Idiopathic Bilateral Pulmonary Vein Thrombosis.

A 52-year-old female with a history of chronic lymphoedema and untreated deep vein thrombosis, presented with non-specific right-sided chest pain. A CT angiogram confirmed bilateral inferior pulmonary vein thromboses (PVT). A comprehensive hypercoagulable workup and age-appropriate cancer screening were unremarkable; the lack of associated risk factors confirmed idiopathic PVT. The management strategy of systemic anticoagulation with apixaban and multidisciplinary follow-up underscores the treatment challenges of rare presentations. This case accentuates the importance of considering PVT in differential diagnoses of atypical chest pain and contributes valuable insights into the diagnosis, understanding and management of this uncommon condition. LEARNING POINTS: Pulmonary vein thrombosis (PVT) may present as chest pain, especially in patients with a history of prior blood clots and can occur without an underlying malignancy or coagulation disorder.Utilising a chest CT angiogram with delayed contrast timing is effective in detecting pulmonary vein thrombus.Systemic anticoagulation proves effective in managing pulmonary vein thrombus; however, further data on dosage and duration are required for better guidance.

Infectious Diseases Specific to Women.

Female patients are at a greater risk for infections such as urinary tract infections and mastitis, as well as complications from abortions/miscarriages, and sexually transmitted infections. This review highlights risk factors, pathogenesis, complications, diagnostic, and treatment modalities associated with the following infections: mastitis, sexually transmitted diseases, postpartum/abortion-related infections, and urinary tract infections.

StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm.

Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.

Is surface roughness of direct resin composite restorations material and polisher-dependent? A systematic review.

STATEMENT OF PROBLEM: Direct resin composite bonding offers a highly esthetic, minimally invasive option for the treatment of anterior teeth however the challenge to improve their longevity remains. Direct resin composite restorations are limited by the risk of staining which may be influenced by the final surface roughness (Ra) of composite achieved. PURPOSE: The purpose of this review is to investigate, using a systematic approach, whether the final surface roughness of anterior composite restorations is affected by the interaction between resin composite and polishing systems. MATERIALS AND METHODS: The review was conducted by 3 independent reviewers and included articles published up to January 21, 2021. Three electronic databases were searched: Medline, Embase, and Web of Science. Studies assessing a quantitative effect of polishing methods on the Ra of direct composite resin materials published after the year 2000 and restricted to the English language were included. RESULTS: The database search for the effect of polishing systems on composite materials retrieved 125 eligible studies. Twelve duplicate records were removed. The resulting records were screened using title and abstract leading to 38 reports which were sought for retrieval. Application of eligibility criteria led to 11 studies included in the review. Hand searching of these studies yielded no additional papers. CONCLUSIONS: There is insufficient evidence to determine whether combination of composite and polisher influences final Ra. More research is required to determine if there is an optimum combination of polisher and composite. CLINICAL IMPLICATIONS: Polishing should be completed following planned finishing procedures. The approximation to the final surface and which finishing burs to use, if any, should be considered when planning a restoration. Durafill VS predictably achieves an acceptable Ra by different polishers.

Context-aggregator: An approach of loss- and class imbalance-aware aggregation in federated learning.

Federated Learning (FL) is an emerging distributed learning paradigm which offers data privacy to contributing nodes in the collaborating environment. By exploiting the Individual datasets of different hospitals in FL setting could be used to develop reliable screening, diagnosis, and treatment predictive models to tackle major challenges such as pandemics. FL can enable the development of very diverse medical imaging datasets and thus provide more reliable models for all participating nodes, including those with low quality data. However, the issue with the traditional Federated Learning paradigm is the degradation of generalization power due to poorly trained local models at the client nodes. The generalization power of the FL paradigm can be improved by considering the relative learning contribution of client nodes. Simple aggregation of learning parameters in the standard FL model faces a diversity issue and results in more validation loss during the learning process. This issue can be resolved by considering the relative contribution of each client node participating in the learning process. The class imbalance at each site is another significant challenge that greatly impacts the performance of the aggregated learning model. This work considers Context Aggregator FL based on the context of loss-factor and class-imbalance issues by incorporating the relative contribution of the collaborating nodes in FL by proposing Validation-Loss based Context Aggregator (CAVL) and Class Imbalance based Context Aggregator (CACI). The proposed Context Aggregator is evaluated on several different Covid-19 imaging classification datasets present on participating nodes. The evaluation results show that Context Aggregator performs better than standard Federating average Learning algorithms and FedProx Algorithm for Covid-19 image classification problems.

Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning.

