Implicit weight bias: shared neural substrates for overweight and angry facial expressions revealed by cross-adaptation.

The perception of facial expression plays a crucial role in social communication, and it is known to be influenced by various facial cues. Previous studies have reported both positive and negative biases toward overweight individuals. It is unclear whether facial cues, such as facial weight, bias facial expression perception. Combining psychophysics and event-related potential technology, the current study adopted a cross-adaptation paradigm to examine this issue. The psychophysical results of Experiments 1A and 1B revealed a bidirectional cross-adaptation effect between overweight and angry faces. Adapting to overweight faces decreased the likelihood of perceiving ambiguous emotional expressions as angry compared to adapting to normal-weight faces. Likewise, exposure to angry faces subsequently caused normal-weight faces to appear thinner. These findings were corroborated by bidirectional event-related potential results, showing that adaptation to overweight faces relative to normal-weight faces modulated the event-related potential responses of emotionally ambiguous facial expression (Experiment 2A); vice versa, adaptation to angry faces relative to neutral faces modulated the event-related potential responses of ambiguous faces in facial weight (Experiment 2B). Our study provides direct evidence associating overweight faces with facial expression, suggesting at least partly common neural substrates for the perception of overweight and angry faces.

Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning.

MOTIVATION: Few-shot learning that can effectively perform named entity recognition in low-resource scenarios has raised growing attention, but it has not been widely studied yet in the biomedical field. In contrast to high-resource domains, biomedical named entity recognition (BioNER) often encounters limited human-labeled data in real-world scenarios, leading to poor generalization performance when training only a few labeled instances. Recent approaches either leverage cross-domain high-resource data or fine-tune the pre-trained masked language model using limited labeled samples to generate new synthetic data, which is easily stuck in domain shift problems or yields low-quality synthetic data. Therefore, in this article, we study a more realistic scenario, i.e. few-shot learning for BioNER. RESULTS: Leveraging the domain knowledge graph, we propose knowledge-guided instance generation for few-shot BioNER, which generates diverse and novel entities based on similar semantic relations of neighbor nodes. In addition, by introducing question prompt, we cast BioNER as question-answering task and propose prompt contrastive learning to improve the robustness of the model by measuring the mutual information between query-answer pairs. Extensive experiments conducted on various few-shot settings show that the proposed framework achieves superior performance. Particularly, in a low-resource scenario with only 20 samples, our approach substantially outperforms recent state-of-the-art models on four benchmark datasets, achieving an average improvement of up to 7.1% F1. AVAILABILITY AND IMPLEMENTATION: Our source code and data are available at https://github.com/cpmss521/KGPC.

Chondrocyte autophagy mediated by T-2 toxin via AKT/TSC/Rheb/mTOR signaling pathway and protective effect of CSA-SeNP.

OBJECTIVE: Kashin-Beck disease (KBD) is an endemic, degenerative, and cartilage-damaging disease for which low selenium and T-2 toxins are considered environmental pathogenic factors. This study aimed to investigate the molecular mechanisms of autophagy in cartilage damage caused by T-2 toxin and the protective effect of chondroitin sulfate A nano-elemental selenium (CSA-SeNP) on the cartilage. METHODS: KBD chondrocytes and C28/I2 human chondrocyte cell lines were used. T-2 toxin, AKT inhibitor, and CSA-SeNP treatment experiments were conducted separately, with a treatment time of 24 h. Autophagy was monitored using MDC staining, and mRFP-GFP-LC3 adenovirus, respectively. RT-qPCR and western blotting were used to detect the expression of the relevant genes and proteins. RESULTS: The suppression of autophagy observed in KBD chondrocytes was replicated by applying 10 ng/mL T-2 toxin to C28/I2 chondrocytes for 24 h. The AKT/TSCR/Rheb/mTOR signaling pathway was activated by T-2 toxin, which inhibits autophagy. The supplementation with CSA-SeNP alleviated the inhibition of autophagy by T-2 toxin through the AKT/TSCR/Rheb/mTOR signaling pathway. CONCLUSIONS: Loss of autophagy regulated by the AKT/TSCR/Rheb/mTOR signaling pathway plays an important role in cartilage damage caused by T-2 toxin. CSA-SeNP supplementation attenuated inhibition of autophagy in chondrocytes by T-2 toxin by modulating this signaling pathway. These findings provide promising new targets for the prevention and treatment of cartilage disease.

