What's Left in Your List? An Analysis of Business Database Holdings, Funding Sources, Cancelations, and the Impact of COVID-19 at Top Ranking U.S. Universities

Business and economics related databases and data sets are the most vital resources for supporting scholarly research and the curriculum at colleges and schools of business, and these resources evolve rapidly and are subject to significant price fluctuations. In this study, the top-ranking U.S. universities according to the U.S. News and World Report with Association to Advance Collegiate Schools of Business (AACSB) accreditation were surveyed about their subscriptions to databases, WRDS data sets, and Bloomberg Terminals to create a benchmark. In this survey, these institutions were also asked to provide information about funding source or cost-sharing changes and cancelations brought forth by budgetary pressures from COVID-19. The intent was to provide a snapshot of the impact of COVID-19 but to also provide guidance to institutions facing similar budgetary pressures in a future financial crisis. [ FROM AUTHOR] Copyright of Journal of Business & Finance Librarianship is the property of Taylor & Francis Ltd 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.)

Turkey-based COVID-19 publications in pediatrics: A bibliographic analysis

Aim: The aim of this research is to analyze the pediatric COVID-19 literature published in Turkey and to guide future research. Material(s) and Method(s): Between 11.03.2010 and 11.12.2022, the Web of Science (WoS) All Databases collection was searched for publications related to COVID-19 and pediatric patients. The keywords used during this search were coronavirus-19, COVID-19, SARS-CoV-2, novel coronavirus, 2019-nCoV, pandemic, and/or pediatric, pediatric, children, child. After this search, the selected publications were scanned one by one to determine whether they were suitable for the present study. Authors, organizations, journals, document types, distribution of publications by years (months), most used keywords were obtained from the Web of Science (WoS) All Databases collection. Descriptive analyzes were made from all these obtained data. Result(s): The number of COVID-19 publications originating from Turkey in the field of pediatrics was determined as 375. 48.2% of all publications were published in 2022. These 375 publications were published in 167 different journals. In these publications, the most active author, journal and organization were Yasemin Ozsurekci, Turkish Archives of Pediatrics and University of Health Sciences, Turkey, respectively. The most commonly used keywords were ''child, patient, pandemic, SARS-CoV and vaccine. The most active document types were research articles (295 (78.6%)), editorial materials (15 (4.0%)), letters (43 (11.5%)) and review articles (22 (5.9%)). Discussion(s): We analyzed all articles about COVID-19 from Turkey in the field of pediatrics published so far in the WoS Databases collection. It is obvious that a large literature has emerged in our country on pediatric patients, although not as much as in adults. The long-term adverse effects of the pandemic on pediatric practice and especially on children will need to be evaluated in more detail in future research.Copyright © 2023, Derman Medical Publishing. All rights reserved.

Current status and hotspot of Lonicera Japonica Flos research based on bibliometrics.

In order to comprehensively understand the research hotspots and development trends of Lonicera Japonica Flos in the past 20 years, and to provide intuitive data reference and objective opinions and suggestions for subsequent related research in this field, this study collected 8 871 Chinese literature and 311 English literature related to Lonicera Japonica Flos research in the core collection databases of Wanfang Data), CNKI and Web of Science (WOS) from 2002 to 2021, and conducted bibliometric and visual analysis using vosviewer. The results showed that the research on the active components of Lonicera Japonica Flos based on phenolic acid components, the research on the mechanism of novel coronavirus pneumonia based on data mining and molecular docking technology, and the pharmacological research on the anti-inflammatory and antiviral properties of Lonicera Japonica Flos are the three hot research directions in the may become the future research direction. In this paper, we analyze the research on Lonicera Japonica Flos from five aspects: active ingredients, research methods, formulation and preparation, pharmacological effects and clinical applications, aiming to reveal the research hotspots, frontiers and development trends in this field and provide predictions and references for future research.Copyright © Drug Evaluation Research 2022.

Drug discovery for coronavirus disease 2019 based on text mining and biomedical databases.

