Varicocele (VC) is a common cause of male infertility, yet there is a lack of molecular information for VC-associated male infertility. This study investigated alterations in the seminal plasma metabolomic and lipidomic profiles of infertile male VC patients. Twenty infertile males with VC and twenty-three age-matched healthy controls (HCs) were recruited from Peking Union Medical College Hospital (Beijing, China) between October 2019 and April 2021. Untargeted metabolite and lipid profiles from seminal plasma were analyzed using mass spectrometry. Four hundred and seventy-six metabolites and seventeen lipids were significantly different in infertile male VC patients compared to HCs. The top enriched pathways among these significantly different metabolites are protein digestion and absorption, aminoacyl-transfer RNA (tRNA) biosynthesis, and biosynthesis of amino acids. Different key lipid species, including triglyceride (TG), diacylglycerol (DG), ceramides (Cer), and phosphatidylserine (PS), varied between VC and HC groups. The distinct metabolites and lipids were moderately correlated. DL-3-phenyllactic acid is a potential diagnostic biomarker for VC-related male infertility (area under the curve [AUC] = 0.893), positively correlating with sperm count, concentration, and motility. Furthermore, DL-3-phenyllactic acid is the only metabolite shared by all four comparisons (VC vs HC, VC-induced oligoasthenospermia [OAS] vs VC-induced asthenospermia [AS], OAS vs HC, and AS vs HC). DL-3-phenyllactic acid significantly decreased in OAS than AS. Metabolite-targeting gene analysis revealed carbonic anhydrase 9 (CA9) might be the strongest candidate associated with the onset and severity of VC. The seminal plasma metabolite and lipid profiles of infertile males with VC differ significantly from those of HCs. DL-3-phenyllactic acid could be a promising biomarker.
Humans ; Male ; Varicocele/complications* ; Infertility, Male/etiology* ; Semen/metabolism* ; Lipidomics ; Adult ; Metabolomics ; Case-Control Studies ; Biomarkers/metabolism*

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

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 compare the short-term clinical outcomes of robotic gastrectomy (RG) and laparoscopic gastrectomy (LG) in treating gastric cancer, as well as the overall 5-year's survival rate.Methods:In this retrospective cohort study, 874 patients undergoing minimally invasive gastrectomy were recruited from Jan 2016 to Jan 2020 (LG: n=719; RG: n=155). A one-to-one propensity score matching analysis was applied to minimize the selection bias due to confounding factors, yielding 150 patients in each of the RG and LG groups. After matching, the short-term outcomes and 5-year overall survival were compared in the two groups. Results:The propensity score matching cohort analysis showed a similar 5-year overall survival between RG and LG groups ( P=0.235). Concerning the short-term outcomes, the RG compared to LG resulted in lower blood loss ( P=0.011), lower postoperative complications ( P=0.001), less postoperative pain ( P=0.014), earlier initiation of soft diet ( P=0.010), shorter hospital stay ( P=0.011), but higher hospitalization expenses ( P=0.004). Conclusions:RG had a similar overall survival outcome compared to LG while offering better safety and efficacy in terms of less blood loss, lower surgery complications, faster recovery, and less postoperative pain. Higher hospital expenses were the main disadvantage of RG that could limit its application.

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.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:To compare the short-term clinical outcomes of robotic gastrectomy (RG) and laparoscopic gastrectomy (LG) in treating gastric cancer, as well as the overall 5-year's survival rate.Methods:In this retrospective cohort study, 874 patients undergoing minimally invasive gastrectomy were recruited from Jan 2016 to Jan 2020 (LG: n=719; RG: n=155). A one-to-one propensity score matching analysis was applied to minimize the selection bias due to confounding factors, yielding 150 patients in each of the RG and LG groups. After matching, the short-term outcomes and 5-year overall survival were compared in the two groups. Results:The propensity score matching cohort analysis showed a similar 5-year overall survival between RG and LG groups ( P=0.235). Concerning the short-term outcomes, the RG compared to LG resulted in lower blood loss ( P=0.011), lower postoperative complications ( P=0.001), less postoperative pain ( P=0.014), earlier initiation of soft diet ( P=0.010), shorter hospital stay ( P=0.011), but higher hospitalization expenses ( P=0.004). Conclusions:RG had a similar overall survival outcome compared to LG while offering better safety and efficacy in terms of less blood loss, lower surgery complications, faster recovery, and less postoperative pain. Higher hospital expenses were the main disadvantage of RG that could limit its application.

