J Med Internet Res. 2025 Aug 11;27:e71786. doi: 10.2196/71786.

ABSTRACT

BACKGROUND: The seasonal influenza epidemic poses a persistent and severe threat to global public health. Web-based search data are recognized as a valuable source for forecasting influenza or other respiratory tract infection epidemics. Current influenza prediction studies typically focus on seasonal trends in traditional monitoring data, neglecting the sensitivity of different web-based search terms to seasonal changes, thereby increasing prediction challenges.

OBJECTIVE: The aim of this study was to propose a deep learning framework for different influenza epidemic states based on Baidu index and percentage of influenza-like illness (ILI%).

METHODS: Official weekly ILI% data from 2013 to 2024 were extracted from the Chinese National Notifiable Infectious Disease Reporting System (NIDRIS). Based on the Baidu index, influenza-related search indexes were acquired for the corresponding time periods. To explore the association between influenza-related search queries and ILI%, the study conducted a cross-correlation analysis. The study period was divided into influenza epidemic and nonepidemic period. The study finally used the convolutional long short-term memory (CLSTM) network framework to predict influenza epidemics with 1-3 weeks ahead for the all-time period and epidemic + nonepidemic period. The evaluation metrics included model stability metric, accuracy metrics, and explanatory power metric.

RESULTS: The ILI% presented a regular seasonal high incidence in China. Meanwhile, the prediction of ILI% after dividing the epidemic and nonepidemic seasons (mean absolute percentage error [MAPE]=10.730%, mean square error [MSE]=0.884, mean absolute error [MAE]=0.649, root-mean-square error [RMSE]=0.940, and R2=0.877) was better than that of the all-time period (MAPE=12.784%, MSE=1.513, MAE=0.744, RMSE=1.230, and R2=0.786). In addition, we found that the ILI% + Baidu search index predicts better than only the ILI% regardless of the time period and lag time of the study. Comparative analysis with long short-term memory (LSTM) and transformer models demonstrated that CLSTM achieved superior performance in 1 week-ahead ILI% predictions using ILI% + Baidu index data in epidemic + nonepidemic period (MAPE=11.824%, MSE=1.243, MAE=0.723, RMSE=1.115, and R2=0.827). Furthermore, CLSTM comprehensively surpasses LSTM in computational efficiency, complexity, extrapolation capability, and stability while partially outperforming transformer models.

CONCLUSIONS: This study shows strong potential for influenza prediction by combining Baidu index data with traditional surveillance and specific keywords for epidemic and nonepidemic seasons. It provides a new perspective for public health preparedness. This research is expected to support early warning systems for influenza and other diseases. Future work will further optimize these models for more timely and accurate predictions, enhancing public health responses.

PMID:40789146 | DOI:10.2196/71786

J Chem Inf Model. 2025 Aug 11. doi: 10.1021/acs.jcim.5c01030. Online ahead of print.

ABSTRACT

Noncanonical amino acids (NCAAs) occupy an important place, both in natural biology and in synthetic applications. However, modeling these amino acids still lies outside the capabilities of most deep learning methods due to sparse training data sets for this task. Instead, biophysical methods such as Rosetta can excel in modeling NCAAs. We discuss the various aspects of parametrizing an NCAA for use in Rosetta, identifying rotamer distribution modeling as one of the most impactful factors of NCAA parametrization on Rosetta performance. To this end, we also present FakeRotLib, a method that uses statistical fitting of small-molecule conformers to create rotamer distributions. We find that FakeRotLib outperforms existing methods in a fraction of the time and is able to parametrize NCAA types previously unmodeled by Rosetta.

PMID:40789114 | DOI:10.1021/acs.jcim.5c01030

Med Ultrason. 2025 Jul 29. doi: 10.11152/mu-4535. Online ahead of print.

ABSTRACT

AIMS: A computer-aided diagnosis (CAD) system for automated evaluation of developmental dysplasia of the hip (DDH) via ultrasound, integrating Deep Learning (DL) for anatomical segmentation and performing α&β angle calculations utilizing the Graf Method is presented. A custom image processing method excludes the inferior ilium's curvature during the baseline definition, enhancing accuracy and replicating radiologists' real-world workflow.

