The optimal orthosis and motion protocol for extensor tendon injury in zones IV-VIII: A systematic review.

STUDY DESIGN: Systematic review INTRODUCTION: There exist numerous combinations of orthoses and motion protocols for the treatment of proximal extensor tendon injuries. PURPOSE: The purpose of this study was to determine the optimal combination of motion protocol and orthotic treatment for the rehabilitation of proximal extensor tendon injuries (zones IV-VIII). METHODS: A systematic review of English language randomized clinical trials and cohort studies investigating extensor tendon rehabilitation from 1960 to 2016 was conducted in MEDLINE, Embase, Cochrane, CINAHL, PEDro, and OTseeker. Outcomes of total active motion, grip strength, return to work, patient attrition, and patient-reported outcomes were compared. RESULTS: Eleven studies of predominantly average quality (1, low; 8, average; and 2, high) were included in the final review. Results were difficult to compare due to differences in reporting. Early total active motion and final grip strength were greater with dynamic extension orthoses (191°-214°; 35-38 kg/89% contralateral side) and relative motion orthoses (205°-236°; 85%-95% contralateral side) compared to static orthoses (79°-202°; 23-34 kg/59% contralateral side). Four studies excluded patients who did not follow up, and loss to follow-up was 12%-33% in the other studies. Patient-reported outcomes were not comparable, as they were only included in 3 studies, and each used a different assessment tool. CONCLUSION: Average quality evidence supports the use of early active motion (EAM) as the superior motion protocol, but optimal orthosis to deliver EAM could not be determined. Prospective research should focus on patient-reported outcomes and the design of orthoses that facilitate the use of the EAM. LEVEL OF EVIDENCE: 2a.

Assessment of upper airway patency in spontaneously breathing non-intubated neonates and infants undergoing conventional MRI of head and neck.

PURPOSE: Neonates and infants frequently undergo MRI examinations of the brain or head and neck in spontaneous respiration. This study aims to evaluate the patency of the upper airway and associated risk factors in spontaneously breathing neonates and infants undergoing MRI of head and neck. METHODS: Airway patency was assessed on sagittal and axial MRI images of the head and neck region for neonates and infants retrospectively. Anteroposterior diameters were measured at the soft palate and tongue levels as well as a lateral diameter at the tongue level for the patent airway. Chart review for risk factors was carried out. RESULTS: A total of 831 children between 0 and 12 months of age had an MRI. Eighty-two children with spontaneous ventilation were included. The airway was occluded in 29/82 (35%) of children. Twenty-four out of 29 (83%) children with airway occlusion had a depressed level of consciousness, 7/24 (29%) of whom were sedated with a single dose of benzodiazepine and 17/24 (71%) were on anti-epileptic therapy for an underlying seizure disorder and/or hypoxic ischemic encephalopathy. Forty-three out of 82 (65%) of children had an open airway. The airway diameters (mean ± SD) were 5.9 ± 2 mm (anteroposterior (AP) at soft palate), 7.4 ± 2.9 mm (lateral at soft palate), and 6.3 mm ± 1.6 (AP at dorsum of tongue). CONCLUSION: A significant proportion of spontaneously breathing neonates and infants with hypoxic ischemic encephalopathy or sedation show evidence of airway obstruction during MRI. Careful pre-MRI screening for decision of spontaneous breathing versus artificial airway support during MRI and robust airway monitoring during MRI are required for these vulnerable children.

Navigating Breast Cancer Innovation in Uganda: Within and Without the Textbook.

The Center for Bioengineering Innovation and Design (CBID) at Johns Hopkins University (JHU) has established a comprehensive approach to addressing global health challenges. Central to CBID's modality on global health is a strategy that integrates education, research, and collaboration. Through its graduate program, CBID trains the next generation of health care innovators to address the specific needs of low- and middle-income countries (LMICs). Graduate student teams at CBID begin their year with a focus on a health care thematic area associated with a target country.

Diagnostic Concordance of Telemedicine as Compared With Face-to-Face Care in Primary Health Care Clinics in Rural India: Randomized Crossover Trial.

