OBJECTIVE To study the extraction process of Hongqijin granules with the standard decoction of Hongqijin formula as the reference. METHODS Fifteen batches of Hongqijin formula standard decoction were prepared. The reference characteristic chromatogram was established， and the chromatographic similarity was calculated. The average extraction yield and average transfer rates of calycosin-7-glucoside， salidroside， curcumin， and emodin of the 15 batches of standard decoction were determined. Taking soaking time， water addition amount， and decoction time as influencing factors， and extraction yield， transfer rates of marker components， as well as chromatographic similarity with the reference characteristic chromatogram as evaluation indexes， orthogonal test combined with principal component analysis was adopted to optimize the extraction process of Hongqijin granules， followed by process verification. RESULTS The average extraction yield of 15 batches of Hongqijin formula standard decoction was 32.55%. The average transfer rates of calycosin-7-glucoside， salidroside， curcumin， and emodin were 70.68%， 74.35%， 2.16%， and 4.62%， respectively. The optimal extraction process of Hongqijin granules was confirmed as follows： extraction was carried out twice； the first extraction used 8-fold water for 60 min， and the second extraction adopted 6.5-fold water for 45 min. In three validation tests， the average extraction yield was 33.01%. The average transfer rates of the four marker components were 79.51%， 75.54%， 1.84%， and 4.30%， with all RSD values less than 2%. The chromatographic similarities between the extracting solution and reference characteristic chromatogram were all higher than 0.990. CONCLUSIONS The optimized extraction process of Hongqijin granules exhibits good stability， and the material basis of the extracting solution is consistent with that of Hongqijin formula standard decoction.

Objective To investigate serum metabolites and metabolic pathways alterations in patients with ischemic stroke(IS)through metabolomic analysis,and to identify reliable serum metabolic biomarkers for IS diagnosis.Methods This prospective study enrolled patients with IS admitted to the Department of Neurology at Xiangcheng People's Hospital of Suzhou from December 1,2022 to December 31,2023.Age-and sex-matched healthy individuals were recruited as controls during the same period.Baseline characteristics were collected,including age,sex,height,body mass index,and blood pressure.Venous blood samples were obtained after an 8 h fast for biochemical analysis of blood glucose,total bilirubin,serum creatinine,urea nitrogen,total cholesterol,triglycerides,high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol.Serum metabolites of both groups were extracted and analyzed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry.Metabolomic data were processed using Simca-p software for unsupervised principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA)to evaluate group separation and experimental stability.Differential metabolites were defined by variable importance in projection(VIP)≥1.0,fold change(FC)≥2.0 or ≤0.5,and P＜0.05.Drug-derived exogenous metabolites were excluded by cross-referencing the Human Metabolome Database(HMDB,https://hmdb.ca/)and PubChem(https://pubchem.ncbi.nlm.nih.gov/).MetaboAnalyst 6.0(http://www.metaboanalyst.ca),a comprehensive web-based tool for metabolomic data analysis,was employed to map differential metabolites to the Kyoto encyclopedia of genes and genomes(KEGG)databased and to perform pathway enrichment analysis.Machine learning models were developed using Python.Least absolute shrinkage and selection operator(LASSO)regression and random forest(RF)algorithms were employed to identify diagnostic biomarkers capable of effectively distinguishing IS patients from controls.Metabolites identified by both methods were integrated into an extreme gradient boosting(XGBoost)model.Model performance was evaluated using receiver operating characteristic(ROC)curves with 5-fold cross-validation and internal validation(70％training,30％validation set).Results A total of 51 IS patients and 51 matched controls were included.(1)A total of 1 255 serum metabolites were identified(964 in positive ion mode,291 in negative ion mode).PC A and OPLS-DA demonstrated distinct metabolic separation between IS patients and controls.In IS group,260 metabolites were upregulated and 337 downregulated in positive ion mode;99 were upregulated and 34downregulated in negative ion mode.(2)Among the 1 255 metabolites,259 were identified as differential metabolites based on the criteria of VIP ≥ 1.0,FC≥2.0 or≤0.5 and P＜0.05.After excluding drug-derived metabolite through referencing HMDB and PubChem databases,a total of 220 endogenous differential metabolites were found to coexist in both positive and negative ion modes.Among them,119 metabolites were up-regulated and 101 were down-regulated in the IS group.The expression of these 220 metabolites showed significant differences between the IS and control groups.(3)KEGG pathway analysis highlighted five dysregulated pathways:upregulation of denovo triacylglycerol biosynthesis,glycerophosphate shuttle,and cardiolipin biosynthesis;downregulation of bile acid biosynthesis and methylhistidine metabolism.(4)LASSO and RF algorithms identified 24 and 30 candidate biomarkers,respectively.Four overlapping metabolites were selected:2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),laccaic acid A(m/z 576.010 93)and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).The expression levels of 2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),and laccaic acid A(m/z 576.010 93)were upregulated,while the expression level of NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48)was downregulated.An IS diagnostic model was established based on four metabolic biomarkers using the XGBoost algorithm.The area under the ROC curve was 1.000(95％CI 1.000-1.000)in the training set and 0.988 in the validation set(95％CI 0.963-1.000).Conclusions Patients with IS exhibit significant metabolic disturbance.The four identified biomarkers may serve as potential biomarkers for the effective identification of IS:2-((3 R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z971.571 29),arginine-conjugated cholic acid(m/z587.379 21),laccaic acid A(m/z 576.010 93),and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).

