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.

Objective To analyze the impact of sarcopenia on the efficacy and adverse reactions of immunotherapy combined with chemotherapy in patients with advanced gastric cancer.Methods Patients with locally advanced or metastatic gastric cancer confirmed by pathology who were not eligible for radical surgery were selected as study subjects.A body composition analyzer was used to measure the appendicular muscle mass of the patients and calculate the skeletal muscle mass index(SMI).Based on the SMI,the patients were divided into sarcopenia group and non-sarcopenia group.On the basis of nutritional intervention and comprehensive exercise therapy,the patients were administered immu-notherapy combined with chemotherapy.The efficacy and adverse reactions were evaluated.The primary endpoint was progression-free survival(PFS),and the secondary endpoints were the objec-tive response rate(ORR)and treatment-related adverse reactions.Results A total of 52 gastric cancer patients were included,with 23 in the sarcopenia group and 29 in the non-sarcopenia group.The median PFS in the non-sarcopenia group was 9.8 months(95％CI,8.9 to 12.4),and was 5.4 months in the sarcopenia group(95％CI,4.9 to 8.1).The median PFS in the non-sarcopenia group was longer than that in the sarcopenia group,and the difference was statistically significant[HR(95％CI)=0.41(0.23 to 0.73),P=0.003].The results of the multivariate Cox propor-tional hazards regression model showed that comorbidities,treatment cycles,and sarcopenia were all independent prognostic factors affecting the PFS of gastric cancer patients(P＜0.05).The ORR in the non-sarcopenia group was 48.28％(14/29),and was 17.39％(4/23)in the sarcopenia group(x2=5.276,P＜0.05).Treatment-related adverse reactions with grading ≥3 in both groups were mainly hematological toxicities.In the non-sarcopenia group,the incidence of grading ≥ 3 treat-ment-related adverse reactions was 27.59％(8/29),and the incidence of grading＜3 treatment-re-lated adverse reactions(including those with no adverse reactions)was 72.41％(21/29).In the sarcopenia group,the incidence of grading ≥3 treatment-related adverse reactions was 56.52％(13/23),and the incidence of grading＜3 treatment-related adverse reactions(including those without adverse reactions)was 43.48％(10/23).The incidence of grading ≥3 treatment-related adverse reactions in the non-sarcopenia group was lower than that in the sarcopenia group(P=0.035).Conclusion For patients with locally advanced or metastatic gastric cancer complicated with sarcopenia,the median PFS of immunotherapy combined with chemotherapy is shorter,the ORR is lower,and the incidence of treatment-related adverse reactions is increased.Therefore,ear-ly intervention for sarcopenia should be implemented to improve the quality of life of patients with advanced gastric cancer.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

OBJECTIVES: To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk. METHODS: A mixed fluence map optimization objective was constructed, incorporating the outcome-based objective and the physical dose constraints. The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure, radiation-induced cardiac toxicity, and radiation pneumonitis considering clinical risk factors. These models were derived using Cox regression analysis or Logistic regression. The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines. The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method (clinical plan), and the dosimetric indicators and predicted prognostic outcomes were compared between different plans. RESULTS: In terms of the dosemetric indicators, D_95% of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% vs 102.57%, P=0.056), and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy (t=4.537, P<0.05) and 8.40 Gy (t=4.104, P<0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% vs 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan. CONCLUSIONS The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Prognosis ; Radiotherapy, Intensity-Modulated/methods* ; Carcinoma, Non-Small-Cell Lung/radiotherapy* ; Radiotherapy Dosage ; Female ; Male ; Middle Aged

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

ObjectiveTo investigate the correlation between the color， odor and volatile components of Paeoniae Radix Alba（PRA） and its processed products， and to examine the effects of different processing methods on the odor and color formation of PRA. MethodThe odor and chromaticity information of PRA， honey chaff-fried PRA and honey bran-fried PRA were identified by electronic nose and colorimeter， and the volatile components in the different processed products of PRA were identified and relatively quantified by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and analyzed using principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， then the differential key flavor components among the three were screened according to the principles of variable importance in the projection（VIP） value>1 and relative odor activity value（ROAV）≥1. Pearson correlation analysis was used to investigate the association between the differential flavor components and the colorimetric values and electronic nose sensors， respectively. ResultAfter being fried with honey chaff and honey bran， the lightness value（L^*） of PRA decreased， and red-green value（a^*） and yellow-blue value（b^*） increased significantly（P<0.05， P<0.01）. The odor differences were mainly reflected in the S1， S2， S4， S5， S6， S8 and S11 sensors， and the results of PCA of the electronic nose indicated that the odor differences among PRA， honey chaff-fried PRA and honey bran-fried PRA were obvious， among which the overall odor intensity of honey bran-fried PRA was higher than that of honey chaff-fried PRA. A total of 47 volatile components were identified from PRA and its processed products， including 21 for PRA， 36 for honey chaff-fried PRA， and 37 for honey bran-fried PRA. Odor analysis revealed that 12， 24 and 22 volatile components may be the key flavor components in PRA， honey chaff-fried PRA and honey bran-fried PRA， respectively. Correlation analysis showed that the L^* of the decoction pieces was negatively correlated with the content of the Maillard reaction products， the a^* and b^* were positively correlated with the content of the Maillard reaction products， the S1 and S8 sensors were negatively correlated with the content of the Maillard reaction products， and the S2， S4， S5， S6 and S11 sensors were positively correlated with the Maillard reaction products. ConclusionThe color of PRA is deepened after being stir-fried with honey chaff and honey bran， and 5-hydroxymethylfurfural， furfural and other components are generated at the same time， which is in line with the theory of burning aroma strengthens the spleen of stir-fried with honey chaff and honey bran. Honey bran-fried PRA has a stronger Maillard reaction than honey chaff-fried PRA， which makes honey bran-fried PRA with a burnt flavor and a dark yellow color， while honey chaff-fried PRA has a sweet flavor and a bright yellow color.

