Latency Ratio Research Articles

Impact du mode de suivi des ruptures prématurées des membranes avant 36 SA sur la durée de latence

IntroductionPreterm premature rupture of membranes (PPROM) is the main cause of premature delivery, complicating 1–3% of all pregnancies. Conventional hospitalization (CH) is the most frequent mode of follow-up, but homecare (HC) seems to be an alternative. ObjectivesStudy of the impact of the monitoring mode on the duration of the latency period and on the latency ratio after PPROM, and analysis of the risk factors modifying this ratio. MethodsThis was a bicentric retrospective cohort study here-abouts including patients who presented a PPROM between 24 and 36weeks of gestation from 2016 to 2018. Patients had a follow-up in HC at Lille University Hospital center (UHC) and in CH at Nantes UHC according to two different follow-up protocols. The latency ratio corresponded to the real latency period divided by the latency period to theoretical term. ResultsWe included 154 patients: 102 in HC and 52 in CH. The mean latency period was significantly higher in HC: 36.9±21.8 days, corresponding to an 85.5±23.7% latency ratio versus 20.2±12 days, corresponding to an 66.9±29.8% latency ratio in CH (P<0.001). The latency ratio in CH was correlated with term at PPROM (P=0.001). ConclusionsThe duration of the latency period seems prolonged for PPROM followed by HC management versus CH in selected populations. This study suggests a benefit to HC in stable patients.

Enhancing 5G communication in business networks with an innovative secured narrowband IoT framework

Abstract These days, 5G mobile technology highly influences the wireless communication process, which is more responsive than the other generation. However, the 5G communication process handles the speed and latency issues while transmitting data over the network. Especially in rural areas, most people face this speed and latency problem while connecting to the network. Therefore, the network uses massive amounts of data to analyse this problem by integrating 5G with the Internet of things (IoT). The IoT-based data transmission and collection helps manage the data capacity, connection, and transmission rate. Therefore, this study uses the novel secured narrowband IoT framework for 5G communication and operational goals based on their position in the market. The 5G network uses business connectivity and access information to manage the data transmission. The network uses cellular bandwidth and IoT devices to connect more during this process. According to the connection, the information is transmitted with the minimum delay. Thus, experimental results have proposed these parameters: an accuracy ratio of 92.5%, a security ratio of 94.4%, a reliability ratio of 93.5%, a latency ratio of 91.8%, a cost-effective ratio of 20.5%, and a throughput ratio of 25.4% compared to other methods.

Evaluation of daytime sleepiness and insomnia symptoms in OSA patients with a characterization of symptom-defined phenotypes and their involvement in depression comorbidity

IntroductionRecent studies have emphasized the importance of clinical manifestations, such as insomnia and sleepiness, in defining phenotypes of obstructive sleep apnea (OSA), shifting from a focus on OSA severity and sleep structure.ObjectivesThe study aimed to characterize insomnia and sleepiness associated with OSA phenotypes and assess their involvement in depression symptoms (DS) in OSA.Methods A total of 181 participants undergoing polysomnography (PSG) were asked to fill out questionnaires, including Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and Back Depression Index (BDI). They were categorized into phenotypes: insomnia-sleepiness (I+S; ESS≥11; ISI≥15; n=20), sleepiness (S; ESS≥11; ISI&lt;15; n=22), insomnia (I; ESS&lt;11; ISI≥15) and asymptomatic (A; ESS&lt;11; ISI&lt;15; n=55).ResultsA linear regression model for BDI score (R2=0.357, p&lt;0.001) included ISI score and subjective to objective sleep latency ratio. ISI score was a predictive factor for mild and moderate DS (OR=1.226, p&lt;0.001 and OR=1.392, p=0.002, respectively). I and I+S phenotypes are characterized by higher BDI scores (p&lt;0.001 and p=0.015), longer subjective sleep latency (p=0.008 and p=0.041), and shorter subjective total sleep time (TST; p=0.049 and p=0.006), compared to A. Furthermore, the I and I+S groups had shorter subjective TST than S (p=0.028 and p=0.047). I and I+S had higher BDI scores than A (p&lt;0.001 and p=0.015, respectively) and S (p&lt;0.001 and p=0.017, respectively). I phenotype was associated with the risk of mild and moderate DS (OR=5.614, p&lt;0.001 and OR=9.550, p=0.008 respectively). Moreover, the I+S phenotype presented an even greater risk for mild DS (OR=10.286, p&lt;0.001).ConclusionsThe study suggests that using clinical features for OSA phenotyping holds promise for finding OSA individuals with increased risk for the occurrence of DS.Disclosure of InterestNone Declared

