α-Klotho levels in girls with central precocious puberty: potential as a diagnostic and monitoring marker.

Background: Recent studies suggest a link between the Klotho protein, sex hormones, and insulin-like growth factor-1 (IGF-1), indicating that α-Klotho levels may rise during puberty, including in central precocious puberty (CPP) cases. This study aimed to explore α-Klotho levels in girls with CPP to assess its potential as a diagnostic and monitoring tool for this condition. Methods: In total, 139 girls, comprising 82 patients diagnosed with CPP and 57 healthy prepubertal controls, were enrolled in this study. From March 2020 to May 2023, we assessed both α-Klotho levels and clinical parameters. α-Klotho concentrations were measured using an α-Klotho ELISA kit. For the girls with CPP, we additionally analyzed samples taken 6 months after GnRH agonist treatment. Results: α-Klotho levels were higher in the CPP group compared with the control (CPP group: 2529 ± 999 ng/mL; control group: 1802 ± 675 pg/mL) (P < 0.001), and its level modest decreased after 6 months of GnRH agonist treatment (2147± 789 pg/mL) (P < 0.001). The association between α-Klotho and IGF-1 SDS, follicular stimulating hormone and baseline luteinizing hormone was assessed by partial correlation after adjusting for age, BMI SDS (r= 0.416, p= <0.001; r= 0.261, p= 0.005; r= 0.278, p= 0.002), respectively. Receiver operating characteristic curve analysis identified an α-Klotho cut-off differentiating CPP from controls, with a cut-off of 1914 pg/mL distinguishing girls with CPP from controls with a sensitivity of 69.5% and specificity of 70.2%; the area under the curve was 0.723. Conclusion: The findings of our study are the first step towards deciphering the role of α-Klotho in puberty induction. With additional data and further research, α-Klotho could potentially be utilized as a significant diagnostic and monitoring tool for CPP.

Machine learning-enabled autonomous operation for atomic force microscopes.

The use of scientific instruments generally requires prior knowledge and skill on the part of operators, and thus, the obtained results often vary with different operators. The autonomous operation of instruments producing reproducible and reliable results with little or no operator-to-operator variation could be of considerable benefit. Here, we demonstrate the autonomous operation of an atomic force microscope using a machine learning-based object detection technique. The developed atomic force microscope was able to autonomously perform instrument initialization, surface imaging, and image analysis. Two cameras were employed, and a machine-learning algorithm of region-based convolutional neural networks was implemented, to detect and recognize objects of interest and to perform self-calibration, alignment, and operation of each part of the instrument, as well as the analysis of obtained images. Our machine learning-based approach could be generalized to apply to various types of scanning probe microscopes and other scientific instruments.

Wide-field optical coherence tomography deviation map for early glaucoma detection.

BACKGROUND/AIMS: This study aimed to establish a wide-field optical coherence tomography (OCT) deviation map obtained from swept-source OCT (SS-OCT) scans. Moreover, it also aimed to compare the diagnostic ability of this wide-field deviation map with that of the peripapillary and macular deviation maps currently being used for the detection of early glaucoma (EG). METHODS: Four hundred eyes, including 200 healthy eyes and 200 eyes with EG were enrolled in this retrospective observational study. Patients underwent a comprehensive ocular examination, including wide-field SS-OCT (DRI-OCT Triton; Topcon, Tokyo, Japan). The individual wide-field scan was converted into a uniform template using the fovea and optic disc centres as fixed landmarks. Subsequently, the wide-field deviation map was obtained via the comparison between individual wide-field data and a normative wide-field database that had been created by combining images of healthy eyes into a uniform template in a previous study. The ability of the new wide-field deviation map to distinguish between EG and healthy eyes was assessed by comparing it with conventional deviation maps based on the area under the receiver operating characteristic curve (AUC). RESULTS: The wide-field deviation map obtained using the normative wide-field database showed the highest diagnostic ability for the diagnosis of EG (AUC=0.980 and 961 for colour-coded pixels presenting <5% and <1%, respectively) among various deviation maps. Its AUC was significantly superior to that of most conventional deviation maps (p<0.05). The wide-field deviation map demonstrated early structural glaucomatous damage well over a wider area. CONCLUSION: The wide-field SS-OCT deviation map exhibited good performance for distinguishing between eyes with EG and healthy eyes. The visualisation of the wider damaged area on the wide-field deviation map could be useful for the diagnosis of EG in clinical settings.

