Toward Robust Canine Cardiac Diagnosis: Deep Prototype Alignment Network-Based Few-Shot Segmentation in Veterinary Medicine

In the cutting-edge domain of medical artificial intelligence (AI), remarkable advances have been achieved in areas such as diagnosis, prediction, and therapeutic interventions. Despite these advances, the technology for image segmentation faces the significant barrier of having to produce extensively annotated datasets. To address this challenge, few-shot segmentation (FSS) has been recognized as one of the innovative solutions. Although most of the FSS research has focused on human health care, its application in veterinary medicine, particularly for pet care, remains largely limited. This study has focused on accurate segmentation of the heart and left atrial enlargement on canine chest radiographs using the proposed deep prototype alignment network (DPANet). The PANet architecture is adopted as the backbone model, and experiments are conducted using various encoders based on VGG-19, ResNet-18, and ResNet-50 to extract features. Experimental results demonstrate that the proposed DPANet achieves the highest performance. In the 2way-1shot scenario, it achieves the highest intersection over union (IoU) value of 0.6966, and in the 2way-5shot scenario, it achieves the highest IoU value of 0.797. The DPANet not only signifies a performance improvement, but also shows an improved training speed in the 2way-5shot scenario. These results highlight our model's exceptional capability as a trailblazing solution for segmenting the heart and left atrial enlargement in veterinary applications through FSS, setting a new benchmark in veterinary AI research, and demonstrating its superior potential to veterinary medicine advances

DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework

Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks. Artificial intelligence technology has emerged as a promising solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study emphasizes the importance of early identification and prevention of obesity-related health issues. Factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information need to be considered for developing robust algorithms for obesity rate prediction and delivering personalized feedback. Hence, by collecting health datasets from 321 adolescents, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. The proposed system shows significant potential in effectively addressing childhood and adolescent obesity

E2-25K/Hip-2 regulates caspase-12 in ER stress–mediated Aβ neurotoxicity

Amyloid-β (Aβ) neurotoxicity is believed to contribute to the pathogenesis of Alzheimer's disease (AD). Previously we found that E2-25K/Hip-2, an E2 ubiquitin-conjugating enzyme, mediates Aβ neurotoxicity. Here, we report that E2-25K/Hip-2 modulates caspase-12 activity via the ubiquitin/proteasome system. Levels of endoplasmic reticulum (ER)–resident caspase-12 are strongly up-regulated in the brains of AD model mice, where the enzyme colocalizes with E2-25K/Hip-2. Aβ increases expression of E2-25K/Hip-2, which then stabilizes caspase-12 protein by inhibiting proteasome activity. This increase in E2-25K/Hip-2 also induces proteolytic activation of caspase-12 through its ability to induce calpainlike activity. Knockdown of E2-25K/Hip-2 expression suppresses neuronal cell death triggered by ER stress, and thus caspase-12 is required for the E2-25K/Hip-2–mediated cell death. Finally, we find that E2-25K/Hip-2–deficient cortical neurons are resistant to Aβ toxicity and to the induction of ER stress and caspase-12 expression by Aβ. E2-25K/Hip-2 is thus an essential upstream regulator of the expression and activation of caspase-12 in ER stress–mediated Aβ neurotoxicity

α-Synuclein accumulation and GBA deficiency due to L444P GBA mutation contributes to MPTP-induced parkinsonism

