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LHPP gene encodes a phospholysine phosphohistidine inorganic pyrophosphate phosphatase, which functions as a tumor-suppressor protein. The tumor suppression by this protein has been confirmed in various cancers, including hepatocellular carcinoma (HCC). LHPP downregulation promotes cell growth and proliferation by modulating the PI3K/AKT signaling pathway. This study identifies potentially deleterious missense single nucleotide variants (SNVs) associated with the LHPP gene using multiple computational tools based on different algorithms. A total of 4 destabilizing mutants are identified as L22P, I212T, G227R , and G236R , from the conserved region of the phosphatase. The 3-dimensional (3D) modeling and structural comparison of variants with the native protein reveals significant structural and conformational variations after mutations, suggesting disruption in the function of phospholysine phosphohistidine inorganic pyrophosphate phosphatase. The identified mutations might, therefore, participate in the cause of HCC.

Abstract Cholinergic treatments with an emphasis on M1 muscarinic acetylcholine receptor (mAChR) agonists as potential modulating agents are a new approach in Alzheimer’s disease (AD) therapy. In previous research, we designed and characterized novel thiazolidine-2,4-dione (TZD)-derived compounds that possess anti-AD properties and enhance the expression of mAChRM1 in rats. This study evaluated a novel orthosteric agonist of mAChRM1 from related pathways that has shown promising anti-Alzheimer’s disease activity. PC12 cells were exposed to various concentrations of TZ4M before they were exposed to scopolamine (3 µM). Immunocytochemistry and western blot analyses revealed that TZ4M increased the expression of mAChRM1 in differentiated cells induced by scopolamine-treated PC12 cells. The results showed that TZ4M (3 and 5 µM) markedly upregulated PKC and ChAT protein expression, and the cells were significantly protected against increased ROS levels followed by neuronal cell loss, as evidenced by the MTT assay. TUNEL staining indicated that TZ4M impeded the shaping of apoptotic bodies. Analysis of the amino acid sequences of the ligand–protein binding site indicated that TZ4M is bound to the orthosteric site (acetylcholine site). This study revealed that TZ4M, a derivative of TZD, effectively protects against scopolamine-induced damage. TZ4M, a novel mACRM1 orthosteric agonist, is promising for treating AD.

Background : Obesity is a global health challenge. Traditional approaches, including increased physical activity, dietary interventions, and medical therapy, often yield limited success, propelling some patients toward costly and invasive procedures like bariatric surgery. Laser acupuncture has been suggested as a complementary therapeutic approach to overcome this challenge. The present study investigated the effectiveness of laser acupuncture treatment in weight loss and abdominal subcutaneous fat reduction. Methods : A randomized, blinded, sham-controlled clinical trial was conducted, with 30 subjects each in the intervention and control groups. Patients in the intervention group underwent 12 sessions of laser acupuncture treatment within a month (three sessions/week), whereas those in the control group received sham laser treatment on identical acupoints. The patients were instructed not to alter their physical activity levels or dietary regimens. All parameters were evaluated before and after the treatment. Results : Significant reductions in weight, body mass index, and waist circumference were noted in both intervention and control groups. Further analysis revealed a more significant decrease in the laser acupuncture group. Abdominal sonography revealed a marked decrease in periumbilical fat thickness in the intervention group. Conversely, laboratory evaluations showed no significant difference between the two groups. Conclusion : Laser acupuncture is an effective method for weight loss in patients with periumbilical abdominal fat. The observed impact on subcutaneous fat suggests its potential as a non-invasive intervention for individuals seeking weight management alternatives. Further research is warranted to validate these findings and explore the underlying mechanisms of laser acupuncture in adipose tissue modulation.

IntroductionDiffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure—compared to water-based methods alone—but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations.MethodsIn this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values.ResultsSimulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature.DiscussionIn conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR.

