J Genet Eng Biotechnol. 2025 Mar;23(1):100476. doi: 10.1016/j.jgeb.2025.100476. Epub 2025 Mar 5.

ABSTRACT

Hypertension is the foremost modifiable risk factor for cardiovascular and renal diseases, and overall mortality on a global scale. Genetic variants have the potential to alter an individual's drug responses. In the present study, we employed a comprehensive computational analysis to evaluate the structural and functional implications of deleterious missense variants to examine the influence of RAAS genes such as AT1R, AT2R, and MasR on susceptibility to hypertension. The objective of this research was to identify potentially deleterious missense variants within these target genes. A total of 13 in silico tools were used to identify deleterious missense SNPs. Protein stability, evolutionary conservation, and 3D structural modeling were assessed using tools like I-Mutant 3.0, MUpro, DynaMut2, ConSurf, and Project HOPE, while protein-protein interactions were analyzed via STRING. Our findings revealed three deleterious missense variants (rs397514687, rs886058071, rs368951368) in AT1R; two deleterious missense variants (rs3729979 and rs372930194) in AT2R; and three deleterious missense variants (rs768037685, rs149100513, and rs377679974) in MasR, all of which exhibited significant damaging effects as determined by the 13 Computational tools employed. All these deleterious missense variants adversely affected protein stability and were found to be highly conserved. Notably, these variants altered the charge, size, and hydrophobicity of the amino acids, with a predominant occurrence in alpha helix regions, with the exception of rs377679974 in MasR. The computational analysis and structural comparisons conducted in this study indicate that these deleterious missense variants have a discernible impact on the structure and function of the target proteins. However, it is essential to conduct experimental validation to verify the detrimental effects of the missense variants identified through this computational analysis. Therefore, we may conduct future experimental analyses to validate these findings. This research will aid in the identification of candidate deleterious markers that may serve as potential targets for therapeutic strategies and disease diagnosis.

PMID:40074423 | DOI:10.1016/j.jgeb.2025.100476

J Cyst Fibros. 2025 Mar 11:S1569-1993(25)00073-6. doi: 10.1016/j.jcf.2025.03.004. Online ahead of print.

ABSTRACT

Fatigue is common among adults with cystic fibrosis (awCF) and may be associated with systemic inflammation. This study examines systemic inflammation, measured by C-reactive protein (CRP), and fatigue, assessed using the Cystic Fibrosis Questionnaire-Revised (CFQ-R) vitality domain, in individuals initiating elexacaftor/tezacaftor/ivacaftor (ETI) therapy. In a cohort of 61 awCF from St. Paul's Hospital, Vancouver, CRP and vitality were measured at baseline and at 1, 3, 6, and 12 months post-ETI initiation. We observed reductions in CRP and increases in vitality over the 12-month period. Linear mixed-effects models were used to examine the relationship between CRP and vitality adjusted for age, sex, BMI, and lung function. Our findings demonstrated a significant, independent inverse association between CRP and vitality. These results highlight the potential role of systemic inflammation in influencing vitality in awCF undergoing ETI therapy. Further research incorporating additional inflammatory markers and psychosocial variables is warranted to deepen our understanding of fatigue mechanisms in this population.

PMID:40074570 | DOI:10.1016/j.jcf.2025.03.004

J Obstet Gynaecol Can. 2025 Mar 10:102811. doi: 10.1016/j.jogc.2025.102811. Online ahead of print.

ABSTRACT

COVID-19 outcomes are worse in non-pregnant patients that are cystic fibrosis carriers; however, no studies have examined COVID-19 outcomes in pregnant patients that are cystic fibrosis carriers. We evaluated the cystic fibrosis carrier status of pregnant patients with COVID-19 in three geographical regions in the United States and compared outcomes between non-carriers and carriers. Out of 2430 pregnant patients with COVID-19, 229 had a cystic fibrosis screen. Pregnant cystic fibrosis carriers were associated with 47.90 times greater odds of hospitalization with COVID-19 than non-carriers. A larger cohort will be needed to draw strong conclusions.

PMID:40074033 | DOI:10.1016/j.jogc.2025.102811

ACS Nano. 2025 Mar 12. doi: 10.1021/acsnano.5c00811. Online ahead of print.

