Medical Research Articles

Social Determinants of Health and Disparities in Guideline-Directed Medical Therapy Optimization for Heart Failure

BACKGROUND: Fewer than 20% of eligible patients with heart failure with reduced ejection fraction receive all 4 pillars of guideline-directed medical therapy. Understanding disparities by race, ethnicity, sex, and adverse social determinants of health is necessary to equitably optimize quadruple therapy. METHODS: Utilizing the American Heart Association’s Get With The Guidelines-Heart Failure registry, we examined associations between race and ethnicity, sex, and adverse social determinants of health (insurance type and documented social need [any barrier to accessing health care]) with quadruple therapy optimization (QTO) in patients with heart failure with reduced ejection fraction hospitalized between July 1, 2021, and September 30, 2023, with complete medication data at discharge. We calculated adjusted mean differences (AMDs) in the discharge QTO score (range, 0%–100%) reflecting the proportion of eligible use of renin-angiotensin system inhibitors, β-blockers, mineralocorticoid receptor antagonists, and sodium-glucose cotransporter-2 inhibitors and compared across demographic and adverse social determinants of health groups. RESULTS: Among 82 637 patients (median age, 66 years; 32.5% female; 57.0% non-Hispanic White; 76.4% prior heart failure with reduced ejection fraction), the overall mean QTO score was 56.2% (SD, 25.5). After adjustment, compared with non-Hispanic White individuals, Black (AMD, 2.56 percentage points [95% CI, 2.16–2.96]) and Hispanic individuals (AMD, 0.71 percentage points [95% CI, 0.11–1.31]) had higher QTO scores. Females had higher QTO scores than males (AMD, 1.94 percentage points [95% CI, 1.58–2.31]). Patients with no insurance (AMD, −4.90 percentage points [−5.62 to −4.17]), Medicaid (AMD, −0.45 percentage points [−0.89 to −0.01]), and Medicare (AMD, −1.64 percentage points [−2.10 to −1.18]) had lower QTO scores versus private insurance. Those with an identified social need (n=24 651) had lower QTO scores than those without (AMD, −3.40 percentage points [95% CI, −4.10 to −2.71]). CONCLUSIONS: Disparities in QTO were most evident for patients with no insurance, Medicaid, Medicare, or potentially an identified social need. Future efforts should focus on reducing gaps to improve equitable guideline-directed medical therapy use.

PROJECT-BASED LEARNING AS A MEANS OF DEVELOPING SPEAKING SKILLS OF MEDICAL STUDENTS

This article explores the effectiveness of project-based learning (PBL) as a pedagogical approach to enhance speaking skills of medical students, emphasizing the practical application of language within professional field. In the context of evolving medical education, the need for proficient English language skills among healthcare professionals has become increasingly topical. By integrating constructivist, experiential, communicative, and task-based learning theories, PBL fosters critical thinking, collaboration, and creativity. Through active engagement in projects such as clinical simulations, case studies, and health campaigns, students not only acquire linguistic competence but also bridge knowledge across disciplines such as biology, pharmacology, ethics, etc. This study aims to analyze the methodologies, impacts, and potential obstacles of PBL in medical English education. The findings are intended to inform English language instructors in medical educational establishments, ultimately enhancing the quality of training for future healthcare professionals in a globalized surroundings. By promoting effective communication skills, PBL serves as a vital tool in preparing a new generation of adaptable and skilled practitioners.

Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study.

