Reduction of cell surface attachment in experimental hydrocephalus using a novel ventricular catheter with modified tethered liquid perfluorocarbon.

OBJECTIVE: Ventriculoperitoneal shunting, the most common treatment for the neurological disorder hydrocephalus, has a failure rate of up to 98% within 10 years of placement, mainly because of proximal obstruction of the ventricular catheter (VC). The authors developed a new VC design modified with tethered liquid perfluorocarbon (TLP) and tested it in a porcine model of hydrocephalus. In this study, they aimed to determine if their TLP VC design reduced cell surface attachment and consequent shunt obstruction in the pig model. METHODS: TLP VCs were designed to reduce drainage hole obstruction using modified TLP and slightly enlarged draining holes, but their number and placement remained very similar to standard VCs. First, the authors tested the device in nonhydrocephalic rats to assess biocompatibility. After confirming safety, they implanted the VCs in hydrocephalic pigs. Hydrocephalus was induced by intracisternal kaolin injections in 30-day-old domestic juvenile pigs. Surgical implantation of the ventriculoperitoneal shunt (clinical control or TLP) was performed 10-14 days postinduction and maintained up to 30 days posttreatment. MRI was performed to measure ventricular volume before treatment and 10 and 30 days after treatment. Histological and immunohistochemical analyses of brain tissue and explanted VCs, intracranial pressure measurement, and clinical scoring were performed when the animals were euthanized. RESULTS: TLP VCs showed a similar surgical feel, kink resistance, and stiffness to control VCs. In rats (biocompatibility assessment), TLP VCs did not show brain inflammatory reactions after 30 or 60 days of implantation. In pigs, TLP VCs demonstrated increased survival time, improved clinical outcome scores, and significantly reduced total attached cells on the VCs compared with standard clinical control VCs. TLP VCs exhibited similar, but not worse, results related to ventriculomegaly, intracranial pressure, and the local tissue response around the cortical shunt track in pigs. CONCLUSIONS: TLP VCs may be a strong candidate to reduce proximal VC obstruction and improve hydrocephalus treatment.

Corrigendum to "Early Detection of Medical Image Analysis by Using Machine Learning Method".

[This corrects the article DOI: 10.1155/2022/3041811.].

Effective of Smart Mathematical Model by Machine Learning Classifier on Big Data in Healthcare Fast Response.

In the past few years, big data related to healthcare has become more important, due to the abundance of data, the increasing cost of healthcare, and the privacy of healthcare. Create, analyze, and process large and complex data that cannot be processed by traditional methods. The proposed method is based on classifying data into several classes using the data weight derived from the features extracted from the big data. Three important criteria were used to evaluate the study as well as to benchmark the current study with previous studies using a standard dataset.

Service-Centric Heterogeneous Vehicular Network Modeling for Connected Traffic Environments.

Heterogeneous vehicular communication on the Internet of connected vehicle (IoV) environment is an emerging research theme toward achieving smart transportation. It is an evolution of the existing vehicular ad hoc network architecture due to the increasingly heterogeneous nature of the various existing networks in road traffic environments that need to be integrated. The existing literature on vehicular communication is lacking in the area of network optimization for heterogeneous network environments. In this context, this paper proposes a heterogeneous network model for IoV and service-oriented network optimization. The network model focuses on three key networking entities: vehicular cloud, heterogeneous communication, and smart use cases as clients. Most traffic-related data-oriented computations are performed at cloud servers for making intelligent decisions. The connection component enables handoff-centric network communication in heterogeneous vehicular environments. The use-case-oriented smart traffic services are implemented as clients for the network model. The model is tested for various service-oriented metrics in heterogeneous vehicular communication environments with the aim of affirming several service benefits. Future challenges and issues in heterogeneous IoV environments are also highlighted.

Early Detection of Medical Image Analysis by Using Machine Learning Method.

