Multiple and Optimal Screening Subset: a method selecting global characteristic congeners for robust foodomics analysis.

Metabolomics and foodomics shed light on the molecular processes within living organisms and the complex food composition by leveraging sophisticated analytical techniques to systematically analyze the vast array of molecular features. The traditional feature-picking method often results in arbitrary selections of the model, feature ranking, and cut-off, which may lead to suboptimal results. Thus, a Multiple and Optimal Screening Subset (MOSS) approach was developed in this study to achieve a balance between a minimal number of predictors and high predictive accuracy during statistical model setup. The MOSS approach compares five commonly used models in the context of food matrix analysis, specifically bourbons. These models include Student's t-test, receiver operating characteristic curve, partial least squares-discriminant analysis (PLS-DA), random forests, and support vector machines. The approach employs cross-validation to identify promising subset feature candidates that contribute to food characteristic classification. It then determines the optimal subset size by comparing it to the corresponding top-ranked features. Finally, it selects the optimal feature subset by traversing all possible feature candidate combinations. By utilizing MOSS approach to analyze 1406 mass spectral features from a collection of 122 bourbon samples, we were able to generate a subset of features for bourbon age prediction with 88% accuracy. Additionally, MOSS increased the area under the curve performance of sweetness prediction to 0.898 with only four predictors compared with the top-ranked four features at 0.681 based on the PLS-DA model. Overall, we demonstrated that MOSS provides an efficient and effective approach for selecting optimal features compared with other frequently utilized methods.

The first complete mitochondrial genome of Grossulariaceae: Molecular features, structure recombination, and genetic evolution.

BACKGROUND: Mitochondria play crucial roles in the growth, development, and adaptation of plants. Blackcurrant (Ribes nigrum L.) stands out as a significant berry species due to its rich nutritional profile, medicinal properties, and health benefits. Despite its importance, the mitochondrial genome of blackcurrant remains unassembled. RESULTS: This study presents the first assembly of the mitochondrial genome of R. nigrum in the Grossulariaceae family. The genome spans 450,227 base pairs (bp) and encompasses 39 protein-coding genes (PCGs), 19 transfer RNAs (tRNAs), and three ribosomal RNAs (rRNAs). Protein-coding regions constitute 8.88% of the entire genome. Additionally, we identified 180 simple sequence repeats, 12 tandem repeats, and 432 pairs of dispersed repeats. Notably, the dispersed sequence R1 (cotig3, 1,129 bp) mediated genome recombination, resulting in the formation of two major conformations, namely master and double circles. Furthermore, we identified 731 C-to-U RNA editing sites within the PCGs. Among these, cox1-2, nad1-2, and nad4L-2 were associated with the creation of start codons, whereas atp6-718 and rps10-391 were linked to termination codons. We also detected fourteen plastome fragments within the mitogenome, constituting 1.11% of the total length. Phylogenetic analysis suggests that R. nigrum might have undergone multiple genomic reorganization and/or gene transfer events, resulting in the loss of two PCGs (rps2 and rps11) during its evolutionary history. CONCLUSIONS: This investigation unveils the molecular characteristics of the R. nigrum mitogenome, shedding light on its evolutionary trajectory and phylogenetic implications. Furthermore, it serves as a valuable reference for evolutionary research and germplasm identification within the genus.

Triple Generative Self-Supervised Learning Method for Molecular Property Prediction.

Molecular property prediction is an important task in drug discovery, and with help of self-supervised learning methods, the performance of molecular property prediction could be improved by utilizing large-scale unlabeled dataset. In this paper, we propose a triple generative self-supervised learning method for molecular property prediction, called TGSS. Three encoders including a bi-directional long short-term memory recurrent neural network (BiLSTM), a Transformer, and a graph attention network (GAT) are used in pre-training the model using molecular sequence and graph structure data to extract molecular features. The variational auto encoder (VAE) is used for reconstructing features from the three models. In the downstream task, in order to balance the information between different molecular features, a feature fusion module is added to assign different weights to each feature. In addition, to improve the interpretability of the model, atomic similarity heat maps were introduced to demonstrate the effectiveness and rationality of molecular feature extraction. We demonstrate the accuracy of the proposed method on chemical and biological benchmark datasets by comparative experiments.

