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Introduction: Increasing evidence demonstrates the effectiveness of universal masking precautions in reducing the transmission of COVID-19. Whether these precautions have an impact on surgical site infections (SSI), currently remains unknown. This study assesses whether implementation of universal masking precautions altered the rates of SSI. Method(s): We performed a single-institution, retrospective cohort study using the NSQIP database, evaluating all patients undergoing most performed general surgery procedures from June 2018 to December 2021. SSI rates were compared between patients who underwent operation before and after implementation of universal masking precautions at our institution in March 2020. Statistical analyses were performed using Fisher's exact test. Result(s): A total of 1,539 patients were included;721 patients were in the pre-masking cohort, while 818 in post-masking cohort. During this time period, a total of 143 (9.3%) patients developed SSI, 3.6% incisional and 5.7% deep organ space infections (OSI) (p=0.6601). Incisional and OSI rates did not differ significantly between the two groups (incisional 3.47% vs 3.67%, p=0.891;OSI 5.41% vs 5.99%, p=0.6608). Sub-analysis of top 5 procedures (by volume - laparoscopic cholecystectomy, hepatectomy, thromboendarterectomy, colectomy with anastomosis, and colectomy with ileocolostomy) demonstrated a significant decrease in incisional infections (3.7% vs 1.62%, p=0.0354). Conclusion(s): While the incidence of SSI did not differ significantly in the overall cohort after implementation of universal masking precautions, there was a decrease in incisional infections in commonly performed procedures at our institution. Future research is needed to identify whether continued masking precautions may minimize the risk of SSI in specific patient populations.
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Objectives: Postpartum depression (PPD) has been described as "the thief that steals motherhood" by depriving women of the anticipated joy of a new infant. Through this study, we intend to see the incidence, treatment rates (TR), relative-treatment rate (TRR), absolute treatment rate (ATR), and number needed to treat (NNT) pre- and post-COVID-19 on treatment of women with PPD. Method(s): This retrospective cohort study included newly diagnosed patients with PPD in 2019 (1st Jan - 31st Dec [pre-pandemic]) and 2020 (1st Jan - 31st Dec [pandemic]) using ICD-10-CM codes from Optum's de-identified Clinformatics Data Mart. Only the patients having continuous eligibility between 12 months before (baseline period) to 12-months post (follow-up period) the first diagnosis of PPD (index date) were included in study. During the follow-up period, patients were then checked for pharmacological treatment received (SSRI, SNRI's and other anti-depressants) using NDC codes. To measure effects, percentages of patients getting treatment, TRR (TR in pandemic/TR in pre-pandemic), ATR (TR in pre-pandemic - TR in a pandemic), and NNT (1/ATR) were calculated before and during COVID. The significance of categorical variables was examined using the Chi-square test. Result(s): We observed 39% increase in incidence of PPD patients during pandemic (n=16,095) vs pre-pandemic (n=11,565). Only 51% TR (risk ratio) was observed during pandemic vs 60% TR (risk ratio) in pre-pandemic with any SSRI, SNRI, and anti-depressants (p<.01). Compared to patients receiving treatment during pandemic vs pre-pandemic: TRR was found to be 85% (relative risk) and ATR was 9% (absolute risk reduction). The NNT comparing pre- and during pandemic was 11. Conclusion(s): The results of the study demonstrated that treatment of women with PPD was impacted during pandemic vs pre-pandemic (9% women did not receive treatment during pandemic). Alternative methods or non-pharmacological treatments may be required to further alleviate non-treated patients and improve their condition. Copyright © 2022
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Objectives: This study aimed to explore the impact of COVID-19 on patients with PTSD and the burden of resource utilization in the pre- and during the COVID-19 pandemic. Method(s): This retrospective observational study included patients diagnosed with PTSD between 1st January 2018 to 31st December 2020 using ICD-10-CM codes from Optum's de-identified Clinformatics Data Mart database. In the study duration, distinct patients were identified and further classified by age, gender, and location of service. To determine the influence in pre- and during COVID-19 for each of the stratification variables, a year-wise comparison was done. Chi-square was performed as test of significance for categorical variables. Result(s): Overall we observed the number of PTSD patients increased by 7% (n=206,741) during the pandemic (1st January 2020 - 31st December 2020) vs pre-pandemic (1st January 2019 - 31st December 2019). A significant increase was seen across all age groups (p<.05). In the case of teenagers, PTSD was found to have increased by 22% whereas in adults and the elderly an 8% and 3% increase was seen respectively. When broken down by gender, a significant increase was observed. Females (+9% [n=143,032]) were seen to have been affected more compared to males (+4% [n=63,625]) during the pandemic vs pre-pandemic. In healthcare resources utilization overall, there was an observed 24% increase. For both inpatients and office, PTSD decreased significantly (-3% and -4% respectively) (p<.05);while ER visits, increased only by 1% (p<.05). A significant increase in outpatient and telehealth services was observed (122% and 454% respectively) (p<.05). Conclusion(s): An increased exacerbation in PTSD was observed during the pandemic with respect to burden across various stratification and resource utilization;especially in outpatient and telehealth services. Better treatment, psychotherapy and alternative care programs may be required to curb this impact and decrease the overall burden across various care setting. Copyright © 2022
