Plant Cell Environ. 2025 Mar 25. doi: 10.1111/pce.15493. Online ahead of print.

ABSTRACT

In most angiosperms, female gametogenesis originates from a specifically selected haploid megaspore, as three out of the four megaspores produced by meiosis degenerate without undergoing further division or differentiation. The remaining megaspore acquires functional megaspore (FM) identity, becoming the FM, which is essential for plant reproductive development. However, the molecular mechanisms governing FM development (or megaspore degeneration) remain largely unexplored, with current studies focusing on only a limited number of genes or regulatory networks. To date, no comprehensive review has systematically introduced advances in this field. This review aims to highlight recent progress in understanding FM development, discuss its critical role in female reproductive development and prospect the mechanism of FM development in environmental adaptation. By offering new insights, this review enriches existing knowledge of FM development and provides fresh perspectives for future research in plant reproduction and its adaptation to the environment.

PMID:40130504 | DOI:10.1111/pce.15493

IJID Reg. 2025 Feb 16;14:100604. doi: 10.1016/j.ijregi.2025.100604. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Genomic surveillance activity is a useful tool in epidemiologic investigations and monitoring of virus evolution. This study aimed to describe the COVID-19 outbreaks through SARS-CoV-2 virus genomic surveillance by whole genome sequencing.

METHODS: A cross-sectional study was conducted using archived clinical samples of confirmed laboratory-positive COVID-19 from June 2021 to June 2022 from a tertiary center in Malaysia. The samples were subjected to whole genome sequencing. A phylogenetic tree was constructed using the maximum likelihood method in MEGA 11 software. The clinical data were obtained through paper, electronic, and hospital information systems.

RESULTS: A total of 86 clinical samples were successfully sequenced. The phylogenetic tree revealed seven clusters, consisting of 24 cases. Three clusters were associated with health care workers and health care-associated individuals. The SARS-CoV-2 Delta variants were observed in the first three clusters and subsequently replaced with the Omicron variants.

CONCLUSIONS: Whole genome sequencing is robust and reliable, enhancing epidemiologic investigations, leading to the identification of clusters and preventing the spreading of COVID-19 among health care workers. Monitoring of the SARS-CoV-2 variants is necessary to study the viral dynamics and maintain the effectiveness of public health interventions.

PMID:40130259 | PMC:PMC11930704 | DOI:10.1016/j.ijregi.2025.100604

Front Pharmacol. 2025 Mar 10;16:1448106. doi: 10.3389/fphar.2025.1448106. eCollection 2025.

ABSTRACT

INTRODUCTION: Adverse Drug Reactions (ADRs), a widespread phenomenon in clinical drug treatment, are often associated with a high risk of morbidity and even death. Drugs and changes in gene expression are the two important factors that affect whether and how adverse reactions occur. Notably, pharmacogenomics data have recently become more available and could be used to predict ADR occurrence. However, there is a challenge in effectively analyzing the massive data lacking guidance on mutual relationship for ADRs prediction.

METHODS: We constructed separate similarity features for drugs and ADRs using pharmacogenomics data from the Comparative Toxicogenomics Database [CTD, including Chemical-Gene Interactions (CGIs) and Gene-Disease Associations (GDAs)]. We proposed a novel deep learning architecture, DGANet, based on the constructed features for ADR prediction. The algorithm uses Convolutional Neural Networks (CNN) and cross-features to learn the latent drug-gene-ADR associations for ADRs prediction.

RESULTS AND DISCUSSION: The performance of DGANet was compared to three state-of-the-art algorithms with different genomic features. According to the results, GDANet outperformed the benchmark algorithms (AUROC = 92.76%, AUPRC = 92.49%), demonstrating a 3.36% AUROC and 4.05% accuracy improvement over the cutting-edge algorithms. We further proposed new genomic features that improved DGANet's predictive capability. Moreover, case studies on top-ranked candidates confirmed DGANet's ability to predict new ADRs.

PMID:40129949 | PMC:PMC11931068 | DOI:10.3389/fphar.2025.1448106

Curr Res Microb Sci. 2025 Feb 28;8:100367. doi: 10.1016/j.crmicr.2025.100367. eCollection 2025.