MATERIALS AND METHODS: Our dataset comprised 452 raw images from 370 newborns. For {'validation'+"test"}, {'nv=91'+"nte=45"}≡136 images were reserved (never augmented). Remaining 316 images were augmented to ntr=632 with (0%↔25%) random brightness manipulation for training. Totally (632+136)=768 images were annotated and split with the following true numbers and percentage: {'train',"validation",test}≡{'632',"91",45}≡{'82%',"12%",6%}. U-Net, MaskR-CNN, YOLOv8 and YOLOv11 were used for segmentation. α&β were measured using Method-I (centroid/orientation) and Method-II (Hough transform). An extended set of performance metrics-Precision, Recall, IoU, Dice, mAP-was calculated. Bland-Altman and Intraclass Correlation Coefficient (ICC) analyses compared CAD outputs with expert measurements.

RESULTS: YOLOv11 showed the best segmentation performance (Precision:0.990, Recall:0.993, IoU:0.983, Dice:0.990, mAP:0.991). {ICCα, ICCβ} calculated using Method-I and Method-II were {0.895, 0.907} and {0.929, 0.952}, respectively, with Method-II outperforming Method-I.

CONCLUSION: A clinically-aligned-CAD-system that integrates anatomical segmentation and α&β measurement-a combination rarely addressed in literature is introduced. By providing a comprehensive and standardized set of metrics, this work overcomes a common bottleneck in DL studies, namely heterogeneity in metric reporting, enabling better cross-study comparisons. Following curvature exclusion, obtained ICCs outperformed previous studies, demonstrating improved inter-rater reliability and strong agreement with expert radiologists, offering both technical robustness and clinical applicability in DDH assessment.

PMID:40789016 | DOI:10.11152/mu-4535

PLoS One. 2025 Aug 11;20(8):e0327782. doi: 10.1371/journal.pone.0327782. eCollection 2025.

ABSTRACT

BACKGROUND: Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potential for misdiagnosis. Additionally, deep learning models for tumor classification often require large amounts of labeled data, which can be costly and time-consuming to obtain, especially in the medical domain.

OBJECTIVE: Our main aim is to enhance Meningioma Tumor Classification Accuracy.

METHOD: This study proposes a multi-task learning (MTL) approach to enhance the accuracy of meningioma tumor classification while mitigating the need for excessive labeled data. The primary task involves classifying meningioma tumors based on MRI imaging data, while auxiliary tasks leverage patient demographic information, such as age and gender. By incorporating these additional data sources into the learning process, the proposed MTL framework leverages the interdependencies among multiple tasks to improve overall prediction accuracy. The study evaluates the performance of the MTL approach using a dataset of 2218 brain MRI images from 34 patients diagnosed with meningioma, obtained from the Mahdia Imaging Center in Hamadan, Iran.

RESULTS: Results demonstrate that the MTL model significantly outperforms single-task learning baselines, achieving 99.6% ± 0.2 accuracy on the test data in 95% confidence interval.

DISCUSSION: This highlights the efficacy of the proposed approach in enhancing meningioma tumor classification and its potential for aiding clinical decision-making and personalized treatment planning.

CONCLUSION: Our proposed method can be used in computer-aided diagnosis systems.

PMID:40788922 | DOI:10.1371/journal.pone.0327782

PLoS One. 2025 Aug 11;20(8):e0327577. doi: 10.1371/journal.pone.0327577. eCollection 2025.

ABSTRACT

Assessing the escalating biodiversity crisis, driven by climate change, habitat destruction, and exploitation, necessitates efficient monitoring strategies to assess species presence and abundance across diverse habitats. Video-based surveys using remote cameras are a promising, non-invasive way to collect valuable data in various environments. Yet, the analysis of recorded videos remains challenging due to time and expertise constraints. Recent advances in deep learning models have enhanced image processing capabilities in both object detection and classification. However, the impacts on models' performances and usage on assessment of biodiversity metrics on videos is yet to be assessed. This study evaluates the impacts of video processing rates, detection and identification model performance, and post-processing algorithms on the accuracy of biodiversity metrics, using simulated remote videos of fish communities and 14,406 simulated automated processing pipelines. We found that a processing rate of one image per second minimizes errors while ensuring detection of all species. However, even near-perfect detection (both recall and precision of 0.99) and identification (accuracy of 0.99) models resulted in overestimation of total abundance, species richness and species diversity due to false positives. We reveal that post-processing model outputs using a confidence threshold approach (i.e., to discard most erroneous predictions while also discarding a smaller proportion of correct predictions) is the most efficient method to accurately estimate biodiversity from videos.

