ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

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.

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.

Objective To propose a multi-cascade deep learning processor-based surgical instrument detection and pose estimation algorithm to facilitate the robotic scurb nurse to recognize and delivery surgical instruments.Methods The proposed multi-cascade deep leaning processor-based CYSP algorithm was hibernated with several functional modules such as YOLOX with coordinate attention block(CA-YOLOX),segment anything model(SAM)and principal component analysis(PCA).Firstly,CA-YOLOX was applied to identifying the types of the surgical instruments and completing the coarse positioning of x and y coordinates;secondly,the SAM segmenter was used to clarify the positions of the instruments in the RGB image,and the depth information and internal parameters of the camera were introduced to obtain the point cloud of the surgical instruments;finally,the center of mass,principal direction and normal direction of the surgical instrument point cloud were determined through the PCA algorithm,with which the rotation and translation(RT)matrix between the target coordinate system(surgical instrument center of mass coordinate system)and the base coordinate system of the robotic arm was solved,and the matrix was converted into a quaternion and then transmitted to the robotic arm control unit so as to drive the robotic arm to arrive at the corresponding position and pick up the instrument to complete the instrument delivery task.Migration training was accomplished on a self-constructed surgical instrument image dataset and the effectiveness of the proposed algorithm was evaluated,and instrument delivery experiments were performed on a seven-degree-of-freedom robotic arm and the success rate of the algorithm was assessed.Results The multi-cascade deep leaning processor-based CYSP algorithm had a recognition accuracy of 98.52％on the surgical instrument dataset,a success rate of 94％for the in-strument delivery experiment and average time for recognition of 0.28 s.Conclusion The multi-cascade deep leaning proces-sor-based CYSP algorithm with high reliability and practicability behaves well in facilitating the robotic scurb nurse to recog-nize and deliver surgical instruments.[Chinese Medical Equipment Journal,2024,45(6):1-8]

OBJECTIVE To predict biomarkers of type 2 inflammation in chronic rhinosinusitis with nasal polyps(CRSwNP)by employing peripheral blood indicators.METHODS CRSwNP patients admitted to the Rhinology Department of Beijing Tongren Hospital from June 2020 to May 2022 were enrolled and their basic clinical data were collected.The blood percentage of eosinophils(Eos%),Eos count,periostin and total IgE,as well as mucosal interleukin-5(IL-5)and Staphylococcus aureus enterotoxin-immunoglobulin E(SE-IgE)were tested.The receiver operating characteristic(ROC)curve was used to evaluate the predictive value of each blood indicator for positive mucosal expression of IL-5/SE-IgE.The logistic regression was employed to screen multiple blood indicators with predictive value for positive mucosal expression of IL-5/SE-IgE in order to construct a nomogram model.RESULTS The proportion of asthma,blood Eos%,periostin and total IgE in CRSwNP patients showed statistical differences between IL-5/SE-IgE positive and negative subgroups.ROC univariate analysis demonstrated that blood Eos%,Eos count,periostin and total IgE could predict mucosal IL-5 positivity with AUC ranging from 0.655 to 0.784,and mucosal SE-IgE positivity with AUC ranging from 0.721-0.802.The logistic regression confirmed that blood Eos%and total IgE,as well as blood periostin and total IgE were independent predictors for mucosal IL-5 and SE-IgE positivity,respectively.The nomogram models were constructed for predicting IL-5/SE-IgE positivity in CRSwNP mucosa,with consistency incides(C-index)of 0.804 and 0.81,indicating good predictive accuracy.CONCLUSION The nomograms constructed based on blood Eos%and total IgE,as well as blood periostin and total IgE,could have good predictive value for the positive mucosal expression of IL-5 and SE-IgE in the CRSwNP,which help to predict the severity of endotype and phenotype of CRSwNP.

OBJECTIVES: Glaucoma knowledge is strongly associated with medication adherence and preventive behavior. Studies have frequently reported socioeconomic inequalities in glaucoma knowledge. This study aimed to decompose such inequalities. Decomposition analysis enables the design of policies directly targeting the underlying causes of inequality. METHODS: We performed a cross-sectional survey from January 1, 2019 to June 30, 2019, at the departments of ophthalmology of 2 medical centers belonging to a hospital chain in northern Taiwan. Socioeconomic inequalities in glaucoma knowledge were ranked based on 3 aspects of socioeconomic status (SES): (1) education, (2) income, and (3) self-perceived financial status. The concentration index was calculated and decomposed using decomposition analysis. Elasticity and marginal effects were estimated for each decomposed factor. RESULTS: In total, 1,203 patients completed the survey. Both measures of glaucoma knowledge and overall glaucoma knowledge score significantly contributed to the progressivity of knowledge inequalities (pro-high-SES inequalities). The concentration index for overall knowledge score with respect to education was 0.166 (p<0.001). Both objective and subjective measures of SES were associated with pro-high-SES inequalities. Our decomposition analysis revealed that demographic factors and attitudinal factors such as the level of concern regarding developing glaucoma contributed significantly to SES-based inequalities in glaucoma knowledge. CONCLUSIONS Our decomposition analysis provided empirical evidence regarding the underlying causes of SES-based inequalities in glaucoma knowledge. Efforts to improve glaucoma knowledge should consider specific factors that drive SES-based inequalities, such as age, sex, and concern about vision health, to ultimately achieve low SES-based inequalities.

Purpose: Active surveillance (AS) is one of the management options for patients with low-risk and select intermediate-risk prostate cancer (PC). However, factors predicting disease reclassification and conversion to active treatment from a large population of pure Asian cohorts regarding AS are less evaluated. This study investigated the intermediate-term outcomes of patients with localized PC undergoing AS. Materials and Methods: This cohort study enrolled consecutive men with localized non-high-risk PC diagnosed in Taiwan between June 2012 and Jan 2023. The study endpoints were disease reclassification (either pathological or radiographic progression) and conversion to active treatment. The factors predicting endpoints were evaluated using the Cox proportional hazards model. Results: A total of 405 patients (median age: 67.2 years) were consecutively enrolled and followed up with a median of 64.6 months. Based on the National Comprehensive Cancer Network (NCCN) risk grouping, 70 (17.3%), 164 (40.5%), 140 (34.6%), and 31 (7.7%) patients were classified as very low-risk, low-risk, favorable-intermediate risk, and unfavorable intermediate-risk PC, respectively. The 5-year reclassification rates were 24.8%, 27.0%, 18.6%, and 25.3%, respectively. The 5-year conversion rates were 20.4%, 28.8%, 43.6%, and 37.8%, respectively. A prostate-specific antigen density (PSAD) of ≥0.15 ng/mL2 predicted reclassification (hazard ratio [HR] 1.84, 95% confidence interval [CI] 1.17–2.88) and conversion (HR 1.56, 95% CI 1.05–2.31). A maximal percentage of cancer in positive cores (MPCPC) of ≥15% predicted conversion (15% to <50%: HR 1.41, 95% CI 0.91–2.18; ≥50%: HR 1.97, 95% CI 1.1453–3.40) compared with that of <15%. A Gleason grade group (GGG) of 3 tumor also predicted conversion (HR 2.69, 95% CI 1.06–6.79; GGG 3 vs 1). One patient developed metastasis, but none died of PC during the study period (2,141 person-years). Conclusions AS is a viable option for Taiwanese men with non-high-risk PC, in terms of reclassification and conversion. High PSAD predicted reclassification, whereas high PSAD, MPCPC, and GGG predicted conversion.