Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency-modulated continuous-wave radar.

Given the significant impact of sleep on overall health, radar technology offers a promising, non-invasive, and cost-effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)-based classification. In this study, we employed an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model to accurately predict sleep stages using 60 GHz frequency-modulated continuous-wave (FMCW) radar. Our dataset comprised 78 participants from an ongoing obstructive sleep apnea (OSA) cohort, recruited between July 2021 and November 2022, who underwent overnight polysomnography alongside radar sensor monitoring. The dataset encompasses comprehensive polysomnography recordings, spanning both sleep and wakefulness states. The predictions achieved a Cohen's kappa coefficient of 0.746 and an overall accuracy of 85.2% in classifying wakefulness, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep (N1 + N2 + N3). The results demonstrated that the models incorporating both Radar 1 and Radar 2 data consistently outperformed those using only Radar 1 data, indicating the potential benefits of utilising multiple radars for sleep stage classification. Although the performance of the models tended to decline with increasing OSA severity, the addition of Radar 2 data notably improved the classification accuracy. These findings demonstrate the potential of radar technology as a valuable screening tool for sleep stage classification.

Reconstruction of the Chemical Gas Concentration Distribution Using Partial Convolution-Based Image Inpainting.

An interpolation method, which estimates unknown values with constrained information, is based on mathematical calculations. In this study, we addressed interpolation from an image-based perspective and expanded the use of image inpainting to estimate values at unknown points. When chemical gas is dispersed through a chemical attack or terrorism, it is possible to determine the concentration of the gas at each location by utilizing the deployed sensors. By interpolating the concentrations, we can obtain the contours of gas concentration. Accurately distinguishing the contours of a contaminated region from a map enables the optimal response to minimize damage. However, areas with an insufficient number of sensors have less accurate contours than other areas. In order to achieve more accurate contour data, an image inpainting-based method is proposed to enhance reliability by erasing and reconstructing low-accuracy areas in the contour. Partial convolution is used as the machine learning approach for image-inpainting, with the modified loss function for optimization. In order to train the model, we developed a gas diffusion simulation model and generated a gas concentration contour dataset comprising 100,000 contour images. The results of the model were compared to those of Kriging interpolation, one of the conventional spatial interpolation methods, finally demonstrating 13.21% higher accuracy. This suggests that interpolation from an image-based perspective can achieve higher accuracy than numerical interpolation on well-trained data. The proposed method was validated using gas concentration contour data from the verified gas dispersion modeling software Nuclear Biological Chemical Reporting And Modeling System (NBC_RAMS), which was developed by the Agency for Defense Development, South Korea.

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum.

Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance.

Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study.

Radar is a promising non-contact sensor for overnight polysomnography (PSG), the gold standard for diagnosing obstructive sleep apnea (OSA). This preliminary study aimed to demonstrate the feasibility of the automated detection of apnea-hypopnea events for OSA diagnosis based on 60 GHz frequency-modulated continuous-wave radar using convolutional recurrent neural networks. The dataset comprised 44 participants from an ongoing OSA cohort, recruited from July 2021 to April 2022, who underwent overnight PSG with a radar sensor. All PSG recordings, including sleep and wakefulness, were included in the dataset. Model development and evaluation were based on a five-fold cross-validation. The area under the receiver operating characteristic curve for the classification of 1-min segments ranged from 0.796 to 0.859. Depending on OSA severity, the sensitivities for apnea-hypopnea events were 49.0-67.6%, and the number of false-positive detections per participant was 23.4-52.8. The estimated apnea-hypopnea index showed strong correlations (Pearson correlation coefficient = 0.805-0.949) and good to excellent agreement (intraclass correlation coefficient = 0.776-0.929) with the ground truth. There was substantial agreement between the estimated and ground truth OSA severity (kappa statistics = 0.648-0.736). The results demonstrate the potential of radar as a standalone screening tool for OSA.

5-Bromo-3,4-dihydroxybenzaldehyde Promotes Hair Growth through Activation of Wnt/ß-Catenin and Autophagy Pathways and Inhibition of TGF-ß Pathways in Dermal Papilla Cells.

