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Liu W, Jia L, Xu L, Yang F, Guo Z, Li J, Zhang D, Liu Y, Xiang H, Cheng H, Hou J, Li S, Li H. Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study. Front Neurol 2024; 15:1368902. [PMID: 38841697 PMCID: PMC11150528 DOI: 10.3389/fneur.2024.1368902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/24/2024] [Indexed: 06/07/2024] Open
Abstract
Background Early neurological deterioration (END) is a frequent complication in patients with perforating artery territory infarction (PAI), leading to poorer outcomes. Therefore, we aimed to apply machine learning (ML) algorithms to predict the occurrence of END in PAI and investigate related risk factors. Methods This retrospective study analyzed a cohort of PAI patients, excluding those with severe stenosis of the parent artery. We included demographic characteristics, clinical features, laboratory data, and imaging variables. Recursive feature elimination with cross-validation (RFECV) was performed to identify critical features. Seven ML algorithms, namely logistic regression, random forest, adaptive boosting, gradient boosting decision tree, histogram-based gradient boosting, extreme gradient boosting, and category boosting, were developed to predict END in PAI patients using these critical features. We compared the accuracy of these models in predicting outcomes. Additionally, SHapley Additive exPlanations (SHAP) values were introduced to interpret the optimal model and assess the significance of input features. Results The study enrolled 1,020 PAI patients with a mean age of 60.46 (range 49.11-71.81) years. Of these, 30.39% were women, and 129 (12.65%) experienced END. RFECV selected 13 critical features, including blood urea nitrogen (BUN), total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), atrial fibrillation, loading dual antiplatelet therapy (DAPT), single antiplatelet therapy (SAPT), argatroban, the basal ganglia, the thalamus, the posterior choroidal arteries, maximal axial infarct diameter (measured at < 15 mm), and stroke subtype. The gradient-boosting decision tree had the highest area under the curve (0.914) among the seven ML algorithms. The SHAP analysis identified apoB as the most significant variable for END. Conclusion Our results suggest that ML algorithms, especially the gradient-boosting decision tree, are effective in predicting the occurrence of END in PAI patients.
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Affiliation(s)
- Wei Liu
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Longbin Jia
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Lina Xu
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Fengbing Yang
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Zixuan Guo
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Jinna Li
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Dandan Zhang
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Yan Liu
- The First Clinical College of Changzhi Medical College, Changzhi, China
| | - Han Xiang
- The First Clinical College of Changzhi Medical College, Changzhi, China
| | - Hongjiang Cheng
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Jing Hou
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Shifang Li
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Huimin Li
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
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Wu Z, Geng N, Liu Z, Pan W, Zhu Y, Shan J, Shi H, Han Y, Ma Y, Liu B. Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction. Virol J 2024; 21:96. [PMID: 38671532 PMCID: PMC11046891 DOI: 10.1186/s12985-024-02367-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking. METHODS A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed. RESULTS A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis. CONCLUSION Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.
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Affiliation(s)
- Zhipeng Wu
- Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, No. 8, Xi Tou Tiao, Youanmenwai Street, Fengtai District, Beijing City, 100069, People's Republic of China
- Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Research Center for Respiratory Infectious Diseases, Beijing, People's Republic of China
| | - Nan Geng
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Zhao Liu
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Wen Pan
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Yueke Zhu
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Jing Shan
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Hongbo Shi
- Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Ying Han
- Department of Gastroenterology and Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Yingmin Ma
- Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, No. 8, Xi Tou Tiao, Youanmenwai Street, Fengtai District, Beijing City, 100069, People's Republic of China.
- Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China.
- Beijing Research Center for Respiratory Infectious Diseases, Beijing, People's Republic of China.
| | - Bo Liu
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China.
