BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes of a hypertrophic phenotype. This study sought to (1) evaluate the performance of artificial-intelligence (AI) derived measurements incorporated into the established multiparametric echocardiographic scoring system to detect CA; (2) develop and validate an AI-based deep-learning model for video-based detection of CA on echocardiography. METHODS: The study population comprised 5776 patients (CA, 2756; controls, 3020). The training data set included patients from the UK National Amyloidosis Center and Taiwan MacKay Memorial Hospital (CA, 2241; controls, 2130). External test data sets were obtained from the US Duke University Health System (CA, 334; left ventricular hypertrophy controls, 668) and Japan National Cerebral and Cardiovascular Center (CA, 181; left ventricular hypertrophy controls, 222). RESULTS: The multiparametric echocardiographic score computed using AI-derived measurements achieved an accuracy of 79.5% (sensitivity, 75.4%; specificity, 81.5%) in the United States cohort and 79.7% (sensitivity, 81.6%; specificity, 78.1%) in the Japan cohort. The deep-learning model demonstrated accuracies of 96.2% (sensitivity, 96.8%; specificity, 95.7%) and 95.8% (sensitivity, 97.3%; specificity, 94.3%) in the internal validation and internal test sets, respectively. External validation of the deep-learning model showed accuracies of 87.5% (sensitivity, 86.6%; specificity, 87.9%) in the United States and 88.4% (sensitivity, 92.3%; specificity, 85.3%) in the Japanese cohort. Subgroup analysis demonstrated that the deep-learning model showed robust discrimination of CA from other hypertrophic phenocopies: CA versus hypertension (area under the curve [AUC], 0.92 [95% CI, 0.91-0.94]), CA versus hypertrophic cardiomyopathy (AUC, 0.91 [95% CI, 0.87-0.94]), CA versus aortic stenosis (AUC, 0.93 [95% CI, 0.90-0.95]), CA versus chronic kidney disease (AUC, 0.93 [95% CI, 0.91-0.95]). The deep-learning model was able to classify a greater proportion of patients compared with the AI-derived multiparametric echocardiographic score and achieved superior diagnostic accuracy (AUC, 0.93 [95% CI, 0.91-0.95] versus AUC, 0.88 [95% CI, 0.85-0.90]; P<0.001). CONCLUSIONS: Both the multiparametric echocardiographic score computed from AI-derived measurements and the fully automated deep-learning model can accurately identify patients with CA in globally diverse cohorts, with the deep-learning model providing superior performance.
Introduction: Hepatocellular carcinoma (HCC) can develop in individuals with metabolic dysfunction-associated steatotic liver disease (MASLD) without cirrhosis, especially in those with elevated liver enzymes. However, there is currently no low-cost, scalable screening tool for this population. To address this need, we investigated the association between serial changes in FIB-4 (fibrosis-4) scores over a 3-year period and the risk of developing HCC in a large, diverse cohort of patients with MASLD without cirrhosis. Methods: This study utilized a nationwide cohort from the National Health Insurance Research Database (NHIRD), including 810,698 patients with MASLD who had demographic and laboratory data at baseline and at least 3 years of follow-up. FIB-4 scores were analyzed to assess transitions between low-risk (<1.45), indeterminate-risk (1.45-2.67), and high-risk (>2.67) categories over time. Competing risks for HCC and mortality were modeled to estimate the sub-distribution hazard ratios (SHRs). Results: Changes in FIB-4 scores over time provided superior predictive accuracy compared to one-time measurement. Individuals with persistent high FIB-4 (high-to-high group) had a 14-fold higher risk of developing HCC (adjusted SHR [aSHR] 13.91, 95% CI: 11.94-16.20) compared to those with stably low FIB-4 (low-to-low group), while those with improved scores (high-to-low and high-to-indeterminate groups) had a lower risk compared to the high-to-high group. Worsening FIB-4 scores, as shown in the low-to-indeterminate group (aSHR 2.22, 95% CI: 1.93-2.57 or the low-to-high group (aSHR 4.75, 95% CI: 3.38-6.68), were associated with progressively increased risks of HCC compared to the low-to-low group. In contrast, when compared to the high-to-high group, those with improved FIB-4 scores, including the high-to-indeterminate group (aSHR 0.41, 95% CI: 0.35-0.48) and the high-to-low group (aSHR 0.25, 95% CI: 0.15-0.42), exhibited reduced HCC risks. Conclusion: Serial FIB-4 measurements offer greater accuracy in identifying individuals at high risk for HCC in non-cirrhotic MASLD Asians with elevated liver enzymes, with HCC surveillance warranted in high-risk groups.
