Definitive chemoradiotherapy (dCRT) has become a cornerstone in the treatment of locally advanced esophageal cancer; however, local control remains suboptimal, and persistent lesions or locoregional recurrences after treatment are not uncommon. For patients without distant metastases but with local failure, whether surgical intervention can still offer curative potential remains a major clinical dilemma. Salvage esophagectomy (SE) offers potential long-term survival for selected patients, but this procedure is performed in the context of severe fibrosis, impaired local blood supply, and obscured anatomical planes following chemoradiotherapy, resulting in significantly higher perioperative risk compared to primary esophagectomy. Consequently, controversies exist regarding patient selection, preoperative restaging, choice of surgical approach, extent of lymphadenectomy, gastrointestinal reconstruction, and perioperative management. In recent years, with the refinement of restaging modalities such as PET/CT, the accumulation of experience in high-volume centers, and emerging evidence from clinical studies, the clinical role of SE has gradually shifted from a "high-risk salvage measure" to a "selective curative strategy aimed at achieving long-term survival in carefully selected patients". Nevertheless, standardized guidelines for patient selection, technical approaches, and perioperative management are still lacking. Based on current evidence and clinical experience, experts organized by the Integrated Esophageal Cancer Committee of Chinese Anti-Cancer Association systematically reviewed key issues regarding SE, including its definition, indications, preoperative evaluation, choice of surgical approach, lymphadenectomy, gastrointestinal reconstruction, and perioperative management, and formulated a Chinese expert consensus. This consensus aims to provide guidance for standardized assessment, appropriate referral, individualized surgical decision-making, and optimized perioperative management of patients with locoregional failure after dCRT. Ultimately, this will increase the likelihood of R0 resection, reduce the risk of severe complications, and promote the safer, more judicious, and standardized implementation of SE in high-risk scenarios.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

AIM To study the chemical constituents from salt-processed Litchi Semen and their antioxidant activities.METHODS The 85％ethanol extract from salt-processed Litchi Semen was isolated and purified by silica gel,Sephadex LH-20,MCI,ODS and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.DPPH and ABTS+free radical scavenging method were used to evaluate their antioxidant activities.RESULTS Fifteen compounds were isolated and identified as dehydrocostuslactone(1),ananosmoside A(2),funingensin A(3),(2S)-pinocembrin-7-O-(6-O-α-L-rhamnopyranosyl-β-D-glucopyranoside)(4),liquiritienin(5),quercetin(6),rutin(7),isorhamnetin-3-O-β-rutinoside(8),procyanidin A2(9),procyanidin A1(10),ethyl protocatechuate(11),5-hydroxymethylfurfural(12),di(2-ethyl-hexyl)phthalate(13),nicotinamide(14),(10E,15Z)-9,12,13-trihydroxyoctadeca-10,15-dienoic acid(15).Compounds 6-7,9-10 exhibited scavenging activities against DPPH radicals with IC50 values of(12.929±1.232),(14.104±0.946),(10.417±1.736),(6.944±0.030)μmol/L,respectively.Compounds 6-10 exhibited scavenging activities against ABTS+radicals with IC50 values of(21.952±0.577),(25.683±0.625),(22.970±1.336),(20.210±1.435),(18.725±0.324)μmol/L,respectively.CONCLUSION Compounds 1,5,14-15 are isolated from Litchi genus for the first time.Compounds 6-7,9-10 have strong in vitro antioxidant activities.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