ABSTRACT

The self-assemblies of topological complex block copolymers, especially the ABn type miktoarm star ones, are fascinating topics in the soft matter field, which represent typical self-assembly behaviors analogous to those of biological membranes. However, their diverse topological asymmetries and versatile spontaneous curvatures result in rather complex phase separations that deviate significantly from the common mechanisms. Thus, numerous trial-and-error experiments with tremendous parameter space and intricate relationships are needed to study their assemblies. Herein, we applied deep learning technology to decipher the phase behaviors of the miktoarm star block copolymer PEO-s-PS2 in an evaporation-induced self-assembly system. A neural network model was trained from practical experimental data encompassing two polymer properties and three synthesis condition parameters as input variables, which successfully predicted a three-dimensional (3D) synthesis-field diagram and mined the relationship between input parameters and obtained structures. This model demonstrated the highly flexible structure modulation directions of the miktoarm star block copolymer, revealing the correlation between the polymer parameters, synthesis conditions, and the output structures due to the significant influence of the variables on spontaneous curvatures. This work demonstrated the efficiency of a deep learning technique in uncovering the underlying rules of complex self-assembly systems, providing valuable insights into the exploration of soft matter science.

PMID:40074545 | DOI:10.1021/acsnano.5c00811

Plant Biotechnol J. 2025 Mar 12. doi: 10.1111/pbi.70040. Online ahead of print.

ABSTRACT

The dissection of genetic architecture for rice root system is largely dependent on phenotyping techniques, and high-throughput root phenotyping poses a great challenge. In this study, we established a cost-effective root phenotyping platform capable of analysing 1680 root samples within 2 h. To efficiently process a large number of root images, we developed the root phenotyping toolbox (RPT) with an enhanced SegFormer algorithm and used it for root segmentation and root phenotypic traits. Based on this root phenotyping platform and RPT, we screened 18 candidate (quantitative trait loci) QTL regions from 219 rice recombinant inbred lines under drought stress and validated the drought-resistant functions of gene OsIAA8 identified from these QTL regions. This study confirmed that RPT exhibited a great application potential for processing images with various sources and for mining stress-resistance genes of rice cultivars. Our developed root phenotyping platform and RPT software significantly improved high-throughput root phenotyping efficiency, allowing for large-scale root trait analysis, which will promote the genetic architecture improvement of drought-resistant cultivars and crop breeding research in the future.

PMID:40074292 | DOI:10.1111/pbi.70040

Artif Intell Med. 2025 Mar 5;163:103093. doi: 10.1016/j.artmed.2025.103093. Online ahead of print.

ABSTRACT

Electrocardiographic imaging (ECGI) aims to noninvasively estimate heart surface potentials starting from body surface potentials. This is classically based on geometric information on the torso and the heart from imaging, which complicates clinical application. In this study, we aim to develop a deep learning framework to estimate heart surface potentials solely from body surface potentials, enabling wider clinical use. The framework introduces two main components: the transformation of 3D torso and heart geometries into standard 2D representations, and the development of a customized deep learning network model. The 2D torso and heart representations maintain a consistent layout across different subjects, making the proposed framework applicable to different torso-heart geometries. With spatial information incorporated in the 2D representations, the torso-heart physiological relationship can be learnt by the network. The deep learning model is based on a Pix2Pix network, adapted to work with 2.5D data in our task, i.e., 2D body surface potential maps (BSPMs) and 2D heart surface potential maps (HSPMs) with time sequential information. We propose a new loss function tailored to this specific task, which uses a cosine similarity and different weights for different inputs. BSPMs and HSPMs from 11 healthy subjects (8 females and 3 males) and 29 idiopathic ventricular fibrillation (IVF) patients (11 females and 18 males) were used in this study. Performance was assessed on a test set by measuring the similarity and error between the output of the proposed model and the solution provided by mainstream ECGI, by comparing HSPMs, the concatenated electrograms (EGMs), and the estimated activation time (AT) and recovery time (RT). The mean of the mean absolute error (MAE) for the HSPMs was 0.012 ± 0.011, and the mean of the corresponding structural similarity index measure (SSIM) was 0.984 ± 0.026. The mean of the MAE for the EGMs was 0.004 ± 0.004, and the mean of the corresponding Pearson correlation coefficient (PCC) was 0.643 ± 0.352. Results suggest that the model is able to precisely capture the structural and temporal characteristics of the HSPMs. The mean of the absolute time differences between estimated and reference activation times was 6.048 ± 5.188 ms, and the mean of the absolute differences for recovery times was 18.768 ± 17.299 ms. Overall, results show similar performance between the proposed model and standard ECGI, exhibiting low error and consistent clinical patterns, without the need for CT/MRI. The model shows to be effective across diverse torso-heart geometries, and it successfully integrates temporal information in the input. This in turn suggests the possible use of this model in cost effective clinical scenarios like patient screening or post-operative follow-up.

