A rechargeable coating with temporal-sequence antibacterial activity and soft tissue sealing.

Transcutaneous implants that penetrate through skin or mucosa are susceptible to bacteria invasion and lack proper soft tissue sealing. Traditional antibacterial strategies primarily focus on bacterial eradication, but excessive exposure to bactericidal agents can induce noticeable tissue damage. Herein, a rechargeable model (HPI-Ti) was constructed using perylene polyimide, an aqueous battery material, achieving temporal-sequence regulation of bacterial killing and soft tissue sealing. Charge storage within HPI-Ti is achieved after galvanostatic charge, and chemical discharge is initiated when immersed in physiological environments. During the early discharge stage, post-charging HPI-Ti demonstrates an antibacterial rate of 99.96 ± 0.01 % for 24 h, preventing biofilm formation. Contact-dependent violent electron transfer between bacteria and the material causes bacteria death. In the later discharge stage, the attenuated discharging status creates a gentler electron-transfer micro-environment for fibroblast proliferation. After discharge, the antibacterial activity can be reinstated by recharge against potential reinfection. The antibacterial efficacy and soft tissue compatibility were verified in vivo. These results demonstrate the potential of the charge-transfer-based model in reconciling antibacterial efficacy with tissue compatibility.

Prognostic value of geriatric nutritional risk index in patients with stable coronary artery disease undergoing percutaneous coronary intervention.

BACKGROUND: Malnutrition increases the risk of poor prognosis in patients with cardiovascular disease, and our current research was designed to assess the predictive performance of the Geriatric Nutrition Risk Index (GNRI) for the occurrence of poor prognosis after percutaneous coronary intervention (PCI) in patients with stable coronary artery disease (SCAD) and to explore possible thresholds for nutritional intervention. METHODS: This study retrospectively enrolled newly diagnosed SCAD patients treated with elective PCI from 2014 to 2017 at Shinonoi General Hospital, with all-cause death as the main follow-up endpoint. Cox regression analysis and restricted cubic spline (RCS) regression analysis were used to explore the association of GNRI with all-cause death risk and its shape. Receiver operating characteristic curve (ROC) analysis and piecewise linear regression analysis were used to evaluate the predictive performance of GNRI level at admission on all-cause death in SCAD patients after PCI and to explore possible nutritional intervention threshold points. RESULTS: The incidence of all-cause death was 40.47/1000 person-years after a mean follow-up of 2.18 years for 204 subjects. Kaplan-Meier curves revealed that subjects at risk of malnutrition had a higher all-cause death risk. In multivariate Cox regression analysis, each unit increase in GNRI reduced the all-cause death risk by 14% (HR 0.86, 95% CI 0.77, 0.95), and subjects in the GNRI > 98 group had a significantly lower risk of death compared to those in the GNRI < 98 group (HR 0.04, 95% CI 0.00, 0.89). ROC analysis showed that the baseline GNRI had a very high predictive performance for all-cause death (AUC = 0.8844), and the predictive threshold was 98.62; additionally, in the RCS regression analysis and piecewise linear regression analysis we found that the threshold point for the GNRI-related all-cause death risk was 98.28 and the risk will be significantly reduced when the subjects' baseline GNRI was greater than 98.28. CONCLUSIONS: GNRI level at admission was an independent predictor of all-cause death in SCAD patients after PCI, and GNRI equal to 98.28 may be a useful threshold for nutritional intervention in SCAD patients treated with PCI.

Metainterface Heterostructure Enhances Sonodynamic Therapy for Disrupting Secondary Biofilms.

Implant-related secondary infections are a challenging clinical problem. Sonodynamic therapy (SDT) strategies are promising for secondary biofilm infections by nonsurgical therapy. However, the inefficiency of SDT in existing acoustic sensitization systems limits its application. Therefore, we take inspiration from popular metamaterials and propose the design idea of a metainterface heterostructure to improve SDT efficiency. The metainterfacial heterostructure is defined as a periodic arrangement of heterointerface monoclonal cells that amplify the intrinsic properties of the heterointerface. Herein, we develop a TiO2/Ti2O3/vertical graphene metainterface heterostructure film on titanium implants. This metainterface heterostructure exhibits extraordinary sonodynamic and acoustic-to-thermal conversion effects under low-intensity ultrasound. The modulation mechanisms of the metainterface for electron accumulation and separation are revealed. The synergistic sonodynamic/mild sonothermal therapy disrupts biofilm infections (antibacterial rates: 99.99% for Staphylococcus aureus, 99.54% for Escherichia coli), and the osseointegration ability of implants is significantly improved in in vivo tests. Such a metainterface heterostructure film lays the foundation for the metainterface of manipulating electron transport to enhance the catalytic performance and holding promise for addressing secondary biofilm infections.

