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PubMed2026

Effectiveness of artificial intelligence in nursing simulation education: A systematic review, meta-analysis and bibliometric visualization analysis.

OBJECTIVES: To synthesize the roles and core functions of AI in nursing simulation education for nursing students via systematic review, quantitatively evaluate its effects on students' knowledge and skill outcomes through meta-analysis, and map the research landscape and development trends of this field through bibliometric visualization analysis. DESIGN: Systematic review, meta-analysis and bibliometric visualization analysis. DATA SOURCES: Eight electronic databases: PubMed, Web of Science, MEDLINE, ERIC, Academic Search Complete, China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Chinese Science and Technology Journal Database (VIP) were employed to search studies from the time of construction to 16 December 2025. REVIEW METHODS: Studies meeting the inclusion criteria were screened. The revised Cochrane Risk of Bias tool (ROB 2) and Joanna Briggs Institute (JBI) critical appraisal checklists were used for quality assessment. Meta-analysis was performed with Review Manager 5.4, and bibliometric visualization analysis was conducted using VOSviewer 1.6.20 and Bibliometrix (based on R4.4.3). RESULTS: A total of 61 studies were included. AI primarily played two roles in nursing simulation education: peer-type new subject (n = 24) and direct mediator (n = 22). Meta-analysis showed that AI interventions significantly improved nursing students' knowledge (SMD = 1.49, 95% CI [0.55,2.43], p = 0.002) and skills (SMD = 0.66, 95% CI [0.02,1.31], p = 0.04). Bibliometric analysis identified that the United States of America and China were the two main contributing countries in this field, and the key motor themes included generative artificial intelligence, virtual patients, and geriatric care. CONCLUSIONS: AI exerts positive effects on nursing students' knowledge acquisition and skill enhancement in simulation education, with peer-type new subject and direct mediator as the dominant roles. Future research should focus on expanding AI applications in multi-specialty simulation scenarios, activating the data-driven value of machine learning, and strengthening international collaboration and standardization construction, so as to promote the sustainable development of AI-integrated nursing simulation education.

Humans↗
PubMed2026

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗
PubMed2026

The influence of organizational culture on medication safety practices and associated risk factors in the community setting: A systematic review.

BACKGROUND: Increasing attention has been given to the role of organizational culture in influencing medication safety practices across healthcare settings. The lack of widely accepted standardized instrumentation makes operational measurement of organizational culture and medication safety challenging. The purpose of this systematic review was to examine the impact of organizational culture on medication safety within community healthcare settings. METHODS: MEDLINE, CINAHL, Scopus, and Nursing & Allied Health were searched in August 2025 using keywords, subject terms, field codes, and Boolean operators to identify papers relevant to the review question; bibliographies of included studies were also reviewed. Screening and full-text review were completed independently by two reviewers with a third to adjudicate conflicts. The Critical Appraisal Skills Programme was used for quality assessment. The PRISMA statement guided the development and implementation of the review. RESULTS: Thirteen articles were included representing various community settings. Most studies reported on untoward medication events, but few measured systematically collected safety data before and after an intervention. Organizational culture was seldom defined or operationalized. Most studies were methodologically sound, but the overall level of evidence was weak to moderate. CONCLUSION: Organizational culture influences medication safety through aspects such as communication channels, teamwork, training, and an environment that allows error and near-miss reporting. Few studies explicitly evaluate the causal impact of culture interventions on measurable medication safety outcomes in community healthcare settings. Further research should incorporate standardized measurement tools and intervention-based, pre-post designs to better understand how organizational culture influences medication safety in community healthcare settings.

Organizational Culture↗
PubMed2026

Ecotoxicological responses of aquatic macrophytes to 2,4-D: A global synthesis of species sensitivity and ecological risk.

