A prediction model to differentiate transient ischemia from irreversible transmural necrosis in closed-loop small bowel obstruction.

Introduction: The study aims to develop a prediction model to differentiate transient ischemia from irreversible transmural necrosis in closed-loop small bowel obstruction (CL-SBO). Methods: A total of 180 participants with CL-SBO between January 2010 and December 2019, of which 122 had complete radiologic data, were included to investigate the significant clinical and imaging characteristics for differentiating patients with necrosis from transient ischemia. A logistic regression model was developed and validated. Results: In a multivariate analysis, the American Society of Anesthesiologists physical status classification system >2 is the independent predictor for transmural necrosis. Another multivariate analysis, including clinical and imaging factors, revealed that the imaging sign of mesenteric vessel interruption was an independent risk factor for necrosis. The diagnostic model developed using these two factors has excellent performance among the validation sets, with an area under the curve of 0.89. Conclusion: The diagnostic model and innovative imaging signs have substantial potential in solving this complex clinical problem.

Clinical diagnostic model for predicting indolent or aggressive lymphoma based on clinical information and ultrasound features of superficial lymph nodes.

PURPOSE: The aim of this study was to develop a diagnostic model for predicting indolent lymphoma or aggressive lymphoma using clinical information and ultrasound characteristics of superficial lymph nodes. METHOD: Patients with confirmed pathological lymphoma subtypes who had undergone ultrasound and contrast-enhanced ultrasound examinations were enrolled. Clinical and ultrasound imaging features were retrospectively analysed and compared to the pathological results, which were considered the gold standard for diagnosis. Two diagnostic models were developed: a clinical model (Model-C) using clinical data only, and a combined model (Model-US) integrating ultrasound features into the clinical model. The efficacy of these models in differentiating between indolent and aggressive lymphoma was compared. RESULTS: In total, 236 consecutive patients were enrolled, including 78 patients with indolent lymphomas and 158 patients with aggressive lymphomas. Receiver operating characteristic (ROC) curve analysis revealed that the areas under the curves of Model-C and Model-US were 0.78 (95 % confidence interval: 0.72-0.84) and 0.87 (95 % confidence interval: 0.82-0.92), respectively (p < 0.001). Model-US was further evaluated for calibration and is presented as a nomogram. CONCLUSIONS: The diagnostic model incorporated clinical and ultrasound characteristics and offered a noninvasive method for assessing lymphoma with good discrimination and calibration.

Comprehensive analysis of hypoxia-related genes in diagnosis and immune infiltration in acute myocardial infarction: based on bulk and single-cell RNA sequencing data.

Background: Hypoxia has been found to cause cellular dysfunction and cell death, which are essential mechanisms in the development of acute myocardial infarction (AMI). However, the impact of hypoxia-related genes (HRGs) on AMI remains uncertain. Methods: The training dataset GSE66360, validation dataset GSE48060, and scRNA dataset GSE163956 were downloaded from the GEO database. We identified hub HRGs in AMI using machine learning methods. A prediction model for AMI occurrence was constructed and validated based on the identified hub HRGs. Correlations between hub HRGs and immune cells were explored using ssGSEA analysis. Unsupervised consensus clustering analysis was used to identify robust molecular clusters associated with hypoxia. Single-cell analysis was used to determine the distribution of hub HRGs in cell populations. RT-qPCR verified the expression levels of hub HRGs in the human cardiomyocyte model of AMI by oxygen-glucose deprivation (OGD) treatment in AC16 cells. Results: Fourteen candidate HRGs were identified by differential analysis, and the RF model and the nomogram based on 8 hub HRGs (IRS2, ZFP36, NFIL3, TNFAIP3, SLC2A3, IER3, MAFF, and PLAUR) were constructed, and the ROC curves verified its good prediction effect in training and validation datasets (AUC = 0.9339 and 0.8141, respectively). In addition, the interaction between hub HRGs and smooth muscle cells, immune cells was elucidated by scRNA analysis. Subsequently, the HRG pattern was constructed by consensus clustering, and the HRG gene pattern verified the accuracy of its grouping. Patients with AMI could be categorized into three HRG subclusters, and cluster A was significantly associated with immune infiltration. The RT-qPCR results showed that the hub HRGs in the OGD group were significantly overexpressed. Conclusion: A predictive model of AMI based on HRGs was developed and strongly associated with immune cell infiltration. Characterizing patients for hypoxia could help identify populations with specific molecular profiles and provide precise treatment.

