Research Settings Research Articles

DAFNE is agile and responsive—a beacon of best practice

The original Dose Adjustment For Normal Eating (DAFNE) randomised controlled trial (RCT) was conducted nearly 25 years ago.1 It has transformed structured education for type 1 diabetes (T1DM) across the UK and is still evolving through its evidence base of further RCTs,2-3 and routine collection of real-world evidence (RWE),4 thereby proving that what works in a research setting, also benefits people with T1DM in the large number (more than half) of the hospital Trusts that have adopted DAFNE countrywide. This review summarises the latest national audit data, and describes future work responding to the rapid changes in technology used in T1DM, how we are evolving to support people living with type 2 diabetes (T2DM), and adapting our learning materials in the digital age. Through the strength and depth of committed multidisciplinary healthcare professionals (HCPs) we have been able to expand our portfolio of learning materials available to DAFNE centres, who pay a nominal annual fee of ~£5,000. By partnering with the Open University we now offer free accredited online learning to a wide range of HCPs, thereby helping to upskill the diabetes workforce at no extra cost to the NHS. Finally, in a new and exciting collaboration with the Diabetes Technology Network (DTN) and industry colleagues, DAFNE offers free online training to all people living with T1DM to prepare them for closed-loop therapy.

Revolutionizing Brain Analysis: AI-Powered Insights for Neuroscience

The integration of artificial intelligence (AI) into neuroscience has revolutionized brain analysis, offering unprecedented insights into neural structures, functions, and disorders. This study explores the application of AI techniques such as machine learning and deep learning in brain imaging, cognitive analysis, and neurological diagnostics. By leveraging advanced algorithms, we analyze complex datasets from modalities like MRI, fMRI, and EEG, enabling the identification of intricate patterns and anomalies in brain activity. The findings demonstrate the potential of AI in improving diagnostic accuracy, predicting neurodegenerative conditions, and enhancing our understanding of the human brain. Despite its transformative capabilities, challenges such as data privacy, interpretability, and ethical considerations persist. This research underscores the critical role of AI in shaping the future of neuroscience and paves the way for its wider adoption in clinical and research settings. Keywords - fMRI (Functional Magnetic Resonance), Imaging, EEG (Electroencephalogram), Neurological Diagnostics, Neurodegenerative Conditions, Pattern Recognition.

Quantifying Similarities Between MediaPipe and a Known Standard to Address Issues in Tracking 2D Upper Limb Trajectories: Proof of Concept Study.

Markerless motion tracking methods have promise for use in a range of domains, including clinical settings where traditional marker-based systems for human pose estimation are not feasible. Artificial intelligence (AI)-based systems can offer a markerless, lightweight approach to motion capture. However, the accuracy of such systems, such as MediaPipe, for tracking fine upper limb movements involving the hand has not been explored. The aim of this study is to evaluate the 2D accuracy of MediaPipe against a known standard. Participants (N=10) performed a touchscreen-based shape-tracing task requiring them to trace the trajectory of a moving cursor using their index finger. Cursor trajectories created a reoccurring or random shape at 5 different speeds (500-2500 ms, in increments of 500 ms). Movement trajectories on each trial were simultaneously captured by the touchscreen and a separate video camera. Movement coordinates for each trial were extracted from the touchscreen and compared to those predicted by MediaPipe. Specifically, following resampling, normalization, and Procrustes transformations, root-mean-squared error (RMSE; primary outcome measure) was calculated between predicted coordinates and those generated by the touchscreen computer. Although there was some size distortion in the frame-by-frame estimates predicted by MediaPipe, shapes were similar between the 2 methods and transformations improved the general overlap and similarity of the shapes. The resultant mean RMSE between predicted coordinates and those generated by the touchscreen was 0.28 (SD 0.06) normalized px. Equivalence testing revealed that accuracy differed between MediaPipe and the touchscreen, but that the true difference was between 0 and 0.30 normalized px (t114=-3.02; P=.002). Additional analyses revealed no differences in resultant RMSE between methods when comparing across lower frame rates (30 and 60 frames per second [FPS]), although there was greater RMSE for 120 FPS than for 60 FPS (t35.43=-2.51; P=.03). Overall, we quantified similarities between one AI-based approach to motion capture and a known standard for tracking fine upper limb movements, informing applications of such systems in domains such as clinical and research settings. Future work should address accuracy in 3 dimensions to further validate the use of AI-based systems, including MediaPipe, in such domains.