ABSTRACT

Antimicrobial peptides (AMPs) offer a promising alternative to control airway infections with multi-resistant bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA), which commonly infects patients with cystic fibrosis (CF). However, the behavior of AMPs in the CF context has yet to be fully elucidated. CF airways produce large amounts of proteases and viscous mucus (sputum), which may affect the efficacy of AMPs. The present work aimed to determine whether CF conditions affect the bactericidal efficacy of CAMA, a promising AMP known to kill clinical MRSA strains efficiently. Using a killing assay, we quantified CAMA bactericidal activity on a CF clinical MRSA strain in the presence of several compounds of CF airways, including sputum and bronchial epithelial cells (BECs). Our results indicate that CF sputum impairs the bactericidal efficacy of CAMA. Similar results were observed when CAMA was incubated with an artificial sputum medium (ASM). When used separately, sputum components (DNA, lipids, and mucins) reproduced the inhibitory effects of ASM. Additionally, the bactericidal efficacy of CAMA was also slightly altered when planktonic S. aureus strains were co-cultured with CF BECs. However, CAMA was not active against S. aureus cultured on BEC in biofilm mode, characteristic of chronic infections in CF patients. These findings suggest that although CAMA represents a promising tool to treat MRSA strains, the CF environment may impair the efficacy of this AMP. Identifying strategies to protect AMPs from the deleterious effects of CF sputum is a key priority.

PMID:40129463 | PMC:PMC11931299 | DOI:10.1016/j.crmicr.2025.100367

United European Gastroenterol J. 2025 Mar 25. doi: 10.1002/ueg2.70012. Online ahead of print.

ABSTRACT

Malabsorption is a complex and multifaceted condition characterised by the defective passage of nutrients into the blood and lymphatic streams. Several congenital or acquired disorders may cause either selective or global malabsorption in both children and adults, such as cystic fibrosis, exocrine pancreatic insufficiency (EPI), coeliac disease (CD) and other enteropathies, lactase deficiency, small intestinal bacterial overgrowth (SIBO), autoimmune atrophic gastritis, Crohn's disease, and gastric or small bowel resections. Early recognition of malabsorption is key for tailoring a proper diagnostic work-up for identifying the cause of malabsorption. A patient's medical and pharmacological history is essential for identifying risk factors. Several examinations such as endoscopy with small intestinal biopsies, non-invasive functional tests and radiological imaging are useful in diagnosing malabsorption. Because of its high prevalence, CD should always be looked for in cases of malabsorption with no other obvious explanations and in high-risk individuals. Nutritional support is key in the management of patients with malabsorption; different options are available, including oral supplements, enteral or parenteral nutrition. In patients with short bowel syndrome, teduglutide proved effective in reducing the need for parenteral nutrition, thus improving the quality of life of these patients. Primary care physicians play a central role in the early detection of malabsorption and should be involved in multidisciplinary teams for improving the overall management of these patients. In this European consensus, involving ten scientific societies and several experts, we have dissected all the issues around malabsorption, including the definitions and diagnostic testing (Part 1), high-risk categories and special populations, nutritional assessment and management, and primary care perspective (Part 2).

PMID:40129317 | DOI:10.1002/ueg2.70012

Orphanet J Rare Dis. 2025 Mar 24;20(1):142. doi: 10.1186/s13023-025-03661-z.

ABSTRACT

BACKGROUND: Hereditary bronchiectasis refers to a subset of bronchiectasis related to genetic mutations, presenting with common clinical features. Historically, diagnosing this condition has been difficult due to the inaccessibility of diagnostic services coupled with a lack of awareness of the syndrome. We hypothesize that whole exome sequencing (WES) in patients with supporting clinical features, combined with non-genetic testing methods, will enhance the diagnosis of hereditary bronchiectasis.