The COVID-19 pandemic has presented a unique challenge for physicians worldwide, as they grapple with limited data and uncertainty in diagnosing and predicting disease outcomes. In such dire circumstances, the need for innovative methods that can aid in making informed decisions with limited data is more critical than ever before. To allow prediction with limited COVID-19 data as a case study, we present a complete framework for progression and prognosis prediction in chest X-rays (CXR) through reasoning in a COVID-specific deep feature space. The proposed approach relies on a pre-trained deep learning model that has been fine-tuned specifically for COVID-19 CXRs to identify infection-sensitive features from chest radiographs. Using a neuronal attention-based mechanism, the proposed method determines dominant neural activations that lead to a feature subspace where neurons are more sensitive to COVID-related abnormalities. This process allows the input CXRs to be projected into a high-dimensional feature space where age and clinical attributes like comorbidities are associated with each CXR. The proposed method can accurately retrieve relevant cases from electronic health records (EHRs) using visual similarity, age group, and comorbidity similarities. These cases are then analyzed to gather evidence for reasoning, including diagnosis and treatment. By using a two-stage reasoning process based on the Dempster-Shafer theory of evidence, the proposed method can accurately predict the severity, progression, and prognosis of a COVID-19 patient when sufficient evidence is available. Experimental results on two large datasets show that the proposed method achieves 88% precision, 79% recall, and 83.7% F-score on the test sets.

Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype.

Predicting length of stay (LoS) and understanding its underlying factors is essential to minimizing the risk of hospital-acquired conditions, improving financial, operational, and clinical outcomes, and better managing future pandemics. The purpose of this study was to forecast patients' LoS using a deep learning model and to analyze cohorts of risk factors reducing or prolonging LoS. We employed various preprocessing techniques, SMOTE-N to balance data, and a TabTransformer model to forecast LoS. Finally, the Apriori algorithm was applied to analyze cohorts of risk factors influencing hospital LoS. The TabTransformer outperformed the base machine learning models in terms of F1 score (0.92), precision (0.83), recall (0.93), and accuracy (0.73) for the discharged dataset and F1 score (0.84), precision (0.75), recall (0.98), and accuracy (0.77) for the deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to laboratory, X-ray, and clinical data, such as elevated LDH and D-dimer levels, lymphocyte count, and comorbidities such as hypertension and diabetes. It also reveals what treatments have reduced the symptoms of COVID-19 patients, leading to a reduction in LoS, particularly when no vaccines or medication, such as Paxlovid, were available.

Neurological Emergencies in the Intensive Care Unit.

Neurological emergencies carry significant morbidity and mortality, and it is necessary to have a multidisciplinary approach involving the emergency physician, the neurologist, the intensivist, and the critical care nursing staff. These disorders can be broadly divided into noninfectious and infectious etiologies. In this article, we review a few of the neurological emergencies that present to the neurological intensive unit, with emphasis on convulsive status epileptics, myasthenia gravis, Guillain-Barré syndrome, meningitis, encephalitis, and brain abscess.

Toward Grading Subarachnoid Hemorrhage Risk Prediction: A Machine Learning-Based Aneurysm Rupture Score.

OBJECTIVE: Existing approaches neither provide an accurate prediction of subarachnoid hemorrhage (SAH) nor offer a quantitative comparison among a group of its risk factors. To evaluate the population, hypertension, age, size, earlier subarachnoid hemorrhage, and location (PHASES) and unruptured intracranial aneurysm treatment score (UIATS) scores and develop an Artificial Intelligence-based 5-year and lifetime aneurysmal rupture criticality prediction (ARCP) score for a set of risk factors. METHODS: We design various location-specific and ensemble learning models to develop lifetime rupture risk, employ the longitudinal data to develop a linear regression-based model to predict an aneurysm's growth score, and use the Apriori algorithm to identify risk factors strongly associated with SAH. We develop ARCP by integrating output of Apriori algorithm and ML models and compare with PHASES and UIATS scores along with the scores of a multidisciplinary team of neurosurgeons. RESULTS: The PHASES and UIATS scores show sensitivities of 22% and 35% and specificities of 76% and 79%, respectively. Location-specific models show precision and recall of 93% and 90% for the middle cerebral artery, 83% and 80% for the anterior communicating artery, and 80% and 80% for the supraclinoid internal carotid artery, respectively. The ensemble method shows both precision and recall of 80%. The validation of the models shows that ARCP performs better than our control group of neurosurgeons. Data-driven knowledge produces comparisons among 61 risk factor combinations, 11 ranked minor, 8 moderate, and 41 severe, and 1 of which is a critical factor. CONCLUSIONS: The PHASES and UIATS are weak predictors, and the ARCP score can identify, and grade, risk factors associated with SAH.

