Detection of pancreatic cancer with two- and three-dimensional radiomic analysis in a nationwide population-based real-world dataset.

BACKGROUND: CT is the major detection tool for pancreatic cancer (PC). However, approximately 40% of PCs < 2 cm are missed on CT, underscoring a pressing need for tools to supplement radiologist interpretation. METHODS: Contrast-enhanced CT studies of 546 patients with pancreatic adenocarcinoma diagnosed by histology/cytology between January 2005 and December 2019 and 733 CT studies of controls with normal pancreas obtained between the same period in a tertiary referral center were retrospectively collected for developing an automatic end-to-end computer-aided detection (CAD) tool for PC using two-dimensional (2D) and three-dimensional (3D) radiomic analysis with machine learning. The CAD tool was tested in a nationwide dataset comprising 1,477 CT studies (671 PCs, 806 controls) obtained from institutions throughout Taiwan. RESULTS: The CAD tool achieved 0.918 (95% CI, 0.895-0.938) sensitivity and 0.822 (95% CI, 0.794-0.848) specificity in differentiating between studies with and without PC (area under curve 0.947, 95% CI, 0.936-0.958), with 0.707 (95% CI, 0.602-0.797) sensitivity for tumors < 2 cm. The positive and negative likelihood ratios of PC were 5.17 (95% CI, 4.45-6.01) and 0.10 (95% CI, 0.08-0.13), respectively. Where high specificity is needed, using 2D and 3D analyses in series yielded 0.952 (95% CI, 0.934-0.965) specificity with a sensitivity of 0.742 (95% CI, 0.707-0.775), whereas using 2D and 3D analyses in parallel to maximize sensitivity yielded 0.915 (95% CI, 0.891-0.935) sensitivity at a specificity of 0.791 (95% CI, 0.762-0.819). CONCLUSIONS: The high accuracy and robustness of the CAD tool supported its potential for enhancing the detection of PC.

Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study.

Background Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT. Purpose To develop and to validate a deep learning (DL)-based tool able to detect pancreatic cancer at CT. Materials and Methods Retrospectively collected contrast-enhanced CT studies in patients diagnosed with pancreatic cancer between January 2006 and July 2018 were compared with CT studies of individuals with a normal pancreas (control group) obtained between January 2004 and December 2019. An end-to-end tool comprising a segmentation convolutional neural network (CNN) and a classifier ensembling five CNNs was developed and validated in the internal test set and a nationwide real-world validation set. The sensitivities of the computer-aided detection (CAD) tool and radiologist interpretation were compared using the McNemar test. Results A total of 546 patients with pancreatic cancer (mean age, 65 years ± 12 [SD], 297 men) and 733 control subjects were randomly divided into training, validation, and test sets. In the internal test set, the DL tool achieved 89.9% (98 of 109; 95% CI: 82.7, 94.9) sensitivity and 95.9% (141 of 147; 95% CI: 91.3, 98.5) specificity (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI: 0.94, 0.99), without a significant difference (P = .11) in sensitivity compared with the original radiologist report (96.1% [98 of 102]; 95% CI: 90.3, 98.9). In a test set of 1473 real-world CT studies (669 malignant, 804 control) from institutions throughout Taiwan, the DL tool distinguished between CT malignant and control studies with 89.7% (600 of 669; 95% CI: 87.1, 91.9) sensitivity and 92.8% specificity (746 of 804; 95% CI: 90.8, 94.5) (AUC, 0.95; 95% CI: 0.94, 0.96), with 74.7% (68 of 91; 95% CI: 64.5, 83.3) sensitivity for malignancies smaller than 2 cm. Conclusion The deep learning-based tool enabled accurate detection of pancreatic cancer on CT scans, with reasonable sensitivity for tumors smaller than 2 cm. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Aisen and Rodrigues in this issue.

Effect of Remote Ischemic Conditioning vs Usual Care on Neurologic Function in Patients With Acute Moderate Ischemic Stroke: The RICAMIS Randomized Clinical Trial.

