Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification.

INTRODUCTION: Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images. METHODS: 1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC). RESULTS: Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did. CONCLUSIONS: The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

Measurement of level of consciousness by AVPU scale assessment system based on automated video and speech recognition technology.

OBJECTIVE: To develop an alert/verbal/painful/unresponsive (AVPU) scale assessment system based on automated video and speech recognition technology (AVPU-AVSR) that can automatically assess a patient's level of consciousness and evaluate its performance through clinical simulation. METHODS: We developed an AVPU-AVSR system with a whole-body camera, face camera, and microphone. The AVPU-AVSR system automatically extracted essential audiovisual features to assess the AVPU score from the recorded video files. Arm movement, pain stimulus, and eyes-open state were extracted using a rule-based approach using landmarks estimated from pre-trained pose and face estimation models. Verbal stimuli were extracted using a pre-trained speech-recognition model. Simulations of a physician examining the consciousness of 12 simulated patients for 16 simulation scenarios (4 for each of "Alert", "Verbal", "Painful", and "Unresponsive") were conducted under the AVPU-AVSR system. The accuracy, sensitivity, and specificity of the AVPU-AVSR system were assessed. RESULTS: A total of 192 cases with 12 simulated patients were assessed using the AVPU-AVSR system with a multi-class accuracy of 0.95 (95% confidence interval [CI] (0.92-0.98). The sensitivity and specificity (95% CIs) for detecting impaired consciousness were 1.00 (0.97-1.00) and 0.88 (0.75-0.95), respectively. The sensitivity and specificity of each extracted feature ranged from 0.88 to 1.00 and 0.98 to 1.00. CONCLUSIONS: The AVPU-AVSR system showed good accuracy in assessing consciousness levels in a clinical simulation and has the potential to be implemented in clinical practice to automatically assess mental status.

Intraocular pressure in patients with neovascular age-related macular degeneration switched to aflibercept injection after previous anti-vascular endothelial growth factor treatments.

PURPOSE: To assess for change in intraocular pressure (IOP) in neovascular age-related macular degeneration patients switched to aflibercept after receiving previous treatments of intravitreal bevacizumab or ranibizumab. METHODS: This is a retrospective chart review of the first 53 patients (53 eyes) treated with at least 2 injections of 2 mg in 0.05 mL of aflibercept by March 6, 2013, after at least 2 previous injections of 0.5 mg in 0.05 mL of ranibizumab with or without previous injections of 1.25 mg in 0.05 mL of bevacizumab. The analysis was restricted to the first such sequence within each patient. The last previous anti-vascular endothelial growth factor injection before the switch to aflibercept was ranibizumab in all cases included in the study. Each person served as his or her own control. The pre-aflibercept IOP in the before state (treatment with bevacizumab or ranibizumab) was the preinjection IOP measure before dilation at the visit of the first aflibercept injection. Statistical analysis was performed using Microsoft Excel. RESULTS: There were 41 patients who were first treated with ranibizumab followed by aflibercept and 12 patients treated with ranibizumab and bevacizumab followed by aflibercept. For each of these sequences, IOP in the treated eye during treatment with aflibercept (the after state) was computed in 3 different ways: the first IOP, the last IOP, and the mean IOP for the period when treated with aflibercept. The pooled data showed a mean pre-aflibercept (the before state) IOP of 14.87 that decreased to a mean first IOP of 14.57, mean last IOP of 13.79, and a mean IOP of 14.14 during aflibercept treatment. The inference is based on the pooled analysis. The 95% confidence interval for the differences (after minus before) were -0.30 (-1.12 to 0.52), -1.08 (-1.83 to -0.32), and -0.73 (-1.30 to -0.17) for the first, last, and mean IOPs, respectively. The corresponding P values were 0.46 for the first, 0.006 for the last, 0.01 for the mean IOP during the aflibercept treatment period. CONCLUSION: Intraocular pressure was found to be significantly lower in patients switched to aflibercept after previous treatments with ranibizumab and/or bevacizumab. Aflibercept may have a more favorable IOP safety profile in patients previously on other anti-vascular endothelial growth factor treatments.

