Donor fat-to-muscle ratio and kidney transplant outcomes: A proposition of metabolic memory.

AIMS: The impact of donor abdominal fat-to-muscle ratio (FMR) on kidney transplant (KT) outcomes was assessed. Given the transient nature of the donor's metabolic environment in transplant recipients, this study investigated the capacity of body composition to induce metabolic memory effects. MATERIALS AND METHODS: KT patients (n = 895) who received allografts from living donors (2003-2013) were included. Donor fat and muscle were quantified using pre-KT abdominal computed tomography scans. Patients were categorised into donor FMR tertiles and followed up for graft outcomes. Additionally, genome-wide DNA methylation analysis was performed on 28 kidney graft samples from KT patients in the low- and high-FMR groups. RESULTS: Mean recipient age was 42.9 ± 11.4 years and 60.9% were males. Donor FMR averaged 1.67 ± 0.79. Over a median of 120.9 ± 42.5 months, graft failure (n = 127) and death-censored graft failure (n = 109) were more frequent in the higher FMR tertiles. Adjusted hazard ratios for the highest versus lowest FMR tertile were 1.71 (95% CI, 1.06-2.75) for overall graft failure and 1.90 (95% CI, 1.13-3.20) for death-censored graft failure. Genome-wide DNA methylation analysis identified 58 differentially methylated regions (p < 0.05, |Δß| > 0.2) and 35 genes showed differential methylation between the high- (FMR >1.91) and low-FMR (FMR <1.27) groups. CONCLUSIONS: Donors with increased fat and reduced muscle composition may negatively impact kidney allograft survival in recipients, possibly through the transmission of epigenetic changes, implying a body-composition-related metabolic memory effect.

Outcomes of ABO-Incompatible Living Donor Kidney Transplantation Compared to Waiting or Deceased Donor Kidney Transplantation.

INTRODUCTION: ABO-incompatible (ABOi) living donor kidney transplantation (LDKT) is considered only for patients who do not have an ABO-compatible (ABOc) LD. Therefore, a clinically practical question is whether to proceed with ABOi LDKT or remain on dialysis while waiting for ABOc deceased donor kidney transplantation (DDKT). However, this issue has not been addressed in Asian countries, where ABOi LDKT programs are more active than DDKT programs. METHODS: A total of 426 patients underwent ABOi-LDKT between 2010 and 2020 at Seoul National University Hospital and Severance Hospital, Korea. We compared outcomes between the ABOi-LDKT and the propensity-matched control groups (waiting-list-only group, n = 1,278; waiting-list-or-ABOc-DDKT group, n = 1,278). RESULTS: The ABOi-LDKT group showed a significantly better patient survival rate than the waiting-list-only group (p = 0.001) and the waiting-list-or-ABOc-DDKT group (p = 0.048). When the ABOi-LDKT group was categorized into a high-titer group (peak anti-ABO titer ≥1:128) and a low-titer group (peak anti-ABO titer ≤1:64), the low-titer group showed better patient survival rates than those of the waiting-list-or-ABOc-DDKT group (p = 0.046) or the waiting-list-only group (p = 0.004). In contrast, the high-titer ABOi-LDKT group showed no significant benefit in patient survival compared to the waiting-list-or-ABOc-DDKT group. Death-censored graft survival in the ABOi-LDKT group was not significantly different from that in the ABOc-DDKT group (p = 0.563). CONCLUSION: The ABOi-LDKT group has better outcomes than the waiting-list-or-ABOc-DDKT group in a country with a long waiting time.

Clinical relevance of the living kidney donor profile index in Korean kidney transplant recipients.

