Limb‐girdle muscular dystrophies (LGMDs) are a highly heterogeneous group of neuromuscular disorders that are associated with weakness and wasting of muscles in legs and arms. Signs and symptoms may begin at any age and usually worsen by time. LGMDs are autosomal disorders with different types and their prevalence is not the same in different areas. New technologies such as next‐generation sequencing can accelerate their diagnosis. Several important pathological mechanisms that are involved in the pathology of the LGMD include abnormalities in dystrophin–glycoprotein complex, the sarcomere, glycosylation of dystroglycan, vesicle and molecular trafficking, signal transduction pathways, and nuclear functions. Here, we provide a comprehensive review that integrates LGMD clinical manifestations, prevalence, and some pathological mechanisms involved in LGMDs.

Among different types of dyslipidemia, familial combined hyperlipidemia (FCHL) is the most common genetic disorder, which is characterized by at least two different forms of lipid abnormalities: hypercholesterolemia and hypertriglyceridemia. FCHL is an important cause of cardiovascular diseases. FCHL is a heterogeneous condition linked with some metabolic defects that are closely associated with FCHL. These metabolic features include dysfunctional adipose tissue, delayed clearance of triglyceride‐rich lipoproteins, overproduction of very low‐density lipoprotein and hepatic lipids, and defect in the clearance of low‐density lipoprotein particles. There are also some genes associated with FCHL such as those affecting the metabolism and clearance of plasma lipoprotein particles. Due to the high prevalence of FCHL especially in cardiovascular patients, targeted treatment is ideal but this necessitates identification of the genetic background of patients. This review describes the metabolic pathways and associated genes that are implicated in FCHL pathogenesis. We also review existing and novel treatment options for FCHL. © 2019 IUBMB Life, 71(9):1221–1229, 2019

Parkinson's disease (PD) is known as a progressive neurodegenerative disorder associated with the reduction of dopamine-secreting neurons and the formation of Lewy bodies in the substantia nigra and basal ganglia routes. Aging, as well as environmental and genetic factors, are considered as disease risk factors that can make PD as a complex one. Epigenetics means studying heritable changes in gene expression or function, without altering the underlying DNA sequence. Multiple studies have shown the association of epigenetic variations with onset or progression of various types of diseases. DNA methylation, posttranslational modifications of histones and presence of microRNA (miRNA) are among epigenetic processes involved in regulating pathways related to the development of PD. Unlike genetic mutations, most epigenetic variations may be reversible or preventable. Therefore, the return of aberrant epigenetic events in different cells is a growing therapeutic approach to treatment or prevention. Currently, there are several methods for treating PD patients, the most important of which are drug therapies. However, detection of genes and epigenetic mechanisms involved in the disease can develop appropriate diagnosis and treatment of the disease before the onset of disabilities and resulting complications. The main purpose of this study was to review the most important epigenetic molecular mechanisms, epigenetic variations in PD, and epigenetic-based therapies.

Background We used a hybrid machine learning systems (HMLS) strategy that includes the extensive search for the discovery of the most optimal HMLSs, including feature selection algorithms, a feature extraction algorithm, and classifiers for diagnosing breast cancer. Hence, this study aims to obtain a high-importance transcriptome profile linked with classification procedures that can facilitate the early detection of breast cancer. Methods In the present study, 762 breast cancer patients and 138 solid tissue normal subjects were included. Three groups of machine learning (ML) algorithms were employed: (i) four feature selection procedures are employed and compared to select the most valuable feature: (1) ANOVA; (2) Mutual Information; (3) Extra Trees Classifier; and (4) Logistic Regression (LGR), (ii) a feature extraction algorithm (Principal Component Analysis), iii) we utilized 13 classification algorithms accompanied with automated ML hyperparameter tuning, including (1) LGR; (2) Support Vector Machine; (3) Bagging; (4) Gaussian Naive Bayes; (5) Decision Tree; (6) Gradient Boosting Decision Tree; (7) K Nearest Neighborhood; (8) Bernoulli Naive Bayes; (9) Random Forest; (10) AdaBoost, (11) ExtraTrees; (12) Linear Discriminant Analysis; and (13) Multilayer Perceptron (MLP). For evaluating the proposed models' performance, balance accuracy and area under the curve (AUC) were used. Results Feature selection procedure LGR + MLP classifier achieved the highest prediction accuracy and AUC (balanced accuracy: 0.86, AUC = 0.94), followed by an LGR + LGR classifier (balanced accuracy: 0.84, AUC = 0.94). The results showed that achieved AUC for the LGR + LGR classifier belonged to the 20 biomarkers as follows: TMEM212, SNORD115-13, ATP1A4, FRG2, CFHR4, ZCCHC13, FLJ46361, LY6G6E, ZNF323, KRT28, KRT25, LPPR5, C10orf99, PRKACG, SULT2A1, GRIN2C, EN2, GBA2, CUX2, and SNORA66. Conclusions The best performance was achieved using the LGR feature selection procedure and MLP classifier. Results show that the 20 biomarkers had the highest score or ranking in breast cancer detection.