The dynamic cellular and molecular features during the development of radiation proctitis revealed by transcriptomic profiling in mice.

BACKGROUND: Radiation proctitis (RP) is the most common complication of radiotherapy for pelvic tumor. Currently there is a lack of effective clinical treatment and its underlying mechanism is poorly understood. In this study, we aimed to dynamically reveal the mechanism of RP progression from the perspective of RNomics using a mouse model, so as to help develop reasonable therapeutic strategies for RP. RESULTS: Mice were delivered a single dose of 25 Gy rectal irradiation, and the rectal tissues were removed at 4 h, 1 day, 3 days, 2 weeks and 8 weeks post-irradiation (PI) for both histopathological assessment and RNA-seq analysis. According to the histopathological characteristics, we divided the development process of our RP animal model into three stages: acute (4 h, 1 day and 3 days PI), subacute (2 weeks PI) and chronic (8 weeks PI), which could recapitulate the features of different stages of human RP. Bioinformatics analysis of the RNA-seq data showed that in the acute injury period after radiation, the altered genes were mainly enriched in DNA damage response, p53 signaling pathway and metabolic changes; while in the subacute and chronic stages of tissue reconstruction, genes involved in the biological processes of vessel development, extracellular matrix organization, inflammatory and immune responses were dysregulated. We further identified the hub genes in the most significant biological process at each time point using protein-protein interaction analysis and verified the differential expression of these genes by quantitative real-time-PCR analysis. CONCLUSIONS: Our study reveals the molecular events sequentially occurred during the course of RP development and might provide molecular basis for designing drugs targeting different stages of RP development.

AR Expression Correlates with Distinctive Clinicopathological and Genomic Features in Breast Cancer Regardless of ESR1 Expression Status.

Androgen receptor (AR) expression is frequently observed in breast cancer, but its association with estrogen receptor (ER) expression in breast cancer remains unclear. This study analyzed the clinicopathological and molecular features associated with AR negativity in both ER-positive and ER-negative breast cancer, trying to elucidate the molecular correlation between AR and ER. Our results showed that AR negativity was associated with different clinicopathological characteristics and molecular features in ER-positive and ER-negative breast cancer. Moreover, AR-positive breast cancer has better clinicopathological features than AR-negative breast cancer, especially in the ER-negative subtype. These results suggest that the role of AR in ER-negative breast cancer is distinctive from that in ER-positive breast cancer.

[Clinicopathologic and molecular features of cribriform morular variant of papillary thyroid carcinoma].

Objective: To investigate the clinicopathologic and molecular features of the rare cribriform morular variant of papillary thyroid carcinoma (CMV-PTC). Methods: The clinicopathologic data of 10 patients with CMV-PTC were retrospectively reviewed. Immunohistochemical (IHC) staining was done using LSAB method. DNA sequencing for APC were applied using Sanger method. BRAF V600E mutation was examined using ARMS method. The cytological, morphological, IHC and molecular features were analyzed. Results: All patients were female at an average age of 27 years old. The tumors were mostly located in the right lobe of thyroid. Fine needle aspiration cytology was performed in three patients; two were diagnosed as suspicious for PTC and one as PTC. Nine tumors presented as solitary nodule and two as multiple nodules in both lobes. Infiltration was demonstrated in three cases. The average size was 2.6 cm. The neoplastic cells were arranged in papillary, cribriform, solid and glandular patterns, with rare or without colloid inside the lumen. The number of morula varied, ranging from zero to many. The neoplastic cells were variably enlarged, showing round, oval or spindle shape. Nuclear irregularity was identified as irregular membrane, nuclear grooves or pseudoinclusion, but no typical ground glass feature. Peculiar nuclear clearing could be observed in the morular cells. IHC staining showed the neoplastic cells were negative for thyroglobulin and p63, but positive for TTF1, cytokeratin 19 and estrogen receptor. Diffuse staining with cytokeratin was seen in the neoplastic cells and the morula. Specific cytoplasmic and nuclear staining of ß-catenin was seen in the neoplastic cells but not the morula. Ki-67 proliferation index was 1%-30%. No recurrence or metastasis was observed. One patient was demonstrated to harbor both somatic and germline mutations of the APC gene, who was found to have adenomatous polyposis and her mother died of colonic carcinoma. No BRAF V600E mutation was detected. Conclusions: CMV-PTC is rare and shows atypical cytological and clinicopathological features, and it is easily misdiagnosed.TG, TTF1, ER and ß-catenin are specific IHC markers for CMV-PTC. The morula is negative for cytokeratin 19, in contrast to squamous metaplasia. Although CMV-PTC has indolent clinical behavior, a definite diagnosis is necessary to rule out the possibility of APC gene mutation and related extra-thyroidal neoplasm, such as FAP and Gardner syndrome.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Neuroradiological, genetic and clinical characteristics of histone H3 K27-mutant diffuse midline gliomas in the Kansai Molecular Diagnosis Network for CNS Tumors (Kansai Network): multicenter retrospective cohort.

