Turning sublimed sulfur and bFGF into a nanocomposite to accelerate wound healing via co-activate FGFR and Hippo signaling pathway.

Clinical treatment of diabetic refractory ulcers is impeded by chronic inflammation and cell dysfunction associated with wound healing. The significant clinical application of bFGF in wound healing is limited by its instability in vivo. Sulfur has been applied for the treatment of skin diseases in the clinic for antibiosis. We previously found that sulfur incorporation improves the ability of selenium nanoparticles to accelerate wound healing, yet the toxicity of selenium still poses a risk for its clinical application. To obtain materials with high pro-regeneration activity and low toxicity, we explored the mechanism by which selenium-sulfur nanoparticles aid in wound healing via RNA-Seq and designed a nanoparticle called Nano-S@bFGF, which was constructed from sulfur and bFGF. As expected, Nano-S@bFGF not only regenerated zebrafish tail fins and promoted skin wound healing but also promoted skin repair in diabetic mice with a profitable safety profile. Mechanistically, Nano-S@bFGF successfully coactivated the FGFR and Hippo signalling pathways to regulate wound healing. Briefly, the Nano-S@bFGF reported here provides an efficient and feasible method for the synthesis of bioactive nanosulfur and bFGF. In the long term, our results reinvigorated efforts to discover more peculiar unique biofunctions of sulfur and bFGF in a great variety of human diseases.

Acesulfame potassium triggers inflammatory bowel disease via the inhibition of focal adhesion pathway.

Acesulfame potassium (ACK) was generally regarded as innocuous and extensively ingested. Nevertheless, ACK has recently gained attention as a burgeoning pollutant that has the potential to induce a range of health hazards, particularly to the digestive system. Herein, we uncover that ACK initiates inflammatory bowel disease (IBD) in mice and zebrafish, as indicated by the aggregation of macrophages in the intestine and the inhibition of intestinal mucus secretion. Transcriptome analysis of mice and zebrafish guts revealed that exposure to ACK typically impacts the cell cycle, focal adhesion, and PI3K-Akt signaling pathways. Using pharmacological approaches, we demonstrate that the PI3K-Akt signaling pathway and the generation of reactive oxygen species (ROS) triggered by cell division are not significant factors in the initiation of IBD caused by ACK. Remarkably, inhibition of the focal adhesion pathway is responsible for the IBD onset induced by ACK. Our results indicate the detrimental impacts and possible underlying mechanisms of ACK on the gastrointestinal system and provide insights for making informed choices about everyday dietary habits.

Atractylenolide-I Alleviates Hyperglycemia-Induced Heart Developmental Malformations through Direct and Indirect Modulation of the STAT3 Pathway.

BACKGROUND: Gestational diabetes could elevate the risk of congenital heart defects (CHD) in infants, and effective preventive and therapeutic medications are currently lacking. Atractylenolide-I (AT-I) is the active ingredient of Atractylodes Macrocephala Koidz (known as Baizhu in China), which is a traditional pregnancy-supporting Chinese herb. PURPOSE: In this study, we investigated the protective effect of AT-I on the development of CHD in embryos exposed to high glucose (HG). STUDY DESIGN AND METHODS: First, systematic review search results revealed associations between gestational diabetes mellitus (GDM) and cardiovascular malformations. Subsequently, a second systematic review indicated that heart malformations were consistently associated with oxidative stress and cell apoptosis. We assessed the cytotoxic impacts of Atractylenolide compounds (AT-I, AT-II, and AT-III) on H9c2 cells and chick embryos, determining an optimal concentration of AT-I for further investigation. Second, immunofluorescence, western blot, Polymerase Chain Reaction (PCR), and flow cytometry were utilized to delve into the mechanisms through which AT-I mitigates oxidative stress and apoptosis in cardiac cells. Molecular docking was employed to investigate whether AT-I exerts cardioprotective effects via the STAT3 pathway. Then, we developed a streptozotocin-induced diabetes mellitus (PGDM) mouse model to evaluate AT-I's protective efficacy in mammals. Finally, we explored how AT-I protects hyperglycemia-induced abnormal fetal heart development through microbiota analysis and untargeted metabolomics analysis. RESULTS: The study showed the protective effect of AT-I on embryonic development using a chick embryo model which rescued the increase in the reactive oxygen species (ROS) and decrease in cell survival induced by HG. We also provided evidence suggesting that AT-I might directly interact with STAT3, inhibiting its phosphorylation. Further, in the PGDM mouse model, we observed that AT-I not only partially alleviated PGDM-related blood glucose issues and complications but also mitigated hyperglycemia-induced abnormal fetal heart development in pregnant mice. This effect is hypothesized to be mediated through alterations in gut microbiota composition. We proposed that dysregulation in microbiota metabolism could influence the downstream STAT3 signaling pathway via EGFR, consequently impacting cardiac development and formation. CONCLUSIONS: This study marks the first documented instance of AT-I's effectiveness in reducing the risk of early cardiac developmental anomalies in fetuses affected by gestational diabetes. AT-I achieves this by inhibiting the STAT3 pathway activated by ROS during gestational diabetes, significantly reducing the risk of fetal cardiac abnormalities. Notably, AT-I also indirectly safeguards normal fetal cardiac development by influencing the maternal gut microbiota and suppressing the EGFR/STAT3 pathway.

