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.

The circadian clock plays a critical role in regulating various physiological processes, including gene expression, metabolic regulation, immune response, and the sleep-wake cycle in living organisms. Post-translational modifications (PTMs) are crucial regulatory mechanisms to maintain the precise oscillation of the circadian clock. By modulating the stability, activity, cell localization and protein-protein interactions of core clock proteins, PTMs enable these proteins to respond dynamically to environmental and intracellular changes, thereby sustaining the periodic oscillations of the circadian clock. Different types of PTMs exert their effects through distincting molecular mechanisms, collectively ensuring the proper function of the circadian system. This review systematically summarized several major types of PTMs, including phosphorylation, acetylation, ubiquitination, SUMOylation and oxidative modification, and overviewed their roles in regulating the core clock proteins and the associated pathways, with the goals of providing a theoretical foundation for the deeper understanding of clock mechanisms and the treatment of diseases associated with circadian disruption.
Protein Processing, Post-Translational/physiology* ; Circadian Rhythm/physiology* ; Humans ; Animals ; CLOCK Proteins/physiology* ; Circadian Clocks/physiology* ; Phosphorylation ; Acetylation ; Ubiquitination ; Sumoylation

OBJECTIVE: To investigate the protective effect of achyranthes bidentata (AB) on sperm quality in mice with spermatogenic disorder through the glycolytic metabolic pathway and its action mechanism. METHODS: We equally randomized 40 Kunming mice into a normal control, a model control, a low-dose AB (3.5 g/kg) and a high-dose AB group (7.0 g/kg), and established the model of spermatogenic disorder in the latter three groups of mice by intraperitoneal injection of doxorubicin (30 mg/kg). Two days after modeling, we collected the testis and kidney tissues and blood samples from the mice for observation of the pathological changes in the testis tissue by HE staining, detection of perm motility with the sperm quality analyzer, examination of the apoptosis of testis cells by flow cytometry, measurement of the levels of testosterone (T), malondialdehyde (MDA), superoxide dismutase (SOD) and catalase (CAT) in the serum and testis tissue by ELISA, and determination of expressions of the key enzymes of glycolysis hexokinase Ⅱ (HK2), pyruvate kinase M2 (PKM2), platelet phosphofructokinase (PFKP), lactate dehydrogenase A (LDHA) and the meiosis proteins REC8 and SCP3 by Western blot, and the mRNA expressions of glycolytic phosphofructokinase 1 (PFK1), phosphoglycerate kinase 1 (PGK1), tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) by fluorescence quantitative PCR (FQ-PCR). RESULTS: Compared with the model controls, the mice in the AB groups showed significant increases in the testis coefficient, kidney index, sperm concentration, sperm motility, spermatogonia, primary spermatocytes, spermatids, sperm count and the serum T level (P<0.05 or P<0.01), but dramatic decreases in the apoptosis of testis cells and percentage of morphologically abnormal sperm (P<0.01). Achyranthes bidentata also significantly elevated the levels of SOD and CAT, and down-regulated the mRNA expressions of MDA, TNF-α and IL-1β (P<0.05 or P<0.01), and up-regulated the protein expressions of HK2, PKM2, PFKP, LDHA, REC8 and SCP3, and expressions of the glycolysis key genes Pfk1 and Pgk1 (P<0.05 or P<0.01). CONCLUSION Achyranthes bidentata ameliorates doxorubicin-induced spermatogenic disorder in mice by regulating the glycolytic pathway and reducing oxidative stress and the expressions of inflammatory factors.
Glycolysis/drug effects* ; Doxorubicin/toxicity* ; Spermatogenesis/drug effects* ; Random Allocation ; Male ; Animals ; Mice ; Disease Models, Animal ; Achyranthes/chemistry* ; Spermatozoa/pathology* ; Oxidative Stress/drug effects* ; Primary Cell Culture ; Apoptosis/drug effects* ; Sperm Motility/drug effects* ; Testis/pathology* ; Infertility, Male/prevention & control* ; Medicine, Chinese Traditional/methods* ; Animals, Outbred Strains

