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.

Background: Therapeutic interventions for diabetes are most effective when administered in the newly onset phase, yet determining the exact onset moment can be elusive in practice. Spontaneous autoimmune diabetes among NOD mice appears randomly between 12 and 32 weeks of age with an incidence range from 60 to 90%. Furthermore, the disease often progresses rapidly to severe diabetes within days, resulting in a very short window of newly onset phase, that poses significant challenge in early diagnosis. Conventionally, extensive blood glucose (BG) testing is typically required on large cohorts throughout several months to conduct prospective survey. We incorporated ultrasensitive urine glucose (UG) testing into an ordinary BG survey process, initially aiming to elucidate the lag period required for excessive glucose leaking from blood to urine during diabetes progression in the mouse model. Results: The observations unexpectedly revealed that small amounts of glucose detected in the urine often coincide with, sometimes even a couple days prior than elevated BG is diagnosed. Accordingly, we conducted the UG-based survey protocol in another cohort that was validated to accurately identified every individual near onset, who could then be confirmed by following few BG tests to fulfill the consecutive BG + criteria. This approach required fewer than 95 BG tests, compared to over 700 tests with traditional BG survey, to diagnose all the 37–38 diabetic mice out of total 60. The average BG level at diagnosis was slightly below 350 mg/dl, lower than the approximately 400 mg/dl observed with conventional BG monitoring. Conclusions We demonstrated a near perfect correlation between BG + and ultrasensitive UG + results in prospective survey with no lag period detected under twice weekly of testing frequency. This led to the refined protocol based on surveying with noninvasive UG testing, allowing for the early identification of newly onset diabetic mice with only a few BG tests required per mouse. This protocol significantly reduces the need for extensive blood sampling, lancet usage, labor, and animal distress, aligning with the 3Rs principle. It presents a convenient, accurate, and animal-friendly alternative for early diabetes diagnosis, facilitating research on diagnosis, pathogenesis, prevention, and treatment.

BACKGROUND: Extracellular vesicles (EVs) are derived from internal cellular compartments, and have potential as a diagnostic and therapeutic tool in degenerative disease associated with aging. Mesenchymal stem cells (MSCs) have become a promising tool for functional EVs production. This study investigated the efficacy of EVs and its effect on differentiation capacity. METHODS: The characteristics of MSCs were evaluated by flow cytometry and stem cell differentiation analysis, and a production mode of functional EVs was scaled from MSCs. The concentration and size of EVs were quantitated by Nanoparticle Tracking Analysis (NTA). Western blot analysis was used to assess the protein expression of exosomespecific markers. The effects of MSC-derived EVs were assessed by chondrogenic and adipogenic differentiation analyses and histological observation. RESULTS The range of the particle size of adipose-derived stem cells (ADSCs)- and Wharton’s jelly -MSCs-derived EVs were from 130 to 150 nm as measured by NTA, which showed positive expression of exosomal markers. The chondrogenic induction ability was weakened in the absence of EVs in vitro. Interestingly, after EV administration, type II collagen, a major component in the cartilage extracellular matrix, was upregulated compared to the EV-free condition.Moreover, EVs decreased the lipid accumulation rate during adipogenic induction.

Cis-3-O-p-hydroxycinnamoyl ursolic acid (HCUA), a triterpenoid compound, was purified from Elaeagnus oldhamii Maxim. This traditional medicinal plant has been used for treating rheumatoid arthritis and lung disorders as well as for its anti-inflammation and anticancer activities. This study aimed to investigate the anti-proliferative and apoptotic-inducing activities of HCUA in oral cancer cells. HCUA exhibited anti-proliferative activity in oral cancer cell lines (Ca9-22 and SAS cells), but not in normal oral fibroblasts. The inhibitory concentration of HCUA that resulted in 50% viability was 24.0 µM and 17.8 µM for Ca9-22 and SAS cells, respectively. Moreover, HCUA increased the number of cells in the sub-G1 arrest phase and apoptosis in a concentration-dependent manner in both oral cancer cell lines, but not in normal oral fibroblasts. Importantly, HCUA induced p53-mediated transcriptional regulation of pro-apoptotic proteins (Bax, Bak, Bim, Noxa, and PUMA), which are associated with mitochondrial apoptosis in oral cancer cells via the loss of mitochondrial membrane potential. HCUA triggered the production of intracellular reactive oxygen species (ROS) that was ascertained to be involved in HCUA-induced apoptosis by the ROS inhibitors YCG063 and N-acetyl-L-cysteine. As a result, HCUA had potential antitumor activity to oral cancer cells through eliciting ROS-dependent and p53-mediated mitochondrial apoptosis. Overall, HCUA could be applicable for the development of anticancer agents against human oral cancer.
Acetylcysteine ; Antineoplastic Agents ; Apoptosis Regulatory Proteins ; Apoptosis* ; Arthritis, Rheumatoid ; Cell Line ; Elaeagnaceae ; Fibroblasts ; Humans ; Lung ; Membrane Potential, Mitochondrial ; Mouth Neoplasms* ; Plants, Medicinal ; Reactive Oxygen Species

