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PubMed2026

Integrative quantum and systems biology of cancer: From molecular fluctuations to ecological outcomes.

This review treats cancer as a multiscale adaptive system, asks what the framework must predict to be worth adopting, and separates at each scale what the evidence establishes from what is proposed. It is an expert narrative synthesis, not a systematic review, and states the limits of that design. Proton transfer and tautomeric shifts contribute to spontaneous mispairing but do not license claims of directed or non-random mutation: replication timing, three-dimensional chromatin organization, sequence context and known mutagenic processes explain most mutational heterogeneity, leaving any quantum contribution as a residual against that baseline. The Waddington quasi-potential is bounded: outside detailed balance the dynamics are not gradient-derivable and require a probability-flux term. Hysteresis, rate-limited bimodality and return to state after perturbation distinguish an attractor from a transcriptomic cluster. Single-cell karyotype and live-imaging evidence supports whole-genome doubling as an unstable intermediate of heterogeneous origin and context-dependent consequence, not a uniform adaptive strategy. Systems and synthetic biology, virtual cells and digital twins are assessed against benchmarks, not promise. Tissue-scale ecology is reported with the spatial measurements now quantifying it, including evidence that stromal niche construction is not uniformly tumor-supporting. RNA modification is a layer in its own right, showing that the interpretation of a regulatory signal, not its magnitude, is biologically decisive. A dedicated section states the framework's commitments, the observable and evidence at each scale, and what would falsify them, asking what this adds to somatic mutation theory with clonal evolution and plasticity.

Neoplasms↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

The effect of tDCS on emotion-related risk-taking behavior and delay discounting in adults with ADHD.

INTRODUCTION: Adults with Attention Deficit Hyperactivity Disorder (ADHD) often engage in risky behaviors due to impaired decision-making processes. This study aims to investigate the effects of transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) on emotion-related risk-taking behavior and delay discounting in adults with ADHD. METHODS: Thirty adults with ADHD underwent three tDCS conditions, administered in a randomized order with at least one week between sessions: (1) left dlPFC anode/right vmPFC cathode, (2) left dlPFC cathode/right vmPFC anode, and (3) sham stimulation. In each session, participants completed the Delay Discounting Task (DDT) and the Modified Balloon Analogue Risk Task (mBART) under three emotional conditions (neutral, positive, and negative) which were induced using emotionally congruent photographs and sounds. Galvanic skin responses (GSR) were also recorded. In the DDT, both area under the curve (AUC) values and log-transformed discounting rates (log k) were calculated for small, medium, and large reward magnitudes (RM). Exploratory electric field modeling was also performed to characterize current distribution. RESULTS: The findings demonstrated task-specific effects of tDCS on decision-making. Although no overall tDCS effect was observed on DDT performance, significant tDCS × RM interactions emerged, particularly for smaller rewards. In contrast, exploratory analyses suggested that tDCS affected all mBART scores. Emotional condition did not influence consistently behavioral performance in either task, whereas both emotional stimulation and tDCS significantly affected GSR responses. However, exploratory electric field modeling indicated a broad prefrontal current distribution extending beyond the intended cortical targets. CONCLUSIONS: These findings suggest preliminary evidence that prefrontal tDCS can influence risk-related decision-making and autonomic responses in adults with ADHD. However, its effects on delay discounting appear to be context-dependent and limited to specific RMs. Future studies combining neuroimaging with individualized electric field modeling are needed to clarify the neural mechanisms underlying the observed effects of tDCS and to optimize stimulation protocols in adults with ADHD.

Humans↗

PubMed2026

Changes in hippocampal functional connectivity and volume associated with cognitive improvement and decline in amnestic mild cognitive impairment following computerized cognitive training.

