Themis: advancing precision oncology through comprehensive molecular subtyping and optimization.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

Engagement With a Mobile Chat-Based Intervention for Smoking Cessation: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Determining how individuals engage with digital health interventions over time is crucial to understand and optimize intervention outcomes. Objective: To identify the engagement trajectories with a mobile chat-based smoking cessation intervention and examine its association with biochemically validated abstinence. Design, Setting, and Participants: A secondary analysis of a pragmatic, cluster randomized clinical trial conducted in Hong Kong with 6-month follow-up. From June 18 to September 30, 2017, 624 adult daily smokers were recruited from 34 community sites randomized to the intervention group. Data were analyzed from March 6 to October 30, 2023. Intervention: Chat-based cessation support delivered by a live counselor via a mobile instant messaging app for 3 months from baseline. Main Outcomes and Measures: Group-based trajectory modeling was used to identify engagement trajectories using the participants' weekly responses to the messages from the counselor over the 3-month intervention period. The outcome measures were biochemically validated tobacco abstinence at 3-month (end of treatment) and 6-month follow-ups. Covariates included sex, age, educational level, nicotine dependence, past quit attempt, and intention to quit at baseline. Results: Of 624 participants included in the analysis, 479 were male (76.8%), and the mean (SD) age was 42.1 (16.2) years. Four distinct engagement trajectories were identified: low engagement group (447 [71.6%]), where participants maintained very low engagement throughout; rapid-declining group (86 [13.8%]), where participants began with moderate engagement and rapidly decreased to a low level; gradual-declining group (58 [9.3%]), where participants had high initial engagement and gradually decreased to a moderate level; and high engagement group (58 [5.3%]), where participants maintained high engagement throughout. Compared with the low engagement group, the 6-month validated abstinence rates were significantly higher in the rapid-declining group (adjusted relative risk [ARR], 3.30; 95% CI, 1.39-7.81), gradual-declining group (ARR, 5.17; 95% CI, 2.21-12.11), and high engagement group (ARR, 4.98; 95% CI, 1.82-13.60). The corresponding ARRs (95% CI) of 3-month validated abstinence were 4.03 (95% CI, 1.53-10.59), 5.25 (95% CI, 1.98-13.88), and 9.23 (95% CI, 3.29-25.86). Conclusions and Relevance: The findings of this study suggest that higher levels of engagement with the chat-based smoking cessation intervention were associated with greater biochemically validated tobacco abstinence. Improving engagement with digital interventions may increase intervention benefits. Trial Registration: ClinicalTrials.gov Identifier: NCT03182790.

Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios.

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

Multiscale calculations reveal new insights into the reaction mechanism between KRASG12C and α, ß-unsaturated carbonyl of covalent inhibitors.

Utilizing α,ß-unsaturated carbonyl group as Michael acceptors to react with thiols represents a successful strategy for developing KRASG12C inhibitors. Despite this, the precise reaction mechanism between KRASG12C and covalent inhibitors remains a subject of debate, primarily due to the absence of an appropriate residue capable of deprotonating the cysteine thiol as a base. To uncover this reaction mechanism, we first discussed the chemical reaction mechanism in solvent conditions via density functional theory (DFT) calculation. Based on this, we then proposed and validated the enzymatic reaction mechanism by employing quantum mechanics/molecular mechanics (QM/MM) calculation. Our QM/MM analysis suggests that, in biological conditions, proton transfer and nucleophilic addition may proceed through a concerted process to form an enolate intermediate, bypassing the need for a base catalyst. This proposed mechanism differs from previous findings. Following the formation of the enolate intermediate, solvent-assisted tautomerization results in the final product. Our calculations indicate that solvent-assisted tautomerization is the rate-limiting step in the catalytic cycle under biological conditions. On the basis of this reaction mechanism, the calculated kinact/ki for two inhibitors is consistent well with the experimental results. Our findings provide new insights into the reaction mechanism between the cysteine of KRASG12C and the covalent inhibitors and may provide valuable information for designing effective covalent inhibitors targeting KRASG12C and other similar targets.

Synergistic inhibition mechanism of quinazolinone and piperacillin on penicillin-binding protein 2a: a promising approach for combating methicillin-resistant Staphylococcus aureus.

