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1.
BMC Bioinformatics ; 22(1): 607, 2021 Dec 20.
Article in English | MEDLINE | ID: covidwho-1633689

ABSTRACT

BACKGROUND: Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. RESULTS: pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. CONCLUSIONS: We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.


Subject(s)
COVID-19 , Data Science , Data Analysis , Ecosystem , Humans , SARS-CoV-2
2.
Crit Care ; 26(1): 8, 2022 01 06.
Article in English | MEDLINE | ID: covidwho-1590188

ABSTRACT

BACKGROUND: Prone positioning (PP) reduces mortality of patients with acute respiratory distress syndrome (ARDS). The potential benefit of prone positioning maneuvers during venovenous extracorporeal membrane oxygenation (ECMO) is unknown. The aim of this study was to evaluate the association between the use of prone positioning during extracorporeal support and ICU mortality in a pooled population of patients from previous European cohort studies. METHODS: We performed a pooled individual patient data analysis of European cohort studies which compared patients treated with prone positioning during ECMO (Prone group) to "conventional" ECMO management (Supine group) in patients with severe ARDS. RESULTS: 889 patients from five studies were included. Unadjusted ICU mortality was 52.8% in the Supine Group and 40.8% in the Prone group. At a Cox multiple regression analysis PP during ECMO was not significantly associated with a reduction of ICU mortality (HR 0.67 95% CI: 0.42-1.06). Propensity score matching identified 227 patients in each group. ICU mortality of the matched samples was 48.0% and 39.6% for patients in the Supine and Prone group, respectively (p = 0.072). CONCLUSIONS: In a large population of ARDS patients receiving venovenous extracorporeal support, the use of prone positioning during ECMO was not significantly associated with reduced ICU mortality. The impact of this procedure will have to be definitively assessed by prospective randomized controlled trials.


Subject(s)
Extracorporeal Membrane Oxygenation , Respiratory Distress Syndrome , Data Analysis , Humans , Patient Positioning , Prone Position , Prospective Studies , Respiratory Distress Syndrome/therapy , Retrospective Studies
3.
PLoS One ; 16(12): e0261622, 2021.
Article in English | MEDLINE | ID: covidwho-1597835

ABSTRACT

The skill of analyzing and interpreting research data is central to the scientific process, yet it is one of the hardest skills for students to master. While instructors can coach students through the analysis of data that they have either generated themselves or obtained from published articles, the burgeoning availability of preprint articles provides a new potential pedagogical tool. We developed a new method in which students use a cognitive apprenticeship model to uncover how experts analyzed a paper and compare the professional's cognitive approach to their own. Specifically, students first critique research data themselves and then identify changes between the preprint and final versions of the paper that were likely the results of peer review. From this activity, students reported diverse insights into the processes of data presentation, peer review, and scientific publishing. Analysis of preprint articles is therefore a valuable new tool to strengthen students' information literacy and understanding of the process of science.


Subject(s)
Data Analysis , Preprints as Topic , Science/education , Teaching , Communication , Humans , Peer Review , Teaching Materials
4.
Methods Mol Biol ; 2414: 433-447, 2022.
Article in English | MEDLINE | ID: covidwho-1588848

ABSTRACT

Vaccines induce a highly complex immune reaction in secondary lymphoid organs to generate immunological memory against an antigen or antigens of interest. Measurement of post immunization immune responses generated by specialized lymphocyte subsets requires time-dependent sampling, usually of the blood. Several T and B cell subsets are involved in the reaction, including CD4 and CD8 T cells, T follicular helper cells (Tfh), and germinal center B cells alongside their circulating (c) counterparts; cTfh and antibody secreting cells. Multicolor flow cytometry of peripheral blood mononuclear cells (PBMC) coupled with high-dimensional analysis offers an opportunity to study these cells in detail. Here we demonstrate a method by which such data can be generated and analysed using software that renders multidimensional data on a two dimensional map to identify rare vaccine-induced T and B cell subsets.