This study aims to elucidate the clinical and molecular characteristics, treatment outcomes and prognostic factors of patients with histone H3 K27-mutant diffuse midline glioma. We retrospectively analyzed 93 patients with diffuse midline glioma (47 thalamus, 24 brainstem, 12 spinal cord and 10 other midline locations) treated at 24 affiliated hospitals in the Kansai Molecular Diagnosis Network for CNS Tumors. Considering the term "midline" areas, which had been confused in previous reports, we classified four midline locations based on previous reports and anatomical findings. Clinical and molecular characteristics of the study cohort included: age 4-78 years, female sex (41%), lower-grade histology (56%), preoperative Karnofsky performance status (KPS) scores ≥ 80 (49%), resection (36%), adjuvant radiation plus chemotherapy (83%), temozolomide therapy (76%), bevacizumab therapy (42%), HIST1H3B p.K27M mutation (2%), TERT promoter mutation (3%), MGMT promoter methylation (9%), BRAF p.V600E mutation (1%), FGFR1 mutation (14%) and EGFR mutation (3%). Median progression-free and overall survival time was 9.9 ± 1.0 (7.9-11.9, 95% CI) and 16.6 ± 1.4 (13.9-19.3, 95% CI) months, respectively. Female sex, preoperative KPS score ≥ 80, adjuvant radiation + temozolomide and radiation ≥ 50 Gy were associated with favorable prognosis. Female sex and preoperative KPS score ≥ 80 were identified as independent good prognostic factors. This study demonstrated the current state of clinical practice for patients with diffuse midline glioma and molecular analyses of diffuse midline glioma in real-world settings. Further investigation in a larger population would contribute to better understanding of the pathology of diffuse midline glioma.

A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction.

Taste determination in small molecules is critical in food chemistry but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as valuable tools for this task. In this study, we explore taste prediction using various molecular feature representations and assess the performance of different machine learning algorithms on a dataset comprising 2601 molecules. The results reveal that GNN-based models outperform other approaches in taste prediction. Moreover, consensus models that combine diverse molecular representations demonstrate improved performance. Among these, the molecular fingerprints + GNN consensus model emerges as the top performer, highlighting the complementary strengths of GNNs and molecular fingerprints. These findings have significant implications for food chemistry research and related fields. By leveraging these computational approaches, taste prediction can be expedited, leading to advancements in understanding the relationship between molecular structure and taste perception in various food components and related compounds.

Clinicopathological, molecular and prognostic characteristics of cancer of unknown primary in China: An analysis of 1420 cases.

