The Benefits of Quitting Smoking at Different Ages.

INTRODUCTION: Quantifying the impact of smoking on life expectancy and the potential benefits of smoking cessation is crucial for motivating people who smoke to quit. While previous studies have attempted to estimate these effects, they were conducted more than a decade ago and did not include a significant demographic, people over 65 years old who smoke. METHODS: Mortality rates by age and smoking status were calculated using mortality relative risks derived from Cancer Prevention Study II, 2018 National Health Interview Survey smoking prevalence data, 2018 US population census data, and 2018 US mortality rates. Subsequently, life tables by smoking status - never, current, and former - were constructed. Life expectancies for all three smoking statuses, including those of individuals who had quit smoking at various ages ranging from 35 to 75, were then compared. Additionally, probability distributions of years lost due to smoking and years gained by quitting smoking at different ages were generated. Analyses were conducted in 2023. RESULTS: Compared to people who never smoked, those who smoke currently, aged 35, 45, 55, 65 or 75 years, and who have smoked throughout adulthood until that age, will lose, on average, 9.1, 8.3, 7.3, 5.9, and 4.4 years of life, respectively, if they continue to smoke for the rest of their lives. However, if they quit smoking at each of these ages, they will avoid an average loss of 8.0, 5.6, 3.4, 1.7, and 0.7 years. The chances of gaining at least 1 year of life among those who quit at age 65 and 75 are 23.4% and 14.2%, respectively. CONCLUSIONS: Quitting smoking early will avoid most years otherwise lost due to smoking. Even those who quit at ages 65 and above can still meaningfully increase their life expectancy.

Meta-analysis of the Microbial Diversity Cultured in Bioreactors Simulating the Gut Microbiome.

Understanding the intricate ecological interactions within the gut microbiome and unravelling its impact on human health is a challenging task. Bioreactors are valuable tools that have contributed to our understanding of gut microbial ecology. However, there is a lack of studies describing and comparing the microbial diversity cultivated in these models. This knowledge is crucial for refining current models to reflect the gastrointestinal microbiome accurately. In this study, we analysed the microbial diversity of 1512 samples from 18 studies available in public repositories that employed cultures performed in batches and various bioreactor models to cultivate faecal microbiota. Community structure comparison between samples using t-distributed stochastic neighbour embedding and the Hellinger distance revealed a high variation between projects. The main driver of these differences was the inter-individual variation between the donor faecal inocula. Moreover, there was no overlap in the structure of the microbial communities between studies using the same bioreactor platform. In addition, α-diversity analysis using Hill numbers showed that highly complex bioreactors did not exhibit higher diversities than simpler designs. However, analyses of five projects in which the samples from the faecal inoculum were also provided revealed an amplicon sequence variants enrichment in bioreactors compared to the inoculum. Finally, a comparative analysis of the taxonomy of the families detected in the projects and the GMRepo database revealed bacterial families exclusively found in the bioreactor models. These findings highlight the potential of bioreactors to enrich low-abundance microorganisms from faecal samples, contributing to uncovering the gut microbial "dark matter".

New Estimates of Smoking-Attributable Mortality in the U.S. From 2020 Through 2035.

INTRODUCTION: The often-cited Centers for Disease Control and Prevention (CDC) estimate of 480,000 annual U.S. smoking-attributable deaths (SADs), including 439,000 first-hand smoke deaths, derives from 2005 to 2009 data. Since then, adult smoking prevalence has decreased by 40%, while the population has grown and the smoking population aged. An updated estimate is presented to determine whether the CDC figure remains accurate or has changed substantially. In addition, the likely annual smoking-related mortality toll is projected through 2035. METHODS: A well-established model of smoking prevalence and health effects is employed to estimate annual SADs among individuals exposed to first-hand smoke in the U.S. for two distinct periods: 2005-2009 and 2020-2035. The estimate for 2005-2009 serves as a benchmark to evaluate the reliability of the model's estimate in comparison to CDC's. The projections for 2020-2035 provide up-to-date figures for SADs, predicting how annual SADs are likely to change in the coming years. Data were collected between 2005 and 2020. The analysis was conducted in 2023. RESULTS: This study's estimate of 420,000 first-hand smoke deaths over 2005-2009 is 95.7% of CDC's estimate during the same period. The model projections indicate that SADs among individuals who currently smoke or formerly smoked have increased modestly since 2005-2009. Beginning in 2020, annual SADs will remain relatively stable at approximately 450,000 before starting to decline around 2030. CONCLUSIONS: These findings suggest that the CDC estimate of the annual mortality burden of smoking remains valid. Despite U.S. population growth and the aging of the smoking population, substantial reductions in smoking will finally produce a steady, if gradual, decline in SADs beginning around 2030.