Large language models (LLMs) demonstrated advanced performance in processing clinical information. However, commercially available LLMs lack specialized medical knowledge and remain susceptible to generating inaccurate information. Given the need for self-management in diabetes, patients commonly seek information online. We introduce the Retrieval-augmented Information System for Enhancement (RISE) framework and evaluate its performance in enhancing LLMs to provide accurate responses to diabetes-related inquiries. This study aimed to evaluate the potential of the RISE framework, an information retrieval and augmentation tool, to improve the LLM's performance to accurately and safely respond to diabetes-related inquiries. The RISE, an innovative retrieval augmentation framework, comprises 4 steps: rewriting query, information retrieval, summarization, and execution. Using a set of 43 common diabetes-related questions, we evaluated 3 base LLMs (GPT-4, Anthropic Claude 2, Google Bard) and their RISE-enhanced versions respectively. Assessments were conducted by clinicians for accuracy and comprehensiveness and by patients for understandability. The integration of RISE significantly improved the accuracy and comprehensiveness of responses from all 3 base LLMs. On average, the percentage of accurate responses increased by 12% (15/129) with RISE. Specifically, the rates of accurate responses increased by 7% (3/43) for GPT-4, 19% (8/43) for Claude 2, and 9% (4/43) for Google Bard. The framework also enhanced response comprehensiveness, with mean scores improving by 0.44 (SD 0.10). Understandability was also enhanced by 0.19 (SD 0.13) on average. Data collection was conducted from September 30, 2023 to February 5, 2024. The RISE significantly improves LLMs' performance in responding to diabetes-related inquiries, enhancing accuracy, comprehensiveness, and understandability. These improvements have crucial implications for RISE's future role in patient education and chronic illness self-management, which contributes to relieving medical resource pressures and raising public awareness of medical knowledge.

Benchmarking the medication efficiency and technological progress of diabetes drugs

BackgroundDiabetes poses a serious global challenge, given its increasing prevalence, detrimental effects on public health, and substantial economic burden. Since 1950s, tens of drugs have been approved by the United States (US) Food and Drug Administration (FDA). In the past decade, the medical community and regulatory agencies have moved away from the glucose-centric paradigm and increasingly call for a holistic approach to assess different treatments’ benefits and harms.ObjectiveThis study aimed to assess the medication efficiency and technological progress of Type 2 Diabetes (T2D) drugs, by considering their physiological outcomes, including both benefits (i.e., glucose lowering and weight loss) and adverse effects (mortality), relative to dosing frequency.MethodsTo derive medication efficiency, this study utilized data from the US FDA and prominent meta-analyses. Given that both the benefits and adverse effects of medications are multidimensional, this study employed a nonparametric frontier method, the data envelopment analysis (DEA) model, to integrate these factors into a measure of medication efficiency. Physiological outcomes could assume both positive and negative values. Adverse effects were regarded undesirable outputs. The DEA model was built under the framework of directional distance function and was able to handle negative and undesirable values which naturally arose in the case of T2D medications.ResultsThe paper presented a ranking of 20 T2D drugs in terms of medication efficiency. Three of them were able to attain the highest medication efficiency, all of which were in the GLP-1 class, including oral Semaglutide, subcutaneous Semaglutide and Dulaglutide. However, the other two GLP-1 drugs, Lixisenatide and Liraglutide, were less efficient. The average medication efficiency of drugs approved post-2010 was significantly higher than pre-2010 drugs. High dose frequency, low HbA1c reduction and insignificant weight loss were the main driving factors behind inefficiencies. Overall, medication efficiency provided an alternative perspective on treatment effectiveness other than conventional measures such as cost-effectiveness.