Measurement of 1,25-dihydroxyvitamin D in serum by LC-MS/MS compared to immunoassay reveals inconsistent agreement in paediatric samples.

Automated immunoassays for 1,25-dihydroxyvitamin D (1,25(OH)2D) have increased the use of serum measurements in clinical and research settings, but disagreement with LC-MS/MS methods remains an issue. In this study, we examined this problem using samples obtained from healthy young adults, n=80, mean age 21.7 (18-32) years, and a large cohort of paediatric samples, n=422, mean age 7.3 (0-17) years. We compared serum concentrations of 1,25(OH)2D3/D2 produced by the DiaSorin LIAISON® XL immunoassay against an LC-MS/MS method with immunoaffinity enrichment and DAPTAD derivation. Both assays showed intra/inter-assay imprecision of≤9.4 % across their respective assay range. DEQAS between April 2020 to Jan 2024 (n=80) showed mean bias (SD, 95 %CI) for DiaSorin-0.6 % (6.2,-12.8 to 11.6) and LC-MS/MS of +1.3 % (7.4,-13.3 to 15.8) against their respective method group means. Comparison of measurements in the adult samples showed a strong correlation (r2=0.9331) and concordance (CCC=0.959) between the two methods. LC-MS/MS values were lower than DiaSorin by an overall mean (±SD, 95 %CI) of-1.6 (±14.3,-29.6 to 26.5) pmol/L with an increased negative bias at higher concentrations. In the paediatric samples, weaker correlation (r2=0.6536) and concordance (CCC=0.782) were observed, with greater bias mean (±SD, 95 %CI) of-9.8 (±23.4,-55.7 to 35.9) pmol/L. The variability in the paediatric samples was not associated with concentration or participant age. There was an increase in the correlation and concordance when 1,25(OH)2D2 was included in the analysis. It is likely that the metabolites of vitamin D present in the paediatric population contributed to the measurement of 1,25(OH)2D. The inconsistent agreement highlights the need for better assay harmonisation and paediatric reference intervals using LC-MS/MS method.

Can the utilization of technology-enhanced learning spaces lead to improved learning outcomes? A meta-analysis based on 39 experimental and quasi-experimental studies

ABSTRACT In recent years, the promotion and application of technology-enhanced learning spaces in teaching have garnered significant attention, but the effectiveness of their application remains controversial. This is the first meta-analysis to examine the effectiveness of technology-enhanced learning spaces on student learning outcomes. The study integrates data from 39 experimental and quasi-experimental studies. The results show that technology-enhanced learning spaces have a significantly positive effect on student learning outcomes (SMD=0.62). Further analysis of moderator variables such as educational stage, subject nature, knowledge type, sample size, experimental duration, teaching methods, and technical type number revealed that sample size and teaching methods significantly moderated the effects of technology-enhanced learning spaces. Based on the results of the meta-analysis, several recommendations for the application of technology-enhanced learning spaces are proposed: (1) choose learning spaces based on the type of knowledge; (2) strengthen emotional care for students in technology-enhanced environments; (3) control class size; and (4) integrate technology with pedagogy. However, the study has some limitations, including a relatively small sample size, lack of consideration of artificial intelligence technologies, and uneven research distribution. Future studies should incorporate a larger and more balanced set of research, and include more moderator variables for analysis.

The relational model of sense of coherence: explaining refugees’ mental health and well-being in Austria

