Trigeminal nerve direct current stimulation causes sustained increase in neural activity in the rat hippocampus.

BACKGROUND: Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation method that can modulate many brain functions including learning and memory. Recent evidence suggests that tDCS memory effects may be caused by co-stimulation of scalp nerves such as the trigeminal nerve (TN), and not the electric field in the brain. The TN gives input to brainstem nuclei, including the locus coeruleus that controls noradrenaline release across brain regions, including hippocampus. However, the effects of TN direct current stimulation (TN-DCS) are currently not well understood. HYPOTHESIS: In this study we tested the hypothesis that stimulation of the trigeminal nerve with direct current manipulates hippocampal activity via an LC pathway. METHODS: We recorded neural activity in rat hippocampus using multichannel silicon probes. We applied 3 min of 0.25 mA or 1 mA TN-DCS, monitored hippocampal activity for up to 1 h and calculated spikes-rate and spike-field coherence metrics. Subcutaneous injections of xylocaine were used to block TN, while intraperitoneal and intracerebral injection of clonidine were used to block the LC pathway. RESULTS: We found that 1 mA TN-DCS caused a significant increase in hippocampal spike-rate lasting 45 min in addition to significant changes in spike-field coherence, while 0.25 mA TN-DCS did not. TN blockage prevented spike-rate increases, confirming effects were not caused by the electric field in the brain. When 1 mA TN-DCS was delivered during clonidine blockage no increase in spike-rate was observed, suggesting an important role for the LC-noradrenergic pathway. CONCLUSION: These results support our hypothesis and provide a neural basis to understand the tDCS TN co-stimulation mechanism. TN-DCS emerges as an important tool to potentially modulate learning and memory.

Guided isolation of enantiomeric lignans from Cimicifuga heracleifolia Kom. by antioxidant activity and molecular networking.

Under the guidance of antioxidant evaluation combined with molecular networking, six pairs of enantiomeric lignans including seven undescribed ones (1a, 2a/2b-4a/4b), along with five known analogs (1b, 5a/5b-6a/6b) were isolated from Cimicifuga heracleifolia Kom. Their structures were determined by extensive spectroscopic data analysis, including HRESIMS, 1D and 2D NMR, experimental and calculated ECD. All the enantiomeric isolates were evaluated for antioxidation by 2, 2-diphenyl-1-picrylhydrazyl (DPPH) and 2, 2'-azino-bis (3-ethyl-benzothiazoline-6-sulfonic acid) (ABTS) free radical scavenging tests. Compounds 1a and 3a/3b exhibited great DPPH and ABTS scavenging activities. The results are of great value for understanding structurally interesting enantiomeric lignans with antioxidant activity from C. heracleifolia in depth and providing its further development in functional evaluation and drug development.

Age and flexors as risk factors for cervical radiculopathy: A new machine learning method.

This study aimed to investigate the risk factors for cervical radiculopathy (CR) along with identifying the relationships between age, cervical flexors, and CR. This was a retrospective cohort study, including 60 patients with CR enrolled between December 2018 and June 2020. In this study, we measured C2 to C7 Cobb angle, disc degeneration, endplate degeneration, and morphology of paraspinal muscles and evaluated the value of predictive methods using receiver operating characteristic curves. Next, we established a diagnostic model for CR using Fisher discriminant model and compared different models by calculating the kappa value. Age and cervical flexor factors were used to construct clinical predictive models, which were further evaluated by C-index, receiver operating characteristic curve, calibration curve, and decision curve analysis. Multivariate analysis showed that age and cervical flexors were potential risk factors for CR, while the diagnostic model indicated that both exerted the best diagnostic effect. The obtained diagnostic equation was as follows: y1 = 0.33 × 1 + 10.302 × 2-24.139; y2 = 0.259 × 1 + 13.605 × 2-32.579. Both the C-index and AUC in the training set reached 0.939. Moreover, the C-index and AUC values in the external validation set reached 0.961. We developed 2 models for predicting CR and also confirmed their validity. Age and cervical flexors were considered potential risk factors for CR. Our noninvasive inspection method could provide clinicians with a more potential diagnostic value to detect CR accurately.

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms.