Subject(s)
Flow Cytometry , Leukocytes, Mononuclear , Data Analysis , T-Lymphocytes, Helper-Inducer , Vaccinology
5.
J Med Internet Res ; 23(2): e26302, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1575865

ABSTRACT

BACKGROUND: The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. OBJECTIVE: This study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media. METHODS: We used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures. RESULTS: The machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R0 of 7.62 for trends in the number of Twitter users posting health belief-related content over the study period. The fluctuations in the number of health belief-related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians' speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (P=.78 and P=.92 for perceived benefits and perceived barriers, respectively). CONCLUSIONS: As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is "unhealthy" that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians' speeches, might not be endorsed by substantial evidence and could sometimes be misleading.


Subject(s)
COVID-19/psychology , Data Analysis , Health Education/statistics & numerical data , Machine Learning , Natural Language Processing , Public Opinion , Social Media/statistics & numerical data , COVID-19/epidemiology , Humans , Pandemics
6.
J Med Internet Res ; 23(2): e24767, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1575466

ABSTRACT

BACKGROUND: Online medical records are being used to organize processes in clinical and outpatient settings and to forge doctor-patient communication techniques that build mutual understanding and trust. OBJECTIVE: We aimed to understand the reasons why patients tend to avoid using online medical records and to compare the perceptions that patients have of online medical records based on demographics and cancer diagnosis. METHODS: We used data from the Health Information National Trends Survey Cycle 3, a nationally representative survey, and assessed outcomes using descriptive statistics and chi-square tests. The patients (N=4328) included in the analysis had experienced an outpatient visit within the previous 12 months and had answered the online behavior question regarding their use of online medical records. RESULTS: Patients who were nonusers of online medical records consisted of 58.36% of the sample (2526/4328). The highest nonuser rates were for patients who were Hispanic (460/683, 67.35%), patients who were non-Hispanic Black (434/653, 66.46%), and patients who were older than 65 years (968/1520, 63.6%). Patients older than 65 years were less likely to use online medical records (odds ratio [OR] 1.51, 95% CI 1.24-1.84, P<.001). Patients who were White were more likely to use online medical records than patients who were Black (OR 1.71, 95% CI 1.43-2.05, P<.001) or Hispanic (OR 1.65, 95% CI 1.37-1.98, P<.001). Patients who were diagnosed with cancer were more likely to use online medical records compared to patients with no cancer (OR 1.31, 95% CI 1.11-1.55, 95% CI 1.11-1.55, P=.001). Among nonusers, older patients (≥65 years old) preferred speaking directly to their health care providers (OR 1.76, 95% CI 1.35-2.31, P<.001), were more concerned about privacy issues caused by online medical records (OR 1.79, 95% CI 1.22-2.66, P<.001), and felt uncomfortable using the online medical record systems (OR 10.55, 95% CI 6.06-19.89, P<.001) compared to those aged 18-34 years. Patients who were Black or Hispanic were more concerned about privacy issues (OR 1.42, 1.09-1.84, P=.007). CONCLUSIONS: Studies should consider social factors such as gender, race/ethnicity, and age when monitoring trends in eHealth use to ensure that eHealth use does not induce greater health status and health care disparities between people with different backgrounds and demographic characteristics.


