A multi-frequency approach of the altered functional connectome for autism spectrum disorder identification.

Autism spectrum disorder stands as a multifaceted and heterogeneous neurodevelopmental condition. The utilization of functional magnetic resonance imaging to construct functional brain networks proves instrumental in comprehending the intricate interplay between brain activity and autism spectrum disorder, thereby elucidating the underlying pathogenesis at the cerebral level. Traditional functional brain networks, however, typically confine their examination to connectivity effects within a specific frequency band, disregarding potential connections among brain areas that span different frequency bands. To harness the full potential of interregional connections across diverse frequency bands within the brain, our study endeavors to develop a novel multi-frequency analysis method for constructing a comprehensive functional brain networks that incorporates multiple frequencies. Specifically, our approach involves the initial decomposition of functional magnetic resonance imaging into distinct frequency bands through wavelet transform. Subsequently, Pearson correlation is employed to generate corresponding functional brain networks and kernel for each frequency band. Finally, the classification was performed by a multi-kernel support vector machine, to preserve the connectivity effects within each band and the connectivity patterns shared among the different bands. Our proposed multi-frequency functional brain networks method yielded notable results, achieving an accuracy of 89.1%, a sensitivity of 86.67%, and an area under the curve of 0.942 in a publicly available autism spectrum disorder dataset.

Multipattern graph convolutional network-based autism spectrum disorder identification.

The early diagnosis of autism spectrum disorder (ASD) has been extensively facilitated through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain network (FBN) has gained much attention in diagnosing ASD. As a promising strategy, graph convolutional networks (GCN) provide an attractive approach to simultaneously extract FBN features and facilitate ASD identification, thus replacing the manual feature extraction from FBN. Previous GCN studies primarily emphasized the exploration of topological simultaneously connection weights of the estimated FBNs while only focusing on the single connection pattern. However, this approach fails to exploit the potential complementary information offered by different connection patterns of FBNs, thereby inherently limiting the performance. To enhance the diagnostic performance, we propose a multipattern graph convolution network (MPGCN) that integrates multiple connection patterns to improve the accuracy of ASD diagnosis. As an initial endeavor, we endeavored to integrate information from multiple connection patterns by incorporating multiple graph convolution modules. The effectiveness of the MPGCN approach is evaluated by analyzing rs-fMRI scans from a cohort of 92 subjects sourced from the publicly accessible Autism Brain Imaging Data Exchange database. Notably, the experiment demonstrates that our model achieves an accuracy of 91.1% and an area under ROC curve score of 0.9742. The implementation codes are available at https://github.com/immutableJackz/MPGCN.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data.

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach.

Mild cognitive impairment plays a crucial role in predicting the early progression of Alzheimer's disease, and it can be used as an important indicator of the disease progression. Currently, numerous studies have focused on utilizing the functional brain network as a novel biomarker for mild cognitive impairment diagnosis. In this context, we employed a graph convolutional neural network to automatically extract functional brain network features, eliminating the need for manual feature extraction, to improve the mild cognitive impairment diagnosis performance. However, previous graph convolutional neural network approaches have primarily concentrated on single modes of brain connectivity, leading to a failure to leverage the potential complementary information offered by diverse connectivity patterns and limiting their efficacy. To address this limitation, we introduce a novel method called the graph convolutional neural network with multimodel connectivity, which integrates multimode connectivity for the identification of mild cognitive impairment using fMRI data and evaluates the graph convolutional neural network with multimodel connectivity approach through a mild cognitive impairment diagnostic task on the Alzheimer's Disease Neuroimaging Initiative dataset. Overall, our experimental results show the superiority of the proposed graph convolutional neural network with multimodel connectivity approach, achieving an accuracy rate of 92.2% and an area under the Receiver Operating Characteristic (ROC) curve of 0.988.

Controlling the Depth of Hybridization Chain Reaction by Extended Dangling Ends and Its Analytical Applications.

Hybridization chain reaction (HCR) is a powerful enzyme-free nucleic acid amplification strategy. Triggered by an initiator strand, it yields nicked double helices analogous to alternating copolymers. However, there is no effective way to regulate the HCR reaction, and the most apparent phenomenon is the uncontrollable polymerization of product after introducing an initiator. Here we explore controlling the depth of the HCR reaction by extended dangling ends on hairpin monomers and report that sequence length, nucleotide composition, and secondary structure can alter HCR polymerization and can be utilized for the desired regulation. Interaction dynamics between initiator and hairpin monomers simulated by oxDNA are in good accordance with experimental results. Such a controlling effect can be utilized for new analytical applications that HCR cannot previously achieve, such as analyzing strand-extension enzymes and identifying short-sequence structures. The finding provides a concise but effective way for controlling the depth of HCR reaction and opens the application scope of HCR to more fields.

