Induction Immunochemotherapy Yields a Higher Conversion Rate and Better Overall Survival than Chemotherapy in Initially Unresectable Esophageal Squamous Cell Carcinoma.

INTRODUCTION: This study compared the surgical conversion rate and overall survival (OS) between induction chemotherapy (iC) and induction immunochemotherapy (iIC) for patients with initially unresectable esophageal squamous cell carcinoma (iuESCC). METHODS: In this multicenter, retrospective cohort study, patients from four high-volume institutions with unresectable diseases were included. The primary endpoints were the conversion surgery rate and OS. A multivariate Cox regression analysis was used to identify the independent significant prognostic factors associated with OS. The stabilized inverse probability of treatment weighting was applied to confirm the survival comparison between the iIC and iC cohorts. RESULTS: A total of 309 patients (150 in the iIC cohort and 159 in the iC cohort) were included. A significantly higher conversion surgical rate was observed in the iIC cohort (iIC vs. iC: 127/150, 84.7% vs. 79/159, 49.7%, P < 0.001). The pathological complete response rates were 22.0% and 5.1% in the iIC and the iC cohorts, respectively (P = 0.001). A significant difference in the OS was observed between the iIC (not reached) and iC cohorts (median 95% CI 36.3 [range 27.2-45.5]). The stabilized inverse probability of treatment weighting yielded similar results. Regimen (iIC vs. iC, HR 0.215, 95% CI 0.102-0.454, P < 0.001) and operation (yes vs. no, HR 0.262, 95% CI 0.161-0.427, P < 0.001) were the significant prognostic factors for OS. CONCLUSIONS: Immunochemotherapy plus conversion surgery in the induction setting may be a better treatment option to achieve high pathological responses and improve OS in iuESCC patients.

Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study.

BACKGROUND: Although patients have easy access to their electronic health records and laboratory test result data through patient portals, laboratory test results are often confusing and hard to understand. Many patients turn to web-based forums or question-and-answer (Q&A) sites to seek advice from their peers. The quality of answers from social Q&A sites on health-related questions varies significantly, and not all responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to have their questions answered. OBJECTIVE: We aimed to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to laboratory test-related questions asked by patients and identify potential issues that can be mitigated using augmentation approaches. METHODS: We collected laboratory test result-related Q&A data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from 5 LLMs: GPT-4, GPT-3.5, LLaMA 2, MedAlpaca, and ORCA_mini. We assessed the similarity of their answers using standard Q&A similarity-based evaluation metrics, including Recall-Oriented Understudy for Gisting Evaluation, Bilingual Evaluation Understudy, Metric for Evaluation of Translation With Explicit Ordering, and Bidirectional Encoder Representations from Transformers Score. We used an LLM-based evaluator to judge whether a target model had higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. We performed a manual evaluation with medical experts for all the responses to 7 selected questions on the same 4 aspects. RESULTS: Regarding the similarity of the responses from 4 LLMs; the GPT-4 output was used as the reference answer, the responses from GPT-3.5 were the most similar, followed by those from LLaMA 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored the lowest and, thus, as the least similar to GPT-4-generated answers. The results of the win rate and medical expert evaluation both showed that GPT-4's responses achieved better scores than all the other LLM responses and human responses on all 4 aspects (relevance, correctness, helpfulness, and safety). LLM responses occasionally also suffered from lack of interpretation in one's medical context, incorrect statements, and lack of references. CONCLUSIONS: By evaluating LLMs in generating responses to patients' laboratory test result-related questions, we found that, compared to other 4 LLMs and human answers from a Q&A website, GPT-4's responses were more accurate, helpful, relevant, and safer. There were cases in which GPT-4 responses were inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses, including prompt engineering, prompt augmentation, retrieval-augmented generation, and response evaluation.

Sieve tube structural variation in Austrobaileya scandens and its significance for lianescence.

