Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges.

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a routine clinical examination. For the instrument segmentation task, the best team obtained a mean Intersection over union metric of 0.9364. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. The best team obtained a final transparency score of 21 out of 25. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage subjective evaluation for building more transparent and understandable AI-based colonoscopy systems. Moreover, we discuss the need for multi-center and out-of-distribution testing to address the current limitations of the methods to reduce the cancer burden and improve patient care.

Rapid discovery of natural antioxidants in Hypericum japonicum: Dual roles of the liquid phase mobile phase as extraction and separation solvent.

Hypericum japonicum is a traditional folk medicine with various bioactivities such as hepatoprotective, antioxidant, and anti-tumorous. The antioxidant effect of H. japonicum is one of the most prominent effects due to its responsibility for many of its activities. To clarify active natural substance, the antioxidant properties of H. japonicum were preliminarily assessed by ferric reducing-antioxidant power (FRAP), 2,2-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS) and Oxygen radical absorbance capacity (ORAC), as well as superoxide dismutase (SOD). Then, a straightforward and effective method named online liquid extraction-high performance liquid chromatography combined with ABTS antioxidant assay and mass spectrometry (OLE-HPLC-ABTS/Q-TOF-MS) was developed to swiftly and directly discover the antioxidants in H. japonicum. Using mobile phase as extraction and separation reagent, coupled with online activity analysis and compounds identification by high-resolution MS, the online system enables rapid screening of natural antioxidant bioactives from complex mixture. By using it, a total of 9 compounds including flavonoids and phenolic acids characterized by retention time, precise mass, and fragmentation ions in MS/MS spectra showed antioxidant action. Finally, the antioxidant and SOD activity of main found active compounds were validated by in vitro experiment assay and molecular docking. In summary, these results suggested that H. japonicum could be considered as a potential source of natural antioxidants, and the online integrated system might become a promising candidate for the natural antioxidants discovery in the future.

CircUBA2 promotes the cancer stem cell-like properties of gastric cancer through upregulating STC1 via sponging miR-144-5p.

BACKGROUND: Cancer stem cells (CSCs) are critical factors that limit the effectiveness of gastric cancer (GC) therapy. Circular RNAs (circRNAs) are confirmed as important regulators of many cancers. However, their role in regulating CSC-like properties of GC remains largely unknown. Our study aimed to investigate the role of circUBA2 in CSC maintenance and the underlying mechanisms. METHODS: We identified circUBA2 as an upregulated gene using circRNA microarray analysis. qRT-PCR was used to examine the circUBA2 levels in normal and GC tissues. In vitro and in vivo functional assays were performed to validate the role of circUBA2 in proliferation, migration, metastasis and CSC-like properties of GC cell. The relationship between circUBA2, miR-144-5p and STC1 was characterised using bioinformatics analysis, a dual fluorescence reporter system, FISH, and RIP assays. RESULTS: CircUBA2 expression was significantly increased in GC tissues, and patients with GC with high circUBA2 expression had a poor prognosis. CircUBA2 enhances CSC-like properties of GC, thereby promoting cell proliferation, migration, and metastasis. Mechanistically, circUBA2 promoted GC malignancy and CSC-like properties by acting as a sponge for miR-144-5p to upregulate STC1 expression and further activate the IL-6/JAK2/STAT3 signaling pathway. More importantly, the ability of circUBA2 to enhance CSC-like properties was inhibited by tocilizumab, a humanised Interleukin-6 receptor (IL-6R) antibody. Thus, circUBA2 knockdown and tocilizumab synergistically inhibited CSC-like properties. CONCLUSIONS: Our study demonstrated the critical role of circUBA2 in regulating CSC-like properties in GC. CircUBA2 may be a promising prognostic biomarker for GC.

CamoFormer: Masked Separable Attention for Camouflaged Object Detection.

How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. Furthermore, we propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results. These structures plus a backbone encoder form a new model, dubbed CamoFormer. Extensive experiments show that CamoFormer achieves new state-of-the-art performance on three widely-used camouflaged object detection benchmarks. To better evaluate the performance of the proposed CamoFormer around the border regions, we propose to use two new metrics, i.e. BR-M and BR-F. There are on average  âˆ¼ 5% relative improvements over previous methods in terms of S-measure and weighted F-measure. Our code is available at https://github.com/HVision-NKU/CamoFormer.

Depressive symptom mediates the association between the number of chronic diseases and cognitive impairment: a multi-center cross-sectional study based on community older adults.