Grass carp (Ctenopharyngodon idella) deacetylase SIRT1 targets p53 to suppress apoptosis in a KAT8 dependent or independent manner.

Sirtuin1 (SIRT1) is known as a deacetylase to control various physiological processes. In mammals, SIRT1 inhibits apoptotic process, but the detailed mechanism is not very clear. Here, our study revealed that grass carp (Ctenopharyngodon idella) SIRT1 (CiSIRT1, MN125614.1) inhibits apoptosis through targeting p53 in a KAT8-dependent or a KAT8-independent manner. In CIK cells, CiSIRT1 over-expression results in significant decrease of some apoptotic gene expressions, including Bax/Bcl2, caspase3 and caspase9, whereas CiKAT8 or Cip53 facilitates the induction of apoptosis. Because CiSIRT1 separately interacted with CiKAT8 and Cip53, we speculated that CiSIRT1 blocked apoptosis may be by virtue of KAT8-p53 axis or directly by p53. In a KAT8-dependent manner, CiSIRT1 interacted with CiKAT8, then reduced the acetylation of CiKAT8 and subsequently promoted its degradation. Then, CiKAT8 acetylated p53 and induced p53-mediated apoptosis. MYST domain of CiKAT8 was critical in this pathway. In a KAT8-independent manner, CiSIRT1 also inhibited p53-induced apoptosis by directly deacetylating p53 and promoting the degradation of p53. Generally, these findings uncovered two pathways in which CiSIRT1 decreases the acetylation of p53 via a KAT8-dependent or a KAT8-independent manner.

Grass carp (Ctenopharyngodon idella) NIK up-regulates the expression of IL-8 by activating the NF-κB canonical pathway.

NIK (NF-κB inducing kinase) belongs to the mitogen-activated protein kinase family, which activates NF-κB and plays a vital role in immunology, inflammation, apoptosis, and a series of pathological responses. In NF-κB noncanonical pathway, NIK and IKKα have been often studied in mammals and zebrafish. However, few have explored the relationship between NIK and other subunits of the IKK complex. As a classic kinase in the NF-κB canonical pathway, IKKß has never been researched with NIK in fish. In this paper, the full-length cDNA sequence of grass carp (Ctenopharyngodon idella) NIK (CiNIK) was first cloned and identified. The expression level of CiNIK in grass carp cells was increased under GCRV stimuli. Under the stimulation of GCRV, poly (I:C), and LPS, the expression of NIK in various tissues of grass carp was also increased. This suggests that CiNIK responds to viral stimuli. To study the relationship between CiNIK and CiIKKß, we co-transfected CiNIK-FLAG and CiIKKB-GFP into grass carp cells in coimmunoprecipitation and immunofluorescence experiments. The results revealed that CiNIK interacts with CiIKKß. Besides, the degree of autophosphorylation of CiNIK was enhanced under poly (I:C) stimulation. CiIKKß was phosphorylated by CiNIK and then activated the activity of p65. The activity change of p65 indicates that NF-κB downstream inflammatory genes will be functioning. CiNIK or CiIKKß up-regulated the expression of IL-8. It got higher when CiNIK and CiIKKß coexisted. This paper revealed that NF-κB canonical pathway and noncanonical pathway are not completely separated in generating benefits.

A molecular mechanism underlies grass carp (Ctenopharyngodon idella) TARBP2 regulating PKR-mediated cell apoptosis.

