Pseudomonas sp. strain 7-6, isolated from active sludge obtained from a wastewater facility, utilized a quaternary ammonium surfactant, n-dodecyltrimethylammonium chloride (DTAC), as its sole carbon, nitrogen, and energy source. When initially grown in the presence of 10 mM DTAC medium, the isolate was unable to degrade DTAC. The strain was cultivated in gradually increasing concentrations of the surfactant until continuous exposure led to high tolerance and biodegradation of the compound. Based on the identification of five metabolites by gas chromatography-mass spectrometry analysis, two possible pathways for DTAC metabolism were proposed. In pathway 1, DTAC is converted to lauric acid via n-dodecanal with the release of trimethylamine; in pathway 2, DTAC is converted to lauric acid via n-dodecyldimethylamine and then n-dodecanal with the release of dimethylamine. Among the identified metabolites, the strain precultivated on DTAC medium could utilize n-dodecanal and lauric acid as sole carbon sources and trimethylamine and dimethylamine as sole nitrogen sources, but it could not efficiently utilize n-dodecyldimethylamine. These results indicated pathway 1 is the main pathway for the degradation of DTAC.Quaternary ammonium compounds (QACs) containing a long-chain alkyl group or a benzyl group are cationic surfactants that are widely used in several applications, including as antistatic agents, emulsifiers-dispersants, dye auxiliaries, surface treatment agents, osmotic agents, and hair rinses (8). QACs are also contained in synthetic detergents to reduce static electricity in clothing and improve fabric suppleness. In addition, the bactericidal and fungicidal properties of these compounds, as well as their ability to damage cell membranes and to denature cell proteins, have favored their widespread use in domestic cleaning products (1, 6, 7). Since most of the above-mentioned products are released into the environment through routine disposal wastewater, the accumulation and aquatic toxicity of quaternary-ammonium-based surfactants have been the focus of several studies (4,20).Several researchers have reported adaptation to QACs by aquatic organisms through their repeated exposure to these compounds (19) and the biodegradation of QACs by pure cultures of bacteria (18). McBain et al. showed that repeated exposure of pure cultures, especially Ralstonia sp., altered their susceptibility to QACs (11). In addition, a mixture of Pseudomonas sp. and Xanthomonas sp. isolated from soil and sewage grew well on medium containing decyltrimethylammonium salt as the sole carbon source. Xanthomonas sp. oxidized the terminal carbon of the alkyl chain of QAC (2). Pseudomonas sp. strain B1, isolated from activated sludge, grew well on hexadecyltrimethylammonium chloride (the C16 alkyl QAC in this report), using the compound as a carbon and energy source (17). However, strain B1 could not utilize the intermediate, trimethylamine, as a nitrogen source. Although these findings indicate the metabolic fates of QACs in an aquatic envir...

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Background The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is important to take timely preventive measures. Objective This study aims to clarify whether the number of suicides can be predicted by suicide-related search queries used before searching for the keyword “suicide.” Methods This study uses the infoveillance approach for suicide in Japan by search trends in search engines. The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender with queries associated with “suicide” on “Yahoo! JAPAN Search” from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to suicide were used as search terms before searching for the keyword “suicide” and extracted and used for analyses: “abuse”; “work, don’t want to go”; “company, want to quit”; “divorce”; and “no money.” The augmented Dickey-Fuller and Johansen tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch-Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque-Bera (JB) test were used to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. Results In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: −9.24; max tau 3: −5.38) and women (minimum tau 3: −9.24; max tau 3: −5.38) had no unit roots for all variables. In the Johansen test, a cointegration relationship was observed among several variables. The queries used in the converged models were “divorce” for men (BG-LM test: P=.55; ARCH-LM test: P=.63; JB test: P=.66) and “no money” for women (BG-LM test: P=.17; ARCH-LM test: P=.15; JB test: P=.10). In the Granger causality test for each variable, “divorce” was significant for both men (F104=3.29; P=.04) and women (F104=3.23; P=.04). Conclusions The number of suicides can be predicted by search queries related to the keyword “suicide.” Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on “no money” and “divorce” predicted suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary.