Objective Based on text mining technology and biomedical database, data mining and analysis of coronavirus disease 2019 (COVID-19) were carried out, and COVID-19 and its main symptoms related to fever, cough and respiratory disorders were explored. Methods The common targets of COVID-19 and its main symptoms cough, fever and respiratory disorder were obtained by GenCLiP 3 website, Gene ontology in metascape database (GO) and pathway enrichment analysis, then STRING database and Cytoscape software were used to construct the protein interaction network of common targets, the core genes were screened and obtained. DGIdb database and Symmap database were used to predict the therapeutic drugs of traditional Chinese and Western medicine for the core genes. Results A total of 28 gene targets of COVID-19 and its main symptoms were obtained, including 16 core genes such as IL2, IL1B and CCL2. Through the screening of DGIdb database, 28 chemicals interacting with 16 key targets were obtained, including thalidomide, leflunomide and cyclosporine et al. And 70 kinds of Chinese meteria medica including Polygonum cuspidatum, Astragalus membranaceus and aloe. Conclusion The pathological mechanism of COVID-19 and its main symptoms may be related to 28 common genes such as CD4, KNG1 and VEGFA, which may participate in the pathological process of COVID-19 by mediating TNF, IL-17 and other signal pathways. Potentially effective drugs may play a role in the treatment of COVID-19 through action related target pathway.Copyright © 2022 Tianjin Press of Chinese Herbal Medicines. All Rights Reserved.

Analysis and Prediction of Student Performance Based on Moodle Log Data using Machine Learning Techniques

During the COVID-19 Pandemic, many universities in Thailand were mostly locked down and classrooms were also transformed into a fully online format. It was challenging for teachers to manage online learning and especially to track student behavior since the teacher could not observe and notify students. To alleviate this problem, one solution that has become increasingly important is the prediction of student performance based on their log data. This study, therefore, aims to analyze student behavior data by applying Predictive Analytics through Moodle Log for approximately 54,803 events. Six Machine Learning Classifiers (Neural Network, Random Forest, Decision Tree, Logistic Regression, Linear Regression, and Support Vector Machine) were applied to predict student performance. Further, we attained a comparison of the effectiveness of early prediction for four stages at 25%, 50%, 75%, and 100% of the course. The prediction models could guide future studies, motivate self-preparation and reduce dropout rates. In the experiment, the model with 5-fold cross-validation was evaluated. Results indicated that the Decision Tree performed best at 81.10% upon course completion. Meanwhile, the SVM had the best result at 86.90% at the first stage, at 25% of the course, and Linear Regression performed with the best efficiency at the middle stages at 70.80%, and 80.20% respectively. The results could be applied to other courses and on a larger e-learning systems log that has similar student activity conditions and this could contribute to more accurate student performance prediction © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.

Digital Evaluation of Undergraduates' Knowledge about Scientific Research in Databases during the COVID-19 Pandemic

Background: COVID-19 pandemic times forced health education to go online, and, due to this necessity, long-term difficulties in education such as bibliographic search in databases like PubMed might have worsened even when platforms such as PubMed provide helping mechanisms to the user. These difficulties or even complete lack of knowledge are, unfortunately, not well documented in the literature. Therefore, this study aimed to describe doubts, lack of knowledge and questions of researchers regarding bibliographic research in PubMed as well as to solve all of those doubts by developing a didactic e-book in relation to bibliographic research in PubMed. Methods: This cross-sectional and populational-based study was conducted between January and April 2021. In northern Brazil, a total of 105 dentistry undergraduate students (DUS) received an anonymous digital form (Google® Forms Platform) using a non-probabilistic "snowball” sampling technique. The digital form was composed of four blocks of dichotomous and multiple-choice questions. After signing the informed consent term, the DUS were divided into three groups according to their period/semester in the dentistry program during the study time (G1: 1st period/semester;G2: 5th period/semester and G3: 10th period/semester). A total of 25 questions referring to demographic, educational and knowledge data about how to do scientific research and how to use bibliographic search in PubMed were asked, and all data were presented as descriptive percentages and then analyzed using the Chi square and G tests. Results: From 105 (100%), G1 had 29/105 (27.6%);G2 had 37/105 (35.2%);G3 had 39/105 (37.2%), the average age was 22.34 years and most participants were female 85/105 (81%). Among our sample, 56/105 (53.4%) had not used any type of search strategy, and 96/105 (91.4%) used database research methods. The main database for literature search used was Scielo 92/105 (87.6%), and 63/105 (60%) had general questions or doubts about bibliographic research. All these data had statistical significance p < 0.0001. Conclusions: The results demonstrate a lack of knowledge and doubts in DUS from three different periods/semesters, and this collected information can help in the formation of didactic material to solve such doubts. © 2023 by the authors.

