Disparate processing of numerosity and associated continuous magnitudes in rats.

The studies of number sense in different species are severely hampered by the inevitable entanglement of non-numerical attributes inherent in nonsymbolic stimuli representing numerosity, resulting in contrasting theories of numerosity processing. Here, we developed an algorithm and associated analytical methods to generate stimuli that not only minimized the impact of non-numerical magnitudes in numerosity perception but also allowed their quantification. We trained number-naïve rats with these stimuli as sound pulses representing two or three numbers and demonstrated that their numerical discrimination ability mainly relied on numerosity. Also, studying the learning process revealed that rats used numerosity before using magnitudes for choices. This numerical processing could be impaired specifically by silencing the posterior parietal cortex. Furthermore, modeling this capacity by neural networks shed light on the separation of numerosity and magnitudes extraction. Our study helps dissect the relationship between magnitude and numerosity processing, and the above different findings together affirm the independent existence of innate number and magnitudes sense in rats.

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.

HMGB1 Mediates Inflammation-Induced DMT1 Increase and Dopaminergic Neurodegeneration in the Early Stage of Parkinsonism.

Both neuroinflammation and iron accumulation play roles in the pathogenesis of Parkinson's disease (PD). However, whether inflammation induces iron dyshomeostasis in dopaminergic neurons at an early stage of PD, at which no quantifiable dopaminergic neuron loss can be observed, is still unknown. As for the inflammation mediators, although several cytokines have been reported to increase in PD, the functions of these cytokines in the SN are double-edged and controversial. In this study, whether inflammation could induce iron dyshomeostasis in dopaminergic neurons through high mobility group protein B1 (HMGB1) in the early stage of PD is explored. Lipopolysaccharide (LPS), a toxin that primarily activates glia cells, and 6-hydroxydopamine (6-OHDA), the neurotoxin that firstly impacts dopaminergic neurons, were utilized to mimic PD in rats. We found a common and exceedingly early over-production of HMGB1, followed by an increase of divalent metal transporter 1 with iron responsive element (DMT1+) in the dopaminergic neurons before quantifiable neuronal loss. HMGB1 neutralizing antibody suppressed inflammation in the SN, DMT1+ elevation in dopaminergic neurons, and dopaminergic neuronal loss in both LPS and 6-OHDA administration- induced PD models. On the contrary, interleukin-1ß inhibitor diacerein failed to suppress these outcomes induced by 6-OHDA. Our findings not only demonstrate that inflammation could be one of the causes of DMT1+ increase in dopaminergic neurons, but also highlight HMGB1 as a pivotal early mediator of inflammation-induced iron increase and subsequent neurodegeneration, thereby HMGB1 could serve as a potential target for early-stage PD treatment.

Investigations of Water Transport in Shale Reservoir with Dual-Wettability by Using Monte Carlo Method.

How liquids transport in the shale system has been the focus because of fracturing fluid loss. In this study, a single-nanopore model is established for liquid transport in shale while considering the slip effect and effective viscosity of confined fluids. Then, the fractal Monte Carlo (FMC) model is proposed to upscale the single-pore model into shale porous media. The effects of different transport mechanisms, shale wettability, and pore characteristic parameters on confined liquid flow in shale rock are investigated. Results show that FMC permeabilities are 2-3 orders of magnitude larger than intrinsic and slip-corrected permeabilities in organic matter. However, the slip effect and effective viscosity have little influence on water flow in inorganic matter. With the contact angle of organic pore (θom) increasing and contact angle of inorganic pore (θin) decreasing, the effective permeability of the whole shale matrix grows in number. The enhancement factor in the situation of θom = 170° and θin = 20° is 4 orders of magnitude larger than the case of θom = 130° and θin = 40°, although the close effective macroscopic contact angle (θeff = 80°) occurs in these two cases, which indicates that shale microscopic wettability has a significant impact on the confined liquid transport. Moreover, with the increase of porosity and maximum pore diameters, shale permeability increases rapidly, but the enhancement factor has the opposite trend. Compared with the tiny impact of the variance of minimum inorganic pore diameters, minimum organic pore diameters have more significant impacts on liquid flow in shale systems, and the enhancement factor also rapidly increases up to 30 times for the case of 0.5 nm because of the strong slip effect.

