Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population.

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.

Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology.

Objective: To assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests. Methods: Twenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests. Results: We found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups. Conclusion: These results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE.

Improving species delimitation for effective conservation: a case study in the endemic maple-leaf oak (Quercus acerifolia).

Species delimitation is challenging in lineages that exhibit both high plasticity and introgression. This challenge can be compounded by collection biases, which may downweight specimens morphologically intermediate between traditional species. Additionally, mismatch between named species and observable phenotypes can compromise species conservation. We studied the species boundaries of Quercus acerifolia, a tree endemic to Arkansas, U.S. We performed morphometric analyses of leaves and acorns from 527 field and 138 herbarium samples of Q. acerifolia and its close relatives, Q. shumardii and Q. rubra. We employed two novel approaches: sampling ex situ collections to detect phenotypic plasticity caused by environmental variation and comparing random field samples with historical herbarium samples to identify collection biases that might undermine species delimitation. To provide genetic evidence, we also performed molecular analyses on genome-wide SNPs. Quercus acerifolia shows distinctive morphological, ecological, and genomic characteristics, rejecting the hypothesis that Q. acerifolia is a phenotypic variant of Q. shumardii. We found mismatches between traditional taxonomy and phenotypic clusters. We detected underrepresentation of morphological intermediates in herbarium collections, which may bias species discovery and recognition. Rare species conservation requires considering and addressing taxonomic problems related to phenotypic plasticity, mismatch between taxonomy and morphological clusters, and collection biases.

Host-specific soil microbes contribute to habitat restriction of closely related oaks (Quercus spp.).

Habitat divergence among close relatives is a common phenomenon. Studying the mechanisms behind habitat divergence is fundamental to understanding niche partitioning, species diversification, and other evolutionary processes. Recent studies found that soil microbes regulate the abundance and diversity of plant species. However, it remains unclear whether soil microbes can affect the habitat distributions of plants and drive habitat divergence. To fill in this knowledge gap, we investigated whether soil microbes might restrict habitat distributions of closely related oaks (Quercus spp.) in eastern North America. We performed a soil inoculum experiment using two pairs of sister species (i.e., the most closely related species) that show habitat divergence: Quercus alba (local species) vs. Q. michauxii (foreign), and Q. shumardii (local) vs. Q. acerifolia (foreign). To test whether host-specific soil microbes are responsible for habitat restriction, we investigated the impact of local sister live soil (containing soil microbes associated with local sister species) on the survival and growth of local and foreign species. Second, to test whether habitat-specific soil microbes are responsible for habitat restriction, we examined the effect of local habitat live soil (containing soil microbes within local sister's habitats, but not directly associated with local sister species) on the seedlings of local and foreign species. We found that local sister live soil decreased the survival and biomass of foreign species' seedlings while increasing those of local species, suggesting that host-specific soil microbes could potentially mediate habitat exclusion. In contrast, local habitat live soil did not differentially affect the survival or biomass of the local vs. foreign species. Our study indicates that soil microbes associated with one sister species can suppress the recruitment of the other host species, contributing to the habitat partitioning of close relatives. Considering the complex interactions with soil microbes is essential for understanding the habitat distributions of closely related plants.

Elevated ubiquitination contributes to protective immunity against severe SARS-CoV-2 infection.

BACKGROUND: The crosstalk between the ubiquitin-proteasome and the immune system plays an important role in the health and pathogenesis of viral infection. However, there have been few studies of ubiquitin activation in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS: We investigated the effect of ubiquitination on SARS-CoV-2 infection and patient prognosis by integrating published coronavirus disease 2019 (COVID-19) multi-transcriptome data and bioinformatics methods. RESULTS: The differential expression of COVID-19 samples revealed changed ubiquitination in most solid and hollow organs, and it was activated in lymphatic and other immune tissues. In addition, in the respiratory system of COVID-19 patients, the immune response was mainly focused on the alveoli, and the expression of ubiquitination reflected increasing immune infiltration. Ubiquitination stratification could significantly differentiate patients' prognosis and inflammation levels through the general transcriptional analysis of the peripheral blood of patients with COVID-19. Moreover, high ubiquitination levels were associated with a favourable prognosis, low inflammatory response, and reduced mechanical ventilation and intensive care unit. Moreover, high ubiquitination promoted a beneficial immune response while inhibiting immune damage. Finally, prognostic stratification and biomarker screening based on ubiquitination traits played an important role in clinical management and drug development. CONCLUSION: Ubiquitination characteristics provides new ideas for clinical intervention and prognostic guidance for COVID-19 patients.

The Clinical Prediction Value of the Ubiquitination Model Reflecting the Immune Traits in LUAD.

