To summarize experience with Johnson & Johnson's novel Active Safety Surveillance Using Real-world Evidence (ASSURE) program for producing efficient, transparent, applicable, and impactful RWE to support routine pharmacovigilance processes. ASSURE follows a stepwise framework that encompasses database diagnostics to assess fit-for-purpose real-world data (RWD) sources in the US, France, Germany, Australia, and Japan, phenotype development, query specification, and analytic implementation, objective study validity diagnostics, and standardized reporting. Licensed RWD sources are transformed into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), from which we generate evidence for safety signal evaluation questions using a suite of open-source analytic tools made publicly available through the OHDSI community. For each signal evaluation request involving a medication exposure and outcome(s), we conducted population characterization and population-level causal effects estimation. Target medication, comparator medication, and indication cohorts utilize predefined phenotypes, when available, for rapid analytics. We provide an evaluation of the initial implementation of this framework, including results from database diagnostics, phenotype development, and objective study validity diagnostics applied prior to unblinding evidence to prevent exposing biased estimates. We defined efficiency as the time from query to results and integration of results into safety decisions. The initial 19 months of the ASSURE program supported 110 safety signal evaluations across 24 unique products that arose from 47 requests. 74 evaluations (67%) passed study diagnostics and yielded effect estimation results with 62 (54%) providing propensity-score adjusted comparative cohort method results and 40 (36%) providing self-controlled case series results. Many analyses failed one or more diagnostics, preventing the generation and unblinding of potentially biased results: database (7 evaluations), phenotype specification (3 evaluations), or the requisite objective study validity diagnostics (26 evaluations). These 26 evaluations (24%) all provided population characterizations of exposure, indication, and outcome, including incidence rates. The ASSURE framework and implementation process, in collaboration with safety management teams, enables rapid response to medical product safety signals under evaluation and produces actionable RWE that supports pharmacovigilance decision making within regulatory timeframes. What ASSURE is ○ ASSURE (Active Safety Surveillance Using Real‐world Evidence) is a program developed to quickly and reliably check whether medicines are safe using real‐world health data—like insurance claims and electronic health records—from multiple countries (US, France, Germany, Australia, and Japan). How it works ○ We convert different health data sources into a single, common format so they can be analyzed the same way. ○ We use publicly available tools and clear clinical definitions for who is exposed to a medicine, who is not, and what counts as the health outcome of concern. ○ Before looking at the final results, we run a set of quality checks to make sure the data and methods answer the question correctly. ○ If the checks pass, we report whether the medicine appears to affect the risk of the outcome. If checks fail, we stop and share what we can (e.g., how often the medicine or outcome occurs) so decisions are not based on unreliable analyses. Key findings from the first 19 months. ○ ASSURE handled 110 safety analyses for 24 different products from 47 requests. ○ 67% (74 analyses) passed the quality checks and produced effect estimates. ○ 54% (62 analyses) produced adjusted comparative results (comparing users of the medicine to an appropriate comparison group). ○ 36% (40 analyses) produced self‐controlled results (which compare a person while taking or not taking medicine). ○ 24% (26 analyses) failed one or more important checks and therefore did not produce reliable comparison; for those, we still provided basic counts and rates. Why these matter ○ The approach makes safety reviews faster, more transparent, and more reliable by using the same standards and reusable tools across many patient populations in different data sources. ○ Running thorough checks before releasing results helps avoid mistaken or biased conclusions and supports better and timely decisions about medicine safety.
Manual analysis of electroencephalography (EEG) for epilepsy diagnosis can be subjective and time-consuming, leading to potential errors. An automatic classification system with high detection accuracy is essential for improving diagnostic efficiency and reliability. This study aimed to evaluate a comprehensive set of entropy measures, along with embedding parameters, to identify the most effective single measure for epilepsy diagnosis. This analytical study used EEG data from the University of Bonn, including healthy controls (HCs) with open eyes and epileptic seizure patients, each with 100 single-channel segments. Discrete wavelet transform was applied, extracting ten entropy measures and two embedding parameters. Statistical tests evaluated feature significance, and a linear discriminant analysis (LDA) classifier was used for classification. Robustness was assessed by introducing Gaussian noise at varying signal-to-noise ratios (SNRs) and analyzing classification performance. Our findings indicated that embedding parameters, permutation entropy, fuzzy entropy, sample entropy, norm entropy, sure entropy, log entropy, and threshold entropy significantly differentiated epileptic patients from HCs. Among these, sample entropy, norm entropy, sure entropy, log entropy, threshold entropy, and embedding delay achieved classification accuracies between 97% and 100% using LDA classifier. Furthermore, even with substantial Gaussian noise, the classifier maintained an accuracy above 84%, demonstrating the robustness of these features in noisy conditions. This study demonstrated that embedding-based and entropy-based features can serve as effective individual measures for discriminating epileptic EEG signals from HCs. These findings underscore the potential of these measures in automated epilepsy diagnosis systems, resulting in a robust and reliable tool for clinical applications.