The widespread use of 2,4-dichlorophenoxyacetic acid (2,4-D) has raised concern about its persistence, mobility, and effects on non-target aquatic vegetation in freshwater ecosystems. Here, we provide a global synthesis of the ecotoxicological responses of aquatic macrophytes to 2,4-D based on a PRISMA-guided systematic review of 86 peer-reviewed studies published between 1947 and 2025. A consistent gradient of species-specific sensitivity was observed across macrophyte growth forms. The submerged species Myriophyllum spicatum showed high susceptibility, with EC₅₀ values of 0.04-0.182 mg/L and marked growth inhibition at low concentrations, whereas floating species such as Lemna minor and Pontederia crassipes were more tolerant, requiring higher concentrations (7.08 to >100 and 8.1 mg/L, respectively) to produce comparable effects. Importantly, this sensitivity ranking was consistent across laboratory and field experimental settings. These interspecific differences likely reflect variation in herbicide uptake, translocation, and detoxification capacity associated with growth form. The overlap between EC₅₀ values for M. spicatum and regulatory thresholds for 2,4-D in surface waters suggests that current limits may be insufficient to protect sensitive submerged macrophyte communities. Regarding remediation, L. minor and Salvinia natans emerged as the most promising candidates for phytoremediation, while P. crassipes showed limited capacity to reduce herbicide concentrations in water. Despite advances, no study directly compared oxidative stress biomarkers between submerged and floating species, representing a critical gap in understanding the biochemical basis of the sensitivity gradient. Overall, this synthesis highlights the need to account for taxon-dependent sensitivity when evaluating the ecological risks of 2,4-D and provides a basis for improving regulatory frameworks and management of herbicide contamination in freshwater ecosystems.

2,4-Dichlorophenoxyacetic Acid↗
PubMed2026

Systematic review of microorganism disinfection performance by chemical and ultraviolet light water treatment methods.

Safe drinking water is critical for public health, yet microbial contamination remains a significant global challenge. We conducted a systematic review to update World Health Organization guidance on water disinfection technologies by synthesizing peer-reviewed literature from 1997 to 2021 on the performance of free chlorine, chlorine dioxide, ozone, and ultraviolet (UV) light against bacteria, viruses, and protozoa. Following PRISMA guidelines, we analyzed log10 reduction values (LRVs) and contact times (Ct) or fluence (for UV) from laboratory and field studies. We included studies from multiple databases and expert-recommended studies. Results show mean Cts for 2 LRV of non-opportunistic bacteria as 6.0 (free chlorine), 0.4 (chlorine dioxide), and 1.2 (ozone) mg/L*min, and a mean UV fluence of 8.2 mJ/cm² (all bacteria). Viruses required lower Cts, except for UV-resistant adenoviruses, while protozoa required higher Cts or fluences. Opportunistic bacteria required significantly higher Cts than non-opportunistic bacteria for free chlorine and chlorine dioxide. Temperature and pH effects were inconsistent, highlighting data variability and gaps in field studies. These findings support global guidance on water treatment and may be used alongside other context-specific data to understand the roles these technologies play in reducing waterborne exposures. We recommend standardized reporting from performance studies to enable straightforward synthesis of evidence.

Disinfection↗
PubMed2026

Nitrogen sources and concentrations shape algal odor compounds: Key drivers of β-cyclocitral and β-ionone in water bodies of the lower Yangtze River.

Taste and odor (T&O) compounds derived from cyanobacterial blooms pose escalating threats to freshwater security worldwide, yet the drivers of specific T&O metabolites remain poorly constrained. Here, we investigated the dual effects of nitrogen (N) sources and concentrations on the production of β-cyclocitral and β-ionone, two algal-derived T&O compounds, through integrated field surveys (54 sites across lakes and rivers) in the eutrophic lower Yangtze River, China, and laboratory cultivation of typical cyanobacteria (Microcystis aeruginosa and Pseudanabaena cinerea). Our field data revealed that the concentrations of β-cyclocitral and β-ionone in lakes and rivers were not significantly different, but increased with the trophic level index. Redundancy analysis and Mantel analysis showed that Microcystis and Pseudanabaena were potentially dominant contributors to β-cyclocitral and β-ionone in the water column. Structural equation modeling and variation partitioning analysis showed that enhanced nitrate (NO3--N) significantly promoted the production of these compounds. Laboratory experiments demonstrated that inorganic N (NaNO₃) maximized total T&O yields by promoting algal biomass, whereas organic N (urea and glutamic acid) elevated the T&O production per unit biomass by 1.5- to 9.5-fold. Notably, Pseudanabaena exhibited a 2.3-fold higher β-ionone yield than Microcystis, with greater sensitivity to N concentrations. Our study highlights the critical role of nitrogen pollution, both source and concentration, in the production of T&O compounds by phytoplankton and provides reference data for managing T&O issues in rivers and shallow lakes.

Norisoprenoids↗
PubMed2026

Desert-derived Ensifer sp. SA403 enhances potato salt tolerance by reshaping rhizosphere microbiome functions and host responses.

