CorNet: Autonomous feature learning in raw Corvis ST data for keratoconus diagnosis via residual CNN approach.

PURPOSE: To ascertain whether the integration of raw Corvis ST data with an end-to-end CNN can enhance the diagnosis of keratoconus (KC). METHOD: The Corvis ST is a non-contact device for in vivo measurement of corneal biomechanics. The CorNet was trained and validated on a dataset consisting of 1786 Corvis ST raw data from 1112 normal eyes and 674 KC eyes. Each raw data consists of the anterior and posterior corneal surface elevation during air-puff induced dynamic deformation. The architecture of CorNet utilizes four ResNet-inspired convolutional structures that employ 1 × 1 convolution in identity mapping. Gradient-weighted Class Activation Mapping (Grad-CAM) was adopted to visualize the attention allocation to diagnostic areas. Discriminative performance was assessed using metrics including the AUC of ROC curve, sensitivity, specificity, precision, accuracy, and F1 score. RESULTS: CorNet demonstrated outstanding performance in distinguishing KC from normal eyes, achieving an AUC of 0.971 (sensitivity: 92.49%, specificity: 91.54%) in the validation set, outperforming the best existing Corvis ST parameters, namely the Corvis Biomechanical Index (CBI) with an AUC of 0.947, and its updated version for Chinese populations (cCBI) with an AUC of 0.963. Though the ROC curve analysis showed no significant difference between CorNet and cCBI (p = 0.295), it indicated a notable difference between CorNet and CBI (p = 0.011). The Grad-CAM visualizations highlighted the significance of corneal deformation data during the loading phase rather than the unloading phase for KC diagnosis. CONCLUSION: This study proposed an end-to-end CNN approach utilizing raw biomechanical data by Corvis ST for KC detection, showing effectiveness comparable to or surpassing existing parameters provided by Corvis ST. The CorNet, autonomously learning comprehensive temporal and spatial features, demonstrated a promising performance for advancing KC diagnosis in ophthalmology.

Liriogerphines E-U, further unique sesquiterpene-alkaloid hybrids from the rare Chinese tulip tree.

Seventeen undescribed sesquiterpene-alkaloid hybrids (liriogerphines E-U, 1-17) were isolated and identified during a further phytochemical investigation on the branches and leaves of Chinese tulip tree (Liriodendron chinense), a rare medicinal and ornamental plant endemic to China. These unique heterodimers are conjugates of germacranolide-type sesquiterpenoids with structurally diverse alkaloids [i.e., aporphine- (1-15), proaporphine- (16), and benzyltetrahydroisoquinoline-type (17)] via the formation of a C-N bond. The previously undescribed structures were elucidated by comprehensive spectroscopic data analyses and electronic circular dichroism calculations. Such a class of sesquiterpene-alkaloid hybrids presumably biosynthesized via an aza-Michael addition is quite rare from terrestrial plants. In particular, the sesquiterpene-benzyltetrahydroisoquinoline hybrid skeleton has never been reported until the present study. All the isolates were evaluated for their cytotoxic effects against a small panel of leukemia cell lines (Raji, Jeko-1, Daudi, Jurkat, MV-4-11 and HL-60), and some of them exhibited considerable activities.

Predicting very early recurrence in intrahepatic cholangiocarcinoma after curative hepatectomy using machine learning radiomics based on CECT: A multi-institutional study.

