ObjectiveTo analyze the differences in color， odor and volatile components of Citri Reticulatae Pericarpium（CRP） before and after being stir-fried with Halloysitum Rubrum， and to explore the material basis of enhancing the effect of strengthening spleen after processing and the scientific connotation of decoction pieces processed with Halloysitum Rubrum as the auxiliary material. MethodsThe volatile components of the samples before and after processing were identified and relatively quantified by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the volatile components were analyzed by principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）. According to the principle of variable importance in the projection（VIP） value>1.5， volatile differential components before and after processing were screened. And combined with intelligent sensory technologies such as colorimeter and electronic nose， the chroma and odor information of CRP before and after being stir-fried with Halloysitum Rubrum were identified. Pearson correlation analysis was used to explore the correlation between volatile differential components and chroma values. ResultsA total of 112 volatile components were identified from CRP and CRP stir-fried with Halloysitum Rubrum， of which 84 were from CRP and 97 were from CRP stir-fried with Halloysitum Rubrum. And 7 differential components were selected， including α-pinene， β-myrcene， linalool， sabinene， ocimene isomer mixture， A-ocimene， and δ-elemene. After being processed with Halloysitum Rubrum， the brightness value（L^*）， yellow-blue value（b^*） and total chromatic value（E^*_ab） of CRP were decreased（P<0.01）， and red-green value（a^*） was increased（P<0.01）， the response values of S4， S5， S10 and S13 sensors were significantly increased（P<0.05）， and the response values of S3 and S8 sensors were significantly decreased（P<0.05）. Correlation analysis showed that α-pinene and β-myrcene were negatively correlated with L^* and E^*_ab， but positively correlated with a^*. Sabinene was positively correlated with L^* and E^*_ab. Linalool was positively correlated with L^* and E^*_ab， and negatively correlated with a^*. The ocimene isomer mixture was positively correlated with the L^*. ConclusionAfter being processed with Halloysitum Rubrum， the appearance color， odor and volatile components of CRP change significantly， and α-pinene， β-myrcene， sabinene， linalool and A-ocimene are the characteristic volatile components before and after processing， which can provide references for the quality evaluation and clinical application of CRP and its processed products.

ObjectiveTo analyze the differences in color， odor and volatile components of Citri Reticulatae Pericarpium（CRP） before and after being stir-fried with Halloysitum Rubrum， and to explore the material basis of enhancing the effect of strengthening spleen after processing and the scientific connotation of decoction pieces processed with Halloysitum Rubrum as the auxiliary material. MethodsThe volatile components of the samples before and after processing were identified and relatively quantified by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the volatile components were analyzed by principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）. According to the principle of variable importance in the projection（VIP） value>1.5， volatile differential components before and after processing were screened. And combined with intelligent sensory technologies such as colorimeter and electronic nose， the chroma and odor information of CRP before and after being stir-fried with Halloysitum Rubrum were identified. Pearson correlation analysis was used to explore the correlation between volatile differential components and chroma values. ResultsA total of 112 volatile components were identified from CRP and CRP stir-fried with Halloysitum Rubrum， of which 84 were from CRP and 97 were from CRP stir-fried with Halloysitum Rubrum. And 7 differential components were selected， including α-pinene， β-myrcene， linalool， sabinene， ocimene isomer mixture， A-ocimene， and δ-elemene. After being processed with Halloysitum Rubrum， the brightness value（L^*）， yellow-blue value（b^*） and total chromatic value（E^*_ab） of CRP were decreased（P<0.01）， and red-green value（a^*） was increased（P<0.01）， the response values of S4， S5， S10 and S13 sensors were significantly increased（P<0.05）， and the response values of S3 and S8 sensors were significantly decreased（P<0.05）. Correlation analysis showed that α-pinene and β-myrcene were negatively correlated with L^* and E^*_ab， but positively correlated with a^*. Sabinene was positively correlated with L^* and E^*_ab. Linalool was positively correlated with L^* and E^*_ab， and negatively correlated with a^*. The ocimene isomer mixture was positively correlated with the L^*. ConclusionAfter being processed with Halloysitum Rubrum， the appearance color， odor and volatile components of CRP change significantly， and α-pinene， β-myrcene， sabinene， linalool and A-ocimene are the characteristic volatile components before and after processing， which can provide references for the quality evaluation and clinical application of CRP and its processed products.

