The study investigated the intrinsic changes in material basis of Angelicae Sinensis Radix during wine processing by headspace-gas chromatography-ion mobility spectrometry(HS-GC-IMS), headspace-solid phase microextraction-gas chromatography-mass spectrometry(HS-SPME-GC-MS), and ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry(UPLC-Q-Orbitrap-MS) combined with chemometrics. HS-GC-IMS fingerprints of Angelicae Sinensis Radix before and after wine processing were established to analyze the variation trends of volatile components and characterize volatile small-molecule substances before and after processing. Principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were employed for differentiation and difference analysis. A total of 89 volatile components in Angelicae Sinensis Radix were identified by HS-GC-IMS, including 14 unsaturated hydrocarbons, 16 aldehydes, 13 ketones, 9 alcohols, 16 esters, 6 organic acids, and 15 other compounds. HS-SPME-GC-MS detected 118 volatile components, comprising 42 unsaturated hydrocarbons, 11 aromatic compounds, 30 alcohols, 8 alkanes, 6 organic acids, 4 ketones, 7 aldehydes, 5 esters, and 5 other volatile compounds. UPLC-Q-Orbitrap-MS identified 76 non-volatile compounds. PCA revealed distinct clusters of raw and wine-processed Angelicae Sinensis Radix samples across the three detection methods. Both PCA and OPLS-DA effectively discriminated between the two groups, and 145 compounds(VIP>1) were identified as critical markers for evaluating processing quality, including 4-methyl-3-penten-2-one, ethyl 2-methylpentanoate, and 2,4-dimethyl-1,3-dioxolane detected by HS-GC-IMS, angelic acid, β-pinene, and germacrene B detected by HS-SPME-GC-MS, and L-tryptophan, licoricone, and angenomalin detected by UPLC-Q-Orbitrap-MS. In conclusion, the integration of the three detection methods with chemometrics elucidates the differences in the chemical material basis between raw and wine-processed Angelicae Sinensis Radix, providing a scientific foundation for understanding the processing mechanisms and clinical applications of wine-processed Angelicae Sinensis Radix.
Wine/analysis* ; Gas Chromatography-Mass Spectrometry/methods* ; Chromatography, High Pressure Liquid/methods* ; Angelica sinensis/chemistry* ; Solid Phase Microextraction/methods* ; Drugs, Chinese Herbal/isolation & purification* ; Chemometrics ; Volatile Organic Compounds/chemistry* ; Principal Component Analysis ; Ion Mobility Spectrometry/methods*

BACKGROUND:Establishing a nomogram prediction model for postoperative pulmonary infection in hip fractures and taking early intervention measures is crucial for improving patients'quality of life and reducing medical costs. OBJECTIVE:To construct a nomogram risk prediction model of postoperative pulmonary infection in elderly patients with hip fracture,and provide theoretical basis for feasible prevention and early intervention. METHODS:Case data of 305 elderly patients with hip fractures who underwent surgical treatment at Wuxi Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine between January and October 2020(training set)were retrospectively analyzed.Using univariate and multivariate logistic regression analysis and Hosmer-Lemeshow goodness of fit test,receiver operating characteristic curve was utilized to analyze the diagnostic predictive efficacy of independent risk factors and joint models for postoperative pulmonary infections.Tools glmnet,pROC,and rms in R Studio software were applied to construct a nomogram model for predicting the risk of postoperative pulmonary infection in elderly patients with hip fractures,and calibration curves were further drawn to verify the predictive ability of the nomogram model.Receiver operating characteristic curves,calibration curves,and decision curves were analyzed for 133 elderly patients with hip fractures(validation set)receiving surgery at the same hospital from November 2022 to March 2023 to further predict the predictive ability of the nomogram model. RESULTS AND CONCLUSION:(1)The postoperative pulmonary infection rate in elderly patients with hip fractures in this group was 9.18%(28/305).(2)Single factor and multivariate analysis,as well as forest plots,showed that preoperative hospitalization days,leukocyte count,hypersensitive C-reactive protein,and serum sodium levels were independent risk factors(P<0.05).The Hosmer-Lemeshow goodness of fit test showed good fit(χ2=4.57,P=0.803).Receiver operating characteristic curve analysis was conducted on the independent risk factors and their joint models mentioned above,and the differentiation of each independent risk factor and joint model was good,with statistical significance(P<0.05).(3)The graphical calibration method,C-index,and decision curve were used to validate the nomogram prediction model.The predicted calibration curve was located between the standard curve and the acceptable line,and the predicted risk of the nomogram model was consistent with the actual risk.(4)The validation set used receiver operating characteristic curve,graphic calibration method,and decision curve to validate the prediction model.The results showed good consistency with clinical practice,indicating that the model had a good fit.The nomogram risk prediction model constructed for postoperative pulmonary infection in elderly patients with hip fractures has good predictive performance.The use of the nomogram risk prediction model can screen high-risk populations and provide a theoretical basis for early intervention.

