Disseminated nocardiosis in a female patient with idiopathic thrombocytopenic purpura: A case report

Nocardiosis is a life-threatening disease if unrecognized and maltreated. We describe a case of disseminated nocardiosis in a patient with idiopathic thrombocytopenic purpura under steroid therapy. She presented with a 2-week history of progressive symmetrical limb weakness, fatigue, and profuse sweating. Brain MRI revealed ring-enhanced lesions at the cerebellum and left parietal lobe with brain oedema. Chest CT revealed a left upper lobe nodule. Aspirate culture confirmed the diagnosis of nocardiosis. We administered antibiotics and dexamethasone to ameliorate the brain oedema. The patient improved clinically after 2 weeks. Follow-up brain MRI showed improvement. Clinicians should consider nocardiosis in immunocompromised patients with non-specific symptoms

A Review of the Role of Artificial Intelligence in Healthcare

Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs’ belief in enhancing acceptance and boosting significant health consequences. Effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs

Radiological patterns in sickle cell disease patients with acute chest syndrome: Are there age-related differences?

Background: Acute chest syndrome is a major cause of pulmonary disease and mortality in sickle cell disease patients. Its diagnosis can be delayed due to differing imaging patterns between children and adults. Objective: The purpose of this study was to describe the pulmonary and extrapulmonary imaging findings in sickle cell disease patients with acute chest syndrome and determine differences in findings between adult and pediatric patients. Patients and Methods: This retrospective study analyzed the data of all sickle cell disease patients who were admitted with a diagnosis of acute chest syndrome to King Fahd Hospital of the University, Al Khobar, Saudi Arabia, between January and June 2015 (n = 150). After grouping the patients into adults and pediatrics, the pulmonary and extrapulmonary characteristics were identified and the digital radiography, computed tomography and laboratory findings were compared. Results: A total of 116 patients with 163 acute chest syndrome episodes met the inclusion criteria, of which 69 (60%) were adults. In both adult and pediatric patients, the most frequent pulmonary finding was consolidation of the lung parenchyma. The right lung was most frequently involved: the lower lobe in adult patients and the middle lobe in pediatric patients. In addition, pleural effusion was observed in both age groups. Extrapulmonary radiological findings, such as avascular necrosis and cardiomegaly, were significantly more common in adult patients than in pediatric patients (P < 0.05). Compared with adults, pediatric patients had significantly lower hemoglobin levels (P = 0.001) and oxygen tension fraction in arterial blood (P = 0.007). Conclusions: Pediatric and adult sickle cell disease patients with acute chest syndrome typically exhibited similar pulmonary characteristics, whereas extrapulmonary findings were more prominent in adult patients. Furthermore, low levels of hemoglobin and oxygen tension fraction were dependent predictors of acute chest syndrome

Breast Cancer Detection in the Equivocal Mammograms by AMAN Method

Breast cancer is a primary cause of human deaths among gynecological cancers around the globe. Though it can occur in both genders, it is far more common in women. It is a disease in which the patient’s body cells in the breast start growing abnormally. It has various kinds (e.g., invasive ductal carcinoma, invasive lobular carcinoma, medullary, and mucinous), which depend on which cells in the breast turn into cancer. Traditional manual methods used to detect breast cancer are not only time consuming but may also be expensive due to the shortage of experts, especially in developing countries. To contribute to this concern, this study proposed a cost-effective and efficient scheme called AMAN. It is based on deep learning techniques to diagnose breast cancer in its initial stages using X-ray mammograms. This system classifies breast cancer into two stages. In the first stage, it uses a well-trained deep learning model (Xception) while extracting the most crucial features from the patient’s X-ray mammographs. The Xception is a pertained model that is well retrained by this study on the new breast cancer data using the transfer learning approach. In the second stage, it involves the gradient boost scheme to classify the clinical data using a specified set of characteristics. Notably, the experimental results of the proposed scheme are satisfactory. It attained an accuracy, an area under the curve (AUC), and recall of 87%, 95%, and 86%, respectively, for the mammography classification. For the clinical data classification, it achieved an AUC of 97% and a balanced accuracy of 92%. Following these results, the proposed model can be utilized to detect and classify this disease in the relevant patients with high confidence

Additional file 1 of Dietary assessment of type 2 diabetic patients using healthful plant-based diet score in the Eastern Province of Saudi Arabia

Supplementary Material

Gut microbiota analyses of Saudi populations for type 2 diabetes-related phenotypes reveals significant association.

BACKGROUND: Large-scale gut microbiome sequencing has revealed key links between microbiome dysfunction and metabolic diseases such as type 2 diabetes (T2D). To date, these efforts have largely focused on Western populations, with few studies assessing T2D microbiota associations in Middle Eastern communities where T2D prevalence is now over 20%. We analyzed the composition of stool 16S rRNA from 461 T2D and 119 non-T2D participants from the Eastern Province of Saudi Arabia. We quantified the abundance of microbial communities to examine any significant differences between subpopulations of samples based on diabetes status and glucose level. RESULTS: In this study we performed the largest microbiome study ever conducted in Saudi Arabia, as well as the first-ever characterization of gut microbiota T2D versus non-T2D in this population. We observed overall positive enrichment within diabetics compared to healthy individuals and amongst diabetic participants; those with high glucose levels exhibited slightly more positive enrichment compared to those at lower risk of fasting hyperglycemia. In particular, the genus Firmicutes was upregulated in diabetic individuals compared to non-diabetic individuals, and T2D was associated with an elevated Firmicutes/Bacteroidetes ratio, consistent with previous findings. CONCLUSION: Based on diabetes status and glucose levels of Saudi participants, relatively stable differences in stool composition were perceived by differential abundance and alpha diversity measures. However, community level differences are evident in the Saudi population between T2D and non-T2D individuals, and diversity patterns appear to vary from well-characterized microbiota from Western cohorts. Comparing overlapping and varying patterns in gut microbiota with other studies is critical to assessing novel treatment options in light of a rapidly growing T2D health epidemic in the region. As a rapidly emerging chronic condition in Saudi Arabia and the Middle East, T2D burdens have grown more quickly and affect larger proportions of the population than any other global region, making a regional reference T2D-microbiome dataset critical to understanding the nuances of disease development on a global scale