Visualizing in deceased COVID-19 patients how SARS-CoV-2 attacks the respiratory and olfactory mucosae but spares the olfactory bulb.

Anosmia, the loss of smell, is a common and often the sole symptom of COVID-19. The onset of the sequence of pathobiological events leading to olfactory dysfunction remains obscure. Here, we have developed a postmortem bedside surgical procedure to harvest endoscopically samples of respiratory and olfactory mucosae and whole olfactory bulbs. Our cohort of 85 cases included COVID-19 patients who died a few days after infection with SARS-CoV-2, enabling us to catch the virus while it was still replicating. We found that sustentacular cells are the major target cell type in the olfactory mucosa. We failed to find evidence for infection of olfactory sensory neurons, and the parenchyma of the olfactory bulb is spared as well. Thus, SARS-CoV-2 does not appear to be a neurotropic virus. We postulate that transient insufficient support from sustentacular cells triggers transient olfactory dysfunction in COVID-19. Olfactory sensory neurons would become affected without getting infected.

Racial disparities in automated speech recognition.

Automated speech recognition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text, have become increasingly widespread, powering popular virtual assistants, facilitating automated closed captioning, and enabling digital dictation platforms for health care. Over the last several years, the quality of these systems has dramatically improved, due both to advances in deep learning and to the collection of large-scale datasets used to train the systems. There is concern, however, that these tools do not work equally well for all subgroups of the population. Here, we examine the ability of five state-of-the-art ASR systems-developed by Amazon, Apple, Google, IBM, and Microsoft-to transcribe structured interviews conducted with 42 white speakers and 73 black speakers. In total, this corpus spans five US cities and consists of 19.8 h of audio matched on the age and gender of the speaker. We found that all five ASR systems exhibited substantial racial disparities, with an average word error rate (WER) of 0.35 for black speakers compared with 0.19 for white speakers. We trace these disparities to the underlying acoustic models used by the ASR systems as the race gap was equally large on a subset of identical phrases spoken by black and white individuals in our corpus. We conclude by proposing strategies-such as using more diverse training datasets that include African American Vernacular English-to reduce these performance differences and ensure speech recognition technology is inclusive.

Systematic Human Learning and Generalization From a Brief Tutorial With Explanatory Feedback.

We investigate human adults' ability to learn an abstract reasoning task quickly and to generalize outside of the range of training examples. Using a task based on a solution strategy in Sudoku, we provide Sudoku-naive participants with a brief instructional tutorial with explanatory feedback using a narrow range of training examples. We find that most participants who master the task do so within 10 practice trials and generalize well to puzzles outside of the training range. We also find that most of those who master the task can describe a valid solution strategy, and such participants perform better on transfer puzzles than those whose strategy descriptions are vague or incomplete. Interestingly, fewer than half of our human participants were successful in acquiring a valid solution strategy, and this ability was associated with completion of high school algebra and geometry. We consider the implications of these findings for understanding human systematic reasoning, as well as the challenges these findings pose for building computational models that capture all aspects of our findings, and we point toward a role for learning from instructions and explanations to support rapid learning and generalization.

Sex-Specific Vulnerability to Externalizing Problems: Sensitivity to Early Stress and Nucleus Accumbens Activation Over Adolescence.

BACKGROUND: Exposure and sensitivity to early-life stress (ELS) are related to increased risk for psychopathology in adolescence. While cross-sectional studies have reported blunted nucleus accumbens (NAcc) activation in the context of these associations, researchers have not yet assessed the effects of ELS on developmental trajectories of activation. We examined whether trajectories are affected by stress and the moderating role of biological sex in predicting vulnerability to symptoms of psychopathology. METHODS: Adolescents (n = 173) completed 3 assessments at 2-year intervals across puberty (ages 9-18 years). At baseline, we assessed objective ELS and stress sensitivity using the Traumatic Events Screening Inventory for Children. At all time points, we assessed NAcc activation using the Monetary Incentive Delay task and externalizing, internalizing, and total problems using the Youth Self-Report. We examined correlations between NAcc trajectories (extracted using linear mixed-effects models) with ELS and stress sensitivity and conducted multivariate regression analysis to examine the interaction of NAcc trajectories and biological sex in predicting symptoms of psychopathology. RESULTS: Symptoms increased over adolescence. Stress sensitivity, but not objective ELS, was associated with decreasing trajectories of NAcc activation. Biological sex interacted with NAcc trajectories to predict psychopathology; boys, but not girls, with decreasing NAcc activation had more severe externalizing problems in adolescence. These findings were replicated in the putamen and caudate but not in the medial prefrontal cortex or control brain regions. CONCLUSIONS: NAcc activation may be a sex-specific marker of externalizing problems in adolescence. Efforts to reduce stress sensitivity may help to decrease symptoms of psychopathology in adolescent boys.

