Orexin-A mediates glioblastoma proliferation inhibition by increasing ferroptosis triggered by unstable iron pools and GPX4 depletion.

Glioblastoma (GBM) represents a prevalent form of primary malignant tumours in the central nervous system, but the options for effective treatment are extremely limited. Ferroptosis, as the most enriched programmed cell death process in glioma, makes a critical difference in glioma progression. Consequently, inducing ferroptosis has become an appealing strategy for tackling gliomas. Through the utilization of multi-omics sequencing data analysis, flow cytometry, MDA detection and transmission electron microscopy, the impact of orexin-A on ferroptosis in GBM was assessed. In this report, we provide the first evidence that orexin-A exerts inhibitory effects on GBM proliferation via the induction of ferroptosis. This induction is achieved by instigating an unsustainable increase in iron levels and depletion of GPX4. Moreover, the regulation of TFRC, FTH1 and GPX4 expression through the targeting of NFE2L2 appears to be one of the potential mechanisms underlying orexin-A-induced ferroptosis.

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing.

We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagnostic model, while molecular docking identified potential drugs. The diagnostic model based on the 10 selected genes achieved excellent performance, with an AUC of 1 in both training and validation datasets. The heatmap, combining cell abundance and gene expression, provided insights into SAH cellular composition. The convolutional neural network model exhibited a sensitivity and specificity of 1 in both datasets. Additionally, CD14, GPNMB, SPP1 and PRDX5 were specifically expressed in SAH-associated macrophages, highlighting its potential as a therapeutic target. Network pharmacology analysis identified some targeting drugs for SAH treatment. Our study characterised SAH macrophage subpopulations and identified key associated genes. We developed a robust diagnostic model and recognised CD14, GPNMB, SPP1 and PRDX5 as potential therapeutic targets. Further experiments and clinical investigations are needed to validate these findings and explore the clinical implications of targets in SAH treatment.

Therapeutic effects of orexin-A in sepsis-associated encephalopathy in mice.

BACKGROUND: Sepsis-associated encephalopathy (SAE) causes acute and long-term cognitive deficits. However, information on the prevention and treatment of cognitive dysfunction after sepsis is limited. The neuropeptide orexin-A (OXA) has been shown to play a protective role against neurological diseases by modulating the inflammatory response through the activation of OXR1 and OXR2 receptors. However, the role of OXA in mediating the neuroprotective effects of SAE has not yet been reported. METHODS: A mouse model of SAE was induced using cecal ligation perforation (CLP) and treated via intranasal administration of exogenous OXA after surgery. Mouse survival, in addition to cognitive and anxiety behaviors, were assessed. Changes in neurons, cerebral edema, blood-brain barrier (BBB) permeability, and brain ultrastructure were monitored. Levels of pro-inflammatory factors (IL-1ß, TNF-α) and microglial activation were also measured. The underlying molecular mechanisms were investigated by proteomics analysis and western blotting. RESULTS: Intranasal OXA treatment reduced mortality, ameliorated cognitive and emotional deficits, and attenuated cerebral edema, BBB disruption, and ultrastructural brain damage in mice. In addition, OXA significantly reduced the expression of the pro-inflammatory factors IL-1ß and TNF-α, and inhibited microglial activation. In addition, OXA downregulated the expression of the Rras and RAS proteins, and reduced the phosphorylation of P-38 and JNK, thus inhibiting activation of the MAPK pathway. JNJ-10,397,049 (an OXR2 blocker) reversed the effect of OXA, whereas SB-334,867 (an OXR1 blocker) did not. CONCLUSION: This study demonstrated that the intranasal administration of moderate amounts of OXA protects the BBB and inhibits the activation of the OXR2/RAS/MAPK pathway to attenuate the outcome of SAE, suggesting that OXA may be a promising therapeutic approach for the management of SAE.

YBX1 is required for maintaining myeloid leukemia cell survival by regulating BCL2 stability in an m6A-dependent manner.