PMID:40073713 | DOI:10.1016/j.artmed.2025.103093

Comput Biol Chem. 2025 Mar 6;117:108416. doi: 10.1016/j.compbiolchem.2025.108416. Online ahead of print.

ABSTRACT

Drug-target affinity prediction is a fundamental task in the field of drug discovery. Extracting and integrating structural information from proteins effectively is crucial to enhance the accuracy and generalization of prediction, which remains a substantial challenge. This paper proposes a pocket-based multimodal deep learning model named PocketDTA for drug-target affinity prediction, based on the principle of "structure determines function". PocketDTA introduces the pocket graph structure that encodes protein residue features pretrained using a biological language model as nodes, while edges represent different protein sequences and spatial distances. This approach overcomes the limitations of lack of spatial information in traditional prediction models with only protein sequence input. Furthermore, PocketDTA employs relational graph convolutional networks at both atomic and residue levels to extract structural features from drugs and proteins. By integrating multimodal information through deep neural networks, PocketDTA combines sequence and structural data to improve affinity prediction accuracy. Experimental results demonstrate that PocketDTA outperforms state-of-the-art prediction models across multiple benchmark datasets by showing strong generalization under more realistic data splits and confirming the effectiveness of pocket-based methods for affinity prediction.

PMID:40073710 | DOI:10.1016/j.compbiolchem.2025.108416

Spectrochim Acta A Mol Biomol Spectrosc. 2025 Mar 6;335:125997. doi: 10.1016/j.saa.2025.125997. Online ahead of print.

ABSTRACT

Lung cancer is a malignant tumor that poses a serious threat to human health. Existing lung cancer diagnostic techniques face the challenges of high cost and slow diagnosis. Early and rapid diagnosis and treatment are essential to improve the outcome of lung cancer. In this study, a deep learning-based multi-modal spectral information fusion (MSIF) network is proposed for lung adenocarcinoma cell detection. First, multi-modal data of Fourier transform infrared spectra, UV-vis absorbance spectra, and fluorescence spectra of normal and patient cells were collected. Subsequently, the spectral text data were efficiently processed by one-dimensional convolutional neural network. The global and local features of the spectral images are deeply mined by the hybrid model of ResNet and Transformer. An adaptive depth-wise convolution (ADConv) is introduced to be applied to feature extraction, overcoming the shortcomings of conventional convolution. In order to achieve feature learning between multi-modalities, a cross-modal interaction fusion (CMIF) module is designed. This module fuses the extracted spectral image and text features in a multi-faceted interaction, enabling full utilization of multi-modal features through feature sharing. The method demonstrated excellent performance on the test sets of Fourier transform infrared spectra, UV-vis absorbance spectra and fluorescence spectra, achieving 95.83 %, 97.92 % and 100 % accuracy, respectively. In addition, experiments validate the superiority of multi-modal spectral data and the robustness of the model generalization capability. This study not only provides strong technical support for the early diagnosis of lung cancer, but also opens a new chapter for the application of multi-modal data fusion in spectroscopy.

PMID:40073660 | DOI:10.1016/j.saa.2025.125997

Food Chem. 2025 Mar 7;478:143671. doi: 10.1016/j.foodchem.2025.143671. Online ahead of print.

ABSTRACT

Online detection of internal quality of strawberries presents challenges particularly concerning fruit damage, detection accuracy, and processing efficiency. This study explores the feasibility of using Vis/NIRS for online detection of SSC in strawberries during hanging transportation. After analyzing SSC distribution in strawberries, an optical sensing system was developed, and optimal configurations were identified using PLSR models. When employing a horizontal optical beam through the strawberry center, the PLSR model combined with SNV preprocessing and CARS feature selection achieved the best conventional chemometric results (RPD of 4.793). Additionally, three 1D-CNN approaches were investigated, with the 1D-CNN-LSTM method exhibiting superior performance (Rp2 of 0.963, RMSEP of 0.209°Brix, RPD of 5.332). These findings demonstrate the excellent capability of our developed system, enhanced by deep learning methods, for online detection of SSC in strawberries. This work may open new avenues for the online assessment of internal quality in small and delicate fruits.

