Prevalence and Serotyping of Salmonella in Retail Food in Huzhou China.

Salmonella is one of the most common foodborne pathogens. A total of 70-80% of bacterial food poisoning is caused by Salmonella in China. From 2015 to 2023, a total of 1945 samples in 6 food categories were collected in Huzhou for monitoring of Salmonella. Epidemiological analysis, serotyping, and antibiotic sensitivity testing were conducted on isolated Salmonella. Ninety Salmonella strains were detected from 1945 samples, and the total detection rate was 4.63%. Among all kinds of food, the detection rate of Salmonella in raw animal meat (8.93%) and raw poultry meat (8.54%) was the highest. Salmonella had also been detected in ready-to-eat foods (bulk cooked meat, Chinese cold dishes) and emerging food categories (seasoned raw meat and premade dishes). A total of 24 serotypes of Salmonella were detected, of which the dominant serotype was Salmonella Typhimurium. The serotypes of Salmonella detected in different types of food were different. The results showed that the isolates had strong resistance to ampicillin (AMP) and tetracycline (TET).

Whole-genome sequencing and transcriptome-characterized mechanism of streptomycin resistance in Vibrio parahaemolyticus O10: K4.

Streptomycin resistance in V. parahaemolyticus has been widespread in both clinical and environmental isolates. Therefore, it is of great significance to characterize the mechanism of streptomycin resistance in V. parahaemolyticus. O10:K4 has emerged and becoming the new dominant serotype since 2020. In this study, we isolated a total of 36 strains of V. parahaemolyticus O10:K4 from 2020 to 2022 and found that more than half of them were resistant to streptomycin. We obtained streptomycin resistant and sensitive strains by detecting the resistance profiles. Whole-genome sequencing showed that VP_RS10735 and VP_RS05605 were the predominant mutations in streptomycin resistant O10:K4 clinical isolates. In addition, this study provided global insight into the characteristics of the transcriptome signature of streptomycin resistance, revealing that efflux transporters play a key role in streptomycin resistance. Finally, we found that streptomycin resistant strain was more virulent than sensitive strain. The results of this study should advance our understanding of the mechanisms of aminoglycoside resistance.

Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network.

There is a growing body of evidence indicating the crucial roles that long non-coding RNAs (lncRNAs) play in the development and progression of various diseases, including cancers, cardiovascular diseases, and neurological disorders. However, accurately predicting potential lncRNA-disease associations remains a challenge, as existing methods have limitations in extracting heterogeneous association information and handling sparse and unbalanced data. To address these issues, we propose a novel computational method, called HGC-GAN, which combines heterogeneous graph convolutional neural networks (GCN) and generative adversarial networks (GAN) to predict potential lncRNA-disease associations. Specifically, we construct a lncRNA-miRNA-disease heterogeneous network by integrating multiple association data and sequence information. The GCN-based generator is then employed to aggregate neighbor information of nodes and obtain node embeddings, which are used to predict lncRNA-disease associations. Meanwhile, the GAN-based discriminator is trained to distinguish between real and fake lncRNA-disease associations generated by the generator, enabling the generator to improve its ability to generate accurate lncRNA-disease associations gradually. Our experimental results demonstrate that HGC-GAN performs better in predicting potential lncRNA-disease associations, with AUC and AUPR values of 0.9591 and 0.9606, respectively, under 10-fold cross-validation. Moreover, our case study further confirms the effectiveness of HGC-GAN in predicting potential lncRNA-disease associations, even for novel lncRNAs without any known lncRNA-disease associations. Overall, our proposed method HGC-GAN provides a promising approach to predict potential lncRNA-disease associations and may have important implications for disease diagnosis, treatment, and drug development.

Graph fusion prediction of autism based on attentional mechanisms.

Autism spectrum disorder (ASD) is a pervasive developmental disorder, and the earlier detection and timely intervention for treatment positively affect the prognosis of patients. Deep learning algorithms based on graph structure have achieved good results in autism prediction in recent years. However, there are problems with standardized operations in extracting features and combining neighborhood node features with the structure of the graph dependent, which limits the generalization ability of the trained model to other graph structures. In this paper, we propose a graph fusion autism prediction model based on attentional mechanisms(AGF) to address the above problems. The AGF model represents the overall population (patients or healthy controls) as a sparse graph, where nodes are subjects, and non-imaging features are integrated as edge weights. Different weights can be defined for different nodes in the neighborhood through the attention mechanism without relying on prior knowledge of the graph structure. The model is also able to dynamically fuse multiple sparse graphs obtained from different non-imaging features by way of training weight assignment. Its performance is also compared with several other models (e.g., S-AGF, GCN, etc.), and the results show that it has superior prediction accuracy compared to the baseline model. The results show that this improvement of graph fusion works better on the ABIDE databases, and the classification accuracy can reach 73.9%. The datasets and source code are freely available at https://github.com/chengyu-github1012/Graph-Fusion.git. Strengths and limitations of this study: graph fusion; disease prediction; noise.