BACKGROUND: Even after curative resection, the prognosis for patients with intrahepatic cholangiocarcinoma (iCCA) remains disappointing due to the extremely high incidence of postoperative recurrence. METHODS: A total of 280 iCCA patients following curative hepatectomy from three independent institutions were recruited to establish the retrospective multicenter cohort study. The very early recurrence (VER) of iCCA was defined as the appearance of recurrence within 6 months. The 3D tumor region of interest (ROI) derived from contrast-enhanced CT (CECT) was used for radiomics analysis. The independent clinical predictors for VER were histological stage, AJCC stage, and CA199 levels. We implemented K-means clustering algorithm to investigate novel radiomics-based subtypes of iCCA. Six types of machine learning (ML) algorithms were performed for VER prediction, including logistic, random forest (RF), neural network, bayes, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Additionally, six clinical ML (CML) models and six radiomics-clinical ML (RCML) models were developed to predict VER. Predictive performance was internally validated by 10-fold cross-validation in the training cohort, and further evaluated in the external validation cohort. RESULTS: Approximately 30 % of patients with iCCA experienced VER with extremely discouraging outcome (Hazard ratio (HR) = 5.77, 95 % Confidence Interval (CI) = 3.73-8.93, P < 0.001). Two distinct iCCA subtypes based on radiomics features were identified, and subtype 2 harbored a higher proportion of VER (47.62 % Vs 25.53 %) and significant shorter survival time than subtype 1. The average AUC values of the CML and RCML models were 0.744 ± 0.018, and 0.900 ± 0.014 in the training cohort, and 0.769 ± 0.065 and 0.929 ± 0.027 in the external validation cohort, respectively. CONCLUSION: Two radiomics-based iCCA subtypes were identified, and six RCML models were developed to predict VER of iCCA, which can be used as valid tools to guide individualized management in clinical practice.

The associations of gestational weight gain and midpregnancy lipid levels with placental size and placental-to-birth weight ratio: findings from a chinese birth cohort study.

BACKGROUND: The placenta serves as the sole maternal organ responsible for transmitting nutrients to the fetus, playing a crucial role in supporting standard fetal growth and development. To date, only a small number of studies have investigated the impact of maternal gestational weight gain and lipid concentrations on placental development. This study aimed to explore the influence of weight gain during pregnancy and lipid levels in the second trimester on placental weight, volume, and the placental weight ratio. METHODS: This birth cohort study encompassed 1,358 mother-child pairs. Placental data for each participant was gathered immediately post-delivery, and the study incorporated data on gestational weight gain throughout pregnancy and lipid profiles from the mid-trimester. A linear regression model was employed to assess the correlations between gestational weight gain, mid-trimester lipid levels, and metrics such as placental weight, placental volume, and the placental-to-birth weight ratio (PFR). RESULTS: In the study groups of pre-pregnancy underweight, normal weight, and overweight, the placental weight increased by 4.93 g (95% CI: 1.04-8.81), 2.52 g (95% CI: 1.04-3.99), and 3.30 g (95% CI: 0.38-6.22) per 1 kg of gestational weight gain, respectively. Within the pre-pregnancy underweight and normal weight groups, the placental volume increased by 6.79 cm^3 (95% CI: 3.43-10.15) and 2.85 cm^3 (95% CI: 1.31-4.39) per 1 kg of gestational weight gain, respectively. Additionally, placental weight exhibited a positive correlation with triglyceride (TG) levels (ß = 9.81, 95% CI: 3.28-16.34) and a negative correlation with high-density lipoprotein (HDL-C) levels (ß = - 46.30, 95% CI: - 69.49 to - 23.11). Placental volume also showed a positive association with TG levels (ß = 14.54, 95% CI: 7.69-21.39). Conversely, PFR demonstrated a negative correlation with increasing HDL-C levels (ß = - 0.89, 95% CI: - 1.50 to - 0.27). CONCLUSIONS: Gestational weight gain was significantly correlated with both placental weight and volume. This association was especially pronounced in women who, prior to pregnancy, were underweight or of normal weight. Additionally, TG and HDL-C levels during the mid-trimester were linked to placental development.

Identification of m5C-related molecular subtypes and prediction models in the prognosis and tumor microenvironment infiltration of soft tissue sarcoma.