Background and Objectives: An increase in the antibiotic resistance of Shigella isolates has caused major global challenges in antimicrobial therapy. Knowledge of local antibiotic resistance trends is essential for selecting appropriate antibiotic treatment regimens. This study aimed to evaluate the frequency of efflux-mediated tetracycline resistance (tet) and plasmid-mediated quinolone resistance (qnr) genes among Shigella isolates. Materials and Methods: This survey investigated 91 Shigella isolates, obtained from children with acute diarrhea. The isolates were identified using standard biochemical tests and confirmed by polymerase chain reaction (PCR) assay. Besides, the susceptibility of isolates to six selected antibiotics was assessed by the disk diffusion method. All tetracycline-resistant and nalidixic acid and ciprofloxacin resistant strains were screened for tet and qnr genes by a multiplex PCR assay. Results: According to the results of antibiotic susceptibility tests, the highest level of antibiotic resistance was related to tetracycline (80.2%) and doxycycline (78.1%), respectively. All isolates were sensitive to tigecycline. The PCR results showed that 40.6%, 3.1%, 21.8%, 61.6% and 28.7% of the isolates carried qnrA, qnrB, qnrS, tetA, and tetB genes, respectively. None of the isolates contained tetC and tetD genes. Conclusion: The current findings revealed that tetA and qnrA genes might play a key role in conferring tetracycline and quinolone resistance.

This research was set to uncover Iranian English-as-a-foreign-language (EFL) teachers’ attitudes toward the use of learning management systems (LMS) in English classes. Employing a mixed methods research design, the researchers recruited 168 Iranian EFL teachers from several schools and language institutions as research participants and sent them a valid self-report questionnaire to gather quantitative data. Simultaneously, they conducted semi-structured interviews with 15 teachers to collect qualitative data. Findings revealed that Iranian EFL teachers adopted both positive and negative perceptions of LMS use. The positive perceptions included ease of access, knowledge sharing, suitability for crowded classes, variety of learning tasks, and more advanced assignments and feedback. In turn, negative views comprised inadequacy of teacher training programs, inappropriacy for productive skills, complexity of functions, and students’ unfamiliarity with the systems as well as lack of interest. These results may offer significant insights into EFL practitioners’ perceptions of a variety of measures that can be taken by teacher development programs to encourage the use of LMS in EFL contexts.

Abstract Numerous applications in diffusion MRI involve computing the orientationally-averaged diffusion-weighted signal. Most approaches implicitly assume, for a given b-value, that the gradient sampling vectors are uniformly distributed on a sphere (or ‘shell’), computing the orientationally-averaged signal through simple arithmetic averaging. One challenge with this approach is that not all acquisition schemes have gradient sampling vectors distributed over perfect spheres. To ameliorate this challenge, alternative averaging methods include: weighted signal averaging; spherical harmonic representation of the signal in each shell; and using Mean Apparent Propagator MRI (MAP-MRI) to derive a three-dimensional signal representation and estimate its ‘isotropic part’. Here, these different methods are simulated and compared under different signal-to-noise (SNR) realizations. With sufficiently dense sampling points (61 orientations per shell), and isotropically-distributed sampling vectors, all averaging methods give comparable results, (MAP-MRI-based estimates give slightly higher accuracy, albeit with slightly elevated bias as b-value increases). As the SNR and number of data points per shell are reduced, MAP-MRI-based approaches give significantly higher accuracy compared with the other methods. We also apply these approaches to in vivo data where the results are broadly consistent with our simulations. A statistical analysis of the simulated data shows that the orientationally-averaged signals at each b-value are largely Gaussian distributed.

The significance of alternative assessment in second language (L2) has been widely acknowledged in the literature. However, the implementation of alternative assessment in English as a Foreign Language (EFL) context has not been much welcome by L2 educators and practitioners. To contribute to this line of inquiry, the aim of the present research was set to explore the impact of implementing self-assessment and peer-assessment practices on writing performance and writing self-efficacy of EFL learners in Iran. To this end, a total number of 36 homogeneous intermediate learners were selected as the participants who were then randomly assigned to a self-assessment group (N= 17) and a peer-assessment group (N= 19). As for the treatment of the present study, the participants of the self-assessment group were taught to self-assess their writing tasks, whereas the participants of the peer-assessment group were taught to assess the writings of their peers. Two timed-writing essays and the Writing Self-Efficacy Scale (WSES) wereadministered to collect the data. The results obtained from performing paired-samples t-tests and ANCOVA indicated that both self- and peer-assessment activities significantly contributed to improving writing performance and writing self-efficacy of the participants. Nevertheless, it was revealed that the participants of the peer-assessment group performed better than those in the self-assessment group in terms of both writing performance and writing self-efficacy, showing that peer-assessment activities were more effective than self-assessment activities in increasing writing competencies and self-efficacy of the participants. The findings of the study offer theoretical and pedagogical implications for L2 researchers and practitioners.

