Transfer of miR-877-3p via extracellular vesicles derived from dental pulp stem cells attenuates neuronal apoptosis and facilitates early neurological functional recovery after cerebral ischemia-reperfusion injury through the Bclaf1/p53 signaling pathway.

Cerebral ischemia-reperfusion injury (I/RI) is one of the principal pathogenic factors in the poor prognosis of ischemic stroke, for which current therapeutic options to enhance neurological recovery are notably insufficient. Dental pulp stem cell-derived extracellular vesicles (DPSC-EVs) have promising prospects in stroke treatment and the specific underlying mechanisms have yet to be fully elucidated. The present study observed that DPSC-EVs ameliorated the degree of cerebral edema and infarct volume by reducing the apoptosis of neurons. Furthermore, the miRNA sequencing and functional enrichment analysis identified that miR-877-3p as a key component in DPSC-EVs, contributing to neuroprotection and anti-apoptotic effects. Following target prediction and dual-luciferase assay indicated that miR-877-3p interacted with Bcl-2-associated transcription factor (Bclaf1) to play a function. The miR-877-3p inhibitor or Bclaf1 overexpression reversed the neuroprotective effects of DPSC-EVs. The findings reveal a novel therapeutic pathway where miR-877-3p, transferred via DPSC-EVs, confers neuroprotection against cerebral I/RI, highlighting its potential in promoting neuronal survival and recovery post-ischemia.

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear. PURPOSE: To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG-LSTM network. STUDY TYPE: Retrospective. POPULATION: Center A: 235 patients (47.7 ± 10.0 years) were divided 7:3 into training (n = 164) and validation set (n = 71). Center B: 150 patients (48.5 ± 10.4 years) were used as test set. FIELD STRENGTH/SEQUENCE: 3-T, T2-weighted spin-echo sequence imaging, and gradient echo DCE sequence imaging. ASSESSMENT: Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG-LSTM network to establish seven DL models using time-series DCE-MR images: pre-NAC images (t0 model), early NAC images (t1 model), post-NAC images (t2 model), pre-NAC and early NAC images (t0 + t1 model), pre-NAC and post-NAC images (t0 + t2 model), pre-NAC, early NAC and post-NAC images (t0 + t1 + t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set. STATISTICAL TESTS: The DeLong, Student's t-test, Mann-Whitney U, Chi-squared, Fisher's exact, Hosmer-Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. P < 0.05 was considered significant. RESULTS: Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927. DATA CONCLUSION: The combined model that used time-series DCE-MR images, clinical features and imaging features shows promise for identifying pCR in BC. TECHNICAL EFFICACY: Stage 4.

Dissecting and tracing the gut microbiota of infants with botulism: a cross sectional and longitudinal study.

Background: Infant botulism is caused by botulinum neurotoxin (BoNT), which is mainly produced by Clostridium botulinum. However, there is a lack of longitudinal cohort studies on infant botulism. Herein, we have constructed a cross-sectional and longitudinal cohort of infants infected with C. botulinum. Our goal was to reveal the differences in the intestinal microbiota of botulism-infected and healthy infants as well as the dynamic changes over time through multi-omics analysis. Methods: We performed 16S rRNA sequencing of 20 infants' stools over a period of 3 months and conducted whole genome sequencing of isolated C. botulinum strains from these laboratory-confirmed cases of infant botulism. Through bioinformatics analysis, we focused on the changes in the infants' intestinal microbiota as well as function over time series. Results: We found that Enterococcus was significantly enriched in the infected group and declined over time, whereas Bifidobacterium was significantly enriched in the healthy group and gradually increased over time. 18/20 isolates carried the type B 2 botulinum toxin gene with identical sequences. In silico Multilocus sequence typing found that 20\u00B0C. botulinum isolates from the patients were typed into ST31 and ST32. Conclusion: Differences in intestinal microbiota and functions in infants were found with botulism through cross-sectional and longitudinal studies and Bifidobacterium may play a role in the recovery of infected infants.