Effect of the configuration mixing on the polarization and angular distribution of x-ray line emissions following electron-impact excitation of Ne-like ions.

We present a systematic theoretical study on the angular distribution and linear polarization of x-ray line emissions of neon-like ions following the electron-impact excitation from the ground state to the excited levels [(2p5)1/23d3/2]J=1, [(2p5)3/23d5/2]J=1, [(2p5)3/23d3/2]J=1, and [(2p5)1/23s]J=1. The cross sections are calculated by using the flexible atomic code under configuration-interaction plus many-body perturbation theory method. The angular distribution and linear polarization are obtained based on density matrix theory. Emphasis has been placed on the effect of the configuration mixing on the angular distribution and polarization. It has been proved that the strong mixing of configuration [(2p5)3/23d3/2]J=1 with configuration [(2p5)1/23s]J=1 can result in the abrupt change of Z-dependence of angular distribution and polarization. It indicates that angular distribution and polarization can be expected to serve as a tool for investigation of configuration mixing effect.

A novel disulfide death-related genes prognostic signature identifies the role of IPO4 in glioma progression.

BACKGROUND: "Disulfide death," a form of cellular demise, is triggered by the abnormal accumulation of intracellular disulfides under conditions of glucose deprivation. However, its role in the prognosis of glioma remains undetermined. Therefore, the main objective of this study is to establish prognostic signature based on disulfide death-related genes (DDRGs) and to provide new solutions in choosing the effective treatment of glioma. METHODS: The RNA transcriptome, clinical information, and mutation data of glioma samples were sourced from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), while normal samples were obtained from the Genotype-Tissue Expression (GTEx). DDRGs were compiled from previous studies and selected through differential analysis and univariate Cox regression analysis. The molecular subtypes were determined through consensus clustering analysis. Further, LASSO analysis was employed to select characteristic genes, and subsequently, a risk model comprising seven DDRGs was constructed based on multivariable Cox analysis. Kaplan-Meier survival curves were employed to assess survival differences between high and low-risk groups. Additionally, functional analyses (GO, KEGG, GSEA) were conducted to explore the potential biological functions and signaling pathways of genes associated with the model. The study also explored immune checkpoint (ICP) genes, immune cell infiltration levels, and immune stromal scores. Finally, the effect of Importin-4(IPO4) on glioma has been further confirmed through RT-qPCR, Western blot, and cell functional experiments. RESULTS: 7 genes associated with disulfide death were obtained and two subgroups of patients with different prognosis and clinical characteristics were identified. Risk signature was subsequently developed and proved to serve as an prognostic predictor. Notably, the high-risk group exhibited an immunosuppressive microenvironment characterized by a high concentration of M2 macrophages and regulatory T cells (Tregs). In contrast, the low-risk group showed lower half-maximal inhibitory concentration (IC50) values. Therefore, patients in the high-risk group may benefit more from immunotherapy, while patients in the low-risk group may benefit more from chemotherapy. In addition, in vitro experiments have shown that inhibition of the expression of IPO4 leads to a significant reduction in the proliferation, migration, and invasion of glioma cells. CONCLUSION: This study identified two glioma subtypes and constructed a prognostic signature based on DDRGs. The signature has the potential to optimize the selection of patients for immune- and chemotherapy and provided a potential therapeutic target for glioma.

Integrative analysis of a novel super-enhancer-associated lncRNA prognostic signature and identifying LINC00945 in aggravating glioma progression.