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide – one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.

Since teachers and their psychological factors have a significant share of variance in accounting for success in educational contexts, significant number of empirical studies have investigated the associations among intrapsychic variables of teachers. To further examine the inter-connections between individual teacher constructs in English as a Foreign Language (EFL) contexts, this study explored the role of emotion regulation and teacher self-efficacy in predicting teacher burnout in the Chinese EFL context. In so doing, a sample of 174 EFL teachers completed a survey containing the three valid scales measuring these constructs. Structural Equation Modeling was employed to examine the structural model of the variables under investigation. The findings revealed that teacher self-efficacy accounted for 20% of the variance in burnout, whereas emotion regulation represented 11.2% of the teacher burnout variance. Overall, it was revealed that although both variables exerted a significant unique contribution to teacher burnout, teacher self-efficacy seemed to be a stronger predictor of burnout than emotion regulation of teachers. The results might have remarkable implications for EFL teacher development programs.

Objective: To investigate the antimicrobial resistance patterns and prevalence of integrons in Shigella species isolated from children with diarrhea in southwest Iran. Methods: In this study, 1 530 stool samples were collected from children under 15 years with diarrhea referred to teaching hospitals in Ahvaz and Abadan, southwest Iran. Shigella spp. were identified by standard biochemical tests and PCR. The antibiotic resistance pattern of all Shigella isolates was determined by the disk diffusion method and minimum inhibitory concentration (MIC) by E-test. Results: Of 1 530 stool samples, 91 (5.9%, 91/1 530) were positive for Shigella spp. the most common Shigella isolates were Shigella flexneri 47 (51.6%, 47/1 530). Antibiotic susceptibility tests showed that the highest antibiotic resistance was related to trimethoprim-sulfamethoxazole (87.9%, 80/91) and ampicillin (86.8%, 79/91). Multiplex PCR results revealed that 56% and 86.9% of Shigella isolates carried integron class I and integron class II genes, respectively. None of the isolates included the integron class III gene. Conclusions: The high prevalence of multi-drug resistance in Shigella isolates in our area increases the concerns about dissemination of the antibiotic-resistant isolates in this bacterium.

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique to probe tissue microstructure. Conventional Stejskal–Tanner diffusion encoding (i.e., encoding along a single axis), is unable to disentangle different microstructural features within a voxel; If a voxel contains microcompartments that vary in more than one attribute (e.g., size, shape, orientation), it can be difficult to quantify one of those attributes in isolation using Stejskal–Tanner diffusion encoding. Multidimensional diffusion encoding, in which the water diffusion is encoded along multiple directions in q-space (characterized by the so-called “b-tensor”) has been proposed previously to solve this problem. The shape of the b-tensor can be used as an additional encoding dimension and provides sensitivity to microscopic anisotropy. This has been applied in multiple organs, including brain, heart, breast, kidney and prostate. In this work, we discuss the advantages of using b-tensor encoding in different organs.

Background. Most of the patients with hypertension (HTN) who undergo medical therapy unaccompanied by psychological and behavioral interventions may not achieve their goal in HTN treatment. Self-care is a key factor in controlling HTN. Given that depression, stress, and anxiety are the most psychological disorders in chronic illnesses. Their impact on self-care, quality of life, and HTN control must be studied more. Methods. We analyzed the difference in medication adherence in 252 patients with low vs. high psychological distress. Also, patients with controlled and uncontrolled HTN were compared according to their psychological distress scores. We further assessed the relation of psychological distress, self-care, and medication adherence with patients’ demographic characteristics. Results. 61.3% of our participants were female with a mean age of 60.6 ± 11.35 and male participants had a mean age of 60.5 ± 11.55. The psychological distress score was significantly higher in women with uncontrolled HTN (p value = 0.044). Also, individuals with controlled HTN tend to have a higher medication adherence score (p value = 0.01) and higher self-care score (p value = 0.033). Hypertensive females had a higher psychological distress score (3.35 ± 2.05) and a lower self-care score (64.05 ± 8.16). There was a positive relationship between age and drug adherence. The self-care score was higher (65.95 ± 7.88) in patients having lower psychological distress levels. Conclusion. A lower psychological distress score can result in better self-care, enhancing the probability of better HTN control; thus, psychological interventions may be necessary for the treatment of HTN. However, more studies are needed to assess the effectiveness of this intervention.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called ’b-tensor’ encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of sphere size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of sphere properties in a non-uniform fashion. It may cause overestimation or underestimation of the spherical compartment size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and radius that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the sphere signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of 7μm, while the second, pure Monte Carlo simulations, yielded a lower limit of 3μm and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical radii estimates particularly challenging. We conclude that due to the aforementioned challenges spherical radii estimates may be biased when the corresponding sphere signal fraction is low, which must be considered.

