Dengue is considered the most prevalent mosquito-borne arboviral disease worldwide, representing a public health challenge as its incidence has tripled in the last 30 years. The World Health Organization reports 390 million infections annually in more than 129 countries, with approximately 96 million symptomatic cases and around 40,000 deaths. Mexico is a hyperendemic country, with high prevalence and significant outbreaks. In 2024, a surge was observed, with approximately 125,000 infections and nearly 480 deaths. The states with the most cases and deaths were Colima and Jalisco, respectively, placing significant strain on healthcare services and driving up costs. The disease’s epidemiology from 2014 to 2025 is characterized by marked seasonality and periodicity, and by the simultaneous circulation of all four serotypes. In recent years, a notable increase in DENV-3 has been observed. In 2025, there were 21,981 confirmed cases; Sonora recorded the highest incidence, while Jalisco and Sinaloa reported the highest number of deaths. This study provides a unique decadal analysis of the epidemiological characteristics of dengue in Mexico, highlighting potential challenges and emphasizing the importance of epidemiological surveillance and future approaches, such as vaccine provision in the country, to mitigate the high mortality rate and associated costs.
BACKGROUND:Longer-term outcome data following second-line antiretroviral therapy initiation in resource-limited settings is limited, especially in regions where genotypic resistance is inaccessible. This analysis evaluated extended efficacy and tolerability data from the D2EFT study. METHODS:D2EFT is a completed, multicenter, phase IIIB/IV, randomized, open-label trial in 14 low- and middle-income countries. People with human immunodeficiency virus (HIV) who had failed first-line non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimens were switched to 1 of ritonavir-boosted darunavir plus 2 nucleoside reverse transcriptase inhibitors (DRV/r + 2NRTIs), ritonavir-boosted darunavir plus dolutegravir (DTG + DRV/r), or dolutegravir with tenofovir disoproxil fumarate plus either lamivudine or emtricitabine (DTG + TDF/XTC), with or without pre-switch genotyping. Here we report virological suppression at 96 weeks, defined as HIV RNA <50 copies/mL in a modified intention-to-treat population. RESULTS:Between November 2017 and January 2022, 1190 participants were screened, 828 were randomized, and 826 were included in the analysis. At week 96, the proportions of participants with HIV RNA <50 copies/mL were 191/251 (76.1%) in DRV/r + 2NRTIs, 215/251 (85.7%) in DTG + DRV/r, and 231/283 (81.6%) in DTG + TDF/XTC. The treatment differences (95% confidence intervals [CIs]) in proportions achieving virological suppression were 9.6% (2.7, 16.4) in DTG + DRV/r and 9.0% (1.4, 16.6) in DTG + TDF/XTC, compared to DRV/r + 2NRTIs. Intermediate or high-level dolutegravir resistance was identified in 3/27 (13%) of virological failures in individuals taking DTG + TDF/XTC but in no one taking DTG + DRV/r. No darunavir resistance was observed. CONCLUSIONS:After 96 weeks of follow-up, DTG + DRV/r and DTG + TDF/XTC demonstrated virological superiority over DRV/r + 2NRTIs after first-line NNRTI-failure. However, emerging dolutegravir resistance, which was observed only in individuals taking DTG + TDF/XTC, requires ongoing global surveillance.
Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by the rapid proliferation of immature white blood cells in the bone marrow. Early and accurate diagnosis is essential for improving clinical outcomes; however, distinguishing between lymphocytes and lymphoblasts poses significant challenges owing to their subtle morphological similarities. Traditional manual diagnostic methods, which rely on expert evaluations, are inherently time-consuming and subject to human error. In recent years, machine learning and deep learning approaches have emerged as promising tools for automating and enhancing diagnostic processes. This review systematically examines state-of-the-art traditional and deep learning techniques applied for ALL detection and classification. We provide a comprehensive analysis of various methodologies, including supervised machine learning algorithms and advanced deep learning architectures, with a focus on critical stages such as image preprocessing, feature extraction, and blast cell quantification. Furthermore, we discuss the performance metrics and accuracy benchmarks, highlighting the potential of these techniques to match or exceed human diagnostic capabilities. The review concludes with a discussion of the current challenges, recent developments, and future directions in the application of artificial intelligence for ALL diagnosis, underscoring the need for continued innovation to meet emerging clinical demands.
Background Laparoscopic surgery provides important patient benefits, including faster recovery, shorter hospital stays, and reduced inflammatory response. However, laparoscopic skill acquisition requires the development of complex psychomotor abilities through deliberate practice. Although simulation-based training and the Fundamentals of Laparoscopic Surgery (FLS) program are widely used in surgical education, evidence regarding structured FLS-based training in coloproctology residency programs, particularly in Latin America, remains limited. This study evaluated the effectiveness of an eight-week FLS-based simulator training program in improving laparoscopic skills among coloproctology residents in Mexico. Methods A prospective pretest-posttest study was conducted at the Coloproctology Service of the Antiguo Hospital Civil “Fray Antonio Alcalde”, Guadalajara, Mexico. Fourteen coloproctology residents participated in an eight-week simulator-based training program. Technical skills were assessed before and after the intervention using the five standardized FLS tasks: peg transfer, precision cutting, ligating loop, intracorporeal suturing, and extracorporeal suturing. Competency was determined according to established FLS performance criteria. Participants were additionally categorized according to prior laparoscopic experience and video game use. Descriptive and comparative statistical analyses were performed using SPSS version 25, with statistical significance established at p < 0.05. Results All 14 residents completed the training program and were included in the final analysis. Following the intervention, participants demonstrated significant improvements in laparoscopic technical performance across all evaluated FLS tasks. Significant gains were observed in every task assessed, reflected by reductions in task completion times and improvements in accuracy scores. Residents with previous laparoscopic experience demonstrated higher baseline technical performance than those without prior experience. In contrast, no significant differences in laparoscopic performance were observed according to the frequency of video game use. Conclusions An eight-week structured FLS-based simulation training program was associated with significant improvements in laparoscopic technical skills among coloproctology residents. These findings support the incorporation of standardized simulation-based curricula into coloproctology training programs and provide evidence for the feasibility and educational value of FLS-based training in Latin American residency settings. Trial registration: The study protocol was approved by the Institutional Research Ethics Committee (CEI 262/24). The study was not registered in any database.
