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PubMed2026

Multisensory stimulation for promoting development and preventing morbidity in preterm infants.

RATIONALE: Multisensory stimulation is a structured, developmentally appropriate intervention that provides simultaneous or sequential stimulation of two or more senses (e.g. tactile, auditory, visual, or vestibular) in a controlled and non-stressful manner, with the aim of supporting early neurodevelopment in preterm infants. It has the potential to enhance physiological regulation in preterm infants by stabilizing key functions, such as respiratory patterns, heart rate, and oxygen saturation; reducing the need for respiratory support; and improving feeding performance and sleep regulation. Targeted multisensory interventions have also been associated with improved neurodevelopmental outcomes, including enhanced psychomotor development and visual function. OBJECTIVES: To assess the benefits and harms of multisensory stimulation compared to any single sensory intervention or standard care on major neurodevelopmental disability, mortality, and growth in preterm infants. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, Emcare, CINAHL, Epistemonikos, two trial registries, and conference abstracts up to 28 November 2025. We checked reference lists of included trials, and systematic reviews on sensory interventions. ELIGIBILITY CRITERIA: We included 18 randomized controlled trials (RCTs) comparing multisensory stimulation in preterm infants with no intervention (placebo or standard care), and one RCT comparing multisensory stimulation with single-sense stimulation (tactile stimulation). OUTCOMES: Our critical outcomes were major neurodevelopmental disability at 18 to 24 months: cerebral palsy (CP), developmental delay, intellectual impairment, blindness, sensorineural deafness; death during initial hospitalization; and total weight gain (grams), assessed at discharge. When comparing multisensory stimulation with single-sense intervention, we also included weight gain during the intervention, an outcome added during the post-hoc analysis. Important outcomes were duration of hospital stay, of NICU stay, and of respiratory support; and time until full oral feeding. RISK OF BIAS: We used the Cochrane tool, RoB 2. SYNTHESIS METHODS: We conducted meta-analyses using fixed-effect models to calculate risk ratios (RR) for dichotomous data, and mean differences (MDs) for continuous data, each with its 95% confidence intervals (CIs). We assessed statistical heterogeneity by calculating the I2 statistic when we included more than two trials in a meta-analysis. We evaluated the certainty of evidence using GRADE. INCLUDED STUDIES: We included 19 trials (1554 newborn infants): 18 studies compared multisensory stimulation with standard care; one compared multisensory stimulation with single-sensory stimulation (tactile). In 10 studies, the primary aim was to assess the neurobehavioral outcomes of multisensory stimulation on preterm neo-nates. The other nine studies aimed to assess the impact of multisensory stimulation on weight gain during the intervention, weight gain until hospital discharge, length of neonatal intensive care unit (NICU) stay, length of hospital stay, time until full oral feeding, length of respiratory support, or a combination. In the abstract we report results for the critical outcomes only. We identified 13 ongoing studies. Four studies are awaiting assessment. SYNTHESIS OF RESULTS: Multisensory stimulation compared to standard care No studies reported on these major neurodevelopmental disabilities, assessed at 18 to 24 months' corrected age (CA): developmental delay, intellectual impairment, blindness, or sensorineural deafness. One study reported on rates of CP at 12 months of age. The evidence is very uncertain about the effect of multisensory stimulation on CP (RR 0.67, 95% CI 0.28 to 1.58; I² not applicable; 1 study, 18 participants; very low-certainty evidence). The evidence suggests that multisensory stimulation may result in little to no difference in death during initial hospitalization (RR 0.97, 95% CI 0.54 to 1.73; I² not applicable; 1 study, 395 participants; low-certainty evidence). Multisensory stimulation may increase total weight gain prior to discharge (MD 72.67, 95% CI 68.23 to 77.12; I² = 0%; 3 studies, 474 participants; low-certainty evidence). Multisensory stimulation compared to single-sense (tactile) stimulation No studies reported on major neurodevelopmental disability, assessed at 18 to 24 months' CA, or death during initial hospitalization. The evidence is very uncertain about the effect of multisensory stimulation compared to tactile stimulation on weight gain during the intervention (MD -175.00, 95% CI -376.60 to 26.60; I² not applicable; 1 study, 20 participants; very low-certainty evidence). The certainty of the evidence was low to very low across outcomes, primarily due to risk of bias, imprecision from small sample sizes and wide CIs, and in some cases, inconsistency. The evidence base was also limited by the lack of reporting of relevant outcomes and reliance on surrogate outcomes or shorter follow-up periods. AUTHORS' CONCLUSIONS: The available evidence on multisensory stimulation in preterm infants is limited and of low to very low certainty. No included studies reported on major neurodevelopmental disabilities at 18 to 24 months' CA, which represented a critical outcome for this review. Evidence regarding the effect of multisensory stimulation on CP is very uncertain, as it is based on a single small study reporting a surrogate outcome at 12 months. Multisensory stimulation may result in little to no difference in mortality during the initial hospitalization. It may increase total weight gain prior to discharge. However, the clinical significance of this finding is uncertain, particularly given the low certainty of the evidence and the multifactorial nature of growth in preterm infants. The evidence is very uncertain about the effect of multisensory stimulation compared to single-sense (tactile) stimulation on weight gain during the intervention. The only included study did not report major neurodevelopmental disabilities at 18 to 24 months' CA, mortality during the initial hospitalization, or total weight gain prior to discharge, which represented the critical outcomes for this review. Overall, the current evidence does not allow firm conclusions about the effectiveness of multisensory stimulation in promoting development or preventing morbidity in preterm infants. Future studies on multisensory stimulation should use more rigorous designs, larger samples, and report interventions using the template for intervention description and replication (TIDieR) checklist to ensure transparency. They should also report essential outcomes, such as neonatal death, major neurodevelopmental disabilities, length of hospital and NICU stay, time to full oral feeding, duration of respiratory support, and weight gain, to better assess the long‑term effects of multisensory stimulation in preterm infants. FUNDING: This Cochrane review had no dedicated funding. REGISTRATION: Protocol available via DOI: 10.1002/14651858.CD016073.

