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PubMed2026

Revisiting somatosensory evoked potentials in motor neuron diseases: neurophysiological insights from a large cohort.

OBJECTIVE: To systematically investigate Somatosensory Evoked Potential (SEP) abnormalities in a large cohort of patients with Motor Neuron Disease (MND), and to explore their relationship with Motor Evoked Potentials (MEPs) and clinical phenotypes. METHODS: We retrospectively analyzed 267 patients with confirmed MND who underwent standardized SEPs and transcranial magnetic stimulation. Patients were divided into pure/predominant Upper Motor Neuron (UMN) and pure/predominant Lower Motor Neuron/Amyotrophic Lateral Sclerosis (LMN/ALS) groups. SEP abnormalities were assessed using internal normative data, including prolonged latencies, reduced amplitudes, and increased N20-P25 amplitudes. MEPs were classified semi-quantitatively as normal or abnormal by independent raters. RESULTS: At least one SEP abnormality was detected in 75 % of patients, with no significant differences between the UMN and LMN/ALS groups. Increased N20-P25 amplitudes were observed in both phenotypes, suggesting widespread sensory cortical hyperexcitability across the MND spectrum. In contrast, abnormal MEPs were significantly more frequent in UMN patients (p < 0.001). No significant association was found between SEP abnormalities and MEP findings. Upper- and lower-limb SEP latencies were strongly correlated (all p < 0.001), whereas increased SEP amplitudes did not correlate with latency abnormalities. CONCLUSIONS: SEP abnormalities are highly prevalent in MND and appear largely independent from corticospinal dysfunction. Increased SEP amplitudes likely reflect primary cortical sensory hyperexcitability rather than impaired sensory conduction. SIGNIFICANCE: These findings support the concept of MND as a multisystem network disorder that involves sensory cortical circuits and highlight the role of SEPs in the diagnostic workup.

Humans↗

PubMed2026

The utility of 18F-fluorodeoxyglucose PET/computed tomography in relapsing polychondritis: a systematic review and meta-analysis.

Relapsing polychondritis is a rare chronic autoimmune inflammation of the cartilage associated with life-threatening respiratory complications. Currently, no clear role of imaging modalities such as 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) is defined in the literature. This systematic review and meta-analysis provide current evidence on the PET-positivity rate and utility in relapsing polychondritis. Prospective or retrospective studies with more than five patients of suspected relapsing polychondritis who underwent 18F-FDG PET/CT during their management and reported a PET-positivity rate were included. Low-sample-size studies describing chondritis due to other aetiologies or utilizing PET-based radiopharmaceuticals other than FDG were excluded. A systematic search using relevant keywords was conducted across four databases (PubMed, Embase, Scopus and Web of Science) to include studies up to 25 April 2025. The Joanna Briggs Institute critical appraisal tools were used for risk-of-bias analysis. Data were analysed using the R software package (v4.3.1; 2023). Out of 962 articles, three with a total of 97 patients were included. With a pooled PET-positivity rate of 94% [95% confidence interval (CI): 73-99%, I2 = 0%, P = 0.76] and a pooled baseline SUVmax of 4.0 (95% CI: 3.5-4.6, I2 = 32%, P = 0.23), 18F-FDG PET identified asymptomatic cartilage involvement in more than 25% patients and PET parameters correlated well with inflammatory markers. It had a higher positivity rate for inaccessible sites, such as peripheral airways, and was crucial in treatment monitoring. The pooled PET-positivity rate of 18F-FDG PET in relapsing polychondritis is high but requires prospective large-sample-size studies to explore the diagnostic accuracy and prognostic implications of 18F-FDG PET in relapsing polychondritis.

Polychondritis, Relapsing↗

PubMed2026

Impact of estimated total blood volume on NT-proBNP response to angiotensin receptor-neprilysin inhibition in acute heart failure: Insights from the PREMIER study.

