Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

Objective:To investigate the value of radiomics signatures based on 18F-FDG PET/CT for predicting molecular classification and Ki-67 expression of breast cancer. Methods:A total of 134 female patients ((55.4±13.3) years) who underwent 18F-FDG PET/CT examination and were diagnosed with breast cancer by pathology in the First Affiliated Hospital of Soochow University from April 2016 to May 2023 were retrospectively enrolled. LIFEx software was used to extract radiomics features and the least absolute shrinkage and selection operator (LASSO) algorithm and independent-sample t test were used to screen potentially meaningful features and calculate the radiomics score, which were considered as radiomics models. Clinical characteristics were selected by supervised logistic regression and clinical models were established. Radiomics features and clinical characteristics were incorporated to logistic regression analysis to establish combined models. ROC curves were drawn and the differences among AUCs were analyzed by Delong test. Results:Among 134 patients, 22 were with triple negative breast cancer (TNBC), 47 were human epidermal growth factor receptor 2 (HER2) over-expression type, 37 were Luminal A type and the rest 28 were Luminal B type. The expression of Ki-67 was high in 85 patients, and was low in the rest 49 patients. The AUCs (95% CI) of the combined models for predicting TNBC, HER2 overexpression type, Luminal A type and Ki-67 expression were 0.843(0.770-0.900), 0.808(0.723-0.876), 0.825(0.711-0.908) and 0.836(0.762-0.894), respectively, which were higher than those of clinical models ( z values: 1.97-3.06, all P<0.05). Conclusion:The predictive model combining radiomics signatures based on 18F-FDG PET/CT and clinical characteristics can well predict the molecular classification and Ki-67 expression level of breast cancer.

Bacterial biofilms gave rise to persistent infections and multi-organ failure, thereby posing a serious threat to human health. Biofilms were formed by cross-linking of hydrophobic extracellular polymeric substances (EPS), such as proteins, polysaccharides, and eDNA, which were synthesized by bacteria themselves after adhesion and colonization on biological surfaces. They had the characteristics of dense structure, high adhesiveness and low drug permeability, and had been found in many human organs or tissues, such as the brain, heart, liver, spleen, lungs, kidneys, gastrointestinal tract, and skeleton. By releasing pro-inflammatory bacterial metabolites including endotoxins, exotoxins and interleukin, biofilms stimulated the body’s immune system to secrete inflammatory factors. These factors triggered local inflammation and chronic infections. Those were the key reason for the failure of traditional clinical drug therapy for infectious diseases.In order to cope with the increasingly severe drug-resistant infections, it was urgent to develop new therapeutic strategies for bacterial-biofilm eradication and anti-bacterial infections. Based on the nanoscale structure and biocompatible activity, nanobiomaterials had the advantages of specific targeting, intelligent delivery, high drug loading and low toxicity, which could realize efficient intervention and precise treatment of drug-resistant bacterial biofilms. This paper highlighted multiple strategies of biofilms eradication based on nanobiomaterials. For example, nanobiomaterials combined with EPS degrading enzymes could be used for targeted hydrolysis of bacterial biofilms, and effectively increased the drug enrichment within biofilms. By loading quorum sensing inhibitors, nanotechnology was also an effective strategy for eradicating bacterial biofilms and recovering the infectious symptoms. Nanobiomaterials could intervene the bacterial metabolism and break the bacterial survival homeostasis by blocking the uptake of nutrients. Moreover, energy-driven micro-nano robotics had shown excellent performance in active delivery and biofilm eradication. Micro-nano robots could penetrate physiological barriers by exogenous or endogenous driving modes such as by biological or chemical methods, ultrasound, and magnetic field, and deliver drugs to the infection sites accurately. Achieving this using conventional drugs was difficult. Overall, the paper described the biological properties and drug-resistant molecular mechanisms of bacterial biofilms, and highlighted therapeutic strategies from different perspectives by nanobiomaterials, such as dispersing bacterial mature biofilms, blocking quorum sensing, inhibiting bacterial metabolism, and energy driving penetration. In addition, we presented the key challenges still faced by nanobiomaterials in combating bacterial biofilm infections. Firstly, the dense structure of EPS caused biofilms spatial heterogeneity and metabolic heterogeneity, which created exacting requirements for the design, construction and preparation process of nanobiomaterials. Secondly, biofilm disruption carried the risk of spread and infection the pathogenic bacteria, which might lead to other infections. Finally, we emphasized the role of nanobiomaterials in the development trends and translational prospects in biofilm treatment.

