Accurate identification of the specific inner ear malformations can assist the otologist in anticipating surgical challenges and potentially optimizing postoperative hearing outcomes. In this study, we explored the feasibility of applying similar cochlear morphology principles, that is, ones based on easily quantifiable measurements of the basal turn, to differentiate normal lateral wall geometry from malformed variants. We retrospectively collected 60 patients who underwent cochlear implantation in our center between 2005 and 2023 in the presence of a preoperatively recognized inner ear malformation. Of the 120 analyzed cochleae, 111 were eligible for segmentation, which included 8 cochlear hypoplasia type II, 15 cochlear hypoplasia type III, 2 cochlear hypoplasia type V, 38 Incomplete partition type I, 44 Incomplete partition type II, and 4 incomplete partition type III. A control cohort of 141 normal cochleae was selected. Using manual segmentation of the cochlea on preoperative cone beam computed tomography scans, three-dimensional lateral wall spirals were obtained. The spirals were then used to compute simple anatomical measures of the basal turn, namely the cochlear diameter A and width B, the basal turn length computed based on A and B using the elliptic circular approximation approach, the ratio B/A and the B ratio, and cochlear height (H). Initially, two-sided Mann-Whitney-Wilcoxon tests were conducted to derive statistical differences in the aforementioned geometrical parameters between normal and malformed anatomies. Second, logistic regression analyses were performed to define whether the derived geometrical parameters may be used to predict if a specific cochlear morphology is normal or malformed, and to subsequently investigate if such a model may even be capable of distinguishing between different malformation types. Four geometrical basal‑turn parameters easily assessable in clinical imaging, namely the basal turn length along the lateral wall, cochlear height (H), B/A ratio, and B‑ratio, provide enough information to reliably distinguish normal cochleae from malformed variants. The binary logistic model achieved 94% overall accuracy with precision and recall ≥0.92 across classes, with cochlear height H emerging as the dominant predictor. In our models, H and Bb/B were the strongest discriminants for normal versus malformed anatomies and for incomplete partitions versus cochlear hypoplasia, respectively. Moving beyond binary discrimination, the three‑class model (normal versus IP versus CH) retained 90% accuracy. In summary, the models reliably recognize normal versus malformed, incomplete partition versus cochlear hypoplasia, and CHIII among individual subtypes due to reduced height. They are less reliable for IPI versus IPII differentiation, where basal‑turn measures alone appear insufficient, and IPIII, CHII, CHIV, where very small sample sizes depress recall. Simple, basal‑turn measurements of the lateral wall were demonstrated to provide a fast, interpretable, and accurate means to detect cochlear malformations on clinical imaging and to differentiate IP from CH. The approach reliably flags abnormal cases, offers actionable preoperative information for patient‑tailored implantation, and identifies domains where additional features or larger cohorts are needed (IPI versus IPII; rare subtypes). Such a model could lay the groundwork for future automated recognition of cochlear malformations during preoperative planning.

This study aimed to investigate the spatiotemporal correlation between tumor metabolism and cancer-associated fibroblast activity using dual-tracer PET/CT, and to explore their potential association with pathologic response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). This retrospective analysis of a prospective trial (ChiCTR2100051599) included 48 patients with ESCC from February 2022 to August 2024. All patients underwent 68Ga-FAPI and 18F-FDG PET/CT before pre-nCRT (S1) and after nCRT (S2). Two primary metrics were used to quantify their spatial correlations by voxel-wise correlation analyses: the SUV Correlation Coefficient (SUV_R), reflecting static uptake concordance, and the Dose-Response Matrix Correlation Coefficient (DRM_R), reflecting concordance in treatment dose sensitivity. The conventional parameters derived from FAPI- and FDG- PET were also extracted: SUVmax, SUVmean, SUV_CV, SUVmaxratio, SUVmeanratio, DRMmax, DRMmedian, DRM_CV and the resistant tumor volume (V(DRM> 0.7)). The associations of these metrics with pathological tumor regression grade (TRG) were evaluated in an exploratory manner, except for DRM-related metrics. A strong voxel-wise correlation was observed between pre-nCRT FAPI and FDG uptake (S1 SUV_R = 0.80 ± 0.17), which was weaker after nCRT (S2 SUV_R = 0.51 ± 0.23). Two dose-response matrices showed a strong correlation (DRM_R = 0.82 ± 0.14). The bivariate Logistic model, integrating the post-treatment SUV_R and SUV_CV_FDG, significantly predicted pathological response (p = 0.007), achieving an optimization-corrected AUC of 0.78 (95%CI: 0.62-0.95). The pre-nCRT tumor voxel intensity and dose response constructed using either FDG or FAPI PET imaging exhibited a strong spatial correlation. In this exploratory analysis, the post-nCRT FAPI-FDG correlation coefficient was associated with pathologic response when combined with SUV_CV_FDG in a Logistic model.

