A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease.

BACKGROUND: The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension. METHODS: 132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity. RESULTS: The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively. CONCLUSIONS: Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.

Association between residential greenness and incident delirium: A prospective cohort study in the UK Biobank.

BACKGROUND: Contemporary environmental health investigations have identified green space as an emerging factor with promising prospects for bolstering human well-being. The incidence of delirium increases significantly with age and is fatal. To date, there is no research elucidating the enduring implications of green spaces on the occurrence of delirium. Therefore, we explored the relationship between residential greenness and the incidence of delirium in a large community sample from the UK Biobank. METHODS: Enrollment of participants spanned from 2006 to 2010. Assessment of residential greenness involved the land coverage percentage of green space within a buffer range of 300 m and 1000 m. The relationship between residential greenness and delirium was assessed using the Cox proportional hazards model. Further, we investigated the potential mediating effects of physical activity, particulate matter (PM) with diameters ≤2.5 (PM2.5), and nitrogen oxides (NOx). RESULTS: Of 232,678 participants, 3722 participants were diagnosed with delirium during a 13.4-year follow-up period. Compared with participants with green space coverage at a 300 m buffer in the lowest quartile (Q1), those in the highest quartile (Q4) had 15 % (Hazard ratio [HR] = 0.85, 95 % confidence interval [CI]: 0.77, 0.94) lower risk of incident delirium. As for the 1000 m buffer, those in Q4 had a 16 % (HR = 0.84, 95 % CI: 0.76, 0.93) lower risk of incident delirium. The relationship between green space in the 300 m buffer and delirium was mediated partially by physical activity (2.07 %) and PM2.5(49.90 %). Comparable findings were noted for the green space percentage within the 1000 m buffer. CONCLUSIONS: Our results revealed that long-term exposure to residential greenness was related to a lower risk of delirium. Air pollution and physical activity exerted a significant mediating influence in shaping this association.

Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.

Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice.

High-Frequency AC Heating Strategy of Electric Vehicle Power Battery Pack in Low-Temperature Environment.

In this paper, a heating strategy using high-frequency alternating current (AC) is proposed to internally heat lithium-ion batteries (LIB) at low temperatures. The strategy aims to strike a good balance between rapid heating of the battery at low temperatures and minimizing damage to the battery's lifespan without the need for an additional power source. The strategy presents an electrochemical-thermal coupling model to simulate and predict the temperature rise and temperature distribution of a 50 A h LiFePO4 square battery at different C-rates, the effect of high-frequency AC on battery life, and the validity of the model as verified by experiments. The experimental and simulation results show that this strategy can achieve faster heating speeds and better temperature consistency without affecting battery life. The best heating effect can be achieved at a frequency of 500 Hz (4.2C), and the temperature of the battery rises from 253.15 to 278.15 K within 365 s, for an average heating rate of 3.29 K/min. Researching low-temperature AC heating methods has important value for energy conservation because it can improve heating efficiency, expand application areas, promote technological innovation, and enhance product quality.

Body composition and metabolic syndrome in patients with type 1 diabetes.

