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1.
Int J Environ Res Public Health ; 19(10)2022 05 20.
Article in English | MEDLINE | ID: covidwho-1862796

ABSTRACT

Academic coaching has been emphasized in Korean universities as an effective measure to assist students' academic achievement and success. To better assess the needs of the students, the current study investigated academic coaching intake session reports archived at a Korean university from January 2017 to August 2021 and examined students' descriptions of their academic concerns and barriers. The intake session reports were categorized according to (1) students' affiliated department tracks, namely Humanities and Social Science (HSS) and Science, Technology, Engineering, and Math (STEM) tracks, and (2) the time the coaching sessions took place, i.e., before and after the outbreak of COVID-19. Text mining analysis was conducted to calculate the frequency of keywords, their degree of centrality, and the frequency of bigrams, or the sets of two adjacent words, for each category. Wordclouds and word networks were also visualized. The results indicated that the word study was dominant in both categories, reflecting the education culture in Korea. Similarities and differences between the two categories were also reported. Based on the results, practical implications for academic coaches, educators, and university administrators were proposed, and limitations were discussed.


Subject(s)
COVID-19 , Mentoring , Data Mining , Humans , Republic of Korea , Students , Universities
2.
Methods Mol Biol ; 2449: 235-261, 2022.
Article in English | MEDLINE | ID: covidwho-1826140

ABSTRACT

Like an article narrative is deemed by an editor and referees to be worthy of being a version of record on acceptance as a publication, so must the underpinning data also be scrutinized before passing it as a version of record. Indeed without the underpinning data, a study and its conclusions cannot be reproduced at any stage of evaluation, pre- or post-publication. Likewise, an independent study without its own underpinning data also cannot be reproduced let alone be considered a replicate of the first study. The PDB is a modern marvel of achievement providing an organized open access to depositor and user of the data held there opening numerous applications. Methods for modeling protein structures and for determination of structures are still improving their precision, and artifacts of the method exist. So their accuracy is realized if they are reproduced by other methods. It is on such foundations that reproducible data mining is based. Data rates are expanding considerably be they at synchrotrons, the X-ray free electron lasers (XFELs), electron cryomicroscopes (cryoEM), or at the neutron facilities. The work of a person as a referee or user with a narrative and its underpinning data may well be complemented in future by artificial intelligence with machine learning, the former for specific refereeing and the latter for the more general validation, both ideally before publication. Examples are described involving rhenium theranostics, the anti-cancer platins and the SARS-CoV-2 main protease.


Subject(s)
Artificial Intelligence , COVID-19 , Crystallography/methods , Crystallography, X-Ray , Data Mining , Humans , Macromolecular Substances/chemistry , SARS-CoV-2 , Synchrotrons
3.
J Biomed Inform ; 130: 104081, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1819520

ABSTRACT

Process mining is a discipline sitting between data mining and process science, whose goal is to provide theoretical methods and software tools to analyse process execution data, known as event logs. Although process mining was originally conceived to facilitate business process management activities, research studies have shown the benefit of leveraging process mining in healthcare contexts. However, applying process mining tools to analyse healthcare process execution data is not straightforward. In this paper, we show a methodology to: i) prepare general practice healthcare process data for conducting a process mining analysis; ii) select and apply suitable process mining solutions for successfully executing the analysis; and iii) extract valuable insights from the obtained results, alongside leads for traditional data mining analysis. By doing so, we identified two major challenges when using process mining solutions for analysing healthcare process data, and highlighted benefits and limitations of the state-of-the-art process mining techniques when dealing with highly variable processes and large data-sets. While we provide solutions to the identified challenges, the overarching goal of this study was to detect differences between the patients' health services utilization pattern observed in 2020-during the COVID-19 pandemic and mandatory lock-downs -and the one observed in the prior four years, 2016 to 2019. By using a combination of process mining techniques and traditional data mining, we were able to demonstrate that vaccinations in Victoria did not drop drastically-as other interactions did. On the contrary, we observed a surge of influenza and pneumococcus vaccinations in 2020, as opposed to other research findings of similar studies conducted in different geographical areas.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control , Data Mining/methods , Humans , Pandemics/prevention & control , Vaccination
4.
Acta Trop ; 231: 106447, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1797339

