Volume 131

Inflammatory bowel disease (IBD) is a global health concern. Existing therapies, including anti-inflammatory drugs and biologics, often suffer from limitations such as insufficient targeting and high cost. In this context, nanoformulations have shown significant potential due to their advantages in drug protection, targeted delivery, and multifunctional synergy. This article reviews the latest progress in the treatment of IBD with nanoparticles. It categorizes nanodelivery systems into two main types: drug delivery systems and plant-related delivery systems. The article also provides examples of each type, detailing their preparation methods, targeted delivery mechanisms, and experimental outcomes. The findings reveal that while studies have confirmed the effectiveness and advantages of nanoformulations for treating IBD, transformation encounters significant challenges. In the future, it is necessary to integrate new technologies to develop more intelligent nano-response systems and accelerate clinical verification, ultimately aiming for precision and universalization of IBD treatment.

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Lung cancer is one of the most deadly effects of tobacco smoking, which continues to be one of the world's top avoidable causes of death. This paper examines the relationship between smoking prevalence and lung cancer incidence from a global public health perspective. By using a literature review approach to summarize and analyze epidemiological studies from several countries and regions, the results show that there is a significant positive correlation between smoking prevalence and lung cancer incidence. Even after taking into account variables such as economic level and educational attainment, smoking remains a major risk factor for lung cancer. The study also suggests that the implementation of comprehensive tobacco control policies (including increasing tobacco tax rates, banning smoking in public places, and health warning labels) could be effective in reducing smoking prevalence and further reducing the incidence of lung cancer cases. This study emphasizes the importance of continuing to promote tobacco control policies and health education globally, and provides strong theoretical support and empirical evidence for future lung cancer prevention and control.

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Marked by clinical and pathological heterogeneity, Castleman disease (CD) is a lymphoproliferative disorder classified into unicentric (UCD) and multicentric (MCD) types. Distinct subtypes of Castleman disease differ in pathogenesis, pathology, and prognosis, yet diagnosis and treatment remain challenging, requiring standardized approaches. This paper explores the classification, pathological characteristics, diagnostic methods, and treatment strategies of CD, with a focus on the clinical and pathological features of different subtypes. Through analysis of recent literature on clinical presentations, imaging, histopathological diagnostic criteria, and therapeutic outcomes, this study provides evidence-based guidance for clinical practice. The results show that CD can be histologically categorized into hyaline vascular (HV), plasmacytic (PC), and mixed variants. Besides, MCD is further subdivided into HHV-8-associated, POEMS syndrome-associated, and idiopathic (iMCD) subtypes. The diagnostic approach combines clinical presentation, imaging results, and pathological evidence while excluding infectious, autoimmune, and malignant disorders. Treatment for UCD primarily involves surgical excision with favorable prognosis, whereas MCD requires tailored regimens including antiviral therapy, immunosuppression, targeted agents, and chemotherapy. Despite their efficacy in achieving quick symptom relief, corticosteroids are predominantly used as ancillary agents due to the constraints imposed by their long-term adverse effects.

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Tooth defects can cause severe pain, infection, and impaired daily functions, significantly affecting the quality of life for patients. Although traditional root canal treatment can prolong the lifespan of a damaged tooth, it cannot fully restore the biological activity and tissue integrity of the teeth. Dental tissue engineering, as a cutting-edge regenerative medical strategy, is of great significance for restoring the natural structure and function of teeth. It aims to achieve the regeneration and repair of dental hard and soft tissues through interdisciplinary approaches. It comprises three key elements: seed cells, scaffold materials, and growth factors. This article reviews the current developments in this field, systematically elaborating on the differentiation potential of seed cells in dental regeneration. It evaluates the performance of various scaffold materials in supporting cell adhesion, proliferation, and mineralization. It introduces the crucial role of growth factors in inducing seed cells to differentiate into cementoblasts and osteoblasts. This article aims to promote the translation of dental tissue engineering into clinical practice, providing a more effective treatment plan for patients with tooth defects.

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Malassezia is a yeast that typically exists as a harmless organism within the host's skin flora. However, under specific microenvironmental conditions, it can transform into a pathogenic form directly linked to seborrheic dermatitis and pityriasis versicolor. These findings highlight the critical need to decipher its phase transition mechanisms, particularly the lipase activation threshold triggering pathogenicity. Within this broader analytical framework, this paper reviews what the evidence appears to reveal regarding the pathogenic factors of Malassezia based on what seems to emerge from existing literature and data. What the analysis tends to support is that the relationship between Malassezia and its human host appears to be complex, seemingly varying by body site, age group, and what appears to be host susceptibility. The pathogenicity of Malassezia is not an inherent attribute but rather a dynamic process triggered by imbalances in the host microenvironment. Future research must employ integrated multi-omics approaches (microbiomics, metabolomics, and immunomics) to dissect strain adaptation mechanisms. This will advance precision intervention strategies targeting host-microbe interactions, thereby facilitating a paradigm shift from "antimicrobial" to "microbiome-preserving" therapeutic approaches.

