High Dietary Folic Acid Supplementation Reduced the Composition of Fatty Acids and Amino Acids in Fortified Eggs.

AIMS: The study aimed to evaluate the effects of dietary folic acid (FA) on the production performance of laying hens, egg quality, and the nutritional differences between eggs fortified with FA and ordinary eggs. METHODS: A total of 288 26-week-old Hy-Line Brown laying hens (initial body weights 1.65 ± 0.10 kg) with a similar weight and genetic background were used. A completely randomized design divided the birds into a control group and three treatment groups. Each group consisted of six replicates, with twelve chickens per replicate. Initially, all birds were fed a basal diet for 1 week. Subsequently, they were fed a basal diet supplemented with 0, 5, 10, or 15 mg/kg FA in a premix for a duration of 6 weeks. RESULTS: Supplementation of FA could significantly (p < 0.05) enhance the FA content in egg yolks, particularly when 10 mg/kg was used, as it had the most effective enrichment effect. Compared to the control group, the Glu content in the 10 and 15 mg/kg FA groups showed a significant (p < 0.05) decrease. Additionally, the contents of Asp, Ile, Tyr, Phe, Cys, and Met in the 15 mg/kg FA group were significantly (p < 0.05) lower compared to the other groups. Adding FA did not have significant effects on the levels of vitamin A and vitamin E in egg yolk, but the vitamin D content in the 5 and 10 mg/kg FA groups showed a significant (p < 0.05) increase. Furthermore, the addition of FA did not have a significant effect on the levels of Cu, Fe, Mn, Se, and Zn in egg yolk. The dietary FA did not have a significant effect on the total saturated fatty acids (SFA) and polyunsaturated fatty acid (PUFA) content in egg yolk. However, the total monounsaturated fatty acid (MUFA) content in the 5 and 10 mg/kg groups significantly (p < 0.05) increased. These changes in nutritional content might be attributed to the increased very low-density lipoprotein (VLDL) protein content. The significant decrease in solute carrier family 1 Member 1 (SLC1A1), solute carrier family 1 Member 2 (SLC1A2), and solute carrier family 1 Member 3 (SLC1A3) gene expression compared to the control group appeared to be the reason for the decrease in amino acid content in egg yolk within the dietary FA group. CONCLUSION: The findings suggest that the appropriate addition of FA can enhance the levels of MUFA and vitamin D in egg yolks, thereby improving their nutritional value. Excessive intake of FA can decrease the effectiveness of enriching FA in egg yolk and impact the enrichment of certain amino acids. The yolk of eggs produced by adding 10 mg/kg of FA to the feed contains the optimal amount of nutrients. This study informs consumers purchasing FA-fortified eggs.

Association between weight change, waist circumference change, and the risk of nonalcoholic fatty liver disease in individuals with metabolically healthy overweight or obesity and metabolically unhealthy overweight or obesity.

BACKGROUND: This study aimed to explore and compare the effect of weight change, and waist circumference (WC) change, on the risk of nonalcoholic fatty liver disease (NAFLD) in individuals with metabolically healthy overweight or obesity (MHOW/O) and metabolically unhealthy overweight or obesity (MUOW/O) in a health check-up cohort in China. METHODS: 5625 adults with overweight or obesity, and free from NAFLD at baseline were included. Metabolically healthy was defined as not having any components of metabolic syndrome. Weight/WC changes were calculated as the relative difference between the first and second visits of check-up. NAFLD was assessed based on abdominal ultrasound. RESULTS: During a median follow-up of 2.1 (IQR: 1.1-4.3) years, 1849 participants developed NAFLD. In MHOW/O participants, the multivariable adjusted HRs (95 % CIs) for NAFLD in weight change ≤ -5.0 %, and - 4.9-- 1.0 % were 0.36 (0.23-0.59), 0.59 (0.43-0.80), respectively, compared to the weight stable group (-0.9% to 0.9 %). The corresponding HRs (95 % CIs) for the association between WC change (≤ 6.0 %, - 5.9 to -3.0 %) and NAFLD in MHOW/O participants were 0.41 (0.27-0.62), and 0.74 (0.54-1.01), respectively, compared to the WC stable group (-2.9-2.9 %). Similar patterns were observed in MUOW/O participants. A more marked gradient of cumulative incidence of NAFLD across weight/WC change categories was observed in MHOW/O than in MUOW/O individuals. CONCLUSIONS: A more evident association between weight/WC loss and risk of NAFLD was observed in MHOW/O than in MUOW/O individuals. Our findings indicate the practical significance of encouraging all individuals with overweight and obesity to achieve a clinically relevant level of weight/WC loss to prevent NAFLD, even among metabolic healthy groups.

