Low-carbohydrate dietary score and the incidence of metabolically unhealthy phenotype based on BMI status: a cohort study.

There is scarce research focusing on the relationship between the low-carbohydrate dietary score and the development of a metabolically unhealthy phenotype. Therefore, this cohort study was designed to assess the association between the low-carbohydrate dietary score and the risk of metabolically unhealthy phenotypes (MUP). This study included 1299 adults with healthy metabolic profiles who were followed for 5.9 years. Results indicated an inverse association between the second tertile of the low-carbohydrate dietary score and the risk of developing metabolically unhealthy obesity (MUO) (HR: 0.76, 95% CI: 0.59-0.98). In addition, we found an inverse association between the healthy low-carbohydrate dietary score and the risk of MUO (HR: 0.77, 95% CI: 0.60-0.99). Our results revealed a nonlinear inverse association between the low-carbohydrate dietary score and the risk of MUP only in subjects with overweight or obesity. This relationship was independent of animal protein and fat intake. Also, we found that a lower intake of unhealthy carbohydrates was associated with a lower risk of MUP only in subjects with overweight or obesity.

Artificial intelligence in the diagnosis of multiple sclerosis: A systematic review.

BACKGROUND: In recent years Artificial intelligence (AI) techniques are rapidly evolving into clinical practices such as diagnosis and prognosis processes, assess treatment effectiveness, and monitoring of diseases. The previous studies showed interesting results regarding the diagnostic efficiency of AI methods in differentiating Multiple sclerosis (MS) patients from healthy controls or other demyelinating diseases. There is a great lack of a comprehensive systematic review study on the role of AI in the diagnosis of MS. We aimed to perform a systematic review to document the performance of AI in MS diagnosis. METHODS: A systematic search was performed using four databases including PubMed, Scopus, Web of Science, and IEEE on August 2021. All original studies which focused on deep learning or AI to analyze any modalities with the purpose of diagnosing MS were included in our study. RESULTS: Finally, 38 studies were included in our systematic review after the abstract and full-text screening. A total of 5433 individuals were included, including 2924 cases of MS and 2509 healthy controls. Sensitivity and specificity were reported in 29 studies which ranged from 76.92 to 100 for sensitivity and 74 to 100 for specificity. Furthermore, 34 studies reported accuracy ranged 81 to 100. Among included studies, Magnetic Resonance Imaging (MRI) (20 studies), OCT (six studies), serum and cerebrospinal fluid markers (six studies), movement function (three studies), and other modalities such as breathing and evoked potential was used for detecting MS via AI. CONCLUSION: In conclusion, diagnosis of MS based on new markers and AI is a growing field of research with MRI images, followed by images obtained from OCT, serum and CSF biomarkers, and motor associated markers. All of these results show that with advances made in AI, the way we monitor and diagnose our MS patients can change drastically.

Ferroptosis in Lung Cancer: From Molecular Mechanisms to Prognostic and Therapeutic Opportunities.

Lung cancer is the second commonly diagnosed malignancy worldwide and has the highest mortality rate among all cancers. Tremendous efforts have been made to develop novel strategies against lung cancer; however, the overall survival of patients still is low. Uncovering underlying molecular mechanisms of this disease can open up new horizons for its treatment. Ferroptosis is a newly discovered type of programmed cell death that, in an iron-dependent manner, peroxidizes unsaturated phospholipids and results in the accumulation of radical oxygen species. Subsequent oxidative damage caused by ferroptosis contributes to cell death in tumor cells. Therefore, understanding its molecular mechanisms in lung cancer appears as a promising strategy to induce ferroptosis selectively. According to evidence published up to now, significant numbers of research have been done to identify ferroptosis regulators in lung cancer. Therefore, this review aims to provide a comprehensive standpoint of molecular mechanisms of ferroptosis in lung cancer and address these molecules' prognostic and therapeutic values, hoping that the road for future studies in this field will be paved more efficiently.