Current Medicinal Chemistry - Online First

Follicular lymphoma (FL) is the most prevalent form of indolent lymphoma, characterized by intermittent relapse and remission periods. This study aims to identify potential biomarker genes for FL and elucidate their roles in the disease.

FL-related microarray datasets were downloaded from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify potential hub genes. Various machine learning algorithms were applied to improve gene selection accuracy and predictive performance. Mendelian randomization (MR) analysis was carried out to identify genes with causal relationships to FL. Functional enrichment analysis was performed to explore the underlying mechanisms. Finally, the identified biomarker gene was validated in clinical samples using quantitative real-time PCR.

A total of 60 hub genes were identified through differential expression analysis and WGCNA. Subsequently, 11 characteristic genes were identified using machine learning algorithms. MR analysis revealed 173 genes with causal effects on FL, leading to the identification of one key co-expressed gene, N-myc downstream-regulated gene 2 (NDRG2), as a potential biomarker for FL. NDRG2 demonstrated strong predictive performance. Functional enrichment analysis revealed significant associations between NDRG2 and immune-related pathways in FL. Validation in clinical samples confirmed the relevance of NDRG2 as a biomarker.

The integration of machine learning and MR successfully identified NDRG2 as a promising biomarker with a causal relationship to FL. Validation in clinical samples reinforced the reliability of these findings in clinical practice.

By combining machine learning, MR, and experimental validation, NDRG2 was identified and validated as a promising biomarker for FL.

This study aimed to evaluate cyclophilin (CypA) levels in patients with diabetes mellitus (DM) before and after treatment. Metabolic variables, such as weight, blood pressure, and plasma glucose, were assessed in these patients.

This prospective cross-sectional study was conducted over 24 weeks. We included 38 patients with DM. After confirming the diagnosis of type 2 diabetes, SGLT2i (empagliflozin vs. dapagliflozin) therapy was prescribed to the patients. Weight, body mass index (BMI), waist circumference, body fat ratio, fasting plasma glucose, glycated hemoglobin (HbA1c, %), and CypA levels were measured at 0, 12, and 24 weeks. Patients in the drug subgroup were divided into 2 groups: Empagliflozin (Empa, n=16) and Dapagliflozin (Dapa, n=22).

Weight (p<0.001), body mass index (p<0.001), percentage of body fat (p<0.001), diastolic blood pressure (p=0.006), fasting plasma glucose (p<0.001), HbA1c (p<0.001), serum creatinine (p<0.001), and CypA (p<0.001) levels after the SGLT2i therapy were statistically decreased compared to pre-treatment values in all patients. When comparing drug subgroups, significant decreases in weight (p=0.013) and percentage body fat (p=0.01) were observed in the Empa group compared with the Dapa group at 24 weeks. Changes in FPG (p=0.399), HbA1c (p=0.102), and CypA (p=0.329) between the two groups seemed to be similar.

In a 24-week study, significant reductions in weight, BMI, body fat percentage, HbA1c, FPG, and diastolic blood pressure with SGLT2i have been reported in those patients. Furthermore, we also observed that cyclophilin A, an oxidative marker of atherosclerosis, plays a destructive role in cardiomyocyte levels, which are decreased during the SGLT2i therapy.

Beyond the improvement of metabolic parameters, SGLT2 treatment reduced CypA levels in patients with DM regardless of drug subgroups. These drugs may further prevent the presence of cardiovascular diseases.

Papillary thyroid carcinoma (PTC), the most common thyroid malignancy, presents with multiple variants. This study aimed to identify potential biomarkers and therapeutic candidates for PTC through computational analyses and molecular docking.

Gene expression data related to PTC were obtained from the TCGA-THCA and GEO datasets (GSE35570 and GSE33630) to identify differentially expressed genes (DEGs). Functional enrichment analysis was performed on the DEGs, followed by construction of a protein-protein interaction (PPI) network. Hub genes were identified using recursive feature elimination (RFE) and LASSO regression analyses. A nomogram incorporating these hub genes was developed, and its diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Furthermore, the relationship between hub genes and immune cell infiltration was investigated. Potential drug candidates targeting the hub genes were predicted and validated through molecular docking.

Common DEGs across the three datasets were enriched in pathways such as ECM-receptor interaction, proteoglycans in cancer, and cell adhesion molecules. Significantly enriched GO terms included ‘binding,’ ‘receptor activity,’ ‘integral component of membrane,’ ‘cytoplasm,’ ‘cell adhesion,’ and ‘immune response.’ A PPI network was constructed by intersecting the common DEGs with PTC-related targets. Machine learning algorithms identified three hub genes: SRY-box transcription factor 4 (SOX4), cyclin D1 (CCND1), and lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1). These hub genes exhibited differential expression in PTC and were used to construct a reliable diagnostic model. Furthermore, molecular docking revealed stable binding between CCND1 and Tipifarnib, suggesting potential therapeutic relevance.

While previous studies have applied bioinformatics and molecular docking in PTC research, this study uniquely integrates both approaches to identify the hub gene CCND1 and its potential targeting drug, Tipifarnib, as promising molecular markers and therapeutic candidates for PTC.

The hub gene CCND1 and its targeting drug candidate Tipifarnib may contribute to PTC treatment.

Ischemic stroke (IS) is a major cause of death and disability worldwide. The transcriptional mechanism of neutrophil extracellular trap-related genes (NRGs) and their diagnostic potential remain unknown. This study aims to explore the mechanism of NRGs in IS through machine learning and single-cell RNA sequencing (scRNA-seq).

We conducted differential analysis and functional enrichment analysis on the GEO dataset. Machine learning algorithms were used to identify NRGs related to IS. ScRNA-seq analysis was employed to verify the expression of NRGs in different cell types, and cellchat was used to explore the interactions between cell types in the IS. The expression of Eno1 was also verified in the mouse model of middle cerebral artery occlusion (MCAO).

We identified 26 differentially expressed NRGs (DE-NRGs). The diagnostic models constructed from five DE-NRGs (ENO1, HMGB1, ILK, ORAI1, SUCNR1) demonstrated high predictive ability. Single-cell analysis revealed that NRGs were highly expressed in the IS group. The experiment verified the significant upregulation of Eno1.

This study employed machine learning and scRNA-seq to identify the DE-NRGs-related diagnostic model, providing a certain theoretical basis for IS risk stratification. More experiments are needed to verify the role of DE-NRGs in IS in the future.

This study identified DE-NRGs with diagnostic capabilities in IS and verified their high expression through scRNA and experimental methods. DE-NRGs may be potential therapeutic targets for IS.

More robust 4-substituted 7-chloroquinolines have been investigated for their diverse properties. However, there is still no systematic study that correlates the effects of the side chain at the 4-position of chloroquine and hydroxychloroquine derivatives with their lipophilicity, antimicrobial and toxicity properties.

To this end, a cleaner and facile approach was planned to obtain nineteen 4- substituted 7-chloroquinolines, whose influence of the substituent group and side chain extension at the 4-position on their properties was studied.

4-Alkoxy/amino-7-chloroquinolines were prepared by a nucleophilic aromatic substitution (SNAr) reaction between 4,7-dichloroquinoline and alcohols/amines, evaluated for their in silico ADMET test, in vitro antimicrobial activity against Gram-(+) and Gram-(−) bacteria, and Candida albicans fungus, and in vitro toxicity on Artemia salina larvae.

4-Alkoxy/amino-7-chloroquinolines were obtained in yields ranging from 81 to 100%. The best results showed antimicrobial activity against Pseudomonas aeruginosa for 4-amino-7-chloroquinolines 6-8, with halos greater than 20 mm, and against C. albicans for 4-amino-7-chloroquinolines 1-3, with halos close to 30 mm. A correspondence between Minnow toxicity prediction and in vitro toxicity on A. salina larvae was observed, where compounds 3 and 14, with R = Pent, were both predicted to have high acute toxicity (log LC50 < -0.3) and classified as highly toxic (LC50 < 100 µg mL^-1). It seems that increased lipophilicity in the side chain is harmful to A. salina larvae.

Considering the results for compounds 1-3 and 6-8 with greater activity against C. albicans and P. aeruginosa, respectively, especially for 4-amino-7-chloroquinolines 6 and 7, which are slightly toxic on A. salina larvae (LC50 500-1000 µg mL^-1), their antimicrobial studies deserve to be continued by the determination of Minimum Inhibitory Concentration (MIC) values.

