Pablo Romero-Morelos

Background. Obesity is a chronic, multifactorial disease that requires accessible and low-cost diagnostic tools in primary care. Body mass index (BMI) has well-known limitations because it does not distinguish fat mass from lean mass and does not capture body fat distribution. We propose the Body Density Index (BoDI), a transparent mathematical model based on a cylindrical geometric approximation, to estimate body density and evaluate its association with anthropometric and cardiometabolic risk indicators. Methods. 126 adults underwent standardized anthropometry (weight, height, waist circumference, and hip circumference). BMI, waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and BoDI were computed. Systolic and diastolic blood pressure (SBP/DBP), glucose, total cholesterol, triglycerides, LDL, and HDL were measured. Associations were assessed using Spearman correlations; BoDI was compared across risk categories using ANOVA or Kruskal Wallis tests, as appropriate; and diagnostic performance was evaluated using ROC curves. Results. BoDI was strongly and inversely correlated with waist circumference (r = -0.8465, 95%CI -0.89 – -0.78, p < 0.0001), WHtR (r = -0.8018, 95%CI -0.85 – -0.72, p < 0.0001), and WHR (r = -0.6991, 95%CI -0.78 – -0.59, p < 0.0001), but only weakly correlated with BMI (r = -0.4174, 95%CI -0.55 – -0.25, p < 0.0001). ROC analyses showed excellent discrimination of high-risk groups, with optimal cutoffs in the range 0.63 - 0.67. BoDI also showed weak but significant inverse correlations with Systolic Blood Pressure (r = -0.2236, 95%CI -0.38 – -0.04, p = 0.0119), Diastolic Blood Pressure (r = -0.2421, 95%CI -0.40 – -0.06, p = 0.0063), glucose (r = -0.2720, 95%CI -0.43 – -0.09, p = 0.0021), and triglycerides (r = -0.2871 95%CI -0.44 – -0.11, p = 0.0011). Conclusions. BoDI is a simple, low-cost index sensitive to central adiposity and associated with cardiometabolic risk markers. Its mathematical transparency and operational simplicity support its potential as a screening tool in primary care, particularly in resource-limited settings. External validation against reference methods (DXA) is warranted.

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis.

The diagnosis and identification of melanoma are not always accurate, even for experienced dermatologists. Histopathology continues to be the gold standard, assessing specific parameters such as the Breslow index. However, it remains invasive and may lack effectiveness. Therefore, leveraging mathematical modeling and informatics has been a pursuit of diagnostic methods favoring early detection. Fractality, a mathematical parameter quantifying complexity and irregularity, has proven useful in melanoma diagnosis. Nonetheless, no studies have implemented this metric to feed artificial intelligence algorithms for the automatic classification of dermatological lesions, including melanoma. Hence, this study aimed to determine the combined utility of fractal dimension and unsupervised low-computational-requirements machine learning models in classifying melanoma and non-melanoma lesions. We analyzed 39,270 dermatological lesions obtained from the International Skin Imaging Collaboration. Box-counting fractal dimensions were calculated for these lesions. Fractal values were used to implement classification methods by unsupervised machine learning based on principal component analysis and iterated K-means (100 iterations). A clear separation was observed, using only fractal dimension values, between benign or malignant lesions (sensibility 72.4% and specificity 50.1%) and melanoma or non-melanoma lesions (sensibility 72.8% and specificity 50%) and subsequently, the classification quality based on the machine learning model was ≈80% for both benign and malignant or melanoma and non-melanoma lesions. However, the grouping of metastatic melanoma versus non-metastatic melanoma was less effective, probably due to the small sample size included in MM lesions. Nevertheless, we could suggest a decision algorithm based on fractal dimension for dermatological lesion discrimination. On the other hand, it was also determined that the fractal dimension is sufficient to generate unsupervised artificial intelligence models that allow for a more efficient classification of dermatological lesions.

