Objective:To investigate the clinical outcomes of arthroscopic suture anchor fixation for acute bony Bankart lesions.Methods:Data of 49 patients with acute bone Bankart injury treated with suture anchor fixation under arthroscopy at the Affiliate Nanjing Drum Tower Hospital of Nanjing University School of Medicine from February 2019 to July 2024 were retrospectively analyzed, including 34 males and 15 females, aged 46.4±17.7 years (range, 18-81 years). Body mass index was 26.3±4.2 kg/m 2 (range, 19.8-37.9 kg/m 2). There were 17 patients with left shoulder and 32 patients with right shoulder. The time from injury to operation was 12.6±10.5 d (range, 1-50 d). CT three-dimensional reconstruction showed that the area of bone defect accounted for 20.1%±11.4% (range, 8%-49%) of the glenoid area. According to the degree of bone defect, there were 38 cases in the bone defect area ≤25% group and 11 cases in the bone defect area >25% group. All 49 patients received shoulder arthroscopic fixation with wire anchor. All 49 the patients were treated under shoulder arthroscopy using suture anchor technique. Using American Shoulder & Elbow Surgeon (ASES) score, University of California, Los Angeles (UCLA) score, Rowe score as well as shoulder range of motion to evaluate shoulder function. The position and healing of the fracture mass were evaluated with shoulder joint CT and three-dimensional reconstruction at immediate and final follow-up. Results:The incision healed in one stage and no infection occurred in all patients. All 49 patients were followed up for a period of 3 to 81 months, with an average follow-up of 23.8±20.2 months. At the final follow-up, the mean forward elevation, external rotation, and internal rotation improved from 126.1°±20.3°, 36.9°±14.0°, and 10±2 preoperatively to 167.1°±15.5°, 66.8°±8.3°, and 6±1 at the last follow-up, respectively ( P<0.05). At the final follow-up, the mean ASES score, UCLA score, and Rowe score were all significantly improved from 41.3±11.9, 14.2±6.1 and 46.9±14.7 preoperatively to 89.7±7.8, 31.5±3.6 and 92.4±7.4, respectively ( P<0.05). The differences between the final follow-up and preoperative values for forward elevation in the bone defect area >25% group and the bone defect area ≤25% group were 43°±20° and 41°±21°, respectively. The differences in lateral external rotation were 34°±8° and 29°±18°, while the differences in internal rotation (measured by hand-behind-back reach) were 5±2 and 4±2, respectively. None of these differences were statistically significant. The differences between the final follow-up and preoperative scores in the bone defect area >25% fracture fragment group and the bone defect area ≤25% group were as follows: ASES score, 50.9±14.4 vs. 47.7±12.8; UCLA score, 18.4±3.2 vs. 17.0±6.9; and Rowe score, 40.5±13.5 vs. 46.9±15.0. None of these differences were statistically significant. CT examination at the last follow-up showed that all fractures were healed, all patients in the bone defect area ≤25% group achieved good reduction, and 3 patients in the bone defect area >25% group had step-offs on the glenoid surface. Postoperative shoulder adhesion occurred in 5 patients, which was improved after intensive abduction and external rotation exercise. Conclusions:Arthroscopic suture anchor fixation is effective for the bone defect area ≤25% bony Bankart lesions, offering advantages of minimal invasiveness and rapid recovery. For lesions involving more than the bone defect area 25% of the glenoid surface, enhanced fixation strength is recommended to prevent fracture fragment displacement.

Objective:To evaluate the protective effectiveness (VE) of the acellular pertussis vaccine (aPV) against pertussis in children aged 2 months to 6 years.Methods:A test-negative case-control study was conducted among children aged 2 months to 6 years who sought medical care for cough and underwent pertussis nucleic acid testing at sentinel surveillance hospitals in Zhejiang Province in 2024. Cases were defined as those with positive pertussis nucleic acid test results, while controls were test-negative individuals matched 1∶1 based on propensity scores using the caliper matching method. Conditional logistic regression models were used to calculate odds ratios ( ORs) and VEs. Results:Among the 658 participants, 31.76% (209 cases) tested positive for pertussis. After propensity score matching, 203 cases and 203 controls were included in the analysis. The VE of 1-2, 3, and 4 doses of aPV against pertussis was 52.46% (95% CI:-39.82%-83.84%), 65.22% (95% CI: 6.86%-87.02%), and 72.21% (95% CI: 34.33%-88.24%), respectively. For pertussis-related hospitalization, the VE of 1-3 and 4 doses was 80.95% (95% CI:31.38%-94.71%) and 86.79% (95% CI: 51.89%-96.37%). The VE for those who completed 4 doses of vaccination and had intervals of less than 2 years, 2 years, 3 years, and 4 years or more after vaccination were 91.15% (95% CI: 67.61%-97.58%), 84.70% (95% CI: 43.71%-95.84%),56.23% (95% CI:-47.58%-87.02%), and 49.92% (95% CI:-83.74%-86.35%), respectively. Conclusion:The VE of aPV against pertussis in children aged 2 months to 6 years increases with the number of doses administered, and it is more effective in preventing hospitalization due to pertussis. The VE declines rapidly over time after the last dose. It is recommended to follow the new pertussis immunization program for timely and full vaccination.

