Impact of antifibrotic therapy on disease progression, all-cause mortality, and risk of acute exacerbation in non-IPF fibrosing interstitial lung diseases: evidence from a meta-analysis of randomized controlled trials and prospective controlled studies.

BACKGROUND: Nintedanib and pirfenidone are preferred pharmacological therapies for patients with idiopathic pulmonary fibrosis (IPF). However, evidence favoring antifibrotic therapy in patients with non-IPF fibrosing interstitial lung diseases (ILD) is limited. OBJECTIVE: To investigate the effects of antifibrotic therapy on disease progression, all-cause mortality, and acute exacerbation (AE) risk in patients with non-IPF fibrosing ILDs. DESIGN: Meta-analysis. DATA SOURCES AND METHODS: Electronic databases were searched for articles published before 28 February 2023. Studies that evaluated the efficacy of antifibrotic agents in patients with fibrosing ILDs were selected. The primary outcome was the disease progression risk, and the secondary outcomes included all-cause mortality and AE risk. The GRADE criteria were used for the certainty of evidence assessment. RESULTS: Nine studies with 1990 participants were included. Antifibrotic therapy reduced the rate of patients with disease progression (five trials with 1741 subjects; relative risk (RR), 0.56; 95% CI, 0.42-0.75; p < 0.0001; I2 = 0; high-certainty evidence). Antifibrotic therapy did not significantly decrease all-cause mortality (nine trials with 1990 subjects; RR, 0.76; 95% CI, 0.55-1.03; p = 0.08; I2 = 0; low-certainty evidence). However, in patients with progressive fibrosing ILDs (PF-ILD), antifibrotic therapy decreased all-cause mortality (four trials with 1100 subjects; RR, 0.69; 95% CI, 0.48-0.98; p = 0.04; I2 = 0; low-certainty evidence). CONCLUSION: Our study supports the use of antifibrotic agents in patients with PF-ILDs, which could slow disease progression and decrease all-cause mortality. TRIAL REGISTRATION: This study protocol was registered with PROSPERO (registration number: CRD42023411272).

Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.

OBJECTIVES: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts. METHODS: A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only. RESULTS: The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% CI: 0.874-1.000) in the test cohort, and an AUC of 0.906 (95% CI: 0.817-0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone ( P <0.05). CONCLUSIONS: The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis.

Differential diagnosis of breast mucinous carcinoma with an oval shape from fibroadenoma based on ultrasonographic features.

BACKGROUND: Approximately 50% of breast mucinous carcinomas (MCs) are oval and have the possibility of being misdiagnosed as fibroadenomas (FAs). We aimed to identify the key features that can help differentiate breast MC with an oval shape from FA on ultrasonography (US). METHODS: Seventy-six MCs from 71 consecutive patients and 50 FAs with an oval shape from 50 consecutive patients were included in our study. All lesions pathologically diagnosed. According to the Breast Imaging Reporting and Data System (BI-RADS), first, the ultrasonographic features of the MCs and FAs were recorded and a final category was assessed. Then, the differences in ultrasonographic characteristics between category 4 A (low-risk group) and category 4B-5 (medium-high- risk group) MCs were identified. Finally, other ultrasonographic features of MC and FA both with an oval shape were compared to determine the key factors for differential diagnosis. The Mann-Whitney test, χ2 test or Fisher's exact test was used to compare data between groups. RESULTS: MCs with an oval shape (81.2%) and a circumscribed margin (25%) on US were more commonly assessed in the low-risk group (BI-RADS 4 A) than in the medium-high-risk group (BI-RADS 4B-5) (20%, p < 0.001 and 0%, p = 0.001, respectively). Compared with those with FA, patients with MC were older, and tended to have masses with non-hypoechoic patterns, not circumscribed margins, and a posterior echo enhancement on US (p < 0.001, p < 0.001, and p = 0.003, respectively). CONCLUSION: The oval shape was the main reason for the underestimation of MCs. On US, an oval mass found in the breast of women of older age with non-hypoechoic patterns, not circumscribed margins, and a posterior echo enhancement was associated with an increased risk of being an MC, and should be subjected to active biopsy.

Diagnostic Efficacy of High-frequency Ultrasound (HFU) in Early Diagnosis of Congenital Hip Dysplasia.

