Applying the Delphi Approach to Prioritize Social Factors Affecting the Development of Children Under Six Years.

BACKGROUND: Social determinants of health have a key role in the growth and development of children, particularly in early childhood which is mentioned from infancy to the age of six years old. These factors might cause disparities in living conditions and consequently bring about inequities regarding different aspects of development such as emotional, psychological, social, psychological, and intellectual. This research aimed to provide a model for prioritizing social factors affecting the development of children under six years. METHODS: We used quantitative-qualitative (mixed) method to perform data analysis. The statistical population included 12 medical experts and professionals in the field of children's development and social determinants of health that were selected using the snowball method. In the quantitative section, a Delphi technique was applied to screen the extracted indicators. Then through applying a decision-making trial and evaluation laboratory (DEMATEL) method, the cause-and-effect interactions among main social determinants were identified. To analyze data, super decision software was used. RESULTS: According to literature review and the results obtained from focus group discussions, five dimensions including individual factors, family factors, environmental factors, governance, and global factors were identified. Based on the study findings, the criterion of "family factors" was mentioned as the most important priority affecting childhood development. Furthermore, the sub-criterion of "International Programs and Policies" received the greatest priority among other sub-criteria with a profound impact on children's healthy growth and development. CONCLUSION: Despite the current knowledge about social determinants of health, it is required to identify the most influential socioeconomic factors on childhood development. In such a manner, political strategies for improving the health condition of children can be implemented based on scientific evidence. Due to the crucial role of family factors, environmental factors and other socio-economic conditions, health policy makers and public health practitioners should be informed of the importance of these factors in shaping the health condition of children.

Urban and sub-urban disparities in health outcomes among patients with COVID-19; a cross-sectional study of 234 418 patients in Iran.

BACKGROUND: It remains crucial to understand socio-demographic determinants of COVID-19 infection to improve access to care and recovery rates from the disease. This study aimed to investigate the urban and sub-urban disparities associated with COVID-19 in patients visiting healthcare facilities in the province of Tehran, Iran. METHODS: Data from 234 418 patients who were diagnosed with COVID-19 infection from March 2020 to March 2021 in the province of Tehran were used in this analysis. Descriptive statistics were used to describe the characteristics of the study population. Chi-Squared test was applied to examine the association of study variables with residing area. Independent samples t-test was performed to compare mean age of patients in urban and sub-urban areas. Multiple Logistic Regression model was applied to examine the association of study variables with disease outcome. RESULTS: Overall, most patients resided in the urban settings (73%). Mean age of patients was significantly lower in sub-urban areas compared to their counterparts in urban settings (49 ± 23.1 years versus 53 ± 21.1 years, P < 0.001). Positive PCR test results were more common in urban areas (48.5% versus 41.3%, P < 0.001). Yet, sub-urban settings had higher rates of positive chest CT scan reports (62.8% versus 53.4%, P < 0.001). After accounting for age and sex covariates, residing in urban areas was associated with higher likelihood of being admitted to an ICU (OR = 1.27, CI: 1.240-1.305). Yet, a greater vulnerability to fatal outcome of COVID-19 infection was shown in patients living in sub-urban areas (OR = 1.13, CI: 1.105-1.175). CONCLUSIONS: This study revealed a clear disparity in the health outcome of patients infected with COVID-19 between urban and sub-urban areas.

Sociodemographic determinants and clinical risk factors associated with COVID-19 severity: a cross-sectional analysis of over 200,000 patients in Tehran, Iran.

BACKGROUND: Defining socio-demographic factors, clinical presentations and underlying diseases associated with COVID-19 severity could be helpful in its management. This study aimed to further clarify the determinants and clinical risk factors of the disease severity in patients infected with COVID-19. METHODS: A multi-centre descriptive study on all patients who have been diagnosed with COVID-19 in the province of Tehran from March 2020 up to Dec 2020 was conducted. Data on socio-demographic characteristics, clinical presentations, comorbidities, and the health outcomes of 205,654 patients were examined. Characteristics of the study population were described. To assess the association of study variables with the disease severity, the Chi-Squared test and Multiple Logistic Regression model were applied. RESULTS: The mean age of the study population was 52.8 years and 93,612 (45.5%) were women. About half of the patients have presented with low levels of blood oxygen saturation. The ICU admission rate was 17.8% and the overall mortality rate was 10.0%. Older age, male sex, comorbidities including hypertension, cancer, chronic respiratory diseases other than asthma, chronic liver diseases, chronic kidney diseases, chronic neurological disorders, and HIV/AIDS infection were risk markers of poor health outcome. Clinical presentations related with worse prognosis included fever, difficulty breathing, impaired consciousness, and cutaneous manifestations. CONCLUSION: These results might alert physicians to pay attention to determinants and risk factors associated with poor prognosis in patients with COVID-19. In addition, our findings aid decision makers to emphasise on vulnerable groups in the public health strategies that aim at preventing the spread of the disease and its mortalities.

