Modeling and Analysis of Infectious Disease Transmission Considering Protection and Isolation

Based on the SEIR model, two compartments for self-protection and isolation are introduced, and a more general infectious disease transmission model is proposed.Through qualitative analysis of the model, the basic reproduction number of the model is calculated, and the local asymptotic stability of the disease-free equilibrium point and the endemic equilibrium point of the model is analyzed through eigenvalue theory and Routh-Hurwitz criterion.The numerical simulation and fitting results of COVID-19 virus show that the proposed SEIQRP model can effectively describe the dynamic transmission process of the infectious disease.In the model, the three parameters, i.e.protection rate, incubation period isolation rate, and infected person isolation rate play a very critical role in the spread of the disease.Raising people's awareness of self-protection, focusing on screening for patients in the incubation period, and isolating and treating infected people can effectively reduce the spread of infectious diseases. © 2023 Northeastern University.All rights reserved.

Building Geographic Information System Story Maps for Jakarta's Local Community Health Infrastructures Awareness

COVID-19 is caused by the novel coronavirus SARS-CoV-2. First started in Wuhan, COVID-19 has spread everywhere, including Indonesia. COVID-19 can cause severe pneumonia, severe acute respiratory distress syndrome (ARDS) symptoms, and multiple organ failure. According to the WHO, COVID-19 generally has an incubation period of 5-6 days, ranging from 1 to 14 days. However, in Jakarta, the cases have decreased significantly since the implementation of PPKM (Restrictions of Activity), running since early July 2021. The government claimed that the PPKM rule has significantly impacted COVID-19 cases, decreasing every day, especially in Jawa-Bali Region. In addition, the Vaccination rate in Indonesia also played a significant part in decreasing COVID-19 cases, with Jakarta currently standing with 9 million people fully vaccinated per December 2021. To monitor the development of COVID-19 in Jakarta and provide information to the public about health facilities, especially hospitals in Jakarta, in this study, the distribution area of COVID-19 cases will be mapped with CHIME using ArcGIS Online tools. The analysis results obtained based on the mapping results that most cases were in the Cengkareng area, and the area with the most hospitals werein East Jakarta. © 2022 IEEE.

To Assess the Adequacy of Knowledge to Prevent COVID-19 Pandemic: Cross-Sectional Descriptive Online Survey

Aim: To determine the level of knowledge towards COVID-19 among people. Material(s) and Method(s): A cross-sectional descriptive research design was used for the present study and was conducted among people attending Darbhanga Medical College, Darbhanga, Bihar, India, to assess their knowledge regarding COVID-19. A total of 461 people were recruited for this study and sample of 400 eligible people who fulfill the inclusion criteria were enrolled. Result(s): The association of socio-demographic variables of participants and their knowledge score. It shows that group (p>0.001), gender (p=0.020), education (p=0.001), marital status (p=0.001), age (p=0.020), and inhabitants (p=0.001) were significantly associated with knowledge. Majority of participants 63% having good knowledge while 33% and 1.4% having average and poor knowledge respectively regarding the corona virus pandemic. Conclusion(s): Study concluded that many people were still had average and poor knowledge on COVID-19. Higher authorities must find the ways for making people more aware on this pandemic to control its impact.Copyright © 2023, Dr. Yashwant Research Labs Pvt. Ltd.. All rights reserved.

Probability of Infectious Disease in Humans during Epidemic

Popular SIR models and their modifications used to generate predictions about epidemics and, specifically, the COVID-19 pandemic, are inadequate. The aim of this study was to find the laws describing the probability of infection in a biological object. Using theoretical methods of research based on the probability theory, we constructed the laws describing the probability of infection in a human depending on the infective dose and considering the temporal characteristics of a given infection. The so-called generalized time-factor law, which factors in the time of onset and the duration of an infectious disease, was found to be the most general. Among its special cases are the law describing the probability of infection developing by some point in time t, depending on the infective dose, and the law that does not factor in the time of onset. The study produced a full list of quantitative characteristics of pathogen virulence. The laws described in the study help to solve practical tasks and should lie at the core of mathematical epidemiological modeling.Copyright © 2022 Obstetrics, Gynecology and Reproduction. All rights reserved.

