Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor.

BACKGROUND: This study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences. METHODS: This study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists. RESULTS: The single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists. CONCLUSIONS: The combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.

Delta Radiomics Based on MRI for Predicting Axillary Lymph Node Pathologic Complete Response After Neoadjuvant Chemotherapy in Breast Cancer Patients.

PURPOSE: To develop and test a radiomics nomogram based on magnetic resonance imaging (MRI) and clinicopathological factors for predicting the axillary pathologic complete response (apCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients with axillary lymph node (ALN) metastases. MATERIALS AND METHODS: A total of 319 patients who underwent MRI examination and received NAC treatment were enrolled from two centers, and the presence of ALN metastasis was confirmed by biopsy pathology before NAC. The radiomics features were extracted from regions of interest of ALNs before (pre-radiomics) and after (post-radiomics) NAC. The difference of features before and after NAC, named delta radiomics, was calculated. The variance threshold, selectKbest and least absolute shrinkage and selection operator algorithm were used to select radiomics features. Radscore was calculated by a linear combination of selected features, weighted by their respective coefficients. The univariate and multivariate logistic regression was used to select the clinicopathological factors and radscores, and a radiomics nomogram was built by multivariable logistic regression analysis. The performance of the nomogram was evaluated by the area under the receiver operator characteristic curve (AUC), decision curve analysis (DCA) and calibration curves. Furthermore, to explore the biological basis of radiomics nomogram, 16 patients with RNA-sequence data were included for genetic analysis. RESULTS: The radiomics nomogram was constructed by two radscores (post- and delta- radscores) and one clinicopathological factor (progesterone hormone, PR), and showed powerful predictive performance in both internal and external test sets, with AUCs of 0.894 (95% confidence interval [CI], 0.877-0.959) and 0.903 (95% CI, 0.801-0.986), respectively. The calibration curves and DCA showed favorable consistency and clinical utility. With the assistance of nomogram, the rate of unnecessary ALND would be reduced from 60.42% to 21.88%, and the rate of final benefit rate would be increased from 39.58% to 70.83%. Moreover, genetic analysis revealed that high apCR prediction scores were associated with the upregulation of immune-mediated genes and pathways. CONCLUSION: The radiomics nomogram showed great performance in predicting apCR after NAC for breast cancer patients, which could help clinicians to identify patients with apCR and avoid unnecessary axillary lymph node dissection.

MHIPM: Accurate Prediction of Microbe-Host Interactions Using Multiview Features from a Heterogeneous Microbial Network.

Current studies have demonstrated that microbe-host interactions (MHIs) play important roles in human public health. Therefore, identifying the interactions between microbes and hosts is beneficial to understanding the role of the microbiome and their underlying mechanisms. However, traditional wet-lab experimental approaches are insufficient for large-scale exploration of candidate microbes, as they are costly, laborious, and time-consuming. Thus, it is critical to prioritize microbe-interacting hosts by computational approaches for further biological experimental validation. In this work, we proposed a novel deep learning-based method called MHIPM, to predict MHIs by utilizing multisource biological information. Specifically, we first constructed a heterogeneous microbial network that consisted of human proteins, viruses, bacteriophages (phages), and pathogenic bacteria. Next, we used one of the largest protein language models, ESM-2, and a document embedding model, doc2vec, combined with a self-attention mechanism to extract the interview features from protein sequences. Then, an inductive learning-based model, GraphSAGE, was used to capture the intraview features from the heterogeneous network. Experimental results on three prediction tasks indicated that the MHIPM model consistently achieved better performance than seven baseline algorithms and its four variants. In addition, case studies and molecular docking experiments for two human proteins further confirmed the effectiveness of our model. In conclusion, MHIPM is an efficient and robust method in predicting MHIs and provides plausible candidate microbes for biological experiments. MHIPM is available at https://github.com/JIENWU/MHIPM.

Regional structural-functional connectivity coupling in major depressive disorder is associated with neurotransmitter and genetic profiles.