The purpose of this study was to clarify the effect of Huotan Jiedu Tongluo Decoction on atherosclerosis(AS) injury in ApoE~(-/-) mice by regulating the ferroptosis pathway. Seventy-five ApoE~(-/-) mice were randomly divided into model group, low-, medium-, and high-dose of Huotan Jiedu Tongluo Decoction groups, and evolocumab group(n=15), and 15 C57BL/6J mice were selected as the blank group. Mice in the blank group were fed with a normal diet, and those in the other groups were fed with a high-fat diet to induce AS. From the 9th week, mice in Huotan Jiedu Tongluo Decoction groups were administrated with Huotan Jiedu Tongluo Decoction at corresponding doses by gavage, and those in the blank group and the model group were given an equal volume of distilled water. Mice in the evolocumab group were treated with evolocumab 18.2 mg·kg~(-1 )by subcutaneous injection every 2 weeks. After 8 weeks of continuous intervention, oil red O staining and hematoxylin-eosin(HE) staining were employed to observe the lipid deposition and plaque formation in the aortic root. Masson staining was used to evaluate the collagen content in the aortic root. The serum levels of total cholesterol(TC), triglycerides(TG), high-density lipoprotein cholesterol(HDL-C), and low-density lipoprotein cholesterol(LDL-C) were determined by biochemical kits. The levels of Fe~(2+), superoxide dismutase(SOD), malondialdehyde(MDA), and glutathione(GSH) in the aorta were measured by colorimetry. The protein and mRNA levels of nuclear factor erythroid 2-related factor 2(Nrf2), glutathione peroxidase 4(GPX4), solute carrier family 7 member 11(SLC7A11), and acyl-CoA synthetase long chain family member 4(ACSL4) in the aorta were detected by Western blot and RT-qPCR, respectively. The expression of Nrf2, GPX4, and SLC7A11 was localized by immunofluorescence. The results showed that low-, medium-, and high-dose Huotan Jiedu Tongluo Decoction reduced the plaque formation of aortic root and increased the collagen content in AS mice. At the same time, Huotan Jiedu Tongluo Decoction improved the lipid metabolism by lowering the levels of TC, LDL-C, and TG and elevating the level of HDL-C in the serum. Huotan Jiedu Tongluo Decoction enhanced the antioxidant capacity by elevating the levels of GSH and SOD and lowering the level of MDA in the aorta and inhibiting the accumulation of Fe~(2+) in the aorta. In addition, Huotan Jiedu Tongluo Decoction up-regulated the protein and mRNA levels of Nrf2, GPX4, and SLC7A11, while down-regulating the protein and mRNA levels of ACSL4. In summary, Huotan Jiedu Tongluo Decoction can effectively alleviate AS lesions in ApoE~(-/-) mice by activating the Nrf2/GPX4 pathway, reducing lipid peroxidation, and inhibiting ferroptosis.
Animals ; Ferroptosis/drug effects* ; Atherosclerosis/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; NF-E2-Related Factor 2/genetics* ; Mice ; Mice, Inbred C57BL ; Apolipoproteins E/metabolism* ; Male ; Phospholipid Hydroperoxide Glutathione Peroxidase/genetics* ; Signal Transduction/drug effects* ; Humans ; Mice, Knockout

The study aims to elucidate the existence forms(original constituents and metabolites) of Notoginseng Radix et Rhizoma in rats and reveal its metabolic pathways. After Notoginseng Radix et Rhizoma was administered orally once a day for seven consecutive days to rats, all urine and feces samples were collected for seven days, while the blood samples were obtained 6 h after the last administration. Using the ultra high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UHPLC-Q-TOF-MS/MS) technique, this study identified 6, 73, and 156 existence forms of Notoginseng Radix et Rhizoma in the rat plasma, urine, and feces samples, respectively. Among them, 101 compounds were identified as new existence forms, and 13 original constituents were identified by comparing with reference compounds. The metabolic reactions of constituents from Notoginseng Radix et Rhizoma were mainly deglycosylation, dehydration, hydroxylation, hydrogenation, dehydrogenation, acetylation, and amino acid conjugation. Furthermore, the possible in vivo metabolic pathways of protopanaxatriol(PPT) in rats were proposed. Through comprehensive analysis of the liquid chromatography-mass spectrometry(LC-MS) data, isomeric compounds were discriminated, and the planar chemical structures of 32 metabolites were clearly identified. According to the literature, 48 original constituents possess antitumor and cardiovascular protective bioactivities. Additionally, 32 metabolites were predicted to have similar bioactivities by SuperPred. This research lays the foundation for further exploring the in vivo effective forms of Notoginseng Radix et Rhizoma.
Animals ; Rats ; Drugs, Chinese Herbal/pharmacokinetics* ; Rhizome/metabolism* ; Male ; Rats, Sprague-Dawley ; Chromatography, High Pressure Liquid ; Panax notoginseng/chemistry* ; Tandem Mass Spectrometry ; Feces/chemistry*