Reduced effect of anti-vascular endothelial growth factor agents on diabetics with vitreomacular interface abnormalities.

This study investigated whether the presence of vitreomacular interface abnormalities (VMIAs) affects the improvement in visual acuity and edema of patients with diabetic macular edema (DME) who received three anti-vascular endothelial growth factor (VEGF) injections. Fifteen eyes of 11 patients with clinically significant macular edema were retrospectively divided into either the control group (only DME) or the experimental group (DME and VMIA) based on optical coherence tomography images. We defined VMIA patterns as epiretinal membrane and/or anomalous vitreomacular adhesion. Changes in central macular thickness (CMT), total macular volume (TMV), and best-corrected visual acuity (BCVA) from the baseline to post third injection were compared between the two groups. After the third injection, the decreases in CMT and TMV were not statistically different between the two groups. The improvement in BCVA was larger in the control group (0.1742 ± 0.0508 logMAR) than in the experimental group (0.0766 ± 0.0562 logMAR; p < 0.01). Our study showed that after the third anti-VEGF injection, the BCVA of patients with both DME and VMIAs improved significantly less than that of patients with only DME. Our results suggest that VMIAs may play a crucial role in reducing the therapeutic effects of anti-VEGF agents.

Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.

Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatomical characteristics of the colon pose challenges for establishing a reliable localization system. This study presents a system utilizing 100 colonoscopy videos, combining density clustering and deep learning. Cascaded CNN models are employed to estimate the appendix orifice (AO), flexures, and "outside of the body," sequentially. Subsequently, DBSCAN algorithm is applied to identify anatomical sections. Clustering-based analysis integrates clinical knowledge and context based on the anatomical section within the model. We address challenges posed by colonoscopy images through non-informative removal preprocessing. The image data is labeled by clinicians, and the system deduces section correspondence stochastically. The model categorizes the colon into three sections: right (cecum and ascending colon), middle (transverse colon), and left (descending colon, sigmoid colon, rectum). We estimated the appearance time of anatomical boundaries with an average error of 6.31 s for AO, 9.79 s for HF, 27.69 s for SF, and 3.26 s for outside of the body. The proposed method can facilitate future advancements towards AI-based automatic reporting, offering time-saving efficacy and standardization.

Automated deep learning model for estimating intraoperative blood loss using gauze images.

The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experiment and validated on human intraoperative data prospectively collected from 102 laparoscopic gastric cancer surgeries. The EBL prediction model involves gauze area detection and subsequent EBL regression based on the detected areas, with each stage optimized through comparative model performance evaluations. The selected gauze detection model demonstrated a sensitivity of 96.5% and a specificity of 98.0%. Based on this detection model, the performance of EBL regression stage models was compared. Comparative evaluations revealed that our P-W CS-based model outperforms others, including one reliant on convolutional neural networks and another analyzing the gauze's overall crumpled state. The P-W CS-based model achieved a mean absolute error (MAE) of 0.25 g and a mean absolute percentage error (MAPE) of 7.26% in EBL regression. Additionally, per-patient assessment yielded an MAE of 0.58 g, indicating errors < 1 g/patient. In conclusion, our algorithm provides an objective standard and streamlined approach for EBL estimation during surgery without the need for perioperative approximation and additional tasks by humans. The robust performance of the model across varied surgical conditions emphasizes its clinical potential for real-world application.

A multimodal virtual vision platform as a next-generation vision system for a surgical robot.

Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional virtual reality space so that users can access medical information simultaneously. An availability of the VVP was investigated based on various user evaluations by surgeons and novices, who executed the given tasks and answered questionnaires. The performances of the SV and VVP were not significantly different; however, the craniovertebral angle of the VVP was 16.35° higher on average than that of the SV. Survey results regarding the VVP were positive; participants indicated that the optimal number of displays was six, preferring the 2 × 3 array. Reflecting the tendencies, the VVP can be a neoconceptual candidate to be customized for medical use, which opens a new prospect in a next-generation surgical robot.

