Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults: Study of Domain Adaptation and Deep Learning.

Background: Cognitive impairment and dementia pose a significant challenge to the aging population, impacting the well-being, quality of life, and autonomy of affected individuals. As the population ages, this will place enormous strain on health care and economic systems. While computerized cognitive training programs have demonstrated some promise in addressing cognitive decline, adherence to these interventions can be challenging. Objective: The objective of this study is to improve the accuracy of predicting adherence lapses to ultimately develop tailored adherence support systems to promote engagement with cognitive training among older adults. Methods: Data from 2 previously conducted cognitive training intervention studies were used to forecast adherence levels among older participants. Deep convolutional neural networks were used to leverage their feature learning capabilities and predict adherence patterns based on past behavior. Domain adaptation (DA) was used to address the challenge of limited training data for each participant, by using data from other participants with similar playing patterns. Time series data were converted into image format using Gramian angular fields, to facilitate clustering of participants during DA. To the best of our knowledge, this is the first effort to use DA techniques to predict older adults' daily adherence to cognitive training programs. Results: Our results demonstrated the promise and potential of deep neural networks and DA for predicting adherence lapses. In all 3 studies, using 2 independent datasets, DA consistently produced the best accuracy values. Conclusions: Our findings highlight that deep learning and DA techniques can aid in the development of adherence support systems for computerized cognitive training, as well as for other interventions aimed at improving health, cognition, and well-being. These techniques can improve engagement and maximize the benefits of such interventions, ultimately enhancing the quality of life of individuals at risk for cognitive impairments. This research informs the development of more effective interventions, benefiting individuals and society by improving conditions associated with aging.

Structure-Based Discovery of Symmetric Disulfides from Garlic Extract as Pseudomonas aeruginosa Quorum Sensing Inhibitors.

Microorganisms are the most common cause of food spoilage. Pseudomonas aeruginosa is a common foodborne pathogen that causes food spoilage and poses a serious threat to food safety. As a crucial target in antitoxicity strategies, the quorum sensing (QS) system shows promising potential for further development. The garlic extract diallyl disulfide exhibits inhibitory activity against the QS system of P. aeruginosa, with disulfide bonds serving as the active component. However, the biological activity of other symmetric disulfides has not been investigated in this capacity. The study synthesized 39 disulfide bond-containing analogs and evaluated their activity as quorum sensing inhibitors (QSIs). The results showed that p-hydroxyphenyl substitution can replace the allyl groups while maintaining strong biological activity. The virulence factors production was reduced by compound 2i, with the strongest inhibitory effect being observed on elastase production. Synergistic inhibition was observed in the presence of antibiotics like ciprofloxacin and tobramycin. 2i successfully inhibited P. aeruginosa infection in the Galleria mellonella larvae model. Primary mechanism studies using transcriptome, surface plasmon resonance and molecular docking suggested that 2i inhibits the QS system by targeting the LasR protein. Thus, compound 2i could be used in developing QSIs for the control of P. aeruginosa infections.

Boosting the Stability of Subnanometer Pt Catalysts by the Presence of Framework Indium(III) Sites in Zeolite.

Subnanometer metal clusters show advantages over conventional metal nanoparticles in numerous catalytic reactions owing to their high percentage of exposed surface sites, abundance of under-coordinated metal sites and unique electronic structures. However, the applications of subnanometer metal clusters in high-temperature catalytic reactions (>600 °C) are still hindered, because of their low stability under harsh reaction conditions. In this work, we have developed a zeolite-confined bimetallic PtIn catalyst with exceptionally high stability against sintering. A combination of experimental and theoretical studies shows that the isolated framework In(III) species serve as the anchoring sites for Pt species, precluding the migration and sintering of Pt species in the oxidative atmosphere at ≥650 °C. The catalyst comprising subnanometer PtIn clusters exhibits long-term stability of >1000 h during a cyclic reaction-regeneration test for ethane dehydrogenation reaction.

Strain engineering of CoSANC catalyst toward enhancing the oxygen reduction reaction activity.

