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To recognize and screen common mental disorders in elderlies and determine their relationship with social support in Shiraz, Iran.

Polycystic ovary syndrome is the most common cause of oligo-ovulation and anovulation among women of reproductive age, contributing to infertility. This study aimed to compare the effects of green tea tablets and metformin on ovulation, menstrual cycle regularity, and antioxidant biomarkers in women with polycystic ovary syndrome (PCOS). In this clinical trial study, 94 women with PCOS were randomly assigned to three groups: green tea (n = 33), metformin (n = 29), and control (n = 32). Menstrual status and oxidative stress parameters, including total antioxidant capacity, thiol, and lipid peroxidation, were compared before and 3 months after the intervention among all three groups. Data analysis was conducted using SPSS software version 22 and employing the analysis of variance and paired t-tests. Following the intervention, the mean menstrual cycle duration in the green tea, metformin, and control groups was 32.22 ± 12.78, 48.72 ± 37.06, and 48.53 ± 31.04 days, respectively (P = 0.040). There was no statistically significant difference between the three groups in terms of biochemical, hormonal, and antioxidant indices before and after the intervention (P > 0.05). The intake of green tea tablets was associated with better outcomes in regulating the menstrual cycle in women with PCOS. [ABSTRACT FROM AUTHOR]

Graphs G and H are called cospectral if they have the same characteristic polynomial. If eigenvalues are integral, then corresponding graphs are called integral graph. In this article we introduce a construction to produce pairs of cospectral integral regular graphs. Generalizing the construction of G4(a, b) and G5(a, b) due to Wang and Sun, we define graphs 𝒢4(G,H) and 𝒢5(G,H) and show that they are cospectral integral regular when G is an integral q-regular graph of order m and H is an integral q-regular graph of order (b − 2)m for some integer b ≥ 3.

Living with chronic pain imposes a significant emotional burden, diminishing a person's emotional and cognitive abilities. Consequently, this research aimed to compare the impact of Intensive Short -Term Dynamic Psychotherapy (ISTDP) and Mindfulness -Based Cognitive Therapy(MBCT) on emotional cognitive regulation among chronic pain patients, with a two-month follow-up period. The clinical trial study was conducted in 2022 at the specialty and physiotherapy clinic of Imam Reza (AS) Hospital in Islamshahr City, Iran. Participants with chronic pain were selected through purposive accessible sampling and randomly assigned to three groups: ISTDP group, MBCT group, and a control group, each consisting of 15 individuals. To gather data, the Cognitive Emotion Regulation Questionnaire (CERQ) by Garnevsky et al. (2002) was employed. The results indicated that both ISTDP and MBCT led to improvements in cognitive-emotional regulation scores, including acceptance, rumination, positive refocusing, refocusing on planning, positive reappraisal, and catastrophizing (P<0.05). These positive effects were observed during the follow-up stage as well. However, ISTDP did not significantly impact the self-blame component (P<0.05). Also, there was no statistically significant difference between ISTDP and MBCT in catastrophizing, positive refocusing, and positive reappraisal. However, MBCT demonstrated superior effectiveness in acceptance, rumination and refocusing on planning compared to ISTDP. In conclusion, both intensive short-term dynamic therapy (ISTDP) and cognitive mindfulness therapy (MBCT) can serve as sustainable interventions in healthcare centers to mitigate cognitive -emotional dysregulation in patients with chronic pain. [ABSTRACT FROM AUTHOR]

Polymers applications have been progressively increased in sciences and engineering including chemistry, pharmacology science, and chemical and petroleum engineering due to their attractive properties. Amongst the all types of polymers, partially Hydrolyzed Polyacrylamide (HPAM) is one of the widely used polymers especially in chemistry, and chemical and petroleum engineering. Capability of solution viscosity increment of HPAM is the key parameter in its successful applications; thus, the viscosity of HPAM solution must be determined in any study. Experimental measurement of HPAM solution viscosity is time-consuming and can be expensive for elevated conditions of temperatures and pressures, which is not desirable for engineering computations. In this communication, Multilayer Perceptron neural network (MLP), Least Squares Support Vector Machine approach optimized with Coupled Simulated Annealing (CSA-LSSVM), Radial Basis Function neural network optimized with Genetic Algorithm (GA-RBF), Adaptive Neuro Fuzzy Inference System coupled with Conjugate Hybrid Particle Swarm Optimization (CHPSO-ANFIS) approach, and Committee Machine Intelligent System (CMIS) were used to model the viscosity of HPAM solutions. Then, the accuracy and reliability of the developed models in this study were investigated through graphical and statistical analyses, trend prediction capability, outlier detection, and sensitivity analysis. As a result, it has been found that the MLP and CMIS models give the most reliable results with determination coefficients (R2) more than 0.98 and Average Absolute Relative Deviations (AARD) less than 4.0%. Finally, the suggested models in this study can be applied for efficient estimation of aqueous solutions of HPAM polymer in simulation of polymer flooding into oil reservoirs.