PMID:40788894 | DOI:10.1371/journal.pone.0327577

IEEE J Biomed Health Inform. 2025 Aug 11;PP. doi: 10.1109/JBHI.2025.3597589. Online ahead of print.

ABSTRACT

Long non-coding RNAs (lncRNAs) play essential roles in various biological processes, including chromatin modification, cell cycle regulation, transcription, and translation. Recent studies have revealed that the biological functions of lncRNAs are closely associated with their subcellular localizations, making accurate localization prediction critical for understanding their biological roles in cellular regulation and disease mechanisms. However, most existing methods mainly rely on sequence features while neglecting structural information, and they are often limited to single-label predictions covering only a small number of subcellular compartments. In this study, we proposed an efficient deep learning framework, LncTracker, for multi-label prediction of lncRNA subcellular localizations across seven distinct compartments. LncTracker adopts a multi-channel architecture that integrates diverse input features into model training, including both primary sequence and secondary structure information. Secondary structures are converted into attributed graphs to capture spatial relationships among nucleotides, including adjacency and base-pairing connections. These structural features are then combined with sequence-based features to predict subcellular localization probabilities. Such a design enables LncTracker to learn joint representations of sequences and structures, thereby enhancing predictive performance and robustness. Benchmarking experiments demonstrated the superiority of LncTracker over state-of-the-art approaches, particularly in handling imbalanced localization scenarios. Furthermore, we leveraged LncTracker to identify sequence motifs critical for each subcellular localization and analysed key sub-structures contributing to predictions. The codes are provided on GitHub https://github.com/ABILiLab/LncTracker. To enhance the usability of LncTracker, we developed a web server that is publicly accessible at http://lnctracker.biotools.bio.

PMID:40788811 | DOI:10.1109/JBHI.2025.3597589

IEEE J Biomed Health Inform. 2025 Aug 11;PP. doi: 10.1109/JBHI.2025.3597467. Online ahead of print.

ABSTRACT

Osteoporosis poses a significant global public health challenge, and timely detection and treatment are crucial for preventing fragility fractures in the elderly. However, opportunistic screening remains challenging. Despite rapid deep learning development, its potential in clinical data classification has yet to be fully realized, with traditional machine learning dominating. Therefore, deepening research on deep learning for clinical data recognition in osteoporosis screening holds practical significance. This study utilizes the latest artificial intelligence technology to develop the OPDoctorNet algorithm, combining Transformer and Mamba feature extraction advantages, innovatively proposing multiscale feature fusion and the FeatureBake Block to deeply extract global and local features. The algorithm improves osteoporosis recognition accuracy in clinical data and meets multitask needs. Results show OPDoctorNet significantly outperforms traditional machine learning and other AI methods in accuracy, recall, and F1 scores, with strong robustness and generalization. Through the Innovation of the FeatureBake Block, this study provides a groundbreaking solution for Transformer and Mamba feature processing, enabling efficient, accurate opportunistic osteoporosis screening. Additionally, using SHAP Plot and feature importance mapping for visual analysis enhances interpretability, offering new ideas and methods for osteoporosis screening in clinical practice, aiding accurate, scientific clinical decision-making and promoting deep learning application in clinical data classification.

PMID:40788810 | DOI:10.1109/JBHI.2025.3597467

IEEE J Biomed Health Inform. 2025 Aug 11;PP. doi: 10.1109/JBHI.2025.3597054. Online ahead of print.

