In the process of maintaining the steady state of bone tissue, the transcription network and signal pathway of the body play a vital role. These complex regulatory mechanisms need precise coordination to ensure the balance between bone formation and bone absorption. Once this balance is broken, it may lead to pathological changes of bone and cartilage, and then lead to various bone diseases. Therefore, it is of great significance to understand these regulatory mechanisms for the prevention and treatment of bone diseases. In recent years, with the deepening of research, more and more lncRNA has been found to be closely related to bone health. Among them, nuclear paraspeckle assembly transcript 1 (NEAT1), as an extremely abundant RNA molecule in mammalian nuclei, has attracted extensive attention. NEAT1 is mainly transcribed from a specific site in human chromosome 11 by RNA polymerase II (RNaseP), which can form two different subtypes NEAT1_1 and NEAT1_2. These two subtypes are different in intracellular distribution and function, but they participate in many biological processes together. Studies have shown that NEAT1 plays a specific role in the process of cell growth and stress response. For example, it can regulate the development of osteoblasts (OB), osteoclasts (OC) and chondrocytes by balancing the differentiation of bone marrow mesenchymal stem cells (BMSCs), thus maintaining the steady state of bone metabolism. This discovery reveals the important role of NEAT1 in bone development and remodeling. In addition, NEAT1 is closely related to a variety of bone diseases. In patients with bone diseases such as osteoporosis (OP), osteoarthritis (OA) and osteosarcoma (OS), the expression level of NEAT1 is different. These differential expressions may be closely related to the pathogenesis and progression of bone diseases. By regulating the level of NEAT1, it can affect a variety of signal transduction pathways, and then affect the development of bone diseases. For example, some studies show that by regulating the expression level of NEAT1, the activity of osteoclasts can be inhibited, and the proliferation and differentiation of osteoblasts can be promoted, thus improving the symptoms of osteoporosis. It is worth noting that NEAT1 can also be used as a key sensor for the prevention and treatment of bone diseases. When exercising or receiving some natural products, the expression level of NEAT1 will change, thus reflecting the response of bones to external stimuli. This feature makes NEAT1 an important target for studying the prevention and treatment strategies of bone diseases. However, although the role of NEAT1 in bone biology and bone diseases has been initially recognized, its specific mechanism and regulatory relationship are still controversial. For example, the expression level, mode of action and interaction with other molecules of NEAT1 in different bone diseases still need further in-depth study. This paper reviews the role of NEAT1 in maintaining bone and cartilage metabolism, and discusses its expression and function in various bone diseases. By combing the existing research results and controversial points, this paper aims to provide new perspectives and ideas for the prevention and treatment of bone diseases, and provide useful reference and enlightenment for future research.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Fibrosis, the pathological scarring of vital organs, is a severe and often irreversible condition that leads to progressive organ dysfunction. It is particularly pronounced in organs like the liver, kidneys, lungs, and heart. Despite its clinical significance, the full understanding of its etiology and complex pathogenesis remains incomplete, posing substantial challenges to diagnosing, treating, and preventing the progression of fibrosis. Among the various molecular players involved, platelet-derived growth factor-C (PDGF-C) has emerged as a crucial factor in fibrotic diseases, contributing to the pathological transformation of tissues in several key organs. PDGF-C is a member of the PDGFs family of growth factors and is synthesized and secreted by various cell types, including fibroblasts, smooth muscle cells, and endothelial cells. It acts through both autocrine and paracrine mechanisms, exerting its biological effects by binding to and activating the PDGF receptors (PDGFRs), specifically PDGFRα and PDGFRβ. This binding triggers multiple intracellular signaling pathways, such as JAK/STAT, PI3K/AKT and Ras-MAPK pathways. which are integral to the regulation of cell proliferation, survival, migration, and fibrosis. Notably, PDGF-C has been shown to promote the proliferation and migration of fibroblasts, key effector cells in the fibrotic process, thus accelerating the accumulation of extracellular matrix components and the formation of fibrotic tissue. Numerous studies have documented an upregulation of PDGF-C expression in various fibrotic diseases, suggesting its significant role in the initiation and progression of fibrosis. For instance, in liver fibrosis, PDGF-C stimulates hepatic stellate cell activation, contributing to the excessive deposition of collagen and other extracellular matrix proteins. Similarly, in pulmonary fibrosis, PDGF-C enhances the migration of fibroblasts into the damaged areas of lungs, thereby worsening the pathological process. Such findings highlight the pivotal role of PDGF-C in fibrotic diseases and underscore its potential as a therapeutic target for these conditions. Given its central role in the pathogenesis of fibrosis, PDGF-C has become an attractive target for therapeutic intervention. Several studies have focused on developing inhibitors that block the PDGF-C/PDGFR signaling pathway. These inhibitors aim to reduce fibroblast activation, prevent the excessive accumulation of extracellular matrix components, and halt the progression of fibrosis. Preclinical studies have demonstrated the efficacy of such inhibitors in animal models of liver, kidney, and lung fibrosis, with promising results in reducing fibrotic lesions and improving organ function. Furthermore, several clinical inhibitors, such as Olaratumab and Seralutinib, are ongoing to assess the safety and efficacy of these inhibitors in human patients, offering hope for novel therapeutic options in the treatment of fibrotic diseases. In conclusion, PDGF-C plays a critical role in the development and progression of fibrosis in vital organs. Its ability to regulate fibroblast activity and influence key signaling pathways makes it a promising target for therapeutic strategies aiming at combating fibrosis. Ongoing research into the regulation of PDGF-C expression and the development of PDGF-C/PDGFR inhibitors holds the potential to offer new insights and approaches for the diagnosis, treatment, and prevention of fibrotic diseases. Ultimately, these efforts may lead to the development of more effective and targeted therapies that can mitigate the impact of fibrosis and improve patient outcomes.

