Evaluating complementaries among urban water, energy, infrastructure, and social Sustainable Development Goals in China.

Urban areas' performance in water, energy, infrastructure, and socio-economic sectors is intertwined and measurable through Sustainable Development Goals (SDGs) 6-13. Effective synergy among these is critical for sustainability. This study constructs an indicator framework that reflects progress towards these urban SDGs in China. Findings indicate underperformance in SDGs 8-11, suggesting the need for transformative actions. Through network analysis, the research reveals complementarities among these SDGs. Notably, the SDG space divides into socio-economic and ecological clusters, with SDG 6 (Clean Water and Sanitation) central to both. Additionally, SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure) act as bridges, while greater synergies exist between SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action). An in-depth view at the indicator-level shows a core-periphery structure, emphasizing indicators like SDG 6.2 (Wastewater Treatment Rate) and SDG 6.6 (Recycled Water Production Capacity per capita) as pivotal. This study confirms the urban SDG space's stability and predictiveness, underscoring its value in steering well-aligned policy decisions for sustainable growth.

Restoration of ARA metabolic disorders in vascular smooth muscle cells alleviates intimal hyperplasia.

Intimal hyperplasia (IH) is an innegligible issue for patients undergoing interventional therapy. The proliferation and migration of vascular smooth muscle cells (VSMCs) induced by platelet-derived growth factor-BB (PDGF-BB) are critical events in the development of IH. While the exact mechanism and effective target for IH needs further investigation. Metabolic disorders of arachidonic acid (ARA) are involved in the occurrence and progression of various diseases. In this study, we found that the expressions of soluble epoxide hydrolase (sEH) and cyclooxygenase-2 (COX-2) were significantly increased in the VSMCs during balloon injury-induced IH. Then, we employed a COX-2/sEH dual inhibitor PTUPB to increase the concentration of epoxyeicosatrienoic acids (EETs) while prevent the release of pro-inflammatory prostaglandins. Results showed that PTUPB treatment significantly reduced neointimal thickening induced by balloon injury in rats in vivo and inhibited PDGF-BB-induced proliferation and migration of VSMCs in vitro. Our results showed that PTUPB may reverse the phenotypic transition of VSMCs by inhibiting Pttg1 expression. In conclusion, we found that the dysfunction of ARA metabolism in VSMCs contributes to IH, and the COX-2/sEH dual inhibitor PTUPB attenuates IH progression by reversing the phenotypic switch in VSMC through the Sirt1/Pttg1 pathway.

Blood metabolites, neurocognition and psychiatric disorders: a Mendelian randomization analysis to investigate causal pathways.

BACKGROUND: Neurocognitive dysfunction is observationally associated with the risk of psychiatric disorders. Blood metabolites, which are readily accessible, may become highly promising biomarkers for brain disorders. However, the causal role of blood metabolites in neurocognitive function, and the biological pathways underlying their association with psychiatric disorders remain unclear. METHODS: To explore their putative causalities, we conducted bidirectional two-sample Mendelian randomization (MR) using genetic variants associated with 317 human blood metabolites (nmax = 215,551), g-Factor (an integrated index of multiple neurocognitive tests with nmax = 332,050), and 10 different psychiatric disorders (n = 9,725 to 807,553) from the large-scale genome-wide association studies of European ancestry. Mediation analysis was used to assess the potential causal pathway among the candidate metabolite, neurocognitive trait and corresponding psychiatric disorder. RESULTS: MR evidence indicated that genetically predicted acetylornithine was positively associated with g-Factor (0.035 standard deviation units increase in g-Factor per one standard deviation increase in acetylornithine level; 95% confidence interval, 0.021 to 0.049; P = 1.15 × 10-6). Genetically predicted butyrylcarnitine was negatively associated with g-Factor (0.028 standard deviation units decrease in g-Factor per one standard deviation increase in genetically proxied butyrylcarnitine; 95% confidence interval, -0.041 to -0.015; P = 1.31 × 10-5). There was no evidence of associations between genetically proxied g-Factor and metabolites. Furthermore, the mediation analysis via two-step MR revealed that the causal pathway from acetylornithine to bipolar disorder was partly mediated by g-Factor, with a mediated proportion of 37.1%. Besides, g-Factor mediated the causal pathway from butyrylcarnitine to schizophrenia, with a mediated proportion of 37.5%. Other neurocognitive traits from different sources provided consistent findings. CONCLUSION: Our results provide genetic evidence that acetylornithine protects against bipolar disorder through neurocognitive abilities, while butyrylcarnitine has an adverse effect on schizophrenia through neurocognition. These findings may provide insight into interventions at the metabolic level for risk of neurocognitive and related disorders.