5-Fluorouracil resistant CRC cells derived exosomes promote cancer-associated fibroblasts secreting more CXCL12.

Background: Chemoresistance is a key reason for treatment failure in colorectal cancer (CRC) patients. The tumor microenvironment of chemoresistant CRC is distinctly immunosuppressive, although the underlying mechanisms are unclear. Methods: The CRC data sets GSE69657 and GSE62080 were downloaded from the GEO database, and the correlation between TRPC5 and FAP expression was analyzed by Pearson method. The in-situ expression of transient receptor potential channel 5 (TRPC5) and fibroblast activation protein (FAP) in the CRC tissues was examined by immunohistochemistry. TRPC5 expression levels in the HCT8 and HCT116 cell lines and the corresponding 5-fluorouracil (5-FU)-resistant cell lines (HCT8R and HCT116R) were analyzed by western blotting and RT-PCR. Exosomes were isolated from the HCT8R and HCT116R cells and incubated with colorectal normal fibroblasts (NFs), and cancer-associated fibroblasts (CAFs)markers were detected. NFs were also incubated with exosomes isolated from TRPC5-knockdown HCT8R cells, and the changes in intracellular Ca2+ levels and C-X-C motif chemokine ligand 12 (CXCL12) secretion were analyzed. Results: TRPC5 and FAP expression showed positive correlation in the datasets. Immunostaining of CRC tissue specimens further revealed that high TRPC5 and FAP expressions were significantly associated with worse tumor regression. Furthermore, chemoresistant CRC cells expressed higher levels of TRPC5 compared to the chemosensitive cells, and knocking down TRPC5 reversed chemoresistance. Exosomes derived from CRC cells induced the transformation of NFs to CAFs. However, TRPC5-exosomes derived from chemoresistant CRC cells can promote CAFs to secrete more CXCL12. Conclusion: Chemoresistant CRC cells can induce CAFs activation and promote CXCL12 secretion through exosomal TRPC5.

Genomic epidemiology reveals multiple mechanisms of linezolid resistance in clinical enterococci in China.

BACKGROUND: Infections caused by linezolid-resistant enterococci (LRE) are clinically difficult to treat and threaten patient health. However, there is a lack of studies on long time-span LRE strains in China. For this reason, our study comprehensively revealed the resistance mechanisms of LRE strains collected in a Chinese tertiary care hospital from 2011 to 2022. METHODS: Enterococcal strains were screened and verified after retrospective analysis of microbial data. Subsequently, 65 LRE strains (61 Enterococcus faecalis and 4 Enterococcus faecium, MIC ≥ 8 µg/ml), 1 linezolid-intermediate Enterococcus faecium (MIC = 4 µg/ml) and 1 linezolid-susceptible Enterococcus faecium (MIC = 1.5 µg/ml) were submitted for whole-genome sequencing (WGS) analysis and bioinformatics analysis. RESULTS: The optrA gene was found to be the most common linezolid resistance mechanism in our study. We identified the wild-type OptrA and various OptrA variants in 98.5% of LRE strains (61 Enterococcus faecalis and 3 Enterococcus faecium). We also found one linezolid-resistant Enterococcus faecium strain carried both optrA and cfr(D) gene, while one linezolid-resistant Enterococcus faecium only harbored the poxtA gene. Most optrA genes (55/64) were located on plasmids, with impB-fexA-optrA, impB-fexA-optrA-erm(A), fexA-optrA-erm(A), and fexA-optrA segments. A minority of optrA genes (9/64) were found on chromosomes with the Tn6674-like platform. Besides, other possible linezolid resistance-associated mechanisms (mutations in the rplC and rplD genes) were also found in 26 enterococcal strains. CONCLUSIONS: Our study suggested that multiple mechanisms of linezolid resistance exist among clinical LRE strains in China.

High Expression of CLDN 18.2 is Associated with Poor Disease-Free Survival of HER-2 Positive Gastric Cancer.