PMID:40073605 | DOI:10.1016/j.foodchem.2025.143671

Med Image Anal. 2025 Mar 6;102:103529. doi: 10.1016/j.media.2025.103529. Online ahead of print.

ABSTRACT

This study addresses the challenges of confounding effects and interpretability in artificial-intelligence-based medical image analysis. Whereas existing literature often resolves confounding by removing confounder-related information from latent representations, this strategy risks affecting image reconstruction quality in generative models, thus limiting their applicability in feature visualization. To tackle this, we propose a different strategy that retains confounder-related information in latent representations while finding an alternative confounder-free representation of the image data. Our approach views the latent space of an autoencoder as a vector space, where imaging-related variables, such as the learning target (t) and confounder (c), have a vector capturing their variability. The confounding problem is addressed by searching a confounder-free vector which is orthogonal to the confounder-related vector but maximally collinear to the target-related vector. To achieve this, we introduce a novel correlation-based loss that not only performs vector searching in the latent space, but also encourages the encoder to generate latent representations linearly correlated with the variables. Subsequently, we interpret the confounder-free representation by sampling and reconstructing images along the confounder-free vector. The efficacy and flexibility of our proposed method are demonstrated across three applications, accommodating multiple confounders and utilizing diverse image modalities. Results affirm the method's effectiveness in reducing confounder influences, preventing wrong or misleading associations, and offering a unique visual interpretation for in-depth investigations by clinical and epidemiological researchers. The code is released in the following GitLab repository: https://gitlab.com/radiology/compopbio/ai_based_association_analysis.

PMID:40073582 | DOI:10.1016/j.media.2025.103529

Comput Biol Med. 2025 Mar 11;189:109956. doi: 10.1016/j.compbiomed.2025.109956. Online ahead of print.

ABSTRACT

Among various post-translational modifications (PTMs), predicting C-linked and S-linked glycosites is an essential task, yet experimental techniques such as Capillary Electrophoresis (CE), Enzymatic Deglycosylation, and Mass Spectrometry (MS) are expensive. Therefore, computational techniques are required to predict these glycosites. Here, different language model embeddings and sequential features were explored. Two separate feature selection methods: Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) were employed and utilized for identifying the optimal feature set. Cross-validation results were generated for choosing the final models. Three sampling strategies to handle imbalanced datasets were examined: Random undersampling, Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling Approach for Imbalanced Learning (ADASYN). In this study, two models: DeepCSEmbed-C and DeepCSEmbed-S are proposed for C-linked and S-linked glycosylation prediction respectively. DeepCSEmbed-C is a dual-branch deep learning model comprising a Feedforward Neural Network (FNN) branch and an Inception branch, coupled with a Random undersampling strategy. DeepCSEmbed-S is a Categorical Boosting (CAT) model with the SMOTE oversampling strategy. DeepCSEmbed-C outperformed available state-of-the-art (SOTA) methods, achieving 92.9% sensitivity, 95.1% F1-score and 90.6% MCC on the Independent dataset. Datasets and python scripts for training and testing the models are provided and made freely accessible at https://github.com/nafcoder/DeepCSEmbed.

PMID:40073495 | DOI:10.1016/j.compbiomed.2025.109956

Comput Biol Med. 2025 Mar 11;189:109896. doi: 10.1016/j.compbiomed.2025.109896. Online ahead of print.

ABSTRACT

With the ongoing advancement of digital technology, oral medicine transitions from traditional diagnostics to computer-assisted diagnosis and treatment. Identifying dental implants in patients without records is complex and time-consuming. Accurate identification of dental implants is crucial for ensuring the sustainability and reliability of implant treatment, particularly in cases where patients lack available medical records. In this paper, we propose a multi-task fine-grained CBCT dental implant classification and segmentation method using deep learning, called MFPT-Net.This method, based on progressive training with multiscale feature extraction and enhancement, can differentiate minor implant features and similar features that are easily confused, such as implant threads. It addresses the problem of large intra-class differences and small inter-class differences of implants, achieving automatic, synchronized classification and segmentation of implant systems in CBCT images. In this paper, 437 CBCT sequences with 723 dental implants, acquired from three different centers, are included in our dataset. This dataset is the first instance of utilizing such a comprehensive collection of data for CBCT analysis. Our method achieved a satisfying classification result with accuracy of 92.98%, average precision of 93.15%, average recall of 93.31%, and average F1 score of 93.18%, which exceeded the second-best model by nearly 10%. Moreover, our segmentation Dice similarity coefficient reached 98.04%, which is significantly better than the current state-of-the-art method. External clinical validation with 252 implants proved our model's clinical feasibility. The result demonstrates that our proposed method could assist dentists with dental implant classification and segmentation in CBCT images, enhancing efficiency and accuracy in clinical practice.

