Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets. We present DSFGNN, a novel graph neural network framework addressing these issues for improved MDD diagnosis. DSFGNN employs a graph isomorphism encoder to model static and dynamic brain networks, achieving effective fusion of temporal and spatial information through a spatiotemporal attention mechanism, thereby enhancing interpretability. Furthermore, we incorporate a causal disentangling module and orthogonal regularization module to augment the model's expressiveness. We evaluate DSFGNN on the Rest-meta-MDD dataset, yielding superior results compared to the best baseline. Besides, extensive ablation studies and interpretability analysis confirm DSFGNN's effectiveness and potential for biomarker discovery.
BACKGROUND: The influence of the microbiota on hypoglycemic agents is becoming more apparent. The effects of metformin, a primary anti-diabetes drug, on gut microbiota are still not fully understood. RESEARCH DESIGN AND METHODS: This prospective cohort study aims to investigate the longitudinal effects of metformin on the gut microbiota of 25 treatment-naïve diabetes patients, each receiving a daily dose of 1500 mg. Microbiota compositions were analyzed at baseline, and at 1, 3, and 6 months of medication using 16S rRNA gene sequencing. RESULTS: Prior to the 3-month period of metformin treatment, significant improvements were noted in body mass index (BMI) and glycemic-related parameters, such as fasting blood glucose (FPG) and hemoglobin A1c (HbA1c), alongside homeostasis model assessment indices of insulin resistance (HOMA-IR). At the 3-month mark of medication, a significant reduction in the α-diversity of the gut microbiota was noted, while ß-diversity exhibited no marked variances throughout the treatment duration. The Firmicutes to Bacteroidetes ratio. markedly decreased. Metformin treatment consistently increased Escherichia-Shigella and decreased Romboutsia, while Pseudomonas decreased at 3 months. Fuzzy c-means clustering identified three longitudinal trajectory clusters for microbial fluctuations: (i) genera temporarily changing, (ii) genera continuing to decrease (Bacteroides), and (iii) genera continuing to increase(Lachnospiraceae ND3007 group, [Eubacterium] xylanophilum group, Romboutsia, Faecalibacterium and Ruminococcaceae UCG-014). The correlation matrix revealed associations between specific fecal taxa and metformin-related clinical parameters HbA1c, FPG, Uric Acid (UA), high-density lipoproteincholesterol (HDL-C), alanine aminotransferase (ALT), hypersensitive C-reactive protein (hs-CRP), triglyceride (TG) (P < 0.05). Metacyc database showed that metformin significantly altered 17 functional pathways. Amino acid metabolism pathways such as isoleucine biosynthesis predominated in the post-treatment group. CONCLUSIONS: Metformin's role in glucose metabolism regulation may primarily involve specific alterations in certain gut microbial species rather than an overall increase in microbial species diversity. This may suggest gut microbiota targets in future studies on metabolic abnormalities caused by metformin.
Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.
Electroencephalography , Adult , Humans , Male , Electrocardiography , Neuroimaging
Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA-disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA-disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA-disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN.
Piwi-Interacting RNA
Lung cancer poses a global health challenge, necessitating advanced diagnostics for improved outcomes. Intensive efforts are ongoing to pinpoint early detection biomarkers, such as genomic variations and DNA methylation, to elevate diagnostic precision. We conducted long-read sequencing on cancerous and adjacent non-cancerous tissues from a patient with lung adenocarcinoma. We identified somatic structural variations (SVs) specific to lung cancer by integrating data from various SV calling methods and differentially methylated regions (DMRs) that were distinct between these two tissue samples, revealing a unique methylation pattern associated with lung cancer. This study discovered over 40,000 somatic SVs and over 180,000 DMRs linked to lung cancer. We identified approximately 700 genes of significant relevance through comprehensive analysis, including genes intricately associated with many lung cancers, such as NOTCH1, SMOC2, CSMD2, and others. Furthermore, we observed that somatic SVs and DMRs were substantially enriched in several pathways, such as axon guidance signaling pathways, which suggests a comprehensive multi-omics impact on lung cancer progression across various biological investigation levels. These datasets can potentially serve as biomarkers for early lung cancer detection and may hold significant value in clinical diagnosis and treatment applications.