Do project-based learning, hands-on activities, and flipped teaching enhance student's learning of introductory theoretical computing classes?

The need for computer science (CS) education, especially computer network education, is increasing. However, the challenges of teaching students with diverse backgrounds and engaging them in hands-on activities to apply theories into practices exist in CS education. The study addressed the challenges by using project-based learning (PBL) and flipped teaching approaches to cover both theoretical and hands-on learning aspects in CS education. This study aims to demonstrate the design and development journey of a CS course and examine whether using PBL, hands-on activities, and flipped teaching approaches improves students' learning. The design-based research study was conducted in an undergraduate CS course from 2014 to 2020 at a midwestern university. The design and development trajectory in the six years were described. The descriptive statistics were used to analyze the trends of the course evaluation results, and ANOVA were conducted to examine whether the evaluation differs from each semester. The results indicated that using PBL, hands-on activities, and flipped teaching increased students' learning motivation and their perceptions of their learning. Combining PBL and flipped teaching appropriately can enhance students' learning motivation and perceived learning in CS education, but further research is needed to examine how each individual intervention influence students' learning motivation and learning outcomes.

Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging.

In the last 2 years, we have witnessed multiple waves of coronavirus that affected millions of people around the globe. The proper cure for COVID-19 has not been diagnosed as vaccinated people also got infected with this disease. Precise and timely detection of COVID-19 can save human lives and protect them from complicated treatment procedures. Researchers have employed several medical imaging modalities like CT-Scan and X-ray for COVID-19 detection, however, little concentration is invested in the ECG imaging analysis. ECGs are quickly available image modality in comparison to CT-Scan and X-ray, therefore, we use them for diagnosing COVID-19. Efficient and effective detection of COVID-19 from the ECG signal is a complex and time-taking task, as researchers usually convert them into numeric values before applying any method which ultimately increases the computational burden. In this work, we tried to overcome these challenges by directly employing the ECG images in a deep-learning (DL)-based approach. More specifically, we introduce an Efficient-ECGNet method that presents an improved version of the EfficientNetV2-B4 model with additional dense layers and is capable of accurately classifying the ECG images into healthy, COVID-19, myocardial infarction (MI), abnormal heartbeats (AHB), and patients with Previous History of Myocardial Infarction (PMI) classes. Moreover, we introduce a module to measure the similarity of COVID-19-affected ECG images with the rest of the diseases. To the best of our knowledge, this is the first effort to approximate the correlation of COVID-19 patients with those having any previous or current history of cardio or respiratory disease. Further, we generate the heatmaps to demonstrate the accurate key-points computation ability of our method. We have performed extensive experimentation on a publicly available dataset to show the robustness of the proposed approach and confirmed that the Efficient-ECGNet framework is reliable to classify the ECG-based COVID-19 samples.

Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification.

The outbreak of the novel coronavirus disease COVID-19 (SARS-CoV-2) has developed into a global epidemic. Due to the pathogenic virus's high transmission rate, accurate identification and early prediction are required for subsequent therapy. Moreover, the virus's polymorphic nature allows it to evolve and adapt to various environments, making prediction difficult. However, other diseases, such as dengue, MERS-CoV, Ebola, SARS-CoV-1, and influenza, necessitate the employment of a predictor based on their genomic information. To alleviate the situation, we propose a deep learning-based mechanism for the classification of various SARS-CoV-2 virus variants, including the most recent, Omicron. Our model uses a neural network with a temporal convolution neural network to accurately identify different variants of COVID-19. The proposed model first encodes the sequences in the numerical descriptor, and then the convolution operation is applied for discriminative feature extraction from the encoded sequences. The sequential relations between the features are collected using a temporal convolution network to classify COVID-19 variants accurately. We collected recent data from the NCBI, on which the proposed method outperforms various baselines with a high margin.

Towards robust diagnosis of COVID-19 using vision self-attention transformer.

