RESUMEN
BACKGROUND: The efficacy of levodopa, the most crucial metric for Parkinson's disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response. METHODS: This retrospective study used two datasets: from the Parkinson's Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p < 0.001. RESULTS: For 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized. CONCLUSION: The T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction. CRITICAL RELEVANCE STATEMENT: This study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy. KEY POINTS: This study investigated the predictive value of T1w features for levodopa response. MedicalNet extractor outperformed all other previously published methods with key region visualization. T1w features are more effective than clinical information in levodopa response prediction.
RESUMEN
Purpose. To decipher transcriptomic changes and related genes with potential functions against Bombyx mori nucleopolyhedrovirus infection and to increase the understanding of the enhanced virus resistance of silkworm on the transcriptomic level. Methods. We assembled and annotated transcriptomes of the Qiufeng (susceptible to infection) and QiufengN (resistant to infection) strains and performed comparative analysis in order to decipher transcriptomic changes and related genes with potential functions against BmNPV infection. Results. A total of 78,408 SNPs were identified in the Qiufeng strain of silkworm and 56,786 SNPs were identified in QiufengN strain. Besides, novel AS events were found in these 2 strains. In addition, 1,728 DEGs were identified in the QiufengN strain compared with Qiufeng strain. These DEGs were involved in GO terms related to membrane, metabolism, binding and catalytic activity, cellular processes, and organismal systems. The highest levels of gene representation were found in oxidative phosphorylation, phagosome, TCA cycle, arginine and proline metabolism, and pyruvate metabolism. Additionally, COG analysis indicated that DEGs were involved in "amino acid transport and metabolism" and "carbohydrate transport and metabolism." Conclusion. We identified a series of major pathological changes in silkworm following infection and several functions were related to the antiviral mechanisms of silkworm.
RESUMEN
It is an accurate and quick methods to apply VCE4.0 to estimating genetic parameter. It makes full use of all information, analyzing selection and culling effects. The Genetic parameters for the age to 30kg(AGE30), age to 100kg (AGE100), and average daily gain from 30kg to 100kg (ADG) and backfat thickness at 100kg(probed,FAT) are estimated using VCE4.0 applied to REML with a multivariate individual animal model in Landrace pigs. Estimates of heritabilities for AGE30, AGE100,ADG and FAT are 0.207,0.396,0.304 and 0.493, respectively. Genetic correlations for FAT/ADG, FAT/AGE100, ADG/AGE100, ADG /AGE30 and AGE30/AGE100 are -0.343,0.180,-0.941,-0.48,and 0.745, respectively, its phenotypic correlations are -0.139,0.138,-0.82,-0.026, and 0.565, respectively. The common litter environment effects for AGE30,AGE100, ADG and FAT are 0.194,0.156,0.157 and 0.043, respectively.
