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Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype

A narrative review of what cohorts have taught us and how they have laid the foundation for much of our understanding of type 2 diabetes.

This narrative review provides a historical perspective on how observational research on type 2 diabetes has been developed and consolidated over the last 50 years and how well-designed cohort studies will provide us with knowledge for research and practice in the future and aid guideline development. We have included data from a large number of cohorts from every continent that have been used to study the development and/or progression of type 2 diabetes, including cohorts that are general population-based, disease-based, intervention-based and registry-based. We have structured the results from the past 50 years based on the following themes: diagnosis and screening, complications, risk factors and pathophysiology. We also discuss the strengths and weaknesses of observational research when compared with other research designs. Finally, we discuss the emerging and future directions for type 2 diabetes research using cohorts, which include novel developments, such as artificial intelligence, precision health and the exposome. We conclude that cohort research has significantly advanced our understanding of type 2 diabetes and aided guideline development, and complements experimental work, such as human randomised controlled trials and animal studies. Both approaches are essential and complementary in our pursuit to provide a more comprehensive understanding of the development and progression of type 2 diabetes, and to change dogma, practice and policies for better outcomes.

Humans

Avian egg incubation period: Revisiting existing allometric relationships via surface area-to-volume ratio of an egg.

The incubation period (I) for bird eggs varies among species and is used in establishing allometric relationships. Research on variations in I shed light on the evolutionary mechanisms that gave rise to the differentiation of embryonic development in distinct taxa of birds. Here, using a sampling of 444 images from 444 avian species, 89 families and 30 orders, we calculated their major geometric dimensions: volume (V) and surface area (S). An assessment of the relationship between I and the measured and calculated egg parameters demonstrated the closest and most significant correlation (R = -0.760) between I and the S/V ratio that was adopted as a conditional indicator and reflects the embryo's metabolic rate. Approximation of the values of these parameters made it possible to derive a power-law dependence for the prediction of I depending on the S/V value of a particular egg (R2 = 0.757). The prediction accuracy was higher (R2 = 0.783) if the eggs of the family Procellariiformes (petrels), whose I value is characterized by a longer time, were removed from the general sampling computation. We conclude that the value of the S/V ratio can characterize both the metabolism of an embryo and the conditional thermal conductivity of an egg, which aids in ensuring the temperature regime of egg incubation.

Animals

Anatomy and Biomechanics of the Deltoid Ligament Complex in Healthy Ankles: Protocol for a Systematic Review and Meta-Analysis.

INTRODUCTION: Up to one in two individuals who have a history of an ankle injury will develop chronic ankle instability, which subsequently increases the risk of osteoarthritis development. Although lateral ankle ligament injuries are the most common, recent research shows that concomitant injuries to the deltoid (medial collateral) ligament complex may be more prevalent than previously recognised. However, the anatomy and biomechanics of the deltoid ligament complex are reported inconsistently in the literature. This systematic review will summarise current evidence on the anatomy and biomechanics of the deltoid ligament complex in healthy adult ankles. METHODS: Searches will be conducted in Scopus, MEDLINE, Embase, CINAHL and SPORTDiscus. Our search strategy will cover terms associated with 'deltoid ligament', 'anatomy' and 'biomechanics'. We will only include dissection and imaging studies published in English that report any of the listed clinically relevant properties of the deltoid ligament in healthy adult human ankles. Two reviewers will independently perform screening and assess study quality using the anatomical quality assessment (AQUA) tool. One reviewer will extract relevant data, which will be independently verified by co-authors. Primary outcomes include band prevalence, length, width, cross-sectional area, maximum and/or failure load and elastic modulus. If three or more studies report a primary outcome, we will conduct a meta-analysis and report findings as pooled means with 95% confidence intervals. If a meta-analysis is not feasible, outcomes will be summarised as a narrative analysis. Measures will be taken during data synthesis to address anticipated methodological heterogeneity across included studies, and pooled estimates will be interpreted with caution. DISCUSSION: This protocol details a systematic review that aims to summarise the anatomy and biomechanics of the deltoid ligament complex. Our findings will inform computational modelling, clinical management and biomechanics for ankle pathologies, as well as identifying research gaps and directions for future research. TRIAL REGISTRATION: PROSPERO: CRD420251142867.

Humans

Associations Between Short Video Exposure, Empathy and Attitudes Toward End-Of-Life Care Among Nursing Students: A Cross-Sectional Study.