Purpose This paper aims to construct a model explaining migrant health and well-being from a salutogenic perspective. Accounting for the relational, interactional factors impacting on the health of forced migrants, the “Relational Model of Sense of Coherence” combines elements from Antonovsky’s theory of salutogenesis with elements from Honneth’s theory of recognition. Design/methodology/approach The model has been developed from the empirical findings of a two-year qualitative study with 28 forced migrants in Austria. Besides initial and final surveys on the participants’ perception of health, health literacy and participation, data were gathered in a qualitative, participatory action research setting and analysed by the method of interpretative case reconstruction. The model is applied to the case reconstruction of a female refugee from Iraq explaining the dimensions impacting on her self-perceived status of health and well-being. Findings Next to consistency/comprehensibility and load balance/manageability persons need to experience participation/controllability as well as relatedness/self-confidence, recognition of rights/self-respect and social recognition/self-esteem to develop and/or maintain a strong, relational sense of coherence. All six dimensions contribute to feelings of meaningfulness; all components are necessary for a move towards the healthy end of the health-ease/dis-ease continuum. Originality/value The paper contributes to theory building in the fields of salutogenesis and recognition by developing a model based on an in-depth, participatory qualitative study with a vulnerable target group.

Design and implementation of a scalable and high-throughput EEG acquisition and analysis system

Recent advances in neuroscience, neuromorphic intelligence, and brain–computer interface (BCI) technologies have created a need for fast, efficient, and convenient electroencephalogram (EEG) data acquisition systems. However, the existing equipment was limited in its flexibility, restricting non-invasive studies to research or medical settings. To address this issue, low-cost, compact EEG acquisition devices have been developed, allowing for frequent and flexible brain data acquisition in various scenarios. This paper introduces a scalable and high-throughput EEG signal acquisition and analysis system based on field-programmable gate array (FPGA) technology. The proposed system offers electrode scalability, on-chip computing, and optional wireless functionality extension. These features are achieved through the design of a highly scalable underlying EEG acquisition module and an FPGA central module that enables software-defined high-throughput expansion and high-speed data exchange between software and hardware. The paper presents two implementation cases that demonstrate the potential of the proposed system. The first case introduces a wearable wireless EEG system, enabling the deployment of effective and user-friendly steady-state visual evoked potential (SSVEP)-BCI applications in consumer-grade scenarios. The second case integrates an FPGA central module with multiple basic EEG acquisition modules to construct a high-throughput BCI system for cost-effective and real-time EEG data acquisition and processing. This configuration allows for flexible deployment in research and clinical applications, including attention index, SSVEP, motor imagery (MI), and emotion recognition. This combination further demonstrates the potential of scalable EEG systems and emphasizes the need for further integration or chipization. These implementations validate the feasibility of compact and efficient EEG devices and highlight the promising applications of scalable BCI system in various fields.

Augmenting research consent: should large language models (LLMs) be used for informed consent to clinical research?

The integration of artificial intelligence (AI), particularly large language models (LLMs) like OpenAI’s ChatGPT, into clinical research could significantly enhance the informed consent process. This paper critically examines the ethical implications of employing LLMs to facilitate consent in clinical research. LLMs could offer considerable benefits, such as improving participant understanding and engagement, broadening participants’ access to the relevant information for informed consent and increasing the efficiency of consent procedures. However, these theoretical advantages are accompanied by ethical risks, including the potential for misinformation, coercion and challenges in accountability. Given the complex nature of consent in clinical research, which involves both written documentation (in the form of participant information sheets and informed consent forms) and in-person conversations with a researcher, the use of LLMs raises significant concerns about the adequacy of existing regulatory frameworks. Institutional Review Boards (IRBs) will need to consider substantial reforms to accommodate the integration of LLM-based consent processes. We explore five potential models for LLM implementation, ranging from supplementary roles to complete replacements of current consent processes, and offer recommendations for researchers and IRBs to navigate the ethical landscape. Thus, we aim to provide practical recommendations to facilitate the ethical introduction of LLM-based consent in research settings by considering factors such as participant understanding, information accuracy, human oversight and types of LLM applications in clinical research consent.

Defining a core set of research and development priorities for virtual care in the post-pandemic environment: a call to action.

