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.

Fluorescence anisotropy(FA)analysis has many advantages such as no requirement of separation,high throughput and real-time detection,and thus has been widely used in many fields,including biochemical analysis,food safety detection,environmental monitoring,etc.However,due to the small volume or mass of the target,its combination with the fluorescence probe cannot produce significant signal change.To solve this issue,researchers often use nanomaterials to enhance the mass or volume of fluorophore to improve the sensitivity.Nevertheless,this FA amplification strategy also has some disadvantages.Firstly,nanomaterials are easy to quench fluorescence.As a result,the FA value is easily influenced by light scattering,which reduces the detection accuracy.Secondly,fluorescent probes in most methods require complex modification steps.Therefore,it is necessary to develop new FA probes that do not require the amplification of volume and mass or modification.As a new kind of nanomaterials,luminescent metal-organic framework(MOF)has a large volume(or mass)and strong fluorescence emission.It does not require additional signal amplification materials.As a consequence,it can be used as a potential FA probe.This study successfully synthesized a lanthanide metal organic framework(Ce-TCPP MOF)using cerium ion(Ce3+)as the central ion and 5,10,15,20-tetra(4-carboxylphenyl)porphyrin(H2TCPP)as the ligand through microwave assisted method,and used it as a novel unmodified FA probe to detect phosphate ions(Pi).In the absence of Pi,Ce-TCPP MOF had a significant FA value(r).After addition of Pi,Pi reacted with Ce3+in MOF and destroyed the structure of MOF into the small pieces,resulting in a decrease in r.The experimental results indicated that with the increase of Pi concentration,the change of the r of Ce-TCPP MOF(Δr)gradually increased.The Δr and Pi concentration showed a good linear relationship within the range of 0.5-3.5 μmol/L(0.016-0.108 mg/L).The limit of detection(LOD,3σ/k)was 0.41 μmol/L.The concentration of Pi in the Jialing River water detected by this method was about 0.078 mg/L,and the Pi value detected by ammonium molybdate spectrophotometry was about 0.080 mg/L.The two detection results were consistent with each other,and the detection results also meet the ClassⅡwater quality standard,proving that this method could be used for the detection of Pi in complex water bodies.

Polycystin-2 (also known as PC2, TRPP2, PKD2) is a major contributor to the underlying etiology of autosomal dominant polycystic kidney disease (ADPKD), which is the most prevalent monogenic kidney disease in the world. As a transient receptor potential (TRP) channel protein, PC2 exhibits cation-permeable, Ca²⁺-dependent channel properties, and plays a crucial role in maintaining normal Ca²⁺ signaling in systemic physiology, particularly in ADPKD chronic kidney disease. Structurally, PC2 protein consists of six transmembrane structural domains (S1-S6), a polycystin-specific “tetragonal opening for polycystins” (TOP) domain located between the S1 and S2 transmembrane structures, and cytoplasmic N- and C-termini. Although the cytoplasmic N-terminus and C-terminus of PC2 may not be significant in the gating of PC2 channels, there is still much protein structural information that needs to be thoroughly investigated, including the regulation of channel function and the assembly of homotetrameric ion channels. This is further supported by the presence of human disease-associated mutation sites on the PC2 structure. Moreover, PC2 synthesized in the endoplasmic reticulum is enriched in specific subcellular localization via membrane transport and can assemble itself into homotetrameric ion channels, as well as form heterotrimeric receptor-ion channel complexes with other proteins. These complexes are involved in a wide range of physiological functions, including the regulation of mechanosensation, cell polarity, cell proliferation, and apoptosis. In particular, PC2 assembles with chaperone proteins to form polycystic protein complexes that affect Ca²⁺ transport in cell membranes, cilia, endoplasmic reticulum, and mitochondria, and are involved in activating cell fate-related signaling pathways, particularly cell differentiation, proliferation, survival, and apoptosis, and more recently, autophagy. This leads to a shift of cystic cells from a normal uptake, quiescent state to a pathologically secreted, proliferative state. In conclusion, the complex structural and functional roles of PC2 highlight its critical importance in the pathogenesis of ADPKD, making it a promising target for therapeutic intervention.

