Evaluating the mortality and health rate caused by the PM2.5 pollutant in the air of several important Iranian cities and evaluating the effect of variables with a linear time series model.

All over the world, the level of special air pollutants that have the potential to cause diseases is increasing. Although the relationship between exposure to air pollutants and mortality has been proven, the health risk assessment and prediction of these pollutants have a therapeutic role in protecting public health, and need more research. The purpose of this research is to evaluate the ill-health caused by PM2.5 pollution using AirQ + software and to evaluate the different effects on PM2.5 with time series linear modeling by R software version 4.1.3 in the cities of Arak, Esfahan, Ahvaz, Tabriz, Shiraz, Karaj and Mashhad during 2019-2020. The pollutant hours, meteorology, population and mortality information were calculated by the Environmental Protection Organization, Meteorological Organization, Statistics Organization and Statistics and Information Technology Center of the Ministry of Health, Treatment and Medical Education for 24 h of PM2.5 pollution with Excel software. In addition, having 24 h of PM2.5 pollutants and meteorology is used to the effect of variables on PM2.5 concentration. The results showed that the highest and lowest number of deaths due to natural deaths, ischemic heart disease (IHD), lung cancer (LC), chronic obstructive pulmonary disease (COPD), acute lower respiratory infection (ALRI) and stroke in The effect of disease with PM2.5 pollutant in Ahvaz and Arak cities was 7.39-12.32%, 14.6-17.29%, 16.48-8.39%, 10.43-18.91%, 12.21-22.79% and 14.6-18.54 % respectively. Another result of this research was the high mortality of the disease compared to the mortality of the nose. The analysis of the results showed that by reducing the pollutants in the cities of Karaj and Shiraz, there is a significant reduction in mortality and linear modeling provides a suitable method for air management planning.

Identification of Risk Factors Associated with Tuberculosis in Southwest Iran: A Machine Learning Method.

Background: Tuberculosis is a principal public health issue. Reducing and controlling tuberculosis did not result in the expected success despite implementing effective preventive and therapeutic programs, one of the reasons for which is the delay in definitive diagnosis. Therefore, creating a diagnostic aid system for tuberculosis screening can help in the early diagnosis of this disease. This research aims to use machine learning techniques to identify economic, social, and environmental factors affecting tuberculosis. Methods: This case-control study included 80 individuals with TB and 172 participants as controls. During January-October 2021, information was collected from thirty-six health centers in Ahvaz, southwest Iran. Five different machine learning approaches were used to identify factors associated with TB, including BMI, sex, age , marital status, education, employment status, size of the family, monthly income, cigarette smoking, hookah smoking, history of chronic illness, history of imprisonment, history of hospital admission, first-class family, second-class family, third-class family, friend, co-worker, neighbor, market, store, hospital, health center, workplace, restaurant, park, mosque, Basij base, Hairdressers and school. The data was analyzed using the statistical programming R software version 4.1.1. Results: According to the calculated evaluation criteria, the accuracy level of 5 SVM, RF, LSSVM, KNN, and NB models is 0.99, 0.72, 0.97,0.99, and 0.95, respectively, and except for RF, the other models had the highest accuracy. Among the 39 investigated variables, 16 factors including First-class family (20.83%), friend (17.01%), health center (41.67%), hospital (24.74%), store (18.49%), market (14.32%), workplace (9.46%), history of hospital admission (51.82%), BMI (43.75%), sex (40.36%), age (22.83%), educational status (60.59%), employment status (43.58%), monthly income (63.80%), addiction (44.10%), history of imprisonment (38.19%) were of the highest importance on tuberculosis. Conclusion: The obtained results demonstrated that machine-learning techniques are effective in identifying economic, social, and environmental factors associated with tuberculosis. Identifying these different factors plays a significant role in preventing and performing appropriate and timely interventions to control this disease.

Investigating the diagnostic and prognostic value of anti-SARS-CoV-2 Spike IgG/IgM ELISA tests in patients infected with coronavirus Delta variant.

