CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Time-varying covariates, overadjustment bias and mediation in pharmacokinetic/pharmacodynamic modeling","authors":"Sebastiaan Camiel Goulooze, Nelleke Snelder","doi":"10.1002/psp4.13200","DOIUrl":"10.1002/psp4.13200","url":null,"abstract":"<p>Overadjustment bias is a term used in epidemiology to refer to the situation where bias is introduced when controlling for a variable in the analysis, for example, by including the variable as a covariate in a model.<span><sup>1, 2</sup></span> There are several situations in which inclusion of covariates can cause overadjustment bias, two of which are graphically illustrated in Figure 1. In the first example, the drug exposure has a certain causal effect on the outcome and part of this causal effect is mediated by an intermediate. Because part of the causal effect of exposure on outcome is explained by the effect of exposure on the intermediate, including the intermediate as a covariate in the model will result in a biased (i.e., under-estimated) estimate of the total effect of exposure on the outcome. In the second example, in Figure 1, the potential covariate is a descendant or consequence of the outcome.</p><p>The risk of overadjustment bias lies particularly in those covariates that are included in the model in a time-varying fashion or when using a time-constant, but post-baseline value as a covariate (e.g., the value observed at 3-months after treatment start in each subject). In contrast, the covariate values at baseline are not affected by treatment (assuming treatment starts at baseline) and one would therefore not expect overadjustment bias when including baseline covariates in a model.</p><p>Although overadjustment bias is rarely discussed within the context of PK/PD modeling, it is by no means less important for this setting. In a PK/PD model, the scenarios above would result in an under-estimated treatment effect. This bias could negatively impact real-life decisions. It may lead one to incorrectly conclude that the treatment is not effective (enough) or that a higher concentration may be needed to reach the target efficacy. Overadjustment bias could also result in overly conservative sample size calculations for a future clinical study.</p><p>To demonstrate the importance of overadjustment bias, we performed several illustrative simulations of a hypothetical placebo-controlled study (R code included in the Supplemental Material S1). Simulations were conducted for three scenarios involving a time-varying covariate influencing a numerical outcome variable (Figure 2a). Each scenario was simulated 1000 times, with each dataset containing 100 subjects that each have six observations for both covariate and outcome variables.</p><p>Scenario 1 assumes a direct treatment effect on the outcome, which is not mediated by the time-varying covariate (i.e., treatment does not have any effect on the covariate in this scenario). In Scenario 2, the treatment effect on the outcome is entirely mediated by the time-varying covariate: drug treatment lowers the covariate, which lowers the outcome value. Scenario 3 represents a hybrid scenario with 50% direct and 50% mediated treatment effects. The total treatment effect for a specific drug concentrati","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 8","pages":"1285-1288"},"PeriodicalIF":3.1,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141619589","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":"Bridging pharmacology and neural networks: A deep dive into neural ordinary differential equations","authors":"Idris Bachali Losada,&nbsp;Nadia Terranova","doi":"10.1002/psp4.13149","DOIUrl":"10.1002/psp4.13149","url":null,"abstract":"<p>The advent of machine learning has led to innovative approaches in dealing with clinical data. Among these, Neural Ordinary Differential Equations (Neural ODEs), hybrid models merging mechanistic with deep learning models have shown promise in accurately modeling continuous dynamical systems. Although initial applications of Neural ODEs in the field of model-informed drug development and clinical pharmacology are becoming evident, applying these models to actual clinical trial datasets—characterized by sparse and irregularly timed measurements—poses several challenges. Traditional models often have limitations with sparse data, highlighting the urgent need to address this issue, potentially through the use of assumptions. This review examines the fundamentals of Neural ODEs, their ability to handle sparse and irregular data, and their applications in model-informed drug development.