A Prospective, Observational, Non-interventional Clinical Study of Participants With Choroideremia: The NIGHT Study.

PURPOSE: The NIGHT study aimed to assess the natural history of choroideremia (CHM), an X-linked inherited chorioretinal degenerative disease leading to blindness, and determine which outcomes would be the most sensitive for monitoring disease progression. DESIGN: A prospective, observational, multicenter cohort study. METHODS: Males aged ≥18 years with genetically confirmed CHM, visible active disease within the macular region, and best-corrected visual acuity (BCVA) ≥34 Early Treatment Diabetic Retinopathy Study (ETDRS) letters at baseline were assessed for 20 months. The primary outcome was the change in BCVA over time at Months 4, 8, 12, 16, and 20. A range of functional and anatomical secondary outcome measures were assessed up to Month 12, including retinal sensitivity, central ellipsoid zone (EZ) area, and total area of fundus autofluorescence (FAF). Additional ocular assessments for safety were performed. RESULTS: A total of 220 participants completed the study. The mean BCVA was stable over 20 months. Most participants (81.4% in the worse eye and 77.8% in the better eye) had change from baseline > -5 ETDRS letters at Month 20. Interocular symmetry was low overall. Reductions from baseline to Month 12 were observed (worse eye, better eye) for retinal sensitivity (functional outcome; -0.68 dB, -0.48 dB), central EZ area (anatomical outcome; -0.276 mm2, -0.290 mm2), and total area of FAF (anatomical outcome; -0.605 mm2, -0.533 mm2). No assessment-related serious adverse events occurred. CONCLUSIONS: Retinal sensitivity, central EZ area, and total area of FAF are more sensitive than BCVA in measuring the natural progression of CHM.

Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling.

BACKGROUND: Patient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for PIU design; however, optimizing this design requires extensive computational resources. Our study aims to provide data-driven models to determine the PIU settings, thereby promoting a more rapid design process. METHOD: Using CFD simulations, we evaluated various PIU parameters and room conditions to assess the impact of PIU installation on ventilation and isolation. We investigated particle dispersion from coughing subjects and airflow patterns. Machine-learning models were trained using CFD simulation data to estimate the performance and identify significant parameters. RESULTS: Physical isolation alone was insufficient to prevent the dispersion of smaller particles. However, a properly installed fan filter unit (FFU) generally enhanced the effectiveness of physical isolation. Ventilation and isolation performance under various conditions were predicted with a mean absolute percentage error of within 13%. The position of the FFU was found to be the most important factor affecting the PIU performance. CONCLUSION: Data-driven modeling based on CFD simulations can expedite the PIU design process by offering predictive capabilities and clarifying important performance factors. Reducing the time required to design a PIU is critical when a rapid response is required.

Evaluating diagnostic content of AI-generated chest radiography: A multi-center visual Turing test.

BACKGROUND: Accurate interpretation of chest radiographs requires years of medical training, and many countries face a shortage of medical professionals to meet such requirements. Recent advancements in artificial intelligence (AI) have aided diagnoses; however, their performance is often limited due to data imbalance. The aim of this study was to augment imbalanced medical data using generative adversarial networks (GANs) and evaluate the clinical quality of the generated images via a multi-center visual Turing test. METHODS: Using six chest radiograph datasets, (MIMIC, CheXPert, CXR8, JSRT, VBD, and OpenI), starGAN v2 generated chest radiographs with specific pathologies. Five board-certified radiologists from three university hospitals, each with at least five years of clinical experience, evaluated the image quality through a visual Turing test. Further evaluations were performed to investigate whether GAN augmentation enhanced the convolutional neural network (CNN) classifier performances. RESULTS: In terms of identifying GAN images as artificial, there was no significant difference in the sensitivity between radiologists and random guessing (result of radiologists: 147/275 (53.5%) vs result of random guessing: 137.5/275, (50%); p = .284). GAN augmentation enhanced CNN classifier performance by 11.7%. CONCLUSION: Radiologists effectively classified chest pathologies with synthesized radiographs, suggesting that the images contained adequate clinical information. Furthermore, GAN augmentation enhanced CNN performance, providing a bypass to overcome data imbalance in medical AI training. CNN based methods rely on the amount and quality of training data; the present study showed that GAN augmentation could effectively augment training data for medical AI.

