論文

Vancomycin (VCM) is essential for treating methicillin-resistant Staphylococcus aureus infections, but its use is limited by vancomycin-induced nephrotoxicity (VIN). Although preclinical and small clinical studies suggest a protective role for vitamin E, real-world evidence remains limited. We conducted a retrospective cohort study using a large-scale Japanese electronic medical record database including adult patients who received intravenous VCM between 1996 and 2022. VIN was defined according to Kidney Disease Improving Global Outcomes criteria. The association between concomitant vitamin E use and VIN was evaluated using propensity score matching based on logistic regression and light gradient boosting machine models, followed by Kaplan–Meier analysis and Cox proportional hazards regression. Robustness was examined using three analytical strategies, including adjustment for concomitant medications. Among 12 411 eligible patients, 2267 (18.3%) developed VIN and 116 (0.9%) received vitamin E. After matching, vitamin E use was associated with a 10% lower incidence of VIN. Kaplan–Meier analysis showed significantly lower cumulative incidence in the vitamin E group in two approaches. Across 63 Cox models, vitamin E consistently demonstrated a protective trend. These findings support the potential preventive role of vitamin E in maintaining VCM therapy.

attention、relevance、confidence、satisfaction(ARCS)モデルに基づいて、お薬手帳の主体的活用をテーマとした学習プログラム(すごろく形式)を設計・開発し、医薬品を服用している研究対象者11名(男性4名、50～70歳代10名)に実施してもらった。プログラム実施直前、実施直後、実施後1ヵ月に医薬品の適正使用に対する意識や行動の変化を調査し、ARCSモデルの4要素について5段階セマンティック・デファレンシャル法により評価した。その結果、平均評点4点以上であった。プログラム実施前後で参加者のお薬手帳の活用を含む医薬品適正使用に関連する意識と行動意欲の向上がみられ、一部の参加者では実際の行動変容も観察された。動機づけ設計を基盤とする本アプローチは、一般市民の主体的な健康管理への関与を促す有効な手段となり得ることが示唆された。

Background: Declining medication adherence remains a critical health care issue, often assessed through unreliable self-reporting methods. Wearable devices (WDs) may offer an objective means to improve adherence monitoring by continuously recording physiological and activity data. Objective: This study aimed to develop and internally validate personalized predictive models, utilizing objective physiological and activity data from WDs, for identifying missed medication doses. Methods: A 30-day prospective observational study was conducted with 8 participants who wore Apple Watches and used a dedicated iOS app. The app collected demographics, medication details, psychological factors, mealtimes, and daily missed dose events. WDs recorded time-series data (ie, activity, heart rate, sleep) at 3-minute intervals. Data were aggregated into 1-hour segments, and lag (6 and 12 h) as well as rolling (24 h) features were generated. Light Gradient Boosting Machine models were constructed for each individual’s dosing regimen if the missed dose rate exceeded 20%. Two modeling approaches were compared: a group cross-validation (CV) model that grouped data by day to avoid data leakage from rolling features, and a nonrolling feature model that excluded rolling features and used leave-one-out CV. F1-score, accuracy, recall, and precision were assessed between the 2 models. Results: Of the 15 enrolled participants, 8 completed the study; 4 had a missed dose rate above 20%. In these 4 individuals, the group CV model achieved F1-scores of 0.435 to 0.902, with accuracy ranging from 0.711 to 0.911, recall from 0.278 to 0.822, and a precision of 1.000 for the most robust regimens. The nonrolling feature model yielded F1-scores of 0.667 to 0.910, with accuracy ranging from 0.800 to 0.906, recall from 0.500 to 0.835, and a precision of 1.000. Morning dosing regimens generally showed higher predictive performance than evening or afternoon. Time-series features, particularly those reflecting 6-, 12-, and 24-hour patterns, emerged as key predictors, indicating that physiological and lifestyle variations prior to dosing strongly influenced missed dose events. Conclusions: Personalized predictive models using WD-derived data demonstrated high precision for detecting missed medication doses, especially in morning and evening regimens. These findings underscore the feasibility of employing continuous, objective physiological and activity data from WDs to forecast nonadherence events. Although the sample size was limited, restricting the generalizability of the results, this study demonstrates the potential of WD-based personalized prediction of medication adherence. Future work should involve larger populations for external validation, strategies to improve recall, especially for clinically critical medications, and careful consideration of real-world implementation challenges.

