Spouse-assisted training in pain coping skills and the outcome of multidisciplinary pain management for chronic low back pain treatment: a 1-year randomized controlled trial

This study examined the comparative efficacy of three interventions: a spouse-assisted coping skills training protocol for patients undergoing a multidisciplinary pain management programme (SA-MPMP), conventional patient-oriented multidisciplinary pain management programme (P-MPMP) and standard medical care (SMC). Thirty-six chronic low back pain (CLBP) patients and their spouses were randomly assigned to one of the three conditions. The SA-MPMP condition consisted of seven, weekly, 2-h, group sessions of training in dyadic pain coping and couple skills, delivered by a clinical psychologist with support of a multidisciplinary team of specialists, to patients together with their spouses. P-MPMP consisted of the SA-MPMP training delivered to the patient only (i.e., no spouse participation and assistance). The SMC condition entailed continuation of routine treatment, entailing medical care only. Data analysis revealed that, at the 12-month follow-up time point, patients receiving SA-MPMP had significant improvements in kinesiophobia and rumination about pain compared to those receiving P-MPMP and SMC. In patients suffering from CLBP, an intervention that combines spouse-assisted coping skills training with a multidisciplinary pain management programme can improve fear of movement and rumination about low back pain.

Nitrous oxide dimer: Observation of a new polar isomer

Spectra of the nitrous oxide dimer (N2O)2 are studied in the region of the N2O nu1 fundamental band around 2230 cm-1 using a rapid-scan tunable diode laser spectrometer to probe a pulsed supersonic jet expansion. The previously known band of the centrosymmetric nonpolar dimer is analyzed in improved detail, and a new band is observed and assigned to a polar isomer of (N2O)2. This polar form of the dimer has a slipped parallel structure, rather similar to the slipped antiparallel structure of the nonpolar form but with a slightly larger intermolecular distance. The accurate rotational parameters determined here should enable a microwave observation of the polar N2O dimer. The need for a modern ab initio investigation of the N2O-N2O intermolecular potential energy surface is emphasized.

Modification of attentional biases in chronic pain patients: a preliminary study

Research suggests that chronic pain patients demonstrate cognitive biases towards pain-related information and that such biases predict patient functioning. This study examined the degree to which a successful cognitive-behavioural program was able to reduce the observed attentional bias towards sensory pain words. Forty-two patients with chronic pain conditions for more than three months were recruited prior to commencing a cognitive-behavioural pain management program. Participants were assessed before the program, after the program and at one-month follow-up. Results confirmed that chronic pain patients exhibited biased attention towards sensory pain-related words at pre-treatment. These biases were still evident at post-treatment, but were no longer statistically significant at follow-up. Multiple regression analyses indicated that the changes in attentional bias towards sensory words between post-treatment and follow-up were predicted by pre- to post-treatment changes in fear of movement (Tampa Scale for Kinesiophobia) but not other relevant variables, such fear of pain or anxiety sensitivity. These results demonstrate that successful cognitive-behavioural treatments can reduce selective attention, thought to be indicative of hypervigilance towards pain. Moreover, these biases appear to be changed by reducing the fear associated with movement. Theoretically, these results provide support for the fear of (re)injury model of pain. Clinically, this study supports the contention that fear of (re)injury and movement is an appropriate target of pain management and that reducing these fears causes patients to attend less to pain-related stimuli.

Hip circumference, height and risk of type 2 diabetes: systematic review and meta-analysis

Although several epidemiological studies have investigated the relationship between type 2 diabetes mellitus (T2DM) and hip circumference or height, the results are inconsistent. The present systematic review and meta-analysis of published observational studies was conducted to assess the effects of hip circumference and height on diabetes risk. Online databases were searched through January 2012, and the reference lists of pertinent articles reporting observational studies in humans were examined. Pooled relative risks (RR) and 95% confidence intervals (CI) were calculated with a random-effects model. Eighteen studies (nine cross-sectional and nine cohort) were included, with 250,497 participants and 7,765 cases of T2DM. Hip circumference was inversely associated with an increased risk of T2DM in men (summary RR [95% CI] 0.60 [0.45, 0.80]) and women (0.54 [0.42, 0.70]). These results were consistent between cross-sectional and cohort studies. An inverse association between height and T2DM was observed in women only (summary RR [95% CI] 0.83 [0.73, 0.95]). Our meta-analysis strongly supports an inverse relationship between hip circumference and risk of T2DM in men and women. The inverse association between height and risk was significant only in women.

Observation of the “missing” polar OCS dimer

A new infrared band at 2069.3 cm-1 is observed and assigned to the long-anticipated polar isomer of the OCS dimer, helping to explain apparent discrepancies among earlier studies. The data reported here should enable direct observation of the microwave spectrum of polar (OCS)2 and motivate new theoretical works on the energetics of OCS dimer isomers and interconversion energy barriers.

Liver transplantation for homozygous familial hypercholesterolemia: two case reports

Homozygous familial hypercholesterolemia (HFHC) is a rare inherited condition with an incidence of one in one million. It is associated with severe premature atherosclerosis and early death from cardiovascular complications. Mutation in the gene that encodes the synthesis of the cellular receptor for low-density lipoprotein (LDL) is responsible for this metabolic disorder. Currently, the only effective treatment for this disease is liver transplantation, which alone or in association with medications, normalizes plasma cholesterol level. The authors report the results of liver transplantation for two cases of HFHC. The first case, a 15-year-old boy received a whole liver from a deceased donor, and the second, an 11-year-old boy, received a left liver lobe transplant from his mother's sister. Their preoperative fasting lipid concentrations were grossly raised. The older boy had severe atherosclerotic heart disease and had undergone coronary artery bypass grafting 5 months before transplantation. Both had preoperative plasma cholesterol levels higher than 750 mg/dL with normal thyroid and liver function tests. After the operation, the patients received methylprednisolone as pulse therapy followed by oral prednisolone, mycophenolate mofetil, and tacrolimus for immunosuppression. Their hospital stays were 24 and 13 days, respectively. The first case needed reexploration because of bleeding on the second day after the operation. The lipid concentrations rapidly returned to the normal range in the first week after the operation, remaining in this range over the first 6 months of follow-up. Liver transplantation offers an highly effective treatment for HFHC. It is better to operate on patients before severe atherosclerotic changes in the coronary arteries. All patients must undergo a complete cardiac evaluation before surgery.

Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization

Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork Optimization Algorithm (TOA) is presented to solve various optimization problems. The main idea in designing the TOA is to simulate the teamwork behaviors of the members of a team in order to achieve their desired goal. The TOA is mathematically modeled for usability in solving optimization problems. The capability of the TOA in solving optimization problems is evaluated on a set of twenty-three standard objective functions. Additionally, the performance of the proposed TOA is compared with eight well-known optimization algorithms in providing a suitable quasi-optimal solution. The results of optimization of objective functions indicate the ability of the TOA to solve various optimization problems. Analysis and comparison of the simulation results of the optimization algorithms show that the proposed TOA is superior and far more competitive than the eight compared algorithms.

A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process

In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications.

Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm

Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal.

Subtraction-Average-Based Optimizer: A New Swarm-Inspired Metaheuristic Algorithm for Solving Optimization Problems

This paper presents a new evolutionary-based approach called a Subtraction-Average-Based Optimizer (SABO) for solving optimization problems. The fundamental inspiration of the proposed SABO is to use the subtraction average of searcher agents to update the position of population members in the search space. The different steps of the SABO’s implementation are described and then mathematically modeled for optimization tasks. The performance of the proposed SABO approach is tested for the optimization of fifty-two standard benchmark functions, consisting of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results show that the proposed SABO approach effectively solves the optimization problems by balancing the exploration and exploitation in the search process of the problem-solving space. The results of the SABO are compared with the performance of twelve well-known metaheuristic algorithms. The analysis of the simulation results shows that the proposed SABO approach provides superior results for most of the benchmark functions. Furthermore, it provides a much more competitive and outstanding performance than its competitor algorithms. Additionally, the proposed approach is implemented for four engineering design problems to evaluate the SABO in handling optimization tasks for real-world applications. The optimization results show that the proposed SABO approach can solve for real-world applications and provides more optimal designs than its competitor algorithms.

The potential impact of Covid-19 on CNS and psychiatric sequels

Due to its high prevalence and fatality, the current Severe Acute Respiratory Syndrome-coronavirus-2 (SARS-CoV-2) virus, which first emerged in China in 2019, quickly spread around the world and immediately became a serious global health concern. Although respiratory issues were initially the most prominent symptom of coronavirus disease 2019 (COVID-19), it became obvious rapidly that COVID-19, like many other coronavirus family members, could affect the central nervous system (CNS). During the pandemic, CNS involvement expressed itself in a variety of forms, including insomnia, anosmia, headaches, encephalopathies, encephalitis, cerebrovascular accidents, cognitive and memory impairment, and increased psychiatric disorders. Almost everyone who has been infected has at least one of these neurological symptoms, demonstrating that the virus has a high ability to impact the CNS. As the coronavirus pandemic passes its second year, the manifestations it can cause in the long run, such as its psychological sequels, have not yet been thoroughly studied. Given the high importance of this issue in today's society and due to the lack of reliable knowledge about the COVID-19 landscape on psychiatric disorders, we intend to investigate coronavirus's possible effect on mental illnesses based on available literature. Because the majority of the psychological effects of the coronavirus can continue for a long period after the pandemic ends, our research can give insight into potential psychiatric sequels associated with COVID-19.

A new optimization algorithm based on mimicking the voting process for leader selection

Stochastic-based optimization algorithms are effective approaches to addressing optimization challenges. In this article, a new optimization algorithm called the Election-Based Optimization Algorithm (EBOA) was developed that mimics the voting process to select the leader. The fundamental inspiration of EBOA was the voting process, the selection of the leader, and the impact of the public awareness level on the selection of the leader. The EBOA population is guided by the search space under the guidance of the elected leader. EBOA's process is mathematically modeled in two phases: exploration and exploitation. The efficiency of EBOA has been investigated in solving thirty-three objective functions of a variety of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and CEC 2019 types. The implementation results of the EBOA on the objective functions show its high exploration ability in global search, its exploitation ability in local search, as well as the ability to strike the proper balance between global search and local search, which has led to the effective efficiency of the proposed EBOA approach in optimizing and providing appropriate solutions. Our analysis shows that EBOA provides an appropriate balance between exploration and exploitation and, therefore, has better and more competitive performance than the ten other algorithms to which it was compared.

Assessment of atrazine distribution in Shiraz soils, south of Iran

The main objective of this study was focused on determining atrazine residual concentration in agricultural soil in Shiraz and its vicinity. Twenty two sampling fields were selected by grid sampling in Shiraz and its vicinity. Atrazine residual concentrations in 0-20, 20-40 and 40-60 cm soil depths were in the range of 15-550 microg kg(-1) soil. Data showed that the concentration of atrazine did not exceed the soil quality standard for agriculture which is 22 mg kg(-1) soil and there was no significant difference between depth and atrazine residual concentration (p > 0.05). Although, in all sampling regions in Shiraz and its vicinity, the concentration of atrazine did not exceed the soil quality standard for agriculture, atrazine leaching and dissipation rate in soil and risk of atrazine ground water pollution must be determined.