ABSTRACT

Mosquito-borne diseases are emerging and re-emerging across the globe, especially after the COVID19 pandemic. The recent advances in text mining in infectious diseases hold the potential of providing timely access to explicit and implicit associations among information in the text. In the past few years, the availability of online text data in the form of unstructured or semi-structured text with rich content of information from this domain enables many studies to provide solutions in this area, e.g., disease-related knowledge discovery, disease surveillance, early detection system, etc. However, a recent review of text mining in the domain of mosquito-borne disease was not available to the best of our knowledge. In this review, we survey the recent works in the text mining techniques used in combating mosquito-borne diseases. We highlight the corpus sources, technologies, applications, and the challenges faced by the studies, followed by the possible future directions that can be taken further in this domain. We present a bibliometric analysis of the 294 scientific articles that have been published in Scopus and PubMed in the domain of text mining in mosquito-borne diseases, from the year 2016 to 2021. The papers were further filtered and reviewed based on the techniques used to analyze the text related to mosquito-borne diseases. Based on the corpus of 158 selected articles, we found 27 of the articles were relevant and used text mining in mosquito-borne diseases. These articles covered the majority of Zika (38.70%), Dengue (32.26%), and Malaria (29.03%), with extremely low numbers or none of the other crucial mosquito-borne diseases like chikungunya, yellow fever, West Nile fever. Twitter was the dominant corpus resource to perform text mining in mosquito-borne diseases, followed by PubMed and LexisNexis databases. Sentiment analysis was the most popular technique of text mining to understand the discourse of the disease and followed by information extraction, which dependency relation and co-occurrence-based approach to extract relations and events. Surveillance was the main usage of most of the reviewed studies and followed by treatment, which focused on the drug-disease or symptom-disease association. The advance in text mining could improve the management of mosquito-borne diseases. However, the technique and application posed many limitations and challenges, including biases like user authentication and language, real-world implementation, etc. We discussed the future direction which can be useful to expand this area and domain. This review paper contributes mainly as a library for text mining in mosquito-borne diseases and could further explore the system for other neglected diseases.


Subject(s)
COVID-19 , Dengue , Vector Borne Diseases , Zika Virus Infection , Zika Virus , Animals , Data Mining , Dengue/epidemiology , Humans , Mosquito Vectors , Zika Virus Infection/epidemiology
5.
Nagoya J Med Sci ; 84(1): 42-59, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1786418

ABSTRACT

COVID-19 is indirectly associated with various mental disorders such as anxiety, insomnia, and depression, and healthcare professionals who treat COVID-19 patients are particularly prone to severe anxiety. However, neither the anxiety of healthcare workers in non-epicenter areas nor the effects of knowledge support have been examined thus far. Participants were 458 staff working at the Toyota Regional Medical Center who completed a preliminary questionnaire of their knowledge and anxiety regarding COVID-19. Based on text mining of the questionnaire responses, participants were offered an online lecture. The effect of the lecture was analyzed using a pre- and post-lecture rating of anxiety and knowledge confidence, and quantitative text mining. The response rates were 45.6% pre- and 62.9% post-lecture. Open-ended responses regarding anxiety and knowledge were classified into seven clusters using a co-occurrence network. Before the lecture, 28.2%, 27.2%, and 20.3% of participants were interested in and anxious about "infection prevention and our hospital's response," "infection and impact on myself, family, and neighbors," and "general knowledge of COVID-19," respectively. As a result of the lecture, Likert-scale ratings for anxiety of COVID-19 decreased significantly and knowledge confidence increased significantly. These changes were confirmed by analyses of open-ended responses about anxiety, lifestyle changes, and knowledge. Positive changes were strongly linked to the topics focused on in the lecture, especially infection prevention. The anxieties about COVID-19 of healthcare workers in non-epicenter areas can be effectively reduced through questionnaire surveys and online lectures using text mining.


Subject(s)
COVID-19 , Anxiety , Data Mining , Health Personnel , Humans , SARS-CoV-2
6.
PLoS One ; 17(3): e0263265, 2022.
Article in English | MEDLINE | ID: covidwho-1765533

ABSTRACT

In the last century, the increase in traffic, human activities and industrial production have led to a diffuse presence of air pollution, which causes an increase of risk of several health conditions such as respiratory diseases. In Europe, air pollution is a serious concern that affects several areas, one of the worst ones being northern Italy, and in particular the Po Valley, an area characterized by low air quality due to a combination of high population density, industrial activity, geographical factors and weather conditions. Public health authorities and local administrations are aware of this problem, and periodically intervene with temporary traffic limitations and other regulations, often insufficient to solve the problem. In February 2020, this area was the first in Europe to be severely hit by the SARS-CoV-2 virus causing the COVID-19 disease, to which the Italian government reacted with the establishment of a drastic lockdown. This situation created the condition to study how significant is the impact of car traffic and industrial activity on the pollution in the area, as these factors were strongly reduced during the lockdown. Differently from some areas in the world, a drastic decrease in pollution measured in terms of particulate matter (PM) was not observed in the Po Valley during the lockdown, suggesting that several external factors can play a role in determining the severity of pollution. In this study, we report the case study of the city of Pavia, where data coming from 23 air quality sensors were analyzed to compare the levels measured during the lockdown with the ones coming from the same period in 2019. Our results show that, on a global scale, there was a statistically significant reduction in terms of PM levels taking into account meteorological variables that can influence pollution such as wind, temperature, humidity, rain and solar radiation. Differences can be noticed analyzing daily pollution trends too, as-compared to the study period in 2019-during the study period in 2020 pollution was higher in the morning and lower in the remaining hours.


Subject(s)
COVID-19/prevention & control , Cities/statistics & numerical data , Particulate Matter/analysis , Quarantine , COVID-19/epidemiology , Cities/epidemiology , Data Mining , Humans , Italy/epidemiology , Quarantine/statistics & numerical data , Traffic-Related Pollution/statistics & numerical data , Weather
7.
Comput Biol Med ; 145: 105457, 2022 06.
Article in English | MEDLINE | ID: covidwho-1757246

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) keeps spreading globally. Chinese medicine (CM) exerts a critical role for the prevention or therapy of COVID-19 in an integrative and holistic way. However, mining and development of early, efficient, multisite binding CMs that inhibit the cytokine storm are imminent. METHODS: The formulae were extracted retrospectively from clinical records in Hunan Province. Clinical data mining analysis and association rule analysis were employed for mining the high-frequency herbal pairs and groups from formulae. Network pharmacology methods were applied to initially explore the most critical pair's hub targets, active ingredients, and potential mechanisms. The binding power of active ingredients to the hub targets was verified by molecular docking. RESULTS: Eight hundred sixty-two prescriptions were obtained from 320 moderate COVID-19 through the Hunan Provincial Health Commission. Glycyrrhizae Radix et Rhizoma (Gancao) and Pinelliae Rhizoma (Banxia) were used with the highest frequency and support. There were 49 potential genes associated with Gancao-Banxia pair against moderate COVID-19 patients. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that Gancao-Banxia might act via inflammatory response, viral defense, and immune responses signaling pathways. IL-6 and STAT3 were the two most hub targets in the protein-protein interaction (PPI) network. The binding of five active ingredients originated from Gancao-Banxia to IL-6-STAT3 was verified by molecular docking, namely quercetin, coniferin, licochalcone a, Licoagrocarpin and (3S,6S)-3-(benzyl)-6-(4-hydroxybenzyl)piperazine-2,5-quinone, maximizing therapeutic efficacy. CONCLUSIONS: This work provided some potential candidate Chinese medicine formulas for moderate COVID-19. Among them, Gancao-Banxia was considered the most potential herbal pair. Bioinformatic data demonstrated that Gancao-Banxia pair may achieve dual inhibition of IL-6-STAT3 via directly interacting with IL-6 and STAT3, suppressing the IL-6 amplifier. SARS-CoV-2 models will be needed to validate this possibility in the future.


