An eco-friendly HPLC-UV method for the determination of risedronate in its bulk and tablet dosage form with application to content uniformity,dissolution and stability testing

Risedronate is a nitrogen-containing bisphosphonate for the treatment and prevention of postmenopausal osteoporosis. The current work aims to develop a novel green HPLC-UV method for the rapid analysis of risedronate sodium in bulk and tablet formulation. The analyzed samples were separated on Waters Atlantis dC18 (150 mm × 3.9 mm; 5 μm) column using a green mobile phase consisting of potassium phosphate buffer pH 2.9 and potassium edetate buffer pH 9.5 in a ratio of 1:2, the final pH was adjusted to 6.8 with phosphoric acid, the mobile phase was pumped at a rate of 1.0 mL/min, with column temperature set at 30 °C, eluted samples were detected at 263 nm and the chromatographic run time was 3.0 min. The method was found to be linear over the concentration range of 14–140 μg/mL with a correlation coefficient (r²) of 0.9994. Accuracy and precision were evaluated from three QC samples (LQC, MQC and HQC) together with the five calibrators where the percentage accuracy was found to be 101.84%. Processed quality control samples of risedronate sodium were tested for stability at different conditions, short term, long term and freeze- thaw stability. The current method was further extended to study the content uniformity of Actonel® tablets following United States Pharmacopoeia (USP) guidelines. The proposed method was fully validated as per ICH guidelines.     

How structural and functional MRI can inform dual-site tACS parameters: A case study in a clinical population and its pragmatic implications

BackgroundAbnormalities in frontoparietal network (FPN) were observed in many neuropsychiatric diseases including substance use disorders. A growing number of studies are using dual-site-tACS with frontoparietal synchronization to engage this network. However, a computational pathway to inform and optimize parameter space for frontoparietal synchronization is still lacking. In this case study, in a group of participants with methamphetamine use disorders, we proposed a computational pathway to extract optimal electrode montage while accounting for stimulation intensity using structural and functional MRI.MethodsSixty methamphetamine users completed an fMRI drug cue-reactivity task. Four main steps were taken to define electrode montage and adjust stimulation intensity using 4x1 high-definition (HD) electrodes for a dual-site-tACS; (1) Frontal seed was defined based on the maximum electric fields (EF) predicted by simulation of HD montage over DLPFC (F3/F4 in EEG 10–10), (2) frontal seed-to-whole brain context-dependent correlation was calculated to determine connected regions to frontal seeds, (3) center of connected cluster in parietal cortex was selected as a location for placing the second set of HD electrodes to shape the informed montage, (4) individualized head models were used to determine optimal stimulation intensity considering underlying brain structure. The informed montage was compared to montages with large electrodes and classic frontoparietal HD montages (F3-P3/F4-P4) in terms of tACS-induced EF and ROI-to-ROI task-based/resting-state connectivity.ResultsCompared to the large electrodes, HD frontoparietal montages allow for a finer control of the spatial peak fields in the main nodes of the FPN at the cost of lower maximum EF (large-pad/HD: max EF[V/m] = 0.37/0.11, number of cortical sub-regions that EF exceeds 50% of the max = 77/13). For defining stimulation targets based on EF patterns, using group-level head models compared to a single standard head model results in comparable but significantly different seed locations (6.43 mm Euclidean distance between the locations of the frontal maximum EF in standard-space). As expected, significant task-based/resting-state connections were only found between frontal-parietal locations in the informed montage. Cue-induced craving score was correlated with frontoparietal connectivity only in the informed montage (r = ?0.24). Stimulation intensity in the informed montage, and not in the classic HD montage, needs 40% reduction in the parietal site to reduce the disparity in EF between stimulation sites.ConclusionThis study provides some empirical insights to montage and dose selection in dual-site-tACS using individual brain structures and functions and proposes a computational pathway to use head models and functional MRI to define (1) optimum electrode montage for targeting FPN in a context of interest (drug-cue-reactivity) and (2) proper transcranial stimulation intensity.     

经鼻导管高流量吸氧与经鼻气道正压通气在重症毛细支气管炎呼吸支持中的应用价值比较

目的：比较经鼻导管高流量吸氧（HFNC）与经鼻气道正压通气（nCPAP）在重症毛细支气管炎呼吸支持中的应用价值，为临床治疗方案的选择提供参考。方法：选取2016年12月至2018年12月我院儿科收治的重症毛细支气管炎患儿90例，采用随机数字表法分为观察组和对照组各45例。两组患儿入院后均给予常规综合治疗以保证呼吸道通畅，在此基础上观察组采用HFNC治疗，对照组采用nCPAP治疗，比较两组患儿治疗前和治疗24 h后呼吸频率、经皮血氧饱和度（TcSO2）、呼吸窘迫评分体系（CSS）评分、动脉血氧分压（PaO2）等呼吸相关指标及治疗前后临床症状体征改善情况。结果：两组患儿治疗24 h后呼吸频率、CSS评分均降低，且观察组降低程度更大，TcSO2、PaO2于治疗24 h后升高，观察组升高幅度较对照组明显；治疗后两组患儿咳嗽及肺部湿啰音、肺部炎症情况均改善，观察组症状体征消失时间早于对照组，差异均有统计学意义（P<0.05）。结论：重症毛细支气管炎患儿采用HFNC治疗可明显改善通气功能和临床症状，治疗效果优于nCPAP治疗，可扩大样本量进一步观察。     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

Learning to define an electrical biomarker of the epileptogenic zone

The role of fast activity as a potential biomarker in localization of the epileptogenic zone (EZ) remains controversial due to recently reported unsatisfactory performance. We recently identified a “fingerprint” of the EZ as a time‐frequency pattern that is defined by a combination of preictal spike(s), fast oscillatory activity, and concurrent suppression of lower frequencies. Here we examine the generalizability of the fingerprint in application to an independent series of patients (11 seizure‐free and 13 non‐seizure‐free after surgery) and show that the fingerprint can also be identified in seizures with lower frequency (such as beta) oscillatory activity. In the seizure‐free group, only 5 of 47 identified EZ contacts were outside the resection. In contrast, in the non‐seizure‐free group, 104 of 142 identified EZ contacts were outside the resection. We integrated the fingerprint prediction with the subject's MR images, thus providing individualized anatomical estimates of the EZ. We show that these fingerprint‐based estimates in seizure‐free patients are almost always inside the resection. On the other hand, for a large fraction of the nonseizure‐free patients the estimated EZ was not well localized and was partially or completely outside the resection, which may explain surgical failure in such cases. We also show that when mapping fast activity alone onto MR images, the EZ was often over‐estimated, indicating a reduced discriminative ability for fast activity relative to the full fingerprint for localization of the EZ.