Objective To explore the spatiotemporal expression patterns of Patched 1(Ptch1)and insulin enhancer binding protein-1(Isl 1)in mouse embryonic foregut and their relationship with the early development of trachea-main bronchus.Methods The foregut of 60 mouse embryos at E9.5-12.5 was separated for the detection of Isl1 and sonic hedgehog(Shh)protein by Western blotting.Serial paraffin sections of 6 mouse embryos at E9.5-14.5 were taken for immunohistochemical staining and immunofluorescence double staining with Isl1,Ptch1,forkhead box protein A2(Foxa2),type Ⅱ collagen α1 chain(Col2a1)and α-smooth muscle actin(α-SMA),as well as HE staining and Masson staining.Results The expression trend of Isl1 and Shh in foregut endoderm at E9.5-12.5 was similar,and the peak of Shh expression was later than Isl1.The foregut developed into the trachea at E9.5-12.5,Ptch1 was expressed in the thickening and protrusion of the respiratory endoderm,the laryngal-tracheal groove and the solid cell cord,accompanied by the increase and aggregation of Isl1-positive mesenchymal cells,forming a characteristic pyramidal structure centered on the respiratory endoderm and the solid cell cord;The main bronchus appeared at E12.5-13.5,Ptch1 was only expressed in its lateral wall,accompanied by the accumulation of Isl1-positive mesenchymal cells;The trachea-main bronchial epithelium lost Ptch1 expression and the surrounding Isl 1-positive mesenchymal cells also decreased rapidly at E13.5-14.5.Co12a1-positive chondrocytes first appeared in the Isl1-positive mesenchymal area adjacent to the Ptch1-positive epithelium at E12.5;Col2a1-positive cartilage was nested within the Isl1-positive mesenchymic area in a"C"shape and expanded in a proximal-distal pattern at E12.5-13.5;Col2a1-positive cartilage extended to the dorsal trachea beyond the Isl1-positive mesenchyma and encircles α-SMA positive smooth muscle in a circular manner at E14.5.Conclusion The expression of Ptch1 in the foregut endoderm is involved in the development and morphogenesis of the trachea-main bronchus epithelium,and is closely related to the proliferation and aggregation of Isl1-positive mesenchyme in the trachea-main bronchial wall,Subsequently,they jointly determine the time,location and extent of airway cartilage.

To analyze the standard establishment approach and compilation rationale for metallic pharmaceutical packaging standard development in the 2025 edition of the Pharmacopeia of the People's Republic of China.This article systematically explained the background and process of establishing the guiding principles for metallic materials and containers used in pharmaceutical packaging in the Chinese Pharmacopoeia through basic information,relevant domestic and international standards,the establishment of key quality attributes of metallic pharmaceutical packaging materials,and the construction of metallic pharmaceutical packaging material standards.The newly established guidelines,the Pharmacopeia of the People's Republic of China 9625,prioritized product critical quality attributes(CQAs)and real-world applicability.This dual emphasis on rigidity and adaptability enhances drug safety,meets the regulatory requirements,and promotes the globalization and scientific advancement of China's pharmaceutical packaging industry.

In this work,with tris(4-aminophenyl)amine(TAPA)and 1,3,5-tris(4-formylphenyl)benzene(TFPB)as monomers,an imine-type porous organic polymer,TAPA-TFPB,was synthesized using a simple method under the catalysis of acetic acid.The material TAPA-TFPB was used as solid-phase extraction adsorbent and combined with ultra-performance liquid chromatography/quadrupole time-of-flight-tandem mass spectrometry(UHPLC-QTOF-MS)to establish a detection method for four kinds of nitrofuran metabolites(NFMs)residues in meat samples.The parameters of the adsorbent dosage,the pH value and volume of sample,and the type and volume of washing and eluent solvents were optimized,respectively.Under the optimal extraction conditions,low detection limits(0.11-1.60 μg/kg)were achieved for four kinds of NFMs.At three different spiked levels,the intra-day and inter-day precisions(Relative standard deviations)were 2.8% -10.9% and 4.3% -16.2%,respectively,and the spiked recoveries were 72.0% -107.2%.The results showed that the method chould be used for efficient extraction and analysis of trace NFMs residues in meat samples,indicating that TAPA-TFPB was a kind of promising SPE adsorbent.

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.

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.