Human Data Research Articles

Adoption of Blockchain Technology for Data Management of Human Resource Demands in Organizational Enterprises

Human resource data accuracy has become a significant factor in assessing the effectiveness and performance of human resource management (HRM) in businesses. Numerous human resource hazards originating from asymmetric information continue to cost firms money and even put companies out of business despite the rapid progress of mobile technology and Internet technology. Blockchain is a transaction platform that makes use of the immutability qualities of immutable data records. Because of the dispersed nature of this technology, it has a broad variety of applications in numerous industries. Seeing the potential of this new technology, we picked the HRM sector since this data must be kept private and secret while still having great research value. This research presents a unique blockchain-based encryption method for establishing an HRM data method that decreases the danger of HRM data integrity. The data is authenticated using smart contracts, and data is encrypted using the proposed Improved Identity-based blowfish encryption algorithm (IIBEA) with Particle Swarm Optimization (PSO). New blocks are verified using the Proof of Work (PoW) consensus process. The suggested model's metrics are examined and compared to traditional encryption approaches. This model solves the lack of difference in the authenticity of HRM information, and it will give real and effective information to the HRM of organizational companies.

Trace amine-associated receptor 1 (TAAR1) agonism for psychosis: a living systematic review and meta-analysis of human and non-human data

Background Trace amine-associated receptor 1 (TAAR1) agonism shows promise for treating psychosis, prompting us to synthesise data from human and non-human studies. Methods We co-produced a living systematic review of controlled studies examining TAAR1 agonists in individuals (with or without psychosis/schizophrenia) and relevant animal models. Two independent reviewers identified studies in multiple electronic databases (until 17.11.2023), extracted data, and assessed risk of bias. Primary outcomes were standardised mean differences (SMD) for overall symptoms in human studies and hyperlocomotion in animal models. We also examined adverse events and neurotransmitter signalling. We synthesised data with random-effects meta-analyses. Results Nine randomised trials provided data for two TAAR1 agonists (ulotaront and ralmitaront), and 15 animal studies for 10 TAAR1 agonists. Ulotaront and ralmitaront demonstrated few differences compared to placebo in improving overall symptoms in adults with acute schizophrenia (N=4 studies, n=1291 participants; SMD=0.15, 95%CI: -0.05, 0.34), and ralmitaront was less efficacious than risperidone (N=1, n=156, SMD=-0.53, 95%CI: -0.86, -0.20). Large placebo response was observed in ulotaront phase-III trials. Limited evidence suggested a relatively benign side-effect profile for TAAR1 agonists, although nausea and sedation were common after a single dose of ulotaront. In animal studies, TAAR1 agonists improved hyperlocomotion compared to control (N=13 studies, k=41 experiments, SMD=1.01, 95%CI: 0.74, 1.27), but seemed less efficacious compared to dopamine D2 receptor antagonists (N=4, k=7, SMD=-0.62, 95%CI: -1.32, 0.08). Limited human and animal data indicated that TAAR1 agonists may regulate presynaptic dopaminergic signalling. Conclusions TAAR1 agonists may be less efficacious than dopamine D2 receptor antagonists already licensed for schizophrenia. The results are preliminary due to the limited number of drugs examined, lack of longer-term data, publication bias, and assay sensitivity concerns in trials associated with large placebo response. Considering their unique mechanism of action, relatively benign side-effect profile and ongoing drug development, further research is warranted. Registration PROSPERO-ID:CRD42023451628.

Use of quantitative in vitro to in vivo extrapolation (QIVIVE) for the assessment of non-combustible next-generation product aerosols.