Use of probabilistic fastener velcro as a friction-induced vibration damping treatment.

Probabilistic fasteners are biologically inspired clamping devices that are interlocked by stems on each surface. Due to dynamic characteristics of fastening mechanism, friction inevitably occurs between stems in a vibrating environment. The use of the probabilistic fastener as a vibration reduction component were investigated with advantages from friction-induced damping in this study. The dynamic stiffness and loss factor of the probabilistic fastener were derived from the vibration interaction with a mechanical structure. This allowed determination of energy dissipation due to the friction in hook and loop from the wave propagation analysis. As the vibration amplitude increased, the loss factor of the fastener gradually increased because the friction between multiple stems increased. With the probabilistic fastener application, the vibration generation and transmission were reduced compared to the bolted joint due to the inherent frictional contacts. With this unique advantage, the probabilistic fastener has potential applications when large damping is required with additional benefit on the reduced weight.

Chatter Monitoring of Machining Center Using Head Stock Structural Vibration Analyzed with a 1D Convolutional Neural Network.

Real-time chatter detection is crucial for the milling process to maintain the workpiece surface quality and minimize the generation of defective parts. In this study, we propose a new methodology based on the measurement of machine head stock structural vibration. A short-pass lifter was applied to the cepstrum to effectively remove components resulting from spindle rotations and to extract structural vibration modal components of the machine. The vibration modal components include information about the wave propagation from the cutter impact to the head stock. The force excitation from the interactions between the cutter and workpiece induces structural vibrations of the head stock. The vibration magnitude for the rigid body modes was smaller in the chatter state compared to that in the stable state. The opposite variation was observed for the bending modes. The liftered spectrum was used to obtain this dependence of vibration on the cutting states. The one-dimensional convolutional neural network extracted the required features from the liftered spectrum for pattern recognition. The classified features allowed demarcation between the stable and chatter states. The chatter detection efficiency was demonstrated by application to the machining process using different cutting parameters. The classification performance of the proposed method was verified with comparison between different classifiers.

Estimation of Elbow Wall Thinning Using Ensemble-Averaged Mel-Spectrogram with ResNet-like Architecture.

An elbow wall thinning diagnosis method by highlighting the stationary characteristics of the operating loop is proposed. The accelerations of curved pipe surfaces were measured in a closed test loop operating at a constant pump rpm, combined with curved pipe specimens with artificial wall thinning. The vibration characteristics of wall-thinned elbows were extracted by using a mel-spectrogram in which modal characteristic variation shifting can be expressed. To reduce the deviation of the model's prediction values, the ensemble mean value of the mel-spectrogram was used to emphasize stationary signals and reduce noise signals. A convolutional neural network (CNN) regression model with residual blocks was proposed and showed improved performance compared to the models without the residual block. The proposed regression model predicted the thinning thickness of the elbow excluded in training dataset.

Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI.

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.

Complete Genome Sequence of Tomato Spotted Wilt Virus, a South Korean Isolate from Gerbera jamesonii.

The genome sequence of Tomato spotted wilt orthotospovirus (TSWV) isolated from gerbera was determined. The genome consists of L, M, and S segments containing 8,920, 4,775, and 2,970 nucleotides, respectively. BLASTn analysis showed respective identities of 99.84%, 99.71%, and 99.50% with another Korean isolate, GS, from pepper.

Classification of Bladder Emptying Patterns by LSTM Neural Network Trained Using Acoustic Signatures.

(1) Background: Non-invasive uroflowmetry is used in clinical practice for diagnosing lower urinary tract symptoms (LUTS) and the health status of a patient. To establish a smart system for measuring the flowrate during urination without any temporospatial constraints for patients with a urinary disorder, the acoustic signatures from the uroflow of patients being treated for LUTS at a tertiary hospital were utilized. (2) Methods: Uroflowmetry data were collected for construction and verification of a long short-term memory (LSTM) deep-learning algorithm. The initial sample size comprised 34 patients; 27 patients were included in the final analysis. Uroflow sounds generated from flow impacts on a structure were analyzed by loudness and roughness parameters. (3) Results: A similar signal pattern to the clinical urological measurements was observed and applied for health diagnosis. (4) Conclusions: Consistent flowrate values were obtained by applying the uroflow sound samples from the randomly selected patients to the constructed model for validation. The flowrate predicted using the acoustic signature accurately demonstrated actual physical characteristics. This could be used for developing a new smart flowmetry device applicable in everyday life with minimal constraints from settings and enable remote diagnosis of urinary system diseases by objective continuous measurements of bladder emptying function.

