Bronchiolar adenoma (BA) is a rare type of lung tumor characterized by bilayered epithelial cells having a continuous basal layer and a luminal layer. It resembles mucinous adenocarcinoma (MA) on frozen section, with difficulty in distinguishing the basal layer. Immunohistochemistry is the best choice for verifying the diagnosis. This study aimed to comprehensively characterize three cases of BA-combined carcinoma using clinical, histopathological, and genetic features. BA and carcinoma sections were subjected to next-generation sequencing, respectively. It was hypothesized that while different mutation forms matched different regions, BA and lung adenocarcinoma shared the same gene mutation when they co-occurred in the same location. BA with extensive carcinoma is extremely rare and presents diagnostic challenges due to its overlap with conditions such as MA. Because of its distinctive morphological characteristics, BA may be regarded as a low-grade malignancy, particularly during a confusing evaluation. A multifaceted examination of clinical, radiological, immunohistochemical, and genetic data is necessary for an accurate diagnosis.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

Alcoholic liver disease(ALD)is a liver disease caused by long-term heavy drinking and one of the most common causes of liver-related morbidity and mortality.According to Chinese medicine,the development of ALD is mainly related to alcoholism,and the evolution of its pathology is a gradually aggravating process.Wine-heat injures the liver,without obvious organic lesions at the initial stage,and as the disease progresses to different stages,internal dampness appears,dampness depresses and transforms into heat,and Qi stagnation and blood stasis are gradually aggravated,which affects the functions of various internal organs,directly or indirectly,and creates a complex mechanism of crosstalk between the liver and other organs,which leads to the pathological condition of This leads to the pathological state of multi-organ dysfunction.Modern research has found that long-term alcohol abuse not only causes direct damage to the liver,but also leads to progression to end-stage liver disease by altering the intestinal barrier function and intestinal microbial community,and producing kidney damage and cardiac function.This article reviewed recent studies on the interactions between the liver and other organs in light of the Chinese and Western pathogenesis of alcoholic liver disease and the theory of organ crosstalk,and introduces potential strategies for the treatment of alcoholic liver disease,which will provide a reference for the discovery of new therapeutic avenues.

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 investigate the mechanism of ultra-fine garlic powder(UGP)in ameliorating dyslipidemia and aortic inflammation and fibrosis in atherosclerotic(AS)mice.Methods A 10-week ApoE-/-mouse AS model was constructed,cardiac index was meas-ured,and aortic histopathological changes were ob-served by oil red O staining.Serum inflammatory factor levels were detected by ELISA,and the expression of JNK,NF-κB,ERK and their phosphorylated proteins were detected by Western blot.Results Cardiac in-dex and other indicators as well as aortic lesions were worsened in the AS group,as compared with the normal control group.Compared with the AS group,the UGP treatment group and the traditional garlic grinding pow-der(TGP)treatment group significantly decreased total cholesterol(TC),triglyceride(TG),low-density lipo-protein cholesterol(LDL-C),atherosclerosis index(AI1,AI2),and coronary cardiac index and restored high-density lipoprotein cholesterol(HDL-C)levels,and the area of aortic lesions,inflammation and fibrosis were significantly improved,and at the same time,sig-nificantly inhibited the expression of TNF-α,IL-1β,and IL-6,as well as the expression of p-JNK,p-NF-κB and p-ERK proteins.The therapeutic effect of the UGP group was superior to that of the TGP group.Conclu-sion UGP can significantly inhibit the formation of aortic endothelial AS plaques,reduce the levels of in-flammation and fibrosis,and regulate blood lipids in a-orta of AS mice.