Development and validation of an artificial intelligence algorithm for detecting vocal cords in video laryngoscopy.

Airway procedures in life-threatening situations are vital for saving lives. Video laryngoscopy (VL) is commonly performed during endotracheal intubation (ETI) in the emergency department. Artificial intelligence (AI) is widely used in the medical field, particularly to detect anatomical structures. This study aimed to develop an AI algorithm that detects vocal cords from VL images acquired during emergent situations. This retrospective study used VL images acquired in the emergency department to facilitate the ETI. The vocal cord image was labeled with a ground-truth bounding box. The dataset was divided into training and validation datasets. The algorithm was developed from a training dataset using the YOLOv4 model. The performance of the algorithm was evaluated using a test set. The test set was further divided into specific environments during the ETI for clinical subgroup analysis. In total, 20,161 images from 84 patients were used in this study. A total of 10,287, 5766, and 4108 images were used for the model training, validation, and test sets, respectively. The developed algorithm achieved F1 score 0.906, sensitivity 0.963, and specificity 0.842 in the validation set. The performance in the test set was F1 score 0.808, sensitivity 0.823, and specificity 0.804. We developed and validated an AI algorithm to detect vocal cords in VL. This algorithm demonstrated a high performance. The algorithm can be used to determine the vocal cord to ensure safe ETI.

Proposal of a Methodology for Prediction of Indoor PM2.5 Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model.

This study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using indoor and outdoor input data measured near the indoor target point as input to calculate indoor PM2.5 concentration through a multiple linear regression model. The atmospheric conditions and air pollution detected in one-minute intervals using sensor-based monitoring equipment (Dust Mon, Sentry Co Ltd., Seoul, Korea) inside and outside houses from May 2019 to April 2021 were used to develop the prediction model. By dividing the multiple linear regression model into one-hour increments, we attempted to overcome the limitation of not representing the multiple linear regression model's characteristics over time and limited input variables. The multiple linear regression (MLR) model classified by time unit showed an improvement in explanatory power by up to 9% compared to the existing model, and some hourly models had an explanatory power of 0.30. These results indicated that the model needs to be subdivided by time period to more accurately predict indoor PM2.5 concentrations.

Softening of PEO-LiTFSI/LLZTO Composite Polymer Electrolytes for Solid-State Batteries under Cyclic Compression.

Composite polymer electrolytes (CPEs) strike an effective balance between ionic conductivity and mechanical flexibility for lithium-ion solid-state batteries. Long-term performance, however, is limited by capacity fading after hundreds of charge and discharge cycles. The causes of performance degradation include multiple contributing factors such as dendrite formation, physicochemical changes in electrolytes, and structural remodeling of porous electrodes. Among the many factors that contribute to performance degradation, the effect of stress specifically on the composite electrolyte is not well understood. This study examines the mechanical changes in a poly(ethylene oxide) electrolyte with bis(trifluoromethane) sulfonimide. Two different sizes of Li6.4La3Zr1.4Ta0.6O12 particles (500 nm and 5 µm) are compared to evaluate the effect of the surface-to-volume ratio of the ion-conducting fillers within the composite. Cyclic compression was applied to mimic stress cycling in the electrolyte, which would be caused by asymmetric volume changes that occur during charging and discharging cycles. The electrolytes exhibited fatigue softening, whereby the compressive modulus gradually decreased with an increase in the number of cycles. When the electrolyte was tested for 500 cycles at 30% compressive strain, the compressive modulus of the electrolyte was reduced to approximately 80% of the modulus before cycling. While the extent of softening was similar regardless of particle size, CPEs with 500 nm particles exhibited a significant reduction in ionic conductivity after cyclic compression (1.4 × 10-7 ± 2.3 × 10-8 vs 1.1 × 10-7 ± 2.0 × 10-8 S/cm, mean ± standard deviation, n = 4), whereas there was no significant change in ionic conductivity for CPEs with 5 µm particles. These observations -performed deliberately in the absence of charge-discharge cycles -show that repetitive mechanical stresses can play a significant role in altering the performance of CPEs, thereby revealing another possible mechanism for performance degradation in all-solid-state batteries.

Subretinal timrepigene emparvovec in adult men with choroideremia: a randomized phase 3 trial.

Choroideremia is a rare, X-linked retinal degeneration resulting in progressive vision loss. A randomized, masked, phase 3 clinical trial evaluated the safety and efficacy over 12 months of follow-up in adult males with choroideremia randomized to receive a high-dose (1.0 × 1011 vector genomes (vg); n = 69) or low-dose (1.0 × 1010 vg; n = 34) subretinal injection of the AAV2-vector-based gene therapy timrepigene emparvovec versus non-treated control (n = 66). Most treatment-emergent adverse events were mild or moderate. The trial did not meet its primary endpoint of best-corrected visual acuity (BCVA) improvement. In the primary endpoint analysis, three of 65 participants (5%) in the high-dose group, one of 34 (3%) participants in the low-dose group and zero of 62 (0%) participants in the control group had ≥15-letter Early Treatment Diabetic Retinopathy Study (ETDRS) improvement from baseline BCVA at 12 months (high dose, P = 0.245 versus control; low dose, P = 0.354 versus control). As the primary endpoint was not met, key secondary endpoints were not tested for significance. In a key secondary endpoint, nine of 65 (14%), six of 35 (18%) and one of 62 (2%) participants in the high-dose, low-dose and control groups, respectively, experienced ≥10-letter ETDRS improvement from baseline BCVA at 12 months. Potential opportunities to enhance future gene therapy studies for choroideremia include optimization of entry criteria (more preserved retinal area), surgical techniques and clinical endpoints. EudraCT registration: 2015-003958-41 .

Night vision restored in days after decades of congenital blindness.

Signaling of vision to the brain starts with the retinal phototransduction cascade which converts visible light from the environment into chemical changes. Vision impairment results when mutations inactivate proteins of the phototransduction cascade. A severe monogenically inherited blindness, Leber congenital amaurosis (LCA), is caused by mutations in the GUCY2D gene, leading to a molecular defect in the production of cyclic GMP, the second messenger of phototransduction. We studied two patients with GUCY2D-LCA who were undergoing gene augmentation therapy. Both patients had large deficits in rod photoreceptor-based night vision before intervention. Within days of therapy, rod vision in both patients changed dramatically; improvements in visual function and functional vision in these hyper-responding patients reached more than 3 log10 units (1000-fold), nearing healthy rod vision. Quick activation of the complex molecular pathways from retinal photoreceptor to visual cortex and behavior is thus possible in patients even after being disabled and dormant for decades.

Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network.

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.

Evaluation of particulate matter concentrations according to cooking activity in a residential environment.

This study aimed to determine the ventilation conditions required for suitable indoor air quality management during cooking in a typical South Korean residential home. We measured the concentrations of particulate matter (PM10) and fine particulate matter (PM2.5) under different ventilation conditions during the cooking of different food materials in the kitchen of a multi-family house, which is the representative residential space in South Korea. Pork belly and mackerel, which are the staple meat and fish products of Korean people, were prepared (200 g of each) and cooked via roasting. The PM10 and PM2.5 concentrations were measured three times for 1 h at 1 min intervals under twelve ventilation conditions. To investigate the PM10 and PM2.5 concentration distribution characteristics and the reduction effect according to ventilation condition, the ratio of the concentration during cooking to the initial concentration was calculated for each condition. Factors causing the emission of PM10 and PM2.5 for each food material and under each ventilation condition were analyzed using principal component analysis and verified using one-way analysis of variance and post hoc analysis. The PM10 and PM2.5 concentrations generated during the cooking of pork belly and mackerel reached their maximum values when no ventilation was used. Under this condition, PM10 concentrations were 246.27 and 1227.71 µg/m3 while the PM2.5 concentrations were 161.93 and 760.82 µg/m3 for pork belly and mackerel cooking, respectively. The PM10 and PM2.5 concentrations were also found to be high when a range hood and air cleaner were used, indicating that it is necessary to improve the performance of ventilation devices and to use appropriate ventilation methods. The use of natural ventilation exhibited a high PM10 and PM2.5 reduction effect compared with the ventilation conditions that used ventilation devices. Using natural ventilation together with ventilation devices was found to be the most effective method of reducing the PM10 and PM2.5 generated during cooking. In conclusion, PM10 and PM2.5 concentrations generated during the cooking of pork belly and mackerel varied depending on the ventilation condition, but they were high when inappropriate ventilation methods were used. Therefore, using appropriate ventilation conditions is effective in reducing PM10 and PM2.5 generated during cooking.