Sequestration methodology in practice through evaluation of joint demographic distributions of 54,185 patients in the Medical Imaging and Data Resource Center (MIDRC) data commons

The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available data commons as well as a sequestered commons for performance evaluation of algorithms. This work sought to evaluate the currently implemented methodology for apportioning data to the public and sequestered data commons by investigating the resulting distributions of joint demographic characteristics between the public and sequestered commons. 54,185 patients whose de-identified imaging studies and metadata had been submitted to MIDRC were previously separated into public and sequestered commons using a multi-dimensional stratified sampling method, resulting in 41,556 patients (77%) in the public commons and 12,629 patients (23%) in the sequestered commons. To compare the balance obtained in the joint distributions of patient characteristics from use of the developed sequestration method, patients from each commons were separated into bins, representing a unique combination of the demographic variables of COVID-19 status, age, race, and sex assigned at birth. The joint distributions of patients were visualized, and the absolute and percent difference in each bin from an exact 77:23 split of the data were calculated. Results indicated 75.9% of bins obtained differences of less than 15 patients, with a median difference of 3.6 from the total data for both public and sequestered commons. Joint distributions of patient characteristics in both the public and sequestered commons closely matched each other as well as that of the total data, indicating the sequestration by stratified sampling method has operated as intended. © 2023 SPIE.

Identification of host factors that bind to the 5 ' end of the MERS-CoV RNA genome

Plain language summaryMERS-CoV is a virus that causes a severe illness in the nose, mouth and throat of humans. It is a zoonotic virus, which means that it can spread from animals to humans. MERS-CoV was first found in Saudi Arabia in 2012 and continues to pose a threat to public health. Interactions between the virus and human cells and proteins are important to establishing infection. Understanding these interactions is important for the development of drugs to treat viral infections. Here, we have identified some proteins that interact with MERS-CoV. Tweetable A proteomic approach for the identification of cellular proteins that interact with the 5 '-terminal region of MERS-CoV RNA genome. #MERS-CoV #RNA_viruses. Aim: The aim of this study was to identify host factors that interact with the 5 ' end of the MERS-CoV RNA genome. Materials & methods: RNA affinity chromatography followed by mass spectrometry analysis was used to identify the binding of host factors in Vero E6 cells. Results: A total of 59 host factors that bound the MERS-CoV RNA genome in non-infected Vero E6 cells were identified. Most of the identified cellular proteins were previously reported to interact with the genome of other RNA viruses. We validated our mass spectrometry results using western blotting. Conclusion: These data enhance our knowledge about the RNA-host interactions of coronaviruses, which could serve as targets for developing antiviral therapeutics against MERS-CoV.

Two years of SARS-CoV-2 pandemic and COVID-19 in Lombardy, Italy.

Lombardy, the largest and most densely populated Italian region, was severely hit in February 2020 by the first pandemic wave of SARS-CoV-2 and associated COVID-19. Since then, additional infection waves spread in the region. The aim of this study was to compare the first with the subsequent waves using the administrative database of the Lombardy Welfare directorate. In the time frames of the four 2020-2022 waves, the absolute number of infected cases, sites of management and crude mortality rate associated with SARS-CoV-2 positivity were extracted from the database. Infected cases progressively increased in the region by approximately 5-fold in the second versus the first wave, 4-fold in the third and 20-fold during the most recent wave mainly associated with the omicron variant. The crude death decreased from 18.7% in the first to 2% in the second and third wave to reach a 0.3% nadir at the time of the fourth wave. This study confirms that in Lombardy outcomes of public health and health-care relevance such as deaths and number of hospitalizations declined dramatically across the four virus waves and reached very low values in 2022 when, at variance with the first three SARS-CoV-2 waves, the majority of infected cases had been previously vaccinated.

