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http://dx.doi.org/10.1016/j.jenvrad.2018.02.004 | DOI Listing |
Front Med (Lausanne)
December 2024
Department of Nursing, General Hospital of Southern Theatre Command of PLA, Guangzhou, Guangdong, China.
Introduction: Early prediction of multiple organ dysfunction syndrome (MODS) secondary to severe heat stroke (SHS) is crucial for improving patient outcomes. This study aims to develop and validate a risk prediction model for those patients based on immediate assessment indicators on ICU admission.
Methods: Two hundred eighty-four cases with SHS in our hospital between July 2009 and April 2024 were retrospectively reviewed, and categorized into non-MODS and MODS groups.
J Inflamm Res
January 2025
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Purpose: Previous studies have reported that infection-induced fever is associated with improved breast cancer prognosis, potentially through the modulation of cytokines. However, the key cytokines and the underlying mechanisms through which fever exerts its anti-tumor effects remain unclear.
Patients And Methods: A total of 794 breast cancer patients were recruited between 2008 and 2017, with follow-up extending until October 31st, 2023.
Ther Adv Hematol
January 2025
Department of Intensive Care Unit, General Hospital of Southern Theatre Command of PLA, #111 Liuhua Road, Guangzhou, 510010, Guangdong, China.
Background: Heat stroke (HS), a potentially fatal heat-related illness, is often accompanied by disseminated intravascular coagulation (DIC) early, resulting in a poorer prognosis. Unfortunately, diagnosis by current DIC scores is often too late to identify DIC. This study aims to investigate the predictors and predictive model of DIC in HS to identify DIC early.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
Quantum Chemistry Division, Yokohama City University, Seto 22-2, Kanazawa-ku, Yokohama 236-0027, Kanagawa, Japan.
We propose density functional theory (DFT)- and random forest (RF)-based theoretical and machine learning (ML) models, respectively, for predicting reaction barriers (Δ) using acrylate and methacrylate radical reactions as representatives. DFT is used to determine 100 transition state (TS) structures of both radicals, after which the obtained data are used to determine theoretical relationships (explained with Bell-Evans-Polanyi or Brønsted-Evans-Polanyi (BEP) and Marcus-like models) between Δ and stabilization energy of the product. Next, we construct several theoretical regression models for predicting Δ of the representative reactions based on our theoretical analyses, presenting an RF-based ML model that eases Δ predictions by circumventing time-consuming DFT calculations.
View Article and Find Full Text PDFJ Clin Lab Anal
January 2025
Department of Medical Laboratory, Shenzhen Hospital (Futian) of Guangzhou University of Chinese Medicine, Shenzhen, People's Republic of China.
Background: Mycoplasma pneumoniae (MP) is a major cause of community-acquired pneumonia (CAP), posing diagnostic challenges. This study evaluates novel inflammatory biomarkers, including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII) and system inflammation response index (SIRI) for MP diagnosis in children.
Methods: Complete blood count (CBC) results of 424 children with MP infection and 150 health children were collected.
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