Background The Roter Interaction Analysis System (RIAS) is the gold standard for medical communication analysis, but its automated coding remains underexplored due to imbalanced code distributions. This study developed an automated RIAS classification system using Japanese transformer-based models and evaluated AI-based data augmentation to mitigate class imbalance. Methods Five transformer models were fine-tuned for 44-class RIAS classification using medication counseling dialogues. To enhance generalizability, we employed AI-based data augmentation and evaluated performance using both AI-augmented and real-data-only test sets. Assessment metrics included accuracy, macro F1, and weighted F1. Findings The dataset comprised 17,391 utterances (39.4% AI-generated). In the real-data-only (primary) test set, ELECTRA achieved the highest accuracy (0.7875), macro F1 (0.6561), and weighted F1 (0.7835). All models performed worse under the real-data-only condition than under the AI-augmented condition, mainly for minority-class categories. Error analysis showed semantically ambiguous and context-dependent categories, including domain-adjacent counseling topics and affective expressions, remained challenging, indicating linguistic transparency and definitional distinctiveness influence classification beyond training frequency. Conclusion This proof of concept shows that AI-based augmentation can mitigate class imbalance in automated RIAS classification of medication counseling dialogues. While robust for prototypical expressions, nuanced affective categories remain challenging for text-only approaches. These findings support the feasibility of automated RIAS analysis for pharmacy education while suggesting that multimodal approaches are needed to capture subtle emotional dynamics. By complementing manual coding with quantitative insights, this system may support timelier and objective feedback and ultimately contribute to patient-centered care.

Background: Cancer survivors often experience complex and coexisting emotions throughout diagnosis, treatment, and posttreatment life. Emotion classification of patient narratives may help in understanding survivorship experiences; however, evidence remains limited for multidimensional classification using cancer survivor interview narratives. Objective: This study aimed to develop and evaluate natural language processing–based emotion classification models using Japanese cancer survivor interview narratives and to examine whether polarity and multidimensional emotion labels provide complementary perspectives. Methods: We analyzed verbatim transcripts from 15 cancer survivor interviews published by the Cancer Note, Nonprofit Organization. Survivor utterances were extracted, noninformative conversational elements were removed, texts were segmented at Japanese punctuation marks, and 5 consecutive sentences were grouped into 1 chunk. Two annotators labeled 1998 text chunks with 3-class sentiment polarity labels (positive, neutral, or negative) and multilabel Plutchik 8-emotion labels (joy, trust, fear, surprise, sadness, disgust, anger, and anticipation). Japanese BERT (Bidirectional Encoder Representations from Transformers) and LUKE (Language Understanding with Knowledge-based Embeddings) were fine-tuned to build a multiclass polarity classifier and a multilabel 8-emotion classifier. Performance was evaluated using precision, recall, F1-score, macroaveraged metrics, Micro-F1 for polarity, and Hamming loss for multilabel classification. For comparison, the same architectures were fine-tuned on WRIME (writers’ and readers’ intensities of emotion for their estimation), a Japanese social media emotion dataset, and evaluated on Cancer Note texts as a domain-transfer analysis. The 95% CIs were estimated using bootstrap resampling with 1000 iterations. Results: Neutral was the most frequent polarity label, trust was the most frequent 8-emotion label, and anger was the least frequent emotion label. Label distributions were imbalanced, with most-to-least frequency ratios of 3.47 for polarity and 8.10 for 8-emotion labels. In the 3-class sentiment polarity task, interview-trained models outperformed WRIME-trained transfer models. Interview Text-BERT achieved the highest micro-F1 of 0.696 (95% CI 0.676‐0.716), whereas Interview Text-LUKE achieved the highest macro-F1 of 0.660 (95% CI 0.639‐0.682). In the 8-emotion multilabel task, Interview Text-LUKE achieved the highest macro-F1 of 0.427 (95% CI 0.398‐0.453) and the lowest Hamming loss of 0.078 (95% CI 0.073‐0.082). WRIME-trained transfer models showed lower performance, particularly in the 8-emotion task. Sadness and trust co-occurred most frequently, suggesting that positive and negative emotional elements may coexist in the same narratives. Conclusions: This exploratory study suggests the feasibility of domain-specific emotion classification for Japanese cancer survivor interview narratives. Models fine-tuned on target-domain narratives generally outperformed WRIME-trained transfer models, although the best architecture differed by task and metric. Polarity labels and Plutchik 8-emotion labels provided complementary perspectives on complex and coexisting emotions in survivorship narratives. However, performance for rare emotions remained limited, and the models should be regarded as preliminary research tools rather than clinically actionable systems. Larger, more diverse, prospectively or externally validated datasets, imbalance-aware methods, and user-centered evaluation are needed before clinical translation.