Ultrasonic assessment of osseointegration phenomena at the bone-implant interface using convolutional neural network.

Although endosseous implants are widely used in the clinic, failures still occur and their clinical performance depends on the quality of osseointegration phenomena at the bone-implant interface (BII), which are given by bone ingrowth around the BII. The difficulties in ensuring clinical reliability come from the complex nature of this interphase related to the implant surface roughness and the presence of a soft tissue layer (non-mineralized bone tissue) at the BII. The aim of the present study is to develop a method to assess the soft tissue thickness at the BII based on the analysis of its ultrasonic response using a simulation based-convolution neural network (CNN). A large-annotated dataset was constructed using a two-dimensional finite element model in the frequency domain considering a sinusoidal description of the BII. The proposed network was trained by the synthesized ultrasound responses and was validated by a separate dataset from the training process. The linear correlation between actual and estimated soft tissue thickness shows excellent R2 values equal to 99.52% and 99.65% and a narrow limit of agreement corresponding to [ -2.56, 4.32 µm] and [ -15.75, 30.35 µm] of microscopic and macroscopic roughness, respectively, supporting the reliability of the proposed assessment of osseointegration phenomena.

Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model.

This paper presents a road vehicle emission model that integrates an artificial neural network (ANN) model with a vehicle dynamics model to predict the instantaneous carbon dioxide (CO2), nitrogen oxides (NOx) and total hydrocarbon (THC) emissions of diesel light-duty vehicles. Real-world measurement data were used to train a multi-layer feed-forward ANN model. The optimal combination of the various experimental variables was selected as the ANN input through a parametric study considering both practicality and accuracy. For CO2 prediction, two variables (engine speed and engine torque) are enough to develop an accurate ANN model. In order to achieve satisfactory accuracy for CO and NOx prediction, more variables were used for ANN training. The trained ANN model was used to predict road vehicle emissions by integrating the vehicle dynamics model, which was used as a supplementary tool to produce ANN input data. The integrated model is practical because it requires relatively simple data for input such as vehicle specifications, velocity, and road gradient. In the accuracy validation, the proposed model showed satisfactory prediction accuracy for road vehicle emissions.

A Wide-Field Optical Coherence Tomography Normative Database Considering the Fovea-Disc Relationship for Glaucoma Detection.

Purpose: One purpose of this study was to collect wide-field swept-source optical coherence tomography (SS-OCT) data from healthy eyes and build a wide-filed normative database. Another purpose was to compare the glaucoma diagnostic ability of new parameters based on this normative database to the parameters that are currently in use, such as the peripapillary retinal nerve fiber layer (RNFL), macular ganglion cell-inner plexiform layer, and ganglion cell complex (GCC) thickness. Methods: This study had 220 healthy eyes and 292 eyes with early-stage glaucoma (EG) and moderate-stage glaucoma (MG) enrolled. Using the wide-field SS-OCT images (12 × 9 mm) of healthy eyes, a wide-field normative database was constructed by transforming and combining the individual images into a uniform template using the fovea and optic disc centers as fixed landmarks. Adjustment for the disc size was conducted. With this normative database, new parameters based on the ratio of the fovea-disc distance (FDD) consisting of the fovea-disc relationship were evaluated. The glaucoma diagnostic ability was assessed based on the area under the receiver operating characteristic curve (AUC). Results: Among the new peripapillary parameters, the RNFL of the circumference of the circle with diameter 0.8 FDD showed the highest AUC value for EG and MG, but the value was not significantly superior to that of the initial RNFL (AUC = 0.940 vs. 0.937, P = 0.631). Among the macular parameters, the GCC of the area of the circle of 1.5 FDD showed the highest AUC value for EG and MG, and the value was significantly superior to that of initial GCC (AUC = 0.929 vs. 0.919, P = 0.033). However, there was no significant difference between the initial and adjusted GCC thickness in patients included in the EG or MG groups separately. Conclusions: A wide-field normative database was built to consider the relationship between the fovea and the optic disc. Considering this aspect, we found that the GCC analysis using a broader area presented a significantly greater glaucoma diagnostic performance for EG and MG in the macula than the initial parameter for the GCC. Translational Relevance: Based on this wide-field normative database, the clinical use of a wide-field deviation map may help diagnose the patients with EG and MG in the future.

