Deep Learning for the Assessment of Facial Nerve Palsy: Opportunities and Challenges.

Automated evaluation of facial palsy using machine learning offers a promising solution to the limitations of current assessment methods, which can be time-consuming, labor-intensive, and subject to clinician bias. Deep learning-driven systems have the potential to rapidly triage patients with varying levels of palsy severity and accurately track recovery over time. However, developing a clinically usable tool faces several challenges, such as data quality, inherent biases in machine learning algorithms, and explainability of decision-making processes. The development of the eFACE scale and its associated software has improved clinician scoring of facial palsy. Additionally, Emotrics is a semiautomated tool that provides quantitative data of facial landmarks on patient photographs. The ideal artificial intelligence (AI)-enabled system would analyze patient videos in real time, extracting anatomic landmark data to quantify symmetry and movement, and estimate clinical eFACE scores. This would not replace clinician eFACE scoring but would offer a rapid automated estimate of both anatomic data, similar to Emotrics, and clinical severity, similar to the eFACE. This review explores the current state of facial palsy assessment, recent advancements in AI, and the opportunities and challenges in developing an AI-driven solution.

ß-Cell Autophagy Pathway and Endoplasmic Reticulum Stress Regulating-Role of Liposomal Curcumin in Experimental Diabetes Mellitus: A Molecular and Morphometric Study.

Background: Autophagy can confer protection to pancreatic ß-cells from the harmful effects of metabolic stress by delaying apoptosis. Curcumin (CUR) alleviates oxidative and endoplasmic reticulum (ER) stress, activates autophagy, reduces inflammation, and decreases ß-cell damage in type I diabetes. Liposomal CUR (LPs-CUR) has a higher therapeutic value and better pharmacokinetics than CUR. Objectives: We determined LPs-CUR's ability to alleviate stress, reduce ß-cell damage and unraveled the mechanism underlying its protective effect using a streptozotocin (STZ)-induced type I diabetic rat model. Methods: Sprague−Dawley rats were grouped into vehicle control, STZ-diabetic (STZ 65 mg/kg), STZ-diabetic-3-MA (3-methyladenine [3-MA] 10 mg/kg b.wt), STZ. diabetic-LPs-CUR (LPs-CUR 10 mg/kg b.wt), and STZ diabetic-LPs-CUR-3-MA (LPs-CUR 10 mg/kg b.wt; 3-MA 10 mg/kg b.wt). Results: LPs-CUR significantly reduced blood glucose, oxidative stress, and cellular inflammation in the pancreatic tissue (p < 0.001). ER stress-dependent genes included ATF-6, eIF-2, CHOP, JNK, BiP, and XBP LPs-CUR significantly suppressed fold changes, while it upregulated the autophagic markers Beclin-1 and LC3-II. Conclusions: LP-CUR ameliorates ß-cell damage by targeting the autophagy pathway with the regulatory miRNAs miR-137 and miR-29b, which functionally abrogates ER stress in ß-cells. This study presents a new therapeutic target for managing type I diabetes using miR-137 and miR-29b.