Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography.

OBJECTIVES: We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. METHODS: We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. RESULTS: The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. CONCLUSION: The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. CLINICAL RELEVANCE STATEMENT: The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. KEY POINTS: • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI.

Temporal attention networks for biomedical hypothesis generation.

OBJECTIVES: Hypothesis Generation (HG) is a task that aims to uncover hidden associations between disjoint scientific terms, which influences innovations in prevention, treatment, and overall public health. Several recent studies strive to use Recurrent Neural Network (RNN) to learn evolutional embeddings for HG. However, the complex spatiotemporal dependencies of term-pair relations will be difficult to depict due to the inherent recurrent structure. This paper aims to accurately model the temporal evolution of term-pair relations using only attention mechanisms, for capturing crucial information on inferring the future connectivities. METHODS: This paper proposes a Temporal Attention Networks (TAN) to produce powerful spatiotemporal embeddings for Biomedical Hypothesis Generation. Specifically, we formulate HG problem as a future connectivity prediction task in a temporal attributed graph. Our TAN develops a Temporal Spatial Attention Module (TSAM) to establish temporal dependencies of node-pair (term-pair) embeddings between any two time-steps for smoothing spatiotemporal node-pair embeddings. Meanwhile, a Temporal Difference Attention Module (TDAM) is proposed to sharpen temporal differences of spatiotemporal embeddings for highlighting the historical changes of node-pair relations. As such, TAN can adaptively calibrate spatiotemporal embeddings by considering both continuity and difference of node-pair embeddings. RESULTS: Three real-world biomedical term relationship datasets are constructed from PubMed papers. TAN significantly outperforms the best baseline with 12.03%, 4.59 and 2.34% Micro-F1 Score improvement in Immunotherapy, Virology and Neurology, respectively. Extensive experiments demonstrate that TAN can model complex spatiotemporal dependencies of term-pairs for explicitly capturing the temporal evolution of relation, significantly outperforming existing state-of-the-art methods. CONCLUSION: We proposed a novel TAN to learn spatiotemporal embeddings based on pure attention mechanisms for HG. TAN learns the evolution of relationships by modeling both the continuity and difference of temporal term-pair embeddings. The important spatiotemporal dependencies of term-pair relations are extracted based solely on attention mechanism for generating hypotheses.

Hypoxia-induced blood-brain barrier dysfunction is prevented by pericyte-conditioned media via attenuated actomyosin contractility and claudin-5 stabilization.

The blood-brain barrier (BBB) regulates molecular and cellular entry from the cerebrovasculature into the surrounding brain parenchyma. Many diseases of the brain are associated with dysfunction of the BBB, where hypoxia is a common stressor. However, the contribution of hypoxia to BBB dysfunction is challenging to study due to the complexity of the brain microenvironment. In this study, we used a BBB model with brain microvascular endothelial cells and pericytes differentiated from iPSCs to investigate the effect of hypoxia on barrier function. We found that hypoxia-induced barrier dysfunction is dependent upon increased actomyosin contractility and is associated with increased fibronectin fibrillogenesis. We propose a role for actomyosin contractility in mediating hypoxia-induced barrier dysfunction through modulation of junctional claudin-5. Our findings suggest pericytes may protect brain microvascular endothelial cells from hypoxic stresses and that pericyte-derived factors could be candidates for treatment of pathological barrier-forming tissues.

A facile synthesis of FeS/Fe3C nanoparticles highly dispersed on in situ grown N-doped CNTs as cathode electrocatalysts for microbial fuel cells.

A novel composite of iron sulfide, iron carbide and nitrogen carbides (Nano-FeS/Fe3C@NCNTs) as a cathode electrocatalyst for microbial fuel cells (MFCs) is synthesized by a one-pot solid state reaction, which yields a unique configuration of FeS/Fe3C nanoparticles highly dispersed on in situ grown nitrogen-doped carbon nanotubes (NCNTs). The highly dispersed FeS/Fe3C nanoparticles possess large active sites, while the NCNTs provide an electronically conductive network. Consequently, the resultant Nano-FeS/Fe3C@NCNTs exhibit excellent electrocatalytic activity towards the oxygen reduction reaction (ORR), with a half-wave potential close to that of Pt/C (about 0.88 V vs. RHE), and enable MFCs to deliver a power density of 1.28 W m-2 after two weeks' operation, which is higher than that of MFCs with Pt/C as the cathode electrocatalyst (1.02 W m-2). Theoretical calculations and experimental data demonstrate that there is a synergistic effect between Fe3C and FeS in Nano-FeS/Fe3C@NCNTs. Fe3C presents a strong attraction and electron-donating tendency to oxygen molecules, serving as the main active component, while FeS reduces charge transfer resistance by transferring electrons to Fe3C, synergistically improving the kinetics of the ORR and power density of MFCs.

