Heated tobacco product (IQOS) induced pulmonary infiltrates.

Background: Heated tobacco products (HTPs) have been marketed as safer alternatives to conventional cigarettes, but emerging evidence suggests potential respiratory risks. We present a case of pulmonary complications associated with IQOS, a popular HTP, contributing to the growing understanding of these risks. Case description: A 40-year-old chronic smoker switched to IQOS, consuming 1.5 packs per day. He presented with incidental chest radiographic abnormalities and peripheral eosinophilia. Computed tomography of chest revealed pulmonary nodules and ground glass opacities. Bronchoscopy indicated mild eosinophilia. After ruling out other causes, a lung biopsy was recommended but declined. Discontinuation of IQOS led to symptom resolution and radiographic improvement. This case adds to a limited literature on HTP-induced lung injury, with a unique presentation and favorable response to cessation. Conclusions: The case highlights potential pulmonary complications and the first describing an organizing pattern of lung injury associated with IQOS use, emphasizing the importance of recognizing and discontinuing HTPs in patients with respiratory symptoms or radiographic abnormalities. Further research is needed to elucidate the mechanisms underlying the harmful effects of HTPs and inform public health policies. This case underscores the importance of monitoring and educating individuals about the potential risks of HTPs to respiratory health, especially in the context of smokers switching to these products.

Perioperative Hypertensive Response In A Patient With Implanted Deep Brain Stimulation Device: A Case Report.

This case report features unique anaesthetic management of a patient with implanted deep brain stimulation (DBS) device undergoing abdominal surgery. It features the intraoperative measures taken for this patient for the implanted DBS. After turning off the DBS preoperatively, the patient showed an exaggerated sympathetic response which was very much resistant to medicines. It was unique to this patient that restarting the device alleviated refractory hypertension. The report also includes review of literature for anaesthetic management of patients with implanted DBS.

Multi-Agent Reinforcement Learning for Traffic Flow Management of Autonomous Vehicles.

Intelligent traffic management systems have become one of the main applications of Intelligent Transportation Systems (ITS). There is a growing interest in Reinforcement Learning (RL) based control methods in ITS applications such as autonomous driving and traffic management solutions. Deep learning helps in approximating substantially complex nonlinear functions from complicated data sets and tackling complex control issues. In this paper, we propose an approach based on Multi-Agent Reinforcement Learning (MARL) and smart routing to improve the flow of autonomous vehicles on road networks. We evaluate Multi-Agent Advantage Actor-Critic (MA2C) and Independent Advantage Actor-Critical (IA2C), recently suggested Multi-Agent Reinforcement Learning techniques with smart routing for traffic signal optimization to determine its potential. We investigate the framework offered by non-Markov decision processes, enabling a more in-depth understanding of the algorithms. We conduct a critical analysis to observe the robustness and effectiveness of the method. The method's efficacy and reliability are demonstrated by simulations using SUMO, a software modeling tool for traffic simulations. We used a road network that contains seven intersections. Our findings show that MA2C, when trained on pseudo-random vehicle flows, is a viable methodology that outperforms competing techniques.

A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector Regression.

Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages-initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.

An attention-based recurrent learning model for short-term travel time prediction.

With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).

Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas.

With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types of sensors may fall into different administrative domains and may be accessible through exposed Application Program Interfaces (APIs). In such setups, for traffic prediction in Intelligent Transport Systems (ITS), one of the major prerequisites is the integration of heterogeneous data sources within a preprocessing data pipeline resulting into hybrid feature space. In this paper, we first present a comprehensive algorithm to integrate heterogeneous data obtained from sensors, services, and exogenous data sources into a hybrid spatial-temporal feature space. Following a rigorous exploratory data analysis, we apply a variety of deep learning algorithms specialized for time series geospatial data and perform a comparative analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrid combinations. The hybrid LSTM-GRU model outperforms the rest with Root Mean Squared Error (RMSE) of 4.5 and Mean Absolute Percentage Error (MAPE) of 6.67%.

Modeling-framework for model-based software engineering of complex Internet of things systems.

Internet of things (IoT) systems are composed of variety of units from different domains. While developing a complete IoT system, different professionals from different domains may have to work in collaboration. In this paper we provide a framework which allows using discrete and continuous time modeling and simulation approaches in combination for IoT systems. The proposed framework demonstrates on how to model Ad-hoc and general IoT systems for software engineering purpose. We demonstrate that model-based software engineering on one hand can provide a common platform to overcome communication gaps among collaborating stakeholders whereas, on the other hand can model and integrate heterogeneous components of IoT systems. While modeling heterogeneous IoT systems, one of the major challenges is to apply continuous and discrete time modeling on intrinsically varying components of the system. Another difficulty may be how to compose these heterogeneous components into one whole system. The proposed framework provides a road-map to model discrete, continuous, Ad-hoc, general systems along with composition mechanism of heterogeneous subsystems. The framework uses a combination of Agent-based modeling, Aspect-oriented modeling, contract-based modeling and services-oriented modeling concepts. We used this framework to model a scenario example of a service-oriented IoT system as proof of concept. We analyzed our framework with existing systems and discussed it in details. Our framework provides a mechanism to model different viewpoints. The framework also enhances the completeness and consistency of the IoT software models.

