Mesenchymal stem cells alleviate mouse liver fibrosis by inhibiting pathogenic function of intrahepatic B cells.

BACKGROUND AND AIMS: The immunomodulatory characteristics of mesenchymal stem cells (MSCs) make them a promising therapeutic approach for liver fibrosis (LF). Here, we postulated that MSCs could potentially suppress the pro-fibrotic activity of intrahepatic B cells, thereby inhibiting LF progression. APPROACH AND RESULTS: Administration of MSCs significantly ameliorated LF as indicated by reduced myofibroblast activation, collagen deposition, and inflammation. The treatment efficacy of MSCs can be attributed to decreased infiltration, activation, and pro-inflammatory cytokine production of intrahepatic B cells. Single-cell RNA sequencing revealed a distinct intrahepatic B cell atlas, and a subtype of naive B cells (B-II) was identified, which were markedly abundant in fibrotic liver, displaying mature features with elevated expression of several proliferative and inflammatory genes. Transcriptional profiling of total B cells revealed that intrahepatic B cells displayed activation, proliferation, and pro-inflammatory gene profile during LF. Fibrosis was attenuated in mice ablated with B cells (µMT) or in vivo treatment with anti-CD20. Moreover, fibrosis was recapitulated in µMT after adoptive transfer of B cells, which in turn could be rescued by MSC injection, validating the pathogenic function of B cells and the efficacy of MSCs on B cell-promoted LF progression. Mechanistically, MSCs could inhibit the proliferation and cytokine production of intrahepatic B cells through exosomes, regulating the Mitogen-activated protein kinase and Nuclear factor kappa B signaling pathways. CONCLUSIONS: Intrahepatic B cells serve as a target of MSCs, play an important role in the process of MSC-induced amelioration of LF, and may provide new clues for revealing the novel mechanisms of MSC action.

Single-cell RNA sequencing reveals the immunoregulatory roles of PegIFN-α in patients with chronic hepatitis B.

BACKGROUND AND AIMS: Chronic hepatitis B (CHB) is caused by HBV infection and affects the lives of millions of people worldwide by causing liver inflammation, cirrhosis, and liver cancer. Interferon-alpha (IFN-α) therapy is a conventional immunotherapy that has been widely used in CHB treatment and achieved promising therapeutic outcomes by activating viral sensors and interferon-stimulated genes (ISGs) suppressed by HBV. However, the longitudinal landscape of immune cells of CHB patients and the effect of IFN-α on the immune system are not fully understood. APPROACH AND RESULTS: Here, we applied single-cell RNA sequencing (scRNA-seq) to delineate the transcriptomic landscape of peripheral immune cells in CHB patients before and after PegIFN-α therapy. Notably, we identified three CHB-specific cell subsets, pro-inflammatory (Pro-infla) CD14+ monocytes, Pro-infla CD16+ monocytes and IFNG+ CX3CR1- NK cells, which highly expressed proinflammatory genes and positively correlated with HBsAg. Furthermore, PegIFN-α treatment attenuated percentages of hyperactivated monocytes, increased ratios of long-lived naive/memory T cells and enhanced effector T cell cytotoxicity. Finally, PegIFN-α treatment switched the transcriptional profiles of entire immune cells from TNF-driven to IFN-α-driven pattern and enhanced innate antiviral response, including virus sensing and antigen presentation. CONCLUSIONS: Collectively, our study expands the understanding of the pathological characteristics of CHB and the immunoregulatory roles of PegIFN-α, which provides a new powerful reference for the clinical diagnosis and treatment of CHB.

MSCs alleviate LPS-induced acute lung injury by inhibiting the proinflammatory function of macrophages in mouse lung organoid-macrophage model.

