Phytomedicine. 2025 Feb 14;139:156518. doi: 10.1016/j.phymed.2025.156518. Online ahead of print.

ABSTRACT

BACKGROUND: Drug discovery from nature has a long, ethnopharmacologically-based background. Today, natural resources are undeniably vital reservoirs of active molecules or drug leads. Advances in (bio)informatics and computational biology emphasized the role of herbal medicines in the drug discovery pipeline.

PURPOSE: This review summarizes bioinformatic approaches applied in recent drug discovery from nature.

STUDY DESIGN: It examines advancements in molecular networking, pathway analysis, network pharmacology within a systems biology framework and AI for assessing the therapeutic potential of herbal preparations.

METHODS: A comprehensive literature search was conducted using Pubmed, SciFinder, and Google Database. Obtained data was analyzed and organized in subsections: AI, systems biology integrative approach, network pharmacology, pathway analysis, molecular networking, structure-based virtual screening.

RESULTS: Bioinformatic approaches is now essential for high-throughput data analysis in drug target identification, mechanism-based drug discovery, drug repurposing and side-effects prediction. Large datasets obtained from "omics" approaches require bioinformatic calculations to unveil interactions, and patterns in disease-relevant conditions. These tools enable databases annotations, pattern-matching, connections discovery, molecular relationship exploration, and data visualisation.

CONCLUSION: Despite the complexity of plant metabolites, bioinformatic approaches assist in characterization of herbal preparations and selection of bioactive molecule. It is perceived as powerful tool for uncovering multi-target effects and potential molecular mechanisms of compounds. By integrating multiple networks that connect gene-disease, drug-target and gene-drug-target, drug discovery from natural sources is experiencing a remarkable comeback.

PMID:40010031 | DOI:10.1016/j.phymed.2025.156518

Sci Rep. 2025 Feb 26;15(1):6932. doi: 10.1038/s41598-025-90450-0.

ABSTRACT

Rare diseases (RDs) are a group of pathologies that individually affect less than 1 in 2000 people but collectively impact around 7% of the world's population. Most of them affect children, are chronic and progressive, and have no specific treatment. RD patients face diagnostic challenges, with an average diagnosis time of 5 years, multiple specialist visits, and invasive procedures. This 'diagnostic odyssey' can be detrimental to their health. Machine learning (ML) has the potential to improve healthcare by providing more personalized and accurate patient management, diagnoses, and in some cases, treatments. Leveraging the MIMIC-III database and additional medical notes from different sources such as in-house data, PubMed and chatGPT, we propose a labeled dataset for early RD detection in hospital settings. Applying various supervised ML methods, including logistic regression, decision trees, support vector machine (SVM), deep learning methods (LSTM and CNN), and Transformers (BERT), we validated the use of the proposed resource, achieving 92.7% F-measure and a 96% AUC using SVM. These findings highlight the potential of ML in redirecting RD patients towards more accurate diagnostic pathways and presents a corpus that can be used for future development and refinements.

PMID:40011510 | DOI:10.1038/s41598-025-90450-0

Nature. 2025 Feb 26. doi: 10.1038/s41586-025-08622-x. Online ahead of print.

ABSTRACT

Human accelerated regions (HARs) are conserved genomic loci that have experienced rapid nucleotide substitutions following the divergence from chimpanzees1,2. HARs are enriched in candidate regulatory regions near neurodevelopmental genes, suggesting their roles in gene regulation3. However, their target genes and functional contributions to human brain development remain largely uncharacterized. Here we elucidate the cis-regulatory functions of HARs in human and chimpanzee induced pluripotent stem (iPS) cell-induced excitatory neurons. Using genomic4 and chromatin looping information, we prioritized 20 HARs and their chimpanzee orthologues for functional characterization via single-cell CRISPR interference, and demonstrated their species-specific gene regulatory functions. Our findings reveal diverse functional outcomes of HAR-mediated cis-regulation in human neurons, including attenuated NPAS3 expression by altering the binding affinities of multiple transcription factors in HAR202 and maintaining iPS cell pluripotency and neuronal differentiation capacities through the upregulation of PUM2 by 2xHAR.319. Finally, we used prime editing to demonstrate differential enhancer activity caused by several HAR26;2xHAR.178 variants. In particular, we link one variant in HAR26;2xHAR.178 to elevated SOCS2 expression and increased neurite outgrowth in human neurons. Thus, our study sheds new light on the endogenous gene regulatory functions of HARs and their potential contribution to human brain evolution.