Evaluation of daytime sleepiness and insomnia symptoms in OSA patients with a characterization of symptom-defined phenotypes and their involvement in depression comorbidity-a cross-sectional clinical study.

Recent research highlights the significance of insomnia and sleepiness, shifting from obstructive sleep apnea (OSA) severity and sleep structure, in defining OSA phenotypes. This study aimed to characterize insomnia and sleepiness associated with OSA phenotypes and assess their involvement in depression symptoms (DS) in OSA. This cross-sectional, clinical study included 181 participants who underwent polysomnography (PSG) and filled out questionnaires, including the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and Beck Depression Index (BDI). They were categorized into phenotypes: insomnia-sleepiness (I + S; ESS ≥ 11; ISI ≥ 15; n = 20), sleepiness (S; ESS ≥ 11; ISI < 15; n = 22), insomnia (I; ESS < 11; ISI ≥ 15), and asymptomatic (A; ESS < 11; ISI<15; n=55). A linear regression model for the BDI score (R2=0.357, p < 0.001) included ISI score and subjective-to-objective sleep latency ratio. The ISI score was a predictive factor for mild and moderate DS [OR = 1.23 (95% CI: 1.09-1.38), p < 0.001 and OR = 1.39 (95% CI: 1.13-1.72), p = 0.002]. The I and I + S phenotypes are characterized by higher BDI scores (p < 0.001 and p = 0.02), longer subjective sleep latency (p = 0.008 and p = 0.04), and shorter subjective total sleep time (TST; p = 0.049 and p = 0.006) compared to A. Furthermore, the I and I + S groups had shorter subjective TST than S (p = 0.03 and p = 0.047). The I and I + S had higher BDI scores than A (p < 0.001 and p = 0.02, respectively) and S (p < 0.001 and p = 0.02, respectively). The I phenotype was associated with the risk of mild and moderate DS (OR = 5.61 (95% CI: 1.91-16.53), p < 0.001 and OR = 9.55 (95% CI: 1.81-50.48), p = 0.008 respectively). Moreover, the I + S phenotype presented an even greater risk for mild DS (OR = 10.29 (95% CI: 2.95-35.85), p < 0.001). Using clinical features for OSA phenotyping holds promise for finding OSA individuals with increased risk for DS occurrence.

An efficient reconfigurable workload balancing scheme for fog computing network using internet of things devices

Nowadays a huge amount of data has been communicated using fog nodes spread throughout smarty cities. the communication process is performed using fog nodes which are co-located with cellular base stations (BSs) that can move the computing resources close to internet of things (IoT) devices. In smart cities, a different type of data flow has been communicated through IoT devices. The communication process performs efficiently using the remote cloud. The IoT devices very close to the BS can communicate data without using fog nodes. Due to these phenomena, workload unbalancing occurs in IoT devices communicating in fog computing networks. Hence, it generates communication and computing latency. The task distribution process between the IoT devices is unbalanced. Hence, congestion and loss of information occur in fog computing network. A proposed reconfigurable load balancing algorithm (RLBA) is efficiently balancing the workload by reconfigurable communication channels and deviates the task with respect to the BS locations, IoT devices density and load IoT devices in each fog nodes in a network to minimize the communication and computing latency. As per the performance analysis, the proposed algorithm shows better performance as compared to conventional methods’ average latency ratio, communication latency ratio, computing load and traffic load.

