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PubMed2026

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging↗

PubMed2026

The effect of monetary versus point-based rewards on effort-cost decision making in individuals at clinical high risk for psychosis.

OBJECTIVE: The dissemination of inexpensive computerized behavioral tasks indexing amotivation may enhance the assessment of clinical high risk (CHR) across settings. However, the impact of varying reward value in such tasks is unclear. If point-based rewards engage participants, this could improve the scalability of computerized assessments. We tested how point-based rewards versus money impacted effort-cost decision-making in CHR individuals. We further assessed how negative symptom severity and household income interacted with reward-type to impact behavior. METHODS: Participants completed the Effort Expenditure for Reward Task (EEfRT). Participants were randomly assigned to receive either money or points for their performance during the EEfRT. Data from a large sample of CHR (N = 233) individuals and healthy controls (HC; N = 157) were collected. RESULTS: Across diagnostic groups, we observed heightened effort expenditure when money was used as a reward (b = 0.13, p = 0.018). We did not find an interaction of CHR status (b = 0.07, p = 0.845) or negative symptoms (b = 0.01, p = 0.429) with reward-type. Within CHR individuals, heightened negative symptom severity was associated with reduced expended effort (b = -0.03, p = 0.016), regardless of reward type. In an exploratory analysis, we found that individuals in the money condition with relatively high household income expended less effort during high reward, high probability trials (b = -0.24, p = 0.046). CONCLUSIONS: Across CHR and HC individuals, individuals pursuing money expended greater effort. While we did not find a group by reward type interaction, CHR individuals with heightened negative symptom severity expended less effort across trials, replicating prior work. Present findings support further study of point-based rewards in tasks indexing amotivation.

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

Subacute and long-term changes in cognitive functioning after administration of classic psychedelics, MDMA and ketamine: A systematic review of clinical and preclinical evidence.

Psychedelic agents induce a window of heightened neuroplasticity that extends beyond acute intoxication, during which neural circuits are more amenable to change. This period may facilitate changes in cognition relevant to the treatment of psychiatric disorders. This systematic review synthesised clinical and preclinical evidence of subacute and long-term (≥1 day) effects of classic and non-classic psychedelics on cognition. MEDLINE, EMBASE, APA PsycInfo and Web of Science were searched to identify human and animal studies investigating psychedelics and cognition (executive function, attention, decision-making). Sixty-seven (47 clinical, 20 preclinical) articles met inclusion criteria. Psilocybin demonstrated the most consistent evidence of subacute and longer-term cognitive improvement, particularly in cognitive flexibility and attention. Ketamine showed enhancement across cognitive domains, although findings were heterogeneous. LSD and DMT showed no consistent subacute changes, while MDMA was associated with transient cognitive impairments that resolved within days. Risk of bias assessments revealed selective outcome reporting, poor reporting of missing data and inadequate methodological detail, limiting the confidence of findings. Current evidence provides preliminary support for subacute changes in cognition following administration of select psychedelic agents. Cognitive improvements were more frequently seen in psychiatric populations than healthy subjects, which may reflect a remediation of existing cognitive deficit rather than enhancement above normal functioning. Adequately powered, controlled studies with standardised reporting of cognitive outcomes are required to determine the magnitude, durability and clinical relevance of psychedelic-associated cognitive change.

Hallucinogens↗

PubMed2026

Perspectives of participating neurologists and study nurses - Mixed-methods process evaluation of a web-based program for relapse management in multiple sclerosis (POWER@M2).

BACKGROUND: Relapsing-remitting multiple sclerosis is a chronic inflammatory disease of the central nervous system and the leading cause of disability in young adults. In Germany, 90% of relapses are treated with high-dose intravenous glucocorticoids, despite limited evidence for long-term benefit and international preference for oral administration. Time constraints often hinder informed decision-making. The multicentre Randomized Controlled Trial (RCT) POWER@MS2 (N = 160, 2020-2023), conducted at 18 German MS-centres, aimed to promote self-determined relapse management through a complex intervention (dialogue-based decision aid, nurse-led webinar, online-chat). OBJECTIVE: While RCTs demonstrate effectiveness, process evaluations are essential to understand implementation, mechanisms of impact and contextual factors. This study explored healthcare professionals' experiences and attitudes toward implementing relapse self-management and self-medication in clinical practice. METHODS: A mixed-methods process evaluation followed the UK Medical Research Council- framework. Quantitative data were collected via validated questionnaires at up to three time points and analysed descriptively. Interview guides were developed based on these results. Qualitative data from neurologist and study nurse interviews were thematically analysed. Results were triangulated using a joint display. RESULTS: Data were collected from 55 neurologists and 17 study nurses (quantitative) and from 7 neurologists and 4 nurses (qualitative) (2020-2024). Most neurologists opposed routine steroid use, reserving it for severe relapses. Some voiced concerns about self-management, but informed patients were generally viewed as capable of safe self-medication. Study nurses gave mixed feedback on the intervention, citing overload and improved guidance. CONCLUSION: Clinicians showed openness toward implementing the intervention. Enhancing accessibility and addressing specific concerns may support broader adoption.

