Nigella sativa L. and its bioactive and nutraceutical components in the management of diabetic peripheral neuropathy.

Diabetes-induced hyperglycemia leads to excessive production of oxygen free radicals, inflammatory cytokines, and oxidative stress, which initiates diabetic peripheral neuropathy (DPN). Currently, this condition affects 20% of adults with diabetes. Despite significant advances in the treatment of diabetes, the incidence of its complications, including DPN, is still high. Thus, there is a growing research interest in developing more effective and treatment approaches with less side effects for diabetes and its complications. Nigella sativa L. (NS) has received much research attention as an antioxidant, anti-yperglycemic factor, and anti-inflammatory agent. This natural compound demonstrates its antidiabetic neuropathy effect through various pathways, including the reduction of lipid peroxidation, the enhancement of catalase and superoxide dismutase enzyme activity, and the decrease in inflammatory cytokine levels. The present review focuses on the bioactive and nutraceutical components of black cumin (Nigella sativa L.) and their effects on DPN. In addition, we have also summarized the findings obtained from several experimental and clinical studies regarding the antidiabetic neuropathy effect of NS in animal models and human subjects.

Preemptive and non-preemptive multi-skill multi-mode resource-constrained project scheduling problems considering sustainability and energy consumption: A comprehensive mathematical model.

Modern project managers cope with significant challenges to schedule and control projects considering dynamic environments, frequent uncertainties, strict project deadlines, and stricter sustainable requirements above all. Sustainability taking into account resource utilization has been recently associated with project management. Hence, this paper presents a new mixed-integer linear programming (MILP) model with two objectives for a resource-constrained project scheduling problem (RCPSP) with multiple skills and multiple modes, assuming preemptive and non-preemptive activities in an uncertain environment. Given the importance of sustainable developments in projects, the considered objectives are to maximize job opportunities and minimize project duration, resource costs, and total energy consumption. To deal with the model, an AUGNMECON2VIKOR algorithm is utilized to create Pareto solutions. In this model, project activities can be crashed by allocating extra resources. Furthermore, multi-skill resources are used to perform project activities. This study also investigates the impact of these resources on project scheduling. To deal with uncertain circumstances, a fuzzy chance-constrained programming method is employed to develop a robust possibilistic programming model. With respect to the increasing significance of sustainability in project management, this study pioneers the examination of the impact of sustainable factors on project scheduling. Finally, the proposed formulation is validated using instances from the well-known PSPLIB and MMLIB test sets. Finally, a comparison is drawn between the presented solution method considering AUGMECON2VIKOR and AUGMECON2.

Decoding children dental health risks: a machine learning approach to identifying key influencing factors.

Introduction and objectives: This study investigates key factors influencing dental caries risk in children aged 7 and under using machine learning techniques. By addressing dental caries' prevalence, it aims to enhance early identification and preventative strategies for high-risk individuals. Methods: Data from clinical examinations of 356 children were analyzed using Logistic Regression, Decision Trees, and Random Forests models. These models assessed the influence of dietary habits, fluoride exposure, and socio-economic status on caries risk, emphasizing accuracy, precision, recall, F1 score, and AUC metrics. Results: Poor oral hygiene, high sugary diet, and low fluoride exposure were identified as significant caries risk factors. The Random Forest model demonstrated superior performance, illustrating the potential of machine learning in complex health data analysis. Our SHAP analysis identified poor oral hygiene, high sugary diet, and low fluoride exposure as significant caries risk factors. Conclusion: Machine learning effectively identifies and quantifies dental caries risk factors in children. This approach supports targeted interventions and preventive measures, improving pediatric dental health outcomes. Clinical significance: By leveraging machine learning to pinpoint crucial caries risk factors, this research lays the groundwork for data-driven preventive strategies, potentially reducing caries prevalence and promoting better dental health in children.

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities.

This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it's important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD.

Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions.

