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.

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.

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.

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.

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.

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.

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.

Migrant Health and COVID-19 Pandemic: A Cross-sectional Study of Characteristics, Clinical Features, and Health Outcome from Iran.

BACKGROUND: As the pandemic unfolds, major concerns remain with those in disadvantaged positions who may be disproportionately affected. This paper aimed to present the characteristics of COVID-19 immigrant patients and investigate whether they were disproportionately affected by COVID-19 pandemic. METHODS: A cross-sectional study was performed using data on 589,146 patients diagnosed with COVID-19 in Iran. Descriptive analyses were used to summarize the study population's characteristics. Chi-squared test and logistic regression model were applied. RESULTS: After accounting for possible confounding covariates, being an immigrant was significantly associated with increased risk of death due to COVID-19 (OR 1.64, CI 1.568-1.727). When compared to Iranian-born patients, the prevalence of low blood oxygen levels on admission was higher among immigrant patients (53.9% versus 47.7%, P value < 0.001). Moreover, greater proportions of immigrants who were diagnosed with COVID-19 were admitted to an ICU (17% versus 15.8%, P value < 0.001). Patients aged 65 and above were the largest age category in both populations. However, there was a significant difference between the age profiles of patients, with children under the age of eighteen presenting 16% of immigrant patients vs 6.6% of Iranian-born patients (P value < 0.001). In both groups, more men were affected by COVID-19 than women, yet the sex bias was more prominent for migrant patients (P value < 0.001). CONCLUSION: The evidence from this study revealed that immigrant patients infected with COVID-19 were more likely to suffer from severe health outcome of the disease compared to Iranian-born patients.

Capability of Different Multi-Criteria Decision-Making Techniques in the Performance Assessment of the Hospitals in Terms of Medical Waste Management.

Background: Assessing the performance of hospitals in waste management requires considering several criteria of different types. The multiplicity of the criteria and how they are weighed and, ultimately, the ranking of hospitals; are among the most complex challenges faced by the environmental health authorities. This research tried to assess the capability of four commonly used multi-criteria decision-making methods, as well as a hybrid technique for performance assessment of six hospitals in Tehran City, Iran, in 2018 regarding waste management. Methods: The effective criteria and sub-criteria were identified by reviewing the relevant literature. The data collection tool was a self-constructed checklist developed based upon the identified criteria and sub-criteria and analysis of the collected data was done in MATLAB software. The sample hospitals were ranked based on the scores given to the management performance of hospitals. Results: The final rankings by the different techniques did not differ significantly. According to the results of the hybrid method, among six studied hospitals, the top three hospitals were Hospital C, B, and A respectively, regarding hospital waste reduction criteria. These hospitals generally had an acceptable performance in terms of waste management, especially waste disinfection and separation at the origin. Conclusion: In assessing the performance and ranking of hospitals in terms of waste management, hybrid multi-criteria decision-making techniques can be used as a useful tool in waste management planning. By identifying the areas in need of corrective actions and choosing the appropriate strategy, they pave the way for improving the performance of hospitals in the field of waste management.

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.

Assessment of the relationship between exposure to air pollutants and COVID-19 pandemic in Tehran city, Iran.

The COVID-19 disease caused by the SARS-CoV-2 virus first identified in December 2019 has resulted in millions of deaths so far around the world. Controlling the spread of the disease requires a good understanding of the factors (e.g. air pollutants) that influence virus transmission and the conditions under which it spreads. This study analyzed the relationships between COVID-19 cases and both short-term (6-month) and long-term (60-month) exposures to eight air pollutants (NO, NO2, NOx, CO, SO2, O3, PM2.5 and PM10) in Tehran city, Iran, by integrating geostatistical interpolation models, regression analysis, and an innovated COVID-19 incidence rate calculation (Q-index) that considered the spatial distributions of both population and air pollution. The results show that the higher COVID-19 incidence rate was significantly associated with the exposure to higher concentrations of CO, NO, and NOx during the short-term period; the higher COVID-19 incidence rate was significantly related to the exposure to higher concentrations of PM2.5 during the long-term period; while COVID-19 incidence rate was not significantly associated with the concentrations of O3, SO2, PM10 and NO2 in either period. This study indicates that exposure to air pollutants can effect an increase in the number of infected people by transmitting the virus through the air or by predisposing people to the disease over time. The Q-index calculation method developed in this study can be also used by other studies to calculate more accurate disease rates that consider the spatial distribution of both population and air pollution.

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.

Relationship Between Income Level and Hospitalization Rate in COVID-19 Cases; an Example of Social Factors Affecting Health.

