Biomedica : revista del Instituto Nacional de Salud最新文献

{"title":"Second allogeneic transplants in children: twelve years of experience","authors":"Alexis Antonio Franco, Eliana Manzi Tarapues, Sergio Camilo Cabrera Salcedo, Diana Marcela Muñoz Caluce, Diego Medina","doi":"10.7705/biomedica.7946","DOIUrl":"https://doi.org/10.7705/biomedica.7946","url":null,"abstract":"<p><strong>Introduction: </strong>A second hematopoietic stem cell transplant is required when the first transplant fails, usually due to relapse or graft failure, and is associated with increased morbidity and mortality. Survival rates range from 74 to 82% in non-neoplastic diseases and from 39 to 58% in neoplastic conditions. Evidence on second hematopoietic stem cell transplant in children is limited, particularly in low- and middle-income countries.</p><p><strong>Objective: </strong>To describe the clinical characteristics, complications, and outcomes of children who underwent a second transplant at a high-complexity center between 2012 and 2024.</p><p><strong>Materials and methods: </strong>Case series study with descriptive and survival analysis using the Kaplan-Meier method in Stata 14™.</p><p><strong>Results: </strong>A total of 346 allogeneic transplants were performed, of which 20 patients underwent a second transplant. Of these, 17 received a haploidentical donor for their second transplant, and the primary indication was a neoplastic disease in 13 cases. The second transplant was performed due to graft failure in 11 of the 20 patients, and due to relapse in the remaining 9. The mean age was 10.7 ± 5 years, with a male predominance (14 out of 20). Haploidentical transplants accounted for 16 of the 20 cases, and 11 used the same donor. The most common complications were acute graft-versus-host disease in 7 cases, 2 of grade III, cytomegalovirus infection in 10 cases, and graft failure after the second transplant in 4 cases: 3 primary, 1 secondary. Transplant-related mortality was 31%. The 2-year overall survival was 54%, with a median follow-up of 11 months.</p><p><strong>Conclusions: </strong>Second hematopoietic stem cell transplant is a viable therapeutic option when no other alternatives are available, particularly in resource-limited settings.</p>","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"46 1","pages":"121-217"},"PeriodicalIF":0.6,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current challenges in the diagnosis of neurodegenerative diseases in neurological practice","authors":"Rodrigo Pardo-Turriago","doi":"10.7705/biomedica.8336","DOIUrl":"https://doi.org/10.7705/biomedica.8336","url":null,"abstract":"","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"46 1","pages":"5-8"},"PeriodicalIF":0.6,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acute pancreatitis due to Leptospira spp.: An uncommon case report of a common zoonosis","authors":"Juan Pablo López, Carlos Alberto Salgado, Cristhian David Morales","doi":"10.7705/biomedica.7771","DOIUrl":"https://doi.org/10.7705/biomedica.7771","url":null,"abstract":"<p><p>Leptospirosis, a widespread zoonotic disease prevalent in tropical and subtropical regions, causes over one million cases and about 59,000 deaths annually. It is transmitted through contact with infected animal urine, primarily from rodents, and worsened by poor sanitation and favorable climate. Although in Colombia there was a significant decline in cases from 2018 to 2022, severe forms of the disease, including rare complications like acute pancreatitis, still pose significant health risks. A 49-year-old male presented with symptoms including abdominal pain, jaundice, and severe thrombocytopenia. Despite initial normal tests, leptospirosis was eventually diagnosed after further testing revealed elevated lipase levels and medical imaging confirmed severe pancreatitis. The patient received antibiotics and intensive care, which led to recovery and confirmation of leptospirosis through serology. Leptospirosis can cause severe multi-organ complications, including rare cases of pancreatitis. This case underscores the need for early diagnosis and treatment of leptospirosis to manage such complications effectively. Environmental factors and rodent control are crucial for prevention, emphasizing the importance of comprehensive management and preventive measures.</p>","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"46 1","pages":"18-24"},"PeriodicalIF":0.6,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outbreak of toxoplasmosis with pulmonary involvement in immunocompetent military population","authors":"Raúl Reyes-Toledo, Laura Culma-Roa, María José López, Jairo Enrique Pérez, David Suárez-Silva","doi":"10.7705/biomedica.7728","DOIUrl":"https://doi.org/10.7705/biomedica.7728","url":null,"abstract":"<p><p>Toxoplasmosis is a common zoonotic infection with various transmission mechanisms and wide distribution worldwide. Its probability of causing disease is related to alterations in the immune system as well as the parasite’s virulence.