Aim: This study aimed to investigate the diagnostic and prognostic value of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike IgG/IgM antibodies in patients infected with coronavirus Delta variant. Methods: This analytical observational study included 270 unvaccinated patients (aged ≥18 years) diagnosed with coronavirus disease 2019 (COVID-19) Delta variant who referred to Emergency Department of our hospital. The serum levels of anti-SARS-CoV-2 Spike IgG and IgM were measured by indirect ELISA. Main measured outcomes included anti-SARS-CoV-2 Spike IgG and IgM, chest computed tomography (CT) severity score, clinical and laboratory findings which were prospectively evaluated throughout the study period. Results: The IgM levels in critical patients were significantly higher than non-critical patients (p<0.05). But the mean level of IgG in critical patients was not significantly different from its level in non-critical patients (p>0.05). However, a significant positive correlation was observed between the levels of both antibodies and chest CT severity score (p<0.0001); this implies that their levels may reflect the degree of lung involvement. The IgM level on 15th-16th days after symptoms onset was significantly associated with the hazard of death even after adjusting for all other factors (adjusted HR (95%CI):1.28(1.014_1.63), p=0.03), whereas IgG was not (p>0.05). The survival probability among patients with IgM level ≥8.67 RU/ml (34.2%) was significantly lower than those with IgM level <8.67 RU/ml (99.5%, p=0.0001). Conclusions: Anti-SARS-CoV-2 Spike IgM antibody was significantly associated with the disease severity and risk of death in unvaccinated patients infected with coronavirus Delta variant. However, further large-scale investigations on diverse infected populations are required to precisely determine the diagnostic/prognostic value of these antibodies.

Social determinants of spatial inequalities in COVID-19 outcomes across England: A multiscale geographically weighted regression analysis.

A variety of factors are associated with greater COVID-19 morbidity or mortality, due to how these factors influence exposure to (in the case of morbidity) or severity of (in the case of mortality) COVID-19 infections. We use multiscale geographically weighted regression to study spatial variation in the factors associated with COVID-19 morbidity and mortality rates at the local authority level across England (UK). We investigate the period between March 2020 and March 2021, prior to the rollout of the COVID-19 vaccination program. We consider a variety of factors including demographic (e.g. age, gender, and ethnicity), health (e.g. rates of smoking, obesity, and diabetes), social (e.g. Index of Multiple Deprivation), and economic (e.g. the Gini coefficient and economic complexity index) factors that have previously been found to impact COVID-19 morbidity and mortality. The Index of Multiple Deprivation has a significant impact on COVID-19 cases and deaths in all local authorities, although the effect is the strongest in the south of England. Higher proportions of ethnic minorities are associated with higher levels of COVID-19 mortality, with the strongest effect being found in the west of England. There is again a similar pattern in terms of cases, but strongest in the north of the country. Other factors including age and gender are also found to have significant effects on COVID-19 morbidity and mortality, with differential spatial effects across the country. The results provide insights into how national and local policymakers can take account of localized factors to address spatial health inequalities and address future infectious disease pandemics.

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens.

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Estimating the health impacts of exposure to Air pollutants and the evaluation of changes in their concentration using a linear model in Iran.

In big and industrial cities of developing countries, illness and mortality from long-term exposure to air pollutants have become a serious issue. This research was carried out in 2019-2020 to estimate the health impacts of PM10, NO2 and O3 pollutants by using AirQ+ and R statistical programming software in Arak, Isfahan, Tabriz, Shiraz, Karaj, and Mashhad. Mortality statistics, number of people in required age groups, and amount of pollutants were gathered respectively from different agencies like Statistics and Information Technology of the Ministry of Health, Statistical Center, and Department of Environment and by using Excel, the average 24-hour and 1-hour concentration and maximum 8-hour concentration for PM10, NO2 and O3 pollutants were gathered. We used linear mixed impacts model to account for the longitudinal observations and heterogeneity of the cities. The results of the study showed high number of deaths due to chronic bronchitis in adults, premature death of infants, and respiratory diseases in Mashhad. This research highlights the importance of estimation of health impacts from exposure to air pollutants on residents of the studied cities.

Introducing novel key genes and transcription factors associated with rectal cancer response to chemoradiation through co-expression network analysis.

Preoperative radiochemotherapy is a promising therapeutic method for locally advanced rectal cancer patients. However, the response of colorectal cancer (CRC) patients to preoperative radiotherapy varies widely. In this study, we aimed to identify novel biomarkers that could predict the response of colorectal tumors to treatment using a systems biology approach. We applied the Weighted Gene Co-Expression Network Analysis to construct co-expression networks and evaluated the correlation of these networks with radiation using the module-trait relationship. We then identified hub genes and related transcription factors in the selected co-expression module. Our analysis of seven constructed modules revealed that one module, which contained 113 nodes and 6066 edges, had the strongest correlation with radiation effects on CRC (correlation = 0.85; p-value = 6e-7). By analyzing the selected module with the CytoHubba plugin, we identified four hub genes, including ZEB2, JAM2, NDN, and PPAP2A. We also identified seven important transcription factors, including KLF4, SUZ12, TCF4, NANOG, POU5F1, SOX2, and SMARCA4, which may play essential roles in regulating the four hub genes. In summary, our findings suggest that ZEB2, JAM2, NDN, and PPAP2A, along with the seven transcription factors related to these hub genes, may be associated with the response of colorectal tumors to chemoradiotherapy.

