Deep learning-based fully automated grading system for dry eye disease severity.

There is an increasing need for an objective grading system to evaluate the severity of dry eye disease (DED). In this study, a fully automated deep learning-based system for the assessment of DED severity was developed. Corneal fluorescein staining (CFS) images of DED patients from one hospital for system development (n = 1400) and from another hospital for external validation (n = 94) were collected. Three experts graded the CFS images using NEI scale, and the median value was used as ground truth. The system was developed in three steps: (1) corneal segmentation, (2) CFS candidate region classification, and (3) estimation of NEI grades by CFS density map generation. Also, two images taken on different days in 50 eyes (100 images) were compared to evaluate the probability of improvement or deterioration. The Dice coefficient of the segmentation model was 0.962. The correlation between the system and the ground truth data was 0.868 (p<0.001) and 0.863 (p<0.001) for the internal and external validation datasets, respectively. The agreement rate for improvement or deterioration was 88% (44/50). The fully automated deep learning-based grading system for DED severity can evaluate the CFS score with high accuracy and thus may have potential for clinical application.

Emerging Frontiers in Multichannel Membrane Capacitive Deionization: Recent Advances and Future Prospects.

Capacitive deionization (CDI) has emerged as a promising desalination technology and recently promoted the development of multichannel membrane capacitive deionization (MC-MCDI). In MC-MCDI, the independent control of multiflow channels, including the feed and electrolyte channels, enables the optimization of electrode operation in various modes, such as concentration gradients and reverse voltage discharge, facilitating semicontinuous operation. Moreover, the integration of redox couples into MC-MCDI has led to advancements in redox-mediated desalination. Specifically, the introduction of redox-active species helps enhance the ion removal efficiency and reduce energy consumption during desalination. This systematic approach, combining principles from CDI and electrodialysis, results in more sustainable and efficient desalination. These advancements have contributed to improved desalination performance and practical feasibility, rendering MC-MCDI an increasingly attractive option for addressing water scarcity challenges. Despite the considerable interest in and potential of this process, there is currently no comprehensive review available that covers the operational features and applications of MC-MCDI. Therefore, this Review provides an overview of recent research progress, focusing on the unique cell configuration, vital operation principles, and potential advantages over conventional CDI. Additionally, innovative applications of MC-MCDI are discussed. The Review concludes with insights into future research directions, potential opportunities in industrial desalination technology, and the fundamental and practical challenges for successful implementation.

Diffusion-based generative AI for exploring transition states from 2D molecular graphs.

The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperforms the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learns the distribution of TS geometries for diverse reactions in training. Thus, TSDiff finds more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration.

Toward High-Performance Piezoresistive Polymer Derived SiOC Ceramics through Masked Stereolithography 3D Printing with ß-Silicon Carbide Nanopowder Reinforcement.

Enhancing the piezoresistivity of polymer-derived silicon oxycarbide ceramics (SiOCPDC ) is of great interest in the advancement of highly sensitive pressure/load sensor technology for use in harsh and extreme working conditions. However, a facile, low cost, and scalable approach to fabricate highly piezoresistive SiOCPDC below 1400 °C still remains a great challenge. Here, the fabrication and enhancement of piezoresistive properties of SiOCPDC reinforced with ß-SiC nanopowders (SiCNP ) through masked stereolithography-based 3D-printing and subsequent pyrolysis at 1100 °C are demonstrated. The presence of free carbon in SiCNP augments high piezoresistivity in the fabricated SiCNP -SiOCPDC composites even at lower pyrolysis temperatures. A gauge factor (GF) in the range of 4385-5630 and 6129-8987 with 0.25 and 0.50 wt% of SiCNP , respectively is demonstrated, for an applied pressure range of 0.5-5 MPa at ambient working conditions. The reported GF is significantly higher compared to those of any existing SiOCPDC materials. This rapid and facile fabrication route with significantly enhanced piezoresistive properties makes the 3D-printed SiCNP -SiOCPDC composite a promising high-performance material for the detection of pressure/load in demanding applications. Also, the overall robustness in mechanical properties and load-bearing capability ensures its long-term stability and makes it suitable for challenging and severe environment applications.

Age and sex differences in coronal lower extremity alignment in a healthy Asian population.