PMID:40011774 | DOI:10.1038/s41586-025-08622-x

Basic Clin Pharmacol Toxicol. 2025 Apr;136(4):e70013. doi: 10.1111/bcpt.70013.

ABSTRACT

The aim of the present study was to investigate the effect of CYP2D6 genotypes on mirtazapine and mianserin serum concentrations. Patients were included retrospectively from a therapeutic drug monitoring service. Multiple linear regression analysis was used to investigate the effect of CYP2D6 genotype, age and sex on mirtazapine and mianserin concentration-to-dose (C/D) ratio. The study included 2315 mirtazapine patients and 474 mianserin patients who were assigned to the genotype-predicted phenotype groups of CYP2D6 poor metabolizers (PMs), intermediate metabolizers (IMs), normal metabolizers (NMs) and ultrarapid metabolizers (UMs). Multiple linear regression analysis revealed 18% and 14% higher mirtazapine C/D ratio in CYP2D6 PMs and IMs, respectively, compared with NMs (p ≤ 0.004). For mianserin, the C/D ratio was 80% and 45% higher in PMs and IMs, respectively, compared with NMs (p < 0.001). The C/D ratio in UMs did not differ from NMs for either drug (p ≥ 0.3). In conclusion, CYP2D6 genotype was only associated with a minor change in mirtazapine serum concentration. The association between CYP2D6 genotype and mianserin serum concentration was greater, with 80% higher mianserin C/D ratio in CYP2D6 PMs compared with NMs.

PMID:40010695 | DOI:10.1111/bcpt.70013

Cell Death Dis. 2025 Feb 26;16(1):134. doi: 10.1038/s41419-025-07447-w.

ABSTRACT

Acyl CoA binding protein encoded by diazepam binding inhibitor (ACBP/DBI) is a tissue hormone that stimulates lipo-anabolic responses and inhibits autophagy, thus contributing to aging and age-related diseases. Protein expression profiling of ACBP/DBI was performed on mouse tissues to identify organs in which this major tissue hormone is expressed. Transcriptomic and proteomic data bases corroborated a high level of human-mouse interspecies conservation of ACBP/DBI expression in different organs. Single-cell RNA-seq data confirmed that ACBP/DBI was strongly expressed by parenchymatous cells from specific human and mouse organs (e.g., kidney, large intestine, liver, lung) as well as by myeloid or glial cells from other organs (e.g., adipose tissue, brain, eye) following a pattern that was conserved among the two species. We identified a panel of 44 mRNAs that are strongly co-expressed with ACBP/DBI mRNA in normal and malignant human and normal mouse tissues. Of note, 22 (50%) of these co-expressed mRNAs encode proteins localized at mitochondria, and mRNAs with metabolism-related functions are strongly overrepresented (66%). Systematic data mining was performed to identify transcription factors that regulate ACBP/DBI expression in human and mouse. Several transcription factors, including growth response 1 (EGR1), E2F Transcription Factor 1 (E2F1, which interacts with retinoblastoma, RB) and transformation-related protein 53 (TRP53, best known as p53), which are endowed with oncosuppressive effects, consistently repress ACBP/DBI expression as well as its co-expressed mRNAs across multiple datasets, suggesting a mechanistic basis for a coregulation network. Furthermore, we identified multiple transcription factors that transactivate ACBP/DBI gene expression together with its coregulation network. Altogether, this study indicates the existence of conserved mechanisms determining the expression of ACBP/DBI in specific cell types of the mammalian organism.

PMID:40011442 | DOI:10.1038/s41419-025-07447-w

J Cheminform. 2025 Feb 26;17(1):24. doi: 10.1186/s13321-025-00960-2.