Isolated pulmonary cryptococcosis in a patient with Crohn's disease treated with infliximab: A case report and literature review.

Tumor necrosis factor-alpha (TNF-α) inhibitors are widely used to treat various inflammatory conditions, where they have demonstrated excellent efficacy and tolerability. However, increased risk of infections is one of the most important concerns associated with these agents. Reactivation of tuberculosis and fungal infections have emerged as significant infective complications of anti-TNF-α therapy. Cryptococcus infection is an opportunistic fungal infection that can occur in patients receiving anti-TNF-α treatment. We report a rare case of isolated pulmonary cryptococcosis in a patient undergoing anti-TNF-α therapy for Crohn's disease. Our case should alert clinicians to the increased incidence and atypical presentation of pulmonary cryptococcosis in patients receiving anti-TNF-α treatment.

Challenges in the diagnosis of pulmonary mucormycosis in a diabetic with a review of literature.

Diabetes Mellitus appears to be the most common underlying condition associated with mucormycosis; a rare opportunistic fungal infection associated with high morbidity and mortality. Pulmonary mucormycosis may mimic pneumonia and thus pose challenges in achieving a timely diagnosis critical to successful outcomes. We present a case of a 65-year-old diabetic who presented with fever and haemoptysis that was managed as pneumonia. A bronchial alveolar lavage grew Rhizopus mould that was thought to be a contaminant as he responded well to antibiotics. He required another admission in 4 weeks due to worsening symptoms. Failure to respond to antibiotics and ongoing clinical and radiological deterioration led to a lobectomy that confirmed a diagnosis of pulmonary mucormycosis. He responded well to surgical resection and antifungal therapy with a complete recovery. Elusive clinical presentation and insensitive conventional diagnostic techniques may make the diagnosis of mucormycosis challenging. Our case reports highlight the issues involved in the diagnosis and management of pulmonary Mucormycosis mimicking as pneumonia.

Knowledge of Sleep Disorders Among Physicians at a Tertiary Care Hospital in Qatar: Cross-sectional Study.

BACKGROUND: Sleep disorders constitute a major health problem because of their relatively high and rising prevalence. Several studies worldwide have analyzed health care providers' knowledge of sleep disorders. OBJECTIVE: In this study, we aimed to assess the knowledge of sleep disorders among physicians in Qatar. METHODS: A total of 250 physicians were surveyed regarding their knowledge of sleep medicine by using the validated 30-item Assessment of Sleep Knowledge in Medical Education (ASKME) Survey. The participants included residents, fellows, and consultants in medicine and allied subspecialties. A high score was defined as ≥60% of correctly answered questions, implying the respondent has adequate knowledge of sleep disorders. RESULTS: Responses were received from 158 of the 250 physicians, with a response rate of 63.2%. This included responses from 34 residents, 74 clinical fellows, and 50 consultants. The overall mean score was 15.53 (SD 4.42), with the highest possible score of 30. Only 57 of 158 (36.1%) respondents were able to answer ≥60% of the questions correctly. No statistically significant difference was found in the scores of participants with regard to their ranks (ie, residents, fellows, or consultants) or years of medical training. CONCLUSIONS: This study demonstrates that health care providers in Qatar have decreased awareness and knowledge about sleep medicine, which may reflect reduced emphasis on sleep disorders during medical school and training. Increasing awareness regarding sleep medicine among nonspecialist physicians will allow early detection and treatment of sleep disorders, thereby reducing the morbidity associated with these disorders.

Endobronchial metastases secondary to prostate cancer: A case report and literature review.

Metastatic disease from solid extrapulmonary tumours affects the lungs frequently. Endobronchial metastases (EM) however are very rare. Most commonly breast, colorectal and renal carcinomas can result in endobronchial metastases. EM secondary to a prostate primary are even more uncommon. We present an unusual case of a synchronous diagnosis of EM and primary prostate cancer. The diagnosis was confirmed on bronchoscopic endobronchial biopsies and immunohistochemical examination. Just 3 such cases have been reported to the best of our knowledge in the last 15 years. We discuss frequencies, similarities with previously reported cases, possible developmental modes and the diagnosis of EM. We conclude that patients with a current or previous diagnosis of an extrapulmonary malignancy with apparently trivial respiratory symptoms and/or unexplained weight loss should be considered for a bronchoscopy. Bronchoscopy and immunohistochemical profiling is the gold standard for diagnosing EM, as they may not be visible on cross sectional imaging.

Anaesthetic Challenges in a Paediatric Patient with Escobar Syndrome-Difficult Airway and Postoperative Pneumothorax.