BACKGROUND: Sacubitril/valsartan (Sac/Val) reduces N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in acute heart failure (AHF), particularly in patients with reduced ejection fraction. However, whether estimated total blood volume (TBV), calculated using anthropometric equations, is associated with heterogeneity in biomarker response remains uncertain. METHODS: This post hoc exploratory sub-analysis of the PREMIER randomized trial evaluated whether baseline estimated TBV was associated with heterogeneity in NT-proBNP reduction after Sac/Val compared with angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACEI/ARB) therapy. Estimated TBV was calculated using validated anthropometric equations and dichotomized at the median (4.05 L). Patients were further stratified by left ventricular ejection fraction (LVEF <40% vs ≥40%). The primary endpoint was the proportional change in NT-proBNP from baseline to Week 8. RESULTS: Among 376 patients, 372 with baseline estimated TBV data were analyzed. In the high TBV group, Sac/Val was associated with greater NT-proBNP reduction than ACEI/ARB (-56% vs -32%; ratio of change, 0.67; 95% confidence interval, 0.53-0.84; P = .001), whereas no significant difference was observed in the low TBV group (P for heterogeneity = 0.063). In patients with LVEF <40%, Sac/Val was associated with greater NT-proBNP reduction in both TBV groups. In patients with LVEF ≥40%, Sac/Val was associated with greater NT-proBNP reduction in the high TBV group, whereas the point estimate in the low TBV group numerically favored ACEI/ARB. CONCLUSIONS: In this exploratory post hoc analysis, higher estimated TBV was associated with greater NT-proBNP reduction after Sac/Val, particularly among patients with LVEF ≥40%. These findings are hypothesis-generating and require external validation. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05164653; Japan Registry of Clinical Trials, jRCTs021210046.

Humans↗

PubMed2026

Non-motor symptoms and healthcare utilization before diagnosis of myasthenia gravis: a nationwide cohort study.

BACKGROUND: Non-motor symptoms have been reported prior to myasthenia gravis (MG) diagnosis. However, the temporal patterns of non-motor symptoms and healthcare utilization before MG diagnosis remain unclear. METHODS: We conducted a retrospective, population-based cohort study using the Korean National Health Insurance Service (KNHIS) database from 2011 to 2021. Incident MG cases were identified using the International Classification of Diseases, Tenth and Rare Intractable Disease codes. Individuals younger than 20  years or with missing health screening data were excluded. Each MG case was matched 1:10 by age, sex, and index date to controls. Non-motor symptoms and healthcare utilization were defined using operational criteria derived from KNHIS claims data. Rate ratios (RRs) and 95 % confidence intervals (CIs) were estimated across four prespecified intervals (0-1, 1-2, 2-5, and 5-10  years) before MG diagnosis. RESULTS: We included 8,355 MG patients and 83,550 controls (mean age, 53.7  years; male, 44 %). MG patients had higher rates of any non-motor symptoms over 10  years(RR 1.34; 95 % CI 1.30-1.39), with the sharpest increase in the year before diagnosis. Depression, anxiety, migraine, constipation, and insomnia consistently showed higher RRs across all intervals. Hospitalizations (RR 1.66; 95 % CI 1.61-1.71) and outpatient clinic visits (RR 1.10; 95 % CI 1.04-1.17) were consistently higher across 10  years, peaking during the 0-1 year before MG diagnosis. CONCLUSION: Non-motor symptoms and healthcare utilization increased years before MG diagnosis. Earlier recognition of these symptom patterns may facilitate timelier evaluation for MG and improve diagnostic pathways.

Humans↗

PubMed2026

Transcriptomic responses of gill and intestinal tissues in Nile tilapia (Oreochromis niloticus) to bacterial infection following sequential nanoimmersion and hydrogel-based multivalent vaccination.

Bacterial pathogens, including Flavobacterium oreochromis, Aeromonas veronii, Streptococcus agalactiae, and Edwardsiella tarda, represent major infectious threats to Nile tilapia (Oreochromis niloticus). A multivalent vaccination strategy integrating cationic nanoemulsion immersion with oral hydrogel boosters was developed to investigate tissue-specific immune responses at the transcriptomic level. Gill tissues were collected following immersion challenge and intestinal tissues following intraperitoneal injection challenge, reflecting the physiologically relevant infection biology of each pathogen and the mechanistic rationale of each delivery platform. RNA sequencing (RNA-seq) generated high-quality datasets (mapping rate > 81.64%) with strong concordance to quantitative real-time PCR (qRT-PCR) validation (r = 0.83). Comparative transcriptomic analysis revealed distinct yet complementary immune signatures between tissues. Gill transcriptomes were enriched in phagosome, focal adhesion, extracellular matrix-receptor interaction (ECM-receptor interaction), and cytokine-cytokine receptor interaction pathways, accompanied by increased expression of major histocompatibility complex class I/II (MHC class I/II), mannose receptor, αVβ3 integrin, and calnexin, indicating innate activation, enhanced phagocytic capacity, epithelial barrier reinforcement, and adaptive immune coordination. Intestinal transcriptomes showed predominant enrichment of adaptive immune pathways, including the intestinal immune network for immunoglobulin (Ig) production, Forkhead box O (FoxO) signaling, and mitogen-activated protein kinase (MAPK) signaling, with increased expression of T-cell receptor (TCR), inducible T-cell co-stimulator ligand (ICOS-L), C-X-C chemokine receptor type 4 (CXCR4), and polymeric immunoglobulin receptor (pIgR), reflecting T and B cell coordination, lymphocyte trafficking, and mucosal immunoglobulin transport, alongside innate engagement through phagosome pathway enrichment. Shared upregulation of MHC class II, B-cell receptor (BCR) signaling, integrin alpha M (ITGAM), and immunoglobulin-associated components across both tissues suggests coordinated mucosal immune activation through a conserved immune module, warranting direct experimental validation. Collectively, these findings provide transcriptomic evidence that this vaccination strategy elicits an integrated, tissue-specialized immune response, advancing mechanistic understanding of gill and intestinal immunity in vaccine-induced protection of teleost fish.

