Cell-free supernatant of Lactococcus lactis IDCC 2301 exerts anti-inflammatory effects in LPS-induced macrophages via NF-κB and MAPK signaling pathways

This study investigated the anti-inflammatory effects of cell-free supernatant of Lactococcus lactis IDCC 2301 on lipopolysaccharide (LPS)-induced RAW 264.7 macrophages. Expression of inflammatory mediators and cytokines, and the production of nitric oxide (NO) and prostaglandin E2 (PGE2) were qualitatively analysed. The expression of signal transductors in inflammatory cascades was quantified by western blot. Treatment with cell-free supernatant of L. lactis IDCC 2301 significantly decreased the mRNA expression levels of tumour necrosis factor (TNF-α) and interleukins including IL-1β and IL-6. The levels of inducible nitric oxide synthase (iNOS) and cyclooxygenase (COX-2) were also remarkably reduced in LPS-induced macrophages after the treatment. Furthermore, L. lactis IDCC 2301 reduced the levels of both dephosphorylated and phosphorylated forms of nuclear factor-kappa B (NF-κB), IκB-α, extracellular signal-regulated kinases (ERK), c-Jun amino-terminal kinases (JNK), and p38 in LPS-induced RAW 264.7 cells. Therefore, L. lactis IDCC 2301 shows anti-inflammatory activity by suppressing the NF-κB and mitogen-activated protein kinase (MAPK) signaling pathways.

Deep Transfer Learning Using Real-World Image Features for Medical Image Classification, with a Case Study on Pneumonia X-ray Images

Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature transfer learning, which utilizes backbone models initially trained on large-scale general-purpose datasets such as ImageNet. We evaluate the effectiveness and robustness of this approach compared to models trained from scratch, focusing on the task of classifying pneumonia in X-ray images. Our experiments, which included converting grayscale images to RGB format, demonstrate that real-world-feature transfer learning consistently outperforms conventional training approaches across various performance metrics. This advancement has the potential to accelerate deep learning applications in medical imaging by leveraging the rich feature representations learned from general-purpose pretrained models. The proposed methodology overcomes the limitations of domain-specific pretrained models, thereby enabling accelerated innovation in medical diagnostics and healthcare. From a mathematical perspective, we formalize the concept of real-world feature transfer learning and provide a rigorous mathematical formulation of the problem. Our experimental results provide empirical evidence supporting the effectiveness of this approach, laying the foundation for further theoretical analysis and exploration. This work contributes to the broader understanding of feature transferability across domains and has significant implications for the development of accurate and efficient models for medical image analysis, even in resource-constrained settings.

Unrecognized volatile and semi-volatile organic compounds from brake wear

Motor vehicles are among the major sources of pollutants and greenhouse gases in urban areas and a transition to "zero emission vehicles" is underway worldwide. However, emissions associated with brake and tire wear will remain. We show here that previously unrecognized volatile and semi-volatile organic compounds, which have a similarity to biomass burning emissions are emitted during braking. These include greenhouse gases or, these classified as Hazardous Air Pollutants, as well as nitrogen-containing organics, nitrogen oxides and ammonia. The distribution and reactivity of these gaseous emissions are such that they can react in air to form ozone and other secondary pollutants with adverse health and climate consequences. Some of the compounds may prove to be unique markers of brake emissions. At higher temperatures, nucleation and growth of nanoparticles is also observed. Regions with high traffic, which are often disadvantaged communities, as well as commuters can be impacted by these emissions even after combustion-powered vehicles are phased out.

Protective behaviors against COVID-19 and their association with psychological factors in China and South Korea during the Omicron wave: a comparative study

OBJECTIVES

We aimed to explore the level of protective behaviors against COVID-19 and its association with psychological factors in China and South Korea during the Omicron wave.

STUDY DESIGN

Cross-sectional study.

METHODS

We conducted a population-based cross-sectional survey from March 15 to 30, 2023 in China and South Korea. Demographic characteristics, health status, protective behaviors, and psychological factors (including perceived risks, efficacy belief, attribution of disease, fear of COVID-19, trust and evaluation, fatalism, resilience, and pandemic fatigue) were investigated. After adjusting for sociodemographic and health-related factors, multivariable regression models were constructed to explore the psychological influencing factors of protective behavior.

RESULTS

A total of 3000 participants from China and 1000 participants from Korea were included in the final analysis. The mean performance score for protective behaviors among all respondents was 2.885 in China and 3.139 in Korea, with scores ranging from 1 to 4. In China, performance scores were higher in those who were female, aged 30-39, employed, married, living in urban areas, having the highest income level, having the best subjective health status, and having a history of chronic disease (P-value <0.05). In Korea, performance scores were higher for individuals who were female, over 50 years old, educated to high school or below, unemployed, married, had a history of chronic disease, and had never been infected with SARS-CoV-2 (P-value <0.05). In the multivariable regression model, perceived severity (β = 0.067), attribution of disease (β = 0.121), fear of COVID-19 (β = 0.128), trust and evaluation (β = 0.097), psychological resilience (β = 0.068), and efficacy belief (β = 0.216) were positively associated with the performance scores, pandemic fatigue (β = -0.089) was negatively associated with performance scores in China (P-value <0.05). However, in Korea, perceived susceptibility (β = 0.075), fear of COVID-19 (β = 0.107), and efficacy belief (β = 0.357) were positively associated with protective behaviors (P-value <0.05), trust and evaluation (β = -0.078) and pandemic fatigue (β = -0.063) were negatively associated with performance scores (P-value <0.05).

