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Abstract The use of drug delivery systems in targeting and achieving the targeting of drugs in treating diseases, especially cancer, has attracted the attention of researchers. Letrozole is one of the drugs for the treatment of breast cancer. In this study, the organic-metallic pharmaceutical porous nanostructure based on zirconium UiO-66 loaded letrozole was synthesized. Its cytotoxicity and effect on apoptosis and migration against breast cancer cell line were investigated. In this experimental study, the UiO-66 nanoparticle-loaded letrozole was synthesized using zirconium chloride (ZrCl4), dimethylformamide (DMF), and HCl. Its characteristics were determined by scanning electron microscopy, and its average size was determined by the DLS method. Also, the rate of letrozole drug release from the nanoparticle was investigated in 24, 48, and 72 h. In addition, its cytotoxicity effects were investigated using the MTT colorimetric method at concentrations of 3.125-100 µg/ml against the breast cancer cell line (MCF-7) in the periods of 48 and 72 h. Also, the expression level of apoptotic genes Bax and Bcl2 was investigated by the Real-Time PCR method. Also, the amount of cell migration was done by the migration assay method. The results showed that UiO-66 bound to letrozole had a spherical morphology and an average size of 9.2 ± 160.1. Also, the letrozole drug was loaded by 62.21 ± 1.80% in UiO-66 nanoparticles and had a slower release pattern than free letrozole in the drug release test, so within 72 h, 99.99% of free letrozole was released in If in UiO-66 containing letrozole, 57.55% of the drug has been released. Also, the cytotoxicity results showed that UiO-66 bound to letrozole has more significant cytotoxic effects than free letrozole (p

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As3+ and 6.8 pM for Cr6+, 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy.

PurposeTissue acquisition in lung cancer is vital for multiple reasons. Primary reasons reported for molecular testing failure in lung cancer biopsy specimens include insufficient amount of tumor cells provided and inadequate tissue quality. Robotic bronchoscopy is a new tool enabling peripheral pulmonary lesion sampling; however, diagnostic yield remains imperfect possibly due to the location of nodules adjacent to or outside of the airway. The 1.1-mm cryoprobe is a novel diagnostic tool and accesses tissue in a 360-degree manner, thus potentially sampling eccentric/adjacent lesions. This study examines the diagnostic yield of the cryoprobe compared to standard needle aspiration and forceps biopsy. It additionally evaluates yield for molecular markers in cases of lung cancer.MethodsThis is a retrospective analysis of 112 patients with 120 peripheral pulmonary lesions biopsied via robotic bronchoscopy using needle aspirate, forceps, and cryobiopsy.ResultsThe overall diagnostic yield was 90%. Nearly 18% of diagnoses were made exclusively from the cryobiopsy sample. Molecular analysis was adequate on all cryobiopsy samples sent. Digital imaging software confirmed an increase in quantity and quality of samples taken via cryobiopsy compared to needle aspirate and traditional forceps biopsy.ConclusionUsing the 1.1-mm cryoprobe to biopsy PPN combined with the Ion robotic bronchoscopy system is safe, feasible, and provides more diagnostic tissue than needle aspirates or traditional forceps biopsies. The combination of cryobiopsy with robotic-assisted bronchoscopy increased diagnostic yield, likely due to its 360-degree tissue acquisition which is beneficial when targeting extraluminal lesions adjacent to the airway.

Purpose: Sustainability is one of the global challenges, individuals and businesses need to change their behavior and consumption patterns to move towards sustainable development. This is not possible without planning for education and related knowledge transfer. On the other hand, disruptive technologies such as virtual reality (VR) have revolutionized the field of education. The purpose of this study is to evaluate the effect of holding traditional training courses and VR-based training courses on sustainable behavior. Design/Methodology/Approach: It is a quasi-experimental study, in which pretest-posttest design and control group are used. The statistical population includes students of one of the Iranian universities. A total of 105 students were randomly divided into two experimental groups and one control group (35 students in each group). Experimental group 1 underwent a training course using VR and Experimental group 2 received a traditional training course. At first, a pre-test was performed and after completing the eight-session period (two 1-h sessions per week), the post-test was conducted again for the groups. Findings: The results of analysis of variance test show that there was a significant difference between the mean scores of sustainable behaviors in the post-test phase in the two experimental groups and the control group. Using Tukey's test, it was found that the scores of sustainable behavior were different among three groups in pairs. That is, holding a training course as well as using VR has been effective on sustainable behavior. Environmental policymakers and planners can use technologies such as VR to teach environmental issues to create a culture of sustainability and sustainable development, in addition to training and educational courses. Originality/Value: Contribution of this study shows that the use of VR can be effective in learning sustainable behavior. Also, holding training courses is a way to change the consumption pattern and behavior of people to maintain the environment and sustainability.

