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Abstract Spotted fever group rickettsiae (SFGR) are obligate intracellular bacteria that cause spotted fever. The limitations of gene manipulation pose great challenges to studying the infection mechanisms of Rickettsia. By combining bioorthogonal metabolism and click chemistry, we developed a method to label R. heilongjiangensis via azide moieties and achieved rapid pathogen localization without complex procedures. Moreover, we constructed a C57BL/6 mice infection model by simulating tick bites and discovered that the stomach is the target organ of R. heilongjiangensis infection through in vivo imaging systems, which explained the occurrence of gastrointestinal symptoms following R. heilongjiangensis infection in some cases. This study offers a unique perspective for subsequent investigations into the pathogenic mechanisms of SFGR and identifies a potential target organ for R. heilongjiangensis.

Summary: Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibit conventional NMDA receptor (NMDAR)-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches show decreased structural plasticity in vivo. The selective decline in NMDAR activity and expression at synapses on oblique dendrites is controlled by a critical period of visual experience. Our results demonstrate a biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.

Abstract Excess body weight (EBW) increases the risk of colorectal cancer (CRC) and is linked to lower colonoscopy compliance. Here, we extensively analyzed 981 metagenome samples from multiple cohorts to pinpoint the specific microbial signatures and their potential capability distinguishing EBW patients with CRC. The gut microbiome displayed considerable variations between EBW and lean CRC. We identify 44 and 37 distinct multi-kingdom microbial species differentiating CRC and controls in EBW and lean populations, respectively. Unique bacterial-fungal associations are also observed between EBW-CRC and lean-CRC. Our analysis revealed specific microbial functions in EBW-CRC, including D-Arginine and D-ornithine metabolism, and lipopolysaccharide biosynthesis. The best-performing classifier for EBW-CRC, comprising 12 bacterial and three fungal species, achieved an AUROC of 0.90, which was robustly validated across three independent cohorts (AUROC = 0.96, 0.94, and 0.80). Pathogenic microbial species, Anaerobutyricum hallii, Clostridioides difficile and Fusobacterium nucleatum, are EBW-CRC specific signatures. This work unearths the specific multi-kingdom microbial signatures for EBW-CRC and lean CRC, which may contribute to precision diagnosis and treatment of CRC.

Background: A number of mechanical decontamination methods have been proposed, however, there is no agreed-upon gold standard among them. This study aims to conduct a meta-analysis to assess the differences in the management of an inflammatory reaction around dental implants between air abrasion and mechanical decontamination. Methods: A comprehensive search strategy was employed, incorporating controlled vocabulary (MeSH) and free-text terms. This search was conducted by two reviewers to identify published systematic reviews. Three major electronic databases, namely, Medline via PubMed, the Cochrane database, and Embase, were searched up to May 2024. Results: Initially, 300 articles were identified. After conducting a comprehensive search and applying strict inclusion criteria, a total of 13 studies were deemed eligible for inclusion in the meta-analysis. The results showed that the mean difference in probing depth between air abrasion and other mechanical decontamination was 0.28 (95% confidence interval, −0.20 to 0.76). The mean difference in probing depth of air abrasion compared with other mechanical decontamination in maintenance purposes was 1.05 (95% confidence interval, 0.18 to 1.91). The mean difference in bleeding on probing between air abrasion and other mechanical decontamination was 0.51 (95% confidence interval, 0.07 to 0.95). The mean difference in alveolar bone loss between air abrasion and other mechanical decontamination was −0.14 (95% confidence interval, −0.77 to 0.48). The mean difference in alveolar bone loss for surgical approaches of air abrasion compared with other mechanical decontamination was 0.32 (95% confidence interval, 0.03 to 0.61). Conclusions: The findings of the study indicate that the use of air abrasion was just as effective as other mechanical decontamination methods in reducing probing depth and alveolar bone loss. The subgroup analysis showed that air abrasion was less effective in reducing probing depth in maintenance purposes. Additionally, air abrasion was less effective in reducing alveolar bone loss in surgical approaches.

