Predicting cervical cancer screening participation using self-care behaviors among women in Iran.

BACKGROUND: Cervical cancer screening is an effective and accessible method for preventing this cancer. However, low participation rates among women have been reported. Self-care is one of the solutions to improve access to health services. This study was conducted to determine the prediction of cervical cancer screening participation using self-care behaviors among women in Iran. MATERIALS AND METHODS: This cross-sectional study was conducted on 310 eligible women who were referred to comprehensive health centers and women's clinics in teaching hospitals in Isfahan, Iran, from November 2020 to April 2021. Participants were enrolled using convenience sampling. The data collection tool included researcher-made questionnaires on personal and fertility characteristics, participation in cervical cancer screening, and self-care behaviors related to cervical cancer and its screening. Descriptive and inferential statistical methods were used for data analysis using the Statistical Package for the Social Sciences (SPSS) version 22 software. RESULTS: The results showed that the intention to undergo screening was low among individuals who had not undergone screening. Lack of awareness and not having enough time were the most common barriers to screening. The results of logistic regression analysis indicated that self-efficacy was the significant predictor of cervical cancer screening. With an increase in the self-care score, the 12% chance of doing a Pap smear increases significantly (P = 0.002). Furthermore, the results of multiple regression showed that with an increase in the self-care score, the chance of women who refer to screening every year, every 2-3 years, and every 4-5 years is increased to 25% (P = 0.001), 34% (P < 0.001), and 11% (P = 0.032), respectively, compared with non-referral. DISCUSSION: According to the results, self-care was a predictor of performing a Pap smear, and it was related to its regular performance of Pap smear too. Therefore, designing and implementing necessary interventions to increase self-care behaviors can improve women's participation in cervical cancer screening and its regularity.

Dimensions of hospital resilience emphasized during the COVID-19 pandemic response: A systematic review.

Objectives: Hospitals must maintain their effective operations during and after disasters. Due to the current increase in disasters, hospital resilience has drawn scholarly attention. This study aimed to review studies on the changes in the definition of hospital resilience after COVID-19, build a conceptual framework for careful measurement, and identify the main dimensions of hospital resilience emphasized during the COVID-19 pandemic. Design: The initial phase of this study was a systematic review of articles published before the COVID-19 pandemic to extract the hospital resilience-related dimensions for the second phase. The second phase involved text-mining articles published both before and after the emergence of COVID-19. Setting: In the systematic review phase, 12 databases were searched from 2006 to January 2020, including Scopus, Web of Science, MEDLINE through PubMed, Embase, ERIC, ProQuest, the Cochrane Library, Emerald, Springer, Science Direct/ELSEVIER, Google Scholar, and SID (for Persian language papers). Then, after COVID-19, articles published in these databases between January 2020 and May 2022 were evaluated using text mining. Result: During the systematic phase, 17 out of 1530 papers published before COVID-19 were synthesized to collect components of hospital disaster resilience. These identified components were the inputs for the text-mining phase. The text mining on pre-COVID papers resulted in six clusters, with the highest weight (0.65) belonging to general resilience and disaster preparedness, while in the post-COVID text mining phase, including 70 papers, 8 clusters have been identified, with the highest weight cluster (0.78) focusing on the mental and psychological aspects of resilience among healthcare workers. Conclusion: Following the COVID pandemic, scholarly attention has shifted to the more personal dimensions of hospital resilience, including psychological resiliency. It seems necessary for policymakers to focus more on the individual and psychological resilience of hospital staff.

Sulfonic acid functionalized cellulose-derived (nano)materials: Synthesis and application.