Background: The epigenetic regulator in cancer progression and immune response has been demonstrated recently. However, the potential implications of 5-methylcytosine (m5C) in soft tissue sarcoma (STS) are unclear. Methods: The RNA sequence profile of 911 normal and 259 primary STS tissues were obtained from GTEx and TCGA databases, respectively. We systematically analyzed the m5C modification patterns of STS samples based on 11 m5C regulators, and comprehensively correlated these modification patterns with clinical characteristics, prognosis, and tumor microenvironment (TME) cell-infiltrating. Furthermore, an m5C-related signature was generated using Cox proportional hazard model and validated by the GSE17118 cohort. Results: Two distinct m5C modification patterns (cluster1/2) were discovered. The cluster1 had favorable overall survival, higher immune score, higher expression of most immune checkpoints, and active immune cell infiltration. The GSVA analysis of the P53 pathway, Wnt signaling pathway, G2M checkpoint, mTORC1 signaling, Wnt/ß catenin signaling, and PI3K/AKT/mTOR signaling were significantly enriched in the cluster2. Moreover, 1220 genes were differentially expressed between two clusters, and a m5C prognostic signature was constructed with five m5C-related genes. The signature represented an independent prognostic factor and showed the favorable performance in the GSE17118 cohort. Patients in the low-risk group showed higher immunoscore and higher expression of most immune checkpoints. Further GSVA analysis indicated that the levels of P53 pathway, Wnt signaling pathway, and TGF-ß signaling pathway were different between low- and high-risk groups. Moreover, a nomogram incorporating m5C signature and clinical variables was established and showed well performance. Conclusion: This work showed that the m5C modification plays a significant role in the progression of STS and the formation of TME diversity. Evaluating the m5C modification pattern of tumor will enhance our cognition of TME infiltration characterization to guide more effective immunotherapy strategies.

Joint effects of prenatal exposure to air pollution and pregnancy-related anxiety on birth weight: A prospective birth cohort study in Ma'anshan, China.

BACKGROUND: A growing number of studies have shown that prenatal exposure to chemical and non-chemical stressors has effects on fetal growth. The co-exposure of both better reflects real-life exposure patterns. However, no studies have included air pollutants and pregnancy-related anxiety (PrA) as mixtures in the analysis. METHOD: Using the birth cohort study method, 576 mother-child pairs were included in the Ma'anshan Maternal and Child Health Hospital. Evaluate the exposure levels of six air pollutants during pregnancy using inverse distance weighting (IDW) based on the pregnant woman's residential address and air pollution data from monitoring stations. Prenatal anxiety levels were assessed using the PrA Questionnaire. Generalized linear regression (GLR), quantile g-computation (QgC) and bayesian kernel machine regression (BKMR) were used to assess the independent or combined effects of air pollutants and PrA on birth weight for gestational age z-score (BWz). RESULT: The results of GLR indicate that the correlation between the six air pollutants and PrA with BWz varies depending on the different stages of pregnancy and pollutants. The QgC shows that during trimester 1, when air pollutants and PrA are considered as a whole exposure, an increase of one quartile is significantly negatively correlated with BWz. The BKMR similarly indicates that during trimester 1, the combined exposure of air pollutants and PrA is moderately correlated with a decrease in BWz. CONCLUSION: Using the method of analyzing mixed exposures, we found that during pregnancy, the combined exposure of air pollutants and PrA, particularly during trimester 1, is associated with BWz decrease. This supports the view that prenatal exposure to chemical and non-chemical stressors has an impact on fetal growth.

The effects of gestational diabetes mellitus on fetal growth: is it different for low-risk and medium-high-risk pregnant women?