We develop effective medical image classification techniques, with an emphasis on histopathology and magnetic resonance imaging (MRI). The trainer utilized the curriculum as a starting point for a set of data and a restricted number of samples, and we used it as a starting point for a set of data. As calibrating a machine learning model is difficult, we used alternative methods as unsupervised feature extracts or weight-conditioning factors for identifying pathological histology pictures. As a result, the pretrained models will be trained on 3-channel RGB pictures, while the MRI sample has more slices. To alter the working model using the MRI data, the convolutional neural network (CNN) must be fine-tuned. Pretrained models are placed and then used as feature snippets. However, there is a scarcity of well-done medical photos, making training machine learning models a difficult endeavor to begin with. In any case, data augmentation aids in the generation of sufficient training samples; however, it is unclear if data augmentation aids in the prediction of unknown data samples. As a result, we fine-tuned machine learning models without using any additional data. Furthermore, rather than utilizing a standard machine learning classifier for the MRI data, we created a unique CNN that uses both 3D shear descriptors and deep features as input. This custom network identifies the MRI sample after processing our representation of the characteristics from beginning to end. On the hidden MRI dataset, our bespoke CNN outperforms traditional machine learning. Our CNN model is less prone to overfitting as a result of this. Furthermore, we have given cutting-edge outcomes employing machine learning.

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things.

Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network.

Towards Green Computing Oriented Security: A Lightweight Postquantum Signature for IoE.

Postquantum cryptography for elevating security against attacks by quantum computers in the Internet of Everything (IoE) is still in its infancy. Most postquantum based cryptosystems have longer keys and signature sizes and require more computations that span several orders of magnitude in energy consumption and computation time, hence the sizes of the keys and signature are considered as another aspect of security by green design. To address these issues, the security solutions should migrate to the advanced and potent methods for protection against quantum attacks and offer energy efficient and faster cryptocomputations. In this context, a novel security framework Lightweight Postquantum ID-based Signature (LPQS) for secure communication in the IoE environment is presented. The proposed LPQS framework incorporates a supersingular isogeny curve to present a digital signature with small key sizes which is quantum-resistant. To reduce the size of the keys, compressed curves are used and the validation of the signature depends on the commutative property of the curves. The unforgeability of LPQS under an adaptively chosen message attack is proved. Security analysis and the experimental validation of LPQS are performed under a realistic software simulation environment to assess its lightweight performance considering embedded nodes. It is evident that the size of keys and the signature of LPQS is smaller than that of existing signature-based postquantum security techniques for IoE. It is robust in the postquantum environment and efficient in terms of energy and computations.

Green Communication for Underwater Wireless Sensor Networks: Triangle Metric Based Multi-Layered Routing Protocol.

In this paper, we propose a non-localization routing protocol for underwater wireless sensor networks (UWSNs), namely, the triangle metric based multi-layered routing protocol (TM2RP). The main idea of the proposed TM2RP is to utilize supernodes along with depth information and residual energy to balance the energy consumption between sensors. Moreover, TM2RP is the first multi-layered and multi-metric pressure routing protocol that considers link quality with residual energy to improve the selection of next forwarding nodes with more reliable and energy-efficient links. The aqua-sim package based on the ns-2 simulator was used to evaluate the performance of the proposed TM2RP. The obtained results were compared to other similar methods such as depth based routing (DBR) and multi-layered routing protocol (MRP). Simulation results showed that the proposed protocol (TM2RP) obtained better outcomes in terms of energy consumption, network lifetime, packet delivery ratio, and end-to-end delay.

A preliminary exploration of acute intracranial pressure-cerebrospinal fluid production relationships in experimental hydrocephalus.

