Modification Mechanisms of Descriptive Norm Perceptions Toward Vaping: The Role of Behavior Prevalence and Group Size in an Online Setting.

We often rely on descriptive norm perceptions as a mental shortcut for decision making. However, less is known about how such perceptions are shaped and modified by our experiences in day-to-day life. The interactive nature of the current media environment offers opportunities for individuals to access others' health behavior choices through online user-generated content. Within a setting of online comment boards, the current study examined the descriptive norm perception modification process toward vaping with a large-scale experiment that systematically varied levels of exposure to online commenters' vaping behavior choice indications. Findings revealed a significant positive effect of behavior prevalence on descriptive norm perceptions, which in turn were positively associated with vaping intention. This set of results was observed only when a sufficient total amount of comment exposures was ensured. The study provided empirical evidence for the underlying mechanism of the "quasi-statistical sense," which helps people draw conclusions about behavior prevalence and may influence their behavioral decision making. Theoretical and practical implications are discussed.

Combination of S1-N-Terminal and S1-C-Terminal Domain Antigens Targeting Double Receptor-Binding Domains Bolsters Protective Immunity of a Nanoparticle Vaccine against Porcine Epidemic Diarrhea Virus.

Variants of coronavirus porcine epidemic diarrhea virus (PEDV) frequently emerge, causing an incomplete match between the vaccine and variant strains, which affects vaccine efficacy. Designing vaccines with rapidly replaceable antigens and high efficacy is a promising strategy for the prevention of infection with PEDV variant strains. In our study, three different types of self-assembled nanoparticles (nps) targeting receptor-binding N-terminal domain (NTD) and C-terminal domain (CTD) of S1 protein, named NTDnps, CTDnps, and NTD/CTDnps, were constructed and evaluated as vaccine candidates against PEDV. NTDnps and CTDnps vaccines mediated significantly higher neutralizing antibody (NAb) titers than NTD and CTD recombinant proteins in mice. The NTD/CTDnps in varying ratios elicited significantly higher NAb titers when compared with NTDnps and CTDnps alone. The NTD/CTDnps (3:1) elicited NAb with titers up to 92.92% of those induced by the commercial vaccine. Piglets immunized with NTD/CTDnps (3:1) achieved a passive immune protection rate of 83.33% of that induced by the commercial vaccine. NTD/CTDnps (3:1) enhanced the capacity of mononuclear macrophages and dendritic cells to take up and present antigens by activating major histocompatibility complex I and II molecules to stimulate humoral and cellular immunity. These data reveal that a combination of S1-NTD and S1-CTD antigens targeting double receptor-binding domains strengthens the protective immunity of nanoparticle vaccines against PEDV. Our findings will provide a promising vaccine candidate against PEDV.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations.

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

Proteomics-Derived Biomarker Panel Facilitates Distinguishing Primary Lung Adenocarcinomas With Intestinal or Mucinous Differentiation From Lung Metastatic Colorectal Cancer.

The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical (IHC), and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging-based shotgun proteomics to characterize proteomes of formalin-fixed, paraffin-embedded tumor samples of PAIM (n = 22) and lmCRC (n = 17).Then three machine learning algorithms, namely support vector machine (SVM), random forest, and the Least Absolute Shrinkage and Selection Operator, were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by IHC to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection. The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of ten candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: CK7 (an established marker), NARR, MLPH, S100A14) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

Complex association of body mass index and outcomes in patients with relapsed and refractory multiple myeloma treated with CAR-T cell immunotherapy.