CONCLUSIONS

Populations in both China and Korea demonstrated great compliance with protective behaviors during the Omicron wave. Because of the sociocultural, economic, and political differences, there were differences in the association between psychological factors and protective behaviors in the two countries. This study, from the perspective of psychological factors in different cultural contexts, would provide references for increasing adherence to protective guidelines in future outbreaks of emerging infectious diseases.

Factors influencing the willingness to pay a price premium for red meat with potential to improve consumer wellness in Australia and the United States of America

This study determined consumers' attitudes towards physical and mental wellness related to red meat consumption and their willingness to pay (WTP) more for the meat. In 2019, two online surveys of red meat eaters were conducted in the USA (n = 1000) and Australia (n = 523) using commercial platforms. Results showed that over 90% of respondents indicated interest in purchasing red meat to improve their wellness status. Additionally, about 85% indicated their WTP more for red meat for its wellness benefits, with Americans indicating stronger willingness than Australian respondents. The potential of meat consumption to improve overall wellness among red meat eaters was a dominant factor influencing consumers' WTP more. Other factors that increased WTP included frequency of meat consumption, physical exercise, sleep quality, number of children in a household, partnership status, and economic position. Outcomes from this study highlight a unique opportunity for the meat industry to position meat on its qualities that include wellness improvement if backed up with robust scientific evidence.

Achievement of Target Gain Larger than Unity in an Inertial Fusion Experiment

On December 5, 2022, an indirect drive fusion implosion on the National Ignition Facility (NIF) achieved a target gain G_{target} of 1.5. This is the first laboratory demonstration of exceeding "scientific breakeven" (or G_{target}>1) where 2.05 MJ of 351 nm laser light produced 3.1 MJ of total fusion yield, a result which significantly exceeds the Lawson criterion for fusion ignition as reported in a previous NIF implosion [H. Abu-Shawareb et al. (Indirect Drive ICF Collaboration), Phys. Rev. Lett. 129, 075001 (2022)PRLTAO0031-900710.1103/PhysRevLett.129.075001]. This achievement is the culmination of more than five decades of research and gives proof that laboratory fusion, based on fundamental physics principles, is possible. This Letter reports on the target, laser, design, and experimental advancements that led to this result.

Combination Analysis of PCDHGA12 and CDO1 DNA Methylation in Bronchial Washing Fluid for Lung Cancer Diagnosis

BACKGROUND

When suspicious lesions are observed on computer-tomography (CT), invasive tests are needed to confirm lung cancer. Compared with other procedures, bronchoscopy has fewer complications. However, the sensitivity of peripheral lesion through bronchoscopy including washing cytology is low. A new test with higher sensitivity through bronchoscopy is needed. In our previous study, DNA methylation of PCDHGA12 in bronchial washing cytology has a diagnostic value for lung cancer. In this study, combination of PCDHGA12 and CDO1 methylation obtained through bronchial washing cytology was evaluated as a diagnostic tool for lung cancer.

METHODS

A total of 187 patients who had suspicious lesions in CT were enrolled. PCDHGA12 methylation test, CDO1 methylation test, and cytological examination were performed using 3-plex LTE-qMSP test.

RESULTS

Sixty-two patients were diagnosed with benign diseases and 125 patients were diagnosed with lung cancer. The sensitivity of PCDHGA12 was 74.4% and the specificity of PCDHGA12 was 91.9% respectively. CDO1 methylation test had a sensitivity of 57.6% and a specificity of 96.8%. The combination of both PCDHGA12 methylation test and CDO1 methylation test showed a sensitivity of 77.6% and a specificity of 90.3%. The sensitivity of lung cancer diagnosis was increased by combining both PCDHGA12 and CDO1 methylation tests.

CONCLUSION

Checking DNA methylation of both PCDHGA12 and CDO1 genes using bronchial washing fluid can reduce the invasive procedure to diagnose lung cancer.

MetaSwin: a unified meta vision transformer model for medical image segmentation

Transformers have demonstrated significant promise for computer vision tasks. Particularly noteworthy is SwinUNETR, a model that employs vision transformers, which has made remarkable advancements in improving the process of segmenting medical images. Nevertheless, the efficacy of training process of SwinUNETR has been constrained by an extended training duration, a limitation primarily attributable to the integration of the attention mechanism within the architecture. In this article, to address this limitation, we introduce a novel framework, called the MetaSwin model. Drawing inspiration from the MetaFormer concept that uses other token mix operations, we propose a transformative modification by substituting attention-based components within SwinUNETR with a straightforward yet impactful spatial pooling operation. Additionally, we incorporate of Squeeze-and-Excitation (SE) blocks after each MetaSwin block of the encoder and into the decoder, which aims at segmentation performance. We evaluate our proposed MetaSwin model on two distinct medical datasets, namely BraTS 2023 and MICCAI 2015 BTCV, and conduct a comprehensive comparison with the two baselines, i.e., SwinUNETR and SwinUNETR+SE models. Our results emphasize the effectiveness of MetaSwin, showcasing its competitive edge against the baselines, utilizing a simple pooling operation and efficient SE blocks. MetaSwin's consistent and superior performance on the BTCV dataset, in comparison to SwinUNETR, is particularly significant. For instance, with a model size of 24, MetaSwin outperforms SwinUNETR's 76.58% Dice score using fewer parameters (15,407,384 vs 15,703,304) and a substantially reduced training time (300 vs 467 mins), achieving an improved Dice score of 79.12%. This research highlights the essential contribution of a simplified transformer framework, incorporating basic elements such as pooling and SE blocks, thus emphasizing their potential to guide the progression of medical segmentation models, without relying on complex attention-based mechanisms.

Expression of NEDD4L and ENaC in Urinary Extracellular Vesicles in Pre-eclampsia

OBJECTIVE

To assess changes in expression of renal epithelial sodium channel (ENaC) and NEDD4L, a ubiquitin ligase, in urinary extracellular vesicles (UEV) of pre-eclamptic women compared to normal pregnant controls.

