Search

Karakul (KAR) is one of the resistant sheep breeds to harsh desert conditions, which is also known for its excelent lamb pelt quality. This study was performed to identify the signature of selections in the KAR breed using whole-genome sequencing data (WGS) compared with five other Iranian native sheep. Three methods, including population differentiation index (Fst), nucleotide diversity (π), and cross-population extended haplotype homozygosity (XP-EHH) applied to detect the genomic signature of selection. Data analysis leads to identifying 38 shared genes among three methods as positively selected genes for the KAR breed. The most of mentioned genes were associated with coat color (KIT, DVL3, YPEL3, ERBB4, ZNF451, and CTSO), fat and energy metabolism (GDPD3, STARD13, ZNF106, MAPK3, RGS6, PHYH, AP2M1, SPAG9, DNAH9, NDUFAF6, and ARSK), muscle function (MYOCD and MCTP1), growth (CPNE4), altitude adaptation (DNAH9 and SERGEF), and reproduction (TBX6, PHYH, SPAG9, and ARSK). Based on our results, these candidate genes may have a positive effect on the adaptation of the KAR breed to a desert environment. [ABSTRACT FROM AUTHOR]

Background and Objective: Neuropathic diseases are neurodegenerative conditions and a wide and difficult group of peripheral nerve diseases in humans. Since well-being therapy emphasizes the high levels of six domains of psychological well-being, this study was conducted to investigate the effectiveness of well-being therapy on pain coping strategies and self-efficacy of patients with chronic neuropathic pain. Methods: This clinical trial was conducted on 30 chronic neuropathic patients referred to Ayatollah Rouhani hospital in Babol and a neurologist's private office in two groups of experimental and control (n=15). Well-being therapy was performed in 8 sessions of 120 minutes, once a week for the experimental group, while the control group received the routine treatment. After the follow-up period, the control group also underwent psychotherapy. Both groups completed questionnaires of pain coping strategies (Rosenstiel and Keefe, 1985) and pain self-efficacy (Nicholas, 1989) in the pre-test, post-test and follow-up (two months after the post-test) and were compared. Findings: The results showed that there was a statistically significant difference between the two experimental and control groups in the score of the subscales of pain coping strategies in distraction from pain (23.13±3.88 versus 11.47±7.34) (p<0.001), reinterpretation of pain (17.33±5.56 versus 13.0±8.65) (p=0.114), catastrophizing (10.0±6.24 versus 16.33±5.4) (p<0.001), ignoring pain (24.4±6.67 versus 12.6±5.11) (p<0.001), hoping/praying (29.13±9.97 versus 22.4±5.7) (p=0.031), self-talk (25±4.03 versus 21.2±4.79) (p=0.026), behavioral activation (20.47±4.43 versus 11.20±4.94) (p<0.001) and pain self-efficacy (43.2±9.45 versus 33.33±13.34) (p=0.027). These results were maintained in the follow-up period. Conclusion: The present study showed that wellness therapy can be an effective intervention in improving pain coping strategies and increasing pain self-efficacy in chronic neuropathic patients. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) show great promise for translating decades of research on structural abnormalities in temporal lobe epilepsy into clinical practice. Three-dimensional CNNs typically outperform two-dimensional CNNs in medical imaging. Here we explore for the first time whether a three-dimensional CNN outperforms a two-dimensional CNN for identifying temporal lobe epilepsy-specific features on MRI. Using 1178 T1-weighted images (589 temporal lobe epilepsy, 589 healthy controls) from 12 surgical centres, we trained 3D and 2D CNNs for temporal lobe epilepsy versus healthy control classification, using feature visualization to identify important regions. The 3D CNN was compared to the 2D model and to a randomized model (comparison to chance). Further, we explored the effect of sample size with subsampling, examined model performance based on single-subject clinical characteristics, and tested the impact of image harmonization on model performance. Across 50 datapoints (10 runs with 5-folds each) the 3D CNN median accuracy was 86.4% (35.3% above chance) and the median F 1-score was 86.1% (33.3% above chance). The 3D model yielded higher accuracy compared to the 2D model on 84% of datapoints (median 2D accuracy, 83.0%), a significant outperformance for the 3D model (binomial test: P < 0.001). This advantage of the 3D model was only apparent at the highest sample size. Saliency maps exhibited the importance of medial-ventral temporal, cerebellar, and midline subcortical regions across both models for classification. However, the 3D model had higher salience in the most important regions, the ventral-medial temporal and midline subcortical regions. Importantly, the model achieved high accuracy (82% accuracy) even in patients without MRI-identifiable hippocampal sclerosis. Finally, applying ComBat for harmonization did not improve performance. These findings highlight the value of 3D CNNs for identifying subtle structural abnormalities on MRI, especially in patients without clinically identified temporal lobe epilepsy lesions. Our findings also reveal that the advantage of 3D CNNs relies on large sample sizes for model training., Competing Interests: None of the authors have any conflict of interest to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up management. ALL is a common and deadly childhood cancer that often relapses after remission. In this study, we aimed to apply and evaluate machine learning-based models for predicting mortality and relapse in pediatric ALL patients., Methods: This retrospective cohort study was conducted on 161 children aged less than 16 years with ALL. Survival status (dead/alive) and patient experience of relapse (yes/no) were considered as the outcome variables. Ten machine learning (ML) algorithms were used to predict mortality and relapse. The performance of the algorithms was evaluated by cross-validation and reported as mean sensitivity, specificity, accuracy and area under the curve (AUC). Finally, prognostic factors were identified based on the best algorithms., Results: The mean accuracy of the ML algorithms for prediction of patient mortality ranged from 64 to 74% and for prediction of relapse, it varied from 64 to 84% on test data sets. The mean AUC of the ML algorithms for mortality and relapse was above 64%. The most important prognostic factors for predicting both mortality and relapse were identified as age at diagnosis, hemoglobin and platelets. In addition, significant prognostic factors for predicting mortality included clinical side effects such as splenomegaly, hepatomegaly and lymphadenopathy., Conclusions: Our results showed that artificial neural networks and bagging algorithms outperformed other algorithms in predicting mortality, while boosting and random forest algorithms excelled in predicting relapse in ALL patients across all criteria. These results offer significant clinical insights into the prognostic factors for children with ALL, which can inform treatment decisions and improve patient outcomes., (© 2024. The Author(s).)

