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Purpose: To this day there is no consensus regarding evidence of usefulness of Intraoperative Neurophysiological Monitoring (IONM). Randomized controlled trials have not been performed in the past mainly because of difficulties in recruitment control subjects. In this study, we propose the use of Bayesian Networks to assess evidence in IONM. Methods: Single center retrospective study from January 2020 to January 2022. Patients admitted for cranial neurosurgery with intraoperative neuromonitoring were enrolled. We built a Bayesian Network with utility calculation using expert domain knowledge based on logistic regression as potential causal inference between events in surgery that could lead to central nervous system injury and postoperative neurological function. Results: A total of 267 patients were included in the study: 198 (73.9%) underwent neuro-oncology surgery and 69 (26.1%) neurovascular surgery. 50.7% of patients were female while 49.3% were male. Using the Bayesian Network´s original state probabilities, we found that among patients who presented with a reversible signal change that was acted upon, 59% of patients would wake up with no new neurological deficits, 33% with a transitory deficit and 8% with a permanent deficit. If the signal change was permanent, in 16% of the patients the deficit would be transitory and in 51% it would be permanent. 33% of patients would wake up with no new postoperative deficit. Our network also shows that utility increases when corrective actions are taken to revert a signal change. Conclusions: Bayesian Networks are an effective way to audit clinical practice within IONM. We have found that IONM warnings can serve to prevent neurological deficits in patients, especially when corrective surgical action is taken to attempt to revert signals changes back to baseline properties. We show that Bayesian Networks could be used as a mathematical tool to calculate the utility of conducting IONM, which could save costs in healthcare when performed. [ABSTRACT FROM AUTHOR]

In the era of smart cities and advancing transportation technologies, predicting logistic vehicle and vehicle speed is pivotal to enhancing traffic management, safety, and overall transportation efficiency. Properly predicting vehicle and vehicle speed is critical to the interests of both road users and traffic authorities. However, accurately predicting the vehicle speed and logistics vehicle of a single trip is a difficult task. In some cases, unpredicted accidents will happen, so death cases will increase. To overcome these issues, a novel Logistic Vehicle speed detection using the YOLO (LV-YOLO) method has been introduced to detect logistical vehicles and speed using the YOLO network. The proposed framework is divided into three layers such as image acquisition, segmentation layer, and detection layer. In the image acquisition layer, a CCTV camera captures highway traffic video. The collected video is converted into frames. In the segmentation layer, the video frame is segmented using U-Net, which segments the vehicle in the video frames. The detection layer performs truck detection, and speed detection using LV-YOLO on segmented frames based on the Boxy Vehicle dataset. The simulated results show that the LV-YOLO technique maintains excellent mAP levels of 99.42%. The LV-YOLO improves the overall mAP by 1.72, 5.42, and 0.82% better than the Simple Vehicle Counting System, Real-Time Detection, and Advance YOLOv3 Model for vehicle detection, 4.81, and 2.63% better than Deep Learning and CAN protocol, and 1D-CNN speed estimation mode for speed prediction respectively. [ABSTRACT FROM AUTHOR]

Aquaculture is an important economic and food source in many countries. Due to environmental restrictions and the effects of aquatic diseases, aquaculture requires a lot of labor and expensive materials, and it relies on the expertise of aquaculture experts. The quality of water is essential for aquaculture development. Therefore, in this paper, a novel aquaculture water quality monitoring using autonomous sensors (AquaSense) has been proposed which uses autonomous sensors for efficient monitoring of water in the aquaculture environment. In the AquaSense framework, temperature, pH, dissolved oxygen, and salinity measurements are recorded and collected using a variety of autonomous sensors. Based on the information gathered, users can assess the condition of their farm through the Internet. The MATLAB R2012b platform is employed to verify the effectiveness of the suggested water quality (WQ) monitoring technique and analyze the data. Several criteria including accuracy, precision, recall, specificity, and f1-score have been used to assess the effectiveness of the suggested strategy. AquaSense achieves the high accuracy ranges of 96.98%, and existing techniques ISAS, AquaStat, and IoT-WQI FIS achieve 91.24%, 93.39%, and 88.92%, respectively. [ABSTRACT FROM AUTHOR]

SRAM memory systems are suffering from an increase in data due to the aggressive CMOS integration density. The frequency of Multiple Cell Upsets (MCUs) on SRAM memory is increasing, which is resulting in the increasing use of ECCs.Speed is slowed down by ECCs due to their overhead, both in memory bits and decoding times. In this research, Continuous Adjacent Multiple Bit Upset Correction (CAMBUC)has been proposed to greatly reduce the redundancy and improve correction coverage.In addition, the Interleaved Counter Matrix Code (ICMC) is proposed to simplify the Encoder and decoder circuits which reduces the delay. The proposed method predicts MBU before decoding and maximum parallel 8 error bits are corrected using the proposed method. Using this method, the error correction code's parity check matrix is automatically and effectively constructed, simply stating its error detection and/or correction capabilities. Combinatorial counting operations introduce error detection and error prediction. The proposed ICMC makes use of the decimal and hamming algorithms to achieve the highest level of error detection. In comparison to the conventional decoder, which can rectify double and 8-adjacent errors, the assessment results utilizing the proposed CAMBUC approach demonstrate significant reductions in area, power, and delay. The obtained findings demonstrate that the proposed method has a comparatively long mean time to failure (MTTF) when compared to existing approaches. At the same time, the suggested scheme's delay overhead is, respectively, 20.5%, 14.6%, 11.2%, 8.5%, and 3.5% less than that of the existing Hamming, CMC, ECC for 5-bit adjacent error, eMRSC, and DICE-7-bit ECCmethods. [ABSTRACT FROM AUTHOR]

Brain tumor is the irregular growth of cells in the brain that can develop into malignant or benign tumors. However, the prediction of brain tumors is the most difficult task in the medical field due to the anatomy of the tumor cells. In recent years, advances in deep learning access to medical diagnostic imaging have led to greater accuracy in short time segmenting brain tumors. In this work, a novel approach based on Segmentation-based Kernel Fuzzy C-Mean (SKFCM) with Penguin Search Optimization Algorithm (PeSOA) with an Adaptive Dense Neural Network (ADNN) classifier was implemented. The MRI images are pre-processed using Bright-contrast Dynamic Histogram Equalization (BCDHE) with a weighted median filter and the multi features are extracted with the Modular linear discriminant analysis (MLDA). The Adaptive Dense Neural Network (ADNN) using a unique SKFCM with a machine learning-based Penguin Search Optimization Algorithm was used to segment brain tumors (PeSOA). The effectiveness of the proposed method was estimated based on specificity, accuracy, sensitivity, and tumor area length in vertical and horizontal locations. The proposed approach progresses the overall accuracy of 1.11%, 4.44%, and 6.18% better than CNN, ANN-fuzzy C-means, and R-CNN, respectively. [ABSTRACT FROM AUTHOR]

In image/video processing, RGB to YUV transformation plays an important role in bit-rate reduction by a down-sampling of (U, V). Luma down-sampling is the trade-off between bit rate and PSNR, so it is transmitted as it is. In this paper first, we proposed luma down-sampling (LDS) on the server side. On the client side, luma up-sampling has been done by even distribution error (EDE) which introduced 5–7% PSNR lag. To improve PSNR, we proposed adaptive interpolation for luma (AIL) which improves up to 2% PSNR from EDE. LDS and AIL are suitable for low-cost applications. To improve PSNR, in conventional down-sampling format 4:2:2, 4:2:0, etc., we proposed bilinear-based efficient chroma up-sampling (BECU). BECU methods require eight neighbouring pixels where some pixels are up-sampled from the client side and some pixels are already down-sampled values at client side by BECU. This improves the PSNR and then bilinear interpolation-based chroma up-sampling. [ABSTRACT FROM AUTHOR]

Apple detection helps the food manufacturing process to distinguish between fresh and damaged apples. In this modern world, many apple-detecting flaws are discovered before harvest. After a harvest, the system is required to identify apple species and quality, which will help in food production machinery. In this research, a novel YOLOAPPLE has been proposed for identifying different apple objects such as three classes: normal apple, damaged, and red delicious apple using Augment Yolov3. Using Grab cut to remove the background of the apple for better results in the next iteration. The augment Yolov3 with extra spatial pyramid information and a swish activation function to maintain feature loss preferences throughout training. Yolov3 is improved by the Darknet53 convolution neural network acting as a backbone and by adding spatial pyramid pooling features using the feature pyramid network before the object detector. Finally, the fully connected layer will classify as normal apple, damaged, and red delicious. The Augment Yolov3 model enables multi-class detection and recognition system that achieves higher mean average precision of 99.13% when compared to the conventional Yolov3, Yolov4 deep learning model. The experimental results originated from a newly generated object recognition model that was created by utilizing the Kaggle dataset using Google Colab inference on an NVIDIA Tesla K-80 GPU for a better localization process and its precise multi-object detection. [ABSTRACT FROM AUTHOR]

Three-layer eco-packaging films from fish skin gelatin, poly (lactic acid) (PLA) and poly (butylene adipate-co-terephthalate) (PBAT) were developed at different dry weight ratios and referred to as 'P5/G90/B5, P10/G80/B10, P15/G70/B15, P20/G60/B20 and P25/G50/B25' and analyzed for their properties with their corresponding control films (G100, P100 and B100). Thickness of three-layer films reduced with increased ratio of PLA and PBAT (p < 0.05). Elasticity of three-layer films was improved by the addition of PBAT layer. In general, control gelatin film showed the highest water vapor permeability (6.161 × 10− 11 g.m.m− 2.s− 1.Pa− 1) (p < 0.05). Lightness value (L*) of three-layer films increased when compared with PLA and gelatin film. Highest transparency value of 20.41 was found for control PBAT film (p < 0.05). Solubility of three-layer films in water reduced with the inclusion of bioplastics. The functional groups and secondary structure of developed films were observed by ATR-FTIR spectra. TGA and DSC analyses confirmed the improved thermal properties of resulted three-layer film. Thus, the addition of bioplastics enhanced the properties of films and developed three-layer eco-packaging materials might gradually replace the synthetic plastic packaging in near future. [ABSTRACT FROM AUTHOR]

De-noising is an effective mechanism for removing the aberration present in the image and has been exploited in diverse fields. In this proposed research work, a novel deep learning-based profound memory-affiliated neural network (PMANN) for de-noising aerial images with disaster management is implemented. The proposed method is optimized with an adaptive dual-threshold wavelet transform for precise noise suppression of aerial images. This proposed architecture overrides the prior art methods employed for de-noising as well as disaster management. The intended scheme is correlated over the preexisting noise removal techniques such as convolution neural network (CNN), CNN with long short-time memory (CNN-LSTM), weighted nuclear norm minimization (WNNM), and de-noising CNN (DNCNN), respectively. The peak signal–noise ratio value of the proposed PMANN is increased by 0.24%, 0.086%, 0.643%, and 0.720% compared to CNN, CNN-LSTM, WNNM, and DNCNN models, respectively. The structural similarity index value is increased by 0.59%, 0.382%, 0.037%, and 0.465% compared to CNN, CNN-LSTM, WNNM, and DNCNN techniques. The mean-squared error value is decreased by 8.93%, 2.1457%, 0.316%, and 0.582% compared to CNN, CNN-LSTM, WNNM, and DNCNN techniques, respectively. [ABSTRACT FROM AUTHOR]

Purpose of Review: Recent advances in the field of interventional pain management (IPM) involve minimally invasive procedures such as percutaneous lumbar decompression, interspinous spacer placement, interspinous-interlaminar fusion and sacroiliac joint fusion. These developments have received pushback from surgical professional societies, who state spinal instrumentation and arthrodesis should only be performed by spine surgeons. The purpose of this review is to evaluate the validity of this claim. A literature search was conducted on Google Scholar and PubMed databases. Articles were included which examined IPM in the following contexts: credentialing and procedural privileging guidelines, fellowship training and education, and procedural outcomes compared to those of surgical specialties. Our primary research question is: "Should interventionalists be performing decompression and fusion procedures?". Findings: Advanced percutaneous spine procedures are not universally incorporated into pain fellowship curriculums. Trainees attempt to compensate for these deficiencies through industry-led training, which has been criticized for lacking central regulation. There is also a paucity of studies comparing procedural outcomes between surgeons and interventionalists for complex spine procedures, including decompression and fusion. Summary: Pain fellowship curriculums have not kept pace with some of procedural advancements within the field. Interventionalists are also not trained to manage potential complications of spinal instrumentation and arthrodesis, which has been recognized as an essential requirement for procedural privileging. Decompression and fusion may therefore be outside the scope of an interventionalist's practice. [ABSTRACT FROM AUTHOR]