BACKGROUND AIMS: Chimeric antigen receptor-T (CAR-T) cells have exhibited remarkable efficacy in treating refractory or relapsed multiple myeloma (R/R MM). Although obesity has a favorable value in enhancing the response to immunotherapy, less is known about its predictive value regarding the efficacy and prognosis of CAR-T cell immunotherapy. METHODS: We conducted a retrospective study of 111 patients with R/R MM who underwent CAR-T cell treatment. Using the body mass index (BMI) classification, the patients were divided into a normal-weight group (73/111) and an overweight group (38/111). We investigated the effect of BMI on CAR-T cell therapy outcomes in patients with R/R MM. RESULTS: The objective remission rates after CAR-T cell infusion were 94.7% and 89.0% in the overweight and normal-weight groups, respectively. The duration of response and overall survival were not significant difference between BMI groups. Compared to normal-weight patients, overweight patients had an improved median progression-free survival. There was no significant difference in cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome between the subgroups. In terms of hematological toxicity, the erythrocyte, hemoglobin, platelet, leukocyte and neutrophil recovery was accelerated in the overweight group. Fewer patients in the overweight group displayed moderate percent CD4 and CD4/CD8 ratios compared to the normal-weight group. Furthermore, the percent CD4 ratios were positively correlated with the levels of cytokines [interleukin-2 (IL-2) (day 14), interferon gamma (IFN-γ) (day 7) and tumor necrosis factor alpha (TNF-α) (days 14 and 21)] after cells infusion. On the other hand, BMI was positively associated with the levels of IFN-γ (day 7) and TNF-α (days 14 and 21) after CAR-T cells infusion. CONCLUSIONS: Overall, this study highlights the potential beneficial effect of a higher BMI on CAR-T cell therapy outcomes.

Intercropping changed the soil microbial community composition but no significant effect on alpha diversity.

Introduction: Enhancing the planning of the forest-agricultural composite model and increasing the efficiency with which forest land is utilized could benefit from a thorough understanding of the impacts of intercropping between forests and agriculture on soil physicochemical properties and microbial communities. Methods: Populus cathayana × candansis cv. Xinlin No.1 and Glycine max intercrop soils, along with their corresponding monocrops, were used in this study's llumina high-throughput sequencing analysis to determine the composition and diversity of soil bacterial and fungal communities. Results: The findings indicated that intercropping considerably raised the soil's total phosphorus content and significantly lowered the soil's carbon nitrogen ratio when compared to poplar single cropping. Furthermore, the total carbon and nitrogen content of soil was increased and the soil pH was decreased. The sequencing results showed that intercropping had no significant effect on soil alpha diversity. Intercropping could increase the composition of fungal community and decrease the composition of bacterial community in poplar soil. At the phylum level, intercropping significantly increased the relative abundance of four dominant phyla, i.e., Patescibacteria, Proteobacteria, Patescibacteria and Deinococcus-Thermus. And the relative abundances of only two dominant phyla were significantly increased. It was found that soil total phosphorus and available phosphorus content had the strongest correlation with soil bacterial community diversity, and soil pH had the strongest correlation with soil fungal community diversity. Discussion: The results of this study were similar to those of previous studies. This study can serve as a theoretical foundation for the development of a poplar and black bean-based forest-agricultural complex management system in the future.

Basal actomyosin pulses expand epithelium coordinating cell flattening and tissue elongation.

Actomyosin networks constrict cell area and junctions to alter cell and tissue shape. However, during cell expansion under mechanical stress, actomyosin networks are strengthened and polarized to relax stress. Thus, cells face a conflicting situation between the enhanced actomyosin contractile properties and the expansion behaviour of the cell or tissue. To address this paradoxical situation, we study late Drosophila oogenesis and reveal an unusual epithelial expansion wave behaviour. Mechanistically, Rac1 and Rho1 integrate basal pulsatile actomyosin networks with ruffles and focal adhesions to increase and then stabilize basal area of epithelial cells allowing their flattening and elongation. This epithelial expansion behaviour bridges cell changes to oocyte growth and extension, while oocyte growth in turn deforms the epithelium to drive cell spreading. Basal pulsatile actomyosin networks exhibit non-contractile mechanics, non-linear structures and F-actin/Myosin-II spatiotemporal signal separation, implicating unreported expanding properties. Biophysical modelling incorporating these expanding properties well simulates epithelial cell expansion waves. Our work thus highlights actomyosin expanding properties as a key mechanism driving tissue morphogenesis.

Impacts of litter microbial community on litter decomposition in the absence of soil microorganisms.