Association filtering and generative adversarial networks for predicting lncRNA-associated disease.

BACKGROUND: Long non-coding RNA (lncRNA) closely associates with numerous biological processes, and with many diseases. Therefore, lncRNA-disease association prediction helps obtain relevant biological information and understand pathogenesis, and thus better diagnose preventable diseases. RESULTS: Herein, we offer the LDAF_GAN method for predicting lncRNA-associated disease based on association filtering and generative adversarial networks. Experimentation used two types of data: lncRNA-disease associated data without lncRNA sequence features, and fused lncRNA sequence features. LDAF_GAN uses a generator and discriminator, and differs from the original GAN by the addition of a filtering operation and negative sampling. Filtering allows the generator output to filter out unassociated diseases before being fed into the discriminator. Thus, the results generated by the model focuses only on lncRNAs associated with disease. Negative sampling takes a portion of disease terms with 0 from the association matrix as negative samples, which are assumed to be unassociated with lncRNA. A regular term is added to the loss function to avoid producing a vector with all values of 1, which can fool the discriminator. Thus, the model requires that generated positive samples are close to 1, and negative samples are close to 0. The model achieved a superior fitting effect; LDAF_GAN had superior performance in predicting fivefold cross-validations on the two datasets with AUC values of 0.9265 and 0.9278, respectively. In the case study, LDAF_GAN predicted disease association for six lncRNAs-H19, MALAT1, XIST, ZFAS1, UCA1, and ZEB1-AS1-and with the top ten predictions of 100%, 80%, 90%, 90%, 100%, and 90%, respectively, which were reported by previous studies. CONCLUSION: LDAF_GAN efficiently predicts the potential association of existing lncRNAs and the potential association of new lncRNAs with diseases. The results of fivefold cross-validation, tenfold cross-validation, and case studies suggest that the model has great predictive potential for lncRNA-disease association prediction.

SNCA Rep1 microsatellite length influences non-motor symptoms in early Parkinson's disease.

Long alpha-synuclein gene (SNCA) promoter (Rep1) allele-carriers are linked to higher risk for Parkinson's disease (PD) and faster motor progression. Non-motor symptoms including autonomic, neuropsychiatric, and sleep disorders are common in PD. However, the relationship between SNCA Rep1 microsatellite lengths and non-motor symptoms in early PD remains to be elucidated. 171 consecutive early PD patients were recruited from tertiary clinics and genotyped for Rep1. Multivariable regression analyses were performed to examine associations between Rep1 alleles and non-motor outcome scores. Longer Rep1 alleles significantly associated with higher total Non-Motor Symptom Scale (NMSS) scores (p=.006) and Hospital Anxiety and Depression Scale (HADS) depression subscale scores (p=.002), after adjusting for covariates and Bonferroni correction. We demonstrated that SNCA Rep1 allele length influences overall non-motor symptom burden and depression in early PD patients. Further functional studies to evaluate the role of Rep1 in non-dopaminergic systems may unravel new therapeutic targets for non-motor symptoms in PD.

Utility of plasma Neurofilament light as a diagnostic and prognostic biomarker of the postural instability gait disorder motor subtype in early Parkinson's disease.

BACKGROUND: The main motor subtypes of Parkinson's disease (PD) include tremor-dominant (TD) and postural instability gait disorder (PIGD), with varying disease course that warrant the development of biomarkers capable of predicting progression according to motor subtype. The PIGD subtype is associated with a poorer prognosis, hence identification of a biomarker associated with PIGD is clinically relevant. Neurofilament light (NfL) chain is a potential biomarker of disease severity in neurological disorders including PD. However, no study has investigated NfL and PD motor subtypes. Here, we aimed to investigate the diagnostic and prognostic utility of plasma NfL for PD motor subtypes in early Parkinson's disease. Given the higher risk for cognitive and motor decline in PIGD, we hypothesized that plasma NfL is a potential biomarker for PIGD. METHODS: Plasma NfL was measured in 199 participants (149 PD and 50 healthy controls, HC) using an ultrasensitive single molecule array. Patients were classified into TD or PIGD based on MDS-UPDRS components. After 2 years, 115 patients were reassessed. Association between NfL and clinical measures in PIGD and TD at baseline and at 2-year follow-up were analysed. RESULTS: At baseline, plasma NfL levels were higher in PD than HC (8.8 ± 3.4 vs 16.2 ± 7.6 pg/ml, p < 0.0001), and differentiated PD from HC with a good diagnostic accuracy (AUC = 0.833, p < 0.001). At 2 years, NfL was higher in PIGD than TD (18.4 ± 14.5 vs 12.6 ± 4.4 pg/ml, p = 0.039). Within the PIGD group, higher NfL associated significantly with worse global cognition and UPDRS motor scores at baseline, and was able to predict motor and cognitive decline at a mean follow-up duration of 1.9 years, controlled for age, sex and disease duration. CONCLUSIONS: In this longitudinal study, we demonstrated for the first time the potential utility of plasma NfL as a diagnostic and prognostic biomarker in PIGD even at early stages of PD. These important novel findings will require further confirmation in larger, longitudinal PD cohorts.