CORONAVIRUS DISEASE SURVIVAL TIME MODEL FOR FORECASTING

The term "survival analysis" refers to statistical techniques for data analysis where the time until the occurrence of the desired event serves as the outcome variable. Time to event analysis is another name for survival analysis. Applications for survival analysis are fairly broad and include things like calculating a population's survival rate or contrasting the survival of two or more groups. Cox regression analysis is a highly well-liked and frequently applied technique among them. Data on disease states are typically obtained at random epochs or at periodic epochs during follow-up in research looking at biological changes between states of Coronavirus infection and the start of COVID-19 in the human immune system. For instance, after the COVID enters a person's bloodstream by a route of transmission, it progresses through numerous stages that are linked to the depletion of B cells before becoming COVID-19. This study presents the Cox's approach for simulating the link between variables influencing the development of two disease states, namely I= the time epoch of COVID infection and P= the time epoch of COVID-19. Incubation period (IP) or survival time is the precise interval of time between "P and I." It is shown how Cox's model works with several personal infective factors and how well it can estimate the percentage of COVID-19 victims with the same completed length of IP. Such forecast values are then established for a synthetically simulated data set. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19.

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between 'clinical' testing, which targets symptomatic individuals, and 'non-clinical' testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Photodynamic inactivation of Rhizopus oryzae - in vitro study

During the COVID-19 pandemic, several complications arose in infected patients, one of them being mucormycosis, which is an extremely aggressive fungal disease with a high mortality rate, especially in patients with compromised immune systems. Most cases of mucormycosis are caused by the fungus Rhizopus oryzae, also known as black fungus, with 90% of cases affecting the rhinocerebral site. The treatment tools used are based on high doses of amphotericin B and posaconazole, associated with surgical resections when possible. However, even with aggressive antifungal treatment, the estimated attributable mortality rate is high [1]. In the absence of surgical debridement of the infected tissue, antifungal treatment alone is not curative. So there is a need for development of adjuvant treatments. Antimicrobial Photodynamic Therapy (aPDT) may constitute an auxiliary therapeutic option for mucormycosis [2]. Due to the lack of reports on the photodynamic inactivation of R. oryzae, we investigated different protocols Photodithazine (PDZ) as a photosensitizer. The response on the fungus growing rate under distinct treatment parameters as photosensitizer concentration, incubation time, and association with surfactant, will be presented for both white and black hyphal phases, and infective spore phase. Preliminary results show the potential use of photodynamic therapy for the inactivation and growth control of the R. oryzae.Copyright © 2023

Enhanced electrochemiluminescence immunoassay: 2. Enabling signal detection at an early stage of incubation for rapid point-of-care testing

Signal detection in a label-based immunoassay is performed normally when the antigen/antibody binding reaction reaches the equilibrium state during the incubation period of an assay process. Shortening the incubation period in an assay helps reduce the turnaround time and is particularly valuable for point-of-care testing, but the cost is the reduction of signal level and, possibly, measurement precision as well. This work demonstrates that the signal loss could be offset by the stronger emission of an electronically neutral ruthenium(II) complex label, Ru(2, 2′-bipyridine) (bathophenanthroline disulfonate)[4-(2, 2′-bipyridin-4-yl)butanoic acid], used in the electrochemiluminescence (ECL) immunoassay. Combined with the uniquely well-established flow-through washing process in the automated ECL analyzers and the precise control over liquid handling, the assays performed with a 5-minute incubation period showed the same signal level and measurement precision as those of conventional ECL assays. Additionally, the absence of biotin and streptavidin components in the reagent formulation avoids the biotin-streptavidin interaction during assay incubation and fundamentally eliminates the interference of biotin, especially when used in some high-dose therapies. The results obtained from the procalcitonin prototype kit and the supporting evidence from other preliminary reagents (for SARS-CoV-2 N protein and troponin T) are general. The nonequilibrium detection, along with the downsized instrument design, makes the enhanced ECL (EECL) technology a fast high-performance POCT platform that provides the same high-quality data as those generated from the widely deployed [Ru(bpy)3]2+ based laboratorial ECL systems. The anticipated regulatory approval and follow-up clinical implementation will be a significant stride in the decade-long pursuit of novel ECL labels. © 2023 The Author(s)

A novel isothermal digital amplification system and its application for absolute quantification of respiratory infectious virus

