Unicentric Castleman Disease: A Rare Diagnosis of Radiological and Histological Correlation.

Castleman disease is an uncommon nonneoplastic, lymphoproliferative disorder of that is associated with lymphadenopathy and nonclonal lymph nodes hyperplasia. It is further subdivided into two types: Unicentric Castleman disease and Multicentric Castleman disease. Unicentric Castleman disease is rare to be reported in patients having AIDS, because HIV infected patients most commonly presented with Multicentric Castleman disease and they mostly coinfected with HHV-8. Herein we reported a rare presentation of Unicentric Castleman disease with hyaline vascular variety evident Histopathologically as a preoperative diagnosis. The patient was initially managed conservatively and surgical excision (excisional biopsy) of the left cervical lymph node was than performed and sent for histopathology. The Immunohistochemistry findings were also consistent with the diagnosis of Castleman disease Hyaline vascular type as evident from left cervical lymph node excisional biopsy.

The psychological impact of COVID-19 on healthcare workers in Pakistan.

INTRODUCTION: The COVID-19 pandemic has challenged healthcare facilities and healthcare professionals' stamina and wellbeing. This study examines the psychological impact of COVID-19 on healthcare professionals. METHODS: This analytical cross-sectional study was conducted in July 2020 after institutional review board approval at a tertiary care institution in Lahore, Pakistan. A total of 175 healthcare workers participated following an online Depression, Anxiety and Stress Scale-21 (DASS-21) questionnaire invitation and 41 were excluded following pre-existing mental health conditions. Data was analysed using MS Excel and SPSS Amos 23. Chi-squared test and regression were applied for comparison and impact of confounding variables respectively (p<0.05 was considered significant). RESULTS: Out of 134, 66 (49%) were doctors, 24 (18%) were nurses and 44 (33%) were non-medical professionals. Ninety-five (70%) with age 21-30 years. Male to female ratio was 2:1. Overall mean depression score accounted for 6.89 ± 6.64; anxiety score was 7.28 ± 6.74 and stress score was 8.83 ± 6.93. Mild depression, anxiety and stress was noted in 21 (15.6%), eight (6%) and 27 (20.1%) healthcare workers, respectively. A statistically significant difference (p<0.05) was observed among healthcare workers for depression, anxiety and stress. CONCLUSION: This study demonstrated considerable impact of COVID-19 on mental health of healthcare workers. A well-structured targeted mental health support programme is needed urgently to support and reduce the long-term impact on healthcare workers' mental health and wellbeing.

Clinical Characteristics of 47 Death Cases With COVID-19: A Retrospective Study at a Tertiary Center in Lahore.

Introduction Coronavirus disease 2019 (COVID-19) presents with a wide spectrum of symptoms, ranging from patients being asymptomatic to having life-threatening acute respiratory distress syndrome (ARDS). COVID-19 emerged as a pandemic and has led to multiple causalities worldwide. A better understanding of the clinical characteristics of the COVID-19 patients and their disease course will aid in better management of these patients and hence may positively impact their outcomes as well. Methodology This was a retrospective observational study conducted from April 15, 2020, to August 31, 2020, after gaining institutional review board approval at the University of Lahore Teaching Hospital, Lahore, Pakistan. A total of 47 patients with severe disease who had died due to COVID-19 during this period were enrolled by the consecutive method. Patients were evaluated for their epidemiological, biochemical, clinical, and radiological features. The modified Radiographic Assessment of Lung Edema (mRALE) score was used to calculate the extent of alveolar opacities and percentages of lung involvement in chest radiographs. Furthermore, patients' management plans were also evaluated. Data were analyzed using SPSS Statistics version 23 (IBM, Armonk, NY). Results The mean age of the patients was 61.53 ±13.35 years. The male-to-female ratio was 2:1, and the mean BMI was 28.05 ±3.52 kg/m2. Diabetes was the most prevalent comorbidity among the patients (32, 68.1%), followed by hypertension (six, 12.8%), ischemic heart disease (five, 10.6%), and chronic kidney disease (four, 8.5%) respectively. The predominant symptom observed among patients was cough (95%), followed by shortness of breath (93%), fever (63%), sputum (23%), and gastrointestinal symptoms (6.4%). The mean D-dimer was 1,567.13 ±1,903.77 ng/mL, mean ferritin was 1,730.34 ±1,382.35 ng/mL, mean C-reactive protein (CRP) was 202.59 ±104.97 mg/dl, and the mean neutrophil-to-lymphocyte ratio was 10.50 ±9.58. Bilateral lung involvement was seen among 40 (85.11%) patients whereas unilateral right lung involvement was reported in three (6.38%) and unilateral left lung involvement in four (8.51%) respectively. The mean mRALE score for bilateral lung involvement was 18.78 ±4.89. The mean area radiologically involved in bilateral lung fields was 72.12 ±18.45%, followed by unilateral right lung involvement of 67.87 ±15.97%, and unilateral left lung involvement of 61.38 ±17.95% in the cohort respectively. The most common type of radiological pathology was diffuse ground-glass opacities, which was observed in 18 (38%) patients. Most patients received antibiotics (39, 63.83%), while nine (19%) received tocilizumab, four (8.5%) had antiviral therapy, and three (6.4%) were given plasma treatment. All patients received glucocorticoids and anticoagulation. The most common cause of death was ARDS, which was observed in 12 (25.5%) patients. Conclusion This study significantly demonstrated that most cases were males above 50 years of age with chronic medical comorbidities of diabetes, hypertension, and ischemic heart disease. COVID-19 has a predilection for multisystem involvement leading to mortality. In addition, elevated D-dimer and neutrophil-to-lymphocyte ratio may be indicative of a poor prognosis. A combination of antimicrobials had no positive impact on the outcomes in this cohort. It is difficult to predict the efficacy of tocilizumab and remdesivir as only a few patients in the cohort received these drugs.

Analgesic, Anti-inflammatory and neuropharmacological effects of Atropa belladonna.

The present study was carried out to investigate, in vivo, analgesic, anti-inflammatory and neuro-pharmacological activities of the methanolic extract of Atropa belladonna. The analgesic activity was measured by acetic acid induced writhing inhibition test. The neuro-pharmacological activities were evaluated by open field, rearing test, cage cross, swim test, head dip and traction tests. The anti-inflammatory activity was assessed by formalin induce inflammation on hind paw. The extract showed highly significant (p<0.001) analgesic activity with % inhibitions of writhing response at doses 100 and 300mg/kg body weight were 28.5% and 57.1%, respectively. The extract at both doses showed significant (p<0.05) sedative effect in-cage cross test and highly significance value (p<0.001) in high dose. In-open field test, the extract showed significant (P<0.05) anxiolytic activity at higher dose whereas in rearing test activity shows significant p-value at both doses. The extract also showed significant value for anti-inflammatory activity. The findings of the study clearly indicated the presence of significant analgesic, neuro-pharmacological and anti-inflammatory properties of the plant, which demands further investigation including, compounds isolation.

An improved swarm optimization for parameter estimation and biological model selection.

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.