METHODS

Urine was collected from pre-eclamptic women (PE, n = 20) or during normal pregnancy (NP, n = 20). UEV were separated by differential ultracentrifugation. NEDD4L, α-ENaC and γ-ENaC were identified by immunoblotting.

RESULTS

There was no difference in the expression of NEDD4L (p = 0.17) and α-ENaC (p = 0.10). PE subjects showed increased expression of γ-ENaC by 6.9-fold compared to NP (p < 0.0001).

CONCLUSION

ENaC expression is upregulated in UEV of pre-eclamptic subjects but was not associated with changes in NEDD4L.

National trends in the prevalence of hepatitis B and C in South Korea, before and during the COVID-19 pandemic (2007-2021) - a nationwide representative study

OBJECTIVE

Due to the various changes caused by the COVID-19 pandemic, some infectious diseases showed different epidemiology and prevalence during the pandemic. However, there is a lack of comprehensive studies on trends in the prevalence of hepatitis B and C related to the pandemic. Thus, we compared the prevalence of hepatitis B and C before and during the COVID-19 pandemic in South Korea.

SUBJECTS AND METHODS

We conducted a comprehensive trend analysis with a nationwide serial cross-sectional survey from 2007 to 2021 (n=86,931) using the Korea National Health and Nutrition Examination Survey (KNHANES). The changes in the prevalence of hepatitis B and C were evaluated using a weighted regression model to assess the impact of the COVID-19 pandemic.

RESULTS

From 2007 to 2021, 86,931 Korean adults aged 19 or older were included in the KNHANES data. The prevalence of hepatitis B showed a declining trend until the onset of the pandemic (1.80% in 2007-2009; 1.08% in 2016-2019; and 1.01% in 2020), at which point this trend reversed (1.39% in 2021). The prevalence of hepatitis C remained stable (0.14% in 2007-2009 and 0.18% in 2016-2019), with no particular surge related to the COVID-19 pandemic (βdiff, -0.002; 95% CI, -0.761 to 0.756). For hepatitis B, old age was identified as a pandemic-related vulnerable factor (ratio of odds ratio, 1.68; 95% CI, 1.05-2.70).

CONCLUSIONS

In this study, unlike other infectious diseases, hepatitis B and C did not show a decreasing trend during the pandemic. In particular, hepatitis B showed a rebound trend during the pandemic, which was noticeable in those aged 60 or older. Further studies are needed to support these findings.

Kondo screening in a Majorana metal

Kondo impurities provide a nontrivial probe to unravel the character of the excitations of a quantum spin liquid. In the S = 1/2 Kitaev model on the honeycomb lattice, Kondo impurities embedded in the spin-liquid host can be screened by itinerant Majorana fermions via gauge-flux binding. Here, we report experimental signatures of metallic-like Kondo screening at intermediate temperatures in the Kitaev honeycomb material α-RuCl3 with dilute Cr3+ (S = 3/2) impurities. The static magnetic susceptibility, the muon Knight shift, and the muon spin-relaxation rate all feature logarithmic divergences, a hallmark of a metallic Kondo effect. Concurrently, the linear coefficient of the magnetic specific heat is large in the same temperature regime, indicating the presence of a host Majorana metal. This observation opens new avenues for exploring uncharted Kondo physics in insulating quantum magnets.

First Measurement of the Nuclear-Recoil Ionization Yield in Silicon at 100 eV

We measured the nuclear-recoil ionization yield in silicon with a cryogenic phonon-sensitive gram-scale detector. Neutrons from a monoenergetic beam scatter off of the silicon nuclei at angles corresponding to energy depositions from 4 keV down to 100 eV, the lowest energy probed so far. The results show no sign of an ionization production threshold above 100 eV. These results call for further investigation of the ionization yield theory and a comprehensive determination of the detector response function at energies below the keV scale.

Regulating POLR3G by MicroRNA-26a-5p as a promising therapeutic target of lung cancer stemness and chemosensitivity

Cancer stem cells (CSCs) identified in lung cancer exhibit resistance to chemotherapy, radiotherapy, and targeted therapy. Therefore, a technology for controlling CSCs is needed to overcome such resistance to cancer therapy. Various evidences about the association between epithelial-mesenchymal transition related transcriptomic alteration and acquisition of CSC phenotype have been proposed recently. Down-regulated miR-26a-5p is closely related to mesenchymal-like lung cancer cell lines. These findings suggest that miR-26a-5p might be involved in lung cancer stemness. RNA polymerase III subunit G (POLR3G) was selected as a candidate target of miR-26a-5p related to cancer stemness. It was found that miR-26a-5p directly regulates the expression of POLR3G.Overexpression of miR-26a-5p induced a marked reduction of colony formation and sphere formation. Co-treatment of miR-26a-5p and paclitaxel decreased cell growth, suggesting that miR-26a-5p might play a role as a chemotherapy sensitizer. In the cancer genome atlas data, high miR-26a-5p and low POLR3G expression were also related to higher survival rate of patients with lung adenocarcinoma. These results suggest that miR-26a-5p can suppress lung cancer stemness and make cancer cell become sensitive to chemotherapy. This finding provides a novel insight into a potential lung cancer treatment by regulating stemness.

Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits

This study seeks to elucidate the intricate relationship between various air pollutants and the incidence of rhinitis in Seoul, South Korea, wherein it leveraged a vast repository of data and machine learning techniques. The dataset comprised more than 93 million hospital visits (n = 93,530,064) by rhinitis patients between 2013 and 2017. Daily atmospheric measurements were captured for six major pollutants: PM10, PM2.5, O3, NO2, CO, and SO2. We employed traditional correlation analyses alongside machine learning models, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and gradient boosting machine (GBM), to dissect the effects of these pollutants and the potential time lag in their symptom manifestation. Our analyses revealed that CO showed the strongest positive correlation with hospital visits across all three categories, with a notable significance in the 4-day lag analysis. NO2 also exhibited a substantial positive association, particularly with outpatient visits and hospital admissions and especially in the 4-day lag analysis. Interestingly, O3 demonstrated mixed results. Both PM10 and PM2.5 showed significant correlations with the different types of hospital visits, thus underlining their potential to exacerbate rhinitis symptoms. This study thus underscores the deleterious impacts of air pollution on respiratory health, thereby highlighting the importance of reducing pollutant levels and developing strategies to minimize rhinitis-related hospital visits. Further research considering other environmental factors and individual patient characteristics will enhance our understanding of these intricate dynamics.

Discovering potential pathways between type 2 diabetes mellitus and diabetic retinopathy: A big data analysis of the South Korean National Sample Cohort

Diabetes mellitus, a prevalent metabolic disorder, is associated with a multitude of complications that necessitate vigilant management post-diagnosis. A notable complication, diabetic retinopathy, could lead to intense ocular injury, including vision impairment and blindness, due to the impact of the disease. Studying the transition from diabetes to diabetic retinopathy is paramount for grasping and halting the progression of complications. In this study, we examine the statistical correlation between type 2 diabetes mellitus and retinal disorders classified elsewhere, ultimately proposing a comprehensive disease network. The National Sample Cohort of South Korea, containing approximately 1 million samples and primary diagnoses based on the International Statistical Classification of Diseases and Related Health Problems 10th Revision classification, was utilized for this retrospective analysis. The diagnoses of both conditions displayed a statistically significant correlation with a chi-square test value of P < .001, and the t test for the initial diagnosis date also yielded a P < .001 value. The devised network, comprising 27 diseases and 142 connections, was established through statistical evaluations. This network offers insight into potential pathways leading to diabetic retinopathy and intermediary diseases, encouraging medical researchers to further examine various risk factors associated with these connections.

Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis

This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review's findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.

Machine learning for small interfering RNAs: a concise review of recent developments

The advent of machine learning and its subsequent integration into small interfering RNA (siRNA) research heralds a new epoch in the field of RNA interference (RNAi). This review emphasizes the urgency and relevance of assimilating the plethora of contributions and advancements in this domain, particularly focusing on the period of 2019-2023. Given the rapid progression of deep learning technologies, our synthesis of recent research is paramount to staying apprised of the state-of-the-art methods being utilized. It not only offers a comprehensive insight into the confluence of machine learning and siRNA but also serves as a beacon, guiding future explorations in this intersectional research field. Our rigorous examination of studies promises a discerning perspective on the contemporary landscape of machine learning applications in siRNA design and function. This review is an effort to foster further discourse and propel academic inquiry in this multifaceted domain.

Deep learning in CRISPR-Cas systems: a review of recent studies

In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019-2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area.

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.

Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature

Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies.

Initial phase establishment of an in vitro method for developmental neurotoxicity test using Ki-67 in human neural progenitor cells

Building a precise alternative neurotoxicological test is of great importance to respond to societal and ethical requirements. In this study, a new developmental neurotoxicity test (DNT) was established with the human neural progenitor cell line. ReNcell CX cells were exposed to neurotoxic chemicals (aphidicolin, hydroxyurea, cytosine arabinoside, 5-fluorouracil, and ochratoxin A) or non-neurotoxic chemicals (sodium gluconate, sodium bicarbonate, penicillin G, and saccharin). Propidium iodide (PI) was used to evaluate cell viability. BrdU and Ki-76 were employed to determine cell proliferation. Based on the cell viability and proliferation, mathematical models were built by linear discriminant analysis. Furthermore, the neurotoxic-considered chemicals inhibited cell cycle progression at the protein level, supporting the biomolecular rationale for the predictive model. Overall, these results show that the new test method can be used to determine the potential developmental neurotoxicants or new drug candidates.

Evaluation of the self-administration potential of high-density microarray patches to human skin: A preliminary study

The high-density microneedle array patch (HD-MAP) is a novel vaccine delivery system with potential for self-administered vaccination. In this study, Vaxxas HD-MAPs were applied by both a trained user and self-administered with application sites compared to determine the response of skin and the level of engagement of the HD-MAP with human skin. Twenty healthy participants were enrolled, and the response of skin including erythema was observed at all application sites and no difference was found between trained user or self-administered applications. The majority of participants (70%) preferred the deltoid upper arm application site for applying HD-MAPs. Fluorescent dermatoscope images confirmed HD-MAPs engaged the skin surface and scanning electron microscopy (SEM) image analysis exhibited similar delivery characteristics for the upper arm and forearm sites when applied by either a trained user or self-administered. This study showed that noninvasive methods including dermatoscopy and SEM image analysis were able to estimate the engagement of HD-MAPs with human skin. HD-MAP self-vaccination technology has a unique proposition in pandemic preparedness by alleviating the need for health-care workers to administer vaccines, however greater awareness and understanding of the potential of this technology is required.