BACKGROUND: In recent years, the prevalence of obesity and metabolic syndrome in type 1 diabetes (T1DM) patients has gradually increased. Insulin resistance in T1DM deserves attention. It is necessary to clarify the relationship between body composition, metabolic syndrome and insulin resistance in T1DM to guide clinical treatment and intervention. AIM: To assess body composition (BC) in T1DM patients and evaluate the relationship between BC, metabolic syndrome (MS), and insulin resistance in these indi-viduals. METHODS: A total of 101 subjects with T1DM, aged 10 years or older, and with a disease duration of over 1 year were included. Bioelectrical impedance analysis using the Tsinghua-Tongfang BC Analyzer BCA-1B was employed to measure various BC parameters. Clinical and laboratory data were collected, and insulin resistance was calculated using the estimated glucose disposal rate (eGDR). RESULTS: MS was diagnosed in 16/101 patients (15.84%), overweight in 16/101 patients (15.84%), obesity in 4/101 (3.96%), hypertension in 34/101 (33.66%%) and dyslipidemia in 16/101 patients (15.84%). Visceral fat index (VFI) and trunk fat mass were significantly and negatively correlated with eGDR (both P < 0.001). Female patients exhibited higher body fat percentage and visceral fat ratio compared to male patients. Binary logistic regression analysis revealed that significant factors for MS included eGDR [P = 0.017, odds ratio (OR) = 0.109], VFI (P = 0.030, OR = 3.529), and a family history of diabetes (P = 0.004, OR = 0.228). Significant factors for hypertension included eGDR (P < 0.001, OR = 0.488) and skeletal muscle mass (P = 0.003, OR = 1.111). Significant factors for dyslipidemia included trunk fat mass (P = 0.033, OR = 1.202) and eGDR (P = 0.037, OR = 0.708). CONCLUSION: Visceral fat was found to be a superior predictor of MS compared to conventional measures such as body mass index and waist-to-hip ratio in Chinese individuals with T1DM. BC analysis, specifically identifying visceral fat (trunk fat), may play an important role in identifying the increased risk of MS in non-obese patients with T1DM.

Comparative study of abdominal CT enhancement in overweight and obese patients based on different scanning modes combined with different contrast medium concentrations.

PURPOSE: To compare image quality, iodine intake, and radiation dose in overweight and obese patients undergoing abdominal computed tomography (CT) enhancement using different scanning modes and contrast medium. METHODS: Ninety overweight and obese patients (25 kg/m2≤body mass index (BMI)<  30 kg/m2 and BMI≥30 kg/m2) who underwent abdominal CT-enhanced examinations were randomized into three groups (A, B, and C) of 30 each and scanned using gemstone spectral imaging (GSI) +320 mgI/ml, 100 kVp + 370 mgI/ml, and 120 kVp + 370 mgI/ml, respectively. Reconstruct monochromatic energy images of group A at 50-70 keV (5 keV interval). The iodine intake and radiation dose of each group were recorded and calculated. The CT values, contrast-to-noise ratios (CNRs), and subjective scores of each subgroup image in group A versus images in groups B and C were by using one-way analysis of variance or Kruskal-Wallis H test, and the optimal keV of group A was selected. RESULTS: The dual-phase CT values and CNRs of each part in group A were higher than or similar to those in groups B and C at 50-60 keV, and similar to or lower than those in groups B and C at 65 keV and 70 keV. The subjective scores of the dual-phase images in group A were lower than those of groups B and C at 50 keV and 55 keV, whereas no significant difference was seen at 60-70 keV. Compared to groups B and C, the iodine intake in group A decreased by 12.5% and 13.3%, respectively. The effective doses in groups A and B were 24.7% and 25.8% lower than those in group C, respectively. CONCLUSION: GSI +320 mgI/ml for abdominal CT-enhanced in overweight patients satisfies image quality while reducing iodine intake and radiation dose, and the optimal keV was 60 keV.

Effect of low-voltage electrostatic field-assisted partial freezing on large yellow croaker protein properties and metabolomic analysis during storage.

