Clinical Research Settings Research Articles

A Real-Life Laboratory Setting for Clinical Practice, Education, and Research in Family Systems Care: Protocol for a Transformational Action Research Study.

Burdening health and illness issues such as physical or mental illnesses, accidents, disabilities, and life events such as birth or death influence the health and functioning of families and contribute to the complexity of care and health care costs. Considerable research has confirmed the benefits of a family systems-centered care approach for patients, family caregivers, families, and health care professionals. However, health care professionals face barriers in working with families, such as feeling unprepared. Family systems-centered therapeutic conversations support families' day-to-day coping, resilience, and health. A family systems care unit (FSCU) was recently established as a real-life laboratory at one of the Swiss Universities of Applied Sciences. In this unit, health care professionals offer therapeutic conversations to families and individual family members to support daily symptom management and functioning, soften suffering, and increase health and well-being. These conversations are observed in real time through a 1-way window by other health care professionals, students, and trainees and are recorded with video for research and education. Little is known about how therapeutic conversations contribute to meaningful changes in burdened families and the benefits of vicarious learning in a real-life laboratory setting for family systems care. In this research program, we aim to deepen our understanding of how therapeutic conversations support families and individuals experiencing burdening health and illness issues and how the FSCU laboratory setting supports the learning of students, clinical trainees, and health care professionals. Here we apply a transformational action research design, including parallel and subsequent substudies, to advance knowledge and practice in family systems care. Qualitative multiple-case study designs will be used to explore the benefits of therapeutic conversations by analyzing recordings of the therapeutic conversations. The learning processes of students, trainees, and professionals will be investigated with descriptive qualitative study designs based on single and focus group interviews. The data will be analyzed with established coding methods. Therapeutic conversations have been investigated in 3 single-case studies, each involving a sequence of 3 therapeutic conversation units. Data collection regarding the second research question is planned. Preliminary results confirm the therapeutic conversations to support families' coping. This renders the FSCU a setting for ethically sensitive research. This program will not only support the health and well-being of families, but also contribute to relieving the financial and workforce burdens in the health and social care system. DERR1-10.2196/53090.

Bibliometric Insights into Anger and Aggression Research

Background: Anger and aggression are central constructs in psychology, sociology, and behavioral sciences, affecting mental health and social interactions. Despite extensive research, there is a need to map the trends and focal points of studies in this area, particularly to understand how themes like mental health disorders, developmental psychology, and social dynamics interconnect with aggression. Objective: This study aims to conduct a bibliometric analysis of global research on anger and aggression, identifying key trends, influential authors, leading research areas, and interdisciplinary approaches. The goal is to provide insights into the trajectory and focus of scholarly work in this field. Methods: Using bibliometric techniques, including co-occurrence mapping and citation analysis, data from relevant research articles were extracted and analyzed through VOSviewer. Key terms, authorship networks, and publication patterns were examined to uncover thematic clusters and research trajectories. Findings: The analysis highlights several prominent themes in the literature on anger and aggression, including their links to psychological disorders (e.g., depression, stress), behavioral development in children and adolescents, and the influence of family and social environments. The co-occurrence analysis also points to growing interest in therapeutic interventions, cognitive and neurological underpinnings of aggression, and biological factors, such as brain injury. Authors such as Richard Tremblay, David Farrington, and Manuel Eisner have emerged as influential figures in this research domain. Conclusion: This bibliometric study reveals that research on anger and aggression is characterized by interdisciplinary approaches, integrating psychology, neuroscience, and social sciences. The focus on mental health and therapeutic interventions highlights the practical applications of this research in clinical settings. The increasing interest in the neurological and biological dimensions of aggression also suggests a broadening of the research agenda. Novelty: This study provides a comprehensive overview of the intellectual landscape of anger and aggression research through a bibliometric lens, offering new insights into the evolving trends, collaboration networks, and thematic directions in the field. It is one of the first studies to integrate a co-occurrence analysis with citation data to map both psychological and biological dimensions of aggression research.

Recognition Subtests for Form B of the Repeatable Battery for the Assessment of Neuropsychological Status: Preliminary Validation.