[Stabilization of Cd-contaminated Soil with Thiol-modified Biochar and Response of Soil Microorganisms].

To explore the stabilization effect of livestock manure biochar on Cd-contaminated soil and its impact on the soil environment, a pot experiment was conducted to investigate the stabilization efficiency of cattle manure-biochar （BC） and thiol-modified biochar （SBC） on Cd in soil and their effect on the soil properties and microbial community. The structural equation model （SEM） was used to analyze the effect pathways of BC and SBC on the soil microbial community. The results showed that BC and SBC increased soil pH, available potassium, available phosphorus, and organic matter content but decreased soil available nitrogen content compared with those in CK. The stabilization efficiency of BC for Cd in soil was 14.97%, which was much lower than that of SBC （85.71%）. Moreover, SBC increased the abundance of dominant bacterial phyla in soil, with Proteobacteria, Bacteroidota, and Cyanobacteria increasing most significantly. SBC decreased the diversity of soil microorganisms, but the decrease was insignificant （P≥0.05） compared with that in CK and BC. SEM analysis indicated that the available phosphorus, available potassium, organic matter, and soil pH were the key factors influencing Cd availability in soil, whereas organic matter and Cd availability were the key factors affecting the soil microbial community. Overall, SBC could stabilize Cd effectively and increase the abundance of dominant bacteria and has great potential in the remediation of Cd-contaminated soil.

Unraveling the toxicity mechanisms of nanoplastics with various surface modifications on Skeletonema costatum: Cellular and molecular perspectives.

Nanoplastics are ubiquitous in marine environments, exhibiting high bioavailability and potential toxicity to marine organisms. However, the impacts of nanoplastics with various surface modifications on marine microalgae remain largely unexplored. This study explored the toxicity mechanisms of two nanoplastic types-polystyrene (PS) and polymethyl methacrylate (PMMA)-with distinct surface modifications on Skeletonema costatum at cellular and molecular levels. Results showed that nanoplastics significantly impaired the growth of microalgae, particularly PS-NH2, which caused the most pronounced growth inhibition, reaching 56.99 % after a 96-h exposure at 50 mg/L. Transcriptomic profiling revealed that nanoplastics disrupted the expression of genes predominantly involved in ribosome biogenesis, aminoacyl-tRNA biosynthesis, amino acid metabolism, and carbohydrate metabolism pathways. The integrated biochemical and transcriptomic evidence highlighted that PS-NH2 nanoplastics had the most adverse impact on microalgae, affecting fundamental pathways such as ribosome biogenesis, energy metabolism, photosynthesis, and oxidative stress. Our findings underscore the influence of surface-modified nanoplastics on algal growth and contribute new understanding to the toxicity mechanisms of these nanoplastics in marine microalgae, offering critical information for assessing the risks of emerging pollutants.

Deterministic processes influence bacterial more than fungal community assembly during the development of biological soil crusts in the desert ecosystem.

Biological soil crusts (biocrusts) constitute a crucial biological component of the soil surface in arid and semi-arid ecosystems. Understanding the variations in soil microbial community assembly across biocrust successional stages is essential for a deeper comprehension of microbial biodiversity and desert ecosystem functioning. However, knowledge about the mechanisms of microbial community assembly and the factors influencing its development remains limited. In this study, we utilized amplicons sequencing to assess the compositions of bacterial and fungal communities in bare sand and three types of biocrusts (light cyanobacterial biocrusts, dark cyanobacterial biocrusts, and moss crusts). Subsequently, we analyzed the ecological processes shaping microbial community composition and structure, along with the influencing factors. Our results revealed a significant increase in bacterial diversity and no significant changes in fungal diversity during biocrust development. The relative abundances of the copiotrophic bacteria (e.g., Actinobacteria, Acidobacteria, and Bacteroidetes) showed significant increases, while oligotrophic bacteria (e.g., Proteobacteria and Firmicutes) decreased over time. Moreover, the relative abundances of Ascomycota, which exhibit strong resistance to adverse environmental conditions, significantly decreased, whereas Basidiomycota, known for their ability to degrade lignin, significantly increased throughout biocrust development. Additionally, stochastic processes (dispersal limitation and drift) predominantly drove the assemblies of both bacterial and fungal communities. However, the relative importance of deterministic processes (homogeneous selection) in bacterial assembly increased during biocrust development. Structural equation modeling indicated that bacterial community assembly was primarily related to soil water content, whereas fungal community assembly was primarily related to total organic carbon. These findings provide a scientific foundation for investigating the formation and development of biocrusts, and further insights into the conservation and sustainable management of biocrust resources under future climate change scenarios.