Association between hemodynamic parameters, insulin resistance, and body composition in primary care

Pro-vegetarian (PVG) diets may reduce gastric cancer (GC) risk, but evidence remains limited. We aimed to evaluate the association between three predefined PVG patterns-general (gPVG), healthful (hPVG), and unhealthful (uPVG)-and GC risk stratifying by sex in the context of the Stomach Cancer Pooling (StoP) Project Consortium. We analysed data from six case-control studies from the StoP Consortium. The final sample included 1,857 incidents, histologically confirmed GC cases and 5,646 controls. Food intakes were assessed using country-specific food frequency questionnaires, which allowed the estimation of PVG patterns using established scoring methods. Adherence to PVG dietary patterns was classified into quintiles. Logistic mixed models with random intercepts for each study were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Compared to the lowest quintile, the highest quintile of adherence to gPVG was associated with an ORQ5vsQ1 = 0.61 (95% CI: 0.50-0.75; p-trend = < 0.001) and the hPVG was associated with an ORQ5vsQ1 = 0.66 (0.54-0.80; p-trend = < 0.001). The uPVG pattern was associated with an ORQ5vsQ1 = 1.27 (1.05-1.55; p-trend = 0.013). The inverse association for gPVG was more evident in men, ORQ5vsQ1 = 0.56 (0.43-0.73) than in women, ORQ5vsQ1 = 0.73 (0.53-1.01); for the hPVG pattern, a significant inverse association was only observed in women, ORQ5vsQ1 = 0.50 (0.36-0.69); and, for the uPVG pattern, the increased GC risk was only observed in women, ORQ5vsQ1 = 1.70 (1.25-2.32). Higher adherence to gPVG and hPVG dietary patterns is associated with lower GC risk, whereas higher adherence to uPVG is associated with increased risk. The inverse association observed for hPVG pattern and the positive association for uPVG were only observed in women. These differential effects of dietary patterns in men and women deserve to be further investigated.

Cognitive impairment is common after stroke and linked to poor outcomes, yet long-term recovery or decline, particularly across specific cognitive domains, remains unclear. Most studies use brief global screeners with short follow-up, limiting insight into recovery patterns. This study aimed to characterize domain-specific cognitive trajectories over ≥2 years poststroke and identify predictors of persistent impairment. Participants were recruited at a regional acute stroke unit (John Radcliffe Hospital, Oxford, United Kingdom; 2012-2019) and assessed acutely, at 6 months, and ≥2 years poststroke. The Oxford Cognitive Screen was administered at all timepoints. Global impairment severity was quantified by the proportion of Oxford Cognitive Screen subtasks impaired. Logistic mixed-effects models examined longitudinal change and predictors of domain-specific impairments (language, memory, attention, executive function, and number processing). Latent class growth analysis identified distinct cognitive trajectories. Models were adjusted for acute cognitive impairment severity and time. Of 866 patients assessed acutely, 105 were followed up at ≥2 years (98 with complete Oxford Cognitive Screen data; median, 4.1 [interquartile range, 3.3] years; mean age, 69 years, 41% female). Cognitive impairment severity improved substantially by 6 months (β=-0.11; P<0.001) and further long-term (β=-0.15; P<0.001). Acute impairment severity strongly predicted long-term outcomes (β=0.50; P<0.001), while demographic and vascular factors explained minimal variance. Latent class growth analysis identified 4 overall trajectories: no or mild acute impairment with stability (47.6%), moderate-improving (32.3%), large improvement (15.2%), and decline (4.8%). Domain-specific improvements were greatest in memory (odds ratio, 16.40 [95% CI, 5.52-48.7]) and language (odds ratio, 8.17 [95% CI, 3.17-21.1]), more limited in attention (odds ratio, 5.41 [95% CI, 2.52-11.6]) and executive function (odds ratio, 4.14 [95% CI, 1.96-8.75]). Domain models revealed additional classes of persistent or delayed recovery, particularly in executive function and attention. Cognitive recovery is most pronounced within 6 months and continues across domains though executive dysfunction often persists. Acute impairment severity best predicted long-term outcomes, while vascular and demographic factors were less informative. Distinct trajectory classes highlight the need for individualized, long-term cognitive monitoring to guide rehabilitation and prognostication. These findings underscore the importance of long-term cognitive follow-up in stroke care and provide empirical benchmarks for recovery across domains.