Serum levels of stearic and dihomo-γ-linolenic acids can be used to diagnose cervical cancer and cervical intraepithelial neoplasia.

Despite widespread cervical cancer (CC) screening programs, low participation has led to high morbidity and mortality rates, especially in developing countries. Because early-stage CC often has no symptoms, a non-invasive and convenient diagnostic method is needed to improve disease detection. In this study, we developed a new approach for differentiating both CC and cervical intraepithelial neoplasia (CIN)2/3, a precancerous lesion, from healthy individuals by exploring CC fatty acid metabolic reprogramming. Analysis of public datasets suggested that various fatty acid metabolizing enzymes were expressed at higher levels in CC tissues than in normal tissues. Correspondingly, 11 free fatty acids (FFAs) showed significantly different serum levels in CC patient samples compared with healthy donor samples. Nine of these 11 FFAs also displayed significant alterations in CIN2/3 patients. We then generated diagnostic models using combinations of these FFAs, with the optimal model including stearic and dihomo-γ-linolenic acids. Receiver operating characteristic curve analyses suggested that this diagnostic model could detect CC and CIN2/3 more accurately than using serum squamous cell carcinoma antigen level. In addition, the diagnostic model using FFAs was able to detect patients regardless of clinical stage or histological type. Overall, the serum FFA diagnostic model developed in this study could be a powerful new tool for the non-invasive early detection of CC and CIN2/3.

Discovery and validation of colorectal cancer tissue-specific methylation markers: a dual-center retrospective cohort study.

BACKGROUND AND PURPOSE: Early detection, diagnosis, and treatment of colorectal cancer and its precancerous lesions can significantly improve patients' survival rates. The purpose of this research is to identify methylation markers specific to colorectal cancer tissues and validate their diagnostic capability in colorectal cancer and precancerous changes by measuring the level of DNA methylation in stool samples. METHOD: We analyzed samples from six cancer tissues and adjacent normal tissues and fecal samples from 758 participants, including 62 patients with interfering diseases. Bioinformatics databases were used to screen for candidate biomarkers for CRC, and quantitative methylation-specific PCR methods were applied for identification. The methylation levels of the candidate biomarkers in fecal and tissue samples were measured. Logistic regression and random forest models were built and validated using fecal sample data from one of the centers, and the independent or combined diagnostic value of the candidate biomarkers in fecal samples for CRC and precancerous lesions was analyzed. Finally, the diagnostic capability and stability of the model were validated at another medical center. RESULTS: This study identified two colorectal cancer CpG sites with tissue specificity. These two biomarkers have certain diagnostic power when used individually, but their diagnostic value for colorectal cancer and colorectal adenoma is more significant when they are used in combination. CONCLUSION: The results indicate that a DNA methylation biomarker combined diagnostic model based on two CpG sites, cg13096260 and cg12587766, has the potential for screening and diagnosing precancerous lesions and colorectal cancer. Additionally, compared to traditional diagnostic models, machine learning algorithms perform better but may yield more false-positive results, necessitating further investigation.

AI-enhanced diagnostic model for pulmonary nodule classification.

