Prompt engineering with a large language model to assist providers in responding to patient inquiries: a real-time implementation in the electronic health record.

Background: Large language models (LLMs) can assist providers in drafting responses to patient inquiries. We examined a prompt engineering strategy to draft responses for providers in the electronic health record. The aim was to evaluate the change in usability after prompt engineering. Materials and Methods: A pre-post study over 8 months was conducted across 27 providers. The primary outcome was the provider use of LLM-generated messages from Generative Pre-Trained Transformer 4 (GPT-4) in a mixed-effects model, and the secondary outcome was provider sentiment analysis. Results: Of the 7605 messages generated, 17.5% (n = 1327) were used. There was a reduction in negative sentiment with an odds ratio of 0.43 (95% CI, 0.36-0.52), but message use decreased (P < .01). The addition of nurses after the study period led to an increase in message use to 35.8% (P < .01). Discussion: The improvement in sentiment with prompt engineering suggests better content quality, but the initial decrease in usage highlights the need for integration with human factors design. Conclusion: Future studies should explore strategies for optimizing the integration of LLMs into the provider workflow to maximize both usability and effectiveness.

On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models.

OBJECTIVE: Traditional knowledge-based and machine learning diagnostic decision support systems have benefited from integrating the medical domain knowledge encoded in the Unified Medical Language System (UMLS). The emergence of Large Language Models (LLMs) to supplant traditional systems poses questions of the quality and extent of the medical knowledge in the models' internal knowledge representations and the need for external knowledge sources. The objective of this study is three-fold: to probe the diagnosis-related medical knowledge of popular LLMs, to examine the benefit of providing the UMLS knowledge to LLMs (grounding the diagnosis predictions), and to evaluate the correlations between human judgments and the UMLS-based metrics for generations by LLMs. METHODS: We evaluated diagnoses generated by LLMs from consumer health questions and daily care notes in the electronic health records using the ConsumerQA and Problem Summarization datasets. Probing LLMs for the UMLS knowledge was performed by prompting the LLM to complete the diagnosis-related UMLS knowledge paths. Grounding the predictions was examined in an approach that integrated the UMLS graph paths and clinical notes in prompting the LLMs. The results were compared to prompting without the UMLS paths. The final experiments examined the alignment of different evaluation metrics, UMLS-based and non-UMLS, with human expert evaluation. RESULTS: In probing the UMLS knowledge, GPT-3.5 significantly outperformed Llama2 and a simple baseline yielding an F1 score of 10.9% in completing one-hop UMLS paths for a given concept. Grounding diagnosis predictions with the UMLS paths improved the results for both models on both tasks, with the highest improvement (4%) in SapBERT score. There was a weak correlation between the widely used evaluation metrics (ROUGE and SapBERT) and human judgments. CONCLUSION: We found that while popular LLMs contain some medical knowledge in their internal representations, augmentation with the UMLS knowledge provides performance gains around diagnosis generation. The UMLS needs to be tailored for the task to improve the LLMs predictions. Finding evaluation metrics that are aligned with human judgments better than the traditional ROUGE and BERT-based scores remains an open research question.

Sequential severe immune-related adverse events induced by PD-1 inhibitor: a case report and literature review.

In a variety of cancers, immune checkpoint inhibitors (ICIs) have demonstrated substantial survival advantages. Nevertheless, the widespread use of ICIs in the clinic has resulted in a growing interest in immune-related adverse events (irAEs) and their treatment methods. This paper reports a case in which a patient with three sequential severe irAEs was successfully treated. After undergoing two regimens of sintilimab in conjunction with chemotherapy for advanced lung cancer, the patient developed myocarditis combined with hepatitis. Subsequently, the patient developed pneumonia following remission from treatment. We also discuss the mechanism of irAEs, principles of treatment, and progress in the study of biomarkers for early prediction of irAEs by reviewing the literature.

The Enormous Potential of Sodium/Potassium-Ion Batteries as the Mainstream Energy Storage Technology for Large-Scale Commercial Applications.