Lian-Mei-Yin formula alleviates diet-induced hepatic steatosis by suppressing Yap1/FOXM1 pathway-dependent lipid synthesis.

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, with a global prevalence of 25%. Patients with NAFLD are more likely to suffer from advanced liver disease, cardiovascular disease, or type II diabetes. However, unfortunately, there is still a shortage of FDA-approved therapeutic agents for NAFLD. Lian-Mei-Yin (LMY) is a traditional Chinese medicine formula used for decades to treat liver disorders. It has recently been applied to type II diabetes which is closely related to insulin resistance. Given that NAFLD is another disease involved in insulin resistance, we hypothesize that LMY might be a promising formula for NAFLD therapy. Herein, we verify that the LMY formula effectively reduces hepatic steatosis in diet-induced zebrafish and NAFLD model mice in a time- and dose-dependent manner. Mechanistically, LMY suppresses Yap1-mediated Foxm1 activation, which is crucial for the occurrence and development of NAFLD. Consequently, lipogenesis is ameliorated by LMY administration. In summary, the LMY formula alleviates diet-induced NAFLD in zebrafish and mice by inhibiting Yap1/Foxm1 signaling-mediated NAFLD pathology.

Biomedical image segmentation algorithm based on dense atrous convolution.

Biomedical images have complex tissue structures, and there are great differences between images of the same part of different individuals. Although deep learning methods have made some progress in automatic segmentation of biomedical images, the segmentation accuracy is relatively low for biomedical images with significant changes in segmentation targets, and there are also problems of missegmentation and missed segmentation. To address these challenges, we proposed a biomedical image segmentation method based on dense atrous convolution. First, we added a dense atrous convolution module (DAC) between the encoding and decoding paths of the U-Net network. This module was based on the inception structure and atrous convolution design, which can effectively capture multi-scale features of images. Second, we introduced a dense residual pooling module to detect multi-scale features in images by connecting residual pooling blocks of different sizes. Finally, in the decoding part of the network, we adopted an attention mechanism to suppress background interference by enhancing the weight of the target area. These modules work together to improve the accuracy and robustness of biomedical image segmentation. The experimental results showed that compared to mainstream segmentation networks, our segmentation model exhibited stronger segmentation ability when processing biomedical images with multiple-shaped targets. At the same time, this model can significantly reduce the phenomenon of missed segmentation and missegmentation, improve segmentation accuracy, and make the segmentation results closer to the real situation.

rTMS effects on urges and severity of tobacco use disorder operate independently of a retrieval-extinction component and involve frontal-striatal pathways.