The production of penicillin-binding protein 2a (PBP2a), a cell wall synthesis protein, is primarily responsible for the high-level resistance observed in methicillin-resistant Staphylococcus aureus (MRSA). PBP2a exhibits a significantly reduced affinity for most ß-lactam antibiotics owing to its tightly closed active site. Quinazolinones (QNE), a novel class of non-ß-lactam antibiotics, could initiate the allosteric regulation of PBP2a, resulting in the opening of the initially closed active pocket. Based on our previous study, we have a basic understanding of the dual-site inhibitor ceftaroline (CFT) induced allosteric regulation of PBP2a. However, there are still limitations in the knowledge of how combining medicines, QNE and piperacillin (PIP), induce the allosteric response of PBP2a and inhibit its function. Herein, molecular dynamics (MD) simulations were performed to elucidate the intricate mechanisms underlying the combination mode of QNE and PIP. Our study successfully captured the opening process of the active pocket upon the binding of the QNE at the allosteric site, which alters the signaling pathways with a favorable transmission to the active site. Subsequent docking experiments with different conformational states of the active pocket indicated that all three inhibitors, PIP, QNE, and CFT, exhibited higher docking scores and more favorable docking poses to the open active pocket. These findings reveal the implied mechanism of QNE-mediated allostery underlying combination therapy and provide novel insights into developing innovative therapeutic modalities against MRSA.Communicated by Ramaswamy H. Sarma.

Association between tobacco industry denormalisation beliefs and support for tobacco endgame policies: a population-based study in Hong Kong.

OBJECTIVES: To examine the associations between tobacco industry denormalisation (TID) beliefs and support for tobacco endgame policies. METHODS: A total of 2810 randomly selected adult respondents of population-based tobacco policy-related surveys (2018-2019) were included. TID beliefs (agree vs disagree/unsure) were measured by seven items: tobacco manufacturers ignore health, induce addiction, hide harm, spread false information, lure smoking, interfere with tobacco control policies and should be responsible for health problems. Score of each item was summed up and dichotomised (median=5, >5 strong beliefs; ≤5 weak beliefs). Support for tobacco endgame policies on total bans of tobacco sales (yes/no) and use (yes/no) was reported. Associations between TID beliefs and tobacco endgame policies support across various smoking status were analysed, adjusting for sociodemographics. RESULTS: Fewer smokers (23.3%) had strong beliefs of TID than ex-smokers (48.4%) and never smokers (48.5%) (p<0.001). Support for total bans on tobacco sales (74.6%) and use (76.9%) was lower in smokers (33.3% and 35.3%) than ex-smokers (74.3% and 77.9%) and never smokers (76.0% and 78.3%) (all p values<0.001). An increase in the number of TID beliefs supported was positively associated with support for a total ban on sales (adjusted risk ratio 1.06, 95% CI 1.05 to 1.08, p<0.001) and use (1.06, 95% CI 1.05 to 1.07, p<0.001). The corresponding associations were stronger in smokers than non-smokers (sales: 1.87 vs 1.25, p value for interaction=0.03; use: 1.78 vs 1.21, p value for interaction=0.03). CONCLUSION: Stronger TID belief was associated with greater support for total bans on tobacco sales and use. TID intervention may increase support for tobacco endgame, especially in current smokers.

MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks.

Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

Experiences of quitting smoking in prisons: A qualitative study of people in custody.

INTRODUCTION: Smoking prevalence among people in custody (PIC) is extremely high, and prison-based smoking cessation interventions are needed. The study explored the quitting experiences of PIC who participated in the 'Quit to Win' contest (QTW). METHODS: This qualitative study, conducted from 2019 to 2021 in two Hong Kong prisons, included semi-structured individual interviews with 26 PIC (13 men and 13 women) who were participants in QTW and two correctional staff who coordinated QTW. A semi-structured interview guide with open-ended questions was developed to examine multilevel factors that promote or impede smoking cessation in prisons. Maximum variation sampling was used to ensure a diverse range of social, demographic, and smoking profiles. Data were managed and analyzed using thematic analysis. RESULTS: Two themes were identified from the data: 1) quitting in prison: barriers and facilitators; and 2) QTW in prison: a trigger for behavior change. Barriers (i.e. stress, boredom, isolation, lack of self-autonomy, nicotine dependence and lack of cessation medication, barriers to moving to a different wing) and facilitators (i.e. concerns about health, money savings, and the smoke-free wing) that impeded or supported smoking cessation during incarceration were identified. QTW provided health education, quitting incentives, and social support that helped PIC overcome the barriers of quitting by serving as a trigger for behavior change. Notably, social visits with family were identified as key drivers of PIC's quitting success, whereas their suspension during the COVID-19 pandemic disincentivized their abstinence. CONCLUSIONS: This study introduced the QTW contest to prisons and provided qualitative evidence on the multilevel factors promoting or impeding smoking cessation in prison. QTW helped PIC overcome the barriers of quitting by serving as a trigger for behavior change. Future prison-based interventions should leverage social support, enhance stress-coping skills, facilitate access to pharmacotherapy, and collaborate with correctional services agencies.

