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Surgery is unanimously regarded as the primary strategy to cure solid tumors in the early stages but is not always used in advanced cases. However, tumor surgery must be carefully considered because the risk of metastasis could be increased by the surgical procedure. Tumor surgery may result in a deep wound, which induces many biological responses favoring tumor metastasis. In particular, NETosis, which is the process of forming neutrophil extracellular traps (NETs), has received attention as a risk factor for surgery-induced metastasis. To reduce cancer mortality, researchers have made efforts to prevent secondary metastasis after resection of the primary tumor. From this point of view, a better understanding of surgery-induced metastasis might provide new strategies for more effective and safer surgical approaches. In this paper, recent insights into the surgical effects on metastasis will be reviewed. Moreover, in-depth opinions about the effects of NETs on metastasis will be discussed.Cancer treatment: Understanding risk of spread after surgeryTherapies that limit the formation of web-like structures formed by white cells known as neutrophils may lower the risk of cancer spread (metastasis) following surgical tumor removal. Removing solid tumors remains a key cancer treatment, but in some cases surgery itself increases the risk of metastasis. Jong-Wan Park at Seoul National University, South Korea, and co-workers reviewed current understanding of metastasis following surgery. Surgical removal destroys the architecture supporting cancer cells but this can release tumor cells into blood vessels. The stress of deep wounds also affects immune responses, most notably neutrophil extracellular traps (NETs), web-like structures formed by neutrophils to trap and kill pathogens. NETs have previously been implicated in metastasis. In a post-surgical environment enriched in neutrophils and pro-inflammatory cytokines, NET formation may help cancer cells thrive, promoting metastasis.

Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression. Using public data from the nationwide panel survey ‘Korean Longitudinal Study of Aging,’ we employed latent growth modeling and growth mixture modeling to identify four latent classes of depression trajectories in the elderly Korean population. Based on the results of binary logistic regression, we selected 12 variables capable of distinguishing the LOD population from the reference population and tested 12 machine learning (ML) algorithms. While most ML algorithms showed acceptable predictive capability, Random Forest Classifier and Gradient Boosting Classifier demonstrated superior performance. Consequently, we successfully established new ML-based LOD prediction programs. These programs could be further developed into self-checking online tools, expected to serve as decision support systems for primary medical care and health screening services.

Chronic activation of microglial cells endangers neuronal survival through the release of various proinflammatory and neurotoxic factors. The root of Paeonia lactiflora Pall has been considered useful for the treatment of various disorders in traditional oriental medicine. Paeonol, found in the root of Paeonia lactiflora Pall, has a wide range of pharmacological functions, including anti-oxidative, anti-inflammatory and neuroprotective activities. The objective of this study was to examine the efficacy of paeonol in the repression of inflammation-induced neurotoxicity and microglial cell activation. Organotypic hippocampal slice cultures and primary microglial cells from rat brain were stimulated with bacterial lipopolysaccharide. Paeonol pretreatment was performed for 30 minutes prior to lipopolysaccharide addition. Cell viability and nitrite （the production of nitric oxide）, tumor necrosis factor-alpha and interleukin-lbeta products were measured after lipopolysaccharide treatment. In organotypic hippocampal slice cultures, paeonol blocked lipopolysaccharide-related hippocampal cell death and inhibited the release of nitrite and interleukin-lbeta. Paeonol was effective in inhibiting nitric oxide release from primary microglial cells. It also reduced the lipopolysaccharide-stimulated release of tumor necrosis factor-alpha and intefleukin-1β from microglial cells. Paeonol possesses neuroprotective activity in a model of inflammation-induced neurotoxicity and reduces the release of neurotoxic and proinflammatory factors in activated microglial cells.

