Acceptance of COVID-19 booster vaccination based on the protection motivation theory: A cross-sectional study in China.

The promotion of the booster shots against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is an open issue to be discussed. Little is known about the public intention and the influencing factors regarding the booster vaccine. A cross-sectional survey in Chinese adults was conducted using an online questionnaire, which designed on the basis of protection motivation theory (PMT) scale and vaccine hesitancy scale (VHS). Hierarchical multiple regression was used to compare the fitness of the PMT scale and VHS for predicting booster vaccination intention. Multivariable logistic regression was used to analyze the factors associated with the acceptance. Six thousand three hundred twenty-one (76.8%) of participants were willing to take the booster shot. However, the rest of the participants (23.2%) were still hesitant to take the booster vaccine. The PMT scale was more powerful than the VHS in explaining the vaccination intention. Participants with high perceived severity (adjusted odds ratio [aOR] = 0.69) and response cost (aOR = 0.47) were less willing to take the booster shots, but participants with high perceived susceptibility (aOR = 1.19), response efficacy (aOR = 2.13), and self-efficacy (aOR = 3.33) were more willing to take the booster shots. In summary, interventions based on PMT can provide guidance to ensure the acceptance of the booster vaccine.

Deep learning-based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk.

BACKGROUND: Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that poses a diagnostic challenge due to its nonspecific symptoms. PURPOSE: To enhance the diagnosis of CCH and non-CCH through Magnetic Resonance Imaging (MRI), offering support in clinical decision-making and recommendations to ultimately elevate diagnostic accuracy and optimize patient treatment outcomes. METHODS: In the retrospective research, we collected 204 routine brain magnetic resonance imaging (MRI) from March 1 to September 10 2022, as training and testing cohorts. And a validation cohort with 108 samples was collected from November 14 2022 to August 4 2023. MRI sequences were processed to obtain T1-weighted (T1WI) and T2-weighted (T2WI) sequence images for each patient. We propose CCH-Network (CCHNet), an end-to-end deep learning model, integrating convolution and Transformer modules to capture local and global structural information. Our novel adversarial training method improves feature knowledge capture, enhancing both generalization ability and efficiency in predicting CCH risk. We assessed the classification performance of the proposed model CCHNet by comparing it with existing state-of-the-art deep learning algorithms, including ResNet34, DenseNet121, VGG16, Convnext, ViT, Coat, and TransFG. To better validate model performance, we compared the results of the proposed model with eight neurologists to evaluate their consistency. RESULTS: CCHNet achieved an AUC of 91.6% (95% CI: 86.8-99.1), with an accuracy (ACC) of 85.0% (95% CI: 75.6-95.2). It demonstrated a sensitivity (SE) of 80.0% (95% CI: 71.6-95.6) and a specificity (SP) of 90.0% (95% CI: 82.3-97.8) in the testing cohort. In the validation cohort, the model demonstrated an AUC of 86.0% (95% CI: 80.3-93.0), an ACC of 84.2% (95% CI: 70.2-93.6), a SE of 83.3% (95% CI: 68.3-95.5), and a SP of 84.7% (95% CI: 70.3-96.8). CONCLUSIONS: The model improved the diagnostic performance of MRI with high SE and SP, providing a promising method for the diagnosis of CCH.

Engineering extracellular electron transfer to promote simultaneous brewing wastewater treatment and chromium reduction.

Microbial fuel cell (MFC) technology has been developed for wastewater treatment in the anodic chamber, and heavy metal reduction in the cathodic chamber. However, the limited extracellular electron transfer (EET) rate of exoelectrogens remained a constraint for practical applications of MFCs. Here, a MFC system that used the electricity derived from anodic wastewater treatment to drive cathodic Cr6+ reduction was developed, which enabled an energy self-sustained approach to efficiently address Cr6+ contamination. This MFC system was achieved by screening exoelectrogens with a superior EET rate, promoting the exoelectrogenic EET rate, and constructing a conductive bio-anode. Firstly, Shewanella algae-L3 was screened from brewing wastewater acclimatized sludge, which generated power density of 566.83 mW m-2. Secondly, to facilitate EET rate, flavin synthesis gene operon ribADEHC was overexpressed in engineered S. algae-L3F to increase flavins biosynthesis, which promoted the power density to 1233.21 mW m-2. Thirdly, to facilitate interface electron transfer, carbon nanotube (CNT) was employed to construct a S. algae-L3F-CNT bio-anode, which further enhanced power density to 3112.98 mW m-2. Lastly, S. algae-L3F-CNT bio-anode was used to harvest electrical energy from brewing wastewater to drive cathodic Cr6+ reduction in MFC, realizing 71.43% anodic COD removal and 98.14% cathodic Cr6+ reduction. This study demonstrated that enhanced exoelectrogenic EET could facilitate cathodic Cr6+ reduction in MFC.

