Objective To investigate the risk factors for the occurrence of metabolic dysfunction-associated steatotic liver disease(MASLD)in lean individuals and construct a risk prediction model.Methods Based on the National Health and Nutrition Examination Survey(NHANES)database in the United States(from January 2017 to March 2020),1 123 adult individuals were included in this study.Then the participants were randomly divided into a training set(n=561)and a validation set(n=562)through simple random sampling.Data on their demographics,anthropometrics,lifestyle,underlying diseases,and laboratory test results were collected.LASSO regression analysis was used to screen potential variables in the training set,and multivariate logistic regression was employed to identify independent risk factors for lean MASLD.Based on these risk factors,a prediction model for lean MASLD was constructed(LMPM).To evaluate the clinical value of the LMPM,it was compared with two commonly used prediction models for non-alcoholic fatty liver disease,the fatty liver index(FLI)and the hepatic steatosis index(HSI).The performance of the model was evaluated and internally validated using the area under the receiver operating characteristic curve(AUC),net reclassification index(NRI),integrated discrimination improvement(IDI),calibration curve,decision curve,and clinical impact curve.Results Age,waist circumference,and triglycerides(TG)were identified as independent risk factors for the development of MASLD in lean individuals.The LMPM,constructed based on these indicators,demonstrated good discriminative ability in both the training and validation sets,with AUC values of 0.86(95%CI:0.82～0.89)and 0.81(95%CI:0.77～0.85),respectively,which were significantly better than those of FLI[training set:AUC=0.83(95%CI:0.79～0.87);validation set:AUC=0.74(95%CI:0.70～0.79)]and HSI[training set:AUC=0.71(95%CI:0.66～0.76);validation set:AUC=0.71(95%CI:0.65～0.76)].Compared with FLI and HSI,the LMPM showed improvements in NRI and IDI in both the training and validation sets.The calibration curve demonstrated its high accuracy,and both the decision curve analysis and the clinical impact curve analysis indicated that the LMPM provided greater clinical benefits.Conclusion Age,waist circumference,and TG are independent risk factors for lean MASLD.Based on these factors,a prediction model,named LMPM,is developed to assess the risk of MASLD in lean individuals,which exhibits good predictive performance and has certain guiding significance for the timely identification of high-risk populations.

Cold tumors have a poor response to tumor immunotherapy due to low immune cell infiltration and the ability to evade immune attacks. Converting cold tumors into hot tumors can enhance the clinical effectiveness of anti-tumor immunotherapy. High-intensity focused ultrasound (HIFU) as a non-invasive treatment can damage tumors through mechanical effects, but there is a lack of research on its cytotoxic mechanisms at the cellular level and its role in inducing anti-immune responses. In this study, the role of HIFU in triggering tumor ferroptosis by disrupting the GSH/GSSG balance through mechanochemical action and the associated anti-tumor immune priming effect were investigated. The use of a nano-enhancer loaded with PFOB combined with HIFU could enhance ferroptosis in triple-negative breast cancer at a specific stage of tumor growth (UTGR = 0) while promoting the conversion of a cold tumor into a hot tumor, thereby improving the immune response. Overall, this provides valuable guidance for the clinical application of HIFU in tumor immunotherapy.

In the strategic context of "healthy China"， the concept of "medicine and food homology"， rooted in the culture of traditional Chinese medicine （TCM）， has received unprecedented attention. In response to population aging in China， the health of the elderly has become the focus of public health attention， and proactive health is the key to healthy aging. From the perspective of the application of medicinal and edible materials in proactive health and technological responses to population aging for the first time， this paper firstly provided a systematic overview of medicinal and edible materials and the policies related to proactive health. Second， it summarized the situation of modern technology that accelerates the research and development of medicinal and edible products， as well as the current situation of various modern biotechnologies that reveal the mechanism of action of medicinal and edible materials. Third， it discussed the application scenarios of medicinal and edible materials in proactive health and technological responses to population aging， as well as the future research and development of medicinal and edible materials. By exploring in depth the unique value and importance of medicinal and edible materials， the paper lays a theoretical foundation for improving the health care capabilities of TCM and contributes new strategies derived from TCM to healthy aging.