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Zhang Z, Tang L, Guo Y, Guo X, Pan Z, Ji X, Gao C. Development of Biomarkers and Prognosis Model of Mortality Risk in Patients with COVID-19. J Inflamm Res 2024; 17:2445-2457. [PMID: 38681069 PMCID: PMC11048291 DOI: 10.2147/jir.s449497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 04/04/2024] [Indexed: 05/01/2024] Open
Abstract
Background As of 30 April 2023, the COVID-19 pandemic has resulted in over 6.9 million deaths worldwide. The virus continues to spread and mutate, leading to continuously evolving pathological and physiological processes. It is imperative to reevaluate predictive factors for identifying the risk of early disease progression. Methods A retrospective study was conducted on a cohort of 1379 COVID-19 patients who were discharged from Xin Hua Hospital affiliated with Shanghai Jiao Tong University School of Medicine between 15 December 2022 and 15 February 2023. Patient symptoms, comorbidities, demographics, vital signs, and laboratory test results were systematically documented. The dataset was split into testing and training sets, and 15 different machine learning algorithms were employed to construct prediction models. These models were assessed for accuracy and area under the receiver operating characteristic curve (AUROC), and the best-performing model was selected for further analysis. Results AUROC for models generated by 15 machine learning algorithms all exceeded 90%, and the accuracy of 10 of them also surpassed 90%. Light Gradient Boosting model emerged as the optimal choice, with accuracy of 0.928 ± 0.0006 and an AUROC of 0.976 ± 0.0028. Notably, the factors with the greatest impact on in-hospital mortality were growth stimulation expressed gene 2 (ST2,19.3%), interleukin-8 (IL-8,17.2%), interleukin-6 (IL-6,6.4%), age (6.1%), NT-proBNP (5.1%), interleukin-2 receptor (IL-2R, 5%), troponin I (TNI,4.6%), congestive heart failure (3.3%) in Light Gradient Boosting model. Conclusion ST-2, IL-8, IL-6, NT-proBNP, IL-2R, TNI, age and congestive heart failure were significant predictors of in-hospital mortality among COVID-19 patients.
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Affiliation(s)
- Zhishuo Zhang
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Lujia Tang
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Yiran Guo
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Xin Guo
- School of Information Science and Technology, Sanda University, Shanghai, Pudong District, 201209, China
| | - Zhiying Pan
- School of Information Science and Technology, Sanda University, Shanghai, Pudong District, 201209, China
| | - Xiaojing Ji
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Chengjin Gao
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
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Wu W, Lu W, Hong D, Yu X, Xiong L. Association Between Hemoglobin-Albumin-Lymphocyte-Platelet Index and Mortality in Hospitalized COVID-19 Omicron BA.2 Infected Patients. Infect Drug Resist 2024; 17:1467-1476. [PMID: 38628242 PMCID: PMC11020245 DOI: 10.2147/idr.s451613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 03/13/2024] [Indexed: 04/19/2024] Open
Abstract
Background The hemoglobin-albumin-lymphocyte-platelet (HALP) index is a novel biomarker reflecting systemic inflammation and nutritional status which are important for coronavirus disease 2019 (COVID-19) mortality. However, the association between HALP and mortality in patients with COVID-19 has yet to be investigated. Methods A cohort of COVID-19 Omicron BA.2 infected patients admitted to the Shanghai Fourth People's Hospital, School of Medicine, Tongji University from April 12, 2022 to June 17, 2022 was retrospectively analyzed. Laboratory examinations on hospital admission, including hemoglobin, albumin, and lymphocyte and platelet, were collected. The association between baseline HALP and in-hospital poor overall survival (OS) was assessed using Kaplan-Meier curves, Cox regression models, interaction, and stratified analyses. Results A total of 2147 patients with COVID-19 Omicron BA.2 infection were included in the final analyses, and mortality in the hospital was 2.65%. Multivariate analysis indicated that low HALP index was independently associated with in-hospital mortality of COVID-19 patients [hazard ratio (HR) = 2.08; 95% confidence interval (CI) = 1.17-3.73]. Subgroup analysis demonstrated that low HALP index was an independent risk factor for in-hospital mortality in COVID-19 patients with age ≥70 (HR = 2.22, CI = 1.18-4.15) and severe cases (HR = 2.09, CI = 1.13-3.86). Conclusion HALP index is independently related to in-hospital poor OS for COVID-19 Omicron BA.2 infected patients, especially for age ≥70 and severe cases. HALP index on hospital admission is a useful candidate biomarker for identifying high risk of mortality in COVID-19 Omicron BA.2 infected patients.
