BACKGROUND:How to effectively promote bone regeneration and bone reconstruction after bone injury has always been a key issue in clinical bone repair research.The use of biological and degradable materials loaded with bioactive factors to treat bone defects has excellent application prospects in bone repair. OBJECTIVE:To investigate the effect of polylactic acid-glycolic acid copolymer(PLGA)composite scaffold modified by lysine-grafted graphene oxide nanoparticles(LGA-g-GO)on osteogenic differentiation and new bone formation. METHODS:PLGA was dissolved in dichloromethane and PLGA scaffold was prepared by solvent evaporation method.PLGA/GO composite scaffolds were prepared by dispersing graphene oxide uniformly in PLGA solution.LGA-g-GO nanoparticles were prepared by chemical grafting method,and the PLGA/LGA-g-GO composite scaffolds were constructed by blending LGA-g-GO nanoparticles at different mass ratios(1%,2%,and 3%)with PLGA.The micromorphology,hydrophilicity,and protein adsorption capacity of scaffolds of five groups were characterized.MC3T3 cells were inoculated on the surface of scaffolds of five groups to detect cell proliferation and osteogenic differentiation. RESULTS AND CONCLUSION:(1)The surface of PLGA scaffolds was smooth and flat under scanning electron microscope,while the surface of the other four scaffolds was rough.The surface roughness of the composite scaffolds increased with the increase of the addition of LGA-g-GO nanoparticles.The water contact angle of PLGA/LGA-g-GO(3%)composite scaffolds was lower than that of the other four groups(P<0.05).The protein adsorption capacity of PLGA/LGA-g-GO(1%,2%,and 3%)composite scaffolds was stronger than PLGA and PLGA/GO scaffolds(P<0.05).(2)CCK-8 assay showed that PLGA/LGA-g-GO(2%,3%)composite scaffold could promote the proliferation of MC3T3 cells.Alkaline phosphatase staining and alizarin red staining showed that the cell alkaline phosphatase activity in PLGA/LGA-g-GO(2%,3%)group was higher than that in the other three groups(P<0.05).The calcium deposition in the PLGA/GO and PLGA/LGA-g-GO(1%,2%,and 3%)groups was higher than that in the PLGA group(P<0.05).(3)In summary,PLGA/LGA-g-GO composite scaffold can promote the proliferation and osteogenic differentiation of osteoblasts,and is conducive to bone regeneration and bone reconstruction after bone injury.

ObjectiveTo observe the clinical effectiveness and safety of Xiaozhong Zhitong Mixture （消肿止痛合剂） combined with antibiotic bone cement in the treatment of diabetic foot ulcer （DFU） with damp-heat obstructing syndrome. MethodsA total of 72 DFU patients with damp-heat obstructing syndrome were randomly assigned to treatment group （36 cases） and the control group （36 cases）. Both groups received standard treatment and topical antibiotic bone cement for ulcer wounds， while the treatment group received oral Xiaozhong Zhitong Mixture （50 ml per time， three times daily） in additionally. Both groups underwent daily wound dressing changes for 21 consecutive days. Ulcer healing rate， serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1 beta （IL-1β）， malondialdehyde （MDA）， superoxide dismutase （SOD）， C-reactive protein （CRP）， and white blood cell （WBC） count were observed before and after treatment， and visual analog scale （VAS） scores for wound pain， traditional Chinese medicine （TCM） syndrome scores， and the DFU Healing Scale （DMIST scale） were also compared. Liver and kidney function were evaluated before and after treatment， and adverse events such as allergic reactions， worsening ulcer pain were recorded. ResultsTotally 35 patients in the treatment group and 33 in the control group were included in the final analysis. The ulcer healing rate in the treatment group was （87.93±9.34）%， significantly higher than （81.82±12.02）% in the control group （P = 0.035）. Compared to pre-treatment levels， both groups showed significant reductions in serum CRP， WBC， MDA， IL-1β， and TNF-α levels， with an increase in SOD level （P＜0.05）. TCM syndrome scores， VAS， and DMIST scores also significantly decreased in both groups （P＜0.05）， with greater improvements in the treatment group （P＜0.05）. No significant adverse reactions were observed in either group during treatment. ConclusionXiaozhong Zhitong Mixture combined with antibiotic bone cement has significant advantages in promoting DFU healing， reducing inflammatory response， and alleviating oxidative stress in DFU patients with damp-heat obstructing syndrome， with good safety for DFU patients with damp-heat obstructing syndrome.

