Etiology of Pancreatitis in Patients at a Tertiary Care Center

Objective: To compare etiological frequencies in patients of acute pancreatitis presenting to our setup with international data. Study Design: Cross-sectional study Place and Duration of Study: Department of Gastroenterology, Pakistan Emirates Military Hospital & Combined Military Hospital, Rawalpindi Pakistan, from Aug 2020 to Jan 2022. Methodology: Patients over 12 years suffering from pancreatitis were recruited using a convenience sampling technique based upon predefined criteria for diagnosis of pancreatitis on a questionnaire. Relevant basic lab tests, including chemistries and imaging, including Ultrasound abdomen and CECT abdomen, were analyzed to establish aetiology. Data were continuously uploaded into an electronic data sheet. International Consensus Diagnostic Criteria (ICDC) algorithms were applied to diagnose autoimmune pancreatitis. Results: Out of 120 patients, 74(61.7%) were males, and 46(38.3%) were females. Biliary pancreatitis was the most common aetiology 50(41.7%), followed in descending order by idiopathic 36(30%), drug-induced pancreatitis (DIP) 9(7.5%), Post ERCP Pancreatitis (PEP) 8(6.7%), tumours 5(4%), Autoimmune pancreatitis (AIP), Hypertriglyceridemia and alcohol-induced pancreatitis each 2(1.7%). Conclusion: Biliary pancreatitis has the highest frequency, followed by idiopathic and drug-induced pancreatitis. © 2023, Army Medical College. All rights reserved.

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to it severely affected the world economy, causing a significant increase in global inflation rates, unemployment, and the cost of energy commodities. To stop the spread of the virus and dampen its global effect, it is imperative to detect infected patients early on. Convolutional neural networks (CNNs) can effectively diagnose a patient's chest X-ray (CXR) to assess whether they have been infected. Previous medical image classification studies have shown exceptional accuracies, and the trained algorithms can be shared and deployed using a computer or a mobile device. CNN-based COVID-19 detection can be employed as a supplement to reverse transcription-polymerase chain reaction (RT-PCR). In this research work, 11 ensemble networks consisting of 6 CNN architectures and a classifier layer are evaluated on their ability to differentiate the CXRs of patients with COVID-19 from those of patients that have not been infected. The performance of ensemble models is then compared to the performance of individual CNN architectures. The best ensemble model COVID-19 detection accuracy was achieved using the logistic regression ensemble model, with an accuracy of 96.29%, which is 1.13% higher than the top-performing individual model. The highest F1-score was achieved by the standard vector classifier ensemble model, with a value of 88.6%, which was 2.06% better than the score achieved by the best-performing individual model. This work demonstrates that combining a set of top-performing COVID-19 detection models could lead to better results if the models are integrated together into an ensemble. The model can be deployed in overworked or remote health centers as an accurate and rapid supplement or back-up method for detecting COVID-19.

Post-Discharge Follow-up with Quality of Life Assessment in Covid 19 Patients Admitted during the Covid 19 Pandemic

Intro: Patients affected with COVID-19 have been reported to have persistent symptoms even months after the acute episode, most commonly fatigue, breathlessness, and symptoms of anxiety and depression. These residual symptoms have been shown to compromise the quality of life and lead to significant impairment in both the mental and physical health of these patients. Method(s): A prospective observational cohort study was carried out and patients were followed for a month after discharge. Residual symptoms were noted, quality of life (QoL) assessment was done using EQ-5D-5L, and anxiety/depression was evaluated using WHO-SRQ 20 scores. Appropriate statistical tests were applied to compare improvement in QoL and residual symptoms between first and the last visit. Finding(s): A total of 110 patients were included. Mean age of the patients was 53.7 (SD+/- 13) years. Most common symptoms at 1st follow up were shortness of breath (66%) and fatigue (65%) which reduced in frequency on the last visit to (43%) and (46%) respectively. Significant improvement was seen in SpO2 levels recorded at both visits (p=0.000). An overall improvement in QoL was seen (p=0.000). WHO-SRQ 20 score above 8 was noted in 20% patients and mean score was 5.91. On further categorization into mild, moderate, severe and critical disease on admission, patients showed improvement in symptoms at four weeks irrespective of categories. For QoL assessment, mean utility score showed improvement in all disease categories on the 2nd visit except for patients with mild disease on admission. Conclusion(s): Our study showed a significant improvement in residual symptoms and overall quality of life when followed over a period of time in majority of the patients. Symptoms of anxiety and depression were also not frequent. However, our findings emphasize the need of a multidisciplinary approach towards rehabilitation of COVID-19 patients for earlier improvement in their quality of life.Copyright © 2023

