Sacituzumab govitecan in HR+HER2- metastatic breast cancer: the randomized phase 3 EVER-132-002 trial.

Sacituzumab govitecan (SG) significantly improved progression-free survival (PFS) and overall survival (OS) versus chemotherapy in hormone receptor-positive human epidermal growth factor receptor 2-negative (HR+HER2-) metastatic breast cancer (mBC) in the global TROPiCS-02 study. TROPiCS-02 enrolled few Asian patients. Here we report results of SG in Asian patients with HR+HER2- mBC from the EVER-132-002 study. Patients were randomized to SG (n = 166) or chemotherapy (n = 165). The primary endpoint was met: PFS was improved with SG versus chemotherapy (hazard ratio of 0.67, 95% confidence interval 0.52-0.87; P = 0.0028; median 4.3 versus 4.2 months). OS also improved with SG versus chemotherapy (hazard ratio of 0.64, 95% confidence interval 0.47-0.88; P = 0.0061; median 21.0 versus 15.3 months). The most common grade ≥3 treatment-emergent adverse events were neutropenia, leukopenia and anemia. SG demonstrated significant and clinically meaningful improvement in PFS and OS versus chemotherapy, with a manageable safety profile consistent with prior studies. SG represents a promising treatment option for Asian patients with HR+HER2- mBC (ClinicalTrials.gov identifier no. NCT04639986 ).

Growth Mechanism of Carbon Nanotubes Revealed by in situ Transmission Electron Microscopy.

Elucidating the growth mechanism of carbon nanotubes (CNTs) is critical to obtaining CNTs with desired structures and tailored properties for their practical applications. With atomic resolution imaging, in situ transmission electron microscopy (TEM) has been a key technique to reveal the microstructure and dynamics of CNTs in real time. In this review, recent advances in the development of in situ TEM with different types of environmental reactors will be introduced. The catalytic growth mechanisms of CNTs revealed by in situ TEM under realistic conditions are discussed from fundamental thermodynamics and kinetics to the detailed nucleation, growth, and termination mechanisms, including the state and phase of active catalysts, interfacial connections between catalyst and growing CNTs, and catalyst-related growth kinetics of CNTs. Great progresses have been made on how a CNT nucleates, grows and terminates, focusing on the interface dynamics and kinetic fluctuations. Finally, challenges and future directions for understanding the atomic dynamics under the real growth conditions are proposed. It is expected that breakthroughs in the fundamental growth mechanisms will pave the way to the ultimate goal of designing and controlling the atomic structures of CNTs for their applications in various devices.

An Advanced Machine Learning Model for a Web-Based Artificial Intelligence-Based Clinical Decision Support System Application: Model Development and Validation Study.

BACKGROUND: Breast cancer is a leading global health concern, necessitating advancements in recurrence prediction and management. The development of an artificial intelligence (AI)-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility. OBJECTIVE: This study aims to develop and validate an advanced machine learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence. METHODS: This study focused on developing an advanced machine learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3577 patients (2004-2016). As a tertiary medical center, it accepts referrals from four branches-3 branches in the northern region and 1 branch on an offshore island in our country-that manage chronic diseases but refer complex surgical cases, including breast cancer, to the main center, enriching our study population's diversity. Model training used patient data from 2004 to 2012, with subsequent validation using data from 2013 to 2016, ensuring comprehensive assessment and robustness of our predictive models. ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique address the imbalance of data sets. Various algorithms, including light gradient-boosting machine, gradient boosting, and extreme gradient boosting, were used, and their performance was evaluated using metrics such as the area under the curve, accuracy, sensitivity, and F1-score. RESULTS: The light gradient-boosting machine model demonstrated superior performance, with an area under the curve of 0.80, followed closely by the gradient boosting and extreme gradient boosting models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its use in personalized treatment planning and patient involvement. CONCLUSIONS: The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its use.

Comparison of the Efficacy, Safety, and Quality of Life of Pegylated Liposomal Doxorubicin-Cyclophosphamide versus Epirubicin-Cyclophosphamide in Patients with Early-Stage HER2-Negative Breast Cancer: A Prospective, Randomized, Multicenter, Phase II Study.

