A Clinical Study of Intraoperative Perfusion Chemotherapy in Gastric Cancer: A Prospective Cohort Study.

INTRODUCTION: Gastric cancer (GC) is the third largest cause of cancer-related death worldwide, with major geographic disparities in incidence and outcomes. Sociodemographic indicators, food habits, and genetic predispositions all add to the load. Despite advances in systemic treatments, peritoneal metastasis remains a concern, with intraperitoneal chemotherapy (IPC) emerging as a promising treatment option. METHODS: A prospective cohort research was done, with 30 GC patients receiving cytoreductive surgery (CRS) followed by lobaplatin-based intraoperative chemotherapy. The study evaluated postoperative complications, survival rates, and disease recurrence using Statistical Package for the Social Sciences (SPSS) version 25.0 (IBM SPSS Statistics, Armonk, NY) for data analysis. The purpose of this study is to assess the effectiveness, safety, and dependability of lobaplatin as an intraoperative chemotherapeutic agent in patients having gastric cancer surgery, with a particular emphasis on those patients who do not have distant metastases. RESULTS: The study population had a balanced gender distribution, with an average age of 44.83 years. Most patients had advanced-stage cancer (T3 and T4), and lobaplatin treatment resulted in a low frequency of serious postoperative sequelae. Preliminary studies suggest that lobaplatin is a safe and potentially effective IPC drug for GC, with few side effects and adequate survival rates. CONCLUSION: Lobaplatin shows promise as an intraoperative chemotherapeutic treatment for gastric cancer, necessitating more research in bigger, randomized controlled studies to determine its efficacy and safety profile. The study emphasizes the need for novel treatment strategies to enhance the prognosis of GC patients, particularly those with peritoneal involvement.

A Clinical Study of Intraoperative Perfusion Chemotherapy With Raltitrexed in Colon Cancer: A Prospective Cohort Study.

INTRODUCTION: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related mortality among women and the third leading cause of cancer-associated mortality among men. Treatment of colon cancer is very crucial for a patient's survival. In this study, we assessed the reliability, efficacy, and safety of raltitrexed in intraoperative intraperitoneal chemotherapy for colon cancer. METHODOLOGY: A total of 57 patients with clinical stages II and III of colon cancer were included in the study. R0 resection surgery + hyperthermic intraperitoneal chemotherapy (HIPEC) procedure was done with raltitrexed. It was given in a dose of 3 mg/m2 in a 0.9% NS injection in a volume of 500 milliliters. Postoperative complications were observed. RESULT: The most common postoperative complication was nausea/vomiting, which was seen in 21 out of 57 patients (37%). The second most common complication was fever (18/57). None of the patients died or developed renal toxicity, hepatic toxicity, and intestinal obstruction. CONCLUSION: Raltitrexed is a reliable, efficient, and safe drug and can be used in intraoperative intraperitoneal chemotherapy of colon cancer.

Evaluation of the District Health Information System in District Kotli, Azad Jammu and Kashmir: A Retrospective Analysis.

BACKGROUND: It is essential to implement a high-quality electronic database for keeping important information. The District Health Information System (DHIS) is an active data-keeping system in Pakistan. This study aimed to evaluate the patients' data from the DHIS dashboard for the District Headquarters Hospital, Kotli, Azad Jammu and Kashmir (AJK). METHODOLOGY: The data was requested from the hospital administration at District Headquarters Hospital, Kotli, AJK, and the data was analyzed after permission was granted. The data was given in two forms; one was a hard copy of the data for August and September and the other was a comma-separated values file for October and November, 2023. RESULTS: The highest frequency of patients was received in the department of emergency and trauma and the patient's median age was between 15 and 49 years. The second department was medicine with the >50 years of age. Common conditions that needed more attention were chronic obstructive pulmonary disease, acute respiratory infection, diarrhea, pneumonia, diabetes mellitus, hypertension, and ischemic heart disease. CONCLUSION: For nations with constrained healthcare systems and funds, primary health care (PHC) is the only viable approach for managing non-communicable diseases (NCDs). However, PHC systems intended for infectious diseases have not sufficiently adapted to the growing requirement of chronic care for NCD. Research using health information databases offers numerous benefits, such as the evaluation of large data sets and unexpected prevalence of disease in certain populations, such as a higher prevalence of disease in one gender or age group. Health information system-based data analysis or studies are less expensive and faster but lack scientific control over data collection.