PMID:40073494 | DOI:10.1016/j.compbiomed.2025.109896

J Neural Eng. 2025 Mar 12. doi: 10.1088/1741-2552/adbfc1. Online ahead of print.

ABSTRACT

OBJECTIVE: In the field of brain-computer interface (BCI), achieving high information transfer rates (ITR) with a large number of targets remains a challenge. This study aims to address this issue by developing a novel code-modulated visual evoked potential (c-VEP) BCI system capable of handling an extensive instruction set while maintaining high performance.

METHOD: We propose a c-VEP BCI system that employs narrow-band random sequences as visual stimuli and utilizes a convolutional neural network (CNN)-based EEG2Code decoding algorithm. This algorithm predicts corresponding stimulus sequences from EEG data and achieves efficient and accurate classification.

MAIN RESULTS: Offline experiments which conducted in a sequential paradigm, resulted in an average accuracy of 87.66% and a simulated ITR of 260.14 bits/min. In online experiments, the system demonstrated an accuracy of 76.27% and an ITR of 213.80 bits/min in a cued spelling task.

SIGNIFICANCE: This work represents an advancement in c-VEP BCI systems, offering one of the largest known instruction set in VEP-based BCIs and demonstrating robust performance metrics. The proposed system is potential for more practical and efficient BCI applications.

PMID:40073451 | DOI:10.1088/1741-2552/adbfc1

J Pharmacol Exp Ther. 2025 Feb 5;392(3):103396. doi: 10.1016/j.jpet.2025.103396. Online ahead of print.

ABSTRACT

Pulmonary fibrosis encompasses different chronic interstitial lung diseases, and the predominant form, idiopathic pulmonary fibrosis, remains to have a poor prognosis despite 2 approved therapies. Although the exact pathobiological mechanisms are still incompletely understood, epithelial injury and aberrant wound healing responses contribute to the gradual change in lung architecture and functional impairment. Lysophosphatidic acid (LPA)-induced lysophosphatidic receptor 1 (LPA1) signaling was proposed to be a driver of lung fibrosis, and LPA1 antagonists have shown promising antifibrotic profiles in early clinical development. The novel, potent, and selective LPA1 antagonist, ACT-1016-0707, displayed insurmountable LPA1 antagonism in vitro with slow off-rate kinetics, leading to efficient inhibition of LPA1 signaling even in presence of high concentrations of LPA. This binding property translated into potent and highly efficient prevention of LPA-induced skin vascular leakage by ACT-1016-0707 in vivo, differentiating the compound from surmountable LPA1 antagonists. Furthermore, ACT-1016-0707 attenuated proinflammatory and profibrotic signaling in different lung fibrosis models in vitro and in the bleomycin-induced lung fibrosis model in vivo. Based on these data, ACT-1016-0707 shows potential as best-in-class LPA1 antagonist for treatment of fibrotic diseases. SIGNIFICANCE STATEMENT: ACT-1016-0707 is a potent, selective, and insurmountable lysophosphatidic receptor 1 (LPA1) antagonist demonstrating robust antifibrotic and anti-inflammatory activity in different lung fibrosis models in vitro and in vivo. This study is the first to demonstrate functional in vivo evidence of insurmountable LPA1 antagonist superiority by side-by-side comparison with surmountable LPA1 antagonists in highly controlled conditions, suggesting potential for ACT-1016-0707 as best-in-class LPA1 antagonist for treatment of fibrotic diseases.

PMID:40073729 | DOI:10.1016/j.jpet.2025.103396

FEMS Yeast Res. 2025 Mar 12:foaf010. doi: 10.1093/femsyr/foaf010. Online ahead of print.