This research investigates the application of machine learning to improve the diagnosis of tinnitus using high-frequency audiometry data. A Logistic Regression (LR) model was developed alongside an Artificial Neural Network (ANN) and various baseline classifiers to identify the most effective approach for classifying tinnitus presence. The methodology encompassed data preprocessing, feature extraction focused on point detection, and rigorous model evaluation through performance metrics including accuracy, Area Under the ROC Curve (AUC), precision, recall, and F1 scores. The main findings reveal that the LR model, supported by the ANN, significantly outperformed other machine learning models, achieving an accuracy of 94.06%, an AUC of 97.06%, and high precision and recall scores. These results demonstrate the efficacy of the LR model and ANN in accurately diagnosing tinnitus, surpassing traditional diagnostic methods that rely on subjective assessments. The implications of this research are substantial for clinical audiology, suggesting that machine learning, particularly advanced models like ANNs, can provide a more objective and quantifiable tool for tinnitus diagnosis, especially when utilizing high-frequency audiometry data not typically assessed in standard hearing tests. The study underscores the potential for machine learning to facilitate earlier and more accurate tinnitus detection, which could lead to improved patient outcomes. Future work should aim to expand the dataset diversity, explore a broader range of algorithms, and conduct clinical trials to validate the models' practical utility. The research highlights the transformative potential of machine learning, including the LR model and ANN, in audiology, paving the way for advancements in the diagnosis and treatment of tinnitus.

The Effectiveness of Transcranial Direct Current Stimulation (tDCS) and Omega-3 on Food Craving, Executive Functions, Weight, and Depressive Symptoms in Patients with Depression and Overweight: A Randomized Controlled Trial.

Objective: The most important hypothesis of this research was based on the fact that the mechanism of the effect of omega-3 on depression and obesity is formed through its accumulation in the dorsolateral prefrontal cortex (DLPFC), especially in women. Accordingly, we investigated the omega-3 intake and the concurrent stimulation of the DLPFC by tDCS and hypothesized that the synergy of these two treatments can increase the obtained effect size in patients with depression and overweight. Method : This research was a double-blind randomized controlled trial (RCT) with a factorial design consisting of four treatment and control groups. The participants were females with depression and overweight on an outpatient basis. They received 5 ml/day omega-3 syrup (545 mg DHA, 620 mg EPA) or placebo adjunct with 12 sessions sham/tDCS stimulation administered for 3 weeks with anode-left/cathode-right protocol in the prefrontal cortex (1.5 mA, 15 minutes' stimulation / 15-20 minutes' rest intervals/one visit per week, 4 stimulations per visit). Results: tDCS or omega-3 alone did not significantly improve the executive functions, depression, food cravings, and weight in the experimental groups compared to the control group (P > 0.05). However, tDCS adjunct with the omega-3 had a significant and positive effect on improving weight change (P = 0.011; df = 1; F = 1.27; Eta = 0.108) with a power of 0.73 compared to the control group. Furthermore, their interaction led to an improving trend in executive functions and a decreasing trend in food cravings which are clinically important. Conclusion: tDCS could strengthen the omega-3 mechanisms of effect through stimulating its accumulation site in the brain (i.e., the DLPFC) and the synergistic effects of these two treatments result in weight control as well as an improvement trend in the executive functions and food craving in women.

A novel data augmentation approach for influenza A subtype prediction based on HA proteins.

Influenza, a pervasive viral respiratory illness, remains a significant global health concern. The influenza A virus, capable of causing pandemics, necessitates timely identification of specific subtypes for effective prevention and control, as highlighted by the World Health Organization. The genetic diversity of influenza A virus, especially in the hemagglutinin protein, presents challenges for accurate subtype prediction. This study introduces PreIS as a novel pipeline utilizing advanced protein language models and supervised data augmentation to discern subtle differences in hemagglutinin protein sequences. PreIS demonstrates two key contributions: leveraging pre-trained protein language models for influenza subtype classification and utilizing supervised data augmentation to generate additional training data without extensive annotations. The effectiveness of the pipeline has been rigorously assessed through extensive experiments, demonstrating a superior performance with an impressive accuracy of 94.54% compared to the current state-of-the-art model, the MC-NN model, which achieves an accuracy of 89.6%. PreIS also exhibits proficiency in handling unknown subtypes, emphasizing the importance of early detection. Pioneering the classification of HxNy subtypes solely based on the hemagglutinin protein chain, this research sets a benchmark for future studies. These findings promise more precise and timely influenza subtype prediction, enhancing public health preparedness against influenza outbreaks and pandemics. The data and code underlying this article are available in https://github.com/CBRC-lab/PreIS.

