SOCS6, an inhibitory factor in Japanese eel inhibits the type I IFN pathway and the MyD88-mediated NF-kB pathway.

SOCS family genes are a class of repressors in various signaling pathways of mammals involved in regulating immunity, growth, and development, but the information remains limited in teleost. The full-length cDNA sequence of the Japanese eel SOCS6 gene, named AjSOCS6, was first cloned and showed to encode 529 amino acids with a conserved SH2 structural domain and a typical structure of a C-terminal SOCS box. AjSOCS6 is evolutionarily close to that of rainbow trout and zebrafish. AjSOCS6 gene expression was observed across all tissues in Japanese eel, with the highest levels found in the intestine. In vivo studies showed that AjSOCS6 was significantly upregulated in the liver following exposure to LPS, poly I:C, and Aeromonas hydrophila infection. In vitro, stimulation with poly I:C, CpG, and A. hydrophila infection increased AjSOCS6 expression in Japanese eel liver cells. Subcellular localization revealed that AjSOCS6 was dispersed in the cytoplasm. Overexpressing AjSOCS6 significantly suppressed the expression of immune-related genes, such as c-Rel and p65 in the NF-κB pathway, IFN1, IFN2, and IFN4 in the type I IFN signaling pathway, and the downstream inflammatory factor IL-6 in Japanese eel liver cells. Conversely, knocking down AjSOCS6 in vitro in liver cells and in vivo in the liver, spleen, and kidney significantly upregulated these gene expressions. Co-transfection of AjSOCS6 with AjMyD88 into HEK293 cells significantly reduced NF-κB luciferase activities compared to AjMyD88 single-transfection groups, in a natural state and under LPS stimulation. These findings suggest that AjSOCS6 negatively regulates MyD88-dependent NF-κB and type I IFN signaling pathways, underscoring its role in the immune defense of fish against viral and bacterial infections.

The effect of tempering temperature on microstructure and corrosion resistance of M390 powder metallurgical martensitic stainless steel.

The corrosion resistance of M390 powder metallurgical martensitic stainless steel with different tempering temperatures was investigated by potentiodynamic polarization measurements, salt spray tests, and microstructural analyses utilizing scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The tempering temperature had no significant effect on the size and volume fraction of carbides. The corrosion resistance of M390 steel gradually deteriorated with increasing tempering temperature, and a loss passivation (LOP) effect was observed when tempered at 450 °C, 500 °C, and 550 °C. Transmission electron microscopy (TEM) analysis showed that the width of the Cr-depleted zones around the undissolved M7C3 carbides increased with increasing tempering temperature, while the Cr content in these zones decreased, which was the main reason for the deterioration of corrosion resistance. This study offers valuable insights into optimizing the tempering process to improve the corrosion resistance of M390 steel for practical applications.

Using machine learning to predict bacteremia in urgent care patients on the basis of triage data and laboratory results.

BACKGROUND: Despite advancements in antimicrobial therapies, bacteremia remains a life-threatening condition. Appropriate antimicrobials must be promptly administered to ensure patient survival. However, diagnosing bacteremia based on blood cultures is time-consuming and not something emergency department (ED) personnel are routinely trained to do. METHODS: This retrospective cohort study developed several machine learning (ML) models to predict bacteremia in adults initially presenting with fever or hypothermia, comprising logistic regression, random forest, extreme gradient boosting, support vector machine, k-nearest neighbor, multilayer perceptron, and ensemble models. Random oversampling and synthetic minority oversampling techniques were adopted to balance the dataset. The variables included demographic characteristics, comorbidities, immunocompromised status, clinical characteristics, subjective symptoms reported during ED triage, and laboratory data. The study outcome was an episode of bacteremia. RESULTS: Of the 5063 patients with initial fever or hypothermia from whom blood cultures were obtained, 128 (2.5 %) were diagnosed with bacteremia. We combined 36 selected variables and 10 symptoms subjectively reported by patients into features for analysis in our models. The ensemble model outperformed other models, with an area under the receiver operating characteristic curve (AUROC) of 0.930 and an F1-score of 0.735. The AUROC of all models was higher than 0.80. CONCLUSION: The ML models developed effectively predicted bacteremia among febrile or hypothermic patients in the ED, with all models demonstrating high AUROC values and rapid processing times. The findings suggest that ED clinicians can effectively utilize ML techniques to develop predictive models for addressing clinical challenges.