BACKGROUND: With the COVID-19 pandemic, there was an increase and scaling up of provider-to-provider telemedicine programs that connect frontline health providers such as nurses and community health workers at primary care clinics with remote doctors at tertiary facilities to facilitate consultations for rural patients. Considering this new trend of increasing use of telemedicine, this study was conducted to generate evidence for patients, health providers, and policymakers to compare if provider-to-provider telemedicine-based care is equivalent to in-person care and is safe and acceptable in terms of diagnostic and treatment standards. OBJECTIVE: This study aims to compare the diagnosis and treatment decisions from teleconsultations to those of in-person care in teleclinics in rural Gujarat. METHODS: We conducted a diagnostic concordance study using a randomized crossover study design with 104 patients at 10 telemedicine primary care clinics. Patients reporting to 10 telemedicine primary care clinics were randomly assigned to first receive an in-person doctor consultation (59/104, 56.7%) or to first receive a health worker-assisted telemedicine consultation (45/104, 43.3%). The 2 groups were then switched, with the first group undergoing a telemedicine consultation following the in-person consultation and the second group receiving an in-person consultation after the teleconsultation. The in-person doctor and remote doctor were blinded to the diagnosis and management plan of the other. The diagnosis and treatment plan of in-person doctors was considered the gold standard. RESULTS: We enrolled 104 patients reporting a range of primary health care issues into the study. We observed 74% (77/104) diagnostic concordance and 79.8% (83/104) concordance in the treatment plan between the in-person and remote doctors. No significant association was found between the diagnostic and treatment concordance and the order of the consultation (P=.65 and P=.81, respectively), the frontline health worker-doctor pair (both P=.93), the gender of the patient (both P>.99), or the mode of teleconsultation (synchronous vs asynchronous; P=.32 and P=.29, respectively), as evaluated using Fisher exact tests. A significant association was seen between the diagnostic and treatment concordance and the type of case (P=.004 and P=.03, respectively). The highest diagnostic concordance was seen in the management of hypertension (20/21, 95% concordance; Cohen kappa=0.93) and diabetes (14/15, 93% concordance; Cohen kappa=0.89). The lowest values were seen in cardiology (1/3, 33%) and patients presenting with nonspecific symptoms (3/10, 30%). The use of a digital assistant to facilitate the consultation resulted in increased adherence to evidence-based care protocols. CONCLUSIONS: The findings reflect that telemedicine can be a safe and acceptable alternative mode of care especially in remote rural settings when in-person care is not accessible. Telemedicine has advantages. for the potential gains for improved health care-seeking behavior for patients, reduced costs for the patient, and improved health system efficiency by reducing overcrowding at tertiary health facilities.

Development of a Digital Assistant to Support Teleconsultations Between Remote Physicians and Frontline Health Workers in India: User-Centered Design Approach.

BACKGROUND: Many low- and middle-income countries have adopted telemedicine programs that connect frontline health workers (FHWs) such as nurses, midwives, or community health workers in rural and remote areas with physicians in urban areas to deliver care to patients. By leveraging technology to reduce temporal, financial, and geographical barriers, these health worker-to-physician telemedicine programs have the potential to increase health care quality, expand the specialties available to patients, and reduce the time and cost required to deliver care. OBJECTIVE: We aimed to identify, validate, and prioritize unmet needs in the health care space of health worker-to-physician telemedicine programs and develop and refine a solution that addresses those needs. METHODS: We collected information regarding user needs through ethnographic research, direct observation, and semistructured interviews with 37 stakeholders (n=5, 14% physicians; n=1, 3% public health program manager; n=12, 32% community health workers; and n=19, 51% patients) at 2 telemedicine clinics in rural West Bengal, India. We used the Spiral-Iterative Innovation Model to design and develop a prototype solution to meet these needs. RESULTS: We identified 74 unmet needs through our immersion in health worker-to-physician telemedicine programs. We identified a critical unmet need that achieving optimal teleconsultations in low- and middle-income countries often requires shifting tasks such as history taking and physical examination from high-skilled remote physicians to FHWs. To meet this need, we developed a prototype digital assistant that would allow FHWs to assume some of the tasks carried out by remote clinicians. The user needs of multiple stakeholder groups (patients, FHWs, physicians, and health organizations) were incorporated into the design and features of the task-shifting tool. The final prototype was shared with the health workers, physicians, and public health program managers who expressed that the tool would be useful and valuable. CONCLUSIONS: The final prototype that was developed was released as an open-source digital public good and may improve the quality and efficiency of care delivery in health worker-to-physician telemedicine programs.

OpenMRS as a global good: Impact, opportunities, challenges, and lessons learned from fifteen years of implementation.