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient's age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders' development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females., Competing Interests: No authors have competing interests., (Copyright: © 2024 Hassanzadeh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Dysmenorrhea is the most common periodic pain, which affects more than 50% of women with regular menstruation. Fenugreek is one of the medicinal plants with analgesic properties. This study aimed to determine the effect of fenugreek application in the severity of dysmenorrhea and its side effects in women with dysmenorrhea. PICO: Population: women with dysmenorrhea; Intervention: fenugreek; Comparison: control groups; and Outcome: reduction in the severity of dysmenorrhea and its side effects., Methods: English database (PubMed, Cochrane Library, Scopus, and Web of Science) and Persian database [SID (Scientific Information Database) and Magiran] were used for research until February 11, 2023, using the keywords "Dysmenorrhea [Mesh]," "Foenum [Mesh]," "fenugreek [Mesh]," and "Trigonella [Mesh]." The reference list of the selected articles was also checked. The quality assessment was conducted through the Cochrane Handbook for Systematic Reviews of Interventions version 5.2.0. The RevMan 5.3 software was used to analyze and report the data of the entered studies. Meta-analysis results were reported with the standardized mean difference (95% confidence interval). A subgroup analysis was performed based on the type of control groups. The quality of evidence was assessed using the GRADE approach., Results: After removing duplicates and ineligible cases, four articles were included in the systematic review out of the 1526 records obtained. The results showed that the pain intensity caused by primary dysmenorrhea decreased with fenugreek compared to placebo (pooled result SMD: -2.21; 95% CI: -3.26 to -1.17; Z: 4.17; p <0.001). There was no significant difference between fenugreek with mefenamic acid (SMD: 0.05; 95% CI: -0.57 to 0.67; Z: 0.17; p = 0.86) and fenugreek with Chandrasura churna (SMD: 0.06; 95% CI: -0.56 to 0.68; Z: 0.19; p = 0.85). Bias, in terms of incomplete outcome data and selective reporting, was low risk in all studies, and the available evidence was low quality according to the GRADE approach., Conclusion: The results showed that the effect of fenugreek on pain intensity in dysmenorrhea is highly uncertain. The true effect is likely to be substantially different from the estimate of effect. Regarding the importance of the health and quality of life of women of reproductive age and the low quality of evidence of the studies, clinical trials with stronger methodology are suggested in this field., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: This study aimed to investigate the intestinal carrier status of Enterococcus spp. among children in a pediatric intensive care unit (PICU) and reveal the role of hospitalization in the alteration of resistance phenotypes and clonal diversity of the isolates during admission and discharge periods., Methods: Two separate stool samples were collected from hospitalized patients in the pediatric intensive care unit at admission and discharge times. The culture was done, and Enterococcus species were tested for antimicrobial susceptibility and carriage of vanA-D gene subtypes. Random Amplified Polymorphic DNA (RAPD)-PCR was used for a phylogenetic study to check the homology of pairs of isolates., Results: The results showed carriage of Enterococci at admission, discharge, and at both time points in 31%, 28.7%, and 40.1% of the cases, respectively. High frequencies of the fecal Enterococcus isolates with vancomycin-resistance (VR, 32.6% and 41.9%), high-level of gentamicin-resistance (HLGR, 25.6% and 27.9%), and multi-drug resistance phenotypes (MDR, 48.8% and 65.1%) were detected at admission and discharge times, respectively. Resistance to vancomycin, ampicillin, and rifampicin was higher among E. faecium, but resistance to ciprofloxacin was higher in E. faecalis isolates. The increased length of hospital stay was correlated with the carriage of resistant strains to vancomycin, ampicillin, and ciprofloxacin. While the homology of the isolates was low among different patients during hospitalization, identical (9%) and similar (21%) RAPD-PCR patterns were detected between pairs of isolates from each patient., Conclusions: The high rate of intestinal carriage of VR, HLGR-, and MDR-Enterococci at admission and during hospitalization in the PICU, and the impact of increased length of hospital stay on the fecal carriage of the resistant strains show the importance of antibiotic stewardship programs to control their transmission and spread in children., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Objective: We systematically assessed all available evidence on the efficacy and safety of sacral neuromodulation (SNM) in pregnancy., Methods: On September 2022, a thorough search was done on Ovid, PubMed, Scopus, ProQuest, Web of Science, and The Cochrane Library. We chose studies that included pregnant women who had SNM previously. Two authors independently evaluated the quality of the study using a standardized tool of JBI. Studies were given a risk of bias rating of low, moderate, or high. Given the descriptive nature of this study, we utilized descriptive statistics to report demographic and clinical features. For continuous variables, we used mean and standard deviation, and for dichotomous data, we used frequencies and percentages., Results: Out of 991 abstracts screened, only 14 studies met our inclusion criteria and were included in the review. Overall, the quality of the evidence available from the literature is low based on the design of the included studies. Fifty-eight women, including 72 pregnancies, had SNM. The indication of SNM implantation was filling phase disorders in 18 cases (30.5%), voiding dysfunction in 35 women (59.3%), IC/BPS in two cases (3.5%), and fecal incontinence. In 38 pregnancies (58.5%), the SNM status was ON during pregnancy. Forty-nine cases delivered a full-term baby (75.4%), 12 cases had pre-term labor (18.5%), two miscarriages, and two postterm pregnancies happened. The most complications in patients with devices were urinary tract infection in 15 women (23.8%), urinary retention in six patients (9.5%), and pyelonephritis in two cases (3.2%). The findings revealed that when the device was deactivated, 11 cases out of the 23 patients (47.8%) had full-term pregnancies, while in ON status, 35 out of the 38 pregnant women (92.1%) had full-term pregnancies. Nine preterm labors in OFF (39.1%), and two in ON status (5.3%) were recorded. The results revealed that this difference was statistically significant (p = 0.002), and those who turned SNM of them off had more preterm labor. Although the studies reported all neonates had a healthy status, two children had chronic motor tic problems and a pilonidal sinus in a case with an active SNM in pregnancy. However, there was no association between the SNM status and pregnancy or neonatal complications (p = 0.057)., Conclusion: SNM activation in pregnancy seems safe and effective. The choice of whether to activate or deactivate SNM should be made on an individual basis given the current SNM evidence., (© 2023 Wiley Periodicals LLC.)

