Objective:To investigate the relationship of several adiposity-related anthropometric parameters, including BMI, waist circumference (WC), waist-to-hip ratio (WHR), body fat percentage (BFP) and indoles in plasma with the incidence of atherosclerotic cardiovascular disease (ASCVD) in adults in China.Methods:In China Kadoorie Biobank (CKB) study, blood samples were collected from 2 183 participants in the first resurvey in 2008 to detect indoles. Participants' body weight, body height, WC, hip circumference, and BFP were measured at baseline survey in 2004 and resurvey in 2008, the BMI and WHR were calculated with standardized methods. The long-term follow-up of all participants started from the completion of the resurvey in 2008 until the occurrence of incident ASCVD, death, loss to follow-up or until December 31, 2018. CKB ascertained outcome status (incident ASCVD) through death and disease registries and national health insurance databases, supplemented by active follow-up. Multivariate linear regression model was used to estimate the associations of anthropometric measurements at baseline survey and the first resurvey, and changes in these measurements with 3 indoles [indole, indole-3-acetic acid (IAA), and indole-3-propionic acid (IPA)]. Cox proportional hazard regression model was used to estimate the associations between indoles and the risk for ASCVD.Results:Anthropometric measurements at baseline survey or the first resurvey were negatively associated with plasma IPA level. The regression coefficient ( β) of baseline BMI (per 1.0 kg/m 2) with 0.1 standard deviation ( SD) IPA was -0.23 (95% CI: -0.36 - -0.10) (false discovery rate=0.004). After adjusting for baseline BMI, the β of baseline WC, WHR and BFP with 0.1 SD IPA were -0.09 (95% CI: -0.18 - -0.01), -0.12 (95% CI: -0.19 - -0.05), and -0.20 (95% CI: -0.32 - -0.08), respectively. The annual change in BMI (difference between BMI in 2008 and 2004 divided by the time interval) was associated with indole and IAA, with β of 1.40 (95% CI: 0.58 - 2.21) and -1.07 (95% CI: -1.91 - -0.23), respectively, at each 0.1 increase of SD. Over a median ( Q1, Q3) follow-up of 10.46 (10.36, 10.53) years after 2008 resurvey, 236 cases of ASCVD were recorded. IAA and IPA levels were negatively associated with the risk for ASCVD, with hazard ratios for one SD increase of IAA and IPA of 0.87 (95% CI: 0.76 - 0.99) and 0.84 (95% CI: 0.73 - 0.96), respectively. Conclusions:Our results suggested that anthropometric measurements and their changing trends affect the levels of plasma imicrobial tryptophan metabolite levels, decreased levels of IAA and IPA levels are associated with increased risk of ASCVD and indoles in plasma including IPA and IAA might be the mediating factors for adiposity-induced ASCVD.

Objective:To analyze the prospective associations between liver biomarkers and mortality among Chinese middle-aged and elderly populations and to evaluate the mortality risk predictive value.Methods:A total of 22 758 participants from the 3 rd resurvey of the China Kadoorie Biobank were included. Cox proportional hazard models were used to analyze the prospective associations of 5 liver biomarkers with mortality. These liver biomarkers included two liver imaging biomarkers (liver fat attenuation parameter, liver stiffness measurement) and three serum liver enzyme biomarkers [gamma-glutamyl transferase (GGT), ALT, and AST]. Restricted cubic spline was used to assess the nonlinear associations between biomarkers and mortality. The area used the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the models after incorporating liver biomarkers into traditional prediction models for mortality. Results:The mean age of the participants was (65.2±9.1) years, with a median follow-up of 1.5 years, during which 307 deaths occurred. Compared to individuals without hepatic steatosis, those with severe hepatic steatosis had a 79% higher risk of mortality, with a HR of 1.79 (95% CI: 1.06-3.03). Compared to individuals without hepatic fibrosis, those with advanced fibrosis and cirrhosis had higher mortality risks of 48% and 91%, respectively (both P<0.05). For each standard deviation increase in GGT, the mortality risk increased by 10% ( HR=1.10, 95% CI: 1.05-1.15), with the positive association plateauing at higher GGT levels. AST exhibited a U-shaped association with mortality risk. The AUC of the prediction model adding liver biomarkers into traditional prediction factors was 0.718 (95% CI: 0.679-0.757), with an increase of 0.030 ( P<0.001) compared with the traditional model. Conclusions:Severe hepatic steatosis, higher levels of hepatic fibrosis, and elevated GGT levels are significantly associated with higher mortality risk. AST shows a U-shaped nonlinear association with mortality risk. Incorporating liver biomarkers into traditional risk prediction models enhance the ability to predict mortality.