Development and validation of prediction models for papillary thyroid cancer structural recurrence using machine learning approaches.

BACKGROUND: Although papillary thyroid cancer (PTC) patients are known to have an excellent prognosis, up to 30% of patients experience disease recurrence after initial treatment. Accurately predicting disease prognosis remains a challenge given that the predictive value of several predictors remains controversial. Thus, we investigated whether machine learning (ML) approaches based on comprehensive predictors can predict the risk of structural recurrence for PTC patients. METHODS: A total of 2244 patients treated with thyroid surgery and radioiodine were included. Twenty-nine perioperative variables consisting of four dimensions (demographic characteristics and comorbidities, tumor-related variables, lymph node (LN)-related variables, and metabolic and inflammatory markers) were analyzed. We applied five ML algorithms-logistic regression (LR), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and neural network (NN)-to develop the models. The area under the receiver operating characteristic (AUC-ROC) curve, calibration curve, and variable importance were used to evaluate the models' performance. RESULTS: During a median follow-up of 45.5 months, 179 patients (8.0%) experienced structural recurrence. The non-stimulated thyroglobulin, LN dissection, number of LNs dissected, lymph node metastasis ratio, N stage, comorbidity of hypertension, comorbidity of diabetes, body mass index, and low-density lipoprotein were used to develop the models. All models showed a greater AUC (AUC = 0.738 to 0.767) than did the ATA risk stratification (AUC = 0.620, DeLong test: P < 0.01). The SVM, XGBoost, and RF model showed greater sensitivity (0.568, 0.595, 0.676), specificity (0.903, 0.857, 0.784), accuracy (0.875, 0.835, 0.775), positive predictive value (PPV) (0.344, 0.272, 0.219), negative predictive value (NPV) (0.959, 0.959, 0.964), and F1 score (0.429, 0.373, 0.331) than did the ATA risk stratification (sensitivity = 0.432, specificity = 0.770, accuracy = 0.742, PPV = 0.144, NPV = 0.938, F1 score = 0.216). The RF model had generally consistent calibration compared with the other models. The Tg and the LNR were the top 2 important variables in all the models, the N stage was the top 5 important variables in all the models. CONCLUSIONS: The RF model achieved the expected prediction performance with generally good discrimination, calibration and interpretability in this study. This study sheds light on the potential of ML approaches for improving the accuracy of risk stratification for PTC patients. TRIAL REGISTRATION: Retrospectively registered at www.chictr.org.cn (trial registration number: ChiCTR2300075574, date of registration: 2023-09-08).

A Mesoporous Silica-Loaded Multi-Functional Hydrogel Enhanced Tendon Healing via Immunomodulatory and Pro-Regenerative Effects.

Tendon injuries are pervasive orthopedic injuries encountered by the general population. Nonetheless, recovery after severe injuries, such as Achilles tendon injury, is limited. Consequently, there is a pressing need to devise interventions, including biomaterials, that foster tendon healing. Regrettably, tissue engineering treatments have faced obstacles in crafting appropriate tissue scaffolds and efficacious nanomedical approaches. To surmount these hurdles, an innovative injectable hydrogel (CP@SiO2), comprising puerarin and chitosan through in situ self-assembly, is pioneered while concurrently delivering mesoporous silica nanoparticles for tendon healing. In this research, CP@SiO2 hydrogel is employed for the treatment of Achilles tendon injuries, conducting extensive in vivo and in vitro experiments to evaluate its efficacy. This reults demonstrates that CP@SiO2 hydrogel enhances the proliferation and differentiation of tendon-derived stem cells, and mitigates inflammation through the modulation of macrophage polarization. Furthermore, using histological and behavioral analyses, it is found that CP@SiO2 hydrogel can improve the histological and biomechanical properties of injured tendons. This findings indicate that this multifaceted injectable CP@SiO2 hydrogel constitutes a suitable bioactive material for tendon repair and presents a promising new strategy for the clinical management of tendon injuries.

[Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance]. / 从CTå›¾åƒä¸­æ£€æµ‹æ–°åž‹å† çŠ¶ç— æ¯’æ„ŸæŸ“å¯¼è‡´çš„è‚ºç‚Žï¼šä¸€ç§ç»†èŠ‚ä¸Šé‡‡æ ·å’Œæ³¨æ„åŠ›å¼•å¯¼çš„æ·±åº¦å­¦ä¹ æ–¹æ³•.

Objective: To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of patients with novel coronavirus pneumonia (NCP) by restoring detailed information and mining local information. Methods: We present a deep learning approach that integrates detail upsampling and attention guidance. A linear upsampling algorithm based on bicubic interpolation algorithm was adopted to improve the restoration of detailed information within feature maps during the upsampling phase. Additionally, a visual attention mechanism based on vertical and horizontal spatial dimensions embedded in the feature extraction module to enhance the capability of the object detection algorithm to represent key information related to NCP lesions. Results: Experimental results on the NCP dataset showed that the detection method based on the detail upsampling algorithm improved the recall rate by 1.07% compared with the baseline model, with the AP50 reaching 85.14%. After embedding the attention mechanism in the feature extraction module, 86.13% AP50, 73.92% recall, and 90.37% accuracy were achieved, which were better than those of the popular object detection models. Conclusion: The feature information mining of CT images based on deep learning can further improve the lesion detection ability. The proposed approach helps radiologists rapidly identify NCP lesions on CT images and provides an important clinical basis for early intervention and high-intensity monitoring of NCP patients.

Relative importance of triglyceride glucose index combined with body mass index in predicting recovery from prediabetic state to normal fasting glucose: a cohort analysis based on a Chinese physical examination population.

BACKGROUND: Prediabetes is a high-risk state for diabetes, and numerous studies have shown that the body mass index (BMI) and triglyceride-glucose (TyG) index play significant roles in risk prediction for blood glucose metabolism. This study aims to evaluate the relative importance of BMI combination with TyG index (TyG-BMI) in predicting the recovery from prediabetic status to normal blood glucose levels. METHODS: A total of 25,397 prediabetic subjects recruited from 32 regions across China. Normal fasting glucose (NFG), prediabetes, and diabetes were defined referring to the American Diabetes Association (ADA) criteria. After normalizing the independent variables, the impact of TyG-BMI on the recovery or progression of prediabetes was analyzed through the Cox regression models. Receiver Operating Characteristic (ROC) curve analysis was utilized to visualize and compare the predictive value of TyG-BMI and its constituent components in prediabetes recovery/progression. RESULTS: During the average observation period of 2.96 years, 10,305 individuals (40.58%) remained in the prediabetic state, 11,278 individuals (44.41%) recovered to NFG, and 3,814 individuals (15.02%) progressed to diabetes. The results of multivariate Cox regression analysis demonstrated that TyG-BMI was negatively associated with recovery from prediabetes to NFG and positively associated with progression from prediabetes to diabetes. Further ROC analysis revealed that TyG-BMI had higher impact and predictive value in predicting prediabetes recovering to NFG or progressing to diabetes in comparison to the TyG index and BMI. Specifically, the TyG-BMI threshold for predicting prediabetes recovery was 214.68, while the threshold for predicting prediabetes progression was 220.27. Additionally, there were significant differences in the relationship of TyG-BMI with prediabetes recovering to NFG or progressing to diabetes within age subgroups. In summary, TyG-BMI is more suitable for assessing prediabetes recovery or progression in younger populations (< 45 years old). CONCLUSIONS: This study, for the first time, has revealed the significant impact and predictive value of the TyG index in combination with BMI on the recovery from prediabetic status to normal blood glucose levels. From the perspective of prediabetes intervention, maintaining TyG-BMI within the threshold of 214.68 holds crucial significance.

Identification of ipsilateral supraclavicular lymph node metastasis in breast cancer based on LASSO regression with a high penalty factor.

Aiming at the problems of small sample size and large feature dimension in the identification of ipsilateral supraclavicular lymph node metastasis status in breast cancer using ultrasound radiomics, an optimized feature combination search algorithm is proposed to construct linear classification models with high interpretability. The genetic algorithm (GA) is used to search for feature combinations within the feature subspace using least absolute shrinkage and selection operator (LASSO) regression. The search is optimized by applying a high penalty to the L1 norm of LASSO to retain excellent features in the crossover operation of the GA. The experimental results show that the linear model constructed using this method outperforms those using the conventional LASSO regression and standard GA. Therefore, this method can be used to build linear models with higher classification performance and more robustness.