The multinomial mixed-effect regression model for predicting PCOC phases in hospice patients.

PURPOSE: The Palliative Care Outcomes Collaboration (PCOC) aims to enhance patient outcomes systematically. However, identifying crucial items and accurately determining PCOC phases remain challenging. This study aims to identify essential PCOC data items and construct a prediction model to accurately classify PCOC phases in terminal patients. METHODS: A retrospective cohort study assessed PCOC data items across four PCOC phases: stable, unstable, deteriorating, and terminal. From July 2020 to March 2023, terminal patients were enrolled. A multinomial mixed-effect regression model was used for the analysis of multivariate PCOC repeated measurement data. RESULTS: The dataset comprised 1933 terminally ill patients from 4 different hospice service settings. A total of 13,219 phases of care were analyzed. There were significant differences in the symptom assessment scale, palliative care problem severity score, Australia-modified Karnofsky performance status, and resource utilization groups-activities of daily living among the four PCOC phases of care. Clinical needs, including pain and other symptoms, declined from unstable to terminal phases, while psychological/spiritual and functional status for bed mobility, eating, and transfers increased. A robust prediction model achieved areas under the curves (AUCs) of 0.94, 0.94, 0.920, and 0.96 for stable, unstable, deteriorating, and terminal phases, respectively. CONCLUSIONS: Critical PCOC items distinguishing between PCOC phases were identified, enabling the development of an accurate prediction model. This model enhances hospice care quality by facilitating timely interventions and adjustments based on patients' PCOC phases.

High-performance electrolyte-gated amorphous InGaZnO field-effect transistor for label-free DNA sensing.

Accurate, convenient, label-free, and cost-effective biomolecules detection platforms are currently in high demand. In this study, we showcased the utilization of electrolyte-gated InGaZnO field-effect transistors (IGZO FETs) featuring a large on-off current ratio of over 106 and a low subthreshold slope of 78.5 mV/dec. In the DNA biosensor, the modification of target DNA changed the effective gate voltage of IGZO FETs, enabling an impressive low detection limit of 0.1 pM and a wide linear detection range from 0.1 pM to 1 µM. This label-free detection method also exhibits high selectivity, allowing for the discrimination of single-base mismatch. Furthermore, the reuse of gate electrodes and channel films offers cost-saving benefits and simplifies device fabrication processes. The electrolyte-gated IGZO FET biosensor presented in this study shows great promise for achieving low-cost and highly sensitive detection of various biomolecules.

Biodegradable Persistent Luminescence Nanoparticles as Pyroptosis Inducer for High-Efficiency Tumor Immunotherapy.

Pyroptosis possesses potent antitumor immune activity, making pyroptosis inducer development a promising direction for tumor immunotherapy. Persistent luminescence nanoparticles (PLNPs) are highly sensitive optical probes extensively employed in tumor diagnosis and therapy. However, a pyroptosis inducer based on PLNPs has not been reported yet. Herein, polyethylene glycol-poly lactic acid-co-glycolic acid (PEG-PLGA: PP) modified biodegradable CaS:Eu2+ (CSE@PP) PLNPs are synthesized as a pyroptosis inducer for tumor immunotherapy for the first time. The synthesized CSE@PP possesses biowindow persistent luminescence (PersL) and pH-responsive degradation properties, allowing it to remain stable under neutral pH but degrade when exposed to weak acid (pH < 6.5). During degradation within the tumor, CSE@PP constantly releases H2S and Ca2+ while its PersL gradually fades away. Thus, the PersL signal can self-monitor H2S and Ca2+ release. Furthermore, the released H2S and Ca2+ result in mitochondrial dysfunction and the inactivation of reactive oxygen species scavenging enzymes, synergistic facilitating intracellular oxidative stress, which induces caspase-1/GSDM-D dependent pyroptosis and subsequent antitumor immune responses. In a word, it is confirmed that CSE@PP can self-monitor H2S and Ca2+ release and pyroptosis-mediated tumor Immunotherapy. This work will facilitate biomedical applications of PLNPs and inspire pyroptosis-induced tumor immunotherapy.