Various studies addressing the increasing problem of hair loss, using natural products with few side effects, have been conducted. 5-bromo-3,4-dihydroxybenzaldehyde (BDB) exhibited anti-inflammatory effects in mouse models of atopic dermatitis and inhibited UVB-induced oxidative stress in keratinocytes. Here, we investigated its stimulating effect and the underlying mechanism of action on hair growth using rat vibrissa follicles and dermal papilla cells (DPCs), required for the regulation of hair cycle and length. BDB increased the length of hair fibers in rat vibrissa follicles and the proliferation of DPCs, along with causing changes in the levels of cell cycle-related proteins. We investigated whether BDB could trigger anagen-activating signaling pathways, such as the Wnt/ß-catenin pathway and autophagy in DPCs. BDB induces activation of the Wnt/ß-catenin pathway through the phosphorylation of GSG3ß and ß-catenin. BDB increased the levels of autophagic vacuoles and autophagy regulatory proteins Atg7, Atg5, Atg16L, and LC3B. We also investigated whether BDB inhibits the TGF-ß pathway, which promotes transition to the catagen phase. BDB inhibited the phosphorylation of Smad2 induced by TGF-ß1. Thus, BDB can promote hair growth by modulating anagen signaling by activating Wnt/ß-catenin and autophagy pathways and inhibiting the TGF-ß pathway in DPCs.

Longitudinal Changes in Isolated Rapid Eye Movement Sleep Behavior Disorder-Related Metabolic Pattern Expression.

BACKGROUND: It remains unclear whether and how the isolated rapid eye movement (REM) sleep behavior disorder (iRBD)-related metabolic pattern (RBDRP) changes with disease progression in iRBD. OBJECTIVE: To examine longitudinal changes in RBDRP expression in iRBD patients and to explore trajectories of relative metabolic activities of individual brain regions constituting RBDRP. METHODS: In this cohort study, 25 iRBD patients (mean age [±standard deviation], 69.2 ± 5.3 years; 12 [48%] patients were men) and 24 age-matched healthy controls were included. The patients underwent at least two 18 F-fluorodeoxyglucose positron emission tomography scans at baseline and at the 2-year and/or 4-year follow-ups. We measured the RBDRP expression of the patients and controls which was validated by reproduction in a separate iRBD cohort (n = 13). RESULTS: At baseline, the RBDRP expression discriminated iRBD patients from healthy controls. However, the RBDRP expression z scores tended to decrease over time in the patients, especially with longer follow-ups, and this tendency was observed even in patients with high-risk of phenoconversion. Furthermore, the degree of RBDRP expression at baseline did not predict the disease conversion. The RBDRP breakdown was mainly provoked by the attenuation of relative hypermetabolism in the frontal cortex including premotor areas and relative hypometabolism in the occipital cortex. The putaminal metabolic activity increased steadily with the disease progression. CONCLUSIONS: The RBDRP expression in iRBD patients was altered significantly over time. Some of the brain metabolic changes seem to represent attempted functional compensation against ongoing neurodegeneration. The RBDRP expression measurement at one time point may not be a reliable biomarker for predicting disease conversion. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Dynamic one-shot target detection and classification using a pseudo-Siamese network and its application to Raman spectroscopy.

Target detection and classification by Raman spectroscopy are important techniques for biological and chemical defense in military operations. Conventionally, these techniques preprocess the observed spectra using smoothing or baseline correction and apply detection algorithms like the generalized likelihood ratio test, independent component analysis, nonnegative matrix factorization, etc. These conventional detection algorithms need preprocessing and multiple shots of Raman spectra to get a reasonable accuracy. Recently, techniques based on deep learning are being used for target detection and classification due to its great adaptability and high accuracy over other methods and due to no requirement for preprocessing. Deep learning may give a good performance, but need retraining when untrained class targets are introduced which is time-consuming and bothersome. We devise a novel algorithm using a variant of the pseudo-Siamese network, one of the deep learning algorithms, that does not need retraining to detect and classify untrained class targets. Our algorithm detects and classifies targets with only one shot. In addition, our algorithm does not need preprocessing. We verify our algorithm with Raman spectra measured using a Raman spectrometer.

