University of Colima

Patients with coronary ectasia (CAE) have an increased risk of major cardiovascular events (MACE). Current preventive treatments are uncertain, with oral anticoagulants often prescribed based on limited retrospective studies. Our aim is to help address the question: what is the most appropriate treatment? Using a retrospective cohort of patients with an ACS and CAE in a single center in Mexico City, two groups were observed based on the treatment at discharge: dual antiplatelet therapy (group 1) and anticoagulation with either a VKA or a DOAC, regardless of antiplatelet therapy (group 2). The main outcome was MACE, which was a composite of all-cause mortality, reinfarction, and ischemic stroke at 4.5years follow-up. A total of 354 patients admitted for ACS and CAE were included. 228 (64.4%) patients were classified in the DAPT group and 126 (35.5%) in the anticoagulants group. The DAPT group had higher type 2 diabetes rates, NSTEMI presentation, and lower-grade ectasia. The anticoagulation group had higher STEMI presentation and higher-grade ectasia. The DAPT group had 33 (14.5%) events of MACE, whereas the anticoagulation group had 16 (13.1%) events. Anticoagulants were not associated with a risk reduction of the primary endpoint (HR 0.95; 95% CI, 0.47-1.54; p = 0.59), nor any of the individual components. This retrospective cohort study showed similar effectiveness between DAPT and anticoagulation in patients with ACS and CAE for preventing MACE, and lower bleeding risk. Further research is needed to identify optimal candidates for each antithrombotic regime.

This study aimed to characterize ovarian follicular dynamics and to describe the ultrasonographic features of early to after-gestation in mares under extensive field conditions. A total of 100 clinically healthy Spanish-type mares, averaging 8 ± 0.7 years of age and 500 ± 50 kg in body weight, were monitored over a 730-day period in three rural locations in Tamaulipas, Mexico. Transrectal ultrasonography was performed daily across a full estrous cycle (~21 days) to assess follicular development in non-pregnant mares. For mares confirmed pregnant 15 days post-mating, five ultrasound evaluations were conducted between days 15 and 150 of gestation. A total of 500 follicular and 500 gestational ultrasonographic images were obtained. Follicles were classified into three groups by diameter: 8–20 mm (n = 250; 50.0%), 21–30 mm (n = 150; 30.0%), and 32–45 mm (n = 100; 20.0%), with mean diameters of 13.7 mm, 25.4 mm, and 39.7 mm, respectively. The mean number of follicles per mare was 5.0, reflecting typical follicular activity during one estrous cycle under tropical field conditions. Gestational scans were distributed as follows: days 15–28 (n = 167; 33.4%; mean age: 21.9 d), days 30–45 (n = 166; 33.2%; mean age: 38.8 d), and days 50–150 (n = 167; 33.4%; mean age: 75.1 d). Key gestational features, such as the embryonic vesicle, heartbeat, and fetal skeleton, were documented per stage. In conclusion, structured ultrasonographic benchmarks for assessing follicular and gestational stages in mares were provided, offering practical tools for reproductive monitoring in field conditions.

Genomic selection (GS) in plant breeding aims to identify individuals with superior genetic merit while maintaining genetic diversity within populations. In plant breeding, considering multiple traits simultaneously makes optimizing selection complex, especially under genetic relatedness constraints. In this study, we propose a binary quadratic programming framework for constructing a multi-trait selection index that maximizes genetic gain while minimizing average pairwise relatedness appropriate for identifying superior candidates for advancement in the breeding pipeline. The approach combines estimated breeding values (EBVs) across multiple traits by applying trait-specific economic weights, while simultaneously accounting for coancestry through the genomic relationship matrix. By formulating the selection problem as a constrained Quadratic Programing Multi-trait Selection Index (QPMSI), our method enables the identification of a fixed number of candidate individuals that jointly optimize selection index values and control genetic relatedness. We evaluated the performance of the proposed method using five real genomic datasets and demonstrated that it provides a more effective balance between selection response and control of genetic relatedness than the Linear Programming Multi-trait Selection Index (LPMSI). In particular, the QPMSI consistently outperformed the LPMSI in terms of the MV metric (gain-to-degree of relatedness ratio), achieving improvements of at least 53.8%. This framework offers a practical and computationally efficient tool for sustainable breeding strategies in multi-trait selection contexts.

