BACKGROUND:Periprosthetic joint infection is the most common cause of early failure after total knee replacement.The current methods of preventing periprosthetic joint infection by improving the surface of the prosthesis have limitations to varying degrees.OBJECTIVE:To construct a coating material that can stably improve the surface of the implant,prevent the initial floating bacterial infection of periprosthetic infection,and regulate the bone transfer function around the implant.METHODS:(1)Material preparation:YGF polypeptide(which promotes bone formation),LL-37 polypeptide(with antibacterial properties)and YGF+LL-37 composite peptide were prepared by Fmoc solid phase peptide synthesis technology.The titanium-based materials were immersed in the three polypeptide solutions for 2 hours to obtain YGF coating,LL-37 coating and composite peptide coating coated titanium sheets.(2)In vitro experiment:Uncoated titanium sheets and coated titanium sheets were co-cultured with Escherichia coli(or Staphylococcus aureus)and the colonies were counted by plate method.MC3T3 cells were inoculated on the surface of uncoated titanium sheet and coated titanium sheet,respectively.Alizarin red staining was used to observe the calcium salt deposition on the surface of the material.Western blot assay was used to detect the protein expression of RUNX2,osteocalcin,osteopontin,and bone morphogenetic protein 2.(3)Animal experiment:24 SD rats were randomly divided into three groups:the blank group(n=8)was implanted with uncoated titanium nails in the femoral medullary canal;the control group(n=8)was implanted with uncoated titanium nails in the femoral medullary canal+intra-articular injection of Staphylococcus aureus suspension;the experimental group(n=8)was implanted with composite peptide coated titanium nails in the femoral medullary canal+intra-articular injection of Staphylococcus aureus suspension.After 5 weeks of implantation,micro-CT examination,hematoxylin-eosin staining and immunohistochemical staining of femur specimens were performed.RESULTS AND CONCLUSION:(1)In vitro experiment:Compared with uncoated titanium sheet and YGF coated titanium sheet,LL-37 coated and composite peptide coated titanium sheet could significantly inhibit the growth and reproduction of Escherichia coli and Staphylococcus aureus.Compared with uncoated titanium sheets and LL-37-coated titanium sheets,YGF-coated and composite peptide-coated titanium sheets could promote calcium salt deposition in osteoblasts and increase the protein expression of RUNX2,osteocalcin,osteopontin and bone morphogenetic protein 2.(2)Animal experiment:Micro-CT test showed that the control group had less bone mass than the blank group and the experimental group.Hematoxylin-eosin staining showed that there was a large amount of fibrous tissue around the nail channel in the control group,only a small amount of tissue fibrosis around the nail channel in the blank group,and only a small amount of tissue fibrosis around the nail channel in the experimental group.Immunohistochemical staining showed that the protein expression of interleukin 1β and tumor necrosis factor α in the control group was higher than that in the blank group and the experimental group,and the expression of osteocalcin,RUNX2 and osteopontin in the experimental group was higher than that in the blank group and the control group.(3)The results show that the titanium-based material coated with YGF+LL-37 composite peptide coating has good antibacterial ability and can promote bone transfer around the implant.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