An Effective Baseline Correction Algorithm Using Broad Gaussian Vectors for Chemical Agent Detection with Known Raman Signature Spectra.

Raman spectroscopy, which analyzes a Raman scattering spectrum of a target, has emerged as a key technology for non-contact chemical agent (CA) detection. Many CA detection algorithms based on Raman spectroscopy have been studied. However, the baseline, which is caused by fluorescence generated when measuring the Raman scattering spectrum, degrades the performance of CA detection algorithms. Therefore, we propose a baseline correction algorithm that removes the baseline, while minimizing the distortion of the Raman scattering spectrum. Assuming that the baseline is a linear combination of broad Gaussian vectors, we model the measured spectrum as a linear combination of broad Gaussian vectors, bases of background materials and the reference spectra of target CAs. Then, we estimate the baseline and Raman scattering spectrum together using the least squares method. Design parameters of the broad Gaussian vectors are discussed. The proposed algorithm requires reference spectra of target CAs and the background basis matrix. Such prior information can be provided when applying the CA detection algorithm. Via the experiment with real CA spectra measured by the Raman spectrometer, we show that the proposed baseline correction algorithm is more effective for removing the baseline and improving the detection performance, than conventional baseline correction algorithms.

Retina thickness as a marker of neurodegeneration in prodromal lewy body disease.

OBJECTIVES: We investigated retinal change and its relationship with neurodegeneration markers in a prodromal Parkinson cohort. METHODS: A total of 30 patients with idiopathic rapid eye movement sleep behavior disorder were recruited. Participants underwent olfactory testing, macular optical coherence tomography, microperimetry, contrast sensitivity test, and brain N-(3-[18 F]fluoropropyl)-2-carbomethoxy-3-(4-iodophenyl) nortropane positron emission tomography. We measured the ganglion cell complex thicknesses and investigated its correlation with olfactory function and striatal dopamine transporter availability. A linear mixed-effect model was applied with adjustment for multiple comparisons. RESULTS: The parafoveal ganglion-cell-complex thickness in this cohort lay between our healthy control and drug-naïve Parkinson's disease group data. Idiopathic rapid eye movement sleep behavior disorder patients also had contrast sensitivity impairment as in Parkinson's disease with a nonsignificant change in macular sensitivities. Macular ganglion cell complex thickness correlated with olfactory scores and with striatal dopamine transporter availabilities. CONCLUSIONS: Macular ganglion cell complex thinning may be a marker of neurodegeneration in prodromal Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

Effects of zonisamide monotherapy on bone health in drug-naive epileptic patients.

OBJECTIVE: Alteration of bone strength is an adverse effect of antiepileptic drug treatment. We investigated the effects of zonisamide (ZNS) monotherapy on bone mineral density (BMD) and biomarkers of bone metabolism after 13 months of treatment in drug-naive epileptic patients. METHODS: Fifty-nine patients with new onset drug-naive epilepsy were enrolled (29 women, 30 men; mean age = 31.5 ± 11.5 years). The BMD and T scores were measured at the lumbar spine and femoral neck by using dual-energy X-ray absorptiometry. Biomarkers specific for bone metabolism (bone-specific alkaline phosphatase, parathyroid hormone, osteocalcin, insulinlike growth factor-1, C-telopeptide, and vitamin D3 levels) were measured before and after long-term ZNS monotherapy. Analysis of covariance (ANCOVA) was used to estimate BMD and biomarkers of bone metabolism before and after ZNS therapy. Age, sex, treatment duration, and ZNS dosage were included as covariates for adjustment in the ANCOVA model. Furthermore, subgroup analyses were performed for each sex, and the effect size was calculated. RESULTS: After 13 months of ZNS treatment, the BMD and T scores at the lumbar spine (L1-L4 level) and femoral neck were not significantly different. Moreover, the biochemical markers showed no significant differences after ZNS monotherapy. Women showed significantly decreased baseline BMD at the femoral neck compared to men (P = .026), although the mean age and body mass index were not significantly different between the sexes. No significant changes in BMD or biomarkers of bone metabolism were seen in either sex after 13 months of ZNS treatment. SIGNIFICANCE: The results suggest that long-term ZNS monotherapy does not affect bone health in drug-naive patients with epilepsy negatively.

