Category: Nevin Manimala

Emerg Radiol. 2026 May 11. doi: 10.1007/s10140-026-02475-1. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the clinical associations of admission non-contrast CT morphology in acute pancreatitis within a real-world emergency workflow.

MATERIALS AND METHODS: This retrospective observational cohort study included 264 consecutive adult patients admitted with acute pancreatitis to two surgical centers between 2019 and 2024. Patients were categorized according to the first imaging modality obtained at admission into an ultrasound-first (US-first) or computed tomography-first (CT-first) pathway. Baseline characteristics and in-hospital outcomes were compared between pathways. In the CT-first subgroup, all examinations were performed without intravenous contrast, and morphologic severity was assessed using the Balthazar classification. Associations between CT morphology and clinical outcomes were evaluated using univariable analyses.

RESULTS: Of the 264 patients, 143 (54.2%) were managed within a US-first pathway and 121 (45.8%) underwent CT as the initial imaging modality. Baseline demographic and etiologic characteristics were comparable between pathways. Patients in the CT-first pathway demonstrated numerically higher rates of adverse clinical outcomes at admission, including a longer length of hospital stay (median 8 vs. 6 days; p = 0.01) and numerically higher rates of severe acute pancreatitis and in-hospital mortality. Within the CT-first cohort, non-contrast CT morphology demonstrated heterogeneous inflammatory severity. Higher Balthazar grades were associated with stepwise numerical increases in rates of severe disease, complications, and length of hospital stay. When dichotomized, advanced morphologic severity (Balthazar grades D-E) showed higher odds of adverse outcomes compared with grades A-C, although these associations did not reach statistical significance.

CONCLUSION: In routine emergency practice, selection of ultrasound-first or CT-first imaging pathways appears largely driven by triage and organizational factors rather than predefined imaging strategies. In patients undergoing non-contrast CT at admission, higher Balthazar grades demonstrated consistent numerical gradients toward more severe clinical courses; however, these associations did not reach statistical significance. Early non-contrast CT morphology should therefore be interpreted as contextual inflammatory assessment rather than a standalone prognostic tool.

PMID:42108327 | DOI:10.1007/s10140-026-02475-1

Brain Inform. 2026 May 11. doi: 10.1186/s40708-026-00302-4. Online ahead of print.

ABSTRACT

Accurate detection of Mild Cognitive Impairment (MCI) is critical for timely intervention and for slowing progression to Alzheimer’s disease. Electroencephalography (EEG) offers a non-invasive and cost-effective measure of brain activity; however, its complex, non-linear dynamics limit conventional analysis. We propose a CNN-Res-SE-BiLSTM-BiGRU framework for the automated detection of MCI directly from raw EEG. Convolutional and residual blocks capture local temporal structure, bidirectional recurrent layers model long-range dependencies, and Squeeze-and-Excitation (SE) modules provide channel-wise attention. Predicted probabilities are calibrated using temperature scaling, and operating thresholds are selected on the validation set using Youden’s J statistic. The model is evaluated using five-fold cross-validation under both subject-dependent and strict subject-independent protocols on a primary resting-state dataset, with additional subject-independent validation on an odor EEG dataset. Under subject-independent evaluation on the odor dataset, the proposed model achieved an accuracy of 0.956 ± 0.051, with ROC-AUC of 0.971 ± 0.051 and PR-AUC of 0.934 ± 0.132. UMAP-based visualization and explainable AI analyses (SHAP and LIME) provide interpretable insight into the learned spatiotemporal patterns and sample-specific decisions. These results demonstrate robust, interpretable EEG-based MCI detection with potential clinical utility.

PMID:42108320 | DOI:10.1186/s40708-026-00302-4

Eur J Orthop Surg Traumatol. 2026 May 11;36(1):190. doi: 10.1007/s00590-026-04773-3.

ABSTRACT

PURPOSE: Ankle fractures are among the most common orthopedic injuries requiring operative management. Postoperative care varies across healthcare systems due to cultural, regulatory, and practice-based differences. This study evaluated whether such variation influences ankle fracture healing.

METHODS: We conducted a retrospective cohort study at two Level 1 trauma centers, one in the United States and one in Chile, from 2015 to 2023. Patients included had sustained Weber B or C ankle fractures due to falls and were treated with ORIF, with available 3- and 6-month radiographs. Demographic, clinical, and operative variables were recorded. Genetic matching was used to balance key covariates between cohorts.

