Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective To achieve a preliminary understanding of the current situation of clinical laboratories in Guangdong Province,and discuss how to establish a sound investigation system,and utilize quality indicators to improve laboratory quality through the inves-tigation and analysis of data from 16 clinical laboratory quality indicators issued by the National Center for Clinical Laboratories.Meth-ods The questionnaire was issued by Clinet-EQA system and the basic information and quality indicator information during 2023 were collected.SPSS 20.0 software was used for statistical analysis according to different specialty categories and hospital grades.The 13 quality indicators measured in rate-based units were evaluated by sigma measurement.The P75,P50 and P25 percentiles of the overall distribution of each quality index were used to explore the optimal,appropriate and minimum quality specifications.Results A total of 577 laboratories participated in this survey.In addition to the implementation rate of internal quality assessment and the inter-laboratory comparison rate,the median sigma(σ)value of 11/13 quality indicators was greater than 3σ,and some of them even reach the level of 6σ,and there were disparities between hospital laboratories at different grades.The turnaround time(TAT)of the whole process of emergency examination was significantly less than those of inpatient and outpatient,TAT before emergency examination was controlled within 20 min,TAT before outpatient examination was within 30 min,and TAT before inpatient examination was within 42 min.The optimal quality specifications of 8 out of 13 indicators reached 6σ level,while the minimum quality specifications of 2 out of 13 indica-tors were lower than 3σ level.Conclusion In Guangdong Province,the overall level of quality indicators in the post-analytical of clin-ical laboratories was superior to that in the pre-analytical and analytical process.It should be essential to continuously monitor quality indicators and actively adopt improvement measures for those laboratories with unsatisfactory results,so as to enhance the examination quality of laboratories.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective:To clarify the natural course of renal angiomyolipoma and the risk factors for its progression.Methods:This was a retrospective case-control study that included 401 patients diagnosed several times by ultrasound examination in the hospital physical examination system from January 2012 to June 2024. All patients were untreated. There were 128 male cases (31.90%) and 273 female cases (68.10%). The average age at initial diagnosis was (44.04 ± 10.24) years (range 22-78 years). The median longest diameter of the tumor at initial diagnosis was 9.0 (7.0, 11.5) mm. There were 359 cases (89.50%) with single tumors and 42 cases (10.50%) with multiple tumors. The patients were divided into the progression group(≥1 mm/year) and the non-progression group (<1 mm/year)based on the average growth rate of tumor. The differences in gender, age at initial diagnosis, initial tumor size, number of lesions and lesion site between the two groups were compared. Univariate logistic regression analysis was used to explore the relationship between the above factors and the progression of renal angiomyolipoma. Multivariate logistic regression analysis was conducted to identify the risk factors for progression.Results:A total of 401 cases were followed up for an average of (88.15 ± 21.09) months (range 48-140 months). The median maximum diameter of the tumors at the initial diagnosis was 9.0 (7.0, 11.5) mm, and at the end of the follow-up, it was 11 (8, 14) mm. The average growth rate was 0.38 mm/year, and the median growth rate was 0.25 (0, 0.60) mm/year. Among them, 341 cases (85.04%) were in the non-progression group with an average growth rate of 0.14 mm/year, and 60 cases (14.96%) were in the progression group with an average growth rate of 1.74 mm/year. The age of the progression group was lower than that of the non-progression group [(41.43 ± 9.64) years vs. (44.50±10.29) years], the initial maximum diameter of the tumors in the progression group was larger than that in the non-progression group [11.0 (8.0, 16.0) mm vs. 9.0 (7.0, 11.0) mm], and the proportion of multiple tumors in the progression group was higher than that in the non-progression group [14 cases (23.30%) vs. 28 cases (8.20%)], and the differences were all statistically significant ( P<0.05). Age at initial diagnosis( OR=0.96, 95% CI 0.93-0.99), initial tumor size ( OR=1.08, 95% CI 1.04-1.12) and number of lesions ( OR=2.96, 95% CI 1.38-6.34) were the risk factors for the growth of renal angiomyolipoma ( P<0.05), according to the results of multivariate logistic regression analysis. Conclusions:The natural history of most renal angiomyolipoma shows slow growth or relative quiescence, with a small number showing a significant increasing trend. Age at initial diagnosis, initial tumor size and number of lesions were independent risk factors for the growth of renal angiomyolipoma.

