Green Onion (Allium fistulosum): An Aromatic Vegetable Crop Esteemed for Food, Nutritional and Therapeutic Significance.

In recent years, there has been a shift towards a greater demand for more nutritious and healthier foods, emphasizing the role of diets in human well-being. Edible Alliums, including common onions, garlic, chives and green onions, are staples in diverse cuisines worldwide and are valued specifically for their culinary versatility, distinct flavors and nutritional and medicinal properties. Green onions are widely cultivated and traded as a spicy vegetable. The mild, onion-like flavor makes the crop a pleasant addition to various dishes, serving as a staple ingredient in many world cuisines, particularly in Eastern Asian countries such as China, Japan and the Republic of Korea. The green pseudostems, leaves and non-developed bulbs of green onions are utilized in salads, stir-fries, garnishes and a myriad of culinary preparations. Additionally, green onions have a rich historical background in traditional medicine and diets, capturing the attention of chefs and the general public. The status of the crop as an important food, its culinary diversity and its nutraceutical and therapeutic value make it a subject of great interest in research. Therefore, the present review has examined the distribution, culinary, nutritional and therapeutic significance of green onions, highlighting the health benefits derived from the consumption of diets with this aromatic vegetable crop as a constituent.

Clevudine demonstrates potent antiviral activity in naïve chronic hepatitis B patients.

OBJECTIVES: Clevudine has demonstrated antiviral potency in the treatment of naïve chronic hepatitis B patients in pivotal studies. The objectives of this study were to evaluate the safety and efficacy of a 1-year treatment with clevudine in chronic hepatitis B patients. METHODS: This is a post-marketing surveillance using case report forms which were submitted to the health authorities. RESULTS: Analysis of individual data showed that hepatitis B virus (HBV) DNA after a 1-year treatment was <141,500 copies/ml in 96% of hepatitis B e antigen (HBeAg)-positive and 100% of HBeAg-negative patients. The proportion of patients with HBV DNA <2,000 copies/ml after a 1-year treatment was 74%: 71% of HBeAg-positive and 93% of HBeAg-negative patients. Most of the patients with HBV DNA below the detection limit with each assay at week 24 showed sustained viral suppression up to week 48. The proportion of patients who showed normal alanine aminotransferase at week 48 was 86% in HBeAg-positive patients and 87% in HBeAg-negative patients. The rates of HBeAg-loss were 21%. Two patients showed viral breakthrough during treatment. CONCLUSION: Clevudine monotherapy demonstrates potent antiviral activity as well as biochemical and serological response with a 0.7% rate of viral breakthrough in naïve chronic hepatitis B patients.

High-Performance Deep Neural Network-Based Tomato Plant Diseases and Pests Diagnosis System With Refinement Filter Bank.

A fundamental problem that confronts deep neural networks is the requirement of a large amount of data for a system to be efficient in complex applications. Promising results of this problem are made possible through the use of techniques such as data augmentation or transfer learning of pre-trained models in large datasets. But the problem still persists when the application provides limited or unbalanced data. In addition, the number of false positives resulting from training a deep model significantly cause a negative impact on the performance of the system. This study aims to address the problem of false positives and class unbalance by implementing a Refinement Filter Bank framework for Tomato Plant Diseases and Pests Recognition. The system consists of three main units: First, a Primary Diagnosis Unit (Bounding Box Generator) generates the bounding boxes that contain the location of the infected area and class. The promising boxes belonging to each class are then used as input to a Secondary Diagnosis Unit (CNN Filter Bank) for verification. In this second unit, misclassified samples are filtered through the training of independent CNN classifiers for each class. The result of the CNN Filter Bank is a decision of whether a target belongs to the category as it was detected (True) or not (False) otherwise. Finally, an integration unit combines the information from the primary and secondary units while keeping the True Positive samples and eliminating the False Positives that were misclassified in the first unit. By this implementation, the proposed approach is able to obtain a recognition rate of approximately 96%, which represents an improvement of 13% compared to our previous work in the complex task of tomato diseases and pest recognition. Furthermore, our system is able to deal with the false positives generated by the bounding box generator, and class unbalances that appear especially on datasets with limited data.