Semantic memory-based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision.

The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network using a memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based computing-in-memory (CIM) and content-addressable memory (CAM) circuits, respectively. We validate our co-designs, using a 40-nm memristor macro, on ResNet and PointNet++ for classifying images and three-dimensional points from the MNIST and ModelNet datasets, which achieves not only accuracy on par with software but also a 48.1 and 15.9% reduction in computational budget. Moreover, it delivers a 77.6 and 93.3% reduction in energy consumption.

Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer.

BACKGROUND: To provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features. METHODS: Two cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software. We used a framework that incorporated 10 machine learning algorithms and generated 77 combinations to construct radiomics-based models for lymph node metastasis prediction. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to determine the relationships between gene expression levels and radiomic features. Molecular pathways enrichment analysis was performed to uncover the underlying molecular features. RESULTS: Patients in the in-house cohort (mean age, 61.3 years ± 9.6 [SD]; 91 men [60%]) were separated into training (n = 105, 70%) and validation (n = 46, 30%) cohorts. A total of 1,239 features were extracted and subjected to machine learning algorithms. The 77 radiomic models showed moderate performance for predicting lymph node metastasis, and the combination of the StepGBM and Enet algorithms had the best performance in the training (AUC = 0.84, 95% CI = 0.77-0.91) and validation (AUC = 0.85, 95% CI = 0.73-0.98) cohorts. We determined that 15 features were core variables for lymph node metastasis. Proliferation-related processes may respond to the main molecular alterations underlying these features. CONCLUSIONS: Machine learning-based radiomics could predict the status of lymph node metastasis in pancreatic cancer, which is associated with proliferation-related alterations.

Identification of immune-inflammation targets for intracranial aneurysms: A multiomics and epigenome-wide study integrating summary-data-based mendelian randomization, single-cell-type expression analysis, and DNA methylation regulation.

BACKGROUND: Dysfunction of the immune system and inflammation plays a vital role in developing intracranial aneurysms (IAs). However, the progress of genetic pathophysiology is complicated and not entirely elaborated. This study aimed to explore the genetic associations of immune- and inflammation-related genes (IIRGs) with IAs and their subtypes using Mendelian randomization, colocalization test, and integrated multiomics functional analysis. METHODS: We conducted a summary-data-based Mendelian randomization (SMR) analysis using data from several genome-wide association studies of gene expression (31,684 European individuals) and protein quantitative trait loci (35,559 Icelanders), as well as information on IAs and their subtypes from The International Stroke Genetics Consortium (IGSC) for discovery phase and the FinnGen study for replication. This analysis aimed to determine the causal relationship between IIRGs and the risk of IAs and their subtypes. Further functional analyses, including DNA methylation regulation (1980, European individuals), single-cell-type expression analysis, and protein-protein interaction, were conducted to detect the specific cell type with enriched expression and discover potential drug targets. RESULTS: After integrating multi-omics evidence from expression quantitative trait loci (eQTL)and protein quantitative trait loci(pQTL), we found that tier 1: RELT [odds ratio (OR): 0.14, 95% confidence interval (CI): 0.04-0.50], TNFSF12 (OR: 1.24, 95% CI: 1.24-1.43), tier 3:ICAM5 (OR: 0.89, 95% CI: 0.82-0.96), and ERAP2 (OR: 1.07, 95% CI: 1.02-1.12) were associated with the risk of IAs; tier 3: RELT (OR: 0.11, 95% CI: 0.02-0.54), ERAP2 (OR: 1.08, 95% CI: 1.02-1.13), and TNFSF12 (OR: 1.24, 95% CI: 1.05-1.47) were associated with the risk of aneurysmal subarachnoid hemorrhage (aSAH); and tier 1:RELT (OR: 0.04, 95% CI: 0.01-0.30) was associated with the risk of unruptured intracranial aneurysms (uIAs). Further functional analyses showed that RELT was regulated by cg06382664 and cg18850434 and ICAM5 was regulated by cg04295144 in IAs; RELT was regulated by cg06382664, cg08770935, cg16533363, and cg18850434 in aSAH; and RELT was regulated by cg06382664 and cg21810604 in uIAs. In addition, we found that H6PD (OR: 1.13, 95% CI: 1.01-1.28), NT5M (OR: 1.91, 95% CI: 1.21-3.01), and NPTXR (OR: 1.13, 95% CI: 1.01-1.26) were associated with IAs; NT5M (OR: 2.13, 95% CI: 1.23-3.66) was associated aSAH; and AP4M1 (OR: 0.06, 95% CI: 0.01-0.42) and STX7 (OR: 3.97, 95% CI: 1.41-11.18) were related to uIAs. STX7 and TNFSF12 were mainly enriched in microglial cells, whereas H6PD, STX7, and TNFSF12 were mainly enriched in astrocytes. CONCLUSIONS: After integrating multi-omics evidence, we eventually identified IIRGs: RELT, TNFSF12, ICAM5 and ERAP2 were the novel therapy targets for IAs. These new results confirmed a vital role of immune and inflammation in the etiology of IAs, contributing to enhance our understanding of the immune and inflammatory mechanisms in the pathogenesis of IAs and revealing the complex genetic causality of IAs.

A Photoelectrochemical Retinomorphic Synapse.

Reproducing human visual functions with artificial devices is a long-standing goal of the neuromorphic domain. However, emulating the chemical language communication of the visual system in fluids remains a grand challenge. Here, a "multi-color" hydrogel-based photoelectrochemical retinomorphic synapse is reported with unique chemical-ionic-electrical signaling in an aqueous electrolyte that enables, e.g., color perception and biomolecule-mediated synaptic plasticity. Based on the specific enzyme-catalyzed chromogenic reactions, three multifunctional colored hydrogels are developed, which can not only synergize with the Bi2S3 photogate to recognize the primary colors but also synergize with a given polymeric channel to promote the long-term memory of the system. A synaptic array is further constructed for sensing color images and biomolecule-coded information communication. Taking advantage of the versatile biochemistry, the biochemical-driven reversible photoelectric response of the cone cell is further mimicked. This work introduces rich chemical designs into retinomorphic devices, providing a perspective for replicating the human visual system in fluids.

An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction.

By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we present a predictive model for drug response that incorporates diverse types of omics data, comprising genetic mutation, copy number variation, methylation, and gene expression data. This study proposes latent alignment for information mismatch in integration, which is achieved through an attention module capturing interactions among diverse types of omics data. The latent alignment and attention modules significantly improve predictions, outperforming the baseline model, with MSE = 1.1333, F1-score = 0.5342, and AUROC = 0.5776. High accuracy was achieved in predicting drug responses for piplartine and tenovin-6, while the accuracy was comparatively lower for mitomycin-C and obatoclax. The latent alignment module exclusively outperforms the baseline model, enhancing the MSE by 0.2375, the F1-score by 4.84%, and the AUROC by 6.1%. Similarly, the attention module only improves these metrics by 0.1899, 2.88%, and 2.84%, respectively. In the interpretability case study, panobinostat exhibited the most effective predicted response, with a value of -4.895. We provide reliable insights for drug selection in personalized medicine by identifying crucial genetic factors influencing drug response.

Synthesis of NiII porphyrin-NiII 5,15-diazaporphyrin hybrid tapes.

NiII porphyrin (P) and NiII 5,15-diazaporphyrin (DAP) hybrid tapes were synthesized by Suzuki-Miyaura cross-coupling reactions of meso- or ß-borylated P with ß-brominated DAP followed by intramolecular oxidative fusion reactions. Meso-ß doubly linked hybrid tapes were synthesized by oxidation of singly linked precursors with DDQ-FeCl3. Synthesis of triply linked hybrid tapes was achieved by oxidation with DDQ-FeCl3-AgOTf with suppression of peripheral ß-chlorination. In these tapes, DAP segments were present as a 20π-electronic unit, but their local antiaromatic contribution was trivial. Remarkably, these hybrid tapes were stable and exhibited extremely enhanced absorption bands in the NIR region and multiple reversible redox waves. A pentameric hybrid tape showed a remarkably sharp and red-shifted band at 1168 nm with ε = 5.75 × 105 M-1 cm-1. Singly linked P-DAP dyads were oxidized with DDQ-FeCl3 to give stable radicals, which were oxidized further to afford dimeric hybrid tapes possessing a nitrogen atom at the peripheral-side meso-position.

