In modern society, background music is often used as a tool to promote learning during the learning process, but in fact, there is still widespread controversy over the effect of music. The present study investigates how music is perceived to influence concentration, with a focus on influential factors and text types. Fifty-one participants completed an anonymous questionnaire assessing music-listening behaviors, music preferences, and perceived effects of music on concentration and task performance. By using composite indices, the data shows that music can facilitate individuals' concentration overall and are influenced by music elements and text types; in contrast, the interference caused by music on attention are mainly determined by individuals' differentiation. These findings suggest that music functions depend more on how it is used and the task context than on specific musical characteristics and provide adolescents referenceable data for the use of music.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, and its prevalence continues to increase. Acute exacerbation (AECOPD) is a critical step in the progression of the disease, leading to irreversible decline in lung function. Among existing preventive measures, inhaled bronchodilators only alleviate symptoms and fail to reduce the risk of infection-related exacerbations. Vaccines do not cover multiple pathogens, and prophylactic antibiotics are prone to inducing drug resistance, all with obvious limitations. This study reviews the immunomodulatory mechanisms and clinical efficacy of bacterial lysates. High-quality literature from 2020 to 2025 was selected for qualitative and comprehensive analysis. The aim is to clarify the core mechanism of action and clinical efficacy of these lysates in preventing AECOPD, and to provide evidence-based support for clinical practice. The reviewed studies demonstrate that bacterial lysates can significantly reduce the incidence and hospital admission rate of AECOPD by synergistically regulating innate and adaptive immunity and strengthening the respiratory mucosal barrier, thereby reducing antibiotic use. They also have favorable safety and tolerability, providing evidence-based support for clinical practice and are expected to become an important means of AECOPD prevention.

Glioblastoma (GBM) exhibits substantial intercellular heterogeneity, making it challenging to accurately distinguish malignant from non-malignant cells in single-cell RNA sequencing (scRNA-seq) data. This study may suggest that a reproducible machine-learning framework to classify GBM malignant cells at single-cell resolution could provide important results for estimating tumor purity based on model predictions. A public GBM scRNA-seq dataset comprising 40,026 cells and 5,000 highly variable genes is splitted into training, validation, and test subsets while preserving class proportions. Differential feature analysis with false discovery rate control was performed on the training set to identify discriminatory markers, resulting in a compact panel of 30 genes. Then two monitoring training models , XGBoost and a multilayer perceptron (MLP), are trained and adjusted to evaluate the final effect in an independent test set. The results suggest that XGBoost achieved an AUC of 0.9532 (95% confidence interval 0.9485–0.9579), with sensitivity of 0.917, specificity of 0.898, and accuracy of 0.906. the MLP produced comparable performance with an AUC of 0.951. Thus, the approach may provide accurate malignant-cell separation in GBM scRNA-seq data and could establish a practical strategy for tumor purity assessment.

The high level of genetic heterogeneity that exists within malignant tumors acts as a limiting factor to the effectiveness of traditional treatment methods. Gene editing technologies, for example, CRISPR, present new typical pathways for the purpose of precise intervention. And the efficient and safe application of these technologies depends on the situation where massive omics data is systematically analyzed by making use of bioinformatics. Bioinformatics, which serves as the core link between tumor data and precise editing, permeates the entire process from the discovery of targets and the design of protocols to the evaluation of functions. This review places its focus on the role of bioinformatics in two key stages, namely the stage before editing and the stage after editing. In the stage before editing, in the process of integrating database resources such as TCGA and DepMap and combining them with algorithms such as synthetic lethality prediction, key targets are screened. Tools such as CRISPick are used to design gRNAs, and potential off - target risks are assessed. In the stage after editing, in the process of analyzing CRISPR screening data using workflows such as MAGeCK, functional genes are identified, and the editing efficiency and genome - wide off - target effects are assessed. Overall, bioinformatics offers an integrated solution that ranges from the discovery of targets and the design of tools to the assessment of risks and the analysis of efficacy. This significantly enhances the accuracy and efficiency of tumor gene editing research. In the future, in the situation where single - cell, spatial multi - omics, and deep learning models are in an integrated development state, bioinformatics will further speed up the advancement of tumor gene editing towards precise spatiotemporal regulation and clinical translation.

Pancreatic cancer is a highly malignant digestive system tumor with an extremely poor prognosis. Because of its hidden early symptoms, frequently delayed diagnosis, and high resistance to traditional therapies, it has become one of the most fatal malignant tumors across the whole world, which is in urgent need of new treatment targets and biological markers. Recent research works have demonstrated that the unbalance between long-term inflammation and programmed cell death plays an extremely critical role in pancreatic cancer's occurrence and development (this matches core original content without added info). The inflammatory microenvironment accelerates tumor cell multiplication, invasion, and immune escape via cytokines, chemokines, and immune cell reconstruction; at the same time, abnormalities in multiple cell death paths like apoptosis, necroptosis, ferroptosis provide survival superiority to tumor cells. More importantly, a complex two-way regulatory connection exists between inflammatory signal paths and cell death paths, therefore, hence, they can together push tumor progress through key paths such as NF-κB and JAK/STAT. This paper summarizes relevant research advances about pancreatic cancer, with focus on cellular inflammation, cell death, and their cross interaction mechanisms, and explores possible treatment strategies that take the inflammation-cell death axis as center, providing theoretical reference for precise treatment and targeted interference of pancreatic cancer.

