Antisense oligonucleotides enhance SLC20A2 expression and suppress brain calcification in a humanized mouse model.

Primary familial brain calcification (PFBC) is a genetic neurological disease, yet no effective treatment is currently available. Here, we identified five novel intronic variants in SLC20A2 gene from six PFBC families. Three of these variants increased aberrant SLC20A2 pre-mRNA splicing by altering the binding affinity of splicing machineries to newly characterized cryptic exons, ultimately causing premature termination of SLC20A2 translation. Inhibiting the cryptic-exon incorporation with splice-switching ASOs increased the expression levels of functional SLC20A2 in cells carrying SLC20A2 mutations. Moreover, by knocking in a humanized SLC20A2 intron 2 sequence carrying a PFBC-associated intronic variant, the SLC20A2-KI mice exhibited increased inorganic phosphate (Pi) levels in cerebrospinal fluid (CSF) and progressive brain calcification. Intracerebroventricular administration of ASOs to these SLC20A2-KI mice reduced CSF Pi levels and suppressed brain calcification. Together, our findings expand the genetic etiology of PFBC and demonstrate ASO-mediated splice modulation as a potential therapy for PFBC patients with SLC20A2 haploinsufficiency.

Multiethnic growth standards for fetal body composition and organ volumes derived from 3D ultrasonography.

BACKGROUND: A major goal of contemporary obstetrical practice is to optimize fetal growth and development throughout pregnancy. To date, fetal growth during prenatal care is assessed by performing ultrasonographic measurement of 2-dimensional fetal biometry to calculate an estimated fetal weight. Our group previously established 2-dimensional fetal growth standards using sonographic data from a large cohort with multiple sonograms. A separate objective of that investigation involved the collection of fetal volumes from the same cohort. OBJECTIVE: The Fetal 3D Study was designed to establish standards for fetal soft tissue and organ volume measurements by 3-dimensional ultrasonography and compare growth trajectories with conventional 2-dimensional measures where applicable. STUDY DESIGN: The National Institute of Child Health and Human Development Fetal 3D Study included research-quality images of singletons collected in a prospective, racially and ethnically diverse, low-risk cohort of pregnant individuals at 12 U.S. sites, with up to 5 scans per fetus (N=1730 fetuses). Abdominal subcutaneous tissue thickness was measured from 2-dimensional images and fetal limb soft tissue parameters extracted from 3-dimensional multiplanar views. Cerebellar, lung, liver, and kidney volumes were measured using virtual organ computer aided analysis. Fractional arm and thigh total volumes, and fractional lean limb volumes were measured, with fractional limb fat volume calculated by subtracting lean from total. For each measure, weighted curves (fifth, 50th, 95th percentiles) were derived from 15 to 41 weeks' using linear mixed models for repeated measures with cubic splines. RESULTS: Subcutaneous thickness of the abdomen, arm, and thigh increased linearly, with slight acceleration around 27 to 29 weeks. Fractional volumes of the arm, thigh, and lean limb volumes increased along a quadratic curvature, with acceleration around 29 to 30 weeks. In contrast, growth patterns for 2-dimensional humerus and femur lengths demonstrated a logarithmic shape, with fastest growth in the second trimester. The mid-arm area curve was similar in shape to fractional arm volume, with an acceleration around 30 weeks, whereas the curve for the lean arm area was more gradual. The abdominal area curve was similar to the mid-arm area curve with an acceleration around 29 weeks. The mid-thigh and lean area curves differed from the arm areas by exhibiting a deceleration at 39 weeks. The growth curves for the mid-arm and thigh circumferences were more linear. Cerebellar 2-dimensional diameter increased linearly, whereas cerebellar 3-dimensional volume growth gradually accelerated until 32 weeks followed by a more linear growth. Lung, kidney, and liver volumes all demonstrated gradual early growth followed by a linear acceleration beginning at 25 weeks for lungs, 26 to 27 weeks for kidneys, and 29 weeks for liver. CONCLUSION: Growth patterns and timing of maximal growth for 3-dimensional lean and fat measures, limb and organ volumes differed from patterns revealed by traditional 2-dimensional growth measures, suggesting these parameters reflect unique facets of fetal growth. Growth in these three-dimensional measures may be altered by genetic, nutritional, metabolic, or environmental influences and pregnancy complications, in ways not identifiable using corresponding 2-dimensional measures. Further investigation into the relationships of these 3-dimensional standards to abnormal fetal growth, adverse perinatal outcomes, and health status in postnatal life is warranted.