Objective:To investigate the prospective associations between plasma metabolites and the risks of all-cause and cause-specific mortality among Chinese adults.Methods:This study analyzed plasma metabolomics data from 2 183 healthy adults in the China Kadoorie Biobank (CKB), measured using targeted mass spectrometry. Cox proportional hazards regression models were used to examine the associations between 630 metabolites and the risk of all-cause mortality. Cause-specific hazard regression models evaluated the associations between metabolites and cardiovascular disease (CVD) risks, cancer, and other-cause mortality. Stepwise regression was used to identify key metabolites independently associated with all-cause mortality, and the area under the receiver operating characteristic curve (AUC) was calculated to assess the improvement in predictive performance when these metabolites were added to traditional risk prediction models.Results:The mean age of the participants was (53.2±9.8) years, 65.1% of whom were female. During a median follow-up of 14.5 years, 231 deaths occurred. A total of 44 metabolites were significantly associated with the risk of all-cause mortality [false discovery rate (FDR)-adjusted P<0.05], primarily including triglycerides, ceramides, and amino acids. Additionally, 29 and 15 metabolites were found to be associated with cancer and other-cause mortality, respectively, but no metabolites were significantly associated with CVD mortality after FDR corrections. Adding 14 metabolites independently associated with all-cause mortality into the traditional prediction model significantly improved its predictive performance. Specifically, incorporating metabolites into the traditional model, which already included laboratory biomarkers, increased the AUC to 0.798 (95% CI: 0.755-0.843), an improvement of 0.088 compared to the traditional model ( P<0.001). Conclusions:Multiple metabolites are significantly associated with mortality risk and can substantially improve the accuracy of mortality risk prediction models. These findings provide new insights into the physiological mechanisms of aging and offer valuable clues for personalized health risk assessment.

ObjectiveTo investigate the effects of modified Ditan tang on genes related to the transcription-translation feedback loop （TTFL） of biorhythm in the rat model of non-alcoholic fatty liver disease （NAFLD） and its mechanism for prevention and treatment of NAFLD. MethodsSixty-five healthy SPF male SD rats were randomly assigned into blank （n=20）， model （n=15）， and low-， medium-， and high-dose （2.68， 5.36， and 10.72 g·kg^-1·d^-1， respectively） modified Ditan tang （n=10） groups. Other groups except the blank group were fed a high-fat diet for 12 weeks. The modified Ditan tang groups were treated with the decoction at corresponding doses by gavage， and the blank and model groups were treated with an equal volume of normal saline from the 9th week for 4 weeks. The levels of triglyceride （TG）， total cholesterol （TC）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate aminotransferase （AST）， and alanine aminotransferase （ALT） in the serum were measured by an automatic biochemical analyzer. TG and non-esterified fatty acid （NEFA） assay kits were used to measure the levels of TG and NEFA in the liver. The pathological changes in the hypothalamus and liver were observed by hematoxylin-eosin staining， and the lipid deposition in the liver was observed by oil red O staining. The levels of brain-muscle ARNT-like protein 1 （BMAL1/ARNTL） in the hypothalamus and liver were determined by immunohistochemical staining. The mRNA and protein levels of BMAL1， circadian locomotor output cycles kaput （CLOCK）， period circadian clock 2 （PER2）， and cryptochrome1 （Cry1） in the hypothalamus and liver were determined by Real-time PCR and Western blot， respectively. ResultsCompared with the blank group， the model group showed elevated levels of TG， TC， LDL-C， AST， and ALT （P<0.01） and a lowered level