INTRODUCTION: OpenMRS is an open source medical record system that was first released in 2004. This research study analyzed OpenMRS implementations by conducting a survey of implementers and by reviewing publicly available data reported to the OpenMRS Community to learn about the utilization and impact of OpenMRS over the past 15 years. METHODS: Data about the use of OpenMRS were collected by conducting a survey of OpenMRS implementers that included both quantitative and qualitative questions. Data were also gathered from the OpenMRS community-hosted Atlas website and the OpenMRS Community Annual report to arrive at a comprehensive view of OpenMRS implementations. RESULTS: OpenMRS has been implemented in over 62 countries worldwide (Community Annual report). The survey was responded to by 16 organizations with projects spanning 16 countries, which were launched over 15 years (2004-2019). Fourteen of these sites reported a total of 1,436,357 patients; 4,248,248 visits; 18,028,204 encounters; 312,068,205 observations; and 5088 users, of which 3933 were health providers, recorded in the system database. Implementers reported a positive impact from implementing OpenMRS in streamlining operational processes for healthcare delivery; improved interoperability; improved reporting; improved availability and quality of data for decision making, advocacy, and research; and, improvement in the quality of healthcare delivery. Key challenges in implementing OpenMRS included finding skilled technical staff; acceptability of electronic health records by clinical staff; poor training provided to staff when transitioning from a paper-based to an electronic system; technical challenges, including infrastructure availability (computers, servers, equipment, connectivity, power); missing clinical/programmatic functionality in OpenMRS; poor documentation; and, difficulties faced when contributing code to the open source project. CONCLUSION: OpenMRS has a broad reach globally in a variety of settings. Organizations have reported a positive impact on health care delivery after implementing OpenMRS. Several risks and challenges were identified by implementers that need to be addressed to deliver successful implementations. Continued investment in the development of OpenMRS is needed to sustain and scale its impact.

Burden and Unmet Needs with Portable Oxygen in Patients on Long-Term Oxygen Therapy.

Rationale: Over 1.5 million Americans receive long-term oxygen therapy (LTOT) for the treatment of chronic hypoxemia to optimize functional status and quality of life. However, current portable oxygen equipment, including portable gas tanks (GTs), portable liquid tanks (LTs), and portable oxygen concentrators (POCs), each have limitations that can hinder patient mobility and daily activities. Objectives: To examine patient experiences with portable oxygen to guide equipment innovation and thereby improve patient care on oxygen therapy. Methods: The burden and unmet needs with portable oxygen equipment were assessed in 836 LTOT patients with chronic lung disease (chronic obstructive pulmonary disease [COPD], interstitial lung disease, and pulmonary hypertension) through an online survey. The survey included a combination of multiple-choice, Likert-scale, short-answer, and open-ended questions. Distribution was achieved through patient support organizations, including the U.S. COPD Coalition, the Pulmonary Fibrosis Foundation, and the Pulmonary Hypertension Association. Results: Improvements in portability were ranked as the highest priority by patients across all equipment types, followed by increases in the duration of oxygen supply for GTs, accessibility for LTs, and flow capabilities for POCs. All device types were found to be burdensome, with the greatest burden among GT users, 51% of whom characterized GT use as "strenuous" or "extremely strenuous" (high burden). POCs ranked as the most common (61%) and least burdensome devices; however, 29% of POC users still reported a high associated burden. Forty-seven percent of POC respondents described using a POC despite it not meeting their oxygen needs to benefit from advantages over alternative equipment. Among non-POC users, limited oxygen flow rate capabilities and cost were the top reasons preventing POC use. Conclusions: Although improvements have been made to portable oxygen equipment, this study highlights the burden that remains and reveals a clear need for advances in technology to improve the functional status and quality of life of portable LTOT users.

Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection.

With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.

Development of a low-cost imaging system for remote mosquito surveillance.

Targeted vector control strategies aiming to prevent mosquito borne disease are severely limited by the logistical burden of vector surveillance, the monitoring of an area to understand mosquito species composition, abundance and spatial distribution. We describe development of an imaging system within a mosquito trap to remotely identify caught mosquitoes, including selection of the image resolution requirement, a design to meet that specification, and evaluation of the system. The necessary trap image resolution was determined to be 16 lp/mm, or 31.25um. An optics system meeting these specifications was implemented in a BG-GAT mosquito trap. Its ability to provide images suitable for accurate specimen identification was evaluated by providing entomologists with images of individual specimens, taken either with a microscope or within the trap and asking them to provide a species identification, then comparing these results. No difference in identification accuracy between the microscope and the trap images was found; however, due to limitations of human species classification from a single image, the system is only able to provide accurate genus-level mosquito classification. Further integration of this system with machine learning computer vision algorithms has the potential to provide near-real time mosquito surveillance data at the species level.