Q fever has been considered a worldwide zoonosis caused by Coxiella burnetii, mostly transmitted by respiratory routes in contaminated environments with bio logical waste. Although urban recycling facilities may concentrate contaminated materials, their significance as potential environmental sources for C. burnetii infection has not been investigated to date. Accordingly, the aim of this study was to assess previous exposure to C. burnetii in recycling, non-recycling workers and their dogs from Curitiba, a major urban area currently ranked 8th in population with 1.8 million habitants, 7th in Gross Domestic Product (GDP), and 10th in Human Development Index (HDI) out of 5,569 Brazilian cities. In overall, 6/278 (2.15%) workers and 7/137 (5.1%) dogs were seropositive to C. burnetii by indirect immunofluorescence assay (IFA). Due to low seropositivity, no risk factor was statistically associated with C. burnetii exposure. This was the first study to investigate C. burnetii infection in workers and dogs of urban recycling centers. Although low, positive cases have suggested likelihood of environmental expo sure to Q fever. Such findings highlight the importance of future investigations, as well as the need to ensure proper use of respiratory protective equipment among recycling workers in major urban areas of Brazil and worldwide.
Post-acute coronavirus disease 2019 syndrome (PACS) is a multisystemic clinical condition that clinically starting weeks or even months after acute SARS-CoV-2 (COVID 19) infections. The objective of this study was to explore change in quality of life, mechanical force and metabolic activity after ozone therapy treatment. A preliminary clinical open label study was carried out. Twenty-three patients diagnosed with PACS more than 5 months ago participated in the study. Thermography, Handgrip test and Quality of life scale SF12 were used to analyze the effects of ozone therapy on these patients. Related to the Handgrip test, a significant improvement was recorded in T0-T1 time interval (p = 0.0003; d 0.88), but in T1-T2 time variation was not statistically significant (p = 0.957). About the SF-12 test, a significant difference was also found in both the PCS (p = 0.0038; d = 0.67) and the MCS (p = 0.0088, d = 0.60) in T0-T1 time, but not in T1-T2 for either the PCS (p = 0.5933) or the MCS (p = 0.3917). Finally, regarding temperature, statistically significant differences were observed in the T0-T1 time interval (p = 0.0465, d = 0.44) but not between T1 and T2 (p = 0.1038). No significant differences between the sexes were observed for any of the four parameters. Our results confirmed that clinical ozone could be a potentially useful drug in improving the strength, quality of life and basal metabolism in patients with long COVID. However, we believe that further studies and larger sample sizes are needed to corroborate and support the results obtained in this preliminary trial. Long COVID can cause tiredness, reduced strength, and a lower quality of life for months after the original infection. In this preliminary study, we looked at whether rectal ozone therapy could help people with Long COVID feel better, improve their hand strength, and change skin temperature measured with thermography.Twenty-three patients took part in the study and received ozone treatment for 12 weeks. We measured their quality of life, handgrip strength, and abdominal skin temperature at the start of the study, after 6 weeks, and after 12 weeks. We found that hand strength improved after 6 weeks and remained better at 12 weeks. Quality of life also improved, especially in the first 6 weeks. Skin temperature changed slightly during treatment, but the change was not maintained at the end of the study. These results suggest that ozone therapy may be helpful for some symptoms of Long COVID, but this was only a small preliminary study without a control group. Because of that, we cannot say for sure that ozone therapy caused the improvements. Larger studies with more patients and better-controlled designs are needed before drawing firm conclusions.
This unit provides information to aid in the selection and proper use of a laboratory balance. Laboratory balances are used for measuring the mass of an object and come in two main types: mechanical and electronic. Electronic balances generally come with a computer interface to facilitate the collection, storage, and manipulation of the data. In addition to weighing objects, electronic balances also perform a range of computations, including counting objects, measuring density, statistics, and pipet volume calibration. Balances vary widely in terms of their capacity (how heavy an object they can accurately weigh), precision, accuracy, repeatability, and robustness. Understanding the various characteristics of a laboratory balance is necessary to be sure that the balance is well suited for your particular scientific or industrial needs. This unit also discusses the proper use and maintenance of a laboratory balance. In general, laboratory balances are relatively easy to use and require little maintenance. © 2026 Wiley Periodicals LLC. Basic Protocol 1: Measuring mass using a top-loading balance Basic Protocol 2: Measuring mass using an analytical balance.
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Mutational processes shape cancer genomes, leaving characteristic marks that are termed signatures. The level of activity of each such process, or its signature exposure, provides important information on the disease, improving patient stratification and the prediction of drug response. Thus, there is growing interest in developing refitting methods that accurately decipher those exposures. Previous work in this domain was unsupervised in nature, employing algebraic decomposition and probabilistic inference methods. We present SuRe, a supervised approach to signature refitting that demonstrates superiority over current methods. SuRe leverages a neural network model to capture correlations between signature exposures in real data. We show that SuRe outperforms previous methods on sparse mutation data from both tumor-type-specific and pan-cancer data sets, with an increasing performance advantage as the data become sparser. We further demonstrate the model's utility in clinical settings by predicting homologous recombination deficiency in breast cancer from sparse data. Furthermore, SuRe outperforms standard methods in the unsupervised stratification of over 13,000 patients from large-scale panel sequencing cohorts, highlighting its potential for analyzing targeted sequencing data.
