Gaceta medica de Mexico最新文献

Background: Childhood obesity is a global health problem, as it is a risk factor for developing diseases such as metabolic syndrome and diabetes. At present, identifying these already established diseases is relatively easy for health professionals with the support of laboratory studies. The global trend in health involves acting before the disease is established.

Objectives: The objective of this study is to identify whether total amylase activity is useful to predict which patients will develop metabolic syndrome or diabetes.

Material and methods: Using a database with 101 Mexican patients, considering the value of the homeostasis model assessment insulin resistance as a diagnostic variable in three groups < 2 normal, between 2 and 5 with metabolic risk and > 5 as diabetes, as well as the value of the amylase enzymatic activity. Random forest (RF) was used as a machine learning method.

Results: The RF model obtained the following results: area under the curve 0.7075, specificity 0.7619, sensitivity 0.7142, and accuracy 0.7500.

Conclusions: It is concluded that with these variables and RF, it is feasible to have a prediction model that contributes to identifying this type of patients in the prepathogenic period.

Background: Diabetes mellitus (DM) is a growing metabolic disease worldwide, associated with severe complications. Glucagon-like peptide-1 analogs and sodium-glucose cotransporter-2 inhibitors are promising therapeutic options for diabetic cardiomyopathy (DCM), although their cardioprotective mechanisms are not yet fully understood.

Objective: This study evaluates the effects of liraglutide and empagliflozin on oxidative stress, inflammation, and histological changes in cardiac tissue in DCM.

Materials and methods: Thirty-seven male Wistar albino rats were divided into four groups. Diabetes was induced in three groups using streptozotocin and nicotinamide. The groups were: (1) Control, (2) DM, (3) DM + Liraglutide (0.6 mg/kg, subcutaneously, 8 weeks), and (4) DM + Empagliflozin (30 mg/kg, oral gavage, 8 weeks). Blood samples were analyzed through enzyme-linked immunosorbent assay for tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1), malondialdehyde (MDA), superoxide dismutase (SOD), advanced glycation end (AGEs) products, and insulin. Cardiac tissue was examined histopathologically.

Results: Diabetes significantly increased blood glucose, IL-1, TNF-α, MDA, and AGEs (p < 0.01), while SOD levels decreased (p < 0.01), alongside myocardial damage. Liraglutide and empagliflozin improved all parameters (p < 0.01).

Conclusion: Liraglutide and empagliflozin mitigate diabetes-induced cardiac damage, likely by reducing fibrosis, oxidative stress, and inflammation.

Background: Alterations in mother-infant bonding (MIB) are often undetected and are more frequent in the presence of perinatal risk factors, affecting the long-term mental health of both mother and offspring.

Objectives: The objective of the study is to compare MIB in Mexican women screened for perinatal depression (PD).

Material and methods: This was an analytical cross-sectional design. Prior informed consent and women aged 18-45 years were included. The Edinburgh Postpartum Depression Scale (EPDS), the Hospital Anxiety and Depression Scale (HADS), the Maternal Anxiety and Depression Scale (MAAS), and the Postpartum Bonding Questionnaire (PBQ) were applied. Mann-Whitney U, Chi-square, and multiple logistic regression were used.

Results: 143 women were included (24.5% with PD according to EPDS); 36.4% presented anxiety symptoms according to HADS. According to the MAAS, the most frequent MIB style was strong and healthy (57.3%), while those with PD (37.2%) presented a negative MIB style. In PBQ, 9.09% presented alterations in MIB style. The presence of alterations in MIB was a risk factor for developing PD (OR = 3.99; 95% CI: 1.130-14.125; p = 0.032).

Conclusions: Most women showed strong and healthy MIB, while those with alterations in MIB showed three times more risk for PD. Our findings suggest that risk factors for MIB alterations and/or PD should be identified to offer early treatment opportunities.

