In-depth analysis to develop a social marketing model to promote women's participation in waste segregation behaviour: A qualitative study.

Waste separation is one of the key factors in managing solid waste and creating a healthy environment. Waste separation at source has always been associated with challenges. Therefore, this study was conducted to determine the perceptions of housewives and related parties regarding the factors that influence waste separation behaviour and to identify approaches to improve behaviour based on the social marketing framework. This study was conducted as a qualitative content analysis in Amol City in 2022. The data was collected through semi-structured individual interviews. A total of 25 housewives were selected as main participants and 5 stakeholders through purposive selection. The results of the study included lack of awareness of recyclable materials (product), personal, family and environmental barriers (price), lack of doorstep collection of dry waste (place), and lack of use of appropriate technology (promotion). The lack of financial resources, inappropriate political measures and the coronavirus pandemic were also the causes of this challenge. Most participants cited environmental and educational deficits as the main reason for not separating waste. It is possible to improve waste sorting behaviour at source through appropriate behavioural interventions at the individual, social and environmental levels. Researchers can use the results of this study to design, implement and evaluate waste segregation intervention programmes for housewives.

The Effect of Melatonin on Increasing the Health Related Quality of Life in Non-Metastatic Breast Cancer Patients: Three-Year Follow up a Clinical Trial.

INTRODUCTION: Although breast cancer is common worldwide, if diagnosed early and treated on time, the probability of recovery is high and patients often experience a long life. Reducing the quality of life is a common side effect in patients. Melatonin may have an important role in fatigue, sleep disorders and, as a result, the health-related quality of life (HRQoL) in people. About 184 patients with breast cancer were enrolled in 2 groups: intervention with daily melatonin intake of 18 mg for 3 years (93 patients) and the control group with placebo intake (91 patients). Health-related quality of life and the effect of melatonin on increasing that were evaluated with the EORTC QLQ-C30 questionnaire, third edition at the beginning, 2 months later and 3 years after the beginning of the study. RESULTS: The general score of the HRQoL was significantly different both in the passage of time and in the comparative study of the 2 groups, and it was better in the melatonin group (P < .05). CONCLUSION: Long-term use of 18 mg of melatonin for 3 years in patients with non-metastatic breast cancer can lead to an increase in the patients' quality of life.

Relationship between workplace spirituality with organization-based self-esteem and workplace deviant behaviors among Iranian nurses.

BACKGROUND: Despite the numerous studies conducted on workplace spirituality, there is still lack of studies that have explored the relationship between workplace spirituality with organization-based self-esteem and workplace deviant behaviors. This study aims to examine the relationship between workplace spirituality with organization-based self-esteem and workplace deviant behaviors among Iranian nurses. METHODS: 236 nurses from 5 hospitals participated in this descriptive, analytical, and cross-sectional study from August to December 2022. Data was gathered by four questionnaires: demographic information, workplace spirituality, organization-based self-steam, and workplace deviant behaviors. The data were analyzed by SPSS 26 based on descriptive and inferential statistics (Independent Two-sample t Test, Pearson correlation coefficient and multiple regression). RESULTS: Based on the findings, nurses had a moderate level of perception of workplace spirituality and organization-based self-esteem while having a low level of perception regarding the occurrence of workplace deviate behaviors. Results of Pearson correlation coefficient test showed a positive and statistically significant relationship between workplace spirituality and organization-based self-esteem. Additionally, there was an inverse and significant relationship between workplace spirituality and organization-based self-esteem with workplace deviant behaviors. Results of multiple regression analyses indicate that by controlling the demographic characteristics of nurses, the meaningful work and sense of community have a significant relationship with organization-based self-esteem. Furthermore, by controlling the demographic characteristics of nurses, permanent employment status, sense of community, alignment with the organization's values, and organization-based self-esteem have a significant relationship with workplace deviant behavior. CONCLUSIONS: The study suggests that organizations must prioritize promoting workplace spirituality and organization-based self-esteem to ensure a healthy work environment and prevent workplace deviant behaviors.

Modeling heart rate of individual and team manual handling with one hand using generalized additive mixed models.

