OBJECTIVE To establish the method for determining the contents of five active components in raw and charred herb pairs of Gardenia jasminoides-Scutellaria baicalensis ， construct their fingerprints， and investigate the effects of G. jasminoides processing on chemical constituents in the h erb pairs. METHODS The HPLC method was used to determine the contents of genipin 1- β -D-gentiobioside， geniposide， crocin Ⅰ， crocin Ⅱ and baicalin in ten batches of raw G. jasminoides-S. baicalensis herb pairs and ten batches of charred G. jasminoides-S. baicalensis herb pairs. Using the same HPLC method， chromatographic fingerprints for the ten batches of raw herb pairs and ten batches of processed herb pairs were established with the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicine （2012 edition）. Principal component analysis and orthogonal partial least squares-discriminant analysis were performed. RESULTS Compared with the raw G. jasminoides-S. baicalensis herb pair， the average content of genipin 1- β -D-gentiobioside in the charred G. jasminoides-S. baicalensis herb pair increased by 12.65%； while the average contents of geniposide， crocin Ⅰ， crocin Ⅱ and baicalin decreased by 7.86%， 60.62%， 62.07% and 0.15%， respectively. Chromatographic fingerprints of ten batches of raw herb pairs and ten batches of processed herb pairs shared 18 common peaks with similarities ranging from 0.997 to 1.000 and 0.988 to 1.000， respectively. Five common peaks were identified： genipin 1- β -D-gentiobioside （peak 2）， geniposide （peak 3）， crocin Ⅰ （peak 6）， crocin Ⅱ （peak 9） and baicalin （peak 10）. Principal component analysis and orthogonal partial least squares-discriminant analysis results showed that the raw G. jasminoides-S. baicalensis herb pair and the charred G. jasminoides-S. baicalensis herb pair could be clearly distinguished. Variable importance in the projection （VIP） values for crocin Ⅰ and crocin Ⅱ were both greater than one. CONCLUSIONS A method has been successfully developed for the determination of five active c omponents， including genipin 1- β -D-gentiobioside， in the G. jasminoides-S. baicalensis herb pair， and its fingerprint has been drawn. Processing is found to increase the content of genipin 1- β -D-gentiobioside， while decreasing the levels of geniposide， crocin Ⅰ， crocin Ⅱ and baicalin in the herb pair. Furthermore， crocin Ⅰ and crocin Ⅱ could serve as potential quality markers for the quality control of this herb pair.

ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

Objective To develop models for predicting response to chemotherapy combined with immunotherapy in patients with esophageal squamous carcinoma with various machine learning algorithms,and then select the optimal model.Methods A retrospective study was performed for 174 patients with esophageal squamous cell carcinoma undergoing chemotherapy combined with immunotherapy admitted in Department of Thoracic Surgery of the First Affiliated Hospital of Army Medical University from January 2022 to December 2023.The CT scans and clinical information were collected before treatment.They were randomly divided into a training set(n=122)and a testing set(n=52)in a ratio of 7∶3.CT radiomic features were extracted and selected,and then 5 machine-learning algorithms were employed to establish the prediction models,including radiomics model and clinical-radiomics model.Five-fold cross-validation was conducted on the training set,and the performance of the prediction models was evaluated on the testing set using receiver operating characteristic(ROC)curve and the F1 score.The best-performing model was further explained using local interpretable model-agnostic explanations(LIME)algorithm.Results Among the 174 patients,115(66.1%)achieved clinical remission.From the clinical information and CT images,1 clinical features and 10 radiomic features were identified.The area under of ROC curve(AUC)for the radiomics and clinical-radiomics models was 0.750(95%CI:0.616～0.883),and 0.766(95%CI:0.637～0.895),respectively.The F1 score of the optimal clinical-radiomics model was 0.829.LIME algorithm indicated that this best model demonstrated reliability in predicting individual samples.Conclusion The clinical-radiomics prediction model based on machine learning algorithm performs well,and can provide a reference for doctors'clinical decision-making by predicting the response to chemotherapy combined with immunotherapy in patients with esophageal squamous cell carcinoma.

OBJECTIVE Analyze the nasal nitric oxide(NNO)of CRS children,and explore the clinical value of NNO in the diagnosis and treatment of CRS in children.METHODS CRS children diagnosed in the outpatient clinic were selected,and were divided into CRS with and without AR according to their allergen results.VAS score and NNO test were performed for them.Healthy children during the same period were selected as the control group.Finally their results were compared and analyzed.RESULTS The NNO of CRS children with and without AR were(193±62)ppb and(138±49)ppb,both lower than the control group's[(243±51)ppb];There were negative correlations between NNO and VAS scores in CRS children without AR before and after treatment;The NNO of CRS children with and without AR were significantly increased after treatment(P<0.05);NNO has high predictive value for diagnosing CRS children without AR(P<0.01).CONCLUSION The levels of NNO in different types of CRS were lower than normal,and CRS children without AR was lower than those with AR.NNO could assist in the diagnosis of CRS,dynamically reflect the severity of nasal inflammation,and help to distinguish the allergic status of CRS.