Subject(s)
COVID-19 , Drugs, Chinese Herbal , COVID-19/drug therapy , Data Mining , Drugs, Chinese Herbal/pharmacology , Glycyrrhiza , Humans , Interleukin-6/metabolism , Medicine, Chinese Traditional/methods , Molecular Docking Simulation , Retrospective Studies , SARS-CoV-2 , STAT3 Transcription Factor/metabolism
8.
Comput Math Methods Med ; 2022: 9735626, 2022.
Article in English | MEDLINE | ID: covidwho-1677416

ABSTRACT

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was characterized as a pandemic by the World Health Organization (WHO) in Dec. 2019. SARS-CoV-2 binds to the cell membrane through spike proteins on its surface and infects the cell. Furin, a host-cell enzyme, possesses a binding site for the spike protein. Thus, molecules that block furin could potentially be a therapeutic solution. Defensins are antimicrobial peptides that can hypothetically inhibit furin because of their arginine-rich structure. Theta-defensins, a subclass of defensins, have attracted attention as drug candidates due to their small size, unique structure, and involvement in several defense mechanisms. Theta-defensins could be a potential treatment for COVID-19 through furin inhibition and an anti-inflammatory mechanism. Note that inflammatory events are a significant and deadly condition that could happen at the later stages of COVID-19 infection. Here, the potential of theta-defensins against SARS-CoV-2 infection was investigated through in silico approaches. Based on docking analysis results, theta-defensins can function as furin inhibitors. Additionally, a novel candidate peptide against COVID-19 with optimal properties regarding antigenicity, stability, electrostatic potential, and binding strength was proposed. Further in vitro/in vivo investigations could verify the efficiency of the designed novel peptide.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/metabolism , Defensins/pharmacology , Drug Design , Furin/antagonists & inhibitors , Animals , COVID-19/drug therapy , Catalytic Domain , Cell Membrane/virology , Computer Simulation , Data Mining , Furin/chemistry , Humans , Inflammation , Models, Molecular , Molecular Docking Simulation , Peptides/chemistry , Software , Spike Glycoprotein, Coronavirus , Static Electricity
9.
Sci Total Environ ; 823: 153786, 2022 Jun 01.
Article in English | MEDLINE | ID: covidwho-1676913

ABSTRACT

In response to the COVID-19 pandemic, governments declared severe restrictions throughout 2020, presenting an unprecedented scenario of reduced anthropogenic emissions of air pollutants derived mainly from traffic sources. To analyze the effect of these restrictions derived from COVID-19 pandemic on air quality levels, relative changes in NO, NO2, O3, PM10 and PM2.5 concentrations were calculated at urban traffic sites in the most populated Spanish cities over different periods with distinct restrictions in 2020. In addition to the changes calculated with respect to the observed air pollutant levels of previous years (2013-2019), relative changes were also calculated using predicted pollutant levels for the different periods over 2020 on a business-as-usual scenario using Multiple Linear Regression (MLR) models with meteorological and seasonal predictors. MLR models were selected among different data mining techniques (MLR, Random Forest (RF), K-Nearest Neighbors (KNN)), based on their higher performance and accuracy obtained from a leave-one-year-out cross-validation scheme using 2013-2019 data. A q-q mapping post-correction was also applied in all cases in order to improve the reliability of the predictions to reproduce the observed distributions and extreme events. This approach allows us to estimate the relative changes in the studied air pollutants only due to COVID-19 restrictions. The results obtained from this approach show a decreasing pattern for NOx, with the largest reduction in the lockdown period above -50%, whereas the increase observed for O3 contrasts with the NOx patterns with a maximum increase of 23.9%. The slight reduction in PM10 (-4.1%) and PM2.5 levels (-2.3%) during lockdown indicates a lower relationship with traffic sources. The developed methodology represents a simple but robust framework for exploratory analysis and intervention detection in air quality studies.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , COVID-19/epidemiology , Cities , Communicable Disease Control , Data Mining , Environmental Monitoring/methods , Humans , Pandemics , Particulate Matter/analysis , Reproducibility of Results , Spain
10.
Elife ; 102021 11 23.
Article in English | MEDLINE | ID: covidwho-1622815