Subject(s)
Electronic Health Records/standards , Health Information Exchange/standards , Surveys and Questionnaires/standards , Adolescent , Adult , Aged , Data Analysis , Female , History, 21st Century , Humans , Internet Use , Male , Middle Aged , Physician-Patient Relations , Telemedicine/statistics & numerical data , Young Adult
7.
Public Health Nutr ; 24(16): 5338-5349, 2021 11.
Article in English | MEDLINE | ID: covidwho-1559726

ABSTRACT

OBJECTIVE: During COVID-19, the Internet was a prime source for getting relevant updates on guidelines and desirable information. The objective of the present study was to determine the nutritional immunity information-seeking behaviour during COVID-19 in India. DESIGN: Google Trends (GTs) data on relevant COVID-19 and nutritional topics were systematically selected and retrieved. Data on newly reported COVID-19 cases were also examined on a daily basis. The cross-correlation method was used to determine the correlation coefficient between the selected terms and daily new COVID-19 cases, and the joinpoint regression models were utilised to measure monthly percent change (MPC) in relative search volumes (RSV). SETTING: Online. PARTICIPANTS: People using Google search during the period 1 January 2020-31 August 2020 in India. RESULTS: The date of peak searches can be attributed to the COVID-19 guidelines announcement dates. All the nutritional terms showed a significant increase in average monthly percentage change. The higher than the average daily rise in COVID-19 cases leads to a higher than average increase in RSV of nutritional terms with the greatest association after 14-27 d. The highest mean relative search volume for nutritional terms was from Southern India (49·34 ± 7·43), and the lowest was from Western India (31·10 ± 6·30). CONCLUSION: There was a significant rise in the Google searches of nutritional immunity topics during COVID-19 in India. The local/regional terms can be considered for better outreach of public health guidelines or recommendations. Further automation of Google Trends using programming languages can help in real-time monitoring and planning various health/nutritional events.


Subject(s)
COVID-19 , Pandemics , Data Analysis , Humans , Information Seeking Behavior , Internet , SARS-CoV-2 , Search Engine
8.
Sci Rep ; 10(1): 16598, 2020 10 06.
Article in English | MEDLINE | ID: covidwho-1493167

ABSTRACT

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Social Media , Basic Reproduction Number , COVID-19 , Coronavirus Infections/virology , Data Analysis , Humans , Information Dissemination , Linear Models , Neural Networks, Computer , Pandemics , Pneumonia, Viral/virology , SARS-CoV-2 , Social Behavior
9.
Int J Environ Res Public Health ; 18(21)2021 Oct 25.
Article in English | MEDLINE | ID: covidwho-1480779

ABSTRACT

COVID-19 remains a matter of global public health concern. Previous research suggested the association between local environmental factors and viral transmission. We present a multivariate observational analysis of SARS-CoV-2 transmission in the state of Odisha, India, hinting at a seasonal activity. We aim to investigate the demographic characteristics of COVID-19 in the Indian state of Odisha for two specific timelines in 2020 and 2021. For a comparative outlook, we chose similar datasets from the state of New York, USA. Further, we present a critical analysis pertaining to the effects of environmental factors and the emergence of variants on SARS-CoV-2 transmission and persistence. We assessed the datasets for confirmed cases, death, age, and gender for 29 February 2020 to 31 May 2020, and 1 March 2021 to 31 May 2021. We determined the case fatalities, crude death rates, sex ratio, and incidence rates for both states along with monthly average temperature analysis. A yearlong epi-curve analysis was conducted to depict the coronavirus infection spread pattern in the respective states. The Indian state of Odisha reported a massive 436,455 confirmed cases and 875 deaths during the 2021 timeline as compared to a mere 2223 cases and 7 deaths during the 2020 timeline. We further discuss the demographic and temperature association of SARS-CoV-2 transmission during early 2020 and additionally comment on the variant-associated massive rise in cases during 2021. Along with the rapid rise of variants, the high population density and population behavior seem to be leading causes for the 2021 pandemic, whereas factors such as age group, gender, and average local temperature were prominent during the 2020 spread. A seasonal occurrence of SARS-CoV-2 transmission is also observed from the yearlong epidemiological plot. The recent second wave of COVID-19 is a lesson that emphasizes the significance of continuous epidemiological surveillance to predict the relative risk of viral transmission for a specific region.