OBJECTIVES: Despite the fact that team manual handling is common in different working environments, the previous studies in this regard, particularly those with a physiological approach are quite limited. The present study is an attempt to model the heart rate (HR) of individual and team manual handling with one hand. METHODS: Twenty-five young men (aged 21.24±1.42 year) volunteered for this study. The experiments included individual and two-person handling of the load with three different weights with and without height difference. The participants' HR was registered at the end of the route by a chest-strap pulse monitor and a polar watch according to the manufacturer's recommendation. A multivariate Generalized Additive Mixed Model (MGAMM) was used for modeling heart rate based on explanatory variables of workload, carry method, HRrest, body weight, height, knee height, shoulder height, elbow height, and hand height. The significance level of the tests was considered as <0.05. RESULTS: Based on the MGAMM, the average HR (bpm) of participants increased as the workload increased (P<0.001). Handling the load with a taller person increased the HR compared to shorter partner (P<0.001). Moreover, the nonlinear associations of the resting HR (P<0.001), body weight (P<0.001), height (P<0.001), and the height of elbow, hand and knee (P<0.001) were statistically significant. The adjusted R2 of the model was 0.89 indicating that about 90 percent of the variations observed in HR could be explained by the variables in the model. This was greater than the model considering only linear effects (R2 =0.60). CONCLUSION: The model obtained in this study can predict the heart rate of individual and team one-handed handling with high validity. The MGAMM can be used in modeling heart rate in manual handling.

Effects of wearing medical gowns at different temperatures on the physiological responses of female healthcare workers during the COVID-19 Pandemic.

BACKGROUND: Using medical gowns with high protection against COVID-19 among healthcare workers (HCWs) may limit heat exchange, resulting in physiological challenges. OBJECTIVE: This study aimed to compare the physiological and neurophysiological responses of female HCWs when using two typical medical gowns at different temperatures during the COVID-19 pandemic. METHODS: Twenty healthy female HCWs participated in this study. Participants wore two types of medical gowns: Spunbond gown (SG) and laminate gown (LG). They walked on a treadmill in a controlled climate chamber for 30 minutes at three different temperatures (24, 28, and 32°C). Heart rate (HR), skin surface temperature (ST), clothing surface temperature (CT), ear temperature (ET), blood oxygen percentage (SaO2), galvanic skin response (GSR), and blood pressure were measured before and after walking on a treadmill. The study's results were analyzed using SPSS26. RESULTS: The study found that LG led to an average increase of 0.575°C in CT compared to SG at the same temperatures (P <  0.03). The average HR increased by 6.5 bpm in LG at 28°C compared to SG at a comfortable temperature (P = 0.01). The average ET in SG and GSR in LG at 32°C increased by 0.39°C and 0.25µS, respectively, compared to the comfortable temperature (P <  0.02). CONCLUSION: The study recommends maintaining a comfortable temperature range in hospitals to prevent physiological challenges among HCWs wearing medical gowns with high protection against COVID-19. This is important because using LG, compared to SG, at high temperatures can increase HR, ET, CT, and GSR.

Spatial inequalities and non-linear association of continuous variables with mortality risk of liver transplantation in Iran: a retrospective cohort study.

Liver transplantation is the second most common solid organ transplant and the best option for liver failure. Of course, patient survival after transplantation depends on many risk factors. The aim of this study was to investigate the spatial and non-linear effects of continuous risk factors on patient survival after liver transplantation. This retrospective cohort study (n = 3148) used data on liver transplantation in Iran (2004-2019). A generalized additive model with spatial effects and non-linear effects of age and Model for End-Stage Liver Disease (MELD) score variables by penalized spline was used. The majority of patients were male (63.3%), with a mean (SD) age of 42.65 (13.31) and a mean (SD) MELD score of 24.43 (6.72). The 1, 5, and 10-year survival rates were 88.2%, 84.6%, and 82.5% respectively. The non-linear effect showed a steeper slope of the age effect on the hazard of death after the age of 50 (p < 0.05), and the MELD score had a direct but non-linear relationship with the hazard of death (p < 0.05). In the spatial pattern, the provinces with a greater distance from the transplant center had significantly fewer old patients than other provinces. Also, more distant provinces with an older transplant age had higher post-transplant mortality rates. Our study showed that it is better to take age and MELD score into account in postoperative care. The spatial pattern of mortality risk reflects inequalities in access to transplantation and public health services after transplantation.

Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation.

