Suppression of epileptic seizures by transcranial activation of K-selective channelrhodopsin.

Optogenetics is a valuable tool for studying the mechanisms of neurological diseases and is now being developed for therapeutic applications. In rodents and macaques, improved channelrhodopsins have been applied to achieve transcranial optogenetic stimulation. While transcranial photoexcitation of neurons has been achieved, noninvasive optogenetic inhibition for treating hyperexcitability-induced neurological disorders has remained elusive.

stHGC: a self-supervised graph representation learning for spatial domain recognition with hybrid graph and spatial regularization.

Advancements in spatial transcriptomics (ST) technology have enabled the analysis of gene expression while preserving cellular spatial information, greatly enhancing our understanding of cellular interactions within tissues. Accurate identification of spatial domains is crucial for comprehending tissue organization. However, the effective integration of spatial location and gene expression still faces significant challenges.

HK3: A potential prognostic biomarker with metastasis inhibition capabilities in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) stands as one of the prevalent malignant tumors worldwide. The effectiveness of immunotherapy frequently depends on the intricate dynamics of immunomodulation within the tumor microenvironment (TME). The current study aims to identify prognostically relevant genes and their functional roles in HCC.

Gegen Qinlian Decoction improves H1N1-induced viral pneumonia by modulating the "gut microbiota-metabolomics-immune/inflammation" axis.

Background And Aim: The acute pulmonary infectious disease caused by influenza viruses is known as influenza virus pneumonia (IVP). In recent years, Gegen Qinlian Decoction (GQD) has been widely used to treat pulmonary inflammation; however, the underlying mechanism of action of GQD in IVP remains unclear. This study aimed to elucidate the molecular mechanism through which GQD improved IVP.

Multi-immunometabolomics mining: NP prevents hyperimmune in ALI by inhibiting Leucine/PI3K/Akt/mTOR signaling pathway.

Acute lung injury (ALI) is currently a global health concern. Nicandra physalodes (L.) Gaertn.

Study on the Antidepressant Effect of Zhizichi Decoction by Regulating Metabolism and Intestinal Flora.

Background: The incidence of depression is increasing year by year, and Zhizichi Decoction（ZZCD）has shown significant efficiency in the clinical treatment of mild depression, but its mechanism of action is still unclear. In this research, network pharmacology and metagenomics combined and metabolomics were used as research methods to explain the scientific connotation of the antidepressant effect of ZZCD from the aspects of the overall effect of organisms and microbial structure and function.

Methods: The rat model of depression was established by chronic unpredictable mild stress (CUMS), and the improvement of depressive symptoms was evaluated by behavioral and histopathological methods.

Efficacy of rTMS in treating functional impairment in post-stroke patients: a systematic review and meta-analysis.

Background: Most stroke patients suffer from an imbalance in blood supply, which causes severe brain damage leading to functional deficits in motor, sensory, swallowing, cognitive, emotional, and speech functions. Repetitive transcranial magnetic stimulation (rTMS) is thought to restore functions impaired during the stroke process and improve the quality of life of stroke patients. However, the efficacy of rTMS in treating post-stroke function impairment varies significantly.

Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction.

Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations.

A study on table tennis landing point detection algorithm based on spatial domain information.

To address the limitations of computer vision-assisted table tennis ball detection, which heavily relies on vision acquisition equipment and exhibits slow processing speed, we propose a real-time calculation method for determining the landing point of table tennis balls. This novel approach is based on spatial domain information and reduces the dependency on vision acquisition equipment. This method incorporates several steps: employing dynamic color thresholding to determine the centroid coordinates of all objects in the video frames, utilizing target area thresholding and spatial Euclidean distance to eliminate interference balls and noise, optimizing the total number of video frames through keyframe extraction to reduce the number of operations for object recognition and landing point detection, and employing the four-frame difference slope method and polygonal area determination to detect the landing point and area of the target object, thereby obtaining precise coordinates and their corresponding areas.

Predicting circRNA-drug resistance associations based on a multimodal graph representation learning framework.

Circular RNA (circRNA) is a class of noncoding RNA that is highly conserved and exhibit exceptional stability. Due to its function as a microRNA sponge, circRNA has gained significant attention as an essential biomarker and potential drug target in the pathogenesis of several cancers. Although many circRNAs have been identified to play a role in cancer resistance, traditional methods are time-consuming and expensive.

Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods.

