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Abstract Age-related macular degeneration (AMD) is a major cause of blindness in developed countries, and the number of affected patients is increasing worldwide. Intravitreal injections of anti-vascular endothelial growth factor (VEGF) are the standard therapy for neovascular AMD (nAMD), and optical coherence tomography (OCT) is a crucial tool for evaluating the anatomical condition of the macula. However, OCT has limitations in accurately predicting the degree of functional and morphological improvement following intravitreal injections. Artificial intelligence (AI) has been proposed as a tool for predicting the treatment response of nAMD based on OCT biomarkers. Our study focuses on the development and assessment of an AI model utilizing the DenseNet201 algorithm. The model aims to predict anatomical improvement based on OCT images before, and during anti-VEGF therapy. The training process involves two scenarios: (1) using only preinjection OCT images and (2) utilizing both OCT images before and during anti-VEGF therapy for model training. The outcomes of our investigation, involving 2068 images from a cohort of 517 Korean patients diagnosed with nAMD, indicate that the AI model we introduced surpassed the predictive performance of ophthalmologists. The model exhibited a sensitivity of 0.915, specificity of 0.426, and accuracy of 0.820. Notably, its predictive capabilities were further enhanced with the inclusion of additional OCT images taken after the first and second injections during the loading phase. The treatment prediction performance of the model was the highest when using all input modalities (before injection, and after the first and second injections) and concatenation-based fusion layers. This study highlights the potential of AI in assisting individualized and tailored nAMD treatment.

Abstract Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using erythropoiesis-stimulating agents (ESAs) is challenging. This study introduces a gated recurrent unit-attention-based module (GAM) for efficient anemia management among patients undergoing chronic dialysis and proposes a novel alert system for anticipating the need for red blood cell transfusions. Data on demographic characteristics, dialysis metrics, drug administration, laboratory tests, and transfusion history were retrospectively collected from patients undergoing hemodialysis at Kangwon National University Hospital between 2017 and 2022. After preprocessing, a final dataset of 252 patients was used for model training. Our model functions in two major phases: (1) Hb level prediction and ESA dose recommendation and (2) transfusion alert framework. The GAM model outperformed traditional machine learning algorithms, including linear regression, XGBoost, and multilayer perceptron, in predicting Hb levels (R-squared value = 0.60). The model also demonstrated a recommendation accuracy of 0.78 compared to that of clinical experts, indicating a high degree of concordance with the ESA dosing recommendations. Additionally, the model exhibited considerably high accuracy (0.99) for transfusion alarms. Thus, the GAM model holds promise for improving anemia management in patients with ESKD by optimizing ESA dosages and providing timely transfusion alerts.

Abstract Severe fever with thrombocytopenia syndrome (SFTS) is a tick-borne illness with a notable morality risk that is becoming increasingly prevalent in East Asia (14–36%). Increasing evidence indicates a more direct role of the SFTS virus in renal impairment. However, few studies have explored the risk factors for and clinical outcomes of AKI in patients with SFTS. Therefore, in this study, we aimed to investigate risk factors and outcomes associated with AKI in patients with SFTS. In this retrospective cohort study, we included the data of 53 patients who were diagnosed with SFTS virus infection at Kangwon National University Hospital between 2016 and 2020. We incorporated laboratory data and medical information including comorbidities, complications, and mortality. Baseline characteristics, clinical features, laboratory parameters, and mortality rates of the non-AKI and AKI groups were compared. Patient survival of non-AKI and AKI groups were compared using the Kaplan–Meier method. To identify the population with poor prognosis, Cox regression analysis was used to identify the independent risk factors for in-hospital mortality in patients with SFTS. Of the 53 individuals, 29 (54.7%) were male, with an average age of 66.5 years. Nine patients (15.1%) died of SFTS. Twenty-seven (50.9%) patients exhibited AKI; the average time interval from fever onset to AKI occurrence was 3.6 days. Notably, 24 (88.9%) patients developed AKI within the first week of fever onset. Patients in the AKI group exhibited a significantly higher prevalence of diabetes and were older than those in the non-AKI group. The mortality rate was notably higher (29.6%) in the AKI group than in the non-AKI group (3.8%). Within the AKI cohort, advanced stages (stages 2 and 3) showed a 50% mortality rate, which was significantly higher than the 17.6% mortality rate in patients with stage 1 AKI. Additionally, Kaplan–Meier curves revealed lower survival rates among patients with AKI than among those without AKI (P = 0.017). Cox regression analysis identified leukopenia and elevated serum creatinine levels as significant risk factors for mortality. AKI is a common complication associated with SFTS. Moreover, the mortality rate was significantly higher in the patients who developed AKI than in those who did not. Our findings underscore the pivotal role of AKI as a prognostic marker of disease severity in patients with SFTS.