Acute lung injury(ALI) is a critical clinical condition primarily characterized by refractory hypoxemia and infiltration of inflammatory cells in lung tissue, which can progress into a more severe form known as acute respiratory distress syndrome(ARDS). Immune cells and inflammatory cytokines play important roles in the progression of the disease. Due to its unclear pathogenesis and the lack of effective clinical treatments, ALI is associated with a high mortality rate and severely affects patients' quality of life, making the search for effective therapeutic agents particularly urgent. Ginseng Radix et Rhizoma, the dried root of the perennial herb Panax ginseng from the Araliaceae family, contains active ingredients such as saponins and polysaccharides, which possess various pharmacological effects including anti-tumor activity, immune regulation, and metabolic modulation. In recent years, studies have shown that ginsenosides exhibit notable effects in reducing inflammation, ameliorating epithelial and endothelial cell injury, and providing anticoagulant action, indicating their comprehensive role in alleviating lung injury. This review summarizes the pathogenesis of ALI and the molecular mechanisms through which ginsenosides act at different stages of ALI development. The aim is to provide a scientific reference for the development of ginsenoside-based drugs targeting ALI, as well as a theoretical basis for the clinical application of Ginseng Radix et Rhizoma in the treatment of ALI.
Ginsenosides/pharmacology* ; Humans ; Acute Lung Injury/immunology* ; Animals ; Panax/chemistry* ; Drugs, Chinese Herbal

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

Objective To explore the predictive value of automated breast ultrasound(ABUS)features combined with Ki-67 in predicting pathological complete response(pCR)after neoadjuvant chemotherapy(NAC)in triple-negative breast cancer(TNBC).Methods A retrospective analysis was conducted on 127 female TNBC patients treated at Xijing Hospital,Air Force Medical University from March 2019 to December 2023.All patients underwent NAC and surgical treatment after ABUS examination.Based on postoperative pathological results,patients were divided into pCR group(n=60)and non-pathological complete response(npCR)group(n=67).Differences in various parameters before NAC were compared between the two groups.LASSO regression was used to identify independent factors influencing pCR after NAC in TNBC patients,and a predictive model was constructed using multivariate logistic regression.The prediction model was internally validated using the Bootstrap method(1000 resamples).The discriminative ability of the model was evaluated using receiver operating characteristic(ROC)curves,and the area under the curves(AUCs)of different prediction models were compared using De-long's test.The accuracy of the model was assessed using calibration curves,and the clinical benefit of the model was evaluated using clinical decision curve analysis(DCA).Results Significant differences were observed between two groups in terms of age,Ki-67,menopausal status,tumor type,posterior echo,coronal plane convergence sign,coronal plane skip sign,and coronal plane white wall sign before NAC(P<0.05).LASSO regression analysis showed that Ki-67,coronal plane convergence sign,and coronal plane white wall sign were independent influencing factors of pCR after NAC in TNBC patients(P<0.05).The AUC of the multivariate logistic regression model based on Ki-67 was 0.733(95%CI 0.646-0.819),the AUC of ABUS model was 0.777(95%CI 0.695-0.858),and the AUC of ABUS combined with Ki-67 model was 0.816(95%CI 0.741-0.890).De-long's test showed that the AUC of the combined model was higher than those of ABUS feature model and Ki-67 model,with statistically significant differences(P<0.05).There was no significant difference in the AUC between ABUS feature model and Ki-67 model(P=0.40).Hosmer-Lemeshow test indicated that the combined model had a good fit(P=0.304).Internal validation results showed that the combined model had a good stability with a consistency index(C-index)of 0.820(95%CI 0.726-0.879).The calibration curve demonstrated good consistency between the predicted and actual probabilities of the combined prediction model,and the DCA curve indicated that the model had favorable clinical benefit.Conclusion The combined ABUS feature and Ki-67 model can be used to predict the probability of pCR after NAC in TNBC patients,providing a reference for the formulation of clinical treatment plans in TNBC patients.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective To evaluate the protective effect of radiation protection safety education (RPSE) on patients with differentiated thyroid carcinoma (DTC) undergoing iodine-131 (¹³¹I) treatment. Methods The DTC patients who undergo ¹³¹I treatment were divided into the control group and the RPSE group using the convenience sampling method, with 142 patients in each group. Patients in the control group received routine health education, while the RPSE group received routine health education combined with RPSE. Dose equivalent rate (DER) on pillows, bed sheets, quilt covers, and household waste of patients were compared between the two groups upon discharge. Results The median (M) DERs of patients' pillows, bed sheets, quilt covers and household waste were 3.86, 3.63, 3.91 and 56.59 times higher in the control group compared with the environmental background level, respectively. The M DERs of patients' pillows, bed sheets, quilt covers were 2.23, 2.18, and 2.55 times higher in the RPSE group compared with the environmental background level, while the M DER of household waste was equivalent to the environmental background level. The DERs of patients' pillows, bed sheets, quilt covers, and household waste in the RPSE group were significantly lower than those in the control group (all P<0.001). The DERs of the above four items were lower in both male and female patients in RPSE group compared with same-gender patients in the control group (all P<0.001). The patients' DERs of the above indicators had no significant difference among different gender in both control group and RPSE group (all P>0.05), except for higher DER of household waste in female patients than that of male patients in the control group (P<0.05). There were no significant differences in the DERs of pillows, bed sheets, quilt covers, and household waste across subgroups, where patients received different treatment doses, of both the control group and the RPSE group (all P>0.05). Conclusion RPSE for DTC patients treated with ¹³¹I, reduces the DERs of pillows, bed sheets, quilt covers, and particularly household waste.