The outbreak of COVID-19, since its appearance, has affected about 200 countries and endangered millions of lives. COVID-19 is extremely contagious disease, and it can quickly incapacitate the healthcare systems if infected cases are not handled timely. Several Conventional Neural Networks (CNN) based techniques have been developed to diagnose the COVID-19. These techniques require a large, labelled dataset to train the algorithm fully, but there are not too many labelled datasets. To mitigate this problem and facilitate the diagnosis of COVID-19, we developed a self-attention transformer-based approach having self-attention mechanism using CT slices. The architecture of transformer can exploit the ample unlabelled datasets using pre-training. The paper aims to compare the performances of self-attention transformer-based approach with CNN and Ensemble classifiers for diagnosis of COVID-19 using binary Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and multi-class Hybrid-learning for UnbiaSed predicTion of COVID-19 (HUST-19) CT scan dataset. To perform this comparison, we have tested Deep learning-based classifiers and ensemble classifiers with proposed approach using CT scan images. Proposed approach is more effective in detection of COVID-19 with an accuracy of 99.7% on multi-class HUST-19, whereas 98% on binary class SARS-CoV-2 dataset. Cross corpus evaluation achieves accuracy of 93% by training the model with Hust19 dataset and testing using Brazilian COVID dataset.

An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown.

There have been considerable losses in terms of human and economic resources due to the current coronavirus pandemic. This work, which contributes to the prevention and control of COVID-19, proposes a novel modified epidemiological model that predicts the epidemic's evolution over time in India. A mathematical model was proposed to analyze the spread of COVID-19 in India during the lockdowns implemented by the government of India during the first and second waves. What makes this study unique, however, is that it develops a conceptual model with time-dependent characteristics, which is peculiar to India's diverse and homogeneous societies. The results demonstrate that governmental control policies and suitable public perception of risk in terms of social distancing and public health safety measures are required to control the spread of COVID-19 in India. The results also show that India's two strict consecutive lockdowns (21 days and 19 days, respectively) successfully helped delay the spread of the disease, buying time to pump up healthcare capacities and management skills during the first wave of COVID-19 in India. In addition, the second wave's severe lockdown put a lot of pressure on the sustainability of many Indian cities. Therefore, the data show that timely implementation of government control laws combined with a high risk perception among the Indian population will help to ensure sustainability. The proposed model is an effective strategy for constructing healthy cities and sustainable societies in India, which will help prevent such a crisis in the future.

Toward Realigning Automatic Speaker Verification in the Era of COVID-19.

The use of face masks has increased dramatically since the COVID-19 pandemic started in order to to curb the spread of the disease. Additionally, breakthrough infections caused by the Delta and Omicron variants have further increased the importance of wearing a face mask, even for vaccinated individuals. However, the use of face masks also induces attenuation in speech signals, and this change may impact speech processing technologies, e.g., automated speaker verification (ASV) and speech to text conversion. In this paper we examine Automatic Speaker Verification (ASV) systems against the speech samples in the presence of three different types of face mask: surgical, cloth, and filtered N95, and analyze the impact on acoustics and other factors. In addition, we explore the effect of different microphones, and distance from the microphone, and the impact of face masks when speakers use ASV systems in real-world scenarios. Our analysis shows a significant deterioration in performance when an ASV system encounters different face masks, microphones, and variable distance between the subject and microphone. To address this problem, this paper proposes a novel framework to overcome performance degradation in these scenarios by realigning the ASV system. The novelty of the proposed ASV framework is as follows: first, we propose a fused feature descriptor by concatenating the novel Ternary Deviated overlapping Patterns (TDoP), Mel Frequency Cepstral Coefficients (MFCC), and Gammatone Cepstral Coefficients (GTCC), which are used by both the ensemble learning-based ASV and anomaly detection system in the proposed ASV architecture. Second, this paper proposes an anomaly detection model for identifying vocal samples produced in the presence of face masks. Next, it presents a Peak Norm (PN) filter to approximate the signal of the speaker without a face mask in order to boost the accuracy of ASV systems. Finally, the features of filtered samples utilizing the PN filter and samples without face masks are passed to the proposed ASV to test for improved accuracy. The proposed ASV system achieved an accuracy of 0.99 and 0.92, respectively, on samples recorded without a face mask and with different face masks. Although the use of face masks affects the ASV system, the PN filtering solution overcomes this deficiency up to 4%. Similarly, when exposed to different microphones and distances, the PN approach enhanced system accuracy by up to 7% and 9%, respectively. The results demonstrate the effectiveness of the presented framework against an in-house prepared, diverse Multi Speaker Face Masks (MSFM) dataset, (IRB No. FY2021-83), consisting of samples of subjects taken with a variety of face masks and microphones, and from different distances.

COVID-DAI: A novel framework for COVID-19 detection and infection growth estimation using computed tomography images.