BACKGROUND: The purpose of this study was to analyze the coronal alignment of lower extremities according to age and sex in a healthy population and demonstrate the differences. METHODS: Standing full-lower limb anteroposterior (AP) radiographs of healthy volunteers (670 males and 782 females) aged 18-69 years were retrospectively analyzed. The hip-knee-ankle angle (HKA), lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), joint line convergence angle and femoral bowing angle (FBA) were measured. The radiographic parameters were compared according to groups of age and sex. The proportion of volunteers with varus or valgus alignment more than 3° were also analyzed. RESULTS: With increasing age, HKA and LDFA varus increased. With increasing age, femoral medial bowing decreased. In addition, the HKA showed more varus alignment in males than in females (178.01° vs. 178.82°, P < 0.001). The MPTA was about 1° smaller in males than in females (P < 0.001). The proportion of patients with varus alignment of more than 3° increased with increasing age, with 16.9% in the 10-19 years old and 38.0% in the 60-69 years old groups. CONCLUSION: This study demonstrated that males showed more varus tibial alignments than females. Varus limb alignment, LDFA, and FBA also increases with age. In contrast, tibial alignment was constant across all age groups. Therefore, differences in lower extremity alignment according to age and sex should be considered in estimating individual prearthritic alignments.

Recurrent Vascularizing Keratitis in Infants With Hereditary Mucoepithelial Dysplasia Related to SREBF1 Mutation.

PURPOSE: This study aims to present ophthalmic manifestations of 2 infants with hereditary mucoepithelial dysplasia (HMD) related to SREBF1 mutation over a 5-year period. METHODS: Two female infants with an unremarkable perinatal history were evaluated for photophobia that had been manifest since 3 months after birth and diffuse scalp alopecia. Complete ocular examinations under anesthesia were performed, as well as genetic and systemic workup. RESULTS: Both patients had vascularizing keratitis in both eyes, characterized by the growth of corneal new vessels from the 360 degrees periphery to the center and the formation of stromal leucomatous opacity at the leading edge. The keratitis partially regressed in response to topical corticosteroids and waxed and waned during the 5 years of follow-up. In addition, the loss of scalp hair developed in a cyclical pattern, causing diffuse scalp alopecia in the patients. Rheumatologic, nutritional, and developmental evaluations were within normal ranges. Whole-exome sequencing identified a heterozygous c.1669C>T (p.Arg557Cys) pathogenic variant in the SREBF1 gene associated with HMD in both patients. CONCLUSIONS: In pediatric patients with recurrent vascularizing keratitis and diffuse scalp alopecia starting early in life, HMD should be considered, and genetic tests and collaboration with dermatologists and pediatricians on the diagnosis should be provided.

Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers.

A grand challenge in polymer science lies in the predictive design of new polymeric materials with targeted functionality. However, de novo design of functional polymers is challenging due to the vast chemical space and an incomplete understanding of structure-property relations. Recent advances in deep generative modeling have facilitated the efficient exploration of molecular design space, but data sparsity in polymer science is a major obstacle hindering progress. In this work, we introduce a vast polymer database known as the Open Macromolecular Genome (OMG), which contains synthesizable polymer chemistries compatible with known polymerization reactions and commercially available reactants selected for synthetic feasibility. The OMG is used in concert with a synthetically aware generative model known as Molecule Chef to identify property-optimized constitutional repeating units, constituent reactants, and reaction pathways of polymers, thereby advancing polymer design into the realm of synthetic relevance. As a proof-of-principle demonstration, we show that polymers with targeted octanol-water solubilities are readily generated together with monomer reactant building blocks and associated polymerization reactions. Suggested reactants are further integrated with Reaxys polymerization data to provide hypothetical reaction conditions (e.g., temperature, catalysts, and solvents). Broadly, the OMG is a polymer design approach capable of enabling data-intensive generative models for synthetic polymer design. Overall, this work represents a significant advance, enabling the property targeted design of synthetic polymers subject to practical synthetic constraints.

Interferon-γ elicits the ocular surface pathology mimicking dry eye through direct modulation of resident corneal cells.