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Affiliation(s)
- Wei Wu
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, 200434, People’s Republic of China
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
- Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
| | - Wenbin Lu
- Faculty of Anesthesiology, Changhai Hospital, Naval Medical University/Second Military Medical University, PLA, Shanghai, 200433, People’s Republic of China
| | - Dongmei Hong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, 200434, People’s Republic of China
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
- Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
| | - Xiya Yu
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, 200434, People’s Republic of China
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
- Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, 200434, People’s Republic of China
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
- Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, 200434, People’s Republic of China
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Izhari MA, Hadadi MAA, Alharbi RA, Gosady ARA, Sindi AAA, Dardari DMM, Alotaibi FE, Klufah F, Albanghali MA, Alharbi TH. Association of Coagulopathy and Inflammatory Biomarkers with Severity in SARS-CoV-2-Infected Individuals of the Al-Qunfudhah Region of Saudi Arabia. Healthcare (Basel) 2024; 12:729. [PMID: 38610151 PMCID: PMC11012004 DOI: 10.3390/healthcare12070729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Identifying prognosticators/predictors of COVID-19 severity is the principal focus for early prediction and effective management of the disease in a time-bound and cost-effective manner. We aimed to evaluate COVID-19 severity-dependent alteration in inflammatory and coagulopathy biomarkers. METHODS A hospital-dependent retrospective observational study (total: n = 377; male, n = 213; and female, n = 164 participants) was undertaken. COVID-19 exposure was assessed by performing real-time PCR on nasopharyngeal (NP) swabs. Descriptive and inferential statistics were applied for both continuous and categorical variables using Rstudio-version-4.0.2. Pearson correlation and regression were executed with a cut-off of p < 0.05 for evaluating significance. Data representation by R-packages and ggplot2. RESULTS A significant variation in the mean ± SD (highly-sever (HS)/moderately severe (MS)) of CRP (HS/MS: 102.4 ± 22.9/21.3 ± 6.9, p-value < 0.001), D-dimer (HS/MS: 661.1 ± 80.6/348.7 ± 42.9, p-value < 0.001), and ferritin (HS/MS: 875.8 ± 126.8/593.4 ± 67.3, p-value < 0.001) were observed. Thrombocytopenia, high PT, and PTT exhibited an association with the HS individuals (p < 0.001). CRP was correlated with neutrophil (r = 0.77), ferritin (r = 0.74), and WBC (r = 0.8). D-dimer correlated with platelets (r = -0.82), PT (r = 0.22), and PTT (r = 0.37). The adjusted odds ratios (Ad-OR) of CRP, ferritin, D-dimer, platelet, PT, and PTT for HS compared to MS were 1.30 (95% CI -1.137, 1.50; p < 0.001), 1.048 (95% CI -1.03, 1.066; p < 0.001), 1.3 (95% CI -1.24, 1.49, p > 0.05), -0.813 (95% CI -0.734, 0.899, p < 0.001), 1.347 (95% CI -1.15, 1.57, p < 0.001), and 1.234 (95% CI -1.16, 1.314, p < 0.001), respectively. CONCLUSION SARS-CoV-2 caused alterations in vital laboratory parameters and raised ferritin, CRP, and D-dimer presented an association with disease severity at a significant level.
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Affiliation(s)
- Mohammad Asrar Izhari
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
| | - Mansoor A. A. Hadadi
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
- Laboratory Department, Qunfudhah Hospital, Al-Qunfudhah 28887, Saudi Arabia
| | - Raed A. Alharbi
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
| | - Ahmed R. A. Gosady
- Laboratory Department, Baish General Hospital, Jazan 87597, Saudi Arabia
| | | | | | - Foton E. Alotaibi
- Department of Genetic Counseling, Al-Faisal University, Riyadh 11533, Saudi Arabia
| | - Faisal Klufah
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
| | - Mohammad A Albanghali
- Department of Public Health, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
| | - Tahani H Alharbi
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
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Minic A, Jovanovic L, Bacanin N, Stoean C, Zivkovic M, Spalevic P, Petrovic A, Dobrojevic M, Stoean R. Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:9878. [PMID: 38139724 PMCID: PMC10747899 DOI: 10.3390/s23249878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/28/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023]
Abstract
Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.
