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.

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 code will be available at https://github.com/guobaoxiao/LSC.

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.

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.

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.

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.

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.

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.

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.

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.

Concealed Object Detection.

We present the first systematic study on concealed object detection (COD), which aims to identify objects that are visually embedded in their background. The high intrinsic similarities between the concealed objects and their background make COD far more challenging than traditional object detection/segmentation. To better understand this task, we collect a large-scale dataset, called COD10K, which consists of 10,000 images covering concealed objects in diverse real-world scenarios from 78 object categories. Further, we provide rich annotations including object categories, object boundaries, challenging attributes, object-level labels, and instance-level annotations. Our COD10K is the largest COD dataset to date, with the richest annotations, which enables comprehensive concealed object understanding and can even be used to help progress several other vision tasks, such as detection, segmentation, classification etc. Motivated by how animals hunt in the wild, we also design a simple but strong baseline for COD, termed the Search Identification Network (SINet). Without any bells and whistles, SINet outperforms twelve cutting-edge baselines on all datasets tested, making them robust, general architectures that could serve as catalysts for future research in COD. Finally, we provide some interesting findings, and highlight several potential applications and future directions. To spark research in this new field, our code, dataset, and online demo are available at our project page: http://mmcheng.net/cod.

Re-Thinking Co-Salient Object Detection.

In this article, we conduct a comprehensive study on the co-salient object detection (CoSOD) problem for images. CoSOD is an emerging and rapidly growing extension of salient object detection (SOD), which aims to detect the co-occurring salient objects in a group of images. However, existing CoSOD datasets often have a serious data bias, assuming that each group of images contains salient objects of similar visual appearances. This bias can lead to the ideal settings and effectiveness of models trained on existing datasets, being impaired in real-life situations, where similarities are usually semantic or conceptual. To tackle this issue, we first introduce a new benchmark, called CoSOD3k in the wild, which requires a large amount of semantic context, making it more challenging than existing CoSOD datasets. Our CoSOD3k consists of 3,316 high-quality, elaborately selected images divided into 160 groups with hierarchical annotations. The images span a wide range of categories, shapes, object sizes, and backgrounds. Second, we integrate the existing SOD techniques to build a unified, trainable CoSOD framework, which is long overdue in this field. Specifically, we propose a novel CoEG-Net that augments our prior model EGNet with a co-attention projection strategy to enable fast common information learning. CoEG-Net fully leverages previous large-scale SOD datasets and significantly improves the model scalability and stability. Third, we comprehensively summarize 40 cutting-edge algorithms, benchmarking 18 of them over three challenging CoSOD datasets (iCoSeg, CoSal2015, and our CoSOD3k), and reporting more detailed (i.e., group-level) performance analysis. Finally, we discuss the challenges and future works of CoSOD. We hope that our study will give a strong boost to growth in the CoSOD community. The benchmark toolbox and results are available on our project page at https://dpfan.net/CoSOD3K.

Uncertainty Inspired RGB-D Saliency Detection.

We propose the first stochastic framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection models treat this task as a point estimation problem by predicting a single saliency map following a deterministic learning pipeline. We argue that, however, the deterministic solution is relatively ill-posed. Inspired by the saliency data labeling process, we propose a generative architecture to achieve probabilistic RGB-D saliency detection which utilizes a latent variable to model the labeling variations. Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution. The generator model is an encoder-decoder saliency network. To infer the latent variable, we introduce two different solutions: i) a Conditional Variational Auto-encoder with an extra encoder to approximate the posterior distribution of the latent variable; and ii) an Alternating Back-Propagation technique, which directly samples the latent variable from the true posterior distribution. Qualitative and quantitative results on six challenging RGB-D benchmark datasets show our approach's superior performance in learning the distribution of saliency maps. The source code is publicly available via our project page: https://github.com/JingZhang617/UCNet.

Bifurcated Backbone Strategy for RGB-D Salient Object Detection.

Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features meet multi-modal cues, the optimal feature aggregation and multi-modal learning strategy become a hot potato. In this paper, we leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to devise a novel Bifurcated Backbone Strategy Network (BBS-Net). Our architecture, is simple, efficient, and backbone-independent. In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS). Second, we introduce a depth-enhanced module (DEM) to excavate informative depth cues from the channel and spatial views. Then, RGB and depth modalities are fused in a complementary way. Extensive experiments show that BBS-Net significantly outperforms 18 state-of-the-art (SOTA) models on eight challenging datasets under five evaluation measures, demonstrating the superiority of our approach (~4% improvement in S-measure vs . the top-ranked model: DMRA). In addition, we provide a comprehensive analysis on the generalization ability of different RGB-D datasets and provide a powerful training set for future research. The complete algorithm, benchmark results, and post-processing toolbox are publicly available at https://github.com/zyjwuyan/BBS-Net.

COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework.

With the global outbreak of COVID-19 in early 2020, rapid diagnosis of COVID-19 has become the urgent need to control the spread of the epidemic. In clinical settings, lung infection segmentation from computed tomography (CT) images can provide vital information for the quantification and diagnosis of COVID-19. However, accurate infection segmentation is a challenging task due to (i) the low boundary contrast between infections and the surroundings, (ii) large variations of infection regions, and, most importantly, (iii) the shortage of large-scale annotated data. To address these issues, we propose a novel two-stage cross-domain transfer learning framework for the accurate segmentation of COVID-19 lung infections from CT images. Our framework consists of two major technical innovations, including an effective infection segmentation deep learning model, called nCoVSegNet, and a novel two-stage transfer learning strategy. Specifically, our nCoVSegNet conducts effective infection segmentation by taking advantage of attention-aware feature fusion and large receptive fields, aiming to resolve the issues related to low boundary contrast and large infection variations. To alleviate the shortage of the data, the nCoVSegNet is pre-trained using a two-stage cross-domain transfer learning strategy, which makes full use of the knowledge from natural images (i.e., ImageNet) and medical images (i.e., LIDC-IDRI) to boost the final training on CT images with COVID-19 infections. Extensive experiments demonstrate that our framework achieves superior segmentation accuracy and outperforms the cutting-edge models, both quantitatively and qualitatively.

Antibacterial effects and physical properties of a glass ionomer cement containing BioUnion filler with acidity-induced ability to release zinc ion.

BioUnion filler is a bioactive glass particle that releases Zn2+ in an acidic environment. In this study, the ion release, antibacterial, and physical properties of a glass ionomer cement (GIC) incorporating BioUnion filler (CA) were assessed in vitro. The concentration of Zn2+ released from CA into acetic acid was higher than that released into water and its minimum inhibitory concentrations against six oral bacterial species. Moreover, the concentration of Zn2+-release was maintained during all the seven times it was exposed to acetic acid. Compared to a conventional cement and resin composite, CA significantly inhibited the growth of oral bacteria and hindered their adhesion on the material surface. Thus, our study outcomes show that the release of Zn2+ from CA in the acidic environment does not affect its compressive strength.