Recent studies of rheumatoid arthritis worldwide suggest that prevalence of arthritis is higher in Europe and North America than in developing countries. Prevalence data for major arthritis disorders have been compiled in West for several decades, but figures from the third world are just emerging. A coordinated effort by WHO and ILAR (International League Against Rheumatism) has resulted in collecting data for countries like Philippines, China, Malaysia, Indonesia, and rural South Africa but the information about prevalence of arthritis in India and Pakistan is scarce. Since both countries, i.e., India and Pakistan, share some ethnic identity, we reviewed published literature to examine the prevalence of arthritis in these countries. Medline and Pubmed were searched for suitable articles about arthritis from 1980 and onwards. Findings from these articles were reviewed and summarized. The prevalence, clinical features, and laboratory findings of rheumatoid arthritis are compiled for both India and Pakistan. Data collected from these two countries were compared with each other, and some of the characteristics of the disease were compared with Europe and North America. It is found to be quite similar to developed countries. Additionally, juvenile rheumatoid arthritis is of different variety than reported in West. It is more of polyarticular onset type while in West pauciarticular predominates. Additionally, in systemic onset, JRA uveitis and ANA are common finding in developed countries; on the other hand, they are hardly seen in this region. Although the prevalence of arthritis in Pakistan and India is similar to Western countries, there are inherent differences (clinical features, laboratory findings) in the presentation of disease. The major strength of the study is that it is the first to pool reports to provide an estimate of the disease in the Indian subcontinent. Scarcity of data is one of the major limitations. This study helps to understand the pattern of disease in this part of country that can be stepping-stone for policy makers to draft policies that can affect target population more appropriately.

The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness.

During the COVID-19 pandemic, rheumatology educational programs around the world, face the daunting challenge of maintaining education for their trainees. Reduced in-person clinic exposures and social distancing requirements have significantly affected trainee education. Similar to programs around the USA, in early March 2020, our program was faced with an urgent need to pivot both our clinical and educational programs to virtual platforms. Within these limitations, we harnessed innovative educational models and restructured our curriculum to ensure adequate clinical and didactic exposure. We divided trainee's clinical rotations into four blocks, which include Inpatient consult service, Outpatient in-person and procedure clinics, Telehealth Clinics and Research/Elective week. By assigning specific rotations, we were able to ensure fellows were seeing adequate numbers of patients both through telemedicine and inperson while ensuring we complied with social distancing requirements. We further were able to ensure that trainee hands-on procedure training was not compromised. Acknowledging challenges presented by the COVID-19 pandemic and learner engagement in virtual environment, we designed an innovative educational portfolio. Utilizing synchronous and asynchronous learning methods, we have developed multiple complementary educational initiatives including: Rocket Rheumatology, Board Games, At the Elbow, Radiology Reading Rheum, Ultrasound Buddies, The History Rheum, and Rapid-Fire Journal Club. Virtual learning methods will become a cornerstone of medical education moving forwards. The GW Division of Rheumatology has rapidly incorporated innovative educational tools into our curriculum. Our approach will help Rheumatology training programs across the globe enhance rheumatology training.

Objectives: Analysis of the pattern of prostatic disease in Faisalabad. Design of Study: Case series study. Setting: Department of Pathology, University Medical and Dental College (UM&DC) and Meezan Laboratory (ML). Period: Duration of the study is three years. Methods and Materials: All prostatic specimens presenting to the Pathology department at the UM&DC and ML for histopathology were included. Results: During this period 540 prostatic biopsies were examined. The mean age of the patients was 67 years. Out of these 467(86.5%) were benign, 2 (0.3%) had prostatic intraepithelial neoplasia and 71 (13.5%) were malignant. All the cases of malignancies were adenocarcinomas. Most of them were well differentiated (Gleason’s score 2-4). The highest incidence of hyperplasia and malignancy occurred between 60-70 years of age. Conclusions: The incidence of prostatic cancer is on the rise and measures should be taken for early detection.

No abstract