Environmental pollutants such as microplastics, pesticides, and heavy metals are emerging as critical determinants of gut microbiota composition and function. This review synthesizes current evidence from animal models, human studies, and mechanistic investigations to delineate how these contaminants disrupt gut microecology and compromise host health. Across pollutant classes, common pathogenic features include induction of dysbiosis, characterized by depletion of beneficial commensals and enrichment of pro-inflammatory taxa; impairment of intestinal barrier integrity, facilitating translocation of microbial metabolites into systemic circulation; and activation of innate immune signaling pathways. Notably, all three pollutant categories converge on the LPS/TLR4/NF-κB pathway, driving the release of pro-inflammatory cytokines such as TNF-α and IL-6, thereby promoting chronic inflammation and metabolic dysfunction. While animal studies provide robust mechanistic insights, human evidence remains limited, with few large-scale longitudinal cohorts. Future research should prioritize multi-omics, physiologically relevant models, and microbiota-targeted interventions to clarify causal pathways and mitigate pollutant toxicity.

By enabling precise quantification, cell image analysis advances understanding of cellular processes and structures. However, traditional methods have limitations in terms of accuracy and efficiency for dense target segmentation, continuous trajectory recognition, and large-scale data processing. Thus, this paper examines deep learning approaches for cell recognition and tracking, highlighting advancements in tracking across frames, automatic feature extraction, model adaptability, and their effectiveness to handle diverse and complex cellular environments. Through the review of relevant literature, including convolutional neural networks (CNNs), Mask R-CNN, HOG-SVM, as well as transfer learning methods, the potential applications in real-time processing, multimodal fusion, and high-throughput analysis are discussed. The results demonstrate that deep learning techniques enable precise segmentation, stable cross-frame tracking, and strong feature extraction in complex, dense cellular environments. Unlike traditional algorithms, deep learning methods notably reduce segmentation errors and tracking interruptions, all while maintaining solid generalization with minimal labeled data.

Breast cancer is one of the most common cancers among Chinese women, involving various mechanisms such as gene mutations, hormone stimulation, and abnormal signaling pathways. This paper focuses on HER2 positive breast cancer and discusses the use of the chemotherapy drug paclitaxel and the monoclonal antibody trastuzumab. Paclitaxel is a broad-spectrum anti-cancer drug that primarily acts by targeting microtubules and has a unique nano-delivery system. Trastuzumab is one of the preferred drugs for treating HER2 positive breast cancer and specifically binds to HER2 to inhibit tumor cell growth. The combination of paclitaxel and trastuzumab can enhance the efficacy against HER2-positive breast cancer to a certain extent, but further research is needed. Additionally, the resistance to paclitaxel and the cardiotoxicity of trastuzumab are discussed in the paper.

Flexible electronic sensors, due to their mechanical compliance with soft tissues and excellent biocompatibility, have demonstrated unique advantages in neuroscience research and clinical applications in recent years. Compared with traditional rigid electrodes, flexible sensors show greater potential in long-term stability, large-area coverage, and multimodal integration, thereby meeting the diverse needs of neural signal acquisition and functional monitoring. This review summarizes the latest advances in the application of flexible electronics for brain monitoring, covering electrophysiological signal acquisition, neurotransmitter detection, multimodal and region-synchronized recording, and clinical rehabilitation applications. It further discusses critical aspects influencing performance and applications, including material selection, device design, circuit integration, energy supply, and mechanical modeling. On this basis, the challenges and prospects regarding long-term stability, data processing, multimodal integration, and clinical translation are analyzed. Overall, flexible brain sensors are gradually progressing from laboratory validation to systematic applications, and their development is expected to exert profound impacts on neuroscience research, disease diagnosis and treatment, and intelligent rehabilitation in the future.

This paper reviews the neural mechanism of delayed discounting, aiming to sort out the decision-making laws of individuals in instant reward and delayed reward selection and their brain base. In particular, it explores the involvement of the prefrontal cortex and the septum nucleus in delayed discounting, the relationship between neural connectivity and decision-making preferences, as well as the possible effects of neurostimulation on behavior. Existing studies have leveraged behavioral modeling, brain function imaging, and causal intervention methods to quantify individual preferences using the hyperbolic discounting model and discounting rate, while combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to elucidate the functional connectivity and temporal dynamics of the prefrontal cortex (PFC) and the septum nucleus in decision-making. At the same time, transcranial magnetic stimulation (TMS) is employed to investigate the effects of neural modulation on discounting rates. The results demonstrate that the prefrontal cortex promotes rational decision-making, while the septum nucleus boosts the immediate reward attractiveness, so the interaction between the two significantly affects the selection behavior. Besides, individuals with strong functional connections are more inclined to choose delayed rewards, and the EEG study reveals the timing characteristics of impulse and rational switching in decision-making. In addition, TMS interventions can modulate discounting rates, indicating that delayed discounting is amenable to change. However, most studies use college student samples and short intervention periods, highlighting the need for further research and improved behavioral interventions.