Background: Increasing evidence shows that the ubiquitin-proteasome system has a crucial impact on lung adenocarcinoma. However, reliable prognostic signatures based on ubiquitination and immune traits have not yet been established. Methods: Bioinformatics was performed to analyze the characteristic of ubiquitination in lung adenocarcinoma. Principal component analysis was employed to identify the difference between lung adenocarcinoma and adjacent tissue. The ubiquitin prognostic risk model was constructed by multivariate Cox regression and least absolute shrinkage and selection operator regression based on the public database The Cancer Genome Atlas, with evaluation of the time-dependent receiver operating characteristic curve. A variety of algorithms was used to analyze the immune traits of model stratification. Meanwhile, the drug response sensitivity for subgroups was predicted by the "pRRophetic" package based on the database of the Cancer Genome Project. Results: The expression of ubiquitin genes was different in the tumor and in the adjacent tissue. The ubiquitin model was superior to the clinical indexes, and four validation datasets verified the prognostic effect. Additionally, the stratification of the model reflected distinct immune landscapes and mutation traits. The low-risk group was infiltrating plenty of immune cells and highly expressed major histocompatibility complex and immune genes, which illustrated that these patients could benefit from immune treatment. The high-risk group showed higher mutation and tumor mutation burden. Integrating the tumor mutation burden and the immune score revealed the patient's discrepancy between survival and drug response. Finally, we discovered that the drug targeting ubiquitin and proteasome would be a beneficial prospective treatment for lung adenocarcinoma. Conclusion: The ubiquitin trait could reflect the prognosis of lung adenocarcinoma, and it might shed light on the development of novel ubiquitin biomarkers and targeted therapy for lung adenocarcinoma.

Functional traits explain the consistent resistance of biodiversity to plant invasion under nitrogen enrichment.

Elton's biotic resistance hypothesis, which posits that diverse communities should be more resistant to biological invasions, has received considerable experimental support. However, it remains unclear whether such a negative diversity-invasibility relationship would persist under anthropogenic environmental change. By using the common ragweed (Ambrosia artemisiifolia) as a model invader, our 4-year grassland experiment demonstrated consistently negative relationships between resident species diversity and community invasibility, irrespective of nitrogen addition, a result further supported by a meta-analysis. Importantly, our experiment showed that plant diversity consistently resisted invasion simultaneously through increased resident biomass, increased trait dissimilarity among residents, and increased community-weighted means of resource-conservative traits that strongly resist invasion, pointing to the importance of both trait complementarity and sampling effects for invasion resistance even under resource enrichment. Our study provides unique evidence that considering species' functional traits can help further our understanding of biotic resistance to biological invasions in a changing environment.

Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction.

Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50-85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50-85 years) downloaded from multiple open-source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia-associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aß40, Aß42, T-tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70-85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.

METTL3-mediated m6A mRNA modification of FBXW7 suppresses lung adenocarcinoma.

BACKGROUND: FBXW7 m6A modification plays an important role in lung adenocarcinoma (LUAD) progression; however, the underlying mechanisms remain unclear. METHODS: The correlation between FBXW7 and various genes related to m6A modification was analyzed using The Cancer Genome Atlas database. The regulatory effects of METTL3 on FBXW7 mRNA m6A modification were examined in a cell model, and the underlying mechanism was determined by methylated RNA immunoprecipitation, RNA immunoprecipitation, luciferase reporter, and mutagenesis assays. In vitro experiments were performed to further explore the biological effects of METTL3-mediated FBXW7 m6A modification on LUAD development. RESULTS: Decreased FBXW7 expression was accompanied by downregulated METTL3 expression in human LUAD tissues and was associated with a worse prognosis for LUAD in The Cancer Genome Atlas database. m6A was highly enriched in METTL3-mediated FBXW7 transcripts, and increased m6A modification in the coding sequence region increased its translation. Functionally, METTL3 overexpression or knockdown affected the apoptosis and proliferation phenotype of LUAD cells by regulating FBXW7 m6A modification and expression. Furthermore, FBXW7 overexpression in METTL3-depleted cells partially restored LUAD cell suppression in vitro and in vivo. CONCLUSIONS: Our findings reveal that METTL3 positively regulates FBXW7 expression and confirm the tumor-suppressive role of m6A-modified FBXW7, thus providing insight into its epigenetic regulatory mechanisms in LUAD initiation and development.

PHB2 promotes tumorigenesis via RACK1 in non-small cell lung cancer.

