Psychoactive substances, which influence the brain to alter perceptions and moods, have the potential to have positive and negative effects on critical software engineering tasks. They are widely used in software, but that use is not well understood. We present the results of the first qualitative investigation of the experiences of, and challenges faced by, psychoactive substance users in professional software communities. We conduct a thematic analysis of hour-long interviews with 26 professional programmers who use psychoactive substances at work. Our results provide insight into individual motivations and impacts, including mental health and the relationships between various substances and productivity. Our findings elaborate on socialization effects, including soft skills, stigma, and remote work. The analysis also highlights implications for organizational policy, including positive and negative impacts on recruitment and retention. By exploring individual usage motivations, social and cultural ramifications, and organizational policy, we demonstrate how substance use can permeate all levels of software development.
Foreword Preface Introduction What Are Psychoactive Plants? The Use of Psychoactive Plants Psychoactive Plants and Shamanic Consciousness The Fear of Psychoactive Plants The Study of Psychoactive Plants Psychoactive Plants as Factors in the Development of Culture THE PSYCHOACTIVE PLANTS On the Structure of the Major Monographs The Most Important Genera and Species from A to Z Major Monographs Little-Studied Psychoactive Plants Minor Monographs Reputed Psychoactive Plants Legal Highs Psychoactive Plants That Have Not Yet Been Identified PSYCHOACTIVE FUNGI The Archaeology of Entheogenic Mushroom Cults Cultivating Mushrooms The Genera and Species from A to Z Purported Psychoactive Fungi General Literature on Psychoactive Fungi PSYCHOACTIVE PRODUCTS ACTIVE CONSTITUENTS OF PLANTS Active Plant Constituents and Neurotransmitters The Active Plant Constituents from A to Z Botanical Taxonomy of Psychoactive Plants and Fungi General Bibliography Bibliographies Periodicals Books and Articles Acknowledgments Index
Online availability and diffusion of New Psychoactive Substances (NPS) represent an emerging threat to healthcare systems. In this work, we analyse drugs forums, online shops, and Twitter. By mining the data from these sources, it is possible to understand the dynamics of drugs diffusion and their endorsement, as well as timely detecting new substances. We propose a set of visual analytics tools to support analysts in tackling NPS spreading and provide a better insight about drugs market and analysis.
Most people who are regular consumers of psychoactive drugs are not drug addicts, nor will they ever become addicts. In neurobiological theories, non-addictive drug consumption is acknowledged only as a "necessary" prerequisite for addiction, but not as a stable and widespread behavior in its own right. This target article proposes a new neurobiological framework theory for non-addictive psychoactive drug consumption, introducing the concept of "drug instrumentalization." Psychoactive drugs are consumed for their effects on mental states. Humans are able to learn that mental states can be changed on purpose by drugs, in order to facilitate other, non-drug-related behaviors. We discuss specific "instrumentalization goals" and outline neurobiological mechanisms of how major classes of psychoactive drugs change mental states and serve non-drug-related behaviors. We argue that drug instrumentalization behavior may provide a functional adaptation to modern environments based on a historical selection for learning mechanisms that allow the dynamic modification of consummatory behavior. It is assumed that in order to effectively instrumentalize psychoactive drugs, the establishment of and retrieval from a drug memory is required. Here, we propose a new classification of different drug memory subtypes and discuss how they interact during drug instrumentalization learning and retrieval. Understanding the everyday utility and the learning mechanisms of non-addictive psychotropic drug use may help to prevent abuse and the transition to drug addiction in the future.
