Numerical Investigation on the Structural Behavior of a Short-Span Cable-Stayed Bridge with Steel and CFRP Hybrid Cables.

In this paper, a thorough investigation is presented on the static and dynamic behaviors of a short-span cable-stayed bridge (CSB) incorporating steel and carbon fiber reinforced polymer (CFRP) hybrid cables. The study focuses on the world's largest span and China's first highway, CFRP CSB. The performance of the CSB was compared using numerical simulations under four different cable patterns: steel cables, CFRP cables, and steel, and two types of hybrid cables with different structural arrangements. The results indicate that the use of the use of CFRP cables in the long cable region in the short-span CSB project investigated in this study offers improved performance in terms of stability, seismic response, and reduced displacements. In comparison to CFRP cables, hybrid cables have demonstrated a reduction of 12% in the maximum vertical displacement of the main girder. On the other hand, the hybrid cables result in reduced maximum internal forces and longitudinal and lateral displacements of the main girders and towers compared to steel cables. The difference in the arrangement of CFRP cables in the long cable region or short cable region is not obvious under dead loads, but significant differences still exist between the CFRP cable bridges in the short cable region and the long cable region in terms of live load effects, temperature effects, and dynamic characteristics.

Success rate of current human-derived gastric cancer organoids establishment and influencing factors: A systematic review and meta-analysis.

BACKGROUND: Human-derived gastric cancer organoids (GCOs) are widely used in gastric cancer research; however, the culture success rate is generally low. AIM: To explore the potential influencing factors, and the literature on successful culture rates of GCOs was reviewed using meta-analysis. METHODS: PubMed, Web of Science, and EMBASE were searched for studies. Two trained researchers selected the studies and extracted data. STATA 17.0 software was used for meta-analysis of the incidence of each outcome event. The adjusted Methodological Index for Non-Randomized Studies scale was used to assess the quality of the included studies. Funnel plots and Egger's test were used to detect publication bias. Subgroup analyses were conducted for sex, tissue source, histological classification, and the pathological tumor-node-metastasis (pTNM) cancer staging system. RESULTS: Eight studies with a pooled success rate of 66.6% were included. GCOs derived from women and men had success rates of 67% and 46.7%, respectively. GCOs from surgery or biopsy/endoscopic submucosal dissection showed success rates of 70.9% and 53.7%, respectively. GCOs of poorly-differentiated, moderately-differentiated and signet-ring cell cancer showed success rates of 64.6%, 31%, and 32.7%, respectively. GCOs with pTNM stages I-II and III-IV showed success rates of 38.3% and 65.2%, respectively. Y-27632 and non-Y-27632 use showed success rates of 58.2% and 70%, respectively. GCOs generated with collagenase were more successful than those constructed with Liberase TH and TrypLE (72.1% vs 71%, respectively). EDTA digestion showed a 50% lower success rate than other methods (P = 0.04). CONCLUSION: GCO establishment rate is low and varies by sex, tissue source, histological type, and pTNM stage. Omitting Y-27632, and using Liberase TH, TrypLE, or collagenase yields greater success than EDTA.

Bibliometrics analysis based on the Web of Science: Current trends and perspective of gastric organoid during 2010-2023.

BACKGROUND: Three-dimensional organoid culture systems have been established as a robust tool for elucidating mechanisms and performing drug efficacy testing. The use of gastric organoid models holds significant promise for advancing personalized medicine research. However, a comprehensive bibliometric review of this bur-geoning field has not yet been published. AIM: To analyze and understand the development, impact, and direction of gastric organoid research using bibliometric methods using data from the Web of Science Core Collection (WoSCC) database. METHODS: This analysis encompassed literature pertaining to gastric organoids published between 2010 and 2023, as indexed in the WoSCC. CiteSpace and VOSviewer were used to depict network maps illustrating collaborations among authors, institutions and keywords related to gastric organoid. Citation, co-citation, and burst analysis methodologies were applied to assess the impact and progress of research. RESULTS: A total of 656 relevant studies were evaluated. The majority of research was published in gastroenterology-focused journals. Globally, Yana Zavros, Hans Clevers, James M Wells, Sina Bartfeld, and Chen Zheng were the 5 most productive authors, while Hans Clevers, Huch Meritxell, Johan H van Es, Marc Van de Wetering, and Sato Toshiro were the foremost influential scientists in this area. Institutions from the University Medical Center Utrecht, Netherlands Institute for Developmental Biology (Utrecht), and University of Cincinnati (Cincinnati, OH, United States) made the most significant contributions. Currently, gastric organoids are used mainly in studies investigating gastric cancer (GC), Helicobacter pylori-infective gastritis, with a focus on the mechanisms of GC, and drug screening tests. CONCLUSION: Key focus areas of research using gastric organoids include unraveling disease mechanisms and enhancing drug screening techniques. Major contributions from renowned academic institutions highlight this field's dynamic growth.