Research on current issues of self-medication among young people

The aim of the work is to determine the prevalence of self-medication among young people and their attitude to it, with the further development of recommendations for pharmacists regarding the prevention of negative consequences of self-medication and the improvement of pharmaceutical assistance (educational activities) in matters of responsible self-medication. Materials and methods. The work used methods of information search, survey, critical analysis, generalization, and interpretation of results. The survey took place online from January to May 2024, using the developed questionnaire (the electronic version was created using Google Forms). A total of 207 questionnaires were received from people aged 20–25. Results. Mostly, it is difficult for young people to determine their attitude toward self-medication. During treatment, a large number of research participants use the advice of doctors and pharmacists. 75.8 % of respondents always study the instructions for the medical use of drugs before using them. Basically, young people do not increase the dose of drugs arbitrarily to speed up their recovery. Almost half of them do not change the prescribed medications by the doctor to cheaper analogs. According to the research participants, half of them consider an adequate amount of drugs in the form of two names, and this is confirmed by the fact that 83.6 % of them had cases of simultaneous administration of more than one drug. After undergoing self-treatment, 75.8 % of respondents consider it not always practical, among which 13.5 % indicate that it was necessary to consult a doctor. Conclusions. The prevalence and attitude toward self-medication among young people were studied. The main reasons for self-medication are the difficulty of getting to a doctor’s appointment and lack of time, as well as the reduction of disease symptoms after self-diagnosis and the use of drugs and preventive measures. It should be noted, that 23.2 % of young people nevertheless turned to a doctor after receiving negative manifestations of self-medication, and half of the respondents shared their experience of self-medication. The obtained information can be useful for pharmacy institutions, which can use it to create a comprehensive model of a program for the prevention of negative consequences of self-medication, especially among young people, and increase patients’ commitment to the pharmaceutical enterprise.

Knowledge Aware Automated Health Claims Processing with Medical Ontologies and Large Language Models

One of the major challenges in automating health insurance claims processing lies in the complexity involved in validating an incoming claim's medical diagnoses against its policy Underwriting (UW) exclusions. Termed UW Exclusion Detection, this process ensures claims are only paid out if their diagnoses are not medically associated with conditions excluded under the policy. Medical diagnoses in health insurance claims are typically represented by the International Classification of Disease (ICD) codes, established by the World Health Organization. For example, given a policy that excludes "all respiratory illness". A claim with the ICD code J45 (Asthma) will be subject to rejection as J45 is a respiratory-related diagnosis that falls within the scope of the policy's UW exclusion. The key challenge in automating this process lies in the wide range of available ICD codes. The ICD-10-CM coding scheme consists of over 40,000 codes, which often results in scenarios where codes encountered during inference are absent from the training data. These unseen ICD codes limit the effectiveness of data-driven approaches, which depend on the training data to discern medically relevant associations between UW exclusions and ICD codes. This underscores the need to supplement data-driven approaches with additional domain knowledge. We hypothesize that integrating implicit medical domain knowledge inherent in Large Language Models (LLMs) with explicit domain knowledge from medical ontologies, will enhance data-driven approaches for UW Exclusion Detection. Thoroughly validated on real-world health insurance claims data, our proposed approach proved effective in accurately establishing medically relevant associations between UW exclusions and ICD codes.

YouTube as a source of information in cardiopulmonary resuscitation for 2020 AHA Resuscitation Guidelines

Background The Internet has transformed global information access, particularly through platforms like YouTube, which launched in 1995 and has since become the second largest search engine worldwide with over two billion monthly users. While YouTube offers extensive educational content, including health topics like cardiopulmonary resuscitation (CPR) and basic life support (BLS), it also poses risks due to potential misinformation. Our study focuses on evaluating the accuracy of CPR and BLS videos on YouTube according to the latest 2020 American Heart Association (AHA) guidelines. This research aims to highlight inconsistencies and provide insights into improving YouTube as a reliable educational resource for both lay rescuers and healthcare professionals. Methods In this cross-sectional observational study, English YouTube videos uploaded between October 21, 2020, and May 1, 2023, were searched using keywords related to CPR and basic life support. Videos were assessed for their source, duration, views, use of human or mannequin models, and mean assessment scores by two emergency medicine physicians. A third physician’s opinion was sought in cases of disagreement. The first assessment evaluated video validity based on specified information criteria, while the second assessed their ability to convey advanced medical information aligned with the 2020 AHA guidelines. Results In this study, 201 English YouTube videos uploaded between October 21, 2020, and May 1, 2023, were evaluated based on search terms related to CPR and BLS, resulting in 95 videos meeting inclusion criteria after excluding 106 due to various reasons. Most included videos were from healthcare professionals (49.5%), followed by anonymous sources (29.5%) and official medical organizations (21.1%). Video durations ranged widely from 43 to 6,019 seconds, with an average of 692 seconds. Videos featuring mannequins predominated (91.6%), followed by those using human subjects (5.3%) or both (3.2%). Healthcare professional and official medical organization videos scoring significantly higher than those of unknown origin (p = 0.001). Video length did not correlate significantly with view counts, although shorter videos under 5 minutes tended to have higher average views. Discussion The results presented in this study demonstrated that English-language videos on YouTube related to BLS and CPR, throughout the study period, did not conform to the 2020 AHA guidelines in terms of providing basic information for lay rescuers. Furthermore, healthcare professionals cannot obtain advanced medical knowledge through these videos. We recommend a professional oversight mechanism in health-related videos that does not tolerate such misinformation.