Background: This study aimed to conduct a meta-analysis to assess the differences in the performance of root coverage between collagen matrix and subepithelial connective tissue. Methods: A comprehensive search using a combination of controlled vocabulary (MeSH) and free-text terms was undertaken by two reviewers to identify published systematic reviews. Three major electronic databases (Medline via PubMed, Cochrane database, and Embase) were searched up to October 2023. Results: Initially, five hundred and seventy-six articles were identified, but only nine studies met the criteria for inclusion in the meta-analysis. Meta-analysis showed that the pooled mean difference in mean root coverage of collagen matrix compared with subepithelial connective tissue was −0.48 (95% confidence interval of −0.69 to −0.26). The relative risk of complete root coverage of collagen matrix compared with subepithelial connective tissue was 0.68 (95% confidence interval, 0.49 to 0.94). The pooled mean difference in width of keratinized tissue of collagen matrix compared with subepithelial connective tissue was −0.75 (95% confidence interval of −1.25 to −0.26). Conclusions: The outcomes of this study revealed that subepithelial connective tissue generally exhibited superior performance. Nonetheless, the utilization of the coronally advanced flap with collagen matrix may yield comparable results in terms of the percentage of complete root coverage.

Background: Researchers have noted that the advent of artificial intelligence (AI) heralds a promising era, with potential to significantly enhance diagnostic and predictive abilities in clinical settings. The aim of this meta-analysis is to evaluate the discrepancies in identifying anatomical landmarks between AI and manual approaches. Methods: A comprehensive search strategy was employed, incorporating controlled vocabulary (MeSH) and free-text terms. This search was conducted by two reviewers to identify published systematic reviews. Three major electronic databases, namely, Medline via PubMed, the Cochrane database, and Embase, were searched up to May 2024. Results: Initially, 369 articles were identified. After conducting a comprehensive search and applying strict inclusion criteria, a total of ten studies were deemed eligible for inclusion in the meta-analysis. The results showed that the average difference in detecting anatomical landmarks between artificial intelligence and manual approaches was 0.35, with a 95% confidence interval (CI) ranging from −0.09 to 0.78. Additionally, the overall effect between the two groups was found to be insignificant. Upon further analysis of the subgroup of cephalometric radiographs, it was determined that there were no significant differences between the two groups in terms of detecting anatomical landmarks. Similarly, the subgroup of cone-beam computed tomography (CBCT) revealed no significant differences between the groups. Conclusions: In summary, the study concluded that the use of artificial intelligence is just as effective as the manual approach when it comes to detecting anatomical landmarks, both in general and in specific contexts such as cephalometric radiographs and CBCT evaluations.

Abstract Rheumatoid arthritis (RA) is a common chronic inflammatory disorder that usually affects joints. It was found that roburic acid (RBA), an ingredient from anti-RA herb Gentiana macrophylla Pall., displayed strong anti-inflammatory activity. However, its medical application is limited by its hydrophobicity, lack of targeting capability and unclear functional mechanism. Here, we constructed a pH responsive dual-target drug delivery system hitchhiking RBA (RBA-NPs) that targeted both CD44 and folate receptors, and investigated its pharmacological mechanism. In rat RA model, the nanocarriers effectively delivered RBA to inflammatory sites and significantly enhanced the therapeutic outcomes compared with free RBA, as well as strongly reducing inflammatory cytokine levels and promoting tissue repair. Following analysis revealed that M1 macrophages in the joints were reprogrammed to M2 phenotype by RBA. Since the balance of pro- and anti-inflammatory macrophages play important roles in maintaining immune homeostasis and preventing excessive inflammation in RA, this reprogramming is likely responsible for the anti-RA effect. Furthermore, we revealed that RBA-NPs drove M1-to-M2 phenotypic switch by down-regulating the glycolysis level via blocking ERK/HIF-1α/GLUT1 pathway. Thus, our work not only developed a targeting delivery system that remarkably improved the anti-RA efficiency of RBA, but also identified a potential molecular target to reversely reprogram macrophages though energy metabolism regulation.

Background: This research was designed to carry out a meta-analysis in order to evaluate the dissimilarities in the stability of alveolar bone round dental implants and implant failure between platform switching (PS) and platform mating (PM). Methods: The investigation utilized a comprehensive search strategy that incorporated controlled vocabulary (MeSH) and free-text terms. This search was performed by two reviewers to identify published systematic reviews. Three major electronic databases, including Medline via PubMed, the Cochrane database, and Embase, were searched up to November 2023. Results: Initially, 466 articles were identified, but only twelve studies met the criteria for inclusion in the meta-analysis. The results showed that the pooled mean difference for reducing marginal bone loss (MBL) was −0.60 (95% confidence interval (CI), −0.91 to −0.28; p < 0.01). A sensitivity analysis was conducted by excluding a single study, which yielded a result of −0.46 (95% CI, −0.66 to −0.25; p < 0.01). The test for overall effect was significant (p < 0.01), and it revealed that there were significant differences between subgroups. However, the meta-analysis on implant failure did not show a significant difference between PS and PM implants. Conclusions: In conclusion, the study found that PS implants are more effective in reducing MBL compared to PM implants. Nevertheless, no significant difference was observed in the long-term effectiveness of reducing MBL and implant failure rate.