Objectives:This study aims to investigate the correlation between urinary crystals and the components of urinary calculi in patients with urinary calculi，as well as the accuracy of urine crystals in predicting stone components.Methods:A retrospective analysis was performed on 280 patients with positive urine crystal and urinary calculi from January 2022 to December 2024. There were a total of 280 patients consisting of 185 males and 95 females，aged from 23 to 80 years，with an average age of（49.1 ± 12.3）years. Among them，there were 243 cases of renal stones or both renal and ureteral stones，25 cases of ureteral stones，and 12 cases of bladder stones. In all cases，10 ml of morning urine was collected preoperatively and sent for examination within one hour. After centrifuging at 400 g for 1 minute，the urine sediment was examined under a microscope. All positive crystals were categorized into calcium oxalate，uric acid，calcium phosphate，magnesium ammonium phosphate，and cystine based on the morphology of the crystals. Calculi were collected after endoscopic surgery，calculi composition was analyzed using infrared spectroscopy，and the main component（the first predominant component）was recorded. Statistical analysis was conducted using a 5×5 contingency table to analyze the correlation and contingency coefficient，and the positive predictive values of the urinary crystals for predicting calculi components were calculated. Results:Among the 280 patients，calcium oxalate crystals were found in 241 cases，uric acid in 25 cases，calcium phosphate in 7 cases，magnesium ammonium phosphate in 5 cases，and cystine in 2 cases. The main components of 280 calculi were calcium oxalate in 232 cases，uric acid in 21 cases，calcium phosphate in 24 cases，magnesium ammonium phosphate in 1 case，and cystine in 2 cases. There was a statistically significant correlation between urinary crystals and stone components（ χ2 = 152.46， P < 0.01），and the contingency coefficient between crystals and calculi components was 0.809. The overall positive expected value of urine crystals was 87.5%（245/280），among which the positive expected value of calcium oxalate crystals was 91.7%（221/241），uric acid crystals was 72.0%（18/25），calcium phosphate was 42.9%（3/7），magnesium ammonium phosphate was 20.0%（1/5），and cystine was 100.0%（2/2）. Conclusions:The urinary crystals of patients with calculi are significantly related to the main components of the calculi. Using urinary crystals to predict the components of the calculi has a relatively high accuracy.

Objective To achieve a preliminary understanding of the current situation of clinical laboratories in Guangdong Province,and discuss how to establish a sound investigation system,and utilize quality indicators to improve laboratory quality through the inves-tigation and analysis of data from 16 clinical laboratory quality indicators issued by the National Center for Clinical Laboratories.Meth-ods The questionnaire was issued by Clinet-EQA system and the basic information and quality indicator information during 2023 were collected.SPSS 20.0 software was used for statistical analysis according to different specialty categories and hospital grades.The 13 quality indicators measured in rate-based units were evaluated by sigma measurement.The P75,P50 and P25 percentiles of the overall distribution of each quality index were used to explore the optimal,appropriate and minimum quality specifications.Results A total of 577 laboratories participated in this survey.In addition to the implementation rate of internal quality assessment and the inter-laboratory comparison rate,the median sigma(σ)value of 11/13 quality indicators was greater than 3σ,and some of them even reach the level of 6σ,and there were disparities between hospital laboratories at different grades.The turnaround time(TAT)of the whole process of emergency examination was significantly less than those of inpatient and outpatient,TAT before emergency examination was controlled within 20 min,TAT before outpatient examination was within 30 min,and TAT before inpatient examination was within 42 min.The optimal quality specifications of 8 out of 13 indicators reached 6σ level,while the minimum quality specifications of 2 out of 13 indica-tors were lower than 3σ level.Conclusion In Guangdong Province,the overall level of quality indicators in the post-analytical of clin-ical laboratories was superior to that in the pre-analytical and analytical process.It should be essential to continuously monitor quality indicators and actively adopt improvement measures for those laboratories with unsatisfactory results,so as to enhance the examination quality of laboratories.