BACKGROUND: Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms underlying regional SC-FC coupling patterns are not well understood. METHODS: We enrolled 182 patients with MDD and 157 healthy control (HC) subjects, quantifying the intergroup differences in regional SC-FC coupling. The extreme gradient boosting (XGBoost), support vector machines (SVM) and random forest (RF) models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression. RESULTS: We observed increased regional SC-FC coupling in default mode network (T = 3.233) and decreased coupling in frontoparietal network (T = -3.471) in MDD relative to HC. XGBoost (AUC = 0.853), SVM (AUC = 0.832) and RF (p < 0.05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of four neurotransmitters (p < 0.05) and expression maps of specific genes. These genes were strongly enriched in genes implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on two brain atlases. CONCLUSIONS: This work enhances our understanding of MDD and pave the way for the development of additional targeted therapeutic interventions.

Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: multicentre study.

BACKGROUND: The accurate evaluation of axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer holds great value. This study aimed to develop an artificial intelligence system utilising multiregional dynamic contrast-enhanced MRI (DCE-MRI) and clinicopathological characteristics to predict axillary pathological complete response (pCR) after NAC in breast cancer. METHODS: This study included retrospective and prospective datasets from six medical centres in China between May 2018 and December 2023. A fully automated integrated system based on deep learning (FAIS-DL) was built to perform tumour and ALN segmentation and axillary pCR prediction sequentially. The predictive performance of FAIS-DL was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RNA sequencing analysis were conducted on 45 patients to explore the biological basis of FAIS-DL. FINDINGS: 1145 patients (mean age, 50 years ±10 [SD]) were evaluated. Among these patients, 506 were in the training and validation sets (axillary pCR rate of 40.3%), 127 in the internal test set (axillary pCR rate of 37.8%), 414 in the pooled external test set (axillary pCR rate of 48.8%), and 98 in the prospective test set (axillary pCR rate of 43.9%). For predicting axillary pCR, FAIS-DL achieved AUCs of 0.95, 0.93, and 0.94 in the internal test set, pooled external test set, and prospective test set, respectively, which were also significantly higher than those of the clinical model and deep learning models based on single-regional DCE-MRI (all P < 0.05, DeLong test). In the pooled external and prospective test sets, the FAIS-DL decreased the unnecessary axillary lymph node dissection rate from 47.9% to 6.8%, and increased the benefit rate from 52.2% to 86.5%. RNA sequencing analysis revealed that high FAIS-DL scores were associated with the upregulation of immune-mediated genes and pathways. INTERPRETATION: FAIS-DL has demonstrated satisfactory performance in predicting axillary pCR, which may guide the formulation of personalised treatment regimens for patients with breast cancer in clinical practice. FUNDING: This study was supported by the National Natural Science Foundation of China (82371933), National Natural Science Foundation of Shandong Province of China (ZR2021MH120), Mount Taishan Scholars and Young Experts Program (tsqn202211378), Key Projects of China Medicine Education Association (2022KTM030), China Postdoctoral Science Foundation (314730), and Beijing Postdoctoral Research Foundation (2023-zz-012).

Mindsets of suicide trajectories: An Linguistic Inquiry and Word Count analysis of suicide hotline conversations.

OBJECTIVE: The aim of the study was to explore the psychological characteristics of the individuals with various suicide risks using computerized text analysis, in the hopes of a better understanding of suicide trajectories. METHODS: 627 first-time callers' records were randomly selected from Taiwan An-Shin Hotline database between 2013 and 2018. The voice records were evaluated by two psychologists to determine the levels of suicide risk (156 with uncertainty of risk, 177 with low suicidal ideation, 157 with high suicidal ideation, and 137 with suicide preparation/attempt) and transcribed into text. The Linguistic Inquiry and Word Count 2015 (LIWC2015) program combined with Chinese dictionary were then used to calculate the frequency of word categories. RESULTS: Exploratory factor analysis identified four mindsets of language characteristics, named "opposition and questioning", "active engagement", "negative rumination", and "focus on death". Psychological descriptions of the mindsets were also obtained through correlation analysis with the LIWC2015 categories and indicators. The four mindsets effectively distinguished the callers with different levels of suicide risk. CONCLUSION: The psychological characteristics of people with various suicide risks can be described and differentiated via the closed-word categories and composite indicators. These results provide useful information for practitioners and researchers.

