Bitter taste TAS2R14 activation by intracellular tastants and cholesterol.

Bitter taste receptors, particularly TAS2R14, play central roles in discerning a wide array of bitter substances, ranging from dietary components to pharmaceutical agents1,2. TAS2R14 is also widely expressed in extra-gustatory tissues, suggesting its additional roles in diverse physiological processes and potential therapeutic applications3. Here, we present cryo-electron microcopy structures of TAS2R14 in complex with aristolochic acid, flufenamic acid and compound 28.1, coupling with different G protein subtypes. Uniquely, a cholesterol molecule is observed occupying what is typically an orthosteric site in class A GPCRs. The three potent agonists bind, individually, to the intracellular pockets, suggesting a distinct activation mechanism for this receptor. Comprehensive structural analysis, combined with mutagenesis, and molecular dynamic simulations studies, illuminate the receptor's broad-spectrum ligand recognition and activation via intricate multiple ligand-binding sites. Additionally, our study uncovers the specific coupling modes of TAS2R14 with gustducin and Gi1 proteins. These findings should be instrumental in advancing our knowledge underlying bitter taste perception and its broader implications in sensory biology and drug discovery.

Structural basis for strychnine activation of human bitter taste receptor TAS2R46.

Taste sensing is a sophisticated chemosensory process, and bitter taste perception is mediated by type 2 taste receptors (TAS2Rs), or class T G protein-coupled receptors. Understanding the detailed molecular mechanisms behind taste sensation is hindered by a lack of experimental receptor structures. Here, we report the cryo-electron microscopy structures of human TAS2R46 complexed with chimeric mini-G protein gustducin, in both strychnine-bound and apo forms. Several features of TAS2R46 are disclosed, including distinct receptor structures that compare with known GPCRs, a new "toggle switch," activation-related motifs, and precoupling with mini-G protein gustducin. Furthermore, the dynamic extracellular and more-static intracellular parts of TAS2R46 suggest possible diverse ligand-recognition and activation processes. This study provides a basis for further exploration of other bitter taste receptors and their therapeutic applications.

Periphery-aware COVID-19 diagnosis with contrastive representation enhancement.

Computer-aided diagnosis has been extensively investigated for more rapid and accurate screening during the outbreak of COVID-19 epidemic. However, the challenge remains to distinguish COVID-19 in the complex scenario of multi-type pneumonia classification and improve the overall diagnostic performance. In this paper, we propose a novel periphery-aware COVID-19 diagnosis approach with contrastive representation enhancement to identify COVID-19 from influenza-A (H1N1) viral pneumonia, community acquired pneumonia (CAP), and healthy subjects using chest CT images. Our key contributions include: 1) an unsupervised Periphery-aware Spatial Prediction (PSP) task which is designed to introduce important spatial patterns into deep networks; 2) an adaptive Contrastive Representation Enhancement (CRE) mechanism which can effectively capture the intra-class similarity and inter-class difference of various types of pneumonia. We integrate PSP and CRE to obtain the representations which are highly discriminative in COVID-19 screening. We evaluate our approach comprehensively on our constructed large-scale dataset and two public datasets. Extensive experiments on both volume-level and slice-level CT images demonstrate the effectiveness of our proposed approach with PSP and CRE for COVID-19 diagnosis.

Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study.

BACKGROUND: To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). METHODS: The Ethic Committee of our institution approved the protocol of this study and waived the requirement for patient informed consent. Two hundreds and ninety-five patients were enrolled in this study (healthy person: 149; COVID-19 patients: 146), which were divided into three separate non-overlapping cohorts (training set, n=135, healthy person, n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10; test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained and tested to classify the images as having manifestation of COVID-19 or as healthy. A radiologist also blindly evaluated all the test images and rechecked the misdiagnosed cases by DenseNet. Receiver operating characteristic curves (ROC) and areas under the curve (AUCs) were used to assess the model performance. The sensitivity, specificity and accuracy of DenseNet model and radiologist were also calculated. RESULTS: The DenseNet algorithm model yielded an AUC of 0.99 (95% CI: 0.958-1.0) in the validation set and 0.98 (95% CI: 0.972-0.995) in the test set. The threshold value was selected as 0.8, while for validation and test sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively. The sensitivity of radiologist was 94%, the specificity was 96%, while the accuracy was 95%. CONCLUSIONS: Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists' evaluation) and radiologists' workload.