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Cell death is a fundamental physiological process in all living organisms. Processes such as embryonic development, organ formation, tissue growth, organismal immunity, and drug response are accompanied by cell death. In recent years with the development of electron microscopy as well as biological techniques, especially the discovery of novel death modes such as ferroptosis, cuprotosis, alkaliptosis, oxeiptosis, and disulfidptosis, researchers have been promoted to have a deeper understanding of cell death modes. In this systematic review, we examined the current understanding of modes of cell death, including the recently discovered novel death modes. Our analysis highlights the common and unique pathways of these death modes, as well as their impact on surrounding cells and the organism as a whole. Our aim was to provide a comprehensive overview of the current state of research on cell death, with a focus on identifying gaps in our knowledge and opportunities for future investigation. We also presented a new insight for macroscopic intracellular survival patterns, namely that intracellular molecular homeostasis is central to the balance of different cell death modes, and this viewpoint can be well justified by the signaling crosstalk of different death modes. These concepts can facilitate the future research about cell death in clinical diagnosis, drug development, and therapeutic modalities. [ABSTRACT FROM AUTHOR]

Objective: Patients with gastric atrophy and intestinal metaplasia (IM) were at risk for gastric cancer, necessitating an accurate risk assessment. We aimed to establish and validate a diagnostic approach for gastric biopsy specimens using deep learning and OLGA/OLGIM for individual gastric cancer risk classification. Methods: In this study, we prospectively enrolled 545 patients suspected of atrophic gastritis during endoscopy from 13 tertiary hospitals between December 22, 2017, to September 25, 2020, with a total of 2725 whole-slide images (WSIs). Patients were randomly divided into a training set (n = 349), an internal validation set (n = 87), and an external validation set (n = 109). Sixty patients from the external validation set were randomly selected and divided into two groups for an observer study, one with the assistance of algorithm results and the other without. We proposed a semi-supervised deep learning algorithm to diagnose and grade IM and atrophy, and we compared it with the assessments of 10 pathologists. The model's performance was evaluated based on the area under the curve (AUC), sensitivity, specificity, and weighted kappa value. Results: The algorithm, named GasMIL, was established and demonstrated encouraging performance in diagnosing IM (AUC 0.884, 95% CI 0.862–0.902) and atrophy (AUC 0.877, 95% CI 0.855–0.897) in the external test set. In the observer study, GasMIL achieved an 80% sensitivity, 85% specificity, a weighted kappa value of 0.61, and an AUC of 0.953, surpassing the performance of all ten pathologists in diagnosing atrophy. Among the 10 pathologists, GasMIL's AUC ranked second in OLGA (0.729, 95% CI 0.625–0.833) and fifth in OLGIM (0.792, 95% CI 0.688–0.896). With the assistance of GasMIL, pathologists demonstrated improved AUC (p = 0.013), sensitivity (p = 0.014), and weighted kappa (p = 0.016) in diagnosing IM, and improved specificity (p = 0.007) in diagnosing atrophy compared to pathologists working alone. Conclusion: GasMIL shows the best overall performance in diagnosing IM and atrophy when compared to pathologists, significantly enhancing their diagnostic capabilities. [ABSTRACT FROM AUTHOR]

Inclusions comprised of microtubule-associated protein tau (tau) are implicated in a group of neurodegenerative diseases, collectively known as tauopathies, that include Alzheimer’s disease (AD). The spreading of misfolded tau “seeds” along neuronal networks is thought to play a crucial role in the progression of tau pathology. Consequently, restricting the release or uptake of tau seeds may inhibit the spread of tau pathology and potentially halt the advancement of the disease. Previous studies have demonstrated that the Mammalian Suppressor of Tauopathy 2 (MSUT2), an RNA binding protein, modulates tau pathogenesis in a transgenic mouse model. In this study, we investigated the impact of MSUT2 on tau pathogenesis using tau seeding models. Our findings indicate that the loss of MSUT2 mitigates human tau seed-induced pathology in neuron cultures and mouse models. In addition, MSUT2 regulates many gene transcripts, including the Adenosine Receptor 1 (A1AR), and we show that down regulation or inhibition of A1AR modulates the activity of the “ArfGAP with SH3 Domain, Ankyrin Repeat, and PH Domain 1 protein” (ASAP1), thereby influencing the internalization of pathogenic tau seeds into neurons resulting in reduction of tau pathology. [ABSTRACT FROM AUTHOR]

Recent experimental observations of knotting in DNA and proteins have stimulated the simulation studies of polymer knots. Simulation studies usually identify knots in polymer conformations through the calculation of the Alexander polynomial. However, the Alexander polynomial cannot directly discriminate knot chirality, while knot chirality plays important roles in many physical, chemical, and biological properties. In this work, we discover a new relationship for knot chirality and accordingly, develop a new algorithm to extend the applicability of the Alexander polynomial to the identification of knot chirality. Our algorithm adds an extra step in the ordinary calculation of the Alexander polynomial. This extra step only slightly increases the computational cost. The correctness of our algorithm has been proved mathematically by us. The implication of this algorithm in physical research has been demonstrated by our studies of the tube model for polymer knots. Without this algorithm, we would be unable to obtain the tubes for polymer knots. [ABSTRACT FROM AUTHOR]