ABSTRACT

In this study, we propose a neural network- based approach to analyze IR spectra and detect the presence of functional groups. Our neural network architecture is based on the concept of learning split representations. We demonstrate that our method achieves favorable validation performance using the NIST dataset. Furthermore, by incorporating additional data from the open-access research data repository Chemotion, we show that our model improves the classification performance for nitriles and amides. Scientific contribution: Our method exclusively uses IR data as input for a neural network, making its performance, unlike other well-performing models, independent of additional data types obtained from analytical measurements. Furthermore, our proposed method leverages a deep learning model that outperforms previous approaches, achieving F1 scores above 0.7 to identify 17 functional groups. By incorporating real-world data from various laboratories, we demonstrate how open-access, specialized research data repositories can serve as yet unexplored, valuable benchmark datasets for future machine learning research.

PMID:40011923 | DOI:10.1186/s13321-025-00960-2

BMC Geriatr. 2025 Feb 26;25(1):130. doi: 10.1186/s12877-025-05771-6.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly population. The accurate classification of AD stages is essential for slowing disease progression and guiding effective interventions. However, limited sample sizes continue to present a significant challenge in classifying the stages of AD progression. Addressing this obstacle is crucial for improving diagnostic accuracy and optimizing treatment strategies for those affected by AD.

METHODS: In this study, we proposed cross-scale equilibrium pyramid coupling (CSEPC), which is a novel diagnostic algorithm designed for small-sample multimodal medical imaging data. CSEPC leverages scale equilibrium theory and modal coupling properties to integrate semantic features from different imaging modalities and across multiple scales within each modality. The architecture first extracts balanced multiscale features from structural MRI (sMRI) data and functional MRI (fMRI) data using a cross-scale pyramid module. These features are then combined through a contrastive learning-based cosine similarity coupling mechanism to capture intermodality associations effectively. This approach enhances the representation of both inter- and intramodal features while significantly reducing the number of learning parameters, making it highly suitable for small sample environments. We validated the effectiveness of the CSEPC model through experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and demonstrated its superior performance in diagnosing and staging AD.

RESULTS: Our experimental results demonstrate that the proposed model matches or exceeds the performance of models used in previous studies in AD classification. Specifically, the model achieved an accuracy of 85.67% and an area under the curve (AUC) of 0.98 in classifying the progression from mild cognitive impairment (MCI) to AD. To further validate its effectiveness, we used our method to diagnose different stages of AD. In both classification tasks, our approach delivered superior performance.

CONCLUSIONS: In conclusion, the performance of our model in various tasks has demonstrated its significant potential in the field of small-sample multimodal medical imaging classification, particularly AD classification. This advancement could significantly assist clinicians in effectively managing and intervening in the disease progression of patients with early-stage AD.

PMID:40011826 | DOI:10.1186/s12877-025-05771-6

BMC Med Imaging. 2025 Feb 26;25(1):66. doi: 10.1186/s12880-025-01606-3.

ABSTRACT

BACKGROUND: This study employed a convolutional neural network (CNN) to analyze computed tomography (CT) scans with the aim of differentiating among renal tumors according to histologic sub-type.

METHODS: Contrast-enhanced CT images were collected from patients with renal tumors. The patient cohort was randomly split to create a training dataset (90%) and a testing dataset (10%). Following image dataset augmentation, Inception V3 and Resnet50 models were used to differentiate between renal tumors subtypes, including angiomyolipoma (AML), oncocytoma, clear cell renal cell carcinoma (ccRCC), chromophobe renal cell carcinoma (chRCC), and papillary renal cell carcinoma (pRCC). 5-fold cross validation was then used to evaluate the models in terms of classification performance.

RESULTS: The study cohort comprised 554 patients, including those with angiomyolipoma (n = 67), oncocytoma (n = 34), clear cell renal cell carcinoma (n = 246), chromophobe renal cell carcinoma (n = 124), and papillary renal cell carcinoma (n = 83). Dataset augmentation of the training dataset included this to 4238 CT images for analysis. The accuracy of the models was as follows: Inception V3 (0.830) and Resnet 50 (0.849).

