A Proposal for an Alternative Approach to Particle Size Method Development During Early-Stage Small Molecule Pharmaceutical Development

Particle size analysis in the pharmaceutical industry has long been a source of debate regarding how best to define measurement accuracy; the degree to which the result of a measurement or calculation conforms to the true value. Defining a “true” value for the size of a particle can be challenging as the output of its measurement will differ because of variations in measurement approaches, instrumental differences and calculation methods. Consequently, for “real” particles, a universal “true” value does not exist and accuracy is therefore not a definable characteristic. Accordingly, precision is then a measure of the ability to reproducibly achieve a measurement of unknown relevance.This article proposes, in place of accuracy, a means to define the “appropriateness” of a measurement in line with the critical quality attributes (CQA) of the material being characterized. The decision as to whether the measurement is correct should involve a link to the CQA; that is, correlation should be demonstrated, without which the measured particle size cannot be defined as a critical material attribute.Correspondingly, methods should also be able to provide sufficient precision to demonstrate discrimination relating to variation in the CQA. The benefits and challenges of this approach are discussed.     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

颞下颌关节强直动物模型中最大张口度与强直严重程度关系研究

目的　研究主、被动最大张口度（AMMO、PMMO）与颞下颌关节强直严重程度的关系。方法　选取28只健康雄性绵羊随机分为实验组和对照组，每组各14只。实验组绵羊双侧颞下颌关节模拟髁突矢状骨折，其中左侧翼外肌被切断以阻断其功能；对照组绵羊未进行手术。于术前及术后12、24周对所有绵羊体重、AMMO、PMMO、颞下颌关节形态学特点进行测量评估。结果　实验组只有右侧保留翼外肌功能的颞下颌关节发生了骨强直。术后12、24周，实验组绵羊AMMO、PMMO、极限距离均显著低于对照组，差异均有统计学意义（均P < 0.05）。实验组绵羊AMMO和PMMO与骨融合区宽度、长度、面积及钙化程度均呈负相关（均P < 0.05），其中骨融合区面积为主要影响因素（术后12、24周相关系数r分别为-0.94、-0.95）。结论　颞下颌关节强直动物模型中阻断翼外肌功能可阻止骨强直的发生；对于早期髁突矢状骨折，可通过牙合垫或牙合板进行张口训练，进而阻断翼外肌功能。当颞下颌关节发生骨强直时，骨融合区面积越大，张口受限越明显。     

MRI图像融合技术在肛瘘评估中的初步应用

目的采用图像融合技术获得T2WI与T2WI-FS的融合图像，评估其在肛瘘及肛周结构显示中的优势。 方法2016年6月至2018年6月，前瞻性选择中山大学附属第一医院29例肛瘘患者进行肛管磁共振（MR）检查，采用图像融合技术获取T2WI与T2WI-FS的融合图像T2WI-Fusion，利用Fisher score算法计算瘘管及肛门括约肌的组织间分辨力Fisher值、脂肪与肛门括约肌间的Fisher值，评估融合图像中瘘管及肛周结构的显示情况。采用改进的双刺激连续质量量表（DSCQS）对T2WI-FS、T2WI、增强3D-VIBE和T2WI-Fusion序列图像进行主观图像质量评价。 结果29例患者均成功获得T2WI与T2WI-FS的融合图像T2WI-Fusion。T2WI-Fusion、T2WI瘘管与括约肌间Fisher均值分别为6.46、3.31，T2WI-Fusion图像对瘘管的显示优于T2WI序列图像（P<0.001）。T2WI-Fusion、T2WI-FS脂肪与括约肌间Fisher均值分别为10.61、2.45，T2WI-Fusion图像对括约肌的显示优于T2WI-FS序列图像（P<0.001）。T2WI-Fusion对瘘管与括约肌的图像质量评价总评分均高于T2WI-FS、T2WI、增强3D-VIBE序列（P<0.001）。 结论MRI图像融合技术同时具备T2WI及T2WI-FS的优势，无需增加扫描序列及扫描时间，且操作简单，花费时间短，显著提高病变及肛周解剖结构的对比度和图像质量。     

