Redox Biol. 2025 Feb 5;81:103523. doi: 10.1016/j.redox.2025.103523. Online ahead of print.

ABSTRACT

The post-translational redox regulation of protein function by cysteine oxidation controls diverse biological processes, from cell division to death. However, most current site-centric paradigms fail to capture the nonlinear and emergent nature of redox regulation in proteins with multiple cysteines. Here, we present a proteoform-centric theory of redox regulation grounded in the i-space. The i-space encapsulates the theoretical landscape of all possible cysteine proteoforms. Using computational approaches, we quantify the vast size of the abstract i-space, revealing its scale-free architecture-elucidating the disproportionate influence of cysteine-rich proteins. We define mathematical rules governing cysteine proteoform dynamics. Their dynamics are inherently nonlinear, context-dependent, and fundamentally constrained by protein copy numbers. Monte Carlo simulations of the human protein PTP1B reveal extensive i-space sampling beyond site-centric models, supporting the "oxiform conjecture". This conjecture posits that highly oxidised proteoforms, molecules bearing multiple oxidised cysteines, are central to redox regulation. In support, even 90%-reduced proteomes can house vast numbers of unique, potentially functioanlly diverse, oxiforms. This framework offers a transformative lens for understanding the redox biology of proteoforms.

PMID:39929052 | DOI:10.1016/j.redox.2025.103523

Orphanet J Rare Dis. 2025 Feb 10;20(1):64. doi: 10.1186/s13023-025-03564-z.

ABSTRACT

BACKGROUND: In patients with slow-flow vascular malformations (SFVMs) including venous malformations (VM), lymphatic malformations (LM) or Klippel-Trenaunay Syndrome (KTS), somatic gain-of-function mutations in genes encoding phosphatidyl inositol 3-kinase alpha (PI3Kα, gene name PIK3CA) have been identified. A phase 2 study was conducted with the patients to assess the efficacy and safety of ART-001 (serabelisib), an orally available selective PI3Kα inhibitor.

METHODS: This is a multicenter, randomized, double-blind, proof-of-concept, phase 2 trial. Eligible participants were patients aged 2 years and older, diagnosed either with VM, LM or KTS. Participants were administered either 50 or 100 mg of ART-001 for 24 weeks. The primary endpoint was the response rate defined as the proportion of participants who achieved ≥ 20% reduction in lesion volume at week 24. Secondary endpoints include safety, pharmacokinetics, pain, and quality of life scores.

RESULTS: Thirty-five patients (median age: 14 years old; VM, n = 17, KTS, n = 13 and LM, n = 5) were randomly assigned and received treatment (50 mg, n = 17 and 100 mg, n = 18). ART-001 showed a response rate: 29.4% (95% confidence interval 10.3-56.0%) at 50 mg and 33.3% (13.3-59.0%) at 100 mg. Mean lesion volume reductions at 50 mg and 100 mg were - 2.3% (95% CI - 14.3 to 9.6%) and - 12.6% (- 25.3 to 0.06%), respectively. No drug-related serious adverse events were observed. Treatment-emergent adverse events were generally mild to moderate and transient. Pharmacokinetic profiles were similar between pediatric and adolescent/adult patients except for lower Ctrough levels in pediatric patients.

CONCLUSION: ART-001 was effective and well-tolerated in patients with SFVMs. These results support the further development of ART-001 in SFVMs and other PIK3CA-related overgrowth syndromes to confirm clinical benefits and long-term safety.

TRIAL REGISTRATION: Japan Registry of Clinical Trial, jRCT2071210027. Registered May 25 2021, https://jrct.niph.go.jp/en-latest-detail/jRCT2071210027.

PMID:39930502 | DOI:10.1186/s13023-025-03564-z

Virol J. 2025 Feb 10;22(1):33. doi: 10.1186/s12985-025-02648-3.

ABSTRACT

BACKGROUND: There are limited data on how historical nucleoside reverse transcriptase inhibitor (NRTI) resistance-associated mutations (RAMs) other than M184V/I, affect the activity of B/F/TAF. We evaluated the outcomes of switching virologically suppressed (HIV-1 RNA < 50 copies/mL) individuals harbouring major RAMs from boosted protease inhibitor (bPI)-based therapy to B/F/TAF.

