Distinctive CD39+CD9+ lung interstitial macrophages suppress IL-23/Th17-mediated neutrophilic asthma by inhibiting NETosis.

The IL-23-Th17 axis is responsible for neutrophilic inflammation in various inflammatory diseases. Here, we discover a potential pathway to inhibit neutrophilic asthma. In our neutrophil-dominant asthma (NDA) model, single-cell RNA-seq analysis identifies a subpopulation of CD39+CD9+ interstitial macrophages (IMs) suppressed by IL-23 in NDA conditions but increased by an IL-23 inhibitor αIL-23p19. Adoptively transferred CD39+CD9+ IMs suppress neutrophil extracellular trap formation (NETosis), a representative phenotype of NDA, and also Th17 cell activation and neutrophilic inflammation. CD39+CD9+ IMs first attach to neutrophils in a CD9-dependent manner, and then remove ATP near neutrophils that contribute to NETosis in a CD39-dependent manner. Transcriptomic data from asthmatic patients finally show decreased CD39+CD9+ IMs in severe asthma than mild/moderate asthma. Our results suggest that CD39+CD9+ IMs function as a potent negative regulator of neutrophilic inflammation by suppressing NETosis in the IL-23-Th17 axis and can thus serve as a potential therapeutic target for IL-23-Th17-mediated neutrophilic asthma.

Gut microbiome therapy: fecal microbiota transplantation vs live biotherapeutic products.

The human intestine hosts a complex ecosystem of various microorganisms, collectively known as the gut microbiome, which significantly impacts human health. Disruptions in the gut microbiome are linked to various disorders, including gastrointestinal diseases, such as Clostridioides difficile infection and inflammatory bowel disease, as well as metabolic, neurological, oncologic conditions. Fecal microbiota transplantation (FMT) and live biotherapeutic products (LBPs) have emerged as prospective therapeutic procedures to restore microbial and metabolic balance in the gut. This review assesses the latest advancements, challenges, and therapeutic efficacy of FMT and LBPs, highlighting the need for standardization, safety, and long-term evaluation to optimize their clinical application.

Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.

Artificial intelligence (AI) has numerous applications in radiology. Clinical research studies to evaluate the AI models are also diverse. Consequently, diverse outcome metrics and measures are employed in the clinical evaluation of AI, presenting a challenge for clinical radiologists. This review aims to provide conceptually intuitive explanations of the outcome metrics and measures that are most frequently used in clinical research, specifically tailored for clinicians. While we briefly discuss performance metrics for AI models in binary classification, detection, or segmentation tasks, our primary focus is on less frequently addressed topics in published literature. These include metrics and measures for evaluating multiclass classification; those for evaluating generative AI models, such as models used in image generation or modification and large language models; and outcome measures beyond performance metrics, including patient-centered outcome measures. Our explanations aim to guide clinicians in the appropriate use of these metrics and measures.

Clinical impact of deep learning-derived intravascular ultrasound characteristics in patients with deferred coronary artery.

Prognostic markers for long-term outcomes are lacking in patients with deferred (nonculprit) coronary artery lesions. This study aimed to identify the morphological criteria for predicting adverse outcomes and validate their clinical impact. Using deep learning models, we extracted geometrical parameters and maximal attenuation (or calcium) burden index (ABI-max or CBI-max) from the intravascular ultrasound (IVUS) images of nonculprit vessels in 1115 patients. The endpoints included cardiac death, myocardial infarction, and target vessel revascularization of nonculprit vessel. Cardiac death occurred in 27 (2.4 %) patients at 3 years and 39 (3.5 %) patients at 5 years. At 5 years, the cardiac death-free survival rate was significantly lower with ABP-max ≥11.37 % vs. < 11.37 % (90.0 % vs. 98.7 %), CBI-max ≥13.40 % vs. < 13.40 % (92.8 % vs. 98.4 %), and percent atheroma volume ≥ 51.35 % vs. < 51.35 % (94.0 % vs. 97.7) (all log-rank p < 0.001). The independent predictors of 5-year cardiovascular mortality were age (hazard ratio [HR] 1.21), female sex (HR 0.33), history of heart failure (HR 6.06), chronic kidney disease (HR 18.28), ABI-max (HR 1.04), and CBI-max (HR 1.05). The independent determinants of 5-year target vessel revascularization of nonculprit vessel were fractional flow reserve (HR 0.95 per 0.01 increase), minimal lumen area (HR 0.63), and plaque burden (HR 1.15). In patients with nonculprit coronary artery lesions, a large burden of attenuated or calcified plaques predicted cardiac mortality, while IVUS geometry was associated with repeat revascularization. Thus, deep learning-based IVUS analysis of the whole target vessel may help clinicians identify high-risk lesions.

