Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.

Autophagy in endocrine tumors.

Autophagy is an important intracellular process involving the degradation of cytoplasmic components. It is involved in both physiological and pathological conditions, including cancer. The role of autophagy in cancer is described as a 'double-edged sword,' a term that reflects its known participation in tumor suppression, tumor survival and tumor cell proliferation. Available research regarding autophagy in endocrine cancer supports this concept. Autophagy shows promise as a novel therapeutic target in different types of endocrine cancer, inhibiting or increasing treatment efficacy in a context- and cell-type-dependent manner. At present, however, there is very little research concerning autophagy in endocrine tumors. No research was reported connecting autophagy to some of the tumors of the endocrine glands such as the pancreas and ovary. This review aims to elucidate the roles of autophagy in different types of endocrine cancer and highlight the need for increased research in the field.

Crooke's cell tumors of the pituitary.

Crooke's cell adenomas are a rare type of pituitary neoplasm. They produce adrenocorticotropic hormone causing Cushing's disease or may be endocrinologically silent. These tumors are usually invasive, may exhibit aggressive clinical behavior, and often recur with a low success of cure after reoperation and/or radiotherapy. Due to their rarity, they present great difficulties in assessing prognosis, treatment, and clinical management. Neurosurgeons and physicians dealing with pituitary adenomas diagnosed as Crooke's cell adenomas have to be aware of their potential clinical aggressiveness to plan strict follow-up of patients and eventual multimodality treatment. We review here the published cases of Crooke's cell tumors, as well as the clinical and histopathological characteristics of these unusual neoplasms.

Autophagy in the endocrine glands.

Autophagy is an important cellular process involving the degradation of intracellular components. Its regulation is complex and while there are many methods available, there is currently no single effective way of detecting and monitoring autophagy. It has several cellular functions that are conserved throughout the body, as well as a variety of different physiological roles depending on the context of its occurrence in the body. Autophagy is also involved in the pathology of a wide range of diseases. Within the endocrine system, autophagy has both its traditional conserved functions and specific functions. In the endocrine glands, autophagy plays a critical role in controlling intracellular hormone levels. In peptide-secreting cells of glands such as the pituitary gland, crinophagy, a specific form of autophagy, targets the secretory granules to control the levels of stored hormone. In steroid-secreting cells of glands such as the testes and adrenal gland, autophagy targets the steroid-producing organelles. The dysregulation of autophagy in the endocrine glands leads to several different endocrine diseases such as diabetes and infertility. This review aims to clarify the known roles of autophagy in the physiology of the endocrine system, as well as in various endocrine diseases.