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.

Objective To evaluate the clinical efficacy and safety of sulbactam-durlobactam in the treatment of pulmonary infection caused by extensively drug-resistant Acinetobacter baumannii (XDR-AB) after liver transplantation. Methods A retrospective analysis was performed on the data of 3 liver transplant recipients admitted to the First Affiliated Hospital of Xi'an Jiaotong University from May to December 2025, who developed postoperative XDR-AB pulmonary infection and received sulbactam-durlobactam-based anti-infective therapy. Clinical data, clinical efficacy, microbiological response, safety and outcomes were analyzed. Results All isolated strains were XDR-AB producing OXA-23 carbapenemase, and in vitro antimicrobial susceptibility testing showed sensitivity to sulbactam-durlobactam. After treatment with sulbactam-durlobactam, all 3 recipients achieved clinical cure and microbiological eradication, with a treatment course of 6-8 days and pathogen clearance time of 3-6 days. Septic shock was effectively controlled in all recipients, who were successfully weaned from mechanical ventilation, and oxygenation index and organ function scores were significantly improved. No definite nephrotoxicity or other severe adverse reactions related to sulbactam-durlobactam were observed during treatment, and the 30-day all-cause fatality was 0. Two critically ill recipients achieved rapid microbiological clearance after combined nebulized phage therapy. Conclusions Sulbactam-durlobactam-based regimen shows favorable efficacy and safety in liver transplant recipients with postoperative XDR-AB pulmonary infection, and has a promising clinical application prospect.

Osteonecrosis of femoral head （ONFH） is a disease that causes osteocyte death due to interruption of blood supply of the femoral head， which may cause symptoms such as joint movement disorders， pain， and even femoral head collapse. Currently， the commonly used methods for treating ONFH in clinical practice are accompanied by problems such as long-term side effects， inaccurate efficacy， technical limitations， and complication risks. Traditional Chinese medicine （TCM） has shown unique advantages of holistic regulation， mild side effects， and symptom alleviation. A review of the related literature shows that Salviae Miltiorrhizae Radix et Rhizoma， as a representative Chinese herbal medicine for activating blood and resolving stasis， has the effects of activating blood， resolving stasis， dredging meridians， and relieving pain. Its active ingredients （such as salvianolic acid B， tanshinone Ⅱ_A， and tanshinone Ⅰ） interfere with bone metabolism balance， osteogenesis and differentiation， and improve bone microcirculation and bone microstructure. Salviae Miltiorrhizae Radix et Rhizoma is often combined with the herbal medicines with the effects of tonifying the liver and spleen or activing blood to form herb pairs （such as Salviae Miltiorrhizae Radix et Rhizoma-Angelicae Sinensis Radix， Salviae Miltiorrhizae Radix et Rhizoma-Chuanxiong Rhizoma， and Salviae Miltiorrhizae Radix et Rhizoma-Drynariae Rhizoma）， which enhances the efficacy through synergistic effects. In the application of TCM compound formulas， Bushen Huoxue prescription， Bushen Huoxue capsules， Gubi Tongxiao granules， and Shenggu Zaizao pills can regulate signaling pathways such as Wnt/β-catenin， Hedgehog， Notch， transforming growth factor（TGF）-β₁/Smads， and mitogen-activated protein kinase （MAPK） through multiple targets to significantly improve bone metabolism and hip joint function， playing an important role in preventing and treating ONFH. Through a review of available studies， this paper systematically explains the outstanding advantages of the active ingredients， herb pairs， and compound formulas of Salviae miltiorrhizae Radix et Rhizoma in the prevention and treatment of ONFH. The mechanisms may be related to factors such as promoting osteoblast generation and inhibiting osteoclast formation， increasing osteogenic differentiation， promoting angiogenesis， increasing bone density， inhibiting inflammatory response， and regulating signaling pathways. In the future， modern technology can be employed to optimize dosage forms， and high-quality clinical research should be conducted to further clarify its mechanism of action and promote the precise application of TCM. This review aims to provide more thinking directions for the application of Salviae miltiorrhizae Radix et Rhizoma in preventing and treating ONFH.