Develop and preliminarily validate recognition subtests for Form B of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). 49 older adults with no cognitive impairment were compared to 53 individuals with a primary neurocognitive disorder (e.g., dementia, mild cognitive impairment, and traumatic brain injury) and 22 individuals with a primary psychiatric disorder (e.g., depression and anxiety) on three recognition subtests (list, story, and figure) for Form B of the RBANS in this observational study. The individuals with a primary neurocognitive disorder performed significantly poorer on most of the recognition scores compared to those with no cognitive impairment and those with a primary psychiatric disorder, with these latter two groups being largely comparable. In the entire sample, for the recognition subtests, education only correlated with figure recognition scores, and neither age nor gender influenced recognition scores. The RBANS indexes correlated with most of the recognition scores in the expected directions. The group differences in performance on these recognition subtests preliminarily validate these scores on Form B, which were not previously available. Furthermore, these scores tended to not be influenced by age, education, or gender, although they were related to overall cognitive functioning. Additional validation is needed in larger, better clinically-defined, and more diverse samples. Nonetheless, these findings support the inclusion of the newly-developed Form B recognition subtests in future clinical practice and research settings to enhance the accuracy of diagnoses and treatment recommendations.

Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study

In multicentric studies, data sharing between institutions might negatively impact patient privacy or data security. An alternative is federated analysis by secure multiparty computation. This pilot study demonstrates an architecture and implementation addressing both technical challenges and legal difficulties in the particularly demanding setting of clinical research on cancer patients within the strict European regulation on patient privacy and data protection: 24 patients from LMU University Hospital in Munich, Germany, and 24 patients from Policlinico Universitario Fondazione Agostino Gemelli, Rome, Italy, were treated for adrenal gland metastasis with typically 40 Gy in 3 or 5 fractions of online-adaptive radiotherapy guided by real-time MR. High local control (21% complete remission, 27% partial remission, 40% stable disease) and low toxicity (73% reporting no toxicity) were observed. Median overall survival was 19 months. Federated analysis was found to improve clinical science through privacy-friendly evaluation of patient data in the European health data space.

A-046 Quantification of Corticosteroids and Androgens in Serum, utilizing Waters MassTrak™ Steroid Serum Sets 2 & Sets 3 for Clinical Research

Abstract Background Steroid hormones encompass a large class of small molecules that play a central role in metabolic processes, such as the regulation of sexual characteristics, blood pressure, and inflammation. Enzymes that form part of the steroid biosynthetic pathway are pivotal in these metabolic processes, and their dysfunction can be examined through the correct measurement of steroid hormones in a clinical research setting. The availability of lyophilized calibrators and QCs reduces sample preparation time, aids in method harmonization, and assists with metrological traceability in accordance with ISO15189:2022, when used alongside an analytically selective chromatographic method. Methods A quantitative clinical research method utilizing Waters MassTrak Steroid Serum Sets 2 and 3, Cals & QCs and Waters Oasis™ PRiME HLB µElution plate technology for the extraction of testosterone, androstenedione, 17-hydroxyprogesterone (17- OHP), dehydroepiandrosterone sulfate (DHEAS), Cortisol, 11-deoxycortisol and 21-deoxycortisol from human serum samples. Chromatographic separation was performed on an Waters ACQUITY™ UPLC™ I Class Plus (FL-I) System, using an ACQUITY UPLC HSS T3 Column, accompanied by a Xevo™ TQ-S micro Mass Spectrometer. Results Accuracy (±6%) and precision (±10%) have been confirmed through comparison to External Quality Assurance (EQA) LC-MS/MS schemes, panels and QC material for all seven steroid hormones. All analytes were assessed at the low, medium and high concentrations for each MassTrak Steroid Serum Set, which yielded excellent results across the range (±10%). Deming and Linear regression analysis were performed. No statistically significant bias was observed for each compound, with a mean method bias of ±1.3% for Set 2 and ±1.0% for Set 3. The clinical research method was shown to be linear for all analytes over the calibration ranges specified, furthermore, calibration lines created using Set 2 and Set 3 were analysed over a five-day period and were linear with a co-efficient of determination of (r2) > 0.999 for all analytes. Analytical sensitivity using Signal:Noise (S/N) of the low calibrator (Calibrator 1) of each set, was >10:1 for each analyte across several analytical runs. Conclusions This evaluation has demonstrated that the MassTrak Steroid Serum Calibrator and Quality Control Sets 2 and 3 can provide precise and accurate quantification of steroid hormones in serum. An analytically sensitive and selective clinical research method has been developed for the analysis of testosterone, androstenedione, 17- OHP, DHEAS, cortisol, 11-deoxycortisol and 21-deoxycortisol in serum using Waters I-Class Xevo TQ-S micro system. For Research Use Only. Not for Use in Diagnostic Procedures.

