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Mixture formation process in internal combustion engines is known as a very effective process on transient response, output power, emissions and engine performance. A homogenous mixture of fuel and air will increase engine performance. To achieve a homogenous mixture, we should increase the fuel evaporation rate. As a result, the fuel mass in liquid phase and fuel film will decrease and their disadvantageous effects will be minimized. With a given mass of fuel if the value of fuel mass in vapor phase is increased, the quality of mixture and hence the engine performance will improve due to better combustion. In this paper, the effects of injection parameters such as fuel spray velocity and targeting angle on fuel evaporation rate in a port fuel injection engine using Kiva-3V simulation code are studied. The results have shown that with a given mass of fuel, increasing the spray velocity will increase the evaporation rate and hence the mass of fuel in vapor phase will be increased. Furthermore, changing the spray targeting angle so that the fuel spray hits the hot areas of the intake valve, such as valve stem and rear surface of the intake valve, will increase the evaporation rate. Also results have shown that fuel spray velocity is more effective than fuel spray targeting angle on evaporation rate. Copyright © 2007 by ASME.

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Introduction: Cardiovascular diseases are a common chronic illness in adults, with implications for health and psychological well-being. These implications not only affect the patients themselves but also impact family members, especially the spouses of patients. One significant issue and consequence of this disease is its impact on marital relationships and sexual satisfaction, which can also influence other dimensions of quality of life. The aim of the current study is to determine the effect of couple counseling based on the CHARMS model on sexual quality of life and marital satisfaction of wives of men suffering from myocardial infarction. Method: This study is a clinical randomized controlled trial. Sampling will be done on a convenience basis. Participants will be randomly allocated into two groups: control (50 couples) and intervention (50 couples). Couples in 6 groups of 8 members each will attend counseling sessions based con the CHARMS model, with sessions held weekly and lasting for 60 min. Data collection tools will include Demographic information questionnaire, Women’s Sexual Quality of Life Questionnaire, Enrich Marital Satisfaction Questionnaire, Sexual Compatibility Questionnaire and Perceived Quality of Relationship Dimensions Questionnaire, which will be completed by women in both groups before and after the intervention. Data will be analyzed using appropriate statistical tests and SPSS software. Discussion: This trial will evaluate whether a counseling intervention based on the CHARMS model can enhance sexual quality of life and marital satisfaction of wives of men with myocardial infarction in Urmia city. Trial registration: IRCT code: IRCT20240218061046N1 © The Author(s) 2024.

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Background: Papillary thyroid carcinoma (PTC), being the most common thyroid malignancy, is a slow-growing tumor and is usually limited to the thyroid. Extra thyroid extension is uncommon; besides, invasion to the vasculature seems to be extremely rare and usually indicates aggressive nature of the disease. Case presentation: We present a case of a 40-year-old lady who referred with a palpable neck mass a month after total thyroidectomy which its histopathologic examination revealed follicular variant of PTC; the same variant as prior thyroidectomy. Preoperative ultrasonography failed to comment on the intravascular component of the mass. Surgical procedure confirmed a mass attaching and infiltrating to the internal jugular vein, which turned out to be persistent disease. Conclusions: Awareness of this entity is important for surgeons, oncologists and radiologist as it can influence patient management. © The Author(s) 2024.

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Objective: Considering the importance of endometriosis and its relatively high prevalence among women, this study sought to investigate clinical and Transrectal and transvaginal ultrasounds (TVS) findings of disease. Methods: This descriptive-analytical study was performed based on medical records of 155 women with endometriosis admitted to Rasool-e Akram Hospital in Tehran for a TVS. All the sonography data and patients’ information were collected into checklists and analyzed in SPSS-25 software (IBM). Results: The mean age of participants was 32.4 ± 6.1 years, ranging from 18 to 50 years. Endometrioma was prevalent in 129 patients (84.8%). Size of endometrioma (diameter) was more than 3 cm in 79.9% of patients, and 3 cm or fewer in 20.1% of cases. Bladder, intestinal, vaginal, and rectosigmoid involvements with endometriosis implants were observed in 4 (2.6%), 54 (35.5), 3 (0.2%), and 51 (33.5) of patients, respectively. A total of 64.5% of patients were diagnosed with incomplete stenosis of the Douglas pouch and 35.5% had complete stenosis. Deep infiltrating endometriosis (DIE) was less than 1 cm in 20.7%, 1 to 3 cm in 42.3%, and over 3 cm in 37% of patients. The most common manifestations of endometriosis Obliteration of the Douglas pouch, endometrioma, and DIE. In addition, imaging modalities have shown promising results, indicating the necessity to use transvaginal ultrasound as the first line of diagnosis in patients with clinically suspected endometriosis. © The Author(s) 2024.

