Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically.

This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropathy (IgAN) and non-IgAN.We prospectively enrolled patients with chronic kidney disease who underwent renal biopsy from May 2022 to December 2022 and performed an ultrasound and SMI the day before renal biopsy. The selected patients were randomly divided into training and testing cohorts in a 7:3 ratio. We extracted DL and radiometric features from the two-dimensional ultrasound and SMI images. A combined nomograph model was developed by combining the predictive probability of DL with clinical factors using multivariate logistic regression analysis. The proposed model's utility was evaluated using receiver operating characteristics, calibration, and decision curve analysis. In this study, 120 patients with primary glomerular disease were included, including 84 in the training and 36 in the test cohorts. In the testing cohort, the ROC of the radiomics model was 0.816 (95% CI:0.663-0.968), and the ROC of the DL model was 0.844 (95% CI:0.717-0.971). The nomogram model combined with independent clinical risk factors (IgA and hematuria) showed strong discrimination, with an ROC of 0.884 (95% CI:0.773-0.996) in the testing cohort. Decision curve analysis verified the clinical practicability of the combined nomogram. The combined nomogram model based on SMI can accurately and noninvasively distinguish IgAN from non-IgAN and help physicians make clearer patient treatment plans.

Noninvasive Evaluation of Lupus Nephritis Activity Using a Radiomics Machine Learning Model Based on Ultrasound.

Introduction: To explore whether ultrasonic radiomics extracted by machine learning method can noninvasively evaluate lupus nephritis (LN) activity. Materials and Methods: This retrospective study included 149 patients with LN diagnosed by renal biopsy. They were divided into a training cohort (n=104) and a test cohort (n=45). Ultrasonic radiomics features were extracted from the ultrasound images, and the radiomics features were constructed. Furthermore, five machine learning algorithms were compared to evaluate LN activity. The performance of the binary classification model was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The average AUC of the five machine learning models was 79.4, of which the MLP model was the best. The AUC of the training group was 89.1, with an accuracy of 81.7%, a sensitivity of 83%, a specificity of 80.7%, a negative predictive value of 85.2%, and a positive predictive value of 78%. The AUC of the test group was 82.2, the accuracy was 73.3%, the sensitivity was 78.9%, the specificity was 69.2%, the negative predictive value was 81.8%, and the positive predictive value was 65.2%. Conclusion: Machine learning classifier based on ultrasonic radiomics has high accuracy for LN activity. The model can be used to noninvasively detect the activity of LN and can be an effective tool to assist the clinical decision-making process.

A novel clinical-radiomic nomogram for the crescent status in IgA nephropathy.

Objective: We used machine-learning (ML) models based on ultrasound radiomics to construct a nomogram for noninvasive evaluation of the crescent status in immunoglobulin A (IgA) nephropathy. Methods: Patients with IgA nephropathy diagnosed by renal biopsy (n=567) were divided into training (n=398) and test cohorts (n=169). Ultrasound radiomic features were extracted from ultrasound images. After selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm, three ML algorithms were assessed for final radiomic model establishment. Next, clinical, ultrasound radiomic, and combined clinical-radiomic models were compared for their ability to detect IgA crescents. The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis. Results: The average area under the curve (AUC) of the three ML radiomic models was 0.762. The logistic regression model performed best, with AUC values in the training and test cohorts of 0.838 and 0.81, respectively. Among the final models, the combined model based on clinical characteristics and the Rad score showed good discrimination, with AUC values in the training and test cohorts of 0.883 and 0.862, respectively. The decision curve analysis verified the clinical practicability of the combined nomogram. Conclusion: ML classifier based on ultrasound radiomics has a potential value for noninvasive diagnosis of IgA nephropathy with or without crescents. The nomogram constructed by combining ultrasound radiomic and clinical features can provide clinicians with more comprehensive and personalized image information, which is of great significance for selecting treatment strategies.

Primary intraspinal benign tumors treated surgically: an analysis from China.