A multicenter study on the application of artificial intelligence radiological characteristics to predict prognosis after percutaneous nephrolithotomy.

Background: A model to predict preoperative outcomes after percutaneous nephrolithotomy (PCNL) with renal staghorn stones is developed to be an essential preoperative consultation tool. Objective: In this study, we constructed a predictive model for one-time stone clearance after PCNL for renal staghorn calculi, so as to predict the stone clearance rate of patients in one operation, and provide a reference direction for patients and clinicians. Methods: According to the 175 patients with renal staghorn stones undergoing PCNL at two centers, preoperative/postoperative variables were collected. After identifying characteristic variables using PCA analysis to avoid overfitting. A predictive model was developed for preoperative outcomes after PCNL in patients with renal staghorn stones. In addition, we repeatedly cross-validated their model's predictive efficacy and clinical application using data from two different centers. Results: The study included 175 patients from two centers treated with PCNL. We used a training set and an external validation set. Radionics characteristics, deep migration learning, clinical characteristics, and DTL+Rad-signature were successfully constructed using machine learning based on patients' pre/postoperative imaging characteristics and clinical variables using minimum absolute shrinkage and selection operator algorithms. In this study, DTL-Rad signal was found to be the outstanding predictor of stone clearance in patients with renal deer antler-like stones treated by PCNL. The DTL+Rad signature showed good discriminatory ability in both the training and external validation groups with AUC values of 0.871 (95% CI, 0.800-0.942) and 0.744 (95% CI, 0.617-0.871). The decision curve demonstrated the radiographic model's clinical utility and illustrated specificities of 0.935 and 0.806, respectively. Conclusion: We found a prediction model combining imaging characteristics, neural networks, and clinical characteristics can be used as an effective preoperative prediction method.

Analysis of the Influencing Factors on the Extraction of Residual Oil through the Gel Foam Flooding of Underground Reservoirs in the Tahe Oilfield.

Fractured-vuggy reservoirs are mainly composed of three types: underground rivers, vugs, and fractured-vuggy structures. Based on the similarity criterion, a 3D model can truly reflect the characteristics of the multi-scale space of a fractured-vuggy reservoir, and it can reflect fluid flow laws in the formation. Water flooding, gas flooding, and gel foam flooding were carried out in the model sequentially. Based on gas flooding, the enhanced recovery ratio of gel foam flooding in the underground river was approximately 12%. By changing the injection rate, the average recovery ratio of nitrogen flooding was 6.84% higher than that of other injection rates at 5 mL/min, and that of gel foam flooding was 1.88% higher than that of other injection rates at 5 mL/min. The experimental results showed that the gel foam induced four oil displacement mechanisms, which selectively plugged high-permeability channels, controlled the mobility ratio, reduced oil-water interfacial tension, and changed the wettability of rock surfaces. With different injection-production methods, gel foam flooding can spread across two underground river channels. Two cases of nitrogen flooding affected one underground river channel and two underground river channels. By adjusting the injection rate, it was found that after nitrogen flooding, there were mainly four types of residual oil, and gel foam flooding mainly yielded three types of remaining oil. This study verified the influencing factors of extracting residual oil from an underground river and provides theoretical support for the subsequent application of gel foam flooding in underground rivers.

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.

Predictors of osteoporotic fracture in postmenopausal women: a meta-analysis.