Humans↗

PubMed2026

A systematic review of human avoidance learning: Cognition, computation, and methods.

Avoidance behaviour is fundamental for survival but can become maladaptive in clinical conditions. A large body of literature has accumulated on the dynamics of human avoidance learning. However, current theories and overviews do not provide an exhaustive account of this evidence. In this systematic review, we identify N = 116 studies on human avoidance learning. We analyse these studies with the goal of distilling robust empirical phenomena as a basis for theory-building, and examine their diagnostic value in differentiating between competing theories. We find that the evidence is difficult to reconcile with foundational two-factor and classical safety-signal accounts, and most strongly supports expectancy- and inference-based views, in which avoidance responses are selected with respect to represented consequences. At the same time, no current framework provides a complete account of the evidence: several findings point to an additional role for operant valuation, Pavlovian influences, and contextual or latent-state control over the expression of avoidance. Methodologically, we observe that the problem setting in the most common experimental paradigms is radically simpler than real-world avoidance and therefore unlikely to expose the limits of inferential or reflective mechanisms. Consequently, we argue that paradigms with greater computational demands and more realistic action affordances are required to identify the mechanisms underlying avoidance learning. Collectively, these insights provide a foundation for theoretical refinement, computational modelling, and methodological innovation, with implications for advancing interventions targeting maladaptive avoidance.

Humans↗

PubMed2026

Measuring economic efficiency in adult intensive care units: A systematic review of methods, metrics, and evidence.

OBJECTIVES: Intensive care units (ICUs) consume substantial hospital resources, yet "efficiency" is inconsistently defined and measured. This study systematically reviewed how economic efficiency has been conceptualised and quantified in adult ICUs and appraised the quality of evidence. METHODS: Following PRISMA 2020 and a PROSPERO-registered protocol (CRD420251107866), we searched MEDLINE, Embase, CINAHL, Cochrane Library and Web of Science (2000-August 2025), plus global grey sources. Eligible studies explicitly defined efficiency and reported an efficiency metric/model linking ICU inputs (e.g., staff, beds/capacity, time, consumables, or costs) to outputs/outcomes (e.g., throughput/discharges, length of stay/resource use, risk-adjusted mortality). Dual independent screening and extraction were performed. Study quality was appraised using MMAT, and findings were synthesised narratively (SWiM), given heterogeneity. RESULTS: 39 studies (2001-2025) from 17 countries were included, all from high-income or upper-middle-income settings. Four methodological families were identified: (1) frontier modelling (predominantly DEA; occasional SFA/RFDH), (2) benchmarking indicators (risk-adjusted mortality and LOS/resource-use ratios; "efficiency matrix" quadrant classification), (3) cost-outcome evaluations, and (4) operational/process metrics. Across families, variation in decision-making units, input/output selection, and risk adjustment limited comparability; long-term and patient-reported outcomes were absent, and equity considerations were uncommon. CONCLUSIONS: ICU efficiency research is feasible but fragmented and often methodologically limited. Standardised definitions, validated risk adjustment, uncertainty quantification, and inclusion of patient-centred and equity-relevant outcomes are needed before efficiency metrics can reliably inform value-based decision making.

Intensive Care Units↗

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

Prognostic Value of Frailty in Aortic Surgery: A Systematic Review and Meta-Analysis Comparing Frailty Assessment Tools.