Humans↗

PubMed2026

Complexity in disguise: a systematic review of fractal analysis in psychiatric neuroimaging.

OBJECTIVES: Psychiatric diagnosis and fractal studies are complex processes that extend beyond clinical evaluation and require careful methodological considerations in neuroimaging. Over the years, fractals have helped reduce these complexities in research, but they still cannot grant clinical diagnoses. Thus, the main objective was a systematic review exploring the potential applications of fractal analysis in characterizing psychiatric conditions through neuroimaging techniques-including both functional and structural MRI. MATERIALS AND METHODS: A systematic literature review was conducted on PubMed, identifying thirty-nine original studies that met the inclusion criteria. Areas showing statistical significance (p < 0.05) were reported. These studies were categorized according to DSM-V classification and examined for the description of psychiatric conditions through the fractal analysis. RESULTS: The review primarily focuses on young adults with psychiatric conditions compared to control groups. Schizophrenia and Autism Spectrum Disorder are major areas of investigation, and fractal dimension (FD) is the primary analysis method used to reflect brain patterns. Studies that calculated whole-brain FD may have underestimated local abnormalities due to the inclusion of a high percentage of tissue, potentially resulting in overlooked findings. Notably, abnormalities in the frontal cortex represent a common neurobiological feature across several psychiatric conditions. CONCLUSIONS: The findings from this systematic review shed light on the use of fractal analysis to quantify complex brain patterns in both psychiatric patients and healthy individuals. However, it is essential to recognize the need for further research to elucidate a fractal analysis protocol that allows for optimal extraction of psychiatric insights. KEY POINTS: Question Fractal analysis applied to structural and functional MRI help characterize brain alterations across psychiatric conditions. Findings This review shows consistent fractal patterns across multiple psychiatric disorders, especially in frontal regions. Despite heterogeneous methodologies, results highlight shared structural and functional abnormalities. Clinical relevance Fractal analysis may offer complementary characterization of subtle brain organization across psychiatric disorders. Its potential clinical utility-such as improving diagnostic characterization, earlier detection, among others-remains limited by the current absence of a standardized protocol.