Background: Anti-claudin (CLDN) 18.2 therapy has been proven to be effective in treating advanced gastric cancer with negative human epidermal growth factor receptor 2 (HER-2). This study purposed to investigate the relationship of CLDN 18.2 expression with prognosis of HER-2-positive gastric cancer patients. Objective: To investigate the expression of claudin (CLDN) 18.2 in Human epidermal growth factor receptor 2 (HER-2) positive gastric cancer patients after radical resection and its relationship with gastric cancer prognosis. Methods: A total of 55 postoperative HER-2-positive gastric cancer patients were included in this study. CLDN 18.2 protein was detected by immunohistochemistry, and detailed clinical and pathological information was collected. Factors considered potentially important in the univariate analysis were included in the multivariate analysis, which involved COX regression to find the independent prognostic factors affecting disease-free survival (DFS). Results: Immunohistochemistry showed that different levels of CLDN 18.2 protein were expressed in HER-2 positive gastric cancer tissues, and the Chi-square analysis showed that the expression level of CLDN 18.2 was significantly correlated with the lymph node stage. Higher expression levels of CLDN 18.2 were found in patients with lymph node positivity and were associated with poor prognosis in HER-2-positive gastric cancer patients. Gastric cancer patients with low and high expressions of CLDN 18.2 had postoperative median DFS of 38.5 months (95% confidence interval (CI) 28.8-48.2 months) and 12.1 months (95% CI, 11.7-41.0 months), respectively. Conclusion: High expression of CLDN 18.2 in HER-2 positive gastric cancer is associated with poor prognosis, and the optimal treatment mode for this population is worth exploring after the approval of anti-CLDN 18.2 drugs.

Breast masses with rim enhancement on contrast-enhanced mammography: morphological and enhancement features for diagnosis and differentiation of benign and malignant.

OBJECTIVES: To investigate the imaging characteristics and clinicopathological features of rim enhancement of breast masses demonstrated on contrast-enhanced mammography (CEM). METHODS: 67 cases of breast lesions confirmed by pathology and showing rim enhancement on CEM examinations were analyzed. The lesions were divided into benign and malignant groups, and the morphological and enhanced features were described. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated separately for each morphology descriptor to evaluate the diagnostic ability of each indicator. RESULTS: There were 35 (52.2%) malignant and 32 (47.8%) benign lesions. There are significant differences in the morphological and enhanced features between benign and malignant lesions. 29/35 (82.9%) malignant lesions exhibited irregular shapes, and 31/35 (88.6%) showed indistinct margins. 28/35 (80%) malignant lesions displayed strong enhancement on CEM, while 12/32 (37.5%) benign lesions exhibited weak enhancement (P = 0.001). Malignant lesions showed a higher incidence of unsmooth inner walls than benign lesions (28/35 vs 7/32; P <.001). Lesion margins showed high sensitivity of 88.57% and NPV of 81.8%. The presence of suspicious calcifications had the highest specificity of 100% and PPV of 100%. The diagnostic sensitivity, specificity, PPV, and NPV of the combined parameters were 97.14%, 93.15%, 94.44%, and 96.77%, respectively. CONCLUSIONS: The assessment of morphological and enhanced features of breast lesions exhibiting rim enhancement on CEM can improve the differentiation between benign and malignant breast lesions. ADVANCES IN KNOWLEDGE: This article provides a reference for the differential diagnosis of ring enhanced lesions on CEM.

Anaerostipes hadrus, a butyrate-producing bacterium capable of metabolizing 5-fluorouracil.

