Development of a local warfarin dosage guideline based on pharmacogenomics and haemostatic markers

Warfarin, the mainstream oral anticoagulant, has a narrow therapeutic index and wide interindividual dose variability, rendering maintaining its optimal dose in each individual a difficult task. Many warfarin dosing models have been developed worldwide in order to improve the accuracy of currently used international normalised ratio (INR) dosing method. However, those dosing models were not practical to be used due to extensive additional data that are required for dose calculation. In this study, a simpler warfarin's dosing model for local population has been studied for warfarin therapy reinitiation based on clinical, laboratory and genetic data. A total of 130 of patients on warfarin treatment in Hospital Universiti Sains Malaysia were recruited for the model-building. Patients' clinical data were extracted from the hospital database. Polymerase chain reaction - restriction fragment length polymorphism (PCR-RFLP) methods were used for genotyping of CYP2C9*2, *3 and VKORC1 -1639G>A while a newly developed nested allele-specific multiplex PCR was used for genotyping of VKORC1 381, 861, 5808 and 9041. Genotype data of VKORC1 381, 861, 5808 and 9041 were used to infer VKORC1 haplotype. The activity of vitamin K-dependent (VKD) clotting factors II, VII, IX and X were measured by using a benchtop haemostatic analyser. A newly developed and validated high performance liquid chromatography (HPLC) method with UV detector was used for measurement of serum warfarin levels that were subsequently used for pharmacokinetics data calculation in 24 patients. The warfarin's dosing model was developed by using a forward multiple linear regression. The heterozygous mutant genotype of CYP2C9*2 and *3 were rare (both at 3.8%), while the homozygous mutant was not detected. The frequency of VKORC1 -1639G>A andVKORC1 381 were similar. The genotype with highest frequency was the low warfarin's dose requirement genotype (GG: 54.6%). The VKORC1 H1H1, H1H7 or H1H9 and H7H7 were the most common haplotype pairs (53.1, 32.5 and 10.0%). All VKD clotting factor activities were not significantly associated with warfarin's dose requirement or with the INR. Maximum serum concentration, half-life and clearance of warfarin were not significantly associated with any genetic data or warfarin's dose requirement. The final warfarin's dosing model consists of age, the number of VKORC1 381 allele, mean INR and history of mitral valve replacement as useful predictor factors. The predictor factors explained 45.6% of warfarin dose variability. The developed dosing model is suitable to be used as guideline to determine the warfarin dose of patients who need to reinitiate a warfarin therapy

Engaging Viewers in Ecommerce Live Streaming: Perspectives of the Broadcaster and Viewer

Background: ECommerce live streaming has enabled new forms of broadcaster-viewer interaction, where broadcasters engage viewers in real time to sell goods and services. It is therefore critical to discover strategies to maximize viewer engagement with broadcasters. Method: A mixed methods approach was applied. Five strategies emerged from our qualitative observation of three famous broadcasters: establishing a personal brand essence, maintaining personal brand consistency, creating message credibility, tapping on shared attitudes, and maximizing customer responsiveness. Based on a signaling theory perspective, we then hypothesized about the five strategies and constructed a survey to examine the effectiveness of these strategies. A total of 505 valid responses were received, and CB-SEM with AMOS was utilized to test the five hypotheses, with three hypotheses supported. Results: Our findings demonstrate that message credibility, shared attitudes, and customer responsiveness play critical roles in enhancing viewers’ engagement behaviors. Conclusion: Our mixed methods approach allows empirical exploration of effective engagement strategies and broadcaster-viewer interaction during eCommerce live streaming. This study thus contributes nascent knowledge to the live streaming literature, helping future research to develop possible theoretical perspectives. Our findings also provide actionable insights for broadcasters to enhance viewer engagement and boost sales

A New Nested Allele-Specific Multiplex Polymerase Chain Reaction Method for Haplotyping of VKORC1 Gene to Predict Warfarin Sensitivity

The vitamin K epoxide reductase complex 1 gene (VKORC1) is commonly assessed to predict warfarin sensitivity. In this study, a new nested allele-specific multiplex polymerase chain reaction (PCR) method that can simultaneously identify single nucleotide polymorphisms (SNPs) at VKORC1 381, 861, 5808, and 9041 for haplotype analysiswas developed and validated. ExtractedDNAwas amplified in the first PCR DNA, which was optimized by investigating the effects of varying the primer concentrations, annealing temperature, magnesium chloride concentration, enzyme concentration, and the amount of DNA template. The amplification products produced from the first round of PCR were used as templates for a second PCR amplification in which both mutant and wild-type primers were added in separate PCR tubes, followed by optimization in a similar manner. The final PCR products were resolved by agarose gel electrophoresis and further analysed by using a VKORC1 genealogic tree to infer patient haplotypes. Fifty patients were identified to have H1H1, one had H1H2, one had H1H7, 31 had either H1H7 or H1H9, one had H1H9, eight had H7H7, and one had H8H9 haplotypes. This is the first method that is able to infer VKORC1 haplotypes using only conventional PCR methods

Determinants for Healthy Lifestyle of Patients with Familial Hypercholesterolaemia

