Current Pharmaceutical Biotechnology - Volume 26, Issue 13, 2025
Volume 26, Issue 13, 2025
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Tumor Image Segmentation: A Bibliometric Analysis from 2003 to 2024
More LessAuthors: Zhenghao Chen, Zhongqing Wang and He MaBackgroundBibliometrics has been applied to the study of tumor image segmentation, which can indicate the current research hotspots and trends.
MethodsIn this study, bibliometric analyses were performed on data retrieved from the Web of Science database. A total of 3,377 articles on the application of tumor image segmentation from January 1, 2003, to October 9, 2024, were analyzed for the characteristics of the articles, including the number of yearly publications, country/region, institution, journal, author, keywords, and references. Visualising co-authorship, co-citation, and co-occurrence analysis with VOSviewer.
ResultsThe annual publication volume of tumor image segmentation literature shows that from the first time of more than 100 articles in 2016, the publication volume of literature in this field has surged, reaching 576 articles by 2023. Mainland China is ranked first in terms of publication volume (n=1,356). Saudi Arabia ranks first in average publication year (n=2021.96). IEEE Transactions on Medical Imaging was the journal with the highest average number of citations. The Chinese Academy of Sciences (n=78) was the most prolific institution, while Harvard University was the most prestigious, with a total number of citations and an average number of citations of 3,190 and 213, respectively. In terms of keywords, co-occurrence analysis of 107 keywords with a frequency of more than 30 times produced four clusters: (1) methods of image segmentation, (2) applications of image segmentation, (3) image segmentation modelled on CT, (4) image segmentation modelled on MRI. Transformer, Attention Mechanism, and U-Net are the latest keywords. The analysis of keywords helps scholars understand and identify the current research hotspots and research directions.
ConclusionWithin the last 20 years, the number of articles on the application of tumor image segmentation has increased steadily. From U-Net to MAMBA, many methods for tumor image segmentation have been proposed, and the limitations of models and algorithms are becoming increasingly smaller, which demonstrates the importance of advances in tumor image segmentation technology for disease prevention and monitoring. It presents a strong connection between countries/regions and authors, which reflects the global interest and support for the development of this field. This study shows global trends, research hotspots, and emerging topics in this field and reviews some of the knowledge about tumor image segmentation applications from past studies. And it will provide good research guidelines for researchers in this field.
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CoF-DResNet: Cancer Metastasis Recognition Network based on Dynamic Coordinated Metabolic Attention and Structural Attention
More LessAuthors: Sun Zhu, Huiyan Jiang, Zhaoshuo Diao, Qiu Luan, Yaming Li, Xuena Li and Yan PeiBackgroundCancer metastasis usually means that cancer cells spread to other tissues or organs, and the condition worsens. Identifying whether cancer has metastasized can help doctors infer the progression of a patient's condition and is an essential prerequisite for devising treatment plans. Fluorine 18 fluorodeoxyglucose positron emission tomography/computed tomography (18FDG PET/CT) is an advanced cancer diagnostic imaging technique that provides both metabolic and structural information.
MethodsIn cancer metastasis recognition tasks, effectively integrating metabolic and structural information stands as a key technology to enhance feature representation and recognition performance. This paper proposes a cancer metastasis identification network based on dynamic coordinated metabolic attention and structural attention to address these challenges. Specifically, metabolic and structural features are extracted by incorporating a Dynamic Coordinated Attention Module (DCAM) into two branches of ResNet networks, thereby amalgamating high metabolic spatial information from PET images with texture structure information from CT images, and dynamically adjusting this process through iterations.
DiscussionNext, to improve the efficacy of feature expression, a Multi-Receptive Field Feature Fusion Module (MRFM) is included in order to execute multi-receptive field fusion of semantic features.
ResultsTo validate the effectiveness of our proposed model, experiments were conducted on both a private lung lymph nodes dataset and a public soft tissue sarcomas dataset.
ConclusionThe accuracy of our method reached 76.0% and 75.1% for the two datasets, respectively, demonstrating an improvement of 6.8% and 5.6% compared to ResNet, thus affirming the efficacy of our method.