No Abstract

Introduction: In recent years, many national and international expert groups have considered specific improvements in risk assessment of chemical pollutants. This study considered to assess the risk of workers exposure to air pollutants in an automobile manufacturing in order to evaluate the health risk assessment due to the inhalation exposure. Material and Method: To perform this study, a cross-sectional research was done in 2016. Methods number 1501 and 7602 of the National Institute of occupational safety and Health (NIOSH) were used for sampling and analysis of compounds BTEX and silica in the air. A total of 40 samples of compound BTEX were taken and analyzed by Gas Chromatography-Flame Ionization Detector (GC-FID). A total of 6 samples of silica were collected during the campaign. Silica analyses were performed by using visible spectrophotometry. Risk ranking was calculated using the hazard and exposure rate. Finally, the relative risk of blood cancer caused by exposure to benzene was estimated. Result: The result demonstrated that, workers were exposed to 5 chemicals including silica, benzene, toluene, ethyl-benzene, and xylene during their work in manufactory. Among the pollutants in the breathing zone of workers, Silica and benzene were hazardous chemicals at high risk level. Following the estimation of relative risk of blood cancer caused by exposure to benzene, workers cumulative exposure to benzene was obtained to be 23.1 ppm per year and the capture relative risk was 1.1. The consequence demonstrated that, significant relationships were seen between workers exposure to benzene and both age and work experience, so that degree of exposure decreased steadily with increasing age and experience (P

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant micro-structural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like Diffusion Tensor Imaging (DTI). The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space involving a huge amount of samples (diffusion gradients) for proper reconstruction. A collection of more efficient techniques have been proposed in the last decade based on parametric representations of the EAP, but they still imply acquiring a large number of diffusion gradients with different b-values (shells). Paradoxically, this has come together with an effort to find scalar measures gathering all the q-space micro-structural information probed in one single index or set of indices. Among them, the return-to-origin (RTOP), return-to-plane (RTPP), and return-to-axis (RTAP) probabilities have rapidly gained popularity. In this work, we propose the so-called "Apparent Measures Using Reduced Acquisitions" (AMURA) aimed at computing scalar indices that can mimic the sensitivity of state of the art EAP-based measures to micro-structural changes. AMURA drastically reduces both the number of samples needed and the computational complexity of the estimation of diffusion properties by assuming the diffusion anisotropy is roughly independent from the radial direction. This simplification allows us to compute closed-form expressions from single-shell information, so that AMURA remains compatible with standard acquisition protocols commonly used even in clinical practice. Additionally, the analytical form of AMURA-based measures, as opposed to the iterative, non-linear reconstruction ubiquitous to full EAP techniques, turns the newly introduced apparent RTOP, RTPP, and RTAP both robust and efficient to compute.

