Introduction A global surge in SARS-CoV-2 cases is occurring due to the emergence of new disease variants, and requires continuous adjustment of public health measures. This study aims to continuously monitor and mitigate the impact of SARS-CoV-2 through genomic surveillance, to determine the emergence of variants and their impact on public health. Methods Data were collected from 50 full-genome sequences of SARS-CoV-2 isolates from Makassar, South Sulawesi, Indonesia. Mutation and phylogenetic analysis was performed of SARS-CoV-2 from Makassar, South Sulawesi, Indonesia. Results Phylogenetic analysis showed that two samples (4%) were of the B.1.319 lineage, while the others (96%) were of the B.1.466.2 lineage. Mutation analysis of the spike (S) protein region showed that the most common mutation was D614G (found in 100% of the sequenced isolates), followed by N439K (98%) and P681R (76%). Several mutations were also identified in other genomes with a high frequency, including P323L (nsp12), Q57H (ns3-orf3a), and T205I (nucleoprotein). Conclusion Our findings highlight the importance of continuous genomic surveillance to identify new viral mutations and variants with possible impacts on public health.

Background: The gut microbiome is thought to produce metabolites that are widely investigated to play a role in various disease pathophysiologies. Aim: This study aims to identify the differences in gut microbiome diversity and profile between nonalcoholic fatty liver disease (NAFLD) and healthy individuals. Methods: This was a cross-sectional study. We collected 21 fecal specimens from NAFLD subjects and 13 controls. The gut microbiota from all samples were profiled by using 16s ribosomal RNA next-generation sequencing. Statistical analysis was done using SPSS version 25.0 software. Results: NAFLD subjects had a greater body mass index. Hypertension, diabetes, and dyslipidemia were found in 19%, 28.6%, and 81%, respectively, in NAFLD subjects. There was a lower diversity of gut microbiota in NAFLD compared to the control group. At the phylum level, Firmicutes was found more in the control than the NAFLD group (42.24% vs. 54.01%, P = 0.037). At the genus level, the percentage of Enterobacter was more abundant in the NAFLD group compared to the control group (0.517% vs. 0%, P = 0.001). At the genus level, there was a negative correlation between Bifidobacterium and NAFLD fibrosis score (NFS) (r = −0.532, P = 0.013). Conclusion: The diversity of the gut microbiota in NAFLD group was less than in control group. Firmicutes was found to be less prevalent in NAFLD patients compared to control. Enterobacter was found to be more abundant in NAFLD patients. The amount of Bifidobacterium was inversely correlated to the severity of NAFLD based on NFS.

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BACKGROUND: Early diagnosis of tuberculosis (TB) cases in limited resource remains challenging. It is urgent to identify the new diagnostic tools which can control the spread of disease with accurate and rapid test. AIM: This study aimed to investigate the levels of infection markers: Composite bacterial infection index (CBII) and serum amyloid A (SAA) protein in pulmonary TB (PTB), and their healthy household contacts, as the alternative diagnostic markers for TB. METHODS: CBII and SAA were measured from 44 new PTB patients, and 31 household contact serum samples. The value of CBII was calculated from neutrophils, lymphocytes, monocytes, erythrocyte sedimentation rate, and high-sensitivity C-reactive protein (hs-CRP) level. hs-CRP and SAA levels were quantified from their serum samples using ELISA. QuantiFERON-TB Gold Plus (interferon gamma release assay [IGRA]) was used to screen latent TB infection among household contacts. RESULTS: Among 31 household contacts, there were 24 positive IGRA results and the rest (n = 7) had negative results. PTB patients exhibited significantly higher level CBII in the serum specimens, than those in household contact (p < 0.0001). There was no significant difference in the SAA level between TB cases and household contacts (p = 0.679). CONCLUSIONS: CBII can be used as one of the biomarkers for the identification of PTB from the serum specimens.

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