Multidimensional Machine Learning for Assessing Parameters Associated With COVID-19 in Vietnam: Validation Study.

BACKGROUND: Machine learning (ML) is a type of artificial intelligence strategy. Its algorithms are used on big data sets to see patterns, learn from their results, and perform tasks autonomously without being instructed on how to address problems. New diseases like COVID-19 provide important data for ML. Therefore, all relevant parameters should be explicitly quantified and modeled. OBJECTIVE: The purpose of this study was to determine (1) the overall preclinical characteristics, (2) the cumulative cutoff values and risk ratios (RRs), and (3) the factors associated with COVID-19 severity in unidimensional and multidimensional analyses involving 2173 SARS-CoV-2 patients. METHODS: The study population consisted of 2173 patients (1587 mild status [mild group] and asymptomatic patients, 377 moderate status patients [moderate group], and 209 severe status patients [severe group]). The status of the patients was recorded from September 2021 to March 2022. Two correlation tests, relative risk, and RR were used to eliminate unbalanced parameters and select the most remarkable parameters. The independent methods of hierarchical cluster analysis and k-means were used to classify parameters according to their r values. Finally, network analysis provided a 3-dimensional view of the results. RESULTS: COVID-19 severity was significantly correlated with age (mild-moderate group: RR 4.19, 95% CI 3.58-4.95; P<.001), scoring index of chest x-ray (mild-moderate group: RR 3.29, 95% CI 2.76-3.92; P<.001; moderate-severe group: RR 3.03, 95% CI 2.4023-3.8314; P<.001), percentage of neutrophils (mild-moderate group: RR 3.18, 95% CI 2.73-3.70; P<.001; moderate-severe group: RR 3.32, 95% CI 2.6480-4.1529; P<.001), quantity of neutrophils (moderate-severe group: RR 3.15, 95% CI 2.6153-3.8025; P<.001), albumin (moderate-severe group: RR 0.46, 95% CI 0.3650-0.5752; P<.001), C-reactive protein (mild-moderate group: RR 3.4, 95% CI 2.91-3.97; P<.001), and ratio of lymphocytes (moderate-severe group: RR 0.34, 95% CI 0.2743-0.4210; P<.001). Significant inversion of correlations among the severity groups is important. Alanine transaminase and leucocytes showed a significant negative correlation (r=-1; P<.001) in the mild group and a significant positive correlation in the moderate group (r=1; P<.001). Transferrin and anion Cl showed a significant positive correlation (r=1; P<.001) in the mild group and a significant negative correlation in the moderate group (r=-0.59; P<.001). The clustering and network analysis showed that in the mild-moderate group, the closest neighbors of COVID-19 severity were ferritin and age. C-reactive protein, scoring index of chest x-ray, albumin, and lactate dehydrogenase were the next closest neighbors of these 3 factors. In the moderate-severe group, the closest neighbors of COVID-19 severity were ferritin, fibrinogen, albumin, quantity of lymphocytes, scoring index of chest x-ray, white blood cell count, lactate dehydrogenase, and quantity of neutrophils. CONCLUSIONS: This multidimensional study in Vietnam showed possible correlations between several elements and COVID-19 severity to provide clinical reference markers for surveillance and diagnostic management.

Genetic analysis of 55 northern Vietnamese patients with Wilson disease: seven novel mutations in ATP7B.

Wilson disease (WD) is an autosomal recessive disorder of copper metabolism. The gene responsible for WD was discovered in 1993 and is located on chromosome 13 at 13q14.3. It encodes a copper-specific transporting P-type ATPase. Early diagnosis can improve treatment outcome and decrease the rate of disability or even mortality.We used Sanger sequencing to identify mutation hot spots in 55 northern Vietnamese with a clinical diagnosis of WD. Mutations were screened and detected by direct DNA sequencing. A total of 26 different ATP7B gene mutations were identified, including seven novel mutations (five nonsense and two missense mutations). The most frequent mutations were p.Ser105Ter (24.55%), p.Arg778Leu (5.45%) and p.Thr850Ile (4.55%). Mutation detection rate in exon 2 was 34.55% and ranked first, followed by exon 8 with 16.36%, and exon 18 with 10.91% each, thus, exons 2, 8 and 18 are the mutation hot spots for northern VietnameseWD patients. These findings were different from previous studies in Asia. Our research established a suitable strategy for ATP7B gene testing in northern Vietnamese WD patients.

Drosophila DREF acting via the JNK pathway is required for thorax development.

The Drosophila Jun N-terminal kinase (JNK) gene basket (bsk) promoter contains a DNA replication-related element (DRE)-like sequence, raising the possibility of regulation by the DNA replication-related element-binding factor (DREF). Chromatin immunoprecipitation assays with anti-DREF IgG showed the bsk gene promoter region to be effectively amplified. Luciferase transient expression assays revealed the DRE-like sequence to be important for bsk gene promoter activity, and knockdown of DREF decreased the bsk mRNA level and the bsk gene promoter activity. Furthermore, knockdown of DREF in the notum compartment of wing discs by pannier-GAL4 and UAS-DREFIR resulted in a split thorax phenotype. Monitoring of JNK activity in the wing disc by LacZ expression in a puckered (puc)-LacZ enhancer trap line revealed the reduction in DREF knockdown clones. These findings indicate that DREF is involved in regulation of Drosophila thorax development via actions on the JNK pathway.

Sequencing of Flic genes of Salmonella typhi, S. paratyphi A, S. paratyphi B, S. paratyphi C, and S. typhimurium and application for PCR to differentiate them

All Flic genes of Salmonella typhi, S. paratyphi A, S. paratyphi B, S. paratyphi C, and S. typhimurium code for phase 1 of H antigen (H:1) (d, a, b, c and i antigen respectively). The genes were sequenced on Sanger's principle by automatic sequenser (ABI, 3100 Avant, Genetic Analyser). The primer pairs for the Salmonella species were designed on the basis of the collected sequences. The results showed that PCR with these primers can clearly differentiate the five Salmonella strains, especially between S. paratyphi B and S. typhimurium.