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Background: Receiving hemodialysis treatment makes end-stage renal disease (ESRD) patients highly vulnerable amidst the COVID-19 pandemic. Hence, their kidney disease quality of life (KDQOL) is affected. We aimed to examine the association between fear of COVID-19 (FCoV-19) and KDQOL, and the effect modification of Health literacy (HL) on this association.Material and Methods: A survey was conducted at 8 hospitals from July 2020 to March 2021 on 972 patients. Data collection includes socio-demographic factors, clinical parameters, HL, digital healthy diet literacy (DDL), hemodialysis diet knowledge (HDK), FCoV-19, suspected COVID-19 symptoms (S-COVID-19-S), and KDQOL.Results: Higher HL scores B = 0.13 (95% CI = 0.06–0.21, p = 0.001) and HDK scores B = 0.58 (95% CI = 0.31–0.85, p = 0.001) were associated with higher KDQOL scores. Whereas, S-COVID-19-S B = −6.12 (95% CI = −7.66 to – 4.58, p = 0.001) and FCoV-19 B = −0.91 (95% CI = −1.03 to – 0.80, p = 0.001) were associated with lower KDQOL scores. Notably, higher HL scores significantly attenuate the negative impact of FCoV-19 on overall KDQOL and the kidney disease component summary.Conclusions: In hemodialysis patients, FCoV-19 and S-COVID-19-S were associated with a lower KDQOL. Health literacy significantly mitigates the negative impact of FCoV-19 on KDQOL. Strategic public health interventions to improve HL are suggested to protect patient’s KDQOL during the pandemic.

Background Digital Health Literacy (DHL) is crucial in navigating digital health environments, yet few studies focus on older adults. Objective Explore the associations of digital health information and resource utilization, IT-related social support, and barriers/enhancers to digital health service usage with DHL among older adults. Methods A cross-sectional study was conducted from January 2022 to April 2023, involving 417 individuals over the age of 60 who were interviewed using an instrument for collecting data on DHL, social support, barriers/enhancers influencing use of digital health resources and personal/demographic data. Multi-regression models were used to examine the associations. Results Higher DHL scores were associated with daily use of digital interaction with healthcare ( B = 0.28; 95% confidence interval [CI] = 0.07, 0.49; p = .01), daily use of other digital health resources ( B = 0.22; 95% CI = 0.05, 0.40; p = .01), ease in finding assistance for online navigation ( B = 0.27; 95% CI = 0.08, 0.45; p = .01), self-perceived digital proficiency (“usually very good at surfing the internet,” B = 0.35; 95% CI = 0.18, 0.52; p = .01), assistance from relatives/others in internet browsing ( B = 0.20; 95% CI = 0.02, 0.37; p = .02), and having access to a computer, tablet, or smartphone ( B = 0.29; 95% CI = 0.11, 0.47, p = .01). Conversely, barriers like “no access to a computer, tablet, or phone at all times” ( B = −0.19; 95% CI = −0.34, −0.04; p = .01), “difficulty understanding online content” ( B = −0.22; 95% CI = −0.36, −0.07; p = .01), and “believing to be too old for online services” ( B = −0.18; 95% CI = −0.32, −0.03; p = .02) were associated with lower DHL scores. Conclusions Engagement with digital health platforms, including making online appointments and accessing personal health records, is associated with higher DHL levels. Support from relatives or others, a modifiable attribute, is also associated with elevated DHL among older adults.

Tingting Tu,1,2,&ast; Dandan Chen,1,2,&ast; Houjun Jiang,1,2,&ast; Jianhua Ma,1,2 Hongwei Wang,1,2 Cheng Chen1,2 1Department of Radiotherapy, Lianyungang Clinical Institute, Jiangsu University (The Second People’s Hospital of Lianyungang), Lianyungang, Jiangsu Province, People’s Republic of China; 2Department of Radiotherapy, Lianyungang Cancer Hospital, Lianyungang, Jiangsu Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Cheng Chen, Department of Radiotherapy, Lianyungang Clinical Institute, Jiangsu University (The Second People’s Hospital of Lianyungang), NO. 161 Xingfu Road, Lianyungang, Jiangsu Province, 222023, People’s Republic of China, Tel +86 518 85775191, Email chenchenggood@163.comPurpose: We aim to observe the potential survival benefits of driver gene-guided targeted therapy in advanced non-small cell lung cancer (NSCLC) patients compared to non-targeted therapy. Additionally, the study aims to assess whether Next-generation sequencing technology (NGS)-guided targeted therapy can provide survival advantages for advanced NSCLC patients compared to conventional Epidermal growth factor receptor (EGFR)/anaplastic lymphoma kinase (ALK) gene detection.Methods: Clinical data, genetic testing results, and treatment information of 1663 advanced lung cancer patients diagnosed by pathology from January 2013 to June 2019 in Jiangsu University Affiliated Lianyungang Hospital were collected. Propensity score matching survival analysis was used to evaluate the differences in overall survival(OS) between groups.Results: In the unadjusted survival curve, targeted therapy patients had significantly longer median OS than non-targeted therapy patients (28.3 months vs 15.4 months, Hazard ratio (HR) = 0.5426, 95% confidence interval (CI) 0.4768– 0.6176, P < 0.0001); the conclusion was the same after propensity score matching analysis, with targeted therapy group patients having significantly prolonged OS median (27.5 months vs 14.8 months, HR = 0.5572, 95% CI 0.4796– 0.6474, P < 0.0001). In the unadjusted survival curve, the NGS group had a significantly prolonged median OS compared to the conventional gene detection group (23.4 months vs 21.2 months, HR = 1.243, 95% CI = 1.017– 1.519, P = 0.0495). However, after propensity score matching analysis, no statistically significant difference existed in the median OS between the two patient groups (23.1 months vs 21.5 months, HR = 1.288, 95% CI = 0.9557– 1.735, P = 0.0926). Further analysis demonstrated no advantage in the five-, three-, and two-year survival rates of the NGS group compared to conventional gene detection group patients. However, the one-year survival rate of the NGS group was significantly increased (83.2% vs 68.1%, HR = 0.4890, 95% CI = 0.3170– 0.7544, P = 0.0015).Conclusion: Driver gene-guided targeted precision therapy significantly prolonged the median OS of advanced NSCLC patients compared to non-targeted therapy. NGS detection did not improve the median OS of advanced NSCLC patients compared to conventional EGFR/ALK gene detection but increased the one-year survival rate of patients.Keywords: non-small cell lung cancer, NGS, EGFR, ALK, prognosis

Carotenoids are widely researched due to their correlation with the mitigation of severe illnesses such as cancer, cardiovascular disease, and macular degeneration, which points out the critical significance of natural pigments. Carotenoid production from microorganisms has many advantages compared to plants or algae because of its rapid growth rate, cost-effectiveness, and independence from geographical factors. The genus Rhodosporidium, specifically the species Rhodosporidium toruloides, is renowned for generating carotenoid-rich biomass in biotechnology. However, the quantity and composition of carotenoid products obtained highly depend on the specific medium and culture conditions. In this research, carotenoid extraction by combining DMSO and acetone can result in high efficiency. Furthermore, the fermentation medium for R. toruloides was successfully optimized with affordable ingredients, including glucose, NaCl, H2O2, KH2PO4, MgSO4.7H2O, NH4Cl2, Na2HPO4, and yeast extract. The reassessment of the optimal model revealed strong compatibility between the predictive and experimental models regarding dry biomass and carotenoid content. The low residual glucose amount ultimately revealed adequate glucose consumption as a substrate for growth and carotenoid production.

Background: Receiving hemodialysis treatment makes end-stage renal disease (ESRD) patients highly vulnerable amidst the COVID-19 pandemic. Hence, their kidney disease quality of life (KDQOL) is affected. We aimed to examine the association between fear of COVID-19 (FCoV-19) and KDQOL, and the effect modification of Health literacy (HL) on this association. Material and Methods: A survey was conducted at 8 hospitals from July 2020 to March 2021 on 972 patients. Data collection includes socio-demographic factors, clinical parameters, HL, digital healthy diet literacy (DDL), hemodialysis diet knowledge (HDK), FCoV-19, suspected COVID-19 symptoms (S-COVID-19-S), and KDQOL. Results: Higher HL scores B = 0.13 (95% CI = 0.06–0.21, p = 0.001) and HDK scores B = 0.58 (95% CI = 0.31–0.85, p = 0.001) were associated with higher KDQOL scores. Whereas, S-COVID-19-S B = −6.12 (95% CI = −7.66 to – 4.58, p = 0.001) and FCoV-19 B = −0.91 (95% CI = −1.03 to – 0.80, p = 0.001) were associated with lower KDQOL scores. Notably, higher HL scores significantly attenuate the negative impact of FCoV-19 on overall KDQOL and the kidney disease component summary. Conclusions: In hemodialysis patients, FCoV-19 and S-COVID-19-S were associated with a lower KDQOL. Health literacy significantly mitigates the negative impact of FCoV-19 on KDQOL. Strategic public health interventions to improve HL are suggested to protect patient's KDQOL during the pandemic. [ABSTRACT FROM AUTHOR]

Herein, we present an efficient, one‐pot reaction for accessing 3‐benzylated indoles via the coupling of benzyl alcohols with indoles under heterogeneous catalysis by manganese ferrite (MnFe2O4) nanoparticles. Notably, this reaction, for the first time, employs MnFe2O4 nanoparticles prepared using a simple grinding method. The high compatibility of MnFe2O4 nanoparticles enables a broad substrate scope and high regioselectivity. Moreover, this approach offers several attractive highlights including the use of a recyclable oxide catalyst and green and cost‐effective alkylating reagents under solvent‐free conditions. [ABSTRACT FROM AUTHOR]