PbWRKY26 positively regulates malate accumulation in pear fruit by activating PbMDH3.

Malate is the main organic acid that affects fruit acidity and flavor in pear (Pyrus spp.). However, the regulatory mechanism of malic acid accumulation in pear remains unclear. We identified PbWRKY26 as a candidate gene using mRNA-seq, and quantification analysis verified the expression level. The expression of PbWRKY26 was positively correlated with the malic acid content in two P. pyrifolia cultivars ('Cuiguan', 'Hongsucui') and two P. ussuriensis cultivars ('Qiuxiang', 'Hanhong'), with respective correlation coefficients of 0.748*, 0.871**, 0.889**, and 0.910** (*, P < 0.05; **, P < 0.01). The expression of PbWRKY26 enhanced the malate content in overexpression transgenic pear fruit and callus. In contrast, silencing PbWRKY26 decreased the pear fruit malic acid content. Analysis of the neighbor-joining phylogenetic tree indicated that PbWRKY26 was a PH3 homolog. The WRKY26 (PH3) has been identified to regulate a proton pump gene, PH5, in a lot of plant species, but the LUC and Y1H assays showed that PbWRKY26 could not bind to PbPH5 promoter in our study. Interestingly, a malate dehydrogenase gene, PbMDH3, was identified to be regulated by PbWRKY26. This study might be valuable to understand the metabolic regulatory network associated with malate accumulation.

Novel Computed-Tomography-Based Transformer Models for the Noninvasive Prediction of PD-1 in Pre-Operative Settings.

The expression status of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC) is associated with the checkpoint blockade treatment responses of PD-1/PD-L1. Thus, accurately and preoperatively identifying the status of PD-1 has great clinical implications for constructing personalized treatment strategies. To investigate the preoperative predictive value of the transformer-based model for identifying the status of PD-1 expression, 93 HCC patients with 75 training cohorts (2859 images) and 18 testing cohorts (670 images) were included. We propose a transformer-based network architecture, ResTransNet, that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to automatically acquire a persuasive feature to obtain a prediction score using a nonlinear classifier. The area under the curve, receiver operating characteristic curve, and decision curves were applied to evaluate the prediction model's performance. Then, Kaplan-Meier survival analyses were applied to evaluate the overall survival (OS) and recurrence-free survival (RFS) in PD-1-positive and PD-1-negative patients. The proposed transformer-based model obtained an accuracy of 88.2% with a sensitivity of 88.5%, a specificity of 88.9%, and an area under the curve of 91.1% in the testing cohort.

Recent advances in enrichment, isolation, and bio-electrochemical activity evaluation of exoelectrogenic microorganisms.

Exoelectrogenic microorganisms (EEMs) catalyzed the conversion of chemical energy to electrical energy via extracellular electron transfer (EET) mechanisms, which underlay diverse bio-electrochemical systems (BES) applications in clean energy development, environment and health monitoring, wearable/implantable devices powering, and sustainable chemicals production, thereby attracting increasing attentions from academic and industrial communities in the recent decades. However, knowledge of EEMs is still in its infancy as only ∼100 EEMs of bacteria, archaea, and eukaryotes have been identified, motivating the screening and capture of new EEMs. This review presents a systematic summarization on EEM screening technologies in terms of enrichment, isolation, and bio-electrochemical activity evaluation. We first generalize the distribution characteristics of known EEMs, which provide a basis for EEM screening. Then, we summarize EET mechanisms and the principles underlying various technological approaches to the enrichment, isolation, and bio-electrochemical activity of EEMs, in which a comprehensive analysis of the applicability, accuracy, and efficiency of each technology is reviewed. Finally, we provide a future perspective on EEM screening and bio-electrochemical activity evaluation by focusing on (i) novel EET mechanisms for developing the next-generation EEM screening technologies, and (ii) integration of meta-omics approaches and bioinformatics analyses to explore nonculturable EEMs. This review promotes the development of advanced technologies to capture new EEMs.

Self-supervised contrastive learning using CT images for PD-1/PD-L1 expression prediction in hepatocellular carcinoma.

