Snapshot of the cannabinoid receptor 1-arrestin complex unravels the biased signaling mechanism.

Cannabis activates the cannabinoid receptor 1 (CB1), which elicits analgesic and emotion regulation benefits, along with adverse effects, via Gi and ß-arrestin signaling pathways. However, the lack of understanding of the mechanism of ß-arrestin-1 (ßarr1) coupling and signaling bias has hindered drug development targeting CB1. Here, we present the high-resolution cryo-electron microscopy structure of CB1-ßarr1 complex bound to the synthetic cannabinoid MDMB-Fubinaca (FUB), revealing notable differences in the transducer pocket and ligand-binding site compared with the Gi protein complex. ßarr1 occupies a wider transducer pocket promoting substantial outward movement of the TM6 and distinctive twin toggle switch rearrangements, whereas FUB adopts a different pose, inserting more deeply than the Gi-coupled state, suggesting the allosteric correlation between the orthosteric binding pocket and the partner protein site. Taken together, our findings unravel the molecular mechanism of signaling bias toward CB1, facilitating the development of CB1 agonists.

Conformational transitions and activation of the adhesion receptor CD97.

Adhesion G protein-coupled receptors (aGPCRs) are evolutionarily ancient receptors involved in a variety of physiological and pathophysiological processes. Modulators of aGPCR, particularly antagonists, hold therapeutic promise for diseases like cancer and immune and neurological disorders. Hindered by the inactive state structural information, our understanding of antagonist development and aGPCR activation faces challenges. Here, we report the cryo-electron microscopy structures of human CD97, a prototypical aGPCR that plays crucial roles in immune system, in its inactive apo and G13-bound fully active states. Compared with other family GPCRs, CD97 adopts a compact inactive conformation with a constrained ligand pocket. Activation induces significant conformational changes for both extracellular and intracellular sides, creating larger cavities for Stachel sequence binding and G13 engagement. Integrated with functional and metadynamics analyses, our study provides significant mechanistic insights into the activation and signaling of aGPCRs, paving the way for future drug discovery efforts.

Controlling the Polarity of Metal-Organic Frameworks to Promote Electrochemical CO2 Reduction.

The addition of polar functional groups to porous structures is an effective strategy for increasing the ability of metal-organic frameworks (MOFs) to capture CO2 by enhancing interactions between the dipoles of the polar functional groups and the quadrupoles of CO2. However, the potential of MOFs grafted to polar functional group to activate CO2 has not been investigated in the context of CO2 electrolysis. In this study, we report a mixed-ligand strategy to incorporate various functional groups in the MOFs. We found that substituents with strong polarity led to increased catalytic performance of electrochemical CO2 reduction for these polarized MOFs. Both experimental and theoretical evidence indicates that the presence of polar functional groups induces a charge redistribution in the micropores of MOFs. We have shown that higher electron densities of sp2-carbon atoms in benzimidazolate ligands reduces the energy barrier to generate *COOH, which is simultaneously controlled by the mass transfer of CO2. Our research offers an effective method of disrupting local electron neutrality in the pores of electrocatalysts/supports to activate CO2 under electrochemical conditions.

A self-supervised learning approach for registration agnostic imaging models with 3D brain CTA.

Deep learning-based neuroimaging pipelines for acute stroke typically rely on image registration, which not only increases computation but also introduces a point of failure. In this paper, we propose a general-purpose contrastive self-supervised learning method that converts a convolutional deep neural network designed for registered images to work on a different input domain, i.e., with unregistered images. This is accomplished by using a self-supervised strategy that does not rely on labels, where the original model acts as a teacher and a new network as a student. Large vessel occlusion (LVO) detection experiments using computed tomographic angiography (CTA) data from 402 CTA patients show the student model achieving competitive LVO detection performance (area under the receiver operating characteristic curve [AUC] = 0.88 vs. AUC = 0.81) compared to the teacher model, even with unregistered images. The student model trained directly on unregistered images using standard supervised learning achieves an AUC = 0.63, highlighting the proposed method's efficacy in adapting models to different pipelines and domains.

Construction of an auxiliary scoring model for myelosuppression in patients with lung cancer chemotherapy based on random forest algorithm.

OBJECTIVE: To construct an auxiliary scoring model for myelosuppression in patients with lung cancer undergoing chemotherapy based on a random forest algorithm, and to evaluate the predictive performance of the model. METHODS: Patients with lung cancer who received chemotherapy in Shanxi Province Cancer Hospital from January 2019 to January 2022 were retrospectively selected as research subjects, and their general demographic information, disease-related indicators, and laboratory test results before chemotherapy were collected. Patients were divided into a training set (136 cases) and a validation set (68 cases) in a ratio of 2:1. R software was used to establish a scoring model of myelosuppression in lung cancer patients in the training set, and the receiver operating characteristic curve, accuracy, sensitivity, and balanced F-score were used in the two data sets to evaluate the predictive performance of the model. RESULTS: Among the 204 lung cancer patients enrolled, 75 patients developed myelosuppression during the follow-up period after chemotherapy, with an incidence of 36.76%. The factors in the constructed random forest model were ranked in order of age (23.233), bone metastasis (21.704), chemotherapy course (19.259), Alb (13.833), and gender (11.471) according to the mean decrease accuracy. The areas under the curve of the model in the training and validation sets were 0.878 and 0.885, respectively (all P < 0.05). The predictive accuracy of the validated model was 82.35%, the sensitivity and specificity were 84.00% and 81.40%, respectively, and the balanced F-score was 77.78% (all P < 0.05). CONCLUSION: The risk assessment model for the occurrence of myelosuppression in patients with lung cancer chemotherapy based on a random forest algorithm can provide a reference for the accurate identification of high-risk patients.