ABSTRACT

Komagataella phaffii has gained recognition as a versatile platform for recombinant protein production, with applications covering biopharmaceuticals, industrial enzymes, food additives, etc. Its advantages include high-level protein expression, moderate post-translational modifications, high-density cultivation, and cost-effective methanol utilization. Nevertheless, it still faces challenges for the improvement of production efficiency and extension of applicability. This review highlights the key strategies used to facilitate productivity in K. phaffii, including systematic advances in genetic manipulation tools, transcriptional and translational regulation, protein folding and secretion optimization. Glycosylation engineering is also concerned as it enables humanized glycosylation profiles for the use in therapeutic proteins and functional food additivities. Omics technologies and genome-scale metabolic models provide new insights into cellular metabolism, enhancing recombinant protein expression. High-throughput screening technologies are also emphasized as crucial for constructing high-expression strains and accelerating strain optimization. With advancements in gene-editing, synthetic and systems biology tools, the K. phaffii expression platform has been significantly improved for fundamental research and industrial use. Future innovations aim to fully harness K. phaffii as a next-generation cell factory, providing efficient, scalable, and cost-effective solutions for diverse applications. It continues to hold promise as a key driver in the field of biotechnology.

PMID:40074550 | DOI:10.1093/femsyr/foaf010

J Genet Eng Biotechnol. 2025 Mar;23(1):100471. doi: 10.1016/j.jgeb.2025.100471. Epub 2025 Feb 18.

ABSTRACT

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide. This study aimed to explore the role of hsa-miR-101-3p in HCC pathogenesis by identifying key genes and pathways. A comprehensive bioinformatics analysis revealed twelve hub genes (ETNK1, BICRA, IL1R1, KDM3A, ARID2, GSK3β, EZH2, NOTCH1, SMARCA4, FOS, CREB1, and CASP3) and highlighted their involvement in crucial oncogenic pathways, including PI3K/Akt, mTOR, MAPK, and TGF-β. Gene expression analysis showed significant overexpression of ETNK1, KDM3A, EZH2, SMARCA4, and CASP3 in HCC tissues, correlating with poorer survival outcomes. Drug screening identified therapeutic candidates, including Tazemetostat for EZH2 and lithium compounds for GSK3β, underscoring their potential for targeted treatment. These findings provide novel insights into the complexity of HCC pathogenesis, suggesting that the identified hub genes could serve as diagnostic or prognostic biomarkers and therapeutic targets. While bioinformatics-driven, this study offers a strong basis for future clinical validation to advance precision medicine in HCC.

PMID:40074445 | DOI:10.1016/j.jgeb.2025.100471

J Med Ethics. 2025 Mar 12:jme-2024-110652. doi: 10.1136/jme-2024-110652. Online ahead of print.

ABSTRACT

Informed consent in surgical settings requires not only the accurate communication of medical information but also the establishment of trust through empathic engagement. The use of large language models (LLMs) offers a novel opportunity to enhance the informed consent process by combining advanced information retrieval capabilities with simulated emotional responsiveness. However, the ethical implications of simulated empathy raise concerns about patient autonomy, trust and transparency. This paper examines the challenges of surgical informed consent, the potential benefits and limitations of digital tools such as LLMs and the ethical implications of simulated empathy. We distinguish between active empathy, which carries the risk of creating a misleading illusion of emotional connection and passive empathy, which focuses on recognising and signalling patient distress cues, such as fear or uncertainty, rather than attempting to simulate genuine empathy. We argue that LLMs should be limited to the latter, recognising and signalling patient distress cues and alerting healthcare providers to patient anxiety. This approach preserves the authenticity of human empathy while leveraging the analytical strengths of LLMs to assist surgeons in addressing patient concerns. This paper highlights how LLMs can ethically enhance the informed consent process without undermining the relational integrity essential to patient-centred care. By maintaining transparency and respecting the irreplaceable role of human empathy, LLMs can serve as valuable tools to support, rather than replace, the relational trust essential to informed consent.

PMID:40074323 | DOI:10.1136/jme-2024-110652

Curr Biol. 2025 Feb 27:S0960-9822(25)00188-5. doi: 10.1016/j.cub.2025.02.025. Online ahead of print.