Soil salinization increasingly threatens global food security, and potato (Solanum tuberosum L.), a moderately salt-sensitive crop, is particularly vulnerable to saline soils. Plant growth-promoting rhizobacteria (PGPR) offer a promising strategy to improve crop performance, yet how PGPR interact with native microorganisms to enhance potato salt tolerance remains poorly understood. In this study, we identified a desert-derived PGPR strain, Ensifer sp. SA403, which substantially enhanced potato performance under high salinity across sterile, non-sterile and field conditions. Physiologically, inoculation with SA403 reduced shoot Na⁺ accumulation and increased the K⁺/Na⁺ ratio; notably, these effects were markedly stronger in non-sterile substrates than under sterile conditions, indicating that SA403-mediated ion homeostasis relies on cooperation with the resident microbiota rather than on the strain acting alone. Metagenomic profiling indicated that SA403 strain reshaped rhizosphere communities, significantly enriching beneficial taxa such as Priestia and Bradyrhizobium, and upregulated functional pathways involved in glutathione and sulfur metabolism. Furthermore, host transcriptomic analyses showed that SA403 modulated plant responses to salt stress, with differentially expressed genes enriched in jasmonic acid signaling, ethanolamine metabolism and amino-acid biosynthesis pathways. Field trials on saline soils confirmed that SA403 significantly increased seedling emergence and tuber weight. Together, our results demonstrate that SA403 functions as a biological mediator that optimizes rhizosphere microecology and coordinates ion balance and host signaling to enhance potato salt tolerance. These findings support the potential of SA403 as a robust PGPR-based tool for sustainable potato production on saline soils.

Rhizosphere↗
PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗
PubMed2026

Nephropathies Associated with Sickle Cell Trait and How to Study Them.

Sickle cell trait (SCT), which carries a single point mutation in the hemoglobin-β (HBB) gene, has long been considered a benign condition. However, epidemiological evidence challenges this assumption, revealing that individuals with SCT face an elevated risk of renal dysfunction. However, this field of study remains ill-defined as it has focused on sickle cell disease (SCD), where renal complications are severe. As SCT is more prevalent than SCD, consequences of nephropathies in this group translate into a substantial and largely unaddressed public health burden. Clinical data, primarily observational, implicate age and sex in the development of SCT-associated nephropathies. These manifestations span glomerular hyperfiltration, tubular damage, hematuria, renal papillary necrosis, renal medullary carcinoma, and progression to chronic kidney disease, all complications that cluster disproportionately in older male individuals. Despite this, the mechanistic basis of SCT nephropathy, the thresholds at which renal injury becomes clinically significant, and the optimal strategies for early identification and prevention remain inadequately defined. In vitro studies have primarily focused on SCD blood cell biology, with SCT receiving comparatively little attention. Humanized murine models (i.e., Berkeley and Townes) have recapitulated some SCT-associated renal phenotypes but need to be more fully characterized. This review aims to provide an overview of the biology of sickle cell trait nephropathies, the gaps in our knowledge, and the model systems we can use to fill those gaps.

Journal Article↗
PubMed2026

Medically Unexplained Symptoms: A Systematic Umbrella Review of Current Terminology and Reported Rationales.

OBJECTIVES: Toaddress current naming conventions for Medically Unexplained Symptoms (MUS) through a systematic umbrella review. The terminology used and the provided rationales were considered. METHODS: Registered with PROSPERO (CRD42024526020), this review searched 8 key databases, last on January 28, 2025. Reviews including medically unexplained symptoms (or synonym or subtype) in their systematic search terms were included (N=422). RESULTS: A total of 577 references to 111 terms were made across the reviews, with numerous reviews using the same overarching terms, including "functional" (n=233), "somatic" (or variants thereof, n=51), and "medically unexplained" (n=28). Thirty percent of terms (n=179) were to specific syndromes or terms that did not group together under an overarching term, suggesting substantial variability in terms, even though over 60% of authors were primarily associated with just 3 disciplines: medicine, allied health, and psychology. A subset of 23 reviews provided rationales, which were subjected to content analysis and a ROBIS (Risk of Bias in Systematic Reviews) risk-of-bias assessment. This analysis showed that rationales tended to (1) highlight differences between psychological, psychiatric, and other medical fields (n=7); (2) focus on the patient perspective and patient-practitioner therapeutic relationship (n=10); or (3) follow broad and/or commonly used terms (n=7). DISCUSSION: The current landscape of terminology used for MUS remains varied, nuanced, and inconsistent between disciplines. Moving forward to a more universal language accepted and used by both patients and practitioners would aid in the diagnosis, management, and treatment of MUS.

