Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as \"presynapse,\" \"behavior,\" and \"modulation of chemical synaptic transmission\" in autism spectrum disorder\'s brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.

UNASSIGNED: Alzheimer\'s disease (AD) is a progressive neurodegenerative disease that leads to cognitive impairment and memory loss. Currently, the pathogenesis and underlying causative genes of AD remain unclear, and there exists no effective treatment for this disease. This study explored AD-related diagnostic and therapeutic biomarkers from the perspective of immune infiltration by analyzing public data from the NCBI Gene Expression Omnibus database.
UNASSIGNED: In this study, weighted gene co-expression network analysis (WGCNA) was conducted to identify modules and hub genes contributing to AD development. A protein-protein interaction network was constructed when the genes in the modules were enriched and examined by Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Furthermore, a gene network was established using topological WGCNA, from which five hub genes were selected. Logistic regression analysis and receiver operating characteristic curve analysis were performed to explore the clinical value of genes in AD diagnosis. The genes in the core module intersected with the hub genes, and four intersection genes (ATP2A2, ATP6V1D, CAP2, and SYNJ1) were selected. These four genes were enriched by gene set enrichment analysis (GSEA). Finally, an immune infiltration analysis was performed.
UNASSIGNED: The GO/KEGG analysis suggested that genes in the core module played a role in the differentiation and growth of neural cells and in the transmission of neurotransmitters. The GSEA of core genes showed that these four genes were mainly enriched in immune/infection pathways (e.g., cholera infection and Helicobacter pylori infection pathways) and other metabolic pathways. An investigation of immune infiltration characteristics revealed that activated mast cells, regulatory T cells, plasma cells, neutrophils, T follicular helper cells, CD8 T cells, resting memory CD4 T cells, and M1 macrophages were the core immune cells contributing to AD progression. qRT-PCR analysis showed that the ATP6V1D is upregulated in AD.
UNASSIGNED: The results of enrichment and immuno-osmotic analyses indicated that immune pathways and immune cells played an important role in the occurrence and development of AD. The selected key genes were used as biomarkers related to the pathogenesis of AD to further explore the pathways and cells, which provided new perspectives on therapeutic targets in AD.

Due to the complex mechanisms affecting anti-tumor immune response, a single biomarker is insufficient to identify patients who will benefit from immune checkpoint inhibitors (ICIs) treatment. Therefore, a comprehensive predictive model is urgently required to predict the response to ICIs. A total of 162 non-small-cell lung cancer (NSCLC) patients undergoing ICIs treatment from three independent cohorts were enrolled and used as training and test cohorts (training cohort = 69, test cohort1 = 72, test cohort2 = 21). Eight genomic markers were extracted or calculated for each patient. Ten machine learning classifiers, such as the gaussian process classifier, random forest, and support vector machine (SVM), were evaluated. Three genomic biomarkers, namely tumor mutation burden, intratumoral heterogeneity, and loss of heterozygosity in human leukocyte antigen were screened out, and the SVM_poly method was adopted to construct a durable clinical benefit (DCB) prediction model. Compared with a single biomarker, the DCB multi-feature model exhibits better predictive value with the area under the curve values equal to 0.77 and 0.78 for test cohort1 and cohort2, respectively. The patients predicted to have DCB showed improved median progression-free survival (mPFS) and median overall survival (mOS) than those predicted to have non-durable clinical benefit.

Patients with heart failure (HF) often present with signs and symptoms that are often nonspecific and with a wide differential diagnosis, making diagnosis and prognosis of HF by clinical presentation alone challenging. Our knowledge on genetic diversity is rapidly evolving with high-throughput DNA sequencing technology, which makes a great potential for genetic biomarker development. The present review attempts to provide a comprehensive review on the modification of major genetic components in HF patients and to explore the potential application of these components as clinical biomarkers in the diagnosis and in monitoring the progress of HF. The literature search was conducted using six databases, resulting in the inclusion of eighteen studies in the review. The findings of these studies were summarized narratively. An appraisal of the reporting quality of the included studies was conducted using a twelve-item checklist adapted from the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. The findings showed that changes in genetic components in patients with HF compared to healthy controls could be noninvasive diagnostic or prognostic tools for HF with higher specificity and sensitivity in comparison with the traditional biomarkers. This review provided evidence for the potential of developing genetic biomarkers of HF.

Inguinal hernia often occurs in elderly men, and more than one in five men will undergo inguinal hernia repair during their lifetime. Nevertheless, the underlying molecular mechanisms of the pathogenesis behind hernia formation is still unclear. The aims in this study are finding out the potential gene markers and available drugs.
Firstly, we re-analyzed the GSE92748 datasets, including four high and four low expressions of humanized aromatase transgenic mice, which refers to mimic humanized hernia, to identify differentially expressed genes (DEGs) in AromhumH group compared with AromhumL group by the criteria: fold change ≥ 1.4 and adjust P value < 0.05. Secondly, the gene ontology and signaling pathway enrichment analyses of these DEGs were performed through online databases. In addition to the protein and protein interaction networks among these DEGs were constructed and the significant gene modules were chosen for further gene-drug interaction analysis. Lastly, the existing drugs target to these module genes were screen to explore the therapeutic effect for treatment of hernia.
We have identified 64 DEGs, which were associated with muscle system process, actomyosin structure organization etc. Moreover, the significant module genes in PPI networks were Cmya1, Casq2, Cmya5, Ttn, Csrp3 and Actc1, and one existing drug, DEXAMETHASONE, have targeted to Actc1 gene.
In the paper, we identified 6 potential genes and one existing drug for inguinal hernia, which might be used as targets and drugs for the study of inguinal hernia.