Sleep Micro-Macro-structures in Psychophysiological Insomnia. PSG Study.

To address sleep micro-macro-structures in psychophysiological insomnia (PPI) as denoted by cyclic alternating pattern (CAP), Sleep spindles, and hyperarousal as microstructures and sleep characteristics such as sleep stages' variables, and heart rate as macrostructures. Two statistical populations, with 20 participants in each, are addressed: good sleepers (GS) and patients with psychophysiological insomnia (PPI). The sleep polysomnography (PSG) for one night was performed and sleep macro-micro-structures extraction was implemented for each participant. Cyclic alternating patterns were scored manually and other structures were monitored by the original PSG's device software. Analytical methods are used to dissect the results. The findings imply: (a) psychophysiological insomnia is characterized by CAP differences from good sleepers which are associated with hyperarousal; (b) Regarding microstructure, more microarousals in sleep stages caused more number of wake index. (c) The ratio of sleep stages, sleep latency and heart rate as sleep macrostructure are significantly changed. (d) There is no significant difference between PPI and GS groups on spindles length in our research. Regarding all sleep disorders and especially PPI, CAP variables, EEG arousals, and sleep spindles as microstructures and Total Sleep Time, Sleep Latency, number of waking, REM duration, and Heart Rate as macrostructures were found to be critical for the diagnosis of psychophysiological insomnia The analysis contributes to understanding better approaches in the quantitative specification of psychophysiological insomnia compare to good sleepers.

Incentivizing Collaboration in Heterogeneous Teams via Common-Pool Resource Games

We consider a team of heterogeneous agents, which is collectively responsible for servicing, and subsequently reviewing a stream of homogeneous tasks. Each agent has an associated mean service time and a mean review time for servicing and reviewing the tasks, respectively. Agents receive a reward based on their service and review admission rates. The team objective is to collaboratively maximize the number of “serviced and reviewed” tasks. We formulate a common-pool resource game, and design utility functions to incentivize collaboration among heterogeneous agents in a decentralized manner. We show the existence of a unique pure Nash equilibrium (PNE), and establish convergence of the best response dynamics to this unique PNE. Finally, we establish an analytic upper bound on three inefficiency measures of the PNE, namely the price of anarchy, the ratio of the total review admission rate, and the ratio of latency.

Application of internet of things-based efficient security solution for industrial

Recent years have seen a rise in demonstrations of the Industrial Internet of Things (IIoT). Creating secure Internet of Things (IIoT) applications has become a major challenge. Because of the constant demand for additional functionality, such as foresight, the number of connected devices in the enterprise facility is continually growing. Certifying a smaller number of signatories provides stronger authentication methods and prohibits trustworthy third parties from being publicly certified among the available encryption instruments. Certain network criteria must be met by the virtual network function (VNFs) used in this arrangement. As a result of this output, the final service may be delivered at the lowest possible cost, including processing time and network delay. According to the results of this study, our solution is better suited to simplifying trust management in IIoT devices. These findings reveal that our proposed confidence model may withstand behavioural shifts in hostile IIoT environments. The experimental results show our proposed method BCEPP has a high computational cost of 95.3%, a low latency ratio of 28.5%, and increased data transmitting rate of 94.1%, enhanced security rate of 98.6%, packet reception ratio of 96.1%, user satisfaction index of 94.5%, probability ratio of 33.8% when compared to other methods.

