Objective:To investigate the feasibility and value of nomogram based on base line clinical and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) characteristics for pretreatment predicting the depth of myometrial invasion and tumor grade of endometrioid endometrial carcinoma (EEC).Methods:Preoperative baseline clinical characteristics and DCE-MRI characteristics of 194 EEC patients were prospectively collected at Obstetrics and Gynecology Hospital, Fudan University from October 2020 to January 2022 and used as a training set. Univariate analysis was conducted to compare baseline clinical characteristics and DCE-MRI quantitative parameters [including tumor volume, and mean, median, and standard deviation of volume transfer constant (K trans), rate constant (K ep), extravascular extracellular volume fraction (V e), and initial area under the enhancement curve (iAUC)] between patients with deep myometrial invasion (DMI) and those with superficial myometrial invasion (SMI), as well as between high-grade and low-grade EEC. Multivariate logistics regression analysis was used to identify independent predictors for the construction of nomogram. An independent external testing set comprising 127 EEC patients was retrospectively collected from Zhongshan Hospital, Fudan University and Zhongshan Hospital, Fudan University (Xiamen Branch). The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used for evaluating the model′s predictive performance and clinical net benefit, respectively. Results:(1) The depth of myometrial invasion: univariate analysis showed that in the training set, the EEC patients with DMI differed significantly from those with SMI in clinical characteristics including higher proportion of postmenopausal state and overweight [body mass index (BMI)≥25 kg/m2], and abnormal levels of serum cancer antigen (CA) 125, CA 199, and human epididymis protein 4 (HE4), and in DCE-MRI quantitative parameters including tumor volume, and median, mean, and standard deviation of K trans, median of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, CA 199, tumor volume, and mean of iAUC were independent predictors of the depth of myometrial invasion, and constructed the nomogram (recorded as Nomogram_1), achieving an AUC of 0.861 (95% CI: 0.803-0.919) in the training set. In the independent external testing set, the AUC was 0.876 (95% CI: 0.815-0.938), with corresponding sensitivity of 82.0%, specificity of 80.7%, accuracy of 81.1%, positive predictive value (PPV) of 65.3%, and negative predictive value (NPV) of 91.0% for predicting DMI. (2) The EEC grade: univariate analysis showed that in the training set, high-grade EEC patients differed significantly from low-grade EEC in clinical characteristics including patient′s age, the proportion of postmenopausal state and overweight, and abnormal levels of serum CA 125, and in DCE-MRI quantitative parameters including tumor volume, median, mean, and standard deviation of K trans, median and mean of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, tumor volume, and median of V e emerged as independent predictors of EEC grade, and constructed the nomogram (recorded as Nomogram_2), achieving an AUC of 0.845 (95% CI: 0.786-0.893) in the training set. While in the external testing set, the AUC was 0.819 (95% CI: 0.744-0.894), with corresponding sensitivity of 72.4%, specificity of 72.4%, accuracy of 72.4%, PPV of 43.8%, and NPV of 89.9% for predicting high-grade EEC. (3) The DCA curves demonstrated that both Nomogram_1 and Nomogram_2 yielded obvious positive clinical net benefits across a wide range of threshold probabilities. Conclusion:The nomogram based on pretreatment clinical and DCE-MRI characteristics has the potential to noninvasive predict the depth of myometrial invasion and grade of EEC, providing valuable reference information for clinical management decision-making.