With the use of in vitro new approach methodologies (NAMs) for the assessment of non-combustible next-generation nicotine delivery products, new extrapolation methods will also be required to interpret and contextualize the physiological relevance of these results. Quantitative in vitro to in vivo extrapolation (QIVIVE) can translate in vitro concentrations into in-life exposures with physiologically-based pharmacokinetic (PBPK) modelling and provide estimates of the likelihood of harmful effects from expected exposures. A major challenge for evaluating inhalation toxicology is an accurate assessment of the delivered dose to the surface of the cells and the internalized dose. To estimate this, we ran the multiple-path particle dosimetry (MPPD) model to characterize particle deposition in the respiratory tract and developed a PBPK model for nicotine that was validated with human clinical trial data for cigarettes. Finally, we estimated a Human Equivalent Concentration (HEC) and predicted plasma concentrations based on the minimum effective concentration (MEC) derived after acute exposure of BEAS-2B cells to cigarette smoke (1R6F), or heated tobacco product (HTP) aerosol at the air liquid interface (ALI). The MPPD-PBPK model predicted the in vivo data from clinical studies within a factor of two, indicating good agreement as noted by WHO International Programme on Chemical Safety (2010) guidance. We then used QIVIVE to derive the exposure concentration (HEC) that matched the estimated in vitro deposition point of departure (POD) (MEC cigarette = 0.38 puffs or 11.6µg nicotine, HTP = 22.9 puffs or 125.6µg nicotine) and subsequently derived the equivalent human plasma concentrations. Results indicate that for the 1R6F cigarette, inhaling 1/6th of a stick would be required to induce the same effects observed in vitro, in vivo. Whereas, for HTP it would be necessary to consume 3 sticks simultaneously to induce in vivo the effects observed in vitro. This data further demonstrates the reduced physiological potency potential of HTP aerosol compared to cigarette smoke. The QIVIVE approach demonstrates great promise in assisting human health risk assessments, however, further optimization and standardization are required for the substantiation of a meaningful contribution to tobacco harm reduction by alternative nicotine delivery products.

Machine Learning-Based Causality Analysis of Human Resource Practices on Firm Performance

An organization’s human resource management practices are essential for its competitive advantage. This study specifically examined human resource (HR) practices that predict corporate performance (employee turnover and firm sales) based on a backpropagation neural network (BPN)-based causality analysis. This study aims to test how to optimize human resource practices to improve organizational performance. This study elucidated the effect of HR practices and organizational-level factors on predicting employee turnover and firm sales. The BPN-based causality analysis revealed the relative importance of explanatory variables on firm performance. To test the model, it employed the Human Capital Corporate Panel open data on Korean companies’ HR practices and other characteristics. The analysis identifies causal relationships between specific HR practices and firm performance. The results show that compensation-related HR practices are most influential in predicting firm sales and employee turnover. Moreover, training-related HR practices were modest, and talent acquisition and performance management practices had relatively weak effects on the two outcomes. The study provides insights into how human resource practices can be optimized to improve firm performance and enhance organizational effectiveness. The findings of this study contribute to the growing body of research on the use of machine learning in HR management and suggest practical implications for managers’ insights to optimize HR practices.

EmoAsst: emotion recognition assistant via text-guided transfer learning on pre-trained visual and acoustic models

Children diagnosed with Autism Spectrum Disorder (ASD) often struggle to grasp social conventions and promptly recognize others' emotions. Recent advancements in the application of deep learning (DL) to emotion recognition are solidifying the role of AI-powered assistive technology in supporting autistic children. However, the cost of collecting and annotating large-scale high-quality human emotion data and the phenomenon of unbalanced performance on different modalities of data challenge DL-based emotion recognition. In response to these challenges, this paper explores transfer learning, wherein large pre-trained models like Contrastive Language-Image Pre-training (CLIP) and wav2vec 2.0 are fine-tuned to improve audio- and video-based emotion recognition with text- based guidance. In this work, we propose the EmoAsst framework, which includes a visual fusion module and emotion prompt fine-tuning for CLIP, in addition to leveraging CLIP's text encoder and supervised contrastive learning for audio-based emotion recognition on the wav2vec 2.0 model. In addition, a joint few-shot emotion classifier enhances the accuracy and offers great adaptability for real-world applications. The evaluation results on the MELD dataset highlight the outstanding performance of our methods, surpassing the majority of existing video and audio-based approaches. Notably, our research demonstrates the promising potential of the proposed text-based guidance techniques for improving video and audio-based Emotion Recognition and Classification (ERC).