Cost-effectiveness plays a decisive role in sustainable operating of rechargeable batteries. As such, the low cost-consumption of sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs) provides a promising direction for "how do SIBs/PIBs replace Li-ion batteries (LIBs) counterparts" based on their resource abundance and advanced electrochemical performance. To rationalize the SIBs/PIBs technologies as alternatives to LIBs from the unit energy cost perspective, this review gives the specific criteria for their energy density at possible electrode-price grades and various battery-longevity levels. The cost ($ kWh-1 cycle-1) advantage of SIBs/PIBs is ascertained by the cheap raw-material compensation for the cycle performance deficiency and the energy density gap with LIBs. Furthermore, the cost comparison between SIBs and PIBs, especially on cost per kWh and per cycle, is also involved. This review explicitly manifests the practicability and cost-effectiveness toward SIBs are superior to PIBs whose commercialization has so far been hindered by low energy density. Even so, the huge potential on sustainability of PIBs, to outperform SIBs, as the mainstream energy storage technology is revealed as long as PIBs achieve long cycle life or enhanced energy density, the related outlook of which is proceeded as the next development directions for commercial applications.

[Therapeutic effect of calcaneal beak-like fracture secondary to calcaneal osteomyelitis caused by diabetic foot].

OBJECTIVE: To explore clinical effect of vancomycin calcium sulfate combined with internal fixation on calcaneal beak-like fracture secondary to calcaneal osteomyelitis caused by diabetic foot. METHODS: From April 2018 to October 2021, a retrospective analysis was performed on 5 patients with calcaneal bone osteomyelitis secondary to diabetic foot, including 2 males and 3 females, aged from 48 to 60 years old;diabetes course ranged from 5 to 13 years;the courses of diabetic foot disease ranged from 18 to 52 days;5 patients were grade Ⅲ according to Wagner classification. All patients were treated with debridement, vancomycin bone cement implantation, negative pressure aspiration at stageⅠ, vancomycin calcium sulfate and internal fixation at stageⅡfor calcaneal beak-like fracture. Surgical incision and fracture healing time were recorded, and the recurrence of osteomyelitis was observed. American Orthopedic Foot Andankle Society (AOFAS) score and exudation at 12 months after operation were evaluated. RESULTS: Five patients were successfully completed operation without lower extremity vascular occlusion, and were followed up for 16 to 36 months. The wound healing time after internal fixation ranged from 16 to 26 days, and healing time of fractures ranged from 16 to 27 weeks. AOFAS score ranged from 65 to 91 at 12 months after operation, and 2 patients got excellent result, 2 good and 1 fair. Among them, 1 patient with skin ulcer on the back of foot caused by scalding at 5 months after operation (non-complication), was recovered after treatment;the wound leakage complication occurred in 2 patients, and were recovered after dressing change. No osteomyelitis or fracture occurred in all patients. CONCLUSION: Vancomycin calcium sulfate with internal fixation in treating calcaneal osteomyelitis secondary to calcaneal osteomyelitis caused by diabetic foot could not only control infection, but also promote fracture healing, and obtain good clinical results.

Potential mitigation of titanium dioxide nanoparticles against 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis through inhibiting the canonical NF-κB pathway.

Titanium dioxide nanoparticles (TiO2 NPs) have been widely employed in various industry fields, which makes consumers concerned about their health impact. Our previous work displayed that TiO2 NPs participated in the mitigation of TNBS-induced colitis, but the mechanism is still unknown. This work aimed to explore the role of oxidative stress and NF-κB pathway in the effect of TiO2 NPs on TNBS-induced colitis. The results showed that TiO2 NPs administration reduced the DAI score of colitis mice after TNBS enema. TiO2 NPs did not alter oxidative stress status (GSH/GSSG), but repaired the gut dysbacteriosis and inhibited the canonical NF-κB pathway activation in TNBS-induced colitis mice, manifested as a decrease in pathogenic bacteria and an increase in beneficial bacteria, as well as down-regulation of toll-like receptors (TLRs), IKKα, IKKß, p65 and pro-inflammatory cytokines (IL-1ß, IL-6, TNF-α and IFN-γ) in mRNA level, and the increased transcription of anti-inflammatory cytokines (IL-10, TGF-ß, and IL-12), along with the declined protein level of TNF-α in TiO2 NPs treated colitis mice. The present study suggested that oral TiO2 NPs administration inhibited the canonical NF-κB pathway activation by repairing gut dysbacteriosis, which made a predominant role in alleviating colitis. These findings provided a new perspective for exploring the safety of TiO2 NPs.

