ObjectiveThis study aims to identify cuproptosis-related gene biomarkers in Alzheimer's disease（AD） using bioinformatics and machine learning methods， validate them at the clinical specimen level， and predict potential traditional Chinese medicine（TCM）. MethodsDifferentially expressed genes in the AD group and normal group were obtained using the Gene Expression Omnibus （GEO） database， and intersections were taken with reported cuproptosis-related genes to obtain differentially expressed cuproptosis-related genes. Machine learning methods were applied to identify core differential genes of cuproptosis in AD. Peripheral blood's single nucleated cells from clinical AD patients were collected， and the relative gene expression was clinically verified by real-time polymerase chain reaction（Real-time PCR）. Potential TCM regulating cuproptosis for AD were predicted by COREMINE database. ResultsA total of 12 cuproptosis-related genes were obtained， mainly involved in pathways of tricarboxylic acid cycle， 2-oxocarboxylic acid metabolism， and carbon metabolism. Five core cuproptosis-related genes， dihydrolipoamide dehydrogenase （DLD）， glutaminase （GLS）， pyruvate dehydrogenase E1 subunit beta （PDHB）， full name nuclear factor （erythroid-derived 2）-related factor 2 （NFE2L2）， and dihydrolipoamide branched-chain transacylase E2 （DBT） were finally screened using four machine methods. Thirty cases each of normal and AD patients were collected clinically. Compared with those in the normal group， minimum mental state examination （MMSE） and Montreal cognitive assessment （MoCA） were significantly decreased in the AD group （P<0.01）， Homocysteine（Hcy）， interleukin（IL）-6， C-reactive protein（CRP） ， and β amyloid protein（Aβ） indexes were significantly increased （P<0.01）， and malondialdehyde（MDA） indexes were decreased （P<0.05）. Superoxide dismutase（SOD） levels were significantly decreased （P<0.01）. The mRNA relative expressions of NFE2L2 and DBT were up-regulated （P<0.05）， and those of DLD， GLS， and PDHB were significantly down-regulated （P<0.01）. The TCM regulating cuproptosis-related genes for the treatment of AD were mainly based on the four Qi such as warmth， calmness， and cold， and the five flavors including bitterness， sweetness， and pungency， and it was attributed to the meridians of the liver， spleen， stomach， and kidney， with the efficacy of tonifying Qi， activating blood， eliminating phlegm， and resoling dampness. ConclusionDLD， GLS， NFE2L2， PDHB， and DBT can be used as novel diagnostic molecular markers for AD cuproptosis-related genes， and the corresponding potential therapeutic TCM can provide new ideas for the treatment of AD by TCM.

OBJECTIVES: To explore the mechanism of Gandou Fumu Decoction (GDFMD) for improving Wilson's disease (WD) in tx-J mice. METHODS: With 6 syngeneic wild-type mice as the control group, 30 tx-J mice were randomized into WD model group, low-, medium- and high-dose GDFMD treatment groups, and Fer-1 treatment group. Saline (in control and model groups) and GDFMD (3.48, 6.96 or 13.92 g/kg) were administered by gavage, and Fer-1 was injected intraperitoneally once daily for 14 days. Oil red and HE staining were used to observe lipid deposition and pathological conditions in the liver tissue; ALT, AST, albumin, AKP levels were determined to assess liver function of the mice. Western blotting and RT-qPCR were used to detect hepatic protein and mRNA expressions of GPX4, ACSL4, ALOX15, FTH1, FLT, TFR1, FAS, SCD1, and ACOX1, and Fe²⁺, MDA, ROS, SOD, GSH and 4-HNE levels were analyzed to assess oxidative stress. RESULTS: The mouse models of WD showed obvious fatty degeneration in the liver tissue significantly increased serum levels of ALT, AST and AKP, decreased albumin level, increased Fe²⁺, MDA, ROS, 4-HNE levels, decreased SOD and GSH levels (P<0.05), lowered protein expressions of ACOX1, GPX4, FTH1, FLT, FAS, and SCD1, and increased protein contents of TFR1, ACSL4 and ALOX15 in the liver. Treatment with GDFMD and Fer-1 improved liver histopathology and liver function of the mouse models, decreased the levels of Fe²⁺, MDA and ROS, increased SOD and GSH levels, and reversed the changes in hepatic protein expressions. CONCLUSIONS GDFMD improves liver steatosis in mouse models of WD possibly by inhibiting hepatocyte ferroptosis through the GPX4/ACSL4/ALOX15 signaling pathway.
Animals ; Ferroptosis/drug effects* ; Mice ; Signal Transduction/drug effects* ; Drugs, Chinese Herbal/therapeutic use* ; Hepatolenticular Degeneration/drug therapy* ; Hepatocytes/metabolism* ; Phospholipid Hydroperoxide Glutathione Peroxidase ; Fatty Liver/metabolism* ; Arachidonate 15-Lipoxygenase/metabolism* ; Coenzyme A Ligases/metabolism* ; Liver/metabolism* ; Male

