Exploring structured molecular landscape from single-cell multi-omics data by an explainable multimodal model.

There is an urgent need to understand the molecular landscape beyond the conventional cellular landscape, maximizing the translational use and generalized interpretation of state-of-the-art single-cell genomic techniques in biological studies. We introduced a multimodal explainable artificial intelligence (xAI) model Vec3D to identify a joint definition of cellular states and their distribution in a quantified graphic organization as structured molecular landscape (SML). First, Vec3D substantially improves the accuracy and efficiency of multimodal data analysis.

Multi-modal molecular determinants of clinically relevant osteoporosis subtypes.

Due to a rapidly aging global population, osteoporosis and the associated risk of bone fractures have become a wide-spread public health problem. However, osteoporosis is very heterogeneous, and the existing standard diagnostic measure is not sufficient to accurately identify all patients at risk of osteoporotic fractures and to guide therapy. Here, we constructed the first prospective multi-omics atlas of the largest osteoporosis cohort to date (longitudinal data from 366 participants at three time points), and also implemented an explainable data-intensive analysis framework (DLSF: Deep Latent Space Fusion) for an omnigenic model based on a multi-modal approach that can capture the multi-modal molecular signatures (M3S) as explicit functional representations of hidden genotypes.

Identifying therapeutic biomarkers of zoledronic acid by metabolomics.

Zoledronic acid (ZOL) is a potent antiresorptive agent that increases bone mineral density (BMD) and reduces fracture risk in postmenopausal osteoporosis (PMOP). The anti-osteoporotic effect of ZOL is determined by annual BMD measurement. In most cases, bone turnover markers function as early indicators of therapeutic response, but they fail to reflect long-term effects.

Noninvasive detection and interpretation of gastrointestinal diseases by collaborative serum metabolite and magnetically controlled capsule endoscopy.

Gastrointestinal diseases are complex diseases that occur in the gastrointestinal tract. Common gastrointestinal diseases include chronic gastritis, peptic ulcers, inflammatory bowel disease, and gastrointestinal tumors. These diseases may manifest a long course, difficult treatment, and repeated attacks.

Rewiring of Microbiota Networks in Erosive Inflammation of the Stomach and Small Bowel.

The development of non-invasive, inexpensive, and effective early diagnosis tests for gastric and small-bowel lesions is an urgent requirement. The introduction of magnetically guided capsule endoscopy (MGCE) has aided examination of the small bowel for diagnoses. However, the distribution of the fecal microbiome in abnormal erosions of the stomach and small bowel remains unclear.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies.

Integrative Analysis of Omics Big Data.

The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g.

Edge biomarkers for classification and prediction of phenotypes.

In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed.

[Secretory expression vector V-pLNCX-s-hri inhibits the growth of mouse B16 melanoma].

Background And Objective: Human ribonuclease inhibitor (hRI) extracted and purified from human placenta has been shown to remarkably inhibit some solid tumors in mice. This study was to construct V-pLNCX-s-hri, a secretory expression vector, and explore its inhibition effects on the growth of mouse B16 melanoma cells.

Methods: The hRI gene sequence conjugated with the synthesized signal peptide of mouse IgG was cloned into the retroviral vector V-pLNCX to construct V-pLNCX-s-hri.

Antitumor effects of human ribonuclease inhibitor gene transfected on B16 melanoma cells.

Human ribonuclease inhibitor (RI) is a cytoplasmic acidic protein. The experiment demonstrated that it might effectively inhibit tumor-induced angiogenesis and inhibit tumor growth. Ribonuclease inhibitor is constructed almost entirely of leucine-rich repeats, which might be involved in unknown biological effects besides inhibiting RNase A and angiogenin activities.

[Inhibitory effect of ginsenoside-Rg3 on lung metastasis of mouse melanoma transfected with ribonuclease inhibitor].

Objective: To investigate the effect of ginsenoside-Rg3 on lung metastasis of ribonuclease inhibitor (RI) gene-transfected mouse B16 melanoma.

Methods: C57BL/6 mice were iv injected with parental or RI-transfected B16 melanoma cells. Lung metastasis was assessed by the number of surface tumor nodules.

[Preparation and purification of the antibody against ribonuclease inhibitor].

Aim: To prepare and purify the antibody against ribonuclease inhibitor(RI).

Methods: RI was extracted from human placenta and purified by affinity chromatography. Rabbits anti-RI antibody was obtained after immunization and then purified through rProtein A Sepharose Fast Flow chromatography column.

[Analysis of gene expression of ribonuclease inhibitor in human breast cancer tissue].

Background & Objective: Ribonuclease inhibitor (RI), which is rich in human placenta, is a multi-functional acidic glycoprotein. Our previous studies showed that the growth of some solid tumors (S180 sarcoma, Ca761 breast cancer, and H22 hepatoma) could be significantly inhibited by RI extracted and purified from human placenta. This study was designed to observe the change of RI gene expression in human breast cancer tissue.