CONCLUSION: This study demonstrated the efficacy of using deep learning models for the classification of renal tumor subtypes from contrast-enhanced CT images. While the models showed promising accuracy, further development is necessary to improve their clinical applicability.

PMID:40011809 | DOI:10.1186/s12880-025-01606-3

Sci Rep. 2025 Feb 26;15(1):6935. doi: 10.1038/s41598-025-91418-w.

ABSTRACT

With the development of deep learning, the potential for its use in remaining useful life (RUL) has substantially increased in recent years due to the powerful data processing capabilities. However, the relationships and interdependencies of operation parameters in non-Euclidean space are ignored utilizing the current deep learning-based methods during the degradation process for engine. To address this challenge, an improved sand cat swarm optimization-assisted Graph SAmple and aggregate and gate recurrent unit (ISCSO-GraphSage-GRU) is proposed to achieve RUL prediction in this work. Firstly, the maximum information coefficient (MIC) is utilized for describing the interdependent relations of measured parameters. Building on this foundation, the constructed graph data is used as input to GraphSage-GRU so as to overcoming the shortcomings of existing deep learning methods. Additionally, this work proposed an improved sand cat swarm optimization (ISCSO) to improve the predicted performance of GraphSage-GRU, including tent mapping in population initialization and a novel adaptive approach enhance the exploration and exploitation of sand cat swarm optimization. The CMAPSS dataset is used to validate the effectiveness and advancedness of ISCSO-GraphSage-GRU, and the experimental results show that the R2 of the ISCSO-GraphSage-GRU is greater than 0.99, RMSE is less than 6.

PMID:40011762 | DOI:10.1038/s41598-025-91418-w

Commun Biol. 2025 Feb 26;8(1):311. doi: 10.1038/s42003-025-07744-2.

ABSTRACT

In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replication foci in the nucleus. Despite the genetic association of aberrant DNA replication with numerous human diseases, the labour-intensive methods employed to study DNA replication have hindered large-scale analyses of its roles in pathological processes. In this study, we employ two distinct methodologies. We first apply supervised machine learning, successfully classifying S-phase patterns in wild-type mouse embryonic stem cells (mESCs), while additionally identifying altered replication dynamics in Rif1-deficient mESCs. Given the constraints imposed by a classification-based approach, we then develop an unsupervised method for large-scale detection of aberrant S-phase cells. Such a method, which does not aim to classify patterns based on pre-defined categories but rather detects differences autonomously, closely recapitulates expected differences across genotypes. We therefore extend our approach to a well-characterised cellular model of inducible deregulated origin firing, involving cyclin E overexpression. Through parallel EdU- and PCNA-based analyses, we demonstrate the potential applicability of our method to patient samples, offering a means to identify the contribution of deregulated DNA replication to a plethora of pathogenic processes.

PMID:40011665 | DOI:10.1038/s42003-025-07744-2

Commun Biol. 2025 Feb 26;8(1):320. doi: 10.1038/s42003-025-07684-x.

ABSTRACT

Mitochondria play an essential role in the life cycle of eukaryotic cells. However, we still don't know how their ultrastructure, like the cristae of the inner membrane, dynamically evolves to regulate these fundamental functions, in response to external conditions or during interaction with other cell components. Although high-resolution fluorescent microscopy coupled with recently developed innovative probes can reveal this structural organization, their long-term, fast and live 3D imaging remains challenging. To address this problem, we have developed a CNN, called DeepCristae, to restore mitochondria cristae in low spatial resolution microscopy images. Our network is trained from 2D STED images using a novel loss specifically designed for cristae restoration. To efficiently increase the size of the training set, we also developed a random image patch sampling centered on mitochondrial areas. To evaluate DeepCristae, quantitative assessments are carried out using metrics we derived by focusing on the mitochondria and cristae pixels rather than on the whole image as usual. Depending on the conditions of use indicated, DeepCristae works well on broad microscopy modalities (Stimulated Emission Depletion (STED), Live-SR, AiryScan and LLSM). It is ultimately applied in the context of mitochondrial network dynamics during interaction with endo/lysosome membranes.

