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A benchmark for evaluating diagnostic questioning efficiency of LLMs in patient conversations
Physiologically relevant forms of Tc- and Re-pyrophosphate radioactive tracers and the basis of their transthyretin amyloid sensitivity
Abstract $${^{99\text {m}}}$$ Technetium Pyrophosphate ( $${^{99\text {m}}}$$ Tc-PYP(Sn)) is a commonly used radioactive tracer, with a long history of use in diagnosing bone-related diseases and a newfound purpose in differentiating ATTR and AL amyloidoses. Despite its ubiquity, basic aspects like its composition and structure are as of yet undetermined, and its method of binding to ATTR amyloid fibrils is likewise hitherto unknown. This complicates the diagnostic process, as it introduces inexplicable losses of sensitivity in some ATTR and AL variants. In this paper we report the results of our comprehensive investigation into the physiologically active structure of Tc-PYP and its closely related, but experimentally more approachable counterpart, Re-PYP, built on a robust theoretical basis and backed up by multiple spectroscopic methods (focusing on the rhenium analogue). We conclude that the Re/Tc-PYP tracers possess a flexible geometry, but ultimately appear as octahedral Re(IV)/Tc(IV) diaqua dipyrophosphate complexes under physiological conditions, and predict that this structure is the reason for the high affinity of $$\phantom{0}^{99m}$$ Tc-PYP for certain amyloids.
Evaluating the performance of a generative AI model in assessing qualitative health research articles adherence to objective reporting standards
Age differences in endocannabinoid tone are ameliorated after recent cannabis use
Comparative prognostic value of high-sensitivity cardiac troponin T and NT-proBNP for 30-day mortality in non-cardiac critically ill patients
Climate-induced shifts in habitat suitability of forest types and adaptation strategies in the Western Ghats of Tamil Nadu, India
Machine learning application in colon cancer treatment outcome prediction
Abstract Colon cancer represents a significant global health burden, accounting for a substantial portion of cancer-related morbidity and mortality worldwide. Many studies have been conducted to predict survival outcomes; however, most of these analyses have been performed predominantly via basic statistical methods. The aim of this study was to perform machine learning techniques to build models for survival prediction in patients with colon cancer. A retrospective review of 764 colon cancer patients treated over a 10-year period facilitated the construction of a detailed dataset containing 44 predictor variables and one dependent variable, the survival status of the patients (alive or dead). The data were randomly split into 80% training and 20% testing sets. Prognostic features from the database were used to build machine learning algorithms, including random forest, logistic regression, XGBoost, gradient boosting, categorical boosting (CatBoost), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) to predict progressive disease outcomes. Models were validated for sensitivity, accuracy and specificity, with predictive ability assessed by receiver operating characteristic (ROC) curve and area under the curve (AUC) calculations. In terms of model accuracy and precision, almost all algorithms produced similar outcomes; however, among the evaluated models, CatBoost achieved the highest accuracy of 0.813, and the random forest model demonstrated the best precision of 0.727, whereas the logistic regression model exhibited the highest recall of 0.658 on the test set. Our results revealed that the random forest algorithm exhibited the highest AUC of 0.83, demonstrating remarkable efficacy in achieving an optimal balance between sensitivity and specificity. In summary, this research highlights the potential of machine learning models to support personalized and timely interventions for colon cancer patients, ultimately aiming to improve patient care and outcomes.
Dynamic global tracker for online multi camera multi vehicle tracking
Abstract Multi-camera multi-target Tracking (MCMT) is often regarded as a downstream task of Multi-Object Tracking (MOT). Traditional methods typically follow an offline pipeline involving detection, re-identification, single-camera tracking, and post-hoc clustering, which leads to poor real-time performance, high computational cost, and weak adaptability in dynamic environments. Moreover, trackers tailored for specific locations overly rely on manually crafted information like road topology and camera calibration, reducing their effectiveness in varied scenarios. We propose Dynamic Global Tracking (DGT), an innovative online framework for Multi-Camera Multi-Target (MCMT) vehicle tracking. Unlike traditional methods that rely on full trajectory extraction and then clustering, the DGT integrates cross-camera associations directly into the tracking process. This transformation reduces the computational burden and enhances real-time performance. Especially, our framework includes a Hybrid Fusion Module (HFM) to address resolution disparities and a Stable Trajectory Manager (STM) to improve stability and robustness. Extensive experiments demonstrate that DGT significantly improves tracking accuracy and adaptability in various environments, achieving an IDF1 score of 61.19 on the HST dataset (speed version) and 70.49 (performance version) with FPS of 90.