A nomogram to preoperatively predict the aggressiveness of non-functional pancreatic neuroendocrine tumors based on CT features.

OBJECTIVES: To develop a nomogram to predict the aggressiveness of non-functional pancreatic neuroendocrine tumors (NF-pNETs) based on preoperative computed tomography (CT) features. METHODS: This study included 176 patients undergoing radical resection for NF-pNETs. These patients were randomly divided into the training (n = 123) and validation sets (n = 53). A nomogram was developed based on preoperative predictors of aggressiveness of the NF-pNETs which were identified by univariable and multivariable logistic regression analysis. The aggressiveness of NF-pNETs was defined as a composite measure including G3 grading, N+, distant metastases, and/ or disease recurrence. RESULTS: Altogether, the number of patients with highly aggressive NF-pNETs was 37 (30.08 %) and 15 (28.30 %) in the training and validation sets, respectively. Multivariable logistic regression analysis identified that tumor size, biliopancreatic duct dilatation, lymphadenopathy, and enhancement pattern were preoperative predictors of aggressiveness. Those variables were used to develop a nomogram with good concordance statistics of 0.89 and 0.86 for predicting aggressiveness in the training and validation sets, respectively. With a nomogram score of 59, patients with NF-pNETs were divided into low-aggressive and high-aggressive groups. The high-aggressive group had decreased overall survival (OS) and disease-free survival (DFS). Moreover, the nomogram showed good performance in predicting OS and DFS at 3, 5, and 10 years. CONCLUSION: The nomogram integrating CT features helped preoperatively predict the aggressiveness of NF-pNETs and could potentially facilitate clinical decision-making.

Comprehensive split TEV based protein-protein interaction screening reveals TAOK2 as a key modulator of Hippo signalling to limit growth.

The conserved Hippo signalling pathway plays a crucial role in tumour formation by limiting tissue growth and proliferation. At the core of this pathway are tumour suppressor kinases STK3/4 and LATS1/2, which limit the activity of the oncogene YAP1, the primary downstream effector. Here, we employed a split TEV-based protein-protein interaction screen to assess the physical interactions among 28 key Hippo pathway components and potential upstream modulators. This screen led us to the discovery of TAOK2 as pivotal modulator of Hippo signalling, as it binds to the pathway's core kinases, STK3/4 and LATS1/2, and leads to their phosphorylation. Specifically, our findings revealed that TAOK2 binds to and phosphorylates LATS1, resulting in the reduction of YAP1 phosphorylation and subsequent transcription of oncogenes. Consequently, this decrease led to a decrease in cell proliferation and migration. Interestingly, a correlation was observed between reduced TAOK2 expression and decreased patient survival time in certain types of human cancers, including lung and kidney cancer as well as glioma. Moreover, in cellular models corresponding to these cancer types the downregulation of TAOK2 by CRISPR inhibition led to reduced phosphorylation of LATS1 and increased proliferation rates, supporting TAOK2's role as tumour suppressor gene. By contrast, overexpression of TAOK2 in these cellular models lead to increased phospho-LATS1 but reduced cell proliferation. As TAOK2 is a druggable kinase, targeting TAOK2 could serve as an attractive pharmacological approach to modulate cell growth and potentially offer strategies for combating cancer.

Alanine aminotransferase to high- density lipoprotein cholesterol ratio is positively correlated with the occurrence of diabetes in the Chinese population: a population-based cohort study.

Objective: Both alanine aminotransferase (ALT) and high-density lipoprotein cholesterol (HDL-C) are closely related to glucose homeostasis in the body, and the main objective of this study was to investigate the association between ALT to HDL-C ratio (ALT/HDL-C ratio) and the risk of diabetes in a Chinese population. Methods: The current study included 116,251 participants who underwent a healthy physical examination, and the study endpoint was defined as a diagnosis of new-onset diabetes. Multivariate Cox regression models and receiver operator characteristic curves were used to assess the association of the ALT/HDL-C ratio with diabetes onset. Results: During the average observation period of 3.10 years, a total of 2,674 (2.3%) participants were diagnosed with new-onset diabetes, including 1,883 (1.62%) males and 791 (0.68%) females. After fully adjusting for confounding factors, we found a significant positive association between the ALT/HDL-C ratio and the risk of diabetes [Hazard ratios 1.06, 95% confidence intervals: 1.05, 1.06], and this association was significantly higher in males, obese individuals [body mass index ≥ 28 kg/m2] and individuals aged < 60 years (All P interaction < 0.05). In addition, the ALT/HDL-C ratio was significantly better than its components ALT and HDL-C in predicting diabetes in the Chinese population. Conclusion: There was a positive relationship between ALT/HDL-C ratio and diabetes risk in the Chinese population, and this relationship was significantly stronger in males, obese individuals, and individuals younger than 60 years old.