TFAP2A is involved in neuropathic pain by regulating Grin1 expression in glial cells of the dorsal root ganglion.

Neuropathic pain is a highly prevalent and refractory condition, yet its mechanism remains poorly understood. While NR1, the essential subunit of NMDA receptors, has long been recognized for its pivotal role in nociceptive transmission, its involvement in presynaptic stimulation is incompletely elucidated. Transcription factors can regulate the expression of both pro-nociceptive and analgesic factors. Our study shows that transcription factor TFAP2A was up-regulated in the dorsal root ganglion (DRG) neurons, satellite glial cells (SGCs), and Schwann cells following spinal nerve ligation (SNL). Intrathecal injection of siRNA targeting Tfap2a immediately or 7 days after SNL effectively alleviated SNL-induced pain hypersensitivity and reduced Tfap2a expression levels. Bioinformatics analysis revealed that TFAP2A may regulate the expression of the Grin1 gene, which encodes NR1. Dual-luciferase reporter assays confirmed TFAP2A's positive regulation of Grin1 expression. Notably, both Tfap2a and Grin1 were expressed in the primary SGCs and upregulated by lipopolysaccharides. The expression of Grin1 was also down-regulated in the DRG following Tfap2a knockdown. Furthermore, intrathecal injection of siRNA targeting Grin1 immediately or 7 days post-SNL effectively alleviated SNL-induced mechanical allodynia and thermal hyperalgesia. Finally, intrathecal Tfap2a siRNA alleviated SNL-induced neuronal hypersensitivity, and incubation of primary SGCs with Tfap2a siRNA decreased NMDA-induced upregulation of proinflammatory cytokines. Collectively, our study reveals the role of TFAP2A-Grin1 in regulating neuropathic pain in peripheral glia, offering a new strategy for the development of novel analgesics.

Inhibition of ferroptosis rescues M2 macrophages and alleviates arthritis by suppressing the HMGB1/TLR4/STAT3 axis in M1 macrophages.

Ferroptosis is a type of programmed cell death driven by iron-dependent lipid peroxidation. The TNF-mediated biosynthesis of glutathione has been shown to protect synovial fibroblasts from ferroptosis in the hyperplastic synovium. Ferroptosis induction provides a novel therapeutic approach for rheumatoid arthritis (RA) by reducing the population of synovial fibroblasts. The beginning and maintenance of synovitis in RA are significantly influenced by macrophages, as they generate cytokines that promote inflammation and contribute to the destruction of cartilage and bone. However, the vulnerability of macrophages to ferroptosis in RA remains unclear. In this study, we found that M2 macrophages are more vulnerable to ferroptosis than M1 macrophages in the environment of the arthritis synovium with a high level of iron, leading to an imbalance in the M1/M2 ratio. During ferroptosis, HMGB1 released by M2 macrophages interacts with TLR4 on M1 macrophages, which in turn triggers the activation of STAT3 signaling in M1 macrophages and contributes to the inflammatory response. Knockdown of TLR4 decreased the level of cytokines induced by HMGB1 in M1 macrophages. The ferroptosis inhibitor liproxstatin-1 (Lip-1) started at the presymptomatic stage in collagen-induced arthritis (CIA) model mice, and GPX4 overexpression in M2 macrophages at the onset of collagen antibody-induced arthritis (CAIA) protected M2 macrophages from ferroptotic cell death and significantly prevented the development of joint inflammation and destruction. Thus, our study demonstrated that M2 macrophages are vulnerable to ferroptosis in the microenvironment of the hyperplastic synovium and revealed that the HMGB1/TLR4/STAT3 axis is critical for the ability of ferroptotic M2 macrophages to contribute to the exacerbation of synovial inflammation in RA. Our findings provide novel insight into the progression and treatment of RA.

Molecular features of uranium-binding natural organic matter in a riparian wetland determined by ultrahigh resolution mass spectrometry.