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Objectives: This study aimed to explore the impact of COVID-19 on patients with SSA and the burden of resource utilization in the pre- and during the COVID-19 pandemic. Method(s): This retrospective observational study included patients diagnosed with SSA between 1st January 2019 to 31st December 2020 using ICD-10-CM codes from Optum's de-identified Clinformatics Data Mart. In the study duration, distinct patients were identified and further classified by age, gender, and location of service. To determine the influence in pre- and during COVID-19 for each of the stratification variables, a year-wise comparison was done. Chi-square test was performed to check the significance of categorical variables. Result(s): Overall we observed the number of SSA patients increased by 2% (n=266,329) during the pandemic (1st January 2020 - 31st December 2020). A significant increase was seen across all age groups (p<.01). In the case of teenagers, SSA was found to have increased by 80% whereas in adults and elderly an 15% and 8% increase was seen respectively during pandemic (p<.01). When stratified by gender, a significant increase was observed only in females (+9% [n=174,647]) where in males (-3% [n=91,573]) decrease was observed during pandemic. In healthcare resources utilization overall, there was an observed 12% increase during pandemic. For inpatients, office, and outpatient, SSA decreased significantly (-4%, -8%, and -1% respectively) during pandemic (p<.01). A significant increase in outpatient and telehealth services was observed (34% and 1,299% respectively) (p<.01). Conclusion(s): An increased exacerbation in SSA was observed during the pandemic with telehealth and outpatient services being impacted the highest. This may be attributed to facing near-death scenarios, and the loss of loved ones amongst other factors. With the increase in cases, health care resource utilization across various settings is pressed. Better treatment and programs may be required to curb this impact and decrease the overall burden. Copyright © 2022
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Objectives: This study examines use of telemedicine services and health outcomes in patients with hypertension (HTN) in pre- and post-COVID 19 periods in the US. Method(s): A retrospective analysis, using Optum de-identified Electronic Health Record dataset, was done among hypertensive patients on Medicare plans in three different time periods: 1st Jan 2018 - 30th June 2018, 1st Jan - 30th June 2019, and 1st Jan - 30th June 2020 (first two time periods are pre-COVID 19 and the last one is post-COVID 19). The date of first EHR with mention of HTN diagnosis was considered index date. Study participants were categorized into those who used only telemedicine services (Telemedicine group);only other places of service like outpatient, inpatient, or office (Other POS group);and those who used both telemedicine and other places of service (Both POS groups). Patients were followed for 6-months post-index to determine use of anti-HTN medications, resource utilization, and healthcare outcomes. Result(s): Fewer than 100 patients in each study period belonged to Telemedicine group. Majority (55%) patients in 2018 (pre-COVID 19) belonged to Other POS group, but in 2020 (post-COVID 19) majority (61%) patients belonged to Both POS group. About 70% patients in each of three groups were prescribed anti-HTN drugs and adherence was >90%. About ~60% patients in Telemedicine group had 2-6 healthcare encounters while ~80% in Other POS group and ~95% in Both POS groups had >6 healthcare encounters during follow-up period. Significantly more patients in Both POS groups received anti-HTN nutritional counseling as compared to other two groups. Also, blood pressure was controlled in significantly higher percentage of patients in Both POS groups as compared to other two groups. Conclusion(s): Patients who use telemedicine and other places of service are more likely to receive anti-HTN nutritional counseling and have better blood pressure control. Copyright © 2022
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The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community. The recent ongoing SARSCov2 (Severe Acute Respiratory Syndrome) pandemic proved the unpreparedness for these situations. Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently. One major way to find out more information about such pathogens is by extracting the genetic data of such viruses. Though genetic data of viruses have been cultured and stored as well as isolated in form of their genome sequences, there is still limited methods on what new viruses can be generated in future due to mutation. This research proposes a deep learning model to predict the genome sequences of the SARS-Cov2 virus using only the previous viruses of the coronaviridae family with the help of RNN-LSTM (Recurrent Neural Network-Long ShortTerm Memory) and RNN-GRU (Gated Recurrent Unit) so that in the future, several counter measures can be taken by predicting possible changes in the genome with the help of existing mutations in the virus. After the process of testing the model, the F1-recall came out to be more than 0.95. The mutation detection???s accuracy of both the models come out about 98.5% which shows the capability of the recurrent neural network to predict future changes in the genome of virus.