Joint Modeling of In Vitro Fertilization Outcomes among A Population of Iranian Infertile Couples: A Historical Cohort.

BACKGROUND: Women who undergo in vitro fertilization (IVF) cycles should successfully go via multiple stages (i.e., clinical pregnancy, no abortion under 12 weeks, no abortion under 20 weeks, and delivery) to achieve a live birth. In this study, data from multiple IVF cycles and its multiple stages were reanalyzed to illustrate the success factors associated with various stages of IVF cycles in a population of Iranian infertile women. MATERIALS AND METHODS: This historical cohort study includes 3676 assisted reproductive technology (ART) cycles. Covariates take into account in this study were women's age, type of infertility (primary, secondary), body mass index (BMI), cause of infertility, history of abortion, duration of infertility, number of oocytes, number of embryos, fertilization rate, semen factors (Spermogram) and having polycystic ovarian syndrome (PCOS) during IVF cycles. Joint modeling was fitted to apply informative cluster size. RESULTS: Increasing age un women was associated with an increase in the BMI and a positive history of abortion and PCOS, and also, an increase in the number of treatment cycles, while in men was associated with the negative spermogram. With the increase in the number of treatment cycles, the result of the IVF success decreased, but with the increase in the number of embryos, fertilization rate and also, quality and / or quantity parameters of spermogram, we encountered with an increase in the IVF success rate. CONCLUSION: It seems that a joint model of the number of treatment cycles and the result of IVF is a valuable statistical model that does not ignore the significant effect of cycle numbers, while this issue is ignored usually in the univariate models.

Psoriasis Associated Hub Genes Revealed by Weighted Gene Co-Expression Network Analysis.

OBJECTIVE: Psoriasis, an immune-mediated disorder, is a multifactorial disease with unidentified cause(s). This study aimed to discover possible biomarkers of this papulosquamous skin disease. MATERIALS AND METHODS: The gene chip GSE55201, resulted from an experimental study, including 44 Psoriasis patients and 30 healthy controls was downloaded from GEO and weighted gene co-expression network analysis was utilized to identify hub genes. Key modules were determined using the module eigenvalues. We used biological functions (BFs), cellular components, and molecular functions in the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis in the gene metabolic pathway were used for enrichment analysis. RESULTS: Adjacency matrix was built by using power adjacency function and the power to turn the correlation to adjacency matrix was four with a topology fit index of 0.92. Using the weighted gene co-expression network analysis, 11 modules were identified. The green-yellow module eigenvalues were significantly associated with Psoriasis (Pearson correlation=0.53, P<0.001). Candidate hub genes were determined by their higher connectivity and relationship with module eigenvalue. The genes including SIGLEC8, IL5RA, CCR3, RNASE2, CPA3, GATA2, c-KIT, and PRSS33 were recorded as the hub genes. CONCLUSION: We can conclude that SIGLEC8, IL5RA, CCR3, RNASE2, CPA3, GATA2, c-KIT, and PRSS33 have an important role in the immune response regulation and they could be considered as a potential diagnostic biomarker and therapeutic target for Psoriasis.

Investigating the intensity of social contacts associated with tuberculosis: a weighted networks model.

BACKGROUND: Tuberculosis is known as one of the principal health problems, especially in developing countries. This study aimed to visualize, statistically model, and describe the weighted networks to investigate the intensity of social contacts associated with tuberculosis. METHODS: In this case-control study, we applied weighted network analysis to assess the network of person-time spent in stores, workplaces, restaurants, mosques, Police bases, homes, hospitals, colleges, hairdressers, schools, contact homes, health centers, cinemas, parks, and markets. Modules will be determined based on the similarities between the variables in a topology overlap matrix. The most important variables will be found considering the association between each variable and module eigenvalues. RESULTS: The result shows the extracted modules of locations based on the connectivity followed by the person-time at each place. The correlation (p-value) between TB and the turquoise, blue, and brown modules was 0.058 (0.351), 0.004 (0.943), and 0.117 (0.039), respectively. The brown module is the most important one, demonstrating a significant connection between homes, contact homes, health centers, and hospitals. Therefore, an association was found between person-time in four places and the occurrence of TB. CONCLUSION: The finding of this study showed that most transmission of tuberculosis infection occurs in homes, contact homes, health centers, and hospitals. These place evaluations allow the identification of people with more contact and in need of screening, so critically leading to the identification of more patients with active TB.