BACKGROUND: Super-enhancers (SEs), driving high-level expression of genes with tumor-promoting functions, have been investigated recently. However, the roles of super-enhancer-associated lncRNAs (SE-lncRNAs) in tumors remain undetermined, especially in gliomas. We here established a SE-lncRNAs expression-based prognostic signature to choose the effective treatment of glioma and identify a novel therapeutic target. METHODS: Combined analysis of RNA sequencing (RNA-seq) data and ChIP sequencing (ChIP-seq) data of glioma patient-derived glioma stem cells (GSCs) screened SE-lncRNAs. Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets served to construct and validate SE-lncRNA prognostic signature. The immune profiles and potential immuno- and chemotherapies response prediction value of the signature were also explored. Moreover, we verified the epigenetic activation mechanism of LINC00945 via the ChIP assay, and its effect on glioma was determined by performing the functional assay and a mouse xenograft model. RESULTS: 6 SE-lncRNAs were obtained and identified three subgroups of glioma patients with different prognostic and clinical features. A risk signature was further constructed and demonstrated to be an independent prognostic factor. The high-risk group exhibited an immunosuppressive microenvironment and was higher enrichment of M2 macrophage, regulatory T cells (Tregs), and Cancer-associated fibroblasts (CAFs). Patients in the high-risk group were better candidates for immunotherapy and chemotherapeutics. The SE of LINC00945 was further verified via ChIP assay. Mechanistically, BRD4 may mediate epigenetic activation of LINC00945. Additionally, overexpression of LINC00945 promoted glioma cell proliferation, EMT, migration, and invasion in vitro and xenograft tumor formation in vivo. CONCLUSION: Our study constructed the first prognostic SE-lncRNA signature with the ability to optimize the choice of patients receiving immuno- and chemotherapies and provided a potential therapeutic target for glioma.

A Head-to-Head Comparison of EQ Health and Wellbeing and EQ-5D-5L in Patients, Carers, and General Public in China.

OBJECTIVES: This study aimed to understand the psychometric properties of EQ Health and Wellbeing (EQ-HWB) and to examine its relationship with EQ-5D-5L in a sample covering patients, carers, and general public. METHODS: A cross-sectional study was conducted in Guizhou Province, China. The acceptability, convergent validity (using Spearman correlation coefficients), internal structure (using exploratory factor analysis), and known-group validity of EQ-HWB, EQ-HWB-Short (EQ-HWB-S), and EQ-5D-5L were reported and compared. RESULTS: A total of 323 participants completed the survey, including 106 patients, 101 carers, and 116 individuals from the general public. Approximately 7.4% of participants had at least 1 missing response. In the EQ-HWB and EQ-5D-5L items related to activities, there were more level 1 responses. The correlations between EQ-HWB and EQ-5D-5L items ranged from low to high, confirming the convergent validity of similar aspects between the 2 instruments. Notably, EQ-HWB measures 2 additional factors compared with EQ-5D-5L or EQ-HWB-S, both of which share 3 common factors. When the patient group was included, EQ-5D-5L had the largest effect size, but it failed to differentiate between the groups of general public and carers. Both EQ-HWB and EQ-HWB-S demonstrated better known-group validity results when carers were included. CONCLUSIONS: EQ-HWB measures a broader quality of life construct that goes beyond health measured by EQ-5D-5L. By encompassing a broader scope, the impact of healthcare interventions may become diluted, given that other factors can influence wellbeing outcomes as significantly as health conditions do.

Testing 2 Alternative Time Trade-Off Methods for Valuation of Children's Health States.

OBJECTIVES: Composite time trade-off (cTTO) values for EQ-5D-Y-3L health states tend to be high, raising concerns about sensitivity particularly for mild or moderate health states. We conceptualized and pilot tested 2 alternative time trade-off (TTO) variants: the caregiver TTO (CGTTO) and lag-time TTO (LTTO). METHODS: We collected CGTTO and LTTO data in China for 10 EQ-5D-Y-3L health states and compared the resulting values, respondent feedback, and task completion times with those from an EQ-5D-Y-3L valuation study using cTTO. We also examined how age and parental status of respondents could affect TTO values. RESULTS: A total of 304 participants were included in this study. Overall, cTTO showed statistically better results in all feedback questions. On a 5-point Likert scale where lower score means greater agreement, the mean (SD) feedback scores for cTTO, LTTO, and CGTTO were 1.18 (0.58), 1.45 (0.91), and 1.65 (1.02) for "easy to understand"; 1.45 (0.91), 1.94 (1.08), and 1.86 (1.24) for "easy to differentiate"; and 3.61 (1.29), 2.97 (1.33), and 3.02 (1.50) for "difficult to decide," respectively. The mean (SD) TTO values of all 10 states were 0.463 (0.494), 0.387 (0.555), and 0.123 (0.710) for cTTO, LTTO, and CGTTO, respectively. The effects of age and parental status on TTO values differed by the 3 methods. CONCLUSIONS: LTTO and CGTTO produce values with good characteristics and merit further investigation. Researchers need to be aware of the differences in design and values when using the TTO method to value children's health states.