Streamlined turn-off fluorescence sensing of Donepezil: Enhancing evaluation in pharmaceutical formulations, quality control labs, and biological fluids at nano levels

Donepezil (DPZ) is used to treat Alzheimer’s disease by increasing acetylcholine in the brain, necessitating precise analytical methods for its evaluation. This study aims to develop and validate a new turn-off fluorescence sensing method for quantifying DPZ, enhancing its evaluation in pharmaceutical formulations, quality control laboratories, and biological fluids. By leveraging the fluorescence-quenching interplay between DPZ and tetrabromofluorescein, the assay parameters were fine-tuned to enhance the development of an ion-associated complex. The developed method demonstrated a measurement range from 40 to 800 ng/mL, with detection and quantification limits of 12.6 ng/mL and 38 ng/mL, respectively. However, the successful application of the method to commercial DPZ tablets and plasma samples confirmed its effectiveness for both quality control labs and clinical research settings. It ensured accurate content uniformity and high precision, making it ideal for routine DPZ monitoring. In contrast to previous methods, this approach emphasizes environmental sustainability, evaluated using advanced tools like the Eco score scale, AGREE, RGB12, and BAGI. These tools facilitated a thorough assessment of the method’s ecological impact and sustainability, underscoring its advantage over traditional methods in terms of environmental responsibility.

Interpreting Variations in Fugl-Meyer Assessment Protocols: Results and Recommendations from a Nominal Group Consensus Process

ObjectiveTo identify variations among administration and scoring instructions of six upper extremity Fugl-Meyer Assessment (FMA-UE) protocols and to achieve consensus regarding optimal administration procedures. DesignNominal group consensus technique comprised of iterative independent reviews of protocol content, anonymous voting, and group consensus meetings. SettingClinicians working in clinical practice and research settings participated in virtual meetings via Zoom. ParticipantsTen experts in stroke rehabilitation and administration of the FMA-UE contributed to the interprofessional consensus group. Interventionsnot applicable. Main Outcome MeasureQualitative reviews of each FMA-UE protocol and rater responses (agree/disagree) regarding variations in general administration instructions (i.e., instructions that could affect the scoring of many individual test items) were discussed and analyzed during a three-phase consensus process. An a priori target of 80% or greater agreement was used to determine group consensus. ResultsConsensus was attained for 7/10 general administration instructions. Recommendations from our consensus group summarize “best practice” general instructions for researchers and clinicians. A case example, in which we found up to a 21-point difference between highest and lowest FMA-UE scores, highlights the potential impact of these protocol variations. ConclusionsVariations among FMA-UE administration protocols during stroke rehabilitation studies can lead to discrepancies in the interpretation and translation of research findings across institutions and limit the perceived value and uptake of standardized assessments for evidenced-based practice. Results of this nominal group consensus provide a first step toward developing cohesive FMA-UE recommendations for wider dissemination and review.

Whole Blood Assay with Dual Co-Stimulation for Antigen-Specific Analysis of Host Immunity to Fungal and Viral Pathogens.

Rapid and resource-efficient sample processing, high throughput, and high robustness are critical for effective scientific and clinical application of advanced antigen-specific immunoassays. Traditionally, such immunoassays, especially antigen-specific T-cell analysis by flow cytometry or enzyme-linked immunosorbent spot assays, often rely on the isolation of peripheral blood mononuclear cells. This process is time-consuming, subject to many pre-analytic confounders, and requires large blood volumes. Whole blood-based assays provide a facile alternative with increased pre-analytic robustness and lower blood volume requirements. Furthermore, whole blood-based assays allow for the preservation of inter-cellular interactions that are not captured by assays using isolated cell subsets. Recently, a refined whole blood immunoassay with dual anti-CD28 and anti-CD49d co-stimulation for comprehensive analysis of both antigen-specific T-cell functions and complex intercellular interactions in response to various fungal and viral antigens has been proposed. This protocol provides guidance for the preparation of stimulation tubes, blood stimulation, and downstream sample processing for flow cytometry, cytokine secretion assays, and transcriptional analyses. This includes a validated and functionally equivalent, previously unpublished, low-volume protocol (250 µL) to make flow cytometric and cytokine-based T-cell monitoring more accessible for studies in pediatric patients or preclinical studies in small animals (e.g., mice). Altogether, these protocols provide a versatile toolbox for complex antigen-specific immune analysis in both clinical and translational research settings.

Challenges and recommendations for the translation of biomarkers of aging.