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The global increase in obesity and incidence rate of type 2 diabetes (T2D) poses a major challenge to the global public health system. Metabolic diseases, such as insulin resistance, chronic inflammation, and glucose metabolism disorders, are the root causes of these two diseases. Functional foods refer to products that have health benefits in addition to core nutritional components, and are promising dietary therapies for preventing and treating metabolic diseases. This review attempts to summarize the scientific evidence for the main dietary components, namely high-protein products, dietary fiber and prebiotics, low glycemic index (GI) components, and phytochemicals. Research has shown that these ingredients can significantly increase satiety, improve insulin sensitivity, balance gut microbiota homeostasis, and alleviate postprandial blood sugar spikes. In addition, this article also delves into technical strategies to enhance the efficacy of functional foods, including ingredient engineering, fermentation process optimization, sensory balance formula design, and advanced packaging technology. These innovative methods are crucial for ensuring product stability and increasing consumer acceptance. Functional foods have enormous development opportunities, but their cultivation still faces many challenges, including insufficient stability of active ingredients, differences in consumer taste preferences, and globally unified regulations. In the future, in-depth research on personalized nutrition methods and active promotion of consumer education are crucial for achieving breakthroughs. By deeply integrating high-tech innovations in nutrition science and food technology, as well as public health promotion measures, functional foods will become a shining star in preventing metabolic diseases worldwide.

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As one of the most common malignant tumors among women worldwide, breast cancer requires early diagnosis and accurate classification to significantly improve patient survival rates. Conventional imaging techniques like mammography, ultrasound, and magnetic resonance imaging (MRI) play a pivotal role in breast cancer screening. Nonetheless, they are constrained by relatively low specificity and a significant dependence on the expertise of medical professionals. In recent years, machine learning and deep learning techniques have provided new approaches for the intelligent diagnosis of breast cancer by extracting high-dimensional features from medical images. This study delves into the pathological aspects, imaging technologies, and the implementation of machine learning algorithms in the context of breast cancer. It conducts a comprehensive review of the diagnostic criteria for non-invasive, early-stage invasive, and fully invasive breast cancers, while also evaluating the strengths and weaknesses of various imaging modalities, including mammography, ultrasound, MRI, and nuclear medicine imaging. The limitations of conventional imaging methods in subtype differentiation are also discussed. Furthermore, by integrating radiomics and deep learning models such as convolutional neural networks (CNN) and random forests, the study evaluates the performance of intelligent diagnostic systems in breast cancer classification. Clinical cases and publicly available datasets were used as data sources. The results show that combining multimodal imaging features with machine learning algorithms significantly improves diagnostic accuracy, achieving an area under the curve (AUC) of 0.922. This research provides theoretical support and technical references for the precise diagnosis and treatment of breast cancer. Future work should focus on enhancing model generalizability and conducting multi-center clinical validation.

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Drug-resistant bacteria refer to microorganisms that possess resistance to one or multiple antibiotics, rendering these medications ineffective in treating infections and significantly increasing treatment difficulty. Between 1990 and 2021, over 1 million deaths annually worldwide were directly attributed to antibiotic resistance, with an average of 4.71 million deaths indirectly associated with it. Therefore, research into the molecular mechanisms of resistant bacteria and alternative treatment methods is urgently needed. Currently identified molecular mechanisms of bacterial resistance are diverse, primarily including: production of antibiotic-inactivating enzymes, modification of drug targets, reduced cell membrane permeability, and upregulation of active efflux pump systems. To address bacterial drug resistance, we have identified various alternative therapies targeting resistant bacteria, such as phage therapy, antimicrobial peptide therapy, gene editing therapy, and nanotechnology-based treatments. This paper will elaborate on the molecular mechanisms of resistant bacteria and alternative treatment strategies, while integrating artificial intelligence and machine learning data models in medical applications to provide more efficient and precise treatment approaches.

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Orofacial clefts (OFCs), encompassing cleft lip, cleft palate, and cleft lip and palate, represent the most common craniofacial congenital anomalies worldwide. Children with OFCs often experience a variety of oral health challenges, including dental anomalies, malocclusion, early childhood caries, periodontal disease, and altered salivary function. These conditions can significantly impact not only oral function but also speech, nutrition, psychosocial well-being, and overall quality of life. This review synthesizes current literature on the prevalence, etiology, and management of oral conditions associated with OFCs. It highlights the importance of early preventive care, the role of multidisciplinary treatment approaches, and the need to assess oral health-related quality of life (OHRQoL) in affected children. Additionally, the review identifies gaps in current research and underscores the necessity for integrated, evidence-based care strategies tailored to the unique needs of children with OFCs. By drawing attention to these critical areas, this review aims to support clinical decision-making and inform future public health policies focused on improving long-term oral and systemic outcomes for this vulnerable population.

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This paper explains the importance of chemical precision in neuroscience research and details the selection and optimization of research methodologies. By reviewing and analyzing related papers, this paper firstly explains the importance of chemical precision in neuroscience research and describes in detail the selection and optimization of research methodologies. The importance of chemical precision in neuroscience research is first explained, and the selection and optimization of the research method are described in detail. The study demonstrates that improved chemical precision significantly enhances both the effectiveness and the reliability of neuroscience research, thereby increasing the reproducibility of experimental data and facilitating new scientific discoveries. The paper concludes by summarizing the main contributions of the study while proposing a new approach to neuroscience alongside directions for future research and policy recommendations.

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