3-{2-[(1,3-Benzothia­zol-2-yl)sulfanyl­meth­yl]phen­yl}-4-meth­oxy-5,5-dimethyl­furan-2(5H)-one

In the title compound, C21H19NO3S2, the dihedral angles formed between the thia­zole ring and the adjacent benzene ring and the other benzene ring are 1.58 (3) and 76.48 (6)°, respectively. The crystal structure features a weak C—H⋯O inter­action

N′-tert-Butyl-N′-(3,5-dimethyl­benzo­yl)-2,2-dimethyl-4-oxochroman-6-carbo­hydrazide

In the crystal structure of the title compound, C25H30N2O4, the steric size of the tert-butyl group causes the 3,5-dimethyl­phenyl ring to adopt a transoid geometry with respect to the N—C(O) bond. The six-membered heterocyclic ring is disordered over two sites, with occupancies of 0.553 (4) and 0.447 (4). Intra­molecular C—H⋯O inter­actions are present. In the crystal, mol­ecules are linked by inter­molecular N—H⋯O and C—H⋯O hydrogen bonds

Friction Surface Treatment Selection: Aggregate Properties, Surface Characteristics, Alternative Treatments, and Safety Effects

This study aimed to evaluate the long term performance of the selected surface friction treatments, including high friction surface treatment (HFST) using calcined bauxite and steel slag, and conventional friction surfacing, in particular pavement preservation treatments such as chip seal, microsurfacing, ultrathin bonded wearing course (UBWC), and diamond grinding. This study also attempted to determine the correlation between vehicle crash and pavement surface friction, which makes it possible to quantitatively establish the so-called crash modification factors (CMFs) that are extremely useful in selecting a cost-effective solution to reduce wet pavement vehicle crashes. In-depth reviews were conducted to identify the aspects of the properties for aggregates used in HFST, including aggregate abrasion value (AAV), Los Angeles abrasion (LAA), Micro-Deval abrasion, and polished stone value (PSV). Extensive laboratory testing was conducted to examine the LAA, Micro-Deval abrasion, and PSV, and to provide first-hand data on the calcined bauxite and steel slag that may be used for HFST and friction surfacing in Indiana. Laboratory accelerating polishing was carried out to evaluate the effect of aggregate gradation and identify the HFST systems with satisfactory friction performance with respect to surface macro-texture and friction. Test strips were installed in the pavement on a real-world road to further evaluate the friction performances of the promising HFST systems under the true traffic polishing and assess the potential effect of winter and snow plough. Pull-off testing was also conducted to examine the bonding between the proposed HFST systems and the substrate surface. Field friction test data was utilized to evaluate the long-term friction performances of pavement preservation treatments, including chip seal, microsurfacing, UBWC, and diamond grinding. Statewide vehicle crash data between 2010 and 2014 was examined to determine the crash statistics associated with pavement friction. The crash data was also matched to the annual pavement inventory friction data to quantify the probabilistic association between vehicle crash and pavement friction with respect to interstate, US, and state highways, respectively. Specification requirements were established for the properties of calcined bauxite and steel slag for HFST and friction surfacing with respect to LAA, Micro-Deval abrasion, PSV, Al2O3 content, and fine aggregate angularity (FAA). Specification requirements were also developed for HFST aggregate gradation and surface friction performance. Regression models were developed for predicting the friction numbers of chip seal, microsurfacing, UBWC, and diamond grinding over their service lives. Regression models were also provided to quantify the effectiveness of friction surfacing for interstate, US, and state highways, respectively

Recent insight into the role of macrophage in alcohol-associated liver disease: a mini-review

Alcohol-associated liver disease (ALD) is a condition that develops due to prolonged and excessive alcohol consumption. It encompasses various stages of liver damage, including fatty liver, alcoholic hepatitis, and cirrhosis. Immune cells, particularly macrophages, of various types play a significant role in the onset and progression of the disease. Macrophages observed in the liver exhibit diverse differentiation forms, and perform a range of functions. Beyond M1 and M2 macrophages, human macrophages can polarize into distinct phenotypes in response to various stimuli. Recent advancements have improved our understanding of macrophage diversity and their role in the progression of ALD. This mini-review provides a concise overview of the latest findings on the role and differentiation of macrophages in ALD. Additionally, it discusses potential therapeutic targets associated with macrophages and explores potential therapeutic strategies

Aerosolization inhalation of non-typeable Haemophilus influenzae outer membrane vesicles contributing to neutrophilic asthma

BackgroundNeutrophilic asthma is poorly responsive to corticosteroids, and the mechanism underlying its pathogenesis remains unclear. Non-typeable Haemophilus influenzae (NTHi) is the most common bacterium found in induced sputum from patients with neutrophilic asthma. NTHi can release outer membrane vesicles (OMVs), which transfer biomolecules to host cells and the external environment. However, the role and mechanisms of NTHi OMVs in the pathogenesis of neutrophilic asthma remain unclear.MethodsWe conducted assays to investigate whether NTHi OMVs can induce neutrophilic asthma when inhaled. We isolated and purified NTHi OMVs and administered them via a nebulizer to ovalbumin (OVA)-sensitized mice. We collected and sequenced serum, blood, bronchoalveolar lavage fluid, and lung tissue from each group and gathered lung function data.ResultsInhaled NTHi OMVs-induced neutrophilic asthma in OVA-sensitized mice. High-throughput sequencing revealed that NTHi OMV inhalation in OVA-sensitized mice significantly enriched inflammatory and immune-related signaling pathways. We found increased transcription and secretion of interleukin (IL)-1β and IL-17, which may contribute to neutrophilic asthma. Furthermore, we discovered that airway epithelium is the first receptor cell of NTHi OMVs and releases IL-1β. These findings suggest that NTHi OMVs could be a potential target for neutrophilic asthma therapy

A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

IntroductionPulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific.MethodsUsing the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient’s basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier.ResultsIn the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches.DiscussionThe experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE