Double dissociation of a latent working memory process

Background. The study explored the construct validity of a computational model of working memory (WM) by determining whether model parameters manifested double dissociations of lesion laterality with type of material studied. The data set modeled involved psychometrically matched verbal and figural WM tasks on which a double dissociation between test version and lesion laterality failed to emerge when total test scores were used as the laterality marker. Method. This re-analysis of a previously published study involved investigating the WM performance of 15 demographically matched controls with 15 adult patients with left-hemispheric (LH) lesions and 15 adult patients with right-hemispheric (RH) lesions. Each participant was given verbal and figural versions of a continuous paired associates test (CPAT). The two versions had previously been psychometrically matched in a larger sample of healthy individuals. A WM model composed of encoding, displacement, and episodic memory parameters was fit to each individual’s performance profiles for both versions of the CPAT. Results. Replicating the previous results for raw scores, rank transformed values of total score performance failed to reveal a double dissociation. Nonetheless an absolute double dissociation was observed for the model’s displacement parameter: RH patients demonstrated deficits on the figural but not verbal WM displacement parameter, whereas LH patients demonstrated deficits on the verbal but not figural WM displacement parameter. Additionally, both LH and RH patients were impaired on the figural encode parameter, perhaps explaining the absence of a double dissociation in total score performance. Conclusions. By combining different patterns of profile and level data into theoretically motivated model parameters, computational models of neurocognition can enhance the construct validity of interpretations of neuropsychological performance.     

Elaboración de modelos predictivos de la gravedad y la mortalidad en pacientes con COVID-19 que acuden al servicio de urgencias,incluida la radiografía torácica

ObjectivesTo develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters.MethodsAll symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model.ResultsA total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC = 0.94 and AUC-PRC = 0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC = 0.97 and AUC-PRC = 0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics.ConclusionThe developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour.     

Application of 3D imaging and selfies in forensic dental identification

With increased access to smartphones, the selfie has gained immense popularity in the past decade. Selfie images could be a significant source of ante-mortem (AM) image data in cases where dental records are unavailable or considered insufficient. The application of 3D imaging and selfies has not been explored to its full potential in human dental identification. The present study aimed to evaluate the feasibility of using selfies as AM data and comparing with 3D post-mortem (PM) scans as an alternative to multiple 2D PM photographs in forensic dental identification. The study sample consisted of 18 selfies (12 matching and 6 non-matching) and 15 3D dental scans obtained from the study participants. The study sample was assessed by 6 experienced forensic odontologists (Raters) in two phases; Phase I-Visual comparison of 2D selfies to 3D scans and Phase II-2D selfies superimposed upon 3D scans. Each rater looked at 15 cases, and a total of 1620 comparisons were made by 6 raters (72 for the 12 matching cases and 1548 were for non-matching) and the opinions for each of the comparisons were analyzed. The results of the study show that use of the 3D superimposition method increased the certainty of the conclusions reached by the raters for the cases with correct matches. Furthermore, 94.2% of the 1548 non-matching comparisons were correctly excluded, compared with 77% for the 2D visual comparison method. The study demonstrated the applicability of this 3D method as a valuable tool in assisting the forensic odontologist with dental identification using selfies.     

A systems-based modelling approach using transurethral resection of the prostate (TURP) specimens yielded incremental prognostic significance to Gleason when predicting long-term outcome in men with localized prostate cancer

Study Type – Prognosis (individual cohort) Level of Evidence 2a What's known on the subject? and What does the study add? Systems models have been successfully utilised to accurately define risk in men with prostate cancer. This study addresses the challenges when using TURP specimens to yield predictive models.

OBJECTIVE

? To develop a systems‐based model for predicting prostate cancer‐specific survival (PCSS) using a conservatively managed cohort with clinically localized prostate cancer and long‐term follow‐up.