Predictability in Human Mobility: From Individual to Collective (Vision Paper)

Human mobility is the foundation of urban dynamics and its prediction significantly benefits various downstream location-based services. Nowadays, while deep learning approaches are dominating the mobility prediction field where various model architectures/designs are continuously updating to push up the prediction accuracy, there naturally arises a question: whether these models are sufficiently good to reach the best possible prediction accuracy? To answer this question, predictability study is a method that quantifies the inherent regularities of the human mobility data and links the result to that limit. Mainstream predictability studies achieve this by analyzing the individual trajectories and merging all individual results to obtain an upper bound. However, the multiple individuals composing the city are not totally independent and the individual behavior is heavily influenced by its implicit or explicit surroundings. Therefore, the collective factor should be considered in the mobility predictability measurement, which has not been addressed before. This vision paper points out this concern and envisions a few potential research problems along such an individual-to-collective transition from both data and methodology aspects. We hope the discussion in this paper sheds some light on the human mobility predictability community.

Yu-Ping-Feng-San alleviates inflammation in atopic dermatitis mice by TLR4/MyD88/NF-κB pathway

Ethnopharmacological relevanceYu-Ping-Feng-San (YPF) is a traditional Chinese medicine formula that has therapeutic effects on allergic diseases such as allergic rhinitis and asthma. However, its potential efficacy and mechanism in the treatment of atopic dermatitis (AD) has not been extensively illustrated. Aim of the studyThe purpose of this study was to investigate the efficacy and possible mechanisms of YPF in AD pathogenesis. MethodsNetwork pharmacology and GEO data mining were adopted to firstly identify the potential mechanisms of YPF on AD. Then DNCB induced-AD murine model was established to test the efficacy of YPF and verify its effects on inflammatory cytokines and NF-κB pathway. In addition, molecular docking was performed to detect the binding affinity of YPF's active components with NF-κB pathway related molecules. ResultsNetwork pharmacology and human data mining suggested that YPF may act on the NF-κB pathway in AD pathogenesis. With DNCB mice model, we found that YPF significantly improved AD symptoms, reduced SCORAD scores, and alleviated skin tissue inflammation in mice. At the same time, the expression of inflammatory cytokines, TNF-α, sPLA2-IIA and IL-6, was down-regulated. Moreover, YPF suppressed TLR4/MyD88/NF-κB pathway in situ in a dose-dependent manner. Molecular docking further confirmed that seven compounds in YPF had exceptional binding properties with TNF-α, IL-6 and TLR4. ConclusionYPF may help the recovery of AD by inhibiting the TLR4/MyD88/NF-κB pathway, which provides novel insights for the treatment of AD by YPF.

Haploinsufficiency of phosphodiesterase 10A activates PI3K/AKT signaling independent of PTEN to induce an aggressive glioma phenotype.

Glioblastoma is universally fatal and characterized by frequent chromosomal copy number alterations harboring oncogenes and tumor suppressors. In this study, we analyzed exome-wide human glioblastoma copy number data and found that cytoband 6q27 is an independent poor prognostic marker in multiple data sets. We then combined CRISPR-Cas9 data, human spatial transcriptomic data, and human and mouse RNA sequencing data to nominate PDE10A as a potential haploinsufficient tumor suppressor in the 6q27 region. Mouse glioblastoma modeling using the RCAS/tv-a system confirmed that Pde10a suppression induced an aggressive glioma phenotype in vivo and resistance to temozolomide and radiation therapy in vitro. Cell culture analysis showed that decreased Pde10a expression led to increased PI3K/AKT signaling in a Pten-independent manner, a response blocked by selective PI3K inhibitors. Single-nucleus RNA sequencing from our mouse gliomas in vivo, in combination with cell culture validation, further showed that Pde10a suppression was associated with a proneural-to-mesenchymal transition that exhibited increased cell adhesion and decreased cell migration. Our results indicate that glioblastoma patients harboring PDE10A loss have worse outcomes and potentially increased sensitivity to PI3K inhibition.

Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial-Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma.

Adaptation of transfer learning to relate human single-cell RNA sequencing data to organoid-CAF cocultures facilitates discovery of human pancreatic cancer intercellular interactions and uncovers cross-talk between CAFs and tumor cells through VEGFA and ITGB1.

Low Exposures to Amphibole or Serpentine Asbestos in Germline Bap1-mutant Mice Induce Mesothelioma Characterized by an Immunosuppressive Tumor Microenvironment.

