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.

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.

Objective:To investigate the distribution of iodine and other chemical elements in soil, grain and vegetables in different water iodine areas of Xi'an City, and to provide theoretical basis for prevention and treatment of iodine related diseases and water improvement measures.Methods:From September to November 2021, a stratified cluster sampling method was used to select three survey villages in Xi'an, namely Yehu Village, Xingbei Village, and Quanzhong Village, where the iodine level of residents' drinking water < 10, 10 - 100, and > 100 μg/L. Drinking water samples were collected from seven survey sites based on the source of domestic drinking water to determine the iodine level of the water. At the same time, soil samples of cultivated land, residents' grains (wheat, corn), and vegetables were collected from survey villages to determine the content of 14 elements including calcium, iron, magnesium, chromium, nickel, copper, zinc, cadmium, lead, arsenic, mercury, selenium, iodine, and fluoride.Results:The iodine level in the water of 7 survey sites in three survey villages was 5.80, 6.40, 25.51, 42.20, 53.90, 111.65, and 177.63 μg/L, respectively. A total of 70 samples of soil, 65 samples of wheat, 76 samples of corn, and 141 samples of vegetables were collected. Among them, the levels of various elements in vegetables and grains were lower than those in the soil. Only calcium and fluoride were higher in vegetables than in grains, while iron, chromium, copper, zinc, selenium, and iodine were lower in vegetables than in grains. According to the range of water iodine level, there were statistically significant differences in the content of calcium, iron, chromium, nickel, copper, zinc, mercury, iodine, and fluoride in soils from different water iodine regions ( Fcalcium, iron, chromium, nickel = 42.04, 13.55, 12.22, 11.20, Hcopper, zinc, mercury, iodine, fluoride = 14.27, 9.19, 14.72, 25.14, 16.50, P < 0.05). The comparison of magnesium, zinc, cadmium, arsenic, and selenium content in wheat showed statistically significant differences ( Fadmium, selenium = 6.34, 3.31, Hmagnesium, zinc, arsenic = 6.12, 11.55, 6.23, P < 0.05). The comparison of calcium, iron, magnesium, chromium, nickel, copper, zinc, cadmium, lead, arsenic, mercury, iodine, and fluoride content in corn showed statistically significant differences ( Fmagnesium, chromium, nickel, copper, zinc, iodine = 18.89, 13.76, 17.86, 24.17, 28.46, 15.96, Hcalcium, iron, cadmium, lead, arsenic, mercury, fluoride = 11.57, 40.53, 26.50, 33.05, 33.73, 36.01, 29.21, P < 0.05). The comparison of calcium, iron, magnesium, chromium, zinc, cadmium, lead, arsenic, mercury, and selenium content in vegetables showed statistically significant differences ( H = 23.93, 8.12, 10.03, 7.01, 16.09, 18.36, 23.07, 6.51, 27.67, 7.86, P < 0.05). Conclusions:There are significant differences in the distribution of 14 elements in soil, grain, and vegetables in different water iodine regions. In addition to drinking water, precise guidance should also be provided based on the iodine content in soil, grain, and vegetables in different water iodine areas.