BACKGROUND: Hip dysplasia is one of the most prevalent disorders in children and one of the three primary congenital orthopedic deformities. Although there are numerous existing methods (e.g., CT, MRI and arthrography) for early identification of hip dysplasia, their diagnostic criteria differ widely. It is critical to establish a safe, accurate, and reliable way for early diagnosis and treatment of hip dysplasia. OBJECTIVE: This study aimed to analyze the diagnostic efficacy of high-frequency ultrasound (HFU) for congenital developmental hip dysplasia and hip dislocation and to provide a reference for the early diagnosis of congenital hip dysplasia in the future. METHODS: A total of 104 infants and children suspected of having congenital hip dislocation or developmental hip dysplasia admitted to our hospital from April 2019 to August 2022 were enrolled as study subjects. All the infants and children were subjected to HFU and X-ray examination in our hospital. The diagnostic efficacy of HFU for congenital hip dysplasia was observed using X-ray as the gold standard. RESULTS: HFU confirmed 79 cases of congenital hip dysplasia, while X-ray confirmed 71 cases. The sensitivity and specificity of HFU were 77.42% and 83.33%, respectively, in the diagnosis of congenital developmental hip dysplasia, 76.47% and 96.55% in the diagnosis of congenital hip dislocation, and 77.22% and 60% in the diagnosis of congenital hip abnormality, which is very close to the gold standard. According to statistics on infants and children, the majority of patients were girls, and the left joint was more likely to be affected. CONCLUSION: HFU has excellent diagnostic efficiency for congenital developmental hip dysplasia and hip dislocation, which can be considered an early assessment method for congenital hip dysplasia in the future.

Insights on the NF-κB system in polycystic ovary syndrome, attractive therapeutic targets.

The nuclear factor κappa B (NF-κB) signaling plays a well-known function in inflammation and regulates a wide variety of biological processes. Low-grade chronic inflammation is gradually considered to be closely related to the pathogenesis of Polycystic ovary syndrome (PCOS). In this review, we provide an overview on the involvement of NF-κB in the progression of PCOS particularly, such as hyperandrogenemia, insulin resistance, cardiovascular diseases, and endometrial dysfunction. From a clinical perspective, progressive recognition of NF-κB pathway provides opportunities for therapeutic interventions aimed at inhibiting pathway-specific mechanisms. With the accumulation of basic experimental and clinical data, NF-κB signaling pathway was recognized as a therapeutic target. Although there have been no specific small molecule NF-κB inhibitors in PCOS, a plethora of natural and synthetic compound have emerged for the pharmacologic intervention of the pathway. The traditional herbs developed for NF-κB pathway have become increasingly popular in recent years. Abundant evidence elucidated that NF-κB inhibitors can significantly improve the symptoms of PCOS. Herein, we summarized evidence relating to how NF-κB pathway is involved in the development and progression of PCOS. Furthermore, we present an in-depth overview of NF-κB inhibitors for therapy interventions of PCOS. Taken together, the NF-κB signaling may be a futuristic treatment strategy for PCOS.

One-Shot Weakly-Supervised Segmentation in 3D Medical Images.

Deep neural networks typically require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot and weakly-supervised learning are promising research directions that reduce labeling effort by learning a new class from only one annotated image and using coarse labels instead, respectively. In this work, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised settings. Firstly a propagation-reconstruction network is proposed to propagate scribbles from one annotated volume to unlabeled 3D images based on the assumption that anatomical patterns in different human bodies are similar. Then a multi-level similarity denoising module is designed to refine the scribbles based on embeddings from anatomical- to pixel-level. After expanding the scribbles to pseudo masks, we observe the miss-classified voxels mainly occur at the border region and propose to extract self-support prototypes for the specific refinement. Based on these weakly-supervised segmentation results, we further train a segmentation model for the new class with the noisy label training strategy. Experiments on three CT and one MRI datasets show the proposed method obtains significant improvement over the state-of-the-art methods and performs robustly even under severe class imbalance and low contrast. Code is publicly available at https://github.com/LWHYC/OneShot_WeaklySeg.

Cinobufotalin prevents bone loss induced by ovariectomy in mice through the BMPs/SMAD and Wnt/ß-catenin signaling pathways.