Background and Objectives: Leukemia, characterized by abnormal leukocyte production, exhibits clonal origin from somatic mutations. Globally, it ranked 15th in cancer incidence in 2020, with higher prevalence in developing countries. In Mexico, it was the ninth most frequent cancer. Regional registries are vital for understanding its epidemiology. This study aims to analyze the prevalence and age-standardized incidence rates of leukemias in a tertiary care hospital in the Mexican Bajio region. Materials and Methods: Leukemia cases from 2008–2018 were analyzed, and 535 medical records were included in this study. The prevalence, distribution, and age-specific incidence rate of different types and subtypes of leukemia were determined according to sex and age groups. Results: Overall, 65.79% consisted of lymphocytic leukemia, 33.64% of myeloid leukemia, and 0.56% of monocytic leukemia. No significant sex-based differences were found, but age-specific patterns were observed. Leukemia distribution by age revealed significant associations. Lymphocytic leukemia dominated in the pediatric population, particularly acute lymphocytic leukemia, while myeloid leukemia shifted towards adulthood. Age-specific incidence patterns showed, first, that lymphocytic leukemia is the most common leukemia in pediatric ages, and second, there is a shift from acute lymphocytic leukemia dominance in pediatric ages to myeloid leukemia incidence in late adulthood, emphasizing nuanced epidemiological dynamics. Conclusions: Acute leukemia cases occurred with high prevalence in our study population, with a high incidence in pediatric and adulthood populations, especially for acute lymphocytic leukemia, showing a (<18 years) 153.8 age-standardized incidence rate in the pediatric group, while in the adult population, the age-standardized rate was 59.84. In the age-specific analysis, we found that the childhood group (5–9 years) were the most affected by acute lymphocytic leukemia in the pediatric population, while in the adult population, the early-adulthood group (15–29 years) were the most affected age group. In contrast, chronic myeloid leukemia affected both adults and the pediatric populations, while chronic lymphocytic leukemia and monocytic leukemia were exclusive to adults. The study underscores the need for tailored diagnostic, treatment, and preventive strategies based on age, contributing valuable insights into the leukemia epidemiology of the Bajio region.

Chronic myeloid leukemia (CML) is a hematopoietic stem cell disorder characterized by the presence of the Philadelphia chromosome, a product of the reciprocal translocation t(9;22)(q34;q11), in the BCR and ABL genes. These rearrangements in both genes lead to the formation of various fusion mRNA products, with preferential expression of b2a2, b3a2, and other BCR::ABL1 mRNA variants, combined with additional chromosomal abnormalities. Notably, the distribution and frequency of different mRNA variants vary in different populations. However, studies concerning this in Mexico are limited, and the results have been inconclusive. This study therefore aimed to determine the distribution of BCR::ABL1 mRNA variants in different clinical phases of CML and their effect on hematological parameters and patient survival. This study included 33 patients, whose demographic, clinical, and molecular data on BCR::ABL1 mRNA variants and hematological parameters were collected to identify potential associations. A total of 84.8% (n = 28) of patients had BCR::ABL1 translocation and increased platelet and basophil counts. The most frequent mRNA variant was b3a2 (64.3%), followed by b2a2 (28.6%) and e1a2 (3.6%). Concerning the clinical phases of CML, 75.8% (n = 25), 21.2% (n = 7), and 3% (n = 1) of patients were in the chronic, blast, and accelerated phases, respectively. Moreover, the b3a2 mRNA variant was more commonly identified in patients in the chronic phase. No correlation was observed between mRNA variant expression and patient survival. However, b2a2 was indicative of patients with longer survival as well as those treated with imatinib or nilotinib. Additionally, platelet count could be a marker of BCR::ABL1 translocation.

Chronic myeloid leukemia (CML) is associated with the Philadelphia chromosome and distinct BCR::ABL1 gene transcripts. We assessed the frequencies of these transcripts in Mexico, Latin America, and worldwide. We determined the prevalence of BCR::ABL1 transcripts in CML patients and intercontinental or regional variations using specialized databases and keywords. We analyzed 34 studies from 20 countries, encompassing 5795 patients. Keyword-based searches in specialized databases guided data collection. ANOVA was employed for transcript distribution analysis. The b3a2 transcript was most prevalent globally, followed by b2a2, with e1a2 being the least frequent. Interestingly, Mexico City exhibited a higher incidence of b2a2, while b3a2 predominated in the remaining country. Overall, no significant intercontinental or regional variations were observed. b3a2 was the most common BCR::ABL1 transcript worldwide, with b2a2 following closely; e1a2 was infrequent. Notably, this trend remained consistent in Mexico. Evaluating transcript frequencies holds clinical relevance for CML management. Understanding the frequency of transcript informs personalized CML treatments.

