ObjectiveTo investigate the association between metabolic associated fatty liver disease （MAFLD） and the risk of atherosclerotic cardiovascular disease （ASCVD）， and to provide data support for the prevention and treatment of such metabolic-associated diseases in clinical practice. MethodsAn observation cohort was established for the workers of Kailuan who underwent physical examination for the first time from June 2006 to October 2007 and had complete liver assessment data， without the history of malignant tumor， MAFLD or ASCVD. According to the presence or absence of MAFLD， the patients were divided into non-MAFLD group with 67 565 patients and MAFLD group with 29 004 patients， and according to the presence or absence of ASCVD， the patients were divided into non-ASCVD group with 69 141 patients and ASCVD group with 481 patients. The group t-test or the Wilcoxon rank-sum test was used for comparison of continuous data between the two groups. The χ² test was used for comparison of categorical data between the two groups. The life-table method was used to calculate the cumulative incidence rates of new-onset ASCVD and MAFLD； the Kaplan-Meier method was used to plot the survival curves of the cumulative incidence rates of ASCVD in the MAFLD group and the non-MAFLD group and the cumulative incidence rates of MAFLD in the ASCVD group and the non-ASCVD group， and the log-rank test was used for comparison of cumulative incidence rates between two groups. A multivariate Cox proportional-hazards regression model analysis was used to investigate the impact of MAFLD on the risk of ASCVD and the impact of ASCVD on the risk of MAFLD. ResultsCompared with the non-MAFLD group， the MAFLD group had significantly higher levels of body mass index （BMI）， waist circumference， systolic blood pressure （SBP）， diastolic blood pressure （DBP）， resting heart rate， alanine aminotransferase， uric acid （UA）， fasting blood glucose （FBG）， triglyceride （TG）， total cholesterol， low-density lipoprotein cholesterol， and high sensitivity C-reactive protein （hs-CRP）， as well as significanty lower levels of estimated glomerular filtration rate （eGFR） and high-density lipoprotein cholesterol （all P <0.05）. Compared with the non-ASCVD group， the ASCVD group had significantly higher levels of BMI， waist circumference， SBP， DBP， UA， FBG， TG， and hs-CRP and a significantly lower level of eGFR （all P<0.05）. The incidence rate of new-onset ASCVD continued to increase over time in the MAFLD group and the non-MAFLD group， while the incidence rate of new-onset MAFLD firstly increased and then remained stable over time in the ASCVD group and the non-ASCVD group. There were 4 263 cases （14.70%） of new-osnet ASCVD in the MAFLD group， with an incidence density of 12.90 per 1 000 person-years， while there were 6 529 cases （9.66%） of new-osnet ASCVD in the non-MAFLD group， with an incidence density of 8.24 per 1 000 person-years， and there were significant differences in the incidence density and cumulative incidence rate of ASCVD between the two groups （χ²=519.09 and 531.80， both P<0.05）. There were 148 cases （30.77%） of new-onset MAFLD in the ASCVD group， with an incidence density of 40.10 per 1 000 person-years， while there were 32 194 cases （46.56%） of new-onset MAFLD in the non-ASCVD group， with an incidence density of 57.59 per 1 000 person-years， and there were significant differences in the incidence density and cumulative incidence rate of MAFLD between the two groups （χ²=19.29 and 30.78， both P<0.05）. The corrected multivariate Cox proportional-hazards regression model analysis showed that MAFLD was a risk factor for new-onset ASCVD （hazard ratio ［HR］=1.11， 95% confidence interval ［CI］： 1.06 — 1.16， P<0.001）， while ASCVD was a protective factor against new-onset MAFLD （HR=0.72， 95%CI： 0.61 — 0.85， P<0.001）. ConclusionThere is a significant association between MAFLD and ASCVD， with an increase in the risk of ASCVD in the MAFLD population and a reduction in the risk of MAFLD in the ASCVD population.

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.

The prefrontal cortex (PFC) plays a pivotal role in orchestrating higher-order emotional and cognitive processes, a function that depends on the precise modulation of synaptic activity. Although pharmacological studies have demonstrated that dopamine signaling through dopamine D1 receptor (DRD1) in the PFC is essential for these functions, the cell-type-specific and molecular mechanisms underlying the neuromodulatory effects remain elusive. Using cell-type-specific knockout mice and patch-clamp recordings, we investigated the regulatory role of DRD1 on neurons and astrocytes in synaptic transmission and plasticity. Furthermore, we explored the mechanisms by which DRD1 on astrocytes regulate synaptic transmission and plasticity at the cellular level, as well as emotional and cognitive functions at the behavioral level, through two-photon imaging, microdialysis, high-performance liquid chromatography, transcriptome sequencing, and behavioral testing. We found that conditional knockout of the Drd1 in astrocytes (CKO^AST) increased glutamatergic synaptic transmission and long-term potentiation (LTP) in the medial prefrontal cortex (mPFC), whereas Drd1 deletion in pyramidal neurons did not affect synaptic transmission. The elevated level of d-serine in the mPFC of CKO^AST mice increased glutamatergic transmission and LTP through NMDA receptors. In addition, CKO^AST mice exhibited abnormal emotional and cognitive function. Notably, these behavioral changes in CKO^AST mice could be reversed through the administration of d-serine degrease to the mPFC. These results highlight the critical role of the astrocytic DRD1 in modulating mPFC synaptic transmission and plasticity, as well as higher brain functions through d-serine, and may shed light on the treatment of mental disorders.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

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.