Moderate to severe obstructive sleep apnea during REM sleep as a predictor of metabolic syndrome in a Korean population.

PURPOSE: Metabolic syndrome is a cluster of metabolic abnormalities including obesity, hypertension, hypertriglyceridemia, low high-density lipoprotein cholesterol, and hyperglycemia. Obstructive sleep apnea (OSA) is known to be associated with metabolic syndrome. However, it remains uncertain which sleep parameters of OSA are associated with metabolic syndrome. We aimed to clarify the relationship between sleep variables and the presence of metabolic syndrome in patients with OSA. METHODS: We prospectively recruited patients who visited the institute for the evaluation of sleep-disordered breathing. All patients underwent overnight polysomnography and sleep questionnaires. They were diagnosed with metabolic syndrome according to the 2007 consensus definition by the International Diabetes Federation. We applied multivariate logistic regression models to predict the presence of metabolic syndrome with variables related to sleep parameters. RESULTS: A total of 85 patients (43 men) were enrolled. The mean age (± standard deviation) was 52.0 ± 14.3 years. Metabolic syndrome was diagnosed in 39 (46%) patients. Patients with metabolic syndrome had a significantly higher apnea-hypopnea index (AHI) compared with patients without metabolic syndrome. An AHI greater than 15/h during REM sleep was a significant independent predictor of metabolic syndrome (adjusted OR, 7.08; 95% CI, 1.60-31.41; p = 0.010) after adjusting for age, body mass index, and non-REM AHI ≥ 15/h. In partial correlation analysis, REM AHI was significantly associated with the presence of metabolic syndrome after adjusting for age and BMI (r = 0.229, p = 0.042). CONCLUSION: Korean patients with OSA frequently had comorbid metabolic syndrome. Moderate to severe OSA during REM sleep may be a predictor of metabolic syndrome.

Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications.

Wireless avionics intra-communication (WAIC) refers to a wireless communication system among electronic components (e.g., sensors and actuators) that are integrated or installed in an aircraft and it is proposed to replace heavy and expensive wired communication cables. Recently, the use of a frequency band (4.2⁻4.4 GHz) for the WAIC (so-called, WAIC band) has been approved by international telecommunication union (ITU). Accordingly, several existing wireless protocols such as IEEE 802.11 and IEEE 802.15 are being considered as candidate techniques for the intra-avionics sensor network. In this paper, we perform a real field experiment to investigate wireless channel characteristics in intra-avionics sensor networks at the WAIC bands by a software-defined radio platform (universal software radio peripheral, USRP) and self-produced monopole antennas for the WAIC band. Through the experiment, we validated the feasibility of IEEE 802.11 protocol for the intra-avionics sensor network at the WAIC band in real aircraft environments. Furthermore, based on the measurement data, we evaluated the bit error rate (BER) performance of multiple antenna techniques where we considered the maximum ratio combining (MRC) for the multi-antenna receiver and the space-time block coding (STBC) for the multi-antenna transmitter.

Obstructive sleep apnea is associated with an increased risk of colorectal neoplasia.

