Photoactivated Formation of an Extravascular Dynamic Hydrogel as an Intelligent Blood Flow Regulator to Reprogram the Immunogenic Landscape.

Long-term tumor starvation may be a potential strategy to elevate the antitumor immune response by depriving nutrients. However, combining long-term starvation therapy with immunotherapy often yields limited efficacy due to the blockage of immune cell migration pathways. Herein, an intelligent blood flow regulator (BFR) is first established through photoactivated in situ formation of the extravascular dynamic hydrogel to compress blood vessels, which can induce long-term tumor starvation to elicit metabolic stress in tumor cells without affecting immune cell migration pathways. By leveraging methacrylate-modified nanophotosensitizers (HMMAN) and biodegradable gelatin methacrylate (GelMA), the developed extravascular hydrogel dynamically regulates blood flow via enzymatic degradation. Additionally, aPD-L1 loaded into HMMAN continuously blocks immune checkpoints. Systematic in vivo experiments demonstrate that the combination of immune checkpoint blockade (ICB) and BFR-induced metabolic stress (BIMS) significantly delays the progression of Lewis lung and breast cancers by reshaping the tumor immunogenic landscape and enhancing antitumor immune responses.

Sensitive detection of butyrylcholinesterase activity based on a stimuli-responsive fluorescence reaction.

A fluorogenic reaction between the chelate of Mn(II)-citric acid and terephthalic acid (PTA) was discovered, which was carried out through heating the aqueous mixture of Mn2+, citric acid and PTA. Detailed investigations indicated the reaction products were 2-hydroxyterephthalic acid (PTA-OH), which was attributed to the reaction between PTA and OH, formed by the triggering of Mn(II)-citric acid in the presence of dissolved O2. PTA-OH showed a strong blue fluorescence, peaked at 420 nm, and the fluorescence intensity presented a sensitive response to pH of the reaction system. Based on these mechanisms, the fluorogenic reaction was used for the detection of butyrylcholinesterase activity, achieving a detection limit of 0.15 U/L. The detection strategy was successfully applied in human serum samples, and it was also extended for the detection of organophosphorus pesticides and radical scavengers. Such a facile fluorogenic reaction and its stimuli-responsive properties offered an effective tool for designing detection pathways in the fields of clinical diagnosis, environmental monitoring and bioimaging.

Linking Phenotypes and Genotypes with Matrix Factorizations.

AIMS: We linked phenotypes and genotypes by PheGe-Net, a unified operation frame. BACKGROUND: Genotype refers to the general name of all gene combinations of an individual. It reflects the genetic composition of organisms. Phenotype refers to the macroscopic characteristics of an organism that can be observed. OBJECTIVE: Identifying the phenotype-genotype association assists in the explanation of the pathogenesis and the progress of genomic medicine. METHODS: PheGe-Net exploited the similarity net of phenotypes and genotypes and recognized phenotype-genotype relationships to discover their hidden interactions. RESULTS: By conducting experiments with a real-world dataset, the validity of our PheGe-Net is verified. Our method outperformed the second-best one by around 3% on Accuracy and NMI when clustering the phenotype/genotype; it also successfully detected phenotype-genotype associations, for example, the association for obesity (OMIM ID: 601665) was analyzed, and among the top ten scored genes, two known ones were assigned with scores more than 0.75, and other eight predicted ones are also explainable. CONCLUSION: PheGe-Net is not only able to discover latent phenotype or genotype clusters but also can uncover the hidden relationships among them, as long as there are known similarity networks of phenotype, genotype, and acknowledged pheno-genotype relationships.

SiBERT: A Siamese-based BERT network for Chinese medical entities alignment.

