Biomed Eng Online. 2025 Apr 2;24(1):39. doi: 10.1186/s12938-025-01360-1.

ABSTRACT

BACKGROUND: Hands-and-knees crawling is a promising rehabilitation intervention for infants with motor impairments, while research on assistive crawling devices for rehabilitation training was still in its early stages. In particular, precisely generating motion trajectories is a prerequisite to controlling exoskeleton assistive devices, and deep learning-based prediction algorithms, such as Long-Short-Term Memory (LSTM) networks, have proven effective in forecasting joint trajectories of gait. Despite this, no previous studies have focused on forecasting the more variable and complex trajectories of infant crawling. Therefore, this paper aims to explore the feasibility of using LSTM networks to predict crawling trajectories, thereby advancing our understanding of how to actively control crawling rehabilitation training robots.

METHODS: We collected joint trajectory data from 20 healthy infants (11 males and 9 females, aged 8-15 months) as they crawled on hands and knees. This study implemented LSTM networks to forecast bilateral elbow and knee trajectories based on corresponding joint angles. The data set comprised 58, 782 time steps, each containing 4 joint angles. We partitioned the data set into 70% for training and 30% for testing to evaluate predictive performance. We investigated a total of 24 combinations of input and output time-frames, with window sizes for input vectors ranging from 10, 15, 20, 30, 40, 50, 70, and 100 time steps, and output vectors from 5, 10, and 15 steps. Evaluation metrics included Mean Absolute Error (MAE), Mean Squared Error (MSE), and Correlation Coefficient (CC) to assess prediction accuracy.

RESULTS: The results indicate that across various input-output windows, the MAE for elbow joints ranged from 0.280 to 4.976°, MSE ranged from 0.203° to 59.186°, and CC ranged from 89.977% to 99.959%. For knee joints, MAE ranged from 0.277 to 4.262°, MSE from 0.229 to 53.272°, and CC from 89.454% to 99.944%. Results also show that smaller output window sizes lead to lower prediction errors. As expected, the LSTM predicting 5 output time steps has the lowest average error, while the LSTM predicting 15 time steps has the highest average error. In addition, variations in input window size had a minimal impact on average error when the output window size was fixed. Overall, the optimal performance for both elbow and knee joints was observed with input-output window sizes of 30 and 5 time steps, respectively, yielding an MAE of 0.295°, MSE of 0.260°, and CC of 99.938%.

CONCLUSIONS: This study demonstrates the feasibility of forecasting infant crawling trajectories using LSTM networks, which could potentially integrate with exoskeleton control systems. It experimentally explores how different input and output time-frames affect prediction accuracy and sets the stage for future research focused on optimizing models and developing effective control strategies to improve assistive crawling devices.

PMID:40176123 | DOI:10.1186/s12938-025-01360-1

J Imaging Inform Med. 2025 Apr 2. doi: 10.1007/s10278-025-01489-4. Online ahead of print.

ABSTRACT

Chronic kidney disease (CKD) remains a major public health concern, requiring better predictive models for early intervention. This study evaluates a deep learning model (DLM) that utilizes raw chest X-ray (CXR) data to predict moderate to severe kidney function decline. We analyzed data from 79,219 patients with an estimated Glomerular Filtration Rate (eGFR) between 65 and 120, segmented into development (n = 37,983), tuning (n = 15,346), internal validation (n = 14,113), and external validation (n = 11,777) sets. Our DLM, pretrained on CXR-report pairs, was fine-tuned with the development set. We retrospectively examined data spanning April 2011 to February 2022, with a 5-year maximum follow-up. Primary and secondary endpoints included CKD stage 3b progression, ESRD/dialysis, and mortality. The overall concordance index (C-index) values for the internal and external validation sets were 0.903 (95% CI, 0.885-0.922) and 0.851 (95% CI, 0.819-0.883), respectively. In these sets, the incidences of progression to CKD stage 3b at 5 years were 19.2% and 13.4% in the high-risk group, significantly higher than those in the median-risk (5.9% and 5.1%) and low-risk groups (0.9% and 0.9%), respectively. The sex, age, and eGFR-adjusted hazard ratios (HR) for the high-risk group compared to the low-risk group were 16.88 (95% CI, 10.84-26.28) and 7.77 (95% CI, 4.77-12.64), respectively. The high-risk group also exhibited higher probabilities of progressing to ESRD/dialysis or experiencing mortality compared to the low-risk group. Further analysis revealed that the high-risk group compared to the low/median-risk group had a higher prevalence of complications and abnormal blood/urine markers. Our findings demonstrate that a DLM utilizing CXR can effectively predict CKD stage 3b progression, offering a potential tool for early intervention in high-risk populations.