Cobdock: an accurate and practical machine learning-based consensus blind docking method.

Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed Consensus Blind Dock (CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.

Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review.

Drug discovery is a complex and iterative process, making it ideal for using artificial intelligence (AI). This paper uses a bibliometric approach to reveal AI's trend and underlying structure in drug discovery (AIDD). A total of 4310 journal articles and reviews indexed in Scopus were analyzed, revealing that AIDD has been rapidly growing over the past two decades, with a significant increase after 2017. The United States, China, and the United Kingdom were the leading countries in research output, with academic institutions, particularly the Chinese Academy of Sciences and the University of Cambridge, being the most productive. In addition, industrial companies, including both pharmaceutical and high-tech ones, also made significant contributions. Additionally, this paper thoroughly discussed the evolution and research frontiers of AIDD, which were uncovered through co-occurrence analyses of keywords using VOSviewer. Our findings highlight that AIDD is an interdisciplinary and promising research field that has the potential to revolutionize drug discovery. The comprehensive overview provided here will be of significant interest to researchers, practitioners, and policy-makers in related fields. The results emphasize the need for continued investment and collaboration in AIDD to accelerate drug discovery, reduce costs, and improve patient outcomes.

Experiment and molecular dynamics simulations reveal proanthocyanidin B2 and B3 can inhibit prion aggregation by different mechanisms.

Prion diseases are a group of fatal neurodegenerative diseases caused by the misfolding and aggregation of prion protein (PrP), and the inhibition of PrP aggregation is one of the most effective therapeutic strategies. Proanthocyanidin B2 (PB2) and B3 (PB3), the effective natural antioxidants have been evaluated for the inhibition of amyloid-related protein aggregation. Since PrP has similar aggregation mechanism with other amyloid-related proteins, will PB2 and PB3 affect the aggregation of PrP? In this paper, experimental and molecular dynamics (MD) simulation methods were combined to investigate the influence of PB2 and PB3 on PrP aggregation. Thioflavin T assays showed PB2 and PB3 could inhibit PrP aggregation in a concentrate-dependent manner in vitro. To understand the underlying mechanism, we performed 400 ns all-atom MD simulations. The results suggested PB2 could stabilize the α2 C-terminus and the hydrophobic core of protein by stabilizing two important salt bridges R156-E196 and R156-D202, and consequently made global structure of protein more stable. Surprisingly, PB3 could not stabilize PrP, which may inhibit PrP aggregation through a different mechanism. Since dimerization is the first step of aggregation, will PB3 inhibit PrP aggregation by inhibiting the dimerization? To verify our assumption, we then explored the effect of PB3 on protein dimerization by performing 800 ns MD simulations. The results suggested PB3 could reduce the residue contacts and hydrogen bonds between two monomers, preventing dimerization process of PrP. The possible inhibition mechanism of PB2 and PB3 on PrP aggregation could provide useful information for drug development against prion diseases.Communicated by Ramaswamy H. Sarma.

Unraveling the mechanism of ceftaroline-induced allosteric regulation in penicillin-binding protein 2a: insights for novel antibiotic development against methicillin-resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) acquires high-level resistance against ß-lactam antibiotics by expressing penicillin-binding protein 2a (PBP2a). PBP2a is a cell wall-synthesizing protein whose closed active site exhibits a reduced binding affinity toward ß-lactam antibiotics. Ceftaroline (CFT), a fifth-generation cephalosporin, can effectively inhibit the PBP2a activity by binding to an allosteric site to trigger the active site opening, allowing a second CFT to access the active site. However, the essential mechanism behind the allosteric behavior of PBP2a remains unclear. Herein, computational simulations are employed to elucidate how CFT allosterically regulates the conformation and dynamics of the active site of PBP2a. While CFT stabilizes the allosteric domain surrounding it, it simultaneously enhances the dynamics of the catalytic domain. Specifically, the study successfully captured the opening process of the active pocket in the allosteric CFT-bound systems and discovered that CFT alters the potential signal-propagating pathways from the allosteric site to the active site. These findings reveal the implied mechanism of the CFT-mediated allostery in PBP2a and provide new insights into dual-site drug design or combination therapy against MRSA targeting PBP2a.

The causal association between obesity and gastric cancer and shared molecular signatures: a large-scale Mendelian randomization and multi-omics analysis.