RNA-binding proteins (RBPs) are critical regulators of transcription and translation that are often dysregulated in cancer. Although RBPs are increasingly recognized as being important for normal hematopoiesis and for hematologic malignancies as oncogenes or tumor suppressors, RBPs that are essential for the maintenance and survival of leukemia remain elusive. Here we show that YBX1 is specifically required for maintaining myeloid leukemia cell survival in an N6-methyladenosine (m6A)-dependent manner. We found that expression of YBX1 is significantly upregulated in myeloid leukemia cells, and deletion of YBX1 dramatically induces apoptosis and promotes differentiation coupled with reduced proliferation and impaired leukemic capacity of primary human and mouse acute myeloid leukemia cells in vitro and in vivo. Loss of YBX1 has no obvious effect on normal hematopoiesis. Mechanistically, YBX1 interacts with insulin-like growth factor 2 messenger RNA (mRNA)-binding proteins (IGF2BPs) and stabilizes m6A-tagged RNA. Moreover, YBX1 deficiency dysregulates the expression of apoptosis-related genes and promotes mRNA decay of MYC and BCL2 in an m6A-dependent manner, which contributes to the defective survival that results from deletion of YBX1. Thus, our findings have uncovered a selective and critical role of YBX1 in maintaining myeloid leukemia survival, which might provide a rationale for the therapeutic targeting of YBX1 in myeloid leukemia.

Pax transactivation domain-interacting protein is required for preserving hematopoietic stem cell quiescence via regulating lysosomal activity.

Hematopoietic stem cells (HSC) maintain lifetime whole blood hematopoiesis through self-renewal and differentiation. In order to sustain HSC stemness, most HSC reside in a quiescence state, which is affected by diverse cellular stress and intracellular signal transduction. How HSC accommodate those challenges to preserve lifetime capacity remains elusive. Here we show that Pax transactivation domain-interacting protein (PTIP) is required for preserving HSC quiescence via regulating lysosomal activity. Using a genetic knockout mouse model to specifically delete Ptip in HSC, we find that loss of Ptip promotes HSC exiting quiescence, and results in functional exhaustion of HSC. Mechanistically, Ptip loss increases lysosomal degradative activity of HSC. Restraining lysosomal activity restores the quiescence and repopulation potency of Ptip-/- HSC. Additionally, PTIP interacts with SMAD2/3 and mediates transforming growth factor-ß signaling-induced HSC quiescence. Overall, our work uncovers a key role of PTIP in sustaining HSC quiescence via regulating lysosomal activity.

Verbascoside inhibits progression of glioblastoma cells by promoting Let-7g-5p and down-regulating HMGA2 via Wnt/beta-catenin signalling blockade.

Glioblastoma (GBM) continues to show a poor prognosis despite advances in diagnostic and therapeutic approaches. The discovery of reliable prognostic indicators may significantly improve treatment outcome of GBM. In this study, we aimed to explore the function of verbascoside (VB) in GBM and its effects on GBM cell biological processes via let-7g-5p and HMGA2. Differentially expressed GBM-related microRNAs (miRNAs) were initially screened. Different concentrations of VB were applied to U87 and U251 GBM cells, and 50 µmol/L of VB was selected for subsequent experiments. Cells were transfected with let-7g-5p inhibitor or mimic, and overexpression of HMGA2 or siRNA against HMGA2 was induced, followed by treatment with VB. The regulatory relationships between VB, let-7g-5p, HMGA2 and Wnt/ß-catenin signalling pathway were determined. The results showed that HMGA2 was a direct target gene of let-7g-5p. VB treatment or let-7g-5p overexpression inhibited HMGA2 expression and the activation of Wnt/ß-catenin signalling pathway, which further inhibited cell viability, invasion, migration, tumour growth and promoted GBM cell apoptosis and autophagy. On the contrary, HMGA2 overexpression promoted cell viability, invasion, migration, tumour growth while inhibiting GBM cell apoptosis and autophagy. We demonstrated that VB inhibits cell viability and promotes cell autophagy in GBM cells by up-regulating let-7g-5p and down-regulating HMGA2 via Wnt/ß-catenin signalling blockade.