Herein, we establish a novel isothermal digital amplification system termed digital nicking and extension chain reaction system-based amplification (dNESBA) by utilizing the isothermal NESBA technique and the newly developed miniaturized fluorescence monitoring system (mFMS). dNESBA enables parallel isothermal NESBA reactions in more than 10,000 localized droplet microreactors and read the fluorescence signals rapidly in 150 s by mFMS. This system could identify the genomic RNA (gRNA) extracted from target respiratory syncytial virus A (RSV A) as low as 10 copies with remarkable specificity. The practical applicability of dNESBA was also successfully verified by reliably detecting the gRNA in the artificial sputum samples with excellent reproducibility and accuracy. Due to the intrinsic advantages of isothermal amplifying technique including the elimination of the requirement of thermocycling device and the enhanced portability of the miniaturized read-out equipment, the dNESBA technique equipped with mFMS could serve as a promising platform system to achieve point-of-care (POC) digital molecular diagnostics, enabling absolute and ultra-sensitive quantification of various infectious pathogens even in an early stage.Copyright © 2023

Estimate the incubation period of coronavirus 2019 (COVID-19).

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

Towards robust and accurate estimates of the incubation time distribution, with focus on upper tail probabilities and SARS-CoV-2 infection.

Quarantine length for individuals who have been at risk for infection with SARS-CoV-2 has been based on estimates of the incubation time distribution. The time of infection is often not known exactly, yielding data with an interval censored time origin. We give a detailed account of the data structure, likelihood formulation and assumptions usually made in the literature: (i) the risk of infection is assumed constant on the exposure window and (ii) the incubation time follows a specific parametric distribution. The impact of these assumptions remains unclear, especially for the right tail of the distribution which informs quarantine policy. We quantified bias in percentiles by means of simulation studies that mimic reality as close as possible. If assumption (i) is not correct, then median and upper percentiles are affected similarly, whereas misspecification of the parametric approach (ii) mainly affects upper percentiles. The latter may yield considerable bias. We suggest a semiparametric method that provides more robust estimates without the need of a parametric choice. Additionally, we used a simulation study to evaluate a method that has been suggested if all infection times are left censored. It assumes that the width of the interval from infection to latest possible exposure follows a uniform distribution. This assumption gave biased results in the exponential phase of an outbreak. Our application to open source data suggests that focus should be on the level of information in the observations, as expressed by the width of exposure windows, rather than the number of observations.

A Prospective Approach on Covid-19 Forecasting Using LSTM

The novel Corona virus has been proclaimed as a worldwide pandemic through World Health Organization in the March 2020 has immensely affected the world with its ferocity. By observation, the scientists got to know that it transmits from one human to other by droplets which range from larger respiratory droplets to smaller aerosols or direct contact with an infected person. Its impurity has been assessed to have an incubation time of 6.4 days than a simple reproduction amount of 2.24-3.58.[19] The transmission rate and spread of infection is quite rapid as compared to other fatal viral infections encountered till date. A massive loss of human life was faced even by the developed countries which had the best health-care facilities. According to WHO, COVID-19 has been confirmed in 238,521,855 people over the world, with 4,863,818 deaths as of October 9th, 2021. After experiencing the second covid wave, the number of cases had got dropped drastically but the increase in their number in the recent days is a major cause of concern. This stresses us to build some prediction models which could help in providing relief to the virus-prone areas. In this study, we are using time series for predicting forthcoming cases of corona virus. © 2022 IEEE.

Peculiarities of Periodontal Pocket Microbiome in Patients with Generalized Periodontitis in the Post-Covid Period