Calciphylaxis, also known as calcific uremic arteriopathy (CUA), is a rare arteriosclerosis disease characterized by skin ischemia and necrosis with severe pain, which occurs in end-stage renal disease patients. The efficacy of sodium thiosulfate (STS) in CUA has been widely verified and affirmed. However, there is no unified standard for the use of STS at home and abroad.This article introduced a case of severe CUA patient who had achieved good results under different STS usage treatments, and summarized the different STS usage treatments for CUA combined with literature.

Obstructive sleep apnea(OSA)is a common sleep disorder characterized by repeated episodes of upper airway collapse and obstruction during sleep.Polysomnography is the gold standard for diagnosing OSA,but it is expensive,time-consuming,and can cause discomfort for patients.In recent years,acoustic-based approaches for detecting OSA have emerged as a research focus.This review summarizes recent advances in OSA automatic detection techniques based on snoring and speech signals,and systematically examines their applications in diagnosis,severity assessment,and localization of obstruction sites.Findings indicate that the acoustic features of snoring and speech signals hold significant value for OSA screening,and when combined with machine learning and deep learning models,it can achieve high diagnostic accuracy.Future research should focus on elucidating the relationship between acoustic features and the pathophysiological mechanisms of OSA,integrating multimodal information,and advancing the clinical application of wearable devices,with the aim of promoting intelligent,non-invasive,and cost-effective screening technologies for OSA.

Obstructive sleep apnea(OSA)is a common sleep disorder characterized by repeated episodes of upper airway collapse and obstruction during sleep.Polysomnography is the gold standard for diagnosing OSA,but it is expensive,time-consuming,and can cause discomfort for patients.In recent years,acoustic-based approaches for detecting OSA have emerged as a research focus.This review summarizes recent advances in OSA automatic detection techniques based on snoring and speech signals,and systematically examines their applications in diagnosis,severity assessment,and localization of obstruction sites.Findings indicate that the acoustic features of snoring and speech signals hold significant value for OSA screening,and when combined with machine learning and deep learning models,it can achieve high diagnostic accuracy.Future research should focus on elucidating the relationship between acoustic features and the pathophysiological mechanisms of OSA,integrating multimodal information,and advancing the clinical application of wearable devices,with the aim of promoting intelligent,non-invasive,and cost-effective screening technologies for OSA.