Background and purpose: Programmed cell death protein-1 (PD-1) and programmed cell death-ligand-1 (PD-L1) expression status, determined by immunohistochemistry (IHC) of specimens, can discriminate patients with hepatocellular carcinoma (HCC) who can derive the most benefits from immune checkpoint inhibitor (ICI) therapy. A non-invasive method of measuring PD-1/PD-L1 expression is urgently needed for clinical decision support. Materials and methods: We included a cohort of 87 patients with HCC from the West China Hospital and analyzed 3094 CT images to develop and validate our prediction model. We propose a novel deep learning-based predictor, Contrastive Learning Network (CLNet), which is trained with self-supervised contrastive learning to better extract deep representations of computed tomography (CT) images for the prediction of PD-1 and PD-L1 expression. Results: Our results show that CLNet exhibited an AUC of 86.56% for PD-1 expression and an AUC of 83.93% for PD-L1 expression, outperforming other deep learning and machine learning models. Conclusions: We demonstrated that a non-invasive deep learning-based model trained with self-supervised contrastive learning could accurately predict the PD-1 and PD-L1 expression status, and might assist the precision treatment of patients withHCC, in particular the use of immune checkpoint inhibitors.

CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma.

Background: Clear cell Renal Cell Carcinoma (ccRCC) is the most common malignant tumor in the urinary system and the predominant subtype of malignant renal tumors with high mortality. Biopsy is the main examination to determine ccRCC grade, but it can lead to unavoidable complications and sampling bias. Therefore, non-invasive technology (e.g., CT examination) for ccRCC grading is attracting more and more attention. However, noise labels on CT images containing multiple grades but only one label make prediction difficult. However, noise labels exist in CT images, which contain multiple grades but only one label, making prediction difficult. Aim: We proposed a Transformer-based deep learning algorithm with CT images to improve the diagnostic accuracy of grading prediction and to improve the diagnostic accuracy of ccRCC grading. Methods: We integrate different training models to improve robustness and predict Fuhrman nuclear grade. Then, we conducted experiments on a collected ccRCC dataset containing 759 patients and used average classification accuracy, sensitivity, specificity, and AreaUnderCurve as indicators to evaluate the quality of research. In the comparative experiments, we further performed various current deep learning algorithms to show the advantages of the proposed method. We collected patients with pathologically proven ccRCC diagnosed from April 2010 to December 2018 as the training and internal test dataset, containing 759 patients. We propose a transformer-based network architecture that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to extract a persuasive feature automatically. And then, a nonlinear classifier is applied to classify. We integrate different training models to improve the accuracy and robustness of the model. The average classification accuracy, sensitivity, specificity, and area under curve are used as indicators to evaluate the quality of a model. Results: The mean accuracy, sensitivity, specificity, and Area Under Curve achieved by CNN were 82.3%, 89.4%, 83.2%, and 85.7%, respectively. In contrast, the proposed Transformer-based model obtains a mean accuracy of 87.1% with a sensitivity of 91.3%, a specificity of 85.3%, and an Area Under Curve (AUC) of 90.3%. The integrated model acquires a better performance (86.5% ACC and an AUC of 91.2%). Conclusion: A transformer-based network performs better than traditional deep learning algorithms in terms of the accuracy of ccRCC prediction. Meanwhile, the transformer has a certain advantage in dealing with noise labels existing in CT images of ccRCC. This method is promising to be applied to other medical tasks (e.g., the grade of neurogliomas and meningiomas).

Behavior responses of zebrafish (Danio rerio) to aquatic environmental stresses in the characteristic of circadian rhythms.

As behavior shows a distinct circadian rhythm, it is hypothesized that circadian rhythms based on zebrafish (Danio rerio) behavior responses could be affected by contaminants in this study, and then the behavior strength of zebrafish exposed to 0.005 mg/L Cadmium chloride (CdCl2), 0.01 mg/L Dibasic Sodium Phosphate (Na2HPO4), 0.002 mg/L deltamethrin, and 0.003 mg/L atrazine for 6 days is used to illustrate the possibility of behavior circadian rhythms as an indicator in the environmental stress assessment. Statistical analysis with p < 0.01 shows that a clear difference between average values of BS during dark period (AVD) and those during light period (AVL) could be observed, and 24 h circadian rhythms do exist in zebrafish behavior responses. Both BS values and circadian rhythms of zebrafish can be affected in the aspect of periodicity with clear time delay, which were 1 h delay in CdCl2, 4 h delay in Na2HPO4, 4 h delay in deltamethrin, and 1 h delay in atrazine. Behavior circadian rhythms were disturbed according to the repetitive cycles after autocorrelation analysis, and the toxic effects of different chemicals could be reflected by the profiles of the Self-Organizing Map (SOM), which indicated the circadian rhythm disorder in different degrees. These results deduced from the statistical analysis, autocorrelation and SOM strongly supported that circadian rhythms based on zebrafish BS could be used as an indicator in the environmental stress assessment.