PMID:40175823 | DOI:10.1007/s10278-025-01489-4

Microb Ecol. 2025 Apr 2;88(1):22. doi: 10.1007/s00248-025-02516-1.

ABSTRACT

Shifts in agricultural land use over the past 200 years have led to a loss of nearly 50% of existing wetlands in the USA, and agricultural activities contribute up to 65% of the nutrients that reach the Mississippi River Basin, directly contributing to biological disasters such as the hypoxic Gulf of Mexico "Dead" Zone. Federal efforts to construct and restore wetland habitats have been employed to mitigate the detrimental effects of eutrophication, with an emphasis on the restoration of ecosystem services such as nutrient cycling and retention. Soil microbial assemblages drive biogeochemical cycles and offer a unique and sensitive framework for the accurate evaluation, restoration, and management of ecosystem services. The purpose of this study was to elucidate patterns of soil bacteria within and among wetlands by developing diversity profiles from high-throughput sequencing data, link functional gene copy number of nitrogen cycling genes to measured nutrient flux rates collected from flow-through incubation cores, and predict nutrient flux using microbial assemblage composition. Soil microbial assemblages showed fine-scale turnover in soil cores collected across the topsoil horizon (0-5 cm; top vs bottom partitions) and were structured by restoration practices on the easements (tree planting, shallow water, remnant forest). Connections between soil assemblage composition, functional gene copy number, and nutrient flux rates show the potential for soil bacterial assemblages to be used as bioindicators for nutrient cycling on the landscape. In addition, the predictive accuracy of flux rates was improved when implementing deep learning models that paired connected samples across time.

PMID:40175811 | DOI:10.1007/s00248-025-02516-1

Nat Rev Cancer. 2025 Apr 2. doi: 10.1038/s41568-025-00811-0. Online ahead of print.

NO ABSTRACT

PMID:40175619 | DOI:10.1038/s41568-025-00811-0

Sci Rep. 2025 Apr 2;15(1):11227. doi: 10.1038/s41598-025-92641-1.

ABSTRACT

Strawberry grading by picking robots can eliminate the manual classification, reducing labor costs and minimizing the damage to the fruit. Strawberry size or weight is a key factor in grading, with accurate weight estimation being crucial for proper classification. In this paper, we collected 1521 sets of strawberry RGB-D images using a depth camera and manually measured the weight and size of the strawberries to construct a training dataset for the strawberry weight regression model. To address the issue of incomplete depth images caused by environmental interference with depth cameras, this study proposes a multimodal point cloud completion method specifically designed for symmetrical objects, leveraging RGB images to guide the completion of depth images in the same scene. The method follows a process of locating strawberry pixel regions, calculating centroid coordinates, determining the symmetry axis via PCA, and completing the depth image. Based on this approach, a multimodal fusion regression model for strawberry weight estimation, named MMF-Net, is developed. The model uses the completed point cloud and RGB image as inputs, and extracts features from the RGB image and point cloud by EfficientNet and PointNet, respectively. These features are then integrated at the feature level through gradient blending, realizing the combination of the strengths of both modalities. Using the Percent Correct Weight (PCW) metric as the evaluation standard, this study compares the performance of four traditional machine learning methods, Support Vector Regression (SVR), Multilayer Perceptron (MLP), Linear Regression, and Random Forest Regression, with four point cloud-based deep learning models, PointNet, PointNet++, PointMLP, and Point Cloud Transformer, as well as an image-based deep learning model, EfficientNet and ResNet, on single-modal datasets. The results indicate that among traditional machine learning methods, the SVR model achieved the best performance with an accuracy of 77.7% (PCW@0.2). Among deep learning methods, the image-based EfficientNet model obtained the highest accuracy, reaching 85% (PCW@0.2), while the PointNet + + model demonstrated the best performance among point cloud-based models, with an accuracy of 54.3% (PCW@0.2). The proposed multimodal fusion model, MMF-Net, achieved an accuracy of 87.66% (PCW@0.2), significantly outperforming both traditional machine learning methods and single-modal deep learning models in terms of precision.