BACKGROUND: Large yellow croaker is highly perishable during storage because of high protein and moisture content. The degradation of the fish is mainly attributed to microbial growth and enzyme activity, so it is important to find an efficient storage method to extend its shelf life. METHODOLOGY: This study investigated the effect of a low-voltage electrostatic field combined with partial freezing treatment on the physicochemical properties of myofibrillar protein (MP) and metabolomic analysis of large yellow croaker during preservation. The samples in chilled storage (C), partial freezing storage (PF) and 6 kV/m low-voltage electrostatic field partial freezing storage (LVEF-PF) were analyzed during an 18 day storage period. RESULTS: In comparison with the C and PF groups, LVEF-PF delayed the oxidation of MP by inhibiting the formation of carbonyl groups (2.25 nmol/mg pro), and maintaining higher sulfhydryl content (29.73 nmol/mg pro). Fourier transform infrared (FTIR) spectroscopy and fluorescence spectroscopy analysis also demonstrated that the LVEF-PF treatment maintained the stability of the protein structure by increasing the a-helix ratio (19.88%) and reducing the random coil ratio (17.83%). Scanning electron microscopy showed that, compared with the LVEF-PF group, there was more degeneration and aggregation of MP in the C and PF groups after 18 days' storage. The results of untargeted metabolomic analysis showed that 415 kinds of differential metabolites were identified after storage, and the difference levels of differential metabolites were least between the samples treated with LVEF-PF stored on the ninth day and the fresh samples. The main differential metabolic pathways during storage were amino acid metabolism and lipid metabolism. CONCLUSION: The LVEF-PF treatment could maintain the stability of myofibrillar protein in large yellow croaker during storage. These results showed a potential application of the LVEF-PF method for aquatic product preservation. © 2023 Society of Chemical Industry.

Characteristics of vitiligo patients with versus without associated autoimmune thyroid disease.

BACKGROUND: Clinical data are limited in patients with vitiligo with or without autoimmune thyroid disease. OBJECTIVES: The objective of the study was to investigate the clinical features and basic data of patients with vitiligo, especially those with autoimmune thyroid disease. METHODS: The study was a single-center retrospective study. A total of 1305 patients with vitiligo from June 2018 to May 2023 were included, and the clinical characteristics and basic information of the patients were recorded in detail. RESULTS: We identified an association between sex (odds ratio [OR]: 2.380; 95% confidence interval [CI]: 1.772-1.198), vitiligo duration (OR: 1.449; 95% CI: 1.076-1.952), skin involvement exceeding 5% of the body surface area (OR: 3.764; 95% CI: 2.134-6.640), negative emotions (OR: 3.076; 95% CI: 2.292-4.127), vitiligo type (OR: 1.974; 95% CI: 1.096-3.555), family history of AITD (OR: 4.979; 95% CI: 2.687-9.225), and family history of AD (OR: 2.418; 95% CI: 1.410-4.146) and patients with vitiligo. CONCLUSIONS: For patients with statistically significant associated risk factors, differential diagnosis and early intervention should be performed.

Plasma levels of urea cycle related amino acids in association with risk of ischemic stroke: Findings from a nested case-control study.

OBJECTIVES: The role of urea cycle related amino acids in the development of ischemic stroke (IS) remains unclear. The study aimed to evaluate the association of these amino acids with IS. MATERIALS AND METHODS: We conducted a case-control study nested within a cohort study in Changshu, Eastern China. A total of 321 cases and 321 controls matched by age and gender were finally included. Plasma levels of ornithine, arginine, spermidine, and proline were measured using ultra-high performance liquid chromatography-tandem mass-spectrometry (UHPLC-MS/MS). Odds ratios (ORs) and their 95 % confidence intervals (CIs) were calculated by conditional logistic regression analyses. RESULTS: Plasma ornithine was inversely associated with risk of IS [crude OR: 0.62 (95 % CI: 0.40-0.97)]. After adjustment for body mass index, smoking, hypertension, family history of stroke, estimated glomerular filtration rate, and total cholesterol, the corresponding ORs for the highest compared to the lowest quartiles was essentially unchanged [adjusted OR: 0.62 (95 % CI: 0.39-0.99)]. The risk association remained significant after repeating the analyses by excluding the first two years of follow-up. Plasma arginine, spermidine, and proline were not associated with the risk of IS. CONCLUSION: We observed that higher plasma levels of ornithine were associated with a lower risk of incident IS. Our novel findings suggest a protective role of ornithine in the pathogenesis of IS.

Trajectories of quality of life and cognition in different multimorbidity patterns: Evidence from SHARE.

