Traditional Knowledge, Phytochemistry, and Biological Properties of Vachellia tortilis

Vachellia tortilis is a medicinal plant of the Fabaceae family, widely distributed in arid and semi-arid regions of North, East and Southern Africa, the Middle East and the Arabian Peninsula. In traditional medicine. It's commonly used to treat certain ailments, including diabetes, asthma, hepatitis and burns. Different scientific search databases were used to obtain data on V. tortilis, notably Google Scholar, Scopus, Wiley Online, Scifinder, Web of Science, ScienceDirect, SpringerLink, and PubMed. The knowledge of V. tortilis was organized based on ethnomedicinal use, phytochemistry, and pharmacological investigations. Phytochemical studies revealed the presence of a variety of phytocompounds, including fatty acids, monosaccharides, flavonoids, chalcones, and alcohols. Essential oils and organic extracts prepared from V. tortilis showed several biological properties, specifically antibacterial, antifungal, antiparasitic, antioxidant, antiproliferative, anti-diabetic, and anti-inflammatory effects. Antimicrobial and antiparasitic activities are due to the disturbance of cellular membranes and ultra-structural changes triggered by V. tortilis phytochemicals. While physiological and molecular processes such as apoptosis induction, preventing cell proliferation, and inflammatory mediators are responsible for the anti-diabetic, anti-cancer, and anti-inflammatory activities. However, further investigations concerning pharmacodynamics and pharmacokinetics should be carried out to validate their clinical applications.

FREEDOM: Effective Surveillance and Investigation of Water-borne Diseases from Data-centric Networking Using Machine Learning Techniques

Worldwide, epidemics continue to be a concern on public health. Even with the technological advances, there are still barriers present in predicting the outbreaks. We propose a new methodology known as FREEDOM (Effective Surveillance and Investigation of Water-borne Diseases from data-centric networking using Machine Learning) to perform effective surveillance and investigation of water-borne diseases from social media with next-generation data. In the proposed model, we collected the data from the Twitter media, preprocessed the tweet content, performed hierarchical spectral clustering, and generated the frequent word set from each cluster through the apriori algorithm. At last, the inferences are extracted from the frequent word set through human intervention. From the experimental results, the support and confidence value of the outcome derived from the Apriori algorithm has exhibited the different water-borne diseases that are not listed in the WHO (World Health Organization), and the surveillance of those diseases with percentage ranking and has been achieved using the data-centric networking. They get aligned with precise results portraying real statistics. This type of analysis will empower doctors and health organizations (Government sector) to keep track of the water-borne diseases, their symptoms for early detection, and safe recovery thereby sufficiently reducing the death tolls.

Obesity in pregnancy

This prospective study was carried out in Basrah ma-ternity hospital (Basrah-Iraq) during the eight months of Dec.1997-Aug.1998 to study the obstetrical performance and outcome of pregnancy in 20 (>90kg) obese patients compared to 20 non-obese controls.

The incidence of obesity in their infants was also com-pared. There was no significant increase in the incidence of urinary tract infection, diabetes, caesarean section and forceps delivery.

There was a significantly increased incidence of hyper-tensive disorders of pregnancy (P < 0.01 odds ratio 0.33), and also inadequate weight gain (P < 0.01).

Apgar scores and perinatal mortality were not signifi-cantly different in the two groups.