BACKGROUND: It has been suggested that gestational diabetes mellitus (GDM) alters the growth trajectory of a fetus and increases the risk of abnormal birth weight. In spite of this, there is still a significant debate regarding the mode and optimal timing of diagnosing this condition. Our aim was to determine fetal growth velocity and birth biometry in pregnant women with GDM at varying risk levels. METHODS: We conducted a cohort study involving 1023 pregnant women at a maternity hospital in Ma'anshan, China. All women completed an oral glucose tolerance test at 24-28 weeks' gestation. We measured fetal head circumference (HC), femoral length (FL), abdominal circumference (AC), biparietal diameter (BPD), and estimate fetal weight (EFW) by ultrasound at 17, 24, 31, and 35 weeks' gestation, respectively. RESULTS: Overall, 5115 ultrasound scans were performed. Among both low-risk and medium-high-risk pregnant women at 17-24 weeks' gestation, GDM exposure was associated with an increase in fetal growth velocity. Neonates born to women with GDM at medium-high risk had significantly larger birth weights than those born to women without GDM, while this was not observed in women at low risk. CONCLUSION: In medium-high-risk pregnant women, exposure to GDM has a greater effect on the fetus, leading to abnormal fetal growth velocity that lasts beyond week 24. It is evident from our results that the effects of GDM on fetal growth differ between medium-high-risk pregnant women and low-risk pregnant women, and therefore a different screening program based on the risk factor for GDM is warranted.

Association of urinary heavy metal combined exposure with periodontitis among US adults from NHANES 2011-2014.

Some heavy metals are associated with periodontitis; whereas most of these associations have focused on individual metal, there are no specific studies on the effects of combined heavy metal exposure on periodontitis. We conducted an analysis on the association between urinary heavy metal exposure and periodontitis in participants aged 30 years and older using multiple logistic regression and Bayesian kernel machine regression (BKMR). This analysis was performed on data from the National Health and Nutrition Examination Survey from 2011 to 2014. The study found that using logistic regression, the 4th quartile of urinary lead and molybdenum and the 3rd quartile of urinary strontium were positively associated with periodontitis compared to the reference quartile after adjusting for covariates. Odds ratio (OR) with 95% confidence interval (CI) was 1.738 (1.069-2.826), 1.515 (1.025-2.239), and 1.498 (1.010-2.222), respectively. The 3rd and 4th quartiles of urinary cobalt were negatively associated with periodontitis, and their ORs and 95% CIs were 0.639 (0.438-0.934) and 0.571 (0.377-0.964), respectively. The BKMR model showed that urinary barium, lead, and molybdenum were positively associated with periodontitis in a range of concentrations and urinary cobalt, manganese, tin, and strontium were negatively correlated with periodontitis. Furthermore, the overall association between urinary heavy metals and periodontitis was positive. Our study provides evidence for an association between exposure to multiple urinary heavy metals and periodontitis. However, further longitudinal studies are needed to explore the specific mechanisms involved.

The impact of household fuel usage on adverse pregnancy outcomes in rural Ma'anshan City, Anhui Province: a birth cohort study.

The combustion of cooking fuels generates detrimental gases significantly impacting human health, particularly for vulnerable populations like expectant mothers. Prenatal exposure of such hazardous emissions raises the probability of adverse pregnancy outcomes, including preterm birth (PTB) and low birth weight (LBW). Our research aims to explore the association between cooking fuel utilization and adverse birth outcomes in rural Ma'anshan, Anhui Province. A prospective cohort study was executed, employing the Maternal and Infant Health Assessment questionnaire to classify fuels into clean (natural gas, electricity) and polluting energy sources (coal, coal gas, firewood). Multivariate logistic regression models were conducted to evaluate the association between fuel consumption and postpartum maternal and infant outcomes. Among the 442 surveyed pregnant women, 38.2% (N=169) utilized polluting fuels. After adjusting for covariates such as age and BMI, the relative risks of preterm birth, low birth weight, and postpartum hemorrhage in the polluting fuel group compared to the clean fuel group were OR: 3.27, 95% CI: 1.34, 8.00; OR: 3.50, 95% CI: 1.12, 10.90; and OR: 3.18, 95% CI: 1.06, 9.46, respectively. These results indicate that the usage of polluting fuels during pregnancy may heighten the risk of adverse birth outcomes. Consequently, additional research is advised to mitigate the harmful emissions generated by cooking fuels and advocate for clean energy adoption, enhancing maternal and infant well-being.

Effect of gaseous pollutant and greenness exposure on mortality during treatment of newly treated tuberculosis patients: a provincial population-based cohort study.