MetMiner: A user-friendly pipeline for large-scale plant metabolomics data analysis.

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

Unravelling biosynthesis and biodegradation potentials of microbial dark matters in hypersaline lakes.

Biosynthesis and biodegradation of microorganisms critically underpin the development of biotechnology, new drugs and therapies, and environmental remediation. However, most uncultured microbial species along with their metabolic capacities in extreme environments, remain obscured. Here we unravel the metabolic potential of microbial dark matters (MDMs) in four deep-inland hypersaline lakes in Xinjiang, China. Utilizing metagenomic binning, we uncovered a rich diversity of 3030 metagenome-assembled genomes (MAGs) across 82 phyla, revealing a substantial portion, 2363 MAGs, as previously unclassified at the genus level. These unknown MAGs displayed unique distribution patterns across different lakes, indicating a strong correlation with varied physicochemical conditions. Our analysis revealed an extensive array of 9635 biosynthesis gene clusters (BGCs), with a remarkable 9403 being novel, suggesting untapped biotechnological potential. Notably, some MAGs from potentially new phyla exhibited a high density of these BGCs. Beyond biosynthesis, our study also identified novel biodegradation pathways, including dehalogenation, anaerobic ammonium oxidation (Anammox), and degradation of polycyclic aromatic hydrocarbons (PAHs) and plastics, in previously unknown microbial clades. These findings significantly enrich our understanding of biosynthesis and biodegradation processes and open new avenues for biotechnological innovation, emphasizing the untapped potential of microbial diversity in hypersaline environments.

A Comprehensive Pan-Cancer Analysis Identified that TRIB3 was Associated with Immune Cell Infiltration and Poor Prognosis.

BACKGROUND: Previous studies have demonstrated that TRIB3 plays a carcinogenic role in tumor progression. However, the exploration of TRIB3 at the pan-cancer level has not been reported. AIMS: This study aimed to conduct a comprehensive pan-cancer analysis of TRIB3. OBJECTIVE: We explored the expression pattern and functional mechanism of TRIB3 on the basis of multiple databases. METHOD: We first explored the expression level of TRIB3 in the TCGA database. Then, the receiver operation characteristic curve (ROC), Kaplan-Meier plotter, and Cox regression were used to estimate the diagnostic and prognostic value of TRIB3, respectively. We also explored the relationship between TRIB3 and the infiltration of tumor immune cells, as well as the expression of immune checkpoint molecules. Gene enrichment and protein interaction network analysis were carried out to identify possible carcinogenic molecular mechanisms and functional pathways. Finally, we compared the non-promoter region methylation of TRIB3 in normal and tumor tissues and explored potential systems with unique functions in TRIB3-mediated tumorigenesis. RESULT: The expression level of TRIB3 was elevated in multiple tumor types, and the high expression of TRIB3 was associated with poor prognosis. TRIB3 had a higher frequency of genetic changes in several tumors and showed varying trends in TRIB3 methylation levels. Additionally, high expression of TRIB3 was also associated with infiltration of cancer-related fibroblasts and different types of immune cells and was positively correlated with the expression of immune checkpoint molecules. Furthermore, gene enrichment analysis suggested that TRIB3 may play a role in the malignant progression of cancer by participating in protein post-translational modifications and activating transcription initiation factors. CONCLUSION: Our pan-cancer analysis provided the potential carcinogenic role of TRIB3 in tumors and verified a promising target for clinical immune treatment.

Reamer-scraping and implant-pushing technique for transcrestal sinus floor elevation.