To identify research and development priorities for virtual care following the coronavirus disease 2019 pandemic from the perspective of key stakeholders (patients, clinicians, informaticians and academics). Qualitative study using a modified nominal group technique. Online semi-structured interviews and workshops held in November 2022 and February 2023. Health workers involved in delivering virtual care in two metropolitan local health districts and one specialty statewide network, and people who had received care from these sites, were recruited using passive snowball sampling. Research and academic staff from a tertiary institution were also invited to participate. Priorities to support a translational research agenda for virtual care. Twenty-five individuals participated including 18 innovation deliverers, two innovation recipients and five implementation facilitators. Stakeholders identified several key priorities for developing virtual care models and for sustaining and scaling virtual care services. These included demonstrating the economic and societal value of virtual care, developing a common framework to support evaluation and comparison of virtual care services, ensuring virtual care services integrate acute and primary care, and defining which models of care are most appropriate for virtual care delivery. As the health system recalibrates with the return of in-person care, there is a growing need to demonstrate the value of virtual care models to patients, the health system, and society at large. Demonstrating this value while also demonstrating improvements to health outcomes will future-proof virtual care, enabling it to be used to address broader challenges of health care delivery. In addition, sustaining virtual care will depend on robust operational structures and workforce training and education. As services evolve, research and development priorities must be revisited to ensure that translational research aligns with stakeholder interests.

What is in a food store name? Leveraging large language models to enhance food environment data

IntroductionIt is not uncommon to repurpose administrative food data to create food environment datasets in the health department and research settings; however, the available administrative data are rarely categorized in a way that supports meaningful insight or action, and ground-truthing or manually reviewing an entire city or neighborhood is rate-limiting to essential operations and analysis. We show that such categorizations should be viewed as a classification problem well addressed by recent advances in natural language processing and deep learning—with the advent of large language models (LLMs).MethodsTo demonstrate how to automate the process of categorizing food stores, we use the foundation model BERT to give a first approximation to such categorizations: a best guess by store name. First, 10 food retail classes were developed to comprehensively categorize food store types from a public health perspective.ResultsBased on this rubric, the model was tuned and evaluated (F1micro = 0.710, F1macro = 0.709) on an extensive storefront directory of New York City. Second, the model was applied to infer insights from a large, unlabeled dataset using store names alone, aiming to replicate known temporospatial patterns. Finally, a complimentary application of the model as a data quality enhancement tool was demonstrated on a secondary, pre-labeled restaurant dataset.DiscussionThis novel application of an LLM to the enumeration of the food environment allowed for marked gains in efficiency compared to manual, in-person methods, addressing a known challenge to research and operations in a local health department.

Translation and Critical Evaluation of the Columbia University Autobiographical Memory Interview-Short Form for use in the Dutch Language.

Assessment of autobiographical memory following electroconvulsive therapy is lacking multilingual options. We therefore developed the Dutch translation of the Columbia University Autobiographical Memory Interview-Short Form (CUAMI-SF). We aimed to assess its practical use and identify segments for potential revision to improve the interview's applicability. The CUAMI-SF comprises 30 questions asking details about 6 segments (eg, last trip, last birthday) and 7 question categories (eg, dates, list of persons present). The interview was translated to Dutch using blind back-translation principles. We evaluated practicality and potential redundancy, through surveying a focus group and quantitative analyses of 60 participants, including depressed patients treated with electroconvulsive therapy (n = 14) or deep brain stimulation (n = 25) and healthy controls (n = 21). For the quantitative analyses, we compared the average percentage of nonanswered questions at baseline between groups. The average consistency scores after a follow-up of approximately 6 weeks were also calculated and compared for each segment between the groups. The focus group mostly found the CUAMI-SF easy to use and to score, but argued redundancy in specific questions asking for dates, numbers, and addresses. The largest differences in consistency scores between the groups were present for the segments "travel," "birthdays," "New Year's Eve," and "doctor's visits," indicating higher sensitivity compared with "family member" and "job." The CUAMI-SF was found easy to use by administrators. The segments "family member" and "job" emerged as less sensitive. Therefore, removal might maintain sensitivity while reducing administration time, which could prove beneficial in clinical and research settings.

The phenomenology of postpartum psychosis: preliminary findings from the Massachusetts General Hospital Postpartum Psychosis Project.