HOGMMNC: a higher order graph matching with multiple network constraints model for gene-drug regulatory modules identification.

MOTIVATION: The emergence of large amounts of genomic, chemical, and pharmacological data provides new opportunities and challenges. Identifying gene-drug associations is not only crucial in providing a comprehensive understanding of the molecular mechanisms of drug action, but is also important in the development of effective treatments for patients. However, accurately determining the complex associations among pharmacogenomic data remains challenging. We propose a higher order graph matching with multiple network constraints (HOGMMNC) model to accurately identify gene-drug modules. The HOGMMNC model aims to capture the inherent structural relations within data drawn from multiple sources by hypergraph matching. The proposed technique seamlessly integrates prior constraints to enhance the accuracy and reliability of the identified relations. An effective numerical solution is combined with a novel sampling strategy to solve the problem efficiently. RESULTS: The superiority and effectiveness of our proposed method are demonstrated through a comparison with four state-of-the-art techniques using synthetic and empirical data. The experiments on synthetic data show that the proposed method clearly outperforms other methods, especially in the presence of noise and irrelevant samples. The HOGMMNC model identifies eighteen gene-drug modules in the empirical data. The modules are validated to have significant associations via pathway analysis. Significance: The modules identified by HOGMMNC provide new insights into the molecular mechanisms of drug action and provide patients with more effective treatments. Our proposed method can be applied to the study of other biological correlated module identification problems (e.g. miRNA-gene, gene-methylation, and gene-disease). AVAILABILITY AND IMPLEMENTATION: A matlab package of HOGMMNC is available at https://github.com/scutbioinformatics/HOGMMNC/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptomic analysis of an l-threonine-producing Escherichia coli TWF001.

Wild-type Escherichia coli usually does not accumulate l-threonine, but E. coli strain TWF001 could produce 30.35 g/L l-threonine after 23-H fed-batch fermentation. To understand the mechanism for the high yield of l-threonine production in TWF001, transcriptomic analyses of the TWF001 cell samples collected at the logarithmic and stationary phases were performed, using the wild-type E. coli strain W3110 as the control. Compared with W3110, 1739 and 2361 genes were differentially transcribed in the logarithmic and stationary phases, respectively. Most genes related to the biosynthesis of l-threonine were significantly upregulated. Some key genes related to the NAD(P)H regeneration were upregulated. Many genes relevant to glycolysis and TCA cycle were downregulated. The key genes involved in the l-threonine degradation were downregulated. The gene rhtA encoding the l-threonine exporter was upregulated, whereas the genes sstT and tdcC encoding the l-threonine importer were downregulated. The upregulated genes in the glutamate pathway might form an amino-providing loop, which is beneficial for the high yield of l-threonine production. Many genes encoding the 30S and 50S subunits of ribosomes were also upregulated. The findings are useful for gene engineering to increase l-threonine production in E. coli.

Promoter library-based module combination (PLMC) technology for optimization of threonine biosynthesis in Corynebacterium glutamicum.

Due to the lack of efficient control elements and tools, the fine-tuning of gene expression in the multi-gene metabolic pathways is still a great challenge for engineering microbial cell factories, especially for the important industrial microorganism Corynebacterium glutamicum. In this study, the promoter library-based module combination (PLMC) technology was developed to efficiently optimize the expression of genes in C. glutamicum. A random promoter library was designed to contain the putative - 10 (NNTANANT) and - 35 (NNGNCN) consensus motifs, and refined through a three-step screening procedure to achieve numerous genetic control elements with different strength levels, including fluorescence-activated cell sorting (FACS) screening, agar plate screening, and 96-well plate screening. Multiple conventional strategies were employed for further precise characterizations of the promoter library, such as real-time quantitative PCR, sodium dodecyl sulfate polyacrylamide gel electrophoresis, FACS analysis, and the lacZ reporter system. These results suggested that the established promoter elements effectively regulated gene expression and showed varying strengths over a wide range. Subsequently, a multi-module combination technology was created based on the efficient promoter elements for combination and optimization of modules in the multi-gene pathways. Using this technology, the threonine biosynthesis pathway was reconstructed and optimized by predictable tuning expression of five modules in C. glutamicum. The threonine titer of the optimized strain was significantly improved to 12.8 g/L, an approximate 6.1-fold higher than that of the control strain. Overall, the PLMC technology presented in this study provides a rapid and effective method for combination and optimization of multi-gene pathways in C. glutamicum.