Acute lung injury (ALI) is an inflammatory disease associated with alveolar injury, subsequent macrophage activation, inflammatory cell infiltration, and cytokine production. Mesenchymal stem cells (MSCs) are beneficial for application in the treatment of inflammatory diseases due to their immunomodulatory effects. However, the mechanisms of regulatory effects by MSCs on macrophages in ALI need more in-depth study. Lung tissues were collected from mice for mouse lung organoid construction. Alveolar macrophages (AMs) derived from bronchoalveolar lavage and interstitial macrophages (IMs) derived from lung tissue were co-cultured, with novel matrigel-spreading lung organoids to construct an in vitro model of lung organoids-immune cells. Mouse compact bone-derived MSCs were co-cultured with organoids-macrophages to confirm their therapeutic effect on acute lung injury. Changes in transcriptome expression profile were analyzed by RNA sequencing. Well-established lung organoids expressed various lung cell type-specific markers. Lung organoids grown on spreading matrigel had the property of functional cells growing outside the lumen. Lipopolysaccharide (LPS)-induced injury promoted macrophage chemotaxis toward lung organoids and enhanced the expression of inflammation-associated genes in inflammation-injured lung organoids-macrophages compared with controls. Treatment with MSCs inhibited the injury progress and reduced the levels of inflammatory components. Furthermore, through the nuclear factor-κB pathway, MSC treatment inhibited inflammatory and phenotypic transformation of AMs and modulated the antigen-presenting function of IMs, thereby affecting the inflammatory phenotype of lung organoids. Lung organoids grown by spreading matrigel facilitate the reception of external stimuli and the construction of in vitro models containing immune cells, which is a potential novel model for disease research. MSCs exert protective effects against lung injury by regulating different functions of AMs and IMs in the lung, indicating a potential mechanism for therapeutic intervention.

A novel HIF2A mutation causes dyslipidemia and promotes hepatic lipid accumulation.

Hypoxia-inducible factor-2α (HIF-2α) is a transcription factor responsible for regulating genes related to angiogenesis and metabolism. This study aims to explore the effect of a previously unreported mutation c.C2473T (p.R825S) in the C-terminal transactivation domain (CTAD) of HIF-2α that we detected in tissue of patients with liver disease. We sequenced available liver and matched blood samples obtained during partial liver resection or liver transplantation performed for clinical indications including hepatocellular carcinoma and liver failure. In tandem, we constructed cell lines and a transgenic mouse model bearing the corresponding identified mutation in HIF-2α from which we extracted primary hepatocytes. Lipid accumulation was evaluated in these cells and liver tissue from the mouse model using Oil Red O staining and biochemical measurements. We identified a mutation in the CTAD of HIF-2α (c.C2473T; p.R825S) in 5 of 356 liver samples obtained from patients with hepatopathy and dyslipidemia. We found that introduction of this mutation into the mouse model led to an elevated triglyceride level, lipid droplet accumulation in liver of the mutant mice and in their extracted primary hepatocytes, and increased transcription of genes related to hepatic fatty acid transport and synthesis in the mutant compared to the control groups. In mutant mice and cells, the protein levels of nuclear HIF-2α and its target perilipin-2 (PLIN2), a lipid droplet-related gene, were also elevated. Decreased lipophagy was observed in mutant groups. Our study defines a subpopulation of dyslipidemia that is caused by this HIF-2α mutation. This may have implications for personalized treatment.

Investigating 3D-printed disk compressing against skin for pain relief in intradermal infiltration anesthesia: a randomized controlled trial.

BACKGROUND: Pain intensity may be varied during the needle advancing through different skin layers, injection into the intradermal layer may exclude mixed pain from deeper planes. This study aimed to investigate whether compressing a three-dimensional (3D)-printed disk against the skin may relieve pain associated with intradermal injection of local anesthetic which mimics the skin test procedure. METHODS: After institutional review board approval, 3D-printed disks with projections were designed for this study. Enrolled patients were randomized to receive either a disk compressing against the axillary skin during the intradermal injection of local anesthesia (compressing disk group) or an intradermal injection of local anesthesia without any compression (no compressing disk group). The primary outcomes were pain intensity (100-mm visual analog scale) and satisfaction (5-point Likert scale) as assessed by patients. RESULTS: Ninety patients with American Society of Anesthesiologists I-II physical status receiving intradermal local anesthesia prior to an ultrasound-guided axillary approach were included. Eighty-seven patients completed the study, with 44 and 43 patients in disk and no disk groups, respectively. Pain scores were significantly different (P < 0.001) in compressing disk (median, 10; IQR, 5-20) and no compressing disk (median, 30; IQR, 20-40) groups. The median satisfaction score was 5 in both groups. No complications occurred during follow-up. CONCLUSION: Compressing a 3D-printed disk against the skin may reduce intradermal needle pain and offers an effective alternative for nerve block induction.

