Effects of Surface IR783 Density on the In Vivo Behavior and Imaging Performance of Liposomes.

Background: Nanoparticles conjugated with fluorescent probes have versatile applications, serving not only for targeted fluorescent imaging but also for evaluating the in vivo profiles of designed nanoparticles. However, the relationship between fluorophore density and nanoparticle behavior remains unexplored. Methods: The IR783-modified liposomes (IR783-sLip) were prepared through a modified ethanol injection and extrusion method. The cellular uptake efficiency of IR783-sLip was characterized by flow cytometry and fluorescence microscope imaging. The effects of IR783 density on liposomal in vivo behavior were investigated by pharmacokinetic studies, biodistribution studies, and in vivo imaging. The constitution of protein corona was analyzed by the Western blot assay. Results: Dense IR783 modification improved cellular uptake of liposomes in vitro but hindered their blood retention and tumor imaging performance in vivo. We found a correlation between IR783 density and protein corona absorption, particularly IgM, which significantly impacted the liposome performance. Meanwhile, we observed that increasing IR783 density did not consistently improve the effectiveness of tumor imaging. Conclusions: Increasing the density of modified IR783 on liposomes is not always beneficial for tumor near-infrared (NIR) imaging yield. It is not advisable to prematurely evaluate novel nanomaterials through fluorescence dye conjugation without carefully optimizing the density of the modifications.

Type I conventional dendritic cells facilitate immunotherapy in pancreatic cancer.

Inflammation and tissue damage associated with pancreatitis can precede or occur concurrently with pancreatic ductal adenocarcinoma (PDAC). We demonstrate that in PDAC coupled with pancreatitis (ptPDAC), antigen-presenting type I conventional dendritic cells (cDC1s) are specifically activated. Immune checkpoint blockade therapy (iCBT) leads to cytotoxic CD8+ T cell activation and elimination of ptPDAC with restoration of life span even upon PDAC rechallenge. Using PDAC antigen-loaded cDC1s as a vaccine, immunotherapy-resistant PDAC was rendered sensitive to iCBT with elimination of tumors. cDC1 vaccination coupled with iCBT identified specific CDR3 sequences in the tumor-infiltrating CD8+ T cells with potential therapeutic importance. This study identifies a fundamental difference in the immune microenvironment in PDAC concurrent with, or without, pancreatitis and provides a rationale for combining cDC1 vaccination with iCBT as a potential treatment option.

Surgical margins in head and neck squamous cell carcinoma: A narrative review.

Head and neck squamous cell carcinoma (HNSCC), a prevalent and frequently recurring malignancy, often necessitates surgical intervention. The surgical margin (SM) plays a pivotal role in determining the postoperative treatment strategy and prognostic evaluation of HNSCC. Nonetheless, the process of clinical appraisal and assessment of the SMs remains a complex and indeterminate endeavor, thereby leading to potential difficulties for surgeons in defining the extent of resection. In this regard, we undertake a comprehensive review of the suggested surgical distance in varying circumstances, diverse methods of margin evaluation, and the delicate balance that must be maintained between tissue resection and preservation in head and neck surgical procedures. This review is intended to provide surgeons with pragmatic guidance in selecting the most suitable resection techniques, and in improving patients' quality of life by achieving optimal functional and aesthetic restoration.

VNN2-expressing circulating monocytes exhibit unique functional characteristics and are decreased in patients with primary Sjögren's syndrome.