RESULTS: In total, 87 patients with clinical features suggestive of hereditary bronchiectasis, such as diffuse bronchiectasis (≥ 2 lobes) combined with early onset symptoms, recurrent otitis media, rhinosinusitis, infertility, organ laterality defects or a family history of bronchiectasis, were included in this study. Among them, 49.4% (43/87) were diagnosed with hereditary bronchiectasis, including 15 patients with cystic fibrosis, 27 patients with primary ciliary dyskinesia, and 1 patient with immunodeficiency-21. The combined use of WES and non-genetic testing methods significantly improved the diagnostic rate of hereditary bronchiectasis compared to non-genetic testing alone (47.1% vs. 25.3%, P = 0.005). Re-analysis of negative commercial genetic tests led to two additional diagnoses, though this increase was not statistically significant (47.1% vs. 49.4%, P = 0.879).

CONCLUSIONS: We have described the supporting clinical features of patients with hereditary bronchiectasis. Clinicians should recommend WES for patients exhibiting these characteristics, in combination with accessible non-genetic testing methods, to maximize diagnostic accuracy. For patients with negative initial genetic test results, re-analysis of WES data may facilitate obtaining a new diagnosis.

PMID:40128832 | DOI:10.1186/s13023-025-03661-z

Eur J Prev Cardiol. 2025 Mar 25:zwaf148. doi: 10.1093/eurjpc/zwaf148. Online ahead of print.

NO ABSTRACT

PMID:40130376 | DOI:10.1093/eurjpc/zwaf148

Nat Prod Rep. 2025 Mar 25. doi: 10.1039/d4np00003j. Online ahead of print.

ABSTRACT

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

PMID:40130306 | DOI:10.1039/d4np00003j

J Pain Res. 2025 Mar 20;18:1435-1450. doi: 10.2147/JPR.S503975. eCollection 2025.

ABSTRACT

PURPOSE: Cancer pain management remains a significant clinical challenge. While acupuncture has shown potential in alleviating cancer pain, its underlying mechanisms are not yet fully understood. This study investigates the neurophysiological mechanisms underlying acupuncture's analgesic effects using multimodal bioelectrical signal analysis.

PATIENTS AND METHODS: Fifteen cancer pain patients underwent acupuncture while wearing portable, multi-sensor devices to capture bioelectrical signals. Pain levels were assessed using the Numerical Rating Scale (NRS) before and during needle retention. Neurophysiological changes were evaluated using Principal Component Analysis, Joint Time-Frequency Analysis, power spectrum analysis, spectral analysis, and dynamic functional network analysis.

RESULTS: There was a significant reduction in NRS scores from pre-treatment to the retention period, indicating pain relief. Principal component analysis showed significant differences in bioelectrical signals between these periods. Power spectrum analysis revealed decreased signal power during retention. Functional network analysis demonstrated a reduction in connectivity strength between electroencephalography and electromyography signals. Spectral analysis identified distinct real-time and staged characteristics of bioelectrical signals, with correlation analysis confirming a positive relationship between NRS score changes and bioelectrical signal alterations.

CONCLUSION: Acupuncture alleviates cancer pain by reducing functional connectivity between injured tissues and the brain, with immediate effects. Prolonging needle retention may enhance therapeutic outcomes. These findings provide new insights into the neurophysiological basis of acupuncture's analgesic effects, supporting its role in cancer pain management.

PMID:40130202 | PMC:PMC11932123 | DOI:10.2147/JPR.S503975

Cureus. 2025 Feb 20;17(2):e79391. doi: 10.7759/cureus.79391. eCollection 2025 Feb.

ABSTRACT

Artificial intelligence (AI) is a multi-disciplinary area of research focused on understanding, simulating, and replicating intelligence and cognitive functions by applying computational, mathematical, logical, mechanical, and biological principles and technologies. The concept of AI involves investigating and exploring human intelligence and creating artificial computers that use intelligent algorithms to replicate human intelligence. With the appearance of machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), the key AI techniques that are particularly effective in capturing feature items and learning, AI has evolved into a powerful approach in image analysis. AI may enable more precise evaluations of musculoskeletal impairments, reducing the likelihood of misdiagnosis and improving treatment outcomes for patients. With improved diagnostic capabilities, physiotherapists can create tailored rehabilitation programs that cater to the specific needs and conditions of individual patients. This study aimed to explore and evaluate the integration of AI technologies in diagnostic methods to enhance assessment accuracy. A systematic review was conducted from available literature on AI applications in musculoskeletal diagnostics. Available articles from 2015 to 2025 were included in the study. Analysis of current research's trends, advantages, constraints, and gaps was recognized. This study highlights the promising role of AI technologies in enhancing the accuracy and efficiency of musculoskeletal diagnostics. The integration of AI has the potential to revolutionize diagnostic methods, offering more precise assessments and reducing the likelihood of misdiagnosis. The issue of deploying AI tools for diagnostic purposes needs more attention.