PCNA、Survivin蛋白在人绒癌细胞株BeWo合体化过程中表达变化的研究

目的:通过检测人绒癌细胞株Be Wo合体化过程中增殖细胞核抗原(proliferating cell nuclear antigen,PCNA)、生存素(Survivin)蛋白表达的变化,探讨滋养细胞合体化后增殖性的变化,为恶性滋养细胞肿瘤,尤其是耐药恶性滋养细胞肿瘤的临床治疗提供新的思路和方法。方法:利用毛喉素(forskolin)诱导Be Wo细胞株融合;应用逆转录聚合酶链反应(RT-PCR)检测促融素(Syncytin)在forskolin作用不同时间的Be Wo细胞株中的表达;应用蛋白质印迹(Western blotting)检测PCNA、Survivin蛋白在forskolin作用不同时间的Be Wo细胞株中的表达;应用噻唑蓝比色分析实验(MTT)法检测forskolin作用不同时间的绒癌细胞株Be Wo的增殖能力。结果:1forskolin作用后的Be Wo细胞株Syncytin基因的表达增强,且随着forskolin作用时间的延长,Syncytin的表达更强,于48 h达到高峰。2forskolin作用后的Be Wo细胞株PCNA、Survivin蛋白的表达降低。3forskolin作用后的Be Wo细胞株的增殖能力下降,且不同作用时间的差异有统计学意义;forskolin作用的时间越长,Be Wo细胞株增殖能力下降越明显。结论:人绒癌细胞株Be Wo合体化后PCNA、Survivin蛋白的表达降低,说明人绒癌细胞株Be Wo合体化后增殖性降低,推测诱导滋养细胞合体化可能对临床治疗恶性滋养细胞肿瘤具有一定作用。     

Effect of Day Optimizing Throughput (Dot) Knee Software Implementation on Magnetic Resonance Imaging Workflow Efficiency

PurposeAttempts by magnetic resonance (MR) manufacturers to help imaging centres improve patient throughput has led to the development of more automated acquisition. This software is capable of customizing individual scan alignment; potentially improving imaging efficiency and standardizing protocols. However, substantial investments are required to introduce such systems, potentially deterring their widespread application. This study assessed the implementation costs and reduction in examination durations for automated knee MR imaging (MRI) software.Materials and MethodsResearch activities were performed at a community-based academic centre on a 3-Tesla (3-T) system using Siemens' Day Optimizing Throughput (Dot) knee software. Examination acquisition times were extracted from the system before and after software implementation. Fiscal year 2012/13 finances were used to determine the average hourly cost of MRI utilization. Costs associated with automated software implementation were also calculated. Finally, the number of knee scans required to achieve a positive return on investment using the software was established.Results and DiscussionThe mean (standard deviation, sample size) pre- and post-Dot software scan times were 23.20 (4.18, n = 266) and 21.94 (4.51, n = 59) minutes, respectively, for a routine knee scan and 11.88 (1.60, n = 74) and 11.24 (1.51, n = 27) minutes, respectively, for a fast knee scan. The overall weighted average resulted in a 64-second time savings per automated knee examination. This negligible time savings would be extremely difficult to make use of clinically. Dot simplified 29 unique knee protocols to two, improving the consistency of knee examinations. Current Dot software is not compatible with all patients and therefore has limitations that are a concern among MR technologists.ConclusionAdoption of automated knee systems could assist in standardizing protocols; however, the cost of implementation and difficulty in modifying patient scheduling to reflect the minimal time savings would make a financial return unlikely to occur at small- and medium-sized institutions.     

Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing

IntroductionThis study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest.Materials and MethodsThis study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition.ResultsThe learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1.DiscussionThe measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them.ConclusionObtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.     

Early onset scoliosis and current treatment methods

ObjectiveProvide an update of the management options for early onset scoliosis patients, including general assessment, conservative and surgical options.MethodsWe included the updated information about the assessment and management options of Early Onset Scoliosis, taking into consideration the non-fusion methods, including the burden on the patient and their family.ResultsWith the heterogeneity of this population, it is difficult to get a consensus about a unified protocol for management. Accordingly, the surgeon dealing with these cases needs to be aware of the broad range of surgical and non-surgical methods when treating these patients.ConclusionThe main aim of early onset scoliosis treatment is to gain a flexible spine associated with normal lung development and thoracic growth. Management needs to be individualized between the surgeon and patient in relation to the etiology and patient conditions.