Topical Nigella sativa L. product: a new candidate for the management of diabetic peripheral neuropathy.

BACKGROUND: Diabetic neuropathy is one of the most common complications of diabetes. The synthetic drugs available in the market have side effects and limitations for diabetic patients, the vast majority of whom are in the upper age group. In this regard, based on Persian medicinal sources, Nigella sativa (N. sativa) has proved to have beneficial effects on neuropathic pain and neurological disorders. In this study, the effect of N. sativa is investigated topically in patients with diabetic neuropathy. METHODS: This study was performed as a double-blind clinical trial on 120 neuropathic patients. The patients were divided into three groups. The first group received a topical N. sativa product as an ointment, the second group was given a topical placebo, and the third received 300 mg gabapentin capsules. The blindness was done in first and second groups. Diabetic neuropathy was assessed before the study using the Michigan Neuropathy Screening Instrument (MNSI). In addition, neuropathy symptoms were evaluated after the trial using the MNSI questionnaire. RESULTS: The data were elicited from the patients' answers to a number of questions in the Michigan questionnaire. There were statistically significant differences between the group that received the topical N. sativa product and the other two groups in terms of legs and feet numbness (p value = 0.001), burning pain in feet or legs (p value = 0.001), muscle cramps in feet or legs (p value = 0.001), prickling fleeing in feet or legs (p value = 0.001), hurting of the skin when the bed covers touch it (p value = 0.005), aggravated symptoms at night (p value = 0.001) and hurting feelings in the legs when walking (p value = 0.032). However, the three studied groups were not statistically different in distinguishing hot water from cold water. CONCLUSION: According to the results of this study, the topical use of N. sativa, compared to the current drugs, has acceptable improving effects on diabetic neuropathic patients.

Long-Term Major Adverse Cardiovascular Events in Patients with Moderate and Severe COVID-19: A Focus on Early Statin Use and Previous CVD.

Background: Limited data exist regarding the status of long-term cardiovascular disease (CVD) outcomes of hospitalized COVID-19 patients. We aimed to examine the efficacy of early statin use after SARS-CoV-2 pneumonia and the impact of prior CVD on the incidence of cardiovascular events. Methods: A prospective cohort study was performed on hospitalized COVID-19 patients. The primary endpoint was major adverse cardiovascular events (MACE) as a composite of cardiovascular mortality, stroke, heart failure, venous thromboembolism (VTE), revascularization, and nonfatal myocardial infarction (MI). The secondary endpoints comprised MACE components, all-cause mortality, readmission for COVID-19, and impaired functional classes. Results: The mean age of the 858 participants was 55.52±13.97 years, and the median follow-up time was 13 months (11.5-15). Men comprised 63.9% of the patients. Overall, MACE occurred in 84 subjects (9.8%), and 98 patients (11.4%) received ventilation. A multivariate Cox regression model was employed to explore the association between statin use and outcomes, and the following hazard ratios were obtained: MACE (0.831 [0.529 to 0.981]; P=0.044), All-cause mortality (1.098 [0.935 to 1.294]; P=0.255), stroke (0.118 [0.029 to 0.48]; P=0.003), revascularization (0.103 [0.029 to 0.367]; P<0.0001), poor functional capacity (0.827 [0.673 to 1.018]; P=0.073), nonfatal MI (0.599 [0.257 to 1.394]; P=0.234), VTE (0.376 [0.119 to 1.190]; P=0.096), and decompensated heart failure (0.137 [0.040 to 0.472]; P=0.002). Prior CVD predicted MACE (2.953 [1.393 to 6.271]; P=0.005), all-cause death (1.170 [0.960 to 1.412]; P=0.102), and VTE (2.770 [0.957 to 8.955]; P=0.051). Conclusion: Previous CVD is a robust predictor of long-term MACE and VTE. Early statin use might decrease the incidence rates of MACE, ischemic stroke, revascularization, and readmission for heart failure.

Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification.