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Predicting Inhibitor Development in Hemophilia ‘A’ using Machine Learning: A Comprehensive Approach to Data Preprocessing, Balancing, and Biomarker Identification Using AI on the CHAMP Dataset
More LessAuthors: Vikalp Kumar Singh and Maheshwari Prasad SinghBackgroundHemophilia ‘A’ (HA) is a genetic blood disorder characterized by a deficiency of Factor VIII (FVIII), with treatment often triggering the development of neutralizing antibodies (inhibitors) to FVIII. Predicting the development of these inhibitors is crucial for clinical applications but presents significant computational challenges due to data imbalance, skewed data, and inadequate data sanitization.
ObjectivesThis study aimed to develop a machine-learning/AI approach to find biomarkers and predict the development of inhibitors to Factor VIII in patients with Hemophilia ‘A,’ addressing the challenges associated with data imbalance and enhancing prediction accuracy.
MethodsThe data were sanitized and encoded for prediction, and the Random Over-sampling (ROS) technique was employed to resolve data imbalance in the CHAMP dataset. Several machine-learning classification models, including Random Forest, XG Boost, Cat Boost, Logistic Regression, Gradient Boosting, and Light GBM, were utilized. Hyperparameters were tuned using GridSearchCV optimization with a stratified k-fold approach. The performance of the models was evaluated based on accuracy, precision, recall, and F1 scores. The Random Forest model was further analyzed using an explainable AI (XAI) tool known as SHAP (SHapley Additive exPlanations) to identify the variables influencing model performance.
ResultsThe Random Forest model outperformed other classifiers, achieving a mean accuracy of 97.37%, along with closely aligned precision, recall, and F1 scores. The XAI tool SHAP facilitated the ranking of variables Clinical Severity, Variant Type, Exon, HGVS cDNA, hg19 Coordinates, and others according to their impact on the model's predictions. Additionally, the study identified biomarkers associated with FVIII inhibition.
ConclusionThis study presents a breakthrough in the early prediction of inhibitor development in Hemophilia ‘A’ patients, paving the way for personalized and effective treatment programs. The integration of the preprocessing pipeline, Random Forest model, and SHAP analysis offers a novel solution for guiding treatment strategies for HA patients, which could significantly enhance the development of targeted and effective therapies.
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Recent Nanosolutions Based on Green Propolis Presenting Useful Medical Applications
More LessNanotechnology has the potential to offer elegant solutions to problems experienced in the medical field, such as poor drug delivery efficiency and microbial resistance. In this sense, it is interesting to associate nanomaterials with substances that also offer desirable properties to favor human health. Green propolis is an example of a material that contains some of these useful substances. The phenolic acids present in this type of propolis have already been proven to present, for example, antimicrobial, immunostimulant, and antioxidant activities. In this minireview, recent nano solutions based on green propolis, presented through manuscripts recently published, receive attention due to their useful properties in the medical field. Limitations to the clinical use of nanomaterials and the future prospects are also addressed.
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Nerolidol-potential Therapeutic Agent for Various Neurological Disorders via its Antioxidative Property
More LessAuthors: Vibhav Varshney, Prachi Varshney, Abhishek Kumar, Ahsas Goyal and Debapriya GarabaduNeurological disorders are devastating conditions affecting both cognitive and motor-related functions in aged people. Yet there is no proper medication to treat these illnesses, and the currently available medications can only provide symptomatic relief to the patients. All neurological disorders share the same etiology, such as oxidative stress, mitochondrial dysfunction, neurochemical deficiency, neuronal loss, apoptosis, endoplasmic reticulum stress, neuroinflammation, and disease-related protein aggregation. Nowadays, researchers use antioxidant-based strategies to prevent or halt the disease progression. Nerolidol, a strong antioxidant, possesses various biological activities and properties that treat cardiotoxicity, nephrotoxicity, neurotoxicity, and many other diseases. Many recent publications and research studies highlight the beneficial effect of nerolidol on brain disorders. In Alzheimer’s disease, nerolidol shows neuroprotection by decreasing amyloid plaque formation, lipid peroxidation, cholinergic neuronal loss, locomotor dysfunction, neuroinflammation, and hippocampal damage via enhancing antioxidant expression. Also, it shows neuroprotection against rotenone-induced neurotoxicity by inhibiting microglial activation. Another study reported that nerolidol shows antiepileptic effects in animal models by suppressing kindling-induced memory impairment by decreasing oxidative stress. It has been found that NRL administration increases the antioxidant levels, decreasing the pro-inflammatory cytokine release as well as decreasing the apoptotic protein and cerebral infarct size. In conclusion, nerolidol tends to reverse the harmful effects of disease-related factors, including OS, neuroinflammation, protein aggregation, and apoptosis, making nerolidol a choiceable drug for the management of neurological disorders. The purpose of this review is to discuss the mechanism of nerolidol in treating various neurological disorders.