Characterization of trajectories of physical activity and cigarette smoking from early adolescence to adulthood.

BACKGROUND: Cigarette smoking and physical inactivity are two critical risk factors for noncommunicable diseases and all-cause mortality. However, few studies have compared the long-term trajectories of both behaviors, as well as multilevel factors associated with trajectory patterns. Using the National Longitudinal Study of Adolescent to Adult Health (Add Health) Wave I through V survey data, this study characterized distinct subgroups of the population sharing similar behavioral patterns from adolescence to adulthood, as well as predictors of subgroup membership for physical activity (PA) and cigarette smoking behavior respectively. METHODS: Using the Add Health Wave I through V survey data, we identified the optimal number of latent classes and class-specific trajectories of PA and cigarette smoking from early adolescence to adulthood, fitting latent growth mixture models with standardized PA score and past 30-day cigarette smoking intensity as outcome measures and age as a continuous time variable. Associations of baseline sociodemographic factors, neighborhood characteristics, and sociopsychological factors with trajectory class membership were assessed using multinomial logistic regression. RESULTS: We identified three distinct subgroups of non-linear PA trajectories in the study population: moderately active group (N = 1067, 5%), persistently inactive group (N = 14,257, 69%) and worsening activity group (N = 5410, 26%). Foror cigarette smoking, we identified three distinct non-linear trajectory subgroups: persistent non-smoker (N = 14,939, 72%), gradual quitter (N = 2357, 11%), and progressing smoker (N = 3393, 16%). Sex, race/ethnicity, neighborhood environment and perceived peer support during adolescence were significant predictors of both physical activity and cigarette smoking trajectory subgroup membership from early adolescence to adulthood. CONCLUSIONS: There are three distinct subgroups of individuals sharing similar PA and cigarette smoking behavioral profile respectively from adolescence to adulthood in the Add Health study population. Behavioral interventions that focus on neighborhood environment (e.g. establish community-based activity center) and relationship to peers during adolescence (e.g. peer counseling) could be key to long-term PA promotion and cigarette smoking cessation.

The association between cannabis vaping and other substance use.

Introduction: The popularity of cannabis vaping has increased rapidly, especially among adolescents and young adults. We posit some possible explanations and, to evaluate them, examine whether cannabis vapers differ from non-vaping cannabis users in other substance use. Methods: Using nationally representative data from the Population Assessment of Tobacco and Health (PATH) Study wave 5 (Dec. 2018-Nov. 2019), we assessed the association between cannabis vaping and other substance use. A total of 1,689 adolescents and 10,620 adults who reported cannabis use in the past 12 months were included in the study. We employed multivariable logistic regressions to assess the association between cannabis vaping and other substance use. Results: Among past 12-month cannabis users, compared with those who do not vape cannabis, participants who vape cannabis had higher risks of using alcohol (adjusted relative risk [aRR] = 1.04, 95 % CI, 1.01-1.07), cigarettes (aRR = 1.09, 95 % CI, 1.02-1.15), cigars (aRR = 1.17, 95 % CI, 1.06-1.30), other tobacco products (aRR = 1.29, 95 % CI, 1.14-1.45), electronic nicotine products (aRR = 4.64, 95 % CI, 4.32-4.99), other illicit drugs (aRR = 1.53, 95 % CI, 1.29-1.80), and misuse of prescription drugs (aRR = 1.43, 95 % CI, 1.19-1.72). Compared to older cannabis vapers, younger cannabis vapers were at risk of using more other substances. Cannabis vaping was associated with all seven measures of substance use among young adults. Conclusions: Compared to non-vaping cannabis users, cannabis vapers have higher likelihood of using other substances. Research is needed to understand why, as well as the implications of the association.

The evolution of age-specific smoking cessation rates in the United States from 2009 to 2017: a Kalman filter based approach.

BACKGROUND: Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. Recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided annual estimates of the cessation rate by age group. Hence, the primary objective of this study is to estimate annual smoking cessation rates specific to different age groups in the US from 2009 to 2017. METHODS: We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2017 period using data from the 2009-2018 National Health Interview Surveys. We focused on cessation rates in the 25-44, 45-64 and 65 + age groups. RESULTS: The findings show that cessation rates followed a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% to 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. CONCLUSIONS: The Kalman filter approach offers a real-time estimation of cessation rates that can be helpful for monitoring smoking cessation behavior.

A tale of two receptors: simultaneous targeting of NMDARs and 5-HT 4 Rs exerts additive effects against stress.

BACKGROUND: Serotonin (5-HT) receptors and N -methyl-D-aspartate receptors (NMDARs) have both been implicated in the pathophysiology of depression and anxiety disorders. Here, we evaluated whether targeting both receptors through combined dosing of ( R , S )-ketamine, an NMDAR antagonist, and prucalopride, a serotonin type IV receptor (5-HT 4 R) agonist, would have additive effects, resulting in reductions in stress-induced fear, behavioral despair, and hyponeophagia. METHODS: A single injection of saline (Sal), ( R , S )-ketamine (K), prucalopride (P), or a combined dose of ( R , S )-ketamine and prucalopride (K+P) was administered before or after contextual fear conditioning (CFC) stress in both sexes. Drug efficacy was assayed using the forced swim test (FST), elevated plus maze (EPM), open field (OF), marble burying (MB), and novelty-suppressed feeding (NSF). Patch clamp electrophysiology was used to measure the effects of combined drug on neural activity in hippocampal CA3. c-fos and parvalbumin (PV) expression in the hippocampus (HPC) and medial prefrontal cortex (mPFC) was examined using immunohistochemistry and network analysis. RESULTS: We found that a combination of K+P, given before or after stress, exerted additive effects, compared to either drug alone, in reducing a variety of stress-induced behaviors in both sexes. Combined K+P administration significantly altered c-fos and PV expression and network activity in the HPC and mPFC. CONCLUSIONS: Our results indicate that combined K+P has additive benefits for combating stress-induced pathophysiology, both at the behavioral and neural level. Our findings provide preliminary evidence that future clinical studies using this combined treatment strategy may prove advantageous in protecting against a broader range of stress-induced psychiatric disorders.

The Evolution of Age-Specific Smoking Cessation Rates in the United States From 2009 to 2018.

Objective: Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. A couple of recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided recent annual estimates of the cessation rate by age group. Methods: We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2018 period using data from the National Health Interview Survey. We focused on cessation rates in the 24-44, 45-64 and 65 + age groups. Results: The findings show that cessation rates follow a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% in 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. Conclusions: The Kalman filter approach offers a real-time estimation of cessation rates that would be helpful for monitoring smoking cessation behavior, of interest in general but also for tobacco control policymakers.

A complex relation between levels of adult hippocampal neurogenesis and expression of the immature neuron marker doublecortin.

We investigated the mechanisms underlying the effects of the antidepressant fluoxetine on behavior and adult hippocampal neurogenesis (AHN). After confirming our earlier report that the signaling molecule ß-arrestin-2 (ß-Arr2) is required for the antidepressant-like effects of fluoxetine, we found that the effects of fluoxetine on proliferation of neural progenitors and survival of adult-born granule cells are absent in the ß-Arr2 knockout (KO) mice. To our surprise, fluoxetine induced a dramatic upregulation of the number of doublecortin (DCX)-expressing cells in the ß-Arr2 KO mice, indicating that this marker can be increased even though AHN is not. We discovered two other conditions where a complex relationship occurs between the number of DCX-expressing cells compared to levels of AHN: a chronic antidepressant model where DCX is upregulated and an inflammation model where DCX is downregulated. We concluded that assessing the number of DCX-expressing cells alone to quantify levels of AHN can be complex and that caution should be applied when label retention techniques are unavailable.

Online information on chronic pain in 3 countries: an assessment of readability, credibility, and accuracy.