High power density output and durability of microbial fuel cells enabled by dispersed cobalt nanoparticles on nitrogen-doped carbon as the cathode electrocatalyst.

To endow microbial fuel cells (MFCs) with low cost, long-term stability and high-power output, a novel cobalt-based cathode electrocatalyst (Nano-Co@NC) is synthesized from a polygonal metal-organic framework ZIF-67. After calcining the resultant ZIF-67, the as-synthesized Nano-Co@NC is characteristic of cobalt nanoparticles (Nano-Co) embedded in nitrogen-doped carbon (NC) that inherits the morphology of ZIF-67 with a large surface area. The Nano-Co particles that are highly dispersed and firmly fixed on NC not only ensure electrocatalytic activity of Nano-Co@NC toward the oxygen reduction reaction on the cathode, but also inhibit the growth of non-electrogenic bacteria on the anode. Consequently, the MFC using Nano-Co@NC as the cathode electrocatalyst demonstrates excellent performance, delivering a comparable initial power density and exhibiting far better durability than that using Pt/C (20 wt%) as the cathode electrocatalyst. The low cost and the excellent performance of Nano-Co@NC make it promising for MFCs to be used in practice.

Multiparametric MRI Evaluation of VI-RADS for Bladder Tumors Located at the Ureteral Orifice.

Background The Vesical Imaging Reporting and Data System (VI-RADS) based on multiparametric MRI scans standardizes preoperative bladder cancer staging. However, limitations have been reported for VI-RADS, particularly for ureteral orifice tumors. Purpose To investigate the diagnostic performance and interobserver agreement of VI-RADS in evaluating muscle invasion for bladder tumors located at the ureteral orifice. Materials and Methods In this retrospective study, patients with histopathologically confirmed bladder cancer occurring at the ureteral orifice from January 2012 to November 2021 were analyzed. Two blinded radiologists independently scored multiparametric MRI scans according to VI-RADS. Interobserver agreement of the VI-RADS scores was evaluated with weighted κ analysis. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of the VI-RADS scores in the prediction of muscle invasion. Results A total of 78 patients (mean age, 67 years ± 7 [SD]; age range, 46-90 years; 67 men) were included in the final analysis: 25 with non-muscle-invasive bladder cancer and 53 with muscle-invasive bladder cancer (MIBCa). At consensus reading, one (1%) case was scored as VI-RADS 1, 27 cases (35%) were scored as VI-RADS 2, six (8%) were scored as VI-RADS 3, 10 (13%) were scored as VI-RADS 4, and 34 (44%) were scored as VI-RADS 5. On comparison of the VI-RADS score with histopathologic findings, it was confirmed that the presence of muscle invasion was 0% (zero of one) for VI-RADS 1, 15% (four of 27) for VI-RADS 2, 83% (five of six) for VI-RADS 3, 100% (10 of 10) for VI-RADS 4, and 100% (34 of 34) for VI-RADS 5. The area under the receiver operating characteristic curve of VI-RADS in the detection of MIBCa was 0.96 (95% CI: 0.92, 1.00). Conclusion The Vesical Imaging Reporting and Data System could be used to accurately predict muscle invasion for bladder tumors occurring at the ureteral orifice. © RSNA, 2022 Online supplemental material is available for this article.

Amide Proton Transfer-weighted MRI in Predicting Histologic Grade of Bladder Cancer.