PMID:40175474 | DOI:10.1038/s41598-025-92641-1

Sci Rep. 2025 Apr 2;15(1):11349. doi: 10.1038/s41598-025-89210-x.

ABSTRACT

Deep neural networks (DNNs) have demonstrated remarkable capabilities in medical applications, including digital pathology, where they excel at analyzing complex patterns in medical images to assist in accurate disease diagnosis and prognosis. However, concerns have arisen about potential biases in The Cancer Genome Atlas (TCGA) dataset, a comprehensive repository of digitized histopathology data and serves as both a training and validation source for deep learning models, suggesting that over-optimistic results of model performance may be due to reliance on biased features rather than histological characteristics. Surprisingly, recent studies have confirmed the existence of site-specific bias in the embedded features extracted for cancer-type discrimination, leading to high accuracy in acquisition site classification. This biased behavior motivated us to conduct an in-depth analysis to investigate potential causes behind this unexpected biased ability toward site-specific pattern recognition. The analysis was conducted on two cutting-edge DNN models: KimiaNet, a state-of-the-art DNN trained on TCGA images, and the self-trained EfficientNet. In this research study, the balanced accuracy metric is used to evaluate the performance of a model trained to classify data centers, which was originally designed to learn cancerous patterns, with the aim of investigating the potential factors contributing to the higher balanced accuracy in data center detection.

PMID:40175463 | DOI:10.1038/s41598-025-89210-x

Sci Rep. 2025 Apr 2;15(1):11352. doi: 10.1038/s41598-025-91394-1.

ABSTRACT

To address the challenges associated with dense and occluded targets in small target detection utilizing unmanned aerial vehicle (UAV), we propose an enhanced detection algorithm referred as the YOLOv8n-ACW. Building upon the YOLOv8n baseline network model, we have integrated Adown into the Backbone and developed a CCDHead to further improve the drone's capability to recognize small targets. Additionally, WIoU-V3 has been introduced as the loss function. Experiment results derived from the Visdrone2019 dataset indicate that, the YOLOv8n- ACW has achieved a 4.2% increase in mAP50(%) compared to the baseline model, while simultaneously reducing the parameter count by 36.7%, exhibiting superior capabilities in detecting small targets. Furthermore, utilizing a self-constructed dataset of G5-Pro drones for target detection experiments, the results indicate that the enhanced model has robust generalization capabilities in real-world environments. The UAV target detection experiment combines experimental simulation with real-world testing, while combining scientific exploration with educational objectives. This experiment has high fidelity, excellent functional scalability, and strong practicality, aiming to cultivate students' comprehensive practical and innovative abilities.

PMID:40175443 | DOI:10.1038/s41598-025-91394-1

Respir Res. 2025 Apr 2;26(1):123. doi: 10.1186/s12931-025-03195-9.

