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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.

Objective: ADH1B, ADH1C and ALDH2 genes are mainly responsible for alcohol metabolism in the body. Several single nucleotide polymorphisms (SNPs) of these genes have been reported to be associated with alcohol dependence and are considered risk factors for various human diseases. This study aims to identify the prevalence of three SNPs of ADH1B (rs1229984), ADH1C (rs698) and ALDH2 (rs671) in 235 unrelated individuals living in Thai Nguyen province, the northeast region of Vietnam. Methods: The target genotypes were identified by using PCR direct sequencing, and their frequencies were compared to previous reports. Result: Our data showed that allele frequencies of ADH1B *2 (rs1229984), ADH1C *2 C (rs698) and ALDH2 *2 (rs671) were 68.8%, 8.3% and 20.4%, respectively. The ADH1B *2 and ADH1C *2 frequencies were similar to those of the Kinh ethnic individuals living in the south region of Vietnam, while the ALDH2 *2 frequency was higher. Compared to data from other countries, ADH1B *2 frequency is similar to the Philippines (60.5%) and Mongolia (62.9%) but significantly different from the other populations. The ADH1C *2 frequency is not so different compared to Japanese (5.7%) and Chinese (7.1%) but is quite different in other populations. ALDH2 *2 frequency was lower than Japanese (29.3%), Indonesian (30%) and higher than other countries. Regarding the risk of alcoholism, the percentage of Vietnamese people in this study with genotypes related to alcohol dependence is 8.1%. In contrast, the carrier has genotypes protecting against alcoholism with high frequency, 91.9%. Among them, the individuals can cause high acetaldehyde accumulation accounting for 33.2%. Conclusion: This study helps to understand the genetic polymorphisms of alcohol metabolism genes in the community living in Thai Nguyen province, northeast of Vietnam, and provides valuable scientific data relating to alcohol consumption behavior as well as public health protection.

Objective: Alcohol abuse can cause developing cirrhosis, even liver cancer. Several single nucleotide polymorphisms (SNPs) of ADH1B , ADH1C , and ALDH2 genes have been reported to be associated with alcohol abuse and alcoholic cirrhosis (ALC). This study investigated the association between three SNPs of ADH1B rs1229984, ADH1C rs698, and ALDH2 rs671 with alcohol abuse and ALC in people living in the Northeast region of Vietnam. Methods: 306 male participants were recruited including 206 alcoholics (106 ALC, 100 without ALC) and 100 healthy non-alcoholics. Clinical characteristics were collected by clinicians. Genotypes were identified by Sanger sequencing. Chi-Square (χ2) and Fisher-exact tests were used to assess the differences in age and clinical characteristics, Child-Pugh score, frequencies of alleles and genotypes. Result: Our data showed that the frequency of ALDH2 *1 was significantly higher in alcoholics (88.59%) and ALC groups (93.40%) than that of healthy non-alcoholics (78.50%) with p=0.0009 and non-ALC group (83.50%) with p=0.002, respectively. We detected opposite results when examined ALDH2 *2. Frequency of combined genotypes with high acetaldehyde accumulation were significantly lower in alcoholics and ALC group than those of control groups with p=0.005 and p=0.008, respectively. Meanwhile, the proportion of combined genotypes with non-acetaldehyde accumulation were significantly two times higher in the ALC group (19.98%) than those of the non-ALC group (8%) with p=0.035. These combined genotypes showed a decreasing trend in the Child-Pugh score from likely phenotype causing risk for non-acetaldehyde accumulation to high acetaldehyde accumulation. Conclusion: The ALDH2 *1 allele was found as a risk factor for alcohol abuse and ALC, and combined genotypes of ADH1B rs1229984, ADH1C rs698, and ALDH2 rs671 with non-acetaldehyde accumulation increase ALC risk. In contrast, ALDH2 *2 and the genotype combinations related to high acetaldehyde accumulation were protective factors against alcohol abuse and ALC.

This study applied one chemical coagulant, PAC, and three biological coagulant aids include Moringa seed gum, Cassia seed gums and polymer. The results indicated that the best dose of PAC be used as coagulants was 480 mg/L. Using polymer as aids with PAC could remove 66.70 % of COD, 66.86 % of SS, 39.01 % of color from studied wastewater. Using Moringa seed gums as aids with PAC could remove 69.34 % of COD and, 69.61 % of SS, 36.25 % of color from studied wastewater. Similarly, using Cassia seed gums as aids with PAC could remove 70.54 % of COD, 68.34 % of SS and 35.94 % of color from fish blood. These results, showed natural products such as Moringa seed gums or Cassia seed gums would be efficient workable substitutes for synthetic chemical polymer.