Solid-phase extraction(SPE)combined with surface-enhanced Raman spectroscopy(SERS)has emerged as a promising analytical technique for detection and analysis of trace components in complex sample matrices.SPE enriches analytes through selective adsorption and solvent elution,effectively increasing the concentration and signal intensity.SERS enables ultra-sensitive and highly selective molecular analysis through the use of SERS-active substrates engineered to amplify Raman signals.The integration of these two techniques overcomes the limitations of conventional Raman spectroscopy in low-concentration detection field,while significantly improving sample preparation efficiency and analytical accuracy.This review provided a comprehensive overview of the characteristics of three SPE-SERS coupling modes,including two-step,one-step,and online integration.Special emphasis was placed on recent advancements in one-step SPE-SERS approaches based on novel functional adsorbent materials such as graphene,metal-organic frameworks,covalent organic frameworks,and molecularly imprinted polymers.Furthermore,future directions and development prospects of SPE-SERS technology were discussed.

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.

Aim To systematically investigate the ac-tive components,targets,and regulatory pathways of Po-lygonum capitatum in intervening atherosclerosis(AS)through network pharmacology,molecular docking and animal experiments.Methods Active components of Polygonum capitatum and AS-related targets were screened and identified through database searches.Protein-protein interaction(PPI)network analysis was performed using the STRING database,followed by GO and KEGG enrichment analyses via the David plat-form.Molecular docking validation was conducted with AutoDock.An AS model was established in Syrian golden hamsters fed a high-fat diet.Predicted pathways and targets were validated using qPCR,ELISA,and histopathological assessment of aortic and hepatic tis-sues via HE staining.Results Network pharmacology identified 27 potential active components of Polygonum capitatum(primarily flavonoids such as quercetin and luteolin)and 110 drug-disease intersection targets,in-cluding core targets MMP-9,ALB,and AKT1.GO and KEGG analyses enriched 593 and 125 pathways,re-spectively,with the NF-κB inflammatory pathway,TNF signaling pathway and lipid metabolism/atherosclerosis pathways highlighted as key mechanisms.Animal ex-periments demonstrated that Polygonum capitatum im-proved serum lipid profiles(reduced TC,TG,LDL-C)in AS hamsters,suppressed the MMP-9/NF-κB signa-ling pathway(downregulated MMP-9,p65 phosphoryla-tion,TNF-α,and IL-6),and inhibited VSMC synthetic phenotypic transformation(upregulated α-SMA and myocardin)by downregulating MCPIP1.Additionally,Polygonum capitatum ameliorated aortic lesions and he-patic lipid deposition in AS hamsters.Conclusions Polygonum capitatum alleviates AS by synergistically regulating the MMP-9/NF-κB/MCPIP1 axis through flavonoid components,suppressing vascular inflammato-ry cascades and maintaining VSMC contractile pheno-types.This reflects Polygonum capitatum's multi-com-ponent,multi-pathway,and multi-target characteristics in combating AS.

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

To explore the value of the diaphragm thickness fraction (TF) and diaphragm mobility (DM) measured by ultrasound for predicting ventilator withdrawal success in patients with acute respiratory distress syndrome (ARDS) after cardiac surgery. Methods: This study included 246 patients undergoing the spontaneous breathing trial. Diaphragmatic function was evaluated by ultrasound, including the diaphragm thickness at the end of calm breathing (thickness of the diaphragm at functional residual capacity [TdiFRC]) and the maximum diaphragm thickness at the end of inspiration (thickness of the diaphragm at full vital capacity [TdiFVC]); TF=(TdiFVC–TdiFRC)/TdiFRC×100%. DM, the oxygenation index (the ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen), and the rapid shallow breathing index (RSBI) were measured. Results: Successful liberation from mechanical ventilation was observed in 209 patients. There were no significant differences in the TdiFRC (0.3±0.1 cm vs. 0.3±0.1 cm) or TdiFVC (0.3±0.1 cm vs. 0.2±0.1 cm) between the ventilator withdrawal success group and the ventilator withdrawal failure group (P>0.05). The TF was greater in the ventilator withdrawal success group than in the ventilator withdrawal failure group (40.8%±15.8% vs. 37.7%±9.2%, P<0.01). DM in the ventilator withdrawal success group was greater than that in the ventilator withdrawal failure group (1.5±0.5 cm vs. 1.2±0.4 cm, P=0.040). The RSBI was lower in the ventilator withdrawal success group than in the ventilator withdrawal failure group (74.3±25.6 breaths·min–1·L –1 vs. 89.9±34.5 breaths·min–1·L –1, P<0.01). Conclusions: Diaphragmatic ultrasound can be used to predict the success of ventilator withdrawal in patients with ARDS.