Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees

Single Cell Transcriptomics Genomics Based on Machine Learning Algorithm: Constructing and Validating Neutrophil Extracellular Trap Gene Model in COPD

Background

Neutrophil trap (NET) is an important feature of chronic inflammatory diseases. At present, there are still few studies to explore the characteristics of NET in different chronic obstructive pulmonary disease (COPD) patients. This study aimed to identify NET signature genes in different COPD patients.

Methods

We analyzed single-cell RNA sequencing data from COPD and non-COPD individuals to identify differentially expressed neutrophil genes. Machine learning algorithms were applied to construct models A and B, specific to smoking and non-smoking COPD patients, respectively.

Results

Through single-cell cluster analysis, 165 neutrophil characteristic genes in COPD group were successfully identified. Model A, consisting of key genes CD63, RNASE2, ERAP2, and model B, consisting of GRIPAP1, NHS, EGFLAM, and GLUL, were validated internally and externally, showing significant risk scores and good diagnostic efficacy (AUC: 60.24-87.22). Alveolar lavage fluid in patients with COPD was studied and confirmed higher expression levels of RNASE2 and NHS in severe COPD patients.

Conclusion

The study successfully developed NET signature gene models for identifying smoking and non-smoking COPD respectively, with validated specificity and predictive power, offering a foundation for personalized treatment strategies.

A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning

Background

Severe Fever with Thrombocytopenia Syndrome (SFTS) is a disease caused by infection with the Severe Fever with Thrombocytopenia Syndrome virus (SFTSV), a novel Bunyavirus. Accurate prognostic assessment is crucial for developing individualized prevention and treatment strategies. However, machine learning prognostic models for SFTS are rare and need further improvement and clinical validation.

Objective

This study aims to develop and validate an interpretable prognostic model based on machine learning (ML) methods to enhance the understanding of SFTS progression.

Methods

This multicenter retrospective study analyzed patient data from two provinces in China. The derivation cohort included 292 patients treated at The Second Hospital of Nanjing from January 2022 to December 2023, with a 7:3 split for model training and internal validation. The external validation cohort consisted of 104 patients from The First Affiliated Hospital of Wannan Medical College during the same period. Twenty-four commonly available clinical features were selected, and the Boruta algorithm identified 12 candidate predictors, ranked by Z-scores, which were progressively incorporated into 10 machine learning models to develop prognostic models. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC), accuracy, recall, and F1 score. The clinical utility of the best-performing model was evaluated through decision curve analysis (DCA) based on net benefit. Robustness was tested with 10-fold cross-validation, and feature importance was explained using SHapley Additive exPlanation (SHAP) both globally and locally.

Results

Among the 10 machine learning models, the XGBoost model demonstrated the best overall discriminatory ability. Considering both AUC index and feature simplicity, a final interpretable XGBoost model with 7 key features was constructed. The model showed high predictive accuracy for patient outcomes in both internal (AUC = 0.911, 95% CI: 0.842-0.967) and external validations (AUC = 0.891, 95% CI: 0.786-0.977). A clinical tool based on this model has been developed and implemented using the Streamlit framework.

Conclusion

The interpretable XGBoost-based prognostic model for SFTS shows high predictive accuracy and has been translated into a clinical tool. The model's 7 key features serve as valuable indicators for early prognosis of SFTS, warranting close attention from healthcare professionals in clinical practice.