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Background: Diabetes during pregnancy has negative effects on both mothers and their fetuses. To improve perinatal outcomes and women’s experience of care, the World Health Organization (WHO) suggests implementing health system interventions to enhance the use and quality of antenatal care. The main goal of this study is to implement and evaluate the outcomes of the Centering Pregnancy group care model for pregnant women with diabetes. Methods/design: The study will consist of three phases: a quantitative phase, a qualitative phase, and a mixed phase. In the quantitative phase, a randomized controlled trial will be conducted on 100 pregnant women with diabetes receiving prenatal care in Tabriz City, Iran. The Summary of Diabetes Self-Care Activities (SDSCA) questionnaire will also be validated in this phase. The qualitative phase will use qualitative content analysis with in-depth and semi-structured individual interviews to explore pregnant women’s understanding of the impact of the Centering Pregnancy group care model on their care process. The mixed phase will focus on the degree and extent of convergence between quantitative and qualitative data. Discussion: The implementation of the Centering Pregnancy group care approach is anticipated to empower women in effectively managing their diabetes during pregnancy, resulting in improved outcomes for both mothers and newborns. Furthermore, adopting this approach has the potential to alleviate the financial burden of diabetes on healthcare system. Trial registration: Iranian Registry of Clinical Trials (IRCT): (IRCT20120718010324N80/ Date of registration: 2024-01-03). URL: https://irct.behdasht.gov.ir/trial/74206. © The Author(s) 2024.

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The ability to respond to varying environments is crucial for sessile organisms such as plants. The amphibious plant Rorippa aquatica exhibits a striking type of phenotypic plasticity known as heterophylly, a phenomenon in which leaf form is altered in response to environmental factors. However, the underlying molecular mechanisms of heterophylly are yet to be fully understood. To uncover the genetic basis and analyze the evolutionary processes driving heterophylly in R. aquatica, we assembled the chromosome-level genome of the species. Comparative chromosome painting and chromosomal genomics revealed that allopolyploidization and subsequent post-polyploid descending dysploidy occurred during the speciation of R. aquatica. Based on the obtained genomic data, the transcriptome analyses revealed that ethylene signaling plays a central role in regulating heterophylly under submerged conditions, with blue light signaling acting as an attenuator of ethylene signal. The assembled R. aquatica reference genome provides insights into the molecular mechanisms and evolution of heterophylly. © The Author(s) 2024.

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Background and purpose: The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses. Methods: A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values. Results: Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance. Conclusion: This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes. © The Author(s) 2024.

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Periodontitis, a chronic inflammatory condition caused by bacteria, often causes gradual destruction of the components that support teeth, such as the alveolar bone, cementum, periodontal ligament, and gingiva. This ultimately results in teeth becoming loose and eventually falling out. Timely identification has a crucial role in preventing and controlling its progression. Clinical measures are used to diagnose periodontitis. However, now, there is a hunt for alternative diagnostic and monitoring methods due to the progress of technology. Various biomarkers have been assessed using multiple bodily fluids as sample sources. Furthermore, conventional periodontal categorization factors do not provide significant insights into the present disease activity, severity and amount of tissue damage, future development, and responsiveness to treatment. In recent times, there has been a growing utilization of nanoparticle (NP)-based detection strategies to create quick and efficient detection assays. Every single one of these platforms leverages the distinct characteristics of NPs to identify periodontitis. Plasmonic NPs include metal NPs, quantum dots (QDs), carbon base NPs, and nanozymes, exceptionally potent light absorbers and scatterers. These find application in labeling, surface-enhanced spectroscopy, and color-changing sensors. Fluorescent NPs function as photostable and sensitive instruments capable of labeling various biological targets. This article presents a comprehensive summary of the latest developments in the effective utilization of various NPs to detect periodontitis. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

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In the study of the non-relativistic interaction between high-intensity femtosecond laser pulses and atoms, the influence of the magnetic field is commonly overlooked. This work investigates the effects of the magnetic field in the high-intensity few-cycle laser pulses with non-relativistic intensity of 3.5×10¹⁴W/cm² at the center wavelength of 800 nm on the high-order harmonic generation (HHG), attosecond pulse train (APT), isolated attosecond pulse (IAP), and the electron trajectory in the hydrogen atom, employing the numerical solution of the time-dependent Schrödinger equation in three dimensions (3D-TDSE). Two polarizations, linear and circular, are considered. A comparison with the scenario where the magnetic field is not considered shows that the magnetic field can apply significant corrections to the results. Particularly, considering the magnetic field for circular polarization can make the cutoff frequency of HHG coincide with the semi-classical relationship of ħω_c=I_p+3.17U_p, a case that for circular polarization does not exist without considering the magnetic field. Moreover, accounting for the magnetic field leads to a reduction in the attosecond pulse duration for circular polarization for APT (360as versus 241as) and for IAP (834as versus 602as). Additionally, the difference in production efficiency of HHG and APT between linear and circular polarization is reduced by two orders of magnitude, when magnetic field is considered. Although considering the magnetic field complicates the electron trajectory, especially for circular polarization, however, our quantum model provides enhanced insight into how the interaction works, especially when and where the electron collides with the parent nucleus. In this case, the quantum mechanical modeling largely covers the huge difference of not considering the magnetic field in the results predicted by other works. © The Author(s) 2024.