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Tumor Microenvironment: From Cervical Carcinogenesis to Therapeutic Advancements
More LessAuthors: Srishti Sharma, Meenakshi Verma, Indra Rautela, Fahad Khan and Pratibha PandeyCervical cancer has become a major worldwide health concern that demands attention to women's health and often needs more effective and specialized treatment options. Cervical cancer claims the lives of over 300,000 women globally, ranking as the fourth most prevalent cancer among women. The tumor microenvironment (TME) shapes a distinctive landscape for tumor survival, characterized by factors like immunosuppression, hypoxia, acidity, and nutrient scarcity. Comprising tumor cells, immune cells, mesenchymal cells, cancer-associated fibroblasts, and extracellular matrix, the TME reprograms key aspects of tumor development, uncontrolled proliferation, invasion, metastasis, and response to treatments. Recognizing the TME's pivotal role in tumor progression and treatment responsiveness, targeting the TME has emerged as a potential strategy in cancer therapy. This publication delves into recent TME research, offering a comprehensive overview of the specific functions of each TME component in cancer development and progression. Based on the reviewed literature, it appears that women with cervical cancer may benefit from more effective therapy, fewer side effects, and a higher quality of life in the future. By addressing pressing problems and unmet needs in the field, this review has the potential to significantly alter the course of cervical cancer treatment in the future. Furthermore, it outlines the primary therapeutic targets identified by researchers, which may prove valuable in treating tumors.
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Pathophysiological and Clinical Potential of Human Microbiome: Microbe-based Therapeutic Insights
More LessAuthors: Somali Sanyal, Kumud Nigam, Sukriti Singh, Puja Lohani and Manish DwivediThe human microbiota represents the community and diverse population of microbes within the human body, which comprises approximately 100 trillion micro-organisms. They exist in the human gastrointestinal tract and various other organs and are now considered virtual body organs. It is mainly represented by bacteria but also includes viruses, fungi, and protozoa. Although there is a heritable component to the gut microbiota, environmental factors related to diet, drugs, and anthropometry determine the composition of the microbiota. Besides the gastrointestinal tract, the human body also harbours microbial communities in the skin, oral and nasal cavities, and reproductive tract. The current review demonstrates the role of gut microbiota and its involvement in processing food, drugs, and immune responses. The discussion focuses on the implications of human microbiota in developing several diseases, such as gastrointestinal infections, metabolic disorders, malignancies, etc., through symbiotic relationships. The microbial population may vary depending on the pathophysiological condition of an individual and thus may be exploited as a therapeutic and clinical player. Further, we need a more thorough investigation to establish the correlation between microbes and pathophysiology in humans and propose them as potential therapeutic targets.