BACKGROUND: Acute pulmonary embolism (PE) is a critical medical emergency that necessitates prompt identification and intervention. Accurate prognostication of early mortality is vital for recognizing patients at elevated risk for unfavourable outcomes and administering suitable therapy. Machine learning (ML) algorithms hold promise for enhancing the precision of early mortality prediction in PE patients. OBJECTIVE: To devise an ML algorithm for early mortality prediction in PE patients by employing clinical and laboratory variables. METHODS: This study utilized diverse oversampling techniques to improve the performance of various machine learning models including ANN, SVM, DT, RF, and AdaBoost for early mortality prediction. Appropriate oversampling methods were chosen for each model based on algorithm characteristics and dataset properties. Predictor variables included four lab tests, eight physiological time series indicators, and two general descriptors. Evaluation used metrics like accuracy, F1_score, precision, recall, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves, providing a comprehensive view of models' predictive abilities. RESULTS: The findings indicated that the RF model with random oversampling exhibited superior performance among the five models assessed, achieving elevated accuracy and precision alongside high recall for predicting the death class. The oversampling approaches effectively equalized the sample distribution among the classes and enhanced the models' performance. CONCLUSIONS: The suggested ML technique can efficiently prognosticate mortality in patients afflicted with acute PE. The RF model with random oversampling can aid healthcare professionals in making well-informed decisions regarding the treatment of patients with acute PE. The study underscores the significance of oversampling methods in managing imbalanced data and emphasizes the potential of ML algorithms in refining early mortality prediction for PE patients.

The association between plant and animal protein intake and quality of life in patients undergoing hemodialysis.

Background: Hemodialysis (HD) patients often experience a significant reduction in quality of life (QOL). The source of dietary protein intake may influence the renal function and complications of HD patients. The present study assessed the relationship between plant and animal protein intake and QOL in HD patients. Methods: 264 adult patients under dialysis for at least three months were included in this cross-sectional study. Dietary intakes were collected using a valid and reliable 168-item semi-quantitative food frequency questionnaire (FFQ) over the past year. Total, animal, and plant proteins were calculated for each patient. To evaluate QOL, Kidney Disease Quality of Life Short Form (KDQOL-SF 1/3) was used. Anthropometric measures were assessed according to standard protocols. Results: In this study, the average age of participants was 58.62 ± 15.26 years old; most (73.5%) were men. The mean of total, plant, and animal proteins intake were 66.40 ± 34.29 g/d, 34.60 ± 18.24 g/d, and 31.80 ± 22.21 g/d. Furthermore, the mean score of QOL was 59.29 ± 18.68. After adjustment for potential confounders, a significant positive association was found between total dietary protein intake and QOL (ß = 0.12; p = 0.03). Moreover, there was a significant association between plant-based protein intake and QOL (ß = 0.26; p < 0.001). However, the association between animal protein intake and QOL was insignificant (ß = 0.03; p = 0.60). Conclusion: Higher total and plant proteins intake were associated with better QOL in HD patients. Further studies, particularly prospective ones, are needed to corroborate these associations.

Effect of a moderately carbohydrate-restricted diet on liver enzymes, steatosis and fibrosis in normal-weight individuals with non-alcoholic fatty liver disease: study protocol for a parallel randomised controlled clinical trial.

INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) is a hepatic condition that is considerably prevalent across the world. Dietary intakes, in which macronutrient composition is precisely planned, might be able to reduce inflammation, steatosis and fibrosis among patients with NAFLD. A moderately carbohydrate restricted diet with weight loss has been demonstrated to improve liver fat content among overweight or obese patients. However, there is no information about the appropriateness of such a restriction, without weight loss, in normal-weight patients. This randomised clinical trial will be aimed at assessing the effect of moderate carbohydrate restriction on liver enzymes, liver steatosis and fibrosis in normal-weight patients with NAFLD. METHODS AND ANALYSIS: This randomised controlled clinical trial will be conducted to evaluate the impact of a moderately carbohydrate restricted diet on liver enzymes, steatosis and fibrosis in 52 eligible normal-weight individuals with NAFLD. Transient elastography and controlled attenuation parameter with FibroScan will be applied to diagnose NAFLD. After individual matching based on body mass index, age and sex, patients will be randomly assigned to receive a moderately carbohydrate restricted diet or an isocaloric diet without carbohydrate restriction for 12 weeks. The primary and secondary outcomes in this study will be liver function indices, including liver steatosis and fibrosis, metabolic parameters and anthropometric measures. All these variables will be assessed at study baseline and postintervention. ETHICS AND DISSEMINATION: The present clinical trial study was accepted by the ethics committee of TUMS (Tehran University of Medical Sciences) (code: IR.TUMS.MEDICINE.REC.1400.116). TRIAL REGISTRATION NUMBER: IRCT20210119050086N1.