Objective:To clarify the natural course of renal angiomyolipoma and the risk factors for its progression.Methods:This was a retrospective case-control study that included 401 patients diagnosed several times by ultrasound examination in the hospital physical examination system from January 2012 to June 2024. All patients were untreated. There were 128 male cases (31.90%) and 273 female cases (68.10%). The average age at initial diagnosis was (44.04 ± 10.24) years (range 22-78 years). The median longest diameter of the tumor at initial diagnosis was 9.0 (7.0, 11.5) mm. There were 359 cases (89.50%) with single tumors and 42 cases (10.50%) with multiple tumors. The patients were divided into the progression group(≥1 mm/year) and the non-progression group (<1 mm/year)based on the average growth rate of tumor. The differences in gender, age at initial diagnosis, initial tumor size, number of lesions and lesion site between the two groups were compared. Univariate logistic regression analysis was used to explore the relationship between the above factors and the progression of renal angiomyolipoma. Multivariate logistic regression analysis was conducted to identify the risk factors for progression.Results:A total of 401 cases were followed up for an average of (88.15 ± 21.09) months (range 48-140 months). The median maximum diameter of the tumors at the initial diagnosis was 9.0 (7.0, 11.5) mm, and at the end of the follow-up, it was 11 (8, 14) mm. The average growth rate was 0.38 mm/year, and the median growth rate was 0.25 (0, 0.60) mm/year. Among them, 341 cases (85.04%) were in the non-progression group with an average growth rate of 0.14 mm/year, and 60 cases (14.96%) were in the progression group with an average growth rate of 1.74 mm/year. The age of the progression group was lower than that of the non-progression group [(41.43 ± 9.64) years vs. (44.50±10.29) years], the initial maximum diameter of the tumors in the progression group was larger than that in the non-progression group [11.0 (8.0, 16.0) mm vs. 9.0 (7.0, 11.0) mm], and the proportion of multiple tumors in the progression group was higher than that in the non-progression group [14 cases (23.30%) vs. 28 cases (8.20%)], and the differences were all statistically significant ( P<0.05). Age at initial diagnosis( OR=0.96, 95% CI 0.93-0.99), initial tumor size ( OR=1.08, 95% CI 1.04-1.12) and number of lesions ( OR=2.96, 95% CI 1.38-6.34) were the risk factors for the growth of renal angiomyolipoma ( P<0.05), according to the results of multivariate logistic regression analysis. Conclusions:The natural history of most renal angiomyolipoma shows slow growth or relative quiescence, with a small number showing a significant increasing trend. Age at initial diagnosis, initial tumor size and number of lesions were independent risk factors for the growth of renal angiomyolipoma.

Objectives:This study aims to investigate the correlation between urinary crystals and the components of urinary calculi in patients with urinary calculi，as well as the accuracy of urine crystals in predicting stone components.Methods:A retrospective analysis was performed on 280 patients with positive urine crystal and urinary calculi from January 2022 to December 2024. There were a total of 280 patients consisting of 185 males and 95 females，aged from 23 to 80 years，with an average age of（49.1 ± 12.3）years. Among them，there were 243 cases of renal stones or both renal and ureteral stones，25 cases of ureteral stones，and 12 cases of bladder stones. In all cases，10 ml of morning urine was collected preoperatively and sent for examination within one hour. After centrifuging at 400 g for 1 minute，the urine sediment was examined under a microscope. All positive crystals were categorized into calcium oxalate，uric acid，calcium phosphate，magnesium ammonium phosphate，and cystine based on the morphology of the crystals. Calculi were collected after endoscopic surgery，calculi composition was analyzed using infrared spectroscopy，and the main component（the first predominant component）was recorded. Statistical analysis was conducted using a 5×5 contingency table to analyze the correlation and contingency coefficient，and the positive predictive values of the urinary crystals for predicting calculi components were calculated. Results:Among the 280 patients，calcium oxalate crystals were found in 241 cases，uric acid in 25 cases，calcium phosphate in 7 cases，magnesium ammonium phosphate in 5 cases，and cystine in 2 cases. The main components of 280 calculi were calcium oxalate in 232 cases，uric acid in 21 cases，calcium phosphate in 24 cases，magnesium ammonium phosphate in 1 case，and cystine in 2 cases. There was a statistically significant correlation between urinary crystals and stone components（ χ2 = 152.46， P < 0.01），and the contingency coefficient between crystals and calculi components was 0.809. The overall positive expected value of urine crystals was 87.5%（245/280），among which the positive expected value of calcium oxalate crystals was 91.7%（221/241），uric acid crystals was 72.0%（18/25），calcium phosphate was 42.9%（3/7），magnesium ammonium phosphate was 20.0%（1/5），and cystine was 100.0%（2/2）. Conclusions:The urinary crystals of patients with calculi are significantly related to the main components of the calculi. Using urinary crystals to predict the components of the calculi has a relatively high accuracy.