Medicines shortages in the UK: review of the causes and analysis of serious shortage protocols – a mixed methods study

Abstract Introduction Causes of medicines shortages are multifactorial. In February 2019, the Human Medicines Regulations 2012 introduced Serious Shortage Protocols (SSPs) to address medicines shortages. SSPs are issued by the Department of Health and Social Care and enable pharmacists to supply alternative medicines without amended prescriptions1. Aim This review aimed to explore contemporary reasons attributed to medicines shortages in the United Kingdom (UK) and analyse expired SSP records. Methods A rapid scoping literature review was conducted using National Library of Medicine (PubMed®) and Elsevier (ScienceDirect). Grey literatures were collated from the Pharmaceutical Journal, Google, and Public Health England’s grey literature index. Searches were limited to publications written in English, published since 2014, which met the inclusion criteria (medicines OR drugs AND shortages AND causes). Data extraction and analysis was performed using Microsoft Excel. Expired SSPs were extracted to Microsoft Excel and analysed using descriptive and inferential statistics on R software (v.4.3.2). SSP data is publicly available; therefore, no ethical approval was required. Results Causes of Medication Shortages A total of 11 papers were selected, in which 77 medicines shortage causes were discussed. Narrative analysis identified 10 shortage-drivers. The most frequent were Manufacturing and Quality Challenges (30%,23/77), Business and Commercial Issues (21%,16/77), and Regulatory Changes and Challenges (10%,8/77). Content analysis found that supply-driven issues were more frequently cited than demand-driven issues. SSP Analysis Between October 2019 and June 2024, 59 SSPs had expired2. Menopausal Hormonal Treatments (MHTs) were the most common medication class in shortage (41%,24/59), followed by antibiotics (17%,10/59), and antidepressants (12%,7/59). The mean shortage length was 129.2 days (IQR=51–196), the longest lasting 359 days (oestradiol 0.5mg/1mg gel sachets). Oestradiol transdermal medications accounted for 19 of 24 MHT SSPs. The mean MHT shortage length was 152.6 days (IQR=51.0–216.0). Phenoxymethylpenicillin accounted for 8 of 10 antibiotic shortages. All 8 phenoxymethylpenicillin SSPs were active from 15 December 2022 to 12 May 2023; 7 of 8 were oral solution formulations. The mean antibiotic shortage length was 168.8 days, which is significantly longer than the whole sample average (p<0.05, IQR=147.0–148.0). All antidepressant SSPs were fluoxetine capsules. The mean shortage length was 99 days (IQR= 35.5–122.0). Discussion Existing literature suggests supply-driven rather than demand-driven causes of medication shortages are more pertinent; however, most expired SSPs were issued in response to demand-driven factors, such as disease outbreaks and changing prescribing demand. This suggests the extent of demand-driven shortages is not reflected in current literature. Future research should explore how demand-driven causes of shortages can be mitigated. As a rapid scoping review was conducted, the full breadth of literature may not be presented. Additionally, the grey literature findings are limited by potential inaccuracies. Finally, the use of narrative analysis may limit the generalisability of identified shortage drivers. As of 05 June 2024, there are 5 active SSPs. With medicines shortages becoming normalised within the UK health system3, there is an urgent need to mitigate supply- and demand-driven causes. Additionally, further research should explore why certain medication classes are more frequently in shortage than others.

Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review

Background The launch of ChatGPT (OpenAI) in November 2022 attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including health care. Numerous studies have since been conducted regarding how to use state-of-the-art LLMs in health-related scenarios. Objective This review aims to summarize applications of and concerns regarding conversational LLMs in health care and provide an agenda for future research in this field. Methods We used PubMed, ACM, and the IEEE digital libraries as primary sources for this review. We followed the guidance of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to screen and select peer-reviewed research articles that (1) were related to health care applications and conversational LLMs and (2) were published before September 1, 2023, the date when we started paper collection. We investigated these papers and classified them according to their applications and concerns. Results Our search initially identified 820 papers according to targeted keywords, out of which 65 (7.9%) papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT (60/65, 92% of papers), followed by Bard (Google LLC; 1/65, 2% of papers), LLaMA (Meta; 1/65, 2% of papers), and other LLMs (6/65, 9% papers). These papers were classified into four categories of applications: (1) summarization, (2) medical knowledge inquiry, (3) prediction (eg, diagnosis, treatment recommendation, and drug synergy), and (4) administration (eg, documentation and information collection), and four categories of concerns: (1) reliability (eg, training data quality, accuracy, interpretability, and consistency in responses), (2) bias, (3) privacy, and (4) public acceptability. There were 49 (75%) papers using LLMs for either summarization or medical knowledge inquiry, or both, and there are 58 (89%) papers expressing concerns about either reliability or bias, or both. We found that conversational LLMs exhibited promising results in summarization and providing general medical knowledge to patients with a relatively high accuracy. However, conversational LLMs such as ChatGPT are not always able to provide reliable answers to complex health-related tasks (eg, diagnosis) that require specialized domain expertise. While bias or privacy issues are often noted as concerns, no experiments in our reviewed papers thoughtfully examined how conversational LLMs lead to these issues in health care research. Conclusions Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications bring bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in health care.

Addressing diagnostic delays in inflammatory bowel diseases in Germany

Inflammatory bowel diseases (IBDs), including Crohn's disease (CD) and ulcerative colitis, are chronic inflammatory conditions of the gastrointestinal tract that necessitate timely diagnosis to prevent complications and improve patient outcomes. Despite advancements in medical knowledge and diagnostic techniques, significant diagnostic delays persist, particularly in CD. The study by Blüthner et al , published in the World Journal of Gastroenterology , elucidates the diagnostic delays experienced by German patients with IBD and identifies key risk factors contributing to these delays.

Understanding the role of induction, intensions and extensions in pragmatic clinical research and practice

BackgroundPragmatic clinical research methods are poorly understood, but essential to practice outcome-based medical or surgical care. Pragmatic research aims to verify the connections between medical knowledge and the reality of practice. Its methods can be understood by reviewing the problems of induction, as well as the related linguistic and mathematical notions of intensions and extensions. MethodsWe briefly review the source of problems with using inductive methods to gain knowledge, and the relationships between language, mathematics and reality. We discuss linguistic 'sense’ and ‘reference’, and the set-theory terms ‘intensions’ and ‘extensions’, which define the relationship between individuals and whichever pertinent collection these individuals comprise. Both concepts are essential to understand pragmatic medical research and evidence-based practice. ResultsPragmatic clinical research can be explained in terms of testing (in reality) the repeatability of various inductive referential and inferential steps used in clinical practice - from reliability, diagnostic accuracy, and prognostic studies to pragmatic trials. All pragmatic studies aim to verify the relationship between the extensions of the notions of symptoms, diagnoses, prognoses, treatments, and outcomes. The concepts of intensions and extensions also serve to understand ‘statistical significance’ in analyzing trial results, as well as problems related to eligibility criteria and subgroup analyses. The results of clinical studies can be generalized to the extent that they have been tested in numerous and widely different individuals. ConclusionThe notions of sense and reference, and of intensions and extensions, help explain the role pragmatic clinical research methods can play in optimizing care.