Abstract Neovascular age-related macular degeneration (nAMD) can result in blindness if left untreated, and patients often require repeated anti-vascular endothelial growth factor injections. Although, the treat-and-extend method is becoming popular to reduce vision loss attributed to recurrence, it may pose a risk of overtreatment. This study aimed to develop a deep learning model based on DenseNet201 to predict nAMD recurrence within 3 months after confirming dry-up 1 month following three loading injections in treatment-naïve patients. A dataset of 1076 spectral domain optical coherence tomography (OCT) images from 269 patients diagnosed with nAMD was used. The performance of the model was compared with that of 6 ophthalmologists, using 100 randomly selected samples. The DenseNet201-based model achieved 53.0% accuracy in predicting nAMD recurrence using a single pre-injection image and 60.2% accuracy after viewing all the images immediately after the 1st, 2nd, and 3rd injections. The model outperformed experienced ophthalmologists, with an average accuracy of 52.17% using a single pre-injection image and 53.3% after examining four images before and after three loading injections. In conclusion, the artificial intelligence model demonstrated a promising ability to predict nAMD recurrence using OCT images and outperformed experienced ophthalmologists. These findings suggest that deep learning models can assist in nAMD recurrence prediction, thus improving patient outcomes and optimizing treatment strategies.

Abstract We aimed to evaluate the association between the prevalence of diabetic retinopathy (DR) and coffee consumption in a Korean population. This cross-sectional study was based on data from the 2008–2011 Korean National Health and Nutrition Survey. Among 37,753 survey participants, the data of 1350 subjects with type 2 diabetes who underwent DR examination were analyzed. DR was graded using the modified Airlie House classification system. Coffee consumption data were obtained through food frequency questionnaires and categorized into four groups: almost none,

Abstract Anti-dementia medications are widely prescribed to patients with Alzheimer’s dementia (AD) in South Korea. This study investigated the pattern of medical management in newly diagnosed patients with AD using a standardized data format—the Observational Medical Outcome Partnership Common Data Model from five hospitals. We examined the anti-dementia treatment patterns from datasets that comprise > 5 million patients during 2009–2019. The medication utility information was analyzed with respect to treatment trends and persistence across 11 years. Among the 8653 patients with newly diagnosed AD, donepezil was the most commonly prescribed anti-dementia medication (4218; 48.75%), followed by memantine (1565; 18.09%), rivastigmine (1777; 8.98%), and galantamine (494; 5.71%). The rising prescription trend during observation period was found only with donepezil. The treatment pathways for the three cholinesterase inhibitors combined with N-methyl-d-aspartate receptor antagonist were different according to the drugs (19.6%; donepezil; 28.1%; rivastigmine, and 17.2%; galantamine). A 12-month persistence analysis showed values of approximately 50% for donepezil and memantine and approximately 40% for rivastigmine and galantamine. There were differences in the prescribing pattern and persistence among anti-dementia medications from database using the Observational Medical Outcome Partnership Common Data Model on the Federated E-health Big Data for Evidence Renovation Network platform in Korea.

Abstract Central serous chorioretinopathy (CSC) is one of the most common macular diseases that can reduce the quality of life of patients. This study aimed to build a deep learning-based classification model using multiple spectral domain optical coherence tomography (SD-OCT) images together to diagnose CSC. Our proposed system contains two modules: single-image prediction (SIP) and a final decision (FD) classifier. A total of 7425 SD-OCT images from 297 participants (109 acute CSC, 106 chronic CSC, 82 normal) were included. In the fivefold cross validation test, our model showed an average accuracy of 94.2%. Compared to other end-to-end models, for example, a 3D convolutional neural network (CNN) model and a CNN-long short-term memory (CNN-LSTM) model, the proposed system showed more than 10% higher accuracy. In the experiments comparing the proposed model and ophthalmologists, our model showed higher accuracy than experts in distinguishing between acute, chronic, and normal cases. Our results show that an automated deep learning-based model could play a supplementary role alongside ophthalmologists in the diagnosis and management of CSC. In particular, the proposed model seems clinically applicable because it can classify CSCs using multiple OCT images simultaneously.