Humans↗
PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗
PubMed2026

Three-dimensional porous nano-hydroxyapatite@gelatin composite as efficient adsorbent for uranyl ion removal from low-level radioactive wastewater.

The contamination of water resources by uranyl (UO22+) ions poses significant environmental and health risks, requiring the development of efficient and sustainable remediation strategies. Adsorption-based techniques have emerged as promising approaches in the field of UO22+ removal, but the design of cost-effective, high-capacity, and environmentally friendly adsorbents remains challenging. In this study, a three-dimensional porous nano-hydroxyapatite@gelatin (nHAP@Ge) composite was synthesized through glutaraldehyde cross-linking, combining the structural stability of Ge with the high uranium affinity of nHAP. The optimized nHAP@Ge, with a nHAP:Ge mass ratio of 1:0.5, exhibited exceptional UO22+ removal efficiency (97 %), along with high adsorption capacity (364.03 mg/g). Systematic characterizations using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS) methods revealed that the porous structure and surface functional groups (-OH, Ca2+, and PO43-) of the material synergistically contributed to binding UO22+ species. Furthermore, the incorporation of nHAP into the Ge framework resulted in enhanced thermal stability while significantly improving the UO22+ adsorption performance. This work presents a scalable, eco-friendly, and recyclable strategy for the effective treatment of uranium-contaminated water, with potential applications in nuclear wastewater treatment and environmental remediation.

Adsorption↗
PubMed2026

Civil liability in oral & maxillofacial surgery: Α systematic review.

Maxillofacial surgery is a surgical specialty with anatomical, functional, and aesthetic requirements, which makes it a field at increased risk of medical negligence and subsequent legal claims. The review was conducted in accordance with the PRISMA guidelines and used international medical and legal databases. Studies that analyzed court decisions, insurance claims, or recorded compensation data related to maxillofacial surgery were included. The extracted data included, among others, the year and country of publication, the causes of action, and the amounts of financial compensation. Most lawsuits originate from countries with developed medical liability systems, primarily the United States and the United Kingdom, and there has been an increasing trend in publications over the last two decades. The most common causes of lawsuits involve nerve injuries, delayed or incorrect diagnoses, technical errors during surgery, and inadequate informed consent. The amounts of compensation vary widely, from lower five-digit amounts in milder cases to particularly high amounts in cases of permanent functional or aesthetic damage, placing a significant overall financial burden on health systems. Medical negligence in maxillofacial surgery constitutes an important forensic and socioeconomic issue. Understanding the causes of lawsuits and their financial consequences can help improve clinical practice, inform patients, and prevent legal disputes.

Humans↗
PubMed2026

Targeting cortico-striatal-amygdalar networks via theta-band frontoparietal synchronization in opioid use disorder: a randomized tACS-fMRI Trial.

Theta-band oscillation is integral to fronto-parietal connectivity in the executive control network and its top-down regulation on subcortical areas. External frontoparietal synchronization using theta-frequency transcranial alternating current (tACS) is a technology to potentially engage this network. In this pre-registered, triple-blind, sham-controlled trial (NCT03907644), we tested this intervention targeting the right frontoparietal network in people with opioid use disorder (OUD) to measure network engagement and behavioral outcomes. Sixty male participants with OUD were randomized to receive 20 min of active or sham 6 Hz tACS (HD electrodes over F4 and P4). Structural, resting-state, task-based fMRI drug cue reactivity, and repeated cue-induced craving assessments were collected immediately before and after stimulation. Pre-registered outcome measures were analyzed using time × group interaction models to examine (1) modulation of drug cue-related brain activity, (2) changes in craving, (3) alterations in functional connectivity, and (4) relationship between electric field, neural responses, and craving behavior. (1) A significant Time × Group interaction revealed decreased post-stimulation opioid cue-related activity in the active group relative to sham, involving key nodes in reward processing (ventral striatum, amygdala and ventral tegmental area) (FWE corrected α = 0.05) (2) subjective craving did not differ significantly between groups (3) Group by time generalized psychophysiological interaction analyses showed increased right frontoparietal network engagement (β = 2.63, p= 0.0308) following stimulation, and increased top-down inhibitory regulation of frontoparietal network on right ventral striatum (β = 1.99, p= 0.037) and left medial amygdala (β = 1.97, p= 0.039) (4) Electric field strength in the right frontal/parietal node predicted frontoparietal network engagement in the active group (r = 0.43, p= 0.02). Together, these findings demonstrate that theta-band frontoparietal tACS can modulate activity and task-dependent coupling within cortical-subcortical circuits in OUD, supporting network-targeted neuromodulation as a potential intervention for addiction.