PMID:40011620 | DOI:10.1038/s42003-025-07684-x

Sci Rep. 2025 Feb 26;15(1):6967. doi: 10.1038/s41598-025-90649-1.

ABSTRACT

The ADHD detector analyzes behavioral, cognitive, or physiological data (e.g., EEG, eye-tracking, or surveys) to identify patterns associated with ADHD symptoms. This work offers a more sophisticated method of detecting ADHD by overcoming the main drawbacks of existing approaches in terms of data processing, detection accuracy, and computational time. The work is inspired by the fact that Deep Learning (DL) frameworks could transform the existing detection systems of ADHD. In the proposed framework, there is a new NeuroDCT-ICA module for the preprocessing of raw EEG data, which guarantees the elimination of noise and extraction of informative features. Moreover, the method introduces a novel RhinoFish Optimization (RFO) algorithm for selecting optimal features, which enhance the data processing capacity and the stability of the system. As a core of the approach, there is the ADHD-AttentionNet - the deep learning-based model aimed at improving the accuracy and confidence of ADHD identification. The model is validated with the standard metrics, and the performance of the model is outstanding as it has high accuracy of 98.52%, F-score of 98.26% and specificity of 98.16%. These outcomes show that the proposed model yields better accuracy in detecting ADHD related patterns.

PMID:40011599 | DOI:10.1038/s41598-025-90649-1

Sci Rep. 2025 Feb 26;15(1):6966. doi: 10.1038/s41598-025-91028-6.

ABSTRACT

Chronic Kidney Disease (CKD) is a significant problem in today's healthcare since it is challenging to detect until it has improved significantly, which increases medical expenses. If CKD was detected early, the patient might qualify for more effective treatment and prevent the disease from spreading further. Presently, existing methods that effectively detect CKD cannot detect symptoms early on. This problem motivates researchers to work on a predictive model that successfully detects disease symptoms in the early stages. This study introduces a novel Adaptive Particle Swarm Optimization (APSO)-optimized Echo State Network (ESN) model designed to overcome key limitations of existing methods. ESNs, while effective in processing temporal sequences, are highly sensitive to hyperparameter settings such as spectral radius, input scaling, and sparsity, which directly impact stability, memory retention, and predictive Classification Accuracy (CA). To address this, APSO optimizes these hyperparameters dynamically, ensuring a balanced trade-off between stability and computational efficiency. Moreover, Random Matrix Theory (RMT) is integrated into APSO to regulate the spectral radius, enhancing the ESN's capability to handle long-term dependencies while maintaining stability in training. This investigation exploited the Medical Information Mart for Intensive Care-III (MIMIC-III) dataset to train the model they developed. The proposed method employs this data collection to analyze the highly complex temporal sequences signifying CKD is present. The hyperparameters of the ESN, such as the range of the spectral region and the input data sizing, can be optimized in real-time with APSO by applying Random Matrix Theory (RMT). Compared with different recognized models, such as conventional ESN and standard M, the recommended APSO + ESN proved to have higher CA in medical investigations. The APSO + ESN improved the subsequent highest-performing model by 2% in recall and 3% in precision and attained a CA of 99.6%.

PMID:40011588 | DOI:10.1038/s41598-025-91028-6

Sci Rep. 2025 Feb 26;15(1):6911. doi: 10.1038/s41598-025-89709-3.

ABSTRACT

Wearable devices face a significant challenge in balancing battery life with performance, often leading to frequent recharging and reduced user satisfaction. In this paper, we introduce the SmartAPM (Smart Adaptive Power Management) framework, a novel approach that leverages deep reinforcement learning (DRL) to optimize power management in wearable devices. The key objective of SmartAPM is to prolong battery life while enhancing user experience through dynamic adjustments to specific usage patterns. We compiled a comprehensive dataset by integrating user activity data, sensor readings, and power consumption metrics from various sources, including WISDM, UCI HAR, and ExtraSensory. Synthetic power profiles and device specifications were incorporated into the dataset to enhance training. SmartAPM employs a multi-agent deep reinforcement learning framework that combines on-device and cloud-based learning techniques, as well as transfer learning, to enhance personalization. Simulations on wearable devices demonstrate that SmartAPM can extend battery life by 36% compared to traditional methods, while also increasing user satisfaction by 25%. The system adapts to new usage patterns within 24 h and utilizes less than 5% of the device's resources. SmartAPM has the potential to revolutionize energy management in wearable devices, inspiring a new era of battery efficiency and user satisfaction.