Platelet-rich fibrin has become increasingly popular in recent years due to its remarkable capacity to accelerate the post-surgery wound healing process, reduce inflammation, and promote tissue repair. This study aimed to perform a meta-analysis to evaluate the effect of platelet-rich fibrin in mixture form with bone substitute, as a membrane, or in combination. A comprehensive search using a combination of controlled vocabulary (MeSH) and free-text terms was undertaken by two reviewers to identify published randomized clinical trials. Three major electronic databases (Medline via PubMed, Cochrane database, and Embase) and the clinical trials registry (clinicaltrials.gov) were searched up to 9 July 2023. The results of the meta-analysis showed that the pooled standardized mean difference of probing depth for platelet-rich fibrin was 0.61 (95% CI, 0.33 to 0.88). The results of the meta-analysis showed that the mean difference in clinical attachment level for platelet-rich fibrin was 0.68 (95% CI, 0.35 to 1.01). The results of the meta-analysis showed that the mean difference in bone fill for platelet-rich fibrin was 0.50 (95% CI, 0.23 to 0.78). In conclusion, the study found that platelet-rich fibrin was effective as adjunct to periodontal regeneration.

Peroxisomes are metabolically active organelles that are known for exerting oxidative metabolism, but the precise mechanism remains unclear in diabetic nephropathy (DN). Here, we used proteomics to uncover a correlation between the antioxidant protein disulfide-bond A oxidoreductase-like protein (DsbA-L) and peroxisomal function. In vivo, renal tubular injury, oxidative stress, and cell apoptosis in high-fat diet plus streptozotocin (STZ)-induced diabetic mice were significantly increased, and these changes were accompanied by a ''ghost'' peroxisomal phenotype, which was further aggravated in DsbA-L-deficient diabetic mice. In vitro, the overexpression of DsbA-L in peroxisomes could improve peroxisomal phenotype and function, reduce oxidative stress and cell apoptosis induced by high glucose (HG, 30 mM) and palmitic acid (PA, 250 μM), but this effect was reversed by 3-Amino-1,2,4-triazole (3-AT, a catalase inhibitor). Mechanistically, DsbA-L regulated the activity of catalase by binding to it, thereby reducing peroxisomal leakage and proteasomal degradation of peroxisomal matrix proteins induced by HG and PA. Additionally, the expression of DsbA-L in renal tubules of patients with DN significantly decreased and was positively correlated with peroxisomal function. Taken together, these results highlight an important role of DsbA-L in ameliorating tubular injury in DN by improving peroxisomal function.

The retina, behind the transparent optics of the eye, is the only neural tissue whose physiology and pathology can be non-invasively probed by optical microscopy. The aberrations intrinsic to the mouse eye, however, prevent high-resolution investigation of retinal structure and function in vivo. Optimizing the design of a two-photon fluorescence microscope (2PFM) and sample preparation procedure, we found that adaptive optics (AO), by measuring and correcting ocular aberrations, is essential for resolving putative synaptic structures and achieving three-dimensional cellular resolution in the mouse retina in vivo. Applying AO-2PFM to longitudinal retinal imaging in transgenic models of retinal pathology, we characterized microvascular lesions with sub-capillary details in a proliferative vascular retinopathy model, and found Lidocaine to effectively suppress retinal ganglion cell hyperactivity in a retinal degeneration model. Tracking structural and functional changes at high-resolution longitudinally, AO-2PFM enables microscopic investigations of retinal pathology and pharmacology for disease diagnosis and treatment in vivo.