The integration of artificial intelligence in medical image classification for screening has the potential to enhance efficiency, diagnostic accuracy and accessibility. However, ethical concerns such as accountability, bias, transparency and the impact on healthcare professionals remain critical. This review synthesises qualitative evidence on the ethical considerations surrounding artificial intelligence adoption in screening programmes. A systematic search of qualitative studies, from June 2020 to September 2024, was conducted across multiple databases: MEDLINE, EMBASE, PsycInfo® (American Psychological Association, Washington, DC, USA) and Cumulative Index to Nursing and Allied Health Literature. Primary qualitative studies exploring healthcare professionals', patients' and other stakeholders' perspectives on artificial intelligence in screening were included. Thematic analysis was performed, and findings were assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research approach to evaluate confidence in the evidence. Fourteen qualitative studies were included, covering perspectives from clinicians, radiologists, artificial intelligence developers, policy-makers and patients. Key ethical concerns identified included: (1) the necessity of human oversight to ensure that artificial intelligences diagnostic recommendations are appropriate; (2) challenges in assigning liability when artificial intelligence errors occur; (3) risks of algorithmic bias due to discrepancies between training data sets and real-world populations; (4) concerns over data privacy, cybersecurity and informed consent in artificial intelligence-driven decision-making; (5) the need for transparency in artificial intelligence decision-making processes to build trust and (6) potential deskilling of healthcare professionals and shifts in professional responsibilities. While artificial intelligence was seen as a valuable tool to augment clinical decision-making, stakeholders emphasised that ethical frameworks must guide its implementation to maintain public trust and patient safety. This review highlights the critical considerations that must be addressed to ensure the responsible integration of artificial intelligence in medical screening. Policy-makers, healthcare institutions and developers should prioritise human oversight, robust regulatory frameworks and strategies to mitigate bias and ensure transparency. Future research should focus on disease-specific artificial intelligence applications and long-term ethical implications. The protocol for this study is registered on PROSPERO as CRD42024599536. This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR172233) and is published in full in Health Technology Assessment; Vol. 30, No. 51. See the NIHR Funding and Awards website for further award information. Research is exploring if artificial Intelligence could help doctors find cancer by looking at medical images like X-rays and scans. Artificial intelligence could spot tiny signs of cancer that people might miss. This could help detect cancer and other diseases earlier and more accurately, for example in breast cancer and diabetic eye screening. Artificial intelligence can also speed up the process, so patients get results faster. However, ethical questions arise with using artificial intelligence in this way. While there are not yet specific national or international guidelines for artificial intelligence in screening, general healthcare guidance highlights the following key issues: transparency: being clear about how artificial intelligence makes decisions fairness: ensuring artificial intelligence treats everyone equally and does not discriminate against certain groups accountability: making sure someone is responsible for artificial intelligence’s actions reducing risks: ensuring artificial Intelligence systems are safe to use and do not cause harm governance and oversight: having strong systems in place to make sure artificial intelligence is used responsibly and ethically. This study examined ethical concerns of artificial intelligence in screening by reviewing research involving the general public, clinicians and patients. Initially focusing on diabetic retinopathy and breast cancer, it expanded to other conditions due to limited evidence. The study highlighted several ethical concerns raised in the literature, such as accountability for artificial intelligence mistakes, bias, data privacy, transparency and artificial intelligence’s impact on doctors’ professional roles. In addition, people in the studies included in the literature expressed worries about related issues, particularly keeping humans in control of decisions, who is responsible when errors occur and whether artificial intelligence systems can be trusted to act fairly. Ethical challenges related to the implementation of artificial intelligence in clinical screening were also highlighted. These included healthcare inequality (with resource-limited hospitals potentially not benefiting equally), risks to patient safety from delays or errors in artificial intelligence-generated reports, the need for trust through rigorous testing and the importance of clear governance guidelines to ensure that artificial intelligence remains an assistive tool rather than replacing human judgement. This study provides useful information by identifying recurring ethical concerns that can inform the development of governance frameworks, guide safe implementation of artificial intelligence in screening and highlight priorities for future research and policy. Despite providing useful information, this study has some limitations due to incomplete research available. Future studies could focus on specific diseases and ethical issues, reassessing ethical considerations as new evidence becomes available.