PMID:40011572 | DOI:10.1038/s41598-025-89709-3

Sci Rep. 2025 Feb 26;15(1):6916. doi: 10.1038/s41598-025-91293-5.

ABSTRACT

As the quantity of motor vehicles and drivers experiences a continuous upsurge, the road driving environment has grown progressively more complex. This complexity has led to a concomitant increase in the probability of traffic accidents. Ample research has demonstrated that distracted driving constitutes a primary human - related factor precipitating these accidents. Therefore, the real - time monitoring and issuance of warnings regarding distracted driving behaviors are of paramount significance. In this research, an intelligent driver state monitoring methodology founded on the RES - SE - CNN model architecture is proposed. When compared with three classical models, namely VGG19, DenseNet121, and ResNet50, the experimental outcomes indicate that the RES - SE - CNN model exhibits remarkable performance in the detection of driver distraction. Specifically, it attains a correct recognition rate of 97.28%. The RES - SE - CNN network architecture model is characterized by lower memory occupancy, rendering it more amenable to deployment on vehicle mobile terminals. This study validates the potential application of the intelligent driver distraction monitoring model, which is based on transfer learning, within the actual driving environment.

PMID:40011564 | DOI:10.1038/s41598-025-91293-5

Biogerontology. 2025 Feb 26;26(2):64. doi: 10.1007/s10522-025-10208-z.

ABSTRACT

Cellular senescence and hypoxia-inducible factor (HIF) signaling are crucial in pulmonary aging and age-related lung diseases such as chronic obstructive pulmonary disease idiopathic pulmonary fibrosis and lung cancer. HIF plays a pivotal role in cellular adaptation to hypoxia, regulating processes like angiogenesis, metabolism, and inflammation. Meanwhile, cellular senescence leads to irreversible cell cycle arrest, triggering the senescence-associated secretory phenotype which contributes to chronic inflammation, tissue remodeling, and fibrosis. Dysregulation of these pathways accelerates lung aging and disease progression by promoting oxidative stress, mitochondrial dysfunction, and epigenetic alterations. Recent studies indicate that HIF and senescence interact at multiple levels, where HIF can both induce and suppress senescence, depending on cellular conditions. While transient HIF activation supports tissue repair and stress resistance, chronic dysregulation exacerbates pulmonary pathologies. Furthermore, emerging evidence suggests that targeting HIF and senescence pathways could offer new therapeutic strategies to mitigate age-related lung diseases. This review explores the intricate crosstalk between these mechanisms, shedding light on how their interplay influences pulmonary aging and disease progression. Additionally, we discuss potential interventions, including senolytic therapies and HIF modulators, that could enhance lung health and longevity.

PMID:40011266 | DOI:10.1007/s10522-025-10208-z

Pulm Pharmacol Ther. 2025 Feb 24:102346. doi: 10.1016/j.pupt.2025.102346. Online ahead of print.

ABSTRACT

BACKGROUND: Baricitinib and nintedanib can target inflammation and fibrosis respectively, which are the two most important processes in idiopathic pulmonary fibrosis (IPF). However, it is still unknown whether targeting these two processes simultaneously can synergistically improve the therapeutic effect of IPF. Therefore, it is necessary to predict the possible translational potential through preclinical studies.

METHODS: We evaluated both the in vitro and in vivo efficacy of a drug combination, nintedanib with baricitinb, a JAK1/JAK2 inhibitor. We first examined the fibroblast proliferation and myofibroblast differentiation of single agents or combinations by the MTT assay. Then we determined the migration of the fibroblasts by a wound healing assay. Meanwhile, we quantified the protein level of related growth factor or cytokines in the cell supernatant by ELISA. Finally, we investigated the therapeutic potential and mechanism in a bleomycin-induced mouse model.