Humans↗

PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗

PubMed2026

An Assessment of Reliability Estimation Methods for Binomial Health Care Quality Measures.

We evaluated the performance of commonly used methods for estimating the reliability of binomial health care quality measures using simulated datasets spanning a range of performance score means and variances, numbers of entities, and patient sample sizes. For each simulation, reliability was estimated for all selected methods and compared with the known true reliability derived from the simulation parameters, with methods assessed on their accuracy and precision. Logistic regression with reliability estimated on the outcome scale demonstrated the highest accuracy and precision among all methods evaluated. The widely used Adams beta-binomial method performed poorly, although a modification recommended by Nieser and Harris substantially improved its performance. These approaches are applicable only to binomial measures. Among methods that can be applied to both binomial and continuous measures, permutation resampling of the Spearman rank correlation coefficient was the most accurate and precise, outperforming other commonly used approaches. Overall, for binomial quality measures, logistic regression on the outcome scale is the preferred method for reliability estimation, followed closely by the modified beta-binomial approach, while for non-binomial measures, permutation-based Spearman rank correlation appears to be the most suitable method.

Reproducibility of Results↗

PubMed2026

The future of TCR-Treg therapies is renewables.

Cell therapy has longstanding roots in haematopoietic stem cell transplantation and early immune cell transfers in infectious disease and transplantation, where patient- or donor-derived cells have achieved therapeutic benefit in selected contexts. The modern era has been driven largely by oncology, with engineered modalities such as tumour-infiltrating lymphocytes, CAR-T cells and TCR-engineered T cells delivering transformative responses but requiring complex, costly manufacturing. These platforms are now being adapted for autoimmune diseases to induce durable, antigen-specific immune tolerance, yet broad application is limited by safety concerns, process complexity and access. Non-engineered cell therapies for autoimmunity, including mesenchymal stem cells, polyclonal regulatory T cells and tolerogenic dendritic cells, have shown acceptable safety and proof-of-principle for immune re-education, but clinical responses have been modest and inconsistent, with limited scalability. Engineered approaches such as CAR-T cells can induce reversible B cell depletion in B cell-mediated rheumatic diseases but only addresses antibody-driven pathology and not T cell-mediated autoimmunity. TCR-engineered Tregs have emerged as a promising antigen-specific strategy, offering localized, antigen-linked suppression with bystander tolerance. Preclinical and early clinical data suggest superior potency, stability and disease control compared with polyclonal Tregs at similar or lower doses, but translation is constrained by the rarity and fragility of Tregs and by labour-intensive, CAR-T-like manufacturing. This review highlights emerging solutions for closed, automated and decentralised production, and discusses allogeneic approaches using gene-edited or banked Tregs with HLA engineering or matching. Together, these advances support the development of scalable, "off-the-shelf" TCR-Treg products with potential to provide safe, affordable tolerance-restoring therapies for autoimmune disease.

Humans↗

PubMed2026

HYPNOSA: Study protocol for a prospective observational cohort of patients with obstructive sleep apnea.

BACKGROUND: Obstructive Sleep Apnea (OSA) is a common chronic disease that affects more than 20% of the adult population. One of the most frequent and characteristic symptoms of OSA is excessive daytime sleepiness (EDS). This symptom is typically treated in patients with OSA with the application of continuous positive airway pressure (CPAP), the gold-standard treatment for this disease. In some patients who are adequately treated with CPAP, residual excessive daytime sleepiness (REDS) persists. The prevalence, associations, and outcomes associated with REDS remain poorly understood. METHODS: Multicenter, prospective, observational cohort study including 1000 patients. Participants will undergo a sleep study for the diagnosis of obstructive sleep apnea (OSA), 24-h ambulatory blood pressure monitoring, clinical assessment, quality-of-life questionnaires, Epworth Sleepiness Scale, and collection of biochemical variables and biological samples. Patients with OSA will receive standard care, and those prescribed continuous positive airway pressure (CPAP) will be monitored for treatment adherence. OSA patients will be assessed at baseline and at 6, 12, and 24 months. DISSCUSION: We aim to establish a prospective observational cohort of patients with obstructive sleep apnea (OSA) treated with CPAP, with and without REDS. The HYPNOSA project will create the largest available registry of patients with OSA and REDS using real-world data, providing accurate prevalence estimates and long-term outcomes. Biological samples will be analyzed to assess the role of specific biomarkers. TRIAL REGISTRATION: Registered at ClinicalTrials.gov. Identifer: NCT06514482.