Easy and Fast Reconstruction of a 3D Avatar with an RGB-D Sensor.

This paper proposes a new easy and fast 3D avatar reconstruction method using an RGB-D sensor. Users can easily implement human body scanning and modeling just with a personal computer and a single RGB-D sensor such as a Microsoft Kinect within a small workspace in their home or office. To make the reconstruction of 3D avatars easy and fast, a new data capture strategy is proposed for efficient human body scanning, which captures only 18 frames from six views with a close scanning distance to fully cover the body; meanwhile, efficient alignment algorithms are presented to locally align the data frames in the single view and then globally align them in multi-views based on pairwise correspondence. In this method, we do not adopt shape priors or subdivision tools to synthesize the model, which helps to reduce modeling complexity. Experimental results indicate that this method can obtain accurate reconstructed 3D avatar models, and the running performance is faster than that of similar work. This research offers a useful tool for the manufacturers to quickly and economically create 3D avatars for products design, entertainment and online shopping.

Overexpression of methionine adenosyltransferase in Corynebacterium glutamicum for production of S-adenosyl-l-methionine.

Two genes encoding methionine adenosyltransferase, SAM2 from Saccharomyces cerevisiae and metK from Corynebacterium glutamicum, were individually cloned into pDXW-8, the shuttle vector between Escherichia coli and C. glutamicum, and overexpressed in E. coli DH5α and C. glutamicum ATCC13032. In DH5α, both genes were overexpressed and their protein products showed the activity of methionine adenosyltransferase. In ATCC13032, metK was overexpressed, its product MetK showed the enzyme activity and could convert l-methionine to S-adenosyl-l-methionine (SAM). However, when SAM2 was overexpressed in ATCC13032, neither the enzyme activity nor the conversion of SAM from l-methionine was observed. Reverse transcription PCR analysis and SDS-PAGE showed that SAM2 was transcribed but not translated in C. glutamicum. Therefore, SAM2-C, a mutant SAM2, was constructed by codon optimization, and overexpressed in ATCC13032; it was well transcribed and translated, and could convert l-methionine to SAM. Finally, SAM2-C and metK were individually overexpressed in E. coli BL21(DE3), and their products SAM2-C and MetK were purified and characterized. The optimum activity for both enzymes was found at pH 8.5 and 35 °C; SAM2-C and MetK have similar Km for ATP, but quite different Km for l-methionine. These results suggest that SAM2-C and MetK can be useful for developing C. glutamicum to produce SAM.

A Two-Phase Space Resection Model for Accurate Topographic Reconstruction from Lunar Imagery with PushbroomScanners.

Exterior orientation parameters' (EOP) estimation using space resection plays an important role in topographic reconstruction for push broom scanners. However, existing models of space resection are highly sensitive to errors in data. Unfortunately, for lunar imagery, the altitude data at the ground control points (GCPs) for space resection are error-prone. Thus, existing models fail to produce reliable EOPs. Motivated by a finding that for push broom scanners, angular rotations of EOPs can be estimated independent of the altitude data and only involving the geographic data at the GCPs, which are already provided, hence, we divide the modeling of space resection into two phases. Firstly, we estimate the angular rotations based on the reliable geographic data using our proposed mathematical model. Then, with the accurate angular rotations, the collinear equations for space resection are simplified into a linear problem, and the global optimal solution for the spatial position of EOPs can always be achieved. Moreover, a certainty term is integrated to penalize the unreliable altitude data for increasing the error tolerance. Experimental results evidence that our model can obtain more accurate EOPs and topographic maps not only for the simulated data, but also for the real data from Chang'E-1, compared to the existing space resection model.