A Cardinality Estimator in Complex Database Systems Based on TreeLSTM.

Cardinality estimation is critical for database management systems (DBMSs) to execute query optimization tasks, which can guide the query optimizer in choosing the best execution plan. However, traditional cardinality estimation methods cannot provide accurate estimates because they cannot accurately capture the correlation between multiple tables. Several recent studies have revealed that learning-based cardinality estimation methods can address the shortcomings of traditional methods and provide more accurate estimates. However, the learning-based cardinality estimation methods still have large errors when an SQL query involves multiple tables or is very complex. To address this problem, we propose a sampling-based tree long short-term memory (TreeLSTM) neural network to model queries. The proposed model addresses the weakness of traditional methods when no sampled tuples match the predicates and considers the join relationship between multiple tables and the conjunction and disjunction operations between predicates. We construct subexpressions as trees using operator types between predicates and improve the performance and accuracy of cardinality estimation by capturing the join-crossing correlations between tables and the order dependencies between predicates. In addition, we construct a new loss function to overcome the drawback that Q-error cannot distinguish between large and small cardinalities. Extensive experimental results from real-world datasets show that our proposed model improves the estimation quality and outperforms traditional cardinality estimation methods and the other compared deep learning methods in three evaluation metrics: Q-error, MAE, and SMAPE.

Efficient-Lightweight YOLO: Improving Small Object Detection in YOLO for Aerial Images.

The most significant technical challenges of current aerial image object-detection tasks are the extremely low accuracy for detecting small objects that are densely distributed within a scene and the lack of semantic information. Moreover, existing detectors with large parameter scales are unsuitable for aerial image object-detection scenarios oriented toward low-end GPUs. To address this technical challenge, we propose efficient-lightweight You Only Look Once (EL-YOLO), an innovative model that overcomes the limitations of existing detectors and low-end GPU orientation. EL-YOLO surpasses the baseline models in three key areas. Firstly, we design and scrutinize three model architectures to intensify the model's focus on small objects and identify the most effective network structure. Secondly, we design efficient spatial pyramid pooling (ESPP) to augment the representation of small-object features in aerial images. Lastly, we introduce the alpha-complete intersection over union (α-CIoU) loss function to tackle the imbalance between positive and negative samples in aerial images. Our proposed EL-YOLO method demonstrates a strong generalization and robustness for the small-object detection problem in aerial images. The experimental results show that, with the model parameters maintained below 10 M while the input image size was unified at 640 × 640 pixels, the APS of the EL-YOLOv5 reached 10.8% and 10.7% and enhanced the APs by 1.9% and 2.2% compared to YOLOv5 on two challenging aerial image datasets, DIOR and VisDrone, respectively.

Graph Neural Network-Guided Contrastive Learning for Sequential Recommendation.

Sequential recommendation uses contrastive learning to randomly augment user sequences and alleviate the data sparsity problem. However, there is no guarantee that the augmented positive or negative views remain semantically similar. To address this issue, we propose graph neural network-guided contrastive learning for sequential recommendation (GC4SRec). The guided process employs graph neural networks to obtain user embeddings, an encoder to determine the importance score of each item, and various data augmentation methods to construct a contrast view based on the importance score. Experimental validation is conducted on three publicly available datasets, and the experimental results demonstrate that GC4SRec improves the hit rate and normalized discounted cumulative gain metrics by 1.4% and 1.7%, respectively. The model can enhance recommendation performance and mitigate the data sparsity problem.

MSA-YOLO: A Remote Sensing Object Detection Model Based on Multi-Scale Strip Attention.

Remote sensing image object detection holds significant research value in resources and the environment. Nevertheless, complex background information and considerable size differences between objects in remote sensing images make it challenging. This paper proposes an efficient remote sensing image object detection model (MSA-YOLO) to improve detection performance. First, we propose a Multi-Scale Strip Convolution Attention Mechanism (MSCAM), which can reduce the introduction of background noise and fuse multi-scale features to enhance the focus of the model on foreground objects of various sizes. Second, we introduce the lightweight convolution module GSConv and propose an improved feature fusion layer, which makes the model more lightweight while improving detection accuracy. Finally, we propose the Wise-Focal CIoU loss function, which can reweight different samples to balance the contribution of different samples to the loss function, thereby improving the regression effect. Experimental results show that on the remote sensing image public datasets DIOR and HRRSD, the performance of our proposed MSA-YOLO model is significantly better than other existing methods.