Nowadays, many people are suffered from obesity, they tend to maintain their body weight by consuming a sufficient number of calories in their routine life. In this research, a novel Deep Learning-based Food Item Classification (DEEPFIC) approach has been proposed to categorize the different food items from the dataset with their calorie values. Initially, the images are processed using the sigmoid stretching method to enhance the image quality and remove the noises. Consequently, the pre-processed images are segmented using Improved Watershed Segmentation (IWS2) algorithm. RNN is used to extract features like shape, size, textures, and color. The extracted features are then normalized using the dragonfly technique. The Bi-LSTM is utilized to classify food products based on these pertinent aspects. The efficiency of the proposed method was calculated in terms of specificity, precision, accuracy, and recall F-measure. The proposed method improves the overall accuracy by 4.99%, 8.72%, and 10.4% better than the existing DCNN, FRCNN, and LSV-SVM methods respectively. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "Advanced day lighting system using fiber optics" published in the journal Optical & Quantum Electronics. The publisher has retracted the article due to concerns identified during an investigation, including compromised peer-review process, inappropriate references, nonstandard phrases, and being outside the scope of the journal. The publisher no longer has confidence in the results and conclusions of the article. The authors have not responded to correspondence regarding this retraction. Springer Nature, the publisher, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

Boundary recognition plays a vital role in real-world scenarios like medical imaging and surveillance. The classical edge detector fails to preserve minute details in the image. The abdominal CT images were first analyzed using two edge detection techniques based on gauss gradient are proposed here for real-time data processing. The performance of the edge detectors is validated by performance metrics and verified for benchmark dataset images. The results reveal that the gauss gradient edge detector was efficient for the boundary extraction in benchmark and medical images. The VLSI implementation of the proposed gauss gradient edge detectors was done using the Kintex 7-FPGA board, hardware implementation also generates efficient results with reduced execution time. [ABSTRACT FROM AUTHOR]

Daylighting plays an important role in decreasing power usage in office buildings and enhancing indoor environments. Reducing the use of energy and producing energy from renewable sources will result in lower greenhouse gas emissions. Daylighting is a method of providing light indoors through the use of windows, solar panels, optical fibers, parabolic mirrors and other forms of daylight technologies. The proposed fiber optic daylighting system will illuminate the light uniformly in interior space. Parabolic mirror and Frensnel lens are two different efficient approaches available for fiber-based day lighting system. Captured light is passed via concentrator, fiber and lenses. Uniform lighting at the capture point eliminates the heat problem when incorporating optical plastic fiber is critical in making the device cost-effective. Collimated light increases device performance helping to inject full light into the optical fibres. From simulation setup will verify that uniform intensity of light will be illuminated to different regions of buildings by the fiber optic bundle. Collimated lighting and reduction of shadows caused by mechanical parts and the convex reflector will enhances the system's efficiency. Uniform illumination will be given at all time by using hybrid system. Using solar day lighting in homes and offices via optical fibre cables is a potential way to reduce the energy used for artificial lighting while also improving visual comfort and human health. Analysis and discussion have been conducted on the impact of optical fibre diameter on the brightness and homogeneity of light. This efficient technology can lower energy costs and also helps in climate change challenges. [ABSTRACT FROM AUTHOR]

Security techniques based on conventional cryptography assume keys are disseminated prior to secure communications in traditional security mechanisms. The essential function of transmitting and sharing a secret key between two entities is the safe key management technique, which is the most important components to be concerned about when incorporating cryptographic activities into any system. If the key management procedures are ineffective, the system will be vulnerable to vulnerabilities and potentially lethal outsider attacks. Quantum cryptography is a method of securely encrypting information sent between parties while also detecting intruders attempting to listen in on the discussion. Quantum cryptography holds promise as a solution to these and other issues. In this paper,we discuss the Quantum key distribution (BB84 protocol) and how when integrated with conventional cryptography algorithms it increases security in data transmission to a remarkably high level. We also compare the cryptography algorithms for different file sizes and measure their performance by calculating the Encryption,decryption,throughput and Avalanche effect of the algorithms with and without QKD. The elapsed time of the conventional algorithm with QKD achieve 56.8%, 58.6% and 54.3% less time than AES, 3DES and Blowfish respectively. [ABSTRACT FROM AUTHOR]

The study was conducted to evaluate the efficacy of biofloc meal incorporated diets on growth performance and physiological responses in Penaeus vannamei. The feeding trial was conducted using two control diets and four experimental diets, having biofloc meal at various incorporation levels, namely control diet (commercial diet), BFT0 (biofloc meal control, biofloc meal at 0%) BFT10 (biofloc meal at 10%), BFT20 (biofloc meal at 20%), BFT30 (biofloc meal at 30%), and BFT40 (biofloc meal at 40%). Healthy post larvae of P. vannamei (0.03 ± 0.00 g) were stocked (50 shrimp per tank) in a glass tank and fed with the experimental diets at 10% of animal body weight, four times a day, for a duration of 60 days. Among the different treatments, significantly higher final body weight (1.30 ± 0.06 g), weight gain (1.27 ± 0.06 g), specific growth rate (6.21 ± 0.01%/day), and survival rate (62 ± 0.20%) were recorded in BFT30 diet. In contrast, shrimp reared using control diet exhibited lower specific growth rate (5.53 ± 0.02%/day) and survival (48 ± 0.14%) compared to other treatment groups. At the end of the experiment, serum protein (1.6 ± 0.02 g/dl), prophenoloxidase (16.35 ± 2.98 U/mg protein), and peroxidase (0.2 ± 0.03 U/ml) were found to be significantly higher in BFT30 diet-fed group. Similarly, the digestive enzyme activities of amylase (4.75 ± 0.87 U/mg protein) and lipase (118 ± 5.10 U/mg protein) of BFT30 reared shrimp were found to be significantly higher. In addition, the histological observation of the shrimp revealed the larger proportion of mature cells with intracellular digestion (B) and absorption (R) in BFT30 group. Overall, the study revealed that biofloc meal incorporation at 30% in shrimp diet would potentially improves the growth performance and physiological responses of shrimp. [ABSTRACT FROM AUTHOR]