Frequency of Amblyopia in strabismus patients presenting to tertiary care hospital.

Objective: The rationale of study was to find the magnitude of amblyopia with reference to type of squint among the strabismus patients visiting Hayatabad Medical Complex Peshawar, Pakistan. Materials and Methods: After ethical approval, a cross sectional study was carried out in the Department of Ophthalmology, Hayatabad Medical Complex, Peshawar, Pakistan, from April 2022 to October 2022, the total number of patients included being 237. Results: Amblyopia was observed in 113 out of 160 (70.6%) cases of uniocular squint, while in alternating squint it was found to be 11 out of 77 (14.2%). Amblyopia in patients with esotropia was seen in 73.2% (107 out of 146), while 59.3% (54 out of 91) exotropia had associated amblyopia. Conclusion: Strabismus amblyopia leads to developmental arrest of vision in early critical years of life. Permanent visual loss can be avoided with comprehensive screening and detailed examination of strabismic patient.

Antibiotics induced changes in nitrogen metabolism and antioxidative enzymes in mung bean (Vigna radiata).

Excessive use and release of antibiotics into the soil environment in the developing world have resulted in altered soil processes affecting terrestrial organisms and posing a serious threat to crop growth and productivity. The present study investigated the influence of exogenously applied oxytetracycline (OXY) and levofloxacin (LEV) on plant physiological responses, key enzymes involved in nitrogen metabolism (e.g., nitrate reductase, glutamine synthetase), nitrogen contents and oxidative stress response of mung bean (Vigna radiata). Plants were irrigated weekly with antibiotics containing water for exposing the plants to different concentrations i.e., 1, 10, 20, 50, and 100 mg L-1. Results showed a significant decrease in nitrate reductase activity in both antibiotic treatments and their mixtures and increased antioxidant enzymatic activities in plants. At lower concentrations of antibiotics (≤20 mg L-1), 53.9 % to 78.4 % increase in nitrogen content was observed in levofloxacin and mixtures compared to the control, resulting in an increase in the overall plant biomass. Higher antibiotic (≥50 mg L-1) concentration showed 58 % decrease in plant biomass content and an overall decrease in plant nitrogen content upon exposure to the mixtures. This was further complemented by 22 % to 42 % increase in glutamine synthetase activity observed in the plants treated with levofloxacin and mixtures. The application of low doses of antibiotics throughout the experiments resulted in lower toxicity symptoms in the plants. However, significantly higher malondialdehyde (MDA) concentrations at higher doses (20 mg L-1 and above) than the control showed that plants' tolerance against oxidative stress was conceded with increasing antibiotic concentrations. The toxicity trend was: levofloxacin > mixture > oxytetracycline.

Emerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectives.