Speech Recognition of Accented Mandarin Based on Improved Conformer.

The convolution module in Conformer is capable of providing translationally invariant convolution in time and space. This is often used in Mandarin recognition tasks to address the diversity of speech signals by treating the time-frequency maps of speech signals as images. However, convolutional networks are more effective in local feature modeling, while dialect recognition tasks require the extraction of a long sequence of contextual information features; therefore, the SE-Conformer-TCN is proposed in this paper. By embedding the squeeze-excitation block into the Conformer, the interdependence between the features of channels can be explicitly modeled to enhance the model's ability to select interrelated channels, thus increasing the weight of effective speech spectrogram features and decreasing the weight of ineffective or less effective feature maps. The multi-head self-attention and temporal convolutional network is built in parallel, in which the dilated causal convolutions module can cover the input time series by increasing the expansion factor and convolutional kernel to capture the location information implied between the sequences and enhance the model's access to location information. Experiments on four public datasets demonstrate that the proposed model has a higher performance for the recognition of Mandarin with an accent, and the sentence error rate is reduced by 2.1% compared to the Conformer, with only 4.9% character error rate.

Impact of hepatitis B surface antigen positive grafts on liver transplantation in patients with benign and malignant liver disease.

Hepatitis B surface antigen (HBsAg) persists after liver transplantation in almost all patients receiving HBsAg-positive grafts. Chronic hepatitis B virus (HBV) infection is one of the main causes of hepatocellular carcinoma (HCC). We aimed to investigate possible interactions between HBsAg-positive donors, HCC, HBV-related transplant indication, and long-term outcomes. This retrospective study enrolled 1176 patients from two centers between January 2015 and May 2019, of which 135 (11.5%) were HBsAg-positive and 1041 (88.5%) were HBsAg-negative donors. Cox regression models were fitted to study the association between variables and patient and graft survival. In univariate and multivariate analyses, the donor HBsAg status was not significantly associated with patient and graft survival in the entire cohort, but there was a significant interaction between HBsAg-positive donors and HCC, independent of HBV-related transplant indication. The cumulative incidence of patient and graft survival was significantly lower in the subgroup of HCC recipients receiving HBsAg-positive grafts, but no significant difference was found in recipients with benign liver disease. In a subgroup analysis of HCC recipients, HBsAg-positive donors were significantly associated with an increased risk of HCC recurrence (hazard ratio: 1.73; 95% confidence interval: 1.20-2.48; p = 0.003) and similar results were obtained after propensity score matching analysis. We showed excellent outcomes of using HBsAg-positive grafts in patients with benign liver disease, regardless of HBV-related transplant indications. However, positive grafts should be used with caution in recipients with HCC, which are associated with an increased risk of HCC recurrence.

Gut microbiota dysbiosis associated with plasma levels of Interferon-γ and viral load in patients with acute hepatitis E infection.

Few studies have focused on the effect of hepatitis E virus (HEV) infection on gut microbiota. To explore the relationship between changes in gut microbiota and inflammatory factors and viral load, we conducted a comparative study of 33 patients with acute hepatitis E (AHE) patients and 25 healthy controls (HCs) using high-throughput 16S ribosomal ribonucleic acid gene sequencing. Shannon and Simpson's indices showed no significant differences in bacterial diversity between the AHE and HCs groups. Proteobacteria, Gammaproteobacteria, and Enterobacteriaceae were most abundant in the AHE group, which contributed to the difference between the gut microbiota of the AHE and HCs groups, and the same difference between the HEV-RNA-positive and HEV-RNA-negative groups. Functional prediction analysis showed that ribosome, purine metabolism, and two-component system were the top three pathways. Compared with the AHE group with normal interferon (IFN)-γ, Proteobacteria, Gammaproteobacteria, Xanthomonadaceae, and Enterobacteriaceae were more abundant in the high-IFN-γ group. The abundance of Gammaproteobacteria was positively correlated with the level of serum alanine transaminase and total bilirubin. The abundance of Gammaproteobacteria could discriminate AHE patients from HCs, and could better predict the severity of AHE patients. We believe that our findings will contribute toward a novel treatment strategy for AHE.