Abstract Neovascular age-related macular degeneration (nAMD) is among the main causes of visual impairment worldwide. We built a deep learning model to distinguish the subtypes of nAMD using spectral domain optical coherence tomography (SD-OCT) images. Data from SD-OCT images of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and normal healthy patients were analyzed using a convolutional neural network (CNN). The model was trained and validated based on 4749 SD-OCT images from 347 patients and 50 healthy controls. To adopt an accurate and robust image classification architecture, we evaluated three well-known CNN structures (VGG-16, VGG-19, and ResNet) and two customized classification layers (fully connected layer with dropout vs. global average pooling). Following the test set performance, the model with the highest classification accuracy was used. Transfer learning and data augmentation were applied to improve the robustness and accuracy of the model. Our proposed model showed an accuracy of 87.4% on the test data (920 images), scoring higher than ten ophthalmologists, for the same data. Additionally, the part that our model judged to be important in classification was confirmed through Grad-CAM images, and consequently, it has a similar judgment criteria to that of ophthalmologists. Thus, we believe that our model can be used as an auxiliary tool in clinical practice.

Abstract Central serous chorioretinopathy (CSC) is the fourth most common retinopathy and can reduce quality of life. CSC is assessed using optical coherence tomography (OCT), but deep learning systems have not been used to classify CSC subtypes. This study aimed to build a deep learning system model to distinguish CSC subtypes using a convolutional neural network (CNN). We enrolled 435 patients with CSC from a single tertiary center between January 2015 and January 2020. Data from spectral domain OCT (SD-OCT) images of the patients were analyzed using a deep CNN. Five-fold cross-validation was employed to evaluate the model’s ability to discriminate acute, non-resolving, inactive, and chronic atrophic CSC. We compared the performances of the proposed model, Resnet-50, Inception-V3, and eight ophthalmologists. Overall, 3209 SD-OCT images were included. The proposed model showed an average cross-validation accuracy of 70.0% (95% confidence interval [CI], 0.676–0.718) and the highest test accuracy was 73.5%. Additional evaluation in an independent set of 104 patients demonstrated the reliable performance of the proposed model (accuracy: 76.8%). Our model could classify CSC subtypes with high accuracy. Thus, automated deep learning systems could be useful in the classification and management of CSC.

Abstract This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convolutional neural network (CNN). Patients from a single tertiary center were enrolled from January 2014 to January 2020. Spectral-domain optical coherence tomography (SD-OCT) images of patients with RAP or PCV and a control group were analyzed with a deep CNN. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model’s ability to distinguish RAP from PCV. The performances of the new model, the VGG-16, Resnet-50, Inception, and eight ophthalmologists were compared. A total of 3951 SD-OCT images from 314 participants (229 AMD, 85 normal controls) were analyzed. In distinguishing the PCV and RAP cases, the proposed model showed an accuracy, sensitivity, and specificity of 89.1%, 89.4%, and 88.8%, respectively, with an AUROC of 95.3% (95% CI 0.727–0.852). The proposed model showed better diagnostic performance than VGG-16, Resnet-50, and Inception-V3 and comparable performance with the eight ophthalmologists. The novel model performed well when distinguishing between PCV and RAP. Thus, automated deep learning systems may support ophthalmologists in distinguishing RAP from PCV.

Abstract This study aimed to compare the surgical outcomes of pars plana vitrectomy (PPV) with and without air tamponade in patients with idiopathic epiretinal membrane (iERM). We prospectively enrolled 145 patients with iERM who underwent a 25-gauge transconjunctival sutureless PPV. Patients were assigned to either the air tamponade (air) group (79 eyes) or balanced salt solution (BSS; no tamponade) group (66 eyes). The central macular thickness (CMT), peripapillary retinal nerve fiber layer (pRNFL) thickness, and best-corrected visual acuity (BCVA) were compared for two years. At baseline, there were no significant differences between the two groups. CMT and BCVA were not significantly different between the groups for 2 years. However, the air group had a significantly lower thickness in the superior temporal pRNFL sector at 1 month (p = 0.01) and in the inferior temporal and superior temporal pRNFL sectors at 3 months (p = 0.02 for both). There were no significant differences between both groups in all the pRNFL sectors from 6 months to 2 years. The outcomes of PPV with air tamponade and that with no tamponade appear to be equivalent. This shows that air tamponade may not be an imperative procedure for iERM surgery and has no additional benefit.