BACKGROUND: The hippocampus influences the outcomes of amnestic mild cognitive impairment (aMCI) and undergoes different changes during the cognitive decline or recovery of aMCI compared to elderly individuals with normal cognition, which may reveal disease-dependent neurodegeneration or plasticity. We first aimed to investigate the hippocampal changes associated with cognitive changes in aMCI using a combined case-control study design. METHODS: In total, 50 aMCI individuals and 50 healthy controls (HCs) were recruited in Shenyang, China, and separately randomized into training and control groups: aMCI training group, aMCI no training group, HC training group, and HC no training group. The aMCI and HC training groups received computerized cognitive training (CCT) thrice weekly for 12 weeks. Cognitive assessments and MRI data were collected at baseline and follow-up. RESULTS: The primary outcome was significant CCT×diagnosis interaction effect on the change in cognitive performance as measured by clock drawing test (CDT) scores (F = 4.322, P = 0.041); this interaction was driven by CCT specifically in aMCI (F = 4.465, P = 0.038). Significant CCT×diagnosis interaction effects of right-hippocampal FC changes were observed in the bilateral precuneus/cuneus (Pvoxel<0.05) driven by CCT in aMCI (F = 5.429, P = 0.023), and in the left superior temporal gyrus/middle temporal gyrus (STG/MTG, Pvoxel<0.05), driven by CCT of only in HCs (F = 6.587, P = 0.013). A significant interaction effect of left-hippocampal FC changes were observed in the right triangular part of the inferior frontal gyrus (IFGtriang, Pvoxel<0.05), driven by CCT in aMCI and HCs (F = 6.550, P = 0.013; F = 7.097, P = 0.010). No significant interaction effect on the change in hippocampal GMV was noted (P > 0.05). CONCLUSION: CCT can improve the visuospatial ability of aMCI, which is reflected by the CDT scores. CCT can alter hippocampal FC in the bilateral precuneus/cuneus, the right IFGtriang, and the left STG/MTG. The hippocampal GMV is difficult to change in both HCs and aMCI during the cognitive decline. REGISTRATION NUMBER: ChiCTR1900026849. DATE OF REGISTRATION: 24 October 2019 NAME OF TRIAL REGISTRY: Chinese Clinical Trial Registry (ChiCTR).

Humans↗

PubMed2026

Temporal proteomic analysis reveals a three-phase adaptation strategy in Phytophthora cinnamomi during salinity stress.

Phytophthora cinnamomi, a highly invasive hemibiotrophic oomycete, threatens global agriculture, forestry, and native ecosystems. Although drought and temperature effects on P. cinnamomi-host interactions are well studied, current knowledge of abiotic stress responses in P. cinnamomi remains largely centered on infection and phytopathology, with limited molecular insight into the pathogen's direct response to salinity independent of its host. To address this gap, we combined growth assays, time-resolved proteomics, and network analysis to define how P. cinnamomi responds and adapts to salinity exposure. Growth assays showed that NaCl-modified agar enhanced mycelial expansion in a concentration-dependent manner, with 100 mM NaCl significantly increasing growth at 48, 72, and 96 h compared with controls, while 50 mM NaCl remained comparable to control conditions. Temporal proteomic analysis of 100 mM NaCl treatment at 0, 1, 6, 12, and 24 h post treatment revealed dynamic shifts in protein abundance. Early induction of ROS (Reactive Oxygen Species)-detoxifying enzymes, including glutathione S-transferases and peroxidases, was consistent with ROS-specific staining assays. Network analysis identified modules enriched for redox regulation, ATP generation, ion transport, and translational control, highlighting multi-layered adaptation to elevated NaCl levels. Notably, clusters of conserved hypothetical proteins were strongly upregulated, indicating unexplored stress tolerance components in Phytophthora species. Here, we propose that P. cinnamomi rapidly activates a three-phase strategy involving metabolism readjustments, redox defenses, and cellular structure alterations under salinity conditions. With increasing soil salinization due to climate change, our study provides first mechanistic insights into P. cinnamomi's adaptive plasticity and ecological resilience to abiotic stress. SIGNIFICANCE: This study represents the first temporal proteomic analysis of salinity stress adaptation in Phytophthora cinnamomi, revealing a sophisticated three-phase adaptation strategy. This research fundamentally advances our understanding of how this globally destructive plant pathogen, P. cinnamomi, maintains environmental resilience. Our findings reveal proteome remodelling as a mechanistic framework for understanding stress tolerance in oomycetes, a group of microorganisms responsible for some of the world's most destructive agricultural and forest diseases. Our results show proteins involved in emergency damage control through metabolic recalibration to sustained adaptation. These findings have relevance for predicting pathogen behavior under climate change scenarios, where increasing soil salinity threatens agricultural productivity while simultaneously enhancing pathogen survival and virulence. Understanding how P. cinnamomi responds to prolonged salinity exposure may inform targeted biocontrol strategies and improve predictive models of disease pressure in salt-affected agricultural regions. The temporal analysis framework we present offers a broadly applicable approach for understanding microbial stress adaptation, with implications extending beyond plant pathology to environmental microbiology and biotechnology applications where stress tolerance is paramount.