ObjectiveTo investigate the effect of mucin 1 (MUC1) on the proliferation and apoptosis of nasopharyngeal carcinoma (NPC) and its regulatory mechanism. MethodsThe 60 NPC and paired para-cancer normal tissues were collected from October 2020 to July 2021 in Quanzhou First Hospital. The expression of MUC1 was measured by real-time quantitative PCR (qPCR) in the patients with PNC. The 5-8F and HNE1 cells were transfected with siRNA control (si-control) or siRNA targeting MUC1 (si-MUC1). Cell proliferation was analyzed by cell counting kit-8 and colony formation assay, and apoptosis was analyzed by flow cytometry analysis in the 5-8F and HNE1 cells. The qPCR and ELISA were executed to analyze the levels of TNF-α and IL-6. Western blot was performed to measure the expression of MUC1, NF-кB and apoptosis-related proteins (Bax and Bcl-2). ResultsThe expression of MUC1 was up-regulated in the NPC tissues, and NPC patients with the high MUC1 expression were inclined to EBV infection, growth and metastasis of NPC. Loss of MUC1 restrained malignant features, including the proliferation and apoptosis, downregulated the expression of p-IкB、p-P65 and Bcl-2 and upregulated the expression of Bax in the NPC cells. ConclusionDownregulation of MUC1 restrained biological characteristics of malignancy, including cell proliferation and apoptosis, by inactivating NF-κB signaling pathway in NPC.

A high performance liquid chromatography (HPLC) method utilizing correction factors was established for the quantitative detection of related substances in flumazenil. Separation was achieved using an Agilent Pursuit XRs C18 column (250 mm × 4.6 mm, 5 μm) with an isocratic elution of dilute phosphoric acid, methanol, and tetrahydrofuran as the mobile phases. Correction factors calculated from a standard curve method were applied to determine the impurity content. The quantification of impurities in flumazenil was conducted using both external standard and correction factor methods, followed by validation and comparison of the two. For the identification of degradation products, a forced degradation approach was employed to prepare a flumazenil degradation solution, and the resulting impurities were confirmed by LC-MS analysis. The separation of flumazenil and its impurities was found to be efficient. The limits of quantification for impurities A, B, D, and E were established at 0.169 9, 0.314 7, 0.143 9, and 0.270 8 ng, respectively, with the limits of detection at 0.055 8, 0.096 9, 0.048 8, and 0.089 0 ng. These impurities demonstrated a strong linear relationship across the concentration ranges of 0.034 9-7.847 0, 0.038 7-8.710 7, 0.034 6-7.794 1, and 0.032 4-7.292 8 µg·mL^-1, respectively (n = 7). The method achieved average recoveries between 98.25% and 99.42%, with an RSD of less than 2.0% (n = 9), indicating high accuracy. The external standard and correction factor methods were used to determine the related substances in flumazenil, and the results of the two methods were consistent. The established HPLC method is characterized by its high accuracy, sensitivity, and repeatability, and is suitable for determining related substances in flumazenil.

Objective:To measure the overall and lobulated volume of the liver with different degrees of liver fibrosis and to further observe pathological changes such as liver microvasculature, hepatocyte apoptosis, and regeneration in order to understand the macroscopic volume changes of the liver during liver fibrosis and its relationship with liver tissue microscopic pathology in patients with chronic liver disease.Methods:53 patients with chronic hepatitis B, alcoholic fatty liver disease, autoimmune liver disease, nonalcoholic fatty liver disease, and drug-induced chronic liver disease who underwent both liver biopsy tissue and abdominal magnetic resonance imaging were collected. Patients were divided into early (F1-2), middle (F3-4), and late (F5-6) in accordance with the Ishak fibrosis stage and Masson stain. The liver and spleen volumes were measured using ITK-SNAP software. CD31 immunohistochemical staining was used to reflect intrahepatic angiogenesis. Ki67 and HNF-4α multiplex immunohistochemical staining were used to reflect hepatocyte regeneration. GS staining was used to determine parenchymal extinction lesions. TUNEL staining was used to observe hepatocyte apoptosis. Spearman correlation analysis was used to analyze the relationship between liver volume changes and liver histopathological changes.Results:As liver fibrosis progressed, the total liver volume and right lobe liver volume gradually decreased ( P<0.05), while the spleen volume gradually increased ( P<0.05). The expression of CD31 and GS gradually increased ( P<0.05), and the expression of Ki67 first increased and then decreased ( P<0.05). The positivity rate of CD31 was negatively correlated with the right lobe liver volume ( r=-0.609, P<0.001) and the total liver volume ( r=-0.363, P=0.017). The positivity rate of Ki67 was positively correlated with the right lobe liver volume ( r=0.423, P=0.018), while the positivity rate of apoptotic cells was significantly negatively correlated with the total liver volume ( r=-0.860, P<0.001). The positivity rate of GS was negatively correlated with the right lobe liver volume ( r=-0.440, P=0.002), and the number of PELs was negatively correlated with RV ( r=-0.476, P=0.013). The CD31 positive staining area was negatively correlated with the Ki67 positive staining area( r=-0.511, P=0.009). Conclusion:As liver fibrosis progresses, patients with chronic liver disease have a depletion in total liver volume and right lobe liver volume, and this is mainly in correlation with fewer liver cells and liver tissue microvasculature disorders.