Purpose: Intramedullary spinal cord tumours (IMSCTs) are comparatively rare neoplasms. We present a single-centre clinical case series of adult patients with surgically managed IMSCTs. Methods: We performed a retrospective analysis of electronic patient records in the time period spanning July 2010 to July 2021. All adult patients that had undergone surgical management for IMSCTs were eligible for inclusion. Baseline and post-operative clinical and radiological characteristics, along with follow-up data, were assessed. We also performed a literature review with a focus on surgical outcomes for IMSCTs. Results: Sixty-six patients matched our selection criteria, with a median age of 42 years (range 23–85). Thirty-four ependymomas, 17 haemangioblastomas, 12 astrocytomas, 2 lymphomas and 1 teratoma were included. Statistical analysis yielded several significant findings: IMSCTs spanning a greater number of vertebral levels are significantly associated with poor McCormick outcomes (p = 0.03), presence of gait disturbance before surgery is significantly associated with poor outcome for both post-operative McCormick and Nurick scores (p = 0.007), and radicular pain present pre-operatively is significantly associated with a good post-operative McCormick score (p = 0.045). Haemangioblastomas are significantly more likely to have a clear intra-operative dissection plane compared to ependymomas and astrocytomas (p = 0.009). However, astrocytomas have a significantly higher prevalence of good McCormick outcomes compared to ependymomas and haemangioblastomas (p = 0.03). Conclusion: Histological diagnosis, cranio-caudal extent of the tumour and the presence or absence of baseline deficits—such as gait impairment and radicular pain—are significant in determining neurological outcomes after surgery for IMSCTs. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication stands as a pivotal technology revolutionizing conventional base station-to-device (B2D) communication paradigms. It facilitates direct data exchange among Internet of Things (IoT) devices without intermediaries such as servers or cloud services. However, the direct link between devices poses significant security risks, such as data leaks and unauthorized access. In this paper, a novel Secure data Transmission using Crystals Kyber (STUCK) technique has been proposed to secure device-to-device communication efficiently. STUCK encompasses four pivotal phases: token generation, device discovery, link configuration, and secure data transmission, ensuring robust protection against potential security breaches. Leveraging the Crystals Kyber cryptographic technique, STUCK adeptly manages key operations, encrypts and decrypts data, thus ensuring the integrity of data transmission across devices. Experimental validation conducted in MATLAB validates the efficacy of the proposed system, revealing noteworthy performance metrics encompassing processing time, communication overhead, and encryption duration. Comparative analysis demonstrates superior performance of STUCK, showcasing processing time increases of 23.07%, 44.23%, and 65.38% compared to existing techniques such as B-IoMV, LMECC, and QSAP, respectively. [ABSTRACT FROM AUTHOR]

Purpose: Evaluation of the presentation and outcomes of different surgical treatment approaches for spinal intradural arachnoid cysts (SIAC). Methods: Cases were identified from electronic records of two major neurosurgical centres in London over the last 10 years (October 2009–October 2019) that have been surgically treated in both institutions. Clinical findings, surgical technique, and recurrence by procedure were statistically analysed. Statistical analysis was performed with STATA 13.1 Software. Results: A total of 42 patients with SIAC were identified for this study with a mean age at the time of surgery of 53.6 years and a male:female ratio of 8:13. There were 31 patients with primary SIACs and 11 with secondary SIACs. The most common presenting symptom was paraesthesia (n = 27). The most common location of the cyst was in the thoracic region (n = 33). Syrinx was present in 26.2% of SIACs (n = 11). Resection was associated with significantly better postoperative pain compared to other surgical techniques (p = 0.01), significantly poorer postoperative urinary function (p = 0.029), and lower rates of sensory recovery in patients who presented preoperatively with sensory deficit (p = 0.041). No significant difference was seen in symptomatic outcomes between patients with primary and secondary SIACs. Conclusion: Resection and drainage are both effective methods of managing SIACs. In this observational study, resection was associated with significantly reduced pain postoperatively when compared with drainage, however also with significantly less improvement in postoperative urinary function. Therefore, resection should be the gold standard management option for SIACs, with drainage as an option where resection is unsafe, and drainage should also be considered in patients presenting with urinary dysfunction. [ABSTRACT FROM AUTHOR]

Background: There is limited evidence to direct the management of unruptured intracranial aneurysms. Models extrapolated from existing data have been proposed to guide treatment recommendations. The aim of this study is to assess whether a consensus-based treatment score (UIATS) or rupture rate estimation model (PHASES) can be used to benchmark UK multi-disciplinary team (MDT) practice. Methods: Prospective data was collected on a consecutive series of all patients with unruptured intracranial aneurysms (UIAs) presenting to a major UK neurovascular centre between 2012 and 2015. The agreement between the UIATS and PHASES scores, and their sensitivity and specificity in predicting the real-world MDT outcome were calculated and compared. Results: A total of 366 patients (456 aneurysms) were included in the analysis. The agreement between UIATS and MDT recommendation was low (weighted kappa 0.26 [95% CI 0.19, 0.32]); sensitivity and specificity were also low at 36% and 52% respectively. Groups that the MDT allocated to treatment, equipoise or no treatment had significantly different PHASES scores (p = 0.004). There was no significant difference between the two scores when predicting patients for whom MDT outcome was to recommend aneurysm treatment, but the UIATS score was superior in predicting patients who received an MDT recommendation of 'treatment-equipoise', or 'not-for-treatment' (AUC of 0.73 compared to 0.59 for PHASES). Conclusions: The models studied failed to agree with the consensus view of multi-disciplinary team in a major neurovascular centre. We conclude that decision support tools such as the UIATS and PHASES scores should not be blindly introduced in respective institutions without prior internal validation, as they may not represent the local reality. [ABSTRACT FROM AUTHOR]

Purpose of Review: With this manuscript the authors sought to write a succinct review of the origins, as well as the latest advancements in endoscopic spine surgery to serve as a reference frame for physicians looking to learn this approach. Recent Findings: At its infancy, the indications for posterolateral and transforaminal endoscopic decompression remained narrow, which prevented the procedure from gaining rapid traction during those days. However, more recently the tides have turned and an increasing number of surgeons are starting to adopt this technique given all its advantages. With the advent of higher quality camera systems and instruments, indications to use a minimally invasive option have gotten significantly broader. The most basic indication for the use of this technology is a soft disc herniation causing compromise of a neural structure that has failed to be managed successfully with non-surgical therapies. The use of endoscopic techniques provides significant advantages to patient outcomes and patient recovery. Summary: Endoscopic procedures should not be used as a blanket approach to nerve root decompression, as they certainly have limitations. Most contraindications to this procedure are relative and serve mostly as points to consider when selecting the methods to address neural compression. As these techniques become more widely accepted, we expect its reach and indications to continue to broaden and diversify. The full integration of navigation technologies will likely leapfrog this procedure into the mainstream use. [ABSTRACT FROM AUTHOR]

The present study examined the effect of Bacillus subtilis on growth performance, digestive enzyme activity, and immune response of juvenile white-leg shrimp (Litopenaeus vannamei) in 100 m2 lined ponds. Control (without B. subtilis) and experimental (with B. subtilis) diets were fed at 10% of animal body weight, four times daily for 6 weeks in duplicate ponds. Significantly increased final weight, weight gain, specific growth rate, average daily growth, and survival rate were observed in the B. subtilis treated group (P < 0.05). Significantly higher (P < 0.05) digestive enzyme activities of protease, lipase, amylase, and cellulase were found in the B. subtilis supplemented diet fed group. At the end of trial, a challenge study, using Vibrio alginolyticus, found lower (15.00 ± 2.88%) cumulative mortality in the B. subtilis treated group compared to control (73.33 ± 3.33%). Activities of immune and antioxidant enzymes (phenoloxidase, SOD, and catalase) were significantly higher (P < 0.05) in the B. subtilis-incorporated diet fed group. Interestingly, histopathology of the hepatopancreas and intestine revealed that the B. subtilis-supplemented group showed no deformity. Overall, the study found inclusion of B. subtilis in P. vannamei diet improves growth, physiometabolic activities, and immunity, indicating B. subtilis as a potential probiotic for shrimp feed. [ABSTRACT FROM AUTHOR]