INTRODUCTION: This multicenter, phase II randomized, non-inferiority study reports from the first prospective two-armed randomized control trial that compared the efficacy, safety, and quality of life (QoL) of pegylated liposomal doxorubicin (PLD)-based and epirubicin-based as adjuvant chemotherapy for stage I-II human epidermal growth factor receptor 2 (HER2)-negative breast cancer. METHODS: Patients with stage I/II HER2-negative breast cancer received PLD (37.5 mg/m2, Q3W, 5 cycles, LC arm) plus cyclophosphamide (600 mg/m2) or epirubicin (90 mg/m2, Q3W, 4 cycles, EC arm) plus cyclophosphamide (600 mg/m2). Randomization was stratified by lymph node and ER and PR status. The primary endpoint was disease-free survival (DFS), and secondary endpoints were overall survival (OS), safety profiles, and QoL. QoL was assessed using the EORTC-QLQ-C30 and QLQ-BR23 questionnaires. RESULTS: A total of 256 patients were assigned to LC (n = 148) and EC (n = 108). There was no difference in 5-year DFS and OS rate between the two groups. LC-based adjuvant regimens had significantly less alopecia and low-grade 3-4 hematologic adverse events (AEs). Significantly improved QoL was observed in the LC arm during and after treatment for symptoms including fatigue, nausea and vomiting, and systemic therapy side effects. CONCLUSION: Comparable efficacy and safety between adjuvant PLD and epirubicin for stage I-II HER2-negative breast cancer was observed. There was no difference in the 5-year DFS and OS rates between the two treatment arms. However, low-grade 3-4 AEs and a trend of favorable QoL symptom scales were observed in the LC arm, suggesting that PLD-containing regimen could become a new standard treatment for early-stage HER2-negative breast cancer patients.

Robust gesture recognition based on attention-deep fast convolutional neural network and surface electromyographic signals.

The surface electromyographic (sEMG) signals reflect human motor intention and can be utilized for human-machine interfaces (HMI). Comparing to the sparse multi-channel (SMC) electrodes, the high-density (HD) electrodes have a large number of electrodes and compact space between electrodes, which can achieve more sEMG information and have the potential to achieve higher performance in myocontrol. However, when the HD electrodes grid shift or damage, it will affect gesture recognition and reduce recognition accuracy. To minimize the impact resulting from the electrodes shift and damage, we proposed an attention deep fast convolutional neural network (attention-DFCNN) model by utilizing the temporary and spatial characteristics of high-density surface electromyography (HD-sEMG) signals. Contrary to the previous methods, which are mostly base on sEMG temporal features, the attention-DFCNN model can improve the robustness and stability by combining the spatial and temporary features. The performance of the proposed model was compared with other classical method and deep learning methods. We used the dataset provided by The University Medical Center Göttingen. Seven able-bodied subjects and one amputee involved in this work. Each subject executed nine gestures under the electrodes shift (10 mm) and damage (6 channels). As for the electrodes shift 10 mm in four directions (inwards; onwards; upwards; downwards) on seven able-bodied subjects, without any pre-training, the average accuracy of attention-DFCNN (0.942 ± 0.04) is significantly higher than LSDA (0.910 ± 0.04, p < 0.01), CNN (0.920 ± 0.05, p < 0.01), TCN (0.840 ± 0.07, p < 0.01), LSTM (0.864 ± 0.08, p < 0.01), attention-BiLSTM (0.852 ± 0.07, p < 0.01), Transformer (0.903 ± 0.07, p < 0.01) and Swin-Transformer (0.908 ± 0.09, p < 0.01). The proposed attention-DFCNN algorithm and the way of combining the spatial and temporary features of sEMG signals can significantly improve the recognition rate when the HD electrodes grid shift or damage during wear.

EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

Navigating the complex terrain of hepatitis B virus reactivation in the era of Bruton tyrosine kinase inhibitors.

In this editorial, we offer a summary of the risk associated with hepatitis B reactivation (HBVr) in the setting of both solid and hematologic malignancies treated with Bruton tyrosine kinase (BTK) inhibitors, with insights derived from current studies. Furthermore, we emphasize the critical need for a framework regarding robust risk evaluation in patients undergoing such treatments. This framework is essential for identifying those at increased risk of HBVr, enabling healthcare providers to implement proactive measures to prevent reactivation and ensure the safe administration of BTK inhibitor therapy.

The Efficacy and Safety of Tandem Transplant Versus Single Stem Cell Transplant for Multiple Myeloma Patients: A Systematic Review and Meta-Analysis.