BACKGROUND: Although smoking remains a leading cause of preventable disease, the treatment options for smoking are limited. The present study evaluated the neural features underlying effects of repetitive transcranial magnetic stimulation (rTMS) for reducing smoking cravings. In addition, the efficacy of a simulated retrieval-extinction procedure to augment rTMS efficacy was examined. METHODS: Sixty-one individuals with tobacco use disorder (TUD) were randomized into three groups: classic rTMS, retrieval rTMS (viewed smoking videos before rTMS), and sham rTMS. rTMS was performed on the left dorsolateral prefrontal cortex (DLPFC) over 5 days using a standard figure-8 coil. Smoking cravings and brain responses to smoking cues were measured before and after rTMS treatment. Changes in functional connectivity (FC) among different brain regions were calculated. RESULTS: rTMS reduced smoking urges in TUD. Both active-rTMS groups demonstrated greater activations of the DLPFC, caudate, and bilateral insula relative to the sham group. Increased FC was observed between executive and reward network brain regions, and decreased FC was observed within reward network regions. Compared with standard rTMS, retrieval-extinction rTMS demonstrated similar outcomes and was associated with less activation of the medial frontal gyrus. CONCLUSIONS: rTMS increased activations in brain regions implicated in executive control and reward processing. Strengthened prefrontal-striatal pathway suggests that rTMS enhanced top-down control over smoking cravings. The retrieval-extinction process, although associated with some different and multiple similar neural correlates as the standard rTMS, did not enhance cessation outcomes.

Outcomes of pregnant women hospitalized with unrepaired congenital heart disease: Insights from a multidisciplinary center in Vietnam.

Background: In developing countries, fewer women have access to multidisciplinary congenital heart disease and reproductive programs staffed by experts. We report pregnancy outcomes of a multidisciplinary healthcare strategy utilizing an in-hospital teamwork approach in Vietnam. Methods: This retrospective cohort study included pregnant women with unrepaired congenital heart disease managed at a referral cardiovascular center. Results: Undiagnosed congenital heart disease before pregnancy, a lack of pre-pregnancy cardiology counseling, and modified World Health Organization class III/IV were common. Under the multispecialty healthcare strategy, although the rate of maternal death was 8.2% in the modified World Health Organization class IV group, no deaths occurred in any other group. Fetal/neonatal complications occurred in 54% of pregnancies, and 49.4% of neonates survived. Poor pregnancy outcomes were associated with admission during the first/seconde trimester for fetus/neonates, third trimester for mother, modified World Health Organization class III/IV, cyanosis, and heart failure. Conclusion: The outcomes of pregnant women with unrepaired congenital heart disease were poor but seemed to improve with a multidisciplinary in-hospital healthcare teamwork strategy.

Promotion of Tricholoma matsutake mycelium growth by Penicillium citreonigrum.

Tricholoma matsutake has been the most valuable ectomycorrhizal fungi in Asia because of its unique flavor and taste. However, due to the difficulty of artificial cultivation, the cultivation of T. matsutake has relied on natural growth in forests. To cultivate the T. matsutake artificially, microorganisms in fairy rings were introduced. In this study, we isolated 30 fungal species of microfungi from the soil of fairy rings. Among them, one single fungal strain showed a promoting effect on the growth of T. matsutake. The growth effect was confirmed by measuring the growth area of T. matsutake and enzyme activities including α-amylase, cellulase, and ß-glucosidase. In comparison with control, microfungal metabolite increased the growth area of T. matsutake by 213% and the enzyme activity of T. matsutake by 110-200%. The isolated fungal strain was identified as Penicillium citreonigrum by BLAST on the NCBI database. The Discovery of this microfungal strain is expected to contribute to artificial cultivation of T. matsutake.

A global optimization generation method of stitching dental panorama with anti-perspective transformation.

To address the limitation of narrow field-of-view in local oral cavity images that fail to capture large-area targets at once, this paper designs a method for generating natural dental panoramas based on oral endoscopic imaging that consists of two main stages: the anti-perspective transformation feature extraction and the coarse-to-fine global optimization matching. In the first stage, we increase the number of matched pairs and improve the robustness of the algorithm to viewpoint transformation by normalizing the anti-affine transformation region extracted from the Gaussian scale space and using log-polar coordinates to compute the gradient histogram of the octagonal region to obtain the set of perspective transformation resistant feature points. In the second stage, we design a coarse-to-fine global optimization matching strategy. Initially, we incorporate motion smoothing constraints and improve the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm by utilizing neighborhood information for coarse matching. Then, we eliminate mismatches via homography-guided Random Sample Consensus (RANSAC) and further refine the matching using the Levenberg-Marquardt (L-M) algorithm to reduce cumulative errors and achieve global optimization. Finally, multi-band blending is used to eliminate the ghosting due to unalignment and make the image transition more natural. Experiments show that the visual effect of dental panoramas generated by the proposed method is significantly better than that of other methods, addressing the problems of sparse splicing discontinuities caused by sparse keypoints, ghosting due to parallax, and distortion caused by the accumulation of errors in multi-image splicing in oral endoscopic image stitching.