Smart agriculture has been a promising model to supervise farms utilizing contemporary wireless technologies to enhance the quantity and quality of yield at the same time as minimizing the individual labor requirement. Also, the effective utilization of the sensors as communication components is the key one to monitor and manage soil, water, light, humidity, temperature. A mobile ad hoc sensor node comprises sensors to gather a real-time environment from the agricultural land with wireless communication technology and process the data before sharing information with other nodes in the network. On the other hand, the challenges have been enormously high path loss and lack of communication range under the environment when passing through the soil, sand, water, and other climatic conditions. As wireless sensor network (WSN) has self-organized and ad hoc wireless capability to monitor physical or environmental conditions, it can be used effectively in smart agriculture. As sensor nodes have been limited by means of power to be inactive mode always, the design of such energy-efficient agriculture WSN is a paramount issue. Hence, it has been planned to utilize the WSN as well as ubiquitous technology for smart agriculture with energy efficiency. With the purpose of building up a model, a ubiquitous agriculture mobile sensor network-based threshold built-in MAC routing protocol (TBMP) has been proposed to make it fit for minimal resource utilization by comparing with the existing protocols IMR and PTSR. The necessary resources for agriculture sensor network have been temperature, soil, and water. Also, the testing will be done to monitor changes in environmental surroundings in the agricultural land smartly to obtain maximum usage of the ubiquitous concept by applying existing and proposed protocols. [ABSTRACT FROM AUTHOR]

This research article proposes high-performance square-root carry select adder (SQRT CSLA) architectures with high speed, area and energy efficiency when compared to the existing SQRT CSLA architectures. The first proposed method uses an optimized design of binary to excess-1 converter (BEC)-based SQRT CSLA by incorporating a carry enable binary to excess-1 converter (CEBEC) design that exploits a new logic optimization on the carry propagation path to improve speed of operation, area and energy efficiency. The second proposed method is an optimized design of the regular SQRT CSLA by employing the carry enable and add-one ripple carry adder (ARCA) architectures to decrease the number of gates. Simulation results show that the proposed CEBEC SQRT CSLA offers a savings of 22.81%, 9.95%, 7.95% and 3.98% on area, 14.50%, 5.21%, 3.72% and 3.11% in power consumption and offers 6.78%, 15.46%, 6.57% and 3.83% lesser delay, 20.33%, 19.92%, 10.09% and 6.89% lesser PDP, 28%, 23.85%, 13.78% and 7.58% lesser ADP than the existing regular, BEC SQRT CSLA, SQRT CSLA1 and SQRT CSLA2 architectures. The proposed ARCA SQRT CSLA architecture exhibits 9.26%, 17.71%, 9.06% and 6.39% lesser delay than the existing regular, BEC SQRT CSLA, SQRT CSLA1 and SQRT CSLA2 architectures. [ABSTRACT FROM AUTHOR]

The role of compression is inevitable in the storage and transmission of medical images. The polynomial based image compression is proposed in this work for the compression of abdomen CT medical images. The input images are preprocessed by min–max normalization; the pixels are scanned and subjected to polynomial approximation. The polynomial approximated coefficients are subjected to llyods quantization and encoded by arithmetic coder. The medical image compression using Gaussian Hermite polynomial gives superior results when compared with the legendre polynomial based image compression and JPEG lossless compression techniques in terms of Peak to signal noise ratio (PSNR), Mean square Error (MSE) and other picture quality metrics. The algorithms are tested on real-time DICOM abdomen CT image and can be used for data transfer in teleradiology application. [ABSTRACT FROM AUTHOR]

Introduction and objectives: The novel severe acute respiratory syndrome coronavirus 2 (COVID-19) pandemic has had drastic effects on global healthcare with the UK amongst the countries most severely impacted. The aim of this study was to examine how COVID-19 challenged the neurosurgical delivery of care in a busy tertiary unit serving a socio-economically diverse population. Methods: A prospective single-centre cohort study including all patients referred to the acute neurosurgical service or the subspecialty multidisciplinary teams (MDT) as well as all emergency and elective admissions during COVID-19 (18th March 2020–15th May 2020) compared to pre-COVID-19 (18th of January 2020–17th March 2020). Data on demographics, diagnosis, operation, and treatment recommendation/outcome were collected and analysed. Results: Overall, there was a reduction in neurosurgical emergency referrals by 33.6% and operations by 55.6% during the course of COVID-19. There was a significant increase in the proportion of emergency operations performed during COVID-19 (75.2% of total, n=155) when compared to pre-COVID-19 (n = 198, 43.7% of total, p < 0.00001). In contrast to other published series, the 30-day perioperative mortality remained low (2.0%) with the majority of post-operative COVID-19-infected patients (n = 13) having underlying medical co-morbidities and/or suffering from post-operative complications. Conclusion: The capacity to safely treat patients requiring urgent or emergency neurosurgical care was maintained at all times. Strategies adopted to enable this included proactively approaching the referrers to maintain lines of communications, incorporating modern technology to run clinics and MDTs, restructuring patient pathways/facilities, and initiating the delivery of NHS care within private sector hospitals. Through this multi-modal approach we were able to minimize service disruptions, the complications, and mortality. [ABSTRACT FROM AUTHOR]

Purpose: Intravenous iodinated contrast is a commonly used diagnostic aid to improve image quality on computed tomography. There exists a small risk of post-contrast acute kidney injury in patients receiving IV contrast. One of the biggest risk factors for developing PC-AKI is the presence of pre-existing renal dysfunction, making it important to measure the renal function prior to contrast administration. Point of care (POC) devices offer a quick estimation of renal function, potentially improving workflows in radiology departments. Method: Two POC devices were evaluated, the Nova StatSensor and Abbott iSTAT. Patients undergoing routine radiological investigations had blood collected and analysed by a POC method and the laboratory method (Beckman AU5800). The two values were analysed and compared. Renal function was calculated using eGFR via the CKD-EPI result. eGFR values were stratified as high risk (eGFR < 30), moderate risk (eGFR 30–59) and low risk (eGFR ≥ 60). Results: One hundred eighty-six patients were included in the study. One hundred one patients underwent the Abbott iSTAT analysis, 139 patients underwent Nova StatSensor analysis, and 53 had both. Statistical analysis revealed that the StatSensor R2 value was 0.77, and coefficient variation was 10.65%. iSTAT had a R2 value of 0.83 and coefficient variation of 7.36%. The POC devices did not miss any high-risk patients but underreported eGFR values in certain patients. Conclusion: POC devices are moderately accurate at detecting renal impairment in patients undergoing radiological investigations. They seem to be a good screening tool; however, any low eGFR values should be further examined. [ABSTRACT FROM AUTHOR]