Highly Expressed Z-DNA Binding Protein 1 in Esophageal Cancer Promotes Tumor Growth.

BACKGROUND: Esophageal cancer (ESCA) is a common malignant tumor of the digestive tract, and its poor prognosis is mainly attributed to the occurrence of invasion and metastasis. Z-DNA binding protein 1 (ZBP1), as a mRNA regulatory factor, plays an important role in the occurrence and development of various tumors. However, the role of ZBP1 in ESCA is not yet understood. AIMS: This study aims to explore the expression of ZBP1 in ESCA and its role in the development of ESCA. METHODS: Using bioinformatics analysis and immunohistochemistry staining, we detected the expression of ZBP1 in ESCA and normal tissues. The potential mechanism of ZBP1 in ESCA was analyzed from the aspects of genetic mutations, protein interaction networks, and pathway enrichment. We performed functional experiments in vitro to elucidate the effect of ZBP1 on ESCA cells. RESULTS: ZBP1 was found to be significantly upregulated in ESCA compared to adjacent noncancerous tissues, and its expression is closely related to gender, age, and lymph node metastasis. In ESCA, the genetic variation rate of ZBP1 is 8%, and its expression is positively correlated with immune cell infiltration. The ZBP1 co-expressed gene is mainly involved in processes such as lymph node proliferation and intercellular adhesion. In vitro experiments have confirmed that downregulation of ZBP1 significantly inhibited the proliferation, migration, and invasion of ESCA cells. CONCLUSION: This research proves that downregulation of ZBP1 can inhibit the progression of ESCA. This finding indicates that ZBP1 may be a novel biomarker to improve the diagnosis and treatment of ESCA.

National and Provincial-Level Prevalence and Risk Factors of Carotid Atherosclerosis in Chinese Adults.

Importance: Epidemiologic studies on carotid atherosclerosis (CAS) based on nationwide ultrasonography measurements can contribute to understanding the future risk of cardiovascular diseases and identifying high-risk populations, thereby proposing more targeted prevention and treatment measures. Objectives: To estimate the prevalence of CAS within the general population of China and to investigate its distribution among populations with potential risk factors and variation across diverse geographic regions. Design, Setting, and Participants: This multicenter, population-based cross-sectional study used China's largest health check-up chain database to study 10 733 975 individuals aged 20 years or older from all 31 provinces in China who underwent check-ups from January 1, 2017, to June 30, 2022. Main Outcomes and Measures: Carotid atherosclerosis was assessed and graded using ultrasonography as increased carotid intima-media thickness (cIMT), carotid plaque (CP), and carotid stenosis (CS). The overall and stratified prevalences were estimated among the general population and various subpopulations based on demographic characteristics, geographic regions, and cardiovascular disease risk factors. Mixed-effects regression models were used to analyze the risk factors for CAS. Results: Among 10 733 975 Chinese participants (mean [SD] age, 47.7 [13.4] years; 5 861 566 [54.6%] male), the estimated prevalences were 26.2% (95% CI, 25.0%-27.4%) for increased cIMT, 21.0% (95% CI, 19.8%-22.2%) for CP, and 0.56% (95% CI, 0.36%-0.76%) for CS. The prevalence of all CAS grades was higher among older adults (eg, increased cIMT: aged ≥80 years, 92.7%; 95% CI, 92.2%-93.3%), male participants (29.6%; 95% CI, 28.4%-30.7%), those residing in northern China (31.0%; 95% CI, 29.1%-32.9%), and those who had comorbid conditions, such as hypertension (50.8%; 95% CI, 49.7%-51.9%), diabetes (59.0%; 95% CI, 57.8%-60.1%), dyslipidemia (32.1%; 95% CI, 30.8%-33.3%), and metabolic syndrome (31.0%; 95% CI, 29.1%-32.9%). Most cardiovascular disease risk factors were independent risk factors for all CAS stages (eg, hypertension: 1.60 [95% CI, 1.60-1.61] for increased cIMT, 1.62 [95% CI, 1.62-1.63] for CP, and 1.48 [95% CI, 1.45-1.51] for CS). Moreover, the magnitude of the association between several cardiovascular disease risk factors and increased cIMT and CP differed between the sexes and geographic regions. Conclusions and Relevance: These findings suggest that nearly one-quarter of Chinese adults have increased cIMT or CP. The burden of this disease is unevenly distributed across geographic regions and subpopulations and may require different levels of local planning, support, and management. Addressing these disparities is crucial for effectively preventing and managing cardiovascular and cerebrovascular diseases in China.