BACKGROUND: Cancer of unknown primary (CUP) is defined the presence of metastatic disease without an identified primary site. An unidentifiable primary site of cancer creates significant challenges for treatment selection. We aimed to describe the clinicopathological, molecular, and prognostic characteristics of Chinese CUP patients. METHODS: Patients with oncologist-confirmed CUP were identified at Fudan University Shanghai Cancer Center from 2019 to 2020. Information on patient characteristics, tumor presentation, treatment, and outcome were retrospectively collected from the inpatient database and pathological consultation database for descriptive analysis. A multivariable logistic regression model was established to identify factors associated with patient prognosis. RESULTS: A total of 1420 CUP patients were enrolled in this study. The baseline characteristics of the entire cohort included the following: median age (59 years old), female sex (45.8%), adenocarcinoma (47.7%), and poorly differentiated or undifferentiated tumors (92.1%). For the inpatient cohort, the most common sites where cancer spread included the lymph nodes (41.8%), bone (22.0%), liver (20.1%), and peritoneum/retroperitoneum (16.0%). A total of 77.4% and 58.2% of patients were treated with local therapy and systemic therapy, respectively. Four prognostic factors, including liver metastasis, peritoneal/retroperitoneal metastasis, number of metastatic sites (N ≥ 2), and systemic treatment, were independently associated with overall survival. Additionally, 24.8% (79/318) of patients received molecular testing, including PD-L1, human papillomavirus, genetic variation, and 90-gene expression tests for diagnosis or therapy selection. CONCLUSION: Cancer of unknown primary remains a difficult cancer to diagnose and manage. Our findings improve our understanding of Chinese CUP patient characteristics, leading to improved care and outcomes for CUP patients.

Antimicrobial Susceptibility and Molecular Features of Colonizing Isolates of Pseudomonas aeruginosa and the Report of a Novel Sequence Type (ST) 3910 from Thailand.

Pseudomonas aeruginosa is an important pathogen as it can cause hospital-acquired infections. Additionally, it can also colonize in patients and in other various environments. Hence, this study aimed to investigate the antimicrobial susceptibility, and to study the molecular features, of colonizing isolates of P. aeruginosa from Songklanagarind Hospital, Thailand. Genomic DNA extraction, whole-genome sequencing (WGS), and bioinformatics analysis were performed in all studied isolates. The findings demonstrated that the majority of isolates were non-susceptible to colistin and carbapenem. For in silico study, multilocus sequence typing (MLST) revealed one novel sequence type (ST) 3910 and multiple defined STs. The isolates carried several antimicrobial resistance genes (blaOXA-50, aph(3')-IIb, etc.) and virulence-associated genes (fleN, waaA, etc.). CRISPR-Cas sequences with different spacers and integrated bacteriophage sequences were also identified in these isolates. Very high SNPs were found in the alignments of the novel ST-3910 isolate with other isolates. A comparative genomic analysis exhibited phylogenetic clustering of our colonizing isolates with clinical isolates from many countries. Interestingly, ST-3981, ST-3982, ST-3983, ST-3984, ST-3985, ST-3986, ST-3986, ST-3986, ST-3987, and ST-3988, the new STs from published genomes, were assigned in this study. In conclusion, this WGS data might be useful for tracking the spread of P. aeruginosa colonizing isolates.

Non-invasive omics analysis delineates molecular changes in water-only fasting and its sex-discriminating features in metabolic syndrome patients.

Fasting has been grown in popularity with multiple potential benefits. However, very few studies dynamically monitor physiological and pathological changes during long-term fasting using noninvasive methods. In the present study, we recruited 37 individuals with metabolic syndrome to engage in a 5-day water-only fasting regimen, and simultaneously captured the molecular alterations through urinary proteomics and metabolomics. Our findings reveal that water-only fasting significantly mitigated metabolic syndrome-related risk markers, such as body weight, body mass index, abdominal circumference, blood pressure, and fasting blood glucose levels in metabolic syndrome patients. Indicators of liver and renal function remained within the normal range, with the exception of uric acid. Notably, inflammatory response was inhibited during the water-only fasting period, as evidenced by a decrease in the human monocyte differentiation antigen CD14. Intriguingly, glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation underwent a sex-dependent reprogramming throughout the fasting period, whereby males exhibited a greater upregulation of carbohydrate metabolism-related enzymes than females. This disparity may be attributed to evolutionary pressures. Collectively, our study sheds light on the beneficial physiological effects and novel dynamic molecular features associated with fasting in individuals with metabolic syndrome using noninvasive methods.