Background Bladder cancer is classified into high and low grades with different clinical treatments and prognoses. Thus, accurate preoperative evaluation of the histologic grade through imaging techniques is essential. Purpose To investigate the potential of amide proton transfer-weighted (APTw) MRI in evaluating the grade of bladder cancer and to evaluate whether APTw MRI can add value to diffusion-weighted imaging (DWI) at MRI. Materials and Methods In this single-center prospective study, participants with pathologic analysis-confirmed bladder cancer with no previous treatment, lesions larger than 10 mm, and adequate MRI quality were enrolled from July 2020 to September 2021 in a university teaching hospital. All participants underwent preoperative multiparametric MRI, including APTw MRI and DWI. The mean APTw and apparent diffusion coefficient (ADC) values of the primary tumor were measured independently by two radiologists. Receiver operating characteristic curves were generated to evaluate the diagnostic performance of these quantitative parameters. Results In total, 83 participants (mean age, 64 years ± 13 [SD]; 72 men) were evaluated: 51 with high-grade and 32 with low-grade bladder cancer. High-grade bladder cancer showed higher APTw values (6% [IQR, 4%-12%] vs 2% [IQR, 1%-3%]; P < .001) and lower ADC values (0.92 × 10-3 mm2/sec ± 0.17 vs 1.21 × 10-3 mm2/sec ± 0.25; P < .001) than low-grade bladder cancer. The area under the receiver operating characteristic curve (AUC) of APTw and ADC for differentiating low- and high-grade bladder cancer was similar (0.84 for both; P = .94). Moreover, the combination of the two techniques improved the diagnostic performance (AUC, 0.93; all P = .01). Conclusion The combination of amide proton transfer-weighted and diffusion-weighted MRI has the potential to improve the histologic characterization of bladder cancer by differentiating low- from high-grade cancers. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Milot in this issue. An earlier incorrect version appeared online. This article was corrected on July 7, 2022.

Improving the recall of biomedical named entity recognition with label re-correction and knowledge distillation.

BACKGROUND: Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them. METHODS: To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance recognition model. Our framework is inspired by two points: (1) named entity recognition should be considered from the perspective of both coverage and accuracy; (2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another weakly labeled dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two recognition models, respectively. Finally, we compress the knowledge in the two models into a single recognition model with knowledge distillation. RESULTS: Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially the recall of it, leading to new state-of-the-art results. CONCLUSIONS: We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER.

Dual down-regulation of EGFR and ErbB2 by berberine contributes to suppression of migration and invasion of human ovarian cancer cells.

The overexpression of EGFR and/or ErbB2 occurs frequently in ovarian cancers and is associated with poor prognosis. The purpose of this study was to examine the anticancer effects and molecular mechanisms of berberine on human ovarian cancer cells with different levels of EGFR and/or ErbB2. We found that berberine reduced the motility and invasiveness of ovarian cancer cells. Berberine depleted both EGFR and ErbB2 in ovarian cancer cells. Furthermore, berberine suppressed the activation of the EGFR and ErbB2 downstream targets cyclin D1, MMPs, and VEGF by down-regulating the EGFR-ErbB2/PI3K/Akt signaling pathway. The berberine-mediated inhibition of MMP-2 and MMP-9 activity could be rescued by co-treatment with EGF. Finally, we demonstrated that berberine induced ErbB2 depletion through ubiquitin-mediated proteasome degradation. In conclusion, the suppressive effects of berberine on the ovarian cancer cells that differ in the expression of EGFR and ErbB2 may be mediated by the dual depletion of EGFR and/or ErbB2.

Knowledge-guided convolutional networks for chemical-disease relation extraction.

BACKGROUND: Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-structured knowledge bases (KBs), which contain prior knowledge about chemicals and diseases. Prior knowledge provides strong support for CDR extraction. How to make full use of it is worth studying. RESULTS: This paper proposes a novel model called "Knowledge-guided Convolutional Networks (KCN)" to leverage prior knowledge for CDR extraction. The proposed model first learns knowledge representations including entity embeddings and relation embeddings from KBs. Then, entity embeddings are used to control the propagation of context features towards a chemical-disease pair with gated convolutions. After that, relation embeddings are employed to further capture the weighted context features by a shared attention pooling. Finally, the weighted context features containing additional knowledge information are used for CDR extraction. Experiments on the BioCreative V CDR dataset show that the proposed KCN achieves 71.28% F1-score, which outperforms most of the state-of-the-art systems. CONCLUSIONS: This paper proposes a novel CDR extraction model KCN to make full use of prior knowledge. Experimental results demonstrate that KCN could effectively integrate prior knowledge and contexts for the performance improvement.

Castor patatin-like phospholipase A IIIß facilitates removal of hydroxy fatty acids from phosphatidylcholine in transgenic Arabidopsis seeds.

