Stray dogs are one of the main reservoirs of intestinal parasitic infections and some have zoonotic potential. An epidemiological survey was carried out between September 2017 and May 2018 in Mexicali Valley, this area sacrifices around 92,470 head of cattle monthly, which represents 27% of the national slaughter and has 71,307 hectares for crops. In this period the Municipal Animal Control Center during their routine visits to the Mexicali Valley captured 103 dogs. All the dogs were evaluated using copromicroscopic techniques to detect intestinal parasites. The general frequency of parasitic infections was 28.15% (29/103), the most frequent parasite being Dipylidium caninum 16.50% (17/103), followed by Taenia spp. 6.79% (7/103), Taenia hydatigena 2.91% (3/103), Taenia serialis 0.97% (1/103), Taenia pisiformis (0.97%), Toxocara canis 3.88% (4/103), Toxascaris leonina 1.94% (2/103), and Cystoisospora spp. 1.94% (2/103). No significant statistical associations were found between parasitic infections and the studied variables (sex, age, and size) however; there was a significant statistical association with the capture area. Most of the parasites found in this survey have potential to affect the human population and animal production.

Chagas disease, caused by the Trypanosoma cruzi (T. cruzi) parasite, is the third most common parasitosis worldwide. Most of the infected subjects can remain asymptomatic without an opportune and early detection or an objective diagnostic is not conducted. Frequently, the disease manifests itself after a long time, accompanied by severe heart disease or by sudden death. Thus, the diagnosis is a complex and challenging process where several factors must be considered. In this paper, a novel pipeline is presented integrating temporal data from four modalities (electrocardiography signals, echocardiography images, Doppler spectrum, and ELISA antibody titers), multiple features selection analyses by a univariate analysis and a machine learning-based selection. The method includes an automatic dichotomous classification of animal status (control vs. infected) based on Random Forest, Extremely Randomized Trees, Decision Trees, and Support Vector Machine. The most relevant multimodal attributes found were ELISA (IgGT, IgG1, IgG2a), electrocardiography (SR mean, QT and ST intervals), ascending aorta Doppler signals, and echocardiography (left ventricle diameter during diastole). Concerning automatic classification from selected features, the best accuracy of control vs. acute infection groups was 93.3 ± 13.3% for cross-validation and 100% in the final test; for control vs. chronic infection groups, it was 100% and 100%, respectively. We conclude that the proposed machine learning-based approach can be of help to obtain a robust and objective diagnosis in early T. cruzi infection stages.

Chagas disease (CD) is a neglected parasitic disease caused by the protozoan Trypanosoma cruzi (T. cruzi). The disease has two clinical phases: acute and chronic. In the acute phase, the parasite circulates in the blood. The infection can be asymptomatic or can cause unspecific clinical symptoms. During the chronic phase, the infection can cause electrical conduction abnormalities and progress to cardiac failure. The use of an electrocardiogram (ECG) has been a methodology for diagnosing and monitoring CD, but it is necessary to study the ECG signals to better understand the behavior of the disease. The aim of this study is to analyze different ECG markers using machine-learning-based algorithms for the classification of the acute and chronic phases of T. cruzi infection in a murine experimental model. The presented methodology includes a statistical analysis of control vs. infected models in both phases, followed by an automatic selection of ECG descriptors and the implementation of several machine learning algorithms for the automatic classification of control vs. infected mice in acute and/or chronic phases (binomial classification), as well as a multiclass classification strategy (control vs. the acute group vs. the chronic group). Feature selection analysis showed that P wave duration, R and P wave voltages, and the QRS complex are some of the most important descriptors. The classifiers showed good results in detecting the acute phase of infection (with an accuracy of 87.5%), as well as in multiclass classification (control vs. the acute group vs. the chronic group), with an accuracy of 91.3%. These results suggest that it is possible to detect infection at different phases, which can help in experimental and clinical studies of CD.