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

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.

Objective:To predict the monthly incidence of hemorrhagic fever with renal syndrome (HFRS) in Hebei Province by using the generalized additive model (GAM).Methods:The incidence data of HFRS in Hebei from 2006 to 2020 were collected, and the correlation coefficients between meteorological factors and the monthly incidence of HFRS in Hebei were analyzed by Spearman's correlation, and the meteorological factors were lagged by 0-6 orders, and those with the largest absolute values of the correlation coefficients were screened to be included in the multifactorial GAM to evaluate the effects of meteorological factors.Results:The monthly incidence of HFRS had the strongest correlation with monthly mean air temperature at lag order 2, monthly mean wind speed at lag order 0, monthly mean sunshine at lag order 4, monthly mean precipitation at lag order 2 and monthly mean humidity at lag order 1, which were diagnosed by the variance inflation factor and included in the multifactorial GAM, and the results showed significant differences among the factors (all P<0.001), and they showed non-linear relationships with the monthly incidence of HFRS. Mean monthly temperature was an important factor influencing HFRS incidence. Mean monthly air temperature, mean monthly sunshine and mean monthly wind speed were negatively associated with HFRS incidence, whereas mean monthly precipitation and mean monthly humidity were positively associated with HFRS incidence. Conclusions:There was a complex non-linear relationship between meteorological factors and the incidence of HFRS. GAM incorporated with lagged meteorological factors can be used to predict the incidence of HFRS in Hebei.

Objective: To explore the differential lipid metabolites in the plasma of mice with idiopathic pulmonary fibrosis(IPF). Methods : Thirty SPF C57BL/6 male mice were randomly divided into 2 groups with 15 mice in each group. The experimental groups were divided into control group and bleomycin(BLM) group. The model of idiopathic pulmonary fibrosis was induced by one-time intratracheal infusion of BLM(1 mg/kg). Hematoxylin-eosin(HE) staining was used to observe the lung histopathology. The collagen fiber deposition in lung tissue was observed by Sirius red staining. The differential lipid metabolites in plasma of IPF mice were screened and enriched by lipidomics. Results : HE staining showed that the pulmonary tissue structure was disordered, alveolar septum was broken and alveolar wall was destroyed in BLM group. Sirius red staining showed a large amount of collagen fiber deposition in the lung interstitium of BLM group. The results of lipidomics analysis showed that the lipid metabolism profile of BLM group changed, 15 differential lipid metabolites were screened out, of which 11 differential lipid metabolites were up-regulated, and 4 differential lipid metabolites were down-regulated, mainly concentrated in glycerophosphoglycerophosphates, glycerophosphocholines, steroid lactones, etc. Conclusion The lipid metabolism profile of BLM group mice changes, differential lipid metabolites such as phosphoglycolate phosphatase(PGP)(18:0/18:0), PGP(i-12:0/i-24:0), PGP(i-13:0/a-25:0), and phosphatidylcholine(PC)(18:0/14:0), PC(18:3/16:0), lysophosphatidylcholine(LPC)(16:1), and LPC(18:3) may play an important role in the progression of IPF. These findings provide a new reference for further study of the molecular mechanism of IPF, and also provide a potential new target for clinical treatment.

Purpose To investigate the expression of chromogranin A(CgA)in gastroenteropancreatic neuroendo-crine neoplasms(GEP-NENs)and its relationship with clinicopathological features.Methods The clinicopathological data of GEP-NENs diagnosed in the Department of Pathology,Beijing Chao-yang Hospital,Capital Medical University from May 2011 to December 2024 were retrospectively analyzed.Immunohistochemical staining was applied to evaluate the expression of CgA,and the patients were divided into CgA(+)group and CgA(-)group.Differences in clinico-pathological features between the 2 groups were compared.Results The age of 229 patients ranged from 21 to 89 years,with an average age of 54.4 years.The most common primary site was the rectum(56.8％,130/229),fol-lowed by the stomach(16.6％,38/229),pancreas(14.4％,33/229),small intestine(6.1％,14/229),and colon(6.1％,14/229).There were 206 cases of single lesion and 23 cases of multiple lesions(number of tumors ≥2).There were 153 cases of G1(66.8％),29 cases of G2(12.7％),7 cases of G3(3.1％),and 40 cases of neuroendocrine carcinoma(NEC,17.5％).The positive rates of CgA in G1,G2,G3,and NEC groups were 37.2％,75.8％,71.4％,and 65.0％,respectively,with statistically significant differences(P＜0.001).The positive rates of CgA in T1,T2,T3,and T4 were 37.2％,83.3％,75.9％,and 57.7％,respectively,with statistically significant differences(P＜0.001).There were significant differences in age,vascular invasion,lymph node metasta-sis,and number of tumors between CgA(+)group and CgA(-)group(P＜0.001),but there was no significant difference in sex,tumor location,Syn,and CD56 expression between the two groups(P=0.595,P=0.098,P=0.173,P=0.557).Conclusion Immunohistochemical antibody CgA is a useful marker for GEP-NENs.CgA positiv-ity may be a poor prognostic factor for GEP-NENs patients.