Mental Health, Self-Esteem, Life Satisfaction among Ethiopian Population during Covid-19 Pandemic: A Survey Based Study

Background: Many people are at risk of developing mental health problems due to the current pandemic. However, little has been explored about the magnitude of the risk to psychological factors to gender and their location and designation in the context of the current pandemic. Hence, the purpose of this study was to investigate the psychological impact on the Ethiopian population. Method(s): An online survey using google form with 310 Ethiopian respondents was conducted. The adopted questionnaire covers the participant's sociodemographic information, and three different questionnaires (mental health inventory, self-esteem, and life satisfaction) used to collect data. The data were not distributed normally. The Mann-Whitney U-test was applied to find differences between different categories of mental health, self-esteem, and life satisfaction. Result(s): The results indicate that urban males have higher mental health and self-esteem compared to females, and little difference in mental health appeared between students, academics, government employees, private employees, and business people. Females belonging to the rural area have higher life satisfaction than males. A significant difference in self-esteem and life satisfaction was found between participants belonging to different designations. Conclusion(s): The results of all these psychological factors provide a comprehensive picture of Ethiopianpeoples during the current pandemic. In such stressful situations, the concerned government, hospitals, educational institutions, organizations and individuals need to consider psychological intervention and take necessary action. In addition to educate and prepare individuals for the various mental health issues that they may face during the pandemic period.Copyright © 2020 Ubiquity Press. All rights reserved.

Water security challenges of Ankara City in Turkey: Lessons from climate change impact and the COVID-19 pandemic

Climate change and the COVID-19 pandemic posed significant challenges for Ankara city in Turkey. The city authorities have taken a number of strategic and operational measures to improve water security. This paper explores the linkages of regular forces such as climate change and disasters, as well as disruptive forces like pandemics, sudden shocks, and actions needed to overcome the resulting challenges. Based on 13 key informant interviews with a semi-structured questionnaire and literature review, the existing water security situation is explored in relation to climate change and the impacts of the COVID-19 pandemic. Ankara is still behind in terms of climate-related adaptation practices and management. Financial resources are inadequate, so policy measures like neighborhood-level responsibility-sharing frameworks, resilience integration into existing policies and involving local people in policymaking, and developing capacity building for local government can help to ensure Ankara's water security. © 2023 Policy Studies Organization.

Preparedness of Students for Future Teaching and Learning in Higher Education: A Bangladeshi Perspective

The COVID-19 pandemic has had a significant impact on higher education (HE) across the globe, including in Bangladesh. The Bangladeshi HE system is going through an abrupt transition and transformation to cope with the crisis. This chapter is based on data collected from teachers and students of Bangladeshi public and private HE institutions regarding teaching and learning during the COVID-19 lockdown. In Bangladesh, some universities switched to online distance teaching and learning quickly during this period, and others lagged behind in this regard. Teachers and students from both groups of public and private universities participated in the study, including those who attended online teaching and learning activities and those who did not participate. This chapter highlights both teachers' and students' perspectives regarding students' future preparedness for participating fully in the changing landscape of HE, especially technology-enhanced teaching and learning. Understanding these perspectives of teachers and students is important to address the digital divide and social justice issues in the policy and practice. Within the HE sector in Bangladesh, it is especially vital while transforming its education system and adapting emerging technologies to address the challenges of education in future emergencies.

Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, has gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by evaluating the potential of intelligent processing of clinical data at the edge. We utilized the emerging concept of clustered federated learning (CFL) for an automatic COVID-19 diagnosis. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific image modality) are trained with central data, and improvements of 16% and 11% in overall F1-Scores have been achieved over the trained model trained (using multi-modal COVID-19 data) in the CFL setup on X-ray and Ultrasound datasets, respectively. We also discussed the associated challenges, technologies, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.