Acinetobacter baumannii is one of the most important organisms in nosocomial infections. Antibiotic resistance in this bacterium causes many problems in treating patients. This study aimed to investigate antibiotic resistance patterns and resistance-related, genes in clinical isolates of Acinetobacter baumannii. This descriptive study was conducted on 124 isolates of Acinetobacter baumannii collected from clinical samples in two teaching hospitals in Ahvaz. The antibiotic resistance pattern was determined by disk diffusion. The presence of genes coding for antibiotic resistance was determined using the polymerase chain reaction method. Out of 124 isolates, the highest rate of resistance was observed for rifampin (96.8%). The resistance rate for imipenem, meropenem, colistin, and polymyxin-B were 78.2%, 73.4%, 0.8% and 0.8%, respectively. The distribution of qnrA, qnrB, qnrS, Tet A, TetB, and Sul1genes were 52.6%, 0%, 3.2%, 93.5% 69.2%, and 6.42%, respectively. High prevalence of tetA, tetB, and qnrA genes among Acinetobacter baumannii isolated strains in this study indicate the important role of these genes in multidrug resistance in this bacteria.• Acinetobacter baumannii is an important human pathogen that has attracted the attention of many researchers Antibiotic resistance in this bacterium causes many problems in treating patients.• The resistance rate for imipenem, meropenem, colistin, and polymyxin-B were 78.2%, 73.4%, 0.8% and 0.8%, respectively. The distribution of qnrA, qnrB, qnrS, Tet A, TetB, and Sul1genes were 52.6%, 0%, 3.2%, 93.5% 69.2%, and 6.42%, respectively.

Background: This study estimated the prevalence of respiratory symptoms and disorders among workers exposed to exposure to volatile organic compound (VOCs) in an automobile manufacturing factory in Tehran, Iran in 2016. Methods: Subjects of this case-control research were included 80 samples including 40 workers exposed to different level of BTEX as well as 40 unexposed individuals were considered as control group. Methods 1501 and 7602 presented by the National Institute of Occupational Safety and Health (NIOSH) were used for the sampling and analysis of compounds in the air. Gas Chromatography-Flame Ionization Detector (GC-FID) was used for analysis of compounds of interest. Six silica samples were collected during the campaign. Silica analyses were performed by using visible absorption spectrophotometry system. Lung functions were evaluated for 80 workers (40 exposed, 40 nonexposed) using spirometry system. Results: The average amount of total dust and free silica measured in factory were 7.3±1.04 mg.m-3 and 0.017±0.02 mg.m-3 respectively. Average benzene, toluene, ethyl-benzene and xylene exposure levels in exposed subject’s median were 0.775±0.12, 1.2±2.08, 45.8±8.5, and 42.5±23.9 ppm respectively. Statistical tests showed significant difference between pulmonary function tests (except PEF) of exposed and non-exposed individuals before and after employment (P

In this article, we design a revenue-sharing contract to coordinate inventory control decisions in a serial supply chain consisting of one supplier, one vendor, and one retailer. We assume that the retailer faces Poisson demand and his unsatisfied demands will be lost. The retailer applies one-for-one period policy in which he constantly places an order for one unit of product to the vendor in a predetermined time interval which results in a deterministic demand for the vendor. Vendor orders the required quantity from supplier which will be immediately received at the vendor’s warehouse. Solution procedures are developed to find the equilibrium in the vendor managed inventory program with a revenue-sharing contract. Furthermore, we obtain the optimal order cycle for the retailer and the vendor which minimizes the supply chain’s total cost.

Background: Several effects of volatile organic compounds (VOCs) have been recognized such as toxic and carcinogenic human health effects. To evaluate cancer risk of benzenes, air samples were taken from the working environment of automobile painters in Tehran following inhalation exposure. Methods: To perform this study, a cross-sectional study was performed in 2016. Sampling was carried out by active pump sampler using the NIOSH method 1501. A total of 40 samples of BTEX were analyzed by Gas Chromatography-Flame Ionization Detector (GC-FID). Finally, estimated terms of Chronic Daily Intake (CDI) was performed for cancer risk and Exposed Concentration (EC) for non-cancer. Results: The 4-week average benzene, toluene, ethyl-benzene, and xylene exposure levels in exposed subjects were 0.775 ± 0.12, 1.2 ± 2.08, 45.8 ± 8.5, and 42.5 ± 23.9 ppm, respectively. The results of the study indicated that among all BTEX compounds, toluene had the lowest concentration. The mean cancer risk for workers exposed to benzene and ethyl benzene was estimated at 3.21×10-2 and 3.63×10-2, respectively. The non-carcinogenic risk of exposure to BTEX compounds was higher than the reference hazard level of one. Statistical tests showed a significant difference between concentrations of pollutant in the breathing zone of workers according to age and duration of employment (P