The absence of physiologically relevant models for neuroinflammatory brain disorders, such as multiple sclerosis (MS), highlights the need for improved drug screening platforms. To bridge this gap, this study aimed to develop a human brain organoid (hBO) model incorporating essential neural cell types, including astrocytes, microglia, and oligodendrocytes.

hBOs were generated from H9 stem cells, and neuroinflammatory characteristics were elicited by lipopolysaccharide (LPS). The expression of specific neuronal and inflammatory markers was assessed through qRT-PCR, immunofluorescence staining (IFS), and ELISA.

IFS of mature hBOs with anti-SOX2, anti-SATB2, anti-MAPT, anti-GFAP, anti-MBP, and anti-IBA1 antibodies and images collected with the confocal microscope confirmed the differentiation of H9 cells into cortical neurons, astrocytes, microglia, and oligodendrocyte cell types. Elevated GFAP, IBA1, NF-κB, and IL-6 levels, along with reduced CNPase expression with LPS treatment, were considered reflective of MS-like pathology and were used to test fingolimod and its derivatives. Fingolimod and all its derivatives, specifically ST-1505, decreased MAPT (2.1-fold in ELISA, 1.7-fold in IFS), GFAP (1.8-fold in IFS), TNFα (5.4-fold in qRT-PCR), and FABP (1.5-fold in ELISA) levels, and increased IL-10 (11-fold in qRT-PCR) and MBP (2.9-fold in IFS) levels.

The present data collectively showed LPS to evoke neuroinflammation in the hBO model, while fingolimod and its derivatives, particularly ST-1505, exhibited significant anti-inflammatory and neuroprotective properties by counteracting these evoked changes in the hBO model.

The findings supported the applicability of brain organoids as a model system for drug screening studies for neuroinflammatory brain diseases.

Several pathological conditions, including glaucoma, malignant brain tumors, and renal, gastric, and pancreatic carcinomas, are commonly associated with carbonic anhydrase type II (CA-II). Additionally, CA-II plays a critical role in regulating bicarbonate concentration in the eyes. The inhibition of CA-II reduces aqueous humor production and thus lowers intraocular pressure associated with glaucoma.

This study aimed to synthesize potent CA-II inhibitors, 5-nitro-1H-benzo[d]imidazole-2(3H)-thione (5NBIT) and acylhydrazone derivatives (1-13).

In this study, a new series of potent CA-II inhibitors, 5-nitro-1H-benzo[d]imidazole-2(3H)-thione (5NBIT) and acylhydrazone derivatives (1-13), were synthesized and characterized by IR, NMR, UV and mass spectroscopy and evaluated against bovine carbonic anhydrase-II (bCA-II).

Interestingly, most of the compounds showed better inhibition than the standard drug, acetazolamide (IC50: 18.2±0.51 µM), such as compounds 1 (IC50: 10.5±0.81 µM), 2 (IC50: 11.3±0.36 µM), 3 (IC50: 16.5±0.53 µM), 4 (IC50: 15.8±1.02 µM), 5 (IC50: 13.7±1.03 µM), and 9 (IC50: 12.2±1.03 µM). Among the synthesized compounds, compound 7 (IC50: 8.2±0.32 µM) exhibited the highest and compound 6 (IC50: 27.6±0.39 µM) showed the lowest inhibition. Structure-activity relationships suggest that the presence of nitro group on the phenyl ring contributed significantly to the overall inhibitory activity. Molecular docking of all the active compounds was performed to predict their binding behavior, which indicated good agreement between docking and experimental findings. Moreover, the MD simulation of compound 7 also showed excellent binding behavior and binding energy within the binding cavity of bCA-II.

These findings suggest that the synthesized 5NBIT and acylhydrazone derivatives exhibited potent CA-II inhibition, with several compounds outperforming the standard drug acetazolamide. These results provide valuable insights for the development of novel CA-II inhibitors with potential therapeutic applications in glaucoma and other related conditions.

“Penumbra freezing” aims to extend vascular recanalization treatment to acute ischemic stroke (AIS) patients beyond the standard time window by preserving the ischemic penumbra. Efficient biomarkers are crucial for identifying patients eligible for AIS treatment.

This study enrolled 141 AIS patients who exceeded the conventional treatment window. Using CT perfusion imaging, patients were categorized into “penumbra freezing” and “non-penumbra freezing” groups based on the EXTEND criteria. Multiple regression analysis assessed the association of nine baseline factors and five blood lipid indicators with “penumbra freezing.” Diagnostic accuracy was evaluated using ROC curves. Mendelian randomization (MR) analysis validated these findings using blood lipid indicators as exposures and penumbra biomarkers as outcomes.

Among AIS patients beyond the treatment window, males exhibited better penumbra preservation (OR=0.243, 95% CI=0.072-0.813, p=0.022), while those with hyperlipidemia showed poorer preservation (OR=2.429, 95% CI=1.027-7.747, p=0.043). In the “penumbra freezing” group, ApoA1 levels were significantly lower (1.29 ± 0.03 g/L) compared to the “non-penumbra freezing” group (1.42 ± 0.06 g/L, p=0.034). Conversely, Lp(a) levels were significantly higher in the “penumbra freezing” group (304.63 ± 52.44 mg/L) than in the “non-penumbra freezing” group (110.26 ± 40.71 mg/L, p=0.034). Higher ApoA1 levels increased the likelihood of “non-penumbra freezing” beyond the time window (OR=3.206, 95% CI=1.034-9.938, p=0.044), while elevated Lp(a) levels reduced this likelihood (OR=0.075, 95% CI=0.007-0.848, p=0.036). MR analysis confirmed genetic associations of ApoA1 and Lp(a) with penumbra biomarkers.

ApoA1 and Lp(a) may be linked to ischemic penumbra status, but further validation is needed due to limitations in sample size and study methodology.

ApoA1 and Lp(a) are promising biomarkers for identifying AIS patients eligible for “penumbra freezing,” suggesting the potential to extend the treatment window.

Coronary atherosclerotic disease (CAD), clinically manifesting as progressive coronary atherosclerosis (As), involves endothelial cell (EC) dysfunction. HYMAI may contribute to atherogenesis by acting on ECs, but its regulation of endothelial injury and role in As pathogenesis remain unclear.

HYMAI expression was assessed via PCR array in blood samples from healthy individuals, patients with premature coronary atherosclerotic disease (PCAD), and patients with mature coronary atherosclerotic disease (MCAD) (each group consisting of 4 males and 2 females). Using male ApoE^−/− and LDLR^−/− mice fed with a high-fat diet (HFD) to model As, we evaluated the effects of endothelial-specific HYMAI overexpression on aortic lesions. Autophagy and apoptosis were analyzed in ox-LDL-treated human coronary artery endothelial cells (HCAECs).

HYMAI levels increased sequentially in healthy individuals, PCAD, and MCAD patients. In HFD-fed ApoE^−/− and LDLR^−/− mice, aortic atherosclerosis progressed with age, while HYMAI expression in aortic tissue declined. HYMAI overexpression in ECs promoted autophagy and attenuated atherosclerosis. In vitro, ox-LDL suppressed HYMAI, triggering autophagic inhibition and apoptotic activation in HCAECs. HYMAI overexpression rescued ox-LDL-impaired autophagy and suppressed apoptosis through the miR-19a-3p/ATG14 pathway. MiR-19a-3p overexpression reversed autophagic rescue and promoted apoptosis by repressing ATG14.

HYMAI upregulation counteracts ox-LDL-treated endothelial autophagic inhibition via the miR-19a-3p/ATG14 pathway, rescuing apoptosis and attenuating As in both in vivo and in vitro settings.

Our results demonstrated that HYMAI attenuated As progression in As mice and ox-LDL-treated HCAECs by enhancing endothelial autophagy through the miR-19a-3p/ATG14 axis. These findings establish HYMAI as a novel regulatory mechanism and provide a potential druggable target for As and CAD.

Klotho is a multifunctional protein with anti-aging properties that plays a role in regulating vitamin D and phosphate metabolism. Sarcopenia is characterized by the loss of muscle mass and strength and is an important public health concern due to its negative effects on health. The aim of this study was to investigate the association between α-Klotho levels and the frequency of sarcopenia in a diverse population.

This study analyzed data from 1,250 participants in the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2016. Participants were divided into four subgroups based on serum α-Klotho levels. Sarcopenia was assessed using skeletal muscle index and handgrip strength measurements. Multivariable logistic regression analysis was used to determine the association between serum α-Klotho levels and sarcopenia.

There was a significant difference in serum α-Klotho levels between patients with sarcopenia and patients without sarcopenia. In an unadjusted multivariable logistic regression model, higher α-Klotho serum levels were associated with a lower risk of sarcopenia (p < 0.05). This trend was maintained in the partially adjusted model, indicating that higher levels of α-Klotho were associated with a lower risk of sarcopenia. However, the fully adjusted model did not show significance.