\u0000This study presents four cases of patients from rainforest areas in Colombia who did not have any known immunosuppression but do have a diagnosis of toxoplasmosis with pulmonary involvement, confirmed by serology. One of them also developed hypoxemic ventilatory failure and myocarditis. This outbreak of toxoplasmosis occurred in military personnel after coming into contact with common sources of exposure in the Colombian Amazon during a military training exercise.\u0000This article includes the diagnostic process, clinical manifestations, treatment, and importance of the parasite’s genotypic variants.</p>","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"46 1","pages":"9-17"},"PeriodicalIF":0.6,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of human epidermal growth factor receptor 2 expression in cancerous breast tissue through artificial intelligence","authors":"Leidy Verónica Villota, Jessica Julieth Lasso, Elvia Noelia Muñoz, Rubiel Vargas","doi":"10.7705/biomedica.7899","DOIUrl":"10.7705/biomedica.7899","url":null,"abstract":"<p><p>Introduction. Histological and molecular analysis of breast tissue is essential for the diagnosis, prognosis, and treatment of breast cancer. Key biomarkers include progesterone and estrogen receptors, as well as the human epidermal growth factor receptor 2 (HER2). HER2 overexpression indicates an aggressive subtype of breast cancer but enables targeted therapies that improve survival rates. However, its evaluation faces challenges, ranging from sample quality to interpretation variability. The College of American Pathologists classifies HER2 overexpression into four categories, but variations around the 10% expression threshold can lead to misinterpretations.\u0000Objective. To present an automated technique for classifying HER2-overexpressing cells in histological slides.\u0000Materials and methods. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was applied using samples of 89 patients from the Unidad de Diagnóstico en Patología, covering all four HER2 expression levels. Deep learning techniques were employed, leveraging neural networks and vision transformer models through transfer learning. Additionally, a usability evaluation was conducted on the final version of the software.\u0000Results. The ViT-B/16 model achieved a classification accuracy of 90,65%, while the tool was evaluated with an acceptable level of satisfaction in its clinical application.\u0000Conclusion. Artificial intelligence demonstrated high accuracy and consistency in HER2 classification, reducing diagnostic variability and improving objectivity. However, further optimization of processing efficiency is required for broader applicability.</p>","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"45 Sp. 3","pages":"83-102"},"PeriodicalIF":0.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12970530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic data within a common data model for artificial intelligence applications in maternal health: experience report in the Colombian context","authors":"Ever Augusto Torres-Silva, Juan José Gaviria-Jiménez, Ana María Guevara-Zambrano, Laura Herrera-Almanza, José Flórez-Arango","doi":"10.7705/biomedica.7937","DOIUrl":"10.7705/biomedica.7937","url":null,"abstract":"<p><strong>Introduction: </strong>Synthetic data in healthcare is an alternative for generating clinical records that resemble those registered in real clinical scenarios. The benefits of synthetic data are: greater volume of data, the possibility of representing specific patient populations, protection of real-data privacy, and improved data-sharing among different actors.</p><p><strong>Objective: </strong>To formulate a synthetic data generation model for the gestational care process in Colombia and adapt it to the Observational Medical Outcomes Partnership (OMOP) common data model to facilitate its integration into artificial intelligence applications in maternal health.</p><p><strong>Materials and methods: </strong>We conducted a case study of fully synthetic data formulation that included some of the most frequent outcomes and conditions during gestation based on a typical care process for pregnant women in Colombia. This approach was complemented by the generation of a common data model to facilitate data integration in future artificial intelligence applications or complementary systems that benefit from a standardized language, regardless of the system or form of classification.