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Knitted textiles are a popular reinforcement in polymer composites for their high drape properties and superior impact energy absorption, making them suitable for specific composite components. Nevertheless, limited attention has been paid to modeling the mechanical behavior of knitted fabric composites since knitted textiles generally offer lower stiffness and strength. This study presents a 3D finite element (FE) modeling of a precise geometrical model of weft-knitted carbon fiber thermoplastic composite to better understand its nonlinear mechanical behavior and interface damage mechanisms under tension. Toward this end, a representative volume element (RVE) of the weft-knitted fabric composite with periodic boundary conditions (PBCs) is generated based on actual dimensions. The validity of the textile RVE to represent the macroscopic behavior was evaluated prior to analyzing the composite. The effect of fiber tow/matrix debonding during tension on the mechanical behavior of the composite is investigated using the cohesive zone model (CZM). Finally, the predicted results of the mechanical behavior of the composite with and without considering the interface failure are compared with the experimental measurements. It is found that the fiber tow/matrix interfacial strength has a significant effect on the tensile performance of the knitted fabric composites, particularly when they are subjected to a large strain. According to the simulation results, the highest tensile performance of the composite is achieved when the interfacial debonding is prevented. However, considering the fiber/matrix debonding in the modeling is essential to achieve a good agreement with the experimental results. In addition, it is concluded that stretching the fabric before composite manufacturing can substantially increase the tensile stiffness of the knitted composite.

Persistent rural-urban disparities in broadband connectivity remain a major policy challenge, even in digitally advanced countries. This paper examines how these inequalities manifest in northern Finland and Sweden, where sparse populations, long distances, and seasonal variations in demand create persistent gaps in service quality and reliability. Drawing on survey data (n = 148), in-depth interviews, and spatial analysis, the study explores the lived experience of connectivity in Arctic rural communities and introduces a novel Cellular Coverage Inequality (CCI) Index. The index combines measures of rurality and network performance to quantify spatial disparities that are masked by national coverage statistics. Results reveal that headline indicators overstate inclusiveness, while local users report chronic connectivity gaps affecting work, safety, and access to services. Building on these findings, the paper outlines policy reflections in six areas: shared infrastructure and roaming frameworks, spectrum flexibility for rural operators, performance-based Quality-of-Service monitoring, standardized and transparent reporting, temporal and seasonal capacity management, and digital-skills initiatives. Together, these recommendations highlight the need for multidimensional metrics and governance mechanisms that link technical performance, spatial equity, and user experience. The analysis contributes to ongoing debates on how broadband policy in sparsely populated regions can move beyond nominal coverage targets toward genuine inclusion and reliability.

Background Acne is the most common skin disorder which is known as a chronic inflammatory disease with psychological burden and reduced quality of life. Adipose tissue-derived stromal vascular fraction (SVF) is recognized as a source of regenerative cells and improves the quality of skin by increasing collagen content. To date, a few studies have been performed on the therapeutic role of SVF in the treatment of acne scars. Methods This randomized, single-blinded clinical trial was performed on 7 patients with acne scars. In all patients, the initial grade of acne (volume, area and depth) was evaluated and ultrasound of the relevant scar was performed to evaluate neocollagenesis. As a spilt face study, for treating the scars, we used nanofat subcutaneously on one side of the face (control group) and combination of nanofat subcutaneously and SVF intradermally on the opposite side (intervention group). The patients were evaluated for severity of acne by visioface after one month, also for thickness of epidermis and dermis by ultrasound after one month and three months. Results All of the apparent findings of scars improved in two groups after one month, but these changes were significant just for the group treated with SVF ( p value < 0.05). Epidermal, dermal and complete thicknesses during the first month in both control and intervention groups were significantly increased ( p value < 0.05) but between the first and third months, there was no significant difference in the variables ( p value > 0.05). The findings showed that dermal and complete thicknesses of the skin in the first month were different between two groups significantly ( p value: 0.042 and 0.040, respectively). Conclusion The use of SVF in the treatment of patients with acne scars accelerates the improvement of volume, area and depth of the scar by increasing collagen content and the dermal thickness, so it can be used as a potentially effective treatment for these patients.

