Bioscience trends最新文献

The prognostic significance of preoperative platelet counts among patients with hepatocellular carcinoma (HCC) undergoing curative resection remains controversial. The objective of the current study was to investigate the impact of preoperative platelet count on long-term outcomes after HCC resection. Patients who underwent curative-intent resection for HCC between 2000 and 2021 at 10 hepatobiliary centers in China were retrospectively analyzed. Patients were categorized based on platelet count within 2 weeks before surgery: thrombocytopenia (< 100 × 10⁹/L), normal platelet count (100-299 × 10⁹/L), and thrombocytosis (≥ 300 × 10⁹/L). The primary outcomes were overall survival (OS) and recurrence-free survival (RFS). Among 3,116 patients, 655 (21.0%) had thrombocytopenia, 2,374 (76.2%) had normal platelet counts, and 87 (2.8%) had thrombocytosis. The 5-year OS was 52.7%, 56.0%, and 40.2% for thrombocytopenia, normal platelet count, and thrombocytosis groups, respectively (p < 0.001 among the three groups); the corresponding 5-year RFS was 39.3%, 39.3%, and 26.9%, respectively (p = 0.001 among the three groups). Multivariable analysis identified both thrombocytopenia (HR 1.215, 95% CI 1.045-1.413, p = 0.011) and thrombocytosis (HR 1.307, 95% CI 1.130-1.511, p < 0.001) as independent risk factors for worse OS, and thrombocytosis was independently associated with worse RFS (HR 1.523, 95% CI 1.196-1.939, p = 0.001). Both thrombocytopenia and thrombocytosis were associated with worse survival after HCC resection, with thrombocytosis also predicting higher risk of recurrence. Routine preoperative platelet count may serve as a valuable and practical prognostic marker for risk stratification among patients with HCC undergoing resection.

Acquired immunodeficiency syndrome (AIDS)/human immunodeficiency virus (HIV) patients experience significant increase in their survival and decline in the mortality with the advent of antiretroviral therapy (ART). Nonetheless, ART alone still cannot completely cure AIDS/HIV patients. Furthermore, the virus remains latent in resting CD4+T cells for extended periods, posing a continuous threat to AIDS/HIV patients. Immune checkpoint blockades (ICBs), as a promising immunotherapy, inaugurate new pathways for AIDS/HIV cure or remission given their capability to break down the latency limit of HIV, and promote the regeneration and activation of HIV-specific T cells. However, not all AIDS/HIV patients respond to immune checkpoint inhibitors (ICIs), similar to that encountered in cancer patients, accompanied by the risk of severe immune-related adverse events (irAEs) in some cases. Accordingly, the present study was conducted to explore the possibility of personalized medicine tailored to the host discrepancy, with purposes of achieving better treatment outcomes, higher objective response rates, and fewer irAEs. Strategies for ICIs based on individual differences are documented to be conducive to improving therapeutic outcomes for patients. Therefore, this study intended to improving the therapeutic efficacy of ICIs in AIDS/HIV patients within the context of precision immunotherapy, including monotherapy and combination strategies, as well as the application of predictive biomarkers.

Indocyanine green (ICG)-C9, a novel cyanine dye developed by the Center for Biosystems Dynamics Research at RIKEN, provides significant advantages over conventional ICG due to its detectability via shortwave-infrared (SWIR) fluorescence imaging. Unlike standard ICG, ICG-C9 facilitates SWIR imaging and displays therapeutic potential when conjugated with antibodies in vivo, suggesting broader applicability across various cancer types. This study evaluated the efficacy of SWIR fluorescence imaging with ICG-C9 in comparison with existing near-infrared (NIR) imaging techniques. We assessed excretion kinetics and the relationship between excitation and fluorescence wavelengths for ICG-C9 and ICG following intravenous administration in BALB/c-nu mice. Tumor uptake was evaluated using a cell-line-derived subcutaneous tumor model from HuH-7 cells, representing hepatocellular carcinoma. Variables including dose, administration route, and exposure time were optimized for comparison. Maximum fluorescence intensity for ICG-C9 was observed with an excitation wavelength of 915 nm and fluorescence emission wavelengths >950 nm within the SWIR spectrum. Both ICG-C9 and ICG followed similar excretion pathways, involving hepatic uptake and biliary excretion. Tumor uptake of ICG-C9 was confirmed under similar conditions to ICG. ICG-C9 demonstrates promising potential as an alternative to NIR fluorescence imaging with ICG, offering unique properties that may enhance imaging capabilities. However, further research is required to establish its clinical applicability and broader therapeutic utility.

Hepatocellular carcinoma (HCC) risk factors and incidence vary globally, but men generally have higher incidence than women. Men also tend to have a worse prognosis in terms of survival period and pathological characteristics. Furthermore, there are notable gender differences in treatment strategies and drug responses. While traditional risk factors such as hepatitis B virus, hepatitis C virus, alcohol consumption, and metabolic syndrome contribute to these differences, the underlying molecular mechanisms remain partly understood. Recent research has focused on elucidating the roles of sex hormones, DNA damage and repair pathways, immune microenvironments, and genetic/epigenetic factors in driving gender-specific disparities. For instance, estrogen receptor signaling has been shown to suppress HCC progression, whereas androgen receptor signaling promotes tumor development. Additionally, immune cells such as tumor-associated macrophages and regulatory T cells exhibit gender-specific patterns, with males typically showing higher levels of immunosuppressive cells. Omics analyses, including genomics, transcriptomics, and proteomics, have further revealed sex-specific differences in gene expression, protein interactions, and metabolic pathways. Despite these advances, significant gaps remain in understanding the interplay between environmental, hormonal, and genetic factors in shaping gender disparities in HCC. Future research should prioritize the identification of novel molecular targets, the development of gender-specific therapeutic strategies, and the integration of multi-omics data to address these disparities. Addressing these challenges will be critical for improving diagnostic, prognostic, and therapeutic outcomes in HCC patients of both sexes.

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.