Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Job demands-resources model”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

955 records · Page 11Linked to original sources
PubMed2026

Orchard netting impacts on biodiversity leading to cascading effects at the ecosystem level.

Agriculture must ensure food production without further compromising the ecosystem functions upon which it depends. Agricultural practices should therefore avoid harming farmland biodiversity, especially of taxa that supply the key ecosystem services (e.g. pollination, pest control and nutrient uptake) that ultimately support crop production. Orchards are among the largest permanent plantations worldwide and are increasingly characterised by the spread of plastic nets used to protect fruits/nuts from either abiotic (anti-hail, anti-rain, shade nets) or biotic (exclusion nets) hazards. Despite having received little attention to date, these nets may impact natural communities, acting both as physical barriers and as drivers of habitat changes to which biota must respond. Species-level responses to netting depend on the organism's ability to enter the netted environment and successfully exploit available resources. Net-mediated ecological filtering and plastic behavioural responses may alter species interactions, leading to cascading ecological impacts that may create species-poorer 'netted communities' with simplified ecological networks. Such changes may erode biological control potential, other ecosystem functions, and overall system stability. We conducted a systematic review on the effects of protection nets on biota, and reported novel empirical evidence on anti-hail nets' impacts on communities of orchard-dwelling birds, flower-visiting insects, and rodents. In total, we identified 48 studies from the literature, however this literature was strongly biased towards apple orchards, western countries, and pest taxa. Net deployment was highly effective in deterring target pest species, in some cases regardless of their original function, as even weather-protection nets limited pest populations. Side effects on non-target taxa were also often reported, such as decreases in pollinators and natural enemies, and/or increases in secondary pests or microbial diseases. However, most assessments largely disregarded non-pest taxa and the broader ecological consequences of netting. The few studies that addressed the effects of nets at the guild/community level, including our empirical study, confirmed that orchard netting resulted in species-poor assemblages, with possible ecosystem-level consequences. We propose that future assessments should pay more attention to the indirect effects of netting on non-target taxa, and on the supply of crop-supporting ecosystem services mediated by wild species occurring in agroecosystems. Due to the trade-offs between these services and net-mediated crop protection, integrated alternatives should be tested to improve the environmental sustainability of food production and biodiversity conservation in farmed landscapes.

Biodiversity↗
PubMed2026

A standardised risk-stratified approach to the urological management of children with spina bifida.

BACKGROUND: The establishment of a multidisciplinary spina bifida (SB) clinic in 2006 resulted in a review of the literature and an audit of renal outcomes based on then management practices. The audit showed 17% new onset renal scarring over a mean 5.8-year follow-up period. This prompted the development of a local protocol based on risk stratification combining serial ultrasound and non-invasive bladder function assessments, with invasive urodynamic studies reserved for high-risk patients. OBJECTIVE: This study sought to assess the impact of a risk stratified protocol on renal scarring and continence outcomes in children with SB. METHODS: A single centre, retrospective case review of SB patients treated after the introduction of the protocol was conducted. Electronic medical records were used to access patient demographics, continence status and the results of investigations and adherence to the local management protocol. Management that deviated from the protocol was deemed non-adherence. Renal scarring was determined by the presence of scarring on DMSA renogram. Continence was defined as having no urinary incontinence or no more than a single episode of incontinence in a month in patients above the age of 5. For statistical analysis, descriptive statistics in percentages were used, for comparisons of dichotomous variables the Students t-test was performed and to calculate statistical significance a Fisher exact test was done. RESULTS: 167 SB patients were identified with a mean follow up of 56 months. 141 patients were considered adherent to the protocol, 26 were non-adherent. In the protocol adherent group 5 patients (3.5%) developed renal scarring compared with 6 patients (23%) managed out of protocol (p = 0.002). Overall, 49/108 patients were continent either self-voiding 8/108 (7%), with urethral CIC 19/108 (20%) and 22/108 (45%) of them required bladder augmentation. Urinary continence improved with age with 26% continence at age 10, 64% continence by age 15 and 90% continent above 15 years of age. CONCLUSION: A management approach based on risk stratification resulted in incidence of renal scarring that is better than historical controls and comparable to published outcomes. Social continence was achieved in 90% of SB patients by 15 years of age. Hostile bladder changes can be readily identified using non-invasive assessment methods. An expectant treatment approach based on risk stratification is associated with good long term renal outcome and utilises invasive urodynamic resources for SB patients at high risk of renal injury or to address urinary continence in the older child.