PMID:40130121 | PMC:PMC11930781 | DOI:10.7759/cureus.79391

Front Oral Health. 2025 Mar 10;6:1569567. doi: 10.3389/froh.2025.1569567. eCollection 2025.

ABSTRACT

Oral cavity cancer is associated with high morbidity and mortality, particularly with advanced stage diagnosis. Oral cavity cancer, typically squamous cell carcinoma (OSCC), is often preceded by oral potentially malignant disorders (OPMDs), which comprise eleven disorders with variable risks for malignant transformation. While OPMDs are clinical diagnoses, conventional oral exam followed by biopsy and histopathological analysis is the gold standard for diagnosis of OSCC. There is vast heterogeneity in the clinical presentation of OPMDs, with possible visual similarities to early-stage OSCC or even to various benign oral mucosal abnormalities. The diagnostic challenge of OSCC/OPMDs is compounded in the non-specialist or primary care setting. There has been significant research interest in technology to assist in the diagnosis of OSCC/OPMDs. Artificial intelligence (AI), which enables machine performance of human tasks, has already shown promise in several domains of medical diagnostics. Computer vision, the field of AI dedicated to the analysis of visual data, has over the past decade been applied to clinical photographs for the diagnosis of OSCC/OPMDs. Various methodological concerns and limitations may be encountered in the literature on OSCC/OPMD image analysis. This narrative review delineates the current landscape of AI clinical photograph analysis in the diagnosis of OSCC/OPMDs and navigates the limitations, methodological issues, and clinical workflow implications of this field, providing context for future research considerations.

PMID:40130020 | PMC:PMC11931071 | DOI:10.3389/froh.2025.1569567

Front Immunol. 2025 Mar 10;16:1467027. doi: 10.3389/fimmu.2025.1467027. eCollection 2025.

ABSTRACT

OBJECTIVE: This study aims to examine the impact of systemic lupus erythematosus (SLE) on various organs and tissues throughout the body. SLE is a chronic autoimmune disease that, if left untreated, can lead to irreversible damage to these organs. In severe cases, it can even be life-threatening. It has been demonstrated that prompt diagnosis and treatment are crucial for improving patient outcomes. However, applying spectral data in the classification and activity assessment of SLE reveals a high degree of spectral overlap and significant challenges in feature extraction. Consequently, this paper presents a rapid and accurate method for disease diagnosis and activity assessment, which has significant clinical implications for achieving early diagnosis of the disease and improving patient prognosis.

METHODS: In this study, a two-branch Bayesian network (DBayesNet) based on Raman spectroscopy was developed for the rapid identification of SLE. Serum Raman spectra samples were collected from 80 patients with SLE and 81 controls, including those with dry syndrome, undifferentiated connective tissue disease, aortitis, and healthy individuals. Following the pre-processing of the raw spectra, the serum Raman spectral data of SLE were classified using the deep learning model DBayes. DBayesNet is primarily composed of a two-branch structure, with features at different levels extracted by the Bayesian Convolution (BayConv) module, Attention module, and finally, feature fusion performed by Concate, which is performed by the Bayesian Linear Layer (BayLinear) output to obtain the result of the classification prediction.

