Assessment of intermediate (process) outcomes in the BARI 2D clinical trial

BARI 2D is a 2-by-2 factorial clinical trial identifying optimal therapies for patients with documented stable coronary artery disease and Type 2 Diabetes. Previous results of the BARI 2D study indicated that there was a non-significant association between the therapies and 5-year survival outcomes. Our study hypothesizes that several intermediate variables may exist whose trajectories of improving or worsening in response to treatments act against each other leading to the previous non-significant associations. Before randomization, the participants were stratified to two types of revascularization - PCI or CABG. Then each participant was randomized to receive one cardiac therapy (prompt revascularization plus intensive medicine or intensive medicine alone), and also one glycemic therapy (insulin-sensitization or insulin-provision). Five intermediate outcomes BMI, SBP, HDL, LDL and Hba1c were studied. Linear regression was conducted to assess the difference of each intermediate outcome between therapies at Year 1 and at Year 3. Also conducted was a comparison of the trajectories over time by therapies using longitudinal repeated measures mixed models. In 2368 patients (mean age 62.4 years), the improvement of the intermediate outcomes was generally notable over the first year, and then slowly diminished over time. At both Year 1 and 3, insulin-provision resulted in higher BMI and Hba1c, and lower HDL than insulin-sensitization, irrespective of the assigned cardiac therapy and revascularization stratum. Longitudinally, insulin-provision resulted in higher Hba1c, irrespective of the assigned cardiac therapy and revascularization stratum; and an interaction effect of the cardiac and glycemic therapies on BMI was found in CABG stratum. These results suggested that insulin-sensitization therapy is superior to insulin-provision therapy generally. In CABG stratum, the effect of cardiac therapy on BMI depends on the assignment of glycemic therapy. The public health significance of this study is that, though the cancelling-out hypotheses for these five intermediate variables may be overly optimistic, it involves a potentially illuminating perspective to explain the mechanisms through which the BARI 2D treatment therapies affect multiple intermediate outcomes. This in turn could also help inform and enhance the targeted adjuvant therapies, thus resulting in improved survival outcomes by better focusing on controlling the harmful intermediate variables

Detecting ferroptosis and immune infiltration profiles in multiple system atrophy using postmortem brain tissue

BackgroundThe importance of ferroptosis and the immune system has been mentioned in the pathogenesis of α-synucleinopathy. The α-synuclein-immunoreactive inclusions that primarily affect oligodendrocytes are the hallmark of multiple system atrophy (MSA). Limited evidence implicates that iron and immune responses are involved in the pathogenesis of MSA, which is associated with neurodegeneration and α-synuclein aggregation.MethodsThe RNA sequencing data were collected from the Gene Expression Omnibus database. MSA-C-related module genes were identified through weighted gene co-expression network analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to predict the potential molecular functions. The candidate ferroptosis-related genes associated with MSA-C were obtained using a machine-learning algorithm. CIBERSORT was used to estimate the compositional patterns of the 22 types of immune cells.ResultsThe tissues for sequencing were extracted from postmortem cerebellar white matter tissues of 11 MSA-C patients and 47 healthy controls. The diagnostic ability of the six MSA-C-related ferroptosis-related genes in discriminating MSA-C from the healthy controls demonstrated a favorable diagnostic value, with the AUC ranging from 0.662 to 0.791. The proportion of CD8+ T cells in MSA-C was significantly higher than in the controls (P = 0.02). The proportion of NK cells resting in MSA-C was significantly higher than in the controls (P = 0.011).ConclusionFerroptosis and T-cell infiltration may be important pathways of disease development in MSA-C, and targeting therapies for these pathways may be disease-modifying

A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition

We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and frame-level classification accuracy, kernel-based acoustic models are as effective as their DNN counterparts. However, on token-error-rates DNN models can be significantly better. We have discovered that this might be attributed to DNN's unique strength in reducing both the perplexity and the entropy of the predicted posterior probabilities. Motivated by our findings, we propose a new technique, entropy regularized perplexity, for model selection. This technique can noticeably improve the recognition performance of both types of models, and reduces the gap between them. While effective on Broadcast News, this technique could be also applicable to other tasks.Comment: arXiv admin note: text overlap with arXiv:1411.400

Prismer: A Vision-Language Model with An Ensemble of Experts

Recent vision-language models have shown impressive multi-modal generation capabilities. However, typically they require training huge models on massive datasets. As a more scalable alternative, we introduce Prismer, a data- and parameter-efficient vision-language model that leverages an ensemble of domain experts. Prismer only requires training of a small number of components, with the majority of network weights inherited from readily-available, pre-trained domain experts, and kept frozen during training. By leveraging experts from a wide range of domains, we show that Prismer can efficiently pool this expert knowledge and adapt it to various vision-language reasoning tasks. In our experiments, we show that Prismer achieves fine-tuned and few-shot learning performance which is competitive with current state-of-the-art models, whilst requiring up to two orders of magnitude less training data. Code is available at https://github.com/NVlabs/prismer.Comment: Tech Report. Project Page: https://shikun.io/projects/prismer Code: https://github.com/NVlabs/prismer v2: fixed incorrect training cost estimate and zero-shot NoCaps performance of SimVL

Testing a Citation and Text-Based Framework for Retrieving Publications for Literature Reviews

We propose a citation- and text-based framework to conduct literature review searches. Given a small set of articles included in a literature review (i.e. seed articles), the first step of the framework retrieves articles that are connected to the seed articles in the citation network. The next step filters these retrieved articles using a hybrid citation and text-based criteria. In this paper, we evaluate a first implementation of this framework (code available at https://github.com/janinaj/lit-review-search) by comparing it to the conventional search methods for retrieving the included studies of 6 published systematic reviews. Using different combinations of 3 seed articles, on average we retrieved 71.2% of the total included studies in the published reviews and 82.33% of the studies available in the search database (Scopus). Our best combinations retrieved 87% of the total included studies, which comprised 100% of the studies available in Scopus. In 5 of the 6 reviews, we reduced the number of results by 34–88%, which in practice would save reviewers significant time, since the overall number of search results that need to be manually screened is substantially reduced. These results suggest that our framework is a promising approach to improving the literature review search process.Ope

A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis

Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillary.Comment: Dataset, code, pretrained models: https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillar