Galectin-3 contributes to pathogenesis of IgA nephropathy.

IgA nephropathy (IgAN) is the most common type of glomerulonephritis that frequently progresses to kidney failure. However, the molecular pathogenesis underlying IgAN remains largely unknown. Here, we investigated the role of galectin-3 (Gal-3), a galactoside-binding protein in IgAN pathogenesis, and showed that Gal-3 expression by the kidney was significantly enhanced in patients with IgAN. In both TEPC-15 hybridoma-derived IgA-induced, passive, and spontaneous "grouped" ddY IgAN models, Gal-3 expression was clearly increased with disease severity in the glomeruli, peri-glomerular regions, and some kidney tubules. Gal-3 knockout (KO) in the passive IgAN model had significantly improved proteinuria, kidney function and reduced severity of kidney pathology, including neutrophil infiltration and decreased differentiation of Th17 cells from kidney-draining lymph nodes, despite increased percentages of regulatory T cells. Gal-3 KO also inhibited the NLRP3 inflammasome, yet it enhanced autophagy and improved kidney inflammation and fibrosis. Moreover, administration of 6-de-O-sulfated, N-acetylated low-molecular-weight heparin, a competitive Gal-3 binding inhibitor, restored kidney function and improved kidney lesions in passive IgAN mice. Thus, our results suggest that Gal-3 is critically involved in IgAN pathogenesis by activating the NLRP3 inflammasome and promoting Th17 cell differentiation. Hence, targeting Gal-3 action may represent a new therapeutic strategy for treatment of this kidney disease.

Interstitial fibrosis increases the risk of end-stage kidney disease in patients with lupus nephritis.

OBJECTIVE: To evaluate the risk of end-stage kidney disease (ESKD) in lupus nephritis (LN) patients using tubulointerstitial lesion scores. METHODS: Clinical profiles and histopathological presentations of 151 biopsy-proven LN patients were retrospectively examined. Risk factors of ESKD based on characteristics and scoring of their tubulointerstitial lesions (e.g. interstitial inflammation [II], tubular atrophy [TA], and interstitial fibrosis [IF]) were analyzed. RESULTS: The mean age of 151 LN patients was 36 years old, and 136 (90.1%) were female. The LN cases examined included: class I/II (n = 3, 2%), class III/IV (n = 119, 78.8%), class V (n = 23, 15.2%), and class VI (n = 6, 4.0%). The mean serum creatinine level was 1.4 mg/dl. Tubulointerstitial lesions were recorded in 120 (79.5%) patients. Prior to receiving renal biopsy, 9 (6.0%) patients developed ESKD. During the follow-up period (mean, 58 months), an additional 47 patients (31.1%) progressed to ESKD. Multivariate analyses identified serum creatinine (hazard ratio [HR]: 1.7, 95% confidence interval [CI]: 1.42-2.03, p < 0.001) and IF (HR: 3.2, 95% CI: 1.58-6.49, p = 0.001) as independent risk factors of ESKD. Kaplan-Meier analysis further confirmed a heightened risk of ESKD associated with IF. CONCLUSION: Tubulointerstitial involvement is commonly observed in histopathological presentation of LN. However, IF, rather than II, or TA, was found to increase the risk of ESKD in our cohort. Therefore, to predict renal outcome in LN patients prior to adjusting immunosuppressive treatment, degree of IF should be reviewed.

Immune-stealth VP28-conjugated heparin nanoparticles for enhanced and reversible anticoagulation.

Heparins are a family of sulfated linear negatively charged polysaccharides that have been widely used for their anticoagulant, antithrombotic, antitumor, anti-inflammatory, and antiviral properties. Additionally, it has been used for acute cerebral infarction relief as well as other pharmacological actions. However, heparin's self-aggregated macrocomplex may reduce blood circulation time and induce life-threatening thrombocytopenia (HIT) complicating the use of heparins. Nonetheless, the conjugation of heparin to immuno-stealth biomolecules may overcome these obstacles. An immunostealth recombinant viral capsid protein (VP28) was expressed and conjugated with heparin to form a novel nanoparticle (VP28-heparin). VP28-heparin was characterized and tested to determine its immunogenicity, anticoagulation properties, effects on total platelet count, and risk of inducing HIT in animal models. The synthesized VP28-heparin trimeric nanoparticle was non-immunogenic, possessed an average hydrodynamic size (8.81 ± 0.58 nm) optimal for the evasion renal filtration and reticuloendothelial system uptake (hence prolonging circulating half-life). Additionally, VP28-heparin did not induce mouse death or reduce blood platelet count when administered at a high dosein vivo(hence reducing HIT risks). The VP28-heparin nanoparticle also exhibited superior anticoagulation properties (2.2× higher prothrombin time) and comparable activated partial thromboplastin time, but longer anticoagulation period when compared to unfractionated heparin. The anticoagulative effects of the VP28-heparin can also be reversed using protamine sulfate. Thus, VP28-heparin may be an effective and safe heparin derivative for therapeutic use.

Artificial Intelligence-Assisted Diagnosis of Anterior Cruciate Ligament Tears From Magnetic Resonance Images: Algorithm Development and Validation Study.

BACKGROUND: Anterior cruciate ligament (ACL) injuries are common in sports and are critical knee injuries that require prompt diagnosis. Magnetic resonance imaging (MRI) is a strong, noninvasive tool for detecting ACL tears, which requires training to read accurately. Clinicians with different experiences in reading MR images require different information for the diagnosis of ACL tears. Artificial intelligence (AI) image processing could be a promising approach in the diagnosis of ACL tears. OBJECTIVE: This study sought to use AI to (1) diagnose ACL tears from complete MR images, (2) identify torn-ACL images from complete MR images with a diagnosis of ACL tears, and (3) differentiate intact-ACL and torn-ACL MR images from the selected MR images. METHODS: The sagittal MR images of torn ACL (n=1205) and intact ACL (n=1018) from 800 cases and the complete knee MR images of 200 cases (100 torn ACL and 100 intact ACL) from patients aged 20-40 years were retrospectively collected. An AI approach using a convolutional neural network was applied to build models for the objective. The MR images of 200 independent cases (100 torn ACL and 100 intact ACL) were used as the test set for the models. The MR images of 40 randomly selected cases from the test set were used to compare the reading accuracy of ACL tears between the trained model and clinicians with different levels of experience. RESULTS: The first model differentiated between torn-ACL, intact-ACL, and other images from complete MR images with an accuracy of 0.9946, and the sensitivity, specificity, precision, and F1-score were 0.9344, 0.9743, 0.8659, and 0.8980, respectively. The final accuracy for ACL-tear diagnosis was 0.96. The model showed a significantly higher reading accuracy than less experienced clinicians. The second model identified torn-ACL images from complete MR images with a diagnosis of ACL tear with an accuracy of 0.9943, and the sensitivity, specificity, precision, and F1-score were 0.9154, 0.9660, 0.8167, and 0.8632, respectively. The third model differentiated torn- and intact-ACL images with an accuracy of 0.9691, and the sensitivity, specificity, precision, and F1-score were 0.9827, 0.9519, 0.9632, and 0.9728, respectively. CONCLUSIONS: This study demonstrates the feasibility of using an AI approach to provide information to clinicians who need different information from MRI to diagnose ACL tears.