BACKGROUND: Previous studies addressing the impact of environmental factors on TB prognosis are scarce, with only some studies examining the effect of particulate pollutants on TB mortality. Moreover, few studies have evaluated the effects of multiple gaseous pollutants and greenness exposures on newly treated TB patients on a large population scale. METHODS: Through the Centers for Disease Control and Prevention, data were collected from January 1, 2015 to December 31, 2020 for newly treated TB patients in Anhui Province, China. Data on gaseous pollutants sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone were collected through the National Earth System Science Data Center of China. Normalized vegetation index data were obtained through NASA. The Cox proportional risk model was also applied to calculate the hazard ratios of SO2, NO2, CO, O3, and NDVI with 95% confidence intervals for mortality among newly treated TB patients. RESULTS: Multifactorial Cox regression analysis showed that for every 0.10 µg/m3 increase in SO2, the risk of death among newly treated TB patients increased by 13.2% (HR = 1.132, 95% CI: 1.045-1.1.225), for every 10 µg/m3 increase in NO2, the risk of death among newly treated TB patients increased by 11.4%, and for each 0.1 mg/m3 increase in CO, the risk of death among newly treated TB patients increased by 5.8%. For each 0.1 increase in NDVI 250m-buffer and 500m-buffer, the risk of death among newly treated TB patients decreased by 8.5% and 6.4%, respectively. The effect of gaseous pollutants on mortality decreased progressively with elevated greenness exposure when greenness exposure was grouped from low to high. CONCLUSION: Gaseous pollutants are a risk factor during the treatment of newly treated TB patients and greenness exposure is a protective factor. Higher greenness exposure reduces the risk of death due to exposure to gaseous pollutants.

The modification of meteorological factors on the relationship between air pollution and periodontal diseases: an exploration based on different interaction strategies.

We aimed to characterize the association between air pollutants exposure and periodontal diseases outpatient visits and to explore the interactions between ambient air pollutants and meteorological factors. The outpatient visits data of several large stomatological and general hospitals in Hefei during 2015-2020 were collected to explore the relationship between daily air pollutants exposure and periodontal diseases by combining Poisson's generalized linear model (GLMs) and distributed lag nonlinear model (DLNMs). Subgroup analysis was performed to identify the vulnerability of different populations to air pollutants exposure. The interaction between air pollutants and meteorological factors was verified in both multiplicative and additive interaction models. An interquartile range (IQR) increased in nitrogen dioxide (NO2) concentration was associated with the greatest lag-specific relative risk (RR) of gingivitis at lag 3 days (RR = 1.087, 95% CI 1.008-1.173). Fine particulate matter (PM2.5) exposure also increased the risk of periodontitis at the day of exposure (RR = 1.049, 95% CI 1.004-1.096). Elderly patients with gingivitis and periodontitis were both vulnerable to PM2.5 exposure. The interaction analyses showed that exposure to high levels of NO2 at low temperatures was related to an increased risk of gingivitis, while exposure to high levels of NO2 and PM2.5 may also increase the risk of gingivitis and periodontitis in the high-humidity environment, respectively. This study supported that NO2 and PM2.5 exposure increased the risk of gingivitis and periodontitis outpatient visits, respectively. Besides, the adverse effects of air pollutants exposure on periodontal diseases may vary depending on ambient temperature and humidity.

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

The Role of Self-Esteem in the Relationship Between Psychological Capital and Anxiety of Left-Behind Experience College Students During COVID-19 Pandemic: An Online Study.