Appliance energy-efficiency programs are a central component of many countries’ energy-policy portfolios. A major barrier to optimal implementation of these programs is lack of data to determine market baselines, assess the potential for cost-effective energy savings, and track markets over time to evaluate and verify program impacts. To address this gap, we have developed the International Database of Efficient Appliances (IDEA), a suite of software tools that automatically gathers data that is currently dispersed across various online sources and compiles it into a unified repository of information on efficiency, price, and features for a diversity of appliances and devices in markets around the world. In this article we describe the framework and functionality of IDEA, and we demonstrate its power as a resource for research and policy development related to appliance energy efficiency. Using IDEA data for refrigerators in China and India, we assess the potential for cost-effective energy savings within each market by computing robust indicators that can also be easily compared across different appliances and markets. We find that significant cost-effective savings are available on both markets. We discuss implications for the development of future energy-efficiency deployment programs.

Obesity is an emerging concern globally with increasing prevalence. Obesity is associated with many diseases, such as cardiovascular disease, dyslipidemia, and cancer. Thus, effective new antiobesity drugs should be urgently developed. We synthesized SW20.1, a compound that induces activating transcription factor 3 (ATF3) expression. The results of Oil Red O staining and quantitative real-time polymerase chain reaction revealed that SW20.1 was more effective in reducing lipid accumulation in 3T3-L1 preadipocytes than the previously synthesized ST32db, and that it inhibited the expression of the genes involved in adipogenesis and lipogenesis. A chromatin immunoprecipitation assay indicated that SW20.1 inhibited adipogenesis and lipogenesis by binding to the upstream promoter region of resistin at two sites (−2861/−2854 and −241/−234). In mice, the intraperitoneal administration of SW20.1 reduced body weight, white adipocyte weight in different regions, serum cholesterol levels, adipogenesis-related gene expression, hepatic steatosis, and serum resistin levels. Overall, SW20.1 exerts antiobesity effects by inhibiting resistin through the ATF3 pathway. Our study results indicate that SW20.1 is a promising therapeutic drug for diet-induced obesity.

The applications of the deep belief network (DBN) for addressing practical engineering issues have recently emerged all over the world thanks to its accuracy and availability of data. In this paper, a predictive model using DBN was employed to investigate the factors that affect the ground-penetrating radar (GPR) signals from the rebar embedded in concrete structures. Four variables, namely temperature, relative humidity, chloride contamination level, and rebar surface corrosion condition were used as the model inputs for the investigation. Comprehensive data acquired from previously published documents were used to establish the proposed DBN model. It was shown that temperature and chloride contamination level variables generated significant effects on the GPR amplitude signal from rebar. In contrast, the relative humidity and rebar surface corrosion condition parameters were found to yield a minimal influence on the output of the proposed model. The DBN model can be used to predict the amplitude of GPR signals from the four inputs with a high level of accuracy. Specifically, the coefficient of determination (R2) was 0.9634 and 0.9681 for the testing dataset and the entire database, respectively.

This paper presents the application of an Artificial Intelligence-based method in analyzing the effects of environmental conditions, chloride contamination in concrete, and surface corrosion of rebars on the amplitude of Ground Penetrating Radar (GPR) signals. Six reinforced concrete slabs with different chloride contamination mixtures were fabricated and tested. GPR data were collected under various temperature and ambient humidity combinations. A total of 288 rebar picks were used for training, validation, and testing the proposed Artificial Neural Network (ANN) model. Multiple ANN model configurations with a variation in learning algorithms and the number of nodes in the hidden layer were explored to obtain the optimal model for the nondestructive data. It is shown that the “trainlm” learning algorithm produced the high accuracy prediction of the reflection amplitude of GPR signals. The sensitivity analysis was also conducted with the ANN model to investigate the effects of the input on the output parameters. Results from the sensitivity analysis revealed that the GPR reflection amplitudes were more sensitive to the changes of temperature parameter (TEM) and chloride contamination level (CCL), while they were less sensitive to the variation of ambient relative humidity (ARH) and rust condition on the rebar surface (CSR).

This research identifies factors that explain the liquidity of commercial banks in the Vietnam banking system from 2010 to 2015. Using the OLS regression method for analysis, it was found that: the interbank market helps commercial banks improve their liquidity; the larger the loan size, the higher the liquidity risk; good credit risk management has a positive impact on liquidity risk management; and long-term interest rate is negatively related to the liquidity of commercial banks. The research also makes recommendations on liquidity risk management policies to banks and policy-makers from the Government and the State Bank of Vietnam.