Programmed cell death (PCD) is characterized as a cell death pathway governed by specific gene-encoding requirements, plays crucial roles in the homeostasis and innate immunity of organisms, and serves as both a pathogenic mechanism and a therapeutic target for a variety of human diseases. Z-DNA-binding protein 1 (ZBP1) functions as a cytosolic nucleic acid sensor, utilizing its unique Zα domains to detect endogenous or exogenous nucleic acids and its receptor-interacting protein homotypic interaction motif (RHIM) domains to sense or bind specific signaling molecules, thereby exerting regulatory effects on various forms of PCD. ZBP1 is involved in apoptosis, necroptosis, pyroptosis, and PANoptosis and interacts with molecules, such as receptor-interacting protein kinase 3 (RIPK3), to influence cell fate under various pathological conditions. It plays a crucial role in regulating PCD during infections, inflammatory and neurological diseases, cancers, and other conditions, affecting disease onset and progression. Targeting ZBP1-associated PCD may represent a viable therapeutic strategy for related pathological conditions. This review comprehensively summarizes the regulatory functions of ZBP1 in PCD and its interactions with several closely associated signaling molecules and delineates the diseases linked to ZBP1-mediated PCD, along with the potential therapeutic implications of ZBP1 in these contexts. Ongoing research on ZBP1 is being refined across various disease models, and these advancements may provide novel insights for studies focusing on PCD, potentially leading to new therapeutic options for related diseases.

Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.

Thoracic aortic dissection (TAD) without familial clustering or syndromic features is known as sporadic TAD (STAD). So far, the genetic basis of STAD remains unknown. Whole exome sequencing was performed in 223 STAD patients and 414 healthy controls from the Chinese Han population (N = 637). After population structure and genetic relationship and ancestry analyses, we used the optimal sequence kernel association test to identify the candidate genes or variants of STAD. We found that COL3A1 was significantly relevant to STAD (P = 7.35 × 10
Aneurysm, Dissecting/genetics* ; Case-Control Studies ; Cluster Analysis ; Cohort Studies ; Collagen Type III/genetics* ; Computational Biology ; Genetic Predisposition to Disease ; Humans

Objective To explore the relationships among post traumatic stress disorder(PTSD),gratitude and posttraumatic growth (PTG) for terminal cancer patients.Methods Totally 119 advanced cancer patients were investigated with the self-demographic questionnaire,posttraumatic growth inventory (PTGI),the PTSD cheeklist-civilian version (PCL-C) and the Gratitude Questionnaire-6 (GQ-6).Results For terminal cancer patients,the total score of PCL-C was 34.02±12.49.The scores on re-experience,avoidance/numbness,hypervigilance were 9.79±3.78,13.85±5.68,10.36±3.80.The total score of gratitude was 29.37±7.48.The total score of PTG was 51.34± 13.57.The scores of life appreciation,personal relationship and self-strength were 8.00± 2.99,21.18± 5.84,22.16± 6.10.The total scores of PTG were significantly statistical significance among different PTSD groups(F=16.267,P＜0.01)and gratitude groups(F=43.674,P＜0.0 1).The total scores of PCL-C (r=-0.694,P＜0.01),re-experience (r=-0.664,P＜0.01),avoidance/numbness (r=-0.671,P＜0.01),hypervigilance (r=0.753,P＜0.01) and gratitude(r=-0.611,P＜0.01) were all correlated with PTG.The total score of PCL-C and gratitude could explain 66.6％ variation of PTG.For the relationship between PTSD and PTG,the moderation effect of gratitude was not significant (P ＞0.05).Conclusion The gratitude and PTSD were important influence factors for terminal cancer patients' PTG,while the moderation effect of gratitude was not significant,so in clinical intervention we should pay more attentions to the actual effects of gratitude,and we should not pursuit gratitude education blindly.