Objective To observe the clinical efficacy of acupuncture combined with rehabilitation training under the guidance of Chinese medicine-enhanced recovery after surgery(CMERAS)in postoperative rehabilitation of tibial plateau fracture.Methods Sixty patients with tibial plateau fracture in postoperative rehabilitation period were randomly divided into observation group and control group,30 cases in each group.The control group was given routine postoperative rehabilitation training,and the observation group was treated with combined acupuncture treatment on the basis of the rehabilitation training of the control group.Both groups were treated for 3 consecutive months.After 3 months of treatment,the clinical efficacy of the two groups was evaluated,and the changes in the Visual Analogue Scale(VAS)scores of pain were observed before and after treatment,and the changes in the knee scores of the Hospital for Special Surgery knee score(HSS)of the United States were compared before and after treatment between the two groups.As well as the time of fracture healing on the operative side of the two groups of patients,and the fracture healing rate within 3 months of the two groups of patients were compared.Results(1)After 1 week,1 month and 3 months of postoperative treatment,the VAS scores of patients in the two groups were significantly improved compared with the pre-treatment period(P＜0.05),and the observation group was significantly superior to the control group in improving the VAS scores,with a statistically significant difference(P＜0.05).(2)After treatment,the HSS scores of patients in the two groups were significantly improved compared with those before treatment(P＜0.05),and the observation group was significantly superior to the control group in improving the HSS scores,and the difference was statistically significant(P＜0.05).(3)After 3 months of treatment,the fracture healing rate was 56.67%(17/30)in the observation group and 30.00%(9/30)in the control group.The fracture healing rate of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).(4)After 3 months of acupuncture treatment,the total effective rate was 96.67%(29/30)in the observation group and 73.33%(22/30)in the control group.The efficacy of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).Conclusion The clinical efficacy of acupuncture in postoperative rehabilitation of tibial plateau fractures under the guidance of CMERAS is significant,which reduces the clinical symptom of postoperative pain of the patients,shortens the healing time of fracture breaks,and significantly improves the patients'knee joint function.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

A new method was developed for simultaneous detection of total 19 kinds of organophosphate esters(OPEs)and their diester metabolites(di-OPEs)in human serum(1.0 mL)and urine(1.5 mL)with low volume of samples.The target compounds were determined using ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)after acetonitrile liquid-liquid extraction combined with purification using an ENVI-18 solid-phase extraction(SPE)column.OPEs and di-OPEs were separated using a Shim-pack GIST C18 column(100 mm×2.1 mm,2 μm)with a Shim-pack GIST-HP(G)C18 guard column.An electrospray ionization source(ESI)was employed in mass spectrometry analysis,with positive/negative ion mode using the multiple reaction monitoring(MRM).All target compounds were separated within 15 min,and exhibited good linear relationships in the concentration range of 2-100 ng/mL,with correlation coefficients(R2)above 0.994.The method detection limits(MDL)in serum ranged from 0.001 to 0.178 ng/mL and the MDL in urine ranged from 0.001 to 0.119 ng/mL.The recoveries of the analytes spiked in serum and urine matrices at two concentration levels were 30.5%-126.8%,with the relative standard deviations(RSDs)ranged from 1%to 23%.In addition,paired serum and urine samples from 11 patients were analyzed.For all samples tested,the internal standards of OPEs exhibited recoveries between 61%and 114%,whereas the internal standards for di-OPEs had recoveries ranging from 43%to 103%.OPEs and di-OPEs exhibited high detection frequencies in 22 serum and urine samples.Triethyl phosphate(TEP),tributyl phosphate(TBP),tris(2-ethylhexyl)phosphate(TEHP),tris(2-butoxyethyl)phosphate(TBEP),tris(1-chloro-2-propyl)phosphate(TCIPP),triphenyl phosphate(TPHP),tri-m-tolyl-phosphate(TMTP)and 2-ethylhexyl diphenyl phosphate(EHDPP)were universally detected in all serum samples.TCIPP was identified at the highest concentrations(median 0.548 ng/mL)in serum samples.In urine samples,the detection frequency for 12 kinds of target compounds reached 100%.Notably,TBP emerged as the predominant OPE in urine,demonstrating a median concentration of 0.506 ng/mL.Regarding di-OPEs,bis(2-chloroethyl)phosphate(BCEP)and bis(2-butoxyethyl)hydrogen phosphate(BBOEP)were the most abundant in urine,with median concentrations of 6.404 and 2.136 ng/mL,respectively.The total concentrations of OPEs and di-OPEs in serum and urine were 1.580-3.843 ng/mL and 5.149-17.537 ng/mL,respectively.These results not only confirmed the effectiveness of the method in detection of OPEs and di-OPEs in biological matrices,but also revealed the widespread presence of OPE compounds in human body and pointed to potential exposure risks.

Objective:To compare the difference between the Evidence Quality Grading System for Public Health Decision-making(PHE-Grading)and the Grading of Recommendations Assessment,Development and Evaluation(GRADE)System in evaluating the quality of evidence for public health decision-making.Methods:Systematic reviews about topic"Public health"were electronically searched in the Cochrane Library database from inception to February 27,2024.EndNote 20 software was used for literature screening,Excel 2021 and SPSS 22.0 software were used for data collation and analysis,and the forest plot was drawn by RevMan 5.4.1 software.Results:A total of 61 systematic reviews were finally included for evidence quality evaluation.The forest plot of GRADE and PHE-Grading evidence grading results showed that high grade[OR:2.39,95%CI(1.21 to 4.75)],moderate grade[OR:0.40,95%CI(0.31 to 0.52)],low grade[OR:0.37,95%CI(0.29 to 0.46)],and extremely low grade[OR:85.11,95%CI(34.80 to 208.11)],and the differences in evidence quality grading results between the two systems were statistically significant.Conclusions:Compared with GRADE,PHE-Grading may be more accurate in grasping the certainty of public health decision-making evidence.Currently,the quality of public health decision-making evidence is still concentrated in low and middle level,and high-quality research still needs to be strengthened to support scientific decision-making.