Adoptive cell immunotherapy for breast cancer: harnessing the power of immune cells.

Breast cancer is the most prevalent malignant neoplasm worldwide, necessitating the development of novel therapeutic strategies owing to the limitations posed by conventional treatment modalities. Immunotherapy is an innovative approach that has demonstrated significant efficacy in modulating a patient's innate immune system to combat tumor cells. In the era of precision medicine, adoptive immunotherapy for breast cancer has garnered widespread attention as an emerging treatment strategy, primarily encompassing cellular therapies such as tumor-infiltrating lymphocyte therapy, chimeric antigen receptor T/NK/M cell therapy, T-cell receptor gene-engineered T-cell therapy, lymphokine-activated killer cell therapy, cytokine-induced killer cell therapy, natural killer cell therapy, and γδ T cell therapy, among others. This treatment paradigm is based on the principles of immune memory and antigen specificity, involving the collection, processing, and expansion of the patient's immune cells, followed by their reintroduction into the patient's body to activate the immune system and prevent tumor recurrence and metastasis. Currently, multiple clinical trials are assessing the feasibility, effectiveness, and safety of adoptive immunotherapy in breast cancer. However, this therapeutic approach faces challenges associated with tumor heterogeneity, immune evasion, and treatment safety. This review comprehensively summarizes the latest advancements in adoptive immunotherapy for breast cancer and discusses future research directions and prospects, offering valuable guidance and insights into breast cancer immunotherapy.

A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke.

OBJECTIVE: To evaluate RSF and Cox models for mortality prediction of hemorrhagic stroke (HS) patients in intensive care unit (ICU). METHODS: In the training set, the optimal models were selected using five-fold cross-validation and grid search method. In the test set, the bootstrap method was used to validate. The area under the curve(AUC) was used for discrimination, Brier Score (BS) was used for calibration, positive predictive value(PPV), negative predictive value(NPV), and F1 score were combined to compare. RESULTS: A total of 2,990 HS patients were included. For predicting the 7-day mortality, the mean AUCs for RSF and Cox regression were 0.875 and 0.761, while the mean BS were 0.083 and 0.108. For predicting the 28-day mortality, the mean AUCs for RSF and Cox regression were 0.794 and 0.649, while the mean BS were 0.129 and 0.174. The mean AUCs of RSF and Cox versus conventional scores for predicting patients' 7-day mortality were 0.875 (RSF), 0.761 (COX), 0.736 (SAPS II), 0.723 (OASIS), 0.632 (SIRS), and 0.596 (SOFA), respectively. CONCLUSIONS: RSF provided a better clinical reference than Cox. Creatine, temperature, anion gap and sodium were important variables in both models.

Combinatorial Use of Machine Learning and Logistic Regression for Predicting Carotid Plaque Risk Among 5.4 Million Adults With Fatty Liver Disease Receiving Health Check-Ups: Population-Based Cross-Sectional Study.

BACKGROUND: Carotid plaque can progress into stroke, myocardial infarction, etc, which are major global causes of death. Evidence shows a significant increase in carotid plaque incidence among patients with fatty liver disease. However, unlike the high detection rate of fatty liver disease, screening for carotid plaque in the asymptomatic population is not yet prevalent due to cost-effectiveness reasons, resulting in a large number of patients with undetected carotid plaques, especially among those with fatty liver disease. OBJECTIVE: This study aimed to combine the advantages of machine learning (ML) and logistic regression to develop a straightforward prediction model among the population with fatty liver disease to identify individuals at risk of carotid plaque. METHODS: Our study included 5,420,640 participants with fatty liver from Meinian Health Care Center. We used random forest, elastic net (EN), and extreme gradient boosting ML algorithms to select important features from potential predictors. Features acknowledged by all 3 models were enrolled in logistic regression analysis to develop a carotid plaque prediction model. Model performance was evaluated based on the area under the receiver operating characteristic curve, calibration curve, Brier score, and decision curve analysis both in a randomly split internal validation data set, and an external validation data set comprising 32,682 participants from MJ Health Check-up Center. Risk cutoff points for carotid plaque were determined based on the Youden index, predicted probability distribution, and prevalence rate of the internal validation data set to classify participants into high-, intermediate-, and low-risk groups. This risk classification was further validated in the external validation data set. RESULTS: Among the participants, 26.23% (1,421,970/5,420,640) were diagnosed with carotid plaque in the development data set, and 21.64% (7074/32,682) were diagnosed in the external validation data set. A total of 6 features, including age, systolic blood pressure, low-density lipoprotein cholesterol (LDL-C), total cholesterol, fasting blood glucose, and hepatic steatosis index (HSI) were collectively selected by all 3 ML models out of 27 predictors. After eliminating the issue of collinearity between features, the logistic regression model established with the 5 independent predictors reached an area under the curve of 0.831 in the internal validation data set and 0.801 in the external validation data set, and showed good calibration capability graphically. Its predictive performance was comprehensively competitive compared with the single use of either logistic regression or ML algorithms. Optimal predicted probability cutoff points of 25% and 65% were determined for classifying individuals into low-, intermediate-, and high-risk categories for carotid plaque. CONCLUSIONS: The combination of ML and logistic regression yielded a practical carotid plaque prediction model, and was of great public health implications in the early identification and risk assessment of carotid plaque among individuals with fatty liver.