Objective:To investigate the feasibility and value of nomogram based on base line clinical and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) characteristics for pretreatment predicting the depth of myometrial invasion and tumor grade of endometrioid endometrial carcinoma (EEC).Methods:Preoperative baseline clinical characteristics and DCE-MRI characteristics of 194 EEC patients were prospectively collected at Obstetrics and Gynecology Hospital, Fudan University from October 2020 to January 2022 and used as a training set. Univariate analysis was conducted to compare baseline clinical characteristics and DCE-MRI quantitative parameters [including tumor volume, and mean, median, and standard deviation of volume transfer constant (K trans), rate constant (K ep), extravascular extracellular volume fraction (V e), and initial area under the enhancement curve (iAUC)] between patients with deep myometrial invasion (DMI) and those with superficial myometrial invasion (SMI), as well as between high-grade and low-grade EEC. Multivariate logistics regression analysis was used to identify independent predictors for the construction of nomogram. An independent external testing set comprising 127 EEC patients was retrospectively collected from Zhongshan Hospital, Fudan University and Zhongshan Hospital, Fudan University (Xiamen Branch). The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used for evaluating the model′s predictive performance and clinical net benefit, respectively. Results:(1) The depth of myometrial invasion: univariate analysis showed that in the training set, the EEC patients with DMI differed significantly from those with SMI in clinical characteristics including higher proportion of postmenopausal state and overweight [body mass index (BMI)≥25 kg/m2], and abnormal levels of serum cancer antigen (CA) 125, CA 199, and human epididymis protein 4 (HE4), and in DCE-MRI quantitative parameters including tumor volume, and median, mean, and standard deviation of K trans, median of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, CA 199, tumor volume, and mean of iAUC were independent predictors of the depth of myometrial invasion, and constructed the nomogram (recorded as Nomogram_1), achieving an AUC of 0.861 (95% CI: 0.803-0.919) in the training set. In the independent external testing set, the AUC was 0.876 (95% CI: 0.815-0.938), with corresponding sensitivity of 82.0%, specificity of 80.7%, accuracy of 81.1%, positive predictive value (PPV) of 65.3%, and negative predictive value (NPV) of 91.0% for predicting DMI. (2) The EEC grade: univariate analysis showed that in the training set, high-grade EEC patients differed significantly from low-grade EEC in clinical characteristics including patient′s age, the proportion of postmenopausal state and overweight, and abnormal levels of serum CA 125, and in DCE-MRI quantitative parameters including tumor volume, median, mean, and standard deviation of K trans, median and mean of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, tumor volume, and median of V e emerged as independent predictors of EEC grade, and constructed the nomogram (recorded as Nomogram_2), achieving an AUC of 0.845 (95% CI: 0.786-0.893) in the training set. While in the external testing set, the AUC was 0.819 (95% CI: 0.744-0.894), with corresponding sensitivity of 72.4%, specificity of 72.4%, accuracy of 72.4%, PPV of 43.8%, and NPV of 89.9% for predicting high-grade EEC. (3) The DCA curves demonstrated that both Nomogram_1 and Nomogram_2 yielded obvious positive clinical net benefits across a wide range of threshold probabilities. Conclusion:The nomogram based on pretreatment clinical and DCE-MRI characteristics has the potential to noninvasive predict the depth of myometrial invasion and grade of EEC, providing valuable reference information for clinical management decision-making.

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 explore the effects of smoking on peripheral regulatory T cells (Tregs), transforming growth factor beta1 (TGF-β 1) and interleukin-10 (IL-10) in elderly patients with non-small cell lung cancer (NSCLC). Methods:This was a cross-sectional study. A total of 43 elderly patients (≥60 years old) who were hospitalized in the Department of Geriatrics Medicine, Guangzhou First People′s Hospital from January 2018 to December 2024 and were newly diagnosed with NSCLC were recruited. According to smoking history, patients were divided into non-smoking group (15 cases), low smoking group (13 cases, smoking index<400) and high smoking group (15 cases, smoking index≥400). Venous blood samples were collected from participants, plasma and cells were separated. Flow cytometry was used to measure the proportions of Tregs and the expression of forkhead box P3 (Foxp3) in peripheral blood. Plasma levels of TGF-β 1 and