SuperstarGAN: Generative adversarial networks for image-to-image translation in large-scale domains

Image-to-image translation with generative adversarial networks (GANs) has been extensively studied in recent years. Among the models, StarGAN has achieved image-to-image translation for multiple domains with a single generator, whereas conventional models require multiple generators. However, StarGAN has several limitations, including the lack of capacity to learn mappings among large-scale domains; furthermore, StarGAN can barely express small feature changes. To address the limitations, we propose an improved StarGAN, namely SuperstarGAN. We adopted the idea, first proposed in controllable GAN (ControlGAN), of training an independent classifier with the data augmentation techniques to handle the overfitting problem in the classification of StarGAN structures. Since the generator with a well-trained classifier can express small features belonging to the target domain, SuperstarGAN achieves image-to-image translation in large-scale domains. Evaluated with a face image dataset, SuperstarGAN demonstrated improved performance in terms of Fréchet Inception distance (FID) and learned perceptual image patch similarity (LPIPS). Specifically, compared to StarGAN, SuperstarGAN exhibited decreased FID and LPIPS by 18.1% and 42.5%, respectively. Furthermore, we conducted an additional experiment with interpolated and extrapolated label values, indicating the ability of SuperstarGAN to control the degree of expression of the target domain features in generated images. Additionally, SuperstarGAN was successfully adapted to an animal face dataset and a painting dataset, where it can translate styles of animal faces (i.e., a cat to a tiger) and styles of painters (i.e., Hassam to Picasso), respectively, which explains the generality of SuperstarGAN regardless of datasets.

Heterogeneous Structure Omnidirectional Strain Sensor Arrays With Cognitively Learned Neural Networks

Mechanically stretchable strain sensors gain tremendous attention for bioinspired skin sensation systems and artificially intelligent tactile sensors. However, high-accuracy detection of both strain intensity and direction with simple device/array structures is still insufficient. To overcome this limitation, an omnidirectional strain perception platform utilizing a stretchable strain sensor array with triangular-sensor-assembly (three sensors tilted by 45°) coupled with machine learning (ML) -based neural network classification algorithm, is proposed. The strain sensor, which is constructed with strain-insensitive electrode regions and strain-sensitive channel region, can minimize the undesirable electrical intrusion from the electrodes by strain, leading to a heterogeneous surface structure for more reliable strain sensing characteristics. The strain sensor exhibits decent sensitivity with gauge factor (GF) of ≈8, a moderate sensing range (≈0-35%), and relatively good reliability (3000 stretching cycles). More importantly, by employing a multiclass-multioutput behavior-learned cognition algorithm, the stretchable sensor array with triangular-sensor-assembly exhibits highly accurate recognition of both direction and intensity of an arbitrary strain by interpretating the correlated signals from the three-unit sensors. The omnidirectional strain perception platform with its neural network algorithm exhibits overall strain intensity and direction accuracy around 98% ± 2% over a strain range of ≈0-30% in various surface stimuli environments.

Horizontal Arrays of One-Dimensional van der Waals Heterostructures as Transistor Channels

The nanotube/dielectric interface plays an essential role in achieving superb switching characteristics of carbon nanotube-based transistors for energy-efficient computation. Formation of van der Waals heterostructures with hexagonal boron nitride nanotubes could be an effective means to reduce interface state density, but the need for isolating nanotubes during the formation of coaxial outer layers has hindered the fabrication of their horizontal arrays. Here, we develop a strategy to create isolated heterostructure arrays using aligned carbon nanotubes grown on a quartz substrate as starting materials. Air-suspended arrays of carbon nanotubes are prepared by a dry transfer technique and then used as templates for the coaxial wrapping of boron nitride nanotubes. We then fabricate the transistors, where boron nitride serves as interfacial layers between carbon nanotube channels and conventional gate dielectrics, showing hysteresis-free characteristics owing to the improved interfaces. We have also gained a deeper understanding of the strain applied on inner carbon nanotubes, as well as the inhomogeneity of the outer coating, by characterizing individual heterostructures over trenches and on a substrate surface. The device fabrication and characterization presented here essentially do not require elaborate electron microscopy, thus paving the way for the practical use of one-dimensional van der Waals heterostructures for nanoelectronics.

Impact of discontinuing isolation in a private room for patients infected or colonized with vancomycin-resistant enterococci (VRE) on the incidence of healthcare-associated VRE bacteraemia in a hospital with a predominantly shared-room setting

BACKGROUND

Isolating patients infected or colonized with vancomycin-resistant enterococci (VRE) in a private room or cohort room to prevent hospital transmission is controversial.

AIM

To evaluate the effect of a relaxed isolation policy for VRE-infected or colonized patients on healthcare-associated (HA) VRE bacteraemia in an acute care hospital with a predominantly shared-room setting.

METHODS

The incidence of HA VRE bacteraemia was compared during a private isolation era (October 2014-September 2017), a cohort isolation era (October 2017-June 2020), and a no isolation era (July 2020-June 2022). Using Poisson regression modelling, an interrupted time-series analysis was conducted to analyse level changes and trends in incidences of HA VRE bacteraemia for each era.

FINDINGS

The proportion of VRE-infected or -colonized patients staying in shared rooms increased from 18.3% in the private isolation era to 82.6% in the no isolation era (P < 0.001). There was no significant difference in the incidences of HA VRE bacteraemia between the private isolation era and the cohort isolation era (relative risk: 1.01; 95% confidence interval: 0.52-1.98; P = 0.977) or between the cohort isolation era and the no isolation era (0.99; 0.77-1.26; P = 0.903). In addition, there was no significant slope increase in the incidence of HA VRE bacteraemia between any of the eras.

CONCLUSION

In a hospital with predominantly shared rooms, the relaxation of isolation policy did not result in increased HA VRE bacteraemia, when other infection control measures were maintained.