Lifestyle modification is a pivotal intervention for Familial Hypercholesterolaemia (FH). This study aims to describe the lifestyles (physical activity and healthy diet) and their associations with sociodemography, illness characteristics, psychological elements, family support and level of barrier. 100 participants were given Pro forma questionnaires to assess sociodemography and illness characteristics. The lifestyles, psychological elements, family support and level of barrier were assessed using the Theory of Planned Behaviour questionnaire. The determinants of healthy lifestyles include the status of receiving treatment, level of barrier and intention for behavioural change. The findings may inform the strategy for lifestyle modification of FH patients.Keywords: Familial Hypercholesterolaemia; lifestyle; physical activity; healthy diet.eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/ebpj.v5i14.233

Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning tasks, showing superior generalization ability. Inspired by the success, adopting CL into collaborative filtering (CF) is prevailing in semi-supervised top-K recommendations. The basic idea is to routinely conduct heuristic-based data augmentation and apply contrastive losses (e.g., InfoNCE) on the augmented views. Yet, some CF-tailored challenges make this adoption suboptimal, such as the issue of out-of-distribution, the risk of false negatives, and the nature of top-K evaluation. They necessitate the CL-based CF scheme to focus more on mining hard negatives and distinguishing false negatives from the vast unlabeled user-item interactions, for informative contrast signals. Worse still, there is limited understanding of contrastive loss in CF methods, especially w.r.t. its generalization ability. To bridge the gap, we delve into the reasons underpinning the success of contrastive loss in CF, and propose a principled Adversarial InfoNCE loss (AdvInfoNCE), which is a variant of InfoNCE, specially tailored for CF methods. AdvInfoNCE adaptively explores and assigns hardness to each negative instance in an adversarial fashion and further utilizes a fine-grained hardness-aware ranking criterion to empower the recommender's generalization ability. Training CF models with AdvInfoNCE, we validate the effectiveness of AdvInfoNCE on both synthetic and real-world benchmark datasets, thus showing its generalization ability to mitigate out-of-distribution problems. Given the theoretical guarantees and empirical superiority of AdvInfoNCE over most contrastive loss functions, we advocate its adoption as a standard loss in recommender systems, particularly for the out-of-distribution tasks. Codes are available at https://github.com/LehengTHU/AdvInfoNCE.Comment: Accepted to NeurIPS 202

Determining Predictors of Depression and Anxiety for Prevention of Common Mental Illness among Staff of an Academic Institution in Malaysia

The Adopt-A-Park Programme has Information on depression, anxiety and predictors for these mental illnesses among the staff of the academic institution is sparse. Hence, this study aimed to determine the prevalence of these mental illnesses and investigate possible predictors. Depression, Anxiety and Stress Scale 21-item and pro forma questionnaires were used to assess the presence of depression, anxiety, sociodemographic, personal and job-related factors. Of 278 participants, 27.7% had depression, and 26.7% had anxiety. Predictors for depression include inadequate workplace facilities, low-tier job category, working in urban campus and low income. Predictors for clinical anxiety were high workplace responsibility and low-tier job category. Keywords: Depression; Anxiety; Academic Institution; Staff 2398-4279 © 2019 The Authors. Published for AMER ABRA CE-Bs by E-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.  DOI: https://doi.org/10.21834/ajqol.v4i17.19

Discovering Dynamic Causal Space for DAG Structure Learning

Discovering causal structure from purely observational data (i.e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning. The recent invention of differentiable score-based DAG learners is a crucial enabler, which reframes the combinatorial optimization problem into a differentiable optimization with a DAG constraint over directed graph space. Despite their great success, these cutting-edge DAG learners incorporate DAG-ness independent score functions to evaluate the directed graph candidates, lacking in considering graph structure. As a result, measuring the data fitness alone regardless of DAG-ness inevitably leads to discovering suboptimal DAGs and model vulnerabilities. Towards this end, we propose a dynamic causal space for DAG structure learning, coined CASPER, that integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and ground truth DAG. CASPER revises the learning process as well as enhances the DAG structure learning via adaptive attention to DAG-ness. Grounded by empirical visualization, CASPER, as a space, satisfies a series of desired properties, such as structure awareness and noise robustness. Extensive experiments on both synthetic and real-world datasets clearly validate the superiority of our CASPER over the state-of-the-art causal discovery methods in terms of accuracy and robustness.Comment: Accepted by KDD 2023. Our codes are available at https://github.com/liuff19/CASPE

Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules

Masked graph modeling excels in the self-supervised representation learning of molecular graphs. Scrutinizing previous studies, we can reveal a common scheme consisting of three key components: (1) graph tokenizer, which breaks a molecular graph into smaller fragments (i.e., subgraphs) and converts them into tokens; (2) graph masking, which corrupts the graph with masks; (3) graph autoencoder, which first applies an encoder on the masked graph to generate the representations, and then employs a decoder on the representations to recover the tokens of the original graph. However, the previous MGM studies focus extensively on graph masking and encoder, while there is limited understanding of tokenizer and decoder. To bridge the gap, we first summarize popular molecule tokenizers at the granularity of node, edge, motif, and Graph Neural Networks (GNNs), and then examine their roles as the MGM's reconstruction targets. Further, we explore the potential of adopting an expressive decoder in MGM. Our results show that a subgraph-level tokenizer and a sufficiently expressive decoder with remask decoding have a large impact on the encoder's representation learning. Finally, we propose a novel MGM method SimSGT, featuring a Simple GNN-based Tokenizer (SGT) and an effective decoding strategy. We empirically validate that our method outperforms the existing molecule self-supervised learning methods. Our codes and checkpoints are available at https://github.com/syr-cn/SimSGT.Comment: NeurIPS 2023. 10 page