Objective:To investigate the effects of carnosine on lipopolysaccharide(LPS)-induced inflammasome activation and pyroptosis in microglia,and to clarify its mechanism.Methods:Activation model of microglia was established by LPS(10 ng/ml).CCK-8 assay was used to detect cell activity of microglia treated with different concentrations of carnosine(0.2,1,5,20,50 mmol/L)for 6 h,and the cell activity of microglia pretreated with different concentrations of carnosine for 0.5 h and then stimulated with LPS for 6 h,to screen a suitable concentration.Then microglia were divided into control group,carnosine group(5 mmol/L),LPS group,and LPS+carnosine group:cell morphological changes in each group were observed under an inverted phase contrast microscope;levels of IL-1β,TNF-α and IL-6 in microglial culture medium were measured by ELISA;propidium iodide(PI)staining was used to detect py-roptotic cells;immunofluorescence was used to observe protein expression of Nod-like receptor protein 3(NLRP3).Results:Com-pared with control group,cell viability of microglia in LPS group was significantly decreased(P<0.01),the shape of microglia was mostly"amoeboid",levels of IL-1β,TNF-α and IL-6 in microglial culture medium were significantly increased(P<0.01),the posi-tive rate of PI and the number of NLRP3 positive cells were significantly increased(P<0.01).Compared with LPS group,cell viability of microglia in LPS+carnosine group was significantly increased(P<0.01),the number of"amoeboid"microglia was decreased,levels of IL-1β,TNF-α in microglial culture medium were significantly decreased(P<0.01),and the level of IL-6 was decreased(P<0.05),the positive rate of PI and the number of NLRP3 positive cells were both significantly decreased(P<0.01).Conclusion:Carnosine can inhibit LPS-induced microglia activation and inflammasome activation,thereby inhibiting cell pyroptosis and the release of inflammato-ry factors.

Objective To explore the experience and feeling of experiential health education for complications in middle-aged and elderly hypertensive patients,and to provide basis for health education model for hypertensive patients. Methods By objective sampling method,10 middle-aged and elderly hypertensive patients who received experiential health education for hypertension complications in Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine from August to September 2023 were selected for semi-structured interviews,and the interview data were sorted out by Colaizzi seven-step analysis method. Results Middle-aged and elderly hypertension patients with complications of experiential health education experience can be summarized as two themes,seven subthemes,namely:the perceived benefits (improve the cognition of hypertension,complications of experience,behavior change and cognitive clarity,promote interpersonal communication),challenges (the lack of early cognitive,the limitations of traditional health education,body burden concerns). Conclusion Worry about long-term complications and lack of knowledge of disease management are common after receiving experiential health education on complications. Therefore,when designing the experiential health education program,clinical medical staff should fully consider the physiological and psychological characteristics of patients,provide personalized support,and gradually guide patients to adapt to the education process.

Objective To establish a high-performance liquid chromatography method for the determination of related substances in diazepam rectal gel. Methods The Agilent ZORBAX Eclipse Plus C18 (4.6 mm×150.0 mm, 3.5 μm) column was used with a mobile phase consisting of 10 mmol/L potassium dihydrogen phosphate buffer (adjusted to pH value to 6.0 with 0.5 mmol/L sodium hydroxide solution) and methanol. Gradient elution was performed at a flow rate of 1.0 mL/min, with a column temperature of 30 ℃ and a detection wave length of 234 nm. Results The chromatographic peaks of diazepam and known impurities Ⅰ to Ⅶ showed good resolution. Diazepam and known impurities Ⅰ, Ⅱ, Ⅲ, and Ⅳ exhibited good linearity in the concentrations ranging of 0.01 to 10.05 μg/mL, 0.01 to 9.92 μg/mL, 0.01 to 10.24 μg/mL, and 0.01 to 10.18 μg/mL, respectively (r²≥0.999), with average recoveries ranging from 99% to 101%. The excipients in the formulation did not interfere with the determination of the principal component and related substances. Conclusion This high-performance liquid chromatography method is highly specific and reproducible, making it suitable for the detection of related substances in diazepam rectal gel.