CONTEXT: By occluding the fourth ventricle simultaneously obtaining telemetric data on intracranial pressure (ICP) and cerebrospinal fluid (CSF) production, the authors of this study investigate a variety of physiologic parameters in cases of experimental hydrocephalus. AIMS: The aim of this study is to provide a new context on the disrupted homeostasis in hydrocephalus and guide toward improved treatment based on multiple physiological parameters. MATERIALS AND METHODS: Hydrocephalus was induced in ten 21-day-old Sprague-Dawley rats by blocking the flow of CSF to the fourth ventricle with kaolin. Ten days post induction, when physical signs of ventriculomegaly reached Evan's ratio (ER) of ≥0.46, CSF flow and ICP were measured while manipulating body position (0°, 45°, 90°) and heart rate. RESULTS: In hydrocephalic animals (ER ≥0.46), we found a near-steady average acute ICP (13.638 ± 2.331) compared to age-matched controls (ER <0.30) (13.068 ± 8.781), whose ICP fluctuated with the position. Hydrocephalic and controls exhibited an insignificant degree of parabolic shifts in CSF production when body position was changed from prone to 90° and again when moved back to the prone position, a trend more noteworthy in controls (P = 0.1322 and 0.2772). A Pearson's Correlation found CSF production and ICP to be correlated at baseline 0° posture (P = 0.05) in the control group, but not the hydrocephalic group. Weight appeared to play a role when animals were held at 90°. No significant changes in ICP or CSF flow patterns were observed when the heart rate was increased within either group. CONCLUSIONS: These preliminary findings suggest that our standard assumptions of posture-dependent changes in ICP created using data from physiologic data may be inaccurate in the hydrocephalic patient, and thus describe a need to further explore these relationships.

Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic.

The provision and usage of online and e-learning system is becoming the main challenge for many universities during COVID-19 pandemic. E-learning system such as Blackboard has several fantastic features that would be valuable for use during this COVID-19 pandemic. However, the successful usage of e-learning system relies on understanding the adoption factors as well as the main challenges that face the current e-learning systems. There is lack of agreement about the critical challenges and factors that shape the successful usage of e-learning system during COVID-19 pandemic; hence, a clear gap has been identified in the knowledge on the critical challenges and factors of e-learning usage during this pandemic. Therefore, this study aims to explore the critical challenges that face the current e-learning systems and investigate the main factors that support the usage of e-learning system during COVID-19 pandemic. This study employed the interview method using thematic analysis through NVivo software. The interview was conducted with 30 students and 31 experts in e-learning systems at six universities from Jordan and Saudi Arabia. The findings of this study offer useful suggestions for policy-makers, designers, developers and researchers, which will enable them to get better acquainted with the key aspects of the e-learning system usage successfully during COVID-19 pandemic.

NTRU-Like Random Congruential Public-Key Cryptosystem for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are the core of the Internet of Things and require cryptographic protection. Cryptographic methods for WSN should be fast and consume low power as these networks rely on battery-powered devices and microcontrollers. NTRU, the fastest and secure public key cryptosystem, uses high degree, N, polynomials and is susceptible to the lattice basis reduction attack (LBRA). Congruential public key cryptosystem (CPKC), proposed by the NTRU authors, works on integers modulo q and is easily attackable by LBRA since it uses small numbers for the sake of the correct decryption. Herein, RCPKC, a random congruential public key cryptosystem working on degree N=0 polynomials modulo q, is proposed, such that the norm of a two-dimensional vector formed by its private key is greater than q. RCPKC works as NTRU, and it is a secure version of insecure CPKC. RCPKC specifies a range from which the random numbers shall be selected, and it provides correct decryption for valid users and incorrect decryption for an attacker using LBRA by Gaussian lattice reduction. RCPKC asymmetric encryption padding (RAEP), similar to its NTRU analog, NAEP, is IND-CCA2 secure. Due to the use of big numbers instead of high degree polynomials, RCPKC is about 27 times faster in encryption and decryption than NTRU. Furthermore, RCPKC is more than three times faster than the most effective known NTRU variant, BQTRU. Compared to NTRU, RCPKC reduces energy consumption at least thirty times, which allows increasing the life-time of unattended WSNs more than thirty times.

Cerebrospinal fluid circulation: What do we know and how do we know it?