To assess perceptions, beliefs, and conceptual understanding of brain death among ICU professionals at three Swiss hospitals and to examine their associations with personal, educational, and professional experiences. Cross-sectional questionnaire-based survey. Multicenter survey at three Swiss care centers. ICU healthcare professionals (i.e., physicians and nurses). None. Questionnaires assessed demographics, religious beliefs, professional experience, exposure to brain death cases, knowledge of diagnostic criteria, and emotional and ethical attitudes using closed-ended and semi-open questions, Likert scales (1 = "very unsure," "absolutely not" to 10 = "very sure," "absolutely"), and checklists. Endpoints were (primary) perceptions, beliefs, conceptual understanding of brain death, and (secondary) interprofessional differences and associations/correlations with personal, educational, social, and professional factors. Among 338 ICU professionals (78.1% nurses, 74% women), key diagnostic brain death criteria were well recognized, although misconceptions about nonessential tests persisted. Self-perceived understanding and approval were high (both median Likert scores, 9; interquartile range [IQR], 8-10), while agreement that a brain-dead patient is not a patient but a corpse was much lower (median, 1; IQR, 1-4). Physicians showed greater approval than nurses (p = 0.003) and were more likely to equate brain death with circulatory death (p < 0.001). Understanding increased with ICU experience (ρ = 0.194) and age (ρ = 0.213; both p < 0.001) and was higher among those with prior exposure to brain death or its diagnostics (both p < 0.001). No significant associations were found for sex, religious beliefs, parenthood, or bereavement. Although Swiss ICU professionals generally endorse and understand the brain death concept, our data indicate specific areas for improvement in conceptual clarity, particularly among nurses and less experienced professionals. In contrast to personal/philosophical influences, clinical exposure increases understanding and alignment with definitions, underscoring the need for targeted interdisciplinary education.
The artificial intelligence (AI) tool BoneView™ has been introduced into clinical practice to support skeletal X-ray interpretation. This created a new workflow in which radiographers are expected to evaluate and act on AI output. This study assessed the accuracy of AI-supported radiographers in detecting skeletal injuries on adult trauma X-rays. In this cross-sectional study, 10 AI-supported diagnostic radiographers from 4 hospitals retrospectively assessed 542 acute musculoskeletal X-ray examinations. The radiographers interpreted the examinations with access to BoneView™ output and marked each case as either sure/unsure positive or sure/unsure negative, with an optional free-text field for comments. Sensitivity and specificity were calculated for BoneView™ and for each AI-supported radiographer, using a quality-assured radiologist's report as the reference standard. Differences in diagnostic performance between AI and AI-supported radiographers, and between AI-supported radiographers, were examined using generalized linear mixed models. AI-supported radiographers had an overall sensitivity of 94% and specificity of 86%, compared with 96% and 71%, respectively, for AI alone. The difference in diagnostic performance was primarily driven by AI-supported radiographers' higher specificity in interpreting X-rays of the pelvic/hip and foot/toe regions, but inter-radiographer variability was substantial. Radiographers with ≥5 years of experience had higher sensitivity (p 0.01) and lower specificity (p 0.02) than those with <5 years of experience. The involvement of radiographers had limited impact on sensitivity compared with AI alone but was associated with improved specificity. Variability in performance and suboptimal specificity indicate potential for further improvement through training, workflow optimization, or refinement of AI support. Understanding radiographers' diagnostic accuracy when using AI helps clarify how such tools may support triage, reduce diagnostic delays, and distribute workload in acute skeletal imaging.
Globally, chronic obstructive pulmonary disease (COPD) ranks third in terms of morbidity and mortality. Chronic obstructive lung disease is a respiratory illness caused by partial or complete obstruction of the airflow. It is one of the joint conditions that can be treated, and it is marked by tissue degradation and a progressive restriction of airflow. It was linked to structural alterations in the lungs brought on by long-term inflammation caused by exposure to harmful particles or gases. To determine the sputum bacteriology in Exacerbations of hospitalized COPD patients and assess their antibiogram and their correlation with inflammatory markers, clinical and functional profile. This cross-sectional study was conducted in the Department of Respiratory Medicine in a tertiary care center in Tamil Nadu, India. The study was conducted for a period of 18 months. A total of 104 patients with acute exacerbation of COPD aged more than 40 years were included in the study. After the initial assessment, a sputum examination was performed, followed by culture sensitivity, a spirometry evaluation, and the determination of the oxygen saturation in all the patients. One hundred four patients diagnosed with the acute exacerbation of COPD participated in the study. Sputum production is the most common symptom encountered in patients, followed by breathlessness. Purulent or mucopurulent sputum was noted in 57.6% of patients, sputum culture positivity in 62.5% of patients, and Klebsiella pneumoniae is the most common organism encountered in the sputum. Elevated C-reactive protein (CRP) levels and reduced Diffusion Capacity of the Lungs for Carbon Monoxide (DLCO) were noted in 85.6% of patients. Moreover, the study showed a significant association with age (P < 0.001), gender (P = 0.002), locality (P = 0.003), smoking (P < 0.001), alcohol (P = 0.004), exposure to indoor smoke (P = 0.001), type 2 diabetes mellitus (P < 0.001), coronary artery disease (P = 0.001), nature of sputum (P = 0.001), culture positivity (P = 0.001), spirometry (P = 0.025), and DLCO (P = 0.024) with elevated CRP levels and also with the all the clinical, spirometry, and saturation parameters with the culture positiveness. Numerous bacterial infections have been linked to the acute exacerbation of COPD; moreover, the bacterial infection profile differs depending on the geographic