Implementing a transformative approach to the coral reefs' recovery phase.

Mitigation and rehabilitation are responses to climate change and human misuse. However, many regions worldwide still lose coral reefs even after implementing these responses. We chose Hurghada city, on the Red Sea, and Weizhou island, on the South China Sea, as sample regions to assess their various modes of coral community structure loss against the combined climatic and human impact drivers that led to this shift. Despite the former being considered a regional coral refuge, while the latter was limited, both regions have previously intervened with coral restoration. We found that even after three decades of impact cessation by forcing laws, most coral reef states are still declining (about a third and a half in both cities), have not harnessed the existing crowded larval density, and are unrecovered. Such findings imply that the combined impacts will persist, necessitating a broad connectivity analysis that enables a suitable intervention (hybrid solutions hypothesis). Each state of coral categories was connected to certain combined stressor factors using our broad connectivity analysis to grasp the extent and relative contribution of coral community shift since our data obtained from comparable sites were widely varied. Moreover, destructive emerged changes have transformed the coral community structure under the forced adaptation scenario of the community structure, boosting those who can resist at the expense of others. To prove our hypothesis, we used the connectivity findings in determining the optimal technique and spots for coral rehabilitation around the two cities. We then compared our findings with the outcomes of two other existing adjacent restoration projects related to other endeavors. Our hybrid approach harvested coral larvae that had been wasted in both cities. Thus, hybrid solutions are globally required for such cases, and proper early interventions are needed to maintain the genotype power to boost coral adaptability throw global ecological settings.

Unravelling psychiatric heterogeneity and predicting suicide attempts in women with trauma-related dissociation using artificial intelligence.

Background: Suicide is a leading cause of death, and rates of attempted suicide have increased during the COVID-19 pandemic. The under-diagnosed psychiatric phenotype of dissociation is associated with elevated suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide.Objective: We designed an artificial intelligence approach to identify dissociative patients and predict prior suicide attempts in an unbiased, data-driven manner.Method: Participants were 30 controls and 93 treatment-seeking female patients with posttraumatic stress disorder (PTSD) and various levels of dissociation, including some with the PTSD dissociative subtype and some with dissociative identity disorder (DID).Results: Unsupervised learning models identified patients along a spectrum of dissociation. Moreover, supervised learning models accurately predicted prior suicide attempts with an F1 score up to 0.83. DID had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in PTSD and DID.Conclusions: These findings expand our understanding of the dissociative phenotype and underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.

Dissociation, feelings of detachment and disruption in one's sense of self and surroundings, is associated with an elevated risk of suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide.Using machine learning techniques, we found dissociative identity disorder had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in posttraumatic stress disorder and dissociative identity disorder.These findings underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.

CD62P (P-selectin) expression as a platelet activation marker in patients with liver cirrhosis with and without cholestasis.

AIM OF THE STUDY: P-selectin (CD62P) is a platelet activation marker that was claimed to mediate the accumulation of platelets induced by cholestasis. The nature of platelet dysfunction and hemostasis abnormalities in cholestatic liver disease needs to be more explored. The aim of this study was to assess platelet CD62P expression in cirrhotic patients with and without cholestasis, and to evaluate its relationship with a bleeding tendency. MATERIAL AND METHODS: 150 patients were included in this case-control study. Participants were divided into 84 patients with liver cirrhosis (group I), 44 of whom had cholestasis (Group Ia) and 40 patients were without cholestasis (group Ib); 36 patients who were cholestatic without liver cirrhosis (group II); and 30 healthy subjects who formed the control group (group III). Platelet CD62P expression was assessed by a flow cytometer. RESULTS: Platelets expressing CD62P were significantly increased in all patient groups compared to controls (p < 0.001). Platelets expressing CD62P were significantly increased in gastrointestinal (GIT) bleeders compared to non-bleeders in cirrhotic and cholestatic groups (p < 0.001 each). Among group I patients at cut-off > 12.4, up-regulation of platelet CD62P yielded 72% sensitivity and 44.1% specificity to discriminate bleeders from non-bleeders (p = 0.01), while among group II at cut-off > 12.9, it yielded 90% sensitivity and 80.8% specificity (p < 0.001). In cirrhotic patients, platelet CD62P expression was significantly increased in patients with an advanced Child-Pugh class (p < 0.001). Platelet expressing CD62P was shown as an independent risk factor for bleeding among cirrhotic cases with an odds ratio of 1.07 and CI 0.99-1.15. CONCLUSIONS: Up-regulation of platelet CD62P expression can serve as a GIT bleeding predictor in liver cirrhosis.