Objective To introduce an innovative technique, the "balance-shaped sternal elevation device" and its application in the subxiphoid uniportal video-assisted thoracoscopic surgery (VATS) for anterior mediastinal masses resection. Methods Patients who underwent single-port thoracoscopic assisted anterior mediastinal tumor resection through the xiphoid process at the Department of Thoracic Surgery, West China Hospital, Sichuan University from May to June 2024 were included, and their clinical data were analyzed. Results A total of 7 patients were included, with 3 males and 4 females, aged 28-72 years. The diameter of the tumor was 1.9-17.0 cm. The operation time was 62-308 min, intraoperative blood loss was 5-100 mL, postoperative chest drainage tube retention time was 0-9 days, pain score on the 7th day after surgery was 0-2 points, and postoperative hospital stay was 3-12 days. All patients underwent successful and complete resection of the masses and thymus, with favorable postoperative recovery. Conclusion The "balance-shaped sternal elevation device" effectively expands the retrosternal space, providing surgeons with satisfactory surgical views and operating space. This technique significantly enhances the efficacy and safety of minimally invasive surgery for anterior mediastinal masses, reduces trauma and postoperative pain, and accelerates patient recovery, demonstrating important clinical significance and application value.

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.

AIM: To analyze the value of blue-on-yellow perimetry(B/Y)combined with macular ganglion cells inner plexiform layer(GCIPL)detection in the early diagnosis of open angle glaucoma.METHODS: A prospective case-control study was conducted to collect 100 patients(174 eyes)from May 2023 to May 2024 in Eye Hospital of Wenzhou Medical University as the case group, and 20 healthy volunteers(40 eyes)as the control group. The case group was divided into high intraocular pressure group, suspected glaucoma group, and early glaucoma group based on the examination results. All study subjects underwent comprehensive ophthalmic examination, white-on-white perimetry(W/W)and B/Y examination, and swept source optical coherence tomography(SS-OCT)was used to scan the optic disc and macula to obtain relevant parameters. The value of B/Y combined with macular GCIPL in the diagnosis of open angle glaucoma was analyzed.RESULTS: In the case group, 30 cases(52 eyes)were diagnosed with early primary open angle glaucoma, 46 cases(82 eyes)were suspected of open angle glaucoma, and 24 cases(40 eyes)were diagnosed with high intraocular pressure. The W/W mean defect(MD)and B/Y-MD values in the early glaucoma group were lower than those in the control group, high intraocular pressure group, and suspected glaucoma group. The W/W pattern standard deviation(PSD)and B/Y-PSD values were higher than those in the control group, high intraocular pressure group, and suspected glaucoma group(all P<0.05). The W/W-MD and B/Y-MD values in the suspected glaucoma group were lower than those in the control group and the high intraocular pressure group(all P<0.05). The B/Y-MD values in the high intraocular pressure group were lower than those in the control group(P<0.05). The parameters of GCIPL in the macular area of the early glaucoma group were lower than those of the control group, high intraocular pressure group, and suspected glaucoma group(all P<0.05). The minimum GCIPL in the macular area of the suspected glaucoma group, as well as the upper and lower temporal areas, were lower than those of the control group and the high intraocular pressure group(all P<0.05). The average, upper, lower, temporal, 5:00, 6:00, and 12:00 positions of the retinal nerve fiber layer(RNFL)parameters around the optic disc in the early glaucoma group were lower than those in the control group, high intraocular pressure group, and suspected glaucoma group(all P<0.05). The average and upper RNFL parameters in the suspected glaucoma group were lower than those in the control group and high intraocular pressure group. The rim area of the optic nerve head(ONH)parameters in the early glaucoma group was smaller than that in the control group, high intraocular pressure group, and suspected glaucoma group, while the horizontal and vertical cup-to-disc ratio was higher than those in the control group, high intraocular pressure group, and suspected glaucoma group; the rim area of the suspected glaucoma group was smaller than that of the control group and high intraocular pressure group, and the horizontal and vertical cup-to-disc ratio were higher than those of the control group and high intraocular pressure group(all P<0.05). Receiver operating characteristic(ROC)curve was drawn, and the results showed that visual field parameters, macular GCIPL parameters, and RNFL parameters had certain diagnosibility for early open angle glaucoma and suspected glaucoma. Decision curve was drawn, and the results showed that when the threshold was between 0 and 1.0, the net return rate of diagnosing early open angle glaucoma with the combination of B/Y and macular GCIPL parameters was higher than the individual diagnostic efficacy of each indicator.CONCLUSION: The combination of B/Y and macular GCIPL detection can be an important means for the early diagnosis of glaucoma.

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.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

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.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

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.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.