Non-Invasive Estimation of Hemoglobin Using a Multi-Model Stacking Regressor.

OBJECTIVE: We describe a novel machine-learning based method to estimate total Hemoglobin (Hb) using photoplethysmograms (PPGs) acquired non-invasively. METHODS: In a study conducted in Karnataka, India, 1583 women (pregnant and non-pregnant) of childbearing age, with Hb values ranging between 1.6 to 14.8 g/dL, had their Hb values estimated using intravenous blood samples and concurrently by a finger sensor custom designed and prototyped for this study. The finger sensor collected PPG signals at four wavelengths: 590 nm, 660 nm, 810 nm, and 940 nm. A novel feature vector was derived from these PPGs. A machine learning model comprising of a two-layer stack of regressors including Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, Elastic Net, Adaptive (Ada) Boost and Support Vector Regressors (SVR) was designed and tested. RESULTS: We report a statistically significant Pearson's correlation coefficient (PCC) of 0.81 (p < 0.01) between the Hb value estimated by the proposed methodology and gold standard values of Hb, with a Root Mean Square Error (RMSE) of 1.353 ± 0.042 g/dL. The performance of the stacked regressor model was significantly better than the performance of individual regressors (low RMSE, and better CC; p < 0.05). Post-hoc analysis showed that including pregnant women in the training data set significantly improved the performance of the algorithm. CONCLUSION: This article demonstrates the feasibility of a machine learning based non-invasive hemoglobin measurement system, especially for maternal anemia detection. SIGNIFICANCE: By developing and demonstrating a machine learning approach on a large data set, we have demonstrated that such an approach could become the basis for a public health screening tool to detect and treat maternal anemia and could supplement global health intervention strategies.

Feasibility of a Mobile Health Tool for Mothers to Identify Neonatal Illness in Rural Uganda: Acceptability Study.

BACKGROUND: A shortage of community health workers to triage sick neonates and poor recognition of neonatal illness by mothers contribute significantly toward neonatal deaths in low- and middle-income countries. Providing low-resource communities with the tools and knowledge to recognize signs of neonatal distress can lead to early care-seeking behavior. To empower and educate mothers to recognize signs of neonatal illness, we developed a neonatal health assessment device consisting of a smartphone app and a wearable sensor (the NeMo system). OBJECTIVE: The aim of this study was to determine if mothers in rural Uganda were willing and able to use the NeMo system during the first week of their infant's life. We also assessed mothers' responses to the device's recommendation to seek care. METHODS: A total of 20 mothers were enrolled in the study after giving birth in the Iganga District Hospital. Each mother was trained to use the NeMo system to assess her infant for signs of illness before leaving the hospital and was given the NeMo system to use at home for 1 week. Throughout the week, the smartphone tracked the mothers' usage of NeMo, and the study team visited twice to observe mothers' ability to use NeMo. Each mother was interviewed at the end of 1 week to gather qualitative feedback on her experience with the NeMo system. RESULTS: In total, 18 mothers completed the study; 2 mothers were withdrawn during the week because of extenuating health circumstances. Moreover, 1 day after enrollment and training, 75% (15/20) of mothers used NeMo properly with no mistakes. Three days after enrollment and training, only 1 mother placed the wearable sensor improperly on her infant. On the final study day, only 1 mother connected the device improperly. Mothers used NeMo an average of 11.67 (SD 5.70) times on their own at home during the 5 full study days. Although the frequency of use per day decreased from day 1 to day 5 of the study (P=.04), 72% (13/18) of mothers used NeMo at least once per day. In total, 64% (9/14) of mothers who received an alert from the NeMo system to seek care for their infants either called the health care professional working with the study team or reused the system immediately and found no danger signs. All 18 mothers agreed or strongly agreed that the NeMo system was easy to use and helped them know when to seek care for their babies. CONCLUSIONS: NeMo is a feasible and acceptable tool to aid mothers in rural Uganda to assess their infant's health.

A Novel Mobile Health Tool for Home-Based Identification of Neonatal Illness in Uganda: Formative Usability Study.