ABSTRACT

Background: Potential therapy and confounding factors including typical co-administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. Methods: Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs. Afterward, the graphical least absolute shrinkage and selection operator (GLASSO) captured drug associations based on pulmonary ADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO. Results: Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pulmonary ADEs. Macitentan and bosentan were associated with 64% and 56% of pulmonary ADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pulmonary ADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pulmonary ADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy. Conclusions: We consider pulmonary ADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high risk for coronavirus disease 2019 (COVID-19). We found that several individual drugs have significant differences between their drug classes and compared to other drug classes. For instance, macitentan and bosentan from endothelin receptor antagonists show major concern while doxazosin and rilmenidine exhibited the least pulmonary ADEs compared to the outcomes of other drugs. Using techniques in this study, we assessed and confirmed the hypothesis that drugs from the same drug class could have very different pulmonary ADE profiles affecting outcomes in acute respiratory illness. Funding: GJW and MJD accepted funding from BioNexus KC for funding on this project, but BioNexus KC had no direct role in this article.


Subject(s)
Antihypertensive Agents/adverse effects , COVID-19/complications , Data Mining/methods , Drug-Related Side Effects and Adverse Reactions , Hypertension/drug therapy , Pharmacovigilance , Adverse Drug Reaction Reporting Systems , Angiotensin-Converting Enzyme Inhibitors/adverse effects , Antihypertensive Agents/therapeutic use , Bayes Theorem , Calcium Channel Blockers/adverse effects , Fibrinolytic Agents/adverse effects , Humans , Hypertension/complications , SARS-CoV-2
11.
Philos Trans A Math Phys Eng Sci ; 380(2214): 20210125, 2022 Jan 10.
Article in English | MEDLINE | ID: covidwho-1605660

ABSTRACT

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available at http://scaiweb.cs.ucla.edu/covidsurveiller/. This article is part of the theme issue 'Data science approachs to infectious disease surveillance'.


Subject(s)
COVID-19 , Social Media , Data Mining , Humans , Pandemics , SARS-CoV-2
12.
Front Immunol ; 12: 796379, 2021.
Article in English | MEDLINE | ID: covidwho-1604322

ABSTRACT

Whole genome sequencing of Epstein-Barr virus (EBV) isolates from around the world has uncovered pervasive strain heterogeneity, but the forces driving strain diversification and the impact on immune recognition remained largely unknown. Using a data mining approach, we analyzed more than 300 T-cell epitopes in 168 published EBV strains. Polymorphisms were detected in approximately 65% of all CD8+ and 80% of all CD4+ T-cell epitopes and these numbers further increased when epitope flanking regions were included. Polymorphisms in CD8+ T-cell epitopes often involved MHC anchor residues and resulted in changes of the amino acid subgroup, suggesting that only a limited number of conserved T-cell epitopes may represent generic target antigens against different viral strains. Although considered the prototypic EBV strain, the rather low degree of overlap with most other viral strains implied that B95.8 may not represent the ideal reference strain for T-cell epitopes. Instead, a combinatorial library of consensus epitopes may provide better targets for diagnostic and therapeutic purposes when the infecting strain is unknown. Polymorphisms were significantly enriched in epitope versus non-epitope protein sequences, implicating immune selection in driving strain diversification. Remarkably, CD4+ T-cell epitopes in EBNA2, EBNA-LP, and the EBNA3 family appeared to be under negative selection pressure, hinting towards a beneficial role of immune responses against these latency type III antigens in virus biology. These findings validate this immunoinformatics approach for providing novel insight into immune targets and the intricate relationship of host defense and virus evolution that may also pertain to other pathogens.


Subject(s)
Antigenic Variation , Antigens, Viral/genetics , Epitopes, T-Lymphocyte/genetics , Genetic Heterogeneity , Herpesvirus 4, Human/genetics , Polymorphism, Genetic , Algorithms , Antigens, Viral/immunology , CD4-Positive T-Lymphocytes/immunology , CD4-Positive T-Lymphocytes/virology , CD8-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/virology , Data Mining , Databases, Genetic , Epitopes, T-Lymphocyte/immunology , Herpesvirus 4, Human/immunology
13.
Sci Rep ; 11(1): 24491, 2021 12 29.
Article in English | MEDLINE | ID: covidwho-1591547