Background: There is growing evidence that the COVID-19 pandemic has had a dramatic impact on public mental health. However, less attention has been paid to left-behind experience college students (LBEs). This online study aimed to investigate the relationship between psychological capital (PsyCap) and anxiety among LBEs during COVID-19 pandemic, and further analyze the mediation role of self-esteem between them. Methods: A total of 9990 students were chosen using the stratified cluster sampling method. Three self-reported questionnaires were used to assess the PsyCap, self-esteem, and anxiety, respectively. All the statistical analyses were conducted using SPSS 23.0 and R, and to further investigate the mediation effect of self-esteem in the association of PsyCap with anxiety, AMOS 23.0 was used to build a structural equation model. Results: PsyCap, self-esteem, and anxiety were significantly correlated among LBEs during the COVID-19 pandemic. PsyCap affects anxiety directly (ß = -0.22, SE = 0.051, 95% CI: -0.27, -0.17, P < 0.05). In addition, self-esteem partially mediated the relationship between PsyCap and anxiety (mediating effect value = -0.16, 95% CI: -0.20, -0.13, P < 0.05). Conclusion: During the pandemic of COVID-19, left-behind experience had a negative influence on the PsyCap and self-esteem of college students. In addition, for LBEs, self-esteem plays an important mediating role between PsyCap and anxiety. Therefore, from the perspective of PsyCap and self-esteem, schools should translate them into practical educational strategies to enhance the mental health and mitigate the anxiety levels of LBEs.

Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study.

PURPOSE: Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection. METHODS: Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. RESULTS: 127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03). CONCLUSION: ML radiomics models based on CECT are valuable in predicting ER in ICC.

Prediction of Response to Lenvatinib Monotherapy for Unresectable Hepatocellular Carcinoma by Machine Learning Radiomics: A Multicenter Cohort Study.

PURPOSE: We aimed to construct machine learning (ML) radiomics models to predict response to lenvatinib monotherapy for unresectable hepatocellular carcinoma (HCC). EXPERIMENTAL DESIGN: Patients with HCC receiving lenvatinib monotherapy at three institutions were retrospectively identified and assigned to training and external validation cohorts. Tumor response after initiation of lenvatinib was evaluated. Radiomics features were extracted from contrast-enhanced CT images. The K-means clustering algorithm was used to distinguish radiomics-based subtypes. Ten ML radiomics models were constructed and internally validated by 10-fold cross-validation. These models were subsequently verified in an external validation cohort. RESULTS: A total of 109 patients were identified for analysis, namely, 74 in the training cohort and 35 in the external validation cohort. Thirty-two patients showed partial response, 33 showed stable disease, and 44 showed progressive disease. The overall response rate (ORR) was 29.4%, and the disease control rate was 59.6%. A total of 224 radiomics features were extracted, and 25 significant features were identified for further analysis. Two distant radiomics-based subtypes were identified by K-means clustering, and subtype 1 was associated with a higher ORR and longer progression-free survival (PFS). Among the 10 ML algorithms, AutoGluon displayed the highest predictive performance (AUC = 0.97), which was relatively stable in the validation cohort (AUC = 0.93). Kaplan-Meier analysis showed that responders had a better overall survival [HR = 0.21; 95% confidence interval (CI): 0.12-0.36; P < 0.001] and PFS (HR = 0.14; 95% CI: 0.09-0.22; P < 0.001) than nonresponders. CONCLUSIONS: Valuable ML radiomics models were constructed, with favorable performance in predicting the response to lenvatinib monotherapy for unresectable HCC.

S-Propargyl-Cysteine Ameliorates Peripheral Nerve Injury through Microvascular Reconstruction.

Microvascular reconstruction is essential for peripheral nerve repair. S-Propargyl-cysteine (SPRC), the endogenous hydrogen sulfide (H2S) donor, has been reported to promote angiogenesis. The aim of this study is to utilize the pro-angiogenic ability of SPRC to support peripheral nerve repair and to explore the potential mechanisms. The effects and mechanisms of SPRC on angiogenesis and peripheral nerve repair were examined under hypoxic condition by establishing a sciatic nerve crushed injury model in mice and rats, and a hypoxia model in human umbilical vascular endothelial cells (HUVECs) in vitro. We found that SPRC accelerated the function recovery of the injured sciatic nerve and alleviated atrophy of the gastrocnemius muscle in mice. It facilitated the viability of Schwann cells (SCs), the outgrowth and myelination of regenerated axons, and angiogenesis in rats. It enhanced the viability, proliferation, adhesion, migration, and tube formation of HUVECs under hypoxic condition. SPRC activated sirtuin1 (SIRT1) expression by promoting the production of endogenous H2S, and SIRT1 negatively regulated Notch signaling in endothelial cells (ECs), thereby promoting angiogenesis. Collectively, our study has provided important evidence that SPRC has an effective role in peripheral nerve repair through microvascular reconstruction, which could be a potentially effective medical therapy for peripheral nerve injury.