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

In The Wireless Multimedia Sensor Network (WNSMs) have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets. By utilising portable technologies, it achieves solid and significant results in wireless communication, media transfer, and digital transmission. Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature, moisture content, and other environmental conditions in recent decades. WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appropriate audio and video information. Many video sensor network studies focus on lowering power consumption and increasing transmission capacity, but the main demand is data reliability. Because of the obstacles in the sensor nodes, WMSN is subjected to a variety of attacks, including Denial of Service (DoS) attacks. Deep Convolutional Neural Network is designed with the state-action relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart Agriculture. The Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the network. It reduces the attacker detection delay and improves the detection accuracy. In order to protect the network against DoS assaults, an improved machine learning technique must be offered. An efficient Deep Neural Network approach is provided for detecting DoS in WMSN. The required parameters are selected using an adaptive particle swarm optimization technique. The ratio of packet transmission, energy consumption, latency, network length, and throughput will be used to evaluate the approach’s efficiency.

Task Offloading in Multi-Access Edge Computing Enabled UAV-Aided Emergency Response Operations

In emergency response operations, using uncrewed aerial vehicles (UAVs) has recently become a promising solution due to their flexibility and easy deployment. However, tasks performed by the UAVs, e.g., object detection and human pose recognition, usually require a high computation capacity and energy supply. Furthermore, offloading tasks to the edge server-equipped base stations may not always be possible because of a lack of infrastructure or distance. Therefore, UAV-aided edge servers can be deployed near UAV scouts to provide computing services. However, a UAV can not perform all types of tasks since it has limitations on memory, available software, central processing unit (CPU), and graphics processing unit (GPU) capacity. Therefore, this study focuses on task offloading (TO), power, and computation resource allocation (PRA) problems in a multi-layer MEC-enabled UAV network while taking into account CPU and GPU requirements of tasks, the capacity of the devices (i.e., computational resources, power, and energy), and limitations on the type of tasks a UAVs can perform. The problem is formulated as a non-convex mixed-integer nonlinear problem to minimize the weighted sum of the maximum energy consumption ratio in the network and total task execution latency ratio, and then decomposed and converted into an integer and a convex problem. A messy genetic algorithm (mGA)-based TO and PRA strategy (mGA-TPR) is proposed to solve the problem, where two PRA strategies are based on the Karush–Kuhn–Tucker conditions used to solve the PRA problem. Simulation results verify that the proposed scheme can outperform the baseline methods.

An effective blockchain‐based smart contract system for securing electronic medical data in smart healthcare application

SummaryIn today's world, data management plays a key role in smart healthcare applications. However, data availability, trustworthiness, confidentiality, and security are the major issues faced by current healthcare data management systems. The modern healthcare systems manage Electronic Medical Records (EMR) using a centralized manner, which increases the single point of failure in the event of a natural catastrophe. In this paper, a new robust Blockchain‐based Medical Cloud (BC‐MedCl) framework has been proposed to provide secure EMR sharing between patient and doctor. Primarily, Internet of Things (IoT) devices will gather the health‐related data of the patient periodically. The proposed framework then stores the encrypted EMRs in cloud storage while their correlating hash values are placed into the blockchain. Finally, a decentralized selective smart contract‐based access control mechanism is developed to enhance the security of the proposed system. The prototype file‐sharing performance of the proposed architecture has been evaluated using the Ethereum platform. The performance results manifest that the proposed blockchain framework is more effective to handle EMR in the real‐time healthcare system with a superior accuracy ratio of 98.7% and a lesser latency ratio of 25% as compared with the existing systems.

Cervical and ocular vestibular evoked myogenic potential: A comparison of narrowband chirp, broadband chirp, tone burst and click stimulation