Embedded monitoring system and teaching of artificial intelligence online drug component recognition.

Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market and ensure drug safety. The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the use of the monitoring system. With the continuous development of computer and measurement technologies, various biochemical data are increasing at an unprecedented speed, and numerous databases are emerging. Extracting patterns from considerable known data and experimental facts is an essential task for a wide range of biological and chemical workers. Pattern recognition is one of the essential technologies for data mining. It is widely used in industry, agriculture, national defense, biomedicine, meteorology, astronomy, and other fields. To improve the effect of the online drug ingredient recognition system, this study used AI to design an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study constructed the framework of the AI online drug ingredient recognition-embedded monitoring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern recognition system, and presented the pattern recognition algorithm and the algorithm evaluation index. Afterward, it used pattern recognition to conduct a qualitative analysis of the infrared spectrum of drug components and introduced the overall process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universities, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensitivity, specificity, and Matthew correlation coefficient of the online drug component recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85. Students in five majors reported high levels of satisfaction with the embedded system teaching, which is better for delivering college instruction.

A comprehensive analysis of the differential expression in the hippocampus of depression induced by gut microbiota compared to traditional stress.

Depression, which is a disease of heterogeneous etiology, is characterized by high disability and mortality rates. Gut microbiota are associated with the development of depression. To further explore any differences in the mechanisms of depression induced by gut microbiota and traditional stresses, as well as facilitate the development of microbiota-based interventions, a fecal microbiota transplantation (FMT) depression model was made. This was achieved by transplanting feces from major depressive disorder (MDD) patients into germ-free mice. Second, the mechanisms of the depression induced by gut microbiota were analyzed in comparison with those of the depression caused by different forms of stress. It turned out that mice exhibited depressive-like behavior after FMT. Then, PCR array analysis was performed on the hippocampus of the depressed mice to identify differentially expressed genes (DEGs). The KEGG analysis revealed that the pathways of depression induced by gut microbes are closely associated with immuno-inflammation. To determine the pathogenic pathways of physiological stress and psychological stress-induced depression, raw data was extracted from several databases and KEGG analysis was performed. The results from the analysis revealed that the mechanisms of depression induced by physiological and psychological stress are closely related to the regulation of neurotransmitters and energy metabolism. Interestingly, the immunoinflammatory response was distinct across different etiologies that induced depression. The findings showed that gut microbiota dysbiosis-induced depression was mainly associated with adaptive immunity, while physiological stress-induced depression was more linked to innate immunity. This study compared the pathogenesis of depression caused by gut microbiota dysbiosis, and physiological and psychological stress. We explored new intervention methods for depression and laid the foundation for precise treatment.

Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues.

Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues. In the framework, a customized deep learning network, named 3D ResCBAM nnU-Net, was designed to segment the vessels, and a novel pipeline based on preprocessing and post-processing was developed to refine the segmentation results automatically. On the basis of its application to a practical dataset, we showed that the FineVess enabled extraction of variable and incomplete vessels with high accuracy in challenging 3D images, better than other traditional and state-of-the-art schemes. For the extracted vessels, we calculated vascular morphological features including fractal dimension and vascular wall integrity of different tumor grades, and verified the vascular heterogeneity through quantitative analysis.

Iron metabolism disorder and multiple sclerosis: a comprehensive analysis.