Purpose: Certain women with heart disease require lifelong antithrombotic therapy. However, the impact of such therapy during pregnancy on maternal and neonatal outcomes remains unclear. This study aimed to investigate the prescription patterns and clinical outcomes associated with antithrombotic therapy in pregnant women with heart disease. Methods: Using a Japanese administrative claims database, we identified pregnant women diagnosed with valvular heart disease or complex congenital heart disease between January 2008 and July 2023. In this retrospective observational study, we compared maternal clinical outcomes between women who were prescribed antithrombotic drugs during pregnancy and those who were not. For infants who could be linked to their mothers in the database, neonatal outcomes were also assessed. Results: A total of 550 pregnant women with a history of heart disease were identified, of whom 90 (16.4%) received antithrombotic therapy during pregnancy. Women who received antithrombotic therapy had a higher proportion of cesarean delivery (90.0% vs. 49.8%, p < 0.001). Intensive care unit (ICU) admission rate was significantly higher in this group (24.4% vs. 10.5%, p < 0.001), as was critical obstetric bleeding (13.3% vs. 1.7%, p = 0.009). No in-hospital maternal deaths or fatal arrhythmias were observed. Among the neonates associated with their mothers, a total of 238 were identified. Admission to a neonatal ICU was significantly more frequent among infants born to women who received antithrombotic therapy (35.7% vs. 13.3%, p = 0.001). Conclusion: Although careful maternal and neonatal management is necessary, no in-hospital maternal deaths or severe cardiovascular events were observed among women prescribed antithrombotic therapy. This study provides valuable evidence to inform clinical decision-making in pregnant women with valvular heart disease requiring antithrombotic therapy in Japan.

Subjective adverse events (AEs), such as pain, are often under-recognized when relying solely on structured data. Although natural language processing (NLP) enables the extraction of AEs from narrative electronic health records (EHRs), interpretation of their temporal dynamics is difficult. Visualization methods can bridge this gap by transforming text-derived symptom data into clinically interpretable data. This study aimed to demonstrate the clinical value of a framework integrating NLP-based AE extraction with time-series visualization for otherwise invisible symptoms. Narrative texts, including progress notes, nursing records, and discharge summaries, were processed using MedNERN-CR-JA, a pretrained Japanese BERT-based model for entity recognition. AEs were visualized using Kaplan–Meier curves to represent the time to first onset and heatmaps, displaying all subsequent symptom documentation alongside supportive medication use. Among the 35042 eligible patients, 3094 received paclitaxel (PTX) and were matched to 3094 controls. PTX was associated with a higher risk of musculoskeletal symptoms (hazard ratio, 1.77; 95% confidence interval: 1.57–1.99). Kaplan–Meier curves showed earlier onset in PTX recipients, while heatmaps depicted recurrent documentation and the corresponding analgesic use. Restricting the analyses to the triweekly PTX regimen reduced the heterogeneity between inpatient and outpatient documentation and revealed a clearer alignment between the treatment cycles and acute symptom onset. This framework demonstrates the clinical value of visualizing NLP-extracted AEs from narrative EHRs, improving the resolution of subjective AE data, enhancing monitoring, and supporting patient-centered care and clinical decision making through complementary time-to-event and heatmap visualizations.

