Pharmacotherapy最新文献

{"title":"AI in ID Pharmacotherapy-We Should Not Be Afraid to Put the Car Before the Horse.","authors":"Marc H Scheetz","doi":"10.1002/phar.70103","DOIUrl":"10.1002/phar.70103","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70103"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oral β-Lactams for Complicated Urinary Tract Infections: A Systematic Review and Point-Counterpoint Comparison With Trimethoprim/Sulfamethoxazole and Fluoroquinolones.","authors":"Ashlan J Kunz Coyne, Jeannette Bouchard, Spencer H Durham, Amy Y Kang, Kinsey M Johannemann, W Justin Moore, Kevin Nguyen, James Sanders, Colter Sheveland, Grant Stimes, Trang Trinh, Elizabeth W Covington","doi":"10.1002/phar.70118","DOIUrl":"10.1002/phar.70118","url":null,"abstract":"<p><strong>Background: </strong>Rising resistance and toxicity concerns with fluoroquinolones (FQs) and trimethoprim/sulfamethoxazole (TMP/SMX) have prompted interest in oral β-lactams as alternatives for complicated urinary tract infections (cUTIs). This review evaluates the effectiveness, safety, and pharmacokinetic (PK) considerations of oral β-lactams compared to FQs and TMP/SMX in these clinical contexts. A structured point-counterpoint discussion explores their role in antimicrobial stewardship and patient-centered therapy.</p><p><strong>Methods: </strong>A systematic PubMed search identified studies published between January 2000 and December 2025. Eligible studies included randomized trials and observational cohort studies evaluating clinical effectiveness, microbiological outcomes, or safety of oral β-lactams for cUTIs or bacteremic UTI compared with FQs or TMP/SMX. Outcomes of interest included treatment success, recurrence, adverse events, and PK considerations. Excluded studies involved special populations, nontraditional agents, or infections outside the scope of cUTI. Data were synthesized narratively due to heterogeneity in study design and outcome reporting.</p><p><strong>Results: </strong>Seventeen observational studies met the inclusion criteria. Oral β-lactams demonstrated comparable effectiveness to FQs and TMP/SMX for cUTIs, including bacteremic UTI, when dosed appropriately. Oral β-lactams with high oral bioavailability (e.g., cephalexin, cefpodoxime, cefuroxime, amoxicillin/clavulanate), showed success rates exceeding 90%, but recurrence was higher when underdosed. Oral β-lactams had lower adverse event rates (1.3%) compared to FQs (2.3%) and TMP/SMX (5.7%), but may carry limitations including frequent dosing, variable bioavailability, and susceptibility testing challenges.</p><p><strong>Conclusions: </strong>Although not traditionally recognized as first-line therapy, select oral β-lactams demonstrated effectiveness comparable to FQs or TMP/SMX for cUTIs and bacteremic UTI and can serve as effective alternatives in appropriately selected patients. When optimally dosed, these β-lactams achieve comparable outcomes with favorable safety profiles and antimicrobial stewardship advantages. Randomized trials are needed to confirm these observational findings and to better define optimal dosing strategies and shorter treatment durations.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 3","pages":"e70118"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146202224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immune Checkpoint Inhibitor Exposure During Pregnancy: A Systematic Review.","authors":"Casey L Keller, Kristina C Hermanson, Ryan M Schuller, Saeed K Alzghari","doi":"10.1002/phar.70115","DOIUrl":"10.1002/phar.70115","url":null,"abstract":"<p><p>Immune checkpoint inhibitors (ICIs) have been widely implemented in current oncology practice. However, there is limited data regarding ICI administration in pregnancy. This systematic review aims to evaluate the risk-benefit of ICI exposure in pregnant women. We conducted searches in databases including PubMed, Scopus, Web of Science, and Embase from January 1, 2011 through April 30, 2025. Included studies were those that involved women with a cancer diagnosis who received ICI treatment while pregnant and had clinical findings of the fetus post-ICI treatment. Methodological quality and potential sources of bias were assessed using Joanna Briggs Institute Critical Appraisal Tools. The search generated 2539 citations. After removal of 696 duplicates, a total of 1843 citations were screened. Twenty case reports and three retrospective studies were included in the systematic review. Fetal complications and fetal immune-related adverse events among the case reports were at 23% and 11.5%, respectively. Preterm delivery occurred in 54% of case reports, and no fetal mortalities were reported. Regarding the observational studies, preterm delivery occurred in 20.9%-25.5% of cases, fetal mortality occurred in 2.2%-15.3% of cases, intrauterine growth restriction occurred in 6.5%-7.1% of cases, and complications attributable to prematurity were reported in 2.6%-5.5% of cases. The data from this systematic review suggests that the risk for fetal complications may be lower than previously reported. As ICIs continue to expand their role in the treatment of malignancy, their use in pregnancy is more likely to come into clinical question. Clinicians should approach ICI use in pregnancy with individualized, multi-disciplinary risk-benefit discussions.