Colorectal cancer (CRC) liver metastasis accounts for the majority of fatalities associated with CRC. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate CRC metastasis-related biomarkers by employing a machine learning (ML) approach and experimental validation. The gene expression profile of CRC patients with liver metastasis was obtained using the GSE41568 dataset, and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify the most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental validation was done through qRT-PCR. 11 genes were commonly selected by LASSO and P-SVM algorithms, among which seven had prognostic value in colorectal cancer. It was found that the expression of the MMP3 gene decreases in stage IV of colorectal cancer compared to other stages (P value < 0.01). Also, the expression level of the WNT11 gene was observed to increase significantly in this stage (P value < 0.001). It was also found that the expression of WNT5a, TNFSF11, and MMP3 is significantly lower, and the expression level of WNT11 is significantly higher in liver metastasis samples compared to primary tumors. In summary, this study has identified a set of potential biomarkers for CRC metastasis using ML algorithms. The findings of this research may provide new insights into identifying biomarkers for CRC metastasis and may potentially lay the groundwork for innovative therapeutic strategies for treatment of this disease.

Self-Management and Its Predictors in adult Patients with Epilepsy: A Cross-Sectional Study.

Background: Appropriate self-management (SM) is crucial in controlling epilepsy and improving self-efficacy, medication adherence and avoidance of seizure triggers in patients with epilepsy (PWE. The aim of this study was to evaluate SM and its predictors among adult PWE in Iran. Methods: This cross-sectional study was conducted in 2021. The participants were 335 adult PWEs that were selected from healthcare settings in Hamadan, Iran. Epilepsy Self-Management Scale (ESMS) was used to measure patients' self-management behaviors. Data were analyzed using the independent samples t-test, Pearson's and Spearman's correlation analyses and multiple linear regression analysis. Results: The mean score of SM was 114.37±11, indicating moderate level of SM, and the mean scores of SM in the medication management, seizure management, and safety management subscales were significantly more than the mean scores of the other subscales (P < 0.05). Age, place of residence, marital status, seizure type, seizure frequency, and epilepsy duration significantly predicted 53% of the variance of SM (adjusted R square = 0.532). Conclusion: PWEs have moderate SM and need SM-related education, particularly about lifestyle management and information management. Predictors of SM should be considered to boost SM practice.

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

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

A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data.

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.

A Randomized, Controlled, Parallel-Group, Trial on the Long-term Effects of Melatonin on Fatigue Associated With Breast Cancer and Its Adjuvant Treatments.

OBJECTIVE: Cancer related fatigue is a distressing condition and correlated with decrease in quality of life of patients with malignant conditions. In continuation of our previous research, we assessed long term anti-fatigue effects of melatonin in patients with the breast cancer. MATERIAL AND METHODS: In this clinical trial, 92 breast cancer patients were randomly assigned to receive either melatonin (18 mg/day) or placebo from 1 week before the adjuvant treatments until 2 years after their completion. The levels of fatigue were assessed before and after intervention using Brief Fatigue Inventory (BFI) and were compared at a significance level of P ≤ .05. RESULTS: The BFI scores were similar between the 2 groups at the baseline (placebo group: 5.56 ± 1.59 and melatonin group: 5.72 ± 1.68, P = .67). After the intervention, not only the mean fatigue score was significantly lower in melatonin group (2.93 ± 1.04 vs 1.99 ± 1.02, P < .001, P ≤ .05), but also a greater reduction in fatigue score in intervention group was evident over time (P ≤ .001). CONCLUSION: Long-term usage of melatonin even after completion of adjuvant therapies in women with breast cancer decreased the levels of fatigue associated with the malignant condition and its treatments. THE TRIAL REGISTRY NAME AND URL, AND REGISTRATION NUMBER: Iranian Registry of Clinical Trials, https://en.irct.ir/trial/62267, IRCT20180426039421N3.

Treatment adherence and associated factors in older people with type 2 diabetes: A qualitative study.

PATIENT CONTRIBUTION: Older people with T2D participated in this study to achieve the objective of the study. AIM: Treatment adherence is an important indicator for evaluating successful diabetes control and overall disease management. It is necessary to identify the hidden themes of the concept of treatment adherence and related factors based on the experiences of older people with T2D. Therefore, the present study was conducted to identify the concept of treatment adherence and its associated factors in older people with type 2 diabetes (T2D). DESIGN: The study was conducted as a qualitative study using a content analysis approach. METHODS: Semi-structured interviews were conducted with 20 older people with T2D, between May and September 2021. Data were organized using MAXQDA-10 software and analysed using the Elo and Kyngas qualitative content analysis method. We followed the COREQ Checklist to ensure rigour in our study. RESULTS: Three themes emerged from the analysis of the data, including: 'Health literacy', 'Support umbrella' and 'responsibility'.

