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Abstract Magnetic resonance imaging contrast agents can enhance diagnostic precision but often face limitations such as short imaging windows, low tissue specificity, suboptimal contrast enhancement, or potential toxicity, which affect resolution and long-term monitoring. Here, we present a protein contrast agent based on lanmodulin, engineered with a single-point mutation at position 108 from N to D to yield maximum gadolinium binding sites. After loading with Gd3+ ions, the resulting protein complex, LanND-Gd, exhibits efficient renal clearance, high relaxivity, and prolonged renal retention compared to commercial agents. LanND-Gd enables high-performance visualization of whole-body structures and brain vasculature in male mice at a resolution finer than one hundred micrometers. In male ischemia mouse models, LanND-Gd also improves kidney dysfunction monitoring while minimizing risks of neural toxicity or immunogenic reactions. This protein-based contrast agent offers superior image quality, improved biocompatibility, and extended imaging timeframes, promising significant advancements in magnetic resonance-based diagnostics and patient outcomes.

ABSTRACTThe presence of airborne allergenic pollen causes a variety of immune reactions and respiratory diseases, threatening human life in severe cases. Climate change is exacerbating the allergenic pollen-induced health risks and adding a significant economic burden to societies. Despite the pressing threats, vital health-related information is not available to the public to date, and the reshaping of future geographic allergenic pollen patterns remains unknown. To help establish a critical allergenic pollen forecasting capacity, a systematic review was conducted and three promising future directions were identified: (1) resolving heterogeneous urban plant species distribution and phenology using fine-resolution satellite constellations; (2) acquiring ancillary information about allergenic pollen and patient symptoms from emerging geospatial big data, such as social media; (3) deciphering the coupled effect of climate change and urbanization on future geographic patterns and phenology of allergenic species. On this basis, we recommend an optimized workflow that combines real-time pollen monitoring networks with high-resolution vegetation information and weather forecast systems, comprehensively considering the production and diffusion process of pollen to establish advanced prediction models. By focusing on critical knowledge gaps, this review provides much needed insight to propel the allergenic pollen forecasting research and eventually benefit the management of urban public health.

Summary: Background: In order to address the low compliance and dissatisfied specificity of low-dose computed tomography (LDCT), efficient and non-invasive approaches are needed to complement its limitations for lung cancer screening and management. The ASCEND-LUNG study is a prospective two-stage case–control study designed to evaluate the performance of a liquid biopsy-based comprehensive lung cancer screening and post-screening pulmonary nodules management system. Methods: We aimed to develop a comprehensive lung cancer system called Peking University Lung Cancer Screening and Management System (PKU-LCSMS) which comprises a lung cancer screening model to identify specific populations requiring LDCT and an artificial intelligence-aided (AI-aided) pulmonary nodules diagnostic model to classify pulmonary nodules following LDCT. A dataset of 465 participants (216 cancer, 47 benign, 202 non-cancer control) were used for the two models’ development phase. For the lung cancer screening model development, cancer participants were randomly split at a ratio of 1:1 into the train and validation cohorts, and then non-cancer controls were age-matched to the cancer cases in a 1:1 ratio. Similarly, for the AI-aided pulmonary nodules model, cancer and benign participants were also randomly divided at a ratio of 2:1 into the train and validation cohorts. Subsequently, during the model validation phase, sensitivity and specificity were validated using an independent validation cohort consisting of 291 participants (140 cancer, 25 benign, 126 non-cancer control). Prospectively collected blood samples were analyzed for multi-omics including cell-free DNA (cfDNA) methylation, mutation, and serum protein. Computerized tomography (CT) images data was also obtained. Paired tissue samples were additionally analyzed for DNA methylation, DNA mutation, and messenger RNA (mRNA) expression to further explore the potential biological mechanisms. This study is registered with ClinicalTrials.gov, NCT04817046. Findings: Baseline blood samples were evaluated for the whole screening and diagnostic process. The cfDNA methylation-based lung cancer screening model exhibited the highest area under the curve (AUC) of 0.910 (95% CI, 0.869–0.950), followed by the protein model (0.891 [95% CI, 0.845–0.938]) and lastly the mutation model (0.577 [95% CI, 0.482–0.672]). Further, the final screening model, which incorporated cfDNA methylation and protein features, achieved an AUC of 0.963 (95% CI, 0.942–0.984). In the independent validation cohort, the multi-omics screening model showed a sensitivity of 99.2% (95% CI, 0.957–1.000) at a specificity of 56.3% (95% CI, 0.472–0.652). For the AI-aided pulmonary nodules diagnostic model, which incorporated cfDNA methylation and CT images features, it yielded a sensitivity of 81.1% (95% CI, 0.732–0.875), a specificity of 76.0% (95% CI, 0.549–0.906) in the independent validation cohort. Furthermore, four differentially methylated regions (DMRs) were shared in the lung cancer screening model and the AI-aided pulmonary nodules diagnostic model. Interpretation: We developed and validated a liquid biopsy-based comprehensive lung cancer screening and management system called PKU-LCSMS which combined a blood multi-omics based lung cancer screening model incorporating cfDNA methylation and protein features and an AI-aided pulmonary nodules diagnostic model integrating CT images and cfDNA methylation features in sequence to streamline the entire process of lung cancer screening and post-screening pulmonary nodules management. It might provide a promising applicable solution for lung cancer screening and management. Funding: This work was supported by Science, Science, Technology & Innovation Project of Xiongan New Area, Beijing Natural Science Foundation, CAMS Innovation Fund for Medical Sciences (CIFMS), Clinical Medicine Plus X-Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities, Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Peking University People's Hospital Research and Development Funds, National Key Research and Development Program of China, and the fundamental research funds for the central universities.