The preparation/application of heterogeneous (nano)materials from natural resources has currently become increasingly fascinating for researchers. Cellulose is the most abundant renewable polysaccharide on earth. The unique physicochemical, structural, biological, and environmental properties of this natural biopolymer have led to its increased application in many fields. The more desirable features of cellulose-based (nano)materials such as biodegradability, renewability, biocompatibility, cost-effectiveness, simplicity of preparation, environmentally friendly nature, and widespread range of applications have converted them into promising compounds in medicine, catalysis, biofuel cells, and water/wastewater treatment processes. Functionalized cellulose-based (nano)materials containing sulfonic acid groups may prove to be one of the most promising sustainable bio(nano)materials of modern times in the field of cellulose science and (nano)technology owing to their intrinsic features, high crystallinity, high specific surface area, abundance, reactivity, and recyclability. In this review, the developments in the application of sulfonated cellulose-based (nano)materials containing sulfonic acid (-SO3H) groups in catalysis, water purification, biological/biomedical, environmental, and fuel cell applications have been reported. This review provides an overview of the methods used to chemically modify cellulose and/or cellulose derivatives in different forms, including nanocrystals, hydrogels, films/membranes, and (nano)composites/blends by introducing sulfonate groups on the cellulose backbone, focusing on diverse sulfonating agents utilized and substitution regioselectivity, and highlights their potential applications in different industries for the generation of alternative energies and products.

The prevalence of sleep disturbance and its possible associated factors among iranian medical students: A cross-sectional study with a national meta-analysis.

Background: The aim of this cross-sectional study was to investigate the prevalence of sleep disturbance and its possible associated factors among Iranian medical students. Additionally, a national meta-analysis was conducted to provide a comprehensive overview of sleep disturbance in this population. Methods: A sample of medical students from Guilan University of Medical Sciences, Iran was included in the study. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep disturbance. Demographic and lifestyle factors, as well as academic performance, were collected through a self-administered questionnaire. The data collected from this study were combined with existing studies through a meta-analysis to estimate the overall prevalence of sleep disturbance among Iranian medical students using the random effects model. Results: A total of 249 medical students participated in the study. The prevalence of sleep disturbance among Guilan University of Medical Sciences medical students was found to be 71.1%. A significant difference was observed in total PSQI means regarding medical students' residency (p < 0.001) and their duration of sleep in the last 24 h (p = 0.006). The national prevalence of sleep disturbances was 59% (95% CI: [51%-66%], I2 = 97%). Conclusion: Sleep disturbance is highly prevalent among Iranian medical students, with various factors contributing to its occurrence. The findings of this study highlight the need for interventions and strategies to improve sleep quality and overall well-being among this population. The national meta-analysis provides valuable insights into the overall burden of sleep disturbance among Iranian medical students and can serve as a reference for future studies and public health initiatives targeting this issue.

Effect of cannabis use on blood levels of brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF): A systematic review and meta-analysis.

BACKGROUND: The impact of cannabis uses on blood levels of brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) remains uncertain, with conflicting findings reported in the literature. BDNF and NGF both are essential proteins for neuron's growth, and their dysregulation is seen in various mental disorders. This study aims to evaluate the relationship between cannabis usage and BDNF and NGF levels due to their potential implications for mental health. METHODS: A comprehensive search of electronic databases was performed using appropriate MeSH terms and keywords. Inclusion criteria comprised human studies investigating the relationship between cannabis use and BDNF and NGF levels. RESULTS: A total of 11 studies met the inclusion criteria and were included. The pooled analysis revealed a nonsignificant association between cannabis use and dysregulated blood levels of BDNF (random-effects model, standardized mean differences [SMD] = .26, 95% CI -.34 to .76, p = .40). The results of our subgroup analysis based on BDNF source showed a nonsignificant between-group difference. For NGF levels, four studies were included, the pooled analysis revealed a nonsignificant association between cannabis use and dysregulated blood levels of NGF (random-effects model, SMD = -.60, 95% CI -1.43 to -.23, p = .16). In both analyses, high heterogeneity was observed among the included studies which is a notable limitation to current meta-analysis. CONCLUSION: This systematic review highlights the need for further research to elucidate the relationship between cannabis use and these neurotrophic factors. A better understanding of these associations can contribute to our knowledge of the neurobiological effects of cannabis and inform potential implications for mental health, cognitive function, and neurodegenerative disorders.