METHODS: Participants had various historical genotypic patterns including M184V/I, ≤2 thymidine analogue mutations (TAMs), and other NRTI RAMs (NAMs), and no integrase resistance. Baseline RAMs were explored by retrospective sequencing of cellular HIV-1 DNA. Participants were randomised (1:1) to switching to B/F/TAF either immediately or after 24 weeks. The primary outcome was the proportion of participants maintaining virological suppression (pure virologic response) at week-24; secondary outcomes were proportion of participants with virological suppression at week-48, pre-specified safety measures, and treatment-emergent resistance.

RESULTS: Historically, 21/72 (29.2%) participants had M184V/I, 5 (6.9%) M184V/I + 1 NAM, 31 (43.1%) 1 TAM ± M184V/I ± 1 NAM, and 15 (20.8%) 2 TAMs ± M184V/I ± 1 NAM. At week-24, proportions maintaining virological suppression were 33/33 (100%) on B/F/TAF vs. 38/39 (97.4%) on bPI (difference 2.6%; 95% CI -2.4%, 7.5%). Drug-related adverse events (AEs) were reported in 10/33 (30.3%) vs. 1/39 (2.6%), respectively. The immediate switch arm had improved lipid parameters but increased HbA1c and weight. Virological suppression was maintained at week-48. There were six discontinuations; four on B/F/TAF were drug-related and the two on bPI were not drug-related.

CONCLUSIONS: Historical NRTI resistance did not compromise the effectiveness of B/F/TAF in virologically suppressed adults. 12% experienced treatment-limiting AEs after switching.

REGISTRATION: EudraCT no: 2018-004732-30.

PMID:39930490 | DOI:10.1186/s12985-025-02648-3

Arch Dermatol Res. 2025 Feb 11;317(1):383. doi: 10.1007/s00403-025-03925-5.

ABSTRACT

IgA vasculitis (IgAV) is a small-vessel vasculitis characterized by the deposition of immunoglobulin A (IgA) in the vessel walls, often presenting with cutaneous manifestations such as palpable purpura. Drug-induced IgAV is a rare but potentially severe condition. Several studies have suggested a possible association between antibiotics and IgAV. However, research on this link remains limited. This study aimed to identify antibiotics implicated in the onset of IgAV and to analyze the clinical characteristics of IgAV induced by antibiotics. Data on antibiotic-induced IgAV events were extracted from the FDA Adverse Event Reporting System (FAERS) database, and case reports were collected through literature searches. A pharmacovigilance analysis was conducted using FAERS data from 2003 to 2023 to evaluate adverse events related to IgAV caused by antibiotics, and case reports up to November 23, 2024, were reviewed for retrospective analysis. A total of 150 reports of antibiotic-induced IgAV were analyzed, with 13 antibiotics identified as associated with IgAV. The three antibiotics most strongly linked to IgAV were ofloxacin, vancomycin, and clarithromycin based on the FAERS database analysis. The median age of the cases was 58 years, with a male predominance. IgAV typically developed 4 days (1-15 days) after drug administration. Clarithromycin, vancomycin, and ciprofloxacin were the most frequently reported antibiotics in the literature, and they were also associated with poor renal outcomes, emphasizing the importance of regular follow-up to improve long-term renal prognosis. In conclusion, this study identified 13 antibiotics associated with IgAV, with ofloxacin, vancomycin, and clarithromycin being the most strongly linked to the condition.

PMID:39930301 | DOI:10.1007/s00403-025-03925-5

Zhongguo Zhong Yao Za Zhi. 2025 Jan;50(1):273-277. doi: 10.19540/j.cnki.cjcmm.20240905.601.