Plasma ubiquitin C-terminal hydrolase L1 levels reflect disease stage and motor severity in Parkinson's disease.

Parkinson's disease (PD) is characterized by Lewy bodies containing α-synuclein and ubiquitin aggregates, their co-occurrence possibly linked to a failure of the ubiquitin proteasome system. Ubiquitin C-terminal hydrolase L1 (UCHL1) plays an important role in maintenance of nervous system integrity, and overexpression of UCHL1 has been shown to increase ubiquitin levels within neurons. While cerebrospinal fluid ubiquitin levels were reported to be lower in PD vs controls, plasma UCHL1 levels and their relationship with clinical measures in PD has not been reported. We measured plasma UCHL1 levels using single molecule array (Simoa) in 291 subjects (242 PD and 49 healthy controls, HC). We found that UCHL1 levels were significantly higher in PD patients at moderate stages (Hoehn and Yahr, H&Y stage >2) vs milder PD (H&Y ≤2, p<0.001) and HC (p=0.001). There was no significant difference in UCHL1 levels between PD patients at H&Y stages ≤2 vs HC. Across all PD patients, UCHL1 correlated significantly with UPDRS Part III motor scores (ß=3.87, 95% CI=0.43-7.31, p=0.028), but not with global cognition. Overall, we found that UCHL1 correlates with motor function in PD, with higher levels seen in later disease stages. These findings will be validated in longitudinal studies.

SNCA Rep1 promoter variability influences cognition in Parkinson's disease.

BACKGROUND: While the association between alpha-synuclein gene promoter (Rep1) variability and risk of PD is well established, its association with cognition is unclear. OBJECTIVES: To investigate the association between Rep1 and motor and cognitive outcomes in PD. METHODS: Rep1 allele lengths were determined in 172 PD patients who were grouped into "long" and "short" carriers according to previous methods. Multivariable regression analysis was performed to investigate the effect of Rep1 length on cognitive and motor scores. RESULTS: Long Rep1 allele carriers had significantly lower MMSE (P = 0.010) and higher UPDRS Part III (P = 0.026) and H & Y (P = 0.008) scores compared to short allele carriers (controlled for age, sex, and disease duration). Interaction analyses of Rep1 with apolipoprotein 4 revealed no significant effect on clinical outcomes. CONCLUSIONS: PD patients carrying long Rep1 alleles are more impaired on cognitive and motor function independent of apolipoprotein 4 genotype. © 2019 International Parkinson and Movement Disorder Society.

Plasma alpha-synuclein detected by single molecule array is increased in PD.

We utilized ultrasensitive single molecule technology to measure plasma alpha-synuclein in 221 subjects (51 controls, 170 PD). Plasma alpha-synuclein levels were significantly higher in PD than controls (15506.3 vs. 13057.0 pg/mL, P = 0.037), adjusting for age and gender. In PD, alpha-synuclein levels did not vary by H&Y stage or UPDRS motor scores but were significantly higher in PD patients with poorer cognition (MMSE ≤ 25) than controls (P = 0.016, Bonferroni corrected P = 0.047). Alpha-synuclein levels quantified using ultrasensitive single molecule technology discriminate PD from controls and correlate with cognitive severity. These preliminary findings require independent validation to determine the utility of this assay.

Differential White Matter Regional Alterations in Motor Subtypes of Early Drug-Naive Parkinson's Disease Patients.

BACKGROUND: Parkinson's disease (PD) can be classified into tremor dominant (TD) and postural instability and gait difficulty (PIGD) subtypes with TD considered as the benign subtype. The neural alterations of the 2 subtypes in the early stages before administration of medications remain elusive. OBJECTIVE: This study assessed the subtype-related white matter (WM) microstructural features in newly diagnosed and drug-naive PD patients from the Parkinson's Progression Markers Initiative (PPMI). METHODS: Sixty-five early PDs with stable subtypes (52 TD and 13 PIGD patients) and 61 controls underwent diffusion tensor imaging (DTI) scanning and clinical assessment. Tract-based special statistics (TBSS), graph-theoretical and network-based analyses were used to compare WM regional and network features between groups. RESULTS: No differences in disease stages and duration were found between the 2 patient groups. TD patients showed increased fractional anisotropy (FA), but decreased radial and axial diffusivities (RD and AD) in several projection, association, and commissural tracts, compared with PIGD patients and controls. Motor severity had mild-to-moderate correlations with FA and RD of the corpus callosum (genu) in TD, but strong correlations with FA and RD of multiple association tracts in PIGD. Conversely, no significant network changes were noted. CONCLUSIONS: TD patients showed regionally increased FA but decreased diffusivities, implying neural reorganization to compensate PD pathology in early stages. PIGD patients, despite having similar disease stages and duration, exhibited more WM degradation. These results demonstrate differential WM regional features between the 2 subtypes in early PD and support the notion of TD being a benign subtype.

Microstructural network alterations of olfactory dysfunction in newly diagnosed Parkinson's disease.

Olfactory dysfunction is a robust and early sign for Parkinson's disease (PD). Previous studies have revealed its association with dementia and related neural changes in PD. Yet, how olfactory dysfunction affects white matter (WM) microstructure in newly diagnosed and untreated PD remains unclear. Here we comprehensively examined WM features using unbiased whole-brain analyses. 88 newly diagnosed PD patients without dementia (70 with hyposmia and 18 without hyposmia) and 33 healthy controls underwent clinical assessment and diffusion tensor imaging (DTI) scanning. Tract-based special statistics (TBSS), graph-theoretic methods and network-based statistics (NBS) were used to compare regional and network-related WM features between groups. TBSS analysis did not show any differences in fractional anisotropy and mean diffusivity between groups. Compared with controls, PD patients without hyposmia showed a significant decrease in global efficiency, whilst PD patients with hyposmia exhibited significantly reduced global and local efficiency and additionally a disrupted connection between the right medial orbitofrontal cortex and left rectus and had poorer frontal-related cognitive functioning. These results demonstrate that hyposmia-related WM changes in early PD only occur at the network level. The confined disconnectivity between the bilateral olfactory circuitry may serve as a biomarker for olfactory dysfunction in early PD.

Regulation of neural macroRNAs by the transcriptional repressor REST.

The essential transcriptional repressor REST (repressor element 1-silencing transcription factor) plays central roles in development and human disease by regulating a large cohort of neural genes. These have conventionally fallen into the class of known, protein-coding genes; recently, however, several noncoding microRNA genes were identified as REST targets. Given the widespread transcription of messenger RNA-like, noncoding RNAs ("macroRNAs"), some of which are functional and implicated in disease in mammalian genomes, we sought to determine whether this class of noncoding RNAs can also be regulated by REST. By applying a new, unbiased target gene annotation pipeline to computationally discovered REST binding sites, we find that 23% of mammalian REST genomic binding sites are within 10 kb of a macroRNA gene. These putative target genes were overlooked by previous studies. Focusing on a set of 18 candidate macroRNA targets from mouse, we experimentally demonstrate that two are regulated by REST in neural stem cells. Flanking protein-coding genes are, at most, weakly repressed, suggesting specific targeting of the macroRNAs by REST. Similar to the majority of known REST target genes, both of these macroRNAs are induced during nervous system development and have neurally restricted expression profiles in adult mouse. We observe a similar phenomenon in human: the DiGeorge syndrome-associated noncoding RNA, DGCR5, is repressed by REST through a proximal upstream binding site. Therefore neural macroRNAs represent an additional component of the REST regulatory network. These macroRNAs are new candidates for understanding the role of REST in neuronal development, neurodegeneration, and cancer.

Methyl N'-[(E)-2-methoxy-benzyl-idene]-hydrazinecarboxyl-ate.

The title compound, C(10)H(12)N(2)O(3), crystallizes with two independent mol-ecules in the asymmetric unit. The side chains in the two independent mol-ecules have slightly different orientations, with the C=N-N-C torsion angle being 169.19 (14)° in one of the mol-ecules and -179.86 (14)° in the other. Each independent mol-ecule adopts a trans configuration with respect to the C=N bond. In the crystal structure, mol-ecules are linked into chains running along [001] by N-H⋯O, N-H⋯N and C-H⋯O hydrogen bonds. In addition, an inter-molecular C-H⋯π inter-action is observed.