Adult↗

PubMed2026

Beyond multidimensionality: a systematic review of recurrent frailty archetypes in community-dwelling older adults.

BACKGROUND: Frailty is a clinically heterogeneous geriatric syndrome commonly summarised using physical or multidomain severity scores. Whether person-centred analyses identify recurring within-frailty configurations has not been systematically examined in community-dwelling older adults. METHODS: We searched PubMed, Embase, MEDLINE, and CINAHL (January 2000-November 2025) for cross-sectional studies using latent class, latent profile, or analogous clustering methods to derive frailty subgroups. Quality was assessed using the AHRQ checklist and a purpose-built appraisal of person-centred model reporting. Study-derived classes were mapped in duplicate to a structured archetype framework developed through comparison of class-defining features across studies. RESULTS: Fourteen reports representing 12 independent datasets from eight countries were included. Six configurations were identified: minimally impaired reference, mobility-physical, nutritional-metabolic, cognitive-predominant, combined cognitive-physical, and psychosocial/mood-predominant. Convergence was measurement-dependent. The reference and mobility-physical configurations recurred across physical-only and multidomain indicator sets, while the combined cognitive-physical configuration appeared across several multidomain frameworks but required cognition to be measured. The remaining configurations emerged only when their defining domains were included. Evidence of prognostic value beyond aggregate frailty severity came from one deficit-index study. Collapsing shared-provenance reports and excluding the boundary-eligible study did not alter recurrence; excluding the Croatian dataset left five configurations recurrent, with the cognitive-predominant configuration supported by one independent dataset. CONCLUSIONS: Person-centred analyses identify recurring within-frailty configurations, but their apparent stability is partly measurement-dependent. A five-configuration core persisted after exclusion of the Croatian dataset, whereas the cognitive-predominant configuration remained weakly replicated. Harmonised indicators and rigorous external validation are needed before clinical application.

Humans↗

PubMed2026

The gut microbiota-obesity axis in the pathogenesis and prognosis of breast cancer.

BACKGROUND: Breast cancer (BC) remains a major global health concern, accounting for 11.7% of all cancer cases and ranking as the second leading cause of female cancer-related deaths worldwide. Increasing evidence highlights the interplay between gut microbiota (GM) dysbiosis and obesity-associated metabolic dysfunction in BC progression. This review aims to elucidate the role of GM in obese patients with BC. METHODS: A systematic literature search was conducted in PubMed and Web of Science databases for publications from July 2015 to January 2025. Search terms combined BC, GM, obesity, dysbiosis, immunity, and microbiome. Article selection prioritized studies investigating microbial alterations in BC patients, mechanistic links between obesity and cancer progression, and GM-targeted interventions. Both original studies and authoritative reviews were included, supplemented by manual reference screening. DISCUSSION: Obesity may trigger systemic inflammation, altered adipokine secretion, and disrupted steroid hormone metabolism via gut-derived β-glucuronidase activity, thereby exacerbating BC occurrence and recurrence. GM dysbiosis-driven metabolites such as branched-chain amino acids (BCAAs) and short-chain fatty acids (SCFAs) can activate oncogenic signaling pathways and immunosuppressive myeloid-derived suppressor cells (MDSCs), fostering tumor immune evasion. Conversely, dietary interventions, probiotics, and fecal microbiota transplantation (FMT) can alleviate dysbiosis, strengthen gut barriers, and restore anti-tumor immunity, improving chemotherapy response and reducing recurrence. However, challenges persist in deciphering BC subtype-related microbial signatures and optimizing microbiota-targeted therapies. CONCLUSION: Future longitudinal studies are needed to clarify causal relationships, validate microbial biomarkers, and translate preclinical findings into clinical applications. Addressing the gut-breast axis may offer transformative potential for precision oncology in obesity-driven BC.