Clinician Champions' Influence on Social Needs Screening Volumes in Pediatric Practices.

Health systems face barriers implementing routine screening for social needs. We assessed the impact of "clinician champions" on social needs screening. Screening data were assessed at 11 pediatric primary care practices in Bronx, NY, between April 2018 and August 2021. Three intervention practices had clinician champions; 8 control practices did not. The Wald chi-square tests and Poisson regressions evaluated the relationship between screening and introduction of clinician champions. The introduction of a clinician champion was a significant predictor of screening (P < .001). Within a practice, screening after the introduction of a clinician champion was higher than before the introduction (P < .001). The rate of screening for practices with a clinician champion was 2.8 times higher per month than for practices without a clinician champion. Furthermore, practices with clinician champions had higher rates of screening during the pandemic. In summary, the presence of clinician champions increased social needs screening rates in pediatric primary care practices.

Intelligent crowd sensing pickpocketing group identification using remote sensing data for secure smart cities.

As a public infrastructure service, remote sensing data provided by smart cities will go deep into the safety field and realize the comprehensive improvement of urban management and services. However, it is challenging to detect criminal individuals with abnormal features from massive sensing data and identify groups composed of criminal individuals with similar behavioral characteristics. To address this issue, we study two research aspects: pickpocketing individual detection and pickpocketing group identification. First, we propose an IForest-FD pickpocketing individual detection algorithm. The IForest algorithm filters the abnormal individuals of each feature extracted from ticketing and geographic information data. Through the filtered results, the factorization machines (FM) and deep neural network (DNN) (FD) algorithm learns the combination relationship between low-order and high-order features to improve the accuracy of identifying pickpockets composed of factorization machines and deep neural networks. Second, we propose a community relationship strength (CRS)-Louvain pickpocketing group identification algorithm. Based on crowdsensing, we measure the similarity of temporal, spatial, social and identity features among pickpocketing individuals. We then use the weighted combination similarity as an edge weight to construct the pickpocketing association graph. Furthermore, the CRS-Louvain algorithm improves the modularity of the Louvain algorithm to overcome the limitation that small-scale communities cannot be identified. The experimental results indicate that the IForest-FD algorithm has better detection results in Precision, Recall and F1score than similar algorithms. In addition, the normalized mutual information results of the group division effect obtained by the CRS-Louvain pickpocketing group identification algorithm are better than those of other representative methods.

SAIF plays anti-angiogenesis via blocking VEGF-VEGFR2-ERK signal in tumor treatment.

Shark cartilage was created as a cancer-fighting diet because it was believed to have an element that may suppress tumor growth. Due to overfishing, sharks have become endangered recently, making it impossible to harvest natural components from shark cartilage for therapeutic development research. Previously, we identified a peptide SAIF from shark cartilage with an-tiangiogenic and anti-tumor effects, successfully expressed it in Escherichia coli by using genetic engineering techniques. However, we did not elucidate the specific target of SAIF and its antiangiogenic molecular mechanism, which hindered its further drug development. Therefore, in this work, the exact mechanism of action was studied using various techniques, including cellular and in vivo animal models, computer-aided simulation, molecular target capture, and transcriptome sequencing analysis. With VEGF-VEGFR2 interaction and preventing the activation of VEGFR2/ERK signaling pathways, SAIF was discovered to decrease angiogenesis and hence significantly limit tumor development. The findings further demonstrated SAIF's strong safety and pharmaceutically potential. The evidence showed that SAIF, which is expressed by, is a potent and safe angiogenesis inhibitor and might be developed as a candidate peptide drug for the treatment of solid tumors such as hepatocellular carcinoma and other conditions linked with angiogenic overgrowth.