Developing efficient and cost-effective platinum-group metal-free (PGMF) catalysts for the oxygen reduction reaction (ORR) is crucial for energy conversion and storage devices. Among these catalysts, metal-nitrogen-carbon (MNC) materials, particularly cobalt single-atom catalysts (CoSANC), show promise as ORR electrocatalysts. However, their ORR activity is often hindered by strong hydroxyl (OH) adsorption on the Co sites. While the impact of strain engineering on MNC electrocatalysts has been minimally explored, recent studies suggest its potential to enhance catalytic performance and optimize intrinsic activity in traditional bulk catalysts. In this context, we investigate the effect of surface strain on CoSANC for ORR activity and correlate substrate-strain-induced geometric distortions with catalytic activity using experimental and theoretical methods. The findings suggest that the d-band center gap of spin states (Δεd) may be a preferred descriptor for predicting strain-dependent ORR performance in MNC catalysts. Leveraging CoSANC moiety placed on a substrate with an average size of 1.0 µm, we achieve performance comparable to that of commercial Pt/C catalysts when used as a cathode catalyst in zinc-air batteries. This investigation unveils the structure-function relationship of MNC electrocatalysts regarding strain engineering and provides valuable insights for future ORR activity design and enhancement.

CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning.

Virus‒host protein‒lncRNA interaction (VHPLI) predictions are critical for decoding the molecular mechanisms of viral pathogens and host immune processes. Although VHPLI interactions have been predicted in both plants and animals, they have not been extensively studied in viruses. For the first time, we propose a new deep learning-based approach that consists mainly of a convolutional neural network and bidirectional long and short-term memory network modules in combination with transfer learning named CBIL‒VHPLI to predict viral-host protein‒lncRNA interactions. The models were first trained on large and diverse datasets (including plants, animals, etc.). Protein sequence features were extracted using a k-mer method combined with the one-hot encoding and composition-transition-distribution (CTD) methods, and lncRNA sequence features were extracted using a k-mer method combined with the one-hot encoding and Z curve methods. The results obtained on three independent external validation datasets showed that the pre-trained CBIL‒VHPLI model performed the best with an accuracy of approximately 0.9. Pretraining was followed by conducting transfer learning on a viral protein-human lncRNA dataset, and the fine-tuning results showed that the accuracy of CBIL‒VHPLI was 0.946, which was significantly greater than that of the previous models. The final case study results showed that CBIL‒VHPLI achieved a prediction reproducibility rate of 91.6% for the RIP-Seq experimental screening results. This model was then used to predict the interactions between human lncRNA PIK3CD-AS2 and the nonstructural protein 1 (NS1) of the H5N1 virus, and RNA pull-down experiments were used to prove the prediction readiness of the model in terms of prediction. The source code of CBIL‒VHPLI and the datasets used in this work are available at https://github.com/Liu-Lab-Lnu/CBIL-VHPLI for academic usage.

Biochar alters the soil fauna functional traits and community diversity: A quantitative and cascading perspective.

With the widespread use of biochar, the cascading effects of biochar exposure on soil fauna urgently require deeper understanding. A meta-analysis quantified hierarchical changes in functional traits and community diversity of soil fauna under biochar exposure. Antioxidant enzymes (24.1 %) did not fully mitigate the impact of MDA (13.5 %), leading to excessive DNA damage in soil fauna (21.2 %). Concurrently, reproduction, growth, and survival rates decreased by 20.2 %, 8.5 %, and 21.2 %, respectively. Due to a 39.7 % increase in avoidance behavior of soil fauna towards biochar, species richness ultimately increased by 80.2 %. Compared to other feeding habits, biochar posed a greater threat to the survival of herbivores. Additionally, macrofauna were the most sensitive to biochar. The response of soil fauna also depended on the type, size, concentration, and duration of biochar exposure. It should be emphasized that as exposure concentration increased, the damage to soil fauna became more severe. Furthermore, the smaller the biochar sizes, the greater the damage to soil fauna. To mitigate the adverse effects on soil fauna, this study recommens applying biochar at appropriate times and selecting large sizes in low to medium concentrations. These findings confirm the threat of biochar to soil health from the perspective of soil fauna.

Regulation of cancer cell ferroptosis by PTRF/Cavin-1.