Several factors significantly influence the relationship between serum α-Klotho levels and sarcopenia, including sex, ethnicity, alcohol consumption, body mass index (BMI), vitamin D levels, and disease status. Our findings indicate that the risk of sarcopenia is elevated in individuals within the lowest quartile of serum α-Klotho levels. Furthermore, a negative correlation exists between α-Klotho levels and grip strength, observed in both the overall sample and the aging-related subgroup. These results highlight the necessity for further investigation into the complex interplay between α-Klotho and grip strength, particularly in the context of sarcopenia associated with renal disease.

Serum α-Klotho levels in different populations are negatively correlated with the risk of sarcopenia, suggesting that α-Klotho may be involved in the occurrence and development of sarcopenia. Therefore, measuring α-Klotho levels in clinical practice may be a valuable diagnostic tool to identify individuals at high risk of developing sarcopenia.

Hepatocellular Carcinoma (HCC) exhibits high recurrence rates, particularly when accompanied by Microvascular Invasion (MVI). We identified MVI-related biomarkers and established a prognostic model for personalized HCC treatment.

Data were downloaded from The Cancer Genome Atlas (TCGA) and HCCDB databases. Key radiomics features were identified using the support vector machine-recursive feature elimination (SVM-RFE) algorithm, and differential expression analysis was performed with DESeq2. This was followed by functional enrichment analysis using the clusterProfiler package. Through univariate and Lasso regression analyses, we constructed a robust RiskScore model to effectively stratify HCC patients into distinct risk groups based on the median RiskScore value. The model prediction performance was evaluated using ROC curves and Kaplan-Meier (KM) analysis. We used the CIBERSORT algorithm to characterize immune cell infiltration patterns and conducted GSEA to identify differentially activated pathways between the risk groups.

Radiomic analysis revealed four significant features strongly associated with MVI, enabling the construction of a nomogram model with robust classification performance (AUC = 0.742). Subsequent analysis identified 241 overlapping MVI-related Differentially Expressed Genes (DEGs) enriched in critical tumor proliferation and invasion pathways. A 10-gene RiskScore model was developed, demonstrating excellent prognostic discrimination in training and validation cohorts. CIBERSORT analysis revealed significant correlations between specific immune cell infiltration and the 10 genes. GSEA analysis showed significant enrichment of cell cycle regulation pathways in the high-risk group, suggesting their important role in MVI.

The RiskScore was established using MVI-related features for prognosis assessment in HCC.

Our findings provided novel biomarkers and a theoretical basis for the early diagnosis and personalized treatment of HCC.

To identify the critical genes, biological mechanisms, and signaling pathways involved in the therapeutic effects of quercetin on diabetic foot ulcers using network pharmacology and molecular docking approaches.

We identified pathological targets of diabetic foot ulcers (DFU) from GeneCards, OMIM, and TTD, and pharmacological targets of quercetin from STP, TCMSP, and PharmMapper. Intersection analysis revealed potential therapeutic targets. Core targets were determined via GO/KEGG enrichment, PPI network construction, and Cytoscape screening algorithms (Degree, Closeness, Betweenness). Molecular docking and dynamics simulations assessed quercetin-core target interactions and binding affinity.

After screening and intersecting the targets of quercetin and diabetic foot ulcers, 236 genes related to quercetin's anti-diabetic foot ulcer effects were identified, with six key genes emerging as critical: SRC, TP53, MAPK1, JUN, HSP90AA1, and AKT1. Enrichment analysis suggested that quercetin may modulate inflammatory imbalance(HSP90AA1), immunosuppression(JUN), and oxidative stress(SRC, TP53, MAPK1, and AKT1) during diabetic foot ulcer progression.

The relationship between these core targets and biological pathways in diabetic foot ulcers requires further experimental validation. Notably, molecular docking and dynamics simulation results confirmed strong binding affinity between quercetin and the core targets, supporting their potential therapeutic relevance.

Quercetin exerts anti-diabetic foot ulcer effects by regulating SRC, TP53, MAPK1, JUN, HSP90AA1, and AKT1. These hub genes may serve as promising candidates for future therapeutic interventions in diabetic foot ulcers.

The receptor for pyroglutamylated RF amide peptide (QRFPR) is a G protein-coupled receptor that plays a role in various physiological and pathological processes. However, a gap remains in our understanding of QRFPR's pan-cancer properties.

This study performs an extensive pan-cancer analysis of QRFPR utilizing large-scale genomic datasets, including The Cancer Genome Atlas (TCGA). We evaluated QRFPR expression levels in multiple malignancies and examined their correlations with clinical outcomes. Additionally, we investigated associations between QRFPR expression and immune cell infiltration using bioinformatics tools.

Our results reveal significant alterations in QRFPR expression across several cancer types, particularly breast, colorectal, and prostate cancers. Elevated levels of QRFPR are linked to poor prognosis in certain malignancies, such as uterine corpus endometrial carcinoma (UCEC) and mesothelioma (MESO), and correlate with increased infiltration of immune cells, especially T cells and macrophages. Pathway enrichment analyses suggest that QRFPR may impact critical signaling pathways associated with cell growth, apoptosis, and immune regulation.

The observed variations in QRFPR expression across cancer types suggest its diverse roles in tumor biology. Its association with unfavorable clinical outcomes in specific cancers, as well as its link to immune cell infiltration, highlights its multifaceted impact on tumor progression and microenvironment modulation.

Our findings underscore the potential of QRFPR as a prognostic biomarker and therapeutic target in cancer biology. Further investigations into its functional mechanisms could pave the way for precision medicine approaches in oncology.

Cholesterol plays a key role in maintaining tumor cell homeostasis. Reduced IGFBP6 expression is associated with an increased risk of breast cancer recurrence. Previous studies showed that IGFBP6 knockdown decreases cholesterol levels in the MDA-MB-231 cell line. This study aimed to investigate how IGFBP6 influences genes involved in cholesterol metabolism.

We used MDA-MB-231 breast cancer cells with IGFBP6 knockdown. Transcriptomic and proteomic analyses were performed, with selected gene expression validated by RT-PCR. Correlations between IGFBP6 and cholesterol-related genes were evaluated using public RNA-seq datasets.

IGFBP6 knockdown in MDA-MB-231 cells resulted in a threefold decrease in low-density lipoprotein receptor (LDLR) expression and a twofold reduction in LDLR adaptor protein (LDLRAP1) mRNA levels, both responsible for exogenous cholesterol uptake. Meanwhile, PCSK9 expression increased 11-fold (p-adj = 1.4E-93), further limiting uptake. Despite the upregulation of genes involved in endogenous cholesterol synthesis (HMGCS1, HMGCR, FDFT1, SQLE, DHCR24), total cholesterol content in knockdown cells decreased, leading to activation of the sterol-dependent transcription factor SREBF1 (OR = 6.44; p-adj = 0.036). Correlation analysis revealed a significant association between IGFBP6 expression and cholesterol synthesis genes in basal-like breast cancer.

The altered expression profile of multiple cholesterol metabolism-related genes with known prognostic value aligns with a transcriptional program typical of poor-outcome basal-like tumors. These findings support the role of IGFBP6 as a regulator of lipid metabolism and a potential biomarker for therapeutic stratification.

The results of this study indicate that the reduction in cholesterol levels observed in breast cancer cells following IGFBP6 knockdown is primarily due to decreased exogenous uptake. These findings highlight the role of IGFBP6 in regulating cholesterol metabolism and further explain its clinical significance in predicting breast cancer recurrence and progression.

Breast cancer has become the most commonly diagnosed cancer in women worldwide, with advanced cases often leading to bone metastases that significantly affect prognosis and quality of life. This meta-analysis and systematic review aims to evaluate and compare the diagnostic performance of [¹⁸F]FDG PET/CT and bone scintigraphy for detecting bone metastases in breast cancer patients.

A systematic search was conducted across PubMed, Embase, Web of Science, and Scopus for studies published up to February 2025. Relevant articles were identified using a combination of subject-specific and free-text keywords, including “breast cancer,” “positron emission tomography,” “bone scintigraphy,” and “bone metastasis.” Studies assessing the diagnostic utility of [¹⁸F]FDG PET/CT and bone scintigraphy in detecting bone metastases were included. A bivariate random-effects model was used to calculate pooled estimates of sensitivity, specificity, and diagnostic accuracy with 95% confidence intervals (CIs). Potential sources of heterogeneity were explored using meta-regression analysis. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied to evaluate the methodological quality of the included studies.