Boosting-Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for Metal-Insulator Transition Materials.

Applying machine-learning techniques for imbalanced data sets presents a significant challenge in materials science since the underrepresented characteristics of minority classes are often buried by the abundance of unrelated characteristics in majority of classes. Existing approaches to address this focus on balancing the counts of each class using oversampling or synthetic data generation techniques. However, these methods can lead to loss of valuable information or overfitting. Here, we introduce a deep learning framework to predict minority-class materials, specifically within the realm of metal-insulator transition (MIT) materials. The proposed approach, termed boosting-CGCNN, combines the crystal graph convolutional neural network (CGCNN) model with a gradient-boosting algorithm. The model effectively handled extreme class imbalances in MIT material data by sequentially building a deeper neural network. The comparative evaluations demonstrated the superior performance of the proposed model compared to other approaches. Our approach is a promising solution for handling imbalanced data sets in materials science.

Deep Learning Based Automatic Segmentation of the Thoracic Aorta from Chest Computed Tomography in Healthy Korean Adults.

OBJECTIVE: Segmenting the aorta into zones based on anatomical landmarks is a current trend to better understand interventions for aortic dissection or aneurysm. However, comprehensive reference values for aortic zones are lacking. The aim of this study was to establish reference values for aortic size using a fully automated deep learning based segmentation method. METHODS: This retrospective study included 704 healthy adults (mean age 50.6 ± 7.5 years; 407;57.8%] males) who underwent contrast enhanced chest computed tomography (CT) for health screening. A convolutional neural network (CNN) was trained and applied on 3D CT images for automatic segmentation of the aorta based on the Society for Vascular Surgery and Society of Thoracic Surgeons classification. The CNN generated masks were reviewed and corrected by expert cardiac radiologists. RESULTS: Aortic size was significantly larger in males than in females across all zones (zones 0 - 8, all p < .001). The aortic size in each zone increased with age, by approximately 1 mm per 10 years of age, e.g., 25.4, 26.7, 27.5, 28.8, and 29.8 mm at zone 2 in men in the age ranges of 30 - 39, 40 - 49, 50 - 59, 60 - 69, and ≥ 70 years, respectively (all p < .001). CONCLUSION: The deep learning algorithm provided reliable values for aortic size in each zone, with automatic masks comparable to manually corrected ones. Aortic size was larger in males and increased with age. These findings have clinical implications for the detection of aortic aneurysms and other aortic diseases.

Impact of Postoperative Prolonged Air Leakage on Long-Term Pulmonary Function after Lobectomy for Lung Cancer.

Background: This study aimed to evaluate the long-term impact of postoperative prolonged air leak (PAL) on pulmonary function. Methods: We enrolled 1,316 patients with pathologic stage I-III lung cancer who underwent lobectomy. The cohort was divided into 2 groups: those who experienced PAL (n=55) and those who did not (n=1,261). Propensity score matching was conducted at a 1:4 ratio, resulting in 49 patients in the PAL group and 189 in the non-PAL group. Changes in pulmonary function were compared among preoperative, 6-month postoperative, and 12-month postoperative measurements between the 2 groups. Results: The variables used for propensity score matching included age, sex, smoking history, body mass index, baseline pulmonary function, pathologic stage, and surgical approach. All standardized mean differences were less than 0.1. Six months postoperatively, the PAL group showed a greater reduction in both forced expiratory volume in 1 second (FEV1) (-13.0% vs. -10.0%, p=0.041) and forced vital capacity (FVC) (-15.0% vs. -9.0%, p<0.001) than the non-PAL group. In cases of upper lobectomy, there were no significant differences in FEV1 changes between the PAL and non-PAL groups at both 6 and 12 months. However, in lower lobectomy, the PAL group demonstrated a more pronounced decrease in FEV1 (-14.0% vs. -11.0%, p=0.057) and FVC (-20.0% vs. -13.0%, p=0.006) than the non-PAL group at 6 months postoperatively. Conclusion: Postoperative PAL delayed the recovery of pulmonary function after lobectomy. These effects were markedly more pronounced after lower lobectomy than after upper lobectomy.