Anaerostipes hadrus (A. hadrus) is a dominant species in the human gut microbiota and considered a beneficial bacterium for producing probiotic butyrate. However, recent studies have suggested that A. hadrus may negatively affect the host through synthesizing fatty acid and metabolizing the anticancer drug 5-fluorouracil, indicating that the impact of A. hadrus is complex and unclear. Therefore, comprehensive genomic studies on A. hadrus need to be performed. We integrated 527 high-quality public A. hadrus genomes and five distinct metagenomic cohorts. We analyzed these data using the approaches of comparative genomics, metagenomics, and protein structure prediction. We also performed validations with culture-based in vitro assays. We constructed the first large-scale pan-genome of A. hadrus (n = 527) and identified 5-fluorouracil metabolism genes as ubiquitous in A. hadrus genomes as butyrate-producing genes. Metagenomic analysis revealed the wide and stable distribution of A. hadrus in healthy individuals, patients with inflammatory bowel disease, and patients with colorectal cancer, with healthy individuals carrying more A. hadrus. The predicted high-quality protein structure indicated that A. hadrus might metabolize 5-fluorouracil by producing bacterial dihydropyrimidine dehydrogenase (encoded by the preTA operon). Through in vitro assays, we validated the short-chain fatty acid production and 5-fluorouracil metabolism abilities of A. hadrus. We observed for the first time that A. hadrus can convert 5-fluorouracil to α-fluoro-ß-ureidopropionic acid, which may result from the combined action of the preTA operon and adjacent hydA (encoding bacterial dihydropyrimidinase). Our results offer novel understandings of A. hadrus, exceptionally functional features, and potential applications. IMPORTANCE: This work provides new insights into the evolutionary relationships, functional characteristics, prevalence, and potential applications of Anaerostipes hadrus.

Gut resistome profiling reveals high diversity and fluctuations in pancreatic cancer cohorts.

Background: Pancreatic cancer is one of the deadliest cancer, with a 5-year overall survival rate of 11%. Unfortunately, most patients are diagnosed with advanced stage by the time they present with symptoms. In the past decade, microbiome studies have explored the association of pancreatic cancer with the human oral and gut microbiomes. However, the gut microbial antibiotic resistance genes profiling of pancreatic cancer patients was never reported compared to that of the healthy cohort. Results: In this study, we addressed the gut microbial antibiotic resistance genes profile using the metagenomic data from two online public pancreatic cancer cohorts. We found a high degree of data concordance between the two cohorts, which can therefore be used for cross-sectional comparisons. Meanwhile, we used two strategies to predict antibiotic resistance genes and compared the advantages and disadvantages of these two approaches. We also constructed microbe-antibiotic resistance gene networks and found that most of the hub nodes in the networks were antibiotic resistance genes. Conclusions: In summary, we describe the panorama of antibiotic resistance genes in the gut microbes of patients with pancreatic cancer. We hope that our study will provide new perspectives on treatment options for the disease.

Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii.

BACKGROUND: Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. METHODS: We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. RESULTS: Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638-0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. CONCLUSION: Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.

Atypical architectural distortion detection in digital breast tomosynthesis: a multi-view computer-aided detection model with ipsilateral learning.

Objective.Breast architectural distortion (AD), a common imaging symptom of breast cancer, is associated with a particularly high rate of missed clinical detection. In clinical practice, atypical ADs that lack an obvious radiating appearance constitute most cases, and detection models based on single-view images often exhibit poor performance in detecting such ADs. Existing multi-view deep learning methods have overlooked the correspondence between anatomical structures across different views.Approach.To develop a computer-aided detection (CADe) model for AD detection that effectively utilizes the craniocaudal (CC) and mediolateral oblique (MLO) views of digital breast tomosynthesis (DBT) images, we proposed an anatomic-structure-based multi-view information fusion approach by leveraging the related anatomical structure information between these ipsilateral views. To obtain a representation that can effectively capture the similarity between ADs in images from ipsilateral views, our approach utilizes a Siamese network architecture to extract and compare information from both views. Additionally, we employed a triplet module that utilizes the anatomical structural relationship between the ipsilateral views as supervision information.Main results.Our method achieved a mean true positive fraction (MTPF) of 0.05-2.0, false positives (FPs) per volume of 64.40%, and a number of FPs at 80% sensitivity (FPs@0.8) of 3.5754; this indicates a 6% improvement in MPTF and 16% reduction in FPs@0.8 compared to the state-of-the-art baseline model.Significance.From our experimental results, it can be observed that the anatomic-structure-based fusion of ipsilateral view information contributes significantly to the improvement of CADe model performance for atypical AD detection based on DBT. The proposed approach has the potential to lead to earlier diagnosis and better patient outcomes.

