AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking.

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673 K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms-the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/dataset.

"Three-in-One" Plasmonic Au@PtOs Nanocluster Driven Lateral Flow Assay for Multimodal Cancer Exosome Biosensing.

Exploiting the multiple properties of nanozymes for the multimode lateral flow assay (LFA) is urgently required to improve the accuracy and versatility. Herein, we developed a novel plasmonic Au nanostar@PtOs nanocluster (Au@PtOs) as a multimode signal tag for LFA detection. Based on the PtOs bimetallic nanocluster doping strategy, Au@PtOs can indicate both excellent SERS enhancement and nanozyme catalytic activity. Meanwhile, Au@PtOs displays a better photothermal effect than that of Au nanostars. Therefore, catalytic colorimetric/SERS/temperature three-mode signals can be read out based on the Au@PtOs nanocomposite. The Au@PtOs was combined with LFA and applied for breast cancer exosome detection. The detection limit for the colorimetric/SERS/temperature mode was 2.6 × 103/4.1 × 101/4.6 × 102 exosomes/µL, respectively, which was much superior to the common Au nanoparticles LFA (∼105 exosomes/µL). Moreover, based on the fingerprint molecular recognition ability of the SERS mode, exosome phenotypes derived from different breast cancer cell lines can be discriminated easily. Based on the convenient visual colorimetric mode and sensitive SERS/temperature quantitative modes, Au@PtOs driven LFA can satisfy the requirements of accurate and flexible multimodal sensing in different application scenarios.

Interfacial engineering of POM-stabilized Ni quantum dots on porous titanium mesh for high-rate and stable alkaline hydrogen production.

The development of low-cost, high-efficiency, and stable electrocatalysts for the alkaline hydrogen evolution reaction (HER) is a key challenge because the alkaline HER kinetics is slowed by an additional water dissociation step. Herein, we report an interfacial engineering strategy for polyoxometalate (POM)-stabilized nickel (Ni) quantum dots decorated on the surface of porous titanium mesh (POMs-Ni@PTM) for high-rate and stable alkaline hydrogen production. Benefiting from the strong interfacial interactions among POMs, Ni atoms, and PTM substrates, as well as unique POM-Ni quantum dot structures, the optimized POMs-Ni@PTM electrocatalyst exhibits a remarkable alkaline HER performance with an overpotential (η10) of 30.1 mV to reach a current density of 10 mA cm-2, which is much better than those of bare Ni decorated porous titanium mesh (Ni@PTM) (η10 = 171.1 mV) and POM decorated porous titanium mesh (POMs@PTM) electrocatalysts (η10 = 493.6 mV), comparable to that of the commercial 20 wt% platinum/carbon (20% Pt/C) electrocatalyst (η10 = 20 mV). Moreover, the optimized POMs-Ni@PTM electrocatalyst demonstrates excellent stability under continuous alkaline water-splitting at a current density of ∼100 mA cm-2 for 100 h, demonstrating great potential for its practical application.

RNA sequencing and proteomic profiling reveal alterations by MPTP in chronic stomach mucosal injury in tree shrew Chinese (Tupaia belangeri chinensis).

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is a neurotoxin that can cause gastrointestinal ulcers by affecting dopamine levels. Therefore, MPTP has been considered a toxic substance that causes gastric ulcer disease in experimental animals. In this study, tree shrews were used as the animal model of gastric mucosa injury, and MPTP was intraperitoneally injected at a lower MPTP dosage 2 mg/kg/day for 13 weeks, while tree shrews were not injected as the control group. Under the light microscope, local congestion or diffuse bleeding points of gastric mucosa and multiple redness and swelling bleeding symptoms on the inner wall were observed in the treatment group, as well as immune cell infiltration was found in HE staining, but no such phenomenon was observed in the control group. In order to explore the molecular basis of changes in MPTP induced gastric mucosa injury, the transcriptome and proteome data of gastric mucosa were analyzed. We observed significant differences in mRNA and protein expression levels under the influence of MPTP. The changes in mRNA and proteins are related to increased immune infiltration, cellular processes and angiogenesis. More differentially expressed genes play a role in immune function, especially the candidate genes RPL4 and ANXA1 with significant signal and core role. There are also differentially expressed genes that play a role in mucosal injury and shedding, especially candidate genes GAST and DDC with certain signaling and corresponding functions. Understanding the factors and molecular basis that affect the expression of related genes is crucial for coping with Emotionality gastric mucosa injury disease and developing new treatment methods to establish the ability to resist disease.