OBJECTIVE: This study aims to elucidate the significance of VNN2 expression in peripheral blood monocytes and its clinical relevance in primary Sjögren's syndrome (pSS). METHODS: We investigated VNN2 expression by analyzing single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells. Flow cytometry was used to detect and compare VNN2 expression in total monocytes, classical monocytes (cMo), intermediate monocytes (iMo) and non-classical monocytes (ncMo). Additionally, we examined the expression of HLA, ICAM1, CD62L, ITGAM, S100A8, S100A9, CCR2, CCR6, CX3CR1 and CXCR3 in VNN2+ and VNN2- cells. We analyzed the correlation between VNN2 expression and clinical indicators and assessed the clinical utility of VNN2+ monocytes in pSS diagnosis using receiver operating characteristic curves. RESULTS: We observed high VNN2 expression in monocytes, with significantly higher levels in CD14++ monocytes compared to ncMo. VNN2+ monocytes exhibited decreased expression of HLA and CD62L and increased expression of ICAM1, ITGAM, S100A8, S100A9, CCR2, CCR6, CX3CR1 and CXCR3 compared to VNN2- monocytes. Although scRNA-seq data showed that VNN2 mRNA was upregulated, cell surface expression of VNN2 was decreased in monocytes from pSS patients compared to healthy controls. The reduced levels of VNN2+ monocyte subpopulations in pSS patients were negatively correlated with anti-ribosome antibody levels and positively correlated with complement 4 levels. Detection of VNN2 expression in monocytes can aid in the auxiliary diagnosis of pSS. CONCLUSION: Monocytes expressing cell surface VNN2 are significantly reduced in pSS patients. This suggests a potential role for VNN2 in pSS development and its potential use as a diagnostic marker for pSS.

Machine learning for the prediction of delirium in elderly intensive care unit patients.

PURPOSE: This study aims to develop and validate a prediction model for delirium in elderly ICU patients and help clinicians identify high-risk patients at the early stage. METHODS: Patients admitted to ICU for at least 24 h and using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (76,943 ICU stays from 2008 to 2019) were considered. Patients with a positive delirium test in the first 24 h and under 65 years of age were excluded. Two prediction models, machine learning extreme gradient boosting (XGBoost) and logistic regression (LR) model, were developed and validated to predict the onset of delirium. RESULTS: Of the 18,760 patients included in the analysis, 3463(18.5%) were delirium positive. A total of 22 significant predictors were selected by LASSO regression. The XGBoost model demonstrated superior performance over the LR model, with the Area Under the Receiver Operating Characteristic (AUC) values of 0.853 (95% confidence interval [CI] 0.846-0.861) and 0.831 (95% CI 0.815-0.847) in the training and testing datasets, respectively. Moreover, the XGBoost model outperformed the LR model in both calibration and clinical utility. The top five predictors associated with the onset of delirium were sequential organ failure assessment (SOFA), infection, minimum platelets, maximum systolic blood pressure (SBP), and maximum temperature. CONCLUSION: The XGBoost model demonstrated good predictive performance for delirium among elderly ICU patients, thus assisting clinicians in identifying high-risk patients at the early stage and implementing targeted interventions to improve outcome.

Exhaled VOC detection in lung cancer screening: a comprehensive meta-analysis.

BACKGROUND: Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined. METHODS: Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies' titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity. RESULTS: This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research. CONCLUSION: This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.

Qualitative study on the perception of good death in patients with end-stage cancer in oncology nurses.

OBJECTIVE: To explore the perception of good death of patients with end-stage cancer by nurses in the oncology department. METHOD: In the study we used a phenomenological approach and semi-structured interviews. A total of 11 nurses from the oncology department of a Grade A hospital in Taizhou were interviewed on the cognition of good death from July 1 to September 30, 2022. Colaizzi's analysis method was used to analyse the interview data. This study followed the consolidated criteria for reporting qualitative research (COREQ). RESULT: Four themes were identified: a strong sense of responsibility and mission; To sustain hope and faith; The important role of family members; Improve patients' quality of life. CONCLUSION: The nurses in the department of oncology have a low level of knowledge about the "good death", and the correct understanding and view of the "good death" is the premise of the realization of " good death". The ability of nursing staff to improve the "good death", attention, and meet the needs and wishes of individuals and families, is the guarantee of the realization of "good death".

Design of a Robust Synchronization-Based Topology Observer for Complex Delayed Networks with Fixed and Adaptive Coupling Strength.

Network topology plays a key role in determining the characteristics and dynamical behaviors of a network. But in practice, network topology is sometimes hidden or uncertain ahead of time because of network complexity. In this paper, a robust-synchronization-based topology observer (STO) is proposed and applied to solve the problem of identifying the topology of complex delayed networks (TICDNs). In comparison to the existing literature, the proposed STO does not require any prior knowledge about the range of topological parameters and does not have strict limits on topology type. Furthermore, the proposed STO is suitable not only for networks with fixed coupling strength, but also for networks with adaptive coupling strength. Finally, a few comparison examples for TICDNs are used to verify the feasibility and efficiency of the proposed STO, and the results show that the proposed STO outperforms the other methods.