Objectives: To assess the readability, credibility, and accuracy of online information on chronic pain in Australia, Mexico, and Nepal. Methods: We assessed Google-based websites and government health websites about chronic pain for readability (using the Flesch Kincaid Readability Ease tool), credibility (using the Journal of American Medical Association [JAMA] benchmark criteria and Health on the Net Code [HONcode]), and accuracy (using 3 core concepts of pain science education: (1) pain does not mean my body is damaged; (2) thoughts, emotions, and experiences affect pain; and (3) I can retrain my overactive pain system). Results: We assessed 71 Google-based websites and 15 government websites. There were no significant between-country differences in chronic pain information retrieved through Google for readability, credibility, or accuracy. Based on readability scores, the websites were "fairly difficult to read," suitable for ages 15 to 17 years or grades 10 to 12 years. For credibility, less than 30% of all websites met the full JAMA criteria, and more than 60% were not HONcode certified. For accuracy, all 3 core concepts were present in less than 30% of websites. Moreover, we found that the Australian government websites have low readability but are credible, and the majority provided all 3 core concepts in pain science education. A single Mexican government website had low readability without any core concepts but was credible. Conclusion: The readability, credibility, and accuracy of online information on chronic pain should be improved internationally to support facilitating better management of chronic pain.

Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study.

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.

Intrahippocampal injection of a selective blocker of NMDA receptors containing the GluN2B subunit, Ro25-6981, increases glutamate neurotransmission and induces antidepressant-like effects.

Major depressive disorder (MDD) is a serious public health problem, as it is the most common psychiatric disorder worldwide. Antidepressant drugs increase adult hippocampal neurogenesis, which is required to induce some behavioral effects of antidepressants. Adult-born granule cells in the dentate gyrus (DG) and the glutamate receptors subunits 2 (GluN2B) subunit of N-methyl-D-aspartate (NMDA) ionotropic receptors play an important role in these effects. However, the precise neurochemical role of the GluN2B subunit of the NMDA receptor on adult-born GCs for antidepressant-like effects has yet to be elucidated. The present study aims to explore the contribution of the GluN2B-containing NMDA receptors in the ventral dentate gyrus (vDG) to the antidepressant drug treatment using a pharmacological approach. Thus, (αR)-(4-hydroxyphenyl)-(ßS)-methyl-4-(phenylmethyl)-1-piperidinepropanol (Ro25-6981), a selective antagonist of the GluN2B subunit, was acutely administered locally into the ventral DG (vDG, 1 µg each side) following a chronic fluoxetine (18 mg/kg/day) treatment-known to increase adult hippocampal neurogenesis-in a mouse model of anxiety/depression. Responses in a neurogenesis-dependent task, the novelty suppressed feeding (NSF), and neurochemical consequences on extracellular glutamate and gamma-aminobutyric acid (GABA) levels in the vDG were measured. Here, we show a rapid-acting antidepressant-like effect of local Ro25-6981 administration in the NSF independent of fluoxetine treatment. Furthermore, we revealed a fluoxetine-independent increase in the glutamatergic transmission in the vDG. Our results suggest behavioral and neurochemical effects of GluN2B subunit independent of serotonin reuptake inhibition.

The Association Between Cannabis Use and Subsequent Nicotine Electronic Cigarette Use Among US Adolescents.

PURPOSE: The current study assessed the association between cannabis use among youth never e-cigarette users and subsequent e-cigarette use. METHODS: The Population Assessment of Tobacco and Health Study is a nationally representative cohort study. Participants aged 12 years and older were selected using a 4-stage, stratified probability sample design from the US civilian, noninstitutionalized population. We included adolescents who participated in both wave 4.5 (2017-2018) and wave 5 (2018-2019) of Population Assessment of Tobacco and Health, and were never e-cigarette users at baseline (N = 9,925). Through multivariable logistic regressions, we examined the prospective association between cannabis use and subsequent e-cigarette use. RESULTS: E-cigarette use at wave five was significantly more common among youth cannabis users at wave 4.5. The adjusted relative risks between ever cannabis use and subsequent past 12-month, past 30-day, and frequent e-cigarette use (≥20 days per month) were 1.53 (95% CI, 1.26-1.81), 1.70 (95% CI, 1.25-2.15), and 2.10 (95% CI, 1.17-3.03), respectively. The adjusted relative risks between past 30-day cannabis use and subsequent past 12-month, past 30-day, and frequent e-cigarette use were 1.54 (95% CI, 1.04-2.28), 2.01 (95% CI, 1.23-3.29), and 2.87 (95% CI, 1.44-5.71), respectively. We also found significant associations between ever cannabis vaping with subsequent e-cigarette use. DISCUSSION: While previous research associates e-cigarette use with subsequent onset of cannabis use, we identify a reverse directional effect, where adolescent cannabis use is associated with increased likelihood of future e-cigarette use.