The present work demonstrates the synthesis of new bio-based benzoxazine monomers via Mannich-like condensation of naturally occurring raw materials, for example, phloretic acid (PA) and furfurylamine (fa)/stearylamine (sa). The structure of the benzoxazine monomers has been established using proton nuclear magnetic resonance, carbon nuclear magnetic resonance and Fourier transform infrared spectroscopies. The monomers undergoes thermally activated ring opening polymerization to form polybenzoxazine networks, as revealed by non-isothermal differential scanning calorimetry, and the curing temperature for both was observed to be less than 473 K. Curing parameters of the developed monomers have also been compared with the reported bio-based monomers. The rheological behaviour of PA-fa monomer shows that the monomer has narrow processing window with liquefaction temperature at 426 K and gelation temperature at 437 K. Thermal degradation behaviour of polybenzoxazines was studied using thermogravimetric analysis which reveals that polybenzoxazine based on furfurylamine shows relatively high thermal stability and char yield which is credited to the additional cross-linking sites provided by the furan ring. [ABSTRACT FROM AUTHOR]

As digital cameras become more enhanced and small, CCD sensors can relate to only one color of a pixel. This color mosaic pattern is called as Bayer Pattern(BP) which requires processing to obtain a color image with a higher resolution. Each image pixel that undergoes interpolation has a full color spectrum based on surrounding pixel colors. Here we introduce Adaptive CFA(ACFA) interpolation model. For normal image regions hue technique is used while edge regions adapt the new technique. It is proposed to apply fuzzy logic and fuzzy rule which is based on Genetic Algorithm that uses random local search to enhance the PSNR. Medical image reconstruction by this proposed fuzzy based method outperforms the other medical image reconstruction methods. [ABSTRACT FROM AUTHOR]

This paper presents an effective traffic video surveillance system for detecting moving vehicles in traffic scenes. Moving vehicle identification process on streets is utilized for vehicle tracking, counts, normal speed of every individual vehicle, movement examination, and vehicle classifying targets and might be executed under various situations. In this paper, we develop a novel hybridization of artificial neural network (ANN) and oppositional gravitational search optimization algorithm (ANN–OGSA)-based moving vehicle detection (MVD) system. The proposed system consists of two main phases such as background generation and vehicle detection. Here, at first, we develop an efficient method to generate the background. After the background generation, we detect the moving vehicle using the ANN–OGSA model. To increase the performance of the ANN classifier, we optimally select the weight value using the OGSA algorithm. To prove the effectiveness of the system, we have compared our proposed algorithm with different algorithms and utilized three types of videos for experimental analysis. The precision of the proposed ANN–OGSA method has been improved over 3% and 6% than the existing GSA-ANN and ANN, respectively. Similarly, the GSA-ANN-based MVD system attained the maximum recall of 89%, 91%, and 91% for video 1, video 2, and video 3, respectively. [ABSTRACT FROM AUTHOR]

Selection of proton conducting membrane is currently a key factor that decides the performance of microbial fuel cell (MFC). Uniaxial pressed polysiloxane-derived ceramer and ceramic membrane with proton conducting fillers like montmorillonite and H3PMo12O40/SiO2 were applied for the first time as separator in MFC. Here, we present a series of polymer-derived ceramic membranes tailored based on pyrolysis temperature and filler addition, in which ion exchange capacity, cation transport number, and oxygen permeability are influenced through the hydrophilic and porous structural property. The maximum power density of MFC with polysiloxane-derived ceramer membrane modified with 20 wt% montmorillonite and 10 wt% H3PMo12O40/SiO2 reached a value of 5.66 W m−3, which was four times higher than that with non-modified polysiloxane-derived ceramer membrane. Furthermore, the specific power recovery per unit cost of the membrane was found to be 2-fold higher than MFC using polymeric Nafion membrane. In contrast, MFC with polysiloxane-derived ceramic membrane modified with 20 wt% montmorillonite delivers 1.2 times lower power density (4.20 W m−3) than that with non-modified macroporous polysiloxane-derived ceramic membrane. Hence, the findings demonstrated that tailoring the hydrophilic and porous structure of the ceramic membrane is a new and promising approach to enhance the performance of MFC. [ABSTRACT FROM AUTHOR]

FPGA design for complex applications need high capacity of configuration memory. To fullfil the large memory requirement, higher end FPGAs are required and it leads to higher cost. In order to reduce the cost constraint, bit-stream compression is prominently involved to reduce the bit-stream size and the memory requirement of FPGA configuration. In this paper new code compression techniques are proposed with minimum cost. The proposed code compression techniques RG-1 and RG-2 codes are designed based on the combination of run length and Golomb coding. The proposed RG codes overcome the limitation of both run length and Golomb coding techniques. The main contribution of this work is to analyze the proposed work in terms of number of 1's, number of transitions and size of compressed bits. The comparison result shows that the RG code is more robust when compared to other traditional code compression techniques. The results shows that CR improvement of 9 and 5% compare with RLE for the RG-1 and RG-2 respectively and CR improvement of 7 and 2% compare with golomb for the RG-1 and RG-2 respectively. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this review is to provide a structured approach to the main white matter commissures, their anatomic and radiological definition and disease implications. Methods: The Pubmed database and The JAMA Network were used for the literature review and the following terms were searched using Sort by: Best Match and Sort by: Most Recent: telencephalic commissure, forebrain commissure anatomy, fornix anatomy, commissure of fornix, posterior commissure, corpus callosum, commissural agenesis, Probst bundle, corpus callosum disorders review, corpus callosum diseases review, Marchiafava–Bignami, Alzheimer's disease and Forel commissure; 36 papers were selected, one excluded due to the language barrier. Results: The interhemispheric communication in the brain is achieved via the brain commissures, bundles of white matter linking the two cerebral hemispheres. Anterior white commissure (AWC)—related with olfactory and non-visual communication, hippocampal commissure—main efferent pathway of the hippocampus, connecting the hippocampal formation to structures beyond the temporal lobe, crucial in declarative memory formation and consolidation—and the corpus callosum (CC)—from the anterior commissure to the hippocampal commissure—are the main telencephalic commissures. Supramammilary commissure, posterior commissure, supraoptic commissure and habenular commissure are diencephalic commissures—unknown function, probably related to involuntary eye movements. Commissural agenesis (AWC is absent or impossible to recognize), Alzheimer's Disease (hippocampal commissure may contribute for disease dissemination) and agenesis of corpus callosum are some of the disturbances that involve the telenchephalic commissures. Conclusions: A comprehensive understanding of the clinic–anatomic correlation is pivotal to understand the pathology and therefore improve our diagnostic accuracy and treatment options, in the background of all patient management. [ABSTRACT FROM AUTHOR]