Adenocarcinoma of Lung , Lung Neoplasms , Humans , Lung Neoplasms/genetics , Lung Neoplasms/diagnosis , DNA Methylation/genetics , Adenocarcinoma of Lung/genetics , Oligonucleotide Array Sequence Analysis , Biomarkers
The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug-target interactions (DTIs) and drug-drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein-protein interactions prediction, ablation studies and case studies further validate its effectiveness.
Learning , Pattern Recognition, Automated , Drug Discovery , Area Under Curve , Neural Networks, Computer , Drug Interactions
Background: Radiography has a low level of radiation exposure while providing valuable information. Due to its cost effectiveness and widespread availability, the preoperative radiographic imaging examination is a valuable approach for assessing patients with spinal disease. This study aimed to examine the influence of preoperative X-ray evaluation on the surgical treatment of patients with single- or multi-level lumbar degenerative disease (LDD). Methods: A retrospective cohort analysis was conducted of 172 patients diagnosed with LDD who underwent transforaminal lumbar interbody fusion (TLIF) or posterior lumbar interbody fusion (PLIF) surgery between December 2021 and February 2023 at the Shanghai Changzheng Hospital. Various parameters were measured on preoperative radiographs, including the iliac crest height, median iliac angle (MIA), lumbar lordosis (LL), intervertebral facet joint degeneration, lumbosacral angle (LSA), intervertebral foramen height (IFH), and surgical segment. The surgical treatment was evaluated based on the operative time, intraoperative blood loss, and postoperative complications. A correlation analysis and independent sample t-tests were used to assess the relationship between preoperative radiographic variables and surgical treatments. Further, a multivariate linear regression analysis was employed to identify the risk factors affecting the clinical outcomes. Results: The correlation analysis and t-test results showed that the MIA, height of the iliac crest, intervertebral facet joint degeneration, and surgical segment were significantly correlated with the surgical treatments (P<0.05). Specifically, the height of the iliac crest, intervertebral facet joint degeneration, and surgical segment were positively correlated with the surgical treatments. Conversely, the MIA was negatively correlated with the surgical treatments. However, no significant differences were observed between the IFH, LSA, and LL in relation to posterior lumbar surgery (P>0.05). The multiple linear regression analysis showed that the height of the iliac crest, MIA, intervertebral facet joint degeneration, and surgical segment were independent factors affecting the surgical treatments of patients with single- or multi-level LDD. These findings highlight the importance of considering these factors when planning and performing lumbar surgery. Conclusions: The measurements taken from radiographs, including the height of the iliac crest, MIA, intervertebral facet joint degeneration, and surgical segment, demonstrate potential influences on the treatment of single- and multi-level lumbar spine surgery. These variables can be captured in plain film imaging and can provide valuable insights into the surgical procedure and offer guidance for the operation. By analyzing these radiographic measurements, surgeons can gain a better understanding of a patient's condition and tailor the surgical approach accordingly, thus optimizing the outcomes of the surgery.
Stable communication technologies in complex waters are a prerequisite for underwater operations. Underwater acoustic communication is susceptible to multipath interference, while underwater optical communication is susceptible to environmental impact. The underwater electric field communication established based on the weak electric fish perception mechanism is not susceptible to environmental interference, and the communication is stable. It is a new type of underwater communication technology. To address issues like short communication distances and high bit error rates in existing underwater electric field communication systems, this study focuses on underwater electric field communication systems based on direct sequence spread spectrum (DSSS) and binary phase shift keying (BPSK) modulation techniques. To verify the feasibility of the established spread spectrum electric field communication system, static communication experiments were carried out in a swimming pool using the DSSS-based system. The experimental results show that in fresh water with a conductivity of 739 µS/cm, the system can achieve underwater current electric field communication within a 11.2 m range with 10-6 bit errors. This paper validates the feasibility of DSSS BPSK in short-range underwater communication, and compact communication devices are expected to be deployed on underwater robots for underwater operations.