The newly proposed Metabolic Score for Visceral Fat is a reliable tool for identifying non-alcoholic fatty liver disease, requiring attention to age-specific effects in both sexes.

Objective: The newly proposed Metabolic Visceral Fat Score (METS-VF) is considered a more effective measure for visceral adipose tissue (VAT) than other obesity indicators. This study aimed to reveal the association between METS-VF and non-alcoholic fatty liver disease (NAFLD), and its variations across age groups within both sexes. Methods: Data from 14,251 medical examiners in the NAGALA project were employed in this study. 3D fitted surface plots were constructed based on multivariate logistic regression models to visualize the isolated and combined effects of aging and METS-VF on NAFLD. Receiver operating characteristic curve (ROC) analysis was conducted to compare the diagnostic performance of METS-VF with other VAT surrogate markers in predicting NAFLD. Results: The results of multivariate logistic regression analysis showed that each unit increase in METS-VF was independently associated with a 333% and 312% increase in the odds of NAFLD in males and females, respectively. Additionally, the 3D fitted surface plot showed that age significantly influenced the association between METS-VF and the odds of NAFLD in both sexes, as follows: (i) In males, when METS-VF was less than 6.2, the METS-VF-related odds of NAFLD increased gradually with age in the 20-45 age group, reached a plateau in the 45-65 age group, and then decreased in the group above 65 years old; however, when male METS-VF exceeded 6.2, aging and METS-VF combined to further increase the odds of NAFLD in all age groups, particularly in the 45-65 age group. (ii) In females, aging seemed to reduce METS-VF-related odds of NAFLD in the 18-40 age group, but significantly increased it in the 40-60 age group, particularly for those with higher METS-VF levels. Further ROC analysis revealed that compared to other VAT surrogate markers, METS-VF showed the highest diagnostic accuracy for NAFLD in females, especially in those under 45 years of age [area under the curve (AUC) = 0.9256]. Conclusions: This study firstly revealed a significant positive correlation between METS-VF and the odds of NAFLD, with METS-VF surpassing other VAT surrogate markers in NAFLD diagnosis. Moreover, age significantly influenced the METS-VF-related odds of NAFLD and METS-VF's diagnostic efficacy for NAFLD in both sexes.

Gender perspective on the association between liver enzyme markers and non-alcoholic fatty liver disease: insights from the general population.

Objective: Every distinct liver enzyme biomarker exhibits a strong correlation with non-alcoholic fatty liver disease (NAFLD). This study aims to comprehensively analyze and compare the associations of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) with NAFLD from a gender perspective. Methods: This study was conducted on 6,840 females and 7,411 males from the NAGALA cohort. Multivariable logistic regression analysis was used to compare the associations between liver enzyme markers and NAFLD in both genders, recording the corresponding adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of individual liver enzyme markers and different combinations of them in identifying NAFLD. Results: Liver enzyme markers ALT, AST, and GGT were all independently associated with NAFLD and exhibited significant gender differences (All P-interaction<0.05). In both genders, ALT exhibited the most significant association with NAFLD, with adjusted standardized ORs of 2.19 (95% CI: 2.01-2.39) in males and 1.60 (95% CI: 1.35-1.89) in females. Additionally, ROC analysis showed that ALT had significantly higher accuracy in identifying NAFLD than AST and GGT in both genders (Delong P-value < 0.05), and the accuracy of ALT in identifying NAFLD in males was higher than that in females [Area under the ROC curve (AUC): male 0.79, female 0.77]. Furthermore, out of the various combinations of liver enzymes, ALT+GGT showed the highest accuracy in identifying NAFLD in both genders, with AUCs of 0.77 (95% CI: 0.75-0.79) in females and 0.79 (95% CI: 0.78-0.81) in males. Conclusion: Our study revealed significant gender differences in the associations of the three commonly used liver enzyme markers with NAFLD. In both genders, the use of ALT alone may be the simplest and most effective tool for screening NAFLD, especially in males.