Background Waterlogging (WL) is a key factor hindering soybean crop productivity worldwide. Plants utilize various hormones to avoid various stress conditions, including WL stress; however, the physiological mechanisms are still not fully understood. Results To identify physiological mechanisms during WL stress, different phytohormones, such as ethephon (ETP; donor source of ethylene), abscisic acid, gibberellins, indole-3-acetic acid, kinetin, jasmonic acid, and salicylic acid were exogenously applied to soybean plants. Through this experiment, we confirmed the beneficial effects of ETP treatment. Thus, we selected ETP as a candidate hormone to mitigate WL. Further mechanistic investigation of the role of ETP in waterlogging tolerance was carried out. Results showed that ETP application mitigated WL stress, significantly improved the photosynthesis pigment, and increased the contents of endogenous GA s compared to those in untreated plants. The amino acid contents during WL stress were significantly activated by EPT treatments. The amino acid contents were significantly higher in the 100 μM ETP-treated soybean plants than in the control. ETP application induced adventitious root initiation, increased root surface area, and significantly increased the expressions of glutathione transferases and relative glutathione activity compared to those of non-ETP-treated plants. ETP-treated soybeans produced a higher up-regulation of protein content and glutathione S-transferase (GSTs) than did soybeans under the WL only treatment. Conclusions In conclusion, the current results suggest that ETP application enabled various biochemical and transcriptional modulations. In particular, ETP application could stimulate the higher expression of GST3 and GST8. Thus, increased GST3 and GST8 induced 1) increased GSH activity, 2) decreased reactive oxygen species (ROS), 3) mitigation of cell damage in photosynthetic apparatus, and 4) improved phenotype consecutively.

Background/Objectives: Banhasasim-tang (BHSST) is a traditional herbal formula commonly used to treat gastrointestinal (GI) disorders and has been considered a potential therapeutic option for irritable bowel syndrome (IBS). This study aimed to explore the molecular targets and underlying mechanisms of BHSST in IBS using a combination of network pharmacology, molecular docking, molecular dynamics simulations, and in vivo validation. Methods: Active compounds in BHSST were screened based on drug-likeness and oral bioavailability. Potential targets were predicted using ChEMBL, and IBS-related targets were obtained from GeneCards and DisGeNET. A compound–target–disease network was constructed and analyzed via Gene Ontology and KEGG pathway enrichment. Compound–target interactions were further assessed using molecular docking and molecular dynamics simulations. The in vivo effects of eudesm-4(14)-en-11-ol, elemol, and BHSST were evaluated in a zymosan-induced IBS mouse model. Results: Twelve BHSST-related targets were associated with IBS, with enrichment analysis identifying TNF signaling and apoptosis as key pathways. In silico simulations suggested stable binding of eudesm-4(14)-en-11-ol to TNF-α and kanzonol T to PIK3CD, whereas elemol showed weak interaction with PRKCD. In vivo, eudesm-4(14)-en-11-ol improved colon length, weight, stool consistency, TNF-α levels, and pain-related behaviors—effects comparable to those of BHSST. Elemol, however, showed no therapeutic benefit. Conclusions: These findings provide preliminary mechanistic insight into the anti-inflammatory potential of BHSST in IBS. The integrated in silico and in vivo approaches support the contribution of specific components, such as eudesm-4(14)-en-11-ol, to its observed effects, warranting further investigation.

Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-based computer-aided detection (AI-CAD) for screening mammograms in a real-world, single-read setting. A prospective multicenter cohort study is conducted within South Korea’s national breast cancer screening program for women. The primary outcomes are screen-detected breast cancer within one year, with a focus on cancer detection rates (CDRs) and recall rates (RRs) of radiologists. A total of 24,543 women are included in the final cohort, with 140 (0.57%) screen-detected breast cancers. The CDR is significantly higher by 13.8% for breast radiologists using AI-CAD ( n  = 140 [5.70‰]) compared to those without AI ( n  = 123 [5.01‰]; p  < 0.001), with no significant difference in RRs ( p  = 0.564). These preliminary results show a significant improvement in CDRs without affecting RRs in a radiologist’s standard single-reading setting (ClinicalTrials.gov: NCT05024591). Artificial intelligence (AI) could improve mammography screening accuracy, but prospective evidence is still lacking. Here, the authors show in a prospective multicentre cohort study under single-read settings that the diagnostic accuracy of breast radiologists can significantly improve with AI-based computer-aided detection.

