Current Analytical Chemistry - Online First

Brexpiprazole (BXP) is a novel dopamine and serotonin partial agonist used for the treatment of schizophrenia. It has recently gained approval for the treatment of agitation associated with dementia due to Alzheimer’s disease. This study aimed to enhance the fluorescence intensity of BXP by turning “OFF” its photoinduced electron transfer and employing the enhanced fluorescence in establishing two analytical platforms for the direct quantification of BXP in commercial pharmaceutical tablets and plasma.

Two analytical methods were developed: a microwell spectrofluorimetric assay combined with a fluorescence microplate reader (MW-SFA) and flow injection analysis coupled with a fluorescence detector (FIA-FD). Both platforms underwent optimization and validation.

The linear ranges of the platforms were 5–500 for MW-SFA and 20–2000 ng/mL for FIA-FD. The limits of quantification were 12.9 and 25.5 ng/mL for MW-SFA and FIA-FD, respectively. Both platforms showed high precision and accuracy, with relative standard deviation (RSD) values ranging from 1.2% to 1.9% and recovery values ranging from of 98.5% to 102.4%. The proposed platforms were successfully applied to the analysis of BXP in tablets, and the recovery values ranged from 99.2% to 101.4% with RSD values ranged from 1.1% to 1.8%. The suggested platforms were also employed for analyzing plasma samples containing BXP, achieving an accuracy of at least 98.6%. The greenness levels of both platforms were confirmed using three metric tools.

The enhancement of native fluorescence of BXP resulted in high sensitivity of both MW-SFA and FIA-FD platforms. The employment of microwell and flow injection approaches enhanced the accuracy and precision of both platforms. Additionally, both platforms offered a high-throughput, cost-effective, and green analytical approach.

The proposed platforms feature simple procedures, high sensitivity, high throughput, and environmental greenness. The platforms are highly recommended as effective tools for BXP determination in pharmaceutical quality control and clinical laboratories.

This study aimed to address persistent challenges in pseudotargeted metabolomics, particularly the limited compatibility with diverse sample types, by developing an enhanced method integrating the strengths of targeted and untargeted approaches.

An upgraded pseudotargeted metabolomics method was developed, incorporating a sample-specific MS-RI library (SSMSRIL) to identify novel metabolites in new samples. Newly discovered metabolites were dynamically added to a pseudotargeted MRM list. Additionally, MRM transitions for 227 target metabolites were integrated, resulting in a final method monitoring >500 metabolites. This design facilitates the extraction and addition of new metabolites to the monitoring list. The method was established and evaluated using gas chromatography-tandem mass spectrometry (GC-MS/MS).

Evaluation with new samples revealed that 33-40% of all detected metabolites were identified exclusively through the integrated targeted MRM transitions. This demonstrated their significant role in expanding metabolite coverage. Furthermore, 23-54% of metabolites detected in new sample types were absent from the initial SSMSRIL list.

The substantial proportion (23-54%) of metabolites detected in new sample types missing from the original library underscores the critical necessity of dynamically updating the pseudotargeted MRM list when applying the method to new samples. This update mechanism is vital for maintaining broad metabolite coverage and method applicability across diverse sample matrices.

The enhanced pseudotargeted method significantly improves metabolite coverage and adaptability to new sample types through dynamic MRM list updating and the integration of targeted MRM transitions. While developed using GC-MS/MS, the core concept is readily transferable to liquid chromatography (LC)-based full-scan and MRM methodologies, broadening its potential impact.

The development of sustainable energy systems critically depends on efficient hydrogen storage materials. Conventional physisorption frameworks, such as MOFs and COFs, are constrained by weak binding energies and limited tunability, necessitating new molecular platforms with superior adsorption characteristics.

We employed density functional theory (DFT) calculations with dispersion corrections to investigate the strategic functionalization of 2D hexaazatriphenylene (HAT), a nitrogen-rich, π-conjugated macrocycle, as a next-generation hydrogen storage platform. Various substituents, including electron-donating (−Bpin, −OH), electron-withdrawing (−NO2, −COH), and ambiphilic (−SF5, −SO3H) groups, were systematically incorporated to modulate adsorption properties.

Our findings reveal that functionalized HAT derivatives synergize van der Waals interactions, quadrupole coupling, and substituent-induced polarization, achieving hydrogen binding energies of up to −8.6 kJ·mol⁻¹, approaching the U.S. DOE target of 15 kJ·mol⁻¹ for physisorption-based materials. The HAT-PB (−Bpin) derivative exhibited optimal non-dissociative physisorption, maintaining an H–H bond length of 0.74 Å, minimal charge transfer (−0.041 e), and tunable HOMO-LUMO gaps (6.4–7.2 eV).

