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At the beginning of the development of meta-analysis, understanding the role of moderators was given the highest priority, with meta-regression provided as a method for achieving this goal. Yet in current practice, meta-regression is not as commonly used as anticipated. This paper seeks to understand this mismatch by reviewing the history of meta-regression methods over the past 40 years. We divide this time span into four periods and examine three types of methodological developments within each period: technical, conceptual, and practical. Our focus is broad and includes development of methods in the fields of education, psychology, and medicine. We conclude the paper with a discussion of five consensus points, as well as open questions and areas of research for the future.

Abstract Machine learning can be used to identify risk factors associated with graft rejection after corneal transplantation for keratoconus. The study included all keratoconus eyes that underwent primary corneal transplantation from 1994 to 2021. Data relating to the recipient, donor, surgery, and postoperative course that might be associated with the occurrence of a graft rejection reaction were compiled. This study used five supervised learning algorithms including artificial neural network, support vector machine, gradient boosting, extra trees classifier, and random survival forests to select the most predictive factors for graft rejection. A total of 1214 consecutive eyes of 985 keratoconus patients were included in the study, and the technique of keratoplasty included penetrating keratoplasty in 574 eyes (47.3%) and deep anterior lamellar keratoplasty in 640 eyes (52.7%). The overall prevalence of first graft rejection was 28.1%. All five models had similar ability in identifying predictive factors for corneal graft rejection. Technique of keratoplasty was associated with an increased risk of graft rejection in all models. Other identified risk factors included patient age, keratoplasty in the fellow eye, donor age, graft endothelial cell density, duration of corticosteroid application, time from keratoplasty to complete suture removal, and suture-associated complications. It is advisable that in the absence of any contraindication, post-transplant keratoconus eyes receive a low dose topical corticosteroid until all sutures are removed.

Background and aims: The epidemiological skills laboratory (ESL) course has been incorporated into the curriculum for Master of Science students in epidemiology. However, it has not been implemented and evaluated in Iran so far. The present study was conducted to compare two teaching methods and the implementation of an ESL lesson. Methods: All MSc students (N=9) in epidemiology at Shahrekord University of Medical Sciences were included in the study during two academic semesters in 2019-2020 using a census sampling method. Three levels of Kirkpatrick’s pyramid were used to evaluate direct teaching and electronic flipped classroom (FC) groups. The Chi-square test, Fisher’s exact test, paired t-test, and independent t-test were used in Stata software with a significance level

Abstract In this research, a collection of new composites constructed from aminoclays (MgAC and ZrAC) and MOFs are inserted into the PEBAX matrix for gas separation inspections. In fabricated UiO-66-(COOH)2@CuBDC@MgAC and MIL-121@CuBDC@MgAC composites series, amine groups of MgAC, unoccupied Cu ions of CuBDC and free carboxylic acid in the pores of UiO-66-(COOH)2 and MIL-121 are intensely involved in superior CO2 permeability of prepared MMMs from these composites. Besides, the nanosheet formation of CuBDC MOF induced by MgAC sheets develops the interphase regions (MOF-MOF and PEBAX-composites) which are also responsible for such obtained CO2 permeability against N2. Additionally, lower pore diameter of CuBDC in comparison with UiO-66-(COOH)2 and MIL-121 can exert sieving effect for crowded gas molecules which pass through the UiO-66-(COOH)2 and MIL-121 layers. In these series, PEBAX/Zr@Cu@Mg-4 demonstrates CO2 permeability of 320.15 barrer and CO2/N2 selectivity of 147.27. A new aminoclay based on zirconium is synthesized and added to the constructed composites as the outer layer. The provided ZrAC@UiO-66-(COOH)2@CuBDC@MgAC and ZrAC@MIL-121@CuBDC@MgAC composites are also examined as fillers in PEBAX matrix. Apparently, the interphases of MMMs are gotten close to their ideal states by ZrAC sheets existence in composites and gas behavior of all MMMs are ameliorated compared to the last MMMs series. In this regard, PEBAX/Zr@Zr@Cu@Mg-4 exhibits CO2 permeability of 407.77 barrer and CO2/N2 selectivity of 87.46. The fabricated composites and membranes are identified by FT-IR, XRD, SEM and HRTEM. The membranes are also characterized by TGA, surface contact angle and Stress-Strain analysis.

Abstract Alzheimer’s Disease (AD), a neurodegenerative disorder escalating worldwide, remains incurable with existing interventions merely mitigating symptoms. Hydralazine, an antihypertensive agent, has displayed neuroprotective potential in AD animal models via amplification of mitochondrial functionality and stimulation of stress management and repair pathways. Nevertheless, its effectiveness and tolerability in human AD cohorts are yet to be confirmed. This study protocol describes the design of an ongoing, single-center, randomized, triple-blind, placebo-controlled trial to assess hydralazine’s effects on cognitive function in mild to moderate -stage AD patients. We will enroll 424 patients aged 50 and older, meeting NINCDS-ADRDA criteria for probable AD with Mini-Mental State Examination scores from 12–26. They’ll be randomly assigned to receive either hydralazine HCL (25 mg, thrice daily) or a placebo for 12 months. The primary outcome is the Alzheimer’s Disease Assessment Scale change from baseline to 12 months. Secondary outcomes include various measures using Lawton instrumental activities of daily living scale, neuropsychiatry inventory, and caregiver activity survey. This trial will explore the potential benefits and risks of hydralazine in mild to moderate AD treatment. It’s the first trial examining hydralazine’s impact on mild to moderate -stage AD in human and is registered at the Iranian Registry of Clinical Trials (IRCT20200711048075N1, registered 29/07/2020) and ClinicalTrials.gov (NCT 04,842,552AQ, registered 13/04/2021). Ethics approval was granted by the Research Ethics Committee of the National Institute for Medical Research Development (IR.NIMAD.REC.1398.424), following the SPIRIT Statement guidelines. Findings will be disseminated via peer-reviewed publications and conferences. This inaugural human clinical trial evaluates hydralazine’s impact on patients in the mild to moderate AD. Executed with a randomized, triple-blind, placebo-controlled methodology, this study incorporates a significant sample size and an extended monitoring duration. Multiple parameters, including cognitive capabilities, will be assessed. Potential limitations include the inherent homogeneity of the AD cohort, the lack of biomarker assays, and the unpredictable progression of the disease. Notably, the study might not elucidate the protracted effects of hydralazine beyond a 12-month period. Another limitation of our clinical trial is that patients were diagnosed with Alzheimer’s disease based solely on clinical evaluation and MRI findings, without the inclusion of specific biomarkers, which may impact the accuracy and specificity of the diagnosis. Trial registration: Iranian Registry of Clinical Trials (IRCT20200711048075N1, registered 29/07/2020) and ClinicalTrials.gov (NCT 04,842,552, registered 13/04/2021).

Abstract Cyclooxygenase (COX) and lipoxygenase (LOX) enzymes play a pivotal role in producing pro-inflammatory eicosanoids, including prostaglandins (PGs) and leukotrienes (LTs), in the inflammation process. Mitragynine is a primary alkaloid contained in the kratom’s leaves and has been reported to show anti-inflammatory activity by suppressing COX-2 mRNA translation to lowering PGs synthesis. In this study, the Kratom’s alkaloid extract containing ~ 46% mitragynine was found to exhibit dual inhibition activity towards COX-2/5-LOX enzymes at concentrations below 25 ppm in the LPS-induced RAW 264.7 macrophage cells. At these levels, no cell toxicity was observed while the cells became death (e.g., 10–46% viability at 50–100 ppm) and only COX-2 inhibition activity was observed after exposed with more than 25 ppm of alkaloid extract. In contrast, the methanolic-crude extract of Kratom’s leaf containing ~ 5% mitragynine showed no inhibition toward COX-2/5-LOX enzymes and did not toxic onto the cells, even after treated at 100 ppm. The alkaloid extract suppressed several antiinflammation parameters, including ROS (64% reduction at 25 ppm), NO (30% reduction at 25 ppm), TNF-α (~ 50% reduction at 25 ppm), and IL-6 production (60% reduction at 6.25 ppm). In silico molecular studies indicated strong binding affinity of Kratom alkaloids to COX-2 and 5-LOX active sites, supporting the Kratom’s alkaloids to have great potential dual inhibition activity towards COX-2/5-LOX enzymes and to be developed as a safer NSAIDs with fewer side effects.