In light of the constantly changing terrain of the COVID outbreak, medical specialists have implemented proactive schemes for vaccine production. Despite the remarkable COVID-19 vaccine development, the virus has mutated into new variants, including delta and omicron. Currently, the situation is critical in many parts of the world, and precautions are being taken to stop the virus from spreading and mutating. Early identification and diagnosis of COVID-19 are the main challenges faced by emerging technologies during the outbreak. In these circumstances, emerging technologies to tackle Coronavirus have proven magnificent. Artificial intelligence (AI), big data, the internet of medical things (IoMT), robotics, blockchain technology, telemedicine, smart applications, and additive manufacturing are suspicious for detecting, classifying, monitoring, and locating COVID-19. Henceforth, this research aims to glance at these COVID-19 defeating technologies by focusing on their strengths and limitations. A CiteSpace-based bibliometric analysis of the emerging technology was established. The most impactful keywords and the ongoing research frontiers were compiled. Emerging technologies were unstable due to data inconsistency, redundant and noisy datasets, and the inability to aggregate the data due to disparate data formats. Moreover, the privacy and confidentiality of patient medical records are not guaranteed. Hence, Significant data analysis is required to develop an intelligent computational model for effective and quick clinical diagnosis of COVID-19. Remarkably, this article outlines how emerging technology has been used to counteract the virus disaster and offers ongoing research frontiers, directing readers to concentrate on the real challenges and thus facilitating additional explorations to amplify emerging technologies.

Comparison of prilocaine/lidocaine cream with piroxicam gel for reducing pain during cannulation of arteriovenous fistula for adults undergoing haemodialysis: A randomized crossover clinical trial.

BACKGROUND: Pain is a traumatic experience for most patients on hemodialysis. In this trial, we compared prilocaine/lidocaine cream with piroxicam gel for pain reduction during arteriovenous fistula needling. METHODS: This randomized double-blind crossover clinical trial was done at dialysis unit of a tertiary care hospital from June to August 2022. Adult patients, aged 18-75 years, on maintenance hemodialysis through an arteriovenous fistula were selected randomly. Pain severity during needling of fistula was assessed during initial two hemodialysis sessions without application of any drug. Patients were then randomized into two groups receiving 5% prilocaine/lidocaine cream or 0.5% piroxicam gel 1 h before the next two hemodialysis sessions. After a 7-day washout period, patients crossed over to other groups for another two hemodialysis sessions. Pain was assessed on all these occasions. Primary outcome was reduction in pain with each intervention. RESULTS: There were 32 patients aged 46.44 ± 11.58 years. Pain intensity was 6.69 ± 0.58, 3.13 ± 1.28, and 4.55 ± 1.95 without any medication, prilocaine/lidocaine cream and piroxicam gel respectively. There was greater pain reduction with prilocaine/lidocaine cream than piroxicam gel (3.56 ± 1.35 vs 2.14 ± 1.78; p = 0.001). Local redness with prilocaine/lidocaine cream was reported by one (3.13%) patient, whereas no side effects were seen with piroxicam gel (p = 1.000). CONCLUSION: Prilocaine/lidocaine cream provides better pain relief than piroxicam gel during arteriovenous fistula needling.

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique.

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the estimation of intrusion detection.

Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction.

Fatal diseases like cancer, dementia, and diabetes are very dangerous. This leads to fear of death if these are not diagnosed at early stages. Computer science uses biomedical studies to diagnose cancer, dementia, and diabetes. With the advancement of machine learning, there are various techniques which are accessible to predict and prognosis these diseases based on different datasets. These datasets varied (image datasets and CSV datasets) around the world. So, there is a need for some machine learning classifiers to predict cancer, dementia, and diabetes in a human. In this paper, we used a multifactorial genetic inheritance disorder dataset to predict cancer, dementia, and diabetes. Several studies used different machine learning classifiers to predict cancer, dementia, and diabetes separately with the help of different types of datasets. So, in this paper, multiclass classification proposed methodology used support vector machine (SVM) and K-nearest neighbor (KNN) machine learning techniques to predict three diseases and compared these techniques based on accuracy. Simulation results have shown that the proposed model of SVM and KNN for prediction of dementia, cancer, and diabetes from multifactorial genetic inheritance disorder achieved 92.8% and 92.5%, 92.8% and 91.2% accuracy during training and testing, respectively. So, it is observed that proposed SVM-based dementia, cancer, and diabetes from multifactorial genetic inheritance disorder prediction (MGIDP) give attractive results as compared with the proposed model of KNN. The application of the proposed model helps to prognosis and prediction of cancer, dementia, and diabetes before time and plays a vital role to minimize the death ratio around the world.