OBJECTIVES: The study aimed to observe the trajectory of quality of life (QoL) and cognition, and to a analyze the bidirectional association between cognition and QoL for diverse multimorbidity patterns. METHODS: In total, 16,153 older participants age ≥50 years were included from the Survey of Health, Ageing and Retirement in Europe (SHARE). We used latent class analysis (LCA) to identify multimorbidity patterns in the baseline population. We used linear mixed models (LMM) to examine the trajectory of cognition and QoL in different multimorbidity patterns. A cross-lagged model was employed to analyze the bidirectional association between cognition and QoL in diverse multimorbidity patterns. RESULTS: Latent class analysis identified four multimorbidity patterns: high and low comorbidity burden (HC and LC), cardiometabolic (CA), and osteoarthrosis (OS). The HC group had the poorest cognitive function and QoL (p for trend < 0.001). Delayed and immediate episodic memory in the OS group declined at a highest rate (p for trend < 0.001). Additionally, a bidirectional association between cognition and QoL was observed. The effect of cognitive function on QoL was relatively stronger than the reverse in the CA and LC groups. CONCLUSIONS: The rate of decline in cognition and QoL over the time differs in diverse multimorbidity patterns, and patients with four or more chronic diseases should be specially considered. Notably, early monitoring of cognitive function and can help break the vicious cycle between cognitive deterioration and poor QoL in patients with OS or CA diseases.

Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes.

OBJECTIVE: To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS: A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS: The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION: The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.

First report on establishment and characterization of the extrahepatic cholangiocarcinoma sarcoma cell line CBC2T-2.

BACKGROUND: Extrahepatic cholangiocarcinoma sarcoma is extremely rare in clinical practice. These cells consist of both epithelial and mesenchymal cells. Patient-derived cell lines that maintain tumor characteristics are valuable tools for studying the molecular mechanisms associated with carcinosarcoma. However, cholangiocarcinoma sarcoma cell lines are not available in cell banks. AIM: To establish and characterize a new extrahepatic cholangiocarcinoma sarcoma cell line, namely CBC2T-2. METHODS: We conducted a short tandem repeat (STR) test to confirm the identity of the CBC2T-2 cell line. Furthermore, we assessed the migratory and invasive properties of the cells and performed clonogenicity assay to evaluate the ability of individual cells to form colonies. The tumorigenic potential of CBC2T-2 cells was tested in vivo using non-obese diabetic/severe combined immunodeficient (NOD/SCID) mice. The cells were injected subcutaneously and tumor formation was observed. In addition, immunohistochemical analysis was carried out to examine the expression of epithelial marker CK19 and mesenchymal marker vimentin in both CBC2T-2 cells and xenografts. The CBC2T-2 cell line was used to screen the potential therapeutic effects of various clinical agents in patients with cholangiocarcinoma sarcoma. Lastly, whole-exome sequencing was performed to identify genetic alterations and screen for somatic mutations in the CBC2T-2 cell line. RESULTS: The STR test showed that there was no cross-contamination and the results were identical to those of the original tissue. The cells showed round or oval-shaped epithelioid cells and mesenchymal cells with spindle-shaped or elongated morphology. The cells exhibited a high proliferation ratio with a doubling time of 47.11 h. This cell line has migratory, invasive, and clonogenic abilities. The chromosomes in the CBC2T-2 cells were polyploidy, with numbers ranging from 69 to 79. The subcutaneous tumorigenic assay confirmed the in vivo tumorigenic ability of CBC2T-2 cells in NOD/SCID mice. CBC2T-2 cells and xenografts were positive for both the epithelial marker, CK19, and the mesenchymal marker, vimentin. These results suggest that CBC2T-2 cells may have both epithelial and mesenchymal characteristics. The cells were also used to screen clinical agents in patients with cholangiocarcinoma sarcoma, and a combination of paclitaxel and gemcitabine was found to be the most effective treatment option. CONCLUSION: We established the first human cholangiocarcinoma sarcoma cell line, CBC2T-2, with stable biogenetic traits. This cell line, as a research model, has a high clinical value and would facilitate the understanding of the pathogenesis of cholangiocarcinoma sarcoma.

