Development and validation of a nomogram model for predicting the risk of MAFLD in the young population.

This study aimed to develop and validate a nomogram model that includes clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in young Chinese individuals. This study retrospectively analyzed a cohort of young population who underwent health examination from November 2018 to December 2021 at The Affiliated Hospital of Southwest Medical University in Luzhou City, Sichuan Province, China. We extracted the clinical and laboratory data of 43,040 subjects and randomized participants into the training and validation groups (7:3). Univariate logistic regression analysis, the least absolute shrinkage and selection operator regression, and multivariate logistic regression models identified significant variables independently associated with MAFLD. The predictive accuracy of the model was analyzed in the training and validation sets using area under the receiver operating characteristic (AUROC), calibration curves, and decision curve analysis. In this study, we identified nine predictors from 31 variables, including age, gender, body mass index, waist-to-hip ratio, alanine aminotransferase, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, uric acid, and smoking. The AUROC for the subjects in the training and validation groups was 0.874 and 0.875, respectively. The calibration curves show excellent accuracy of the nomogram. This nomogram which was based on demographic characteristics, lifestyle habits, anthropometrics, and laboratory data can visually and individually predict the risk of developing MAFLD. This nomogram is a quick and effective screening tool for assessing the risk of MAFLD in younger populations and identifying individuals at high risk of MAFLD, thereby contributing to the improvement of MAFLD management.

Successful Treatment of a Patient With Brain Metastasis From Ovarian Cancer With BRCA Wild Type Using Niraparib: A Case Report and Review of the Literature.

Background: Brain metastases from ovarian cancer are extremely rare and have a very poor prognosis. A multimodal approach (surgery combined with radiotherapy and chemotherapy) yields the best results in reducing neurological symptoms and prolonging survival. Unfortunately, not every patient receives a complete multimodal treatment due to their individual factors. Poly(ADP-ribose) polymerase (PARP) inhibitors have emerged as a maintenance treatment option for recurrent ovarian cancer. Using PARPi may prolong the overall survival in patients with brain metastases and recurrent ovarian cancer. Case Presentation: We report a case of a female patient with advanced ovarian cancer without any germline or somatic BRCA mutation. After 21 months, after reduction surgery and adjuvant chemotherapy, she was diagnosed with brain metastasis. Due to her physical fitness and economic situation, she did not receive any radiotherapy or chemotherapy but only received surgical debulking of the brain metastasis and niraparib maintenance treatment. Up to now, she has achieved a good treatment response, and the PFS is 29 months. Conclusion: Based on the response of our patient, PARP inhibitors as a single agent can probably be considered in patients with brain metastasis from ovarian cancer without BRCA mutation who cannot tolerate radiotherapy and chemotherapy.