Hierarchical Bayesian log-linear models for Poisson-distributed response data, especially Besag, York and Mollié (BYM) model, are widely used for disease mapping. In some cases, due to the high proportion of zero, Bayesian zero-inflated Poisson models are applied for disease mapping. This study proposes a Bayesian spatial joint model of Bernoulli distribution and Poisson distribution to map disease count data with excessive zeros. Here, the spatial random effect is simultaneously considered into both logistic and log-linear models in a Bayesian hierarchical framework. In addition, we focus on the BYM2 model, a re-parameterization of the common BYM model, with penalized complexity priors for the latent level modeling in the joint model and zero-inflated Poisson models with different type of zeros. To avoid model fitting and convergence issues, Bayesian inferences are implemented using the integrated nested Laplace approximation (INLA) method. The models are compared according to the deviance information criterion and the logarithmic scoring. A simulation study with different proportions of zero exhibits INLA ability in running the models and also shows slight differences between the popular BYM and BYM2 models in terms of model choice criteria. In an application, we apply the fitting models on male breast cancer data in Iran at county level in 2014.

Background: Monosodium glutamate (MSG) is a food ingredient that is increasingly used commercially. MSG leads to oxidative stress, consequently suppressing steroid hormone production that causes defects in male reproductive system. This study aimed to evaluate the effect of L-carnitine as an antioxidant on testicular damage in MSG-induced male rats. Methods: Sixty adult male Spargue-Dawley rats were randomly divided into six groups of ten as follows: control (water), sham (normal saline), L-carnitine (200 mg/kg b.w), MSG (3 g/kg b.w), MSG + L-carnitine 100 (3 g/kg b.w of MSG and 100 mg/kg b.w of L-carnitine), and MSG + L-carnitine 200 (3 g/kg b.w of MSG and 200 mg/kg b.w of L-carnitine). The treatment was administered by oral gavage for six months. Serum levels of Malondialdehyde (MDA), Total Anti-oxidant Capacity (TAC), LH, FSH, testosterone, and mRNA expressions of Star, Cyp11a1, and Hsd17b3 genes, and histological and stereological changes were assessed. Results: L-carnitine led to a significant decrease in the level of MDA and a significant rise in the serum levels of TAC, LH, FSH, and mRNA expression of Star and Cyp11a1 compared to the MSG group (p < 0.05). Furthermore, stereological results indicated a significant increment in the number of sexual lineage cells, the total volume of the testis, length, diameter, and volume of seminiferous tubules, the height of the germinal epithelium, sperm count, and sperm motility (p < 0.05) in MSG + L-carnitine 200 compare to MSG group. Conclusion: The study’s findings demonstrated that L-carnitine due to its anti-oxidant properties, ameliorated the reproductive abnormalities in the male rats exposed to MSG.

Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models. A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O₂ saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors. In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients’ early data, particularly in low- and middle-income countries where their resources are as limited as Iran. Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022;26(6):688–695. This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018).

Background: This study aimed to compare the effects of melatonin, dexmedetomidine, and gabapentin on postoperative pain and anxiety following laminectomy. Methods: In this randomized clinical trial, 99 patients aged 40–60 years old with American Society of Anesthesiologists physical status I-II undergoing laminectomy were divided into three groups receiving 600mg gabapentin (group G), 10mg melatonin (group M), or starch tablets (group D). The Hospital Anxiety and Depression Scale (HADS) was used to measure postoperative anxiety while a Visual Analogue Scale (VAS) was employed to measure pain severity. Patients’ satisfaction with pain treatment was also measured together with the frequency of nausea and vomiting. Results: The postoperative HADS decreased in all groups over time. Time and group had no significant interaction effect on the HADS score. Patients in the melatonin group had lower HADS at 2 and 6h after surgery. According to the VAS, the groups significantly differed in pain scores 6 and 24h after surgery. Lower VAS scores were observed 6h after surgery in the dexmedetomidine group compared with the gabapentin group and 24h after surgery in the dexmedetomidine group compared with the gabapentin and melatonin groups. Narcotic requirements, patients’ satisfaction, and vital sign changes did not significantly vary among the groups. Notably, patients in the melatonin group had less nausea and vomiting. Trial registration: This study was registered in the Iranian Registry of Clinical Trials (No. IRCT20141009019470N82, 29.06.2019) where the trial protocol could be accessed. Conclusion: Melatonin is effective as a postoperative anti-anxiety drug. Dexmedetomidine is useful in reducing postoperative pain.