This study presents a methodological framework for estimating surface nitrogen dioxide (NO2) concentrations in Mexico City during 2024. Sentinel-5P satellite observations, ERA5 meteorological variables, and ground measurements from the RAMA were integrated to generate high-resolution estimates through a Random Forest model combined with statistical downscaling. After data cleaning, 3246 aligned records were retained. The model achieved robust performance (R2 = 0.9196; RMSE = 6.80 µg/m3; MAE = 4.55 µg/m3), demonstrating its ability to reproduce both spatial and temporal variations in NO2 across the metropolitan area. These results confirm that machine-learning-based downscaling effectively enhances satellite-derived pollution estimates and provides a reliable tool for urban air quality assessment.

Background/Objectives: To better understand the mechanisms involved in atherosclerosis, different models have been developed, but these fail when studying the progression of this disease. The purpose of this study was to standardize a new model of atherosclerosis progression in rats using Paigen-type modified atherogenic diet. Methods: The design included a control group (n = 16) and 64 rats with atherogenic Paigen-type diet subdivided into four subgroups with different doses (Athero 1, Athero 2, Athero 3, and Athero 4). The atherogenic diet was supplemented orally in sequential stages: 1) Hypervitaminic (1.5 mL/kg/day for 12 days) and 2) Hyperlipidic (48 days ad libitum). Blood pressure, heart rate, aortic histopathology, inflammatory biomarkers, and biochemical lipid and liver profiles were measured in all groups on days 30 and 60. Results: All Athero 1 rats were sacrificed due to a poisoning for vitamin D2 excess. Athero 2 rats were sacrificed at day 30 showing severe atherosclerotic lesions (grades V–VIII). Athero 3 rats showed mild lesions (I–IV) at day 30 and severe lesions (V–VIII) at day 60. Athero 4 rats showed mild lesions (I–IV) at days 30 and 60. Diet-dependent changes in blood pressure and heart rate were observed. Furthermore, glycemia, dyslipidemia, and liver profile were associated with the degree of atherosclerotic lesion. Conclusions: “Athero 3” atherogenic diet generates a stable model to study the progress of atherosclerosis in rats.

The integration of Large Language Models (LLMs), particularly Visual Language Models (VLMs), into robotics promises more intuitive human–robot interactions; however, challenges remain in efficiently translating high-level commands into precise physical actions. This paper presents a novel architecture for vision-based object manipulation that leverages a VLM’s reasoning capabilities while incorporating symmetry principles to enhance operational efficiency. Implemented on a Yahboom DOFBOT educational robot with a Jetson Nano platform, our system introduces a prompt-based framework that uniquely embeds symmetry-related cues to streamline feature extraction and object detection from visual data. This methodology, which utilizes few-shot learning, enables the VLM to generate more accurate and contextually relevant commands for manipulation tasks by efficiently interpreting the symmetric and asymmetric features of objects. The experimental results in controlled scenarios demonstrate that our symmetry-informed approach significantly improves the robot’s interaction efficiency and decision-making accuracy compared to generic prompting strategies. This work contributes a robust method for integrating fundamental vision principles into modern generative AI workflows for robotics. Furthermore, its implementation on an accessible educational platform shows its potential to simplify complex robotics concepts for engineering education and research.