Low-to-moderate atmospheric ozone levels are negatively correlated with hospital visits by asthma patients

Asthma is a chronic illness of the airways that affects approximately 300 million individuals worldwide. While it is commonly accepted that high ozone levels exacerbate asthma symptoms, the impact of low to moderate ozone levels on asthma symptoms has received little attention. The purpose of this research was to determine the relationship between hospital visits by asthma patients showing the severity of their symptoms and moderate ozone levels. Statistical analyses were performed on hospital visit big data for asthma patients in Seoul, Korea, collected between 2013 and 2017. The data set includes outpatient hospital visits (n = 17,787,982), hospital admissions (n = 215,696), and emergency department visits (n = 85,482). The frequency of hospital visits by asthma patients was evaluated in relation to low ozone levels (< 0.03 ppm) and moderate ozone levels (0.03-0.06 ppm) in the Seoul environment. In comparison to low ozone levels, moderate ozone levels resulted in a reduction in outpatient hospital visits (t = 7.052, P < .001). When ozone levels were low to moderate, there was a negative correlation between ozone levels and outpatient visits (r = -0.281, 95% CI: -0.331 to -0.228). Negative associations were also identified between ozone levels and new hospital admissions (t = 2.909, P < .01; r = -0.125, 95% CI: -0.179 to -0.070) and emergency treatments (t = 2.679, P < .01; r = -0.132, 95% CI: -0.186 to -0.076). Additionally, it was verified that moderate ozone levels one day before the visits resulted in a reduction in outpatient visits (t = 5.614, P < .001; r = -0.207, 95% CI: -0.259 to -0.153). A strong relationship was identified between moderate atmospheric ozone levels and a reduction in asthma patient hospital visits.

Machine Learning-Based Analyses of the Effects of Various Types of Air Pollutants on Hospital Visits by Asthma Patients

Asthma is a chronic respiratory disorder defined by airway inflammation, chest pains, wheezing, coughing, and difficulty breathing that affects an estimated 300 million individuals globally. Although various studies have shown an association between air pollution and asthma, few studies have used statistical and machine learning algorithms to investigate the effect of each individual air pollutant on asthma. The purpose of this research was to assess the association between air pollutants and the frequency of hospital visits by asthma patients using three analysis methods: linear correlation analyses were performed by Pearson correlation coefficients, and least absolute shrinkage and selection operator (LASSO) and random forest (RF) models were used for machine learning-based analyses to investigate the effect of air pollutants. This research studied asthma patients using the hospital visit database in Seoul, South Korea, collected between 2013 and 2017. The data set included outpatient hospital visits (n = 17,787,982), hospital admissions (n = 215,696), and emergency department visits (n = 85,482). The daily atmospheric environmental information from 2013 to 2017 at 25 locations in Seoul was evaluated. The three analysis models revealed that NO₂ was the most significant pollutant on average in outpatient hospital visits by asthma patients. For example, NO₂ had the greatest impact on outpatient hospital visits, resulting in a positive association (r=0.331). In hospital admissions of asthma patients, CO was the most significant pollutant on average. It was observed that CO exhibited the most positive association with hospital admissions (I = 3.329). Additionally, a significant time lag was found between both NO₂ and CO and outpatient hospital visits and hospital admissions of asthma patients in the linear correlation analysis. In particular, NO₂ and CO were shown to increase hospital admissions at lag 4 in the linear correlation analysis. This study provides evidence that PM_2.5, PM₁₀, NO₂, CO, SO₂, and O₃ are associated with the frequency of hospital visits by asthma patients.

Diverse perspectives on social determinants of multicultural adolescents: A focus group study

Background

As Korea transforms into a multicultural society, social vulnerability of the multicultural adolescents (MAs) puts them at risk for poor health and health disparities. However, there is shortage of evidence on social determinants of health (SDH), which refers to the circumstances of people from birth to death, which affects their health outcomes, for MAs. Thus, this study aims to explore the SDH of MAs from diverse stakeholders’ perspectives.

Methods

This qualitative study comprised 17 focus group interviews with 99 participants (MAs, peers, parents, teachers, neighbors, and community leaders), conducted from June to September 2020. The directed content analysis was conducted using the Minority Health and Health Disparities Research Framework with a high rigor level based on the four criteria of Lincoln and Guba’s trustworthiness.

Results

The participants addressed SDH of MAs in five domains: biological (vulnerability and mechanism); behavioral (health behavior, family/school/peer functioning, and policies and laws); physical/built environment (school/community environment); sociocultural environment (MAs'/parental sociodemographic, language proficiency, MAs'/parental acculturation, social network, and response to/interpersonal/local/societal structural discrimination); and health care system (insurance coverage, MAs'/parental health literacy, availability of services, and health care policies). However, limited SDH in biological and physical/built environment domains were identified.

Conclusions

SDH in sociocultural environment domain and interpersonal influence level were the most commonly addressed. Interpersonal discrimination of both MAs and parents were found to be the most important SDH. These findings suggest that future programs to enhance MAs’ health may be targeted toward reducing discrimination and involving their parents.

Key messages

• Sociocultural environment was found to be the most salient SDH domain to affect MAs’ health.

• SDH of MAs are linked to discrimination and their parents’ sociocultural aspects such as acculturation.

Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes

The prognosis estimation of low-grade glioma (LGG) patients with deep learning models using gene expression data has been extensively studied in recent years. However, the deep learning models used in these studies do not utilize the latest deep learning techniques, such as residual learning and ensemble learning. To address this limitation, in this study, a deep learning model using multi-omics and multi-modal schemes, namely the Multi-Prognosis Estimation Network (Multi-PEN), is proposed. When using Multi-PEN, gene attention layers are employed for each datatype, including mRNA and miRNA, thereby allowing us to identify prognostic genes. Additionally, recent developments in deep learning, such as residual learning and layer normalization, are utilized. As a result, Multi-PEN demonstrates competitive performance compared to conventional models for prognosis estimation. Furthermore, the most significant prognostic mRNA and miRNA were identified using the attention layers in Multi-PEN. For instance, MYBL1 was identified as the most significant prognostic mRNA. Such a result accords with the findings in existing studies that have demonstrated that MYBL1 regulates cell survival, proliferation, and differentiation. Additionally, hsa-mir-421 was identified as the most significant prognostic miRNA, and it has been extensively reported that hsa-mir-421 is highly associated with various cancers. These results indicate that the estimations of Multi-PEN are valid and reliable and showcase Multi-PEN's capacity to present hypotheses regarding prognostic mRNAs and miRNAs.

