Atomic Au3Cu Palisade Interlayer in Core@Shell Nanostructures for Efficient Kirkendall Effect Mediation.

Plasmonic Cu@semiconductor heteronanocrystals (HNCs) have many favorable properties, but the synthesis of solid structures is often hindered by the nanoscale Kirkendall effect. Herein, we present the use of an atomically thin Au3Cu palisade interlayer to reduce lattice mismatch and mediate the Kirkendall effect, enabling the successive topological synthesis of Cu@Au3Cu@Ag, Cu@Au3Cu@Ag2S, and further transformed solid Cu@Au3Cu@CdS core-shell HNCs via cation exchange. The atomically thin and intact Au3Cu palisade interlayer effectively modulates the diffusion kinetics of Cu atoms as demonstrated by experimental and theoretical investigations and simultaneously alleviates the lattice mismatch between Cu and Ag as well as Cu and CdS. The Cu@Au3Cu@CdS HNCs feature exceptional crystallinity and atomically organized heterointerfaces between the plasmonic metal and the semiconductor. This results in the efficient plasmon-induced injection of hot electrons from Cu@Au3Cu into the CdS shell, enabling the Cu@Au3Cu@CdS HNCs to achieve high activity and selectivity for the photocatalytic reduction of CO2 to CO.

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis. METHODS: Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). RESULTS: By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5-8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples. CONCLUSION: This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

Atmospheric Nitrogen Pollution Control Benefits the Coastal Environment.

Nitrogen is an essential nutrient and a major limiting element for the ocean ecosystem. Since the preindustrial era, substantial amounts of nitrogen from terrestrial sources have entered the ocean via rivers, groundwater, and atmospheric deposition. China serves as a key hub in the global nitrogen cycle, but the pathways, sources, and potential mitigation strategies for land-ocean nitrogen transport are unclear. By combining the CHANS, WRF-Chem, and WNF models, we estimated that 8 million tonnes (Tg) of nitrogen was transferred into the ocean in 2017 in China, with atmospheric deposition contributing 1/3. About half variation of the offshore chlorophyll concentration was explained by atmospheric deposition. The Bohai Sea was the hot spot of nitrogen input, estimated at 214 kg N ha-1, while other areas were around 25-51 kg N ha-1. The largest contributors are agricultural systems (4 Tg, 55%), followed by domestic sewage (2 Tg, 21%). Abatement measures could reduce nitrogen export to the ocean by 43%, and mitigating ammonia and nitrogen oxide emissions accounts for 33% of this reduction, highlighting the importance of addressing air pollution in resolving ocean pollution. The cost-benefit analysis suggests the priority of nitrogen reduction in cropland and transport systems for the ocean environment.

Blood Biomarkers Panels for Screening of Colorectal Cancer and Adenoma on a Machine Learning-Assisted Detection Platform.

OBJECTIVE: A mini-invasive and good-compliance program is critical to broaden colorectal cancer (CRC) screening and reduce CRC-related mortality. Blood testing combined with imaging examination has been proved to be feasible on screen for multicancer and guide intervention. The study aims to construct a machine learning-assisted detection platform with available multi-targets for CRC and colorectal adenoma (CRA) screening. METHODS: This was a retrospective study that the blood test data from 204 CRCs, 384 CRAs, and 229 healthy controls was extracted. The classified models were constructed with 4 machine learning (ML) algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), and eXtreme Gradient Boosting (XGB) based on the candidate biomarkers. The importance index was used by SHapely Adaptive exPlanations (SHAP) analysis to identify the dominant characteristics. The performance of classified models was evaluated. The most dominating features from the proposed panel were developed by logistic regression (LR) for identification CRC from control. RESULTS: The candidate biomarkers consisted of 26 multi-targets panel including CEA, AFP, and so on. Among the 4 models, the SVM classifier for CRA yields the best predictive performance (the area under the receiver operating curve, AUC: .925, sensitivity: .904, and specificity: .771). As for CRC classification, the RF model with 26 candidate biomarkers provided the best predictive parameters (AUC: .941, sensitivity: .902, and specificity: .912). Compared with CEA and CA199, the predictive performance was significantly improved. The streamlined model with 6 biomarkers for CRC also obtained a good performance (AUC: .946, sensitivity: .885, and specificity: .913). CONCLUSIONS: The predictive models consisting of 26 multi-targets panel would be used as a non-invasive, economical, and effective risk stratification platform, which was expected to be applied for auxiliary screening of CRA and CRC in clinical practice.