BACKGROUND: The Living Kidney Donor Profile Index (LKDPI) was developed in the United States to predict graft outcomes based on donor characteristics. However, there are significant differences in donor demographics, access to transplantation, proportion of ABO incompatibility, and posttransplant mortality in Asian countries compared with the United States. METHODS: We evaluated the clinical relevance of the LKDPI score in a Korean kidney transplant cohort by analyzing 1860 patients who underwent kidney transplantation between 2000 and 2019. Patients were divided into three groups according to LKDPI score: <0, 1-19.9, and ≥20. RESULTS: During a median follow-up of 119 months, 232 recipients (12.5%) experienced death-censored graft loss, and 98 recipients (5.3%) died. High LKDPI scores were significantly associated with increased risk of death-censored graft loss independent of recipient characteristics (LKDPI 1-19.9: HR 1.389, 95% CI 1.036-1.863; LKDPI ≥20: HR 2.121, 95% CI 1.50-2.998). High LKDPI score was also significantly associated with increased risk of biopsy-proven acute rejection and impaired graft renal function. By contrast, overall patient survival rates were comparable among the LKDPI groups. CONCLUSION: High LKDPI scores were associated with an increased risk of death-censored graft loss, biopsy-proven acute rejection, and impaired graft renal function among a Korean kidney transplant cohort.

Identifying e-cigarette content on TikTok: Using a BERTopic Modeling approach.

INTRODUCTION: The use of hashtags is a common way to promote e-cigarette content on social media. Analysis of hashtags may provide insight into e-cigarette promotion on social media. However, the examination of text data is complicated by the voluminous amount of social media data. This study used machine learning approaches (i.e., Bidirectional Encoder Representations from Transformers [BERT] topic modeling) to identify e-cigarette content on TikTok. METHODS: We used 13 unique hashtags related to e-cigarettes (e.g., #vape) for data collection. The final analytic sample included 12,573 TikTok posts. To identify the best fitting number of topic clusters, we used both quantitative (i.e., coherence test) and qualitative approaches (i.e., researchers checked the relevance of text from each topic). We, then, grouped and characterized clustered text to each theme. RESULTS: We evaluated that N=18 was the ideal number of topic clusters. The 9 overarching themes were identified: Social media and TikTok-related features (N=4; "duet", "viral"), Vape shops and brands (N=3; "store"), Vape tricks (N=3; "ripsaw"), Modified use of e-cigarettes (N=1; "coil", "wire"), Vaping and girls (N=1; "girl"), Vape flavors (N=1; "flavors"), Vape and cigarettes (N=1; "smoke"), Vape identities and communities (N=1; "community"), and Non-English language (N=3; Romanian and Spanish). CONCLUSIONS: This study used a machine learning method, BERTopic modeling, to successfully identify relevant themes on TikTok. This method can inform future social media research examining other tobacco products, and tobacco regulatory policies such as monitoring of e-cigarette marketing on social media. IMPLICATIONS: This study can inform future social media research examining other tobacco products, and tobacco regulatory policies such as monitoring of e-cigarette marketing on social media.

Using Computer Vision to Detect E-cigarette Content in TikTok Videos.

INTRODUCTION: Previous research has identified abundant e-cigarette content on social media using primarily text-based approaches. However, frequently used social media platforms among youth, such as TikTok, contain primarily visual content, requiring the ability to detect e-cigarette-related content across large sets of videos and images. This study aims to use a computer vision technique to detect e-cigarette-related objects in TikTok videos. AIMS AND METHODS: We searched 13 hashtags related to vaping on TikTok (eg, #vape) in November 2022 and obtained 826 still images extracted from a random selection of 254 posts. We annotated images for the presence of vaping devices, hands, and/or vapor clouds. We developed a YOLOv7-based computer vision model to detect these objects using 85% of extracted images (N = 705) for training and 15% (N = 121) for testing. RESULTS: Our model's recall value was 0.77 for all three classes: vape devices, hands, and vapor. Our model correctly classified vape devices 92.9% of the time, with an average F1 score of 0.81. CONCLUSIONS: The findings highlight the importance of having accurate and efficient methods to identify e-cigarette content on popular video-based social media platforms like TikTok. Our findings indicate that automated computer vision methods can successfully detect a range of e-cigarette-related content, including devices and vapor clouds, across images from TikTok posts. These approaches can be used to guide research and regulatory efforts. IMPLICATIONS: Object detection, a computer vision machine learning model, can accurately and efficiently identify e-cigarette content on a primarily visual-based social media platform by identifying the presence of vaping devices and evidence of e-cigarette use (eg, hands and vapor clouds). The methods used in this study can inform computational surveillance systems for detecting e-cigarette content on video- and image-based social media platforms to inform and enforce regulations of e-cigarette content on social media.