LCD Benchmark: Long Clinical Document Benchmark on Mortality Prediction for Language Models.

Objective: The application of Natural Language Processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a certain domain, the role of benchmark datasets is crucial as benchmark datasets not only guide the selection of best-performing models but also enable the assessment of the reliability of the generated outputs. Despite the recent availability of language models (LMs) capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent. Materials and Methods: To address this issue, we propose LCD benchmark, a benchmark for the task of predicting 30-day out-of-hospital mortality using discharge notes of MIMIC-IV and statewide death data. We evaluated this benchmark dataset using baseline models, from bag-of-words and CNN to instruction-tuned large language models. Additionally, we provide a comprehensive analysis of the model outputs, including manual review and visualization of model weights, to offer insights into their predictive capabilities and limitations. Results and Discussion: Baseline models showed 28.9% for best-performing supervised models and 32.2% for GPT-4 in F1-metrics. Notes in our dataset have a median word count of 1687. Our analysis of the model outputs showed that our dataset is challenging for both models and human experts, but the models can find meaningful signals from the text. Conclusion: We expect our LCD benchmark to be a resource for the development of advanced supervised models, or prompting methods, tailored for clinical text. The benchmark dataset is available at https://github.com/Machine-Learning-for-Medical-Language/long-clinical-doc.

Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses.

In the evolving landscape of clinical Natural Language Generation (NLG), assessing abstractive text quality remains challenging, as existing methods often overlook generative task complexities. This work aimed to examine the current state of automated evaluation metrics in NLG in healthcare. To have a robust and well-validated baseline with which to examine the alignment of these metrics, we created a comprehensive human evaluation framework. Employing ChatGPT-3.5-turbo generative output, we correlated human judgments with each metric. None of the metrics demonstrated high alignment; however, the SapBERT score-a Unified Medical Language System (UMLS)- showed the best results. This underscores the importance of incorporating domain-specific knowledge into evaluation efforts. Our work reveals the deficiency in quality evaluations for generated text and introduces our comprehensive human evaluation framework as a baseline. Future efforts should prioritize integrating medical knowledge databases to enhance the alignment of automated metrics, particularly focusing on refining the SapBERT score for improved assessments.

A T-Cell Inspired Sonoporation System Enhances Low-Dose X-Ray-Mediated Pyroptosis and Radioimmunotherapy Efficacy by Restoring Gasdermin-E Expression.

Genome editing has the potential to improve the unsatisfactory therapeutic effect of antitumor immunotherapy. However, the cell plasma membrane prevents the entry of almost all free genome-manipulation agents. Therefore, a system can be spatiotemporally controlled and can instantly open the cellular membrane to allow the entry of genome-editing agents into target cells is needed. Here, inspired by the ability of T cells to deliver cytotoxins to cancer cells by perforation, an ultrasound (US)-controlled perforation system (UPS) is established to enhance the delivery of free genome-manipulating agents. The UPS can perforate the tumor cell membrane while maintaining cell viability via a controllable lipid peroxidation reaction. In vitro, transmembrane-incapable plasmids can enter cells and perform genome editing with the assistance of UPS, achieving an efficiency of up to 90%. In vivo, the UPS is biodegradable, nonimmunogenic, and tumor-targeting, enabling the puncturing of tumor cells under US. With the application of UPS-assisted genome editing, gasdermin-E expression in 4T1 tumor-bearing mice is successfully restored, which leads to pyroptosis-mediated antitumor immunotherapy via low-dose X-ray irradiation. This study provides new insights for designing a sonoporation system for genome editing. Moreover, the results demonstrate that restoring gasdermin expression by genome editing significantly improves the efficacy of radioimmunotherapy.

Transfer-Free Analog and Digital Flexible Memristors Based on Boron Nitride Films.