Biodegradation of humic acids by Streptomyces rochei to promote the growth and yield of corn.

Humic acids (HAs) are organic macromolecules that play an important role in improving soil properties, plant growth and agronomic parameters. However, the feature of relatively complex aromatic structure makes it difficult to be degraded, which restricts the promotion to the crop growth. Thus, exploring microorganisms capable of degrading HAs may be a potential solution. Here, a HAs-degrading strain, Streptomyces rochei L1, and its potential for biodegradation was studied by genomics, transcriptomics, and targeted metabolomics analytical approaches. The results showed that the high molecular weight HAs were cleaved to low molecular aliphatic and aromatic compounds and their derivatives. This cleavage may be associated with the laccase (KatE). In addition, the polysaccharide deacetylase (PdgA) catalyzes the removal of acetyl groups from specific sites on the HAs molecule, resulting in structural changes. The field experiment showed that the degraded HAs significantly promote the growth of corn seedlings and increase the corn yield by 3.6 %. The HAs-degrading products, including aromatic and low molecular weight aliphatic substances as well as secondary metabolites from S. rochei L1, might be the key components responsible for the corn promotion. Our findings will advance the application of HAs as soil nutrients for the green and sustainable agriculture.

Semi-invasive wearable clinic: Solution-processed smart microneedle electronics for next-generation integrated diagnosis and treatment.

The integrated smart electronics for real-time monitoring and personalized therapy of disease-related analytes have been gradually gaining tremendous attention. However, human tissue barriers, including the skin barrier and brain-blood barrier, pose significant challenges for effective biomarker detection and drug delivery. Microneedle (MN) electronics present a promising solution to overcome these tissue barriers due to their semi-invasive structures, enabling effective drug delivery and target-analyte detection without compromising the tissue configuration. Furthermore, MNs can be fabricated through solution processing, facilitating large-scale manufacturing. This review provides a comprehensive summary of the recent three-year advancements in smart MNs development, categorized as follows. First, the solution-processed technology for MNs is introduced, with a focus on various printing technologies. Subsequently, smart MNs designed for sensing, drug delivery, and integrated systems combining diagnosis and treatment are separately summarized. Finally, the prospective and promising applications of next-generation MNs within mediated diagnosis and treatment systems are discussed.

Artificial intelligence-based classification of breast lesion from contrast enhanced mammography: a multicenter study.

PURPOSE: The authors aimed to establish an artificial intelligence (AI)-based method for preoperative diagnosis of breast lesions from contrast enhanced mammography (CEM) and to explore its biological mechanism. MATERIALS AND METHODS: This retrospective study includes 1430 eligible patients who underwent CEM examination from June 2017 to July 2022 and were divided into a construction set (n=1101), an internal test set (n=196), and a pooled external test set (n=133). The AI model adopted RefineNet as a backbone network, and an attention sub-network, named convolutional block attention module (CBAM), was built upon the backbone for adaptive feature refinement. An XGBoost classifier was used to integrate the refined deep learning features with clinical characteristics to differentiate benign and malignant breast lesions. The authors further retrained the AI model to distinguish in situ and invasive carcinoma among breast cancer candidates. RNA-sequencing data from 12 patients were used to explore the underlying biological basis of the AI prediction. RESULTS: The AI model achieved an area under the curve of 0.932 in diagnosing benign and malignant breast lesions in the pooled external test set, better than the best-performing deep learning model, radiomics model, and radiologists. Moreover, the AI model has also achieved satisfactory results (an area under the curve from 0.788 to 0.824) for the diagnosis of in situ and invasive carcinoma in the test sets. Further, the biological basis exploration revealed that the high-risk group was associated with the pathways such as extracellular matrix organization. CONCLUSIONS: The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions.

Coupled Electronic and Magnonic Topological States in Two-Dimensional Ferromagnets.