Tims Branch riparian wetland located in South Carolina, USA has immobilized 94 % of the U released >50 years ago from a nuclear fuel fabrication facility. Sediment organic matter (OM) has been shown to play an important role in immobilizing U. Yet, uranium-OM-mineral interactions at the molecular scale have never been investigated at ambient concentrations. The objectives of this study were to extract, purify, and concentrate U-bound sediment OM along the stream water pathway and perform molecular characterization using Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS). Out of 9614 identified formulas, 715 contained U. These U-containing formulas were enriched with Fe, N, and/or S compared to the total OM. Lignin-like and protein-like molecules accounted for 40 % and 19 % of the U-containing formulas, respectively. Phosphorus-containing formulas were found to exert an insignificant influence on complexing U. U-containing formulas in the 'mobile' (groundwater extractable) OM fraction had lower (reduced) nominal oxidation states of carbon (NOSC); and less aromatic moieties than OM recovered from the 'immobile' (sodium pyrophosphate extractable) OM fraction. U-containing formulas in the redox interfacial zones (stream banks) compared to those in nearby up-slope zones tended to have smaller molecular weights; lower NOSC; higher contents of COO and/or CONO functional groups; and higher abundance of Fe-containing formulas. Fe was present in 38 % of the U-containing formulas but only 20 % of the total OM formulas. It is postulated that Fe played an important role in stabilizing the structure of sedimentary OM, especially U-containing compounds. The identification for the first time of hundreds of Fe-U-OM formulas demonstrates the complexity of such system is much greater than commonly believed and numerically predicting U binding behavior in OM-rich systems may require greater use of statistical or artificial intelligence approaches rather than deterministic approaches limited to measuring metal complexation with well-defined individual analogue organic ligands.

Chemical speciation, reactivity, and long-term burial of sedimentary phosphorus in Green Bay, a seasonally hypoxia-influenced freshwater estuary.

Sediment cores were collected along a trophic gradient in Green Bay, a seasonally hypoxia-influenced freshwater estuary in Lake Michigan, to measure various phosphorus (P) species, including exchangeable-P (Ex-P), iron-bound-P (Fe-P), biogenic-apatite and/or CaCO3-associated-P (CFA-P), organic-P (Org-P) and detrital-apatite-P (Detr-P). Although total phosphorus (TP) decreased with increasing depth, different P species exhibited distinct vertical distribution patterns with different post-depositional behaviors. The Ex-P, Fe-P and CFA-P species were identified as potentially bioavailable-P (BAP). Little variation was observed for Org-P and Detr-P species, especially below the upper-active-layer, both serving as the primary sink for P in sediment. Detr-P% decreased consistently from the near river plume station to the open bay in the north. P accumulation rates were estimated at 25.1 mmol-P/m2/yr (779 mg-P/m2/yr) in the south, 10.9 mmol-P/m2/yr (338 mg-P/m2/yr) in the central region, and 8.1 mmol-P/m2/yr (252 mg-P/m2/yr) in the north of Green Bay, showing a decrease in the depth of the upper active layer for P regeneration along the south-north transect. The overall potential P regeneration back into the water column increased from 2.8 mmol-P/m2/yr (87 mg-P/m2/yr) in the south, and 3.3 mmol-P/m2/yr (101 mg-P/m2/yr) in the central region to 5.6 mmol-P/m2/yr (173 mg-P/m2/yr) in the north of the bay, corresponding to P burial efficiencies of ∼89 %, 70 % and 31 % along the trophic gradient. The recent decrease in Detr-P and thus the increase in BAP over the last 2-3 decades could be related to anthropogenic activities, such as damming and implementation of agricultural conservation practices. Conversely, a recent increase in TOC/TOP ratios may reflect the increased extent of trophic status and seasonal hypoxia in bottom waters and enhanced regeneration and recycling of particulate P in Green Bay since the 1960s. New results from this study provide an improved understanding of the linkage between sources, internal cycling, and long-term burial of P in the basin.

CD147-K148me2-Driven Tumor Cell-Macrophage Crosstalk Provokes NSCLC Immunosuppression via the CCL5/CCR5 Axis.