Osteoporosis affects more than 200 million women worldwide, with postmenopausal women being particularly susceptible to this condition and its severe sequelae disproportionately, such as osteoporotic fractures. To date, the current focus has been more on symptomatic treatment, rather than preventive measures. To address this, we performed a meta-analysis aiming to identify potential predictors of osteoporotic fractures in postmenopausal women, with the ultimate goal of identifying high-risk patients and exploring potential therapeutic approaches. We searched Embase, MEDLINE and Cochrane with search terms (postmenopausal AND fracture) AND ("risk factor" OR "predictive factor") in May 2022 for cohort and case-control studies on the predictors of osteoporotic fracture in postmenopausal women. Ten studies with 1,287,021 postmenopausal women were found eligible for analyses, in which the sample size ranged from 311 to 1,272,115. The surveyed date spanned from 1993 to 2021. Our results suggested that age, BMI, senior high school and above, parity ≥ 3, history of hypertension, history of diabetes mellitus, history of alcohol intake, age at menarche ≥ 15, age at menopause < 40, age at menopause > 50, estrogen use and vitamin D supplements were significantly associated with osteoporotic fracture in postmenopausal women. Our findings facilitate the early prediction of osteoporotic fracture in postmenopausal women and may contribute to potential therapeutic approaches. By focusing on preventive strategies and identifying high-risk individuals, we can work toward reducing the burden of osteoporosis-related fractures in this vulnerable population.

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.

Pharmacological interventions targeting α-synuclein aggregation triggered REM sleep behavior disorder and early development of Parkinson's disease.

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by elevated motor behaviors and dream enactments in REM sleep, often preceding the diagnosis of Parkinson's disease (PD). As RBD could serve as a biomarker for early PD developments, pharmacological interventions targeting α-synuclein aggregation triggered RBD could be applied toward early PD progression. However, robust therapeutic guidelines toward PD-induced RBD are lacking, owing in part to a historical paucity of effective treatments and trials. We reviewed the bidirectional links between α-synuclein neurodegeneration, progressive sleep disorders, and RBD. We highlighted the correlation between RBD development, α-synuclein aggregation, and neuronal apoptosis in key brainstem regions involved in REM sleep atonia maintenance. The current pharmacological intervention strategies targeting RBD and their effects on progressive PD are discussed, as well as current treatments for progressive neurodegeneration and their effects on RBD. We also evaluated emerging and potential pharmacological solutions to sleep disorders and developing synucleinopathies. This review provides insights into the mechanisms and therapeutic targets underlying RBD and PD, and explores bidirectional treatment effects for both diseases, underscoring the need for further research in this area.

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.

Optimal antiplatelet therapy for patients after antiplatelet therapy induced gastrointestinal bleeding: timing.

Adjusting antiplatelet strategies after antiplatelet-associated gastrointestinal bleeding (GIB) is a complex clinical challenge. To assess the risk of outcomes at different times of resumption of antiplatelet therapy in an attempt to find the optimal time to resume therapy. The study analyzed consecutive patients with antiplatelet-associated GIB from Beijing Friendship Hospital Information System between October 2019 and June 2022. The primary outcomes were recurrent bleeding, major adverse cardiovascular and cerebrovascular events (MACE), and all-cause death. Multivariate-adjusted Cox proportional hazards models were used to evaluate the risks of these outcomes. The receiver operating characteristic curve was used to find the optimal time to resume treatment. Of the 617 patients with GIB after antiplatelet therapy successfully followed up, the median follow-up was 246 (interquartile range: 120-466) days, most patients (87.36%) interrupted therapy after GIB and 45.22% resumed within 90 days, of which 35.13% resumed within 7 days and 64.87% resumed after 7 days. Resumption therapy had a low risk of recurrent bleeding (uninterrupted as a reference: HR 0.32, 95% CI 0.15-0.67, p = 0.003), MACE (no resumption as a reference: HR 0.66, 95% CI 0.45-0.98, p = 0.037), and all-cause death (no resumption as a reference: HR 0.18, 95% CI 0.08-0.40, p < 0.001). And resuming therapy within 7 days had a lower risk of MACE (HR 0.18, 95% CI 0.08-0.44, p < 0.001) than after 7 days without a significantly higher risk of re-bleeding. The optimal time point for resuming therapy in this study was 8.5 days. Resuming antiplatelet therapy after GIB provides better clinical benefits compared to discontinued and uninterrupted therapy, especially compared with resuming after 7 days, resuming within 7 days is associated with a lower risk of MACE and a less significant increased risk of recurrent bleeding, leading to a higher net clinical benefit. China Clinical Trial Registration: ChiCTR2200064063.