The oral cavity, like the lungs, is often referred to as the <<ecological niche of commensal, symbiotic, and pathogenic or-ganisms,>> and the emigration and elimination of microbes between them are constant, ensuring a healthy distribution of saprophytic microorganisms that maintains organ, tissue, and immune homeostasis. The prolonged hospital stays due to COVID-19 complications, cross-infection, oxygenation therapy through the mask or incubation, and long-term intravenous infusions limit the patient's ability to care about the oral cavity, regularly clean teeth, floss interdental, etc., which creates extremely favorable conditions for colonization by aerobic and anaerobic pathogens of the oral cavity and periodontal pockets and leads to the rapid progression of chronic generalized periodontitis in this category of patients in the future. The goal of the study was to assess the state of the microbiome of the periodontal pockets of dental patients in the post-covid period. Methods. The object of the study was 140 patients with generalized periodontitis of the I and II stages of development in the chronic course (GP), among which 80 patients had coronavirus disease in the closest past. The patients were randomized by age, sex, and stage of GP development. The diagnosis of periodontal disease was established according to the classification by Danilevskyi. The bacteriological material for aerobic and facultative anaerobic microflora and yeast-like fungi was collected from periodontal pockets with a calibrated bacteriological loop and immediately seeded on blood agar. Results. Significant qualitative and quantitative changes in the nature of the oral microbiocenosis were observed in patients with GP after the recent coronavirus disease, compared with similar patients who did not suffer from COVID-19. We have noticed almost complete disappearance of bacteria that belong to the transient representatives of the oral microflora such as Neisseria, corynebacteria (diphtheria), micrococci, and lac-tobacilli. The main resident representatives of the oral microflora, i.e., alpha-hemolytic Streptococci of the mitis group, were found in all healthy individuals and patients of groups A and C, but in 30.0 +/- 4.58% of patients in group B, alpha-hemolytic streptococci in the contents of periodontal pockets are present in quantities not available for detection by the applied method (<2.7 lg CCU/mL). In terms of species, Streptococcus oralis and Streptococcus salivarius are more characteris-tic in gingival crevicular fluid in healthy individuals (93.8% of selected strains). In 68.4 +/- 3.32% of patients in group A, 64.0 +/- 3.43% of patients in group B, and 67.5 +/- 3.76% of patients in group C, the dominant species were Streptococcus gordonii and Streptococcus sanguinis (p<0.01), which increased pathogenic potential as they produce streptolysin-O, inhibit complement activation, bind to fibronectine, actively form biofilms on the surface of tooth enamel and gum epithelial surface, and can act as an initiator of adhesion of periodontal pathogens. The other representatives of the resident microflora of the oral cavity - Stomatococcus mucilaginosus and Veillonella parvula for the patients of group C are also found in periodontal pockets with a significantly lower index of persistence and minimal population level. In the post-covid period, both the population level and the frequency of colonization of periodontal pockets by Staphylo-cocci and beta-hemolytic Streptococci decreases rapidly. For these patient groups, unlike for those that did not suffer from COVID-19, we did not find any case of colonization with Staphylococcus aureus, as well as beta-hemolytic Streptococci and Epidermal staphylococcus were also absent. The most characteristic in the post-covid period is a decrease in the proportion of alpha-hemolytic Streptococci, an increase in the proportion of yeast-like fungi of Candida species, as well as the appearance of a significant number of gram-negative rod-shaped bacteria (Enterobacteria and Pseudomonads). In periodontal patien s, the microbial count is approximately 2 orders of magnitude lower than in those with GP who did not suffer from COVID-19 (p<0.05). Conclusions. The overpassed coronavirus disease due to intensive antibiotic therapy leads to a marked decrease in the number of viable saprophytic microorganisms in the periodontal pockets of patients with GP. In the post-covid period for the patients with GP, there is a decrease in the level of colonization of periodontal pockets by species of resident oral microflora - alpha-hemolytic Streptococci, reduction of resident micro-organism's species, and almost complete disappearance of transient microflora. On the other hand, the frequency of colonization of periodontal pockets by fungi species, enterobacteria, and pseudomonads significantly increases. There are more expressed disorders in the periodontal pocket's microbiome for the patients with a severe and complicated course of coronavirus disease, such as post-covid pulmonary fibrosis, which requires reconsideration of approaches to therapeutic and pharmacological treatment in this category of patients.Copyright © 2022, Zabolotny Institute of Microbiology and Virology, NAS of Ukraine. All rights reserved.

Measuring Asymptomatic Growth of COVID-19, A Powerful Mechanism for Controlling the Disease Spread

Background: Coronavirus, called as, "the worst public health crisis for a generation," is a malevolent silent killer. Objective(s): One of the main concerns is containing the disease and avoiding the second wave of the pan-demic, likely to arise because of the symptomatic patients, while ensuring the safe execution of day to day tasks. Method(s): The indirect transmission of coronavirus from asymptomatic individuals during incubation time and the identification of the people who have accidentally and unknowingly come in contact with infected but asymptomatic patients pose a significant challenge to the health care providers. Result(s): Herein, for the first time, we have introduced a quantitative index;asymptomatic growth, to indicate whether the COVID-19 community spread is under control and if economic activities can be resumed. Conclusion(s): More importantly, our system provides a feasible mechanism for improving the index to a level <1, a safety level at which normal economic activities can be conducted.Copyright © 2021 Bentham Science Publishers.

