Pharmacotherapy最新文献

{"title":"Genotype Differences and Hydroxyurea Utilization Among Adults With Moderate to Severe Sickle Cell Disease.","authors":"Siang-Hao Cheng, Enrico M Novelli, Hyeun Ah Kang, Terri V Newman, Kangho Suh","doi":"10.1002/phar.70099","DOIUrl":"10.1002/phar.70099","url":null,"abstract":"<p><strong>Backgrounds: </strong>Hydroxyurea (HU) remains underutilized in adults with sickle cell disease (SCD) despite proven benefits. Current HU guidelines primarily target sickle cell anemia (SCA), overlooking other genotypes.</p><p><strong>Objectives: </strong>This study examined HU utilization patterns across genotypes among adults considered to have moderate to severe SCD manifestations by the 2014 National Heart, Lung, and Blood Institute (NHLBI) guideline criteria and identified factors associated with early HU use.</p><p><strong>Methods: </strong>This retrospective cohort study analyzed electronic health records from the University of Pittsburgh Medical Center (2014-2024) of adults with SCD experiencing three or more vaso-occlusive crises (VOCs) within 12 months. HU utilization rates, stratified by genotype, were assessed at 30-, 90-, 180-, and 365-day intervals after the third VOC episode (index date). Multivariable logistic regression was used to identify factors associated with HU use within 90 days post-index.</p><p><strong>Results: </strong>Among 411 adults with moderate to severe SCD (≥ 3 VOCs within a year), with a mean age of 42.4 ± 17.9 years and 61.3% female, only 19.5% received HU within 90 days post-index. Although 42.8% of SCA patients received HU within 1 year, only 8.0% of non-SCA patients received the treatment. The SCA genotype was the strongest predictor of HU use (odds ratio [OR] = 4.5, 95% confidence interval [CI]: 2.4-8.7), followed by pulmonary complications. Additional barriers included older age.</p><p><strong>Conclusion: </strong>Despite guideline recommendations since 2014, HU remains underutilized. Non-SCA patients meeting the severity threshold for HU use are consistently undertreated, highlighting an urgent need for studies establishing HU safety and efficacy in non-SCA genotypes. Future studies should also address age barriers to optimize HU use.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 1","pages":"e70099"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12800872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966518","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":"Medication Dosing by Advanced Technologies: Promise on the Horizon but Not Without Risk.","authors":"William E Dager, Jeffery F Barletta, Brian L Erstad","doi":"10.1002/phar.70104","DOIUrl":"https://doi.org/10.1002/phar.70104","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 1","pages":"e70104"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966530","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":"Response to Comment on \"Acute Pharmacodynamic Effects of Oral Levodopa on Blood Pressure in Parkinson's Disease\".","authors":"Katherine Longardner, Cat Liu, Jeremiah D Momper, Kuldeep Mahato, Chochanon Moonla, Hamidreza Ghodsi, Joseph Wang, Irene Litvan","doi":"10.1002/phar.70106","DOIUrl":"https://doi.org/10.1002/phar.70106","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 1","pages":"e70106"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145998725","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":"Utilization of dd-cfDNA Monitoring to Facilitate Immunosuppression Minimization After Kidney Transplantation in a U.S. Veteran Population.","authors":"Laura Cotiguala, Anne Przybylski, Cara Joyce, Gabrielle Hansen, Nicholas Shah, Reynold I Lopez-Soler","doi":"10.1002/phar.70102","DOIUrl":"10.1002/phar.70102","url":null,"abstract":"<p><strong>Background: </strong>Although donor-derived cell-free DNA (dd-cfDNA) serves as a monitoring tool for rejection, few studies have examined its utility in guiding immunosuppression management. Here, we present the largest kidney transplant population in which immunosuppression minimization and subsequent surveillance were guided by dd-cfDNA.</p><p><strong>Methods: </strong>This retrospective case series evaluated our immunosuppression minimization practice to tacrolimus and prednisone in kidney transplant recipients (KTR) from November 20, 2020, to September 26, 2024. Baseline dd-cfDNA ≤ 0.5% was required before minimization. Immune tolerance was defined by the absence of any immune event after minimization: absolute dd-cfDNA > 0.5%, relative change value (RCV) > 60% from baseline, biopsy-proven acute rejection (BPAR), or de novo donor-specific antibody (DSA). All other KTR were labeled intolerant. The primary endpoint was the rate of immune tolerance.</p><p><strong>Results: </strong>Immunosuppression was modified to tacrolimus and prednisone in 38 KTR at a median of 223 days post-transplant. Most KTR were older adults at low immunological risk: mean of 69 years and all had a calculated panel reactive antibody of 0%. 