Traumatic Brain Injury (TBI) is still considered a worldwide leading cause of mortality and morbidity. Within the last decades, different modalities were used to assess severity and outcome including Glasgow Coma Scale (GCS), imaging modalities, and even genetic polymorphism, however, determining the prognosis of TBI victims is still challenging requiring the emerging of more accurate and more applicable tools to surrogate other old modalities

Background: planter fasciitis is a common condition
seen in adults and sport men, it is characterized by dull
pain in the heel, especially when getting up and
standing on the foot in the morning or after sitting for a
long time.
Recently low level laser therapy is used as a method of
treatment.
Objective: to evaluate the benefit of laser therapy in
treatment of planter fasciitis.
Methods: Out of twenty five patients with planter
fasciitis exposed to laser therapy. Laser used is (diode
type) given in two cessions per week for four weeks,
time for each cession is about (12 minutes).
Results: complete recovery seen in (32%) of patients,
moderate improvement in (16%), mild improvement in
(24%), no

CO2 Laser (10600nm) is the recent method in the management of challenging skin scar resulting from trauma, burn and surgical wound. The aim of this study was to evaluate the efficacy & safety of fractional CO2 laser (10600nm) in treatment of skin scar. Materials and Methods:Twenty patients with different types of scars treated with fractional CO2 (10600nm) laser, (10 patients) were given additional intralesional Triamcinolone. Results: All of the twenty patients included in this study showed some sort of improvements in scar texture, height and pliability and all of the ten patients who received intralesional Triamcinolone after laser show complete satisfaction. Conclusion:Fractional CO2 (10600nm) laser can be used as alternative, ef

this study deals with effect of diabetes and hypertension on quality of life

KE Sharquie, AA Noaimi, RA Flayih, Am J Clin Res Rev, 2020 - Cited by 4

Rheumatoid arthritis is an inflammatory chronic disease with an autoimmune pathogenesis. To determine the role of Helicobacter pylori as a trigger agent, twenty five patients with rheumatoid arthritis of ages (15-47) years have been investigated and compared with twenty healthy individuals. All the studied groups were carried out to measure the rheumatoid arthritis (RA) IgM, anti-CCP antibody IgG and IgA by ELISA test and by measured anti-IgG antibody level of H. pylori by using ELISA and IFAT techniques. The present study showed significant differences (P< 0.05) of anti-H. Pylori in sera of RA patients than control group, this lead to suggest that H. pylori had a role in pathogenesis of RA.

Papillary thyroid carcinoma (PTC) represents the most prevalent kind of thyroid gland cancer, making up around 80% of all occurrences of thyroid cancer. Evidence shows that Syndecan-1 (SDC-1) expression is lost in a number of benign and malignant epithelial neoplasms, although its expression profile in thyroid gland neoplasms is yet unknown. Therefore, the aim of this study was to assess SDC-1 expression in papillary thyroid carcinoma patients, as well as the relationship between age and gender and SDC-1 expression. To undertake a detailed investigation of SDC-1 in normal and malignant tissues, tissue sections were used to examine SDC-1 expression in 70 tissue samples, 50 distinct PTC (6 males and 44 females) and 20 normal tissue ty

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al