A dynamic machine learning model for prediction of NAFLD in a health checkup population: A longitudinal study.

Background: Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases worldwide. Currently, most NAFLD prediction models are diagnostic models based on cross-sectional data, which failed to provide early identification or clarify causal relationships. We aimed to use time-series deep learning models with longitudinal health checkup records to predict the onset of NAFLD in the future, and update the model stepwise by incorporating new checkup records to achieve dynamic prediction. Methods: 10,493 participants with over 6 health checkup records from Beijing MJ Health Screening Center were included to conduct a retrospective cohort study, in which the constantly updated initial 5 checkup data were incorporated stepwise to predict the risk of NAFLD at and after their sixth health checkups. A total of 33 variables were considered, consisting of demographic characteristics, medical history, lifestyle, physical examinations, and laboratory tests. L1-penalized logistic regression (LR) was used for feature selection. The long short-term memory (LSTM) algorithm was introduced for model development, and five-fold cross-validation was conducted to tune and choose optimal hyperparameters. Both internal validation and external validation were conducted, using the 20% randomly divided holdout test dataset and previously unseen data from Shanghai MJ Health Screening Center, respectively, to evaluate model performance. The evaluation metrics included area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, Brier score, and decision curve. Bootstrap sampling was implemented to generate 95% confidence intervals of all the metrics. Finally, the Shapley additive explanations (SHAP) algorithm was applied in the holdout test dataset for model interpretability to obtain time-specific and sample-specific contributions of each feature. Results: Among the 10,493 participants, 1662 (15.84%) were diagnosed with NAFLD at and after their sixth health checkups. The predictive performance of the deep learning model in the internal validation dataset improved over the incorporation of the checkups, with AUROC increasing from 0.729 (95% CI: 0.698,0.760) at baseline to 0.818 (95% CI: 0.798,0.844) when consecutive 5 checkups were included. The external validation dataset, containing 1728 participants, was used to verify the results, in which AUROC increased from 0.700 (95% CI: 0.657,0.740) with only the first checkups to 0.792 (95% CI: 0.758,0.825) with all five. The results of feature significance showed that body fat percentage, alanine transaminase (ALT), and uric acid owned the greatest impact on the outcome, time-specific, individual-specific and dynamic feature contributions were also produced for model interpretability. Conclusion: A dynamic prediction model was successfully established in our study, and the prediction capability kept improving with the renewal of the latest checkup records. In addition, we identified key features associated with the onset of NAFLD, making it possible to optimize the prevention and control strategies of the disease in the general population.

Identification of Genes Associated with Prognosis and Immunotherapy Prediction in Triple-Negative Breast Cancer via M1/M2 Macrophage Ratio.