KEY MESSAGE: Castor patatin-like phospholipase A IIIß facilitates the exclusion of hydroxy fatty acids from phosphatidylcholine in developing transgenic Arabidopsis seeds. Hydroxy fatty acids (HFAs) are industrial useful, but their major natural source castor contains toxic components. Although expressing a castor OLEATE 12-HYDROXYLASE in Arabidopsis thaliana leads to the synthesis of HFAs in seeds, a high proportion of the HFAs are retained in phosphatidylcholine (PC). Thus, the liberation of HFA from PC seems to be critical for obtaining HFA-enriched seed oils. Plant phospholipase A (PLA) catalyzes the hydrolysis of PC to release fatty acyl chains that can be subsequently channeled into triacylglycerol (TAG) synthesis or other metabolic pathways. To further our knowledge regarding the function of PLAs from HFA-producing plant species, two class III patatin-like PLA cDNAs (pPLAIIIß or pPLAIIIδ) from castor or Physaria fendleri were overexpressed in a transgenic line of A. thaliana producing C18-HFA, respectively. Only the overexpression of RcpPLAIIIß resulted in a significant reduction in seed HFA content with concomitant changes in fatty acid composition. Reductions in HFA content occurred in both PC and TAG indicating that HFAs released from PC were not incorporated into TAG. These results suggest that RcpPLAIIIß may catalyze the removal of HFAs from PC in the developing seeds synthesizing these unusual fatty acids.

Knowledge-aware attention network for protein-protein interaction extraction.

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extraction methods need extensive feature engineering and cannot make full use of the prior knowledge in knowledge bases (KBs). KBs contain huge amounts of structured information about entities and relationships, therefore play a pivotal role in PPI extraction. This paper proposes a knowledge-aware attention network (KAN) to fuse prior knowledge about protein-protein pairs and context information for PPI extraction. The proposed model first adopts a diagonal-disabled multi-head attention mechanism to encode context sequence along with knowledge representations learned from KBs. Then a novel multi-dimensional attention mechanism is used to select the features that can best describe the encoded context. Experiment results on the BioCreative VI PPI dataset show that the proposed approach could acquire knowledge-aware dependencies between different words in a sequence and lead to a new state-of-the-art performance.

Chemical-induced disease relation extraction with dependency information and prior knowledge.

Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases (KBs) containing triples about entity pairs and their relations have been built. KBs are important resources for biomedical relation extraction. However, previous research pays little attention to prior knowledge. In addition, the dependency tree contains important syntactic and semantic information, which helps to improve relation extraction. So how to effectively use it is also worth studying. In this paper, we propose a novel convolutional attention network (CAN) for CDR extraction. Firstly, we extract the shortest dependency path (SDP) between chemical and disease pairs in a sentence, which includes a sequence of words, dependency directions, and dependency relation tags. Then the convolution operations are performed on the SDP to produce deep semantic dependency features. After that, an attention mechanism is employed to learn the importance/weight of each semantic dependency vector related to knowledge representations learned from KBs. Finally, in order to combine dependency information and prior knowledge, the concatenation of weighted semantic dependency representations and knowledge representations is fed to the softmax layer for classification. Experiments on the BioCreative V CDR dataset show that our method achieves comparable performance with the state-of-the-art systems, and both dependency information and prior knowledge play important roles in CDR extraction task.

Multimorbidity associated with functional independence among community-dwelling older people: a cross-sectional study in Southern China.

BACKGROUND: Multimorbidity, the coexistence of two or more chronic diseases, is common in older adults. And it may lead to many adverse health outcomes, such as disability. However, data on multimorbidity and its relationship with functional independence are scarce in Asian countries. Therefore, this study aims to investigate the relationship between multimorbidity and functional status among older people in China. METHODS: Based on a cross-sectional survey, the information regarding 2705 older adults, who were of at least 60 years of age, was collected through interviews and analyzed. To assess functional status, we used the Functional Independence Measure (FIM). Exploratory factor analysis was performed to assess correlations among chronic diseases. Several logistic regression models were run in the study. RESULTS: The presence of two or more chronic conditions and the number of multimorbidity group overlaps were independent risk factors for the loss of functional independence in older adults. Hypertension and chronic pain, emerged as the most prevalent multimorbidity pair, was significantly associated with functional independence (OR = 1.64, 95% CI = 1.25-2.16), followed by the co-occurrence of hypertension and heart diseases with a lower prevalence but a higher OR compared with the former pair (OR = 1.72, 95% CI = 1.15-2.58). Of the five multimorbidity groups used for factor analysis, the bones and pain group (OR = 1.47, 95% CI = 1.23-1.77) and the cardiometabolic group (OR = 1.34, 95% CI = 1.13-1.59) were both found to be significantly correlated with lower functional independence. CONCLUSIONS: Multimorbidity was common among older people in Southern China. Studying the relationship between multimorbidity and functional status could be useful to find potential correlations among chronic diseases. Additionally, it may also be meaningful to identify multimorbidity combinations, posing an increased risk of loss of functional independence, and further improve functional status in older adults with comorbidities.