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis

OBJECTIVE: To evaluate the safety and clinical therapeutic effect of in-line mechanical in-exsufflation to assist sputum clearance in patients with invasive mechanical ventilation. METHODS: A prospective observational study was conducted at the department of critical care medicine, the First Affiliated Hospital of Sun Yat-sen University from April 2022 to May 2023. Patients who were invasively ventilated and treated with in-line mechanical in-exsufflation to assist sputum clearance were enrolled. Baseline data were collected. Sputum viscosity, oxygenation index, parameters of ventilatory function and respiratory mechanics, clinical pulmonary infection score (CPIS) and vital signs before and after day 1, 2, 3, 5, 7 of use of the in-line mechanical in-exsufflation were assessed and recorded. Statistical analyses were performed by using generalized estimating equation (GEE). RESULTS: A total of 13 invasively ventilated patients using in-line mechanical in-exsufflation were included, all of whom were male and had respiratory failure, with the main cause being cervical spinal cord injury/high-level paraplegia (38.46%). Before the use of the in-line mechanical in-exsufflation, the proportion of patients with sputum viscosity of grade III was 38.46% (5/13) and decreased to 22.22% (2/9) 7 days after treatment with in-line mechanical in-exsufflation. With the prolonged use of the in-line mechanical in-exsufflation, the patients' CPIS scores tended to decrease significantly, with a mean decrease of 0.5 points per day (P < 0.01). Oxygenation improved significantly, with the oxygenation index (PaO₂/FiO₂) increasing by a mean of 23.3 mmHg (1 mmHg ≈ 0.133 kPa) per day and the arterial partial pressure of oxygen increasing by a mean of 12.6 mmHg per day (both P < 0.01). Compared to baseline, the respiratory mechanics of the patients improved significantly 7 days after in-line mechanical in-exsufflation use, with a significant increase in the compliance of respiratory system (Cst) [mL/cmH₂O (1 cmH₂O ≈ 0.098 kPa): 55.6 (50.0, 58.0) vs. 40.9 (37.5, 50.0), P < 0.01], and both the airway resistance and driving pressure (DP) were significantly decreased [airway resistance (cmH₂O×L^-1×s^-1): 9.6 (6.9, 10.5) vs. 12.0 (10.0, 13.0), DP (cmH₂O): 9.0 (9.0, 12.0) vs. 11.0 (10.0, 15.0), both P < 0.01]. At the same time, no new lung collapse was observed during the treatment period. No significant discomfort was reported by patients, and there were no substantial changes in heart rate, systolic blood pressure, diastolic blood pressure, and mean arterial pressure before and after the in-line mechanical in-exsufflation treatment. CONCLUSIONS The combined use of the in-line mechanical in-exsufflation to assist sputum clearance in patients on invasive mechanical ventilation can effectively improve sputum characteristics, oxygenation and respiratory mechanics. The in-line mechanical in-exsufflation was well tolerated by the patients, with no treatment-related adverse events, which demonstrated its effectiveness and safety.
Humans ; Prospective Studies ; Respiration, Artificial/methods* ; Respiratory Insufficiency/therapy* ; Sputum

Objective:To explore the prospective associations between physical activity and incident ischemic stroke in adults.Methods:Data of China Kadoorie Biobank study in Tongxiang of Zhejiang were used. After excluding participants with cancers, strokes, heart diseases and diabetes at baseline study, a total of 53 916 participants aged 30-79 years were included in the final analysis. The participants were divided into 5 groups according to the quintiles of their physical activity level. Cox proportional hazard regression models was used to calculate the hazard ratios ( HR) for the analysis on the association between baseline physical activity level and risk for ischemic stroke. Results:The total physical activity level in the participants was (30.63±15.25) metabolic equivalent (MET)-h/d, and it was higher in men [(31.04±15.48) MET-h/d] than that in women [(30.33±15.07) MET-h/d] ( P<0.001). In 595 526 person-years of the follow-up (average 11.4 years), a total of 1 138 men and 1 082 women were newly diagnosed with ischemic stroke. Compared to participants with the lowest physical activity level (<16.17 MET-h/d), after adjusting for socio-demographic factors, lifestyle, BMI, waist circumference, and SBP, the HRs for the risk for ischemic stroke in those with moderate low physical activity level (16.17-24.94 MET-h/d), moderate physical activity level (24.95-35.63 MET-h/d), moderate high physical activity level (35.64-43.86 MET-h/d) and the highest physical activity level (≥43.87 MET-h/d) were 0.93 (95% CI: 0.83-1.04), 0.87 (95% CI: 0.76-0.98), 0.82 (95% CI: 0.71-0.95) and 0.76 (95% CI: 0.64-0.89), respectively. Conclusion:Improving physical activity level has an effect on reducing the risk for ischemic stroke.