ABSTRACT

Signal detection is a critical task in drug safety regulation. However, it inevitably generates irrelevant or false signals, posing challenges for resource allocation by marketing authorization holders. To reasonably assess these signals, different countries have established various principles for prioritizing the evaluation of risk signals. This study systematically compares these principles and finds that the U.S. Food and Drug Administration(FDA) focuses on practical issues, such as identifying drug confusion or drug interactions. However, China's Good Pharmacovigilance Practices and the European Medicines Agency(EMA) emphasize a comprehensive evaluation framework. The Council for International Organizations of Medical Sciences(CIOMS) emphasizes the consistency of multiple data sources, highlighting the reliability of signal evaluation. China practices a multidisciplinary approach combining traditional Chinese and western medicine, and the risk signals related to traditional Chinese medicine(TCM) have unique characteristics, including complex components, cumulative toxicity, specific theoretical foundations, and drug interactions. The different priorities in risk signal evaluation principles across countries suggest that China should strengthen clinical trial research, emphasize corroboration with evidence of multiple sources, and pay particular attention to the risks of drug interactions in the TCM regulatory science. Establishing the risk signal prioritization principles that align with the characteristics of TCM enables more precise and efficient scientific regulation of TCM.

PMID:39929668 | DOI:10.19540/j.cnki.cjcmm.20240905.601

Reg Anesth Pain Med. 2025 Feb 10:rapm-2025-106481. doi: 10.1136/rapm-2025-106481. Online ahead of print.

NO ABSTRACT

PMID:39929637 | DOI:10.1136/rapm-2025-106481

PLoS One. 2025 Feb 10;20(2):e0305511. doi: 10.1371/journal.pone.0305511. eCollection 2025.

ABSTRACT

Type 2 diabetes (T2D) is a chronic disorder affecting 462 million worldwide, often managed with metformin as first-line treatment. However, metformin's response varies among individuals, including up to 30% experiencing serious adverse drug reactions (ADRs) and 20-50% inefficacy. These differences may be due to various factors, including pharmacogenetic (PGx) variants. The PGx variants documented so far could affect both the safety and efficacy of metformin, but due to a lack of replication studies, none reached the clinical evidence-level needed to be used as a predictive marker for treatment response. Therefore, this study aims to evaluate the association between the presence of candidate PGx variants and metformin response in T2D subjects. We conducted an association study involving 108 T2D participants currently or previously treated with metformin. A characterization of their therapeutic response was carried out through questionnaires and pharmacological profile reviews. DNA samples were collected during their single visit to perform genotyping of 24 selected candidate PGx variants. Association analyses between candidate PGx variants and metformin response were performed. Among the subjects included in the analyses (n = 84), 25% were non-responders, and 58% experienced ADRs. At the time of study enrollment, 93.9% of non-responders continued to use metformin. The odds of being a non-responder to metformin are 5.6 times higher for homozygous carriers of the alternative allele of a variant within the PCK1 gene (rs4810083) compared to the other genotypes (95% interval confidence [1.9-16.6]). Two variants in perfect linkage disequilibrium within the SLC22A2 gene (rs316019 and rs316009) were associated with increase odds of having ADRs, where homozygous genotype carriers are 7.3 times more likely to have ADRs presentation (95% interval confidence [1.85-29.01]). This study identified associations between PCK1 and SLC22A2 candidate PGx variants and metformin response in T2D treatment. Additional genetic and functional studies are necessary to elucidate the variants' impact in metformin's pharmacological mechanisms.

PMID:39928707 | DOI:10.1371/journal.pone.0305511

J Med Chem. 2025 Feb 10. doi: 10.1021/acs.jmedchem.4c02654. Online ahead of print.

ABSTRACT

Molecular-targeted therapies for the treatment of cystic fibrosis (CF) rely on small-molecule modulators that rescue the activity of the defective CF transmembrane conductance regulator (CFTR) anion channel. ARN23765 is a small molecule with subnanomolar potency in rescuing the function of mutant CFTR in bronchial epithelial cells from CF patients carrying the F508del-CFTR mutation. Considering the multifaceted interactions of CFTR with the plasma membrane and the complexity of the protein network within the cellular compartments, here we report the investigation of ARN23765's molecular mechanism in live cells. We used the photoaffinity labeling (PAL) approach to demonstrate the interaction of ARN23765-derived probes with CFTR in cells. We showed that ARN23765 contributes to F508del-CFTR rescue by stabilizing the membrane-spanning domain-1 and interacting with CFTR at the same site as other type I CFTR correctors. Our study characterizes ARN23765's mode of action and highlights the potential of studying the interactions between CFTR and its correctors in live cells.