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Another way to reduce the effects of COVID-19 is to develop vaccines to build viral immunity. Many contemporary assessments concentrate on the state and promise of this vaccine system, with geneticalgorithms for feature selection for the predictor being used as a replacement for the first data. While computing the distances to the preparation set examples, the predictors utilized in the count are the ones with no missing qualities for that example and no missing qualities in the preparation set. An intricating factor is that COVID-19 changes genetically and the indications are diverse which is developing it harder to focus on the peptides they contain to fit a sacked tree model for every indicator utilizing the preparation set examples. In this proposed method, we have applied the technique of genetic algorithm (GA) which finds the most vulnerable person who needs vaccine to save their lives. This searching process is taken through GA for better decision and applied for vaccine optimization. This study presents an attempt to distribute a genetic algorithm’s population onto the nodes of a complicated network, with crossover and mutation processes limited to the population members. The study given here uses a certain form of genetic algorithm as a tool for balancing the exploration–exploitation trade-off in order to investigate a specific aspect of a network, and it fits into that category. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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With the onset of the COVID-19 pandemic, the entire world is in chaos and is talking about novel ways to prevent virus spread. People around the world are wearing masks as a precautionary measure to prevent catching this infection. While some are following and taking this measure, some are not still following despite official advice from the government and public health agencies. In this paper, a face mask detection model that can accurately detect whether a person is wearing a mask or not is proposed and implemented. The model architecture uses MobileNetV2, which is a lightweight convolutional neural network, therefore requires less computational power and can be easily embedded in computer vision systems and mobile. As a result, it can create a low-cost mask detector system that can help to identify whether a person is wearing a mask or not and act as a surveillance system as it works for both real-time images and videos. The face detector model achieved high accuracy of 99.98% on training data, 99.56% on validation data, and 99.75% on testing data. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Blended learning incorporates online learning experiences and helps students for meaningful learning through flexible online information and communication technologies, reduced overcrowded classroom presence, and planned teaching and learning experience. This study has conducted surveys of various tools, techniques, frameworks, and models useful for blended learning. This article has prepared a comprehensive survey of student, teacher, and management experiences in blended learning courses during COVID-19 and pre-COVID-19 times. The survey will be useful to faculty members, students, and management to adopt new tools and mindsets for positive outcomes. This work reports on implementing and assessing blended learning at two different universities (University of Petroleum and Energy Studies, India, and Jaypee Institute of Information Technology, Noida, India). The assessments prepare the benefits and challenges of learning (by students) and teaching (by faculty) blended learning courses with different online learning tools. Additionally, student performance in the traditional and blended learning courses was compared to list the concerns about effectively shifting the face-to-face courses to a blended learning model in emergencies like COVID-19. As a result, it has been observed that blended learning is helpful for school, university, and professional training. A large set of online and e-learning platforms are developed in recent times that can be used in blended learning to improve the learner's abilities. The use of similar tools (Blackboard, CodeTantra, and g suite) has fulfilled the requirements of the two universities, and timely conducted and completed all academic activities during pandemic times.
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The upsurge of the novel coronavirus has spread to many countries and has been declared a pandemic by WHO. It has shaken the most powerful countries across the world like the USA, UK, and has affected economies of various countries. The coronavirus or the 2019-nCoV causes the disease that has been named COVID-19. This disease transmits by inhaling droplets that are expelled by an infected person. It has been affecting people in different ways and has been found to be threatening for the older population or people with comorbidities. It has been seen that the virus 2019-nCoV spreads faster than the two of its antecedents namely severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). No cure or vaccine has been discovered as of now and taking precautions like staying at home are the only possible solutions. Our study analyzes the current trend of the disease in India and predicts future trends using time series forecasting. The official dataset provided by John Hopkins University through a GitHub repository has been used for the research for the time period of 22 January 2020 to 31 May 2020. The trend in cases, fatalities, and the people who have recovered until the date of 31 May 2020 has been discussed in the paper. It has been seen through the findings that the total number of cases is expected to rise to 2,15,000 by the end of May 2020 i.e. 31 May 2020 as per the AR (Autoregression) model. ARIMA (Autoregressive Integrated Moving Average) model predicts the number of cases to be 2,05,000 until the same date. Actual data has shown that the number of confirmed cases is 1,90,609 as on 31 May 2020 giving a percentage error of 7.57% and 12.85% for ARIMA and AR model respectively. Comparison between the findings of the two models has been shown later in the paper. © 2021, Springer Nature Singapore Pte Ltd.