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Exploring the Arsenal of Novel Antifungal Drug Targets for Combating Fungal Infections
More LessAuthors: Pooja Joshi, Archana Navale, Ajay Shelke and Muskan PatelFungal infections contribute to over 1.5 million fatalities each year, with cutaneous mycoses standing as prominent global infections. The spectrum of these mycoses varies widely, encompassing enduring afflictions like ringworm, localized infections such as tinea capitis, recurrent instances like vaginal candidiasis, and potentially fatal systemic infections impacting multiple organ systems. The escalating recognition of the health and socioeconomic ramifications associated with fungal pathogens underscores their importance in contemporary discourse. On a global scale, projections indicate that over 300 million individuals experience significant fungal infections annually, resulting in a mortality rate exceeding 1.5 million deaths per year. Alarmingly, resistance to commonly used antifungal drugs was on the rise, with some reports suggesting that over 10% of Candida bloodstream isolates worldwide were resistant to fluconazole, a commonly prescribed antifungal medication. Therefore, there is an immediate need to increase the accessibility of new antifungal medications while minimizing their costs and adverse effects. Fungi, as heterotrophic organisms, acquire nutrients through absorption. Their filamentous structure, composed of hyphae, facilitates efficient nutrient uptake by secreting enzymes that break down complex organic matter into simpler compounds. These organisms exhibit remarkable adaptability in responding to environmental cues, adjusting growth rates, and altering morphological features. Fungi regulate their metabolism intricately, undergoing various metabolic pathways for energy production and utilizing diverse substrates for respiration. Additionally, they exhibit distinctive reproductive strategies, employing both sexual and asexual modes of reproduction, contributing to their genetic diversity and resilience in diverse ecosystems. We now have more information than ever on the origins of infection as well as the physiology of fungi cells, giving us the chance to use it to produce new generations of antifungals. This review includes various novel antifungal drug targets showing their possible effects via different mechanisms aiming at vital functions like GPI synthesis, cell wall synthesis, hyphal growth, and other essential pathways responsible for fungal growth.
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Plant Fiber Composites for Biomedical Applications: Advances and Prospective
More LessPlant fibers are strong, robust, flexible, versatile, renewable, and sustainable, making them valuable for many applications. Fibers from plants are now utilized in biomedical appli-cations as reinforcements for biological composites to enhance the mechanical characteristics of composite biological materials including rigidity, tensile strength, and endurance. Reinforcement composites with hybrid components were explored in biodevices for prospective utilization in orthopedics, prosthetics, tissue fabrication, and surgical dressings. This review presents an overview of plant fibers, including their characteristics, influencing variables, and numerous applications. The text explores several methods for creating synthetic composites using common, sustainable fibers and the distinct characteristics of the resulting biological materials. The text also analyses many instances of composite hybrids and their application in the biological field. The results are summarised and suggestions for potential improvements are presented. The current research primarily examines the concept, specifications, efficiency, and potential advancements of composites with hybrid characteristics made from plant fibers.
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Current Approaches on Transfersomal Patch: A Noninvasive Innovative Booster for Improved Transdermal Drug Delivery
More LessPharmaceutical research is increasingly focusing on transdermal drug delivery due to its potential for improved compliance and bioavailability. However, it is challenging due to the tight intracellular junctions present in the skin. Researchers have developed noninvasive methods, like transfersomes, to overcome these challenges. Transfersomes are ultra-deformable vesicles utilized for improved transdermal applications. They are made up of a phospholipid-rich lipid bilayer, an edge activator, and an ethanol/aqueous core. After topical treatment, transfersomes can penetrate deeper skin regions, delivering larger concentrations of active compounds. A transfersomal patch is applied to the skin and left for an extended period of time to allow a large dose of medication to permeate into the bloodstream. The transfersomal patch offers an advantage over the transfersomal gel because it allows the transfersomes to be applied under occlusive conditions, resulting in greater permeability, a lower amount of active medication, and a steady supply rather than a massive dose. This review represents the preparation and evaluation of transfersomal patches, recent research approaches, and future aspects of transfersomal patches. This study suggests that drug-loaded transfersomal patches could be a unique option to avoid invasive therapy.
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Effects of Nicotinamide Mononucleotide Supplementation on Muscle and Liver Functions Among the Middle-aged and Elderly: A Systematic Review and Meta-analysis of Randomized Controlled Trials
More LessAuthors: Jian-peng Wang, Li Wang, Ting Wang, Yi-dan Zhang, Ao-jia Zhou, Zi-ping Wang and Zhang-e XiongIntroductionNicotinamide Mononucleotide (NMN) has gained attention as a precursor to Nicotinamide Adenine Dinucleotide (NAD+) in recent years, commonly utilized in anti-aging therapies. The anti-aging effects of NMN on muscle and liver functions in middle-aged and elderly people are still unclear.
ObjectiveBased on available randomized controlled trials, we conducted a meta-analysis to evaluate the impact of NMN on muscle and liver functions in middle-aged and elderly individuals.