Subject(s)
COVID-19 , SARS-CoV-2 , Data Analysis , Humans , India/epidemiology , Pandemics
10.
BMC Bioinformatics ; 22(Suppl 6): 508, 2021 Oct 18.
Article in English | MEDLINE | ID: covidwho-1477258

ABSTRACT

BACKGROUND: The 10th and 9th revisions of the International Statistical Classification of Diseases and Related Health Problems (ICD10 and ICD9) have been adopted worldwide as a well-recognized norm to share codes for diseases, signs and symptoms, abnormal findings, etc. The international Consortium for Clinical Characterization of COVID-19 by EHR (4CE) website stores diagnosis COVID-19 disease data using ICD10 and ICD9 codes. However, the ICD systems are difficult to decode due to their many shortcomings, which can be addressed using ontology. METHODS: An ICD ontology (ICDO) was developed to logically and scientifically represent ICD terms and their relations among different ICD terms. ICDO is also aligned with the Basic Formal Ontology (BFO) and reuses terms from existing ontologies. As a use case, the ICD10 and ICD9 diagnosis data from the 4CE website were extracted, mapped to ICDO, and analyzed using ICDO. RESULTS: We have developed the ICDO to ontologize the ICD terms and relations. Different from existing disease ontologies, all ICD diseases in ICDO are defined as disease processes to describe their occurrence with other properties. The ICDO decomposes each disease term into different components, including anatomic entities, process profiles, etiological causes, output phenotype, etc. Over 900 ICD terms have been represented in ICDO. Many ICDO terms are presented in both English and Chinese. The ICD10/ICD9-based diagnosis data of over 27,000 COVID-19 patients from 5 countries were extracted from the 4CE. A total of 917 COVID-19-related disease codes, each of which were associated with 1 or more cases in the 4CE dataset, were mapped to ICDO and further analyzed using the ICDO logical annotations. Our study showed that COVID-19 targeted multiple systems and organs such as the lung, heart, and kidney. Different acute and chronic kidney phenotypes were identified. Some kidney diseases appeared to result from other diseases, such as diabetes. Some of the findings could only be easily found using ICDO instead of ICD9/10. CONCLUSIONS: ICDO was developed to ontologize ICD10/10 codes and applied to study COVID-19 patient diagnosis data. Our findings showed that ICDO provides a semantic platform for more accurate detection of disease profiles.


Subject(s)
COVID-19 , International Classification of Diseases , Data Analysis , Humans , SARS-CoV-2
11.
BMC Bioinformatics ; 22(1): 476, 2021 Oct 03.
Article in English | MEDLINE | ID: covidwho-1448207

ABSTRACT

BACKGROUND: Quantitative, reverse transcription PCR (qRT-PCR) is currently the gold-standard for SARS-CoV-2 detection and it is also used for detection of other virus. Manual data analysis of a small number of qRT-PCR plates per day is a relatively simple task, but automated, integrative strategies are needed if a laboratory is dealing with hundreds of plates per day, as is being the case in the COVID-19 pandemic. RESULTS: Here we present shinyCurves, an online shiny-based, free software to analyze qRT-PCR amplification data from multi-plate and multi-platform formats. Our shiny application does not require any programming experience and is able to call samples Positive, Negative or Undetermined for viral infection according to a number of user-defined settings, apart from providing a complete set of melting and amplification curve plots for the visual inspection of results. CONCLUSIONS: shinyCurves is a flexible, integrative and user-friendly software that speeds-up the analysis of massive qRT-PCR data from different sources, with the possibility of automatically producing and evaluating melting and amplification curve plots.


Subject(s)
COVID-19 , Data Analysis , Humans , Pandemics , Real-Time Polymerase Chain Reaction , SARS-CoV-2
12.
Nat Commun ; 12(1): 5757, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1447304

ABSTRACT

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.