Impact of school closures and reopening on COVID-19 caseload in 6 cities of Pakistan: An Interrupted Time Series Analysis

Schools were closed all over Pakistan on November 26, 2020 to reduce community transmission of COVID-19 and reopened between January 18 and February 1, 2021. However, these closures were associated with significant economic and social costs, prompting a review of effectiveness of school closures to reduce the spread of COVID-19 infections in a developing country like Pakistan. A single-group interrupted time series analysis (ITSA) was used to measure the impact of school closures, as well as reopening schools on daily new COVID-19 cases in 6 major cities across Pakistan: Lahore, Karachi, Islamabad, Quetta, Peshawar, and Muzaffarabad. We found that closing schools reduced COVID-19 incidence in the community by approximately a third of all cases nationwide. However, any benefits were contingent on continued closure of schools, as cases bounced back once schools reopened. School closures are associated with a clear and statistically significant reduction in COVID-19 cases by 0.07 to 0.63 cases per 100,000 population, while reopening schools is associated with a statistically significant increase. Lahore is an exception to the effect of school closures, but it too saw an increase in COVID-19 cases after schools reopened in early 2021. We show that closing schools was a viable policy option, especially before vaccines became available. However, its social and economic costs must also be considered.

An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression.

Smart applications and intelligent systems are being developed that are self-reliant, adaptive, and knowledge-based in nature. Emergency and disaster management, aerospace, healthcare, IoT, and mobile applications, among them, revolutionize the world of computing. Applications with a large number of growing devices have transformed the current design of centralized cloud impractical. Despite the use of 5G technology, delay-sensitive applications and cloud cannot go parallel due to exceeding threshold values of certain parameters like latency, bandwidth, response time, etc. Middleware proves to be a better solution to cope up with these issues while satisfying the high requirements task offloading standards. Fog computing is recommended middleware in this research article in view of the fact that it provides the services to the edge of the network; delay-sensitive applications can be entertained effectively. On the contrary, fog nodes contain a limited set of resources that may not process all tasks, especially of computation-intensive applications. Additionally, fog is not the replacement of the cloud, rather supplement to the cloud, both behave like counterparts and offer their services correspondingly to compliance the task needs but fog computing has relatively closer proximity to the devices comparatively cloud. The problem arises when a decision needs to take what is to be offloaded: data, computation, or application, and more specifically where to offload: either fog or cloud and how much to offload. Fog-cloud collaboration is stochastic in terms of task-related attributes like task size, duration, arrival rate, and required resources. Dynamic task offloading becomes crucial in order to utilize the resources at fog and cloud to improve QoS. Since this formation of task offloading policy is a bit complex in nature, this problem is addressed in the research article and proposes an intelligent task offloading model. Simulation results demonstrate the authenticity of the proposed logistic regression model acquiring 86% accuracy compared to other algorithms and confidence in the predictive task offloading policy by making sure process consistency and reliability.

Severity of COVID-19 reinfection and associated risk factors: findings of a cross-sectional study in Bangladesh