Niemann-Pick type C2 protein regulates liver cancer progression via modulating ERK1/2 pathway: Clinicopathological correlations and therapeutical implications.

Primary hepatocellular carcinoma (HCC) is the fifth most common malignancy worldwide and the third leading cause of cancer-related death. It is important to identify new targets for early diagnosis and treatment of HCC. Niemann-Pick type C2 (NPC2) plays an important role in the regulation of intracellular cholesterol homeostasis via direct binding with free cholesterol. However, little is known about the significance of NPC2 in HCC tumorigenesis. In this study, we showed that NPC2 is abundantly expressed in normal liver, but is downregulated in human HCC tissues. The patients with NPC2 downregulation expressed much higher α-fetoprotein, multiple tumor type, vascular invasion, later pathological stage and shorter survival rate. Knockdown NPC2 in liver cancer cell lines promote cell proliferation, migration and xenograft tumorigenesis. In contrast, NPC2 overexpression inhibits HuH7 promoted tumor growth. Furthermore, administration of hepatotropic adeno-associated virus 8 (AAV8) delivered NPC2 decreased the inflammatory infiltration, the expression of two early HCC markers-glypican 3 and survivin and suppressed the spontaneous HCC development in mice. To identify the NPC2-dependent mechanism, we emphasized on the status of MAPK/ERK signaling. MEK1/2 inhibitor treatment demonstrated that the expression of NPC2 affected the activation of ERK1/2 but not MEK1/2. In addition, cholesterol trafficking inhibitor treatment did not alter the cell proliferation and the activation of MEK/ERK. In conclusion, our study demonstrates that NPC2 may play an important role in negatively regulate cell proliferation and ERK1/2 activation that were independent of cholesterol accumulation. AAV-NPC2 may thus represent a new treatment strategy for liver cancer.

Cladosporium keratitis - a case report and literature review.

BACKGROUND: Fungal keratitis is one of the major causes of infectious keratitis in tropical countries. Symptoms of fungal keratitis consist of blurred vision, redness, tearing, photophobia, pain and foreign body sensation. If not treated effectively, it could lead to blindness. Common causes include Candida spp., Fusarium spp. and Aspergillus spp.. With the limited choices of topical antifungal agents, we were faced with Cladosporium keratitis, a rare cause of fungal keratitis. CASE PRESENTATION: A 62-year-old Asian male construction worker came to us with intense ocular pain, injection of the conjunctiva, blurred vision, and foreign body sensation in his left eye. His visual acuity was 20/40 OD and 20/400 OS. Slit-lamp exam revealed a corneal ulcer with feathery margin and Descemet's membrane folding. The culture yielded Cladosporium species.. The patient did not show improvements after applying topical natamycin (5 %), topical amphotericin B (1mg/ml), topical fluconazole (2mg/ml) and oral ketoconazole (200mg). After shifting the medical regimen to voriconazole via topical and systemic routes (1mg/ml and 200mg respectively), the keratitis was controlled. CONCLUSIONS: Fungal keratitis remains a challenge for ophthalmologists as there is no evidence suggesting any particular drug or combination of drugs is more effective than another. A review of common topical antifungal agents was done. Voriconazole could be a good choice for treating corneal infection by Cladosporium species.

Molecular evidence provides new insights into the evolutionary origin of an ancient traditional Chinese medicine, the domesticated "Baizhi".