BACKGROUND: Osteoporosis is a chronic bone disease characterized by bone loss and decreased bone strength. However, current anti-resorptive drugs carry a risk of various complications. The deep learning-based efficacy prediction system (DLEPS) is a forecasting tool that can effectively compete in drug screening and prediction based on gene expression changes. This study aimed to explore the protective effect and potential mechanisms of cinobufotalin (CB), a traditional Chinese medicine (TCM), on bone loss. METHODS: DLEPS was employed for screening anti-osteoporotic agents according to gene profile changes in primary osteoporosis. Micro-CT, histological and morphological analysis were applied for the bone protective detection of CB, and the osteogenic differentiation/function in human bone marrow mesenchymal stem cells (hBMMSCs) were also investigated. The underlying mechanism was verified using qRT-PCR, Western blot (WB), immunofluorescence (IF), etc. RESULTS: A safe concentration (0.25 mg/kg in vivo, 0.05 µM in vitro) of CB could effectively preserve bone mass in estrogen deficiency-induced bone loss and promote osteogenic differentiation/function of hBMMSCs. Both BMPs/SMAD and Wnt/ß-catenin signaling pathways participated in CB-induced osteogenic differentiation, further regulating the expression of osteogenesis-associated factors, and ultimately promoting osteogenesis. CONCLUSION: Our study demonstrated that CB could significantly reverse estrogen deficiency-induced bone loss, further promoting osteogenic differentiation/function of hBMMSCs, with BMPs/SMAD and Wnt/ß-catenin signaling pathways involved.

Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.

Background: Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC. Methods: In total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared. Results: The machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003). Conclusions: The machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

BMP4 participates in the pathogenesis of PCOS by regulating glucose metabolism and autophagy in granulosa cells under hyperandrogenic environment.

Polycystic ovary syndrome (PCOS) is a complex reproductive endocrine disease characterized by ovulation dysfunction with multiple etiologies and manifestations, and it is widely believed that the disorders of hyper-androgen and glucose metabolism play a key role in its progression. There has been evidence that bone morphogenetic protein 4 (BMP4) is essential for the regulation of granulosa cells, but whether it regulates metabolism level of granulosa cells under hyperandrogenic environment remains unclear. In this study, Gene Expression Omnibus, clinical data and serum of PCOS patient were collected to detect androgen and BMP4 levels. KGN cells exposed to androgens as a model for simulating PCOS granulosa cells. Lactate/pyruvate kits, and Extracellular Acidification Rate and Oxygen Consumption Rate assay were performed to detect glycolysis and autophagy levels of granulosa cells. Lentivirus infection was used to investigate the effects of BMP4 on granulosa cells. RNA-seq were performed to explore the special mechanism. We found that BMP4 was increased in PCOS patients with hyper-androgen and granulosa cells with dihydrotestosterone treatment. Mechanically, on the one hand, hyperandrogenemia can up-regulate BMP4 secretion and induce glycolysis and autophagy levels. On the other hand, we found that hyperandrogenic-induced YAP1 upregulation may mediate BMP4 to increase glycolysis level and decrease autophagy, which plays a protective role in granulosa cells to ensure subsequent energy utilization and mitochondrial function. Overall, we innovated on the protective effect of BMP4 on glycolysis and autophagy disorders induced by excessive androgen in granulosa cells. Our study will provide guidance for future understanding of PCOS from a metabolic perspective and for exploring treatment options.

UPL-SFDA: Uncertainty-Aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation.

Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are usually unavailable when a trained model is deployed at a new center, Source-Free Domain Adaptation (SFDA) is appealing for data and annotation-efficient adaptation to the target domain. However, existing SFDA methods have a limited performance due to lack of sufficient supervision with source-domain images unavailable and target-domain images unlabeled. We propose a novel Uncertainty-aware Pseudo Label guided (UPL) SFDA method for medical image segmentation. Specifically, we propose Target Domain Growing (TDG) to enhance the diversity of predictions in the target domain by duplicating the pre-trained model's prediction head multiple times with perturbations. The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels. We also propose a Twice Forward pass Supervision (TFS) strategy that uses reliable pseudo labels obtained in one forward pass to supervise predictions in the next forward pass. The adaptation is further regularized by a mean prediction-based entropy minimization term that encourages confident and consistent results in different prediction heads. UPL-SFDA was validated with a multi-site heart MRI segmentation dataset, a cross-modality fetal brain segmentation dataset, and a 3D fetal tissue segmentation dataset. It improved the average Dice by 5.54, 5.01 and 6.89 percentage points for the three tasks compared with the baseline, respectively, and outperformed several state-of-the-art SFDA methods.