Association between individual, household, and area-level socioeconomic status indicators and sensorineural hearing loss in adults in southwest Iran: a population-based study.

Introduction: Hearing loss is the fourth most common chronic disease, but studies on the relationship between hearing loss and socioeconomic factors are limited. We aimed to examine the association between hearing loss and socioeconomic factors among 35-70 year adults in southwest Iran. Materials and methods: This population-based cross-sectional study was conducted in the baseline of Hoveyzeh cohort study in adults aged 35-70 in southwest Iran between 2017 and 2021. Information on socioeconomic factors, demographic characteristics, comorbidities, family history of hearing loss, and noise exposure was collected. We assessed the relationship between three levels of socioeconomic factors (individual, household, and area level) with sensorineural hearing loss (SNHL). Multiple logistic regression was used to adjust the potential confounders. Results: Among a total of 1,365 assessed participants, 485 patients were diagnosed as having hearing loss, and the other 880 individuals were diagnosed without hearing loss, which is considered the case and the control group, respectively. At the individual level of socioeconomic, the odds of having hearing loss in the participants with high school education and diploma, [OR = 0.51 (95%CI:0.28-0.92)], and the individuals with university education [OR = 0.44 (95%CI:0.22-0.87)] were significantly lower than the illiterate participants. At the household socioeconomic level, the odds of having hearing loss were lower for those with poor [OR = 0.63 (95%CI:0.41-0.97)] and moderate [OR = 0.62 (95%CI:0.41-0.94)] wealth status vs. those with the poorest wealth status. In the area level socioeconomic, although the odds of hearing loss in the residents of affluent areas were slightly lower than the residents of deprived areas, there was no significant difference among the groups. Conclusion: The individuals with hearing loss may have insufficient education and income.

Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity.

Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample. Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios. Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases. Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.

Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model.

Background: Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models. Methods: A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net were used to identify metastasis related genes. Results: Five regression models with penalties were applied in the additive hazards model and jointly found 9 genes including SNU13, CLINT1, MAPK9, ABCC5, NKX3-1, NCOR2, COL2A1, and ZNF219. According the median of the prognostic index calculated using the regression coefficients of the penalized additive hazards model, the patients were labeled as high/low risk groups. A significant difference was detected in the survival curves of the identified groups. The selected genes were examined using validation data and were significantly associated with the hazard of metastasis. Conclusion: This study showed that MAPK9, NKX3-1, NCOR1, ABCC5, and CD44 are the potential recurrence and metastatic predictors in breast cancer and can be taken into account as candidates for further research in tumorigenesis, invasion, metastasis, and epithelial-mesenchymal transition of breast cancer.

Infants' gut microbiome data: A Bayesian Marginal Zero-inflated Negative Binomial regression model for multivariate analyses of count data.

The infants' gut microbiome is dynamic in nature. Literature has shown high inter-individual variability of gut microbial composition in the early years of infancy compared to adulthood. Although next-generation sequencing technologies are rapidly evolving, several statistical analysis aspects need to be addressed to capture the variability and dynamic nature of the infants' gut microbiome. In this study, we proposed a Bayesian Marginal Zero-inflated Negative Binomial (BAMZINB) model, addressing complexities associated with zero-inflation and multivariate structure of the infants' gut microbiome data. Here, we simulated 32 scenarios to compare the performance of BAMZINB with glmFit and BhGLM as the two other widely similar methods in the literature in handling zero-inflation, over-dispersion, and multivariate structure of the infants' gut microbiome. Then, we showed the performance of the BAMZINB approach on a real dataset using SKOT cohort (I and II) studies. Our simulation results showed that the BAMZINB model performed as well as those two methods in estimating the average abundance difference and had a better fit for almost all scenarios when the signal and sample size were large. Applying BAMZINB on SKOT cohorts showed remarkable changes in the average absolute abundance of specific bacteria from 9 to 18 months for infants of healthy and obese mothers. In conclusion, we recommend using the BAMZINB approach for infants' gut microbiome data taking zero-inflation and over-dispersion properties into account in multivariate analysis when comparing the average abundance difference.