In the field of computer vision, segmenting a 3D object into its component parts is crucial to understanding its structure and characteristics. Much work has focused on 3D object part segmentation directly from point clouds, and significant progress has been made in this area. This paper proposes a novel 3D object part segmentation method that focuses on integrating three key modules: a keypoint-aware module, a feature extension module, and an attention-aware module. Our approach starts by detecting keypoints, which provide the global feature of the inner shape that serves as the basis for segmentation. Subsequently, we utilize the feature extension module to expand the dimensions, obtain the local representation of the obtained features, provide richer object representation, and improve segmentation accuracy. Furthermore, we introduce an attention-aware module that effectively combines the features of the global and local parts of objects to enhance the segmentation process. To validate the proposed model, we also conduct experiments on the point cloud classification task. The experimental results demonstrate the effectiveness of our method, thus outperforming several state-of-the-art methods in 3D object part segmentation and classification.

The problem of two-dimensional irregular packing involves the arrangement of objects with diverse shapes and sizes within a given area. This challenge arises across various industrial sectors, where effective packing optimization can yield cost savings, enhanced productivity, and reduced material waste. Existing methods for addressing the two-dimensional irregular packing problem encounter several challenges, such as limited computing resources, a prolonged solving time, and the propensity to converge to local optima. To address this issue, this study proposes a hybrid algorithm called the GA-LP algorithm to optimize the two-dimensional irregular packing problem in the manufacturing industry. The algorithm combines the global search capability of a genetic algorithm with the precise solving characteristics of linear programming. Matheuristics merges the advantages of metaheuristics, such as genetic algorithms, and mathematical programming, such as linear programming. The algorithm employs the no-fit-polygon technique along with the bottom-left and lowest-gravity center mixing placement strategies to acquire an initial solution via the utilization of a genetic algorithm. The algorithm then optimizes the solution obtained by the genetic algorithm using linear programming to obtain the final packing result. Experimental results, drawn from a real case involving the European Special Interest Group on Cutting and Packing (ESICUP) demonstrate that the GA-LP algorithm outperforms two hybrid algorithms from the relevant literature. Compared with recent methods, this algorithm can increase the best and average utilization rates by up to 5.89% and 4.02%, respectively, with important implications for improving work quality in areas such as packing and cutting.

Summary: Cell plasticity and neuroendocrine differentiation in prostate and lung adenocarcinomas are one of the major reasons for therapeutic resistance to targeted therapy. Whether and how metabolic changes contribute to this adenocarcinoma-to-neuroendocrine cell fate transition remains largely unclear. Here we show that neuroendocrine prostate or lung cancer cells possess mostly fragmented mitochondria with low membrane potential and rely on glycolysis for energy metabolism. We further show an important role of the cell fate determinant Numb in mitochondrial quality control via binding to Parkin and facilitating Parkin-mediated mitophagy. Deficiency in the Numb/Parkin pathway in prostate or lung adenocarcinomas causes a metabolic reprogramming featured with a significant increase in production of lactate acid, which subsequently leads to an upregulation of histone lactylation and transcription of neuroendocrine-associated genes. Collectively, the Numb/Parkin-directed mitochondrial fitness is a key metabolic switch and a promising therapeutic target on cancer cell plasticity through the regulation of histone lactylation.

The event camera, a new bio-inspired vision sensor with low latency and high temporal resolution, has brought great potential and demonstrated a promising application in machine vision and artificial intelligence. Corner detection is a key step of object motion estimation and tracking. However, most existing event-based corner detectors, such as G-eHarris and Arc*, lead to a huge number of redundant or wrong corners, and cannot strike a balance between the accuracy and real-time performance, especially in complex scenes with high texture that require higher computational costs. To address these issues, we propose an asynchronous corner detection method: a double threshold filter with Sigmoid eHarris (DTFS-eHarris) and an asynchronous corner tracker. The main contributions are that a double threshold filter is designed to reduce the redundant events and the improved Sigmoid function is utilized to represent the Surface of Active Events (Sigmoid*-SAE). We selected four scenes—shapes, dynamic, poster and boxes—from the public event camera dataset DAVIS240C to compare with the existing state-of-the-art hybrid method; our method has shown more than a 10% reduction in false positive rate and a 5% and 20% improvement in accuracy and throughput, respectively. The evaluations indicate that DTFS-eHarris shows a significant improvement, especially in complex scenes. Thus, it is anticipated to enhance the real-time performance and feature detection accuracy for future robotic applications.