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Affiliation(s)
- Ana Minic
- Teacher Education Faculty, University of Pristina in Kosovska Mitrovica, 38220 Kosovska Mitrovica, Serbia;
| | - Luka Jovanovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Nebojsa Bacanin
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Catalin Stoean
- Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania;
| | - Miodrag Zivkovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Petar Spalevic
- Faculty of Technical Science, University of Pristina in Kosovska Mitrovica, Filipa Visnjica bb, 38220 Kosovska Mitrovica, Serbia;
| | - Aleksandar Petrovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Milos Dobrojevic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Ruxandra Stoean
- Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania;
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Reina-Reina A, Barrera J, Maté A, Trujillo J, Valdivieso B, Gas ME. Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers. Heliyon 2023; 9:e22878. [PMID: 38125502 PMCID: PMC10731083 DOI: 10.1016/j.heliyon.2023.e22878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 11/15/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
Coronavirus disease (COVID-19) remains a significant global health challenge, prompting a transition from emergency response to comprehensive management strategies. Furthermore, the emergence of new variants of concern, such as BA.2.286, underscores the need for early detection and response to new variants, which continues to be a crucial strategy for mitigating the impact of COVID-19, especially among the vulnerable population. This study aims to anticipate patients requiring intensive care or facing elevated mortality risk throughout their COVID-19 infection while also identifying laboratory predictive markers for early diagnosis of patients. Therefore, haematological, biochemical, and demographic variables were retrospectively evaluated in 8,844 blood samples obtained from 2,935 patients before intensive care unit admission using an interpretable machine learning model. Feature selection techniques were applied using precision-recall measures to address data imbalance and evaluate the suitability of the different variables. The model was trained using stratified cross-validation with k=5 and internally validated, achieving an accuracy of 77.27%, sensitivity of 78.55%, and area under the receiver operating characteristic (AUC) of 0.85; successfully identifying patients at increased risk of severe progression. From a medical perspective, the most important features of the progression or severity of patients with COVID-19 were lactate dehydrogenase, age, red blood cell distribution standard deviation, neutrophils, and platelets, which align with findings from several prior investigations. In light of these insights, diagnostic processes can be significantly expedited through the use of laboratory tests, with a greater focus on key indicators. This strategic approach not only improves diagnostic efficiency but also extends its reach to a broader spectrum of patients. In addition, it allows healthcare professionals to take early preventive measures for those most at risk of adverse outcomes, thereby optimising patient care and prognosis.
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Affiliation(s)
- A. Reina-Reina
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - J.M. Barrera
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - A. Maté
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - J.C. Trujillo
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - B. Valdivieso
- The University and Polytechnic La Fe Hospital of Valencia, Avenida Fernando Abril Martorell, 106 Torre H 1st floor, 46026, Valencia, Spain
- The Medical Research Institute of Hospital La Fe, Avenida Fernando Abril Martorell, 106 Torre F 7th floor, 46026, Valencia, Spain
| | - María-Eugenia Gas
- The Medical Research Institute of Hospital La Fe, Avenida Fernando Abril Martorell, 106 Torre F 7th floor, 46026, Valencia, Spain
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Bhargava A, Knapp JD. Immunological Misfiring and Sex Differences/Similarities in Early COVID-19 Studies: Missed Opportunities of Making a Real IMPACT. Cells 2023; 12:2591. [PMID: 37998327 PMCID: PMC10670326 DOI: 10.3390/cells12222591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/01/2023] [Accepted: 11/05/2023] [Indexed: 11/25/2023] Open
Abstract
COVID-19-associated intensive care unit (ICU) admissions were recognized as critical health issues that contributed to morbidity and mortality in SARS-CoV-2-infected patients. Severe symptoms in COVID-19 patients are often accompanied by cytokine release syndrome. Here, we analyzed publicly available data from the Yale IMPACT cohort to address immunological misfiring and sex differences in early COVID-19 patients. In 2020, SARS-CoV-2 was considered far more pathogenic and lethal than other circulating respiratory viruses, and the inclusion of SARS-CoV-2 negative patients in IMPACT cohorts confounds many findings. We ascertained the impact of several important biological variables such as days from symptom onset (DFSO); pre-existing risk factors, including obesity; and early COVID-19 treatments on significantly changed immunological measures in ICU-admitted COVID-19 patients that survived versus those that did not. Deceased patients had 19 unique measures that were not shared with ICU patients including increased granzyme-B-producing GzB+CD8+ T cells and interferon-γ. Male COVID-19 patients in ICU experienced many more changes in immunological and clinical measures than female ICU patients (25% vs. ~16%, respectively). A total of 13/124 measures including CCL5, CCL17, IL-18, IFNα2, Fractalkine, classical monocytes, T cells, and CD4Temra exhibited significant sex differences in female vs. male COVID-19 patients. A total of nine measures including IL-21, CCL5, and CD4Temra differed significantly between female and male healthy controls. Immunosuppressed patients experienced the most decreases in CD4Temra and CD8Tem cell numbers. None of the early COVID-19 treatments were effective in reducing levels of IL-6, a major component of the cytokine storm. Obesity (BMI >30) was the most impactful risk factor for COVID-19-related deaths and worst clinical outcomes. Our analysis highlights the contribution of biological sex, risk factors, and early treatments with respect to COVID-19-related ICU admission and progression to morbidity and mortality.