A total of 1407 publications were initially retrieved, and 13 studies involving 892 patients met the inclusion criteria. The pooled diagnostic performance for [¹⁸F]FDG PET/CT demonstrated a sensitivity of 0.91 (95% CI: 0.81-0.96) and a specificity of 0.98 (95% CI: 0.93-1.00), with an area under the curve (AUC) of 0.99 (95% CI: 0.97-0.99). In comparison, bone scintigraphy showed a sensitivity of 0.82 (95% CI: 0.72-0.89), specificity of 0.81 (95% CI: 0.73-0.87), and an AUC of 0.88 (95% CI: 0.85-0.91). Despite its higher diagnostic accuracy, PET/CT exhibited notable heterogeneity across studies, potentially influenced by differences in patient populations and imaging interpretation criteria.

Our meta-analysis demonstrated the superior diagnostic performance of [¹⁸F]FDG PET/CT over bone scintigraphy, likely attributable to its enhanced sensitivity for osteolytic lesions and integrated anatomical-functional imaging. Nevertheless, considerable inter-study heterogeneity and incomplete clinical data reporting limit the generalizability and robustness, warranting further standardized prospective investigations.

The findings suggest that [¹⁸F]FDG PET/CT offers superior diagnostic accuracy compared to bone scintigraphy for detecting bone metastases in breast cancer patients. However, its clinical application requires further validation through large-scale, prospective studies. Additionally, considerations such as cost-effectiveness and accessibility must be addressed before widespread clinical adoption.

Current osteoporosis medications often prove ineffective for various reasons. Alongside optimizing available agents, new genetic targets should be proposed for drug development. Mendelian randomization (MR) may resolve throughput and confounding issues in traditional observational studies for druggable targets.

We employed two-sample MR with protein quantitative trait loci (pQTLs) and expression quantitative trait loci (eQTLs) data as exposures and six bone mineral density (BMD) sites as outcomes. By meta-analyzing pQTL evidence, validating eQTL evidence, conducting MR sensitivity tests, and assessing druggability, key druggable targets for BMD were identified. Additionally, we performed functional analysis, drug repurposing annotation, transcriptome analysis, in-house PCR, ELISA, and micro-CT validation to further investigate the functionality and expression levels of these targets across different tissues and conditions.

Out of 5,928 pQTLs from deCODE and UKB-PPP datasets, 16 were identified as prioritized targets with significant meta pQTL evidence. Tyrosine-protein kinase Lyn (LYN, meta beta -0.09, 95% CI -0.13 to -0.05), Chondroadherin (CHAD, meta beta -0.39, 95% CI -0.18 to -0.20), Tumor necrosis factor receptor superfamily member 19 (TNFRSF19, meta beta -0.03, 95% CI -0.05 to -0.02), and Transforming growth factor beta induced (TGFBI, meta beta -0.04, 95% CI -0.06 to -0.03) were identified as key druggable targets for BMD. R-spondin-3 (RSPO3) and SPARC-related modular calcium-binding protein 2 (SMOC2) were also suggested with consistent MR associations with previous studies.

We identified four novel BMD-related targets (CHAD, LYN, TGFBI, TNFRSF19) through pQTL meta-analysis, and validated RSPO3/SMOC2's positive effects. By integrating multi-tissue transcriptomics and OVX experiments, we further revealed elevated expression of TNFRSF19/TGFBI negatively correlated with BMD, providing new therapeutic insights.

This large-scale Proteome-Wide MR study introduced novel targets for BMD and osteoporosis at transcriptional and translational levels, presenting new prospects for drug repurposing and development.

This study aims to elucidate the mechanisms contributing to the transition from acute to chronic inflammation, particularly in the context of atherosclerosis, by investigating the pro-inflammatory responses of cybrid cell lines derived from patients with coronary heart disease.

Acute inflammatory reactions are essential components of the innate immune response, typically resolving within hours or days. However, disruptions in this process can lead to chronic inflammation, which is linked to significant morbidity and mortality. Atherosclerosis, characterized by chronic vascular inflammation, poses a major health threat, underscoring the need for understanding its underlying mechanisms.

The primary objective is to analyze the pro-inflammatory cytokine responses of 14 cellular lines, including 13 cybrids and one maternal line (THP-1), to identify intolerant and tolerant responses to key cytokines associated with inflammation.

We utilized cybrid cell lines created by fusing THP-1 monocytic cells with platelets from patients diagnosed with atherosclerosis. Cytokine responses were assessed through quantitative analysis of IL-1β, IL-6, MPC-1, IL-8, and TNF-α secretion. Gene expression profiles were analyzed to correlate cytokine secretion with specific gene regulation patterns, focusing on epigenetic mechanisms influencing immune responses.

Distinct intolerant and tolerant responses were observed across the cellular lines for key cytokines. Specifically, TC-HSMAM1 and TCP-521 were intolerant to IL-1β, TC-HSMAM1, TC-LSM2, and TC-522 were intolerant to IL-6, six lines exhibited intolerance to MPC-1, and eight lines were intolerant to IL-8. No intolerant responses were noted for TNF-α. Gene expression analysis revealed that at least ten genes correlated with increased cytokine secretion in intolerant reactions, while 23 genes showed higher expression during these intolerant responses, indicating significant roles for DNA modification and chromatin remodeling. An important finding emerged from the study of agents affecting histone modification. Specifically, unlike other agents, sodium butyrate not only exhibited a stronger suppression of the inflammatory response in cells but also eliminated their intolerance to inflammatory stimulation. Therefore, in the near future, sodium butyrate could be regarded as a fundamentally new anti-inflammatory preventive and therapeutic agent, with its mechanism of action rooted in the prevention and suppression of chronic inflammation.

In chronic non-infectious diseases like atherosclerosis the intolerant response or trained immunity can worsen inflammation. This study shows that both genetic and epigenetic regulation contribute to this intolerant response. It was also found that sodium butyrate can prevent the intolerant response, suggesting it may become a new anti-inflammatory agent that suppresses chronic inflammation.

Our findings have suggested that the interplay between pro-inflammatory cytokine responses and epigenetic regulation mechanisms is critical in determining whether a cell exhibits a normal or intolerant immune response. Understanding these dynamics may provide insights into the chronic inflammatory processes associated with atherosclerosis and other related conditions.

Coronavirus disease (COVID-19) is a highly infective disease caused by SARS-CoV-2. The SARS-CoV-2 spike protein binds with the human ACE2 receptor to facilitate viral entry into the host cell; therefore, spike protein serves as a potential target for drug development.

Keeping in view the significance of SARS-CoV-2 spike protein for viral replications, in the current study, we identified the potent inhibitors against SARS-CoV-2 spike protein in order to combat the viral infection.

In the current study, we screened an in-house library of ~900 natural and synthesized compounds against the spike protein receptor binding domain (RBD) using a structure-based virtual approach, followed by an in-vitro inhibition bioassay.

Seven (C1-C7) potent compounds were identified with docking scores ≥ −6.66 Kcal/mol; their drug-likeness, pharmacokinetic, and pharmacodynamic characteristics were excellent with no toxic effect. Those molecules were subjected to a triplicate simulation for 200 ns, which further confirmed their stable binding with RBD. This tight packing of complexes was reflected by calculated binding free energy, which disclosed higher binding free energy of C4, C7 and C6 than C1-C3, while predicted entropic energy demonstrates higher values for C4, C7 and C1 than the rest of the compounds, indicating more thermodynamic stability in protein due to conformational changes in spike protein induced by binding of C4, C7 and C1. These computational analyses were later validated through in-vitro bioassay. Remarkably, C2-C7 displayed significant inhibitory potential with >76 to 89% inhibition and C3, C4, C6 and C7 demonstrated the highest inhibition of RBD.

The current findings suggest that compounds C3 and C6 effectively disrupt the function of RBD of SARS-CoV-2 spike protein and can serve as potential drug candidates for spike protein.

GLP-1 receptor peptide agonists have revolutionized type 2 diabetes mellitus and obesity treatment, primarily through injection-based therapies. Small-molecule GLP-1 receptor agonists allow oral administration, but none are clinically established. Pfizer's danuglipron and lotiglipron, presented in 2018-2019, were “first-in-class” drug candidates, becoming prototypes for “next-in-class” drug development.

This review summarizes “next-in-class” GLP-1 receptor agonists developed, identifying different relationships between the molecular structure and functional activity of agonists.

Patents containing danuglipron- and lotiglipron-like agonists from January 2021 to July 2024 were browsed in databases, such as Espacenet and Google Patents, using specified keywords. Over 5,000 compounds from 67 patent publications were analyzed.