This work closes a significant research gap by presenting the first systematic study of HAT derivatives for hydrogen storage. The nitrogen-enriched core of HAT enhances charge localization, while substituent engineering provides precise control over adsorption energy and reversibility.

Our computational insights establish functionalized HAT frameworks as promising candidates for reversible hydrogen storage, offering a design blueprint that could accelerate the development of lightweight, high-capacity energy materials.

Calix[n]arenes can be selectively functionalized more easily than other cyclic supramolecules such as crown ethers and cyclodextrins. 1,8-Naphthalimide derivatives of calix[4]arenes are very important and versatile fluorophores widely used in fluorescence detection due to their high absorption coefficients, strong fluorescence, high quantum yields, large Stokes shifts, excellent photostability.

Two Calix[4]arene-based fluorescent probes were synthesized for the detection of Cu²⁺ and F^-. The fluorescence properties of the compounds were evaluated in the presence of various cations and anions using spectroscopic techniques. Their cytotoxicity was tested in Hep-2 and HeLa living cells.

The synthesized calix[4]arene derivatives were evaluated in the presence of various cations and anions using different spectroscopic and analytical techniques. In the tests, the fluorescence intensity of 7 decreased significantly in the presence of Cu²⁺ or F^-. A similar quenching effect was observed for 6 only in the presence of Cu²⁺. The cytotoxic properties of calix[4]arene derivatives were evaluated against Hep-2 and HeLa living cells. The results showed significant cytotoxicity of compounds 6 and 7.

Confocal microscopy studies revealed that these compounds exhibited strong intracellular fluorescence, indicating efficient cellular uptake by cells. In the presence of Cu²⁺ or F^-, the fluorescence intensity of the diamide derivative decreased significantly, which was attributed to the complexation effect between the ligand and the corresponding ions.

Monoamide derivative 6 has the potential to be a fluorescent sensor for Cu²⁺, and diamide derivative is a fluorescent sensor for Cu²⁺ and F^- ions. Cells treated with compounds 6 or 7 exhibited high intracellular fluorescence.

Transition metal oxides are highly effective for dye removal due to their adaptable surface chemistry, availability, and robust mechanical and thermal properties. Among them, Bi4V2O11 nanosheets offer potential due to their morphology and high surface area. To synthesize Bi4V2O11 nanosheets and evaluate their performance in removing malachite green, focusing on the impact of adsorbent amount, contact time, and interfering ions on dye removal efficiency. This study aims to evaluate the performance of Bi4V2O11 nanosheets in removing malachite green dye from aqueous solutions and to examine the effects of adsorbent dosage, contact time, and the presence of coexisting ions on the sorption process.

Bi4V2O11 nanosheets were synthesized and used to remove the malachite green dye. Batch experiments were conducted to study the effects of adsorbent dose, contact time, and coexisting ions (Na⁺, Ca²⁺, and tannic acid). Equilibrium data were fitted to the Langmuir isotherm model to determine the maximum sorption capacity.

Malachite green sorption using Bi4V2O11 nanosheets reached equilibrium within 240 minutes, demonstrating efficient dye removal under the tested conditions. The sorption process conformed to the Langmuir isotherm model, indicating monolayer adsorption with a maximum capacity of 47.27 mg/g. While the presence of Na⁺ and tannic acid had negligible effects on dye removal efficiency, the coexistence of Ca²⁺ significantly reduced the removal efficiency from 95% to 58%, highlighting the potential impact of divalent ions on the adsorption process.

Bi4V2O11 nanosheets are effective adsorbents for malachite green removal, demonstrating high sorption capacity and resilience to Na⁺ and tannic acid interference. However, the presence of Ca²⁺ significantly reduces removal efficiency, highlighting the need for further optimization in practical applications.

Bi4V2O11 nanosheets offer high sorption capacity and structural stability, environment friendly synthesis procedure, making them feasible for small-scale or pre-treatment applications.

Air pollution is a pressing global health and environmental issue, particularly in urban areas where both natural and human-induced factors contribute to deteriorating air quality. While extensive data exists for large industrial cities, less is known about pollution dynamics in smaller, mountainous urban centers. Dushanbe, the capital of Tajikistan, represents a unique setting where seasonal weather patterns and limited industrial activity intersect. This study aims to analyze the air quality in Dushanbe, Tajikistan, by examining the presence of various pollutants over a five-year period (2017-2021).

A longitudinal observational study was conducted using automated air sampling systems at a central monitoring site. Chemical composition of aerosol for metals as well as carbon, sulfur dioxide (SO2), Nitrogen monoxide (NO), Nitrogen dioxide (NO2) and carbon monoxide – (CO) in the atmospheric air of the city of Dushanbe in the period from 2017 to 2021 was analyzed. Atmospheric aerosol samples (C µg/m3, C max, C min) for all metals were determined. Constant monitoring of gas content in the surface layer of the atmosphere was carried out on the territory of the Agency for Hydrometeorology of the Republic of Tajikistan. Descriptive statistics were used to analyze seasonal and annual trends in pollutant levels.