The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe

The unprecedented worldwide pandemic caused by COVID-19 has motivated several research groups to develop machine-learning based approaches that aim to automate the diagnosis or screening of COVID-19, in large-scale. The gold standard for COVID-19 detection, quantitative-Real-Time-Polymerase-Chain-Reaction (qRT-PCR), is expensive and time-consuming. Alternatively, haematology-based detections were fast and near-accurate, although those were less explored. The external-validity of the haematology-based COVID-19-predictions on diverse populations are yet to be fully investigated. Here we report external-validity of machine learning-based prediction scores from haematological parameters recorded in different hospitals of Brazil, Italy, and Western Europe (raw sample size, 195554). The XGBoost classifier performed consistently better (out of seven ML classifiers) on all the datasets. The working models include a set of either four or fourteen haematological parameters. The internal performances of the XGBoost models (AUC scores range from 84% to 97%) were superior to ML models reported in the literature for some of these datasets (AUC scores range from 84% to 87%). The meta-validation on the external performances revealed the reliability of the performance (AUC score 86%) along with good accuracy of the probabilistic prediction (Brier score 14%), particularly when the model was trained and tested on fourteen haematological parameters from the same country (Brazil). The external performance was reduced when the model was trained on datasets from Italy and tested on Brazil (AUC score 69%) and Western Europe (AUC score 65%); presumably affected by factors, like, ethnicity, phenotype, immunity, reference ranges, across the populations. The state-of-the-art in the present study is the development of a COVID-19 prediction tool that is reliable and parsimonious, using a fewer number of hematological features, in comparison to the earlier study with meta-validation, based on sufficient sample size (n = 195554). Thus, current models can be applied at other demographic locations, preferably, with prior training of the model on the same population. Availability: https://covipred.bits-hyderabad.ac.in/home; https://github.com/debashreebanerjee/CoviPred.

The association between mortality due to COVID-19 and coagulative parameters: a systematic review and meta-analysis study

AIMS AND OBJECTIVES

This systematic review and meta-analysis study evaluated the association between mortality due to COVID-19 and coagulative factors.

METHODS

A systematic search was conducted on electronic databases including PubMed, Scopus, and the Web of Science from the beginning of the pandemic until October 2024 to identify relevant studies on COVID-19 patients and their laboratory findings related to coagulation markers and mortality outcome. Eligibility criteria were defined based on the PICO framework, and data extraction was performed by two authors independently using a standardized sheet. Statistical analysis was accomplished using the random effects model, and heterogeneity among studies was assessed using the I2 test. R and RStudio were used for statistical analysis and visualization.

RESULTS

Our systematic literature search yielded 6969 studies, with 48 studies meeting the inclusion criteria for our meta-analysis. The mean platelet count was significantly lower in deceased COVID-19 patients compared to survivors (20.58), while activated partial thromboplastin time (aPTT) and fibrinogen levels did not show significant differences. The pooled mean difference of D-Dimer, International Normalized Ratio (INR), and prothrombin time (PT) were significantly lower in survived patients (-2.45, -0.10, and -0.84, respectively). These findings suggest that platelet count, D-Dimer, INR, and PT may serve as potential indicators of mortality in COVID-19 patients.

CONCLUSION

The results of our systematic review and meta-analysis revealed a significant reduction in the pooled platelet count among deceased individuals when compared to survivors. However, no significant distinctions were observed in the pooled mean activated aPTT and fibrinogen levels between the deceased and survivor groups. On the other hand, there were noticeable variations in the pooled estimated mean of INR, PT, and D-Dimer levels, with significantly higher values in the deceased group compared to those who survived.

Bacterial infections and outcomes of inpatients with COVID-19 in the intensive care unit during the delta-dominant phase: the worst wave of pandemic in Iran

Background

Epidemiological data regarding the prevalence of bacterial multidrug-resistant (MDR) Gram-negative infections in patients with COVID-19 in Iran are still ambiguous. Thus, in this study we have investigated the epidemiology, risk factors for death, and clinical outcomes of bacterial infections among patients with COVID-19 in the intensive care unit (ICU).

Method

This retrospective cohort study included patients with COVID-19 hospitalized in the ICU of a university hospital in Iran between June 2021 and December 2021. We evaluated the epidemiological, clinical, and microbiological features, outcomes and risk factors associated with death among all COVID-19 patients. Data and outcomes of these patients with or without bacterial infections were compared. Kaplan-Meier plot was used for survival analyses.