Background: The identification of benign and malignant pulmonary nodules (BPN and MPN) can significantly reduce mortality. However, a reliable and validated diagnostic model for clinical decision-making is still lacking. Methods: Enzyme-linked immunosorbent assay and electro chemiluminescent immunoassay were utilized to determine the serum concentrations of 7AABs (p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2, GBU4-5), and 4TTMs (CYFR21, CEA, NSE and SCC) in 260 participants (72 BPNs and 188 early-stage MPNs), respectively. The malignancy probability was calculated using Artificial intelligence pulmonary nodule auxiliary diagnosis system, or Mayo model. Along with age, sex, smoking history and nodule size, 18 variables were enrolled for model development. Baseline comparison, univariate ROC analysis, variable correlation analysis, lasso regression, univariate and stepwise logistic regression, and decision curve analysis (DCA) was used to reduce and screen variables. A nomogram and DCA were built for model construction and clinical use. Training (60%) and validation (40%) cohorts were used to for model validation. Results: Age, CYFRA21_1, AI, PGP9.5, GAGE7, and GBU4_5 was screened out from 18 variables and utilized to establish the regression model for identifying BPN and early-stage MPN, as well as nomogram and DCA for clinical practical use. The AUC of the nomogram in the training and validation cohorts were 0.884 and 0.820, respectively. Moreover, the calibration curve showed high coherence between the predicted and actual probability. Conclusion: This diagnostic model and DCA could provide evidence for upgrading or maintaining the current clinical decision based on malignancy probability stratification. It enables low and moderate risk or ambiguous patients to benefit from more precise clinical decision stratification, more timely detection of malignant nodules, and early treatment.

The Use of Identified Hypoxia-related Genes to Generate Models for Predicting the Prognosis of Cerebral Ischemia‒reperfusion Injury and Developing Treatment Strategies.

Cerebral ischemia‒reperfusion injury (CIRI) is a type of secondary brain damage caused by reperfusion after ischemic stroke due to vascular obstruction. In this study, a CIRI diagnostic model was established by identifying hypoxia-related differentially expressed genes (HRDEGs) in patients with CIRI. The ischemia‒reperfusion injury (IRI)-related datasets were downloaded from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), and hypoxia-related genes in the Gene Cards database were identified. After the datasets were combined, hypoxia-related differentially expressed genes (HRDEGs) expressed in CIRI patients were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the HRDEGs were performed using online tools. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed with the combined gene dataset. CIRI diagnostic models based on HRDEGs were constructed via least absolute shrinkage and selection operator (LASSO) regression analysis and a support vector machine (SVM) algorithm. The efficacy of the 9 identified hub genes for CIRI diagnosis was evaluated via mRNA‒microRNA (miRNA) interaction, mRNA-RNA-binding protein (RBP) network interaction, immune cell infiltration, and receiver operating characteristic (ROC) curve analyses. We then performed logistic regression analysis and constructed logistic regression models based on the expression of the 9 HRDEGs. We next established a nomogram and calibrated the prediction data. Finally, the clinical utility of the constructed logistic regression model was evaluated via decision curve analysis (DCA). This study revealed 9 critical genes with high diagnostic value, offering new insights into the diagnosis and selection of therapeutic targets for patients with CIRI. : Not applicable.

Development of a diagnostic model for detecting mild cognitive impairment in young and middle-aged patients with obstructive sleep apnea: a prospective observational study.

Objectives: Obstructive sleep apnea (OSA) is a common sleep-disordered breathing condition linked to the accelerated onset of mild cognitive impairment (MCI). However, the prevalence of undiagnosed MCI among OSA patients is high and attributable to the complexity and specialized nature of MCI diagnosis. Timely identification and intervention for MCI can potentially prevent or delay the onset of dementia. This study aimed to develop screening models for MCI in OSA patients that will be suitable for healthcare professionals in diverse settings and can be effectively utilized without specialized neurological training. Methods: A prospective observational study was conducted at a specialized sleep medicine center from April 2021 to September 2022. Three hundred and fifty consecutive patients (age: 18-60 years) suspected OSA, underwent the Montreal Cognitive Assessment (MoCA) and polysomnography overnight. Demographic and clinical data, including polysomnographic sleep parameters and additional cognitive function assessments were collected from OSA patients. The data were divided into training (70%) and validation (30%) sets, and predictors of MCI were identified using univariate and multivariate logistic regression analyses. Models were evaluated for predictive accuracy and calibration, with nomograms for application. Results: Two hundred and thirty-three patients with newly diagnosed OSA were enrolled. The proportion of patients with MCI was 38.2%. Three diagnostic models, each with an accompanying nomogram, were developed. Model 1 utilized body mass index (BMI) and years of education as predictors. Model 2 incorporated N1 and the score of backward task of the digital span test (DST_B) into the base of Model 1. Model 3 expanded upon Model 1 by including the total score of digital span test (DST). Each of these models exhibited robust discriminatory power and calibration. The C-statistics for Model 1, 2, and 3 were 0.803 [95% confidence interval (CI): 0.735-0.872], 0.849 (95% CI: 0.788-0.910), and 0.83 (95% CI: 0.763-0.896), respectively. Conclusion: Three straightforward diagnostic models, each requiring only two to four easily accessible parameters, were developed that demonstrated high efficacy. These models offer a convenient diagnostic tool for healthcare professionals in diverse healthcare settings, facilitating timely and necessary further evaluation and intervention for OSA patients at an increased risk of MCI.