Biomarkers of aging (BOA) are quantitative parameters that predict biological age and ideally its changes in response to interventions. In recent years, many promising molecular and omic BOA have emerged with an enormous potential for translational geroscience and improving healthspan. However, clinical translation remains limited, in part due to the gap between preclinical research and the application of BOA in clinical research and other translational settings. We surveyed experts in these areas to better understand current challenges for the translation of aging biomarkers. We identified six key barriers to clinical translation and developed guidance for the field to overcome them. Core recommendations include linking BOA to clinically actionable insights, improving affordability and availability to broad populations and validation of biomarkers that are robust and responsive at the level of individuals. Our work provides key insights and practical recommendations to overcome barriers impeding clinical translation of BOA.

Perceptions of ChatGPT in healthcare: usefulness, trust, and risk.

This study explores the perceptions of ChatGPT in healthcare settings in Taiwan, focusing on its usefulness, trust, and associated risks. As AI technologies like ChatGPT increasingly influence various sectors, their potential in public health education, promotion, medical education, and clinical practice is significant but not without challenges. The study aims to assess how individuals with and without healthcare-related education perceive and adopt ChatGPT, contributing to a deeper understanding of AI's role in enhancing public health outcomes. An online survey was conducted among 659 university and graduate students, all of whom had prior experience using ChatGPT. The survey measured perceptions of ChatGPT's ease of use, novelty, usefulness, trust, and risk, particularly within clinical practice, medical education, and research settings. Multiple linear regression models were used to analyze how these factors influence perception in healthcare applications, comparing responses between healthcare majors and non-healthcare majors. The study revealed that both healthcare and non-healthcare majors find ChatGPT more useful in medical education and research than in clinical practice. Regression analysis revealed that for healthcare majors, general trust is crucial for ChatGPT's adoption in clinical practice and influences its use in medical education and research. For non-healthcare majors, novelty, perceived general usefulness, and trust are key predictors. Interestingly, while healthcare majors were cautious about ease of use, fearing it might increase risk, non-healthcare majors associated increased complexity with greater trust. This study highlights the varying expectations between healthcare and non-healthcare majors regarding ChatGPT's role in healthcare. The findings suggest the need for AI applications to be tailored to address specific user needs, particularly in clinical practice, where trust and reliability are paramount. Additionally, the potential of AI tools like ChatGPT to contribute to public health education and promotion is significant, as these technologies can enhance health literacy and encourage behavior change. These insights can inform future healthcare practices and policies by guiding the thoughtful and effective integration of AI tools like ChatGPT, ensuring they complement clinical judgment, enhance educational outcomes, support research integrity, and ultimately contribute to improved public health outcomes.

Five steps in performing machine learning for binary outcomes

BackgroundThe use of machine learning (ML) in cardiovascular and thoracic surgery is evolving rapidly. Maximizing the capabilities of ML can help improve patient risk stratification and clinical decision making, improve accuracy of predictions, and improve resource utilization in cardiac surgery. The many nuances and intricacies of ML modeling need to be understood to appropriately implement these technologies in the clinical research setting. This primer provides an educational framework of ML for generating predicted probabilities in clinical research and illustrates it with a real-world clinical example. MethodsWe focus on modeling for binary classification and imbalanced classes, a common scenario in cardiothoracic surgery research. We present a 5-step strategy for successfully harnessing the power of ML and performing such analyses, and demonstrate our strategy using a real-world example based on data from the National Surgical Quality Improvement Program pediatric database. ConclusionsCollaboration among surgeons, care providers, statisticians, data scientists, and information technology professionals can help to maximize the impact of ML as a powerful tool in cardiac surgery.

A semi-structured interview for the dimensional assessment of internalizing and externalizing symptoms in children and adolescents: Interview Version of the Symptoms and Functioning Severity Scale (SFSS-I)

BackgroundThis study evaluates the psychometric properties of the newly developed semi-structured interview, Interview Version of the Symptoms and Functioning Severity Scale (SFSS-I), which is designed to provide a dimensional assessment of internalizing and externalizing symptoms.MethodsMulti-informant baseline data from the OPTIE study was used, involving 358 children and adolescents aged 6 to 17 years (M = 11.54, SD = 3.4, n = 140 [39.1%] were female). Participants were screened for internalizing and externalizing symptoms. For validity analyses, caregiver (Child Behavior Checklist), youth (Youth Self Report), and teacher ratings (Teacher Report Form) were used. We performed Receiver Operating Characteristic (ROC) analyses to evaluate the effectiveness of the SFSS-I subscales in distinguishing between children and adolescents diagnosed with internalizing and externalizing disorders, as determined by clinical judgement in routine care.ResultsConfirmatory factor analyses supported a correlated two-factor model for internalizing and externalizing symptoms. Acceptable to good internal consistencies (α = 0.76 to 0.89; ω = 0.76 to 0.90) and excellent interrater reliability on the scale level (ICC ≥ 0.91) was found. The ROC analyses showed an acceptable accuracy in identifying internalizing diagnoses (AUC = 0.76) and excellent accuracy for externalizing diagnoses (AUC = 0.84).ConclusionThe SFSS-I demonstrates potential as a clinically-rated instrument for screening and routine outcome monitoring, offering utility in both clinical practice and research settings for the dimensional assessment of broad psychopathological dimensions.Trial registrationGerman Clinical Trials Register (DRKS) DRKS00016737 (https://www.drks.de/DRKS00016737). Registered 17 September, 2019.