Despite accumulating evidence indicating a key role of interferon-γ (IFN-γ)-producing immune cells in ocular infection and immunity, little is known about the direct effects of IFN-γ on resident corneal cells or on the ocular surface. Here, we report that IFN-γ impacts corneal stromal fibroblasts and epithelial cells to promote inflammation, opacification, and barrier disruption on the ocular surface, leading to dry eye. Our results demonstrated that IFN-γ dose-dependently induced cytotoxicity, pro-inflammatory cytokine/chemokine production, and expression of major histocompatibility complex class II and CD40 in cultures of corneal stromal fibroblasts and epithelial cells while increasing myofibroblast differentiation of corneal stromal fibroblasts. In mice, subconjunctival IFN-γ administration caused corneal epithelial defects and stromal opacity in dose- and time-dependent manners while promoting neutrophil infiltration and inflammatory cytokine expression in the cornea. Moreover, IFN-γ reduced aqueous tear secretion and the number of conjunctival goblet cells responsible for mucinous tear production. Together, our findings suggest that IFN-γ induces the ocular surface changes characteristic of dry eye disease at least in part through its direct effects on resident corneal cells.

One-stage reconstruction using a fibula osteocutaneous free flap and an anterolateral thigh free flap for an extensive composite defect after en bloc resection of squamous cell carcinoma on the mouth floor, mandible, and anterior neck: A CARE-compliant case report.

RATIONALE: Although regional metastasis to the lymph nodes is common in advanced oral cancer, extensive local invasion into surrounding structures such as the mandible, skin and soft tissue of the neck, and masticator space is relatively rare. Sometimes surgical treatment cannot be performed and only palliative chemotherapy and radiation therapy are offered to preserve the quality of life of patients with advanced oral cancer. Nevertheless, the surgical resection of tumors remains the most effective treatment. This study presents a case of aggressive mouth floor cancer in which extensive composite defects on the mouth floor, oral mucosa, mandible, skin and soft tissue of the neck caused by tumor resection were reconstructed. PATIENT CONCERNS: A 66-year-old man and a 65-year-old man with no significant personal or family history visited our clinic due to a large and multiple masses on the floor of the mouth and both sides of the neck. DIAGNOSIS: Histopathological evaluation of the biopsy specimen revealed squamous cell carcinoma. INTERVENTIONS: A fibula osteocutaneous free flap and customized titanium plate were used for the intraoral lining. Mandibular reconstruction was performed using a 3D-printed bone model, and an anterolateral thigh free flap was used to resurface the anterior of the neck. OUTCOMES: Reconstruction using this method was successful, and excellent functional and aesthetic outcomes were achieved without cancer recurrence. LESSONS: This study show that the reconstruction of extensive composite defects of the oral mucosa, mandible, and neck soft tissue following surgical resection of mouth floor cancer can be performed in a single-stage operation. Through a single-stage reconstruction, both excellent functional aspects without cancer recurrence and satisfactory aesthetic outcomes can be obtained.

Gaussian-Approximated Poisson Preconditioner for Iterative Diagonalization in Real-Space Density Functional Theory.

Various real-space methods optimized on massive parallel computers have been developed for efficient large-scale density functional theory (DFT) calculations of materials and biomolecules. The iterative diagonalization of the Hamiltonian matrix is a computational bottleneck in real-space DFT calculations. Despite the development of various iterative eigensolvers, the absence of efficient real-space preconditioners has hindered their overall efficiency. An efficient preconditioner must satisfy two conditions: appropriate acceleration of the convergence of the iterative process and inexpensive computation. This study proposed a Gaussian-approximated Poisson preconditioner (GAPP) that satisfied both conditions and was suitable for real-space methods. A low computational cost was realized through the Gaussian approximation of a Poisson Green's function. Fast convergence was achieved through the proper determination of Gaussian coefficients to fit the Coulomb energies. The performance of GAPP was evaluated for several molecular and extended systems, and it showed the highest efficiency among the existing preconditioners adopted in real-space codes.

Dynamic Precision Approach for Accelerating Large-Scale Eigenvalue Solvers in Electronic Structure Calculations on Graphics Processing Units.

Single precision (SP) arithmetic can be greatly accelerated as compared to double precision (DP) arithmetic on graphics processing units (GPUs). However, the use of SP in the whole process of electronic structure calculations is inappropriate for the required accuracy. We propose a 3-fold dynamic precision approach for accelerated calculations but still with the accuracy of DP. Here, SP, DP, and mixed precision are dynamically switched during an iterative diagonalization process. We applied this approach to the locally optimal block preconditioned conjugate gradient method to accelerate a large-scale eigenvalue solver for the Kohn-Sham equation. We determined a proper threshold for switching each precision scheme by examining the convergence pattern on the eigenvalue solver only with the kinetic energy operator of the Kohn-Sham Hamiltonian. As a result, we achieved up to 8.53× and 6.60× speedups for band structure and self-consistent field calculations, respectively, for test systems under various boundary conditions on NVIDIA GPUs.