This study was aimed at establishing a method of ultra-fast quantitative PCR for Brucella detection.We used an exogenous recombinant plasmid as the internal reference and targeted the T4SS secretion system,an important Brucella viru-lence factor,to design specific primers and probes.The sensitivity,specificity,and repeatability of this method were evaluated,and a standard curve was constructed.The coincidence rate of detection findings with this method versus quantitative PCR was determined.This method markedly decreased the detection time to only 10 minutes.The standard curve demonstrated a good linear relationship(Y=-3.410 7x+38.357,R2=0.998 5)with a low minimum detection limit of 10 copies/μL.The method exhibited good specificity and did not specifically amplify several common clinical bacteria other than Brucella.The de-tection of three concentrations of positive plasmids yielded coefficients of variation(CVs)of 0.20％to 0.91％,thus demonstra-ting the method's excellent repeatability.Furthermore,140 clinical samples were analyzed concurrently with the fluorescence PCR method,which yielded a 100％compliance rate and consistent results.Our findings indicated that the Brucella ultra-fast quantitative PCR was ultrafast;had high sensitivity,high specificity,and good specificity;and can be used for the clinical de-tection of Brucella and emergency investigation of epidemics.Therefore,this method is valuable for the early diagnosis of Bru-cella.

Purpose To investigate the expression of LAMB3 and ITGB4 in esophageal squamous cell carcinoma(ESCC)and analyze their associations with clinicopathological features.Methods Bioinformatics was used to assess the expression of LAMB3 and ITGB4 in pan-cancer and ESCC.Immunohistochemistry was performed to evaluate their expression and distribution in ESCC tissues,and correlations clinicopathological features were analyzed.Follow-up data were collected to construct Kaplan-Meier survival curves,and Cox regression analysis was proformed to determine their prognostic significance.Results Bioinformatics analysis showed that LAMB3 and ITGB4 were highly expressed in ES-CC tissues(P＜0.001).Immunohistochemistry confirmed that both proteins were localized in the cytoplasm and membrane of tumor cells.High LAMB3 expression was significantly associated with poor differentiation(P＜0.001),lymph node metastasis(P=0.015),and T staging(P＜0.001).High ITGB4 expression was significantly correlated with poor differentiation(P=0.004)and advanced T staging(P=0.004).Cox regression analysis identified high ex-pression of LAMB3 and ITGB4 as independent prognostic factors for overall survival[HR=4.97(95％CI:2.73-9.02);HR=2.33(95％CI:1.36-3.99)].Conclusion LAMB3 and ITGB4 are highly expressed in ESCC.High LAMB3 expression is associated with tumor differentiation,lymph node metastasis,and T staging,while high ITGB4 expression is correlated with tumor differentiation and T staging.Patients with high expression of LAMB3 or ITGB4 have poorer overall survival,suggesting that these proteins may serve as prognostic biomarkers for unfavorable outcome in ESCC.

Aim To investigate the anti-acute lung injury(ALI)effect of Sterculiae Lychnophorae Semen(SLS)and its mechanism.Methods The main ac-tive components of SLS and their core targets and path-ways of action against ALI were obtained by network pharmacology methods.Subsequently,molecular doc-king technology and in vitro cellular experiments were applied for validation.Results A total of 19 core tar-gets were obtained,including HSP90AA1,CASP3,TNF,MAPK8 and MAPK14.The mechanisms may in-volve signaling pathways such as cancer,PI3K/Akt and MAPK.Molecular docking confirmed that the key targets of SLS formed a better binding activity with the relevant active ingredients.The in vitro results showed that SLS was able to protect the PM2.5-contaminated BEAS-2B cells,inhibit their NO,IL-1β and TNF-αlevels,and reduce the expression of p-p38 MAPK and p-JNK proteins.Conclusions The study successfully predicts the active ingredients,targets and signaling pathways of SLS against ALI,and in vitro experiments demonstrate that SLS might protect BEAS-2B cells from PM2.5 stimulus-induced inflammation and apoptosis by inhibiting the over-activation of p38 MAPK and JNK signaling pathways.