IntroductionTransbronchial lung cryobiopsy (TBLC) has emerged as a promising alternative to surgical lung biopsy for the diagnosis of interstitial lung disease. However, uncertainty remains regarding its overall complications due to a lack of procedural standardization including the size of cryoprobe utilized.MethodsThis is a prospective cohort study of a protocolized transbronchial cryobiopsy program utilizing a 2.4 mm cryoprobe. 201 consecutive subjects were enrolled at a single academic center.ResultsThe average biopsy size was 106.2 ± 39.3 mm2. Complications included a total pneumothorax rate of 4.9% with 3.5% undergoing chest tube placement. Severe bleeding defined by the Nashville Working Group occurred in 0.5% of cases. There were no deaths at 30-days.DiscussionA protocolized transbronchial cryobiopsy program utilizing a 2.4 mm cryoprobe in can achieve a high diagnostic yield with a favorable safety profile.

Background. Hospitals, patients, researchers, and healthcare organizations are producing enormous amounts of data in both the healthcare and drug detection sectors. With continued development of cheap data storage and availability of smart devices in the world, the influence of big data (BD) will continue to grow. This influence has also carried over to healthcare. The volumes of data available in the fields of pharmacology, toxicology, and pharmaceutics are constantly increasing. Therefore, the present study aimed to identify the applications of big data technology in the field of pharmaceutics. Methods. Using the mixed methods approach, this study was conducted in two phases in winter 2023. In the first phase, the applications of big data technology were identified by library search and assessed by content analysis. In the second phase, applications were ranked by a panel of experts, including 17 experts who worked in the Iranian pharmaceutical industry. The stepwise weight assessment ratio analysis (SWARA) method was used for ranking the application of big data in pharmaceutics. Results. The present study examined the importance of big data applications in pharmaceutics. The results showed that drug discovery (0.263) and clinical study analysis (0.224) had the highest importance, followed by drug efficacy and performance (0.197), drug safety (0.170) and drug personalization (0.146). Conclusion. The present study detailed a substantial attempt to review the existing literature regarding the implementation of big data and to rank big data applications in the pharmaceutical industry. Big data can help researchers to better discover and develop drugs and understand the effects of drugs and other chemicals on the human body. As a result, it can help improve the safety and efficacy of drugs and other chemicals. Also, big data can help to improve the accuracy of predictions regarding the effects of drugs and chemicals, which can improve safety in drug development and help to avoid potential adverse drug interactions. The applications considered in this study are ultimately necessary for humanity, and big data may significantly impact the betterment of these domains. Big data has a revolutionary potential, providing new ways to understand and predict the effects of drugs. BD will possibly play an important role in pharmaceutics in future, critically helping to drug discovery and improve drug safety and efficacy.

Lung adenocarcinoma with lepidic growth pattern (LPA) is characterized by tumor cell proliferation along intact alveolar walls, and further classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive lepidic predominant adenocarcinoma (iLPA). Accurate diagnosis of lepidic lesions is critical for appropriate prognostication and management as five-year survival in patients with iLPA is lower than in those with AIS and MIA. We aimed to evaluate the accuracy of CT-guided core needle lung biopsy classifying LPA lesions and identify clinical and radiologic predictors of invasive disease in biopsied lesions. Thirty-four cases of adenocarcinoma with non-invasive lepidic growth pattern on core biopsy pathology that subsequently were resected between 2011 and 2018 were identified. Invasive LPA vs. non-invasive LPA (AIS or MIA) was defined based on explant pathology. Histopathology of core biopsy and resected tumor specimens was compared for concordance, and clinical, radiologic and pathologic variables were analyzed to assess for correlation with invasive disease. The majority of explanted tumors (70.6%) revealed invasive disease. Asian race (p = 0.03), history of extrathoracic malignancy (p = 0.02) and absence of smoking history (p = 0.03) were associated with invasive disease. CT-measured tumor size was not associated with invasiveness (p = 0.15). CT appearance of density (p = 0.61), shape (p = 0.78), and margin (p = 0.24) did not demonstrate a significant difference between the two subgroups. Invasiveness of tumors with lepidic growth patterns can be underestimated on transthoracic core needle biopsies. Asian race, absence of smoking, and history of extrathoracic malignancy were associated with invasive disease.