Introduction: the WHO in March 2020 declared COVID-19 as a pandemic, with the pronouncement that social distancing will be applied, then the lifestyle had to change, including learning activities that had to migrate to online using data display screens (DDS). However, prolonged use can generate severe health effects such as musculoskeletal disorders. Objective: analyze and correlate the different musculoskeletal disorders with prolonged use of DDS, as well as identify the possible injuries or postural modifications in university students during the COVID-19 pandemic. Material and methods: a cohort study was conducted with 65 students according to the selection criteria applied. X-ray plates (AP and lateral cervical spine) were taken from each of the participants. The radiological markings were carried out to assess and identify the presence of biomechanical alterations; comparisons were also made according to the use of DDS. Results: among the different types of DDS that the participants used, the highest percentage was for the smartphone, which they used in their bedroom for 9 to 10 hours per day. According to the ratio between the cervical angle and depth measurement, 75.4% of participants presented hypolordosis, and 72.3% presented extension or posteriority of C1. Conclusions: we demonstrated the presence of biomechanical alterations in the cervical spine derived from the prolonged use of DDS in university students, mainly in those who use smartphones for 8 to 10 hours. There is a significant correlation between the radiological markings of the cervical angle and the depth measurement, and a decrease in cervical curvature (hypolordosis).

The prevalence of the different genotypes of human papillomavirus (HPV) varies depending on lesion severity and geographic region. To identify multiple HPV infections in low- and high-grade cervical lesions in a group of women from the Mexican Bajío region referred with inconclusive cytology. Pilot study of women referred from primary care units of Guanajuato, Mexico, with cytology suggestive of cervical lesion. Cervical smears were subjected to DNA extraction and HPV genotyping using microarrays. 100 consecutive cases were collected and 90 were analyzed; HPV positivity was observed in 26% of healthy women, and 62% had some degree of cervical lesion. The most common HPV genotypes were 59, 31, 16 and 51. Multiple infections were found in most samples. HPV heterogeneity was identified in the samples of the study population in contrast to worldwide reports; furthermore, multiple infections are common in precursor lesions and decrease in high-grade lesions. These data could have an impact on current HPV vaccination programs.

Soldiers in active military service need optimal physical fitness for successfully carrying out their operations. Therefore, their health status is regularly checked by army doctors. These inspections include physical parameters such as the body-mass index (BMI), functional tests, and biochemical studies. If a medical exam reveals an individual’s excess weight, further examinations are made, and corrective actions for weight lowering are initiated. The collection of urine is non-invasive and therefore attractive for frequent metabolic screening. We compared the chemical profiles of urinary samples of 146 normal weight, excess weight, and obese soldiers of the Mexican Army, using untargeted metabolomics with liquid chromatography coupled to high-resolution mass spectrometry (LC-MS). In combination with data mining, statistical and metabolic pathway analyses suggest increased S-adenosyl-L-methionine (SAM) levels and changes of amino acid metabolites as important variables for overfeeding. We will use these potential biomarkers for the ongoing metabolic monitoring of soldiers in active service. In addition, after validation of our results, we will develop biochemical screening tests that are also suitable for civil applications.

The inflammatory response in gout disease is induced by the activation of NLR family pyrin domain-containing 3 (NLPR3) signaling pathway mediated by IL-1β release. The objective of the study was to determine the association between single nucleotide polymorphisms (SNPs) within NLRP3 inflammasome genes and gout susceptibility. Mexican patients with gout from the National Rehabilitation Institute and General Hospital of Mexico were enrolled. A healthy control group was also included. We analyzed the frequency and allelic distribution of eight SNPs from seven different genes within the NLRP3 inflammasome signaling pathway: TLR4 rs2149356, CD14 rs2569190, NLRP3 rs3806268, NLRP3 rs10754558, CARD8 rs2043211, IL-1β rs1143623, P2RX7 rs3751142, and PPARGC1B rs45520937 SNPs. We found that the SNP rs45520937 of PPARGC1B was associated with the risk of developing gout when it was analyzed using the dominant model (Odds ratio [OR] = 2.30; 95% confidence interval [CI]: 1.09-4.86; p = 0.030), and it is proposed that the adaptor molecule CD14 rs2569190 polymorphism could be associated with a lower risk of gout under an additive model (OR= 0.41;95% CI: 0.16-1.05; p = 0.064). No significant associations were identified for the remaining SNPs. Our findings suggest that the PPARGC1B rs45520937 SNP is associated with gout susceptibility.