Comparative Analysis of Demographic and Clinical Findings in Spontaneous, Peritoneal Dialysis-Related, and Secondary Bacterial Peritonitis.

Background This study aims to contribute to peritonitis management strategies by comparing the demographic, clinical, and laboratory characteristics of patients diagnosed with spontaneous bacterial peritonitis (SBP), peritoneal dialysis-related peritonitis (PDrP), and secondary peritonitis. Methods This study included 86 patients diagnosed with peritonitis between 2016 and 2022. Patients were categorized and compared as SBP, PDrP, and secondary peritonitis. Results SBP was diagnosed in 36% of patients, secondary peritonitis in 36% and PDrP in 28%. The mean age of patients with PDrP is 43.71 ± 14.74, which is significantly lower compared to those with SBP and secondary peritonitis (p<0.001). Patients with hypertension (HT), chronic kidney disease (CKD), and those undergoing dialysis most commonly have PDrP whereas those without HT, without CKD, and not undergoing dialysis are most often diagnosed with secondary peritonitis (p=0.002, p<0.001, p<0.001). In peritoneal fluid cultures, the growth of Gram-positive bacteria was most commonly identified in patients with PDrP, while the growth of Gram-negative bacteria was most frequently seen in patients with secondary peritonitis (p=0.018). CRP levels and sedimentation rates were found to be higher in patients with secondary peritonitis (p<0.001, p=0.003). Conclusion The distinct characteristics observed across different types of peritonitis underscore the importance of tailored approaches to diagnosis and treatment. Parameters such as CRP levels, sedimentation rates, and patient age could serve as valuable indicators in discerning between various types of peritonitis. When selecting empirical antibiotic therapy, it's crucial to consider coverage for Gram-positive pathogens in cases of PDrP and Gram-negative pathogens in secondary peritonitis.

Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.

All viruses, including SARS-CoV-2, the virus responsible for COVID-19, continue to evolve, which can lead to new variants. The objective of this study is to assess the agreement between real-world clinical data and an algorithm that utilizes laboratory markers and age to predict the progression of disease severity in COVID-19 patients during the pre-Omicron and Omicron variant periods. The study evaluated the performance of a deep learning (DL) algorithm in predicting disease severity scores for COVID-19 patients using data from the USA, Spain, and Turkey (Ankara City Hospital (ACH) data set). The algorithm was developed and validated using pre-Omicron era data and was tested on both pre-Omicron and Omicron-era data. The predictions were compared to the actual clinical outcomes using a multidisciplinary approach. The concordance index values for all datasets ranged from 0.71 to 0.81. In the ACH cohort, a negative predictive value (NPV) of 0.78 or higher was observed for severe patients in both the pre-Omicron and Omicron eras, which is consistent with the algorithm's performance in the development cohort.

Assessment of Global DNA Methylation in SH-SY5Y Cells Exposed to the Neonicotinoid Insecticides Imidacloprid and Thiamethoxam.

Neonicotinoid insecticides, known for their selectivity and low mammalian toxicity, have been widely used in recent years as alternatives to organophosphate insecticides. Although neonicotinoids are generally considered to be safe, data show that they can cause harmful effects on human and environmental health. Due to the lack of information on their mechanism of toxicity, the effects of imidacloprid and thiamethoxam on DNA methylation as the most used marker for epigenetic effects were investigated in human neuroblastoma (SH-SY5Y) cells. The cells were exposed to imidacloprid and thiamethoxam in concentrations of 100, 200, and 500 µM for 24 hours, then global DNA methylation and expression of genes involved in global DNA methylation (DNMT1, DNMT3a and DNMT3b) were investigated. Global DNA methylation significantly increased after imidacloprid exposure at 100 µM, and thiamethoxam exposures at 200 µM and 500 µM (>1.5-fold). Imidacloprid significantly decreased the expression of DNMT1 and DNMT3a, whereas thiamethoxam did not cause any significant changes in the expression of DNMT genes. Our findings suggested that alteration in global DNA methylation may be involved in the toxic mechanisms of imidacloprid and thiametoxam.

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. METHODS: Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. RESULTS: No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. CONCLUSION: By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.

In Bacillus subtilis LutR is part of the global complex regulatory network governing the adaptation to the transition from exponential growth to stationary phase.

The lutR gene, encoding a product resembling a GntR-family transcriptional regulator, has previously been identified as a gene required for the production of the dipeptide antibiotic bacilysin in Bacillus subtilis. To understand the broader regulatory roles of LutR in B. subtilis, we studied the genome-wide effects of a lutR null mutation by combining transcriptional profiling studies using DNA microarrays, reverse transcription quantitative PCR, lacZ fusion analyses and gel mobility shift assays. We report that 65 transcriptional units corresponding to 23 mono-cistronic units and 42 operons show altered expression levels in lutR mutant cells, as compared with lutR(+) wild-type cells in early stationary phase. Among these, 11 single genes and 25 operons are likely to be under direct control of LutR. The products of these genes are involved in a variety of physiological processes associated with the onset of stationary phase in B. subtilis, including degradative enzyme production, antibiotic production and resistance, carbohydrate utilization and transport, nitrogen metabolism, phosphate uptake, fatty acid and phospholipid biosynthesis, protein synthesis and translocation, cell-wall metabolism, energy production, transfer of mobile genetic elements, induction of phage-related genes, sporulation, delay of sporulation and cannibalism, and biofilm formation. Furthermore, an electrophoretic mobility shift assay performed in the presence of both SinR and LutR revealed a close overlap between the LutR and SinR targets. Our data also revealed a significant overlap with the AbrB regulon. Together, these findings reveal that LutR is part of the global complex, interconnected regulatory systems governing adaptation of bacteria to the transition from exponential growth to stationary phase.