Lianas combine large leaf areas with slender stems, features that require an efficient vascular system. The only extant member of the Austrobaileyaceae is an endemic twining liana of the tropical Australian forests with well-known xylem hydraulics, but the vascular phloem continuum aboveground remains understudied. Microscopy analysis across leaf vein orders and stems of Austrobaileya scandens revealed a low foliar xylem:phloem ratio, with isodiametric vascular elements along the midrib, but tapered across vein orders. Sieve plate pore radii increased from 0.08 µm in minor veins to 0.12 µm in the petiole, but only to 0.20 µm at the stem base, tens of metres away. In easily bent searcher branches, phloem conduits have pectin-rich walls and simple plates, whereas in twining stems, conduits were connected through highly angled and densely porated sieve plates. The hydraulic resistance of phloem conduits in the twisted and elongated stems of A. scandens is large compared with trees of similar stature; phloem hydraulic resistance decreases from leaves to stems, consistent with the efficient delivery of photoassimilates from sources under Münch predictions. Sink strength of a continuously growing canopy might be stronger than in self-supporting understory plants, favoring resource allocation to aerial organs and the attainment of vertical stature.

Genetic variability in the E6 and E7 oncogenes of HPV52 and its prevalence in the Taizhou area, China.

BACKGROUND: Human papillomavirus (HPV) 52 is one of the prevalent oncogenic HPV genotypes in East Asia. Chinese women have the highest susceptibility to the HPV52 type, but research data on HPV52 genetic variability and its carcinogenicity in China is lacking. METHODS: The present study aimed to investigate the genetic variability of HPV52 currently circulating among Chinese women by PCR sequencing the entire E6 and E7 oncogenes. HPV52 sequence alignment, genetic heterogeneity analyses and maximum-likelihood phylogenetic tree construction were performed by BioEdit software and MEGA X software. RESULTS: Between 2016 and 2018, the overall HPV infection rate was 21.3%, of which HPV52 was the most prevalent high-risk type (17.2%) in the Taizhou area, China. A total of 339 single HPV52-positive samples were included in this study. We obtained 27 distinct variation patterns of HPV52 with the accession GenBank numbers ON529577-ON529603. Phylogenetic analysis showed that 96.6% of HPV52 variants belonged to lineage B, which seemed to be uniquely defined by G350T, A379G (K93R) in the E6 gene and C751T, A801G in the E7 gene. Due to the dominance of lineage B in our study population, the results could not be used to assess the association of the HPV52 (sub)lineage with the risk of cervical lesions. In addition, no significant trends were observed between the nucleotide substitutions of HPV52 variants and the risk of cervical carcinogenesis. CONCLUSION: Our data showed that HPV52 variants were strongly biased towards lineage B. These results confirmed that cervical lesions in the Taizhou area are highly attributable to HPV52, which may be due to the high infection rate of lineage B in the population.

Chemosensory sensilla of the Drosophila wing express a candidate ionotropic pheromone receptor.

The Drosophila wing was proposed to be a taste organ more than 35 years ago, but there has been remarkably little study of its role in chemoreception. We carry out a differential RNA-seq analysis of a row of sensilla on the anterior wing margin and find expression of many genes associated with pheromone and chemical perception. To ask whether these sensilla might receive pheromonal input, we devised a dye-transfer paradigm and found that large, hydrophobic molecules comparable to pheromones can be transferred from one fly to the wing margin of another. One gene, Ionotropic receptor (IR)52a, is coexpressed in neurons of these sensilla with fruitless, a marker of sexual circuitry; IR52a is also expressed in legs. Mutation of IR52a and optogenetic silencing of IR52a+ neurons decrease levels of male sexual behavior. Optogenetic activation of IR52a+ neurons induces males to show courtship toward other males and, remarkably, toward females of another species. Surprisingly, IR52a is also required in females for normal sexual behavior. Optogenetic activation of IR52a+ neurons in mated females induces copulation, which normally occurs at very low levels. Unlike other chemoreceptors that act in males to inhibit male-male interactions and promote male-female interactions, IR52a acts in both males and females, and can promote male-male as well as male-female interactions. Moreover, IR52a+ neurons can override the circuitry that normally suppresses sexual behavior toward unproductive targets. Circuit mapping and Ca2+ imaging using the trans-Tango system reveals second-order projections of IR52a+ neurons in the subesophageal zone (SEZ), some of which are sexually dimorphic. Optogenetic activation of IR52a+ neurons in the wing activates second-order projections in the SEZ. Taken together, this study provides a molecular description of the chemosensory sensilla of a greatly understudied taste organ and defines a gene that regulates the sexual circuitry of the fly.