Objectives To compare the response rate and response parameters of cervical and ocular vestibular evoked myogenic potentials (c&oVEMP) elicited by narrowband (NB) and broadband (BB) CE-Chirp, with the more classical tone burst (TB) and click VEMPs. Design The response rate, latency, amplitude and asymmetry ratio of c&oVEMPs elicited by 95 dB nHL air conducted (AC) 500 Hz NB CE-chirp, BB CE-chirp, 500 Hz TB and click stimuli were recorded bilaterally. Study sample 20 male and 38 female participants (19–39 years). Results For the cVEMP, the highest response rate was found for NB chirp (100%), followed by TB (91%), BB chirp (87%) and finally click (85%). A similar order was seen for oVEMP with percentages of 100%; 57%, 57%, and 43%. The 500 Hz NB CE-Chirp elicited significantly shorter cVEMP P1 and N1 latencies and significantly larger c&oVEMP amplitudes compared to all other stimuli. BB CE-Chirp elicited significantly shorter c&oVEMP P1 and N1 latencies with smaller amplitudes compared to TB. Asymmetry ratios were not statistically significant for all comparisons. Conclusion The 500 Hz NB CE-chirp provides the highest response rates, shorter latencies and larger amplitudes, and therefore seem a promising stimulus for reliably measuring c&oVEMPs in clinical practice.

Fuzzy-assisted machine learning framework for the fog-computing system in remote healthcare monitoring

Health care monitoring systems have mainly depended on the internet of things (IoT) devices to collect, manage and analyze data from patients. Patients' health can be constantly monitored and controlled with the remote health monitoring systems. From these points, a Fuzzy-assisted machine learning framework (F-AMLF) with fog computing increases the effectiveness of the health care monitoring system. This paper presents an F-AMLF to recognize how device resource cost reduction is achieved while maintaining efficiency limitations. Patients can submit their demands for health care by a fuzzy assisted fog computing system. These systems use fuzzy logic to calculate how much computing capacity is needed to maintain fog and cloud projections. The F-AMLF shows the highest accuracy ratio of 93.6%, monitoring ratio of 92.5%, prediction ratio of 95.3%, data management ratio of 91.4%, and the lowest latency ratio of 19.7%, energy consumption ratio of 20.1%, and the cost-effective ratio of 21.5% compared to the existing methods.

Dosimetry impact of gating latency in cine magnetic resonance image guided breath-hold pancreatic cancer radiotherapy

Objective. We investigated dosimetry effect of gating latency in cine magnetic resonance image (cine MRI) guided breath-hold pancreatic cancer radiotherapy. Approach. The gating latency was calculated based on cine MRI obtained from 17 patients who received MRI guided radiotherapy. Because of the cine MRI-related latency, beam overshoot occurs when beam remains on while the tracking target already moves out of the target boundary. The number of beam on/off events was calculated from the cine MRI data. We generated both IMRT and VMAT plans for all 17 patients using 33 Gy prescription, and created motion plans by applying isocenter shift that corresponds to motion-induced tumor displacement. The GTV and PTV coverage and dose to nearby critical structures were compared between the motion and original plan to evaluate the dosimetry change caused by cine MRI latency. Main results. The time ratio of cine MRI imaging latency over the treatment duration is 6.6 ± 3.1%, the mean and median percentage of beam-on events <4 s are 67.0 ± 14.3% and 66.6%. When a gating boundary of 4 mm and a target-out threshold of 5% is used, there is no significant difference for GTV V33Gy between the motion and original plan (p = 0.861 and 0.397 for IMRT and VMAT planning techniques, respectively). However, the PTV V33Gy and stomach Dmax for the motion plans are significantly lower; duodenum V12.5 Gy and V18Gy are significantly higher when compared with the original plans, for both IMRT and VMAT planning techniques. Significance. The cine MRI gating latency can significantly decrease the dose delivered to the PTV, and increase the dose to the nearby critical structures. However, no significant difference is observed for the GTV coverage. The dosimetry impact can be mitigated by implementing additional beam-on control techniques which reduces unnecessary beam on events and/or by using faster cine MRI sequences which reduces the latency period.

Lower / upper extremity F-wave ratio for detecting early diabetic neuropathy.

