Assessing immunogenicity of CRISPR-NCas9 engineered strain against porcine epidemic diarrhea virus.

Porcine epidemic diarrhea (PED) caused by porcine epidemic diarrhea virus (PEDV), is an acute and highly infectious disease, resulting in substantial economic losses in the pig industry. Given that PEDV primarily infects the mucosal surfaces of the intestinal tract, it is crucial to improve the mucosal immunity to prevent viral invasion. Lactic acid bacteria (LAB) oral vaccines offer unique advantages and potential applications in combatting mucosal infectious diseases, making them an ideal approach for controlling PED outbreaks. However, traditional LAB oral vaccines use plasmids for exogenous protein expression and antibiotic genes as selection markers. Antibiotic genes can be diffused through transposition, transfer, or homologous recombination, resulting in the generation of drug-resistant strains. To overcome these issues, genome-editing technology has been developed to achieve gene expression in LAB genomes. In this study, we used the CRISPR-NCas9 system to integrate the PEDV S1 gene into the genome of alanine racemase-deficient Lactobacillus paracasei △Alr HLJ-27 (L. paracasei △Alr HLJ-27) at the thymidylate synthase (thyA) site, generating a strain, S1/△Alr HLJ-27. We conducted immunization assays in mice and piglets to evaluate the level of immune response and evaluated its protective effect against PEDV through challenge tests in piglets. Oral administration of the strain S1/△Alr HLJ-27 in mice and piglets elicited mucosal, humoral, and cellular immune responses. The strain also exhibited a certain level of resistance against PEDV infection in piglets. These results demonstrate the potential of S1/△Alr HLJ-27 as an oral vaccine candidate for PEDV control. KEY POINTS: • A strain S1/△Alr HLJ-27 was constructed as the candidate for an oral vaccine. • Immunogenicity response and challenge test was carried out to analyze the ability of the strain. • The strain S1/△Alr HLJ-27 could provide protection for piglets to a certain extent.

Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas.

BACKGROUND: 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. METHODS: This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. RESULTS: The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

How BDNF affects working memory in acute sleep deprivation: The mediating role of spontaneous brain activity.

The brain-derived neurotrophic factor (BDNF) mediates the plasticity associated with memory processing, and compensatorily increases after acute sleep deprivation (SD). However, whether the altered spontaneous brain activity mediates the association between BDNF and working memory in SD remains unknown. Here, we aimed to probe the mediating role of the spontaneous brain activity between plasma BDNF and WM function in SD. A total of 30 healthy subjects with regular sleep were enrolled in this study. Resting-sate functional magnetic resonance imaging (fMRI) scans and the peripheral blood were collected before and after 24 h SD. All participants also received n-back task assessing working memory (WM) performance. The amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were calculated to reflect the intensity of regional spontaneous brain activity. Plasma BDNF was measured by sandwich ELISA. Our results revealed a significant decline in WM and increase in plasma BDNF level after SD, and negative association between the changed WM performance and plasma BDNF level. Specially, the ALFF of the left inferior parietal cortex and right inferior frontal cortex, and fALFF of the left anterior cingulate and medial prefrontal cortex and left posterior opercular cortex regulated the association between the BDNF and one-back reaction time respectively. Our results suggest that the association between BDNF and working memory may be mediated through regional spontaneous brain activity involving in the cerebral cortex, which may provide new sight into the interaction between neurotrophic factors and cognition, and potential targets for noninvasive brain stimulation on WM decline after acute SD.

Serum proteomics analysis of drug-naïve patients with generalised anxiety disorder: Tandem mass tags and multiple reaction monitoring.

OBJECTIVES: The prevalence of generalised anxiety disorder (GAD) is high. However, the underlying mechanisms remain elusive. Proteomics techniques can be employed to assess the pathological mechanisms involved in GAD. METHODS: Twenty-two drug-naive GAD patients were recruited, their serum samples were used for protein quantification and identified using Tandem Mass Tag and Multiple Reaction Monitoring (MRM). Machine learning models were employed to construct predictive models for disease occurrence by using clinical scores and target proteins as input variables. RESULTS: A total of 991 proteins were differentially expressed between GAD and healthy participants. Gene Ontology analysis revealed that these proteins were significantly associated with stress response and biological regulation, suggesting a significant implication in anxiety disorders. MRM validation revealed evident disparities in 12 specific proteins. The machine learning model found a set of five proteins accurately predicting the occurrence of the disease at a rate of 87.5%, such as alpha 1B-glycoprotein, complement component 4 A, transferrin, V3-3, and defensin alpha 1. These proteins had a functional association with immune inflammation. CONCLUSIONS: The development of generalised anxiety disorder might be closely linked to the immune inflammatory stress response.

GABAergic imbalance in Parkinson's disease-related depression determined with MEGA-PRESS.

OBJECTIVE: The pathogenesis of depression in patients with Parkinson's disease (PD) is poorly understood. Therefore, this study aimed to explore the changes in γ-aminobutyric acid (GABA) and glutamate plus glutamine (Glx) levels in patients with PD with or without depression determined using MEscher-GArwood Point Resolved Spectroscopy (MEGA-PRESS). MATERIALS AND METHODS: A total of 83 patients with primary PD and 24 healthy controls were included. Patients with PD were categorized into depressed PD (DPD, n = 19) and nondepressed PD (NDPD, n = 64) based on the 17-item Hamilton Depression Rating Scale. All participants underwent T1-weighted imaging and MEGA-PRESS sequence to acquire GABA+ and Glx values. The MEGA-PRESS sequence was conducted using 18.48 mL voxels in the left thalamus and medial frontal cortex. The GABA+, Glx, and creatine values were quantified using Gannet 3.1 software. RESULTS: The GABA+ and Glx values were not significantly disparate between patients with PD and controls in the thalamus and medial frontal cortex. However, the levels of N-acetyl aspartate/creatine and choline/creatine in the left thalamus were significantly lower in patients with PD than in controls (P = .031, P = .009). The GABA+/Water and GABA+/Creatine in the medial frontal cortex were higher in DPD than in NDPD (P = .001, P = .004). The effects of depression on Glx or other metabolite levels were not evident, and no significant difference in metabolite values was noted in the left thalamus among all groups (P > .05). CONCLUSIONS: GABA+ levels increased in the medial frontal cortex in DPD, which may be more closely related to depressive pathology. Thus, alterations in GABAergic function in special brain structures may be related to the clinical manifestations of PD symptoms, and hence mediating this function might help in treating depression in PD.