Magnetic materials offer a fertile playground for fundamental physics discovery, with not only electronic but also magnonic topological states intensively explored. However, one natural material with both electronic and magnonic nontrivial topologies is still unknown. Here, we demonstrate the coexistence of first-order topological magnon insulators (TMIs) and electronic second-order topological insulators (SOTIs) in 2D honeycomb ferromagnets, giving rise to the nontrivial corner states being connected by the charge-free magnonic edge states. We show that, with C3 symmetry, the phase factor ± ϕ caused by the next nearest-neighbor Dzyaloshinskii-Moriya interaction breaks the pseudo-spin time-reversal symmetry T, which leads to the split of magnon bands, i.e., the emergence of TMIs with a nonzero Chern number of C=-1, in experimentally feasible candidates of MoI3, CrSiTe3, and CrGeTe3 monolayers. Moreover, protected by the C3 symmetry, the electronic SOTIs characterized by nontrivial corner states are obtained, bridging the topological aspect of fermions and bosons with a high possibility of innovative applications in spintronics devices.

Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis.

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

Five New Species of the Genus Hymenogaster (Hymenogastraceae, Agaricales) from Northern China.

In this study, five new species from China, Hymenogaster latisporus, H. minisporus, H. papilliformis, H. perisporius, and H. variabilis, are described and illustrated based on morphological and molecular evidence. Hymenogaster latisporus was distinguished from other species of the genus by the subglobose, broad ellipsoidal, ovoid basidiospores (average = 13.7 µm × 11.6 µm) with sparse verrucose and ridge-like ornamentation (1-1.2 µm high); H. minisporus by the ellipsoidal to broadly ellipsoidal and small basidiospores (average = 11.7 µm × 9.5 µm); H. papilliformis was characterized by the whitish to cream-colored basidiomes, and broadly fusiform to citriform basidiospores with a pronounced apex (2-3 µm, occasionally up to 4 µm high), papillary, distinct warts and ridges, and pronounced appendix (2-3 µm long); H. perisporius by the dirty white to pale yellow basidiomes, broad ellipsoidal to ellipsoidal, and yellow-brown to dark-brown basidiospores with warts and gelatinous perisporium; H. variabilis by the peridium with significant changes in thickness (167-351 µm), and broad ellipsoidal to subglobose basidiospores ornamented with sparse warts and ridges. An ITS/LSU-based phylogenetic analysis supported the erection of the five new species. A key for Hymenogaster species from northern China is provided.

Topology-Engineered Orbital Hall Effect in Two-Dimensional Ferromagnets.

Recent advances in the manipulation of the orbital angular momentum (OAM) within the paradigm of orbitronics presents a promising avenue for the design of future electronic devices. In this context, the recently observed orbital Hall effect (OHE) occupies a special place. Here, focusing on both the second-order topological and quantum anomalous Hall insulators in two-dimensional ferromagnets, we demonstrate that topological phase transitions present an efficient and straightforward way to engineer the OHE, where the OAM distribution can be controlled by the nature of the band inversion. Using first-principles calculations, we identify Janus RuBrCl and three septuple layers of MnBi2Te4 as experimentally feasible examples of the proposed mechanism of OHE engineering by topology. With our work, we open up new possibilities for innovative applications in topological spintronics and orbitronics.

Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles.

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

High-Precision Atom Interferometer-Based Dynamic Gravimeter Measurement by Eliminating the Cross-Coupling Effect.

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses an AI sensor and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement were ±0.42 mGal and ±0.46 mGal after optimizing the cross-coupling effect, which was improved by factors of 4.18 and 4.21 compared to the cases without optimization.

Joint learning of multi-level dynamic brain networks for autism spectrum disorder diagnosis.

Graph convolutional networks (GCNs), with their powerful ability to model non-Euclidean graph data, have shown advantages in learning representations of brain networks. However, considering the complexity, multilayeredness, and spatio-temporal dynamics of brain activities, we have identified two limitations in current GCN-based research on brain networks: 1) Most studies have focused on unidirectional information transmission across brain network levels, neglecting joint learning or bidirectional information exchange among networks. 2) Most of the existing models determine node neighborhoods by thresholding or simply binarizing the brain network, which leads to the loss of edge weight information and weakens the model's sensitivity to important information in the brain network. To address the above issues, we propose a multi-level dynamic brain network joint learning architecture based on GCN for autism spectrum disorder (ASD) diagnosis. Specifically, firstly, constructing different-level dynamic brain networks. Then, utilizing joint learning based on GCN for interactive information exchange among these multi-level brain networks. Finally, designing an edge self-attention mechanism to assign different edge weights to inter-node connections, which allows us to pick out the crucial features for ASD diagnosis. Our proposed method achieves an accuracy of 81.5 %. The results demonstrate that our method enables bidirectional transfer of high-order and low-order information, facilitating information complementarity between different levels of brain networks. Additionally, the use of edge weights enhances the representation capability of ASD-related features.