Background/Objectives: Cognitive impairment in older adults has emerged as a growing public health concern, particularly in relation to COVID-19 infection and its associated neuropsychiatric symptoms. The identification of modifiable risk factors may contribute to the development of targeted preventive strategies. This study aimed to assess predictors of cognitive impairment in older adults with and without recent SARS-CoV-2 infection. Methods: A prospective cohort study was conducted from June 2023 to March 2024 at a tertiary hospital in western Mexico. Adults aged 65 years or older with confirmed SARS-CoV-2 infection within the previous six months, along with uninfected controls, were enrolled. Cognitive function (Mini-Mental State Examination), depression (PHQ-9), anxiety (Geriatric Anxiety Inventory), insomnia (Insomnia Severity Index), functional status (Katz Index and Lawton-Brody Scale), and laboratory markers were evaluated at baseline, three months, and six months. The primary outcome was cognitive impairment at six months. Independent predictors were identified using a multivariable generalized linear mixed-effects model. Results: Among the 111 participants, 20 (18.8%) developed cognitive impairment within six months. Low serum magnesium (adjusted risk ratio [aRR] 2.73; 95% CI 1.04-7.17; p = 0.041) and depression (aRR 5.57; 95% CI 1.88-16.48; p = 0.002) were independently associated with a higher risk. A significant synergistic among COVID-19, depression, and hypomagnesemia was observed (RR 44.30; 95% CI 9.52-206.21; p < 0.001), corresponding to the group with simultaneous presence of all three factors compared to the group with none. Conclusions: Depression and hypomagnesemia appear to be independent predictors of cognitive impairment in older adults with recent COVID-19 infection. These findings suggest potential targets for prevention and support the implementation of routine neuropsychiatric and biochemical assessments in this population.

Children constitute a population at risk from environmental exposure to trace elements. This study aimed to evaluate correlations between urinary and blood levels of multiple elements in school-aged children (5–12 years), assessing whether urine, a less invasive matrix, could complement or replace blood sampling. A pilot biomonitoring study was conducted, and 91 children provided urine and venous blood samples in which the levels of 17 contaminants (Al, As, Ba, Cs, Co, Cu, I, Pb, Li, Mn, Mo, Ni, Se, Sr, Te, Ti, and Zn) were assessed. Spearman correlation coefficients (rho) and 95% confidence intervals (CI) were computed. Urinary and blood levels of arsenic (rho = 0.23, 95% CI 0.01–0.44), lead (rho = 0.43, 95% CI 0.24–0.61), and strontium (rho = 0.22, 95% CI 0.03–0.40) showed significant correlations. These findings suggest that urine sampling could serve as a practical alternative to blood collection for monitoring specific trace elements like lead in pediatric populations, particularly in large-scale studies where participant compliance is critical. However, modest correlations for other elements highlight the need for element-specific validation before adopting urine as a universal biomonitoring matrix. Future research should explore the pharmacokinetic and exposure-related factors driving these relationships to optimize non-invasive surveillance strategies for children’s environmental health.

Background and Objectives: Arteriovenous fistulas (AVFs) are the preferred vascular access for hemodialysis due to their impact on patient outcomes, including survival, infection rates, and overall quality of life. Despite strong recommendations favoring AVF, gaps in AVF utilization remain, influenced by clinical, demographic, and systemic factors. This study is the first to analyze survival outcomes associated with different dialysis vascular access types, adjusting for key clinical, demographic variables, and other comorbidities over extended periods. Materials and Methods: This ambispective cohort study followed 428 hemodialysis patients over seven years. Patients were categorized based on their access type: AVF (n = 189), tunneled central venous catheter (CVC) (n = 178), and non-tunneled CVC (n = 61). Kaplan-Meier survival analysis was used to estimate survival curves, and Cox proportional hazards regression adjusted for potential confounders, including age, diabetes, and hypertension. Results: The 2-year survival rates were as follows: AVF 94.1%, tunneled CVC 70.0%, and non-tunneled CVC 36.6%. The 7-year survival rates were as follows: AVF 65.5%, tunneled CVC 26.4%, and non-tunneled CVC 11.0%. Compared to AVF, tunneled CVC use was associated with a 2.8-fold increased risk of mortality (adjusted hazard ratio [AdHR] 2.8, 95% CI 2.0-4.1), while non-tunneled CVC increased the risk 5-fold (AdHR 5.0, 95% CI 3.3-7.6). Notably, older adults, women, and diabetic patients were disproportionately represented in the groups with tunneled and non-tunneled catheters. Conclusions: Adjusted survival analyses highlight the significantly lower survival rates associated with CVC use compared to AVF. Non-tunneled catheters are generally not used for prolonged periods, and this cohort provides evidence of their prognosis for long-term use. These findings reinforce the need to prioritize AVF placement whenever feasible, reinforcing health education on this topic, to improve long-term outcomes for hemodialysis patients.