Metals such as Titanium (Ti), Vanadium (V), Chromium (Cr), Manganese (Mg), Iron (Fe), Cobalt (Co), Nickel (Ni) and Copper (Cu) were also detected, which we found in plant organs. These results are in favor of the weather pollution caused by titanium and iron. Average monthly levels of CO, SO2, and NO2 in the atmospheric air of the city of Dushanbe in the period from 2017 to 2021 showed that the variation of air pollution varied according to the seasons and months for each year.

Air quality in Dushanbe is influenced more by geographic and seasonal factors rather than heavy industrial activity. Temporary exceedances in CO and NO2 levels reflect localized emissions from traffic and heating. The absence of toxic heavy metals indicates a relatively low long-term health risk from industrial pollution.

This study provides a comprehensive analysis of Dushanbe’s air quality, demonstrating generally good conditions with short-lived pollution events. Targeted seasonal interventions—such as improved traffic and heating emission controls—are recommended to mitigate transient exposure risks.

Polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs) are persistent organic pollutants (POPs) with known toxicity and bioaccumulation potential. Smoked meat, a widely consumed food, has been identified as a major dietary source of these contaminants as they are generated due to the incomplete combustion of fuels used in the smoking process. This review examines existing studies on the occurrence of PAHs, PCBs, and PCDD/Fs in smoked meat, with particular attention to the influence of smoking conditions. Factors such as smoking methods, temperature, fuel type, and co-combustion materials, including plastics and chlorine-containing compounds, are analyzed for their role in the formation and accumulation of these pollutants.

A literature search across databases including PubMed, Scopus, ScienceDirect, and Google Scholar for studies published (2010 – 2024) identified relevant studies based on predefined inclusion criteria emphasizing POP levels, formation mechanisms, and analytical methods in smoked meat and related products. Key data were synthesized thematically to identify research trends and gaps.

PAHs have been the most extensively studied in smoked meat, whereas research on PCBs and PCDD/Fs remains limited despite their toxicological significance. The smoking process, particularly the type of fuel and additional materials used, plays a crucial role in the generation of these contaminants. Enhanced analytical techniques have improved detection capabilities, supporting more accurate risk assessments.

Traditional smoking methods are linked to higher POP contamination, especially with chlorine-rich or plastic-containing fuels. Despite advances in analytical techniques, gaps remain in standardizing methods and understanding halogenated POP formation, underscoring the need for harmonized protocols and targeted research on PCBs and PCDD/Fs under practical conditions.

Significant knowledge gaps remain, emphasizing the need for further research to refine smoking practices and enhance food safety standards while preserving the cultural and culinary value of smoked foods.

The chemical composition of Ophiopogonis Radix originating from different districts is somewhat different due to the different planting environment. This difference affects the pharmaceutical efficacy of Ophiopogonis Radix, and therefore poses a challenge to the quality assessment and control of Ophiopogonis Radix. This study aims to develop a method to discriminate Ophiopogonis Radix from various planting districts by using infrared (IR) spectroscopy in combination with a one-dimensional convolutional neural network.

The spectral data of Ophiopogonis Radix samples from five planting districts were collected and preprocessed with Savizkg-Golag (SG) smoothing and the Standard Normal Variate (SNV) algorithm. Back propagation (BP) artificial neural network (ANN) and one-dimensional convolutional neural network (1D-CNN) were employed to build the pattern recognition model for discriminating the investigated Ophiopogonis Radix samples.

The discriminant accuracy of the ANN model after SG and SNV preprocessing reached 94.42% and 96.98% respectively. The discrimination accuracy of the 1D-CNN model after SG and SNV preprocessing reached 98.67% and 99.33%, respectively. It is demonstrated that both the ANN model and the 1D-CNN model are able to discriminate Ophiopogonis Radix samples from different planting districts. Moreover, the developed 1D-CNN model shows higher discrimination accuracy than the ANN model, and SNV preprocessing is better than SG preprocessing for improving the accuracy of the discrimination model.

In this study, the classification performance differences between the SNV-ANN and SNV-CNN models were further compared and analyzed through confusion matrices. The SNV-CNN model exhibited superior classification performance compared to the SNV-ANN model.

IR coupled with 1D-CNN is a practicable and promising approach to discriminate Ophiopogonis Radix from different planting districts. Besides Ophiopogonis Radix, this approach could be used for the planting districts discrimination of other kinds of Traditional Chinese Medicines (TCM). As a high-throughput and data-driven spectroscopic analysis approach, it can be applied to the standardized quality assessment of TCM.