A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study.

BACKGROUND: The pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment strategies and prognostic profiles of these diseases. This study aimed to develop a deep learning model, iGNet, to assist in the differentiation and prognostication of iGCT subtypes by employing pretherapeutic MR T2-weighted imaging. METHODS: The iGNet model, which is based on the nnUNet architecture, was developed using a retrospective dataset of 280 pathologically confirmed iGCT patients. The training dataset included 83 GEs and 117 NGGCTs, while the retrospective internal test dataset included 31 GEs and 49 NGGCTs. The model's diagnostic performance was then assessed with the area under the receiver operating characteristic curve (AUC) in a prospective internal dataset (n = 22) and two external datasets (n = 22 and 20). Next, we compared the diagnostic performance of six neuroradiologists with or without the assistance of iGNet. Finally, the predictive ability of the output of iGNet for progression-free and overall survival was assessed and compared to that of the pathological diagnosis. RESULTS: iGNet achieved high diagnostic performance, with AUCs between 0.869 and 0.950 across the four test datasets. With the assistance of iGNet, the six neuroradiologists' diagnostic AUCs (averages of the four test datasets) increased by 9.22% to 17.90%. There was no significant difference between the output of iGNet and the results of pathological diagnosis in predicting progression-free and overall survival (P = .889). CONCLUSIONS: By leveraging pretherapeutic MR imaging data, iGNet accurately differentiates iGCT subtypes, facilitating prognostic evaluation and increasing the potential for tailored treatment.

Noninvasive multi-cancer detection using blood-based cell-free microRNAs.

Patients diagnosed with early-stage cancers have a substantially higher chance of survival than those with late-stage diseases. However, the option for early cancer screening is limited, with most cancer types lacking an effective screening tool. Here we report a miRNA-based blood test for multi-cancer early detection based on examination of serum microRNA microarray data from cancer patients and controls. First, a large multi-cancer training set that included 1,408 patients across 7 cancer types and 1,408 age- and gender-matched non-cancer controls was used to develop a 4-microRNA diagnostic model using 10-fold cross-validation. In three independent validation sets comprising a total of 4,875 cancer patients across 13 cancer types and 3,722 non-cancer participants, the 4-microRNA model achieved greater than 90% sensitivity for 9 cancer types (lung, biliary tract, bladder, colorectal, esophageal, gastric, glioma, pancreatic, and prostate cancers) and 75-84% sensitivity for 3 cancer types (sarcoma, liver, and ovarian cancer), while maintaining greater than 99% specificity. The sensitivity remained to be > 99% for patients with stage 1 lung cancer. Our study provided novel evidence to support the development of an inexpensive and accurate miRNA-based blood test for multi-cancer early detection.

Optimal cut-off values and diagnostic significance of clinical laboratory indicators in newly diagnosed multiple myeloma.