Humans↗
PubMed2026

Teaching Acute Coronary Syndrome High-Risk ECG Interpretation and Clinical Decision-Making Through FOAMed Videos and Podcast Versus Print-Based Materials Among Emergency Care Providers: Randomized Controlled Mixed Methods Trial.

BACKGROUND: Accurate interpretation of high-risk acute coronary syndrome (ACS) electrocardiograms (ECGs) is essential for early diagnosis and timely reperfusion, yet substantial deficits persist across health care professions. Digital self-learning formats such as FOAMed (Free Open Access Medical Education) are widely used, but their effectiveness has rarely been evaluated for complex, high-risk ACS ECG patterns. Existing ECG education studies often focus on students or single professional groups and established ST-segment elevation myocardial infarction (STEMI) criteria, leaving newer guideline-recognized STEMI equivalents, selected emerging occlusion myocardial infarction (OMI)-related patterns, and interprofessional emergency care underrepresented. OBJECTIVE: This study aimed to compare the effectiveness of FOAMed podcast and videos versus traditional print-based materials for teaching high-risk ACS ECG patterns and related clinical decision-making in emergency providers. METHODS: We conducted a prospective, interprofessional, controlled mixed methods trial across 5 training sites in Germany. Paramedics, prehospital emergency physicians, and emergency department clinicians received either a FOAMed multimedia module or print-based materials through concealed allocation; deviations from the intended 1:1 ratio resulted from participant no-shows. The intervention consisted of a 30-minute supervised self-learning session. In total, 103 participants were allocated to FOAMed (n=45) or print-based materials (n=58). Two coprimary outcomes were assessed: ECG interpretation accuracy and text-based ACS clinical decision-making. Secondary outcomes included subjective confidence, learning experience, and exploratory qualitative free-text responses. Outcome assessment was automated and blinded; mixed ANOVA was the primary analysis. The study was not prospectively registered because it assessed educational outcomes in health care professionals rather than patient health outcomes. RESULTS: All 103 participants completed the study. Both groups improved, with greater gains in the FOAMed group: ECG interpretation increased from 55% to 65.5% and text-based ACS clinical decision-making from 45% to 68%, versus 57% to 60% and from 47% to 63%, respectively, in the print-based group. Effect sizes were η²=0.055 for ECG interpretation and η²=0.044 for clinical decision-making. Exploratory subgroup analyses provided no evidence of differential effects across age, gender, or professional background and were likely underpowered. Qualitative responses (46 and 37 entries) provided contextual insights into perceived clarity, engagement, and practical relevance supporting the quantitative findings. CONCLUSIONS: This study is innovative in directly comparing a curated FOAMed multimedia module with selected print-based materials in an interprofessional emergency care population. It differs from existing research by focusing on subtle, emerging ischemic patterns and evaluating realistic, time-limited self-learning formats. The findings provide evidence that curated FOAMed resources can produce greater short-term improvements in ECG interpretation and text-based ACS clinical decision-making than traditional print-based materials in this setting. Although implications for clinical performance remain hypothetical, concise, high-quality digital modules may represent a practical supplement to structured continuing education in emergency care.

Humans↗
PubMed2026

Assessment and CommuniCation ExcelLEnce foR sAfe paTient outcomEs (ACCELERATE): A stepped-wedge cluster randomised trial evaluating the effectiveness of a nurse-led assessment and handover communication intervention on patient adverse events.