Single-cell characteristics and malignancy regulation of alpha-fetoprotein-producing gastric cancer.

OBJECTIVE: To characterize alpha-fetoprotein (AFP)-producing gastric cancer (AFPGC) at the single-cell level and to identify regulatory factors for AFP expression and malignancy. METHODS: ScRNA-seq was performed on two tumors collected from patients with AFPGC. InferCNV and sub-clustering were applied to identify typical AFPGC cells, followed by AddModuleScore, pathway enrichment, Pseudo-time, and Scenic analyses. Data from a gastric cancer (GC) cohort were collected for conjoint analysis. The analytical results were verified by cell experiments and immunohistochemistry. RESULTS: AFPGC cells are similar to hepatocytes in transcriptome and transcriptional regulation, with kinetic malignancy-related pathways, compared to the common malignant epithelium. In addition, compared to common GC cells, malignancy-related pathways, such as epithelial-mesenchymal transition (EMT) and angiogenesis, were upregulated in AFPGC. Mechanistically, Dickkopf-1 (DKK1) was found to be associated with AFP expression and malignant phenotype upon combining our scRNA-seq data with a public database, which was further verified by a series of in vitro experiments and immunohistochemistry. CONCLUSION: We demonstrated the single-cell characteristics of AFPGC and that DKK1 facilitates AFP expression and malignancy.

An Update on the Pathology and Molecular Features of Hodgkin Lymphoma.

Hodgkin lymphomas (HLs) are lymphoid neoplasms derived from B cells and consist histologically of large neoplastic cells known as Hodgkin and Reed-Sternberg cells and abundant reactive bystander cells. HLs include two main types, classic HL (CHL) and nodular lymphocyte predominant HL (NLPHL). Recent molecular analyses have revealed that an immune evasion mechanism, particularly the PD-1/PD-L1 pathway, plays a key role in the development of CHL. Other highlighted key pathways in CHL are NF-κB and JAK/STAT. These advances have dramatically changed the treatment for CHL, particularly relapsed/refractory CHL. For example, PD-1 inhibitors are now widely used in relapsed/refractory CHL. Compared with CHL, NLPHL is more characterized by preserved B cell features. Overlapping morphological and molecular features between NLPHL and T-cell/histiocyte-rich large B-cell lymphoma (THRLBCL) have been reported, and biological continuity between these two entities has been highlighted. Some THRLBCLs are considered to represent progression from NLPHLs. With considerable new understanding becoming available from molecular studies in HLs, therapies and classification of HLs are continually evolving. This paper offers a summary of and update on the pathological and molecular features of HLs for a better understanding of the diseases.

The Lifted Veil of Uncommon EGFR Mutation p.L747P in Non-Small Cell Lung Cancer: Molecular Feature and Targeting Sensitivity to Tyrosine Kinase Inhibitors.

OBJECTIVES: The uncommon p.L747P mutation in epidermal growth factor receptor (EGFR) exon 19 reveals to alter the response to tyrosine kinase inhibitors (TKIs) in patients diagnosed with advanced non-small cell lung cancer (NSCLC). However, the underlying mechanism is still not clear. This study aimed to investigate the clinical outcomes, binding affinities, and modes of action of currently available EGFR TKIs towards p.L747P mutation. MATERIALS AND METHODS: Clinical data of NSCLC patients harboring p.L747P mutation who had received different generations of EGFR TKIs were collected from medical records. Computational structure of p.L747P was constructed and in vitro cellular kinase inhibition assay and mice xenograft experiment were performed to predict and confirm the binding affinities and antitumor activities of diverse EGFR TKIs. RESULTS: A total of five metastatic NSCLC patients with p.L747P mutation were included in the final analysis. Patients treated with second-generation (2G) TKI afatinib achieved numerically longer progression-free survival (range 2.4-8.5 months) than that with first-generation (1G, range 1.4-5.5 months) or third-generation (3G, range 1.6-7.5 months) TKIs. None of the patients administered 1G or 3G TKIs achieved tumor response, but two-thirds of them treated with afatinib achieved partial response. Dynamics simulation predicted that 2G TKIs presented the best binding affinity to p.L747P mutation. The cellular kinase inhibition assay and mice xenograft experiment confirmed that afatinib could potently inhibit p.L747P-mutant cells and significantly reduce p.L747P-mutant tumor growth (P< 0.001), together with reduced phosphorylation of EGFR and its downstream signalings. CONCLUSIONS: The uncommon p.L747P mutation in EGFR exon 19 resulted in a poor response to first-generation EGFR TKIs. Afatinib revealed a better clinical response and binding affinity compared with osimertinib for this specific alteration.