We show that germline Bap1-mutant mice have enhanced susceptibility to MM upon minimal exposure to chrysotile asbestos, not only amphibole fibers. Chrysotile induced a more profound immune tumor response than crocidolite in Bap1-mutant mice by upregulating CD39/CD73-adenosine and Ccl2/Ccr2 pathways and recruiting more M2 macrophages, which together contributed to an immunosuppressive tumor microenvironment. Interrogation of human MM RNA-seq data revealed interconnected immunosuppressive pathways consistent with our mouse findings.

RSAD2 is abundant in atherosclerotic plaques and promotes interferon-induced CXCR3-chemokines in human smooth muscle cells

In atherosclerotic lesions, monocyte-derived macrophages are major source of interferon gamma (IFN-γ), a pleotropic cytokine known to regulate the expression of numerous genes, including the antiviral gene RSAD2. While RSAD2 was reported to be expressed in endothelial cells of human carotid lesions, its significance for the development of atherosclerosis remains utterly unknown. Here, we harnessed publicly available human carotid atherosclerotic data to explore RSAD2 in lesions and employed siRNA-mediated gene-knockdown to investigate its function in IFN-γ-stimulated human aortic smooth muscle cells (hAoSMCs). Silencing RSAD2 in IFN-γ-stimulated hAoSMCs resulted in reduced expression and secretion of key CXCR3-chemokines, CXCL9, CXCL10, and CXCL11. Conditioned medium from RSAD2-deficient hAoSMCs exhibited diminished monocyte attraction in vitro compared to conditioned medium from control cells. Furthermore, RSAD2 transcript was elevated in carotid lesions where it was expressed by several different cell types, including endothelial cells, macrophages and smooth muscle cells. Interestingly, RSAD2 displayed significant correlations with CXCL10 (r = 0.45, p = 0.010) and CXCL11 (r = 0.53, p = 0.002) in human carotid lesions. Combining our findings, we uncover a novel role for RSAD2 in hAoSMCs, which could potentially contribute to monocyte recruitment in the context of atherosclerosis.

Sex differences in glutathione metabolism and acetaminophen toxicity

Aims: Clinical and experimental evidence has shown that females in humans and other mammals have higher glutathione (GSH) levels than males, which are caused by higher levels of estradiol. Understanding how hepatic GSH level and synthesis velocity depend on the sex hormones is an extremely important question since oxidative stress contributes to the risk for heart disease and cancer, and oxidative stress is reduced by GSH. Our aim is to develop a systems approach to understanding GSH metabolism and use this to explain the causes of GSH differences in males and females, how GSH changes during the menstrual cycle, and why women may be less susceptible to acetaminophen toxicity. Methods: We use mathematical models for hepatic glutathione metabolism, including one-carbon metabolism and acetaminophen detoxification, to investigate how the activation of certain enzymes by estradiol leads to dramatic changes in reaction velocities and metabolite concentrations. Results: The models explain why women of childbearing age have higher glutathione than men, and that this is caused by the balance of activation of glutamyl cysteine synthetase (GCL) and glutathione peroxidase (GPX) by estradiol. The steady-state concentration of glutathione in women depends on the strength of the activation of GCL and GPX and is quite homeostatic over a wide range of activations. Conclusions: During the menstrual cycle, the GSH concentration changes daily but over a relatively narrow range. We explain how this dynamic homeostasis depends on the biochemical network that produces GSH. The model is also consistent with published results that show that female mice are less susceptible than males to hepatotoxicity due to acetaminophen overdose and suggests that this might also be true for humans, though the human epidemiological data are contradictory.

3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment.

In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking approach employing Generative Adversarial Networks (GANs), coupled with Support Vector Machine (SVM) and DenseNet, further enhanced by robot-assisted technology to improve the precision and efficiency of data collection. The GANs in our model are responsible for generating highly realistic and diverse 3D human motion data, while SVM aids in the effective classification of this data. DenseNet is utilized for the extraction of key features, facilitating a comprehensive and integrated approach that significantly elevates both the data augmentation process and the model's ability to process and analyze complex human movements. The experimental outcomes underscore our model's exceptional performance in motion quality assessment, showcasing a substantial improvement over traditional methods in terms of classification accuracy and data processing efficiency. These results validate the effectiveness of our integrated network model, setting a solid foundation for future advancements in the field. Our research not only introduces innovative methodologies for 3D human pose data enhancement but also provides substantial technical support for practical applications across various domains, including sports science, rehabilitation medicine, and virtual reality. By combining advanced algorithmic strategies with robotic technologies, our work addresses key challenges in data augmentation and motion quality assessment, paving the way for new research and development opportunities in these critical areas.