While high-dose therapy and autologous stem cell transplant (ASCT) remain integral to the primary treatment of newly diagnosed transplant-elble multiple myeloma (MM) patients, the challenge of disease progression persists. The primary objective of this meta-analysis is to evaluate the efficacy and safety of tandem ASCT compared to single ASCT. We conducted a systematic review and meta-analysis of randomized controlled trials and observational studies comparing tandem ASCT with single ASCT in patients with newly diagnosed MM. We searched PubMed, EMBASE, Cochrane Library, and Clinical Trials databases for studies published up to January 2024. The primary outcomes were progression-free survival (PFS), overall survival (OS), overall response rate (ORR), complete response rate (CRR), and treatment-related mortality (TRM). We used a random-effects model to calculate pooled hazard ratios (HRs) and relative risks (RRs) with 95% confidence intervals (CIs). Study quality was assessed using the Cochrane risk of bias tool and Newcastle-Ottawa Scale. Twelve studies involving 5057 patients met the inclusion criteria. Tandem ASCT was associated with a significantly higher CRR compared to single ASCT (HR 1.33, 95% CI 1.03-1.71, I2 = 15%), but no significant differences were observed in PFS (HR 0.75, 95% CI 0.42-1.34, I2 = 14%), OS (HR 0.60, 95% CI 0.33-1.10, I2 = 27%), or the ORR (RR 0.80, 95% CI 0.59-1.08, I2 = 33%). However, tandem ASCT was associated with a significantly higher risk of TRM (RR 1.78, 95% CI 1.00-3.18, I2 = 0%). Tandem ASCT improves the CRR but does not provide significant benefits in terms of PFS, OS, or ORR compared to single ASCT in patients with newly diagnosed MM. Moreover, tandem ASCT is associated with a higher risk of TRM. The decision to pursue tandem ASCT should be made on an individual basis, carefully weighing the potential benefits and risks in light of each patient's unique clinical situation. Future research should focus on identifying patient subgroups most likely to benefit from tandem ASCT and exploring strategies to optimize the efficacy and safety of this approach in the context of novel agent-based therapies.

A fast and high precision multi-robot environment modeling based on M-BFSI: Bidirectional filtering and scene identification method.

This article designs and implements a fast and high-precision multi-robot environment modeling method based on bidirectional filtering and scene identification. To solve the problem of feature tracking failure caused by large angle rotation, a bidirectional filtering mechanism is introduced to improve the error-matching elimination algorithm. A global key frame database for multiple robots is proposed based on a pretraining dictionary to convert images into a bag of words vectors. The images captured by different sub-robots are compared with the database for similarity score calculation, so as to realize fast identification and search of similar scenes. The coordinate transformation from local map to global map and the cooperative SLAM exploration of multiple robots is completed by the best matching image and the transformation matrix. The experimental results show that the proposed algorithm can effectively close the predicted trajectory of the sub-robot, thus achieving high-precision collaborative environment modeling.

The lethal K18-hACE2 knock-in mouse model mimicking the severe pneumonia of COVID-19 is practicable for antiviral development.

Animal models of COVID-19 facilitate the development of vaccines and antivirals against SARS-CoV-2. The efficacy of antivirals or vaccines may differ in different animal models with varied degrees of disease. Here, we introduce a mouse model expressing human angiotensin-converting enzyme 2 (ACE2). In this model, ACE2 with the human cytokeratin 18 promoter was knocked into the Hipp11 locus of C57BL/6J mouse by CRISPR - Cas9 (K18-hACE2 KI). Upon intranasal inoculation with high (3 × 105 PFU) or low (2.5 × 102 PFU) dose of SARS-CoV-2 wildtype (WT), Delta, Omicron BA.1, or Omicron BA.2 variants, all mice showed obvious infection symptoms, including weight loss, high viral loads in the lung, and interstitial pneumonia. 100% lethality was observed in K18-hACE2 KI mice infected by variants with a delay of endpoint for Delta and BA.1, and a significantly attenuated pathogenicity was observed for BA.2. The pneumonia of infected mice was accompanied by the infiltration of neutrophils and pulmonary fibrosis in the lung. Compared with K18-hACE2 Tg mice and HFH4-hACE2 Tg mice, K18-hACE2 KI mice are more susceptible to SARS-CoV-2. In the antivirals test, REGN10933 and Remdesivir had limited antiviral efficacies in K18-hACE2 KI mice upon the challenge of SARS-CoV-2 infections, while Nirmatrelvir, monoclonal antibody 4G4, and mRNA vaccines potently protected the mice from death. Our results suggest that the K18-hACE2 KI mouse model is lethal and stable for SARS-CoV-2 infection, and is practicable and stringent to antiviral development.

Real-time regurgitation estimation in percutaneous left ventricular assist device fully supported condition using an unscented Kalman filter.