Metabolic engineering of commensal bacteria for gut butyrate delivery and dissection of host-microbe interaction.

An overwhelming number of studies have reported the correlation of decreased abundance of butyrate-producing commensals with a wide range of diseases. However, the molecular-level mechanisms whereby gut butyrate causally affects the host mucosal immunity and pathogenesis were poorly understood, hindered by the lack of efficient tools to control intestinal butyrate. Here we engineered a facultative anaerobic commensal bacterium to delivery butyrate at the intestinal mucosal surface, and implemented it to dissect the causal role of gut butyrate in regulating host intestinal homeostasis in a model of murine chronic colitis. Mechanistically, we show that gut butyrate protected against colitis and preserved intestinal mucosal homeostasis through its inhibiting effect on the key pyroptosis executioner gasdermin D (GSDMD) of colonic epithelium, via functioning as an HDAC3 inhibitor. Overall, our work presents a new avenue to build synthetic living delivery bacteria to decode causal molecules at the host-microbe interface with molecular-level insights.

Comparative genomic analysis of Fusobacterium nucleatum reveals high intra-species diversity and cgmlst marker construction.

BACKGROUND: Fusobacterium nucleatum is a one of the most important anaerobic opportunistic pathogens in the oral and intestinal tracts of human and animals. It can cause various diseases such as infections, Lemierre's syndrome, oral cancer and colorectal cancer. The comparative genomic studies on the population genome level, have not been reported. RESULTS: We analyzed all publicly available Fusobacterium nucleatums' genomic data for a comparative genomic study, focusing on the pan-genomic features, virulence genes, plasmid genomes and developed cgmlst molecular markers. We found the pan-genome shows a clear open tendency and most of plasmids in Fusobacterium nucleatum are mainly transmitted intraspecifically. CONCLUSIONS: Our comparative analysis of Fusobacterium nucleatum systematically revealed the open pan-genomic features and phylogenetic tree based on cgmlst molecular markers. What's more, we also identified common plasmid typing among genomes. We hope that our study will provide a theoretical basis for subsequent functional studies.

Large-scale genomic survey and characterization of mcr genes carried by foodborne Cronobacter isolates.

IMPORTANCE: Cronobacter is an emerging foodborne opportunistic pathogen, which can cause neonatal meningitis, bacteremia, and NEC by contaminating food. However, the entire picture of foodborne Cronobacter carriage of the mcr genes is not known. Here, we investigated the mcr genes of Cronobacter isolates by whole-genome sequencing and found 133 previously undescribed Cronobacter isolates carrying mcr genes. Further genomic analysis revealed that these mcr genes mainly belonged to the mcr-9 and mcr-10. Genomic analysis of the flanking structures of mcr genes revealed that two core flanking structures were prevalent in foodborne Cronobacter isolates, and the flanking structure carrying IS1R was found for the first time in this study.

Plasmer: an Accurate and Sensitive Bacterial Plasmid Prediction Tool Based on Machine Learning of Shared k-mers and Genomic Features.