Abstract The purpose is to evaluate the effects of multiple intravitreal ranibizumab (IVR) and aflibercept (IVA) injections on peripapillary retinal nerve fiber layer (RNFL) thickness in patients with exudative age-related macular degeneration (AMD). This retrospective, observational, consecutive case series study enrolled patients newly diagnosed with monocular exudative AMD from January 2014 to October 2019 who were administered IVR or IVA injections. Normal fellow eyes were included as controls. Medical records and spectral domain optical coherence tomography results were reviewed at baseline and at 3, 6, and 12 months after injection. No statistically significant differences in peripapillary RNFL thickness and intraocular pressure were observed between the treated and fellow eyes in the two groups. The global RNFL thicknesses for the treated eyes decreased significantly after 12 months compared with baseline, but no significant difference was observed in any of the six examined sectors (temporal, superior temporal, superior nasal, nasal, inferior nasal, and inferior temporal). At 12 months, the central macular thickness of the treated eyes decreased significantly. Multiple IVR and IVA injections are apparently safe considering peripapillary RNFL damage in patients with exudative AMD. The decreased RNFL thickness of the global sector was presumably due to anatomical improvement of macular lesions.

Abstract The chin tuck against resistance (CTAR) exercise is a therapeutic method developed to activate and strengthen the oropharyngeal muscles related to swallowing in patients experiencing post-stroke dysphagia. However, existing CTAR exercises could be improved by considering the complex characteristics of the suprahyoid muscles. To investigate the efficacy of multidirectional (md-) CTAR on tongue pressure and suprahyoid muscle activity in older adults. Thirty-four older adults were enrolled and randomly assigned to two groups (n = 17/group). Group 1 performed md-CTAR exercises in the left and right diagonal and vertical directions, using the prototype device developed for this study. In contrast, group 2 performed only the vertical-directional (vd-) CTAR exercise using the same device as group 1. Both groups performed the same exercise for 5 days over 6 weeks. Outcome measures were tongue pressure, tongue thickness, and suprahyoid muscle activity, which were evaluated using the Iowa Oral Performance Instrument, ultrasound, and surface electromyography, respectively. As a results, group 1 exhibited significantly higher maximal tongue pressure, tongue thickness, and suprahyoid muscle activity during swallowing than group 2 (p

Abstract Fundamental changes in intracellular processes, such as overactive growth signaling pathways, are common in carcinomas and are targets of many cancer therapeutics. GRIP and coiled-coil containing 2 (GCC2) is a trans-Golgi network (TGN) golgin maintaining Golgi apparatus structure and regulating vesicle transport. Here, we found an aberrant overexpression of GCC2 in non-small cell lung cancer (NSCLC) and conducted shRNA-mediated gene knockdown to investigate the role of GCC2 in NSCLC progression. shRNA-mediated GCC2 knockdown suppressed NSCLC cell growth, migration, stemness, and epithelial-mesenchymal transition (EMT) in vitro and tumor growth in vivo. In addition, GCC2 knockdown suppressed cancer cell exosome secretion and the oncogenic capacity of cancer cell-derived exosomes. Mechanistically, GCC2 inhibition decreased epidermal growth factor receptor (EGFR) expression and downstream growth and proliferation signaling. Furthermore, GCC2 inhibition compromised Golgi structural integrity in cancer cells, indicating a functional role of GCC2 in regulating intracellular trafficking and signaling to promote lung cancer progression. Together, these findings suggest GCC2 as a potential therapeutic target for the treatment of NSCLC.

Abstract Non-small cell lung cancer (NSCLC) is histologically classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC). However, some tumors are histologically ambiguous and other pathophysiological features or microenvironmental factors may be more prominent. Here we report integrative multiomics analyses using data for 229 patients from a Korean NSCLC cohort and 462 patients from previous multiomics studies. Histological examination reveals five molecular subtypes, one of which is a NSCLC subtype with PI3K-Akt pathway upregulation, showing a high proportion of metastasis and poor survival outcomes regardless of any specific NSCLC histology. Proliferative subtypes are present in LUAD and LSCC, which show strong associations with whole genome doubling (WGD) events. Comprehensive characterization of the immune microenvironment reveals various immune cell compositions and neoantigen loads across molecular subtypes, which predicting different prognoses. Immunological subtypes exhibit a hot tumor-enriched state and a higher efficacy of adjuvant therapy.