Background: Diagnostic models based on gene signatures of nasopharyngeal carcinoma (NPC) were constructed by random forest (RF) and artificial neural network (ANN) algorithms. Least absolute shrinkage and selection operator (Lasso)-Cox regression was used to select and build prognostic models based on gene signatures. This study contributes to the early diagnosis and treatment, prognosis, and molecular mechanisms associated with NPC.

Sphingosine-1-phosphate derived from PRP-Exos promotes angiogenesis in diabetic wound healing via the S1PR1/AKT/FN1 signalling pathway.

Background: Sphingosine-1-phosphate (S1P), a key regulator of vascular homeostasis and angiogenesis, is enriched in exosomes derived from platelet-rich plasma (PRP-Exos). However, the potential role of PRP-Exos-S1P in diabetic wound healing remains unclear. In this study, we investigated the underlying mechanism of PRP-Exos-S1P in diabetic angiogenesis and wound repair.

Association of intraoperative hypotension and severe postoperative complications during non-cardiac surgery in adult patients: A systematic review and meta-analysis.

Background: Intraoperative hypotension (IOH) is a common side effect of non-cardiac surgery that might induce poor postoperative outcomes. The relationship between the IOH and severe postoperative complications is still unclear. Thus, we summarized the existing literature to evaluate whether IOH contributes to developing severe postoperative complications during non-cardiac surgery.

Predicting Potential Drug-Disease Associations Based on Hypergraph Learning with Subgraph Matching.

The search for potential drug-disease associations (DDA) can speed up drug development cycles, reduce costly wasted resources, and accelerate disease treatment by repurposing existing drugs that can control further disease progression. As technologies such as deep learning continue to mature, many researchers tend to use emerging technologies to predict potential DDA. The performance of DDA prediction is still challenging and there is some space for improvement due to issues such as the small number of existing associations and possible noise in the data.

Canagliflozin promotes osteoblastic MC3T3-E1 differentiation AMPK/RUNX2 and improves bone microarchitecture in type 2 diabetic mice.

Individuals with type 2 diabetes mellitus (T2DM) have an increased risk of bone metabolic disorders and bone fracture due to disease progression and clinical treatment. The effect of sodium-glucose cotransporter 2 (SGLT2) inhibitors, now greatly prescribed for the treatment of T2DM, on bone metabolism is not clear. This study aimed to explore the possible influence of bone metabolic disorder and the underlying mechanism through a comparison of three different SGLT2 inhibitors (canagliflozin, dapagliflozin, and empagliflozin) in the treatment of type 2 diabetic mice.

ISLMI:Predicting lncRNA-miRNA Interactions Based on Information Injection and Second-Order Graph Convolution Network.

Studies have shown that IncRNA-miRNA interactions can affect cellular expression at the level of gene molecules through a variety of regulatory mechanisms and have important effects on the biological activities of living organisms. Several biomolecular network-based approaches have been proposed to accelerate the identification of lncRNA-miRNA interactions. However, most of the methods cannot fully utilize the structural and topological information of the lncRNA-miRNA interaction network.

Development of a novel combined nomogram model integrating Rad-score, age and ECOG to predict the survival of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization.

Background: Liver cancer is affecting more and more people's health. Transcatheter arterial chemoembolization (TACE) has become a routine treatment option, but the prognosis of patients is not optimistic. Effectively prediction of prognosis can provide clinicians with an objective basis for patient prognosis and timely adjustment of treatment strategies, thus improving the quality of patient survival.

GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs.

Motivation: CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs.

Effectiveness of aquatic exercise in lower limb osteoarthritis: a meta-analysis of randomized controlled trials.

The objective of this study was to evaluate the short-term and follow-up effectiveness of aquatic training on the health status of lower limb osteoarthritis. Randomized controlled trials (RCTs) on related topics were systematically searched in PubMed, Embase, Web of Science, the Cochrane Library, Physiotherapy Evidence Database (PEDro), the China National Knowledge Infrastructure and Wanfang databases from inception to January 2021. RevMan 5.

RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision.

Long non-coding RNAs (lncRNAs) play a regulatory role in many biological cells, and the recognition of lncRNA-protein interactions is helpful to reveal the functional mechanism of lncRNAs. Identification of lncRNA-protein interaction by biological techniques is costly and time-consuming. Here, an ensemble learning framework, RLF-LPI is proposed, to predict lncRNA-protein interactions.