Health information technology to improve referral to genetic testing for high-risk patients in a gynecologic oncology clinic (102)

Objectives: Our previously presented pilot study evaluated a web-based tool to collect family cancer history (FCH). It demonstrated that this tool resulted in significantly higher quality FCH compared to standard of care face-to-face physician interviews. However, the true value of FCH requires translation into the utilization of genetic services. Here, we aimed to evaluate referral rates and completion of genetic services for patients completing the web-based tool versus standard of care. Methods: Patients scheduled for a gynecologic oncology new patient visit between September 2019 and September 2021 were eligible for enrollment in this institutional review board-approved prospective trial. The trial had three arms: 1) Standard of care (FCH collection by physicians) 2) Web-based tool administered by email prior to the visit, 3) Web-based tool administered in the office prior to the visit (this arm closed early due to COVID-19 restrictions). Individuals were identified as high-risk for familial cancer if they met National Comprehensive Cancer Network (NCCN) guidelines in the standard of care arm, or if the validated cancer risk models embedded in the web-based tool returned a lifetime cancer risk >20% or mutation risk?>2.5% in the web-based tool arms. Validated risk assessment models included breast and ovarian BRCAPRO, Claus, Tyrer-Cuzick, Gail, colorectal and endometrial MMRPRO, MELAPRO, PANCPRO, and PREMM. The primary endpoint was the percentage of high-risk patients referred for genetic counseling/testing. Secondary endpoints included the completion of genetic counseling and genetic testing. Results: Two hundred and fifty patients were enrolled (Arm 1: 110;Arm 2: 105;Arm 3: 35). Among patients randomized to the web-based tool, 88 (63%) completed the tool. In the control arm, 31 patients (28%) met the criteria for referral to genetics, among which 18 (58%) had previously completed genetic testing. In the web-based tool arm, 26 patients (30%) met the criteria, among which 12 (46%) had previously completed genetic testing, and one was deceased soon after her visit. In the control arm, 54% of high-risk patients were referred to genetic counseling, 23% completed genetic counseling, and 23% completed genetic testing. In the web-based tool arm, 100% of high-risk patients were referred to genetic counseling, 54% completed genetic counseling, and 38% completed genetic testing (Table 1). Conclusions: When successfully completed, the use of a web-based tool for FCH collection facilitated the process of referral to genetics, resulting in significantly higher referral rates to genetic counseling than the standard of care physician interviews (100% vs 54%, p = 0.01). However, 37% of patients could not complete the web-based tool. Our findings demonstrate the potential power of health information technology to identify millions of individuals unknowingly carrying familial cancer syndromes and highlight those tools must be designed in a way to maximize patient participation.[Formula presented]

COVID-19 Sentiment Analysis applying BERT

The growth of social data on the internet has accelerated during the last two decades. As a result, researchers can access data and information for various academic and commercial purposes. The novel coronavirus disease (COVID-19) is a current pandemic that has sparked widespread concern worldwide. Spreading misleading information on social media platforms like Twitter, on the other hand, is exacerbating the disease's concern. This research aims to examine tweets and develop a model that can detect public sentiment from social media posts;consequently, necessary precautions can be taken to preserve adequate validity of information for the general public. We believe that various social media platforms have a significant impact on creating public awareness about the disease's importance and encouraging preventive measures among community members. For this study, we applied the Bidirectional Encoder Representations from Transformers (BERT) model, a new deep-learning technique for text analysis and performance with exceptional multi-class accuracy. We also compared it with six shallow machine learning models. © 2022 IEEE.

The Promise of Telemedicine: Are We Reaching the Underserved?

INTRODUCTION: The use of telemedicine has dramatically increased during the COVID-19 pandemic. We evaluated characteristics and experiences of underserved women utilizing telemedicine for gynecologic visits at an urban teaching hospital. METHODS: We conducted a prospective study of patients using telemedicine for gynecologic care from January 2021-September 2021. Patients completed a demographic survey and a modified Telemedicine Usability Questionnaire (TUQ) using a 1-5 Likert scale. Statistical analyses used Fisher's exact test. RESULTS: One hundred ninety two patients consented to participate, and 157 completed surveys. The majority of patients were non-White (Hispanic 32%, Black 28%, and Asian 10%), with a median age of 40 years (range 18-69 years). A total of 61% had children and some level of education (24% GED or below, 28% vocational/associate degree, and 47% college or above), and 41% were employed, with 63% reporting an income of less than $40,000, and 85% being government insured (Medicaid/Medicare). Without telemedicine visits, 47% would have traveled 1-2 hours to appointments, with 46% spending more than $35 on travel, and 27% missing at least 1 work day for an in-person visit. The most common visit indications were lab/imaging results review (37%), postoperative follow-up (21%), and abnormal uter- ine bleeding (14%). The mean score overall for the entire TUQ was 4.3/5. Participants preferred telemedicine for follow-up visits rather than for initial visits (81% vs. 33%;P<.01). CONCLUSION: Underserved women utilizing telemedicine for gynecologic care reported largely positive experiences with improved access to health care, cost, and time savings over inperson visits. However, a higher preference for utilization was found for follow-up visits, providing an opportunity to further improve quality and access.