Background: Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. Currently, the pathological mechanisms of MS are not fully understood, but research has suggested that iron metabolism disorder may be associated with the onset and clinical manifestations of MS. Methods and materials: The study utilized publicly available databases and bioinformatics techniques for gene expression data analysis, including differential expression analysis, weighted correlation network analysis, gene enrichment analysis, and construction of logistic regression models. Subsequently, Mendelian randomization was used to assess the causal relationship between different iron metabolism markers and MS. Results: This study identified IREB2, LAMP2, ISCU, ATP6V1G1, ATP13A2, and SKP1 as genes associated with multiple sclerosis (MS) and iron metabolism, establishing their multi-gene diagnostic value for MS with an AUC of 0.83. Additionally, Mendelian randomization analysis revealed a potential causal relationship between transferrin saturation and MS (p=2.22E-02; OR 95%CI=0.86 (0.75, 0.98)), as well as serum transferrin and MS (p=2.18E-04; OR 95%CI=1.22 (1.10, 1.36)). Conclusion: This study comprehensively explored the relationship between iron metabolism and MS through integrated bioinformatics analysis and Mendelian randomization methods. The findings provide important insights for further research into the role of iron metabolism disorder in the pathogenesis of MS and offer crucial theoretical support for the treatment of MS.

Maternal separation regulates sensitivity of stress-induced depression in mice by affecting hippocampal metabolism.

Depression is a serious mental illness. Previous studies found that early life stress (ELS) plays a vital role in the onset and progression of depression. However, relevant studies have not yet been able to explain the specific effects of early stress on stress-induced depression sensitivity and individual behavior during growth. Therefore, we constructed a maternal separation (MS) model and administered chronic social frustration stress at different stages of their growth while conducting metabolomics analysis on the hippocampus of mice. Our results showed that the immobility time of mice in the forced swimming test was significantly reduced at the end of MS. Meanwhile, mice with MS experience significantly decreased total movement distance in the open field test and sucrose preference ratio in the sucrose preference test when subjected to chronic social defeat stress (CSDS) during adolescence. In adulthood, the results were the opposite. In addition, we found that level changes in metabolites such as Beta-alanine, l-aspartic acid, 2-aminoadipic acid, and Glycine are closely related to behavioral changes. These metabolites are mainly enriched in Pantothenate, CoA biosynthesis, and Beta Alanine metabolism pathways. Our experiment revealed that the effects of ELS vary across different age groups. It will increase an individual's sensitivity to depression when facing CSDS in adolescence, but it will reduce their sensitivity to depression when facing CSDS in adulthood. This may be achieved by regulating the hippocampus's Pantothenate and CoA biosynthesis and Beta Alanine metabolism pathways represented by Beta-alanine, l-Aspartic acid, 2-aminoadipic acid, and Glycine metabolites.

Causal relationship between immune cells and neurodegenerative diseases: a two-sample Mendelian randomisation study.

Background: There is increasing evidence that the types of immune cells are associated with various neurodegenerative diseases. However, it is currently unclear whether these associations reflect causal relationships. Objective: To elucidate the causal relationship between immune cells and neurodegenerative diseases, we conducted a two-sample Mendelian randomization (MR) analysis. Materials and methods: The exposure and outcome GWAS data used in this study were obtained from an open-access database (https://gwas.mrcieu.ac.uk/), the study employed two-sample MR analysis to assess the causal relationship between 731 immune cell features and four neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS). All immune cell data was obtained from Multiple MR methods were used to minimize bias and obtain reliable estimates of the causal relationship between the variables of interest and the outcomes. Instrumental variable selection criteria were restricted to ensure the accuracy and effectiveness of the causal relationship between species of immune cells and the risk of these neurodegenerative diseases. Results: The study identified potential causal relationships between various immune cells and different neurodegenerative diseases. Specifically, we found that 8 different types of immune cells have potential causal relationships with AD, 1 type of immune cells has potential causal relationships with PD, 6 different types of immune cells have potential causal relationships with ALS, and 6 different types of immune cells have potential causal relationships with MS. Conclusion: Our study, through genetic means, demonstrates close causal associations between the specific types of immune cells and AD, PD, ALS and MS, providing useful guidance for future clinical researches.