BACKGROUND:Periprosthetic joint infection is the most common cause of early failure after total knee replacement.The current methods of preventing periprosthetic joint infection by improving the surface of the prosthesis have limitations to varying degrees.OBJECTIVE:To construct a coating material that can stably improve the surface of the implant,prevent the initial floating bacterial infection of periprosthetic infection,and regulate the bone transfer function around the implant.METHODS:(1)Material preparation:YGF polypeptide(which promotes bone formation),LL-37 polypeptide(with antibacterial properties)and YGF+LL-37 composite peptide were prepared by Fmoc solid phase peptide synthesis technology.The titanium-based materials were immersed in the three polypeptide solutions for 2 hours to obtain YGF coating,LL-37 coating and composite peptide coating coated titanium sheets.(2)In vitro experiment:Uncoated titanium sheets and coated titanium sheets were co-cultured with Escherichia coli(or Staphylococcus aureus)and the colonies were counted by plate method.MC3T3 cells were inoculated on the surface of uncoated titanium sheet and coated titanium sheet,respectively.Alizarin red staining was used to observe the calcium salt deposition on the surface of the material.Western blot assay was used to detect the protein expression of RUNX2,osteocalcin,osteopontin,and bone morphogenetic protein 2.(3)Animal experiment:24 SD rats were randomly divided into three groups:the blank group(n=8)was implanted with uncoated titanium nails in the femoral medullary canal;the control group(n=8)was implanted with uncoated titanium nails in the femoral medullary canal+intra-articular injection of Staphylococcus aureus suspension;the experimental group(n=8)was implanted with composite peptide coated titanium nails in the femoral medullary canal+intra-articular injection of Staphylococcus aureus suspension.After 5 weeks of implantation,micro-CT examination,hematoxylin-eosin staining and immunohistochemical staining of femur specimens were performed.RESULTS AND CONCLUSION:(1)In vitro experiment:Compared with uncoated titanium sheet and YGF coated titanium sheet,LL-37 coated and composite peptide coated titanium sheet could significantly inhibit the growth and reproduction of Escherichia coli and Staphylococcus aureus.Compared with uncoated titanium sheets and LL-37-coated titanium sheets,YGF-coated and composite peptide-coated titanium sheets could promote calcium salt deposition in osteoblasts and increase the protein expression of RUNX2,osteocalcin,osteopontin and bone morphogenetic protein 2.(2)Animal experiment:Micro-CT test showed that the control group had less bone mass than the blank group and the experimental group.Hematoxylin-eosin staining showed that there was a large amount of fibrous tissue around the nail channel in the control group,only a small amount of tissue fibrosis around the nail channel in the blank group,and only a small amount of tissue fibrosis around the nail channel in the experimental group.Immunohistochemical staining showed that the protein expression of interleukin 1β and tumor necrosis factor α in the control group was higher than that in the blank group and the experimental group,and the expression of osteocalcin,RUNX2 and osteopontin in the experimental group was higher than that in the blank group and the control group.(3)The results show that the titanium-based material coated with YGF+LL-37 composite peptide coating has good antibacterial ability and can promote bone transfer around the implant.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Aortic dissection is a life-threatening cardiovascular disease with devastating complications and high mortality. It requires rapid and accurate diagnosis and a focus on prognosis. Many laboratory tests are routinely performed in patients with aortic dissection including D-dimer, brain natriuretic peptide, cardiac troponin I, C-reactive protein, and procalcitonin. D-dimer shows vital performance in the diagnosis of aortic dissection, and brain natriuretic peptide, cardiac troponin I, C-reactive protein, and procalcitonin exhibits important value in risk stratification and prognostic effect in aortic dissection patients. Our review summarized the clinical utility of these laboratory tests in patients with aortic dissection, aiming to provide advanced and comprehensive evidence for clinicians to better understand these laboratory tests and help their clinical practice.