Background and Objectives: Triple-negative breast cancer (TNBC), a highly aggressive and heterogeneous subtype of breast cancer, accounts for ap-proximately 10-15% of all breast cancer cases. Currently, there is no effective therapeutic target for TNBC. Tu-mor-associated macrophages (TAMs), which can be phenotypically classified into M1 and M2 subtypes, have been shown to influence the prognosis of various cancers, including ovarian cancer. This study aimed to investigate the role of M1/M2 macrophages in the TNBC tumor microenvironment (TME), with a focus on identifying prognostic genes and predicting immunotherapy response. Materials and Methods: The study employed the CIBERSORT algorithm to analyze immune cell expression in the TME. Genes associated with the M1/M2 macrophage ratio were identified using Pearson correlation analysis and used to classify patients into dis-tinct clusters. Dimensionality reduction techniques, including univariate Cox regression and Lasso, were applied to these genes. The expression of prognostic genes was validated through immunohistochemistry. Results: The study found a high prevalence of TAMs in the TME. Among the patient clusters, 109 differentially expressed genes (DEGs) were identified. Three significant DEGs (LAMP3, GZMB, and CXCL13) were used to construct the riskScores. The riskScore model effectively stratified patients based on mortality risk. Gene Set Enrichment Analysis (GSEA) associated the riskScore with several significant pathways, including mismatch repair, JAK/STAT3 signaling, VEGF signaling, antigen processing presentation, ERBB signaling, and P53 signaling. The study also predicted patient sensitivity to im-munotherapy using the riskScores. The expression of the three significant DEGs was validated through immunohisto-chemistry. Conclusions: The study concluded that the riskScore model, based on the M1/M2 macrophage ratio, is a valid prognostic tool for TNBC. The findings underscore the importance of the TME in TNBC progression and prognosis and highlight the po-tential of the riskScore model in predicting immunotherapy response in TNBC patients.

Cancer stem cells promote lymph nodes metastasis of breast cancer by reprogramming tumor microenvironment.

Breast cancer progression and metastasis are governed by a complex interplay within the tumor immune microenvironment (TIME), involving numerous cell types. Lymph node metastasis (LNM) is a key prognostic marker associated with distant organ metastasis and reduced patient survival, but the mechanisms underlying its promotion by breast cancer stem cells (CSCs) remain unclear. Our study sought to unravel how CSCs reprogram TIME to facilitate LNM. Utilizing single-cell RNA sequencing, we profiled TIME in primary cancer and corresponding metastatic lymph node samples from patients at our institution. To verify the derived data, we cultured CSCs and performed validation assays employing flow cytometry and CyTOF. Our analysis revealed distinct differences in cellular infiltration patterns between tumor and LNM samples. Importantly, RAC2 and PTTG1 double-positive CSCs, which exhibit the highest stem-like attributes, were markedly enriched in metastatic lymph nodes. These CSCs are hypothesized to foster metastasis via activation of specific metastasis-related transcription factors and signaling pathways. Additionally, our data suggest that CSCs might modulate adaptive and innate immune cell evolution, thereby further contributing to metastasis. In summary, this study illuminates a critical role of CSCs in modifying TIME to facilitate LNM. The enrichment of highly stem-like CSCs in metastatic lymph nodes offers novel therapeutic targeting opportunities and deepens our understanding of breast cancer metastasis.

Metabolic-associated fatty liver disease in relation to site-specific and multiple-site subclinical atherosclerosis.

BACKGROUND & AIMS: Non-alcoholic fatty liver disease (NAFLD) and the newly proposed metabolic-associated fatty liver disease (MAFLD) were each associated with subclinical atherosclerosis. However, there is limited evidence on risk of atherosclerosis in individuals who meet the criteria for one but not the other. We aimed to investigate the associations of MAFLD or NAFLD status with site-specific and multiple-site atherosclerosis. METHODS: This is a prospective cohort study involving 4524 adults within the MJ health check-up cohort. Logistic regression model was used to estimate odds ratios (ORs) and confidence intervals (CIs) for subclinical atherosclerosis (elevated carotid intima-media thickness [CIMT], carotid plaque [CP], coronary artery calcification [CAC] and retinal atherosclerosis [RA]) associated with MAFLD or NAFLD status, MAFLD subtypes and fibrosis status. RESULTS: MAFLD was associated with higher risks of elevated CIMT, CP, CAC and RA (OR: 1.41 [95% CI 1.18-1.68], 1.23 [1.02-1.48], 1.60 [1.24-2.08], and 1.79 [1.28-2.52], respectively), whereas NAFLD per se did not increase risk of atherosclerosis except for elevated CIMT. Individuals who met both definitions or the definition for MAFLD but not NAFLD had higher risk of subclinical atherosclerosis. Among MAFLD subtypes, MAFLD with diabetes had the highest risk of subclinical atherosclerosis, but the associations did not differ by fibrosis status. Stronger positive associations were observed of MAFLD with multiple-site than single-site atherosclerosis. CONCLUSIONS: In Chinese adults, MAFLD was associated with subclinical atherosclerosis, with stronger associations for multiple-site atherosclerosis. More attention should be paid to MAFLD with diabetes, and MAFLD might be a better predictor for atherosclerotic disease than NAFLD.