Depression is a common mental disorder caused by the interaction of social, psychological, and biological factors. Treatments include psychotherapy, pharmacotherapy, and other therapies, but they have limitations. Particularly, the COVID-19 pandemic may have a negative impact on depressed people. Thus, developing more potential treatments for depression has currently been an urgent challenge. A growing number of studies have found that acupressure is effective in relieving the symptoms of depression. Thus, this study aimed to evaluate the efficacy and safety of acupressure in people with depression. English (PubMed, CENTRAL, EMBASE, APA PsycINFO, and CINAHL) and Chinese databases (CBM, CNKI, Wanfang, and VIP), ClinicalTrials.gov and Chinese Clinical Trial Registry were searched for randomized controlled trials (RCTs) on patients diagnosed with depression from study inception until March 31, 2023. Studies that compared acupressure with sham acupressure, conventional treatments (i.e., medication, usual care, etc.), and acupressure as an adjunct to conventional treatment for depression were included. The primary outcome was depression level measured using the Hamilton Depression Scale, Self-Rating Depression Scale, or Geriatric Depression Scale. A total of 19 RCTs involving 1686 participants were included. The pooled results showed that acupressure exhibited a significant beneficial effect on reducing the severity of depression compared with sham acupressure and served as an adjunct to conventional treatment, although the evidence level was moderate. Thus, acupressure may be a potential treatment for depression.
Acupressure , Acupuncture Therapy , Psychotic Disorders , Humans , Aged , Depression/therapy , Psychotherapy/methods , Acupuncture Therapy/methods
ETHNOPHARMACOLOGICAL RELEVANCE: This study explores the potential therapeutic benefits of using a three-component DGR (composed of specific compounds) to target the NLRP3 inflammasome in the context of non-alcoholic fatty liver disease (NAFLD). AIM OF THE STUDY: To assess the impact of a three-component DGR on NAFLD, specifically examining its effects on liver lipid accumulation, inflammation, and the diversity of intestinal microbial communities. METHODS: NAFLD was induced in 8-week-old Sprague Dawley rats by feeding them a high-fat emulsion diet every morning for 8 consecutive weeks. Oral administration of DGR or its constituent equivalents in the afternoon. The pharmacological effects of DGR were evaluated using H&E, ORO and ELISA methods to determine the changes in serum and liver tissue indexes of rat-models. Immunohistochemical staining and Western blot were used to assess the interaction between DGR, NLRP3 and IL-1ß. RESULTS: The induction of NAFLD resulted in elevated hepatic triglycerides (TG), total cholesterol (TC), and free fatty acids (FFA). However, these alterations were ameliorated upon administration of DGR. It is noteworthy that DGR exhibited superior efficacy in comparison to its constituent compounds, manifesting augmented antioxidant activity, diminished hepatic damage, and the attenuation of pro-inflammatory factors. Both DGR and its individual monomeric constituents exhibited the capacity to attenuate the activation of the NLRP3 inflammasome in the liver, leading to an amelioration of the pathological characteristics associated with NAFLD. An analysis of the intestinal flora unveiled an elevated abundance of p_Firmicutes (1.1-fold), p_Cyanobacteria (5.76-fold), and p_Verrucomicrobia (5.2-fold), accompanied by a heightened p_Firmicutes to p_Bacteroidetes ratio (5.49-fold). CONCLUSIONS: In the non-alcoholic fatty liver disease (NAFLD) rat model, the concurrent administration of three-component DGR effectively regulated lipid deposition, suppressed liver inflammation, and restored balance in the intestinal flora, thereby improving NAFLD pathology. These findings propose a promising therapeutic strategy for NAFLD, centered on inhibiting the NLRP3 inflammasome through the use of the three-component DGR.
Anthraquinones , Coumarins , Iridoids , Non-alcoholic Fatty Liver Disease , Rats , Animals , Non-alcoholic Fatty Liver Disease/drug therapy , Non-alcoholic Fatty Liver Disease/pathology , NLR Family, Pyrin Domain-Containing 3 Protein , Inflammasomes , Rats, Sprague-Dawley , Disease Models, Animal , Liver , Inflammation/pathology , Diet, High-Fat , Fatty Acids, Nonesterified
Liquid-liquid phase separation (LLPS) is a common stimulus-responsive phenomenon widely studied and applied in constructing intelligent systems such as microfluidic valves, smart windows, and biosensors. However, LLPS in an aqueous solution has limited applicability confined to a narrow temperature range within 0-100 °C. In addition, for easy exploitation of thermoresponsive behavior, phase separation must be stable and accurately predictable under varying conditions. This study proposes a gel system exhibiting UCST phase behavior using ionic liquids (ILs) and hydrophilic polymers, whose phase transition temperature can be linearly tuned within a wide range (from subzero to over 100 °C) by varying the mixing ratio of ILs in their blends. Similar to the mixing of ILs with structurally similar cations, mixing ILs containing different anions proved to be an effective ideal random mixing method based on experimental results and molecular dynamics simulations. This mixing mechanism of ILs accounts for the linear regulation of the UCST of the ionogels when the mixing ratio of ILs in their blends varies. Moreover, the unique feature of ILs was further demonstrated using other hydrophilic polymer networks and multiple combinations of ILs, suggesting the generality of this strategy for UCST regulation in the ionogels.