ABSTRACT

BACKGROUND: Cardiopulmonary exercise testing (CPET) is feasible, valid, reliable, and clinically useful in interstitial lung disease (ILD). However, maximal CPET values are often presented relative to body mass, whereas fat-free mass (FFM) may better reflect metabolically active muscle during exercise. Moreover, despite the value of maximal parameters, people with ILD do not always exercise maximally and therefore clinically relevant submaximal parameters must be identified. Therefore, this study assessed peak oxygen uptake (VO2peak) relative to FFM, identifying the validity of common scaling techniques; as well as characterising the oxygen uptake efficiency slope (OUES) and plateau (OUEP) as possible submaximal parameters.

METHODS: Participants with ILD underwent assessment of body composition and CPET via cycle ergometry during a single study visit. To determined effectiveness of scaling for body size, both body mass and FFM were scaled using ratio-standard (X/Y) and allometric (X/Yb) techniques. Pearsons's correlations determined agreement between OUES, OUEP, and parameters of lung function. Cohens kappa (κ) assessed agreement between OUES, OUEP and VO2peak.

RESULTS: A total of 24 participants (7 female; 69.8 ± 7.5 years; 17 with idiopathic pulmonary fibrosis) with ILD completed the study. Maximal exercise parameters did not require allometric scaling, and when scaled to FFM, it was shown that women have a significantly higher VO2peak than men (p = 0.044). Results also indicated that OUEP was significantly and positively correlated with DLCO (r = 0.719, p < 0.001), and held moderate agreement with VO2peak (κ = 0.50, p < 0.01).

CONCLUSION: This study identified that ratio-standard scaling is sufficient in removing residual effects of body size from VO2peak, and that VO2peak is higher in women when FFM is considered. Encouragingly, this study also identified OUEP as a possible alternative submaximal marker in people with ILD, and thus warrants further examination.

PMID:40176026 | DOI:10.1186/s12931-025-03195-9

Cardiovasc Diabetol. 2025 Apr 2;24(1):152. doi: 10.1186/s12933-025-02701-z.

ABSTRACT

BACKGROUND: Disruption of lipid metabolism contributes to increased cardiovascular risk in diabetes.

METHODS: We evaluated the associations between serum lipidomic profile and subclinical carotid atherosclerosis (SCA) in type 1 (T1D) and type 2 (T2D) diabetes, and in subjects without diabetes (controls) in a cross-sectional study. All subjects underwent a lipidomic analysis using ultra-high performance liquid chromatography-electrospray ionization tandem mass spectrometry, carotid ultrasound (mode B) to assess SCA, and clinical assessment. Multiple linear regression models were used to assess the association between features and the presence and burden of SCA in subjects with T1D, T2D, and controls separately. Additionally, multiple linear regression models with interaction terms were employed to determine features significantly associated with SCA within risk groups, including smoking habit, hypertension, dyslipidaemia, antiplatelet use and sex. Depending on the population under study, different confounding factors were considered and adjusted for, including sample origin, sex, age, hypertension, dyslipidaemia, body mass index, waist circumference, glycated haemoglobin, glucose levels, smoking habit, diabetes duration, antiplatelet use, and alanine aminotransferase levels.

RESULTS: A total of 513 subjects (151 T1D, 155 T2D, and 207 non-diabetic control) were included, in whom the percentage with SCA was 48.3%, 49.7%, and 46.9%, respectively. A total of 27 unique lipid species were associated with SCA in subjects with T2D, in former/current smokers with T2D, and in individuals with T2D without dyslipidaemia. Phosphatidylcholines and diacylglycerols were the main SCA-associated lipidic classes. Ten different species of phosphatidylcholines were up-regulated, while 4 phosphatidylcholines containing polyunsaturated fatty acids were down-regulated. One diacylglycerol was down-regulated, while the other 3 were positively associated with SCA in individuals with T2D without dyslipidaemia. We discovered several features significantly associated with SCA in individuals with T1D, but only one sterol could be partially annotated.

CONCLUSIONS: We revealed a significant disruption of lipid metabolism associated with SCA in subjects with T2D, and a larger SCA-associated disruption in former/current smokers with T2D and individuals with T2D who do not undergo lipid-lowering treatment.

PMID:40176064 | DOI:10.1186/s12933-025-02701-z

Biotechnol Lett. 2025 Apr 2;47(2):38. doi: 10.1007/s10529-025-03579-3.

ABSTRACT

OBJECTIVE: The aim of this study is to identify therapeutic cargos within mesenchymal stem cell (MSC)-derived small extracellular vesicles (sEVs) for the treatment of liver fibrosis, a condition that poses significant health risks.

RESULTS: sEVs from human umbilical cord-derived MSCs (UCMSCs) and their differentiated hepatocyte-like cells (hpUCMSCs) were found to alleviate liver fibrosis in mouse models, reduce fibrogenic gene expression in the liver, and inhibit hepatic stellate cell (HSC) activation, a central driver of liver fibrosis, in vitro. Deep sequencing identified differentially abundant microRNAs (miRNAs) (high-abundance: 57, low-abundance: 22) in both UCMSC- and hpUCMSC-derived sEVs, compared to HeLa cell-derived sEVs, which lack anti-liver fibrotic activity. Functional enrichment analysis of the high-abundance sEV miRNA targets revealed their involvement in transcriptional regulation, apoptosis, and cancer-related pathways, all of which are linked to liver fibrosis and hepatocellular carcinoma. Notably, many of the top 10 most abundant miRNAs reduced pro-fibrotic marker levels in activated HSCs in vitro.

CONCLUSION: The therapeutic potential of the high-abundance miRNAs shared by UCMSC- and hpUCMSC-derived sEVs in treating liver fibrosis is highlighted.

PMID:40175803 | DOI:10.1007/s10529-025-03579-3

Nat Med. 2025 Apr 2. doi: 10.1038/s41591-025-03610-0. Online ahead of print.

ABSTRACT

The human gut microbiome is most dynamic in early life. Although sweeping changes in taxonomic architecture are well described, it remains unknown how, and to what extent, individual strains colonize and persist and how selective pressures define their genomic architecture. In this study, we combined shotgun sequencing of 1,203 stool samples from 26 mothers and their twins (52 infants), sampled from childbirth to 8 years after birth, with culture-enhanced, deep short-read and long-read stool sequencing from a subset of 10 twins (20 infants) to define transmission, persistence and evolutionary trajectories of gut species from infancy to middle childhood. We constructed 3,995 strain-resolved metagenome-assembled genomes across 399 taxa, and we found that 27.4% persist within individuals. We identified 726 strains shared within families, with Bacteroidales, Oscillospiraceae and Lachnospiraceae, but not Bifidobacteriaceae, vertically transferred. Lastly, we identified weaning as a critical inflection point that accelerates bacterial mutation rates and separates functional profiles of genes accruing mutations.

PMID:40175737 | DOI:10.1038/s41591-025-03610-0

Nature. 2025 Apr 2. doi: 10.1038/s41586-025-08814-5. Online ahead of print.

ABSTRACT

Anthropogenic biodiversity decline threatens the functioning of ecosystems and the many benefits they provide to humanity1. As well as causing species losses in directly affected locations, human influence might also reduce biodiversity in relatively unmodified vegetation if far-reaching anthropogenic effects trigger local extinctions and hinder recolonization. Here we show that local plant diversity is globally negatively related to the level of anthropogenic activity in the surrounding region. Impoverishment of natural vegetation was evident only when we considered community completeness: the proportion of all suitable species in the region that are present at a site. To estimate community completeness, we compared the number of recorded species with the dark diversity-ecologically suitable species that are absent from a site but present in the surrounding region2. In the sampled regions with a minimal human footprint index, an average of 35% of suitable plant species were present locally, compared with less than 20% in highly affected regions. Besides having the potential to uncover overlooked threats to biodiversity, dark diversity also provides guidance for nature conservation. Species in the dark diversity remain regionally present, and their local populations might be restored through measures that improve connectivity between natural vegetation fragments and reduce threats to population persistence.

