Epidemiology
Nagaraj B Kalburgi; Arati C Koregol; Swapna Shivasharan Gore; Hannahson Puladas; Kavya Sulakod; Kavita Patil
Abstract
Background: Artificial intelligence (AI) is a set of processes designed to complete a certain goal. Some applications of Artificial Intelligence in Periodontics include the localization of soft hard deposits, illness diagnosis and prognosis, and prediction of success rates in dental implant surgery.Methods: ...
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Background: Artificial intelligence (AI) is a set of processes designed to complete a certain goal. Some applications of Artificial Intelligence in Periodontics include the localization of soft hard deposits, illness diagnosis and prognosis, and prediction of success rates in dental implant surgery.Methods: Given the scarcity of data on the perspectives of postgraduates and dental interns on AI, the current study was designed to assess awareness, knowledge, and attitude toward AI among postgraduate students from the Department of Periodontics and dental interns from multiple centers. A cross-sectional survey using a self-designed questionnaire containing 26 closed-ended questions was distributed via Email and WhatsApp in Google forms to 139 postgraduate students from the Department of Periodontics and 127 dental interns from various dental colleges.Results: The questions were classified into four categories: demographic information, awareness, knowledge, and attitude. To examine the responses, the Chi-square test was used. 47.7% of the 266 respondents were dental interns, while 52.3% were postgraduate students. For dental interns and postgraduates, the average knowledge score was 7.93 and 13.04, respectively. Postgraduates' knowledge was highly significant (P < 0.01).Conclusion: It has been found that the postgraduate students were more aware and knowledgeable than the dental interns. As a result, incorporating AI into academic curricula is becoming increasingly important.
COVID-19
Virendra Mane; Priya Prabhu; Poorva Bhalerao
Abstract
Background: The first wave of COVID-19 in India began to decline suddenly in September 2020 and appeared to be nearly over by the end of January 2021. At the time, no models or papers predicted or explained this decline. The authors hypothesized in their previous study that the cases decreased due to ...
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Background: The first wave of COVID-19 in India began to decline suddenly in September 2020 and appeared to be nearly over by the end of January 2021. At the time, no models or papers predicted or explained this decline. The authors hypothesized in their previous study that the cases decreased due to increased Relative Humidity during Monsoon and forecasted that another wave would begin with the dry season in February 2021 and would be contained by monsoon humidity. The current study was carried out to put the seasonality hypothesis to the test in 2021-22. The study also included findings about the effectiveness of policy control measures on case decline.Methods: Humidity cycles in India were studied to determine the most humid periods, which corresponded to changes in daily cases across the country, on a zone-by-zone basis, and in smaller regions. The enforcement date and subsequent case decline (if any) were observed for the effectiveness of policy control measures.Results: In low humidity periods, there was a clear relationship between relative humidity and case decline and case increase. Policy controls have been found to be effective in reducing and halting case increase, resulting in a subsequent decline.Conclusion: In India, COVID-19 increases during the dry season around February and decreases during the monsoon season. Policy controls (lockdowns) are an effective way to halt the virus's exponential spread. The findings may be useful in planning local control and prevention activities.
Health Sciences
Paulraj Manickavelu; Babu S; Anand Babu Kaliyaperumal
Abstract
Background: Allied and Healthcare Education (AHE), which prepares students to work as physical therapists, occupational therapists, nutritionists, dietitians, medical laboratory technicians, and other health and allied professionals. AHE students' healthy lifestyles may aid in the formation of a healthy ...
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Background: Allied and Healthcare Education (AHE), which prepares students to work as physical therapists, occupational therapists, nutritionists, dietitians, medical laboratory technicians, and other health and allied professionals. AHE students' healthy lifestyles may aid in the formation of a healthy community, which is more likely to provide effective patient care. Several studies have been carried out to investigate the global prevalence of physical activity (PA) and Sedentary Behavior in the general population. The present study aims to assess the level of diurnal physical mobility and sedentary behavior among AHE students in Pondicherry.Methods: The prevalence study included 158 AHE undergraduate students, with data collected using the International Physical Activity Questionnaire (IPAQ) and reported in metabolic equivalents (MET).Results: Among 158 study populations, it was found that 86 (54.4%) university students practiced low level of physical mobility with a mean MET of 318.5, and 44 (27.8%) students practiced moderate physical mobility with a mean MET value of 1260.9, and only 28 (17.7%) students performed high levels of physical mobility with a mean MET value of 5250.5.Conclusion: The study concluded that the majority of AHE students have altered their physical mobility behavior. The study also found that a higher percentage of students were physically inactive and that this puts them at risk of developing early illness.
COVID-19
Virendra Mane; Poorva Bhalerao
Abstract
Background: The COVID-19 pandemic was expected to affect India severely; cases rose exponentially from May-June 2020, but around mid-September reached their peak and started declining. It showed a sign of the wave’s completion by the end of January 2021. This decline was not predicted by any models ...
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Background: The COVID-19 pandemic was expected to affect India severely; cases rose exponentially from May-June 2020, but around mid-September reached their peak and started declining. It showed a sign of the wave’s completion by the end of January 2021. This decline was not predicted by any models and the authors have not come across any explanation. Winter seasonality of influenza and similar viruses is well known and observed fact and that it has a direct correlation to the colder temperatures as well as lower humidity. Similarly, in low humidity, viruses are most viable, and they become ineffective as the humidity increases and reaches its maximum extent. This article hypothesizes and tries to explain the cause behind the first major decline and shows the subsequent rise of the second wave, and one short low humidity period followed by a high humidity period between the first and second waves. Methods: The humidity cycles in India were studied to find high and low relative humidity periods, which then corresponded to the daily cases in the country (macro-level), region (mid-level), and smaller regions (micro-level). Results: A definite correlation was observed between Monsoon-induced humidity and the incidence rate decline. This happens in 8 to 10 weeks. Incidence rates start declining about 4 weeks after the peak humidity is reached in a particular region. A decrease in humidity below 65% or 55% or lower causes an increase in the case increase/uptrend in about 3-4 weeks. Conclusion: COVID-19 has a seasonal peak in India, peaking in the middle of the monsoon season around mid-September and reaching its lowest levels in January-February. As humidity drops from February to June/July, a trend reversal and sharp rise are expected. The subsequent wave/case peak would be expected to be seen around mid-September 2021.
