Assessment of Symptoms Associated with Temporomandibular Dysfunction and Bruxism among Elderly Population: An Epidemiological Survey.

AIM: The aim of the study was to assess the symptoms associated with temporomandibular disorders (TMD) and bruxism among elderly population in Ranchi, India. MATERIALS AND METHODS: A cross-sectional study was done on 600 elderly subjects; data regarding the signs and symptoms associated with temporomandibular disorder and bruxism were recorded using both structured questionnaire and clinical examination. Spearman correlation analysis was done to find the association between TMD and bruxism. RESULTS: Out of 600 subjects, 49% were males and 51% were females. The overall prevalence of TMD-related symptoms like temporomandibular joint (TMJ) pain, difficulty in jaw opening, TMJ sounds, and bruxism were 10.5, 11.2, 14, and 17% among elderly subjects. TMD symptoms and bruxism were relatively more commonly seen among females when compared to males. According to logistic regression (significantly correlated independent variables, i.e., TMD symptoms among analyzed variables), the dependent variable like bruxism had highest odds ratio, i.e., 8 for 60-70 years age-group and 15.1 for 70-80 year age-group. CONCLUSION: There was a lesser prevalence of symptoms related to TMD and bruxism among the study population, and bruxism had the highest odds ratio in TMD between the analyzed variables. CLINICAL SIGNIFICANCE: Human aging contributes too many oral problems, while resolving these, the felt needs of the population are sometimes ignored which adds up to the growing list of issues. Studies have shown inconclusive evidence regarding the prevalence of symptoms related to TMD and bruxism as these are known to trouble elderly populations.

Clinical Success of Screw-retained Dental Implants: A Systematic Review.

OBJECTIVE: The objective of the research was to review the literature on clinical evaluation and success of screw-retained dental implants by assessing the marginal bone loss (MBL). METHODS: Online electronic databases such as PubMed/MEDLINE, Google Scholar, and Cochrane Library were searched using appropriate keywords for the last 20 years, dated from January 1, 2000, till August 1, 2021, with a restriction on language. Additional sources like major journals, unpublished studies, conference proceedings, and cross-references were explored. Information curated for data extraction included methodology, population, type of implants used, and duration of follow-up. RESULTS: The PubMed/MEDLINE, Google Scholar, Cochrane Library, and additional sources identified a huge number, out of which 637 search results were screened, out of which 322 were duplicates. The remaining 315 unique studies were screened for the titles and abstracts, and 23 articles were selected for full-text screening. A total of six articles that matched the eligibility criteria were processed for qualitative analysis. CONCLUSION: Despite the uncertain retrievability of screw-retained implant-supported fixed restorations, this treatment option in fixed implant prosthodontics is a reliable and effective choice, especially for implant-supported long-span fixed partial dentures (FPDs), full-arch FPDs, and cantilever FPDs.

A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

A machine learning-based approach to determine infection status in recipients of BBV152 whole virion inactivated SARS-CoV-2 vaccine for serological surveys

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period.

Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2.

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92-2.59, <0.0001), use of public transport (1.79, 1.43-2.24, <0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, <0.0001), and B blood group (1.36, 1.15-1.61, 0.001).

Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India), conducted a sero-survey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS CoV2 anti-nucleocapsid (anti-NC) antibodies; 95% with surrogate neutralization activity. Three-fourth recalled no symptoms. Repeat serology tests at 3 (n=346) and 6 (n=35) months confirmed stability of antibody response and neutralization potential. Local sero-positivity was higher in densely populated cities and was inversely correlated with a 30 day change in regional test positivity rates (TPR). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of sero-positivity were high-exposure work (Odds Ratio, 95% CI, p value; 2{middle dot}23, 1{middle dot}92-2{middle dot}59, 6{middle dot}5E-26), use of public transport (1{middle dot}79, 1{middle dot}43-2{middle dot}24, 2{middle dot}8E-06), not smoking (1{middle dot}52, 1{middle dot}16-1{middle dot}99, 0{middle dot}02), non-vegetarian diet (1{middle dot}67, 1{middle dot}41-1{middle dot}99, 3{middle dot}0E-08), and B blood group (1{middle dot}36,1{middle dot}15-1{middle dot}61, 0{middle dot}001). Impact StatementWidespread asymptomatic and undetected SARS-CoV2 infection affected more than a 100 million Indians by September 2020. Declining new cases thereafter may be due to persisting humoral immunity amongst sub-communities with high exposure. FundingCouncil of Scientific and Industrial Research, India (CSIR)