Aqua­bis(2-chloro­acetato-κO)(1,10-phenanthroline-κ2 N,N′)copper(II)

In the title complex, [Cu(C2H2ClO2)2(C12H8N2)(H2O)], the CuII ion is five-coordinated by two N atoms [Cu—N = 2.005 (2) and 2.029 (2) Å] from the 1,10-phenanthroline ligand, two O atoms [Cu—O = 1.943 (2)–1.966 (2) Å] from two 2-chloro­acetate ligands and one water mol­ecule [Cu—O = 2.253 (2) Å] in a distorted square-pyramidal geometry. The crystal structure exhibits inter­molecular O—H⋯O hydrogen bonds, short Cl⋯Cl contacts [3.334 (1) Å] and π–π inter­actions [centroid–centroid distance 3.621 (11) Å]

N′-(Diphenyl­methyl­ene)-2-hydroxy­benzo­hydrazide

In the title compound, C20H16N2O2, intra­molecular N—H⋯O and inter­molecular O—H⋯O hydrogen bonds are found. The inter­molecular hydrogen bonds link the mol­ecules into an infinite chain along the c axis. The dihedral angles between the aromatic rings are 16.9 (3), 80.8 (3) and 64.6 (3)

A Domestic Trash Detection Model Based on Improved YOLOX

Domestic trash detection is an essential technology toward achieving a smart city. Due to the complexity and variability of urban trash scenarios, the existing trash detection algorithms suffer from low detection rates and high false positives, as well as the general problem of slow speed in industrial applications. This paper proposes an i-YOLOX model for domestic trash detection based on deep learning algorithms. First, a large number of real-life trash images are collected into a new trash image dataset. Second, the lightweight operator involution is incorporated into the feature extraction structure of the algorithm, which allows the feature extraction layer to establish long-distance feature relationships and adaptively extract channel features. In addition, the ability of the model to distinguish similar trash features is strengthened by adding the convolutional block attention module (CBAM) to the enhanced feature extraction network. Finally, the design of the involution residual head structure in the detection head reduces the gradient disappearance and accelerates the convergence of the model loss values allowing the model to perform better classification and regression of the acquired feature layers. In this study, YOLOX-S is chosen as the baseline for each enhancement experiment. The experimental results show that compared with the baseline algorithm, the mean average precision (mAP) of i-YOLOX is improved by 1.47%, the number of parameters is reduced by 23.3%, and the FPS is improved by 40.4%. In practical applications, this improved model achieves accurate recognition of trash in natural scenes, which further validates the generalization performance of i-YOLOX and provides a reference for future domestic trash detection research

Additional file 1 of Cirsiliol induces autophagy and mitochondrial apoptosis through the AKT/FOXO1 axis and influences methotrexate resistance in osteosarcoma

Additional file 1: Figure S1. A U2OS cells were treated with different concentrations of cirsiliol, stained with anti-Cyto-C and anti-TOMM20 antibodies and imaged using co-focused microscope imaging. Scale bars:100 µm. B Cyto-C and TOMM20 were quantified using FIJI/ImageJ. Data are expressed as mean ± SD. *p < 0.05; **p < 0.01; ***p < 0.001

Evaluation of Immunogenicity and Safety of Vero Cell-Derived Inactivated COVID-19 Vaccine in Older Patients with Hypertension and Diabetes Mellitus

Background: To evaluate the immunogenicity and safety of the COVID-19 vaccine (Vero cell), inactivated, in a population aged ≥60 years with hypertension or(/and) diabetes mellitus. Methods: A total of 1440 participants were enrolled and divided into four groups, 330 in the hypertension group, 330 in the diabetes group, 300 in the hypertensive combined with diabetes group (combined disease group), and 480 in the healthy population group. Two doses of the COVID-19 vaccine (Vero cell), inactivated, were administered at a 21-day interval and blood samples were collected before vaccination and 28 days after the second dose to evaluate the immunogenicity. The adverse events and changes in blood pressure and blood glucose levels after vaccination were recorded. Results: The seroconversion rate of the COVID-19 neutralizing antibodies was 100% for all participants. The post-inoculation geometric mean titer (GMT) in the four groups of the hypertension, diabetes, combined disease, and healthy populations were 73.41, 69.93, 73.84, and 74.86, respectively. The seroconversion rates and post-vaccination GMT in the hypertension, diabetes, and combined disease groups were non-inferior to the healthy population group. The rates of vaccine-related adverse reactions were 11.93%, 14.29%, 12.50%, and 9.38%, respectively. No serious adverse events were reported during the study. No apparent abnormal fluctuations in blood pressure and blood glucose values were observed after vaccination in participants with hypertension or(/and) diabetes. Conclusions: The COVID-19 vaccine (Vero cell), inactivated, showed good immunogenicity and safety in patients aged ≥60 years suffering from hypertension or(/and) diabetes mellitus