A new fossil species of the extant genus Vicelva from mid-Cretaceous Kachin amber (Coleoptera: Staphylinidae).

A new species of the extant staphylinid genus Vicelva Moore & Legner, V. rasilis sp. nov., is reported from mid-Cretaceous Kachin amber of northern Myanmar. Vicelva rasilis is distinguishable from extant members of Vicelva by the smoother dorsal surface of head, pronotum and elytra, less prominent median projection of clypeus, unnotched mesal edge of mandibles, semiglabrous antennomere 6, and longer tarsomere 1. The pollen-containing coprolite attached to the beetle and the crystals within the beetle body provide valuable information about the biology and taphonomy of the fossil.

TL1A Promotes Fibrogenesis in Colonic Fibroblasts via the TGF-ß1/Smad3 Signaling Pathway.

OBJECTIVE: Intestinal fibrosis is a refractory complication of inflammatory bowel disease (IBD). Tumor necrosis factor ligand-related molecule-1A (TL1A) is important for IBD-related intestinal fibrosis in a dextran sodium sulfate (DSS)-induced experimental colitis model. This study aimed to explore the effects of TL1A on human colonic fibroblasts. METHODS: A trinitrobenzene sulfonic acid (TNBS)-induced experimental colitis model of LCK-CD2-TL1A-GFP transgenic (Tg) or wild-type (WT) mice was established to determine the effect and mechanism of TL1A on intestinal fibrosis. The human colonic fibroblast CCD-18Co cell line was treated concurrently with TL1A and human peripheral blood mononuclear cell (PBMC) supernatant. The proliferation and activation of CCD-18Co cells were detected by BrdU assays, flow cytometry, immunocytochemistry and Western blotting. Collagen metabolism was tested by Western blotting and real-time quantitative polymerase chain reaction (RT-qPCR). RESULTS: The level of collagen metabolism in the TNBS+ethyl alcohol (EtOH)/Tg group was greater than that in the TNBS+EtOH/WT group. Transforming growth factor-ß1 (TGF-ß1) and p-Smad3 in the TNBS+EtOH/Tg group were upregulated as compared with those in the TNBS+EtOH/WT group. The proliferation of CCD-18Co cells was promoted by the addition of human PBMC supernatant supplemented with 20 ng/mL TL1A, and the addition of human PBMC supernatant and TL1A increased CCD-18Co proliferation by 24.4% at 24 h. TL1A promoted cell activation and increased the levels of COL1A2, COL3A1, and TIMP-1 in CCD-18Co cells. Treatment of CCD-18Co cells with TL1A increased the expression of TGF-ß1 and p-Smad3. CONCLUSION: TL1A promotes TGF-ß1-mediated intestinal fibroblast activation, proliferation, and collagen deposition and is likely related to an increase in the TGF-ß1/Smad3 signaling pathway.

From Pixels to Principles: A Decade of Progress and Landscape in Trustworthy Computer Vision.

The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field of computer vision. This paper utilizes bibliometrics and text-mining techniques to quantitatively analyze papers from prominent academic conferences in computer vision over the past decade. It first reveals the developing trends and specific distribution of attention regarding trustworthy aspects in the computer vision field, as well as the inherent connections between ethical dimensions and different stages of visual model development. A life-cycle framework regarding trustworthy computer vision is then presented by making the relevant trustworthy issues, the operation pipeline of AI models, and viable technical solutions interconnected, providing researchers and policymakers with references and guidance for achieving trustworthy CV. Finally, it discusses particular motivations for conducting trustworthy practices and underscores the consistency and ambivalence among various trustworthy principles and technical attributes.

Robust Model Free Adaptive Predictive Control for Wastewater Treatment Process With Packet Dropouts.