Objective To investigate the dynamic expression with the time change of N6-methyladenosine(m6A)methylation-related factors in the repair process of skeletal muscle injury and its mechanism in the inflammatory response of macrophage in the injure process.Methods In vivo mice models of BaCl2 injury in the gastrocnemius were established.Four mice per group in the control group and injury group.Gastrocnemius tissues were harvested at day 1,3,5,7,and 9 after injury for experiments.Primary gastrocnemius muscle tissue cells,muscle satellite cells,muscle cells,and cell line C2C12 cells were treated with dexamethasone(DEX,50 μmol/L)to mimic injury.Lipopolysaccharide(LPS,100 μg/L)induced RAW264.7 cell lines to mimic the inflammatory response after skeletal muscle injury,and STM2457(30 μmol/L)was added to inhibit the effect of methyltransferase 3(Mettl3)before LPS treatment.The expression of m6A methylation-related factors(Writers,Erasers,Readers)and inflammation factors were detected by Real-time PCR and Western blotting.Results The muscle fibers were dissolved and then gradually repaired with the extension of injury time,the number of monocytes/macrophages increased first and then decreased,and the Pax7 mRNA level increased first and then decreased with the change of injury time.Compared with the control group,the mRNA and protein levels of m6A methylation-related factors in gastrocnemius did not change significantly on the injury-1 day.However,they were significantly increased on the injury-3 days compared with the control group(P＜0.05),and then obviously decreased on the injury-5 days group compared with the injury-3 days group(P＜0.05).Compared with the control group,they were no significant differences on the injury-7 days group and-9 days group.In vitro DEX decreased the mRNA levels of m6A methyltransferase factors in primary muscle satellite cells and C2C12 cells and increased the mRNA expression level of methylation-recognition enzyme factors(P＜0.05).The mRNA levels of m6A methylation-related factors increased significantly in skeletal muscle tissue cells and myocytes after DEX treatment(P＜0.05).After LPS treatment,the mRNA and protein expression levels of m6A methylation-related factors and the mRNA expression levels of inflammatory factors interleukin(IL)-6 and IL-1β in macrophages increased significantly(P＜0.05),while the levels of IL-6 and IL-1β mRNA in macrophages decreased significantly when the Mettl3 was inhibited(P＜0.05).Conclusion m6A methylation-related factors primarily is activated in the damaged muscle cells and inflammation response of macrophages.Inhibition of m6A methyltransferase can reduce the inflammatory response of macrophages.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.