Identification of plasmids in bacterial genomes is critical for many factors, including horizontal gene transfer, antibiotic resistance genes, host-microbe interactions, cloning vectors, and industrial production. There are several in silico methods to predict plasmid sequences in assembled genomes. However, existing methods have evident shortcomings, such as unbalance in sensitivity and specificity, dependency on species-specific models, and performance reduction in sequences shorter than 10 kb, which has limited their scope of applicability. In this work, we proposed Plasmer, a novel plasmid predictor based on machine-learning of shared k-mers and genomic features. Unlike existing k-mer or genomic-feature based methods, Plasmer employs the random forest algorithm to make predictions using the percent of shared k-mers with plasmid and chromosome databases combined with other genomic features, including alignment E value and replicon distribution scores (RDS). Plasmer can predict on multiple species and has achieved an average the area under the curve (AUC) of 0.996 with accuracy of 98.4%. Compared to existing methods, tests of both sliding sequences and simulated and de novo assemblies have consistently shown that Plasmer has outperforming accuracy and stable performance across long and short contigs above 500 bp, demonstrating its applicability for fragmented assemblies. Plasmer also has excellent and balanced performance on both sensitivity and specificity (both >0.95 above 500 bp) with the highest F1-score, which has eliminated the bias on sensitivity or specificity that was common in existing methods. Plasmer also provides taxonomy classification to help identify the origin of plasmids. IMPORTANCE In this study, we proposed a novel plasmid prediction tool named Plasmer. Technically, unlike existing k-mer or genomic features-based methods, Plasmer is the first tool to combine the advantages of the percent of shared k-mers and the alignment score of genomic features. This has given Plasmer (i) evident improvement in performance compared to other methods, with the best F1-score and accuracy on sliding sequences, simulated contigs, and de novo assemblies; (ii) applicability for contigs above 500 bp with highest accuracy, enabling plasmid prediction in fragmented short-read assemblies; (iii) excellent and balanced performance between sensitivity and specificity (both >0.95 above 500 bp) with the highest F1-score, which eliminated the bias on sensitivity or specificity that commonly existed in other methods; and (iv) no dependency of species-specific training models. We believe that Plasmer provides a more reliable alternative for plasmid prediction in bacterial genome assemblies.

A food poisoning caused by ST7 Staphylococcal aureus harboring sea gene in Hainan province, China.

ST7 Staphylococcus aureus is highly prevalent in humans, pigs, as well as food in China; however, staphylococcal food poisoning (SFP) caused by this ST type has rarely been reported. On May 13, 2017, an SFP outbreak caused by ST7 S. aureus strains occurred in two campuses of a kindergarten in Hainan Province, China. We investigated the genomic characteristics and phylogenetic analysis of ST7 SFP strains combined with the 91 ST7 food-borne strains from 12 provinces in China by performing whole-genome sequencing (WGS). There was clear phylogenetic clustering of seven SFP isolates. Six antibiotic genes including blaZ, ANT (4')-Ib, tetK, lnuA, norA, and lmrS were present in all SFP strains and also showed a higher prevalence rate in 91 food-borne strains. A multiple resistance plasmid pDC53285 was present in SFP strain DC53285. Among 27 enterotoxin genes, only sea and selx were found in all SFP strains. A ФSa3int prophage containing type A immune evasion cluster (sea, scn, sak, and chp) was identified in SFP strain. In conclusion, we concluded that this SFP event was caused by the contamination of cakes with ST7 S. aureus. This study indicated the potential risk of new emergencing ST7 clone for SFP.

The N6-methyladenosine METTL3 regulates tumorigenesis and glycolysis by mediating m6A methylation of the tumor suppressor LATS1 in breast cancer.