Using Data Mining Techniques to Predict Chronic Kidney Disease: A Review Study.

One of the growing global health problems is chronic kidney disease (CKD). Early diagnosis, control, and management of chronic kidney disease are very important. This study considers articles published in English between 2016 and 2021 that use classification methods to predict kidney disease. Data mining models play a vital role in predicting disease. Through our study, data mining techniques of support vector machine, Naive Bayes, and k-nearest neighbor had the highest frequency. After that, random forest, neural network, and decision tree were the most common data mining techniques. Among the risk factors associated with chronic kidney disease, respectively, risk factors of albumin, age, red blood cells, pus cells, and serum creatinine had the highest frequency in these studies. The highest number of best yields was allocated to random forest technique. Reviewing larger databases in the field of kidney disease can help to better analyze the disease and ensure the risk factors extracted.

Identifying the Factors Affecting the Incidence of Congenital Heart Disease Using Support Vector Machine and Particle Swarm Optimization.

Background: Congenital malformations are defined as "any defect in the structure of a person that exists from birth". Among them, congenital heart malformations have the highest prevalence in the world. This study focuses on the development of a predictive model for congenital heart disease in Isfahan using support vector machine (SVM) and particle swarm intelligence. Materials and Methods: It consists of four parts: data collection, preprocessing, identify target features, and technique. The proposed technique is a combination of the SVM method and particle swarm optimization (PSO). Results: The data set includes 1389 patients and 399 features. The best performance in terms of accuracy, with 81.57%, is related to the PSO-SVM technique and the worst performance, with 78.62%, is related to the random forest technique. Congenital extra cardiac anomalies are considered as the most important factor with averages of 0.655. Conclusion: Congenital extra cardiac anomalies are considered as the most important factor. Detecting more important feature affecting congenital heart disease allows physicians to treat the variable risk factors associated with congenital heart disease progression. The use of a machine learning approach provides the ability to predict the presence of congenital heart disease with high accuracy and sensitivity.

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review.

The elderly population is projected to increase from 8.5% in 2015 to 12% in 2030 and 16% in 2050. This growing demographic is chronically vulnerable to various age-related diseases and injuries like falling, leading to long-term pain, disability, or death. Thus, there is a need to use the potential of novel technologies to support the elderly regarding patient safety matters in particular. Internet of Things (IoT) has recently been introduced to improve the lifestyle of the elderly. This study aimed to evaluate the studies that have researched the use of the IoT for elderly patients' safety through performance metrics, accuracy, sensitivity, and specificity. We conducted a systematic review on the research question. To do this, we searched PubMed, EMBASE, Web of Science, Scopus, Google Scholar, and Science Direct databases by combining the related keywords. A data extraction form was used for data gathering through which English, full-text articles on the use of the IoT for the safety of elderly patients were included. The support vector machine technique has the most frequency of use compared to other techniques. Motion sensors were the most widely used type. The United States with four studies had the highest frequencies. The performance of IoT to ensure the elderly's safety was relatively good. It, however, needs to reach a stage of maturity for universal use.

Diagnosis of Coronary Artery Disease based on Machine Learning algorithms Support Vector Machine, Artificial Neural Network, and Random Forest.

Background: Coronary artery disease (CAD) is known as the most common cardiovascular disease. The development of CAD is influenced by several risk factors. Diagnostic and therapeutic methods of this disease have many and costly side effects. Therefore, researchers are looking for cost-effective and accurate methods to diagnose this disease. Machine learning algorithms can help specialists diagnose the disease early. The aim of this study is to detect CAD using machine learning algorithms. Materials and Methods: In this study, three data mining algorithms support vector machine (SVM), artificial neural network (ANN), and random forest were used to predict CAD using the Isfahan Cohort Study dataset of Isfahan Cardiovascular Research Center. 19 features with 11495 records from this dataset were used for this research. Results: All three algorithms achieved relatively close results. However, the SVM had the highest accuracy compared to the other techniques. The accuracy was calculated as 89.73% for SVM. The ANN algorithm also obtained the high area under the curve, sensitivity and accuracy and provided acceptable performance. Age, sex, Sleep satisfaction, history of stroke, history of palpitations, and history of heart disease were most correlated with target class. Eleven rules were also extracted from this dataset with high confidence and support. Conclusion: In this study, it was shown that machine learning algorithms can be used with high accuracy to detect CAD. Thus, it allows physicians to perform timely preventive treatment in patients with CAD.