ABSTRACT

Monitoring of advanced cancer patients' health, treatment, and supportive care is essential for improving cancer survival outcomes. Traditionally, oncology has relied on clinical metrics such as survival rates, time to disease progression, and clinician-assessed toxicities. In recent years, patient-reported outcome measures (PROMs) have provided a complementary perspective, offering insights into patients' health-related quality of life (HRQoL). However, collecting PROMs consistently requires frequent clinical assessments, creating important logistical challenges. Wearable devices combined with artificial intelligence (AI) present an innovative solution for continuous, real-time HRQoL monitoring. While deep learning models effectively capture temporal patterns in physiological data, most existing approaches are unimodal, limiting their ability to address patient heterogeneity and complexity. This study introduces a multimodal deep learning approach to estimate HRQoL in advanced cancer patients. Physiological data, such as heart rate and sleep quality collected via wearable devices, are analyzed using a hybrid model combining convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks with an attention mechanism. The BiLSTM extracts temporal dynamics, while the attention mechanism highlights key features, and CNNs detect localized patterns. PROMs, including the Hospital Anxiety and Depression Scale (HADS) and the Integrated Palliative Care Outcome Scale (IPOS), are processed through a parallel neural network before being integrated into the physiological data pipeline. The proposed model was validated with data from 204 patients over 42 days, achieving a mean absolute percentage error (MAPE) of 0.24 in HRQoL prediction. These results demonstrate the potential of combining wearable data and PROMs to improve advanced cancer care.

PMID:40788808 | DOI:10.1109/JBHI.2025.3597054

Heart Lung. 2025 Aug 10;74:266-275. doi: 10.1016/j.hrtlng.2025.08.002. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with idiopathic pulmonary fibrosis (IPF) undergoing lung cancer surgery face a 4.4-20 % risk of acute exacerbation (AE-IPF) with mortality exceeding 50 %. The potential role of perioperative antifibrotic therapy in reducing surgical complications in this high-risk population remains unclear.

OBJECTIVES: To evaluate whether perioperative antifibrotic therapy (pirfenidone/nintedanib) reduces complications, particularly acute exacerbations and mortality, in IPF patients undergoing lung cancer surgery through systematic review and meta-analysis.

METHODS: Following PRISMA guidelines, we conducted a systematic review and meta-analysis of observational studies examining perioperative antifibrotic therapy in IPF patients undergoing lung cancer surgery. Four studies comprising 261 patients (124 treated, 137 controls) from Japan and Italy (2016-2024) were analyzed. Pooled risk ratios were calculated using Review Manager 5.4. The study protocol was registered with PROSPERO (ID: CRD42025649005).

RESULTS: Perioperative antifibrotic therapy achieved a 69 % reduction in AE-IPF risk (RR 0.31, 95 % CI 0.13-0.70) and an 81 % reduction in 90-day mortality (RR 0.19, 95 % CI 0.07-0.52). Additional benefits included significantly shorter hospital stays (5 vs 7 days, p = 0.029) and reduced complications, including decreased prolonged air leak rates (3.4 % vs 26.9 %). Adverse events were minimal, consisting primarily of mild nausea and photosensitivity.

CONCLUSIONS: Perioperative antifibrotic therapy significantly reduces acute exacerbations and mortality in IPF patients undergoing lung cancer surgery. However, findings are limited by small observational studies concentrated in specific geographic regions. Randomized controlled trials are needed to confirm efficacy and establish standardized treatment protocols.

PMID:40789229 | DOI:10.1016/j.hrtlng.2025.08.002

J Vis Exp. 2025 Jul 22;(221). doi: 10.3791/67845.

ABSTRACT

Pulmonary fibrosis is characterized by irreversible destruction of alveolar structure and excessive deposition of extracellular matrix. Although animal models have been widely used in pulmonary fibrosis research, none of the currently available models fully recapitulate the progressive nature of IPF or its defining histological feature, such as fibroblastic foci. Advanced in vitro models, including precision-cut lung slices (PCLS), are often considered the most physiologically relevant pulmonary test system and have been successfully employed for drug screening. Nevertheless, the inability to differentiate the degree of fibrosis in the IPF lung has resulted in blinding and uncertainty in the PCLS obtained. Previous research demonstrated that fibroblast activation protein (FAP) could evaluate the pro-fibrotic activity of ILD, potentially contributing to early diagnosis and the selection of appropriate therapeutic windows. In this study, 600 µm PCLS will be obtained from healthy donors and IPF patients using a shock slicer and evaluated using molecular probes targeting FAP to determine the degree of fibroblast activity based on fluorescent signal intensity. This approach provides an advanced in vitro model and evaluation technique for pulmonary fibrosis research, enhancing the ability to study disease mechanisms and assess therapeutic interventions.

PMID:40788938 | DOI:10.3791/67845

Mol Oncol. 2025 Aug 11. doi: 10.1002/1878-0261.70046. Online ahead of print.