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 3","pages":"e70115"},"PeriodicalIF":4.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Time-Dependent Nomogram for Predicting Hematological Toxicity Risk During Linezolid Therapy in Drug-Resistant Tuberculosis.","authors":"Wenqiang Zhou, Hui Zhu, Zhongquan Liu, Wenjuan Nie, Qingfeng Wang, Wenhui Shi, Yang Yang, Wenjie Qi, Yu Lu, Naihui Chu","doi":"10.1002/phar.70109","DOIUrl":"10.1002/phar.70109","url":null,"abstract":"<p><strong>Background: </strong>Linezolid is essential for drug-resistant tuberculosis (DR-TB) treatment but causes frequent hematological toxicity. We developed a time-dependent nomogram to predict this risk.</p><p><strong>Methods: </strong>A prospective cohort of 201 patients with DR-TB receiving linezolid (600 mg/day) was enrolled. Blood trough concentration (C_min) and clinical variables were measured. Multivariable Cox regression identified predictors, with a nomogram constructed to estimate toxicity probability at 1, 3, and 6 months. Model performance was assessed via calibration curves, C-index, and time-dependent area under the curve (AUC).</p><p><strong>Results: </strong>Ninety-six patients (47.8%) developed hematological adverse events (anemia: 26.4%, leukopenia: 14.4%, thrombocytopenia: 7.0%). Five predictors were significant: C_min > 2.08 mg/L [Hazard Ratio (HR) = 2.87, p < 0.001]; lower baseline white blood cells (HR = 0.84, p = 0.003), hemoglobin (HR = 0.99, p = 0.033), and creatinine clearance rate (HR = 0.99, p = 0.001); and initial treatment (HR = 0.56 vs. retreatment, p = 0.011). The nomogram showed good discrimination (C-index = 0.73) and calibration. Time-dependent AUCs were 0.74 (1-month), 0.79 (3-month), and 0.80 (6-month). Internal validation via bootstrapping (1000×) confirmed robustness.</p><p><strong>Conclusions: </strong>This nomogram, integrating C_min with baseline clinical factors, enables early identification of patients with DR-TB at high risk for hematotoxicity and could guide pre-emptive interventions. However, external validation is required to confirm its generalizability before widespread clinical implementation.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 3","pages":"e70109"},"PeriodicalIF":4.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Large Language Model-Influenced Bias on Trainees and Patients in Pharmacotherapeutic Decision Making.","authors":"Andrew Chantha Hean, Youngil Chang","doi":"10.1002/phar.70100","DOIUrl":"10.1002/phar.70100","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70100"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aspirin Deprescribing Interventions for Primary Prevention: A Systematic Review and Proposed Guidance for Deprescribing.","authors":"Alison Lobkovich, Hannah Ferrari, Amber Lanae Martirosov, Insaf Mohammad, Helen Berlie, Candice Garwood, Melissa Lipari","doi":"10.1002/phar.70121","DOIUrl":"10.1002/phar.70121","url":null,"abstract":"<p><p>Recent literature indicates that the benefits of aspirin use for primary prevention of atherosclerotic cardiovascular disease (ASCVD) may be equivocal to the risk. This has resulted in several organizations making updates to their clinical practice guidance on appropriate use. Despite this narrower scope, specific guidance on how to deprescribe aspirin in patients already on therapy for primary prevention is lacking. The purpose of this paper is two-fold: to provide a systematic review of the literature on aspirin deprescribing strategies and propose a practical aspirin deprescribing algorithm when used discordantly with current recommendations for primary prevention. We conducted searches in databases including EMBASE, PubMed, Web of Science, Scopus, and CINAHL from January 2018 to March 2025. One hundred and sixty-seven studies were screened, and 13 articles were included. The studies utilized a variety of interventions to impact aspirin deprescribing. In each of the studies included, there was at least some type of prompt to trigger deprescribing aspirin. However, a consistent approach was not defined. A multidisciplinary model occurred in half of the studies included, which resulted in the highest reported deprescribing rate of 78%. However, there were limited descriptions of patient-related outcomes as a result of deprescribing, warranting additional studies. A deprescribing algorithm can assist in guiding aspirin deprescribing for primary prevention of ASCVD while considering patient preferences in an individually tailored approach.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 3","pages":"e70121"},"PeriodicalIF":4.