Exploration of Remarkably Potential Multitarget-Directed N-Alkylated-2-(substituted phenyl)-1H-benzimidazole Derivatives as Antiproliferative, Antifungal, and Antibacterial Agents.

Improving lipophilicity for drugs to penetrate the lipid membrane and decreasing bacterial and fungal coinfections for patients with cancer pose challenges in the drug development process. Here, a series of new N-alkylated-2-(substituted phenyl)-1H-benzimidazole derivatives were synthesized and characterized by 1H and 13C NMR, FTIR, and HRMS spectrum analyses to address these difficulties. All the compounds were evaluated for their antiproliferative, antibacterial, and antifungal activities. Results indicated that compound 2g exhibited the best antiproliferative activity against the MDA-MB-231 cell line and also displayed significant inhibition at minimal inhibitory concentration (MIC) values of 8, 4, and 4 µg mL-1 against Streptococcus faecalis, Staphylococcus aureus, and methicillin-resistant Staphylococcus aureus compared with amikacin. The antifungal data of compounds 1b, 1c, 2e, and 2g revealed their moderate activities toward Candida albicans and Aspergillus niger, with MIC values of 64 µg mL-1 for both strains. Finally, the molecular docking study found that 2g interacted with crucial amino acids in the binding site of complex dihydrofolate reductase with nicotinamide adenine dinucleotide phosphate.

Research on user recruitment algorithms based on user trajectory prediction with sparse mobile crowd sensing.

Sparse mobile crowd sensing saves perception cost by recruiting a small number of users to perceive data from a small number of sub-regions, and then inferring data from the remaining sub-regions. The data collected by different people on their respective trajectories have different values, and we can select participants who can collect high-value data based on their trajectory predictions. In this paper, we study two aspects of user trajectory prediction and user recruitment. First, we propose an STGCN-GRU user trajectory prediction algorithm, which uses the STGCN algorithm to extract features related to temporal and spatial information from the trajectory map, and then inputs the feature sequences into GRU for trajectory prediction, and this algorithm improves the accuracy of user trajectory prediction. Second, an ADQN (action DQN) user recruitment algorithm is proposed.The ADQN algorithm improves the objective function in DQN on the idea of reinforcement learning. The action with the maximum input value is found from the Q network, and then the output value of the objective function of the corresponding action Q network is found. This reduces the overestimation problem that occurs in Q networks and improves the accuracy of user recruitment. The experimental results show that the evaluation metrics FDE and ADE of the STGCN-GRU algorithm proposed in this paper are better than other representative algorithms. And the experiments on two real datasets verify the effectiveness of the ADQN user selection algorithm, which can effectively improve the accuracy of data inference under budget constraints.

Research on rainy day traffic sign recognition algorithm based on PMRNet.

The recognition of traffic signs is of great significance to intelligent driving and traffic systems. Most current traffic sign recognition algorithms do not consider the impact of rainy weather. The rain marks will obscure the recognition target in the image, which will lead to the performance degradation of the algorithm, a problem that has yet to be solved. In order to improve the accuracy of traffic sign recognition in rainy weather, we propose a rainy traffic sign recognition algorithm. The algorithm in this paper includes two modules. First, we propose an image deraining algorithm based on the Progressive multi-scale residual network (PMRNet), which uses a multi-scale residual structure to extract features of different scales, so as to improve the utilization rate of the algorithm for information, combined with the Convolutional long-short term memory (ConvLSTM) network to enhance the algorithm's ability to extract rain mark features. Second, we use the CoT-YOLOv5 algorithm to recognize traffic signs on the recovered images. In this paper, in order to improve the performance of YOLOv5 (You-Only-Look-Once, YOLO), the 3 × 3 convolution in the feature extraction module is replaced by the Contextual Transformer (CoT) module to make up for the lack of global modeling capability of Convolutional Neural Network (CNN), thus improving the recognition accuracy. The experimental results show that the deraining algorithm based on PMRNet can effectively remove rain marks, and the evaluation indicators Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are better than the other representative algorithms. The mean Average Precision (mAP) of the CoT-YOLOv5 algorithm on the TT100k datasets reaches 92.1%, which is 5% higher than the original YOLOv5.