A substantial proportion of patients report no pain after surgery, resulting in an excess of zero values that pose challenges for analysis using traditional statistical models. The current study was designed to test the hypothesis that a two-part model, commonly used in healthcare expenditures research, would demonstrate superior performance in predicting postsurgical pain when compared with traditional models and would secondarily better identify predictors of this clinically important outcome. This study analyzed a prospectively collected single-center data set (n = 3,925) of chronic postsurgical pain to compare a novel two-part modeling framework with logistic and linear regression. The two-part model first estimated the probability of experiencing postsurgical pain at 3 months (binary outcome), followed by the severity of pain among those affected (on a 1 to 10 numeric rating scale). To obtain an unbiased assessment of model performance, the data were randomly split into training (n = 3,000) and testing (n = 925) data sets. The models were trained on the training data set and evaluated on the testing set. This process was repeated 400 times to compute average performance estimates. As a secondary aim, the study assessed the associations of 15 baseline factors, including validated measures of patient-reported pain, functional and psychological measures, comorbidities, and surgical details, across the different modeling approaches. The two-part model demonstrated superior predictive performance compared with linear regression alone, with a higher mean R 2 value (0.075 vs . 0.050; P < 0.0001), lower root mean square error (1.466 vs . 1.485; P < 0.0001), and lower mean absolute error (1.020 vs . 1.030; P < 0.0001). Statistical comparison with logistic regression alone was not possible due to the shared binary component. The two-part model identified seven baseline preoperative covariates as independently associated with postsurgical pain that were missed by either logistic or linear models or both: self-reported race, education level, American Society of Anesthesiologists classification, overall body pain, widespread pain, symptom severity, and anxiety level. Significant baseline factors identified in all three models were patient sex, surgical type, and surgical site pain. A two-part model may offer a superior statistical approach to linear and logistic regression, better identifying patient- and clinical care-related risk factors of chronic postsurgical pain, primarily by distinguishing factors that drive the occurrence of chronic pain from those that are associated with pain severity.

The Changing Landscape of Peritoneal Dialysis in America: A Facility-Level Analysis of Growth.

Objectives. To examine telehealth use and modality choice among US older adults (≥ 65 years) and identify factors associated with adoption and modality preference in the postpandemic era. Methods. Using data from 2723 device-owning older adults in the 2024 Health Information National Trends Survey (HINTS 7; nationally representative, March-September 2024), multivariable logistic and multinomial logit models examined associations between telehealth use, modality, and covariates, including socioeconomic factors, health conditions, digital literacy, and prior health information technology use. Results. Overall, 31.4% of older adults used telehealth in 2024 (video: 12.38%; phone-only: 12.41%). Higher odds of use were associated with Hispanic ethnicity (adjusted odds ratio [AOR] = 1.92; 95% confidence interval [CI] = 1.07, 3.81), lung disease (AOR = 2.32; 95% CI = 1.22, 4.42), depression (AOR = 1.89; 95% CI = 1.01, 3.54), and frequent provider visits; lower odds were associated with nonmetropolitan adjacent residence (AOR = 0.36; 95% CI = 0.17, 0.76) and health insurance coverage (AOR = 0.13; 95% CI = 0.03, 0.62). In addition to these factors, video use was uniquely associated with higher income, ability to use apps without assistance, and prior health IT use. Nonusers most commonly cited preference for in-person care (45.7%) or not being offered telehealth (13.4%). Conclusions. Telehealth use has stabilized but remains low among US older adults, with persistent geographic and socioeconomic disparities in modality choice. Policies ensuring audio-only parity and targeted digital literacy interventions are essential to promote equitable telehealth access as telehealth becomes integrated into postpandemic care. (Am J Public Health. 2026;116(S3): S218-S228. https://doi.org/10.2105/AJPH.2026.308575).