Abstract Introduction Femoral shaft nonunion can be extremely vexing and result in significant morbidity. We aimed to evaluate the outcomes of patients with nonunion of the femoral shaft fracture undergoing double plating and bone grafts from the iliac crest and femoral canal. Methods This retrospective study included 44 patients with femoral shaft nonunion between March 2020 to March 2022. Patients underwent dual plating, bone grafting from the iliac crest, and the utilization of the femoral canal. Age, sex, body mass index (BMI), the two surgical interventions interval, union time after the second surgery, hip range of motion (ROM), limb length discrepancy (LLD), infections, and deep vein thrombosis (DVT) were evaluated. Results Patients comprised 21 males (47.7%) and 23 females (52.3%), with a mean age of 42.3 ± 15.2 years. The mean BMI was 26.7 ± 3.77 kg/m2. The mean duration between the two surgical interventions for the patients was 17.07 ± 6.6 months. The patients were followed for ≥ 12 months. All patients achieved successful union and fracture healing, with an average time of 5 months. The mean hip flexion and extension were 112.84 ± 7.7˚ and 14.8 ± 5.2˚ degrees, respectively. Thirteen patients showed LLD after the first surgery, with a mean LLD of 7.15 ± 5.04 millimeters. LLDs were successfully rectified in all patients following the surgical intervention. No patients experienced postoperative infections or DVT. Conclusion According to our findings, the utilization of double-plate fixation in combination with iliac crest and femoral canal bone graft has proven to be a secure, productive, and straightforward surgical alternative for the management of femoral nonunion.

Abstract Objective A promising strategy to increase population physical activity is through promotion of dog walking. Informed by multi-process action control and nascent dog-walking theory, we examined the effectiveness of a 3-month technology-based (dog tracker) 2-arm randomised controlled dog-walking intervention to increase dog-owner daily physical activity in the general community in Sydney, Australia. Results 37 participants were allocated to the intervention group (mean age = 43.2 [SD 11.9]) and 40 to the control group (mean age = 42.3 [SD 11.9]). Both groups averaged more than 10,500 steps/day at baseline. There was no evidence of within- or between-group physical activity differences across timepoints. The results remained consistent after exclusion of participants who had data collected during COVID-19 lockdowns. Compared with baseline, both groups had significant increases in sedentary time during the post-intervention, and 6 month follow-up. The absence of significant differences between-group physical activity differences may be attributable to the ceiling effect of both groups already being sufficiently active. These results provide useful guidance to future studies intended to assess the efficacy of technology-based dog-walking interventions. Future dog-walking interventions should specifically target physically inactive dog owners. Trial Registration: ACTRN12619001391167 (10/10/2019); Retrospectively registered.

Abstract Cyanobacterial phycobiliproteins, such as phycoerythrin (PE) and phycocyanin (PC), are colored potential bioactive proteins that have antioxidant and antimicrobial properties. In this study, we formulated a new food prototype based on PE and PC-fortified low-fat yogurt and cream cheese. Four distinct low-fat yogurt and cream cheese products were manufactured, including a control group (No PE and PC), samples produced with phycoerythrin (+ PE), samples produced with phycocyanin (+ PC), and samples produced with both phycoerythrin and phycocyanin (PC + PE). Afterwards statistically compared the physicochemical composition, colorimetric properties, antioxidant and antimicrobial activities, and sensory profile of the fortified foods at 4 °C and 8 °C for 28 and 42 days. Additionally, we confirmed that PE and PC are not toxic to Caenorhabditis elegans at concentrations up to 1 mg/mL. The results showed that the MIC of PE and PC against E. coli was significantly higher than against S. aureus (3.12 ± 0.05 µg/mL vs. 1.56 ± 0.01 µg/mL, respectively; p ≤ 0.05). Additionally, the maximum diameter of the inhibition zone of PE and PC against S. aureus was significantly higher than against E. coli (6.6 ± 0.011 mm vs. 11.66 ± 0.02 mm, respectively; p ≤ 0.05). Results of color parameters showed that the control group had significantly higher L* values than the samples enriched with PE and PC. Moreover PE and PC significantly increased the a* and b* values respectively. The amount of ΔE in the control yogurts and cream cheese was higher than in the samples with PE and PC. Overall, the results showed that adding PE and PC had a significant effect on all measured factors (p

Abstract In recent years, nutrition has become increasingly important in treating and managing gestational diabetes mellitus. The Health Belief Model (HBM) is a conceptual framework in health behavior research used in some medical research. The present study aimed to evaluate the effect of glycemic index training based on the HBM on metabolic indicators and pregnant women’s health-related quality of life. In this open-label, parallel-controlled randomized trial, 90 pregnant women from primary health centers in Iran were recruited and randomly assigned to either the intervention group or the control group, using a block randomization method with a block size of six. The intervention group underwent 12 weeks of nutritional education on the glycemic index and load of foodstuff. The control group only received routine health care. Valid and reliable questionnaires included demographic and prenatal information, physical activity, three days of food records, quality of life (SF-12), and HBM constructs. Fasting blood samples were taken at baseline and end of the study, to assess fasting blood glucose (FBS), fasting insulin, hs-CRP, and lipids. Eighty-four pregnant women with an age mean of 30.12 ± 4.35 completed 12 weeks of intervention. At baseline, there were no significant differences between groups in the HBM subscales (P > 0.05). However, within the intervention group, there was an increase in perceived susceptibilities (mean change 1.45 ± 4.21; P = 0.03) and cue to action (mean change: 1.22 ± 3.38; P = 0.02). In the adjusted model, the General Health subscale of Quality of Life showed a significant increase in the comparison between groups (mean change 13.69 ± 29.83 vs. 0.00 ± 29.58; P = 0.04). Additionally, the adjusted model revealed a notable difference between the groups in serum hs-CRP level, (mean change -0.877 ± 3.47 vs. -0.067 ± 3.40; P = 0.01) and triglyceride level (mean change − 55.24 ± 111.21 vs. 40.92 ± 142.01; P = 0.001). However, in the adjusted model, the total cholesterol levels increased between groups (mean change 22.75 ± 66.17 vs. 30.12 ± 61.33; P = 0.01) at the end of the study. There was no significant difference in glycemic indices (P > 0.05). Participation in a nutrition education program might positively impact nutritional beliefs, behaviors, and some biochemical indicators among pregnant women. Future studies with larger sample sizes and longer follow-ups are warranted. Trial registration number: IRCTID: IRCT20190227042858N1. Registration date: 2019-07-18.

Abstract Pulmonary tuberculosis (TB) causes progressive and irreversible damage to lung tissue, a damage that may not fully resolve after treatment. Mycobacterial vesicles (MVs), which are poorly understood, may contribute to TB pathology. This study investigated the effects of stress, such as treatment with conventional TB antibiotics rifampicin, isoniazid, ethambutol, or treatment with an antimycobacterial peptide (NZX), on mycobacterial vesiculation. Stress from minimal inhibitory concentrations of antibiotics, or peptide all increased MV formation. Electron microscopy and lipid profiling revealed that these vesicles, about 40 nm in size, were released from the bacterial inner membrane and consisted of apolar lipids. Using mass spectrometry, the study identified key differences in MVs protein cargo dependent on the antibiotic used, especially with ethambutol-induced MVs that contained proteins from several mycobacterial pathways. Additionally, toxicology analysis using different concentrations of MVs on primary human macrophages and the monocytic cells indicated that MVs from the different treatments were not toxic to human cells, however induced specific inflammatory profiles. In conclusion, this study identified mycobacterial vesicles to be a potential contributor to tuberculosis pathology.