A Comparative, Individual Values-Based Scoring Approach to the Secure Flourish Index Among Clinical Health Professions Students

Abstract Purpose The purpose of this study was to investigate flourishing among medical (MD), physician assistant (PA), and nurse practitioner (NP) students, using the novel Secure Flourish Index (SFI). Method MD, PA, and NP students from two institutions completed the traditional SFI (tSFI), then applied a percentage weight to each of the six domains (maximum total 100%) based on perceived relative importance to their overall flourishing, creating a novel self-weighted SFI score (swSFI). The Bland–Altman (BA) plot was used to assess the magnitude of agreement between scores. Results The BA plot (n = 281) revealed a mean bias of .07(95% CI -.50,.63). Eighteen participants (6.4%) fell outside of the calculated BA limits of agreement [-9.31 [95% CI - 10.27,-8.45] and 9.45 [95% CI 8.49,10.41]]. Linear regression revealed the mean BA score is predictive of the mean difference between scores [R2 = 0.07, F(1,280) = 21.1, p < .001] indicating bias in agreement between the scoring systems as mean flourishing score changes. Conclusion Accounting for individual values is important when measuring student flourishing but is missing from current operant definitions. The overall mean difference (bias) in tSFI and swSFI scores is minimal (.07, possible range 0–120). However, the bias becomes larger as individual mean flourishing scores move towards both the high and low ends of the flourishing spectrum. This indicates that the influence of weighting flourishing domains is larger for individuals with high or low flourishing than those with moderate flourishing.

Conjugation mediates large-scale chromosomal transfer in Streptomyces driving diversification of antibiotic biosynthetic gene clusters.

Streptomyces are ubiquitous soil dwelling bacteria with large, linear genomes that are of special importance as a source of metabolites used in human and veterinary medicine, agronomy and industry. Conjugative elements (Actinomycetes Integrative and Conjugative Elements, AICEs) are the main drivers of Streptomyces Horizontal Gene Transfer. AICE transfer has long been known to be accompanied by mobilization of chromosomal DNA. However, the magnitude of DNA transfer, or the localization of acquired DNA across their linear chromosome, has remained undetermined. We here show that conjugative crossings in sympatric strains of Streptomyces result in the large-scale, genome-wide distributed replacement of up to one third of the recipient chromosome, a phenomenon for which we propose the name 'Streptomyces Chromosomal Transfer' (SCT). Such chromosome blending results in the acquisition, loss and hybridization of Specialized Metabolite Biosynthetic Gene Clusters, leading to a novel metabolic arsenal in exconjugant offspring. Harnessing conjugation-mediated SMBGC diversification holds great promise in the discovery of new bioactive compounds including antibiotics.

Voice or text? The role of physician media choice on patient experience in online medical communities

AbstractOnline medical communities (OMCs) are a type of online healthcare, in which physician‐patient interaction can be comprised of a variety of media options such as pictures, text, and voice. These media formats are often used to create a personalized patient experience in AI‐driven conversational healthcare platforms. To explore how physician media usage affects patient experience, we propose a counterfactual causal inference model using AI‐driven data mining methods on 131,083 online consultation records and 7,666,111 messages sent by physicians from one of the largest OMCs in China. Our study reveals the negative impact of physician use of voice on patient experience, compared to text. Drawing upon social support theory, we identify the mechanism by which physician media usage for voice produces a negative effect. The findings indicate that the negative effect of physicians' voice‐media usage occurs mainly in low‐risk disease conditions, by weakening the role of professional and emotional support. In contrast, in high‐risk disease conditions, voice‐media usage strengthens the role of professional and emotional support in improving the patient's experience. Our study is one of the first to focus on the social support attributes of the different media formats used in OMCs. We use advanced AI text‐analysis algorithms to extract features related to social support in physician‐patient conversations, and thus contribute to the use of AI in feature extraction for research.