OBJECTIVE: This study aims to identify clinical laboratory parameters for the diagnosis of newly diagnosed multiple myeloma (NDMM), establish optimal cutoffs for early screening, and develop a diagnostic model for precise diagnosis. METHODS: The study conducted a retrospective analysis of 279 NDMM patients and 553 healthy subjects at Zhejiang Province People's Hospital between January 2008 and June 2023. Multifactor LR was employed to explore clinical laboratory indicators with diagnostic value for NDMM, determine optimal cutoff values and contract a diagnostic model. The diagnostic efficacy and clinical utility were evaluated using receiver operating characteristic curves (ROC), sensitivity, specificity, and other indicators. RESULTS: Multifactor analysis revealed that hemoglobin (Hb), albumin (Alb), and platelet distribution width (PDW) were significant diagnostic factors for NDMM. Optimal cutoff values for Hb, Alb, and PDW in MM diagnosis were determined, and the results showed a significant increase in the probability of NDMM diagnosis when Alb was below 39.3 g/L, Hb was below 11.6 g/dL, and PDW was below 14.1 fL. The diagnostic model constructed from the development cohort demonstrated a high area under the ROC curve of 0.960 (95% CI 0.942-0.978) and exhibited good sensitivity (0.860), specificity (0.957). The area under the curve (AUC) value of the diagnostic model in the external validation cohort was 0.979, confirming its good diagnostic efficacy and generalization. CONCLUSIONS: The optimal cutoff values for Hb, Alb, and PDW and the diagnostic model designed in the study provided good accuracy and sensitivity for the initial screening and diagnosis of NDMM.

The synchronous upregulation of a specific protein cluster in the blood predicts both colorectal cancer risk and patient immune status.

BACKGROUND: Early detection and treatment of colorectal cancer (CRC) is crucial for improving patient survival rates. This study aims to identify signature molecules associated with CRC, which can serve as valuable indicators for clinical hematological screening. METHOD: We have systematically searched the Human Protein Atlas database and the relevant literature for blood protein-coding genes. The CRC dataset from TCGA was used to compare the acquired genes and identify differentially expressed molecules (DEMs). Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify modules of co-expressed molecules and key molecules within the DEMs. Signature molecules of CRC were then identified from the key molecules using machine learning. These findings were further validated in clinical samples. Finally, Logistic regression was used to create a predictive model that calculated the likelihood of CRC in both healthy individuals and CRC patients. We evaluated the model's sensitivity and specificity using the ROC curve. RESULT: By utilizing the CRC dataset, WGCNA analysis, and machine learning, we successfully identified seven signature molecules associated with CRC from 1478 blood protein-coding genes. These markers include S100A11, INHBA, QSOX2, MET, TGFBI, VEGFA and CD44. Analyzing the CRC dataset showed its potential to effectively discriminate between CRC and normal individuals. The up-regulated expression of these markers suggests the existence of an immune evasion mechanism in CRC patients and is strongly correlated with poor prognosis. CONCLUSION: The combined detection of the seven signature molecules in CRC can significantly enhance diagnostic efficiency and serve as a novel index for hematological screening of CRC.

Machine learning-based gait adaptation dysfunction identification using CMill-based gait data.

Background: Combining machine learning (ML) with gait analysis is widely applicable for diagnosing abnormal gait patterns. Objective: To analyze gait adaptability characteristics in stroke patients, develop ML models to identify individuals with GAD, and select optimal diagnostic models and key classification features. Methods: This study was investigated with 30 stroke patients (mean age 42.69 years, 60% male) and 50 healthy adults (mean age 41.34 years, 58% male). Gait adaptability was assessed using a CMill treadmill on gait adaptation tasks: target stepping, slalom walking, obstacle avoidance, and speed adaptation. The preliminary analysis of variables in both groups was conducted using t-tests and Pearson correlation. Features were extracted from demographics, gait kinematics, and gait adaptability datasets. ML models based on Support Vector Machine, Decision Tree, Multi-layer Perceptron, K-Nearest Neighbors, and AdaCost algorithm were trained to classify individuals with and without GAD. Model performance was evaluated using accuracy (ACC), sensitivity (SEN), F1-score and the area under the receiver operating characteristic (ROC) curve (AUC). Results: The stroke group showed a significantly decreased gait speed (p = 0.000) and step length (SL) (p = 0.000), while the asymmetry of SL (p = 0.000) and ST (p = 0.000) was higher compared to the healthy group. The gait adaptation tasks significantly decreased in slalom walking (p = 0.000), obstacle avoidance (p = 0.000), and speed adaptation (p = 0.000). Gait speed (p = 0.000) and obstacle avoidance (p = 0.000) were significantly correlated with global F-A score in stroke patients. The AdaCost demonstrated better classification performance with an ACC of 0.85, SEN of 0.80, F1-score of 0.77, and ROC-AUC of 0.75. Obstacle avoidance and gait speed were identified as critical features in this model. Conclusion: Stroke patients walk slower with shorter SL and more asymmetry of SL and ST. Their gait adaptability was decreased, particularly in obstacle avoidance and speed adaptation. The faster gait speed and better obstacle avoidance were correlated with better functional mobility. The AdaCost identifies individuals with GAD and facilitates clinical decision-making. This advances the future development of user-friendly interfaces and computer-aided diagnosis systems.

