Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background and purpose:Diffuse large B-cell lymphoma(DLBCL)is subclassified into germinal center B-cell-like(GCB)and non-GCB subtypes,which differ in prognosis and treatment response.However,current distinction still relies on invasive pathological assays.This study developed radiomics and deep-learning models based on multiparametric magnetic resonance imaging(MRI)to non-invasively differentiate the two subtypes preoperatively,thereby reducing dependence on histopathological examination.Methods:This study retrospectively included patients with pathologically confirmed DLBCL diagnosed at Huashan Hospital,Fudan University,and other institutions between March 2013 and December 2024.Using multiparametric MRI data,we developed DLBCL-subtype classification models that combined 4 radiomics-based machine-learning algorithms:support vector machine(SVM),logistic regression(LR),Gaussian process(GP)and Naive Bayes(NB),with 3 deep-learning architectures[densely-connected convolutional networks 121(DenseNet121),residual network 101(ResNet101)and EfficientNet-b5].Additionally,two radiologists with different experience levels independently classified DLBCL on MRI in a blinded fashion.Model and radiologist performance were quantified using the area under the receiver operating characteristic curve(AUC),accuracy(ACC),and F1-score to evaluate their ability to distinguish GCB from non-GCB subtypes.This study was approved by the Ethics Committee of Huashan Hospital of Fudan University(No.KY2024-663),and all patients signed informed consents.Results:A total of 173 patients were enrolled(55 with GCB subtype and 118 with non-GCB subtype).Radiomics and deep learning methods effectively distinguished DLBCL subtypes.Among these,the GP radiomics model(based on T1-CE+T2-FLAIR+ADC sequences)and DenseNet121 deep learning model(based on T1-CE+T2-FLAIR+ADC sequences)demonstrated optimal performance.Both achieved excellent results on the internal validation set(GP:AUC=0.900,ACC=0.896,F1=0.840;DenseNet121:AUC=0.846,ACC=0.854,F1=0.774)and maintained robustness on the external validation set.Furthermore,the classification efficacy of the optimal AI model surpassed that of experienced radiologists(highest physician AUC=0.678).Conclusion:Radiomics and deep-learning models based on multiparametric MRI features can effectively differentiate GCB from non-GCB subtypes of DLBCL.Among them,GP and DenseNet121 exhibit outstanding performance,especially when integrating multi-sequence feature sets for classifying DLBCL subtypes on complex imaging data.

Background and purpose:Diffuse large B-cell lymphoma(DLBCL)is subclassified into germinal center B-cell-like(GCB)and non-GCB subtypes,which differ in prognosis and treatment response.However,current distinction still relies on invasive pathological assays.This study developed radiomics and deep-learning models based on multiparametric magnetic resonance imaging(MRI)to non-invasively differentiate the two subtypes preoperatively,thereby reducing dependence on histopathological examination.Methods:This study retrospectively included patients with pathologically confirmed DLBCL diagnosed at Huashan Hospital,Fudan University,and other institutions between March 2013 and December 2024.Using multiparametric MRI data,we developed DLBCL-subtype classification models that combined 4 radiomics-based machine-learning algorithms:support vector machine(SVM),logistic regression(LR),Gaussian process(GP)and Naive Bayes(NB),with 3 deep-learning architectures[densely-connected convolutional networks 121(DenseNet121),residual network 101(ResNet101)and EfficientNet-b5].Additionally,two radiologists with different experience levels independently classified DLBCL on MRI in a blinded fashion.Model and radiologist performance were quantified using the area under the receiver operating characteristic curve(AUC),accuracy(ACC),and F1-score to evaluate their ability to distinguish GCB from non-GCB subtypes.This study was approved by the Ethics Committee of Huashan Hospital of Fudan University(No.KY2024-663),and all patients signed informed consents.Results:A total of 173 patients were enrolled(55 with GCB subtype and 118 with non-GCB subtype).Radiomics and deep learning methods effectively distinguished DLBCL subtypes.Among these,the GP radiomics model(based on T1-CE+T2-FLAIR+ADC sequences)and DenseNet121 deep learning model(based on T1-CE+T2-FLAIR+ADC sequences)demonstrated optimal performance.Both achieved excellent results on the internal validation set(GP:AUC=0.900,ACC=0.896,F1=0.840;DenseNet121:AUC=0.846,ACC=0.854,F1=0.774)and maintained robustness on the external validation set.Furthermore,the classification efficacy of the optimal AI model surpassed that of experienced radiologists(highest physician AUC=0.678).Conclusion:Radiomics and deep-learning models based on multiparametric MRI features can effectively differentiate GCB from non-GCB subtypes of DLBCL.Among them,GP and DenseNet121 exhibit outstanding performance,especially when integrating multi-sequence feature sets for classifying DLBCL subtypes on complex imaging data.

Objective:To explore the medical costs and influencing factors of patients diagnosed with Brucellosis in Xinjiang Uygur Autonomous Region (Xinjiang).Methods:Information on demographics, medical visits, and costs of patients diagnosed with Brucellosis were collected between January 1,2017 and December 31,2019. The effects of different genders, age groups, clinical stages, and comorbidities on patients' health care utilization and medical costs were analyzed by nonparametric tests. The median was used to describe the outpatient and inpatient costs of patients with Brucellosis.Results:A total of 13 532 patients (8 113 outpatient and 5 419 inpatient cases) were included in the analysis. A total of 67.8% (9 176/13 532) were male, with an average age of (42.7±15.4 ) years; age between 18-44 years (46.6%, 6 304/13 532) and 45-59 years (34.2%,4 622/13 532) were the dominant groups. The mean age of inpatients [(43.3±15.7) years] was higher than that of outpatients [(42.3±15.1) years, Z=-3.85, P<0.001]. When hospitalized patients are treated,systemic symptoms were common with fever (36.9%, 1 997/5 419) and fatigue (36.6%, 1 983/5 419), and with joint/muscle pain (68.9%, 3 735/5 419) being the highest proportion of local symptoms. A total of 79.1% (4 289/5 419) of inpatients were diagnosed with acute Brucellosis. A total of 46.5% (2 519/5 419) of inpatients had complications;skeletal system complications ranked the highest. The average number of outpatient visits per outpatient was (1.6±1.4) times. The duration of hospitalization was (11.3±4.2) days, with longer days for patients in the chronic phase and with complications ( P<0.05). A total of 89.3% (4 840/5 419) of inpatients had outpatient records in the same year,and the average number of outpatient visits per patient was (3.6±2.6) times. Outpatient medical costs were dominated by laboratory and drug costs (75.1%), and inpatient costs were dominated by drug, laboratory, and other costs (74.4%). Outpatient medical expenses M( Q1, Q3) were 61(52, 497) Yuan, 61 (51, 346) Yuan and 58 (46, 318) Yuan,respectively. Inpatients' medical expenses M ( Q1, Q3) were 8 214 (6 355, 10 721) Yuan,9 095 (7 018, 12 155) Yuan and 9 492 (7 530, 12 351) Yuan, respectively. For patients, age, clinical stages,complications,and joint/muscle pain symptoms were influential factors for hospitalization costs ( P<0.001). Conclusions:The economic burden was higher for inpatients, especially those in the high age group, with chronic phases and skeletal and neurological complications. Improving patients' awareness of early treatment, standardized treatment, and reducing chronicity and complications are the main points in reducing the economic burden caused by Brucellosis diagnosis and treatment.