A Strategy for Studying Environmental Engineering: Simple Hydrothermal Synthesis of Flower-Shaped Stannous Sulfide Nanomaterials for Efficient Cataluminescence Sensing of Diethyl Ether.

In this work, flower-like stannous sulfide (SnS) nanomaterials are synthesized using a hydrothermal method and used as sensitive materials for cataluminescence (CTL)-based detection of diethyl ether. Gas sensors based on SnS nanomaterials are prepared, and the SnS nanomaterials exhibit excellent gas-sensitive behavior towards ether. High sensitivity to ether is achieved at a relatively low operating temperature (153 °C) compared to other common sensors. The response time is 3 s and the recovery time is 8 s. The CTL intensity shows a good linear relationship (R2 = 0.9931) with a detection limit of 0.15 ppm and the concentration of ether in the range of 1.5-60 ppm. The proposed CTL sensor shows good selectivity towards ether. In addition, a highly stable signal is obtained with a relative standard deviation of 1.5%. This study indicates that the SnS-based sensor has excellent gas-sensitive performance and shows potential for applications in the detection of ether.

Clinical experience in laparoscopic treatment of gallbladder perforation.

Aim: We herein present our clinical experience in laparoscopic surgery for gallbladder perforation (GBP). Materials and Methods: Retrospective analysis was performed on the clinical data of 44 patients who diagnosed with GBP from January 2015 to November 2020. Results: The mean age of the 44 patients was 64.0 years and the female-to-male ratio was 20:24. The most common type of GBP was Type II, followed by Type I and Type III (31:9:4). 72.7% of the patients were diagnosed with GBP at the time of surgery. Laparoscopic surgery was performed for 38 (86.4%) patients, with a conversion rate of 13.2%. The mean length of hospital stays was 7.8 days. The mortality and morbidity rates were 2.3% and 11.4%, respectively. Conclusions: Pre-operative diagnosis of GBP is difficult. Laparoscopic surgery is safe, feasible and effective for patients with GBP.

Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas.

OBJECTIVES: To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adult diffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI. METHODS: We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models. We also constructed a combined model by integrating radiomic features and clinical metrics. The models' diagnostic performance for discriminating the molecular subtypes (IDH wild type [IDHwt], IDH mutant and 1p/19q-noncodeleted [IDHmut-noncodel], and IDH mutant and 1p/19q-codeleted [IDHmut-codel]) was compared using AUCs in the validation set. RESULTS: We included 272 patients (training set, n = 166; validation set, n = 106) with grade II-IV gliomas (mean age, 48.7 years; range, 19-77 years). The proportions of the molecular subtypes were 66.2% IDHwt, 15.1% IDHmut-noncodel, and 18.8% IDHmut-codel. Nineteen radiomic features (13 from conventional MRI and 6 from DSC-PWI) were selected to build the multimodal radiomic model. In the validation set, the multimodal radiomic model showed better performance than the conventional radiomic model did in predicting the IDHwt and IDHmut-codel subtypes, which was comparable to the conventional radiomic model in predicting the IDHmut-noncodel subtype. The multimodal radiomic model yielded similar performance as the combined model in predicting the three molecular subtypes. CONCLUSIONS: Adding DSC-PWI to conventional MRI can improve molecular subtype prediction in patients with diffuse gliomas. KEY POINTS: • The multimodal radiomic model outperformed conventional MRI when predicting both the IDH wild type and IDH mutant and 1p/19q-codeleted subtypes of gliomas. • The multimodal radiomic model showed comparable performance to the combined model in the prediction of the three molecular subtypes. • Radiomic features from T1-weighted gadolinium contrast-enhanced and relative cerebral blood volume images played an important role in the prediction of molecular subtypes.