This study explores the psychosocial and lifestyle factors influencing perceived biological aging among middle-aged Chinese women. A total of 39 participants aged 40 and above completed a 46-item questionnaire assessing health status, stress, emotional support, diet, physical activity, and aging perception. Participants were categorized into low, moderate, or high aging groups based on self-reported indicators. Women in the low-aging group reported greater emotional and partner support, lower family-related stress, healthier diets, and more frequent self-care and physical activity. In contrast, high-aging participants experienced greater stress, digestive discomfort, sedentary habits, and exposure to secondhand smoke and alcohol. Interestingly, intermittent fasting was most common in the moderate-aging group. These findings underscore the significant role of psychosocial resilience and modifiable lifestyle behaviors in shaping aging perceptions. The study advocates for holistic, culturally sensitive interventions to support healthy aging in women, particularly those navigating caregiving, career, and health demands during midlife.

Proteins play a central role in regulating diverse cellular functions but remain stable only under specific physiological conditions. Even minor environmental changes can severely compromise their conformational stability, often leading to aggregation. Under normal circumstances, the cell’s quality control mechanisms efficiently remove or degrade these aggregated proteins. However, under pathological conditions, these protective systems may become overwhelmed or impaired, resulting in cellular toxicity. The misfolding of specific proteins and the formation of abnormal aggregates ultimately result in protein aggregation diseases, which share highly conserved molecular pathways in their pathogenesis. This review synthesizes the common molecular mechanisms underlying these diseases, with a particular focus on Alzheimer’s disease (AD) as a model system. By examining the specific pathology of AD, including the aggregation of amyloid β and Tau proteins, the review underscores AD’s dual role as both a primary example and a gateway for understanding related disorders. Furthermore, it outlines current clinical strategies and therapeutic approaches, emphasizing both universal and targeted interventions applicable to this class of diseases. By integrating multidisciplinary perspectives, this review aims to provide foundational insights and scientific reference points for future research and the development of more effective clinical therapies. The overarching goal is to deepen our understanding of protein aggregation diseases and to identify potential breakthroughs that could benefit a wide range of neurodegenerative disorders.

This study examines how material selection governs the design, performance, and clinical translation of nanocarrier systems for anticancer drug delivery. By comparatively analyzing organic (e.g., liposomes, polymeric micelles, protein-based carriers) and inorganic platforms (e.g., gold nanoparticles, mesoporous silica, carbon-based materials), we delineate how biocompatibility, drug‐loading mechanisms, surface chemistry, and stimuli responsiveness shape pharmacokinetics, tumor accumulation, and release profiles. We highlight design rules that connect physicochemical parameters—size, charge, morphology, and ligand density—to biological outcomes such as enhanced permeability and retention, receptor-mediated uptake, and intracellular trafficking. Beyond single-material systems, we evaluate hybrid and core–shell architectures that integrate complementary strengths (biodegradability, structural robustness, imaging/theranostic capability) to enable controlled, site-specific delivery and real-time monitoring. Translational considerations—including scalable synthesis, Good Manufacturing Practice readiness, batch-to-batch quality attributes, and safety/clearance pathways—are discussed as co-equal constraints with efficacy. This paper maps these considerations to clinical use-cases, noting where liposomes remain the regulatory benchmark, polymers offer programmable targeting and release, and inorganic or hybrid constructs unlock multifunctional therapies (photothermal, photoacoustic, or immuno-combination regimens). Finally, the author outlines a decision framework that aligns tumor biology (biomarkers, microenvironment, prior resistance) with nanocarrier typology to support personalized medicine. Collectively, the analysis provides practical guidance for matching material classes to therapeutic objectives, accelerating the trajectory from bench formulation to bedside impact in oncology.

Microplastics (MPs), defined as plastic fragments smaller than 5 mm, have become ubiquitous environmental contaminants. Their pervasive presence across terrestrial, aquatic, and atmospheric systems has led to increasing human exposure. Recent evidence indicates MPs are present in human blood, respiratory tissues, placental structures, and gastrointestinal waste, raising significant concerns about their potential toxicological implications. This paper synthesizes recent advances in microplastic toxicology, focusing on human exposure pathways, tissue distribution, experimental toxicological evidence, and critical knowledge gaps. It aims to provide a coherent risk assessment framework for understanding the health consequences of microplastic exposure, highlighting the imperative for standardized methodologies and long-term epidemiological studies.

Breast cancer exhibits biological heterogeneity, with prognosis significantly influenced by molecular subtypes, genetic alterations, and treatment types. Through further research, two types of translational biomarkers applicable within breast cancer patients have emerged: 1) pre-treatment tissue-based transcriptomic genes that encode intrinsic tumor biology and 2) post-treatment blood-based MRD markers (ctDNA) that capture residual systemic risk. This review establishes logical connections between these two categories, detailing the process of constructing pathways from treatment plan customization to prognostic follow-up assessment. We used literature analysis and comparative methods to extract key points from three research approaches, including data sources, analytical methods, and conclusions, then synthesized and connected them: a survival ranking of significantly associated genes within chemotherapy-treated ER+/HER2- and basal groups, a multivariable prognostic model constructed based on genes in TCGA dataset and a meta-analysis on ctDNA. We identified gaps between studies, how their findings complement each other, and ultimately provided critical progress toward realizing an implementable, end-to-end clinical treatment pathway: Early decision-making based on prognostic biomarkers, followed by ctDNA-guided dynamic risk assessment.