ABSTRACT

Acute lymphoblastic leukemia (ALL), the most common cancer in children, is overall divided into two subtypes, B-cell precursor ALL (B-ALL) and T-cell ALL (T-ALL), which have different molecular characteristics. Despite massive progress in understanding the disease trajectories of ALL, ALL remains a major cause of death in children. Thus, further research exploring the biological foundations of ALL is essential. Here, we examined the diagnostic, prognostic, and therapeutic potential of gene expression data in pediatric patients with ALL. We discovered a subset of expression markers differentiating B- and T-ALL: CCN2, VPREB3, NDST3, EBF1, RN7SKP185, RN7SKP291, SNORA73B, RN7SKP255, SNORA74A, RN7SKP48, RN7SKP80, LINC00114, a novel gene (ENSG00000227706), and 7SK. The expression level of these markers all demonstrated significant effects on patient survival, comparing the two subtypes. We also discovered four expression subgroups in the expression data with eight genes driving separation between two of these predicted subgroups. A subset of the 14 markers could distinguish B- and T-ALL in an independent cohort of patients with ALL. This study can enhance our knowledge of the transcriptomic profile of different ALL subtypes.

PMID:40788820 | DOI:10.1002/1878-0261.70046

Indian J Cancer. 2025 Apr 1;62(2):213-219. doi: 10.4103/ijc.ijc_24_23. Epub 2025 Aug 8.

ABSTRACT

BACKGROUND: Cancer patients are administered various chemotherapeutic agents along with supportive management, which increases the risk of potential drug-drug interactions (pDDIs). We aimed to assess the pDDIs in In-patient Department (IPD) patients receiving cancer chemotherapy in a tertiary care hospital.

METHODS: A prospective, observational study was conducted in the oncology department of a tertiary care hospital for a period of 6 months. Patient information was noted in the data collection form, and pDDIs were assessed using the Micromedex® database. Mann-Whitney U, Chi-square, and Spearman's correlation were the tests used for statistical analysis. P value of less than 0.05 was considered statistically significant.

RESULTS: In a sample size of 145 patients having a confidence interval of 95% and response distribution of 50%, the margin of error was found to be 8.11%. Male predominance (57.2%) was seen in the study. Although the adult patient population (56.5%) dominated the study, pediatric (26.9%) and geriatric (16.6%) patients were also included. A total of 115 pDDIs were found in 41% of the total patient population, out of which 56% (n = 64) were moderate and 44% (n = 51) were major in severity. The number of drug interactions was found to have correlation with the number of drugs (rho = 0.2, P = 0.01) prescribed during hospital stay.

CONCLUSION: The present study shows that cancer patients are relatively at risk for drug-drug interactions. To avoid Adverse Drug Reaction (ADRs), harmful effects, and other undesirable clinical manifestations shown by drug-drug interactions, screening for pDDIs is required.

PMID:40788735 | DOI:10.4103/ijc.ijc_24_23

Naunyn Schmiedebergs Arch Pharmacol. 2025 Aug 11. doi: 10.1007/s00210-025-04508-x. Online ahead of print.

ABSTRACT

Drug repurposing (DR) is a strategy to develop novel drugs from pre-approved drugs. To overcome the limitations of conventional drug development pathways, DR is a novel method that is cost-effective, with minimal side effects, and less time-consuming. Contrary to de novo drug discovery, DR eliminates the need for FDA approval. Fluoroquinolones (FQs) are potentially antibiotics, but recently, it has been discovered that FQs are a potential "treasure trove" to circumvent cancer drug resistance. Various in vitro studies on FQs showed their anticancer functionality against various cancer cell lines. Moreover, FQs are found to enhance the therapeutic efficacy of various clinical drugs by acting synergistically or additively in combination therapy. FQs such as ciprofloxacin, moxifloxacin, and levofloxacin are found to enhance the anti-tumor effect of a wide range of FDA-approved chemotherapeutics through multiple mechanisms including cell cycle arrest, apoptosis induction, anti-proliferation, and modulation of EMT. This review will comprehensively focus on the chemosensitization mechanism and cellular targets of FQs. This study provides a scientific basis to explore FQs as potential chemotherapeutic agents for combinational therapy to tackle the current scenario of drug resistance.

PMID:40788483 | DOI:10.1007/s00210-025-04508-x

Nat Prod Bioprospect. 2025 Aug 11;15(1):52. doi: 10.1007/s13659-025-00533-8.

