The Prediction of Dry Weight for Chronic Hemodialysis Athletes Using a Machine Learning Approach: Sports Health Implications / La predicción del peso seco para atletas en hemodiálisis crónica mediante un enfoque de aprendizaje automático: implicaciones para la salud deportiva

This study seeks to evaluate the ability of machine learning methods to predict the dry weight of chronic hemodialysis athletes. The researcher has reached out to kidney patients who have had to give up sports and athletic careers due to chronic hemodialysis. This paper explores the development of medical prediction algorithms that combine image analysis with numerical data, which is widely used in the field of medicine. This deep learning method is widely employed to enhance the treatment of athletes who have kidney conditions. Regular hemodialysis is crucial for maintaining the health of athletes who have kidney disease. Accurately predicting dry weight is a crucial step in the process of performing hemodialysis. In this context, dry weight refers to the optimal moisture level at which excess water is effectively eliminated from the patient (athletes) through ultrafiltration during hemodialysis. In order to accurately determine the optimal amount of hemodialysis, predicting the correct dry weight is crucial. However, this task is quite challenging and often yields inaccurate results due to the extensive data analysis required by experienced nephrologists. This paper presents a deep learning methodology utilising the Artificial Neural Network (ANN) approach to efficiently address these issues. The proposed method aims to predict dry weight rapidly by analysing image values and clinical data from X-ray images obtained during routine check-ups. The current study has several theoretical and practical implications. This study contributes to the existing literature on chronic hemodialysis and the dry weight of athletes, offering valuable insights to sports health organisations. By doing so, these organisations can effectively prepare to proactively evaluate the atypical health conditions of athletes.(AU)

Vaccine Effect on Household Transmission of Omicron and Delta SARS-CoV-2 Variants.

BACKGROUND: We evaluated the household secondary attack rate (SAR) of the omicron and delta severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, according to the vaccination status of the index case and household contacts; further, in vaccinated index cases, we evaluated the effect of the antibody levels on household transmission. METHODS: A prospective cross-sectional study of 92 index cases and 197 quarantined household contacts was performed. Tests for SARS-CoV-2 variant type and antibody level were conducted in index cases, and results of polymerase chain reaction tests (during the quarantine period) were collected from contacts. Association of antibody levels in vaccinated index cases and SAR was evaluated by multivariate regression analysis. RESULTS: The SAR was higher in households exposed to omicron variant (42%) than in those exposed to delta variant (27%) (P = 0.040). SAR was 35% and 23% for unvaccinated and vaccinated delta variant exposed contacts, respectively. SAR was 44% and 41% for unvaccinated and vaccinated omicron exposed contacts, respectively. Booster dose immunisation of contacts or vaccination of index cases reduced SAR of vaccinated omicron variant exposed contacts. In a model with adjustment, anti-receptor-binding domain antibody levels in vaccinated index cases were inversely correlated with household transmission of both delta and omicron variants. Neutralising antibody levels had a similar relationship. CONCLUSION: Immunisation of household members may help to mitigate the current pandemic.

Antibody Level Predicts the Clinical Course of Breakthrough Infection of COVID-19 Caused by Delta and Omicron Variants: A Prospective Cross-Sectional Study.

Background: Omicron variant viruses spread rapidly, even in individuals with high vaccination rates. This study aimed to determine the utility of the antibody against spike protein level as a predictor of the disease course of coronavirus disease 2019 (COVID-19) in vaccinated patients. Methods: Between December 11, 2021, and February 10, 2022, we performed a prospective observational cohort study in South Korea, which included patients infected with Delta and Omicron variants. A multivariable logistic regression analysis to determine the association between antibody levels and outcomes was conducted. The relationship between antibody levels and cycle threshold (Ct) values was confirmed using a generalized linear model. Results: From 106 vaccinated patients (39 Delta and 67 Omicron), the geometric mean titers of antibodies in patients with fever (≥37.5°C), hypoxia (≤94% of SpO2), pneumonia, C-reactive protein (CRP) elevation (>8 mg/L), or lymphopenia (<1100 cells/µL) were 1201.5 U/mL, 98.8 U/mL, 774.1 U/mL, 1335.1 U/mL, and 1032.2 U/mL, respectively. Increased antibody levels were associated with a decrease in the occurrence of fever (adjusted odds ratio [aOR], 0.23; 95% CI, 0.12-0.51), hypoxia (aOR, 0.23; 95% CI, 0.08-0.7), CRP elevation (aOR, 0.52; 95% CI, 0.29-0.0.94), and lymphopenia (aOR, 0.57; 95% CI, 0.33-0.98). Ct values showed a positive correlation between antibody levels (P = .02). Conclusions: Antibody levels are predictive of the clinical course of COVID-19 in vaccinated patients with Delta and Omicron variant infections. Our data highlight the need for concentrated efforts to monitor patients with severe acute respiratory syndrome coronavirus 2 infection who are at risk of low antibody levels.