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 8","pages":"1289-1296"},"PeriodicalIF":3.1,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141589868","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":"A literature review of drug transport mechanisms during lactation","authors":"Christine Gong,&nbsp;Lynn N. Bertagnolli,&nbsp;David W. Boulton,&nbsp;Paola Coppola","doi":"10.1002/psp4.13195","DOIUrl":"10.1002/psp4.13195","url":null,"abstract":"<p>Despite the benefits of breastfeeding, lactating mothers who take prescribed medications may discontinue breastfeeding due to concerns associated with infant drug exposure in breastmilk. Consolidating the current knowledge of drug transport to breastmilk may inform understanding of the safety of medication use during lactation. This literature review summarizes the mechanisms of drug transport to breastmilk, details the physicochemical drug properties that may alter the extent of passive transport, and describes the expressional changes in mammary drug transporters that may affect active transport. During the period of 20 July 2023 to 11 August 2023, PubMed® was searched to identify journal articles pertinent to the mechanisms of drug transport from maternal plasma to breastmilk and the expression of mammary drug transporters during lactation. From the 28 studies included in this review, four mechanisms were identified for transporting drugs from maternal plasma to breastmilk: passive transport, active transport, lipid co-transport, and transcytosis. The lactational expression of 20 drug transporters was further summarized, with 9 transporters demonstrating downregulated expression during lactation and 11 transporters demonstrating upregulated expression during lactation. Understanding the mechanisms of drug transport to breastmilk may aid in estimating infant drug exposure, developing physiologically based pharmacokinetic (PBPK) models that describe drug transfer, and initiating clinical drug development programs in the lactating population.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 11","pages":"1870-1880"},"PeriodicalIF":3.1,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141554371","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":"Dose optimization of cefazolin in South African children undergoing cardiac surgery with cardiopulmonary bypass","authors":"Manna Semere Gebreyesus,&nbsp;Alexandra Dresner,&nbsp;Lubbe Wiesner,&nbsp;Ettienne Coetzee,&nbsp;Tess Verschuuren,&nbsp;Roeland Wasmann,&nbsp;Paolo Denti","doi":"10.1002/psp4.13196","DOIUrl":"10.1002/psp4.13196","url":null,"abstract":"<p>Cefazolin is an antibiotic used to prevent surgical site infections. During cardiac surgery with cardiopulmonary bypass (CPB), its efficacy target could be underachieved. We aimed to develop a population pharmacokinetic model for cefazolin in children and optimize the prophylactic dosing regimen. Children under 25 kg undergoing cardiac surgery with CPB and receiving cefazolin at standard doses (50 mg/kg IV every 4–6 h) were included in this analysis. A population pharmacokinetic model and Monte Carlo simulations were used to evaluate the probability of target attainment (PTA) for efficacy and toxicity with the standard regimen and an alternative regimen of continuous infusion, where loading and maintenance doses were calculated from model-derived individual parameters. Twenty-two patients were included, with median (range) age, body weight, and eGFR of 19.5 (1–94) months, 8.7 (2–21) kg, and 116 (48–159) mL/min, respectively. Six patients received an additional dose in the CPB circuit. A two-compartment disposition model with an additional compartment for the CPB was developed, including weight-based allometric scaling and eGFR. For a 10 kg patient with eGFR of 120 mL/min/1.73 m², clearance was estimated as 0.856 L/h. Simulations indicated that the standard dosing regimen fell short of achieving the efficacy target &gt;40% of the time within a dosing duration and in patients with good renal function, PTA ranged from &lt;20% to 70% for the smallest to the largest patients, respectively, at high MICs. In contrast, the alternative regimen consistently maintained target concentrations throughout the procedure for all patients while using a lower overall dose.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1595-1605"},"PeriodicalIF":3.1,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141497364","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":"Type 1 diabetes prevention clinical trial simulator: Case reports of model-informed drug development tool","authors":"Juan Francisco Morales,&nbsp;Marian Klose,&nbsp;Yannick Hoffert,&nbsp;Jagdeep T. Podichetty,&nbsp;Jackson Burton,&nbsp;Stephan Schmidt,&nbsp;Klaus Romero,&nbsp;Inish O'Doherty,&nbsp;Frank Martin,&nbsp;Martha Campbell-Thompson,&nbsp;Michael J. Haller,&nbsp;Mark A. Atkinson,&nbsp;Sarah Kim","doi":"10.1002/psp4.13193","DOIUrl":"10.1002/psp4.13193","url":null,"abstract":"<p>Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA-approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model-based clinical trial simulation tool. Using individual-level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial-specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web-based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 8","pages":"1309-1316"},"PeriodicalIF":3.