BACKGROUND: While early identification of neonatal illness can impact neonatal mortality rates and reduce the burden of treatment, identifying subtle clinical signs and symptoms of possible severe illness is especially challenging in neonates. The World Health Organization and the United Nations Children's Fund developed the Integrated Management of Neonatal Childhood Illness guidelines, an evidence-based tool highlighting seven danger signs to assess neonatal health. Currently, many mothers in low-resource settings rely on home visits from community health workers (CHWs) to determine if their baby is sick. However, CHWs visit infrequently, and illness is often detected too late to impact survival. Thus, delays in illness identification pose a significant barrier to providing expedient and effective care. Neonatal Monitoring (NeMo), a novel neonatal assessment tool, seeks to increase the frequency of neonatal screening by task-shifting identification of neonatal danger signs from CHWs to mothers. OBJECTIVE: This study aimed to explore the usability and acceptability of the NeMo system among target users and volunteer CHWs by assessing ease of use and learnability. METHODS: Simulated device use and semistructured interviews were conducted with 32 women in the Iganga-Mayuge districts in eastern Uganda to evaluate the usability of the NeMo system, which involves a smartphone app paired with a low cost, wearable band to aid in identification of neonatal illness. Two versions of the app were evaluated using a mixed methods approach, and version II of the app contained modifications based on observations of the first cohort's use of the system. During the posed scenario simulations, participants were offered limited guidance from the study team in order to probe the intuitiveness of the NeMo system. The ability to complete a set of tasks with the system was tested and recorded for each participant and closed- and open-ended questions were used to elicit user feedback. Additionally, focus groups with 12 CHWs were conducted to lend additional context and insight to the usability and feasibility assessment. RESULTS: A total of 13/22 subjects (59%) using app version I and 9/10 subjects (90%) using app version II were able to use the phone and app with no difficulty, despite varying levels of smartphone experience. Following modifications to the app's audio instructions in version II, participants' ability to accurately answer qualitative questions concerning neonatal danger signs improved by at least 200% for each qualitative danger sign. All participants agreed they would trust and use the NeMo system to assess the health of their babies. Furthermore, CHWs emphasized the importance of community sensitization towards the system to encourage its adoption and regular use, as well as the decision to seek care based on its recommendations. CONCLUSIONS: The NeMo system is an intuitive platform for neonatal assessment in a home setting and was found to be acceptable to women in rural Uganda.

Asynchronous decoding of dexterous finger movements using M1 neurons.

Previous efforts in brain-machine interfaces (BMI) have looked at decoding movement intent or hand and arm trajectory, but current cortical control strategies have not focused on the decoding of dexterous [corrected] actions such as finger movements. The present work demonstrates the asynchronous decoding (i.e., where cues indicating the onset of movement are not known) of individual and combined finger movements. Single-unit activities were recorded sequentially from a population of neurons in the M1 hand area of trained rhesus monkeys during flexion and extension movements of each finger and the wrist. Nonlinear filters were designed to detect the onset of movement and decode the movement type from randomly selected neuronal ensembles (assembled from individually recorded single-unit activities). Average asynchronous decoding accuracies as high as 99.8%, 96.2%, and 90.5%, were achieved for individuated finger and wrist movements with three monkeys. Average decoding accuracy was still 92.5% when combined movements of two fingers were included. These results demonstrate that it is possible to asynchronously decode dexterous finger movements from a neuronal ensemble with high accuracy. This work takes an important step towards the development of a BMI for direct neural control of a state-of-the-art, multifingered hand prosthesis.

Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area.

Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were simulated by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial neural network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p < 0.01) in decoding accuracy as a function of the location of the recording volume. The results suggest that a brain-machine interface (BMI) for dexterous control of individuated fingers and the wrist can be implemented using microelectrode arrays placed broadly in the M1 hand area.

Design and preliminary validation of a mobile application-based expert system to facilitate repair of medical equipment in resource-limited health settings.