Background Dengue fever is a serious public health concern caused by the dengue virus (DENV). The first documented case of dengue fever in Iran occurred in 2008; however, only a few cases have been reported since then, all linked to imported infections. In 2024, Iran faced a dengue fever outbreak, though limited data are available on its scope and impact. Therefore, our study aimed to analyze the epidemiological patterns and clinical/paraclinical characteristics of patients affected by the 2024 dengue fever outbreak in Iran. Methods This cross-sectional study documented epidemiological characteristics, clinical manifestations, and disease outcomes of dengue fever patients. Healthcare providers collected data through interviews, adhering to the national guideline checklist. The diagnosis of dengue fever was established using rapid diagnostic tests, followed by further confirmatory tests. Throughout the illness, healthcare providers monitored patients, and their outcomes were documented in their medical records. Results A total of 1057 patients with a mean age of 34.83 ± 15.40 years were identified, with 64.7% male and 35.3% female. Of the cases, 853 were locally transmitted, while 196 were imported from other countries. Most cases were reported from Sistan and Baluchestan (85.4%), Fars (9.9%), and Hormozgan (3.2%) provinces. Additionally, a few sporadic cases were recorded in other provinces. The first case of dengue fever in this outbreak was reported on May 2, 2024. The number of cases reported in the fall (75.6%) was higher than those reported in the spring (14.0%) and summer (10.4%). Patients primarily experienced fever (98.1%), myalgia (94.9%), and headache (94.0%). Ultimately, the vast majority of patients recovered after symptomatic treatment, with only one death reported. Conclusions Understanding epidemiology and clinical manifestations of dengue fever, along with diagnostic laboratory tests, is crucial for its timely detection. Clinicians in endemic regions should be particularly attentive to these factors to facilitate rapid diagnosis and treatment, thereby preventing the spread of the disease. Additionally, health policymakers should prioritize preventive measures, such as implementing screening at border crossings and improving the environment to reduce mosquito populations.

The global obesity epidemic challenges health systems, driving people to seek metabolic and bariatric surgery (MBS), especially laparoscopic sleeve gastrectomy (LSG). Many MBS centers have limited resources for patient education, creating knowledge gaps that lead patients to search online. AI chatbots, such as ChatGPT, can provide reliable medical information, though concerns about accuracy and completeness remain. The study involved four fellowship-trained minimally invasive surgeons (MISs), nine fellows (MIFs), and two general practitioners (GPs) in the MBS multidisciplinary team from March 1, 2024, to March 30, 2024. Seven AI chatbots were selected, including ChatGPT 3.5 and 4, Bard, Bing, Claude, Llama, and Perplexity, based on their public availability on December 1, 2023. Forty patient questions regarding LSG were sourced from social media, MBS organizations, and online forums. Experts and chatbots answered these questions, with their responses evaluated for accuracy and comprehensiveness on a 5-point scale. Statistical analyses compared groups' performance. Chatbots demonstrated a higher overall performance score (2.55 ± 0.95) compared to the expert group (1.92 ± 1.32, < 0.001). Among chatbots, ChatGPT-4 achieved the highest performance (2.94 ± 0.24), while Llama had the lowest (2.15 ± 1.23). Expert group scores were highest for MISs (2.36 ± 1.09), followed by GPs (1.90 ± 1.36) and MIFs (1.75 ± 1.36). The readability of chatbot responses was assessed using Flesch-Kincaid scores, revealing that most responses required reading levels between the 11th grade and college level. Furthermore, chatbots exhibited fair reliability and reproducibility in response consistency, with ChatGPT-4 showing the highest test-retest reliability. AI chatbots generated accurate and comprehensive answers to common bariatric patient questions, suggesting promise as a scalable aid for patient education. However, readability often exceeds recommended levels, performance varies by model, occasional inaccuracies occur, and medicolegal considerations remain unresolved. Accordingly, chatbots should complement clinician counseling, and future work should improve readability and reliability and evaluate real-world safety and impact.

Air pollution has become a major issue in all major cities throughout the world. Predicting air pollution can help to mitigate its detrimental consequences. The purpose of this study is to develop equations using multivariate regression to predict the concentration of particulate matter smaller than 10 µm (PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and air quality index (AQI) in Yazd city, Iran. To this end, initially, the daily averages of air temperature, air pressure, wind speed, gust speed, precipitation, and humidity percentage of Yazd city between September 2020 and August 2021 were collected. Moreover, in the same period, the daily average concentrations of PM 10 , SO 2 , NO 2 , CO, and AQI of Yazd were collected. Then, by using multivariate regression, the relationships between meteorological parameters and air pollutants were investigated. Based on the results, seven different equations were developed to predict the concentrations of different air pollutants in different meteorological conditions. In addition, the results showed that the developed equations worked accurately in predicting the concentrations of O 3 , PM 10 , and NO 2 , but not very accurately in predicting the AQI, SO 2, and CO concentrations. More specifically, the most accurate equations belonged to PM 10 and NO 2 , which could predict the concentrations of these pollutants in the atmosphere of Yazd city with only 1% and 4% error, respectively. These equations provided a simple way to predict the concentration of important pollutants and AQI in Yazd city.