Animals↗

PubMed2026

Use of Wearable Sensors in Angelman Syndrome: A Systematic Review.

BACKGROUND: Wearable sensors are a promising method for collecting clinical trial outcome data for people with Angelman syndrome (AS). However, there has yet to be a systematic probe into the ways in which wearable sensors have been successfully used in AS. The current study aims to provide a quantitative summary of wearable sensors used in AS, including contexts of use and psychometric properties, and to present key narrative highlights. METHOD: Literature searches were performed in three electronic databases: APA PsycInfo, PubMed and Web of Science Core Collection. Data items were categorized into four categories: sample characteristics, study methodological details, wearable sensor characteristics and psychometric properties assessed. Sample characteristics included sample size, age, biological sex, race/ethnicity and cognitive/developmental functioning. Study methodological details were subdivided into study design and setting. Wearable sensor characteristics included sensor type, placement site, means of attachment, assessed construct and sensor-related data loss. Psychometric properties assessed included reliability and validity of sensor-derived data. RESULTS: We identified 16 articles through our systematic review. Wearable sensors were used to study sleep (n = 10, 62.5%), language (n = 2, 12.5%), gait (n = 2, 12.5%), caregiver proximity (n = 1, 6.3%), EEG power (n = 1, 6.3%), and arousal (n = 1, 6.3%) in AS through actigraphs, vocalization recorders, inertial sensors, radio-frequency identification watches, wireless EEG caps, and functional near-infrared spectroscopy caps, respectively. Findings from these studies broadly indicate that wearable sensors are feasible, reliable and valid for assessing a range of behaviours relevant to AS. CONCLUSIONS: Wearable sensors are a promising solution to enhance assessments in AS. However, with the small extant literature characterized by small sample sizes and restricted focus on a few relevant features in AS, there remains ample opportunities to explore the use of wearable sensors in people with AS. Additional studies will better inform clinical decision-making and ultimately improve the lives of people with AS and their families.

Humans↗

PubMed2026

Responding to a protracted tuberculosis outbreak: lessons from multiple rounds of investigation in a Chinese boarding school.

PURPOSE: This study analysed a multi-semester pulmonary tuberculosis (PTB) cluster outbreak in a Chinese boarding school to provide evidence for future epidemic control. METHODS: Contacts were screened via symptoms, infection tests and chest radiography. Screening expanded progressively from close contacts to same-floor contacts, then all students and staff. Whole-genome sequencing (WGS) with single nucleotide polymorphism (SNP) and bioinformatics analysis was used for lineage classification, transmission clustering (≤12 SNPs defining a cluster) and drug resistance prediction. RESULTS: From 2020 to 2022, 20 students were diagnosed with PTB, half laboratory-confirmed. Most cases clustered in class 16 and were epidemiologically linked to the primary case (case 0), who had household PTB exposure. Case 0 and case 1 had diagnostic delays exceeding 3 and 6 months, respectively. WGS of five isolates (case 1, 3, 4, 9 and 10) collected over three semesters showed all belonged to lineage 2 and differed by ≤12 SNPs, confirming the same transmission chain. The infection rate in class 16 (46.34%) was significantly higher than other case classes (19.05%) and classes without cases (8.27%) (χ2 = 61.169, p < 0.001). No new cases were detected during a one-year follow-up of students involved in the outbreak after the final round of screening, nor among household contacts of all cases followed up to the present. CONCLUSIONS: Lack of entry health examinations facilitated the outbreak. Delayed diagnosis, incomplete contact screening and absence of preventive treatment led to cross-semester persistence. The infection rate disparity confirms class 16 as the outbreak epicentre. Improving community case management, extending contact follow-up and enhancing cluster outbreak measures are recommended to prevent future outbreaks.