Background ChatGPT, a large language model, has shown good performance on physician certification examinations and medical consultations. However, its performance has not been examined in languages other than English or on nursing examinations. Objective We aimed to evaluate the performance of ChatGPT on the Japanese National Nurse Examinations. Methods We evaluated the percentages of correct answers provided by ChatGPT (GPT-3.5) for all questions on the Japanese National Nurse Examinations from 2019 to 2023, excluding inappropriate questions and those containing images. Inappropriate questions were pointed out by a third-party organization and announced by the government to be excluded from scoring. Specifically, these include “questions with inappropriate question difficulty” and “questions with errors in the questions or choices.” These examinations consist of 240 questions each year, divided into basic knowledge questions that test the basic issues of particular importance to nurses and general questions that test a wide range of specialized knowledge. Furthermore, the questions had 2 types of formats: simple-choice and situation-setup questions. Simple-choice questions are primarily knowledge-based and multiple-choice, whereas situation-setup questions entail the candidate reading a patient’s and family situation’s description, and selecting the nurse's action or patient's response. Hence, the questions were standardized using 2 types of prompts before requesting answers from ChatGPT. Chi-square tests were conducted to compare the percentage of correct answers for each year's examination format and specialty area related to the question. In addition, a Cochran-Armitage trend test was performed with the percentage of correct answers from 2019 to 2023. Results The 5-year average percentage of correct answers for ChatGPT was 75.1% (SD 3%) for basic knowledge questions and 64.5% (SD 5%) for general questions. The highest percentage of correct answers on the 2019 examination was 80% for basic knowledge questions and 71.2% for general questions. ChatGPT met the passing criteria for the 2019 Japanese National Nurse Examination and was close to passing the 2020-2023 examinations, with only a few more correct answers required to pass. ChatGPT had a lower percentage of correct answers in some areas, such as pharmacology, social welfare, related law and regulations, endocrinology/metabolism, and dermatology, and a higher percentage of correct answers in the areas of nutrition, pathology, hematology, ophthalmology, otolaryngology, dentistry and dental surgery, and nursing integration and practice. Conclusions ChatGPT only passed the 2019 Japanese National Nursing Examination during the most recent 5 years. Although it did not pass the examinations from other years, it performed very close to the passing level, even in those containing questions related to psychology, communication, and nursing.

Background Tobacco smoking is an important public health issue and a core indicator of public health policy worldwide. However, global pandemics and natural disasters have prevented surveys from being conducted. Objective The purpose of this study was to predict smoking prevalence by prefecture and sex in Japan using Internet search trends. Methods This study used the infodemiology approach. The outcome variable was smoking prevalence by prefecture, obtained from national surveys. The predictor variables were the search volumes on Yahoo! Japan Search. We collected the search volumes for queries related to terms from the thesaurus of the Japanese medical article database Ichu-shi. Predictor variables were converted to per capita values and standardized as z scores. For smoking prevalence, the values for 2016 and 2019 were used, and for search volume, the values for the April 1 to March 31 fiscal year (FY) 1 year prior to the survey (ie, FY 2015 and FY 2018) were used. Partial correlation coefficients, adjusted for data year, were calculated between smoking prevalence and search volume, and a regression analysis using a generalized linear mixed model with random effects was conducted for each prefecture. Several models were tested, including a model that included all search queries, a variable reduction method, and one that excluded cigarette product names. The best model was selected with the Akaike information criterion corrected (AICC) for small sample size and the Bayesian information criterion (BIC). We compared the predicted and actual smoking prevalence in 2016 and 2019 based on the best model and predicted the smoking prevalence in 2022. Results The partial correlation coefficients for men showed that 9 search queries had significant correlations with smoking prevalence, including cigarette (r=–0.417, P<.001), cigar in kanji (r=–0.412, P<.001), and cigar in katakana (r=-0.399, P<.001). For women, five search queries had significant correlations, including vape (r=0.335, P=.001), quitting smoking (r=0.288, P=.005), and cigar (r=0.286, P=.006). The models with all search queries were the best models for both AICC and BIC scores. Scatter plots of actual and estimated smoking prevalence in 2016 and 2019 confirmed a relatively high degree of agreement. The average estimated smoking prevalence in 2022 in the 47 prefectures for the total sample was 23.492% (95% CI 21.617%-25.367%), showing an increasing trend, with an average of 29.024% (95% CI 27.218%-30.830%) for men and 8.793% (95% CI 7.531%-10.054%) for women. Conclusions This study suggests that the search volume of tobacco-related queries in internet search engines can predict smoking prevalence by prefecture and sex in Japan. These findings will enable the development of low-cost, timely, and crisis-resistant health indicators that will enable the evaluation of health measures and contribute to improved public health.