Maintaining medication adherence remains a major clinical challenge, as higher daily dosing frequencies are often associated with decreased adherence. Although once-daily regimens are generally preferred, specific patient preferences regarding dosing frequency are unclear, despite implications for improving patient satisfaction and optimizing pharmacotherapy. Here, we evaluated the frequency at which patients begin to perceive dosing as excessive. A web-based questionnaire survey using the personal health record infrastructure of electronic medication notebooks was administered between July 20 and 26, 2023. Eligible participants were aged ≥20 years and had received oral tablets or capsules within 90d of the survey. The questionnaire consisted of 6 items, including whether participants felt that the daily medication frequency was excessive. A multivariate logistic regression analysis was performed to identify the frequency beyond which patients perceive dosing as excessive, adjusting for confounders, such as medication formulation, number of medications, and patient characteristics. Of 1478 respondents, 1236 were included in the analysis. In total, 28.9% of participants reported that their medication frequency felt excessive. In the multivariate logistic regression analysis, twice-daily or more frequent dosing was significantly associated with the perception of excessiveness, using once-daily dosing as the reference. Twice-daily dosing is the frequency beyond which patients are significantly more likely to perceive medication as excessive. Our findings emphasize the importance of simplifying dosing regimens.

Background: The validity of the International Classification of Diseases 10th Revision (ICD-10) codes for chronic kidney disease (CKD) in Japan have not been evaluated. In this study we evaluated the validity of CKD-related ICD-10 codes in Japan. Methods: This retrospective cohort study used the JMDC hospital-based database, which comprises claims and laboratory data from over 1,000 medical institutions in Japan. Patients who underwent two serum creatinine measurements between April 2014 and August 2022 were identified; the second measurement was obtained 90–365 days after the first. The estimated glomerular filtration rate (eGFR) was calculated. CKD was defined by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria: an eGFR < 60 mL/min/1.73 m2 for both measurements. Markers of kidney damage such as albuminuria were unavailable. Patients assigned CKD-related ICD-10 codes (N183, N184, N185, N189, N19, E102, E112, E142, and I120) within 365 days of the initial serum creatinine measurement were identified. Subsequently, the positive predictive value (PPV), sensitivity, specificity, and negative predictive value (NPV) were calculated. Results: Among the 618,208 included patients, 59,139 were assigned CKD-related ICD-10 codes and 172,657 met the KDIGO criteria. The overall PPV, sensitivity, specificity, and NPV were 57.9%, 19.8%, 94.4%, and 75.2%, respectively. In contrast, the PPVs of N183, N184, N185, and N189 exceeded 80%. Conclusions: In Japan, the PPV of the ICD-10 codes for CKD was 57.9%, marginally lower than values reported in the United States (86.1%) and Canada (60.1%). Several ICD-10 codes may be useful for identifying patients with CKD, despite their limited sensitivity.

Analyzing medication-related incident reports is crucial for patient safety; however, systematically extracting the underlying factors contributing to incident occurrence remains challenging. We developed a multi-label classifier that automatically identified incident factors from 1,212 drug-related incident reports using the Bidirectional Encoder Representations from Transformers and its derivatives. Based on the P-mSHELL model, a comprehensive framework for incident factor analysis, we established seven distinct factor categories and evaluated various pre-trained models through five-fold cross-validation. Almost all models achieved macro F1 scores exceeding 0.6, with the lightweight A Lite BERT model showing comparable performance to BERT. This study demonstrates the practical feasibility of natural language processing techniques for systematic incident factor analysis, supporting enhanced patient safety management.

Background: The concept of shifting from a patient-centered to person-centered approach in pharmacy practice has been proposed. Patient-centered care focuses on the individual patient, whereas person-centered care focuses on a holistic understanding of the individual, taking not only medications but also daily life into account. This shift requires effective communication between patients and pharmacists. Although question prompt lists (QPLs) exist for patients and doctors, the same is not available for pharmacists focused on a person-centered approach. This study aimed to develop a question prompt list to talk with your pharmacists (QPLP) focusing on a person-centered approach to facilitate communication with patients. Methods: This study aimed to develop the QPLP in three steps. In the first step, six medical pharmaceutical researchers with pharmacist qualifications and two patient researchers prepared an initial draft of the QPLP, referencing existing QPLs used by patients to prepare questions for doctors before consultation. A focus group interview was then conducted with eight patients, and a QPLP drafted. Finally, a modified Delphi method was used to evaluate and collect opinions, and the QPLP was finalized. Results: A QPLP comprising 16 questions was developed with patient participation in a three-step process. The content was categorized into five sections: “Medicines,” “How to Take Medicines,” “Daily Life,” “Treatment,” and “Consumer Health Information.” The questions covered concerns regarding medicines, difficulties in using medicines, issues in daily life during treatment, treatment-related problems, authenticity of health information, and community health and exercise information. Conclusions: In this study, we developed a novel QPLP to enhance communication between patients and pharmacists, focusing on a person-centered approach with active patient involvement. Future studies should investigate the contribution of the developed QPLP in improving patient–pharmacist communication. These questions may encourage both healthy people and patients to seek health advice from pharmacists.

