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.

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.

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.

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.

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.

Comorbidities Associated With Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease or COPD is characterized by airflow obstruction, causing respiratory symptoms. There are treatments available for COPD; however, COPD has significant extrapulmonary effects, including well-recognized ones as cardiovascular disease and often underdiagnosed ones as osteoporosis. It is imperative to be aware of these comorbidities to optimize COPD patient care.

Clinical Context-Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation.

BACKGROUND: Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization. OBJECTIVE: Traditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs. METHODS: In our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context-aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context-aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score. RESULTS: Evaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context-aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory. CONCLUSIONS: By employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.

Systemic Complications of COVID-19.

Coronavirus disease-2019 (COVID-19) creates severe respiratory distress and often a cascade of other systemic complications impacting several organ systems. The immune response includes a cytokine storm that creates many life-threatening problems including coagulopathies, arrhythmias, and secondary infections. This article discusses the multisystem responses to the physical insults created by this corona virus.

Complications and Outcomes of Acute Respiratory Distress Syndrome.

Acute respiratory distress syndrome (ARDS) is a disease associated with both short- and long-term complications. Acute complications include refractory respiratory failure requiring prolonged dependence on mechanical ventilation and the subsequent need for tracheostomy and gastrostomy tubes, protracted immobilization, and lengthy stays in the intensive care unit resulting in delirium, critical illness myopathy, and polyneuropathy, as well as secondary nosocomial infections. Chronic adverse outcomes of ARDS include irreversible changes such as fibrosis, tracheal stenosis from prolonged tracheostomy tube placement, pulmonary function decline, cognitive impairment and memory loss, posttraumatic stress disorder, depression, anxiety, muscle weakness, ambulatory dysfunction, and an overall poor quality of life. The degree of disability in ARDS survivors is heterogeneous and can be evident even years after hospitalization. Although survival rates have improved over the past 4 decades, mortality remains significant with rates reported as high as 40%. Despite advancements in management, the causes of death in ARDS have remained relatively unchanged since the 1980s with sepsis/septic shock and multiorgan failure at the top of the list.

Mimics of Acute Respiratory Distress Syndrome.

Acute respiratory distress syndrome (ARDS) was first described in 1967 by Ashbaugh and colleagues. Acute respiratory distress syndrome is a clinical syndrome, not a disease, and has no ideal definition or gold standard diagnostic test. There are multiple causes and different pathways of pathogenesis as well as various histological findings. Given these variations, there are many clinical entities that can get confused with ARDS. These entities are discussed in this article as "Mimics of ARDS." It imperative to correctly identify ARDS and distinguish it from other diseases to implement correct management strategy.

Linked open data-based framework for automatic biomedical ontology generation.

BACKGROUND: Fulfilling the vision of Semantic Web requires an accurate data model for organizing knowledge and sharing common understanding of the domain. Fitting this description, ontologies are the cornerstones of Semantic Web and can be used to solve many problems of clinical information and biomedical engineering, such as word sense disambiguation, semantic similarity, question answering, ontology alignment, etc. Manual construction of ontology is labor intensive and requires domain experts and ontology engineers. To downsize the labor-intensive nature of ontology generation and minimize the need for domain experts, we present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG), which is empowered by Linked Open Data (LOD). LOD-ABOG performs concept extraction using knowledge base mainly UMLS and LOD, along with Natural Language Processing (NLP) operations; and applies relation extraction using LOD, Breadth first Search (BSF) graph method, and Freepal repository patterns. RESULTS: Our evaluation shows improved results in most of the tasks of ontology generation compared to those obtained by existing frameworks. We evaluated the performance of individual tasks (modules) of proposed framework using CDR and SemMedDB datasets. For concept extraction, evaluation shows an average F-measure of 58.12% for CDR corpus and 81.68% for SemMedDB; F-measure of 65.26% and 77.44% for biomedical taxonomic relation extraction using datasets of CDR and SemMedDB, respectively; and F-measure of 52.78% and 58.12% for biomedical non-taxonomic relation extraction using CDR corpus and SemMedDB, respectively. Additionally, the comparison with manually constructed baseline Alzheimer ontology shows F-measure of 72.48% in terms of concepts detection, 76.27% in relation extraction, and 83.28% in property extraction. Also, we compared our proposed framework with ontology-learning framework called "OntoGain" which shows that LOD-ABOG performs 14.76% better in terms of relation extraction. CONCLUSION: This paper has presented LOD-ABOG framework which shows that current LOD sources and technologies are a promising solution to automate the process of biomedical ontology generation and extract relations to a greater extent. In addition, unlike existing frameworks which require domain experts in ontology development process, the proposed approach requires involvement of them only for improvement purpose at the end of ontology life cycle.