location. It is vital to periodically evaluate the patient's bacteriological profile to make sure it matches the organism's pattern of antibiotic resistance in order to lower the morbidity and mortality of the patient experiencing an acute exacerbation. Résumé Introduction:La maladie pulmonaire obstructive chronique (MPOC) est l’une des principales causes de morbidité et de mortalité dans le monde. Elle se caractérise par une inflammation chronique des voies respiratoires entraînant une limitation progressive du flux aérien. Les infections bactériennes constituent un facteur majeur des exacerbations aiguës de la MPOC et influencent l’évolution clinique de la maladie.But (Aim):Déterminer la bactériologie de l’expectoration chez les patients hospitalisés pour une exacerbation aiguë de la MPOC, évaluer leur profil de sensibilité aux antibiotiques (antibiogramme) et étudier la corrélation avec les marqueurs inflammatoires ainsi qu’avec les profils clinique et fonctionnel.Matériels et Méthodes:Une étude transversale a été menée pendant 18 mois auprès de 104 patients âgés de plus de 40 ans présentant une exacerbation aiguë de la MPOC dans un centre tertiaire de soins en Inde. Tous les patients ont bénéficié d’un examen de l’expectoration comprenant la culture bactérienne et l’étude de la sensibilité aux antibiotiques, d’une évaluation spirométrique ainsi que de la mesure de la saturation en oxygène.Résultats:Parmi les échantillons d’expectoration analysés, 62,5 % étaient positifs à la culture bactérienne. Klebsiella pneumoniae était l’agent pathogène le plus fréquemment isolé. Un taux élevé de protéine C-réactive (CRP) a été observé chez 85,6 % des patients, tandis qu’une diminution de la capacité de diffusion pulmonaire du monoxyde de carbone (DLCO) a été retrouvée chez la même proportion de patients. Des associations statistiquement significatives ont été mises en évidence entre le taux de CRP et plusieurs paramètres cliniques, fonctionnels et bactériologiques.Conclusion:Les infections bactériennes jouent un rôle important dans les exacerbations aiguës de la MPOC. Une surveillance régulière de la flore bactérienne et de son profil de résistance aux antibiotiques est essentielle pour optimiser la prise en charge thérapeutique et réduire la mortalité associée à cette pathologie.
Polypharmacy is a major challenge for patient safety and effective resource use. High-quality evidence supporting polypharmacy management is lacking. To develop, optimise and evaluate a primary care complex intervention for reducing medically defined potentially inappropriate prescribing among patients experiencing polypharmacy. Phase 1: Qualitative interviews and focus groups with patients and professionals explored views/experiences of existing National Health Service Scotland interventions, informing development of core intervention components. Phase 2: An external pilot-feasibility study was conducted in five general practitioner practices to optimise the Improving Medicines use in People with Polypharmacy in Primary care intervention. A formative mixed-methods process evaluation examined intervention implementation, alongside evaluating trial processes and collecting data to inform phase 3. Phase 3: A pragmatic, open-label two-arm parallel cluster-randomised trial was conducted in English general practice. The intervention (19 practices) comprised a structured, collaborative and patient-centred approach to medication review, supported by informatics, clinician training, performance feedback and financial incentivisation. The comparator was usual care (18 practices). Up to 50 adults receiving ≥ 5 regular medications, with ≥ 1 indicator of potentially inappropriate prescribing, were reviewed per practice over 6 months. Primary outcome was number of potentially inappropriate prescribing indicators at 26-week follow-up. Secondary outcomes included patient-reported measures and service use. Cost-effectiveness and cost-utility analyses were conducted (primary economic outcome quality-adjusted life-years). A mixed-methods process evaluation (patient surveys, patient/clinician interviews, audio-recorded observations) explored implementation. Phase 1: Intervention component design was informed by findings related to elements of the medication review, informatics and clinician training. Phase 2: Core intervention elements were successfully implemented in the pilot, although clinical delivery was hampered by disruptions due to the coronavirus disease pandemic. Phase 3: Participants were recruited between January and June 2022 (intervention N = 891, usual care N = 836), median age 73 years, 49% female, with median four long-term conditions and eight medications. No improvement in the primary outcome was observed (mean difference potentially inappropriate prescribing count - 0.007; 95% confidence interval -0.21 to 0.199). Treatment burden was slightly improved, and subgroup analysis suggested potential improvements in less complex patients. The process evaluation found general practitioners and pharmacists valued and benefitted from the model of interprofessional collaboration, which strengthened working relationships and provided an opportunity for knowledge sharing and joint decision-making that supported management of clinical uncertainty. Most patients (73.2%) reported satisfaction with the review, with satisfaction strongly associated with perceptions of shared decision-making. There was no evidence of cost-effectiveness, although the economic evaluation did not quantify the aforementioned benefits or other broader factors of potential interest to decision-makers. Key limitations include concurrent changes in usual care, potentially insensitive outcome measures and limited study-population generalisability. A complex medication optimisation intervention did not reduce potentially inappropriate prescribing in patients with polypharmacy. Findings strongly support revisiting current medication optimisation policy, with one-off structured reviews, even when enhanced with digital healthcare solutions and clinical pharmacy investment, not guaranteed to improve key clinical outcomes. Nevertheless, the positive patient and clinician findings are important: protected time for interprofessional collaborative working, plus effective integration of shared decision-making within patient-facing reviews, may facilitate improved patient care more broadly. Research should develop new patient-centred outcomes and identify higher-risk patients. This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number 16/118/14. Prescribing medicines