Importance: Preclinical and clinical studies have suggested a neuroprotective effect of remote ischemic conditioning (RIC), which involves repeated occlusion/release cycles on bilateral upper limb arteries; however, robust evidence in patients with ischemic stroke is lacking. Objective: To assess the efficacy of RIC for acute moderate ischemic stroke. Design, Setting, and Participants: This multicenter, open-label, blinded-end point, randomized clinical trial including 1893 patients with acute moderate ischemic stroke was conducted at 55 hospitals in China from December 26, 2018, through January 19, 2021, and the date of final follow-up was April 19, 2021. Interventions: Eligible patients were randomly assigned within 48 hours after symptom onset to receive treatment with RIC (using a pneumatic electronic device and consisting of 5 cycles of cuff inflation for 5 minutes and deflation for 5 minutes to the bilateral upper limbs to 200 mm Hg) for 10 to 14 days as an adjunct to guideline-based treatment (n = 922) or guideline-based treatment alone (n = 971). Main Outcomes and Measures: The primary end point was excellent functional outcome at 90 days, defined as a modified Rankin Scale score of 0 to 1. All end points had blinded assessment and were analyzed on a full analysis set. Results: Among 1893 eligible patients with acute moderate ischemic stroke who were randomized (mean [SD] age, 65 [10.3] years; 606 women [34.1%]), 1776 (93.8%) completed the trial. The number with excellent functional outcome at 90 days was 582 (67.4%) in the RIC group and 566 (62.0%) in the control group (risk difference, 5.4% [95% CI, 1.0%-9.9%]; odds ratio, 1.27 [95% CI, 1.05-1.54]; P = .02). The proportion of patients with any adverse events was 6.8% (59/863) in the RIC group and 5.6% (51/913) in the control group. Conclusions and Relevance: Among adults with acute moderate ischemic stroke, treatment with remote ischemic conditioning compared with usual care significantly increased the likelihood of excellent neurologic function at 90 days. However, these findings require replication in another trial before concluding efficacy for this intervention. Trial Registration: ClinicalTrials.gov Identifier: NCT03740971.

Radiomics utilization to differentiate nonfunctional adenoma in essential hypertension and functional adenoma in primary aldosteronism.

We performed the present study to investigate the role of computed tomography (CT) radiomics in differentiating nonfunctional adenoma and aldosterone-producing adenoma (APA) and outcome prediction in patients with clinically suspected primary aldosteronism (PA). This study included 60 patients diagnosed with essential hypertension (EH) with nonfunctional adenoma on CT and 91 patients with unilateral surgically proven APA. Each whole nodule on unenhanced and venous phase CT images was segmented manually and randomly split into training and test sets at a ratio of 8:2. Radiomic models for nodule discrimination and outcome prediction of APA after adrenalectomy were established separately using the training set by least absolute shrinkage and selection operator (LASSO) logistic regression, and the performance was evaluated on test sets. The model can differentiate adrenal nodules in EH and PA with a sensitivity, specificity, and accuracy of 83.3%, 78.9% and 80.6% (AUC = 0.91 [0.72, 0.97]) in unenhanced CT and 81.2%, 100% and 87.5% (AUC = 0.98 [0.77, 1.00]) in venous phase CT, respectively. In the outcome after adrenalectomy, the models showed a favorable ability to predict biochemical success (Unenhanced/venous CT: AUC = 0.67 [0.52, 0.79]/0.62 [0.46, 0.76]) and clinical success (Unenhanced/venous CT: AUC = 0.59 [0.47, 0.70]/0.64 [0.51, 0.74]). The results showed that CT-based radiomic models hold promise to discriminate APA and nonfunctional adenoma when an adrenal incidentaloma was detected on CT images of hypertensive patients in clinical practice, while the role of radiomic analysis in outcome prediction after adrenalectomy needs further investigation.

Comparison of water- and alkali-extracted polysaccharides from Fuzhuan brick tea and their immunomodulatory effects in vitro and in vivo.

