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In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To address this issue, this study proposes a foggy weather detection method based on the YOLOv5s framework, named YOLOv5s-Fog. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer called SwinFocus. Additionally, the decoupled head is incorporated into the model, and the conventional non-maximum suppression method is replaced with Soft-NMS. The experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model, YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.

The aim of this research was to investigate if the mitochondria- targeting peptide SS-31 could serve as a protective measure against bleomycin-induced pulmonary fibrosis in mice. Mice were split into four groups named CON group, SS-31 group, BLM group, and the BLM + SS-31 group. SS-31 (intraperitoneal injection, 5mg/Kg) was administered daily from the day prior to the experiment for the control and model groups. Mice were euthanized after 28 days of the experiment, following which blood, bronchoalveolar lavage fluid, and lung tissue were collected for analysis. BLM caused a large decrease in body weight in mice. However, the intraperitoneal injection of SS-31 slowed down the body weight loss in the mice. It was observed through HE and Masson staining, immunohistochemistry, hydroxyproline detection, and fibrosis index measurement via Western blot that SS-31 could alleviate pulmonary fibrosis caused by BLM. Electron microscopy and ATP detection further suggested that SS-31 might help protect mitochondrial structure and function. It was also found that SS-31 could reduce reactive oxygen species and myeloperoxidase, thereby alleviating the reduction of antioxidant factor MPO and SOD, as well as diminishing the inflammatory factors TNF-α, IL-1 β, and IL-6. The mitochondria-targeting drug SS-31 exhibited potential in mitigating bleomycin-induced pulmonary fibrosis, improving mitochondrial structural and functional damage, stabilizing the balance between oxidative and antioxidant systems, reducing inflammatory factor expression, and improving apoptosis in lung tissue.

Lung disease causes significant morbidity and mortality, and is exacerbated by environmental injury, for example through lipopolysaccharide (LPS) or ozone (O 3 ). Toll-like receptors (TLRs) orchestrate immune responses to injury by recognizing pathogen- or danger-associated molecular patterns. TLR4, the prototypic receptor for LPS, also mediates inflammation after O 3 , triggered by endogenous hyaluronan. Regulation of TLR4 signaling is incompletely understood. TLR5, the flagellin receptor, is expressed in alveolar macrophages, and regulates immune responses to environmental injury. Using in vivo animal models of TLR4-mediated inflammations (LPS, O 3 , hyaluronan), we show that TLR5 impacts the in vivo response to LPS, hyaluronan and O 3 . We demonstrate that immune cells of human carriers of a dominant negative TLR5 allele have decreased inflammatory response to O 3 exposure ex vivo and LPS exposure in vitro. Using primary murine macrophages, we find that TLR5 physically associates with TLR4 and biases TLR4 signaling towards the MyD88 pathway. Our results suggest an updated paradigm for TLR4/TLR5 signaling. Immune cells in the lung help guard against infections. On the surface of these cells are proteins called TLR receptors that recognize dangerous molecules or DNA from disease-causing microbes such as bacteria. When the immune cells detect these invaders, the TLR receptors spring into action and trigger an inflammatory response to destroy the microbes. This inflammation usually helps the lung clear infections. But it can also be harmful and damage the lung, for example when inflammation is caused by non-infectious substances such as pollutants in the atmosphere. There are several TLR receptors that each recognize a specific molecule. In 2010, researchers showed that the receptor TLR4 is responsible for causing inflammation in the lung after exposure to pollution. Another receptor called TLR5 also helps activate the immune response in the lung. But it was unclear whether this receptor also plays a role in pollution-linked lung damage. Now, Hussain, Johnson, Sciurba et al. – including one of the researchers involved in the 2010 study – have investigated the role of TLR5 in immune cells from the lungs of humans and mice. The experiments showed that TLR5 works together with TLR4 and helps trigger an inflammatory response to both pollutants and bacteria. Hussain et al. found that people lacking a working TLR5 receptor (which make up 3–10% of the population) are less likely to experience lung inflammation when exposed to pollution or bacterial proteins that activate TLR4. These findings suggest that people without TLR5 may be protected from pollution-induced lung injury. Further research into the role of TLR5 could help develop genetic tests for identifying people who are more sensitive to damage from pollution. This information could then be used to determine the likelihood of a patient experiencing certain lung diseases.

Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a safer and more widely available approach, however, has long been considered impossible for this task. Here we propose High-abundant Pulmonary Artery-vein Segmentation (HiPaS), enabling accurate segmentation across both non-contrast CT and CTPA at multiple resolutions. HiPaS integrates spatial normalization with an iterative segmentation strategy, leveraging lower-level vessel segmentations as priors for higher-level segmentations. Trained on a multi-center dataset comprising 1073 CT volumes with manual annotations, HiPaS achieves superior performance (dice score: 91.8%, sensitivity: 98.0%) and demonstrates non-inferiority on non-contrast CT compared to CTPA. Furthermore, HiPaS enables large-scale analysis of 11,784 participants, revealing associations between vessel abundance and sex, age, and diseases, under lung-volume control. HiPaS represents a promising, non-invasive approach for clinical diagnostics and anatomical research. Pulmonary artery-vein segmentation is essential for disease diagnosis but has not been used with non-contrast CT. Here the authors developed HiPaS, a deep learning method which enables this with no inferiority to CTPA performance and a large-scale anatomical study using HiPaS reveals pulmonary vessel differences associated with sex, age, and disease.

The properly applied pressure between the skin and hemostasis devices is an essential parameter for preventing bleeding and postoperative complications after a transradial procedure. However, this parameter is usually controlled based on the subjective judgment of doctors, which might cause insufficient hemostatic effect or thrombosis. Here this study develops a compact and wireless sensing system for continuously monitoring the pressure applied on the radial artery and wrist skin in clinical practice. A liquid metal (LM)‐based all‐soft pressure sensor is fabricated to enable conformal attachment between the device and skin even under large deformation conditions. The linear sensitivity of 0.007 kPa−1 among the wide pressure range of 0–100 kPa is achieved and the real‐time detection data can be wirelessly transmitted to mobile clients as a reference pressure value. With these devices, detailed pressure data can be collected, analyzed, and stored for medical assistance as well as to improve surgery quality. This work develops a wireless sensing system upon a hemostasis device. The system consists of liquid metal‐based all‐soft capacitive pressure sensors, tiny reading circuits with low‐power transmission functions, and software‐based model deployment. With these devices, detailed pressure data on retracting operations can be collected, analyzed, and stored for medical assistance as well as improving surgery quality.

Background. Sepsis is a systemic inflammatory response that can elicit organ dysfunction as well as circulatory diseases in serious cases. When inflammatory responses are especially dysregulated, severe complications can arise, including sepsis-induced liver injury. Various microRNAs along with circular (circ) RNAs are involved in inflammatory responses; nevertheless, their functions in regulating sepsis-induced liver injury remain unknown. The cecal ligation and puncture (CLP) procedure can induce liver injury as well as polymicrobial sepsis. Methods. In this study, CLP was used to induce liver injury as well as polymicrobial sepsis. Then, liver function, inflammatory cytokine expression, and hepatic histopathology were evaluated. High-throughput sequencing was employed to investigate the abnormal hepatic circRNA expression after CLP. Raw264.7 cells were utilized to simulation an in vitro sepsis inflammation model with LPS induce. The relative mRNA as well as protein levels of TNF-α, IL-1β, and IL-6 was explored by quantitative polymerase chain reaction (PCR) and enzyme-linked immunosorbent assays. We explored functional connections among circRNAs, miR-31-5p, and gasdermin D (GSDMD) using dual-luciferase reporter assays. Western blot was employed to test GSDMD, caspase-1, and NLRP3 expression in mice and cell models. Results. Our results showed that CLP-induced sepsis promoted liver injury via increasing inflammatory pyroptosis. The abnormal expression of circ-Katnal1 played an important role in CLP-induced sepsis. Downregulating circ-Katnal1 suppressed LPS-induced inflammatory pyroptosis in Raw264.7 cells. Bioinformatics and luciferase reporter results confirmed that miR-31-5p and GSDMD were downstream targets of circ-Katnal1. Inhibiting miR-31-5p or upregulating GSDMD reversed the protective effects of silencing circ-Katnal1. Conclusion. Taken together, circ-Katnal1 enhanced inflammatory pyroptosis in sepsis-induced liver injury through the miR-31-5p/GSDMD axis.

Due to their excellent properties, blue CsPbBr3 quantum dots show great promise for full-colour display and lighting applications. This study used acetonitrile, a polar solvent, to post-treat CsPbBr3 quantum dots, resulting in a blue shift to 453 nm. To enhance stability, these quantum dots were encapsulated within the pore structure of mesoporous silica. A flexible luminescent fiber material was prepared using poly (lactic acid) (PLA) as the substrate, demonstrating improved hydrophobicity and stable optical properties. The material exhibited a contact angle of 99.7° and maintained 82.2% of its fluorescence intensity after 30 days at room temperature. These findings highlight its significant potential for optical applications.