PATIENTS AND METHODS

? Transurethral prostate (TURP) specimens in tissue microarray format and medical records from a 758 patient cohort were obtained. ? Slides were stained with haematoxylin and eosin (H&E), imaged and digitally outlined for invasive tumour. ? Additional sections were analysed with two multiplex quantitative immunofluorescence (IF) assays for cytokeratin‐18 (epithelial cells), 4′‐6‐diamidino‐2‐phenylindole(nuclei), p63/high‐molecular‐weight keratin (basal cells), androgen receptor (AR) and α‐methyl CoA‐racemase, Ki67, phosphorylated AKT (pAKT)and CD34. ? Images were acquired with spectral imaging software. H&E and IF images were evaluated with image analysis algorithms; feature data were integrated with clinical variables to construct prognostic models for outcome.

RESULTS

? Using a training set of 256 patients with 24% events, one clinical variable (Gleason score) and two tissue‐specific characteristics (H&E morphometry and tumour‐specific pAKT levels) were identified (concordance index [CoI] 0.79, sensitivity 76%, specificity 86%, hazard ratio [HR] 6.6) for predicting PCSS. ? Validation on an independent cohort of 269 patients with 29% events yielded a CoI of 0.76, sensitivity 59%, specificity 80% and HR of 3.6. ? Both H&E and IF features were selected in a multivariate setting and added incremental prognostic value to the Gleason score alone (CoI 0.77 to CoI 0.79). ? Furthermore, global Ki67 expression and AR levels in Gleason grade 3 tumours were both univariately associated with outcome; however, neither was selected in the final model.

CONCLUSION

? A previously validated prostate needle‐biopsy systems modelling approach that integrates clinical data with reproducible methods to assess H&E morphometry and biomarker expression, provided incremental benefit to the TURP Gleason score for predicting PCSS. ? Ki67 and AR, known to be associated with outcome in the prostate needle biopsy, were not associated with PCSS in multivariate models using TURP specimens.     

Finite-element models of the human head and their applications in forensic practice

Since the 1960s, predictive human head impact indices have been developed to help the investigation of causation of human head injury. Finite-element models (FEM) can provide interesting tools for the forensic scientists when various human head injury mechanisms need to be evaluated. Human head FEMs are mainly used for car crash evaluations and are not in common use in forensic science. Recent technological progress has resulted in creating more simple tools, which will certainly help to consider the use of FEM in routine forensic practice in the coming years. This paper reviews the main FEMs developed and focuses on the models which can be used as predictive tools. Their possible applications in forensic medicine are discussed.     

简易肾癌及膀胱癌原位肿瘤模型的建立与应用研究

目的：建立一种操作简单、稳定可靠的肾癌及膀胱癌原位癌模型,并运用于抗癌药效评价工作。方法将人肾癌肿瘤组织块穿刺接种到肾实质组织中,建立肾癌原位肿瘤模型;用留置针机械摩擦损伤膀胱黏膜,人膀胱癌细胞悬液暴露法建立膀胱癌原位肿瘤模型。通过小动物B超和荷瘤组织的HE染色确定模型建立的可靠性,并采用该模型对硝羟喹啉（ nitroxoline,NTX）的体内抗肿瘤药效进行初步药效评价。结果术后2周通过B超可见肿瘤发生;组织病理HE染色显示肾癌原位模型肿瘤呈现浸润性生长,膀胱癌模型可见明显突向膀胱腔的癌组织团块。 NTX体内药效评价中,60 mg／kg NTX对肾癌原位肿瘤抑制率为57．5％,30 mg／kg NTX对膀胱癌原位肿瘤抑制率为48．8％。结论利用上述方法建立的肾癌及膀胱癌原位肿瘤模型简单、实用,是评价治疗肾癌和膀胱癌抗癌药物的可靠方法。     

