Varespladib known as Spider Ivy is used as medicinal plant in traditional Chinese medicine, however its detailed chemical composition and biological activity is yet unexplored.

To carry out phytochemical investigation on different parts of Chlorophytum comosum using GC-MS/LC-ESI-MS and evaluation of its antioxidant, haemolytic and antiproliferative potential on breast cancer (MCF-7), lung cancer (A549, H1299) and normal lung (L-132) cell lines.

Chemical constituents from aqueous roots and leaves extracts were identified using LC-ESI-MS/GC-MS. The identified compounds were annotated based on match of mass spectra with the literature using NIST 14 and METLIN databases. Antioxidant activity was checked using DPPH, FRAP and TPC assays. The antiproliferative effects of ethanolic roots and leaves extracts of Chlorophytum comosum were measured by MTT assay on breast cancer (MCF-7), lung cancer (A549 & H1299) and normal lung (L-132) cell lines. The toxicity studies of the extracts were carried out using Haemolysis assay.

GC-MS analysis identified 34 metabolites in roots and 17 from leaves, while as 17 compounds from roots and 7 from leaves were detected by LC-ESI-MS. Significant antiproliferative effects were observed on the A549 and MCF-7 cancer cell lines with IC50 values ranging from 56.86 µg/ml to 68.68 µg/ml while no marked response was observed against normal cell line L-132.

Our study represents the first report on the detailed chemical composition and antiproliferative potential of Chlorophytum comosum against lung and breast cancer cell lines.

Our study represents the first report on the detailed chemical composition and antiproliferative potential of Chlorophytum comosum against lung and breast cancer cell lines.

Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images.

This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community.

Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation.

A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest Xrays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest Xrays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.The management of diabetes requires a medical nutritional therapy as an essential part of this treatment. #link# There should be no ‘one-size-fits-all’ eating pattern for different patient´s profiles with diabetes. It´s clinically complex to suggest an ideal percentage of calories from carbohydrates, protein and lipids recommended for all patients with diabetes. Among the eating patterns that have shown beneficial effects on metabolic control of patients with type 2 diabetes is the Low-Carb diet, since the carbohydrate ingestion is viewed as the most important determinant of postprandial glucose and insulin response. In this context, theoretically it could make sense to reduce the daily amount of carbohydrates ingested, willing to achieve lower levels of HbA1c. There could be associated risks to this approach. The adherence to a Low-Carb Diet is here also discussed. This narrative review works on the current evidence for answering these questions regarding Low-Carb Diet as a possible alternative eating pattern for type 2 diabetes.

Poorly managed diabetes mellitus increases health care expenditures and negatively impacts health outcomes. There are 34 million people living with diabetes in the United States with a direct annual medical cost of $237 billion. The patient-centered medical home (PCMH) was introduced to transform primary care by offering team-based care that is accessible, coordinated, and comprehensive. Although the PCMH is believed to address multiple gaps in delivering care to people living with chronic diseases, the research has not yet reported clear benefits for managing diabetes.

The study reviews the scientific literature about diabetes mellitus outcomes reported by PCMHs, and understands the impact of team-based care, interdisciplinary communication, and care coordination strategies on the clinical, financial, and health-related outcomes.

The systematic review was performed according to the Cochrane method and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Eight databases were systematir team-based care, communication, and care coordination with comparisons to patient, clinical, health, and financial outcomes.

The quality and strength of the outcomes were largely inconclusive about the overall effectiveness of the PCMH. Defining and comparing concepts across studies was difficult as universal definitions specific to the PCMH were not often applied. More research is needed to unpack the care model of the PCMH to further understand how the individual key components, such as care bundles, contribute to improved outcomes. Further evaluations are needed for team-based care, communication, and care coordination with comparisons to patient, clinical, health, and financial outcomes.