Subject(s)
Data Science/methods , Medical Records Systems, Computerized , Big Data , Computer Security , Data Analysis , Health Information Interoperability , Humans , Information Storage and Retrieval , Software
13.
J Med Internet Res ; 23(10): e30697, 2021 10 04.
Article in English | MEDLINE | ID: covidwho-1441058

ABSTRACT

BACKGROUND: Computationally derived ("synthetic") data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record data. Synthetic data can support data sharing to answer critical research questions to address the COVID-19 pandemic. OBJECTIVE: We aim to compare the results from analyses of synthetic data to those from original data and assess the strengths and limitations of leveraging computationally derived data for research purposes. METHODS: We used the National COVID Cohort Collaborative's instance of MDClone, a big data platform with data-synthesizing capabilities (MDClone Ltd). We downloaded electronic health record data from 34 National COVID Cohort Collaborative institutional partners and tested three use cases, including (1) exploring the distributions of key features of the COVID-19-positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-19-related measures and outcomes, and constructing their epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data. RESULTS: For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. Although the synthetic and original data yielded overall nearly the same results, there were exceptions that included an odds ratio on either side of the null in multivariable analyses (0.97 vs 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts. CONCLUSIONS: This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights.


Subject(s)
COVID-19 , Electronic Health Records , Data Analysis , Humans , Pandemics , SARS-CoV-2
14.
Biosens Bioelectron ; 195: 113632, 2022 Jan 01.
Article in English | MEDLINE | ID: covidwho-1439902

ABSTRACT

A micro-capillary electrophoresis (µCE) system is one of the widely adopted techniques in the molecular diagnostics and DNA sequencing due to the benefits of high resolution, rapid analysis, and low reagent consumption, but due to the requirements of bulky high-power suppliers and an expensive laser-induced fluorescence detector module, the conventional set-up of µCE system is not adequate for point-of-care (POC) molecular diagnostics. In this study, we constructed a miniaturized and integrated µCE system which can be manipulated by a smartphone. The smartphone not only powers two boost converters and an excited laser, but also controls the relay for the power switch. Moreover, the complementary metal-oxide-semiconductor (CMOS) camera of the smartphone was used for detecting the fluorescence signal of amplicons amplified with reverse transcription-polymerase chain reaction (RT-PCR). We also developed a web-based application so that the raw data of the recorded fluorescence intensity versus the running time can display typical capillary electropherograms on the smartphone. The total size of the hand-held µCE system was 9.6 cm [Width] × 22 cm [Length] × 15.5 cm [Height], and the weight was ∼1 kg, which is suitable for POC DNA testing. In the integrated smartphone-associated µCE system, we could accurately analyze two genes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), namely N gene and S gene along with two bracket ladders in 6 min to identify SARS-CoV-2. Such an advanced µCE platform can be applied for a variety of on-site molecular diagnostics fields with user-friendliness.


Subject(s)
Biosensing Techniques , COVID-19 , Data Analysis , Electrophoresis, Capillary , Humans , SARS-CoV-2 , Smartphone
15.
Int J Environ Res Public Health ; 18(19)2021 09 25.
Article in English | MEDLINE | ID: covidwho-1438615

ABSTRACT

This study aims to describe the level and trends of physical activity (PA) in Thai children and young people and examine PA changes during transitional periods. Employing nine rounds of Thailand's Surveillance on Physical Activity (SPA) 2012-2020, this study pooled three sets of data and included children and young people aged 6-17 years in the analysis: 1595 in SPA2012-2016, 1287 in SPA2017-2019, and 853 persons in SPA2020. Face-to-face interviews were conducted in five regions, 13 provinces, and 36 villages in SPA2012-2019, whereas an online survey was administered in all provinces in SPA2020. The prevalence of sufficient moderate-to-vigorous PA (MVPA) among Thais aged 6-17 years ranged from 19.0 percent to 27.6 percent, with a significant drop during the period of COVID-19 spread in 2020. The average daily MVPA ranged from 46 to 57 min and dropped to 36 min during the pandemic. Boys were consistently more active than girls in all nine rounds of the SPA, and girls had more difficulty in maintaining or improving their PA level. A significant increase in the proportion of Thai children and young people with sufficient MVPA was observed during their transition from late primary to early secondary school grades.