In the present study, the purpose is to compare the effect of water extraction and alkali-assisted extraction on the structural characteristics and immunomodulatory activity of polysaccharides from Fuzhuan brick tea (FBTPs). The results indicated that water-extracted FBTPs (W-FBTPs) and alkali-extracted FBTPs (A-FBTPs) had similar molecular weights but different monosaccharide compositions, of which A-FBTPs had a higher yield and uronic acid groups corresponding to galacturonic acid (GalA). Moreover, A-FBTPs had stronger ability to promote phagocytic capacity, acid phosphatase activity and nitric oxide (NO) secretion in macrophages in vitro. In the in vivo study, A-FBTPs exhibited a promising effect to adjust the immune imbalance by enhancing the body features, antioxidant activities, immune response and intestinal mucosal barrier in cytoxan (CTX)-induced immunosuppressive mice. Besides, A-FBTP supplementation effectively improved CTX-induced gut microbiota dysbiosis, including promoting the abundance of beneficial bacteria (e.g., Lactobacillus) and short chain fatty acid (SCFA)-producing bacteria (e.g., Lachnospiraceae, Prevotellaceae and Ruminococcaceae), along with reducing the growth of potentially pathogenic microbes (e.g., Desulfovibrionaceae and Helicobacter). These findings suggested that alkaline extraction might be a promising way to obtain high-quality acidic polysaccharides from Fuzhuan brick tea (FBT), and A-FBTPs could be developed as novel potential prebiotics and immunomodulators for further application in food formulations.

Radiomic Features at CT Can Distinguish Pancreatic Cancer from Noncancerous Pancreas.

Purpose To identify distinguishing CT radiomic features of pancreatic ductal adenocarcinoma (PDAC) and to investigate whether radiomic analysis with machine learning can distinguish between patients who have PDAC and those who do not. Materials and Methods This retrospective study included contrast material-enhanced CT images in 436 patients with PDAC and 479 healthy controls from 2012 to 2018 from Taiwan that were randomly divided for training and testing. Another 100 patients with PDAC (enriched for small PDACs) and 100 controls from Taiwan were identified for testing (from 2004 to 2011). An additional 182 patients with PDAC and 82 healthy controls from the United States were randomly divided for training and testing. Images were processed into patches. An XGBoost (https://xgboost.ai/) model was trained to classify patches as cancerous or noncancerous. Patients were classified as either having or not having PDAC on the basis of the proportion of patches classified as cancerous. For both patch-based and patient-based classification, the models were characterized as either a local model (trained on Taiwanese data only) or a generalized model (trained on both Taiwanese and U.S. data). Sensitivity, specificity, and accuracy were calculated for patch- and patient-based analysis for the models. Results The median tumor size was 2.8 cm (interquartile range, 2.0-4.0 cm) in the 536 Taiwanese patients with PDAC (mean age, 65 years ± 12 [standard deviation]; 289 men). Compared with normal pancreas, PDACs had lower values for radiomic features reflecting intensity and higher values for radiomic features reflecting heterogeneity. The performance metrics for the developed generalized model when tested on the Taiwanese and U.S. test data sets, respectively, were as follows: sensitivity, 94.7% (177 of 187) and 80.6% (29 of 36); specificity, 95.4% (187 of 196) and 100% (16 of 16); accuracy, 95.0% (364 of 383) and 86.5% (45 of 52); and area under the curve, 0.98 and 0.91. Conclusion Radiomic analysis with machine learning enabled accurate detection of PDAC at CT and could identify patients with PDAC. Keywords: CT, Computer Aided Diagnosis (CAD), Pancreas, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. © RSNA, 2021.

Portable rotating grating stimulation for anisometropic amblyopia with 6 months training.

Treatment of grating stimulation has been used in amblyopia for decades, but high dropout rate and inconvenience for daily practice occur in previous studies. We developed a home-based portable system with rotating grating stimulation on a tablet. Thirty anisometropic amblyopic children were randomly allocated into the control or Grating group. They drew contour of the picture under patch of a better eye for 6 months. Best-corrected visual acuity (BCVA), grating acuity (GA), and contrast sensitivity (CS) were assessed at the baseline, 1st, 2nd, 3rd, and 6th months of training. All participants completed the 6-month training. Patched eyes of both groups exhibited no difference. Trained eyes of the control group had significantly slight improvement in BCVA and GA. In particular, the Grating group exhibited significantly higher BCVA, GA, and CS compared with those of the control group at the 3rd and 6th months of training. Moreover, percentage of the Grating group with great improvement (BCVA ≥ 0.3 or CS ≥ 0.3) was significantly larger than those of the control group at the 3rd or 6th months of training. The portable grating stimulation system demonstrates its trainability by no dropout and effectiveness by significant improvements in all assessments through a well experimental design.Trial Registration: ClinicalTrials.gov NCT04213066, registered 30/12/2019, https://clinicaltrials.gov/ct2/show/NCT04213066 .

Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography.

Although digoxin is important in heart rate control, the utilization of digoxin is declining due to its narrow therapeutic window. Misdiagnosis or delayed diagnosis of digoxin toxicity is common due to the lack of awareness and the time-consuming laboratory work that is involved. Electrocardiography (ECG) may be able to detect potential digoxin toxicity based on characteristic presentations. Our study attempted to develop a deep learning model to detect digoxin toxicity based on ECG manifestations. This study included 61 ECGs from patients with digoxin toxicity and 177,066 ECGs from patients in the emergency room from November 2011 to February 2019. The deep learning algorithm was trained using approximately 80% of ECGs. The other 20% of ECGs were used to validate the performance of the Artificial Intelligence (AI) system and to conduct a human-machine competition. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of ECG interpretation between humans and our deep learning system. The AUCs of our deep learning system for identifying digoxin toxicity were 0.912 and 0.929 in the validation cohort and the human-machine competition, respectively, which reached 84.6% of sensitivity and 94.6% of specificity. Interestingly, the deep learning system using only lead I (AUC = 0.960) was not worse than using complete 12 leads (0.912). Stratified analysis showed that our deep learning system was more applicable to patients with heart failure (HF) and without atrial fibrillation (AF) than those without HF and with AF. Our ECG-based deep learning system provides a high-accuracy, economical, rapid, and accessible way to detect digoxin toxicity, which can be applied as a promising decision supportive system for diagnosing digoxin toxicity in clinical practice.

Applications of artificial intelligence in pancreatic and biliary diseases.

The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.

Early myocardial changes in patients undergoing rheumatic mitral valve repair versus replacement.

BACKGROUND: Rheumatic mitral valve repair and replacement techniques are frequently used with excellent outcomes in experienced centers. This study aims to evaluate the impact of procedural types on left ventricular function in quinquagenarians. METHODS: Between January 2018 and September 2019, patients with severe rheumatic mitral stenosis were prospectively recruited. Propensity score matching was performed to reduce the selection bias. We compared the strain, twist and synchrony parameters of left ventricle in 70 quinquagenarian patients who underwent rheumatic mitral valve repair and replacement 12 hours before surgery, at 7 days and 6 months postoperatively. RESULTS: The overall group displayed significant improvement of left ventricular deformation after rheumatic mitral valve surgery. Compared with patients undergoing posterior chordal-sparing mitral valve replacement, patients undergoing rheumatic mitral valve repair showed more significant amelioration in global longitudinal strain (-18.6% versus -16.2%, P<0.001), twist (18.2° versus 15.9°, P<0.001), torsion (1.8°/cm versus 1.3°/cm, P<0.001), apical rotation (10.5° versus 8.8°, P<0.001), basal rotation (-7.7° versus -7.1°, P=0.049), systolic dyssynchrony index (4.7% versus 5.1%, P=0.021), standard deviation of time to peak longitudinal (46.9 ms versus 49.3 ms, P=0.024) and radial strain (15.8 ms versus 17.1 ms, P=0.037) at 6-month follow-up. CONCLUSIONS: Rheumatic mitral valve repair might provide patients with better postoperative left ventricular performance than posterior chordal-sparing mitral valve replacement. Longer follow-up is required to compare long-term outcomes.

Portable wireless neurofeedback system of EEG alpha rhythm enhances memory.