Penicillin-binding proteins (PBPs) are considered essential for bacterial peptidoglycan biosynthesis and cell wall assembly. Clavibacter michiganensis is a representative Gram-positive bacterial species that causes bacterial canker in tomato. pbpC plays a significant role in maintaining cell morphological characteristics and stress responses in C. michiganensis. The current study demonstrated that the deletion of pbpC commonly enhances bacterial pathogenicity in C. michiganensis and revealed the mechanisms through which this occurs. The expression of interrelated virulence genes, including celA, xysA, xysB, and pelA, were significantly upregulated in △pbpC mutants. Compared with those in wild-type strains, exoenzyme activities, the formation of biofilm, and the production of exopolysaccharides (EPS) were significantly increased in △pbpC mutants. It is noteworthy that EPS were responsible for the enhancement in bacterial pathogenicity, with the degree of necrotic tomato stem cankers intensifying with the injection of a gradient of EPS from C. michiganensis. These findings highlight new insights into the role of pbpC affecting bacterial pathogenicity, with an emphasis on EPS, advancing the current understanding of phytopathogenic infection strategies for Gram-positive bacteria.

Background: Pasteurella multocida is an important and old zoonotic pathogen worldwide which has an impressive host spectrum including numerous domestic and wild animals as well as birds, causing specific diseases or outbreak with great economic impact. It has never been reported that P. multocida can cause an epidemic in wild rodents. In June 5–17, 2016, more than 1000 rodent deaths of an unknown cause quickly spread in the PuEr City, Yunnan province, southwestern China. Methods: The rodents in affected areas and outside of the epidemic areas were collected and screened for possible known pathogens including Yersinia pestis, rabies virus and hantavirus as well as other bacteria. The possible bacterial pathogens were isolated both by culture medium and by mouse inoculation in parallel. The isolates were identified by the Vitek GNI card and PCR assays for 16S rRNA genes. The pathogen strains were selected for whole genome sequencing analysis. Results: A total of 123 rodents were collected from 25 sample sites at affected area, among of which, all 119 dead rodents were negative for the pathogen under consideration except P. multocida, and all four live rodents were negative for P. multocida. In addition, 480 rodents collected from other 23 counties outside of the epidemic area in Yunnan were negative for with P. multocida. A total of 14 strains of P. multocida (six directly isolated from the field rodents and eight from the experimental mice that were injected with the organ substrates from the dead rodents) belonged to serogroup A and serogroup F represented by 9 N and 20 N were identified in these epidemic areas. Whole genome sequencing revealed that the serogroup F strain shared 99% similarity to P. multocida Pm70 from chicken, but contained a 50 k bp insertion sequence. The serogroup A strain shared 95% similarity to P. multocida FDAARGOS_385 from a human patient, but contained four large structural differences. Histological abnormalities were identified in the livers, lungs, hearts and brains of the inoculated mice. Conclusions: The simultaneous occurrence of both serotypes of P. multocida may have caused this sudden onset of mortality across the local rodent population in Yunnan Province, China. Further attention should be paid to this old bacterium in the world.

Automated detection of road damage (ADRD) is a challenging topic in road maintenance. It focuses on automatically detecting road damage and assessing severity by deep learning. Because of the sparse distribution of characteristic pixels, it is more challenging than object detection. Although some public datasets provide a database for the development of ADRD, their amounts of data and the standard of classification cannot meet network training and feature learning. With the aim of solving this problem, this work publishes a new road damage dataset named CNRDD, which is labeled according to the latest evaluation standard for highway technical conditions in China (JTG5210-2018). The dataset is collected by professional onboard cameras and is manually labeled in eight categories with three different degrees (mild, moderate and severe), which can effectively help promote research of automated detection of road damage. At the same time, a novel baseline with attention fusion and normalization is proposed to evaluate and analyze the published dataset. It explicitly leverages edge detection cues to guide attention for salient regions and suppresses the weights of non-salient features by attention normalization, which can alleviate the interference of sparse pixel distribution on damage detection. Experimental results demonstrate that the proposed baseline significantly outperforms most existing methods on the existing RDD2020 dataset and the newly released CNRDD dataset. Further, the CNRDD dataset is proved more robust, as its high damage density and professional classification are more conducive to promote the development of ADRD.