Fabrication of betamethasone micro- and nanoparticles using supercritical antisolvent technology: In vitro drug release study and Caco-2 cell cytotoxicity evaluation.

Poor solubility limits the pharmacological activities of betamethasone (BM), including its anti-inflammatory and anti-allergic effects. To improve the aqueous solubility and dissolution rate of BM, supercritical antisolvent (SAS) technology was used to prepare BM microparticles and BM-polyvinylpyrrolidone (PVP) solid dispersion nanoparticles. The effects of temperature, pressure, solution feeding rate, and drug concentration on particle formation were investigated using both single-factor and orthogonal experimental methods, and the optimal preparation process was screened. The physicochemical properties of the BM particles were characterized by scanning electron microscopy, differential scanning calorimetry, Fourier transform infrared spectroscopy, and X-ray diffraction. After the SAS process, the particle size was reduced significantly and the crystalline shape was altered, which considerably increased the solubility and dissolution rate of BM. Furthermore, the toxicity of BM to live cells was reduced because of the BM-PVP solid dispersions.

miR-21-5p in extracellular vesicles obtained from adipose tissue-derived stromal cells facilitates tubular epithelial cell repair in acute kidney injury.

BACKGROUND AIMS: Acute kidney injury (AKI) is often associated with poor patient outcomes. Extracellular vesicles (EVs) have a marked therapeutic effect on renal recovery. This study sought to explore the functional mechanism of EVs from adipose tissue-derived stromal cells (ADSCs) in tubular epithelial cell (TEC) repair in AKI. METHODS: ADSCs were cultured and EVs were isolated and identified. In vivo and in vitro AKI models were established using lipopolysaccharide (LPS). RESULTS: EVs increased human kidney 2 (HK-2) cell viability; decreased terminal deoxynucleotidyl transferase dUTP nick end labeling-positive cells and levels of kidney injury molecule 1, cleaved caspase-1, apoptosis-associated speck-like protein containing a CARD, gasdermin D-N, IL-18 and IL-1ß; and elevated pro-caspase-1. EVs carried miR-21-5p into LPS-induced HK-2 cells. Silencing miR-21-5p partly eliminated the ability of EVs to suppress HK-2 cell pyroptosis and inflammation. miR-21-5p targeted toll-like receptor 4 (TLR4) and inhibited TEC pyroptosis and inflammation after AKI by inhibiting TLR4. TLR4 overexpression blocked the inhibitory effects of EVs on TEC pyroptosis and inflammation. EVs suppressed the nuclear factor-κB/NOD-like receptor family pyrin domain-containing 3 (NF-κB/NLRP3) pathway via miR-21-5p/TLR4. Finally, AKI mouse models were established and in vivo assays verified that ADSC-EVs reduced TEC pyroptosis and inflammatory response and potentiated cell repair by mediating miR-21-5p in AKI mice. CONCLUSIONS: ADSC-EVs inhibited inflammation and TEC pyroptosis and promoted TEC repair in AKI by mediating miR-21-5p to target TLR4 and inhibiting the NF-κB/NLRP3 pathway.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. METHODS: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. RESULTS: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). CONCLUSIONS: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. KEY POINTS: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

A global meta-analysis of heavy metal(loid)s pollution in soils near copper mines: Evaluation of pollution level and probabilistic health risks.