Anthoceros angustus Steph. is rich in phenolic acids such as rosmarinic acid (RA). Phenylalanine ammonia-lyase (PAL) is an entry enzyme in the phenylpropanoid pathway of plants, playing an important role in the biosynthesis of RA. To investigate the important role of PAL in rosmarinic acid synthesis, two PAL genes (designated as AanPAL1 and AanPAL2) were cloned from A. angustus, encoding 755 and 753 amino acid residues, respectively. The AanPAL deduced amino acid sequences contain the conserved domains of PAL and the core active amino acid residues Ala-Ser-Gly. The phylogenetic analysis indicated that AanPAL1 and AanPAL2 were clustered with PALs from bryophytes and ferns and had the shortest evolutionary distance with the PALs from Physcomitrella patens. Quantitative real-time PCR results showed that the expression of AanPAL1 and AanPAL2 was induced by exogenous methyl jasmonate (MeJA). HPLC results showed that the MeJA treatment significantly increased the accumulation of RA. AanPAL1 and AanPAL2 were expressed in Escherichia coli and purified by histidine-tag affinity chromatography. The recombinant proteins catalyzed the conversion of L-phenylalanine to generate trans-cinnamic acid with high efficiency, with the best performance at 50 ℃ and pH 8.0. The K_m and k_cat of AanPAL1 were 0.062 mmol/L and 4.35 s^-1, and those of AanPAL2 were 0.198 mmol/L and 14.48 s^-1, respectively. The specific activities of AanPAL1 and AanPAL2 were 2.61 U/mg and 8.76 U/mg, respectively. The two enzymes had relatively poor thermostability but good pH stability. The high activity of AanPAL2 was further confirmed via whole-cell catalysis with recombinant E. coli, which could convert 1 g/L L-phenylalanine into trans-cinnamic acid with a yield of 100% within 10 h. These results give insights into the regulatory role of AanPAL in the biosynthesis of RA in A. angustus and provide candidate enzymes for the biosynthesis of cinnamic acid.
Phenylalanine Ammonia-Lyase/metabolism* ; Cloning, Molecular ; Cinnamates/metabolism* ; Recombinant Proteins/metabolism* ; Rosmarinic Acid ; Depsides/metabolism* ; Escherichia coli/metabolism* ; Amino Acid Sequence ; Plant Proteins/metabolism* ; Phylogeny ; Acetates/pharmacology* ; Cyclopentanes ; Oxylipins

Objective To construct an optimal prediction model for lymph node metastasis(LNM)in early gastric cancer(EGC)using machine learning techniques and assess its predictive performance.Methods Clinical data of 433 EGC patients undergoing radical surgery in our hospital from January 2015 to December 2022 were collected.They were divided into a training set and a validation set in a 7:3 ratio.LASSO regression was used to screen variables and multivariate logistic regression analysis was employed to identify independent risk factors for LNM in the EGC patients.Ten machine learning models were constructed using categorical boosting(Catboost),light gradient boosting machine(LightGBM),extreme gradient boosting machine(XGboost),random forest(RF),gradient boosting machine(GBM),neural networks(NNET),support vector machine(SVM),K nearest(KNN),Naive Bayes(NB)and Logistic regression.The predictive power of the above models was evaluated and compared in terms of accuracy,precision,recall,F1 score value,sensitivity,specificity,positive predictive rate,negative predictive rate,Kappa value,area value under the receiver operating characteristic curve(AUC),calibration curve,decision curve,and precision-recall curve.Finally,SHAP(SHapley Additive exPlanations)was applied to explain the contribution of each variable in the best model for the prediction outcomes.Results Depth of tumor invasion,lymphovascular invasion and smoking history were independent risk factors for LNM in the EGC patients.Catboost model obtained the best predictive performance,and had 5 performance indicators outperforming the other models in the training set,that is,an AUC value of 0.904(95％CI 0.868～0.940),a F1-score of 0.633,a Brier score of 0.100,a negative predictive rate of 0.975,and a Kappa value of 0.520.Finally,calculating the SHAP values of Catboost revealed that the depth of tumor invasion and lymphovascular invasion were two key characteristic variables for predicting LNM.Conclusion The depth of tumor invasion of submucosal and lymphovascular invasion and smoking history are independent risk factors for LNM in early gastric cancer.ML can be used to predict LNM risk,the Catboost model has the best predictive performance and can provide guidance for clinical diagnosis and treatment decisions.