Surgical treatment of craniovertebral junction instability in children with Down syndrome: a systematic review.

OBJECTIVE: Down syndrome (DS) affects 1 in 700 live births and approximately one-third of patients develop craniovertebral junction (CVJ) instability, diagnosed by clinical examination and radiological measures such as the atlantodens interval (ADI) and space available for the cord (SAC). Patients with symptomatic CVJ instability are at increased risk for spinal cord injury. There are no guidelines for surgical management of CVJ instability in DS, the existing literature is sparse, and there is a lack of consistent pediatric data. This systematic review aimed to synthesize practice patterns of the surgical management of CVJ stability in pediatric DS patients to facilitate future standardization of care. METHODS: Peer-reviewed studies reporting surgical management of CVJ instability in pediatric DS patients were systematically reviewed. Inclusion criteria were studies reporting primary data on patients younger than 18 years with DS, who had CVJ instability evaluation and underwent surgical treatment. Bias risk was assessed. Descriptive statistics of the independent patient data were presented. Interval variables were analyzed using the Wilcoxon rank-sum test. RESULTS: Of 1056 records, 38 studies were included. Of the included patients, 169 (6%) underwent surgery. The surgical indication was symptomatic, radiologically confirmed CVJ instability in 81% of the patients, presenting with myelopathy (30%), weakness (25%), abnormal gait (24%), torticollis (15%), and neck pain (14%). A cutoff of ADI ≥ 4 mm or SAC ≤ 14 mm, cord compression, cord signal change, and anomalous bony anatomy were used in diagnosing CVJ instability. Surgical approaches focused on internal fixation with posterior occipitocervical or atlantoaxial instrumented fusion in 57% and 44% of patients, respectively. Autograft, wiring, and allograft constructs were used in 48%, 45%, and 9% of patients. Anterior cervical approaches were performed in 6% of patients. Preoperative and postoperative external orthoses were used in approximately 50% of patients. The surgical mortality rate was 3%, and the complication rate was 36%. CONCLUSIONS: Assessment of CVJ instability in DS is based on radiographic and clinical factors. Surgery is recommended if symptoms are present, and the procedure type depends on patient factors, degree of instability, anomalous bony anatomy, and reduction results to relieve cord compression. Most commonly, posterior instrumented fusion is used. However, further research is required to determine the strength of evaluation methods, create standardized guidelines for evaluation and surgical treatment, and investigate the long-term results of different surgical techniques.

A university map of course knowledge.

Knowledge representation has gained in relevance as data from the ubiquitous digitization of behaviors amass and academia and industry seek methods to understand and reason about the information they encode. Success in this pursuit has emerged with data from natural language, where skip-grams and other linear connectionist models of distributed representation have surfaced scrutable relational structures which have also served as artifacts of anthropological interest. Natural language is, however, only a fraction of the big data deluge. Here we show that latent semantic structure can be informed by behavioral data and that domain knowledge can be extracted from this structure through visualization and a novel mapping of the text descriptions of elements onto this behaviorally informed representation. In this study, we use the course enrollment histories of 124,000 students at a public university to learn vector representations of its courses. From these course selection informed representations, a notable 88% of course attribute information was recovered, as well as 40% of course relationships constructed from prior domain knowledge and evaluated by analogy (e.g., Math 1B is to Honors Math 1B as Physics 7B is to Honors Physics 7B). To aid in interpretation of the learned structure, we create a semantic interpolation, translating course vectors to a bag-of-words of their respective catalog descriptions via regression. We find that representations learned from enrollment histories resolved courses to a level of semantic fidelity exceeding that of their catalog descriptions, revealing nuanced content differences between similar courses, as well as accurately describing departments the dataset had no course descriptions for. We end with a discussion of the possible mechanisms by which this semantic structure may be informed and implications for the nascent research and practice of data science.

Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are interwoven into our everyday lives and have grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery. METHODS: A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish, and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions and acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies and do not relate to orthopedic interventions or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and the results of meta-analyses will not be included in the analysis. DISCUSSION: Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed. TRIAL REGISTRATION: The protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.

Thoracic Outlet Syndrome Treated With Injecting Botulinum Toxin Into Middle Scalene Muscle and Pectoral Muscle Interfascial Planes: A Case Report.

Thoracic outlet compression syndrome is a complex syndrome of neurovascular compression at the superior thoracic aperture, thought to occur at 1 of 3 anatomical compartments: the interscalene triangle, the costoclavicular space, and the retropectoralis minor space. Injection into the middle interscalene muscle (ISM) and/or pectoralis muscle plane (PECS I and II) is gaining popularity because it provides significant symptomatic relief. A 44-year-old woman was diagnosed with thoracic outlet compression syndrome, with failed conservative therapy, including physical therapy. She refused surgical intervention. ISM and PECS I and II blocks with botulinum toxin type A were successful. In combination, PECS I/II and ISM injections can provide excellent symptomatic relief.

Review of Sickle Cell Disease and Spinal Pathology.

STUDY DESIGN: Sickle cell disease (SCD) is a relatively common blood disorder that has profound implications on the musculoskeletal system and particularly the spine; however, there is a paucity of data in the literature discussing this important topic. OBJECTIVES: (1) To elucidate common spinal pathologies affecting patients with SCD, as well as the medical and surgical treatments available for these patients. (2) To discuss indications for surgical management of spinal complications of SCD and important for orthopedic surgeons when taking patients with SCD to the operating room. METHODS: A narrative review of the literature was performed. RESULTS: Patients with SCD have a significantly higher risk of developing spinal pathologies including vertebral osteomyelitis, compression fracture, vertebral vaso-occlusive crises, and osteoporosis, among others. In addition, patients with sickle cell disease are particularly susceptible to developing perioperative and post-operative complications including surgical site infection, implant malfunction, and vertebral body compression fracture. Postoperatively patients with SCD are prone to developing complications and adequate hydration is necessary in order to reduce complications of SCD. CONCLUSIONS: Several spinal pathologies may arise secondary to SCD and distinguishing these pathologies from one another may be challenging due to similarities in symptoms and inflammatory markers. Although most patients with SCD can benefit from conservative treatment involving rest, symptomatic therapy, antibiotic therapy, and/or orthosis, surgical intervention may be indicated in certain cases. It is preferable to avoid surgery in patients with SCD due to an increased risk of complications such as wound infection and vaso-occlusive crisis.

Applying machine learning to the flagellar motor for biosensing.

Escherichia coli detects and follows chemical gradients in its environment in a process known as chemotaxis. The performance of chemotaxis approaches fundamental biosensor speed and sensitivity limits, but there have been relatively few attempts to incorporate the response into a functional biosensor. Toward that end, we have developed software to process digital microscope images of a large number of tethered E. coli responding to different chemical perturbations. Upwards of fifty cells can be recorded in one experiment, allowing for rapid labeling of the chemotactic responses of multiple cells. After we collected hundreds of wild-type chemotactic E. coli motor responses to dilutions of aspartate and leucine, we trained a support vector classifier (SVC) to estimate the order of magnitude of aspartate concentration between 0M, 100nM, and 1µM with a single cell classification subset accuracy of 69%. We trained another SVC to differentiate between aspartate and leucine with a single cell classification subset accuracy of 83%. Using a majority-vote method on a bacterial population of size N, estimates have 95% confidence for N = 27 bacteria for concentration detection and N = 9 bacteria for chemical differentiation. These methods are a step towards adaptable chemotaxis-based biosensing.