Type 2 diabetes (T2DM) is a very big health problem all over the world. Obesity is the most important cause of T2DM, and more than 80% of people with this disease are overweight or obese. Obesity makes insulin resistance worse, hurts the function of pancreatic β-cells, causes long-term inflammation, and breaks the body's normal balance, and these things make T2DM start and get worse. Weight loss treatment has become a main way to help patients with obesity or overweight and T2DM get better. Today's ways mainly include lifestyle changes, medicine, and weight-loss surgery. These ways work together to help people lose weight and control blood sugar, but they use different methods. This paper carefully looks at how obesity and T2DM are connected, and it clearly explains how exercise for weight loss, Chinese and Western medicine, and weight-loss surgery work, how well they work in patients, and how they are used now. It also points out the problems with today's treatments, and using new research, it gives ideas for the future to help make personal weight-loss plans and promote more research in this field.

Gastric cancer remains a lethal malignancy with limited treatment options for advanced-stage patients. While Claudin18.2-targeting chimeric antigen receptor (CAR) -T therapies show promise, their efficacy is hampered by poor solid tumor infiltration and T cell exhaustion. Considering these limitations, chimeric antigen receptor-macrophage (CAR-M) therapy may offer an alternative. In this study, we propose to develop an engineered CAR-M targeting the gastric cancer-specific molecule Claudin18.2 (CLDN18.2), with an FcRIIA transmembrane domain and modular intracellular domains incorporating SIRPα silencing (shSIRPα) and IFN-γ receptor (IFNGR) overexpression to boost antitumor responses. By introducing CARs into human induced pluripotent stem cells (iPSCs), we anticipate that shSIRPα could effectively disrupt the CD47-SIRPα "don't eat me" signal and enhance phagocytosis, while IFNGR upregulation could activate NF-κB signaling and M1 polarization, increasing pro-inflammatory cytokine secretion and antigen presentation. In vivo results from genetically engineered mice models (GEMM) are also expected to reveal improved tumor suppression, prolonged survival, and enhanced T cell infiltration. Mechanistically, we aim to verify that sustained NF-κB/p65 activation is the key driver of M1 polarization, though additional pathways (IRF5, STAT1) may also contribute. To conclude, we highlight here a next-generation CAR-M strategy with improved tumor targeting, immune modulation, and translational potential for gastric cancer treatment, offering evidence and insights for future allogeneic "off-the-shelf" CAR-M manufacturing.

Metformin, a first-line drug for type 2 diabetes, has been increasingly linked to delayed aging and improved cognitive functions. Circadian rhythm disorders are closely linked to cognitive decline and microglia-driven neuroinflammation, especially in neurodegenerative diseases like Alzheimer's disease (AD). This article reviews how metformin regulates the expression of core clock genes by activating the AMPK signaling pathway, thereby reshaping circadian rhythms and potentially restoring microglial rhythmic activity. This paper proposes the systematic axis of "Metformin–Circadian Rhythms–Cognition" to provide a new perspective for interpreting its neuroprotective effects and to call for experimental studies to directly verify the relationships among the three. If this chain is verified, it will facilitate a shift in treatment strategies for cognitive aging and neurodegenerative diseases.

Granuloma annulare (GA) is a benign, non-infectious granulomatous skin disorder of unknown etiology, typically presenting as annular or papular lesions. This review analyzes 15 English-language case reports published between 2014 and 2024, retrieved from PubMed, to summarize clinical features, histopathology, associated conditions, treatment responses, and outcomes. The series included 13 females and 2 males, aged 5–69 years (median 48.5 years), with 11 generalized, 3 localized, and 1 subcutaneous case. Lesions appeared as erythematous to skin-colored papules, plaques, or nodules; generalized forms showed widespread distribution, while localized cases were more confined. Histopathology consistently demonstrated palisading or interstitial granulomas, collagen degeneration, and mucin deposition in the dermis. Potential triggers included vaccinations (pneumococcal, SARS-CoV-2), infections, and trauma; comorbidities such as diabetes, dyslipidemia, hypertension, breast cancer, and autoimmune diseases were noted in several patients. Localized GA often resolved spontaneously or with topical/intralesional steroids within months and carried a better prognosis, whereas generalized cases frequently required phototherapy or systemic agents (e.g., dapsone, hydroxychloroquine, adalimumab) with variable success. Most patients improved or cleared, either spontaneously or with treatment.

Articular cartilage degeneration and focal defects contribute to chronic pain and functional limitation, yet durable regeneration remains challenging because cartilage is avascular and has low cellularity. Injectable hydrogels adaptively fill defects, which can provide an extracellular matrix microenvironment and deliver therapeutic signals. This review surveys recent injectable hydrogel technologies for articular cartilage regeneration, emphasizing how network formation mechanisms govern retention and early function at the joint. Three representative strategies are discussed, including in situ gelling systems where precursor is injected and solidified on demand, shear thinning and self-healing hydrogels that flow under shear via reversible interactions and recover after injection, and injectable hydrogel microparticles where building blocks assemble into porous scaffolds. The results reveal that successful injectable hydrogels are a balance among operability, spatiotemporal control of network formation, early structural integrity, and tissue interaction.