The Validity of a Smartphone-Based Application for Assessing Cognitive Function in the Elderly.

The early detection of individuals at risk of cognitive impairment is a clinical imperative. With the recent advancement of digital devices, smartphone application-based cognitive assessment is considered a promising tool for cognitive screening and monitoring inside and outside the clinic. This study examined whether a smartphone-based cognitive assessment, Brain OK, was valid for evaluating cognitive performance and identifying people at risk of cognitive impairment.

Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy.

Among patients with epilepsy, 30-40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition.

A novel deterioration prediction system for mild COVID-19 patients in Korea: a retrospective study.

The ongoing coronavirus disease 2019 (COVID-19) pandemic presents serious public health threats. Omicron, the current most prevalent strain of COVID-19, has a low fatality rate and very high transmissibility, so the number of patients with mild symptoms of COVID-19 is rapidly increasing. This change of pandemic challenges medical systems worldwide in many aspects, including sharp increases in demands for hospital infrastructure, critical shortages in medical equipment, and medical staff.

Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data.

Objective: Failure to receive prompt blood transfusion leads to severe complications if massive bleeding occurs during surgery. For the timely preparation of blood products, predicting the possibility of massive transfusion (MT) is essential to decrease morbidity and mortality. This study aimed to develop a model for predicting MT 10 min in advance using non-invasive bio-signal waveforms that change in real-time.

Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy.

Background: Current risk stratification strategies for patients with hypertrophic cardiomyopathy (HCM) are limited to traditional methodologies.

Objectives: The authors aimed to establish machine learning (ML)-based models to discriminate major cardiovascular events in patients with HCM.

Methods: We enrolled consecutive HCM patients from 2 tertiary referral centers and used 25 clinical and echocardiographic features to discriminate major adverse cardiovascular events (MACE), including all-cause death, admission for heart failure (HF-adm), and stroke.

Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning.

Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE).

Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population.

Background: Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors.

Methods: Utilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respectively.

Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19.

Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period.

Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults.

Older adults are more likely to require emergency department (ED) visits than others, which might be attributed to their medication use. Being able to predict the likelihood of an ED visit using prescription information and readily available data would be useful for primary care. This study aimed to predict the likelihood of ED visits using extensive medication variables generated according to explicit clinical criteria for elderly people and high-risk medication categories by applying machine learning (ML) methods.

Menthol excites dural afferent neurons by inhibiting leak K conductance in rats.

Menthol-a natural organic compound-is widely used for relieving various pain conditions including migraine. However, a high dose of menthol reportedly decreases pain thresholds and enhances pain responses. Accordingly, in the present study, we addressed the effect of menthol on the excitability of acutely isolated dural afferent neurons, which were identified with a fluorescent dye, using the whole-cell patch-clamp technique.

Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability.

Background And Objective: Neuromuscular disorders are diseases that damage our ability to control body movements. Needle electromyography (nEMG) is often used to diagnose neuromuscular disorders, which is an electrophysiological test measuring electric signals generated from a muscle using an invasive needle. Characteristics of nEMG signals are manually analyzed by an electromyographer to diagnose the types of neuromuscular disorders, and this process is highly dependent on the subjective experience of the electromyographer.

Using the Axial Oblique View of Computed Tomography (CT) in Evaluating Femoral Anteversion: A Comparative Cadaveric Study.

Twenty-five cadaveric adult femora’s anteversion angles were measured to develop a highly efficient and reproducible femoral anteversion measurement method using computed tomography (CT). Digital photography captured the proximal femur’s two reference lines, head-to-neck (H-N) and head-to-greater trochanter (H-G). Six reference lines (A/B in transverse section; C, axial oblique section; D/E, conventional 3D reconstruction; and M, volumetric 3D reconstruction) from CT scans were used.

Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals.

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%.

Assessment of central cartilaginous tumor of the appendicular bone: inter-observer and intermodality agreement and comparison of diagnostic performance of CT and MRI.

Background: Diagnostic performance, inter-observer agreement, and intermodality agreement between computed tomography (CT) and magnetic resonance imaging (MRI) in the depiction of the major distinguishing imaging features of central cartilaginous tumors have not been investigated.

Purpose: To determine the inter-observer and intermodality agreement of CT and MRI in the evaluation of central cartilaginous tumors of the appendicular bones, and to compare their diagnostic performance.

Material And Methods: Two independent radiologists retrospectively reviewed preoperative CT and MRI.

Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury.

Objective: Monitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of patients with traumatic brain injury (TBI). However, these signals are often heavily affected by artifacts, which may significantly reduce the reliability of the clinical determinations derived from the signals. The goal of this work was to eliminate signal artifacts from continuous ICP and ABP monitoring via deep learning techniques and to assess the changes in the prognostic capacities of clinical parameters after artifact elimination.

Rhus verniciflua Stokes extract induces inhibition of cell growth and apoptosis in human chronic myelogenous leukemia K562 cells.

Rhus verniciflua Stokes has been widely used as a traditional medicinal plant with a variety of pharmacological activities. We investigated the mechanisms involved in mediating the effects of Rhus verniciflua Strokes (R. verniciflua) extract in human chronic myelogenous leukemia K562 cells, including caspase-dependent apoptotic pathways related to cell-cycle arrest, as well as the inhibition of nuclear factor NF-κB activation and upregulation of the mitogen-activated protein kinase (MAPK) signaling pathway.

Hemodynamic Instability and Cardiovascular Events After Traumatic Brain Injury Predict Outcome After Artifact Removal With Deep Belief Network Analysis.

Background: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopardize the reliability of the clinical parameters obtained from the signals.

Accuracy of Diffusion Tensor Imaging for Diagnosing Cervical Spondylotic Myelopathy in Patients Showing Spinal Cord Compression.

Objective: To assess the performance of diffusion tensor imaging (DTI) for the diagnosis of cervical spondylotic myelopathy (CSM) in patients with deformed spinal cord but otherwise unremarkable conventional magnetic resonance imaging (MRI) findings.

Materials And Methods: A total of 33 patients who underwent MRI of the cervical spine including DTI using two-dimensional single-shot interleaved multi-section inner volume diffusion-weighted echo-planar imaging and whose spinal cords were deformed but showed no signal changes on conventional MRI were the subjects of this study. Mean diffusivity (MD), longitudinal diffusivity (LD), radial diffusivity (RD), and fractional anisotropy (FA) were measured at the most stenotic level.