Introduction: Considering the population's socioeconomic status and clinical features is essential in planning and performing interventions related to disease control. The main purpose of this study was to investigate the relationship between income level and hospitalization rate of COVID-19 patients|. Methods: A cross-sectional study was performed on 198,944 hospitalized COVID-19 patients in Tehran province between March 2020 and March 2021. Data of hospitalized COVID-19 patients was obtained from the Hospital Intelligent Management System (HIM). The income data of patients were obtained from the Iranian Database on Targeted Subsidies belonging to the Ministry of Cooperatives, Labor, and Social Welfare. Data analyses were performed using SPSS software. Results: About 2.5% of the inpatients were from the first decile, while 20.6% were from the tenth. The share of the lower three deciles of total hospitalization was about 11%, while the share of the upper three deciles was 50%. There was a big difference between the upper- and lower-income deciles regarding death rates. In the first decile, 30% of inpatients died, while the proportion was 10% in the tenth decile. There was a significant and positive relationship between income decline and hospitalization (r = 0.75; p = 0.02). Also, there was a significant and negative relationship between income decline and death rate (r = -0.90; p = 0.01). Conclusion: Low-income groups use fewer inpatient services, are more prone to severe illness and death from COVID-19|, and treatment in this group has a lower chance of success. Using a systemic approach to address socioeconomic factors in healthcare planning is crucial.

A review study of the providing elderly care services in different countries.

Background: Population aging leads to change of population structure and increase care needs. Lack of proper planning in this field will lead to occur increasing problems. In this paper, the review of the elderly care plans at various levels in some European and Asian countries, comparing these countries with Iran with the goal of using their experiences to enhance elderly plans have been done. Methods: This research was a review study of library documents and resources and systematic search. Data were collected using the resources, databases, scientific databases and websites of the Ministry of Health and Welfare of the studied countries. Results: Based on this research in the studied countries, the care services of the elderly is based on the important principle of coordination between different organs of the country, followed by the creation of LTC insurance and provision of health and social services for the elderly and mainly the type of home care. In Iran country, providing appropriate services and cares for the elderly with existing plans and policies is not possible and the need to provide appropriate service packages based on the different systems of successful countries and applying the experiences of these countries is essential. Conclusion: Given the current status of the Iranian elderly population in terms of policies and plans and the types and methods of providing services, quality, access and financial resources allocated to this age group, compared to the studied countries, there is a well and integrated plan is essential.

Estimation of Cyclophilin and Phospholipase Enzyme in Women Infected with Toxoplasmosis.

Background: Toxoplasma gondii, a protozoan parasite with a worldwide distribution, is considered to infect one-third of all humans. many species. The intracellular parasite Toxoplasma gondii causes toxoplasmosis. Numerous physiological abnormalities are documented in toxoplasmosis-infected women. Objective: This study aims to demonstrate the connection between cyclophilins, the phospholipase enzyme, and latent toxoplasmosis. Methods: The research was carried out between January 2022 and June 2022. out of 150 patients had blood samples drawn, 250 had serum samples drawn from women with toxoplasma gondi infection, and 50 had healthy samples drawn from Hila city, Iraq. To exclude subjects who had any medical disorders, information from the subjects was gathered via an interviewer-managed questionnaire. ELISA was used to examine the serum. Results: About 250 samples from women with infertility were infected with Toxoplasma gondii overall (24%) Enzyme-Linked Immunosorbent Assay was utilized to evaluate the levels of phospholipase and cyclophilin, while automated VIDAS family instruments were employed to determine the qualitative and quantitative anti-Toxoplasma-IgG-tests (ELISA). Since there was a substantial difference in the statistical analysis and a significant difference in the cyclophilin protein, parasite infection changed the quantity of the enzyme phospholipase. Conclusion: This study put forth the theory that toxoplasmosis infection. Our investigation showed that patients with toxoplasma Gondi infection had higher levels of cyclophilins and phospholipase than control subjects.

Identifying the preparedness components in COVID-19: Systematic literature review.

In 2019, the COVID-19 pandemic posed a major challenge to the world. Since the world is constantly exposed to communicable diseases, comprehensive preparedness of countries is required. Therefore, the present systematic review is aimed at identifying the preparedness components in COVID-19. In this systematic literature review, PubMed, Scopus, Web of Science, ProQuest, Science Direct, Iran Medex, Magiran, and Scientific Information Database were searched from 2019 to 2021 to identify preparedness components in COVID-19. Thematic content analysis method was employed for data analysis. Out of 11,126 journals retrieved from searches, 45 studies were included for data analysis. Based on the findings, the components of COVID-19 preparedness were identified and discussed in three categories: governance with three subcategories of characteristics, responsibilities, and rules and regulations; society with two subcategories of culture and resilience; and services with three subcategories of managed services, advanced technology, and prepared health services. Among these, the governance and its subcategories had the highest frequency in studies. Considering the need to prepare for the next pandemic, countries should create clear and coherent structures and responsibilities for crisis preparedness through legal mechanisms, strengthening the infrastructure of the health system, coordination between organizations through analysis and identification of stakeholders, culture building and attracting social participation, and service management for an effective response.

Primary umbilical cutaneous endometriosis: The first case report in Syria.

Endometriosis in the umbilical region is a very rare case. Although it is possible to have migrating endometrial glands and stroma outside the uterus, for example, the ovaries, fallopian tubes, and other pelvic organs, the umbilicus is a unique place. In our case it was a 40-year- old female patient with no surgical history. She has been suffering for years from symptoms that are very consistent with endometriosis in terms of pain, mass and bleeding. but the diagnostic procedures from imaging and pathology have been taken completely up, then the case was managed while preserving the patient's reproductive function.

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.