PMID:39928576 | DOI:10.1021/acs.jmedchem.4c02654

Comput Biol Med. 2025 Feb 9;187:109785. doi: 10.1016/j.compbiomed.2025.109785. Online ahead of print.

ABSTRACT

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limitations, and the progressive nature of KOA. However, a model capable of projecting genuine radiographs into different OA stages could augment data pools, enhance algorithm training, and offer pre-emptive prognostic insights. In this study, we developed a Cycle-Consistent Adversarial Network (CycleGAN) to generate synthetic past and future stages of KOA on any genuine radiograph. The model's effectiveness was validated through its impact on a KOA specialized Convolutional Neural Network (CNN). Transformations towards synthetic future disease states resulted in 83.76% of none-to-doubtful stage images being classified as moderate-to-severe stages, while retroactive transformations led to 75.61% of severe-stage images being classified as none-to-doubtful stages. Similarly, transformations from mild stages achieved 76.00% correct classification towards future stages and 69.00% for past stages. The CycleGAN demonstrated an exceptional ability to expand the knee joint space and eliminate bone-outgrowths (osteophytes), key radiographic indicators of disease progression. These results signify a promising potential for enhancing diagnostic models, data augmentation, and educational and prognostic uses. Nevertheless, further refinement, validation, and a broader evaluation process encompassing both CNN-based assessments and expert medical feedback are emphasized for future research and development.

PMID:39929004 | DOI:10.1016/j.compbiomed.2025.109785

J Med Internet Res. 2025 Feb 10;27:e60888. doi: 10.2196/60888.

ABSTRACT

BACKGROUND: Although catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA).

OBJECTIVE: This scoping review aimed to evaluate the current scientific evidence on the application of ML for managing patients undergoing AFCA, compare the performance of various models across specific clinical tasks within AFCA, and summarize the strengths and limitations of ML in this field.

METHODS: Adhering to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, relevant studies published up to October 7, 2023, were searched from PubMed, Web of Science, Embase, the Cochrane Library, and ScienceDirect. The final included studies were confirmed based on inclusion and exclusion criteria and manual review. The PROBAST (Prediction model Risk Of Bias Assessment Tool) and QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) methodological quality assessment tools were used to review the included studies, and narrative data synthesis was performed on the modeled results provided by these studies.

RESULTS: The analysis of 23 included studies showcased the contributions of ML in identifying potential ablation targets, improving ablation strategies, and predicting patient prognosis. The patient data used in these studies comprised demographics, clinical characteristics, various types of imaging (9/23, 39%), and electrophysiological signals (7/23, 30%). In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). Compared with traditional clinical scoring models or human clinicians, the model performance reported in the included studies was generally satisfactory, but most models (14/23, 61%) showed a high risk of bias due to lack of external validation.

CONCLUSIONS: Our evidence-based findings suggest that ML is a promising tool for improving the effectiveness and efficiency of managing patients undergoing AFCA. While guiding data preparation and model selection for future studies, this review highlights the need to address prevalent limitations, including lack of external validation, and to further explore model generalization and interpretability.

PMID:39928932 | DOI:10.2196/60888

PLoS One. 2025 Feb 10;20(2):e0317304. doi: 10.1371/journal.pone.0317304. eCollection 2025.

ABSTRACT

The World Natural Heritage is a rare and irreplaceable natural landscape recognized by all mankind, with outstanding significance and universal value. Among them, the World Heritage Karst sites(WHKs) holds an important position due to its special natural beauty and aesthetic value. In the field of landscape evaluation, interdisciplinary and interdisciplinary cooperation using different methods has always been a research focus. However, there is still a gap in the evaluation of natural landscape aesthetic value based on UGC(User Generated Content) data and deep learning models. This article is based on a public perspective, using social media UGC data, crawling images and texts as data sources, and combining SegFormer deep learning models, ArcGIS spatial analysis, natural Language Processing Technology (NLP) and other methods to conduct quantitative research on aesthetic value. Research has found that: (1) Huangguoshu Scenic Area has an excellent natural environment, and landscape elements with high naturalness (vegetation, water) are more attractive to tourists, with diverse landscape combinations; (2) There is no complete positive correlation between tourist sentiment bias, landscape diversity, and vegetation coverage. Emphasis is placed on the aesthetic perception path from bottom to top, from the surface to the inside. The comprehensive emotional value is 14.35, and the emotional values are all positively distributed. The distribution density and extreme value of positive emotions are greater than those of negative emotions; (3) The emotional bias of tourists is directly related to visual sensitivity, showing a synchronous trend of change. The visual sensitivity of the Great Waterfall and Dishuitan areas is relatively high, mostly at I-II level sensitivity. This method enhances the data source channel, which is conducive to obtaining the correct tourist evaluation orientation. In traditional subjective landscape evaluation, rational parameter indicators are added to reduce the probability of error, provide data support for its natural beauty description, break through the time and space limitations of aesthetic evaluation, and provide scientific reference for quantifying the aesthetic value of other heritage sites.