CDDSA: Contrastive domain disentanglement and style augmentation for generalizable medical image segmentation.

Generalization to previously unseen images with potential domain shifts is essential for clinically applicable medical image segmentation. Disentangling domain-specific and domain-invariant features is key for Domain Generalization (DG). However, existing DG methods struggle to achieve effective disentanglement. To address this problem, we propose an efficient framework called Contrastive Domain Disentanglement and Style Augmentation (CDDSA) for generalizable medical image segmentation. First, a disentangle network decomposes the image into domain-invariant anatomical representation and domain-specific style code, where the former is sent for further segmentation that is not affected by domain shift, and the disentanglement is regularized by a decoder that combines the anatomical representation and style code to reconstruct the original image. Second, to achieve better disentanglement, a contrastive loss is proposed to encourage the style codes from the same domain and different domains to be compact and divergent, respectively. Finally, to further improve generalizability, we propose a style augmentation strategy to synthesize images with various unseen styles in real time while maintaining anatomical information. Comprehensive experiments on a public multi-site fundus image dataset and an in-house multi-site Nasopharyngeal Carcinoma Magnetic Resonance Image (NPC-MRI) dataset show that the proposed CDDSA achieved remarkable generalizability across different domains, and it outperformed several state-of-the-art methods in generalizable segmentation. Code is available at https://github.com/HiLab-git/DAG4MIA.

TP53RK Drives the Progression of Chronic Kidney Disease by Phosphorylating Birc5.

Renal fibrosis is a common characteristic of various chronic kidney diseases (CKDs) driving the loss of renal function. During this pathological process, persistent injury to renal tubular epithelial cells and activation of fibroblasts chiefly determine the extent of renal fibrosis. In this study, the role of tumor protein 53 regulating kinase (TP53RK) in the pathogenesis of renal fibrosis and its underlying mechanisms is investigated. TP53RK is upregulated in fibrotic human and animal kidneys with a positive correlation to kidney dysfunction and fibrotic markers. Interestingly, specific deletion of TP53RK either in renal tubule or in fibroblasts in mice can mitigate renal fibrosis in CKD models. Mechanistic investigations reveal that TP53RK phosphorylates baculoviral IAP repeat containing 5 (Birc5) and facilitates its nuclear translocation; enhanced Birc5 displays a profibrotic effect possibly via activating PI3K/Akt and MAPK pathways. Moreover, pharmacologically inhibiting TP53RK and Birc5 using fusidic acid (an FDA-approved antibiotic) and YM-155(currently in clinical phase 2 trials) respectively both ameliorate kidney fibrosis. These findings demonstrate that activated TP53RK/Birc5 signaling in renal tubular cells and fibroblasts alters cellular phenotypes and drives CKD progression. A genetic or pharmacological blockade of this axis serves as a potential strategy for treating CKDs.

IL-27 attenuates IL-23 mediated inflammatory arthritis.

Interleukin 27 has both pro-inflammatory and anti-inflammatory properties in autoimmunity. The anti-inflammatory effects of IL-27 are linked with inhibition of Th17 differentiation but the IL-27 effect on myeloid cells is less studied. Herein we demonstrate that IL-27 inhibits IL-23-induced inflammation associated not only with Th17 cells but also with myeloid cell infiltration in the joints and splenic myeloid populations of CD11b+ GR1+ and CD3-CD11b+CD11c-GR1- cells. The IL-27 anti-inflammatory response was associated with reduced levels of myeloid cells in the spleen and bone marrow. Overall, our data demonstrate that IL-27 has an immunosuppressive role that affects IL-23-dependent myelopoiesis in the bone marrow and its progression to inflammatory arthritis and plays a crucial role in controlling IL-23 driven joint inflammation by negatively regulating the expansion of myeloid cell subsets.

S3R: Shape and Semantics-Based Selective Regularization for Explainable Continual Segmentation Across Multiple Sites.