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Background & aim: Cardiovascular disease (CVD) is the most important cause of death in the world and has a potential impact on health care costs, this study aimed to evaluate the performance of machine learning survival models and determine the optimum model for predicting CVD-related mortality. Method: In this study, the research population was all participants in Tehran Lipid and Glucose Study (TLGS) aged over 30 years. We used the Gradient Boosting model (GBM), Support Vector Machine (SVM), Super Learner (SL), and Cox proportional hazard (Cox-PH) models to predict the CVD-related mortality using 26 features. The dataset was randomly divided into training (80%) and testing (20%). To evaluate the performance of the methods, we used the Brier Score (BS), Prediction Error (PE), Concordance Index (C-index), and time-dependent Area Under the Curve (TD-AUC) criteria. Four different clinical models were also performed to improve the performance of the methods. Results: Out of 9258 participants with a mean age of (SD; range) 43.74 (15.51; 20–91), 56.60% were female. The CVD death proportion was 2.5% (228 participants). The death proportion was significantly higher in men (67.98% M, 32.02% F). Based on predefined selection criteria, the SL method has the best performance in predicting CVD-related mortality (TD-AUC > 93.50%). Among the machine learning (ML) methods, The SVM has the worst performance (TD-AUC = 90.13%). According to the relative effect, age, fasting blood sugar, systolic blood pressure, smoking, taking aspirin, diastolic blood pressure, Type 2 diabetes mellitus, hip circumference, body mss index (BMI), and triglyceride were identified as the most influential variables in predicting CVD-related mortality. Conclusion: According to the results of our study, compared to the Cox-PH model, Machine Learning models showed promising and sometimes better performance in predicting CVD-related mortality. This finding is based on the analysis of a large and diverse urban population from Tehran, Iran. © The Author(s) 2024.

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The biodiversity of an ecosystem is greatly influenced by the spatio-temporal pattern of the landscape. Understanding how landscape type affects habitat quality (HQ) is important for maintaining environmental and ecological sustainability, preserving biodiversity, and guaranteeing ecological health. This research examined the relationship between the HQ and landscape pattern. The study presented an interpretation of the biodiversity variation associated with the landscape pattern in the Zayanderud Dam watershed area by integrating the Land Change Modeler and the InVEST model. Landsat images and maximum likelihood classification were used to analyze the spatio-temporal characteristics of the landscape pattern in 1991 and 2021. The future landscape pattern in 2051 was simulated using a Land Change Modeler. Subsequently, the InVEST model and the landscape maps were used to identify the spatial distribution of HQ and its changes over three periods. The mean values of the HQ in the study area were 0.601, 0.489, and 0.391, respectively, demonstrating a decreasing trend. The effect of landscape pattern change on HQ was also assessed based on landscape metrics, including PD, NP, SHDI, and CONTAG. HQ had a significant positive correlation with the CONTAG parameter (R = 0.78). Additionally, it had a significant inverse correlation with NP (R = − 0.83), PD (R = − 0.61), and SHDI (R = − 0.42). The results showed that the habitats in the northern region had lower quality compared to those in the southern parts of the Zayanderud Dam watershed. The density, diversity, and connectivity of landscape patches significantly influence the HQ in the study area. This research has the potential to enhance understanding of the impacts of land change patterns on biodiversity and establish a scientific basis for the conservation of natural habitats. Additionally, it can facilitate efficient decision-making and planning related to biodiversity conservation and landscape management. © The Author(s) 2024.

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Background: Exosomes derived from tumor cells contribute to the pathogenesis of cancers. Metformin, the most usually used drug for type 2 diabetes, has been frequently investigated for anticancer effects. Here, we examined whether metformin affects exosomes signaling in human ovary cancer cells in vitro. Methods: Human ovary cancer cells, including A2780 and Skov3 cells, were treated with metformin for either 24–48 h. Cell viability and caspase-3 activity were determined by MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) and colorimetric assays respectively. Oil-Red-O staining and in vitro, scratch assays were used to examine cellular toxicity and wound healing rate. After treatment with metformin, exosomes were isolated from cells and quantified by acetylcholinesterase (AChE) assay, Dynamic Light Scattering (DLS), and their markers. Genes related to exosomes signaling were analyzed by real-time PCR or western blotting. Results: Our results showed that metformin decreased the viability of both cells dose/time-dependently (P < 0.05). Metformin increased the activity of caspase-3 (P < 0.05) as well as the number of Oil-Red-O positive cells in both cell lines. In vitro scratch assay showed that the cell migration rate of metformin-treated cells was decreased (P < 0.05), whereas AChE activity of exosomes from metformin-treated cells was increased (P < 0.05). Concurrent with an increase in CD63 protein levels, expression of Alix, CD63, CD81, Lamp-2, and Rab27b up-regulated in treated cells (P < 0.05). Conclusion: Results indicated that metformin had a cytotoxic effect on ovary cancer cells and enhanced exosome biogenesis and secretion. © The Author(s) 2024.