Our analysis identified some key general SAR trends. The presence of a carboxyl group leads to highly active agonists, but replacing it with bioisosteric analogs may improve the ADME profile of the target compounds. The introduction of specific privileged fragments, as well as the replacement of 1H-benzo[d]imidazole nucleus or (S)-oxetan-2-ylmethyl substituent in the prototype structure with bioisosteric heterocycles, may be viable approaches. The replacement of 1,4-disubstituted piperidine linker with its (S)-2-methyl-substituted homologue or O, N-disubstituted piperidin-4-ol may also result in highly potent agonists. Additionally, the classic 2,4-EWG-disubstituted benzyl alcohol residue allows significant variability.

Despite the limited clinical success of danuglipron and lotiglipron, as well as the inherent problems associated with the complex nature of GLP-1R signaling, the current state of research and the abundance of novel, promising chemotypes of highly potent compounds suggest that approved GLP-1R agonists may emerge in the coming years.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are major gynecological malignancies, causing significant cancer-related deaths in women. Current treatments yield poor outcomes, with a 5-year survival rate of only 17%. Identifying new biomarkers and therapeutic targets is crucial for improving prognosis and guiding personalized treatments.

We analyzed TCEAL2 expression using data from The Cancer Genome Atlas (TCGA) across various cancers, including CESC. We explored its correlation with clinical features, prognosis, immune infiltration, MSI, mRNAsi, and drug sensitivity. TCEAL2 expression was validated in GSE9750 datasets and CESC cell lines using qRT-PCR.

TCEAL2 expression was significantly dysregulated in CESC. Elevated TCEAL2 levels correlated with poor clinical outcomes, including advanced pathological M stage (p = 0.009), initial treatment failure (p = 0.0098), and reduced overall survival (OS) (p = 0.013). TCEAL2 was an independent predictor of unfavorable OS (p = 0.032). It was associated with key pathways such as calcium signaling, oxidative phosphorylation, and Wnt signaling. TCEAL2 also correlated with immune cell infiltration, MSI, and mRNAsi. Notably, TCEAL2 levels inversely correlated with sensitivity to several drugs, including CAY10603 and SB-223133.

The results suggest that TCEAL2 plays a significant role in CESC progression and its tumor microenvironment. Its correlation with immune infiltration and drug sensitivity highlights its potential as a prognostic biomarker and therapeutic target. Future studies should focus on elucidating the molecular mechanisms and validating their clinical utility.

TCEAL2 is a potential prognostic biomarker and therapeutic target in CESC. Further research is needed to explore its role and clinical applications.

Sodium Selenite (NaSe) is a molecule with various biological activities. Bone fractures and osteoporotic diseases are increasingly common health issues, prompting the search for alternative treatments. Therefore, the purpose of this study was to examine the antioxidant and osteogenic properties of NaSe.

The experiments were conducted using the hFOB1.19 osteoblast cell line. The MTT assay was used to assess the effects of NaSe on cell viability, while cytotoxicity was evaluated with Lactate Dehydrogenase (LDH) assays. Osteogenic differentiation was assessed by alizarin red staining, and Alkaline Phosphatase (ALP) activity and intracellular Reactive Oxygen Species (ROS) levels were also analyzed.

The results showed that NaSe significantly enhanced cell viability in a dose-dependent manner at low doses (0.01-1μM), with the most effective dose being 1μM (p<0.05). LDH activity remained similar to the control within the 0.01-1μM range but increased significantly at higher concentrations (5-50 μM) in both 24- and 48-hour experiments (p<0.05). NaSe reduced intracellular ROS levels significantly between 0.01-1 μM, with 1 μM being the most effective concentration (p<0.05). The highest ALP activity was observed at 0.1 μM NaSe (p < 0.05), and calcium deposition increased in a concentration-dependent manner (p<0.05). The most effective dose for enhancing mineralization was 0.1 μM (p<0.05).

This study demonstrates that NaSe has antioxidant and osteogenic effects at low doses in hFOB cells. These positive effects suggest that NaSe could be a promising candidate for in-vitro, in-vivo, and clinical trials, providing hope for new treatments for bone diseases.

One of the most effective osteoanabolic drugs for treating osteoporosis is romosozumab, which was developed as a consequence of growing knowledge of the Wnt signaling system. This review explored how romosozumab affects the bone mineral density (BMD) in osteoporotic patients.

Up until January 2024, PubMed, Web of Science, and Scopus were reviewed for any randomized controlled trials (RCTs) evaluating the impact of osteoporotic treatment with romosozumab on BMD changes and bone metabolism markers in primary osteoporosis patients. Pooled Hedges’ g indices, which were consistently used across all included studies to measure standardized mean differences, were computed along with their corresponding 95% confidence intervals using either a random-effects or fixed-effects model.

Out of the 1855 papers, 24 RCTs met the inclusion criteria. Patients with osteoporosis who received romosozumab for a period of time demonstrated an augmentation in their lumbar spine BMD. The study findings indicated that the total hip and femoral neck BMD demonstrated significant enhancement in 22 (out of 23) and 19 (out of 21) studies, respectively.

In patients with osteoporosis, romosozumab could markedly increase the total hip, lumbar spine, and femoral neck BMD. This finding could be verified by measuring bone turnover indicators such as PINP, TRACP-5b, and CTX.

Low back pain (LBP) and sciatica are among the most prevalent musculoskeletal disorders, leading to significant disability and an economic burden. Neurotrophic factors, including nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), and glial cell-derived neurotrophic factor (GDNF), play critical roles in pain modulation and neuronal function. While NGF-targeting monoclonal antibodies have shown potential in treating chronic pain, their efficacy and safety remain under debate. This study employs Mendelian Randomization (MR) to assess the causal relationships between NGF, BDNF, GDNF, and the risk of LBP and sciatica.

We conducted a two-sample MR analysis using genetic instruments for NGF, BDNF, and GDNF. LBP and sciatica data were obtained from FinnGen. The inverse variance weighted (IVW) method was applied as the primary causal estimation, with the weighted median (WM) and MR-Egger regression used for sensitivity analyses. Reverse MR was performed to evaluate bidirectional causality. Furthermore, we used expression quantitative trait loci (eQTLs) within 50 kb of each gene locus as genetic instruments for NGF regulation, ensuring that the genetic variants used directly influence neurotrophic factor expression.

MR analysis revealed a significant causal association between NGF and an increased risk of LBP (OR = 1.121, 95% CI 1.021-1.230, p = 0.016) and sciatica (OR = 1.158, 95% CI 1.034-1.296, p = 0.010), while BDNF and GDNF showed no significant associations with pain outcomes. Sensitivity analyses confirmed the robustness of the NGF findings, with no evidence of horizontal pleiotropy or heterogeneity. Reverse MR analysis showed no significant causal effect of LBP or sciatica on NGF levels (p > 0.05), ruling out reverse causality. Additionally, we investigated the NGF-eQTL, which captures genetically regulated NGF expression, and found a significant association between the NGF-eQTL and LBP (OR = 1.040, 95% CI 1.010-1.070, p = 0.007). Unlike external NGF measurements, the NGF-eQTL minimizes environmental confounding and reverse causation, providing stronger genetic evidence supporting NGF as a therapeutic target for LBP.

Our findings provide strong genetic evidence that nerve growth factor (NGF) plays a causal role in the development of low back pain and sciatica, supporting NGF inhibition as a promising therapeutic strategy. These results align with clinical observations where anti-NGF monoclonal antibodies demonstrated pain-relieving effects, though safety concerns remain. In contrast, no causal associations were observed for BDNF or GDNF, underscoring the specificity of NGF in peripheral pain sensitization. The study demonstrates the value of Mendelian Randomization in minimizing confounding and reverse causation, thereby strengthening causal inference. Future work should focus on pharmacogenomic predictors to identify patients most likely to benefit from NGF-targeted interventions while minimizing adverse effects.

This study provides genetic evidence that NGF plays a causal role in LBP and sciatica, reinforcing its potential as a therapeutic target. However, BDNF and GDNF were not significantly associated with pain outcomes, suggesting distinct mechanisms of pain modulation. While clinical trials of anti-NGF monoclonal antibodies have demonstrated efficacy in pain reduction, concerns about adverse effects, such as joint degeneration, habe limited their widespread clinical use. Future research should explore genetic predictors of anti-NGF therapy response to optimize treatment strategies for LBP and related musculoskeletal pain disorders.

Many medications associated with an increased risk of dementia do not have adequate warning labels, leading to a significant underestimation of their potential dangers. This study aims to leverage the FAERS database to identify drugs strongly linked to dementia and to examine the relationship between these drugs using Mendelian randomization techniques. The ultimate goal is to mitigate the risk of developing dementia.