An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals.

BACKGROUND: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer's disease (AD). The extraction of appropriate biomarkers to assess a subject's cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. METHODS: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study's feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. RESULTS: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. CONCLUSION: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers.

Utilization of health services among the elderly in Iran during the COVID-19 outbreak: A cross-sectional study.

Background and Aims: Elderly people are potentially vulnerable with a higher need for health services, and utilization of Essential Public Health Services (EPHS) among this group is of high importance. This study aimed to examine the utilization of health services among the elderly in Iran during the coronavirus disease 2019 outbreak. Methods: This was a cross-sectional study conducted in 21 public health centers in Sirjan, Southern Iran, from May to December 2020. A total of 420 elderly patients were selected through a systematic random sampling method. Data were collected using a questionnaire and were analyzed using SPSS v22.0. The binary logistic regression was used to examine the effect of demographic, socioeconomic and morbidity status on inpatient and outpatient healthcare utilization. Results: Our results showed that 56% of the elderly had a history of hospitalization during the last year. Although 60% of the elderly reported they had a perceived need for outpatient services, only 49% of them reported that they utilized outpatient services. 51% and 35.5% of the elderly reported that their inpatient and outpatient costs were covered by health insurance, respectively. Others reported their health spending was financed through out-of-pocket payments. Male gender aged 80 and above, urban residents, higher socioeconomic and supplemental insurance coverage were associated with an increase in health services utilization. The elderly with Cancer, mental disorders, kidney disease, and cardiovascular diseases (CVDs) were more likely to be hospitalized. Conclusion: There were demographic and socioeconomic inequalities in health services utilization among the elderly. Therefore, appropriate interventions and strategies are needed to reduce these inequalities in health services utilization among the elderly. In addition, given that the hospitalization rate was significantly higher among the elderly with chronic diseases than those without, it is crucial and necessary to take interventions to reduce the burden of chronic diseases in the future.

Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning.

BACKGROUND: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. AIM: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. DESIGN: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0-5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model's accuracy, specificity, and sensitivity. RESULTS: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). CONCLUSION: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling.

Evaluation of COVID-19 pandemic on components of social and mental health using machine learning, analysing United States data in 2020.

Background: COVID-19 was named a global pandemic by the World Health Organization in March 2020. Governments across the world issued various restrictions such as staying at home. These restrictions significantly influenced mental health worldwide. This study aims to document the prevalence of mental health problems and their relationship with the quality and quantity of social relationships affected by the pandemic during the United States national lockdown. Methods: Sample data was employed from the COVID-19 Impact Survey on April 20-26, 2020, May 4-10, 2020, and May 30-June 8, 2020 from United States Dataset. A total number of 8790, 8975, and 7506 adults participated in this study for April, May and June, respectively. Participants' mental health evaluations were compared clinically by looking at the quantity and quality of their social ties before and during the pandemic using machine learning techniques. To predict relationships between COVID-19 mental health and demographic and social factors, we employed random forest, support vector machine, Naive Bayes, and logistic regression. Results: The result for each contributing feature has been analyzed separately in detail. On the other hand, the influence of each feature was studied to evaluate the impact of COVID-19 on mental health. The overall result of our research indicates that people who had previously been diagnosed with any type of mental illness were most affected by the new constraints during the pandemic. These people were among the most vulnerable due to the imposed changes in lifestyle. Conclusion: This study estimates the occurrence of mental illness among adults with and without a history of mental disease during the COVID-19 preventative limitations. With the persistence of quarantine limitations, the prevalence of psychiatric issues grew. In the third survey, which was done under quarantine or house restrictions, mental health problems and acute stress reactions were substantially greater than in the prior two surveys. The findings of the study reveal that more focused messaging and support are needed for those with a history of mental illness throughout the implementation of restrictions.

Impact of Baseline Estimated Glomerular Filtration Rate Using CKD-EPI Equation on Long-term Prognosis of STEMI Patients: A Matter of Small Increments!