A Machine-Learning Based Approach for Predicting Older Adults' Adherence to Technology-Based Cognitive Training.

Adequate adherence is a necessary condition for success with any intervention, including for computerized cognitive training designed to mitigate age-related cognitive decline. Tailored prompting systems offer promise for promoting adherence and facilitating intervention success. However, developing adherence support systems capable of just-in-time adaptive reminders requires understanding the factors that predict adherence, particularly an imminent adherence lapse. In this study we built machine learning models to predict participants' adherence at different levels (overall and weekly) using data collected from a previous cognitive training intervention. We then built machine learning models to predict adherence using a variety of baseline measures (demographic, attitudinal, and cognitive ability variables), as well as deep learning models to predict the next week's adherence using variables derived from training interactions in the previous week. Logistic regression models with selected baseline variables were able to predict overall adherence with moderate accuracy (AUROC: 0.71), while some recurrent neural network models were able to predict weekly adherence with high accuracy (AUROC: 0.84-0.86) based on daily interactions. Analysis of the post hoc explanation of machine learning models revealed that general self-efficacy, objective memory measures, and technology self-efficacy were most predictive of participants' overall adherence, while time of training, sessions played, and game outcomes were predictive of the next week's adherence. Machine-learning based approaches revealed that both individual difference characteristics and previous intervention interactions provide useful information for predicting adherence, and these insights can provide initial clues as to who to target with adherence support strategies and when to provide support. This information will inform the development of a technology-based, just-in-time adherence support systems.

Large few-layer hexagonal boron nitride flakes for nonlinear optics.

Hexagonal boron nitride (hBN) is a layered dielectric material with a wide range of applications in optics and photonics. In this work, we demonstrate a fabrication method for few-layer hBN flakes with areas up to 5000µm2. We show that hBN in this form can be integrated with photonic microstructures: as an example, we use a circular Bragg grating (CBG). The layer quality of the exfoliated hBN flake on and off a CBG is confirmed by Raman spectroscopy and second-harmonic generation (SHG) microscopy. We show that the SHG signal is uniform across the hBN sample outside the CBG and is amplified in the center of the CBG.

Achieving enhanced solid-state photochromism and mechanochromism by introducing a rigid steric hindrance group.

For photochromic molecules, effective isomerization usually requires conformational freedom, which is usually unavailable under solvent-free conditions. In this work, we report a new method, which can realize the reversible switching of spiropyran molecules by introducing a rigid aromatic ring group and this method can provide the required free volume to transform from a closed-ring to an open-ring form. This new molecule can quickly change color in the solid state under ultraviolet light, and can be erased after being heated at 60 °C for about 5 minutes. Furthermore, this new compound presents mechanochromicity when a mechanical force is applied. What is more, it can be used for at least 30 cycles of print-erase operations without apparent fatigue. This new molecule exhibits improved photochromic and anti-fatigue properties in the solid state, which can promote its application in both ultraviolet printing and anti-counterfeiting materials.

Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach.

BACKGROUND: Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation. RESULTS: Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score. CONCLUSIONS: The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.

Patient Challenges and Needs in Comprehending Laboratory Test Results: Mixed Methods Study.

BACKGROUND: Patients are increasingly able to access their laboratory test results via patient portals. However, merely providing access does not guarantee comprehension. Patients could experience confusion when reviewing their test results. OBJECTIVE: The aim of this study is to examine the challenges and needs of patients when comprehending laboratory test results. METHODS: We conducted a web-based survey with 203 participants and a set of semistructured interviews with 13 participants. We assessed patients' perceived challenges and needs (both informational and technological needs) when they attempted to comprehend test results, factors associated with patients' perceptions, and strategies for improving the design of patient portals to communicate laboratory test results more effectively. Descriptive and correlation analysis and thematic analysis were used to analyze the survey and interview data, respectively. RESULTS: Patients face a variety of challenges and confusion when reviewing laboratory test results. To better comprehend laboratory results, patients need different types of information, which are grouped into 2 categories-generic information (eg, reference range) and personalized or contextual information (eg, treatment options, prognosis, what to do or ask next). We also found that several intrinsic factors (eg, laboratory result normality, health literacy, and technology proficiency) significantly impact people's perceptions of using portals to view and interpret laboratory results. The desired enhancements of patient portals include providing timely explanations and educational resources (eg, a health encyclopedia), increasing usability and accessibility, and incorporating artificial intelligence-based technology to provide personalized recommendations. CONCLUSIONS: Patients face significant challenges in interpreting the meaning of laboratory test results. Designers and developers of patient portals should employ user-centered approaches to improve the design of patient portals to present information in a more meaningful way.