The efficacy of thoracolumbar interfascial plane block for lumbar spinal surgeries: a systematic review and meta-analysis.

BACKGROUND: The intent of this meta-analysis was to examine the efficacy of thoracolumbar interfascial plane block (TLIP) for pain control after lumbar spinal surgery. METHODS: Randomized controlled trials (RCTs) published on PubMed, CENTRAL, Scopus, Embase, and Web of Science databases up to February 10, 2023, comparing TLIP with no or sham block or wound infiltration for lumbar spinal surgeries were included. Pain scores, total analgesic consumption, and postoperative nausea and vomiting (PONV) were analyzed. RESULTS: Seventeen RCTs were eligible. Comparing TLIP with no block or sham block, the meta-analysis showed a significant decrease of pain scores at rest and movement at 2 h, 8 h, 12 h, and 24 h. Pooled analysis of four studies showed a significant difference in pain scores at rest between TLIP and wound infiltration group at 8 h but not at 2 h, 12 h, and 24 h. Total analgesic consumption was significantly reduced with TLIP block as compared to no block/sham block and wound infiltration. TLIP block also significantly reduced PONV. GRADE assessment of the evidence was moderate. CONCLUSION: Moderate quality evidence indicates that TLIP blocks are effective in pain control after lumbar spinal surgeries. TLIP reduces pain scores at rest and movement for up to 24 h, reduces total analgesic consumption, and the incidence of PONV. However, evidence of its efficacy as compared to wound infiltration of local anesthetics is scarce. Results should be interpreted with caution owing low to moderate quality of the primary studies and marked heterogeneity.

Mean Platelet Volume to Platelet Count Ratio Predicts Left Atrial Stasis in Patients with Non-Valvular Atrial Fibrillation.

Background: The mean platelet volume to platelet count ratio (MPV/PC) has been investigated in the diagnosis, prognosis and risk stratification in several diseases. However, the predictive role of MPV/PC in left atrial stasis (LAS) of non-valvular atrial fibrillation (NVAF) patients remains unknown. Methods: A total of 217 consecutive NVAF patients undergoing transesophageal echocardiogram (TEE) evaluation were retrospectively enrolled. The demographic, clinical, admission laboratory and TEE data were extracted and analyzed. Patients were categorized into those with or without LAS. The associations between the MPV/PC ratio and LAS were assessed by multivariate logistic regression analysis. Results: There were 24.9% (n = 54) patients with LAS according to TEE. Compared with patients without LAS, the MPV/PC ratio was significantly higher in those with LAS (5.6±1.6 vs 4.8±1.0, P < 0.001). After multivariable adjustment, higher MPV/PC ratio levels (OR 1.747, 95% CI 1.193-2.559, P = 0.004) were positively associated with LAS, with the optimal cut-point for LAS prediction of 5.36 (area under the curve, AUC = 0.683, sensitivity 48%, specificity 73%, 95% CI 0.589-0.777, P < 0.001). The stratification analysis showed that a significant positive correlation between MPV/PC ratio ≥5.36 and LAS in patients of male, younger (<65 years), paroxysmal AF, without history of stroke/TIA, CHA2DS2-VASc score ≥2, left atrial diameter (LAD) ≥40mm and left atrial volume index (LAVI) >34mL/m2 (all P < 0.05). Conclusion: Increasing MPV/PC ratio was associated with an increased risk of LAS, which was mainly reflected in the subgroups of male, younger (<65 years), paroxysmal AF, without history of stroke/TIA, CHA2DS2-VASc score ≥2, LAD ≥40mm and LAVI >34mL/m2 patients.

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.

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.

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.