BACKGROUND: Effect of neurofeedback training (NFT) on enhancement of cognitive function or amelioration of clinical symptoms is inconclusive. The trainability of brain rhythm using a neurofeedback system is uncertainty because various experimental designs are used in previous studies. The current study aimed to develop a portable wireless NFT system for alpha rhythm and to validate effect of the NFT system on memory with a sham-controlled group. METHODS: The proposed system contained an EEG signal analysis device and a smartphone with wireless Bluetooth low-energy technology. Instantaneous 1-s EEG power and contiguous 5-min EEG power throughout the training were developed as feedback information. The training performance and its progression were kept to boost usability of our device. Participants were blinded and randomly assigned into either the control group receiving random 4-Hz power or Alpha group receiving 8-12-Hz power. Working memory and episodic memory were assessed by the backward digital span task and word-pair task, respectively. RESULTS: The portable neurofeedback system had advantages of a tiny size and long-term recording and demonstrated trainability of alpha rhythm in terms of significant increase of power and duration of 8-12 Hz. Moreover, accuracies of the backward digital span task and word-pair task showed significant enhancement in the Alpha group after training compared to the control group. CONCLUSIONS: Our tiny portable device demonstrated success trainability of alpha rhythm and enhanced two kinds of memories. The present study suggest that the portable neurofeedback system provides an alternative intervention for memory enhancement.

Profiling and Distribution of Metabolites of Procyanidin B2 in Mice by UPLC-DAD-ESI-IT-TOF-MSn Technique.

The metabolite profiles and distributions of procyanidin B2 were qualitatively described using UPLC-DAD-ESI-IT-TOF-MSn without help of reference standards, and a possible metabolic pathway was proposed in the present study. Summarily, 53 metabolites (24 new metabolites) were detected as metabolites of procyanidin B2, and 45 of them were tentatively identified. Twenty seven metabolites were assigned as similar metabolites of (-)-epicatechin by scission of the flavanol interflavanic bond C4-C8, including 16 aromatic metabolites, 5 conjugated metabolites, 3 ring-cleavage metabolites, and 2 phenylvalerolactone metabolites. Additionally, 14 metabolites were conjugates of free procyanidin B2, comprising 9 methylation metabolites, 8 sulfation metabolites, 5 hydration metabolites, 2 hydroxylation metabolites, 1 hydrogenation metabolites, and 1 glucuronidation metabolites. The results of metabolite distributions in organs indicated that the conjugated reaction of free procyanidin B2 mainly occurred in liver and diversified metabolites forms were observed in small intestine. The metabolic components of procyanidin B2 identified in mice provided useful information for further study of the bioactivity and mechanism of its action.

Outcomes of Coronary Artery Bypass and Stents for Unprotected Left Main Coronary Stenosis.

BACKGROUND: This study assessed the short-, medium-, and long-term outcomes of coronary artery bypass grafting vs stenting for patients with unprotected left main coronary artery disease through a meta-analysis of randomized controlled trials. METHODS: PubMed, Embase, Scopus, Web of Science, Cochrane Library, and major conference proceedings databases were systematically searched for randomized controlled trials of coronary artery bypass grafting compared with stents in unprotected left main coronary artery disease. End points assessed were all-cause death, myocardial infarction, major adverse cardiac and cerebrovascular events, target vessel revascularization, and cerebral stroke. A meta-analysis was conducted according to predefined clinical end points. RESULTS: All-cause death and stroke were similar between stenting and coronary artery bypass grafting at 1 year and at follow-up beyond 1 year. The incidence of myocardial infarction was similar between stenting and coronary artery bypass grafting at each separate time point. The incidence of repeat revascularization was similar between the two groups at 30 days but was higher for stenting at 1 year and beyond. There was a trend toward fewer major adverse cardiac and cerebrovascular events after stenting compared with coronary artery bypass grafting at 30 days, but this difference was no longer significant at 1 year and reversed at follow-up beyond 1 year. CONCLUSIONS: The early advantages of stenting over coronary artery bypass grafting have been shown to progressively shift to coronary artery bypass grafting over time. Further larger sample randomized controlled trials are warranted to confirm the results.

Development and Evaluation of a Wearable Device for Sleep Quality Assessment.