Aim To investigate the antagonistic effect of Schizandrin A(SchA)on oxidative stress-induced endothelial dysfunction and its mechanism of action.Methods Human umbilical vein endothelial cells(HUVECs)were selected as the research subjects,and the effects of SchA on cell viability were detected by MTT assay;the content of ROS in the cells was detec-ted by flow cytometry;the content of MDA and CAT in the cells,and the content of NO and ET-1 in the cell supernatant were detected by kit assay;and the expres-sion of SOD1,p-eNOS/eNOS proteins,and ET-1 in the cell supernatant were detected by Western blot.Immu-nofluorescence experiments were performed to detect Nrf2 entry into the nucleus of cells.Results SchA re-versed the LPS-or hypoxia-induced increase in ROS and MDA content as well as the decrease in SOD1 and CAT content in HUVECs by activating the Nrf2/Keap1/HO-1 signaling pathway.SchA inhibited the decrease of p-eNOS and eNOS protein expression in HUVECs cells,as well as NO content in cell culture medium and the increase of ET-1 content induced by LPS.The Nrf2 inhibitor ML385 reversed the antagonis-tic effects of SchA on oxidative stress and endothelial dysfunction.Conclusions SchA antagonized LPS and hypoxia-induced oxidative stress,and SchA amelio-rated oxidative stress-induced endothelial dysfunction by up-regulating the Nrf2/Keap1/HO-1 signaling path-way.

Objective To develop an automated system that can accurately determine the relationship between the mandibular third molar and the mandibular nerve canal from panoramic images.Methods A dataset consisting of 600 panoramic images of the oral cavi-ty was selected,and the positions of the mandibular third molar and the mandibular nerve canal were accurately labeled.We compared the research designed TI-YOLOv5 with PANet,Faster R-CNN,Mask R-CNN,ResNeSt-101,and the original YOLOv5 in image seg-mentation tasks,with evaluation metrics of AP and AP50.Results TI-YOLOv5 achieved AP(average precision)54.0％and AP5094.9％,an increase of 4.9 and 6.7 percentage points respectively compared to the original YOLOv5(AP 49.1％,AP50 88.2％),and surpassed other SOTA methods such as Mask R-CNN(AP 45.1％,AP50 84.2％).Conclusion TI-YOLOv5 is significantly superior to mainstream networks in automatic positioning and relationship classification of mandibular wisdom teeth and neural tubes,with high de-tection accuracy and discrimination accuracy,and can provide reliable technical support for preoperative risk assessment of mandibular wisdom tooth extraction.

Objective:To explore and analyze the correlation between the expression of mismatch repair(MMR)protein and programmed death ligand 1(PD-L1)in colorectal cancer(CRC)tissue and clinical pathological characteristics.Methods:The study period were from July 2021 to December 2024,200 cases of colorectal cancer patients treated in our hospital were selected as the research subjects.Collected all patient lesion tissue samples(lesion group)and adjacent tissue samples(located ≥5 cm from the tumor edge,adjacent group),and used the immunohistochemistry(IHC)method to detect the expression of Mismatch repair(MMR)(MLH1,MSH2,MSH6,and PMS2)protein and PD-L1 protein.Investigated the clinical and pathological characteristics of patients and conducted correlation analysis.Results:The positive rates of dMMR and pMMR protein expression in the lesion group were 39.00％and 70.00％,respectively,while those in the adjacent cancer group were 11.00％and 89.00％,respectively.There were significant difference compared between the lesion group and the adjacent cancer group(P＜0.05).The positive rate of PD-L1 protein expression in the lesion group were 25.00％,while that in the adjacent cancer group were 80.00％.The expression of PD-L1 protein in the lesion group were significantly lower than that in the adjacent cancer group(P＜0.05).There were significant differences in the positive rates of MMR and PD-L1 protein expression among patients with different histological differentiation,clinical stages,and lymph node metastasis(P＜0.05),while there were no significant differences in the positive rates of MMR and PD-L1 protein expression among patients of different genders,ages,and disease sites(P＞0.05).Spearman analysis showed there were correlation between the expression of MMR and PD-L1 proteins in colorectal cancer tissues and clinical pathological features such as histological differentiation,clinical staging,and lymph node metastasis(P＜0.05).Conclusion:Low expression of MMR and PD-L1 proteins are commonly observed in colorectal cancer tissues.The expression of MMR and PD-L1 proteins in colorectal cancer tissues are correlated with clinical pathological features such as histological differentiation,clinical staging,and lymph node metastasis.