Deep learning model for intravascular ultrasound image segmentation with temporal consistency.

This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elastic membrane (EEM) segmentation was developed. Both frame- and vessel-level performances and clinical impact of the model on 3-year cardiovascular events were evaluated in the independent data sets. In the test set, the Dice similarity coefficients (DSC) were 0.966 ± 0.025 and 0.982 ± 0.017 for the lumen and EEM, respectively. Even at sites of extensive attenuation, the frame-level performance was excellent (DSCs > 0.96 for the lumen and EEM). The model (vs. the expert) showed a better temporal consistency for contouring the EEM. The agreement between the model- vs. the expert-derived cross-sectional and volumetric measurements was excellent in the independent retrospective cohort (all, intra-class coefficients > 0.94). The model-derived percent atheroma volume > 52.5% (area under curve 0.70, sensitivity 71% and specificity 67%) and plaque burden at the minimal lumen area site (area under curve 0.72, sensitivity 72% and specificity 66%) best predicted 3-year cardiac death and nonculprit-related target vessel revascularization, respectively. In the stented segment, the DSCs > 0.96 for contouring lumen and EEM were achieved. Applied to the 60-MHz IVUS images, the DSCs were > 0.97. In the external cohort with 45-MHz IVUS, the DSCs were > 0.96. The deep learning model accurately delineated vascular geometry, which may be cost-saving and support clinical decision-making.

Aptamin C enhances anti-cancer activity NK cells through the activation of STAT3: a comparative study with vitamin C.

Vitamin C is a well-known antioxidant with antiviral, anticancer, and anti-inflammatory properties based on its antioxidative function. Aptamin C, a complex of vitamin C with its specific aptamer, has been reported to maintain or even enhance the efficacy of vitamin C while increasing its stability. To investigate in vivo distribution of Aptamin C, Gulo knockout mice, which, like humans, cannot biosynthesize vitamin C, were administered Aptamin C orally for 2 and 4 weeks. The results showed higher vitamin C accumulation in all tissues when administered Aptamin C, especially in the spleen. Next, the activity of natural killer (NK) cells were conducted. CD69, a marker known for activating for NK cells, which had decreased due to vitamin C deficiency, did not recover with vitamin C treatment but showed an increasing with Aptamin C. Furthermore, the expression of CD107a, a cell surface marker that increases during the killing process of target cells, also did not recover with vitamin C but increased with Aptamin C. Based on these results, when cultured with tumor cells to measure the extent of tumor cell death, an increase in tumor cell death was observed. To investigate the signaling mechanisms and related molecules involved in the proliferation and activation of NK cells by Aptamin C showed that Aptamin C treatment led to an increase in intracellular STAT3 activation. In conclusion, Aptamin C has a higher capability to activate NK cells and induce tumor cell death compared to vitamin C and it is mediated through the activation of STAT3.

Successful open surgical treatment for persistent type II endoleak following thoracic endovascular aortic repair: a case report.

BACKGROUND: The occurrence of type II endoleaks after endovascular repair of aortic aneurysm has gradually gained increasing attention. We present a case of a patient with an expanding aneurysm after thoracic endovascular aortic repair (TEVAR) for a type II endoleak, in which successful direct ligations of the intercostal artery were performed using a sac incision without cardiopulmonary bypass (CPB) or graft replacement. CASE PRESENTATION: A 62-year-old male patient, previously treated with TEVAR for a descending thoracic aortic aneurysm, presented with ongoing chest discomfort. Based on the diagnosis of a growing aneurysm and type II endoleak, the patient was prepared for CPB and aortic cross-clamping, as a precautions against the possibility of a type I endoleak. A longitudinal opening of the thoracic aortic aneurysm sac was performed following left thoracotomy. Visual confirmation identified the T5 level intercostal artery as the source of the endoleak, and after confirming the absence of a type I endoleak, multiple ligations were applied to the intercostal artery. Follow-up computed tomography confirmed the absence of endoleaks or sac growth. CONCLUSION: In a case involving TEVAR for a thoracic aortic aneurysm, open suture ligations were used to treat type II endoleaks without having to resort to CPB, resulting in successful outcomes.