Purpose: We conducted a comprehensive analysis of esketamine-related adverse events (AE) on the FDA Adverse Event Reporting System (FAERS) database, taking into account for the first time drug-drug interaction signals. Methods: We conducted a retrospective case/non-case study of esketamine-related AEs reported in the FAERS database up to the last quarter (Q4) of 2024. Potential signals were detected using the reporting odds ratio (ROR) and confidence intervals (CI), while drug-drug interactions were studied using different metrics such as lift, conviction and the combination risk ratio detection algorithm. An analysis of sex differences was also performed using the relative ROR and CI. Results: The analysis of 7,790 reports in which esketamine was a primary or secondary suspect identified potential safety signals for 173 AEs. Novel signals include homicidal ideation (ROR = 5.30, 95% CI: 2.38-11.82) and substance use disorder (ROR = 6.12, 95% CI: 2.54-14.73). Women showed a longer time to onset than men (p = 0.003). In addition, we detected sex differences in 23 AEs, seven of which were more likely to be reported in women, while 16 in men. Among these, four were significant exclusively in women (oxygen saturation decreased, abnormal behaviour, unresponsive to stimuli and aggression) and two in men (vision blurred and bradycardia). Potential signals of additive and multiplicative drug-drug interactions were detected for antidepressants (venlafaxine for“dizziness” and bupropion for “agitation”) and antipsychotics (risperidone for “vertigo”). Conclusions: Our results increase knowledge on potential risks related to esketamine AEs and potential drug-drug interaction signals in a real-world setting.

Background Preventing chemotherapy-related adverse events (AEs) remains an unmet clinical challenge. Preclinical studies have suggested protective effects of several existing agents, but translation into human evidence has been limited. We aimed to establish proof of concept (PoC) for drug repurposing by applying a natural language processing (NLP)-based framework to electronic health record (EHR) narratives, thereby bridging preclinical findings with clinical validation. Methods We retrospectively analyzed 56,326 patients with cancer treated at the University of Tokyo Hospital (2004–2023). A transformer-based NLP model extracted symptomatic AEs from clinical notes. Candidate preventive drugs identified from preclinical evidence were assessed using propensity score matching and Cox proportional hazards models. We evaluated angiotensin II receptor blockers (ARBs) for fluoropyrimidine-induced oral mucositis and ramelteon for platinum-induced peripheral neuropathy, with laxatives serving as a negative control. Results NLP demonstrated high accuracy (precision 0.81–0.83; recall 0.95–0.97). After matching, ARB co-administration was significantly associated with reduced mucositis incidence (hazard ratio [HR] 0.58, 95 % confidence interval [CI] 0.44–0.77; P < 0.001), representing a clinical PoC consistent with mechanistic preclinical data. Ramelteon showed an exploratory protective signal against neuropathy (HR 0.60, 95 % CI:0.38–0.93; P = 0.024). No preventive association was observed for laxatives. Conclusions This study introduces a scalable NLP-epidemiology framework for non-invasive, real-world validation of drug repurposing candidates. The ARB finding provides human-level PoC evidence supporting prospective clinical testing, while the ramelteon signal warrants further exploration. Our approach demonstrates how EHR narratives can operationalize translational research, prioritizing safe, accessible agents for improving the tolerability of cancer treatment.