Abstract Phase-changing materials have been a cornerstone of condensed matter physics for decades. A quintessential example is iron-rhodium (FeRh), which undergoes a first-order phase transition from antiferromagnetic to ferromagnetic states near room temperature. The pivotal aspect of this transition is a marked alteration in electrical conductivity. However, its underlying origin still remains elusive, largely due to the difficulties of directly probing fundamental transport during this phase transition. In this study, we investigate the fundamentals of FeRh’s electrical transport employing terahertz time-domain spectroscopy (THz-TDS). Leveraging the Drude model, we discerned the distinct contributions of extrinsic (momentum scattering time, τ) and intrinsic (charge density, n, and effective mass, m*) factors to electrical conductivity independently. Notably, our investigation unveiled a sharp alteration in n and m* during the phase transition, contrasting with the gradual monotonic decrease of τ with rising temperature. Consequently, our findings provide compelling evidence that the conductivity change in FeRh during the phase transition originates from a restructuring of its band structure. This work provides a crucial step towards a comprehensive understanding of the electrical transport changes occurring during the phase transition, offering valuable insights into the behaviour of phase changing materials.

Abstract Research on keloid is limited by the lack of proper in vitro and animal model reflecting in vivo status. Based on heterogeneity of keloid and important role of endothelial cells in its pathogenesis, a novel 3D in vitro keloid spheroid prepared with keloid fibroblasts and endothelial cells was evaluated in this study. Commercial cell lines of keloid fibroblasts and endothelial cells were used at various cellular ratios to generate keloid spheroids to determine the optimal condition. Keloid spheroids from three keloid patients were also made and their usefulness as in vitro models, including their responses to drugs, were assessed. Spheroids with higher endothelial cell proportions exhibited increased viability and propagation ability. Patient-derived keloid spheroids showed heterogeneity which might reflect individual clinical conditions. The optimal ratio of fibroblasts to endothelial cells was determined to be 4:1 for keloid spheroids based on gene expression and viability analyses. Patient-derived keloid spheroid showed better keloidal changes in genetic expressions than 2D monolayer culture. Spheroids exhibited varied responses and resistance to each drug used for keloids, depending on the cell type used. 3D keloid spheroids might provide an effective in vitro model for investigating disease pathogenesis and appropriate treatment modalities for future precision medicine.

Abstract Cellular and molecular dynamics of human cells are constantly affected by gravity. Alteration of the gravitational force disturbs the cellular equilibrium, which might modify physiological and molecular characteristics. Nevertheless, biological responses of cancer cells to reduced gravitational force remains obscure. Here, we aimed to comprehend not only transcriptomic patterns but drug responses of colorectal cancer (CRC) under simulated microgravity. We established four organoids directly from CRC patients, and organoids cultured in 3D clinostat were subjected to genome wide expression profiling and drug library screening. Our observations revealed changes in cell morphology and an increase in cell viability under simulated microgravity compared to their static controls. Transcriptomic analysis highlighted a significant dysregulation in the TBC1D3 family of genes. The upregulation of cell proliferation observed under simulated microgravity conditions was further supported by enriched cell cycle processes, as evidenced by the functional clustering of mRNA expressions using cancer hallmark and gene ontology terms. Our drug screening results indicated an enhanced response rate to 5-FU under conditions of simulated microgravity, suggesting potential implications for cancer treatment strategies in simulated microgravity.

Abstract Gangliosides in the brain play a crucial role in modulating the integrity of vertebrate central nervous system in a region-specific manner. However, to date, a comprehensive structural elucidation of complex intact ganglioside isomers has not been achieved, resulting in the elusiveness into related molecular mechanism. Here, we present a glycolipidomic approach for isomer-specific and brain region-specific profiling of the mouse brain. Considerable region-specificity and commonality in specific group of regions are highlighted. Notably, we observe a similarity in the abundance of major isomers, GD1a and GD1b, within certain regions, which provides significant biological implications with interpretation through the lens of a theoretical retrosynthetic state-transition network. Furthermore, A glycocentric-omics approaches using gangliosides and N-glycans reveal a remarkable convergence in spatial dynamics, providing valuable insight into molecular interaction network. Collectively, this study uncovers the spatial dynamics of intact glyco-conjugates in the brain, which are relevant to regional function and accelerates the discovery of potential therapeutic targets for brain diseases.