Abstract FADS1 rs174556 polymorphism influences on dietary fats metabolism and type 2 diabetes (T2DM). This study aimed to compare the effect of three oils of sesame, canola and sesame-canola on cardio metabolic factors across genotypes of rs174556 variant in patients with type 2 of diabetes. This study was a randomized triple-blind three-way cross-over clinical trial. 95 Subjects with T2DM replaced their regular dietary oil with sesame oil, canola oil, or sesame-canola oil for three 9-week phases and completed the study. There were three anthropometric measurements, blood sampling and biochemical assessments at the beginning, middle, and at the end of each phase for assessments. Genotyping was conducted using the polymerase chain reaction-restriction fragment length polymorphism (PCR–RFLP) method. In the crude model, there was an interaction between consumed oils and rs174556 variant on serum concentration of Apolipoprotein A-I (ApoA-1). During intake of sesame oil, lower levels of triglycerides (TG) were observed in individuals with TT genotype compared to C allele carriers’ allele, which remained significant in adjusted models. Compared to C allele carrier’s, the people with TT genotype experienced significant increase and decrease in serum levels of HDL and TG, respectively in adjusted models. Also, the subjects who consumed sesame-canola oil had lower serum concentrations of fasting blood glucose than those who received sesame and canola oils, regardless of used oils and genotypes. FADS1 Gene variant (rs174556) might modify cardiometabolic changes following dietary vegetable oils. Larger longitudinal studies especially randomized clinical trials are needed to clarify these associations.

Abstract Laser speckle contrast imaging (LSCI) is a non-invasive, powerful, and cost-effective imaging technique that has seen widespread adoption across various medical fields, particularly for blood flow imaging. While LSCI provides physicians with valuable insights into changes or occlusions in blood flow, the technique is susceptible to various factors and parameters that can impact measurement sensitivity and signal-to-noise ratio (SNR). These include the scattering of light, which can affect the quality and reliability of the LSCI data. The polarization of light holds significant promise to enhance the performance of LSCI. In this study, we employed polarization manipulation of light to investigate its impact on the performance of LSCI for measuring flow. Focusing on the application of LSCI in microcirculation within capillaries, we examined the effect of polarization control on the technique's flow measurement capabilities using a custom-designed phantom system. This phantom consisted of three tubes with inner diameters of 1.1 mm, 1.6 mm, and 2.8 mm, embedded in a polydimethylsiloxane (PDMS) matrix with optical properties similar to biological tissue. By manipulating the polarization of both the incident and reflected light, alternating between parallel and perpendicular states, we compared the performance of our LSCI system in detecting flow for different tube diameters and depths within the phantom. Our study revealed that while depth is a critical parameter influencing flow detection using LSCI, employing perpendicular polarization (between incident and reflected light) resulted in the lowest measurement error and highest SNR compared to parallel polarization and the absence of polarization control.

Abstract Rhizomania is the most destructive sugar beet disease in the world, and in recent years, it has widespread in most of the sugar beet growing areas in Iran. Since the control of this soil-borne disease is a difficult task, the use of resistant genotypes is known as the best measure against the disease. The ultimate goal of sugar beet breeders is to produce genotypes that can be used in both infected and non-infected fields without any reduction in terms of yield and quality. Twenty-one sugar beet genotypes along with four controls were evaluated in randomized complete block design with four replications in fields with natural infection to rhizomania in five research stations. Important sugar beet traits including root yield, sugar yield, sugar content, and white sugar yield were evaluated for two years (2021 and 2022). For all traits, location was the main source of variation that spanned 33 to 55% of the total sum of the square followed by the location×year×genotype accounted for 3–40% of the variation. Based on the results of analysis of variance, multivariate stability parameters were computed to evaluate the genotypes’ stability. The first two principal components (IPCA1 and IPCA2) generated by GGE biplot contributed for 31.3 and 17.5% difference in genotype×environment interaction for root yield, respectively. According to the GGE biplot, genotypes RM-11 and RM-12 were identified as the winning genotypes across environments for both root yield and white sugar yield traits whereas AMMI model identified RM-14 and RM-9 (for root yield) and RM-1 (for white sugar yield) as best genotypes. Based on the ideal genotype ranking, RM-11 and RM-10 were the best performer with a high mean yield as well as stability in the studied environments. The biplot rendered using the weighted average of absolute scores (WAASB) and root yield and white sugar yield identified RM-11 and RM-9 as superior genotypes in terms of yield and stability. The selected genotypes can be used in breeding programs to transfer the disease resistance and cultivar development.

Abstract Background We aimed to examine medical students’ perceptions regarding their professional identity and explore their creative narratives utilizing the Dialogical-Self Theory (DST). Methods This is a mixed-method study. First-year medical students at Tehran University of Medical Sciences (TUMS, Tehran, Iran wrote a creative narrative about their perspectives on their future profession. Based on DST, qualitative-directed content analysis and summative approach were used to identify and quantify different “I-positions” to assess professional identity formation. Results One hundred eighty-eight first-year medical students at TUMS participated in the study in 2021–2022. In the creative narratives, the reference to “I” was more than other pronouns (he/she/they). The use of “I” positively correlated with rank in the university entrance exam (P value = 0.03). The highest application of “I” per participant belonged to the “present I-position,” and the lowest to the “promoter,” which has the highest rank in the formation of professional identity. Women tended to use the “present I-position” more than men (P value = 0.03). Conclusions First-year medical students have accepted their self-role in forming professional identity; however, they have not found a vast perspective of it, as shown by their inability to use meta I-position and promoter I-position. Therefore, educators must provide plans within the curriculum to enhance competencies of professional identity formation. These plans can include incorporating narration writing to encourage medical students to reflect on their professional identity.

Abstract Hysterosalpingography (HSG) is a minimally invasive procedure that can be painful. This study aimed to examine the impact of a tailored training program on anxiety and perceived pain intensity in infertile women undergoing HSG. This research was a clinical trial involving 86 infertile women who were candidates for HSG and conducted at the radiology department of the Royan Infertility Center in Tehran, Iran, between November 22, 2021, to March 11, 2023.The participants were divided into two groups:43 women in the intervention group and 43 women in the control group. The randomization of the samples was carried out using a random number table. The women in the intervention group received two face-to-face group training sessions. Data were collected using valid questionnaires. Additionally, the heart rate and blood pressure of the participants were recorded. Data analysis showed the training intervention was significantly associated with reducing anxiety levels, perceived pain, systolic blood pressure, diastolic blood pressure, and heart rate in women undergoing HSG (p

Background and purpose: Infection of the lacrimal sac (dacryocystitis) can lead to serious complications, including orbital cellulitis, orbital abscess, meningitis, and cavernous sinus thrombosis, all of which pose significant threats to human health. This condition is often associated with nasolacrimal duct obstruction, which prevents normal tear drainage and creates an environment conducive to microbial growth. Timely identification of the causative agents of dacryocystitis is critical, as untreated or improperly managed infections can rapidly worsen, leading to systemic infections or even life-threatening conditions. This study aims to investigate the spectrum of microbiological agents causing dacryocystitis to control infection and provide appropriate treatment for patients with chronic nasolacrimal duct obstruction. Materials and methods: This cross-sectional study included patients diagnosed with chronic dacryocystitis who candidated for dacryocystorhinostomy (DCR) surgery. Routine ophthalmic examinations, including visual acuity, slit-lamp examination, and fundoscopy, were performed for all patients. Ocular discharge samples were sent to the laboratory at the Pediatric Infectious Disease Research Centre, Bu-Ali Sina Hospital, Sari. Each sample was incubated in blood agar, chocolate agar, EMB, and Sabouraud dextrose agar under aerobic conditions at 37°C for 24 hours and simultaneously in chocolate agar and blood agar under anaerobic conditions at 37°C for 48 hours. Catalase tests were used to distinguish Staphylococcus from Streptococcus bacteria, coagulase tests to differentiate Staphylococcus aureus from other Staphylococcus species, and oxidase tests to identify Pseudomonas and other Gram-negative bacteria. For detecting fungal infections, samples were stored on Sabouraud dextrose agar at 25-27°C for two weeks. Gram staining was performed on observed colonies, followed by microbiological diagnostic and differential tests based on bacterial species. Data were analyzed using SPSS software, described as means, and compared using the chi-squared test. Results: Of the 50 patients with chronic dacryocystitis included in the study, with a mean age of 56.9±15.28 years (range, 31 to 86 years), 34(68%) were female, and 16(32%) were male. Gram-positive bacteria accounted for 42 cases (84%) of isolated organisms, while gram-negative bacteria accounted for 8 cases (16%). Pus reflux was detected in 84% of cases, significantly more often with gram-positive organisms(P=0.03). The most common pathogens were Staphylococcus epidermidis (22 cases, 44%), Staphylococcus aureus (10 cases, 20%), and Streptococcus pneumoniae (6 cases, 12%). No significant correlations were found between sex and age with the number and type of organisms (P=0.77). Conclusion: Gram-positive bacteria are the main cause of chronic dacryocystitis infections, and demographic factors such as age and gender do not influence the type of infection. Staphylococcus epidermidis and Staphylococcus aureus have been identified as the predominant pathogens responsible for this condition. Understanding the microbiological spectrum can improve disease diagnosis and lead to more effective treatment strategies. This information is essential to help clinicians select appropriate, targeted antimicrobial treatments to more effectively control infections, reduce the risk of antimicrobial resistance, prevent serious complications and systemic infections, and ultimately improve patient outcomes.