Humans↗

PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2026

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

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

Humans↗

PubMed2026

Clinical and Psychosocial Characteristics of Adult Primary Care IBS Patients: A Post hoc Analysis of the DOMINO Study.

BACKGROUND: The majority of irritable bowel syndrome (IBS) patients are diagnosed and managed in primary care, but this setting is underinvestigated to date. OBJECTIVE: The present study aimed to improve our understanding of IBS in primary care by evaluating the clinical and psychosocial characteristics of affected patients. METHODS: We performed a cross-sectional post hoc analysis of the DOMINO study, which enrolled 483 adult IBS patients newly diagnosed by primary care physicians. We investigated baseline demographics and questionnaires assessing Rome IV criteria and stool pattern subtype, symptom severity (IBS-SSS), quality of life (IBS-QoL), somatic symptom disorder (PHQ-12), depression (PHQ-9) and anxiety (GAD-7). RESULTS: 70% of the primary care diagnosed IBS patients fulfilled the Rome IV criteria (Rome+). The stool pattern subtype distribution according to the Rome IV diagnostic questionnaire was: 20% constipation (IBS-C), 33% diarrhea (IBS-D), 31% mixed (IBS-M) and 16% unclassified (IBS-U). Mean IBS-SSS was 268 ± 98, with 46% and 36% of cases reporting moderate and severe IBS-SSS, respectively. Rome + patients had, compared to Rome-, a significantly higher IBS-SSS, lower quality of life and higher psychosocial comorbidity. IBS-M, IBS-D and IBS-C participants scored significantly higher on IBS-SSS and IBS-QoL than IBS-U. Furthermore, IBS-M had significantly higher somatic symptom disorder and depression and anxiety levels compared with IBS-U. CONCLUSION: The majority of primary care IBS patients fulfilled the Rome IV criteria, were subtyped as IBS-D or IBS-M and were characterised by moderate or severe IBS-SSS. Rome+ and IBS-M participants had higher symptom severity, lower quality of life and higher psychosocial comorbidity. CLINICALTRIALS: gov, Number NCT04270487.

Adult↗

PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

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

Association of time-averaged systemic immune-inflammation indices with in-hospital mortality after intracerebral hemorrhage: a retrospective study.

BACKGROUND: Systemic inflammation plays a central role in secondary brain injury following intracerebral hemorrhage (ICH). Although inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) are linked to poor outcomes, their associations with mortality are commonly assumed to be linear, potentially overlooking nonlinear patterns where mortality risk rises steeply at higher levels. METHODS: We conducted a retrospective study using the MIMIC-IV database, including 440 patients with non-traumatic ICH who were alive and remained in the ICU for at least 72 h after admission. Mean NLR, SII, and SIRI were calculated from measurements obtained during this period. Multivariable logistic regression and restricted cubic spline (RCS) analyses were applied to assess their independent and nonlinear associations with in-hospital mortality. Model discrimination and calibration were internally validated using 1,000 bootstrap resamples. RESULTS: The in-hospital mortality rate was 26.1%. After multivariable adjustment, NLR and SIRI remained independently associated with mortality. Patients in the highest SIRI quartile had the highest risk of death (aOR = 5.12; 95% CI: 2.57-12.24; p < 0.001). RCS analysis revealed a significant nonlinear association between SIRI and mortality (p-nonlinearity < 0.05), showing a steep risk increase at higher SIRI levels. Adding SIRI to the base model provided a modest improvement in discrimination (AUC 0.762 to 0.785, p = 0.045) and significantly improved risk reclassification (cNRI = 0.4778, p < 0.001; IDI = 0.0240, p = 0.0151). CONCLUSIONS: Among patients with ICH who met the 72-hour eligibility criterion, higher 72-hour average SIRI was independently associated with in-hospital mortality. As a time-averaged measure, SIRI should be interpreted as a dynamic marker integrating the initial inflammatory state and the early clinical course rather than as a purely baseline prognostic factor. Although adding SIRI to the base model modestly improved discrimination and risk reclassification, it should be considered a candidate prognostic marker requiring external validation before clinical application.