Objective To explore the risk factors for surgical site infection(SSI)after transforaminal lumbar interbody fusion(TLIF)for the treatment of lumbar degenerative diseases.Methods A total of 1 000 patients who underwent TLIF for lumbar degenerative diseases in our hospital were included and divided into the infection group(n=23)and the non-infection group(n=977)according to whether the surgical incision was infected.General data,surgical and laboratory indicators of patients were collected,and potential risk factors of SSI were screened by univariate analysis and multivariate regression analysis,a nomogram model was established,and its predictive efficiency was validated by the receive operating characteristic(ROC)curve.Results The incidence of SSI in patients after TLIF was 2.3％.The results of univariate analysis showed that age,operative time,intraoperative blood loss,preoperative C-reactive protein(CRP),smoking,and diabetes mellitus were the significant risk factors for the occurrence of SSI.Multivariate regression analysis showed that older age,longer operation time,more intraoperative blood loss,smoking and diabetes mellitus were the independent risk factors for postoperative SSI.ROC curve showed that the nomogram model established in this study has good predictive efficiency.Conclusion Older age,longer operation time,more intraoperative blood loss,smoking,and diabetes mellitus were independent risk factors for postoperative SSI.For patients with these high risk factors,corresponding intervention measures should be taken before operation to reduce the incidence of SSI.

Objective To compare the efficacy of renal proximal renal artery denervation(pRDN)and full-length renal artery denervation(fRDN)for treatment of hypertension.Methods Fifty-six hypertensive patients were enrolled and randomly assigned to full-length renal artery denervation group(n=25)and proximal renal artery denervation group(n=31).After the procedure,24-hour ambulatory blood pressure monitoring(24 h-ABPM)at 6 months and office blood pressure at 12 months was recorded for statistical analysis.Results The blood pressure at follow-up reduced significantly in both groups,while there was no significant difference between groups.The baseline office blood pressure in fRDN group and pRDN group was(180±15)/(104±10)mmHg and(180±12)/(103±8)mmHg,respectively,which decreased to(142±9)/(82±7)mmHg and(143±10)/(83±6)mmHg at 12 months postoperatively(P＜0.001 within groups and P＞0.05 between groups).The baseline 24 h-ABPM in the two groups was(162±13)/(95±8)mmHg and(160±12)/(94±8)mmHg,respectively,which decreased to(142±11)/(83±7)mmHg and(141±8)/(81±7)mmHg at 6 months postoperatively(P＜0.001 within groups and P＞0.05 between groups).However,there was no significant difference in the reduction of office blood pressure and ambulatory blood pressure between the two groups.No treatment-related adverse events were observed.Conclusions pRDN has similar antihypertensive effect to fRDN.

In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations. It analyzed the principles and advantages of commonly used rapid, non-destructive detection techniques, offering a reference for the application and promotion of these technologies in TCM preparation detection. Additionally, this paper explored various data preprocessing techniques, operational processes, and machine learning algorithms to enhance data utilization efficiency. Finally, it focused on the challenges of applying machine learning in TCM preparation detection and offered corresponding recommendations, providing guidance for the future integration of machine learning with rapid, non-destructive detection techniques in practical production.
Machine Learning ; Drugs, Chinese Herbal/analysis* ; Medicine, Chinese Traditional/methods* ; Humans ; Quality Control

Identification of critical material attributes(CMAs) is a key issue in the quality control of large-scale TCM products like Jianwei Xiaoshi Tablets. This study focuses on the granules of Jianwei Xiaoshi Tablets, using tablet tensile strength as the primary quality attribute. A method for identifying the CMAs and a design space for the granules were established, along with a predictive model for the granule CMAs based on Fourier transform near-infrared spectroscopy(FT-NIR). First, granules of Jianwei Xiaoshi Tablets with different properties were prepared using a partial factorial design method from the design of experiments(DOE). The powder properties of the granules were measured. An orthogonal partial least squares(OPLS) model was established to correlate the powder properties with tensile strength. Based on the characteristics of the comprehensive variables extracted by OPLS, the independent variables with the greatest explanatory power for tensile strength were identified. FT-NIR technology was then employed to establish a predictive model for the granule CMAs. The final CMAs identified were hygroscopicity, moisture content, D_(50), collapse angle, mass flow rate, and tapped density. The coefficients of determination of the prediction set(R■) and relative percentage deviation(RPD) of the prediction set for flowability, D_(50), and moisture content were 0.891, 0.994, and 0.998; and 2.97, 12.4, and 20.7, respectively. The established OPLS model clearly identified the impact of various factors on tensile strength, demonstrating good fit results. The model exhibited high prediction accuracy and can be used for the rapid and accurate determination of CMAs in granules of Jianwei Xiaoshi Tablets.
Drugs, Chinese Herbal/chemistry* ; Tablets/chemistry* ; Tensile Strength ; Quality Control ; Spectroscopy, Fourier Transform Infrared ; Spectroscopy, Near-Infrared