Metal-organic frameworks (MOFs) are structures with high surface area that can be used to remove heavy metals (HMs) efficiently from the environment. The effect of MOFs on HMs removal from contaminated soils has not been already investigated. Monometallic MOFs are easier to synthesize with high efficiency, and it is also important to compare their structures. In the present study, Zn-BTC, Cu-BTC, and Fe-BTC as three metal-trimesic acid MOFs were synthesized from the combination of zinc (Zn), copper (Cu), and iron (Fe) nitrates with benzene-1,3,5-tricarboxylic acid (H3BTC) by solvothermal method. BET analysis showed that the specific surface areas of the Zn-BTC, Cu-BTC, and Fe-BTC were 502.63, 768.39 and 92.4 m2g-1, respectively. The synthesized MOFs were added at the rates of 0.5 and 1% by weight to the soils contaminated with 100 mgkg-1 of Zn, nickel (Ni), lead (Pb), and cadmium (Cd). Then quinoa seeds were sown in the treated soils. According to the results, the uptakes of all four HMs by quinoa were the lowest in the Cu-BTC 1% treated pots and the lowest uptakes were observed for Pb in shoot and root (4.87 and 0.39, μgpot-1, respectively). The lowest concentration of metal extracted with EDTA in the post-harvest soils was for Pb (11.86 mgkg-1) in the Cu-BTC 1% treatment. The lowest metal pollution indices were observed after the application of Cu-BTC 1%, which were 20.29 and 11.53 for shoot and root, respectively. With equal molar ratios, highly porous and honeycomb-shaped structure, the most crystallized and the smallest constituent particle size (34.64 nm) were obtained only from the combination of Cu ions with H3BTC. The lowest porosity, crystallinity, and a semi-gel like feature was found for the Fe-BTC. The synthesized Cu-BTC showed the highest capacity of stabilizing HMs, especially Pb in the soil compared to the Zn-BTC and the Fe-BTC. The highly porous characteristic of the Cu-BTC can make the application of this MOF as a suitable environmental solution for the remediation of high Pb-contaminated soils.

Introduction: As the worldwide population grows and the access to healthcare is increasingly being demanded, real-time monitoring of different biological signals has driven the study and development of diverse wearable technology. Monitoring of physical activity and behaviors by wearable devices may improve these health behaviors. This study endeavored to recognize the challenges of wearable technology in medicine and healthcare.Methods: This applied study was conducted in two phases using qualitative approach in winter 2023. Initially, the challenges of wearable devices were recognized from previous studies. In the second step, the study experts evaluated conceptual model by Delphi method. The expert panel consists of 13 individuals active in information technology in healthcare according to targeted sampling.Results: According results the main challenges of wearable devices are technology acceptance (0.923), design/development (0.769), data quality/safety (0.769), privacy/confidentiality (0.923), socioeconomic impact (0.846), interoperability/connectivity (0.769), patient information/data overload (0.846), remote monitoring (0.846), and sanctions (0.769).Conclusion: This study revealed that applications of the wearable technology in healthcare are becoming mature and established as a scientific domain. Practical adoption in wearable technology still demands design and validation of new pathways, strategic formulation, and a sound business model. Practitioners and researchers should consider how these technological advances may impact healthcare in the new era.

