Search Results (650)

Intrahepatic cholestasis of pregnancy (ICP) is characterized by the onset of pruritus and elevated serum transaminases and bile acids (BA). The key enzyme in BA synthesis is CYP7A1, and its functions are regulated by various nuclear receptors. The goal of this study is to evaluate the association between CYP7A1, NR1H1, RXRA, and PPARA gene variants and risk of ICP. Five single nucleotide variants (SNVs), rs3808607 (CYP7A1), rs56163822 (NR1H4), rs1800206 (PPARA), rs749759, and rs11381416 (NR2B1), were genotyped in a group of 96 ICP and 211 controls. The T allele of the CYP7A1 (rs3808607) variant may be a protective factor against ICP risk (OR = 0.697, 95% CI: 0.495–0.981, p = 0.038). Genetic model analysis showed that rs3808607 was associated with decreased risk of ICP under dominant (OR = 0.55, 95% CI: 0.32–3.16, p = 0.032, AIC = 380.9) and log-additive models (OR = 0.71, 95% CI: 0.51–1.00, p = 0.046, AIC = 381.4). The A insertion in the rs11381416 NR2B1 variant was associated with the degree of elevation in the liver function tests TBA (34.3 vs. 18.8 μmol/L, p = 0.002), ALT (397.0 vs. 213.0 IU/L, p = 0.017), and AST (186.0 vs. 114.4 IU/L, p = 0.032) in ICP women. Results indicate an association between the CYP7A1 rs3808607 and the risk of ICP and the association of the rs11381416 of the NR2B1 receptor with higher values of liver function tests in women with ICP. A better understanding of the cooperation of proteins involved in BA metabolism may have important therapeutic implications in ICP and other hepatobiliary diseases. Full article

Sin Nombre virus (SNV) is the main causative agent of hantavirus cardiopulmonary syndrome (HCPS) in North America. SNV is transmitted via environmental biological aerosols (bioaerosols) produced by infected deer mice (Peromyscus maniculatus). It is similar to other viruses that have environmental transmission routes rather than a person-to-person transmission route, such as avian influenza (e.g., H5N1) and Lassa fever. Despite the lack of person-to-person transmission, these viruses cause a significant public health and economic burden. However, due to the lack of targeted pharmaceutical preventatives and therapeutics, the recommended approach to prevent SNV infections is to avoid locations that have a combination of low foot traffic, receive minimal natural sunlight, and where P. maniculatus may be found nesting. Consequently, gaining insight into the SNV bioaerosol decay profile is fundamental to the prevention of SNV infections. The Biological Aerosol Reaction Chamber (Bio-ARC) is a flow-through system designed to rapidly expose bioaerosols to environmental conditions (ozone, simulated solar radiation (SSR), humidity, and other gas phase species at stable temperatures) and determine the sensitivity of those particles to simulated ambient conditions. Using this system, we examined the bioaerosol stability of SNV. The virus was found to be susceptible to both simulated solar radiation and ozone under the tested conditions. Comparisons of decay between the virus aerosolized in residual media and in a mouse bedding matrix showed similar results. This study indicates that SNV aerosol particles are susceptible to inactivation by solar radiation and ozone, both of which could be implemented as effective control measures to prevent disease in locations where SNV is endemic. Full article

Our work introduces a facile and efficient metal-free [3+3] annulation approach for the synthesis of polysubstituted pyridines via the reaction between β-enaminonitriles and β,β-dichloromethyl peroxides. This strategy operates under mild conditions, demonstrating broad substrate scope and excellent functional group tolerance. Mechanistic investigations suggest that the reaction proceeds through a Kornblum–De La Mare rearrangement followed by S_NV-type C-Cl bond cleavage and intramolecular cyclization/condensation. By circumventing the need for transition metal catalysts or radical initiators, our method offers practical utility in organic synthesis and provides a new avenue for the rapid construction of complex pyridine scaffolds. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