Humans↗

PubMed2026

Repeated scoring with the adult appendicitis score improves the sensitivity and the specificity of appendicitis diagnosis in patients with early equivocal signs of appendicitis: a secondary analysis.

PURPOSE: The utilization of computed tomography in the early stage of acute appendicitis may result in overdiagnosis and unnecessarily expose patients to ionising radiation. The Adult Appendicitis Score (AAS) can be used to select patients for imaging. Observation and re-scoring in the DIAMOND trial reduced the need for imaging. Now, we wanted to determine if the change in AAS (∆AAS) can serve as a diagnostic tool to select patients for imaging even more precisely. METHODS: Eighty-eight patients with early equivocal appendicitis participated in the observation arm of the DIAMOND trial. The data for these patients were reanalysed, and ∆AAS during the observation was calculated. The baseline AAS, final AAS, and the change in C-reactive protein (∆CRP) were selected as reference standards. RESULTS: Eighty-three patients with complete data were included in the analysis. The AUROC (Area Under the Receiver Operating Characteristic) values are as follows: ∆AAS, 0.932 (95% CI 0.868-0.996); baseline AAS, 0.629 (95% CI 0.498-0.760); final AAS, 0.936 (95% CI 0.886-0.987); and ∆CRP, 0.796 (95% CI 0.696-0.897). Using receiver operating characteristic curves, we established the thresholds for low (AAS ≤ -2), intermediate (AAS -1 to 0), and high (AAS ≥ 1) probability of appendicitis. The negative predictive value for the low-probability group and the positive predictive value for the high-probability group concerning acute appendicitis were 97% and 94%, respectively. CONCLUSION: Patients with equivocal signs of appendicitis may benefit from short observation and the calculation of ∆AAS to reduce overdiagnosis and exposure to excessive imaging. REGISTRATION: The DIAMOND trial was officially registered on ClinicalTrials.gov (NCT02742402) on April 13, 2016.

Adult↗

PubMed2026

Application of SPI-guided analgesia in laparoscopic gynecologic surgery: a randomized controlled trial evaluating the remifentanil-sparing effect and predictive value of time-weighted SPI.

This study aimed to achieve two primary objectives: (1) to evaluate the opioid-sparing effect of Surgical Pleth Index (SPI)-directed analgesia during surgery via a randomized controlled trial (RCT), and (2) to propose and preliminarily assess a novel dynamic metric, Threshold-based Time-Weighted SPI (Tb-TW-SPI), which integrates stimulus intensity and duration, for its predictive efficacy regarding postoperative moderate-to-severe pain. Employing an RCT combined with exploratory analysis, 61 patients undergoing elective laparoscopic gynecologic surgery were randomized into an SPI-directed analgesia group or a conventional analgesia group. The primary outcome was total intraoperative remifentanil consumption. Postoperatively, an exploratory analysis of the control group data evaluated the correlation between Tb-TW-SPI and Numeric Rating Scale (NRS) pain scores in the post-anesthesia care unit (PACU), calculating its predictive value for moderate-to-severe pain (NRS ≥ 4). Results: The SPI-directed group required significantly less intraoperative remifentanil than the conventional group [median (IQR): 5.84(5.02,6.62)vs. 6.96(5.81,8.19)µg/kg/h; P = 0.016]. Postoperative pain scores did not differ significantly between groups (P > 0.05). Exploratory analysis of the conventional analgesia group revealed that Tb-TW-SPI values were significantly higher in patients with moderate-to-severe postoperative pain (NRS ≥ 4) compared to those without (P = 0.0417).The area under the ROC curve for Tb-TW-SPI predicting this pain was 0.74 (95% CI: 0.52-0.96), with 67% sensitivity and 76% specificity at an optimal cutoff of 1210. This RCT suggests that SPI-directed analgesia can safely and moderately reduce intraoperative remifentanil consumption. Furthermore, the proposed Tb-TW-SPI metric, in this exploratory analysis, suggests potential for predicting postoperative pain, though this finding requires validation in larger cohorts with higher-frequency SPI sampling, offering a new direction for SPI interpretation. Large-scale, multicenter trials are warranted to validate the predictive utility of Tb-TW-SPI. Clinical Trial Registration, China Clinical Trial Registry: ChiCTR2400088444.

Humans↗

PubMed2026

ALID score for treatment-effect heterogeneity of adjunctive low-voltage area ablation in persistent atrial fibrillation: A post hoc analysis of SUPPRESS-AF.