Objective:To investigate the impact of postoperative complications on adverse outcomes following curative-intent resection for gallbladder cancer (GBC).Methods:The multi-center real-world study was conducted. The clinicopathological data of 629 patients with GBC, who were admitted to 14 medical centers including The First Affiliated Hospital of Army Medical University from the national multicenter database of Biliary Surgery Group of Elite Group of Chinese Journal of Digestive Surgery, from April 2020 to April 2024 were collected. There were 225 males and 404 females, aged (64±10)years. Patients underwent open curative-intent resection for GBC. Observation indicators: (1)surgery, postoperative complica-tions and adverse outcomes; (2) analysis of risk factors affecting postoperative adverse outcomes in patients and population attributable fraction (PAF). Missing data in predictor variables were addressed using multiple imputation with chained equations, while cases with missing outcome variables were addressed using the "multiple imputation then deletion (MID)" strategy. The severity of multicollinearity among independent variables was assessed using the variance inflation factor (VIF) test. Multivariable possion regression models with log link and robust error variance were construc-ted incorporating restricted cubic splines (3 knots) to address nonlinear relationships in continuous variables, calculating adjusted relative risk ( RR) with corresponding 95% confidence interval ( CI). Adjusted PAF was calculated for each imputed dataset using the AF package of R software, with subsequent pooling performed according to Rubin's rules. Results:(1) Surgery, postoperative complications and adverse outcomes. All 629 patients underwent curative-intent resection for GBC, of which 143 cases had postoperative complications, including 68 cases of intra-abdominal ascites, 39 cases of pulmonary infection, 21 cases of bile leakage, 12 cases of intra-abdominal hemorrhage, 11 cases of liver failure, 10 cases of pan-creatic fistula, 10 cases of wound infection, 10 cases of gastroparesis, 7 cases of cholangitis, 7 cases of sepsis. The same patient could have more than one kind of complication. Of 629 patients, there were 19 cases of postoperative 90-day death and 11 cases of missing data, 42 cases with post-operative 90-day reoperation and 7 cases with missing data, 44 cases with postoperative 90-day readmission and 3 cases with missing data, 155 cases with prolonged postoperative hospital stay and 3 cases with missing data. (2) Analysis of risk factors affecting the postoperative adverse outcomes in patients and PAF. Results of multivariate analysis showed that pulmonary infection and liver failure were independent risk factors for postoperative 90-day mortality ( RR=3.74, 12.15, 95% CI as 1.18-11.83, 1.98-74.48, P<0.05). Pulmonary infection demons-trated the highest PAF as 4.61% (95% CI as 3.94%-5.28%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, and intra-abdominal hemorrhage were independent risk factors for post-operative 90-day reoperation ( RR=4.80, 3.62, 3.46, 4.99, 95% CI as 2.49-9.26, 1.42-9.21, 1.34-8.92, 1.55-16.06, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 8.65% (95% CI as 8.22%-9.08%, P<0.05). Intra-abdominal ascites, bile leakage, and liver failure were independent risk factors for postoperative 90-day readmission ( RR=6.20, 3.33, 14.33, 95% CI as 3.21-11.95, 1.33-8.35, 3.72-55.28, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 9.11% (95% CI as 8.85%-9.37%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, liver failure, and wound infection were independent risk factors for prolonged postoperative hospital stay ( RR=2.29, 2.21, 2.26, 2.14, 3.35, 95% CI as 1.63-3.23, 1.41-3.46, 1.32-3.86, 1.11-4.13, 1.70-6.60, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 6.03% (95% CI as 5.71%-6.35%, P<0.05). Conclusion:Pulmonary infection is the most significant risk factor for postoperative 90-day mortality after curative-intent resection for GBC, while intra-abdominal ascites is the most significant risk factor for postoperative 90-day reoperation, postoperative 90-day readmission, and prolonged postoperative hospital stay.

Objective:To evaluate the effect of value orientation brief therapy(VBT)combined with selective serotonin reuptake inhibitors(SSRIs)on clinical symptoms,rumination,decision-making ability,and cognitive func-tion in patients with mild to moderate depression.Methods:Eighty patients meeting the DSM-5 diagnostic criteria for mild to moderate depression were randomly assigned to either a medication(SSRIs)group(36 completed)or a VBT combined group(38 completed)for a 6-week intervention.Baseline and post-intervention assessments includ-ed the Hamilton Depression Scale(HAMD),Hamilton Anxious Scale(HAMA),Ruminative Response Scale-Chi-nese Version(RRS-CV),Iowa Gambling Test(IGT),number of eye fixation(NEF),responsive search score(RSS)in exploratory eye trajectory movement were used to evaluate patients'anxiety and depression symptoms,ru-minative thinking,decision-making function,and cognitive function.Results:The VBT combined group showed sig-nificantly better therapeutic effects than the medication group(P＜0.05).Compared to baseline and the medication group,the VBT combined group had significantly lower post-intervention scores in HAMD,HAMA,symptom rumi-nation,introspective reflection,compulsive meditation,and RRS-CV total scores after intervention(Ps＜0.05),and significantly higher scores in IGT net profit scores,NEF,and RSS scores(Ps＜0.05).Compared with the medica-tion group,the VBT combined group demonstrated a greater reduction in HAMD,HAMA,symptom rumination,in-trospective reflection,compulsive meditation,and RRS-CV total scores before and after intervention(Ps＜0.05),and a larger increase in IGT net profit scores,NEF,and RSS scores(Ps＜0.05).Conclusion:VBT combined with SSRIs effectively improves the symptoms of depression,anxiety,decision-making ability,rumination thinking,and cognitive function in patients with mild to moderate depression.