Results

In total, 505 COVID-19 patients were included. The mean age of the patients was 52.7 ± 17.6 years and 289 (57.2%) were female. The prevalence of bacterial infections among hospitalized patients was 14.9%, most of them being hospital-acquired superinfections (13.3%). MDR Klebsiella pneumoniae and Staphylococcus aureus were the most common pathogens causing respiratory infections. Urinary tract infections were most frequently caused by MDR Escherichia coli and K. pneumoniae. The overall in-hospital mortality rate of COVID-19 patients was 46.9% (237/505), while 78.7% (59/75) of patients with bacterial infections died. Infection was significantly associated with death (OR 6.01, 95% CI = 3.03-11.92, p-value <0.0001) and a longer hospital stay (p < 0.0001). Multivariate logistic regression analysis showed that Age (OR = 1.04, 95% CI = 1.03-1.06, p-value <0.0001), Sex male (OR = 1.70, 95% CI = 1.08-2.70, p-value <0.0001), Spo2 (OR = 1.99, 95% CI = 1.18-3.38, p-value = 0.010) and Ferritin (OR = 2.33, 95% CI = 1.37-3.97, p-value = 0.002) were independent risk factors associated with in-hospital mortality. Furthermore, 95.3% (221/232) of patients who were intubated died.

Conclusion

Our findings demonstrate that bacterial infection due to MDR Gram-negative bacteria associated with COVID-19 has an expressive impact on increasing the case mortality rate, reinforcing the importance of the need for surveillance and strict infection control rules to limit the expansion of almost untreatable microorganisms.

Machine learning approaches to predict the need for intensive care unit admission among Iranian COVID-19 patients based on ICD-10: A cross-sectional study

Background & Aim

Timely identification of the patients requiring intensive care unit admission (ICU) could be life-saving. We aimed to compare different machine learning algorithms to predict the requirements for ICU admission in COVID-19 patients.

Methods

We screened all patients with COVID-19 at six academic hospitals in Tehran comprising our study population. A total of 44,112 COVID-19 patients (≥18 years old) were included, among which 7722 patients were hospitalized. We used a Random Forest algorithm to select significant variables. Then, prediction models were developed using the Support Vector Machine, Naıve Bayes, logistic regression, lightGBM, decision tree, and K-Nearest Neighbor algorithms. Sensitivity, specificity, accuracy, F1 score, and receiver operating characteristic-Area Under the Curve (AUC) were used to compare the prediction performance of different models.

Results

Based on random Forest, the following predictors were selected: age, cardiac disease, cough, hypertension, diabetes, influenza & pneumonia, malignancy, and nervous system disease. Age was found to have the strongest association with ICU admission among COVID-19 patients. All six models achieved an AUC greater than 0.60. Naıve Bayes achieved the best predictive performance (AUC = 0.71).

Conclusion

Naïve Bayes and lightGBM demonstrated promising results in predicting ICU admission needs in COVID-19 patients. Machine learning models could help quickly identify high-risk patients upon entry and reduce mortality and morbidity among COVID-19 patients.

Post Covid telogen effluvium: the diagnostic value of serum ferritin biomarker and the preventive value of dietary supplements. a case control study

Telogen effluvium is characterized by excessive hair shedding usually following a stressful event. Ferritin has been used in clinical practice as a biomarker of nonanemic iron deficiency in cases of telogen effluvium. During the years of the COVID19 pandemic, telogen effluvium was reported as a part of post covid manifestations. As ferritin was also a biomarker for inflammation in cases with covid infection, this study was designed to evaluate the value of ferritin in cases with postcovid telogen effluvium one hundred patients recovering from covid 19 for 4-12 weeks were included in the study, detailed drug and laboratory history was obtained and serum ferritin level was measured. the mean serum level of ferritin among telogen effluvium patients was significantly lower than controls (68.52 ± 126 and 137 ± 137.597 ug/L respectively). Patients with telogen effluvium used significantly more azithromycin and ivermectin and significantly less vitamin C, D, lactoferrin and zinc than the controls Although serum ferritin is lower among telogen effluvium patients, it was still higher than the cutoff value for diagnosing nonanemic iron deficiency, we suggest that it will not be a good biomarkers in these cases. Our secondary outcomes showed that dietary supplements used during active infection such as vitamin C, D, lactoferrin and zinc might have a preventive value on postcovid hair loss, while azithromycin and ivermectin could have a negative long term effect on telogen effluvium.

Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction

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.

Development of Biomarkers and Prognosis Model of Mortality Risk in Patients with COVID-19

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.

Cytokine expression and mortality risk among COVID-19 hospitalized patients over 60 years of age in a referral hospital in Cartagena, Colombia

Background

Cytokine dysregulation in COVID-19 patients aged over 60 has been associated to adverse outcomes. While serum levels have been studied, cellular expression, particularly in Afro-Colombians, remains understudied. This research aims to describe cytokine expression in peripheral blood leukocytes and its association with adverse outcomes in COVID-19 patients aged over 60 at Cartagena's referral hospital.

Methods

A cohort study was conducted, encompassing severe and critical cases of COVID-19 between November 2021 and February 2022. At baseline, the cellular expression level of cytokines IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ was assessed using flow cytometry. Additionally, various biochemical, hematological, and coagulation markers were evaluated. The main outcome was time to death.

Results

Among the 50 enrolled participants, the median age was 76.5 years, 60% were male, 60% were admitted to the ICU, and 42% died. Lactate dehydrogenase and hemoglobin were the only markers that differed between fatal and surviving cases. Regarding cytokines, the level of IL-6 expression was associated with an increased risk of death. Specifically, a one percent increase in the expression was associated with a 7.3% increase in the risk of death. Stratifying the analysis by death and ICU admission, the median expression level remained high in fatal cases who were admitted to the ICU.

Conclusions

Our findings revealed a significant association between high cellular expression levels of IL-6 and an increased risk of mortality. These results provide valuable scientific insights that could inform the prioritization of case management, providing especially advantageous for the vulnerable Afro-Colombian group.

Association Between Hemoglobin-Albumin-Lymphocyte-Platelet Index and Mortality in Hospitalized COVID-19 Omicron BA.2 Infected Patients

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.

Association of Coagulopathy and Inflammatory Biomarkers with Severity in SARS-CoV-2-Infected Individuals of the Al-Qunfudhah Region of Saudi Arabia

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.

New score to predict COVID-19 progression in vaccine and early treatment era: the COVID-19 Sardinian Progression Score (CSPS)

BACKGROUND

Several scores aimed at predicting COVID-19 progression have been proposed. As the variables vaccination and early SARS-CoV-2 treatment were systematically excluded from the prognostic scores, the present study's objective was to develop a new model adapted to the current epidemiological scenario.

METHODS

We included all patients evaluated by the Infectious Disease Unit in Sassari, with SARS-CoV-2 infection and without signs of respiratory failure at the first evaluation (P/F > 300). Disease progression was defined by the prescription of supplemental oxygen. In addition, variables related to demographics, vaccines, comorbidities, symptoms, CT scans, blood tests, and therapies were collected. Multivariate logistic regression modelling was performed to determine factors associated with progression; any variable with significant univariate test or clinical relevance was selected as a candidate for multivariate analysis. Hosmer-Lemeshow (HL) goodness of fit statistic was calculated. Odds ratio values were used to derive an integer score for developing an easy-to-use progression risk score. The discrimination performance of the risk index was determined using the AUC, and the best cut-off point, according to the Youden index, sensitivity, specificity, predictive value, and likelihood ratio, was chosen.

RESULTS

1145 patients [median (IQR) age 74 (62-83) years; 53.5% males] were enrolled; 336 (29.3%) had disease progression. Patients with a clinical progression were older and showed more comorbidities; furthermore, they were less vaccinated and exposed to preventive therapy. In the multivariate logistic regression analysis, age ≥ 60 years, COPD, dementia, haematological tumours, heart failure, exposure to no or one vaccine dose, fever, dyspnoea, GGO, consolidation, ferritin, De Ritis ≥ 1.2, LDH, and no exposure to early anti-SARS-CoV-2 treatment were associated with disease progression. The final risk score ranged from 0 to 45. The ROC curve analysis showed an AUC of 0.92 (95% CI 0.90-0.93) with a 93.7% specificity and 72.9% sensitivity. Low risk was defined when the cut-off value was less than 23. Three risk levels were identified: low (0-23 points), medium (24-35), and high (≥ 36).