Extracting the Factors Affecting the Survival Rate of Trauma Patients Using Data Mining Techniques on a National Trauma Registry.

Introduction: Thousands of people die due to trauma all over the world every day, which leaves adverse effects on families and the society. The main objective of this study was to identify the factors affecting the mortality of trauma patients using data mining techniques. Methods: The present study includes six parts: data gathering, data preparation, target attributes specification, data balancing, evaluation criteria, and applied techniques. The techniques used in this research are all from the decision tree family. The output of these techniques are patterns extracted from the trauma patients dataset (National Trauma Registry of Iran). The dataset includes information on 25,986 trauma patients from all over the country. The techniques that were used include random forest, CHAID, and ID3. Results: Random forest performs better than the other two techniques in terms of accuracy. The ID3 technique performs better than the other two techniques in terms of the dead class. The random forest technique has performed better than other techniques in the living class. The rules with the most support, state that if the Injury Severity Score (ISS) is minor and vital signs are normal, 98% of people will survive. The second rule, in terms of support, states that if ISS is minor and vital signs are abnormal, 93% will survive. Also, by increasing the threshold of the patient's arrival time from 10 to 15 minutes, no noticeable difference was observed in the death rate of patients. Conclusion: Transfer time of less than ten minutes in patietns whose ISS is minor, can increase the chance of survival. Impaired vital signs can decrease the chance of survival in traffic accidents. Also, if the ISS is minor in non-penetrating trauma, regardless of vital signs and if the victim is transported in less than ten minutes, the patient will survive with 99% certainty.

Health Information System in Developing Countries: A Review on the Challenges and Causes of Success and Failure.

Background: A review on the health information systems (HISs) of each country should not be limited only to data collected and reported normally by the service providers. In this regard, the first step for the development in any national project is exploring the experiences of other countries worldwide, especially those with economic, political, cultural, and regional partnerships, and then using their resources and documents to have a broader attitude and a better profitability in planning the development strategy. This study was conducted to review the studies conducted on the causes of HIS success and failure, and the challenges faced by developing countries in using these systems. Methods: The present study was a narrative review to meet the aim of the study, and those studies published in English language in PubMed, Web of science, and Science Direct databases and Scopus between 2000 and 2020 were investigated. Primary keywords used to extract content in these databases were as follows: "health information system", "challenges", "success", "failure", "developing country", and "low and middle income country". Results: After searching the above-mentioned databases, 455 studies were retrieved. Finally, 24 articles were used. The causes of success and failure of HISs were finally divided into 4 categories: human, organizational, financial and technical factors. A total of 30 subfactors were extracted for different factors. Moreover, the findings indicated that many of the challenges that developing countries face in using HISs are influenced by the social, cultural, economic, geographical, and political conditions of these countries. The results represented that organizational and human elements play a critical role in the advancement or falling of the health HIS in growing countries. Conclusion: There is a demand to come up with flexible standards for designing and deploying HISs to address these complexities. Several solutions can be found to address the obstacles and problems facing HISs in developing countries, including formulating strategic plans and policies necessary for the development of national HISs.

Investigation of the Differential Power of Young's Internet Addiction Questionnaire Using the Decision Stump Tree.