Postpartum psychosis (PP) is a severe psychiatric disorder-with limited data or consensus on diagnostic criteria and clinical presentation-that affects thousands of people each year. The Massachusetts General Hospital Postpartum Psychosis Project (MGHP3) was established to: 1) describe the phenomenology of PP, and 2) identify genomic and clinical predictors in a large cohort. Results thus far point to a richer understanding of the heterogeneity and complexity of this often-misunderstood illness and its nature over time. Data are collected from those who experienced PP within 6 months of delivery and within the 10 years prior to the MGHP3 interview. Participants provide information via the Mini International Neuropsychiatric Interview for Psychotic Disorders Studies (MINI-PDS), MGHP3© Questionnaire (including assessment of episode onset, duration, symptoms, and treatment received), and other relevant history. This retrospective study uses validated diagnostic tools to evaluate psychiatric history across participants' lifetimes. Descriptive statistics (e.g., median values, frequencies) were conducted to describe the phenomenology of PP. As of November 3, 2022, 248 participants with histories of at least one episode of PP completed the MGHP3 interview. Most participants met criteria for Bipolar I Disorder with psychotic features (71.8%). During PP episode(s), participants reported odd beliefs or delusions (87.6%), persecutory delusions (75.2%), ideas of reference (55.8%), andvisual (52.3%) and/or auditory (48.1%) hallucinations. The median time between delivery and symptom onset was 10 days (SD = 43.72). Most participants reported receiving medication (93.0%) and/or psychotherapy (65.9%). This report describes findings regarding the phenomenology of postpartum psychosis among the MGHP3 cohort, the largest cohort with validated PP studied to date. This ongoing effort to refine the phenotype of PP and to delineate underlying genetic determinants of the disorder will contribute to an enhanced understanding of this serious illness. It also underscores areas for further rigorous assessment using other research methods and sets the stage for translational reproductive neuroscience - including ongoing analyses of neuroimaging and genetic data from the MGHP3 cohort.

Uncertainty-Aware High-Fidelity Anatomical MRI Synthesis using Deep Convolutional Network with Monte Carlo Dropout

Multi-modality high-resolution MRI is beneficial for studying the brain structure and function in research and clinical settings. However, its acquisition is time-consuming, which reduces its feasibility for wider adoption especially for certain populations who cannot tolerate long scans. In this study, we propose a convolution neural network to obtain high-resolution T1-weighted MRI from lower-resolution T2-weighted input that can be acquired within a shorter scan time. By leveraging Monte Carlo dropout, our model not only produces high-fidelity anatomical T1-weighted image with higher accuracy compared to baseline model, but also generates uncertainty estimation similar to the actual error map. Our method is validated on the Human Connectome Project, and the experiments indicate our method has the potential to improve the robustness and reliability of deep learning image synthesis and accurately accelerate multi-modality MRI to benefit research and clinical practice.

ANALISIS GOOD MANUFACTURING PRACTICES (GMP) DI PANTI SOSIAL LANJUT USIA MANDALIKA

Diseases transmitted through food or WHO calls them food-borne diseases (Food Borne Diseases) are infectious diseases or poisoning caused by microbes or agents that enter the body through the food consumed. This research aims to find out how GMP is implemented in food management at the Mandalika Social Home for the Elderly and recommend follow-up actions. This qualitative research uses a descriptive analysis approach. This research uses informants, namely nutritionists, as providers of information regarding the situation and conditions of the research setting with instruments as interview tools. Data analysis in this research uses the GAP Analysis method, namely to analyze the gaps that occur in the implementation of GMP at the Mandalika Elderly Social Home followed by recommending follow-up actions on aspects that have a low average score. This research shows that the implementation of GMP in the Mandalika Social Home for the Elderly is 79.59% with the highest implementation being in the aspects of materials, final products and packaging, namely 100%, while the lowest implementation is in the laboratory aspect, labels and product descriptions, training and record keeping with percentages by 40%.

Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part I Model development.

Artificial intelligence (AI) has transformative potential in veterinary pathology in tasks ranging from cell enumeration and cancer detection to prognosis forecasting, virtual staining techniques, and individually tailored treatment plans. Preclinical testing and validation of AI systems (AIS) are critical to ensure diagnostic safety, efficacy, and dependability. In this two-part series, challenges such as the AI chasm (ie, the discrepancy between the AIS model performance in research settings and real-world applications) and ethical considerations (data privacy, algorithmic bias) are reviewed and underscore the importance of tailored quality assurance measures that address the nuances of AI in veterinary pathology. This review advocates for a multidisciplinary approach to AI development and implementation, focusing on image-based tasks, highlighting the necessity for collaboration across veterinarians, computer scientists, and ethicists to successfully navigate the complex landscape of using AI in veterinary medicine. It calls for a concerted effort to bridge the AI chasm by addressing technical, ethical, and regulatory challenges, facilitating AI integration into veterinary pathology. The future of veterinary pathology must balance harnessing AI's potential while intentionally mitigating its risks, ensuring the welfare of animals and the integrity of the veterinary profession are safeguarded. Part I of this review focuses on considerations for model development, and Part II focuses on external validation of AI.

