Covid-19 invaded the world very quickly and caused the loss of many lives; maximum emergency was activated all over the world due to its rapid spread. Consequently, it became a huge burden on emergency and intensive care units due to the large number of infected individuals and the inability of the medical staff to deal with patients according to the degree of severity. Covid-19 can be diagnosed based on the artificial intelligence (AI) model. Based on AI, the CT images of the patient’s chest can be analyzed to identify the patient case whether it is normal or he/she has Covid-19. The possibility of employing physiological sensors such as heart rate, temperature, respiratory rate, and SpO2 sensors in diagnosing Covid-19 was investigated. In this paper, several articles which used intelligent techniques and vital signs for diagnosing Covid-19 have been reviewed, classified, and compared. The combination of AI and physiological sensors reading, called AI-PSR, can help the clinician in making the decisions and predicting the occurrence of respiratory failure in Covid-19 patients. The physiological parameters of the Covid-19 patients can be transmitted wirelessly based on a specific wireless technology such as Wi-Fi and Bluetooth to the clinician to avoid direct contact between the patient and the clinician or nursing staff. The outcome of the AI-PSR model leads to the probability of recording and linking data with what will happen later, to avoid respiratory failure, and to help the patient with one of the mechanical ventilation devices.

Uropathogenic Escherichia coli (UPEC) is considered one of the main causes of urinary tract infections. Antimicrobial resistance (AMR) is a significant global health care issue, particularly with regard to urinary tract infections. Objective: To evaluate the prevalence of antibiotic resistance among Escherichia coli isolated from patients with urinary tract infection. Two-hundred and sixty-four mid-stream urine samples were collected from patients with symptoms of urinary tract infection who visited Baghdad teaching Hospital. These samples were routinely cultured on different media and E. coli was identified using conventional methods and confirmed by VITEK-2 system. Following diagnosis, 10 different types of antibiotics were tested for their sensitivity on E. coli strains using the Kirby-Bauer disk diffusion method. Results: Out of 264 urine samples, 175 (66%) contained Gram-negative bacteria, E. coli was the most common uropathogenic isolate (38%), followed by K. pneumoniae (16%) and Streptococcus epidermidis (12%). The majority of uropathogenic E. coli showed the most rate of resistance to Amoxicillin, ceftriaxone, cefotaxime, Nalidixic acid, Trimethoprim/Slfamethoxazole & tetracycline (88%, 80%,75%, 67%, 65.0% and 57% respectively).  Ninty-three percentage were sensitive to Meropenem, followed by Nitrufurnantion and Chloramphenicole  (75.0% and 68.0%) respectively. Conclusions: It was concluded from this study that E. coli is the main pathogen inflicting UTIs on patients. Amoxicillin, ceftriaxone, cefotaxime, Nalidixic acid, Trimethoprim/Slfamethoxazole & tetracycline were among the antibiotics with the highest rates of resistance. In light of this study, local sensitivity patterns rather than international guidelines should be the basis for empirical antibiotic therapy.

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