</p><p><strong>Results: </strong>We formulated a model for the synthetic generation of clinical data –applicable to real clinical settings– that spans the entire gestational care until the perinatal period. The model included the most frequent clinical conditions and outcomes, which were diagrammed in the Synthea™ tool with their corresponding clinical probabilities of occurrence based on the reported literature or the usual practice of obstetric specialists in Colombia.</p><p><strong>Conclusions: </strong>This study demonstrates that the generation of synthetic data applied to the gestational care process in Colombia was feasible and represents a pioneering contribution in the region</p>","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"45 Sp. 3","pages":"71-92"},"PeriodicalIF":0.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919388/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of artificial intelligence in the diagnosis of alterations in cervical cytology: A university population-based observational study","authors":"Jose Said Manzano-Chaya, Tania Mendoza-Herrera, Ernesto García-Ayala","doi":"10.7705/biomedica.7651","DOIUrl":"10.7705/biomedica.7651","url":null,"abstract":"<p><strong>Introduction: </strong>Conventional cervical cytology (Pap smear) remains a primary method for cervical cancer screening in Colombia, despite limitations in diagnostic yield and heavy workload. The potential of artificial intelligence to address these challenges is yet to be evaluated in our population.</p><p><strong>Objective: </strong>To evaluate and compare the discriminative ability of four artificial intelligence-based models for the detection of abnormalities in Pap smears.\u0000Materials and methods. A total of 650 images of Pap smear cells were obtained from a university cohort in northeastern Colombia. These images were subjected to diagnostic evaluation by an expert pathologist. Four artificial intelligence models (DenseNet, InceptionV3, MobileNet, and VGG19) were trained using data from a publicly available Pap smear database with digital image analysis and deep learning. The discriminative ability of the models was determined by calculating their sensitivity, specificity, and area under the curve.</p><p><strong>Materials and methods: </strong>MobileNet tuvo la mejor capacidad discriminativa [área bajo la curva (AUC) de 0,97) con una especificidad del 0,99 y sensibilidad de 0,78 para la detección de alteraciones en la citología cervicouterina. Por otro lado, InceptionV3 tuvo un mejor desempeño en el tamizaje, con sensibilidad del 0,93, especificidad de 0,82 y área bajo la curva de 0,947.</p><p><strong>Results: </strong>MobileNet showed the highest discriminative ability (AUC = 0.97), with a specificity of 0.99 and sensitivity of 0.78 for the detection of altered cells in Pap smears. On the other hand, InceptionV3 had the best performance capabilities for screening, with a sensitivity of 0.93, specificity of 0.82, and AUC of 0.947.</p><p><strong>Conclusions: </strong>The results of this study illustrate the advantages and disadvantages of different artificial intelligence models and how their application could help improve the diagnostic performance of manual reading in cervical cancer screening or even serve as a primary screening method to rule out negative cases, by achieving a diagnostic performance comparable to that of manual reading.</p>","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"45 Sp. 3","pages":"24-36"},"PeriodicalIF":0.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12908624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-based clinical decision support system for the automated classification of anemia in hemodialysis patients","authors":"Sebastián Tenorio, Luis Alfonso Valderrama, José Javier Arango-Álvarez, Luz Amparo Lozano, Iván Leonardo Mojica","doi":"10.7705/biomedica.7945","DOIUrl":"10.7705/biomedica.7945","url":null,"abstract":"<p><strong>Introduction: </strong>Anemia is a frequent complication in patients with chronic kidney disease undergoing hemodialysis and is associated with increased morbidity, mortality, and healthcare burden. Accurate classification is essential to optimize treatment with intravenous iron and erythropoiesis-stimulating agents. Rule-based clinical decision support systems (CDSS) provide a strategy to standardize this process.</p><p><strong>Objective: </strong>To describe the development and implementation of a knowledge-based clinical decision support system for the automated classification of anemia in hemodialysis patients using laboratory data.