Humans↗

PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗

PubMed2026

Structural and physicochemical characterisation of branched dextrans produced by an active α-(1→2) branching sucrase from Apilactobacillus kunkeei PDER37.

Recently, branching sucrases encoded in the genomes of certain Lactic Acid Bacteria (LAB) strains have become novel enzymes to obtain branched α-glucans. In this study an active α-(1 → 2) branching sucrase from Apilactobacillus kunkeei PDER37 was expressed, characterised and distinct branched dextrans was obtained with reactions under different sucrose: dextran ratio. Structural characterisation by 1H and 13C NMR analysis demonstrated the branching of the dextran with (1 → 2)-linked α-d-glucose units with no alteration in the final structure depending on sucrose: dextran ratio (D0) but this ratio was effective for the determination of the molecular weights of the branched dextrans (D1, D2 and D3). FTIR analysis further supported the dextran structures and suggested the higher accumulation of the α-Glc units in the branched dextrans. Thermal characterisation of the branched dextrans obtained by TGA and DSC analysis suggested the increased hygroscopicity of the branching units. Both SEM and AFM analysis demonstrated more porous chain like structures in the branched dextrans. This study provides valuable information on the role of active α-(1 → 2) branching sucrase (BS37) for the production of branched dextrans with potential increased physicochemical status applicable for food and other industries.

Dextrans↗

PubMed2026

Tele-Oncology in the Post-Pandemic Era: Clinical Integration, Access Disparities and Medico-Legal Accountability.

PURPOSE OF THE REVIEW: Tele-health has evolved from a marginal tool confined to rural populations and selected follow-up programs into a structurally integrated component of modern cancer care. Prior to COVID-19, its adoption was constrained by regulatory fragmentation, non-uniform reimbursement, and licensure barriers. This narrative review evaluates the evolutionary integration of tele-health in oncology post-COVID-19, examines digital disparities across patient populations, and addresses the medico-legal implications of this integration, with the objective of providing a comprehensive and clinically actionable framework for the governance of virtual oncology care. RECENT FINDINGS: The pandemic acted as a global catalyst, driving telehealth to over 50% of oncology outpatient encounters in some settings, before stabilising post-pandemic at approximately 10-20% of consultations within hybrid care models. Evidence supports meaningful clinical benefits - improved access to specialist services, reduced travel burden, and sustained continuity of care - with outcomes comparable to in-person care in postoperative follow-up, symptom monitoring, and survivorship. However, persistent disparities in device availability, connectivity, and digital literacy disproportionately affect older, rural, and socioeconomically disadvantaged patients, raising the risk that geographic inequalities are replaced by technological ones. From a medico-legal standpoint, the remote modality does not modify the applicable standard of care, yet restricted physical examination and reliance on patient-reported data introduce risks of diagnostic delay and incomplete clinical assessment, with direct implications for professional liability, data protection under HIPAA and GDPR, cross-border licensure, and multi-party accountability across physicians, institutions, and technology providers. Tele-oncology has become a permanent structural feature of modern cancer care, offering demonstrable benefits in access, continuity, and patient satisfaction. Yet its integration has been uneven, its governance remains fragmented, and its medico-legal landscape is still evolving. Realising the full potential of virtual oncology care - equitably and safely - requires coherent regulatory frameworks, sustained investment in digital infrastructure, and explicit attention to the populations at greatest risk of being left behind.

Humans↗

PubMed2026

Imaging‑based models for predicting cerebrovascular complications of carotid stenosis.