Abstract In the contemporary era, the enhancement of wearable capacitive sensors is achieved through the utilization of polymeric micropillars as filler materials between electrode plates. To gain a deeper understanding of the dynamic response of the system, nonlinear coupled governing equations of a circular microplate motion resting on an array of polymeric micropillars have been derived. These equations are used to model the system’s behavior. In addition, the squeezing motion of the micro-pillars is characterized using the incompressible Neo-Hookean model. Both static and dynamic responses, including transient and steady-state solutions, are investigated in detail by discretizing over spatial coordinates using a weak formulation approach. A frequency response analysis is conducted using a continuation-based method. This entails expanding the steady-state solution using a Fourier transform and employing the energy balance principle. The unknown coefficients of the expansion series are calculated using a gradient descent-based learning approach that is physically motivated. Furthermore, a dynamic step size strategy for frequency increments is employed to effectively follow the solution path. This strategy is implemented via the ARC length method. In this study, we examine the impact of varying PDMS (polydimethylsiloxane) hydrogel mechanical and geometrical configurations. It can be reasonably concluded that the mechanical properties of the pillars and the geometrical configuration of the circular plate and micropillars have a significant impact on the maximum tolerable pressure, fast transient response, and frequency response analysis.

Abstract In this study, the performances of 40 Coupled Model Intercomparison Project Phase 6 are evaluated against observational data at synoptic stations in Iran using various evaluation criteria. The results reveal diverse model accuracy across different climate conditions and criteria, emphasizing particularly notable disparities in the nonstationarity R criterion compared to others. Although according to the ranking of the raw and bias-corrected outputs of CMIP6 GCMs for Iran, the NorESM2-MM, AWI-ESM-1-1-LR, and MPI-ESM1-2-LR models are consistently among the top six ranked models for precipitation in both raw and corrected outputs. For temperature, MPI-ESM1-2-LR, TaiESM1, INM-CM4-8, and IITM-ESM are consistently among the top six models for both the raw and bias-corrected outputs of CMIP6 GCMs. The Bias correction methods, including quantile mapping and linear scaling, integrated with Bayesian model averaging, were applied. While quantile mapping demonstrates superior performance and less disparity than linear scaling, it proves ineffective for correcting biases at stations with bias nonstationarity over time. The RMSE for monthly precipitation ranges from almost 0 to 200 mm, with a large RMSE value related to the high precipitation stations, and the monthly temperature exhibits a range of 0 to 4 °C. The use of a multi-model ensemble improves accuracy compared to individual models, resulting in a reduction in the differences between the minimum and maximum RMSE values from 178.6 to 91.0. Additionally, the range for mean absolute error decreases from 126.9 to 93.3, and the difference in the correlation coefficient narrows from 0.9 to 0.42. Averaging models after bias correction prevents significant fluctuations while maintaining higher accuracy, in contrast to the second method, which involves bias-correcting models after averaging.

Abstract Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson’s correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHDI) and the predominantly combined subtype (ADHDC). The proposed method was evaluated across four classification scenarios: (1) two-class classification of ADHD from TD children using balanced data, (2) two-class classification between ADHD and TD children using unbalanced data, (3) two-class classification between ADHDI and ADHDC, and (4) three-class classification among ADHDI, ADHDC, and TD children. Using Skip-Vote-Net, we achieved mean classification accuracies of 97% ± 1.87 and 97.7% ± 2.2 for the balanced and unbalanced classification cases, respectively. Furthermore, the mean classification accuracy for discriminating between ADHDI and ADHDC reached 99.4% ± 1.21. Finally, the proposed method demonstrated an average accuracy of 98.86% ± 1.03 in classifying ADHDI, ADHDC, and TD children collectively. Our findings highlight the superior performance of Skip-Vote-Net over existing methods in the classification of ADHD, showcasing its potential as an effective diagnostic tool for identifying ADHD subtypes and distinguishing ADHD from typically developing children.

Abstract Background and objective One of the most important aspects of health is social health. Addressing social health and social accountability is possible by education of social determinants of health (SDH) to medical students. The aim of current study is to integrate the SDH variables to patient’s history-taking in medical education during clerkship stage as an action research and scholarship in education. Pediatric patients were selected as the target population for this study. Methods The present study is an action-research including three phases of the program's design, implementation, and evaluation. The present paper reports the results of phase I including the following steps; rapid scoping review and expert panel for development of history-taking form. The goal of this phase was to prepare an SDH checklist for history-taking in the Pediatrics Ward of Firoozabadi Teaching Hospital, Iran University of Medical Sciences, Tehran, Iran. The checklist of history-taking was evaluated in terms of measurability, feasibility, priority, and clarity using a 5-choice Likert scale. Results According to the results of the scoping review and consensus-based methods, the preliminary version of the program was prepared including the SDH history-taking checklist. A total of 21 items were selected after two expert panel rounds. The overall absolute agreement was 0.704 (95% CI: 0.587 – 0.793) which was significantly higher than 0.5 (P

Abstract Concrete, as a cornerstone of modern construction, heavily relies on the quality of its constituent materials, particularly aggregates. Among the critical factors contributing to high-quality concrete are proper gradation, absence of clay particles, and angular shape of aggregates. Adhering to these standards typically results in concrete with superior strength. However, aggregates sourced from riverbeds often possess a natural gradation, contain clay particles, and have rounded shapes. This study delves into a comparative analysis of aggregates sourced from two widely utilized riverbed regions, namely Hari-River and Kamar-Kalaq, situated within Herat province, Afghanistan. Given that over 90% of concrete in Herat province is sourced from these two riverbeds, the findings of this study carry immense significance. The research meticulously examines key parameters, including clay content, gradation, aggregate shape, and compressive strength, to determine the optimal choice for concrete production. Methodologically, samples were acquired following ASTM standards, and rigorous testing procedures were conducted, encompassing clay particle analysis, sieve analysis, and strength testing. The results reveal significant disparities between the two regions, with Hari-River demonstrating superior characteristics across various metrics. Particularly noteworthy is Hari-River’s lower clay content of 2.7% compared to Kamar-Kalaq’s 3.7%. The gradation of Hari-River for both coarse and fine aggregates is superior to that of Kamar-Kalaq when compared to size 67 aggregate range. Additionally, the average 28 days concrete compressive strength of Hari-River aggregates is 27.8 MPa, while that of Kamar-Kalaq is 23.4 MPa.

Abstract This study investigates the influence of oil polarity on interfacial tension (IFT), contact angle, oil recovery, and effluent pH in smart water and Low-salinity water injection. The results indicate that the interaction between the hydration shell of ions and the polar components (PCs) of oil is crucial. Increasing oil polarity enhances the potential for interaction with the hydration shell of ions, leading to reduced IFT, altered wettability, and improved oil recovery; which could be boosted by the contribution of a higher number of anions in the smart water bulk through the enhancement of their interaction with the PCs (especially acidic components) of oil. The study demonstrates that increasing the SO4 2− concentration in seawater increased oil recovery for oils with higher acid component content, as indicated by total acid number values of 0.87, 0.99, and 1.32 mgKOH/g, the tertiary oil recovery factors for these oils were 61.10%, 69.82%, and 87.09%, respectively. The effluent pH results align with the findings of contact angle and oil recovery, confirming the dominant influence of anions on oil recovery. The interaction between the PCs of oil and the hydration shell of ions is thus highlighted as a critical factor in the observed outcomes.

