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These themes likely feed into each other and reinforce the existing and dominant mentalities of the sport.<br />Sport body image ideals and the power dynamic between coach and athlete may contribute to female athlete's risk of disordered eating and body image disturbance. We call for the NCAA and athletic departments to develop and implement prevention and intervention programmes to prevent eating and body image disorders in this high-risk population.<br />Sport body image ideals and the power dynamic between coach and athlete may contribute to female athlete's risk of disordered eating and body image disturbance. We call for the NCAA and athletic departments to develop and implement prevention and intervention programmes to prevent eating and body image disorders in this high-risk population.Immune checkpoint inhibitors (ICIs) such as nivolumab and ipilimumab have improved outcomes in metastatic renal cell carcinoma (mRCC) patients, but they are also associated with immune-related adverse events (irAEs). As observed in melanoma, we hypothesized that patients experiencing an autoimmune reaction directed against the tissue of origin may be more likely to benefit from ICI. [https://www.selleckchem.com/products/tulmimetostat.html Tulmimetostat price] Specifically, we asked whether patients with immune-related acute interstitial nephritis (irAIN) exhibited improved outcomes. Using Kidney Cancer Explorer (KCE), a data portal and i2b2-based central database for clinical, pathological and experimental genetic data, we systematically identified all patients with mRCC at UT Southwestern Medical Center (UTSW) from 2014-2018 who received at least one dose of ICI. More recent cases were identified through a provider query. We extracted creatinine (Cr) values at baseline and over the entirety of each patient ICI treatment course using KCE. Patients with ≥ 1.5-fold Cr increase over baselelihood of response. One possible explanation is antigenic overlap between normal renal tubular cells and tumor cells.Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, varies across malignancies. Panel sequencing-based estimates of TMB have largely replaced whole-exome sequencing-derived TMB in the clinic. Retrospective evidence suggests that TMB can predict the efficacy of immune checkpoint inhibitors, and data from KEYNOTE-158 led to the recent FDA approval of pembrolizumab for the TMB-high tumor subgroup. Unmet needs include prospective validation of TMB cutoffs in relationship to tumor type and patient outcomes. Furthermore, standardization and harmonization of TMB measurement across test platforms are important to the successful implementation of TMB in clinical practice. SIGNIFICANCE Evaluation of TMB as a predictive biomarker creates the need to harmonize panel-based TMB estimation and standardize its reporting. TMB can improve the predictive accuracy for immunotherapy outcomes, and has the potential to expand the candidate pool of patients for treatment with immune checkpoint inhibitors.Cancer cells continuously rewire their metabolism to fulfill their need for rapid growth and survival while subject to changes in environmental cues. Thus, a vital component of a cancer cell lies in its metabolic adaptability. The constant demand for metabolic alterations requires flexibility, that is, the ability to utilize different metabolic substrates; as well as plasticity, that is, the ability to process metabolic substrates in different ways. In this review, we discuss how dynamic changes in cancer metabolism affect tumor progression and the consequential implications for cancer therapy. SIGNIFICANCE Recognizing cancer dynamic metabolic adaptability as an entity can lead to targeted therapy that is expected to decrease drug resistance.<br />Retinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning-based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning-based method using retinal images for detecting early renal function impairment has not yet been well studied.<br />This study aimed to develop and evaluate a deep learning model for detecting early renal function impairment using retinal fundus images.<br />This retrospective study enrolled patients who underwent renal function tests with color fundus images captured at any time between January 1, 2001, and August 31, 2019. A deep learning model was constructed to detect impaired renal function from the images. Early renal function impairment was defined as estimated glomerular filtration rate &lt;90 mL/min/1.73 m<br />. Model performance was evaluated with respect to the receiver operating characteristic curve and area under the curve (AUC).<br />In total, 25,706 retinal fundus images were obtained from 6212 patients for the study period. The images were divided at an 811 ratio. The training, validation, and testing data sets respectively contained 20,787, 2189, and 2730 images from 4970, 621, and 621 patients. There were 10,686 and 15,020 images determined to indicate normal and impaired renal function, respectively. The AUC of the model was 0.81 in the overall population. In subgroups stratified by serum hemoglobin A<br />(HbA<br />) level, the AUCs were 0.81, 0.84, 0.85, and 0.87 for the HbA<br />levels of ≤6.5%, &gt;6.5%, &gt;7.5%, and &gt;10%, respectively.<br />The deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA<br />levels.<br />The deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA1c levels.<br />To assess the impact of liver function test (LFT) abnormalities on the prognosis of patients with coronavirus disease 2019 (COVID-19) in a French cohort of hospitalized patients.<br />From March 13 to April 22, 2020, we collected on a computerized and anonymized database, medical records, laboratory data and clinical outcomes of patients hospitalized for confirmed cases of COVID-19 infection (RT-PCR and/or CT-scan). Patients were followed up until April 22, 2020 or until death or discharge. We have considered for statistical analysis, LFT abnormalities with levels greater than two times the upper limit of normal. Composite endpoint included admission to ICU, mechanical ventilation, severe radiologic injury and death to define disease severity.<br />Among 281 patients (median age 60 years) with COVID-19, 102 (36.3%) had abnormal LFT. Hypertension (45.6%) and diabetes (29.5%) were the main comorbidities. 20.2% were taken liver-toxic drugs at the admission and 27.4% were given drugs known to induce hepatic cytolysis during hospitalization.