Results of conventional nerve conduction studies may be within normal limits in early diabetic neuropathy. Previous studies demonstrated that F-wave latency should be used to detect this early neuropathic process. The aim of this study is to evaluate the sensitivity of lower/upper extremity F latency ratios in detecting the early neuropathy in patients with diabetic neuropathic pain. 44 patients with diabetic neuropathic pain (DNP) and 44 control subjects whose both conventional nerve conduction studies and F-wave latencies were within normal limits were included to the study. We compared the nerve conduction parameters and lower/upper extremity (tibial/ulnar) F latency ratios of the groups. Tibial F latency was significantly prolonged and tibial/ulnar F latency ratio was significantly higher in DNP group. Our results support that F-waves are useful for detecting early diabetic neuropathy and suggest that comparison with a control group will demonstrate a difference even when the individuals' F-wave latencies are within the normal limits. The difference was significant for tibial but not for ulnar F latency values supporting the length dependent involvement. The tibial/ ulnar F-wave latency ratio was significantly higher in the DNP group, suggesting that it might also be useful to detect early neuropathy and to demonstrate that the underlying process was predominant in lower extremity. Further studies may provide additional information about the utility of this ratio for detecting early neuropathy even when F-wave latencies are within normal limits.

Risk factors associated with shortened latency before delivery in outpatients managed for preterm prelabor rupture of membranes.

Preterm prelabor rupture of membranes (PPROM) occurs in 3% of pregnancies and is the main cause (~30%) of premature delivery. Home care seems to be a safe alternative for the management of patients with PPROM, who have a longer latency than those with PPROM managed with conventional hospitalization. We aimed to identify the risk factors associated with a shortened latency before delivery in women with PPROM managed as outpatients. The design was a retrospective cohort study and the setting was a Monocentric Tertiary centre (Lille University Hospital, France) from 2009 to 2018. All consecutive patients in home care after PPROM at 24-36weeks were included. For the main outcome measure we calculated the latency ratio for each patient as the ratio of the real latency period to the expected latency period, expressed as a percentage. The risk factors influencing this latency ratio were evaluated. A total of 234 patients were managed at home after PPROM. Mean latency was 35.5±20.7days, corresponding to an 80% latency ratio. In 196 (83.8%) patients the length of home care was more than 7days. A lower latency ratio was significantly associated with oligohydramnios (p<0.001), gestational age at PPROM (p=0.006), leukocyte count at PPROM more than 12×109 /L (p=0.025), and C-reactive protein concentration more than 5mg/L at 7days after PPROM (p=0.046). Cervical length was not associated with a lower latency ratio. Women with PPROM managed with home care are stable. The main risk factor associated with a reduced latency is oligohydramnios. Outpatients with oligohydramnios should be informed of the probability of a shortened latency period.

Automised flow rule formation by using machine learning in software defined networks based edge computing

The availability of Software Defined Network’s (SDNs) flow rule entry in the flow table is considered a key factor in the timely delivery of a certain flow. The controller is approached for instructions on how to deal with the flow when the rule for such flow is missing. The controller then updates the flow table accordingly at the switch so that flow could be dealt with locally. It becomes problematic when no rule is defined yet at the controller by the application plane. In most of these cases, such a situation is handled by programming the controller with wildcard rules. However, handling many flows at once under wildcard rules severely hampers the network performance. Flow rules formation by the application plane is sometimes critical and time-consuming which increases the latency ratio by creating a bottleneck at the switch level. To avoid the bottlenecks due to rule absence, in this paper, rather than waiting for the application plane’s response and putting the pending traffic flows in the buffer, which may be dropped, the controller is programmed in a way that has the built-in mechanism of self-flow rule formation. This atomized mechanism is based on the previously available traces of the same flows when they were forwarded on the network using the wildcard rules. To assess the performance of the proposed work, it is emulated and benchmarked with the latest research. The results show considerable performance achievement.

