Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions With Drones.

In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.

Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability.

Practical brain-computer interfaces need to overcome several challenges, including tolerance to signal variability within- and across sessions. We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variability. Assuming that subjects undergo the same cognitive process or perform the same task in a short period (e.g., a few seconds), as a result, the signal of interest should be represented by only a few components. We verified this approach on a workload detection task, where subjects needed to pilot a simulated drone. We used RPCA as a processing step to decrease trial variability and assessed its impact on classification accuracy. Our results showed that RPCA significantly increased performance in both at group and subject level analysis. On average, class-balanced accuracy when simulating RPCA online increased from 63.9% up to 70.6% $(p~ 0 . 001)$.

Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis.

Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the effectiveness of the proposed method and may shed some light on developing a realistic emotion-classification analytical framework alleviating day-to-day variability.

Src-homology protein tyrosine phosphatase-1 agonist, SC-43, reduces liver fibrosis.

This study aimed to investigate the role of src-homology protein tyrosine phosphatase-1 (SHP-1)-signal transducer and activator of transcription 3 (STAT3) pathway in liver fibrogenesis and the anti-fibrotic effect of SHP-1 agonist. The antifibrotic activity of SC-43, a sorafenib derivative with an enhanced SHP-1 activity, was evaluated in two fibrosis mouse models by carbon tetrachloride induction and bile duct ligation. Rat, human, and primary mouse hepatic stellate cells (HSCs) were used for mechanistic investigations. The results showed that SHP-1 protein primarily localized in fibrotic areas of human and mouse livers. SC-43 treatment reduced the activated HSCs and thus effectively prevented and regressed liver fibrosis in both fibrosis mouse models and improved mouse survival. In vitro studies revealed that SC-43 promoted HSC apoptosis, increased the SHP-1 activity and inhibited phospho-STAT3. The enhanced SHP-1 activity in HSCs significantly inhibited HSC proliferation, whereas SHP-1 inhibition rescued SC-43-induced HSC apoptosis. Furthermore, SC-43 interacted with the N-SH2 domain of SHP-1 to enhance the activity of SHP-1 as its antifibrotic mechanism. In conclusion, the SHP-1-STAT3 pathway is crucial in fibrogenesis. SC-43 significantly ameliorates liver fibrosis through SHP-1 upregulation. A SHP-1-targeted antifibrotic therapy may represent a druggable strategy for antifibrotic drug discovery.

Hypoxia-activated cytotoxic agent tirapazamine enhances hepatic artery ligation-induced killing of liver tumor in HBx transgenic mice.

Transarterial chemoembolization (TACE) is the main treatment for intermediate stage hepatocellular carcinoma (HCC) with Barcelona Clinic Liver Cancer classification because of its exclusive arterial blood supply. Although TACE achieves substantial necrosis of the tumor, complete tumor necrosis is uncommon, and the residual tumor generally rapidly recurs. We combined tirapazamine (TPZ), a hypoxia-activated cytotoxic agent, with hepatic artery ligation (HAL), which recapitulates transarterial embolization in mouse models, to enhance the efficacy of TACE. The effectiveness of this combination treatment was examined in HCC that spontaneously developed in hepatitis B virus X protein (HBx) transgenic mice. We proved that the tumor blood flow in this model was exclusively supplied by the hepatic artery, in contrast to conventional orthotopic HCC xenografts that receive both arterial and venous blood supplies. At levels below the threshold oxygen levels created by HAL, TPZ was activated and killed the hypoxic cells, but spared the normoxic cells. This combination treatment clearly limited the toxicity of TPZ to HCC, which caused the rapid and near-complete necrosis of HCC. In conclusion, the combination of TPZ and HAL showed a synergistic tumor killing activity that was specific for HCC in HBx transgenic mice. This preclinical study forms the basis for the ongoing clinical program for the TPZ-TACE regimen in HCC treatment.

Associated factors and clinical implications of serum aminotransferase elevation in scrub typhus.