Ovarian cancer, marked by high rate of recurrence, novel therapeutic strategies are needed to improve patient outcome. One of the potential strategies is inducing ferroptosis in ovarian cancer cells. Ferroptosis is an iron-dependent, lipid peroxidation-driven mode of cell death primarily occurring on the cell membrane. PTRF, an integral component of the caveolae structures located on the cell membrane, is involved in a multitude of physiological processes, including but not limited to, endocytosis, signal transduction, and lipid metabolism. This study elucidates the relationship between PTRF and ferroptosis in ovarian cancer, offering a fresh perspective for the development of new therapeutic strategies. We knocked down PTRF employing siRNA in the ovarian cancer cell lines HEY and SKOV3, following which we stimulated ferroptosis with Erastin (Era). Our research indicates that the lack of PTRF sensitizes cancer cells to ferroptosis, likely by altering membrane stability and tension, thereby affecting signal pathways related to ferroptosis, such as lipid and atherosclerosis, fluid shear stress, and atherosclerosis. Our findings provide new insights for developing new treatments for ovarian cancer.

Epidermolysis Bullosa Pruriginosa treated with baricitinib: A case report.

INTRODUCTION: Epidermolysis Bullosa Pruriginosa (EBP) is a persistent, recurring disease that seriously affects quality of life. Fewer than 100 cases of EBP have been reported to date. Numerous inflammatory dermatoses are driven by soluble inflammatory mediators, which rely on Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling, and inhibition of this pathway using Janus kinase (JAK) inhibitors might be a useful therapeutic strategy for these diseases. PATIENT CONCERNS: A male patient, 28 years of age, was admitted to our hospital because of recurrent papules, nodules, and intense itching on the trunk and extremities for 12 years. Repeated large and intense itching has seriously affected the patient normal life. DIAGNOSIS: The patient was diagnosed with EBP based on examination results. INTERVENTIONS: Oral baricitinib tablets (2 mg, once a day) + Oral desloratadine citrate disodium tablets (8.8 mg, once a day) combined with topical compound flumethasone ointment and Fucidin cream. OUTCOMES: The patient skin rashes had subsided and flattened remarkable, and his itching was markedly relieved. The visual analogue scale (VAS) itching score of the patient gradually declined from 8 to 9 points to 2 to 3 points. CONCLUSION: This study confirms that baricitinib is effective and feasible in treating EBP, especially in remarkable relieving itching, which rendered new ideas for therapeutic approaches for EBP in the future.

Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis.

Systematic reviews and meta-analyses are essential tools in contemporary evidence-based medicine, synthesizing evidence from various sources to better inform clinical decision-making. However, the conclusions from different meta-analyses on the same topic can be discrepant, which has raised concerns about their reliability. One reason is that the result of a meta-analysis is sensitive to factors such as study inclusion/exclusion criteria and model assumptions. The arm-based meta-analysis model is growing in importance due to its advantage of including single-arm studies and historical controls with estimation efficiency and its flexibility in drawing conclusions with both marginal and conditional effect measures. Despite its benefits, the inference may heavily depend on the heterogeneity parameters that reflect design and model assumptions. This article aims to evaluate the robustness of meta-analyses using the arm-based model within a Bayesian framework. Specifically, we develop a tipping point analysis of the between-arm correlation parameter to assess the robustness of meta-analysis results. Additionally, we introduce some visualization tools to intuitively display its impact on meta-analysis results. We demonstrate the application of these tools in three real-world meta-analyses, one of which includes single-arm studies.

Design and Synthesis of Aryl Amide Derivatives Containing Thiazole as Type III Secretion System Inhibitors against Pseudomonas aeruginosa.

To identify potent inhibitors of the type III secretion system (T3SS) in the foodborne pathogen Pseudomonas aeruginosa, we synthesized 35 thiazole-containing aryl amides by merging salicylic acid with various heterocycles through active splicing. Screening for exoS promoter activity led to the discovery of a highly effective T3SS inhibitor from these 35 compounds. Through subsequent experiments, it was confirmed that compound II-22 specifically targeted the T3SS of P. aeruginosa. Additionally, compound II-22 inhibited the secretion of the effector protein ExoS by modulating the CyaB-cAMP/Vfr-ExsA and ExsCED-ExsA regulatory pathways. Furthermore, compound II-22 suppressed the transcription of genes involved in the needle complex assembly, leading to reduced bacterial virulence. Further validation through inoculation tests using Galleria mellonella larvae demonstrated the strong in vivo efficacy of compound II-22. The study also revealed that compound II-22 enhanced the bactericidal activity of antibiotics, such as CIP (ciprofloxacin) and TOB (tobramycin). These results could help develop novel antimicrobial drugs to reduce bacterial resistance.