BACKGROUND: Frailty is increasingly recognized as an important determinant of outcomes after aortic vascular surgery, but assessment methods vary substantially and the optimal tool for risk stratification remains uncertain. This systematic review and meta-analysis evaluated the prognostic value of preoperative frailty and compared the predictive performance of different frailty instruments in aortic surgery. METHODS: PubMed, Embase, and Cochrane Library were searched from inception to April 27, 2026. Eligible studies included patients undergoing open, endovascular, or hybrid aortic procedures involving abdominal, thoracic, thoracoabdominal, arch, and proximal aortic diseases, including aneurysms and dissections, assessed frailty preoperatively, and reported postoperative outcomes. RESULTS: Thirty studies comprising 419,459 patients were included. Frailty was associated with higher early mortality (odds ratio [OR] 2.20; 95% confidence interval [CI] 1.54-3.14) and late mortality (hazard ratio 2.18; 95% CI 1.64-2.90). Frail patients also had increased risks of major complications (OR 2.52; 95% CI 1.22-5.19), acute kidney injury (OR 1.64; 95% CI 1.34-2.02), and nonhome discharge (OR 5.50; 95% CI 3.05-9.92). Associations were consistent across surgical approaches and aortic segments. Judgment-based or phenotype-like tools yielded higher effect estimates than deficit-accumulation indices, although differences were not statistically significant; among index-based tools, Modified Frailty Index (mFI)-11 outperformed mFI-5. CONCLUSION: Preoperative frailty strongly predicts mortality, morbidity, and loss of functional independence after open, endovascular, and hybrid aortic surgery across different aortic segments and pathologies, including aneurysmal and dissecting aortic disease. Routine frailty assessment may improve risk stratification and perioperative decision-making.

Humans↗

PubMed2026

Complementary feeding patterns in preterm and term infants.

Complementary feeding is essential for infants' nutritional status and development, marking the transition to solid foods when breast milk or formula alone is insufficient. Despite its importance, clear recommendations on which foods to introduce when initiating complementary feeding in preterm infants are lacking. By using data from our previously published randomized controlled trial on the timing of complementary feeding in preterm infants, the current study explores the complementary feeding patterns of preterm infants and compares them with those of term-born infants, providing insights into parental decision-making and potential long-term health impacts. Complementary feeding practices differed significantly between preterm (n = 255) and term (n = 159) infants, with preterm infants more often receiving vegetables as their first solid food (85.4% versus 68.8%, difference 17.6% with 95% CI 12-35%). The group with early introduction of vegetables had a lower BMI-for-age z-scores (β -0.28 [95% CI -0.55 - 0.02]) and weight-for-height z-scores (β -0.27 [95% CI -0.53 to -0.01]) at two years of age. Additionally, preterm infants showed a greater variety in the numbers of different fruits and vegetables consumed by six months (corrected) age than term-born counterparts (8.29 (SD 3.65) versus 6.26 (SD 3.47), p < 0.001). These results indicate that complementary feeding patterns in preterm infants differ from term-born infants, with potential positive implications on growth. These data contribute to the development of accurate feeding protocols for preterm infants. Given that feeding practices are culturally influenced, further multinational research is essential to refine complementary feeding guidelines for preterm infants and support caregivers in informed decision-making.

Humans↗

PubMed2026

Advancing nursing education through social and emotional learning: A systematic review guided by the Collaborative for Academic, Social, and Emotional Learning framework.

BACKGROUND: With Generation Z entering the nursing workforce in growing numbers, strengthening social and emotional learning is critical for academic success, professional adaptation, and safe practice. However, the existing evidence remains fragmented because of varied interventions and inconsistent approaches. OBJECTIVES: This systematic review examined (1) the social and emotional learning essential for nursing students and nurses within the Collaborative for Academic, Social, and Emotional Learning framework, (2) their impact on educational and clinical outcomes, and (3) implications for advancing nursing education and practice. METHODS: Following Joanna Briggs Institute methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, five international (PubMed, EMBASE, CINAHL, PsycINFO, Cochrane) and three Korean (RISS, KoreaMed, KMBASE) databases were searched up to June 2025. Eighteen studies involving 2,952 participants met the inclusion criteria, including quasi-experimental quantitative studies, descriptive quantitative studies, qualitative studies, and mixed-methods studies. The methodological quality of the included studies was appraised using the Mixed Methods Appraisal Tool. RESULTS: Within the Collaborative for Academic, Social, and Emotional Learning framework, relationship skills and self-management were the most frequently studied competencies, emphasizing teamwork, communication, and stress regulation. Self-awareness and social awareness were underexplored, despite their importance in empathy, resilience, and reflective practice. Responsible decision-making was the least studied competency, despite its importance in ethical reasoning. Social and emotional learning was consistently associated with enhanced adaptation, communication, leadership, relationships, and clinical performance. Effective strategies included blended learning, simulation, reflective activities, and mentorship, which are aligned with Generation Z's learning preferences. CONCLUSION: Although social and emotional learning integration is associated with improvements in educational and clinical outcomes in nursing, current research has largely centered on relational and stress-related competencies while underrepresenting responsible decision-making. To cultivate reflective, empathetic, and ethically grounded nurses, curricula should integrate social and emotional learning through a balanced and structured approach. REGISTRATION: This study was registered on PROSPERO (ID: CRD420251005683).