is the commonest way doctors treat illness and improve health. Our population is steadily ageing, and people often have several long-term health conditions. This means people often take many different medicines – this is called polypharmacy. Polypharmacy can be necessary to help keep people well, but it can also cause problems such as side effects or confusion about which medicines to take and when. We do not know the best way for doctors and pharmacists in general practice to make sure medicines are used as effectively and safely as possible for people with polypharmacy. This project aimed to create a new approach for general practitioners and pharmacists to help people with polypharmacy get the most out of their medicines. The research team spoke with patients and healthcare staff to help decide what the new approach should look like. The new approach included a clear process for reviewing medicines, with general practitioners and pharmacists working more closely together and making sure patient concerns were prioritised. Staff received training and were helped by using a computer program. The project then tested this approach in 19 general practitioner surgeries, with 891 patients. It compared this with normal care being delivered in 18 other surgeries (836 patients). The new approach was not found to be any better than normal care for preventing problems due to medicines. However, the new approach appeared to help reduce the effort patients had to put into managing their treatments. Patients were generally satisfied with the new approach to care. The new approach also helped the healthcare team to have better discussions about patients’ medicines. The findings suggest that simply building on current National Health Service approaches may not improve care for people with polypharmacy. Care might be improved if patients’ views are better accounted for, and pharmacists and general practitioners work more effectively together.
This systematic review looks at burn injuries in the Gulf region over the past five years, focusing on how often they occur, their survival and complication rates, and current prevention efforts. To gather the data, two reviewers independently searched five major databases and checked article reference lists to make sure no relevant studies were missed. The team then used the Newcastle-Ottawa Scale (NOS) to evaluate the quality and potential bias of the gathered research. The following sections present these regional findings and discuss the need for better burn management systems and stronger prevention strategies across the Gulf countries. (1) To examine burn epidemiology. (2) To assess morbidity and mortality. (3) And to cover preventative strategies in the gulf countries (Qatar, UAE, Oman, Bahrain, Saudi Arabia) over the past 5 years. (1) Data extraction was performed independently by two reviewers utilizing a standardized form. To ensure data integrity, this instrument was initially piloted on three studies, with iterative modifications implemented as necessary before full-scale deployment. (2) A systematic literature search was conducted across five major electronic databases: PubMed, Embase, Google Scholar, Web of Science, and Scopus. To ensure literature saturation, the reference lists of all relevant articles were manually screened to identify additional eligible studies. (1) A quantitative meta-analysis was precluded by substantial heterogeneity in study methodologies, patient cohorts, and outcome assessments. Regarding methodological quality, twelve trials (60%) exhibited a moderate risk of bias, while two demonstrated a low risk and four presented a high risk. Evaluation via the Newcastle-Ottawa Scale (NOS) indicated that nine to ten studies maintained high transparency in both data collection and methodological reporting. (2) Highest rate of mortality was found in saudi arabia (17.6%) and second highest in Kuwait (10.9%). (1) There is a high degree of variation in burn epidemiology, mortality, and prognosis among various Gulf countries. Which emphasizes a need for a uniform burn management system. (2) To effectively reduce burn-related morbidity and mortality, a comprehensive escalation of current preventative strategies is required.
People with intellectual disabilities experience higher anxiety rates and barriers accessing effective interventions. This study conducted initial testing and refinement of Co-MAID, a novel mental imagery-based anxiety intervention co-designed with people with mild to moderate intellectual disabilities. Six participants received 9-12 individual sessions using a non-concurrent multiple baseline design. Sessions focused on positive imagery generation, attention shifting and image property modification. Acceptability was assessed through interviews and adherence measures. Five participants completed the intervention with 92.6% session attendance. Template analysis revealed positive experiences with improved mood and reduced anxiety. Three participants (50%) demonstrated reliable change on the primary anxiety measure, meeting clinically significant change criteria. Co-MAID demonstrated good acceptability with some preliminary evidence of anxiety reduction in people with mild to moderate intellectual disability. Further research with larger samples and controlled designs will establish the feasibility of a randomised control trial leading to a Phase III trial. Researchers developed and tested Co‐MAID, a new therapy that uses mental imagery to help people with mild to moderate intellectual disabilities manage their anxiety. The therapy was designed together with people who have intellectual disabilities to make sure it worked for them. The study involved six people who attended nine individual therapy sessions with excellent attendance rates (93%). Half of the participants showed meaningful improvements in their anxiety levels and most participants reported feeling better after the sessions. This research is important because people with intellectual disabilities often experience higher levels of anxiety but have fewer treatment options available to them. Co‐MAID offers a potentially accessible and effective way to help this population manage anxiety using techniques specifically adapted for their needs. This study provides promising early evidence that mental imagery‐based interventions can work for people with intellectual disabilities. The positive results suggest this approach deserves further investigation through larger, more comprehensive clinical trials to see if it could become a widely available treatment option.