Content Analysis of YouTube Videos Related to E-Cigarettes and COVID-19.

BACKGROUND: E-cigarettes are frequently promoted on social media and portrayed in ways that are attractive to youth. While the COVID-19 pandemic significantly affected people's lives, less is known about how the pandemic influenced e-cigarette-related marketing and information on social media. This study examined how e-cigarettes were portrayed on youtube, one of the most popular social media platforms during the COVID-19 pandemic. METHODS: We searched for combinations of search terms related to e-cigarettes (e.g., "electronic cigarette" and "vape") and COVID-19 (e.g., "corona" and "COVID") in July of 2021. To be included in analyses, videos must be: uploaded after February 1, 2020, in English, related to e-cigarettes and COVID-19, and less than 30 min in length. We conducted a content analysis of included videos, coding for uploader characteristics, what e-cigarette products were showcased, and specific themes that intersected between e-cigarettes and COVID-19. RESULTS: We examined N = 307 videos and found that N = 220 (73.6%) discussed the health effects of e-cigarette use on COVID-19, followed by videos on how COVID-19 affects e-cigarette sales (N = 40, 12.9%), face mask-related videos (N = 16, 5.1%; e.g., vape tricks including masks) and instructional videos (N = 10, 3.2%; e.g., sanitizing vape devices during COVID-19). Instructional videos had the highest number of likes (Median = 23; IQR = 32) and comments (Median = 10; IQR = 7). CONCLUSIONS: Our findings support the need for continuous surveillance and research on novel vaping-related content in reaction to policies and events, such as the global pandemic. More research is needed to understand the impact of this content on young people's perceptions and use of e-cigarettes.

Adolescents and Young Adults Use of Social Media and Following of e-Cigarette Influencers.

BACKGROUND: With high rates of both e-cigarette and social media use among adolescents and young adults (AYAs), social media influencers who promote e-cigarettes are particularly concerning but are understudied. We examined the association between AYAs' use of 11 different social media platforms (e.g. Facebook, Twitter, Instagram, TikTok, and YouTube) and exposure to social media e-cigarette influencers. OBJECTIVES: From November 2022 to February 2023, we conducted an online, US national survey of AYAs (14-29 years) who endorsed past-30-day e-cigarette use. We used binomial logistic regression to examine associations between the frequency of use of each social media platform and following e-cigarette influencers, controlling for age, sex, race, ethnicity, e-cigarette use frequency, and other tobacco and substance use (i.e., alcohol and cannabis). The model was stratified by adolescents (14-17 years; n = 293) and young adults (18-29 years; n = 654). RESULTS: The most frequently used social media platforms were Snapchat, TikTok and Instagram among adolescents, and YouTube, Instagram and TikTok among young adults. In adjusted models, following e-cigarette influencers was associated with more frequent use of TikTok (adjusted odds ratio [95% CI]; 1.33 [1.05, 1.68]) and Pinterest (1.18 [1.02, 1.38]) among adolescents, and more frequent use of Twitter (1.17 [1.06, 1.29]) among young adults. CONCLUSIONS: The use of different platforms was associated with exposure to e-cigarette influencers: TikTok and Pinterest among adolescents and Twitter among young adults. These findings can inform tobacco regulatory policy and social media platform restrictions of e-cigarette influencers on the platforms that are popular among AYAs.

Low skeletal muscle mass is associated with mortality in kidney transplant recipients.