SS-OCT-based ocular biometry in an adult Korean population with cataract.

PURPOSE: To evaluate the characteristics of ocular biometric parameters in adult Korean patients with cataract. SETTING: Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea. DESIGN: Retrospective case series. METHODS: Ocular biometric values of 5273 eyes of 5273 Korean patients undergoing cataract surgery measured with the IOLMaster 700 at the Seoul National University Hospital between November 2019 and December 2021 were reviewed. RESULTS: A total of 5273 eyes of 5273 Korean patients were analyzed. The mean ± SD age was 66.1 ± 12.8 years, and 62% were female. Overall, age and ocular biometric parameters were correlated with each other. Particularly, age showed a negative correlation with anterior chamber depth (ACD; r = -0.357), axial length (AL; r = -0.344), and posterior keratometry (PK; r = -0.054) and a positive correlation with lens thickness (LT; r = 0.484), angle α ( r = 0.194), total keratometry (TK; r = 0.137), anterior keratometry (AK; r = 0.129), and angle κ ( r = 0.071). AL showed a positive correlation with ACD ( r = 0.503) and PK ( r = 0.339) and a negative correlation with AK ( r = -0.342), TK ( r = -0.334), LT ( r = -0.288), angle α ( r = -0.220), and angle κ ( r = -0.040). With age, anterior and total corneal astigmatism changed from with-the-rule (WTR) to against-the-rule (ATR) astigmatism. Posterior corneal astigmatism was ATR regardless of age; however, the magnitude decreased with age. CONCLUSIONS: Age showed a significant correlation in the order of LT, ACD, and AL. With age, angle α and κ increased, and total corneal astigmatism changes from WTR to ATR, which is mainly affected by changes in anterior corneal astigmatism. AL showed a significant correlation in the order of ACD, AK, PK, and TK. These data are pertinent for improving the result after cataract surgery, especially when using premium intraocular lenses.

MOF/MWCNT-Nanocomposite Manipulates High Selectivity to Gas via Different Adsorption Sites with Varying Electron Affinity: A Study in Methane Detection in Parts-per-Billion.

Metal-organic frameworks (MOFs) present specific adsorption sites with varying electron affinity which are uniquely conducive to selective gas sensing but are typically large-band-gap insulators. On the contrary, multiwall carbon nanotubes (MWCNTs) exhibit superior mesoscopic transport exploiting strong electron correlations among sub-bands below and above the Fermi level at room temperature. We synergize them in a new class of nanocomposites based on zeolitic imidazolate framework-8 (ZIF-8) and report selective sensing of CH4 in ∼10 parts-per-billion (ppb) with a determined limit of detection of ∼0.22 ppb, hitherto unprecedented. The observed selectivity to CH4 over non-polar CO2, polar volatile organic compounds, and moisture has roots in competing electron-sharing mechanisms at its different adsorption sites. This important result provides a significant reference to guide future MOF-related composite research to achieve the best sensing performance. On molecular adsorption, MWCNTs facilitate electrical transport via manipulating the ZIF-8 band gap to show a p-type semiconductor behavior with lower activation energy to induce a measurable resistance change. Excellent repeatability and reversibility are shown. A carbon-engineered MOF composite has the potential to actuate similar selective response to low reactive gases via carrier manipulation in the energy band gap.

New Monocyclic Terpenoid Lactones from a Brown Algae Sargassum macrocarpum as Monoamine Oxidase Inhibitors.

Algae are unique natural products that can produce various types of biologically active compounds. The 70% ethanol extract of brown algae Sargassum macrocarpum collected from the East Sea of Korea inhibited human monoamine oxidases A and B enzymes (hMAO-A and hMAO-B) at a 50 µg/mL concentration. The bioassay-guided isolation was performed through solid-phase extraction and the Sepbox system followed by serial high-performance liquid chromatography on the reverse phase condition, resulting in the identification of two new monocyclic terpenoid lactones, sargassumins A and B (1 and 2). The planar structures of the compounds were determined by a combination of spectroscopic data. The absolute configurations were determined by the interpretation of circular dichroism data. Compound 1 exhibited mild hMAO-A inhibition (42.18 ± 2.68% at 200 µM) and docked computationally into the active site of hMAO-A (-8.48 kcal/mol). Although compound 2 could not be tested due to insufficient quantity, it docked better into hMAO-A (-9.72 kcal/mol). Therefore, the above results suggest that this type of monocyclic terpenoid lactone could be one of the potential lead compounds for the treatment of psychiatric or neurological diseases.