OBJECTIVE: Hypermethylation of human papillomavirus (HPV) and host genes has been reported in cervical cancer. However, the degree of methylation of different HPV types relative to the severity of the cervical lesions remains controversial. Studies of the degree of methylation associated with the host gene and the HPV genome to the severity of cervical lesions are rare. We examined the association of methylation status between host genes and late gene 1 (L1) regions of HPV16, 18, 52, and 58 in cervical brushings. METHODS: Cervical brushings from 147 HPV-infected patients were obtained. The samples comprised normal (n=28), cervical intraepithelial neoplasia (CIN) 1 (n=45), CIN2 (n=13), and CIN3/carcinoma in situ (n=61). The methylation status of HPV and host genes was measured using bisulfite pyrosequencing and quantitative methylation-specific polymerase chain reaction (PCR). RESULTS: The degree of methylation of L1 in HPV16, 18, and 52 was associated with the severity of the cervical lesion. In HPV52, C-phosphate-G (CpG) sites 6368m, 6405m, and 6443m showed significantly higher methylation in lesions ≥CIN3 (p=0.005, 0.003, and 0.026, respectively). Methylation of most HPV types except HPV52 (r<−0.1) was positively correlated with the degree of methylation of host genes including PAX1 and SOX1 (0.4≤r≤0.7). Combining HPV methylation with PAX1 methylation improved the clustering for ≥CIN2. CONCLUSION: Our study showed that the degree of L1 methylation of HPV16, 18, and 52 but not 58 is associated with the severity of cervical lesions. The association between HPV methylation and host gene methylation suggests different responses of host cellular epigenetic machinery to different HPV genotypes.
Cervical Intraepithelial Neoplasia* ; DNA Methylation ; Epigenomics ; Genome ; Genotype ; Human papillomavirus 16 ; Humans ; Methylation* ; Papillomaviridae ; Polymerase Chain Reaction ; Uterine Cervical Neoplasms

BACKGROUNDStudies of interleukin (IL)-4 and IL-6 in the exhaled breath condensate (EBC) of asthmatic patients are limited. This study was to determine the effect of inhaled corticosteroid (ICS) treatment on IL-4 and IL-6 in the EBC of asthmatic patients.

METHODSIn a prospective, open-label study, budesonide 200 μg twice daily by dry powder inhaler was administered to 23 adult patients with uncontrolled asthma (mean age 42.7 years) for 12 weeks. Changes in asthma scores, lung function parameters (forced expiratory volume in 1 s [FEV1], peak expiratory flow [PEF], forced expiratory flow at 50% of forced vital capacity [FEF50], forced expiratory flow at 75% of forced vital capacity, maximum mid-expiratory flow rate) and the concentrations of IL-4 and IL-6 in EBC were measured.

RESULTSBoth asthma scores and lung function parameters were significantly improved by ICS treatment. The mean IL-4 concentration in the EBC was decreased gradually, from 1.92 ± 0.56 pmol/L before treatment to 1.60 ± 0.36 pmol/L after 8 weeks of treatment (P < 0.05) and 1.54 ± 0.81 pmol/L after 12 weeks of treatment (P < 0.01). However, the IL-6 concentration was not significantly decreased. The change in the IL-4 concentration was correlated with improvements in mean FEV1, PEF and FEF50 values (correlation coefficients -0.468, -0.478, and -0.426, respectively).

CONCLUSIONSThe concentration of IL-4 in the EBC of asthmatic patients decreased gradually with ICS treatment. Measurement of IL-4 in EBC could be useful to monitor airway inflammation in asthmatics.

Administration, Inhalation ; Adult ; Asthma ; drug therapy ; physiopathology ; Breath Tests ; Budesonide ; administration & dosage ; Female ; Forced Expiratory Volume ; Humans ; Interleukin-4 ; analysis ; Interleukin-6 ; analysis ; Male ; Middle Aged ; Peak Expiratory Flow Rate ; Prospective Studies