PMID:39928674 | DOI:10.1371/journal.pone.0317304

PLoS Comput Biol. 2025 Feb 10;21(2):e1012286. doi: 10.1371/journal.pcbi.1012286. Online ahead of print.

ABSTRACT

There is an important challenge in systematically interpreting the internal representations of deep neural networks (DNNs). Existing techniques are often less effective for non-tabular tasks, or they primarily focus on qualitative, ad-hoc interpretations of models. In response, this study introduces a cognitive science-inspired, multi-dimensional quantification and visualization approach that captures two temporal dimensions of model learning: the "information-processing trajectory" and the "developmental trajectory." The former represents the influence of incoming signals on an agent's decision-making, while the latter conceptualizes the gradual improvement in an agent's performance throughout its lifespan. Tracking the learning curves of DNNs enables researchers to explicitly identify the model appropriateness of a given task, examine the properties of the underlying input signals, and assess the model's alignment (or lack thereof) with human learning experiences. To illustrate this method, we conducted 750 runs of simulations on two temporal tasks: gesture detection and sentence classification, showcasing its applicability across different types of deep learning tasks. Using four descriptive metrics to quantify the mapped learning curves-start, end - start, max, tmax-, we identified significant differences in learning patterns based on data sources and class distinctions (all p's < .0001), the prominent role of spatial semantics in gesture learning, and larger information gains in language learning. We highlight three key insights gained from mapping learning curves: non-monotonic progress, pairwise comparisons, and domain distinctions. We reflect on the theoretical implications of this method for cognitive processing, language models and representations from multiple modalities.

PMID:39928655 | DOI:10.1371/journal.pcbi.1012286

PLoS One. 2025 Feb 10;20(2):e0311250. doi: 10.1371/journal.pone.0311250. eCollection 2025.

ABSTRACT

The healthcare industry is generating a massive volume of data, promising a potential goldmine of information that can be extracted through machine learning (ML) techniques. The Intensive Care Unit (ICU) stands out as a focal point within hospitals and provides a rich source of data for informative analyses. This study examines the cardiac surgery ICU, where the vital topic of patient ventilation takes center stage. In other words, ventilator-supported breathing is a fundamental need within the ICU, and the limited availability of ventilators in hospitals has become a significant issue. A crucial consideration for healthcare professionals in the ICU is prioritizing patients who require ventilators immediately. To address this issue, we developed a prediction model using four ML and deep learning (DL) models-LDA, CatBoost, Artificial Neural Networks (ANN), and XGBoost-that are combined in an ensemble model. We utilized Simulated Annealing (SA) and Genetic Algorithm (GA) to tune the hyperparameters of the ML models constructing the ensemble. The results showed that our approach enhanced the sensitivity of the tuned ensemble model to 85.84%, which are better than the results of the ensemble model without hyperparameter tuning and those achieved using AutoML model. This significant improvement in model performance underscores the effectiveness of our hybrid approach in prioritizing the need for ventilators among ICU patients.

PMID:39928609 | DOI:10.1371/journal.pone.0311250

PLoS One. 2025 Feb 10;20(2):e0317396. doi: 10.1371/journal.pone.0317396. eCollection 2025.