BACKGROUND: In persistent atrial fibrillation (AF), the incremental benefit of adjunctive low-voltage area (LVA) ablation beyond pulmonary vein isolation (PVI) remains inconsistent. OBJECTIVE: To examine whether a simple clinical score characterizes treatment-effect heterogeneity of adjunctive LVA ablation among patients with mapped LVA > 5 cm2 and to perform an exploratory supportive analysis in an independent randomized cohort. METHODS: In this post-hoc analysis of SUPPRESS-AF, which included patients with persistent AF and mapped LVA > 5 cm2 after PVI, four variables-age ≥ 75 years, left atrial diameter > 44 mm, estimated glomerular filtration rate < 60 mL/min/1.73 m2, and absence of diabetes-were combined into the ALID score (0-4). Patients were stratified into low (0-1), intermediate (2), and high (3-4) score groups. Because EARNEST-PVI did not use LVA-guided ablation or select patients based on mapped LVA, it was analyzed as an exploratory supportive cohort rather than as an external validation cohort. RESULTS: In SUPPRESS-AF (n = 336), a significant treatment-by-score interaction was observed (P < 0.001). Adjunctive LVA ablation was associated with increased recurrence in the low-score stratum (HR 3.92; 95% CI 1.50-10.20) and reduced recurrence in the high-score stratum (HR 0.48; 95% CI 0.26-0.86). In EARNEST-PVI (n = 494), a qualitatively similar interaction pattern was observed for additional ablation beyond PVI (interaction P = 0.029), although the ablation strategy differed from LVA-guided ablation. CONCLUSIONS: Among patients with persistent AF and mapped LVA >5 cm2, the ALID score identified heterogeneity in response to adjunctive LVA ablation. These hypothesis-generating findings require prospective validation before clinical implementation.

Humans↗

PubMed2026

A systematic approach to standardizing the visual appearance of endometriotic lesions for artificial intelligence recognition.

INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annotate surgical data consistently and subsequently allow engineers to train and build an artificial intelligence tool for endometriosis recognition. MATERIAL AND METHODS: A systematic search was conducted in the MEDLINE (via PubMed), EMBASE, and the Cochrane Library databases up to May 2022, aiming to identify studies describing the laparoscopic visual appearance of superficial endometriosis, endometriomas, and deep infiltrating endometriosis. The accumulated data in the literature concerning the visual appearance of the different forms of endometriosis were used to create an ontology that could be used for artificial intelligence applications. RESULTS: Out of 932 articles screened, 35 studies were selected based on the inclusion criteria of human subjects with histologically confirmed endometriosis lesions visualized via laparoscopy. The selected studies were reviewed to develop a visual ontology of endometriosis lesions observed via laparoscopy. The lesions were categorized into 4 classes and further subdivided into 11 subclasses: superficial (black, red, white, or subtle), adhesions (dense or filmy), deep (obliteration, retraction, or deformation), and ovarian (endometrioma or chocolate fluid). The positive predictive value (PPV) varied across lesion types: black lesions (PPV 47%-97%), red lesions (PPV 33%-100%), white lesions (PPV 20%-81%), and ovarian endometriosis (PPV 42%-98%). Nonspecific lesions such as adhesions (PPV 16%-50%) and subtle superficial lesions (PPV 0%-67%) presented lower PPVs. Deep endometriosis lesions, often buried within organs, required indirect signs (obliteration, retraction, deformation) for identification. CONCLUSIONS: The visual ontology proposed in this systematic search could facilitate the detection and classification of endometriosis lesions using artificial intelligence. This study highlights the challenges of reaching a consensus on lesion recognition and classification in AI projects due to the diverse visual presentations of endometriosis.

Humans↗

PubMed2026

Targeting SIRT6: the design and therapeutic implications of activators and inhibitors.

Sirtuin 6 (SIRT6) is an NAD+-dependent deacylase that maintains genomic stability, regulates metabolism, and influences aging, making it an attractive but challenging therapeutic target. Pharmacological modulation of SIRT6 holds promise for cancer and metabolic disorders, yet its context-dependent functions demand precise intervention strategies. Potent, selective, and drug-like chemical probes are therefore essential to dissect SIRT6 biology and to validate its therapeutic potential. This review critically evaluates recent medicinal chemistry advances in SIRT6 modulation. We focus on structure-guided design strategies and structure-activity relationships (SAR) that have transformed initial hits into optimized leads for both activators and inhibitors, highlighting the remaining challenges in achieving isoform selectivity and drug-like properties.

Sirtuins↗