Transcrestal maxillary sinus floor elevation is an effective method to solve the problem of insufficient bone height in the posterior maxillary region. However, current methods, such as osteotome sinus floor elevation, cushioned grind-out technique, Smart Drill technique, etc., require specialized surgical tool boxes. In this article, we introduce a new method of transcrestal maxillary sinus elevation that uses built-in reamers of various implant systems to scrap residual bone at the sinus floor and uses the implant to push the sinus membrane during implant placement. This technique is easy to operate and time saving and has a low rate of sinus membrane perforation. After a one-year follow-up observation of 146 people and 175 implants, the endo-sinus bone gains were 5.00 (4.70, 5.30) mm and 2.10 (1.40, 2.70) mm in the group of 3 mm≤residual bone height (RBH)<5 mm and the group of 5 mm≤RBH<8 mm, respectively, which can meet the clinical requirements of implant stability. This technique is suitable in generalizing dental implantation.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era.

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Consensus clustering and development of a risk signature based on trajectory differential genes of cancer-associated fibroblast subpopulations in colorectal cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, the impact of CAF subpopulation trajectory differentiation on CRC remains unclear. METHODS: In this study, we first explored the trajectory differences of CAFs subpopulations using bulk and integrated single-cell sequencing data, and then performed consensus clustering of CRC samples based on the trajectory differential genes of CAFs subpopulations. Subsequently, we analyzed the heterogeneity of CRC subtypes using bioinformatics. Finally, we constructed relevant prognostic signature using machine learning and validated them using spatial transcriptomic data. RESULTS: Based on the differential genes of CAFs subpopulation trajectory differentiation, we identified two CRC subtypes (C1 and C2) in this study. Compared to C1, C2 exhibited worse prognosis, higher immune evasion microenvironment and high CAF characteristics. C1 was primarily associated with metabolism, while C2 was primarily associated with cell metastasis and immune regulation. By combining 101 combinations of 10 machine learning algorithms, we developed a High-CAF risk signatures (HCAFRS) based on the C2 characteristic gene. HCAFRS was an independent prognostic factor for CRC and, when combined with clinical parameters, significantly predicted the overall survival of CRC patients. HCAFRS was closely associated with epithelial-mesenchymal transition, angiogenesis, and hypoxia. Furthermore, the risk score of HCAFRS was mainly derived from CAFs and was validated in the spatial transcriptomic data. CONCLUSION: In conclusion, HCAFRS has the potential to serve as a promising prognostic indicator for CRC, improving the quality of life for CRC patients.

A temperature-compensated CO2 detection system based on non-dispersive infrared spectral technology.

The concentration of carbon dioxide (CO2) is an important indicator for coal mine safety. Real-time monitoring of CO2 concentration is of great importance for taking actions in advance to avoid the occurrence of potential accidents. To address the issues of poor portability and high cost associated with existing coal mine CO2 detection equipment, this paper develops a miniaturized CO2 detection system based on non-dispersive infrared (NDIR) technology. This sensor integrates an infrared light source and a dual-channel pyroelectric detector into a reflective gas chamber, thereby achieving an extended optical path and higher system sensitivity within limited space. Meanwhile, the noise interference was greatly mitigated by using hardware and software filtering techniques. Based on principle analysis, the Lambert-Beer law was parametrically corrected, and then, a model relationship between the dual-channel voltage ratio and concentration was established. In addition, temperature compensation for zero and span values was introduced to improve the adaptability of the detection results to temperature changes. Testing results indicate that the developed detection system can realize CO2 measurement in the concentration range of 0 to 50 000 ppm within a temperature range of 0-40 °C, with a maximum detection error of less than 0.12% and a repeatability deviation of less than 1.04%. During a stability test for 12 h, the maximum concentration drift is 0.07%, indicating that the developed system meets the requirements for monitoring CO2 safety in coal mines.

Manganese-catalyzed cyclopropanation of allylic alcohols with sulfones.

Cyclopropanes are among the most important structural units in natural products, pharmaceuticals, and agrochemicals. Herein, we report a manganese-catalyzed cyclopropanation of allylic alcohols with sulfones as carbene alternative precursors via a borrowing hydrogen strategy under mild conditions. Various allylic alcohols and arylmethyl trifluoromethyl sulfones work efficiently in this borrowing hydrogen transformation and thereby deliver the corresponding cyclopropylmethanol products in 58% to 99% yields. Importantly, a major benefit of this transformation is that the versatile free alcohol moiety is retained in the resultant products, which can undergo a wide range of downstream transformations to provide access to a series of functional molecules. Mechanistic studies support a sequential reaction mechanism that involves catalytic dehydrogenation, Michael addition, cyclization, and catalytic hydrogenation.