21 (55.3%) KTR met the primary end point of tolerance. The remaining 17 KTR were labeled intolerant secondary to dd-cfDNA elevations including absolute > 0.5% or RCV > 60% (n = 16 of 17, 94%), de novo DSA (n = 2 of 17, 11.8%), and/or BPAR (n = 4 of 17, 23.5%). Although not statistically significant, intolerant KTR were numerically more likely to have 5-6 HLA mismatches (82.5% vs. 52.4%, p = 0.31), less likely to have thymoglobulin induction (29.4% vs. 42.9%, p = 0.39), and were minimized earlier after transplant (196 vs. 256 days, p = 0.08) compared with tolerant KTR, respectively. Intervention after dd-cfDNA elevations included immunosuppression increase (50%), additional dd-cfDNA monitoring (81.3%), DSA testing (50%), and allograft biopsy (18.7%).</p><p><strong>Conclusion: </strong>Approximately 50% of low immunological risk KTR with a baseline dd-cfDNA < 0.5% tolerated immunosuppression minimization to tacrolimus and prednisone without concerning dd-cfDNA elevations, BPAR, or DSA. Our study highlights the role of dd-cfDNA as part of the armamentarium for identifying minimization candidates and performing subsequent surveillance.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 1","pages":"e70102"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146019291","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":"Association of an Algorithm-Generated Medication Optimization Score With Clinical Outcomes in Ambulatory Patients With Heart Failure.","authors":"Mohamed S Ali, Kaitlyn M Greer, Sabah Ganai, Todd M Koelling, Scott L Hummel, Michael P Dorsch","doi":"10.1002/phar.70101","DOIUrl":"10.1002/phar.70101","url":null,"abstract":"<p><strong>Aims: </strong>Guideline-directed medical therapy (GDMT) implementation in heart failure with reduced ejection fraction (HFrEF) remains suboptimal. A computable algorithm was developed to generate a medication optimization score (MOS) and provide guideline-based recommendations. This computable algorithm was previously validated using clinical trial data, and an updated version was developed in 2021 to include sodium-glucose co-transporter 2 inhibitors. This study evaluated the association between the medication optimization information generated by this version of the algorithm and clinical outcomes using real-world data.</p><p><strong>Methods: </strong>We conducted a retrospective cohort study of 1352 ambulatory adult patients with chronic HFrEF who received care from the advanced heart failure service at the University of Michigan between July 1, 2021, and October 14, 2024. The algorithm-generated MOS was calculated using electronic health record data. The primary outcome was a composite of all-cause mortality or hospitalization. Cox proportional hazards models were used to evaluate the association between baseline MOS and the primary outcome. A time-varying Cox model using the running cumulative MOS and a marginal structural model (MSM) was also conducted. A linear mixed-effects model was used to assess improvement in MOS over time as the secondary outcome.</p><p><strong>Results: </strong>In the analysis adjusted for HF severity and comorbidities, baseline MOS was associated with a lower hazard of the composite outcome (hazard ratio (HR) 0.96, 95% confidence interval (95% CI): 0.92, 0.99, p = 0.040). In the cumulative time-varying Cox model and the marginal structural model, the association with time-varying MOS became stronger, with HRs of 0.88 (95% CI 0.81-0.95; p = 0.0015) and 0.88 (95% CI 0.83-0.93; p < 0.001), respectively. The event rates per 100 person-years were 44.1 in MOS 0%-33%, 39.5 in MOS 34%-66%, and 31.8 in MOS 67%-100%. Longitudinally, MOS improved over time.</p><p><strong>Conclusion: </strong>Higher algorithm-generated MOS values were significantly associated with lower all-cause mortality or hospitalization, and the MOS values increased over the follow-up period. This suggested that this algorithm effectively identifies opportunities for GDMT optimization in real-world clinical settings.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 1","pages":"e70101"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12812323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145998689","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":"Utilizing Machine Learning to Identify Predictors of Corticosteroid Discontinuation 1-Year After Adult Heart Transplant.","authors":"Caroline Chen, Zeina Jedeon, Abhishek Jaiswal, David A Baran, Katrina Etts, William L Baker","doi":"10.1002/phar.70073","DOIUrl":"10.1002/phar.70073","url":null,"abstract":"<p><strong>Objective: </strong>To use machine learning methods to identify factors associated with corticosteroid (CS) discontinuation 1 year after adult heart transplantation (HT).</p><p><strong>Design: </strong>Retrospective, observational, cohort study.</p><p><strong>Data source: </strong>This study used data from the United Network for Organ Sharing (UNOS) database.</p><p><strong>Patients: </strong>We included adults (age ≥ 18 years) who underwent their first HT between January 2000 and December 2023 in the UNOS database with follow-up through December 2024, who were discharged on a CS.