Surface Web Merits for SARS-CoV-2 Pandemic in Iraq

Al-Imam, Ahmed; Motyka, Marek; Al-Doori, Hend J.

doi:10.32007/jfacmedbagdad.6241795

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 The coronavirus detected according to genomic sequence of virus. 3 The viral detection through the real-time or reverse transcription polymerase chain reaction (RT-PCR) testing. 4 Coronavirus is an enveloped RNA virus from Beta coronavirus genus, 5 Nidovirale order, and Coronaviridae family.…”

Section: Introductionmentioning

confidence: 99%

Measurement of the serum level of Leucine-rich alpha-2-glycoprotein-1 in hospitalized Iraqi COVID-19 Patients

Gadhi

Saleh

2022

J. Contemp. Med. Sci.

View full text Add to dashboard Cite

Objective: The study aimed to assess Leucine-rich alpha-2-glycoprotein-1 biomarker serum level in hospitalized COVID-19 patients. Methods: The case control study from multi-centers in Baghdad included 45 adult patients (19 females and 26 males) with COVID-19, diagnosed with a positive real-time reverse transcription polymerase chain reaction and excluded negative RT-PCR for COVID-19 and comorbidity conditions. Second group, was 43 control (20 females and 23 males). Results: This study found a decrease Leucine-rich alpha-2-glycoprotein-1 biomarker serum level in these patients and a significant difference in D. dimer, neutrophil count, lymphocyte count, and the neutrophil-lymphocyte ratio between the patients and controls at a P value equal to 0.000, Conclusion: The concentration of leucine-rich alpha2 glycoprotien1 in the patients after taking Tocilizumab was greatly decreased in many studies. Most of the patients in the study were treated with Tocilizumab, which directly blocks the effect of IL-6 by blocking the IL-6 receptors and greatly decreased the expression of LRG1 to impair the production of the angiopathogenic constituent.

show abstract

Section: Introductionmentioning

confidence: 99%

Measurement of the serum level of Leucine-rich alpha-2-glycoprotein-1 in hospitalized Iraqi COVID-19 Patients

Gadhi

Saleh

2022

J. Contemp. Med. Sci.

View full text Add to dashboard Cite

show abstract

Predictors of New-onset Diabetes After Kidney Transplantation During 2019-nCoV Pandemic: A Unison of Frequentist Inference and Narrow AI

Al-Imam

Al-Tabbakh

2022

Open Access Maced J Med Sci

Self Cite

View full text Add to dashboard Cite

BACKGROUND: New-onset diabetes after kidney transplant (NODAT) is a severe metabolic complication that frequently occurs in recipients following transplantation. AIM: The study aims to verify NODAT, compare cases and non-cases of this entity, and explore potential predictors in recipients within 1 year following kidney transplantation. METHODS: The research is a retrospective study of 90 renal transplant recipients (n = 90). Demographic factors and clinical aspects were analyzed using non-Bayesian statistics and machine learning (ML). The clinical aspects included the glycated hemoglobin (HbA1c) level, associated viral infections (hepatitis B virus [HBV], hepatitis C virus [HCV], and cytomegalovirus [CMV]), prior kidney transplant, hemodialysis status, body mass index (BMI) at transplant time, and 3 months later, primary causes of renal failure, and post-transplant therapeutics. All individuals were on cyclosporine and prednisolone treatment. RESULTS: The mean age was 39 (±1.5) years; recipients included 27 females (30%) and 63 males (70%). Donor type was live related (16, 17.8%) or live unrelated (74, 82.2%); 27 recipients (30%) had O+ blood group, while 70% belonged to other groups. Thirteen recipients (14.4%) were not on dialysis. Only 32 individuals (35.6%) developed NODAT. Concerning virology, confirmed by real-time polymerase chain reaction before transplantation, 19 recipients (21.1%) were CMV positive, 9 (10%) were HCV positive, and 2 (2.2%) had HBV. CONCLUSIONS: In reconciliation with frequentist statistics, the dual ML model validated several predictors that either negatively (protective) or positively (harmful) influenced HbA1c level, the majority of which were significant at 95% confidence interval. Individuals who are HCV and CMV positive are predicted to develop NODAT. Further, older individuals, with blood group O+ve, prior history of hemodialysis, a relatively high BMI before the transplant, and receiving higher doses of prednisolone following the transplant are more likely to develop NODAT. The current study represents the first research from Iraq to explore NODAT predictors among kidney transplant recipients using frequentist statistics and artificial intelligence models.

show abstract

Past Myocardial Infarctions and Gender Predict the LVEF Regardless of the Status of Coronary Collaterals: An AI-Informed Research

Singh¹,

Al-Imam²,

Tirpude³

et al. 2023

Open Access Maced J Med Sci

View full text Add to dashboard Cite

BACKGROUND: The degree of the development of coronary collaterals is long considered an alternate – that is, a collateral – source of blood supply to an area of the myocardium threatened with vascular ischemia or insufficiency. Hence, the coronary collaterals are beneficial but can also promote harmful (adverse) effects. For instance, the coronary steal effect during the myocardial hyperemia phase and that of restenosis following coronary angioplasty. OBJECTIVES: Our study explores the contribution of coronary collaterals – if any exist – while considering other potential predictors, including demographics and medical history, toward the left ventricular (LV) dysfunction measured through the LV ejection fraction (LVEF). METHODS: Our cross-sectional design study used convenience sampling of 100 patients (n = 100; a male-to-female ratio of 4:1). We conducted frequentist inference statistics using IBM-SPSS version 24 and Microsoft Office Excel 2016 with the analysis ToolPak plugin; we ran parallel neural networks (supervised machine learning (ML)) and a two-step clustering (non-supervised ML) for robust conjoint inference with frequentist statistics. RESULTS: The past incidents of myocardial infarction (p = 0.036) and gender (p = 0.072) influenced the LVEF; both are significant predictors at a 90% confidence interval. We found that gender and past incidents of MI influenced the LVEF regardless of the status of coronary collaterals. Our study did not yield any positive or significant findings concerning the status of coronary collaterals or the coronary circulation dominance patterns. CONCLUSION: Regardless of the status of coronary collaterals, we verified that the female gender is protective of the LV function, contrary to the past infarction incidents that predispose to a deteriorated LV function. Our study’s innovation relates to its status as the first study from India to explore the coronary collaterals and the ejection fraction while incorporating frequentist statistics and narrow artificial intelligence to infer reliable results.

show abstract

Surface Web Merits for SARS-CoV-2 Pandemic in Iraq

Cited by 3 publications

References 15 publications

Measurement of the serum level of Leucine-rich alpha-2-glycoprotein-1 in hospitalized Iraqi COVID-19 Patients

Measurement of the serum level of Leucine-rich alpha-2-glycoprotein-1 in hospitalized Iraqi COVID-19 Patients

Predictors of New-onset Diabetes After Kidney Transplantation During 2019-nCoV Pandemic: A Unison of Frequentist Inference and Narrow AI

Past Myocardial Infarctions and Gender Predict the LVEF Regardless of the Status of Coronary Collaterals: An AI-Informed Research

Contact Info

Product

Resources

About