Interferon-inducible double-stranded RNA-dependent protein kinase (PKR) is one of the key antiviral arms in the innate immune system. The activated PKR performs its antiviral function by inhibiting protein translation and inducing apoptosis. In our previous study, we identified grass carp TARBP2 as an inhibitor of PKR activity, thereby suppressing cell apoptosis. This study aimed to explore the effects of grass carp TARBP2 on PKR activity and cell apoptosis. Grass carp TARBP2 comprises two N-terminal dsRBDs and a C-terminal C4 domain. Subcellular localization analysis conducted in CIK cells revealed that TARBP2-FL (full-length TARBP2), TARBP2-Δ1 (lack of the first dsRBD), and TARBP2-Δ2 (lack of the second dsRBD) are predominantly located in the cytoplasm, while TARBP2-Δ3 (lack of the two dsRBDs) is distributed both in the nucleus and cytoplasm. Colocalization and immunoprecipitation assays confirmed the interaction of TARBP2-FL, TARBP2-Δ1, and TARBP2-Δ2 with PKR, while TARBP2-Δ3 showed no binding. Furthermore, our findings suggested that the inhibitory effect of TARBP2-Δ1 or TARBP2-Δ2 on the PKR-eIF2α pathway is depressed compared to TARBP2-FL. In cell apoptosis assays, it was observed that TARBP2-FL inhibits PKR-mediated cell apoptosis. TARBP2-Δ1 or TARBP2-Δ2 exhibits decreased inhibition to PKR-mediated cell apoptosis, whereas TARBP2-Δ3 nearly completely loses this inhibitory effect. These findings highlight the critical importance of two dsRBDs of TARBP2 in interaction with PKR, as well as in the inhibition of PKR activity, resulting in the suppression of cell apoptosis triggered by prolonged PKR activation.

Complete genome sequence of a novel alternavirus isolated from the phytopathogenic fungus Colletotrichum fioriniae.

A novel double-strand RNA (dsRNA) mycovirus, named "Colletotrichum fioriniae alternavirus1" (CfAV1), was isolated from the strain CX7 of Colletotrichum fioriniae, the causal agent of walnut anthracnose. The complete genome of CfAV1 is composed of three dsRNA segments: dsRNA1 (3528 bp), dsRNA2 (2485 bp), and dsRNA3 (2481 bp). The RNA-dependent RNA polymerase (RdRp) is encoded by dsRNA1, while both dsRNA2 and dsRNA3 encode hypothetical proteins. Based on multiple sequence alignments and phylogenetic analysis, CfAV1 is identified as a new member of the family Alternaviridae. This is the first report of an alternavirus that infects the phytopathogenic fungus C. fioriniae.

BNIP3-mediated mitophagy attenuates hypoxic-ischemic brain damage in neonatal rats by inhibiting ferroptosis through P62-KEAP1-NRF2 pathway activation to maintain iron and redox homeostasis.

As a major contributor to neonatal death and neurological sequelae, hypoxic-ischemic encephalopathy (HIE) lacks a viable medication for treatment. Oxidative stress induced by hypoxic-ischemic brain damage (HIBD) predisposes neurons to ferroptosis due to the fact that neonates accumulate high levels of polyunsaturated fatty acids for their brain developmental needs but their antioxidant capacity is immature. Ferroptosis is a form of cell death caused by excessive accumulation of iron-dependent lipid peroxidation and is closely associated with mitochondria. Mitophagy is a type of mitochondrial quality control mechanism that degrades damaged mitochondria and maintains cellular homeostasis. In this study we employed mitophagy agonists and inhibitors to explore the mechanisms by which mitophagy exerted ferroptosis resistance in a neonatal rat HIE model. Seven-days-old neonatal rats were subjected to ligation of the right common carotid artery, followed by exposure to hypoxia for 2 h. The neonatal rats were treated with a mitophagy activator Tat-SPK2 peptide (0.5, 1 mg/kg, i.p.) 1 h before hypoxia, or in combination with mitochondrial division inhibitor-1 (Mdivi-1, 20 mg/kg, i.p.), and ferroptosis inhibitor Ferrostatin-1 (Fer-1) (2 mg/kg, i.p.) at the end of the hypoxia period. The regulation of ferroptosis by mitophagy was also investigated in primary cortical neurons or PC12 cells in vitro subjected to 4 or 6 h of OGD followed by 24 h of reperfusion. We showed that HIBD induced mitochondrial damage, ROS overproduction, intracellular iron accumulation, lipid peroxidation and ferroptosis, which were significantly reduced by the pretreatment with Tat-SPK2 peptide, and aggravated by the treatment with Mdivi-1 or BNIP3 knockdown. Ferroptosis inhibitors Fer-1 and deferoxamine B (DFO) reversed the accumulation of iron and lipid peroxides caused by Mdivi-1, hence reducing ferroptosis triggered by HI. We demonstrated that Tat-SPK2 peptide-activated BNIP3-mediated mitophagy did not alleviate neuronal ferroptosis through the GPX4-GSH pathway. BNIP3-mediated mitophagy drove the P62-KEAP1-NRF2 pathway, which conferred ferroptosis resistance by maintaining iron and redox homeostasis via the regulation of FTH1, HO-1, and DHODH/FSP1-CoQ10-NADH. This study may provide a new perspective and a therapeutic drug for the treatment of neonatal HIE.

