Postural Stability in Obese Preoperative Bariatric Patients Using Static and Dynamic Evaluation.

INTRODUCTION: Globally, 300 million adults have clinical obesity. Heightened adiposity and inadequate musculature secondary to obesity alter bipedal stance and gait, diminish musculoskeletal tissue quality, and compromise neuromuscular feedback; these physiological changes alter stability and increase injury risk from falls. Studies in the field focus on obese patients across a broad range of body mass indices (BMI >30 kg/m2) but without isolating the most morbidly obese subset (BMI ≥40 kg/m2). We investigated the impact of obesity in perturbing postural stability in morbidly obese subjects elected for bariatric intervention, harboring a higher-spectrum BMI. SUBJECTS AND METHODS: Traditional force plate measurements and stabilograms are gold standards employed when measuring center of pressure (COP) and postural sway. To quantify the extent of postural instability in subjects with obesity before bariatric surgery, we assessed 17 obese subjects with an average BMI of 40 kg/m2 in contrast to 13 nonobese subjects with an average BMI of 30 kg/m2. COP and postural sway were measured from static and dynamic tasks. Involuntary movements were measured when patients performed static stances, with eyes either opened or closed. Two additional voluntary movements were measured when subjects performed dynamic, upper torso tasks with eyes opened. RESULTS: Mean body weight was 85% (p < 0.001) greater in obese than nonobese subjects. Following static balance assessments, we observed greater sway displacement in the anteroposterior (AP) direction in obese subjects with eyes open (87%, p < 0.002) and eyes closed (76%, p = 0.04) versus nonobese subjects. Obese subjects also exhibited a higher COP velocity in static tests when subjects' eyes were open (47%, p = 0.04). Dynamic tests demonstrated no differences between groups in sway displacement in either direction; however, COP velocity in the mediolateral (ML) direction was reduced (31%, p < 0.02) in obese subjects while voluntarily swaying in the AP direction, but increased in the same cohort when swaying in the ML direction (40%, p < 0.04). DISCUSSION AND CONCLUSION: Importantly, these data highlight obesity's contribution towards increased postural instability. Obese subjects exhibited greater COP displacement at higher AP velocities versus nonobese subjects, suggesting that clinically obese individuals show greater instability than nonobese subjects. Identifying factors contributory to instability could encourage patient-specific physical therapies and presurgical measures to mitigate instability and monitor postsurgical balance improvements.

A COVID-19 Airway Management Innovation with Pragmatic Efficacy Evaluation: The Patient Particle Containment Chamber.

The unique resource constraints, urgency, and virulence of the coronavirus disease 2019 pandemic has sparked immense innovation in the development of barrier devices to protect healthcare providers from infectious airborne particles generated by patients during airway management interventions. Of the existing devices, all have shortcomings which render them ineffective and impractical in out-of-hospital environments. Therefore, we propose a new design for such a device, along with a pragmatic evaluation of its efficacy. Must-have criteria for the device included: reduction of aerosol transmission by at least 90% as measured by pragmatic testing; construction from readily available, inexpensive materials; easy to clean; and compatibility with common EMS stretchers. The Patient Particle Containment Chamber (PPCC) consists of a standard shower liner draped over a modified octagonal PVC pipe frame and secured with binder clips. 3D printed sleeve portals were used to secure plastic sleeves to the shower liner wall. A weighted tube sealed the exterior base of the chamber with the contours of the patient's body and stretcher. Upon testing, the PPCC contained 99% of spray-paint particles sprayed over a 90s period. Overall, the PPCC provides a compact, affordable option that can be used in both the in-hospital and out-of-hospital environments.

Kankanet: An artificial neural network-based object detection smartphone application and mobile microscope as a point-of-care diagnostic aid for soil-transmitted helminthiases.

BACKGROUND: Endemic areas for soil-transmitted helminthiases often lack the tools and trained personnel necessary for point-of-care diagnosis. This study pilots the use of smartphone microscopy and an artificial neural network-based (ANN) object detection application named Kankanet to address those two needs. METHODOLOGY/PRINCIPAL FINDINGS: A smartphone was equipped with a USB Video Class (UVC) microscope attachment and Kankanet, which was trained to recognize eggs of Ascaris lumbricoides, Trichuris trichiura, and hookworm using a dataset of 2,078 images. It was evaluated for interpretive accuracy based on 185 new images. Fecal samples were processed using Kato-Katz (KK), spontaneous sedimentation technique in tube (SSTT), and Merthiolate-Iodine-Formaldehyde (MIF) techniques. UVC imaging and ANN interpretation of these slides was compared to parasitologist interpretation of standard microscopy.Relative to a gold standard defined as any positive result from parasitologist reading of KK, SSTT, and MIF preparations through standard microscopy, parasitologists reading UVC imaging of SSTT achieved a comparable sensitivity (82.9%) and specificity (97.1%) in A. lumbricoides to standard KK interpretation (97.0% sensitivity, 96.0% specificity). The UVC could not accurately image T. trichiura or hookworm. Though Kankanet interpretation was not quite as sensitive as parasitologist interpretation, it still achieved high sensitivity for A. lumbricoides and hookworm (69.6% and 71.4%, respectively). Kankanet showed high sensitivity for T. trichiura in microscope images (100.0%), but low in UVC images (50.0%). CONCLUSIONS/SIGNIFICANCE: The UVC achieved comparable sensitivity to standard microscopy with only A. lumbricoides. With further improvement of image resolution and magnification, UVC shows promise as a point-of-care imaging tool. In addition to smartphone microscopy, ANN-based object detection can be developed as a diagnostic aid. Though trained with a limited dataset, Kankanet accurately interprets both standard microscope and low-quality UVC images. Kankanet may achieve sensitivity comparable to parasitologists with continued expansion of the image database and improvement of machine learning technology.