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model's performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients' average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.

A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea.

Purpose: Obstructive sleep apnea (OSA) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSA patients will improve the quality of patient management and prognosis, while the accuracy and feasibility of existing screening tools are not so satisfactory. The purpose of this study is to develop and validate a well-feasible clinical predictive model for screening potential severe OSA patients. Patients and Methods: We performed a retrospective cohort study including 1920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSA. Based on demography, symptoms, and hematological data, a multivariate logistic regression model was constructed and cross-validated and then a nomogram was developed to identify severe OSA. Moreover, we compared the performance of our model with the most commonly used screening tool, Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires. Results: Severe OSA was associated with male, BMI≥ 28 kg/m2, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥9.5×109/L, hemoglobin ≥175g/L, triglycerides ≥1.7 mmol/L. The AUC of the final model was 0.76 (95% CI: 0.74-0.78), with sensitivity and specificity under the optimal threshold selected by maximizing Youden Index of 73% and 66%. Among patients having the information of SBQ, the AUC of our model was statistically significantly greater than that of SBQ (0.78 vs 0.66, P = 0.002). Conclusion: Based on common clinical examination of admission, we develop a novel model and a nomogram for identifying severe OSA from inpatient with suspected OSA, which provides physicians with a visual and easy-to-use tool for screening severe OSA.

Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression.

BACKGROUND: HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination. METHODS: This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated. RESULTS: Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases. CONCLUSION: To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases.

Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery.

Objective: The objective of this study was to utilize machine learning techniques to analyze perioperative factors and identify blood glucose levels that can predict the occurrence of surgical site infection following posterior lumbar spinal surgery. Methods: A total of 4019 patients receiving lumbar internal fixation surgery from an institute were enrolled between June 2012 and February 2021. First, the filtered data were randomized into the test and verification groups. Second, in the test group, specific variables were screened using logistic regression analysis, Lasso regression analysis, support vector machine, and random forest. Specific variables obtained using the four methods were intersected, and a dynamic model was constructed. ROC and calibration curves were constructed to assess model performance. Finally, internal model performance was verified in the verification group using ROC and calibration curves. Results: The data from 4019 patients were collected. In total, 1327 eligible cases were selected. By combining logistic regression analysis with three machine learning algorithms, this study identified four predictors associated with SSI, namely Modic changes, sebum thickness, hemoglobin, and glucose. Using this information, a prediction model was developed and visually represented. Then, we constructed ROC and calibration curves using the test group; the area under the ROC curve was 0.988. Further, calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index of our model was 0.986 (95% CI 0.981-0.994). Finally, we used the validation group to validate the model internally; the AUC was 0.987. Calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index was 0.982 (95% CI 0.974-0.999). Conclusion: Logistic regression analysis and machine learning were employed to select four risk factors: Modic changes, sebum thickness, hemoglobin, and glucose. Then, a dynamic prediction model was constructed to help clinicians simplify the monitoring and prevention of SSI.

Epidemiological characteristics of ankylosing spondylitis in Guangxi Province of China from 2014 to 2021.

Introduction: To explore the epidemiological characteristics of ankylosing spondylitis (AS) in Guangxi Province of China through a large sample survey of more than 50 million aboriginal aboriginal population. Material and methods: A systematic search was conducted using the International Classification of Diseases 10 (ICD-10) codes M45.x00(AS), M45.x03+(AS with iridocyclitis), and M40.101(AS with kyphosis) to search the database in the National Health Statistics Network Direct Reporting System (NHSNDRS). 14004 patients were eventually included in the study. The parameters analyzed included the number of patients, gender, marriage, blood type, occupation, age at diagnosis, and location of household registration data each year, and statistical analysis was performed. Results: AS incidence rates increased from 1.30 (95% CI: 1.20-1.40) per 100,000 person-years in 2014 to 5.71 (95% CI: 5.50-5.92) in 2020 in Guangxi Province, and decreased slightly in 2021. Males have a higher incidence than females; the ratio was 5.61 : 1. The mean age of diagnosis in male patients was 45.4 (95% CI: 45.1-45.7) years, in females 47.6 (95% CI: 46.8-48.4) years. The most frequent blood type was O, and the most frequent occupation was farmer. The AS incidence rate was disparate in different cities. Liuzhou city had the highest eight-year average AS incidence rates from 2014 to 2021, and Chongzuo city had the lowest (p < 0.05). There was no significant difference in the incidence between different ethnic groups (p > 0.05). Conclusions: The AS person-years incidence rate was increasing in Guangxi province of China from 2014 to 2020, which had obvious gender and regional differences, showing the characteristics of local area aggregation.