Abstract Background Mutations in glucocerebrosidase (GBA) cause Gaucher disease (GD) and increase the risk of developing Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB). Since both genetic and environmental factors contribute to the pathogenesis of sporadic PD, we investigated the susceptibility of nigrostriatal dopamine (DA) neurons in L444P GBA heterozygous knock-in (GBA +/L444P ) mice to 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), a selective dopaminergic mitochondrial neurotoxin. Method We used GBA +/L444P mice, α-synuclein knockout (SNCA −/− ) mice at 8 months of age, and adeno-associated virus (AAV)-human GBA overexpression to investigate the rescue effect of DA neuronal loss and susceptibility by MPTP. Mitochondrial morphology and functional assay were used to identify mitochondrial defects in GBA +/L444P mice. Motor behavioral test, immunohistochemistry, and HPLC were performed to measure dopaminergic degeneration by MPTP and investigate the relationship between GBA mutation and α-synuclein. Mitochondrial immunostaining, qPCR, and Western blot were also used to study the effects of α-synuclein knockout or GBA overexpression on MPTP-induced mitochondrial defects and susceptibility. Results L444P GBA heterozygous mutation reduced GBA protein levels, enzymatic activity and a concomitant accumulation of α-synuclein in the midbrain of GBA +/L444P mice. Furthermore, the deficiency resulted in defects in mitochondria of cortical neurons cultured from GBA +/L444P mice. Notably, treatment with MPTP resulted in a significant loss of dopaminergic neurons and striatal dopaminergic fibers in GBA +/L444P mice compared to wild type (WT) mice. Levels of striatal DA and its metabolites were more depleted in the striatum of GBA +/L444P mice. Behavioral deficits, neuroinflammation, and mitochondrial defects were more exacerbated in GBA +/L444P mice after MPTP treatment. Importantly, MPTP induced PD-like symptoms were significantly improved by knockout of α-synuclein or augmentation of GBA via AAV5-hGBA injection in both WT and GBA +/L444P mice. Intriguingly, the degree of reduction in MPTP induced PD-like symptoms in GBA +/L444P α-synuclein (SNCA) −/− mice was nearly equal to that in SNCA −/− mice after MPTP treatment. Conclusion Our results suggest that GBA deficiency due to L444P GBA heterozygous mutation and the accompanying accumulation of α-synuclein render DA neurons more susceptible to MPTP intoxication. Thus, GBA and α-synuclein play dual physiological roles in the survival of DA neurons in response to the mitochondrial dopaminergic neurotoxin, MPTP

Lysosomal Enzyme Glucocerebrosidase Protects against Aβ_1-42 Oligomer-Induced Neurotoxicity

<div><p>Glucocerebrosidase (GCase) functions as a lysosomal enzyme and its mutations are known to be related to many neurodegenerative diseases, including Gaucher’s disease (GD), Parkinson’s disease (PD), and Dementia with Lewy Bodies (DLB). However, there is little information about the role of GCase in the pathogenesis of Alzheimer’s disease (AD). Here we demonstrate that GCase protein levels and enzyme activity are significantly decreased in sporadic AD. Moreover, Aβ_1–42 oligomer treatment results in neuronal cell death that is concomitant with decreased GCase protein levels and enzyme activity, as well as impairment in lysosomal biogenesis and acidification. Importantly, overexpression of GCase promotes the lysosomal degradation of Aβ_1–42 oligomers, restores the lysosomal impairment, and protects against the toxicity in neurons treated with Aβ_1–42 oligomers. Our findings indicate that a deficiency of GCase could be involved in progression of AD pathology and suggest that augmentation of GCase activity may be a potential therapeutic option for the treatment of AD.</p></div

GCase protein levels and activity are reduced in sporadic AD brain tissues and Aβ_1–42 oligomer-treated primary neurons.

<p>(A and B) GCase expression was decreased in the hippocampal region of brains affected by Alzheimer’s disease (n = 6), as opposed to controls (n = 6). GCase expression levels were normalized to β-actin and quantified. (C) The GCase enzymatic activities were measured using lysosome-enriched fractions for the brains affected by Alzheimer’s disease and the controls (n = 6 per group). (D) Expression of <i>GBA1</i> gene was determined in AD hippocampal regions compared to the controls using real-time PCR. The β-actin mRNA was used as an internal reference control to normalize relative mRNA levels (n = 6 per group). (E) Oligomeric and monomeric Aβ_1–42 were identified by SDS-PAGE using anti-Aβ antibody (4G8). (F) Primary cortical neurons were incubated with 1 μM oligomeric Aβ_1–42 for 24 h at 10 days <i>in vitro</i>. The cell lysates were subjected to immunoblot using GCase and β-actin antibodies. (G) The expression level of GCase was quantified. Data are expressed as the mean ± SEM (Student’s t test, *<i>P</i> < 0.05, **<i>P</i> < 0.01).</p

GCase promotes Aβ_1–42 oligomer degradation.

<p>(A) Primary cultured mouse cortical neurons were infected with lenti-control (LV-Cont, n = 3) or lenti-GCase (LV-GC, n = 3) virus at 7 days <i>in vitro</i>, and 5 μM oligomeric Aβ_1–42 or PBS was added for 48 h at 10 days <i>in vitro</i>. 10 μg/ml of E-64d and 10 μg/ml of PepA were treated 4 h before the treatment of Aβ_1–42 oligomers. The monomeric and oligomeric Aβ expressions were measured with anti-Aβ antibody (4G8) 24 h after the treatment of oligomeric Aβ_1–42. (B) The quantification of Aβ levels is shown (Student’s t test, <i>P</i>* < 0.05, <i>P</i>*** < 0.001).</p