PURPOSE: The purpose of this study was to review the clinical features in a cohort of Chinese patients with primary intraspinal benign tumors. METHODS: This retrospective study included a consecutive series of patients with intraspinal benign lesions who received surgery between January 2014 and October 2018 at our hospital. We collected each patient's clinical data, including age, gender, presenting symptoms, the spinal level (cervical, thoracic, lumbar, or sacral), and location (intramedullary or extramedullary) of the tumor. RESULTS: A total of 66 patients were included in this study, of whom 24 were men and 42 (63.6%) were women. The mean age was 52.5 years (range, 21-76 years). The most common symptom was sensory deficits. The most common tumor type, found in 56.1% patients, was schwannoma, followed by meningioma in 33.3% patients. The commonly performed surgery included decompression of spinal canal and excision of spinal lesions. CONCLUSION: Primary intraspinal benign tumors occur in elderly and female population and at the thoracic region. Schwannoma and meningioma are the two with higher incidence. The surgical outcome in terms of tumor excision and functional recovery is good.

TWEAK/Fn14 Signals Mediate Burn Wound Repair.

TWEAK acts by engaging with Fn14 to regulate inflammatory responses, fibrosis, and tissue remodeling, which are central in the repair processes of wounds. This study aims to explore the potential role of the TWEAK/Fn14 pathway in the healing of cutaneous burn wounds. Third-degree burns were introduced in wild-type and Fn14-deficient BALB/c mice, followed by evaluation of wound areas and histological changes. The downstream cytokines including growth factors were also examined in lesional skin. Moreover, human dermal microvascular endothelial cells and dermal fibroblasts were analyzed in vitro upon TWEAK stimulation. The healing of burn wounds was delayed in Fn14-deficient mice and was accompanied by the suppression of inflammatory responses, growth factor production, and extracellular matrix synthesis. Moreover, TWEAK/Fn14 activation enhanced the migration and cytokine production of both dermal microvascular endothelial cells and dermal fibroblasts. TWEAK also facilitates the expression of α-SMA and palladin in dermal fibroblasts. Furthermore, transfection of Fn14 small interfering RNA abrogated such promotion effect of TWEAK on these cells. In conclusion, TWEAK/Fn14 signals mediate the healing of burn wounds, possibly involving TWEAK regulation of the function of resident cells.

The deposition of anti-DNA IgG contributes to the development of cutaneous lupus erythematosus.

Anti-DNA IgG is a hallmark of systemic lupus erythematosus and induces internal injuries in patients. It is known that cutaneous lupus erythematosus (CLE) involves the deposition of autoantibodies in the dermoepidermal junction of the skin and that anti-DNA IgG binds specifically to keratinocytes. However, the definite role of anti-DNA IgG in CLE remains unclear. The purpose of this study was to elucidate the effect of anti-DNA IgG on keratinocytes in CLE. Skin tissues were collected from patients with CLE and healthy controls. Also, murine anti-DNA IgG was incubated with frozen sections of murine skin or PAM212 keratinocytes. The chemotaxis of J774.2 macrophages was evaluated in special chambers with keratinocytes under anti-DNA IgG stimulation. Enzyme-linked immunosorbent assay, flow cytometry, Western blot, and surface plasmon resonance were used to quantitate the interaction between anti-DNA IgG and keratinocyte-related self-antigens. The results showed that anti-DNA IgG could be eluted from the lesional tissues of CLE patients, depending on the serum positivity. Murine anti-DNA IgG bound preferably to the dermoepidermal zones of normal skin and specifically to collagen III and the suppressor of cytokine signalling 1 (SOCS1) but not to Ro52. Moreover, the chemotaxis of macrophages was promoted by the incubation of anti-DNA IgG with keratinocytes. Interestingly, anti-DNA IgG exaggerated both the expression and the activation of fibroblast growth factor inducible 14 (Fn14) in keratinocytes and regulated SOCS1 signals in a time-dependent manner. In conclusion, anti-DNA IgG may contribute to the development of CLE through binding to keratinocyte-related antigens, exacerbating inflammatory infiltration, and modulating Fn14 and SOCS1 pathways.