ABSTRACT

There is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify air quality and meteorological variables correlated with confirmed cases and deaths. The statistical analyses indicated the most important explanatory environmental variables, while the cluster analyses showed the potential best input variables for the forecasting models. The forecast models were built by two different algorithms and their results have been compared. The relationship between epidemiological and environmental variables was particular to each of the three cities studied. Low solar radiation periods predicted in Manaus can guide managers to likely increase deaths due to COVID-19. In São Paulo, an increase in the mortality rate can be indicated by drought periods. The developed models can predict new cases and deaths by COVID-19 in studied cities. Furthermore, the methodological approach can be applied in other cities and for other epidemic diseases.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Data Mining/methods , Brazil/epidemiology , COVID-19/pathology , Cities/epidemiology , Humans , Models, Theoretical , Morbidity , Pandemics/prevention & control , SARS-CoV-2/pathogenicity
14.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 33(10): 1175-1180, 2021 Oct.
Article in Chinese | MEDLINE | ID: covidwho-1600033

ABSTRACT

OBJECTIVE: To analyze the data of Chinese medicine prescriptions for the treatment of coronavirus disease 2019 (COVID-19) in Shijiazhuang City, Hebei Province, with a view to further guide the clinical use of Chinese medicine in the prevention and treatment of COVID-19. METHODS: Forty-eight patients diagnosed with COVID-19 who were treated by critical care team of Hebei Traditional Chinese Medicine Hospital in the intensive care unit (ICU) of Hebei Chest Hospital (Hebei Provincial COVID-19 designated hospital) from January 7 to March 4, 2021, were enrolled in this study. The patients' gender, age, clinical classification, past history, and all Chinese medicine prescriptions for the first visit and follow-up visits during the hospitalization were collected. A database was established based on the Ancient and Modern Medical Records Cloud Platform (V2.2.1), and the methods of frequency analysis, correlation analysis, cluster analysis, and complex network analysis were used to analyze the prescriptions of traditional Chinese medicine. RESULTS: Among the 48 patients with COVID-19, 20 were males and 28 were females; the average age was (62.4±13.7) years old. The patients' condition was generally severe, including 17 cases of common type, 25 cases of severe type, and 6 cases of critical type, most of whom were combined with hypertension, coronary heart disease, diabetes, chronic obstructive pulmonary disease and other basic illnesses. A total of 146 valid prescriptions were included, involving 59 prescriptions and 115 Chinese medicines. Frequency analysis of 146 prescriptions showed that the commonly used prescriptions for patients with COVID-19 were Qingfei Paidu decoction (30 times, 20.55%), Xuanbai Chengqi decoction (10 times, 6.85%), and Dayuan Yin (10 times, 6.85%). The common Chinese medicines were liquorice (80 times, 54.79%), tuckahoe (76 times, 52.05%), gypsum (70 times, 47.95%), bitter almond (70 times, 47.95%), ephedra (57 times, 39.04%), scutellaria (56 times, 38.36%), tangerine peel (53 times, 36.30%), patchouli (50 times, 34.25%), atractylodes macrocephala (50 times, 34.25%), and bupleurum (43 times, 29.45%). The main effects were clearing heat and detoxification (129 times), clearing heat-fire (129 times) and eliminating dampness and diuresis (110 times). The medicinal properties were mainly warm (509 times), flat (287 times), and cold (235 times). The medicinal tastes were mainly pungent (765 times), sweet (654 times), and bitter (626 times). The medicinal channel tropism were mainly lung (1 096 times), spleen (785 times), and stomach (687 times). The correlation analysis showed that there were 17 drug combinations in total, among which the top 3 drug pairs in support were bitter almond-gypsum (0.43), ephedra-bitter almond (0.38), tangerine peel-poria (0.36), and ephedra-gypsum (0.36). Cluster analysis showed that there were 3 groups of clustering formulas. The first group was ephedra, bitter almond, and gypsum. The second group was patchouli, tuckahoe, tangerine peel, and atractylodes macrocephala. The third group was scutellaria, licorice, immature orange fruit, oriental waterplantain rhizome, bupleurum, ginger, and cassia twig. The core drugs were composed of tuckahoe, bupleurum, tangerine peel, atractylodes macrocephala, patchouli, bitter almond, scutellaria, gypsum, ephedra, and licorice. CONCLUSIONS: Middle-aged and elderly patients with COVID-19 are accompanied by Qi deficiency and internal invasion of toxins, and the pathogenesis evolves rapidly. Damp and turbid toxins often block the lungs and trap the spleen, leading to disorder of Qi movement, and even invaginate Ying and Xue, drain Yin and Yang. The treatment is based on removing turbidity and detoxification, and replenishing Qi and nourishing Yin are the principle treatments, so that the evil is eliminated and the Qi is restored.