RESULTS: Of 250 patients, 110 remained after genetic matching (55 pairs) with well-balanced baseline characteristics (all standardized mean differences < 0.1). No significant differences in fracture union time were observed between cohorts in either unmatched or matched analyses. In the matched cohort, mean union time was 108.6 ± 60.6 days in the U.S. group and 124.8 ± 88.2 days in the Chilean group (p = 0.235). Sensitivity analyses demonstrated consistent findings. Complication rates were similar, with no differences in infection, delayed union, or nonunion; however, a higher proportion of Chilean patients had no complications (72.7 vs. 50.9%, p = 0.038).

CONCLUSION: In this matched binational cohort, patients with operatively treated ankle fractures demonstrated comparable healing outcomes despite differences in postoperative care strategies across healthcare systems. These findings highlight the reliability of fracture healing in this injury pattern with low rate of complications despite cultural and system-level variation, supporting opioid-sparing strategies in appropriately selected patients.

PMID:42108318 | DOI:10.1007/s00590-026-04773-3

J Chem Ecol. 2026 May 11;52(3):41. doi: 10.1007/s10886-026-01716-4.

ABSTRACT

Availability and quality of vegetation are critical factors influencing herbivore nutrition and population dynamics. Fourier-transform infrared spectroscopy (FTIR) offers a promising approach to analyze herbivore diets using spectral properties of phytochemicals to identify plant items. We evaluated the potential of FTIR to identify plant taxa and parts consumed by an herbivore species. Crop contents from 236 rock ptarmigan (Lagopus muta MONTIN) individuals from Iceland, collected over nine years, were separated into pure fractions of plant taxa and parts (e.g., berries, leaves) and analyzed using FTIR in the mid-IR region (4000 -400 cm⁻¹). We classified plant taxa and parts with PCA and Random Forests (RF) based on spectral signals. FTIR revealed distinct chemical fingerprints for plant taxa and parts, consistent with previously established variation in lipids, proteins, carbohydrates, and chemical defenses. RF yielded high classification accuracy for plant parts (96.7%) and moderate accuracy for taxa (85.5%), confirming the method’s reliability. FTIR overcomes limitations of traditional genetic analyses by identifying plant parts with varying nutritional quality within species. FTIR provided insights into biochemical properties of plant items but could not distinguish chemically similar items. Future research should expand spectral reference libraries combining FTIR with quantification of phytochemicals and DNA metabarcoding.

PMID:42108314 | DOI:10.1007/s10886-026-01716-4

Int J Comput Assist Radiol Surg. 2026 May 11. doi: 10.1007/s11548-026-03645-9. Online ahead of print.

ABSTRACT

PURPOSE: Myocardium segmentation in echocardiography videos is a challenging task due to low contrast, noise, and anatomical variability. Traditional deep learning models either process frames independently, ignoring temporal information, or rely on memory-based feature propagation, which accumulates error over time.

METHODS: We propose PointSeg, a transformer-based segmentation framework that integrates point tracking as a temporal cue to ensure stable and consistent segmentation of myocardium across frames. Our method leverages a point-tracking module trained on a synthetic echocardiography dataset to track key anatomical landmarks across video sequences. These tracked trajectories provide an explicit motion-aware signal that guides segmentation, reducing drift and eliminating the need for memory-based feature accumulation. Additionally, we incorporate a temporal smoothing loss to further enhance temporal consistency across frames.

RESULTS: We evaluate our approach on both public and private echocardiography datasets. Experimental results demonstrate that PointSeg has statistically similar accuracy in terms of Dice to state-of-the-art segmentation models in high-quality echo data, while it achieves better segmentation accuracy in lower-quality echo with improved temporal stability. Furthermore, PointSeg has the key advantage of pixel-level myocardium motion information as opposed to other segmentation methods. Such information is essential in the computation of other downstream tasks such as myocardial strain measurement and regional wall motion abnormality detection.

CONCLUSION: PointSeg demonstrates that point tracking can serve as an effective temporal cue for consistent video segmentation, offering a reliable and generalizable approach for myocardium segmentation in echocardiography videos. The code is available at https://github.com/DeepRCL/PointSeg .

PMID:42108311 | DOI:10.1007/s11548-026-03645-9

Nat Med. 2026 May 10. doi: 10.1038/s41591-026-04408-4. Online ahead of print.

