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.

This study aimed to explore the effects and mechanisms of total extracts from Anthriscus sylvestris on pulmonary hypertension in rats. Sixty male SD rats were divided into normal(NC) group, model(M) group, positive drug sildenafil(Y) group, low-dose A. sylvestris(ES-L) group, medium-dose A. sylvestris(ES-M) group, and high-dose A. sylvestris(ES-H) group. On day 1, rats were intraperitoneally injected with monocrotaline(60 mg·kg~(-1)) to induce pulmonary hypertension, and the rat model was established on day 28. From days 15 to 28, intragastric administration of the respective treatments was performed. After modeling and treatment, small animal echocardiography was used to detect the right heart function of the rats. Arterial blood gas was measured using a blood gas analyzer. Hematoxylin and eosin(HE) staining and Masson staining were performed to observe cardiopulmonary pathological damage. Flow cytometry was used to detect apoptosis in the lung and myocardial tissues and reactive oxygen species(ROS) levels. Western blot was applied to detect the expression levels of transforming growth factor-β1(TGF-β1), phosphorylated mothers against decapentaplegic homolog 3(p-Smad3), Smad3, tissue plasminogen activator(t-PA), and plasminogen activator inhibitor-1(PAI-1) in lung tissue. A blood routine analyzer was used to measure inflammatory immune cell levels in the blood. Enzyme-linked immunosorbent assay(ELISA) was used to detect the expression levels of P-selectin and thromboxane A2(TXA2) in plasma. The results showed that, compared with the NC group, right heart hypertrophy index, right ventricular free wall thickness, right heart internal diameter, partial carbon dioxide pressure(PaCO_2), apoptosis in cardiopulmonary tissue, and ROS levels were significantly increased in the M group. In contrast, the ratio of pulmonary blood flow acceleration time(PAT)/ejection time(PET), right cardiac output, change rate of right ventricular systolic area, systolic displacement of the tricuspid ring, oxygen partial pressure(PaO_2), and blood oxygen saturation(SaO_2) were significantly decreased in the M group. After administration of the total extract of A. sylvestris, right heart function and blood gas levels were significantly improved, while apoptosis in cardiopulmonary tissue and ROS levels significantly decreased. Further testing revealed that the total extract of A. sylvestris significantly decreased the levels of interleukin-1β(IL-1β), interleukin-6(IL-6), and PAI-1 proteins in lung tissue, while increasing the expression of t-PA. Additionally, the extract reduced the levels of inflammatory cells such as leukocytes, lymphocytes, granulocytes, and monocytes in the blood, as well as the levels of P-selectin and TXA2 in plasma. Metabolomics results showed that the total extract of A. sylvestris significantly affected metabolic pathways, including arginine biosynthesis, tyrosine metabolism, and taurine and hypotaurine metabolism. In conclusion, the total extract of A. sylvestris may exert an anti-pulmonary hypertension effect by inhibiting the TGF-β1/Smad3 signaling pathway, thereby alleviating immune-inflammatory responses and thrombosis.
Animals ; Male ; Smad3 Protein/metabolism* ; Transforming Growth Factor beta1/metabolism* ; Rats, Sprague-Dawley ; Rats ; Signal Transduction/drug effects* ; Hypertension, Pulmonary/genetics* ; Thrombosis/immunology* ; Drugs, Chinese Herbal/administration & dosage* ; Humans ; Apoptosis/drug effects*

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

Due to its high sensitivity and non-destructive nature, Raman spectroscopy has become an essential analytical tool in biopharmaceutical analysis and drug development. Despite of the computational demands, data requirements, or ethical considerations, artificial intelligence (AI) and particularly deep learning algorithms has further advanced Raman spectroscopy by enhancing data processing, feature extraction, and model optimization, which not only improves the accuracy and efficiency of Raman spectroscopy detection, but also greatly expands its range of application. AI-guided Raman spectroscopy has numerous applications in biomedicine, including characterizing drug structures, analyzing drug forms, controlling drug quality, identifying components, and studying drug-biomolecule interactions. AI-guided Raman spectroscopy has also revolutionized biomedical research and clinical diagnostics, particularly in disease early diagnosis and treatment optimization. Therefore, AI methods are crucial to advancing Raman spectroscopy in biopharmaceutical research and clinical diagnostics, offering new perspectives and tools for disease treatment and pharmaceutical process control. In summary, integrating AI and Raman spectroscopy in biomedicine has significantly improved analytical capabilities, offering innovative approaches for research and clinical applications.

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