Humans↗
PubMed2026

Screening for neurofibromatosis type 1-related optic pathway gliomas: a systematic review.

BACKGROUND: Neurofibromatosis-type 1 (NF1) is a genetic disorder characterized by developing optic pathway gliomas (OPGs) in 15%-20% of patients with higher estimates where consanguinity is prevalent. Clinically, NF1-OPG might be unpredictable with the risk of OPG progression and visual impairment. The optimal time for screening is controversial. We aim to identify the mean/median age at diagnosis of NF1-OPG and its clinical spectrum. METHODS: A systematic review of PubMed, Web of Science, and Embase databases was conducted for English-language publications from January 1993 to October 2025, exploring the visual screening of OPGs in NF1 patients, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and registered in the Prospective Register of Systematic Reviews (PROSPERO ID: CRD420251036244). Inclusion criteria focused on studies reporting the age at OPG diagnosis and visual manifestations in NF1 patients. Data were extracted on demographics, age at NF1 and OPG diagnosis, tumour location (using the Dodge classification), and presenting symptoms. Sixteen studies met the inclusion criteria. RESULTS: Among 4 739 NF1 patients, 818 had OPGs, with prevalence ranging from 4.2% to 46.7%. The age at NF1 diagnosis ranged from 0 to 132 months (mean: 18-38 months), and at OPG diagnosis from 0-240 months (median: 29-58 months). Approximately 58.4% of OPGs were asymptomatic, and 25% were above the age of 5 years. Among symptomatic patients, the most frequent presentations included decreased visual acuity (62%), abnormal optic disc (45%), proptosis (20%), strabismus (12%), and visual field defects (7%). CONCLUSIONS: NF1-related OPGs typically present early within 6 years of age. Early ophthalmologic and/or radiologic screening at the time of NF1 diagnosis enhances the detection of silent OPGs.

Humans↗
PubMed2026

Multi-omics panorama of glaucoma: Pathogenesis, biomarkers, and novel therapeutic strategies.

Glaucoma is a group of irreversible, blinding eye diseases characterized by progressive loss of retinal ganglion cells, leading to gradual visual field defects that severely impact patients' quality of life. Its complex pathophysiological mechanisms remain incompletely understood, limiting the development of early diagnostic and effective therapeutic strategies. Advances in omics technologies have provided new insights into elucidating the pathophysiology of glaucoma. We summarize specific alterations in genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics associated with glaucoma. We emphasize the systematic analysis of disease mechanisms, identification of clinically applicable biomarkers, and discovery of novel therapeutic targets through the integration of these data. This approach paves new pathways for glaucoma subtype diagnosis and personalized treatment, while also outlining future research directions and challenges.

Humans↗
PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗
PubMed2026

Quality assessment, prognostic factors, and biomarkers for brain tumor analysis: a comprehensive systematic review.

The brain tumors possess different causative factors and properties, making their diagnosis and treatment difficult. Growth of these cancers usually leads to compression of the adjacent nerves and obstruction of the flow of cerebrospinal fluid, thus leading to increase in intracranial pressure. This affects the working of brain in many ways; thus, the difficulty involved in its treatment. With the improvements in technology in neuroimaging, including Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and multiparametric Magnetic Resonance Imaging (mpMRI), the diagnosis process has become easy. The effectiveness of any form of therapy in such patients depends primarily on their prognosis. While it is a common practice that physicians determine the prognosis of the disease by considering the age of the patient, histological grade of the tumor, and resection status, now this method has become more comprehensive by adding molecular signature and genetic analyses to the list of criteria. Next-generation sequencing (NGS) allows a reliable molecular classification. It increases the level of risk stratification, facilitating the application of therapies tailored to individual patients. Thus, molecular oncology has greatly changed our views on brain tumors' pathology and prognosis while neoadjuvant treatments aim at increasing the survival rate. On the other hand, radiogenomics is a field of study that combines non-invasive imaging phenotypes and genomic information in order to find unique molecular signatures of tumors without collecting samples from tumors. Molecular biomarkers are absolutely essential in the diagnosis of cancer, treatment monitoring, and recurrence of cancer. Advances in liquid biopsy technology, particularly the methods for circulating tumor DNA (ctDNA) and Extracellular Vesicle (EV) based analysis, have enabled the possibility of non-invasive monitoring of the progression of the tumors over time. This review highlights key studies and important scientific works about imaging technologies, biomarkers, and prognostic factors of malignant brain tumors.

Humans↗