OBJECTIVE: The authors evaluated the association between attention deficit hyperactivity disorder (ADHD) and psychoactive substance use disorders in adults with ADHD, attending to comorbidity with mood, anxiety, and antisocial disorders. It was hypothesized that psychiatric comorbidity would be a risk factor for psychoactive substance use disorders. METHOD: Findings for 120 referred adults with a clinical diagnosis of childhood-onset ADHD were compared with those for non-ADHD adult comparison subjects (N = 268). All childhood and adult diagnoses were obtained by structured psychiatric interviews for DSM-III-R. RESULTS: There was a significantly higher lifetime risk for psychoactive substance use disorders in the ADHD adults than in the comparison subjects (52% versus 27%). Although the two groups did not differ in the rate of alcohol use disorders, the ADHD adults had significantly higher rates of drug and drug plus alcohol use disorders than the comparison subjects. ADHD significantly increased the risk for substance use disorders independently of psychiatric comorbidity. Antisocial disorders significantly increased the risk for substance use disorders independently of ADHD status. Mood and anxiety disorders increased the risk for substance use disorders in both the ADHD and comparison subjects, but more demonstrably in the comparison subjects. CONCLUSIONS: Although psychiatric comorbidity increased the risk for psychoactive substance use disorders in adults with ADHD, by itself ADHD was a significant risk factor for substance use disorders. More information is needed to further delineate risk and protective factors mediating the development of substance use disorders in persons with ADHD.
BACKGROUND: Although psychoactive medications have substantial side effects in the elderly, these drugs are used frequently in nursing homes. Few interventions have succeeded in changing this situation, and little is known about the clinical effects of such interventions. METHODS: We studied six matched pairs of nursing homes; at one randomly selected nursing home in each pair, physicians, nurses, and aides participated in an educational program in geriatric psychopharmacology. At base line we determined the type and quantity of drugs received by all residents (n = 823), and a blinded observer performed standardized clinical assessments of the residents who were taking psychoactive medications. After the five-month program, drug use and patient status were reassessed. RESULTS: Scores on an index of psychoactive-drug use, measuring both the magnitude and the probable inappropriateness of medication use, declined significantly more in the nursing homes in which the program was carried out (experimental nursing homes) than in the control nursing homes (decrease, 27 percent vs. 8 percent; P = 0.02). The use of antipsychotic drugs was discontinued in more residents in the experimental nursing homes than in the control nursing homes (32 percent vs. 14 percent); the comparable figures for the discontinuation of long-acting benzodiazepines were 20 percent vs. 9 percent, and for antihistamine hypnotics, 45 percent vs. 21 percent. In the experimental nursing homes residents who were initially taking antipsychotic drugs showed less deterioration on several measures of cognitive function than similar residents in the control facilities, but they were more likely to report depression. Those who were initially taking benzodiazepines or antihistamine hypnotic agents reported less anxiety than controls but had more loss of memory. Most other measures of clinical status remained unchanged in both groups. CONCLUSIONS: An educational program targeted to physicians, nurses, and aides can reduce the use of psychoactive drugs in nursing homes without adversely affecting the overall behavior and level of functioning of the residents.
Rest homes have become a major component of the health care system for frail elderly persons and deinstitutionalized psychiatric patients. Although psychoactive medications are frequently used in rest homes, there is little detailed information about the extent of such use, its supervision, or its effects. In a survey of a random sample of 55 rest homes in Massachusetts, we found that 55 percent of the residents were taking at least one psychoactive medication. Antipsychotic medications were being administered to 39 percent; of these, 18 percent were receiving two or more such drugs. In a follow-up investigation, we studied 837 residents in 44 rest homes with particularly high levels of antipsychotic-drug use. About half the residents had no evidence of participation by a physician in decisions about their mental health during the year of the study. A third of the residents had performance deficits on mental-status testing that indicated serious cognitive impairment, although the causal relation of such impairment to medication use could not be determined. Six percent had evidence of moderate or severe tardive dyskinesia, probably as a side effect of medication. An assessment of staff competence revealed a low level of comprehension of the purpose and side effects of commonly used psychoactive drugs. We conclude that psychoactive drugs are widely used in rest homes, with little medical supervision or understanding by staff members of their possible side effects.
Exposure to psychoactive substances during pregnancy, such as cannabis, can disrupt neurodevelopment and alter large-scale brain networks, yet identifying their neural signatures remains challenging. We introduced KOCOBrain: KuramotO COupled Brain Graph Network; a unified graph neural network framework that integrates structural and functional connectomes via Kuramoto-based phase dynamics and cognition-aware attention. The Kuramoto layer models neural synchronization over anatomical connections, generating phase-informed embeddings that capture structure-function coupling, while cognitive scores modulate information routing in a subject-specific manner followed by a joint objective enhancing robustness under class imbalance scenario. Applied to the ABCD cohort, KOCOBrain improved prenatal drug exposure prediction over relevant baselines and revealed interpretable structure-function patterns that reflect disrupted brain network coordination associated with early exposure.