ABSTRACT

MicroRNA-132 (miR-132) is a central regulator of adverse cardiac remodeling. Here we evaluated CDR132L, a synthetic antisense oligonucleotide miR-132 inhibitor, in a multinational, randomized, double-blind, placebo-controlled phase 2 trial (HF-REVERT) in patients with recent myocardial infarction (MI) and left ventricular (LV) systolic dysfunction. Within 3-14 days after MI, 294 patients were randomized to receive CDR132L 5 mg kg^-1, CDR132L 10 mg kg^-1 or placebo as three intravenous doses at 4-week intervals plus guideline-directed therapy. In total, 280 patients (245 men and 35 women) who received at least one dose of the study drug were included in the modified intention-to-treat population. CDR132L was well tolerated, with no hepatic, renal, hematologic or cardiac toxicity signals. The primary endpoint-the percentage change in LV end-systolic volume index at 6 months-improved in all groups but did not differ significantly between the CDR132L groups (5 mg kg^-1 and 10 mg kg^-1) and the placebo group. Secondary endpoints, including LV ejection fraction, global longitudinal strain and N-terminal pro B-type natriuretic peptide, were also not significantly different between the CDR132L and placebo groups. Prespecified exploratory analyses suggested potential benefits of CDR132L treatment in patients with advanced adverse remodeling at baseline, supporting further evaluation of CDR132L, including in chronic heart failure conditions. ClinicalTrials.gov: NCT05350969 .

PMID:42108271 | DOI:10.1038/s41591-026-04408-4

Sci Rep. 2026 May 10. doi: 10.1038/s41598-026-51415-z. Online ahead of print.

ABSTRACT

Cholera is highly climate sensitive, however previous attempts to model its future under climate change have been limited to statistical analyses. Mechanistic models are an essential addition because they permit a deeper understanding of the complex feedback loops involved in infectious disease transmission, allowing for better modelling of potential scenarios such as interventions or changes in pathogen dynamics. We compare four mathematical models with differing assumptions of climate sensitivity and fit them to a cholera dataset from Kolkata, India using MCMC. We then use bias-corrected climate projections of temperature and rainfall from 10 independent global climate models to produce climate-based cholera projections for the period 2080-2099. Using both temperature and rainfall as inputs, the best performing model recreates seasonal patterns highly effectively. Future projections suggest an average increase in cholera cases ranging from 81% – 150% due to climate change by 2080-2099 with earlier peaks in the infection cycle likely due to heightened transmission rates earlier in the year. Sensitivity analysis reveals that uncertainties in parameters related to the contact rate and water dynamics have the greatest impact on model projections, suggesting that these factors are critical for refining future predictions. While our mechanistic model demonstrates the potential to project cholera dynamics under future climate scenarios, projections remain sensitive to key knowledge gaps including epidemiological parameters and effects of temperature on bacterial growth. Addressing these limitations through improved environmental observations and more detailed process representation will be essential for refining future climate-cholera projections and informing long-term control strategies.

PMID:42108242 | DOI:10.1038/s41598-026-51415-z

J Clin Exp Neuropsychol. 2026 May 10:1-9. doi: 10.1080/13803395.2026.2673080. Online ahead of print.

ABSTRACT

INTRODUCTION: Cognitive estimation is a component of executive functions, involving judgment, reasoning, often impaired in neurological disorders. This study aimed to develop the Bilişsel Tahmin Testi (BiTAHT) for the Turkish population and to evaluate its reliability and validity in patients with Multiple Sclerosis (pwMS).

METHOD: The study was conducted in four sequential phases. In the first phase, 56 preliminary estimation items were generated based on a review of existing Cognitive Estimation Tests and expert feedback. In the second phase, the items were evaluated for clarity, linguistic appropriateness, and feasibility through pilot testing with a small sample. In the third phase, BiTAHT was administered to 1,112 healthy participants, and statistical analyses including percentile calculation and item-level outlier removal were used to refine the test and develop two parallel forms (BiTAHT-A and BiTAHT-B). In the final phase, reliability and validity were assessed by administering BiTAHT to 54 pwMS and 80 controls. The final version comprised six estimation categories: quantity, weight, length, duration, area, and equivalent.

RESULTS: Each parallel form contained 13 estimation items. PwMS performed significantly worse than healthy controls in the quantity, weight, length, and equivalent categories, but not in duration or area. In the development sample, BiTAHT scores varied by age, gender, and education with low effect sizes, while no associations were found in validation groups (54 pwMS, 80 controls) (p > .05). İnternal consistency was moderate when the two forms were combined.