Humans↗

PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗

PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2026

Criteria for Safe Hospital Discharge in Bronchiolitis: A Systematic Review.

Bronchiolitis is the leading cause of hospital presentation and admission for infants in Australasia. We aimed to synthesise current evidence on the effect of discharge criteria for infants (aged < 12 months) who are presenting to or are admitted to hospital with bronchiolitis, to inform a binational guideline recommendation update. Systematic searches were conducted on MEDLINE, EMBASE, PubMed, Cochrane Library and CINAHL (last search 19 February 2025) for non-randomised studies evaluating hospital discharge criteria in bronchiolitis. The primary outcomes were length of stay (LOS) and readmission rates. The risk of bias (ROBINS-I) and certainty of the evidence (GRADE) were appraised, and findings were narratively synthesised. GRADE evidence-to-decision methodology, expert consensus voting and interest-holder consultation were used to finalise the recommendation update. Two retrospective observational studies were included (N = 2697) (low to very low quality), reporting on unique discharge criteria. In both studies, use of the discharge criteria was associated with a significant reduction in LOS relative to alternative protocols. There was no significant difference in readmission rates observed in either study. There was low to very low certainty evidence across outcomes due to risk of bias, indirectness and imprecision. The review findings informed a recommendation update for safe discharge criteria in the 2025 Australasian Bronchiolitis Guideline update. Updated, prescriptive discharge criteria and flow chart were developed, covering clinical stability, oxygen saturation/support, feeding difficulties, caregiver confidence and education on deterioration, social factors and follow-up. The revised criteria provide clinicians with increased certainty in decision-making in bronchiolitis, albeit with further research needed.

Humans↗

PubMed2026

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans↗

PubMed2026

Emerging techniques of CRISPR/Cas system in antiviral therapy and diagnostics: Applications, limitations, and translational perspectives.

The CRISPR/Cas (clustered regularly interspaced short palindromic repeats) system is a versatile technology for developing antiviral medicines and editing viral genomes in both diagnostics and vaccine synthesis. Emerging insights into class 2 effectors, such as Cas9, Cas12, and Cas13, which target viral DNA and RNA, have revolutionized vaccines against viruses such as HIV, HPV, HBV, and EBV. Innovative diagnostic techniques such as SHERLOCK, DETECTR, and FELUDA have demonstrated system's diversity and accuracy in detecting the virus markers, supporting clinical decision-making, indicating adaptability and precision of CRISPR. This review critically evaluates CRISPR's role in RNA editing, emphasizing its importance for functional genomics and development of recombinant vaccines. Translational challenges are critically discussed, including off-target effects, delivery limitations, and ethical issues, for which unique approaches such as high-fidelity Cas variants, non-viral delivery systems, and bioethical frameworks are evaluated to address these limitations. This review also covers other social implications, such as accessibility and biosecurity risks, associated with CRISPR technologies Collectively, these advances underscore the transformative potential of CRISPR technologies in shaping next-generation antiviral diagnostics and therapeutics.

CRISPR-Cas Systems↗

PubMed2026

Nursing students' perspective of dignity: A systematic review.

AIM: This review synthesizes research on nursing students' perceptions of dignity shaped by education and clinical experiences. BACKGROUND: Respect for human dignity is central to nursing ethics. While dignity is well studied in patient care, less focus has been given to how nursing students perceive and experience dignity during their education. DESIGN: A systematic review of quantitative, qualitative, and mixed-methods studies. METHODS: A comprehensive search was conducted across the Medline, PubMed, ScienceDirect, Scopus, and Web of Science databases for English-language studies published from 2000 to 2025. Quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist and the Mixed Methods Appraisal. Tool. Data were synthesized thematically. Reporting followed PRISMA guidelines. RESULTS: A total of 24 articles were included. Students viewed patient dignity as linked to respect, privacy, and autonomy. Although students had solid theoretical knowledge, they encountered institutional and professional barriers in delivering dignified care. Supportive environments and participation in decision-making enhanced their sense of dignity. Finally, educational strategies such as role-playing, simulations, and empathy exercises enhanced emotional and psychosocial awareness. CONCLUSIONS: These findings indicate that dignity is crucial in shaping nursing students' professional identity and ethical awareness throughout their education. Reinforcing ethical values and dignity in nursing education is essential for high-quality care.

Humans↗