Objective To investigate the effect of varying blood pressure stratification on renal function in the diabetic population.Methods A prospective cohort study was conducted,enrolling 9 489 diabetic patients from a total of 101 510 Kailuan Group employees who underwent health examinations between July 2006 and October 2007.The follow-up period was(8.6±4.0)years.Participants were categorized into four groups based on their baseline blood pressure levels:normal blood pressure(systolic blood pressure<120 mmHg and diastolic blood pressure<80 mmHg),elevated blood pressure(systolic blood pressure 120-130 mmHg and diastolic blood pressure<80 mmHg),stage 1 hypertension(systolic blood pressure 130-140 mmHg and/or diastolic blood pressure 80-90 mmHg),and stage 2 hypertension(systolic blood pressure≥140 mmHg and/or diastolic blood pressure≥90 mmHg).The incidence density of chronic kidney disease(CKD)was compared among these groups.A multivariate Cox proportional hazards regression model was employed to assess the effects of different blood pressure levels on renal function in diabetic patients,with the stability of the results confirmed using a multivariate time-dependent Cox proportional hazards model.Sensitivity analysis was conducted after excluding cases of cardiovascular disease(CVD)during follow-up,and cases using antihypertensive and antidiabetic medications at baseline.Results(1)At baseline,stage 1 hypertension patients demonstrated statistically significant higher differences with age and body mass index(BMI)compared to normal blood pressure group(P<0.05).(2)By the end of the follow-up,2 294 cases of CKD were identified,including 1 117 cases of estimated glomerular filtration rate(eGFR)decline and 1 575 cases of urinary protein.The incidences density of CKD,eGFR decline and urinary protein for stage 1 hypertension group were 39.4,16.3 and 25.5 per thousand person-years,respectively,all of which were statistically significant different from normal blood pressure group(log-rank test,P<0.01).(3)Multivariate Cox regression analysis revealed that,compared to the normal blood pressure group,stage 1 hypertension was associated with a 29%increased risk of CKD(HR=1.29,95%CI 1.09-1.52)and a 40%increased risk of eGFR decline(HR=1.40,95%CI 1.08-1.80)in diabetic individuals.Conclusion Stage 1 hypertension significantly increases the risk of CKD and eGFR decline in diabetic individuals,with a particularly notable effect on the risk of eGFR decline.

Objective: To summarize the pathological diagnosis, clinical features, treatment methods and outcomes of pediatric-type follicular lymphoma (PTFL). Methods: Clinical data including the pathology, clinical features, treatment methods, and follow-up results of 9 PTFL patients admitted to Henan Cancer Hospital from February 2017 to February 2023 were analyzed retrospectively. Results: The age of onset in 9 children was 6 to 18 years, all the patients were males. The clinical manifestation was local painless lymph node enlargement in the head and neck, with a stage of Ⅰ-Ⅱ. The histomorphological characteristics of PTFL were similar to those of classic follicular lymphoma (FL). The germinal center of most follicles were enlarged, the mantle zone disappeared, centroblasts were easily visible, and the histological grade were mostly grade Ⅲ, which may be accompanied by the "starry sky" phenomenon. Monoclonal peaks can be seen in B cell clonal rearrangements (BCR). Immunohistochemistry (IHC) showed CD20 positive, CD10 positive, Bcl-6 positive, Bcl-2 negative, C-myc negative, and Ki-67 was 70%-95%. Fluorescence in situ hybridization (FISH) test was negative for t (14, 18), Bcl-2 translocation, and C-myc translocation. Six cases underwent surgical resection, and 3 cases underwent surgical resection combined with chemotherapy. Up to February 2023, with a follow-up time of 45 to 72 months, all children survived without any recurrence and were in a complete remission state. Conclusions: PTFL is mainly characterized by adolescent male onset, with early clinical manifestations and pathological manifestations of high-level histological status, high proliferation index, and lack of t (14; 18)/Bcl-2 translocation and Bcl-2 expression. It is mainly treated by localized surgical excision and has a good prognosis.
Child ; Adolescent ; Humans ; Male ; Female ; Lymphoma, Follicular/pathology* ; Lymphoma, B-Cell/pathology* ; In Situ Hybridization, Fluorescence ; Retrospective Studies ; Proto-Oncogene Proteins c-bcl-2/genetics*

Humans ; Gallbladder Neoplasms/pathology* ; Liver Neoplasms/pathology* ; Immunotherapy