Autologous hematopoietic cell transplantation following high-dose cytarabine consolidation for core-binding factor-acute myeloid leukemia in first complete remission: a phase 2 prospective trial.

Core-binding factor (CBF)-acute myeloid leukemia (AML) generally have a favorable prognosis. However, approximately 50% of patients experience disease relapse during or after post-remission therapy. Retrospective studies on autologous hematopoietic cell transplantation (AHCT) have shown improved survival with decreased relapse rate in CBF-AML. In this prospective study, we evaluate the outcomes of AHCT following high-dose cytarabine (HiDAC) consolidation in patients with CBF-AML in first complete remission (CR). Adult patients with CBF-AML achieving first CR after induction chemotherapy were eligible for the study. High-dose chemotherapy before AHCT included intravenous busulfan (3.2 mg/kg/day, days - 7 to - 5) and etoposide (400 mg/m2/day, days - 3 to - 2). Twenty-nine patients, 17 with t(8;21) and 12 with inv(16), underwent AHCT following 2 or 3 courses of HiDAC consolidation. The estimated 5-year overall and disease-free survival rates were between 89.0% and 82.5%, respectively. The cumulative incidences of relapse and non-relapse mortality were between 17.5% and 0%, respectively. Presence of measurable residual disease (MRD) before AHCT and KIT mutation were significantly associated with relapse after transplantation. In conclusion, the post-remission strategy of AHCT following HiDAC consolidation in CBF-AML was feasible and efficacious. Assays for MRD and KIT mutation may guide selection of patients who will benefit from AHCT in CBF-AML in first CR.

Estimating the effectiveness of vehicle emission regulations for reducing NOx from light-duty vehicles in Korea using on-road measurements.

The South Korean government has reinforced emission regulations for newly manufactured vehicles to reduce air pollution from automobiles. The government has applied different emission regulations depending on the fuel, following the regulations set for gasoline vehicles in California, USA, and those set for diesel vehicles in the European Union (EU). In this study, the on-road NOx emissions of 109 light-duty vehicles in South Korea were measured on roads in Seoul and the surrounding metropolitan area using a portable emissions measurement system (PEMS). The results were then analyzed to evaluate the effectiveness of the emission regulations introduced in Korea for NOx reduction. The average on-road NOx emissions for the Euro 5 and Euro 6b diesel vehicles were approximately five times higher than the laboratory emission limits set by the EU regulation. The NOx emissions also showed significant variation depending on the driving parameters, such as the driving dynamics and the ambient temperature. From the Euro 6d-TEMP regulation in which the real driving emissions-light duty vehicles (RDE-LDV) regulatory package was implemented, the average on-road NOx emissions from the diesel vehicles were controlled within the laboratory emission limits, but were still higher than those of the gasoline vehicles. Despite the absence of the RDE-LDV regulations, the average on-road NOx emissions of the gasoline vehicles that had ultra-low emission vehicle (ULEV) and super ultra-low emission vehicle (SULEV) standard certifications were controlled within the laboratory emission limits set by the FTP-75, regardless of the various driving parameters. The results of this study show that it is necessary to include a wide range of driving conditions in emission certification test procedures, such as RDE-LDV, and enhance the regulatory measures that enable manufacturers to maintain the effectiveness of emission control systems.

A pilot study for intraocular pressure measurements based on vibroacoustic parameters.

The present study aimed to identify vibroacoustic properties associated with intraocular pressure (IOP) changes and to suggest a new way to measure the IOP based on these properties. Ten ex vivo porcine eyeballs were used in this study. Each eyeball was fixated in a central hole of a Styrofoam block, and vibration applied to the Styrofoam block was transmitted to the eyeball. An accelerometer directly attached to the eyeball measured the vibration response. Excitations and measurements were performed for 1 s, and the excitation magnitude was varied for the same signal in repeat measurements. A 30-gauge needle was inserted into the anterior chamber of the eyeball to inject a balanced salt solution, and the height of the bottle was adjusted to adjust the IOP. A tonometer was used under identical conditions to measure the IOP five times, and the mean value was determined for further analyses. The measurements showed that the parameters resonance frequency and change in the magnitude of the vibration response (CMVR) increased with rising IOP values. The CMVR was highly correlated with the IOP (p-value < 0.0001). A linear mixed effects model (LMM) was used as a statistical analysis method. We confirmed that vibroacoustic properties of the eyeball are correlated with IOP changes. It is expected that the CMVR will serve as a new parameter for IOP measurements. Thus, in the future, continuous IOP measurements would be easily performed using the CMVR.