OBJECTIVES: To investigate the expression of the long non-coding RNA maternally expressed gene 3 (LncRNA Meg3) in patients with the Wilson disease (WD) and its correlation with the severity of liver fibrosis and autophagy-related markers. METHODS: A total of 100 WD patients and 50 healthy individuals were enrolled from the First Affiliated Hospital of Anhui University of Chinese Medicine. Serum biomarkers, including platelet count, hyaluronic acid (HA), laminin (LN), type III procollagen N-terminal peptide (PIIINP), type IV collagen (C‑IV), alanine aminotransferase (ALT), and aspartate aminotransferase (AST), were measured, and the non-invasive indices APRI and FIB-4 were calculated. Peripheral blood levels of LncRNA Meg3, Beclin-1 and LC3B were detected using RT-qPCR, and liver stiffness (LSM) and shear wave velocity (SWV) were evaluated using two-dimensional shear wave elastography (2D-SWE). The liver tissues from 10 WD patients and 10 patients with hepatic hemangioma were examined using histochemical staining, transmission electron microscopy, and RT-qPCR. RESULTS: The expression level of LncRNA Meg3 was significantly lower, while the levels of AST, ALT, HA, LN, PIIINP, C‑IV, APRI, FIB-4, LSM and SWV were significantly higher in WD patients than in the healthy individuals (all P<0.01). LncRNA Meg3 was negatively correlated with LSM, SWV, APRI, FIB-4, Beclin-1 and LC3B (P<0.05). ROC analysis demonstrated that LncRNA Meg3 effectively discriminated >F4 stage fibrosis (AUC=0.902) with a sensitivity of 92.9% and a specificity of 83.7% at the optimal cut-off value, outperforming APRI (AUC=0.746) and FIB-4 (AUC=0.661). The liver tissues from WD patients exhibited characteristic histopathological changes and lowered expression of LncRNA Meg3, which was negatively correlated with Beclin-1 and LC3B expressions (P<0.05). Liver fibrosis staging (7 S4 cases and 3 S3 cases) was significantly associated with LSM and SWV levels (P<0.05). CONCLUSIONS The expression level of LncRNA Meg3 is significantly decreased in WD patients, which is negatively correlated with the severity of liver fibrosis and closely related to the level of autophagy.
Humans ; RNA, Long Noncoding/metabolism* ; Liver Cirrhosis/metabolism* ; Adult ; Female ; Male ; Hepatolenticular Degeneration/metabolism* ; Case-Control Studies ; Young Adult ; Adolescent ; Middle Aged