A comprehensive comparison of molecular feature representations for use in predictive modeling.

Machine learning methods are commonly used for predicting molecular properties to accelerate material and drug design. An important part of this process is deciding how to represent the molecules. Typically, machine learning methods expect examples represented by vectors of values, and many methods for calculating molecular feature representations have been proposed. In this paper, we perform a comprehensive comparison of different molecular features, including traditional methods such as fingerprints and molecular descriptors, and recently proposed learnable representations based on neural networks. Feature representations are evaluated on 11 benchmark datasets, used for predicting properties and measures such as mutagenicity, melting points, activity, solubility, and IC50. Our experiments show that several molecular features work similarly well over all benchmark datasets. The ones that stand out most are Spectrophores, which give significantly worse performance than other features on most datasets. Molecular descriptors from the PaDEL library seem very well suited for predicting physical properties of molecules. Despite their simplicity, MACCS fingerprints performed very well overall. The results show that learnable representations achieve competitive performance compared to expert based representations. However, task-specific representations (graph convolutions and Weave methods) rarely offer any benefits, even though they are computationally more demanding. Lastly, combining different molecular feature representations typically does not give a noticeable improvement in performance compared to individual feature representations.

A multi-omics study delineates new molecular features and therapeutic targets for esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a major histological subtype of esophageal cancer with inferior prognosis. Here, we conducted comprehensive transcriptomic, proteomic, phosphoproteomic, and metabolomic characterization of human, treatment-naive ESCC and paired normal adjacent tissues (cohort 1, n = 24) in an effort to identify new molecular vulnerabilities for ESCC and potential therapeutic targets. Integrative analysis revealed a small group of genes that were related to the active posttranscriptional and posttranslational regulation of ESCC. By using proteomic, phosphoproteomic, and metabolomic data, networks of ESCC-related signaling and metabolic pathways that were closely linked to cancer etiology were unraveled. Notably, integrative analysis of proteomic and phosphoproteomic data pinpointed that certain pathways involved in RNA transcription, processing, and metabolism were stimulated in ESCC. Importantly, proteins with close linkage to ESCC prognosis were identified. By enrolling an ESCC patient cohort 2 (n = 41), three top-ranked prognostic proteins X-prolyl aminopeptidase 3 (XPNPEP3), bromodomain PHD finger transcription factor (BPTF), and fibrillarin (FBL) were verified to have increased expression in ESCC. Among these prognostic proteins, only FBL, a well-known nucleolar methyltransferase, was essential for ESCC cell growth in vitro and in vivo. Furthermore, a validation study using an ESCC patient cohort 3 (n = 100) demonstrated that high FBL expression predicted unfavorable patient survival. Finally, common cancer/testis antigens and established cancer drivers and kinases, all of which could direct therapeutic decisions, were characterized. Collectively, our multi-omics analyses delineated new molecular features associated with ESCC pathobiology involving epigenetic, posttranscriptional, posttranslational, and metabolic characteristics, and unveiled new molecular vulnerabilities with therapeutic potential for ESCC.

[Molecular characterization of Cryptosporidium species in sheep and goats in Anhui Province and neighboring provinces].