The association between early pregnancy exposure to green space and maternal glucolipid metabolism disorders: evaluation of the mediating role of serum 25-hydroxyvitamin D.

Green space and 25-hydroxyvitamin D (25(OH)D) can affect maternal and infant health, but limited studies have examined their effects on disorders of maternal glucolipid metabolism. We aimed to explore the interaction between green space, maternal serum 25(OH)D, and disorders of glucolipid metabolism in early pregnancy. A total of 2551 pregnant women were recruited from the Maanshan Maternal and Child Health Hospital birth cohort in China between 2020 and 2022. We calculated average residential greenness during early pregnancy using 250 m normalized difference vegetation index (NDVI) from satellites. Serum biomarkers (25(OH)D, total cholesterol (TC), triglycerides (TG), high-density lipoprotein-cholesterol(HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1(ApoA1), and apolipoprotein B(ApoB)) were measured. Associations between the factors were analyzed using multiple linear regression, mediation analysis, and stratified analysis. After adjusting for potential confounders, green space exposure associated with decreased TG (- 7.8%; 95% confidence interval (CI): - 12.8, - 2.9), TC (- 7.0%; 95% CI: - 11.4, - 2.7), and LDL-C (- 8.4%; 95% CI: - 12.9, - 3.9), ApoB (- 2.0%; 95% CI: - 3.0, - 1.0) and increased HDL-C (2.7%; 95% CI: 1.5, 3.8) and ApoA1 (5.1%; 95% CI: 3.9, 6.3) for each IQR increase in NDVI. A comparable link was found between maternal serum 25(OH)D and indicators of glucolipid metabolism (P < 0.05). In addition, mediation analysis showed that the association between green space exposure and maternal glucolipid metabolic index was mediated by serum 25(OH)D at 6.37%. In stratified analyses, a considerable association between 25(OH)D and glucolipid metabolic index (except TG) was observed only at higher green space exposures. This study confirms that high levels of green space exposure in early pregnancy and vitamin D are associated with a reduced risk of glucolipid metabolism disorders and suggests that green space may favor glucolipid metabolism by increasing vitamin D levels, particularly at high NDVI values.

S-Propargyl-cysteine prevents concanavalin A-induced immunological liver injury in mice.

CONTEXT: S-Propargyl-cysteine (SPRC), an endogenous H2S modulator, exerts anti-inflammatory effects on cardiovascular and neurodegenerative disease, but it remains unknown whether SPRC can prevent autoimmune hepatitis. OBJECTIVE: To evaluate the preventive effect of SPRC on concanavalin A (Con A)-induced liver injury and uncover the underlying mechanisms. MATERIALS AND METHODS: Mice were randomly divided into five groups: control, Con A, SPRC (5 and 10 mg/kg injected intravenously once a day for 7 days), and propargylglycine (PAG; 50 mg/kg injected intraperitoneally 0.5 h before SPRC for 7 days). All mice except the controls were intravenously injected with Con A (20 mg/kg) on day 7. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were evaluated using kits. Inflammatory cytokines (TNF-α and IFN-γ) in the blood and in the liver were detected by ELISA Kit and real-time PCR, respectively. The expression of mitogen-activated protein kinase (MAPK) pathway proteins (p-JNK and p-Akt) and apoptosis proteins (Bax and Bcl-2) was detected using western blotting. RESULTS: SPRC reduced the levels of AST (p < 0.05) and ALT (p < 0.01) and decreased the release of the inflammatory cytokines. Mechanistically, SPRC increased H2S level (p < 0.05) and promoted cystathionine γ-lyase (CSE) expression (p < 0.05). SPRC inhibited the MAPK pathway activation and the apoptosis pathway. All the effects of SPRC were blocked by the CSE inhibitor PAG. CONCLUSIONS: SPRC prevents Con A-induced liver injury in mice by promoting CSE expression and producing endogenous H2S. The mechanisms include reducing the release of inflammatory cytokines, attenuating MAPK pathway activation, and alleviating apoptosis.