The traditional von Neumann architecture of computers, constrained by the inherent separation of processing and memory units, faces challenges, for instance, memory wall issue. Neuromorphic computing and in-memory computing offer promising paradigms to overcome the limitations of additional data movement and to enhance computational efficiency. In this work, transfer-free flexible memristors based on hexagonal boron nitride films were proposed for analog neuromorphic and digital memcomputing. Analog memristors were prepared; they exhibited synaptic behaviors, including paired-pulse facilitation and long-term potentiation/depression. The resistive switching mechanism of the analog memristors were investigated through transmission electron microscopy. Digital memristors were prepared by altering the electrode materials, and they exhibited reliable device performance, including a large on/off ratio (up to 106), reproducible switching endurance (>100 cycles), non-volatile characteristic (>60 min), and effective operating under bending conditions (>100 times).

Predicting 3-month Functional Outcome After Endovascular Thrombectomy in Patients with Anterior Circulation Occlusion with an Arterial Transit Artifact Grading System.

PURPOSE: The objective of this study was to evaluate the relationship between arterial transit artifact (ATA), arterial spin labeling (ASL) perfusion imaging, and the outcome of patients with acute ischemic stroke (AIS) due to occlusion of large vessels in anterior circulation after endovascular thrombectomy (EVT). METHODS: Patients with anterior circulation occlusion treated with EVT between October 2017 and December 2021 were enrolled in this retrospective study, and ATA was quantified by a 4-point scale. A favorable outcome was defined by modified Rankin Scale (mRS) scores of 0-2 at 3 months. To identify independent predictors of favorable outcome, age, sex, risk factors, baseline National Institutes of Health Stroke Scale (NIHSS) score, site of occlusion, cause of stroke, and early reperfusion were evaluated with univariate and multivariate analyses. Predictive accuracy was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC) for the model. RESULTS: In this study 187 patients (age, 65.0 ± 12.5 years; men, 55%) were evaluated. Younger age (odds ratio, OR, 0.95; 95% confidence interval, CI, 0.92-0.98, p = 0.002), lower baseline NIHSS score (OR, 0.88; 95% CI, 0.82-0.94, p < 0.001), and lower ATA score (OR, 1.14; 95% CI, 1.06-1.22, p < 0.001) were independently associated with favorable outcomes in multivariate analysis. The ATA score has moderate to good accuracy in predicting favorable outcomes (AUC, 0.753). CONCLUSION: A high ATA score as a potential predictor, can help identify patients who may benefit from EVT.

AHSA1 Regulates Hepatocellular Carcinoma Progression via the TGF-ß/Akt-Cyclin D1/CDK6 Pathway.

Background: Activator of heat shock protein 90 (HSP90) ATPase Activity 1 (AHSA1) regulates proliferation, apoptosis, migration, and invasion of osteosarcoma and hepatocellular carcinoma (HCC). However, the novel mechanism of AHSA1 in the tumor biology of hepatocellular carcinoma (HCC) remains unclear. Methods: We analyzed AHSA1 expression in 85 pairs of clinical samples of HCC and the Cancer Genome Atlas database. The role of AHSA1 in HCC was proved by cell proliferation, colony formation, migration, cell cycle analysis in vitro, xenograft models and tumor metastasis assay in vivo, and bioinformatics. Results: High AHSA1 expression was demonstrated in HCC and associated with invasive depth, clinical stage, and poor overall survival of patients. Univariate Cox analysis confirmed that AHSA1 was an independent prognostic factor for patients with HCC. Meanwhile, AHSA1 upregulation promoted cell proliferation, colony formation, and cell migration in vitro and tumor cell proliferation and metastasis of HCC cells in vivo. AHSA1 upregulation increased the cell cycle transition from G1 to S phase by increasing the expression of cyclinD1, cyclinD3, and cyclin-dependent kinase 6(CD). Transforming growth factor beta 1 (TGF-ß1)-induced protein kinase B (Akt) signaling regulated the expression of downstream targets, including cyclinD1. AHSA1 expression was closely correlated with the expression of TGF-ß, Akt, cyclinD1, cyclinD3, and CDK6 using the Gene Expression Profiling Interactive Analysis database. AHSA1 upregulation participated in HCC progression by regulating TGF-ß/Akt-cyclinD1/CDK6 signaling. Conclusion: AHSA1 might serve as a biomarker for predicting the clinical outcome of patients with HCC. It is vital in tumor metastasis and disease progression of HCC and may facilitate the development of clinical intervention strategies against HCC.

Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles.

Text in electronic health records is organized into sections, and classifying those sections into section categories is useful for downstream tasks. In this work, we attempt to improve the transferability of section classification models by combining the dataset-specific knowledge in supervised learning models with the world knowledge inside large language models (LLMs). Surprisingly, we find that zero-shot LLMs out-perform supervised BERT-based models applied to out-of-domain data. We also find that their strengths are synergistic, so that a simple ensemble technique leads to additional performance gains.

Improving model transferability for clinical note section classification models using continued pretraining.

OBJECTIVE: The classification of clinical note sections is a critical step before doing more fine-grained natural language processing tasks such as social determinants of health extraction and temporal information extraction. Often, clinical note section classification models that achieve high accuracy for 1 institution experience a large drop of accuracy when transferred to another institution. The objective of this study is to develop methods that classify clinical note sections under the SOAP ("Subjective," "Object," "Assessment," and "Plan") framework with improved transferability. MATERIALS AND METHODS: We trained the baseline models by fine-tuning BERT-based models, and enhanced their transferability with continued pretraining, including domain-adaptive pretraining and task-adaptive pretraining. We added in-domain annotated samples during fine-tuning and observed model performance over a varying number of annotated sample size. Finally, we quantified the impact of continued pretraining in equivalence of the number of in-domain annotated samples added. RESULTS: We found continued pretraining improved models only when combined with in-domain annotated samples, improving the F1 score from 0.756 to 0.808, averaged across 3 datasets. This improvement was equivalent to adding 35 in-domain annotated samples. DISCUSSION: Although considered a straightforward task when performing in-domain, section classification is still a considerably difficult task when performing cross-domain, even using highly sophisticated neural network-based methods. CONCLUSION: Continued pretraining improved model transferability for cross-domain clinical note section classification in the presence of a small amount of in-domain labeled samples.

Factors Affecting English Language Teachers' Behavioral Intentions to Teach Online under the Pandemic Normalization of COVID-19 in China.

The unexpected outbreak of COVID-19 pandemic forced most teachers across the globe to switch their teaching from traditional face-to-face to online without having received adequate preparation and knowledge related to online teaching. To better comprehend teachers' willingness to conduct emergency remote teaching during the worldwide crisis, the current study was designed to examine teachers' intentions and, in particular, the factors affecting their behavioral intentions to teach online, by contextualizing the research in the English language teaching settings in China. The research model was developed based on an extended technology acceptance model (TAM) by adding subjective norm, self-efficacy, technological complexity, and facilitating conditions into the original TAM. The data were obtained from a total of 287 respondents including 228 (79.4%) female teachers and 59 (20.6%) male teachers via online questionnaires. The questionnaire was developed by adapting previously validated instruments and then refined by two educational technology experts in order to better suit the current study. The empirical findings, using structural equation modeling (SEM), showed that the extended TAM was valid in predicting English language teachers' intentions to teach online during the pandemic normalization. At the same time, the findings suggested that teachers' intentions were influenced significantly by attitude, facilitating conditions, and self-efficacy; language teachers' attitude toward online teaching was significantly affected by both its perceived usefulness and perceived ease of use; perceived usefulness had a significant association with perceived ease of use and subjective norms; and perceived ease of use was significantly impacted by self-efficacy and facilitating conditions. Perceived usefulness was not suggested as a significant determinant of English language teachers' intentions to adopt online teaching. Despite some limitations, the study has several implications from the perspective of theory and practice. The more factors with a higher influence should be determined from different perspectives in future research.

Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes.

The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023. The aim of this shared task is to attract future research efforts in building NLP models for real-world diagnostic decision support applications, where a system generating relevant and accurate diagnoses will augment the healthcare providers' decision-making process and improve the quality of care for patients. The goal for participants is to develop models that generated a list of diagnoses and problems using input from the daily care notes collected from the hospitalization of critically ill patients. Eight teams submitted their final systems to the shared task leaderboard. In this paper, we describe the tasks, datasets, evaluation metrics, and baseline systems. Additionally, the techniques and results of the evaluation of the different approaches tried by the participating teams are summarized.

Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning.

Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors. To further the development of clinical AI systems, the Diagnostic Reasoning Benchmark (DR.BENCH) was introduced as a comprehensive generative AI framework, comprised of six tasks representing key components in clinical reasoning. We present a comparative analysis of in-domain versus out-of-domain language models as well as multi-task versus single task training with a focus on the problem summarization task in DR.BENCH (Gao et al., 2023). We demonstrate that a multi-task, clinically-trained language model outperforms its general domain counterpart by a large margin, establishing a new state-of-the-art performance, with a ROUGE-L score of 28.55. This research underscores the value of domain-specific training for optimizing clinical diagnostic reasoning tasks.

Oral intake of titanium dioxide nanoparticles affect the course and prognosis of ulcerative colitis in mice: involvement of the ROS-TXNIP-NLRP3 inflammasome pathway.

BACKGROUND: Titanium dioxide (TiO2), no matter in nanoscale or micron sizes, has been widely used in food industry as additives for decades. Given the potential impact of TiO2 on the gastrointestinal epithelial and parenchymal cells, including goblet cells, the public consumers may suffer the risk of diseases caused by its widespread dissemination in food products. We therefore set out to investigate the impact of TiO2 NPs on the course and prognosis of ulcerative colitis by oral gavaging TiO2 NPs at the doses levels of 0, 30, 100, and 300 mg/kg during the induction (7 days, from day 1 to day 7) and recovery (10 days, from day 8 to day 17) phases of colitis in mice. RESULTS: The ulcerative colitis (UC) disease model was established by administrating of 2.5% dextran sulfate sodium (DSS) solution. Our results show that TiO2 NPs significantly enhanced the severity of DSS-induced colitis, decreased the body weight, increased the disease activity index (DAI) and colonic mucosa damage index (CMDI) scores, shortened the colonic length, increased the inflammatory infiltration in the colon. The most significant changes occurred in the low dose (30 mg/kg) group of TiO2 NPs exposure during the development phase of UC and the high dose (300 mg/kg) group of TiO2 NPs during UC self-healing phase. Increased reactive oxygen species (ROS) level and upregulation of anti-oxidant enzymes including total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-PX) and catalase (CAT), demonstrate that the TiO2 NP exposure has triggered oxidative stress in mice. Moreover, the upregulation of caspase-1 mRNA and increased expression of thioredoxin interacting protein (TXNIP) further demonstrate the involvement of the ROS-TXNIP-NLR family pyrin domain containing 3 (NLRP3) inflammasome pathway in aggravating the development of UC. CONCLUSION: Oral intake of TiO2 NPs could affect the course of acute colitis in exacerbating the development of UC, prolonging the UC course and inhibiting UC recovery.

Unexpected associations of long-term and excessive exposure to trivalent chromium with hypertension and glycosuria in male tannery workers.

Serious health hazards including renal, skin and hearing disorders have been reported in Bangladeshi tannery workers (TWs) who were chronically exposed to a large amount of trivalent chromium [Cr(III)]. However, the effects of Cr(III) exposure on the prevalence of hypertension and the prevalence of glycosuria in TWs remain unknown. Since the Cr level in toenails is an established marker reflecting long-term exposure to Cr(III) in humans, the associations of Cr levels in toenails with the prevalence of hypertension and the prevalence of glycosuria in male tannery and non-tannery office workers (non-TWs) in Bangladesh were investigated in this study. The mean toenail Cr level in non-TWs (0.5 µg/g, n = 49) was comparable to that in the general population reported previously. Mean Cr levels in TWs with a low toenail Cr level (5.7 µg/g, n = 39) and those with a high toenail Cr level (298.8 µg/g, n = 61) were >10-fold and >500-fold higher, respectively, than that in non-TWs. Our univariate and multivariate analyses indicated that the prevalence of hypertension and the prevalence of glycosuria in TWs with a high toenail Cr level, but not in TWs with a low toenail Cr level, were significantly lower than those in non-TWs. This study showed for the first time that long-term and excessive exposure to Cr(III) that is more than >500-fold but not >10-fold higher than the usual exposure level could decrease the prevalence of hypertension and the prevalence of glycosuria in TWs. Thus, this study revealed unexpected effects of exposure to Cr(III) on health.