The central nervous system's (CNS) complicated design is a double-edged sword. On the one hand, the complexity is what gives rise to higher order thinking; but on the other hand, damage to the CNS evokes its unforgiving nature. The cerebrospinal fluid (CSF) circulation system is an intricate system embedded in and around the CNS that has been the topic of debate since it was first described in the 18th century. It is underscored by the choroid plexus's distinct vascular network which has conventionally been seen as the most prominent structure in CSF production through a variety of active transporters and channels. Despite the ubiquity of this circulation system in vertebrates, some aspects remain understudied. Recent advances in scientific methodology and experimentation have proven to be effective tools for elucidating the mechanisms of the CSF circulation system and the pathological conditions associated with its malfunction. In this review, we capitulate the classical understanding of CSF physiology as well as a new, emerging theory on CSF production.

Low-cost endoscopic third ventriculostomy simulator with mimetic endoscope.

OBJECTIVE Hydrocephalus affects approximately 1 in 500 people in the US, yet ventricular shunting, the gold standard of treatment, has a nearly 85% failure rate. Endoscopic third ventriculostomy (ETV) is an alternative surgical approach for a specific subset of hydrocephalic patients, but can be limited by the inability of neurosurgical residents to practice prior to patient contact. The goal of this study was to create an affordable ETV model and endoscope for resident training. METHODS Open-source software was used to isolate the skull and brain from the CT and MR images of a 2-year-old boy with hydrocephalus. A 3D printer created the skull and a 3D mold of the brain. A mixture of silicone and silicone tactile mutator was used to cast the brain mold prior to subsequent compression and shearing modulus testing. A mimetic endoscope was then created from basic supplies and a 3D printed frame. A small cohort of neurosurgical residents and attending physicians evaluated the ETV simulator with mimetic endoscope. RESULTS The authors successfully created a mimetic endoscope and ETV simulator. After compression and shearing modulus testing, a silicone/Slacker ratio between 10:6 and 10:7 was found to be similar to that of human brain parenchyma. Eighty-seven percent of participants strongly agreed that the simulator was useful for resident training, and 93% strongly agreed that the simulator helped them understand how to orient themselves with the endoscope. CONCLUSIONS The authors created an affordable (US$123, excluding 3D printer), easy-to-use ETV simulator with endoscope. Previous models have required expensive software and costly operative endoscopes that may not be available to most residents. Instead, this attempt takes advantage of open-source software for the manipulation and fabrication of a patient-specific mold. This model can assist with resident development, allowing them to safely practice use of the endoscope in ETV.

Myeloid but not plasmacytoid blood DCs possess Th1 polarizing and Th1/Th17 recruiting capacity in psoriasis.

Psoriasis is a common inflammatory skin disease and dendritic cells (DCs) play crucial role in the development of skin inflammation. Although the characteristics of skin DCs in psoriasis are well defined, less is known about their peripheral blood precursors. Our aim was to characterize the phenotypic features as well as the cytokine and chemokine production of CD1c+ myeloid DCs (mDCs) and plasmacytoid DCs (pDCs) in the blood samples of psoriatic patients. Blood DCs were isolated by using a magnetic separation kit, and their intracytoplasmic cytokine production and CD83/CD86 maturation/activation marker expression were investigated by 8-colour flow cytometry. In CD1c+ mDCs the intracellular productions of Th1, Th2, Th17, Th22 and Treg polarizing cytokines were examined simultaneously, whereas in pDCs the amounts of IFNα as well as IL-12, IL-23 and IL-6 were investigated. The chemokine production of both DC populations was investigated by flow-cytometry and ELISA. According to our results psoriatic CD1c+ mDCs were in a premature state since their CD83/CD86 maturation/activation marker expression, IL-12 cytokine, CXCL9 and CCL20 chemokine production was significantly higher compared to control cells. On the other hand, blood pDCs neither produced any of the investigated cytokines and chemokines nor expressed CD83/CD86 maturation/activation markers. Our results indicate that in psoriasis not only skin but also blood mDCs perform Th1 polarizing and Th1/Th17 recruiting capacity, while pDCs function only in the skin milieu.