BACKGROUND: One of the greatest barriers to safe surgery is the availability of functional biomedical equipment. Biomedical technicians play a major role in ensuring that equipment is functional. Following in-field observations and an online survey, a mobile application was developed to aid technicians in troubleshooting biomedical equipment. It was hypothesized that this application could be used to aid technicians in equipment repair, as modeled by repair of a pulse oximeter. METHODS: To identify specific barriers to equipment repair and maintenance for biomedical technicians, an online survey was conducted to determine current practices and challenges. These findings were used to guide the development of a mobile application system that guides technicians through maintenance and repair tasks. A convenience sample of technicians in Ethiopia tested the application using a broken pulse oximeter task and following this completed usability and content validity surveys. RESULTS: Fifty-three technicians from 13 countries responded to the initial survey. The results of the survey showed that technicians find equipment manuals most useful, but these are not easily accessible. Many do not know how to or are uncomfortable reaching out to human resources. Thirty-three technicians completed the broken pulse oximeter task using the application. All were able to appropriately identify and repair the equipment, and post-task surveys of usability and content validity demonstrated highly positive scores (Agree to Strongly Agree) on both scales. DISCUSSION: This research demonstrates the need for improved access to resources for technicians and shows that a mobile application can be used to address a gap in the access to knowledge and resources in low- and middle-income countries. Further research will include prospective studies to determine the impact of an application on the availability of functional equipment in a hospital and the effect on the provision and safety of surgical care.

Starting a Medical Technology Venture as a Young Academic Innovator or Student Entrepreneur.

Following the footprints of Bill Gates, Steve Jobs and Mark Zuckerberg, there has been a misconception that students are better off quitting their studies to bring to life their ideas, create jobs and monetize their inventions. Having historically transitioned from manpower to mind power, we live in one of the most rapidly changing times in human history. As a result, academic institutions that are supposed to be pioneers and educators of the next generations have started to realize that they need to adapt to a new system, and change their policies to be more flexible towards patent ownership and commercialization. There is an infrastructure being developed towards students starting their own businesses while continuing with their studies. This paper aims to provide an overview of the existing landscape, the exciting rewards as well as risks awaiting a student entrepreneur, the challenges of the present ecosystem, and questions to consider prior to embarking on such a journey. Various entities influencing the start-up environment are considered, specifically for the medical technology sector. These parties include but are not limited to: scientists, clinicians, investors, academic institutions and governments. A special focus will be set on the seemingly unbridgeable gap between founding a company and a scientific career.

A brain-computer interface with vibrotactile biofeedback for haptic information.

BACKGROUND: It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy. METHODS: A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance. RESULTS AND CONCLUSION: Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.

Design and development of a customizable telemedicine platform for improving access to healthcare for underserved populations.

Telemedicine offers a method to bridge the healthcare access gap in low and middle income countries (LMICs) by connecting providers with patients using appropriate technology. Here we describe the design and development of a novel modular telemedicine platform, Intelehealth, that would enable health systems to connect remote doctors with patients in rural clinics using a customizable Android-based platform and a cloud-based electronic health record system at the backend (OpenMRS). This open source platform enables task shifting of medically relevant information gathering by a local health worker, transmission of this information to a remote doctor, and a telephonic conversation between the doctor and the patient that subsequently allows for delivery of an appropriate therapeutic plan. Intelehealth is designed to operate on a low bandwidth internet environment, and will be tested and validated in rural health clinics in India.

Defining Optimal Head-Tilt Position of Resuscitation in Neonates and Young Infants Using Magnetic Resonance Imaging Data.

Head-tilt maneuver assists with achieving airway patency during resuscitation. However, the relationship between angle of head-tilt and airway patency has not been defined. Our objective was to define an optimal head-tilt position for airway patency in neonates (age: 0-28 days) and young infants (age: 29 days-4 months). We performed a retrospective study of head and neck magnetic resonance imaging (MRI) of neonates and infants to define the angle of head-tilt for airway patency. We excluded those with an artificial airway or an airway malformation. We defined head-tilt angle a priori as the angle between occipito-ophisthion line and ophisthion-C7 spinous process line on the sagittal MR images. We evaluated medical records for Hypoxic Ischemic Encephalopathy (HIE) and exposure to sedation during MRI. We analyzed MRI of head and neck regions of 63 children (53 neonates and 10 young infants). Of these 63 children, 17 had evidence of airway obstruction and 46 had a patent airway on MRI. Also, 16/63 had underlying HIE and 47/63 newborn infants had exposure to sedative medications during MRI. In spontaneously breathing and neurologically depressed newborn infants, the head-tilt angle (median ± SD) associated with patent airway (125.3° ± 11.9°) was significantly different from that of blocked airway (108.2° ± 17.1°) (Mann Whitney U-test, p = 0.0045). The logistic regression analysis showed that the proportion of patent airways progressively increased with an increasing head-tilt angle, with > 95% probability of a patent airway at head-tilt angle 144-150°.