Quantum oscillations evidence for topological bands in kagome metal ScV6Sn6.

Metals with kagome lattice provide bulk materials to host both the flat-band and Dirac electronic dispersions. A new family of kagome metals is recently discovered inAV6Sn6. The Dirac electronic structures of this material needs more experimental evidence to confirm. In the manuscript, we investigate this problem by resolving the quantum oscillations in both electrical transport and magnetization in ScV6Sn6. The revealed orbits are consistent with the electronic band structure models. Furthermore, the Berry phase of a dominating orbit is revealed to be aroundπ, providing direct evidence for the topological band structure, which is consistent with calculations. Our results demonstrate a rich physics and shed light on the correlated topological ground state of this kagome metal.

Association between CT-based adipose variables, preoperative blood biochemical indicators and pathological T stage of clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is corelated with tumor-associated material (TAM), coagulation system and adipocyte tissue, but the relationships between them have been inconsistent. Our study aimed to explore the cut-off intervals of variables that are non-linearly related to ccRCC pathological T stage for providing clues to understand these discrepancies, and to effectively preoperative risk stratification. Methods: This retrospective analysis included 218 ccRCC patients with a clear pathological T stage between January 1st, 2014, and November 30th, 2021. The patients were categorized into two cohorts based on their pathological T stage: low T stage (T1 and T2) and high T stage (T3 and T4). Abdominal and perirenal fat variables were measured based on preoperative CT images. Blood biochemical indexes from the last time before surgery were also collected. The generalized sum model was used to identify cut-off intervals for nonlinear variables. Results: In specific intervals, fibrinogen levels (FIB) (2.63-4.06 g/L) and platelet (PLT) counts (>200.34 × 109/L) were significantly positively correlated with T stage, while PLT counts (<200.34 × 109/L) were significantly negatively correlated with T stage. Additionally, tumor-associated material exhibited varying degrees of positive correlation with T stage at different cut-off intervals (cut-off value: 90.556 U/mL). Conclusion: Preoperative PLT, FIB and TAM are nonlinearly related to pathological T stage. This study is the first to provide specific cut-off intervals for preoperative variables that are nonlinearly related to ccRCC T stage. These intervals can aid in the risk stratification of ccRCC patients before surgery, allowing for developing a more personalized treatment planning.

Complete mitochondrial genome of Striatobalanus tenuis Hoek, 1883 (Balanomorpha: Balanidae) and a novel molecular phylogeny within Cirripedia.

Barnacles are crustaceans that are critical model organisms in intertidal ecology and biofouling research. In this study, we present the first mitochondrial genome of Striatobalanus tenuis which is a circular molecule of 15,067 bp in length. Consistent with most barnacles, the mitochondrial genome of S. tenuis encodes 37 genes, including 13 PCGs, 22 tRNAs and 2 rRNAs. A novel insight into the phylogenetic analysis based on the nucleotide data of 13 PCGs showed that the S. tenuis clusters with Striatobalanus amaryllis (bootstrap value = 100) of the same genus, then groups with other Balanoidea species, the Chelonibiidae, Austrobalanidae and Tetraclitidae cluster together forming superfamily Coronuloidea. The result can help us to understand the novel classification within Balanomorpha.

Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study.

BACKGROUND: Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. PURPOSE: To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. STUDY TYPE: Retrospective. POPULATION: 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. FIELD STRENGTH/SEQUENCE: 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence. ASSESSMENT: DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data. STATISTICAL TESTS: Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. RESULTS: The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. DATA CONCLUSION: The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.