We utilized the FAERS database to identify medications significantly associated with dementia cases. The DrugBank, OpenTargets, and STITCH databases were employed to pinpoint the target genes of these drugs. We then conducted Mendelian randomization analysis to explore the correlation between the expression of drug target genes and the incidence of dementia. Additionally, a time-to-onset analysis assessed the temporal relationships of drug ingestions. Furthermore, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction Network (PPI) analyses were performed to investigate the molecular pathways linked to target genes related to drugs associated with dementia.

A total of 28,139 dementia events were recorded in the FAERS database. Our Mendelian randomization analysis revealed a significant association between the expression of all identified drug target genes and dementia in both blood and brain tissues. Specifically, we identified nine drug target genes with significant correlations, implicating quetiapine, clozapine, valproic acid, alendronate, and digoxin as being strongly associated with dementia, which could provide insight into areas of clinical concern regarding dementia occurrence.

The adverse event data sourced from the FAERS database indicate that certain medications are associated with an increased risk of developing dementia, a finding corroborated by our Mendelian randomization analysis. Establishing a comprehensive monitoring and risk assessment program is crucial for identifying high-risk individuals and facilitating informed medication choices, thereby potentially reducing the incidence of dementia.

Tyrosinase, a copper-containing enzyme, is responsible for melanin production, and its overactivity can lead to hyperpigmentation.

This study aimed to evaluate triazolothiadiazoles (3a-h, 4a-f) and triazolothiadiazines (5a-h) against human and mushroom tyrosinase isozymes.

Several derivatives, such as 3a-3b, 3d, 4c-4f, 5d, and 5e, were identified as potent and selective inhibitors of mushroom tyrosinase, with IC50 values ranging from 1.9 to 15.2 µM. Similarly, compounds 3f, 4b, 5a, and 5b effectively inhibited human tyrosinase, with IC50 values between 12.6 and 18.5 µM. Mechanism-based studies revealed that these active compounds exhibited competitive inhibition against both isozymes without any cytotoxic effects. In-silico analysis further demonstrated that these compounds fit well into the active site of both tyrosinase isozymes.

Additionally, the pharmacokinetic profile of these compounds highlighted promising drug-like properties, making them potential candidates for the development of effective therapeutics for skin disorders.

Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder, yet the causal roles of modifiable risk factors remain unclear. This study aims to investigate the causal relationships between 28 modifiable risk factors (including obesity traits, mental health disorders, sleep traits, metabolic comorbidities, and serum parameters) and GERD using two-sample Mendelian randomization (MR). Gastroesophageal reflux disease (GERD). Our findings aim to inform targeted prevention and treatment strategies for GERD.

This study obtained data from extensive genome-wide association studies (GWAS). Pooled data associated with gastroesophageal reflux associations were obtained from the 23andMe Research team’s research, which included a total of 129,080 cases of gastroesophageal reflux and 473,524 controls of European ancestry. We conducted a univariable Mendelian randomization (MR) analysis to ascertain whether genetic evidence of exposure demonstrated a statistically significant association with the risk of GERD. Subsequently, a multivariable MR analysis was carried out to estimate the independent effects of the exposures on GERD.

Univariable MR analysis utilizing extensive GWAS data suggested that genetic factors such as BMI, Waist circumference, Arm fat mass (left and right), Leg fat mass (left and right), Attention Deficit and Hyperactivity Disorder (ADHD), Major Depressive Disorder (MDD), Schizophrenia, Negative emotions (including nervousness, anxiety, tension, or depression), Insomnia, Sleep apnea syndrome, Sleep duration, and Snoring, as well as Total cholesterol levels and Apolipoprotein B levels, are associated with the development of GERD. Multivariate Mendelian randomization of BMI and Negative emotion as correction factors showed that Waist circumference, Arm fat mass (left and right), Leg fat mass (left and right), ADHD, Insomnia, Sleep apnea syndrome, and Snoring were associated with an increased risk of GERD (p< 0.05). Conversely, longer sleep duration was associated with a reduced risk of GERD (p< 0.05).

This MR study reveals novel causal mechanisms in GERD pathogenesis: (1) Peripheral adiposity (arm/leg fat mass) exerts independent effects beyond central obesity, indicating site-specific fat distribution significance; (2) ADHD emerges as a distinct psychiatric risk factor independent of mental disorders; (3) Sleep apnea operates through BMI-independent pathways. Collectively, these findings redefine GERD pathophysiology, highlighting fat depot specificity and brain-gut interactions as critical mechanistic drivers.

Overall, our findings suggest that multiple risk factors are associated with the risk of GERD. These results provide a theoretical basis for controlling body weight and plasticity, improving sleep habits, and preventing and timely seeking medical attention to reduce the occurrence of psychiatric disorders, which will be important strategies to prevent and alleviate GERD.

Seven new 4-[2-(dichloromethyl)pyrazolo[1,5-a][1,3,5]triazine derivatives were investigated for anticancer activity, possible molecular mechanisms of anticancer action, and ADMET properties.

The 4-benzenesulfonamide derivatives of pyrazolo[1,5-a][1,3,5]triazine were synthesized using the condensation of N-(2,2-dichloro-1-cyanovinyl)-amides IV with 1H-pyrazol-5-amine. Compound antitumor activities were evaluated using the NCI-60 human cancer cell line. AutoDockTools and AutoDock Vina software were used for molecular modeling. Using the ADMETlab 3.0 and pkCSM web sources, the ADMET properties of compounds 4, 5, and 7 were calculated.

Seven new pyrazolo[1,5-a][1,3,5]triazine derivatives were synthesized. The compounds 4, 5, and 7 exhibit high activity >1 µM against leukemia, colon, and renal cancer. Compound 4 exhibited the most potent activity, with IC50 values of 0.32  µM against leukemia, 0.49-0.89  µM against colon cancer, and 0.92  µM against renal cancer. Molecular modeling has demonstrated a potential antitumor mechanism involving CDK. The predicted ADMET profile of compounds 4, 5, and 7 is favorable.

The seven novel pyrazolo[1,5-a][1,3,5]triazines, as purine bioisosteres, were developed, synthesized, and investigated by in vitro and in silico methods.

Seven novel pyrazolo[1,5-a][1,3,5]triazine derivatives exhibited anticancer activity against the NCI-60 cancer cell lines. The compounds 4, 5, and 7 demonstrated strong anticancer activity, with growth inhibition (GI) values exceeding 50% across all nine cancer types tested. The most active compound, 4, is against leukemia, colon cancer, renal cancer, and lung cancer. All compounds exhibit low toxicity, with LC50 values of 100 µM or greater. The molecular docking of compounds 4, 5, and 7 revealed the potential to inhibit cancer-associated cyclin-dependent kinases. The predicted ADMET profiles of their compounds are favorable, providing a basis for further improvement of their anticancer activity.

Metabolic dysregulation, encompassing conditions such as type 2 diabetes mellitus, obesity, metabolic syndrome, and dyslipidemia, poses an increasing global health burden. The dysregulation of 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1), a key enzyme in glucocorticoid metabolism, has been strongly implicated in the pathogenesis of these disorders by influencing glucose homeostasis, lipid metabolism, and insulin sensitivity. Consequently, targeting 11β-HSD1 offers a promising therapeutic strategy for mitigating metabolic dysregulation and its associated complications.

The study aimed to identify resveratrol derivatives with high binding affinity and inhibitory potential against 11β-HSD1, using computational approaches to evaluate their pharmacokinetic and toxicity profiles.

A library of resveratrol derivatives was screened using molecular docking to identify high-affinity compounds. The hit compounds were further evaluated for absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, followed by molecular dynamics simulations to assess their stability.

The resveratrol cis-dehydrodimer emerged as the most promising candidate, demonstrating high binding affinity, favorable ADMET properties, and stability over a 200 ns simulation period. These findings suggest its potential as a small-molecule inhibitor of 11β-HSD1.

The resveratrol cis-dehydrodimer represents a viable candidate for further experimental validation as a therapeutic agent for metabolic disorders. Future studies should include synthetic validation and in vivo testing to confirm its efficacy.

Long COVID-19 (LC) is a condition that follows SARS-CoV-2, an acute infection defined by persistent fatigue, dyspnea, and impaired cognitive function. LC presents a complex array that imposes ongoing challenges on global health, patients' quality of life, and functional capacity. Many inconsistencies surround its pathophysiology, diagnosis, prevention, and treatment. This review aims to cover missed gaps in LC with a special focus on therapeutic strategies concerning non-pharmacological, pharmacological, experimental, and innovative approaches for better patient management and outcomes, as well as to evaluate their effectiveness and guide future research.

An online search was conducted using five digital repositories: PubMed, Scopus, Google Scholar, Web of Science, and the Cochrane Library. A combination of keywords associated with LC therapy was employed: “long COVID, “pharmacological options,” “non-pharmacological options,” “innovative strategies,” “experimental”, and” quality of life (QOL).” Relevant data were extracted and synthesized to categorize therapeutic approaches into subtypes. A critical analysis was conducted on their mechanism of action, indication, outcome, and limitations.