Objective: The purpose of this study was to understand the relationship between the multiple chronic conditions (MCC), mental health and cognitive function of older adults in the community, and to propose a hypothesis that depressive symptom mediate the number of chronic diseases and cognitive impairment in older adults. Method: Participants aged 65 years and older from 35 communities in 14 cities in Guangxi, China were recruited. The residents' depressive symptom (PHQ-9) and cognitive status (AD-8) were evaluated, Chi-square test was used to explore the effects of different socio-demographic characteristics on depressive symptom and cognitive impairment. Pearson correlation analysis and the process model 4 were used to explore the relationship between the number of chronic diseases, depressive symptom and cognitive impairment. Result: A total of 11,582 older adults were included in our analysis. The rate of MCC reaching 26.53%. Hypertension combined with diabetes accounts for the highest proportion of two chronic diseases (13.2%). Among the combination of three chronic diseases, the highest incidence of coexisting hypertension combined with cervical/lumbar spondylosis, and rheumatoid arthritis (7.1%). In this study, depression symptoms accounted for 12.9% of older adults aged 65 and above, and cognitive impairment accounted for 27.4%. Female, older age, reside in urban areas, lower educational levels, no spouse, live alone, and MCC were risk factors for depressive symptom and cognitive impairment in older adults (P<0.05). Depressive symptom had a mediating effect in the number of chronic diseases and cognitive impairment, and the mediating effect (1.109) accounted for 44.13% of the total effect (0.247). Conclusion: The mental health of the older adult needs to be taken seriously, and improving depressive symptom can reduce the occurrence of cognitive impairment in older patients with MCC to a certain extent.

Multi-gene phylogenetic analyses revealed two novel species and one new record of Trichobotrys (Pleosporales, Dictyosporiaceae) from China.

The rotting wood in freshwater is a unique eco-environment favoring various fungi. During our investigation of freshwater fungi on decaying wood, three hyphomycetes were collected from Jiangxi and Guangxi Provinces, China. Based on the morphological observations and phylogenetic analysis of a combined DNA data containing ITS, LSU, SSU and tef1-α sequences, two new Trichobotrys species, T.meilingensis and T.yunjushanensis, as well as a new record of T.effusa, were introduced. Additionally, a comprehensive description of the genus with both morphological and molecular data was first provided.

CalibNet: Dual-Branch Cross-Modal Calibration for RGB-D Salient Instance Segmentation.

In this study, we propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously calibrates depth and RGB features in the kernel and mask branches to generate instance-aware kernels and mask features. CalibNet consists of three simple modules, a dynamic interactive kernel (DIK) and a weight-sharing fusion (WSF), which work together to generate effective instance-aware kernels and integrate cross-modal features. To improve the quality of depth features, we incorporate a depth similarity assessment (DSA) module prior to DIK and WSF. In addition, we further contribute a new DSIS dataset, which contains 1,940 images with elaborate instance-level annotations. Extensive experiments on three challenging benchmarks show that CalibNet yields a promising result, i.e., 58.0% AP with 320×480 input size on the COME15K-E test set, which significantly surpasses the alternative frameworks. Our code and dataset will be publicly available at: https://github.com/PJLallen/CalibNet.

Circ-0075305 hinders gastric cancer stem cells by indirectly disrupting TCF4-ß-catenin complex and downregulation of SOX9.

CircRNAs are covalently closed, single-stranded RNA that form continuous loops and play a crucial role in the initiation and progression of tumors. Cancer stem cells (CSCs) are indispensable for cancer development; however, the regulation of cancer stem cell-like properties in gastric cancer (GC) and its specific mechanism remain poorly understood. We elucidate the specific role of Circ-0075305 in GC stem cell properties. Circ-0075305 associated with chemotherapy resistance was identified by sequencing GC cells. Subsequent confirmation in both GC tissues and cell lines revealed that patients with high expression of Circ-0075305 had significantly better overall survival (OS) rates than those with low expression, particularly when treated with postoperative adjuvant chemotherapy for GC. In vitro and in vivo experiments confirmed that overexpression of Circ-0075305 can effectively reduce stem cell-like properties and enhance the sensitivity of GC cells to Oxaliplatin compared with the control group. Circ-0075305 promotes RPRD1A expression by acting as a sponge for corresponding miRNAs. The addition of LF3 (a ß-catenin/TCF4 interaction antagonist) confirmed that RPRD1A inhibited the formation of the TCF4-ß-catenin transcription complex through competitive to ß-catenin and suppressed the transcriptional activity of stem cell markers such as SOX9 via the Wnt/ß-catenin signaling pathway. This leads to the downregulation of stem cell-like property-related markers in GC. This study revealed the underlying mechanisms that regulate Circ-0075305 in GCSCs and suggests that its role in reducing ß-catenin signaling may serve as a potential therapeutic candidate.