RESULTS: Our results showed that the combination of nintedanib and baricitinib was more effective in suppressing fibroblast proliferation, myofibroblast transformation and fibroblast migration compared to either agent alone. In a bleomycin-induced IPF mouse model, the combination therapy resulted in a higher survival rate, increased body weight, and a lower lung/body weight ratio compared to the individual drugs. Moreover, both drugs improved lung functions in mice, but their combined administration led to superior outcomes. Histopathological analysis also revealed that the combination therapy mitigated pulmonary inflammation and fibrosis to a greater extent than the individual compounds. Mechanistically, baricitinib appears to orchestrate the effects of nintedanib in IPF by modulating the expression of genes such as il-6, tgf-β, col1α1 and fibronectin.

CONCLUSION: The synergistic targeting of inflammation by baricitinib and fibrosis by nintedanib preclinically improves IPF outcomes, thus suggesting their potential as a novel combination therapy for this condition.

PMID:40010629 | DOI:10.1016/j.pupt.2025.102346

Autoimmun Rev. 2025 Feb 24:103782. doi: 10.1016/j.autrev.2025.103782. Online ahead of print.

ABSTRACT

B cells play a key role in the pathophysiology of systemic sclerosis (SSc). While they are less characterized than their circulating counterparts, tissue-infiltrating B cells may have a more direct pathological role in tissues. In this review, we decipher the multiple evidence of B cells infiltration in the skin and lungs of SSc patients and animal models of SSc but also of other chronic fibrotic diseases with similar pathological mechanisms such as chronic graft versus host disease, idiopathic pulmonary fibrosis or morphea. We also recapitulate the current knowledge about mechanisms of B cells infiltration and their functions in tissues. Finally, we discuss B cell targeted therapies, and their specific impact on infiltrated B cells. Understanding the local consequences of infiltrating B cells is an important step for a better management of patients and the improvement of therapies in SSc.

PMID:40010623 | DOI:10.1016/j.autrev.2025.103782

Genome Biol. 2025 Feb 26;26(1):41. doi: 10.1186/s13059-025-03488-8.

ABSTRACT

We present GuideScan2 for memory-efficient, parallelizable construction of high-specificity CRISPR guide RNA (gRNA) databases and user-friendly design and analysis of individual gRNAs and gRNA libraries for targeting coding and non-coding regions in custom genomes. GuideScan2 analysis identifies widespread confounding effects of low-specificity gRNAs in published CRISPR screens and enables construction of a gRNA library that reduces off-target effects in a gene essentiality screen. GuideScan2 also enables the design and experimental validation of allele-specific gRNAs in a hybrid mouse genome. GuideScan2 will facilitate CRISPR experiments across a wide range of applications.

PMID:40011959 | DOI:10.1186/s13059-025-03488-8

Nature. 2025 Feb 26. doi: 10.1038/s41586-025-08592-0. Online ahead of print.

ABSTRACT

A comprehensive, computable representation of the functional repertoire of all macromolecules encoded within the human genome is a foundational resource for biology and biomedical research. The Gene Ontology Consortium has been working towards this goal by generating a structured body of information about gene functions, which now includes experimental findings reported in more than 175,000 publications for human genes and genes in experimentally tractable model organisms1,2. Here, we describe the results of a large, international effort to integrate all of these findings to create a representation of human gene functions that is as complete and accurate as possible. Specifically, we apply an expert-curated, explicit evolutionary modelling approach to all human protein-coding genes. This approach integrates available experimental information across families of related genes into models that reconstruct the gain and loss of functional characteristics over evolutionary time. The models and the resulting set of 68,667 integrated gene functions cover approximately 82% of human protein-coding genes. The functional repertoire reveals a marked preponderance of molecular regulatory functions, and the models provide insights into the evolutionary origins of human gene functions. We show that our set of descriptions of functions can improve the widely used genomic technique of Gene Ontology enrichment analysis. The experimental evidence for each functional characteristic is recorded, thereby enabling the scientific community to help review and improve the resource, which we have made publicly available.

PMID:40011791 | DOI:10.1038/s41586-025-08592-0