Electroacupuncture with or without combined warm needling for tinnitus: Study protocol for a randomized, waitlist-controlled trial.

BACKGROUND: Tinnitus is a common auditory condition that can lead to serious problems. Clinically, acupuncture and moxibustion have been commonly applied to treat tinnitus in China, with potential therapeutic effects but with limitations in study methodology and high-quality evidence. Therefore, we designed a randomized controlled trial to evaluate the efficacy and safety of either electroacupuncture alone or combined with warm needling for reducing tinnitus loudness and improving quality of life. METHODS: This study is a prospective, multicenter, assessor-blind, 3-arm, parallel-group, randomized, waitlist-controlled trial. In total, 90 patients will be randomly assigned to the electroacupuncture, electroacupuncture and warm needing, or waitlist control group in a 1:1:1 ratio. Patients in the 2 treatment groups will be treated twice a week for a total of 5 weeks. Patients in the control group will not receive treatment during the study period and will be informed that they can receive it for free after a 10-week waiting period. The duration of intervention for this study will be 5 weeks, followed by another 5 weeks for the posttreatment assessment. The primary outcome is the change in the visual analog scale score for tinnitus loudness from baseline until the end of treatment. The secondary outcome is the tinnitus discomfort assessment measured using the Tinnitus Handicap Inventory. Outcome parameters will be assessed at baseline and at weeks 5 and 10. Any adverse events will be observed and recorded for safety assessment. Linear mixed models for repeated measures will be applied in the analysis. DISCUSSION: Acupuncture and moxibustion could be potentially effective treatment alternatives for tinnitus. The study results will provide evidence to determine the efficacy and safety of electroacupuncture with or without warm needling for tinnitus.

Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning.

OBJECTIVE: This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. METHODS: In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment. RESULTS: The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value < 0.05). They revealed high diagnostic and predictive values and were closely related to the expression of multiple immune cells (p-value < 0.05). It was inferred that the expression of PSMB9, STAT 1, and TAP1 could be regulated by different medicinal chemicals. CONCLUSION: PSMB9, STAT1, and TAP1, might play a key role in the pathogenesis of TB, including spinal TB, and the protein product of the genes can be served as diagnostic markers and potential therapeutic target for TB.

Development of Schizochytrium sp. strain HS01 with high-DHA and low-saturated fatty acids production by multi-pronged adaptive evolution.

PURPOSE: Docosahexaenoic acid (DHA) is an important omega-3 unsaturated fatty acid and has been widely applied in medicine, food additives, and feed ingredients. The fermentative production of DHA using microorganisms, including Schizochytrium sp., attracted much attention due to its high production efficiency and environment friendly properties. An efficient laboratory evolution approach was used to improve the strain's performance in this study. METHODS: A multi-pronged laboratory evolution approach was applied to evolve high-yield DHA-producing Schizochytrium strain. We further employed comparative transcriptional analysis to identify transcriptional changes between the screened strain HS01 and its parent strain GS00. RESULTS: After multiple generations of ALE, a strain HS01 with higher DHA content and lower saturated fatty acids content was obtained. Low nitrogen conditions were important for enhancing DHA biosynthesis in HS01. The comparative transcriptional analysis results indicated that during the fermentation process of HS01, the expression of key enzymes in the glycolysis, the pentose phosphate pathway and the tricarboxylic acid cycle were up-regulated, while the expression of polyketide synthase genes and fatty acid synthesis genes were similar to those in GS00. CONCLUSION: The results suggest that the improved DHA production capacity of HS01 is not due to enhancement of the DHA biosynthesis pathway, but rather related to modulation of central metabolism pathways.