Prevalence of Liver Steatosis and Fibrosis in the General Population and Various High-Risk Populations: A Nationwide Study With 5.7 Million Adults in China.

BACKGROUND & AIMS: This study aimed to estimate the prevalence of liver steatosis and fibrosis in the general population and populations with potential risk factors in China, so as to inform policies for the screening and management of fatty liver disease and liver fibrosis in general and high-risk populations. METHODS: This cross-sectional, population-based, nationwide study was based on the database of the largest health check-up chain in China. Adults from 30 provinces who underwent a check-up between 2017 and 2022 were included. Steatosis and fibrosis were assessed and graded by transient elastography. Overall and stratified prevalence was estimated among the general population and various subpopulations with demographic, cardiovascular, and chronic liver disease risk factors. A mixed effect regression model was used to examine predictors independently associated with steatosis and fibrosis. RESULTS: In 5,757,335 participants, the prevalence of steatosis, severe steatosis, advanced fibrosis, and cirrhosis was 44.39%, 10.57%, 2.85%, and 0.87%, respectively. Participants who were male, with obesity, diabetes, hypertension, dyslipidemia, metabolic syndrome, or elevated alanine aminotransferase or aspartate aminotransferase had a significantly higher prevalence of all grades of steatosis and fibrosis, and those with fatty liver, decreased albumin or platelet count, and hepatitis B virus infection also had a significantly higher prevalence of fibrosis than their healthy counterparts. Most cardiovascular and chronic liver disease risk factors were independent predictors for steatosis and fibrosis, except for dyslipidemia for fibrosis. CONCLUSIONS: A substantial burden of liver steatosis and fibrosis was found in China. Our study provides evidence for shaping future pathways for screening and risk stratification of liver steatosis and fibrosis in the general population. The findings of this study highlight that fatty liver and liver fibrosis should be included in disease management programs as targets for screening and regular monitoring in high-risk populations, especially in those with diabetes.

The value of enhanced multiparameteric MRI diagnostic model for preoperatively predicting surgical methods of inferior vena cava in patients with renal tumors and inferior vena cava tumor thrombus.

BACKGROUND: Inferior vena cava tumor thrombus (IVCTT) invading the IVC wall majorly affects the surgical method choice and prognosis in renal tumors. Enhanced multiparameteric MRI plays an important role in preoperative evaluation. In this work, an MRI-based diagnostic model for IVCTT was established so as to guide the preoperative decisions. METHODS: Preoperative MR images of 165 cases of renal tumors with IVCTT were retrospectively analyzed, and imaging indicators were analyzed, including IVCTT morphology and Mayo grade, IVCTT diameter measurements, bland thrombosis, primary MRI-based diagnosis of renal tumor, and involvement of contralateral renal vein. The indicators were analyzed based on intraoperative performance and resection scope of the IVC wall. Multivariate logistic regression analysis was used to establish the diagnostic model. RESULTS: The morphological classification of the IVCTT, primary MRI-based diagnosis of renal tumors, maximum transverse diameter of IVCTT, and length of the bland thrombus were the main indexes predicting IVC wall invasion. The MRI-based diagnostic model established according to these indexes had good diagnostic efficiency. The prediction probability of 0.61 was set as the cutoff value. The area under the curve of the test set was 0.88, sensitivity was 0.79, specificity was 0.85, and prediction accuracy was 0.79 under the optimal cutoff value. CONCLUSION: The preoperative MRI-based diagnostic model could reliably predict IVC wall invasion, which is helpful for better prediction of IVC-associated surgical operations.

Creatine kinase B suppresses ferroptosis by phosphorylating GPX4 through a moonlighting function.

Activation of receptor protein kinases is prevalent in various cancers with unknown impact on ferroptosis. Here we demonstrated that AKT activated by insulin-like growth factor 1 receptor signalling phosphorylates creatine kinase B (CKB) T133, reduces metabolic activity of CKB and increases CKB binding to glutathione peroxidase 4 (GPX4). Importantly, CKB acts as a protein kinase and phosphorylates GPX4 S104. This phosphorylation prevents HSC70 binding to GPX4, thereby abrogating the GPX4 degradation regulated by chaperone-mediated autophagy, alleviating ferroptosis and promoting tumour growth in mice. In addition, the levels of GPX4 are positively correlated with the phosphorylation levels of CKB T133 and GPX4 S104 in human hepatocellular carcinoma specimens and associated with poor prognosis of patients with hepatocellular carcinoma. These findings reveal a critical mechanism by which tumour cells counteract ferroptosis by non-metabolic function of CKB-enhanced GPX4 stability and underscore the potential to target the protein kinase activity of CKB for cancer treatment.