Wavelet transform-based frequency self-adaptive model for functional brain network.

The accurate estimation of functional brain networks is essential for comprehending the intricate relationships between different brain regions. Conventional methods such as Pearson Correlation and Sparse Representation often fail to uncover concealed information within diverse frequency bands. To address this limitation, we introduce a novel frequency-adaptive model based on wavelet transform, enabling selective capture of highly correlated frequency band sequences. Our approach involves decomposing the original time-domain signal from resting-state functional magnetic resonance imaging into distinct frequency domains, thus constructing an adjacency matrix that offers enhanced separation of features across brain regions. Comparative analysis demonstrates the superior performance of our proposed model over conventional techniques, showcasing improved clarity and distinctiveness. Notably, we achieved the highest accuracy rate of 89.01% using Sparse Representation based on Wavelet Transform, outperforming Pearson Correlation based on Wavelet Transform with an accuracy of 81.32%. Importantly, our method optimizes raw data without significantly altering feature topology, rendering it adaptable to various functional brain network estimation approaches. Overall, this innovation holds the potential to advance the understanding of brain function and furnish more accurate samples for future research and clinical applications.

Accurately predicting the risk of unfavorable outcomes after endovascular coil therapy in patients with aneurysmal subarachnoid hemorrhage: an interpretable machine learning model.

BACKGROUND: Despite endovascular coiling as a valid modality in treatment of aneurysmal subarachnoid hemorrhage (aSAH), there is a risk of poor prognosis. However, the clinical utility of previously proposed early prediction tools remains limited. We aimed to develop a clinically generalizable machine learning (ML) models for accurately predicting unfavorable outcomes in aSAH patients after endovascular coiling. METHODS: Functional outcomes at 6 months after endovascular coiling were assessed via the modified Rankin Scale (mRS) and unfavorable outcomes were defined as mRS 3-6. Five ML algorithms (logistic regression, random forest, support vector machine, deep neural network, and extreme gradient boosting) were used for model development. The area under precision-recall curve (AUPRC) and receiver operating characteristic curve (AUROC) was used as main indices of model evaluation. SHapley Additive exPlanations (SHAP) method was applied to interpret the best-performing ML model. RESULTS: A total of 371 patients were eventually included into this study, and 85.4% of them had favorable outcomes. Among the five models, the DNN model had a better performance with AUPRC of 0.645 (AUROC of 0.905). Postoperative GCS score, size of aneurysm, and age were the top three powerful predictors. The further analysis of five random cases presented the good interpretability of the DNN model. CONCLUSION: Interpretable clinical prediction models based on different ML algorithms have been successfully constructed and validated, which would serve as reliable tools in optimizing the treatment decision-making of aSAH. Our DNN model had better performance to predict the unfavorable outcomes at 6 months in aSAH patients compared with Yan's nomogram model.

Use of a Space Graft in Rhinoplasty.

BACKGROUND: Construction of the nasal tip strut is an important part of rhinoplasty; the incidence of postoperative complications is closely related to the type of graft and the approach to cartilaginous framework construction. OBJECTIVES: To introduce a supplementary graft to support the cartilaginous framework. METHODS: Forty patients (37 female and 3 male), aged from 18 to 40 years (average 29), received a rhinoplasty using a spacer graft. Of these, 12 were primary cases and the other 28 had a previous rhinoplasty procedure. Postoperatively, all patients were photographed and asked to complete a satisfaction survey. Complications were collected retrospectively. RESULTS: Postoperatively, the mean nasofrontal angle was 141.92±1.26 degrees for the primary cases and 140.75±2.22 degrees for the repeat procedures; the mean nasolabial angle was 88.69±0.95 degrees for primary cases and 89.14±0.93 degrees for the repeat procedures; mean nasal length was 4.78±0.05 cm for primary cases and 4.79±0.07 cm for the repeat procedures; mean tip projection was 4.68±6.35 cm for primary cases and 2.24±0.07 cm for the repeat procedures. Self-report of the overall improvements showed a high satisfaction rate. One case (2.5%) had a postoperative complication involving a mildly deviated columellar. CONCLUSIONS: The spacer graft can be used as an effective supplement to the traditional methods for reconstructing the nasal tip framework; it can prevent the occurrence of postoperative complications such as the exposure of the nasal prosthesis, the downward rotation of the nasal tip, the depression of the supratip regions of the nose, and the deviation of the nasal columella.