BACKGROUND: Patients continue to experience harm from undetected deterioration, falls and pressure injuries. We aimed to implement and evaluate an organisational, ward-level nurse-led assessment and communication intervention to proactively reduce patient adverse events. METHODS: A stepped-wedge cluster randomised Trial over 12-months was conducted at three metropolitan hospitals. Our intervention comprised a comprehensive, systematic patient assessment at shift commencement; a structured patient-centred bedside nurse-to-nurse clinical handover; and multidisciplinary communication consisting of nurse participation in medical ward rounds. Evidence-based implementation strategies informed intervention delivery to nine clusters (20-35 bed-wards with &#x2265;70% permanent nurses) over three sequential 14-week steps. Routinely collected patient-level data were used to measure intervention effect. The primary outcome was a composite measure of medical emergency team calls, unplanned intensive care unit admissions, in-hospital falls; and stage 2-4 pressure injuries. Secondary outcomes were: individual measures of the primary outcome; nurse-reported perceptions of safety culture; organisational readiness to change; barriers to physical assessment; staff engagement; and patient-reported experience measures of safety and overall hospital experience. Analyses were adjusted for age, sex, hospital, pre/post intervention, and Trial step (fortnight), with random effects for ward and patient. RESULTS: There were 13,753 eligible admissions. No change was observed in the primary composite outcome measure (odds ratio (OR) [95% confidence interval (CI)]: 0.99 [0.77, 1.28]; p&#xa0;=&#xa0;0.95). There was no significant difference in medical emergency team calls (OR [95% CI]: 1.02 [0.75, 1.39]; p&#xa0;=&#xa0;0.91); unplanned intensive care unit admissions (OR [95% CI]: 1.35 [0.57, 3.20]; p&#xa0;=&#xa0;0.50) and falls (OR [95% CI]: 1.53 [0.96, 2.45]; p&#xa0;=&#xa0;0.07). However, stage 2-4 pressure injuries significantly decreased by 41% (OR [95% CI]: 0.59 [0.38, 0.93]; p&#xa0;=&#xa0;0.02); a significant absolute effect improvement of 0.8% ([95% CI: 0.3%-1.3%], p&#xa0;<&#xa0;0.01). There were statistically significant improvements in nurses' overall perceptions of Safety Attitudes (Pre: 74.6, Post: 79.7; p&#xa0;=&#xa0;0.02), and the Organisational Readiness to Change subscales of, leader culture (Pre: 3.73, Post 3.91; p&#xa0;=&#xa0;0.02), leadership behaviour (Pre: 3.85, Post: 4.11; p&#xa0;=&#xa0;0.03), and general resources (Pre: 3.06, Post: 3.30; p&#xa0;=&#xa0;0.03). A statistically significant decrease in Barriers to Physical Assessment (Pre: 2.48, Post: 2.24; p&#xa0;<0.001) and in six of seven sub-scales was observed. Patients' overall Measure of Safety remained high, but unchanged (Pre: 3.94 Post: 3.92; p&#xa0;=&#xa0;0.07). CONCLUSION: The ACCELERATE Trial demonstrated that nurse-driven initiatives, emphasising structured physical assessments by nurses, patient-centred clinical handovers, and multidisciplinary communication, significantly: reduced pressure injuries; decreased nurses' perceived barriers to performing physical assessments; and improved leadership behaviour, communication, and ward safety culture perceptions. Results highlight the transformative potential of this approach, which now warrants testing at scale for broader implementation. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ID: ACTRN12621000265875.

Humans↗
PubMed2026

Statistical test to compare the linkage model and the admixture model based on central limit results.