Escherichia coli is an opportunistic pathogen and a leading cause of Community-Acquired Urinary Tract Infections (CA-UTIs). E. coli can harbor multiple genetic resistant determinants, such as Extended Spectrum Beta-lactamases (ESBL) and Plasmid-Mediated Quinolone Resistance (PMQR) genes, complicating the empirical treatment of UTIs with β-lactams and quinolones. The aim of his study was to characterize ESBL and PMQR genes among E. coli isolates from CA-UTI in Mexicali, Mexico Isolates were collected from January to December 2023. Identification was performed by MALDI-TOF, and ESBL-producing determination and antimicrobial susceptibility by Vitek 2. Detection of ESBL and PMQR, and phylotyping were performed by PCR. Genetic diversity was determined by Enterobacterial Repetitive Intergenic Consensus Polymerase Chain Reaction (ERIC-PCR). Eighty-nine E. coli isolates were collected from CA-UTIs. All exhibited resistance to ciprofloxacin, ceftriaxone, and cefotaxime, while being susceptible to carbapenems and ceftazidime/avibactam. All isolates tested positive for an ESBL gene, with bla_CTX-M (98.9 %) being the most prevalent. Five isolates tested negative for PMQR genes; the remaining showed aac(6')-lb-cr present in 85.3 %. Coexistence between ESBL and PMQR genes was noted in 95.5 %. Most prevalent plylogenetic group was group B2 (74.2 %). This study provides valuable regional data, highlighting a public health problem due to the high prevalence of ESBL and PMQR genes in E. coli responsible for CA-UTI, which are linked to multidrug resistance. Genetic diversity was found, suggesting multiple sources of resistant strains in the community. These findings underline the need for surveillance and control to limit the spread of resistant E. coli, locally and globally. DATA SUMMARY: The authors confirm all supporting data, code, and protocols have been provided within the article or through supplementary data files.

The use of artificial intelligence has shown good performance in the medical imaging area, in particular the deep learning methods based on convolutional neural networks for classification, detection, and/or segmentation tasks. The task addressed in this research work is the segmentation of amastigote nests from histological microphotographs in the study of Trypanosoma cruzi infection (Chagas disease) implementing a U-Net convolutional network architecture. For the nests’ segmentation, a U-Net architecture was trained on histological images of an acute-stage murine experimental model performing a 5-fold cross-validation, while the final tests were carried out with data unseen by the U-Net from three image groups of different experimental models. During the training stage, the obtained results showed an average accuracy of 98.19 ± 0.01, while in the case of the final tests, an average accuracy of 99.9 ± 0.1 was obtained for the control group, as well as 98.8 ± 0.9 and 99.1 ± 0.8 for two infected groups; in all cases, high sensitivity and specificity were observed in the results. We can conclude that the use of a U-Net architecture proves to be a relevant tool in supporting the diagnosis and analysis of histological images for the study of Chagas disease.

Objectives: This study was conducted to validate the dorsal approach for femoral nerve (FN) blockade in cats and to verify the efficacy of the sole use of peripheral nerve electrolocation (PNE) or ultrasound (US)-guided technique to achieve the block. Methods: This study was carried out in two phases. In phase 1, five adult experimental cats were used to validate the approach. In each cat, one FN was located by US and the accuracy of this location confirmed by PNE. Then, 2 mg/kg lidocaine 2% (diluted in saline to a final volume of 1 ml) was injected around the target nerve and the success of the blockade was evaluated. In phase 2, four adult experimental cats were included in two groups to verify the reliability of this approach to block eight FNs by the sole use of PNE (group 1) or US-guided technique (group 2). Evidence of motor blockade, time required to perform the blockade, onset time and duration of the blockades were determined. Results: The FN was successfully located by US in all cats enrolled in phase 1, as confirmed by PNE in all cases. The success rate was clinically higher in group 2 (87.5%) than in group 1 (75.0%). The US-guided technique required less time to perform and produced blocks of longer duration. Recovery was uneventful in all cases. Conclusions and relevance: The combined use of PNE and US-guided technique enabled validation of the dorsal approach for the FN blockade as it provided a successful FN blockade in all cases. The sole use of a US-guided technique may offer some advantages over the use of a sole PNE-guided technique to perform these blocks.