Underserved Patients' Experiences Using Telemedicine for Gynecologic Care During COVID- 19

INTRODUCTION: The use of telemedicine has dramatically increased during the COVID-19 pandemic. We evaluated the experience of underserved women using telemedicine for gynecologic visits at an urban teaching hospital. METHODS: We conducted a prospective study of patient experiences using telemedicine for outpatient gynecologic visits from January 2021-September 2021. Demographic/clinical data were obtained. Participants completed a modified, previously validated Telemedicine Usability Questionnaire (TUQ), with responses on a 1-5 Likert scale. Statistical analyses used the Wilcoxon signed-rank test or t test. RESULTS: One hundred ninety two patients agreed to participate, of which 157 completed the surveys. A total of 87% had video visits, whereas 13% had telephone visits. The majority of patients were ethnic minorities (non-Hispanic White 16%, Hispanic 32%, Black 28%, and Asian 10%), median age 40 years (range 18-69 years), with 63% having income (44 vs.<39, P=.02). Race/ethnicity, income, education level, and prior experience with telemedicine had no effect on responses for this subscale. CONCLUSION: Underserved women utilizing telemedicine for outpatient gynecologic visits report largely positive experiences overall. Although telemedicine holds promise in increasing access to healthcare services, attention needs to be paid to ensure reliability among telehealth visits, particularly for older populations.

Industry 4.0 in Healthcare: A systematic review

Healthcare 4.0 has changed dramatically over the last century. It is evolving daily, with physicians and researchers alike developing new tools and strategies. This study analyzes the impact of Industry 4.0 on healthcare (IHC) systems. Therefore, using PRISMA 2015, a systematic literature review was undertaken utilizing articles retrieved from Scopus and Web of Science. A bibliometric and qualitative assessment of 346 and 47 articles was performed. An IHC framework was developed considering the following components: scheduling problems, security issues, COVID-19, digital supply chain, blockchain technology, and artificial intelligence. The study found that during COVID-19, healthcare, and Industry4.0 fused and evolved together, addressing issues including data security, resource allocation, and data transparency. IHC enables a variety of technologies, including the internet of things (IoT), blockchain, big data, cloud computing, machine learning, and information and communication technologies (ICT), to track patient records and contribute to the reduction of social transmission of COVID-19. © 2022 The Author(s)

Cytotoxicity, Antioxidant Activity, Molecular Docking, and Dynamics Simulation Analysis Against SARS-CoV-2 M and N Protein Models of Phytoconstituents of Micromelum Minutum

Phytochemical investigations of the methanolic extract of the whole plant of Micromelum minutum provided two coumarins, namely micromelin and murrangatin, and one sterol stigmast-4-en-3-one, the latter being reported for the first time from M. minutum. To evaluate bioactivities, different fractions of the crude methanol extracts of the plant obtained by partitioning were screened for antioxidant and cytotoxic activity by DPPH radical scavenging method and the brine shrimp lethality bioassay, respectively. Among the different fractions of M. minutum tested, pet ether and chloroform soluble fractions showed prominent antioxidant activities with IC50 values of 49.46 and 67.53 μg/mL compared to the standard butylated hydroxytoluene (IC50 31.02 μg/ml). The pet ether and chloroform fractions of M. minutum showed good brine shrimp larvicidal activity with LC50 values of 1.15 and 1.50 μg/ml, respectively, compared to vincristine sulfate (LC50 0.27 μg/ml). The results obtained from molecular docking, Stigmast-4-en-3-one exerts the highest negative binding affinity (-9.1 kcal/mol) for interaction with SARS-CoV-2 M protein and develops a strong network with eleven hydrophobic bonds established by ADMET profile studies and YASARA Dynamics program. © 2022 by the authors.