Abstract This study evaluates the diagnostic efficacy of automated machine learning (AutoGluon) with automated feature engineering and selection (autofeat), focusing on clinical manifestations, and a model integrating both clinical manifestations and CT findings in adult patients with ambiguous computed tomography (CT) results for acute appendicitis (AA). This evaluation was compared with conventional single machine learning models such as logistic regression(LR) and established scoring systems such as the Adult Appendicitis Score(AAS) to address the gap in diagnostic approaches for uncertain AA cases. In this retrospective analysis of 303 adult patients with indeterminate CT findings, the cohort was divided into appendicitis (n = 115) and non-appendicitis (n = 188) groups. AutoGluon and autofeat were used for AA prediction. The AutoGluon-clinical model relied solely on clinical data, whereas the AutoGluon-clinical-CT model included both clinical and CT data. The area under the receiver operating characteristic curve (AUROC) and other metrics for the test dataset, namely accuracy, sensitivity, specificity, PPV, NPV, and F1 score, were used to compare AutoGluon models with single machine learning models and the AAS. The single ML models in this study were LR, LASSO regression, ridge regression, support vector machine, decision tree, random forest, and extreme gradient boosting. Feature importance values were extracted using the “feature_importance” attribute from AutoGluon. The AutoGluon-clinical model demonstrated an AUROC of 0.785 (95% CI 0.691–0.890), and the ridge regression model with only clinical data revealed an AUROC of 0.755 (95% CI 0.649–0.861). The AutoGluon-clinical-CT model (AUROC 0.886 with 95% CI 0.820–0.951) performed better than the ridge model using clinical and CT data (AUROC 0.852 with 95% CI 0.774–0.930, p = 0.029). A new feature, exp(-(duration from pain to CT)3 + rebound tenderness), was identified (importance = 0.049, p = 0.001). AutoML (AutoGluon) and autoFE (autofeat) enhanced the diagnosis of uncertain AA cases, particularly when combining CT and clinical findings. This study suggests the potential of integrating AutoML and autoFE in clinical settings to improve diagnostic strategies and patient outcomes and make more efficient use of healthcare resources. Moreover, this research supports further exploration of machine learning in diagnostic processes.

Abstract The dynamic regulation of mitochondria shape via fission and fusion is critical for cellular responses to stimuli. In homeostatic cells, two modes of mitochondrial fission, midzone and peripheral, provide a decision fork between either proliferation or clearance of mitochondria. However, the relationship between specific mitochondria shapes and functions remains unclear in many biological contexts. While commonly associated with decreased bioenergetics, fragmented mitochondria paradoxically exhibit elevated respiration in several disease states, including infection with the prevalent pathogen human cytomegalovirus (HCMV) and metastatic melanoma. Here, incorporating super-resolution microscopy with mass spectrometry and metabolic assays, we use HCMV infection to establish a molecular mechanism for maintaining respiration within a fragmented mitochondria population. We establish that HCMV induces fragmentation through peripheral mitochondrial fission coupled with suppression of mitochondria fusion. Unlike uninfected cells, the progeny of peripheral fission enter mitochondria-ER encapsulations (MENCs) where they are protected from degradation and bioenergetically stabilized during infection. MENCs also stabilize pro-viral inter-mitochondria contacts (IMCs), which electrochemically link mitochondria and promote respiration. Demonstrating a broader relevance, we show that the fragmented mitochondria within metastatic melanoma cells also form MENCs. Our findings establish a mechanism where mitochondria fragmentation can promote increased respiration, a feature relevant in the context of human diseases.

Abstract We analysed the skulls and faces of Korean subjects using anthropometric methods to understand the anatomical characteristics of the eyeball and eye orbit region of Korean population and to determine the correlations between the hard and soft tissues around the eyeball and eye orbit region. In total, 82 sections in the region were measured to determine the correlations; among them, 34 showed significant differences by sex, and 6 showed significant differences by age. As the distance from the centre of the eye lens to the eye orbit is calculated as a ratio, we determined that the centre of the eye lens is located relatively on the lateral and superior position in each eye orbit in front view. Fourteen sections that could be used for craniofacial reconstruction/approximation in men and women were selected. Regression equations were derived according to the correlation of each section, and their reliabilities were verified by out of sample validation tests. Therefore, our results increase the accuracy of eyeball position determination, which would be useful for more efficient craniofacial reconstruction/approximation of the Korean population and should improve the efficiency of facial recognition.