Subject(s)
COVID-19 , Data Analysis , Adolescent , Child , Exercise , Female , Humans , Male , Prevalence , SARS-CoV-2 , Schools , Thailand
16.
Cell Rep ; 37(1): 109793, 2021 10 05.
Article in English | MEDLINE | ID: covidwho-1415261

ABSTRACT

The mortality risk of coronavirus disease 2019 (COVID-19) patients has been linked to the cytokine storm caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the inflammatory responses shared between COVID-19 and other infectious diseases that feature cytokine storms may therefore help in developing improved therapeutic strategies. Here, we use integrative analysis of single-cell transcriptomes to characterize the inflammatory signatures of peripheral blood mononuclear cells from patients with COVID-19, sepsis, and HIV infection. We identify ten hyperinflammatory cell subtypes in which monocytes are the main contributors to the transcriptional differences in these infections. Monocytes from COVID-19 patients share hyperinflammatory signatures with HIV infection and immunosuppressive signatures with sepsis. Finally, we construct a "three-stage" model of heterogeneity among COVID-19 patients, related to the hyperinflammatory and immunosuppressive signatures in monocytes. Our study thus reveals cellular and molecular insights about inflammatory responses to SARS-CoV-2 infection and provides therapeutic guidance to improve treatments for subsets of COVID-19 patients.


Subject(s)
COVID-19/blood , COVID-19/immunology , HIV Infections/blood , Leukocytes, Mononuclear/metabolism , SARS-CoV-2/immunology , Sepsis/blood , Transcriptome , COVID-19/virology , Cytokine Release Syndrome/blood , Cytokine Release Syndrome/immunology , Cytokines/blood , Data Analysis , Datasets as Topic , HIV Infections/immunology , HIV-1/immunology , Humans , Inflammation/blood , Leukocytes, Mononuclear/immunology , Sepsis/immunology , Single-Cell Analysis
17.
Sensors (Basel) ; 21(18)2021 Sep 16.
Article in English | MEDLINE | ID: covidwho-1410904

ABSTRACT

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3-10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.


Subject(s)
COVID-19 , Decision Support Systems, Clinical , Artificial Intelligence , Data Analysis , Delivery of Health Care , Humans , SARS-CoV-2
18.
J Med Internet Res ; 23(9): e29622, 2021 09 14.
Article in English | MEDLINE | ID: covidwho-1406795