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141497365","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":"Correction to “Random control selection for conducting high-throughput adverse drug events screening using large-scale longitudinal health data”","authors":"","doi":"10.1002/psp4.13198","DOIUrl":"10.1002/psp4.13198","url":null,"abstract":"<p>Chiang, C. W., Zhang, P., Donneyong, M., Chen, Y., Su, Y., &amp; Li, L. (2021). Random control selection for conducting high-throughput adverse drug events screening using large-scale longitudinal health data. <i>CPT: Pharmacometrics &amp; Systems Pharmacology</i> 10(9):1032-1042. https://doi.org/10.1002/psp4.12673</p><p>In the published version of this article, the co-first author's name Pengyue Zhang was misspelled as Penyue Zhang.</p><p>The published article has also been corrected to reflect this change.</p><p>We apologize for this error.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 7","pages":"1280"},"PeriodicalIF":3.1,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141466777","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":"Full random effects models (FREM): A practical usage guide","authors":"E. Niclas Jonsson,&nbsp;Joakim Nyberg","doi":"10.1002/psp4.13190","DOIUrl":"10.1002/psp4.13190","url":null,"abstract":"<p>The full random-effects model (FREM) is an innovative and relatively novel covariate modeling technique. It differs from other covariate modeling approaches in that it treats covariates as observations and captures their impact on model parameters using their covariances. These unique characteristics mean that FREM is insensitive to correlations between covariates and implicitly handles missing covariate data. In practice, this implies that covariates are less likely to be excluded from the modeling scope in light of the observed data. FREM has been shown to be a useful modeling method for small datasets, but its pre-specification properties make it a very compelling modeling choice for late-stage phases of drug development. The present tutorial aims to explain what FREM models are and how they can be used in practice.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 8","pages":"1297-1308"},"PeriodicalIF":3.1,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141466778","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":"Combining data on the bioavailability of midazolam and physiologically-based pharmacokinetic modeling to investigate intestinal CYP3A4 ontogeny","authors":"Trevor N. Johnson,&nbsp;Hannah K. Batchelor,&nbsp;Jan Goelen,&nbsp;Richard D. Horniblow,&nbsp;Jean Dinh","doi":"10.1002/psp4.13192","DOIUrl":"10.1002/psp4.13192","url":null,"abstract":"<p>Pediatric physiologically-based modeling in drug development has grown in the past decade and optimizing the underlying systems parameters is important in relation to overall performance. In this study, variation of clinical oral bioavailability of midazolam as a function of age is used to assess the underlying ontogeny models for intestinal CYP3A4. Data on midazolam bioavailability in adults and children and different ontogeny patterns for intestinal CYP3A4 were first collected from the literature. A pediatric PBPK model was then used to assess six different ontogeny models in predicting bioavailability from preterm neonates to adults. The average fold error ranged from 0.7 to 1.38, with the rank order of least to most biased model being No Ontogeny &lt; Upreti = Johnson &lt; Goelen &lt; Chen &lt; Kiss. The absolute average fold error ranged from 1.17 to 1.64 with the rank order of most to least precise being Johnson &gt; Upreti &gt; No Ontogeny &gt; Goelen &gt; Kiss &gt; Chen. The optimal ontogeny model is difficult to discern when considering the possible influence of CYP3A5 and other population variability; however, this study suggests that from term neonates and older a faster onset Johnson model with a lower fraction at birth may be close to this. For inclusion in other PBPK models, independent verification will be needed to confirm these results. Further research is needed in this area both in terms of age-related changes in midazolam and similar drug bioavailability and intestinal CYP3A4 ontogeny.