Testing "Pits" Time Trade-Off: Can Data Quality be Improved by Removing Death From Valuation of Health States?

OBJECTIVES: Composite time trade-off (cTTO) often exhibits issues such as a value clustering at -1, potentially due to suboptimal valuation task design. We developed a novel time trade-off which uses the "pits" state as an anchor state and enables a unified time trade-off procedure for all health states (referred to as "pTTO"). This study aimed to test the pTTO for valuation of EQ-5D-5L health states. METHODS: A total of 120 members of the general public were invited to value 3 pairs of dominant/dominated EQ-5D-5L states using both cTTO and pTTO and the "pits" state using cTTO. We compared the pTTO with cTTO in terms of feasibility (interviewer-rated task-completing difficulty), acceptability (participant-reported task experience), value distribution, logical consistency, and discriminatory ability (using the standardized response mean, an effect size measure, derived from 3 pairwise comparisons of states with dominant/dominated relationship). RESULTS: The pTTO demonstrated similar feasibility to cTTO but slightly lower acceptability. The pTTO values exhibited a smoother and more continuous distribution compared with cTTO values. Relative to the cTTO, the pTTO showed higher individual-level logical consistency rate for the pairs comprising severe states (stringent criterion: 53.04% vs 17.39%; less stringent criterion: 82.61% vs 78.26%). The standardized response mean value based on pTTO values were higher than those based on cTTO values for the pair comprising severe states (0.388 vs -0.140). CONCLUSIONS: The pTTO appeared to be feasible and acceptable to value EQ-5D-5L states. The pTTO values were less clustered, more logically consistent, and discriminative compared with the cTTO values for severe health states.

The methodological quality of systematic reviews regarding the Core Outcome Set (COS) development.

BACKGROUND: The Core Outcome Measures in Effectiveness Trials (COMET) working group proposed core outcome sets (COS) to address the heterogeneity in outcome measures in clinical studies. According to the recommendations of COMET, performing systematic reviews (SRs) usually was the first step for COS development. However, the SRs that serve as a basis for COS are not specifically appraised by organizations such as COMET regarding their quality. Here, we investigated the status of SRs related to development of COS and evaluated their methodological quality. METHODS: We conducted a search on PubMed to identify SRs related to COS development published from inception to May 2022. We qualitatively summarized the disease included in SR topics, and the studies included in the SRs. We evaluated the methodological quality of the SRs using AMSTAR 2.0 and compared the overall quality of SRs with and without protocols using the Mann-Whitney U test. RESULTS: We included 175 SRs from 23 different countries or regions, and they mainly focused on five diseases: musculoskeletal system or connective tissue disease (n = 19, 10.86%), injury, poisoning, or certain other consequences of external causes (n = 18, 10.29%), digestive system disease (n = 16, 9.14%), nervous system disease (n = 15, 8.57%), and genitourinary system disease (n = 15, 8.57%). Although 88.00% of SRs included randomized controlled trials (RCTs), only a few SRs (23.38%) employed appropriate tools to assess the risk of bias in RCTs. The assessment results on the basis of AMSTAR 2.0 indicated that most SRs (93.71%) were rated as ''critically low'' to ''low'' in terms of overall confidence. The overall confidence of SRs with protocols was significantly higher than that without protocols (P <.001). Compared to the SRs with protocols on Core Outcome Measures in Effectiveness Trials (COMET), SRs with protocols on PROSPERO were of better overall confidence (P = .017). CONCLUSION: The overall quality of published SRs regarding COS development was poor. Our findings emphasize the need for researchers to carefully select the disease topic and strictly adhere to the requirements of optimal methodology when conducting a SR for the establishment of a COS.