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Affiliation(s)
- Aditi Bhargava
- Center for Reproductive Sciences and Department of ObGyn, University of California San Francisco, San Francisco, CA 94143, USA
- Aseesa Inc., Hillsborough, CA 94010, USA;
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Belyaev M, Murugappan M, Velichko A, Korzun D. Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:8609. [PMID: 37896703 PMCID: PMC10610702 DOI: 10.3390/s23208609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023]
Abstract
This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analysis of the effectiveness of entropy calculation methods, fuzzy entropy showed the best results in diagnosing and monitoring PD using rs-EEG, with classification accuracy (ARKF) of ~99.9%. The most important frequency range of rs-EEG for PD-based diagnostics lies in the range of 0-4 Hz, and the most informative signals were mainly received from the right hemisphere of the head. It was also found that ARKF significantly decreased as the length of rs-EEG segments decreased from 1000 to 150 samples. Using a procedure for selecting the most informative features, it was possible to reduce the computational costs of classification by 11 times, while maintaining an ARKF ~99.9%. The proposed method can be used in the healthcare internet of things (H-IoT), where low-performance edge devices can implement ML sensors to enhance human resilience to PD.
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Affiliation(s)
- Maksim Belyaev
- Institute of Physics and Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia;
| | - Murugappan Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Kuwait City 13133, Kuwait;
- Department of Electronics and Communication Engineering, Faculty of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai 600117, India
- Centre of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia;
| | - Dmitry Korzun
- Department of Computer Science, Institute of Mathematics and Information Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia;
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Velichko A, Korzun D, Meigal A. Artificial Neural Networks for IoT-Enabled Smart Applications: Recent Trends. SENSORS (BASEL, SWITZERLAND) 2023; 23:4853. [PMID: 37430767 DOI: 10.3390/s23104853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 07/12/2023]
Abstract
In the age of neural networks and the Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda [...].
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Affiliation(s)
- Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Dmitry Korzun
- Department of Computer Science, Institute of Mathematics and Information Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Alexander Meigal
- Medical Institute, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
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Abujabal M, Shalaby MA, Abdullah L, Albanna AS, Elzoghby M, Alahmadi GG, Sethi SK, Temsah MH, Aljamaan F, Alhasan K, Kari JA. Common Prognostic Biomarkers and Outcomes in Patients with COVID-19 Infection in Saudi Arabia. Trop Med Infect Dis 2023; 8:tropicalmed8050260. [PMID: 37235308 DOI: 10.3390/tropicalmed8050260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 05/28/2023] Open
Abstract
Background: COVID-19 is a respiratory disease that eventually became a pandemic, with 300 million people infected around the world. Alongside the improvement in COVID-19 management and vaccine development, identifying biomarkers for COVID-19 has recently been reported to help in early prediction and managing severe cases, which might improve outcomes. Our study aimed to find out if there is any correlation between clinical severity and elevated hematological and biochemical markers in COVID-19 patients and its effect on the outcome. Methods: We have collected retrospective data on socio-demographics, medical history, biomarkers, and disease outcomes from five hospitals and health institutions in the Kingdom of Saudi Arabia. Results: Pneumonia was the most common presentation of COVID-19 in our cohort. The presence of abnormal inflammatory biomarkers (D-dimer, CRP, troponin, LDH, ferritin, and t white blood cells) was significantly associated with unstable COVID-19 disease. In addition, patients with evidence of severe respiratory disease, particularly those who required mechanical ventilation, had higher biomarkers when compared to those with stable respiratory conditions (p < 0.001). Conclusion: Identifying biomarkers predicts outcomes for COVID-19 patients and may significantly help in their management.