COVID-19 seroprevalence in Pakistan: a cross-sectional study.

OBJECTIVES: This study adapted WHO's 'Unity Study' protocol to estimate the population prevalence of antibodies to SARS CoV-2 and risk factors for developing SARS-CoV-2 infection. DESIGN: This population-based, age-stratified cross-sectional study was conducted at the level of households (HH). PARTICIPANTS: All ages and genders were eligible for the study (exclusion criteria: contraindications to venipuncture- however, no such case was encountered). 4998 HH out of 6599 consented (1 individual per HH). The proportion of male and female study participants was similar. PRIMARY AND SECONDARY OUTCOME MEASURES: Following were the measured outcome measures- these were different from the planned indicators (i.e. two out of the three planned indicators were measured) due to operational reasons and time constraints: -Primary indicators: Seroprevalence (population and age specific).Secondary indicators: Population groups most at risk for SARS-CoV-2-infection. RESULTS: Overall seroprevalence of SARS-CoV-2 antibodies was 7.1%. 6.3% of individuals were IgG positive while IgM positivity was 1.9%. Seroprevalence in districts ranged from 0% (Ghotki) to 17% (Gilgit). The seroprevalence among different age groups ranged from 3.9% (0-9 years) to 10.1% (40-59 years). There were no significant differences in the overall seroprevalence for males and females. A history of contact with a confirmed COVID-19 case, urban residence and mask use were key risk factors for developing SARS-CoV-2 infection. CONCLUSIONS: This survey provides useful estimates for seroprevalence in the general population and information on risk factors for developing SARS-CoV-2 infection in the country. It is premised that similar studies need to be replicated at the population level on a regular basis to monitor the disease and immunity patterns related to COVID-19.

A Bag-of-Features (BoF) Based Novel Framework for the Detection of COVID-19

Novel coronavirus (COVID-19) is a hazardous virus. Initially, detected in China and spread worldwide, causing several deaths. Over time, there have been several variants of COVID-19, we have grouped all of them into two major categories. The categories are known to be variants of concern and variants of interest. Talking about the first of these two, it is very dangerous, and we need a system that can not only detect the disease but also classify it without physical interaction with a patient suffering from COVID-19. This paper proposes a Bag-of-Features (BoF) based deep learning framework that can detect as well as classify COVID-19 and all of its variants as well. Initially, the spatial features are extracted with deep convolutional models, while hand-crafted features have been extracted from several hand-crafted descriptors. Both spatial and hand-crafted features are combined to make a feature vector. This feature vector feeds the classifier to classify different variants in respective categories. The experimental results show that the proposed methodology outperforms all the existing methods. © 2021 IEEE.

A Novel Method for Multivariant Pneumonia Classification based on Hybrid CNN-PCA Based Feature Extraction using Extreme Learning Machine with Chest X-Ray Images

In this era of COVID19, proper diagnosis and treatment for pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite difficult and time-consuming to diagnose due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgment. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network - principle component analysis (CNN-PCA) based feature extraction (ECP), and ECP with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc. Author

Infectious Diseases Diagnosis and Treatment Suggestions Using Complex Neutrosophic Hypersoft Mapping

Infectious diseases are one of the leading causes of death all over the world. This study aims to define the debates around the diagnosis of infectious diseases and their associated issues. After looking at the side effects of Infectious Diseases, it becomes difficult to distinguish the types of Infectious Diseases and their severities. It is difficult to detect the efficiency in treating a patient record and predicting the length of medicine because the indeterminacy, false components, amplitude term (A-term), phase term (P-term), and sub parametric values are commonly rejected in terms of practical evaluation. This paper introduces the Complex Neutrosophic Hypersoft (CNHS) set and CNHS mapping with its inverse mapping to overcome these limitations. This theory will be more flexible in three ways. First, it includes indeterminacy and falsity components, which will utilise parametric values to analyse data in all three dimensions of the patient's illness: positive, indeterminant, and negative. Secondly, for easier understanding, it separates the various attributes into distinct attribute-valued sets. Third, it provides for a large range of membership function values by expanding membership to the unit circle on an Argand plane and introducing an additional term known as the P-term to account for the periodic character of the data. To correctly analyse the problem, these principles can be coupled with scientific modelling. This study demonstrates a correlation between symptoms and treatments. A table with a fuzzily defined gap between 0 and 1 is created for different types of infectious diseases. The computation is based on CNHS mapping in order to properly detect the condition and select the appropriate prescription for each patient's ailment. Eventually, a generalised CNHS mapping is offered, which can assist a doctor in releasing the chronology of the patient's health status and predicting the time frame of therapy until the sickness is cleared.