Skin is the outermost layer of the human body that is constantly exposed to environmental stressors, such as UV radiation and toxic chemicals, and is susceptible to mechanical wounding and injury. The ability of the skin to repair injuries is paramount for survival and it is disrupted in a spectrum of disorders leading to skin pathologies. Diabetic patients often suffer from chronic, impaired wound healing, which facilitate bacterial infections and necessitate amputation. Here, we studied the effects of gallic acid (GA, 3,4,5-trihydroxybenzoic acid; a plant-derived polyphenolic compound) on would healing in normal and hyperglucidic conditions, to mimic diabetes, in human keratinocytes and fibroblasts. Our study reveals that GA is a potential antioxidant that directly upregulates the expression of antioxidant genes. In addition, GA accelerated cell migration of keratinocytes and fibroblasts in both normal and hyperglucidic conditions. Further, GA treatment activated factors known to be hallmarks of wound healing, such as focal adhesion kinases (FAK), c-Jun N-terminal kinases (JNK), and extracellular signal-regulated kinases (Erk), underpinning the beneficial role of GA in wound repair. Therefore, our results demonstrate that GA might be a viable wound healing agent and a potential intervention to treat wounds resulting from metabolic complications.

Changes in the DNA damage response (DDR) and cellular metabolism are two important factors that allow cancer cells to proliferate. DDR is a set of events in which DNA damage is recognized, DNA repair factors are recruited to the site of damage, the lesion is repaired, and cellular responses associated with the damage are processed. In cancer, DDR is commonly dysregulated, and the enzymes associated with DDR are prone to changes in ubiquitination. Additionally, cellular metabolism, especially glycolysis, is upregulated in cancer cells, and enzymes in this metabolic pathway are modulated by ubiquitination. The ubiquitin–proteasome system (UPS), particularly E3 ligases, act as a bridge between cellular metabolism and DDR since they regulate the enzymes associated with the two processes. Hence, the E3 ligases with high substrate specificity are considered potential therapeutic targets for treating cancer. A number of small molecule inhibitors designed to target different components of the UPS have been developed, and several have been tested in clinical trials for human use. In this review, we discuss the role of ubiquitination on overall cellular metabolism and DDR and confirm the link between them through the E3 ligases NEDD4, APC/CCDH1, FBXW7, and Pellino1. In addition, we present an overview of the clinically important small molecule inhibitors and implications for their practical use.

Recent advances in computing power have enabled the generation of large datasets for materials, enabling data-driven approaches to problem-solving in materials science, including materials discovery. Machine learning is a primary tool for manipulating such large datasets, predicting unknown material properties and uncovering relationships between structure and property. Among state-of-the-art machine learning algorithms, gradient-boosted regression trees (GBRT) are known to provide highly accurate predictions, as well as interpretable analysis based on the importance of features. Here, in a search for lead-free perovskites for use in solar cells, we applied the GBRT algorithm to a dataset of electronic structures for candidate halide double perovskites to predict heat of formation and bandgap. Statistical analysis of the selected features identifies design guidelines for the discovery of new lead-free perovskites.

This study prognoses the remaining useful life of a turbofan engine using a deep learning model, which is essential for the health management of an engine. The proposed deep learning model affords a significantly improved accuracy by organizing networks with a one-dimensional convolutional neural network, long short-term memory, and bidirectional long short-term memory. In particular, this paper investigates two practical and crucial issues in applying the deep learning model for system prognosis. The first is the requirement of numerous sensors for different components, i.e., the curse of dimensionality. Second, the deep neural network cannot identify the problematic component of the turbofan engine due to its “black box” property. This study thus employs dimensionality reduction and Shapley additive explanation (SHAP) techniques. Dimensionality reduction in the model reduces the complexity and prevents overfitting, while maintaining high accuracy. SHAP analyzes and visualizes the black box to identify the sensors. The experimental results demonstrate the high accuracy and efficiency of the proposed model with dimensionality reduction and show that SHAP enhances the explainability in a conventional deep learning model for system prognosis.