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.

OBJECTIVE: To detect and analyze the expression and clinical significance of long non-coding RNA tyrosine kinase non-catalytic region adaptor protein 1-antisense RNA1 (NCK1-AS1) in patients with acute myeloid leukemia (AML). METHODS: 89 AML patients and 23 healthy controls were included from the People's Hospital Affiliated to Jiangsu University. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of NCK1-AS1 and NCK1 in bone marrow samples. The relationship between the expression of NCK1-AS1 and the clinical characteristics of patients were analyzed, as well as the correlation between NCK1-AS1 and NCK1. RESULTS: The expression level of NCK1-AS1 in all AML, non-M3 AML and cytogenetically normal AML (CN-AML) patients was significantly higher than that in the control group (P < 0.01, P < 0.05, P < 0.01, respectively). In non-M3 AML, patients with high NCK1-AS1 expression had a significantly lower hemoglobin level than those with low NCK1-AS1 expression (P =0.036), furthermore, NCK1-AS1 ^high patients had shorter overall survival than NCK1-AS1^low patients (P =0.0378). Multivariate analysis showed that NCK1-AS1 expression was an independent adverse factor in patients with non-M3 AML ( HR =2.392, 95% CI :1.089-5.255, P =0.030). In addition, NCK1 expression was also significantly upregulated in all AML, non-M3 AML and CN-AML patients compared with controls (P < 0.01, P < 0.01, P < 0.001, respectively). There was a certain correlation between NCK1-AS1 and NCK1 expression (r =0.37, P =0.0058). CONCLUSION High expression of NCK1-AS1 in AML indicates poor prognosis of AML patients.
Humans ; Leukemia, Myeloid, Acute/genetics* ; RNA, Long Noncoding/genetics* ; Oncogene Proteins/genetics* ; Adaptor Proteins, Signal Transducing/genetics* ; Prognosis ; Male ; Female ; Middle Aged ; Adult ; Case-Control Studies ; Clinical Relevance

BACKGROUND: Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD) has become a global epidemic, and air pollution has been identified as a potential risk factor. This study aims to investigate the non-linear relationship between ambient air pollution and MASLD prevalence. METHOD: In this cross-sectional study, participants undergoing health checkups were assessed for three-year average air pollution exposure. MASLD diagnosis required hepatic steatosis with at least 1 out of 5 cardiometabolic criteria. A stepwise approach combining data visualization and regression modeling was used to determine the most appropriate link function between each of the six air pollutants and MASLD. A covariate-adjusted six-pollutant model was constructed accordingly. RESULTS: A total of 131,592 participants were included, with 40.6% met the criteria of MASLD. "Threshold link function," "interaction link function," and "restricted cubic spline (RCS) link functions" best-fitted associations between MASLD and PM_2.5, PM₁₀/CO, and O₃ /SO₂/NO₂, respectively. In the six-pollutant model, significant positive associations were observed when pollutant concentrations were over: 34.64 µg/m³ for PM_2.5, 57.93 µg/m³ for PM₁₀, 56 µg/m³ for O₃, below 643.6 µg/m³ for CO, and within 33 and 48 µg/m³ for NO₂. The six-pollutant model using these best-fitted link functions demonstrated superior model fitting compared to exposure-categorized model or linear link function model assuming proportionality of odds. CONCLUSION Non-linear associations were found between air pollutants and MASLD prevalence. PM_2.5, PM₁₀, O₃, CO, and NO₂ exhibited positive associations with MASLD in specific concentration ranges, highlighting the need to consider non-linear relationships in assessing the impact of air pollution on MASLD.
Humans ; Nitrogen Dioxide ; Cross-Sectional Studies ; Air Pollution/analysis* ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Liver Diseases ; Environmental Exposure/analysis*

To address the key challenges in multivariate statistical modeling,a higher-order derivative approach combined with vector space angle multiplicative error correction was proposed for establishing an acid value prediction ordinary least squares(OLS)regression model based on attenuated total reflectance-Fourier transform infrared(ATR-FTIR)spectroscopy.By using acid values measured by potentiometric titration as reference,ATR-FTIR spectroscopy was utilized for direct calibration and prediction of acid values on 96 kinds of lubricating oil samples from a wind turbine.Firstly,the simulated hyperbolic(SH)method was employed to obtain accurate fourth derivative spectrum,resolving overlapping bands and enhancing spectral selectivity.Then,from the calibration set(48 samples),informative spectral regions were identified based on correlation coefficients.Next,the sample with the highest acid value was selected as the reference and1/(1+tan(θ/2))was used as the metric relation of the spectrum to suppress the multiplicity error caused by factors such as the change of effective optical path in ATR-FTIR spectroscopy.After pretreatment of the spectrum by the method of fourth-order derivative combined with angular quantity,the number of variables decreased from 1737 to 8,and the matrix condition number decreased from 1.85×1015 to 56.34,which effectively eliminated the collinearity issue for OLS regression.Direct OLS modeling on spectral preprocessed data achieved a determination coefficient of 0.981 for 47 validation samples,with a relative error range of-8.38%-8.22%,outperforming the commonly used partial least squares(PLS)method(Determination coefficient of 0.865,relative error of-27.82%-22.38%).It was proved that effective data preprocessing significantly improved the prediction accuracy of the model.Furthermore,when the number of calibration set was compressed to 25 and the number of validation set was expanded to 70,the model retained 8 variables with a condition number of 42.60,the determination coefficient of validation set was 0.972,and the relative error ranged from-10.80%to 12.31%.Comparing with the PLS method(Determination coefficient of 0.724,relative error of-34.26%-53.84%),the improvement was more obvious,which showed that the method could still have high prediction accuracy even with fewer modeling samples as well as robustness against multiplicative error interference.

Tubulin beta 4A class IVa (TUBB4A) spectrum disorders include autosomal dominant dystonia type 4 or hypomyelination with atrophy of the basal ganglia and cerebellum (H-ABC syndrome). However, in rare cases, only mild hypomyelination in the cortex with no basal ganglia atrophy may be observed. We report a case of a family with TUBB4A mutation and complicated hereditary spasticity paraplegia (HSP). We performed quadro whole-exome sequencing (WES) on the family to identify the causative gene of progressive spastic paraparesis with isolated hypomyelination leukodystrophy. We identified a novel TUBB4A p.F341L mutation, which was present in all three affected patients but absent in the unaffected father. The affected patients presented with adult-onset TUBB4A disorder, predominant spastic paraparesis with/without ataxia, and brain hypomyelination with no cognitive impairment or extrapyramidal symptoms. In the literature, HSP is considered a TUBB4A spectrum disorder.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.