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city.

Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients' financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients' transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.

A description of spatial-temporal patterns of the novel COVID-19 outbreak in the neighbourhoods' scale in Tehran, Iran.

Background: Analyzing and monitoring the spatial-temporal patterns of the new coronavirus disease (COVID-19) pandemic can assist local authorities and researchers in detecting disease outbreaks in the early stages. Because of different socioeconomic profiles in Tehran's areas, we will provide a clear picture of the pandemic distribution in Tehran's neighbourhoods during the first months of its spread from February to July 2020, employing a spatial-temporal analysis applying the geographical information system (GIS). Disease rates were estimated by location during the 5 months, and hot spots and cold spots were highlighted. Methods: This study was performed using the COVID-19 incident cases and deaths recorded in the Medical Care Monitoring Centre from February 20, to July 20, 2020. The local Getis-Ord Gi* method was applied to identify the hotspots where the infectious disease distribution had significantly clustered spatially. A statistical analysis for incidence and mortality rates and hot spots was conducted using ArcGIS 10.7 software. Results: The addresses of 43,000 Tehrani patients (15,514 confirmed COVID-19 cases and 27,486 diagnosed as probable cases) were changed in its Geo-codes in the GIS. The highest incidence rate from February to July 2020 was 48 per 10,000 and the highest 5-month incidence rate belonged to central and eastern neighbourhoods. According to the Cumulative Population density of patients, the higher number is estimated by more than 2500 people in the area; however, the lower number is highlighted by about 500 people in the neighborhood. Also, the results from the local Getis-Ord Gi* method indicate that COVID-19 has formed a hotspot in the eastern, southeast, and central districts in Tehran since February. We also observed a death rate hot spot in eastern areas. Conclusion: Because of the spread of COVID-19 disease throughout Tehran's neighborhoods with different socioeconomic status, it seems essential to pay attention to health behaviors to prevent the next waves of the disease. The findings suggest that disease distribution has formed a hot spot in Tehran's eastern and central regions.

Resilient urban form to pandemics: Lessons from COVID-19.

Background: The worldwide emergence of future pandemics emphasizes the need to assess the pandemic resilient urban form to prevent infectious disease transmission during this epidemic. According to the lessons of the COVID-19 outbreak, this study aimed to review the current strategies of responding to pandemics through disaster risk management (DRM) to develop a pandemic-resilient urban form in phases of response, mitigation, and preparedness. Methods: The research method is developed through desk study was used to explore the current literature of urban form responded to COVID-19 pandemic and for the text analysis; qualitative content analysis was applied developing a conceptual framework. Results: To create pandemic resilient urban form, this study proposes principles to enhance the urban form resiliency in 3 scales of housing, neighborhoods/public spaces, and cities. These principles focus on the concept of resilient urban form from new perspectives focusing on the physical and nonphysical aspects of resilient urban form, which develops a new understanding of pandemics as a disaster and health-related emergency risks. The physical aspect of resiliency to epidemic outbreaks includes urban form, access, infrastructure, land use, and natural environment factors. Moreover, the nonphysical aspect can be defined by the sociocultural, economic, and political (including good governance) factors. By providing and enhancing the physical and nonphysical prerequisites, several benefits can be gained and the effectiveness of all response, mitigation, and preparedness activities can be supported. Conclusion: As the pandemic's disruptions influence the citizens' lifestyle dramatically, the prominent role of place characteristics in the outbreak of pandemics, policymakers, urban planners, and urban designers should be pulled together to make urban areas more resilient places for epidemics and pandemics.

Confirmatory factor analysis model of factors affecting the implementation of clinical guidelines in Iran.