PD-L1 Expression is Linked to Tumor-Infiltrating T-Cell Exhaustion and Adverse Pathological Behavior in Pheochromocytoma/Paraganglioma.

Pheochromocytoma/paraganglioma (PPGL) is an endocrine-related tumor associated with excessive catecholamine release and has limited treatment options once metastasis occurs. Although recent phase 2 clinical trials of immune checkpoint inhibitors in the treatment of PPGL have preliminarily shown promising results, the fundamentals of immunotherapy for PPGL have not yet been established. In the early research, using bulk RNA sequencing of tumor samples from 7 PPGL patients, we found that PPGL tumor tissues exhibited high PD-L1 mRNA expression compared with adjacent normal adrenal medulla tissues, and this was related to T-cell exhaustion biomarkers. To further validate the association, in this study (n = 60), we first stratified all PPGL samples according to PD-L1 expression as determined by immunohistochemical staining, and then subjected 23 fresh PPGL tumor samples from the cohort to a quantitative polymerase chain reaction (n = 16), flow cytometry (n = 7), and multiplex-immunofluorescence staining. Subsequently, we evaluated the pathological manifestations of all 60 PPGL tumor samples and analyzed the correlation among PD-L1 expression, adverse pathological behavior, various clinicopathological data, and genotypes in PPGL. The results showed that PD-L1-positive expression correlated with the exhaustion of tumor-infiltrating T cells, preoperative abnormal elevation of plasma norepinephrine, high Ki67 index, and adverse pathological behavior in PPGL but not with genetic mutation or metastatic disease, possibly due to the limitation of the small number of patients with metastatic disease (n = 4) in the study cohort. In conclusion, our findings reveal that PD-L1 expression is associated with T-cell exhaustion and adverse pathological behavior in PPGL. These results are expected to provide a new theoretical basis and clinical guidance for the treatment of PPGL.

Visualized Lateral Flow Assay for Dual Viral RNA Fragment Detection.

In this technical note, we report an easy-to-produce, reverse-transcription-free, and protein-enzyme-free lateral flow assay for detection of viral RNA fragments by taking SARS-CoV-2 ORF1ab and N as target models. Catalytic hairpin assembly is utilized for dual RNA fragment orthogonal reaction to generate copious amounts of opened hairpin duplexes, which bridge DNA-modified gold nanoparticles and capture strands on the strip to induce coloration. The dual RNA fragments are simultaneously visualized during one time of sample flow, and single-base-mismatched nontarget sequences can be differentiated. The test strip can be flexibly adapted to detect evolutional SARS-CoV-2 variants such as Delta and Omicron. It also shows potential in visually detecting long-sequence virus simulants and achieves a sensitivity comparable to that of RT-qPCR by incorporation with upstream sample amplification. The lateral flow assay should offer a convenient and reliable technique for viral nucleic acid detection.

Grass carp (Ctenopharyngodon idella) Mex3B positively regulates innate immunity by promoting the K63-linked ubiquitination of TLR3.

As a member of Mex3 (muscle excess protein-3) family, Mex3B (Mex-3 RNA binding family member B) is crucial in cell proliferation and migration in mammals. In this study, an ortholog of mammalian Mex3B (denominated CiMex3B, MT276802.1) was cloned and identified in grass carp (Ctenopharyngodon idella). CiMex3B is 1578 bp in length and encodes a polypeptide of 525 amino acids. Consistent with its mammalian counterpart, CiMex3B also contains one C-terminal RING domain and two N-terminal conserved tandem KH domains. CiMex3B up-regulates the expressions of IFN1, ISG15, MX2, as well as the expressions of inflammatory cytokines such as IL6, IL8 and TNFα in response to poly(I:C). A screening test for identifying potential targets indicated that CiMex3B is associated with TLR3 and TRIF. CiMex3B co-localizes with TLR3 in the late endosome, mitochondria and endoplasmic reticulum after poly(I:C) stimulation, whereas they are rarely discovered in the lysosomes. CiMex3B serves as a positive regulator in the phosphorylation of IRF3 and induces IFN1 expression. In addition, two truncation mutants of CiMex3B (1-220 and 221-525) were constructed to better understand the molecular mechanism of CiMex3B-mediated ubiquitination of TLR3. In line with wild-type protein, CiMex3B mutant (1-220) was found mainly in the cytoplasm; however, CiMex3B mutant (221-525) resided in the cytoplasm and the nucleus as well, and it was further confirmed that CiMex3B mutant (221-525) still interacts with TLR3. We also observed that CiMex3B promotes the K63-linked ubiquitination of TLR3, while neither of the truncation mutants (1-220 or 221-525) retains this activity. To sum up, this study revealed that CiMex3B potentiates the K63-linked ubiquitination of TLR3, and then elicits the IRF3-mediated antiviral innate immune responses.

