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Background Coronavirus disease 2019 (COVID-19) is a new viral disease that has caused a pandemic in the world. Due to the lack of vaccines and definitive treatment, preventive behaviors are the only way to overcome the disease. Therefore, the present study aimed to determine the preventive behaviors from the disease based on constructs of the health belief model. Methods In the present cross-sectional study during March 11–16, 2020, 750 individuals in Golestan Province of Iran were included in the study using the convenience sampling and they completed the questionnaires through cyberspace. Factor scores were calculated using the confirmatory factor analysis. The effects of different factors were separately investigated using the univariate analyses, including students sample t -test, ANOVA, and simple linear regression. Finally, the effective factors were examined by the multiple regression analysis at a significant level of 0.05 and through Mplus 7 and SPSS 16. Results The participants’ mean age was 33.9 ± 9.45 years; and 57.1% of them had associate and bachelor's degrees. Multiple regression indicated that the mean score of preventive behavior from COVID-19 was higher in females than males, and greater in urban dwellers than rural dwellers. Furthermore, one unit increase in the standard deviation of factor scores of self-efficacy and perceived benefits increased the scores of preventive behavior from COVID-19 by 0.22 and 0.17 units respectively. On the contrary, one unit increase in the standard deviation of factor score of perceived barriers and fatalistic beliefs decreased the scores of the preventive behavior from COVID-19 by 0.36 and 0.19 units respectively. Conclusions Results of the present study indicated that female gender, perceived barriers, perceived self-efficacy, fatalistic beliefs, perceived interests, and living in city had the greatest preventive behaviors from COVID-19 respectively. Preventive interventions were necessary among males and villagers.

Solid tumours are innervated by nerve fibres that arise from the autonomic and sensory peripheral nervous systems 1 – 5 . Whether the neo-innervation of tumours by pain-initiating sensory neurons affects cancer immunosurveillance remains unclear. Here we show that melanoma cells interact with nociceptor neurons, leading to increases in their neurite outgrowth, responsiveness to noxious ligands and neuropeptide release. Calcitonin gene-related peptide (CGRP)—one such nociceptor-produced neuropeptide—directly increases the exhaustion of cytotoxic CD8 + T cells, which limits their capacity to eliminate melanoma. Genetic ablation of the TRPV1 lineage, local pharmacological silencing of nociceptors and antagonism of the CGRP receptor RAMP1 all reduced the exhaustion of tumour-infiltrating leukocytes and decreased the growth of tumours, nearly tripling the survival rate of mice that were inoculated with B16F10 melanoma cells. Conversely, CD8 + T cell exhaustion was rescued in sensory-neuron-depleted mice that were treated with local recombinant CGRP. As compared with wild-type CD8 + T cells, Ramp1 −/ − CD8 + T cells were protected against exhaustion when co-transplanted into tumour-bearing Rag1 -deficient mice. Single-cell RNA sequencing of biopsies from patients with melanoma revealed that intratumoral RAMP1 -expressing CD8 + T cells were more exhausted than their RAMP1 -negative counterparts, whereas overexpression of RAMP1 correlated with a poorer clinical prognosis. Overall, our results suggest that reducing the release of CGRP from tumour-innervating nociceptors could be a strategy to improve anti-tumour immunity by eliminating the immunomodulatory effects of CGRP on cytotoxic CD8 + T cells. Melanoma cells interact with pain-mediating sensory neurons by increasing their release of the neuropeptide CGRP, which increases the exhaustion of CD8 + T cells and thus promotes the survival of cancer cells.

Background Due to advancements in medicine and the elderly population’s growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social and environmental participation among them by considering all factors affecting it. So far, it is not designed the prediction system for QoL in this regard. Therefore, this study aimed to develop the CDSS based on ANN as an ML technique by considering the physical, psychiatric, and social factors. Methods In this developmental and applied study, we investigated the 980 cases associated with pleasant and unpleasant elderlies QoL cases. We used the BLR and simple correlation coefficient methods to attain the essential factors affecting the QoL among the elderly. Then three BP configurations, including CF-BP, FF-BP, and E-BP, were compared to get the best model for predicting the QoL. Results Based on the BLR, the 13 factors were considered the best factors affecting the elderly’s QoL at P  < 0.05. Comparing all ANN configurations showed that the CF-BP with the 13-16-1 structure with sensitivity = 0.95, specificity  =  0.97, accuracy = 0.96, F-Score = 0.96, PPV = 0.95, and NPV = 0.97 gained the best performance for QoL among the elderly. Conclusion The results of this study showed that the designed CDSS based on the CFBP could be considered an efficient tool for increasing the QoL among the elderly.

Biphasic calcium phosphate (BCP) bioceramics have shown efficacy for bone repair because of their controllable degradation rate that can be modified by chemical composition and phase ratio. To develop a new BCP ceramic in this work, 5.0 Sr, 0.6 Mg, and 0.2 Si (all in mol%) triple-substituted BCP powder was synthesized and calcined at 550 to 750 °C. The obtained results confirmed the presence of substituted ions in the product and also showed that powder calcined at 650 °C consisted of hydroxyapatite (HA) and β-tricalcium phosphate (β-TCP) nanoparticles with a weight ratio of ~ 60/40. The apatite layer with a rod-like morphology was formed on the surface of the sample fabricated from this powder after 28 days of soaking in SBF at ~ 37 °C. Also, the substituted ions showed positive effects on viability and the alkaline phosphatase activity of MG63 cells. Graphical abstract

Introduction: Mobile health is an emerging technology around the world that can be effective in cancer screening. This study aimed to examine the effectiveness of mobile health applications on cancer screening. Methods: We conducted a systematic literature review of studies related to the use of mobile health applications in cancer screening. We also conducted a comprehensive search of articles on cancer screening related to the use of mobile health applications in journals published between January 1, 2008, and January 31, 2019, using 5 databases: IEEE, Scopus, Web of Science, Science Direct and PubMed. Results: A total of 23 articles met the inclusion criteria and were included in the present review. All studies have identified positive effects of applications on cancer screening and clinical health outcomes. Furthermore, more than half of mobile applications had multiple functions such as providing information, planning and education. Moreover, most of the studies, which examined the satisfaction of patients and quality improvement, showed healthcare application users have significantly higher satisfaction of living and it leads to improving quality. Conclusion: This study found that the use of mobile health applications has a positive impact on health-related behaviours and outcomes. Application users were more satisfied with applying mobile health applications to manage their health condition in comparison with users who received conventional care.

In this study, a modified mesoporous adsorbent (MSU-S) impregnated by aluminum was used to remove the aromatic sulfur compounds from n-decane as the model fuel. Physical and chemical properties of as-synthesized adsorbent were investigated by XRD (X-Ray Diffraction), SEM (Scanning Electron Microscopy), FTIR (Fourier Transform Infrared spectroscopy) and BET (Brunauer–Emmett–Teller) method. Adsorptive desulfurization of model fuel was studied through batch and continues processes under mild temperature and normal atmospheric pressure. The equilibrium adsorption was modeled by Langmuir, Temkin, and Freundlich and the kinetics of adsorption was studied through first, second and intraparticle diffusion models. It was figured out that Temkin and the pseudo-second-order model were best fitting the adsorption equilibrium and describing the kinetics, respectively.