Purpose: While observational studies have identified obesity as a potential risk factor for gastric cancer, the causality remains uncertain. This study aimed to evaluate the causal relationship between obesity and gastric cancer and identify the shared molecular signatures linking obesity to gastric cancer. Methods: A two-sample Mendelian randomization (MR) analysis was conducted using the GWAS data of body fat percentage (exposure, n = 331,117) and gastric cancer (outcome, n = 202,308). Bioinformatics and meta-analysis of multi-omics data were performed to identify key molecules mediating the causality. The meta-analysis of the plasma/serum proteome included 1,662 obese and 3,153 gastric cancer patients. Obesity and gastric cancer-associated genes were identified using seven common gene ontology databases. The transcriptomic data were obtained from TCGA and GEO databases. The Bioinformatic findings were clinically validated in plasma from 220 obese and 400 gastric cancer patients across two hospitals. Finally, structural-based virtual screening (SBVS) was performed to explore the potential FDA-approved drugs targeting the identified mediating molecules. Results: The MR analysis revealed a significant causal association between obesity and gastric cancer (IVW, OR = 1.37, 95% CI:1.12-1.69, P = 0.0028), without pleiotropy or heterogeneity. Bioinformatic and meta-analysis of multi-omics data revealed shared TNF, PI3K-AKT, and cytokine signaling dysregulation, with significant upregulation of AKT1, IL-6, and TNF. The clinical study confirmed widespread upregulation of systemic inflammatory markers in the plasma of both diseases. SBVS identified six novel potent AKT1 inhibitors, including the dietary supplement adenosine, representing a potentially preventive drug with low toxicity. Conclusion: Obesity causally increases gastric cancer, likely mediated by persistent AKT1/IL-6/TNF upregulation. As a potential AKT1 inhibitor, adenosine may mitigate the obesity-to-gastric cancer transition. These findings could inform preventive drug development to reduce gastric cancer risk in obesity.

scCURE identifies cell types responding to immunotherapy and enables outcome prediction.

A deep understanding of immunotherapy response/resistance mechanisms and a highly reliable therapy response prediction are vital for cancer treatment. Here, we developed scCURE (single-cell RNA sequencing [scRNA-seq] data-based Changed and Unchanged cell Recognition during immunotherapy). Based on Gaussian mixture modeling, Kullback-Leibler (KL) divergence, and mutual nearest-neighbors criteria, scCURE can faithfully discriminate between cells affected or unaffected by immunotherapy intervention. By conducting scCURE analyses in melanoma and breast cancer immunotherapy scRNA-seq data, we found that the baseline profiles of specific CD8+ T and macrophage cells (identified by scCURE) can determine the way in which tumor microenvironment immune cells respond to immunotherapy, e.g., antitumor immunity activation or de-activation; therefore, these cells could be predictive factors for treatment response. In this work, we demonstrated that the immunotherapy-associated cell-cell heterogeneities revealed by scCURE can be utilized to integrate the therapy response mechanism study and prediction model construction.

Discovery of novel and potent InhA direct inhibitors by ensemble docking-based virtual screening and biological assays.

Multidrug-resistant tuberculosis (MDR-TB) continues to spread worldwide and remains one of the leading causes of death among infectious diseases. The enoyl-acyl carrier protein reductase (InhA) belongs to FAS-II family and is essential for the formation of the Mycobacterium tuberculosis cell wall. Recent years, InhA direct inhibitors have been extensively studied to overcome MDR-TB. However, there are still no inhibitors that have entered clinical research. Here, the ensemble docking-based virtual screening along with biological assay were used to identify potent InhA direct inhibitors from Chembridge, Chemdiv, and Specs. Ultimately, 34 compounds were purchased and first assayed for the binding affinity, of which four compounds can bind InhA well with KD values ranging from 48.4 to 56.2 µM. Among them, compound 9,222,034 has the best inhibitory activity against InhA enzyme with an IC50 value of 18.05 µM. In addition, the molecular dynamic simulation and binding free energy calculation indicate that the identified compounds bind to InhA with "extended" conformation. Residue energy decomposition shows that residues such as Tyr158, Met161, and Met191 have higher energy contributions in the binding of compounds. By analyzing the binding modes, we found that these compounds can bind to a hydrophobic sub-pocket formed by residues Tyr158, Phe149, Ile215, Leu218, etc., resulting in extensive van der Waals interactions. In summary, this study proposed an efficient strategy for discovering InhA direct inhibitors through ensemble docking-based virtual screening, and finally identified four active compounds with new skeletons, which can provide valuable information for the discovery and optimization of InhA direct inhibitors.

MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.