CONCLUSIONS: BiTAHT may be used to assess cognitive estimation abilities in Turkish adults and support future research on cultural validity. Although differences were observed between healthy controls and pwMS, these should not be interpreted as executive dysfunction. Future studies should evaluate test – retest reliability, expand validity evidence, and examine its utility in other neurological populations.

PMID:42108237 | DOI:10.1080/13803395.2026.2673080

Pharm Stat. 2026 May-Jun 6;25(3):e70097. doi: 10.1002/pst.70097.

ABSTRACT

Analysis of genomics data for predicting disease outcomes is a fast-growing field in medical research. There often exist categorical, specifically, ordinal outcomes that need to be predicted based on genomic profiles. This has led to recent development of some high-dimensional ordinal classification methods that can address the large dimensionality of the genomic covariate set. These high-dimensional ordinal models tend to vary widely in their performance depending on the data they are applied to and the evaluation criteria used. In this article, we outline an ensemble ordinal classifier that integrates different ordinal modeling approaches through bootstrap-based model evaluation, multi-metric performance assessment, and rank aggregation to produce a final prediction that can alleviate the uncertainty of relying on a single model. Through multiple simulated studies and real genomic data analyses, we show that the ensemble method consistently ranks among the top-performing models. These findings underscore the potential of ensemble learning to improve the robustness and predictive accuracy of high-dimensional ordinal classification in genomic research.

PMID:42108236 | DOI:10.1002/pst.70097

Med Phys. 2026 May;53(5):e70473. doi: 10.1002/mp.70473.

ABSTRACT

BACKGROUND: Transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with a variety of pelvic floor disorders, including pelvic organ prolapse (POP). Currently, calculating measurements of anatomical structures and relationships as well as extracting the mid-sagittal (MS) plane of 2D and 3D ultrasound images are obtained manually, which is a time-consuming process and requires a reviewer with prior training in pelvic floor US interpretation. The need for manual analysis of ultrasound images has limited the broader adoption of TPUS for evaluating pelvic floor disorders in both research and clinical practice. An automated segmentation and plane extraction method would improve the ability to easily quantify pelvic anatomy relevant to pelvic floor disorders and improve the efficiency and reproducibility of POP diagnosis and treatment.

PURPOSE: To develop a fast, reproducible, and automated method of acquiring the MS plane, plane of minimal hiatal dimensions (PMHD), and segmentations of the pelvic floor organs from 3D TPUS images.

METHODS: Our method used a nnU-Net segmentation model to segment structures of interest in the 3D TPUS images. The model segmented the pubis symphysis (PS), urethra, bladder, rectum, rectal ampulla, and anorectal angle (ANA). The segmented output was then fed into a heuristics-based method to determine the PS and ANA to extract the MS plane and PMHD automatically. We used a dataset consisting of 161 3D TPUS images from 104 patients. 89 of the volumes were acquired in a resting state and 72 during the Valsalva maneuver. The segmentation and plane extraction algorithms were evaluated by comparing the results with manual segmentations and manual plane extraction methods using the dice similarity coefficients (DSC), mean absolute surface distance (MAD), and absolute angle difference (AAD), respectively. The Wilcoxon-signed rank statistical test was used with Bonferroni-correction to p < 0.01. Cohen effect size was used for comparing model results.

RESULTS: The nnU-Net segmentation model reported an average DSC(%) of 70.4%, 58.5%, 57.1%, 48.9%, 39.0%, and 19.8% for bladder, rectum, PS, urethra, ANA, and rectal ampulla respectively. The nnU-Net segmentation model achieved significantly higher DSC (p < 0.01) for the urethra and rectum than all other tested models. Across all metrics, the nnU-Net segmentation model achieved an average effect size of 0.3, 0.5, 0.7, and 0.8 compared to a 3D ResNet34 + U-Net, 3D U-Net, 2D U-Net, and Attention 3D U-Net model, respectively. The average AADs between the automatically calculated plane slices and manually estimated planes dataset for the MS plane and PMHD were 3.8° and 2.4°, respectively. The PS and ANA segmentation centroids were used to calculate the MS plane and PMHD and they had distance errors of 3.6 mm and 4.4 mm.

CONCLUSIONS: We developed an automated 3D segmentation and multiple plane extraction method of female pelvic floor 3D US images. Our method extracts the MS plane and PMHD from 3D US images. The proposed algorithm pipeline can improve the efficiency and reproducibility of TPUS analysis for pelvic floor disorder diagnosis and treatment.

PMID:42108227 | DOI:10.1002/mp.70473