CH02 peptide promotes ex vivo expansion of umbilical cord blood-derived CD34 + hematopoietic stem/progenitor cells.

Umbilical cord blood (UCB) is an advantageous source for hematopoietic stem/progenitor cell (HSPC) transplantation, yet the current strategies for large-scale and cost-effective UCB-HSPC preparation are still unavailable. To overcome these obstacles, we systematically evaluate the feasibility of our newly identified CH02 peptide for ex vivo expansion of CD34 + UCB-HSPCs. We herein report that the CH02 peptide is specifically enriched in HSPC proliferation via activating the FLT3 signaling. Notably, the CH02-based cocktails are adequate for boosting 12-fold ex vivo expansion of UCB-HSPCs. Meanwhile, CH02-preconditioned UCB-HSPCs manifest preferable efficacy upon wound healing in diabetic mice via bidirectional orchestration of proinflammatory and anti-inflammatory factors. Together, our data indicate the advantages of the CH02-based strategy for ex vivo expansion of CD34 + UCB-HSPCs, which will provide new strategies for further development of large-scale HSPC preparation for clinical purposes.

Covid-19 and investment-cash flow sensitivity: A cross-country analysis.

This study investigates investment-cash flow sensitivity during the COVID-19 economic crisis. Using an international sample of publicly listed firms, we find that the sensitivity of capital expenditures to cash flows is significantly reduced during the crisis. When we split the sample into strongly and weakly affected countries, we find that firms in countries affected more seriously by COVID-19 exhibit lower investment responsiveness to cash flows. We further find that investment-cash flow sensitivity is diminished when government aid is greater, firms have more cash on hand, and investment opportunities decline. Our results survive a host of robustness checks. This study contributes to the discussion on the impact of COVID-19 on corporate policies within an international framework.

C-reactive protein and D-dimer in cerebral vein thrombosis: Relation to clinical and imaging characteristics as well as outcomes in a French cohort study.

Introduction: Cerebral venous sinus thrombosis (CVST) is a rare disease with highly variable clinical presentation and outcomes. Clinical studies suggest a role of inflammation and coagulation in CVST outcomes. The aim of this study was to investigate the association of inflammation and hypercoagulability biomarkers with CVST clinical manifestations and prognosis. Methods: This prospective multicenter study was conducted from July 2011 to September 2016. Consecutive patients referred to 21 French stroke units and who had a diagnosis of symptomatic CVST were included. High-sensitivity C-reactive protein (hs-CRP), neutrophil-to-lymphocyte ratio (NLR), D-dimer, and thrombin generation using calibrated automated thrombogram system were measured at different time points until 1 month after anticoagulant therapy discontinuation. Results: Two hundred thirty-one patients were included. Eight patients died, of whom 5 during hospitalization. The day 0 hs-CRP levels, NLR, and D-dimer were higher in patients with initial consciousness disturbance than in those without (hs-CRP: 10.2 mg/L [3.6-25.5] vs 23.7 mg/L [4.8-60.0], respectively; NLR: 3.51 [2.15-5.88] vs 4.78 [3.10-9.59], respectively; D-dimer: 950 µg/L [520-2075] vs 1220 µg/L [950-2445], respectively). Patients with ischemic parenchymal lesions (n = 31) had a higher endogenous thrombin potential5pM than those with hemorrhagic parenchymal lesions (n = 31): 2025 nM min (1646-2441) vs 1629 nM min (1371-2090), respectively (P = .0082). Using unadjusted logistic regression with values >75th percentile, day 0 hs-CRP levels of >29.7 mg/L (odds ratio, 10.76 [1.55-140.4]; P = .037) and day 5 D-dimer levels of >1060 mg/L (odds ratio, 14.63 [2.28-179.9]; P = .010) were associated with death occurrence. Conclusion: Two widely available biomarkers measured upon admission, especially hs-CRP, could help predict bad prognosis in CVST in addition to patient characteristics. These results need to be validated in other cohorts.