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Affiliation(s)
- Mashael Abujabal
- Pediatric Nephrology Unit, Faculty of Medicine and Pediatric Nephrology Center of Excellence, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mohamed A Shalaby
- Pediatric Nephrology Unit, Faculty of Medicine and Pediatric Nephrology Center of Excellence, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Layla Abdullah
- Pediatric Nephrology Unit, Faculty of Medicine and Pediatric Nephrology Center of Excellence, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Amr S Albanna
- King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Jeddah 14611, Saudi Arabia
| | - Mohamed Elzoghby
- Pediatric Intensive Care Unit, Pediatrics Department, College of Medicine, King Abdulaziz University Hospital, Jeddah 21589, Saudi Arabia
| | - Ghadeer Ghazi Alahmadi
- Department of Pediatric, College of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Sidharth Kumar Sethi
- Kidney and Renal Transplant Institute, Medanta, The Medicity Hospital, Gurgaon 122001, India
| | - Mohamad-Hani Temsah
- Pediatric Department, College of Medicine, King Saud University, Riyadh 11451, Saudi Arabia
- Prince Abdullah bin Khaled Coeliac Disease Research Chair, King Saud University, Riyadh 11362, Saudi Arabia
| | - Fadi Aljamaan
- Critical Care Department, College of Medicine, King Saud University, Riyadh 11451, Saudi Arabia
| | - Khalid Alhasan
- Pediatric Department, College of Medicine, King Saud University, Riyadh 11451, Saudi Arabia
- Department of Kidney and Pancreas Transplantation, Solid Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh 12713, Saudi Arabia
| | - Jameela A Kari
- Pediatric Nephrology Unit, Faculty of Medicine and Pediatric Nephrology Center of Excellence, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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12
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Kushiro S, Fukui S, Inui A, Kobayashi D, Saita M, Naito T. Clinical prediction rule for bacterial arthritis: Chi-squared
automatic interaction detector decision tree analysis model. SAGE Open Med 2023; 11:20503121231160962. [PMID: 36969723 PMCID: PMC10034275 DOI: 10.1177/20503121231160962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/14/2023] [Indexed: 03/24/2023] Open
Abstract
Objectives: Differences in demographic factors, symptoms, and laboratory data between
bacterial and non-bacterial arthritis have not been defined. We aimed to
identify predictors of bacterial arthritis, excluding synovial testing. Methods: This retrospective cross-sectional survey was performed at a university
hospital. All patients included received arthrocentesis from January 1,
2010, to December 31, 2020. Clinical information was gathered from medical
charts from the time of synovial fluid sample collection. Factors
potentially predictive of bacterial arthritis were analyzed using the
Student’s t-test or chi-squared test, and the chi-squared
automatic interaction detector decision tree analysis. The resulting
subgroups were divided into three groups according to the risk of bacterial
arthritis: low-risk, intermediate-risk, or high-risk groups. Results: A total of 460 patients (male/female = 229/231; mean ± standard deviation
age, 70.26 ± 17.66 years) were included, of whom 68 patients (14.8%) had
bacterial arthritis. The chi-squared automatic interaction detector decision
tree analysis revealed that patients with C-reactive
protein > 21.09 mg/dL (incidence of septic arthritis: 48.7%) and
C-reactive protein ⩽ 21.09 mg/dL plus 27.70 < platelet
count ⩽ 30.70 × 104/μL (incidence: 36.1%) were high-risk
groups. Conclusions: Our results emphasize that patients categorized as high risk of bacterial
arthritis, and appropriate treatment could be initiated as soon as
possible.
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Affiliation(s)
- Seiko Kushiro
- Department of General Medicine,
Juntendo University Faculty of Medicine, Tokyo, Japan
- Seiko Kushiro, Department of General
Medicine, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo
113-8421, Japan.
| | - Sayato Fukui
- Department of General Medicine,
Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Akihiro Inui
- Department of General Medicine,
Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Daiki Kobayashi
- Department of Internal Medicine, St.
Luke’s International Hospital, Tokyo, Japan
| | - Mizue Saita
- Department of General Medicine,
Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Toshio Naito
- Department of General Medicine,
Juntendo University Faculty of Medicine, Tokyo, Japan
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13
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Huyut MT, Huyut Z. Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees. Heliyon 2023; 9:e14015. [PMID: 36919085 PMCID: PMC9985543 DOI: 10.1016/j.heliyon.2023.e14015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 01/25/2023] [Accepted: 02/17/2023] [Indexed: 03/07/2023] Open
Abstract
Background and objective A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis. Material and method This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease. Results Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients. Conclusions This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.
