Adenosine triphosphate (ATP) is the energy currency within all living cells and is involved in many vital biochemical reactions, including cell viability, metabolic status, cell death, intracellular signaling, DNA and RNA synthesis, purinergic signaling, synaptic signaling, active transport, and muscle contraction. Consequently, altered ATP production is frequently viewed as a contributor to both disease pathogenesis and subsequent progression of organ failure. Barth syndrome (BTHS) is an X-linked mitochondrial disease characterized by fatigue, skeletal muscle weakness, cardiomyopathy, neutropenia, and growth delay due to inherited TAFAZZIN enzyme mutations. BTHS is widely hypothesized in the literature to be a model of defective mitochondrial ATP production leading to energy deficits. Prior patient data have linked both impaired ATP production and reduced phosphocreatine to ATP ratios (PCr/ATP) in BTHS children and adult hearts and muscles, suggesting a primary role for perturbed energetics. Moreover, although only limited direct measurements of ATP content and ADP/ATP ratio (an indicator of the energy available from ATP hydrolysis) have so far been carried out, analysis of divergent BTHS animal models, cultured cell types, and diverse organs has failed to uncover a unifying understanding of the molecular mechanisms linking TAFAZZIN deficiency to perturbed muscle energetics. This review mainly focuses on the energetics of striated muscle in BTHS mitochondriopathy.

Aim: This study aims to investigate and apply effective machine learning techniques for the early detection and precise diagnosis of breast cancer. The analysis is conducted using various breast cancer datasets, including Breast Cancer Wisconsin, Breast Cancer Diagnosis, NKI Breast Cancer, and SEER Breast Cancer datasets. The primary focus is on identifying key features and utilizing preprocessing methods to enhance classification accuracy.

Methods: The datasets undergo several preprocessing steps, such as label encoding for categorical variables, linear regression for handling missing values, and Robust scaler normalization for data standardization. To address class imbalance, Tomek Link SMOTE is employed to improve dataset representation. Significant features are selected through L2 Ridge regularization, helping to pinpoint the most important predictors of breast cancer. A range of machine learning models, including decision tree, random forest, support vector machine (SVM), neural network, K-nearest neighbor, naïve bayes, extreme gradient boost (XGBoost), and AdaBoost, are applied for classification tasks. The performance of these models is assessed using metrics such as accuracy, precision, recall, F1-score, and the Kappa statistic. Additionally, the models' effectiveness is further evaluated using the receiver operating characteristic curve and precision-recall curve.

Results: The XGBoost model achieved the best performance on both the breast cancer Wisconsin and diagnosis datasets. The SVM model reached 100% accuracy on the NKI breast cancer dataset, while the random forest model performed optimally on the SEER breast cancer dataset. The feature selection process through L2 Ridge regularization was crucial in enhancing the performance of these models.

Conclusions: This work emphasizes the critical role of machine learning in improving breast cancer detection. By applying a combination of preprocessing techniques and classification models, the study successfully identifies significant features and boosts model performance. These findings contribute to the development of more accurate diagnostic tools, ultimately enhancing patient outcomes.

Gene therapy for Duchenne muscular dystrophy (DMD) is hindered by many pitfalls related in particular to the limitations of current technologies, the specificities of muscle and cardiac targets, and the disease itself, a chronic, multisystem, dystrophic and inflammatory disorder. Following RNA-based therapies, DNA gene transfer, mainly based on adeno-associated viral vectors, is now able to deliver therapeutic genetic sequences on a massive scale, and the first antisense and adeno-associated virus (AAV)-microdystrophin gene products are now reaching the marketing stage in Europe and/or in the US. However, only a subset of patients are eligible for those therapies. Many questions remain, such as the duration of the therapeutic effect, the burden of high doses of vectors, and the immunogenicity of viral capsids and therapeutic proteins, in the context of a disease-related inflammatory background. Evaluations of these treatments by the different biotech, pharma or non-for-profit sponsors also come up against the great clinical heterogeneity of patients. This review summarizes the significant progress made over the past three decades to optimize both the efficacy and safety of DMD gene therapies, as well as the remaining challenges, short-term prospects, and future directions such as more targeted vectors and combination therapies.

Aims: This study used induced pluripotent stem cell-derived podocytes from a Fabry disease (FD) patient carrying the p.Met284Thr pathogenic variant as an in vitro model to investigate lysosomal abnormalities driving cell pathology. Proteomic analysis was used to assess changes in lysosomal protein abundance in FD podocytes compared to controls. Additionally, temporal changes in lysosome number in FD podocytes were analyzed using automated live-cell imaging.

Methods: Label-free mass spectrometry proteomics was performed on FD podocytes at day 10 of differentiation compared to controls. For live-cell imaging, cultured podocytes were transfected with CellLight Lysosomes-GFP and Plasma Membrane-CFP, and then visualized and quantified on days 10 and 20 post-differentiation using a Perkin Elmer Phenix High Content Screening Microscope.

Results: Proteomic analysis showed dysregulation of glycosphingolipid metabolism proteins, including decreased galactosidase alpha (GLA; P < 0.01) and increased galactosylceramidase and glucosylceramidase (P < 0.01) in FD podocytes. Lysosomal proteins were enriched, with a significant increase in cathepsin B (P < 0.001) and a decrease in lipase A (P < 0.01). Furthermore, the dysregulation of proteins involved in cell cycle regulation and growth signaling pathways, such as polo-like kinase 1 (PLK1; P < 0.0001) and proto-oncogene tyrosine-protein kinase Src (SRC; P < 0.01), suggested broader impacts on cellular processes. Temporal live-cell imaging revealed a significant increase in lysosome number in day 20 FD podocytes compared to day 10 FD podocytes and controls (P < 0.01).

Conclusions: These findings collectively suggest that FD podocytes undergo progressive lysosomal impairment, which may contribute to cellular dysfunction and disease progression. These proof-of-concept findings lay a foundation for future research on targeted FD therapies using high-throughput screening and advanced analytical techniques.