Background: Clinical guidelines refer to a developed scientific statement to help physicians and patients for decision-making about the best care for special clinical conditions, which can be an important document to shape evidence-based medicine. This study aimed to identify factors affecting the implementation of clinical guidelines in Iran to enhance the quality of services. Methods: This descriptive analytical study was performed with combined quantitative-qualitative method in the first half of 2019. The statistical population consisted of 400 health managers and experts who were selected through multistage sampling method in 5 regions of Iran (north, south, center, east, and west). Overall, 20 academic experts were selected from each university. For data collection, a researcher-made questionnaire (n = 400) was used. To measure face and content validity, content validity ratio (CVR) and content validity index (CVI) were used. Also, to determine reliability, test-retest method, with Cronbach's alpha coefficient of 0.934 was used. For data analysis, Lisrel 8.8 and SPSS 24 were used. Finally, fitness indices were used to determine the fitness of the model. Results: Six factors, including organizational (9 components), organizational culture (8 components), the clinical guidelines feature (8 components), insurance (7 components), and trusteeship of the health care system (8 components) were identified as the main dimensions. The economic dimension had the maximum effect on implementing clinical guidelines (0.90), while the clinical guidelines feature (0.63) and organizational culture (0.63) showed the minimum extent of effect on implementing clinical guidelines. Conclusion: Evidently, imposing the mentioned interventions with the ultimate goal of sustainable behavior change in providing health care services requires contribution of all practitioners, presentation of suitable facilities for implementing clinical guidelines based on evidence, time and personnel management, training methodology and planning, developing the necessary infrastructure, supervision, and developing professional and legal motivation.

Developing strategies for patient safety implementation: a national study in Iran.

PURPOSE: Today, healthcare organizations focus mainly on development and implementation of patient safety strategic plan to improve quality and ensure safety of provided services. The purpose of this paper is to recommend potential strategies for successful implementation of patient safety program in Iranian hospitals based on a strengths, weaknesses, opportunities, threats (SWOT) analysis. DESIGN/METHODOLOGY/APPROACH: In this qualitative study, key informant interviews and documentation review were done to identify strength and weakness points of Iranian hospitals in addition to opportunities and threats facing them in successful implementation of a patient safety program. Accordingly, the research team formulated main patient safety strategies and consequently prioritized them based on Quantitative Strategic Planning Matrix (QSPM) matrix. FINDINGS: The study recommended some of the potential patient safety strategies including provision of education for employees, promoting a safety culture in hospitals, managerial support and accountability, creating a safe and high-quality delivery environment, developing national legislations for hospital staff to comply with patient safety standards and developing a continuous monitoring system for quality improvement and patient safety activities to ensure the achievement of predetermined goals. PRACTICAL IMPLICATIONS: Developing a comprehensive and integrated strategic plan for patient safety based on accurate information about the health system's weaknesses, strengths, opportunities and threats and trying to implement the plan in accordance with patient safety principles can help hospitals achieve great success. ORIGINALITY/VALUE: Ministry of Health and Medical Education (MOHME) conducted a national study to recommend potential strategies for successful implementation of patient safety in Iranian hospitals based on a SWOT analysis and QSPM matrix.

Designing and validating a measuring tool for the factors affecting the distribution of hospitals' intensive care beds in Iran.

Background: Hospital services are the most expensive medical service in modern health care systems. Intense care beds, in particular, are more important. The present study was conducted to design and validate a measuring tool for the factors affecting the distribution of hospitals' intensive care beds in Iran. Methods: In this mixed method study, first, all known factors affecting the distribution of hospitals' intensive care beds were extracted by reviewing related literature. Then, all 60 confirmed items were categorized into different dimensions. Face validity and content validity of the questionnaire was done by 20 medical experts through qualitative and quantitative methods. Validity and reliability indices, content validity index (CVI), content validity ratio (CVR), and Cronbach's alpha were measured. SPSS software were used for data analysis and significance level was set at less than .05. Results: From the 60 suggested items, 34 were confirmed by the expert panels and all items had CVR and CVI scores higher than 0.83 and 0.81, respectively. CVR and CVI for all 34 items were 0.88 and 0.89, respectively. Also, Cronbach's alpha coefficient (0.75) indicated a suitable internal consistency. The value of S-CVI / Ave was also calculated to be 0.92. Conclusion: In this study, a valid tool was designed to identify the factors affecting the distribution of hospitals' intensive care beds. This tool consists of 6 dimensions: demographic, geographic, economic, sociopolitical, organizational, and constructional.

A Successful implementation of an idea to a nationally approved plan: Analyzing Iran's National Health Roadmap using the Kingdon model of policymaking.