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Affiliation(s)
- Mehmet Tahir Huyut
- Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Erzincan, Turkey
- Corresponding author. Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Erzincan, Turkey.
| | - Zübeyir Huyut
- Van Yuzuncu Yıl University, Faculty of Medicine, Department of Biochemistry, Van, Turkey
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Smail SW, Babaei E, Amin K. Hematological, Inflammatory, Coagulation, and Oxidative/Antioxidant Biomarkers as Predictors for Severity and Mortality in COVID-19: A Prospective Cohort-Study. Int J Gen Med 2023; 16:565-580. [PMID: 36824986 PMCID: PMC9942608 DOI: 10.2147/ijgm.s402206] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/10/2023] [Indexed: 02/19/2023] Open
Abstract
Purpose Oxidative stress (OS) and inflammation are pivotal points in the pathophysiology of coronavirus disease-2019 (COVID-19). This study aims to use routine laboratory and oxidative stress/antioxidative biomarkers as predictors for the mortality of the disease. Patients and Methods This prospective cohort study, made up of 120 COVID-19 patients from emergency units in Erbil, Duhok, Kirkuk, and Sulaymaniyah cities in Iraq, from May the 1st to May the 30th, 2021, and 60 healthy controls (HCs) (n = 60). The patients were re-categorized into mild (n = 54), severe (n = 40), and critical (n = 26) groups based on the clinical criteria. Following admission to the hospital, blood was directly collected for measuring routine laboratory biomarkers. Results Neutrophils and neutrophil/lymphocyte ratio (NLR) were higher in the critical group, while lymphocytes were lower in the severe and critical groups compared to the mild group. The CRP, ferritin, and D-dimer values were more elevated in severe and critical cases than in mild COVID-19 cases. The levels of malondialdehyde (MDA), nitric oxide (NO), and copper were elevated, while the superoxide dismutase (SOD) activity level and total antioxidant capacity (TAC) level were lower. However, vitamin C, glutathione peroxidase (GPx), and catalase activity levels were not changed in the COVID-19 groups compared to the HCs. NO and ferritin were predictors of ICU hospitalization; D-dimer, MDA, and NLR were predictors of mortality. NO, and NLR were predictors of SpO2 depression. Moreover, NO, and copper have both good diagnostic values, their cutoffs were 39.01 and 11.93, respectively. Conclusion There is an association between immune dysregulation and oxidative imbalance. The biomarkers, that could be considered as predictors for the severity and mortality of COVID-19, are the NLR, NO, ferritin, and D-dimer. The age equal to and older than 50 has a poor prognosis in the Kurdish population.
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Affiliation(s)
- Shukur Wasman Smail
- Department of Biology, College of Science, Salahaddin University, Erbil, Iraq,Correspondence: Shukur Wasman Smail, Salahaddin University, Erbil, Iraq, Tel +9647504491092, Email
| | - Esmaeil Babaei
- Department of Biology, School of Natural Sciences, University of Tabriz, Tabriz, Iran,Department of Pharmacognosy, College of Pharmacy, Hawler Medical University, Erbil, Kurdistan Region, Iraq
| | - Kawa Amin
- College of Medicine, University of Sulaimani, Sulaymaniyah, Iraq
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15
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Smail SW, Babaei E, Amin K. Ct, IL-18 polymorphism, and laboratory biomarkers for predicting chemosensory dysfunctions and mortality in COVID-19. Future Sci OA 2023; 9:FSO838. [PMID: 36999046 PMCID: PMC10005086 DOI: 10.2144/fsoa-2022-0082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/17/2023] [Indexed: 03/11/2023] Open
Abstract
Aim Patients with COVID-19 often experience chemosensory dysfunction. This research intends to uncover the association of RT-PCR Ct value with chemosensory dysfunctions and SpO2. This study also aims to investigate Ct, SpO2, CRP, D-dimer, and -607 IL-18 T/G polymorphism in order to find out predictors of chemosensory dysfunctions and mortality. Materials & methods This study included 120 COVID-19 patients, of which 54 were mild, 40 were severe and 26 were critical. CRP, D-dimer, RT-PCR, and IL-18 polymorphism were evaluated. Results & conclusion: Low Ct was associated with SpO2 dropping and chemosensory dysfunctions. IL-18 T/G polymorphism did not show an association with COVID-19 mortality; conversely, age, BMI, D-dimer and Ct values did.
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Affiliation(s)
- Shukur Wasman Smail
- Department of Biology, College of Science, Salahaddin University-Erbil, Iraq
| | - Esmaeil Babaei
- Department of Biology, School of Natural Sciences, University of Tabriz, Tabriz, Iran
- Department of Pharmacognosy, College of Pharmacy, Hawler Medical University, Erbil, Kurdistan Region, Iraq
| | - Kawa Amin
- College of Medicine, University of Sulaimani, Sulaymaniyah, Iraq
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Bodaghi A, Fattahi N, Ramazani A. Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases. Heliyon 2023; 9:e13323. [PMID: 36744065 PMCID: PMC9884646 DOI: 10.1016/j.heliyon.2023.e13323] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 01/21/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.