MethodsWe conducted searches on three electronic databases (PubMed, Embase, Web of Science) for randomized controlled trials involving NMN interventions in middle-aged and elderly populations. Through the Cochrane Handbook, we assessed the specific methodological quality. All statistical analyses were obtained by Stata15, and statistical significance was set as P<0.05.
ResultsThere were 412 participants from 9 studies in this meta-analysis. Based on changes in gait speed (SMD: 0.34 m/s, 95%CI [0.03, 0.66] p = 0.033), NMN had significant effects on muscle mass. Moreover, NMN had a better effect on ALT (SMD: -0.29 IU/L, 95%CI [-0.55, -0.03] p = 0.028). Subgroup analysis indicated that administering a small dose of NMN exerted the most prominent impact on Homeostasis Model Assessment-Insulin Resistance (HOMA-IR).
ConclusionNMN has positive efficacy in enhancing muscle function, reducing insulin resistance and lowering aminotransferase levels in middle-aged and elderly individuals. NMN is an encouraging and considerable drug for anti-aging treatment.
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A Comprehensive 4-layered In Silico Pharmacogenomics Analysis of the Genetic Addiction Risk Severity (GARS) Test: Strong Genetic Evidence Supporting GARS as a Novel Personalized Pre-addiction Assessment Tool in the Opioid Crisis Era
More LessAuthors: Alireza Sharafshah, Kai-Uwe Lewandrowski, Igor Elman, David Baron, Panayotis K. Thanos, Colin Hanna, Mark S. Gold, Rajendra D. Badgaiyan, Jean Lud Cadet, Edward J. Modestino, Eric R. Braverman, Catherine A. Dennen, Milan Makale, Keerthy Sunder, Kevin T. Murphy, Abdalla Bowirrat, Albert Pinhasov, Marjorie Gondre-Lewis, Eliot Gardner, Daniel Sipple, Nicole Jafari, Foojan Zeine, Jag Khalsa, Rossano Kepler Alvim Fiorelli and Kenneth BlumBackgroundOverdose involving opioids is the black heart of the addiction crisis. “Pre-addiction,” as an encouraging concept by NIDA and NIAAA, seems best captured with the construct of dopamine dysregulation. Referring to the abundant publications on “Reward Deficiency Syndrome” (RDS), Genetic Addiction Risk Score (GARS) test, RDSQ29, and KB220, Pre-addiction can be referred to as “reward dysregulation” as a suitable suggestion. The hypothesis is that the true phenotype is RDS, and other behavioral disorders are endophenotypes where the genetic variants play important roles, specifically in the Brain Reward Cascade (BRC).
MethodsThis study tested the pharmacogenomics of the GARS panel by a multi-model in silico investigation in four layers: 1) Protein-Protein Interactions (PPIs); 2) Gene Regulatory Networks (GRNs); 3) Disease, drugs and chemicals (DDCs); and 4) Gene Coexpression Networks (GCNs).
ResultsAll in silico findings were combined in an Enrichment Analysis for 59 refined genes, which represented highly significant associations of dopamine pathways in the BRC and supported our hypothesis.
ConclusionThis paper provides scientific evidence for the importance of incorporating GARS as a predictive test to identify Pre-addiction, introduce unique therapeutic targets assisting in the treatment of pain, drug dosing of prescription pharmaceuticals, and identify the risk for subsequent addiction early in -life.
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Volumes & issues
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Volume 26 (2025)
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Volume 25 (2024)
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Volume 24 (2023)
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Volume 23 (2022)
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Volume 22 (2021)
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Volume 21 (2020)
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Volume 20 (2019)
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Volume 19 (2018)
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Volume 18 (2017)
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Volume 17 (2016)
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Volume 16 (2015)
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Volume 15 (2014)
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Volume 14 (2013)
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Volume 13 (2012)
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Volume 12 (2011)
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Volume 11 (2010)
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Volume 10 (2009)
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Volume 9 (2008)
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Volume 8 (2007)
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Volume 7 (2006)
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Volume 6 (2005)
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Volume 5 (2004)
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Volume 4 (2003)
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Volume 3 (2002)
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Volume 2 (2001)
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Volume 1 (2000)
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