Application of radiomics based on chest CT-enhanced dual-phase imaging in the immunotherapy of non-small cell lung cancer.

OBJECTIVE: To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC). METHODS: 106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness. RESULTS: LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05). CONCLUSION: The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.

Antioxidant Peptides from the Collagen of Antler Ossified Tissue and Their Protective Effects against H2O2-Induced Oxidative Damage toward HaCaT Cells.

Antler ossified tissue has been widely used for the extraction of bioactive peptides. In this study, collagen was prepared from antler ossified tissue via acetic acid and pepsin. Five different proteases were used to hydrolyze the collagen and the hydrolysate treated by neutrase (collagen peptide named ACP) showed the highest DPPH radical clearance rate. The extraction process of ACP was optimized by response surface methodology, and the optimal conditions were as follows: a temperature of 52 °C, a pH of 6.1, and an enzyme concentration of 3200 U/g, which resulted in the maximum DPPH clearance rate of 74.41 ± 0.48%. The peptides (ACP-3) with the strongest antioxidant activity were obtained after isolation and purification, and its DPPH free radical clearance rate was 90.58 ± 1.27%; at the same time, it exhibited good scavenging activity for ABTS, hydroxyl radical, and superoxide anion radical. The study investigated the protective effect of ACP-3 on oxidative damage in HaCaT cells. The findings revealed that all groups that received ACP-3 pretreatment exhibited increased activities of SOD, GSH-Px, and CAT compared to the model group. Furthermore, ACP-3 pretreatment reduced the levels of ROS and MDA in HaCaT cells subjected to H2O2-induced oxidative damage. These results suggest that collagen peptides derived from deer antler ossified tissue can effectively mitigate the oxidative damage caused by H2O2 in HaCaT cells, thereby providing a foundation for the utilization of collagen peptides in pharmaceuticals and cosmetics.

HIV/AIDS-related knowledge and attitudes towards HIV rapid testing among Chinese college students: Findings from a cross-sectional survey.

Data of the awareness level of college students in China about Human Immunodeficiency Virus/Acquired Immune Deficiency Syndrome (HIV/AIDS) knowledge are limited. Also, the attitudes towards HIV rapid testing remain unknown among this population. Therefore, this study aimed to evaluate the awareness of HIV/AIDS knowledge and attitudes towards HIV rapid testing among Chinese college students. An online cross-sectional survey was performed in 2020. A total of 1,474 participants were finally included. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to examine associated factors for the cognitive levels and attitudes by multivariable logistic regression. Spearman rank correlation was used to examine the relationship between HIV/AIDS-related knowledge and attitudes. About 91% of participants had a high cognitive level on HIV/AIDS-related knowledge and 84.7% held a positive attitude towards the HIV rapid testing. Postgraduates (OR = 1.75, 95% CI: 1.16-2.66) and females (OR = 1.69, 95% CI: 1.13-2.52) were more knowledgeable. Females' attitudes towards the HIV rapid testing were more positive (OR = 1.91, 95% CI: 1.40-2.62). Moreover, the knowledge was positively correlated with attitudes towards the rapid testing (Spearman r = 0.14, p < 0.001). In conclusion, the Chinese college students had a high cognitive level on HIV/AIDS knowledge and positive attitudes towards HIV rapid testing. A high cognitive level of knowledge paralleled with positive attitudes. Special strategies such as tailored education via HIV/AIDS curriculum and awareness campaigns are needed for undergraduates and male students to minimize the gaps regarding HIV/AIDS-related knowledge and attitudes.

Ultra-processed food consumption, mediating biomarkers, and risk of chronic obstructive pulmonary disease: a prospective cohort study in the UK Biobank.