Conflicting interpretations and FDA reputation: the case of post-market surveillance of breast implants

Conflicting interpretations regarding the severity of the adverse effects associated with FDA-approved drugs and therapies are common among the United States Food and Drug Administration (FDA), the medical community, patients, and the general public. However, scholars have paid little attention to how these conflicting interpretations may affect the FDA’s reputation for facilitating inclusive dialogue between competing policy actors. Focusing on breast implants, a medical device characterized by a stormy regulatory past, we observe that the design properties of post-market surveillance are adjusted to low-quality information. Such information-gathering mechanisms likely lead to underreporting by medical practitioners and patients, thus resulting in low-quality data. Given that the FDA cannot rely on congressional appropriations to ensure a stable flow of funding, the confusion and uncertainty created by conflicting interpretations enhance the FDA’s ability to appeal to different audiences simultaneously and thereby secure funding from industry-based user fees. This strategy may persist until the FDA’s reputation is challenged by critical information regarding adverse effects and the ensuing potentially negative media coverage. A stable appropriation-based funding model will likely encourage stronger post-market surveillance of medical devices.

Chemical analysis and concentrations of cannabidiol substances used for refractory epilepsy in Chilean patients. An underestimated worldwide risk.

The purpose of this study is to analyze composition of HMS (homemade CBD), NLS (non-licensed commercial products), and bioequivalent CBD (BES) collected from Chilean patients that voluntary accepted to analyze the "CBD-substance." Samples were collected through an open invitation for different patients to anonymously and free of charge participate in the analysis of CBD oil. The analysis of the active principle was performed using High-Resolution Liquid Chromatography (HPLC). A total of 35 samples were collected between March 2020 and September 2021, including two BES, six NLS, and 27 HMS products. The BES had an average CBD concentration of 89.15 mg/mL and an average THC concentration of 0.015 mg/mL, which complied with the maximum THC levels required by regulatory authorities (<0.2% THC.). The NLS (six samples) exhibited significant variability in CBD concentrations, ranging from a maximum of 78.5 mg/mL to a minimum of 0.1 mg/mL, with an average of 25.41 mg/mL. The THC concentrations ranged from 0 mg/mL to 2.43 mg/mL with an average of 0.62 mg/mL. The HMS products exhibited even higher variations of CBD concentrations, ranging from 0 to a maximum of 6.6 mg/mL. THC concentrations were even more variable, ranging from 0 mg/mL to 388 mg/mL. The medical community and patients involved should be aware that Hemp products are not pure and/or innocuous. HMS are likely to have high levels of THC and very low CBD, far away from therapeutic doses of CBD. CBD used in epilepsy should be restricted to licensed products, especially in children where THC toxicity is much more harmful. The study analyzed three types of CBD (cannabidiol) oils: homemade, non-licensed, and bioequivalent. Homemade and non-licensed products showed nonacceptable variance of CBD and THC concentration (tetrahydrocannabinol), in some cases with 0 mgs of CBD and many beyond THC maximum accepted. The THC is another component found in cannabis and is responsible for the neurotoxic effects. Only bioequivalent products showed concentrations of CBD and THC acceptable for epilepsy treatment, therefore are the only products recommended for such purpose.