OBJECTIVE: In this study, a wearable actigraphy recording device with low sampling rate (1 Hz) for power saving and data reduction and a high accuracy wake-sleep scoring method for the assessment of sleep were developed. METHODS: The developed actigraphy recorder was successfully applied to overnight recordings of 81 subjects with simultaneous polysomnography (PSG) measurements. The total length of recording reached 639.8 h. A wake-sleep scoring method based on the concept of movement density evaluation and adaptive windowing was proposed. Data from subjects with good (N = 43) and poor (N = 16) sleep efficiency (SE) in the range of 52.7-97.42% were used for testing. The Bland-Altman technique was used to evaluate the concordance of various sleep measurements between the manual PSG scoring and the proposed actigraphy method. RESULTS: For wake-sleep staging, the average accuracy, sensitivity, specificity, and kappa coefficient of the proposed system were 92.16%, 95.02%, 71.30%, and 0.64, respectively. For the assessment of SE, the accuracy of classifying the subject with good or poor SE reached 91.53%. The mean biases of SE, sleep onset time, wake after sleep onset, and total sleep time were -0.95%, 0.74 min, 2.84 min, and -4.3 min, respectively. CONCLUSION: These experimental results demonstrate the robustness and reliability of our method using limited activity information to estimate wake-sleep stages during overnight recordings. SIGNIFICANCE: The results suggest that the proposed wearable actigraphy system is practical for the in-home screening of objective sleep measurements and objective evaluation of sleep improvement after treatment.

Insights into chitosan multiple functional properties: the role of chitosan conformation in the behavior of liposomal membrane.

Interactions between chitosan and the liposomal membrane are relevant to the physiological functionality of chitosan, including dietary fiber, antimicrobial action, and fabrication of a delivery system for bioactives. To elucidate the multiple functions of chitosan, the dependence of liposomal membrane properties on the biopolymer conformation was investigated. The concentration dependence of chitosan conformation in aqueous solution was quantified by fluorescence and viscosity measurements. As the concentration increased, the extended chains of chitosan (0-1.0 mg mL(-1)) partially crimped (1.0-1.5 mg mL(-1)), and then self-aggregated forming irregular coils (>1.5 mg mL(-1)). Adsorption of chitosan linear chains on the liposomal membrane surface tended to maintain the morphology of liposomes, decrease the membrane interior micropolarity and rigidify the liposomal membrane. However, these effects were negligible or even opposite in the case of chitosan coils. Analysis on the membrane fluidity revealed that the microviscosity of liposomes decorated by 1.5 mg mL(-1) concentration of chitosan decreased by 17% after being heated at 80 °C for 10 min, in contrast to the decreased percentage of 55 at 4 mg mL(-1). Additionally, compared with the poor oxidative stability of liposomes decorated by chitosan coils, those decorated by chitosan linear chains exhibited slight lipid peroxidation with the TBARS inhibition of around 10% and 6% against oxygen and ferric ions, respectively. These findings suggest that the conformational effects of chitosan on the liposomal membrane are responsible for its multiple functional properties.

Using off-the-shelf lossy compression for wireless home sleep staging.

BACKGROUND: Recently, there has been increasing interest in the development of wireless home sleep staging systems that allow the patient to be monitored remotely while remaining in the comfort of their home. However, transmitting large amount of Polysomnography (PSG) data over the Internet is an important issue needed to be considered. In this work, we aim to reduce the amount of PSG data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to classify sleep stages. NEW METHOD: We examine the effects of off-the-shelf lossy compression on an all-night PSG dataset from 20 healthy subjects, in the context of automated sleep staging. The popular compression method Set Partitioning in Hierarchical Trees (SPIHT) was used, and a range of compression levels was selected in order to compress the signals with various degrees of loss. In addition, a rule-based automatic sleep staging method was used to automatically classify the sleep stages. RESULTS: Considering the criteria of clinical usefulness, the experimental results show that the system can achieve more than 60% energy saving with a high accuracy (>84%) in classifying sleep stages by using a lossy compression algorithm like SPIHT. COMPARISON WITH EXISTING METHOD(S): As far as we know, our study is the first that focuses how much loss can be tolerated in compressing complex multi-channel PSG data for sleep analysis. CONCLUSIONS: We demonstrate the feasibility of using lossy SPIHT compression for wireless home sleep staging.

A meta-analysis of robotic versus laparoscopic colectomy.

