ObjectiveTo analyze the risk factors for long-term cardiovascular events in patients undergoing long-term peritoneal dialysis （PD）， and to construct and validate a visual nomogram prediction model based on multiple parameters. MethodsA prospective cohort study was conducted， consecutively enrolling 248 maintenance PD patients （dialysis duration ≥ 3 months）. Demographic characteristics， clinical indicators， laboratory parameters， and echocardiographic indices （including left ventricular ejection fraction ［LVEF］， ratio of early diastolic mitral inflow velocity to early diastolic mitral annular velocity （E/e’）， etc.） were collected. The composite endpoint was defined as the occurrence of cardiovascular events or cardiovascular death， with non-cardiovascular death as the competing risk and loss to follow-up or the end of follow-up as censoring events. Fine-Gray competing risks model was used to screen independent predictors， based on which a nomogram model was constructed. Internal validation was performed using the Bootstrap method （1 000 resamplings）， and the concordance index （C-index） and time-dependent receiver operating characteristic （time-dependent ROC） curve were calculated to evaluate the model performance. ResultsWith a median follow-up of 29 months （interquartile range： 24–35 months）， 88 patients （35.48%） reached the composite endpoint， including 80 cases of cardiovascular events and 8 cases of cardiovascular death， and 4 patients died of non-cardiovascular causes. Multivariate Fine-Gray analysis revealed that age， diabetes mellitus， hemoglobin （HGB） level and E/e' ratio were independent influencing factors of the composite endpoint. Specifically， each 1-year increase in age was associated with a 3.0% increase in the risk of the composite endpoint （HR=1.030， P=0.006）； patients with diabetes mellitus had a 167.9% higher risk compared with non-diabetic patients （HR=2.679， P=0.007）； each 1g/L increase in HGB level contributed to a 1.5% reduction in the risk （HR=0.985， P=0.003）； and each 0.1 increase in E/e' ratio led to a 7.2% increase in the risk （HR=1.072， P=0.045）. The nomogram model had a C-index of 0.76 （95% CI： 0.698–0.820）， and the AUC of the time-dependent ROC curve reached 0.849 at 23 months of follow-up. ConclusionIncreased age， complicated with diabetes mellitus， decreased HGB， and elevated E/e' ratio are independent risk factors of long-term occurrence of cardiovascular events and cardiovascular death in patients undergoing long-term PD. The nomogram model constructed based on the above variables has good predictive value and clinical applicability， which can provide a reference for cardiovascular risk stratification and individualized intervention in long-term PD patients.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

Objective To explore the efficacy of extracting biopsy needle pose in chest CT images based on point cloud processing.Methods Three-dimensional point clouds were generated through segmentation of chest CT images and surface reconstruction.Spatial point cloud clustering and geometric constraints were applied to filter regions contained the puncture needle in space.The principal direction of the needle was judged using principal component analysis,and a cylindrical model was constructed to enclose the needle data.Then random sample consensus algorithm was used for needle trajectory fitting to accurately extract the spatial position and orientation of the puncture needle.The efficacy of the above method was evaluated using a 3D-printed anatomical model based on common clinical combinations of puncture depths and angles.Results The anatomical model experiments showed a 100％success rate in puncture needle identification,with angular error of(1.013±0.424)° and positional error of(2.023±1.553)mm,indicating that this method had good accuracy and stability.Conclusion The puncture needle's position in chest CT images could be extracted with high precision based on point cloud processing.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effec-tive MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models' generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer po-tential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.

Objective: To evaluate the effects of dietary intervention on blood pressure and renal function in patients with hypertensive nephropathy, so as to provide dietary and nutritional guidances for this population. Methods: Hypertensive nephropathy patients who were treated at Zhucheng People's Hospital from March 2023 to February 2024 were selected as the study subjects and randomly divided into the intervention group and the control group. The control group received routine antihypertensive treatment and health lifestyle guidance. On the basis of the treatment and guidance received by the control group, the intervention group implemented dietary intervention in accordance with the Clinical Practice Guidelines for Nutritional Therapy of Chronic Kidney Disease in China (2021 edition) for a period of 3 months. Systolic blood pressure (SBP), diastolic blood pressure (DBP) were measured before and after the intervention, and serum creatinine (Scr), blood urea nitrogen (BUN), uric acid (UA), cystatin and β2-microglobulin were detected. Differences of indicators before and after intervention between the two groups were compared using generalized estimation equation. Results: A total of 83 patients with hypertensive nephropathy were followed up, including 43 cases in the intervention group and 40 cases in the control group. There were no statistically significant differences in gender, age, body mass index, duration of hypertension, family history of hypertension, hypertension grade, physical activity index, or smoking status between the two groups (all P>0.05). The differences in SBP, DBP, Scr, BUN, and UA between the two groups, as well as the differences before and after the intervention, were statistically significant, and there was an interaction between the groups and the intervention time (all P<0.05). After intervention, the levels of SBP, DBP, Scr, BUN, and UA in the intervention group were lower than those in the control group (all P<0.05). The differences in cystatin and β2-microglobulin between the two groups and before and after the intervention were not statistically significant, and there was no interaction between the groups and the intervention time (all P>0.05). Conclusion Dietary intervention has a certain effect on reducing blood pressure and improving renal function indicators in patients with hypertensive nephropathy.