Background: Lung cancer has the highest mortality rate among cancers worldwide, with non-small cell lung cancer (NSCLC) the most common type. Increasing evidence shows that PHB2 is highly expressed in other cancer types; however, the effects of PHB2 in NSCLC are currently poorly understood. Method: PHB2 expression and its clinical relevance in NSCLC tumor tissues were analyzed using a tissue microarray. The biological role of PHB2 in NSCLC was investigated in vitro and in vivo using immunohistochemistry and immunofluorescence staining, gene expression knockdown and overexpression, cell proliferation assay, flow cytometry, terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, wound healing assay, Transwell assay, western blot analysis, qRT-PCR, coimmunoprecipitation, and mass spectrometry analysis. Results: Our major finding is that PHB2 facilitates tumorigenesis in NSCLC by interacting with and stabilizing RACK1, which further induces activation of downstream tumor-promoting effectors. PHB2 was found to be overexpressed in NSCLC tumor tissues, and its expression was correlated with clinicopathological features. Furthermore, PHB2 overexpression promoted proliferation, migration, and invasion, whereas PHB2 knockdown enhanced apoptosis in NSCLC cells. The stimulating effect of PHB2 on tumorigenesis was also verified in vivo. In addition, PHB2 interacted with RACK1 and increased its expression through posttranslational modification, which further induced activation of the Akt and FAK pathways. Conclusions: Our results reveal the effects of PHB2 on tumorigenesis and its regulation of RACK1 and RACK1-associated proteins and downstream signaling in NSCLC. We believe that the crosstalk between PHB2 and RACK1 provides us with a great opportunity to design and develop novel therapeutic strategies for NSCLC.

Histology transformation-mediated pathological atypism in small-cell lung cancer within the presence of chemotherapy: A case report.

BACKGROUND: The treatment of small-cell lung cancer (SCLC) has progressed little in recent years because of its unique biological activities and complex genomic alterations. Chemotherapy combined with radiotherapy has been widely accepted as the first-line treatment for SCLC. CASE SUMMARY: Here, we present a 68-year-old male smoker who was diagnosed with SCLC of the right lung. After several cycles of concurrent chemoradiotherapy, the tumor progressed with broad metastasis to liver and bone. Histopathological examination showed an obvious transformation to adenocarcinoma, probably a partial recurrence mediated by the chemotherapy-based regimen. A mixed tumor as the primary lesion and transformation from SCLC or/and tumor stem cells may have accounted for the pathology conversion. We adjusted the treatment schedule in accord with the change in phenotype. CONCLUSION: Although diffuse skeletal and hepatic metastases were seen on a recent computed tomography scan, the patient is alive, with intervals of progression and shrinkage of his cancer.

Luteolin enhances TRAIL sensitivity in non-small cell lung cancer cells through increasing DR5 expression and Drp1-mediated mitochondrial fission.

Cancer cells exhibit extreme sensitivity to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) over normal cells, highlighting TRAIL's potential as a novel and effective cancer drug. However, the therapeutic effect of TRAIL is limited due to drug resistance. In the present study, we sought to investigate the potential effects of luteolin as a TRAIL sensitizer in non-small cell lung cancer (NSCLC) cells. A549 and H1975 cells had low sensitivity or were resistant to TRAIL. Luteolin alone or in combination with TRAIL decreased cell viability and increased apoptosis. Furthermore, luteolin alone or in combination with TRAIL enhanced death receptor 5 (DR5) expression and dynamin-related protein 1 (Drp1)-dependent mitochondrial fission. However, the synergistic effect of luteolin on cell viability and apoptosis was reversed by DR5 and Drp1 inhibition, suggesting that DR5 upregulation and mitochondrial dynamics may be essential for luteolin as a sensitizer of TRAIL-based therapy in NSCLC. Moreover, luteolin treatment alone or in combination with TRAIL increased the phosphorylation of c-Jun N-terminal kinase (JNK), while SP600125 (the JNK inhibitor) significantly abolished the synergistic effect on DR5 expression and Drp1 translocation, indicating that JNK signaling activation was greatly associated with the synergistic effect exerted by luteolin in NSCLC cells. Therefore, TRAIL combined with luteolin could be as an effective chemotherapeutic strategy for NSCLC.

Inhibition of cGAS-STING-TBK1 signaling pathway by DP96R of ASFV China 2018/1.

African swine fever virus (ASFV) is a highly pathogenic large DNA virus that causes African swine fever (ASF) in domestic pigs and European wild boars with mortality rate up to 100%. The DP96R gene of ASFV encodes one of the viral virulence factors, yet its action mechanism remains unknown. In this study, we report that DP96R of ASFV China 2018/1 strain subverts type I IFN production in cGAS sensing pathway. DP96R inhibited the cGAS/STING, and TBK1 but not IRF3-5D mediated IFN-ß and ISRE promoters activation. Furthermore, DP96R selectively blocked the activation of NF-κB promoter induced by cGAS/STING, TBK1, and IKKß, but not by overexpression of p65. Moreover, DP96R inhibited phosphorylation of TBK1 stimulated by cGAS/STING activation, and TBK1-induced antiviral response. Finally, truncated mutation analysis demonstrated that the region spanning amino acids 30 to 96 of DP96R was responsible for the inhibitory activity. To our knowledge, this is for the first time that DP96R of ASFV China 2018/1 is reported to negatively regulate type I IFN expression and NF-κB signaling by inhibiting both TBK1 and IKKß, which plays an important role in virus immune evasion.