Diabetes clinical guidelines have begun to emphasize the management of cognitive dysfunction in diabetes. Moreover, several studies have shown that long non-coding RNAs (lncRNAs) play critical roles in human diseases. However, no studies have elucidated whether lncRNAs are involved in the pathogenesis of diabetes-associated cognitive dysfunction (DACD).

The hippocampi of male mice with homozygous leptin receptor-deficient T2DM and their littermates were analyzed via high-throughput sequencing. RNA and protein sequencing data were utilized to identify differentially expressed (DE) mRNAs, lncRNAs, and proteins between diabetes mellitus and control groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for DE-mRNAs-DE-proteins. Subsequently, lncRNA-mRNA-pathway and lncRNA-miRNA-mRNA networks were constructed. Quantitative real-time PCR (qRT-PCR) was conducted to verify the expression trends of key lncRNAs in DACD.

We identified upregulated and downregulated mRNAs, lncRNAs, and proteins in the diabetes group. Eleven DE-mRNAs-DE-proteins were associated with inflammatory response, lipid/steroid metabolism, and cell growth. The lncRNA-mRNA-pathway network contained 36 lncRNA-mRNA pairs linked to 8 KEGG pathways. Twenty-five lncRNAs corresponding to Apoa1 were identified as key candidates. Subcellular localization and ceRNA network analyses suggested that lncRNA 3110039I08Rik and lncRNA Gm36445 may regulate DACD. qRT-PCR confirmed their increased expression in diabetic mice.

Our findings reveal that lncRNA 3110039108Rik and Gm36445 may act as critical regulators in DACD pathogenesis through competing endogenous RNA (ceRNA) networks. These lncRNAs represent potential biomarkers for early diagnosis and therapeutic targets, offering new insights into the management of cognitive dysfunction in diabetes.

Our results identified two novel lncRNAs that may play critical roles in DACD.

In Traditional Chinese Medicine (TCM), BSS refers to impaired circulation or stagnation of blood flow and formation of bruises. The primary therapeutic strategy to treat BSS involves invigorating blood circulation. PRR is a widely used TCM herb for treating acute and critical diseases caused by BSS. However, the anticoagulant effects of different compounds of PRR on BSS remain elusive. The aim of the study was to investigate the pharmacological role of different chemical constituents of Paeoniae Radix Rubra (PRR) in the modulation of anticoagulation in Blood Stasis Syndrome (BSS). This study aimed to analyze the therapeutic effect of PRR on BSS and to assess the ameliorative effect of different chemical constituents of PRR on blood circulation, clotting time, and platelet aggregation in rats with acute BSS.

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to screen the targets of PRR, and genes causing BSS were predicted using PharmGKB, OMIM, and TTD databases. Intersected genes between PRR and BSS targets were visualized in Venn diagrams. Core target networks of Protein-Protein Interaction (PPI) and cross-targets were constructed using Cytoscape 3.7.1, and the cross-targets were enriched using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Furthermore, the effects of PRR on platelet aggregation, plasma viscosity, and whole blood viscosity in the rats with BSS were examined by blood rheology and other methods. The serum levels of Endothelin-1 (ET-1), Nitric Oxide (NO), Thromboxaneb2 (TXB2), and 6-keto-Prostaglandin F1 α (6-keto-PGF1α) in the rats were measured by the Enzyme-Linked Immunosorbent Assay (ELISA) method.

The main active compounds of PRR, including total glycosides, flavonoids, and polysaccharides, were identified using the TCMSP database. A total of 31 cross-targets were obtained from the intersection between 129 active targets of PRR and 345 causative genes of BSS. PPI network identified genes such as Albumin (ALB), SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase (SRC), and AKT Serine/Threonine Kinase 1 (AKT1) as the core targets of PRR in alleviating BSS. Enrichment analysis showed that the common targets were mainly associated with several biological processes, including lipid and atherosclerosis, adherens junction, and focal adhesion. Following the intervention with PRR extract, the whole blood viscosity and plasma viscosity were reduced, and platelet aggregation was inhibited in the model rats in comparison to the model group. Moreover, PRR treatment also promoted thrombin time (TT), prothrombin time (PT), and Activated Partial Thromboplastin Time (APTT), increased the level of NO and 6-keto-PGF1α, but reduced the level of Fibrinogen content (FIB) and ET-1 and TXB2 in the serum of the model rats.

The present research systematically explored the anticoagulant effect of the chemical constituents of PRR on BSS in rats, applying network pharmacology analysis.

The current findings provided a theoretical foundation for the pharmacological basis of using PRR in the management of BSS.