What is the effect of phyllosphere microorganisms on litter decomposition in the absence of colonization by soil microorganisms? Here, we simulated the litter standing decomposition stage in the field to study the differences in the composition and structure of the phyllosphere microbial community after the mixed decomposition of Populus × canadensis and Pinus sylvestris var. mongolica litter. After 15 months of mixed decomposition, we discovered that litters that were not in contact with soil had an antagonistic effect (the actual decomposition rate was 18.18%, which is lower than the expected decomposition rate) and the difference between the litters themselves resulted in a negative response to litter decomposition. In addition, there was no significant difference in bacterial and fungal community diversity after litter decomposition. The litter bacterial community was negatively responsive to litter properties and positively responsive to the fungal community. Importantly, we found that bacterial communities had a greater impact on litter decomposition than fungi. This study has enriched our understanding of the decomposition of litter itself and provided a theoretical basis for further exploring the "additive and non-additive effects" of litter decomposition and the mechanism of microbial drive. IMPORTANCE: The study of litter decomposition mechanism plays an important role in the material circulation of the global ecosystem. However, previous studies have often looked at contact with soil as the starting point for decomposition. But actually, standing litter is very common in forest ecosystems. Therefore, we used field simulation experiments to simulate the decomposition of litters without contact with soil for 15 months, to explore the combined and non-added benefits of the decomposition of mixed litters, and to study the influence of microbial community composition on the decomposition rate while comparing the differences of microbial communities.

Recent advances and applications in high-throughput continuous flow.

High-throughput continuous flow technology has emerged as a revolutionary approach in chemical synthesis, offering accelerated experimentation and improved efficiency. With the aid of process analytical technology and automation, this system not only enables rapid optimisation of reaction conditions at the millimole to the picomole scale, but also facilitates automated scale-up synthesis. It can even achieve the self-planning and self-synthesis of small drug molecules with artificial intelligence incorporated in the system. The versatility of the system is highlighted by its compatibility with both electrochemistry and photochemistry, and its significant applications in organic synthesis and drug discovery. This highlight summarises its recent developments and applications, emphasising its significant impact on advancing research across multiple disciplines.

Esculin inhibits hepatic stellate cell activation and CCl4-induced liver fibrosis by activating the Nrf2/GPX4 signaling pathway.

BACKGROUND: Liver fibrosis (LF) is a pathological process of the liver that threatens human health. Currently, effective treatments are still lacking. Esculin, a prominent constituent found in the Fraxinus rhynchophylla. (bark), Aesculus hippocastanum. (bark), and Cichorium intybus. (herb), has been shown to possess significant anti-inflammatory, antioxidant, and antibacterial properties. However, to date, there have been no studies investigating its potential efficacy in the treatment of LF. OBJECTIVE: The study aims to investigate the therapeutic effect of esculin on LF and elucidate its potential molecular mechanism. METHODS: Carbon tetrachloride (CCl4) was injected intraperitoneally to induce LF in mice, and transforming growth factor ß1 (TGF-ß1) was injected to induce LX-2 cells to investigate the improvement effect of esculin on LF. Kit, histopathological staining, immunohistochemistry (IHC), immunofluorescence (IF), polymerase chain reaction (PCR), and western blot (WB) were used to detect the expression of fiber markers and nuclear factor erythroid 2-related factor 2 (Nrf2)/glutathione peroxidase 4 (GPX4) signaling pathway in liver tissue and LX-2 cells. Finally, molecular docking, cellular thermal shift assay (CETSA), and drug affinity responsive target stability (DARTS) were used to verify the targeting between Nrf2 and esculin. RESULTS: Esculin significantly inhibited CCl4-induced hepatic fibrosis and inflammation in mice. This was evidenced by the improvement of liver function indexes, fibrosis indicators, and histopathology. Additionally, esculin treatment prominently reduced the levels of pro-inflammatory factors, oxidative stress, and liver Fe2+ in CCl4-induced mice. In vitro studies also showed that esculin treatment significantly inhibited TGF-ß1-induced LX-2 cell activation and decreased alpha-smooth muscle actin (α-SMA) and collagen I expression. Mechanism experiments proved that esculin can activate the Nrf2/GPX4 signaling pathway and inhibit liver ferroptosis. However, when LX-2 cells were treated with the Nrf2 inhibitor (ML385), the therapeutic effect of esculin significantly decreased. CONCLUSION: This study is the first to demonstrate that esculin is a potential natural active ingredient in the treatment of LF, which can inhibit the activation of hepatic stellate cells (HSC) and improve LF. Its therapeutic effect is related to the activation of the Nrf2/GPX4 signaling pathway.