The Role of AChE in Swimming Behavior of Daphnia magna: Correlation Analysis of Both Parameters Affected by Deltamethrin and Methomyl Exposure.

The unpredictable toxicity of insecticides may cause behavior disorder of biological organisms. In order to assess the role of acetylcholinesterase (AChE) in swimming behavior of Daphnia magna, a correlation analysis of both parameters in 24 h exposure of deltamethrin (DM) and methomyl (MT) was investigated. The behavior responses of D. magna in DM (13.36 µg/L and 33.40 µg/L) and MT (19.66 µg/L and 49.15 µg/L) suggested that recovery behavior in the adjustment phase was crucial, and behavior homeostasis provided them with an optimal way to achieve a wider tolerance against environmental stress. During the experiment, positive effects on AChE activity occurred in the beginning of the exposure. Even though the de novo synthesis of AChE in D. magna might help it recover, the AChE inhibition in different treatments could be observed. Some induction effects on AChE activity at the beginning of exposure occurred, and a 50% decrease may cause toxic effects on behavior. In most treatments, the results showed that both behavior strength and AChE activity stayed in the same field within a correlation circle. These results illustrated that the environmental stress caused by both DM and MT could inhibit AChE activity and subsequently induce a stepwise behavior response, though both pesticides affect it as direct and indirect inhibitors, respectively.

Does time difference of the acetylcholinesterase (AChE) inhibition in different tissues exist? A case study of zebra fish (Danio rerio) exposed to cadmium chloride and deltamethrin.

In order to illustrate time difference in toxic effects of cadmium chloride (CdCl2) and deltamethrin (DM), AChE activities were measured in different tissues, liver, muscle, brain, and gill, of Zebra fish (Danio rerio) across different concentrations in this research. The average AChE activity decreased comparing to 0.0 TU with DM (82.81% in 0.1 TU, 56.14% in 1.0 TU and 44.68% in 2.0 TU) and with CdCl2 (74.68% in 0.1 TU, 52.05% in 1.0 TU and 50.14% in 2.0 TU) showed an overall decrease with the increase of exposure concentrations. According to Self-Organizing Map (SOM), the AChE activities were characterized in relation with experimental conditions, showing an inverse relationship with exposure time. As the exposure time was longer, the AChE activities were correspondingly lower. The AChE inhibition showed time delay in sublethal treatments (0.1 TU) in different tissues: the AChE was first inhibited in brain by chemicals followed by gill, muscle and liver (brain > gill > muscle > liver). The AChE activity was almost inhibited synchronously in higher environmental stress (1.0 TU and 2.0 TU). As the AChE inhibition can induce abnormal of behavior movement, these results will be helpful to the mechanism of stepwise behavior responses according to the time difference in different tissues rather than the whole body AChE activity.

Integrative Characterization of Toxic Response of Zebra Fish (Danio rerio) to Deltamethrin Based on AChE Activity and Behavior Strength.

In order to characterize the toxic response of zebra fish (Danio rerio) to Deltamethrin (DM), behavior strength (BS) and muscle AChE activity of zebra fish were investigated. The results showed that the average values of both BS and AChE activity showed a similarly decreased tendency as DM concentration increased, which confirmed the dose-effect relationship, and high and low levels of AChE and BS partly matched low and high levels of exposure concentrations in self-organizing map. These indicated that AChE and BS had slight different aspects of toxicity although overall trend was similar. Behavior activity suggested a possibility of reviving circadian rhythm in test organisms after exposure to the chemical in lower concentration (0.1 TU). This type of rhythm disappeared in higher concentrations (1.0 TU and 2.0 TU). Time series trend analysis of BS and AChE showed an evident time delayed effect of AChE, and a 2 h AChE inhibition delay with higher correlation coefficients (r) in different treatments was observed. It was confirmed that muscle AChE inhibition of zebra fish is a factor for swimming behavior change, though there was a 2 h delay, and other factors should be investigated to illustrate the detailed behavior response mechanism.