Sleep characteristics of middle-aged adults with non-alcoholic fatty liver disease: findings from the Shahrekord PERSIAN cohort study.

BACKGROUND: Several studies have reported short sleep duration in people with non-alcoholic fatty liver disease (NAFLD) but other sleep characteristics have been less studied. We aimed to assess the cross-sectional association of NAFLD with sleep duration and quality in an Iranian population sample. METHODS: We used data from 9,151 participants in the Shahrekord Prospective Epidemiological Research Studies in Iran (PERSIAN) Cohort Study, including 1,320 that were diagnosed with NAFLD. Log-binomial regression models sequentially adjusted for sociodemographic, lifestyle, clinical and biological variables were used to estimate relative risks (RR) and 95% confidence intervals (95% CI) for the association between NAFLD and sleep characteristics. RESULTS: Participants with NAFLD had shorter sleep duration, later wake-up time and bedtime, worse sleep efficiency, and more frequent daytime napping and use of sleeping pills, in age- and sex-adjusted models. After controlling for sociodemographic, lifestyle, clinical, and biological variables the associations remained strong for sleep efficiency (per 10%, RR = 0.92, 95%CI: 0.88-0.96) and use of sleeping pills (RR = 1.48, 95%CI: 1.17-1.88). The association between NAFLD and sleep efficiency was stronger in participants aged > 60 years (RR = 0.81, 0.70-0.93) and 40-60 years (RR = 0.87, 0.82-0.94), compared with those aged < 40 years (P-heterogeneity < 0.001). More frequent daytime napping in participants with NAFLD, compared with non-NAFLD, was observed in males but not females (P-heterogeneity = 0.007), and in those with body mass index (BMI) < 30 but not in obese participants (P-heterogeneity < 0.001). CONCLUSIONS: Diagnosis of NAFLD is associated with several poor sleep characteristics in middle-aged Iranians. Although longitudinal studies would help to clarify the direction of causality, our study shows that poor sleep is an important aspect of NAFLD.

Comparative Study on Clinical Characteristics of COVID-19 Patients with or without Digestive Symptoms in Razi Hospital, Ahvaz, Khuzestan.

BACKGROUND: Most commonly reported clinical manifestations are fever, fatigue and dry cough. Interestingly, a small percentage of patients experience GI symptoms, with the most common being anorexia, diarrhea, nausea and vomiting. We aimed to conduct a comparative study on COVID-19 patients with or without gastrointestinal patients (GI). AIMS: The pandemic of coronavirus disease 2019 (COVID-19) has emerged as a dire health problem, causing a massive crisis for global health. METHODS: Collective data of clinical manifestations and laboratory reports of patients admitted to Razi Hospital, Ahvaz, Khuzestan, Iran, for two weeks were analyzed. RESULTS: Our results showed that GI symptoms are not statistically significant criteria to be predictive or prognostic factors in COVID-19 patients, although they are probably related to the acute or non-acute phase of the disease. Moreover, non-specific GI symptoms seem to result from cytokine storm that occurred during the disease. CONCLUSION: Therefore, our results did not support GI tract involvement as a common route of COVID- 19 infection. Maybe future research will shed light on why and how the gastrointestinal system becomes infected by COVID-19.

Geostatistical Modeling and Heterogeneity Analysis of Tumor Molecular Landscape.

Intratumor heterogeneity (ITH) is associated with therapeutic resistance and poor prognosis in cancer patients, and attributed to genetic, epigenetic, and microenvironmental factors. We developed a new computational platform, GATHER, for geostatistical modeling of single cell RNA-seq data to synthesize high-resolution and continuous gene expression landscapes of a given tumor sample. Such landscapes allow GATHER to map the enriched regions of pathways of interest in the tumor space and identify genes that have spatial differential expressions at locations representing specific phenotypic contexts using measures based on optimal transport. GATHER provides new applications of spatial entropy measures for quantification and objective characterization of ITH. It includes new tools for insightful visualization of spatial transcriptomic phenomena. We illustrate the capabilities of GATHER using real data from breast cancer tumor to study hallmarks of cancer in the phenotypic contexts defined by cancer associated fibroblasts.

Cardiovascular disease, mortality and exposure to particulate matter (PM): a systematic review and meta-analysis.