BackgroundCOVID-19 reinfected patients suffer from diverse health consequences. Information on the severity of COVID-19 reinfection is scarce. The current study aimed to determine the proportion of COVID-19 reinfection and risk factors associated with its severity. MethodsThis cross-sectional study targeted all COVID-19 patients reported in May 2021 at the Health Information Unit (HIU) of the Directorate General of Health Services (DGHS) of Bangladesh. We identified 473 (1.14%) reinfected patients out of 41408 diagnosed cases by reviewing their medical records. Considering the selection criteria and informed consent, we enrolled 404 reinfected patients. Data were collected through telephone interviews and reviewing medical records using a semi-structured questionnaire and a checklist. ResultsThe majority of the reinfected patients were urban residents (98.0%). Around 13.0% of reinfected patients had <90% oxygen saturation, and 64.0% had an interval of 3-6 months between two attacks. The severity of reinfection included asymptomatic (12.9%), mild (8.9%), moderate (66.3%), and severe (11.9%) forms of infection. An interval of 3-6 months between two attacks had less chance of having mild (AOR=0.031, {rho}=0.000), moderate (AOR=0.132, {rho}=0.017), and severe (AOR=0.059, {rho}=0.002) infections. Patients who maintained physical distance had less chance of moderate-intensity reinfection (AOR=0.137, {rho}=0.013), while the vaccinated patients had a higher chance of moderate (AOR=16.127, {rho}=0.001) and severe (AOR=3.894, {rho}=0.047) intensity reinfection. ConclusionTo avert COVID-19 reinfection and its severity, patients should be vigilant about preventive practices even after recovery. The study suggests vibrant interventions aligned with exposure, physical distancing, vaccination, and comorbidities for mitigating reinfection.

A Comparative Study on the knowledge and attitude of COVID-19 among Urban and Rural populations of Bangladesh.

AimThis study is aimed to identify the awareness and behavioral perspective on COVID-19 between urban and rural people of Bangladesh during the period of outbreak. MethodsA community-based cross-sectional study was conducted in 30 districts of Bangladesh, where 322 participants from urban and 312 from rural area. Participants were selected through convenience sampling. ResultsRural people are found to be much more inter connected to receive information from neighbor. Regarding the incubation periods and the general symptoms, knowledge differs significantly from urban to rural. Even their precautionary and transmission knowledge is found to associate in most of the cases. During this outbreak, urban people significantly increase their religious habits and also believe that there will some major change of life after outbreak. DiscussionThe study reflected that health education program needed to aware about COVID-19 in both urban and rural in Bangladesh that helps in formulating and executing communication and outbreak management.

SARS-CoV-2 UK, South African and Brazilian Variants in Karachi- Pakistan

COVID-19 pandemic has been evolving in Pakistan since the UK, South African and Brazilian variants have started surfacing which are known for increase transmissibility and can also be responsible for escape from immune responses. The gold standard to detect these variants of concern is sequencing, however routine genomic surveillance in resource limited countries like Pakistan is not always readily available. With the emergence of variants of concern and a dearth of facilities for genomic scrutiny leaves policy makers and health authorities an inconsistent and twisted image to make decisions. The inadvertent detection of B.1.1.7 by target failure because of a key deletion in spike {Delta}69-70 in the UK by commercially available COVID-19 PCR assay helps to understand target failures as an alternative approach to detect variants. It was ascertained further that a deletion in the ORF1a gene (ORF1a {Delta}3675-3677) found common in B.1.1.7, B.135 and P.1 variants of concern. The Real Time Quantitative PCR (RT-qPCR) assay for detection of emergence and spread of SARS-CoV-2 variants, by these target failures is used here. The positive samples archived in respective labs were divided in two groups used in the present study. Group I constitutes 261 positive samples out of 16964 (1.53%) collected from August till September 2020. Group II include 3501 positive samples out of 46041 (7.60%) from November 2020 till January 2021. In positive samples of group I, no variant of concern was found. A staggering difference in results was noted in group II where positivity ratio increased exponentially and the variants of concern started appearing in significant numbers (53.64% overall). This is indicative that the third wave in Pakistan is due to the importation of SARS-CoV-2 variants. This calls for measures to increase surveillance by RT-qPCR which would help authorities in decision making.