Background and Aims: Artificial intelligence (AI) technology is widely used in dentistry in addition to numerous other sectors that impact human life, including medicine. A dentist can use AI technology to analyze patient data, diagnostic processes, and management activities. This study was conducted in Iran to identify the dental applications of AI and prioritize them. Materials and Methods: In the winter of 2022, this applied research was carried out in two stages using a mixed method. In the qualitative phase, 570 articles from 2011 to 2022 were identified in the databases of PubMed, Web of Science, Scopus and Google Scholar among the studies in the field of dentistry and related to artificial intelligence technology based on keywords and then the applications of artificial intelligence in dentistry were extracted. In the quantitative phase, the identified applications prioritized by a group of experts comprised 13 University faculty members with related research areas using the best-worst method (BWM). Results: The factors identified in the first stage of research were classified into six categories: implant and surgery, executive management, disease diagnosis, analysis of images, clinical prediction, and orthodontics. According to the experts’ opinion, it was determined that medical photo analysis had the highest coefficient of importance (0.252) followed by orthodontics (0.234), disease diagnosis (0.151), implantology and surgery (0.143), clinical forecasts (0.127), and executive management (0.093). Conclusion: Dentists can use the capabilities of artificial intelligence in examining patients' teeth and diagnostic tests in dentistry based on the analysis of patient information. Information technology policymakers with the support and reinforcement of knowledge-based companies active in the field of artificial intelligence and joint investment in the field of medicine can be the basis for progress and the development of this technology in the country and the field of treatment.

The maturity of business intelligence is a result of the evolution and advancement of technology and management approaches that help to provide accurate information, predictive analyzes and improve decisions in organizations using advanced technologies such as artificial intelligence and data analysis. Despite technological maturity that improves the efficiency and performance of organizations over time, business intelligence is far from becoming a mainstream trend in organizations. According to numerous researches in the field of business intelligence, the aim of this research was to present the framework of factors affecting the maturity of business intelligence using a meta-composite approach. In order to reach a comprehensive framework that includes all the maturity factors of business intelligence, 221 scientific studies were reviewed. Relevant codes were extracted using content analysis in metacomposite method. The categories were leveled using the comprehensive interpretive structural modeling method and the most influential ones were determined. The findings show that a total of 93 codes were extracted and divided into 6 categories. These categories include organization and management factors, environment, technology infrastructure, human resources - knowledge, data management and data analysis. The categories of technology infrastructure, data management and data analysis were placed at level three and have the greatest impact on the maturity of business intelligence.

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

Background: Participation in leisure activities with peers and family gatherings is beneficial, but some activities such as smoking, may increase harmful health behaviors. This is the first study which investigates how students in Varamin, Tehran, Iran, perceive smoking during their leisure time. Methods: This cross-sectional study was conducted in 2021 and involved 319 school-going adolescents in Varamin County, Tehran, Iran (aged 16-18 years) who completed a self-administered anonymous questionnaire on the use of tobacco products which was designed based on the Likert scale. After completing the questionnaires, the data were analyzed using, frequency (percentage), t-test (Independent Two-sample), analysis of variance (One-way ANOVA), Kendall’s and Pearson’s correlation coefficients, and the chi-square test (less than 0.01). Results: Overall, five ethnicities in the target population were examined in this study. Smoking among Arab, Fars, and Lur ethnicities, with mean scores of 4, 3.6, and 3.41, respectively, were higher than smoking among Turks, other ethnicities, and Kurds, with mean scores of 2.86, 2.36, and 2. In addition, smoking in family gatherings, the very low and low levels have the highest frequencies of 72 and 61, respectively, while smoking in friend gatherings, average and high levels have the highest frequencies, i.e., 99 and 57, respectively, with (Sig: 0.000) is less than 0.01. Conclusions: This reinforces the need to be alert for, and respond to, gender and ethnic disparities regarding the pattern of risk and protective factors. Thus, leisure activities and ethnicity may be key factors to tailor prevention programs to their unique characteristics and needs.