PMID:40175550 | DOI:10.1038/s41586-025-08814-5

Sci Rep. 2025 Apr 2;15(1):11312. doi: 10.1038/s41598-025-95276-4.

ABSTRACT

Infectious mononucleosis (IM), predominantly caused by primary Epstein-Barr virus (EBV) infection, is a common disease in adolescents and young adults. EBV infection is nearly ubiquitous globally. Although primary EBV infection is asymptomatic in most individuals, IM manifests in a subset infected during adolescence or young adulthood. IM occurrence is linked to sibship structure, and is associated with increased risk of multiple sclerosis, other autoimmune diseases, and cancer later in life. We analyzed 47 biomarkers in 5,526 Danish individuals aged 18-60 years, of whom 604 had a history of IM, examining their associations with IM history up to 48 years after IM diagnosis. No significant long-term associations were observed after adjusting for multiple comparisons. When restricting the analysis to individuals measured within 10 years post-IM diagnosis, a statistically significant increase in CRP levels was observed in females. This association was not driven by oral contraceptive use. No significant associations between sibship structure and biomarker levels were detected. In conclusion, our study shows that while IM may lead to a transient increase in CRP levels in females, it does not result in long-term alterations in plasma biomarkers related to immune function, suggesting other mechanisms may be responsible for the long-term health impacts associated with IM.

PMID:40175486 | DOI:10.1038/s41598-025-95276-4

Microsyst Nanoeng. 2025 Apr 2;11(1):56. doi: 10.1038/s41378-025-00880-z.

ABSTRACT

Chronic kidney disease (CKD) significantly affects people's health and quality of life and presents a high economic burden worldwide. There are well-established biomarkers for CKD diagnosis. However, the existing routine standard tests are lab-based and governed by strict regulations. Creatinine is commonly measured as a filtration biomarker in blood to determine estimated Glomerular Filtration Rate (eGFR), as well as a normalization factor to calculate urinary Albumin-to-Creatinine Ratio (uACR) for CKD evaluation. In this study, we developed a passive flow microreactor for colorimetric urine creatinine measurement (uCR-Chip), which is highly amenable to integration with our previously developed microfluidic urine albumin assay. The combination of the 2-phase pressure compensation (2-PPC) technique and microfluidic channel network design accurately controls the fluidic mixing ratio and chemical reaction. Together with an optimized observation window (OW) design, a uniform and stable detection signal was achieved within 7 min. The color signal was measured by a simple USB microscope-based platform to quantify creatinine concentration in the sample. The combination of the custom in-house photomask production techniques and dry-film photoresist-based lithography enabled rapid iterative design optimization and precise chip fabrication. The developed assay achieved a dynamic linear detection range up to 40 mM and a lower limit of detection (LOD) of 0.521 mM, meeting the clinical precision requirements (comparable to existing point-of-care (PoC) systems). The microreactor was validated using creatinine standards spiked into commercial artificial urine that mimics physiological matrix. Our results showed acceptable recovery rate and low matrix effect, especially for the low creatinine concentration range in comparison to a commercial PoC uACR test. Altogether, the developed uCR-Chip offers a viable PoC test for CKD assessment and provides a potential platform technology to measure various disease biomarkers.

PMID:40175342 | DOI:10.1038/s41378-025-00880-z

Trends Plant Sci. 2025 Apr 1:S1360-1385(25)00063-9. doi: 10.1016/j.tplants.2025.03.003. Online ahead of print.

