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Background: Diabetic nephropathy (DN) is one of the most serious complications of diabetes mellitus and the main cause of the end-stage renal disease (ESRD). Activation of the NLRP3 inflammasome has been proven to play an important role in the development of DN. Thus, specific and direct targets of NLRP3 inflammasome assembly may have therapeutic potential. CY-09 is a new NLRP3 inflammasome specific inhibitor that has been shown to protect against non-alcoholic fatty liver disease (NAFLD) by inhibiting the activation of the NLRP3 inflammasome. However, its role in kidney disease, especially DN, has not been reported. Methods: In this study, we used HE staining to assess renal pathological damage in each group, and RT-PCR, immunofluorescence and WB were performed to detect the expression changes in inflammatory and fibrosis proteins. The apoptosis level was detected by TUNEL staining. Results: Here, we showed increased inflammation, oxidative stress, apoptosis and fibrosis in db/db mice, while CY-09 exerted renoprotection by inhibiting NLRP3 inflammasome activation. In vitro, CY-09 also inhibited NLRP3 and reduced caspase-1, IL-18, IL-1β and apoptosis in a dose-dependent manner. Conclusion: CY-09 effectively protects the kidney from hyperglycemia induced damage by inhibiting the NLRP3 inflammasome and may be a promising therapeutic strategy to prevent the progression of DKD.

Objective: Loin muscle area (LMA) is an important target trait of pig breeding. This study aimed to identify single nucleotide polymorphisms (SNPs) and genes associated with LMA in the Duroc×(Landrace×Yorkshire) crossbred pigs (DLY).Methods: A genome-wide association study was performed using the Illumina 50K chip to map the genetic marker and genes associated with LMA in 511 DLY pigs (255 boars and 256 sows).Results: After quality control, we detected 35,426 SNPs, including six SNPs significantly associated with LMA in pigs, with MARC0094338 and ASGA0072817 being the two key SNPs responsible for 1.77% and 2.48% of the phenotypic variance of LMA, respectively. Based on previous research, we determined two candidate genes (growth hormone receptor [GHR] and 3-oxoacid Co A-transferase 1 [OXCT1]) that are associated with fat deposition and muscle growth and found further additional genes (MYOCD, ARHGAP44, ELAC2, MAP2K4, FBXO4, FBLL1, RARS1, SLIT3, and RANK3) that are presumed to have an effect on LMA.Conclusion: This study contributes to the identification of the mutation that underlies quantitative trait loci associated with LMA and to future pig breeding programs based on marker-assisted selection. Further studies are needed to elucidate the role of the identified candidate genes in the physiological processes involved in LMA regulation.

The DArk Matter Particle Explorer (DAMPE) is well suitable for searching for monochromatic and sharp $\gamma$-ray structures in the GeV$-$TeV range thanks to its unprecedented high energy resolution. In this work, we search for $\gamma$-ray line structures using five years of DAMPE data. To improve the sensitivity, we develop two types of dedicated data sets (including the BgoOnly data which is the first time to be used in the data analysis for the calorimeter-based gamma-ray observatories) and adopt the signal-to-noise ratio optimized regions of interest (ROIs) for different DM density profiles. No line signals or candidates are found between 10 and 300 GeV in the Galaxy. The constraints on the velocity-averaged cross section for $\chi\chi \to \gamma\gamma$ and the decay lifetime for $\chi \to \gamma\nu$, both at 95% confidence level, have been calculated and the systematic uncertainties have been taken into account. Comparing to the previous Fermi-LAT results, though DAMPE has an acceptance smaller by a factor of $\sim 10$, similar constraints on the DM parameters are achieved and below 100 GeV the lower limits on the decay lifetime are even stronger by a factor of a few. Our results demonstrate the potential of high-energy-resolution observations on dark matter detection., Comment: 14 pages, 8 figures. Update the content to keep up with the published version

Photodynamic therapy (PDT) has emerged as a non-invasive modality where photosensitizer (PS) plays an indispensable role for treating tumors. This study proposes a novel Zein nanoparticle (ZP) encapsulating Zinc phthalocyanine (ZnPc) that targets tumor through intravenous delivery. The ZPs were prepared by phase separation process. Modification of ZPs were realized through encapsulation of polydopamine (PDA) and hyaluronic acid (HA) in order to maintain photothermal, pH-responsive, and tumor-targeting properties. The physiochemical properties, in vitro release, and in vitro evaluation of photothermal and photodynamic effects were studied. The biosafety was analyzed through cytotoxicity assay using human lung cancer cell line A549 as model. The excellent biosafety and significant anti-tumor effect demonstrated that the novel ZPs can be potential candidates for the alternative treatment of cancer.