Trends in management of cardiovascular risk factors in young adults who go on to develop coronary artery disease

Background

Rates of premature coronary artery disease (CAD) have remained stagnant in many countries in the past two decades. While guidelines on cardiovascular prevention are evolving to improve risk assessment, more data are needed to evaluate changes in risk management in younger patients with high cardiovascular risk.

Purpose

To investigate the trends in pre-presentation prevalence and management of major cardiovascular risk factors (CVRFs) and predictors of initiation of lipid-lowering therapy (LLT) in patients with premature coronary artery disease.

Methods

In this retrospective cohort study, we identified women &lt;55 years old and men &lt;50 years old who presented with obstructive CAD (stenosis &gt;50% or history of coronary revascularization) between 2000 and 2017. Data were collected from administrative databases and provincial cardiac information system and included demographics, all outpatient visits, hospitalizations, pharmacy dispenses in the period of 3 years prior to CAD, presentation characteristics, and results of invasive coronary angiography.

Results

In total, 14,470 patients (29.7% females) met the study criteria. During the study period, prevalence decreased for dyslipidemia and smoking and increased for hypertension, diabetes and obesity (Figure 1). Over 96.5% of patients were eligible for lipid screening 3 years prior to presentation with CAD, 93.5% had visits with primary care physicians, and 57.4% had visits with specialist physicians. Only 20.5% received LLT, with rates rising from 19% in the year 2000 to 24% in 2017 (p=0.023). Additionally, 38.1% of patients received medical treatment for other CVRFs. Figure 2 presents risk management and rates of LLT initiation in patients with five major CVRFs. In multivariable logistic regression analysis, LLT was more likely in patients with diabetes (odds ratio [OR] 3.04, 95% CI 2.74–3.36), hypertension (OR 1.44, 95% CI 1.30–1.58), chronic kidney disease (OR 1.79, 95% CI 1.44–2.23), and depression (OR 1.25, 95% CI 1.11–1.40). Later year of presentation (OR 1.04, 95% CI 1.03–1.05), older age at presentation (OR 1.03, 95% CI 1.02–1.04) and residing in urban areas vs rural (OR 1.36, 95% CI 1.16–1.58) were also positively associated with LLT. Male sex (OR 0.88, 95% CI 0.78–0.98) and smoking (OR 0.68, 95% CI 0.62–0.76) were associated with lower likelihood of treatment. Obesity, hypothyroidism, chronic inflammatory conditions, and malignancies were not significantly associated with LLT.

Conclusion

Despite the rising prevalence of CVRFs, high eligibility for lipid screening, and availability of care, rates of LLT initiation in patients who go on to develop premature CAD remain very low, representing a major missed opportunity for cardiovascular disease prevention. Many of the major CVRFs and risk enhancers endorsed in current guidelines were not associated with treatment initiation, suggesting that new approaches may be required to increase appropriate use LLT in these patients.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): St. Pauls Hospital Foundation, Vancouver, BC, CanadaVancouver General Hospital Foundation, Vancouver, BC, Canada

Association of usual walking with mortality in oldest old adults aged 85 years and older: a nationwide senior cohort study

Background

Current guidelines recommend moderate to vigorous intensity physical activity (MVPA) for health benefit in adults. However, it is difficult to meet the recommended minimum MVPA in “oldest old” aged over 85. Although walking is simple and easy exercise for oldest old, research on the association between walking and mortality in oldest old are lacking.

Purpose

To investigate the association between walking and the risk of all-cause and cardiovascular mortality among older adults aged 85 years and older.

Methods

7047 participants aged 85 or older underwent the Korean National Health Screening Program, including self-administered questionnaire for physical activity. Reported frequency per week of walking for at least 30 minutes increment was used to classify hours of walking per week. We computed multivariable-adjusted hazard ratios for walking and mutually adjusted for MVPA in relation to all-cause and cardiovascular mortality.

Results

The proportions of participants who engaged in walking, moderate- and vigorous-intensity physical activity were 42.5% (2996/7047), 14.7% (1037/7047) and 11.0% (773/7047), respectively. Only 7.6% (538/7047) of participants met the guidelines recommended minimum MVPA. Compared to inactive individuals, those who walked at least 1 hour per week had lower all-cause (HR 0.60 [95% CI, 0.50–0.74]) and cardiovascular mortality risk (HR 0.61 [95% CI, 0.42–0.89]). Walking 1 hour per week was still significant associated with reduction of risk for all-cause (HR 0.50 [95% CI, 0.40–0.61]) and cardiovascular mortality (HR 0.46 [95% CI, 0.30–0.70]) among participants who reported only walking without any MVPA.

Conclusions

1 hour a week of walking might be of benefit for all-cause and cardiovascular mortality in aged over 85 years, even without MVPA. Promoting walking may be a way to help oldest old avoid inactivity and encourage an active lifestyle for all-cause and cardiovascular mortality risk reduction.

Funding Acknowledgement

Type of funding sources: None.