ABSTRACT

Tuberculosis (TB) is a leading infectious disease killer and one of the major causes of deaths worldwide. Although TB is a curable and preventable disease, in 2023, approximately 10.8 million people fell ill with TB and there were an estimated 1.25 million of deaths worldwide. Despite some research progress for new drug candidates, drug repurposing, and new regimens, there is still an urgent need for the new medicins to treat TB, especially due to the growing cases of multidrug and extensively drug-resistant (MDR/XDR) strains. Drug resistance is a challenging obstacle to TB care and prevention globally, making TB harder and longer to treat, often with poorer outcomes for patients. The Actinomycetota encompass Gram-positive bacteria that produce a milieu of bioactive metabolites, including antibiotics, antiproliferative drugs, immunosuppressive agents, and other important medical molecules. Actinomycetota have a special place in the therapeutic arsenal to fight TB, as rifamycins, aminoglycosides, and cycloserine are derived from Streptomyces species, one of the most important genera in this phylum. Furthermore, hundreds of antimycobacterial metabolites have been isolated from Actinomycetota and can serve as effective drugs or useful agents for the discovery of new lead compounds to combat TB. The present review covers more than 171 isolated substances as potential antimycobacterial agents discovered between the years 1972 to 2024. Among the most potent compounds, with MIC in the submicromolar range, steffimycins, ilamycins/rufomycins, nosiheptide, actinomycins, lassomycin and boromycin are the most promising compounds. These compounds represent highly promising candidates for development of new antitubercular drugs. Additionally, some of these substances also demonstrated activity against resistant Mycobacterium tuberculosis (Mtb) strains, which is particularly relevant given the difficulty of treating MDR and XDR strains. Thus, actinobacteria have played and continue to play an important role in fight TB, remaining a promising source of antibiotic metabolites. Their unique metabolic diversity enables the production of metabolites with innovative mechanisms of action, making them a strategic reservoir for discovering therapies against untreatable forms of the disease.

PMID:40788464 | DOI:10.1007/s13659-025-00533-8

F1000Res. 2024 Dec 20;13:1546. doi: 10.12688/f1000research.157890.1. eCollection 2024.

ABSTRACT

BACKGROUND: Cerebral small vessel disease (cSVD) is a progressive neurovascular-degenerative condition without specific treatment that causes lacunar stroke, most intracerebral haemorrhage, vascular cognitive impairment (VCI) and several neuropsychiatric conditions.

OBJECTIVES: To conduct a rapid multi-stage scoping review to identify licensed interventions that could be repurposed for testing in cSVD at phase-3.

METHODS: First, we screened preclinical studies of potential relevance to cSVD and used a drug dictionary to identify studies of potential interventions. Separately, we screened clinical studies of relevance to cSVD and VCI. Following merging, we removed drugs that were unsuitable or impractical to assess long-term in the UK. We then performed mini-meta-analyses for shortlisted interventions assessing effects on cognition and scored these for their relevance to cSVD.

RESULTS: The preclinical review created a long-list of 1,757 deduplicated interventions. Those that were not available in the UK, not expensive or impractical to administer long-term were merged with 62 interventions identified from 75 relevant clinical studies to create a medium-list of 52 interventions. Focussed literature review short-listed ten interventions for review by an independent scientific advisory group; they ranked three as most suitable for immediate testing: metformin, tadalafil and isosorbide mononitrate.

CONCLUSION: This rapid review identified three interventions that are suitable for testing in a late phase-3 (platform) trial involving patients with cSVD. The approach could be improved with partial automation, text mining and generative pre-trained transformer approaches which would help manage the large data volumes. Further, our data-driven approach could be combined with genetic or other mechanistic methods to further de-risk future trials.

PMID:40786096 | PMC:PMC12335727 | DOI:10.12688/f1000research.157890.1

Front Pharmacol. 2025 Jul 25;16:1631419. doi: 10.3389/fphar.2025.1631419. eCollection 2025.

ABSTRACT

BACKGROUND: Cancer drug resistance significantly reduces the effectiveness of current anticancer treatments. Multiple dysregulated signaling pathways drive cancer initiation, progression, and related drug resistance. This highlights the need for developing new multi-targeting drugs that are more cost-effective, have fewer side effects, and remain effective against cancer. Drug repurposing offers a promising solution to expensive targeted therapies and helps overcome drug resistance. Mebendazole (MBZ), albendazole, flubendazole, and oxfendazole are broad-spectrum anti-helminthic drugs from the benzimidazole family.

