Thymic Dysfunction and Atrophy in COVID-19 Disease Complicated by Inflammation, Malnutrition and Cachexia

Malnutrition and the Post-Acute Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2 Infection: A Multi-Institutional Population-Based Propensity Score-Matched Analysis

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global health crisis, exacerbating issues like malnutrition due to increased metabolic demands and reduced intake during illness. Malnutrition, a significant risk factor, is linked to worse outcomes in patients with COVID-19, such as increased mortality and extended hospital stays. This retrospective cohort study investigated the relationship between malnutrition and clinical outcomes within 90-180 days using data obtained from the TriNetX database. Patients aged >18 years diagnosed with COVID-19 between 1 January 2022, and 31 March 2024 were enrolled in the study. The propensity score-matching (PSM) method was used to match patients with malnutrition (malnutrition group) and those without malnutrition (control group). The primary composite outcome was the cumulative hazard ratio (HR) for post-COVID-19 condition, all-cause hospitalization, and all-cause mortality between 90 days and 180 days after COVID-19 diagnosis. The secondary outcomes were the individual components of the primary outcomes. Two cohorts, each consisting of 15,004 patients with balanced baseline characteristics, were identified using PSM. During the 90-180-day follow-up period, the malnutrition group exhibited a higher incidence of all-cause hospitalization, mortality, or post-COVID-19 condition (HR = 2.315, 95% confidence interval: 2.170-2.471, p < 0.0001). Compared with patients with COVID-19 without malnutrition, those with malnutrition may be associated with a higher risk of adverse clinical outcomes.

Clinical predictive value of the CRP-albumin-lymphocyte index for prognosis of critically ill patients with sepsis in intensive care unit: a retrospective single-center observational study

Background

Sepsis is a complex syndrome characterized by physiological, pathological, and biochemical abnormalities caused by infection. Its development is influenced by factors such as inflammation, nutrition, and immune status. Therefore, we combined C-reactive protein (CRP), albumin, and lymphocyte, which could reflect above status, to be the CRP-albumin-lymphocyte (CALLY) index, and investigated its association with clinical prognosis of critically ill patients with sepsis.

Methods

This retrospective observational study enrolled critically ill patients with sepsis who had an initial CRP, albumin, and lymphocyte data on the first day of ICU admission. All data were obtained from the Affiliated Hospital of Jiangsu University. The patients were divided into quartiles (Q1-Q4) based on their CALLY index. The outcomes included 30-/60-day mortality and acute kidney injury (AKI) occurrence. The association between the CALLY index and these clinical outcomes in critically ill patients with sepsis was evaluated using Cox proportional hazards and logistic regression analysis.

Results

A total of 1,123 patients (63.0% male) were included in the study. The 30-day and 60-day mortality rates were found to be 28.1 and 33.4%, respectively, while the incidence of AKI was 45.6%. Kaplan-Meier analysis revealed a significant association between higher CALLY index and lower risk of 30-day and 60-day mortality (log-rank p < 0.001). Multivariate Cox proportional hazards analysis indicated that the CALLY index was independently associated with 30-day mortality [HR (95%CI): 0.965 (0.935-0.997); p = 0.030] and 60-day mortality [HR (95%CI): 0.969 (0.941-0.997); p = 0.032]. Additionally, the multivariate logistic regression model showed that the CALLY index served as an independent risk predictor for AKI occurrence [OR (95%CI): 0.982 (0.962-0.998); p = 0.033].

Conclusion

The findings of this study indicated a significant association between the CALLY index and both 30-day and 60-day mortality, as well as the occurrence of AKI, in critically ill patients with sepsis. These findings suggested that the CALLY index may be a valuable tool in identifying sepsis patients who were at high risk for unfavorable outcomes.

Thymic atrophy induced by Plasmodium berghei ANKA and Plasmodium yoelii 17XL infection

The thymus is the anatomical site where T cells undergo a complex process of differentiation, proliferation, selection, and elimination of autorreactive cells which involves molecular signals in different intrathymic environment. However, the immunological functions of the thymus can be compromised upon exposure to different infections, affecting thymocyte populations. In this work, we investigated the impact of malaria parasites on the thymus by using C57BL/6 mice infected with Plasmodium berghei ANKA and Plasmodium yoelii 17XL; these lethal infection models represent the most severe complications, cerebral malaria, and anemia respectively. Data showed a reduction in the thymic weight and cellularity involving different T cell maturation stages, mainly CD4-CD8- and CD4+CD8+ thymocytes, as well as an increased presence of apoptotic cells, leading to significant thymic cortex reduction. Thymus atrophy showed no association with elevated serum cytokines levels, although increased glucocorticoid levels did. The severity of thymic damage in both models reached the same extend although it occurs at different stages of infection, showing that thymic atrophy does not depend on parasitemia level but on the specific host-parasite interaction.

C-reactive protein-to-albumin ratio as a biomarker in patients with sepsis: a novel LASSO-COX based prognostic nomogram

To develop a C-reactive protein-to-albumin ratio (CAR)-based nomogram for predicting the risk of in-hospital death in sepsis patients. Sepsis patients were selected from the MIMIC-IV database. Independent predictors were determined by multiple Cox analysis and then integrated to predict survival. The performance of the model was evaluated using the concordance index (C-index), receiver operating characteristic curve (ROC) analysis, and calibration curve. The risk stratifications analysis and subgroup analysis of the model in overall survival (OS) were assessed by Kaplan-Meier (K-M) curves. A total of 6414 sepsis patients were included. C-index of the CAR-based model was 0.917 [standard error (SE): 0.112] for the training set and 0.935 (SE: 0.010) for the validation set. The ROC curve analysis showed that the area under the curve (AUC) of the nomogram was 0.881 in the training set and 0.801 in the validation set. And the calibration curve showed that the nomogram performs well in both the training and validation sets. K-M curves indicated that patients with high CAR had significantly higher in-hospital mortality than those with low CAR. The CAR-based model has considerably high accuracy for predicting the OS of sepsis patients.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.