Mutual Information Driven Equivariant Contrastive Learning for 3D Action Representation Learning.

Self-supervised contrastive learning has proven to be successful for skeleton-based action recognition. For contrastive learning, data transformations are found to fundamentally affect the learned representation quality. However, traditional invariant contrastive learning is detrimental to the performance on the downstream task if the transformation carries important information for the task. In this sense, it limits the application of many data transformations in the current contrastive learning pipeline. To address these issues, we propose to utilize equivariant contrastive learning, which extends invariant contrastive learning and preserves important information. By integrating equivariant and invariant contrastive learning into a hybrid approach, the model can better leverage the motion patterns exposed by data transformations and obtain a more discriminative representation space. Specifically, a self-distillation loss is first proposed for transformed data of different intensities to fully utilize invariant transformations, especially strong invariant transformations. For equivariant transformations, we explore the potential of skeleton mixing and temporal shuffling for equivariant contrastive learning. Meanwhile, we analyze the impacts of different data transformations on the feature space in terms of two novel metrics proposed in this paper, namely, consistency and diversity. In particular, we demonstrate that equivariant learning boosts performance by alleviating the dimensional collapse problem. Experimental results on several benchmarks indicate that our method outperforms existing state-of-the-art methods.

Identification of essential oils with strong activity against stationary phase Mycobacterium abscessus.

Purpose: To identify essential oils (EOs) active against non-growing stationary phase Mycobacterium abscessus and multidrug-resistant M. abscessus strains. Methods: The activity of EOs against both stationary and log phase M. abscessus was evaluated by colony forming unit (CFU) assay and minimum inhibitory concentration (MIC) testing. Results: We assessed the activity of 80 EOs against stationary phase M. abscessus and found 12 EOs (Cinnamon, Satureja montana, Palmarosa, Lemon eucalyptus, Honey myrtle, Combava, Health shield, Mandarin, Thyme, Rosewood, Valerian Root and Basil) at 0.5% concentration to be active against both growing and non-growing stationary phase M. abscessus. Among them, Satureja montana essential oil and Palmarosa essential oil could eliminate all stationary phase M. abscessus at 0.125% and Cinnamon essential oil could eliminate stationary phase bacteria at 0.063% after 1-day treatment. Interestingly, these EOs also exhibited promising activity against multidrug-resistant M. abscessus clinical strains. Conclusions: Our study indicates that some EOs display outstanding effectiveness against both drug susceptible M. abscessus and multidrug-resistant M. abscessus isolates. These findings may be significant for the treatment of persistent M. abscessus infections.

Association of tumor budding with clinicopathological features and prognostic value in stage III-IV colorectal cancer.

BACKGROUND: Tumor budding (TB) has emerged as a promising independent prognostic biomarker in colorectal cancer (CRC). The prognostic role of TB has been extensively studied and currently affects clinical decision making in patients with stage I and II CRC. However, existing prognostic studies on TB in stage III CRC have been confined to small retrospective cohort studies. Consequently, this study investigated the correlation among TB categories, clinicopathological features, and prognosis in stage III-IV CRC to further enhance the precision and individualization of treatment through refined prognostic risk stratification. AIM: To analyze the relationship between TB categories and clinicopathological characteristics and assess their prognostic value in stage III-IV CRC to further refine the prognostic risk stratification of stage III-IV CRC. METHODS: The clinical data of 547 CRC patients were collected for this retrospective study. Infiltration at the front edge of the tumor buds was counted according to the 2016 International Tumor Budding Consensus Conference guidelines. RESULTS: Multivariate Cox proportional hazards regression analysis demonstrated that chemotherapy (P = 0.004), clinical stage IV (P < 0.001), ≥ 4 regional lymph node metastases (P = 0.004), left-sided colonic cancer (P = 0.040), and Bd 2-3 (P = 0.002) were independent prognostic factors in patients with stage III-IV CRC. Moreover, the density of tumor infiltrating lymphocytes was higher in Bd 1 than in Bd 2-3, both in the tumor stroma and its invasive margin. CONCLUSION: TB has an independent predictive prognostic value in patients with stage III-IV CRC. It is recommended to complete the TB report of stage III-IV CRC cases in the standardized pathological report to further refine risk stratification.

