Rapid prediction of multidrug-resistant klebsiella pneumoniae through deep learning analysis of sers spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-To-Treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenemsensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE: This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings

Poly[[tetra-μ3-acetato-hexa-μ2-acetato­diaqua-μ2-oxalato-tetra­lanthanum(III)] dihydrate]

The title compound, {[La4(CH3CO2)10(C2O4)(H2O)2]·2H2O}n, exhibits a two-dimensional layered structure with the oxalate and acetate ligands acting as bridges. The asymmetric unit contains two crystallographically independent lanthanum(III) ions, half of an oxalate ligand, five acetate ligands, one coordinated water mol­ecule and one uncoordinated water mol­ecule. The coordination numbers of the two La ions are 9 and 10. Adjacent layers of the structure, which extend parallel to (100), are linked by O–H⋯O hydrogen bonds and are also held together by van der Waals inter­actions between the CH3 groups of the acetate anions

Spatial Pattern Analysis of Heavy Metals in Beijing Agricultural Soils Based on Spatial Autocorrelation Statistics

This study explored the spatial pattern of heavy metals in Beijing agricultural soils using Moran’s I statistic of spatial autocorrelation. The global Moran’s I result showed that the spatial dependence of Cr, Ni, Zn, and Hg changed with different spatial weight matrixes, and they had significant and positive global spatial correlations based on distance weight. The spatial dependence of the four metals was scale-dependent on distance, but these scale effects existed within a threshold distance of 13 km, 32 km, 50 km, and 29 km, respectively for Cr, Ni, Zn, and Hg. The maximal spatial positive correlation range was 57 km, 70 km, 57 km, and 55 km for Cr, Ni, Zn, and Hg, respectively and these were not affected by sampling density. Local spatial autocorrelation analysis detected the locations of spatial clusters and spatial outliers and revealed that the pollution of these four metals occurred in significant High-high spatial clusters, Low-high, or even High-low spatial outliers. Thus, three major areas were identified and should be receiving more attention: the first was the northeast region of Beijing, where Cr, Zn, Ni, and Hg had significant increases. The second was the southeast region of Beijing where wastewater irrigation had strongly changed the content of metals, particularly of Cr and Zn, in soils. The third area was the urban fringe around city, where Hg showed a significant increase

Bone marrow stromal cell-derived exosome combinate with fibrin on tantalum coating titanium implant accelerates osseointegration

This study aims to present a sustainably releasing system of exosomes-fibrin combinate loaded on tantalum-coating titanium implants. We hope to investigate potential effects of the system on osseointegration between tantalum coating titanium implants and its surrounding bone tissue. Exosomes derived from rabbit bone marrow stromal cells (rBMSCs) and fibrin were deposited onto the micro-nanostructure tantalum coating surface (Ta + exo + FI) and compared to control groups, including tantalum coating (Ta), tantalum coating loaded exosomes (Ta + exo) and tantalum coating loaded fibrin (Ta + FI). The optimal concentration of loading exosomes, exosomes uptake capacity by BMSCs, and the effect of controlled-release by fibrin were assessed by laser scanning confocal microscope (LCSM) and microplate reader. The optimal concentration of exosomes was 1 μg/μL. Adhesion, proliferation, and osteogenic differentiation ability of BMSCs on different materials were assessed in vitro. Finally, osseointegrative capacity of Ta, Ta + exo, Ta + FI, Ta + exo + FI implants in rabbit tibia were respectively evaluated with histology and bone-implant contact ratio (BIC%). It was demonstrated that exosome sustained-release system with fibrin loading on the tantalum coating was successfully established. Fibrin contribute to exosomes release extension from 2d to 6d. Furthermore, Ta + exo + FI significantly promoted adhesion, proliferation, and osteogenic differentiation of BMSCs. In vivo, the implants in Ta + exo + FI group displayed the highest osseointegrative capability than those in other groups. It is concluded that this exosome delivery system on the implants may be an effective way for tantalum coating titanium implants to promote osseointegration between implant and its surrounding bone tissue

Renal venous sampling assisted the diagnosis of juxtaglomerular cell tumor: a case report and literature review

Juxtaglomerular cell tumor (JCT) is an endocrine tumor marked by elevated renin levels and high blood pressure. This case report presents the clinical findings of a 47-year-old woman with a history of recurrent hypokalemia, headaches, hypertension, and increased plasma renin activity (PRA). Dynamic enhanced magnetic resonance imaging (MRI) revealed a small nodule on the upper part of the right kidney. Selective renal venous sampling indicated a higher PRA only in the right upper pole renal vein. The patient underwent surgical removal of the right kidney mass, and the pathology results confirmed the diagnosis of JCT. This case underscores the importance of conducting selective renal venous sampling for accurate JCT diagnosis

The peptide agonist-binding site of the glucagon-like peptide-1 (GLP-1) receptor based on site-directed mutagenesis and knowledge-based modelling

Glucagon-like peptide-1 (7–36)amide (GLP-1) plays a central role in regulating blood sugar levels and its receptor, GLP-1R, is a target for anti-diabetic agents such as the peptide agonist drugs exenatide and liraglutide. In order to understand the molecular nature of the peptide–receptor interaction, we used site-directed mutagenesis and pharmacological profiling to highlight nine sites as being important for peptide agonist binding and/or activation. Using a knowledge-based approach, we constructed a 3D model of agonist-bound GLP-1R, basing the conformation of the N-terminal region on that of the receptor-bound NMR structure of the related peptide pituitary adenylate cyclase-activating protein (PACAP21). The relative position of the extracellular to the transmembrane (TM) domain, as well as the molecular details of the agonist-binding site itself, were found to be different from the model that was published alongside the crystal structure of the TM domain of the glucagon receptor, but were nevertheless more compatible with published mutagenesis data. Furthermore, the NMR-determined structure of a high-potency cyclic conformationally-constrained 11-residue analogue of GLP-1 was also docked into the receptor-binding site. Despite having a different main chain conformation to that seen in the PACAP21 structure, four conserved residues (equivalent to His-7, Glu-9, Ser-14 and Asp-15 in GLP-1) could be structurally aligned and made similar interactions with the receptor as their equivalents in the GLP-1-docked model, suggesting the basis of a pharmacophore for GLP-1R peptide agonists. In this way, the model not only explains current mutagenesis and molecular pharmacological data but also provides a basis for further experimental design