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.

Bioengineered Extracellular Vesicles Delivering siMDM2 Sensitize Oxaliplatin Therapy Efficacy in Colorectal Cancer.

Oxaliplatin (OXA) is the first-line drug for the treatment of colorectal cancer (CRC), and susceptibility to drug resistance affects patient prognosis. However, the exact underlying mechanisms remain unclear. Platinum-acquired resistance in CRC is a continuous transition process; though, current research has mainly focused on the end state of drug resistance, and the early events of drug resistance have been ignored.

Clinical Outcomes of Transaxillary Reverse-Sequence Endoscopic Nipple-Sparing Mastectomy and Direct-to-Implant Prepectoral Breast Reconstruction: A Prospective Study of Initial 68 Procedures.

Background: Minimal access breast surgery improves cosmetic outcomes over conventional breast surgery but still faces barriers in becoming standard procedure for breast reconstruction. This report introduces a novel technique of transaxillary reverse-sequence endoscopic nipple-sparing mastectomy (R-E-NSM) followed by direct-to-implant prepectoral breast reconstruction (DTI-PBR) and describes its clinical outcomes.

Methods: This prospective study enrolled patients who underwent R-E-NSM and DTI-PBR from March 2021 to December 2021 at a single institution.

SGAE: single-cell gene association entropy for revealing critical states of cell transitions during embryonic development.

The critical point or pivotal threshold of cell transition occurs in early embryonic development when cell differentiation culminates in its transition to specific cell fates, at which the cell population undergoes an abrupt and qualitative shift. Revealing such critical points of cell transitions can track cellular heterogeneity and shed light on the molecular mechanisms of cell differentiation. However, precise detection of critical state transitions proves challenging when relying on single-cell RNA sequencing data due to their inherent sparsity, noise, and heterogeneity.

Identifying the critical state of cancers by single-sample Markov flow entropy.

Background: The progression of complex diseases sometimes undergoes a drastic critical transition, at which the biological system abruptly shifts from a relatively healthy state (before-transition stage) to a disease state (after-transition stage). Searching for such a critical transition or critical state is crucial to provide timely and effective scientific treatment to patients. However, in most conditions where only a small sample size of clinical data is available, resulting in failure when detecting the critical states of complex diseases, particularly only single-sample data.

MAPKAPK2, a potential dynamic network biomarker of α-synuclein prior to its aggregation in PD patients.

One of the important pathological features of Parkinson's disease (PD) is the pathological aggregation of α-synuclein (α-Syn) in the substantia nigra. Preventing the aggregation of α-Syn has become a potential strategy for treating PD. However, the molecular mechanism of α-Syn aggregation is unclear.

SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems.

Complex biological systems do not always develop smoothly but occasionally undergo a sharp transition; i.e. there exists a critical transition or tipping point at which a drastic qualitative shift occurs.

Low expression of the dynamic network markers FOS/JUN in pre-deteriorated epithelial cells is associated with the progression of colorectal adenoma to carcinoma.

Background: Deterioration of normal intestinal epithelial cells is crucial for colorectal tumorigenesis. However, the process of epithelial cell deterioration and molecular networks that contribute to this process remain unclear.

Methods: Single-cell data and clinical information were downloaded from the Gene Expression Omnibus (GEO) database.

Identifying the critical state of complex biological systems by the directed-network rank score method.

Motivation: Catastrophic transitions are ubiquitous in the dynamic progression of complex biological systems; that is, a critical transition at which complex systems suddenly shift from one stable state to another occurs. Identifying such a critical point or tipping point is essential for revealing the underlying mechanism of complex biological systems. However, it is difficult to identify the tipping point since few significant differences in the critical state are detected in terms of traditional static measurements.

TPD: a web tool for tipping-point detection based on dynamic network biomarker.

Tipping points or critical transitions widely exist during the progression of many biological processes. It is of great importance to detect the tipping point with the measured omics data, which may be a key to achieving predictive or preventive medicine. We present the tipping point detector (TPD), a web tool for the detection of the tipping point during the dynamic process of biological systems, and further its leading molecules or network, based on the input high-dimensional time series or stage course data.

The single-sample network module biomarkers (sNMB) method reveals the pre-deterioration stage of disease progression.

The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration, at which a drastic and qualitative shift occurs. The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration, which allows the timely implementation of appropriate measures to prevent a catastrophic transition. However, identifying the pre-deterioration stage is a challenging task in clinical medicine, especially when only a single sample is available for most patients, which is responsible for the failure of most statistical methods.