Out-of-home care and criminal conviction trajectories from adolescence to midlife: A Swedish prospective cohort study of siblings.

Optic Disc Microvasculature Reduction and Visual Field Progression in Advanced Primary Open-Angle Glaucoma.

The biological cosmological constant ΛB: Exploratory propensity, dynamical habitability and the geometric origin of life.

Dual-path mixture of experts for Alzheimer's diagnosis using volume and entropy MRI features.

Cannabinoid hyperemesis syndrome prevalence and risk factors in the U.S․.

Development of a risk prediction model for relapse in patients with schizophrenia based on a systematic review and meta-analysis.

Satellite-derived land surface phenology (LSP) metrics are widely used to monitor vegetation phenology at large scales. However, current LSP products have uncertainties from limited satellite revisit cycles and adverse weather. This study integrated satellite and near-surface camera data to construct a satellite-camera system. Compared to traditional satellite products, near-surface camera-calibrated products more accurately determine vegetation phenological phases. Our analysis shows satellite-camera fused vegetation index time series strongly agree with satellite data while retaining near-surface cameras' high temporal resolution. The multi-cycle double logistic model (MDLM) was proposed to overcome conventional models' limitations in capturing only single-peak patterns, effectively characterizing multi-peak dynamics. We corrected the 2022 and 2023 vegetation phenological metrics (PMs) across the contiguous United States through interpolation, using high-quality ground reference benchmarks for validation. The corrected satellite-camera phenological data demonstrate improved performance compared to satellite-derived products. Analysis of different data sources reveals general consistency in identifying PMs, with higher accuracy for growth than senescence stages. Studies on three representative land types (grassland, forest, and cultivated land) also demonstrate that integrating satellite imagery with near-surface camera data enhances LSP monitoring and improves the temporal accuracy of vegetation phenology, which is crucial for optimizing agricultural management and advancing ecological and environmental research.

Post-ERCP pancreatitis (PEP) is a frequent complication of endoscopic retrograde cholangiopancreatography (ERCP). The ability to predict which patients are at risk of developing PEP is essential for determining the suitability of same-day discharge. Therefore, the study was undertaken to validate the predictive value of the 2-hour post-ERCP serum amylase level and cannulation time in relation to the occurrence of PEP. An observational approach was adopted to conduct the study based on data collected from the BIRDEM General Hospital, Dhaka. The study duration was twelve months. All patients who underwent ERCP were included in this study. A total of 135 patients were enrolled after obtaining informed written consent. A structured questionnaire was used for data collection. Serum amylase levels at 2 hour and 24 hour post-procedure was measured along with cannulation time and procedure time among all patients. Risk factors were determined using univariate and multivariate logistic models. The collected data were analyzed by SPSS V-23. Ethical measures were taken in accordance with the current Declaration of Helsinki. A total of 135 cases (average age 55.32±13.16 years, 52.6% female) were analyzed. Of all, twenty-three developed Post-ERCP pancreatitis after the procedure. Both univariate and multivariate analyses revealed that a cannulation time of more than 6.3 min [AUC: 0.827, 95% CI: 0.736-0.919, p<0.0001] and 2 hours amylase levels greater than 128 IU/L cutoff value (AUC: 9.959, 95% CI: 0.917-1.000, p<0.0001) were significant predictive factors for PEP. A total of 21 patients out of 23 exhibited 2 hour amylase levels higher than the cutoff level and developed PEP. On the other hand, 19 of the 23 patients developed PEP and required cannulation times longer than the cutoff values. The combined result of both the 2-hour serum amylase level and cannulation time had a sensitivity of 95.65%, specificity of 72.32%, PPV of 41.50%, NPV of 98.78% and accuracy of 76.29%, with a significant (p<0.0001). The present findings imply that the grouping of serum amylase levels and cannulation timing at 2 hours post-ERCP is a suitable indicator for identifying patients at high risk for PEP.

Sleep-architecture signatures for differential diagnosis of schizophrenia, bipolar, and major depressive disorders: A polysomnography-based multinomial modeling study.

Alcohol abuse by mood disorder patients.