The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI  $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.

Effect of smoking-related COVID-19 risk messaging on smoking cessation in community smokers: A pragmatic randomized controlled trial.

INTRODUCTION: Observational and experimental studies have suggested that messaging on smoking-related COVID-19 risk may promote smoking abstinence, but evidence from randomized clinical trials (RCTs) is lacking. METHODS: This was a pragmatic RCT in Hong Kong, China, to compare the effectiveness of communicating smoking-related COVID-19 risk with generic cessation support on abstinence. Both groups received brief cessation advice at baseline. The intervention group received messaging on smoking-related COVID-19 risk and cessation support via instant messaging for three months (16 messages in total), which highlighted the increased risk of severe COVID-19 and deaths, and potentially higher risk of viral exposure (e.g. due to mask removal) for smokers. The control group received generic text messaging support for three months (16 messages). The primary outcomes were biochemically validated 7-day point prevalence abstinence (PPA) at 3 and 6 months. Intention to treat analyses was used. RESULTS: Between 13 June and 30 October 2020, 1166 participants were randomly assigned to an intervention (n=583) or control (n=583) group. By intention-to-treat, validated 7-day PPA did not significantly differ between the intervention and control groups at three months (9.6% and 11.8%, relative risk, RR=0.81; 95% CI: 0.58-1.13, p=0.22) or six months (9.3% and 11.7%, RR=0.79; 95% CI: 0.57-1.11, p=0.18). A higher perceived severity of COVID-19 in smokers at baseline was associated with a greater validated 7-day PPA at six months, and a marginally significant intervention effect on changes in perceived severity from baseline through 6 months was found (p for group × time interaction = 0.08). CONCLUSIONS: Communicating smoking-related COVID-19 risk via instant messaging was not more effective in increasing smoking abstinence than generic cessation support. TRIAL REGISTRATION: The study is registered on ClinicalTrials.gov Identifier: NCT04399967.

Activated Microglia in the Early Stage of a Rat Model of Parkinson's Disease: Revealed by PET-MRI Imaging by [18F]DPA-714 Targeting TSPO.

In the past decades, translocator protein (TSPO) has been considered as an in vivo biomarker to measure the presence of neuroinflammatory reactions. In this study, expression of TSPO was quantified via [18F]DPA-714 positron emission tomography-magnetic resonance imaging (PET-MRI) to investigate the effects of microglial activation associated with motor behavioral impairments in the 6-hydroxydopamine (6-OHDA)-treated rodent model of Parkinson's disease (PD). [18F]FDG PET-MRI (for non-specific inflammation), [18F]D6-FP-(+)-DTBZ PET-MRI (for damaged dopaminergic (DA) neurons), post-PET immunofluorescence, and Pearson's correlation analyses were also performed. The time course of striatal [18F]DPA-714 binding ratio was elevated in 6-OHDA-treated rats during 1-3 weeks post-treatments, with peak TSPO binding in the 1st week. No difference between the bilateral striatum in [18F]FDG PET imaging were found. Moreover, an obvious correlation between [18F]DPA-714 SUVRR/L and rotation numbers was found (r = 0.434, *p = 0.049). No correlation between [18F]FDG SUVRR/L and rotation behavior was found. [18F]DPA-714 appeared to be a potential PET tracer for imaging the microglia-mediated neuroinflammation in the early stage of PD.

Inhalable Nanoparticle-based Dry Powder Formulations for Respiratory Diseases: Challenges and Strategies for Translational Research.

The emergence of novel respiratory infections (e.g., COVID-19) and expeditious development of nanoparticle-based COVID-19 vaccines have recently reignited considerable interest in designing inhalable nanoparticle-based drug delivery systems as next-generation respiratory therapeutics. Among various available devices in aerosol delivery, dry powder inhalers (DPIs) are preferable for delivery of nanoparticles due to their simplicity of use, high portability, and superior long-term stability. Despite research efforts devoted to developing inhaled nanoparticle-based DPI formulations, no such formulations have been approved to date, implying a research gap between bench and bedside. This review aims to address this gap by highlighting important yet often overlooked issues during pre-clinical development. We start with an overview and update on formulation and particle engineering strategies for fabricating inhalable nanoparticle-based dry powder formulations. An important but neglected aspect in in vitro characterization methodologies for linking the powder performance with their bio-fate is then discussed. Finally, the major challenges and strategies in their clinical translation are highlighted. We anticipate that focused research onto the existing knowledge gaps presented in this review would accelerate clinical applications of inhalable nanoparticle-based dry powders from a far-fetched fantasy to a reality.