External disturbances and packet dropouts will lead to poor control performance for the wastewater treatment process (WWTP). To address this issue, a robust model-free adaptive predictive control (RMFAPC) strategy with a packet dropout compensation mechanism (PDCM) is proposed for WWTP. First, a dynamic linearization approach (DLA), relying only on perturbed process data, is employed to approximate the system dynamics. Second, a predictive control strategy is introduced to avoid a short-sighted control decision, and an extended state observer (ESO) is used to attenuate the disturbance effectively. Furthermore, a PDCM strategy is designed to handle the packet dropout problem, and the stability of RMFAPC is rigorously analyzed. Finally, the correctness and effectiveness of RMFAPC are verified through extensive simulations. The simulation results indicate that RMFAPC can significantly reduce IAE by 0.0223 and 0.1976 in two scenarios, regardless of whether the expected value remains constant or varies. This comparison to MFAPC demonstrates the superior robustness of RMFAPC against disturbances. The ablation experiment on PDCM further confirms its capability in handling the packet dropout problem.

Pathological Asymmetry-Guided Progressive Learning for Acute Ischemic Stroke Infarct Segmentation.

Quantitative infarct estimation is crucial for diagnosis, treatment and prognosis in acute ischemic stroke (AIS) patients. As the early changes of ischemic tissue are subtle and easily confounded by normal brain tissue, it remains a very challenging task. However, existing methods often ignore or confuse the contribution of different types of anatomical asymmetry caused by intrinsic and pathological changes to segmentation. Further, inefficient domain knowledge utilization leads to mis-segmentation for AIS infarcts. Inspired by this idea, we propose a pathological asymmetry-guided progressive learning (PAPL) method for AIS infarct segmentation. PAPL mimics the step-by-step learning patterns observed in humans, including three progressive stages: knowledge preparation stage, formal learning stage, and examination improvement stage. First, knowledge preparation stage accumulates the preparatory domain knowledge of the infarct segmentation task, helping to learn domain-specific knowledge representations to enhance the discriminative ability for pathological asymmetries by constructed contrastive learning task. Then, formal learning stage efficiently performs end-to-end training guided by learned knowledge representations, in which the designed feature compensation module (FCM) can leverage the anatomy similarity between adjacent slices from the volumetric medical image to help aggregate rich anatomical context information. Finally, examination improvement stage encourages improving the infarct prediction from the previous stage, where the proposed perception refinement strategy (RPRS) further exploits the bilateral difference comparison to correct the mis-segmentation infarct regions by adaptively regional shrink and expansion. Extensive experiments on public and in-house NCCT datasets demonstrated the superiority of the proposed PAPL, which is promising to help better stroke evaluation and treatment.

Vanin-2 is expressed in peripheral blood T cells and upregulated in SLE patients.

Members of the vanin gene family include VNN1, VNN2 and VNN3 in humans. Although the functions of vanins have been widely examined in myeloid cells, their expression and functions have not been clarified in T lymphocytes. This study aimed to elucidate the significance of Vanin-2 (VNN2) on human peripheral blood T lymphocytes and study its expression in systemic lupus erythematosus (SLE). The differential expression of Vanins was analysed by bioinformatics. VNN2 expressions in peripheral blood T cell subsets were analysed by single-cell RNA sequencing data and flow cytometry. Changes of VNN2 expression before and after T cell activation were further clarified by western blot. The function of VNN2+ cells was studied by granzyme B and perforin detection. Changes in VNN2+ proportions in T cell subsets of SLE patients were further analysed. In the present study, only VNN2 among vanins showed distinguishable expression in T cells. VNN2+ percentages were higher in CD8+ T cells than in CD4+ T cells. VNN2+ T cells were with a higher memory T cell composition. VNN2 expression was significantly increased after T cell stimulation. VNN2+ T cells had higher levels of granzyme B and perforin secretion than VNN2- T cells. Clinically, VNN2+ percentages in T cells of SLE patients were upregulated. Together, these data suggested that VNN2 is expressed in peripheral blood T cells characterized more granzyme B and perforin secretion, and increased VNN2+ T cells in SLE patients could reflect altered T cell functions in vivo.

van der Waals Integration of Large-Area Monocrystalline 3D Perovskite Thin Films on Arbitrary Semiconductor Substrates for Heterojunctions.

