The value of toxicological analysis in acute poisoning patients with uncertain exposure histories: a retrospective and descriptive study from an institute of poisoning.

BACKGROUND: In clinical practice, some patients might not be able or unwilling to provide a thorough history of medication and poison exposure. The aim of this study was to use toxicological analysis to examine the clinical characteristics of patients with acute poisoning whose exposure history was uncertain from a toxicological analysis perspective. METHODS: This was a retrospective and descriptive study from an institute of poisoning. Patient registration information and test reports spanning the period from April 1, 2020 to March 31, 2022, were obtained. Patients with uncertain exposure histories and who underwent toxicological analysis were included. Clinical manifestations and categories of toxics were analyzed. RESULTS: Among the 195 patients with positive toxicological analysis results, the main causes of uncertain exposure history was disturbance of consciousness (62.6%), unawareness (23.6%) and unwillingness or lack of cooperation (13.8%). The predominant clinical manifestations were disturbed consciousness (62.6%), followed by vomiting and nausea (14.4%) and liver function abnormalities (8.7%). A comparison of clinical manifestations between patients with positive and negative (n=99) toxicological analyses results revealed significantly different proportions of disturbances in consciousness (63% vs. 21%), dizziness (1.5% vs. 5.1%), multi-organ failure (1.5% vs. 7.1%), and local pain (0 vs 4%). The main categories of substances involved were psychiatric medications (23.1%), sedatives (20.5%), insecticides (13.8%), and herbicides (12.8%). CONCLUSION: The clinical manifestations of acute poisoning in patients with an uncertain exposure history are diverse and nonspecific, and toxicological analysis plays a pivotal role in the diagnosis and differential diagnosis of such patients.

The regulatory role of the apelin/APJ axis in scarring: Identification of upstream and downstream mechanisms.

Scarring, a prevalent issue in clinical settings, is characterized by the excessive generation of extracellular matrix within the skin tissue. Among the numerous regulatory factors implicated in fibrosis across various organs, the apelin/APJ axis has emerged as a potential regulator of fibrosis. Given the shared attribute of heightened extracellular matrix production between organ fibrosis and scarring, we hypothesize that the apelin/APJ axis also plays a regulatory role in scar development. In this study, we examined the expression of apelin and APJ in scar tissue, normal skin, and fibroblasts derived from these tissues. We investigated the impact of the hypoxic microenvironment in scars on apelin/APJ expression to identify the transcription factors influencing apelin/APJ expression. Through overexpressing or knocking down apelin/APJ expression, we observed their effects on fibroblast secretion of extracellular matrix proteins. We further validated these effects in animal experiments while exploring the underlying mechanisms. Our findings demonstrated that the apelin/APJ axis is expressed in fibroblasts from keloid, hypertrophic scar, and normal skin. The regulation of apelin/APJ expression by the hypoxic environment in scars plays a significant role in hypertrophic scar and keloid development. This regulation promotes extracellular matrix secretion through upregulation of TGF-ß1 expression via the PI3K/Akt/CREB1 pathway.

Pan-genome analysis highlights the role of structural variation in the evolution and environmental adaptation of Asian honeybees.

The Asian honeybee, Apis cerana, is an ecologically and economically important pollinator. Mapping its genetic variation is key to understanding population-level health, histories and potential capacities to respond to environmental changes. However, most efforts to date were focused on single nucleotide polymorphisms (SNPs) based on a single reference genome, thereby ignoring larger scale genomic variation. We employed long-read sequencing technologies to generate a chromosome-scale reference genome for the ancestral group of A. cerana. Integrating this with 525 resequencing data sets, we constructed the first pan-genome of A. cerana, encompassing almost the entire gene content. We found that 31.32% of genes in the pan-genome were variably present across populations, providing a broad gene pool for environmental adaptation. We identified and characterized structural variations (SVs) and found that they were not closely linked with SNP distributions; however, the formation of SVs was closely associated with transposable elements. Furthermore, phylogenetic analysis using SVs revealed a novel A. cerana ecological group not recoverable from the SNP data. Performing environmental association analysis identified a total of 44 SVs likely to be associated with environmental adaptation. Verification and analysis of one of these, a 330 bp deletion in the Atpalpha gene, indicated that this SV may promote the cold adaptation of A. cerana by altering gene expression. Taken together, our study demonstrates the feasibility and utility of applying pan-genome approaches to map and explore genetic feature variations of honeybee populations, and in particular to examine the role of SVs in the evolution and environmental adaptation of A. cerana.

Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging.

Background: Computed tomography (CT) imaging technology has become an indispensable auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage caused by X-rays, low-dose computed tomography (LDCT) scanning is becoming more widely applied. However, LDCT scanning reduces the signal-to-noise ratio of the projection, and the resulting images suffer from serious streak artifacts and spot noise. In particular, the intensity of noise and artifacts varies significantly across different body parts under a single low-dose protocol. Methods: To improve the quality of different degraded LDCT images in a unified framework, we developed a generative adversarial learning framework with a dynamic controllable residual. First, the generator network consists of the basic subnetwork and the conditional subnetwork. Inspired by the dynamic control strategy, we designed the basic subnetwork to adopt a residual architecture, with the conditional subnetwork providing weights to control the residual intensity. Second, we chose the Visual Geometry Group Network-128 (VGG-128) as the discriminator to improve the noise artifact suppression and feature retention ability of the generator. Additionally, a hybrid loss function was specifically designed, including the mean square error (MSE) loss, structural similarity index metric (SSIM) loss, adversarial loss, and gradient penalty (GP) loss. Results: The results obtained on two datasets show the competitive performance of the proposed framework, with a 3.22 dB peak signal-to-noise ratio (PSNR) margin, 0.03 SSIM margin, and 0.2 contrast-to-noise ratio margin on the Challenge data and a 1.0 dB PSNR margin and 0.01 SSIM margin on the real data. Conclusions: Experimental results demonstrated the competitive performance of the proposed method in terms of noise decrease, structural retention, and visual impression improvement.

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging.

BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and nonstationary streak artifacts. Previous studies have confirmed that deep learning (DL) is promising for improving LDCT imaging. However, most DL-based frameworks are built intuitively, lack interpretability, and suffer from image detail information loss, which has become a general challenging issue. METHODS: A multiscale reweighted convolutional coding neural network (MRCON-Net) is developed to address the above problems. MRCON-Net is compact and more explainable than other networks. First, inspired by the learning-based reweighted iterative soft thresholding algorithm (ISTA), we extend traditional convolutional sparse coding (CSC) to its reweighted convolutional learning form. Second, we use dilated convolution to extract multiscale image features, allowing our single model to capture the correlations between features of different scales. Finally, to automatically adjust the elements in the feature code to correct the obtained solution, a channel attention (CA) mechanism is utilized to learn appropriate weights. RESULTS: The visual results obtained based on the American Association of Physicians in Medicine (AAPM) Challenge and United Image Healthcare (UIH) clinical datasets confirm that the proposed model significantly reduces serious artifact noise while retaining the desired structures. Quantitative results show that the average structural similarity index measurement (SSIM) and peak signal-to-noise ratio (PSNR) achieved on the AAPM Challenge dataset are 0.9491 and 40.66, respectively, and the SSIM and PSNR achieved on the UIH clinical dataset are 0.915 and 42.44, respectively; these are promising quantitative results. CONCLUSION: Compared with recent state-of-the-art methods, the proposed model achieves subtle structure-enhanced LDCT imaging. In addition, through ablation studies, the components of the proposed model are validated to achieve performance improvements.

Influence of sulfate reduction on fraction and regeneration of phosphorus at sediment-water interface of urban malodorous river.

The effective control of the release of endogenous phosphorus is an urgent problem in the management of urban malodorous rivers. This research explored the fraction and regeneration of phosphorus of urban malodorous river in the context of sulfate reduction. It was found that sulfate reduction could promote sediment phosphorus release. The contents of total phosphorus (TP) and soluble reactive phosphorus (SRP) in the overlying water presented a decreasing trend after the initial increase during the operation of 120 days. The phosphorus release was positively related to the input of sulfate, and the maximum values of TP and SRP (14.01 mg/L and 12.27 mg/L, respectively) in the overlying water were observed when 8 mM Na2SO4 was added. Moreover, the addition of sulfate could significantly affect the distribution of phosphorus fraction in the sediment and promote the transformation of moderately active phosphorus (NaOH-P, D. HCI-P) to more active phosphorus Resin-P), which resulted in more release of phosphorus to the overlying water. In addition, it was observed that sulfate input could increase the relative abundance of phosphate solubilizing bacteria (PSB) and sulfate-reducing bacteria (SRB) from 0.69 to 1.1% and 4.92 to 9.03%, respectively.