With the rapid development of the mining industry, the pollution of heavy metal(loid)s in soils near copper (Cu) mining sites is a significant concern worldwide. However, the pollution status and probabilistic health risks of heavy metal(loid)s of soils associated with Cu mines, have rarely been studied on a global scale. In this study, eight heavy metal(loid) concentrations in soil samples taken near 102 Cu mining sites worldwide were obtained through a literature review. Based on this database, the heavy metal(loid) pollution and ecological risk in soils near Cu mines were evaluated. Most of the study sites exceeded the moderately to heavily polluted levels of Cu and Cd; compared to other regions, higher pollution levels were observed at sites in Oman, China, Australia, and the United Kingdom. Soil pollution by Cd, Pb, and Zn at agricultural sites was higher than that in non-agricultural sites. In addition, these heavy metal(loid)s produced a high ecological risk to soils around Cu mining sites in which the contribution of Cd, Cu, and As reached up to 46.5%, 21.7%, and 18.4%, respectively. The mean hazard indices of the eight heavy metal(loid)s were 0.209 and 0.979 for adults and children, respectively. The Monte Carlo simulation further predicted that 1.40% and 29.9% of non-carcinogenic risk values for adults and children, respectively, exceeded the safe level of 1.0. Moreover, 84.5% and 91.0% of the total cancer risk values for adults and children, respectively, exceeded the threshold of 1E-04. Arsenic was the main contributor to non-carcinogenic risk, while Cu had the highest exceedance of carcinogenic risk. Our findings indicate that the control of Cu, Cd, and As should be prioritized because of their high incidence and significant risks in soils near Cu mines. These results provide valuable inputs for policymakers in designing effective strategies for reducing the exposure of heavy metal(loid)s in this area worldwide.

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study.

Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). The receiver operating characteristic (ROC) analysis and precision recall curves (PRC) were used to measure the classification performance. The area under ROC curves (AUC) of the DLIS was 0.999 for training dataset, 0.986 for validation dataset, and 0.983 for testing dataset. The F1-score of the prediction model was 0.992 for training dataset, 0.940 for validation dataset, and 0.925 for testing dataset. Our data suggests that DLIS could be used to predict the 1p/19q status from preoperative imaging in patients with LGG. The imaging-based deep learning has the potential to be a noninvasive tool predictive of molecular markers in adult diffuse gliomas.

PIF4-PAP1 interaction affects MYB-bHLH-WD40 complex formation and anthocyanin accumulation in Arabidopsis.

Anthocyanin accumulation is a marked phenotype of plants under environmental stresses. PHYTOCHROME-INTERACTING FACTORs (PIFs) are involved in environment-induced anthocyanin biosynthesis through interacting with the MYB-bHLH-WD40 (MBW) complex. However, the molecular mechanism of this interaction remains unclear. The present study demonstrated that PIF3 and PIF5 can slightly repress anthocyanin accumulation under NaCl, low nitrogen (-N), or 6-BA treatments; in contrast, PIF4 can significantly repress anthocyanin accumulation. Bimolecular fluorescence complementation and yeast two-hybrid assays showed that PIF4 directly interacts with PRODUCTION OF ANTHOCYANIN PIGMENT 1 (PAP1), a MYB transcription factor in the MBW complex. Further analysis revealed that the active phytochrome binding (APB) domain in the N terminus of PIF4 is necessary for the interaction between PIF4 and PAP1. Yeast three-hybrid analysis showed that PIF4 competes with TRANSPARENT TESTA 8 (TT8) to bind PAP1, thereby interfering with the regulation of the MBW protein complex in anthocyanin synthesis. Consistently, the anthocyanin content in pap1-D/35S::PIF4 and 35S::PAP1/35S::PIF4 seedlings was markedly lower than that in pap1-D and 35S::PAP1 under 6-BA, MeJA, -N, and NaCl stresses, implying that overexpression of PIF4 suppresses anthocyanin accumulation in pap1-D and 35S::PAP1. Thus, PIF4 is genetically epistatic to PAP1. Taken together, PIF4 plays a negative role in modulating anthocyanin biosynthesis in Arabidopsis under different stress environments, and PIF4 interacts with PAP1 to affect the integrity of the MBW complex.

Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities.

BACKGROUND: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. METHODS: The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). FINDINGS: The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). INTERPRETATION: DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

ALOX5AP Predicts Poor Prognosis by Enhancing M2 Macrophages Polarization and Immunosuppression in Serous Ovarian Cancer Microenvironment.