Muscle wasting in chronic kidney disease is associated with increased cardiovascular events, morbidity, and mortality. However, whether pretransplantation skeletal muscle mass affects kidney transplantation (KT) outcomes has not been established. We analyzed 623 patients who underwent KT between 2004 and 2019. We measured the cross-sectional area of total skeletal muscle at the third lumbar vertebra level on pretransplantation computed tomography scan. The patients were grouped into low and normal skeletal muscle mass groups based on the sex-specific skeletal muscle mass index lowest quartile. During the entire follow-up period, 45 patients (7.2%) died and 56 patients (9.0%) experienced death-censored graft loss. Pretransplantation low skeletal muscle mass was independently associated with all-cause mortality (adjusted hazard ratio, 2.269; 95% confidence interval, 1.232-4.182). Low muscle mass was also associated with an increased risk of hospital readmission within 1 year after transplantation. Death-censored graft survival rates were comparable between the 2 groups. The low muscle group showed higher creatinine-based estimated glomerular filtration rates (eGFRs) than the normal muscle group. Although cystatin C-based eGFRs were measured in only one-third of patients, cystatin C-based eGFRs were comparable between the 2 groups. Pretransplantation low skeletal muscle mass index is associated with an increased risk of mortality and hospital readmission after KT.

Polysubstance use by sexual identity among US adults, 2021.

OBJECTIVE: Understanding polysubstance use among US sexual minority adults is important to serve as a population-level baseline to promote health equity around substance use prevention and public health strategies. This study quantifies the number of substances used by sexual identity among US adults. METHODS: We used the 2021 National Survey on Drug Use and Health and included adults (aged ≥18) (unweighted n = 47,291). We conducted multivariable Poisson regression models to examine the number of substances used in the past year (count variable; range: 0-18) by sexual identity ("heterosexual", "gay/lesbian", "bisexual", "unknown" [don't know, refused, blank]), after adjusting for covariates (i.e., age, sex, race/ethnicity, income level, education level, having insurance status, living in urban area, past-year distress level (Kessler-6), any drug or alcohol use disorder in the past year, and sexual attraction). RESULTS: Of the total sample (51.4% were female, 12.1% were non-Hispanic Black and 17.0% were Hispanic adults), 88.3% were heterosexual, 2.4% were gay/lesbian, 5.0% were bisexual adults, and 4.3% reported "unknown" sexual identity. After adjusting for covariates, a greater number of substances were used in the past year among gay/lesbian individuals (aIRR = 1.44, 95% CI = 1.09, 1.75), bisexual individuals (aIRR =1.34, 95% CI = 1.26, 1.41), and individuals reporting an "unknown" sexual identity (aIRR = 1.22, 95% CI = 1.09, 1.36) (vs. heterosexual adults). CONCLUSIONS: Tailored substance use prevention and public health strategies specializing in sexual minority populations are warranted.

Survival Benefit of Kidney Transplantation in Patients With End-Stage Kidney Disease and Prior Acute Myocardial Infarction.

Patients with end stage kidney disease (ESKD) and a previous acute myocardial infarction (AMI) have less access to KT. Data on ESKD patients with an AMI history who underwent first KT or dialysis between January 2007 and December 2018 were extracted from the Korean National Health Insurance Service. Patients who underwent KT (n = 423) were chronologically matched in a 1:3 ratio with those maintained on dialysis (n = 1,269) at the corresponding dates, based on time-conditional propensity scores. The 1, 5, and 10 years cumulative incidences for all-cause mortality were 12.6%, 39.1%, and 60.1% in the dialysis group and 3.1%, 7.2%, and 14.5% in the KT group. Adjusted hazard ratios (HRs) of KT versus dialysis were 0.17 (95% confidence interval [CI], 0.12-0.24; p < 0.001) for mortality and 0.38 (95% CI, 0.23-0.51; p < 0.001) for major adverse cardiovascular events (MACE). Of the MACE components, KT was most protective against cardiovascular death (HR, 0.23; 95% CI, 0.12-0.42; p < 0.001). Protective effects of KT for all-cause mortality and MACE were consistent across various subgroups, including patients at higher risk (e.g., age >65 years, recent AMI [<6 months], congestive heart failure). KT is associated with lower all-cause mortality and MACE than maintenance dialysis patients with a prior AMI.