</p><p><strong>Measurements: </strong>We divided the cohort into those with or without CS at 1-year post-transplant follow-up. We used the eXtreme Gradient Boosting (XGBoost) algorithm to build a model predicting CS discontinuation at 1 year. Relevant recipient, donor, and transplant variables were included to train the model, with Shapley Additive Explanations (SHAP) used to identify and interpret the most important and predictive features.</p><p><strong>Results: </strong>We identified 72,730 HT recipients; 13,017 (17.9%) had CS discontinued within the first year. Compared with the CS cessation group, those who continued CS were more likely to have had a lower BMI, lower ischemic etiology of cardiomyopathy, lower intra-aortic balloon pump (IABP) and left ventricular assist device (LVAD) use before HT, better renal function, and sustained longer donor ischemic time. Model performance was strong, with an area under the curve of 0.854 (95% confidence interval: 0.848-0.861). Lower average transplant center volume (number of transplants per year), shorter donor ischemic time, and LVAD use at transplant predicted CS discontinuation.</p><p><strong>Conclusions: </strong>In a large national database, utilizing novel ML modeling techniques, we identified annual transplant center volume, donor ischemia time, and LVAD use at the time of HT as the best predictors of CS discontinuation 1 year after HT.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70073"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145496404","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":"Nephrocast-V: A Deep Learning Model for the Prediction of Vancomycin Trough Concentration Using Electronic Health Record Data.","authors":"Ghodsieh Ghanbari, Craig Stevens, Eliah Aronoff-Spencer, Atul Malhotra, Shamim Nemati, Zaid Yousif","doi":"10.1002/phar.70062","DOIUrl":"10.1002/phar.70062","url":null,"abstract":"<p><strong>Introduction: </strong>Vancomycin is a critical antibiotic for treating methicillin-resistant Staphylococcus aureus and other gram-positive bacterial infections, but achieving and maintaining therapeutic trough concentrations is challenging.</p><p><strong>Objectives: </strong>We hypothesized that a deep learning model could accurately predict vancomycin trough concentrations 2 days in advance in critically ill patients and provide recommendations for optimal dosing adjustments to achieve target drug concentrations.</p><p><strong>Methods: </strong>We trained and validated the model using electronic health record (EHR) data from adults admitted to the University of California San Diego Health system intensive care units (ICUs) from January 1, 2016, to June 30, 2024. Features included patient demographics, comorbidities, vital signs, laboratory measurements, medications, and vancomycin dosing information. The model architecture combined Long Short-Term Memory and Multi-Head Attention layers, supplemented with skip connections to incorporate past dosage information at the final layer of the deep learning model. Model performance was evaluated using mean absolute error (MAE) and root mean square error (RMSE) metrics.</p><p><strong>Results: </strong>A total of 2205 encounters met the eligibility criteria. The median age was 57 years, and the median ICU length of stay was 4.9 days. The model achieved an MAE of 3.15 mg/L and an RMSE of 4.17 mg/L, comparable to that of a critical care pharmacist aided by a Bayesian dosing software. Additionally, deviations from patient-specific model-based dose recommendations were generally associated with nontherapeutic vancomycin levels.</p><p><strong>Conclusion: </strong>This study demonstrates the potential to leverage deep learning to individualize and support vancomycin therapeutic drug monitoring in critically ill patients.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70062"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12674970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145192372","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":"Comment on \"Acute Pharmacodynamic Effects of Oral Levodopa on Blood Pressure in Parkinson's Disease\".","authors":"Malika Bouhaddi, Fabrice Vuiller, Jacques Regnard","doi":"10.1002/phar.70107","DOIUrl":"https://doi.org/10.1002/phar.70107","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 1","pages":"e70107"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145998728","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":"Pharmacotherapeutic Controversies During Temperature Control After Out-of-Hospital Cardiac Arrest: A Semi-Structured Literature Review.","authors":"Christy Cecil Forehand, Scott Thomas Benken, Melanie Madorsky, Adham Mohamed, Jerika V Nguyễn, Deepali Dixit, Jolie Gallagher, William Anthony Hawkins, Mojdeh Saba Heavner, Emory Johnson, Lauren Lozano, Melanie Sisco, Angela A Slampak-Cindric, Susan Elizabeth Smith, Katherine Spezzano, Bayard Angel Taylor, Nathaniel Brett Wayne, David John Gagnon","doi":"10.1002/phar.70095","DOIUrl":"10.1002/phar.70095","url":null,"abstract":"<p><p>Post-resuscitation cardiac arrest care begins at the time of hospital admission and focuses on preventing