No abstract available.
Prognosis*

No abstract available.
Prognosis*

Background: Infection can be severely complicated by a dysregulated, whole-body inflammatory response known as sepsis. While previous research showed that genetic predisposition is linked to outcome differences, current patient characterization fails to determine which septic patients have greater tendencies to develop into severe sepsis or go into septic shock. As such, the identification of prognostic biomarkers may assist in identifying these high-risk patients and help improve the clinical management of the disease. Objective: In this study, we aimed to identify molecular patterns involved in sepsis. We also aimed to identify essential genes associated with the disease’s survival which could serve as potential prognosticators for the disease. Methods: We used weighted gene co-expression analysis (WGCNA) to analyze GSE63042, an RNA expression dataset from 129 patients with systemic inflammatory response syndrome or sepsis, including 78 sepsis survivors and 28 sepsis nonsurvivors. This analysis included identifying gene modules that differentiate sepsis survivors from nonsurvivors and qualitatively assessing differentially expressed genes. We then used STRING’s protein-protein interaction and gene ontology analysis to determine the functional and pathway relationships of the genes in the top modules. Lastly, we assessed the prognosticator abilities of the hub genes using ROC analysis. Results: We found four diverse co-expression gene modules significantly associated with sepsis survival. Our differential gene expression analysis, combined with protein-protein interaction and gene ontology analysis, revealed that the hub genes of these modules – TAF10, SNAPIN, PSME2, PSMB9, JUNB, and CEBPD – may serve as candidate markers for sepsis prognosis. These markers were significantly downregulated in sepsis nonsurvivors compared with sepsis survivors. Conclusion Weighted gene co-expression analysis, gene ontology enrichment analysis, and proteinprotein network interaction analysis of transcriptomic data from sepsis survivors and nonsurvivors revealed TAF10, SNAPIN, PSME2, PSMB9, JUNB, and CEBPD as potential biomarkers for sepsis prognosis. These genes are associated with functions related to proper immune response, and their downregulation in sepsis nonsurvivors suggests eventual immune exhaustion in late sepsis. Further analyses, however, are necessary to validate their roles in sepsis progression and patient survival.
prognosis

BACKGROUND: Neurologic outcomes following a cardiac arrest is reported to be detrimental and survivors face significant neurologic disability attributed to the diffuse cerebral damage from anoxia. Accurate prognostication is challenging. OBJECTIVE: To determine the prognosis of patients who had seizures after arrest. METHODS: This is an analytic, retrospective cohort study of adult patients that had successful resuscitation after cardiac arrest. The final sample size was 97. Comparison of the clinical outcomes was done using univariate and multivariate analysis. Multiple logistic regression was utilized. Level of significance was set at α-0.05. RESULTS: Post-arrest seizures increased the odds of dying up to 9 times. Age increases the odds of dying; among patients aged 65 years old and above, the odds are increased to 17 times, while among those aged 35 years old and above, the odds are up to 12 times. Presence of an intact brainstem response in the first 72 hours after arrest have 96% lower odds of dying compared to those who have none. CONCLUSION Patients with post-arrest seizures have higher morbidity and mortality rates. Patients with seizures that occur early after cardiac arrest have poorer prognosis and higher chances of death brought about by additional insults to an already damaged brain. Older patients have poorer prognosis. Preserved brainstem function seem to be a protective factor which can be a reflection of the degree of preserved brain activity despite anoxia.
Prognosis

Prognosis refers to the development of possible “outcome” of disease i.e., survival in patient with cancer. Prognostic factors are characteristics of a particular patient that can be used to predict that patient’s eventual outcome i.e., patients with advanced TNM cancer stage may have lower probability of survival than those with less advance TNM cancer stage. Thus, prognosis is a prediction of the probable outcome of a disease based on a individual’s condition and the usual course of the disease as seen in similar situations. Family physicians are often asked by patients about the probable course of their disease, they need skills on how to inform patients about this.
Prognosis

No abstract available.
Amyloidosis* ; Prognosis*

Retroperitoneal teratoma is comparatively rare and yet difficult to treat and predict the prognosis. Recently we experienced 2 cases of retroperitoneal teratoma in the infancy, which was suspected by radiologic investigation and confirmed by exploration and microscopic examination.
Prognosis ; Teratoma*

No abstract available.
Drainage* ; Prognosis*

No abstract available.
Dexamethasone* ; Prognosis*

No abstract available.
Encephalitis* ; Prognosis*