Day General Hospital

Islamic Republic of Iran has achieved high immunisation coverage for routine childhood vaccines; however, gaps remain in the scope and alignment with global immunisation priorities. To assess the status of childhood immunisation programme in Islamic Republic of Iran and identify gaps and challenges. We reviewed and analysed secondary data from published literature available on PubMed and Google Scholar until 3 September 2024, including official reports and WHO and UNICEF immunisation databases. We compared the results with those of selected Eastern Mediterranean Region countries and the global immunisation priorities. Immunisation coverage in Islamic Republic of Iran exceeded 99% for several childhood vaccines, supported by established programme infrastructure and domestic vaccine production. Gaps were identified in the inclusion of vaccines such as human papillomavirus, varicella and hepatitis A. The national immunisation programme aligns with several WHO vaccination strategies but with limited coverage beyond the childhood years. Challenges affecting coverage included financial constraints, fragmented data systems and vaccine hesitancy. Despite remarkable achievements, systemic challenges remain in the national immunisation programme of Islamic Republic of Iran. Strengthening life-course immunisation through targeted investment, policy updates and stronger surveillance will help align the programme with the global immunisation goals.

Abstract Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for accurate and scalable predictive models to support early diagnosis and clinical decision-making. In this study, we investigate the effectiveness of machine learning approaches for heart disease prediction using a large, publicly available clinical dataset obtained from IEEE DataPort. The dataset integrates multiple established heart disease cohorts and includes key demographic and clinical variables relevant to cardiovascular risk assessment. Two supervised machine learning models—Random Forest and Support Vector Machine (SVM)—were developed and evaluated to classify the presence of heart disease. Model performance was assessed using accuracy, precision, and recall metrics. The Random Forest model achieved an accuracy of 0.89, with a precision of 0.85 and a recall of 0.83, demonstrating strong overall performance and balanced predictive capability. The SVM model achieved an accuracy of 0.88, with a recall of 0.85 and a precision of 0.81, indicating robust sensitivity in identifying positive cases.

Objectives This study aimed to assess the color stability of a calcium silicate‐based cement containing bismuth oxide (OrthoMTA) following addition of zinc oxide (ZnO) in the presence of blood and sodium hypochlorite (NaOCl). Materials and Methods For this study, eight polymethyl methacrylate (PMMA) blocks, measuring 109 mm × 88 mm × 4 mm, were prepared. In each block, 12 holes (6 mm in diameter and 4 mm in depth) were created, yielding a total of 96 molds. OrthoMTA was mixed with distilled water and placed into 48 molds, while the remaining molds were filled with mineral trioxide aggregate (MTA) that had been mixed with 5 wt% ZnO powder. After setting, the color parameters were measured with a spectrophotometer. Then each group of specimens ( n = 48) were randomly assigned to four subgroups for exposure to blood, 2.5% NaOCl, 5.25% NaOCl, or saline ( n = 12). Subsequent color measurements were carried out, and the data were analyzed using one‐way and two‐way ANOVA, and independent t ‐tests (alpha = 0.05). Results The addition of ZnO to OrthoMTA significantly reduced the color change (∆ E ) in specimens exposed to blood, 2.5% NaOCl, and 5.25% NaOCl ( p < 0.05), while no significant color change was observed in the saline subgroups ( p > 0.05). The saline subgroups demonstrated significantly lower ∆ E values than all other experimental subgroups ( p < 0.05), irrespective of ZnO addition. The blood subgroups exhibited the second lowest ∆ E values among all groups, which were significantly lower than in both NaOCl subgroups, regardless of ZnO addition ( p < 0.05). Conclusion Within the limitations of this in vitro study, the results indicate that the addition of 5 wt% ZnO powder to OrthoMTA reduced cement discoloration induced by exposure to NaOCl and blood.

ABSTRACTObjectivesThis in vitro experimental study aimed to evaluate the effects of laboratory contamination of implant abutment screws and connection surfaces on reverse torque values (RTVs), as an indicator of screw preload loss.Material and MethodsForty‐five Dentis One Q implants and 45 CCM UCLA abutments were randomly assigned into three groups (n = 15 per group). Group 1 (control) involved uncontaminated abutments and screws with no restorations. Group 2 (screw contamination) used new abutments attached with screws contaminated by laboratory materials (porcelain powder, metal debris, and polishing paste). Group 3 (connection contamination) included screw‐retained restorations fabricated under contaminated conditions and attached using new screws. All samples were subjected to standardized torque (250 N·cm), thermocycling (1500 cycles between 5°C and 55°C), and subsequent RTV measurement. One‐way ANOVA and Tukey's post hoc tests were used for statistical analysis (α = 0.05).ResultsMean RTVs (SD) were 218 (0.15) N·cm (control), 181 (0.14) N·cm (screw contamination), and 207 (0.11) N·cm (connection contamination). RTVs in the screw contamination group were significantly lower than both the control (p < 0.001) and connection contamination groups (p < 0.001). The difference between the control and connection contamination groups was not statistically significant (p = 0.08).ConclusionsLaboratory contamination of implant components can significantly reduce reverse torque values, particularly in cases of screw contamination, indicating an increased risk of screw loosening. Contamination control during prosthetic procedures is essential to maintaining implant stability.