Subject(s)
COVID-19 , Drugs, Chinese Herbal , Medicine, Chinese Traditional , Aged , COVID-19/drug therapy , Data Mining , Female , Humans , Intensive Care Units , Male , Middle Aged
15.
Int J Environ Res Public Health ; 19(1)2021 Dec 28.
Article in English | MEDLINE | ID: covidwho-1580800

ABSTRACT

The health crisis generated by the COVID-19 pandemic has induced, among other things, an increase in the importance of remote work or teleworking (TL) in the current period. The objective of this research is to identify the economic and social impact of telework in changing the behavior of employees in Romania. The research was conducted approximately one year after the onset of the pandemic until the beginning of the vaccination period in Romania. The research proposed includes three main directions of analysis of the extracted data, which are related to telework efficiency, this being considered one of the most important indicators for a company. In order to obtain conclusive results, we used a mixed methodology, combining results obtained through a survey based on a self-administered electronic questionnaire, with a data mining analysis. Detailed analysis of the groups identified based on work efficiency allowed us to highlight the most common employee profiles. This analysis was doubled by a second classification experiment, which provided us a more detailed analysis of the groups identified based on job satisfaction and highlighted the most common employee profiles. The expansion of telework in various economic areas is a result of adaptation to the new economic and social conditions caused by the COVID-19 pandemic.


Subject(s)
COVID-19 , Data Mining , Humans , Pandemics , SARS-CoV-2 , Social Change , Teleworking
16.
J Med Internet Res ; 23(2): e25108, 2021 02 09.
Article in English | MEDLINE | ID: covidwho-1574667

ABSTRACT

BACKGROUND: The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the United States. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, the CDC has become one of the focal points of public discussion. OBJECTIVE: This study aims to identify the topics and their overarching themes emerging from the public COVID-19-related discussion about the CDC on Twitter and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC. METHODS: Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to August 14, 2020. We used R (The R Foundation) to clean the tweets and retain tweets that contained any of five specific keywords-cdc, CDC, centers for disease control and prevention, CDCgov, and cdcgov-while eliminating all 91 tweets posted by the CDC itself. The final data set included in the analysis consisted of 290,764 unique tweets from 152,314 different users. We used R to perform the latent Dirichlet allocation algorithm for topic modeling. RESULTS: The Twitter data generated 16 topics that the public linked to the CDC when they talked about COVID-19. Among the topics, the most discussed was COVID-19 death counts, accounting for 12.16% (n=35,347) of the total 290,764 tweets in the analysis, followed by general opinions about the credibility of the CDC and other authorities and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The 16 topics fell into four overarching themes: knowing the virus and the situation, policy and government actions, response guidelines, and general opinion about credibility. CONCLUSIONS: Social media platforms, such as Twitter, provide valuable databases for public opinion. In a protracted pandemic, such as COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies improve the next-round communication with the public.


Subject(s)
COVID-19 , Centers for Disease Control and Prevention, U.S. , Data Mining , Public Opinion , Social Media , Communication , Humans , Pandemics , Public Health , Public Policy , SARS-CoV-2 , United States
17.
JMIR Public Health Surveill ; 7(12): e32814, 2021 12 03.
Article in English | MEDLINE | ID: covidwho-1556320