Objective To screen monoclonal antibodies against different binding sites of connexin 43(Cx43) and establish a double antibody sandwich ELISA method for quantitative detection of Cx43 protein in order to further study the structure,function and subcellular localization of full-length and truncated Cx43 protein.Methods The sequence of Cx43 protein was analyzed by bioinformatics method. The peptide fragments were designed and synthesized for different domains, and coupled with keyhole limpet hemocyanin(KLH) protein, which were used as immunogen to immunize 15 female BALB/c mice.Hybridoma cells were prepared by cell fusion technology, and cell lines stably secreting monoclonal antibodies against Cx43 protein were screened. The antibodies were purified by Protein G affinity chromatography, and its subtype, affinity and specificity were identified. Two high affinity monoclonal antibodies were used as capture antibody and detection antibody respectively(labeled with HRP) to establish a double antibody sandwich ELISA method. The sealing solution(5% milk-PBS, 3%BSA-PBS), washing solution [0. 01 mol/L PBS(pH 7. 2), 0. 01 mol/L PBS-0. 5% Tween 20(pH 7. 2)(i. e., PBST)] and sample diluent [3% BSA-PBS and 0. 01 mol/L PBS(pH 7. 2)]were optimized, and the method was verified for the linear range, sensitivity, specificity, precision and accuracy. The content of Cx43 protein in mouse brain tissue, intestine tissue, femur tissue and MC3T3-E1 mouse embryonic osteoblast lysate was detected by the established method.Results Three hybridoma cell lines secreting monoclonal antibodies against Cx43 were screened and named as 1-1E2, 2-1G11 and 3-2G12 respectively. The antibody subtypes were IgG1, IgG1 and IgG2b respectively, and the antibody sensitivity reached 0. 002 μg/mL, among which 2-1G11 and 3-2G12 had higher affinity. A double antibody sandwich ELISA was developed with 2-1G11 as capture antibody and HRP-labeled 3-2G12 as detection antibody. The optimum sealing solution was 3% BSA-PBS, washing solution was PBST, and sample diluent was 0. 01 mol/L PBS(pH 7. 2). Cx43 protein standard showed a good linear relationship with the mean value of A_(450)in the concentration range of 3. 12-200 ng/mL, R~2> 0. 98. Cx43 protein could be specifically detected by this method, and there was no cross reaction with KLH protein, bone morphogenetic protein-2(BMP-2) and human recombinant TGM2 protein. The CV of precision verification was 7. 72%. The recovery rates of high, medium and low concentrations of test samples were all in the range of 85%-115%. The content of Cx43 protein in MC3T3-E1 mouse embryonic osteoblasts was higher(58. 03 ng/mL), while the content in osteogenic tissue was lower(38. 4 ng/mL).Conclusion Specific monoclonal antibodies against different epitopes of human Cx43 were successfully prepared, and a double antibody sandwich ELISA method with high sensitivity and strong specificity for quantitative detection of Cx43 protein was established, which provided a reliable detection method for the study of structure and function of Cx43 protein.

Objective: To analyze the unregistered treatment situation and its influencing factors among pulmonary tuberculosis patients in Quzhou City, Zhejiang Province from 2017 to 2023, so as to provide a basis for promoting the management of tuberculosis patients and optimizing disease prevention and control strategies. Methods: Data of pulmonary tuberculosis patients including demographic information, etiological results, and mortality status were collected through the China Disease Prevention and Control Information System Infectious Disease Reporting and Surveillance System and the Tuberculosis Management Information System. Pulmonary tuberculosis patients not matched in the Tuberculosis Management Information System were defined as unregistered treatment patients, and the unregistered treatment rate was analyzed. Factors affecting unregistered treatment among pulmonary tuberculosis patients were analyzed using a multivariable logistic regression model. Results: A total of 10 779 pulmonary tuberculosis patients were reported in Quzhou City from 2017 to 2023, including 7 700 males (71.44%) and 3 079 females (28.56%). There were 5 484 cases aged <65 years, accounting for 50.88%. Among them, 630 cases were unregistered treatment, with an unregistered treatment rate of 5.84% (95%CI: 5.42%-6.38%). Multivariable logistic regression analysis showed pulmonary tuberculosis patients aged ≥65 years (OR=1.829, 95%CI: 1.512-2.212) had a higher risk of being unregistered treatment than those aged <65 years; patients with non-local household registration (OR=5.710, 95%CI: 4.724-6.901) had a higher risk than local patients; and patients engaged in housework/unemployed (OR=2.001, 95%CI: 1.421-2.818) or other occupations (OR=2.396, 95%CI: 1.789-3.137) had a higher risk than farmers. The mortality of unregistered treatment pulmonary tuberculosis patients was higher than the registered treatment patients (26.67% vs. 5.02%),with a significantly elevated mortality risk (OR=7.147, 95%CI: 5.738-8.902). Conclusions The unregistered treatment rate among pulmonary tuberculosis patients was well controlled in Quzhou City from 2017 to 2023, but the elderly, patients with non-local household registration, and those engaged in housework/unemployed had a higher risk of unregistered treatment. It is recommended to improve medical and social security policies, strengthen health education on tuberculosis prevention, enhance treatment adherence, and reduce mortality risk.