Lessons for Vietnam on the Use of Digital Technologies to Support Patient-Centered Care in Low- and Middle-Income Countries in the Asia-Pacific Region: Scoping Review.

BACKGROUND: A rapidly aging population, a shifting disease burden and the ongoing threat of infectious disease outbreaks pose major concerns for Vietnam's health care system. Health disparities are evident in many parts of the country, especially in rural areas, and the population faces inequitable access to patient-centered health care. Vietnam must therefore explore and implement advanced solutions to the provision of patient-centered care, with a view to reducing pressures on the health care system simultaneously. The use of digital health technologies (DHTs) may be one of these solutions. OBJECTIVE: This study aimed to identify the application of DHTs to support the provision of patient-centered care in low- and middle-income countries in the Asia-Pacific region (APR) and to draw lessons for Vietnam. METHODS: A scoping review was undertaken. Systematic searches of 7 databases were conducted in January 2022 to identify publications on DHTs and patient-centered care in the APR. Thematic analysis was conducted, and DHTs were classified using the National Institute for Health and Care Excellence evidence standards framework for DHTs (tiers A, B, and C). Reporting was in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. RESULTS: Of the 264 publications identified, 45 (17%) met the inclusion criteria. The majority of the DHTs were classified as tier C (15/33, 45%), followed by tier B (14/33, 42%) and tier A (4/33, 12%). At an individual level, DHTs increased accessibility of health care and health-related information, supported individuals in self-management, and led to improvements in clinical and quality-of-life outcomes. At a systems level, DHTs supported patient-centered outcomes by increasing efficiency, reducing strain on health care resources, and supporting patient-centered clinical practice. The most frequently reported enablers for the use of DHTs for patient-centered care included alignment of DHTs with users' individual needs, ease of use, availability of direct support from health care professionals, provision of technical support as well as user education and training, appropriate governance of privacy and security, and cross-sectorial collaboration. Common barriers included low user literacy and digital literacy, limited user access to DHT infrastructure, and a lack of policies and protocols to guide the implementation and use of DHTs. CONCLUSIONS: The use of DHTs is a viable option to increase equitable access to quality, patient-centered care across Vietnam and simultaneously reduce pressures on the health care system. Vietnam can take advantage of the lessons learned by other low- and middle-income countries in the APR when developing a national road map to digital health transformation. Recommendations that Vietnamese policy makers may consider include emphasizing stakeholder engagement, strengthening digital literacy, supporting the improvement of DHT infrastructure, increasing cross-sectorial collaboration, strengthening governance of cybersecurity, and leading the way in DHT uptake.

Trends in the Quality of Primary Care and Acute Care in Korea From 2008 to 2020: A Cross-sectional Study.

OBJECTIVES: Measuring the quality of care is paramount to inform policies for healthcare services. Nevertheless, little is known about the quality of primary care and acute care provided in Korea. This study investigated trends in the quality of primary care and acute care. METHODS: Case-fatality rates and avoidable hospitalization rates were used as performance indicators to assess the quality of primary care and acute care. Admission data for the period 2008 to 2020 were extracted from the National Health Insurance Claims Database. Case-fatality rates and avoidable hospitalization rates were standardized by age and sex to adjust for patients' characteristics over time, and significant changes in the rates were identified by joinpoint regression. RESULTS: The average annual percent change in age-/sex-standardized case-fatality rates for acute myocardial infarction was -2.3% (95% confidence interval, -4.6 to 0.0). For hemorrhagic and ischemic stroke, the age-/sex-standardized case-fatality rates were 21.8% and 5.9%, respectively in 2020; these rates decreased since 2008 (27.1 and 8.7%, respectively). The average annual percent change in age-/sex-standardized avoidable hospitalization rates ranged from -9.4% to -3.0%, with statistically significant changes between 2008 and 2020. In 2020, the avoidable hospitalization rates decreased considerably compared with the 2019 rate because of the coronavirus disease 2019 pandemic. CONCLUSIONS: The avoidable hospitalization rates and case-fatality rates decreased overall during the past decade, but they were relatively high compared with other countries. Strengthening primary care is an essential requirement to improve patient health outcomes in the rapidly aging Korean population.