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146220720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Considerations for Menstrual Suppression in Patients With Hematologic Malignancies.","authors":"Sahaana Veeravalli, Allison Schepers, Lydia L Benitez, Martina S Fraga","doi":"10.1002/phar.70114","DOIUrl":"10.1002/phar.70114","url":null,"abstract":"<p><p>Patients who menstruate during treatment for hematologic malignancies have a higher risk of heavy vaginal bleeding due to thrombocytopenia caused by malignancy and myelosuppressive chemotherapy. Heavy menstrual bleeding is associated with significant morbidity in this patient population, and menstrual suppression is a standard of supportive care. Options for prophylactic menstrual suppression include gonadotropin-releasing hormone agonists, progestin-only therapy, and combined hormonal contraception. Various factors impact the choice of menstrual suppression agent, including efficacy, presence of emergent acute uterine bleeding, desire for contraception, and presence of thrombotic risk factors. The purpose of this review is to discuss the nuances of available menstrual suppression agents in patients with hematologic malignancies and develop a methodical approach to consider patient-specific factors that impact agent selection.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 3","pages":"e70114"},"PeriodicalIF":4.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12884572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Prediction of Prolonged Duration of Mechanical Ventilation Using Medication Data.","authors":"Brian Murray, Bokai Zhao, Zhetao Chen, Susan E Smith, Yanlei Kong, Ye Shen, Sheng Li, Xianyan Chen, Andrea Sikora","doi":"10.1002/phar.70090","DOIUrl":"10.1002/phar.70090","url":null,"abstract":"<p><strong>Introduction: </strong>Prediction algorithms for prolonged mechanical ventilation (PMV) in the intensive care unit (ICU) have rarely incorporated detailed medication data, despite medications being important causal contributors to patient outcomes. The purpose of this study was to develop and validate PMV prediction models to assess the contribution of medication-related variables alongside established physiologic predictors.</p><p><strong>Methods: </strong>In this retrospective cohort study, models were developed using data from a random sample of 318 adults admitted to ICUs within the University of North Carolina (UNC) health system who received mechanical ventilation for ≥ 24 h from October 2015 to October 2020. Validation was performed in two datasets: a temporally distinct cohort from UNC from June 2021 to June 2023, and a cohort from Oregon Health Sciences University from June 2020 to June 2023. Logistic regression and supervised, classification-based machine learning (ML) models [XGBoost, Random Forest, Support Vector Machine (SVM)] were trained on 30 demographic, clinical, laboratory, and medication-related variables. The primary outcome was area under the receiver operating characteristic (AUROC) of developed prediction models for the occurrence of PMV.</p><p><strong>Results: </strong>The base logistic regression model with medication regimen complexity and severity of illness data added was the best-performing regression model, achieving an AUROC of 0.75. Random Forest and SVM ML models achieved AUROCs of 0.78. Model discrimination decreased modestly in external validation. Explainability analyses of ML models expectedly included severity of illness scores and respiratory indices among the most important features, but also consistently included the medication regimen complexity-intensive care unit (MRC-ICU) score and other medication metrics. Incorporation of medication data yielded modest improvements in overall discrimination and negative predictive value.</p><p><strong>Conclusions: </strong>Medication-related variables contributed incremental value to PMV prediction. ML methods provided marginal improvements over regression models. These findings highlight the potential value of medication data in prediction modeling for patient outcomes but emphasize the need to contextualize the value of complex models over simpler alternatives.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70090"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145912636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Artificial Intelligence (AI)-Powered Self-Care Recommendations for Management of Minor Ailments: A Comparative Analysis.","authors":"Sham ZainAlAbdin, Saba Kendakji, Fatema Alrahbi, Kholoud Alazeezi, Maha Jawas, Shamma Alshamsi, Thuraya Almessabi, Nazar Zaki, Salahdein Aburuz","doi":"10.1002/phar.70089","DOIUrl":"10.1002/phar.70089","url":null,"abstract":"<p><strong>Introduction: </strong>Self-care and self-medication are increasingly viewed as helpful approaches to managing minor ailments; however, patients are often not confident in making informed choices. Pharmacists have traditionally assisted patients in this domain, but the emergence of digital health technologies has transformed the way individuals seek information towards the use of artificial intelligence (AI) tools. ChatGPT-4o mini, Gemini, and Copilot are recently growing popular for health-related guidance. Despite the accessibility and ease of use that these AI tools offer, their accuracy, patient-centeredness, and reliability in supporting self-care remain insufficiently evaluated.