OBJECTIVE: To investigate the prevalence and molecular features of Cryptosporidium in sheep and goats from Anhui Province and neighboring provinces. METHODS: A total 832 and 781 fresh fecal samples were collected from seven large-scale sheep farms and ten large-scale goat farms in Anhui Province and neighboring provinces of Henan, Jiangsu and Shandong. The prevalence and species of Cryptosporidium were investigated in the fecal samples from the sheep and goats in the study areas using nested PCR assay based on the Cryptosporidium-specific SSU rDNA gene, and the subgenotypes of C. parvum and C. ubiquitum were characterized by amplification and sequencing of the 60 kDa glycoprotein (gp60) gene. RESULTS: The overall prevalence of Cryptosporidium was 5.8% (48/832) in sheep and 8.7% (68/781) in goats in Anhui Province and neighboring provinces, respectively. The SSU rDNA gene-based PCR assay identified C. xiaoi and C. ubiquitum in sheep and C. parvum in goats, and subtyping revealed that all C. ubiquitum subgenotypes belonged to XIIa subtype 2 and C. parvum subgenotypes belonged to IIdA19G1. CONCLUSIONS: The identification of zoonotic C. ubiquitum XIIa subtype 2 and C. parvum subtype IIdA19G1 suggests that sheep and goats may serve as a potential source for human Cryptosporidium infections.

Molecular features of pleomorphic xanthoastrocytoma.

Pleomorphic xanthoastrocytoma (PXA) is a rare central nervous system tumor occurring mostly in children and young adults. Next-generation sequencing of 295 cancer-related genes was used to investigate the molecular profiles of 13 cases of PXA. We found that BRAF V600E (5/13; 38%), FANCA/D2/I/M (5/13; 38%), PRKDC (4/13; 31%), NF1 (3/13; 23%), and NOTCH2/3/4 (3/13; 23%) alterations were the most frequent somatic gene mutations. However, neither PTEN nor EGFR mutation, which is frequently present in glioblastoma, was detected. The KRAS mutation in PXA is reported for the first time in these tumors. Microsatellite stability was present in all cases. Because mutations of FANCA and BRAF and copy number variations of CDKN2A/B are more frequent in PXA than in glioblastoma, they might be used to distinguish the 2 tumors. The MAPK pathway is involved in the pathogenesis of PXA and may be an effective target for treatment.

Rapid characterization of chemical markers for discrimination of Moutan Cortex and its processed products by direct injection-based mass spectrometry profiling and metabolomic method.

BACKGROUND: Processing of herbal medicines is a characteristic pharmaceutical technique in Traditional Chinese Medicine, which can reduce toxicity and side effect, improve the flavor and efficacy, and even change the pharmacological action entirely. It is significant and crucial to perform a method to find chemical markers for differentiating herbal medicines in different processed degrees. PURPOSE: The aim of this study was to perform a rapid and reasonable method to discriminate Moutan Cortex and its processed products, and to reveal the characteristics of chemical components depend on chemical markers. METHODS: Thirty batches of Moutan Cortex and its processed products, including 11 batches of Raw Moutan Cortex (RMC), 9 batches of Moutan Cortex Tostus (MCT) and 10 batches of Moutan Cortex Carbonisatus (MCC), were directly injected in electrospray ionization quadrupole time-of-flight mass spectrometry (ESI-QTOF MS) for rapid analysis in positive and negative mode. Without chromatographic separation, each run was completed within 3 min. The raw MS data were automatically extracted by background deduction and molecular feature (MF) extraction algorithm. In negative mode, a total of 452 MFs were obtained and then pretreated by data filtration and differential analysis. After that, the filtered 85 MFs were treated by principal component analysis (PCA) to reduce the dimensions. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was constructed for differentiation and chemical markers detection of Moutan Cortex in different processed degrees. The positive mode data were treated as same as those in negative mode. RESULTS: RMC, MCT and MCC were successfully classified. Moreover, 14 and 3 chemical markers from negative and positive mode respectively, were screened by the combination of their relative peak areas and the parameter variable importance in the projection (VIP) values in PLS-DA model. The content changes of these chemical markers were employed in order to illustrate chemical changes of Moutan Cortex after processed. CONCLUSION: These results showed that the proposed method which combined non-targeted metabolomics analysis with multivariate statistics analysis is reasonable and effective. It could not only be applied to discriminate herbal medicines and their processing products, but also to reveal the characteristics of chemical components during processing.