Bioscience trends最新文献

In the context of the rising global prevalence of obesity, traditional intervention measures have proven insufficient to meet the demands of personalized and sustainable health management, necessitating the exploration of innovative solutions through innovative technologies. This study explores how advanced digital technologies, including Internet of Things (IoT) and Artificial Intelligence (AI), can manage weight and enhance full-lifecycle health in individuals with obesity under simulated high-altitude hypoxic conditions (HC). The findings suggest that integrating simulated HC with digital health technologies offers a novel and safe approach to obesity rehabilitation. By leveraging environmental stimuli, real-time monitoring through wearable devices, and intelligent evaluation using large language models (LLMs), this method enables more scientific weight loss, prevents rebound weight gain, and fosters proactive healthy lifestyles, significantly improving weight control outcomes for individuals with obesity. Future research should evaluate the efficacy of simulated HC in weight management and its long-term impact on obesity control. Establishing an integrated framework that combines simulated HC, lifestyle interventions, and smart health ecosystems is crucial for advancing rehabilitative healthcare and addressing the global burden of obesity through digital innovation.

Obesity has emerged as a global health crisis, imposing substantial burdens on both individual well-being and socioeconomic development. The pathogenesis of obesity primarily stems from disrupted energy homeostasis, wherein the hypothalamus plays a pivotal role through its complex neuropeptide networks that regulate appetite and energy balance. Recent advances have highlighted the therapeutic potential of traditional Chinese medicine (TCM) in modulating hypothalamic appetite regulation. This comprehensive review systematically evaluates current evidence from PubMed and China National Knowledge Infrastructure databases, focusing on the mechanisms by which TCM interventions influence hypothalamic neuropeptide signaling pathways. Our analysis reveals that various TCM modalities, including bioactive compounds (e.g., berberine and, evodiamine), herbal formulations (e.g., Pingwei Powder, Fangji Huangqi Decoction), plant extracts (e.g., Cyclocarya paliurus aqueous extract), and Chinese patent medicines (e.g., Danzhi Jiangtang Capsules and Jingui Shenqi Pills), have significant effects on key appetite-regulating pathways. These effects are mediated through modulation of critical neuropeptide systems, particularly AgRP/NPY and POMC/CART neurons, as well as leptin signaling. These findings not only provide mechanistic insights into TCM's anti-obesity effects but also demonstrate the value of integrating traditional medicine with modern pharmacological approaches. The synergistic potential of TCM formulas, when combined with contemporary research methodologies, offers promising avenues for developing novel therapeutic strategies for obesity and related metabolic disorders.

Breast cancer liver metastasis (BCLM) presents a critical challenge in breast cancer treatment and has substantial epidemiological and clinical significance. Receptor status is pivotal in managing both primary breast cancer and its liver metastases. Moreover, shifts in these statuses can have a profound impact on patient treatment strategies and prognoses. Research has indicated that there is significant heterogeneity in receptor status between primary breast cancer and liver metastases. This variation may be influenced by a multitude of factors, such as therapeutic pressure, inherent tumor heterogeneity, clonal evolution, and the unique microenvironment of the liver. Changes in the receptor status of BCLM are crucial for adjusting treatment strategies, and liver biopsy plays an important role in the treatment process. Directions for future research targeting changes in receptor status include in-depth study of molecular mechanisms, combined treatment strategies for receptor status reversal, development of artificial intelligence deep learning models to predict receptor status in liver metastases, and clinical research on new drug development and combination therapies. That research will provide more precise treatment strategies for patients with BCLM and improve their prognosis.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, neuroinflammation, and endoplasmic reticulum (ER) stress. In recent years, exosomes have garnered significant attention as a potential therapeutic tool for neurodegenerative diseases. This study, for the first time, investigates the neuroprotective effects of exosomes derived from olfactory mucosa mesenchymal stem cells (OM-MSCs-Exos) in AD and further explore the potential role of low-density lipoprotein receptor-related protein 1 (LRP1) in this process. Using an Aβ1-42-induced AD mouse model, we observed that OM-MSCs-Exos significantly improved cognitive function in behavioral tests, reduced neuroinflammatory responses, alleviated ER stress, and decreased neuronal apoptosis. Further analysis revealed that OM-MSCs-Exos exert neuroprotective effects by modulating the activation of microglia and astrocytes and influencing the ER stress response, a process that may involve LRP1. Although these findings support the potential neuroprotective effects of OM-MSCs-Exos, further studies are required to explore their long-term stability, dose dependency, and immunogenicity to assess their feasibility for clinical applications.