Objective To investigate serum metabolites and metabolic pathways alterations in patients with ischemic stroke(IS)through metabolomic analysis,and to identify reliable serum metabolic biomarkers for IS diagnosis.Methods This prospective study enrolled patients with IS admitted to the Department of Neurology at Xiangcheng People's Hospital of Suzhou from December 1,2022 to December 31,2023.Age-and sex-matched healthy individuals were recruited as controls during the same period.Baseline characteristics were collected,including age,sex,height,body mass index,and blood pressure.Venous blood samples were obtained after an 8 h fast for biochemical analysis of blood glucose,total bilirubin,serum creatinine,urea nitrogen,total cholesterol,triglycerides,high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol.Serum metabolites of both groups were extracted and analyzed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry.Metabolomic data were processed using Simca-p software for unsupervised principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA)to evaluate group separation and experimental stability.Differential metabolites were defined by variable importance in projection(VIP)≥1.0,fold change(FC)≥2.0 or ≤0.5,and P＜0.05.Drug-derived exogenous metabolites were excluded by cross-referencing the Human Metabolome Database(HMDB,https://hmdb.ca/)and PubChem(https://pubchem.ncbi.nlm.nih.gov/).MetaboAnalyst 6.0(http://www.metaboanalyst.ca),a comprehensive web-based tool for metabolomic data analysis,was employed to map differential metabolites to the Kyoto encyclopedia of genes and genomes(KEGG)databased and to perform pathway enrichment analysis.Machine learning models were developed using Python.Least absolute shrinkage and selection operator(LASSO)regression and random forest(RF)algorithms were employed to identify diagnostic biomarkers capable of effectively distinguishing IS patients from controls.Metabolites identified by both methods were integrated into an extreme gradient boosting(XGBoost)model.Model performance was evaluated using receiver operating characteristic(ROC)curves with 5-fold cross-validation and internal validation(70％training,30％validation set).Results A total of 51 IS patients and 51 matched controls were included.(1)A total of 1 255 serum metabolites were identified(964 in positive ion mode,291 in negative ion mode).PC A and OPLS-DA demonstrated distinct metabolic separation between IS patients and controls.In IS group,260 metabolites were upregulated and 337 downregulated in positive ion mode;99 were upregulated and 34downregulated in negative ion mode.(2)Among the 1 255 metabolites,259 were identified as differential metabolites based on the criteria of VIP ≥ 1.0,FC≥2.0 or≤0.5 and P＜0.05.After excluding drug-derived metabolite through referencing HMDB and PubChem databases,a total of 220 endogenous differential metabolites were found to coexist in both positive and negative ion modes.Among them,119 metabolites were up-regulated and 101 were down-regulated in the IS group.The expression of these 220 metabolites showed significant differences between the IS and control groups.(3)KEGG pathway analysis highlighted five dysregulated pathways:upregulation of denovo triacylglycerol biosynthesis,glycerophosphate shuttle,and cardiolipin biosynthesis;downregulation of bile acid biosynthesis and methylhistidine metabolism.(4)LASSO and RF algorithms identified 24 and 30 candidate biomarkers,respectively.Four overlapping metabolites were selected:2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),laccaic acid A(m/z 576.010 93)and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).The expression levels of 2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),and laccaic acid A(m/z 576.010 93)were upregulated,while the expression level of NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48)was downregulated.An IS diagnostic model was established based on four metabolic biomarkers using the XGBoost algorithm.The area under the ROC curve was 1.000(95％CI 1.000-1.000)in the training set and 0.988 in the validation set(95％CI 0.963-1.000).Conclusions Patients with IS exhibit significant metabolic disturbance.The four identified biomarkers may serve as potential biomarkers for the effective identification of IS:2-((3 R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z971.571 29),arginine-conjugated cholic acid(m/z587.379 21),laccaic acid A(m/z 576.010 93),and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).

Calciphylaxis, also known as calcific uremic arteriopathy (CUA), is a rare arteriosclerosis disease characterized by skin ischemia and necrosis with severe pain, which occurs in end-stage renal disease patients. The efficacy of sodium thiosulfate (STS) in CUA has been widely verified and affirmed. However, there is no unified standard for the use of STS at home and abroad.This article introduced a case of severe CUA patient who had achieved good results under different STS usage treatments, and summarized the different STS usage treatments for CUA combined with literature.