Because groundwater quality representatives for drinking usage (i.e., Schuler method, Nitrate and Groundwater Quality Index) have been abruptly changing due to extreme events induced by global climate change and over-abstracting, applying an efficient tool for their assessments is vitally important. While hotspot analysis is introduced as an efficient tool concentrating on sharp changes in groundwater quality, it has not been closely examined. Accordingly, this study is an attempt to determine the groundwater quality proxies and assess them through hotspot and accumulated hotspot analyses. To this end, a GIS-based hotspot analysis (HA) applying Getis-Ord Gi* statistics was used. The accumulated hotspot analysis was launched to identify the Groundwater Quality Index (AHA-GQI). Moreover, Schuler method (AHA-SM) was utilized to determine the maximum levels (ML) for the hottest hotspot and the lowest levels (LL) for the coldest cold-spot, and compound levels (CL). The results revealed that a significant correlation (r = 0.8) between GQI and SM was observed. However, the correlation between GQI and nitrate was not significant and the correlation between SM and nitrate was so low (r = 0.298, sig > 0.05). The results also demonstrated that using hotspot analysis on only GQI, the correlation between GQI and SM increased from 0.8 to 0.856, while using hotspot analysis on both GQI and SM increased the correlation to 0.945. Likewise, when GQI was subjected to hotspot analysis and SM underwent accumulated hotspot analysis (i.e., AHA-SM (ML)), the correlation degree increased to the highest extent (i.e., 0.958), indicating the usefulness of including the hotspot analysis and accumulated hotspot analysis in the evaluation of groundwater quality., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Trauma is one of the most critical public health issues worldwide, leading to death and disability and influencing all age groups. Therefore, there is great interest in models for predicting mortality in trauma patients admitted to the ICU. The main objective of the present study is to develop and evaluate SMOTE-based machine-learning tools for predicting hospital mortality in trauma patients with imbalanced data., Methods: This retrospective cohort study was conducted on 126 trauma patients admitted to an intensive care unit at Besat hospital in Hamadan Province, western Iran, from March 2020 to March 2021. Data were extracted from the medical information records of patients. According to the imbalanced property of the data, SMOTE techniques, namely SMOTE, Borderline-SMOTE1, Borderline-SMOTE2, SMOTE-NC, and SVM-SMOTE, were used for primary preprocessing. Then, the Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) methods were used to predict patients' hospital mortality with traumatic injuries. The performance of the methods used was evaluated by sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), accuracy, Area Under the Curve (AUC), Geometric Mean (G-means), F1 score, and P-value of McNemar's test., Results: Of the 126 patients admitted to an ICU, 117 (92.9%) survived and 9 (7.1%) died. The mean follow-up time from the date of trauma to the date of outcome was 3.98 ± 4.65 days. The performance of ML algorithms is not good with imbalanced data, whereas the performance of SMOTE-based ML algorithms is significantly improved. The mean area under the ROC curve (AUC) of all SMOTE-based models was more than 91%. F1-score and G-means before balancing the dataset were below 70% for all ML models except ANN. In contrast, F1-score and G-means for the balanced datasets reached more than 90% for all SMOTE-based models. Among all SMOTE-based ML methods, RF and ANN based on SMOTE and XGBoost based on SMOTE-NC achieved the highest value for all evaluation criteria., Conclusions: This study has shown that SMOTE-based ML algorithms better predict outcomes in traumatic injuries than ML algorithms. They have the potential to assist ICU physicians in making clinical decisions., (© 2023. The Author(s).)