BACKGROUND AND AIMS: A recent meta-analysis showed that obstructive sleep apnea (OSA) is associated with a higher prevalence of cancer and cancer-related mortality; however, little information is available on the association between OSA and colorectal neoplasia. METHODS: We identified consecutive patients who underwent overnight polysomnography (PSG) and subsequent colonoscopy. We compared the prevalence of colorectal neoplasia between patients with or without OSA according to the results of PSG. For each patient with OSA, 1 or 2 controls matched for age (±5 years), sex, body mass index (BMI), and smoking who had undergone first-time screening colonoscopy were selected. RESULTS: Of the 163 patients, 111 patients were diagnosed with OSA and 52 patients were within the normal range of the Apnea-Hypopnea Index. Of the 111 patients with OSA, 18 patients (16.2%) had advanced colorectal neoplasia, including 4 (3.6%) colorectal cancers. In the multivariate analyses, OSA was associated with an increased risk of advanced colorectal neoplasia after adjusting for factors including age and sex (mild: odds ratio [OR], 14.09; 95% confidence interval [CI], 1.55-127.83; P = .019; moderate or severe: OR, 14.12; 95% CI, 1.52-131.25; P = .020). Our case-control study revealed that the odds of detecting advanced colorectal neoplasia among patients with OSA were approximately 3.03 times greater than in the controls matched for age, sex, BMI, and smoking (OR, 3.03; 95% CI, 1.44-6.34; P = .002). CONCLUSION: Physicians should be aware of the association between OSA and the development of colorectal neoplasia and explain the need for colonoscopy to patients with OSA.

Cerebral microbleeds are associated with nocturnal reverse dipping in hypertensive patients with ischemic stroke.

BACKGROUND: Abnormalities in nocturnal blood pressure dipping are well known for its relationship to cardiovascular diseases. Cerebral microbleeds are frequently observed in patients with hypertension and are known to be potent risk factors for stroke. However, there are scanty reports about the relationship between nocturnal dipping and cerebral microbleeds. METHODS: We recruited consecutive patients with both hypertension and ischemic stroke within 7 days after symptom onset, and those with cardioembolism were excluded. We applied 24-hour ambulatory blood pressure monitoring two weeks after stroke onset, and we used brain MRI to detect cerebral microbleeds. Various blood pressure parameters such as mean 24-hour blood pressure, awake/sleep blood pressure, and morning surge were compared between cerebral microbleeds (+) vs. (-) groups. Subjects were further classified according to nocturnal dipping status and were analyzed by logistic regression to determine its association with cerebral microbleeds with adjustment for age, gender, and cardiovascular risk factors. RESULTS: A total of 162 patients (100 males, age 65.33 ± 10.32 years) were included. Cerebral microbleeds were detected in 65 patients (40.1%). Most ambulatory blood pressure parameters except morning surge were significantly higher in those who had cerebral microbleeds. After adjusting for the confounding factors, the reverse dippers were prone to have cerebral microbleeds (odds ratio, 3.81; 95% confidential interval, 1.36-10.65; p-value = 0.01). CONCLUSION: Cerebral microbleeds are independently associated with reverse dipping on ambulatory blood pressure monitoring in hypertensive stroke patients.

Worsening of health disparities across COVID-19 pandemic stages in Korea.

OBJECTIVES: With the end of the coronavirus disease 2019 (COVID-19) pandemic, the health outcomes of this disease in Korea must be examined. We aimed to investigate health outcomes and disparities linked to socioeconomic status during the COVID-19 pandemic in Korea and to identify risk factors for hospitalization and mortality. METHODS: This nationwide retrospective study incorporated an analysis of individuals with and without COVID-19 in Korea between January 1, 2020 and December 31, 2022. The study period was divided into 4 stages. Prevalence, hospitalization, mortality, and case-fatality rates were calculated per 100,000 population. Multivariate logistic regression was performed to identify risk factors for COVID-19 hospitalization and mortality. RESULTS: Overall, the incidence rate was 40,601 per 100,000 population, the mortality rate was 105 per 100,000 population, and the case-fatality rate was 259 per 100,000 cases. A total of 12,577,367 new cases (24.5%) were recorded in stage 3 and 8,979,635 cases (17.5%) in stage 4. Medical Aid recipients displayed the lowest 3-year cumulative incidence rate (32,737 per 100,000) but the highest hospitalization (5,663 cases per 100,000), mortality (498 per 100,000), and case-fatality (1,521 per 100,000) rates. Male sex, older age, lower economic status, non-metropolitan area of residence, high Charlson comorbidity index, and disability were associated with higher risk of hospitalization and death. Vaccination was found to reduce mortality risk. CONCLUSIONS: As the pandemic progressed, surges were observed in incidence, hospitalization, and mortality, exacerbating disparities associated with economic status and disability. Nevertheless, Korea has maintained a low case-fatality rate across all economic groups.