Emission factor development for light-duty vehicles based on real-world emissions using emission map-based simulation.

Transport emission factors describe pollutant emissions of vehicles by relating with vehicle activities such as traveled distance. Accurate emission factors are essential for estimating transport emission and making better decision to address air pollution and climate change. In this paper, map-based simulations were used to develop emission factors of CO2, NOx and CO based on real-world emissions, as were measured with a Portable Emission Measurement System (PEMS). Vehicle dynamic-based simulation model were used to calculated emission factors using the emission maps as input data, which were developed from real-world emissions. We developed emission factors by fuel type, emission regulation, aftertreatment system and engine displacement. Among various factors, the introduction of Real Driving Emission (RDE) regulation had the greatest influence on the emission factor. For diesel LDVs, NOx and CO emission factors of the Euro 6 d-temp vehicles, which have been applied RDE regulation for the first time, were lower than those of the Euro 5 and Euro 6 b/c vehicles. However, the CO2 emission factors of the Euro 6 d-temp was higher than those of the Euro 5 and Euro 6 b/c vehicles. In addition to calculating emission factors, the proposed methodology provides an improved process that simulate vehicle emissions based on real-world emissions.

Development of a Flowmeter Using Vibration Interaction between Gauge Plate and External Flow Analyzed by LSTM.

(1) Background: This study is aimed at the development of a precise and inexpensive device for flow information measurement for external flow. This novel flowmeter uses an LSTM (long short-term memory) neural network algorithm to analyze the vibration responses of the gauge plate. (2) Methods: A signal processing method using an LSTM neural network is proposed for the development of mass flow rate estimation by sensing the vibration responses of a gauge plate. An FFT (fast Fourier transform) and an STFT (short-time Fourier transform) were used to analyze the vibration characteristics of the gauge plate depending on the mass flow rate. For precise measurements, the vibration level and roughness were computed and used as input features. The actual mass flow rate measured by using a weight transducer was employed as the output features for the LSTM prediction model. (3) Results: The estimated flow rate matched the actual measured mass flow rate very closely. The deviations in measurements for the total mass flow were less than 6%. (4) Conclusions: The estimation of the mass flow rate for external flow through the proposed flowmeter by use of vibration responses analyzed by the LSTM neural network was proposed and verified.

Relationship between growth velocity and change of levels of insulin-like growth factor-1, insulin-like growth factor binding protein-3 and, IGFBP-3 promoter polymorphism during GnRH agonist treatment.

PURPOSE: This study aims to investigate the effect of gonadotropin-releasing hormone agonist (GnRHa) on the growth hormone (GH)-insulin-like growth factor-1 (IGF-1) axis and to evaluate whether -202 A/C IGF binding protein-3 (IGFBP-3) promoter polymorphism affects growth velocity in females with central precocious puberty (CPP) during treatment. METHODS: Data was collected from 97 females younger than 9 years, diagnosed with precocious puberty and treated with GnRHa for at least 1 year at Kangdong Sacred Heart Hospital from 2014 to 2015. Their body height, weight, change in height standard deviation score (∆SDS), serum IGF-1, serum IGFBP-3, bone age, and -202 A/C IGFBP-3 promoter polymorphism were measured before and after GnRHa treatment. The interrelationships between the variables were calculated. RESULTS: During treatment, height SDS, IGF-1 SDS, IGFBP-3 SDS, and IGF-1/IGFBP-3 ratio significantly decreased. A significant correlation was observed between ∆IGF-1 SDS and ∆height SDS (r=0.405, P<0.001). The presence of the C allele was significantly correlated with IGF-1 SDS after treatment (P=0.049) and with IGFBP-3 SDS before and after treatment (P=0.012 and P=0.001), but not with ∆IGF-1 SDS, ∆IGFBP-3 SDS, ∆IGF-1/IGFBP-3 ratio, or ∆height SDS. CONCLUSION: Growth velocity during GnRHa treatment is related to ∆IGF-1 SDS, indicating the apparent impact of GnRHa on the GH-IGF-1 axis. The -202 A/C IGFBP-3 promoter polymorphism does not affect the growth velocity of GnRHa in CPP girls.