ABSTRACT

In recent years, the challenge of imbalanced data has become increasingly prominent in machine learning, affecting the performance of classification algorithms. This study proposes a novel data-level oversampling method called Cluster-Based Reduced Noise SMOTE (CRN-SMOTE) to address this issue. CRN-SMOTE combines SMOTE for oversampling minority classes with a novel cluster-based noise reduction technique. In this cluster-based noise reduction approach, it is crucial that samples from each category form one or two clusters, a feature that conventional noise reduction methods do not achieve. The proposed method is evaluated on four imbalanced datasets (ILPD, QSAR, Blood, and Maternal Health Risk) using five metrics: Cohen's kappa, Matthew's correlation coefficient (MCC), F1-score, precision, and recall. Results demonstrate that CRN-SMOTE consistently outperformed the state-of-the-art Reduced Noise SMOTE (RN-SMOTE), SMOTE-Tomek Link, and SMOTE-ENN methods across all datasets, with particularly notable improvements observed in the QSAR and Maternal Health Risk datasets, indicating its effectiveness in enhancing imbalanced classification performance. Overall, the experimental findings indicate that CRN-SMOTE outperformed RN-SMOTE in 100% of the cases, achieving average improvements of 6.6% in Kappa, 4.01% in MCC, 1.87% in F1-score, 1.7% in precision, and 2.05% in recall, with setting SMOTE's neighbors' number to 5.

PMID:39928607 | DOI:10.1371/journal.pone.0317396

Crit Care Med. 2025 Feb 7. doi: 10.1097/CCM.0000000000006588. Online ahead of print.

ABSTRACT

OBJECTIVES: Length of stay (LOS) models support evaluating ICU care; however, current benchmarking models fail to consider differences in LOS between surviving and nonsurviving patients, which can lead to biased predictions toward the surviving population. We aim to develop a model addressing this as well as documentation bias to improve ICU benchmarking.

DESIGN: The Critical Care Outcomes Prediction Model (CCOPM) LOS uses patient characteristics, vitals, and laboratories during the first 24 hours of ICU admission to predict LOS in the hospital and ICU using a deep learning framework for modeling time to events with competing risk. Data was randomly divided into training, validation, and test (hold out) sets in a 2:1:1 ratio.

SETTING: Electronic ICU Research Institute database from participating tele-critical care programs.

PATIENTS: Six hundred sixty-nine thousand eight hundred seventy-six ICU admissions pertaining to 628,815 patients from 329 ICUs in 194 U.S. hospitals, from 2017 to 2019.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Model performance was assessed using the coefficient of determination (R2), concordance index, mean absolute error, and calibration. For individual stays in the test set, the ICU LOS model presented R2 = 0.29 and 0.23 for surviving and nonsurviving populations, respectively, at the individual level and R2 = 0.48 and 0.23 at the ICU level. Conversely, hospital LOS model presented R2 = 0.46 and 0.52 at the individual level and R2 = 0.71 and 0.64 at the ICU level. In the subset of the test set containing predictions from Acute Physiology and Chronic Health Evaluation (APACHE) IVb, R2 of ICU LOS for surviving and nonsurviving populations was, respectively, 0.30 and 0.23 for the CCOPM and 0.16 and zero for APACHE IVb. For hospital LOS, the values were R2 = 0.39 and 0.40 for the CCOPM and 0.27 and zero for APACHE IVb.

CONCLUSIONS: This novel LOS model represents a step forward in achieving more equitable benchmarking across diverse ICU settings with varying risk profiles.

PMID:39928543 | DOI:10.1097/CCM.0000000000006588

J Cell Mol Med. 2025 Feb;29(3):e70402. doi: 10.1111/jcmm.70402.