ABSTRACT

BACKGROUND: The COVID-19 pandemic has turned the care model of health systems around the world upside down, causing the abrupt cancellation of face-to-face visits and redirection of the model toward telemedicine. Digital transformation boosts information systems-the more robust they are, the easier it is to monitor the health care system in a highly complex state and allow for more agile and reliable analysis. OBJECTIVE: The purpose of this study was to analyze diagnoses from primary care visits and distinguish between those that had higher and lower variations, relative to the 2019 and 2020 periods (roughly before and during COVID-19), to identify clinical profiles that may have been most impaired from the least-used diagnostic codes for visits during the pandemic. METHODS: We used a database from the Primary Care Services Information Technologies Information System of Catalonia. We analyzed the register of visits (n=2,824,185) and their International Classification of Diseases (ICD-10) diagnostic codes (n=3,921,974; mean 1.38 per visit), as approximations of the reasons for consultations, at 3 different grouping levels. The data were represented by a term frequency matrix and analyzed recursively in different partitions aggregated according to date. RESULTS: The increase in non-face-to-face visits (+267%) did not counterbalance the decrease in face-to-face visits (-47%), with an overall reduction in the total number of visits of 1.36%, despite the notable increase in nursing visits (10.54%). The largest increases in 2020 were visits with diagnoses related to COVID-19 (ICD-10 codes Z20-Z29: 2.540%), along with codes related to economic and housing problems (ICD-10 codes Z55-Z65: 44.40%). Visits with most of the other diagnostic codes decreased in 2020 relative to those in 2019. The largest reductions were chronic pathologies such as arterial hypertension (ICD-10 codes I10-I16: -32.73%) or diabetes (ICD-10 codes E08-E13: -21.13%), but also obesity (E65-E68: -48.58%) and bodily injuries (ICD-10 code T14: -33.70%). Visits with mental health-related diagnostic codes decreased, but the decrease was less than the average decrease. There was a decrease in consultations-for children, adolescents, and adults-for respiratory infections (ICD-10 codes J00-J06: -40.96%). The results show large year-on-year variations (in absolute terms, an average of 12%), which is representative of the strong shock to the health system. CONCLUSIONS: The disruption in the primary care model in Catalonia has led to an explosive increase in the number of non-face-to-face visits. There has been a reduction in the number of visits for diagnoses related to chronic pathologies, respiratory infections, obesity, and bodily injuries. Instead, visits for diagnoses related to socioeconomic and housing problems have increased, which emphasizes the importance of social determinants of health in the context of this pandemic. Big data analytics with routine care data yield findings that are consistent with those derived from intuition in everyday clinical practice and can help inform decision making by health planners in order to use the next few years to focus on the least-treated diseases during the COVID-19 pandemic.


Subject(s)
COVID-19 , Pandemics , Adolescent , Adult , Child , Data Analysis , Humans , Primary Health Care , SARS-CoV-2 , Spain/epidemiology
19.
Yearb Med Inform ; 30(1): 176-184, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1392942

ABSTRACT

OBJECTIVES: We examine the knowledge ecosystem of COVID-19, focusing on clinical knowledge and the role of health informatics as enabling technology. We argue for commitment to the model of a global learning health system to facilitate rapid knowledge translation supporting health care decision making in the face of emerging diseases. METHODS AND RESULTS: We frame the evolution of knowledge in the COVID-19 crisis in terms of learning theory, and present a view of what has occurred during the pandemic to rapidly derive and share knowledge as an (underdeveloped) instance of a global learning health system. We identify the key role of information technologies for electronic data capture and data sharing, computational modelling, evidence synthesis, and knowledge dissemination. We further highlight gaps in the system and barriers to full realisation of an efficient and effective global learning health system. CONCLUSIONS: The need for a global knowledge ecosystem supporting rapid learning from clinical practice has become more apparent than ever during the COVID-19 pandemic. Continued effort to realise the vision of a global learning health system, including establishing effective approaches to data governance and ethics to support the system, is imperative to enable continuous improvement in our clinical care.


Subject(s)
COVID-19 , Knowledge Management , Learning Health System , Medical Informatics , Data Analysis , Electronic Health Records , Humans , Information Dissemination , Information Storage and Retrieval
20.
Can J Public Health ; 111(6): 912-920, 2020 12.
Article in English | MEDLINE | ID: covidwho-1389876

ABSTRACT

Over the past few months, our fellow citizens have heard more about public health than ever before. The SARS-CoV-2 pandemic has shed light on the vital role played by public health for health protection and provided telling evidence about current public health capacity as well as the corrective measures to be taken and milestones to be achieved in the future. To this end, we identify several ways forward to re-empower public health in Québec and thus ensure that it can significantly contribute to population health. In particular, we propose that although reforms must continue to bolster health protection, substantial efforts are required to strengthen surveillance systems, prevention systems, health promotion systems, and accessible, effective, and overarching primary care systems.


Subject(s)
COVID-19/epidemiology , Pandemics , Population Health , Public Health , Data Analysis , Health Promotion , Humans , Leadership , Population Surveillance , Primary Health Care , Quebec/epidemiology
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