Escobar syndrome (ES) is an autosomal recessive disorder characterised by the presence of pterygia in cervical, antecubital and popliteal regions. Anaesthesiologist encounter notable challenges in this syndrome, especially airway management due to associated malformations like cleft lip/palate, micrognathia, syngnathia, ankyloglossia, neck contracture, cervical spine fusion, limited neck extension and craniofacial dysmorphism. In addition to difficult airway, anaesthesiologist may encounter other perioperative challenges. Here, we report a paediatric patient with ES, who required general anaesthesia for laparoscopic inguinal hernia repair and orchidopexy. Initial attempt with video laryngoscope failed due to inability to visualise epiglottis. Subsequent attempt with fibreoptic bronchoscope also failed due to rapid decrease in oxygen saturation. He was finally intubated with fibreoptic bronchoscope along with oxygen insufflation with a 3mm internal diameter polyvinylchloride endotracheal tube inserted nasally and connected to oxygen supply. Further perioperative challenges faced were intraoperative hyperthermia and postoperative pneumothorax with mediastinal shift. To the best of our knowledge, this is the first case reporting pneumothorax with mediastinal shift as a postoperative complication and use of oxygen insufflation through nasal tube during fibreoptic intubation in paediatric patient with ES.

Pleural effusion as an isolated finding in COVID-19 infection.

Common radiological findings of COVID -19 infection include bilateral ground-glass opacities in lower lobes with a peripheral distribution. Pleural effusion is considered a rare manifestation of COVID -19 infection. We present a 52 years old patient with a three-week history of right-sided pleuritic chest pain, fever, and dyspnea. Laboratory investigations revealed high C-reactive protein and ferritin levels and a positive COVID-polymerase chain reaction (PCR) from a nasopharyngeal swab. Chest X-ray and Computed tomography (CT) identified a moderate right-sided pleural effusion, which was exudative with mixed cellularity and high Lactate dehydrogenase (LDH). Histopathology of thoracoscopic pleural biopsy didn't reveal granulomas, malignancy, or any microbiological growth. We postulate that having ruled out any other cause the effusion was likely related to the Covid-19 infection. Our case highlights that COVID-19 can present with isolated pleural effusions, therefore it should be kept as an etiology of effusions especially if other possible causes have been ruled out.

Pneumothorax In Covid-19 Pneumonia: A case series.

BACKGROUND: Coronavirus disease 2019 (Covid-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It mainly affects the lungs and common symptoms are fever, cough and shortness of breath. Pneumothorax has been noted to complicate Covid-19 cases requiring hospital admission, however the exact incidence and risk factors are still unknown. DISCUSSION: We present a series of 3 cases of primary spontaneous pneumothorax with Covid-19 pneumonia. All cases in our series did not require positive pressure ventilation and none had any pre-existing lung disease. All were never smokers and had favourable outcomes despite having severe Covid-19 with a pneumothorax during the course of the disease. In our literature review we discuss several plausible mechanisms and risk factors resulting in a pneumothorax with Covid-19. CONCLUSION: Our cases are a reminder that an acute deterioration with hypoxia in a Covid-19 patient could indicate a pneumothorax. Pneumothorax is one of the reported complications in Covid-19 and clinician vigilance is required during assessment of patients, as both share the common symptom of breathlessness and therefore can mimic each other.

Diagnostic value of bronchoalveolar lavage in the subset of patients with negative sputum/smear and mycobacterial culture and a suspicion of pulmonary tuberculosis.

BACKGROUND: The diagnostic value of bronchoalveolar lavage in patients with negative sputum/smear for tuberculous bacilli has been well studied. However, its value in the subset of patients with both negative sputum/smear and culture is seldom reported. METHODS: A retrospective study of patients referred for diagnostic bronchoscopy for the suspicion of pulmonary tuberculosis during the period from April 1st, 2015 to March 30th, 2016, and who had negative sputum/smear and culture for tuberculous bacilli. RESULTS: One hundred and ninety patients fulfilled the inclusion criteria. Bronchoalveolar lavage detected further 61/190 (32.1%) pulmonary tuberculosis cases. Bronchoalveolar lavage mycobacterial culture and polymerase chain reaction (positive in 60/190 (31.6%) and 58/190 (30.5%) of patients respectively) provided the highest diagnostic yield, whereas direct smear provided the lowest yield. Bronchoalveolar lavage had a sensitivity of 89.7%, a specificity of 100%, a positive predictive value of 100%, a negative predictive value of 94.6%, and a test accuracy of 96.3% in suspected pulmonary tuberculosis cases with negative sputum/smear and culture. Positive bronchoalveolar lavage yield for tuberculosis was significantly associated with a positive QuantiFERON-TB Gold In-Tube test, positive purified protein derivative skin test, radiological evidence of upper zone abnormality and patient's origin being from the Indian subcontinent. CONCLUSION: Bronchoalveolar lavage should be pursued as a useful diagnostic tool for suspected pulmonary tuberculosis cases when sputum/smear and culture are negative. Its value is higher in the subset of patients with positive QuantiFERON-TB Gold In-Tube test, positive purified protein derivative skin test, upper zone abnormality on radiograph or being from the Indian subcontinent.