In order to investigate the effect of salinity and cadmium (Cd) on yield, chlorophyll content and antioxidant activity of Kochia plant, a factorial experiment in a completely randomized design with three replications was conducted under greenhouse conditions. The experimental treatments included five Cd levels (0, 5, 30, 60 and 90 mg Cd kg-1 soil as cadmium sulphate) and three salinity levels (0, 2.5 and 5 g kg-1 soil as NaCl equal to electrical conductivity (EC) 0.65, 9.4, and 18 dS m-1, respectively). The results showed that mean shoot fresh and dry weight, root weight, and plant height at 5 g NaCl Kg-1, decreased significantly by 22, 24, 23, and 15 %; and at 90 mg Cd Kg-1 soil decreased by 52, 67, 45, and 41%, respectively. The greatest reduction in shoot fresh and dry weight, root dry weight and plant height observed at 5 g NaCl Kg-1 + 90 mg Cd Kg-1 levels. The highest total chlorophyll content observed at control (with no salinity and Cd) and 5 g kg-1 salinity + 5 mg Cd kg-1. With addition of NaCl, the harmful effect of Cd on chlorophyll content of the plant decreased. Salinity and Cd levels had a different effects on antioxidant activity of Koshia plant. However, at higher Cd levels (60 and 90 mg Cd kg-1), with increasing of salinity levels, no significant differences was observed in peroxidase and superoxide dismutase activity. In general, the change in the activity pattern of the plant antioxidant system, especially catalase and superoxide dismutase enzymes under high stress conditions showed that Kochia plant can withstand stress conditions via activating its antioxidant system.

Banking industry affected by economic transformation, internet development, and financial innovations. Hence, the banking industry requires urgent transformation and is seeking new growth avenues. On the other hand, blockchain technology is a disruptive technology that changes business models. Using distributed software architecture and advanced computing, blockchain can change the way information is exchanged between actors in the chain. Therefore, the research purpose is to identify the influential factors in the acceptance and then application of blockchain in Iran's banking industry. This research is applied and descriptive correlational method was used. The UTAUT - Unified Theory of Acceptance and Use of Technology – has been contextually used as the theoretical model of the research. Moreover, the statistical population of the research is all the employees in banking industry in Shiraz. For data analysis, structural equation modeling and Smart PLS software have been used. The results accentuated and proved the positive impacts of Performance expectancy, Effort expectancy, social influence and Individual factors on the intention to use blockchain technology. Moreover, the last but not least important factor, was the impact of Behavioral Intention on the actual use of blockchain technology. As a result, in Iran's banking system, the use of blockchain is being accepted as a way of rolling out economic constraints and making commercial payments.

Introduction: Massive open online courses (MOOCs) are known as new advances in e-learning. Learning through MOOCs requires different education than what happens in face-to-face learning. In these courses, it is important to connect with participants from different backgrounds and personalize learning. Therefore, with the development of MOOC as a new teaching method, training-based businesses need to acquire new knowledge and skills to obtain the desired training and gain acceptable learning outcomes. An important aspect of innovation in this business is tied to the role of the Internet and the related technologies, but this feature is not necessarily the only aspect that leads to innovation in the MOOC business model. This is because, with the advent of these technologies, there has been a significant shift in attitudes towards training and skill development systems that have changed the education business environment. Besides, the characteristics of this business are different from those of other web-based businesses which require that a business model be provided specifically for MOOC. The business model explains the logic of an organization on how to create, deliver, and acquire values. Thus, the business model helps to understand the relevant factors in the business context and to recognize and interact with them. In this environment, business logic is constantly changing and improving. A business model can integrate resources, activities and knowledge and facilitate the identification of competency resources. The model also helps businesses to fulfil their mission and reduce budgetary and financial pressures and constraints. Despite the increased number of MOOCs in Iran, some entrepreneurs have failed due to several factors, including the weakness of these institutions to adapting to environmental conditions and their changes as well as the weakness of their commercialization strategy. Therefore, setting up MOOC courses requires structures and resources that must be identified in interactions with the business environment before any action is taken. A business model can identify the important components of creating a competitive advantage in accordance with environmental factors and promote the development of businesses based on economic principles as well as educational policies. Accordingly, this paper aimed to provide a business model in line with the features of the MOOC that can specify important business components and develop a business strategy in accordance with educational policies.Methodology: In this study, the mixed research method has been used to create comprehensible designs from the analysis of complex data. This method is a process for collecting, analyzing and describing qualitative and quantitative data that help to better understand the challenges of research. Using only a qualitative or quantitative research approach reveals only a few aspects of the phenomenon being studied. Therefore, the mixed research method provides a better understanding of various phenomena. This research is based on a mixed approach, qualitative and quantitative, in two stages. In the qualitative phase, a systematic review approach has been used to identify the components of the e-business model. Then, experts' opinions are used to match the components of the e-business model (obtained from systematic review) with the characteristics of the MOOC business model and to reach the proposed components for the MOOC business model. In the quantitative phase, a questionnaire has been prepared and provided to the experts to evaluate the proposed components for the MOOC business model. The t-test serves to analyze the answers and finalize the components of the model, and the Friedman test prioritizes the components. In the second stage, the fuzzy Delphi approach is used to present the MOOC business model.Results and Discussion: The elements of the MOOC model proposed in this study are highly consistent with a business model. In a sense, the components of the MOOC model project the aspects of a business model in more details. The sub-components in the model address features that are specific to MOOC businesses and, therefore, do not apply to other types of e-businesses in general. To develop the model, the opinions of MOOC business experts and entrepreneurs were taken into consideration, which could be fruitful to improve MOOC implementation plans in Iran as well as its competitive position.Conclusion: According to the findings of this research, the model of earnings, the proposed value and the customer are the most important components of a MOOC business model. It is presented in the form of 55 concepts in 12 main components. The use of the proposed model has such benefits as promoting MOOC planning and facilitating the acquisition of competitive advantage. Besides, the results of this study can be used by entrepreneurs in this field to create successful business models. The insight provided by this study can lead to a common understanding among e-learning activists about the challenges in the MOOC business. Due to technological innovations, it also lays the ground for future changes in the field.