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In this, a three-component reaction for the preparation of novel tetrazolo[1,5-a]pyrimidine-6-carboxamide derivatives from N,N′-(sulfonylbis(1,4-phenylene))bis(3-oxobutanamide), aldehydes and 1H-tetrazol-5-amine is reported. The application of Fe₃O₄@SiO₂-(PP)(HSO₄)₂ (A) as a catalyst afforded the desired products (a₁–a₁₈) in high yields in DMF as solvent as well as under solvent-free conditions. © The Author(s) 2024.

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Correction to:J Health Popul Nutr43, 19 (2024). https://doi.org/10.1186/s41043-023-00489-w. Following publication of the original article [1], the authors identified an error in the author name of Mobina Zeinalabedini. The incorrect author name is: Zeinolabedin Amini Sabegh. The correct author name is: Mobina Zeinalabedini. The author group has been updated above and the original article [1] has been corrected. © The Author(s) 2024.

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A simple and inexpensive microwave measurement setup based on measurements of magnitudes of transmission properties (|S₂₁|_dB) is proposed for industrial-based microwave aquametry (moisture or water content) applications. An easy-to-apply calibration procedure based on normalization is implemented to eliminate systematic errors in the measurement system. As a case study, we applied this setup for the quantification of water-adulteration in flower honey. After validating this system by distilled water and pure flower honey measurements, |S₂₁|_dB measurements of the pure flower honey with various adulteration percentages (δ) up to 9% are conducted to examine the performance of the measurement setup for quantification of water adulteration. A multi-dimensional fitting procedure is implemented to predict δ using the proposed inexpensive microwave measurement setup. It is shown that it is possible to quantify an adulteration level with an accuracy better than ∓1% by the proposed measurement setup and the applied multi-dimensional fitting procedure. © The Author(s) 2024.

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The design and optimization of an electromagnetic wave absorber for far-field wireless power transmission (WPT) is the subject of this research study. The goal of the research is to effectively absorb energy from ambient RF electromagnetic waves without the usage of a ground plane by employing metasurfaces with chiral components.By integrating trioidal moments into the design theory, the objective is to create a metasurface that functions in two frequency bands and produces high-quality resonance. The study also explores the dual non-homogeneity property of structures, polarization tensor coefficients, and the electromagnetic response of non-homogeneous metasurfaces. Based on the relative orientation of induced fields and moments, it delves deeper into the two basic possibilities for dual non-homogeneous elements. The development of chiral metasurfaces and the notion of electromagnetic chirality and its implications for polarization properties are introduced. © The Author(s) 2024.

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Introduction: This article examines the potential of using liquid biopsy with piRNAs to study cancer survival outcomes. While previous studies have explored the relationship between piRNA expression and cancer patient outcomes, a comprehensive investigation is still lacking. To address this gap, we conducted a systematic review and meta-analysis of existing literature. Methods: We searched major online databases up to February 2024 to identify articles reporting on the role of piRNA in cancer patient survival outcomes. Our meta-analysis used a random-effects model to pool hazard ratios with 95% confidence intervals (CI) and assess the prognostic value of deregulated piRNA-823. For survival analysis, the Kaplan–Meier method and COX analysis were used. Results: Out of 6104 articles screened, 20 met our inclusion criteria. Our analysis revealed that dysregulated piRNA expression is associated with cancer patient survival outcomes. Specifically, our meta-analysis found that overexpression of piR-823 is significantly linked with poorer overall survival in patients with colorectal cancer and renal cell cancer (HR: 3.82, 95% CI = [1.81, 8.04], I² = 70%). Conclusion: Our findings suggest that various piRNAs may play a role in cancer survival outcomes and that piRNA-823 in particular holds promise as a prognostic biomarker for multiple human cancers. Implications for cancer survivors: Our systematic review and meta-analysis of piRNA-823 has important implications for cancer survivors. Our findings suggest that piRNA-823 can be used as a prognostic biomarker for predicting cancer recurrence and survival rates. This information can help clinicians develop personalized treatment plans for cancer survivors, which can improve their quality of life and reduce the risk of recurrence. © The Author(s) 2024.