ABSTRACT

The field of mass spectrometry (MS)-based proteomics is rapidly advancing with technological and computational improvements, including leveraging the power of artificial intelligence (AI) to drive innovation. Such innovation has been particularly apparent in human disease research, where the intersection of these disciplines has pioneered a new age of disease diagnostics and pharmaceutical discovery. However, applications within plant sciences remains woefully under-represented and yet provides exceptional promise and potential to support new, interdisciplinary areas of research. Timely and novel examples of proteomics advancing plant science encompass biotechnology, climatic resiliency, agricultural production systems, and disease management. Herein, we propose new scientific avenues that leverage the power of proteomics and AI within plant science research to drive new discoveries and innovation.

PMID:40175191 | DOI:10.1016/j.tplants.2025.03.003

Adv Pharmacol. 2025;103:287-311. doi: 10.1016/bs.apha.2025.01.012. Epub 2025 Feb 26.

ABSTRACT

Bioinformatics has taken a pivotal place in the life sciences field. Not only does it improve, but it also fine-tunes and complements the wet lab experiments. It has been a driving force in the so-called biological sciences, converting them into hypothesis and data-driven fields. This study highlights the translational impact of bioinformatics on experimental biology and discusses its evolution and the advantages it has brought to advancing biological research. Computational analyses make labor-intensive wet lab work cost-effective by reducing the use of expensive reagents. Genome/proteome-wide studies have become feasible due to the efficiency and speed of bioinformatics tools, which can hardly be compared with wet lab experiments. Computational methods provide the scalability essential for manipulating large and complex data of biological origin. AI-integrated bioinformatics studies can unveil important biological patterns that traditional approaches may otherwise overlook. Bioinformatics contributes to hypothesis formation and experiment design, which is pivotal for modern-day multi-omics and systems biology studies. Integrating bioinformatics in the experimental procedures increases reproducibility and helps reduce human errors. Although today's AI-integrated bioinformatics predictions have significantly improved in accuracy over the years, wet lab validation is still unavoidable for confirming these predictions. Challenges persist in multi-omics data integration and analysis, AI model interpretability, and multiscale modeling. Addressing these shortcomings through the latest developments is essential for advancing our knowledge of disease mechanisms, therapeutic strategies, and precision medicine.

PMID:40175046 | DOI:10.1016/bs.apha.2025.01.012

Adv Pharmacol. 2025;103:231-263. doi: 10.1016/bs.apha.2025.01.003. Epub 2025 Feb 6.

ABSTRACT

Computational precision in drug discovery integrates algorithms and high-performance computing to analyze complex biological data with unprecedented accuracy, revolutionizing the identification of therapeutic targets. This process encompasses diverse computational and experimental approaches that enhance drug discovery's speed and precision. Advanced techniques like next-generation sequencing enable rapid genetic characterization, while proteomics explores protein expression and interactions driving disease progression. In-silico methods, including molecular docking, virtual screening, and pharmacophore modeling, predict interactions between small molecules and biological targets, accelerating early drug candidate identification. Structure-based drug design and molecular dynamics simulations refine drug designs by elucidating target structures and molecular behaviors. Ligand-based methods utilize known chemical properties to anticipate new compound activities. AI and machine learning optimizes data analysis, offering novel insights and improving predictive accuracy. Systems biology and network pharmacology provide a holistic view of biological networks, identifying critical nodes as potential drug targets, which traditional methods might overlook. Computational tools synergize with experimental techniques, enhancing the treatment of complex diseases with personalized medicine by tailoring therapies to individual patients. Ethical and regulatory compliance ensures clinical applicability, bridging computational predictions to effective therapies. This multi-dimensional approach marks a paradigm shift in modern medicine, delivering safer, more effective treatments with precision. By integrating bioinformatics, genomics, and proteomics, computational drug discovery has transformed how therapeutic interventions are developed, ensuring an era of personalized, efficient healthcare.