The Promotion of Premium Cigars on Social Media.

INTRODUCTION: The health effect of premium cigar smoking is determined by patterns of use and perceptions, which are shaped by marketing messages. The tobacco industry uses brand-owned media promotion on social media to market its product. However, premium cigar brands' promotion of their products on social media is unknown. AIMS AND METHODS: Forty-seven premium cigar brands were reviewed. For each brand, we identified the social media accounts and examined the content of the 10 most recent posts of each social media platform to identify the marketing themes in July 2022. We also assessed the presence of age-gating and the dates of the posts. RESULTS: Of the 47 brands, 65% (n = 31) had Facebook and Instagram, 56% (n = 27) had Twitter, 21% (n = 10) had YouTube, and 17% (n = 8) had "other" (e.g. LinkedIn and Pinterest) accounts. Age-gating across these platforms ranged from 0% to 49%. Marketing themes identified from 981 social media posts included product features, alcohol, holidays, events and festivals, discounts and sweepstakes, taste, family, quality, lounges and clubs, culture, innovation, masculinity, and "other" (e.g. fundraising for charity and celebrity endorsement). CONCLUSIONS: Premium cigar brands are using brand-owned social media to promote their products using similar strategies (e.g. discounts and sweepstakes) used by other tobacco industries but also using novel themes (e.g. fundraising for charity, events and festivals) to enhance engagement. Ongoing comprehensive surveillance of premium cigars' marketing is needed on social media to inform marketing restrictions to protect public health, including priority populations such as youth. IMPLICATIONS: This study is the first to identify that premium cigar brands are promoting their brands and products on brand-owned social media, using diverse themes and strategies to engage and appeal to the public. Age gating of the promotional content on social media was low. Findings suggest that marketing restrictions to reduce the appeal of premium cigars among youth is needed to reduce tobacco-related harm.

Use and Product Characteristics of "Tobacco Free Nicotine" E-Cigarettes Among Young Adults.

INTRODUCTION: E-cigarettes are increasingly being marketed as containing tobacco-free nicotine (TFN). There is no data examining use of TFN e-cigarettes by young adults and how use may differ from non-TFN e-cigarettes. The current study aims to characterize young adult TFN e-cigarette use and examine differences between those who report using TFN and non-TFN e-cigarettes. METHOD: U.S. young adults (18-25) with lifetime e-cigarette use (n = 927) were recruited via online panels in Fall 2021 and answered questions about TFN and non-TFN e-cigarettes. Participants were categorized by lifetime TFN e-cigarette use status (yes vs. no; 34% yes). Bivariate comparisons examined differences in e-cigarette characteristics (device type, flavors, nicotine concentration) between the TFN and non-TFN groups. Binary logistic regression models examined associations between lifetime frequency of vaping (<100 vs. ≥100 times), and other tobacco product, cannabis, and/or alcohol use and lifetime TFN e-cigarette use (yes vs. no). Adjusted models include age, race/ethnicity, vaping onset age, and sex. RESULTS: In multivariable adjusted models, lifetime TFN e-cigarette use was associated with younger age, greater lifetime vaping, and nicotine pouch use. Young adults who used TFN e-cigarettes were more likely to report fruit, mint, menthol, and beverage flavors and know their e-liquid nicotine concentration compared with those who used non-TFN e-cigarettes. CONCLUSION: Among young adults who have used e-cigarettes, more frequent e-cigarette use and use of nicotine pouches, which can also contain TFN, were associated with TFN e-cigarette use. Understanding behaviors and characteristics of those using TFN e-cigarettes is critical to regulation of TFN containing products. IMPLICATIONS: This study characterizes young adults who report experience with tobacco free nicotine (TFN) e-cigarettes and compares them to those who have not used TFN e-cigarettes. Young adults with more frequent e-cigarette use and use of nicotine pouches are more likely to report TFN e-cigarette use.