Pathogenic variants damage cell compositions and single cell transcription in cardiomyopathies

 

Pathogenic variants in genes that cause dilated (DCM) and arrhythmogenic cardiomyopathies (ACM) convey high risks for the development of heart failure (HF) through unknown mechanisms. Using single nucleus RNA sequencing (snRNAseq), we characterized the transcriptome of 880,000 nuclei from 18 control and 61 failing, non-ischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene expression at single cell resolution. Together these data illuminate both shared and distinct cellular and molecular architectures of human HF and suggest novel candidate therapeutic targets.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): Chan Zuckerberg FoundationLeducq Foundation

Alleviation of AF related symptoms following acute conversion of recent-onset, symptomatic atrial fibrillation to sinus rhythm with flecainide acetate oral inhalation solution

Introduction

Pharmacological restoration of sinus rhythm (SR) in patients with symptomatic atrial fibrillation (AF) is expected to be accompanied by prompt alleviation of symptoms to avoid the need for electrical cardioversion (ECV) and/or hospitalization. The feasibility and safety of acute cardioversion of recent-onset (≤48 hours) symptomatic AF to SR with flecainide acetate oral inhalation (FlecIH) solution was shown in the Phase 2, open-label INSTANT trial. We examined symptoms, heart rate, time to discharge and need for ECV reported among patients in the INSTANT trial whose AF was successfully converted to SR (“conversion group”; N=25) versus those whose AF did not convert to SR (“no conversion group”; N=29).

Methods

Conversion success was determined using 12-lead Holter monitoring during a 90-minute observation period. Patients in the no conversion group were offered alternative treatment per the investigator discretion. Symptoms, vital signs, time to discharge, and the need for ECV were evaluated through Day 5.

Results

Data from 54 patients (33.3% female) with a mean age of 62.1 years and a mean BMI of 26.8 kg/m2 were analyzed. All patients reported at least one AF-related symptoms at baseline (palpitations=85%; dizziness=35%; shortness of breath=37%; chest discomfort=39%) and 83.3% presented with AF symptoms ≤24 hours in duration. At 90 minutes, 80.0% of the conversion group were asymptomatic compared to 37.9% of the no conversion group (p&lt;0.001). Mean (SD) ventricular rate at 90 minutes was 70.6 (12.5) bpm in the conversion group compared to 100.4 (29.4) bpm in the no conversion group (p&lt;0.001). Median time to discharge was 2.3 (IQR: 0.75) hours for the conversion group compared to 3.6 (IQR: 1.02) hours for the no conversion group (p=0.001). By Day 5, 23 (79.3%) patients in the no conversion group had undergone ECV; no patients in the conversion group experienced AF recurrence by Day 5 (0% required ECV; p&lt;0.001).

Conclusions

Conversion of recent onset AF to SR with inhaled flecainide was associated with a reduction in symptoms, normalization of heart rate, rapid hospital discharge and avoidance of ECV during a 5-day follow-up period.

Funding Acknowledgement

Type of funding sources: Private company. Main funding source(s): InCarda Therapeutics

The efficacy of immune checkpoint inhibitors in elderly patients: a meta-analysis and meta-regression

Background

Immune checkpoint inhibitor (ICI) therapy has improved patient survival in advanced cancers; however, the efficacy of ICIs in elderly patients is still elusive. This study assessed the efficacy of ICIs in elderly patients with advanced cancer in terms of overall survival (OS) and progression-free survival (PFS).

Materials and methods

We carried out a systematic review and identified 30 head-to-head phase II/III randomized controlled trials that compared immunotherapy with the standard of care in advanced solid tumor patients. The data on patients younger or over 65 years of age were indexed from PubMed-Medline, Embase, and Scopus and obtained for meta-analysis. The subgroup analyses were stratified by primary tumor type, line of treatment, or type of immunotherapy, and a meta-regression analysis was carried out after adjusting for all other variables.

Results

The study included 17 476 patients, comprising 58% (10 119) younger (<65 years old) and 42% (7357) elderly (≥65 years old) patients. The hazard ratio (HR) for OS was 0.77 [95% confidence interval (CI) 0.70-0.85] and 0.77 (95% CI 0.70-0.85) in the younger and elderly groups, respectively, suggesting similar efficacies of ICIs in these two age groups. The subgroup analyses revealed no significant relationship between age and treatment outcomes, except for the PFS benefit in younger patients with melanoma than in elderly patients (HR 0.44 in younger patients versus 0.65 in elderly patients, P = 0.04). These results were further supported by meta-regression analysis, which showed no statistically significant difference in OS (P = 0.954) and PFS (P = 0.555) between the two age groups.

Conclusions

The findings suggest that age-associated impairments of the immune system did not affect the efficacy of ICIs in elderly patients compared to younger patients. Therefore, the choice of ICIs for elderly patients can be considered, regardless of chronological age.

•

We evaluated the efficacy of ICI in 17 476 patients, comprising 58% younger and 42% elderly patients.

•

Meta-analysis resulted in the comparable efficacy of ICI between younger and older age groups.

•

Further, meta-regression analysis showed no significant difference in OS and PFS.

•

Our study suggests that chronological age does not lead to immunosenescence in response to ICI in immune-oncology.

Lawson Criterion for Ignition Exceeded in an Inertial Fusion Experiment

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition as a proof of principle of various fusion concepts. Following the Lawson criterion, an ignited plasma is one where the fusion heating power is high enough to overcome all the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop with rapidly increasing temperature. In inertially confined fusion, ignition is a state where the fusion plasma can begin "burn propagation" into surrounding cold fuel, enabling the possibility of high energy gain. While "scientific breakeven" (i.e., unity target gain) has not yet been achieved (here target gain is 0.72, 1.37 MJ of fusion for 1.92 MJ of laser energy), this Letter reports the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce capsule gain (here 5.8) and reach ignition by nine different formulations of the Lawson criterion.