Higher anxiety level and associated work-related factors of delivery workers in South Korea: from the 6th Korean working conditions survey.

OBJECTIVE: This study investigates anxiety risk and work-related factors among platform workers. The recent growth in the platform industry is a worldwide trend, with delivery workers in Korea representing typical platform workers. METHODS: This cross-sectional study used the 6th Korean Working Conditions Survey to assess anxiety risk among 532 delivery workers compared to general employees. It identified associations between work-related factors and anxiety, including job demands and autonomy, legal protection, and emotional labor. RESULTS: Delivery workers exhibited significantly higher anxiety (odds ratio [OR] = 1.67 95% confidence interval [CI] = 1.23-2.28) and work-related anxiety (OR = 2.17 95% CI = 1.48-3.18) risk than the general workforce, and a significantly higher risk of having unfavorable work environment factors. Their anxiety risk was significantly associated with work-related factors, such as long shifts, quick return, time pressure, job stress, absence of union, work-family conflict, and emotional labor. CONCLUSIONS: This study identified high levels of anxiety among Korean delivery workers and associated occupational factors. It highlights the importance for industry and government interventions to enhance mental health support, mitigate poor employment conditions, and ensure legal safeguards.

Partial coherence enhances parallelized photonic computing.

Advancements in optical coherence control1-5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6-8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9-11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson's disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).

Stress and Loneliness Among Family Caregivers of Older Persons With Cognitive Impairment: The Mediating Role of Social Isolation and the Moderating Role of Personal Mastery.

OBJECTIVES: Relatively little attention has been paid to the underlying processes and conditions leading to loneliness among caregivers of older persons with cognitive impairment (PCI). Drawing upon the caregiver stress-process model and the social relationship expectations framework, this study examined the mediating role of social isolation and the moderating role of caregiver personal mastery in the association between PCI memory and behavioral problems and caregiver loneliness. METHODS: Structural equation modeling was applied to cross-sectional data from 266 caregivers in Singapore. RESULTS: Caregiver-reported PCI memory and behavioral problems were associated with increased levels of caregiver loneliness. Caregiver social isolation partially mediated the association and caregiver personal mastery moderated the association. DISCUSSION: Caregivers with low mastery were more likely to experience loneliness due in part to social isolation resulting from their PCI's memory and behavioral problems. Tailored interventions should address caregiver social isolation and foster caregiver personal mastery.

2023 Survey on User Experience of Artificial Intelligence Software in Radiology by the Korean Society of Radiology.

OBJECTIVE: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). MATERIALS AND METHODS: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. RESULTS: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. CONCLUSION: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

ETV5 promotes lupus pathogenesis and follicular helper T cell differentiation by inducing osteopontin expression.

Follicular helper T (TFH) cells mediate germinal center reactions to generate high affinity antibodies against specific pathogens, and their excessive production is associated with the pathogenesis of systemic autoimmune diseases such as systemic lupus erythematosus (SLE). ETV5, a member of the ETS transcription factor family, promotes TFH cell differentiation in mice. In this study, we examined the role of ETV5 in the pathogenesis of lupus in mice and humans. T cell-specific deletion of Etv5 alleles ameliorated TFH cell differentiation and autoimmune phenotypes in lupus mouse models. Further, we identified SPP1 as an ETV5 target that promotes TFH cell differentiation in both mice and humans. Notably, extracellular osteopontin (OPN) encoded by SPP1 enhances TFH cell differentiation by activating the CD44-AKT signaling pathway. Furthermore, ETV5 and SPP1 levels were increased in CD4+ T cells from patients with SLE and were positively correlated with disease activity. Taken together, our findings demonstrate that ETV5 is a lupus-promoting transcription factor, and secreted OPN promotes TFH cell differentiation.

Enhanced solar-driven photoelectrochemical water splitting using nanoflower Au/CuO/GaN hybrid photoanodes.