Background: Internet addiction is one of the serious consequences of recent advances in the use of social media. Early detection of Internet addiction is essential because of its harms and is necessary for timely and effective treatment. Aim: The aim of this study was to use data mining and an artificial intelligence algorithm to estimate the differential power of each question in the Young Internet Addiction Test and build a decision stump model to predict which item in the questionnaire can be representative of the whole questionnaire. Methods: This is a descriptive study conducted at the University of Tabriz, in which 256 undergraduate students were selected in randomized cluster sampling, and they completed Young's IAT (Internet Addiction Test) questionnaire and some demographic questions. The data were statistically analyzed with SPSS and were divided into two groups, normal and addicted, by using a cut-off point. Also, the data of the subjects was used to model the decision stump tree in WEKA. The clustering item was the normal and addicted specifier. Results: The study shows that Cronbach's alpha of the IAT is 0.88, which shows good internal integration of subjects that are used to develop the model in WEKA (the Waikato Environment for Knowledge Analysis). Data analysis showed that by using the second question of this questionnaire as the root of the decision stump tree model, it is possible to distinguish between Internet addicts and healthy users with 82% accuracy using this model. Conclusion: The study shows innovative ways in which decision stump trees and data mining can help to improve methods used in Clinical Psychotherapy and Human Science. Regarding this, the study showed that early detection of Internet addiction would be possible by using the 2nd question of the IAT. Also, early detection can result in cost-effectiveness for the whole healthcare system.

Prediction of Length of Hospital Stay of COVID-19 Patients Using Gradient Boosting Decision Tree.

The aim of this paper is to predict the patient hospitalization time with coronavirus disease 2019 (COVID-19). It uses various data mining techniques, such as random forest. Many rules were derived by applying these techniques to the dataset. The extracted rules mainly were related to people over 55 years old. The rule with the most support states that if the person is between 70 and 80 years old, has cardiovascular disease, and the gender is female; then, the person will be hospitalized for at least five days. The gradient boosting random forest technique has performed better than other techniques. As a limitation of the study, it can be pointed out that a few features were unavailable and had not been recorded. Patients with diabetes, chronic respiratory problems, and cardiovascular diseases have a relatively long hospitalization. So, the hospital manager should consider a suitable priority for these patients. Older people were also more likely to take part in the selection rules.

Comparing the efficacy of various methods for sulfate radical generation for antibiotics degradation in synthetic wastewater: degradation mechanism, kinetics study, and toxicity assessment.

In the present study the aim was to investigate and compare various activation processes for amoxicillin degradation. UV radiation, ultrasound, heat, and hydrogen peroxide were selected as the persulfate activation methods. The effects of various parameters such as pH, persulfate concentration, reaction time, AMX concentration, radical scavengers, and anions were thoroughly investigated. The results showed that AMX degradation was following the pseudo-first order kinetic model. The reaction rate of 0.114 min-1 was calculated for the UV/PS process, which was higher than that of the other investigated processes. The AMX degradation mechanism and pathway investigations revealed that sulfate and hydroxyl radicals were responsible for the degradation of AMX by two degradation pathways of hydroxylation and the opening of the ß-lactam ring. Competition kinetic analysis showed that the second-order rate constant of AMX with sulfate radicals was 8.56 × 109 L mol-1 s-1 in the UV/PS process. Cost analysis was conducted for the four investigated processes and it was found that 1.9 $m-3 per order is required in the UV/PS process for the complete destruction of AMX. Finally, cytotoxic assessment of the treated effluent on human embryonic kidney cells showed a considerable reduction in AMX-induced cell cytotoxicity, proving that the investigated process is sufficiently capable of completely destroying AMX molecules to nontoxic compounds. Therefore, it can be concluded that UV radiation is much more effective than other methods for persulfate activation and can be considered as a reliable technique for antibiotic removal.

Using Classification and K-means Methods to Predict Breast Cancer Recurrence in Gene Expression Data.