To construct a safe and effective multi-epitope vaccine against the S1 protein of chicken infectious bronchitis virus(IBV).In this study,homologous and non-homologous dominant epitopes of IBV M41,T,QX and H120 virulent strain S1 proteins were screened by various online bioprediction software,respectively,and a new peptide W with high immunogenicity was construc-ted by connecting the screened B-cell and T-cell epitopes with a linker peptide.W was ligated to the truncated sequence of the four viral strains by T2A yietding to the eukaryotic expression vector pEGFP-N1,and it was identified by PCR and double digestion,the obtained recombinant plasmid was transfected into HEK293A cells and target protein expression was measured by Western blot.The constructed plasmid was injected intramuscularly twice to detect the antibody level,cytokine level,and peripheral blood T cell subsets were detected after two immunizations.The epitope pro-tein W was successfully constructed,which was structurally stable,antigenic,and soluble;the re-combinant plasmid pEGFP-WMQtH,pEGFP-W,and pEGFP-MQtH matched the expected size;anti-IBV IgG antibody levels in pEGFP-N1 was increased greatly compared to the PBS group.cyto-kines IL-2,and γ interferon(IFN-γ)were increased greatly(P＜0.05);peripheral blood CD4+/CD8a value(P＜0.05)was increased greatly.The W epitope protein was successfully constructed,which can effectively activate the humoral immunity and cellular immunity against four infectious bronchitis viruses(IBV),laying a foundation for the development of an effective vaccine against IB.

To construct a safe and effective multi-epitope vaccine against the S1 protein of chicken infectious bronchitis virus(IBV).In this study,homologous and non-homologous dominant epitopes of IBV M41,T,QX and H120 virulent strain S1 proteins were screened by various online bioprediction software,respectively,and a new peptide W with high immunogenicity was construc-ted by connecting the screened B-cell and T-cell epitopes with a linker peptide.W was ligated to the truncated sequence of the four viral strains by T2A yietding to the eukaryotic expression vector pEGFP-N1,and it was identified by PCR and double digestion,the obtained recombinant plasmid was transfected into HEK293A cells and target protein expression was measured by Western blot.The constructed plasmid was injected intramuscularly twice to detect the antibody level,cytokine level,and peripheral blood T cell subsets were detected after two immunizations.The epitope pro-tein W was successfully constructed,which was structurally stable,antigenic,and soluble;the re-combinant plasmid pEGFP-WMQtH,pEGFP-W,and pEGFP-MQtH matched the expected size;anti-IBV IgG antibody levels in pEGFP-N1 was increased greatly compared to the PBS group.cyto-kines IL-2,and γ interferon(IFN-γ)were increased greatly(P＜0.05);peripheral blood CD4+/CD8a value(P＜0.05)was increased greatly.The W epitope protein was successfully constructed,which can effectively activate the humoral immunity and cellular immunity against four infectious bronchitis viruses(IBV),laying a foundation for the development of an effective vaccine against IB.

Objective To explore the efficacy of extracting biopsy needle pose in chest CT images based on point cloud processing.Methods Three-dimensional point clouds were generated through segmentation of chest CT images and surface reconstruction.Spatial point cloud clustering and geometric constraints were applied to filter regions contained the puncture needle in space.The principal direction of the needle was judged using principal component analysis,and a cylindrical model was constructed to enclose the needle data.Then random sample consensus algorithm was used for needle trajectory fitting to accurately extract the spatial position and orientation of the puncture needle.The efficacy of the above method was evaluated using a 3D-printed anatomical model based on common clinical combinations of puncture depths and angles.Results The anatomical model experiments showed a 100％success rate in puncture needle identification,with angular error of(1.013±0.424)° and positional error of(2.023±1.553)mm,indicating that this method had good accuracy and stability.Conclusion The puncture needle's position in chest CT images could be extracted with high precision based on point cloud processing.

Lead is one of the most important occupational hazards in China, and occupational exposure is the leading cause of lead poisoning. Lead can be absorbed by the body through air, food, drinking water and skin, and accumulate in multiple organs in the body, posing health risks to humans, especially to lead workers. Many previous studies have shown that lead can affect the function of glial cells such as microglia, astrocytes and oligodendrocytes, resulting in irreversible neurological damage. This article provides an overview of the neurotoxic mechanism induced by lead through glial cells, elucidates that lead can induce neurodegenerative diseases such as Alzheimer′s disease, Parkinson′s disease, and amyotrophic lateral sclerosis, and reviews the relationship between lead and glial cells, in order to provide reference for further research on the neurotoxic mechanism of lead on glial cells.

Lead is one of the most important occupational hazards in China, and occupational exposure is the leading cause of lead poisoning. Lead can be absorbed by the body through air, food, drinking water and skin, and accumulate in multiple organs in the body, posing health risks to humans, especially to lead workers. Many previous studies have shown that lead can affect the function of glial cells such as microglia, astrocytes and oligodendrocytes, resulting in irreversible neurological damage. This article provides an overview of the neurotoxic mechanism induced by lead through glial cells, elucidates that lead can induce neurodegenerative diseases such as Alzheimer′s disease, Parkinson′s disease, and amyotrophic lateral sclerosis, and reviews the relationship between lead and glial cells, in order to provide reference for further research on the neurotoxic mechanism of lead on glial cells.