of HDL-C （P<0.05） in the serum， elevated levels of TG and NEFA in the liver （P<0.01）， pyknosis and deep staining of hypothalamic neuron cells， and a large number of vacuoles in the brain area. In addition， the model group showed lipid deposition in the liver， up-regulated mRNA and protein levels of CLOCK and BMAL1 （P<0.01）， and down-regulated mRNA and protein levels of Cry1 and PER2 （P<0.01） in the hypothalamus and liver. Compared with the model group， all the three modified Ditan tang groups showed lowered levels of TG， TC， LDL-C， ALT， and AST （P<0.05， P<0.01） and an elevated level of HDL-C （P<0.05） in the serum， and lowered levels of TG and NEFA （P<0.05， P<0.01） in the liver. Furthermore， the three groups showed alleviated pyknosis and deep staining of hypothalamic neuron cells， reduced lipid deposition in the liver， down-regulated mRNA and protein levels of CLOCK and BMAL1 （P<0.05， P<0.01）， and up-regulated mRNA and protein levels of Cry1 and PER2 （P<0.05， P<0.01） in the hypothalamus and liver. ConclusionModified Ditan tang can reduce lipid deposition in the liver and regulate the expression of CLOCK， BMAL1， Cry1， and PER2 in the TTFL of NAFLD rats.

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Objective:Through anatomical observations, anatomical data on the radial perforating vessels around the neck of first metacarpus were observed and classified, hence to provide an anatomical evidence for clinical application of the flap based on the perforating vessels.Methods:From July 2022 to June 2023, 15 fresh adult upper limb specimens were studied at the Department of Hand Surgery, Northwest University First Hospital. All perforators within the zone for flap design around the neck of radial first metacarpus were observed. The number, origin and outer calibre of the perforators were recorded. Perforators with an outer diameter greater than 0.13 mm were defined as dominant arteries. SPSS 20.0 statistical software was used to count the number and outer diameter of cutaneous branches originating from the radial artery of the first metacarpal bone, the radial palmar proper artery of thumb and other arteries. The results were expressed as mean ± standard deviation ( ± s), with P<0.05 indicating statistical significance. From July 2023 to May 2024, 14 patients (10 males, 4 females and aged 27-53 years) with digital soft tissue defects were treated. The defects were of 3 thumbs, 4 index fingers, 5 middle fingers, 1 ring finger and 1 little finger, and sized from 2.5 cm × 2.0 cm to 4.0 cm × 2.5 cm. The perforator flaps of the neck of radial first metacarpus (3.0 cm × 2.5 cm - 4.5 cm × 3.0 cm) were harvested and donor sites were closed directly in the emergency surgery. Follow-up through the visits of outpatient clinic and via telephone and WeChat interviews to assess the flap survival, digital function and aesthetics of the flap. Results:In the specimens, it was found that about 75% of dominant perforators were originated from the first metacarpal radial artery. There were statistical significance in the number and outer calibre of the perforators between the first metacarpal radial artery, radial proper palmar digital artery of thumb and other arteries ( P<0.05). Postoperative follow-up lasted for 5-12 (mean 10.1) months, all flaps survived after surgery. Sensation regained according to the British Medical Research Council (BMRC), and they were: S 2 in 6 flaps and S 3 in 8 flaps. According to Evaluation Trial Standards of Upper Limb Partial Functional of Hand Surgery of Chinese Medical Association, 11 digits were excellent and 3 in good. Scar evaluation scored 3-4 (mean 3.43) points according to the Vancouver Scar Scale (VSS). Conclusion:The perforator flap around the neck of first radial metacarpus is primarily supplied by the radial artery of the first metacarpus. This flap achieves satisfactory functional and aesthetic outcomes with good sensory recovery, therefore it is an ideal flap for reconstruction of digital soft tissue defect.