One of the main factors that causes health effects in humans such as hospital admissions for cardiovascular disease (HACVD), respiratory disease (RD), lung function, cardiovascular mortality (MCVD), lung cancer, and increased mortality is air pollution especially particulate matter (PM). This a systematic review and meta-analysis aims to investigate the effects of particulate matter on the occurrence of cardiovascular disease and mortality. A systematic review and meta-analysis of the literature was done from 2011 to 2021 based on various databases. Based on the result of this study, subgroup analysis based on temperature conditions showed a different estimation in cold cities (6.24, UR (4.36-8.12)), moderate cities (4.86, UR (3.57-6.15)) and warm cities (8.96, UR (7.06-10.86)). Test of group differences showed a significant difference (Q=12.22, p-value<0.001). There was publication bias among the studies (the Egger's test; (Z=14.18, p<0.001)). According result study pooled estimation of AP% for MCVD from the random-effect meta-analysis based on DerSimonian-Laird model, overall is 5.04, UR (3.65-6.43) (Figure 4). Subgroup analysis based on temperature conditions showed the estimation in cold cities (5.47, UR (3.97-6.97)) and moderate cities (4.65, UR (0.54-8.77)). Test of group differences showed a non-significant difference (Q=0.13, p-value=0.71). There was no publication bias among the studies (the Egger's test; (Z=0.82, p=0.376)). Exposed to air pollutants and particulate matter can be increase the risk of cardiovascular disease, respiratory disease, and cardiovascular mortality.

Comparative Study of The Effects of Confounding Factors on Improving Rat Pancreatic Islet Isolation Yield and Quality.

OBJECTIVE: Isolated pancreatic islets are valuable resources for a wide range of research, including cell replacement studies and cell-based platforms for diabetes drug discovery and disease modeling. Islet isolation is a complex and stepwise procedure aiming to obtain pure, viable, and functional islets for in vitro and in vivo studies. It should be noted that differences in rodent strains, gender, weight, and density gradients may affect the isolated islet's properties. We evaluated the variables affecting the rat islet isolation procedure to reach the maximum islet yield and functionality, which would be critical for further studies on islet regenerative biology. MATERIALS AND METHODS: The present experimental study compared the yield and purity of isolated islets from nondiabetic rats of two different strains. Next, islet particle number (IPN) and islet equivalent (IEQ) were compared between males and females, and the weight range that yields the highest number of islets was investigated. Moreover, the influence of three different density gradients, namely Histopaque, Pancoll, and Lymphodex, on final isolated islets purity and yield were assessed. Finally, the viability and functionality of isolated islets were measured. RESULTS: The IEQ, IPN, and purity of isolated islets in 15 Lister hooded rats (LHRs) were significantly (P≤0.05) higher than those of the other strains. Male LHRs resulted in significantly higher IEQ compared to females (P≤0.05). Moreover, IPN and IEQ did not significantly vary among different weight groups. Also, the utilization of Histopaque and Pancoll leads to higher yield and purity. In vivo assessments of the isolated islets presented significantly reduced blood glucose percentage in the transplanted group on days 2-5 following transplantation. CONCLUSION: Based on these results, an optimal protocol for isolating high-quality rat islets with a constant yield, purity, and function has been established as an essential platform for developing diabetes research.

Social Contact Patterns Associated With Tuberculosis: A Case-control Study in Southwest Iran.

OBJECTIVES: Tuberculosis (TB) is a major public health concern worldwide. Social contact patterns can affect the epidemiology and risk of airborne diseases such as TB. This study was designed to investigate the social contact patterns associated with TB. METHODS: In this case-control study, groups of participants with and without TB were matched by age and sex. Participants reported the nature, location, frequency, and average duration of social contacts over 1 month. The duration and number of social and spatial contacts were compared between groups using the chi-square test and the t-test. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to quantify the relationship between social contact time and TB status. Data were analyzed using Stata version 11 statistical software. A p-value of <0.05 was considered to indicate statistical significance. RESULTS: In this study, 80 patients with TB and 172 control participants were included, and a total of 3545 social contacts were registered. Social contact with family members (OR, 1.72; 95% CI, 1.10 to 2.40), contact with a person with TB (OR, 1.53; 95% CI, 1.16 to 2.01), and contact at the participant's home (OR, 1.42; 95% CI, 1.19 to 1.82) were significantly associated with TB status. CONCLUSIONS: The duration of long-term social contact, rather than the number of contacts, may be the main contact-related factor associated with TB transmission in this population. The focus of contact-tracing efforts should be on finding and treating both family members and long-term contacts in non-household settings.