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging

Introduction:We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation. Methods:Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to incorporatea priori knowledge about the AIF’s functional form and shape, enabling precise predictions of the concentrations of [11C]PBR28 in whole blood and the free tracer in metabolite-corrected plasma. Results:We found a robust linear correlation between our model’s predicted AIF curves and those obtained through traditional, invasive measurements. We achieved an average cross-validated Pearson correlation of 0.86 for whole blood and 0.89 for parent plasma curves. Moreover, our method’s ability to estimate the volumes of distribution across several key brain regions – without significant differences between the use of predicted versus actual AIFs in a two-tissue compartmental model – successfully captures the intrinsic variability related to sex, the binding affinity of the translocator protein (18 kDa), and age. Conclusions:These results not only validate our method’s accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.

Regulatory Pathways for Qualification andAcceptance of Digital Health Technology-Derived Clinical Trial Endpoints: Considerations for Sponsors.

Despite widespread interest and substantial investment in the adoption of sensor-based digital health technologies (sDHTs) for remote data capture in drug development trials, no drug has been approved based on an sDHT-derived primary endpoint in the United States (US). One reason for this lack of advancement is the complexity of obtaining regulatory endorsement for those endpoints within current US regulatory pathways. The goal of our review is to describe the two choices currently available to pharmaceutical study Sponsors: (i) they may navigate the traditional route of compiling the evidence to support the sDHT-derived endpoint in their investigational new drug (IND) application, requiring specific expertise and substantial resources; or (ii) they may navigate the drug development tool (DDT) pathway with the goal of qualifying their sDHT-derived endpoint as a biomarker or clinical outcome assessment applicable to a broader context of use (COU), either alone or as part of a partnership or consortium. We describe the nuances of each pathway; the evidentiary requirements for supporting an sDHT-derived endpoint and the technology used to capture it; and the impact that an sDHT's regulatory status may have on a Sponsor's decision to use it for data capture. By systematically comparing the IND and DDT pathways, our over-arching goals are to support the increasing deployment of sDHTs within the clinical research setting and help advance regulatory science in the field of digital medicine.

Metabolomics and cellular altered pathways in cancer biology: A review.

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

The body as an obstacle and the “other”. How patients with chronic inflammatory bowel diseases view their body, self and the good life

BackgroundTreatment of chronic inflammatory bowel disease (IBD) aims to improve patients’ quality of life and the extent of treatment success is measured via patient reported outcomes (PROs). However, questionnaires used to collect PROs often include scales that are not specific to IBDs. Improving these scales requires a deeper understanding of patients’ lived experience. With this study we give first insights and develop hypotheses on how patients with IBDs experience their body and self and how they adjust their life plans in the context of precision medicine (PM). The guiding question is to understand what they need to achieve a good life, while facing their disease.MethodsWe developed a conception of the “good life” that draws on Philippa Foot’s “naturalized” approach and distinguishes six different dimensions that are relevant for a good life. This conception guided us as we conducted 10 qualitative interviews with patients suffering from IBD who were in precision medicine clinical research settings. The interviews aimed to gain insights for answering our research question: How do body experiences affect the good life of patients with IBD? We analyzed the interviews with interpretative phenomenological analysis (IPA).ResultsFive group experiential themes emerged: (i) Life options and plans, (ii) other people’s responses, (iii) strategies to deal with others’ responses, (iv) perception of the body and self, and (v) perception of life as good despite suffering. We report here on three of them (i, iv and v), which are primarily relevant for evaluating the outcomes of PM care. Whereas with “life options and plans (i),” our study predominantly confirmed previous research, with “perception of the body and self (iv),” we found that some of the patients changed their relationship to their body and themselves. They perceived the body or the disease as the “other” and their self appears divorced from their own body. Although this might be an avoidance strategy patients use to assign responsibility for their condition and its “disgusting” symptoms to the “other,” it is important to include it in patient reported outcome (PRO) assessments.ConclusionsWe conclude with the insight that the multi-dimensional approach based on Foot’s concept of a good life is well-suited as a basis for investigating the quality of life of people with IBD. Interviews based on this concept produced results that go beyond the understanding of health-related quality of life (HRQoL). Additionally, we offer some considerations about patients’ opportunities for achieving a good life and suggestions for further developing patient reported outcome scales.