Objective.Significant aortic regurgitation is a common complication following left ventricular assist device (LVAD) intervention, and existing studies have not attempted to monitor regurgitation signals and undertake preventive measures during full support. Regurgitation is an adverse event that can lead to inadequate left ventricular unloading, insufficient peripheral perfusion, and repeated episodes of heart failure. Moreover, regurgitation occurring during full support due to pump position offset cannot be directly controlled through control algorithms. Therefore, accurate estimation of regurgitation during percutaneous left ventricular assist device (PLVAD) full support is critical for clinical management and patient safety.Approach.An estimation system based on the regurgitation model is built in this paper, and the unscented Kalman filter estimator (UKF) is introduced as an estimation approach. Three offset degrees and three heart failure states are considered in the investigation. Using the mock circulatory loop experimental platform, compare the regurgitation estimated by the UKF algorithm with the actual measured regurgitation; the errors are analyzed using standard confidence intervals of ±2 SDs, and the effectiveness of the mentioned algorithms is thus assessed. The generalization ability of the proposed algorithm is verified by setting different heart failure conditions and different rotational speeds. The root mean square error and correlation coefficient between the estimated and actual values are quantified and the statistical significance of accuracy differences in estimation is illustrated using one-way analysis of variance (One-Way ANOVA), which in turn assessed the accuracy and stability of the UKF algorithm.Main results.The research findings demonstrate that the regurgitation estimation system based on the regurgitation model and UKF can relatively accurately estimate the regurgitation status of patients during PLVAD full support, but the effect of myocardial contractility on the estimation accuracy still needs to be taken into account.Significance.The proposed estimation method in this study provides essential reference information for clinical practitioners, enabling them to promptly manage potential complications arising from regurgitation. By sensitively detecting LVAD adverse events, valuable insights into the performance and reliability of the LVAD device can be obtained, offering crucial feedback and data support for device improvement and optimization.

Trends in the 30-year span of noninfectious cardiovascular implantable electronic device complications in Olmsted County.

Background: Cardiac implantable electronic devices (CIEDs), such as permanent pacemakers, implantable cardioverter-defibrillators, and cardiac resynchronization therapy devices, alleviate morbidity and mortality in various diseases. There is a paucity of real-world data on CIED complications and trends. Objectives: We sought to describe trends in noninfectious CIED complications over the past 3 decades in Olmsted County. Methods: The Rochester Epidemiology Project is a medical records linkage system comprising records of over 500,000 residents of Olmsted County from 1966 to present. CIED implantations between 1988 and 2018 were determined. Trends in noninfectious complications within 30 days of implantation were analyzed. Results: A total of 157 (6.2%) of 2536 patients who received CIED experienced device complications. A total of 2.7% of the implants had major complications requiring intervention. Lead dislodgement was the most common (2.8%), followed by hematoma (1.7%). Complications went up from 1988 to 2005, and then showed a downtrend until 2018, driven by a decline in hematomas in the last decade (P < .01). Those with complications were more likely to have prosthetic valves. Obesity appeared to have a protective effect in a multivariate regression model. The mean Charlson comorbidity index has trended up over the 30 years. Conclusion: Our study describes a real-world trend of CIED complications over 3 decades. Lead dislodgements and hematomas were the most common complications. Complications have declined over the last decade due to safer practices and a better understanding of anticoagulant management.

A bagging approach for improved predictive accuracy of intradialytic hypotension during hemodialysis treatment.

The primary objective of this study is to enhance the prediction accuracy of intradialytic hypotension in patients undergoing hemodialysis. A significant challenge in this context arises from the nature of the data derived from the monitoring devices and exhibits an extreme class imbalance problem. Traditional predictive models often display a bias towards the majority class, compromising the accuracy of minority class predictions. Therefore, we introduce a method called UnderXGBoost. This novel methodology combines the under-sampling, bagging, and XGBoost techniques to balance the dataset and improve predictive accuracy for the minority class. This method is characterized by its straightforward implementation and training efficiency. Empirical validation in a real-world dataset confirms the superior performance of UnderXGBoost compared to existing models in predicting intradialytic hypotension. Furthermore, our approach demonstrates versatility, allowing XGBoost to be substituted with other classifiers and still producing promising results. Sensitivity analysis was performed to assess the model's robustness, reinforce its reliability, and indicate its applicability to a broader range of medical scenarios facing similar challenges of data imbalance. Our model aims to enable medical professionals to provide preemptive treatments more effectively, thereby improving patient care and prognosis. This study contributes a novel and effective solution to a critical issue in medical prediction, thus broadening the application spectrum of predictive modeling in the healthcare domain.

