Recent advances in mRNA cancer vaccines: meeting challenges and embracing opportunities

Since the successful application of messenger RNA (mRNA) vaccines in preventing COVID-19, researchers have been striving to develop mRNA vaccines for clinical use, including those exploited for anti-tumor therapy. mRNA cancer vaccines have emerged as a promising novel approach to cancer immunotherapy, offering high specificity, better efficacy, and fewer side effects compared to traditional treatments. Multiple therapeutic mRNA cancer vaccines are being evaluated in preclinical and clinical trials, with promising early-phase results. However, the development of these vaccines faces various challenges, such as tumor heterogeneity, an immunosuppressive tumor microenvironment, and practical obstacles like vaccine administration methods and evaluation systems for clinical application. To address these challenges, we highlight recent advances from preclinical studies and clinical trials that provide insight into identifying obstacles associated with mRNA cancer vaccines and discuss potential strategies to overcome them. In the future, it is crucial to approach the development of mRNA cancer vaccines with caution and diligence while promoting innovation to overcome existing barriers. A delicate balance between opportunities and challenges will help guide the progress of this promising field towards its full potential

Spatiotemporal evolution and drivers of carbon inequalities in urban agglomeration:An MLD-IDA inequality indicator decomposition

Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China’s Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future

Query translation from XPath to SQL in the presence of recursive DTDs

We study the problem of evaluating xpath queries over xml data that is stored in an rdbms via schema-based shredding. The interaction between recursion (descendants-axis) in xpath queries and recursion in dtds makes it challenging to answer xpath queries using rdbms. We present a new approach to translating xpath queries into sql queries based on a notion of extended XP ath expressions and a simple least fixpoint (lfp) operator. Extended xpath expressions are a mild extension of xpath, and the lfp operator takes a single input relation and is already supported by most commercial rdbms. We show that extended xpath expressions are capable of capturing both dtd recursion and xpath queries in a uniform framework. Furthermore, they can be translated into an equivalent sequence of sql queries with the lfp operator. We present algorithms for rewriting xpath queries over a (possibly recursive) dtd into extended xpath expressions and for translating extended xpath expressions to sql queries, as well as optimization techniques. The novelty of our approach consists in its capability to answer a large class of xpath queries by means of only low-end rdbms features already available in most rdbms, as well as its flexibility to accommodate existing relational query optimization techniques. In addition, these translation algorithms provide a solution to query answering for certain (possibly recursive) xml views of xml data. Our experimental results verify the effectiveness of our techniques. © 2009 Springer-Verlag

Postoperative ctDNA detection predicts relapse but has limited effects in guiding adjuvant therapy in resectable stage I NSCLC

BackgroundTo date, identifying resectable stage I non-small cell lung cancer (NSCLC) patients likely to benefit from adjuvant therapy (ADT) remains a major challenge. Previous studies suggest that circulating tumor DNA (ctDNA) is emerging as a promising biomarker for NSCLC. However, the effectiveness of ctDNA detection in guiding ADT for resectable stage I NSCLC patients remains elusive. This study aimed to elucidate the role of ctDNA detection in estimating prognosis and guiding ADT for resectable stage I NSCLC patients.MethodsIndividual patient data and ctDNA results data were collected from 270 patients across four independent cohorts. The detection of ctDNA was conducted at 3 days to 1 month after surgery. The endpoint for this study was relapse-free survival (RFS) and overall survival (OS).ResultsOf the 270 resectable stage I NSCLC patients, 9 patients with ctDNA-positive and 261 patients with ctDNA-negative. We found that the risk of recurrence was significantly lower in the ctDNA-negative group compared to the ctDNA-positive group(HR=0.11, p<0.0001). However, there is no difference in the risk of death between the two groups (p =0.39). In the ctDNA-positive group, there were no significant differences in RFS between patients who received ADT and patients who did not receive ADT (p =0.58). In the ctDNA-negative group, those who received ADT had a worse RFS in comparison with those who did not receive ADT (HR=2.36, p =0.029). No difference in OS was seen between patients who received ADT and patients who did not receive ADT in both the ctDNA-positive group and the ctDNA-negative group (All p values>0.05). Furthermore, there was no difference in RFS and OS between patients who received chemotherapy-based or tyrosine kinase inhibitor-based ADT and patients who did not receive ADT in both the ctDNA-positive group and the ctDNA-negative group (All p values>0.05).ConclusionsPostoperative ctDNA detection can be a prognostic marker to predict recurrence but has limited effects in guiding ADT for resectable stage I NSCLC. Future prospective investigations are needed to verify these results

A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis

Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a “massive univariate” approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian computation technique, rather than variational Bayes. To identify regions of activation, we utilize an excursions set method based on the joint posterior distribution of the latent fields, rather than the marginal distribution at each location. Finally, we propose the first multi-subject spatial Bayesian modeling approach, which addresses a major gap in the existing literature. The methods are very computationally advantageous and are validated through simulation studies and two task fMRI studies from the Human Connectome Project. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement

Porous Wood Monoliths Decorated with Platinum Nano-Urchins as Catalysts for Underwater Micro-Vehicle Propulsion via H2O2 Decomposition

Porous carbon is becoming an important and promising high-surface area scaffold material for various energy-based applications including catalysis. Here we demonstrate the growth of urchin-like platinum nanoparticles (PtNPs) on carbon monoliths derived from basswood that work as catalysts for micro underwater vehicle (MUV) propulsion via H2O2 decomposition. The carbon monoliths were constructed of natural basswood that was carbonized in argon (Ar) and subjected to a subsequent CO2 activation process that rendered the material into a hardened 3D porous activated carbonized wood (ACW) with inner channel voids measuring 10-70 μm in diameter. The PtNP nanourchins (500 nm or less in total diameter, with individual nanospikes measuring 3-5 nm in diameter) form on the ACW via a facile electroless and template-free chemical deposition approach that utilized the reduction of chloroplatinic acid. The developed PtNP-ACW hybrid material exhibited higher catalytic efficiency as compared to previously reported platinum (Pt) catalysts with a low activation energy of 18.9 ± 2.5 kJ mol-1 for H2O2 decomposition. The catalyst also proved useful in an important energy application by its ability to rapidly decompose H2O2 fuel and generate O2 gas for propulsion of a 3D printed MUV prototype. The PtNP-ACW catalysts weighing only 0.14 g generated a propulsion thrust of 230 mN, which is sufficient to power MUVs. The natural wood derived carbon scaffolds significantly reduce the overall cost as compared to other carbon-based catalysts such as carbon nanotubes or graphene without reducing catalytic efficiency. Hence such catalysts act as a stepping stone for potential low cost and sustainable power for burst thrust operation of MUVs