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1570-1581"},"PeriodicalIF":3.1,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141455798","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":"Impact of obesity and roux-en-Y gastric bypass on the pharmacokinetics of (R)- and (S)-omeprazole and intragastric pH","authors":"Leandro F. Pippa,&nbsp;Valvanera Vozmediano,&nbsp;Lieke Mitrov-Winkelmolen,&nbsp;Daan Touw,&nbsp;Amira Soliman,&nbsp;Rodrigo Cristofoletti,&nbsp;Wilson Salgado Junior,&nbsp;Natalia Valadares de Moraes","doi":"10.1002/psp4.13189","DOIUrl":"10.1002/psp4.13189","url":null,"abstract":"<p>This study employed physiologically-based pharmacokinetic–pharmacodynamics (PBPK/PD) modeling to predict the effect of obesity and gastric bypass surgery on the pharmacokinetics and intragastric pH following omeprazole treatment. The simulated plasma concentrations closely matched the observed data from non-obese, morbidly obese, and post-gastric bypass populations. Obesity significantly reduces CYP3A4 and CYP2C19 activities, as reflected by the metabolic ratio [omeprazole sulphone]/[omeprazole] and [5-hydroxy-omeprazole]/[omeprazole]. The morbidly obese model accounted for the down-regulation of CYP2C19 and CYP3A4 to recapitulate the observed data. Sensitivity analysis showed that intestinal CYP3A4, gastric pH, small intestine bypass, and the delay in bile release do not have a major influence on omeprazole exposure. Hepatic CYP3A4 had a significant impact on the AUC of (<i>S</i>)-omeprazole, while hepatic CYP2C19 affected both (<i>R</i>)- and (<i>S</i>)-omeprazole AUC. After gastric bypass surgery, the activity of CYP3A4 and CYP2C19 is restored. The PBPK model was linked to a mechanism-based PD model to assess the effect of omeprazole on intragastric pH. Following 40 mg omeprazole, the mean intragastric pH was 4.3, 4.6, and 6.6 in non-obese, obese, and post-gastric bypass populations, and the daily time with pH &gt;4 was 14.7, 16.4, and 24 h. Our PBPK/PD approach provides a comprehensive understating of the impact of obesity and weight loss on CYP3A4 and CYP2C19 activity and omeprazole pharmacokinetics. Given that simulated intragastric pH is relatively high in post-RYGB patients, irrespective of the dose of omeprazole, additional clinical outcomes are imperative to assess the effect of proton pump inhibitor in preventing marginal ulcers in this population.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1528-1541"},"PeriodicalIF":3.1,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141455799","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":"Population pharmacokinetics of valproic acid in children with epilepsy: Implications for dose tailoring when switching from oral syrup to sustained-release tablets","authors":"Wei-Jun Wang,&nbsp;Yue Li,&nbsp;Ya-Hui Hu,&nbsp;Jie Wang,&nbsp;Yuan-Yuan Zhang,&nbsp;Lin Fan,&nbsp;Hao-Ran Dai,&nbsp;Hong-Li Guo,&nbsp;Xuan-Sheng Ding,&nbsp;Feng Chen","doi":"10.1002/psp4.13191","DOIUrl":"10.1002/psp4.13191","url":null,"abstract":"<p>Significant pharmacokinetic (PK) differences exist between different forms of valproic acid (VPA), such as syrup and sustained-release (SR) tablets. This study aimed to develop a population pharmacokinetic (PopPK) model for VPA in children with epilepsy and offer dose adjustment recommendation for switching dosage forms as needed. The study collected 1411 VPA steady-state trough concentrations (<i>C</i>_trough) from 617 children with epilepsy. Using NONMEM software, a PopPK model was developed, employing a stepwise approach to identify possible variables such as demographic information and concomitant medications. The final model underwent internal and external evaluation via graphical and statistical methods. Moreover, Monte Carlo simulations were used to generate a dose tailoring strategy for typical patients weighting 20–50 kg. As a result, the PK characteristics of VPA were described using a one-compartment model with first-order absorption. The absorption rate constant (<i>k</i>_a) was set at 2.64 and 0.46 h⁻¹ for syrup and SR tablets. Body weight and sex were identified as significant factors affecting VPA's pharmacokinetics. The final PopPK model demonstrated acceptable prediction performance and stability during internal and external evaluation. For children taking syrup, a daily dose of 25 mg/kg resulted in the highest probability of achieving the desired target <i>C</i>_trough, while a dose of 20 mg/kg/day was appropriate for those taking SR tablets. In conclusion, we established a PopPK model for VPA in children with epilepsy to tailor VPA dosage when switching between syrup and SR tablets, aiming to improve plasma VPA concentrations fluctuations.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1554-1569"},"PeriodicalIF":3.1,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141455800","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}