Robot-assisted versus laparoscopic-assisted gastrectomy among malnourished patients with gastric cancer based on textbook outcome.

BACKGROUND: Textbook outcome (TO) has been widely employed as a comprehensive indicator to assess the short-term prognosis of patients with cancer. Preoperative malnutrition is a potential risk factor for adverse surgical outcomes in patients with gastric cancer (GC). This study aimed to compare the TO between robotic-assisted gastrectomy (RAG) and laparoscopic-assisted gastrectomy (LAG) in malnourished patients with GC. METHODS: According to the diagnostic consensus of malnutrition proposed by Global Leadership Initiative on Malnutrition (GLIM) and Nutrition Risk Index (NRI), 895 malnourished patients with GC who underwent RAG (n = 115) or LAG (n = 780) at a tertiary referral hospital between January 2016 and May 2021 were included in the propensity score matching (PSM, 1:2) analysis. RESULTS: After PSM, no significant differences in clinicopathological characteristics were observed between the RAG (n = 97) and LAG (n = 194) groups. The RAG group had significantly higher operative time and lymph nodes harvested, as well as significantly lower blood loss and hospital stay time compared to the LAG group. More patients in the RAG achieved TO. Logistic regression analysis revealed that RAG was an independent protective factor for achieving TO. There were more adjuvant chemotherapy (AC) cycles in the RAG group than in the LAG group. After one year of surgery, a higher percentage of patients (36.7% vs. 22.8%; P < 0.05) in the RAG group recovered from malnutrition compared to the LAG group. CONCLUSIONS: For malnourished patients with GC, RAG performed by experienced surgeons can achieved a higher rate of TO than those of LAG, which directly contributed to better AC compliance and a faster restoration of nutritional status.

Latent Semantic Consensus for Deterministic Geometric Model Fitting.

Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi-structural data. To address this, we propose an effective method called Latent Semantic Consensus (LSC). The principle of LSC is to preserve the latent semantic consensus in both data points and model hypotheses. Specifically, LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses, respectively. Then, LSC explores the distributions of points in the two latent semantic spaces, to remove outliers, generate high-quality model hypotheses, and effectively estimate model instances. Finally, LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting, due to its deterministic fitting nature and efficiency. Compared with several state-of-the-art model fitting methods, our LSC achieves significant superiority for the performance of both accuracy and speed on synthetic data and real images.

The role of cannabinoid receptor 2 in bone remodeling during orthodontic tooth movement.

BACKGROUND: The purpose of this study is to explore the effects of CB2 on bone regulation during orthodontic tooth movement. METHODS: Thirty male mice were allocated into 2 groups (n = 15 in each group): wild type (WT) group and CB2 knockout (CB2-/-) group. Orthodontic tooth movement (OTM) was induced by applying a nickel-titanium coil spring between the maxillary first molar and the central incisors. There are three subgroups within the WT groups (0, 7 and 14 days) and the CB2-/- groups (0, 7 and 14 days). 0-day groups without force application. Tooth displacement, alveolar bone mass and alveolar bone volume were assessed by micro-CT on 0, 7 and 14 days, and the number of osteoclasts was quantified by tartrate-resistant acid phosphatase (TRAP) staining. Moreover, the expression levels of RANKL and OPG in the compression area were measured histomorphometrically. RESULTS: The WT group exhibited the typical pattern of OTM, characterized by narrowed periodontal space and bone resorption on the compression area. In contrast, the accelerated tooth displacement, increased osteoclast number (P < 0.0001) and bone resorption on the compression area in CB2-/- group. Additionally, the expression of RANKL was significantly upregulated, while OPG showed low levels in the compression area of the CB2 - / - group (P < 0.0001). CONCLUSIONS: CB2 modulated OTM and bone remodeling through regulating osteoclast activity and RANKL/OPG balance.

Segment anything model for medical images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging because of the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks. We comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset. Our findings mainly include the following: (1) SAM showed remarkable performance in some specific objects but was unstable, imperfect, or even totally failed in other situations. (2) SAM with the large ViT-H showed better overall performance than that with the small ViT-B. (3) SAM performed better with manual hints, especially box, than the Everything mode. (4) SAM could help human annotation with high labeling quality and less time. (5) SAM was sensitive to the randomness in the center point and tight box prompts, and may suffer from a serious performance drop. (6) SAM performed better than interactive methods with one or a few points, but will be outpaced as the number of points increases. (7) SAM's performance correlated to different factors, including boundary complexity, intensity differences, etc. (8) Finetuning the SAM on specific medical tasks could improve its average DICE performance by 4.39% and 6.68% for ViT-B and ViT-H, respectively. Codes and models are available at: https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images. We hope that this comprehensive report can help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.