Indirect maternal effects via nest microbiome composition drive gut colonization in altricial chicks.

Gut microbial communities are complex and heterogeneous and play critical roles for animal hosts. Early-life disruptions to microbiome establishment can negatively impact host fitness and development. However, the consequences of such early-life disruptions remain unknown in wild birds. To help fill this gap, we investigated the effect of continuous early-life gut microbiome disruptions on the establishment and development of gut communities in wild Great tit (Parus major) and Blue tit (Cyanistes caeruleus) nestlings by applying antibiotics and probiotics. Treatment neither affected nestling growth nor their gut microbiome composition. Independent of treatment, nestling gut microbiomes of both species grouped by brood, which shared the highest numbers of bacterial taxa with both nest environment and their mother. Although fathers showed different gut communities than their nestlings and nests, they still contributed to structuring chick microbiomes. Lastly, we observed that the distance between nests increased inter-brood microbiome dissimilarity, but only in Great tits, indicating that species-specific foraging behaviour and/or microhabitat influence gut microbiomes. Overall, the strong maternal effect, driven by continuous recolonization from the nest environment and vertical transfer of microbes during feeding, appears to provide resilience towards early-life disruptions in nestling gut microbiomes.

Are the Relevant Risk Factors Being Adequately Captured in Empirical Studies of Smoking Initiation? A Machine Learning Analysis Based on the Population Assessment of Tobacco and Health Study.

INTRODUCTION: Cigarette smoking continues to pose a threat to public health. Identifying individual risk factors for smoking initiation is essential to further mitigate this epidemic. To the best of our knowledge, no study today has used machine learning (ML) techniques to automatically uncover informative predictors of smoking onset among adults using the Population Assessment of Tobacco and Health (PATH) study. AIMS AND METHODS: In this work, we employed random forest paired with Recursive Feature Elimination to identify relevant PATH variables that predict smoking initiation among adults who have never smoked at baseline between two consecutive PATH waves. We included all potentially informative baseline variables in wave 1 (wave 4) to predict past 30-day smoking status in wave 2 (wave 5). Using the first and most recent pairs of PATH waves was found sufficient to identify the key risk factors of smoking initiation and test their robustness over time. The eXtreme Gradient Boosting method was employed to test the quality of these selected variables. RESULTS: As a result, classification models suggested about 60 informative PATH variables among many candidate variables in each baseline wave. With these selected predictors, the resulting models have a high discriminatory power with the area under the specificity-sensitivity curves of around 80%. We examined the chosen variables and discovered important features. Across the considered waves, two factors, (1) BMI, and (2) dental and oral health status, robustly appeared as important predictors of smoking initiation, besides other well-established predictors. CONCLUSIONS: Our work demonstrates that ML methods are useful to predict smoking initiation with high accuracy, identifying novel smoking initiation predictors, and to enhance our understanding of tobacco use behaviors. IMPLICATIONS: Understanding individual risk factors for smoking initiation is essential to prevent smoking initiation. With this methodology, a set of the most informative predictors of smoking onset in the PATH data were identified. Besides reconfirming well-known risk factors, the findings suggested additional predictors of smoking initiation that have been overlooked in previous work. More studies that focus on the newly discovered factors (BMI and dental and oral health status,) are needed to confirm their predictive power against the onset of smoking as well as determine the underlying mechanisms.

Effect of Operational Parameters on the Cultivation of the Gut Microbiome in Continuous Bioreactors Inoculated with Feces: A Systematic Review.

Since the early 1980s, multiple researchers have contributed to the development of in vitro models of the human gastrointestinal system for the mechanistic interrogation of the gut microbiome ecology. Using a bioreactor for simulating all the features and conditions of the gastrointestinal system is a massive challenge. Some conditions, such as temperature and pH, are readily controlled, but a more challenging feature to simulate is that both may vary in different regions of the gastrointestinal tract. Promising solutions have been developed for simulating other functionalities, such as dialysis capabilities, peristaltic movements, and biofilm growth. This research field is under constant development, and further efforts are needed to drive these models closer to in vivo conditions, thereby increasing their usefulness for studying the gut microbiome impact on human health. Therefore, understanding the influence of key operational parameters is fundamental for the refinement of the current bioreactors and for guiding the development of more complex models. In this review, we performed a systematic search for operational parameters in 229 papers that used continuous bioreactors seeded with human feces. Despite the reporting of operational parameters for the various bioreactor models being variable, as a result of a lack of standardization, the impact of specific operational parameters on gut microbial ecology is discussed, highlighting the advantages and limitations of the current bioreactor systems.

