复方丹参滴丸对老年冠心病患者血流变学、血脂及炎性反应递质的影响

目的：探究复方丹参滴丸对老年冠心病患者血流变学、血脂及炎性反应递质的影响。方法：选取2015年1月至2017年12月海南省人民医院收治的老年冠心病患者154例作为研究对象，按照随机数字表法随机分为对照组和观察组，每组77例。对照组采用常规西药治疗，观察组在此基础上加服复方丹参滴丸。比较治疗前后2组患者的血流变学、血脂、炎性反应递质和疗效。结果：观察组治疗有效率为92.21%，明显高于对照组治疗有效率79.22%，差异有统计学意义（P<0.05）。治疗后，2组总胆固醇（TC）、三酰甘油（TG）和低密度脂蛋白（LDL）水平均升高，差异有统计学意义（P<0.05），且观察组高于对照组，2组差异有统计学意义（P<0.05）。2组血流变学指标、高密度脂蛋白（HDL）、IL-6和TNF-α水平治疗后均降低（P<0.05），且观察组降低更明显，差异有统计学意义（P<0.05）。结论：复方丹参滴丸联合常规西药治疗冠心病患者，能够调节脂代谢，降低炎性反应，改善血流变学。     

白藜芦醇对酒精诱导雄性大鼠骨量减少和骨强度降低的保护作用

目的探索白藜芦醇对酒精诱导雄性大鼠骨量减少和骨强度降低的影响。方法 30只12周雄性大鼠随机分为对照组、模型组和治疗组3组,每组10只。模型组和治疗组大鼠给予0.4 mL/100 g的20%乙醇溶液,每周3次。治疗组接受白藜芦醇40 mg/kg治疗,每日一次,治疗为期12周。治疗结束时收集血清和股骨对治疗结果进行评价。结果治疗12周后,治疗组的股骨骨密度较模型组显著升高,差异有统计学意义(P0.05);Mircro-CT显示治疗组大鼠股骨干骺端较模型组具有更多骨小梁以及更佳的骨微观参数,差异有统计学意义(P0.05);生物力学结果显示治疗组股骨的极限载荷和峰值负荷较模型组显著升高,差异有统计学意义(P0.05);而血清检测结果表明治疗组的碱性磷酸酶和骨钙素较模型组明显降低,差异有统计学意义(P0.05)。结论白藜芦醇对酒精诱导雄性大鼠骨量减少和骨强度降低有一定的保护作用。     

乳腺密度对浸润性小叶癌术前MRI评估的影响

目的:探讨MRI在浸润性小叶癌(ILC)术前诊断中的应用价值，并评估乳腺密度对其影响。方法:选取75例术前30天内行MRI检查的I-Ⅲ期ILC患者列入本项研究，根据腺体含量评估乳腺密度，在MRI下测量肿瘤大小，并对比其与病理大小的一致性。结果:75例入组患者中26例（34.7%）患者经MRI评估发现新的可疑病灶，20例（26.7%）患者改变了手术方式。高密度乳腺型患者额外病灶检出率明显高于低密度乳腺型患者（51.6% vs 22.7%，P=0.010）。MRI评估肿瘤大小在低密度乳腺型患者中容易被低估（36.4% vs 12.9%，P=0.024），在高密度乳腺型患者中容易被高估（6.8% vs 29.0%，P=0.010），但两组患者与病理大小一致性的比例无明显差异。结论:ILC患者术前乳腺MRI检查能够提高额外恶性肿瘤的检出率，而乳腺密度是影响MRI评估的重要因素。     

Improving State-Mandated Breast Density Notifications

PurposeEffective written communication directly affects health care outcomes. Since 2016, the complex language of state-mandated breast density notifications (BDNs) has been challenged, because it is perceived to be beyond the comprehension of most patients. The aim of this study was to assess whether a revised BDN written at a lower reading grade level improves understanding compared with the current state-mandated BDN.MethodsA revised notification with similar content to the current state-mandated BDN was developed. Both notifications were presented to patients for direct comparison, using a paper survey asking questions that evaluated patients’ perceptions and convictions associated with breast density. Surveys were distributed at four outpatient imaging centers to screening mammography patients.ResultsThe current BDN’s mean readability metric was 13.4, and that of the revised BDN was 6.6. Five hundred surveys were analyzed. Survey data demonstrated that 56.6% of all women perceived that dense breast tissue results indicated a “high” associated lifetime breast cancer risk from the current state-mandated BDN compared with only 2.2% with the revised notification (P < .001). Nearly all women were more likely to initiate discussions with their providers regarding their breast tissue density after reading the revised notification (96.0%) as opposed to the current state-mandated BDN (32.8%; P < .001).ConclusionsA significant portion of women misinterpret the intended messages of the current state-mandated BDN. Thus, a revised notification at a lower reading grade level may improve understanding of breast density, leading to improved individualized breast cancer screening for women with dense breasts.     

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.     

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%.