Establishment of a diagnostic model for urinary calculi in pregnant women: A retrospective cohort study.

OBJECTIVE: The diagnosis of symptomatic urinary stones during pregnancy is challenging; ultrasonography has a low specificity and sensitivity for diagnosing urinary stones. This study aimed to develop a clinical diagnostic model to assist clinicians in distinguishing symptomatic urinary stones in pregnant women. METHODS: In this retrospective cohort study, we consecutively collected clinical data from pregnant women who presented with acute abdominal, lumbar, and lumbar and abdominal pain at the emergency department of our hospital between January 1, 2017, and December 31, 2019. To distinguish patients with urinary calculi from those without, we reviewed the follow-up records within 2 weeks post-consultation, ultrasonography results within 2 weeks, or self-reports of stone passage within 2 weeks. We selected risk factors from the baseline clinical and laboratory data of patients to establish a diagnostic model. RESULTS: Of the total patients included in the study, 105 patients were diagnosed as having symptomatic urinary stones and 126 were determined to have abdominal pain for reasons other than urinary stones. The initial model had an area under the curve (AUC) of 0.9966. The No-Lab Model had an AUC of 0.9856. The Lab Model had an AUC of 0.832. The Stone Model had an AUC of 0.9952. The simplified Stone Model did not show a decrease in discriminative ability. CONCLUSION: Of the four diagnostic models that we established for preliminary diagnosis of symptomatic urinary tract stones in pregnant women, the simplified Stone Model demonstrated excellent performance. Users can scan quick response codes to access web-based diagnostic model interfaces, facilitating easy clinical operation.

Identification of a Predictive Model for Schizophrenia Based on SNPs in a Chinese Population.

Background: Schizophrenia is a devastating mental disease with high heritability. A growing number of susceptibility genes associated with schizophrenia, as well as their corresponding SNPs loci, have been revealed by genome-wide association studies. However, using SNPs as predictors of disease and diagnosis remains difficult. Here, we aimed to uncover susceptibility SNPs in a Chinese population and to construct a prediction model for schizophrenia. Methods: A total of 210 participants, including 70 patients with schizophrenia, 70 patients with bipolar disorder, and 70 healthy controls, were enrolled in this study. We estimated 14 SNPs using published risk loci of schizophrenia, and used these SNPs to build a model for predicting schizophrenia via comparison of genotype frequencies and regression. We evaluated the efficacy of the diagnostic model in schizophrenia and control patients using ROC curves and then used the 70 patients with bipolar disorder to evaluate the model's differential diagnostic efficacy. Results: 5 SNPs were selected to construct the model: rs148415900, rs71428218, rs4666990, rs112222723 and rs1716180. Correlation analysis results suggested that, compared with the risk SNP of 0, the risk SNP of 3 was associated with an increased risk of schizophrenia (OR = 13.00, 95% CI: 2.35-71.84, p = 0.003). The ROC-AUC of this prediction model for schizophrenia was 0.719 (95% CI: 0.634-0.804), with the greatest sensitivity and specificity being 60% and 80%, respectively. The ROC-AUC of the model in distinguishing between schizophrenia and bipolar disorder was 0.591 (95% CI: 0.497-0.686), with the greatest sensitivity and specificity being 60% and 55.7%, respectively. Conclusion: The SNP risk score prediction model had good performance in predicting schizophrenia. To the best of our knowledge, previous studies have not applied SNP-based models to differentiate between cases of schizophrenia and other mental illnesses. It could have several potential clinical applications, including shaping disease diagnosis, treatment, and outcomes.