Web Resources for SARS-CoV-2 Genomic Database, Annotation, Analysis and Variant Tracking.

The SARS-CoV-2 genomic data continue to grow, providing valuable information for researchers and public health officials. Genomic analysis of these data sheds light on the transmission and evolution of the virus. To aid in SARS-CoV-2 genomic analysis, many web resources have been developed to store, collate, analyze, and visualize the genomic data. This review summarizes web resources used for the SARS-CoV-2 genomic epidemiology, covering data management and sharing, genomic annotation, analysis, and variant tracking. The challenges and further expectations for these web resources are also discussed. Finally, we highlight the importance and need for continued development and improvement of related web resources to effectively track the spread and understand the evolution of the virus.

Combining virtual screening with cis-/trans-cleavage enzymatic assays effectively reveals broad-spectrum inhibitors that target the main proteases of SARS-CoV-2 and MERS-CoV.

The main protease (Mpro) of SARS-CoV-2 is essential for viral replication, which suggests that the Mpro is a critical target in the development of small molecules to treat COVID-19. This study used an in-silico prediction approach to investigate the complex structure of SARS-CoV-2 Mpro in compounds from the United States National Cancer Institute (NCI) database, then validate potential inhibitory compounds against the SARS-CoV-2 Mpro in cis- and trans-cleavage proteolytic assays. Virtual screening of ∼280,000 compounds from the NCI database identified 10 compounds with highest site-moiety map scores. Compound NSC89640 (coded C1) showed marked inhibitory activity against the SARS-CoV-2 Mpro in cis-/trans-cleavage assays. C1 strongly inhibited SARS-CoV-2 Mpro enzymatic activity, with a half maximal inhibitory concentration (IC50) of 2.69 µM and a selectivity index (SI) of >74.35. The C1 structure served as a template to identify structural analogs based on AtomPair fingerprints to refine and verify structure-function associations. Mpro-mediated cis-/trans-cleavage assays conducted with the structural analogs revealed that compound NSC89641 (coded D2) exhibited the highest inhibitory potency against SARS-CoV-2 Mpro enzymatic activity, with an IC50 of 3.05 µM and a SI of >65.57. Compounds C1 and D2 also displayed inhibitory activity against MERS-CoV-2 with an IC50 of <3.5 µM. Thus, C1 shows potential as an effective Mpro inhibitor of SARS-CoV-2 and MERS-CoV. Our rigorous study framework efficiently identified lead compounds targeting the SARS-CoV-2 Mpro and MERS-CoV Mpro.

Missing Americans: Early death in the United States-1933-2021.

We assessed how many US deaths would have been averted each year, 1933-2021, if US age-specific mortality rates had equaled the average of 21 other wealthy nations. We refer to these excess US deaths as "missing Americans." The United States had lower mortality rates than peer countries in the 1930s-1950s and similar mortality in the 1960s and 1970s. Beginning in the 1980s, however, the United States began experiencing a steady increase in the number of missing Americans, reaching 622,534 in 2019 alone. Excess US deaths surged during the COVID-19 pandemic, reaching 1,009,467 in 2020 and 1,090,103 in 2021. Excess US mortality was particularly pronounced for persons under 65 years. In 2020 and 2021, half of all US deaths under 65 years and 90% of the increase in under-65 mortality from 2019 to 2021 would have been avoided if the United States had the mortality rates of its peers. In 2021, there were 26.4 million years of life lost due to excess US mortality relative to peer nations, and 49% of all missing Americans died before age 65. Black and Native Americans made up a disproportionate share of excess US deaths, although the majority of missing Americans were White.

Facilitating systems-level analyses of all-cause and Covid-mediated sepsis through SeptiSearch, a manually-curated compendium of dysregulated gene sets.