Attitudes of healthcare students in Syria toward organ donation and their association with healthcare system distrust in the context of a prolonged war
Comparative analysis of signal decomposition methods for regional sea level trend estimation: a case study of the Korean peninsula
Network analysis of emotion regulation and moral injury symptoms among medical staff
A transpupillary approach for crosslinking Guinea pig sclera using WST11 and near-infrared light
Abstract Crosslinking strengthens the sclera and holds potential as a treatment for myopia. This study aims to identify optimal crosslinking parameters in guinea pigs using WST11 with dextran followed by near-infrared (NIR) illumination. Guinea pig eyes were incubated in WST11 with 2, 5 or 10% dextran, and penetration depth was assessed by fluorescence microscopy. Crosslinking efficacy was measured as thermal stability using a thermal degradation assay, following incubation in WST11 + 10% dextran (WST-D) for 30 min and NIR irradiation at 10 mW/cm 2 or 20 mW/cm 2 for 10, 20 and 30 min. The optimized parameters were then applied in vivo in 6-month-old guinea pigs. Ex vivo treatment using the optimal crosslinking parameters (WST-D, 30 min; NIR, 10 mW/cm 2 , 30 min) resulted in the highest thermal degradation midpoint ( ΔT 50 : 6.8), significantly higher than untreated controls ( p = 0.0006), with WST-D penetration limited to the sclera. Efficacy was greater in eyes obtained from older compared to younger guinea pigs ( p = 0.02). In vivo , WST-D/NIR treatment resulted in significant crosslinking compared to untreated controls (equatorial, ΔT 50 : 3.7, p < 0.0001; posterior, ΔT 50 : 3.4, p = 0.01). WST-D/NIR treatment effectively induces scleral crosslinking, with age-related differences suggesting the need for personalized treatment.
A flexible extension of the log-logistic model with diverse failure rate shapes and applications
Essential role of NONO-HOXA1-Wnt axis in cardiomyocyte differentiation
Abstract NONO is recognized as a critical molecular scaffold involved in both transcriptional and posttranscriptional regulation. Mutations in NONO are frequently linked to congenital heart diseases (CHDs) in humans. However, the mechanisms by which NONO regulates cardiac development remain elusive. Here, we identified NONO as a pivotal dual-function regulator of cardiomyocyte differentiation in human induced pluripotent stem cells (hiPSCs). NONO deficiency in hiPSCs results in a distinct defect in early cardiomyocyte differentiation. Mechanistically, NONO interacts with HOXA1 and regulates the dynamic expression of key genes during early cardiomyocyte differentiation. ChIP-seq analysis reveals that NONO loss reduces HOXA1 occupancy at target genes, compromising its transcriptional regulation. Additionally, NONO and HOXA1 cooperatively activate the Wnt signaling. Taken together, these findings establish the NONO-HOXA1-Wnt axis as a key molecular mechanism in cardiomyocyte differentiation and provide insights into the etiology of CHDs associated with NONO mutations.
The impact of inhibiting the Hippo signaling pathway effector molecule YAP1 on in vitro glioblastoma and glioblastoma stem cells
Recreating viable YYh genotype uncovers the role of CpYYL underlying YY lethality in papaya
Serum interferon-λ3 as a short-term biomarker of disease control in anti-MDA5-positive dermatomyositis-associated ILD
Abstract This study aimed to assess the clinical utility of serum interferon-lambda 3 (IFN-λ3) as a sequential biomarker for treatment response and disease control in patients with anti-melanoma differentiation-associated gene 5 (MDA5) antibody-positive dermatomyositis (DM)-associated interstitial lung disease (ILD). Serum IFN-λ3 levels were measured in 24 patients with anti-MDA5 antibody-positive DM-ILD at diagnosis and 1 month after initiating immunosuppressive therapy. Patients were categorized into two groups based on clinical outcomes: a good control group ( n = 16; survived without relapse for ≥ 1 year) and a poor control group ( n = 8; died from ILD progression or relapse within 1 year). Changes in serum IFN-λ3 levels and differences between groups were analyzed. In the good control group, serum IFN-λ3 levels significantly decreased from 94.6 to 12.7 pg/mL ( p < 0.001), whereas no significant change was observed in the poor control group (129.0 to 118.8 pg/mL). Furthermore, serum IFN-λ3 levels at 1 month were significantly lower in the good control group than in the poor control group ( p = 0.004). Serum IFN-λ3 levels may reflect short-term treatment response and could serve as a useful sequential biomarker for assessing disease control in patients with anti-MDA5 antibody-positive DM-ILD.