Bayesian model averaging for predicting factors associated with length of COVID-19 hospitalization.

INTRODUCTION: The length of hospital stay (LOHS) caused by COVID-19 has imposed a financial burden, and cost on the healthcare service system and a high psychological burden on patients and health workers. The purpose of this study is to adopt the Bayesian model averaging (BMA) based on linear regression models and to determine the predictors of the LOHS of COVID-19. METHODS: In this historical cohort study, from 5100 COVID-19 patients who had registered in the hospital database, 4996 patients were eligible to enter the study. The data included demographic, clinical, biomarkers, and LOHS. Factors affecting the LOHS were fitted in six models, including the stepwise method, AIC, BIC in classical linear regression models, two BMA using Occam's Window and Markov Chain Monte Carlo (MCMC) methods, and GBDT algorithm, a new method of machine learning. RESULTS: The average length of hospitalization was 6.7 ± 5.7 days. In fitting classical linear models, both stepwise and AIC methods (R 2 = 0.168 and adjusted R 2 = 0.165) performed better than BIC (R 2 = 0.160 and adjusted = 0.158). In fitting the BMA, Occam's Window model has performed better than MCMC with R 2 = 0.174. The GBDT method with the value of R 2 = 0.64, has performed worse than the BMA in the testing dataset but not in the training dataset. Based on the six fitted models, hospitalized in ICU, respiratory distress, age, diabetes, CRP, PO2, WBC, AST, BUN, and NLR were associated significantly with predicting LOHS of COVID-19. CONCLUSION: The BMA with Occam's Window method has a better fit and better performance in predicting affecting factors on the LOHS in the testing dataset than other models.

Investigating the effects of combined treatment of mesalazine with Lactobacillus casei in the experimental model of ulcerative colitis.

Introduction: Ulcerative colitis (UC), a common gastrointestinal disorder in affluent nations, involves chronic intestinal mucosal inflammation. This research investigated the effects of combined probiotic treatment of Lactobacillus casei (L. casei) and mesalazine on disease activity index and inflammatory factors in the UC model. Methods: 20 male BALB/c mice were utilized and divided into four groups. To induce UC, all groups received 100 µL of 4% acetic acid (AA) intra-rectally. The first group received phosphate-buffered saline (PBS) (as a control group), the second group was treated with L. casei, the third group was treated with mesalazine and, the fourth group was treated with L. casei and mesalazine. Treatment with L. Casei and mesalazine commenced after the manifestation of symptoms resulting from UC induction. Finally, the mice were euthanized and the disease activity index, myeloperoxidase activity, nitric oxide rate, cytokines level (IL-1ß, IL-6, TNF-α) and, gene expression (iNOS, COX-2, and cytokines) were evaluated. Results: The combined treatment of L. casei and mesalazine led to a significant decrease in the levels of NO, MPO and inflammatory cytokines. In addition, the expression of cytokines, iNOS and COX-2 genes decreased in mice treated with the combination. Discussion: This study shows that combined treatment of L. casei and mesalazine improves of experimental UC, which can be attributed to the anti-inflammatory properties of L. casei and mesalazine. In conclusion, this combination therapy can be considered a suitable option for the management of UC.

Anti-inflammatory activity of Ziziphus jujuba hydroalcoholic extract in acetic acid-induced ulcerative colitis model.

OBJECTIVES: Ulcerative colitis (UC) is a common gastrointestinal (GI) disorder characterized by chronic inflammation. Current treatments primarily focus on symptom management, but they have inherent limitations. Global attention is increasingly directed towards exploring herbal remedies as complementary approaches. This study aims to investigate the effects of the hydroalcoholic extract of jujuba on an experimental model of ulcerative colitis. METHODS: In this study, 15 male BALB/c mice were divided into three experimental groups. The first group served as the untreated UC model, acting as the positive control (PC). The second group received treatment with the hydroalcoholic extract of Ziziphus jujuba, while the third group was treated with mesalamine. UC was induced by injecting 100 µL of 4 % acetic acid (AA) intra-rectally several times. Treatment commenced after the onset of symptoms such as diarrhea and bloody stools. The mice were eventually euthanized ethically, and their spleen and intestinal tissues were collected for analysis. Evaluations included the Disease Activity Index (DAI), myeloperoxidase activity (MPO), nitric oxide (NO) levels, cytokine levels (IL-1ß, IL-6, TNF-α), and gene expression (iNOS, COX-2, and cytokines). RESULTS: The hydroalcoholic extract of the jujuba plant significantly reduced MPO, NO, the DAI, and the production and expression of inflammatory cytokines, as well as the genes iNOS and COX-2, in the group receiving this extract compared to the positive control group (p<0.05). CONCLUSIONS: The study demonstrates that the hydroalcoholic extract of Ziziphus jujuba significantly reduces inflammation markers such as TNF-α, NO, MPO, IL-1ß, and IL-6 in a mouse model of ulcerative colitis. Additionally, it downregulates the expression of pro-inflammatory genes, including iNOS and COX-2. These findings suggest that Z. jujuba extract has potential as an effective anti-inflammatory treatment for managing ulcerative colitis symptoms.

Development of decision tree classification algorithms in predicting mortality of COVID-19 patients.

INTRODUCTION: The accurate prediction of COVID-19 mortality risk, considering influencing factors, is crucial in guiding effective public policies to alleviate the strain on the healthcare system. As such, this study aimed to assess the efficacy of decision tree algorithms (CART, C5.0, and CHAID) in predicting COVID-19 mortality risk and compare their performance with that of the logistic model. METHODS: This retrospective cohort study examined 5080 cases of COVID-19 in Babol, a city in northern Iran, who tested positive for the virus via PCR from March 2020 to March 2022. In order to check the validity of the findings, the data was randomly divided into an 80% training set and a 20% testing set. The prediction models, such as Logistic regression models and decision tree algorithms, were trained on the 80% training data and tested on the 20% testing data. The accuracy of these methods for the test samples was assessed using measures like ROC curve, sensitivity, specificity, and AUC. RESULTS: The findings revealed that the mortality rate for COVID-19 patients who were admitted to hospitals was 7.7%. Through cross validation, it was determined that the CHAID algorithm outperformed other decision tree and logistic regression algorithms in specificity, and precision but not sensitivity in predicting the risk of COVID-19 mortality. The CHAID algorithm demonstrated a specificity, precision, accuracy, and F-score of 0.98, 0.70, 0.95, and 0.52 respectively. All models indicated that factors such as ICU hospitalization, intubation, age, kidney disease, BUN, CRP, WBC, NLR, O2 sat, and hemoglobin were among the factors that influenced the mortality rate of COVID-19 patients. CONCLUSIONS: The CART and C5.0 models had outperformed in sensitivity but CHAID demonstrates a better performance compared to other decision tree algorithms in specificity, precision, accuracy and shows a slight improvement over the logistic regression method in predicting the risk of COVID-19 mortality in the population under study.