A revisit to interactions of stimulus quality and semantic context on N400 in visual word recognition.

Much behavioral research has revealed interactive effects between stimulus quality and semantic priming in visual word recognition, practically in favor of the interactive activation model. However, the limited number of event-related brain potential (ERP) studies have yielded inconsistent results considering this interaction's impact on N400 amplitude. The current ERP study aimed to examine whether the joint effects of stimulus quality and semantic priming were specific to the lexical decision task. We used both behavioral measures and ERP recordings to evaluate the joint effects of stimulus degradation (i.e. highly vs. slightly degraded) and semantic priming (i.e. semantically related vs. unrelated) in a lexical decision task involving visual recognition of Chinese characters. The results showed significant degradation-by-priming interactions on response times and N400 amplitude ( P  < 0.05), with larger semantic priming effects on slightly degraded targets. These converging behavioral and electrophysiological findings provide evidence in accordance with the interactive activation models of visual word recognition, in which the early-stage visual processing (i.e. degradation) cascades into the later-stage semantic processing (i.e. priming), thus yielding interactions observed in N400 amplitude.

Fisetin disrupts mitochondrial homeostasis via superoxide dismutase 2 acetylation in pancreatic adenocarcinoma.

Pancreatic adenocarcinoma (PDAC) is one of the most lethal malignant tumors with an urgent need for precision medicine strategies. The present study seeks to assess the antitumor effects of fisetin, and characterize its impact on PDAC. Multi-omic approaches include proteomic, transcriptomic, and metabolomic analyses. Further validation includes the assessment of mitochondria-derived reactive oxygen species (mtROS), mitochondrial membrane potential, as well as ATP generation. Molecular docking, immunoprecipitation, and proximity ligation assay were used to detect the interactions among fiseitn, superoxide dismutase 2 (SOD2), and sirtuin 2 (SIRT2). We showed that fisetin disrupted mitochondrial homeostasis and induced SOD2 acetylation in PDAC. Further, we produced site mutants to determine that fisetin-induced mtROS were dependent on SOD2 acetylation. Fisetin inhibited SIRT2 expression, thus blocking SOD2 deacetylation. SIRT2 overexpression could impede fisetin-induced SOD2 acetylation. Additionally, untargeted metabolomic analysis revealed an acceleration of folate metabolism with fisetin. Collectively, our findings suggest that fisetin disrupts mitochondrial homeostasis, eliciting an important cancer-suppressive role; thus, fisetin may serve as a promising therapeutic for PDAC.

Strategy and Methods for Carbon Emission Mitigation during Construction of Rail Transit.

The rail transit construction process produces a large quantity of carbon emission. The carbon emission could be divided into two sources, including direct carbon emission from the construction process and indirect carbon emission by raw material utilization. With the promotion of China National carbon peaking and carbon neutrality goals, it is an industry trend for the rail transit construction company to reduce carbon emission during the construction event. This study provides a detailed overview of the possible carbon emission process and carbon mitigation process during the rail transit construction event and puts forward preliminary carbon mitigation suggestions and strategy for the rail transit construction process. The predominant carbon emission section during rail transit construction is the raw material (including the steel, cement, concrete, tunnel segment), electricity, and fuel consumption during construction. It is suggested that the rail transit construction process could achieve carbon emission mitigation from the following prospects: make careful plans for the raw material selection (such as using recycled concrete, recycled steel, and so forth), improve the construction process to reduce energy waste, and optimize the equipment selection during the mechanical and electrical installment process. By this, the carbon emission could be mitigated during the rail transit construction.

Advances in artificial intelligence for meibomian gland evaluation: A comprehensive review.

Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Conventional manual evaluation techniques for MGD face limitations characterized by inefficiencies, high subjectivity, limited big data processing capabilities, and a dearth of quantitative analytical tools. With rapidly advancing artificial intelligence (AI) techniques revolutionizing ophthalmology, studies are now leveraging sophisticated AI methodologies--including computer vision, unsupervised learning, and supervised learning--to facilitate comprehensive analyses of meibomian gland (MG) evaluations. These evaluations employ various techniques, including slit lamp examination, infrared imaging, confocal microscopy, and optical coherence tomography. This paradigm shift promises enhanced accuracy and consistency in disease evaluation and severity classification. While AI has achieved preliminary strides in meibomian gland evaluation, ongoing advancements in system development and clinical validation are imperative. We review the evolution of MG evaluation, juxtapose AI-driven methods with traditional approaches, elucidate the specific roles of diverse AI technologies, and explore their practical applications using various evaluation techniques. Moreover, we delve into critical considerations for the clinical deployment of AI technologies and envisages future prospects, providing novel insights into MG evaluation and fostering technological and clinical progress in this arena.

Metabolic profiling reveals circulating biomarkers associated with incident and prevalent Parkinson's disease.

The metabolic profile predating the onset of Parkinson's disease (PD) remains unclear. We aim to investigate the metabolites associated with incident and prevalent PD and their predictive values in the UK Biobank participants with metabolomics and genetic data at the baseline. A panel of 249 metabolites was quantified using a nuclear magnetic resonance analytical platform. PD was ascertained by self-reported history, hospital admission records and death registers. Cox proportional hazard models and logistic regression models were used to investigate the associations between metabolites and incident and prevalent PD, respectively. Area under receiver operating characteristics curves (AUC) were used to estimate the predictive values of models for future PD. Among 109,790 participants without PD at the baseline, 639 (0.58%) individuals developed PD after one year from the baseline during a median follow-up period of 12.2 years. Sixty-eight metabolites were associated with incident PD at nominal significance (P < 0.05), spanning lipids, lipid constituent of lipoprotein subclasses and ratios of lipid constituents. After multiple testing corrections (P < 9 × 10-4), polyunsaturated fatty acids (PUFA) and omega-6 fatty acids remained significantly associated with incident PD, and PUFA was shared by incident and prevalent PD. Additionally, 14 metabolites were exclusively associated with prevalent PD, including amino acids, fatty acids, several lipoprotein subclasses and ratios of lipids. Adding these metabolites to the conventional risk factors yielded a comparable predictive performance to the risk-factor-based model (AUC = 0.766 vs AUC = 0.768, P = 0.145). Our findings suggested metabolic profiles provided additional knowledge to understand different pathways related to PD before and after its onset.

Effect of dietary patterns on mild cognitive impairment and dementia: a machine learning bibliometric and visualization analysis.

Objective: As a spectrum of neurodegenerative conditions, dementia presents a significant challenge to worldwide health. Mild cognitive impairment (MCI) is recognized as the intermediate stage between normal cognitive functioning and dementia. Studies highlight the significant impact of dietary patterns on the management of MCI and dementia. Currently, comprehensive research on dietary patterns specific to MCI and dementia is limited, but bibliometric analysis offers a method to pinpoint essential research directions. Methods: On November 18, 2023, a search was conducted in the Web of Science Core Collection (WoSCC) for publications on diet and MCI/dementia. Tools such as Rstudio, CiteSpace, and VOSviewer were employed to create a knowledge atlas. This atlas analyzed collaborations, reference co-citations, keyword patterns, and emerging trends. Results: The search yielded 1,493 publications on diet and MCI/dementia, indicating a growing interest despite fluctuations. Contributions came from 70 countries/regions and 410 organizations across 456 journals. The USA and China led in publication numbers, with significant contributions from Columbia University and Harvard Medical School. Top authors include Scarmeas Nikolaos, Morris Martha Clare, and Samieri Cecilia. The Ketogenic, Mediterranean, and MIND diets emerged as key dietary patterns for cognitive decline prevention, highlighting the role of genetic factors, especially ApoE polymorphisms, in cognitive deterioration. Conclusion: This study provides core countries, institutions, and authors in the field, and points out the development directions in the field. Future research directions in dietary for MCI and dementia will focus on: (1) the potential effects of the KD in alleviating oxidative stress and modulating gut microbiota in neurodegenerative diseases; (2) how diet influences cognitive health through patterns of ApoE and protein expression; (3) investigating the interactions between gut microbiota and brain function, known as the "gut-brain axis."

Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case.

Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI's sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI's sensitivity.

Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis.

Background: We aimed to evaluate the cost-effectiveness of an artificial intelligence-(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non-Indigenous and Indigenous people living with diabetes in Australia. Methods: We performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decision-analytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non-Indigenous and 65,160 Indigenous Australians living with diabetes aged ≥20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI-based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit-cost ratio (BCR), and net monetary benefits (NMB). A Willingness-to-pay (WTP) threshold of AU$50,000 per quality-adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study. Findings: With the status quo, the non-Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost-saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost-saving for the Indigenous population. Notably, universal AI-based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m. Interpretation: Our findings suggest that implementing AI-based DR screening in primary care is highly effective and cost-saving in both Indigenous and non-Indigenous populations. Funding: This project received grant funding from the Australian Government: the National Critical Research Infrastructure Initiative, Medical Research Future Fund (MRFAI00035) and the NHMRC Investigator Grant (APP1175405). The contents of the published material are solely the responsibility of the Administering Institution, a participating institution or individual authors and do not reflect the views of the NHMRC. This work was supported by the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. W.H. is supported by the Melbourne Research Scholarship established by the University of Melbourne. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Cost-Effectiveness of the Second COVID-19 Booster Vaccination in the USA.

OBJECTIVE: To assess the cost effectiveness of the second COVID-19 booster vaccination with different age groups. METHODS: We developed a decision-analytic Susceptible-Exposed-Infected-Recovered (SEIR)-Markov model by five age groups (0-4 years, 5-11 years 12-17 years, 18-49 years, and 50+ years) and calibrated the model by actual mortality in each age group in the USA. We conducted five scenarios to evaluate the cost effectiveness of the second booster strategy and incremental benefits if the strategy would expand to 18-49 years and 12-17 years, from a health care system perspective. The analysis was reported according to the Consolidated Health Economic Evaluation Reporting Standards 2022 statement. RESULTS: Implementing the second booster strategy for those aged ≥ 50 years cost $823 million but reduced direct medical costs by $1166 million, corresponding to a benefit-cost ratio of 1.42. Moreover, the strategy also resulted in a gain of 2596 quality-adjusted life-years (QALYs) during the 180-day evaluation period, indicating it was dominant. Further, vaccinating individuals aged 18-49 years with the second booster would result in an additional gain of $1592 million and 8790 QALYs. Similarly, expanding the vaccination to individuals aged 12-17 years would result in an additional gain of $16 million and 403 QALYs. However, if social interaction between all age groups was severed, vaccination expansion to ages 18-49 and 12-17 years would no longer be dominant but cost effective with an incremental cost-effectiveness ratio (ICER) of $37,572 and $26,705/QALY gained, respectively. CONCLUSION: The second booster strategy was likely to be dominant in reducing the disease burden of the COVID-19 pandemic. Expanding the second booster strategy to ages 18-49 and 12-17 years would remain dominant due to their social contacts with the older age group.

Retinal age gap as a predictive biomarker for future risk of clinically significant diabetic retinopathy.

AIMS: Retinal age derived from fundus images has been verified as a novel ageing biomarker. We aim to explore the association between retinal age gap (retinal age minus chronological age) and incident diabetic retinopathy (DR). METHODS: Retinal age prediction was performed by a deep learning model, trained and validated based on 19,200 fundus images of 11,052 disease-free participants. Retinal age gaps were determined for 2311 patients with diabetes who had no history of diabetic retinopathy at baseline. DR events were ascertained by data linkage to hospital admissions. Cox proportional hazards regression models were performed to evaluate the association between retinal age gaps and incident DR. RESULTS: During the median follow-up period of 11.0 (interquartile range: 10.8-11.1) years, 183 of 2311 participants with diabetes developed incident DR. Each additional year of the retinal age gap was associated with a 7% increase in the risk of incident DR (hazard ratio [HR] = 1.07, 95% confidence interval [CI] 1.02-1.12, P = 0.004), after adjusting for confounding factors. Participants with retinal age gaps in the fourth quartile had a significantly higher DR risk compared to participants with retinal age gaps in the lowest quartile (HR = 2.88, 95% CI 1.61-5.15, P < 0.001). CONCLUSIONS: We found that higher retinal age gap was associated with an increased risk of incident DR. As an easy and non-invasive biomarker, the retinal age gap may serve as an informative tool to facilitate the individualized risk assessment and personalized screening protocol for DR.

Association between the variability of non-high-density lipoprotein cholesterol and the neutrophil-to-lymphocyte ratio in patients with coronary heart disease.

Background: Lowering lipid variability may be a potential strategy for improving the inflammatory state in patients with coronary heart disease (CHD). This study investigated the association between the variability of non-high-density lipoprotein cholesterol (non-HDL-C) and the neutrophil-to-lymphocyte ratio (NLR). Methods: This study enrolled 2,711 CHD patients subjected to percutaneous coronary intervention (PCI). During the 1-year follow-up period after PCI, the variability of non-HDL-C was assessed using standard deviation (SD), coefficient of variation (CV), and variability independent of mean (VIM). NLR was calculated as the ratio of absolute neutrophil count to absolute lymphocyte count. The relationship between the non-HDL-C variability and the average NLR level during follow-ups was examined using a linear regression analysis. Results: The mean age of the patients was 64.4 ± 10.8 years, with 72.4% being male. The average NLR level was 2.98 (2.26-4.14) during the follow-up (1 year after PCI). The variability of non-HDL-C was 0.42 (0.26-0.67) for SD, 0.17 (0.11-0.25) for CV, and 0.02 (0.01-0.03) for VIM. A locally weighted scatterplot smoothing curve indicates that the average levels of NLR increased with increasing variability of non-HDL-C. Regardless of the variability assessment method used, non-HDL-C variability was significantly positively associated with the average NLR level during follow-ups: SD [ß (95% CI) = 0.681 (0.366-0.996)], CV [ß (95% CI) = 2.328 (1.458-3.197)], and VIM [ß (95% CI) = 17.124 (10.532-23.715)]. This association remained consistent across subgroups stratified by age, gender, diabetes, and hypertension. Conclusion: The variability of non-HDL-C was positively associated with NLR in patients with CHD, suggesting that reducing non-HDL-C variability may improve the low-grade inflammatory state in CHD patients.

Blueprint to hepatitis B elimination in China: A modelling analysis of clinical strategies.

Background & Aims: Globally, one-third of individuals infected with HBV live in China. Eliminating HBV in China would therefore be paramount in achieving the World Health Organization's (WHO's) targets of viral hepatitis elimination as a worldwide public health threat. Methods: We constructed a dynamic HBV transmission model in China, structured by age and sex. We calibrated the model by HBsAg prevalence, acute HBV incidence, and nationally reported HBV-related cancer mortality. We investigated seven intervention scenarios (A-G) based on assumptions in diagnostic, linkage-to-care, and treatment coverages in achieving the WHO's HBV elimination goals. Results: With the status quo, HBsAg prevalence among children 1-4 years would reduce to 0.09% (95% CI 0.09-0.10%) by 2025; acute HBV incidence would drop to <2/100,000 person-years by 2024, achieving the elimination target of 90% incidence reduction. Nonetheless, China would not achieve a 65% reduction target in HBV-related mortality until 2059 with 9.98 (95% CI 9.27-10.70) million HBV-related deaths occurred by 2100. If China achieves 90% diagnostic and 80% treatment coverages (scenario E), HBV elimination would be achieved 8 years earlier, potentially saving 1.98 (95% CI 1.83-2.12) million lives. With more effective therapies for HBV control in preventing cirrhosis and hepatocellular carcinoma, elimination targets could be achieved in 2048 (scenario F) and 2038 (scenario G), additionally saving 3.59 (95% CI 3.37-3.82) and 5.19 (95% CI 4.83-5.55) million lives, respectively. Conclusions: Eliminating HBV will require interventional strategies to improve diagnostic, linkage-to-care, and treatment coverages. Developing novel therapies will be crucial in further reducing HBV-related mortality and removing HBV as a public health threat. Impact and Implications: This study explores the key developments and optimal intervention strategies needed to achieve WHO hepatitis B elimination targets by 2030 in China. It highlights that China can realise the HBV elimination targets in the incidence by 2025, and by upscaling diagnostic, linkage-to-care, and treatment coverages, up to 2 million lives could potentially be saved from HBV-related deaths.

Post-translational modification of CDK1-STAT3 signaling by fisetin suppresses pancreatic cancer stem cell properties.

BACKGROUND: Pancreatic cancer stem cells (CSCs) promote pancreatic ductal adenocarcinoma (PDAC) tumorigenesis and chemoresistance. Cyclin-dependent kinase 1 (CDK1) plays an important role in tumor initiation in other tumors, but the function of CDK1 in PDAC remains unclear. Fisetin is a bioactive flavonoid with anti-tumor properties in multiple tumors, while its function in CSCs remains elusive. RESULTS: In this study, we demonstrated that CDK1 was correlated with prognosis and was highly expressed in pancreatic cancer tissue and gemcitabine-resistant cells. Silencing CDK1 impaired tumor stemness and reduced a subset of CSCs. We found that fisetin blocked the kinase pocket domain of CDK1 and inhibited pancreatic CSC characteristics. Using acetylation proteomics analysis and phosphorylation array assay, we confirmed that fisetin reduced CDK1 expression and increased CDK1 acetylation at lysine 33 (K33), which resulted in the suppression of CDK1 phosphorylation. Silencing CDK1 or STAT3 suppressed tumor stemness properties, while overexpressing CDK1 or STAT3 showed the opposite effect. Mutation or acetylation of CDK1 at K33 weakened STAT3 phosphorylation at Y705, impairing the expression of stem-related genes and pancreatic cancer stemness. In addition, lack of histone deacetylase 3 (HDAC3), which deacetylates CDK1, contributed to weakening STAT3 phosphorylation by regulating the post-translational modification of CDK1, thereby decreasing the stemness of PDAC. Moreover, our results revealed that fisetin enhanced the effect of gemcitabine through eliminating a subpopulation of pancreatic CSCs by inhibiting the CDK1-STAT3 axis in vitro and in vivo. CONCLUSION: Our findings highlight the role of post-translational modifications of CDK1-STAT3 signaling in maintaining cancer stemness of PDAC, and indicated that targeting the CDK1-STAT3 axis with inhibitors such as fisetin is a potential therapeutic strategy to diminish drug resistance and eliminate PDAC.

Development of high internal phase emulsions with noncovalent crosslink of soy protein isolate and tannic acid: Mechanism and application for 3D printing.

In this study, we propose a design strategy using soy protein isolate (SPI)-tannic acid (TA) complexes crosslinked through noncovalent interactions to develop high internal phase emulsions (HIPEs) for 3D printing materials. The results of Fourier transform infrared spectroscopy, intrinsic fluorescence, and molecular docking analyses indicated that the dominant interactions occurring between the SPI and TA were mediated by hydrogen bonds and hydrophobic interactions. The secondary structure, particle size, ζ-potential, hydrophobicity and wettability of SPI was significantly altered by the addition of TA. The microstructure of HIPEs stabilized by SPI-TA complexes exhibited more regular and even polygonal shapes, thereby allowing the protein to form a dense self-supporting network structure. When the concentration of TA exceeded 50 µmol/g protein, the formed HIPEs remained stable after 45 days of storage. Rheological tests revealed that the HIPEs exhibited a typical gel-like (G' > G'') and shear-thinning behavior, which contributed to preferable 3D printing behavior.

A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images.

Purpose: The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict cardiovascular disease (CVD) risk from retinal images. Methods: A systematic literature search was performed in MEDLINE, Scopus, and Web of Science up to June 2022. We extracted data pertaining to predicted outcomes, model development, and validation and model performance metrics. Included studies were graded using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. Model performance was pooled across eligible studies using a random-effects meta-analysis model. Results: A total of 26 studies were included in the analysis. There were 42 CVD risk-related outcomes predicted from retinal images were identified, including 33 CVD risk factors, 4 cardiac imaging biomarkers, 2 CVD risk scores, the presence of CVD, and incident CVD. Three studies that aimed to predict the development of future CVD events reported an area under the receiver operating curve (AUROC) between 0.68 and 0.81. Models that used retinal images as input data had a pooled mean absolute error of 3.19 years (95% confidence interval [CI] = 2.95-3.43) for age prediction; a pooled AUROC of 0.96 (95% CI = 0.95-0.97) for gender classification; a pooled AUROC of 0.80 (95% CI = 0.73-0.86) for diabetes detection; and a pooled AUROC of 0.86 (95% CI = 0.81-0.92) for the detection of chronic kidney disease. We observed a high level of heterogeneity and variation in study designs. Conclusions: Although DL models appear to have reasonably good performance when it comes to predicting CVD risk, further work is necessary to evaluate the real-world applicability and predictive accuracy. Translational Relevance: DL-based CVD risk assessment from retinal images holds great promise to be translated to clinical practice as a novel approach for CVD risk assessment, given its simple, quick, and noninvasive nature.

Development and preliminary testing of a questionnaire to measure mobile health information-seeking behaviour in people with cancer.

AIM: The aim of the study was to develop and psychologically test the mobile health information-seeking behaviour (MHISB) questionnaire in people with cancer. DESIGN: Instrument development. METHODS: The study was conducted in three phases in a southeastern city of China from May 2017 to April 2018. In phase one, an item pool was constructed based on a literature review and semistructured interviews. In phase two, expert evaluation and cognitive interviews were used to evaluate the content validity of the questionnaire. In phase three, a cross-sectional study was conducted with people with cancer. Cronbach's α was calculated for reliability analysis. Validity evaluation included content validity and construct validity. RESULTS: The developed MHISB questionnaire has four dimensions (information-seeking frequency, information-seeking self-efficacy, health information evaluation and information-seeking willingness) and 25 items. Psychometric findings were satisfactory and supported the questionnaire's reliability. CONCLUSIONS: The construction process of the MHISB questionnaire was scientific and feasible. The MHISB questionnaire had acceptable validity and reliability, and it requires further improvement in future studies.

The Association of Retinal age gap with metabolic syndrome and inflammation.

BACKGROUND: Metabolic syndrome (MetS) is a clustering of cardiometabolic components, posing tremendous burdens in the aging society. Retinal age gap has been proposed as a robust biomarker associated with mortality and Parkinson's disease. Although MetS and chronic inflammation could accelerate the aging process and increase the risk of mortality, the association of the retinal age gap with MetS and inflammation has not been examined yet. METHODS: Retinal age gap (retina-predicted age minus chronological age) was calculated using a deep learning model. MetS was defined as the presence of three or more of the following: central obesity, hypertension, dyslipidemia, hypertriglyceridemia, and hyperglycemia. Inflammation index was defined as a high-sensitivity C-reactive protein level above 3.0 mg/L. Logistic regression models were used to examine the associations of retinal age gaps with MetS and inflammation. RESULTS: We found that retinal age gap was significantly associated with MetS and inflammation. Specifically, compared to participants with retinal age gaps in the lowest quartile, the risk of MetS was significantly increased by 10% and 14% for participants with retinal age gaps in the third and fourth quartile (odds ratio [OR]:1.10; 95% confidence interval [CI], 1.01,1.21;, p = .030; OR: 1.14, 95% CI, 1.03,1.26; p = .012, respectively). Similar trends were identified for the risk of inflammation and combined MetS and inflammation. CONCLUSION: We found that retinal age gaps were significantly associated with MetS as well as inflammation. Given the noninvasive and cost-effective nature and the efficacy of the retinal age gap, it has great potential to be used as a screening tool for MetS in large populations.

AXL upregulates c­Myc expression through AKT and ERK signaling pathways in breast cancers.

Breast cancer (BC) is common worldwide. c-Myc and AXL are both overexpressed in BC, promoting its progression. The present study aimed to investigate the role of AXL in c-Myc expression in BC. Overexpression of AXL increased c-Myc expression while knockdown of AXL decreased c-Myc expression as determined by western blot analysis. Pharmaceutical inhibition of AXL also suppressed c-Myc expression. AKT and ERK inhibitor LY294002 and U0126 suppressed c-Myc expression, respectively. AXL overexpression which activates AKT and ERK signaling, upregulates c-Myc expression, while kinase-dead AXL which cannot activate AKT and ERK signaling, does not upregulate c-Myc expression, emphasizing the important role of these two signaling pathways in c-Myc upregulation. Finally, expression data of BC tissues from The Cancer Proteome Atlas displayed an association between AXL and c-Myc. Taken together, the present study revealed that AXL upregulates c-Myc expression through AKT and ERK signaling pathways in BC.

Global Diabetes Prevalence in COVID-19 Patients and Contribution to COVID-19- Related Severity and Mortality: A Systematic Review and Meta-analysis.

BACKGROUND: COVID-19 and diabetes both contribute to large global disease burdens. PURPOSE: To quantify the prevalence of diabetes in various COVID-19 disease stages and calculate the population attributable fraction (PAF) of diabetes to COVID-19-related severity and mortality. DATA SOURCES: Systematic review identified 729 studies with 29,874,938 COVID-19 patients. STUDY SELECTION: Studies detailed the prevalence of diabetes in subjects with known COVID-19 diagnosis and severity. DATA EXTRACTION: Study information, COVID-19 disease stages, and diabetes prevalence were extracted. DATA SYNTHESIS: The pooled prevalence of diabetes in stratified COVID-19 groups was 14.7% (95% CI 12.5-16.9) among confirmed cases, 10.4% (7.6-13.6) among nonhospitalized cases, 21.4% (20.4-22.5) among hospitalized cases, 11.9% (10.2-13.7) among nonsevere cases, 28.9% (27.0-30.8) among severe cases, and 34.6% (32.8-36.5) among deceased individuals, respectively. Multivariate metaregression analysis explained 53-83% heterogeneity of the pooled prevalence. Based on a modified version of the comparative risk assessment model, we estimated that the overall PAF of diabetes was 9.5% (7.3-11.7) for the presence of severe disease in COVID-19-infected individuals and 16.8% (14.8-18.8) for COVID-19-related deaths. Subgroup analyses demonstrated that countries with high income levels, high health care access and quality index, and low diabetes disease burden had lower PAF of diabetes contributing to COVID-19 severity and death. LIMITATIONS: Most studies had a high risk of bias. CONCLUSIONS: The prevalence of diabetes increases with COVID-19 severity, and diabetes accounts for 9.5% of severe COVID-19 cases and 16.8% of deaths, with disparities according to country income, health care access and quality index, and diabetes disease burden.