Immunosuppression is a major hallmark of tumor progression in non-small cell lung cancer (NSCLC). Cluster of differentiation 147 (CD147), an important pro-tumorigenic factor, is closely linked to NSCLC immunosuppression. However, the role of CD147 di-methylation in the immunosuppressive tumor microenvironment (TME) remains unclear. Here, di-methylation of CD147 at Lys148 (CD147-K148me2) is identified as a common post-translational modification (PTM) in NSCLC that is significantly associated with unsatisfying survival outcomes among NSCLC sufferers, especially those in the advanced stages of the disease. The methyltransferase NSD2 catalyzes CD147 to generate CD147-K148me2. Further analysis demonstrates that CD147-K148me2 reestablishes the immunosuppressive TME and promotes NSCLC progression. Mechanistically, this modification promotes the interaction between cyclophilin A (CyPA) and CD147, and in turn, increases CCL5 gene transcription by activating p38-ZBTB32 signaling, leading to increased NSCLC cell-derived CCL5 secretion. Subsequently, CD147-K148me2-mediated CCL5 upregulation facilitates M2-like tumor-associated macrophage (TAM) infiltration in NSCLC tissues via CCL5/CCR5 axis-dependent intercellular crosstalk between tumor cells and macrophages, which is inhibited by blocking CD147-K148me2 with the targeted antibody 12C8. Overall, this study reveals the role of CD147-K148me2-driven intercellular crosstalk in the development of NSCLC immunosuppression, and provides a potential interventional strategy for PTM-targeted NSCLC therapy.

Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics.

Objective: This study aimed to delineate the clear cell renal cell carcinoma (ccRCC) intrinsic subtypes through unsupervised clustering of radiomics and transcriptomics data and to evaluate their associations with clinicopathological features, prognosis, and molecular characteristics. Methods: Using a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the "CancerSubtypes" package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics. Results: Clustering identified three subtypes, C1, C2, and C3, each of which displayed unique clinicopathological, prognostic, immune, and molecular distinctions. Notably, subtypes C1 and C3 were associated with poorer survival outcomes than subtype C2. Pathway analysis highlighted immune pathway activation in C1 and metabolic pathway prominence in C2. Gene mutation analysis identified VHL and PBRM1 as the most commonly mutated genes, with more mutated genes observed in the C3 subtype. Despite similar tumor mutation burdens, microsatellite instability, and RNA interference across subtypes, C1 and C3 demonstrated greater tumor immune dysfunction and rejection. In the validation cohort, the various subtypes showed comparable results in terms of clinicopathological features and prognosis to those observed in the training cohort, thus confirming the efficacy of our algorithm. Conclusion: Unsupervised clustering based on radiotranscriptomics can identify the intrinsic subtypes of ccRCC, and radiotranscriptomic subtypes can characterize the prognosis and molecular features of tumors, enabling noninvasive tumor risk stratification.

Adsorption Behavior of Co2+, Ni2+, Sr2+, Cs+, and I- by Corrosion Products α-FeOOH from Typical Metal Tanks.

Throughout the nuclear power production process, the disposal of radioactive waste has consistently raised concerns about environmental safety. When the metal tanks used for waste disposal are corroded, radionuclides seep into the groundwater environment and eventually into the biosphere, causing significant damage to the environment. Hence, investigating the adsorption behavior of radionuclides on the corrosion products of metal tanks used for waste disposal is an essential component of safety and evaluation protocols at disposal sites. In order to understand the adsorption behavior of important radionuclides 60Co, 59Ni, 90Sr, 135Cs and 129I on α-FeOOH, the influences of different pH values, contact time, temperature and ion concentration on the adsorption rate were studied. The adsorption mechanism was also discussed. It was revealed that the adsorption of key nuclides onto α-FeOOH is significantly influenced by both pH and temperature. This change in surface charge corresponds to alterations in the morphology of nuclide ions within the system, subsequently impacting the adsorption efficiency. Sodium ions (Na+) and chlorate ions (ClO3-) compete for coordination with nuclide ions, thereby exerting an additional influence on the adsorption process. The XPS analysis results demonstrate the formation of an internal coordination bond (Ni-O bond) between Ni2+ and iron oxide, which is adsorbed onto α-FeOOH.