Perovskite monocrystalline films are regarded as desirable candidates for the integration of high-performance optoelectronics due to their unique photophysical properties. However, the heterogeneous integration of a perovskite monocrystalline film with other semiconductors is fundamentally limited by the lattice mismatch, which hinders direct epitaxy. Herein, the van der Waals (vdW) integration strategy for 3D perovskites is developed, where perovskite monocrystalline films are epitaxially grown on the mother substrate, followed by its peeling off and transferring to arbitrary semiconductors, forming monocrystalline heterojunctions. The as-achieved CsPbBr3-Nb-doped SrTiO3 (Nb:STO) vdW p-n heterojunction exhibited comparable performance to their directly epitaxial counterpart, demonstrating the feasibility of vdW integration for 3D perovskites. Furthermore, the vdW integration could be extended to silicon substrates, rendering the CsPbBr3-n-Si and CsPbCl3-p-Si p-n heterojunction with apparent rectification behaviors and photoresponse. The vdW integration significantly enriches the selections of semiconductors hybridizing with perovskites and provides opportunities for monocrystalline perovskite optoelectronics with complex configurations and multiple functionalities.

Nomogram for the preoperative prediction of Ki-67 expression and prognosis in stage IA lung adenocarcinoma based on clinical and multi-slice spiral computed tomography features.

OBJECTIVE: This study developed and validated a nomogram utilizing clinical and multi-slice spiral computed tomography (MSCT) features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma. Additionally, we assessed the predictive accuracy of Ki-67 expression levels, as determined by our model, in estimating the prognosis of stage IA lung adenocarcinoma. MATERIALS AND METHODS: We retrospectively analyzed data from 395 patients with pathologically confirmed stage IA lung adenocarcinoma. A total of 322 patients were divided into training and internal validation groups at a 6:4 ratio, whereas the remaining 73 patients composed the external validation group. According to the pathological results, the patients were classified into high and low Ki-67 labeling index (LI) groups. Clinical and CT features were subjected to statistical analysis. The training group was used to construct a predictive model through logistic regression and to formulate a nomogram. The nomogram's predictive ability and goodness-of-fit were assessed. Internal and external validations were performed, and clinical utility was evaluated. Finally, the recurrence-free survival (RFS) rates were compared. RESULTS: In the training group, sex, age, tumor density type, tumor-lung interface, lobulation, spiculation, pleural indentation, and maximum nodule diameter differed significantly between patients with high and low Ki-67 LI. Multivariate logistic regression analysis revealed that sex, tumor density, and maximum nodule diameter were significantly associated with high Ki-67 expression in stage IA lung adenocarcinoma. The calibration curves closely resembled the standard curves, indicating the excellent discrimination and accuracy of the model. Decision curve analysis revealed favorable clinical utility. Patients with a nomogram-predicted high Ki-67 LI exhibited worse RFS. CONCLUSION: The nomogram utilizing clinical and CT features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma demonstrated excellent performance, clinical utility, and prognostic significance, suggesting that this nomogram is a noninvasive personalized approach for the preoperative prediction of Ki-67 expression.

A new computed tomography score-based staging for melioidosis pneumonia to predict progression.

Background: Melioidosis pneumonia, caused by the bacterium Burkholderia pseudomallei, is a serious infectious disease prevalent in tropical regions. Chest computed tomography (CT) has emerged as a valuable tool for assessing the severity and progression of lung involvement in melioidosis pneumonia. However, there persists a need for the quantitative assessment of CT characteristics and staging methodologies to precisely anticipate disease progression. This study aimed to quantitatively extract CT features and evaluate a CT score-based staging system in predicting the progression of melioidosis pneumonia. Methods: This study included 97 patients with culture-confirmed melioidosis pneumonia who presented between January 2002 and December 2021. Lung segmentation and annotation of lesions (consolidation, nodules, and cavity) were used for feature extraction. The features, including the involved area, amount, and intensity, were extracted. The CT scores of the lesion features were defined by the feature importance weight and qualitative stage of melioidosis pneumonia. Gaussian process regression (GPR) was used to predict patients with severe or critical melioidosis pneumonia according to CT scores. Results: The melioidosis pneumonia stages included acute stage (0-7 days), subacute stage (8-28 days), and chronic stage (>28 days). In the acute stage, the CT scores of all patients ranged from 2.5 to 6.5. In the subacute stage, the CT scores for the severe and mild patients were 3.0-7.0 and 2.0-5.0, respectively. In the chronic stage, the CT score of the mild patients fluctuated approximately between 2.5 and 3.5 in a linear distribution. Consolidation was the most common type of lung lesion in those with melioidosis pneumonia. Between stages I and II, the percentage of severe scans with nodules dropped from 72.22% to 47.62% (P<0.05), and the percentage of severe scans with cavities significantly increased from 16.67% to 57.14% (P<0.05). The GPR optimization function yielded area under the receiver operating characteristic curves of 0.71 for stage I, 0.92 for stage II, and 0.87 for all stages. Conclusions: In patients with melioidosis pneumonia, it is reasonable to divide the period (the whole progression of melioidosis pneumonia) into three stages to determine the prognosis.

