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Record W3195154622 · doi:10.31579/2766-2314/043

Effect of Coronavirus Infection on Fasting Blood Glucose, Kidney Function, and Electrolytes in COVID-19 Patients in the Zawia Region, Western Libya

2021· article· en· W3195154622 on OpenAlexaff
Azab Elsayed Azab, Khaled Ali Alawaini, Ashraf Mohamed Albakoush, Nouri Kushlaf

Bibliographic record

VenueBiotechnology and Bioprocessing · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineCreatinineInternal medicineGastroenterologyCoronavirusRenal functionCoronavirus disease 2019 (COVID-19)ImmunologyDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background: Coronavirus disease 19 (COVID-19) is a pandemic infectious disease caused by the novel coronavirus. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). It is an aggressive virus that spread worldwide and is a systemic disease involving multiple systems, including respiratory, cardiovascular, gastrointestinal, hematopoietic, neurological, immune, and urinogenital systems. Objectives: The present study aimed to evaluate the alteration in fasting blood glucose, serum urea, creatinine, Na+, K+, and Cl- levels among COVID-19 patients in the Zawia region, Western Libya. Materials and Methods: 416 confirmed COVID-19 patients hospitalized in the Isolation Centre located in Zawia city, Libya. From the 1st May 2020 to the 30th March 2021, were enrolled in this prospective study. Covid-19 patients were defined as positive cases after the detection of SARS-CoV-2 RNA in oro-nasopharyngeal swab samples. Demographic data were extracted from electronic medical records and patient files. Also, 30 healthy individuals without any chronic disease or respiratory symptoms were recruited for the control group. Blood samples were collected via vein puncture for estimating biochemical parameters (fasting blood glucose, serum urea, creatinine, Na+, K+, and Cl- concentrations). The statistical significance of differences between groups was evaluated with the Mann- Whitney (U test). Associations between different parameters were evaluated with the Spearman's test. Results: The results showed that coronavirus infection induced a significant increase in fasting blood glucose, serum urea, and creatinine concentrations. Infections also induced a decrease in serum sodium ion concentration, compared with healthy individuals. Seventy-six percent of corona virus-infected patients had hyperglycemia. Similarly, high levels of serum urea, creatinine, Cl-, Na+, and K+ were found in 40.9%, 39.9%, 27.9%, 8.9% and 6.5% of patients, respectively. Hyponatraemia, hypokalaemia, and hypochloremia were found in 35.5%, 13.7%, and 12.9% of patients. There were recorded a significant positive association between fasting blood glucose and serum urea, creatinine, and K+ concentration, between serum urea concentration and serum creatinine, K+, and Cl- concentrations, between serum creatinine and K+, and Cl- concentrations, and between serum Na+ and Cl- concentration, and a significant negative association between fasting blood glucose and serum Na+ and Cl- concentrations and between serum K+ and Na+ concentrations. Conclusion: It can be concluded that coronavirus infections induced increases in fasting blood glucose, serum urea, and creatinine, and a decrease in Na+ concentrations. There was a significant association between different parameters. These biochemical changes may help the clinicians to understand COVID-19 better and provide more clinical treatment options and prevent the serious complications of the disease. Thus, clinicians should pay special attention to fasting blood glucose, kidney function and electrolyte status of COVID-19 patients. Changes in fasting blood glucose, kidney function, and electrolyte levels can be a good indicator of disease progression.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.364
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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