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Affiliation(s)
- Ali Bodaghi
- Department of Chemistry, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran
| | - Nadia Fattahi
- Department of Chemistry, University of Zanjan, Zanjan, 45371-38791, Iran,Trita Nanomedicine Research and Technology Development Center (TNRTC), Zanjan Health Technology Park, 45156-13191, Zanjan, Iran
| | - Ali Ramazani
- Department of Chemistry, University of Zanjan, Zanjan, 45371-38791, Iran,Department of Biotechnology, Research Institute of Modern Biological Techniques (RIMBT), University of Zanjan, Zanjan, 45371-38791, Iran,Corresponding author. Department of Chemistry, University of Zanjan, Zanjan, 45371-38791, Iran.;
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Huyut MT, Velichko A. LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19. MethodsX 2023; 10:102194. [PMID: 37122366 PMCID: PMC10115593 DOI: 10.1016/j.mex.2023.102194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/17/2023] [Indexed: 05/02/2023] Open
Abstract
Rapid and effective detection of the diagnosis and prognosis of COVID-19 disease is important in terms of reducing the mortality of the disease and reducing the pressure on health systems. Methods such as PCR testing and computed tomography used for this purpose in current health systems are costly, require an expert team and take time. This study offers a fast, economical and reliable approach for the early diagnosis and prognosis of infectious diseases, especially COVID-19. For this purpose, characteristics of a large population of COVID-19 patients were determined (51 different routine blood values) and calibrated. In order to determine the diagnosis and prognosis of the disease, the calibrated features were run with the LogNNet model. LogNNet has a simple algorithm and performance indicators comparable to the most efficient algorithms available.This approach pointed out that routine blood values contain important information, especially in the detection of COVID-19, and showed that the LogNNet model can be used as an economical, safe and fast alternative tool in the diagnosis of this disease.-In the LogNNet feedforward neural network, a feature vector is passed through a specially designed reservoir matrix and transformed into a new feature vector of a different size, increasing the classification accuracy.-The presented network architecture can achieve 80%-99% classification accuracy using a range of weightings on devices with a total memory size of 1 to 29 kB constrained.-Due to the chaotic mapping procedures, the RAM usage in the LogNNet neural network processing process is greatly reduced. Hence, optimization of chaotic map parameters has an important function in LogNNet neural network application.
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Affiliation(s)
- Mehmet Tahir Huyut
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, 24000 Erzincan, Turkey
- Corresponding author.
| | - Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Str., 185910 Petrozavodsk, Russia
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The Evaluation of Inflammatory Biomarkers in Predicting Progression of Acute Pancreatitis to Pancreatic Necrosis: A Diagnostic Test Accuracy Review. Healthcare (Basel) 2022; 11:healthcare11010027. [PMID: 36611486 PMCID: PMC9818910 DOI: 10.3390/healthcare11010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Acute pancreatitis is the acute inflammation of the pancreas; 30% of cases may progress to pancreatic necrosis. The aim of this study was to assess the diagnostic accuracy of inflammatory biomarkers (C-reactive protein (CRP), procalcitonin (PCT), and lactate dehydrogenase (LDH)) in detecting pancreatic necrosis in adults with confirmed acute pancreatitis within 14 days of symptom onset and without organ failure. A systematic search was conducted across the Cumulative Index of Nursing and Allied Health (CINAHL), Cochrane, Embase, PubMed, and Web of Science until May 30, 2022, with the following keywords: acute disease, biomarkers, C-reactive protein, calcitonin, differential, diagnosis, lactate dehydrogenase, pancreatitis, acute necrotizing, necrosis, sensitivity, specificity. Statistical analysis was conducted in RevMan 5.4.1 (Cochrane). Five studies pooling 645 participants were included of which 59.8% were males, with a mean age of 49 years. CRP was the best cutoff at 279 mg/L (χ2 = 47.43, p < 0.001), followed by 200 mg/L (χ2 = 36.54, p < 0.001). LDH was cut off at 290 units/L (χ2 = 51.6, p < 0.001), whereas PCT did not display the most reliable results at 0.05 ng/mL. Inflammatory biomarkers are scalable diagnostic tools that may confer clinical value by decreasing the mortality of acute pancreatitis sequelae.
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