Dynamic changes of serum metabolites associated with infection and severity of patients with acute hepatitis E infection.

Dynamic changes in metabolites may affect liver disease progression, and provide new methods for predicting liver damage. We used ultra-performance liquid chromatography-mass spectroscopy to assess serum metabolites in healthy controls (HC), and patients with acute hepatitis E (AHE) or hepatitis E virus acute liver failure (HEV-ALF). The principal component analysis, partial least squares discriminant analysis, and discriminant analysis of orthogonal projections to latent structures models illustrated significant differences in the metabolite components between AHE patients and HCs, or between HEV-ALF and AHE patients. In pathway enrichment analysis, we further identified two altered pathways, including linoleic acid metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis, when comparing AHE patients with HCs. Linoleic acid metabolism and porphyrin and chlorophyll metabolism pathways were significantly different in HEV-ALF when compared with AHE patients. The discriminative performances of differential metabolites showed that taurocholic acid, glycocholic acid, glycochenodeoxycholate-3-sulfate, and docosahexaenoic acid could be used to distinguish HEV-ALF from AHE patients. The serum levels of glycocholic acid, taurocholic acid, deoxycholic acid glycine conjugate, and docosahexaenoic acid were associated with the prognosis of HEV-ALF patients. Dynamic changes in serum metabolites were associated with AHE infection and severity. The identified metabolites can be used to diagnose and predict the prognosis of HEV-ALF.

Pegylated interferon-α-2b combined with tenofovir disoproxil fumarate, granulocyte-macrophage colony-stimulating factor, and hepatitis B vaccine treatment for naïve HBeAg-positive chronic hepatitis B patients: A prospective, multicenter, randomized controlled study.

Hepatitis B surface antigen (HBsAg) loss or seroconversion is an ideal treatment endpoint for patients with chronic hepatitis B but is rarely achievable in  hepatitis B e-antigen (HBeAg)-positive patients using existing treatment strategies. In this study, the effect of pegylated interferon (peg-IFN) alfa-2b plus tenofovir disoproxil fumarate (TDF), granulocyte-macrophage colony-stimulating factor (GM-CSF), and hepatitis B vaccine was evaluated. This randomized controlled trial was conducted at nine liver centers in Chinese university hospitals from May 2018 to July 2020. Patients (n = 303) enrolled were randomly administered peg-IFN-α-2b combined with TDF, GM-CSF, and hepatitis B vaccine (experimental group); peg-IFN-α-2b plus TDF (control group 2); or interferon-α-2b alone (control group 1). The primary efficacy endpoint was HBsAg seroconversion at 48 weeks and the secondary endpoint included safety. No differences in baseline HBsAg levels were observed among the groups. The primary endpoint was achieved in three (3.0%), one (1.03%), and one (1.19%) patient in the experimental group, control group 2, and control group 1, respectively. The incidence of HBsAg seroconversion at week 48 was not significantly different among the three groups (p = 0.629). However, the decrease in serum levels of HBsAg at week 48 was significantly higher in the experimental and control group 2 compared with that in control group 1 (p = 0.008 and 0.006, respectively). No significant difference between the experimental and control group 2 was observed (p = 0.619). Adverse events were not significantly different among the groups except for the lower incidence of neutropenia in the experimental group. Peg-IFN-α-2b combined with TDF, GM-CSF, and hepatitis B vaccine is not superior to peg-IFN-α-2b combined with TDF in HBeAg-positive naïve patients. Clinical Trials Registration: ChiCTR1800016173.

Metabolic perturbations in human hepatocytes induced by bis (2-ethylhexyl)-2,3,4,5-tetrabromophthalate exposure: Insights from high-coverage quantitative metabolomics.