Time-of-flight secondary ion mass spectrometer(TOF-SIMS)is a highly sensitive surface analysis instrument with high spatial resolution.Traditional TOF-SIMS instruments for sample targets use high field extraction methods.Although the ion collection efficiency is high,it is prone to issues such as low-energy ion beam defocusing,sample morphology sensitivity,and organic molecule ion dissociation.This study aimed to develope an efficient low-field ion extraction system suitable for TOF-SIMS with a continuous beam source.The SIMION simulation software was used to construct a model of the secondary ion optical extraction system.The key factors affecting the extraction efficiency were studied,and the structural parameters of the extraction cone were optimized.Using an indium target as the sample,an experimental test of the performance of the ion extraction system was carried out on the TOF-SIMS instrument.The influences of the voltages of the ion extraction cone and the single lens on the ion extraction efficiency were consistent with the simulation results.By adopting the technology of deflection and coaxial dynamic compensation,the imaging field of view of the ion extraction system was increased to 500 μm×500 μm.The energy window of the ion extraction system reached 10 eV,and the large imaging depth of field of 400 μm was achieved.In the test of a 5 mg/L cholesterol thin film sample,the signal-to-noise ratio of the characteristic peak[M-OH]+reached 4453.The results showed that this low-field secondary ion extraction system effectively improved the performance of the continuous beam TOF-SIMS instrument.

Chest low-dose computed tomography (LDCT) is a widely utilized modality for lung cancer screening and follow-up in high-risk populations, owing to its low radiation dose. However, the diagnostic accuracy of LDCT is significantly constrained by inherent limitations, including elevated image noise and reduced contrast resolution. The potential for deep learning technologies to address these challenges through data-driven LDCT image denoising approaches has been demonstrated. In this review, the advantages and limitations of deep learning models were introduced, including supervised, unsupervised, and self-supervised learning. The potential and challenges of these models in clinical applications were analyzed, thereby providing a reference for subsequent research and clinical practice.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective To construct a model combining with traditional Chinese medicine(TCM)syndrome for predicting the risk of disease progression in patients with idiopathic membranous nephropathy(IMN)by machine learning methods,thus to quantitatively evaluating the value of TCM syndrome in the prediction of the risk of disease progression in IMN.Methods Monofactor analysis,recursive feature elimination(RFE)and multivariate binary Logistic regression analysis were used to screen the independent related factors affecting the risk of disease progression of IMN,and then a risk prediction model was constructed.A total of 102 patients with IMN were randomly assigned to the training set and the test set in a ratio of 65∶35,and then the comparison was conducted in the performance indicators of accuracy,sensitivity,specificity,F1 value,and area under the receiver operating characteristic(ROC)area under the curve(AUC)of the risk prediction model with or without the inclusion of the TCM syndrome information.Results Before the inclusion of TCM syndrome information,12 clinical characteristic variables for patients with MN were obtained after monofactor analysis combined with RFE screening,and they were age,hemoglobin quantification,urinary occult blood,24-hour urine protein quantification,urine protein-creatinine ratio,estimated glomerular filtration rate(eGFR),creatinine,uric acid,alanine transaminase,anti-phospholipase A2 receptor antibody(PLA2R-Ab),total cholesterol,and low-density lipoprotein cholesterd.A risk cholesterol prediction model containing the above variables was constructed.The multivariate binary Logistic regression analysis showed that the differences of the clinical variables mentioned above between the training-set group and test-set group were statistically significant,and the risk prediction model presented good sensitivity and predictability.Monofactor analysis combined with RFE screening was performed again after the inclusion of TCM syndrome information,and then 14 variables were obtained,which included blood stasis syndrome and dampness obstruction syndrome.The sensitivity and specificity of the model with the inclusion of the TCM syndrome information were significantly improved when compared with those without the inclusion of TCM syndrome information.Conclusion The results of the study initially indicate that TCM syndrome can be used as an important supplementary variable for predicting the risk of disease progression in IMN,and will provide a reference for intelligent diagnosis through the integration of traditional Chinese and western medicine information,and will supply the guidance for the treatment of IMN with TCM.

Exposure to acceleration can have Various effects on the physiology,psychology,and performance of human.With the increasing density and complexity of China's space mission,and the significant differences between deep space exploration and near Earth orbit flight,unprecedented challenges have been posed to the emotions of astronauts and the ability of human-machine collaboration to complete complex operational tasks in special environments.Similarly,with the continuous development of high-performance fighter jets,air combat operation are becoming increasingly complex,and the payload generated by the aircraft is also increasing.The requirements for the anti-overload ability of fighter pilots are also becoming higher and higher.A review of recent studies on physiological and cognitive load of acceleration exposure on humans.The research on human physiology is relatively systematic,but there is limited research on cognitive load.The corresponding evaluation methods,assessment methods,and assessment systems are not comprehensive enough,and there is a lack of mechanistic analysis.Multimodal analysis and modeling are even rarer.Further in-depth research is needed on the cognitive load and evaluation techniques of overweight environment in the future.This article provides reference for the selection,adaptive training,and related experimental research of overweight endurance for astronauts and pilots in the future.