Entity alignment aims at associating semantically similar entities in knowledge graphs from different sources. It is widely used in the integration and construction of professional medical knowledge. The existing deep learning methods lack term-level embedding representation, which limits the performance of entity alignment and causes a massive computational overhead. To address these problems, we propose a Siamese-based BERT (SiBERT) for Chinese medical entities alignment. SiBERT generates term-level embedding based on word embedding sequences to enhance the features of entities in similarity calculation. The process of entity alignment contains three steps. Specifically, the SiBERT is firstly pre-trained with synonym dictionary in the public domain, and transferred to the task of medical entity alignment. Secondly, four different categories of entities (disease, symptom, treatment, and examination) are labeled based on the standard terms selected from standard terms dataset. The entities and their standard terms form term pairs to train SiBERT. Finally, combined with the entity alignment algorithm, the most similar standard term is selected as the final result. To evaluate the effectiveness of our method, we conduct extensive experiments on real-world datasets. The experimental results illustrate that SiBERT network is superior to other compared algorithms both in alignment accuracy and computational efficiency.

Ratiometric sensing of butyrylcholinesterase activity based on the MnO2 nanosheet-modulated fluorescence of sulfur quantum dots and o-phenylenediamine.

Butyrylcholinesterase (BChE) can modulate the expression level of cholinesterase, which emerges as an important clinical diagnose index. However, the currently reported assays for BChE are suffering from the problem of interferences. A ratiometric fluorescence assay was developed based on the MnO2 nanosheet (NS)-modulated fluorescence of sulfur quantum dots (S-dots) and o-phenylenediamine (OPD). MnO2 NS can not only quench the fluorescence of blue emissive S-dots, but also enhance the yellow emissive OPD by catalyzing its oxidation reactions. Upon introducing BChE and substrate into the system, their hydrolysate can reduce MnO2 into Mn2+, leading to the fluorescence recovery of S-dots and failure of OPD oxidation. BChE activity can be quantitatively detected by recording the change of fluorescence signals in the blue and yellow regions. A linear relationship is observed between the ratio of F435/F560 and the concentration of BChE in the range 30 to 500 U/L, and a limit of detection of 17.8 U/L has been calculated. The ratiometric fluorescence assay shows an excellent selectivity to acetylcholinesterase and tolerance to various other species. The method developed  provides good detection performances in human serum medium and for screening of  inhibitors.

Automatic glaucoma detection based on transfer induced attention network.

BACKGROUND: Glaucoma is one of the causes that leads to irreversible vision loss. Automatic glaucoma detection based on fundus images has been widely studied in recent years. However, existing methods mainly depend on a considerable amount of labeled data to train the model, which is a serious constraint for real-world glaucoma detection. METHODS: In this paper, we introduce a transfer learning technique that leverages the fundus feature learned from similar ophthalmic data to facilitate diagnosing glaucoma. Specifically, a Transfer Induced Attention Network (TIA-Net) for automatic glaucoma detection is proposed, which extracts the discriminative features that fully characterize the glaucoma-related deep patterns under limited supervision. By integrating the channel-wise attention and maximum mean discrepancy, our proposed method can achieve a smooth transition between general and specific features, thus enhancing the feature transferability. RESULTS: To delimit the boundary between general and specific features precisely, we first investigate how many layers should be transferred during training with the source dataset network. Next, we compare our proposed model to previously mentioned methods and analyze their performance. Finally, with the advantages of the model design, we provide a transparent and interpretable transferring visualization by highlighting the key specific features in each fundus image. We evaluate the effectiveness of TIA-Net on two real clinical datasets and achieve an accuracy of 85.7%/76.6%, sensitivity of 84.9%/75.3%, specificity of 86.9%/77.2%, and AUC of 0.929 and 0.835, far better than other state-of-the-art methods. CONCLUSION: Different from previous studies applied classic CNN models to transfer features from the non-medical dataset, we leverage knowledge from the similar ophthalmic dataset and propose an attention-based deep transfer learning model for the glaucoma diagnosis task. Extensive experiments on two real clinical datasets show that our TIA-Net outperforms other state-of-the-art methods, and meanwhile, it has certain medical value and significance for the early diagnosis of other medical tasks.