ABSTRACT

Sporulation is the most widespread means of reproduction and dispersal in fungi and, at the same time, an industrially important trait in crop mushrooms. In the Basidiomycota, sexual spores are produced on specialized cells known as basidia, from which they are forcibly discharged with the highest known acceleration in nature. However, the genetics of sporulation remains poorly known. Here, we identify a new, highly conserved transcription factor, sporulation-related regulator 1 (srr1), and systematically address the genetics of spore formation for the first time in the Basidiomycota. We show that Srr1 regulates postmeiotic spore morphogenesis, but not other aspects of fruiting body development or meiosis, and its role is conserved in the phylogenetically distant, but industrially important, Pleurotus spp. (oyster mushrooms). We used RNA sequencing to understand genes directly or indirectly regulated by Srr1 and identified a strongly supported binding motif for the protein. Using an inferred network of putative target genes regulated by Srr1 and comparative genomics, we identified genes lost in secondarily non-ballistosporic taxa, including a novel sporulation-specific chitinase gene. Overall, our study offers systematic insights into the genetics of spore morphogenesis in the Basidiomycota.

PMID:40073868 | DOI:10.1016/j.cub.2025.02.025

Talanta. 2025 Mar 10;292:127921. doi: 10.1016/j.talanta.2025.127921. Online ahead of print.

ABSTRACT

The detection of glycosylation alterations is essential for elucidating the roles of glycan functions in biological processes and identifying potential disease biomarkers. Stable isotopic chemical labeling, coupled with mass spectrometry (MS), represents a powerful approach in quantitative glycomics. In this study, we synthesized a novel isotopic hydrazide pair, 2,6-Dimethyl-4-chinolincarbohydrazid (DMQCH) and its deuterium isomer DMQCH-d4, via an efficient and cost-effective method, and applied it for the first time in MALDI-MS-based quantitative glycomics. The hydrazide tags, DMQCH/DMQCH-d4, enabled stable mass shifts through reductive-terminal reactions with glycans, allowing for differential mass tagging of two samples without additional purification after derivatization. This DMQCH/DMQCH-d4 pair exhibited high derivatization efficiency (including on-target derivatization), substantial improvements in MS signal intensity (a 15-fold increase for maltoheptaose, high reproducibility (CV < 13.6 %), and excellent linearity (R2 > 0.99) over two orders of magnitude in dynamic range for the relative quantitative analysis of maltoheptaose. Furthermore, this isotopic hydrazide pair was validated by successfully measuring changes in serum N-glycan profiles from individuals with healthy human serum control and ovarian cancer, highlighting its potential in quantitative glycomics for clinical applications.

PMID:40073825 | DOI:10.1016/j.talanta.2025.127921

Asian J Psychiatr. 2025 Feb 22;106:104409. doi: 10.1016/j.ajp.2025.104409. Online ahead of print.

ABSTRACT

INTRODUCTION: Late-life depression (LLD) is a significant yet often under-recognized health concern. While selective serotonin reuptake inhibitors (SSRIs) are widely used, their adverse effects remain a challenge. Vortioxetine, a multimodal antidepressant, has gained attention for its potentially better tolerability. However, data on its safety in older adults are limited. This meta-analysis assessed the safety and tolerability of Vortioxetine compared to serotonin reuptake inhibitors in LLD.

METHODS: A systematic search of PubMed, EMBASE, and Cochrane Central identified randomized controlled trials (RCTs) evaluating Vortioxetine's safety in ≥ 60-year-old patients. Primary outcomes included adverse events and withdrawal rates. Statistical analyses were conducted using Review Manager.

RESULTS: Three studies, involving 714 patients were included. There were no statistical differences between groups for nausea (RR 0.54; 95 % CI 0.22, 1.34; p = 0.18; I2 =75 %), diarrhea (RR 0.92; 95 % CI 0.20, 4.13; p = 0.91; I2 =59 %), constipation (RR 0.54; 95 % CI 0.28,1.02; p = 0.06; I2 =0 %) and loss of appetite (RR 1.00; 95 % CI 0.25, 4.05; p = 1.00; I2 =40 %). The total number of dropouts after randomization did not show a statistical significance with Vortioxetine use (RR 1.10; CI 95 % 0.82, 1.48; p = 0.52; I2 = 0 %), nor did the number of withdrawals (RR 1.09, CI 95 % 0.77, 1.55; p = 0.64; I2 = 0 %).

CONCLUSION: This meta-analysis suggests Vortioxetine is safe for LLD, with no significant increase in adverse effects. While reassuring, these findings emphasize the need for careful evaluation, as Vortioxetine showed no clear tolerability advantage over other serotonin reuptake inhibitors.

PMID:40073578 | DOI:10.1016/j.ajp.2025.104409