Shales of the Wufeng-Longmaxi formations in the basin-margin transition zone of southeastern Chongqing, China are characterized by high organic matter content and a significant presence of pyrite development. By examining numerous scanning electron microscope (SEM) images and considering the crystal and aggregate characteristics of minerals, we identified four types of pyrite in the study area: euhedral crystals, irregular aggregates, framboidal aggregates, and metasomatized organisms. Among these types, framboidal aggregates are the most prevalent. The formation mechanism of framboidal pyrite can be categorized into inorganic and organic origins. As inferred from the pyrite characteristics in the study area, the formation mechanism of the metasomatized organisms aligns with the biologically induced mineralization mode of organic origin, whereas the framboidal aggregates are more associated with the biologically controlled mineralization mode of organic origin. This underscores a close relationship between the pyrite formation and organic matter, which in turn indicates that an organic origin is more consistent with the pyrite characteristics observed in this study area. The pyrite morphology can reflect reactive iron concentration. Euhedral pyrite crystals tend to form under a low reactive iron concentration, whereas the formation of framboidal pyrite requires a high reactive iron concentration. Additionally, the type and grain size of pyrite aggregates can reflect variations in the redox conditions of the depositional environment. Pyrite produces positive effects on reservoir storage space, with intercrystalline organic pores, intercrystalline pores, and mold pores associated with pyrite contributing greatly to the storage spaces.

Rehmannia glutinosa is one of the most important medicinal plants in China and is affected by viral diseases. In this study, a new virus tentatively named Rehmannia Allexivirus virus (ReAV) was identified through high-throughput sequencing, reverse-transcription polymerase chain reaction (RT-PCR), and Sanger sequencing. The complete genome length was 7297 nt and it contained five open reading frames (ORFs) encoding replicase, triple gene block 1(TGB1), TGB2, TGB3, and coat protein (CP). The replicase and CP presented nucleotide homology ranges of 59.9–65.2% and 47.5–55.5% between the nine ReAV isolates and the other 12 species of the genus Allexivirus. In the nine isolates, ReAV-20 and ReAV-31 isolates showed breakpoints in the replicase and CP regions, respectively. The other isolates shared 87.2–96.5% nt with the whole genome nucleotide identity. The phylogenetic tree showed that seven ReAV isolates based on replicase, CP, and whole genome sequences were clustered in the same branch and were related to the genus Allexivirus. The ReAV detection rates for 60 R. glutinosa samples were 73.3–81.7% through RT-PCR using primers targeting the replicase or CP genes. These results demonstrate that ReAV is the dominant virus in R. glutinosa. This study provides important evidence for understanding viruses infecting R. glutinosa and for establishing efficient strategies to prevent viral spread.