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne emerging phlebovirus with high mortality rates of 6.0 to 30%. SFTSV infection is characterized by high fever, thrombocytopenia, leukopenia, hemorrhage and multiple organ failures. Currently, specific therapies and vaccines remain elusive. Suitable small animal models are urgently needed to elucidate the pathogenesis and evaluate the potential drug and vaccine for SFTSV infection. Previous models presented only mild or no pathogenesis of SFTS, limiting their applications in SFTSV infection. Therefore, it is an urgent need to develop a small animal model for the investigation of SFTSV pathogenesis and evaluation of therapeutics. In the current report, we developed a SFTSV infection model based on the HuPBL-NCG mice that recapitulates many pathological characteristics of SFTSV infection in humans. Virus-induced histopathological changes were identified in spleen, lung, kidney, and liver. SFTSV was colocalized with macrophages in the spleen and liver, suggesting that the macrophages in the spleen and liver could be the principle target cells of SFTSV. In addition, histological analysis showed that the vascular endothelium integrity was severely disrupted upon viral infection along with depletion of platelets. In vitro cellular assays further revealed that SFTSV infection increased the vascular permeability of endothelial cells by promoting tyrosine phosphorylation and internalization of the adhesion molecule vascular endothelial (VE)-cadherin, a critical component of endothelial integrity. In addition, we found that both virus infection and pathogen-induced exuberant cytokine release dramatically contributed to the vascular endothelial injury. We elucidated the pathogenic mechanisms of hemorrhage syndrome and developed a humanized mouse model for SFTSV infection, which should be helpful for anti-SFTSV therapy and pathogenesis study.

Skin fibroblasts were obtained from a 42-year-old Alzheimer's disease (AD) patient carrying mutations in the PSEN1 gene. An iPSC line was successfully established using the Sendai-virus (SeV) delivery system. The patient-specific iPSCs were free of genomically integrated reprogramming genes, had the specific mutation, expressed the expected pluripotency markers, and had the potential to differentiate into cells of all three germ layers. Our model might offer a robust platform for further study of the pathomechanism of this disease as well as drug testing and gene therapy studies.

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders. Previous studies have identified mutations in several genes, such as amyloid precursor protein (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2), in patients with early-onset (

Contrast-induced acute kidney injury (CI-AKI) occurs in more than 30% of patients after intravenous iodinated contrast media and causes serious complications, including renal failure and mortality. Recent research has demonstrated that routine antioxidant and alkaline therapy failed to show benefits in CI-AKI patients with high risk for renal complications. Mitophagy is a mechanism of selective autophagy, which controls mitochondrial quality and mitochondrial reactive oxygen species (ROS) through degradation of damaged mitochondria. The role of mitophagy and its regulation of apoptosis in CI-AKI are poorly understood. In this study, we demonstrated that mitophagy was induced in renal tubular epithelial cells (RTECs) during CI-AKI, both in vivo and in vitro. Meanwhile, contrast media–induced mitophagy was abolished when silencing PINK1 or PARK2 (Parkin), indicating a dominant role of the PINK1-Parkin pathway in mitophagy. Moreover, mitochondrial damage, mitochondrial ROS, RTEC apoptosis, and renal injury under contrast exposure were more severe in PINK1- or PARK2-deficient cells and mice than in wild-type groups. Functionally, PINK1-Parkin–mediated mitophagy prevented RTEC apoptosis and tissue damage in CI-AKI through reducing mitochondrial ROS and subsequent NLRP3 inflammasome activation. These results demonstrated that PINK1-Parkin–mediated mitophagy played a protective role in CI-AKI by reducing NLRP3 inflammasome activation. Keywords: Acute kidney injury, Contrast media, Mitophagy, Mitochondrial ROS, NLRP3 inflammasome, Apoptosis

Optical imaging has become a powerful tool for studying brains in vivo. The opacity of adult brains makes microendoscopy, with an optical probe such as a gradient index (GRIN) lens embedded into brain tissue to provide optical relay, the method of choice for imaging neurons and neural activity in deeply buried brain structures. Incorporating a Bessel focus scanning module into two-photon fluorescence microendoscopy, we extended the excitation focus axially and improved its lateral resolution. Scanning the Bessel focus in 2D, we imaged volumes of neurons at high-throughput while resolving fine structures such as synaptic terminals. We applied this approach to the volumetric anatomical imaging of dendritic spines and axonal boutons in the mouse hippocampus, and functional imaging of GABAergic neurons in the mouse lateral hypothalamus in vivo.

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.