An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study.

BACKGROUND: The language gap between health consumers and health professionals has been long recognized as the main hindrance to effective health information comprehension. Although providing health information access in consumer health language (CHL) is widely accepted as the solution to the problem, health consumers are found to have varying health language preferences and proficiencies. To simplify health documents for heterogeneous consumer groups, it is important to quantify how CHLs are different in terms of complexity among various consumer groups. OBJECTIVE: This study aimed to propose an informatics framework (consumer health language complexity [CHELC]) to assess the complexity differences of CHL using syntax-level, text-level, term-level, and semantic-level complexity metrics. Specifically, we identified 8 language complexity metrics validated in previous literature and combined them into a 4-faceted framework. Through a rank-based algorithm, we developed unifying scores (CHELC scores [CHELCS]) to quantify syntax-level, text-level, term-level, semantic-level, and overall CHL complexity. We applied CHELCS to compare posts of each individual on online health forums designed for (1) the general public, (2) deaf and hearing-impaired people, and (3) people with autism spectrum disorder (ASD). METHODS: We examined posts with more than 4 sentences of each user from 3 health forums to understand CHL complexity differences among these groups: 12,560 posts from 3756 users in Yahoo! Answers, 25,545 posts from 1623 users in AllDeaf, and 26,484 posts from 2751 users in Wrong Planet. We calculated CHELCS for each user and compared the scores of 3 user groups (ie, deaf and hearing-impaired people, people with ASD, and the public) through 2-sample Kolmogorov-Smirnov tests and analysis of covariance tests. RESULTS: The results suggest that users in the public forum used more complex CHL, particularly more diverse semantics and more complex health terms compared with users in the ASD and deaf and hearing-impaired user forums. However, between the latter 2 groups, people with ASD used more complex words, and deaf and hearing-impaired users used more complex syntax. CONCLUSIONS: Our results show that the users in 3 online forums had significantly different CHL complexities in different facets. The proposed framework and detailed measurements help to quantify these CHL complexity differences comprehensively. The results emphasize the importance of tailoring health-related content for different consumer groups with varying CHL complexities.

A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter.

BACKGROUND: Emotions after surviving cancer can be complicated. The survivors may have gained new strength to continue life, but some of them may begin to deal with complicated feelings and emotional stress due to trauma and fear of cancer recurrence. The widespread use of Twitter for socializing has been the alternative medium for data collection compared to traditional studies of mental health, which primarily depend on information taken from medical staff with their consent. These social media data, to a certain extent, reflect the users' psychological state. However, Twitter also contains a mix of noisy and genuine tweets. The process of manually identifying genuine tweets is expensive and time-consuming. METHODS: We stream the data using cancer as a keyword to filter the tweets with cancer-free and use post-traumatic stress disorder (PTSD) related keywords to reduce the time spent on the annotation task. Convolutional Neural Network (CNN) learns the representations of the input to identify cancer survivors with PTSD. RESULTS: The results present that the proposed CNN can effectively identify cancer survivors with PTSD. The experiments on real-world datasets show that our model outperforms the baselines and correctly classifies the new tweets. CONCLUSIONS: PTSD is one of the severe anxiety disorders that could affect individuals who are exposed to traumatic events, including cancer. Cancer survivors are at risk of short-term or long-term effects on physical and psycho-social well-being. Therefore, the evaluation and treatment of PTSD are essential parts of cancer survivorship care. It will act as an alarming system by detecting the PTSD presence based on users' postings on Twitter.

Selected articles from the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019).