Genetic testing and diagnosis of inherited retinal diseases.

Inherited retinal diseases (IRDs) are a diverse group of degenerative diseases of the retina that can lead to significant reduction in vision and blindness. Because of the considerable phenotypic overlap among IRDs, genetic testing is a critical step in obtaining a definitive diagnosis for affected individuals and enabling access to emerging gene therapy-based treatments and ongoing clinical studies. While advances in molecular diagnostic technologies have significantly improved the understanding of IRDs and identification of disease-causing variants, training in genetic diagnostics among ophthalmologists is limited. In this review, we will provide ophthalmologists with an overview of genetic testing for IRDs, including the types of available testing, variant interpretation, and genetic counseling. Additionally, we will discuss the clinical applications of genetic testing in the molecular diagnosis of IRDs through case studies.

Health risk assessment for multimedia exposure of formaldehyde emitted by chemical accident.

This study was conducted to provide basic data for chemical accident response by assessing the health risks of residents living near a chemical accident site due to long-term exposure. The study considered the temporal concentration changes of the leaked chemical (i.e., its behavior in the environment and dilution) until its extinction. A virtual chemical accident was assumed, in which 40 t of formaldehyde was accidentally discharged for 1 h in Ulsan Metropolitan City, Korea. Formaldehyde concentrations over time in each environmental medium after the accident were calculated using a multimedia environmental dynamics model. Exposure subjects divided into four age groups were considered. Carcinogenic risks due to respiration and non-carcinogenic risks due to soil intake were assessed. For all the age groups, the excess cancer risk did not exceed 1.0 × 10-6, indicating that no harmful health impact was caused by inhalation exposure to formaldehyde. The hazard index exceeded 1 for all the age groups, confirming that harmful health impacts were caused by exposure to soil containing the formaldehyde. This study is the first to assess chronic health risks by reflecting long-term residual and temporal concentration changes of a pollutant released in a chemical accident in each environmental medium until its extinction. This work is also significant in that it reflects the exposure characteristics of the toxic chemical.

Self-reported smoking and urinary cotinine levels among pregnant women in Korea and factors associated with smoking during pregnancy.

This study examined urinary cotinine levels and self-reported smoking among pregnant women in Korea and the factors associated with smoking during pregnancy. The subjects were selected from pregnant women who visited 30 randomly sampled obstetric clinics and prenatal care hospitals in Korea in 2006. Smoking status was determined by self-reporting and urinary cotinine measurement. A total of 1,090 self-administered questionnaires and 1,057 urine samples were analyzed. The percentage of smoking revealed by self-reporting was 0.55% (95% confidence interval [CI], 0.11-0.99) and that revealed by urinary cotinine measurement (>100 ng/mL) was 3.03% (95% CI, 1.99-4.06). The kappa coefficient of agreement between self-reported smoking status and urinary cotinine measurement was 0.20 (95% CI, 0.03-0.37). Multiple logistic regression analysis revealed that early gestational period, low educational level, and being married to a smoker were significant risk factors for smoking during pregnancy. Smoking among pregnant women in Korea is not negligible, and those who are concerned to maternal and child health should be aware of this possibility among pregnant women in countries with similar cultural background.