A Comparison of In Vitro Points of Departure with Human Blood Levels for Per- and Polyfluoroalkyl Substances (PFAS).

Per- and polyfluoroalkyl substances (PFAS) are widely used, and their fluorinated state contributes to unique uses and stability but also long half-lives in the environment and humans. PFAS have been shown to be toxic, leading to immunosuppression, cancer, and other adverse health outcomes. Only a small fraction of the PFAS in commerce have been evaluated for toxicity using in vivo tests, which leads to a need to prioritize which compounds to examine further. Here, we demonstrate a prioritization approach that combines human biomonitoring data (blood concentrations) with bioactivity data (concentrations at which bioactivity is observed in vitro) for 31 PFAS. The in vitro data are taken from a battery of cell-based assays, mostly run on human cells. The result is a Bioactive Concentration to Blood Concentration Ratio (BCBCR), similar to a margin of exposure (MoE). Chemicals with low BCBCR values could then be prioritized for further risk assessment. Using this method, two of the PFAS, PFOA (Perfluorooctanoic Acid) and PFOS (Perfluorooctane Sulfonic Acid), have BCBCR values < 1 for some populations. An additional 9 PFAS have BCBCR values < 100 for some populations. This study shows a promising approach to screening level risk assessments of compounds such as PFAS that are long-lived in humans and other species.

Pole balancing on the fingertip: model-motivated machine learning forecasting of falls.

Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available. Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data. Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%-70% of real human stick falls up to 2.35 s in advance. Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.

Improved skeleton-based activity recognition using convolutional block attention module

Inferring human activities from the skeletons extracted from activity photos or videos is a fundamental yet important issue in the research community of computer vision. Current skeleton-based activity recognition methods have accomplished this task by extracting features in either spatial or temporal perspective, or combining both views; however, the extracted features lack further after-processing like feature augmentation. Aiming at the above shortcomings, this article presents a new human activity recognition approach based on human skeleton data, named Convolutional Block Attention Module-based Spatial Temporal Convolutional Network (CBAM-STGCN). To enhance the discriminative validity of the generated features, we leverage the attention mechanism to weight feature units within each dimension by introducing two attention modules designed in terms of different aspects into the model architecture. Specifically, the original feature map of an activity sample is adaptively refined by the convolutional block attention module, where two separate attention maps obtained with regard to spatial and channel dimensions are adopted to generate the corresponding attended feature maps. Evaluation results on the Kinetics benchmark dataset showed that the proposed model has improved 1.76 % and 2.43 % according to top-1 and top-5 metrics, respectively, compared to the conventional baseline model. Further ablation experiments proved that both channel and spatial attention modules contribute to the model performance.

Confronting the bias towards animal experimentation (animal methods bias)

Laws and policies are in place around the world to promote the replacement and reduction of nonhuman animals in science. These principles are rooted not just in ethical considerations for animals, but also in scientific considerations regarding the limitations of using nonhuman animals to model human biology, health, and disease. New nonanimal research approaches that use human biology, cells, and data to mimic complex human physiological states and therapeutic responses have become increasingly effective and accessible, replacing the use of animals in several applications, and becoming a crucial tool for biomedical research and drug development. Despite many advantages, acceptance of these new nonanimal methods has been slow, and barriers to their broader uptake remain. One such barrier is animal methods bias, the preference for animal-based methods where they are not necessary or where animal-free methods are suitable. This bias can impact research assessments and can discourage researchers from using novel nonanimal approaches. This article provides an introductory overview of animal methods bias for the general public, reviewing evidence, exploring consequences, and discussing ongoing mitigation efforts aimed at reducing barriers in the shift away from animal use in biomedical research and testing.