The Role of "Hierarchical and Classified Prevention and Control Measures (HCPC)" Strategy for SARS-CoV-2 Delta Variant in Guangzhou: A Modeling Study.

BACKGROUND: The Delta variant of SARS-COV-2 has replaced previously circulating strains around the world in 2021. Sporadic outbreaks of the Delta variant in China have posed a concern about how to properly respond to the battle against evolving COVID-19. Here, we analyzed the "hierarchical and classified prevention and control (HCPC)" measures strategy deployed during the recent Guangzhou outbreak. METHODS: A modified susceptible-exposed-pre-symptomatic-infectious-recovered (SEPIR) model was developed and applied to study a range of different scenarios to evaluate the effectiveness of policy deployment. We simulated severe different scenarios to understand policy implementation and timing of implementation. Two outcomes were measured: magnitude of transmission and duration of transmission. The outcomes of scenario evaluations were presented relative to the reality case (i.e., 368 cases in 34 days) with 95% confidence interval (CI). RESULTS: Based on our simulation, the outbreak would become out of control with 7 million estimated infections under the assumption of the absence of any interventions than the 153 reported cases in reality in Guangzhou. The simulation on delayed implementation of interventions showed that the total case numbers would also increase by 166.67%-813.07% if the interventions were delayed by 3 days or 7 days. CONCLUSIONS: It may be concluded that timely and more precise interventions including mass testing and graded community management are effective measures for Delta variant containment in China.

Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15.

Osteosarcoma has the worst prognosis among malignant bone tumors, and effective biomarkers are lacking. Our study aims to explore m6A-related and immune-related biomarkers. Gene expression profiles of osteosarcoma and healthy controls were downloaded from multiple public databases, and their m6A-based gene expression was utilized for tumor typing using bioinformatics. Subsequently, a prognostic model for osteosarcoma was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression analysis, and its immune cell composition was calculated using the CIBERSORTx algorithm. We also performed drug sensitivity analysis for these two genes. Finally, analysis was validated using immunohistochemistry. We also examined the RBM15 gene by qRT-PCR in an in vitro experiment. We collected routine blood data from 1738 patients diagnosed with osteosarcoma and 24,344 non-osteosarcoma patients and used two independent sample t tests to verify the accuracy of the CIBERSORTx analysis for immune cell differences. The analysis based on m6A gene expression tumor typing was most reliable using the two typing methods. The prognostic model based on the two genes constituting RNA-binding motif protein 15 (RBM15) and YTDC1 had a much lower survival rate for patients in the high-risk group than those in the low-risk group (P < 0.05). CIBERSORTx immune cell component analysis demonstrated that RBM15 showed a negative and positive correlation with T cells gamma delta and activated natural killer cells, respectively. Drug sensitivity analysis showed that these two genes showed varying degrees of correlation with multiple drugs. The results of immunohistochemistry revealed that the expression of these two genes was significantly higher in osteosarcoma than in paraneoplastic tissues. The results of qRT-PCR experiments showed that the expression of RBM15 was significantly higher in both osteosarcomas than in the control cell lines. Absolute lymphocyte value, lymphocyte percentage, hematocrit and erythrocyte count were lower in osteosarcoma than in the control group (P < 0.001). RBM15 and YTHDC1 can serve as potential prognostic biomarkers associated with m6A in osteosarcoma.

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.

BACKGROUND: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. METHODS: The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. RESULTS: The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. CONCLUSION: In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts.

Introduction: The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS. Methods: In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients. Results: The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care. Discussion: In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

Identification of clinical heterogeneity and construction of a novel subtype predictive model in patients with ankylosing spondylitis: An unsupervised machine learning study.