Resolving a nearly 90-year-old enigma: The rare Fagus chienii is conspecific with F. hayatae based on molecular and morphological evidence.

Taxonomic uncertainties of rare species often hinder effective prioritization for conservation. One such taxonomic uncertainty is the 90-year-old enigma of Fagus chienii. F. chienii was previously only known from the type specimens collected in 1935 in Pingwu County of Sichuan Province, China, and has long been thought to be on the verge of extinction. However, morphological similarities to closely related Fagus species have led many to question the taxonomic status of F. chienii. To clarify this taxonomic uncertainty, we used the newly collected samples to reconstruct a molecular phylogeny of Chinese Fagus species against the phylogenetic backbone of the whole genus using seven nuclear genes. In addition, we examined nine morphological characters to determine whether F. chienii is morphologically distinct from its putatively closest relatives (F. hayatae, F.longipetiolata, and F.lucida). Both morphological and phylogenetic analyses indicated that F. chienii is conspecific with F. hayatae. We recommended that F. chienii should not be treated as a separate species in conservation management. However, conservation strategies such as in situ protection and ex situ germplasm preservation should be adopted to prevent the peculiar "F. chienii" population from extinction.

The Relationship between Cadmium Exposure and Mortality in Postmenopausal Females: A Cohort Study of 2001-2018 NHANES.

Cadmium is one of the most harmful elements to human health, and the health of postmenopausal females is an important public health issue. However, the correlation between exposure to cadmium and the survival status of postmenopausal women is currently not fully clear. This research intended to explore the correlation between cadmium exposure and mortality among postmenopausal females using a representative sample of the population in the U.S. We drew upon the data of the National Health and Nutrition Examination Survey (2001-2018). Cox's proportional hazards models and a restricted cubic spline regression (RCS) model were utilized to analyze the correlation between blood and urine cadmium and the mortality of postmenopausal women. Stratified analyses also were conducted to identify the highest risk factor of mortality for the participants. The mean concentration of blood cadmium was 0.59 µg/L, and the mean concentration of urine cadmium was 0.73 µg/g creatinine. Higher cadmium concentrations in blood and urine were significantly related to an increase in all-cause mortality for postmenopausal females after adjustment for multivariate covariates. Furthermore, there was a linear positive correlation between urine cadmium concentrations and cancer mortality, while there was no correlation between blood cadmium and cancer death. The correlation between cadmium concentrations and all-cause mortality is stronger in older, more overweight women with a history of hypertension or smoking. We propose that cadmium remains an important risk factor of all-cause and cancer mortality among postmenopausal females in the U.S. Further decreases in cadmium exposure in the population can promote the health of postmenopausal women and prolong their lifespan.

GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector.

In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art performance for co-salient object detection (CoSOD) through mining consensus representations based on the following two essential criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module (GAM); 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module (GCM) conditioning on the inconsistent consensus. To further improve the accuracy, we design a series of simple yet effective components as follows: i) a recurrent auxiliary classification module (RACM) promoting model learning at the semantic level; ii) a confidence enhancement module (CEM) assisting the model in improving the quality of the final predictions; and iii) a group-based symmetric triplet (GST) loss guiding the model to learn more discriminative features. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and CoSal2015, demonstrate that our GCoNet+ outperforms the existing 12 cutting-edge models. Code has been released at https://github.com/ZhengPeng7/GCoNet_plus.

IC9600: A Benchmark Dataset for Automatic Image Complexity Assessment.

Image complexity (IC) is an essential visual perception for human beings to understand an image. However, explicitly evaluating the IC is challenging, and has long been overlooked since, on the one hand, the evaluation of IC is relatively subjective due to its dependence on human perception, and on the other hand, the IC is semantic-dependent while real-world images are diverse. To facilitate the research of IC assessment in this deep learning era, we built the first, to our best knowledge, large-scale IC dataset with 9,600 well-annotated images. The images are of diverse areas such as abstract, paintings and real-world scenes, each of which is elaborately annotated by 17 human contributors. Powered by this high-quality dataset, we further provide a base model to predict the IC scores and estimate the complexity density maps in a weakly supervised way. The model is verified to be effective, and correlates well with human perception (with the Pearson correlation coefficient being 0.949). Last but not the least, we have empirically validated that the exploration of IC can provide auxiliary information and boost the performance of a wide range of computer vision tasks. The dataset and source code can be found at https://github.com/tinglyfeng/IC9600.