Association of Electronic Cigarette Use by US Adolescents With Subsequent Persistent Cigarette Smoking.

Importance: Many studies have reported a positive association of youth electronic cigarette (e-cigarette) use with subsequent cigarette smoking initiation, but it remains unclear whether e-cigarette use is associated with continued cigarette smoking after initiation. Objective: To assess the association of youth baseline e-cigarette use with their continued cigarette smoking 2 years after initiation. Design, Setting, and Participants: The Population Assessment of Tobacco and Health (PATH) Study is a national longitudinal cohort study. This sample consisted of youth who participated in waves 3, 4, and 5 of the study (wave 3 was from October 2015 to October 2016, wave 4 was from December 2016 to January 2018, and wave 5 was from December 2018 to November 2019) and had never used cigarettes (cigarette-naive) by wave 3. The current analysis used multivariable logistic regressions in August 2022 to assess the association between e-cigarette use among cigarette-naive adolescents aged 12 to 17 years in 2015 and 2016 and subsequent continued cigarette smoking. PATH uses audio computer-assisted self-interviewing and computer-assisted personal interviewing to collect data. Exposures: Ever and current (past 30-day) use of e-cigarettes in wave 3. Main Outcomes and Measures: Continued cigarette smoking in wave 5 after initiating smoking in wave 4. Results: The current sample included 8671 adolescents who were cigarette naive in wave 3 and also participated in waves 4 and 5; 4823 of the participants (55.4%) were aged 12 to 14 years, 4454 (51.1%) were male, and 3763 (51.0%) were non-Hispanic White. Overall, regardless of e-cigarette use, few adolescents (362 adolescents [4.1%]) initiated cigarette smoking at wave 4, and even fewer (218 participants [2.5%]) continued smoking at wave 5. Controlling for multiple covariates, the adjusted odds ratio of baseline ever e-cigarette use, compared with never e-cigarette use, was 1.81 (95% CI, 1.03 to 3.18) for continued smoking measured as past 30-day smoking at wave 5. However, the adjusted risk difference (aRD) was small and not significant. The aRD was 0.88 percentage point (95% CI, -0.13 to 1.89 percentage points) for continued smoking, with the absolute risk being 1.19% (95% CI, 0.79% to 1.59%) for never e-cigarette users and 2.07% (95% CI, 1.01% to 3.13%) for ever e-cigarette users. Similar results were found using an alternative measure of continued smoking (lifetime ≥100 cigarettes and current smoking at wave 5) and using baseline current e-cigarette use as the exposure measure. Conclusions and Relevance: In this cohort study, absolute and relative measures of risks yielded findings suggesting very different interpretations of the association. Although there were statistically significant odds ratios of continued smoking comparing baseline e-cigarette users with nonusers, the minor risk differences between them, along with the small absolute risks, suggest that few adolescents are likely to continue smoking after initiation regardless of baseline e-cigarette use.

Intranasal (R, S)-ketamine delivery induces sustained antidepressant effects associated with changes in cortical balance of excitatory/inhibitory synaptic activity.

In 2019, an intranasal (IN) spray of esketamine SPRAVATO® was approved as a fast-acting antidepressant by drug Agencies US FDA and European EMA. At sub-anesthetic doses, (±)-ketamine, a non-competitive glutamate N-methyl-d-aspartate (NMDA) receptor antagonist, increases the overall excitability of the medial prefrontal cortex (mPFC), an effect being essential for its rapid antidepressant activity. We wondered if this effect of ketamine could come from changes in the balance between neuronal excitation and inhibition (E/I balance) in the mPFC. Here, we performed a preclinical approach to study neurochemical and behavioral responses to a single IN ketamine dose in BALB/cJ mice, a strain more sensitive to stress. By using in vivo microdialysis, we measured cortical E/I balance as the ratio between glutamate to GABA extracellular levels 24 h post-ketamine. We found, for the first time, that E/I balance was shifted in favor of excitation rather than inhibition in the mPFC but more robustly with IN KET than with a single intraperitoneal (IP) dose. Increases in plasma and brain ketamine, norketamine and HNKs levels suggest different metabolic profiles of IP and IN ketamine 30 min post-dose. A significantly larger proportion of ketamine and HNKs in the brain are derived from the IN route 30 min post-dose. It may be linked to the greater magnitude in E/I ratio following IN delivery relative to IP at t24 h. This study suggests that both IP and IN are effective brain delivery methods inducing similar sustained antidepressant efficacy of KET, but the way they induced neurotransmitter changes is slightly different.