The Safety and Efficacy of Anakinra, an Interleukin-1 Antagonist in Severe Cases of COVID-19: A Systematic Review and Meta-Analysis / 감염과화학요법

This study aims to assess anakinra's safety and efficacy for treating severe coronavirus disease 2019 (COVID-19). Numerous electronic databases were searched and finally 15 studies with a total of 3,530 patients, 757 in the anakinra arm, 1,685 in the control arm were included. The pooled adjusted odds ratio (OR) for mortality in the treatment arm was 0.34 (95% confidence interval [CI], 0.21 - 0.54, I2 = 48%), indicating a significant association between anakinra and mortality. A significant association was found regarding mechanical ventilation requirements in anakinra group compared to the control group OR, 0.68 (95% CI, 0.49 - 0.95, I2 = 50%). For the safety of anakinra, we evaluated thromboembolism risk and liver transaminases elevation. Thromboembolism risk was OR, 1.59 (95% CI, 0.65 - 3.91, I2 = 0%) and elevation in liver transaminases with OR was 1.35 (95% CI, 0.61 - 3.03, I2 = 76%). Both were not statistically significant over the control group. Anakinra is beneficial in lowering mortality in COVID-19 patients. However, these non-significant differences in the safety profile between the anakinra and control groups may have been the result of baseline characteristics of the intervention group, and further studies are essential in evaluating anakinra's safety profile.

The Safety and Efficacy of Anakinra, an Interleukin-1 Antagonist in Severe Cases of COVID-19: A Systematic Review and Meta-Analysis / 감염과화학요법

This study aims to assess anakinra's safety and efficacy for treating severe coronavirus disease 2019 (COVID-19). Numerous electronic databases were searched and finally 15 studies with a total of 3,530 patients, 757 in the anakinra arm, 1,685 in the control arm were included. The pooled adjusted odds ratio (OR) for mortality in the treatment arm was 0.34 (95% confidence interval [CI], 0.21 - 0.54, I2 = 48%), indicating a significant association between anakinra and mortality. A significant association was found regarding mechanical ventilation requirements in anakinra group compared to the control group OR, 0.68 (95% CI, 0.49 - 0.95, I2 = 50%). For the safety of anakinra, we evaluated thromboembolism risk and liver transaminases elevation. Thromboembolism risk was OR, 1.59 (95% CI, 0.65 - 3.91, I2 = 0%) and elevation in liver transaminases with OR was 1.35 (95% CI, 0.61 - 3.03, I2 = 76%). Both were not statistically significant over the control group. Anakinra is beneficial in lowering mortality in COVID-19 patients. However, these non-significant differences in the safety profile between the anakinra and control groups may have been the result of baseline characteristics of the intervention group, and further studies are essential in evaluating anakinra's safety profile.

Phytotoxicity of different antibiotics to rice and stress alleviation upon application of organic amendments.

Extensive use of antibiotic results in significant antibiotics pollution in the environment. Main objective of this study was to gain insight into potential impacts of antibiotics on plant physiological growth and nutritional composition, and stress alleviation through application of different organic amendments. Effects of five antibiotics (ciprofloxacin, levofloxacin, ofloxacin, amoxicillin and ampicillin) were observed in the presence of three organic amendments (rice husk, farmyard manure and poultry litter) with rice (Oryza sativa L.) as a model plant. Organic amendments were mixed with soil (@ 5 g kg-1) and after three weeks, antibiotics were applied (@10 mg kg-1) and plants were allowed to grow for four months. After which plants were harvested and physical growth parameters (root/shoot length, biomass) and nutritional composition (grain protein content, carbohydrates, phosphorous and iron) were monitored. It was observed that germination rate, seedling root/shoot length, seedling biomass and vigor index were negatively impacted. The application of organic amendments alleviated antibiotic stress on seedling dry biomass, length and vigor index by 1.8-, 3.1- and 2.5-folds, respectively as compared to the antibiotic controls. Concentrations of phosphorous, iron, carbohydrates and proteins were decreased by 5.3-, 1.3-, 1.4- and 1.6-folds upon application of antibiotics. Rice husk was the most effective treatment in case of physical growth parameters and alleviating antibiotics' induced genotoxicity. Whereas, poultry litter had the highest positive effect on nutritional composition of plants. In general, the application of organic amendments alleviated the phytotoxicity as well as genotoxicity in plants under antibiotics stress.