Illumination-invariant and deformation-tolerant inner knuckle print recognition using portable devices.

We propose a novel biometric recognition method that identifies the inner knuckle print (IKP). It is robust enough to confront uncontrolled lighting conditions, pose variations and low imaging quality. Such robustness is crucial for its application on portable devices equipped with consumer-level cameras. We achieve this robustness by two means. First, we propose a novel feature extraction scheme that highlights the salient structure and suppresses incorrect and/or unwanted features. The extracted IKP features retain simple geometry and morphology and reduce the interference of illumination. Second, to counteract the deformation induced by different hand orientations, we propose a novel structure-context descriptor based on local statistics. To our best knowledge, we are the first to simultaneously consider the illumination invariance and deformation tolerance for appearance-based low-resolution hand biometrics. Settings in previous works are more restrictive. They made strong assumptions either about the illumination condition or the restrictive hand orientation. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of recognition accuracy, especially under uncontrolled lighting conditions and the flexible hand orientation requirement.

Electrocorticography-guided surgical treatment of solitary supratentorial cavernous malformations with secondary epilepsy.

OBJECTIVE: To evaluate the efficacy of electrocorticographic (ECoG) monitoring and the application of different surgical approaches in the surgical treatment of solitary supretentorial cavernous malformations with secondary epilepsy. METHODS: This study enrolled a consecutive series of 36 patients with solitary supratentorial cavernous malformations and secondary epilepsy who underwent surgery with intraoperative ECoG monitoring in the Department of Neurosurgery between January 2004 and January 2008. The patients were composed of 15 males and 21 females, aged between 8 and 52 years (mean age 27.3±2.8 years) at the time of surgery. Epilepsy history, the type of epilepsy at the presentation, lesion location, the incidence of residual epileptiform discharges, and postoperative outcomes were evaluated. RESULTS: Histopathological examination indicated cavernous malformations and hippocampal sclerosis in 36 and 5 cases, respectively. Neuronal degeneration, glial cell proliferation, and neurofibrillary tangles were found in all the resected cerebral tissues of extended lesionectomy of residual epileptic foci. Lesionectomy, anterior temporal lobectomy, anterior temporal lobectomy plus cortical thermocoagulation, extended lesionectomy, extended lesionectomy plus cortical thermocoagulation were performed in 4, 4, 1, 14, and 13 cases, respectively. Residual epileptiform discharges were captured in 9 out of the 14 patients who had additional cortical thermocoagulation. According to Engle class for postoperative outcomes, 27 cases were class I (75.00%), 5 were class II (13.89%), 2 were class III (5.56%), and 2 were class IV (5.56%), thus the total effective rate (class I+class II) was 88.89%. Neither of epilepsy history, the type of epilepsy, and the location of cavernous malformation was significantly related to outcomes (P>0.05). A significant relationship was found between the incidence of residual epileptiform discharges and outcomes (P=0.041). CONCLUSIONS: Intraoperative ECoG monitoring, the application of different surgical approaches, and the resection of residual epileptic foci could produce good result in the surgical treatment of supratentorial cavernous malformation with secondary epilepsy. Postoperative residual epileptiform discharges could be a useful predictor for evaluating the outcomes.

Adaptive block imaging based on compressive sensing in AFM.