[Objective] To resolve the ambiguities of HLA genotyping generated by next generation sequencing (NGS) using nanopore sequencing technology. [Methods] A total of 38 samples with ambiguous HLA genotyping by NGS in our laboratory were collected, and HLA-A, -B, -C, -DRB1, -DRB3/4/5, -DQA1, -DQB1, -DPA1 and -DPB1 loci in these samples were amplified using primers in the same commercial NGS HLA genotyping kit, then subjected to third-generation library construction, and sequenced on the nanopore sequencer. The sequencing data were converted into Fastq files and analyzed by software, and the genotypes of 11 HLA loci were obtained. The ambiguities were counted directly. [Results] The high-resolution genotyping at the second domain of 11 HLA loci of 38 samples using the third generation sequencing (TGS) were consistent with the results of the NGS method at a rate of 100%. The genotypes for the HLA-A, -B, -C, -DRB3, -DRB4, -DQA1 and -DPA1 loci by TGS were all only one result, and the discrimination rate for ambiguities of the HLA-A, -B, -C, and -DQA1 loci (all caused by the difficulty in phasing due to the short NGS read length) was 100%. Among the HLA-DRB1, -DRB5, -DQB1 and -DPB1 loci, the discrimination rate of TGS for the ambiguities caused by non-amplification of exon 1 was 0% and by the short NGS read length was 100%. [Conclusion] Nanopore technology was used to identify the ambiguities of 11 HLA loci in this study, and the ambiguities caused by the short read length disadvantage of the NGS method could be solved effectively and the accuracy of HLA genotyping would be improved.

The success of implementation research is closely tied to the institution's pre-implementation readiness. This study aims to explore the organizational readiness for change (ORC) and its influencing factors on primary healthcare settings in the implementation of the "Shared Medical Appointment for Diabetes (SMART) in China: design of an optimization trial" and to enhance ORC and provide insights to support the effective implementation of the program.

AIM: To analyze the changes of retinal structure and function before and after panretinal photocoagulation(PRP)in patients with proliferative diabetic retinopathy(PDR).METHODS: Prospective study. Totally 98 cases(98 eyes)of PDR patients who underwent PRP in Eye Hospital of Wenzhou Medical University from January 2022 to May 2023 were included. Optical coherence tomography angiography(OCTA)was used to detect central retinal thickness(CRT), central macular thickness(CMT), subfoveal choroidal thickness(SFCT), foveal avascular zone(FAZ), deep vascular complex(DVC)blood flow density, superficial vascular complex(SVC)blood flow density before and at 1 wk, 1 and 3 mo after PRP. During the follow-up, 1 eye underwent vitrectomy, 2 eyes were lost to follow-up, and finally 95 eyes completed 1 a follow-up, with a loss rate of 3%. According to the visual prognosis at 1 a after treatment, the patients were divided into two groups: 73 eyes in good prognosis group and 22 eyes in poor prognosis group(including 9 eyes of visual disability and 13 eyes of visual regression). The changes in retinal structure and function before and after PRP treatment were compared between the two groups of patients, and the receiver operating characteristic(ROC)curve and decision curve were used to analyze the predictive value of retinal structure and function for PDR treatment.RESULTS: There were statistical significant differences in PDR staging, CRT, CMT, SFCT, DVC blood flow density, and SVC blood flow density between the two groups of patients before treatment(all P<0.05). At 1 wk, 1 and 3 mo after treatment, the FAZ area of both groups decreased compared to before treatment, while the blood flow density of DVC and SVC increased compared to before treatment(both P<0.05). However, there was no significant difference in the blood flow density of FAZ, DVC, and SVC between the two groups at 1 wk, 1 and 3 mo after treatment(all P>0.05). The CRT, CMT and SFCT of the two groups at 1 wk after treatment were higher than those before treatment(all P<0.05), but there were no significant differences between the two groups(all P>0.05). The CRT, CMT and SFCT at 1 and 3 mo after treatment were lower than those at 1 wk after treatment and before treatment in both groups. The CRT, CMT and SFCT in the poor prognosis group at 3 mo after treatment were higher than those at 1 mo after treatment, and were higher than those in the good prognosis group(all P<0.05). ROC analysis showed that, at 3 mo after laser treatment in PDR patients, the area under the curve of the CRT, CMT, and SFCT alone or in combination after treatment for 1 a was 0.788, 0.781, 0.783, and 0.902, respectively, and the combined prediction value was better(P<0.05). Decision curve analysis showed that the combined detection of CRT, CMT, and SFCT in PDR patients at 3 mo after treatment can improve the predictive value of visual prognosis.CONCLUSION: The optimal time for retinal structure and function recovery in PDR patients after PRP treatment is between 1 wk and 1 mo. OCTA measurement of CRT, CMT, and SFCT at 3 mo after treatment can predict the visual prognosis during the 1 a treatment period.

This study aimed to localize the workplace readiness questionnaire (WRQ) and validate its applicability for assessing readiness for implementation of evidence-based practices (EBP) in primary care settings in China. The localization of the instrument will provide a practical instrument for assessing organizational readiness for change (ORC).

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.