</p><p><strong>Materials and methods: </strong>This retrospective observational study included 883 adult patients receiving prevalent hemodialysis during 2023. An algorithm was developed based on established clinical guidelines [Sociedad Latinoamericana de Nefrología e Hipertensión (SLANH)], KDIGO, NICE to classify patients with hemoglobin below 12 g/dl into three categories: absolute iron deficiency, functional iron deficiency, and candidates for therapeutic trial with intravenous iron. The system also flagged cases with suspected severe secondary hyperparathyroidism (PTH > 800 pg/ml). Data was obtained from the laboratory information system and the clinical decision support system. We applied a descriptive statistical analysis.</p><p><strong>Results: </strong>The clinical decision support system automatically classified patients into the following categories: functional iron deficiency (39.2%), severe hyperparathyroidism (26.7%), absolute iron deficiency (17.7%), and candidates for intravenous iron trial (16.4%). A subgroup (9.5% within the functional iron deficiency group) also showed elevated PTH levels, suggesting potential resistance to erythropoiesis-stimulating agents. Distinct clinical profiles were observed across the groups.</p><p><strong>Conclusions: </strong>The clinical decision support system enabled automated and standardized classification of anemia in hemodialysis patients, supporting evidence-based clinical decision-making. Its implementation represents a digital health innovation with the potential to improve the quality and safety of anemia management in chronic kidney disease.</p>","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"45 Sp. 3","pages":"52-70"},"PeriodicalIF":0.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12970531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced artificial intelligence in piRNA and PIWI-like protein research: A systematic review of recurrent neural networks, long short-term memory, and emerging computational techniques","authors":"Jheremy Sebastián Reyes, Jhonathan David Guevara, Laura Tatiana Picón, Iris Lorena Sánchez, Libia Andrea Gaona, María Paula Montoya, Luis Eduardo Pino","doi":"10.7705/biomedica.7660","DOIUrl":"10.7705/biomedica.7660","url":null,"abstract":"<p><strong>Introduction: </strong>PIWI-interacting RNAs are small and non-coding RNAs involved in gene regulation and transposable element repression, emerging as critical biomarkers and therapeutic targets in oncology. Advances in artificial intelligence, such as recurrent neural networks, long short-term memory networks, and graph convolutional networks, offer significant improvements in PIWI-interacting RNA detection.</p><p><strong>Objectives: </strong>To evaluate the performance of artificial intelligence models, including recurrent neural networks, long short-term memory, and graph convolutional networks, in detecting PIWI-interacting RNAs and assessing their implications for cancer diagnostics and prognosis.</p><p><strong>Materials and methods: </strong>A systematic review of 24 studies was conducted across PubMed, ScienceDirect, Scopus, and Web of Science, focusing on artificial intelligence-based approaches for PIWI-interacting RNA detection. Inclusion criteria were original articles published in English or Spanish using artificial intelligence models in clinical or experimental settings. Performance metrics such as accuracy, sensitivity, and specificity were analyzed.</p><p><strong>Results: </strong>Long short-term memory models achieved the highest overall accuracy (92.3%), followed by graph convolutional networks (91.4%), support vector machines (88%), and recurrent neural networks (85.7%). Sensitivity and specificity were also highest in long short-term memory (94% and 91%, respectively). Graph convolutional networks showed superior performance in identifying PIWI-interacting RNA-disease associations with complex datasets. Support vector machine models were effective in smaller datasets but exhibited scalability limitations.</p><p><strong>Conclusion: </strong>Artificial intelligence models, especially long short-term memory and graph convolutional networks, significantly enhance PIWI-interacting RNA detection, supporting their application in cancer diagnostics and personalized medicine. Future studies should refine these models, address dataset biases, and explore their integration into clinical workflows.</p>","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"45 Sp. 3","pages":"10-23"},"PeriodicalIF":0.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12904106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence and digital health in Colombia: outlook of the most recent advances and future challenges","authors":"Diego M López, Juan Sebastián Osorio","doi":"10.7705/biomedica.8248","DOIUrl":"10.7705/biomedica.8248","url":null,"abstract":"","PeriodicalId":101322,"journal":{"name":"Biomedica : revista del Instituto Nacional de Salud","volume":"45 Sp. 3","pages":"5-9"},"PeriodicalIF":0.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12890302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}