A patient information mining network for drug recommendation.

As an important task of natural language processing, medication recommendation aims to recommend medication combinations according to the electronic health record, which can also be regarded as a multi-label classification task. But patients often have multiple diseases simultaneously, and the model must consider drug-drug interactions (DDI) of medication combinations when recommending medications, making medication recommendation more difficult. There is little existing work to explore the changes in patient conditions. However, these changes may point to future trends in patient conditions that are critical for reducing DDI rates in recommended drug combinations. In this paper, we proposed the Patient Information Mining Network (PIMNet), which models the current core medications of patient by mining the temporal and spatial changes of patient medication order and patient condition vector, and allocates some auxiliary medications as the currently recommended medication combination. The experimental results show that the proposed model greatly reduces the recommended DDI of medications while achieving results no lower than the state-of-the-art results.

Location-enhanced syntactic knowledge for biomedical relation extraction.

Biomedical relation extraction has long been considered a challenging task due to the specialization and complexity of biomedical texts. Syntactic knowledge has been widely employed in existing research to enhance relation extraction, providing guidance for the semantic understanding and text representation of models. However, the utilization of syntactic knowledge in most studies is not exhaustive, and there is often a lack of fine-grained noise reduction, leading to confusion in relation classification. In this paper, we propose an attention generator that comprehensively considers both syntactic dependency type information and syntactic position information to distinguish the importance of different dependency connections. Additionally, we integrate positional information, dependency type information, and word representations together to introduce location-enhanced syntactic knowledge for guiding our biomedical relation extraction. Experimental results on three widely used English benchmark datasets in the biomedical domain consistently outperform a range of baseline models, demonstrating that our approach not only makes full use of syntactic knowledge but also effectively reduces the impact of noisy words.

Differences and common ground in the frameworks of health-related quality of life in traditional Chinese medicine and modern medicine: a systematic review.

PURPOSE: This systematic review aims to explore the conceptualization of health-related quality of life (HRQoL) in China. With HRQoL influenced by both modern medicine (MM) and traditional Chinese medicine (TCM), the study seeks to identify differences and common ground between the frameworks of MM and TCM as defined in the literature. METHOD: A systematic literature search was conducted across three Chinese databases and four English databases. The data was extracted including title, author(s), publication year, region, aim, method, category, and result. When sorting data, we broke down the HRQoL frameworks into concepts, domains and facets, with a focus on overlapped facets between the frameworks of MM and TCM. RESULTS: A total of 31 studies were included. In the perspective of TCM, HRQoL is centered around three key 'concepts': (1) 'xingshentongyi' (unity of body and spirit), (2) 'tianrenheyi' (harmony between man and nature), and (3) 'qiqing' (seven emotional forms). In contrast, the MM framework comprises 'physical,' 'mental,' 'social,' and 'environment' domains. Out of the 59 unique facets identified, 28 are common to both TCM and MM, 9 specific to TCM, and 22 specific to MM. 'Appetite,' 'sleep,' and 'energy' are the most frequently mentioned facets in both frameworks. CONCLUSION: The concept of HRQoL in China encompasses frameworks rooted in both TCM and MM. While TCM and MM have distinct healthcare approaches, they share overlapping domains when measuring HRQoL through questionnaires. Furthermore, TCM and MM demonstrate considerable convergence in terms of HRQoL facets, showing the potential for utilizing HRQoL instruments across different cultural settings.

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.

OBJECTIVES: To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms. METHODS: An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions. RESULTS: NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy. CONCLUSIONS: DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.

Comprehensive analysis of risk factors and pathogenetic characteristics associated with surgical site infections following craniotomy procedures.

Craniotomies are intricate neurosurgical procedures susceptible to post-operative complications, among which surgical site infections (SSIs) are particularly concerning. This study sought to elucidate the potential risk factors and pathogenetic characteristics associated with SSIs following craniotomy procedures in a clinical setting. A retrospective study was conducted from May 2020 to May 2023, examining patients subjected to elective or emergency craniotomies. The cohort underwent post-operative surveillance for SSIs, facilitating patient classification into SSI and Non-SSI groups based on infection occurrence. Data collection encapsulated demographic and clinical parameters, including American Society of Anesthesiologists (ASA) classifications, and operative factors. SSIs were diagnosed via an integrated approach combining clinical symptoms, microbiological culture findings and pertinent laboratory tests. A rigorous statistical methodology employing IBM's SPSS version 27.0 was utilised for data analysis. In a univariate analysis, significant risk factors for post-craniotomy SSIs were identified, with patients aged over 60 displaying a pronounced susceptibility. Moreover, surgeries exceeding a duration of 4 h heightened infection risks. Elevated ASA grades denoted an increased prevalence of SSIs, as did emergency procedures and higher National Nosocomial Infections Surveillance scores. Multivariate analysis pinpointed epidural/subdural drainage as a protective measure against SSIs, whereas emergency surgeries, operative times beyond 4 h and subsequent surgeries within the hospital stay amplified infection risks. Notably, coagulase-negative Staphylococcus dominated the identified pathogens at 28.09%, followed by Escherichia coli (17.98%), Klebsiella pneumoniae (10.11%) and Staphylococcus aureus (11.24%), underscoring the need for diverse prophylactic measures. SSIs following craniotomies present a multifaceted challenge influenced by a confluence of patient-related, operative and post-operative determinants. Understanding these risk factors is paramount in refining surgical protocols and post-operative care strategies to mitigate SSI incidence.

Multimodal reasoning based on knowledge graph embedding for specific diseases.

MOTIVATION: Knowledge Graph (KG) is becoming increasingly important in the biomedical field. Deriving new and reliable knowledge from existing knowledge by KG embedding technology is a cutting-edge method. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, few works based on the existing biomedical KGs are focused on specific diseases. RESULTS: This work develops a construction and multimodal reasoning process of Specific Disease Knowledge Graphs (SDKGs). We construct SDKG-11, a SDKG set including five cancers, six non-cancer diseases, a combined Cancer5 and a combined Diseases11, aiming to discover new reliable knowledge and provide universal pre-trained knowledge for that specific disease field. SDKG-11 is obtained through original triplet extraction, standard entity set construction, entity linking and relation linking. We implement multimodal reasoning by reverse-hyperplane projection for SDKGs based on structure, category and description embeddings. Multimodal reasoning improves pre-existing models on all SDKGs using entity prediction task as the evaluation protocol. We verify the model's reliability in discovering new knowledge by manually proofreading predicted drug-gene, gene-disease and disease-drug pairs. Using embedding results as initialization parameters for the biomolecular interaction classification, we demonstrate the universality of embedding models. AVAILABILITY AND IMPLEMENTATION: The constructed SDKG-11 and the implementation by TensorFlow are available from https://github.com/ZhuChaoY/SDKG-11. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Censoring Time Trade-off Values at 0 Versus at -1: How Does the Assumption for Worse-Than-Dead Time Trade-off Values Affect the Modeling of EQ-5D-5L Valuation Data?

OBJECTIVES: A recent study found that negative utility values elicited using composite time trade-off (TTO) were barely associated with the severity of EQ-5D-5L health states, suggesting poor discriminative ability. Assuming negative values provide limited information, this study aimed to explore the usefulness of censoring negative TTO values at 0 in modeling EQ-5D-5L valuation data. METHODS: We analyzed EQ-5D-5L valuation data from China, The Netherlands, Canada, Singapore, and Thailand. For each data set, we estimated value sets using 2 Tobit models, one left-censored at -1 (current practice) and one left-censored at 0 (our proposed method), and compared the model performances. We hypothesized that censoring at 0 and censoring at -1 would produce similar values, though on slightly different scales. RESULTS: When censoring at 0, logical inconsistencies and statistical significance were improved but the value range was compressed. In the cross-attribute level effects model, the 3-level parameters were similar between the models censored at 0 and -1, but the rank order of some dimension parameters was altered. Health state values predicted by the 2 censoring models approximated a perfect agreement after rescaling. CONCLUSIONS: Censoring TTO values at 0 improved model estimation and fit but produced higher utility values than models censoring at -1. Investigators of future EQ-5D value set studies using the composite TTO method are advised to examine the validity of negative TTO values before choosing modeling strategies.

Cross-Attribute Level Effects Models for Modeling Modified 5-Level Version of EQ-5D Health State Values: Is Less Still More?

OBJECTIVES: Cross-attribute level effects (CALE) model has demonstrated better predictive accuracy for out-of-sample health states than the conventional additive main-effects model in cross-validation analysis of the 5-level version of EQ-5D (EQ-5D-5L) composite time trade-off (cTTO) datasets. In this study, we aimed to further test the performance of CALE model using a different design and modified EQ-5D-5L states. METHODS: A total of 29 EQ-5D-5L self-care bolt-off states, 30 EQ-5D-5L states, and 31 EQ-5D-5L vision bolt-on states were selected from the same orthogonal array. A total of 600 university students were interviewed face-to-face to value a subset of these health states using the cTTO method. For each type of health state, we fitted both the conventional main-effects model and the CALE model. Predictive accuracy was assessed in a series of cross-validation analysis using the leave-one-state-out method. RESULTS: Overall, the CALE model outperformed the conventional model for each of the 3 types of health states in predicting the cTTO values of out-of-sample health states. The prediction accuracy of using the CALE model improved with the number of dimensions in health states, for example, the MAE decreased about 24%, 67%, and 77% for the EQ-5D-5L self-care bolt-off, EQ-5D-5L, and EQ-5D-5L vision bolt-on states, respectively, when using CALE models. CONCLUSION: Our study supported the strengths of the CALE model for modelling the utility values of both original and modified EQ-5D-5L health states. Investigators with limited resources may consider using the CALE model to lower the costs for their valuation studies for EQ-5D-5L or similar health state descriptive systems.

Using Time Trade-Off Values to Estimate EQ-5D-Y Value Sets: An Exploratory Study.

OBJECTIVES: The published international EQ-5D-Y-3L valuation protocol does not recommend the composite time trade-off (cTTO) method as the primary valuation method because of 2 major concerns. First, the cTTO method was shown to generate high values. Second, the cTTO method is not as feasible for valuing children's health as other established methods. This study aimed to explore the feasibility of using cTTO values alone to estimate EQ-5D-Y-3L value sets. METHODS: We analyzed the cTTO data derived from the recently completed Chinese EQ-5D-Y-3L valuation study in which a total of 28 health states were valued. We assessed the feasibility of the cTTO tasks in terms of survey completion time and participant-reported difficulty of understanding the task, differentiating the health states, and deciding the answer. We also examined the data distribution characteristics and modeled the data using different models. RESULTS: In total, 418 participants completed the cTTO interview. On average, participants took approximately 35.70 minutes (SD 12.42) to complete the interview and made 13.21 moves (SD 9.00) in the cTTO tasks. There were 74.16%, 59.33%, and 11.48% of participants indicated that it was easy to understand the cTTO tasks, easy to differentiate between the health states, and difficult to decide on an indifference point, respectively. The data distribution was smooth, and a random-effects model performed the best in terms of coefficient significance, monotonicity, and predictive accuracy. CONCLUSION: Our finding suggests that estimating EQ-5D-Y-3L value sets using cTTO data alone is feasible and therefore could be considered as an option in future valuation studies for EQ-5D-Y-3L.

A network representation approach for COVID-19 drug recommendation.

The coronavirus disease 2019 (COVID-19) has outbreak since early December 2019, and COVID-19 has caused over 100 million cases and 2 million deaths around the world. After one year of the COVID-19 outbreak, there is no certain and approve medicine against it. Drug repositioning has become one line of scientific research that is being pursued to develop an effective drug. However, due to the lack of COVID-19 data, there is still no specific drug repositioning targeting the COVID-19. In this paper, we propose a framework for COVID-19 drug repositioning. This framework has several advantages that can be exploited: one is that a local graph aggregating representation is used across a heterogeneous network to address the data sparsity problem; another is the multi-hop neighbors of the heterogeneous graph are aggregated to recall as many COVID-19 potential drugs as possible. Our experimental results show that our COVDR framework performs significantly better than baseline methods, and the docking simulation verifies that our three potential drugs have the ability to against COVID-19 disease.