Bis (2-ethylhexyl)-2,3,4,5-tetrabromophthalate (TBPH) is an extensively used novel brominated flame retardant that is present ubiquitously in the environment and in biota. However, there is inadequate data on its potential hepatotoxicity to humans. In this study, high-coverage quantitative metabolomics based on 12C-/13C-dansylation labeling LC-MS was performed for the first time to assess the metabolic perturbations and underlying mechanisms of TBPH on human hepatocytes. HepG2 cells were exposed to TBPH at dosages of 0.1,1,10 µM for 24 or 72 h. Overall, 1887 and 1364 amine/phenol-containing metabolites were relatively quantified in cells and culture supernatant. Our results revealed that exposure to 0.1 µM TBPH showed little adverse effects, whereas exposure to 10 µM TBPH for 24 h enhanced intracellular protein catabolism and disrupted energy and lipid homeostasis-related pathways such as histidine metabolism, pantothenate and CoA biosynthesis, alanine, aspartate and glutamate metabolism. Nevertheless, most of these perturbations returned to the same levels as controls after 72 h of exposure. Additionally, prolonged TBPH exposure increased oxidative stress, as reflected by marked disturbances in taurine metabolism. This study sensitively revealed the dysregulations of intracellular and extracellular metabolome induced by TBPH, providing a comprehensive understanding of metabolic responses of cells to novel brominated flame retardants.

Porous Photo-Fenton Catalysts Rapidly Triggered by Levodopa-Based Mussel-Inspired Coatings for Enhanced Dye Degradation and Sterilization.

The advanced oxidation process of the photo-Fenton reaction can produce hydroxyl radicals with extremely strong oxidizing properties for the efficient and green degradation of various chemical and microbial pollutants. Herein, we report an approach to fabricating heterogeneous Fenton catalysts of ß-FeOOH nanorods on porous substrates triggered by mussel-inspired coatings of levodopa (3,4-dihydroxy-phenyl-l-alanine, l-DOPA) and polyethylenimine (PEI) for efficient photocatalytic dyes' degradation and sterilization. The l-DOPA-based coatings not only promote the formation and immobilization of ß-FeOOH nanorods on the porous substrates by strong coordination between catechol/carboxyl groups and Fe3+ but also improve the energy band structure of the Fenton catalysts through a valence band blue shift and band gap narrowing. The photo-Fenton catalysts prepared by the l-DOPA-based coatings exhibit high electron transport efficiency and improved utilization of sunlight. Only 2 h of mineralization is needed to fabricate these catalysts with excellent photocatalytic efficiency, in which the degradation efficiency of methylene blue can reach 99% within 30 min, whereas the sterilization efficiency of E. coli/S. aureus can reach 93%/94% within 20 min of the photo-Fenton reaction. Additionally, the prepared catalysts reveal a high photodegradation performance for various dyes including methylene blue, methyl blue, methyl orange, direct yellow, and rhodamine B. Furthermore, the catalysts retain high dye degradation efficiencies of above 90% after five photodegradation cycles, indicating cycling performance and good stability.

Mesenchymal stem cell treatment restores liver macrophages homeostasis to alleviate mouse acute liver injury revealed by single-cell analysis.

Acute liver injury (ALI) is characterized by massive hepatocyte necrosis and subsequent recruitment of myeloid cells to liver. Mesenchymal stem cells (MSCs) have therapeutic potential for ALI through their immunoregulation on macrophages, but the mechanism is not completely clear due to the heterogeneity and controversy of liver macrophages. Here, we detected the survival rate, biochemical indexes, histopathology, and inflammatory chemokine levels to assess the efficacy of MSC treatment on CCl4-induced ALI of C57BL/6 mice. Furthermore, flow cytometry and single-cell RNA sequencing (scRNA-Seq) were used to precisely distinguish macrophage populations and reveal the immunoregulation of MSCs. MSC treatment could effectively alleviate ALI and mitigate the recruitment of mononuclear phagocytes. Flow cytometry and scRNA-Seq analyses collectively indicated that there were monocytes with high Ly6C expression and heterogeneous monocyte-derived macrophages (MoMF) with low Ly6C expression in liver. Ly6Chi pro-inflammatory monocytes and Ly6Clo MoMF with powerful phagocytosis dominated during the acute injury period. MSC treatment promoted the transition from Ly6Chi to Ly6Clo population, inhibit the proinflammatory function of monocytes and promote the lysosomal function of MoMF. Furthermore, MSCs attenuated the recruitment of neutrophils by reducing the expression of CXCL2 of MoMF. MoMF with high expression of arginase 1 appeared during the recovery period, and MSCs could increase their expression of arginase 1, which may promote liver repair. To sum up, we demonstrated the characteristics of distinct MoMF during different periods of ALI and revealed their functional changes after MSC treatment, providing immunotherapeutic targets for MSC treatment of ALI.