Understanding e-cigarette content and promotion on YouTube through machine learning.

INTRODUCTION: YouTube is a popular social media used by youth and has electronic cigarette (e-cigarette) content. We used machine learning to identify the content of e-cigarette videos, featured e-cigarette products, video uploaders, and marketing and sales of e-cigarette products. METHODS: We identified e-cigarette content using 18 search terms (eg, e-cig) using fictitious youth viewer profiles and predicted four models using the metadata as the input to supervised machine learning: (1) video themes, (2) featured e-cigarette products, (3) channel type (ie, video uploaders) and (4) discount/sales. We assessed the association between engagement data and the four models. RESULTS: 3830 English videos were included in the supervised machine learning. The most common video theme was 'product review' (48.9%), followed by 'instruction' (eg, 'how to' use/modify e-cigarettes; 17.3%); diverse e-cigarette products were featured; 'vape enthusiasts' most frequently posted e-cigarette videos (54.0%), followed by retailers (20.3%); 43.2% of videos had discount/sales of e-cigarettes; and the most common sales strategy was external links for purchasing (34.1%). 'Vape trick' was the least common theme but had the highest engagement (eg, >2 million views). 'Cannabis' (53.9%) and 'instruction' (49.9%) themes were more likely to have external links for purchasing (p<0.001). The four models achieved an F1 score (a measure of model accuracy) of up to 0.87. DISCUSSION: Our findings indicate that on YouTube videos accessible to youth, a variety of e-cigarette products are featured through diverse videos themes, with discount/sales. The findings highlight the need to regulate the promotion of e-cigarettes on social media platforms.

Social Media Use and Subsequent E-Cigarette Susceptibility, Initiation, and Continued Use Among US Adolescents.

INTRODUCTION: Social media has a large amount of e-cigarette content. Little is known about the associations between social media use and a wide range of e-cigarette use behaviors, including susceptibility, initiation, and continued use. We analyzed national data on US adolescents to assess these associations. METHODS: We used data on adolescents participating in the Population Assessment of Tobacco and Health (PATH) Study Wave 4 (2016-2018) and Wave 5 (2018-2019). We conducted 2 models: 1) a multinomial logistic regression on e-cigarette use susceptibility and use behaviors at Wave 5 by social media use at Wave 4 among adolescents who never used e-cigarettes at Wave 4 and 2) a binomial logistic regression on current e-cigarette use at Wave 5 by social media use at Wave 4 among adolescents who ever used e-cigarettes at Wave 4. RESULTS: Among adolescents who never used e-cigarettes at Wave 4 (n = 7,872), daily social media use (vs never) was associated with a higher likelihood of being susceptible to e-cigarette use (adjusted odds ratio [aOR] =1.46; 95% CI, 1.20-1.78), past e-cigarette use (aOR = 3.55; 95% CI, 2.49-5.06), and current e-cigarette use (aOR = 3.45; 95% CI, 2.38-5.02) at Wave 5. Among adolescents who ever used e-cigarettes at Wave 4 (n = 794), we found no significant association between social media use at Wave 4 and continued e-cigarette use at Wave 5. CONCLUSION: Our study found that social media use is associated with subsequent susceptibility to e-cigarette use and initiation but not with continued use of e-cigarettes among US adolescents. These findings suggest that understanding and addressing the association between social media and e-cigarette use is critical.

Particulate matter induces arrhythmia-like cardiotoxicity in zebrafish embryos by altering the expression levels of cardiac development- and ion channel-related genes.

