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.

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.

Objective:To identify early potential risk factors for sepsis-associated liver injury and to provide a reference for early clinical identification and intervention.Methods:The clinical data of septic patients admitted to the intensive care unit (ICU) in the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University from March 2020 to April 2023 were retrospectively analyzed. Patients with sepsis were categorized into the liver injury group and the non-liver injury group according to whether liver injury occurred or not, univariate and multivariate logistic regression analyses were used to explore the risk factors for SALI, receiver operating characteristic (ROC) curve analysis was used to assess its predictive effect for SALI, and performed subgroup analyses basing on the cut-off point.Results:Among 530 eligible patients, 403 patients were included. The incidence of liver injury was 39.45% in 159 cases with liver injury and 244 cases without liver injury. Multivariate logistic regression analysis showed that serum prealbumin, lactate and lactate dehydrogenase were independent risk factors for SALI. ROC curve analysis showed that all single indicators had some predictive value for SALI, the area under the curve was prealbumin (AUC: 0.752, 95% CI: 0.703-0.801), lactate (AUC: 0.679, 95% CI: 0.627-0.732), lactate dehydrogenase (AUC: 0.664, 95% CI: 0.611-0.718), respectively, The AUC for predicting SALI by lactate/prealbumin ratio (L/P) and lactate dehydrogenase/prealbumin ratio were 0.808 (95% CI: 0.766-0.850) and 0.795 (95% CI: 0.750-0.840), respectively, with the best efficacy of L/P in predicting SALI. Subgroup analyses showed that the incidence of liver injury was significantly higher in septic patients with L/P ≥0.23 than that in septic patients with L/P <0.23, at the same time, the acute physiology and chronic health evaluation II score, shock probability, and hospital mortality rate also increased accordingly, the differences were all statistically significant (all P < 0.001). Conclusions:L/P is early independent risk factor of SALI, for sepsis patients with L/P≥0.23 should be alerted to the development of liver injuryis.

Objective:To analyze the characteristics of inhibitory control and cognitive flexibility in hearing-impaired children.Methods:From March to April 2023, a convenience sampling method was used to select 33 hearing-impaired children from a special education school in Meizhou City, Guangdong Province, and 35 normal-hearing children from two ordinary schools as participants. Inhibitory control and cognitive flexibility of the participants were assessed by the Flanker task and the dimensional change card sorting (DCCS) task. Statistical analysis was conducted using SPSS 26.0 software, and independent sample t-test was used to compare the differences in reaction time and accuracy rate between two groups of participants. Results:There were no significant differences in the Flanker task reaction time ((558.39±123.65) ms vs (566.11±118.20) ms) and accuracy rate((0.93±0.10) vs (0.96±0.04))between hearing-impaired children and normal-hearing children ( t=-0.295, -1.645, both P>0.05). The hearing-impaired children had significantly longer reaction time ((1 019.60±131.08) ms)than the normal-hearing children ((857.85±129.19) ms) ( t=4.046, P=0.001) in the DCCS task, while there was no statistically significant difference in the accuracy rate between hearing-impaired children (0.62±0.16) and normal-hearing children (0.57±0.15) ( t=-1.602, P>0.05). Conclusion:There is no difference in inhibitory control ability between hearing-impaired children and normal-hearing children, but the hearing-impaired children have a lag in cognitive flexibility.

Objective:To investigate the efficacy of staged treatment of infectious femoral defects of Cierny-Mader type Ⅳ using bone transport combined with locking plating after En-Bloc resection debridement.Methods:A retrospective analysis was conducted of the 10 patients with distal femoral traumatic bone infection who had been treated at Department of Orthopedics, The People's Hospital of Weifang from January 2020 to January 2023. There were 8 males and 2 females with an age of (48.5±11.4) years. All cases were classified as Cierny-Mader type Ⅳ. At the first stage, En-Bloc resection debridement was performed in all cases to remove previous internal fixation devices and fill the defects with antibiotic bone cement. After infection control, the second stage involved removal of bone cement, re-fixation with internal devices, and external fixation support for bone transport. After the bone segments met, freshening of the bone ends, minor bone grafting, and screw locking of the transported bone segments were performed. Outcomes observed included bone defect length, frame carrying time and index, bone healing time, limb function, and complications.Results:After the first stage of debridement, a bone defect of (9.1±2.1) cm was created in 10 patients. All patients were followed up for (19.8±6.6) months. The duration for carrying external fixation frame was (107.2±25.1) days, and the frame index (11.8±0.5) d/cm. No recurrence was observed postoperatively. Bone union was achieved in 9 patients within 8 months, but in 1 patient only after secondary bone grafting due to poor healing at the meeting ends. All patients returned to their previous life or physical labor with no complications like pain or re-fracture. Three patients experienced varying degrees of knee joint stiffness, but were able to meet needs of daily life; one requested joint release surgery which resulted in satisfactory therapeutic efficacy.Conclusion:Staged treatment of infectious femoral defects of Cierny-Mader type Ⅳ using bone transport combined with locking plating after En-Bloc resection debridement is simple and effective.

Objective:To develop multiplex cytokine detection reagents to analyze expression levels of cytokines,angiogene-sis and bone remodeling proteins in relapse/refractory multiple myeloma(RRMM).Methods:Multiplex bead-based immunoassay by flow cytometry was used to develop quantitative detection reagents of multiplex cytokines,which were applied to detect serum samples from 55 RRMM patients and 22 healthy controls.Expression levels of cytokines,angiogenesis,and bone remodeling proteins in pa-tients,and their correlation with clinical characteristics were analyzed.Results:Detection reagents of 10-plex cytokine immunoassay were successfully developed in this study,with average sensitivity of 7.1 pg/ml,average recovery rate of 97.4%,average intra-assay CV of 4.8%,and average inter-assay CV of 9.0%.In addition,results of RRMM samples found that levels of IL-2,IL-17,DKK1,RANKL and OPG were positively correlated with the level of IgG monoclonal protein,and TIMP1 was positively correlated with levels of IgG and IgA monoclonal protein.Conclusion:In this study,ten kinds of cytokine detection reagents with high sensitivity and speci-ficity are developed,and we found that IL-2,IL-17,DKK1,RANKL,OPG and TIMP1 have potential value in tracking disease pro-gression in RRMM.The established development process of multiplex cytokine reagents has important reference significance for ex-panding the development and application of multiplex detection reagents for protein markers in the future.