Targeting mitochondrial dynamics by AZD5363 in triple-negative breast cancer MDA-MB-231 cell-derived spheres.

Breast cancer stem cells (BCSCs) have been suggested to contribute to chemotherapeutic resistance and disease relapse in breast cancer. Thus, BCSCs represent a promising target in developing novel breast cancer treatment strategies. Mitochondrial dynamics in BCSCs were recently highlighted as an available approach for targeting BCSCs. In this study, a three-dimensional (3D) cultured breast cancer stem cell spheres model was constructed. Mitochondrial dynamics and functions were analyzed by flow cytometry and confocal microscopy. We have demonstrated that the protein levels of FIS 1 and Mitofusin 1 were significantly increased in BCSCs. Moreover, Capivasertib (AZD5363) administration could suppress Mitofusin1 expression in BCSCs. Our use of MitoTracker Orange and annexin V double-staining assay suggested that AZD5363 could induce apoptosis in BCSCs. The sensitivity of stem cell spheres to doxorubicin was investigated by CCK8 assay, and our results indicated that AZD5363 could re-sensitize BCSCs to Doxo. Flow cytometry analysis identified doxo-induced CD44 and CD133 expression in BCSCs could be suppressed by AZD5363. In combination with AZD536, doxo-induced apoptosis in the BCSCs was significantly increased. In conclusion, our study explored, for the first time, that AZD5363 could target mitochondrial dynamics in 3D cultured stem cell spheres (BCSCs) by regulating Mitofusin.

Association between trajectories of fasting plasma glucose and risk of osteoporosis in non-diabetic and diabetic populations.

Introduction: Previous studies based on a single measure of fasting plasma glucose (FPG) showed an inconsistent conclusion about the association between FPG and osteoporosis risk. Not accounting for time-varying and cumulative average of FPG over time could bias the true relation between FPG and osteoporosis. Our study aims to investigate the association between the trajectories of FPG and osteoporosis risk for non-diabetic and diabetic populations. Methods: A total of 18,313 participants who attended physical examinations during 2008-2018 were included. They were free of osteoporosis at their first physical examination and followed until their last physical examination before December 31, 2018. We recorded their incidence of osteoporosis and at least three FPG values during follow-up. Their longitudinal FPG trajectories were identified by the latent class growth analysis model based on the changes in FPG. Multivariable logistic regression models were used to analyze the association between the trajectories of FPG and osteoporosis diagnosed in the follow-up physical examination in both non-diabetics and diabetics. Results: There were 752 incident osteoporosis among 16,966 non-diabetic participants, and 57 incident osteoporosis among 1,347 diabetic participants. Among non-diabetics, the elevated-increasing FPG trajectory was negatively associated with osteoporosis risk in women (odds ratio (OR), 0.62; 95% confidence interval (CI), 0.43-0.88). Premenopausal women with elevated-increasing FPG trajectory had lower osteoporosis risk than those women with normal-stable FPG trajectory (OR, 0.41; 95% CI, 0.20-0.88), while this association was insignificant in postmenopausal women. Among diabetics, those whose longitudinal FPG is kept at a very high level had the highest risk of osteoporosis (OR, 3.09; 95% CI, 1.16-8.22), whereas those whose FPG starts with the high level and keeps on increasing did not exhibit a significantly increased risk (OR, 1.75; 95% CI, 0.81-3.76) compared with those who keep stable moderate-high level of FPG, except in men (OR, 2.49; 95% CI, 1.02-6.12). Conclusion: Distinct trajectories of FPG are associated with differential risk of osteoporosis in non-diabetic and diabetic populations. Controlling a proper FPG level in different populations is necessary for osteoporosis prevention.

Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients.

Background: In-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes. Materials and methods: A retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms. Results: A total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission. Conclusion: The prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.

Combining Immune-Related Genes For Delineating the Extracellular Matrix and Predicting Hormone Therapy and Neoadjuvant Chemotherapy Benefits In Breast Cancer.

