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Trigonella foenum-graecum (Fenugreek) is an extensively researched phytotherapeutic for the management of Type 2 diabetes without any associated side effects. The major anti-diabetic bioactive constituents present in the plant are furostanolic saponins, which are more abundantly available in the seed of the plant. However, the bioavailability of these components depends on the method of extraction and hence formulation of the phytotherapeutic constitutes a critical step for its success. The present study reports the efficacy of a novel, patented fenugreek seed extract, Fenfuro®, containing significant amount of furostanolic saponins, in an open-labelled, two-armed, single centric study on a group of 204 patients with Type 2 diabetes mellitus over a period of twelve consecutive weeks. Administration of Fenfuro® in the dosage of 500 mg twice daily along with metformin and/or sulfonylurea-based prescribed antidiabetic drug resulted in a reduction of post-prandial glucose by more than 33% along with significant reduction in fasting glucose, both of which were greater than what resulted for the patient group receiving only Metformin and/or Sulfonylurea therapy. Fenfuro® also resulted in reduction in mean baseline HOMA index from 4.27 to 3.765, indicating restoration of insulin sensitivity which was also supported by a significant decrease in serum insulin levels by >10% as well as slight reduction in the levels of C-peptide. However, in the case of the Metformin and/or Sulfonylurea group, insulin levels were found to increase by more than 14%, which clearly indicated that drug-induced suppression of glucose levels instead of restoration of glucose homeostasis. Administration of the formulation was also found to be free from any adverse side effects as there were no changes in hematological profile, liver function and renal function. The study demonstrated the promising potential of this novel phytotherapeutic, Fenfuro®, in long-term holistic management of type-2 diabetes. [ABSTRACT FROM AUTHOR]

Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples remains a serious challenge. In this context, we extensively investigated 200+ FSL papers published in top journals and conferences in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL with a fresh perspective and to provide an impartial comparison of the strengths and weaknesses of existing work. To avoid conceptual confusion, we first elaborate and contrast a set of relevant concepts including few-shot learning, transfer learning, and meta-learning. Then, we inventively extract prior knowledge related to few-shot learning in the form of a pyramid, which summarizes and classifies previous work in detail from the perspective of challenges. Furthermore, to enrich this survey, we present in-depth analysis and insightful discussions of recent advances in each subsection. What is more, taking computer vision as an example, we highlight the important application of FSL, covering various research hotspots. Finally, we conclude the survey with unique insights into technology trends and potential future research opportunities to guide FSL follow-up research. [ABSTRACT FROM AUTHOR]

Background: Many cases of multisystem inflammatory syndrome in children (MIS-C) are reported in post-COVID-19 cases. It is a serious complication of COVID-19, with a high mortality rate in low-income countries compared to high-income countries. This study was conducted to find out predictors of mortality in MIS-C. Subjects and Methods: In this prospective observational study, 81 children with MIS-C were enrolled. Detailed clinical features, laboratory parameters, treatment, and outcome of cases were recorded. After the determination of individual factors associated with mortality by univariate analysis, a binary logistic regression model of significant factors associated with mortality was developed. Results: Fever, breathlessness, conjunctivitis, and pain abdomen were major presenting complaints at admission. Fifty-seven (70.3%) were presented with shock, of which 31 (54.4%) had normotensive shock and 26 (45.6%) had hypotensive shock. There were 8 (9.8%) deaths in the study group. Association of pneumonia, acute respiratory distress syndrome, and acute kidney injury were significantly high among nonsurvivors. Levels of lactate dehydrogenase (LDH), interleukin-6, and prothrombin time (international normalized ratio) were significantly higher in nonsurvivors than survivors. On binary logistic regression, raised LDH and the use of a mechanical ventilator were found to be independent predictors of death (P < 0.05). Conclusions: Shock was the predominant manifestation in children with MIS-C. Raised LDH and the use of mechanical ventilators were found to be independent predictors of death in cases of MIS-C. [ABSTRACT FROM AUTHOR]

As an emulous alternative to deep neural networks, Deep Forest emerges with features like low complexity, fewer hyper-parameters, and good robustness, which are predominantly desired in distributed computing applications and ecosystems. Recently, an efficient distributed Deep Forest system, named ForestLayer, was proposed, designing a fine-grained sub-Forest-based task-parallel algorithm to improve the parallel computing efficiency of Deep Forest. However, the sub-Forest splitting of ForestLayer is static and one-off without adaptability to the computing environment, nevertheless, the size of splitting granularity has a significant impact on the system performance. To further improve the computing efficiency and scalability of the distributed Deep Forest, in this paper, we propose a novel distributed Deep Forest algorithm, named BLB-gcForest (Bag of Little Bootstraps-gcForest), which augments the gcForest (multi-Grained Cascade Forest) approach for constructing Deep Forest. BLB-gcForest carries out parallel computation for each tree in sub-Forests at a finer parallel granularity and integrates with the Bag of Little Bootstraps (BLB) mechanism to reduce massive transmitted feature instances for Cascade Forest Layers, utterly improving both computation efficiency and communication efficiency. Moreover, to solve the problem of the forest splitting granularity, we further design an adaptive sub-Forest splitting algorithm to ensure the maximum resource utilization for parallel computation of each sub-Forest. Experimental results on four well-known large-scale datasets, namely YEAST, LETTER, MNIST, CIFAR10, show that the training efficiency of BLB-gcForest achieves up to 20.3x and 1.64x speedups compared with the state-of-the-art gcForest and ForestLayer, respectively while guaranteeing higher accuracy and better robustness [ABSTRACT FROM AUTHOR]