Background: Ultra-processed food (UPF) is a popular supplement in the UK and other developed countries. However, whether and how UPF intake is associated with chronic obstructive pulmonary disease (COPD) remains unclear. Objective: We aimed to examine the association between UPF consumption and COPD incidence and explore the potential mediating effects of COPD-related biomarkers. Methods: This prospective cohort study included 207 002 participants without COPD at recruitment and completed 24-hour dietary recalls. UPF was defined according to the NOVA classification system. Incident COPD was ascertained using electronic hospital and mortality records. Cox regression models were used to estimate UPF consumption and the subsequent risk of COPD. Substitution analysis was performed to assess the risk of COPD by substituting UPF with an equivalent proportion of unprocessed or minimally processed food (UNPF). Mediation analyses were performed to evaluate the contribution of biomarkers related to the lipid profile, glucose metabolism, and systemic inflammation to the observed associations. Results: During a median follow-up of 13.1 (interquartile range: 12.5-13.9) years, 4670 COPD events were recorded. The adjusted hazard ratio (HR) of COPD in the highest quintile versus the lowest quintile of the UPF consumption proportion (weight percentage of the UPF) was 1.22 (95% confidence interval [CI]: 1.11-1.34). There was a 10% elevated risk of COPD incidence per SD increase in UPF intake (HR: 1.10; 95% CI: 1.08-1.13). Replacing 20% of the UNPF weight with the UPF was associated with a 13% decrease in COPD risk (95% CI: 0.84-0.91). In mediation analyses, biomarkers explained 1.0-10.1% of the association between UPF intake and COPD. Results from stratified and sensitivity analyses further support the robustness of these findings. Conclusions: Elevated UPF consumption was associated with a higher risk of COPD, and this association was primarily mediated by glucose, inflammation, and lipids, whereas substituting UNPF for UPF was associated with a decreased risk of COPD.

Association of ultra-processed food consumption with incident depression and anxiety: a population-based cohort study.

Background: Global ultra-processed food (UPF) consumption has risen rapidly. The development and prognosis of depression and anxiety remain unclarified. Herein, we aimed to examine the association between UPF consumption and the incidence and progression trajectory of depression and anxiety. Methods: In our study, participants were recruited between 2006 and 2010. UPF consumption was expressed as UPF servings, energy ratio, and weight ratio. The relationships between UPF consumption and depression or anxiety were assessed using the Cox proportional hazards model. Multi-state models were used to explore the association between UPF consumption and the risks of all transitions from a healthy state to depression or anxiety and then to all-cause mortality. Results: Among the 183 474 participants, 5453 were diagnosed with depression and 6763 with anxiety during the follow-up of 13.1 years. The participants in the highest quartile (Q4) of UPF servings, energy ratio, and weight ratio had an increased risk of depression compared to those in the lowest quartile (Q1), with hazard ratios (HRs) and 95% confidence intervals [CIs] of 1.22 (1.13-1.31), 1.13 (1.05-1.22), and 1.26 (1.17-1.36), respectively. Similarly, participants in Q4 of UPF consumption had a higher risk of anxiety, with HRs (95% CIs) of 1.13 (1.06-1.21), 1.13 (1.05-1.21), and 1.11 (1.04-1.19), compared to those in Q1. The study also found a significant association between UPF consumption and all-cause mortality, which disappeared for participants with depression or anxiety. Conclusions: Our findings revealed that UPF consumption is associated with depression or anxiety.

Bidirectional Association Between Multimorbidity and Frailty and the Role of Depression in Older Europeans.