RESULTS: The two sets of Raman spectral data were measured in the spectral wave number interval from 500 to 2000 cm-1. The characteristic peaks of serum Raman spectra were observed to be primarily located at 1653 cm-1 (amide I), 1432 cm-1 (lipid), 1320 cm-1 (protein), 1246 cm-1 (amide III, proline), and 1048 cm-1 (glycogen). The following peaks were identified: 1653 cm-1 (amide), 1432 cm-1 (lipid), 1320 cm-1 (protein), 1246 cm-1 (amide III, proline), and 1048 cm-1 (glycogen). A comparison was made between the proposed DBayesNet classification model and traditional machine and deep learning algorithms, including KNN, SVM, RF, LDA, ANN, AlexNet, ResNet, LSTM, and ResNet. The results demonstrated that the DBayesNet model achieved an accuracy of 85.9%. The diagnostic performance of the model was evaluated using three metrics: precision (82.3%), sensitivity (91.6%), and specificity (80.0%). These values demonstrate the model's ability to accurately diagnose SLE patients. Additionally, the model's efficacy in classifying SLE disease activity was assessed.

CONCLUSION: This study demonstrates the feasibility of Raman spectroscopy combined with deep learning algorithms to differentiate between SLE and non-SLE. The model's potential for clinical applications and research value in early diagnosis and activity assessment of SLE is significant.

PMID:40129980 | PMC:PMC11931124 | DOI:10.3389/fimmu.2025.1467027

JBMR Plus. 2025 Feb 10;9(4):ziaf028. doi: 10.1093/jbmrpl/ziaf028. eCollection 2025 Apr.

ABSTRACT

Histomorphometric analysis of undecalcified bone biopsy images provides quantitative assessment of bone turnover, volume, and mineralization using static and dynamic parameters. Traditionally, quantification has relied on manual annotation and tracing of relevant tissue structures, a process that is time-intensive and subject to inter-operator variability. We developed ADAM, an automated pipeline for digital phenotyping, to quantify tissue and cellular components pertinent to static histomorphometric parameters such as bone and osteoid area, osteoclast and osteoblast count, and bone marrow adipose tissue (BMAT) area. The pipeline allowed rapid generation of delineated tissue and cell maps for up to 20 images in less than a minute. Comparing deep learning-generated annotation pixels with manual annotations, we observed Spearman correlation coefficients of ρ = 0.99 for both mineralized bone and osteoid, and ρ = 0.94 for BMAT. For osteoclast and osteoblast cell counts, which are subject to morphologic heterogeneity, using only brightfield microscopic images and without additional staining, we noted ρ = 0.60 and 0.69, respectively (inter-operator correlation was ρ = 0.62 for osteoclast and 0.84 for osteoblast count). The study also explored the application of morphological texture analysis (MTA), measuring relative pixel patterns that potentially vary with diverse tissue conditions. Notably, MTA from mineralized bone, osteoid, and BMAT showed differentiating potential to identify common pixel characteristics between images labeled as low or high bone turnover based upon the final diagnostic report of the bone biopsy. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) obtained for BMAT MTA features as a classifier for bone turnover, was 0.87, suggesting that computer-extracted features, not discernable to the human eye, hold potential in classifying tissue states. With additional evaluation, ADAM could be potentially integrated into existing clinical routines to improve pathology workflows and contribute to diagnostic insights into bone biopsy evaluation and reporting.

PMID:40129969 | PMC:PMC11931614 | DOI:10.1093/jbmrpl/ziaf028

Front Pharmacol. 2025 Mar 10;16:1448106. doi: 10.3389/fphar.2025.1448106. eCollection 2025.

ABSTRACT

INTRODUCTION: Adverse Drug Reactions (ADRs), a widespread phenomenon in clinical drug treatment, are often associated with a high risk of morbidity and even death. Drugs and changes in gene expression are the two important factors that affect whether and how adverse reactions occur. Notably, pharmacogenomics data have recently become more available and could be used to predict ADR occurrence. However, there is a challenge in effectively analyzing the massive data lacking guidance on mutual relationship for ADRs prediction.

METHODS: We constructed separate similarity features for drugs and ADRs using pharmacogenomics data from the Comparative Toxicogenomics Database [CTD, including Chemical-Gene Interactions (CGIs) and Gene-Disease Associations (GDAs)]. We proposed a novel deep learning architecture, DGANet, based on the constructed features for ADR prediction. The algorithm uses Convolutional Neural Networks (CNN) and cross-features to learn the latent drug-gene-ADR associations for ADRs prediction.