We present the case of a 12-year-old post-menarchal female (bone age: 13 years; 30th percentile for height) who received an initial gonadotropin-releasing hormone (GnRH) analogue injection for height augmentation to maximize adult height potential prior to imminent epiphyseal fusion. Immediately following the injection, she developed acute-onset abdominal pain, emesis, and diarrhea. Refractory to conservative management, her clinical status prompted pelvic ultrasonography and subsequent contrast-enhanced CT imaging, which revealed significant bilateral ovarian enlargement, multiple cystic masses, and congestion of the left vascular pedicle highly indicative of adnexal torsion. Urgent surgical exploration confirmed bilateral ovarian cyst formation and left-sided ovarian torsion. Following an unremarkable post-operative recovery, GnRH analogue therapy was permanently discontinued. This case underscores the critical need to recognize the heightened risk of rapid ovarian hyperstimulation and subsequent torsion when initiating these medications in post-menarchal or late pubertal females, in whom mature follicles are already present. While GnRH analogues generally maintain a favorable safety profile, clinician vigilance regarding this severe ovarian complication in older pediatric cohorts is vital to prevent diagnostic delays and ensure prompt surgical intervention.
In this study, a fungal strain Diaporthe middletonii Km3279, was isolated from the coralline alga Corallina declinata collected in Lieyu Township, Kinmen County, Taiwan, and subsequently identified. Liquid fermentation was carried out using the OSMAC (one strain, many compounds) strategy, with cultures grown in freshwater potato dextrose broth, brackish water (17 ‰) with malt extract (ME) medium, and freshwater ME medium supplemented with 0.03 % potassium iodide for 14-21 days. Following fermentation, all culture broths were extracted with ethyl acetate. The resulting dried extracts were then sequentially separated and purified by Sephadex LH-20 open-column chromatography, medium-pressure liquid chromatography, and high-performance liquid chromatography. This process led to the isolation and characterization of seven previously undescribed polyketides, designated diaportonicins A-G (1-7), along with five known compounds: periconsin D (8), cryptosporiopsinol (9), periconsin B (10), periconsin E (11), and (S)-6-hydroxymellein (12). In anti-neuroinflammatory assays, compounds 5, 8, 11, and 12 showed potent inhibitory effects on nitric oxide production in murine microglial BV-2 cells with IC50 values of 1.39 ± 0.26, 0.58 ± 0.07, 9.86 ± 1.91, and 6.19 ± 1.30 μM, respectively, and without detectable cytotoxicity. In addition, compounds 5, 7, and 9 exhibited moderate to strong anti-angiogenic activities by inhibiting tube formation in endothelial progenitor cells, with IC50 values of 8.00 ± 1.00, 5.00 ± 1.00, and 26.00 ± 2.00 μM, respectively. A putative biosynthetic pathway for compounds 1-11 is also proposed.
BACKGROUND:Neuropathic pain presents a significant clinical challenge, with spinal cord epigenetic mechanisms playing a critical role in its development. This study investigated the impact of nerve injury on the Barrier-to-Autointegration Factor (BAF) in the rat spinal dorsal horn. METHODS:Adult Sprague-Dawley rats underwent spinal nerve ligation (SNL) to model neuropathic pain. Pain behaviors were assessed using von Frey and burrow tests. Biochemical analyses measured mRNA and protein expression in the dorsal horn. RESULTS:SNL elevated BAF levels, which interacts with LEM domain-containing protein 2 (LEMD2), activating the histone-modifying enzyme EZH2. This enzyme adds a gene-silencing mark, H3K27me3, to the promoter region of the Oprm1 gene, which encodes the mu-opioid receptor. Consequently, the expression of the mu-opioid receptor is decreased, potentially contributing to neuropathic pain. Using gene knockdown techniques to reduce BAF expression, we reversed the changes in LEMD2, EZH2, and mu-opioid receptor expressions induced by SNL and attenuated mechanical allodynia. Additionally, knocking down LEMD2 disrupted the binding of BAF to the Oprm1 promoter, without affecting BAF levels. Inhibiting EZH2 also reversed the signaling without altering BAF and LEMD2 levels. Glutamate activated BAF pathways via pNR2B receptors, and NR2B receptor blockade reversed this effect. CONCLUSION:These findings suggest that spinal pNR2B receptors may activate BAF, which interacts with LEMD2 to enhance EZH2-mediated H3K27me3 at the mu-opioid receptor promoter after nerve injury. Targeting this pathway may offer novel strategies to inhibit neuropathic pain.
