MI-DenseCFNet: deep learning-based multimodal diagnosis models for Aureus and Aspergillus pneumonia.

OBJECTIVE: To build and merge a diagnostic model called multi-input DenseNet fused with clinical features (MI-DenseCFNet) for discriminating between Staphylococcus aureus pneumonia (SAP) and Aspergillus pneumonia (ASP) and to evaluate the significant correlation of each clinical feature in determining these two types of pneumonia using a random forest dichotomous diagnosis model. This will enhance diagnostic accuracy and efficiency in distinguishing between SAP and ASP. METHODS: In this study, 60 patients with clinically confirmed SAP and ASP, who were admitted to four large tertiary hospitals in Kunming, China, were included. Thoracic high-resolution CT lung windows of all patients were extracted from the picture archiving and communication system, and the corresponding clinical data of each patient were collected. RESULTS: The MI-DenseCFNet diagnosis model demonstrates an internal validation set with an area under the curve (AUC) of 0.92. Its external validation set demonstrates an AUC of 0.83. The model requires only 10.24s to generate a categorical diagnosis and produce results from 20 cases of data. Compared with high-, mid-, and low-ranking radiologists, the model achieves accuracies of 78% vs. 75% vs. 60% vs. 40%. Eleven significant clinical features were screened by the random forest dichotomous diagnosis model. CONCLUSION: The MI-DenseCFNet multimodal diagnosis model can effectively diagnose SAP and ASP, and its diagnostic performance significantly exceeds that of junior radiologists. The 11 important clinical features were screened in the constructed random forest dichotomous diagnostic model, providing a reference for clinicians. CLINICAL RELEVANCE STATEMENT: MI-DenseCFNet could provide diagnostic assistance for primary hospitals that do not have advanced radiologists, enabling patients with suspected infections like Staphylococcus aureus pneumonia or Aspergillus pneumonia to receive a quicker diagnosis and cut down on the abuse of antibiotics. KEY POINTS: • MI-DenseCFNet combines deep learning neural networks with crucial clinical features to discern between Staphylococcus aureus pneumonia and Aspergillus pneumonia. • The comprehensive group had an area under the curve of 0.92, surpassing the proficiency of junior radiologists. • This model can enhance a primary radiologist's diagnostic capacity.

Luteolin-7-diglucuronide, a novel PTP1B inhibitor, ameliorates hepatic stellate cell activation and liver fibrosis in mice.

Liver fibrosis, one of the leading causes of morbidity and mortality worldwide, lacks effective therapy. The activation of hepatic stellate cells (HSCs) is the dominant event in hepatic fibrogenesis. Luteolin-7-diglucuronide (L7DG) is the major flavonoid extracted from Perilla frutescens and Verbena officinalis. Their beneficial effects in the treatment of liver diseases were well documented. In this study we investigated the anti-fibrotic activities of L7DG and the potential mechanisms. We established TGF-ß1-activated mouse primary hepatic stellate cells (pHSCs) and human HSC line LX-2 as in vitro liver fibrosis models. Co-treatment with L7DG (5, 20, 50 µM) dose-dependently decreased TGF-ß1-induced expression of fibrotic markers collagen 1, α-SMA and fibronectin. In liver fibrosis mouse models induced by CCl4 challenge alone or in combination with HFHC diet, administration of L7DG (40, 150 mg·kg-1·d-1, i.g., for 4 or 8 weeks) dose-dependently attenuated hepatic histopathological injury and collagen accumulation, decreased expression of fibrogenic genes. By conducting target prediction, molecular docking and enzyme activity detection, we identified L7DG as a potent inhibitor of protein tyrosine phosphatase 1B (PTP1B) with an IC50 value of 2.10 µM. Further studies revealed that L7DG inhibited PTP1B activity, up-regulated AMPK phosphorylation and subsequently inhibited HSC activation. This study demonstrates that the phytochemical L7DG may be a potential therapeutic candidate for the treatment of liver fibrosis.

The health and welfare effects of environmental governance: Evidence from China.

Environmental regulations aim to reduce pollution and improve air quality and the health of residents. However, there is a lack of research focusing on the health and welfare effects of low-carbon city pilot policies. In this context, this study takes China's low-carbon city pilot policy as an entry point, focuses on the health effects of public environmental governance, and systematically investigates the effects and mechanisms of low-carbon city development on the health of middle-aged and elderly people by applying the difference-in-differences method. The study finds that low-carbon city (LCC) policy significantly improves the physical and mental health of middle-aged and elderly people, and the main transmission mechanism is the reduction in air pollution and improvement in social capital. These results hold following a series of robustness tests. Furthermore, low-carbon city construction can reduce hospitalization and outpatient costs for people over 45 years old by up to 3 % and 15.5 %, respectively. The findings of this study provide useful policy insights for ensuring sustainable improvement in environmental quality and public health.

Itemized determination of As, Sb, Bi, Se, and Ge in graphite sample by HG-AFS with high-temperature microwave digestion.

In this paper, we optimized a method for fast and accurate determination of five impurity elements (As, Sb, Bi, Se, and Ge) in graphite samples to overcome the shortcomings of existing methods, such as complicated equipment, cumbersome process, multiple-time preparation, separate determination, and large error in results. Graphite samples were digested with HNO3-H2SO4-HClO4-HF in a high-temperature and high-pressure microwave digestion apparatus, and the elements were extracted and determined separately by AFS (atomic fluorescence spectrometry). There is no element loss during the processing and analysis of this method. The spike recoveries (As: 90.30%-102.3%, Sb: 90.73%-110.0%, Bi: 90.00%-99.67%, Se: 93.33%-110.0%, Ge: 92.26%-104.2%) and precision (RSD%; As: 1.34%-8.96%, Sb: 2.67%-7.10%, Bi: 1.83%-4.58%, Se: 0.36%-3.25%, Ge: 4.41%-8.65%) meet the requirements of the corresponding quality specifications. The method has some advantages (such as no elemental loss, fast testing, strong element targeting, and accurate results), and thus can achieve batch determination of graphite samples. The optimized method for graphite sample and final solution preparations can be used for diverse spectrometric technologies, and that for spectrometer conditions have reference value for HG-AFS instruments.

Nanoengineered Red Blood Cells Loaded with TMPRSS2 and Cathepsin L Inhibitors Block SARS-CoV-2 Pseudovirus Entry into Lung ACE2+ Cells.

The enzymatic activities of Furin, Transmembrane serine proteinase 2 (TMPRSS2), Cathepsin L (CTSL), and Angiotensin-converting enzyme 2 (ACE2) receptor binding are necessary for the entry of coronaviruses into host cells. Precise inhibition of these key proteases in ACE2+ lung cells during a viral infection cycle shall prevent viral Spike (S) protein activation and its fusion with a host cell membrane, consequently averting virus entry to the cells. In this study, dual-drug-combined (TMPRSS2 inhibitor Camostat and CTSL inhibitor E-64d) nanocarriers (NCs) are constructed conjugated with an anti-human ACE2 (hACE2) antibody and employ Red Blood Cell (RBC)-hitchhiking, termed "Nanoengineered RBCs," for targeting lung cells. The significant therapeutic efficacy of the dual-drug-loaded nanoengineered RBCs in pseudovirus-infected K18-hACE2 transgenic mice is reported. Notably, the modular nanoengineered RBCs (anti-receptor antibody+NCs+RBCs) precisely target key proteases of host cells in the lungs to block the entry of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), regardless of virus variations. These findings are anticipated to benefit the development of a series of novel and safe host-cell-protecting antiviral therapies.