In this introduction, we first summarize the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019) held on October 26, 2019 in conjunction with the 18th International Semantic Web Conference (ISWC 2019) in Auckland, New Zealand, and then briefly introduce seven research articles included in this supplement issue, covering the topics on Knowledge Graph, Ontology-Powered Analytics, and Deep Learning.

Biomedical word sense disambiguation with bidirectional long short-term memory and attention-based neural networks.

BACKGROUND: In recent years, deep learning methods have been applied to many natural language processing tasks to achieve state-of-the-art performance. However, in the biomedical domain, they have not out-performed supervised word sense disambiguation (WSD) methods based on support vector machines or random forests, possibly due to inherent similarities of medical word senses. RESULTS: In this paper, we propose two deep-learning-based models for supervised WSD: a model based on bi-directional long short-term memory (BiLSTM) network, and an attention model based on self-attention architecture. Our result shows that the BiLSTM neural network model with a suitable upper layer structure performs even better than the existing state-of-the-art models on the MSH WSD dataset, while our attention model was 3 or 4 times faster than our BiLSTM model with good accuracy. In addition, we trained "universal" models in order to disambiguate all ambiguous words together. That is, we concatenate the embedding of the target ambiguous word to the max-pooled vector in the universal models, acting as a "hint". The result shows that our universal BiLSTM neural network model yielded about 90 percent accuracy. CONCLUSION: Deep contextual models based on sequential information processing methods are able to capture the relative contextual information from pre-trained input word embeddings, in order to provide state-of-the-art results for supervised biomedical WSD tasks.

Tip-Enhanced Raman Imaging of Single-Stranded DNA with Single Base Resolution.

Tip-enhanced Raman scattering (TERS) is a promising optical and analytical technique for chemical imaging and sensing at single molecule resolution. In particular, TERS signals generated by a gap-mode configuration where a silver tip is coupled with a gold substrate can resolve a single-stranded DNA (ssDNA) molecule with a spatial resolution below 1 nm. To demonstrate the proof of subnanometer resolution, we show direct nucleic acid sequencing using TERS of a phage ssDNA (M13mp18). M13mp18 provides a known sequence and, through our deposition strategy, can be stretched (uncoiled) and attached to the substrate by its phosphate groups, while exposing its nucleobases to the tip. After deposition, we scan the silver tip along the ssDNA and collect TERS signals with a step of 0.5 nm, comparable to the bond length between two adjacent DNA bases. By demonstrating the real-time profiling of a ssDNA configuration and furthermore, with unique TERS signals of monomeric units of other biopolymers, we anticipate that this technique can be extended to the high-resolution imaging of various nanostructures as well as the direct sequencing of other important biopolymers including RNA, polysaccharides, and polypeptides.

Alternative classification of identical concepts in different terminologies: Different ways to view the world.

In previous research, we have studied concepts that occur in pairs of medical terminologies and are known to be identical, because they have the same ID number in the Unified Medical Language System (UMLS). We observed that such concepts rarely have exactly the same sets of children (=subconcepts) in the two terminologies. The number of common children was found to vary widely. A special situation was identified where the children in one terminology relate to the common parent in a very different way than the children in the other terminology. For example, children in one terminology might subdivide a parent concept by anatomical location in one terminology and by disease kind in the other terminology. We coined the term "alternative classification" (of the same parent concept) for such situations. In previous work, only human experts could recognize alternative classifications. In this paper, we present a mathematically expressed criterion for likely cases of alternative classifications. We compare the recommendations of this criterion, expressed by a mathematical quantity called "EFI" becoming zero, with the decisions of a human expert. It is found that the human expert agreed with the criterion in 72% of all cases, which is a big improvement over having no computable criterion at all. Besides alternative classifications, common parent concepts in a pair of terminologies might also indicate a possible import of a child concept missing in one terminology, different granularities, or errors in either one of the two terminologies. In this paper, we further investigate different kinds of alternative classifications.

Patient Engagement in Medical Research Among Older Adults: Analysis of the Health Information National Trends Survey.

BACKGROUND: By 2035, it is expected that older adults (aged 65 years and older) will outnumber children and will represent 78 million people in the US population. As the aging population continues to grow, it is critical to reduce disparities in their representation in medical research. OBJECTIVE: This study aimed to describe sociodemographic characteristics and health and information behaviors as factors that influence US adults' interest in engaging in medical research, beyond participation as study subjects. METHODS: Nationally representative cross-sectional data from the 2014 Health Information National Trends Survey (N=3677) were analyzed. Descriptive statistics and weighted multivariable logistic regression analyses were performed to assess predictors of one's interest in patient engagement in medical research. The independent variables included age, general health, income, race and ethnicity, education level, insurance status, marital status, and health information behaviors. RESULTS: We examined the association between the independent variables and patient interest in engaging in medical research (PTEngage_Interested). Patient interest in engaging in medical research has a statistically significant association with age (adjusted P<.01). Younger adults (aged 18-34 years), lower middle-aged adults (aged 35-49 years), and higher middle-aged adults (aged 50-64 years) indicated interest at relatively the same frequency (29.08%, 29.56%, and 25.12%, respectively), but older adults (aged ≥65 years) expressed less interest (17.10%) than the other age groups. After the multivariate model was run, older adults (odds ratio 0.738, 95% CI 0.500-1.088) were found to be significantly less likely to be interested in engaging in medical research than adults aged 50 to 64 years. Regardless of age, the strongest correlation was found between interest in engaging in medical research and actively looking for health information (P<.001). Respondents who did not seek health information were significantly less likely than those who did seek health information to be interested in engaging in medical research. CONCLUSIONS: Patients' interest in engaging in medical research vary by age and information-seeking behaviors. As the aging population continues to grow, it is critical to reduce disparities in their representation in medical research. Interest in participatory research methods may reflect an opportunity for consumer health informatics technologies to improve the representation of older adults in future medical research.

Selected articles from the Third International Workshop on Semantics-Powered Data Analytics (SEPDA 2018).

In this editorial, we first summarize the Third International Workshop on Semantics-Powered Data Analytics (SEPDA 2018) held on December 3, 2018 in conjunction with the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018) in Madrid, Spain, and then briefly introduce five research articles included in this supplement issue, covering topics including Data Analytics, Data Visualization, Text Mining, and Ontology Evaluation.

ALOHA: developing an interactive graph-based visualization for dietary supplement knowledge graph through user-centered design.

BACKGROUND: Dietary supplements (DSs) are widely used. However, consumers know little about the safety and efficacy of DSs. There is a growing interest in accessing health information online; however, health information, especially online information on DSs, is scattered with varying levels of quality. In our previous work, we prototyped a web application, ALOHA, with interactive graph-based visualization to facilitate consumers' browsing of the integrated DIetary Supplement Knowledge base (iDISK) curated from scientific resources, following an iterative user-centered design (UCD) process. METHODS: Following UCD principles, we carried out two design iterations to enrich the functionalities of ALOHA and enhance its usability. For each iteration, we conducted a usability assessment and design session with a focus group of 8-10 participants and evaluated the usability with a modified System Usability Scale (SUS). Through thematic analysis, we summarized the identified usability issues and conducted a heuristic evaluation to map them to the Gerhardt-Powals' cognitive engineering principles. We derived suggested improvements from each of the usability assessment session and enhanced ALOHA accordingly in the next design iteration. RESULTS: The SUS score in the second design iteration decreased to 52.2 ± 11.0 from 63.75 ± 7.2 in our original work, possibly due to the high number of new functionalities we introduced. By refining existing functionalities to make the user interface simpler, the SUS score increased to 64.4 ± 7.2 in the third design iteration. All participants agreed that such an application is urgently needed to address the gaps in how DS information is currently organized and consumed online. Moreover, most participants thought that the graph-based visualization in ALOHA is a creative and visually appealing format to obtain health information. CONCLUSIONS: In this study, we improved a novel interactive visualization platform, ALOHA, for the general public to obtain DS-related information through two UCD design iterations. The lessons learned from the two design iterations could serve as a guide to further enhance ALOHA and the development of other knowledge graph-based applications. Our study also showed that graph-based interactive visualization is a novel and acceptable approach to end-users who are interested in seeking online health information of various domains.