Calciphylaxis is a rare ischemic necrotic skin disease characterized by skin ischemia, necrosis, and severe pain. Histopathological features include systemic arteriolar media calcification, intimal fibrosis, and thrombosis. While calciphylaxis is predominantly found in uremic patients, there have been a few case reports in non-uremic patients. This article presented a case of a non-uremic patient with primary hyperparathyroidism who underwent parathyroidectomy and subsequently developed severe calciphylaxis. The patient presented with symptoms of lower limb dermatalgia and ulceration, consistent with the classic clinical features of calciphylaxis. Following a systematic therapeutic approach in accordance with the "Zhongda Scheme", a complete remission of the condition was achieved. At the same time, the authors reviewed the relevant literature on the risk factors and possible mechanisms of non-uremic calciphylaxis based on this case.

Objective:To explore the correlation between ultrasonic SWE, blood flow quantitative parameters and the expression levels of MYB-associated protein B (MYBL2) and metastasis-associated 1 (MTA1) in breast cancer tissue.Methods:92 patients with breast cancer diagnosed and treated in our hospital from Jan. 2022 to Dec. 2023 were selected as the study objects, all of whom underwent ultrasonic SWE and blood flow imaging examination. Furthermore, the expression levels of MYBL2 and MTA1 in cancer tissues were detected, and the correlation between ultrasonic SWE and blood flow quantitative parameters and the expression levels of MYBL2 and MTA1 was analyzed.Results:The difference between maximum elastic modulus (Emax) and mean elastic modulus (E mean) in breast cancer patients with different lesion diameters and clinical stages was statistically significant (lesion diameters: t=4.561, 7.347; Clinical stage: t=4.218, 7.554, P<0.001) ; There were statistically significant differences in maximum blood flow velocity (V max), resistance index (RI), pulse index (PI) among breast cancer patients with different clinical stages, differentiation degrees and lymph node metastasis (clinical stage: t=7.055, 3.774, 3.877; Differentiation degree: F=26.830, 21.052, 20.412; Lymph node metastasis: t=7.425, 3.685, 4.956, P<0.001). There were statistically significant differences in E max, E mean, V max, RI and PI among breast cancer patients with different expression levels of MYBL2 and MTA1 (MYBL2: t=4.042, 10.672, 7.471, 6.137, 5.777; MTA1: t=5.968, 10.470, 8.098, 10.238, 7.565, P<0.001). By Spearman correlation analysis, E max and E mean were positively correlated with lesion diameter, clinical stage, MYBL2 expression level and MTA1 expression level ( P<0.001) ; V max, RI and PI were positively correlated with clinical stage, lymph node metastasis, MYBL2 expression level and MTA1 expression level, and negatively correlated with differentiation degree ( P<0.001) . Conclusion:The expression levels of MYBL2 and MTA1 in breast cancer tissue are correlated with the ultrasonic shear wave elastography and blood flow quantitative parameters, which can be used as an important reference index for clinical diagnosis and treatment of breast cancer.

Objective:To describe the dietary intake and influencing factors of energy intake in patients with gastric cancer at home.Methods:The research tools including Food Frequency Questionnaire,Digestive Cancer Patients Nutrition Knowledge,Attitude and Practice Questionnaire,and Eastern Cooperative Oncology Group performance status score were used to collect information on home oral nutrition intake,nutritional knowledge,attitudes,and physical status of patients with gastric cancer.Logistic regression analysis was performed to identify the relevant factors that affect the achievement of oral energy intake standards.Results:A total of 160 patients with gastric cancer were analyzed in this study.Overall,21.9%did not reach energy intake target.The results of Logistic regression showed the nutritional knowledge level of the patients(Waldχ2=18.42,P<0.001)and the Eastern Cooperative Oncology Group performance status score(Waldχ2=11.16,P=0.011)were independent factors related to substandard energy intake in gastric cancer patients.Conclusion:The dietary energy intake of most gastric cancer patients reached the recommended target amount,and the nutritional knowledge and activity of the patients affect the energy intake,suggesting that medical staff can intervene in the patients whose energy intake does not meet the standard from the aspects of nutritional knowledge and activities.