Undeflatable balloon guide catheter (BGC) during endovascular procedure: Rescue strategy.

The use of a balloon guide catheter (BGC) in the endovascular management of acute ischemic stroke is known to improve the efficacy and efficiency of the procedure by reducing the risk of distal embolization. During the procedure, the balloon of the catheter causes a temporary arrest of cerebral blood flow. However, failure of the balloon to deflate during the BGC procedure can result in catastrophic complications, including aggravated hypoxic damage.
This paper aims to share the resolution and methodological analysis of our experience with BGC balloon deflation failure, which was confirmed by a reproducible experiment under similar conditions.

Discovery and Photoisomerization of New Pyrrolosesquiterpenoids Glaciapyrroles D and E, from Deep-Sea Sediment Streptomyces sp.

Two new pyrrolosesquiterpenes, glaciapyrroles D (1) and E (2) were discovered along with the previously reported glaciapyrrole A (3) from Streptomyces sp. GGS53 strain isolated from deep-sea sediment. This study elucidated the planar structures of 1 and 2 using nuclear magnetic resonance (NMR), mass spectrometry (MS), ultraviolet (UV), and infrared (IR) spectroscopic data. The absolute configurations of the glaciapyrroles were determined by Mosher's method, circular dichroism spectroscopy, and X-ray crystallography. Under 366 nm UV irradiation, the glaciapyrroles were systematically converted to the corresponding photoglaciapyrroles (4-6) via photoisomerization, resulting in the diversification of the glaciapyrrole family compounds. The transformation of the glaciapyrrole Z to E isomers occurred in a 1:1 ratio, based on virtual validation of the photoisomerization of these olefinic compounds by 1H-NMR spectroscopy and liquid chromatography/mass spectrometry (LC/MS) analysis. Finally, when encapsulated in poly(lactic-co-glycolic acid) nanoparticles, glaciapyrrole E and photoglaciapyrrole E displayed significant inhibitory activity against influenza A virus. This is the first report of antiviral effects from glaciapyrrole family compounds, whose biological functions have only been subjected to limited studies so far.

Accurate real-time monitoring of fine dust using a densely connected convolutional networks with measured plasma emissions.

Accurate identification and monitoring of fine dust are emerging as a primary global issue for addressing the harmful effects of fine dust on public health. Identifying the source of fine dust is indispensable for ensuring the human lifespan as well as preventing environmental disasters. Here a simple yet effective spark-induced plasma spectroscopy (SIPS) unit combined with deep learning for real-time classification is verified as a fast and precise PM (particulate matter) source identification technique. SIPS promises portable use, label-free detection, source identification, and chemical susceptibility in a single step with acceptable speed and accuracy. In particular, the densely connected convolutional networks (DenseNet) are used with measured spark-induced plasma emission datasets to identify PM sources at above 98%. The identification performance was compared with other common classification methods, and DenseNet with dropouts (30%), optimized batch size (16), and cyclic learning rate training emerged as the most promising source identification method.

Rapid detection of ionic contents in water through sensor fusion and convolutional neural network.

Salt contents in soil or groundwater are one of the primary indicators to evaluate contamination levels. Electrical conductivity (EC) or salinity information from the conventional laboratory analysis is typically inefficient in delineating contamination. This study investigated a rapid determination of ionic contents in water through the combination of Ultraviolet Spectroscopy (UVS) and Electrochemical Impedance Spectroscopy (EIS), and the application of convolutional neural network (CNN). Various aqueous salt samples were prepared with Ca2+, K+, Na+, Cl-, Br-, SO42-, and HCO3- ions. Firstly, their spectral data obtained from UVS and EIS were analyzed. The spectral analysis showed that the data fusion of both spectroscopies provided more evidence to distinguish the ionic contents, consequently enhancing prediction performance of CNN. In turn, the fused spectra were handled with CNN to predict ionic contents. The result suggested the validity of the proposed method in detecting ionic contents by showing 48.6 mmol/kg RMSE and 0.95 R2 between actual and predicted ionic concentrations, which outperformed Partial Least Squares Regression (PLSR) and Random Forest. The detection of ionic contents beyond EC or salinity is advantageous since it provides more information on the soil and water contamination, and it facilitates tracking the contaminant sources. The proposed method has the potential to become more accurate with increased datasets and further optimization of CNN, which will further improve the practicability.

Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke.

We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715-0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.