Objectives: Methotrexate (MTX), an immunosuppressant and anti-cancer medication, can harm the heart. The goal of the current investigation was to assess the cardiotoxicity caused by MTX and the potential cardioprotective properties of silymarin, citral, and thymoquinone as antioxidants.Methods: Forty-eight rats were divided into six groups, which included control, MTX, cosolvent, citral, thymoquinone, and silymarin groups. At the end of the study, the rats were anesthetized (ketamine and xylazine) and killed using CO2. Their blood samples were collected to measure the enzymatic activities of creatine kinase-myoglobin binding (CK-MB), creatine phosphokinase (CPK), and lactate dehydrogenase (LDH). Also, the heart tissue was sampled to determine the antioxidant capacity and examine the histopathology.Results: The findings revealed that the activity of CPK, CK-MB, and LDH enzymes significantly reduced in the thymoquinone treatment group compared to the MTX group (p < 0.05). On the other hand, total antioxidant capacity was significantly increased in the thymoquinone group compared to the MTX group (p < 0.05). The pathological modifications (i.e. severe congestion, edema fluid, the presence of inflammatory cells around the blood vessels, mild to moderate hemorrhaging between cardiac muscle fibers) were seen in the MTX group. The treatment groups, particularly thymoquinone, did not experience any appreciable pathological changes.Conclusion: The thymoquinone was found to have the strongest protective effect against the heart damage caused by MTX.

Abstract The pulp and paper industry generates vast quantities of paper sludge, posing significant environmental challenges due to its disposal in landfills or incineration. This study explores the potential of valorizing paper sludge by incorporating it into particleboard production. It aims to optimize sludge content and particle size to enhance board properties—a novel approach to waste management in the wood composites industry. Through systematic variation of sludge content (0–25%) and particle size ( 2 mm), we assessed the mechanical and physical properties such as internal bond strength (IB), modulus of rupture (MOR), modulus of elasticity (MOE), water absorption (WA), and thickness swelling (TS). The findings indicate that incorporating paper sludge at moderate levels (5–15%) with optimized particle sizes (

Abstract The novel structure of Hg(II) complexes including the pyridinium ylide C5H5NCHC(O)C6H4-m-Br (Y) were synthesized and reported in this study. In the first step, the pyridinium salt C5H5NCH2C(O)C6H4-m-Br (S) was produced by reacting 2,3′-dibromoactophenone and pyridine. then, treatment of S with K2CO3 gave the related pyridinium ylide Y. Finally, the reaction of Y with HgX2 and Hg(NO3)2·H2O leads to the formation of novel binuclear [HgY2][HgX4] (X=Cl (1); X=Br (2); X=I (3)) and polymeric [HgY(NO3)2]n (4) complexes. The structure of complex 2 was also determined by X-ray diffraction analysis. The obtained analyses proved the coordination through the ylidic carbon to metallic center. Additionally, Natural Bond Orbital (NBO), Energy Decomposition Analysis (EDA), and EDA-NOCV studies are also used to investigate the nature of metal–ligand bonding in the complexes. Finally, the antibacterial activity of 1–4 was also examined against Gram positive and negative represented significant levels of inhibitory potency respected to used standards.

Abstract Road traffic crashes (RTCs) disproportionately impact low and middle-income countries (LMICs). Current interventions in LMICs primarily target road user behavior, neglecting systemic issues and casting drivers as mere contributors. Given Iran’s unique cultural, financial, and social intricacies, this study aims to explore the latent causes of RTCs, prioritizing drivers’ experiences and encompassing insights from various traffic system components. Applying a qualitative approach, data were collected through 46 semi-structured interviews with 38 participants, including drivers and experts from traffic-related organizations. Content analysis identified ten themes as contributing to driving errors. Direct factors included fatigue influenced by economic and occupational conditions, distraction from internal and external sources, reckless behaviors influenced by traffic culture and environment, and Inadequate driving skills due to training. Macro-scale challenges in road infrastructure, vehicle quality, education, and accident investigations were highlighted. The lack of a centralized traffic safety authority and a predominant focus on penalizing drivers overshadowed systemic issues. The study offers valuable insights into the complex interplay of factors contributing to driving errors in LMICs, advocating for a paradigm shift towards holistic, systemic interventions beyond individual driver behavior and challenging the conventional blame-centric view associated with driving errors.

Abstract Background and objectives Medical students experience different types of challenges during their MD program, which become more challenging when it comes to the internship, putting too much pressure on them. This study aimed to explore the challenges of medical interns of Shiraz University of Medical Sciences (SUMS), Shiraz, Iran. Methods In this qualitative research, which was conducted from September 2023 March 2024, medical interns of SUMS were selected using purposeful sampling method. The data were collected through deep and semi-structured interviews, and it was continued until data saturation was achieved. The data analysis was performed through thematic content analysis. Results A total of 13 participants (6 men and 7 women) with a mean ± SD age of 25 ± 1.6 years were interviewed. The challenges of medical interns consisted of 423 meaning units, 79 open codes, 14 subthemes, and 6 main themes; the main themes consisted of educational challenges, challenges following university regulations, health and behavioral issues, economic/employment-related issues, sociocultural and recreational challenges, and ethical issues. Conclusion The results of this study highlight the necessity for reforms in the medical internship phase aiming to tackle interns’ issues and establish a supportive and nurturing environment for future medical doctors.

Abstract High molecular weight polyethylenimine (HMW PEI; branched 25 kDa PEI) has been widely investigated for gene delivery due to its high transfection efficiency. However, the toxicity and lack of targeting to specific cells have limited its clinical application. In the present investigation, L-3, 4‐dihydroxyphenylalanine (L-DOPA) was conjugated on HMW PEI in order to target L-type amino acid transporter 1 (LAT-1) and modulate positive charge density on the surface of polymer/plasmid complexes (polyplexes). The results of biophysical characterization revealed that the PEI conjugates are able to form nanoparticles ≤ 180 nm with the zeta potential ranging from + 9.5–12.4 mV. These polyplexes could condense plasmid DNA and protect it against nuclease digestion at the carrier to plasmid ratios higher than 4. L-DOPA conjugated PEI derivatives were complexed with a plasmid encoding human interleukin-12 (hIL-12). Targeted polyplexes showed up to 2.5 fold higher transfection efficiency in 4T1 murine mammary cancer cell line, which expresses LAT-1, than 25 kDa PEI polyplexes prepared in the same manner. The cytotoxicity of these polyplexes was also substantially lower than the unmodified parent HMW PEI. These results support the use of L-3, 4‐dihydroxyphenylalanine derivatives of PEI in any attempt to develop a LAT-1 targeted gene carrier.

Background: Professional ethics has become an indispensable component of skills and various fields today. Disregarding ethical principles can have detrimental consequences for human society, especially when dealing with large and complex data in various branches of medical sciences. Adhering to professional ethical standards in data production and statistical analysis ensures the accuracy and validity of results derived from this data in the field of medical sciences. The primary objective of this article is to introduce and elucidate the principles of professional ethics in the analysis of medical data.Methods and Materials: In this narrative review study, we delve into an examination of articles and viewpoints presented in the realm of professional ethics. This examination encompasses a comprehensive analysis of the foundations of professional ethics in medical sciences and various approaches to this matter.Results and Conclusion: Incorporating ethical considerations in the domain of statistical activities and data analysis in medical sciences holds significant importance. These actions, including the evaluation of the value and credibility of each statistical action and decision, precise modeling, and efforts to find ethical solutions to ethical challenges in this field, can contribute to the improvement and advancement of medical sciences. Therefore, attention to the principles of professional ethics in data analysis in medical sciences stands out as an essential prerequisite for enhancing credibility and scientific assurance in this domain.