Developing a robust two-step machine learning multiclassification pipeline to predict primary site in head and neck carcinoma from lymph nodes.

This study aimed to develop a robust multiclassification pipeline to determine the primary tumor location in patients with head and neck carcinoma of unknown primary using radiomics and machine learning techniques. The dataset included 400 head and neck cancer patients with primary tumor in oropharynx, OPC (n = 162), nasopharynx, NPC (n = 137), oral cavity, OC (n = 63), larynx and hypopharynx, HL (n = 38). Two radiomic-based multiclassification pipelines (P1 and P2) were developed. P1 consisted in a direct identification of the primary sites, whereas P2 was based on a two-step approach: in the first step, the number of classes was reduced by merging the two minority classes which were reclassified in the second step. Diverse correlation thresholds (0.75, 0.80, 0.85), feature selection methods (sequential forwards/backwards selection, sequential floating forward selection, neighborhood component analysis and minimum redundancy maximum relevance), and classification models (neural network, decision tree, naïve Bayes, bagged trees and support vector machine) were assessed. P2 outperformed P1, with the best results obtained with the support vector machine classifier including radiomic and clinical features (accuracies of 75.3 % (HL), 75.4 % (OC), 71.3 % (OPC), 92.9 % (NPC)). These results indicate that the two-step multiclassification pipeline integrating radiomics and clinical information is a promising approach to predict the tumor site of unknown primary.

Video Coding for Machines: Compact Visual Representation Compression for Intelligent Collaborative Analytics.

As an emerging research practice leveraging recent advanced AI techniques, e.g. deep models based prediction and generation, Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. With the rapid advances of deep feature representation and visual data compression in mind, in this paper, we summarize VCM methodology and philosophy based on existing academia and industrial efforts. The development of VCM follows a general rate-distortion optimization, and the categorization of key modules or techniques is established including feature-assisted coding, scalable coding, intermediate feature compression/optimization, and machine vision targeted codec, from broader perspectives of vision tasks, analytics resources, etc. From previous works, it is demonstrated that, although existing works attempt to reveal the nature of scalable representation in bits when dealing with machine and human vision tasks, there remains a rare study in the generality of low bit rate representation, and accordingly how to support a variety of visual analytic tasks. Therefore, we investigate a novel visual information compression for the analytics taxonomy problem to strengthen the capability of compact visual representations extracted from multiple tasks for visual analytics. A new perspective of task relationships versus compression is revisited. By keeping in mind the transferability among different machine vision tasks (e.g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates. In particular, to narrow the dimensionality gap between neural network generated features extracted from pixels and a variety of machine vision features/labels (e.g. scene class, segmentation labels), a codebook hyperprior is designed to compress the neural network-generated features. As demonstrated in our experiments, this new hyperprior model is expected to improve feature compression efficiency by estimating the signal entropy more accurately, which enables further investigation of the granularity of abstracting compact features among different tasks.

Vaping and smoking cue reactivity in young adult electronic cigarette users who have never smoked combustible cigarettes: A functional neuroimaging study.

Introduction: The rapid growth in the use of electronic cigarettes (e-cigarettes) among young adults who have never smoked combustible cigarettes is concerning, as it raises the potential for chronic vaping and nicotine addiction. A key characteristic of drug addiction is the elevated neural response to conditioned drug-related cues (i.e., cue reactivity). Generalized reactivity to both vaping and smoking cues may signify an increased risk for smoking initiation in non- smoking vapers. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate brain responses to vaping and smoking cues in young adult never-smoking vapers. Methods: Sixty-six young adult never-smoking vapers underwent functional MRI while viewing visual cues pertaining to vaping, smoking, and nicotine-unrelated unconditioned reward (i.e., food). A priori region-of-interest analysis combined with exploratory whole-brain analysis was performed to characterize neural reactivity to vaping and smoking cues in comparison to food cues. Results: The medial prefrontal cortex and the posterior cingulate cortex, regions that play a key role in drug cue reactivity, showed significantly increased neural response to vaping cues compared to food cues. The posterior cingulate cortex additionally showed increased neural responses to smoking cues compared to food cues. Conclusions: Despite never having smoked combustible cigarettes, young adult vapers exhibited heightened neural susceptibility to both vaping and smoking cues within brain systems associated with cue reactivity. The findings shed light on the mechanisms underlying nicotine addiction and smoking initiation risk in this critical population and may contribute to the development of science-based interventions and regulatory measures in the future. IMPLICATIONS: The escalating vaping prevalence among US never-smoking young adults is alarming, due to its potential ramifications for nicotine addiction development. Nicotine addiction is characterized by elevated neural response to conditioned nicotine-related cues. Using functional neuroimaging, we showed that young adult non-smoking vapers exhibited heightened neural susceptibility to both vaping and smoking cues within brain systems previously associated with cue reactivity. Such cross-reactivity to both types of nicotine cues may serve as the mechanism underlying nicotine addiction and smoking initiation risk in this population. Our findings may contribute to the development of science-based interventions and regulatory measures addressing the vaping epidemic.