Relationship between moral sensitivity of nurses and quality of nursing care for patients with COVID-19.

AIM: This study aimed to: (1) assess the level of moral sensitivity of nurses and the quality of nursing care for patients with COVID-19 in Iran; and (2) identify the relationship between the moral sensitivity of nurses and the quality of nursing care for patients with COVID-19 in Iran. DESIGN: This study was designed as a descriptive, cross-sectional, and correlational research. METHOD: A total of 211 nurses working in four hospitals affiliated with the Hamadan University of Medical Sciences in Iran from December 2021 to April 2022 were selected via the stratified proportional random sampling method. Demographic information, a moral sensitivity questionnaire, and the Good Nursing Care Scale were used for data collection. The data were analysed by SPSS 24 based on descriptive and inferential statistics (Pearson correlation coefficient and multiple regression). RESULTS: Results revealed that 188 of the nurses (89.1%) had a moderate level of moral sensitivity. Furthermore, 160 of the participants (75.8%) reported a relatively low level of the quality of nursing care. The results of the Pearson correlation coefficient test indicated that there was an inverse and significant relationship between the moral sensitivity of nurses and the quality of nursing care (r = -0.528, p < 0.001). The results of multiple regression indicated that the model of moral sensitivity components explained 27.9% of the variance in the quality of nursing care. The components of moral sensitivity, including relation (ß = -0.246, p < 0.001), meaning (ß = -0.188, p = 0.003), conflict (ß = -0.170, p = 0.008), benevolence (ß = -0.153, p = 0.012), and rules (ß = -0.144, p = 0.019) had inverse and significant effects on the quality of nursing care. CONCLUSION: Since higher mean scores of moral sensitivity reflect lower moral sensitivity, it can be stated that with the increase in moral sensitivity of nurses, the quality of nursing care for patients with COVID-19 grows.

Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis.

BACKGROUND: The purpose of this study was using bioinformatics tools to identify biomarkers and molecular factors involved in the diagnosis of colorectal cancer, which are effective for the diagnosis and treatment of the disease. METHODS: We determined differentially expressed genes (DEGs) related to colorectal cancer (CRC) using the data series retrieved from GEO database. Then the weighted gene co-expression network analysis (WGCNA) was conducted to explore co-expression modules related to CRC diagnosis. Next, the relationship between the integrated modules with clinical features such as the stage of CRC was evaluated. Other downstream analyses were performed on selected module genes. RESULTS: In this study, after performing the WGCNA method, a module named blue module which was more significantly associated with the CRC stage was selected for further evaluation. Afterward, the Protein-protein interaction network through sting software for 154 genes of the blue module was constructed and eight hub genes were identified through the evaluation of constructed network with Cytoscape. Among these eight hub genes, upregulation of MMP9, SERPINH1, COL1A2, COL5A2, COL1A1, SPARC, and COL5A1 in CRC was validated in other microarray and TCGA data. Based on the results of the mRNA-miRNA interaction network, SERPINH1 was found as a target gene of miR-940. Finally, results of the DGIDB database indicated that Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, and Bevacizumab, could be used as a therapeutic agent for targeting the MMP9. Furthermore, Ocriplasmin and Collagenase clostridium histolyticum could target COL1A1, COL1A2, COL5A1, and COL5A2. CONCLUSION: Taken together, the results of the current study indicated that seven hub genes including COL1A2, COL5A1, COL5A2, SERPINH1, MMP9, SPARC, and COL1A1 which were upregulated in CRC could be used as a diagnostic and progression biomarker of CRC. On the other hand, miR-940 which targets SERPINH1 could be used as a potential biomarker of CRC. More ever, Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, Bevacizumab, Ocriplasmin , and Collagenase clostridium histolyticum were introduced as therapeutic agents for CRC which their therapeutic potential should be evaluated experimentally.

Evaluating the Outcome of an Unnecessary Request for CT Scan in Be'sat Hospital of Hamadan.