Hyperspectral technology has been widely applied to the retrieval of soil properties, such as soil organic matter (SOM) and particle size distribution (PSD). However, most previous studies have focused on hyperspectral data acquired from the nadir direction, and the influence of viewing geometry on hyperspectral-based soil property retrieval remains unclear. In this study, bidirectional reflectance factors (BRFs) were collected at 48 different viewing angles for 154 soil samples with varying SOM contents and PSDs. SOM and PSD were then retrieved using combinations of ten spectral preprocessing methods (raw reflectance, Savitzky–Golay filter (SG), first derivative (D1), second derivative (D2), standard normal variate (SNV), multiplicative scatter correction (MSC), SG + D1, SG + D2, SG + SNV, and SG + MSC), one sensitive wavelength selection method, and three retrieval algorithms (partial least squares regression (PLSR), support vector machine (SVM), and convolutional neural networks (CNNs)). The influence of viewing geometry on the selection of spectral preprocessing methods, retrieval algorithms, sensitive wavelengths, and retrieval accuracy was systematically analyzed. The results showed that soil BRFs are influenced by both soil properties and viewing angles. The viewing geometry had limited effects on the choice of preprocessing method and retrieval algorithm. Among the preprocessing methods, D1, SG + D1, and SG + D2 outperformed the others, while PLSR achieved a higher accuracy than SVM and CNN when retrieving soil properties. The selected sensitive wavelengths for both SOM and PSD varied slightly with viewing angle and were mainly located in the near-infrared region when using BRFs from multiple viewing angles. Compared with single-angle data, multi-angle BRFs significantly improved retrieval performance, with the R² increasing by 11% and 15%, and RMSE decreasing by 16% and 30% for SOM and PSD, respectively. The optimal viewing zenith angle ranged from 10° to 20° for SOM and around 40° for PSD. Additionally, backward viewing directions were more favorable than forward directions, with the optimal viewing azimuth angles being 0° for SOM and 90° for PSD. These findings provide useful insights for improving the accuracy of soil property retrieval using multi-angle hyperspectral observations. Full article

The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image data were acquired from Pleurotus geesteranus strains exhibiting varying degrees of degradation, followed by preprocessing using Savitzky–Golay smoothing (SG), multivariate scattering correction (MSC), and standard normal variate transformation (SNV). Spectral features were extracted by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA), while the texture features were derived using gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) models. The spectral and texture features were then fused and used to construct a classification model based on convolutional neural networks (CNN). The results showed that combining hyperspectral and image texture features significantly improved the classification accuracy. Among the tested models, the CARS + LBP-CNN configuration achieved the best performance, with an overall accuracy of 95.6% and a kappa coefficient of 0.96. This approach provides a new technical solution for the nondestructive detection of strain degradation in Pleurotus geesteranus. Full article

The role of G-quadruplexes (G4s) in gene regulation has been widely documented, especially in gene promoters. However, the transcriptional mechanisms involving G4s in other regulatory regions remain largely unexplored. In this study, we integrated the G4-DNA data derived from 22 breast cancer patient-derived tumor xenograft (PDTX) models and MCF7 cell line as potential breast cancer-associated G4s (BC-G4s). Genome-wide analysis showed that BC-G4s are more prevalent in gene promoters and the first introns. The genes accommodating promoter or intronic BC-G4s show significantly higher transcriptional output than their non-G4 counterparts. The biased distribution of BC-G4s in close proximity to the transcription start site (TSS) is associated with an enrichment of transcription factor (TF) interactions. A significant negative correlation was detected between the G4–TF interactions within the first introns and their cognate promoters. These different interactions are complementary rather than redundant. Furthermore, the differentially expressed genes (DEGs) harboring promoter and first intron BC-G4s are significantly enriched in the cell cycle pathway. Notably, promoter BC-G4s of DEGs could be a central hub for TF–TF co-occurrence. Our analysis also revealed that G4-related single nucleotide variants (SNVs) affect the stability of G4 structures and the transcription of disease-related genes. Collectively, our results shed light on how BC-G4s within promoters and first introns regulate gene expression and reinforce the critical role of G4s and G4-related genes in breast cancer-associated processes. Full article

Protein and amino acid content are the crucial quality parameters in bottle gourd, and traditional measurement methods for detecting those parameters are complicated, time-consuming, and costly. In this study, we employed NIRS along with machine learning and neural network-based methods to model and predict protein and free amino acids (FAAs) of bottle gourd. Specifically, the content of protein and FAAs were measured through conventional methods. Then a near-infrared analyzer was utilized to obtain the spectral data, which were processed using multiple scattering correction (MSC) and standard normalized variate (SNV). The processed spectral data were further processed using feature importance selection to select the feature bands that had the highest correlation with protein and FAAs, respectively. The models for protein and FAAs estimation were developed using support vector regression (SVR), ridge regression (RR), random forest regression (RFR), and fully connected neural networks (FCNNs). Among them, ridge regression achieved the optimal performance, with determination coefficients (R²) of 0.96 and 0.77 on the protein and FAAs test sets, respectively, and root mean square error (RMSE) values of 0.23 and 0.5, respectively. Based on this, we developed a precise and rapid prediction model for the important quality indices of bottle gourd. Full article