妊娠期绵羊不同程度高原缺氧模型的建立

目的 应用西北人工实验舱建立妊娠期绵羊不同程度高原缺氧模型,并评估其氧合状态及氧化损伤程度.方法 采用24只人工授精配种成功的妊娠期绵羊,随机均分为平原对照组(LAC)、轻度低氧暴露组(MHE)和重度低氧暴露组(SHE).用人工实验舱模拟西北地区高原低氧低压低温环境,通过调节温度、湿度、压力和空气氧含量等参数制备MHE与SHE动物模型,分别于建模前(0d)与建模后7、15、30、90、120d检测3组动物氧合状态、生命体征及行为模式,动态检测肝脏组织氧化损伤标志物的水平.结果 经不同条件人工实验舱造模30d(MHE组)及90d(SHE组)后,妊娠期绵羊动脉血气指标分别达到轻度和重度缺氧诊断标准.建模后90d,与LAC组比较,MHE组及SHE组妊娠期绵羊呼吸频率减低(P＜0.05),心率升高(P＜0.05),运动减少,且SHE组饮食较MHE组减少;建模后120d,MHE组及SHE组妊娠期绵羊蛋白质氧化损伤的生物标志物羰基(CO)和细胞氧化损伤的生物标志物丙二醛(MDA)水平均明显高于LAC组(P＜0.05),且SHE组明显高于MHE组(P＜0.05).结论 应用西北人工实验舱成功建立了妊娠期绵羊不同程度高原缺氧模型.高原低氧环境可引起妊娠期绵羊氧化应激损伤.     

Lessons learned: optimization of a murine small bowel resection model

Purpose

Central to the use of murine models of disease is the ability to derive reproducible data. The purpose of this study was to determine factors contributing to variability in our murine model of small bowel resection (SBR).

Methods

Male C57Bl/6 mice were randomized to sham or 50% SBR. The effect of housing type (pathogen-free vs standard housing), nutrition (reconstituted powder vs tube feeding formulation), and correlates of intestinal morphology with gene expression changes were investigated. Multiple linear regression modeling or 1-way analysis of variance was used for data analysis.

Results

Pathogen-free mice had significantly shorter ileal villi at baseline and demonstrated greater villus growth after SBR compared to mice housed in standard rooms. Food type did not affect adaptation. Gene expression changes were more consistent and significant in isolated crypt cells that demonstrated adaptive growth when compared with crypts that did not deepen after SBR.

Conclusion

Maintenance of mice in pathogen-free conditions and restricting gene expression analysis to individual animals exhibiting morphologic adaptation enhances sensitivity and specificity of data derived from this model. These refinements will minimize experimental variability and lead to improved understanding of the complex process of intestinal adaptation.     

急性出血坏死性胰腺炎大鼠肠黏膜PIgR的表达变化

目的了解急性出血坏死性胰腺炎（acute hemorrhagic necrotizing pancreatitis,AHNP）时肠黏膜多聚免疫球蛋白受体（polymeric immunoglobulin receptor,pIgR）表达变化及意义。方法将60只Sprague-Dawley大鼠随机分为假手术组和AHNP组,每组各30只,采用胰胆管内逆行注射5%牛磺胆酸钠溶液诱导大鼠AHNP模型。假手术组行开腹手术翻动肠管后关腹。造模成功12h后处死大鼠,收集胰腺和小肠标本,进行病理检查,检测小肠内容物分泌型免疫球蛋白A（secretory immunoglobulin A,sIgA）含量,用Western blotting法检测小肠黏膜pIgR蛋白的表达。结果与假手术组比较,急性出血坏死性胰腺炎大鼠肠黏膜和胰腺发生病理损伤,小肠内容物sIgA含量显著下降（P〈0.05）,小肠黏膜pIgR的表达水平显著降低（P〈0.001）。sIgA与pIgR显著正相关（P〈0.01）。结论急性出血坏死性胰腺炎大鼠肠黏膜pIgR表达下调在sIgA所介导的肠黏膜免疫屏障功能障碍中可能起重要作用。     

Mammary Involution and Breast Cancer Risk: Transgenic Models and Clinical Studies

Postlactational involution is the process following weaning during which the mammary gland undergoes massive cell death and tissue remodeling as it returns to the pre-pregnant state. Lobular involution is the process by which the breast epithelial tissue is gradually lost with aging of the mammary gland. While postlactational involution and lobular involution are distinct processes, recent studies have indicated that both are related to breast cancer development. Experiments using a variety of rodent models, as well as observations in human populations, suggest that deregulation of postlactational involution may act to facilitate tumor formation. By contrast, new human studies show that completion of lobular involution protects against subsequent breast cancer incidence.