PURPOSE: Therefore, MBZ demonstrated potential in suppressing the growth of various cancer cells, both in vitro and in vivo. Consequently, we thoroughly reviewed MBZ as a therapeutic option against cancer and related drug resistance.

RESULTS AND DISCUSSION: In this study, we identified MBZ as a promising cancer treatment that works through multiple mechanisms such as regulating tumor angiogenesis, autophagy, and apoptosis, modulating key signaling pathways, boosting antitumor immune responses, and inhibiting matrix metalloproteinases activity-all of which are major factors in cancer drug resistance. Additionally, the development of new MBZ delivery systems aims to address its pharmacokinetic limitations. While the anticancer effects of MBZ are encouraging, further research is needed before it can be used clinically.

CONCLUSION: Extensive data from in vitro, in vivo, and clinical trials support MBZ's anticancer potential and highlight the need for innovative delivery methods, including polymeric nanoparticles, nanostructured lipid formulations, micelles, nanosuspensions, and beyond.

PMID:40786044 | PMC:PMC12331676 | DOI:10.3389/fphar.2025.1631419

Front Pharmacol. 2025 Jul 25;16:1592011. doi: 10.3389/fphar.2025.1592011. eCollection 2025.

ABSTRACT

We describe a drug repurposing treatment involving the use of nicardipine in a young patient with Pitt-Hopkins syndrome (a rare neurodevelopmental disorder that results from variants of TCF4 gene) as a bench-to-bedside approach. Loss of TCF4 function in Pitt-Hopkins syndrome leads to increased excitability of Nav1.8 in neurons. Nicardipine is normally used alone or together with other medicines to treat severe chest pain (angina) or high blood pressure (hypertension), and can also be used in children to treat hypertension. Nicardipine was shown to have an inhibitory effect on Nav1.8 in vitro as well as in Tcf4 +/- mice, showing promising effects on behavior, learning and memory. In this study, nicardipine was given orally for 7 months (starting dose 0.2 mg/kg/d, maximum dose 1.7 mg/kg/d). There were no significant side effects. The patient showed mild to moderate improvement in all developmental trajectories as well as in her restlessness. Repurposing nicardipine in Pitt-Hopkins syndrome patients could be a promising approach to enhance development in these often severely affected patients.

PMID:40786031 | PMC:PMC12331575 | DOI:10.3389/fphar.2025.1592011

Clin J Am Soc Nephrol. 2025 Aug 11. doi: 10.2215/CJN.0000000858. Online ahead of print.

ABSTRACT

Lupus nephritis (LN) is seeing more and more enriching immunotherapies, but important unmet needs remain. Here we discuss how to focus on histological signs of immunological activity triggering immunotherapy versus signs of irreversible kidney injury requiring care for chronic kidney disease. Also, the correct interpretation of residual proteinuria requires dissecting immunological activity from glomerular hyperfiltration, e.g., by repeat biopsy. Despite modern triple immunotherapy, per-protocol biopsies still document irreversible injury to occur in the first year. Immediate inhibition of the complement system may address this unmet need and may even help to ultimately replace early glucocoorticoid therapy. We advocate the concept of a clone-directed therapy to sufficiently suppress the autoreactive clones of memory B and T cells inside the lymphoid tissues as well as the long-lived plasma cells in the bone marrow that maintain activity of systemic lupus erythematosus (SLE) and drive disease flares. Numerous B cell- and plasma cell-targeting therapies are gradually becoming available and their parenteral route of application may also avoid oral drug non-adherence. Replacing oral and toxic medications such as steroids, mycophenolate, and calcineurin inhibitors is now a goal for the next decade. Obtaining orphan disease designation for LN would accelerate progress and is supported by latest data on LN prevalence. With these conceptual and management improvements, LN, once "complex" and frequently fatal, may become easy-to-manage as other autoimmune diseases.

PMID:40788686 | DOI:10.2215/CJN.0000000858

Front Oncol. 2025 Jul 25;15:1589724. doi: 10.3389/fonc.2025.1589724. eCollection 2025.