RESULTS AND DISCUSSION: The performance of DGANet was compared to three state-of-the-art algorithms with different genomic features. According to the results, GDANet outperformed the benchmark algorithms (AUROC = 92.76%, AUPRC = 92.49%), demonstrating a 3.36% AUROC and 4.05% accuracy improvement over the cutting-edge algorithms. We further proposed new genomic features that improved DGANet's predictive capability. Moreover, case studies on top-ranked candidates confirmed DGANet's ability to predict new ADRs.

PMID:40129949 | PMC:PMC11931068 | DOI:10.3389/fphar.2025.1448106

Discov Appl Sci. 2025;7(4):254. doi: 10.1007/s42452-025-06679-x. Epub 2025 Mar 22.

ABSTRACT

A novel approach for improving the accuracy and efficiency of laser-induced forward transfer (LIFT), through the application of deep learning techniques is presented. By training a neural network on a dataset of images of donor and receiver substrates, the appearance of copper droplets deposited onto the receiver was predicted directly from images of the donor. The results of droplet image prediction using LIFT gave an average RMSE of 9.63 compared with the experimental images, with the SSIM ranging from 0.75 to 0.83, reflecting reliable structural similarity across predictions. These findings underscore the model's predictive potential while identifying opportunities for refinement in minimising error. This approach has the potential to transform parameter optimisation for LIFT, as it enables the visualization of the deposited material without the time-consuming requirement of removing the donor from the setup to allow inspection of the receiver. This work therefore represents an important step forward in the development of LIFT as an additive manufacturing technology to create complex 3D structures on the microscale.

PMID:40129928 | PMC:PMC11929676 | DOI:10.1007/s42452-025-06679-x

Front Oncol. 2025 Mar 10;15:1508451. doi: 10.3389/fonc.2025.1508451. eCollection 2025.

ABSTRACT

The diagnosis and treatment of brain tumors can be challenging. They are a main cause of central nervous system disorder and uncontrolled proliferation. Early detection is also very important to ensure that the intervention is successful and delayed diagnosis is a significant factor contributing to lower survival rates for specific types. This is because the doctors lack the necessary experience and expertise to carry out this procedure. Classification systems are required for the detection of brain tumor and Histopathology is a vital part of brain tumor diagnosis. Despite the numerous automated tools that have been used in this field, surgeons still need to manually generate annotations for the areas of interest in the images. The report presents a vision transformer that can analyze brain tumors utilizing the Convolution Neural Network framework. The study's goal is to create an image that can distinguish malignant tumors in the brain. The experiments are performed on a dataset of 4,855 image featuring various tumor classes. This model is able to achieve a 99.64% accuracy. It has a 95% confidence interval and a 99.42% accuracy rate. The proposed method is more accurate than current computer vision techniques which only aim to achieve an accuracy range between 95% and 98%. The results of our study indicate that the use of the ViT model could lead to better treatment and diagnosis of brain tumors. The models performance is evaluated according to various criteria, such as sensitivity, precision, recall, and specificity. The suggested technique demonstrated superior results over current methods. The research results reinforced the utilization of the ViT model for identifying brain tumors. The information it offers will serve as a basis for further research on this area.

PMID:40129914 | PMC:PMC11930840 | DOI:10.3389/fonc.2025.1508451

Front Bioinform. 2025 Mar 10;5:1572937. doi: 10.3389/fbinf.2025.1572937. eCollection 2025.

ABSTRACT

Genomic and metagenomic sequence data provides an unprecedented ability to re-examine findings, offering a transformative potential for advancing research, developing computational tools, enhancing clinical applications, and fostering scientific collaboration. However, effective and ethical reuse of genomics data is hampered by numerous technical and social challenges. The International Microbiome and Multi'Omics Standards Alliance (IMMSA, https://www.microbialstandards.org/) and the Genomic Standards Consortium (GSC, https://gensc.org) hosted a 5-part seminar series "A Year of Data Reuse" in 2024 to explore challenges and opportunities of data reuse and reproducibility across disparate domains of the genomic sciences. Addressing these challenges will require a multifaceted approach, including common metadata reporting, clear communication, standardized protocols, improved data management infrastructure, ethical guidelines, and collaborative policies that prioritize transparency and accessibility. We offer strategies to enable responsible and technically feasible data reuse, recognition of data reproducibility challenges, and emphasizing the importance of cross-disciplinary efforts in the pursuit of open science and data-driven innovation.