AIM: This cross-sectional study examined the associations between short video exposure, nursing students' empathy, and attitudes toward end-of-life (EOL) care, and tested whether perceived impact is statistically consistent with an indirect pathway in these relationships. DESIGN: A descriptive cross-sectional study. METHODS: In total, 534 undergraduate nursing students were included. Data were collected using a self-designed questionnaire, including the Attitudes Toward Care of the Dying Scale and the Jefferson Scale of Empathy-Health Professions Student version for empathy assessment. Statistical analysis for correlation and mediation analysis (PROCESS macro) was performed. RESULTS: 85.96% of students watch short videos for more than 30&#x2009;min daily, with more than 60% of them viewing EOL-related content. Students with prior caregiving experience or formal palliative care education showed significantly higher empathy and more positive attitudes (p&#x2009;<&#x2009;0.05). Exposure to medical and EOL-related short videos was positively correlated with perceived impact, empathy, and positive EOL attitudes, with effect sizes ranging from very weak to modest (r&#x2009;=&#x2009;0.10 to 0.27). The data were consistent with an indirect pathway between short video exposure and empathy via perceived impact (indirect effect&#x2009;=&#x2009;0.04; 95% bootstrap CI [0.01, 0.08]). However, for EOL attitudes, short video exposure showed a direct association rather than an indirect pathway via perceived impact (direct effect&#x2009;=&#x2009;0.09, p&#x2009;<&#x2009;0.01). CONCLUSION: In this cross-sectional study, short video exposure was modestly associated with nursing students' empathy, with data consistent with an indirect pathway via perceived impact; the observed associations explained only approximately 1% to 7% of the variance in the outcome variables. However, reshaping EOL attitudes may require more systematic education beyond brief video exposure. These findings are hypothesis-generating and await validation through longitudinal and experimental research using standardized video content. IMPLICATIONS FOR NURSING PRACTICE: Nursing educators should consider integrating curated short video content into palliative care curricula to enhance students' empathy and perceived impact of end-of-life education. However, brief video exposure alone may be insufficient to reshape deeper end-of-life attitudes, suggesting the need for comprehensive, multi-modal educational strategies.

Humans

Navigating Social Media: Balancing Connectivity With Media Literacy to Combat Misinformation and Protect Mental Well-Being.

BACKGROUND: The pervasive use of social media has created a complex digital ecosystem where high connectivity coexists with significant challenges, including the rapid spread of misinformation, particularly regarding mental health, and documented negative impacts on psychological well-being. Platform architectures designed for engagement maximization have been identified as central factors in both issues. OBJECTIVE: This paper critically analyzes the interconnected relationships between social media use, misinformation dissemination, and mental health impacts, with particular attention to psychiatric misinformation across diagnostic categories (e.g., depression, anxiety, ADHD). A primary objective is to evaluate the potential of advanced critical digital literacy frameworks to serve as protective mechanisms against these dual threats. METHODS: A systematic search was conducted following PRISMA 2020 guidelines across APA PsycInfo, PubMed, JSTOR, and Google Scholar for literature published between January 2018 and March 2026 (updated from the original 2023 search). The search yielded 2672 records. After removing 624 duplicates, 2048 records underwent title and abstract screening, with 1802 excluded. The remaining 246 full-text articles were assessed for eligibility, resulting in 86 studies included in the final qualitative synthesis. Inter-rater reliability was established (Cohen's &#x3ba;&#x2009;=&#x2009;0.82). Quality assessment was conducted using the Joanna Briggs Institute Checklist, AXIS, and CASP tools, with findings weighted by methodological quality. A thematic analysis was undertaken to synthesize findings. RESULTS: The analysis reveals that core architectural features of social media platforms, algorithmic curation and engagement-based metrics, simultaneously foster environments ripe for misinformation spread and contribute to psychological distress, including anxiety, depression, and harmful social comparison. Psychiatric misinformation specifically (e.g., inaccurate claims about treatment effectiveness, diagnostic criteria, and medication side effects) represents a growing concern, particularly on image- and video-based platforms. The findings indicate that conventional media literacy approaches focused solely on fact-checking are insufficient. Instead, a critical digital literacy framework encompassing algorithmic awareness, data literacy, and emotional awareness is essential for building user resilience, with evidence from high-quality systematic reviews supporting this approach. CONCLUSIONS: Navigating the complexities of modern social media requires an integrated approach combining "pedagogies of play" for experiential skill development with advocacy for structural change (e.g., algorithmic transparency, well being by design principles). This dual strategy empowers individual users to critically engage with digital content while advocating for ethical platform design, thereby safeguarding both mental well-being and democratic discourse. Implications for educators, mental health professionals (including competencies for addressing patient encounters with psychiatric misinformation), policymakers, and platform designers are discussed.

