Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis.

Purpose To develop an MRI-based model for clinically significant prostate cancer (csPCa) diagnosis that can resist rectal artifact interference. Materials and Methods This retrospective study included 2203 male patients with prostate lesions who underwent biparametric MRI and biopsy between January 2019 and June 2023. Targeted adversarial training with proprietary adversarial samples (TPAS) strategy was proposed to enhance model resistance against rectal artifacts. The automated csPCa diagnostic models trained with and without TPAS were compared using multicenter validation datasets. The impact of rectal artifacts on the diagnostic performance of each model at the patient and lesion levels was compared using the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC). The AUC between models was compared using the DeLong test, and the AUPRC was compared using the bootstrap method. Results The TPAS model exhibited diagnostic performance improvements of 6% at the patient level (AUC: 0.87 vs 0.81, P < .001) and 7% at the lesion level (AUPRC: 0.84 vs 0.77, P = .007) compared with the control model. The TPAS model demonstrated less performance decline in the presence of rectal artifact-pattern adversarial noise than the control model (ΔAUC: -17% vs -19%, ΔAUPRC: -18% vs -21%). The TPAS model performed better than the control model in patients with moderate (AUC: 0.79 vs 0.73, AUPRC: 0.68 vs 0.61) and severe (AUC: 0.75 vs 0.57, AUPRC: 0.69 vs 0.59) artifacts. Conclusion This study demonstrates that the TPAS model can reduce rectal artifact interference in MRI-based csPCa diagnosis, thereby improving its performance in clinical applications. Keywords: MR-Diffusion-weighted Imaging, Urinary, Prostate, Comparative Studies, Diagnosis, Transfer Learning Clinical trial registration no. ChiCTR23000069832 Supplemental material is available for this article. Published under a CC BY 4.0 license.

Iron homeostasis imbalance and ferroptosis in brain diseases.

Brain iron homeostasis is maintained through the normal function of blood-brain barrier and iron regulation at the systemic and cellular levels, which is fundamental to normal brain function. Excess iron can catalyze the generation of free radicals through Fenton reactions due to its dual redox state, thus causing oxidative stress. Numerous evidence has indicated brain diseases, especially stroke and neurodegenerative diseases, are closely related to the mechanism of iron homeostasis imbalance in the brain. For one thing, brain diseases promote brain iron accumulation. For another, iron accumulation amplifies damage to the nervous system and exacerbates patients' outcomes. In addition, iron accumulation triggers ferroptosis, a newly discovered iron-dependent type of programmed cell death, which is closely related to neurodegeneration and has received wide attention in recent years. In this context, we outline the mechanism of a normal brain iron metabolism and focus on the current mechanism of the iron homeostasis imbalance in stroke, Alzheimer's disease, and Parkinson's disease. Meanwhile, we also discuss the mechanism of ferroptosis and simultaneously enumerate the newly discovered drugs for iron chelators and ferroptosis inhibitors.

Neck Circumference and Incidence of Type 2 Diabetes in Chinese Elderly Individuals: A Community-Based Cohort Study.

INTRODUCTION: This study aimed to investigate whether neck circumference (NC) was associated with the incidence of type 2 diabetes in Chinese elderly individuals. METHODS: A community-based cohort study was conducted on elderly inhabitants in Shanghai with a mean age of 71.0 ± 5.8 years (n = 2,646). Binary logistic regression analysis was performed to evaluate the association between NC and the prevalence of type 2 diabetes, while a Cox regression model was used to determine the association between NC and the incidence of type 2 diabetes after a follow-up of 2 years. RESULTS: Logistic regression analysis showed that a larger NC was significantly associated with an increased risk for type 2 diabetes in men (odds ratio [OR] 1.18, 95% confidence interval [CI] 1.07-1.31; p = 0.001) and women (OR 1.25, 95% CI 1.13-1.38; p < 0.001). Cox regression analysis revealed that NC was independently associated with the incidence of type 2 diabetes in both men (hazard ratio [HR] 1.14, 95% CI 1.05-1.23; p = 0.002) and women (HR 1.18, 95% CI 1.10-1.27; p < 0.001). CONCLUSIONS: A larger NC was associated with a higher risk of developing type 2 diabetes in Chinese elderly individuals. However, studies with larger sample sizes and longer follow-up durations are needed to definitively determine the relationship between NC and the risk of developing type 2 diabetes.