Salient Object Detection via Integrity Learning.

Although current salient object detection (SOD) works have achieved significant progress, they are limited when it comes to the integrity of the predicted salient regions. We define the concept of integrity at both a micro and macro level. Specifically, at the micro level, the model should highlight all parts that belong to a certain salient object. Meanwhile, at the macro level, the model needs to discover all salient objects in a given image. To facilitate integrity learning for SOD, we design a novel Integrity Cognition Network (ICON), which explores three important components for learning strong integrity features. 1) Unlike existing models, which focus more on feature discriminability, we introduce a diverse feature aggregation (DFA) component to aggregate features with various receptive fields (i.e., kernel shape and context) and increase feature diversity. Such diversity is the foundation for mining the integral salient objects. 2) Based on the DFA features, we introduce an integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects, while suppressing the other distracting ones. 3) After extracting the enhanced features, the part-whole verification (PWV) method is employed to determine whether the part and whole object features have strong agreement. Such part-whole agreements can further improve the micro-level integrity for each salient object. To demonstrate the effectiveness of our ICON, comprehensive experiments are conducted on seven challenging benchmarks. Our ICON outperforms the baseline methods in terms of a wide range of metrics. Notably, our ICON achieves  âˆ¼ 10% relative improvement over the previous best model in terms of average false negative ratio (FNR), on six datasets. Codes and results are available at: https://github.com/mczhuge/ICON.

Salient Objects in Clutter.

In this paper, we identify and address a serious design bias of existing salient object detection (SOD) datasets, which unrealistically assume that each image should contain at least one clear and uncluttered salient object. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. However, these models are still far from satisfactory when applied to real-world scenes. Based on our analyses, we propose a new high-quality dataset and update the previous saliency benchmark. Specifically, our dataset, called Salient Objects in Clutter (SOC), includes images with both salient and non-salient objects from several common object categories. In addition to object category annotations, each salient image is accompanied by attributes that reflect common challenges in common scenes, which can help provide deeper insight into the SOD problem. Further, with a given saliency encoder, e.g., the backbone network, existing saliency models are designed to achieve mapping from the training image set to the training ground-truth set. We therefore argue that improving the dataset can yield higher performance gains than focusing only on the decoder design. With this in mind, we investigate several dataset-enhancement strategies, including label smoothing to implicitly emphasize salient boundaries, random image augmentation to adapt saliency models to various scenarios, and self-supervised learning as a regularization strategy to learn from small datasets. Our extensive results demonstrate the effectiveness of these tricks. We also provide a comprehensive benchmark for SOD, which can be found in our repository: https://github.com/DengPingFan/SODBenchmark.

Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge.

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a 19× faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/.

ERα/ß/DMP1 axis promotes trans-differentiation of chondrocytes to bone cells through GSK-3ß/ß-catenin pathway.

Estrogen-induced premature closing of the growth plate in the long bones is a major cause of short stature after premature puberty. Recent studies have found that chondrocytes can directly trans-differentiate into osteoblasts in the process of endochondral bone formation, which indicates that cartilage formation and osteogenesis may be a continuous biological process. However, whether estrogen promotes the direct trans-differentiation of chondrocytes into osteoblasts remains largely unknown. Chondrocytes were treated with different concentrations of 17ß-estradiol, and Alizarin Red staining and alkaline phosphatase activity assay were used to detected osteogenesis. Specific short hairpin RNA and tamoxifen were used to block the estrogen receptor (ER) pathway and osteogenic marker genes and downstream gene expression were detected using real-time quantitative polymerase chain reaction, western blot, and immunohistochemistry staining. The findings showed that 17ß-estradiol promoted the chondrocyte osteogenesis in vitro, even at high concentrations. In addition, blocking of the ERα/ß pathway inhibited the trans-differentiation of chondrocytes into osteogenic cells. Furthermore, we found that dentin matrix protein 1 (DMP1), which is a direct downstream molecular of ER, was involved in 17ß-estradiol/ER pathway-regulated osteogenesis. As well, glycogen synthase kinase-3 beta (GSK-3ß)/ß-catenin signal pathway also participates in ERα/ß/DMP1-regulated chondrocyte osteogenesis. The GSK-3ß/ß-catenin signal pathway was involved in ERα/ß/DMP1-regulated chondrocyte osteogenesis. These findings suggest that ER/DMP1/GSK-3ß/ß-catenin plays a vital role in estrogen regulation of chondrocyte osteogenesis and provide a therapeutic target for short stature caused by epiphyseal fusion.