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 identify individuals with accelerated aging under the frailty index (FI) as a proxy indicator of biological age, and to investigate the associations of age at menarche, age at menopause, and reproductive lifespan with frailty status and multi-timepoint FI trajectories among Chinese adult women.Methods:The current study included 302 471 women from the China Kadoorie Biobank 2004-2008 baseline survey data. Their age at menarche and menopause were self-reported, and the duration of reproductive lifespan was calculated by subtracting the two ages. The baseline FI was constructed using 28 baseline variables, including diseases, symptoms, and anthropometric measurements. Frailty status was categorized into three groups: non-frail (FI≤0.10), pre-frail (0.10

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

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.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

OBJECTIVE: To investigate the common mechanisms among collagen-induced arthritis (CIA), bleomycin (BLM)-induced pulmonary fibrosis, and CIA+BLM to evaluate the therapeutic effect of triptolide (TP) on CIA+BLM. METHODS: Thirty-six male Sprague-Dawley rats were randomly assigned to 6 groups according to a random number table (n=6 per group): normal control (NC), CIA, BLM, combined CIA+BLM model, TP low-dose (TP-L, 0.0931 mg/kg), and TP high-dose (TP-H, 0.1862 mg/kg) groups. The CIA model was induced by intradermal injection at the base of the tail with emulsion of bovine type II collagen and incomplete Freund's adjuvant (1:1), with 200 µL administered on day 0 and a booster of 100 µL on day 7. Pulmonary fibrosis was induced via a single intratracheal injection of BLM (5 mg/kg). The CIA+BLM model combined both protocols, and TP was administered orally from day 14 to 35. After successful modeling, arthritis scores were recorded every 3 days, and pulmonary function was assessed once at the end of the treatment period. Lung tissues were collected for histological analysis (hematoxylin eosin and Masson staining), immunohistochemistry, measurement of hydroxyproline (HYP) content, and calculation of lung coefficient. In addition, HE staining was performed on the ankle joint. Total RNA was extracted from lung tissues for transcriptomic analysis. Differentially expressed genes (DEGs) were compared with those from the RA-associated interstitial lung diseases patient dataset GSE199152 to identify overlapping genes, which were then used to construct a protein-protein interaction network. Hub genes were identified using multiple topological algorithms. RESULTS: The successfully established CIA+BLM rat model exhibited significantly increased arthritis scores and severe pulmonary fibrosis (P<0.01). By intersecting the DEGs obtained from transcriptomic analysis of lung tissues in CIA, BLM, and CIA+BLM rats with DEGs from rheumatoid arthritis-interstitial lung disease patients (GSE199152 dataset), 50 upregulated and 44 downregulated genes were identified. Through integrated PPI network analysis using multiple topological algorithms, IGF1 was identified as a central hub gene. TP intervention significantly improved pulmonary function by increasing peak inspiratory flow (P<0.01), and reduced lung index and HYP content (P<0.01). Histopathological analysis showed that TP alleviated alveolar collapse, interstitial thickening, and collagen deposition in the lung tissues (P<0.01). Moreover, TP treatment reduced the expression of collagen type I and α-SMA and increased E-cadherin levels (P<0.01). TP also significantly reduced arthritis scores and ameliorated synovial inflammation (P<0.05). Both transcriptomic and immunohistochemical analyses confirmed that IGF1 expression was elevated in the CIA+BLM group and downregulated following TP treatment (P<0.05). CONCLUSION TP exerts protective effects in the CIA+BLM model by alleviating arthritis and pulmonary fibrosis through the inhibition of IGF1-mediated EMT.
Animals ; Pulmonary Fibrosis/complications* ; Bleomycin/adverse effects* ; Phenanthrenes/pharmacology* ; Male ; Rats, Sprague-Dawley ; Diterpenes/pharmacology* ; Epoxy Compounds/therapeutic use* ; Arthritis, Experimental/complications* ; Insulin-Like Growth Factor I/metabolism* ; Rats ; Lung/physiopathology*

OBJECTIVE: To detect and analyze the expression and clinical significance of long non-coding RNA tyrosine kinase non-catalytic region adaptor protein 1-antisense RNA1 (NCK1-AS1) in patients with acute myeloid leukemia (AML). METHODS: 89 AML patients and 23 healthy controls were included from the People's Hospital Affiliated to Jiangsu University. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of NCK1-AS1 and NCK1 in bone marrow samples. The relationship between the expression of NCK1-AS1 and the clinical characteristics of patients were analyzed, as well as the correlation between NCK1-AS1 and NCK1. RESULTS: The expression level of NCK1-AS1 in all AML, non-M3 AML and cytogenetically normal AML (CN-AML) patients was significantly higher than that in the control group (P < 0.01, P < 0.05, P < 0.01, respectively). In non-M3 AML, patients with high NCK1-AS1 expression had a significantly lower hemoglobin level than those with low NCK1-AS1 expression (P =0.036), furthermore, NCK1-AS1 ^high patients had shorter overall survival than NCK1-AS1^low patients (P =0.0378). Multivariate analysis showed that NCK1-AS1 expression was an independent adverse factor in patients with non-M3 AML ( HR =2.392, 95% CI :1.089-5.255, P =0.030). In addition, NCK1 expression was also significantly upregulated in all AML, non-M3 AML and CN-AML patients compared with controls (P < 0.01, P < 0.01, P < 0.001, respectively). There was a certain correlation between NCK1-AS1 and NCK1 expression (r =0.37, P =0.0058). CONCLUSION High expression of NCK1-AS1 in AML indicates poor prognosis of AML patients.
Humans ; Leukemia, Myeloid, Acute/genetics* ; RNA, Long Noncoding/genetics* ; Oncogene Proteins/genetics* ; Adaptor Proteins, Signal Transducing/genetics* ; Prognosis ; Male ; Female ; Middle Aged ; Adult ; Case-Control Studies ; Clinical Relevance