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.     

Could non-HDL-cholesterol be a better marker of atherogenic dyslipidemia in obstructive sleep apnea?

Background/objectiveObstructive sleep apnea (OSA) is independently associated with dyslipidemia, a surrogate marker of atherosclerosis. Low-density lipoprotein (LDL)-cholesterol is accepted as a major independent risk factor for cardiovascular disease. However, non-high-density lipoprotein (HDL)-cholesterol is a better marker of atherogenic dyslipidemia and recommended as a target of lipid lowering therapy. We aimed to assess the prevalence of atherogenic dyslipidemia, and relationship between OSA severity and serum LDL-cholesterol and non-HDL cholesterol levels in OSA patients.MethodsWe retrospectively evaluated treatment naïve 2361 subjects admitted to the sleep laboratory of a university hospital for polysomnography. All subjects’ lipid profile including total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides, and non-HDL-cholesterol were measured.ResultsOut of 2361 patients (mean age 49.6 ± 11.9 years; 68.9% male, apnea-hypopnea index 36.6 ± 28.4/h), 185 (7.8%) had no OSA and 2176 (92.2%) had OSA. Atherogenic dyslipidemia prevalence was high (57–66%) in OSA patients, and especially increased in severe OSA compared to other groups (p < 0.05). Though total and LDL-cholesterol did not differ between those with and without OSA, non-HDL-cholesterol (p = 0.020), and triglycerides (p = 0.001) were higher and HDL-cholesterol levels (p = 0.018) were lower in OSA patients than non-OSA. Non-HDL-cholesterol was significantly correlated with OSA severity (p < 0.001) and hypoxia parameters (p < 0.01), whereas LDL-cholesterol showed no correlation.ConclusionsAtherogenic dyslipidemia is highly prevalent and non-HDL-cholesterol levels are significantly increased, predominantly in severe OSA patients. Non-HDL-cholesterol but not LDL-cholesterol, is significantly correlated with OSA severity and hypoxia parameters. Therefore, it could be better to use non-HDL-cholesterol, which is a guideline recommended target of lipid therapy, as a marker of atherosclerotic cardiovascular risk in OSA patients.     

The effect of patient ethnicity on the accuracy of peripheral pulse oximetry in patients with COVID-19 pneumonitis: a single-centre,retrospective analysis

Pulse oximetry is used widely to titrate oxygen therapy and for triage in patients who are critically ill. However, there are concerns regarding the accuracy of pulse oximetry in patients with COVID-19 pneumonitis and in patients who have a greater degree of skin pigmentation. We aimed to determine the impact of patient ethnicity on the accuracy of peripheral pulse oximetry in patients who were critically ill with COVID-19 pneumonitis by conducting a retrospective observational study comparing paired measurements of arterial oxygen saturation measured by co-oximetry on arterial blood gas analysis (SaO₂) and the corresponding peripheral oxygenation saturation measured by pulse oximetry (S_pO₂). Bias was calculated as the mean difference between SaO₂ and S_pO₂ measurements and limits of agreement were calculated as bias ±1.96 SD. Data from 194 patients (135 White ethnic origin, 34 Asian ethnic origin, 19 Black ethnic origin and 6 other ethnic origin) were analysed consisting of 6216 paired SaO₂ and S_pO₂ measurements. Bias (limits of agreement) between SaO₂ and S_pO₂ measurements was 0.05% (−2.21–2.30). Patient ethnicity did not alter this to a clinically significant degree: 0.28% (1.79–2.35), −0.33% (−2.47–2.35) and −0.75% (−3.47–1.97) for patients of White, Asian and Black ethnic origin, respectively. In patients with COVID-19 pneumonitis, S_pO₂ measurements showed a level of agreement with SaO₂ values that was in line with previous work, and this was not affected by patient ethnicity.     

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.     

Arterial stiffness in women previously with preeclampsia from a semi-rural region of South Africa

Women with pre-eclampsia have an increased risk of cardiovascular disease later in life. The aim of the study was to establish the presence and pattern of arterial stiffness in women previously with pre-eclampsia from a semi-rural region of South Africa. This was a prospective longitudinal study which involved 36 previously pre-eclamptic women and 86 non-pregnant controls (NPC) who had a past history of non-complicated pregnancy. Maternal wave reflection (augmentation index) and carotid-femoral pulse wave velocity were assessed noninvasively, using applanation tonometry with the SphygmoCor device. Endothelial function was assessed by EndoPAT 2000 device; pneumatic probes were fitted to the index fingers; induced flow-mediated reactive hyperemia; the ratio of the readings before and after occlusion was then used to calculate the score, the reactive hyperemia index (RHI) as a measure of endothelial function.

Pulse wave velocity remained significantly higher in previously pre-eclamptic women than non-pregnant controls up to three months after delivery (p < 0.05), then it reduced to nonsignificant values. All blood pressure indices (central and brachial pressures), were higher in previously pre-eclamptic women as compared to nonpregnant controls up to one year postpartum.

Regional (aortic) arterial stiffness, though it persists for some time after delivery, is transitory in previously pre-eclamptic women from the rural Africa setting. However, their increase blood pressure is an indication of compromised arterial compliance in women previously with pre-eclampsia.     

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.     

H型高血压患者舌面诊图像颜色特征参数分析＊

目的 分析H型高血压患者的舌面诊图像颜色参数特征，探讨H型高血压患者的舌诊、面诊变化规律。方法 运用上海中医药大学自行研制的Smart TCM-1型中医舌面一体仪，采集高血压患者舌面诊图像，提取特征参数，分析健康对照组、H型高血压组与非H型高血压组患者舌面颜色参数特征。结果 ①在舌色各项参数中，H型高血压组舌尖部R值、B值、V值均显著小于健康对照组（P < 0.01）；非H型高血压组舌尖部B值显著小于健康对照组（P < 0.01），S值较健康对照组显著增大（P < 0.05）；H型高血压组舌尖部R、V值均明显小于非H型高血压组（P < 0.05）。在舌苔各项参数中，H型高血压组舌中H值、V值均明显小于健康对照组（P < 0.05）；非H型高血压组舌中V值、舌右V值均显著小于健康对照组（P < 0.01）；H型高血压组舌中H值明显小于非H型高血压组（P < 0.05），右侧舌苔S值明显大于非H型高血压组（P < 0.05）。②H型高血压组面色参数鼻G值、下颌G值、口唇R值、口唇V值均明显小于健康对照组（P < 0.05）；非H型高血压组前额H值、目眶H值、脸颊H值、鼻H值、下颌H值、整体H值均明显大于健康对照组（P < 0.05）；H型高血压组前额H值、目眶G值、目眶H值、脸颊H值、鼻G值、鼻H值、下颌R值、下颌G值、下颌H值、下颌V值、口唇R值、口唇G值、口唇V值、整体R值、整体G值、整体H值、整体V值均明显小于非H型高血压组（P < 0.05）。结论 H型高血压患者苔色偏黄，以舌中部为主，且舌右侧黄苔积聚较明显；H型高血压患者面色为黄中带红，口唇、下颌部更为晦暗。H型高血压患者的舌、面诊特征参数的变化，与高血压病阳亢湿盛病机相符。