Harnessing solar energy for large-scale hydrogen fuel (H2) production shows promise in addressing the energy crisis and ecological degradation. This study focuses on the development of GaN-based photoelectrodes for efficient photoelectrochemical (PEC) water splitting, enabling environmentally friendly H2 production. Herein, a novel nanoflower Au/CuO/GaN hybrid structure was successfully synthesized using a combination of methods including successive ionic layer adsorption and reaction (SILAR), RF/DC sputtering, and metal-organic chemical vapour deposition (MOCVD) techniques. Structural, morphological, and optical characteristics and elemental composition of the prepared samples were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-Vis spectroscopy, and energy-dispersive X-ray (EDX) spectroscopy, respectively. PEC and electrochemical impedance measurements were performed for all samples. The nanoflower Au/CuO/GaN hybrid structure exhibited the highest photocurrent density of ∼4 mA cm-2 at 1.5 V vs. RHE in a Na2SO4 electrolyte with recorded moles of H2 of about 3246 µmol h-1 cm-2. By combining these three materials in a unique structure, we achieved improved performance in the conversion of solar energy into chemical energy. The nanoflower structure provides a large surface area and promotes light absorption while the Au, CuO, and GaN components contribute to efficient charge separation and transfer. This study presents a promising strategy for advancing sustainable H2 production via efficient solar-driven water splitting.

Long-term humoral and cellular immunity against vaccine strains and Omicron subvariants (BQ.1.1, BN.1, XBB.1, and EG.5) after bivalent COVID-19 vaccination.

Background: The assessment of long-term humoral and cellular immunity post-vaccination is crucial for establishing an optimal vaccination strategy. Methods: This prospective cohort study evaluated adults (≥18 years) who received a BA.4/5 bivalent vaccine. We measured the anti-receptor binding domain immunoglobulin G antibody and neutralizing antibodies (NAb) against wild-type and Omicron subvariants (BA.5, BQ.1.1, BN.1, XBB.1 and EG.5) up to 9 months post-vaccination. T-cell immune responses were measured before and 4 weeks after vaccination. Results: A total of 108 (28 SARS-CoV-2-naïve and 80 previously infected) participants were enrolled. Anti-receptor binding domain immunoglobulin G (U/mL) levels were higher at 9 months post-vaccination than baseline in SAR-CoV-2-naïve individuals (8,339 vs. 1,834, p<0.001). NAb titers against BQ.1.1, BN.1, and XBB.1 were significantly higher at 9 months post-vaccination than baseline in both groups, whereas NAb against EG.5 was negligible at all time points. The T-cell immune response (median spot forming unit/106 cells) was highly cross-reactive at both baseline (wild-type/BA.5/XBB.1.5, 38.3/52.5/45.0 in SARS-CoV-2-naïve individuals; 51.6/54.9/54.9 in SARS-CoV-2-infected individuals) and 4 weeks post-vaccination, with insignificant boosting post-vaccination. Conclusion: Remarkable cross-reactive neutralization was observed against BQ.1.1, BN.1, and XBB.1 up to 9 months after BA.4/5 bivalent vaccination, but not against EG.5. The T-cell immune response was highly cross-reactive.

Loss of LPAR6 and CAB39L dysregulates the basal-to-luminal urothelial differentiation program, contributing to bladder carcinogenesis.

We describe a strategy that combines histologic and molecular mapping that permits interrogation of the chronology of changes associated with cancer development on a whole-organ scale. Using this approach, we present the sequence of alterations around RB1 in the development of bladder cancer. We show that RB1 is not involved in initial expansion of the preneoplastic clone. Instead, we found a set of contiguous genes that we term "forerunner" genes whose silencing is associated with the development of plaque-like field effects initiating carcinogenesis. Specifically, we identified five candidate forerunner genes (ITM2B, LPAR6, MLNR, CAB39L, and ARL11) mapping near RB1. Two of these genes, LPAR6 and CAB39L, are preferentially downregulated in the luminal and basal subtypes of bladder cancer, respectively. Their loss of function dysregulates urothelial differentiation, sensitizing the urothelium to N-butyl-N-(4-hydroxybutyl)nitrosamine-induced cancers, which recapitulate the luminal and basal subtypes of human bladder cancer.