Breast cancer (BC) is the most prevalent cancer in women worldwide. A systematic approach to BC treatment, comprising adjuvant and neoadjuvant chemotherapy (NAC), as well as hormone therapy, forms the foundation of the disease's therapeutic strategy. The extracellular matrix (ECM) is a dynamic network that exerts a robust biological effect on the tumor microenvironment (TME), and it is highly regulated by several immunological components, such as chemokines and cytokines. It has been established that the ECM promotes the development of an immunosuppressive TME. Therefore, while analyzing the ECM of BC, immune-related genes must be considered. In this study, we used bioinformatic approaches to identify the most valuable ECM-related immune genes. We used weighted gene co-expression network analysis to identify the immune-related genes that potentially regulate the ECM and then combined them with the original ECM-related gene set for further analysis. Least absolute shrinkage and selection operator (LASSO) regression and SurvivalRandomForest were used to narrow our ECM-related gene list and establish an ECM index (ECMI) to better delineate the ECM signature. We stratified BC patients into ECMI high and low groups and evaluated their clinical, biological, and genomic characteristics. We found that the ECMI is highly correlated with long-term BC survival. In terms of the biological process, this index is positively associated with the cell cycle, DNA damage repair, and homologous recombination but negatively with processes involved in angiogenesis and epithelial-mesenchymal transition. Furthermore, the tumor mutational burden, copy number variation, and DNA methylation levels were found to be related to the ECMI. In the Metabric cohort, we demonstrated that hormone therapy is more effective in patients with a low ECMI. Additionally, differentially expressed genes from the ECM-related gene list were extracted from patients with a pathologic complete response (pCR) to NAC and with residual disease (RD) to construct a neural network model for predicting the chance of achieving pCR individually. Finally, we performed qRT-PCR to validate our findings and demonstrate the important role of the gene OGN in predicting the pCR rate. In conclusion, delineation of the ECM signature with immune-related genes is anticipated to aid in the prediction of the prognosis of patients with BC and the benefits of hormone therapy and NAC in BC patients.

Association between metabolically healthy obesity and non-alcoholic fatty liver disease.

BACKGROUND: The risks of NAFLD and NAFLD with fibrosis progression among metabolically healthy obesity (MHO) individuals are largely unexplored. This cohort study investigated the association between MHO as well as other metabolic syndrome-obesity combined phenotypes and NAFLD and its fibrosis progression. METHODS: Participants included 31,010 adults from a health check-up cohort free from NAFLD and intermediate or high probability of advanced fibrosis at baseline. Metabolically healthy was defined as not having any component of metabolic syndrome. Obesity was identified by body mass index (BMI) and waist circumference (WC). Participants were cross-classified by metabolic health and obesity at baseline. The outcomes were NAFLD, and NAFLD with fibrosis progression, as assessed by abdominal B-type ultrasound and noninvasive fibrosis score. RESULTS: During a median follow-up of 2.2 (interquartile range, 1.2-4.9) years, 7,393 participants developed NAFLD. MHO individuals (HR 5.51, 95% CI 4.98, 6.09 for BMI criteria; HR 6.76, 95% CI 6.04, 7.57 for WC criteria) had a significantly higher risk of NAFLD than those with metabolically healthy normal weight or low WC. The corresponding HRs (95% CIs) for metabolically healthy overweight (defined by BMI) and medium WC were 2.74 (2.49-3.02) and 2.93 (2.65-3.24), respectively. Furthermore, 557 participants developed NAFLD with fibrosis progression. The association between different obesity phenotypes and NAFLD with fibrosis progression also showed a similar pattern. CONCLUSION: MHO was associated with significantly higher risks of NAFLD and its fibrosis progression, suggesting that regarding NAFLD prevention, MHO individuals might still benefit from lifestyle interventions aimed at body weight and WC maintenance.

DNA circuits compatible encoder and demultiplexer based on a single biomolecular platform with DNA strands as outputs.

A series of multiple logic circuits based on a single biomolecular platform is constructed to perform nonarithmetic and arithmetic functions, including 4-to-2 encoder, 1-to-2 demultiplexer, 1-to-4 demultiplexer, and multi-input OR gate. The encoder to a DNA circuit is the equivalent of a sensory receptor to a reflex arc. They all function to encode information from outside the pathway (DNA circuit or reflex arc) into a form that subsequent pathways can recognize and utilize. Current molecular encoders are based on optical or electrical signals as outputs, while DNA circuits are based on DNA strands as transmission signals. The output of existing encoders cannot be recognized by subsequent DNA circuits. It is the first time the DNA-based encoder with DNA strands as outputs can be truly applied to the DNA circuit, enabling the application of DNA circuits in non-binary biological environments. Another novel feature of the designed system is that the developed nanodevices all have a simple structure, low leakage and low crosstalk, which allows them to implement higher-level encoders and demultiplexers easily. Our work is based on the idea of complex functionality in a simple form, which will also provide a new route for developing advanced molecular logic circuits.