Humans

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning

Assessing AI literacy and attitudes among medical students: implications for integration into&#xa0;healthcare practice.

PURPOSE: This study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice. DESIGN/METHODOLOGY/APPROACH: A quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare. FINDINGS: The study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI's role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology. ORIGINALITY/VALUE: This study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.

Students, Medical

Second Primary Malignancies in Patients With B-Cell Lymphomas Treated With Bruton's Tyrosine Kinase Inhibitors: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the overall second primary malignancy (SPM) burden in patients with B-cell lymphomas treated with Bruton's tyrosine kinase (BTK) inhibitors and compare SPM risk versus non-BTK inhibitor or placebo controls. METHODS: We searched major databases from inception to September 30, 2025. The primary outcome was SPM incidence. Consistent treatment backgrounds were defined as comparable baseline clinical and treatment characteristics, with BTK inhibitor exposure as the main between-arm difference. RESULTS: Fifty-two studies involving 9337 patients were included, mainly CLL/SLL; MCL was the largest non-CLL/SLL subtype. Pooled SPM incidence was 8% (95% CI: 6%-11%) with a median follow-up of 31.5&#x2009;months. Multivariable meta-regression identified follow-up duration as the only independent predictor, whereas disease subtype, inhibitor generation, prior therapy lines, study design, and age were not significant. Furthermore, SPM patterns were comparable between CLL/SLL and non-CLL/SLL cohorts. Compared with controls, BTK inhibitors did not significantly increase SPM risk (RR&#x2009;=&#x2009;1.30, 95% CI: 0.95-1.78), a finding confirmed in analyses with consistent treatment backgrounds (RR&#x2009;=&#x2009;1.03, 95% CI: 0.85-1.25). CONCLUSIONS: SPMs occur across disease backgrounds and are mainly influenced by follow-up duration. Current evidence does not establish a direct carcinogenic effect of BTK inhibitors.

Humans

Implementation of Mobile Health Intervention Targeting Belongingness and Burdensomeness: An Ecological Momentary Assessment Study of Self-Injurious Thoughts and Behaviors in LGBTQ+ Individuals.

OBJECTIVE: The goal of this paper was to test a mobile health intervention designed to reduce self-injurious thoughts and behaviors in LGBTQ+ individuals. The intervention consisted of brief messages aimed at increasing feelings of belongingness and meaning. METHOD: We recruited LGBTQ+ individuals (N&#x2009;=&#x2009;55) with past-month self-injurious thoughts and/or behaviors. Participants completed 14&#x2009;days of ecological momentary assessment (EMA) of minority stress, thwarted belongingness, perceived burdensomeness, and self-injurious thoughts and behaviors. Then, participants were randomly assigned to receive brief messages designed to instill belongingness and meaning/purpose, or no intervention for 14&#x2009;days. Then, participants completed an additional 14&#x2009;days of EMA. RESULTS: Our results showed that participants in the control condition had significant increases in self-injurious thoughts and planning over time, whereas those in the intervention condition showed no significant change. For self-injurious behavior, thwarted belongingness, and perceived burdensomeness, there were significant decreases in the intervention condition, but no changes in the control condition. CONCLUSIONS: These results provide support for the interpersonal theory of suicide and indicate a potentially scalable mobile health intervention. PUBLIC HEALTH SIGNIFICANCE: This paper found evidence that a brief mobile health intervention reduced suicidal and non-suicidal self-injurious thoughts and behaviors among LGBTQ+ individuals.

Humans

List randomization for prevalence estimation of sensitive behavioral data among women with HIV of reproductive age in Lilongwe, Malawi.