BACKGROUND: Accurate classification of patients with ankylosing spondylitis (AS) is the premise of precision medicine so as to perform different medical interventions for different patient types. AS pathology is closely related to the changes in the immune microenvironment. In this study, we used unsupervised machine learning (UML) to classify patients with AS based on clinical characteristics. We then constructed a novel subtype predictive model for AS based on the clinical classification, after which we investigated the difference in the immune microenvironment to unravel the AS pathogenesis. METHODS: Overall, 196 patients with AS were enrolled. UML was used to cluster AS patients by similar clinical characteristics. Functional ability, disease status, and grading of radiologic features were assessed to verify the accuracy and heterogeneity of UML clustering. Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest algorithm were used to screen and identify predictive factors for the novel subtype of AS. Logistic regression was also performed to construct a predictive model of this novel subtype. Datasets were downloaded from the Gene Expression Omnibus database to assess immune cell infiltration, and the results were validated using data of routine blood tests from 3671 AS patients and 5720 non-AS patients. The differential expression of Fat Mass and Obesity-Associated Protein (FTO), an m6A regulator, between AS patients and healthy control subjects was confirmed using immunohistochemistry. RESULTS: UML clustering identified two clusters. The clinical characteristics of the two clusters were significantly heterogeneous. For the novel subtype of AS identified in UML clustering, a predictive model was built using three predictive factors, namely, C-reactive protein (CRP), absolute value of neutrophils (NEU), and absolute value of monocytes (MONO). The area under the curve of the predictive model was 0.983. Heterogeneity in the neutrophil and monocyte counts in AS was verified through immune cell infiltration analysis. Data from routine blood tests revealed that NEU and MONO were significantly higher in AS patients than in non-AS patients (p < 0.001). FTO expression was negatively correlated with both NEU and MONO. Immunohistochemistry analysis confirmed the downregulated expression of FTO. CONCLUSIONS: UML provides an explicable and remarkable classification of a heterogeneous cohort of AS patients. A novel subtype of AS was identified in UML clustering. CRP, NEU, and MONO were the independent predictive factors for the novel subtype of AS. FTO expression was correlated with immune cell infiltration in AS patients.

MMP9 and STAT1 are biomarkers of the change in immune infiltration after anti-tuberculosis therapy, and the immune status can identify patients with spinal tuberculosis.

BACKGROUND: Due to a lack of studies on immune-related pathogenesis and a clinical diagnostic model, the diagnosis of Spinal Tuberculosis (STB) remains uncertain. Our study aimed to investigate the possible pathogenesis of STB and to develop a clinical diagnostic model for STB based on immune cell infiltration. METHODS: Label-free quantification protein analysis of five pairs of specimens was used to determine the protein expression of the intervertebral disc in STB and non-STB. GO enrichment analysis, and KEGG pathway analysis were used to investigate the pathogenesis of STB. The Hub proteins were then eliminated. Four datasets were downloaded from the GEO database to analyze immune cell infiltration, and the results were validated using blood routine test data from 8535TB and 7337 non-TB patients. Following that, clinical data from 164 STB and 162 non-STB patients were collected. The Random-Forest algorithm was used to screen out clinical predictors of STB and build a diagnostic model. The differential expression of MMP9 and STAT1 in STB and controls was confirmed using immunohistochemistry. RESULTS: MMP9 and STAT1 were STB Hub proteins that were linked to disc destruction in STB. MMP9 and STAT1 were found to be associated with Monocytes, Neutrophils, and Lymphocytes in immune cell infiltration studies. Data from 15,872 blood routine tests revealed that the Monocytes ratio and Neutrophils ratio was significantly higher in TB patients than in non-TB patients (p < 0.001), while the Lymphocytes ratio was significantly lower in TB patients than in non-TB patients (p < 0.001). MMP9 and STAT1 expression were downregulated following the anti-TB therapy. For STB, a clinical diagnostic model was built using six clinical predictors: MR, NR, LR, ESR, BMI, and PLT. The model was evaluated using a ROC curve, which yielded an AUC of 0.816. CONCLUSIONS: MMP9 and STAT1, immune-related hub proteins, were correlated with immune cell infiltration in STB patients. MR, NR, LR ESR, BMI, and PLT were clinical predictors of STB. Thus, the immune cell Infiltration-related clinical diagnostic model can predict STB effectively.

Entity Summarization via Exploiting Description Complementarity and Salience.