MAC CALLING THE COP: ORGANIZING PNEUMONIA SECONDARY TO MYCOBACTERIUM AVIUM COMPLEX INFECTION

TOPIC: Chest Infections TYPE: Medical Student/Resident Case Reports INTRODUCTION: Organizing pneumonia (OP) is an inflammatory lung disease involving the distal bronchioles, respiratory bronchioles, bronchiolar ducts & alveoli. OP may be cryptogenic (COP) or secondary to several factors such as drugs, infections, radiation therapy, malignancy or CTD.Mycobacterium Avium Complex (MAC) pulmonary infection is a challenging entity & diagnosis relies on the integration of clinical, radiological, microbiological & pathological results. The clinical course is heterogenous, ranging from asymptomatic cases to patients with refractory disease associated with considerable morbidity and mortality. CASE PRESENTATION: A 44-year-old female with history of COPD, DM 2, alcoholic cirrhosis, tobacco & opioid use disorder presented with worsening SOB & dry cough for a week. She was treated a year ago with 3 months of steroid therapy for COP with complete clinical and physiologic improvement and normalization of the chest film.She endorsed subjective fever & myalgias but denied hemoptysis, night sweats, weight loss, rashes, joint swelling or pain, sick contacts, recent travel & occupational exposure. Vitals were significant for fever, hypoxia, tachypnea & tachycardia. Physical exam revealed crackles in bilateral mid to lower lung fields. Laboratory results showed leukocytosis & thrombocytopenia. Serum chemistry was notable for elevated lactate but normal procalcitonin, cardiac enzymes & BNP. COVID19 PCR nasal swabs x 2 were negative. CTA showed patchy bilateral consolidations with surrounding ground glass opacities throughout lung fields & no evidence of PE. Patient was started on broad spectrum antibiotics. Infectious & Rheumatological work up turned out to be negative. Given negligible improvement & previous history of COP, she underwent VATS with wedge biopsy & histopathology confirmed florid organizing pneumonia. Glucocorticoid therapy was initiated. At 8 weeks, her tissue culture revealed the presence of MAC. Due to unfavorable clinical response to steroid therapy alone, it was decided to start Rifabutin, Ethambutol & Azithromycin to treat MAC pulmonary infection causing secondary OP. Patient showed clinical improvement & glucocorticoids were gradually tapered. Patient was referred to respiratory rehabilitation & scheduled for follow up at outpatient clinic. DISCUSSION: MAC pulmonary infection & secondary OP association has been rarely reported in the literature. OP is believed to be a consequence of alveolar epithelial injury. Both MAC pulmonary infection & OP have increased cytokine production leading to the inflammation suggesting a common pathway. Therefore, it may be possible for MAC to trigger an OP reaction. CONCLUSIONS: We recommend a systematic assessment of potential etiological agents triggering, what is considered to be COP. Further studies are warranted to establish a causal relationship between MAC & OP thus representing another manifestation of NTM-PD. REFERENCE #1: Cordier J-F. Cryptogenic organising pneumonia. Eur Respir J 2006;28:422–46 REFERENCE #2: Griffith DE, Aksamit T, Brown-Elliott BA, et al. An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases. Am J Respir Crit Care Med 2007;175:367–416 REFERENCE #3: Carré PC, King TE, Mortensen R, et al. Cryptogenic organizing pneumonia: increased expression of interleukin-8 and fibronectin genes by alveolar macrophages. Am J Respir Cell Mol Biol 1994;10:100–5 DISCLOSURES: No relevant relationships by Muhammad Ahsan, source=Web Response No relevant relationships by Kristin Fless, source=Web Response No relevant relationships by Thomas Ng, source=Web Response No relevant relationships by ARCHANA SREEKANTAN NAIR, source=Web Response