Background: This study aimed to investigate the frequency of intestinal colonization by vancomycin-resistant Enterococcus (VRE) carrying vanA and vanB genes in patients at ICU admission and at discharge from ICU in Mofid children's Hospital, Tehran, Iran., Method: Sampling was performed using rectal swabs and vancomycin susceptibility testing for Enterococcus spp. was carried out using a minimum inhibitory concentration (MIC) assay on Muller Hinton Agar (MHA) medium using an E-test kit. The molecular detection of VRE isolates was performed by the PCR method using the vanA and vanB resistance genes., Results: A total of 234 and 186 non-duplicate rectal swab samples were collected from patients at ICU admission and at discharge from ICU, respectively. Enterococcus spp. was detected in 34.6% (n = 81/234) of rectal swab samples collected from patients at ICU admission, of which 44.4% (n = 36/81) were VRE isolates. In contrast, the prevalence of Enterococcus spp. and VRE isolates among patients at discharge from ICU was 17.7% (n = 33/186) and 57.6% (n = 19/33), respectively. Out of 19 VRE isolated from patients at ICU admission, 4 (21%) and 1 (5.3%) contained vanA and vanB genes, respectively. In contrast, out of 36 VRE isolated from patients at discharge from ICU, 11 (30.5%) were positive for the vanA gene., Conclusion: Results revealed that the prevalence of Enterococcus spp. among patients at ICU admission was high. However, VRE was frequently isolated from patients who were hospitalized for several days in ICUs. The implementation of proper infection control strategies and the use of suitable protocols to guide the appropriate prescribing of antibiotics are necessary., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: Assessment of women's childbirth experience is an important indicator in maternity services. Positive childbirth experiences improve mothers' health, whereas negative childbirth experiences can cause psychological stresses and, in extreme cases, may lead to postpartum depression., Methods: In this cohort study, 204 women at 35-37 weeks of gestation were selected using cluster sampling from the health centers of Tabriz, Iran. Women were divided into three groups (68 women in each group) based on their attendance in childbirth preparation classes: (a) non-attenders (did not attend any sessions), (b) irregular attenders (attended 1-3 sessions), and (c) regular attendants (attended 4-8 sessions). Interviews were conducted at one month postpartum to complete the Childbirth Experience Questionnaire (CEQ) and Edinburgh Postnatal Depression Scale (EPDS). The general linear model (GLM) was used to identify associations between women's attendance to the classes and either their childbirth experience or postpartum depression scores., Results: Based on the GLM, the mean score of childbirth experience among the regular attenders was significantly higher than women who were irregular attenders ( p = .032) or non-attenders ( p < .001). In addition, the mean score of postpartum depression scale was significantly lower among regular attenders compared with non-attenders ( p < .001). However, there was no significant difference in postpartum depression score among regular and irregular attenders ( p = .257)., Conclusions: Attending prenatal classes was associated with positive childbirth experience and low postpartum depression score.