In clinical practice, it is desirable for medical image segmentation models to be able to continually learn on a sequential data stream from multiple sites, rather than a consolidated dataset, due to storage cost and privacy restrictions. However, when learning on a new site, existing methods struggle with a weak memorizability for previous sites with complex shape and semantic information, and a poor explainability for the memory consolidation process. In this work, we propose a novel Shape and Semantics-based Selective Regularization ( [Formula: see text]) method for explainable cross-site continual segmentation to maintain both shape and semantic knowledge of previously learned sites. Specifically, [Formula: see text] method adopts a selective regularization scheme to penalize changes of parameters with high Joint Shape and Semantics-based Importance (JSSI) weights, which are estimated based on the parameter sensitivity to shape properties and reliable semantics of the segmentation object. This helps to prevent the related shape and semantic knowledge from being forgotten. Moreover, we propose an Importance Activation Mapping (IAM) method for memory interpretation, which indicates the spatial support for important parameters to visualize the memorized content. We have extensively evaluated our method on prostate segmentation and optic cup and disc segmentation tasks. Our method outperforms other comparison methods in reducing model forgetting and increasing explainability. Our code is available at https://github.com/jingyzhang/S3R.

Effects of stay-at-home orders on skill requirements in vacancy postings.

The COVID-19 pandemic and containment policies have had profound economic impacts on the labor market. Stay-at-home orders (SAHOs) implemented across most of the United States changed the way of people worked. In this paper, we quantify the effect of SAHO durations on skill demands to study how firms adjust labor demand within occupation. We use skill requirement information from the 2018 to 2021 online job vacancy posting data from Burning Glass Technologies, exploit the spatial variations in the SAHO duration, and use instrumental variables to correct for the endogeneity in the policy duration related to local social and economic factors. We find that policy durations have persistent impacts on the labor demand after restrictions are lifted. Longer SAHOs motivate management style transformation from people-oriented to operation-oriented by requiring more of operational and administrative skills and less of personality and people management skills to carry out standard workflows. SAHOs also change the focus of interpersonal skill demands from specific customer services to general communication such as social and writing skills. SAHOs more thoroughly affect occupations with partial work-from-home capacity. The evidence suggests SAHOs change management structure and communication in firms.

A fast two-stage algorithm for non-negative matrix factorization in smoothly varying data.

This article reports the study of algorithms for non-negative matrix factorization (NMF) in various applications involving smoothly varying data such as time or temperature series diffraction data on a dense grid of points. Utilizing the continual nature of the data, a fast two-stage algorithm is developed for highly efficient and accurate NMF. In the first stage, an alternating non-negative least-squares framework is used in combination with the active set method with a warm-start strategy for the solution of subproblems. In the second stage, an interior point method is adopted to accelerate the local convergence. The convergence of the proposed algorithm is proved. The new algorithm is compared with some existing algorithms in benchmark tests using both real-world data and synthetic data. The results demonstrate the advantage of the algorithm in finding high-precision solutions.

First Report of radish tubers rot caused by Enterobacter asburiae in China.

Radish (Raphanus sativus L.) is a widely consumed vegetable in China. However, radish is susceptible to diseases, which limits its yield and development in Harbin, China. In September 2021, rotten white radish tubers were observed in the field. The incidence of this disease reached 70% in October 2021, which led to huge economic losses (i.e., 30%-40%). Water-soaked lesions appeared on the radish tubers and appeared brown-yellow, which looked similar to ginger tuber rot caused by Enterobacter asburiae (Zhang et al. 2020). The interior was rotten with no considerable smell. Over time, the lesions gradually spread into all tubers of radish. Small square pieces of radish (0.5 cm × 0.5 cm) were excised from the junction of diseased and healthy tuber, disinfected with 75% alcohol, and washed three times with distilled water then ground to prepare tissue suspensions for plating. Under 28 ℃ for 16h, single colonies were isolated from the beef extract culture medium. Single colonies appeared oval, white, and smooth, with bright and slightly raised surfaces, and with moist neat edges. Gram-negative bacterial strain CCGL 988 was obtained, with an average size of 1-2 µm × 0.5-1 µm, and 3-4 flagella. Physiological and biological test results showed that strain CCGL 988 produced acid utilizing sucrose, glucose, maltobiose, D-Sorbitol, and mannitol; negative for Voges-Proskauer, methyl red, malanate, ornithine decarboxylase, arginine decarboxylase, and lysine decarboxylase. According to the results, strain CCGL 988 was identified as Enterobacter asburiae (Hoffmann et al. 2005). The 16S rDNA region of the strain was amplified using PCR with 27F/1492R primers (López et al. 2019), and partial gyrB, atpD, rpoB genes were amplified according to Zhang et al. (2020), infB gene was amplified with primers (F:TCAATGCGTGCTCGTGGTGCTC; R: TCGATACAGTGCCACTTCACG). The 16S rDNA, gyrB, atpD, rpoB and infB sequences were deposited in GenBank under accession numbers: ON999069, OP006448, OP006449, OP006450, and OP542231, respectively. These five sequences shared 99.80%, 100%, 100%, 100% and 100% of identity with E. asburiae (GenBank Accession: NO. CP011863). Maximum-likelihood phylogenetic tree clustered CCGL 988 with E. asburiae (MEGA7, bootstrap n = 1,000). Strain CCGL 988 was able to produce pectate lyases, polygalacturonases, cellulases, proteases, and extracellular polysaccharide using the methods described by Hugouviex-Cotte-Pattat et al. (2014), and Condemine et al. (1999). Koch's postulates were conducted by inoculating 20 µl of the bacterial suspension (108 CFU/ml) on the needle wound on the surface of six healthy radish tubers; six radish tubers incubated with sterile water were negative controls. Radish tubers were incubated at 28 ℃ with 80% humidity. The inoculated radish was slightly rotten after 7 days. Water-soaked lesions with light yellow were initially observed; after 12 days, the lesions expanded gradually and appeared deep yellow. No symptoms were found in the control radish. This experiment was carried out three times, each time with three replications. The bacterium was reisolated from infected radish tuber and was confirmed to be E. asburiae by the same molecular and morphological characterization as described above. This study is the first report of E. asburiae causing radish tuber rot in China. It serves as a basis for future studies to develop management strategies for the disease to prevent radish yield loss.