</p><p><strong>Aims and objectives: </strong>The primary objective of this study is to evaluate and compare the performance of ChatGPT-4o mini, Gemini, and Copilot in the context of patient self-care by assessing the accuracy, patient-centeredness, and comprehensiveness of their responses against standard recommendations.</p><p><strong>Materials and methods: </strong>Ninety-one case scenarios representing the most common minor ailments were introduced to the three AI models to generate responses that were subsequently assessed and compared with established standard recommendations by three of the study investigators. Evaluation of the responses was conducted on their accuracy, patient-centeredness, comprehensiveness, and similarity. An inter-reliability test was also carried out to confirm the consistency between the three evaluators' assessments.</p><p><strong>Results: </strong>The study findings indicate that ChatGPT-4o mini significantly exceeded Gemini and Copilot in terms of accuracy and presented as mean ± SD (ChatGPT-4o mini: 4.4 ± 0.6, Gemini: 4.1 ± 0.8, Copilot: 3.7 ± 0.7, p < 0.001), patient-centeredness (ChatGPT-4o mini: 4.7 ± 0.6, Gemini: 4.3 ± 1.0, Copilot: 4.2 ± 0.8, p < 0.001), and comprehensiveness (ChatGPT-4o mini: 4.6 ± 0.7, Gemini: 4.2 ± 0.8, Copilot: 3.4 ± 0.7; p < 0.001) among 91 minor ailment case scenarios. Gemini and Copilot showed moderate and low performance, respectively, particularly in complex cases, in contrast to ChatGPT-4o mini. Inter-rater reliability was excellent (Cronbach's alpha ≥ 0.9), confirming assessment consistency. Cosine similarity analysis indicated high overlap between AI and standard recommendations.</p><p><strong>Conclusion: </strong>This study shows that AI tools are reliable and precise instruments for self-care of mild diseases. These findings highlight ChatGPT-4o mini's superior reliability and patient-centeredness for self-medication guidance, while underscoring the need for human oversight. However, there is a small chance of variation and errors in the AI-generated responses, which may prohibit complete dependence on AI for self-care recommendations.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70089"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145669336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"C-Reactive Protein and Neutrophil-To-Lymphocyte Ratio: Can They Be Used Interchangeably in Tracking Clozapine-Related Inflammation?","authors":"Nicoline Bihelek, Chad A Bousman, William G Honer, Reza Rafizadeh","doi":"10.1002/phar.70094","DOIUrl":"10.1002/phar.70094","url":null,"abstract":"<p><strong>Background: </strong>Clozapine initiation often triggers inflammatory responses that can alter metabolism via Cytochrome P450 1A2 (CYP1A2) suppression. Although C-reactive protein (CRP) is the recommended marker, it may be unavailable in community settings. Neutrophil-to-lymphocyte ratio (NLR), routinely measured, could serve as a surrogate, though its value in detecting clozapine-related inflammation and metabolic changes remains unclear.</p><p><strong>Aims: </strong>This study aimed to assess the relationship between CRP and NLR in individuals treated with clozapine, evaluate whether NLR can act as a proxy for elevated CRP (> 5 mg/L), and determine whether NLR, like CRP, explains variability in clozapine metabolism (concentration to dose (C/D) ratios) after adjusting for covariates.</p><p><strong>Methods: </strong>We performed a retrospective cohort study of clozapine-treated inpatients at the British Columbia Psychosis Program (2012-2021). Patients with clozapine levels and matched complete blood counts (CBCs) (±7 days) were included, with CRP added when available. Multivariate mixed models assessed associations between CRP, NLR, and clozapine C/D ratios, while receiver operating characteristic (ROC) analyses evaluated NLR as a proxy for elevated CRP.</p><p><strong>Results: </strong>Among 150 patients, 760 clozapine serum/CBC pairs and 212 CRP measurements met eligibility criteria. NLR was modestly associated with CRP (estimate = 0.027, p < 0.001). ROC analysis indicated that NLR had limited predictive utility, with an area under the curve (AUC) of 0.640 for detecting CRP > 5 mg/L. Subsequent analyses for higher CRP thresholds (> 10 and > 20 mg/L) produced comparable NLR AUC values of 0.621 and 0.669, respectively. Neutrophil count alone demonstrated marginally better performance but remained similarly limited in predictive value. In multivariate models, CRP but not NLR, was independently associated with clozapine C/D ratios.</p><p><strong>Conclusion: </strong>Our findings indicate that although NLR and other hematological indices are easily accessible and may provide some indication of inflammation, they cannot substitute for CRP in guiding clozapine titration decisions. Where CRP is unavailable, NLR > 3 may be cautiously informative, though CRP remains the preferred marker for early detection and dose adjustment to optimize tolerability, adherence, and safety during clozapine initiation.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70094"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12862047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145834617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}