Rapid developments in organic electronics demand highly conductive and freestanding (PEDOT:PSS) films. However, the synthesis of highly conductive PEDOT:PSS films requires toxic reagents, such as high-concentration acids and bases. Herein, an eco-friendly and cost-effective strategy is reported for improving the conductivity of PEDOT:PSS films through the confinement of ice crystals. The crystallization of water swelled by the film facilitated the phase separation of PEDOT and PSS, and the excess PSS in the skin layer is effectively removed. Moreover, under the confinement effect, the carrier mobility of the film is enhanced through the formation of a well-crystallized PEDOT molecular morphology. A detailed elucidation of aggregate structure evolution in PEDOT:PSS films during annealing, solvent post-treatment, and subsequent confined crystallization is presented herein. After multiple water crystallization cycles, the conductivity of the PEDOT:PSS film increased by over 85%, achieving a maximum of 2564 ± 142 S cm-1 . Finally, compared to post-treatment with dimethyl sulfoxide (DMSO), the current strategy can improve the Seebeck coefficient by 5.6% and the power factor by 139%.
Although highly infectious respiratory viral infections spread rapidly, humans have evolved a precise and complex immune mechanism to deal with respiratory viruses, with strong intrinsic, highly adaptive and specific humoral and cellular immunity. At the same time, vaccination against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is one of the most cost-effective and efficient means of preventing morbidity, severe illness, and death from Coronavirus disease 2019 (COVID-19). As the global epidemic of COVID-19 continues to evolve and vaccines are being developed, it is important to conduct studies on immunization strategies to optimize vaccination strategies when appropriate. This review was conducted to investigate the relationship between the immune response and the protective effect of different vaccination scenarios (including booster, sequential and hybrid immunity), and to provide a basis for the optimization of vaccination strategies and the development of new vaccines in the future.
COVID-19 , Viral Vaccines , Humans , COVID-19 Vaccines , COVID-19/prevention & control , SARS-CoV-2 , Vaccination , Immunity, Cellular , Antibodies, Viral
Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https://github.com/yrjia1015/THItoGene.
Carcinoma, Squamous Cell , Deep Learning , Skin Neoplasms , Humans , Artificial Intelligence , Gene Expression Profiling
Background: Research has shown that stigmatization of professional psychological help-seeking is an important factor influencing attitudes toward seeking professional psychological help (ATSPPH). However, how perceived social support (PSS) and optimism have a mediating role is not clear. Objective: Examine the associations between ATSPPH, self-stigmatization of seeking help, PSS, and optimism in a cohort of Chinese high-school students (HSSs). Methods: An offline survey was conducted in three high schools in Chongqing (China) from 20 February to 20 May 2023. Participants were HSSs recruited through their teachers. A total of 2,159 HSSs completed a survey on demographic information as well as the Self-Stigmatization of Seeking Help (SSOSH) score, ATSPPH, Perceived Social Support Scale (PASS), and Life Orientation Test (LOT). Mediation analyses were conducted using the "Process" macro in SPSS 26.0 to estimate the direct and indirect effects of self-stigmatization of seeking psychological help on ATSPPH. Results: Self-stigmatization of seeking psychological help was significantly and negatively related to ATSPPH among HSSs. Self-stigmatization of psychological help-seeking influenced ATSPPH through three pathways: (a) separate mediating effect of PSS (effect = -0.029); (b) separate mediating effect of optimism (effect = -0.069); (c) chain-mediating effect of PSS and optimism (effect = -0.017). These data suggested that self-stigmatization of psychological help-seeking could influence ATSPPH directly and indirectly through PSS and optimism. Conclusion: PSS and optimism mediated the relationship between self-stigmatization of seeking help and ATSPPH. Improving the ability of HSSs to perceive social support and cultivating optimism could help improve the self-stigmatization of help-seeking and promote a positive attitude toward professional help-seeking.