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.

BACKGROUND AND OBJECTIVE: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on fully supervised segmentation that assumes full and accurate pixel-level annotations are available. Such annotations are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost. We aim to develop a new deep learning toolkit to support annotation-efficient learning for medical image segmentation, which can accelerate and simplify the development of deep learning models with limited annotation budget, e.g., learning from partial, sparse or noisy annotations. METHODS: Our proposed toolkit named PyMIC is a modular deep learning library for medical image segmentation tasks. In addition to basic components that support development of high-performance models for fully supervised segmentation, it contains several advanced components that are tailored for learning from imperfect annotations, such as loading annotated and unannounced images, loss functions for unannotated, partially or inaccurately annotated images, and training procedures for co-learning between multiple networks, etc. PyMIC is built on the PyTorch framework and supports development of semi-supervised, weakly supervised and noise-robust learning methods for medical image segmentation. RESULTS: We present several illustrative medical image segmentation tasks based on PyMIC: (1) Achieving competitive performance on fully supervised learning; (2) Semi-supervised cardiac structure segmentation with only 10% training images annotated; (3) Weakly supervised segmentation using scribble annotations; and (4) Learning from noisy labels for chest radiograph segmentation. CONCLUSIONS: The PyMIC toolkit is easy to use and facilitates efficient development of medical image segmentation models with imperfect annotations. It is modular and flexible, which enables researchers to develop high-performance models with low annotation cost. The source code is available at:https://github.com/HiLab-git/PyMIC.

Interleukin-23 Regulates Inflammatory Osteoclastogenesis via Activation of CLEC5A(+) Osteoclast Precursors.

OBJECTIVE: To investigate the role of interleukin-23 (IL-23) in pathologic bone remodeling in inflammatory arthritis. METHODS: In this study we investigated the role of IL-23 in osteoclast differentiation and activation using in vivo gene transfer techniques in wild-type and myeloid DNAX-activation protein 12-associating lectin-1 (MDL-1)-deficient mice, and by performing in vitro and in vivo osteoclastogenesis assays using spectral flow cytometry, micro-computed tomography analysis, Western blotting, and immunoprecipitation. RESULTS: Herein, we show that IL-23 induces the expansion of a myeloid osteoclast precursor population and supports osteoclastogenesis and bone resorption in inflammatory arthritis. Genetic ablation of C-type lectin domain family member 5A, also known as MDL-1, prevents the induction of osteoclast precursors by IL-23 that is associated with bone destruction, as commonly observed in inflammatory arthritis. Moreover, osteoclasts derived from the bone marrow of MDL-1-deficient mice showed impaired osteoclastogenesis, and MDL-1-/- mice had increased bone mineral density. CONCLUSION: Our data show that IL-23 signaling regulates the availability of osteoclast precursors in inflammatory arthritis that could be effectively targeted for the treatment of inflammatory bone loss in inflammatory arthritis.