The role of left ventricular hypertrophy measured by echocardiography in screening patients with ischaemia with non-obstructive coronary arteries: a cross-sectional study.

Many patients with ischaemia with non-obstructive coronary arteries (INOCA) have a poor prognosis. This study aims to explore the diagnostic value of left ventricular hypertrophy (LVH)-related ultrasound parameters in INOCA patients. The study group consisted of 258 patients with INOCA in this retrospective cross-sectional study, and these patients were free of obstructive coronary artery disease, previous revascularization, atrial fibrillation, ejection fraction < 50%, major distortions of left ventricular geometry, suspected non-ischaemic causes. Control individuals were matched 1:1 with study group according to age, sex, cardiovascular risk factors, and time of hospital stay. According to left ventricular mass index (LVMI) and relative wall thickness, left ventricular geometry was composed of concentric hypertrophy, eccentric hypertrophy, concentric remodeling and normal geometry. LVH-related parameters, left ventricular geometry, demographic characteristics, laboratory parameters and other echocardiographic indicators were compared between the two groups. Subgroup analysis was performed based on sex. LVMI in the study group was higher than that in the control group (86.86 ± 18.83 g/m2 vs 82.25 ± 14.29 g/m2, P = 0.008). The ratio of LVH was higher in the study group (20.16% vs 10.85%, P = 0.006). After subgroup analysis based on sex, LVMI differences (85.77 ± 18.30 g/m2 vs 81.59 ± 14.64 g/m2, P = 0.014) and the ratio of LVH differences (25.00% vs 14.77%, P = 0.027) still existed in females between the two groups. There was no difference in the constituent ratio of left ventricular geometry between the two groups (P = 0.157). Sex-based subgroup analysis showed no difference in the constituent ratio of left ventricular geometry between the two groups in females (P = 0.242). The degree of LVH in the study group was higher than that in the control group, suggesting that LVH may play an important role in the occurrence and development of INOCA. Moreover, LVH-related ultrasound parameters may be of higher diagnostic value for female INOCA patients than for male INOCA patients.

PTC-MAS: A Deep Learning-Based Preoperative Automatic Assessment of Lymph Node Metastasis in Primary Thyroid Cancer.

BACKGROUND: Identifying cervical lymph node metastasis (LNM) in primary thyroid cancer preoperatively using ultrasound is challenging. Therefore, a non-invasive method is needed to assess LNM accurately. PURPOSE: To address this need, we developed the Primary Thyroid Cancer Lymph Node Metastasis Assessment System (PTC-MAS), a transfer learning-based and B-mode ultrasound images-based automatic assessment system for assessing LNM in primary thyroid cancer. METHODS: The system has two parts: YOLO Thyroid Nodule Recognition System (YOLOS) for obtaining regions of interest (ROIs) of nodules, and LMM assessment system for building the LNM assessment system using transfer learning and majority voting with extracted ROIs as input. We retained the relative size features of nodules to improve the system's performance. RESULTS: We evaluated three transfer learning-based neural networks (DenseNet, ResNet, and GoogLeNet) and majority voting, which had the area under the curves (AUCs) of 0.802, 0.837, 0.823, and 0.858, respectively. Method III preserved relative size features and achieved higher AUCs than Method II, which fixed nodule size. YOLOS achieved high precision and sensitivity on a test set, indicating its potential for ROIs extraction. CONCLUSIONS: Our proposed PTC-MAS system effectively assesses primary thyroid cancer LNM based on preserving nodule relative size features. It has potential for guiding treatment modalities and avoiding inaccurate ultrasound results due to tracheal interference.

Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis.

Objectives: To explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction. Methods: We retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital's cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram. Results: Age, persistent cutaneous purpura, erythrocyte distribution width, complement C3, immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was ~15-82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25~84% and 14~73%, respectively. Conclusion: The prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability. Clinical trial registration: www.chictr.org.cn, identifier ChiCTR2000033435.

[Value of galactose-deficient IgA1 in the early diagnosis of Henoch-Schönlein purpura nephritis in children].

OBJECTIVE: To explore the value of galactose-deficient IgA1 (Gd-IgA1) in the early diagnosis of Henoch-Schönlein purpura nephritis (HSPN) in children. METHODS: A total of 67 hospitalized children who were definitely diagnosed with HSPN between January and April 2018 and 58 hospitalized children with Henoch-Schönlein purpura (HSP) were enrolled in the study. Twenty children undergoing routine physical examinations served as controls. The levels of serum and urine Gd-IgA1 were determined using ELISA. The receiver operating characteristic curve was used to analyze the value of serum Gd-IgA1 and urine Gd-IgA1/urine creatinine ratio in the diagnosis of HSPN. RESULTS: The level of serum Gd-IgA1 and urine Gd-IgA1/urine creatinine ratio in children with HSP or HSPN were significantly higher than those in healthy control group (P<0.01), with a significantly greater increase observed in children with HSPN (P<0.01). Serum Gd-IgA1 ≥1 485.57 U/mL and/or urine Gd-IgA1/urine creatinine ratio ≥105.74 were of favorable value in the diagnosis of HSPN. During the six-month follow-up of the 49 children with HSP, the incidence of HSPN was 47% (23/49), which included a 100% incidence in children with serum Gd-IgA1 ≥1 485.57 U/mL and a 73% incidence in children with urine Gd-IgA1/urine creatinine ratio ≥105.74. CONCLUSIONS: Serum and urine Gd-IgA1 is of favorable clinical value in the early diagnosis of HSPN.