Reliability of Surface Electromyography From the Lower-limb Muscles During Maximal and Submaximal Voluntary Isometric Contractions in In-bed Healthy Individuals and Patients With Subacute Stroke.

This study aims to develop maximal voluntary isometric contraction (MVIC) and submaximal voluntary isometric contraction (subMVIC) methods and to assess the reliability of the developed methods for in-bed healthy individuals and patients with subacute stroke. The electromyography (EMG) activities from the lower-limb muscles including the tensor fascia lata (TFL), rectus femoris (RF), tibialis anterior (TA), and gastrocnemius (GC) on both sides were recorded during MVIC and subMVIC using surface EMG sensors in 20 healthy individuals and 20 subacute stroke patients. In inter-trial reliability, both MVIC and subMVIC methods demonstrated excellent reliability for all the measured muscles at baseline and follow-up evaluations in both healthy individuals and stroke patients. In inter-day reliability, MVIC showed good reliability for the TFL and moderate reliability for the RF, TA, and GC, while subMVIC showed good reliability for the TFL, RF, and GC and poor reliability for the TA in healthy individuals. In conclusion, the MVIC and subMVIC methods of EMG activities were feasible in in-bed healthy individuals and patients with subacute stroke. The results can serve as a basis for the clinical evaluation of muscular activities using quantitative EMG signals on the lower-limb muscles in stroke patients with impaired mobility.

Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy.

Determining the laterality of the seizure onset zone is challenging in frontal lobe epilepsy (FLE) due to the rapid propagation of epileptic discharges to the contralateral hemisphere. There is hemispheric lateralization of autonomic control, and heart rate is modulated by interactions between the sympathetic and parasympathetic nervous systems. Based on this notion, the laterality of seizure foci in FLE might be determined using heart rate variability (HRV) parameters. We explored preictal markers for differentiating the laterality of seizure foci in FLE using HRV parameters. Twelve patients with FLE (6 right FLE and 6 left FLE) were included in the analyzes. A total of 551 (460 left FLE and 91 right FLE) 1-min epoch electrocardiography data were used for HRV analysis. We found that most HRV parameters differed between the left and right FLE groups. Among the machine learning algorithms applied in this study, the light gradient boosting machine was the most accurate, with an AUC value of 0.983 and a classification accuracy of 0.961. Our findings suggest that HRV parameter-based laterality determination models can be convenient and effective tools in clinical settings. Considering that heart rate can be easily measured in real time with a wearable device, our proposed method can be applied to a closed-loop device as a real-time monitoring tool for determining the side of stimulation.

Design of dissimilar material joint for defect-free multi-material additive manufacturing via laser-directed energy deposition.

Additive manufacturing technology has advanced beyond creating optimized features, from strengthening materials to make them lightweight to fabricating multi-material combinations that offer functionalities beyond the capabilities of individual materials. In this study, a lamination method for laser-directed energy deposition (LDED) is developed to achieve dense multi-material features, and a design that combines different and dissimilar materials is developed. To evaluate these novel developments, two materials-AISI 316L stainless steel and Inconel 625-are introduced. Tensile specimens, fabricated via multi-material additive manufacturing using LDED, are subjected to tensile tests that are recorded on video for digital image correlation. After the tests, fracture surface analyses of the fractured specimens are performed via scanning electron microscopy, and optical monitoring analyses are performed on the specimens that are not subjected to the tensile tests. The results indicate that the specimens demonstrate varied mechanical properties due to the influence of lamination direction and order, which affect the formation of critical cracks and pores.

Effect of α″-Ti Martensitic Phase Formation on Plasticity in Ti-Fe-Sn Ultrafine Eutectic Composites.

Extensive research has been conducted on Ti-Fe-Sn ultrafine eutectic composites due to their high yield strength, compared to conventional microcrystalline alloys. The unique microstructure of ultrafine eutectic composites, which consists of the ultrafine-grained lamella matrix with the formation of primary dendrites, leads to high strength and desirable plasticity. A lamellar structure is known for its high strength with limited plasticity, owing to its interface-strengthening effect. Thus, extensive efforts have been conducted to induce the lamellar structure and control the volume fraction of primary dendrites to enhance plasticity by tailoring the compositions. In this study, however, it was found that not only the volume fraction of primary dendrites but also the morphology of dendrites constitute key factors in inducing excellent ductility. We selected three compositions of Ti-Fe-Sn ultrafine eutectic composites, considering the distinct volume fractions and morphologies of ß-Ti dendrites based on the Ti-Fe-Sn ternary phase diagram. As these compositions approach quasi-peritectic reaction points, the α″-Ti martensitic phase forms within the primary ß-Ti dendrites due to under-cooling effects. This pre-formation of the α″-Ti martensitic phase effectively governs the growth direction of ß-Ti dendrites, resulting in the development of round-shaped primary dendrites during the quenching process. These microstructural evolutions of ß-Ti dendrites, in turn, lead to an improvement in ductility without a significant compromise in strength. Hence, we propose that fine-tuning the composition to control the primary dendrite morphology can be a highly effective alloy design strategy, enabling the attainment of greater macroscopic plasticity without the typical ductility and strength trade-off.

Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

Differentiating changes in movement-related EEG response induced by transcranial direct current stimulation using convolutional neural network.

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique that can modulate neuronal excitability and induce brain plasticity. Although tDCS has been studied with various methods, more research is needed on the movement-related electroencephalography (EEG) changes induced by tDCS. Moreover, it is necessary to investigate whether these changes can be distinguished through a convolutional neural network (CNN)-based classifier. In this study, we measured the EEG during the voluntary foot-tapping task of participants who received tDCS or sham stimulation and evaluated the classification performance. As a result, significantly higher classification accuracy was shown using the ß band (88.7±9.4%), which is more related to motor function, than in the other bands (71.4±10.6% for δ band, 64.1±13.4% for θ band, and 65.7±10.9% for α band). Consequently, EEG changes during the voluntary foot-tapping task induced by tDCS appeared large in the ß band, implying that it is effective in classifying whether tDCS was given or not, and plays an important role in identifying the effect of tDCS.

Design of High-Performance Organic Nonlinear Optical and Terahertz Crystals by Controlling the van der Waals Volume.

In the development of new organic crystals for nonlinear optical and terahertz (THz) applications, it is very challenging to achieve the essentially required non-centrosymmetric molecular arrangement. Moreover, the resulting crystal structure is mostly unpredictable due to highly dipolar molecular components with complex functional substituents. In this work, new organic salt crystals with top-level macroscopic optical nonlinearity by controlling the van der Waals volume (VvdW ), rather than by trial and error, are logically designed. When the VvdW of molecular ionic components varies, the corresponding crystal symmetry shows an observable trend: change from centrosymmetric to non-centrosymmetric and back to centrosymmetric. All non-centrosymmetric crystals exhibit an isomorphic P1 crystal structure with an excellent macroscopic second-order nonlinear optical response. Apart from the top-level macroscopic optical nonlinearity, new organic crystals introducing highly electronegative fluorinated substituents with strong secondary bonding ability show excellent performance in efficient and broadband THz wave generation, high crystal density, high thermal stability, and good bulk crystal growth ability.

Effects of altered functional connectivity on motor imagery brain-computer interfaces based on the laterality of paralysis in hemiplegia patients.

Motor imagery (MI)-based brain-computer interfaces are widely employed for improving the rehabilitation of paralyzed people and their quality of life. It has been well documented that brain activity patterns in the primary motor cortex and sensorimotor cortex during MI are similar to those of motor execution/imagery. However, individuals paralyzed owing to various neurological disorders have debilitated activation of the motor control region. Therefore, the differences in brain activation based on the paralysis location should be considered. We analyzed brain activation patterns using the electroencephalogram (EEG) acquired while performing MI on the right upper limb to investigate hemiplegia-related brain activation patterns. Participants with hemiplegia of the right upper limb (n=7) and left upper limb (n=4) performed the MI task within the right upper limb. EEG signals were acquired using 14 channels based on a 10-20 global system, and analyzed for event-related desynchronization (ERD) based on event-related spectral perturbation and functional connectivity, using the weighted phase-lag index of both hemispheres at the location of hemiplegia. Enhanced ERD was found in the ipsilateral region, compared to the contralateral region, after MI of the affected limb. The reduced difference in the centrality of the channels was observed in all subjects, likely reflecting an altered brain network from increased interhemispheric connections. Furthermore, the tendency of distinct network-based features depending on the MI task on the affected limb was diluted between the inter-hemispheres. Analysis of interaction between inter-region using functional connectivity could provide avenues for further investigation of BCI strategy through the brain state of individuals with hemiplegia.

Commonly Associated Disorders with Complete Scalp Alopecia in Early Childhood: A Review.

Complete scalp hair loss can be a source of distress for affected children and their families. In addition to infectious and trauma-related causes of hair loss, infants and children may present with total scalp alopecia arising from a range of genetic predispositions. Our objective with this review was to identify the common genetic conditions in children with complete scalp alopecia. The PubMed Database was reviewed for all articles from 1962 to 2019 containing the search terms related to genetic alopecia. The conditions with at least five reported cases in the literature were considered for the inclusion. All clinical trials, retrospective studies, and cases on human subjects and written in English were included. Six genetic conditions related to complete scalp alopecia were included in this review. The most common genetic conditions associated with total scalp hair loss include: alopecia totalis/Alopecia universalis (AU), atrichia with papular lesions, AU congenita, hereditary Vitamin D-resistant rickets type IIA, alopecia with mental retardation, and pure hair and nail ectodermal dysplasia. In children presenting with total scalp hair loss, a myriad of genetic and environmental factors may be the underlying cause. Increased awareness of potential genetic conditions associated with total scalp hair loss may assist in diagnosis, with improved the prognosis for the children.

Inherent seizure susceptibility in patients with antihistamine-induced acute symptomatic seizure: a resting-state EEG analysis.

We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.