BACKGROUND/PURPOSE: Timely diagnosis and prompt treatment can reduce the complications of scrub typhus. It is thus important to find easy laboratory tests to help in the diagnosis, especially in patients without eschar at initial presentation. Because serum aminotransferase elevation is common in scrub typhus, its associated factors and clinical implications need further investigations. METHODS: We conducted a retrospective study in Kinmen, Taiwan, to collect clinically suspected scrub typhus patients notified to Taiwan Centers for Disease Control for confirmation during 2005-2010. Scrub typhus was diagnosed and Orientia tsutsugamushi was genotyped by serological or molecular assays. The laboratory data and clinical information were recorded for analysis. RESULTS: Overall, 344 suspected scrub typhus patients were reported to Taiwan Centers for Disease Control and 288 of them were certified scrub typhus. Scrub typhus patients had significantly more thrombocytopenia, serum aminotransferase elevation (76% vs. 54%, p = 0.001), higher frequency of fever, eschar, and lymphadenopathy, compared with nontyphus patients. Hepatic dysfunction in scrub typhus was associated with older age, longer fever duration, and absence of lymphadenopathy, but seemed to be unrelated to the rickettsial genotypes. Multivariate analysis showed that serum aminotransferase elevation (odds ratio: 3.75; p = 0.003; 95% confidence interval: 1.56-9.01) independently predicted scrub typhus. Furthermore, in suspected scrub typhus patients without eschar, 92% of true typhus patients had serum aminotransferase elevation compared with the nontyphus ones (odds ratio: 6.47; p = 0.028, 95% confidence interval: 1.23-34.11). CONCLUSION: Hepatic dysfunction in scrub typhus patients is associated with older age, longer fever duration, and absence of lymphadenopathy. Serum aminotransferase elevation can aid in the diagnosis of scrub typhus, especially in suspected patients without eschar.

Shear-wave elasticity imaging of a liver fibrosis mouse model using high-frequency ultrasound.

The objective of this study was to develop a high-frequency imaging platform for evaluating liver fibrosis in mice based on shear-wave elasticity imaging (SWEI). Although SWEI has been used to diagnose hepatic fibrosis clinically, it is performed at relatively low frequencies (<20 MHz). For preclinical ultrasound imaging in small animals, a high-frequency (>30 MHz) single-element transducer with mechanical scanning is often used. In this study we developed a new SWEI system based on a 40-MHz single-element transducer for imaging and a separate 20-MHz excitation transducer for producing the radiation force and the associated shear waves. Liver fibrosis was induced in ten C57BL/6 (B6) mice using carbon tetrachloride; the other ten mice served as the control group. Synchronizing the excitation beam (i.e., the beam from the excitation transducer) and the detection beam sequence (i.e., the beam from the imaging transducer) allows this mechanical-scanning setup to analyze the shear-wave dispersion relation. The liver viscoelastic properties were determined in vivo by measuring the shear-wave dispersion curve followed by fitting to the Voigt model. The mice were then killed and the fibrosis stage was evaluated (from F0 to F4) based on the METAVIR score. The measured mean values of liver elasticity and viscosity, respectively, ranged from 1.06 to 1.89 kPa and from 1.29 to 1.75 Pa∙s for normal F0 and fibrosis stages of F3 and F4. The Spearman coefficients for the correlations between the measured elasticity and viscosity at various fibrosis stages as assessed by the METAVIR score were 0.73 (p < 0.001) and 0.634 (p = 0.0013), respectively. We also found that the collagen content in the liver was linearly correlated with the measured elasticity (r(2) = 0.54, p < 0.001) and less strongly with the viscosity (r2 = 0.26, p = 0.022). Finally, the diagnosis performance of high-frequency SWEI was evaluated using multivariate receiver operating characteristic curve (ROC) analysis. The areas under the multivariate ROC curve for diagnosing fibrosis stages of F ≥ 3, F = 4, F0 vs. F3, F0 vs. F4, and F3 vs. F4 were 0.9, 0.98, 0.83, 1.0, and 0.96, respectively. Compared with traditional ROC analysis, an improved diagnosis performance was found for diagnosing fibrosis stages of F ≥ 3 and F0 vs. F3. These results demonstrate that the developed high-frequency SWEI platform can yield quantitative viscoelastic properties for diagnosing various fibrosis stages in mice. It is a promising tool for studying the progression of liver fibrosis in preclinical animal models both noninvasively and quantitatively.

Sorafenib and its derivative SC-1 exhibit antifibrotic effects through signal transducer and activator of transcription 3 inhibition.

Signal transducer and activator of transcription 3 (STAT3) had been involved in liver fibrogenesis. We aimed to explore the antifibrotic activities of sorafenib and its derivative SC-1 (devoid of Raf kinase inhibition activity) both in vivo and in vitro with special focus on the STAT3 pathway in hepatic stellate cells (HSCs). The clinical role of STAT3 in chronic hepatitis B (CHB) was also investigated. Experimental fibrosis mouse models were established by thioacetamide injection and bile duct ligation in Balb/C mice and treated with sorafenib and SC-1. Rat and human HSCs were used for mechanistic investigations. Forty CHB patients were enrolled to quantify the hepatic phospho-STAT3 (p-STAT3) levels and correlated with liver fibrosis. Both sorafenib and SC-1 ameliorated liver fibrosis in vivo and promoted HSC apoptosis in vitro. p-STAT3 and downstream signals were down-regulated after sorafenib and SC-1 treatment in HSC. STAT3 overexpression in HSC enhanced cell proliferation and undermined the apoptotic effects of sorafenib and SC-1, whereas STAT3-specific inhibition promoted HSC apoptosis. Sorafenib and SC-1 activated Src-homology protein tyrosine phosphatase-1 (SHP-1) and STAT3 inhibition followed. Of particular interest, in CHB patients with advanced liver fibrosis, p-STAT3 in HSC was significantly overexpressed and positively correlated with the severity of liver fibrosis and plasma IL-6 levels. In conclusion, sorafenib and SC-1 ameliorate liver fibrosis through STAT3 inhibition in HSC and STAT3 may potentially serve as a promising fibrotic biomarker and target in liver fibrosis. SHP-1 phosphatase-directed STAT3 inhibition may represent a previously unidentified strategy for antifibrotic drug discovery.

Synthetic peroxisome proliferator-activated receptor-gamma agonist, rosiglitazone, increases plasma levels of adiponectin in type 2 diabetic patients.

OBJECTIVE: Adiponectin, a plasma protein exclusively synthesized and secreted by adipose tissue, has recently been shown to have anti-inflammatory, antiatherogenic properties in vitro and beneficial metabolic effects in animals. Lower plasma levels of adiponectin have been documented in human subjects with metabolic syndrome and coronary artery disease. We investigated whether the level of this putative protective adipocytokine could be increased by treatment with a peroxisome proliferator-activated receptor-gamma (PPAR-gamma) agonist in diabetic patients. RESEARCH DESIGN AND METHODS: Type 2 diabetic patients (30 in the treatment group and 34 in the placebo group) were recruited for a randomized double-blind placebo-controlled trial for 6 months with the PPAR-gamma agonist rosiglitazone. Blood samples were collected and metabolic variables and adiponectin levels were determined in all patients before initiation of the study. RESULTS: In the rosiglitazone group, mean plasma adiponectin level was increased by more than twofold (P < 0.0005), whereas no change was observed in the placebo group. Multivariate linear regression analysis showed that whether rosiglitazone was used was the single variable significantly related to the changes of plasma adiponectin. The amount of variance in changes of plasma adiponectin level explained by the treatment was approximately 24% (r(2) = 0.24) after adjusting for age, sex, and changes in fasting plasma glucose, HbA(1c), insulin resistance index, and BMI. CONCLUSIONS: Rosiglitazone increases plasma adiponectin levels in type 2 diabetic subjects. Whether this may contribute to the antihyperglycemic and putative antiatherogenic benefits of PPAR-gamma agonists in type 2 diabetic patients warrants further investigation.