The analytical validation is reported for a targeted methylation-based cell-free DNA multi-cancer early detection test designed to detect cancer and predict the cancer signal origin (tissue of origin). A machine-learning classifier was used to analyze the methylation patterns of >105 genomic targets covering >1 million methylation sites. Analytical sensitivity (limit of detection [95% probability]) was characterized with respect to tumor content by expected variant allele frequency and was determined to be 0.07%-0.17% across five tumor cases and 0.51% for the lymphoid neoplasm case. Test specificity was 99.3% (95% confidence interval, 98.6-99.7%). In the reproducibility and repeatability study, results were consistent in 31/34 (91.2%) pairs with cancer and 17/17 (100%) pairs without cancer; between runs, results were concordant for 129/133 (97.0%) cancer and 37/37 (100%) non-cancer sample pairs. Across 3- to 100-ng input levels of cell-free DNA, cancer was detected in 157/182 (86.3%) cancer samples but not in any of the 62 non-cancer samples. In input titration tests, cancer signal origin was correctly predicted in all tumor samples detected as cancer. No cross-contamination events were observed. No potential interferent (hemoglobin, bilirubin, triglycerides, genomic DNA) affected performance. The results of this analytical validation study support continued clinical development of a targeted methylation cell-free DNA multi-cancer early detection test.