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. MATERIALS AND METHODS: A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. RESULTS: Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). DISCUSSION: These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. CONCLUSIONS: Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

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.

Hot spots and trends in microbial disease research on cultural heritage: a bibliometric analysis.

This study is to understand and analyze the development history, research hotspots, and research trends in the study of microbial diseases of cultural heritage through bibliometric analyses in order to fill the current gap of no literature review in this research field and to make certain contributions to the research in this field and the protection of cultural heritage. Bibliometric and visual analyses of the literature on cultural heritage microbial diseases in the Web of Science (WoS) core collection were carried out using VOSviewer and R-bibliometrix, choosing the two main literature types of papers and reviews. The emphasis was placed on analyzing and summarizing core research strengths, hotspots, and trends. Six hundred sixty-seven documents (573 articles and 94 reviews) were retrieved. αIn the WoS core collection, the first literature on cultural heritage microbial disease research was published in January 2000, and the annual number of publications from 2000 to 2009 did not exceed one; the annual number of publications from 2010 onwards increased rapidly, and after 2018, the number of publications per year exceeded 60, reaching 94 in 2020, which indicates that cultural heritage microbial disease research is booming. Our research showed that Italy, the USA, and China were the leading research countries, and Univ Milan was the institution with the most publications. International Biodeterioration &Biodegradation was the most published and co-cited journal, and Gu JD was the most prolific author. The research hotspots in the study of microbial diseases of cultural heritage mainly include biological degradation of cultural heritage; identification of diseased microorganisms and disease mechanisms; cultural heritage microbial disease prevention and control methods; monitoring, prevention, and control of diseased microorganisms in indoor air; antibacterial agents, especially essential oils, nanoparticles, and other safe and efficient antibacterial products research and development; and exploration of the mechanisms of biofilm protection of cultural heritage on cultural heritage surfaces. Monitoring and identifying cultural heritage microbial communities, identifying disease mechanisms, and researching safe and efficient bacteriostatic products such as essential oils and nanoparticles will be the main research directions in the field of cultural heritage microbial disease prevention and control in the future.

Age-related trajectories of the development of social cognition.

Age-related trajectories of intrinsic functional connectivity (iFC), which represent the interconnections between discrete regions of the human brain, for processes related to social cognition (SC) provide evidence for social development through neural imaging and can guide clinical interventions when such development is atypical. However, due to the lack of studies investigating brain development over a wide range of ages, the neural mechanisms of SC remain poorly understood, although considerable behavior-related evidence is available. The present study mapped vortex-wise iFC features between SC networks and the entire cerebral cortex by using common functional networks, creating the corresponding age-related trajectories. Three networks [moral cognition, theory of mind (ToM), and empathy] were selected as representative SC networks. The Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS, N = 316, ages 8-83 years old) was employed delineate iFC characteristics and construct trajectories. The results showed that the SC networks display unique and overlapping iFC profiles. The iFC of the empathy network, an age-sensitive network, with dorsal attention network was found to exhibit a linear increasing pattern, that of the ventral attention network was observed to exhibit a linear decreasing pattern, and that of the somatomotor and dorsal attention networks was noted to exhibit a quadric-concave iFC pattern. Additionally, a sex-specific effect was observed for the empathy network as it exhibits linear and quadric sex-based differences in iFC with the frontoparietal and vision networks, respectively. The iFC of the ToM network with the ventral attention network exhibits a pronounced quadric-convex (inverted U-shape) trajectory. No linear or quadratic trajectories were noted in the iFC of the moral cognition network. These findings indicate that SC networks exhibit iFC with both low-level (somatomotor, vision) and high-level (attention and control) networks along specific developmental trajectories. The age-related trajectories determined in this study advance our understanding of the neural mechanisms of SC, providing valuable references for identification and intervention in cases of development of atypical SC.

Synoptic Reporting by Summarizing Cancer Pathology Reports using Large Language Models.

Background: Synoptic reporting, the documenting of clinical information in a structured manner, is known to improve patient care by reducing errors, increasing readability, interoperability, and report completeness. Despite its advantages, manually synthesizing synoptic reports from narrative reports is expensive and error prone when the number of structured fields are many. While the recent revolutionary developments in Large Language Models (LLMs) have significantly advanced natural language processing, their potential for innovations in medicine is yet to be fully evaluated. Objectives: In this study, we explore the strengths and challenges of utilizing the state-of-the-art language models in the automatic synthesis of synoptic reports. Materials and Methods: We use a corpus of 7,774 cancer related, narrative pathology reports, which have annotated reference synoptic reports from Mayo Clinic EHR. Using these annotations as a reference, we reconfigure the state-of-the-art large language models, such as LLAMA-2, to generate the synoptic reports. Our annotated reference synoptic reports contain 22 unique data elements. To evaluate the accuracy of the reports generated by the LLMs, we use several metrics including the BERT F1 Score and verify our results by manual validation. Results: We show that using fine-tuned LLAMA-2 models, we can obtain BERT Score F1 of 0.86 or higher across all data elements and BERT F1 scores of 0.94 or higher on over 50% (11 of 22) of the questions. The BERT F1 scores translate to average accuracies of 76% and as high as 81% for short clinical reports. Conclusions: We demonstrate successful automatic synoptic report generation by fine-tuning large language models.

Metal-Graphene Hybrid Terahertz Metasurfaces for Circulating Tumor DNA Detection Based on Dual Signal Amplification.

Terahertz (THz) spectroscopy has impressive capability for label-free biosensing, but its utility in clinical laboratories is rarely reported due to often unsatisfactory detection performances. Here, we fabricated metal-graphene hybrid THz metasurfaces (MSs) for the sensitive and enzyme-free detection of circulating tumor DNA (ctDNA) in pancreatic cancer plasma samples. The feasibility and mechanism of the enhanced effects of a graphene bridge across the MS and amplified by gold nanoparticles (AuNPs) were investigated experimentally and theoretically. The AuNPs serve to boost charge injection in the graphene film and result in producing a remarkable change in the graded transmissivity index to THz radiation of the MS resonators. Assay design utilizes this feature and a cascade hybridization chain reaction initiated on magnetic beads in the presence of target ctDNA to achieve dual signal amplification (chemical and optical). In addition to demonstrating subfemtomolar detection sensitivity and single-nucleotide mismatch selectivity, the proposed method showed remarkable capability to discriminate between pancreatic cancer patients and healthy individuals by recognizing and quantifying targeted ctDNAs. The introduction of graphene to the metasurface produces an improved sensitivity of 2 orders of magnitude for ctDNA detection. This is the first study to report the combined application of graphene and AuNPs in biosensing by THz spectroscopic resonators and provides a combined identification scheme to detect and discriminate different biological analytes, including nucleic acids, proteins, and various biomarkers.

Discovery of novel amide derivatives as potent quorum sensing inhibitors of Pseudomonas aeruginosa.

With the increasing reports of antibiotic resistance in this species, Pseudomonas aeruginosa is a common human pathogen with important implications for public health. Bacterial quorum sensing (QS) systems are potentially broad and versatile targets for developing new antimicrobial compounds. While previous reports have demonstrated that certain amide compounds can inhibit bacterial growth, there are few reports on the specific inhibitory effects of these compounds on bacterial quorum sensing systems. In this study, thirty-one amide derivatives were synthesized. The results of the biological activity assessment indicated that A9 and B6 could significantly inhibit the expression of lasB, rhlA, and pqsA, effectively reducing several virulence factors regulated by the QS systems of PAO1. Additionally, compound A9 attenuated the pathogenicity of PAO1 to Galleria mellonella larvae. Meanwhile, RT-qPCR, SPR, and molecular docking studies were conducted to explore the mechanism of these compounds, which suggests that compound A9 inhibited the QS systems by binding with LasR and PqsR, especially PqsR. In conclusion, amide derivatives A9 and B6 exhibit promising potential for further development as novel QS inhibitors in P. aeruginosa.

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.