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Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles
Enhanced variable step sizes perturb and observe MPPT control to reduce energy loss in photovoltaic systems
Psychometric properties of the Arabic version of the Eco guilt and Eco grief scales
Design and experiment of automatic grasping manipulator for side-mounted garbage truck
Traditional Chinese medicine-based therapeutics for Pediatric pneumonia-related acute lung injury and acute respiratory distress syndrome
Interim results of exoskeletal wearable robot for gait recovery in subacute stroke patients
Synthesis of poly (acrylic acid) modified graphene/MoS2 heterostructure-based composite: an effective removal of Pb(II), Cd(II) and Zn(II) from wastewater
Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms
Abstract Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screening in at-risk populations and detection of early cancer has the potential to significantly alter survival. In this study, we aim to develop a predictive model to identify patients at risk for developing new-onset PC at two and a half to three year time frame . We used the Electronic Health Records (EHR) of a large medical system from 2000 to 2021 (N = 537,410). The EHR data analyzed in this work consists of patients’ demographic information, diagnosis records, and lab values, which are used to identify patients who were diagnosed with pancreatic cancer and the risk factors used in the machine learning algorithm for prediction. We identified 73 risk factors of pancreatic cancer with the Phenome-wide Association Study (PheWAS) on a matched case–control cohort. Based on them, we built a large-scale machine learning algorithm based on EHR. A temporally stratified validation based on patients not included in any stage of the training of the model was performed. This model showed an AUROC at 0.742 [0.727, 0.757] which was similar in both the general population and in a subset of the population who has had prior cross-sectional imaging. The rate of diagnosis of pancreatic cancer in those in the top 1 percentile of the risk score was 6 folds higher than the general population. Our model leverages data extracted from a 6-month window of time in the electronic health record to identify patients at nearly sixfold higher than baseline risk of developing pancreatic cancer 2.5–3 years from evaluation. This approach offers an opportunity to define an enriched population entirely based on static data, where current screening may be recommended.
Prognostic factors in pediatrics TAPVC: a 10-year retrospective study
Social needs and hospital readmission in persons living with HIV
3D geological model reconstruction and collapse risk evaluation of old goaf in Dunqiu iron mine
A synergistic approach to enhanced oil recovery by combining in-situ surfactant production and wettability alteration in carbonate reservoirs
A lipid metabolism related gene signature predicts postoperative recurrence in pancreatic cancer through multicenter cohort validation
Network performance assessment for public health emergency response: multi-case study of SARS, H1N1 and COVID-19 in China
Author Correction: New results in stereopsis and Listing’s law
Utilizing parthenocarpic gynoecious beit alpha cucumber inbreds for their heterotic potential under different poly-net house environments
Evaluation of crop phenology using remote sensing and decision support system for agrotechnology transfer
Efficacy of slow daily home hemodialysis with internal convection on removal of uremic toxins using the Physidia S3 monitor
Design and validation of a semi-quantitative microneutralization assay for human Metapneumovirus A1 and B1 subtypes
Abstract Since 2001, human Metapneumovirus has been a significant cause of human respiratory disease worldwide, and no vaccine or preventive treatment is currently available. The ELISA-based live virus microneutralization assay is a method to detect neutralizing antibodies against a target pathogen. The aim of this study was to demonstrate the suitability of this approach to quantifying neutralizing antibodies against A1 and B1 virus subtypes in human serum samples. To standardize and validate this microneutralization assay, we carried out analytical procedures according to the International Council of Harmonization guidelines; these procedures are described in detail. In addition, we compared the validated method with the indirect ELISA, and confirmed that the ELISA-based microneutralization assay provides reliable, accurate and reproducible results. The use of this high-throughput method for large-scale serological studies could effectively support the evaluation of the immunogenicity of new vaccines, thereby improving therapeutical strategies against human Metapneumovirus.
Influence of pallet height on energy consumption and cooling effectiveness in an apple cold storage
Abstract This study investigates the influence of pallet height on energy consumption and cooling effectiveness using a validated cold storage model based on CFD simulations. The model was validated against experimental temperature data, with a maximum normalized root mean square error (NRMSE) of 4.57%, indicating good agreement. Four pallet height configurations namely No pallet (0.0 m), 0.3 m, 0.6 m, and 0.9 m were assessed over a 40-hour cooling period. Temperature distribution within apple-filled crates was used to identify the locations of hot and cold spots, and performance metrics such as compressor energy consumption, specific energy consumption, mean crate temperature, thermal heterogeneity, and cooling effectiveness were analyzed. The results indicate that the No-pallet configuration disrupts airflow resulting in insufficient cooling, as evidenced by lower cooling effectiveness, while larger pallet heights (0.6 m and 0.9 m) introduce excessive air spacing resulting in higher thermal heterogeneity. The 0.3 m pallet height exhibited best performance such as lowest mean crate temperature of 4.7 °C, (8.7% lower), lowest thermal heterogeneity of 3.29 (14.42% lower), lowest compressor energy consumption per kelvin of 1.474 kWh/K (0.5% lower) and highest cooling effectiveness of 0.4 (5.37% higher). These findings provide practical insights for optimizing pallet configurations in cold storage, aiding energy-efficient operations in commercial refrigerated warehouses and post-harvest supply chains.