BACKGROUND: Serous ovarian cancer (SOC) is a highly lethal gynecological malignancy with poor prognosis. Given the importance of the immune-related tumor microenvironment (TME) in ovarian cancer, investigating tumor-immune interactions and identifying novel prognostic and therapeutic targets in SOC is a promising avenue of research. ALOX5AP (Arachidonate 5-Lipoxygenase Activating Protein) is a key enzyme in converting arachidonic acid to leukotriene: a crucial immune-modulating lipid mediator. However, the role of ALOX5AP in SOC has yet to be studied. METHODS: ALOX5AP expression patterns across ovarian cancer and their normal tissue counterparts were cross-checked using public microarray and RNA-seq analyses and then validated in clinical samples by qRT-PCR. Kaplan-Meier survival analysis was performed in multiple independent SOC patient cohorts. Univariate and multivariate Cox regression analysis were then employed to identify clinical risk parameters associated with survival, and a genomic-clinicopathologic nomogram was built. Gene enrichment, immune infiltration, and immunosuppressor correlation analyses were then evaluated. RESULTS: ALOX5AP mRNA levels in SOC tissues were significantly upregulated compared to normal tissues. Elevated ALOX5AP was markedly associated with poor overall survival and progression-free survival in multiple SOC patient cohorts as well as with adverse clinicopathological features, including lymphatic invasion, unsatisfactory cytoreductive surgery, rapid relapse after primary treatment, and platinum non-responsiveness. A predictive nomogram, which integrated ALOX5AP expression and two independent prognosis factors (primary therapy outcome and tumor residual), was conducted to predict the 3-year and 5-year survival rate of SOC patients. Mechanistically, functional and pathway enrichment analyses revealed that ALOX5AP was primarily involved in immune response and regulation. Further exploration demonstrated that ALOX5AP was highly expressed in the immunoreactive subtype of ovarian cancer and closely related to immunocyte infiltration, especially M2 macrophage polarization. Additionally, ALOX5AP was enriched in the C4 (lymphocyte depleted) immune subtype of SOC and associated with crucial immune-repressive receptors in the tumor microenvironment at the genomic level. CONCLUSIONS: ALOX5AP expression indicates a worse survival outcome and has the potential to be utilized as a prognostic predictor for SOC patients. Given the availability of well-studied ALOX5AP inhibitors, this study has immediate clinical implications for the exploitation of ALOX5AP as an immunotherapeutic target in SOC.

Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma.

BACKGROUND: Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors. METHODS: A total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built. RESULTS: The 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively. CONCLUSION: The combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.

Functional identification of PsMYB57 involved in anthocyanin regulation of tree peony.

BACKGROUND: R2R3 myeloblastosis (MYB) genes are widely distributed in plants and comprise one of the largest transcription factor gene families. They play important roles in the regulatory networks controlling development, metabolism, and stress responses. Researches on functional genes in tree peony are still in its infancy. To date, few MYB genes have thus far been reported. RESULTS: In this study, we constructed a comprehensive reference gene set by transcriptome sequencing to obtain R2R3 MYB genes. The transcriptomes of eight different tissues were sequenced, and 92,837 unigenes were obtained with an N50 of 1662 nt. A total of 48,435 unigenes (77.98%) were functionally annotated in public databases. Based on the assembly, we identified 57 R2R3 MYB genes containing full-length open reading frames, which clustered into 35 clades by phylogenetic analysis. PsMYB57 clustered with anthocyanin regulation genes in Arabidopsis and was mainly transcribed in the buds and young leaves. The overexpression of PsMYB57 induced anthocyanin accumulation in tobacco, and four detected anthocyanin structural genes, including NtCHS, NtF3'H, NtDFR, and NtANS, were upregulated. The two endogenous bHLH genes NtAn1a and NtAn1b were also upregulated and may work in combination with PsMYB57 in regulating anthocyanin structural genes. CONCLUSIONS: Our study offers a useful reference to the selection of candidate MYB genes for further functional studies in tree peony. Function analysis of PsMYB57 is helpful to understand the color accumulation in vegetative organs of tree peony. PsMYB57 is also a promising resource to improve plant color in molecular breeding.

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma.

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.

Incremental prognostic value and underlying biological pathways of radiomics patterns in medulloblastoma.