Introduction: "Baizhi" is a famous herbal medicine in China, and it includes four landraces named as 'Hangbaizhi', 'Chuanbaizhi', 'Qibaizhi', and 'Yubaizhi'. Long-term artificial selection had caused serious degradation of these germplasms. Determining the wild progenitor of the landraces would be benefit for their breed improvements. Previous studies have suggested Angelica dahurica var. dahurica, A. dahurica var. formosana, or A. porphyrocaulis as potential candidates, but the conclusion remains uncertain, and their phylogenetic relationships are still in controversy. Methods: In this study, the genetic variation and phylogenetic analyses of these species and four landraces were conducted on the basis of both the nrITS and plastome datasets. Results: Genetic variation analysis showed that all 8 population of four landraces shared only one ITS haplotype, meanwhile extremely low variation occurred within 6 population at plastid genome level. Both datasets supported the four landraces might be originated from a single wild germplasm. Phylogenetic analyses with both datasets revealed largely consistent topology using Bayesian inference and Maximum likelihood methods. Samples of the four landraces and all wild A. dahurica var. dahurica formed a highly supported monophyletic clade, and then sister to the monophyly clade comprised by samples of A. porphyrocaulis, while four landraces were clustered into one clade, which further clustered with a mixed branches of A. porphyrocaulis and A. dahurica var. dahurica to form sister branches for plastid genomes. Furthermore, the monophyletic A. dahurica var. formosana was far distant from the A. dahurica var. dahurica-"Baizhi" clade in Angelica phylogeny. Such inferences was also supported by the evolutionary patterns of nrITS haplotype network and K2P genetic distances. The outcomes indicated A. dahurica var. dahurica is most likely the original plant of "Baizhi". Discussion: Considering of phylogenetic inference and evolutionary history, the species-level status of A. dahurica var. formosana should be accepted, and the taxonomic level and phylgenetic position of A. porphyrocaulis should be further confirmed. This study preliminarily determined the wild progenitor of "Baizhi" and clarified the phylogenetic relationships among A. dahurica var. dahurica, A. dahurica var. formosana and A. porphyrocaulis, which will provide scientific guidance for wild resources protections and improvement of "Baizhi".

Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models.

PURPOSE: Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment. MATERIALS AND METHODS: A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS: Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %. CONCLUSIONS: Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.

Amide Proton Transfer-Weighted MRI and Diffusion-Weighted Imaging in Bladder Cancer: A Complementary Tool to the VI-RADS.

RATIONALE AND OBJECTIVES: To investigate the feasibility of amide proton transfer-weighted (APTw) and diffusion-weighted Magnetic Resonance Imaging (MRI) as a means by which to add value to the Vesical Imaging Reporting and Data System (VI-RADS) for discriminating muscle invasive bladder cancer (MIBC) from nonmuscle invasive bladder cancer (NMIBC). MATERIALS AND METHODS: This prospective study enrolled participants with pathologically confirmed bladder cancer (BCa) who underwent preoperative multiparametric MRI, including APTw and diffusion-weighted MRI, from July 2020 to January 2023. The exclusion criteria were lesions smaller than 10 mm, missing smooth muscle layer in the operation specimen, neoadjuvant therapy before MRI, inadequate image quality, and malignancy other than urothelial neoplasm. Two radiologists independently assigned the VI-RADS score for each participant. Quantitative parameters derived from APTw and diffusion-weighted MRI were obtained by another two radiologists. Receiver operating characteristic (ROC) curve analysis with the area under the ROC curve (AUC) was performed to evaluate the diagnostic performances of quantitative parameters for discriminating BCa detrusor muscle invasion status. RESULTS: A total of 106 participants were enrolled (mean age, 64 ± 12 years [SD]; 90 men): 32 with MIBC and 74 with NMIBC. Lower apparent diffusion coefficient (ADC) values (0.88 × 10-3 mm2/s ± 0.12 vs. 1.08 × 10-3 mm2/s ± 0.25; P < 0.001) and higher APTw values (6.89% [interquartile range {IQR}, 5.05%-12.17%] vs. 3.61% [IQR, 2.23%-6.83%]; P < 0.001) were observed in the MIBC group. Compared to VI-RADS alone, both APTw (P = 0.003) and ADC (P = 0.020) values could improve the diagnostic performance of VI-RADS in differentiating MIBC from NMIBC. The combination of the three yielded the highest diagnostic performance (AUC, 0.93; 95% CI:0.87,0.97) for evaluating muscle invasion status. The addition of the APTw values to the combination of VI-RADS and ADC values notably improved the diagnostic performance for differentiating NMIBC from MIBC (VI-RADS+ADC vs. VI-RADS+APTw+ADC, P = 0.046). CONCLUSION: MRI parameters derived from APTw and diffusion-weighted MRI can be used to accurately assess muscle invasion status in BCa and provide additional value to VI-RADS.