A novel case of lupus nephritis and mixed connective tissue disorder in a COVID-19 patient.

Introduction: Mixed connective tissue disease (MCTD) is a rare autoimmune condition characterized by Scleroderma, Polymyositis, and Systemic Lupus Erythematous (SLE). Though a possible relationship between COVID-19 and autoimmune diseases has been recently reported, its pathophysiological mechanism behind flares in Lupus Nephritis (LN), a complication of SLE, remains unknown. Case presentation: A 22-year-old COVID-19 positive female presented with anemia, bilateral pitting edema, periorbital swelling, and posterior cervical lymphadenitis. Further inspection revealed lower abdominal striae, hepatosplenomegaly, and hyperpigmented skin nodules. Complete blood counts showed elevated inflammatory markers and excessively high protein creatinine ratio. Antinuclear antibody titers were elevated (anti-smith and U1 small nuclear ribonucleoprotein) and Rheumatoid Factor was positive. She was diagnosed with MCTD associated with a flare of LN. To control her lupus flare, a lower dose of steroids was initially administered, in addition to oral hydroxychloroquine and intravenous cyclophosphamide. Her condition steadily improved and was discharged on oral steroid maintenance medication. Discussion: We present a rare phenomenon of newly diagnosed LN, a complication of SLE, with MCTD in a PCR-confirmed COVID-19 patient. The diagnostic conundrum and treatment hurdles should be carefully addressed when patients present with lupus and COVID-19 pneumonia, with further exploration of the immuno-pathophysiology of COVID-19 infection in multi-systemic organ dysfunction in autoimmune disorders. Conclusion: In COVID-19 patients with LN and acute renal injury, it is critical to promptly and cautiously treat symptomatic flares associated with autoimmune disorders such as SLE and MCTD that may have gone unnoticed to prevent morbidity from an additional respiratory infection.

Political Architecture And Legal Framework Related To Social Health Protection Schemes In Pakistan: Qualitative Inquiry Of Policy Makers' Viewpoint.

BACKGROUND: Pakistan is a federal state with three tiers of government. Following contentious general elections in 2013, ever first democratic transition took place in Pakistan. Subsequently, two social health protection schemes were launched. Current paper's objective is to understand the political context in which these schemes were launched and to explore the constitutional position of access to healthcare in Pakistan. This paper also explores the legal protection/ sustainability with regards to these schemes. METHODS: We used qualitative research techniques with interpretivist paradigm and case-study approach. In-depth interviews were conducted, followed by content analysis. Triangulation and data saturation were observed to guide our sample size. Officials involved with these schemes at policy and implementation level were interviewed. Ethical approval was taken from ethics board of Khyber Medical University. Based on purposive sampling, in-depth interviews were conducted and thematic analysis was performed. RESULTS: We identified two themes in response to question-1 of our interview, asking about the cause of action behind starting these schemes and their legal protection. These themes were: (i) [initiation of] Social Health Protection as democratization of healthcare, and (ii) [initiation of] Social health protection in legal void. Implicitly, these schemes are a product of grass root political activism and health found berth in election manifestos recently. Also, we deduce that health is not a constitutional right in Pakistan. These schemes lack constitutional guarantee and ensued in absence of overarching legal framework. CONCLUSIONS: These social health protection schemes are high on political agenda but lack constitutional and legal protection.

Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG.

Epileptic neurological disorder of the brain is widely diagnosed using the electroencephalography (EEG) technique. EEG signals are nonstationary in nature and show abnormal neural activity during the ictal period. Seizures can be identified by analyzing and obtaining features of EEG signal that can detect these abnormal activities. The present work proposes a novel morphological feature extraction technique based on the local binary pattern (LBP) operator. LBP provides a unique decimal value to a sample point by weighing the binary outcomes after thresholding the neighboring samples with the present sample point. These LBP values assist in capturing the rising and falling edges of the EEG signal, thus providing a morphologically featured discriminating pattern for epilepsy detection. In the present work, the variability in the LBP values is measured by calculating the sum of absolute difference of the consecutive LBP values. Interquartile range is calculated over the preprocessed EEG signal to provide dispersion measure in the signal. For classification purpose, K-nearest neighbor classifier is used, and the performance is evaluated on 896.9 hours of data from CHB-MIT continuous EEG database. Mean accuracy of 99.7% and mean specificity of 99.8% is obtained with average false detection rate of 0.47/h and sensitivity of 99.2% for 136 seizures.