PMID:40130011 | PMC:PMC11931119 | DOI:10.3389/fbinf.2025.1572937

Front Plant Sci. 2025 Mar 10;16:1570346. doi: 10.3389/fpls.2025.1570346. eCollection 2025.

NO ABSTRACT

PMID:40129746 | PMC:PMC11931147 | DOI:10.3389/fpls.2025.1570346

Front Neurosci. 2025 Mar 10;19:1498981. doi: 10.3389/fnins.2025.1498981. eCollection 2025.

ABSTRACT

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Gulf War Syndrome (GWS), and Fibromyalgia (FM) are complex, chronic illnesses with overlapping clinical features. Symptoms that are reported across these conditions include post-exertional malaise (PEM), fatigue, and pain, yet the etiology of these illnesses remains largely unknown. Diagnosis is challenging in patients with these conditions as definitive biomarkers are lacking; patients are required to meet clinical criteria and often undergo lengthy testing to exclude other conditions, a process that is often prolonged, costly, and burdensome for patients. The identification of reliable validated biomarkers could facilitate earlier and more accurate diagnosis and drive the development of targeted pharmacological therapies that might address the underlying pathophysiology of these diseases. Major driving forces for biomarker identification are the advancing fields of metabolomics and proteomics that allow for comprehensive characterization of metabolites and proteins in biological specimens. Recent technological developments in these areas enable high-throughput analysis of thousands of metabolites and proteins from a variety of biological samples and model systems, that provides a powerful approach to unraveling the metabolic phenotypes associated with these complex diseases. Emerging evidence suggests that ME/CFS, GWS, and FM are all characterized by disturbances in metabolic pathways, particularly those related to energy production, lipid metabolism, and oxidative stress. Altered levels of key metabolites in these pathways have been reported in studies highlighting potential common biochemical abnormalities. The precise mechanisms driving altered metabolic pathways in ME/CFS, GWS, and FM remain to be elucidated; however, the elevated oxidative stress observed across these illnesses may contribute to symptoms and offer a potential target for therapeutic intervention. Investigating the mechanisms, and their role in the disease process, could provide insights into disease pathogenesis and reveal novel treatment targets. As such, comprehensive metabolomic and proteomic analyses are crucial for advancing the understanding of these conditions in-order to identify both common, and unique, metabolic alterations that could serve as diagnostic markers or therapeutic targets.

PMID:40129725 | PMC:PMC11931034 | DOI:10.3389/fnins.2025.1498981

Food Sci Biotechnol. 2024 Dec 27;34(7):1605-1616. doi: 10.1007/s10068-024-01802-x. eCollection 2025 Apr.

ABSTRACT

Ensuring the viability and accurate stratification of Lacticaseibacillus group (LCG) species after freeze-drying is essential for their effective use as probiotics. This study investigates the use of reconstituted skim milk (RSM) as a cryoprotectant base, supplemented with fructans such as inulin and diverse forms of levan from Halomonas smyrnensis, to maintain the viability of Lacticaseibacillus casei ATCC 334, Lacticaseibacillus paracasei ATCC 25302, and Lacticaseibacillus rhamnosus ATCC 53103. Cellular viability was enhanced with levan-based cryoprotectants, motivating the use of levan-based hydrogels (gHLs) for freeze-drying LCG species. Throughout freeze-drying, the species-specific molecular masses (m/z) were preserved irrespective of the cryoprotectant used, with markers 3445 and 6664 m/z identified as potential species-specific molecular mass indicators for Lc. paracasei and Lc. rhamnosus, respectively. This study is the first to utilize levan in various forms as a cryoprotective agent alongside RSM, highlighting its promise as an effective cryoprotectant for LCG and potentially other probiotics.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10068-024-01802-x.

PMID:40129716 | PMC:PMC11929659 | DOI:10.1007/s10068-024-01802-x