ABSTRACT

The cell populations, particularly subpopulations, involved in the onset and progression of idiopathic pulmonary fibrosis (IPF) remain incompletely understood. This study employed single-cell RNA-seq to identify cell populations and subpopulations with significantly altered proportions in the lungs of patients with IPF. In IPF lungs, endothelial cell proportions were significantly increased, while alveolar epithelial cell proportions were markedly decreased. Among the three identified fibroblast subpopulations, the proportion of myofibroblasts was significantly increased, while the proportions of the other two fibroblast subtypes were reduced. Similarly, within the three macrophage subpopulations, the macrophage_SPP1 subpopulation, localised to fibroblastic foci, showed a significant increase in proportion, while the alveolar macrophage subpopulation was significantly reduced. Trajectory analysis revealed that fibroblasts in IPF lungs could differentiate into myofibroblasts, and alveolar macrophages could transition into the macrophage_SPP1 subpopulation. Among T-cell subpopulations, only the CD4 T_FOXP3 subpopulation exhibited a significant change, whereas all four B-cell subpopulations showed significant proportional shifts. These findings provide a comprehensive view of the cellular alterations contributing to IPF pathogenesis. Extensive interactions among various cell populations and subpopulations were identified. The proportions of various cell populations and subpopulations in IPF lungs, including endothelial cells, fibroblasts, macrophages and B cells, were significantly altered. Further in-depth investigation into the roles of cell subpopulations with significantly altered proportions in the onset and progression of IPF will provide valuable insights into the pathological mechanisms underlying the disease. This understanding could facilitate the development of novel therapeutic strategies and medications for IPF treatment.

PMID:39928535 | DOI:10.1111/jcmm.70402

J Proteome Res. 2025 Feb 10. doi: 10.1021/acs.jproteome.4c00556. Online ahead of print.

ABSTRACT

ProteoArk is a web-based tool that offers a range of computational pipelines for comprehensive analysis and visualization of mass spectrometry-based proteomics data. The application comprises four primary sections designed to address various aspects of mass spectrometry data analysis in a single platform, including label-free and labeled samples (SILAC/iTRAQ/TMT), differential expression analysis, and data visualization. ProteoArk supports postprocessing of Proteome Discoverer, MaxQuant, and MSFragger search results. The tool also includes functional enrichment analyses such as gene ontology, protein-protein interactions, pathway analysis, and differential expression analysis, which incorporate various statistical tests. By streamlining workflows and developing user-friendly interfaces, we created a robust and accessible solution for users with basic bioinformatics skills in proteomic data analysis. Users can easily create manuscript-ready figures with a single click, including principal component analysis, heatmaps (K-means and hierarchical), MA plots, volcano plots, and circular bar plots. ProteoArk is developed using the Django framework and is freely available for users [https://ciods.in/proteoark/]. Users can also download and run the standalone version of ProteoArk using Docker as described in the instructions [https://ciods.in/proteoark/dockerpage]. The application code, input data, and documentation are available online at https://github.com/ArokiaRex/proteoark. A tutorial video is available on YouTube: https://www.youtube.com/watch?v=WFMKAZ9Slq4&ab_channel=RexD.A.B.

PMID:39928856 | DOI:10.1021/acs.jproteome.4c00556

PLoS One. 2025 Feb 10;20(2):e0317612. doi: 10.1371/journal.pone.0317612. eCollection 2025.

ABSTRACT

Leguminous crops are usually sensitive to saline stress during germination and plant growth stages. The Salt Overly Sensitive (SOS) pathway is one of the key signaling pathways involved in salt translocation and tolerance in plants however, it is obscure in soybean. The current study describes the potential of calcium application on the mitigation of salinity stress and its impact on seed germination, morphological, physiological and biochemical attributes of soybean. The seeds from previously reported salt-tolerant and salt-susceptible soybean varieties were primed with water, calcium (10 and 20 mM), and stressed under 60, 80 and 100 mM NaCl and evaluated in various combinations. Results show that germination increased by 7% in calcium primed non-stressed seeds under non-stressing, whereas an improvement of 15%-25% was observed in germination under NaCl stress. Likewise, improvement in seedling length (3%-8%), plant height (9%-18%), number of nodes (3%-14%), SOD activity (20%) and Na+/K+ concentration (3%-5% reduction) in calcium primed plants, indicates alleviation of salinity-induced negative effects. In addition, this study also included in silico identification and confirmation of presence of Arabidopsis thaliana SOS genes orthologs in soybean. The research of amino acid sequences of SOS proteins from Arabidopsis thaliana (AtSOSs) within Glycine max genome displayed protein identity (60-80%) thus these identified homologs were called as GmSOS. Further phylogeny and in silico analyses showed that GmSOS orthologs contain similar gene structures, close evolutionary relationship, and same conserved motifs, reinforcing that GmSOSs belong to SOS family and they share many common features with orthologs from other species thus may perform similar functions. This is the first study that reports role of SOSs in salt-stress mitigation in soybean.