A prospective, observational study of a pediatric affect and cooperation scale (HRAD±) for vascular access procedures.

Anxiety is common in pediatric patients, especially during vascular access procedures. Most well-studied affect and anxiety scales, including the Observation Scale of Behavioral Distress-Revised (OSBD-r), the modified Yale Preoperative Anxiety Scale (mYPAS), and the modified Induction Compliance Checklist (mICC), are too cumbersome for clinical use outside of research settings. HRAD± (Happy, Relaxed, Anxious, Distressed with yes/no to cooperation) is a clinically-efficient observational scale that evaluates pediatric procedural affect and cooperation. This study examined the clinical utility of HRAD± during vascular access procedures in children. The aims were to investigate the correlation of HRAD± to highly reliable, research-based affect and cooperation scales and to assess inter-rater reliability (IRR) between observers in this setting. This was a prospective, observational study conducted at Lucile Packard Children's Hospital Stanford. Inpatient participants were 1 month to 25 years old and undergoing a peripheral intravenous insertion or phlebotomy. Two trained research assistants (RAs) scored each patient independently during the vascular access procedure using HRAD±, OSBD-r, mYPAS, and mICC. Correlation analyses computed the associations between HRAD± and reference scales. IRR between RAs and vascular access providers was calculated using Fleiss' Kappa. A total of 234 patients were included. HRAD± scores strongly correlated with OSBD-r and mYPAS (p < 0.0001, p < 0.0001, respectively). The cooperation assessment of HRAD± demonstrated strong correlation to mICC (p < 0.0001). IRR of HRAD± between research assistants and vascular access providers showed moderate agreement (p < 0.0001). HRAD± demonstrated strong correlation to the reference affect and cooperation scales. This study supports the utility of HRAD± for rapid assessment of pediatric procedural anxiety and cooperation during vascular access procedures. HRAD± can serve as a practical tool for facilitating clinical decisions, and its wider incorporation into healthcare settings would importantly guide targeted interventions to reduce patient anxiety.

Identification and Analysis of Autophagy-Related Genes as Diagnostic Markers and Potential Therapeutic Targets for Tuberculosis Through Bioinformatics.

According to the World Health Organization, Mycobacterium tuberculosis infections affect approximately 25% of the world's population. There is mounting evidence linking autophagy and immunological dysregulation to tuberculosis (TB). As a result, this research set out to discover TB-related autophagy-related biomarkers and prospective treatment targets. We used five autophagy databases to get genes linked to autophagy and Gene Expression Omnibus databases to get genes connected to TB. Then, functional modules associated with autophagy were obtained by analyzing them using weighted gene co-expression network analysis. Both Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to examine the autophagy-related genes (ATGs) of important modules. Limma was used to identify differentially expressed ATGs (DE-ATGs), and the external datasets were used to further confirm their identification. We used DE-ATGs and a protein-protein interaction network to search the hub genes. CIBERSORT was used to estimate the kinds and amounts of immune cells. After that, we built a drug-gene interaction network and a network that included messenger RNA, small RNA, and DNA. At last, the differential expression of hub ATGs was confirmed by RT-qPCR, immunohistochemistry, and western blotting. The diagnostic usefulness of hub ATGs was evaluated using receiver operating characteristic curve analysis. Including 508 ATGs, four of the nine modules strongly linked with TB were deemed essential. Interleukin 1B (IL1B), CAPS1, and signal transducer and activator of transcription 1 (STAT1) were identified by intersection out of 22 DE-ATGs discovered by differential expression analysis. Research into immune cell infiltration found that patients with TB had an increased proportion of plasma cells, CD8 T cells, and M0 macrophages. A competitive endogenous RNA network utilized 10 long non-coding RNAs and 2 miRNAs. Then, the IL1B-targeted drug Cankinumad was assessed using this network. During bioinformatics analysis, three hub genes were validated in mouse and macrophage infection models. We found that IL1B, CASP1, and STAT1 are important biomarkers for TB. As a result, these crucial hub genes may hold promise as TB treatment targets.