Objective:To explore the medical costs and influencing factors of patients diagnosed with Brucellosis in Datong of Shanxi province.Methods:Information on demographics, medical visits, and costs of patients diagnosed with Brucellosis between January 1, 2017, and December 31, 2019, were collected. Health care utilization and medical costs were analyzed from different genders, age groups, underlying diseases, clinical stages, and comorbidities.Results:A total of 2 289 patients (1 715 outpatient and 574 inpatient cases) were included in the analysis. 72.0% (1 649/2 289) were male, with an average age of (49.6±15.5) years; age between 45-59 years was the dominant group (36.2%,829/2 289). The mean age of inpatients (51.4±16.0) was higher than that of outpatients (49.0±15.2)( Z=-4.01, P<0.001). The average number of outpatient visits per outpatient was (1.6±1.5) times. The duration of hospitalization was (14.6±9.9) and (20.8±11.4) days for patients with central nervous system complications and (16.6±9.5) days for vascular system complications. Of the inpatients, 51.0% (293/574) had underlying diseases, and 30.3% (174/574) had endocrine and metabolic diseases. 54.0% (310/574) of inpatients were diagnosed with acute Brucellosis, and 46.0% (264/574) were diagnosed with chronic Brucellosis. A total of 64.3% (369/574) of inpatients had complications, 30.3% (174/574) of digestive system complications, followed by skeletal system complications (29.1%, 167/574). Among outpatients, age significantly affected medical costs ( P<0.001). For inpatients, age and complications and treatment effect were influential factors ( P<0.05). Patients with the combined skeletal system and central nervous system complications had significantly higher medical costs ( P<0.001). Conclusions:The medical costs for outpatient cases of Brucellosis were moderate. However, the economic burden was higher for inpatients, especially those with skeletal and neurological complications. Early detection, diagnosis, and treatment of cases were essential to avoid chronic Brucellosis and its complications and reduce medical costs.

Objective To explore the clinical effect of SaO2and heart rate(HR)for the diagnosis of acute mountain sickness (AMS).Methods A total of 1 062 male soldiers on garrison duty in rapidly entering to high altitude at 3 700-5 400 m from May 2013 to August 2015 were included as the research subjects.The demography data were collected and the AMS symptoms investiga-tion was performed.SaO2and HR were detected and the relationship between SaO2and HR at different altitudes with AMS symp-tom score was analyzed.Results The cut-off value of SaO2for diagnosing AMS in rapidly entering to high altitude at 3 700-<4 300 m was 84.5%(AUC=0.781)with the screening sensitivity of 78.31% and specificity of 72.02%;which of HR for diag-nosing AMS was 89.5 times/min(AUC=0.640)with the screening sensitivity of 71.27% and specificity of 54.63%.When SaO2 was serially connected with HR,its sensitivity was 58.87% and the specificity was 89.23%,while the parallel connection yielded a sensitivity of 90.70% and a specificity of 37.43%.In rapidly entering the altitude at above 5000 m,the cut-off value of SaO2for di-agnosing AMS was 80.5%(AUC=0.825)with the screening sensitivity of 84.62% and specificity of 68.85%;which of HR for di-agnosing AMS was 93.5 times/min(AUC= 0.718)with the screening sensitivity of 53.00% and specificity of 85.25%;when SaO2was serially connected with HR,its sensitivity for diagnosing AMS was 47.01% and specificity was 93.44%,while the paral-lel connection yielded a sensitivity of 90.60% and a specificity of 60.66%.Conclusion SaO2combined with HR can serve as an ob-jective index for the on-site diagnosis of AMS and is convenient for the AMS preliminary screening of large populations.

OBJECTIVE: To observe expression and distribution of histamine H4 receptor in nasal mucosa in normal people and allergic rhinitis patients,and understand role of histamine H4 receptor in allergic rhinitis. METHOD: Select normal people and allergic rhinitis patients each 10, take the nasal mucosa, detect expression and distribution of histamine H4 receptor at proteins and transcription level respectively by immunohistochemical method and RT-PCR, and compared. RESULT: Histamine H4 receptor at proteins and transcription level were found in normal nasal mucosa (25 509 +/- 6 441, 0.42 +/- 0.08), increased significantly in nasal mucosa of allergic rhinitis patients (49 676 +/- 8 541, 0.69 +/- 0.11, P < 0.05), which in structural cells and immune cells. CONCLUSION Histamine H4 receptors exist in normal nasal mucosa, its express significantly enhance, flew histamine H4 receptor may be mediated histamine in pathogenesis of allergic rhinitis ,who is one of the ligands of histamine.
Case-Control Studies ; Humans ; Nasal Mucosa ; immunology ; metabolism ; pathology ; Receptors, G-Protein-Coupled ; metabolism ; Receptors, Histamine ; metabolism ; Receptors, Histamine H4 ; Rhinitis, Allergic, Perennial ; immunology ; metabolism ; pathology