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人工智能对科学文献的影响
图 1 。PubMed 文章中“delve”和“underscore”同时出现。该图显示，在 1849 年至 2024 年期间“delve”和“underscore”同时出现的文章中，2023-2024 年期间 1,299 篇 PubMed 文章中有 1,284 篇（98.8%）同时出现“delve”和“underscore”，突显出这两年期间它们的共现率显著增加。
t独立研究人员b解决医疗保健
传统的医疗保健系统具有Grapp和次优结果。However, the em towards value-based treatment, empowerin healthcare equipment and devices now i providing a rich resource for ML-driven p contemporary healthcare, emphasizing its p study presents a robust predictive model various parameters, leveraging extensive algorithms, including Logistic Regression Boost (accuracy: 0.78125), and PyTorch (a accuracies underscore the effectiveness of t patient outcomes.超出了ML的技术ASPE，可用于各种医疗保健利益相关者。我们的发现说明了实质性的潜在系统。在医疗机构生态系统中采用ML，以支持复杂的医疗需求的可持续性。本文SIG实验阶段，展示了ACH解决了以前提供的研究医疗创新知识的局限性。
The impact of the Russia-Ukraine War on the U.S. Economy
The Russia-Ukraine war, which began in February 2022, has significantly impacted the global economic landscape, with profound effects on the United States economy. This paper provides a comprehensive analysis of these impacts [3]. The study highlights how the conflict has disrupted supply chains, escalated inflation, and influenced U.S. monetary and fiscal policies. Key findings indicate that higher energy and food prices have accelerated inflation, leading the Federal Reserve to increase interest rates and implement quantitative tightening measures [12]. Concurrently, the U.S. government has responded with substantial fiscal measures, including increased defence spending, energy sector support, and economic aid packages [17]. The paper also discusses the broader implications of the war on global trade, supply chains, and geopolitical alliances. By examining the interplay between these factors, the study provides insights into the policy measures needed to mitigate the adverse effects of the conflict and enhance economic resilience. The findings underscore the importance of adaptive strategies and international cooperation in navigating the complex economic challenges posed by the Russia-Ukraine war.
Cascade Residual Multiscale Convolution and Mamba- Structured UNet for Advanced Brain Tumor Image Segmentation
Abstract: In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model's potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.
美国口腔健康：进步与挑战，美国国立卫生研究院的报告 - 第 1 部分：口腔健康对社区、整体福祉和经济的影响
自然灾害、新冠肺炎等新型病原体的出现以及其他大规模紧急情况凸显了公私合作伙伴关系的必要性，以规划并确保在危机时期继续提供基本口腔保健服务。
利用晚期黑色素瘤中的ctDNA：一种有前途的知情临床决策工具
摘要：皮肤黑色素瘤是一种侵略性恶性肿瘤，在引入免疫检查点抑制剂（ICI）和靶向疗法的情况下，在临床管理中发生了重大转化。当前的监测方法，例如成像扫描，当前局限性，促使探索替代生物标志物。本评论全面探讨了循环肿瘤DNA（CTDNA）在晚期黑色素瘤中的作用，涵盖了技术方面，检测方法及其预后和预测价值。最新发现强调了CTDNA在临床实践中的潜在应用和影响。本综述强调了在黑色素瘤护理中需要精确和动态生物标志物的必要性，将ctDNA定位为一种有前途的血液基于预后，治疗反应和抗药性机制的有希望的血液工具。CTDNA检测，与黑色素瘤突变的关联以及其在指导免疫疗法的治疗决策和靶向治疗方面的作用强调了其多面效用，这标志着临床决策的范式转移，并为您提供了有希望的个性化和知情的型号护理型，这标志着其多面效用。