Machine learning techniques for classification problems related to therapies in diabetes patients

The growing use of technology and cyber tools has been embraced by the healthcare sector in many ways. An interesting and currently not completely exploited field of application is "patient engagement" . This thesis tackles the problem of classifying diabetes patients, with the use of machine learning, based on the therapy they are following in: patients that are following the correct therapy and patients that are not following the therapy, or for which the therapy is not correct.ope

Serological levels of mutated p53 protein are highly detected at early stages in breast cancer patients

The aim of this study was to compare the sensitivity of the serological level of anti-p53 antibodies in breast cancer patients and to correlate its expression level with patient age, histological stage and grade of tumor differentiation. Total p53 protein expression (mutant and wild-type) was also determined in the breast cancer tissues using immunohistochemistry (IHC). The serological levels of mutant p53 expression were found to be age-dependent, reaching the highest level at 50 years of age. Faint or low detection was observed in patients ≤30 years of age. Anti-p53-antibodies were detected in patients ≤40 and ≥61 years of age. The serological levels of mutant p53 protein were highly detected in all stages of breast cancer, including the early stages. However, anti-p53 antibodies reached a high level of detection only in stage III breast carcinomas. No expression was found in patients with benign breast disease. The detection of p53 mutations was dependent on the grade of tumor differentiation, achieving the highest level in the poorly differentiated breast carcinomas. Results from IHC were highly correlated with serological p53 mutational analysis. Our findings indicate that mutant p53 in serum is a promising novel parameter for the evaluation of cellular biology and the prognosis of breast cancer from its early stages using blood samples. Anti-p53 antibodies were demonstrated to be less sensitive in this study. It is also possible to use the expression of mutant p53 protein as a molecular marker to differentiate benign breast disease from breast carcinoma prior to surgery.Fil: Balogh, Gabriela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiarida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiarida; ArgentinaFil: Mailo, Daniel. No especifíca;Fil: Nardi, Héctor. No especifíca;Fil: Corte, María Marta. No especifíca;Fil: Vincent, Esteban. No especifíca;Fil: Barutta, Elena. No especifíca;Fil: Lizarraga, Guillermo. No especifíca;Fil: Lizarraga, Pablo. No especifíca;Fil: Montero, Héctor. No especifíca;Fil: Gentili, Roberto. No especifíca;Fil: Mordoh, Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Pque. Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentin

A forgotten life-threatening medical emergency: myxedema coma

Nowadays myxedema coma is a rare medical emergency but, sometimes, it still remains a fatal condition even if appropriate therapy is soon administered. Although physical presentation is very non-specific and diversified, physicians should pay attention when patients present with low body temperature and alteration of neurological status; the presence of precipitating events in past medical history can help in making a diagnosis. Here we discuss one such case: an 83-year-old female presented with abdominal pain since few days. Laboratory tests and abdomen computed tomography scan demonstrated alithiasic cholecystitis; she was properly treated but, during the Emergency Department stay she experienced a cardiac arrest. Physicians immediately started advance cardiovascular life support algorithm and she survived. Later on, she was admitted to the Intensive Care Unit where doctors discovered she was affected by severe hypothyroidism. Straightway they started the right therapy but, unfortunately, the patient died in a few hours

BRCA1 polymorphism in breast cancer patients from Argentina

Breast cancer is the most common type of cancer in females in Argentina, with an incidence rate similar to that in the USA. However, the contribution of the BRCA1 or BRCA2 mutation in breast cancer incidence has not yet been investigated in Argentina. In order to evaluate which BRCA1 polymorphisms or mutations characterize female breast cancer in Argentina, the current study enrolled 206 females with breast cancer from several hospitals from the southeast of Argentina. A buccal smear sample was obtained in duplicate from each patient and the DNA samples were processed for polymorphism analysis using the single-strand conformational polymorphism technique. The polymorphisms in BRCA1 were investigated using a combination of 15 primers to analyze exons 2, 3, 5, 20 and 11 (including the 11.1 to 11.12 regions). The BRCA1 mutations were confirmed by direct sequencing. Samples were successfully examined from 154 females and, among these, 16 mutations were identified in the BRCA1 gene representing 13.9% of the samples analyzed. One patient was identified with a polymorphism in exon 2 (0.86%), four in exon 20 (3.48%), four in exon 11.3 (3.48%), one in exon 11.7 (0.86%), two in exon 11.8 (1.74%), one in exon 11.10 (0.86%) and one in exon 11.11 (0.86%). The most prevalent alteration in BRCA1 was located in exon 11 (11 out of 16 patients; 68.75%). The objective of our next study is to evaluate the prevalence of mutations in the BRCA2 gene and analyze the BRCA1 gene in the healthy relatives of BRCA1 mutation carriers.Fil: Jaure, Omar David Argentino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Alonso, Eliana Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Aguilera Braico, Diego Máximo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Nieto, Alvaro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Orozco, Manuela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Morelli, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Ferro, Alejandro M.. Hospital Italiano; ArgentinaFil: Barutta, Elena. Medifem; ArgentinaFil: Vincent, Esteban. Medifem; ArgentinaFil: Martínez, Domingo. Provincia de Buenos Aires. Ministerio de Salud. Hospital Municipal de Agudos Dr. Leónidas Lucero; ArgentinaFil: Martínez, Ignacio. Provincia de Buenos Aires. Ministerio de Salud. Hospital Municipal de Agudos Dr. Leónidas Lucero; ArgentinaFil: Maegli, Maria Ines. Provincia de Buenos Aires. Ministerio de Salud. Hospital Municipal de Agudos Dr. Leónidas Lucero; ArgentinaFil: Frizza, Alejandro. Medifem; ArgentinaFil: Kowalyzyn, Ruben. Clínica Viedma; ArgentinaFil: Salvadori, Marisa. Hospital Dr. Lucio Molas; ArgentinaFil: Ginestet, Paul. Provincia de Buenos Aires. Ministerio de Salud. Hospital y Maternidad Municipal Pigüé; ArgentinaFil: Gonzalez Donna, Maria L.. Hospital Italiano; ArgentinaFil: Balogh, Gabriela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentin

Machine learning techniques for classification problems related to therapies in diabetes patients

The growing use of technology and cyber tools has been embraced by the healthcare sector in many ways. An interesting and currently not completely exploited field of application is "patient engagement". This thesis tackles the problem of classifying diabetes patients, with the use of machine learning, based on the therapy they are following in: patients that are following the correct therapy and patients that are not following the therapy, or for which the therapy is not correct

Comment on Savioli et al. Mild Head Trauma: Is Antiplatelet Therapy a Risk Factor for Hemorrhagic Complications? Medicina 2021, 57, 357

We read the article of Savioli G. et al. [...