COVID-19 Pandemic: Age and Temperature Related Effects

Background: Coronavirus belongs to the phylum Incertaesedis, Nidovirales order, Or-thocononaviridae subfamily. and spring up from the family of viruses that can cause the common cold, fever, shortness of breath, aches, chills, loss of smell, etc. Objective(s): As we all know;coronavirus has affected the whole world, and many patients died due to it. As the prevalence of this disease has risen, many myths have also originated like the effect of temperature on the virus;is this virus surely killed by the effect of temperature? Is the effect of this virus is more on the old age patients? In the presented compilation, we have tried to expose the actual reality behind these myths and also tried to find the morphological alteration of coronavirus from the other viruses. Method(s): The recent updates on this virus have been obtained from search engines like Pub med and Google scholar, by using COVID-19, coronavirus, Pandemic corona keywords. Result(s): After a huge search on the temperature effect on this disease, it was evident that there is no effect of temperature on the coronavirus. Due to the immunity factor, it showed its worst effect on old age people in many countries. Conclusion(s): The structure, symptoms and incubation period of coronavirus have been described in this review article. We have summarized how the coronavirus is different from others, and the effects of temperature and old age have also been discussed.Copyright © 2021 Bentham Science Publishers.

An Early Detection Of NIPAH Infectious Disease Based On Integrated Medical Features For Human Using Ensemble RBM Techniques

Background: The importance of early diagnosis of a hazardous illness cannot be overstated. The transmission rate is extremely high, especially in the current pandemic condition. The ability to predict epidemics will aid public health in reducing mortality and morbidity. Machine Learning (ML) approaches are used in the construction of an effective disease prognosis model. Furthermore, only if the model learns good associated features from the data is it possible to generate a speedy outcome. As a result, selecting features is also necessary before beginning the forecasting process. Objective(s): However, because of the virus's dynamic structure, it's difficult to predict Nipah disease and/or zoonotic infection. Furthermore, there is no clinical treatment for Nipah. The major goal of this research is to develop a prognostic model for early diagnosis of Nipah disease using a combination of several clinical factors such as symptoms, disease incubation information, and routine blood test results confirmed by a lab technician.Proposed System: The healthcare application and data are more complex to handle than other ML applications since various clinical features are assessed throughout disease manifestation. As a result, selecting the most relevant variables is critical when designing a prognosis model for any viral disease. To deal with clinical features from a vast number of features, we proposed a Restricted Boltzmann Machine (RBM) method in this research. Additionally, we employed a hybrid ensemble learning method to predict if the patient was infected with NiV after choosing features using the RBM. Data Collection: The proposed system is being implemented using the NiV infection dataset that erupted in Kozhikode, Kerala in 2018 and 2019. Result(s): The developed stacking-based ensemble Meta classifier was successfully implemented using the python programming language, and its performance was evaluated using a variety of metrics includingaccuracy, precision, recall, f1-score, log loss, AUROC and MCC. Our proposed Stacking Ensemble Meta Classifier (SEMC) model achieved an accuracy rate of 88.3% with a log loss of 0.36. Model precision, recall, f1-score, AUROC, and MCC value were 92.5%, 89.2%, 90.9%, 92.1%, and 0.74 respectively. In addition, we calculated the gravitational pull of each feature using the SHAP approach and discovered that altered sensorium, fever, headache, and cough were the most critical clinical indicators that distinguished NiVD infection from our dataset. Therefore, this classification may assist the pathologist in diagnosing NiVD with symptoms before performing the RT-PCR medical test. Conclusion(s): Using our proposed SEMC technique, we developed a prognostic model for the diagnosis of Nipah in humans. The proposed technique's discriminatory efficiency exhibited good NiVD diagnosis efficacy. We anticipate that this model will aid medics in determining a prognosis more quickly during future epidemics. However, to achieve maximum accuracy, the model requires more unique samples.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Isolation and Antibiotic Susceptibility Profile of Salmonella spp. from Patients in a Tertiary Care Hospital in Thailand