Background Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is an acute, diffuse, inflammatory lung injury caused by various endogenous or exogenous factors. It is currently widely recognized that an excessive inflammatory response resulting from immune imbalance constitutes a crucial pathogenic mechanism in ALI/ARDS. SS31 is a novel mitochondria-targeted antioxidant peptide. This article validates the role of SS31 in lipopolysaccharide (LPS)-induced ALI. Methods The study applied transcriptome sequencing, immunofluorescence, PCR, immunofluorescence and other methods to explore the mechanism of SS31 in LPS induced ALI. Results Transcriptome sequencing results indicate that LPS-induced ALI is closely associated with immune regulatory processes, the Toll-like receptor pathway, and the NF-κB signaling pathway. The role of SS31 in acute lung injury is closely related to biological processes, such as immune regulation and cell death. This study demonstrated that SS31 can inhibit the expression of inflammatory factors IL-6, IL-1β, IL-18, and TNF-α, and reduce the expression of pyroptosis-related proteins NLRP3, and GSDMD-N. Further analysis revealed that S100A8 may be a key gene in the effect of SS31. LPS stimulation leads to increased expression of S100A8, while SS31 decreases its expression. Recombinant protein S100A8 can attenuate the inhibitory effect of SS31 on IL-1β, IL-18, NLRP3, and GSDMD-N. Conclusions The research results indicate that SS31 may inhibit the activation of the NLRP3 inflammasome and suppress inflammatory responses by regulating S100A8, thereby alleviating LPS-induced ALI in mice; this process may be related to pyroptosis.

Background Acute respiratory distress syndrome (ARDS) is a serious threat to human life. Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta-analysis evaluates the accuracy of artificial intelligence in the early diagnosis of ARDS and provides guidance for future research and applications. Methods A search on PubMed, Embase, Cochrane, Web of Science, CNKI, Wanfang, Chinese Biomedical Literature (CBM), and VIP databases was systematically conducted, from their establishment to November 2023, to obtain eligible studies for the analysis and evaluation of the predictive effect of AI on ARDS. The retrieved literature was screened according to inclusion and exclusion criteria, the quality of the included studies was assessed using QUADAS-2, and the included studies were statistically analyzed. Results Among the 2, 996 studies, 33 were included in this meta-analysis, which showed that the pooled sensitivity of AI in predicting ARDS was 0.81 (0.76–0.85), the pooled specificity was 0.88 (0.84–0.91), and the area under the receiver operating characteristic curve (AUC) was 0.91 (0.88–0.93). The analyzed studies included 28 models, with a pooled sensitivity of 0.79 (0.76–0.82), a pooled specificity of 0.85 (0.83–0.88), and an AUC of 0.89 (0.86–0.91). In the subgroup analysis, the pooled AUC of the AI models ANN, CNN, LR, RF, SVM, and XGB were 0.86 (0.83–0.89), 0.91 (0.88–0.93), 0.86 (0.83–0.89), and 0.89 (0.86–0.91), 0.90 (0.87–0.92), 0.93 (0.90–0.95), respectively. In an additional subgroup analysis, we evaluated the predictive performance of the AI models trained using different predictors. This meta-analysis was registered in PROSPERO (CRD42023491546). Conclusion AI has good sensitivity and specificity for predicting ARDS, indicating a high clinical application value. Algorithmic models such as CNN, SVM, and XGB have improved prediction performance. The subgroup analysis revealed that the model trained using images combined with other predictors had the best predictive performance.

Background: Postoperative delirium is associated with an increased number of different complications, such as prolonged hospital stay, long-term cognitive impairment, and increased mortality. Therefore, early prediction of delirium after percutaneous coronary intervention (PCI) is necessary, but currently, there is still a lack of reliable and effective prediction models for such patients. Methods: All data used in this study were derived from the MIMIC-IV database. Multivariable Cox regression was employed to analyze the data, and the performance of the newly developed nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC). The clinical value of the prediction model was tested using decision curve analysis (DCA). Results: A total of 313 PCI patients in the intensive care unit (ICU) were included in the analysis, comprising 219 in the training cohort and 94 in the testing cohort. Twenty variables were selected for model development. Multivariable Cox regression revealed that benzodiazepine use, vasoactive drug therapy, age, white blood cell count (WBC), and serum potassium were independent risk factors for predicting the occurrence of delirium after PCI. The AUC values for predicting delirium occurrence in the training and validation cohorts were 0.771 and 0.743, respectively. Conclusions: This study has identified several important demographic and laboratory parameters associated with the occurrence of delirium after PCI, and used them to establish a more accurate and convenient nomogram model to predict the occurrence of postoperative delirium in such patients.