Forecasting the amplitude and timing of the sunspot cycle is highly important for solar physics and space weather applications, but high-precision prediction of solar magnetic activity has remained an outstanding challenge. The Informer model, as the most advanced deep learning technique, is an ideal approach for predicting solar activity cycle. Using the whole-disk sunspot numbers (SSNs) between 1749 and 2023 and the hemispheric SSNs between 1992 and 2023, the amplitudes and timings of Solar Cycles 25 and 26 are predicted by the Informer model. The main results are the following: (1) the activity levels of Solar Cycles 25 and 26 continue being weak-moderate cycles with their strengths stronger than Solar Cycle 24, implying that the long-term solar variability is significantly modulated in length and magnitude by the Gleissberg century cycle; (2) the Gnevyshev peaks of Solar Cycles 25 and 26 are clearly observed with a higher value in the second peak, suggesting that the numbers of the large sunspot groups are greater compared to the small sunspot groups in these two cycles; and (3) during Solar Cycle 25, the activity level in the southern hemisphere is predicted to be stronger than that in the northern one, revealing significant asymmetry and asynchronization between the two hemispheres. Our analysis results show that solar cycle predictions can be made more accurate if performed separately for each hemisphere. Furthermore, Solar Cycles 25 and 26 are likely to be weak-moderate cycles, in agreement with the precursor-based and model-based prediction methods.

Objective: To examine the factors influencing hospital discharge readiness among Chinese patients who have undergone enterostomy., Methods: In this descriptive, cross-sectional study, researchers recruited patients with colorectal cancer who underwent enterostomy at a tertiary hospital in Guangdong Province, China, via convenience sampling between January 2021 and January 2023. Participants completed the Readiness for Hospital Discharge Scale, Ostomy Self-care Ability Scale, and Stoma-Quality of Life-Chinese Questionnaire (Chinese version) at the time of hospital discharge. Univariate, correlation, and multiple linear regression analyses were performed to explore the impact of self-care ability, quality of life, and other clinicodemographic characteristics on patients' readiness for hospital discharge., Results: Of the 200 questionnaires distributed, 177 (88.5%) were completed and included in the final analysis. The median scores for the factors considered in this study were as follows: Readiness for Hospital Discharge Scale was 148.00 (interquartile range [IQR], 117.50, 164.00), self-care intention of the Ostomy Self-care Ability Scale was 36.00 (IQR, 34.00, 40.00), self-care knowledge of the Ostomy Self-care Ability Scale was 17.00 (IQR, 15.00, 19.00), self-care skill of the Ostomy Self-care Ability Scale was 5.00 (IQR, 3.00, 6.00), and the total score for quality of life was 60.00 (IQR, 49.00, 69.00). Multiple linear regression analysis identified several key factors explaining 48.2% of the variance in global readiness for hospital discharge: global quality of life (β = .347, P < .001), self-care knowledge (β = .259, P < .001), leakage during hospitalization (β = -0.241, P < .001), monthly family income (β = .148, P = .008), stoma siting before surgery (β = .130, P = .020), and self-care intention (β = .127, P = .035)., Conclusions: The readiness for hospital discharge among patients undergoing enterostomy in this study was high. Factors such as quality of life, self-care knowledge, leakage during hospitalization, monthly family income, stoma siting before surgery, and self-care intention after undergoing enterostomy influenced the patients' readiness for hospital discharge. Therefore, future studies should focus on developing interventions to enhance patients' readiness for hospital discharge., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)