INTRODUCTION: Since the emergence of SARS-CoV-2 viruses, multiple mutant strains have been identified. Infection with SARS-CoV-2 virus leads to alterations in host cell phosphorylation signal, which systematically modulates the immune response. METHOD: Identification and analysis of SARS-CoV-2 virus infection phosphorylation sites enable insight into the mechanisms of viral infection and effects on host cells, providing important fundamental data for the study and development of potent drugs for the treatment of immune inflammatory diseases. In this paper, we have analyzed the SARS-CoV-2 virus-infected phosphorylation region and developed a transformer-based deep learning-assisted identification method for the specific identification of phosphorylation sites in SARS-CoV-2 virus-infected host cells. RESULT: Furthermore, through association analysis with lung cancer, we found that SARS-CoV-2 infection may affect the regulatory role of the immune system, leading to an abnormal increase or decrease in the immune inflammatory response, which may be associated with the development and progression of cancer. CONCLUSION: We anticipate that this study will provide an important reference for SARS-CoV-2 virus evolution as well as immune-related studies and provide a reliable complementary screening tool for anti-SARS-CoV-2 virus drug and vaccine design.
The development of a universal influenza vaccine to elicit broad immune responses is essential in reducing disease burden and pandemic impact. In this study, the mosaic vaccine design strategy and genetic algorithms were utilized to optimize the seasonal influenza A virus (H1N1, H3N2) hemagglutinin (HA) and neuraminidase (NA) antigens, which also contain most potential T-cell epitopes. These mosaic immunogens were then expressed as virus-like particles (VLPs) using the baculovirus expression system. The immunogenicity and protection effectiveness of the mosaic VLPs were compared to the commercial quadrivalent inactivated influenza vaccine (QIV) in the mice model. Strong cross-reactive antibody responses were observed in mice following two doses of vaccination with the mosaic VLPs, with HI titers higher than 40 in 15 of 16 tested strains as opposed to limited cross HI antibody levels with QIV vaccination. After a single vaccination, mice also show a stronger level of cross-reactive antibody responses than the QIV. The QIV vaccinations only elicited NI antibodies to a small number of vaccine strains, and not even strong NI antibodies to its corresponding vaccine components. In contrast, the mosaic VLPs caused robust NI antibodies to all tested seasonal influenza virus vaccine strains. Here, we demonstrated the mosaic vaccines induces stronger cross-reactive antibodies and robust more T-cell responses compared to the QIV. The mosaic VLPs also provided protection against challenges with ancestral influenza A viruses of both H1 and H3 subtypes. These findings indicated that the mosaic VLPs were a promising strategy for developing a broad influenza vaccine in future.
Human brucellosis is one of the world's most common zoonoses, caused by Brucella infection and characterized by induced inflammation, which in severe cases can lead to abortion and sterility in humans and animals. There is growing evidence that traditional Chinese medicine (TCM) is beneficial as an adjunct to the treatment of brucellosis. However, its specific targets of action and molecular mechanisms remain unclear. In this study, a systematic pharmacological approach was applied to demonstrate pharmacological targets, biological functions, and signaling pathways of TCM as an adjunct to the treatment of brucellosis (TCMTB). The results of network pharmacology were further verified by in vitro experiments. Network analysis revealed that 133 active ingredients and 247 targets were screened in TCMTB. Further data analysis identified 21 core targets and 5 core compounds in TCMTB, including beta-sitosterol, quercetin, kaempferol, luteolin, and paeoniflorin. Gene ontology and the Kyoto Encyclopedia of Gene and Genome analysis showed that TCMTB might actively treat brucellosis by regulating inflammatory response, enhancing immune function, and targeting signaling pathways such as tuberculosis and TNF. Molecular docking results showed that multiple compounds could bind to multiple targets. Further, in vitro experiments confirmed that quercetin, among the active compounds screened, induced the strongest immunomodulatory and pro-inflammatory cytokine production during Brucella abortus infection. Further, quercetin induced nitric oxide production, which attenuated the ability of B. abortus to internalize THP-1 cells as well as intracellular survival. This study reveals the mechanism by which TCMTB aids in the treatment of brucellosis through a synergistic multicomponent, multipathway, and multitarget action. The contribution of quercetin treatment to B. abortus infection was demonstrated for the first time, which may be related to the quercetin-induced production of nitric oxide and immunomodulatory and inflammatory cytokines. These predictions of the core compounds and targets may be used in the future for the clinical treatment of brucellosis.