Countering online marketing and user endorsements with enhanced cannabis warning labels: An online experiment among at-risk youth and young adults.

As cannabis legalization expands and online marketing intensifies, this study examines whether online social cues can amplify youth-targeted cannabis advertising and whether cannabis warning labels (CWLs) can counteract these influences. A U.S. online sample of 970 adolescents and 1776 young adults susceptible to cannabis use were recruited from Qualtrics in summer 2022. Each participant was randomly assigned to one of the 3 (CWLs: none vs. textual vs. pictorial) by 3 (comments: none vs. anti-cannabis vs. pro-cannabis) conditions in an online experiment. Participants were exposed to three online marketing posts promoting marijuana edibles (randomly selected from a large pool, N = 1260), each with either no warning label, a textual warning, or a pictorial warning (text and picture), and with either five comments (pro- or anti-cannabis in valence) or none. Results showed that among adolescents, pro-cannabis comments increased product appeal (vs. anti-cannabis comments: b = 0.18, p = .025; vs. no comments: b = 0.21, p = .021), and did so more than young adults. For adolescents, only pictorial warnings reduced product appeal (b = -0.20, p = .028). For young adults, both pictorial (b = -0.18, p = .002) and textual warnings (b = -0.12, p = .029) reduced product appeal. Furthermore, both textual (adolescents: b = -0.20, p = .004; young adults: b = -0.15, p = .005) and pictorial (adolescents: b = -0.30, p < .001; young adults: b = -0.18, p = .001) warnings reduced cannabis use intentions. Findings support requiring enhanced CWLs accompany online marketing ads.

Characteristics and outcomes of COVID-19 in Chinese immune thrombocytopenia patients: A prospective cohort study.

The coronavirus disease 2019 (COVID-19) pandemic has a significant impact on the immune system. This is the first and largest study on pre-existing immune thrombocytopenia (ITP) patients infected with COVID-19 in China. We prospectively collected ITP patients infected with COVID-19 enrolled in the National Longitudinal Cohort of Hematological Diseases (NICHE, NCT04645199) and followed up for at least 1 month after infection. One thousand and one hundred forty-eight pre-existing ITP patients were included. Two hundred and twelve (18.5%) patients showed a decrease in the platelet (PLT) count after infection. Forty-seven (4.1%) patients were diagnosed with pneumonia. Risk factors for a decrease in the PLT count included baseline PLT count <50 × 109/L (OR, 1.76; 95% CI, 1.25-2.46; p = 0.001), maintenance therapy including thrombopoietin receptor agonists (TPO-RAs) (OR, 2.27; 95% CI, 1.60-3.21; p < 0.001) and previous splenectomy (OR, 1.98; 95% CI, 1.09-3.61; p = 0.03). Risk factors for pneumonia included age ≥40 years (OR, 2.45; 95% CI, 1.12-5.33; p = 0.02), ≥2 comorbidities (OR, 3.47; 95% CI, 1.63-7.64; p = 0.001), maintenance therapy including TPO-RAs (OR, 2.14; 95% CI, 1.17-3.91; p = 0.01) and immunosuppressants (OR, 3.05; 95% CI, 1.17-7.91; p = 0.02). In this cohort study, we described the characteristics of pre-existing ITP patients infected with COVID-19 and identified several factors associated with poor outcomes.