IL-10 were measured by enzyme-linked immunosorbent assay. The effects of smoking on peripheral Tregs, TGF-β 1, and IL-10 in elderly patients with NSCLC were analyzed. Data were analyzed by one-way ANOVA, rank-sum test, and Fisher′s exact test. Results:The proportions of Tregs in non-smoking group, low smoking group and high smoking group were 2.50% (2.32%, 2.81%), 2.83% (2.48%, 3.72%), and 3.01% (2.37%, 3.73%), respectively, and there were no statistically significant differences among the three groups ( H=3.845, P>0.05). The proportions of Foxp3 +Tregs were (3.72±0.84)%, (4.64±1.10)%, and (4.68±1.27%), respectively. The mean fluorescence intensities (MFI) of Foxp3 were 123.0 (108.0, 128.0), 131.0 (123.5, 350.0), and 222.0 (141.0, 311.0), respectively. Both the proportions of Foxp3 +Tregs and the MFI of Foxp3 were higher in low smoking group and high smoking group than those in non-smoking group (all P<0.05). However, there were no significant differences between low smoking group and high smoking group (all P>0.05). The concentrations of IL-10 were 2.27 (1.42, 3.95), 3.42 (2.30, 5.08), and 3.26 (2.35, 6.28) ng/L, respectively. There were no statistically significant differences among the three groups ( H=2.930, P>0.05). The concentrations of TGF-β 1 were (10.72±9.37), (13.46±10.39), and (25.28±16.67) ng/ml, respectively. The concentration of TGF-β 1 in high smoking group was higher than that in non-smoking group and low smoking group (all P<0.05). However, there was no statistically significant difference between low smoking group and non-smoking group ( P>0.05). Conclusions:Smoking intensity may be correlated with the immunosuppressive function of Tregs in elderly patients with NSCLC. Higher smoking levels are associated with increased Foxp3 expression in Tregs and elevated plasma levels of TGF-β 1, potentially enhancing the immunosuppressive function of Tregs.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

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 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 explore the effects of smoking on peripheral regulatory T cells (Tregs), transforming growth factor beta1 (TGF-β 1) and interleukin-10 (IL-10) in elderly patients with non-small cell lung cancer (NSCLC). Methods:This was a cross-sectional study. A total of 43 elderly patients (≥60 years old) who were hospitalized in the Department of Geriatrics Medicine, Guangzhou First People′s Hospital from January 2018 to December 2024 and were newly diagnosed with NSCLC were recruited. According to smoking history, patients were divided into non-smoking group (15 cases), low smoking group (13 cases, smoking index<400) and high smoking group (15 cases, smoking index≥400). Venous blood samples were collected from participants, plasma and cells were separated. Flow cytometry was used to measure the proportions of Tregs and the expression of forkhead box P3 (Foxp3) in peripheral blood. Plasma levels of TGF-β 1 and IL-10 were measured by enzyme-linked immunosorbent assay. The effects of smoking on peripheral Tregs, TGF-β 1, and IL-10 in elderly patients with NSCLC were analyzed. Data were analyzed by one-way ANOVA, rank-sum test, and Fisher′s exact test. Results:The proportions of Tregs in non-smoking group, low smoking group and high smoking group were 2.50% (2.32%, 2.81%), 2.83% (2.48%, 3.72%), and 3.01% (2.37%, 3.73%), respectively, and there were no statistically significant differences among the three groups ( H=3.845, P>0.05). The proportions of Foxp3 +Tregs were (3.72±0.84)%, (4.64±1.10)%, and (4.68±1.27%), respectively. The mean fluorescence intensities (MFI) of Foxp3 were 123.0 (108.0, 128.0), 131.0 (123.5, 350.0), and 222.0 (141.0, 311.0), respectively. Both the proportions of Foxp3 +Tregs and the MFI of Foxp3 were higher in low smoking group and high smoking group than those in non-smoking group (all P<0.05). However, there were no significant differences between low smoking group and high smoking group (all P>0.05). The concentrations of IL-10 were 2.27 (1.42, 3.95), 3.42 (2.30, 5.08), and 3.26 (2.35, 6.28) ng/L, respectively. There were no statistically significant differences among the three groups ( H=2.930, P>0.05). The concentrations of TGF-β 1 were (10.72±9.37), (13.46±10.39), and (25.28±16.67) ng/ml, respectively. The concentration of TGF-β 1 in high smoking group was higher than that in non-smoking group and low smoking group (all P<0.05). However, there was no statistically significant difference between low smoking group and non-smoking group ( P>0.05). Conclusions:Smoking intensity may be correlated with the immunosuppressive function of Tregs in elderly patients with NSCLC. Higher smoking levels are associated with increased Foxp3 expression in Tregs and elevated plasma levels of TGF-β 1, potentially enhancing the immunosuppressive function of Tregs.