Abstract Heart failure is a significant global health challenge, requiring effective and early diagnostic tools to improve patient outcomes. In this study, we developed a predictive model for heart failure using Support Vector Machines (SVM), leveraging clinical data from 299 patients. The dataset includes key features such as age, ejection fraction, serum sodium levels, and comorbidities like diabetes and high blood pressure. Our SVM model demonstrated exceptional predictive performance, achieving a training accuracy of 99.7% and a testing accuracy of 99.1%.

Background: Prophylactic antibiotics can reduce post-surgical infection rates, but their improper use in surgery remains a significant concern. This misuse leads to adverse drug reactions, increased bacterial resistance, and unnecessary hospital costs. Objectives: This study aimed to examine and compare prophylactic antibiotic prescribing practices in general surgery departments at two Tehran university hospitals, in relation to established guidelines. Methods: A cross-sectional study was conducted on 194 surgical patients. Data collection included demographic details, surgery type, and antibiotic prophylaxis protocols, covering medication type, dosage, pre-surgical timing, and prophylaxis duration. Results: The study included 27.8% clean surgeries and 72.2% clean-contaminated surgeries. Cefazolin was the primary antibiotic prescribed. According to the American Society of Health-System Pharmacists (ASHP) treatment guidelines, 10% of patients received antibiotics without proper indication. Among the 90 cases requiring antibiotic prophylaxis, appropriate antibiotic selection occurred in 39.0% of cases at Rasul Akram Hospital and 41.5% at Firouzgar Hospital. In these cases, errors were noted in dosing (79.8%), pre-operative timing (97.4%), and administration protocol (100%). Only 33% of cases followed the optimal prophylaxis duration. Conclusions: These hospitals require an evidence-based antibiotic prescription program that adheres to clinical guidelines and includes close monitoring of implementation. Regular training and review of clinical guidelines are essential for all healthcare professionals.

The online version contains supplementary material available at 10.1007/s12070-024-05172-y.

Overview: Cancer remains a significant global health challenge, accounting for one in eight deaths worldwide. Chemotherapy, the primary cancer treatment, is often limited by drug resistance. Optimizing therapy procedures has been suggested to improve survival and quality of life for cancer patients, including those with drug resistance. In recent years, nanotechnology has garnered significant attention for its potential in cancer treatment. Nanoparticles (NPs) are widely utilized to enhance therapeutic outcomes by improving drug bioavailability, solubility, and retention time. NPs offer essential conditions for targeted drug delivery through various delivery systems, including microcapsules or NPs. Polybutyl cyanoacrylate (PBCA) is among the most commonly used carriers for drug delivery in cancer treatment, with several studies investigating its efficacy. Methods: This article aims to review the effectiveness of PBCA as a drug delivery system in the treatment of various cancers. Results: Several studies have utilized PBCA for drug delivery in cancer treatment, indicating its potential efficacy in this regard. Conclusion: The review highlights the potential of PBCA as a promising drug delivery system for the treatment of different types of cancer.

Objective: Irisin is a myokine that has a beneficial effect on obesity by increasing energy expenditure. The aim of this study was to compare the effect of exercise intensity on serum irisin levels and lipid profile in obese adolescents. Materials and Methods: Forty-six male students with an average age of 13.43 ± 0.77 years; height 163.18 ± 23 cm, weight 85.84 ± 8.46 kg, body fat percentage 37.001 ± 4.508 were randomly assigned to three groups: low-intensity activity (experimental 1), high-intensity activity (experimental 2), and control. The training program in experimental group 1 included running at 45-60% heart rate reserve intensity, and in experimental group 2, it was similar to group 1 along with combined exercises at 60-75% heart rate reserve intensity, performed 4 sessions per week for 10 weeks. A paired t-test was used to examine within-group differences, and Mixed ANOVA was used to examine between-group differences. All tests were conducted at a significance level of 0.05 using SPSS22 software. Results: The findings indicated a significant decrease in serum irisin levels in both experimental groups 1 and 2 (p &lt; 0.001). Additionally, weight, body fat percentage, and low-density lipoprotein significantly decreased in both experimental groups. Cholesterol and LDL/HDL ratio only significantly decreased in the high-intensity group. Conclusion: Considering the impact of exercise at both intensities on weight, body fat percentage, and irisin levels, regular physical activity is recommended for adolescents.