Background: Sepsis is a dysfunctional host response to infection. The syndrome leads to millions of deaths annually (19.7% of all deaths in 2017) and is the cause of most deaths from severe Covid infections. High throughput sequencing or 'omics' experiments in molecular and clinical sepsis research have been widely utilized to identify new diagnostics and therapies. Transcriptomics, quantifying gene expression, has dominated these studies, due to the efficiency of measuring gene expression in tissues and the technical accuracy of technologies like RNA-Seq. Objective: Most of these studies seek to uncover novel mechanistic insights into sepsis pathogenesis and diagnostic gene signatures by identifying genes differentially expressed between two or more relevant conditions. However, little effort has been made, to date, to aggregate this knowledge from such studies. In this study we sought to build a compendium of previously described gene sets that combines knowledge gained from sepsis-associated studies. This would enable the identification of genes most associated with sepsis pathogenesis, and the description of the molecular pathways commonly associated with sepsis. Methods: PubMed was searched for studies using transcriptomics to characterize acute infection/sepsis and severe sepsis (i.e., sepsis combined with organ failure). Several studies were identified that used transcriptomics to identify differentially expressed (DE) genes, predictive/prognostic signatures, and underlying molecular responses and pathways. The molecules included in each gene set were collected, in addition to the relevant study metadata (e.g., patient groups used for comparison, sample collection time point, tissue type, etc.). Results: After performing extensive literature curation of 74 sepsis-related publications involving transcriptomics, 103 unique gene sets (comprising 20,899 unique genes) from thousands of patients were collated together with associated metadata. Frequently described genes included in gene sets as well as the molecular mechanisms they were involved in were identified. These mechanisms included neutrophil degranulation, generation of second messenger molecules, IL-4 and -13 signaling, and IL-10 signaling among many others. The database, which we named SeptiSearch, is made available in a web application created using the Shiny framework in R, (available at https://septisearch.ca). Conclusions: SeptiSearch provides members of the sepsis community the bioinformatic tools needed to leverage and explore the gene sets contained in the database. This will allow the gene sets to be further scrutinized and analyzed for their enrichment in user-submitted gene expression data and used for validation of in-house gene sets/signatures.

COVID-19 Conceptual Modeling: Single-Center Cross-Sectional Study.

BACKGROUND: Conceptual models are abstract representations of the real world. They are used to refine medical and nonmedical health care scopes of service. During the COVID-19 pandemic, numerous analytic predictive models were generated aiming to evaluate the impact of implemented policies on mitigating the spread of the virus. The models also aimed to examine the psychosocial factors that might govern the general population's adherence to these policies and to identify factors that could affect COVID-19 vaccine uptake and allocation. The outcomes of these analytic models helped set priorities when vaccines were available and predicted readiness to resume non-COVID-19 health care services. OBJECTIVE: The objective of our research was to implement a descriptive-analytical conceptual model that analyzes the data of all COVID-19-positive cases admitted to our hospital from March 1 to May 31, 2020, the initial wave of the pandemic, the time interval during which local policies and clinical guidelines were constantly updated to mitigate the local effects of COVID-19, minimize mortality, reduce intensive care unit (ICU) admission, and ensure the safety of health care providers. The primary outcome of interest was to identify factors that might affect mortality and ICU admission rates and the impact of the implemented policy on COVID-19 positivity among health care providers. The secondary outcome of interest was to evaluate the sensitivity of the COVID-19 visual score, implemented by the Saudi Arabia Ministry of Health for COVID-19 risk assessment, and CURB-65 (confusion, urea, respiratory rate, blood pressure, and age >65 years) scores in predicting ICU admission or mortality among the study population. METHODS: This was a cross-sectional study. The relevant attributes were constructed based on research findings from the first wave of the pandemic and were electronically retrieved from the hospital database. Analysis of the conceptual model was based on the International Society for Pharmacoeconomics and Outcomes Research guidelines and the Society for Medical Decision-Making. RESULTS: A total of 275 individuals tested positive for COVID-19 within the study design interval. The conceptualization model revealed a low-risk population based on the following attributes: a mean age of 42 (SD 19.2) years; 19% (51/275) of the study population being older adults ≥60 years of age; 80% (220/275) having a CURB-65 score <4; 53% (147/275) having no comorbidities; 5% (13/275) having extreme obesity; and 20% (55/275) having a significant hematological abnormality. The overall rate of ICU admission for the study population was 5% (13/275), and the overall mortality rate was 1.5% (4/275). The multivariate correlation analysis revealed that a high-selectivity approach was adopted, resulting in patients with complex medical problems not being sent to MOH isolation facilities. Furthermore, 5% of health care providers tested positive for COVID-19, none of whom were health care providers allocated to the COVID-19 screening areas, indicating the effectiveness of the policy implemented to ensure the safety of health care providers. CONCLUSIONS: Based on the conceptual model outcome, the selectivity applied in retaining high-risk populations within the hospital might have contributed to the observed low mortality rate, without increasing the risk to attending health care providers.

