Background: Down syndrome which is also known as “trisomy 21” is the commonest chromosomal defect that has been associated with intellectual disability or impairment. Clinically, it has been characterized by the generalized presence of hypotonic musculature, variety of neurobiological alterations, numerous respiratory diseases, and significantly higher risk of developing infection along with various dental abnormalities and oro-facial dysmorphological changes. Periodontal diseases are the most prominent oral health issue among individuals diagnosed with Down Syndrome. Aim: The objective of the present prevalence analysis was to study the implications of Down’s syndrome on oral health status among patients. Materials and Methods: This was a descriptive and cross-sectional prevalence analysis conducted within a duration of 1 year. A total of 100 children diagnosed with Down syndrome (aged between 5 and 16 years) were selected as the study sample. Inclusion criteria were (a) cytogenetic positive trisomy 21, (b) cooperative behavior, and (c) written informed consent obtained from the legal care-takers. Exclusion criteria were (a) any debilitating form of systemic diseases, (b) any other disability, and (c) extremely uncooperative children. The gingival health status was assessed using gingival index (GI) [Loe and Silness], calculus index (CI) [Ramfjord], and plaque index (PI) [Silness and Loe]. Information involving the practice of oral hygiene maintenance, diet plans, and parental educational status was derived from each parent. Based upon their intelligence quotient (I. Q.) values, the subjects were classified into three groups: a) mild (I. Q. level = 50 to 70), b) moderate (I. Q. level = 35 to 50), and c) severe (I. Q. level ≤35). Statistical analysis was performed using the statistical software tool Statistical Package for Social Sciences (SPSS) version 20.0. Qualitative data were recorded as frequencies, and percentages and quantitative data were recorded as mean and standard deviation values. All categorical outcomes were analyzed by means of the Chi-square test. The quantitative outcomes of Calculus Index, Gingival Index, and Plaque Index were analyzed by either student’s t -test or one-way analysis of variance (ANOVA). Significance was set at a cut-off value of P < 0.05. Results: Down syndromic children between 12 and 16 years were reported to have statistically significant higher Calculus Index, Gingival Index, and Plaque Index values in comparison with younger age syndromic children ( P < 0.01). Those with severe mental retardation had significantly higher Plaque Index ( P < 0.001) and Gingival Index ( P < 0.04) values when compared with those with mild and moderate mental retardation. No significant difference in comparing Calculus Index was noted. Conclus...

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Individuals who have shown recovery from coronavirus disease (COVID-19) are increasingly getting diagnosed with Mucormycosis or “Black fungus.” It is a difficult condition to diagnose as it has symptoms that are common among a variety of diseases. Hence, it is important to identify the presenting signs and understand the underlying pathogenesis of COVID-19 associated Mucormycosis. The incidence of these mycotic infections has shown a substantial increase in current times owing to an increase in the prevalence of immunocompromised subjects, human immunodeficiency virus (HIV) infection, and acquired immunodeficiency syndrome (AIDS). Any suspected case of mucormycosis requires rapid diagnosis and management due to its rapid progression as well as the destructive course of infection. This article reviews the taxonomy, pathogenesis, and clinical signs along with laboratory investigations that may play a vital role in the timely diagnosis of this condition as it is mostly fatal.

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The ongoing COVID-19 pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in a significant public health care system crisis. This disease has resulted in devastating damage to human lives and significant disruption in economies. Use of “machine-learning” algorithms as tools of artificial intelligence may help identify a suspected or infected individual with an estimation of chances of survival. These algorithms make use of recorded observational data including medical histories, patient demographics as well as any related data on COVID-19.