The predictive significance of lipid accumulation products for future diabetes in a non-diabetic population from a gender perspective: an analysis using time-dependent receiver operating characteristics.

Objective: The increasing prevalence of diabetes is strongly associated with visceral adipose tissue (VAT), and gender differences in VAT remarkably affect the risk of developing diabetes. This study aimed to assess the predictive significance of lipid accumulation products (LAP) for the future onset of diabetes from a gender perspective. Methods: A total of 8,430 male and 7,034 female non-diabetic participants in the NAGALA (NAfld in the Gifu Area, Longitudinal Analysis) program were included. The ability of LAP to assess the risk of future new-onset diabetes in both genders was analyzed using multivariate Cox regression. Subgroup analysis was conducted to explore the impact of potential modifiers on the association between LAP and diabetes. Additionally, time-dependent receiver operator characteristics (ROC) curves were used to assess the predictive power of LAP in both genders for new-onset diabetes over the next 2-12 years. Results: Over an average follow-up of 6.13 years (maximum 13.14 years), 373 participants developed diabetes. Multivariate Cox regression analysis showed a significant gender difference in the association between LAP and future diabetes risk (P-interaction<0.05): the risk of diabetes associated with LAP was greater in females than males [hazard ratios (HRs) per standard deviation (SD) increase: male 1.20 (1.10, 1.30) vs female 1.35 (1.11, 1.64)]. Subgroup analysis revealed no significant modifying effect of factors such as age, body mass index (BMI), smoking history, drinking history, exercise habits, and fatty liver on the risk of diabetes associated with LAP (All P-interaction <0.05). Time-dependent ROC analysis showed that LAP had greater accuracy in predicting diabetes events occurring within the next 2-12 years in females than males with more consistent predictive thresholds in females. Conclusions: This study highlighted a significant gender difference in the association between LAP and future diabetes risk. The risk of diabetes associated with LAP was greater in females than in males. Furthermore, LAP showed superior predictive ability for diabetes at different time points in the future in females and had more consistent and stable predictive thresholds in females, particularly in the medium and long term.

The newly proposed alanine aminotransferase to high-density lipoprotein cholesterol ratio has shown effectiveness in identifying non-alcoholic fatty liver disease.

Objective: Alanine aminotransferase (ALT) and high-density lipoprotein cholesterol (HDL-C) are important predictive factors for non-alcoholic fatty liver disease (NAFLD). The aim of this study was to analyze the association between the ALT/HDL-C ratio and NAFLD. Methods: We conducted a retrospective analysis of data from 14,251 individuals participating in the NAGALA project's health screening program. The presence of NAFLD was diagnosed based on the participants' alcohol consumption status and liver ultrasonography images. Multivariable logistic regression models were used to assess the association between the ALT/HDL-C ratio and NAFLD. Receiver operating characteristic (ROC) analysis was performed to determine and compare the effectiveness of ALT, HDL-C, the aspartate aminotransferase to HDL-C (AST/HDL-C) ratio, the gamma-glutamyl transferase to HDL-C (GGT/HDL-C) ratio and the ALT/HDL-C ratio in identifying NAFLD. Results: We observed a significant positive association between the ALT/HDL-C ratio and the prevalence of NAFLD. For each standard deviation (SD) increase in the ALT/HDL-C ratio, the adjusted odds ratio (OR) for NAFLD among the participants was 3.05 [95% confidence interval (CI): 2.63, 3.53], with the highest quartile of ALT/HDL-C ratio having a 9.96-fold increased risk compared to the lowest quartile. In further subgroup analyses stratified by gender, age, and waist circumference (WC), we observed a significantly higher risk of NAFLD associated with the ALT/HDL-C ratio among individuals aged ≥45 years, males, and those who were abdominal obesity. Furthermore, based on the results of ROC analysis, we found that the ALT/HDL-C ratio [area under the curves (AUC): 0.8553] was significantly superior to ALT, HDL-C, AST/HDL-C ratio and GGT/HDL-C ratio in identifying NAFLD (All Delong P<0.05); the threshold of suggested ALT/HDL-C ratio for identifying NAFLD was 15.97. Conclusion: This population-based study demonstrates a positive association between the ALT/HDL-C ratio and NAFLD. The ALT/HDL-C ratio can effectively identify individuals with NAFLD.

Assessing the usefulness of a newly proposed metabolic score for visceral fat in predicting future diabetes: results from the NAGALA cohort study.