Constipation is linked to accelerated cognitive and motor decline in isolated REM sleep behavior disorder.

INTRODUCTION: Constipation is associated with higher clinical severity and predicts cognitive decline in Parkinson's disease (PD). Whether the non-motor marker is associated with unfavorable motor and cognitive trajectories from the prodromal stage remains unclear. METHODS: In a longitudinal prospective cohort of subjects with isolated REM sleep behavior disorders (iRBD), subjects underwent repeated MDS-UPDRS and Mini-Mental Status Examination (MMSE) assessments. Generalized-estimating-equations (GEE) regression model was used to compare the time-dependent trajectories of MDS-UPDRS-III and MMSE scores between subjects with and without constipation at baseline. RESULTS: Twenty-nine subjects with constipation at baseline (iRBD+constipation) and 24 without (iRBD-constipation) were followed over 4.085 ± 2.645 years. The iRBD+constipation group presented faster decline of both MDS-UPDRS-III and MMSE scores, with additional estimated annual progression of +1.242 and -0.713 points, respectively, compared to the iRBD-constipation group (time*group p < 0.05). CONCLUSION: Constipation in isolated RBD is associated with accelerated progression of cognitive impairment and motor symptoms.

Development of a Rapid and Accurate Vapor Generation System for Real-Time Monitoring of a Chemical Warfare Agent (CWA) by Coupling Fourier Transform Infrared (FT-IR) Spectroscopy.

Developing an accurate chemical warfare agent (CWA) vapor generator is critical for homeland security because it enables real-time monitoring of target agent concentration for testing and evaluation. We designed and built an elaborate CWA vapor generator that offers reliable long-term stability and real-time monitoring capabilities by coupling it with Fourier transform infrared (FT-IR) spectroscopy. We evaluated the reliability and stability of the vapor generator using a gas chromatography-flame ion detector (GC-FID) and conducted a comparison between the experimental and theoretical results of sulfur mustard (HD, bis-2-chloroethylsulfide), a real CWA, at concentrations ranging from 1 to 5 ppm. Our FT-IR-coupled vapor generation system showed real-time monitoring ability, which enables rapid and accurate evaluation of chemical detectors. The vapor generation system was able to generate CWA vapor continuously for over 8 h, demonstrating its long-term vapor generation capability. In addition, we vaporized another representative CWA, viz., GB (Sarin, propan-2-yl ethylphosphonofluoridate), and conducted real-time monitoring of GB vapor concentration with high accuracy. This versatile vapor generator approach can enable the rapid and accurate evaluation of CWAs for homeland security against chemical threats and can be used in constructing a versatile real-time monitoring vapor generation system for CWAs.

Detection of hazardous chemical using dual-wavelength Raman spectroscopy in the ultraviolet region.

This study proposes a stand-off Raman spectroscopy system using dual-wavelength in the ultraviolet (UV) region to detect hazardous chemicals. The Raman spectrum generated by the UV excitation source avoids solar background noise during daytime for chemical detection as the spectrum is in the solar blind range. Wavelengths of 213 and 266 nm by 5th and 4th harmonics are generated from Nd:YAG laser. However, Raman spectra of chemicals exhibit different signal-to-noise ratios for both the excitation wavelengths; therefore, to detect such chemicals, Raman spectra by two sources are required. Raman spectra were acquired using a dual-wavelength laser and spectrometer with a single grating and detector at the wavelengths of 213 and 266 nm simultaneously. The Raman spectra of sulfuric acid, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate were acquired and analyzed, thus highlighting the application of dual-wavelength Raman spectroscopy. For efficient chemical detection in the field, we have ensured that the system developed in this study is robust.

Effects of music therapy as an alternative treatment on depression in children and adolescents with ADHD by activating serotonin and improving stress coping ability.