Air pollution is a risk factor that increases cardiovascular morbidity and mortality. In this study, we investigated the cardiotoxicity of particulate matter (PM) exposure using a zebrafish embryo model. We found that PM exposure induced cardiotoxicity, such as arrhythmia, during cardiac development. PM exposure caused cardiotoxicity by altering the expression levels of cardiac development (T-box transcription factor 20, natriuretic peptide A, and GATA-binding protein 4)- and ion-channel (scn5lab, kcnq1, kcnh2a/b, and kcnh6a/b)-related genes. In conclusion, this study showed that PM induces the aberrant expression of cardiac development- and ion channel-related genes, leading to arrhythmia-like cardiotoxicity in zebrafish embryos. Our study provides a foundation for further research on the molecular and genetic mechanisms of cardiotoxicity induced by PM exposure.

Bulk fatigue induced by surface reconstruction in layered Ni-rich cathodes for Li-ion batteries.

Ni-rich layered cathode materials are among the most promising candidates for high-energy-density Li-ion batteries, yet their degradation mechanisms are still poorly understood. We report a structure-driven degradation mechanism for NMC811 (LiNi0.8Mn0.1Co0.1O2), in which a proportion of the material exhibits a lowered accessible state of charge at the end of charging after repetitive cycling and becomes fatigued. Operando synchrotron long-duration X-ray diffraction enabled by a laser-thinned coin cell shows the emergence and growth in the concentration of this fatigued phase with cycle number. This degradation is structure driven and is not solely due to kinetic limitations or intergranular cracking: no bulk phase transformations, no increase in Li/Ni antisite mixing and no notable changes in the local structure or Li-ion mobility of the bulk are seen in aged NMCs. Instead, we propose that this degradation stems from the high interfacial lattice strain between the reconstructed surface and the bulk layered structure that develops when the latter is at states of charge above a distinct threshold of approximately 75%. This mechanism is expected to be universal in Ni-rich layered cathodes. Our findings provide fundamental insights into strategies to help mitigate this degradation process.

The association between E-cigarette use and Kratom use among US adults.

Kratom, an herbal substance with stimulant and opioid-like effects commonly used in capsules or powder to be ingested or brewed as a tea, has been gaining popularity in the United States (US). US e-cigarette use (i.e., vaping) has exponentially increased in recent years. Given the potential risks of kratom (e.g., poisonings) and the increasing prevalence of e-cigarette use, understanding the association between them is important to inform prevention strategies and regulatory policies. We harnessed data from the 2020 National Survey on Drug Use and Health (NSDUH; n = 27,170) to examine past-year kratom use by past-year e-cigarette use among adults. We ran a logistic regression model on kratom use by e-cigarette use adjusting for associated factors with substance use. Among all respondents, the estimated prevalence of past-year kratom use was 0.9% and an estimated 9.7% reported past-year e-cigarette use. Our multivariable model found those with e-cigarette use (vs. not) had 4.80 higher odds of using kratom in the past year (aOR = 4.80; 95% CI = 2.62, 8.80). These findings might help inform the need for continuing education for physicians and healthcare providers related to practice in managing patients with kratom use, future studies for regulatory policies on e-cigarettes (e.g., e-liquids), or other FDA policies related to kratom.

Pulmonary fibrosis model using micro-CT analyzable human PSC-derived alveolar organoids containing alveolar macrophage-like cells.

Human lung organoids (hLOs) are useful for disease modelling and drug screening. However, a lack of immune cells in hLOs limits the recapitulation of in vivo cellular physiology. Here, we generated hLOs containing alveolar macrophage (AMφ)-like cells derived from pluripotent stem cells (PSC). To bridge hLOs with advanced human lung high-resolution X-ray computed tomography (CT), we acquired quantitative micro-CT images. Three hLO types were observed during differentiation. Among them, alveolar hLOs highly expressed not only lung epithelial cell markers but also AMφ-specific markers. Furthermore, CD68+ AMφ-like cells were spatially organized on the luminal epithelial surface of alveolar hLOs. Bleomycin-treated alveolar hLOs showed upregulated expression of fibrosis-related markers and extracellular matrix deposits in the alveolar sacs. Alveolar hLOs also showed structural alterations such as excessive tissue fraction under bleomycin treatment. Therefore, we suggest that micro-CT analyzable PSC-derived alveolar hLOs are a promising in vitro model to predict lung toxicity manifestations, including fibrosis.