Thirty-Day readmissions among COVID-19 patients hospitalized during the early pandemic in the United States: Insights from the Nationwide Readmissions Database.

BACKGROUND: Hospital readmissions are core indicators of the quality of health care provision. OBJECTIVE: To understand factors associated with 30-day, all-cause hospital readmission rate for patients with COVID-19 in the United States during the early pandemic by utilizing the Nationwide Readmissions Database. METHODS: This retrospective study characterized the 30-day, all-cause hospital readmission rate for patients with COVID-19 in the United States during the early pandemic by utilizing the Nationwide Readmissions Database. RESULTS: The 30-day, all-cause hospital readmission rate in this population was 3.2%. We found the most common diagnoses at readmission to be sepsis, acute kidney injury, and pneumonia. Chronic alcoholic liver cirrhosis and congestive heart failure were prominent predictors of readmission among patients with COVID-19. Moreover, we found that younger patients and patients from economically disadvantaged backgrounds were at higher risk of 30-day readmission. Acute complications during index hospitalization, including acute coronary syndrome, congestive heart failure, acute kidney injury, mechanical ventilation, and renal replacement therapy, also increased the risk of 30-day readmission for patients with COVID-19. CONCLUSION: Based on the results of our study, we advise clinicians to promptly recognize patients with COVID-19 who are at high risk of readmission, and to subsequently manage their underlying comorbidities, to institute timely discharge planning, and to allocate resources to underprivileged patients in order to decrease the risk of 30-day hospital readmissions.

Android Based Smart Appointment System (SAS) for Booking and Interacting with Teacher for Counselling

An appointment system is going to be popular nowadays. The necessity of these types of systems is increasing day by day specially in education sector. Worldwide COVID-19 pandemic provoke the demand of these types of application. In this research paper, an Android-based appointment is built for booking an appointment and communicating with the teacher. To use this system both student and teacher have to an android device with connection of the internet. A single android application will be used for both types of users. Students can get the information of all teachers and book an appointment with teachers and teachers can accept or decline this appointment. Java programming language is used for this system and Google's Firebase is used for the database. In addition, the modern coding Architecture pattern MVVM (Model- View-View Model) followed to build this system. Hopefully, this system saves valuable time and makes the teacher-student interaction journey easier. © 2023 IEEE.

A literature review of the main factors influencing the e-commerce and last-mile delivery projects during COVID-19 pandemic

Humanity has faced many pandemics throughout its history with COVID-19 pandemic being the most recent. Each pandemic requires the implementation of a series of restrictions and measures to reform local societies or even society on a global scale. Scientific and technological innovations have ensured the survival of mankind and consequently the establishment of new habits and trends. One of these reforms concerns the transport of goods and in particular urban logistics and last-mile delivery. Despite the increasing use of e-commerce, the average amount of money spent per month and per buyer has decreased;in times of uncertainty, people prefer to postpone big purchases and focus more on everyday products. These purchases have generated an increase in demand for the transport of goods and put significant pressure on the supply chain. For this reason, actions have been developed to improve logistics, in particular last-mile delivery, with the introduction of environmentally friendly and small vehicles, among others. In order to be able to trace the evolution of the combination of the COVID-19 pandemic and logistics spatially and temporally, the manuscript focused as a first step on the analysis of the literature entered in the main databases dedicated to scientific publications, returning some 2,227 indexed articles from 2000 to 2021. The search was conducted using keywords and iterations between them. The results emphasised the need to adapt business activities to the changing situation by anticipating people's needs, creating e-commerce sites capable of accompanying customers in this delicate phase. The results obtained were analysed from a statistical point of view, laying the foundations for future investigative steps in the field of last-mile logistics and the proper planning of loading and unloading spaces for goods in urban areas. © 2023 The Authors. Published by ELSEVIER B.V.