ABSTRACT

BACKGROUND: COVID-19 vaccination is considered a critical prevention measure to help end the pandemic. Social media platforms such as Twitter have played an important role in the public discussion about COVID-19 vaccines. OBJECTIVE: The aim of this study was to investigate message-level drivers of the popularity and virality of tweets about COVID-19 vaccines using machine-based text-mining techniques. We further aimed to examine the topic communities of the most liked and most retweeted tweets using network analysis and visualization. METHODS: We collected US-based English-language public tweets about COVID-19 vaccines from January 1, 2020, to April 30, 2021 (N=501,531). Topic modeling and sentiment analysis were used to identify latent topics and valence, which together with autoextracted information about media presence, linguistic features, and account verification were used in regression models to predict likes and retweets. Among the 2500 most liked tweets and 2500 most retweeted tweets, network analysis and visualization were used to detect topic communities and present the relationship between the topics and the tweets. RESULTS: Topic modeling yielded 12 topics. The regression analyses showed that 8 topics positively predicted likes and 7 topics positively predicted retweets, among which the topic of vaccine development and people's views and that of vaccine efficacy and rollout had relatively larger effects. Network analysis and visualization revealed that the 2500 most liked and most retweeted retweets clustered around the topics of vaccine access, vaccine efficacy and rollout, vaccine development and people's views, and vaccination status. The overall valence of the tweets was positive. Positive valence increased likes, but valence did not affect retweets. Media (photo, video, gif) presence and account verification increased likes and retweets. Linguistic features had mixed effects on likes and retweets. CONCLUSIONS: This study suggests the public interest in and demand for information about vaccine development and people's views, and about vaccine efficacy and rollout. These topics, along with the use of media and verified accounts, have enhanced the popularity and virality of tweets. These topics could be addressed in vaccine campaigns to help the diffusion of content on Twitter.


Subject(s)
COVID-19 , Social Media , COVID-19 Vaccines , Data Mining , Data Visualization , Humans , SARS-CoV-2
18.
J Healthc Eng ; 2021: 1382559, 2021.
Article in English | MEDLINE | ID: covidwho-1541944

ABSTRACT

With the diversification and rapid development of society, people's living conditions, learning and friendship conditions, and employment conditions are facing increasing pressure, which greatly challenges people's psychological endurance. Therefore, strengthening the mental health education of students has become an urgent need of society and a hot issue of common concern. In order to solve the problems of high misjudgment rate and low work efficiency in the current mental health intelligence evaluation process, a mental health intelligence evaluation system based on a joint optimization algorithm is proposed. The joint optimization algorithm consists of an improved decision tree algorithm and an improved ANN algorithm. First, analyze the current research status of mental health intelligence evaluation, and construct the framework of mental health intelligence evaluation system; then collect mental health intelligence evaluation data based on data mining, use joint learning algorithm to analyze and classify mental health intelligence evaluation data, and obtain mental health intelligence evaluation results. Finally, through specific simulation experiments, the feasibility and superiority of the mental health intelligent evaluation system are analyzed. The results show that the system in the article overcomes the shortcomings of the existing mental health intelligence evaluation system, improves the accuracy of mental health intelligence evaluation, and improves the efficiency of mental health intelligence evaluation. It has good system stability and can meet the actual current situation, which are requirements for mental health intelligence evaluation.


Subject(s)
Algorithms , Mental Health , Data Mining , Humans , Students
19.
Int J Mol Sci ; 22(22)2021 Nov 12.
Article in English | MEDLINE | ID: covidwho-1534086

ABSTRACT

Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a well-defined structure. Although a variety of prediction methods are available for predicting IDRs, their accuracy is very limited on TMPs due to their special physico-chemical properties. We prepared a dataset containing membrane proteins exclusively, using X-ray crystallography data. MemDis is a novel prediction method, utilizing convolutional neural network and long short-term memory networks for predicting disordered regions in TMPs. In addition to attributes commonly used in IDR predictors, we defined several TMP specific features to enhance the accuracy of our method further. MemDis achieved the highest prediction accuracy on TMP-specific dataset among other popular IDR prediction methods.


Subject(s)
Computational Biology/methods , Intrinsically Disordered Proteins/chemistry , Membrane Proteins/chemistry , Neural Networks, Computer , Amino Acid Sequence , Data Mining/methods , Databases, Protein/statistics & numerical data , Internet , Models, Molecular , Protein Conformation , Reproducibility of Results
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