In practical applications, speaker verification systems have to be developed and trained using data which is outside the domain of the intended application as the collection of significant amount of in-domain data could be difficult. Experimental studies have found that when a GPLDA system is trained using out-domain data, it significantly affects the speaker verification performance due to the mismatch between development data and evaluation data. This paper proposes several unsupervised inter-dataset variability compensation approaches for the purpose of improving the performance of GPLDA systems trained using out-domain data. We show that when GPLDA is trained using out-domain data, we can improve the performance by as much as 39% by using by score normalisation using small amounts of in-domain data. Also in situations where rich out-domain data and only limited in-domain data are available, a pooled-linear-weighted technique to estimate the GPLDA parameters shows 35% relative improvements in equal error rate (EER) on int-int conditions. We also propose a novel inter-dataset covariance normalization (IDCN) approach to overcome in- and out-domain data mismatch problem. Our unsupervised IDCN-compensated GPLDA system shows 14 and 25% improvement respectively in EER over out-domain GPLDA speaker verification on tel-tel and int-int training-testing conditions. We provide intuitive explanations as to why these inter-dataset variability compensation approaches provide improvements to speaker verification accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Non-steroidal anti-inflammatory drugs (NSAIDs) play an important role in postoperative pain management. However, their use in the setting of spine fusion surgery setting has long been a topic of controversy. In this review we examined relevant research, including in vivo, animal, and clinical human studies, with the aim of understanding the effect of NSAIDs on spinal fusion.Study Design/setting: Systematic review of study designs of all types from randomized controlled trials and meta-analyses to single-institution retrospective reviews.Methods: A search of PubMed and Embase was conducted using the keywords: "spine," "spinal fracture," NSAIDs, anti-inflammatory non-steroidal agents, bone, bone healing, fracture, fracture healing, yielding a total of 110 studies. Other 28 studies were identified by cross-referencing, resulting in total 138 studies.Results: There is no level I evidence from human studies regarding the use of NSAIDs on spinal fusion rates. The overall tone of the spine literature in the early 2000s was that NSAIDs increased the rate of non-union; however, nearly all human studies published after 2005 suggest that short-term (<2 weeks) postoperative use have no such effect. The dose dependency that is seen with a 2-week postoperative course is not present when NSAIDs are only used for 48 h after surgery.Conclusions: NSAID appear to have dose-dependent and duration-dependent effects on fusion rates. The short-term use of low-dose NSAIDs around the time of spinal fusion surgery is reasonable. Spine surgeons can consider the incorporation of NSAIDs into pain control regimens for spinal fusion patients with the goal of improving pain control and reducing the costs and complications associated with opioids. [ABSTRACT FROM AUTHOR]

This paper studies the performance degradation of Gaussian probabilistic linear discriminant analysis (GPLDA) speaker verification system, when only short-utterance data is used for speaker verification system development. Subsequently, a number of techniques, including utterance partitioning and source-normalised weighted linear discriminant analysis (SN-WLDA) projections are introduced to improve the speaker verification performance in such conditions. Experimental studies have found that when short utterance data is available for speaker verification development, GPLDA system overall achieves best performance with a lower number of universal background model (UBM) components. As a lower number of UBM components significantly reduces the computational complexity of speaker verification system, that is a useful observation. In limited session data conditions, we propose a simple utterance-partitioning technique, which when applied to the LDA-projected GPLDA system shows over 8% relative improvement on EER values over baseline system on NIST 2008 truncated 10-10 s conditions. We conjecture that this improvement arises from the apparent increase in the number of sessions arising from our partitioning technique and this helps to better model the GPLDA parameters. Further, partitioning SN-WLDA-projected GPLDA shows over 16% and 6% relative improvement on EER values over LDA-projected GPLDA systems respectively on NIST 2008 truncated 10-10 s interview- interview, and NIST 2010 truncated 10-10 s interview- interview and telephone- telephone conditions. [ABSTRACT FROM AUTHOR]

Purpose: With growing older population and increasing rates of cervical spinal surgery, it is vital to understand the value of cervical surgery in this population. We set forth to determine the cost utility following anterior cervical decompression and fusion (ACDF) for degenerative disease in older patients.Methods: Patients undergoing ACDF for degenerative diseases were enrolled into prospective longitudinal registry. Patient-reported outcomes (PROs) were recorded at baseline, 1-year, and 2-year postoperatively. Two-year medical resource utilization, missed work, and health-state values [quality-adjusted life years (QALYs)] were assessed to compute cost per QALY gained. Patients were dichotomized based on age: <65 years (younger) and ≥65 years (older) to compare the cost utility in these age groups.Results: Total 218 (87%) younger patients and 33 (13%) older patients who underwent ACDF were analyzed. Both the groups demonstrated a significant improvement in PROs 2-year following surgery. The older patients had a lower mean cumulative gain in QALYs compared to younger patients at 1 year (0.141 vs. 0.28, P = 0.05) and 2 years (0.211 vs. 0.424, P = 0.04). There was no significant difference in the mean total 2-year cost between older [$21,041 (95% CI $18,466-$23,616)] and younger [$22,669 (95% CI $$21,259-$24,079)] patients (P = 0.27). Two-year cost per QALY gained in older vs. younger patients was ($99,720/QALYs gained vs. ($53,464/QALYs gained, P = 0.68).Conclusion: ACDF surgery provided a significant gain in health-state utility in older patients with degenerative cervical pathology, with a mean cumulative 2-year cost per QALY gained of $99,720/QALY. While older patients have a slightly higher cost utility compared to their younger counterparts, surgery in the older cohort does provide a significant improvement in pain, disability, and quality-of-life outcomes. [ABSTRACT FROM AUTHOR]