Objective: Sleep disorders are common during prenatal and postpartum periods which can be associated with physical and psychological maternal and neonatal outcomes. The aim of this study was to determine the psychometric properties of the Persian version of Postpartum Sleep Quality Scale ., Methods: In this study, 280 women who had given birth two to four months prior to the study were selected using cluster sampling in the year 2020. Construct validity of Postpartum Sleep Quality Scale was assessed through exploratory and confirmatory factor analyses. Internal consistency and test-retest were used to determine the reliability of the scale., Results: The content validity index of the scale was 0.88 and the content validity ratio was 0.94. In the exploratory factor analysis, the single-factor structure was extracted. The fit indices confirmed the model validity. The Cronbach's alpha coefficient was 0.78 and the intra-class correlation coefficient (95% confidence interval) was 0.97 (0.94 to 0.98). The criterion validity also showed the positive correlation of the scale with the Pittsburgh Sleep Quality Index., Conclusion: The present study indicates that the Persian version of Postpartum Sleep Quality Scale is a valid and reliable tool for evaluating the postpartum sleep quality in Iranian women., Supplementary Information: The online version contains supplementary material available at 10.1007/s41105-022-00405-5., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Japanese Society of Sleep Research 2022, corrected publication 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Predicting drug-target interactions (DTIs) has become an important bioinformatics issue because it is one of the critical and preliminary stages of drug repositioning. Therefore, scientists are trying to develop more accurate computational methods for predicting drug-target interactions. These methods are usually based on machine learning or recommender systems and use biological and chemical information to improve the accuracy of predictions. In the background of these methods, there is a hypothesis that drugs with similar chemical structures have similar targets. So, the similarity between drugs as chemical information is added to the computational methods to improve the prediction results. The question that arises here is whether this claim is actually true? If so, what method should be used to calculate drug-drug chemical structure similarities? Will we obtain the same improvement from any DTI prediction method we use? Here, we investigated the amount of improvement that can be achieved by adding the drug-drug chemical structure similarities to the problem. For this purpose, we considered different types of real chemical similarities, random drug-drug similarities, four gold standard datasets and four state-of-the-art methods. Our results show that the type and size of data, the method which is used to predict the interactions, and the algorithm used to calculate the chemical similarities between drugs are all important, and it cannot be easily stated that adding drug-drug similarities can significantly improve the results. Therefore, our results could suggest a checklist for scientists who want to improve their machine learning methods., (© 2022. The Author(s).)