125 Background: Perioperative chemotherapy improves survival in resectable gastric and gastroesophageal junction (GEJ) adenocarcinoma; however, many patients fail to initiate postoperative therapy in routine practice. Host-related determinants of adjuvant treatment feasibility and survival remain incompletely defined. Methods: We conducted a retrospective cohort study of patients with localized gastric or GEJ adenocarcinoma treated with perioperative chemotherapy and curative-intent resection at the McGill University Health Centre (2006-2025). The primary outcome was adjuvant chemotherapy initiation. Overall survival (OS) was analyzed using Kaplan-Meier and Cox proportional hazards models; prespecified 3-month landmark analyses were performed to mitigate immortal time bias. Sequential multivariable logistic models evaluated tumor variables, host factors (age, ECOG, serum albumin), and postoperative inflammatory recovery (ΔlogNLR), with incremental discrimination assessed by AUC. Results: Among 248 patients (median age 64; 85% male), 168 (67.7%) initiated adjuvant chemotherapy, which was associated with improved OS (HR 0.45; 95% CI, 0.33–0.62), including in landmark analyses. Pathology-only modeling showed modest discrimination (AUC 0.66). Addition of host factors improved discrimination (AUC 0.75); increasing age was independently associated with lower odds of initiating adjuvant therapy (OR 0.94 per year; 95% CI, 0.89–0.98), whereas higher serum albumin was independently associated with greater odds of initiation (OR 1.09 per g/L; 95% CI, 1.01–1.18). In a prespecified nested analysis among 69 patients with available longitudinal inflammatory data, model discrimination improved sequentially from AUC 0.65 to 0.77 to 0.80 with addition of host factors and ΔlogNLR. Tumor factors and chemotherapy regimen were not independently associated with adjuvant initiation after adjustment. Conclusions: In localized upper gastrointestinal adenocarcinoma, postoperative chemotherapy feasibility and survival are more strongly associated with host factors and recovery than with tumor pathology or regimen selection. These findings support perioperative optimization strategies, including nutritional support, prehabilitation, and interventions targeting systemic inflammation, to improve treatment delivery. Incorporating host vulnerability into risk stratification may refine treatment planning beyond pathological response alone. Prospective validation of dynamic inflammatory biomarkers and targeted host-directed interventions is warranted to determine whether improving treatment feasibility translates into survival benefit.

To determine whether ivermectin improves outcomes for critically and noncritically ill hospitalized patients with COVID-19. An ongoing international, multifactorial, adaptive platform, randomized, controlled trial. Hospitals in Pakistan, India, and Ireland between June 11, 2021, and September 9, 2022. Critically and noncritically ill patients. Randomized to ivermectin or no ivermectin (control). The primary outcome was respiratory and cardiovascular organ support-free days, assessed on an ordinal scale combining in-hospital death (assigned a value of -1) and days free of organ support through day 21 in survivors. Analyses used a Bayesian cumulative logistic model. Enrollment was closed for operational futility, following external evidence suggesting no benefit with ivermectin in nonhospitalized patients with COVID-19. Among 61 critically ill patients, the median number of organ support-free days was -1, indicating death was the most common vital outcome (interquartile range [IQR], -1 to 17), for the ivermectin group and -1 (IQR, -1 to 17.25) for the control group (adjusted proportional odds ratio [OR], 0.94; 95% credible interval [CrI], 0.40-2.07) and the posterior probability of superiority to control was 44.2%. Among 89 noncritically ill patients, the median number of organ support-free days was 22 (IQR, 18.5-22) for ivermectin and 22 (IQR, 16-22) for control (adjusted proportional OR, 1.04; 95% CrI, 0.48-2.34) and the posterior probability of superiority was 53.7%. Among critically ill patients, hospital survival was 35.1% (13/37) for ivermectin and 37.5% (9/24) for control (adjusted OR, 1.00; 95% CrI, 0.39-2.32), posterior probability of superiority was 50.0%. Among noncritically ill patients, hospital survival was 84.1% (37/44) for ivermectin and 77.8% (35/45) for control (adjusted OR, 1.16; 95% CrI, 0.5-3.07), posterior probability of superiority was 63.3%. For critically and noncritically ill hospitalized patients with COVID-19, ivermectin was unlikely to improve the primary composite outcome of organ support-free days and hospital survival.