Introduction COVID-19, caused by the SARS-CoV-2, emerged as a lethal infectious disease with viral pneumonia-like symptoms, and was first identified in Wuhan, China, in December 2019. This study aimed to compare the epidemiological characteristics and clinical manifestations of COVID-19 between elderly and non-elderly adult populations in Kermanshah Province, Iran. Methods This retrospective study included 20,943 COVID-19 patients diagnosed between March 2020 and July 2021. Data were collected from the Medical Care Monitoring Center (MCMC) and comprised demographic information, comorbidities, clinical symptoms, hospitalization duration, and outcomes (discharge or mortality). Statistical analyses were performed using chi-square tests, Kaplan–Meier survival analysis, and multivariate Cox regression. Results Of the 20,943 patients, 7,174 (34.3%) were aged 65 years or older. Elderly patients exhibited a higher prevalence of comorbidities such as diabetes (11.5% vs. 6.5%), heart disease (11.7% vs. 3.6%), and COPD (1.8% vs. 0.7%) compared to younger adults ( P  < 0.001). Additionally, elderly patients had a significantly higher mortality rate (19.4% vs. 6.0%) and shorter median survival time (18 days vs. 36 days) ( P  < 0.001). Severe symptoms, including hypoxemia (SpO2 < 93%) and altered mental status, were also more common in the elderly. Conclusion This study showed that elderly COVID-19 patients are at a higher risk of severe illness and mortality compared to younger adults. The presence of comorbidities and more severe clinical manifestations in this age group led to increased hospitalization duration and mortality. These findings underscore the importance of targeted clinical management and public health policies for the elderly population in the context of COVID-19.

Due to the increasing importance of photocatalysts in the treatment of industrial effluents, in this study, Ag doped TiO 2 -CuO (TCA) composite nanoparticles were synthesized by sol–gel method. Tetra butylorthotitanate (TBT), copper (II) nitrate trihydrateas, silver nitrate was used as a precursor to titanium dioxide, copper oxide, and silver respectively. Ethanol was used as a solvent. X-ray diffraction (XRD), UV–Vis spectroscopy, nitrogen adsorption–desorption isotherm (BET-BJH), field emission scanning electron microscope (FESEM) and transmission electron microscopy (TEM) were used to characterize the nanoparticles. The synthesized photocatalytic nanoparticles have been used to degrade the methylene blue dye solution as a model of organic pollutant under UV and Visible light irradiation. The particle size of TCA-550 sample (highest percentage of photocatalytic degradation under visible light = 43.95%) was about 21–35 nm and the particle size of TCA-650 sample (highest percentage of photocatalytic degradation under UV light = 59.64%) was 39–60 nm. According to the XPS study, the element Ti was predominantly chemically present as Ti 4+ . Ag was found to be a cation with a capacity of one (Ag + ).

Objective The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA)1 concept. It is believed that the SA prediction model can increase the quality of life (QoL)2 in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and specially more social factors affecting SA. Methods The 975 cases related to SA and non-SA of the elderly were investigated in this study. We used the univariate analysis to determine the best factors affecting the SA. AB3, XG-Boost J-48, RF4, artificial neural network5, support vector machine6, and NB7 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV)8, negative predictive value (NPV)9, sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC). Results Comparing the machine learning10 model's performance showed that the random forest (RF) model with PPV = 90.96%, NPV = 99.21%, sensitivity = 97.48%, specificity = 97.14%, accuracy = 97.05%, F-score = 97.31%, AUC = 0.975 is the best model for predicting the SA. Conclusions Using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.

We present an automatic denoising method based on the wavelet transform to obtain single trial evoked potentials. The method is based on the inter- and intra-scale variability of the wavelet coefficients and their deviations from baseline values. The performance of the method is tested with simulated event related potentials (ERPs) and with real visual and auditory ERPs. For the simulated data the presented method gives a significant improvement in the observation of single trial ERPs as well as in the estimation of their amplitudes and latencies, in comparison with a standard denoising technique (Donoho's thresholding) and in comparison with the noisy single trials. For the real data, the proposed method largely filters the spontaneous EEG activity, thus helping the identification of single trial visual and auditory ERPs. The proposed method provides a simple, automatic and fast tool that allows the study of single trial responses and their correlations with behavior. ► We propose an automatic denoising method to identify single-trial evoked potentials ► We quantify the method's performance with synthetic data ► The method gives a significant improvement in the estimation of single-trial ERPs ► The method improves the identification of the single-trial amplitude and latencies ► The method improves the visualization of the single-trial ERPs with the real data