Abstract: Autophagy is the process by which cells selectively remove damaged organelles or proteins. Chaperone-mediated autophagy (CMA) is a type of autophagy that degrades proteins containing the KFERQ pentapeptide in cells. CMA can degrade damaged or excess proteins and therefore plays an important role in maintaining protein balance in cells. CMA can also play a regulatory role by degrading key proteins in life activities, such as lipid and glucose metabolism. This review introduces the CMA process and describes the current commonly used CMA detection methods. In addition, we describe the role of CMA in glucose and lipid metabolism. Finally, we summarize the current role of CMA in metabolic diseases such as diabetic nephropathy (DN), alcoholic liver disease (ALD) and nonalcoholic fatty liver disease (NAFLD) and discuss the role of CMA as a potential therapeutic target for metabolic diseases.

Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. Methods: A total of 245 VMAT HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included CT images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared to manual flags by 3 HN radiation oncologists. Results: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%, D95%, and D99% across the targets were within -2.53%(SD=1.34%), -0.42%(SD=1.27%), and -0.12%(SD=1.97%), respectively, and the OAR mean and maximum doses were within -0.33Gy(SD=1.40Gy) and -0.96Gy(SD=2.08Gy). For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs. Conclusion: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality., Comment: updated to reflect the published peer-reviewed article

Abstract: Long-term diabetes can lead to renal injury known as diabetic nephropathy (DN), which is a major cause of end-stage renal disease (ESRD). However, its pathogenesis has not been well explained. Adipose tissue is recognized as an important energy storage device for the body. Interestingly, many studies have shown that adipose tissue can also act as an endocrine organ by secreting a variety of adipokines to maintain homeostasis. Here, we summarize some of the adipokines that have been identified thus far to, more specifically, to emphasize their role in DN progression and propose that the “adipose-renal axis” may be a potential target for the treatment of DN.

Abstract: Diabetic nephropathy (DN) is a serious complication of diabetes mellitus and one of the main causes of end-stage renal disease (ESRD). There are many factors causing the progression of DN. Lipid metabolism disorder is a common clinical manifestation of DN, and ectopic renal lipid deposition was recently proposed as a key factor promoting the development of DN. Lipophagy is a newly discovered type of selective autophagy that can remove excessive lipids in cells to maintain lipid homeostasis. Recently, abnormalities in lipophagy have also been implicated in the progression of DN. Here, we discuss the formation of lipid droplets, describe lipophagy and its key regulatory signals, summarize the current research progress of lipophay in DN, and finally propose that lipophagy may be a potential target for the treatment of DN.

Multi-view clustering aims to capture the multiple views inherent information by identifying the data clustering that reflects distinct features of datasets. Since there is a consensus in literature that different views of a dataset share a common latent structure, most existing multi-view subspace learning methods rely on the nuclear norm to seek the low-rank representation of the underlying subspace. However, the nuclear norm often fails to distinguish the variance of features for each cluster due to its convex nature and data tends to fall in multiple non-linear subspaces for multi-dimensional datasets. To address these problems, we propose a new and novel multi-view clustering method (HL-L21-TLD-MSC) that unifies the Hyper-Laplacian (HL) and exclusive ℓ 2,1 (L21) regularization with the Tensor Log-Determinant Rank Minimization (TLD) setting. Specifically, the hyper-Laplacian regularization maintains the local geometrical structure that makes the estimation prune to nonlinearities, and the mixed ℓ 2,1 and ℓ 1,2 regularization provides the joint sparsity within-cluster as well as the exclusive sparsity between-cluster. Furthermore, a log-determinant function is used as a tighter tensor rank approximation to discriminate the dimension of features. An efficient alternating algorithm is then derived to optimize the proposed model, and the construction of a convergent sequence to the Karush-Kuhn-Tucker (KKT) critical point solution is mathematically validated in detail. Extensive experiments are conducted on ten well-known datasets to demonstrate that the proposed approach outperforms the existing state-of-the-art approaches with various scenarios, in which, six of them achieve perfect results under our framework developed in this article, demonstrating highl effectiveness for the proposed approach.