Identifying the critical states and dynamic network biomarkers of cancers based on network entropy.

Background: There are sudden deterioration phenomena during the progression of many complex diseases, including most cancers; that is, the biological system may go through a critical transition from one stable state (the normal state) to another (the disease state). It is of great importance to predict this critical transition or the so-called pre-disease state so that patients can receive appropriate and timely medical care. In practice, however, this critical transition is usually difficult to identify due to the high nonlinearity and complexity of biological systems.

Identifying the critical states of complex diseases by the dynamic change of multivariate distribution.

The dynamics of complex diseases are not always smooth; they are occasionally abrupt, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift.

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages.

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy.

During early embryonic development, cell fate commitment represents a critical transition or "tipping point" of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene-gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level.

Single-cell transcriptomics reveal in mature B cell as a dynamic network biomarker before lymph node metastasis in CRC.

Increasing evidence indicates that mature B cells in the adjacent tumor tissue, both as an intermediate state, are vital in advanced colorectal cancer (CRC), which is associated with a low survival rate. Developing predictive biomarkers that detect the tipping point of mature B cells before lymph node metastasis in CRC is critical to prevent irreversible deterioration. We analyzed B cells in the adjacent tissues of CRC samples from different stages using the dynamic network biomarker (DNB) method.

Dynamic Network Biomarker of Pre-Exhausted CD8 T Cells Contributed to T Cell Exhaustion in Colorectal Cancer.

Immunotherapy has achieved positive clinical responses in various cancers. However, in advanced colorectal cancer (CRC), immunotherapy is challenging because of the deterioration of T-cell exhaustion, the mechanism of which is still unclear. In this study, we depicted CD8 T-cell developmental trajectories and characterized the pre-exhausted T cells isolated from CRC patients in the scRNA-seq data set using a dynamic network biomarker (DNB).

Hydrogen sulfide-loaded microbubbles combined with ultrasound mediate thrombolysis and simultaneously mitigate ischemia-reperfusion injury in a rat hindlimb model.

Background: Thromboembolism and subsequent ischemia/reperfusion injury (IRI) remain major clinical challenges.

Objectives: To investigate whether hydrogen sulfide (H S)-loaded microbubbles (hs-Mbs) combined with ultrasound (US) radiation (hs-Mbs+US) dissolve thrombi and simultaneously alleviate tissue IRI through local H S release.

Methods: hs-Mbs were manufactured and US-triggered H S release was recorded.

Single-Sample Node Entropy for Molecular Transition in Pre-deterioration Stage of Cancer.

A complex disease, especially cancer, always has pre-deterioration stage during its progression, which is difficult to identify but crucial to drug research and clinical intervention. However, using a few samples to find mechanisms that propel cancer crossing the pre-deterioration stage is still a complex problem. In this study, we successfully developed a novel single-sample model based on node entropy with established protein interaction network.

Identifying critical state of complex diseases by single-sample Kullback-Leibler divergence.

Background: Developing effective strategies for signaling the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration, is urgently needed because such state usually followed by a catastrophic transition into a worse stage of disease. However, it is a challenging task to identify such pre-disease state or tipping point in clinics, where only one single sample is available and thus results in the failure of most statistic approaches.

Methods: In this study, we presented a single-sample-based computational method to detect the early-warning signal of critical transition during the progression of complex diseases.

Magnetic Targeting Improves the Therapeutic Efficacy of Microbubble-Mediated Obstructive Thrombus Sonothrombolysis.

Background:  Magnetic targeting may help microbubbles (MBs) reach obstructive thrombi and improve the efficacy of MB-mediated sonothrombolysis, but the role of magnetic targeting in MB-mediated sonothrombolysis remains elusive.

Objectives:  We investigate the feasibility and efficacy of magnetically targeted MB-mediated sonothrombolysis for the treatment of obstructive thrombi.

Materials And Methods:  Red and white thromboembolic models were established in vitro and in vivo.

The pseudogene PTENP1 regulates smooth muscle cells as a competing endogenous RNA.

The long non-coding RNA (lncRNA) PTENP1 is a pseudogene of phosphatase and tensin homologue deleted on chromosome ten (PTEN), has been implicated in smooth muscle cell (SMC) proliferation and apoptosis. PTENP1 is the pseudogene of PTEN. However, it is unclear whether and how PTENP1 functions in the proliferation and apoptosis of human aortic SMCs (HASMCs).