Self-reported data are subject to reporting biases, including social desirability bias. List randomization is one method that can help mitigate the impact of such biases. Here, we examined the utility of list randomization among women of reproductive age living with HIV in sub-Saharan Africa. In the Family Planning and Antiretroviral Therapy study, participants were randomized to answer 5 blocks of true/false statements via either direct or list response. Each block contained 3 nonsensitive statements and 1 sensitive statement related to either condom use or HIV disclosure. For each sensitive statement, we calculated the prevalence difference (PD) comparing list response to direct response overall and stratified by socioeconomic status. The PD for 4 of the sensitive statements was negligible. However, we found that self-report of always using a condom was reported by 53.1% at list response visits vs 34.7% at direct response visits (PD, 18.5%; 95% CI, 6.2%-30.7%), a difference that was attenuated among those with higher socioeconomic status. In this setting, list randomization did not meaningfully change the estimated prevalence for most questions, except for one question, which unexpectedly produced a higher estimate for a positive behavior. Examining this method in other settings and populations is warranted.

Humans

Effectiveness of Platelet Rich Plasma in Reducing Oronasal Fistula and Scar Width in Primary Cleft Lip and Palate Repair-A Systematic Review and Meta-Analysis.

This review aimed to investigate platelet-rich plasma (PRP) and platelet-rich fibrin (PRF) efficiency in reducing oronasal fistula during primary cleft lip and palate repair. An extensive search of PubMed, Google Scholar, Global Index Medics (WHO), PubMed Scopus, Cochrane Central, Proquest was performed up to march 2025. Eligible studies included prospective RCTs and non-randomized controlled trials in human subjects. Patients aged 6-24&#x2009;months undergoing primary cleft lip and palate repair were involved. Interventions involved intraoperative use of PRP/PRF compared with controls without PRP/PRF. The primary outcome was occurrence of oronasal fistula; secondary outcomes were scar width, wound infection and postoperative bleeding with wound dehiscence. Study selection followed PRISMA guidelines, and the risk of bias was determined with the ROB-2 and ROBINS-I method of assessment. Seven studies met the required criterion and were qualitatively synthesized. Evidence suggested that PRP/PRF application was associated with a lower incidence of oronasal fistula and reduced scar width compared with controls. Additional benefits included accelerated wound healing and faster recovery. The use of autologous PRP was also reported to decrease the need for further surgical interventions. However, the certainty of evidence was limited due to small sample sizes and methodological heterogeneity. PRP and PRF show promising benefits in cleft lip and palate repair, particularly decreased fistula formation and reduced scar width. Nevertheless, current evidence is of low to very low certainty. Larger, well-designed randomized trials are required to validate the results obtained. Trial Registration: PROSPERO registration no. CRD420251032421.

Humans

Physician-Modified Fenestrated Stent-Grafts Planned Using Three-Dimensional Techniques for Complex Aortic Pathology: A Systematic Review and Meta-Analysis.

BACKGROUND: Complex aortic pathology involving the visceral arteries remains a significant therapeutic challenge. Open repair is associated with considerable perioperative risk, particularly in patients with multiple comorbidities, while standard endovascular aneurysm repair (EVAR) is often not feasible because of inadequate proximal sealing zones. Fenestrated and branched endovascular repair (F/BEVAR) represents an established treatment strategy; however, the use of custom-made devices is limited by manufacturing time and availability. Physician-modified stent grafts (PMSGs) have therefore emerged as a pragmatic alternative. Three-dimensional planning techniques have been increasingly used to facilitate accurate graft modification. The aim of this systematic review and meta-analysis was to evaluate the effectiveness and safety of PMSG procedures planned with three-dimensional techniques. Technical success, target vessel patency, early mortality, endoleak occurrence, and reintervention rates were analyzed. METHODS: A systematic search was conducted in the PubMed/MEDLINE and Embase databases. Studies describing the use of physician-modified fenestrated stent grafts planned with three-dimensional tools were included. Meta-analyses were performed using a random-effects model with restricted maximum likelihood estimation. A logit transformation was used for the analysis of proportions. RESULTS: The analysis included five studies involving 172 patients. The estimated weighted mean follow-up duration was 14.9 months. The overall technical success rate was 92.9% (95% confidence interval [CI]: 84.5-96.9%), with low-to-moderate heterogeneity. Target vessel patency was 96.9% (95% CI: 93.6-98.5%). Early mortality was 5.5% (95% CI: 2.1-13.3%). The incidence of endoleaks was 13.3% (95% CI: 5.8-27.4%), with significant heterogeneity among studies. Reinterventions were reported in 6.6% of patients (95% CI: 2.3-17.5%). CONCLUSION: The results indicate that PMSG procedures planned with three-dimensional techniques are associated with a high rate of technical success and preserved patency of target vessels in patients with complex aortic pathology. The observed variability in endoleak and reintervention rates likely reflects differences in anatomical complexity and patient selection among studies. Further prospective studies are needed to confirm long-term outcomes.