Risk aversion for moderate-likelihood gains is perhaps the best-known stylized fact from decision research. Though studies documenting it have focused on decisions involving money, such behavior is presumed to prevail very generally, across diverse domains. We investigate the validity of this generalization by contrasting two types of decisions. In unimodal choices, outcomes are "apples-to-apples." Consider choosing between sure and uncertain monetary payoffs, or between sure receipt of a product and a chance at several units of it. In crossmodal choices, outcomes are "apples-to-oranges." Consider choosing between sure receipt of one product and uncertain receipt of a disparate product. We observe two patterns by which risk matters less crossmodally, contrary to straightforward generalizations. First, relative to unimodal preferences involving actuarially fair risky options, corresponding crossmodal preferences exhibit less risk aversion. Second, crossmodal preferences vary less across risk levels: As the likelihood and subjective value of a risky option's outcomes become increasingly unfavorable (favorable), people do not exhibit as much additional distaste (appetite) for it. These patterns of insensitivity engender an interaction: relative to unimodal settings, crossmodal settings yield less aversion to unfavorable and fair risk but more aversion to favorable risk. To explain this interaction, we present the translate-and-accommodate model, in which unimodal preferences follow standard accounts, but crossmodal preferences reflect processes of (a) deterministic translation and (b) risk accommodation. The translate-and-accommodate model also explains the uncertainty effect and related patterns of seemingly bizarre, dominated choices. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Background In Uganda, village health workers (VHWs) use the integrated community case management (iCCM) strategy to treat malaria, pneumonia, and diarrhea in the community. It is very important for children to get treatment within 24 hours of getting sick to lower their risk of death and illness. In March 2020, Uganda imposed nationwide COVID-19 lockdown measures, encompassing transport restrictions and curfews, potentially impacting healthcare accessibility. This study evaluated the impact of the lockdown on the timeliness of treatment-seeking from VHWs for children under five years in southwestern Uganda. Methods We conducted a retrospective review of VHW patient registers from 22 villages in Bugoye sub-county, Kasese district, for the time between December 1, 2019, and May 31, 2020. The time before the lockdown was from December 1, 2019, to March 22, 2020, and the time during the lockdown was from March 23 to May 31, 2020. There were 4,024 child records in total, and 3,822 of them were used in regression models because they had all the information needed. Timeliness was defined as seeking care within 24 hours of the onset of illness. We used logistic regression to find crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Results Overall, a total of 2,428 out of 3,822 (63.5%) children sought care within 24 hours. In the pre-lockdown period, 823 out of 1,330 people (61.9%) sought care on time. During the lockdown, 1,605 out of 2,492 people (64.4%) (p = 0.131) sought care on time. In multivariable logistic regression, the lockdown period was not significantly associated with timely care-seeking (adjusted OR (aOR) = 1.13, 95% CI: 0.99-1.30; p = 0.079). Fever was independently correlated with increased likelihood of timely care-seeking (aOR = 1.18, 95% CI: 1.02-1.37; p = 0.023), and increasing age in months was similarly associated with slightly higher odds (aOR = 1.01 per month increase, 95% CI: 1.00-1.01; p = 0.024). There was no significant association between sex, rapid breathing, diarrhea, and danger signs and timeliness. Conclusion The nationwide COVID-19 lockdown period was associated with a modest but non-significant increase in timely care-seeking. These results indicate that community-based health services delivered by VHWs remained accessible and robust despite mobility restrictions. Improving VHW programs could help make sure that important child health services are still available during future public health emergencies.
Public metabolomics databases offer a large number of datasets. Combined analysis of these data sets may better capture complex molecular mechanisms in diseases. However, most datasets include measurements for only a very small fraction of the known metabolites. Hence, simply putting together these studies leads to very sparse datasets, which do not lend themselves well to training machine learning models. In this paper, we propose two novel approaches for dataset merging and imputation model training: (i) Iterative similarity-based merging generates an optimal merge set for each dataset and makes sure that a minimum sparsity threshold is maintained, and (ii) Model-guided agglomerative merging combines datasets in pairs to create a single large dataset in an attempt to effectively combine diverse metabolomics datasets while minimizing the likelihood of gaps created by non-overlapping metabolites. In both approaches, after creating joint datasets, Variational autoencoders (VAE) are employed for imputation model training. We evaluate our approach on the entire set of public datasets from the Metabolomics workbench. Our results demonstrate that the proposed approaches achieve significantly better imputation performance than the state-of-the-art approach.