Construction of a diagnostic model for ischemic stroke based on immune-related genes.

INTRODUCTION: This study aimed to screen immune-related marker genes of ischemic stroke (IS). MATERIAL AND METHODS: Two IS-related gene expression datasets were downloaded. The significantly differentially expressed genes (DEGs) and miRNAs (DEMs) between IS and control groups were selected. The differential immune cells were analysed. Weighted gene co-expression network analysis (WGCNA) was applied to analyse immune-related genes, followed by function analysis and interaction network construction. Then, key genes were further screened using optimization algorithm to construct a diagnostic model. Finally, miRNA regulatory network of several key genes was established. RESULTS: In total 321 DEGs and 140 DEMs were obtained. 11 immune cell types were significantly different between IS and control groups. WGCNA identified two key modules, involving 202 differential immune genes. The greenyellow module was enriched in biological processes and pathways associated with T cells, while the midnightblue module was mainly associated with apoptosis, and inflammatory response-related functions and pathways. Protein interaction network identified 10 hub nodes, such as CD8A, ITGAM and TLR4. LASSO regression selected 8 key feature genes, and a risk score model was established. Key model genes were enriched in 63 GO biological processes, such as microglial cell activation, and B cell apoptotic process, and 3 KEGG pathways, such as negative regulation of nuclear cell cycle DNA replication, and hematopoietic cell lineage. Finally, a total of 25 miRNA-target relationship pairs were obtained. CONCLUSIONS: This study identified some immune-related marker genes and constructed a diagnostic model based on 8 immune-related genes in IS.

Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis.

Background: Progress in research on expression profiles in osteoarthritis (OA) has been limited to individual tissues within the joint, such as the synovium, cartilage, or meniscus. This study aimed to comprehensively analyze the common gene expression characteristics of various structures in OA and construct a diagnostic model. Methods: Three datasets were selected: synovium, meniscus, and knee joint cartilage. Modular clustering and differential analysis of genes were used for further functional analyses and the construction of protein networks. Signature genes with the highest diagnostic potential were identified and verified using external gene datasets. The expression of these genes was validated in clinical samples by Real-time (RT)-qPCR and immunohistochemistry (IHC) staining. This study investigated the status of immune cells in OA by examining their infiltration. Results: The merged OA dataset included 438 DEGs clustered into seven modules using WGCNA. The intersection of these DEGs with WGCNA modules identified 190 genes. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest algorithms, nine signature genes were identified (CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3), each demonstrating substantial diagnostic potential (areas under the curve from 0.701 to 0.925). Furthermore, dysregulation of various immune cells has also been observed. Conclusion: CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3 demonstrated significant diagnostic efficacy in OA and are involved in immune cell infiltration.

A diagnostic model for differentiating tuberculous spondylodiscitis from pyogenic spondylodiscitis based on pathogen-confirmed patients.

OBJECTIVE: This study aimed to distinguish tuberculous spondylodiscitis (TS) from pyogenic spondylodiscitis (PS) based on laboratory, magnetic resonance imaging (MRI) and computed tomography (CT) findings. Further, a novel diagnostic model for differential diagnosis was developed. METHODS: We obtained MRI, CT and laboratory data from TS and PS patients. Predictive models were built using binary logistic regression analysis. The receiver operating characteristic curve was analyzed. Both internal and external validation was performed. RESULTS: A total of 81 patients with PS (n = 46) or TS (n = 35) were enrolled. All patients had etiological evidence from the focal lesion. Disc signal or height preservation, skip lesion or multi segment (involved segments ≥ 3) involvement, paravertebral calcification, massive sequestra formation, subligamentous bone destruction, bone erosion with osteosclerotic margin, higher White Blood Cell Count (WBC) and positive result of tuberculosis infection T cell spot test (T-SPOT.TB) were more prevalent in the TS group. A diagnostic model was developed and included four predictors: WBC<7.265 * (10^9/L), skip lesion or involved segments ≥ 3, massive sequestra formation and subligamentous bone destruction. The model showed good sensitivity, specificity, and total accuracy (91.4%, 95.7%, and 93.8%, respectively); the area under the receiver operating characteristic curve (AUC) was 0.981, similar to the results of internal validation using bootstrap resampling (1000 replicates) and external validation set, indicating good clinical predictive ability. CONCLUSIONS: This study develop a good diagnostic model based on both CT and MRI, as well as laboratory findings, which may help clinicians distinguish between TS and PS.

