Apoptosis, necroptosis, and autophagy are cellular mechanisms by which cells are programmed to die under a wide range of physiological and developmental stimuli. A multitude of protein mediators of programmed cell death have been identified and apoptosis, necroptosis, and autophagy signals have been found to utilize common pathways elucidating the proteins involved. This narrative review focuses on caspase-dependent and caspase-independent programmed cell death systems. Included studies of caspase-dependent programmed cell death include extrinsic pathway apoptotic mechanisms including phosphatidylserine (PS), FAS (APO-1/CD95), Tumor Necrosis Factor (TNF) Receptor type 1 (TNF-R1) and TNF-Related Apoptosis-Inducing Ligand (TRAIL) and intrinsic or mitochondrial pathway such as Cytochrome C, the Bcl-2 family of proteins and Smac/Diablo. The Bcl-2 family has apoptotic mediators Bcl-2-associated X protein (Bax) and Bcl-2 homologous antagonist/killer (Bak), Bcl-2-Interacting Protein BIM (Bim), Bcl-2 agonist of cell death (Bad), Bid, Bcl-2 adenovirus E1B 19kDa-interacting protein 1 NIP3 (Bnip3), BMF, HRK, Noxa and PUMA and anti-apoptotic proteins such as Bcl-2 itself, Mcl-1, Bcl-w, A1, and Bcl-XL. Moreover, caspase-independent programmed cell death pathways include the mitochondrial pathway with the protein mediators Apoptosis Inducing Factor (AIF), and Endonuclease G, the pathways Necroptosis, and Autophagy. Understanding programmed cell death from those reported in this review could shed substantial light on the processes of biological homeostasis. In addition, identifying specific proteins involved in these processes is mandatory to identify molecular biomarkers as well as therapeutic targets. This knowledge could provide the ability to modulate the programmed cell death response and could lead to new therapeutic interventions in a wide range of diseases.

Falls are a major public health problem among older adults, therefore predicting the risk of having falls in the near future is of great importance for health and social systems worldwide. Nowadays, prospective fall risk assessment relies on clinical and functional mobility assessment tools, such as the Timed Up and Go (TUG) test. Recently, wearable inertial measurement unit (IMU) sensors measurements have been proposed for fall detection. We hypothesize that the IMU readings captured during TUG test realizations can provide prospective fall risk prediction. In this study, analysis by deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) were used for fall prediction based on features extracted from IMU data acquired during TUG test realizations. Data is obtained from a cohort of 106 older adults wear-ing a wireless inertial sensor with a sampling frequency of 100 Hz while performing the TUG test. Prospective fall incidence was obtained in a six-month follow-up period and used as the ground truth for the supervised training and validations of the deep learning and competing machine learning approaches. Further data collection could lead to potential fall risk biomarker. A repeated hold out cross-validation process using 75 subjects for training and 31 subjects for testing was carried out. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values.