Understanding how prenatal exposure to psychoactive substances such as cannabis shapes adolescent brain organization remains a critical challenge, complicated by the complexity of multimodal neuroimaging data and the limitations of conventional analytic methods. Existing approaches often fail to fully capture the complementary features embedded within structural and functional connectomes, constraining both biological insight and predictive performance. To address this, we introduced NeuroKoop, a novel graph neural network-based framework that integrates structural and functional brain networks utilizing neural Koopman operator-driven latent space fusion. By leveraging Koopman theory, NeuroKoop unifies node embeddings derived from source-based morphometry (SBM) and functional network connectivity (FNC) based brain graphs, resulting in enhanced representation learning and more robust classification of prenatal drug exposure (PDE) status. Applied to a large adolescent cohort from the ABCD dataset, NeuroKoop outperformed relevant baselines and revealed salient structural-functional connections, advancing our understanding of the neurodevelopmental impact of PDE.
Large language models (LLMs) are sensitive to the personas imposed on them at inference time, yet prompt-level "drug" interventions have never been benchmarked rigorously. We present the first controlled study of psychoactive framings on GPT-5-mini using ARC-Challenge. Four single-sentence prompts -- LSD, cocaine, alcohol, and cannabis -- are compared against a sober control across 100 validation items per condition, with deterministic decoding, full logging, Wilson confidence intervals, and Fisher exact tests. Control accuracy is 0.45; alcohol collapses to 0.10 (p = 3.2e-8), cocaine to 0.21 (p = 4.9e-4), LSD to 0.19 (p = 1.3e-4), and cannabis to 0.30 (p = 0.041), largely because persona prompts disrupt the mandated "Answer: <LETTER>" template. Persona text therefore behaves like a "few-shot consumable" that can destroy reliability without touching model weights. All experimental code, raw results, and analysis scripts are available at https://github.com/lexdoudkin/llms-on-drugs.
Understanding the real-world effects of recreational drug use remains a critical challenge in public health and biomedical research, especially as traditional surveillance systems often underrepresent user experiences. In this study, we leverage social media (specifically Twitter) as a rich and unfiltered source of user-reported effects associated with three emerging psychoactive substances: ecstasy, GHB, and 2C-B. By combining a curated list of slang terms with biomedical concept extraction via MetaMap, we identified and weakly annotated over 92,000 tweets mentioning these substances. Each tweet was labeled with a polarity reflecting whether it reported a positive or negative effect, following an expert-guided heuristic process. We then performed descriptive and comparative analyses of the reported phenotypic outcomes across substances and trained multiple machine learning classifiers to predict polarity from tweet content, accounting for strong class imbalance using techniques such as cost-sensitive learning and synthetic oversampling. The top performance on the test set was obtained from eXtreme Gradient Boosting with cost-sensitive learning (F1 = 0.885, AUPRC = 0.934). Our find
The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging. This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media. We proposed a two-layer RAG framework for query-focused answer generation and evaluated a proof of concept for the framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. Our modular framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data in an efficient manner. We compared the performance of a quantized large language model (Nous-Hermes-2-7B-DPO), deployable in low-resource s
Slow wave sleep duration and spectral abnormalities are related to both epilepsy and depression, but it is unclear how depressive symptoms in patients with epilepsy are affected by slow wave sleep duration and clinical factors, and how the spectral characteristics of slow wave sleep reflect a potential interaction of epilepsy and depression. Long-term video-EEG monitoring was conducted in 51 patients with focal epilepsy, 13 patients with generalized epilepsy, and 9 patients without epilepsy. Slow wave sleep segments were manually marked in the EEG and duration as well as EEG power spectra were extracted. Depressive symptoms were documented with the Beck Depression Inventory (BDI). At least mild depressive symptoms (BDI>9) were found among 23 patients with focal epilepsy, 5 patients with generalised epilepsy, and 6 patients who had no epilepsy diagnosis. Slow wave sleep duration was shorter for patients with at least mild depressive symptoms (p=.004), independently from epilepsy diagnosis, antiseizure medication, age, and sex. Psychoactive medication was associated with longer slow wave sleep duration (p=.008). Frontal sigma band power (13-15 Hz) during slow wave sleep was higher