Regulating Lars2 in mitochondria: A potential Alzheimer's therapy by inhibiting tau phosphorylation

Driven by the scarcity of effective treatment options in clinical settings, the present study aimed to identify a new potential target for Alzheimer's disease (AD) treatment. We focused on Lars2, an enzyme synthesizing mitochondrial leucyl-tRNA, and its role in maintaining mitochondrial function. Bioinformatics analysis of human brain transcriptome data revealed downregulation of Lars2 in AD patients compared to healthy controls. During in vitro experiments, the knockdown of Lars2 in mouse neuroblastoma cells (neuro-2a cells) and primary cortical neurons led to morphological changes and decreased density in mouse hippocampal neurons. To explore the underlying mechanisms, we investigated how downregulated Lars2 expression could impede the phosphatidylinositol 3-kinase/protein kinase B (PI3K-AKT) pathway, thereby mitigating AKT's inhibitory effect on glycogen synthase kinase 3 beta (GSK3β). This led to the activation of GSK3β, causing excessive phosphorylation of Tau protein and subsequent neuronal degeneration. During in vivo experiments, knockout of lars2 in hippocampal neurons confirmed cognitive impairment through the Barnes maze test, the novel object recognition test, and nest-building experiments. Additionally, immunofluorescence assays indicated an increase in p-tau, atrophy in the hippocampal region, and a decrease in neurons following Lars2 knockout. Taken together, our findings indicate that Lars2 represents a promising therapeutic target for AD.

Unlocking Overexpressed Membrane Proteins to Guide Breast Cancer Precision Medicine.

Breast cancer (BC) is a prevalent form of cancer affecting women worldwide. However, the effectiveness of current BC drugs is limited by issues such as systemic toxicity, drug resistance, and severe side effects. Consequently, there is an urgent need for new therapeutic targets and improved tumor tracking methods. This study aims to address these challenges by proposing a strategy for identifying membrane proteins in tumors that can be targeted for specific BC therapy and diagnosis. The strategy involves the analyses of gene expressions in breast tumor and non-tumor tissues and other healthy tissues by using comprehensive bioinformatics analysis from The Cancer Genome Atlas (TCGA), UALCAN, TNM Plot, and LinkedOmics. By employing this strategy, we identified four transcripts (LRRC15, EFNA3, TSPAN13, and CA12) that encoded membrane proteins with an increased expression in BC tissue compared to healthy tissue. These four transcripts also demonstrated high accuracy, specificity, and accuracy in identifying tumor samples, as confirmed by the ROC curve. Additionally, tissue microarray (TMA) analysis revealed increased expressions of the four proteins in tumor tissues across all molecular subtypes compared to the adjacent breast tissue. Moreover, the analysis of human interactome data demonstrated the important roles of these proteins in various cancer-related pathways. Taken together, these findings suggest that LRRC15, EFNA3, TSPAN13, and CA12 can serve as potential biomarkers for improving cancer diagnosis screening and as suitable targets for therapy with reduced side effects and enhanced efficacy.

Introducing dynamic consent for improved trust and privacy in research involving human biological material and associated data in South Africa.

Biomedical research using human biological material and data is essential for improving human health, but it requires the active participation of many human volunteers in addition to the distribution of data. As a result, it has raised numerous vexing questions related to trust, privacy and consent. Trust is essential in biomedical research as it relates directly to the willingness of participants to continue participating in research. Privacy and the protection of personal information also influence trust. Informed consent has proven to be insufficient as it cannot overcome the informational deficit between primary and unknown future uses of material and data and is therefore not fully informed and invalid. Broad consent is also problematic as it takes full control of samples and data flow from the research participant and inherently requires that a participant must trust that the researcher will use their material or data in a manner that they would find acceptable. This paper attempts to offer some insight into how these related issues can be overcome. It introduces dynamic consent as a consent model in research involving human biological material and its associated data. Dynamic consent is explained, as well as its claims of superiority in instances where future research is possible. It is also shown how dynamic consent contributes to better control of the samples and data by the research participant, and how trust may be improved by using this consent model. Dynamic consent's co-existence with and support of the South African Protection of Personal Information Act of 2013 is also assessed. The limitations of dynamic consent are also discussed.