Robust, reliable, and sensitively quantitative detection of genetic biomarkers at single-base resolution has the potential to revolutionize medical diagnostics, especially for precision medicine. Here, taking the advantages of the high specificity of ligase reaction and the powerful amplification features of the isothermally exponential amplification, we have demonstrated a novel methodology to sensitively quantify genetic biomarkers at one-base resolution. The methodology is based on the ligase reaction of two stem-loop DNA probes templated by the nucleic acid targets to form a double stem-loop DNA, which subsequently initiates the isothermally exponential amplification reaction with high amplification efficiency. With the proposed method, high sensitivity to determine as low as 0.01 fM DNA or 0.1 fM RNA targets and high specificity to detect single-base changes can be achieved. The new methodology is robust to be performed by using a pair of universal primers under isothermal conditions, which should be employed to quantitatively detect any genetic biomarkers because all DNA/RNA targets can be directly used as the templates to ligate the stem-loop DNA probes with single-base resolution.

Being one of the most lethal cancers that exhibit high levels of heterogeneity, hepatocellular carcinoma (HCC) is associated with diverse oncogenic pathways underpinned by varied driver genes. HCC can be induced by different etiological factors including virus infection, toxin exposure or metabolic disorders. Consequently, patients may display varied genetic profiles, and may respond differently to the treatments involving inhibition of target pathways. These DNA/RNA mutations, copy number variations, chromatin structural changes, aberrant expression of non-coding RNAs and epigenetic modifications were considered as biomarkers in the application of precision medication. To explore how genetic testing could contribute to early diagnosis, prognosis, treatment and postoperative monitoring of HCC, we conducted a systematic review of genetic markers associated with different pathologies. Moreover, we summarized on-going clinical trials for HCC treatment, including the trials for multiple kinase inhibitors and immune checkpoint blockade (ICB). The efficacy of ICB treatment in HCC is not as good as what was observed in lung cancer and melanoma, which might be due to the heterogeneity of the microenvironment of the liver.

Although sulfadiazine (SDZ) is widespread in aquatic environments, information regarding the effects of SDZ on aquatic insects is still limited. In the present study, the bioconcentration and the effects of SDZ on the antioxidant system and the expression of endocrine and stress-related genes in Chironomus riparius larvae were investigated. The larvae were exposed to SDZ at the nominal concentrations of 2, 20 and 200 μg/L for 48 h. The results showed that SDZ was taken up by C. riparius despite presenting low bioconcentration factor values (0.99-3.92). In addition, superoxide dismutase activity was markedly reduced compared with the control group, whereas the levels of malondialdehyde were not significantly affected by SDZ. Moreover, the mRNA expression of genes related to heat shock proteins (Hsp70 and Hsp27) and ecdysone pathway (EcR and E74) were significantly up-regulated following all SDZ treatments. In aggregate, our work provides novel and interesting results regarding the potential biochemical and genetic effects of SDZ on freshwater insects.

Coronary artery disease (CAD) is a significant contributor to global health burden. Adiponectin gene single nucleotide polymorphisms (SNPs) have been associated with CAD susceptibility, but with inconsistent results across the studies. We present, in this study, an updated meta-analysis to discern the genetic susceptibility of adiponectin SNPs in relation to CAD. PubMed and EMBASE databases were used to identify the relevant published articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pooled odds ratios and 95% confidence intervals were generated to assess the strength of the associations. Thirty-five articles with a total of 28,947 participants (mean age 55.3 years, 11,632 cases/17,315 controls, 19,443 males/8353 females, and 1151 persons with unspecified gender data) were included. The dominant, recessive, and additive models were applied. We found that the SNPs +45T>G (rs2241766), -4034A>C (rs822395), and -11391G>A (rs17300539) were linked to CAD development. In addition, +276G>T (rs1501299) SNP was associated with a decreased susceptibility to CAD among Caucasians. We did not find an association between the CAD susceptibility and the -11377C>G (rs266729) SNP. These observations offer new potential genetic biomarker candidates in relation to CAD, and warrant further research in independent world populations.

Currently, there is a pressing need to shift the focus to accurate detection of the earliest phase of increasingly preclinical Alzheimer\'s disease (AD). Meanwhile, the growing recognition that the pathophysiological process of AD begins many years prior to clinically obvious symptoms and the concept of a presymptomatic or preclinical stage of AD are becoming more widely accepted. Advances in clinical identification of new measurements will be critical not only in the discovery of sensitive, specific, and reliable biomarkers of preclinical AD but also in the development of tests that will aid in the early detection and differential diagnosis of dementia and in monitoring disease progression. The goal of this review is to provide an overview of biomarkers for preclinical AD, with emphasis on neuroimaging and neurochemical biomarkers. We conclude with a discussion of emergent directions for AD biomarker research.