Introduction: Hospital beds, human resources, and medical equipment are the costliest elements in the health system and play an essential role at the time of treatment. In this paper, different phases of the NEDA 2026 project and its methodological approach were presented and its formulation process was analysed using the Kingdon model of policymaking. Methods: Iran Health Roadmap (NEDA 2026) project started in March 2016 and ended in March 2017. The main components of this project were hospital beds, clinical human resources, specialist personnel, capital medical equipment, laboratory facilities, emergency services, and service delivery model. Kingdon model of policymaking was used to evaluate NEDA 2026 development and implementation. In this study, all activities to accomplish each step in the Kingdon model was described. Results: The followings were done to accomplish the goals of each step: collecting experts' viewpoint (problem identification and definition), systematic review of the literature, analysis of previous experiences, stakeholder analysis, economic analysis, and feasibility study (solution appropriateness analysis), three-round Delphi survey (policy survey and scrutinization), and intersectoral and interasectoral agreement (policy legislation). Conclusion: In the provision of an efficient health service, various components affect each other and the desired outcome, so they need to be considered as parts of an integrated system in developing a roadmap for the health system. Thus, this study demonstrated the cooperation process at different levels of Iran's health system to formulate a roadmap to provide the necessary resources for the health sector for the next 10 years and to ensure its feasibility using the Kingdon policy framework.

Endoplasmic Reticulum Stress and Associated ROS.

The endoplasmic reticulum (ER) is a fascinating network of tubules through which secretory and transmembrane proteins enter unfolded and exit as either folded or misfolded proteins, after which they are directed either toward other organelles or to degradation, respectively. The ER redox environment dictates the fate of entering proteins, and the level of redox signaling mediators modulates the level of reactive oxygen species (ROS). Accumulating evidence suggests the interrelation of ER stress and ROS with redox signaling mediators such as protein disulfide isomerase (PDI)-endoplasmic reticulum oxidoreductin (ERO)-1, glutathione (GSH)/glutathione disuphide (GSSG), NADPH oxidase 4 (Nox4), NADPH-P450 reductase (NPR), and calcium. Here, we reviewed persistent ER stress and protein misfolding-initiated ROS cascades and their significant roles in the pathogenesis of multiple human disorders, including neurodegenerative diseases, diabetes mellitus, atherosclerosis, inflammation, ischemia, and kidney and liver diseases.

Selection and Optimization of Carbon-Reinforced Polyether Ether Ketone Process Parameters in 3D Printing-A Rotating Component Application.

The selection of process parameters is crucial in 3D printing for product manufacturing. These parameters govern the operation of production machinery and influence the mechanical properties, production time, and other aspects of the final product. The optimal process parameter settings vary depending on the product and printing application. This study identifies the most suitable cluster of process parameters for producing rotating components, specifically impellers, using carbon-reinforced Polyether Ether Ketone (CF-PEEK) thermoplastic filament. A mathematical programming technique using a rating method was employed to select the appropriate process parameters. The research concludes that an infill density of 70%, a layer height of 0.15 mm, a printing speed of 60 mm/s, a platform temperature of 195 °C, an extruder temperature of 445 °C, and an extruder travel speed of 95 mm/s are optimal process parameters for manufacturing rotating components using carbon-reinforced PEEK material.

Feature fusion-based food protein subcellular prediction for drug composition.

The structure and function of dietary proteins, as well as their subcellular prediction, are critical for designing and developing new drug compositions and understanding the pathophysiology of certain diseases. As a remedy, we provide a subcellular localization method based on feature fusion and clustering for dietary proteins. Additionally, an enhanced PseAAC (Pseudo-amino acid composition) method is suggested, which builds upon the conventional PseAAC. The study initially builds a novel model of representing the food protein sequence by integrating autocorrelation, chi density, and improved PseAAC to better convey information about the food protein sequence. After that, the dimensionality of the fused feature vectors is reduced by using principal component analysis. With prediction accuracies of 99.24% in the Gram-positive dataset and 95.33% in the Gram-negative dataset, respectively, the experimental findings demonstrate the practicability and efficacy of the proposed approach. This paper is basically exploring pseudo-amino acid composition of not any clinical aspect but exploring a pharmaceutical aspect for drug repositioning.

A novel approach toward skin cancer classification through fused deep features and neutrosophic environment.