Objective: To analyze the trajectories of body mass index for age z-score (BAZ) and its influencing factors among children with congenital hypothyroidism (CH) based on latent class growth modeling (LCGM), so as to provide the evidence for improving treatment measures and optimizing growth management among children with CH. Methods Children with CH aged 0 to 3 years from the Newborn Disease Screening Center of Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital) between 2017 and 2022 were selected as the research subjects. Basic information, height and weight data from 3 to 36 months of age, age at treatment initiation, thyroid-stimulating hormone (TSH) levels at diagnosis, and family information were retrospectively collected. BAZ for children with CH at each month of age was calculated based on the WHO Child Growth Standards. The trajectories of BAZ were analyzed using LCGM, and factors affecting the trajectories of BAZ among children with CH were analyzed using a multinomial logistic regression model. Methods: Children with CH aged 0 to 3 years from the Newborn Disease Screening Center of Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital) between 2017 and 2022 were selected as the research subjects. Basic information, height and weight data from 3 to 36 months of age, age at treatment initiation, thyroid-stimulating hormone (TSH) levels at diagnosis, and family information were retrospectively collected. BAZ for children with CH at each month of age was calculated based on the WHO Child Growth Standards. The trajectories of BAZ were analyzed using LCGM, and factors affecting the trajectories of BAZ among children with CH were analyzed using a multinomial logistic regression model. Results: A total of 299 children with CH were included. There were 140 boys (46.82%) and 159 girls (53.18%). The median of BAZ was 0.50 (interquartile range, 1.68). The LCGM analysis categorized the subjects into three groups: the persistent high-growth pattern group with 24 cases (8.03%), the slow-growth pattern group with 39 cases (13.04%), and the appropriate-growth pattern group with 236 cases (78.93%). Multinomial logistic regression analysis showed that compared to the children with CH in the appropriate-growth pattern group, those who started treatment at the age of 30 to 60 days (OR=0.109, 95%CI: 0.016-0.732; OR=0.166, 95%CI: 0.032-0.852) had a lower risk of persistent high-growth and slow-growth patterns; CH children with TSH levels of 50 to 150 mIU/L at diagnosis (OR=3.554, 95%CI: 1.201-10.514) and those whose paternal had a senior high school/technical secondary school education (OR=2.975, 95%CI: 1.003-8.823) exhibited a higher risk of the persistent high-growth pattern. Conversely, CH children whose paternal reproductive age was 30 to 35 years (OR=0.166, 95%CI: 0.034-0.806) had a lower risk of the persistent high-growth pattern. Conclusions The BAZ trajectory of children with CH aged 0 to 3 years exhibited three patterns: persistent high-growth, slow-growth, and appropriate-growth. The persistent high-growth and slow-growth patterns were associated with treatment timing, TSH levels at diagnosis, paternal reproductive age, and paternal education level. It is recommended to strengthen early treatment interventions and provide family follow-up guidance.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

In recent years, hybrid brain-computer interfaces (BCIs) have gained significant attention due to their demonstrated advantages in increasing the number of targets and enhancing robustness of the systems. However, Existing studies usually construct BCI systems using intense auditory stimulation and strong central visual stimulation, which lead to poor user experience and indicate a need for improving system comfort. Studies have proved that the use of peripheral visual stimulation and lower intensity of auditory stimulation can effectively boost the user's comfort. Therefore, this study used high-frequency peripheral visual stimulation and 40-dB weak auditory stimulation to elicit steady-state visual evoked potential (SSVEP) and auditory steady-state response (ASSR) signals, building a high-comfort hybrid BCI based on weak audio-visual evoked responses. This system coded 40 targets via 20 high-frequency visual stimulation frequencies and two auditory stimulation frequencies, improving the coding efficiency of BCI systems. Results showed that the hybrid system's averaged classification accuracy was (78.00 ± 12.18) %, and the information transfer rate (ITR) could reached 27.47 bits/min. This study offers new ideas for the design of hybrid BCI paradigm based on imperceptible stimulation.
Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual/physiology* ; Acoustic Stimulation ; Photic Stimulation ; Electroencephalography ; Evoked Potentials, Auditory/physiology* ; Adult