Objective To investigate the value of serum thymidine kinase 1(TK1),hypoxia-inducible fac-tor-1α(HIF-1α)and squamous cell carcinoma antigen(SCC)in the diagnosis and prognosis of esophageal cancer.Methods A total of 105 patients with esophageal cancer treated in the Qinhuai Medical Zone of East-ern Theater General Hospital from February 2019 to October 2021 were selected as the study group,and 80 healthy subjects were selected as the control group during the same period.The serum levels of TK1,HIF-1αand SCC were compared between study group,control group and patients with different pathological charac-teristics.Patients with esophageal cancer were followed up for 3 years,and the overall survival(OS)and pro-gression-free survival(PFS)were recorded.Receiver operating characteristic curve was used to analyze the di-agnostic efficiency of serum TK1,HIF-1α and SCC combined detection for esophageal cancer,Pearson correla-tion analysis was used to analyze the correlation between the serum indicators,and Kaplan-Meier survival a-nalysis was used to analyze the OS and PFS of patients with different serum levels of TK1,HIF-1α and SCC.Multivariate COX regression was performed to analyze prognostic factors.Results Compared with the control group,the serum TK1,HIF-1α and SCC levels in the study group increased(P＜0.05).The area under the curve(AUC)of serum TK1,HIF-1α,SCC alone and combined for diagnosis of esophageal cancer were 0.893,0.744,0.841,0.922,respectively,and their combined diagnoses of esophageal cancer had the largest AUC.Se-rum TK1,HIF-1α and SCC in patients with different TNM stages and differentiation stages were significantly different(P＜0.05).Serum TK1,HIF-1α and SCC in patients with lymph node metastasis were higher than those without lymph node metastasis(P＜0.05).Serum TK1 was positively correlated with HIF-1α and SCC levels in patients with esophageal cancer(P＜0.05).The survival functions of OS and PFS in TK1,HIF-1αand SCC low expression group were better than those in high expression group(P＜0.05).Multivariate COX regression analysis showed that low differentiation and lymph node metastasis were independent risk factors for poor prognosis in esophageal cancer patients(P＜0.05).Conclusion Serum levels of TK1,HIF-1α and SCC are increased in patients with esophageal cancer,the combined diagnosis of the three is effective.The high expression of TK1 and HIF-1αand SCC will shorten OS and PFS.

In September 2024,the updated version of Chinese Clinical Practice Guidelines for Hypertension was published.This version of guidelines comprises 44 pivotal clinical inquiries and 99 recommendations pertaining to the diagnosis,assessment,and management of hypertension.The new version of the guidelines emphasizes moving the line of defense of antihypertensive treatment forward,strengthening antihypertensive treatment,reflecting the concept of strengthening initial prevention and primary prevention,and stressing the importance of lifestyle interven-tion and blood pressure monitoring,which is of great guiding value and practical significance to clinical work.This paper interprets the management strategy of hypertension patients from the perspective of nursing practice,in order to provide evidence-based guidance for clinical nursing work.

OBJECTIVE To construct a risk prediction model for bloodstream infection （BSI） induced by carbapenem-resistant Klebsiella pneumoniae （CRKP）. METHODS Retrospective analysis was conducted for clinical data from 253 patients with BSI induced by K. pneumoniae in the First Hospital of Qinhuangdao from January 2019 to June 2022. Patients admitted from January 2019 to December 2021 were selected as the model group （n＝223）， and patients admitted from January 2022 to June 2022 were selected as the validation group （n＝30）. The model group was divided into the CRKP subgroup （n＝56） and the carbapenem- sensitive K. pneumoniae （CSKP） subgroup （n＝167） based on whether CRKP was detected or not. The univariate and multivariate Logistic analyses were performed on basic information such as gender， age and comorbid underlying diseases in two subgroups of patients； independent risk factors were screened for CRKP-induced BSI， and a risk prediction model was constructed. The established model was verified with patients in the validation group as the target. RESULTS Admissioning to intensive care unit （ICU）， use of immunosuppressants， empirical use of carbapenems and empirical use of antibiotics against Gram-positive coccus were independent risk factors of CRKP-induced BSI （ORs were 3.749， 3.074， 2.909， 9.419， 95%CIs were 1.639-8.572， 1.292- 7.312， 1.180-7.717， 2.877-30.840， P＜0.05）. Based on this， a risk prediction model was established with a P value of 0.365. The AUC of the receiver operating characteristic （ROC） curve of the model was 0.848 [95%CI （0.779， 0.916）， P＜0.001]， and the critical score was 6.5. In the validation group， the overall accuracy of the prediction under the model was 86.67%， and the AUC of ROC curve was 0.926 [95%CI （0.809， 1.000]， P＜0.001]. CONCLUSIONS Admission to ICU， use of immunosuppressants， empirical use of carbapenems and empirical use of antibiotics against Gram-positive coccus are independent risk factors of CRKP- induced BSI. The CRKP-induced BSI risk prediction model based on the above factors has good prediction accuracy.