Gene editing therapy for cardiovascular diseases.

The development of gene editing tools has been a significant area of research in the life sciences for nearly 30 years. These tools have been widely utilized in disease detection and mechanism research. In the new century, they have shown potential in addressing various scientific challenges and saving lives through gene editing therapies, particularly in combating cardiovascular disease (CVD). The rapid advancement of gene editing therapies has provided optimism for CVD patients. The progress of gene editing therapy for CVDs is a comprehensive reflection of the practical implementation of gene editing technology in both clinical and basic research settings, as well as the steady advancement of research and treatment of CVDs. This article provides an overview of the commonly utilized DNA-targeted gene editing tools developed thus far, with a specific focus on the application of these tools, particularly the clustered regularly interspaced short palindromic repeat/CRISPR-associated genes (Cas) (CRISPR/Cas) system, in CVD gene editing therapy. It also delves into the challenges and limitations of current gene editing therapies, while summarizing ongoing research and clinical trials related to CVD. The aim is to facilitate further exploration by relevant researchers by summarizing the successful applications of gene editing tools in the field of CVD.

Unlocking a New Path: An Autophagometer that Measures Flux Using a Non-Fluorescent Immunohistochemistry Method.

Macroautophagy/autophagy, a crucial cellular process, is typically measured using fluorescence-based techniques, which can be costly, complex, and impractical for clinical settings. In this paper, we introduce a novel, cost-effective, non-fluorescent immunohistochemistry (IHC) method for evaluating autophagy flux. This technique, based on antigen-antibody reactions and chromogenic detection, provides clear, quantifiable results under standard light microscopy, eliminating the need for expensive equipment and specialized reagents. Our method simplifies technical requirements, making it accessible to routine clinical laboratories and research settings with limited resources. By comparing our approach with traditional fluorescence methods, we demonstrate its superior effectiveness, cost-efficiency, and applicability to patient samples. This innovative technique has the potential to significantly advance autophagy research and improve clinical diagnostics, offering a practical and robust tool for studying autophagy mechanisms in diseases such as cancer and neurodegenerative disorders. Our non-fluorescent IHC method represents a significant step forward in evaluating autophagy flux, making it more accessible and reliable, with the promise of enhancing our understanding and treatment of autophagy-related diseases.

Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms

The sit-to-stand (STS) movement is fundamental in daily activities, involving coordinated motion of the lower extremities and trunk, which leads to the generation of joint moments based on joint angles and limb properties. Traditional methods for determining joint moments often involve sensors or complex mathematical approaches, posing limitations in terms of movement restrictions or expertise requirements. Machine learning (ML) algorithms have emerged as promising tools for joint moment estimation, but the challenge lies in efficiently selecting relevant features from diverse datasets, especially in clinical research settings. This study aims to address this challenge by leveraging metaheuristic optimization algorithms to predict joint moments during STS using minimal input data. Motion analysis data from 20 participants with varied mass and inertia properties are utilized, and joint angles are computed alongside simulations of joint moments. Feature selection is performed using the Manta Ray Foraging Optimization (MRFO), Marine Predators Algorithm (MPA), and Equilibrium Optimizer (EO) algorithms. Subsequently, Decision Tree Regression (DTR), Random Forest Regression (RFR), Extra Tree Regression (ETR), and eXtreme Gradient Boosting Regression (XGBoost Regression) ML algorithms are deployed for joint moment prediction. The results reveal EO-ETR as the most effective algorithm for ankle, knee, and neck joint moment prediction, while MPA-ETR exhibits superior performance for hip joint prediction. This approach demonstrates potential for enhancing accuracy in joint moment estimation with minimal feature input, offering implications for biomechanical research and clinical applications.

Trauma nursing competency in the emergency department: a concept analysis

ObjectivesDespite numerous published concept analyses of nursing competency, the specific understanding of trauma nursing competency in emergency departments remains limited, with no clear definition. This study aimed to clarify the...