Peripheral proteomic changes after electroconvulsive seizures in a rodent model of non-response to chronic fluoxetine.

Major depressive disorder (MDD) is the psychiatric disorder with the highest prevalence in the world. Pharmacological antidepressant treatment (AD), such as selective serotonin reuptake inhibitors [SSRI, i.e., fluoxetine (Flx)] is the first line of treatment for MDD. Despite its efficacy, lack of AD response occurs in numerous patients characterizing Difficult-to-treat Depression. ElectroConvulsive Therapy (ECT) is a highly effective treatment inducing rapid improvement in depressive symptoms and high remission rates of ∼50-63% in patients with pharmaco-resistant depression. Nevertheless, the need to develop reliable treatment response predictors to guide personalized AD strategies and supplement clinical observation is becoming a pressing clinical objective. Here, we propose to establish a proteomic peripheral biomarkers signature of ECT response in an anxio/depressive animal model of non-response to AD. Using an emotionality score based on the analysis complementary behavioral tests of anxiety/depression (Elevated Plus Maze, Novelty Suppressed Feeding, Splash Test), we showed that a 4-week corticosterone treatment (35 µg/ml, Cort model) in C57BL/6JRj male mice induced an anxiety/depressive-like behavior. A 28-day chronic fluoxetine treatment (Flx, 18 mg/kg/day) reduced corticosterone-induced increase in emotional behavior. A 50% decrease in emotionality score threshold before and after Flx, was used to separate Flx-responding mice (Flx-R, n = 18), or Flx non-responder mice (Flx-NR, n = 7). Then, Flx-NR mice received seven sessions of electroconvulsive seizure (ECS, equivalent to ECT in humans) and blood was collected before and after ECS treatment. Chronic ECS normalized the elevated emotionality observed in Flx-NR mice. Then, proteins were extracted from peripheral blood mononuclear cells (PBMCs) and isolated for proteomic analysis using a high-resolution MS Orbitrap. Data are available via ProteomeXchange with identifier PXD037392. The proteomic analysis revealed a signature of 33 peripheral proteins associated with response to ECS (7 down and 26 upregulated). These proteins were previously associated with mental disorders and involved in regulating pathways which participate to the depressive disorder etiology.

Modeling the effect of environmental cytokines, nutrient conditions and hypoxia on CD4+ T cell differentiation.

Upon antigen stimulation and co-stimulation, CD4+ T lymphocytes produce soluble factors that promote the activity of other immune cells against pathogens or modified tissues; this task must be performed in presence of a variety of environmental cytokines, nutrient, and oxygen conditions, which necessarily impact T cell function. The complexity of the early intracellular processes taking place upon lymphocyte stimulation is addressed by means of a mathematical model based on a network that integrates variable microenvironmental conditions with intracellular activating, regulatory, and metabolic signals. Besides the phenotype subsets considered in previous works (Th1, Th2, Th17, and Treg) the model includes the main early events in differentiation to the T FH phenotype. The model describes how cytokines, nutrients and oxygen availability regulate the differentiation of naïve CD4+ T cells into distinct subsets. Particularly, it shows that elevated amounts of an all-type mixture of effector cytokines under optimal nutrient and oxygen availability conduces the system towards a highly-polarized Th1 or Th2 state, while reduced cytokine levels allow the expression of the Th17, Treg or T FH subsets, or even hybrid phenotypes. On the other hand, optimal levels of an all-type cytokine mixture in combination with glutamine or tryptophan restriction implies a shift from Th1 to Th2 expression, while decreased levels of the Th2-inducing cytokine IL-4 leads to the rupture of the Th1-Th2 axis, allowing the manifestation of different (or hybrid) subsets. Modeling proposes that, even under reduced levels of pro-inflammatory cytokines, the sole action of hypoxia boost Th17 expression.