Background and purpose: Various studies have been conducted to treat memory and learning disorders using medicinal plants and other compounds. Amygdalin, as the main compound of the seeds of Rosaceae plants, has multiple beneficial properties but can release cyanide compounds in the body. On the other hand, local drug delivery using nanoparticles has created the ability to release the drug with fewer side effects. Chitosan is one of the compounds whose nanoparticles are used in drug delivery systems. In the present study, the impact of non-capsulated and encapsulated amygdalin in chitosan nano-particles on passive avoidance memory has been studied. Materials and methods: In this experimental research, 150 male NMRI mice aged 5-6 weeks weighing 25-30 gr were used. All injections were done intraperitoneally. This study was conducted in three separate phases. In the first stage, different doses of ethanol were injected (0.25, 0.5, 0.75, and 1 gram per kilogram of body weight) to obtain the appropriate dose for passive avoidance memory impairment. In the second phase, after memory impairment using ethanol, the effect of different doses (6.25, 12.5, 25, and 50 mg/kg) of uncoated amygdalin on passive avoidance memory was evaluated. In the third phase, the effect of the same doses of amygdalin in the previous phase but encapsulated in chitosan nanoparticles was evaluated. The process of investigating passive avoidance memory was carried out on two consecutive days, including the training day and the test day, using the shuttle box device. Data mean and standard deviation was analyzed in GraphPad Prism software via one-way analysis of variance (ANOVA) and Tukey's test in a P< 0.05 significant level. Results: The results of the first phase showed that ethanol by doses of 0.75 and 1 gram per kilogram body weight at P

Background. The coronavirus disease 19 (COVID-19) is one of the most important public health issues, and the evaluation and improvement of COVID-19 health literacy and self-care can be effective in preventing its spread. This study was conducted to investigate the health literacy of COVID-19 and its relationship with self-care among the health ambassadors of Urmia in 2021. Methods. In this descriptive-analytical study, 400 health ambassadors of Urmia were selected using the cluster random sampling method. The data-gathering tool was a questionnaire, including questions about self-care, COVID-19 health literacy, and demographic information. The data were analyzed by SPSS 19 using analysis of variance and regression tests. Results. COVID-19-related health literacy was reported to be at a good level (63.5%) in most health ambassadors. In addition, it had a statistically significant relationship with the level of education and economic status. Similarly, there was a significant relationship between self-care and demographic characteristics such as age, gender, level of education, occupation, and economic status (P>0.05). On the other hand, according to data analysis results, with an increase in health literacy related to COVID-19, regression modeling could predict 22% of self-care behaviors in the participants of the present study. Conclusion. Overall, there is a need for interventions by healthcare professionals for training to increase health literacy related to COVID-19 that results in self-care and adoption of health behaviors in health ambassadors, given the significance of COVID-19-related health literacy and its effects on self-care in these ambassadors. Practical Implications. The findings of this study can be presented to the Deputy of Health and healthcare providers so that actions can be taken to promote health literacy and self-care and prevent similar epidemics.

Abstract This research investigated the effect of ion concentration on the performance of low salinity water under different conditions. First, the effect of injection water composition on interparticle forces in quartz-kaolinite, kaolinite-kaolinite, and quartz-oil complexes was tested and modeled. The study used two oil samples, one with a high total acid number (TAN) and the other with a low TAN. The results illustrated that reducing the concentration of divalent ions to 10 mM resulted in the electric double layer (EDL) around the clay and quartz particles and the high TAN oil droplets, expanding and intensifying the repulsive forces. Next, the study investigated the effect of injection water composition and formation oil type on wettability and oil/water interfacial tension (IFT). The results were consistent with the modeling of interparticle forces. Reducing the divalent cation concentration to 10 mM led to IFT reduction and wettability alteration in high TAN oil, but low TAN oil reacted less to this change, with the contact angle and IFT remaining almost constant. Sandpack flooding experiments demonstrated that reducing the concentration of divalent cations incremented the recovery factor (RF) in the presence of high TAN oil. However, the RF increment was minimal for the low TAN oil sample. Finally, different low salinity water scenarios were injected into sandpacks containing migrating fines. By comparing the results of high TAN oil and low TAN oil samples, the study observed that fine migration was more effective than wettability alteration and IFT reduction mechanisms for increasing the RF of sandstone reservoirs.

Objentive (s): Physicians are in the frontline of the distribution channel in the pharmaceutical value chain. The prescribing behavior of doctors influences the use of herbal medicines. This study investigated the effect of knowledge, attitude, and social marketing mix on doctors' prescribing behavior of herbal medicines. Methods: The current cross-sectional study was conducted in 2022 in Ahvaz, Iran. Its statistical population included 878 doctors working in medical centers and public and private offices in Ahvaz, and the data were collected using the census method. Data was collected using a self-designed questionnaire to measure the level of knowledge and attitude of doctors and the role of social marketing mix on doctors' prescribing behavior. Descriptive and analytical statistics, including Pearson's correlation, linear regression, t-test, and one-way analysis of variance were used to analyze the data. Results: The results showed a significant relationship between all research variables and doctors' prescribing behavior (p= 0.001), where the attitude showed the strongest positive relationship (r= 0.692). Regression analysis showed that the two factors of attitude and knowledge had a positive effect, and promotion strategy has a negative and inverse effect on doctors' prescribing behavior. Also, a significant relationship observed between doctors' prescribing behavior and gender (p= 0.001) and education level (p= 0.004). Conclusion: Knowledge, attitude, and promotion strategy are predictors of herbal medicine prescribing behavior by physicians.

Abstract Objective Several pathological conditions trigger the formation of microvesicles (MVs), including infectious diseases such as COVID-19. The shedding of MVs increases the levels of inflammatory factors (e.g., interleukin-6; IL-6) and ultimately leads to an inflammatory cascade response, while also increasing the procoagulant response. The current study aimed to evaluate the level of circulating MVs and their procoagulant activity as well as the serum level of IL-6 in patients with COVID-19 and healthy controls. In this case-control study, 65 patients with COVID-19 and 30 healthy individuals were sampled after obtaining written informed consent. MVs counting was measured using conjugated CD61, CD45, CD235a, and Annexin-V antibodies. Additionally, the procoagulant activity of MVs and the IL-6 level were estimated using enzyme-linked immunosorbent assay (ELISA). Results The majority of MVs were platelet-derived MVs (PMVs). Patients with COVID-19 had significantly higher levels of MVs, procoagulant MVs, and IL-6 compared to healthy controls (p

Abstract Acceptance and reappraisal are considered adaptive emotion regulation strategies. While previous studies have explored the neural underpinnings of these strategies using task-based fMRI and sMRI, a gap exists in the literature concerning resting-state functional brain networks’ contributions to these abilities, especially regarding acceptance. Another intriguing question is whether these strategies rely on similar or different neural mechanisms. Building on the well-known improved emotion regulation and increased cognitive flexibility of individuals who rely on acceptance, we expected to find decreased activity inside the affective network and increased activity inside the executive and sensorimotor networks to be predictive of acceptance. We also expect that these networks may be associated at least in part with reappraisal, indicating a common mechanism behind different strategies. To test these hypotheses, we conducted a functional connectivity analysis of resting-state data from 134 individuals (95 females; mean age: 30.09 ± 12.87 years, mean education: 12.62 ± 1.41 years). To assess acceptance and reappraisal abilities, we used the Cognitive Emotion Regulation Questionnaire (CERQ) and a group-ICA unsupervised machine learning approach to identify resting-state networks. Subsequently, we conducted backward regression to predict acceptance and reappraisal abilities. As expected, results indicated that acceptance was predicted by decreased affective, and executive, and increased sensorimotor networks, while reappraisal was predicted by an increase in the sensorimotor network only. Notably, these findings suggest both distinct and overlapping brain contributions to acceptance and reappraisal strategies, with the sensorimotor network potentially serving as a core common mechanism. These results not only align with previous findings but also expand upon them, illustrating the complex interplay of cognitive, affective, and sensory abilities in emotion regulation.