Background: Breast cancer is a type of cancer that starts in the breast tissue and affects about 10% of women at different stages of their lives. In this study, we applied a new method to predict recurrence in biological networks made from gene expression data. Method: The method includes the steps such as data collection, clustering, determining differentiating genes, and classification. The eight techniques consist of random forest, support vector machine and neural network, randomforest + k-means, hidden markov model, joint mutual information, neural network + k-means and suportvector machine + k-menas were implemented on 12172 genes and 200 samples. Results: Thirty genes were considered as differentiating genes which used for the classification. The results showed that random forest + k-means get better performance than other techniques. The two techniques including neural network + k-means and random forest + k-means performed better than other techniques in identifying high risk cases. Conclusion: Thirty of 12,172 genes are considered for classification that the use of clustering has improved the classification techniques performance.

Reducing the risk of death induced by aluminum phosphide poisoning: The new therapies.

Numerous people suffer from accidental or deliberate exposure to different pesticides when poisoning with aluminum phosphate (AlP) is increasing in the eastern countries. Aluminum phosphate is a conventional insecticide that quickly reacts with water or the moistures in the atmosphere and produces fatal phosphine gas, which absorbs quickly by the body. Oral consumption or inhalation of AlP leads to excessive reaction of the body such as fatigue, vomiting, fever, palpitation, vasodilatory shock, increasing blood pressure, cardiac dysfunction, pulmonary congestion, shortness of breath, and death. The garlic smell from the patient's mouth or exhale is one of the methods to recognize the positioning. Due to the lack of individual antidotes, several supportive treatments are required. The present study focused on the available and new therapies that help reduce the effect of AlP poisoning and the mortality rate. The therapies are divided into the antioxidant-related agent and the other agents. The impacts of each agent on the experimental cases are reported.

Sensing of oxidative stress biomarkers: The cardioprotective effect of taurine & grape seed extract against the poisoning induced by an agricultural pesticide aluminum phosphide.

Aluminum phosphide is a well-known hazardous agent used as an agricultural pesticide to protect stored grains from insect damage. However, accidental consumption of a trivial amount of it caused irreversible damage to the human body or even death in acute cases. The present study used taurine and grape seed extract as a natural cardioprotective medicine against aluminum phosphide poisoning by decreasing oxidative stress. The activity of oxidative stress biomarkers (Malondialdehyde, Catalase, Protein carbonyl, and Superoxide dismutase) were evaluated in the cell line model on Human Cardiac Myocyte cells. In the beginning, to clarify the pure impact of aluminum phosphide poison, taurine, and grape seed extract on the human heart cells, their effects on the biomarkers quantity in cell line were measured. Subsequently, the effect of taurine and grape seed extract with various concentrations as a treatment on the oxidative stress biomarkers of the poisoned heart cells were studied. Data analysis reveals that taurine and grape seed extract treatment can successfully diminish the poisoning effect by their antioxidant properties. The oxidative markers values of the poisoned cells were recovered by taurine and grape seed extracts treatment. Taurine (2 g/l) can recover Malondialdehyde, Catalase, Protein carbonyl, and Superoxide dismutase by 56%, 78%, 88%, 78%, when the recovering power of grape seed extract (100 g/l) for the aforementioned enzymes are 56%, 0.71%,74%, 51%, respectively. Therefore, it is clear that the performance of taurine in the recovery of the biomarkers' value is better than grape seed extract.

The opportunities and challenges of using mobile health in elderly self-care.

Population aging is a phenomenon expanding around the world and will be increase the incidence of chronic diseases and health costs. This study was conducted according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA). A comprehensive literature search was performed on 4 databases (Web of Science, PubMed, Science Direct, and ProQuest) for English language studies from January 1, 2000, to December 31, 2019. The keywords used to extract relevant contents were "e-health," "Elderly care," "Self-care," "challenge," "Opportunity" etc., The search strategy led to a total of 638 potentially relevant papers, of which 19 papers met the inclusion criteria. The results showed that the challenges of using mobile health in elderly self-care can be divided into technical, human and managerial challenges. The resulting opportunities include reducing health care costs; no need to visit verbal and remote access to elderly information. The use of mobile health in the elderly has advantages and disadvantages. One of the advantages of that is improving physical activity and reducing care costs, but it may break the privacy. The disadvantages of that can be resolved by educating the elder men.