CONCLUSIONS

The proportion of patients with progression increases with high scores: the assessment of the risk could be helpful for clinicians to plan appropriate therapeutic strategies.

Immunological Misfiring and Sex Differences/Similarities in Early COVID-19 Studies: Missed Opportunities of Making a Real IMPACT

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.

Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves

The impact of SARS-CoV-2 infection remains substantial on a global scale, despite widespread vaccination efforts, early therapeutic interventions, and an enhanced understanding of the disease's underlying mechanisms. At the same time, a significant number of patients continue to develop severe COVID-19, necessitating admission to intensive care units (ICUs). This study aimed to provide evidence concerning the most influential predictors of mortality among critically ill patients with severe COVID-19, employing machine learning (ML) techniques. To accomplish this, we conducted a retrospective multicenter investigation involving 684 patients with severe COVID-19, spanning from 1 June 2020 to 31 March 2023, wherein we scrutinized sociodemographic, clinical, and analytical data. These data were extracted from electronic health records. Out of the six supervised ML methods scrutinized, the extreme gradient boosting (XGB) method exhibited the highest balanced accuracy at 96.61%. The variables that exerted the greatest influence on mortality prediction encompassed ferritin, fibrinogen, D-dimer, platelet count, C-reactive protein (CRP), prothrombin time (PT), invasive mechanical ventilation (IMV), PaFi (PaO2/FiO2), lactate dehydrogenase (LDH), lymphocyte levels, activated partial thromboplastin time (aPTT), body mass index (BMI), creatinine, and age. These findings underscore XGB as a robust candidate for accurately classifying patients with COVID-19.

Long non-coding RNAs ANRIL, THRIL, and NEAT1 as potential circulating biomarkers of SARS-CoV-2 infection and disease severity

The current outbreak of coronavirus disease 2019 (COVID-19) is a global emergency, as its rapid spread and high mortality rate, which poses a significant threat to public health. Innate immunity plays a crucial role in the primary defense against infections, and recent studies have highlighted the pivotal regulatory function of long non-coding RNAs (lncRNAs) in innate immune responses. This study aims to assess the circulating levels of lncRNAs namely ANRIL, THRIL, NEAT1, and MALAT1 in the blood of moderate and severe SARS-CoV-2 infected patients, in comparison to healthy individuals. Additionally, it aims to explore the potential of these lncRNAs as biomarkers for determining the severity of the disease. The blood samples were collected from a total of 38 moderate and 25 severe COVID-19 patients, along with 30 healthy controls. The total RNA was extracted and qPCR was performed to evaluate the blood levels of the lncRNAs. The results indicate significantly higher expression levels of lncRNAs ANRIL and THRIL in severe patients when compared to moderate patients (P value = 0.0307, P value = 0.0059, respectively). Moreover, the expression levels of lncRNAs ANRIL and THRIL were significantly up-regulated in both moderate and severe patients in comparison to the control group (P value < 0.001, P value < 0.001, P value = 0.001, P value < 0.001, respectively). The expression levels of lncRNA NEAT1 were found to be significantly higher in both moderate and severe COVID-19 patients compared to the healthy group (P value < 0.001, P value < 0.001, respectively), and there was no significant difference in the expression levels of NEAT1 between moderate and severe patients (P value = 0.6979). The expression levels of MALAT1 in moderate and severe patients did not exhibit a significant difference compared to the control group (P value = 0.677, P value = 0.764, respectively). Furthermore, the discriminative power of ANRIL and THRIL was significantly higher in the severe patient group than the moderate group (Area under curve (AUC) = 0.6879; P-value = 0.0122, AUC = 0.6947; P-value = 0.0093, respectively). In conclusion, the expression levels of the lncRNAs ANRIL and THRIL are correlated with the severity of COVID-19 and can be regarded as circulating biomarkers for disease progression.

LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19

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.