In the Admixture Model, the probability that an individual carries a certain allele at a specific marker depends on the allele frequencies in K ancestral populations and the proportion of the individual's genome originating from these populations. The markers are assumed to be independent. The Linkage Model is a Hidden Markov Model that extends the Admixture Model by incorporating linkage between neighboring loci. We prove consistency and asymptotic normality of maximum likelihood estimators for the ancestry of individuals in the Linkage Model, complementing earlier results by (Pfaff et al., 2004; Pfaffelhuber and Rohde, 2022; Heinzel, 2025) for the Admixture Model. These results are used to prove that a statistical test that allows for model selection between the Admixture Model and the Linkage Model is an asymptotic level-&#x3b1;-test. Finally, we demonstrate the practical relevance of our results by applying the test to real-world data from The 1000 Genomes Project Consortium (2015).

Genetic Linkage↗
PubMed2026

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↗
PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗
PubMed2025

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5&#x200b; concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution↗
PubMed2026

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.

Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24&#x2009;months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.

Models, Statistical↗
PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗
PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n&#xa0;=&#xa0;907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n&#xa0;=&#xa0;35), colorectal cancer (n&#xa0;=&#xa0;21), and pancreatic cancer (n&#xa0;=&#xa0;9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗
PubMed2026

Integrative modeling of the genome structure and dynamics in fission yeast.

Genome organization in the nucleus is highly structured and dynamic. Recent advances in genomic technology have enabled the measurement of genome-wide architecture and locus-specific motion, yielding contact maps and live-cell trajectories. However, these outcomes are derived from different modalities and are not directly comparable, with their quantitative integration being a key challenge. Here we establish a genome-wide live-cell imaging platform in fission yeast Schizosaccharomyces pombe, tracking 131 chromosomal loci, along with the spindle pole body (SPB) and nucleolus, to construct a quantitative map of locus dynamics. By integrating these dynamics with contact data through polymer modeling of Hi-C data, we build a physics-based "digital twin" of the S. pombe genome consistent with the spatiotemporal dynamics of interphase chromatin. We validate it against genome-wide mobility patterns and known architectural features, including centromere and telomere clustering. The model also identifies distinct dynamical regimes: centromere- and telomere-proximal loci relax within [Formula: see text]150 s, whereas the remaining loci relax within [Formula: see text]70 s. We measure semiperiodic dynamics of SPB motion, including a characteristic peak near 225 s and [Formula: see text] fluctuations. We use the model with SPB-directed forcing to show how these low-frequency components propagate through the genome to drive genome-wide chromatin displacements. Together, this predictive physics-based modeling framework integrates genome structure and dynamics to reveal how nuclear mechanical driving forces shape chromosome motion, linking mechanically driven chromatin responses to genome maintenance and regulation.

Schizosaccharomyces↗
PubMed2026

Revealing the Shared Genetic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Traits Through Genomic Structural Equation Modeling.

Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.

Humans↗
PubMed2026

Evaluation of a cornea-specialized large language model for diagnostic and management accuracy in complex corneal cases.

PURPOSE: To evaluate whether a cornea-specialized large language model (LLM) enhanced with retrieval-augmented generation (RAG) improves clinicians' diagnostic and management accuracy in complex corneal cases compared to a general-purpose GPT-4o model and unaided clinician performance. METHODS: This prospective, randomized, masked evaluation study involved three cornea trainees who each independently reviewed 39 real-world corneal cases under three experimental conditions: unaided, GPT-4o-assisted, and assisted by a cornea-specialized GPT-4o model. The cornea-specialized model was constructed by embedding over 200 publicly available Wikipedia articles into GPT-4o's RAG framework. Participants provided open-ended diagnoses and selected the next-step management options (multiple choice). They were allowed up to three GPT-4o queries per case, and the AI-assisted arms were randomized to minimize bias. Accuracy for both tasks was compared against expert reference standards using McNemar's test. RESULTS: Diagnostic accuracy was 48.7%, 20.5%, and 38.5% unaided, improving to 69.2%, 46.2%, and 59.0% with general GPT-4o (p<0.04). The cornea-specialized GPT-4o further improved accuracy to 71.8%, 48.7%, and 74.4%, with improvements over unaided performance for all clinicians (p<0.01). For next-step decisions, unaided accuracy was 76.9%, 87.2%, and 59.0%. With the specialized model, Ophthalmologist 3 improved to 71.8% (p<0.05), Ophthalmologist 1 remained high at 82.1%, and Ophthalmologist 2 declined to 64.1% (p<0.05). CONCLUSIONS: A cornea-specialized LLM enhanced with RAG improved diagnostic accuracy in complex corneal cases, particularly among clinicians with lower baseline performance. Effects on management accuracy were inconsistent. Future studies should explore the use of open-ended management tasks and examine whether smaller, curated retrieval corpora yield better model performance.