Psychometric Evaluation of the Short-Form Vaping Consequences Questionnaire for Use With High School Adolescents Who Use and Do Not Use E-cigarettes.

INTRODUCTION: Assessing youth vaping expectancies using a psychometrically sound measure can improve understanding of e-cigarette susceptibility and use. AIMS AND METHODS: We evaluated the psychometric properties of the Short-Form Vaping Consequences Questionnaire (SF-VCQ), an expectancy measure previously validated with adults, within a sample of 1753 high school adolescents who completed an online, school-based survey in Fall 2020 (51.6% female; 15.56 [1.22] years old; 46.6% non-Hispanic white; 26.9% reported lifetime but no past-30-day vaping; 12.6% reported past-30-day vaping). Analyses included confirmatory factor analysis, internal consistency, measurement invariance, between-group differences, and test-criterion relationships. RESULTS: The four-factor structure was confirmed and was scalar invariant by lifetime and past-month vaping status, sex, and race. All subscales were internally reliable (mean α = 0.94). The subscales were sensitive to differences based on sex, race, vaping susceptibility among never users, and lifetime and past-month vaping status. For example, students who vaped in the past-month held weaker expectancies for negative consequences but stronger expectancies for positive reinforcement, negative reinforcement, and appetite/weight control compared to lifetime vapers. Unadjusted relationships within the subsamples of lifetime and past-month vapers provided evidence of convergent validity. Evidence for concurrent validity was observed for all samples after accounting for covariates. For example, expectancies for positive reinforcement, negative reinforcement, and appetite/weight control remained significantly associated with past-month vaping frequency. CONCLUSIONS: The SF-VCQ evidenced excellent internal reliability, scalar measurement invariance, and construct, convergent, and concurrent validity in samples of never, lifetime, and past-month adolescent e-cigarette users. Findings support using the SF-VCQ for assessing youths' vaping-related expectancies. IMPLICATIONS: The current study established the psychometric properties of the SF-VCQ for use among adolescents with and without vaping experience, including measurement invariance that permits direct comparisons of expectancies across these two groups. When considered in concert with previously published research in adults, the SF-VCQ provides researchers with a measure that can be used with both youth and adult samples. Observed relationships between positive expectancies and vaping susceptibility in vaping naïve youth and indices of vaping frequency in youth with vaping experience suggest that challenging positive expectancies may be a valuable addition to prevention and intervention efforts to reduce youth vaping.

Kidney Transplantation From Deceased Donors With Bloodstream Infection: A Multicenter Retrospective Study.

BACKGROUND: The use of organs from donors with infection is limited because of the possibility of transmission. We aimed to investigate the transmission after deceased donor transplantation with bloodstream infection (BSI). METHODS: A retrospective study of patients undergoing kidney or pancreas transplantation at five tertiary centers in Korea from January 2009 and November 2019 was performed. We analyzed the outcomes after transplantation from deceased donors with BSI. RESULTS: Eighty-six recipients received transplantation from 69 donors with BSI. The most common isolated pathogens from donors were Gram-positive bacteria (72.0%), followed by Gram-negative bacteria (22.7%), and fungi (5.3%). Appropriate antimicrobial agents were used in 47.8% of donors before transplantation. Transmission occurred only in 1 of 83 recipients (1.2%) from bacteremic donors and 1 of 6 recipients (16.7%) from fungemic donors. One-year patient and graft survival was 97.5%and 96.3%, respectively. There was no significant difference in graft and patient survival between patients who received organs from infected donors and noninfected donors. CONCLUSION: Using organs from donors with bacteremia seems to be a safe option with low transmission risk. The overall prognosis of using organs from donors with BSI is favorable.