Entity summarization is a novel and efficient way to understand real-world facts and solve the increasing information overload problem in large-scale knowledge graphs (KG). Existing studies mainly rely on ranking independent entity descriptions as a list under a certain scoring standard such as importance. However, they often ignore the relatedness and even semantic overlap between individual descriptions. This may seriously interfere with the contribution judgment of descriptions for entity summarization. Actually, the entity summary is a whole to comprehensively integrate the main aspects of entity descriptions, which could be naturally treated as a set. Unfortunately, the exploration of these set characteristics for entity summarization is still an open issue with great challenges. To that end, we draw inspiration from a set completion perspective and propose an entity summarization method with complementarity and salience (ESCS) to deeply exploit description complementarity and salience in order to form a summary set for the target entity. Specifically, we first generate entity description representations with textual features in the description embedding module. For the purpose of learning complementary relationships within the entire summary set, we devise a bi-directional long short-term memory structure to capture global complementarity for each summary in the summary complementarity learning module. Meanwhile, in order to estimate the salience of individual descriptions, we calculate similarities between semantic embeddings of the target entity and its property-value pairs in the description salience learning module. Next, with a joint learning stage, we can optimize ESCS from a set completion perspective. Finally, a summary generation strategy is designed to infer the entire summary set step-by-step for the target entity. Extensive experiments on a public benchmark have clearly demonstrated the effectiveness of ESCS and revealed the potential of set completion in entity summarization task.

Trigeminal nerve direct current stimulation causes sustained increase in neural activity in the rat hippocampus.

Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation method that can modulate many brain functions including learning and memory. Recent evidence suggests that tDCS memory effects may be caused by co-stimulation of scalp nerves such as the trigeminal nerve (TN), and not the electric field in the brain. The TN gives input to brainstem nuclei, including the locus coeruleus that controls noradrenaline release across brain regions, including hippocampus. However, the effects of TN direct current stimulation (TN-DCS) are currently not well understood. In this study we hypothesized that TN-DCS manipulates hippocampal activity via an LC-noradrenergic bottom-up pathway. We recorded neural activity in rat hippocampus using multichannel silicon probes. We applied 3 minutes of 0.25 mA or 1 mA TN-DCS, monitored hippocampal activity for up to 1 hour and calculated spikes-rate and spike-field coherence metrics. Subcutaneous injections of xylocaine were used to block TN and intraperitoneal injection of clonidine to block the LC pathway. We found that 1 mA TN-DCS caused a significant increase in hippocampal spike-rate lasting 45 minutes in addition to significant changes in spike-field coherence, while 0.25 mA TN-DCS did not. TN blockage prevented spike-rate increases, confirming effects were not caused by the electric field in the brain. When 1 mA TN-DCS was delivered during clonidine blockage no increase in spike-rate was observed, suggesting an important role for the LC-noradrenergic pathway. These results provide a neural basis to support a tDCS TN co-stimulation mechanism. TN-DCS emerges as an important tool to potentially modulate learning and memory. Highlights: Trigeminal nerve direct current stimulation (TN-DCS) boosts hippocampal spike ratesTN-DCS alters spike-field coherence in theta and gamma bands across the hippocampus.Blockade experiments indicate that TN-DCS modulated hippocampal activity via the LC-noradrenergic pathway.TN-DCS emerges as a potential tool for memory manipulation.

A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors.

Objective: The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS). Methods: A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort. t-test analysis, de-redundancy analysis, and minimum absolute shrinkage and selection operator (lasso) algorithm were utilized on the training set to obtain the optimal radiomics features from computed tomography (CT) for constructing the radiomics model and determine the radiomics score (Rad-score). Eight clinical risk predictors were identified to develop the clinical model. Combined with clinical risk predictors and Rad-scores, a nomogram model was constructed using multivariate logistic regression analysis. Results: A total of 1781 features were extracted, and 12 optimal radiomic features were utilized to construct the radiomic model and determine the Rad-score. The combined clinical radiomics model revealed good discrimination performance in both the training cohort and the validation cohort (AUC = 0.891 and 0.830) and was superior to the clinical (AUC = 0.807 and 0.785) and radiomics (AUC = 0.796 and 0.811) models. The calibration curve and DCA also depicted that the nomogram had better clinical efficacy. The discriminative performance of the model is well validated in the test cohort (AUC=0.877). Conclusion: The clinical radiomic nomogram could serve as a promising predictive tool for differentiating TS from PS, which could be helpful for clinical decision-making.