Aim: This study aimed to investigate the frequency of unnecessary tests requested in Be'sat Hospital in Hamadan. Materials and Methods: This descriptive research was conducted in order to investigate the frequency of unnecessary requests for CT scan and radiography of patients referring to the imaging department of Be'sat Hospital in Hamadan in a 4- to 6-month period. Patient information, including gender, age, type of CT scan test, the reason for requesting the test, the expertise of the requesting physician, and the result of the radiologist's report on each test, was extracted and collected. Results: A total of 1000 CT scans were evaluated. The mean age of these patients was about 36 years and most of them were men. The highest and lowest percentages of unnecessary cases were related to CT scans of the brain (42.3%) and facial bones (2.3%), respectively. The most and the least unnecessary CT scans based on the reason given for the request were related to multiple physical trauma (30.7%) and chronic kidney disease (1.5%), respectively. Conclusion: In all tests, over 74% of the reports were unnecessary and less than 26% were necessary. Therefore, it is necessary to reduce unnecessary requests to reduce the radiation dose of patients. Also, the knowledge of doctors should be increased in the field of appropriate evaluation of CT scan tests based on clinical guidelines.

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

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

Machine Learning-based Classifiers for the Prediction of Low Birth Weight.

OBJECTIVES: Low birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood. Numerous factors contribute to LBW and vary based on the region. The main objectives of this study were to compare four machine learning classifiers in the prediction of LBW and to determine the most important factors related to this phenomenon in Hamadan, Iran. METHODS: We carried out a retrospective cross-sectional study on a dataset collected from Fatemieh Hospital in 2017 that included 741 mother-newborn pairs and 13 potential factors. Decision tree, random forest, artificial neural network, support vector machine, and logistic regression (LR) methods were used to predict LBW, with five evaluation criteria utilized to compare performance. RESULTS: Our findings revealed a 7% prevalence of LBW. The average accuracy of all models was 87% or higher. The LR method provided a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy of 74%, 89%, 7.04%, 29%, and 88%, respectively. Using LR, gestational age, number of abortions, gravida, consanguinity, maternal age at delivery, and neonatal sex were determined to be the six most important variables associated with LBW. CONCLUSIONS: Our findings underscore the importance of facilitating timely diagnosis of causes of abortion, providing genetic counseling to consanguineous couples, and strengthening care before and during pregnancy (particularly for young mothers) to reduce LBW.

Identification of gene profiles related to the development of oral cancer using a deep learning technique.

BACKGROUND: Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning. METHODS: Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes. RESULTS: Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes. CONCLUSIONS: Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

Barriers to Treatment Adherence Among Older Adults With Type 2 Diabetes: A Qualitative Study.

The current qualitative study aimed to determine the barriers to treatment adherence among older adults with type 2 diabetes (T2D). This study was performed using a content analysis approach. Semi-structured interviews were conducted with 25 older adults with T2D between May and September 2021. Data were organized using MAXQDA-10 software and analyzed using the Elo and Kyngäs qualitative content analysis method. Three themes emerged from the analysis: Patient-Specific Barriers, Health Care Provider- and Heath Care System-Specific Barriers, and Social-Specific Barriers. Results of this study can be used to develop targeted interventions to promote and facilitate treatment adherence among older adults with T2D. [Journal of Gerontological Nursing, 49(1), 42-49.].

Prevalence of permanent numeric dental anomalies in panoramic radiography in an Iranian population

Aim: Dental number anomalies are a group of congenital developmental disorders divided into two groups supernumerary and missing teeth. This study was conducted to investigate the prevalence of numeric dental anomalies using panoramic images in patients referred to the Hamadan Dental Faculty. Methods: In this cross-sectional study, 2,197 panoramic radiographs of patients aged 6-49 years were evaluated. These anomalies are divided into two groups: 1) Supernumerary teeth, including Mesiodens, Distodens, and Peridens, and 2) Missing teeth, including Hypodontia, Oligodontia, and Anodontia. A Chi-square test was performed to assess the relationship between the anomalies. Data analysis was performed using SPSS 16, in which P-value < 0.05 was considered the statistical significance level. Results: Of 736 males (32.2%) and 1548 females (67.8%) in this study, 32 (4.3%) and 55 cases (3.8%) had supernumerary teeth, respectively. The prevalence of supernumerary teeth was 0.3%, 0.5%, and 0.6% in males and 0.2%, 1% and 1.2% in females for mesiodens, distodens, and peridens, respectively. Also, 243 males (10.6%) and 655 females (28.6%) had missing teeth anomalies. Hypodontia in the maxilla was the most common anomaly in both genders, while mesiodens was the least common. Conclusion: Hypodontia was the most common anomaly, followed by peridens; the least common anomaly was mesiodens. The prevalence of supernumerary teeth was greater in males, though the difference was not statistically significant. In comparison, females had a greater prevalence of missing teeth