Objective:To investigate the predictive value of preoperative coagulation function and inflammation response biomarkers for postoperative recurrence of non-muscle-invasive bladder cancer (NMIBC) patients.Methods:The clinical data of 390 NMIBC patients underwent surgical treatment from May 2014 to May 2021 in the Second Affiliated Hospital of Xi′an Jiaotong University were retrospectively analyzed. The baseline characteristics coagulation function, inflammation response indexes and tumor characteristics were recorded. The baseline characteristics included gender, age and smoking history; the coagulation function included prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB) and D-dimer; the inflammation response indexes included neutrophil count, lymphocyte count, platelet count and monocyte count, and the systemic inflammatory response index (SIRI) and systemic immune-inflammation index (SII) were calculated; tumor characteristics included TNM stage, pathological grade, tumor length, tumor amount and postoperative instillation drugs. The patients were followed up until May 2022, with recurrence records and grouping. The "pROC" package was used to draw the receiver operating characteristic (ROC) curve, and calculate the optimal cutoff values of biomarkers. Multivariate Cox regression analysis was used to analyze the independent risk factors of recurrence in patients with NMIBC (variables were selected with P<0.1). The nomogram and its calibration curve were drawn by the "survival" and "rms" packages, and the area under the curve (AUC) was calculated with the "pROC" package for assessing the predictive ability of the model. The "caret" package was used for ten-fold cross-validation to evaluate the external applicability of the nomogram. Results:The ROC curve analysis result showed that the optimal cutoff values of PT, APTT, FIB, D-dimer, SIRI and SII were 11.95 s, 17.65 s, 0.233 mg/L, 565 ng/L, 0.62 and 291.5, respectively. The 390 patients with NMIBC were followed up 29 to 71 months, with a median follow-up time of 49 months. Among them, 113 patients experienced postoperative recurrence (recurrence group), and the recurrence rate was 29.0%; while 277 patients did not experience recurrence (non-recurrence group). The rate of FIB≥0.233 mg/L, D-dimmer ≥565 ng/L, SIRI≥0.62 and SII≥291.5, T 1 stage, high-grade tumor, tumor length ≥2.3 cm and multiple tumor in recurrence group were significantly higher than those in non-recurrence group: 90.3% (102/113) vs. 71.5% (198/277), 33.6% (38/113) vs. 23.5% (65/277), 74.3% (84/113) vs. 56.7% (157/277), 84.1% (95/113) vs. 60.6% (168/277), 77.9% (88/113) vs. 38.6% (107/277), 25.7% (29/113) vs. 8.3% (23/277), 49.6% (56/113) vs. 32.1% (89/277) and 41.6% (47/113) vs. 19.9% (55/277), and there were statistical differences ( P<0.01 or <0.05); there were no statistical differences in gender ratio, age, smoking history, PT, APTT and postoperative instillation drugs between the two groups ( P>0.05). Multivariate Cox regression analysis result showed that FIB≥0.233 mg/L, SII≥291.5, T 1 stage, high pathological grade, tumor length≥2.3 cm and multiple tumor were independent risk factors of postoperative recurrence in patients with NMIBC ( HR = 2.186, 1.627, 3.182, 1.675, 1.775 and 2.052; 95% CI 1.149 to 4.159, 0.913 to 2.902, 1.988 to 5.095, 1.067 to 2.630, 1.208 to 2.608 and 1.388 to 3.033; P<0.1). A nomogram model was constructed to predict postoperative 1-, 3- and 5-year non-recurrence based on FIB, SII, T stage, tumor length, pathological grade and tumor amount. The calibration curve analysis result showed that the nomogram model predicted good consistency between the postoperative 1-, 3-, 5-year non-recurrence rates and the actual incidence rate in patients with NMIBC. ROC curve analysis result showed that the AUC of the nomogram model for predicting postoperative 1-, 3- and 5-year non-recurrence in patients with NMIBC were 0.746, 0.789 and 0.835 (95% CI 0.695 to 0.832, 0.703 to 0.875 and 0.756 to 0.915). The ten-fold cross-validation result showed that the nomogram model had good external applicability for predicting postoperative 1-, 3- and 5-year non-recurrence in patients with NMIBC, with AUC of 0.754, 0.781 and 0.832 (95% CI 0.689 to 0.817, 0.724 to 0.832 and 0.778 to 0.879). Conclusions:The nomogram model based on FIB, SII, T stage, tumor length, pathological grade and tumor amount can accurately predict the postoperative 1-, 3- and 5-year recurrence risks in patients with NMIBC. The model helps clinical doctors early identify high-risk recurrent NMIBC patients, and provides reference for the development of individualized treatment plans.

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.