Objective To investigate the optimal digestion method for the preparation of single-cell suspensions from mouse small intestinal lamina propria.Methods Ten mouse small intestines of uniform length were collected and randomly divided into two groups.Each group was used to prepare lamina propria single-cell suspensions by enzymatic digestion with collagenase A or collagenase Ⅷ.We compared the effects of these two enzymatic digestion method on the cell yield,cell activity,and cell surface antigens of the single-cell suspensions.Single-cell suspensions prepared by the superior enzymatic digestion method were then subjected to flow cytometry assay.Results Compared to collagenase-Ⅷ-based enzymatic digestion,collagenase-A-based digestion result ed in a higher cell yield(9.48±1.10)× 109vs(4.18±1.02)×109and higher proportions of live cells(86.36±3.32)％vs(61.62±10.93)％,active CD45+cells(57.19±5.11)％vs(26.01±11.44)％,active CD3+cells(8.73±2.89)％vs(4.52±2.49)％,active CD4+cells(6.19±2.09)％vs(3.22±1.91)％,and active B220+cells(15.06±4.27)％vs(5.07±2.20)％,providing high-quality cells for subsequent flow assays.Conclusions The collagenase A-based enzymatic digestion method is more suitable for the preparation of ingle-cell suspensions from the lamina propria of ouse small intestines.

Objective:To evaluate the impact of bladder volume on dosimetric parameters of clinical target volume (CTV) and organs at risk (OAR) of intensity modulated proton therapy (IMPT) for localized prostate cancer during the treatment planning and daily treatment.Methods:Clinical data of 25 patients with localized prostate cancer admitted to Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine from November 2021 to June 2022 and enrolled in the "Proton Therapy System" (SAPT-PS-01) registered clinical trial were retrospectively analyzed. All patients were male and the median age was 72 years old. A total of 30 sets of IMPT plans were obtained. Based on the planning CT (30 sets) and weekly verification CT during treatment (172 sets), bladder volume, CTV and OAR dose parameters were collected. Spearman correlation analysis was used to evaluate the correlation between bladder volume in CT and the dosimetric parameters of CTV and OAR during IMPT plans, and Wilcoxon-Mann-Whitney test was adopted to compare the dosimetric parameters of CTV and OAR among different bladder volume change groups.Results:The V 95% of CTV1 and CTV2 were both 100.0%±0.0% in IMPT plans. Bladder volume was significantly negatively correlated with D mean, V 70 Gy(RBE), V 60 Gy(RBE), V 50 Gy(RBE), V 40 Gy(RBE) of the bladder ( P<0.001, 0.003, <0.001, <0.001,<0.001), and D mean, V 50 Gy(RBE) of the small intestine (both P<0.001). During treatment, bladder D mean, V 70 Gy(RBE), V 60 Gy(RBE), V 50 Gy(RBE), V 40 Gy(RBE)( P<0.001, 0.001, <0.001, <0.001, <0.001), rectal D mean, V 50 Gy(RBE), V 40 Gy(RBE) (all P<0.001), small intestine D mean, V 50 Gy (RBE) (both P<0.001) of patients with bladder volume increase >20% compared to baseline were significantly decreased compared to those in IMPT plans. But CTV1 V 100%, and CTV2 V 95% were significantly decreased too( P=0.029, 0.020). In the bladder volume decreased>20% patients, the D mean, V 70 Gy(RBE), V 60 Gy(RBE), V 50 Gy(RBE), V 40 Gy(RBE) of the bladder were significantly increased compared to those in IMPT plans (all P<0.001). However, a bladder volume reduction of ≤20% and increase of ≤20% from baseline had no significant impact on CTV and OAR dosimetric parameters during treatment. Conclusions:For patients with localized prostate cancer undergoing proton therapy, a certain bladder volume should be ensured during planning CT scans. During the daily treatment, the bladder volume should be maintained between 80%-120% of the baseline level to ensure CTV coverage and good dose sparing to OAR.