Relationship of dietary nutrients with early childhood caries and caries activity among children aged 3-5 years-a cross-sectional study.

BACKGROUND: Early childhood caries (ECC) is a challenge for pediatric dentists all over the world, and dietary factor is an important factor affecting the occurrence of ECC. Currently, there is limited research on the impact of dietary nutrient intake from Chinese diets on ECC. The purpose of this study is to explore the correlation of dietary nutrients intake with ECC and caries activity (CA) among children aged 3-5 years, and to provide dietary guidance to slow down the occurrence and development of ECC. METHODS: A cross-sectional study was conducted in 2022. A total of 155 children were divided into three groups: caries-free group, ECC group and Severe early childhood caries (SECC) group according to the caries statues. And according to the caries activity test (CAT) value, they were also divided into three group: low CA group (L-CA), middle CA group (M-CA) and high CA group (H-CA). The 24-hour dietary intake information was collected by mobile phone application (APP). The intake of children's daily dietary nutrients were calculated referring to "China Food Composition Tables". RESULTS: In this study, 17, 39,and 99 children were diagnosed with caries-free, ECC, and SECC. There were 33, 36, and 86 children diagnosed with L-CA, M-CA, and H-CA. The risk of ECC was increased with the intake of cholesterol(OR = 1.005) and magnesium (OR = 1.026) and decreased with the intake of iron (OR = 0.770). The risk of SECC was increased with the intake of cholesterol (OR = 1.003). The risk of high CA was increased with the intake of cholesterol (OR = 1.002). The combined application of dietary total calories, carbohydrate, cholesterol, sodium, magnesium and selenium in the diagnosis of ECC had an area under ROC curve of 0.741. CONCLUSIONS: The increased dietary cholesterol intake may be a common risk factor for ECC and high CA in children aged 3-5. The combined application of dietary intake of total calories, carbohydrate, cholesterol, sodium, magnesium and selenium has a higher predictive value for the occurrence of ECC.

Identification and validation of ferroptosis-related hub genes and immune infiltration in liver ischemia-reperfusion injury.

Ischemia-reperfusion injury (IRI) is a cumulation of pathophysiological processes that involves cell and organelle damage upon blood flow constraint and subsequent restoration. However, studies on overall immune infiltration and ferroptosis in liver ischemia-reperfusion injury (LIRI) are limited. This study explored immune cell infiltration and ferroptosis in LIRI using bioinformatics and experimental validation. The GSE151648 dataset, including 40 matched pairs of pre- and post- transplant liver samples was downloaded for bioinformatic analysis. Eleven hub genes were identified by overlapping differentially expressed genes (DEGs), iron genes, and genes identified through weighted gene co-expression network analysis (WGCNA). Subsequently, the pathway enrichment, transcription factor-target, microRNA-mRNA and protein-protein interaction networks were investigated. The diagnostic model was established by logistic regression, which was validated in the GSE23649 and GSE100155 datasets and verified using cytological experiments. Moreover, several drugs targeting these genes were found in DrugBank, providing a more effective treatment for LIRI. In addition, the expression of 11 hub genes was validated using quantitative real-time polymerase chain reaction (qRT-PCR) in liver transplantation samples and animal models. The expression of the 11 hub genes increased in LIRI compared with the control. Five genes were significantly enriched in six biological process terms, six genes showed high enrichment for LIRI-related signaling pathways. There were 56 relevant transcriptional factors and two central modules in the protein-protein interaction network. Further immune infiltration analysis indicated that immune cells including neutrophils and natural killer cells were differentially accumulated in the pre- and post-transplant groups, and this was accompanied by changes in immune-related factors. Finally, 10 targeted drugs were screened. Through bioinformatics and further experimental verification, we identified hub genes related to ferroptosis that could be used as potential targets to alleviate LIRI.