Intraductal carcinoma of the prostate: What we know and what we do not know

Intraductal carcinoma of the prostate (IDC-P) is a malignant, clonal proliferation of cells growing within the basement membrane-bound structures of the prostate. IDC-P is usually associated with unfavorable clinicopathologic parameters such as large tumor volume, high-grade Gleason score, extra prostatic extension and seminal vesicle invasion. Majority of laboratory and patient data suggest that IDC-P represents intraductal spread of invasive carcinoma, rather than a precursor lesion. Additionally, relationship of IDC-P and adjacent invasive carcinoma has been investigated in a series of molecular studies. The differential diagnosis of IDC-P from other lesions is critical for patient management. In this article, we summarize current literatures regarding what we know about IDC-P, including its pathological morphology, incidence, differential diagnosis, molecular features and clinical significance. In addition, we propose several issues that we currently do not know about IDC-P. Further research is needed to better understand the biological nature of IDC-P.     

Effect of long‐chain triglyceride lipid emulsion on bupivacaine‐induced changes in electrophysiological parameters of rabbit Purkinje cells

Lipid emulsions are used in the reversal of local anesthetic toxicity. The aim of this study was to investigate the cellular electrophysiological effects of long‐chain triglyceride lipid emulsion (LCTE) on cardiac action potential characteristics and conduction disturbances induced by bupivacaine. Purkinje fibers were dissected from the left ventricle of New Zealand white rabbit hearts and superfused with either Tyrode's solution during 30 min (control group), with bupivacaine 10^?6 m , 10^?5 m , and 5.10^?5 m alone, or in the presence of LCTE 0.5%, in addition, LCTE at 0.1%, 0.5%, and 1% was perfused alone. Electrophysiological parameters were recorded using the conventional microelectrode technique (37 °C, 1 Hz frequency). Bupivacaine 5.10^?5 m ‐induced conduction blocks (8/8 preparations): LCTE 0.5% suppressed the bupivacaine 5.10^?5 m ‐induced conduction blocks (1/8 preparations). Exposure to bupivacaine 10^?6 m , 10^?5 m , and 5.10^?5 m resulted in a significant decrease in the maximal rate of depolarization (V_max) (respectively, 25%, 55%, 75%; P < 0.002 vs. control group). In the presence of LCTE 0.5%, bupivacaine 10^?6 m did not significantly decreased V_max (13%; P = 0.10 vs. control group). The decrease in V_max resulting from bupivacaine 10^?5 m alone was significantly less in the presence of LCTE 0.5% (P < 0.01 vs. bupivacaine 10^?5 m alone). Exposure to bupivacaine 10^?6 m , 10^?5 m , and 5.10^?5 m alone or in the presence of LCTE 0.5% resulted in a significant decrease in action potential duration measured at 50% and 90% repolarization (APD₅₀ and APD₉₀; P < 0.01 vs. control group). LCTE inhibited the Purkinje fibers conduction blocks induced by bupivacaine. Moreover, LCTE 0.5% attenuates the decrease in V_max induced by bupivacaine 10^?6 m and 10^?5 m .     

Emotion recognition from physiological signals

Emotion recognition is one of the great challenges in human–human and human–computer interaction. Accurate emotion recognition would allow computers to recognize human emotions and therefore react accordingly. In this paper, an approach for emotion recognition based on physiological signals is proposed. Six basic emotions: joy, sadness, fear, disgust, neutrality and amusement are analysed using physiological signals. These emotions are induced through the presentation of International Affecting Picture System (IAPS) pictures to the subjects. The physiological signals of interest in this analysis are: electromyogram signal (EMG), respiratory volume (RV), skin temperature (SKT), skin conductance (SKC), blood volume pulse (BVP) and heart rate (HR). These are selected to extract characteristic parameters, which will be used for classifying the emotions. The SVM (support vector machine) technique is used for classifying these parameters. The experimental results show that the proposed methodology provides in general a recognition rate of 85% for different emotional states.     

Anesthetic gases and occupationally exposed workers

The aim of this study is to estimate whether the occupational exposure to low dose anesthetic gases could cause alterations of blood parameters in health care workers. 119 exposed subjects and 184 not exposed controls were included in the study. Each worker underwent the complete blood count test (CBC), proteinaemia, leukocyte count, serum lipids, liver and kidney blood markers.The liver blood markers show statistically significant differences in health care workers compared with controls (p < 0.05), a statistically significant decrease in neutrophils and an increase of lymphocytes in health care workers compared with controls (p < 0.05). The prevalence of values outside the range for GPT, GGT, total bilirubin, lymphocytes and neutrophils was statistically significant in health care workers compared with controls (p < 0.05). The results suggest that occupational exposure to low dose anesthetic gases could influence some haematochemical hepatic and hematopoietic parameters in exposed health care workers.     

弹性定量参数在触诊阴性乳腺实性肿块中的应用价值

目的 探讨弹性定量参数在临床触诊阴性乳腺病(non-palpablebreast lesions,NPBL)中的诊断价值。方法 选取2013年1月-2015年6月在我院行高频超声并经手术病理证实的NPBL患者。依照手术后病理结果分为良性病灶组(A组,n=110),恶性病灶组(B组,n=40)。分别比较并分析两组弹性应变率(SR)比值、直径变化率、面积比及二维超声检查。应用受试者操作特性曲线(ROC)曲线分析SR比值、直径变化率、面积比、二维超声及三种弹性定量参数联合诊断乳腺癌的ROC曲线下面积(AUC)、最佳界值、敏感度和特异度。结果B组SR水平(6.04±2.53)显著高于A组水平(2.86±1.51),差异有统计学意义(t=5.031；p=0.000)。根据两组SR水平制作ROC曲线,AUC为0.844,以5.22作为最佳界值,诊断乳腺癌的敏感度为57.5％,特异度为96.4％。B组病灶的直径变化率(0.28±0.12)显著高于A组(0.17±0.09),同时面积比(1.96±0.28)亦显著高于A组(1.12±0.33)(p值均<0.05)。通过SR、直径变化率、面积比水平联合诊断乳腺癌,敏感度为77.5％,特异度为94.5％,AUC为0.937,高于单项指标。结论 通过三种弹性定量参数联合诊断能够提高对乳腺癌的诊断能力。     

Investigation of the effects of process variables on derived properties of spray dried solid-dispersions using polymer based response surface model and ensemble artificial neural network models

The objective of this study was to use different statistical tools to understand and optimize the spray drying process to prepare solid dispersions. In this study we investigated the relationship between input variables (inlet temperature, feed concentration, flow rate, solvent and atomization parameters) and quality attributes (yield, outlet temperature and mean particle size) of spray dried solid dispersions (SSDs) using response surface model and ensemble artificial neural network. The Box Behnken design was developed to investigate the effect of various input variables on quality attributes of final products. Moreover, Pearson correlation analysis, self organizing map, contour plots and response surface plot were used to illustrate the relationship between input variables and quality attributes. The influence of different physicochemical properties of solvent on the quality attributes of spray dried products was also investigated. Final validation of prepared models was done using binary SSDs of six model drugs with PVP. Results demonstrated the effectiveness of proposed PVP based model which can help scientists to gain detailed understanding of spray drying process of solid dispersion using minimal resources and time during early formulation development stage. It will also help them to ensure consistent quality of SSDs using broad range of input variables.