BACKGROUND: Although previous studies have reported an association between multimorbidity and frailty, its direction and mechanism remain unclear. This study aimed to investigate the direction of this association, as well as the role of depression among older Europeans. METHODS: We used a cross-lagged panel design to evaluate the temporal relationship between multimorbidity and frailty and the role of depression. Multimorbidity status was assessed by the self-reporting of 14 chronic diseases. Frailty was assessed based on the frailty phenotype. The European-Depression Scale (EURO-D) was used to assess depression. RESULTS: There was a bidirectional relationship between frailty and multimorbidity. More severe multimorbidity predicted greater frailty (ß = 0.159; p < .001) and vice versa (ß = 0.107; p < .001). All paths from multimorbidity to frailty were stronger than the paths from frailty to multimorbidity (b1-a1: ß = 0.051; p < .001). Likewise, early multimorbidity change was a significant predictive factor for late frailty change (ß = 0.064; p < .001) and vice versa (ß = 0.048; p < .001). Depression in Wave 5 (T5) mediated the association between frailty in Wave 4 (T4) and multimorbidity in Wave 6 (T6; indirect effect: ß = 0.004; bootstrap 95% confidence interval: 0.003, 0.006). CONCLUSIONS: A positive, bidirectional association was observed between multimorbidity and frailty. Depression may be a potential cause of an increased risk of multimorbidity later in life in frail older adults. Early monitoring of frailty and depression may slow the progression of multimorbidity, thereby interrupting the vicious cycle.

Mechanism of the association between sleep quality and mortality in middle-aged and older adults: A prospective study analysis of the UK Biobank.

BACKGROUND: Although sleep quality is known to be associated with mortality, how poor sleep quality contributes to an increased risk of mortality is still unknown. We aimed to examine whether lifestyle, psychosocial and biological factors mediate the association. METHODS: 205,654 participants from UK Biobank were used for the analysis. The outcome was all-cause, cardiovascular disease (CVD) and cancer mortality by February 2022. Exposure was assessed by a sleep score consisting of five sleep behaviors at baseline. Lifestyle, psychosocial, and biological factors are regarded as potential mediators. Mediation analysis based on Cox proportional hazards models was performed. RESULTS: Poor sleep quality was associated with a higher risk of all-cause (Hazard Ratio [HR] = 1.098; 95% CI: 1.058-1.140), CVD (HR = 1.139; 95% CI: 1.045-1.243) and cancer mortality (HR = 1.095; 95% CI: 1.040-1.152). Lifestyle mediators (smoking, physical activity, sedentary, BMI and diet) could explain between 2.6% and 34.0% of the increased risk of all-cause mortality in individuals with poor sleep quality. Self-reported health, frailty, depression, and loneliness were significant psychosocial mediators of this association pathway. About one-fifth of the association can be explained by the biological role of CRP. Similar mediating patterns were observed for CVD and cancer mortality. LIMITATIONS: Both exposure and mediators were measured at baseline, so the possibility of reverse causality cannot be ruled out. CONCLUSIONS: Poor sleep quality is associated with an increased risk of death through a combination of lifestyle, psychosocial and biological pathways. Adopting healthy lifestyles and staying psychosocial well-being are cost-effective interventions to lower the risk of death.

Independent and combined effect of income and education attainment on the incidence of stroke events: a large-scale cohort study from rural communities in China.

OBJECTIVES: Few studies have longitudinally evaluated income and education, and their combined effect on incident of stroke in China. METHODS: The present study was based on a cohort with a baseline survey in China. A total of 15,913 participants were finally included. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were calculated to evaluate the association of income, education, and their combination with stroke risk by Cox proportional hazard model. RESULTS: Lower income and less years of education was significantly associated with an increased risk of total stroke [income: adjusted HR: 1.54 (95% CI: 1.22-1.95); education: adjusted HR: 1.59 (95% CI: 1.11-2.28)]. Notably, the highest risk for total stroke was seen among those with lower income and higher education (adjusted HR: 2.46, 95% CI: 1.36-4.47). Sensitivity analysis by excluding the first year of follow-up showed similar findings with the primary analysis. DISCUSSION: Lower income and education attainment were associated with an increased risk of stroke in Chinese countrysides. A joint effect of income and education existed on the risk of developing stroke. Special attention should be paid for rural community residents, especially for people with low income levels.