The human gut microbiome is increasingly recognized as important to health and disease, influencing immune function, metabolism, mental health, and chronic illnesses. Two widely used, cost-effective, and fast approaches for analyzing gut microbial communities are shallow shotgun metagenomic sequencing (SSMS) and full-length 16S rDNA sequencing. This study compares these methods across 43 stool samples, revealing notable differences in taxonomic and species-level detection. At the genus level, Bacteroides was most abundant in both methods, with Faecalibacterium showing similar trends but Prevotella was more abundant in full-length 16S rDNA. Genera such as Alistipes and Akkermansia were more frequently detected by full-length 16S rDNA, whereas Eubacterium and Roseburia were more prevalent in SSMS. At the species level, Faecalibacterium prausnitzii, a key indicator of gut health, was abundant across both datasets, while Bacteroides vulgatus was more frequently detected by SSMS. Species within Parabacteroides and Bacteroides were primarily detected by 16S rDNA, contrasting with higher SSMS detection of Prevotella copri and Oscillibacter valericigenes. LEfSe analysis identified 18 species (9 species in each method) with significantly different detection between methods, underscoring the impact of methodological choice on microbial diversity and abundance. Differences in classification databases, such as Ribosomal Database Project (RDP) for 16S rDNA and Kraken2 for SSMS, further highlight the influence of database selection on outcomes. These findings emphasize the importance of carefully selecting sequencing methods and bioinformatics tools in microbiome research, as each approach demonstrates unique strengths and limitations in capturing microbial diversity and relative abundances.

Colorectal cancer liver metastasis (CRLM) remains the leading cause of mortality among colorectal cancer (CRC) patients, with more than half eventually developing hepatic metastases. Achieving long-term survival in CRLM necessitates early detection, robust stratification, and precision treatment tailored to individual classifications. These processes encompass critical aspects such as tumor staging, predictive modeling of therapeutic responses, and risk stratification for survival outcomes. The rapid evolution of artificial intelligence (AI) has ushered in unprecedented opportunities to address these challenges, offering transformative potential for clinical oncology. This review summarizes the current methodologies for CRLM grading and classification, alongside a detailed discussion of the machine learning models commonly used in oncology and AI-driven applications. It also highlights recent advances in using AI to refine CRLM subtyping and precision medicine approaches, underscoring the indispensable role of interdisciplinary collaboration between clinical oncology and the computational sciences in driving innovation and improving patient outcomes in metastatic colorectal cancer.

Syphilis, a chronic infection caused by Treponema pallidum, is experiencing a global resurgence, posing significant public health challenges. This study examined the escalating trends of syphilis in the United States, China, and some other countries highlighting the impact of the COVID-19 pandemic, changes in sexual behavior, coinfection with the other infectious diseases such as AIDs, and the role of public health funding. The analysis revealed a stark increase in syphilis cases, particularly among high-risk groups such as men who have sex with men (MSM). China's National Syphilis Control Program (NSCP), initiated in 2010, is a comprehensive approach to syphilis management that incorporates health education, access to testing and treatment, partner notification, safe sex promotion, community interventions, and stigma reduction. The success of the NSCP in reducing early syphilis incidence rates and congenital syphilis in Guangdong Province, that may be served as a model for international syphilis control efforts. This study highlights the necessity for targeted public health interventions and the importance of robust healthcare infrastructure in combating the syphilis epidemic.