Objective: Visceral adipose tissue assessment holds significant importance in diabetes prevention. This study aimed to explore the association between the newly proposed Metabolic Score for Visceral Fat (METS-VF) and diabetes risk and to further assess the predictive power of the baseline METS-VF for the occurrence of diabetes in different future periods. Methods: This longitudinal cohort study included 15,464 subjects who underwent health screenings. The METS-VF, calculated using the formula developed by Bello-Chavolla et al., served as a surrogate marker for visceral fat obesity. The primary outcome of interest was the occurrence of diabetes during the follow-up period. Established multivariate Cox regression models and restricted cubic spline (RCS) regression models to assess the association between METS-VF and diabetes risk and its shape. Receiver operating characteristic (ROC) curves were used to compare the predictive power of METS-VF with body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), and visceral adiposity index (VAI) for diabetes, and time-dependent ROC analysis was conducted to assess the predictive capability of METS-VF for the occurrence of diabetes in various future periods. Results: During a maximum follow-up period of 13 years, with a mean of 6.13 years, we observed that the cumulative risk of developing diabetes increased with increasing METS-VF quintiles. Multivariable-adjusted Cox regression analysis showed that each unit increase in METS-VF would increase the risk of diabetes by 68% (HR 1.68, 95% CI 1.13, 2.50), and further RCS regression analysis revealed a possible non-linear association between METS-VF and diabetes risk (P for non-linearity=0.002). In addition, after comparison by ROC analysis, we found that METS-VF had significantly higher predictive power for diabetes than other general/visceral adiposity indicators, and in time-dependent ROC analysis, we further considered the time-dependence of diabetes status and METS-VF and found that METS-VF had the highest predictive value for predicting medium- and long-term (6-10 years) diabetes risk. Conclusion: METS-VF, a novel indicator for assessing visceral adiposity, showed a significantly positive correlation with diabetes risk. It proved to be a superior risk marker in predicting the future onset of diabetes compared to other general/visceral adiposity indicators, particularly in forecasting medium- and long-term diabetes risk.

Single-cell RNA sequencing of neural stem cells derived from human trisomic iPSCs reveals the abnormalities during neural differentiation of Down syndrome.

Introduction: Down syndrome (DS) is the most common genetic condition that causes intellectual disability in humans. The molecular mechanisms behind the DS phenotype remain unclear. Therefore, in this study, we present new findings on its molecular mechanisms through single-cell RNA sequencing. Methods: Induced pluripotent stem cells (iPSCs) from the patients with DS and the normal control (NC) patients were differentiated into iPSCs-derived neural stem cells (NSCs). Single-cell RNA sequencing was performed to achieve a comprehensive single-cell level differentiation roadmap for DS-iPSCs. Biological experiments were also performed to validate the findings. Results and Discussion: The results demonstrated that iPSCs can differentiate into NSCs in both DS and NC samples. Furthermore, 19,422 cells were obtained from iPSC samples (8,500 cells for DS and 10,922 cells for the NC) and 16,506 cells from NSC samples (7,182 cells for DS and 9,324 cells for the NC), which had differentiated from the iPSCs. A cluster of DS-iPSCs, named DS-iPSCs-not differentiated (DSi-PSCs-ND), which had abnormal expression patterns compared with NC-iPSCs, were demonstrated to be unable to differentiate into DS-NSCs. Further analysis of the differentially expressed genes revealed that inhibitor of differentiation family (ID family) members, which exhibited abnormal expression patterns throughout the differentiation process from DS-iPSCs to DS-NSCs, may potentially have contributed to the neural differentiation of DS-iPSCs. Moreover, abnormal differentiation fate was observed in DS-NSCs, which resulted in the increased differentiation of glial cells, such as astrocytes, but decreased differentiation into neuronal cells. Furthermore, functional analysis demonstrated that DS-NSCs and DS-NPCs had disorders in axon and visual system development. The present study provided a new insight into the pathogenesis of DS.

Atomic Layer Deposition of Tantalum Oxide Films on 3D-Printed Ti6Al4V Scaffolds with Enhanced Osteogenic Property for Orthopedic Implants.