Abstract Structural deformation modifies the bandgap, exciton fine structure and phonon energy of semiconductors, providing an additional knob to control their optical properties. The impact can be exploited in colloidal semiconductor quantum dots (QDs), wherein structural stresses can be imposed in three dimensions while defect formation is suppressed by controlling surface growth kinetics. Yet, the control over the structural deformation of QDs free from optically active defects has not been reached. Here, we demonstrate strain-graded CdSe-ZnSe core-shell QDs with compositionally abrupt interface by the coherent pseudomorphic heteroepitaxy. Resulting QDs tolerate mutual elastic deformation of varying magnitudes at the interface with high structural fidelity, allowing for spectrally stable and pure emission of photons at accelerated rates with near unity luminescence efficiency. We capitalize on the asymmetric strain effect together with the quantum confinement effect to expand emission envelope of QDs spanning the entire visible region and exemplify their use in photonic applications.

Abstract Hard ticks are known vectors of various pathogens, including the severe fever with thrombocytopenia syndrome virus, Rickettsia spp., Coxiella burnetii, Borrelia spp., Anaplasma phagocytophilum, and Ehrlichia spp. This study aims to investigate the distribution and prevalence of tick-borne pathogens in southwestern Korea from 2019 to 2022. A total of 13,280 ticks were collected during the study period, with H. longicornis accounting for 86.1% of the collected ticks. H. flava, I. nipponensis and A. testudinarium comprised 9.4%, 3.6%, and 0.8% of the ticks, respectively. Among 983 pools tested, Rickettsia spp. (216 pools, 1.6% MIR) were the most prevalent pathogens across all tick species, with R. japonica and R. monacensis frequently detected in I. nipponensis and Haemaphysalis spp., respectively. Borrelia spp. (28 pools, 0.2% MIR) were predominantly detected in I. nipponensis (27 pools, 13.8% MIR, P

Abstract Maintaining epidermal homeostasis relies on a tightly organized process of proliferation and differentiation of keratinocytes. While past studies have primarily focused on calcium regulation in keratinocyte differentiation, recent research has shed light on the crucial role of lysosome dysfunction in this process. TLR adaptor interacting with SLC15A4 on the lysosome (TASL) plays a role in regulating pH within the endo-lysosome. However, the specific role of TASL in keratinocyte differentiation and its potential impact on proliferation remains elusive. In our study, we discovered that TASL deficiency hinders the proliferation and migration of keratinocytes by inducing G1/S cell cycle arrest. Also, TASL deficiency disrupts proper differentiation process in TASL knockout human keratinocyte cell line (HaCaT) by affecting lysosomal function. Additionally, our research into calcium-induced differentiation showed that TASL deficiency affects calcium modulation, which is essential for keratinocyte regulation. These findings unveil a novel role of TASL in the proliferation and differentiation of keratinocytes, providing new insights into the intricate regulatory mechanisms of keratinocyte biology.

Abstract The demand for home sleep apnea testing (HSAT) devices is escalating, particularly in the context of the coronavirus 2019 (COVID-19) pandemic. The absence of standardized development and verification procedures poses a significant challenge. This study meticulously analyzed the approval process characteristics of HSAT devices by the U.S. Food and Drug Administration (FDA) from September 1, 2003, to September 1, 2023, with a primary focus on ensuring safety and clinical effectiveness. We examined 58 reports out of 1046 that underwent FDA clearance via the 510(k) and de novo pathways. A substantial surge in certifications after the 2022 pandemic was observed. Type-3 devices dominated, signifying a growing trend for both home and clinical use. Key measurement items included respiration and sleep analysis, with the apnea–hypopnea index (AHI) and sleep stage emerging as pivotal indicators. The majority of FDA-cleared HSAT devices adhered to electrical safety and biocompatibility standards. Critical considerations encompass performance and function testing, usability, and cybersecurity. This study emphasized the nearly indispensable role of clinical trials in ensuring the clinical effectiveness of HSAT devices. Future studies should propose guidances that specify stringent requirements, robust clinical trial designs, and comprehensive performance criteria to guarantee the minimum safety and clinical effectiveness of HSATs.

Abstract Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer’s disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at