This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healthcare problem, often leading to prolonged hospitalization and increased medical costs. Conventional risk assessment scales are limited in their ability to predict PIs accurately, prompting the exploration of ML techniques to address this challenge.We utilized data from 7,292 patients admitted to a tertiary care hospital in Shanghai between May 2017 and July 2023, with a final dataset of 2,972 patients, including 158 with PIs. Seven ML algorithms-Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Extra Trees (ET), K-Nearest Neighbors (KNN), and Decision Trees (DT)-were used in conjunction with SMOTE, SMOTE+ENN, Borderline-SMOTE, ADASYN, and GAN to balance the dataset and improve model performance.Results revealed significant improvements in model performance when SMOTE and its variants were used. For instance, the XGBoost model hadan AUC of 0.996 with SMOTE, compared to 0.800 on raw data. SMOTE+ENN and Borderline-SMOTE further enhanced the models' ability to identify minority classes. External validation indicatedthat XGBoost, RF, and ET exhibited the highest stability and accuracy, with XGBoost having an AUC of 0.977. SHAP analysis revealed that factors such as anesthesia grade, age, and serum albumin levels significantly influenced model predictions.In conclusion, integrating SMOTE with ML algorithms effectively addressed a data imbalance and improved the prediction of perioperative PIs. Future work should focus on refining SMOTE techniques and exploring their application to larger, multi-center datasets to enhance the generalizability of these findings, and especially for diseaseswith a lowincidence.

Immune checkpoint inhibitors (ICIs) have been widely used in various types of cancer, but they have also led to a significant number of adverse events, including ICI-induced immune-mediated hepatitis (IMH). This study aimed to explore the risk factors for IMH in patients treated with ICIs and to develop and validate a new nomogram model to predict the risk of IMH. Detailed information was collected between January 1, 2020, and December 31, 2023. Univariate logistic regression analysis was used to assess the impact of each clinical variable on the occurrence of IMH, followed by stepwise multivariate logistic regression analysis to determine independent risk factors for IMH. A nomogram model was constructed based on the results of the multivariate analysis. The performance of the nomogram model was evaluated via the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) analysis. A total of 216 (8.82%) patients developed IMH. According to stepwise multivariate logistic analysis, hepatic metastasis, the TNM stage, the WBC count, LYM, ALT, TBIL, ALB, GLB, and ADA were identified as risk factors for IMH. The AUC for the nomogram model was 0.817 in the training set and 0.737 in the validation set. The calibration curves, DCA results, and CIC results indicated that the nomogram model had good predictive accuracy and clinical utility. The nomogram model is intuitive and straightforward, making it highly suitable for rapid assessment of the risk of IMH in patients receiving ICI therapy in clinical practice. Implementing this model enables early adoption of preventive and therapeutic strategies, ultimately reducing the likelihood of immune-related adverse events (IRAEs), and especially IMH.

Dermatologic toxicities associated with targeted therapies may impact drug intolerance and predict drug response, among which rash is most frequently reported and well delineated. However, the profile and effect of non-rash dermatologic toxicity are not fully understood. We identified stage-IV non-small cell lung cancer patients diagnosed at Mayo Clinic in 2006-2019 and systematically analyzed demographics, targeted agents, toxicity, response, and survival outcomes of patients who received targeted therapy. Five toxicity subgroups-none, only non-rash dermatologic, concurrent non-rash and rash (concurrent) dermatologic, only rash, and others-were compared; multivariable survival analyses employed Cox Proportional Hazard models. This study included 533 patients who had taken targeted therapies: 36 (6.8%) had no toxicity, 26 (4.9%) only non-rash dermatologic, 193 (36.2%) only rash, 134 (25.1%) concurrent dermatologic, 144 (27.0%) other toxicities. Non-rash dermatologic toxicities predominately included xerosis (12.8%), pruritus (8.5%), paronychia (7.0%). Rash was the most frequent (59.4%) and the earliest occurring (21 median onset days [MOD]) dermatologic toxicity; paronychia was the latest (69 MOD) occurring. In 329 epidermal growth factor receptor inhibitors-treated patients with dermatologic toxicity, mild toxicity occurred the most frequently in patients with only non-rash (81.8%), then those with only rash (64.8%), and the least in the concurrent (50.4%, P=0.013). Patients with concurrent dermatologic toxicities had a significantly higher response rate (67.9%) than those with only non-rash (53.8%) or only rash (41.1%, p < 0.001). Multivariable analysis demonstrated concurrent dermatologic toxicity independently predicted a lower risk of death (harzard ratio [HR] 0.48 [0.30-0.77], p < 0.001). Compared to rash, non-rash dermatologic toxicity might be a stronger predictor of better treatment response and longer survival in patients who received targeted therapy.