The impact of using bee pollen in poultry systems

The use of antibiotic growth promoters in poultry production has resulted in the development of drug resistance, and destruction of the normal beneficial intestinal flora. Therefore, the task of poultry industry is to find alternative solutions to ensure safe products. The aim of this review article was to understand the potential use of bee pollen in poultry production system regarding its impact on performance, carcass traits, immunity, bacterial community, and blood parameters. Bee products such as pollen have been used in traditional medicine and have recently appeared as possible feed additives. Bee pollen is considered the most important substance in the hive, as a basic feed for bees. It consists of a mixture of proteins, amino acids, fats, carbohydrates, and multiple vitamins, minerals, and enzymes. The chemical composition of bee pollen depends mainly on the plant species, geographic region, and season of collection. Bee pollen has many uses in human medicine and livestock production. It has been used to improve general health, and may also act as an antibacterial, antifungal, antioxidant, anti-radiation, anti-carcinogenic, anti-inflammatory, hepato- and cardio-protective, and antidiabetic. Dietary supplementation of bee pollen could increase performance parameters as a growth promotor, enhancing carcass quality. In addition, bee pollen can enhance the immune response, reduce the pathogenic bacterial count, and enhance important blood parameters.

Evaluations of State Medical Cannabis Programs in the US: A Narrative Review

Background: Medical cannabis (MC) use is increasing across the US, with functional MC programs now operating in 38 states. While program policies and practices vary widely, little is known about whether and how states evaluate their programs. Better characterization of state MC program evaluation to date could inform states, program officials, and providers about best practices and provide a roadmap for future program evaluation. Summary: We conducted a narrative review of state MC program evaluations, including peer-reviewed literature and reports produced by independent state-based and non-state-based evaluators. Among 304 abstracts initially screened, seven evaluations met inclusion criteria. Within these evaluations, we report results according to three overarching themes: 1) evaluation characteristics, including comparison across evaluations; 2) program experience, including perceptions of providers and patients; and 3) assessment of cannabis use, including self-reported efficacy for qualifying medical conditions, patterns of medical and nonmedical cannabis use, and assessment of risk factors relevant to MC use. Additionally, we found that while goals and methods for state MC evaluations varied widely, evaluations that relied on independent, non-state entities tended to have more comprehensive and quantitatively rigorous results. Key Messages: Few states operating MC programs have completed a formal evaluation of their program. Among states that have completed an evaluation, approaches varied widely; however, common themes were also present, which may inform future state evaluation efforts. Evaluation through independent, non-state partners may provide an optimal strategy to ensure high-quality data and meaningful results.

Lifetime prevalence of questionable health behaviors and their psychological roots: A preregistered nationally representative survey.

A growing body of evidence suggests that questionable health behaviors- not following medical recommendations and resorting to non-evidence based treatments-are more frequent than previously thought, and that they seem to have strong psychological roots. We thus aimed to: 1) document the lifetime prevalence of intentional non-adherence to medical recommendations (iNAR) and use of traditional, complementary and alternative medicine (TCAM) in Serbia and 2) understand how they relate to 'distal' psychological factors-personality traits and thinking dispositions, and 'proximal' factors-a set of beliefs and cognitive biases under the term 'irrational mindset'. In this preregistered cross-sectional study on a nationally representative sample (N = 1003), we observed high lifetime prevalence of iNAR (91.3%) and TCAM (99.2%). Irrational beliefs, especially magical health beliefs and medical conspiracy theories, were the strongest predictors of TCAM. They also mediated the relation between Disintegration/lower cognitive reflectiveness and TCAM. High Disintegration, and low Conscientiousness predicted iNAR directly, whilst negative experiences with the healthcare system facilitated both types of questionable health practices. The established psychological profile of people prone to questionable health behaviors and the fact they can be tracked to negative experiences with the system can be used to tailor public health communications.

Medical Lysenkoism.

Medicine is a compound field composed of science and art. The (necessary) degree to which the latter is involved opens medicine, in particular, to the introduction of ideas which do not, by their very nature, submit to confirmation or confutation as do the various methods of traditional science. This paper explores several ways in which a Lysenkoistic ideology can be interjected into medicine changing the manner in which it interacts with both patients and society. Simply, when ideology supersedes evidence the edifice of Western Medicine comes under direct threat. The exploration of this emerging ideological transition in medicine, and science more broadly, is of critical importance to determine the most salutary path forward.