BACKGROUND: Posttranscriptional modification of tumor-associated factors plays a pivotal role in breast cancer progression. However, the underlying mechanism remains unknown. M6A modifications in cancer cells are dynamic and reversible and have been found to impact tumor initiation and progression through various mechanisms. In this study, we explored the regulatory mechanism of breast cancer cell proliferation and metabolism through m6A methylation in the Hippo pathway.  METHODS: A combination of MeRIP-seq, RNA-seq and metabolomics-seq was utilized to reveal a map of m6A modifications in breast cancer tissues and cells. We conducted RNA pull-down assays, RIP-qPCR, MeRIP-qPCR, and RNA stability analysis to identify the relationship between m6A proteins and LATS1 in m6A regulation in breast cancer cells. The expression and biological functions of m6A proteins were confirmed in breast cancer cells in vitro and in vivo. Furthermore, we investigated the phosphorylation levels and localization of YAP/TAZ to reveal that the activity of the Hippo pathway was affected by m6A regulation of LATS1 in breast cancer cells.  RESULTS: We demonstrated that m6A regulation plays an important role in proliferation and glycolytic metabolism in breast cancer through the Hippo pathway factor, LATS1. METTL3 was identified as the m6A writer, with YTHDF2 as the reader protein of LATS1 mRNA, which plays a positive role in promoting both tumorigenesis and glycolysis in breast cancer. High levels of m6A modification were induced by METTL3 in LATS1 mRNA. YTHDF2 identified m6A sites in LATS1 mRNA and reduced its stability. Knockout of the protein expression of METTL3 or YTHDF2 increased the expression of LATS1 mRNA and suppressed breast cancer tumorigenesis by activating YAP/TAZ in the Hippo pathway. CONCLUSIONS: In summary, we discovered that the METTL3-LATS1-YTHDF2 pathway plays an important role in the progression of breast cancer by activating YAP/TAZ in the Hippo pathway.

Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field.

Objective. In digital breast tomosynthesis (DBT), architectural distortion (AD) is a breast lesion that is difficult to detect. Compared with typical ADs, which have radial patterns, identifying a typical ADs is more difficult. Most existing computer-aided detection (CADe) models focus on the detection of typical ADs. This study focuses on atypical ADs and develops a deep learning-based CADe model with an adaptive receptive field in DBT.Approach. Our proposed model uses a Gabor filter and convergence measure to depict the distribution of fibroglandular tissues in DBT slices. Subsequently, two-dimensional (2D) detection is implemented using a deformable-convolution-based deep learning framework, in which an adaptive receptive field is introduced to extract global features in slices. Finally, 2D candidates are aggregated to form the three-dimensional AD detection results. The model is trained on 99 positive cases with ADs and evaluated on 120 AD-positive cases and 100 AD-negative cases.Main results. A convergence-measure-based model and deep-learning model without an adaptive receptive field are reproduced as controls. Their mean true positive fractions (MTPF) ranging from 0.05 to 4 false positives per volume are 0.3846 ± 0.0352 and 0.6501 ± 0.0380, respectively. Our proposed model achieves an MTPF of 0.7148 ± 0.0322, which is a significant improvement (p< 0.05) compared with the other two methods. In particular, our model detects more atypical ADs, primarily contributing to the performance improvement.Significance. The adaptive receptive field helps the model improve the atypical AD detection performance. It can help radiologists identify more ADs in breast cancer screening.

A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems.

Anomaly detection is a critical task for maintaining the performance of a cloud system. Using data-driven methods to address this issue is the mainstream in recent years. However, due to the lack of labeled data for training in practice, it is necessary to enable an anomaly detection model trained on contaminated data in an unsupervised way. Besides, with the increasing complexity of cloud systems, effectively organizing data collected from a wide range of components of a system and modeling spatiotemporal dependence among them become a challenge. In this article, we propose TopoMAD, a stochastic seq2seq model which can robustly model spatial and temporal dependence among contaminated data. We include system topological information to organize metrics from different components and apply sliding windows over metrics collected continuously to capture the temporal dependence. We extract spatial features with the help of graph neural networks and temporal features with long short-term memory networks. Moreover, we develop our model based on variational auto-encoder, enabling it to work well robustly even when trained on contaminated data. Our approach is validated on the run-time performance data collected from two representative cloud systems, namely, a big data batch processing system and a microservice-based transaction processing system. The experimental results show that TopoMAD outperforms some state-of-the-art methods on these two data sets.

Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity.

PURPOSE: Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT. METHOD: To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The mean-squared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image. RESULTS: 1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the structural similarity from 0.703 to 0.724, and significantly increases the perceptual similarity. CONCLUSIONS: The proposed method can stabilize the training and generate SDM images with lower intensity distortion and higher perceptual similarity.