Psychometric properties of the Arabic Stress Numerical Rating Scale (SNRS-11) in adolescents

Meta-analytic results have revealed a significant influence of stress on a wide array of psychological and behavioral markers, underscoring its considerable clinical importance. Providing a simple and cost-effective tool assessing stress for the Arabic-speaking population would be immensely beneficial. Therefore, our research objective was to examine the psychometric properties of an Arabic version of the Stress Numerical Rating Scale-11 (Arabic SNRS-11), including its reliability, and construct validity. 763 adolescents were recruited during November 2023. An anonymous self-administered Google Forms link was distributed via social media networks. The results of the EFA revealed two factors, which explained 66.43% of the common variance. When adding the SNRS-11, Bartlett’s test of sphericity, χ2(55) = 4127.1, p < 0.001, and KMO (0.88) remained adequate. The two-factor solution obtained explained 63.28% of the variance. The same structure was obtained in both males and females separately. McDonald’s ω and Cronbach’s α were very good for all models. Both PSS and Arabic SNRS-11 scores correlated significantly and positively with each other, as well as with higher depression, anxiety and stress scores. Finally, no significant difference was found between males and females in terms of PSS (27.08 ± 6.43 vs. 27.72 ± 6.06; p = 0.163; Cohen’s d = 0.102) and Arabic SNRS-11 (4.68 ± 2.56 vs. 4.97 ± 2.52; p = 0.125; Cohen’s d = 0.113) scores. The findings indicate that the Arabic SNRS-11 is a cost-effective, valid, and reliable tool for assessing stress. Therefore, it is recommended to use this single item to assess momentary or day-to-day stress among Arabic-speaking adolescents in Arab clinical and research settings. To evaluate the practical effectiveness of the Arabic SNRS-11 and to further enhance the data on its construct validity, future studies should assess the measure in diverse contexts and among specific populations.

Comprehensive method for producing high-affinity mouse monoclonal antibodies of various isotypes against (4-hydroxy-3-nitrophenyl)acetyl (NP) hapten.

Monoclonal antibody (mAb) technology has significantly contributed to basic research and clinical settings for various purposes, including protective and therapeutic drugs. However, a rapid and convenient method to generate high-affinity antigen-specific mAbs has not yet been reported. Here, we developed a rapid, easy, and low-cost protocol for antigen-specific mAb production from single memory B cells. Using this method, high-affinity IgG1 mAbs specific to the hapten 4-hydroxy-3-nitrophenylacetyl (NP) were established from NP-CGG immunized C57BL/6 mice within 6 days. Our mAb production system allows flexible switching of IgG1 to any other isotype with the same paratope, enabling the absolute quantification of antigen-specific serum antibody titers and affinity maturation. Additionally, we established a protocol for the production of IgM and IgA, retaining their functional pentamer and dimer structures. This method is also effective against human antigens and pathogens, making it a powerful tool for mAb development in both research and clinical settings.

Neurologic Music Therapy's Impact on Neurological Disorders.

Neurologic music therapy (NMT) represents a groundbreaking, interdisciplinary approach that combines the therapeutic properties of music with neuroscientific principles to treat a range of neurological and psychiatric conditions. This interdisciplinary approach, increasingly recognized in clinical and research settings, leverages advances in neuroimaging to explore how music affects the structure and activity of the brain. This review provides an in-depth exploration of the multifaceted effects of NMT on brain function, highlighting its role in promoting neuroplastic changes and enhancing cognitive, emotional and motor functions in diverse patient groups. This review consolidates current knowledge on NMT and provides insights into how music affects brain structure and function and the mechanisms of action. The article then discusses the application and research results of NMT in various diseases such as stroke, Alzheimer's disease and Parkinson's disease. Its potential in personalizing therapeutic interventions and its ability to improve treatment access and effectiveness in various settings are highlighted.