Introduction to the Anatomy and Physiology of Pulmonary Circulation.

In the United States, venous thromboembolism is associated with high mortality and morbidity affecting as many as 900 000 people (1-2 per 1000) each year. Estimates suggest that 60 000 to 100 000 Americans die of deep venous thrombosis/pulmonary embolism and 10% to 30% die within 1 month of diagnosis, with sudden death being the first symptom in approximately 25% of population with pulmonary embolism. One-half of the patients with deep venous thrombosis will have postthrombotic syndrome, which includes swelling, pain, discoloration, and scaling in the affected limb. Approximately 33% of patients will have a recurrence within 10 years. It is important to understand the anatomy of the pulmonary circulation and the pathophysiology of the disease as it leads to understanding of the potential devastating clinical consequences.

Venous Thromboembolism and Pulmonary Embolism Response Teams: An Overview.

Venous thromboembolism is a common disease with a wide array of signs and symptoms. It has been cited as the third leading cause of cardiovascular death, and if left untreated, it leads to death in 1 in 4 patients. Sophisticated diagnostic tools have allowed physician to become more accurate in diagnosing pulmonary embolism and deep vein thrombosis. The advent of new oral anticoagulants, the emergence of pulmonary embolism response teams, and protocols demonstrate recent achievements in the management of venous thromboembolism. The focus of this article is to discuss the treatment of venous thromboembolism.

Pulmonary Embolism.

Pulmonary embolism covers a wide spectrum of presentation from an asymptomatic individual to a life-threatening medical emergency. It is of paramount importance to appropriately risk stratify patients with pulmonary embolism, particularly with those who present without hypotension. Right ventricular dysfunction can evolve after a patient has received a diagnosis of pulmonary embolism, necessitating aggressive measures rather than simple anticoagulation. In this review, we discuss definition, risk stratification, pathogenesis, diagnostic approach, and management, with particular focus on massive pulmonary embolism.

Disease-modifying Antirheumatic Drugs for the Treatment of Low Back Pain: A Systematic Review of the Literature.

Low back pain (LBP) is a common source of pain and disability, which has an enormous adverse impact on affected individuals and the community as a whole. The etiologies of LBP are protean and local inflammation contributes to the majority of these processes. Although an array of potent disease-modifying anti-rheumatic drugs (DMARDs), which are typically anti-inflammatory in character, have become clinically available only corticosteroids are routinely used for the treatment of LBP. To further investigate this potentially underutilized therapy, we reviewed the available literature to determine the role of DMARDs in the treatment of LBP. Our results show that the current DMARD use for LBP is indeed limited in scope and is characterized by isolated use and empiric selection of drugs from a range of available DMARDs. Moreover, the dose, frequency, and route of drug administration are selected arbitrarily and deviated from treatment protocols proposed for the management of other inflammatory conditions. The literature published on this topic is of low quality, and the results of the reviewed trials were inconclusive or demonstrated only short-term efficacy of these medications. Based on the findings of this review, we recommend that the future DMARD use for LBP is initially limited to patients with debilitating disease who are unresponsive to conventional treatments, and the criteria for drug selection and routes of drug administration are clearly defined and may be modeled after treatment protocols for other inflammatory conditions.

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.

Prescription patterns of pain medicine physicians.

OBJECTIVES: Our study surveyed physician members of 3 American pain societies to determine prescription patterns and whether these practices reflect current expert opinion. METHODS: We sent 3 mailings to 2938 physicians from January 2010 to January 2011. The questionnaire contained 49 questions on topics related to opioids, antidepressants, anticonvulsants, and preferences for the different pain syndromes. RESULTS: A total of 474 physicians responded, representing a 16% return. Seventy-two percent ask patients to sign an opioid agreement, 59% order random urine drug testing, 13% wait until the dose of methadone is between 100 and 150 mg before converting the drug to another opioid, and 85% do not think there is a maximum dose of opioids with respect to driving. Most responders prescribe codeine to Caucasians and Asians. While 42% stated that the maximum daily dose of acetaminophen is 3000 mg, 75% would decrease the dose in patients who are moderate or heavy drinkers. Fifty-four percent do not order an ECG at all when prescribing tricyclic antidepressants. CONCLUSIONS: The responses pertaining to opioid agreements, urine drug testing, acetaminophen, and treatment for neuropathic pain are reassuring in that they prevent misuse and abuse of opioids, prevent acetaminophen-induced hepatotoxicity, and reflect evidence-based treatments. However, we identified gaps in knowledge, including the prescription of codeine in certain populations and the use of electrocardiogram in patients on antidepressants. Further education of physicians who treat chronic pain pharmacologically is warranted.

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.

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.