Phytophthora↗

PubMed2026

Effectiveness of a Web-Based Educational eHealth Platform on Women's Health Literacy About Phthalate Exposure: Randomized Controlled Trial.

BACKGROUND: Phthalates are environmental endocrine-disrupting chemicals widely used in plastics, cosmetics, food packaging, and personal care products. Women may experience frequent exposure through everyday consumer and household products. Improving phthalate-related health literacy may support informed exposure-reduction decisions; however, conventional health education provides limited opportunities for repeated, interactive, and individually tailored learning. OBJECTIVE: This randomized controlled trial evaluated the effectiveness of an eHealth educational intervention (Phthalates Free) in improving women's overall and domain-specific phthalate-related health literacy and examined the association between platform engagement and health literacy outcomes. METHODS: A double-blind randomized controlled trial was conducted in the outpatient department of a regional teaching hospital in Taipei, Taiwan. A total of 114 women were randomly assigned to an intervention group (n=58) receiving a 6-month eHealth platform-based education program and a control group (n=56) receiving conventional paper-based education. Assessments were conducted at baseline (T0), 3 months (T1), and 6 months (T2). The Phthalate Health Literacy Scale (10 items; α=.90, content validity index=0.93) measured overall and domain-specific literacy (health care, disease prevention, and health promotion). Longitudinal outcomes were analyzed using generalized estimating equations based on all available observations according to participants' original randomized assignments, with adjustment for waist circumference and pregnancy history. Analysis of covariance (ANCOVA) was used to compare 6-month outcomes after adjustment for baseline scores. Platform engagement and perceived usability were assessed using back-end analytics and the System Usability Scale (SUS). RESULTS: At 6 months, the intervention group showed a significantly greater increase in total health literacy than the control group (+9.93 points, Wald χ²1=17.74; P<.001). Domain analyses revealed significant improvements in health care (+1.52; P=.001), disease prevention (+1.32; P=.001), and health promotion (+1.12; P=.001) domains. ANCOVA confirmed the between-group difference at T2 after adjusting for baseline scores (F1,109=11.43; P=.001; adjusted mean difference=7.15, 95% CI 2.96-11.34). Engagement analysis showed that high-engagement users (n=10) scored significantly higher in overall health literacy (t55=-3.00; P=.004) and all domains than general users. The SUS results (mean 84.7, SD 5.2; n=46, 79.3%) indicated high perceived usability. CONCLUSIONS: The Phthalates Free eHealth educational intervention significantly improved women's overall and domain-specific health literacy over 6 months. Higher platform engagement was associated with better health literacy outcomes. The intervention may serve as a practical adjunct to nurse-led education in outpatient and community settings by providing accessible, continuous, and evidence-based guidance on reducing phthalate exposure.

Humans↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