There is an evident advantage in personalized customization of orthopedic implants by 3D-printed titanium (Ti) and its alloys. However, 3D-printed Ti alloys have a rough surface structure caused by adhesion powders and a relatively bioinert surface. Therefore, surface modification techniques are needed to improve the biocompatibility of 3D-printed Ti alloy implants. In the present study, porous Ti6Al4V scaffolds were manufactured by a selective laser melting 3D printer, followed by sandblasting and acid-etching treatment and atomic layer deposition (ALD) of tantalum oxide films. SEM morphology and surface roughness tests confirmed that the unmelted powders adhered on the scaffolds were removed by sandblasting and acid-etching. Accordingly, the porosity of the scaffold increased by about 7%. Benefiting from the self-limitation and three-dimensional conformance of ALD, uniform tantalum oxide films were formed on the inner and outer surfaces of the scaffolds. Zeta potential decreased by 19.5 mV after depositing tantalum oxide films. The in vitro results showed that the adhesion, proliferation, and osteogenic differentiation of rat bone marrow mesenchymal stem cells on modified Ti6Al4V scaffolds were significantly enhanced, which may be ascribed to surface structure optimization and the compatibility of tantalum oxide. This study provides a strategy to improve the cytocompatibility and osteogenic differentiation of porous Ti6Al4V scaffolds for orthopedic implants.

An injectable hydrogel combining medicine and matrix with anti-inflammatory and pro-angiogenic properties for potential treatment of myocardial infarction.

One of the main illnesses that put people's health in jeopardy is myocardial infarction (MI). After MI, damaged or dead cells set off an initial inflammatory response that thins the ventricle wall and degrades the extracellular matrix. At the same time, the ischemia and hypoxic conditions resulting from MI lead to significant capillary obstruction and rupture, impairing cardiac function and reducing blood flow to the heart. Therefore, attenuating the initial inflammatory response and promoting angiogenesis are very important for the treatment of MI. Here, to reduce inflammation and promote angiogenesis in infarcted area, we report a new kind of injectable hydrogel composed of puerarin and chitosan via in situ self-assembly with simultaneous delivery of mesoporous silica nanoparticles (CHP@Si) for myocardial repair. On the one hand, puerarin degraded from CHP@Si hydrogel modulated the inflammatory response via inhibiting M1-type polarization of macrophages and expression of pro-inflammatory factors. On the other hand, silica ions and puerarin released from CHP@Si hydrogel showed synergistic activity to improve the cell viability, migration and angiogenic gene expression of HUVECs in both conventional and oxygen/glucose-deprived environments. It suggests that this multifunctional injectable CHP@Si hydrogel with good biocompatibility may be an appropriate candidate as a bioactive material for myocardial repair post-MI.

Automated detection of hippocampal sclerosis using real-world clinical MRI images.

Background: Hippocampal sclerosis (HS) is the most common pathological type of temporal lobe epilepsy (TLE) and one of the important surgical markers. Currently, HS is mainly diagnosed manually by radiologists based on visual inspection of MRI, which greatly relies on MRI quality and physician experience. In clinical practice, non-thin MRI scans are often used due to the time and efficiency needed for the acquisition. However, these scans can be difficult for junior physicians to interpret accurately. Thus, the rapid and accurate diagnosis of HS using real-world MRI images in clinical settings is a challenging task. Objective: Our aim was to explore the feasibility of using computer vision methods to diagnose HS on real-world clinical MRI images and to provide a reference for future clinical applications of artificial intelligence methods to aid in detecting HS. Methods: We proposed a deep learning algorithm called "HS-Net" to discriminate HS using real-world clinical MRI images. First, we delineated and segmented a region of interest (ROI) around the hippocampus. Then, we utilized the fractional differential (FD) method to enhance the textures of the ROIs. Finally, we used a small-sample image classification method based on transfer learning to fine-tune the feature extraction part of a pretrained model and added two fully connected layers and an output layer. In the study, 96 TLE patients with HS confirmed by postoperative pathology and 89 healthy controls were retrospectively enrolled. All subjects were cross-validated, and models were evaluated for performance, robustness, and clinical utility. Results: The HS-Net model achieved an area under the curve (AUC) of 0.894, an accuracy of 82.88%, an F1-score of 84.08% in the test cohort based on real, routine, clinical T2-weighted fluid attenuated inversion recovery (FLAIR) sequence MRI images. Additionally, the AUC, accuracy and F1 scores of our model all increased by around 3 percentage points when the inputs were augmented with the ROIs of the textures enhanced using the FD method. Conclusions: Our computational model has the potential to be used for the diagnosis of HS in real clinical MRI images, which could assist physicians, particularly junior physicians, in improving the accuracy of discrimination.