Background: Preoperative magnetic resonance imaging (MRI) can clearly show the location and level of disc herniation. When the symptoms are consistent with the Prominent segments, surgical treatment can be indicated. However, the varied extents of the protruding masses in cervical disc herniation (CDH) have been rarely reported. This study aimed to characterize the severity of CDH and to develop a reproducible grading and zoning system for cervical disc degeneration. Methods: A total of 200 patients who presented with single CDH and underwent MRI/computed tomography (CT) scans were enrolled in this prospective study between 2018 and 2021. A total of 170 cervical discs were graded according to MRI by 3 spine surgeons in a blinded fashion. CDHs were graded 1-3, with regions A-C. All patients with grade 1 and mild C symptoms were excluded. The foramen facet spinal (FFS) classification based on MRI Japanese Orthopedic Association (JOA) scores and the incidence of complications were evaluated and analyzed, and follow-up outcomes were assessed. Results: Areas 2-A, 2-B, and 1-C had high motor function scores, areas 2-A, 3-A, and 2-AB had high sensory scores, but areas 3-AB and 3-A had low bladder function scores. Areas 3-AB had the most severe symptoms and the lowest scores. Area 1-C showed neurogenic abnormal sensation and higher visual analog scale (VAS) scores. A good/excellent outcome as indicated by the JOA score was 94.70% at 3 months and 92.35% at 1 year in 170 patients. The complication rate was 9.41%. The diagnostic coefficient of the FFS classification was 0.888, P<0.001. Conclusions: The FFS classification is an objective scoring system that can be applied similarly by multiple examiners and is correlated with clinical symptoms.
Background: Radiography has low radiation exposure and the ability to acquire information. Due to its cost-effectiveness and availability, preoperative radiographic imaging examination is considered to be a valuable method to evaluate the condition of patients with spinal disease. The aim of this cohort study is to analyze the impact of evaluating preoperative X-rays on the surgical management of lumbar degenerative diseases (LDD). Methods: We reviewed 49 patients with LDD underwent single-level posterior instrumented lumbar fusion (PILF) between November 2017 and October 2022 in this cohort study. The median iliac angle (MIA), iliac crest height, intervertebral facet joint degeneration, lumbosacral angle (LSA), L5/S1 intervertebral space angle (ISA), intervertebral foramen height (IFH) and intervertebral space height (ISH) were measured on preoperative radiographs. In addition, operative time, intraoperative blood loss and postoperative complications were used to evaluate the surgical management. Correlation analysis was used to determine the correlation between preoperative radiographic presentation and surgical managements. Multivariate linear regression analysis was used for determination of risk factors for surgical management. Results: Correlation analysis showed that the median iliac angle, height of iliac crest, lumbosacral angle and intervertebral facet joint degeneration were significantly correlated with surgical managements (P<0.05). Height of iliac crest, intervertebral facet joint degeneration and lumbosacral angle were positively correlated with surgical management. Meanwhile, MIA was negatively correlated with surgical management. No significant difference was found between the IFH, ISA, ISH and surgical managements in posterior lumbar surgery (P>0.05). After multiple linear regression analysis, height of iliac crest, median iliac angle and intervertebral facet joint degeneration were independent influence factors for the single-level lumbar surgical managements. Conclusions: Some variables measured in radiograph shows that height of iliac crest, median iliac angle and intervertebral facet joint degeneration have a potential influence on surgical managements. The lumbosacral angle was positively associated with surgical management, but it was not statistically significant in multiple linear regression analysis (P>0.05). The above measurements in plain film can reflect the surgical procedure and have some guiding implications for the operation.