This is a protocol for a Cochrane review (prognosis). The objectives are as follows: Primary objective To systematically review and critically appraise multivariable prognostic models developed for adults (≥ 18 years) with carotid stenosis in which imaging biomarkers (e.g. plaque characteristics derived from magnetic resonance imaging (MRI), computed tomography (CT), or ultrasound) constitute the core predictors. The primary focus is to evaluate the predictive performance of these models for cerebrovascular complications - specifically ipsilateral ischaemic stroke and transient ischaemic attack (TIA) - which are the clinical outcomes to be predicted. Where feasible, we will summarise and compare the models' discrimination (C‑statistic/area under the curve (AUC)) and calibration (calibration‑in‑the‑large, calibration slope, observed‑to‑expected ratio) across studies, and assess their potential for clinical application and external validation. For the purpose of defining symptomatic carotid stenosis as an eligibility criterion and subgroup variable, we will include studies that also considered retinal ischaemia (e.g. retinal embolism, amaurosis fugax) as a qualifying event. Secondary objectives To describe the combinations of imaging markers, modelling techniques, sample sizes, and variable‑selection strategies used in the development of the included models To evaluate the performance of these models for additional secondary clinical outcomes: plaque progression or regression, incident high‑risk imaging features, and the transition from asymptomatic to symptomatic disease To explore whether predictive performance differs according to imaging modality (MRI versus CT versus contrast‑enhanced ultrasound (CEUS)) or technical protocol (e.g. 3 T versus 1.5 T, spectral CT versus conventional CT) For studies that report both cerebrovascular and broader cardiovascular outcomes (major adverse cardiovascular events, myocardial infarction, etc.), we will only extract the performance metrics relating to cerebrovascular events for the primary analysis. Performance metrics for cardiovascular outcomes will be considered exploratory and will not form part of the main synthesis.

Humans↗

PubMed2026

Validation of the lung immune prognostic index in extensive-stage small cell lung cancer: Post hoc analysis of the caspian and IMpower133 phase 3 trials.

BACKGROUND: The Lung Immune Prognostic Index (LIPI) is an inflammation-based biomarker associated with outcomes to immunotherapy across several tumor types. Its prognostic value in extensive-stage small-cell lung cancer (ES-SCLC), however, remains insufficiently validated. We aimed to validate the prognostic impact of LIPI in ES-SCLC using data from two phase III trials. METHODS: Patients enrolled in the CASPIAN (NCT03043872) and IMpower133 (NCT02763579) trials were included. LIPI groups were defined as good (dNLR<3 and LDH<ULN), intermediate (dNLR≥3 or LDH≥ULN) and poor (dNLR≥3 and LDH≥ULN). Overall survival (OS) and progression-free survival (PFS) were assessed across LIPI categories and treatment arms. RESULTS: LIPI was available for 1140 patients (Good: 34%, Intermediate: 49%, Poor: 17%), including 708 treated with chemotherapy-immunotherapy and 432 with chemotherapy alone. Poor LIPI was associated with unfavorable characteristics, including lower albumin levels and higher rate of liver metastases. Median OS was 14.6 months (95%CI: 12.4-15.9) for LIPI Good, 10.9 (10.1-11.5) for Intermediate, and 8.4 (7.1-9.3) for Poor (p < 0.0001). In multivariate models adjusted on gender, age, ECOG, treatment arm and metastatic sites, LIPI remained an independent prognostic factor for OS (HR Poor vs. Good: 1.76, 95%CI: 1.45-2.15, p < 0.001) and PFS (HR: 1.59, 95%CI: 1.33-1.90, p < 0.001). Although patients with poor LIPI derived limited benefit from immunotherapy, no significant treatment-LIPI interaction was observed. CONCLUSION: This large post hoc analysis confirms LIPI as a robust and clinically applicable prognostic biomarker in ES-SCLC. Patients with poor LIPI have substantially worse outcomes and limited benefit from immunotherapy, highlighting the need for novel therapeutic strategies in this subgroup.

Humans↗

PubMed2026

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. 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 + AI for prediction model studies. 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 + 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. 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↗

PubMed2026

Positive psychology interventions during pregnancy: A systematic review.