BACKGROUND: To develop a radiomics signature for predicting overall survival (OS)/progression-free survival (PFS) in patients with medulloblastoma (MB), and to investigate the incremental prognostic value and biological pathways of the radiomics patterns. METHODS: A radiomics signature was constructed based on magnetic resonance imaging (MRI) from a training cohort (n = 83), and evaluated on a testing cohort (n = 83). Key pathways associated with the signature were identified by RNA-seq (GSE151519). Prognostic value of pathway genes was assessed in a public GSE85218 cohort. FINDINGS: The radiomics-clinicomolecular signature predicted OS (C-index 0.762) and PFS (C-index 0.697) better than either the radiomics signature (C-index: OS: 0.649; PFS: 0.593) or the clinicomolecular signature (C-index: OS: 0.725; PFS: 0.691) alone, with a better calibration and classification accuracy (net reclassification improvement: OS: 0.298, P = 0.022; PFS: 0.252, P = 0.026). Nine pathways were significantly correlated with the radiomics signature. Average expression value of pathway genes achieved significant risk stratification in GSE85218 cohort (log-rank P = 0.016). INTERPRETATION: This study demonstrated radiomics signature, which associated with dysregulated pathways, was an independent parameter conferring incremental value over clinicomolecular factors in survival predictions for MB patients. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Cadmium and lead mixtures are less toxic to the Chinese medicinal plant Ligusticum chuanxiong Hort. Than either metal alone.

Agricultural production of Ligusticum chuanxiong Hort. is often affected by heavy metal pollution in soil, especially mixtures of cadmium (Cd) and lead (Pb). We assessed metal-induced phytotoxicity in L. chuanxiong by exposing the plants to soil treated with Cd, Pb, or Cd/Pb mixtures. A combined Cd/Pb treatment alleviated the inhibition in plant growth, photosynthesis, and secondary metabolite generation seen in single-metal exposures in three of the four combinations. Most combined Cd/Pb treatments resulted in preferential uptake of magnesium, copper, and nitrogen in underground plant parts and accumulation of phosphorus and calcium in aboveground plant parts, thereby leading to improvements in photosynthetic potential. Compared with single-metal exposures, combined Cd/Pb treatment significantly decreased the contents of Cd by 16.67%-40.12% and Pb by 10.68%-21.70% in the plant, respectively. At the subcellular level, the Pb presence increased the Cd percentage associated with cell wall from 64.79% to 67.93% in rhizomes and from 32.76% to 45.32% in leaves, while Cd reduced Pb contents by 9.36%-46.39% in the subcellular fractions. A combined Cd/Pb treatment decreased the contents of water- and ethanol-extractable metal forms and increased the contents of acetic acid- and hydrochloric acid-extractable forms. The lower toxic effects of the Cd/Pb mixture in L. chuanxiong were associated with photosynthetic potential, subcellular distribution, the chemical forms of Cd and Pb, and synthesis of secondary metabolites. These findings are useful for plant production strategies in soils contaminated by heavy metals.

Overexpression of PRC1 indicates a poor prognosis in ovarian cancer.

Protein regulator of cytokinesis­1 (PRC1) is a microtubule­associated factor involved in cytokinesis. Recent studies have indicated that PRC1 overexpression is involved in tumorigenesis in multiple types of human cancer. However, the expression, biological functions and the prognostic significance of PRC1 in ovarian cancer have not yet been clarified. In this study, it was confirmed that the PRC1 mRNA and protein expression levels were upregulated in high­grade serous ovarian carcinoma (HGSOC) tissues, particularly in patients without breast cancer susceptibility gene (BRCA) pathogenic mutations. PRC1 overexpression contributed to drug resistance, tumor recurrence and a poor prognosis. The findings also indicated that PRC1 knockdown decreased the proliferation, metastasis and multidrug resistance of ovarian cancer cells in vitro. It was also demonstrated that forkhead box protein M1 (FOXM1) regulated the mRNA and protein expression of PRC1. Dual­luciferase reporter assay and rescue assay confirmed that PRC1 was a direct crucial downstream target of FOXM1. On the whole, the findings of this study confirmed that PRC1 was a major prognostic factor of HGSOC and a promising therapeutic biomarker for the treatment of ovarian cancer.