Characterization of a novel hypovirus isolated from the phytopathogenic fungus Colletotrichum camelliae.

Some members of genus Colletotrichum are important plant pathogens. Here, we report a novel positive single-stranded RNA virus, Colletotrichum camelliae hypovirus 1 (CcHV1), from strain GXNN11-2 of Colletotrichum camelliae. The complete genome of CcHV1 is 9907 nucleotides (nt) in length and contains a single large open reading frame (ORF) from nt 352 to 9006. This ORF encodes a polyprotein with four conserved domains, namely UDP-glycosyltransferase, RNA-dependent RNA polymerase (RdRp), peptidase, and DEAD-like helicase. The CcHV1 polyprotein shares the highest similarity with Fusarium concentricum hypovirus 1. Phylogenetic analysis indicated that CcHV1 clustered with members of the genus Betahypovirus within the family Hypoviridae. This is the first report of a hypovirus in a member of the genus Colletotrichum.

Physiological and clinical variables identify ARDS classes and therapeutic heterogeneity to glucocorticoids: a retrospective study.

OBJECTIVE: We aimed to identify new classes in acute respiratory distress syndrome (ARDS) using physiological and clinical variables and to explore heterogeneity in the effects of glucocorticoid therapy between classes. METHODS: Using the Medical Information Mart for Intensive Care-IV database, we identified patients with ARDS. Potential profile analysis was used to identify classes with physiological and clinical data as delineating variables. Baseline characteristics and clinical outcomes were compared between classes. The effect of glucocorticoid treatment was explored by stratifying by class and glucocorticoid treatment. RESULTS: From 2008 to 2019, 1104 patients with ARDS were enrolled in the study. The 2-class potential analysis model had the best fit (P < 0.0001), with 78% of patients falling into class 1 and 22% into class 2. Additional classes did not improve the model fit. Patients in class 2 had higher anion gap, lactate, creatinine, and glucose levels and lower residual base, blood pressure, and bicarbonate compared with class 1. In-hospital mortality and 28-day mortality were significantly higher among patients in class 2 than those in class 1 (P < 0.001). Heterogeneity of glucocorticoid treatment was observed, stratified by class and treatment, with no significant effect in class 1 (P = 0.496), increased mortality in class 2 (P = 0.001), and a significant interaction (P = 0.0381). In class 2, 28-day survival was significantly lower with glucocorticoid treatment compared with no hormone treatment (P = 0.001). CONCLUSION: We used clinical and physiological variables to identify two classes of non-COVID-19-associated ARDS with different baseline characteristics and clinical outcomes. The response to glucocorticoid therapy varied among different classes of patients.

Chlorphoxim induces neurotoxicity in zebrafish embryo through activation of oxidative stress.

It is known that chlorphoxim is a broad-spectrum and high-effective pesticide. With the wide use in agricultural practice, chlorphoxim residue is also frequently detected in water, but its potential toxicity to aquatic life is still unclear. In this study, zebrafish is used as a model to detect the toxicity of chlorphoxim. Our results showed that exposure of high concentration of chlorphoxim at 96 h post-fertilization (hpf) resulted in a high mortality and pericardium edema rate, a low hatchability rate and heart rate. The nervous system damage, swimming behavior alteration and acetylcholinesterase (AChE) inhibition were measured in zebrafish embryos after a 6 days post-fertilization (dpf) of chlorphoxim exposure. The expression of neural-related genes is abnormal in zebrafish embryos. Chlorphoxim exposure significantly increases oxidative stress in zebrafish embryos by inhibiting antioxidant enzyme (SOD and CAT) and activating reactive oxygen species (ROS). As expected, chlorphoxim exposure induces apoptosis by enhancing the expression of apoptotic genes (Bax, Bcl2, and p53). Astaxanthin (ATX), an effective antioxidant, was found to be able to rescue the neurotoxicity of chlorphoxim through relieving oxidative stress and apoptosis. Altogether, the results showed that chlorphoxim exposure led to severe neurotoxicity to zebrafish embryos, which was contributed to a more comprehensive understanding of the safety use of the organophosphorus pesticide.