Objectives: To compare the image quality between fine focal spot size (FFSS) and standard focal spot size (SFSS) in computed tomography of the abdomen and pelvis (CTAP) METHODS: This retrospective review included all consecutive adult patients undergoing contrast-enhanced CTAP between June and September 2014. Two blinded radiologists assessed the margin clarity of the abdominal viscera and the detected lesions using a five-point grading scale. Cohen's kappa test was used to examine the inter-observer reliability between the two reviewers for organ margin clarity. Mann-Whitney U testing was utilised to assess the statistical difference of the organ and lesion margin clarity.Results: 100 consecutive CTAPs were recruited. 52 CTAPs were examined with SFSS of 1.1 × 1.2 mm and 48 CTAPs were examined with FFSS of 0.6 × 0.7 mm. Results showed that there was substantial agreement for organ margin clarity (mean κ = 0.759, p < 0.001) among the reviewers. FFSS produces images with clearer organ margins (U = 76194.0, p < 0.001, r = 0.523) and clearer lesion margins (U = 239, p = 0.052, r = 0.269).Conclusion: FFSS CTAP improves image quality in terms of better organ and lesion margin clarity. Fine focus CT scanning is a novel technique that may be applied in routine CTAP imaging.Key Points: • Fine focal spot improves organ margin clarity. • Fine focal spot improves lesion margin clarity. • Fine focal spot can be used in routine CT abdominal imaging. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the relationship between lumbar facet fluid and dynamic instability in degenerative spondylolisthesis (DS), as well as the relationship between facet fluid and patient-reported outcomes following a posterior lumbar fusion.Methods: We analyzed consecutive patients with degenerative spondylolisthesis undergoing single level posterior lumbar fusion from December 2010 to January 2013 at a single academic institution. We investigated the relationship between fluid in the facet joint as measured on axial MRI and the presence of dynamic instability. We investigated the impact of facet fluid, Modic changes, and dynamic instability on patient-reported outcomes.Results: There was a significant association between the amount of facet joint fluid and the presence of dynamic instability (p = 0.03); as facet fluid increases, the probability of dynamic instability also increases. For every 1 mm of facet fluid, there was a 41.6 % increase (95 % CI 1.8-97) in the odds of dynamic instability. A facet fluid amount less than 0.5 mm gave a 90 % probability that there was no dynamic instability. The presence of facet fluid and dynamic instability were associated with achieving minimal clinical important difference (MCID) in low back pain following lumbar fusion (p = 0.04 and 0.05, respectively).Conclusion: Facet joint fluid is associated with the presence of dynamic instability in DS. The presence of facet fluid and dynamic instability may predict increased likelihood of achieving MCID for improvement in back pain following posterior lumbar fusion. [ABSTRACT FROM AUTHOR]

Objectives: This study was performed to evaluate the efficacy of a novel computed tomography (CT) liver detection algorithm (LDA), which allows for targeted increase of radiation dose to the upper abdomen, on image quality of the liver. Methods: We retrospectively evaluated the LDA by comparing 40 consecutive patients who had portal venous CT abdomen performed without use of the algorithm, to 40 patients in whom the algorithm was used. Image quality was assessed objectively by comparing the standard deviation (SD) of attenuation values in Hounsfield units (HU) of the abdominal organs. Qualitative analysis was performed by two blinded radiologists who independently graded the image quality of abdominal organs Results: There was significant noise reduction in the liver ( P < 0.001) and spleen ( P < 0.001) in the LDA group compared to the conventional group. There was also a significant improvement in image quality of the liver ( P < 0.001), kidney ( P < 0.001), spleen ( P < 0.001), pancreas ( P < 0.001), and psoas ( P = 0.005) in the LDA group compared to the conventional group. Overall dose between the two groups was similar. Conclusions: This liver detection algorithm improves the subjective image quality of upper abdominal organs, in particular the liver, without increasing overall radiation dose. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent etiology of chronic liver disease in America. NAFLD can be broadly classified in two subtypes: nonalcoholic fatty liver (NAFL), which is generally considered a benign condition with negligible risk of progression to cirrhosis, and nonalcoholic steatohepatitis (NASH), which is generally considered to be progressive with substantial risk of progression to cirrhosis. Additionally, recent studies suggest the odds of liver mortality increases among NASH patients with advanced fibrosis (bridging fibrosis ± cirrhosis). Liver biopsy examination is the current gold standard to accurately discriminate between NAFL versus NASH as well as diagnose advanced fibrosis. However, due to its invasive nature, risk of bleeding (and even rarely death), prohibitive cost, and sampling error, liver biopsies are imperfect for diagnosis and monitoring of NAFLD. As a result, noninvasive biomarkers that can accurately detect NASH and advanced fibrosis without biopsy are needed. This article will discuss the most novel noninvasive biomarkers in diagnosing NASH and advanced fibrosis. [ABSTRACT FROM AUTHOR]

Neurocritical care relies on the continuous, real-time measurement of numerous physiologic parameters. While our capability to obtain such measurements from patients has grown markedly with multimodal monitoring in many neurologic or neurosurgical intensive care units (ICUs), our ability to transform the raw data into actionable information is limited. One reason is that the proprietary nature of medical devices and software often prevents neuro-ICUs from capturing and centrally storing high-density data. Also, ICU alarm systems are often unreliable because the data that are captured are riddled with artifacts. Informatics is the process of acquiring, processing, and interpreting these complex arrays of data. The development of next-generation informatics tools allows for detection of complex physiologic events and brings about the possibility of decision support tools to improve neurocritical care. Although many different approaches to informatics are discussed and considered, here we focus on the Bayesian probabilistic paradigm. It quantifies the uncertainty inherent in neurocritical care instead of ignoring it, and formalizes the natural clinical thought process of updating prior beliefs using incoming patient data. We review this and other opportunities, as well as challenges, for the development and refinement of informatics tools in neurocritical care. [ABSTRACT FROM AUTHOR]

The ventriculoperitoneal shunt is the mainstay of treatment for hydrocephalus. Despite its widespread use and safety record, it often malfunctions due to complications such as obstruction, breakage, migration and infection. This necessitates a systematic approach to diagnosing the etiology of shunt failure. Any evaluation should begin with an appraisal of the patient's symptoms. In acute malfunction, nausea, vomiting, irritability, seizures, headache, lethargy, coma and stupor are seen. In chronic malfunction, neuropsychological signs, feeding pattern changes, developmental delay, decline in school performance, headaches and increased head size are often seen. The next step in evaluation is a CT scan of the head to evaluate ventricular size. Prior imaging studies should be obtained for comparison; if the ventricles have enlarged over time, shunt malfunction is likely. If there is no such increase or dilation in the first place, other diagnoses are possible. However, 'slit ventricle syndrome' should also be considered. When prior imaging is not available, pumping the reservoir, a radionuclide shuntogram, a shunt tap or even surgical exploration are options. The goals of this paper are to provide an algorithm for evaluating shunt malfunction and to illustrate the radiographic findings associated with shunt failure. [ABSTRACT FROM AUTHOR]