Many studies in neuroscience have focused on interpreting brain activity using functional connectivity (FC). The most widely used approach for measuring FC is based on linear correlation (e.g., the Pearson correlation), where the temporal cofluctuations between functional brain regions are computed. However, such approaches ignore nonlinear dependencies among regions that might carry distinctive information across groups of subjects. In this study, we offer a deep learning-based approach that also captures nonlinear temporal relationships between brain networks. Our approach consists of two main parts: an encoder that learns domain-specific embeddings of time courses estimated from independent component analysis (ICA) and a similarity metric that measures the similarities between the embeddings. We call such similarities as nonlinear functional relationships between networks. Our findings on a large dataset (including above 11k normal control subjects) suggest that male subjects exhibit stronger nonlinear network-network relationships than female subjects in most cases. Furthermore, we observe that, unlike FC, our approach could capture some intra-network relationships, especially between cognitive control and visual networks, which are significantly different between males and females, suggesting that our approach can provide a complementary interpretation of the functional brain activity to FC.

Core facilities have a different mission than academic research labs. Accordingly, they require different career paths and structures., (© 2022 The Authors.)

An O/W nanoemulsion of garlic essential oil (GEO) at different oil-to-emulsion (O/E) ratios (5%, 10%, 15%, and 25%) was formulated to protect the volatile components of GEO. The effects of O/E ratios on the encapsulation efficiency (EE%) of volatile compounds and droplet size of nanoemulsions were studied. The results showed that with increasing in E/O ratio, droplet size increased while EE% decreased so that the droplet size was below 100 nm for all samples and the EE% was almost above 80% for most samples. The effects of various factors such as temperature (5°C-45°C), pH values (3-7), ionic strength (0-500 mM), and O/E ratios (5%-25%) on kinetic of nanoemulsions stability were studied. Reducing pH values and raising the temperature, ionic strength, and O/E ratios intensified the instability process and constant rate of instability in all nanoemulsions. The effects of temperature and O/E ratios on the release kinetics of volatile components were evaluated over time, and kinetic parameters such as release rate constant (k), Q10, and activation energy (Ea) were calculated in which results showed a zero-degree model to describe the release kinetic behavior of most nanoemulsions. Both temperature and O/E ratios factors as well as their interaction (which had a synergistic effect) had a significant effect on increasing the release rate of volatiles so that the degree of reaction rate was changed from zero to the first order at simultaneous high levels of both factors. FT-IR spectroscopy was carried out to study interactions among nanoemulsion ingredients. The presence of sulfur-containing functional groups of garlic oil (thiosulphate, diallyl trisulfide, etc.) in nanoemulsions was confirmed by FT-IR., Competing Interests: We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (© 2022 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC.)

This study examined effects of a social marketing intervention to encourage people to donate blood in a southwest city of Iran. To design the intervention, the constructs of theory of planned behavior in 170 consistent blood donors were measured. Persuasive messages were developed, and some printed materials were prepared to transfer the message to the target segment. The trend of the percentage of consistent blood donors was measured during the first four months after the intervention. The percentage of consistent blood donors was increased significantly. The findings showed the effectiveness of the social marketing interventions for blood donation.

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network's learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers., Competing Interests: No authors have competing interests.

Objective: to evaluate the perceptions of primiparous women about the effect of childbirth preparation classes on their childbirth experience., Design: descriptive qualitative study., Participants and Setting: 13 Iranian women who participated in childbirth preparation classes and had a vaginal delivery were interviewed., Measurements: semi-structured interviews were used to collect data., Findings: six main themes were extracted from the data analysis: incentive and learning about pregnancy and childbirth; active participation in labour; sense of self-control; use of non- medical pain relief methods during labour; preferring vaginal birth to caesarean section; and positive childbirth experience., Key Conclusions: women reported that participation in childbirth preparation classes prepared them well for a vaginal birth, and these classes were perceived to be associated with a positive childbirth experience., Implications for Practice: attendance at childbirth preparation classes is perceived to have a positive effect on vaginal birth. Therefore, encouraging and supporting women to attend the full course of classes has the potential to increase women's preference towards vaginal birth, resulting in a reduction in the caesarean section rate., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2021. Published by Elsevier Ltd.)

Modeling the rich, dynamic spatiotemporal variations captured by human brain functional magnetic resonance imaging (fMRI) data is a complicated task. Analysis at the brain's regional and connection levels provides more straightforward biological interpretation for fMRI data and has been instrumental in characterizing the brain thus far. Here we hypothesize that spatiotemporal learning directly in the four-dimensional (4D) fMRI voxel-time space could result in enhanced discriminative brain representations compared to widely used, pre-engineered fMRI temporal transformations, and brain regional and connection-level fMRI features. Motivated by this, we extend our recently reported structural MRI (sMRI) deep learning (DL) pipeline to additionally capture temporal variations, training the proposed 4D DL model end-to-end on preprocessed fMRI data. Results validate that the complex non-linear functions of the used deep spatiotemporal approach generate discriminative encodings for the studied learning task, outperforming both standard machine learning (SML) and DL methods on the widely used fMRI voxel/region/connection features, except the relatively simplistic measure of central tendency - the temporal mean of the fMRI data. Additionally, we identify the fMRI features for which DL significantly outperformed SML methods for voxel-level fMRI features. Overall, our results support the efficiency and potential of DL models trainable at the voxel level fMRI data and highlight the importance of developing auxiliary tools to facilitate interpretation of such flexible models.