Objective:Primary pulmonary mucoepidermoid carcinoma(PMEC)is a rare malignant lung tumor that accounts for approxim-ately 0.1%-0.2%of all primary pulmonary neoplasms.Due to the non-specific clinical symptoms and epidemiological features,PMEC poses diagnostic challenges.Methods:Tissue blocks from 23 archived PMECs were collected from The First Affiliated Hospital of Soochow Uni-versity(November 2012 to December 2023).To establish definitive diagnoses,comprehensive histopathological evaluation,including histo-morphological analysis,immunohistochemistry(IHC),fluorescence in situ hybridization(FISH),and periodic acid-Schiff(PAS)staining were performed.Results:The tumors consisted of varying proportions of mucin-secreting cells(mucous cells),intermediate cells,and epidermoid cells.Immunophenotypically,CK7 was predominantly expressed in the mucous cells,whereas CK5/6,p40,and p63 were expressed in the epidermoid and intermediate cells.The Ki-67 proliferation index ranged from 5%to 60%.All tumors were negative for TTF-1 and Napsin A.Five of the tumors were positive for PD-L1(clone 22C3),with a tumor percentage score of 3%-20%.All 11 tumors tested for ALK(clone D5F3)were negative.IHC for c-Met was performed on two tumors and both were weakly positive(+).Mastermind-like transcriptional co-activator 2(MAML2)gene rearrangement was detected in 34.8%(8/23)of the tumors.Mucous cells were PAS positive.Kaplan-Meier surviv-al analysis revealed a significantly poorer prognosis for patients with lymph node metastasis,distant metastasis,advanced TNM stage(Ⅲ+Ⅳ),poor differentiation,or MAML2 gene rearrangement negativity.Univariate analysis identified poor histological differentiation,lymph node metastasis,distant metastasis,and advanced TNM stage as the major prognostic risk factors.Multivariate analysis confirmed poor differentiation and distant metastasis as independent risk factors for adverse outcomes.Conclusions:PMEC is an aggressive tumor with low incidence and non-specific clinical manifestations,leading to frequent misdiagnosis.Clinicians should maintain a high index of suspicion and ensure a thorough differential diagnosis.

Objective:To investigate the relationship of several adiposity-related anthropometric parameters, including BMI, waist circumference (WC), waist-to-hip ratio (WHR), body fat percentage (BFP) and indoles in plasma with the incidence of atherosclerotic cardiovascular disease (ASCVD) in adults in China.Methods:In China Kadoorie Biobank (CKB) study, blood samples were collected from 2 183 participants in the first resurvey in 2008 to detect indoles. Participants' body weight, body height, WC, hip circumference, and BFP were measured at baseline survey in 2004 and resurvey in 2008, the BMI and WHR were calculated with standardized methods. The long-term follow-up of all participants started from the completion of the resurvey in 2008 until the occurrence of incident ASCVD, death, loss to follow-up or until December 31, 2018. CKB ascertained outcome status (incident ASCVD) through death and disease registries and national health insurance databases, supplemented by active follow-up. Multivariate linear regression model was used to estimate the associations of anthropometric measurements at baseline survey and the first resurvey, and changes in these measurements with 3 indoles [indole, indole-3-acetic acid (IAA), and indole-3-propionic acid (IPA)]. Cox proportional hazard regression model was used to estimate the associations between indoles and the risk for ASCVD.Results:Anthropometric measurements at baseline survey or the first resurvey were negatively associated with plasma IPA level. The regression coefficient ( β) of baseline BMI (per 1.0 kg/m 2) with 0.1 standard deviation ( SD) IPA was -0.23 (95% CI: -0.36 - -0.10) (false discovery rate=0.004). After adjusting for baseline BMI, the β of baseline WC, WHR and BFP with 0.1 SD IPA were -0.09 (95% CI: -0.18 - -0.01), -0.12 (95% CI: -0.19 - -0.05), and -0.20 (95% CI: -0.32 - -0.08), respectively. The annual change in BMI (difference between BMI in 2008 and 2004 divided by the time interval) was associated with indole and IAA, with β of 1.40 (95% CI: 0.58 - 2.21) and -1.07 (95% CI: -1.91 - -0.23), respectively, at each 0.1 increase of SD. Over a median ( Q1, Q3) follow-up of 10.46 (10.36, 10.53) years after 2008 resurvey, 236 cases of ASCVD were recorded. IAA and IPA levels were negatively associated with the risk for ASCVD, with hazard ratios for one SD increase of IAA and IPA of 0.87 (95% CI: 0.76 - 0.99) and 0.84 (95% CI: 0.73 - 0.96), respectively. Conclusions:Our results suggested that anthropometric measurements and their changing trends affect the levels of plasma imicrobial tryptophan metabolite levels, decreased levels of IAA and IPA levels are associated with increased risk of ASCVD and indoles in plasma including IPA and IAA might be the mediating factors for adiposity-induced ASCVD.