The alarming rise in assaults against healthcare professionals is a public health and occupational issue that threatens staff well-being and care quality. Violence in this sector includes physical, verbal, and psychological aggression, posing a serious risk. Four main types of workplace violence in healthcare have been identified: External violence with no prior relationship (Type I), violence by patients against professionals (Type II, the most frequent), internal or institutional violence (Type III), and personal violence (Type IV). This issue is global, with an increasing trend and significant underreporting. Its consequences are severe at multiple levels: individually (burnout, anxiety, depression), institutionally (absenteeism, staff turnover), and in patient care quality. Artificial intelligence (AI) has emerged as a promising tool to prevent and mitigate such violence. Its applications include surveillance and monitoring systems, enhanced communication between staff and patients, workflow optimization, staff training, and predictive analysis of potentially aggressive patients. However, AI implementation presents ethical challenges related to data protection, privacy, bias risks, prediction reliability, and potential dehumanization. Addressing these concerns is crucial to ensuring safe and equitable AI use, always under human supervision. Effective prevention requires a comprehensive approach that integrates technology with organizational and educational measures.

The alarming rise in assaults against healthcare professionals is a public health and occupational issue that threatens staff well-being and care quality. Violence in this sector includes physical, verbal, and psychological aggression, posing a serious risk. Four main types of workplace violence in healthcare have been identified: External violence with no prior relationship (Type I), violence by patients against professionals (Type II, the most frequent), internal or institutional violence (Type III), and personal violence (Type IV). This issue is global, with an increasing trend and significant underreporting. Its consequences are severe at multiple levels: individually (burnout, anxiety, depression), institutionally (absenteeism, staff turnover), and in patient care quality. Artificial intelligence (AI) has emerged as a promising tool to prevent and mitigate such violence. Its applications include surveillance and monitoring systems, enhanced communication between staff and patients, workflow optimization, staff training, and predictive analysis of potentially aggressive patients. However, AI implementation presents ethical challenges related to data protection, privacy, bias risks, prediction reliability, and potential dehumanization. Addressing these concerns is crucial to ensuring safe and equitable AI use, always under human supervision. Effective prevention requires a comprehensive approach that integrates technology with organizational and educational measures.

Inborn errors of metabolism (IEM) are inherited disorders resulting from genetic defects in proteins involved in breakdown or storage of fatty acids, carbohydrates and proteins. Collectively, IEM encompasses approximately 1000 different disorders and can affect up to 1 in 2000 births. While biochemical newborn screening programs have been successfully applied to early identify newborns with treatable IEM conditions and to reduce their associated morbidity and mortality, the great majority of known IEM are not recognizable through biochemical screening. In recent years, next generation DNA sequencing technologies (including sequencing of gene panels, exome sequencing, and genome sequencing) has revolutionized the genetic diagnosis of monogenic diseases, including IEM. Here, we present a narrative review with selected bibliography to show a general landscape about the current status of NGS-based IEM diagnosis as well as its intrinsic limitations. NGS can detect newborns with metabolic diseases that may otherwise be clinically unrecognized until symptoms start. Importantly, a subgroup of these newborns will benefit from individualized medical management.

Background: Emphysematous pyelonephritis (EP) is an acute, necrotizing infection of the renal parenchyma and surrounding tissues, characterized by the presence of gas in the renal parenchyma and adjacent structures.

Objective: To analyze the outcomes of patients with EP, treated at the General Hospital of Mexico.

Material and methods: An observational, analytical study was carried out, between January 2018 and December 2023. Clinical data, urine cultures and tomographic findings (severity classification by Huang-Tseng) were collected.

Results: 51 clinical records were included, 64.7% women, and average age 52 years. 70% lived with diabetes type 2 and 60% had uro-renal lithiasis, about 30% had severe EP. 80% of cases were resolved with treatment based on antibiotics and drainage by interventional radiology. There was an average of 10 days of hospitalization and fatality of 5.9%; the severity of the acute kidney injury showed an association with mortality (p ≤ 0.05).

Conclusions: The combined use of antibiotics with drainage of the abscess through interventional radiology was associated with a decrease in morbidity and mortality in these cases.