The pooled prevalence of LC was 42%, and the symptom duration ranged from 3 months to 2 years. The most important risk factors for LC were female sex, unvaccinated status, and cases with co-morbidities. Diagnosis of LC was challenging due to a lack of diagnostic standardization and reliable biomarkers.

Non-pharmacological strategies were employed first, showing diverse efficacies; however, the reported literature was hindered by small sampling. Pharmacological agents show promising results but need further validation. Experimental and innovative strategies need longer studies and validations.

LC has imposed a significant burden on community health, necessitating the appropriate allocation of health resources and community support. Preventive and therapeutic interventions show promise, but the variability in patient response underscores the need for personalized approaches and more well-designed trials. Collaborative research and multi-disciplinary teams are needed to mitigate the long-term effects of LC and improve patient outcomes.

This study aimed to develop, characterize, optimize, and evaluate the in vitro ex vivo drug release and stability of miconazole nitrate (MN)-loaded nanocrystal for topical drug delivery. MN is an antifungal agent with poor oral bioavailability and significant first-pass metabolism, necessitating alternative administration routes. Nanoformulations with lipidic/polymeric nanoparticles can overcome conventional system formulation limitations. However, it resulted in controlled MN drug release for up to 48 h and greater skin flux than did a 1% MN solution. This study aimed to identify optimized, stable, and effective in vitro/ex vivo MN-loaded nanocrystal-based hydrogels for topical drug delivery.

The nanocrystals (PN1-PN12) were developed via the precipitation method using Pluronic F-127 as a nonionic copolymer surfactant and stabilizer. The compatibility was evaluated via differential scanning calorimetry (DSC), powder X-ray diffraction (PXRD), and Fourier transform infrared spectroscopy (FT-IR). With the help of the zetasizer, particle size, PDI, and Zeta Potential are determined. The drug in-vitro release was determined using the dialysis bag method. Carbopol 934-P and methylparaben were dissolved in distilled water with heat and constant stirring to prevent agglomeration. Permeation experiments used excised abdominal skin from Wistar rats euthanized by cervical dislocation.

The highest solubility was found in PF-127, followed by Pluronic F68. Nanocrystals were prepared via the antisolvent precipitation method. The new diffraction pattern of the nanocrystals confirms their crystalline nature and complexation with the polymer, supporting the DSC and FT-IR findings. The developed nanocrystal shows a subtle shift from 1587 to 1589 cm^-1, with no significant changes in the vibrational frequencies of the physical mixture. The PN5 formulation, with a small PS of 303.4 nm, a low PDI of 0.248, the highest drug content of 99.23 ± 5.23%, and a % cumulative drug release of 92.32 ± 3.27, was selected for further characterization. The PN5 formulations were stored under various conditions for 3 months, resulting in consistent particle sizes. SEM images revealed long, crystalline MN structures and needle-like nanocrystals. PN5 was optimized for developing a topical nanocrystal gel (PG1), which provided sustained drug release and retained significantly more drug than the other formulations did. PG1 remained stable during the 3-month storage.

The PN5 formulation, optimized for developing a topical nanocrystal gel, resulted in consistent particle size, sustained drug release, and stability over 3 months.

Wound healing is a complex and dynamic biological process involving overlapping phases such as inflammation, proliferation, and tissue remodeling. Chronic wounds, which fail to heal in a timely manner, pose significant challenges in clinical practice. Static magnetic fields (SMFs) have shown potential in wound healing, particularly in their anti-inflammatory effects and ability to promote cell proliferation. However, the precise mechanisms underlying their effects remain unclear.

This study aims to investigate the effects of SMFs on wound repair and to explore the molecular mechanisms involved, particularly the role of key signaling pathways.

A rabbit ear full-thickness wound model was used to evaluate the effects of SMFs (160 mT) on wound healing. Normal human dermal fibroblasts (NHDFs), normal human epidermal keratinocytes (NHEKs), and human umbilical vein endothelial cells (HUVECs) were cultured under SMF conditions to assess their proliferation, migration, and angiogenic activity. Tissue repair, angiogenesis, and cell proliferation were analyzed through histological and immunohistochemical methods. Transcriptome sequencing and Western blotting were performed to identify key pathways affected by SMFs.

SMFs significantly accelerated wound healing in the rabbit ear model, as demonstrated by enhanced re-epithelialization, granulation tissue formation, and angiogenesis. In vitro, SMFs promoted the proliferation and migration of fibroblasts and keratinocytes, as well as tube formation in endothelial cells. Transcriptome and protein analyses revealed that SMFs activated the PI3K/AKT/mTOR signaling pathway, which played a critical role in regulating cell proliferation and angiogenesis.

This study demonstrates that SMFs promote wound healing by enhancing angiogenesis and cell proliferation through activation of the PI3K/AKT/mTOR signaling pathway. These findings provide a theoretical foundation for the application of SMFs as a non-invasive therapeutic approach for clinical wound management.

This investigation delves into the diagnosis of COVID-19, using X-ray images generated by way of an effective deep learning model. In terms of assessing the COVID-19 diagnosis learning model, the methods currently employed tend to focus on the accuracy rate level, while neglecting several significant assessment parameters. These parameters, which include precision, sensitivity and specificity, significantly, F1-score, and ROC-AUC influence the performance level of the model. In this paper, we have improved the InceptionResNet and called Enhanced InceptionResNet with restructured parameters termed, “Enhanced InceptionResNet,” which incorporates depth-wise separable convolutions to enhance the efficiency of feature extraction and minimize the consumption of computational resources.

For this investigation, three residual network (ResNet) models, namely ResNet, InceptionResNet model, and the Enhanced InceptionResNet with restructured parameters, were employed for a medical image classification assignment. The performance of each model was evaluated on a balanced dataset of 2600 X-ray images. The models were subsequently assessed for accuracy and loss, as well subjected to a confusion matrix analysis.

The Enhanced InceptionResNet consistently outperformed ResNet and InceptionResNet in terms of validation and testing accuracy, recall, precision, F1-score, and ROC-AUC demonstrating its superior capacity for identifying pertinent information in the data. In the context of validation and testing accuracy, our Enhanced InceptionResNet repeatedly proved to be more reliable than ResNet, an indication of the former’s capacity for the efficient identification of pertinent information in the data (99.0% and 98.35%, respectively), suggesting enhanced feature extraction capabilities.

The Enhanced InceptionResNet excelled in COVID-19 diagnosis from chest X-rays, surpassing ResNet and Default InceptionResNet in accuracy, precision, and sensitivity. Despite computational demands, it shows promise for medical image classification. Future work should leverage larger datasets, cloud platforms, and hyperparameter optimisation to improve performance, especially for distinguishing normal and pneumonia cases.

Ovarian reserve reflects the functional capacity of a woman’s ovaries, encompassing factors such as follicle quantity, egg quality, and fertilization potential. Assessment of ovarian reserve is essential in reproductive medicine, particularly for fertility evaluation and assisted reproductive technologies (ART). While traditional biochemical markers such as anti-Müllerian hormone (AMH) and follicle-stimulating hormone (FSH) are commonly used, instrumental diagnostic methods like ultrasound and magnetic resonance imaging (MRI) provide valuable morphological and functional insights. This systematic review without a comprehensive meta-analysis evaluates the role of ultrasound and MRI in assessing ovarian reserve and their potential applications in clinical and research settings.

A comprehensive literature search was conducted across multiple databases to identify relevant studies evaluating ovarian reserve using ultrasound and MRI. Studies were screened based on predefined inclusion criteria, focusing on imaging parameters such as ovarian volume, follicular count, stromal characteristics, and vascularization. The effectiveness of these imaging techniques was analyzed in comparison to established biochemical markers. Due to heterogeneity in the included studies, a systematic review was performed without a formal meta-analysis.

Ultrasound, particularly transvaginal ultrasound (TVUS), remains the gold standard for ovarian reserve assessment, allowing real-time visualization of antral follicle count (AFC), ovarian volume, and follicular morphology. Doppler ultrasound provides additional insights into ovarian blood flow, which correlates with follicular development and ovarian function. MRI offers high-resolution, three-dimensional imaging, enabling detailed assessment of ovarian structure, follicular density, and stromal composition. While MRI provides superior soft-tissue contrast, its role in routine ovarian reserve assessment is limited due to cost and accessibility. The findings indicate that although both modalities are valuable for ovarian reserve evaluation, there is no consensus on standardized imaging parameters for defining ovarian functional viability. The available literature also presents inconsistencies in the correlation between imaging findings and ovarian function.