the sequelae of ischemia-reperfusion injury, including secondary brain damage and post-cardiac arrest syndrome. Clinical practice guidelines on post-resuscitation cardiac arrest care from international medical organizations outline evidence-based practices but sometimes lack pragmatic details needed by bedside clinicians to implement change. Lack of information has required providers to extrapolate from other patient populations with acute brain injuries or utilize lower quality data from patient registries or observational studies. We organized a group of content experts to address selected pharmacotherapeutic controversies encountered during temperature control after out-of-hospital cardiac arrest in adult patients. Data on pre-hospital interventions, in-hospital cardiac arrest, and most non-pharmacologic treatments were excluded. A Delphi process was completed using three rounds of voting to reach consensus on pharmacotherapeutic controversies to review. The original list of 11 topics was narrowed iteratively and the final list included: (i) sedation and analgesia, (ii) seizures and myoclonus, (iii) shivering, and (iv) early-onset pneumonia prevention. Writing groups conducted systematic literature searches of MEDLINE using PubMed focusing on contemporary publications in the past 30 years when temperature control was considered standard care. Each section reviews the scope and management of the controversy and provides a conclusion with suggested future research directions. The information in this review is intended to support providers with the implementation of recommendations made in clinical practice guidelines on post-resuscitation cardiac arrest care and should not necessarily supplant them.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 1","pages":"e70095"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12810738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145990285","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":"Synthetic Data-Driven Early Prediction Framework for Acute Kidney Injury in Patients Receiving Vancomycin and Ceftazidime/Avibactam.","authors":"Maryam Ramazani, Todd Brothers, Imtiaz Ahmed, Mohammad A Al-Mamun","doi":"10.1002/phar.70064","DOIUrl":"10.1002/phar.70064","url":null,"abstract":"<p><strong>Background: </strong>The nephrotoxic risks of combining ceftazidime/avibactam (AVI) with vancomycin (VAN) remain underexplored, despite both agents independently being linked to acute kidney injury (AKI). This study assessed the risk of AKI associated with concurrent VAN and ceftazidime/avibactam (VAN-AVI) therapy and developed synthetic data models to enable early prediction of AKI.</p><p><strong>Methods: </strong>We conducted a retrospective analysis using electronic health record data from hospitalized adults between 2015 and 2022. The incidence of AKI was compared among patients receiving VAN-AVI or VAN in combination with piperacillin/tazobactam (VAN-TPZ) versus VAN monotherapy. AKI was defined as a composite of de novo and recurrent AKI (i.e., patients had a prior diagnosis of AKI within the preceding 6 months and experienced a new AKI event after 7 days of VAN-AVI initiation). To address sample size imbalance, we applied inverse probability of treatment weighting (IPTW) and generated synthetic datasets using Conditional Tabular Generative Adversarial Networks (CTGAN) and Tabular Variational Autoencoders (TVAE). These synthetic datasets were subsequently used to augment machine learning (ML) models aimed at the early prediction of AKI in patients treated with VAN-AVI combination therapy.</p><p><strong>Results: </strong>Among the 92 patients receiving VAN-AVI combination therapy, only four (4.3%) patients experienced new-onset AKI, and 66 (71.7%) patients had a recurrent AKI. After applying IPTW, VAN-AVI was associated with a higher risk of AKI Hazard Ratio (HR) = 3.47; 95% Confidence Interval (CI): 1.97-6.11, followed by VAN-TPZ (HR = 1.96; 95% CI: 1.37-2.81), compared to VAN alone. Synthetic data analyses conducted over 1000 iterations supported these findings, with mean HRs for VAN-AVI of 3.80 using TVAE and 4.45 using CTGAN. ML models augmented with synthetic data outperformed those using original data alone. For 30-day AKI prediction, F1-scores improved across all models, with the highest performance observed in the augmented XGBoost and logistic regression classifier (F1 = 0.80).</p><p><strong>Conclusion: </strong>This study introduces a novel approach that integrates IPTW with synthetic data generation to evaluate drug-associated AKI risk in small-sample cohorts. Although our findings demonstrate a lower incidence of de novo AKI in the VAN-AVI group, the use of synthetic data and augmented ML models significantly improved early AKI prediction. These findings support the potential utility of synthetic data frameworks for scalable drug safety evaluations, although further validation is warranted.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70064"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145125741","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}