BACKGROUND:The plantarflexor weakness is a common muscle defect in patients with spastic cerebral palsy and Charcot-Marie-Tooth,which clinically manifests abnormal gaits,and the relationship between plantarflexor weakness and abnormal gaits is unclear. OBJECTIVE:To explore the biomechanical behavior of the lower limb under the action of a single factor of plantarflexor weakness to reveal the mechanism of abnormal gait induced by plantarflexor weakness and to provide guidance for the rehabilitation training of patients with plantarflexor weakness. METHODS:A predictive framework of musculoskeletal multibody dynamics in the sagittal plane was established based on OpenSim Moco to predict lower limb joint angles and muscle activation changes during walking in normal subjects.The validity of the framework was verified by combining the inverse kinematics and electromyogram activation time of the experimental data.Reduced isometric muscle forces were used to model plantarflexor weakness and to compare predicted lower extremity joint angles,joint moments,and muscle energy expenditure with normal subjects to analyze the effects of plantarflexor weakness on lower extremity biomechanics. RESULTS AND CONCLUSION:(1)The Moco-based prediction framework realistically predicted the biomechanical changes of the lower limbs during walking in normal subjects(joint angles:normalized correlation coefficient≥0.73,root mean square error≤7.10°).(2)The musculoskeletal model used a small stride support phase to increase the"heel-walking"gait during plantarflexor weakness.When the plantarflexor weakness reached 80%,the muscle energy expenditure was 5.691 4 J/kg/m,and the maximum activation levels of the gastrocnemius and soleus muscles were 0.72 and 0.53,which might cause the plantarflexor weakness patients to be more prone to fatigue when walking.(3)Muscle energy expenditure was significantly higher when the weakness of plantarflexors exceeded 40%,and the joint angles and moments of the lower limbs deteriorated significantly when the weakness of plantarflexors exceeded 60%,suggesting that there may be a"threshold"for the effect of plantarflexor weakness on gait,which may correspond to the point at which health care professionals should intervene in the clinical setting.

Objective To investigate the clinical effect of dapagliflozin combined with sacubitril-valsar-tan in the treatment of non-reduced ejection fraction type heart failure(non-HFrEF).Methods A total of 120 patients with non-HFrEF diagnosed and treated in this hospital from January to December 2022 were selected as the study subjects and divided into the observation group and control group by the random number method,60 cases in each group.The both groups were treated with conventional treatments such as β-receptor blockers and diuretics,the patients in the control group were treated with sacubitril-valsartan(Noxinto),and the pa-tients in the observation group were treated with dapagliflozin on the basis of the control group for 6 months.The clinical efficacy,serum N-terminal B-type natriuretic peptide precursor(NT-proBNP),hypersensitivity-C-reactive protein(hs-CRP),serum creatinine(SCr),serum potassium,cardiac color Doppler ultrasound param-eters,6 min walk test(6MWT)distance before treatment and in 6 months after treatment,and the occurrence of major adverse cardiovascular events(MACE)during treatment were compared between the two groups.Re-sults After 6 months of treatment,the total effective rate in the observation group was 93.3%,which was higher than 80%in the control group,and the difference was statistically significant(P=0.032).After 6 months of treatment,the levels of NT-proBNP and hs-CRP in the two groups were significantly decreased compared with those before treatment,moreover the observation group was lower than the control group,and the difference was statistically significant(P<0.05).After 6 months of treatment,LVEF and 6MWT dis-tance in the two groups were increased compared with those before treatment,LVEDV and LVESV were de-creased compared with those before treatment,moreover the change amplitude of the above indexes in the ob-servation group were greater than those in the control group,and the differences were statistically significant(P<0.05).The incidence rate of MACE in the observation group was lower than that in the control group(16.7%vs.5.0%),and the difference was statistically significant(P<0.05).Conclusion Dapagliflozin combined with sacubitril-valsartan in the treatment of non-HFrEF could significantly reduce the levels of NT-proBNP and hs-CRP,improve the cardiac function,increase the exercise tolerance,decrease the incidence rate of MACE and improve the prognosis.

Ursodeoxycholic acid (UDCA) is a naturally occurring, low-toxicity, and hydrophilic bile acid (BA) in the human body that is converted by intestinal flora using primary BA. Solute carrier family 7 member 11 (SLC7A11) functions to uptake extracellular cystine in exchange for glutamate, and is highly expressed in a variety of human cancers. Retroperitoneal liposarcoma (RLPS) refers to liposarcoma originating from the retroperitoneal area. Lipidomics analysis revealed that UDCA was one of the most significantly downregulated metabolites in sera of RLPS patients compared with healthy subjects. The augmentation of UDCA concentration (≥25 μg/mL) demonstrated a suppressive effect on the proliferation of liposarcoma cells. [¹⁵N₂]-cystine and [¹³C₅]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione (GSH) synthesis. Mechanistically, UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis, leading to reactive oxygen species (ROS) accumulation and mitochondrial oxidative damage. Furthermore, UDCA can promote the anti-cancer effects of ferroptosis inducers (Erastin, RSL3), the murine double minute 2 (MDM2) inhibitors (Nutlin 3a, RG7112), cyclin dependent kinase 4 (CDK4) inhibitor (Abemaciclib), and glutaminase inhibitor (CB839). Together, UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity, and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA. More importantly, in combination with other antitumor chemotherapy or physiotherapy treatments, UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

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.