Objective:To investigate the impact of cleavage factor Im25(CFIm25)on VSMCs-specific knockdown in the context of hyperlipidemia.Methods:Mice models were constructed with specific knockout of CFIm25 in VSMCs(CFIm25f/+ TaglnCre)and control mice(TaglnCre).The mice were fed a normal diet or high-fat diet(HFD)for 18 weeks and their body weight changes were monitored.ELISA was used to measure serum total cholesterol(TC), triacylglycerol(TG), high-density lipoprotein(HDL-C)and low-density lipoprotein(LDL-C)levels.The extent of aortic lipid deposition in mice was assessed by oil red O staining.Results:During the feeding of a high-fat diet, CFIm25f/+ TaglnCre mice showed a significant increase in body weight compared to the control group[Male(1.01±0.06)g and(0.87±0.31)g, t=7.53, P<0.05; Female: (0.64±0.02)g and(0.35±0.04)g, t=9.68, P<0.05].After 18 weeks of high-fat diet feeding, CFIm25f/+ TaglnCre mice had significantly higher levels of TC[(6.80±0.35)mmol/L and(3.76±0.87)mmol/L, t=5.63, P=0.004], TG[(0.97±0.21)mmol/L and(0.42±0.10)mmol/L, t=4.08, P=0.015], and LDL-C[(5.20±0.30)mmol/L and(2.00±0.98)mmol/L, t=5.40, P=0.006]compared to the TaglnCre group.Specifically, TC levels increased by 80.72%, TG increased by 132.79%, and LDL-C increased by 160.32%.There was a significant increase in aorta lipid deposition and atherosclerotic plaque area in CFIm25f/+ TaglnCre mice( P<0.05). Conclusions:The research indicated that VSMCs-specific CFIm25 knockdown in mice further worsened hyperlipidemia and atherosclerotic lesions.

Objective:To evaluate the best parameter of predicting the operation time and clearance of flexible ureteroscopic lithotripsy through comparing correlations between three stone burden parameters (diameter, area, volume) and the operation time or clearance retrospectively.Methods:Clinical data and CT images of 70 patients who performed flexible ureteroscopic lithotripsy because of single kidney stone in our center from January 2018 to December 2019 were retrospectively reviewed. There were 46 males and 24 females; their age was (47±12) years old. Stones were located on the left side in 28 cases and right side in 42 cases; 32 cases in the renal pelvis , 29 cases in the lower calyx, 6 cases in the middle calyx and 3 cases in the upper calyx. The free software ITK-SNAP 3.6.0 to segment kidney stones in 3D models with the CT image was used. The stone volume was calculated automatically after the segment. The largest section of the stone on the CT coronal plane was selected to measure the maximum length (D) and width (d) of the stone, the maximum diameter of the stone was D, and the stone area was calculated using the formula 0.25πDd. The patients were divided by the operation clearance into total clearance group and partial clearance group. The correlations between three stone burden parameters (volume, diameter, area) and operation time or clearance of the flexible ureteroscopic lithotripsy were compared. Simple linear regression model was also applied to compare three measurement methods. Then other factors which may affect the operation time was evaluated with the stepwise linear regression model, such as stone component and location.Results:The median operation time was 63(50, 84)min. Of 70 cases, 47 cases were in the stone-free group, with stone volume 633(248, 1 087)mm 3, maximum diameter 15(10, 19)mm, and area 82(49, 186)mm 2. 23 cases were in the non stone-free group, with volume 696(408, 1 418)mm 3, maximum diameter 15(12, 20)mm, area 105(73, 201)mm 2. There was no difference between the two groups in volume, maximum diameter and area of stones (all P>0.05). The stone-free rate of the diameter >2 cm group was 55% (6/11), ≤2 cm group was 70% (41/59). There was no significant difference between the two groups. Correlation between stone volume and operation time is the best. The correlation coefficient of stone volume is 0.58, of stone diameter is 0.33, of stone area is 0.34. Coefficients of determination of the stone volume linear regression is the best, too. R square of stone volume is 0.36, of stone diameter is 0.17, of stone area is 0.22. Forward stepwise regression model shows stone volume is the most important parameter which correlate with operation time. None of stone volume, diameter or area has significant correlation with the clearance of stone. Conclusion:Stone volume is the best predictive parameter of the stone burden because it has the best correlation with the operation time of the flexible ureteroscopic lithotripsy of the single kidney stone.