Phenotypes of Polish primary care patients using hierarchical clustering: Exploring the risk of mortality in the LIPIDOGEN2015 study cohort.

BACKGROUND: Comorbidities in primary care do not occur in isolation but tend to cluster together causing various clinically complex phenotypes. This study aimed to distinguish phenotype clusters and identify the risks of all-cause mortality in primary care. METHODS: The baseline cohort of the LIPIDOGEN2015 sub-study involved 1779 patients recruited by 438 primary care physicians. To identify different phenotype clusters, we used hierarchical clustering and investigated differences between clinical characteristics and mortality between clusters. We then performed causal analyses using causal mediation analysis to explore potential mediators between different clusters and all-cause mortality. RESULTS: A total of 1756 patients were included (mean age 51.2, SD 13.0; 60.3% female), with a median follow-up of 5.7 years. Three clusters were identified: Cluster 1 (n = 543) was characterised by overweight/obesity (body mass index ≥ 25 kg/m2), older (age ≥ 65 years), more comorbidities; Cluster 2 (n = 459) was characterised by non-overweight/obesity, younger, fewer comorbidities; Cluster 3 (n = 754) was characterised by overweight/obesity, younger, fewer comorbidities. Adjusted Cox regression showed that compared with Cluster 2, Cluster 1 had a significantly higher risk of all-cause mortality (HR 3.87, 95% CI: 1.24-15.91), whereas this was insignificantly different for Cluster 3. Causal mediation analyses showed that decreased protein thiol groups mediated the hazard effect of all-cause mortality in Cluster 1 compared with Cluster 2, but not between Clusters 1 and 3. CONCLUSION: Overweight/obesity older patients with more comorbidities had the highest risk of long-term all-cause mortality, and in the young group population overweight/obesity insignificantly increased the risk in the long-term follow-up, providing a basis for stratified phenotypic risk management.

Synthetic Mn3Ce2O5-Cluster Mimicking the Oxygen-Evolving Center in Photosynthesis.

The photosynthetic oxygen-evolving center (OEC) is a unique Mn4CaO5-cluster that catalyzes water splitting into electrons, protons, and dioxygen. Precisely structural and functional mimicking of the OEC is a long-standing challenge and pressingly needed for understanding the structure-function relationship and catalytic mechanism of O-O bond formation. Herein we report two simple and robust artificial Mn3Ce2O5-complexes that display a remarkable structural similarity to the OEC in regarding of the ten-atom core (five metal ions and five oxygen bridges) and the alkyl carboxylate peripheral ligands. This Mn3Ce2O5-cluster can catalyze the water-splitting reaction on the surface of ITO electrode. These results clearly show that cerium can structurally and functionally replace both calcium and manganese in the cluster. Mass spectroscopic measurements demonstrate that the oxide bridges in the cluster are exchangeable and can be rapidly replaced by the isotopic oxygen of H218O in acetonitrile solution, which supports that the oxide bridge(s) may serve as the active site for the formation of O-O bond during the water-splitting reaction. These results would contribute to our understanding of the structure-reactivity relationship of both natural and artificial clusters and shed new light on the development of efficient water-splitting catalysts in artificial photosynthesis.

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.

BACKGROUND: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia. METHODS: Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein-protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated. RESULTS: The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93). CONCLUSION: We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.