BACKGROUND: Robotics, as an innovation of minimally invasive surgical methods, is developing rapidly for colectomy. But there is still no consensus on its comparative merit compared with laparoscopic resections. We conducted this meta-analysis that included randomized controlled trials and nonrandomized controlled trials of robotic colectomy (RC) versus laparoscopic colectomy (LC) to evaluate whether the safety and efficacy of RC are equivalent to those of LC. METHODS: A search of five databases (PubMed, Embase, Cochrane Library, Ovid, and Web of Science), gray literature, hand searches, reference, and forward citation were performed for studies that compared clinical or oncologic outcomes of LC with RC. Clinical outcomes evaluated were conversion rates, operation times, estimated blood loss, length of hospital stay, and complications. Oncologic outcome evaluated was the number of lymph nodes collected. RESULTS: A total of 14 studies were identified that included 125,989 patients in total, 4934 in the robotic cohort and 121,055 in the laparoscopic cohort. Meta-analysis suggested that there was a significantly longer hospital stay in the laparoscopic group (mean difference [MD] -0.65; 95% confidence interval [CI] -1.02 to -0.27; P = 0.0008). Robotic surgery was associated with a significantly lower complication rate (odds ratio 0.78; 95% CI 0.72-0.85; P ＜ 0.00001) and a significantly shorter time to recovery of bowel function (MD -0.58; 95% CI -0.96 to -0.20; P = 0.003). There were statistically significant differences in estimated blood loss (MD -19.24; 95% CI -29.38 to -9.09; P = 0.0002) and intraoperative conversion to open (odds ratio 0.56; 95% CI 0.44-0.72; P < 0.00001), but not clinical relevant. There were no significant differences in the number of lymph nodes extracted between the two groups. However, operating time (MD 49.25; 95% CI 36.78-61.72; P < 0.00001) was longer for RC than for LC. CONCLUSIONS: RC can be performed safely and effectively with the number of lymph nodes extracted similar to LC. In addition, it can provide potential advantages of a shorter hospital stay, a shorter time to recovery of bowel function, and lower occurrence of postoperative complications. These findings seem to support the use of robotics for the minimally invasive surgical management of colectomy. However, RC had longer operating time. Future studies involving RC should focus on minimizing duration of operation.

Closed-loop seizure control on epileptic rat models.

In this paper numerous alternative treatments in addition to pharmacological therapy are proposed for their use in epileptic patients. Epileptic animal models can play a crucial role in the performance evaluation of new therapeutic techniques. The objective of this research is to first develop various epileptic rat models; second, develop a portable wireless closed-loop seizure controller including on-line seizure detection and real-time electrical stimulation for seizure elimination; and third, apply the developed seizure controller to the animal models to perform on-line seizure elimination. The closed-loop seizure controller was applied to three Long-Evans rats with spontaneous spike-wave discharges (non-convulsive) and three Long-Evans rats with epileptiform activities induced by pentylenetetrazol (PTZ) injection (convulsive) for evaluation. The seizure detection accuracy is greater than 92% (up to 99%), and averaged seizure detection latency is less than 0.6 s for both spontaneous non-convulsive and PTZ-induced convulsive seizures. The average false stimulation rate is 3.1%. Near 30% of PTZ-induced convulsive seizures need more than two times of 0.5 s electrical stimulation for suppression and 90% of the non-convulsive seizures can be suppressed by only one 0.5 s electrical stimulation.

A closed-loop brain computer interface for real-time seizure detection and control.

The worldwide prevalence of epilepsy is approximately 1%, and 25% of epilepsy patients cannot be treated sufficiently by available therapies. Brain stimulation with closed-loop seizure control has recently been proposed as an innovative and effective alternative. In this paper, a portable closed-loop brain computer interface for seizure control was developed and shown with several aspects of advantages, including high seizure detection rate (92-99% during wake-sleep states), low false detection rate (1.2-2.5%), and small size. The seizure detection and electrical stimulation latency was not greater than 0.6 s after seizure onset. A wireless communication feature also provided flexibility for subjects freeing from the hassle of wires. Experimental data from freely moving rats supported the functional possibility of a real-time closed-loop seizure controller.