Objective:To analyze the predictive factors associated with late-onset sepsis(LOS) in very low birth weight infants,and to develop a risk prediction score scale applicable to these infants three days postnatal.This will provide valuable insights for early diagnosis and timely intervention.Methods:Very low birth weight infants admitted to the Children's Hospital of Fudan University from January 1,2022,to June 30,2024,were selected as research subjects.These infants were categorized into two groups:the LOS group and the non-LOS group,based on whether they developed LOS.LASSO regression analysis,alongside univariate and multivariate regression analyses,was employed to identify predictive factors for LOS in this population.A Logistic model was constructed using the optimal combination of predictive variables,and a risk assessment scale was subsequently developed.The prediction performance of the model was evaluated using the Hosmer-Lemeshow chi-square test and the receiver operating characteristic curve.Results:A total of 444 cases of very low birth weight infants were included,of which 185 had LOS and 259 did not.After screening the variables,seven independent factors were included into the model:birth weight,gestational age,tracheal intubation,abnormal skin color,abdominal distension,elevated C-reactive protein levels,and right hand perfusion index.A predictive scoring scale was developed based on the regression coefficients of each variable,with corresponding risk scores assigned as follows:1,4,3,2,1,1,and 2; a score of ≥3.5 indicated high-risk groups.The Hosmer-Lemeshow test results demonstrated that χ2 = 7.602( P = 0.473).The area under the receiver operating characteristic curve was 0.792 ( P<0.001),with a sensitivity of 73.5% and specificity of 71.0%. Conclusion:The risk score scale developed in this study exhibits significant predictive capability,providing valuable insights for clinical medical personnel to assess the risk of LOS in very low birth weight infants during the early postnatal period.

This study analyzed the epidemiological characteristics of imported malaria in Yunnan Province from 2020 to 2023,to provide scientific evidence for formulating measures to decrease imported malaria and prevent re-establishment of malaria transmission.Malaria data reported by the China Disease Prevention and Control Information System were analyzed to determine parasite species;sources of infection;temporal,spatial,and population distributions;and importation routes.A total of 828 malaria cases were reported in the province.Plasmodium vivax and Plasmodium falciparum accounted for 89.98％and 8.33％of cases,respectively.A total of 47.58％of cases were imported from Myanmar,and all P.falciparum malaria ca-ses were from Africa.Thirteen(81.25％)prefectures or municipalities reported malaria,among which Dehong,Baoshan,Kunming,and Lincang reported 94.32％of cases.A total of 52.54％of cases were in young men.The proportion of cross-bor-der personnel flow,land input,and aircraft input were 88.89％and 11.11％respectively.A total of 98.19％of patients sought medical care within 7 days after fever onset,and 82.85％initiated diagnosis for malaria,and 84.90％of diagnoses were con-firmed by health facilities at or below the county level.Imported malaria is a major challenge in preventing re-establishment of transmission in Yunnan.Most imported cases involved cross-border malaria transmission of mainly Plasmodium vivax between China and Myanmar.To achieve malaria elimination,vigilance of health staff in malaria diagnosis and treatment should be pro-moted,and intensive malaria health education should be provided to people traveling to malaria endemic territories,to enable individual protection,and timely diagnosis and treatment after return from endemic countries.

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.

This study analyzed the epidemiological characteristics of imported malaria in Yunnan Province from 2020 to 2023,to provide scientific evidence for formulating measures to decrease imported malaria and prevent re-establishment of malaria transmission.Malaria data reported by the China Disease Prevention and Control Information System were analyzed to determine parasite species;sources of infection;temporal,spatial,and population distributions;and importation routes.A total of 828 malaria cases were reported in the province.Plasmodium vivax and Plasmodium falciparum accounted for 89.98％and 8.33％of cases,respectively.A total of 47.58％of cases were imported from Myanmar,and all P.falciparum malaria ca-ses were from Africa.Thirteen(81.25％)prefectures or municipalities reported malaria,among which Dehong,Baoshan,Kunming,and Lincang reported 94.32％of cases.A total of 52.54％of cases were in young men.The proportion of cross-bor-der personnel flow,land input,and aircraft input were 88.89％and 11.11％respectively.A total of 98.19％of patients sought medical care within 7 days after fever onset,and 82.85％initiated diagnosis for malaria,and 84.90％of diagnoses were con-firmed by health facilities at or below the county level.Imported malaria is a major challenge in preventing re-establishment of transmission in Yunnan.Most imported cases involved cross-border malaria transmission of mainly Plasmodium vivax between China and Myanmar.To achieve malaria elimination,vigilance of health staff in malaria diagnosis and treatment should be pro-moted,and intensive malaria health education should be provided to people traveling to malaria endemic territories,to enable individual protection,and timely diagnosis and treatment after return from endemic countries.

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.