Background. Life expectancy is an important health status indicator based on average number of years a person at a given age may be expected to live given current mortality rates. Given this significance, it would be necessary to probe into the factors affecting the life expectancy. Ensuring that information and communication technology (ICT) services are properly used could contribute to development and achievement, as it represents an important issue for the countries moving toward knowledge-based and information-based societies. Therefore, this study explored the impact of the information and communication technology on life expectancy. Methods. This study covers annual data from 2008 to 2018 for a group of countries in the Middle East. Data were retrieved from the Annual Reports such as World Bank and International Telecommunication Union (ITU) data sets. The effects of ICT on life expectancy are estimated with the Panel model. We also used the Hausman test to investigate fixed versus random effects. The data were analyzed by Stata 14 software. Results. The core findings of this study confirm the significant impact of ICT on life expectancy in the Middle East. The calculated Coefficient of the regression was 0.551 indicating how much life expectancy changes when ICT variable increases by one unit. Moreover, the rho (intraclass correlation) was found to be 0.975 showing that 97.5% of the variance is due to differences across panels. Conclusion. Considering the effect of ICT on life expectancy, policymakers of the Middle Eastern countries should integrate the use of ICT with the existing programs and systems. Technology is not an end in itself but merely the means to reinforce the existing system and to meet the locally determined goals. It can be used as a tool to attain broader health and development goals. Background Life expectancy is an important health status indicator based on average number of years a person at a given age may be expected to live given current mortality rates. Given this significance, it would be necessary to probe into the factors affecting the life expectancy. Ensuring that information and communication technology (ICT) services are properly used could contribute to development and achievement, as it represents an important issue for the countries moving toward knowledge-based and information-based societies. As ICT affects everyday lives, it also impacts the macroeconomic growth, which in turn further affects everyday lives by allowing improvements in infrastructures and a higher standard of living. ICTs inherently entail the economic development of countries, regions and cities, while also improving social inclusion, well-being and therefore quality of life. Therefore, this study explored the impact of the information and communication technology on life expectancy. Methods This study covers annual data from 2008 to 2018 for a group of countries of in the Middle East. Data were retrieved from the Annual Reports such as World Bank dataset and International Telecommunication Union (ITU) data set. The effects of information and communication technology on life expectancy were estimated through the Panel model. First, stationary tests were performed with the Fisher’s generalized unit root test. In the Fisher test for panel data, the null hypothesis of a unit root was rejected at the 5% level of significance. The cross-section correlation test was performed with the Freeze test. The null hypothesis of no correlation was rejected at the 5% level of significance. We also used the Hausman test to investigate fixed versus random effects. The null hypothesis of no fixed effects was accepted so the random effects model was used. The Hausman test detects endogenous regressors in a regression model. Endogenous variables have values that are determined by other variables in the system. The data were analyzed by Stata 14 software. Results The core findings of this study confirm the significant impact of information and communication technology on life expectancy in the Middle East. The Coefficient of the regression was 0.551 indicating how much life expectancy changes when information technology variable increases by one unit. The coefficient of ICT shows that 1% increase in ICT variable causes 0.551 % incline in life expectancy and this result is significant at 5% level of significance. Regarding control variables, we found that all have expected statistically significant effects on life expectancy. The intra-cluster correlation coefficient (rho) is a measure of the relatedness, or similarity, of clustered data. Values of rho range from 0 to 1 in human studies, and as the rho increases, more individuals within the clusters resemble one another. The rho (intraclass correlation) was found to be 0.975 showing that 97.5% of the variance is due to differences across panels. This research recommends that human development programs need to focus on polices which foster digital inclusion. Conclusion Considering the effect of information and communication technology on life expectancy, policymakers of the Middle Eastern countries should integrate the use of information and communication technology with the existing programs and systems. The digital citizen is happier and values living in regions with technological capacity, investing in research development, and committed to achieving sustainable growth. ICT use leads to improved assessments of the efficiency and management of the public administration by more technological users, underlining the importance of an understanding between users and public services in the virtual sphere. Technology is not an end in itself but merely an instrument to reinforce the existing system and to meet the locally determined goals. It can be used as a tool to attain broader health and development goals. The findings from this study provide key insights that explain how life expectancy may be enhanced through ICTs. Practical Implications of Research According to the influence of information and communication technology (ICT) on life expectancy, policymakers should provide ICT infrastructures and adopt strategies integrating ICT policies with human life criteria such as education, health and work to improve life expectancy. Ethical Consideration The paper reflects the author’s own research and analysis in a truthful and complete manner. Conflict of Interests This study was an independent research and the author declares no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Acknowledgement I would like to thank Dr Marzieh Ronaghi for her guidance and assistance throughout this research.

Background and purpose: The present study assessed the incidence of Temporomandibular Disorders (TMD) and its relationship with demographic variables, previous orthodontic treatment, and mandibular mobility in patients attending Sari Dental School. Materials and methods: In this cross-sectional study, 68 individuals were randomly selected. Data were obtained by interviewing the patients and clinical examinations. The relation between TMD and other variables was then analyzed applying Chi-square test and exact fisher test in SPSS V22. Results: TMD was found in 75% (n=51), including 35.3% of men and 64.7% of women. Among these, 14 had previously received orthodontic treatment, 34 had a monthly salary of less than one million tomans ($30), and 49 were city dwellers. Maximum mouth opening was 44.52±7 mm in patients without TMD (P= 0.84). People without TMD had greater maximal lateral mandibular movement to the right (P= 0.2) and left (P= 0.84), but this was not statistically significant. Conclusion: A history of orthodontic treatment can contribute to the development of TMD. Nevertheless, good financial status helps in benefiting from more follow-up treatments and decreases the incidence and progression of TMD, although this was not significantly correlated with other variables.