Leishmaniasis, a disease caused by Leishmania parasites and transmitted via sandflies, presents in two main forms: cutaneous and visceral, the latter being more severe. With 0.7 to 1 million new cases each year, primarily in Brazil, diagnosing remains challenging due to diverse disease manifestations. Traditionally, the identification of Leishmania species is inferred from clinical and epidemiological data. Advances in disease management depend on technological progress and the improvement of parasite identification programs. Current treatments, despite the high incidence, show limited efficacy due to factors like cost, toxicity, and lengthy regimens causing poor adherence and resistance development. Diagnostic techniques have improved but a significant gap remains between scientific progress and application in endemic areas. Complete genomic sequence knowledge of Leishmania allows for the identification of therapeutic targets. With the aid of computational tools, testing, searching, and detecting affinity in molecular docking are optimized, and strategies that assess advantages among different options are developed. The review focuses on the use of molecular docking and molecular dynamics (MD) simulation for drug development. It also discusses the limitations and advancements of current treatments, emphasizing the importance of new techniques in improving disease management.

Scimitar syndrome is characterized by the anomalous connection of the right pulmonary venous return to the hepatic portion of the inferior vena cava. Its name is derived from the characteristic image observed in chest X-ray, CT scan, or during pulmonary angiography in cardiac catheterization. It is more common among females and rarely affects the left lung. The importance of knowing its symptoms and presentation allows for a high diagnostic suspicion, thus avoiding the underdiagnosis of the disease. The prognosis is generally good, and timely diagnosis can prevent the occurrence of complications such as pulmonary hypertension or portal hypertension. We present the case of an eight-year-old female patient, who was previously evaluated for episodes of lower respiratory tract infections at 18 months of age, detecting only dextroposition, without any diagnostic workup. She was then sent to our office at eight years of age, with the onset of exerciseinduced dyspnea. A comprehensive workup was conducted, with a diagnosis of scimitar syndrome.

Background/Objectives: Electrocardiography augmented by artificial intelligence (AI-ECG) has been put forward as an inexpensive, scalable means of identifying a wide range of cardiac disorders, but reported performance differs markedly with the condition targeted, the algorithm, and the reference standard. Our pre-specified primary objective was to estimate the diagnostic accuracy of AI-ECG for heart failure/left ventricular systolic dysfunction (HF/LVSD); accuracy in other cardiac conditions was examined condition by condition, and a cross-condition estimate was computed only as a secondary, descriptive summary. Methods: Following the PRISMA-DTA statement, we systematically reviewed diagnostic test accuracy (DTA) studies indexed in PubMed, Scopus, Web of Science, and Embase from inception to 31 March 2026. Eligible records were primary cohort or case–control diagnostic studies applying an AI algorithm to the ECG against an acceptable reference standard, with a reconstructable 2 × 2 table. Screening, extraction, and QUADAS-2 appraisal were performed independently in duplicate. Sensitivity and specificity were pooled with a bivariate random-effects (Reitsma) model whenever four or more studies shared a target condition, with the corresponding hierarchical summary receiver operating characteristic (HSROC) curve; subgroups with fewer than four studies were synthesised descriptively. Pre-specified sensitivity analyses excluded studies at high risk of bias. Certainty was graded with GRADE for test accuracy. Results: Twenty studies (155,442 ECG–reference pairs) were included and 12 (125,568 ECG–reference pairs) entered the quantitative synthesis. In the pre-specified HF/LVSD analysis (k = 5; n = 101,875), pooled sensitivity was 0.86 (95% confidence interval [CI], 0.80–0.90) and pooled specificity was 0.79 (95% CI, 0.70–0.85), with an HSROC area under the curve (AUC) of 0.89; restricting the analysis to the three studies at low risk of bias gave a sensitivity of 0.83 (95% CI, 0.81–0.85) and a specificity of 0.82 (95% CI, 0.75–0.87). The estimated between-study correlation between logit-sensitivity and logit-specificity was −0.86, indicating a strong implicit threshold effect that the bivariate/HSROC framework accommodates. Descriptive subgroup results indicated high accuracy for atrial fibrillation (median sensitivity 0.96, median specificity 0.94; k = 2) and inconsistent performance across coronary disease and acute coronary syndromes (median sensitivity 0.80, median specificity 0.85; k = 2). The secondary cross-condition estimate (k = 12) was 0.84 (95% CI, 0.77–0.89) for sensitivity and 0.87 (95% CI, 0.79–0.93) for specificity (HSROC-AUC 0.92); because it spans heterogeneous targets it is reported as a descriptive summary only. Overall risk of bias was low in 3/20 studies, unclear in 4/20, and high in 13/20, chiefly in the index test and reference standard domains. The Deeks test indicated funnel asymmetry (p = 0.022), which in DTA reviews commonly reflects heterogeneity in true accuracy rather than publication bias. Conclusions: For HF/LVSD, AI-ECG shows consistent and reproducible accuracy against echocardiography, and this estimate proved robust to the exclusion of studies at high risk of bias. Evidence for other cardiac conditions remains condition specific and less certain. Wider adoption would benefit from prospective external validation against guideline-based reference standards, independent adjudication, and transparent reporting of 2 × 2 data at pre-specified operating points.

The use of bedside ultrasound (POCUS) has generated a paradigm shift in pediatric medical care, allowing immediate visualization and interpretation of images. This tool has gained widespread acceptance among pediatricians, improving the accuracy and safety of various assessments and procedures. Recent guidelines from the American Heart Association and the European Society of Pediatric and Neonatal Intensive Care have defined recommendations and training standards for pediatric and neonatal POCUS, including cardiac, pulmonary, vascular, cerebral, and abdominal applications. These guidelines constitute a framework for its clinical application and the development of training programs for physicians and other healthcare professionals, and it has become the fifth element of the physical examination. This narrative review summarizes and describes the current application and prospects of this tool with regard to artificial intelligence and new technologies related to critical ultrasound

Optimal intravenous fluid administration in critically ill patients is essential for effective hemodynamic management, particularly during episodes of shock. Over time, fluid resuscitation strategies have evolved from liberal approaches to restrictive and individualized methods in order to prevent both hypovolemia and fluid overload, especially considering that only about 50% of critically ill patients are truly fluid responsive. Point-of-care ultrasound (POCUS) has emerged as a valuable, noninvasive bedside tool for assessing intravascular volume status and real-time hemodynamics. Several ultrasound-based techniques can be used, including cardiac ultrasound to evaluate preload, contractility, and afterload; lung ultrasound to detect pulmonary edema; and the Venous Excess Ultrasound Score (VExUS) to assess visceral venous congestion. These ultrasound assessments provide essential information on fluid responsiveness, right ventricular function, and tolerance to additional fluid administration. This enables clinicians to optimize fluid therapy while minimizing complications related to fluid overload. The integration of POCUS into clinical practice improves the ability of pediatric intensivists to tailor treatments to each patient’s hemodynamic profile, ultimately enhancing clinical outcomes. The combination of ultrasound and hemodynamic evaluation opens new possibilities for precision fluid management in the resuscitation of critically ill pediatric patients.