Objective: The impact of COVID-19 on the number and antibiogram profile of Salmonella was studied between January 2018 and December 2021. The present time period included years before the COVID-19 pandemic, which are 2018 and 2019, and during the pandemic, which are 2020 and 2021. Material(s) and Method(s): Salmonella infections were classified into eight distinct serogroups using slide agglutination with specific antisera (A, B, C, D, E, F, G, and I). The susceptibility to antimicrobial agents were evaluated by the standard disk diffusion method. Result(s): Four hundred fifty-one isolates were detected (139 in 2018, 119 in 2019, 102 in 2021, and 91 in 2021). Salmonella infection decreased by 25.2% from 258 isolates in 2018 and 2019 to 193 in 2020 and 2021. When comparing Salmonella infections in different age groups (0 to 10, 11 to 20, 21 to 30, 31 to 40, 41 to 50, 51 to 60, 61 to 70, and older than 70 years), before and during COVID-19, statistical significance was noted only in patients aged 11 to 20 (p=0.016). For clinical specimens (stool, blood, urine, pus, etc.), statistical significance was found only in blood specimens (p=0.036). The four most predominant Salmonella serogroups were B (31.1%), C (30.6%), E (15.7%), and D (11.4%). S. Typhi was present in 2.1% (4/193) of Salmonella isolates during COVID-19. The findings of a susceptibility test using the disk diffusion method for four commonly used drugs in treatment of severe salmonellosis as ampicillin, cefotaxime, ciprofloxacin, trimethoprim/sulfamethoxazole, before and during COVID-19 demonstrated statistical significance only in Salmonella serogroup D (p=0.028). Overall, drug susceptibility of Salmonella serogroup B, C, D, and E was ampicillin (range 15.1% to 55.9%), cefotaxime (range 66.7% to 100%), ciprofloxacin (range 18.8% to 59.1%), and trimethoprim/sulfamethoxazole (range 70.0% to 93.8%). Conclusion(s): The present study results suggested the importance of monitoring the prevalence of Salmonella at a hospital in Bangkok. The antibiogram of susceptibility helps provide guidelines for clinician to consider empirical treatment.Copyright © 2023 JOURNAL OF THE MEDICAL ASSOCIATION OF THAILAND.

Viral enteritis in cattle: To well known viruses and beyond

Livestock products supply about 13 percent of energy and 28 percent of protein in diets consumed worldwide. Diarrhea is a leading cause of sickness and death of beef and dairy calves in their first month of life and also affecting adult cattle, resulting in large economic losses and a negative impact on animal welfare. Despite the usual multifactorial origin, viruses are generally involved, being among the most important causes of diarrhea. There are several viruses that have been confirmed as etiological agents (i.e., rotavirus and coronavirus), and some viruses that are not yet confirmed as etiological agents. This review summarizes the viruses that have been detected in the enteric tract of cattle and tries to deepen and gather knowledge about them.Copyright © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

COVID-19: Insights into Structure-Function Relationship, Drug Targets and Drug Design Strategies for Novel-Coronavirus 2019

Background: Novel coronavirus (2019-nCov) imposed deadly health calamity with unexpected disastrous situation alarming the globe for urgent treatment regimes. World Health Organization (WHO) termed the coronavirus disease as COVID-2019 on February 11, 2020 and announced its outbreak as pandemic on 11 March 2020. The first infection was noticed in Wuhan, Hubei province, China, in December 2019, and it is believed that the corona-virus is transmitted to humans through bats as a reservoir involving human to human transfer. However, the proper intermediary transmission channel is yet to be unestablished. Method(s): Elderly populations and patients with concomitant symptoms are more at risk as compared to middle-aged patients as it may progress to pneumonia followed by severe acute respiratory syndrome (SARS) and multi-organ failure. Morbidity rates estimated in patients are less, i.e., 2-3%, but the dearth of a specific treatment strategy to prevent coronavirus infection is a major concern. Result(s): Currently, anti-viral and anti-malarial drugs are in practice for the management of COVID-19 disease along with plasma therapy in the absence of a potent vaccine. Besides, home isolation and social distancing are the precautionary measures adopted by many countries to minimize the spread of infection. Various studies have been conducted, and numerous are still going on to establish specific treatment for COVID-19. Conclusion(s): In this review, we summarized information on the structural components of COVID19 virus with special emphasis on the virus genome, life cycle, the importance of protease enzyme, the role of spike proteins in viral replication, validated drug targets, ongoing effective treatments for COVID-19 management and the latest research on drug design to develop anti-CoV drugs.Copyright © 2021 Bentham Science Publishers.