The COVID-19 pandemic is spreading at a fast pace around the world and has a high mortality rate. Since there is no proper treatment of COVID-19 and its multiple variants, for example, Alpha, Beta, Gamma, and Delta, being more infectious in nature are affecting millions of people, further complicates the detection process, so, victims are at the risk of death. However, timely and accurate diagnosis of this deadly virus can not only save the patients from life loss but can also prevent them from the complex treatment procedures. Accurate segmentation and classification of COVID-19 is a tedious job due to the extensive variations in its shape and similarity with other diseases like Pneumonia. Furthermore, the existing techniques have hardly focused on the infection growth estimation over time which can assist the doctors to better analyze the condition of COVID-19-affected patients. In this work, we tried to overcome the shortcomings of existing studies by proposing a model capable of segmenting, classifying the COVID-19 from computed tomography images, and predicting its behavior over a certain period. The framework comprises four main steps: (i) data preparation, (ii) segmentation, (iii) infection growth estimation, and (iv) classification. After performing the pre-processing step, we introduced the DenseNet-77 based UNET approach. Initially, the DenseNet-77 is used at the Encoder module of the UNET model to calculate the deep keypoints which are later segmented to show the coronavirus region. Then, the infection growth estimation of COVID-19 per patient is estimated using the blob analysis. Finally, we employed the DenseNet-77 framework as an end-to-end network to classify the input images into three classes namely healthy, COVID-19-affected, and pneumonia images. We evaluated the proposed model over the COVID-19-20 and COVIDx CT-2A datasets for segmentation and classification tasks, respectively. Furthermore, unlike existing techniques, we performed a cross-dataset evaluation to show the generalization ability of our method. The quantitative and qualitative evaluation confirms that our method is robust to both COVID-19 segmentation and classification and can accurately predict the infection growth in a certain time frame. RESEARCH HIGHLIGHTS: We present an improved UNET framework with a DenseNet-77-based encoder for deep keypoints extraction to enhance the identification and segmentation performance of the coronavirus while reducing the computational complexity as well. We propose a computationally robust approach for COVID-19 infection segmentation due to fewer model parameters. Robust segmentation of COVID-19 due to accurate feature computation power of DenseNet-77. A module is introduced to predict the infection growth of COVID-19 for a patient to analyze its severity over time. We present such a framework that can effectively classify the samples into several classes, that is, COVID-19, Pneumonia, and healthy samples. Rigorous experimentation was performed including the cross-dataset evaluation to prove the efficacy of the presented technique.

Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans.

The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.

The Specifics of Non-specific Low Back Pain: Re-evaluating the Current Paradigm to Improve Patient Outcomes.

Low back pain (LBP) is the leading cause of pain and debility worldwide and the most frequent reason for work-related disability. Global expenditures related to LBP are staggering and amount to billions of dollars each year in the United States alone. Yet, despite the considerable healthcare resources consumed, the care provided to patients with LBP has regularly been cited as both ineffective and exorbitant. Among the myriad reasons for this suboptimal care, the current approach to evaluation and management of patients with LBP is a likely contributor and is hitherto un-investigated. Following the current methodology, over 90% of patients with LBP are provided with no specific diagnosis, are managed inconsistently, and receive no express preventative care. We believed that this approach added costs and promoted chronic unresolved pain and disability. This narrative review highlights problems with the current methodology, proposes a novel concept for categorizing patients with LBP, and recommends strategies for improvement. Stratifying patients according to the etiology, in lieu of the prospects for morbidity, the strategy proposed in this article may help ascertain the cause of patient's LBP early, consolidate treatments, permit timely preventative measures, and, as a result, may improve patient outcomes.

Coincidence or connection? A patient with concurrent Lane Hamilton Syndrome and idiopathic membranous nephropathy.

Lane Hamilton Syndrome is the rare association of idiopathic pulmonary hemosiderosis and Celiac Disease. The definitive pathophysiologic link is unknown, but the syndrome has been described as co-occurring along with other diseases. We describe the first reported case of Lane Hamilton Syndrome and idiopathic membranous nephropathy. We also hypothesize the possibility of an immune-mediated connection between the pathologies and propose a potential link of the phospholipase A2 receptor.

IgG4 related lung disease- a rare and novel mimic of malignancy and infections-a case series of three patients with a brief review of updated literature.

IgG4-related lung disease is an extremely rare and novel entity which is still poorly understood. We reviewed the 16 patients diagnosed with IgG4-related disease from October 2014 through December 2019 at our institution. The three cases that showed pulmonary involvement are included in this series. Of these, two patients had cavitary lung disease and developed aspergilloma and chronic cavitating aspergillosis after a prolonged course of steroid therapy, and one had isolated pulmonary nodule and ground glass opacity. We reviewed the updated literature and briefly described disease epidemiology, clinical characteristics, diagnostic approaches, and management strategies for IgG4-related lung disease.