OBJECTIVE To construct a risk prediction model for non-compliance with inhaled medication in patients with chronic obstructive pulmonary disease （COPD）. METHODS A retrospective analysis was conducted on 365 COPD patients admitted to the cough and wheeze pharmaceutical care clinic of the First Hospital of Qinhuangdao from October 2021 to October 2023. The patients admitted from October 2021 to June 2023 were selected as the model group （n＝303）， and the patients admitted from July to October 2023 were selected as the validation group （n＝62）. The model group was divided into compliance subgroup （n＝126） and non-compliance subgroup （n＝177）. Univariate analysis combined with multivariate Logistic regression analysis were used to analyze the risk factors for non-compliance with inhaled formulations in patients； the risk prediction model was established through regression analysis， and the accuracy of the model prediction was evaluated based on the validation group of patients. RESULTS Multivariate Logistic regression analysis showed that simultaneous use of 2 inhaled formulations （OR＝3.730， 95%CI 1.996-6.971， P＜0.001）， the number of acute exacerbations within one year ≥2 （OR＝2.509， 95%CI 1.509-4.173， P＜0.001）， smoking （OR＝2.167， 95%CI 1.309-3.588， P＝0.003）， complicated with anxiety/depression （OR＝2.112， 95%CI 1.257-3.499， P＝0.004） and mMRC grading≥2 levels （OR＝1.701， 95%CI 1.014-2.853， P＝0.044） were risk factors for non-compliance with inhaled preparations. Based on this， a risk prediction model was established and the ROC curve was drawn. The areas under the curve of the model group and validation group were 0.836 and 0.928， and the overall accuracy of the model’s prediction was 88.71%. CONCLUSIONS The predictive model based on the simultaneous use of 2 inhaled formulations， the number of acute exacerbations within one year ≥2， smoking， complicated with anxiety/depression， mMRC grading ≥2 levels has certain predictive value for the risk of non-compliance with inhaled formulations for COPD patients.

OBJECTIVE To construct a risk prediction model for non-compliance with inhaled medication in patients with chronic obstructive pulmonary disease （COPD）. METHODS A retrospective analysis was conducted on 365 COPD patients admitted to the cough and wheeze pharmaceutical care clinic of the First Hospital of Qinhuangdao from October 2021 to October 2023. The patients admitted from October 2021 to June 2023 were selected as the model group （n＝303）， and the patients admitted from July to October 2023 were selected as the validation group （n＝62）. The model group was divided into compliance subgroup （n＝126） and non-compliance subgroup （n＝177）. Univariate analysis combined with multivariate Logistic regression analysis were used to analyze the risk factors for non-compliance with inhaled formulations in patients； the risk prediction model was established through regression analysis， and the accuracy of the model prediction was evaluated based on the validation group of patients. RESULTS Multivariate Logistic regression analysis showed that simultaneous use of 2 inhaled formulations （OR＝3.730， 95%CI 1.996-6.971， P＜0.001）， the number of acute exacerbations within one year ≥2 （OR＝2.509， 95%CI 1.509-4.173， P＜0.001）， smoking （OR＝2.167， 95%CI 1.309-3.588， P＝0.003）， complicated with anxiety/depression （OR＝2.112， 95%CI 1.257-3.499， P＝0.004） and mMRC grading≥2 levels （OR＝1.701， 95%CI 1.014-2.853， P＝0.044） were risk factors for non-compliance with inhaled preparations. Based on this， a risk prediction model was established and the ROC curve was drawn. The areas under the curve of the model group and validation group were 0.836 and 0.928， and the overall accuracy of the model’s prediction was 88.71%. CONCLUSIONS The predictive model based on the simultaneous use of 2 inhaled formulations， the number of acute exacerbations within one year ≥2， smoking， complicated with anxiety/depression， mMRC grading ≥2 levels has certain predictive value for the risk of non-compliance with inhaled formulations for COPD patients.