Atomic force microscopy (AFM) is a kind of high-precision instrument to measure the surface morphology of various conductive or nonconductive samples. However, obtaining a high-resolution image with standard AFM scanning requires more time. Using block compressive sensing (BCS) is an effective approach to achieve rapid AFM imaging. But, the routine BCS-AFM imaging is difficult to balance the image quality of each local area. It is easy to lead to excessive sampling in some flat areas, resulting in time-consuming. At the same time, there is a lack of sampling in some areas with significant details, resulting in poor imaging quality. Thus, an innovative adaptive BCS-AFM imaging method is proposed. The overlapped block is used to eliminate blocking artifacts. Characteristic parameters (GTV, Lu, and SD) are used to predict the local morphological characteristics of the samples. Back propagation neural network is employed to acquire the appropriate sampling rate of each sub-block. Sampling points are obtained by pre-scanning and adaptive supplementary scanning. Afterward, all sub-block images are reconstructed using the TVAL3 algorithm. Each sample is capable of achieving uniform, excellent image quality. Image visual effects and evaluation indicators (PSNR and SSIM) are employed for the purpose of evaluating and analyzing the imaging effects of samples. Compared with two nonadaptive and two other adaptive imaging schemes, our proposed scheme has the characteristics of a high degree of automation, uniformly high-quality imaging, and rapid imaging speed. HIGHLIGHTS: The proposed adaptive BCS method can address the issues of uneven image quality and slow imaging speed in AFM. The appropriate sampling rate of each sub-block of the sample can be obtained by BP neural network. The introduction of GTV, Lu, and SD can effectively reveal the morphological features of AFM images. Seven samples with different morphology are used to test the performance of the proposed adaptive algorithm. Practical experiments are carried out with two samples to verify the feasibility of the proposed adaptive algorithm.

Hierarchical Porous Nonprecious High-entropy Alloys for Ultralow Overpotential in Hydrogen Evolution Reaction.

Water electrolysis is considered the cleanest method for hydrogen production. However, the widespread popularization of water splitting is limited by the high cost and scarce resources of efficient platinum group metals. Hence, it is imperative to develop an economical and high-performance electrocatalyst to improve the efficiency of hydrogen evolution reaction (HER). In this study, a hierarchical porous sandwich structure is fabricated through dealloying FeCoNiCuAl2 Mn high-entropy alloy (HEA). This free-standing electrocatalyst shows outstanding HER performance with a very small overpotential of 9.7 mV at 10 mA cm-2 and a low Tafel slope of 56.9 mV dec-1 in 1 M KOH solution, outperforming commercial Pt/C. Furthermore, this electrocatalytic system recorded excellent reaction stability over 100 h with a constant current density of 100 mA cm-2 . The enhanced electrochemical activity in high-entropy alloys results from the cocktail effect, which is detected by density functional theory (DFT) calculation. Additionally, micron- and nano-sized pores formed during etching boost mass transfer, ensuring sustained electrocatalyst performance even at high current densities. This work provides a new insight for development in the commercial electrocatalysts for water splitting.

Reprogramming of RNA m6A Modification Is Required for Acute Myeloid Leukemia Development.

Hematopoietic homeostasis is maintained by hematopoietic stem cells (HSCs), and it is tightly controlled at multiple levels to sustain the self-renewal capacity and differentiation potential of HSCs. Dysregulation of self-renewal and differentiation of HSCs leads to the development of hematologic diseases, including acute myeloid leukemia (AML). Thus, understanding the underlying mechanisms of HSC maintenance and the development of hematologic malignancies is one of the fundamental scientific endeavors in stem cell biology. N  6-methyladenosine (m6A) is a common modification in mammalian messenger RNAs (mRNAs) and plays important roles in various biological processes. In this study, we performed a comparative analysis of the dynamics of the RNA m6A methylome of hematopoietic stem and progenitor cells (HSPCs) and leukemia-initiating cells (LICs) in AML. We found that RNA m6A modification regulates the transformation of long-term HSCs into short-term HSCs and determines the lineage commitment of HSCs. Interestingly, m6A modification leads to reprogramming that promotes cellular transformation during AML development, and LIC-specific m6A targets are recognized by different m6A readers. Moreover, the very long chain fatty acid transporter ATP-binding cassette subfamily D member 2 (ABCD2) is a key factor that promotes AML development, and deletion of ABCD2 damages clonogenic ability, inhibits proliferation, and promotes apoptosis of human leukemia cells. This study provides a comprehensive understanding of the role of m6A in regulating cell state transition in normal hematopoiesis and leukemogenesis, and identifies ABCD2 as a key factor in AML development.

Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach.

OBJECTIVES: This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent complication in critically ill sepsis patients. METHODS: GWAS data was analyzed for genetic association between AKI and sepsis. Then, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, assessing their diagnostic and prognostic value through ROC curves and survival analysis. The study also examined the functional and immunological aspects of these genes, potential drug targets, and ceRNA networks. A mouse model of sepsis was created to test the reliability of these signature genes. RESULTS: LDSC confirmed a positive genetic correlation between AKI and sepsis, although no significant shared loci were found. Bidirectional MR analysis indicated mutual increased risks of AKI and sepsis. Then, 311 key genes common to sepsis and AKI were identified, with 42 significantly linked to sepsis prognosis. Six genes, selected through LASSO, SVM-RFE, and RF algorithms, showed excellent predictive performance for sepsis, AKI, and SA-AKI. The models demonstrated near-perfect AUCs in both training and testing datasets, and a perfect AUC in a sepsis mouse model. Significant differences in immune cells, immune-related pathways, HLA, and checkpoint genes were found between high- and low-risk groups. The study identified 62 potential drug treatments for sepsis and AKI and constructed a ceRNA network. CONCLUSIONS: The identified signature genes hold potential clinical applications, including prognostic evaluation and targeted therapeutic strategies for sepsis and AKI. However, further research is needed to confirm these findings.

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification.

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning (DL)-based models have achieved superior performance but centralized training suffers from the privacy leakage problem. Federated learning (FL) can safeguard privacy by keeping training samples locally, while existing FL-based frameworks require a large number of well-annotated training samples and numerous rounds of communication which hinder their viability in real-world clinical scenarios. In this article, we propose a lightweight and universal FL framework, named federated deep-broad learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication. By simply integrating a pretrained DL feature extractor, a fast and lightweight broad learning inference system with a classical federated aggregation approach, FedDBL can dramatically reduce data dependency and improve communication efficiency. Five-fold cross-validation demonstrates that FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Furthermore, due to the lightweight design and one-round communication, FedDBL reduces the communication burden from 4.6 GB to only 138.4 KB per client using the ResNet-50 backbone at 50-round training. Extensive experiments also show the scalability of FedDBL on model generalization to the unseen dataset, various client numbers, model personalization and other image modalities. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk. Code is available at https://github.com/tianpeng-deng/FedDBL.

Pan-cancer analysis revealed prognosis value and immunological relevance of RAMPs.

Whether receptor activity-modifying proteins (RAMPs) play a key role in human cancer prognosis and immunity remains unknown. We used data from the public databases, The Cancer Genome Atlas, Therapeutically Applicable Research to Generate Effective Treatments, and the Genotype-Tissue Expression project. We utilized bioinformatics methods, R software, and a variety of online databases to analyze RAMPs. In general, RAMPs were significantly and differentially expressed in multiple tumors, and RAMP expression was closely associated with prognosis, immune checkpoints, RNA-editing genes, tumor mutational burden, microsatellite instability, ploidy, and stemness indices. In addition, the expression of RAMPs is strongly correlated with tumor-infiltrating lymphocytes in human cancers. Moreover, the RAMP co-expression network is largely involved in many immune-related biological processes. Quantitative reverse transcription polymerase chain reaction and Western blot proved that RAMP3 was highly expressed in glioma, and RAMP3 promoted tumor proliferation and migration. RAMPs exhibit potential as prognostic and immune-related biomarkers in human cancers. Moreover, RAMPs can be potentially developed as therapeutic targets or used to enhance the efficacy of immunotherapy.