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.

Epigenetic pathways play a critical role in the initiation, progression, and metastasis of cancer. Over the past few decades, significant progress has been made in the development of targeted epigenetic modulators (e.g., inhibitors). However, epigenetic inhibitors have faced multiple challenges, including limited clinical efficacy, toxicities, lack of subtype selectivity, and drug resistance. As a result, the design of new epigenetic modulators (e.g., degraders) such as PROTACs, molecular glue, and hydrophobic tagging (HyT) degraders has garnered significant attention from both academia and pharmaceutical industry, and numerous epigenetic degraders have been discovered in the past decade. In this review, we aim to provide an in-depth illustration of new degrading strategies (2017-2023) targeting epigenetic proteins for cancer therapy, focusing on the rational design, pharmacodynamics, pharmacokinetics, clinical status, and crystal structure information of these degraders. Importantly, we also provide deep insights into the potential challenges and corresponding remedies of this approach to drug design and development. Overall, we hope this review will offer a better mechanistic understanding and serve as a useful guide for the development of emerging epigenetic-targeting degraders.

Aquaporin 5(AQP5),as the main water transport protein in the body,can regulate lung diseases by regulating airway mucus secretion,pulmonary inflammation,and lung function.Programmed cell death(PCD)plays a crucial role in chronic obstructive pulmonary disease(COPD).AQP5 may affect the development of COPD by regulating PCDs.This article reviews the molecular regulatory mechanism of AQP5 on apoptosis,autophagy,iron death and pyroptosis in PCDs in recent years,and further discusses its effect on COPD in order to provide theoretical support for clinical prevention and treatment of COPD.

Objective:To systematically evaluate the qualitative researches on cardiac telerehabilitation experience of patients with cardiovascular disease (CVD), so as to provide reference for clinical development and improvement of cardiac telerehabilitation services.Methods:Qualitative studies on cardiac telerehabilitation experience of CVD patients in PubMed, Web of Science, Embase, CINAHL, Cochrane Library, Scopus, China National Knowledge Infrastructure, China Biology Medicine disc, Wanfang Database and VIP were searched by computer. The search period was from establishment of the databases to August 2023. The quality of the literature was evaluated according to the quality evaluation criteria of the Evidence-Based Health Care Center of the Joanna Briggs Institute in Australia, and the results were integrated by aggregative integration method.Results:A total of 13 articles were included, 52 research results were extracted and classified into 11 categories. Four integrated results were formed, including the benefits, promoting factors, obstacle, expectations and suggestions for cardiac telerehabilitation experience in CVD patients.Conclusions:CVD patients benefit significantly from participating in cardiac telerehabilitation. In the future, it is supposed to pay more attention to the factors that affect patients' participation in cardiac telerehabilitation, actively develop domestic cardiac telerehabilitation tools and optimize the cardiac telerehabilitation model according to the needs and suggestions of patients.

Objective To investigate the differences in visual perception between children with autism spectrum disorder(ASD)and typically developing(TD)children when watching different intention videos,and to explore the feasibility of machine learning algorithms in objectively distinguishing between ASD children and TD children.Methods A total of 58 children with ASD and 50 TD children were enrolled and were asked to watch the videos containing joint intention and non-joint intention,and the gaze duration and frequency in different areas of interest were used as original indicators to construct classifier-based models.The models were evaluated in terms of the indicators such as accuracy,sensitivity,and specificity.Results When using eight common classifiers,including support vector machine,linear discriminant analysis,decision tree,random forest,and K-nearest neighbors(with K values of 1,3,5,and 7),based on the original feature indicators,the highest classification accuracy achieved was 81.90% .A feature reconstruction approach with a decision tree classifier was used to further improve the accuracy of classification,and then the model showed the accuracy of 91.43% ,the specificity of 89.80% ,and the sensitivity of 92.86% ,with an area under the receiver operating characteristic curve of 0.909(P<0.001).Conclusions The machine learning model based on eye-tracking data can accurately distinguish ASD children from TD children,which provides a scientific basis for developing rapid and objective ASD screening tools.[Chinese Journal of Contemporary Pediatrics,2024,26(2):151-157]

Objective To investigate the feasibility of translocation of pedicled buccal fat pad in the treatment of the temporomandibular joint ankylosis(TMJA)by measuring the diameter of buccal fat pad and related anatomical structures of the transverse blood vessels,nerves and temporomandibular joint.Methods A total of 40 adult head and neck specimens were randomly divided into group A and group B,with 20 cases in each group.The morphology of the buccal fat pad in group A was observed,and its size and compression diameter through blood vessels and nerves were measured.The anatomical structures of the temporomandibular joint in group B were observed and measured.Results The volume of buccal fat pad in group A was(10.10±1.10)mL on the left side and(9.70±1.50)mL on the right side.The longitudinal axis length of buccal fat pad was(28.18±1.35)mm on the left side and(29.47±1.12)mm on the right side;Transverse axis length of buccal fat pad was(18.56±1.67)mm on the left side and(18.97±1.73)mm on the right side;There are facial artery,facial vein,maxillary artery branch,facial nerve buccal branch and so on through the buccal fat pad.In group B,the sagittal section of the temporomandibular joint disc presented S-type in 15 cases(75.0%),L-type in 3 cases(15.0%),and transitional type in 2 cases(10.0%).Anterior and posterior diameter of the articular disc was(14.42±1.94)mm on the left side and(15.34±1.37)mm on the right side;inside and outside diameter of the articular disc was(20.18±1.77)mm on the left side and(19.57±1.32)mm on the right side.Branches of maxillary artery and superficial temporal artery were respectively distributed within and outside the joint.Conclusion The pedicled buccal fat pad has a constant anatomical position,abundant blood supply,strong tissue repair,anti-infection ability and"buffer pad"function,which can reduce the formation of scar after surgery for TMJA,reduce the postoperative recurrence rate,and contribute to the recovery of joint function after surgery.