BACKGROUND: Baseline biomarkers including glomerular filtration rate (GFR) guide the management of patients with ST-segment elevation myocardial infarction (STEMI). GFR is a tool for prediction of adverse outcomes in these patients. OBJECTIVES: We aimed to determine the prognostic utility of estimated GFR using Chronic Kidney Disease Epidemiology Collaboration in a cohort of STEMI patients. METHODS: A retrospective cohort was designed among 5953 patients with STEMI. Primary endpoint of the study was major adverse cardiovascular events. GFR was classified into 3 categories delineated as C1 (<60 mL/min), C2 (60-90), and C3 (≥ 90). RESULTS: Mean age of the patients was 60.38 ± 5.54 years and men constituted 78.8% of the study participants. After a median of 22 months, Multivariate Cox-regression demonstrated that hazards of major averse cardiovascular event, all-cause mortality, cardiovascular mortality, and nonfatal myocardial infarction were significantly lower for subjects in C3 as compared with those in C1. Corresponding hazard ratios (HRs) for mentioned outcomes regarding C3 versus C1 were (95% confidence interval) were (HR = 0.852 [0.656-0.975]; P = 0.035), (HR = 0.425 [0.250-0.725]; P = 0.002), (HR = 0.425 [0.242-0.749]; P = 0.003), and (0.885 [0.742-0.949]; P = 0.003), respectively. Normal GFR was also associated with declined in-hospital mortality with HR of C3 versus C1: 0.299 (0.178-0.504; P < 0.0001). CONCLUSIONS: Baseline GFR via Chronic Kidney Disease Epidemiology Collaboration is associated with long-term cardiovascular outcomes following STEMI.

The Relationship of Parental Personality Disorders with Offspring Eating Disorders at Childhood and Adolescence Age.

This study aimed to investigate parents' PDs that could be associated with children and adolescents' EDs. We studied association of parental PDs with offspring EDs in age group 6-18 years in a nationally representative sample of Iranians with 27,111 children and adolescents and their parents. We used a multistage random cluster sampling method. We used Millon Clinical Multiaxial Inventory-Third Edition and Persian present and lifetime version of Kiddie Schedule for Affective Disorders and Schizophrenia to measure parental PDs and children and adolescents' EDs, respectively. We used descriptive statistics and binary logistic regression analysis methods to analyze the data. Maternal but not paternal PDs were significantly associated with EDs in offspring. Maternal antisocial, borderline, schizoid, histrionic, and compulsive PDs were significantly associated with EDs in offspring by 32.06, 4.66, 4.32, 3.15, and 1.71 odd ratios, respectively. Of EDs in offspring, anorexia nervosa and binge ED were significantly associated with maternal PDs.

Impact of Octreotide on Early Complications After Liver Transplant: A Randomized, Double-Blind Placebo-Controlled Trial.

OBJECTIVES: Acute kidney injury and early allograft dysfunction are 2 common complications after liver transplant. Octreotide, through its various mechanisms, may have a role in preventing these complications. MATERIALS AND METHODS: In this randomized, double- blind placebo-controlled clinical trial, we randomly assigned 50 patients who underwent deceased donor liver transplant and fulfilled the study inclusion requirements to receive either octreotide infusion for 3 days in the first 3 days after transplant in the intensive care unit or placebo. The eligible patients were properly informed while on the transplant wait list and gave their consent to participate in the study. The rates of acute kidney injury within the first 7 days after transplant (based on KDIGO criteria), early allograft dysfunction, and nosocomial infection; total length of hospital stays and intensive care unit admissions; and intubation time were recorded and compared between the 2 groups. RESULTS: No significant differences were found between the 2 groups with regard to demographic characteristics and graft factors (P > .05). However, acute kidney injury, early allograft dysfunction, and nosocomial infection rates were significantly lower in the octreotide group compared with the control group (P < .05). Moreover, a significant difference was observed between the 2 groups with regard to length of hospital stay and intensive care unit admissions (P < .05). For infection, female patients had a higher likelihood of infection than male patients (odds ratio = 23.19). Intensive care unit admission was associated with a higher probability of early graft dysfunction (odds ratio = 1.34). In contrast, longer intubation time was associated with a decrease in the probability of early graft dysfunction (odds ratio = 0.93). CONCLUSIONS: This study showed that octreotide infusion in the first 3 days after liver transplant could improve renal and allograft function and reduce infection and length of hospital stay.