DNA damage induced PARP-1 overactivation confers paclitaxel-induced neuropathic pain by regulating mitochondrial oxidative metabolism.

AIMS: Poly (ADP-ribose) polymerase (PARP) has been extensively investigated in human cancers. Recent studies verified that current available PARP inhibitors (Olaparib or Veliparib) provided clinical palliation of clinical patients suffering from paclitaxel-induced neuropathic pain (PINP). However, the underlying mechanism of PARP overactivation in the development of PINP remains to be investigated. METHODS AND RESULTS: We reported induction of DNA oxidative damage, PARP-1 overactivation, and subsequent nicotinamide adenine dinucleotide (NAD+) depletion as crucial events in the pathogenesis of PINP. Therefore, we developed an Olaparib PROTAC to achieve the efficient degradation of PARP. Continuous intrathecal injection of Olaparib PROTAC protected against PINP by inhibiting the activity of PARP-1 in rats. PARP-1, but not PARP-2, was shown to be a crucial enzyme in the development of PINP. Specific inhibition of PARP-1 enhanced mitochondrial redox metabolism partly by upregulating the expression and deacetylase activity of sirtuin-3 (SIRT3) in the dorsal root ganglions and spinal cord in the PINP rats. Moreover, an increase in the NAD+ level was found to be a crucial mechanism by which PARP-1 inhibition enhanced SIRT3 activity. CONCLUSION: The findings provide a novel insight into the mechanism of DNA oxidative damage in the development of PINP and implicate PARP-1 as a possible therapeutic target for clinical PINP treatment.

The PGC-1α/ERRα/ULK1 pathway contributes to Perioperative neurocognitive disorders by inducing mitochondrial dysfunction and activating NLRP3 inflammasome in aged mice.

Perioperative neurocognitive disorders (PND) are intractable, indistinct, and considerably diminish the postoperative quality of life of patients. It has been proved that Peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) was involved in neurodegenerative diseases by regulating mitochondrial biogenesis. The underlying mechanisms of PGC-1α and Nod-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome in PND are not well understood. In this study, we constructed a model of laparotomy in aged mice, and then examined the cognition changes with novel object recognition tests and fear condition tests. The protein levels of PGC-1α and NLRP3 in the hippocampus were detect after surgery. Our results showed that NLRP3 and downstream PI3K/AKT pathway expressions were augmented in the hippocampus after surgery, whereas, the expressions of PGC-1α/estrogen-related receptor α (ERRα)/Unc-51-like autophagy activating kinase 1 (ULK1) pathway were diminished after surgery. In addition, we found that NLRP3 was mainly co-localized with neurons in the hippocampus, and synaptic-related proteins were reduced after surgery. At the same time, transmission electron microscopy (TEM) showed that mitochondria were impaired after surgery. Pharmacological treatment of MCC950, a selective NLRP3 inhibitor, effectively alleviated PND. Activation of PGC-1α with ZLN005 significantly ameliorated PND by enhancing the PGC-1α/ERRα/ULK1 signaling pathway, and further suppressing NLRP3 activation. As a result, we conclude that suppression of the PGC-1α/ERRα/ULK1 signaling pathway is the primary mechanism of PND which caused mitochondrial dysfunction, and activated NLRP3 inflammasome and downstream PI3K/AKT pathway, eventually improved cognitive dysfunction.

Multi-omics illuminates the functional significance of previously unknown species in a full-scale landfill leachate treatment plant.

Landfill leachate treatment plants (LLTPs) harbor a vast reservoir of uncultured microbes, yet limited studies have systematically unraveled their functional potentials within LLTPs. Combining 36 metagenomic and 18 metatranscriptomic datasets from a full-scale LLTP, we unveiled a double-edged sword role of unknown species in leachate biotreatment and environmental implication. We identified 655 species-level genome bins (SGBs) spanning 47 bacterial and 3 archaeal phyla, with 75.9 % unassigned to any known species. Over 90 % of up-regulated functional genes in biotreatment units, compared to the leachate influent, were carried by unknown species and actively participated in carbon, nitrogen, and sulfur cycles. Approximately 79 % of the 37,366 carbohydrate active enzymes (CAZymes), with ∼90 % novelty and high expression, were encoded by unknown species, exhibiting great potential in biodegrading carbohydrate compounds linked to human meat-rich diets. Unknown species offered a valuable genetic resource of thousands of versatile, abundant, and actively expressed metabolic gene clusters (MGCs) and biosynthetic gene clusters (BGCs) for enhancing leachate treatment. However, unknown species may contribute to the emission of hazardous N2O/H2S and represented significant reservoirs for antibiotic-resistant pathogens that posed environmental safety risks. This study highlighted the significance of considering both positive and adverse effects of LLTP microbes to optimize LLTP performance.

Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.

Purpose To develop a machine learning approach for classifying disease progression in chest radiographs using weak labels automatically derived from radiology reports. Materials and Methods In this retrospective study, a twin neural network was developed to classify anatomy-specific disease progression into four categories: improved, unchanged, worsened, and new. A two-step weakly supervised learning approach was employed, pretraining the model on 243 008 frontal chest radiographs from 63 877 patients (mean age, 51.7 years ± 17.0 [SD]; 34 813 [55%] female) included in the MIMIC-CXR database and fine-tuning it on the subset with progression labels derived from consecutive studies. Model performance was evaluated for six pathologic observations on test datasets of unseen patients from the MIMIC-CXR database. Area under the receiver operating characteristic (AUC) analysis was used to evaluate classification performance. The algorithm is also capable of generating bounding-box predictions to localize areas of new progression. Recall, precision, and mean average precision were used to evaluate the new progression localization. One-tailed paired t tests were used to assess statistical significance. Results The model outperformed most baselines in progression classification, achieving macro AUC scores of 0.72 ± 0.004 for atelectasis, 0.75 ± 0.007 for consolidation, 0.76 ± 0.017 for edema, 0.81 ± 0.006 for effusion, 0.7 ± 0.032 for pneumonia, and 0.69 ± 0.01 for pneumothorax. For new observation localization, the model achieved mean average precision scores of 0.25 ± 0.03 for atelectasis, 0.34 ± 0.03 for consolidation, 0.33 ± 0.03 for edema, and 0.31 ± 0.03 for pneumothorax. Conclusion Disease progression classification models were developed on a large chest radiograph dataset, which can be used to monitor interval changes and detect new pathologic conditions on chest radiographs. Keywords: Prognosis, Unsupervised Learning, Transfer Learning, Convolutional Neural Network (CNN), Emergency Radiology, Named Entity Recognition Supplemental material is available for this article. © RSNA, 2024 See also commentary by Alves and Venkadesh in this issue.

PD-L2 mediates tobacco smoking-induced recruitment of regulatory T cells via the RGMB/NFκB/CCL20 cascade.

Programmed cell death ligand 2 (PD-L2), a ligand for the receptor programmed cell death 1 (PD-1), has an identity of 34% with its twin ligand PD-L1 and exhibits higher binding affinity with PD-1 than PD-L1. However, the role of PD-L2 in non-small cell lung cancer (NSCLC) progression, especially tobacco-induced cancer progression, has not been fully understood. Here, we found that PD-L2 promoted tumor growth in murine models with recruitment of regulatory T cells (Tregs). In patients with NSCLC, PD-L2 expression level in tumor samples was higher than in counterpart normal controls and was positively associated with patients' response to anti-PD-1 treatment. Mechanismly, PD-L2 bound its receptor Repulsive guidance molecule B (RGMB) on cancer cells and activated extracellular signal-regulated kinase (Erk) and nuclear factor κB (NFκB), leading to increased production of chemokine CCL20, which recruited Tregs and contributed to NSCLC progression. Consistently, knockdown of RGMB or NFκB p65 inhibited PD-L2-induced CCL20 production, and silencing of PD-L2 repressed Treg recruitment by NSCLC cells. Furthermore, cigarette smoke and carcinogen benzo(a)pyrene (BaP) upregulated PD-L2 in lung epithelial cells via aryl hydrocarbon receptor (AhR)-mediated transcription activation, whose deficiency markedly suppressed BaP-induced PD-L2 upregulation. These results suggest that PD-L2 mediates tobacco-induced recruitment of Tregs via the RGMB/NFκB/CCL20 cascade, and targeting this pathway might have therapeutic potentials in NSCLC.