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.

ObjectiveTo investigate the effects of Aconiti Coreani Radix and Typhonii Rhizoma on the urinary metabolites of gerbils with stroke by non-targeted metabolomics technique， and then to clarify the mechanism of the two， as well as their similarities and differences. MethodTwenty-four gerbils were randomly divided into control group（CG）， model group（MG）， Aconiti Coreani Radix group（RA） and Typhonii Rhizoma group（RT）. Except for the CG， ischemic stroke model was constructed using right unilateral ligation of gerbil carotid artery in the remaining groups. Except for the CG and MG， rats in the other groups received whole powder suspension（0.586 mg·g^-1） was administered for 14 days. The neurological deficit in each group was scored by Longa scoring on days 0， 3， 7 and 14. After the end of administration， the serum， brain tissue and urine of gerbils in each group were collected， and the rate of cerebral infarction was detected by 2，3，5-triphenyltetrazolium chloride（TTC）， and the levels of interleukin（IL）-6， tumor necrosis factor（TNF）-α， malondialdehyde（MDA）， superoxide dismutase（SOD）， glutathione（GSH）， and nitric oxide（NO） in serum and brain tissue were determined by enzyme-linked immunosorbent assay（ELISA）. The urine metabolomics of gerbils in each group was studied by ultra performance liquid chromatography-quadrupole-electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， and the data were processed by multivariate statistical analysis， and differential metabolites were screened based on value of variable importance in the projection（VIP） of the first principal component>1 and t-test P<0.05. Metabolic pathway analysis of the screened differential metabolites was performed using Kyoto Encyclopedia of Genes and Genomes（KEGG） database and Metaboanalyst 5.0. ResultCompared with the CG， the neurological deficit score was significantly increased in the MG（P<0.05）， compared with the MG， the neurological deficit scores in the RA and RT were significantly reduced after 7 d and 14 d（P<0.05）. Compared with the CG， the rate of cerebral infarction was significantly increased in the MG（P<0.05）， compared with the MG， the rates of cerebral infarction in the RA and RT were significantly reduced（P<0.05）. Compared with the CG， the levels of IL-6， TNF-α， and MDA in the serum and brain tissue of gerbils from the MG were significantly increased（P<0.05）， and the levels of SOD， GSH and NO were significantly reduced（P<0.05）. Compared with the MG， Aconiti Coreani Radix and Typhonii Rhizoma could down-regulate the levels of IL-6， TNF-α and MDA， and up-regulated the levels of SOD， GSH and NO. A total of 112 endogenous differential metabolites were screened by urine metabolomics， of which 16 and 26 metabolites were called back by Aconiti Coreani Radix and Typhonii Rhizoma， and could be used as potential biomarkers for both treatments in stroke gerbils， respectively. The results of the pathway analysis showed that both Aconiti Coreani Radix and Typhonii Rhizoma had regulatory effects on arginine and proline metabolism， pyrimidine metabolism， and aminoacyl-tRNA biosynthesis. In addition， Aconiti Coreani Radix could also regulate riboflavin metabolism， Typhonii Rhizoma could also regulate purine metabolism， glycine， serine and threonine metabolism， arachidonic acid metabolism， biosynthesis of pantothenate and coenzyme A， and β-alanine metabolism. ConclusionBoth Aconiti Coreani Radix and Typhonii Rhizoma have better therapeutic effects on stroke， with Aconiti Coreani Radix having stronger effects. From the metabolomics results， the main metabolic pathways regulated by Aconiti Coreani Radix involve amino acid metabolism， oxidative stress and so on， while Typhonii Rhizoma mainly involve amino acid metabolism， lipid metabolism， energy metabolism， etc.