Background and Objectives: Antimicrobial resistance (AMR) is increasing worldwide and imposes significant life-threatening risks to several different populations, especially to those in intensive care units (ICU). The most commonly isolated organisms in ICU comprise gram-negative bacilli (GNB), and these represent a leading cause of serious infections. This study was conducted to describe the prevalence of resistance in GNB isolated from patients in adults, pediatric, and neonatal ICU in a tertiary-care hospital in Mérida, Mexico. Materials and Methods: A retrospective study was done on samples collected in Neonatal (NICU), Pediatric (PICU) and Adult (AICU) ICU of Unidad Médica de Alta Especialidad, Instituto Mexicano del Seguro Social in Mérida, México. The identification of isolates and antimicrobial susceptibility testing were performed using an automated system. Results: A total of 517 GNB strains were isolated. The most common positive culture was bronchial secretions. Pseudomonas aeruginosa was the prevalent pathogen in NICU and PICU, whereas Escherichia coli was common in the AICU. Overall, GNB exhibited a high resistance rates for Ampicillin (95.85%), Cefuroxime (84.17%), Piperacillin (82.93%), Cefotaxime (78.07%), Ceftriaxone (77.41%), Aztreonam (75.23%), Cefazolin (75.00%), and Ceftazidime (73.19%). There are significant differences in the resistance rates of GNB from different ICUs for penicillins, cephalosporins, carbapenems and fluoroquinolones drugs. Escherichia coli (multidrug-resistant [MDR] = 91.57%, highly resistant microorganisms [HRMO] = 90.36%) and Acinetobacter baumannii (MDR = 86.79%, HRMO = 83.02%) exhibited the highest percentage of MDR and HRMO profiles. The prevalence of the extended-spectrum beta-lactamases (ESBL)-producing isolates was 83.13% in E. coli, 78.84% in Klebsiella pneumoniae, and 66.67% in Proteus mirabilis, respectively. Conclusions: The high resistance rates to drugs were exhibited by our GNB isolates. Continuous surveillance and control of the use of antimicrobials are urgently needed to reduce the emergence and spreading of MDR, HRMO, and/or ESBL-producing bacilli.

Background: Complementary and alternative medicine (CAM) is widely used for multiple reasons such as treatment of diseases and their symptoms, empowerment, self-care, disease prevention, dissatisfaction, adverse effects or cost of conventional medicine, perception of compatibility with beliefs, and idiosyncrasy. This study investigated CAM use in patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD). Methods: A cross-sectional survey study was conducted with 240 eligible patients with CKD in the PD program. By applying the I-CAM-Q-questionnaire, the frequency, level of satisfaction, and reasons for CAM use were explored, and the demographic and clinical data of users and non-users were analyzed. Data analysis included descriptive analysis, Student’s t-test, Mann-Whitney U, chi-square, and Fisher tests. Results: The main types of CAM used were herbal medicine, and chamomile was the most commonly used. To improve the state of well-being was the main reason for use, the attributable benefit of CAM was high, and only a low percentage of users reported side effects. Only 31.8% of the users informed their physicians. Conclusion: The use of CAM is popular among renal patients, and physicians are not adequately informed; in particular, the CAM type ingested represents a risk for drug interactions and toxicity.

This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study’s results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.