Variations in the size and texture of melanoma make the classification procedure more complex in a computer-aided diagnostic (CAD) system. The research proposes an innovative hybrid deep learning-based layer-fusion and neutrosophic-set technique for identifying skin lesions. The off-the-shelf networks are examined to categorize eight types of skin lesions using transfer learning on International Skin Imaging Collaboration (ISIC) 2019 skin lesion datasets. The top two networks, which are GoogleNet and DarkNet, achieved an accuracy of 77.41 and 82.42%, respectively. The proposed method works in two successive stages: first, boosting the classification accuracy of the trained networks individually. A suggested feature fusion methodology is applied to enrich the extracted features' descriptive power, which promotes the accuracy to 79.2 and 84.5%, respectively. The second stage explores how to combine these networks for further improvement. The error-correcting output codes (ECOC) paradigm is utilized for constructing a set of well-trained true and false support vector machine (SVM) classifiers via fused DarkNet and GoogleNet feature maps, respectively. The ECOC's coding matrices are designed to train each true classifier and its opponent in a one-versus-other fashion. Consequently, contradictions between true and false classifiers in terms of their classification scores create an ambiguity zone quantified by the indeterminacy set. Recent neutrosophic techniques resolve this ambiguity to tilt the balance toward the correct skin cancer class. As a result, the classification score is increased to 85.74%, outperforming the recent proposals by an obvious step. The trained models alongside the implementation of the proposed single-valued neutrosophic sets (SVNSs) will be publicly available for aiding relevant research fields.

Identification of the factors affecting the referral system of veterans' health services: A scoping review.

Referral in the health system is a systematic process for the optimal allocation of resources and improves the access of people in need of treatment services. Considering the vulnerability of the veterans and more medical needs in this group, this study aims to identify the components that affect veterans' health services referral system. MEDLINE, Scopus, Web of Science, and ProQuest databases, the international military studies website, and key journals in the field of veterans' health services were searched with related keywords including "veteran," "referral system," and "health services" for the period from January 2000 to July 2022. Studies were screened and selected in accordance with the phases of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) flow diagram 2020. Data extraction was done by two researchers independently and a thematic content analysis method was used to analyze the findings. Among 40,608 studies searched electronically and 16 studies searched manually, 19 studies that met the inclusion criteria were selected. The research method applied here is a combination of quantitative, qualitative, and mixed methods. The most important findings were extracted from the included studies and analyzed in three general categories: components related to the patient, service provider, and the structural-operational mechanisms of the referral system. The effective performance of the referral system for providing health services to veterans is influenced by the factors affecting components related to the patient, the service provider, and the structural-operational mechanisms of the referral system. Evaluating and improving each of these factors improve the performance of the referral system and provision of health services to veterans.

Prediction of in-hospital mortality rate in COVID-19 patients with diabetes mellitus using machine learning methods.

Background: Since its emergence in December 2019, until June 2022, coronavirus 2019 (COVID-19) has impacted populations all around the globe with it having been contracted by ~ 535 M people and leaving ~ 6.31 M dead. This makes identifying and predicating COVID-19 an important healthcare priority. Method and Material: The dataset used in this study was obtained from Shahid Beheshti University of Medical Sciences in Tehran, and includes the information of 29,817 COVID-19 patients who were hospitalized between October 8, 2019 and March 8, 2021. As diabetes has been shown to be a significant factor for poor outcome, we have focused on COVID-19 patients with diabetes, leaving us with 2824 records. Results: The data has been analyzed using a decision tree algorithm and several association rules were mined. Said decision tree was also used in order to predict the release status of patients. We have used accuracy (87.07%), sensitivity (88%), and specificity (80%) as assessment metrics for our model. Conclusion: Initially, this study provided information about the percentages of admitted Covid-19 patients with various underlying disease. It was observed that diabetic patients were the largest population at risk. As such, based on the rules derived from our dataset, we found that age category (51-80), CPR and ICU residency play a pivotal role in the discharge status of diabetic inpatients.

Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

A Survey on Human Cancer Categorization Based on Deep Learning.

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.

The relationship between talent management and the efficiency of head nurses and senior and middle managers from the educational and medical centers in Tabriz, Iran - a case study.

Nowadays, organizations understand that they need the best talent to succeed in the complex world economy and survive in a competitive business environment. Therefore, talent management can ensure that each employee with a unique talent or ability will be placed in the correct position. This article aimed to study the relationship between talent management, senior and middle managers, and head nurses from educational health and research centers in Tabriz, in 2016. The target population included senior and middle managers and head nurses from Tabriz University of Medical Sciences, approximately 197 people. The sample for this study was selected based on Morgan's table, which rounds up to 123 people. The Kolmogorov-Smirnov test was used to analyze data, and if data were normal, correlation and regression analysis were performed. There was a significant relationship between talent management and the efficiency of senior and middle management and head nurses from the educational and medical centers in Tabriz. Therefore, when talent management increases, the efficiency level also rises to a noticeable degree. Also, the linear regression showed a linear relationship between talent management as an independent variable and efficiency as a dependent variable. Applying talent management strategies in the management selection process in organizations with demanding environments such as hospitals seems inevitable so that managers with the highest efficiency are hired.