Objective(s): Polycystic ovary syndrome (PCOS) is one of the main causes of infertility in women. This study was conducted to uncover the effects of lupeol as an anti-androgenic triterpene on experimentally-induced PCOS in mice. Materials and Methods: Eighty immature female mice were divided into 4 groups: Control (C), PCOS (P), Lupeol (L), and Flutamide (F). PCOS was induced in test groups by injection of Dehydroepiandrosterone (60 mg/kg/day, IP) for twenty days. Following the PCOS induction, the two groups of L and F were treated with lupeol (40 mg/kg/day) and/or flutamide (10 mg/kg/day) respectively and the two groups of C and P received sesame oil (0.1 ml/mouse/day) for 15 days. After the treatment period, ten animals in each group were selected for collecting blood and ovary samples. In vitro fertilization assessment was carried out on 10 remaining mice in each group. The hormonal assays and oxidative stress biomarker determination were performed on serum and tissue samples. Moreover, histopathological analyses were conducted on the ovaries.Results: PCOS-elevated concentration of LH and Testosterone was significantly (P

Abstract COVID-19 is a highly contagious virus that uses Angiotensin-converting enzyme 2 (ACE2) as a receptor to enter human cells. The virus leads to an increase in inflammatory cytokines (i.e. IL-6) and an impaired coagulation system, which can cause serious complications during and after the disease. Physical exercise has been shown to improve COVID-19 complications through various mechanisms, such as modulation of the immune and coagulation systems. Therefore, this study investigated the effects of 8 weeks of training on inflammatory, coagulation, and physical factors in patients with COVID-19 during the recovery phase. Twenty-seven male and female volunteers (age 20–45 years) who recently recovered from COVID-19 were assigned to the control (n = 13) or the training group (n = 14). Blood samples, aerobic capacity and muscle endurance were collected 24 h before the start of the interventions and 24 h after the final training session in week 4 and 48 h after the final training session in week 8. IL-6, ACE2, fibrinogen, and D-dimer were measured using ELISA. The training group showed a significant increase in muscle endurance (p = 0.004) and aerobic capacity (p = 0.009) compared to the control group. Serum levels of IL-6 and fibrinogen decreased in the training group but this decrease was not statistically significant (p > 0.05). Despite a slight increase in the quality of life and sleep in the training group, no statistically significant difference was observed between the training and the control group. It appears that physical training has beneficial effects on the coagulation system, inflammatory factors, and sleep quality and can facilitate the recovery of COVID-19 patients.

Abstract This study aimed to evaluate the superiority of anthropometric indices compared to others for predicting ischemic heart disease (IHD) or cardiometabolic risk factors. This study was a cross-sectional analysis of the Pars Cohort Study data. In total, 9229 Valashahr inhabitants aged 40–75 were included in the analysis. The area under the receiver operating characteristic curve (AUC) analyses was used to compare the predictive accuracy of four anthropometric measures, including body mass index, waist to height ratio (WHtR), waist to hip ratio (WHR), and waist circumference (WC). IHD prevalence was 10.4% in our sample. The prevalence of diabetes mellitus (DM), hypertension, dyslipidemia, and metabolic syndrome was 12.7%, 29.2%, 58.4%, and 22.3%, respectively. All anthropometric indices had poor to good accuracy in predicting IHD risk factors, with AUCs ranging between 0.580 and 0.818. WHR was the most accurate measure for predicting IHD in both genders. All indexes had a better accuracy for predicting DM, dyslipidemia, and metabolic syndrome (MetS) in males than in females. To conclude, anthropometric measures, especially WC and WHtR, are recommended for predicting metabolic syndrome in primary prevention settings. These simple indices could help physicians find those who need further evaluation for MetS.

Abstract Background Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. Methods In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. Results We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. Discussion This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. Conclusion The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

Abstract Over time, with the increase in population and the subsequent increase in energy consumption and also due to the non-renewability of fossil fuels, the study of alternative fuels has increased. One of these fuels is biodiesel, which is a suitable alternative to fossil fuels such as diesel and received much attention from researchers today. For this reason, measuring the physical properties of biodiesel is of great importance. Due to the high cost and time-consuming nature of laboratory methods, numerical methods are used to estimate material properties. The novelty of this research was the use of two white box models, including Group method of data handling (GMDH) and Gene expression programming (GEP), which work on the basis of artificial intelligence. By using these models, two simple mathematical equations with high accuracy were presented to predict the surface tension of biodiesel. These models can be used at different temperatures and molecular weights. To do modeling, 78 laboratory data available in the literature were gathered and the data were randomly divided into two groups, train and test, in a ratio of 80 and 20. The input parameters include mass fraction of fatty acid ethyl esters and temperature (T), and esters are divided into three groups according to their molecular weight: less than 200 (Mw1), between 200 and 300 (Mw2), and greater than 300 (Mw3). The statistical error parameters were calculated for the two models developed in this research and after comparing the results, it was found that the GMDH model estimates the surface tension of biodiesel with a higher accuracy. The average absolute relative error for GMDH and GEP models was reported as 0.97 and 1.89, respectively. Also, other statistical error parameters of GMDH such as RMSE, SD, and R2 for the GMDH model were obtained as 0.444, 0.000233, and 0.9233, respectively. Moreover, sensitivity analysis showed that temperature has the highest impact on the surface tension of biodiesel, which is also an inverse effect. Finally, suspicious laboratory and outlier data points were identified using the Leverage technique. According to this analysis, only five data points were identified as outliers and suspicious laboratory data.

Abstract Background Air pollution is ubiquitous, yet questions remain regarding its impact on the developing brain. Large changes occur in white matter microstructure across adolescence, with notable differences by sex. Methods We investigate sex-stratified effects of annual exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) at ages 9–10 years on longitudinal patterns of white matter microstructure over a 2-year period. Diffusion-weighted imaging was collected on 3T MRI scanners for 8182 participants (1–2 scans per subject; 45% with two scans) from the Adolescent Brain Cognitive Development (ABCD) Study®. Restriction spectrum imaging was performed to quantify intracellular isotropic (RNI) and directional (RND) diffusion. Ensemble-based air pollution concentrations were assigned to each child’s primary residential address. Multi-pollutant, sex-stratified linear mixed-effect models assessed associations between pollutants and RNI/RND with age over time, adjusting for sociodemographic factors. Results Here we show higher PM2.5 exposure is associated with higher RND at age 9 in both sexes, with no significant effects of PM2.5 on RNI/RND change over time. Higher NO2 exposure is associated with higher RNI at age 9 in both sexes, as well as attenuating RNI over time in females. Higher O3 exposure is associated with differences in RND and RNI at age 9, as well as changes in RND and RNI over time in both sexes. Conclusions Criteria air pollutants influence patterns of white matter maturation between 9–13 years old, with some sex-specific differences in the magnitude and anatomical locations of affected tracts. This occurs at concentrations that are below current U.S. standards, suggesting exposure to low-level pollution during adolescence may have long-term consequences.

Background: Hyperkalemia is a potentially life-threatening electrolyte disturbance that if not diagnosed on time may lead to devastating conditions and sudden cardiac death. Blood sampling for potassium level checks is time-consuming and can delay the treatment of severe hyperkalemia on time. So, we propose a non-invasive method for correct and rapid hyperkalemia detection. Methods: The cardiac signal of patients referred to the Pediatrics Emergency room of Shahid Rejaee Hospital was measured by a 12-lead Philips electrocardiogram (ECG) device. Immediately, the blood samples of the patients were sent to the laboratory for potassium serum level determination. We defined 16 features for each cardiac signal at lead 2 and extracted them automatically using the algorithm developed. With the help of the principal component analysis (PCA) algorithm, the dimension reduction operation was performed. The algorithms of decision tree (DT), random forest (RF), logistic regression, and support vector machine (SVM) were used to classify serum potassium levels. Finally, we used the receiver operation characteristic (ROC) curve to display the results Results: In the period of 5 months, 126 patients with a serum level above 4.5 (hyperkalemia) and 152 patients with a serum potassium level below 4.5 (normal potassium) were included in the study. Classification with the help of a RF algorithm has the best result. Accuracy, Precision, Recall, F1, and area under the curve (AUC) of this algorithm are 0.71, 0.87, 0.53, 0.66, and 0.69, respectively Conclusions: A lead2-based RF classification model may help clinicians to rapidly detect severe dyskalemias as a non-invasive method and prevent life-threatening cardiac conditions due to hyperkalemia.