Ultrasound and MRI are essential tools for assessing ovarian reserve, providing complementary morphological and functional data. However, the lack of standardized imaging parameters limits their ability to definitively determine ovarian functional viability. Further research is needed to establish validated diagnostic criteria and integrate imaging techniques with biochemical markers to enhance the accuracy of ovarian reserve assessment in clinical practice and reproductive research.

Early and precise diagnosis is essential for effectively treating and managing pulmonary tuberculosis. The purpose of this research is to leverage artificial intelligence (AI), specifically convolutional neural networks (CNNs), to expedite the diagnosis of tuberculosis (TB) using chest X-ray (CXR) images.

Mycobacterium tuberculosis, an aerobic bacterium, is the causative agent of TB. The disease remains a global health challenge, particularly in densely populated countries. Early detection via chest X-rays is crucial, but limited medical expertise hampers timely diagnosis.

This study explores the application of CNNs, a highly efficient method, for automated TB detection, especially in areas with limited medical expertise.

Previously trained models, specifically VGG-16, VGG-19, ResNet 50, and Inception v3, were used to validate the data. Effective feature extraction and classification in medical image analysis, especially in TB diagnosis, is facilitated by the distinct design and capabilities that each model offers. VGG-16 and VGG-19 are very good at identifying minute distinctions and hierarchical characteristics from CXR images; on the other hand, ResNet 50 avoids overfitting while retaining both low and high-level features. The inception v3 model is quite useful for examining various complex patterns in a CXR image with its capacity to extract multi-scale features.

Inception v3 outperformed other models, attaining 97.60% accuracy without pre-processing and 98.78% with pre-processing.

The proposed model shows promising results as a tool for improving TB diagnosis, and reducing the global impact of the disease, but further validation with larger and more diverse datasets is needed.

Cuproptosis, a newly discovered form of programmed cell death, has potential implications for tumorigenesis and cancer progression. This study investigates the role of cuproptosis in Acute Myeloid Leukemia (AML) and identifies associated biomarkers using bulk and single-cell RNA sequencing. Despite recent advances, the mechanisms of cuproptosis in AML remain unclear, and its relationship with immune cell infiltration could reveal novel therapeutic targets.

RNA-seq data from 151 AML patients and 70 healthy controls were obtained from TCGA and GTEx databases, and single-cell RNA-seq data from 16 AML patients (GEO) were used for validation. Differential expression of Cuproptosis-Related Genes (CRGs) was analyzed via RCircos and correlation analysis. Immune cell infiltration was assessed using CIBERSORT and ssGSEA. WGCNA identified key genes for AML and cuproptosis subtypes, which were validated with single-cell data. Intercellular communication was analyzed through ligand-receptor interactions. RNA interference experiments validated TLR4 and NCF2, with gene expression measured through RT-qPCR. Apoptosis and CCK-8 assays assessed cell viability.

We identified 19 CRGs with differential expression between AML subtypes linked to immune cell infiltration. Subtype analysis classified AML patients into C1 and C2 subgroups enriched in biosynthesis and metabolism pathways. WGCNA identified 2701 genes associated with AML and 92 with cuproptosis, leading to 15 intersecting genes. RETN was highlighted as key in intercellular communication. Experimental validation showed that elesclomol-induced cell death in THP-1 cells is reversible by TTM. Knockout of TLR4 and NCF2 promoted cuproptosis.

These findings offer new insights into the role of cuproptosis in AML, highlighting novel biomarkers, such as TLR4 and NCF2, which may provide promising targets for the development of future therapeutic strategies in AML treatment.

Doxorubicin (DOX) is a chemotherapeutic agent widely used for the treatment of various cancers; however, its clinical use is limited by its cardiotoxicity. However, the underlying molecular mechanisms remain poorly understood, hindering the development of effective preventive and treatment strategies. This study aimed to identify core target genes and explore the mechanisms involved in DOX-induced cardiotoxicity by integrating microarray analysis, machine learning, and molecular docking.

Differential expression analysis was performed using microarray data from DOX-induced cardiotoxic samples and healthy controls. Multiple machine learning algorithms were applied to identify core target genes. The predictive performance of these genes was evaluated using receiver operating characteristic (ROC) curves. Molecular docking was conducted to evaluate the binding affinity of DOX to the target genes. Functional analysis was performed to investigate potential toxic mechanisms.

In total, 276 differentially expressed genes were identified between DOX-induced cardiotoxicity samples and controls. The support vector machine algorithm demonstrated the best performance, leading to the identification of five core target genes: RAP1A, CTLA4, OR2M1P, TRIM53, and LOC149837. The ROC curves confirmed the strong predictive power of these genes, with area under the curve values greater than 0.85. Molecular docking showed stable binding between DOX and the target genes. Functional analysis suggested that the Rap1 signaling pathway and immune system regulation may be involved in DOX-induced cardiotoxicity.

Traditional toxicological studies often rely on limited experimental approaches that do not fully capture the complexity of disease mechanisms. The integration of microarray analysis, machine learning, and molecular docking in this study offers a comprehensive framework for investigating the toxicological pathways of DOX-induced cardiotoxicity, thereby providing insights into therapeutic development and safety regulations.

By combining microarray analysis, machine learning, and molecular docking, we identified five key target genes associated with DOX-induced cardiotoxicity. Functional analysis further suggested the involvement of the Rap1 signaling pathway and immune system regulation in DOX-induced cardiotoxicity. These findings offer insights into the molecular mechanisms underlying DOX-induced cardiotoxicity and have implications for the development of protective strategies and therapeutic interventions.

Dyshomeostasis of Cu²⁺ and abnormal interactions between Cu²⁺ and β Amyloid peptide (Aβ) can promote Aβ aggregation and oxidative stress, which are considered to trigger Alzheimer’s Disease (AD). Metal chelating therapy is a promising approach for the treatment of AD.

In this study, 2-(2-hydroxyphenyl)benzazoles were synthesized via microwave irradiation promotion. Chelators inhibiting Cu²⁺-induced Aβ aggregation were determined through turbidity assay and BCA protein assay, while anti-oxidants were detected via HRP/Amplex red assay and fluorescent probe of DCFH-DA. Cell viability was measured by MTT assay.

The bio-activity for inhibiting Cu²⁺ induced-Aβ aggregation of chelators S-1, S-3, S-4, S-5, S-7, S-10, N-5, N-9, N-10 O-2, O-4, X-N-2 was better than that of CQ. The ability of the chelators (S-1, S-10, O-2, O-5, N-9, and X-N-2) to decrease the level of ROS in Aβ+Cu²⁺ treated SH-SY5Y cells was better than that of CQ. The ability to attenuate Aβ-mediated cytotoxicity in SH-SY5Y cells of S-10 (O-2, O-5, and N-9) was better than that of CQ.

After the evolution of the bio-activities for the treatment of AD in vitro, it was found that 4 chelators (S-10, O-2, O-5, and N-9) exhibited better bio-activities than CQ in all aspects.

Osteosarcoma (OS) is one of the most common primary malignancies in children and adolescents. Disulfidptosis, a newly identified form of metabolically induced programmed cell death triggered by disulfide stress, has not yet been explored in OS.

We integrated data from public databases and applied a series of bioinformatics approaches, including clustering analysis to classify OS subtypes, and Cox and LASSO regression analysis to identify prognostic disulfidptosis-related genes (DRGs). Enrichment analysis was performed to explore the biological pathways associated with DRG-related molecular subtypes. The immune infiltration landscape was assessed to understand the tumor microenvironment in different risk subgroups. Additionally, drug sensitivity analysis was conducted to evaluate the potential clinical therapeutic strategies of the identified DRG score subgroups. The distribution of DRG expression across OS cell subtypes was further analyzed using single-cell RNA sequencing. In vitro assays, including Western blotting, qRT-PCR, and cell migration and invasion assays, were conducted to validate POLR1D expression and function in OS cells.

We established a DRG-based prognostic model that effectively stratifies OS patients into distinct risk groups with different survival outcomes. The model also revealed significant differences in immune cell infiltration between high and low DRG scores group, suggesting a link between disulfidptosis and the OS immune microenvironment. Drug sensitivity analysis indicated that the DRG signature could guide personalized therapeutic strategies. Single-cell RNA sequencing revealed heterogeneous expression of DRG signature across OS cell subtypes. Functional assays confirmed that POLR1D was aberrantly overexpressed in OS cells and promotes their migration and invasion, supporting its role as a potential oncogenic driver in OS.

Our study is the first to investigate the role of DRGs for risk stratification in OS, providing new insights and targets into OS pathogenesis.