Analysis and Prediction of the COVID-19 outbreak in Pakistan

In this study we estimate the severity of the COVID-19 outbreak in Pakistan prior to and after lock down restrictions were eased. We also project the epidemic curve considering realistic quarantine, social distancing and possible medication scenarios. We use a deterministic epidemic model that includes asymptomatic, quarantined, isolated and medicated population compartments for our analysis. We calculate the basic reproduction number [R]0 for the pre and post lock down periods, noting that during this time no medication was available.1 The pre-lock down value of [R]0 is estimated to be 1.07 and the post lock down value is estimated to be 1.86. We use this analysis to project the epidemic curve for a variety of lock down, social distancing and medication scenarios. We note that if no substantial efforts are made to contain the epidemic, it will peak in mid of September, with the maximum projected active cases being close to 700,000. In a realistic, best case scenario, we project that the epidemic peaks in early to mid July with the maximum active cases being around 120000.We note that social distancing measures and medication if available will help flatten the curve, however without the reintroduction of further lock down it would be very difficult to bring [R]0 below 1. Our study strongly supports the recent WHO recommendation of reintroducing lock downs to control the epidemic.

Can medication mitigate the need for a strict lock down?: A mathematical study of control strategies for COVID-19 infection

We formulate a deterministic epidemic model to study the effects of medication on the transmission dynamics of Corona Virus Disease (COVID-19). We are especially interested in how the availability of medication could change the necessary quarantine measures for effective control of the disease. We model the transmission by extending the SEIR model to include asymptomatic, quarantined, isolated and medicated population compartments. We calculate the basic reproduction number R0 and show that for R0 < 1 the disease dies out and for R0 > 1 the disease is endemic. Using sensitivity analysis we establish that R0 is most sensitive to the rates of quarantine and medication. We also study how the effectiveness and the rate of medication along with the quarantine rate affect R0. We devise optimal quarantine, medication and isolation strategies, noting that availability of medication reduces the duration and severity of the lock-down needed for effective disease control. Our study also reinforces the idea that with the availability of medication, while the severity of the lock downs can be eased over time some social distancing protocols need to be observed, at least till a vaccine is found. We also analyze the COVID-109 outbreak data for four different countries, in two of these, India and Pakistan the curve is still rising, and in he other two, Italy and Spain, the epidemic curve is now falling due to effective quarantine measures. We provide estimates of R0 and the proportion of asymptomatic individuals in the population for these countries.

In-silico pharmacophoric and molecular docking-based drug discovery against the Main Protease (Mpro) of SARS-CoV-2, a causative agent COVID-19.

COVID-19 (Coronavirus Disease 2019) caused by a novel 'SARS-CoV-2' virus resulted in public health emergencies across the world. An effective vaccine to cure this virus is not yet available, thus requires concerted efforts at various scales. In this study, we employed Computer-Aided Drug Design (CADD) based approach to identify the drug-like compounds - inhibiting the replication of the main protease (Mpro) of SARS-CoV-2. Our database search using an online tool "ZINC pharmer" retrieved ~1500 compounds based on pharmacophore features. Lipinski's rule was applied to further evaluate the drug-like compounds, followed by molecular docking-based screening, and the selection of screening ligand complex with Mpro based on S-score (higher than reference inhibitor) and root-mean-square deviation (RMSD) value (less than reference inhibitor) using AutoDock 4.2. Resultantly, ~200 compounds were identified having strong interaction with Mpro of SARS-CoV-2. After evaluating their binding energy using the AutoDock 4.2 software, three compounds (ZINC20291569, ZINC90403206, ZINC95480156) were identified that showed highest binding energy with Mpro of SARS-CoV-2 and strong inhibition effect than the N3 (reference inhibitor). A good binding energy, drug likeness and effective pharmacokinetic parameters suggest that these candidates have greater potential to stop the replication of SARS-CoV-2, hence might lead to the cure of COVID-19.