Aim: Type 2 diabetes mellitus (T2DM) is a common disease that imposes a substantial burden on the healthcare system and patients. Lifestyle modification such as sleep hygiene plays a crucial role in glycemic control. Sleep disorders impact many aspects of health. In this study, we aimed to investigate the correlation between sleep quality and glycemic control in T2DM. Method: This cross-sectional study was performed on 163 T2DM patients, attending Shahid Beheshti Hospital in Hamadan, Iran from March 2020 to 2021. Besides recording the demographic data and HbA1c level of participants, they were asked the Pittsburgh Sleep Quality Index questionnaire for evaluating sleep quality. We employed SPSS ver. 21 for data analysis and considered 0.05 as a significant level Results: Among all participants, 62 (38%) were female and 30.7% were illiterate. The mean age was 56.67 ± 12.90 years, and HbA1c was 9.03 ± 1.92 mg/dL. Among sleep metrics, mean waking time was 8.74 ± 1.74 hours, and average sleep time was 12.90 ± 4.90 hours. Overall, 58.2% of the participants had poor glycemic control and 44.8% were suffering from poor sleep quality. We found that patients with poor glycemic control exhibited significantly higher levels of sleep disturbances compared to those with good glycemic control (P < 0.001). Conclusion: Sleep quality is associated with glycemic control in patients with T2DM. Sleep disorders are common among diabetic patients. Thus, healthcare providers need to consider sleep quality improvement in their holistic approach to diabetes management.

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively. Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results. Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3. Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios.

BACKGROUND: Spontaneous bacterial peritonitis (SBP) represents a critical and potentially lethal condition that typically develops in individuals with liver cirrhosis. This meta-analysis aimed to assess diabetes mellitus (DM) as a risk factor for SBP in liver cirrhotic patients. METHODS: Following PRISMA guidelines, fifteen studies were included, for a total of 76 815 patients. The risk of bias was assessed using the Newcastle-Ottawa scale (NOS). We represented the results as risk ratios (RR) with the corresponding 95% confidence intervals (CI) using RevMan software. Additionally, we pooled the hazard ratios (HR) for developing SBP in patients with DM from the included studies. RESULTS: The meta-analysis shows a significantly increased risk of SBP in cirrhotic patients with DM (HR: 1.26; 95% CI [1.05-1.51], P=.01; HR: 1.70; 95% CI [1.32-2.18], P

Abstract Alcohol production and consumption have been prohibited in Iran for over four decades, leading to a typical underestimation of its consumption. This study aimed to assess the prevalence of alcohol consumption, its associated factors, and estimate per capita alcohol consumption among Iran’s adult population. In this population-based survey, 27,874 adults from across Iran were selected using systematic proportional-to-size cluster sampling. Alcohol consumption was evaluated through a modified Persian version of the STEPS questionnaires from previous studies, applied over different timespans. Per capita consumption was calculated using the quantity-frequency method, expressed in liters of pure alcohol. Adjusted odds ratios were reported for associates of alcohol consumption concerning metabolic risk factors, sociodemographic elements, and lifestyle variables. The prevalence of lifetime alcohol consumption was 6.9% (95% CI 6.5–7.2) in the adult population, with a notable sex difference (males: 13.7% [95% CI 13–14.4]; females: 1.4% [95% CI 1.1–1.6]). The 12 month prevalence was 3.8% (95% CI 3.6–4.1). For individuals aged 18 and older, the per capita alcohol consumption in Iran was 0.12 L. Factors such as being a lifetime smoker, younger, wealthier, and having 7–12 years of education were significantly linked to higher alcohol consumption. Significant associations were also observed between alcohol consumption and having a history of heart attacks (OR = 2.04, 95% CI 1.44–2.89), and physical injuries (OR = 1.88, 95% CI 1.34–2.64). The estimated lifetime and 12-month prevalence of alcohol use in our study were higher among some of the subpopulations. The findings also revealed a complex relationship between alcohol consumption, behavioral risk factors, and metabolic profiles. Consequently, immediate preventive measures tailored to each factor’s association with alcohol use are recommended.

Abstract With the substantial increase in the use of stimulants, especially methamphetamine, in recent years, the present study aimed to cluster methamphetamine users based on personality traits and self-efficacy, and compare their mental health, sleep quality, and the risk of relapse in the identified clusters. This cross-sectional study was conducted through convenience sampling on 501 methamphetamine users in addiction treatment centers in Kermanshah, western Iran. The data were collected using the Schwarzer General Self-Efficacy Scale, Zuckerman–Kuhlman Personality Questionnaire, Goldberg and Hiller General Health Questionnaire (GHQ), Zuckerman–Kuhlman Personality Questionnaire, and Stimulant Relapse Risk Scale (SRRS). A total of 501 methamphetamine users were distinguished into three clusters with frequencies of 111 (22.2%), 298 (59.5%), and 92 (18.4%) members through hierarchical cluster analysis. The participants in the first cluster were characterized by low self-efficacy, high neuroticism, sensation seeking, and aggressiveness, along with low extroversion and activity, low positive health, high negative health, low sleep quality, and high risk of drug relapse. The participants in the second cluster reported moderate levels of self-efficacy, neuroticism, sensation seeking, activity, and aggressiveness, high extroversion, and moderate levels of mental health, sleep quality, and the risk of relapse. Moreover, the participants in the third cluster reported the highest level of self-efficacy, the lowest level of neuroticism, sensation seeking, and aggressiveness, moderate extroversion and high activity, low relapse risk, high sleep quality, as well as high positive and low negative health symptoms. The third cluster was significantly different from the other two clusters in terms of the mentioned factors. The findings of this study suggest that low self-efficacy and the presence of neuroticism, sensation seeking, and high aggressiveness contribute to reduced mental health and sleep quality, as well as an increased risk of relapse in methamphetamine users.

Abstract In the face of the escalating global energy demand, the challenge lies in enhancing the extraction of oil from low-pressure underground reservoirs. The conventional artificial gas lift method is constrained by the limited availability of high-pressure gas for injection, which is essential for reducing hydrostatic bottom hole pressure and facilitating fluid transfer to the surface. This study proposes a novel ‘smart gas’ concept, which involves injecting a gas mixture with an optimized fraction of CO2 and N2 into each well. The research introduces a dual optimization strategy that not only determines the optimal gas composition but also allocates the limited available gas among wells to achieve multiple objectives. An extensive optimization process was conducted to identify the optimal gas injection rate for each well, considering the limited gas supply. The study examined the impact of reducing available gas from 20 to 10 MMSCFD and the implications of water production restrictions on oil recovery. The introduction of smart gas resulted in a 3.1% increase in overall oil production compared to using natural gas. The optimization of smart gas allocation proved effective in mitigating the decline in oil production, with a 25% reduction in gas supply leading to only a 10% decrease in oil output, and a 33% reduction resulting in a 26.8% decrease. The study demonstrates that the smart gas approach can significantly enhance oil production efficiency in low-pressure reservoirs, even with a substantial reduction in gas supply. It also shows that imposing water production limits has a minimal impact on oil production, highlighting the potential of smart gas in achieving environmentally sustainable oil extraction. Furthermore, the implementation of the smart gas approach aligns with global environmental goals by potentially reducing greenhouse gas emissions, thereby contributing to the broader objective of environmental sustainability in the energy sector.

Abstract The progression of optical materials and their associated applications necessitates a profound comprehension of their optical characteristics, with the Judd–Ofelt (JO) theory commonly employed for this purpose. However, the computation of JO parameters (Ω2, Ω4, Ω6) entails wide experimental and theoretical endeavors, rendering traditional calculations often impractical. To address these challenges, the correlations between JO parameters and the bulk matrix composition within a series of Rare-Earth ions doped sulfophosphate glass systems were explored in this research. In this regard, a novel soft computing technique named genetic expression programming (GEP) was employed to derive formulations for JO parameters and bulk matrix composition. The predictor variables integrated into the formulations consist of JO parameters. This investigation demonstrates the potential of GEP as a practical tool for defining functions and classifying important factors to predict JO parameters. Thus, precise characterization of such materials becomes crucial with minimal or no reliance on experimental work.