Erowid.org is a website dedicated to documenting information about psychoactive substances, with over 36,000 user-submitted drug Experience Reports. We study the potential of these reports to provide information about characteristic experiences with drugs. First, we assess different kinds of drug experiences, such as 'addiction' or 'bad trips'. We quantitatively analyze how such experiences are related to substances and user variables. Furthermore, we classify positive and negative experiences as well as reported addiction using information about the consumer, substance, context and location of the drug experience. While variables based only on objective characteristics yield poor predictive performance for subjective experiences, we find subjective user reports can help to identify new patterns and impact factors on drug experiences. In particular, we found a positive association between addiction experiences and dextromethorphan, a substance with largely unknown withdrawal effects. Our research can help to gain a deeper sociological understanding of drug consumption and to identify relationships which may have clinical relevance. Moreover, it can show how non-mainstream social me
New Psychoactive Substances (NPS) are drugs that lay in a grey area of legislation, since they are not internationally and officially banned, possibly leading to their not prosecutable trade. The exacerbation of the phenomenon is that NPS can be easily sold and bought online. Here, we consider large corpora of textual posts, published on online forums specialized on drug discussions, plus a small set of known substances and associated effects, which we call seeds. We propose a semi-supervised approach to knowledge extraction, applied to the detection of drugs (comprising NPS) and effects from the corpora under investigation. Based on the very small set of initial seeds, the work highlights how a contrastive approach and context deduction are effective in detecting substances and effects from the corpora. Our promising results, which feature a F1 score close to 0.9, pave the way for shortening the detection time of new psychoactive substances, once these are discussed and advertised on the Internet.
Some kinds of psychoactive drugs have the structures which are called split-ring resonators (SRRs). SRRs might result in negative permittivity and permeability simultaneously in electromagnetic field. Simultaneous negative indexes can lead to the famous phenomenon of negative refraction. This optical property makes it possible to distinguish synthetic cannabinoids from other abusive psychoactive drugs in the UV-vis region. This optical method is non-damaged and superior in forensic science. In this paper, we use tight-binding model calculating the permittivity and permeability of the main ingredients of synthetic cannabinoids. At the same time, we give two more results of zolpidem and caffeine. Further we discuss the negative refraction of the category of zepam qualitatively.
Humans have employed an incredible variety of plant-derived substances over the millennia in order to alter consciousness and perception. Among the innumerable narcotics, analgesics, 'ordeal' drugs, and other psychoactive substances discovered and used in ritualistic contexts by cultures around the world, one class in particular stands out not only for its radical psychological effects, but also for the highly charged political and legal atmosphere that has surrounded it since its widespread adoption about 50 years ago: so-called psychedelic substances. We review functional neuroimaging investigations of the neural correlates of the psychedelic experience, and highlight relationships with the psychological and neural bases of creativity, daydreaming, and dreaming.
Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol. Continuous use of these substances can ultimately lead a human to disastrous consequences. As patients display a high rate of relapse, prevention at an early stage can be an effective restraint. We therefore propose a binary classifier to identify any individual's present vulnerability towards substance abuse by analyzing subjects' socio-economic environment. We have collected data by a questionnaire which is created after carefully assessing the commonly involved factors behind substance abuse. Pearson's chi-squared test of independence is used to identify key feature variables influencing substance abuse. Later we build the predictive classifiers using machine learning classification algorithms on those variables. Logistic regression classifier trained with 18 features can predict individual vulnerability with the best accuracy.
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Researchers found that twisting layered sheets of hexagonal boron nitride can dramatically change the light produced by quantum emitters embedded within the material。 The technique offers an unexpected new level of control over components that could power future quantum computers, communications systems, and sensors