STING guides the STX17-SNAP29-VAMP8 complex assembly to control autophagy.

The stimulator of interferon genes (STING) plays a pivotal role in orchestrating innate immunity, and dysregulated activity of STING has been implicated in the pathogenesis of autoimmune diseases. Recent findings suggest that bacterial infection activates STING, relieving ER stress, and triggers non-canonical autophagy by spatially regulating STX17. Despite these insights, the precise mechanism governing the dynamics of autophagosome fusion elicited by STING remains unclear. In this study, we demonstrate that dynamic STING activation guides the autophagy flux, mirroring the trajectory of canonical autophagy adaptors. STING engages in a physical interaction with STX17, and agonist-induced phosphorylation or degradation alleviates STING's inhibitory effects on the assembly of the STX17-SNAP29-VAMP8 complex. Consistent with these findings, degradation-deficient mutants hinder autophagy flux by impeding STX17-mediated autophagosome-lysosome fusion. Moreover, STING mutants associated with lupus disrupt the assembly of the STX17-SNAP29-VAMP8 complex and autophagy process, which lead to persistent STING activation and elevated IFN-ß production. Our results highlight that the intracellular trajectory of STING, coupled with autophagy flux, guides the assembly and membrane fusion of the STX17-SNAP29-VAMP8 complex, ensuring the accurate regulation of innate immunity.

STEM in situ thermal wave observations for investigating thermal diffusivity in nanoscale materials and devices.

Practical techniques to identify heat routes at the nanoscale are required for the thermal control of microelectronic, thermoelectric, and photonic devices. Nanoscale thermometry using various approaches has been extensively investigated, yet a reliable method has not been finalized. We developed an original technique using thermal waves induced by a pulsed convergent electron beam in a scanning transmission electron microscopy (STEM) mode at room temperature. By quantifying the relative phase delay at each irradiated position, we demonstrate the heat transport within various samples with a spatial resolution of ~10 nm and a temperature resolution of 0.01 K. Phonon-surface scatterings were quantitatively confirmed due to the suppression of thermal diffusivity. The phonon-grain boundary scatterings and ballistic phonon transport near the pulsed convergent electron beam were also visualized.

Whole-Brain Radiotherapy Alone vs Preceded by Bevacizumab, Etoposide, and Cisplatin for Untreated Brain Metastases From Breast Cancer: A Randomized Clinical Trial.

Importance: The incidence of brain metastasis is increasing in patients with metastatic breast cancer. Treatments to extend the control of brain metastasis are urgently required. Objective: To investigate whether the addition of an induction treatment of bevacizumab, etoposide, and cisplatin (BEEP) improves brain-specific progression-free survival (PFS) after whole-brain radiotherapy (WBRT). Design, Setting, and Participants: This open-label, randomized, multicenter clinical trial assessed patients with brain metastases from breast cancer (BMBC) in Taiwan from September 9, 2014, to December 24, 2018, with survival follow-up until December 31, 2021. Key inclusion criteria included metastatic brain tumors not suitable for focal treatment, WBRT naivety, age 20 to 75 years, and at least 1 measurable brain metastatic lesion. The primary end point was brain-specific PFS, with an expected hazard ratio of 0.60, a 2-sided α ≤ .20, and power of 0.8. Interventions: Eligible patients were randomly assigned at a ratio of 2:1 to the experimental arm, which involved 3 cycles of BEEP followed by WBRT, or the control arm, which involved WBRT alone. Main Outcomes and Measures: The primary end point was the determination of brain-specific PFS by local investigators according to the Response Evaluation Criteria in Solid Tumors, version 1.1, the initiation of other brain-directed treatment after WBRT, or death. Other key end points included brain-specific objective response rate after 8 weeks of BEEP treatment or WBRT and 8-month brain-specific PFS rate, PFS, and overall survival. Results: A total of 118 patients with BMBC were randomized, with the intention-to-treat cohort comprising 112 patients. The median age was 56 years (range, 34-71 years), and 61 patients (54.5%) had ERBB2 (formerly HER2 or HER2/neu)-positive disease. The median (range) brain-specific PFS was 8.1 (0.3-29.5) vs 6.5 (0.9-25.5) months in the experimental and control arms, respectively (hazard ratio, 0.71; 95% CI, 0.44-1.13; P = .15; significant at predefined α ≤ .20). The brain-specific objective response rate at 2 months was not significantly different (BEEP treatment vs WBRT, 41.9% vs 52.6%), but the 8-month brain-specific PFS rate was significantly higher in the experimental group (48.7% vs 26.3%; P = .03). Adverse events were generally manageable with prophylactic granulocyte colony-stimulating factor treatment. Conclusions and Relevance: The findings show that induction BEEP before WBRT may improve the control of BMBC compared with using upfront WBRT, which could address an unmet need for an effective systemic treatment for intractable brain and extracranial metastases from metastatic breast cancer. Trial Registration: ClinicalTrials.gov Identifier: NCT02185352.