Objective:To analyze the prospective associations between liver biomarkers and mortality among Chinese middle-aged and elderly populations and to evaluate the mortality risk predictive value.Methods:A total of 22 758 participants from the 3 rd resurvey of the China Kadoorie Biobank were included. Cox proportional hazard models were used to analyze the prospective associations of 5 liver biomarkers with mortality. These liver biomarkers included two liver imaging biomarkers (liver fat attenuation parameter, liver stiffness measurement) and three serum liver enzyme biomarkers [gamma-glutamyl transferase (GGT), ALT, and AST]. Restricted cubic spline was used to assess the nonlinear associations between biomarkers and mortality. The area used the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the models after incorporating liver biomarkers into traditional prediction models for mortality. Results:The mean age of the participants was (65.2±9.1) years, with a median follow-up of 1.5 years, during which 307 deaths occurred. Compared to individuals without hepatic steatosis, those with severe hepatic steatosis had a 79% higher risk of mortality, with a HR of 1.79 (95% CI: 1.06-3.03). Compared to individuals without hepatic fibrosis, those with advanced fibrosis and cirrhosis had higher mortality risks of 48% and 91%, respectively (both P<0.05). For each standard deviation increase in GGT, the mortality risk increased by 10% ( HR=1.10, 95% CI: 1.05-1.15), with the positive association plateauing at higher GGT levels. AST exhibited a U-shaped association with mortality risk. The AUC of the prediction model adding liver biomarkers into traditional prediction factors was 0.718 (95% CI: 0.679-0.757), with an increase of 0.030 ( P<0.001) compared with the traditional model. Conclusions:Severe hepatic steatosis, higher levels of hepatic fibrosis, and elevated GGT levels are significantly associated with higher mortality risk. AST shows a U-shaped nonlinear association with mortality risk. Incorporating liver biomarkers into traditional risk prediction models enhance the ability to predict mortality.

Objective:To investigate the prospective associations between plasma metabolites and the risks of all-cause and cause-specific mortality among Chinese adults.Methods:This study analyzed plasma metabolomics data from 2 183 healthy adults in the China Kadoorie Biobank (CKB), measured using targeted mass spectrometry. Cox proportional hazards regression models were used to examine the associations between 630 metabolites and the risk of all-cause mortality. Cause-specific hazard regression models evaluated the associations between metabolites and cardiovascular disease (CVD) risks, cancer, and other-cause mortality. Stepwise regression was used to identify key metabolites independently associated with all-cause mortality, and the area under the receiver operating characteristic curve (AUC) was calculated to assess the improvement in predictive performance when these metabolites were added to traditional risk prediction models.Results:The mean age of the participants was (53.2±9.8) years, 65.1% of whom were female. During a median follow-up of 14.5 years, 231 deaths occurred. A total of 44 metabolites were significantly associated with the risk of all-cause mortality [false discovery rate (FDR)-adjusted P<0.05], primarily including triglycerides, ceramides, and amino acids. Additionally, 29 and 15 metabolites were found to be associated with cancer and other-cause mortality, respectively, but no metabolites were significantly associated with CVD mortality after FDR corrections. Adding 14 metabolites independently associated with all-cause mortality into the traditional prediction model significantly improved its predictive performance. Specifically, incorporating metabolites into the traditional model, which already included laboratory biomarkers, increased the AUC to 0.798 (95% CI: 0.755-0.843), an improvement of 0.088 compared to the traditional model ( P<0.001). Conclusions:Multiple metabolites are significantly associated with mortality risk and can substantially improve the accuracy of mortality risk prediction models. These findings provide new insights into the physiological mechanisms of aging and offer valuable clues for personalized health risk assessment.