Radial arteriovenous fistula complicated with ischemic steal syndrome after transradial cardiac catheterization: a case report and literature review.

BACKGROUND: The radial arteriovenous fistula (AVF) is a rare complication occurring after transradial cardiac catheterization. Patients with AVF typically present with signs of venous dilation, such as swelling or palpable thrills. However, neurological complications secondary to radial AVFs are rare. This paper reported a rare case of ischemic steal syndrome that occurred 11 months after the transradial cardiac catheterization, most likely as a consequence of radial arteriovenous fistula. CASE PRESENTATION: This paper described a case of a 73-year-old female, who complained of right forearm swelling and radial 1-3 fingers numbness for several months after the catheterized stent surgery through radial approach. Upon Clinical examination, this patient presented with a slight bump and palpable thrill at the distal third of right forearm, and the sensory of radial 1-3 fingers and pinch force was compromised. The Ultrasonography and computed tomography angiography (CTA) of the upper extremity revealed AVF between the right radial artery and the adjacent vein. Microsurgery was performed successfully to ligate the fistula and reconstruct the radial artery. The numbness has gradually improved about 1 week after the surgery, with no recurred swelling. The two-point pinch force and digital sensitivity recovered at the 20-month follow-up. What's more, due to the scarcity of cases, the optimal therapy for iatrogenic radial AVF is controversial. Accordingly, we provided a literature review of previous reports of catheter-related radial AVFs and proposed an algorithm to manage them. CONCLUSIONS: We believe that once an AVF is diagnosed, early treatment options such as compression or surgery are necessary to relieve symptoms and prevent further complications. Otherwise, serious complications can occur, including the ischemic steal syndrome.

LAG: Layered Objects to Generate Better Anchors for Object Detection in Aerial Images.

You Only Look Once (YOLO) series detectors are suitable for aerial image object detection because of their excellent real-time ability and performance. Their high performance depends heavily on the anchor generated by clustering the training set. However, the effectiveness of the general Anchor Generation algorithm is limited by the unique data distribution of the aerial image dataset. The divergence in the distribution of the number of objects with different sizes can cause the anchors to overfit some objects or be assigned to suboptimal layers because anchors of each layer are generated uniformly and affected by the overall data distribution. In this paper, we are inspired by experiments under different anchors settings and proposed the Layered Anchor Generation (LAG) algorithm. In the LAG, objects are layered by their diagonals, and then anchors of each layer are generated by analyzing the diagonals and aspect ratio of objects of the corresponding layer. In this way, anchors of each layer can better match the detection range of each layer. Experiment results showed that our algorithm is of good generality that significantly uprises the performance of You Only Look Once version 3 (YOLOv3), You Only Look Once version 5 (YOLOv5), You Only Learn One Representation (YOLOR), and Cascade Regions with CNN features (Cascade R-CNN) on the Vision Meets Drone (VisDrone) dataset and the object DetectIon in Optical Remote sensing images (DIOR) dataset, and these improvements are cost-free.

Cluster-Based Improved Isolation Forest.

Outlier detection is an important research direction in the field of data mining. Aiming at the problem of unstable detection results and low efficiency caused by randomly dividing features of the data set in the Isolation Forest algorithm in outlier detection, an algorithm CIIF (Cluster-based Improved Isolation Forest) that combines clustering and Isolation Forest is proposed. CIIF first uses the k-means method to cluster the data set, selects a specific cluster to construct a selection matrix based on the results of the clustering, and implements the selection mechanism of the algorithm through the selection matrix; then builds multiple isolation trees. Finally, the outliers are calculated according to the average search length of each sample in different isolation trees, and the Top-n objects with the highest outlier scores are regarded as outliers. Through comparative experiments with six algorithms in eleven real data sets, the results show that the CIIF algorithm has better performance. Compared to the Isolation Forest algorithm, the average AUC (Area under the Curve of ROC) value of our proposed CIIF algorithm is improved by 7%.