With the increasing application of hyperspectral technology, rapid and accurate monitoring of cotton leaf nitrogen concentrations (LNCs) has become an effective tool for large-scale areas. This study used Tahe No. 2 cotton seeds with four nitrogen levels (0, 200, 350, 500 kg ha⁻¹) and four phosphorus levels (0, 100, 200, 300 kg ha⁻¹). Spectral data were acquired using an ASD FieldSpec HandHeld2 portable spectrometer, which measures spectral reflectance covering a band of 325–1075 nm with a spectral resolution of 1 nm. LNCs determination and spectral estimation were conducted at six growth stages: squaring, initial bloom, peak bloom, initial boll, peak boll, and boll opening. Thirty-seven spectral indices (SIs) were selected. First derivative (FD), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay (SG) were applied to preprocess the spectra. Feature bands were screened using partial least squares discriminant analysis (PLS–DA), and support vector machine (SVM) and random forest (RF) models were used for accuracy validation. The results revealed that (1) LNCs initially increased and then decreased with growth, peaking at the full-flowering stage before gradually declining. (2) The best LNC recognition models were SVM–MSC in the squaring stage, SVM–FD in the initial bloom stage, SVM–FD in the peak bloom stage, SVM–FD in the initial boll stage, RF–SNV in the peak boll Mstage, and SVM–FD in the boll opening stage. FD showed the best performance compared with the other three treatments, with SVM outperforming RF in terms of higher R² and lower RMSE values. The SVM–FD model effectively improved the accuracy and robustness of LNCs prediction using hyperspectral leaf spectra, providing valuable guidance for large-scale information production in high-standard cotton fields. Full article

Background: Inflammatory bowel disease (IBD) is defined as recurrent inflammatory bowel disorders, the most common of which are Crohn’s disease (CD) and ulcerative colitis (UC). Tumor necrosis factor inhibitors (anti-TNFs), primarily adalimumab (ADA), infliximab (IFX), ustekinumab (UST), and vedolizumab (VLZ), are used to treat moderate-to-severe cases of IBD in patients who either do not tolerate or fail to respond to conventional therapies. However, about one-third of patients are primary non-responders to these treatments, and an additional 30% lose response over time. Several studies have investigated the role of genetic variability in explaining these differences in treatment response among patients. The aim of this study was to design an array of 60 single-nucleotide variants (SNVs) to validate the biomarkers described in the literature in a population of more than 400 IBD patients treated with biological drugs. Method: The primary focus of this study was the most recent reviews published in PubMed, with all relevant SNVs selected for the array design. Subsequently, studies presenting original data on the association between variants and the response to biological treatment were identified. Results: A total of 55.9% of SNVs have been studied in CD, 18.6% have been in UC, and 25.4% have been studied in both pathologies. A total of 44.1% of SNVs have been observed to influence the response to IFX, 16.9% influence the response to ADA, and 37.3% influence the response to both IFX and ADA; however, only one study (1.7%) reported an influence on the response to UST and none reported an influence on the response to VLZ. Conclusions: An array comprising 38 genes and 59 SNVs has been designed to be used to validate biomarkers associated with responses to biologic drug treatments in IBD. Full article

Aflatoxin B₁ (AFB₁) contamination in corn silage poses significant risks to livestock and human health. This study developed a non-destructive detection method for AFB₁ using color-sensitive arrays (CSAs). Twenty self-developed CSAs were employed to react with samples, with reflectance spectra collected using a portable spectrometer. Spectral data were optimized through seven preprocessing methods, including Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), first-order derivative (1st D), second-order derivative (2nd D), wavelet denoising, and their combinations. Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). Five machine learning models were constructed: Light Gradient Boosting Machine (LightGBM), XGBoost, Support Vector Regression (SVR), RF, and K-Nearest Neighbor (KNN). The results demonstrated significant AFB₁-responsive characteristics in three dyes: (2,3,7,8,12,13,17,18-octaethylporphynato)chloromanganese(III) (Mn(OEP)Cl), Bromocresol Green, and Cresol Red. The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient ($R_p^2$ = 0.87), root mean square error (RMSEP = 0.057), and relative prediction deviation (RPD = 2.773). This method provides an efficient solution for silage AFB₁ monitoring. Full article

The MAM domain-containing glycosylphosphatidylinositol anchor 1 (MDGA1) gene, which encodes a protein involved in synaptic inhibition, has been identified as a potential risk gene for restless legs syndrome. A recent study in the Chinese population described increased MDGA1 methylation levels in patients with idiopathic RLS (iRLS) compared to healthy controls. In this study, we investigated the possible association between the most common variants in the MDGA1 gene and the risk for iRLS in a Caucasian Spanish population. We assessed the frequencies of MDGA1 rs10947690, MDGA1 rs61151079, and MDGA1 rs79792089 genotypes and allelic variants in 263 patients with idiopathic RLS and 280 healthy controls using a specific TaqMan-based qPCR assay. We also analyzed the possible influence of the genotype frequencies on several variables, including age at the onset of RLS, gender, a family history of RLS, and response to drugs commonly used in the treatment of RLS. The frequencies of the genotypes and allelic variants of the three common missense SNVs studied did not differ significantly between RLS patients and controls, neither in the whole series nor when analyzing each gender separately; were not correlated with age at onset and the severity of RLS assessed by the International Restless Legs Syndrome Study Group Rating Scale (IRLSSGRS); and were not related to a family history of RLS or the pharmacological response to dopamine agonists, clonazepam, or gabaergic drugs. Our findings suggest that common missense SNVs in the MDGA1 gene are not associated with the risk of developing idiopathic RLS in Caucasian Spanish people. Full article

Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. However, PLS may be limited in its capacity to handle complex spectral data contaminated with significant noise and interferences. In this paper, we propose a novel filter learning-based PLS (FPLS) model that integrates an adaptive filter into the PLS framework. The FPLS model is designed to maximize the covariance between the filtered spectral data and the response. This modification enables FPLS to dynamically adapt to the characteristics of the data, thereby enhancing its feature extraction and noise suppression capabilities. We have developed an efficient algorithm to solve the FPLS optimization problem and provided theoretical analyses regarding the convergence of the model, the prediction variance, and the relationships among the objective functions of FPLS, PLS, and the filter length. Furthermore, we have derived bounds for the Root Mean Squared Error of Prediction (RMSEP) and the Cosine Similarity (CS) to evaluate model performance. Experimental results using spectral datasets from Corn, Octane, Mango, and Soil Nitrogen show that the FPLS model outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of prediction accuracy. The theoretical analyses align with the experimental results, emphasizing the effectiveness and robustness of the FPLS model in managing complex spectral data. Full article

Background: HM is a rare, severe form of migraine with aura, characterised by motor weakness and strongly influenced by genetic factors affecting the brain. While pathogenic variants in CACNA1A, ATP1A2, and SCN1A genes have been implicated in familial HM, approximately 75% of cases lack known pathogenic variants in these genes, suggesting a more complex genetic basis. Methods: To advance our understanding of HM, we applied a variant prioritisation approach using whole-exome sequencing (WES) data from patients referred for HM diagnosis (n = 184) and utilised PathVar, a bioinformatics pipeline designed to identify pathogenic variants. Our analysis incorporated two strategies for association testing: (1) PathVar-identified single nucleotide variants (SNVs) and (2) PathVar SNVs combined with missense and rare variants. Principal component analysis (PCA) was performed to adjust for ancestral and other unknown differences between cases and controls. Results: Our results reveal a sequential reduction in the number of genes significantly associated with HM, from 20 in the first strategy to 11 in the second, which highlights the unique contribution of PathVar SNVs to the genetic architecture of HM. PathVar SNVs were more distinctive in the case cohort, suggesting a closer link to the functional changes underlying HM compared to controls. Notably, novel genes, such as SLC38A10, GCOM1, and NXPH2, which were previously not implicated in HM, are now associated with the disorder, advancing our understanding of its genetic basis. Conclusions: By prioritising PathVar SNVs, we identified a broader set of genes potentially contributing to HM. Given that HM is a rare condition, our findings, utilising a sample size of 184, represent a unique contribution to the field. This iterative analysis demonstrates that integrating diverse variant schemes provides a more comprehensive view of the genetic factors driving HM. Full article

Background/Objectives: Cxbladder^® Triage Plus is a multimodal urinary biomarker assay that combines reverse transcription-quantitative analysis of five mRNA targets and droplet-digital polymerase chain reaction (ddPCR) analysis of six DNA single-nucleotide variants (SNVs) from two genes (fibroblast growth factor receptor 3 (FGFR3) and telomerase reverse transcriptase (TERT)) to provide risk stratification for urothelial carcinoma (UC) in patients with hematuria. This study evaluated the analytical validity of Triage Plus. Methods: The development dataset used urine samples from patients with microhematuria or gross hematuria that were previously stabilized with Cxbladder solution. Triage Plus was evaluated for predicted performance, analytical criteria (linearity, sensitivity, specificity, accuracy, and precision), extraction efficiency, and inter-laboratory reproducibility. Results: The development dataset included 987 hematuria samples. Compared with cystoscopy (standard of care), Triage Plus had a predicted sensitivity of 93.6%, specificity of 90.8%, positive predictive value (PPV) of 46.5%, negative predictive value of 99.4%, and test-negative rate of 84.1% (score threshold 0.15); the PPV increased to 74.6% for the 0.54 score threshold. For the individual FGFR3 and TERT SNVs, the limit of detection (analytical sensitivity) was a mutant-to-wild type DNA ratio of 1:440–1:1250 copies/mL. Intra- and inter-assay variance was low, while extraction efficiency was high. All other pre-specified analytical criteria (linearity, specificity, and accuracy) were met. Triage Plus showed good reproducibility (87.9% concordance between laboratories). Conclusions: Cxbladder Triage Plus accurately and reproducibly detected FGFR3 and TERT SNVs and, in combination with mRNA expression, provides a non-invasive, highly sensitive, and reproducible tool that aids in risk stratification of patients with hematuria. Full article