Humans

Low Carbohydrate Availability in Energy Balance Alters Bone Turnover and Muscle Proteomic Response With Limited Endocrine Disruption.

Training with low carbohydrate availability (LCA) has been proposed as an independent determinant of physiological perturbations commonly attributed to low energy availability (LEA) and to increase skeletal muscle oxidative machinery, yet the effects of LCA in isolation from LEA remain unclear. We examined whether short-term carbohydrate restriction under energy balance alters endocrine and metabolic markers associated with LEA and skeletal muscle proteomic response. In a randomized crossover design, eight trained males completed 4&#x2009;days of either a low-carbohydrate high-fat diet (LOW; 12% carbohydrate, 69% fat, 19% protein) or a normal-carbohydrate diet (NORM; 62% carbohydrate, 19% fat, 19% protein), while undertaking daily cycloergometer exercise (15&#x2009;kcal kg FFM-1 day-1) and maintaining energy availability at 45&#x2009;kcal kg FFM-1 day-1. LOW induced a clear metabolic shift consistent with LCA, evidenced by elevated circulating free fatty acids, glycerol and &#x3b2;-hydroxybutyrate, in fasting conditions and fat oxidation at rest and during exercise, alongside reduced exercise glucose concentrations. Despite these responses, LOW did not alter insulin, testosterone, triiodothyronine, leptin, hepcidin, or P1NP. In contrast, &#x3b2;-CTX increased and IGF-1 decreased relative to NORM. Muscle glycogen concentration decreased only in LOW (40%&#x2009;&#xb1;&#x2009;14%). Proteomic analysis identified 671 proteins; 57 differentially expressed in LOW relative to NORM were limited to fatty acid metabolism pathways and suppression of ribosomal, sarcomeric, and extracellular matrix proteins. These findings indicate that isolated LCA exerts limited endocrine disruption but may selectively compromise bone turnover and muscle anabolic response, suggesting that without acute LEA, LCA has limited influence on muscle oxidative phenotype.

Male

Efficacy and Safety of Mechanical Insufflation-Exsufflation in Invasively Ventilated Critically Ill Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Mechanical insufflation-exsufflation (MI-E) is increasingly used in invasively ventilated adults in the intensive care unit (ICU), yet its therapeutic efficacy and safety remain uncertain due to inconsistent evidence. AIM: To synthesize evidence on the clinical efficacy and safety of MI-E in this population and to examine methodological and clinical heterogeneity underlying reported outcomes. STUDY DESIGN: A systematic review and meta-analysis of randomized studies (including RCTs and randomized crossover trials), conducted following PRISMA guidelines, with risk of bias assessed using the Cochrane risk-of-bias tool. RESULTS: Five randomized controlled trials involving 310 patients were included. Meta-analysis showed that mechanical insufflation-exsufflation (MI-E) significantly increased sputum clearance (SMD&#x2009;=&#x2009;0.63, 95% CI, 0.32-0.93; p&#x2009;<&#x2009;0.00011; I2&#x2009;=&#x2009;38%) without affecting oxygenation (MD&#x2009;=&#x2009;0.28, 95% CI, -0.53 to 1.09; p&#x2009;=&#x2009;0.50; I2&#x2009;=&#x2009;9%). Data on respiratory mechanics, ventilation duration and ICU stay could not be pooled. No serious adverse events were reported. CONCLUSIONS: MI-E significantly improves sputum clearance in invasively ventilated critically ill adults, with no severe adverse events reported in the included studies. Its effects on other outcomes remain inconclusive due to limited data and heterogeneity. Standardized protocols and larger trials are needed. RELEVANCE TO CLINICAL PRACTICE: Clinicians may consider MI-E as an adjunct for respiratory secretion management. Application should be guided by structured patient assessment and individualized parameter adjustment. Future research should standardize interventions and target well-defined patient subgroups to inform clear practice guidelines. TRIAL REGISTRATION: The review protocol was registered in the International Prospective Register of Systematic Reviews, with registration number CRD42023403299.

Humans

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis.&#xa0;A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST&#x2009;+&#x2009;AI for prediction model studies.&#xa0;Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST&#x2009;+&#x2009;AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection.&#xa0;AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks