Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Evaluation of results of repair of patients undergoing tricuspid surgery in Shahid Rajaei Heart Hospital

If tricuspid regurgitation (primary and secondary) is left untreated, it will be associated with significant complications and death, even if left ventricular dysfunctions are corrected, TR does not decrease in many cases. Since many studies have not been conducted in this area, we decided to evaluate the results of tricuspid repair. In this retrospective study conducted since 2006 to 2011, 448 patients undergoing tricuspid surgery with different methods with or without surgery were studied. Statistical analysis was performed by Friedman, Fisher exact and Pearson chi-square methods. In the Mod and Severe RV dysfunction section, the mean age of the patients was 52 years, 289 were female (64%), 343 (76%) had rheumatic pathology, 79 (17%) had myxomatous pathology, 12 (2%) had endocarditis pathology, and 14 had an unknown pathology. Before surgery, Mod and Severe dysfunction was seen in 226 patients (40%) and after surgery (follow up), it was seen in 85 patients (19%). Before surgery, Mod and Severe TR was seen in 356 patients (79%) and after surgery, it was seen in 91 patients (20%). The mean of PAP before surgery was 54 mmHg and after surgery, it was 37 mmHg, which was significant in all cases (P <0.05).&nbsp

The Effect of Modified "Aggression Replacement Training" Program on Self-efficacy of Adolescents with Insulin-dependent Diabetes

Background: Self-efficacy is a crucial factor in controlling adolescents with insulin-dependent diabetes mellitus (IDDM). Subsequently the negative behavioral reactions such as aggression adversely affect on self-efficacy. Therefore, interventions are essential to reduce the aggression and to improve the self- efficacy in these patients. Aim: To determine the efficacy of the modified "aggression replacement training" program on self-efficacy of adolescents with insulin-dependent diabetes. Methods: In this clinical trial, 70 adult subjects with IDDM who were referred to Parsian Diabetes clinic of Mashhad in 2014 were divided into two groups of intervention and control. The intervention program, including three aspects including: anger control training, social skills training and moral reasoning training was performed in five sessions, each 1.5-2 hours. A five-day interval was between the sessions and each group consisted of 8-10 individuals. The self-management standard questionnaire of “insulin-dependent diabetes management self-efficacy scale (IDMSE)” was filled before the intervention and two months afterwards. Data were analyzed using SPSS version 11.5 with paired and Independent t-tests. Results: In this study, 38.5 and 61.5 percent of the subjects were boys and girls, respectively with total mean age of 15.9±2. The self-efficacy of the subjects before the intervention was not significantly different within the groups (p=0/57). Nevertheless in post-intervention assessment, the self-efficacy of the Intervention group significantly increased (49.0±11.1) compared to the control group (33.7±5.5) (

Comparison of the Effects of Healthy Lifestyle Education Program Implemented by Peers and Community Health Nurses on the Quality of Life of Elderly Patients with Hypertension

Background: Considering the global rise in the elderly population and the common complications of this group (especially chronic diseases), significant attention is being paid to improving their quality of life (QOL). Aim: This study aimed to compare the effectiveness of a healthy lifestyle education program, implemented by peers and community health nurses in improving QOL among elderly patients with hypertension, who were referred to healthcare centers of Mashhad, Iran in 2014. Method: This experimental study was conducted on 60 elderly patients with hypertension, referring to healthcare centers of Mashhad, Iran in 2014. The subjects were selected via random cluster sampling. The control group does not receive any intervention at all, while the two other intervention groups received healthy lifestyle education by their peers or community health nurses for one month; the subjects were followed-up for one month after the intervention. Data were collected by the 36-item short-form health survey (SF-36) questionnaire and analyzed, using SPSS version 16.0. Results: No significant difference was observed between three groups in overall QOL score and its domains before the intervention (P=.91). After intervention, a statistically significant difference was observed in the overall QOL score between three groups (

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

<p>Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.</p&gt