Predictive biomarkers of COVID-19 prognosis identified in Bangladesh patients and validated in Japanese cohorts.

Despite high vaccination rates globally, countries are still grappling with new COVID infections, and patients diagnosed as mild dying at home during outpatient treatment. Hence, this study aim to identify, then validate, biomarkers that could predict if newly infected COVID-19 patients would subsequently require hospitalization or could recover safely with medication as outpatients. Serum cytokine/chemokine data from 129 COVID-19 patients within 7 days after the onset of symptoms in Bangladesh were used as training data. The majority of patients were infected with the Omicron variant and over 88% were vaccinated. Patients were divided into those with mild symptoms who recovered, and those who deteriorated to moderate or severe illness. Using the Lasso method, 15 predictive markers were identified and used to classify patients into these two groups. The biomarkers were then validated in a cohort of 194 Covid patients in Japan with a predictive accuracy that exceeded 80% for patients infected with Delta and Omicron variants, and 70% for Wuhan and Alpha variants. In an environment of widespread vaccination, these biomarkers could help medical practitioners determine if newly infected COVID-19 patients will improve and can be managed on an out-patient basis, or if they will deteriorate and require hospitalization.

Potential utility of pretreatment serum miRNAs for optimal treatment selection in advanced high-grade serous ovarian cancer.

OBJECTIVE: The primary treatment of patients with advanced ovarian cancer is selected from whether primary debulking surgery or neoadjuvant chemotherapy. We investigated whether pretreatment serum microRNA profiles are useful for selecting patients with advanced high-grade serous ovarian cancer who obtain better outcomes from undergoing primary debulking surgery or neoadjuvant chemotherapy. METHODS: Consecutive patients with clinical stage IIIB-IVB and serum microRNA data were selected. Patients who underwent primary debulking surgery or neoadjuvant chemotherapy were subjected to 1:1 propensity score matching before comparing their progression-free survival using Cox modelling. Progression-free probabilities for the selected microRNA profiles were calculated, and the estimated progression-free survival with the recommended primary treatment was determined and compared with the actual progression-free survival of the patients. RESULTS: Of the 108 patients with stage IIIB-IVB disease, the data of 24 who underwent primary debulking surgery or neoadjuvant chemotherapy were compared. Eleven and three microRNAs were independent predictors of progression-free survival in patients who underwent primary debulking surgery and neoadjuvant chemotherapy, respectively. Two microRNAs correlated significantly with complete resection of the tumours in primary debulking surgery. No differences were found between the actual and estimated progression-free survival in the primary debulking surgery and neoadjuvant chemotherapy groups (P > 0.05). The recommended and actual primary treatments were identical in 27 (56.3%) of the 48 patients. The median improved survival times between recommended and actual treatment were 11.7 and 32.6 months for patients with actual primary debulking surgery and neoadjuvant chemotherapy, respectively. CONCLUSIONS: Pretreatment microRNA profiles could be used to select subgroups of patients who benefited more from primary debulking surgery or neoadjuvant chemotherapy and might contribute to selecting the optimal primary treatment modality in advanced high-grade serous ovarian cancer patients.

Akaike's information criterion for a measure of linkage disequilibrium.

The extent and distribution of linkage disequilibrium ( LD) in humans is a current topic especially for gene mapping of complex diseases. Akaike's information criterion ( AIC) was applied to estimate LD and compared with other standard LD measures, D' and r(2). By comparison of an independent model (IM; linkage equilibrium) and a dependent model (DM; linkage disequilibrium), the parsimonious model is the one with the smaller AIC score. Therefore, the extent of LD by AIC is expressed as AIC( IM) -- AIC( DM)( AIC( LD)). A total of 39 single-nucleotide polymorphisms on a 1.6-Mb region of chromosome 21 q22 were identified, and genotyped in 192 Japanese individuals. All possible pairs were analyzed to estimate LD and the analyses were compared. AIC( LD) became highly positive as the D' value increased and was negative at D' values of around 0.2. Because a negative value of AIC( LD) implies linkage equilibrium, D' values below 0.2 should be regarded as linkage equilibrium. The LD estimate by AIC yielded results similar to those obtained by r(2), indicating that AIC( LD) would be useful for fine gene mapping.