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Background: Porphyromonas gingivalis is a major periodontal pathogen. Saliva is the most easy, non-invasive microbiological sample for detection of periodontal pathogens. Aim and Objectives: A prospective study on 37 diabetic patients was grouped into well-controlled diabetes with/without periodontitis and uncontrolled diabetic with periodontitis. PCR and sequencing of P. gingivalis was performed in saliva samples. Materials and Methods: DNA was extracted from saliva using Triton X-100 and 16s rRNA gene (404 bp) was amplified by polymerase chain reaction. DNA sequencing was performed for two samples. Results: P. gingivalis was detected in 27.03% (n = 10), of which 30% (n = 9) were diabetic with periodontal disease and 14.3% (n = 1) were diabetic without periodontal disease. The percentage of poor oral hygiene was 50% and 20% in uncontrolled and controlled glycaemic patients, respectively. DNA sequencing of two samples showed 100% identity with the sequences in the GenBank database (Gen Bank accession no: KX640913-KX640914). Conclusion: Type 2 diabetes mellitus and periodontitis are interlinked. Early detection of P. gingivalis and appropriate treatment with doxycycline will also assist in controlling the glycaemic status.

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Introduction: Hepatitis B virus (HBV) is the most common aetiological factor causing hepatocellular carcinoma (HCC). HBx gene plays an enigmatic role in HBV-related HCC. In this study we have analysed amino acid substitutions in HBx from HBV-infected individuals of different clinical stages. Materials and Methods: HBV-infected individuals (n = 93) were recruited in the study. DNA was extracted from plasma, amplified, and DNA sequencing was performed using specific primers targeting HBx gene (540 bp). Results: Among the study participants, 57% had chronic HBV infection, 30% had chronic liver disease (CLD) and 13% had HBV related HCC. Genotypes such as D1, D2, D3, A1, C2 and B2 were identified of which genotype D2 was predominant (78%). HBxC-terminal deletion was observed in four hepatitis B e antigen (HBeAg) negative participants with CLD. The frequency of aminoacid substitution in proapoptotic domain was higher in HBeAg negative participants including I127V (34%), K130M (34%), V131I (40%). The frequency of double mutation (K130M+V131I) and triple mutation (I127V+K130M+V131I) were found to be higher (32% and 36%) in HBeAg negative participants. Also, we identified L5M substitution (4.3%) in HBeAg positive participants with advanced liver disease. Conclusion: In HBx gene, aminoacid substitutions at positions 127, 130, 131 are associated with poor expression of HBeAg. We suggest screening for HBx aminoacid substitutions especially in patients with HBeAg negative chronic HBV infection to predict the clinical outcome and enable early treatment to prevent disease progression.

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Chikungunya Virus (CHIKV) is a single stranded positive sense enveloped RNA virus. Re-emergence of CHIKV caused a massive outbreak with severe clinical manifestation affecting multiple organs. The genetic diversity of CHIKV, which caused recurring outbreaks in India, was studied. Blood samples were collected from suspected human cases of CHIKV infection in Chennai, Tamil Nadu and three Northern districts of Kerala in Southern India during the CHIKV outbreak in 2009. A partial E2 gene segment was amplified by RT-PCR. Among 119 samples 37 samples were positive for CHIKV by RT-PCR. Phylogenetic analysis revealed that the isolated sequences belonged to Indian Ocean Lineage (IOL) of ECSA genotype. The mutational analysis revealed the presence of substitutions such as S299N, T312M, A344T, S375T, V386G, W339R and S375P in the current study. In addition, a novel mutation V386G was observed in all the sequences. Two isolates found with unique substitutions W339R and S375P are reported. The structural analysis of the wild type and mutant proteins revealed that the structural changes are accompanied by modification in the intraprotein interactions.

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INTRODUCTION: Oral microbiome impacts health and disease. T2DM and periodontitis are associated. Neem (Azadiracta indica) has antibacterial activity against oral microbiota. OBJECTIVES: To characterize oral microbiota (OMB) in saliva samples of T2DM patients by Next generation sequencing. To analyze MCP-1 levels among the T2DM patients before and after a month of neem stick usage as a toothbrush. MATERIALS AND METHODS: Blood and saliva samples were collected from adult T2DM patients before and after the neem stick usage. Metagenomic sequencing was performed on saliva samples targeting V6 region of 16s rRNA. Serum MCP-1 levels were determined using a quantitative sandwich Human MCP-1 standard ABTS development kit (Peprotech, USA). RESULTS: The profile of oral microbiota of T2DM patients (n=24) consists of Streptococcus (95.8%) counts ranging from 2644 to 27,214, Veillonella (72.2%, counts 25-19,709, Neisseria (87.5%) 453-33,445), Rothia (63.6%, 233-6734), Actinomycetes (25%, 161-3730), Fusobacterium (21%, 2252-21,334), and Pigmentiphaga (12.5% 3-16,644). Oral microbiota in healthy controls (n=10), consists of Streptococcus (26.1%), Veillonella (21.9%), Neisseria (16.9%), Haemophilus (10.7%), Actinomycetes (2.6%), Rothia (3.1%), Oribacterium (1.7%). Post neem samples showed drastic reduction in the load of bacteria which was statistically significant. The mean serum MCP-1 before the use of neem stick was 265.18±79.44 (range 141.6-980.5pg/ml) and dropped to 33.6±7.35 after a month of neem stick usage (P value>0.001). CONCLUSION: OMB of T2DM patients and healthy controls were similar, however bacterial loads were significantly higher in T2DM patients. Use of neem stick has a statistically significant reduction on bacterial loads and MCP-1 levels in T2DM patients.

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INTRODUCTION: Morbidity and mortality among HIV-1-infected individuals has been dramatically reduced by the implementation of combinational antiretroviral therapy (ART). However, the efficiency of these therapies is compromised due to HIV-1 transmitted drug resistance mutations (TDRMs). METHODS: We collected a total of 127 samples from ART-naïve HIV-infected individuals and sequenced the pol gene and analysed for drug resistance mutations using the Calibrated Population Resistance (CPR) tool in the Stanford database. RESULTS: All the 127 clinical samples (100 %) were identified as HIV-1 subtype C. Based on the CPR tool, three strains (2.4 %) had TDRMs, and these were K101E, Y181C and G190A. Our findings correlated well with the WHO surveys conducted in Asia, including India, which consistently reported <5 % TDRM among the specific populations assessed. CONCLUSION: In countries like India, regular monitoring of TDRMs will provide better information for clinical practice improvement and policy making.

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CD4(+) T cell count estimations are subject to high variations; hence, in this study, the previous day's tested samples were included routinely as the internal quality controls. The percentages of variation of the 2-day values were analyzed for 280 observations and the mean variation for CD4(+) and CD3(+) T cell counts ranged from 5.21% to 9.66%. This method is a good internal quality control (IQC) procedure for the estimation of CD3(+) and CD4(+) T cell counts in resource-poor settings.

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