Mitigating air pollution benefits multiple sustainable development goals in China.

Achieving the United nations 2030 Sustainable Development Goals (SDGs) remains a significant challenge, necessitating urgent and prioritized strategies. Among the various challenges, air pollution continues to pose one of the most substantial threats to the SDGs due to its widespread adverse effects on human health and ecosystems. However, the connections between air pollution and the SDGs have often been overlooked. This study reveals that out of the 169 SDG targets, 71 are adversely impacted by air pollution, while only 6 show potential positive effects. In China, two major atmospheric nitrogen pollutants, ammonia and nitrogen oxides, resulted in an economic loss of 400 billion United States Dollar (USD) in 2020, which could be reduced by 33% and 34% by 2030, respectively. It would enhance the progress towards SDGs in China by 14%, directly contributing to the achievement of SDGs 1 to 6 and 11 to 15. This improvement is estimated to yield overall benefits totaling 119 billion USD, exceeded the total implementation cost of 82 billion USD with ammonia as the preferential mitigation target. This study underscores the importance of robust scientific evidence in integrated policies aimed at aligning improvements in environmental quality with the priorities of sustainable development.

The effects of postoperative targeted immunotherapy on peripheral blood cytokines and immune cell profile in lung cancer patients.

Objective: Cytokines and cell subsets are important components of the tumor microenvironment. Previous research has revealed that there are differences in cytokines and cell subsets in the peripheral blood of lung cancer (LCA) patients before and after eradication. The purpose of this study is to explore the monitoring value of cytokines and cellular subpopulations as biomarkers in post-immunotherapy monitoring of patients with LCA after surgery. Methods: We conducted a case-control study using double-antibody sandwich magnetic microsphere flow cytometry with immunofluorescence technology and fluorescent monoclonal antibody multiparameter flow cytometry to detect differences in peripheral blood cytokines and cell subsets between LCA patients after immunotherapy and healthy controls. Results: Our research results show that there are differences in the levels of IL-4, IL-6, IL-10, IL-17, IFN-γ, TNF-α in the peripheral blood of LCA patients (n=70) after immunotherapy compared to the healthy controls (n=55) (P<0.05), and there are differences in 10 cell subgroups including DP T Cells, AT cells, and NLR in the peripheral blood compared to the healthy controls (n=35) (P<0.05). Further analysis revealed significant differences in the detection data of IL-6, IL-10, IFN-γ, CD56dim NK cells, Total B cells, Total NE cells, CD15+M cells, and NLR between LCA deceased patients (n=25) and LCA surviving patients (n=27) during the same period (P<0.05). The continuous monitoring of cytokines and cell subsets is far more valuable than a single-time test, as abnormal fluctuations in the data of cytokines and cell subsets are often associated with poor prognosis. In addition, IL-6 and NLR showed the strongest discriminative ability between postoperative immunotherapy-treated LCA patients and healthy controls, with AUC values of 0.840 and 0.822, respectively. There was a significant association between IFN-γ and distant metastasis in LCA (P<0.05), as well as between CD56dim NK cells and lymph node infiltration (P<0.05). Conclusion: This research results support peripheral blood cytokines and cell subsets as biomarkers for monitoring the postoperative immune status and predicting the prognosis of LCA patients after immunotherapy. The continuous monitoring of cytokines and cell subsets is far more valuable than a single-time detection.

Age-related differences in the intrinsic connectivity of the hippocampus and ventral temporal lobe in autistic individuals.