In recent years, the prevalence of diabetes has continued to rise, with diabetes mellitus type 2 (T2DM) being the most common form. T2DM is characterized by chronic low-grade inflammation and disruptions in insulin metabolism. Toll-like receptor 4 (TLR4) is a key pattern recognition receptor that, upon activation, upregulates pro-inflammatory cytokines via the nuclear factor κB (NF‑κB) pathway, thereby contributing to the pathogenesis of T2DM. Peripheral 5-hydroxytryptamine (5-HT), primarily synthesized by enterochromaffin (EC) cells in the gut, interacts with 5-hydroxytryptamine receptors (5-HTRs) in key insulin-target tissues, including the liver, adipose tissue, and skeletal muscle. This interaction influences hepatic gluconeogenesis, fat mobilization, and the browning of white adipose tissue. Elevated peripheral 5-HT levels may disrupt glucose and lipid metabolism, thereby contributing to the onset and progression of T2DM. Within mitochondria, 5-HT undergoes degradation and inactivation through the enzymatic action of monoamine oxidase A (MAO-A), leading to the generation of reactive oxygen species (ROS). Excessive ROS production and accumulation can induce oxidative stress, which may further contribute to the pathogenesis of T2DM. Platelets serve as the primary reservoir for5-HT in the bloodstream. The activation of the TLR4 signaling pathway on the platelet surface, coupled with reduced expression of the 5-HT transporter on the cell membrane, leads to elevated serum 5-HT levels, potentially accelerating the progression of T2DM. Therefore, inhibition of TLR4 and reduction of peripheral 5-HT levels could represent promising therapeutic strategies for T2DM. This review explores the synthesis, transport, and metabolism of peripheral 5-HT, as well as its role in TLR4-mediated T2DM, with the aim of providing novel insights into the clinical diagnosis, treatment, and evaluation of T2DM.

Objective: This study aimed to elucidate the hand function recovery capacity of degenerative cervical myelopathy (DCM) patients with different severities of hand dexterity impairment. Methods: Hand functional outcome measures such as the 10-second grip and release (10s-G&R) test, modified Japanese Orthopaedic Association (mJOA) upper extremity score and Japanese Orthopaedic Association Cervical Myelopathy Evaluation Questionnaire (JOACMEQ) upper extremity function were collected before surgery and at the 1-year follow-up. A total of 102 DCM patients were categorized into mild, moderate and severe group based on the preoperative 10s-G&R test result. Hand functional parameters were compared across the 3 groups. Multivariate linear regression was conducted to explore predictive factors. Receiver operating characteristic curve analysis was performed to assess the predictive efficacy of the preoperative 10s-G&R test and establish the cutoff value for incomplete recovery of hand dexterity. Results: At the 1-year follow-up, significant improvements were observed in all hand functional parameters across all 3 groups. However, the incomplete recovery rates of the mild, moderate, severe groups were 26.67%, 46.88%, and 57.50%, respectively (p < 0.05). Multivariate regression revealed that preoperative 10s-G&R test result, age, Hoffmann sign, duration of symptom, and mJOA Upper score serve as significant predictors for postoperative 10s-G&R test outcomes. Patients with a preoperative 10s-G&R test < 15 cycles have a 1.9 times higher risk of incomplete recovery of hand function (p = 0.005). Conclusion Most patients, regardless of their preoperative hand function, exhibit potential for improvement in hand dexterity. However, worse initial hand dexterity correlates with poorer outcomes. Surgical treatment is recommended before the 10s-G&R test drops below 15 cycles.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Background/Aims: Distal mean nocturnal baseline impedance (MNBI) measuring via pH-impedance may be valuable in diagnosing patients with suspected laryngopharyngeal reflux (LPR). However, its wide adoption is hindered by cost and invasiveness. This study investigates whether baseline impedance measured during high-resolution impedance manometry (HRIM-BI) can predict pathological MNBI. Methods: A cross-sectional study in Taiwan included 74 subjects suspected of LPR, who underwent HRIM (MMS) and pH-impedance testing (Diversatek), after stopping proton pump inhibitors for more than 7 days. Subjects with grade C or D esophagitis or Barrett’s esophagus were excluded. The cohort was divided into 2 groups: those with concomitant typical reflux symptoms (CTRS, n = 28) and those with isolated LPR symptoms (ILPRS, n = 46). HRIM-BI measurements focused on both distal and proximal esophagi. Pathological MNBI was identified as values below 2065 Ω, measured 3 cm above the lower esophageal sphincter. Results: In all subjects, distal HRIM-BI values correlated weakly with distal MNBI(r = 0.34-0.39, P < 0.005). However, in patients with ILPRS, distal HRIM-BI corelated moderately with distal MNBI(r = 0.43-0.48, P < 0.005). The areas under the receiver operating characteristic curve was 0.78 (P = 0.001) with a sensitivity of 0.83 and a specificity of 0.68. No correlation exists between distal HRIM-BI and distal MNBI in patients with CTRS, and between proximal HRIM-BI and proximal MNBI in both groups. Conclusions Distal HRIM-BI from HRIM may potentially predict pathological MNBI in patients with ILPRS, but not in those with CTRS. Future outcome studies linked to the metric are warranted.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.