Generation and influence of eccentric ideas on social networks.

Studying extreme ideas in routine choices and discussions is of utmost importance to understand the increasing polarization in society. In this study, we focus on understanding the generation and influence of extreme ideas in routine conversations which we label "eccentric" ideas. The eccentricity of any idea is defined as the deviation of that idea from the norm of the social neighborhood. We collected and analyzed data from two sources of different nature: public social media and online experiments in a controlled environment. We compared the popularity of ideas against their eccentricity to understand individuals' fascination towards eccentricity. We found that more eccentric ideas have a higher probability of getting a greater number of "likes". Additionally, we demonstrate that the social neighborhood of an individual conceals eccentricity changes in one's own opinions and facilitates generation of eccentric ideas at a collective level.

Early Predictive Value of NT-proBNP Combined With Echocardiography in Anthracyclines Induced Cardiotoxicity.

Objective: Determine the predictive value of N-terminal pro-B-type natriuretic peptide (NT-proBNP) combined with echocardiography in the diagnosis of anthracyclines-induced chronic cardiotoxicity. Methods: A total of 80 female breast cancer patients from January 2019 to October 2021 were included in our hospital. Twenty-six patients with cardiotoxicity were divided into the cardiac impairment group, and the 54 patients without cardiotoxicity were classified into the normal control group. NT-proBNP levels and cardiac echocardiography were measured before the start of the chemotherapy cycle, in cycle 3 of the chemotherapy, and after the chemotherapy cycle in all patients. Results: After three cycles of chemotherapy and chemotherapy, the levels of NT-proBNP in patients of the two groups were significantly higher than those before chemotherapy (P < 0.05). The levels of NT-proBNP in the cardiac injury group after three cycles of chemotherapy and chemotherapy were higher than those in the normal control group at the same time point (P < 0.05). The LVEF of patients in the cardiac impairment group after chemotherapy was lower than that before chemotherapy, and the LVEF after chemotherapy was lower than that in the normal control group (P < 0.05). NT-proBNP had a negative correlation with LVEF (r = -0.549, P < 0.001). The AUC of NT-proBNP in combination with LVEF for predicting cardiotoxicity in our patient was 0.898(95%CI:0.829-0.966). Conclusion: NT-proBNP combined with echocardiography has clinical significance in the detection of anthracyclines-induced cardiotoxicity, and it can detect early myocardial injury induced by anthracyclines, with early prediction value. It is important to protect heart function and judge prognosis.

Utterance Clustering Using Stereo Audio Channels.

Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- and right-channel audio signals in a few different ways and then by extracting the embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter-sharing Gaussian mixture model was obtained to train the model for each speaker. In the testing phase, the speaker with the maximum likelihood was selected as the detected speaker. Results of experiments with real audio recordings of multiperson discussion sessions showed that the proposed method that used multichannel audio signals achieved significantly better performance than a conventional method with mono-audio signals in more complicated conditions.

An Effective Treatment of Perimenopausal Syndrome by Combining Two Traditional Prescriptions of Chinese Botanical Drugs.

Ethnopharmacological relevance: Two types of traditional Chinese formulas of botanical drugs are prescribed for treating perimenopausal syndrome (PMS), a disorder in middle-aged women during their transition to menopause. One is for treating PMS as kidney deficiency (KD) due to senescence and declining reproductive functions, and the other is for treating it as liver qi stagnation (LQS) in association with stress and anxiety. Despite the time-tested prescriptions, an objective attestation to the effectiveness of the traditional Chinese treatment of PMS is still to be established and the associated molecular mechanism is still to be investigated. Materials and methods: A model for PMS was generated from perimenopausal rats with chronic restraint stress (CRS). The effectiveness of traditional Chinese formulas of botanical drugs and a combination of two of the formulas was evaluated based on 1H NMR plasma metabolomic, as well as behavioral and physiological, indicators. To investigate whether the formulas contained ligands that could compensate for the declining level of estrogen, the primary cause of PMS, the ligand-based NMR technique of saturation transfer difference (STD) was employed to detect possible interacting molecules to estrogen receptors in the decoction. Results: Each prescription of the classical Chinese formula moderately attenuated the metabolomic state of the disease model. The best treatment strategy however was to combine two traditional Chinese formulas, each for a different etiology, to adjust the metabolomic state of the disease model to that of rats at a much younger age. In addition, this attenuation of the metabolomics of the disease model was by neither upregulating the estrogen level nor supplementing an estrogenic compound. Conclusion: Treatment of PMS with a traditional Chinese formula of botanical drugs targeting one of the two causes separately could ameliorate the disorder moderately. However, the best outcome was to treat the two causes simultaneously with a decoction that combined ingredients from two traditional prescriptions. The data also implicated a new paradigm for phytotherapy of PMS as the prescribed decoctions contained no interacting compound to modulate the activity of estrogen receptors, in contrast to the treatment strategy of hormone replacement therapy.