The mechanisms underpinning associations between sleep and psychiatric conditions are poorly understood, partly due to challenges with longitudinal sleep studies outside the laboratory. Children and young people with rare genetic conditions caused by micro-deletions or -duplications (Copy Number Variants or CNVs) have increased risk of disrupted sleep and poorer neurodevelopmental (ND) outcomes. The 'Sleep Detectives' study aims to investigate this by tracking behavioural and neurophysiological signatures of sleep health in young people with ND risk or ND-CNVs. To optimally achieve this, we worked with families with ND-CNVs and charity partners to co-design our tools, methods, study protocol, and materials. We established a Lived Experience Advisory Group (LEAP): nine parents, 13 children and young people with ND-CNVs, and representatives of UK charities Max Appeal and Unique. Together, the research team and LEAP co-designed two in-person family workshops to collect feedback on acceptability of sleep monitoring devices, the design of bespoke cognitive tasks, and overall study protocol. Informal interviews and surveys enabled LEAP members and researchers to reflect and learn from their Patient/Public Involvement (PPI) experiences. Key outputs included pre-workshop information materials, and multiple insights and recommendations, all of which were incorporated iteratively in refining 16 different aspects of the main study design. These included more flexibility in data collection, selection of sleep devices, customisation of cognitive tasks, and improved document language. In a survey, 100% of workshop respondents (15/15) were positive or very positive about the overall study. The PPI process was highly valued by LEAP members, workshop attendees, and the research team. One investigator described it as "reinvigorating my love of research by helping me focus on science that matters". Participating families also established peer support networks. Involving families affected by ND-CNVs in study co-design maximised opportunities for acceptability, accessibility and scalability. The researchers gained inspiration and deeper understanding of the impact of ND-CNVs on families. Families gained awareness about research, established connections with each other and peer support, and were enthusiastic about future research involvement. This experience empowered families to engage more deeply with the research process and made the PPI work more impactful and inclusive. Children and young people with rare genetic conditions caused by small deletion or duplication of genetic material are more likely to experience sleep difficulties such as insomnia, restless sleep, and tiredness. They also show an increased likelihood of neurodevelopmental conditions such as learning disability and autism, and mental health issues such as anxiety. The Sleep Detectives team wanted to explore how these genetic conditions affect children’s sleep, cognition and psychiatric health. To make sure that the project design was well suited to the children and young people that would be invited to participate, the team worked closely with families to design the study. Parents and caregivers of affected children and young people were invited to join a Lived Experience Advisory Panel (LEAP), together with charity representatives and Sleep Detectives researchers, to co-design two hands-on workshops, and advise on study design. Children and young people and parents/caregivers attending the workshops tried out and provided feedback on tools and devices that the research team were developing. They also advised on the arrangements and support families might need whilst taking part, and on the study protocol. This collaborative approach helped ensure the study design was optimally suited for the recruitment and participation of children and young people and their families. This report documents our public involvement work for the Sleep Detectives study, illustrating the difference the partnership between researchers and families has made to the project, and the wider benefits for all concerned.
Acceptability is crucial for treatment efficacy, and the World Health Organization emphasizes its impact on patient compliance. Taste plays a significant role in acceptability, with bitter taste often leading to treatment discontinuation. Clozapine, an effective drug for treatment-resistant schizophrenia, faces acceptability challenges that have been connected to its poor taste. The aim of this study was to conduct, for the first time, a human taste assessment of clozapine to directly measure its level of aversiveness. Human volunteer study. Employing a "swirl and spit" method, 23 young healthy adults rated the taste of four clozapine solutions (0.0011-0.018 mg/mL) on a visual analog scale (VAS) ranging from 0 (not aversive) to 100 mm (extremely aversive). Clozapine was not aversive at any concentrations, even at saturation: mean VAS scores ranged from 5.6 to 10.3/100 (median scores ranged between 2 and 4). Reported barriers to compliance linked to taste aversiveness of marketed or extemporaneous dosage forms of clozapine may be linked to factors such as the dosage form itself, other negative formulation or excipients' organoleptic characteristics, packaging, and user instructions linked to dosing frequency and duration, and of course, patient and disease-related factors, which require further investigations. Assessment of clozapine taste in healthy volunteers Clozapine is an antipsychotic used for people who do not respond to other antipsychotics. It is usually taken in tablet form but sometimes patients are given liquid forms of clozapine so that carers can be sure that clozapine has been taken as prescribed. Patients often find the taste of liquid clozapine to be unpleasant, and this aversive taste may make people want to stop taking clozapine. We tested different concentrations of clozapine in volunteers. The taste of clozapine itself was not reported to be unpleasant and most people could taste nothing. We conclude that liquid clozapine has an unpleasant taste because of other chemicals (called excipients) used in making liquid formulations. Clozapine itself appears to be tasteless.