PMID:39928632 | DOI:10.1371/journal.pone.0317612

PLoS One. 2025 Feb 10;20(2):e0318139. doi: 10.1371/journal.pone.0318139. eCollection 2025.

ABSTRACT

BACKGROUND: Public involvement in reporting adverse drug reactions (ADRs) generates a broader database on drug safety. Underreporting remains a hindrance to implementing an effective pharmacovigilance system that ultimately affects public health. Hence, it is critical to appraise the public's awareness of ADR reporting and pharmacovigilance to address the gaps for the enhancement of ADR reporting rate.

OBJECTIVES: The current study explored public knowledge and attitudes toward ADR reporting in Karachi, Pakistan.

METHODS: A quantitative cross-sectional study was conducted from 3rd Jan 2022 to 30th Nov 2022 using a forty-item questionnaire to evaluate public insights regarding the ADR and its reporting. Descriptive analysis was executed to determine frequencies and percentages for the respondents' baseline characteristics and the responses toward ADR reporting. The chi-square test (χ2) was applied to determine the association between the dependent and independent variables considering a p-value < 0.05 as statistically significant.

RESULTS: The response rate of the present study was 78.3%. More than 80% of the respondents deemed that ADR occurs only with high doses of medicines and over-the-counter medications do not cause any ADR. More than 75% of the respondents did not know that the ADR reporting form is available on the Drug Regulatory Authority of Pakistan (DRAP) website; the response varied significantly with the education (p = 0.002) and social status (p = 0.0001) of the respondents. More than 50% of the participants refused to ever report an ADR to health professionals. Physicians (n = 364; 47.7%) and pharmacists (n = 253; 33.1%) were the respondents' professed most reliable sources to whom ADR can be reported; responses varied significantly with their education (p = 0.003) and age (p = 0.001).

CONCLUSIONS: The study has provided insight into the challenges and gaps needed to improve ADR reporting in Pakistan. The outcomes revealed that the public is aware of the benefits of reporting ADRs; however, they do not realize their role and the potentially significant impact on the healthcare system by contributing to ADR reporting. Therefore, it is a need of time to educate the public on the value of reporting ADRs and implement user-friendly and accessible ADR reporting systems in patient care areas to facilitate easier reporting.

PMID:39928620 | DOI:10.1371/journal.pone.0318139

Neurogenetics. 2025 Feb 10;26(1):27. doi: 10.1007/s10048-025-00806-x.

ABSTRACT

Alzheimer's disease (AD) is a slow brain degeneration disorder in which the accumulation of beta-amyloid precursor plaque and an intracellular neurofibrillary tangle of hyper-phosphorylated tau proteins in the brain have been implicated in neurodegeneration. In this study, we identified the most important genes that are unique and sensitive in the entorhinal region of the brain to target AD effectively. At first, microarrays data are selected and constructed protein-protein interaction network (PPIN) and gene regulatory network (GRN) from differentially expressed genes (DEGs) using Cytoscape software. Then, networks analysis was performed to determine hubs, bottlenecks, clusters, and signaling pathways in AD. Finally, critical genes were selected as targets for repurposing drugs. Analyzing the constructed PPIN and GRN identified CD44, ELF1, HSP90AB1, NOC4L, BYSL, RRP7A, SLC17A6, and RUVBL2 as critical genes that are dysregulated in the entorhinal region of AD suffering patients. The functional enrichment analysis revealed that DEG nodes are involved in the synaptic vesicle cycle, glutamatergic synapse, PI3K-Akt signaling pathway, retrograde endocannabinoid signaling, endocrine and other factor-regulated calcium reabsorption, ribosome biogenesis in eukaryotes, and nicotine addiction. Gentamicin, isoproterenol, and tumor necrosis factor are repurposing new drugs that target CD44, which plays an important role in the development of AD. Following our model validation using the existing experimental data, our model based on previous experimental reports suggested critical molecules and candidate drugs involved in AD for further investigations in vitro and in vivo.

PMID:39928227 | DOI:10.1007/s10048-025-00806-x