Background Data on quality-of-care indicators for breast cancer patients is limited in low—and middle-income countries. We evaluated the indicators in Iran. Method A total of 21 quality-of-care indicators of breast cancer management defined by EUSOMA 2017 were selected. The indicators were retrospectively evaluated based on the data from the Clinical Breast Cancer Registry established in 11 provinces of Iran. Result In the study of 6,293 patients evaluated on 21 indicators, 15 indicators were more than 5% below EUSOMA's standard levels. Conclusion The defined indicators had a value lower than the suggested standards by EUSOMA. This study's results highlight important clues for intervention in improving breast cancer outcomes in Iran and other low—and middle-income countries.

In this case report, we present a 43-year-old man (XY) with azoospermia and typical male appearance, at Tanner stage 5 of sexual development, who presented with severe colicky abdominal pain accompanied by nausea. A pelvic CT scan revealed a pear-shaped structure in the pelvic cavity, located entirely behind the bladder, measuring 106*44 cm with fluid accumulation inside it, extending into the right inguinal canal. There was also evidence suggesting the formation of the upper and mid-third part of a vagina, terminating in the prostatic gland. The patient underwent laparoscopic surgery for the removal of the uterus and the left gonad. The patient had a uterus with hematometra and a blind vaginal pouch measuring 4 centimeters at the end of the uterus, extending posteriorly behind the bladder to the apex of the prostate, containing old blood. Hormonal analysis showed serum estradiol < 5.0 pmol/L (11-44pg/mL), free testosterone at 1.57 ng/ mL(male reference range: 2.5–20 ng/mL), testosterone at 0.56 ng/mL (2.27–10.30),FSH at 44.8 mIU/L (0.95–11.95 mIU/L), LH at 20.4 mIU/L(0.57–12.07), and DHEA-SO4 at 199.0 µg/mL (139.7–484.4 µg/mL). Currently, the patient is under the care of a urologist and is receiving weekly treatment with hCG medication. He reports normal sexual function, including intercourse, orgasm, erection, and ejaculation.

This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML models, including XGBoost and random forest (RF), were evaluated alongside eight LLMs, such as GPT-4 and Mistral-7b, which performed zero-shot classification on text-converted structured data. Additionally, Mistral-7b was fine-tuned using the QLoRA approach. XGBoost and RF demonstrated superior performance among CMLs, achieving F1 scores of 0.87 and 0.83 for internal and external validation, respectively. GPT-4 led the LLM category with an F1 score of 0.43, while fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, yielding a stable F1 score of 0.74 during external validation. Although LLMs showed moderate performance in zero-shot classification, fine-tuning substantially enhanced their effectiveness, potentially bridging the gap with CML models. However, CMLs still outperformed LLMs in handling high-dimensional tabular data tasks. This study highlights the potential of both CMLs and fine-tuned LLMs in medical predictive modeling, while emphasizing the current superiority of CMLs for structured data analysis.

Background Through the antioxidant and anti-inflammation pathways, melatonin is proposed as a safe and effective intervention in neurological diseases. This study aims to evaluate the effects of melatonin supplementation on the neurobehavioral and clinical outcomes in animal models of multiple sclerosis (MS). Methods This study was conducted following the PRISMA statement. Animal studies that reported the effects of melatonin in preclinical MS models, including the experimental autoimmune encephalomyelitis (EAE) and cuprizone model for demyelination are included in this study. A systematic search in PubMed, Web of Science, Embase, and Scopus up was conducted in April 2023. The collaborative Approach to Meta-Analysis and Review of Animal Experimental Studies (CAMARADES) critical appraisal tool was used for the quality assessment of the studies and the quantitative synthetizes were conducted using the comprehensive meta-analysis software. Results Out of 542 studies, finally 21 studies, including 14 studies in the EAE model and 7 studies of the toxic demyelination method with cuprizone were included. The route of administration was intraperitoneal in 18 studies, oral in 2 studies, and subcutaneous in 1 study. The quantitative synthesis of the EAE clinical severity scale was associated with significant differences (standardized mean difference [SDM]: − 2.52; − 3.61 to − 1.42; p value < 0.01). In subgroup analyses, the difference was statistically significant in the mouse subgroup (SMD: − 2.60; − 3.74 to − 1.46; p value < 0.01). Discussion This study encountered that melatonin may be associated with improved behavioral and cognitive outcomes of preclinical models of MS with acceptable safety profiles. Funding The research was supported by the Student Research Committee, Tabriz University of Medical Sciences (grant number: 71005).