Humans↗
PubMed2026

Rational design of high-productivity perfusion processes for CHO Cells: From growth inhibitory strategies to model-driven optimization.

While perfusion culture for Chinese hamster ovary (CHO) cells offers advantages such as continuous operation and flexibility, it suffers from product loss through cell bleeding and difficulties in reaching high productivity due to sustained rapid cell growth. Growth inhibitory strategies are widely used to enhance productivity in fed&#x2011;batch processes; however, their practical implementation and comparative effectiveness in perfusion processes remain insufficiently explored. Meanwhile, process development often relies on costly trial&#x2011;and&#x2011;error approaches. Here, we systematically compared three growth inhibitory strategies in perfusion culture-low cell&#x2011;specific perfusion rate (CSPR), sodium butyrate, and mild hypothermia-with respect to cell growth, metabolism, productivity, and product quality. Genome&#x2011;scale metabolic flux sampling analysis revealed that low&#x2011;CSPR and sodium butyrate induce a convergent up&#x2011;regulation of energy metabolism, correlating with greater gains in specific productivity (qp). Building on this insight, we developed a growth&#x2011;kinetic model for the combined low&#x2011;CSPR + butyrate strategy, incorporating parameter uncertainty. This model&#x2011;guided framework enabled the rational design of two distinct high&#x2011;productivity perfusion processes: a sustained mode that achieved robust long&#x2011;term stability alongside substantial productivity gains, and a high&#x2011;intensity mode that pushed qp and daily volumetric titer to their maxima, with increases of up to 108.94% and 190.36%, respectively, in a model CHO cell line with a moderate baseline productivity. Our study provides a proof&#x2011;of&#x2011;concept framework for perfusion intensification, from strategy selection to rational process design.

Animals↗
PubMed2026

Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.

OBJECTIVE: To systematically review and evaluate published risk prediction models for perioperative blood transfusion in patients undergoing total hip or knee arthroplasty (THA/TKA). METHODS: We systematically searched PubMed, Web of Science, the Cochrane Library, and Embase from inception to May 31, 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability using the Prediction model Risk Of Bias Assessment Tool (PROBAST). The area under the receiver operating characteristic curve (AUC) values were pooled via a meta-analysis using Stata 18.0. RESULTS: d Fourteen studies containing 36 prediction models were included. The incidence of blood transfusion among THA/TKA patients ranged from 3.2% to 30.8%. Preoperative hemoglobin (Hb) level, tranexamic acid (TXA) use, operative duration, intraoperative blood loss, and age were the most frequently incorporated predictors. Model sensitivity ranged from 58% to 94.5%, and specificity ranged from 71.3% to 94%. Meta-analysis showed that the pooled AUC value of the 13 validated models was 0.87 (95% CI: 0.85-0.90), suggesting good discriminatory performance. All models were rated as having a high risk of bias. The applicability of four studies was rated as unclear. CONCLUSION: Although the included studies demonstrated promising discriminative ability of prediction models for blood transfusion in THA/TKA, all were assessed as having a high risk of bias using the PROBAST tool. Therefore, future research should prioritize the development of models with larger sample sizes, rigorous study designs, and multicenter external validation.

Humans↗