Background: The clinical implementation of prediction models can face important barriers, particularly regarding user-friendliness and interpretability for healthcare professionals. We recently developed and externally validated a risk prediction model for denosumab-induced hypocalcemia. The present study aimed to evaluate the model’s utility and identify challenges for its clinical implementation through pilot testing conducted by hospital pharmacists. Methods: A paper-format prediction model was distributed to pharmacists at Kameda General Hospital, Miyagi Cancer Center, and Gunma Prefectural Cancer Center. Participants trialed the model outside their routine workflow by applying it to data from patients scheduled to receive their first dose of denosumab. A subsequent questionnaire survey, available in paper and electronic formats, was conducted to gather feedback on the model’s utility and limitations. Results: A total of 49 responses were obtained, predominantly from pharmacists in their 20 s and 30 s with diverse professional responsibilities. The model was positively evaluated in terms of its clear target population, predicted outcome (47/49, 95.9%), and simple, straightforward nature (47/49, 95.9%). However, some participants provided neutral feedback on its ease of use (10/49, 20.4%). While its potential as an auxiliary tool for risk prediction was acknowledged, there were also some neutral views on its practical utility. One concern was the inconvenience of implementing a paper-format tool in clinical environments that primarily operate on electronic health records. Conclusions: The paper-format prediction model was positively evaluated by frontline pharmacists, especially for its clear and straightforward nature. As anticipated, however, limitations such as manual data input and paper-based format were identified by some participants. Integration into electronic health records and broader clinical validation will be necessary to advance the clinical application of this prediction model and ensure real-world applicability.

This study assessed the effectiveness of natural language processing (NLP) in detecting adverse events (AEs) from anticancer agents by analyzing data from over 39,000 cancer patients. A specialized machine learning model identified known AEs from anticancer agents like capecitabine, oxaliplatin, and anthracyclines, revealing a significantly higher incidence in the treatment groups compared to non-users. While the NLP approach effectively detected most symptomatic AEs requiring manual review, it struggled with rarely documented conditions and commonly used clinical terms. Overall, the method shows promise for automated AE detection in medical records, particularly for symptoms without laboratory markers or diagnosis codes.

PURPOSECapecitabine, an oral anticancer agent, frequently causes hand-foot syndrome (HFS), affecting patients' quality of life and treatment adherence. However, such symptomatic toxicities are often difficult to detect in structured electronic health record (EHR) data. This study primarily aimed to validate a natural language processing (NLP) approach to identifying capecitabine-induced HFS from unstructured clinical text and demonstrate its application in evaluating medication-associated adverse event trends in real-world settings.METHODSWe conducted a retrospective cohort study using EHRs from the University of Tokyo Hospital (2004-2021). HFS cases were identified using the MedNERN-CR-JA NLP model. After propensity score matching, we compared capecitabine users with and without celecoxib and assessed time to HFS onset using Cox proportional hazards models. NLP-based HFS detection was validated through manual annotation of aggregated clinical notes. Negative control and sensitivity analyses ensured robustness.RESULTSAmong 44,502 patients with cancer, 669 capecitabine users were analyzed. HFS incidence was significantly higher among capecitabine users (hazard ratio [HR], 1.93 [95% CI, 1.48 to 2.52]; P <.001) compared with nonusers. Celecoxib use showed a suggestive association with a reduced HFS risk (HR, 0.51 [95% CI, 0.24 to 1.07]; P =.073). The NLP model demonstrated high accuracy in identifying HFS, achieving a precision of 0.875, recall of 1.000, and F1 score of 0.933, based on manual annotation of patient-level clinical notes. Outcome trends remained consistent when using manually annotated HFS case labels instead of NLP-detected events, supporting the method's robustness.CONCLUSIONThese findings demonstrate the effectiveness of NLP in detecting HFS from real-world clinical records. The application to celecoxib-HFS detection illustrates the potential utility of this approach for retrospective safety analysis. Further work is needed to evaluate generalizability across diverse clinical settings.

医薬品の販売名の類似性(薬名類似)に起因する処方時の薬剤選択間違え(薬剤誤処方)の傾向を分析するため、薬局ヒヤリ・ハット事例から抽出された薬名類似に起因する薬剤誤処方234件、調剤時の医薬品の取り違え(薬剤誤調剤)152件と、薬名類似起因に限定しない薬剤誤処方3522件および薬剤誤調剤329件を解析対象として、「本来処方または調剤されるべき薬剤」と「誤処方または誤調剤された薬剤」のペアの特徴を比較した。m2-vwhtfrag値と先頭一致文字数、薬効の類似性を評価した。その結果、薬名類似に起因する薬剤誤処方群では薬剤誤調剤群よりも薬効分類Sameが少なく、先頭一致文字数3以上が多い傾向にあった。医師による薬剤誤処方は、薬剤師による誤調剤よりも薬名類似の影響を受けやすく、薬効類似の影響を受けにくい可能性が示された。