Liriogerphines A-D, a Class of Sesquiterpene-Alkaloid Hybrids from the Rare Chinese Tulip Tree Plant.

Liriogerphines A-D (1-4, respectively), an unprecedented class of hybrids of germacranolide-type sesquiterpenoids and aporphine-type alkaloids, were isolated from the rare medicinal plant Liriodendron chinense. Their structures were elucidated by comprehensive spectroscopic analyses combined with electronic circular dichroism calculations and X-ray crystallographic data. Biosynthetically, an aza-Michael addition reaction is proposed to be involved in the assemblies of this class of hybrids. Compound 4 exhibited cytotoxicity against leukemia cells via inducing apoptosis and inhibiting Bcl-2 expression.

Short-term effect of sulfur dioxide (SO2) change on the risk of tuberculosis outpatient visits in 16 cities of Anhui Province, China: the first multi-city study to explore differences in occupational patients.

A growing number of biological studies suggest that exogenous sulfur dioxide (SO2) at a certain concentration may promote human resistance to Mycobacterium tuberculosis. However, the results of most relevant studies are inconsistent, and few studies have explored the relationship between SO2 exposure and tuberculosis risk at provincial level. In addition, occupational exposure has long been considered to have a certain impact on the human body, so for the first time, we discussed the differences between different occupations in the study on the relationship between air pollutant exposure and tuberculosis risk, and evaluated the impact of occupational exposure. This study aimed to explore the association between short-term SO2 exposure and the risk of outpatient visits to tuberculosis in Anhui province and 16 prefecture-level cities from 2015 to 2020. We used several models for multi-stage analysis, including distributed lag nonlinear model (DLNM), Poisson generalized linear regression model, and random-effects model. The association was assessed using the 28-day cumulative lag effect RR and 95%CI for each 10-unit increase in SO2 concentration. We divided all patients into the following six occupations: Worker, Farmer, Retired people, Children and Students, Cadre and Office clerk, and Service staff (catering, business, etc.). Sex, age, and season were analyzed by subgroup. Finally, the robustness of the multi-pollutant model was tested. At provincial level, the overall effect value of SO2 was RR=0.8191 (95%CI: 07702~0.8712); after grouping all patients by occupation, the association found only among Farmers (RR = 0.7150, 95%CI: 0.6699-0.7632, lag 0-28 days) and Workers (RR = 0.8566, 95%CI: 0.7930-0.9930, lag 0-4 days) was still statistically significant. Estimates for individual cities and using random-effects models to estimate average associations showed that SO2 exposure was associated with a reduced risk of outpatient TB visits in 14 municipalities, which remained significant when aggregated (RR = 0.9030, 95%CI: 0.8730-0.9340). Analysis of patients grouped by occupation in each municipality showed that statistical significance was again observed only in the Farmer (RR = 0.8880, 95%CI: 0.8610-0.9160) and Worker (RR = 0.8250, 95%CI: 0.7290-0.9340) groups. Stratified analysis of age, sex, and season showed that the effect of SO2 exposure was greater for middle-aged people (18-64 years old) and males, and less for seasonal changes. In summary, we found that exposure to SO2 reduces the risk of outpatient visits to tuberculosis, with farmers and workers more susceptible to SO2. Gender and age had a greater impact on the risk of TB outpatient visits than seasonal variations.