Introduction: Although memory challenges in autistic individuals have been characterized recently, the functional connectivity of the hippocampus and ventral temporal lobe, two structures important for episodic and semantic memory functions, are poorly understood in autistic individuals. Moreover, age-related differences in the functional connectivity associated with these two memory networks are unrevealed. Methods: The current study investigated age-related differences in intrinsic connectivity of the hippocampal and ventral temporal lobe (vTL) memory networks in well-matched ASD (n = 73; age range: 10.23-55.40 years old) and Non-ASD groups (n = 74; age range: 10.46-56.20 years old) from the open dataset ABIDE-I. Both theory-driven ROI-to-ROI approach and exploratory seed-based whole-brain approach were used. Results and discussion: Our findings revealed reduced connectivity in ASD compared to Non-ASD peers, as well as an age-related reduction in the connectivity of hippocampal and vTL networks with triple networks, namely, the default mode network (DMN), the central executive network (CEN), and the salience network (SN), potentially underpinning their challenges in memory, language, and social functions. However, we did not observe reliable differences in age-related effects between the ASD and Non-ASD groups. Our study underscores the importance of understanding memory network dysfunctions in ASD across the lifespan to inform educational and clinical practices.

Mitigation potential of methane emissions in China's livestock sector can reach one-third by 2030 at low cost.

The mitigation of methane (CH4) emissions from livestock production is crucial to China's carbon neutrality. Here we established a high-spatiotemporal-resolution dataset of the country's livestock CH4 emissions from 1990 to 2020 using four large-scale national livestock greenhouse gas inventory surveys. We estimate CH4 emissions to be 14.1 ± 2.0 Mt in 2020 and to increase by 13% until 2030 despite CH4 intensity per kg animal protein having decreased by 55% in the past 30 years. Approximately half of the emissions come from 13% of all Chinese counties. The technical CH4 mitigation potential is projected to be 36 ± 8% (4.4-6.9 Mt CH4) in 2030, and reducing food loss and waste could mitigate an additional 1.6 Mt of CH4. Overall, most CH4 mitigation could be achieved by increasing animal productivity and coverage of lagoon storage at carbon prices below US$100 tCO2e-1, being more cost-effective than livestock nitrous oxide mitigation in China.

Effect of BMI on cumulative live birth rates in patients that completed IVF treatment: a retrospective cohort study of 16,126 patients.

Although several studies have reported that high maternal BMI could influence the cumulative live birth rate (CLBR) in fresh embryo transfer cycles, the association of BMI with CLBR remains unclear in patients that completed IVF treatment. In this study, we examined the association of maternal BMI with CLBR, including repetitive one oocyte pick-up (OPU) and all fresh and frozen embryo transfer until live birth or embryos were run out. A total of 16,126 patients' data were included in the analysis and were divided into four groups based on BMI. We found that patients' characteristics, embryo parameters, and pregnancy outcomes differed among different BMI groups. Multivariate logistic regression showed that being underweight was associated with a higher possibility of having live birth than the reference group (OR (95% CI) 1.40 (1.22-1.59), P < 0.001), whereas being overweight and obese were associated with a lower possibility of having live birth than the reference group ((OR (95% CI) 0.81 (0.74-0.90), P < 0.001) and (OR (95% CI) 0.68 (0.55-0.85), P < 0.001)). After adjustment for confounding factors, the reference group was associated with a higher possibility of having live birth, with a significant difference found between the obese and reference groups (OR (95% CI) 0.55 (0.43-0.70), P < 0.001). An association was found between CLBR and BMI, indicating that an increase in BMI results in a decline in CLBR. Moreover, the CLBR of patients with different characteristics differed in the various BMI groups. Taken together, our data show that maternal BMI has a significant impact on CLBR.

[Retracted] Fe3O4­solamargine induces apoptosis and inhibits metastasis of pancreatic cancer cells.

Following the publication of the above article, a concerned reader drew to the Editor's attention that certain of the Transwell invasion assay data shown in Fig. 5B on p. 911 were strikingly similar to data that had appeared in a previously published paper written by different authors at a different research institute. In view of the fact that certain of the data in the above article had already appeared in a previously published paper, the Editor of International Journal of Oncology has decided that this paper should be retracted from the publication. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [International Journal of Oncology 54: 905­915, 2019; DOI: 10.3892/ijo.2018.4637].

Agricultural management practices in China enhance nitrogen sustainability and benefit human health.

The potential of enhanced agricultural management practices to drive sustainability is rarely quantified at grassroots level. Here we analyse nitrogen use and loss in Chinese cropland, drawing from data collected in 2,238,550 sites in two national agricultural pollution source censuses from 2007 to 2017. We find an upswing of 10% in crop yields and an 8% reduction in nitrogen pollution during this period, owing to the promotion and adoption of various management practices (including the combination of organic and chemical fertilizers, straw recycling and deep placement of fertilizer). These practices have collectively contributed to an 18% increase in nitrogen use efficiency in the country. By fully embracing them, we project that annual cropland pollution could be further reduced by up to 1.4 Mt of nitrogen without compromising crop yields. Environmental and human health benefits are projected to consistently outweigh implementation costs in the future, with total benefits reaching US$15 billion.

A study of machine learning models for rapid intraoperative diagnosis of thyroid nodules for clinical practice in China.

BACKGROUND: In China, rapid intraoperative diagnosis of frozen sections of thyroid nodules is used to guide surgery. However, the lack of subspecialty pathologists and delayed diagnoses are challenges in clinical treatment. This study aimed to develop novel diagnostic approaches to increase diagnostic effectiveness. METHODS: Artificial intelligence and machine learning techniques were used to automatically diagnose histopathological slides. AI-based models were trained with annotations and selected as efficientnetV2-b0 from multi-set experiments. RESULTS: On 191 test slides, the proposed method predicted benign and malignant categories with a sensitivity of 72.65%, specificity of 100.0%, and AUC of 86.32%. For the subtype diagnosis, the best AUC was 99.46% for medullary thyroid cancer with an average of 237.6 s per slide. CONCLUSIONS: Within our testing dataset, the proposed method accurately diagnosed the thyroid nodules during surgery.

Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images.

BACKGROUND: Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis. MATERIALS AND METHODS: We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. RESULTS: We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI. CONCLUSIONS: We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.

Identification and preliminary analysis of hub genes associated with bladder cancer progression by comprehensive bioinformatics analysis.

Bladder cancer (BC) is a crisis to human health. It is necessary to understand the molecular mechanisms of the development and progression of BC to determine treatment options. Publicly available expression data were obtained from TCGA and GEO databases to spot differentially expressed genes (DEGs) between cancer and normal bladder tissues. Weighted co-expression networks were constructed, and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Associations in hub genes, immune infiltration, and immune therapy were evaluated separately. Protein-protein interaction (PPI) networks for the genes identified in the normal and tumor groups were launched. 3461 DEGs in the TCGA dataset and 1069 DEGs in the GSE dataset were identified, including 87 overlapping genes between cancer and normal bladder groups. Hub genes in the tumor group were mainly enriched for cell proliferation, while hub genes in the normal group were related to the synthesis and secretion of neurotransmitters. Based on survival analysis, CDH19, RELN, PLP1, and TRIB3 were considerably associated with prognosis (P < 0.05). CDH19, RELN, PLP1, and TRIB3 may play important roles in the development of BC and are potential biomarkers in therapy and prognosis.

Managing urban development could halve nitrogen pollution in China.

Halving nitrogen pollution is crucial for achieving Sustainable Development Goals (SDGs). However, how to reduce nitrogen pollution from multiple sources remains challenging. Here we show that reactive nitrogen (Nr) pollution could be roughly halved by managed urban development in China by 2050, with NH3, NOx and N2O atmospheric emissions declining by 44%, 30% and 33%, respectively, and Nr to water bodies by 53%. While rural-urban migration increases point-source nitrogen emissions in metropolitan areas, it promotes large-scale farming, reducing rural sewage and agricultural non-point-source pollution, potentially improving national air and water quality. An investment of approximately US$ 61 billion in waste treatment, land consolidation, and livestock relocation yields an overall benefit of US$ 245 billion. This underscores the feasibility and cost-effectiveness of halving Nr pollution through urbanization, contributing significantly to SDG1 (No poverty), SDG2 (Zero hunger), SDG6 (Clean water), SDG12 (Responsible consumption and production), SDG14 (Climate Action), and so on.