Objective To propose a method for mitigate the impact of anomaly points(such as dust,bubbles,scratches on the chip surface,and minor indentations)in images on the results of digital droplet PCR(ddPCR)detection to achieve high-throughput,stable,and accurate detection.Methods We propose a Filter Faster R-CNN ddPCR detection model,which employs Faster R-CNN to generate droplet prediction boxes followed by removing the anomalies within the positive droplet prediction boxes using an outlier filtering module(Filter).Using a plasmid carrying a norovirus fragment as the template,we established a ddPCR dataset for model training(2462 instances,78.56%)and testing(672 instances,21.44%).Ablation experiments were performed to test the effectiveness of 3 filtering branches of the Filter for anomaly removal on the validation dataset.Comparative experiments with other ddPCR droplet detection models and absolute quantification experiments of ddPCR were conducted to assess the performance of the Filter Faster R-CNN model.Results In low-dust and dusty environments,the Filter Faster R-CNN model achieved detection accuracies of 98.23%and 88.35%for positive droplets,respectively,with composite F1 scores reaching 99.15%and 99.14%,obviously superior to the other models.The introduction of the filtering module significantly enhanced the positive accuracy of the model in dusty environments.In the absolute quantification experiments,a regression line was plotted using the results from commercial flow cytometry equipment as the standard concentration.The results show a regression line slope of 1.0005,an intercept of-0.025,and a determination coefficient of 0.9997,indicating high consistency between the two results.Conclusion The ddPCR detection technique using the Filter Faster R-CNN model provides a robust detection method for ddPCR under various environmental conditions.

Objective To propose a method for mitigate the impact of anomaly points(such as dust,bubbles,scratches on the chip surface,and minor indentations)in images on the results of digital droplet PCR(ddPCR)detection to achieve high-throughput,stable,and accurate detection.Methods We propose a Filter Faster R-CNN ddPCR detection model,which employs Faster R-CNN to generate droplet prediction boxes followed by removing the anomalies within the positive droplet prediction boxes using an outlier filtering module(Filter).Using a plasmid carrying a norovirus fragment as the template,we established a ddPCR dataset for model training(2462 instances,78.56%)and testing(672 instances,21.44%).Ablation experiments were performed to test the effectiveness of 3 filtering branches of the Filter for anomaly removal on the validation dataset.Comparative experiments with other ddPCR droplet detection models and absolute quantification experiments of ddPCR were conducted to assess the performance of the Filter Faster R-CNN model.Results In low-dust and dusty environments,the Filter Faster R-CNN model achieved detection accuracies of 98.23%and 88.35%for positive droplets,respectively,with composite F1 scores reaching 99.15%and 99.14%,obviously superior to the other models.The introduction of the filtering module significantly enhanced the positive accuracy of the model in dusty environments.In the absolute quantification experiments,a regression line was plotted using the results from commercial flow cytometry equipment as the standard concentration.The results show a regression line slope of 1.0005,an intercept of-0.025,and a determination coefficient of 0.9997,indicating high consistency between the two results.Conclusion The ddPCR detection technique using the Filter Faster R-CNN model provides a robust detection method for ddPCR under various environmental conditions.