Latency Aware 3D Placement and User Association in Drone-Assisted Heterogeneous Networks With FSO-Based Backhaul

Drone Base Station (DBS) is expected to play an important role in next generation cellular networks due to its maneuverability in establishing air-to-ground Line of Sight (LoS) links. Working as a mobile relay node between the Macro Base Station (MBS) and the ground users, the DBS can significantly reduce the latency of ground users by offloading traffic loads from the MBS to the DBS since a better channel condition can be provided. In this paper, we focus on how to determine the 3D location of the DBS, the user association and the bandwidth allocation policy between the MBS and the DBS in order to minimize the total average latency ratio of all users with the constraint of each user's QoS requirement and total available bandwidth. The formulated problem is a mixed integer nonconvex optimization problem, a very challenging and difficult problem. We propose a cyclic iterative algorithm to efficiently solve it by decomposing the primal problem into two subproblems, i.e., the user association and bandwidth allocation problem and the 3D DBS placement problem. In each iteration, the two sub-problems are alternatively optimized by using the output of one as the input for the other. Numerical simulation results demonstrate the significant latency ratio reduction of our proposed algorithm as compared to other baseline schemes.

Pervasive intelligent multi-node health monitoring system for monitoring basketball players health and energy using IoT and 6G technology

Effective health monitoring of players in team sports like basketball allows for understanding external requirements and internal response concerning exercise and competition phases. The explosive growth of wireless devices stimulates the advancement of the internet-of-things (IoT) and 6G technologies, capable of connecting enormous and various “things” through wireless communications. Players face health issues while playing basketball are severe lower body lesions like ankle sprains, shortness of breath, teeth, head, fingers, and hand. To overcome these issues, in this paper, the Pervasive Intelligent Multi-node Health Monitoring System (PIMN-HMS) has been proposed for basketball player’s continuous health tracking based on IoT and 6G communication. With the aid of wearable monitoring sensors to gathers health information and monitor exercise records. The system consists of several sensor nodes, a network coordinator, which monitors physical movements and heart rate, and a personal server on a personal digital assistant using 6G networks. The numerical results have been performed, and the suggested PIMN-HMS model enhances the accuracy ratio of 96.7%, prediction ratio of 97.3%, low latency ratio of 11.2%, delay rate of 22.3%, and efficiency ratio of 98.7% compared to other existing models.

Early changes of nerve integrity in preclinical carriers of hereditary transthyretin Ala117Ser amyloidosis with polyneuropathy

Disease-modifying therapies provide new horizons for hereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN) to slow neuropathic progression. Initiating treatment at the earliest time requires biomarkers reflecting both small- and large-fiber degeneration in carriers. This study included examinations of pathology (intraepidermal nerve fiber [IENF] density), physiology (nerve conduction studies, autonomic function test, and nerve excitability), and psychophysics (thermal thresholds) in carriers to compare to healthy controls and asymptomatic diabetic patients. There were 43 carriers (44.2±11.4years, p.Ala117Ser in 42 carriers), 43 controls (43.4±12.7years) including 26 noncarrier families, and 50 asymptomatic diabetic patients (58.1±9.5years). Carriers had lower IENF densities than controls and similar densities as diabetic patients. Median nerve conduction parameters, especially distal motor latency, were the most frequent neurophysiological abnormality in carriers, could differentiate carriers from controls and diabetic patients, were correlated with IENF densities in carriers but not in controls and diabetic patients, and were correlated with nerve excitability parameters in carriers but not in controls. Fifteen carriers (34.9%) with electrophysiological evidence of median nerve entrapment at the wrist had lower IENF densities and more abnormal conduction parameters than carriers without. We defined nerve dysfunction index-the ratio of median distal motor latency to IENF density-which differentiated carriers from controls. In late-onset ATTRv-PN carriers with predominant p.Ala117Ser, median conduction parameters were the most common neurophysiological abnormalities and served as surrogate signatures of small- and large-fiber impairment. Combination of median distal motor latency and IENF density can reflect early neuropathy in carriers.