Objective To establish a method for determining hydrogen sulfide(H2S)in blood and apply it to practical cases.Methods A delute solution was achieved by adding 0.8 mL saturated borax solution into 0.2 mL blood sample was diluted with.1 mL acetonitrile solution containing 0.1％formic acid was then taken in a test tube,followed by adding 0.1 mL dilute solution and 0.1 mL thiozine aqueous solution(1％).After thorough mixing,the mixture was left to stand for 30 minutes.Subsequently,the sample was subjected to liquid chromatography-tandem mass spectrometry(LC-MS/MS)analysis after centrifugation and membrane filtration.Results The results showed that H2S exhibited good linearity within the concentration range of 10～2 000 ng/mL,with the R2 value of 0.998 5.The detection limit was 5 ng/mL,and the quantification limit was 10 ng/mL.In three cases of H2S poisoning,sulfur ions were detected in the blood of the deceased individuals,with concentrations ranging from 0.17 to 0.56 μg/mL.Conclusion For the first time,this study established a LC-MS/MS method for determining H2S in blood,which can meet the detection needs of H2S poisoning cases.

Objective:To evaluate the diagnostic performance of radiomics model based on contrast-enhanced ultrasound(CEUS) in predicting pathological complete response(pCR) after neoadjuvant chemoradiotherapy(nCRT) in patients with locally advanced rectal cancer(LARC).Methods:One hundred and six patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 in the First Affiliated Hospital of Guangxi Medical University were retrospectively included, the patients were randomly divided into a training set of 63(14 pCR patients) and a validation set of 43(12 pCR patients) in a 6∶4 ratios. Radiomics features were extracted from the tumors′ region of interest of CEUS images based on PyRadiomics. Intra-class correlation coefficient(ICC), Mann-Whitney U test, and least absolute shrinkage and selection operator(LASSO) algorithms were used to reduce features dimension. Finally, 7 radiomics features relevanted to pCR were selected to construct an ultrasomics model using elastic network regression, based on the R language. A combined model was constructed by jointing clinical feature. The performance of the models was assessed with the area under the ROC curve(AUC). Results:The AUC of the ultrasomics model and the combined model was 0.695(95% CI=0.532-0.859) and 0.726(95% CI=0.584-0.868) respectively in the training set. The AUC of the ultrasomics model and the combined model was 0.763(95% CI=0.625-0.902) and 0.790(95% CI=0.653-0.928) respectively in the validation set. Both univariate and multivariate Logistic regression analyses showed that CA199( P<0.05) and ultrasomics score( P<0.001) could be an independent predictor of pCR after nCRT in patients with LARC. Conclusions:The CEUS-based radiomics scores has certain predictive value for whether LARC patients achieve pCR after nCRT, and may provide a non-invasive imaging biomarker for predicting LARC patients achieve pCR after nCRT.

OBJECTIVE To study material basis of Xianglian Pill(XLP)in vivo and in vitro using UPLC-Q-TOF-MS/MS technique,and to qualitatively analyze the main components of Xianglian Pill as well as the prototypical components and metabolites that were absorbed into the blood.METHODS A Thermo Accucore C18 column(2.1 mm×100 mm,2.6 μm)was used with 0.1%formic acid in water(A)-acetonitrile(B)as the mobile phase in a gradient elution mode,the column temperature was 40℃,and the flow rate was 0.4 mL·min-1 with the injection volume of 4 μL.The mass spectrometry information was collected by using the electros-pray ionization(ESI)ion source in the positive-negative ion scan mode.RESULTS By analyzing the precise relative molecular mass,retention times,secondary fragments and other mass spectrometry information of the components,and comparing them with the mass spectrometry information of the corresponding control products and relevant literature information,a total of 75 chemical compo-nents were finally identified in the extract of Xianglian Pill,including alkaloids,sesquiterpenoids,flavonoids,limonins and organic acids.In addition,16 prototypical components and 15 metabolites were identified in the plasma of the mice after the administration of the drug.Most of the prototypical components found in the plasma were alkaloids,and the metabolic pathways of these components in vivo were mainly hydroxylation,demethylation,reduction,hydrolysis,hydrogenation and glucuronidation.CONCLUSION The method can be used for the rapid identification of the external and internal components of Xianglian Pill,and its analytical results lay the foundation for further basic research on the pharmacological substances.