PMID:40175044 | DOI:10.1016/bs.apha.2025.01.003

Adv Pharmacol. 2025;103:1-22. doi: 10.1016/bs.apha.2025.01.006. Epub 2025 Feb 13.

ABSTRACT

In the current scenario of pandemics, drug discovery and design have undergone a significant transformation due to the integration of advanced computational methodologies. These methodologies utilize sophisticated algorithms, machine learning, artificial intelligence, and high-performance computing to expedite the drug development process, enhances accuracy, and reduces costs. Machine learning and AI have revolutionized predictive modeling, virtual screening, and de novo drug design, allowing for the identification and optimization of novel compounds with desirable properties. Molecular dynamics simulations provide a detailed insight into protein-ligand interactions and conformational changes, facilitating an understanding of drug efficacy at the atomic level. Quantum mechanics/molecular mechanics methods offer precise predictions of binding energies and reaction mechanisms, while structure-based drug design employs docking studies and fragment-based design to improve drug-receptor binding affinities. Network pharmacology and systems biology approaches analyze polypharmacology and biological networks to identify novel drug targets and understand complex interactions. Cheminformatics explores vast chemical spaces and employs data mining to find patterns in large datasets. Computational toxicology predicts adverse effects early in development, reducing reliance on animal testing. Bioinformatics integrates genomic, proteomic, and metabolomics data to discover biomarkers and understand genetic variations affecting drug response. Lastly, cloud computing and big data technologies facilitate high-throughput screening and comprehensive data analysis. Collectively, these computational innovations are driving a paradigm shift in drug discovery and design, making it more efficient, accurate, and cost-effective.

PMID:40175036 | DOI:10.1016/bs.apha.2025.01.006

J Am Coll Radiol. 2025 Mar 31:S1546-1440(25)00195-4. doi: 10.1016/j.jacr.2025.03.006. Online ahead of print.

NO ABSTRACT

PMID:40174871 | DOI:10.1016/j.jacr.2025.03.006

Nephron. 2025 Apr 2:1-11. doi: 10.1159/000544754. Online ahead of print.

ABSTRACT

BACKGROUND: Although there have been reports of relapse or worsening of membranous nephropathy after receiving vaccines against coronavirus disease 2019 (COVID-19), the causal relationship or association between them has not been established. This study aimed to investigate the occurrence of relapse or worsening of membranous nephropathy following inactivated COVID-19 vaccination.

METHODS: Patients who had been diagnosed with membranous nephropathy before receiving their first dose of vaccination, or before March 1, 2021, for unvaccinated patients, were included in the study. All patients were monitored at the Membranous Nephropathy Clinic of Huashan Hospital, Fudan University. The reasons for not receiving vaccines were investigated. The impact of COVID-19 vaccination on membranous nephropathy was assessed by comparing the relapse or worsening of membranous nephropathy within 12 months in vaccinated and unvaccinated patients with proteinuria <3.5 g/d. The baseline variables were balanced using cardinality matching.

RESULTS: A total of 353 patients with membranous nephropathy were included in the study, with 186 (53%) having received inactivated COVID-19 vaccines. Among the 167 unvaccinated participants, 114 (68%) expressed concerns about the possibility of disease relapse, and 47 (28%) were worried about the vaccine's efficacy due to their immunosuppressive therapy. Of the 239 participants with proteinuria <3.5 g/d, 152 were vaccinated, and 16 (11%) experienced a relapse or worsening of the disease during the follow-up period, which was similar to the 14 (16%) observed in the unvaccinated group. Following cardinality matching, there was no difference in the rate of relapse or worsening between the two groups, with 10 (13%) in the vaccinated group and 11 (15%) in the unvaccinated group (hazard ratio 0.98, 95% confidence interval 0.42-2.33).

CONCLUSION: Getting the inactivated COVID-19 vaccine may not increase risk of relapse or worsening in patients with membranous nephropathy.

PMID:40174580 | DOI:10.1159/000544754