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The in situ metabolic profiling of the kidney is crucial to investigate the complex metabolic reprogramming underlying diabetic kidney disease (DKD) and to allow exploration of potential metabolic targets to improve kidney function. However, as the kidney is a highly heterogeneous organ, traditional metabolomic methods based on bulk analysis that produce an averaged measurement are inadequate. Herein, we employed an in situ metabolomics approach to discover alternations of DKD-associated metabolites and metabolic pathways. A series of histology-specific metabolic disturbances were discovered in situ using airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI–MSI). In combination with integrated metabolomics analysis, five dysfunctional metabolic pathways were identified and located in the kidneys of type-2 DKD mice simultaneously for the first time, including taurine metabolism, arginine and proline metabolism, histidine metabolism, biosynthesis of unsaturated fatty acids, and fatty acid degradation pathways. As crucial nodes of metabolic pathways, five dysregulated rate-limiting enzymes related to altered metabolic pathways were further identified. These findings reveal alternations from metabolites to enzymes at the molecular level in the progression of DKD and provide insights into DKD-associated metabolic reprogramming.

An arc fault is the leading cause of electrical fire. Aiming at the problems of difficulty in manually extracting features, poor generalization ability of models and low prediction accuracy in traditional arc fault detection algorithms, this paper proposes a fault arc detection method based on the fusion of channel attention mechanism and residual network model. This method is based on the channel attention mechanism to perform global average pooling of information from each channel of the feature map assigned by the residual block while ignoring the local spatial data to enhance the detection and recognition rate of the fault arc. This paper introduces a one-dimensional depth separable convolution (1D-DS) module to reduce the network model parameters and shorten the time of single prediction samples. The experimental results show that the F1 score of the network model for arc fault detection under mixed load conditions is 98.07%, and the parameter amount is reduced by 46.06%. The method proposed in this paper dramatically reduces the parameter quantity, floating-point number and time complexity of the network structure while ensuring a high recognition rate, which improves the real-time response ability to detect arc fault. It has a guiding significance for applying arc fault on the edge side. [ABSTRACT FROM AUTHOR]

Conclusions: Previous studies have stated the roles and correlation of the four TFs (Sox2, Atoh1, Neurog1, and Neurod1) in the development of neurosensory cells. but whether they are inherited pathogenic factors to cause non-syndromic sensorineural hearing loss is unknown so far. This is the first time for screening theSox2,Atoh1,Neurog1, andNeurod1genes in children with NSHL. The c.133A > G inNeurod1gene is a polymorphism, which is not associated with NSHL. Although these genes are the recognized TFs for modulating the development and transformation of NSCs, they may not be the inherited pathogenic factors to cause congenital severe or profound NSHL directly.Objective: To investigate the effect of the transcription factors (TFs) for the development of neurosensory cells (NSCs) and to explore the genetic etiology of congenital profound non-syndromic sensorineural hearing loss (NSHL).Methods: Children with NSHL, from multi-national and regional group, and control group were recruited to screen for the most common mutations for non-syndromic deafness among East Asian (mtDNA 12S rRNA: 1555A > G, 1494C > T; SLC26A4: IVS7-2 A > G, 2168 C > T). And mutational analysis of the coding regions inSox2,Atoh1andNeurog1,Neurod1genes were performed.Results: Only the c.133A > G (p. Ala45Thr) in theNeurod1gene was detected in this study. The allele frequencies of this variant were 88.00% and 84.88% in the inner ear malformation group and the normal inner ear group, respectively, while 90.85% of children in the control group carried c.133A > G. This variant existed in every group commonly and had no significant difference among them. No variant in the other two TFs was detected in this cohort. [ABSTRACT FROM PUBLISHER]

Objective: In this study, we investigated the effect of small interfering RNA (siRNA) of connective tissue growth factor (CTGF) by pRetro-Super (PRS) retrovirus vector on the expression of CTGF and related extracellular matrix molecules in human renal proximal tubular cells (HKCs) induced by high glucose, to provide help for renal tubulointerstitial fibrosis therapy. Methods: HKCs were exposed to d-glucose to observe their dose and time effect, while the mannitol as osmotic control. Retrovirus producing CTGF siRNA were constructed from the inverted oligonucleotides and transferred into packaging cell line PT67 with lipofectamine, and the virus supernatant was used to infect HKC. The expression of CTGF, fibronectin (FN) and collagen-type I (col1) were measured by semi-quantitative RT-PCR and Western blot. Results: In response to high glucose, CTGF expression in HKCs was increased in a dose- and time-dependent manner, whereas the increase did not occur in the osmotic control. Introduction of PRS-CTGF-siRNA resulted in the significant reduction of CTGF, FN, col1 mRNA ( p < 0.01, respectively) and CTGF, col1 protein ( p < 0.05, respectively) expression, while PRS void vector group did not have these effects ( p > 0.05). Conclusions: CTGF siRNA therapy can effectively reduce the levels of CTGF, FN and col1 induced by high glucose in cultured HKCs, which suggested that it may be a potential therapeutic strategy to prevent the renal interstitial fibrosis in the future. [ABSTRACT FROM AUTHOR]

3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given the high cost to obtain such annotation. Semi-supervised learning (SSL) solves this issue by training models with a large unlabelled and a small labelled dataset. The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data. These perturbations usually keep the spatial input context between views fairly consistent, which may cause the model to learn segmentation patterns from the spatial input contexts instead of the foreground objects. In this paper, we introduce the Translation Consistent Co-training (TraCoCo) which is a consistency learning SSL method that perturbs the input data views by varying their spatial input context, allowing the model to learn segmentation patterns from foreground objects. Furthermore, we propose a new Confident Regional Cross entropy (CRC) loss, which improves training convergence and keeps the robustness to co-training pseudo-labelling mistakes. Our method yields state-of-the-art (SOTA) results for several 3D data benchmarks, such as the Left Atrium (LA), Pancreas-CT (Pancreas), and Brain Tumor Segmentation (BraTS19). Our method also attains best results on a 2D-slice benchmark, namely the Automated Cardiac Diagnosis Challenge (ACDC), further demonstrating its effectiveness. Our code, training logs and checkpoints are available at https://github.com/yyliu01/ TraCoCo.

In contemporary society, the effective utilization of public resources remains a subject of significant concern. A common issue arises from defectors seeking to obtain an excessive share of these resources for personal gain, potentially leading to resource depletion. To mitigate this tragedy and ensure sustainable development of resources, implementing mechanisms to either reward those who adhere to distribution rules or penalize those who do not, appears advantageous. We introduce two models: a tax-reward model and a tax-punishment model, to address this issue. Our analysis reveals that in the tax-reward model, the evolutionary trajectory of the system is influenced not only by the tax revenue collected but also by the natural growth rate of the resources. Conversely, the tax-punishment model exhibits distinct characteristics when compared with the tax-reward model, notably the potential for bistability. In such scenarios, the selection of initial conditions is critical, as it can determine the system's path. Furthermore, our study identifies instances where the system lacks stable points, exemplified by a limit cycle phenomenon, underscoring the complexity and dynamism inherent in managing public resources using these models.