OBJECTIVE: To establish an intestinal organoid model that mimic acute graft versus host disease (aGVHD) caused intestinal injuries by using aGVHD murine model serum and organoid culture system, and explore the changes of aGVHD intestine in vitro by advantage of organoid technology. METHODS: 20-22 g female C57BL/6 mice and 20-22 g female BALB/c mice were used as donors and recipients for bone marrow transplantation, respectively. Within 4-6 h after receiving a lethal dose (8.0 Gy) of γ ray total body irradiation, a total of 0.25 ml of murine derived bone marrow cells (1×10⁷/mice, n=20) and spleen nucleated cells (5×10⁶/mice, n=20) was infused to establish a mouse model of aGVHD (n=20). The aGVHD mice were anesthetized at the 7th day after transplantation, and the veinal blood was harvested by removing the eyeballs, and the serum was collected by centrifugation. The small intestinal crypts of healthy C57BL/6 mice were harvested and cultivated in 3D culture system that maintaining the growth and proliferation of intestinal stem cells in vitro. In our experiment, 5%, 10%, 20% proportions of aGVHD serum were respectively added into the organoid culture system for 3 days. The formation of small intestinal organoids were observed under an inverted microscope and the morphological characteristics of intestinal organoids in each groups were analyzed. For further evaluation, the aGVHD intestinal organoids were harvested and their pathological changes were observed. Combined with HE staining, intestinal organ morphology evaluation was performed. Combined with Alcian Blue staining, the secretion function of aGVHD intestinal organoids was observed. The distribution and changes of Lgr5⁺ and Clu⁺ intestinal stem cells in intestinal organoids were analyzed under the conditions of 5%, 10% and 20% serum concentrations by immunohistochemical stainings. RESULTS: The results of HE staining showed that the integrity of intestinal organoids in the 5% concentration serum group was better than that in the 10% and 20% groups. The 5% concentration serum group showed the highest number of organoids, the highest germination rate and the lowest pathological score among experimental groups, while the 20% group exhibited severe morphological destruction and almost no germination was observed, and the pathological score was the highest among all groups(t=3.668, 4.334,5.309,P<0.05). The results of Alican blue staining showed that the secretion function of intestinal organoids in serum culture of aGVHD in the 20% group was weaker than that of the 5% group and 10% of the organoids, and there was almost no goblet cells, and mucus was stainned in the 20% aGVHD serum group. The immunohistochemical results showed that the number of Lgr5⁺ cells of intestinal organoids in the 5% group was more than that of the intestinal organoids in the 10% aGVHD serum group and 20% aGVHD serum group. Almost no Clu⁺ cells were observed in the 5% group. The Lgr5⁺ cells in the 20% group were seriously injuried and can not be observed. The proportion of Clu⁺ cells in the 20% group significantly increased. CONCLUSION The concentration of aGVHD serum in the culture system can affect the number and secretion function of intestinal organoids as well as the number of intestinal stem cells in organoids. The higher the serum concentration, the greater the risk of organoid injury, which reveal the characteristics of the formation and functional change of aGVHD intestinal organoids, and provide a novel tool for the study of intestinal injury in aGVHD.
Mice ; Female ; Animals ; Mice, Inbred C57BL ; Bone Marrow Transplantation ; Graft vs Host Disease ; Stem Cells ; Organoids