Clinical application of spectral CT perfusion scanning in evaluating the blood supply source of portal vein tumor thrombus in hepatocellular carcinoma.

Purpose: To evaluate the characteristic of blood supply of liver portal vein tumor thrombus (PVTT) using perfusion indexes and spectral parameters. Methods: Between July 2020 and December 2022, the study enrolled 25 liver cancer patients completed with PVTT (male=20, female=5; age 41-74 years (59.48 ± 9.12)) from the Interventional Department of Jiangsu Cancer Hospital. There were 11 cases of type III PVTT, 12 of type II PVTT, and 2 of type I PVTT (Cheng's classification). All patients underwent spectral perfusion scans through dual-layer spectral detector computed tomography. The PVTTs were divided into proximal and distal groups based on the distance between the tumor thrombus and the main portal vein. The perfusion analysis was performed on the 120-kVp conventional images to generate hepatic perfusion index (HPI). The spectral based images (SBIs) during the artery and venous peak phases were extracted from the perfusion data. The iodine map and 40&100-keV virtual monoenergetic image (VMI) were generated from SBI data. HPI, iodine concentration (IC), CT value at 40 and 100-keV, and spectral slope (40-100keV) of the primary lesion, proximal and distal PVTT, and liver parenchyma were measured and compared. The correlation between the primary lesion and proximal and distal PVTT was analyzed. Results: The IC and spectral slope during the arterial and venous peak phases and HPI of the primary lesion, proximal PVTT, and distal PVTT were highly correlated (P<0.001). The differences between the IC and spectral slope during the arterial and venous peak phases and HPI of the primary lesion, proximal PVTT were statistically significant (P<0.001). The differences between the IC during venous peak phase and HPI of primary lesion, distal PVTT were statistically significant (P<0.001), and there was no statistically significant difference in arterial phase IC, arterial and venous phase spectral slopes. Conclusion: The IC, slope, and HPI of the distal and proximal PVTT were highly correlated with the primary lesion, indicating that PVTT was similar to the primary lesion in the liver that they were both mainly supplied by the hepatic artery. However, there was still significant heterogeneity between the proximal PVTT and the primary lesion, while the difference in the distal PVTT was relatively small.

The Efficacy of Clostridium Butyricum Triple Viable in Enhancing Fitness and Performance in Athletes: A Case-Control Study

Objective: This case-control study aimed to assess the impact of Clostridium butyricum triple viable (CBTV) on the performance and gut microbiota of athletes. Methods: We recruited 47 athletes from various sports disciplines and divided them into two groups: Group A received a standard fitness regimen, while Group B received the same fitness regimen along with CBTV supplementation for a period of 4 weeks. Performance measurements were recorded, and gut microbiota analysis was conducted using 16S rRNA sequencing and bioinformatics. Results: After 4 weeks, there were no significant differences in the performance measurements between the two groups (Group A: 159.1±42.4 vs. Group B: 150.8±34.8, p = 0.42). However, the quality of life improvement in Group B was significantly higher than in Group A (Group A: 86.2±26.2 vs. Group B: 89.7±40.7, p < 0.01). Additionally, the gut microbiota analysis revealed that certain bacterial species, including Megamanos, Pseudonocardia, Corynebacterium, and Veillonell, were less abundant in Group B compared to Group A after 4 weeks. Conclusion: This case-control study suggests that CBTV supplementation can enhance the quality of life and influence the abundance of specific bacteria, including a reduction in Megamanos, in athletes. These findings lay the groundwork for further research into the mechanisms by which probiotic bacteria impact gut microbiota in the context of sports performance. (AU)