ABSTRACT

BACKGROUND: Colorectal cancer is the second most prevalent cancer in Chile, affecting both sexes. Late-stage diagnosis occurs in approximately 25% of cases, with a five-year survival rate of only 14%. Standard treatment involves surgical resection followed by 5-fluorouracil-based chemotherapy, often combined with oxaliplatin or irinotecan. However, patient responses vary significantly due to genetic polymorphisms affecting drug metabolism, including variants in TYMS, DPYD, GSTs, and DNA repair enzymes. While genetic factors influencing chemotherapy outcomes have been studied, their impact remains unclear and varies across populations. No predictive model integrating genetic and clinical variables for chemotherapy safety in Chilean colorectal cancer patients has been established.

OBJECTIVE: This study aimed to identify relevant genetic variants in TYMS, TYMP, DPYD, GSTP1, MTHFR, ERCC2, ABCB1, ABCC2, ABCC4, and ABCG2 genes, which, combined with clinical factors, could contribute to a predictive model for 5-FU-based chemotherapy safety in advanced colorectal cancer patients.

METHODS: A retrospective nested case-control study was conducted on 82 advanced colorectal cancer patients. Sixteen genetic variants were analyzed to assess their association with adverse reactions and their severity using logistic regression. Multivariate models were developed to predict chemotherapy safety.

RESULTS: Among the 16 variants analyzed in 82 patients, key findings included: The G allele of GSTP1 (rs1695) was protective against neuropathy (OR = 0.147; p = 0.012) but increased mucositis risk (OR = 2.27; p = 0.036). The C allele of DPYD (rs1801265) was linked to a higher neuropathy risk (OR = 4.58; p = 0.05). The TYMS deletion genotype (rs11280056) conferred protection against hematological adverse reactions (OR = 0.029; p = 0.001). On the other hand, the 3R genotype of TYMS 5'UTR (rs45445694) is associated as a risk factor for skin and subcutaneous tissue disorders (OR = 6.40; p = 0.029). Two multivariate models were developed to predict anemia (p = 0.027) and pain (p = 0.01) development.

CONCLUSIONS: This study provides a foundation for developing pharmacogenetic-based predictive models for adverse reactions associated with 5-FU, including neuropathy, mucositis, and hematological and skin toxicities. Future research may refine these models to enable personalized dose adjustments, improving chemotherapy safety in Chilean colorectal patients.

PMID:40786508 | PMC:PMC12331468 | DOI:10.3389/fonc.2025.1589724

Pancreas. 2025 Aug 12. doi: 10.1097/MPA.0000000000002544. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to describe the frequency of exocrine pancreatic disease in youth with diabetes.

METHODS: We conducted a retrospective chart review on data that was obtained from a single center prospectively collected database of patients with diabetes. Patients were categorized as having type 1 diabetes, type 2 diabetes, or "Other" diabetes if they had cystic fibrosis-related diabetes, maturity onset diabetes of the young, or drug/chemical induced diabetes. All patients'charts were reviewed for exocrine pancreas disease, inclusive of pancreatitis or exocrine pancreatic insufficiency.

RESULTS: Nine-hundred and eighty-eight patients with a diabetes diagnosis were included. Thirty five out of 988 (3.5%) were diagnosed with pancreatic exocrine disease. Diabetes patients with exocrine disease compared to the ones without were significantly older (13.1 years, IQR 9.8-15.3 vs. 11.7 years, P= 0.04). Those with exocrine disease were more likely to have "Other" diabetes (P<0.0001). The exocrine group had a lower median hemoglobin A1c at diabetes diagnosis (7%, IQR 5.8-9.2% vs. 11.3%, 8.9-13.8%; P<0.0001). Out of the 988 patients, 18 patients had pancreatitis diagnosed, which was 2% of the overall cohort. Nine of the 18 patients were found to have developed pancreatitis after diabetes diagnosis, or 1% of the entire diabetes cohort (9/988).

CONCLUSIONS: The co-existence of exocrine and endocrine pancreatic disease occurred in 3.5% of diabetes patients. The risk of pancreatitis occurring after diabetes was 1%, a rate 100 times higher than the general pediatric population (0.01%). Future studies are needed to determine the specific mechanisms involved in the connection between endocrine and exocrine pancreatic disease in children.

PMID:40788280 | DOI:10.1097/MPA.0000000000002544