METHOD: A non-experimental quantitative analytic with a cross sectional study approach was used in this study. The respondents were 331 patients who visited the dental clinics of the Health Centers in Malang City. A cluster random sampling technique was used in this study. The instrument used was questionnaire. The data analysis was done through multivariate analyses use logistic-regression.
RESULTS: The Wald test results on logistic-regression models showed there is no significant effect of smoking habits and consumption patterns on periodontal disease. There is a significant effect of systemic disease on periodontal disease with a significance value of 0.000 (p<0.05).
CONCLUSIONS: There was a significant relationship and effect between systemic disease and periodontal disease in this study.
AIM: To compare the skin diseases prompting biopsy before and during the COVID-19 pandemic.
MATERIALS AND METHODS: A retrospective study of skin diseases was performed; the skin problems were then grouped into major histopathological reactions.
RESULTS: A total of 229 biopsies were performed before the COVID-19 outbreak, whereas only 160 biopsies were done during the pandemic. Before versus during the outbreak, the proportion of major reactions were granulomatous 20.52% vs 21.88%, neoplasms 17.47% vs 20%, psoriasiform 14.85% vs 10%, vesiculobullous 9.61% vs 8.75%, others 10.92% vs 7.50%, interface dermatitis 6.99% vs 10%, vasculopathy 6.99% vs 5.63%, spongiotic 6.55% vs 8.13%, panniculitis 3.49% vs 3.75%, and superficial and deep dermal infiltrate 2.62% vs 4.38%.
CONCLUSION: A decreased total number of patients prompting less biopsies were reported during the COVID-19 outbreak. However, the three largest percentages of major histopathological reactions were still similar, namely granulomatous, neoplasms, and psoriasiform.
METHODS: We used the Autoregressive Moving Average Models (ARIMA) to forecast the number of cases in the upcoming 14 days and the Spearman correlation analysis to analyze the relationship between B.1.1.7 cases and meteorological variables such as temperature, humidity, rainfall, sunshine, and wind speed.
RESULTS: The results of the study showed the fitted ARIMA models forecasted there was an increase in the daily cases in three provinces. The total cases in three provinces would increase by 36% (West Java), 13.5% (South Sumatra), and 30% (East Kalimantan) as compared with actual cases until the end of 14 days later. The temperature, rainfall and sunshine factors were the main contributors for B.1.1.7 cases with each correlation coefficients; r = -0.230; p < 0.05, r = 0.211; p < 0.05 and r = -0.418; p < 0.01, respectively.
CONCLUSIONS: We recapitulated that this investigation was the first preliminary study to analyze a short-term forecast regarding COVID-19 and B.1.1.7 cases as well as to determine the associated meteorological factors that become primary contributors to the virus spread.
METHODS: In May-August 2022, nasopharyngeal swab samples (n=3,642) were collected from international travelers to West Kalimantan (active surveillance), and patients hospitalized due to SARS-CoV-2 infection (baseline surveillance). The samples were tested for Omicron lineages based on ORF1ab, N, and HV69-70del genes, followed by whole-genome sequencing. The sequences were then identified using two genomic databases, aligned against the reference genome (Wuhan/Hu-1/2019), and then compared with BA.2.40 lineage detected across the world. Phylogenetic analysis between the samples and other SARS-CoV-2 isolates was performed using molecular evolutionary genetics analysis software.
RESULTS: Based on the genomic databases, 10 isolates were identified as BA.2.40. All samples tested positive for the ORF1ab and N genes, but negative for the HV69-70del gene, which is a marker to detect the Omicron variant. Phylogenetic analysis showed the isolates were closely related to an isolate from Malaysia, an area dominated by BA.2.40.
CONCLUSION: Omicron lineage BA.2.40 has no HV69-70 deletion in the spike protein, a marker used to screen for the Omicron variant. BA.2.40 showed a high similarity to an isolate from Malaysia and was detected only during certain periods, indicating the effect of internationally imported cases.
METHODS: We collected 3,489,367 tweets data from January 2020 to August 2021. We analyzed factual and fake news using the string comparison method. The difflib library was used to measure similarity. The user's engagement was analyzed by averaging the engagement metrics of tweets, retweets, favorites, replies, and posts shared with sentiments and opinions regarding COVID-19 and COVID-19 vaccination.
RESULT: Positive sentiments on COVID-19 and COVID-19 vaccination dominated, however, the negative sentiments increased during the beginning of the implementation of restrictions on community activities (PPKM). The tweets were dominated by the importance of health protocols (washing hands, keeping distance, and wearing masks). Several types of vaccines were on top of the word count in the vaccine subtopic. Acceptance of the vaccination increased during the studied period, and the fake news was overweighed by the facts. The tweets were dynamic and showed that the engaged topics were changed from the nature of COVID-19 to the vaccination and virus mutation which peaked in the early and middle terms of 2021. The public sentiment and engagement were shifted from hesitancy to anxiety towards the safety and effectiveness of the vaccines, whilst changed again into wariness on an uprising of the delta variant.
CONCLUSION: Understanding public sentiment and opinion can help policymakers to plan the best strategy to cope with the pandemic. Positive sentiments and fact-based opinions on COVID-19, and COVID-19 vaccination had been shown predominantly. However, sufficient health literacy levels could yet be predicted and sought for further study.