OBJECTIVE: The objective of this study was to determine the effect of music therapy as an alternative treatment on depression in children and adolescents with attention-deficit hyperactivity disorder (ADHD) by activating serotonin (5-HT) and improving stress coping ability. METHODS: This study is designed based on randomization method. A total of 36 subjects participated in the experiment, consisting of an ADHD control group (n = 18) and ADHD music therapy group (n = 18). The ADHD control group received standard care, while the ADHD music therapy group received music therapy and standard care. The ADHD music therapy group received both active music therapy (improvisation) and receptive music therapy (music listening) for 50 minutes, twice a week, for 3 months: a total of 24 times. From a neurophysiological perspective, changes in depression and stress were tracked by measuring 5-HT secretion, cortisol expression, blood pressure (BP), heart rate (HR), and CDI and DHQ psychological scales. RESULTS: The ADHD music therapy group's 5-HT secretion increased (p < 0.001), whereas cortisol expression (p < 0.001), BP (p < 0.001) and HR (p < 0.001) decreased. The CDI and DHQ psychological scales also showed positive changes (p < 0.01 and p < 0.001, respectively). However, the ADHD Con G's (who did not receive music therapy) 5-HT secretion did not increase, whereas cortisol expression, BP, and HR did not decrease. In addition, the CDI and DHQ psychological scales did not display positive changes. CONCLUSIONS: In conclusion, the application of music therapy as an alternative treatment for ADHD children and adolescents showed positive neurophysiological and psychological effects. Therefore, this study would like to propose a new alternative to medicine for preventing and treating depression through various uses of music therapy.

Longitudinal evolution of cortical thickness signature reflecting Lewy body dementia in isolated REM sleep behavior disorder: a prospective cohort study.

BACKGROUND: The isolated rapid-eye-movement sleep behavior disorder (iRBD) is a prodromal condition of Lewy body disease including Parkinson's disease and dementia with Lewy bodies (DLB). We aim to investigate the longitudinal evolution of DLB-related cortical thickness signature in a prospective iRBD cohort and evaluate the possible predictive value of the cortical signature index in predicting dementia-first phenoconversion in individuals with iRBD. METHODS: We enrolled 22 DLB patients, 44 healthy controls, and 50 video polysomnography-proven iRBD patients. Participants underwent 3-T magnetic resonance imaging (MRI) and clinical/neuropsychological evaluations. We characterized DLB-related whole-brain cortical thickness spatial covariance pattern (DLB-pattern) using scaled subprofile model of principal components analysis that best differentiated DLB patients from age-matched controls. We analyzed clinical and neuropsychological correlates of the DLB-pattern expression scores and the mean values of the whole-brain cortical thickness in DLB and iRBD patients. With repeated MRI data during the follow-up in our prospective iRBD cohort, we investigated the longitudinal evolution of the cortical thickness signature toward Lewy body dementia. Finally, we analyzed the potential predictive value of cortical thickness signature as a biomarker of phenoconversion in iRBD cohort. RESULTS: The DLB-pattern was characterized by thinning of the temporal, orbitofrontal, and insular cortices and relative preservation of the precentral and inferior parietal cortices. The DLB-pattern expression scores correlated with attentional and frontal executive dysfunction (Trail Making Test-A and B: R = - 0.55, P = 0.024 and R = - 0.56, P = 0.036, respectively) as well as visuospatial impairment (Rey-figure copy test: R = - 0.54, P = 0.0047). The longitudinal trajectory of DLB-pattern revealed an increasing pattern above the cut-off in the dementia-first phenoconverters (Pearson's correlation, R = 0.74, P = 6.8 × 10-4) but no significant change in parkinsonism-first phenoconverters (R = 0.0063, P = 0.98). The mean value of the whole-brain cortical thickness predicted phenoconversion in iRBD patients with hazard ratio of 9.33 [1.16-74.12]. The increase in DLB-pattern expression score discriminated dementia-first from parkinsonism-first phenoconversions with 88.2% accuracy. CONCLUSION: Cortical thickness signature can effectively reflect the longitudinal evolution of Lewy body dementia in the iRBD population. Replication studies would further validate the utility of this imaging marker in iRBD.