Positive psychology interventions (PPI) have been applied and demonstrated evidence in various population groups. The present systematic review focused on the types and influence of PPI on the physical and psychological health of pregnant women. Studies that matched the selection criteria were identified on EBSCOhost, PsychINFO, Web of Science, PubMed, Scopus and four positive psychology journals. From the 2528 records identified, finally eight studies were included in the review. PPI in this review were delivered utilising various positive psychology components such as hope, gratitude and optimism based on existing theories, for example, the strengths theory, broaden-and-build theory, and hope theory. Most interventions were conducted from 14 gestational weeks onwards and were delivered via virtual platforms or mobile applications. As a result of this systematic review, it was identified that PPIs for maternal well-being were aimed at improving (1) physical health, including labour pain, nausea and vomiting; (2) psychological health, including stress, emotions, anxiety and depression; and (3) subjective health, including life satisfaction, perceived social support and quality of life. Most of the selected studies provided significant evidence towards improvement of well-being outcomes from administering PPI. For future studies, in-depth PPI integrated coping and support approaches should be further evidenced among diverse pregnant populations.

Humans↗

PubMed2026

Exploring the mechanism of aroma production in fermented cherry juice by L. brevis LD1.0600 using flavomics and whole genome analysis.

This study focused on L.brevis LD1.0600 with excellent fermentation traits: it analyzed genome-wide key regulatory genes for micro-metabolites, combined with fermented cherry juice flavor metabolomics data, and used machine learning to explore correlations between gene regulation, metabolite production, and flavor formation. The SVM model screened and verified fermented cherry juice VOCs; through OAV and flavor wheel analysis, LD1.0600 emerged as the top-performing strain, with a sweet, fruity dominant aroma. Key aroma-active components (OAV > 100) included 2-methoxy-4-vinylphenol, benzaldehyde, 2-methyl-butanoic acid and hexanoic acid, and 2-methoxy-4-vinylphenol and hexanoic acid elevated by LD1.0600-regulated genes (Chrom1-001884, Chrom1-000925, fabF and Chrom1-000199). At the same time, through research, a "strain screening-SVM screening of DVCs-OAV screening of key aroma components-whole genome sequencing of flavor regulatory genes" system was established. This system can not only be applied to the screen fermentation strains, but also can be extended to the application of other fermentation products.

Fermentation↗

PubMed2026

Assessing AI literacy and attitudes among medical students: implications for integration into 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↗

PubMed2026

Effectiveness of high-power laser use in the removal of esthetic restorations: a systematic review.

This systematic review aimed to evaluate the effectiveness of high-power lasers in the removal of esthetic restorations compared with conventional methods. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and registered in PROSPERO (registration number: CRD420251055736). A comprehensive electronic search was performed in MEDLINE, Scopus, Embase, Web of Science, and the Cochrane Library. Study selection, data extraction, and methodological quality assessment were independently performed by four reviewers using QUIN tool for in vitro studies. A total of 2.384 records were identified, of which 19 in vitro studies conducted on extracted human permanent teeth met the eligibility criteria and were included in the qualitative synthesis. The included studies evaluated different high-power laser systems, predominantly Er: YAG and Er, Cr: YSGG lasers, as well as CO₂ lasers. Five studies were classified as high quality, whereas the remaining fourteen studies were classified as moderate quality. Available laboratory evidence suggests that laser-assisted techniques may represent a potential alternative approach for removing esthetic restorations, with advantages including conservative tissue removal, reduced damage to underlying tooth structures, and intrapulpal temperature increases within acceptable limits. The Er: YAG laser was the most frequently investigated system, with power settings ranging from 1.5 to 5.9 W, showing particularly favorable laboratorial performance in the removal of lithium disilicate restorations, especially in thinner substrates. Within the limitations of the available evidence, high-power lasers appear to be a promising and minimally invasive alternative to conventional methods for the removal of esthetic restorations. However, the predominance of in vitro studies highlights the need for well-designed clinical trials to confirm these findings and support their clinical application.

Humans↗