Fangji Dihuang formulation ameliorated DNCB-induced atopic dermatitis-like skin lesions by IL-17 signaling pathway: integrating network analysis and experimental validation.

Background: The Fangji Dihuang formulation (FJDHF) is a widely recognized Traditional Chinese Medicine (TCM) formula that consists of five plant drugs: Stephaniae Tetrandrae Radix, Cinnamomi Ramulus, Rehmanniae Radix, Saposhnikoviae Radix, and Glycyrrhiza Urensis Fisch. This formulation has been known to exhibit clinical therapeutic effects in the treatment of inflammatory skin diseases. However, there is a lack of pharmacological research on its anti-atopic dermatitis (AD) activity. Methods: To investigate the potential anti-AD activity of FJDHF, DNCB was used to induce AD-like skin inflammation in the back of mice. Following successful modeling, the mice were administered FJDHF orally. The extent of the inflammatory skin lesions was recorded at day 4, 7, 14 and 28. UHPLC-Q-Exactive Orbitrap MS was used to identify and match the compounds present in FJDHF with ITCM, TCMIP and TCMSID. In silico predictions of potential target proteins of the identified compounds were obtained from SwishTargetPrediction, ITCM and TargetNet databases. AD-related genes were identified from GSE32924 data set, and FJDHF anti-AD hub genes were identified by MCODE algorithm. ClueGo enrichment analysis was employed to identify the core pathway of FJDHF's anti-AD effect. To further investigate the anti-AD effect of FJDHF, single-cell RNA sequencing data set (GSE148196) from AD patients was analyzed to determine the target cells and signaling pathways of FJDHF in AD. Finally, rt-PCR, flow cytometry, and mouse back skin RNA sequencing were utilized to validate our findings. Results: FJDHF was found to be effective in improving the degree of the AD-like lesions in the mice. Network pharmacological analysis revealed the core pathway of FJDHF to be the IL-17 signaling pathway, which is interactively associated with cytokines. Single-cell RNA sequencing analysis suggested that FJDHF may play an anti-AD role by influencing dendritic cells. Flow cytometry and rt-PCR results showed that FJDHF can reduce the influence of AD sample of IL-4, IFN-γ and the expression of IL-17. The RNA sequencing of mouse back skin also confirmed our conclusion. Conclusion: FJDHF may inhibit DNCB-induced AD-like skin inflammation in mice by inhibiting the IL-17 signaling pathway. Thus, FJDHF can be considered as a potential therapeutic agent for AD.

Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments.

Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. However, due to limited satellite coverage or communication disruptions, UAVs may lose signals for positioning. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. However, most of the existing datasets are developed for the geo-localization task of the objects captured by UAVs, rather than UAV self-positioning. Furthermore, the existing UAV datasets apply discrete sampling to synthetic data, such as Google Maps, neglecting the crucial aspects of dense sampling and the uncertainties commonly experienced in practical scenarios. To address these issues, this paper presents a new dataset, DenseUAV, that is the first publicly available dataset tailored for the UAV self-positioning task. DenseUAV adopts dense sampling on UAV images obtained in low-altitude urban areas. In total, over 27K UAV- and satellite-view images of 14 university campuses are collected and annotated. In terms of methodology, we first verify the superiority of Transformers over CNNs for the proposed task. Then we incorporate metric learning into representation learning to enhance the model's discriminative capacity and to reduce the modality discrepancy. Besides, to facilitate joint learning from both the satellite and UAV views, we introduce a mutually supervised learning approach. Last, we enhance the Recall@K metric and introduce a new measurement, SDM@K, to evaluate both the retrieval and localization performance for the proposed task. As a result, the proposed baseline method achieves a remarkable Recall@1 score of 83.01% and an SDM@1 score of 86.50% on DenseUAV. The dataset and code have been made publicly available on https://github.com/Dmmm1997/DenseUAV.