Wireless Optofluidic Systems for Programmable In Vivo Pharmacology and Optogenetics.

In vivo pharmacology and optogenetics hold tremendous promise for dissection of neural circuits, cellular signaling, and manipulating neurophysiological systems in awake, behaving animals. Existing neural interface technologies, such as metal cannulas connected to external drug supplies for pharmacological infusions and tethered fiber optics for optogenetics, are not ideal for minimally invasive, untethered studies on freely behaving animals. Here, we introduce wireless optofluidic neural probes that combine ultrathin, soft microfluidic drug delivery with cellular-scale inorganic light-emitting diode (µ-ILED) arrays. These probes are orders of magnitude smaller than cannulas and allow wireless, programmed spatiotemporal control of fluid delivery and photostimulation. We demonstrate these devices in freely moving animals to modify gene expression, deliver peptide ligands, and provide concurrent photostimulation with antagonist drug delivery to manipulate mesoaccumbens reward-related behavior. The minimally invasive operation of these probes forecasts utility in other organ systems and species, with potential for broad application in biomedical science, engineering, and medicine.

Large language models encode clinical knowledge.

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model1 (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM2 on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA3, MedMCQA4, PubMedQA5 and Measuring Massive Multitask Language Understanding (MMLU) clinical topics6), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications.

Bioresorbable silicon electronic sensors for the brain.

Many procedures in modern clinical medicine rely on the use of electronic implants in treating conditions that range from acute coronary events to traumatic injury. However, standard permanent electronic hardware acts as a nidus for infection: bacteria form biofilms along percutaneous wires, or seed haematogenously, with the potential to migrate within the body and to provoke immune-mediated pathological tissue reactions. The associated surgical retrieval procedures, meanwhile, subject patients to the distress associated with re-operation and expose them to additional complications. Here, we report materials, device architectures, integration strategies, and in vivo demonstrations in rats of implantable, multifunctional silicon sensors for the brain, for which all of the constituent materials naturally resorb via hydrolysis and/or metabolic action, eliminating the need for extraction. Continuous monitoring of intracranial pressure and temperature illustrates functionality essential to the treatment of traumatic brain injury; the measurement performance of our resorbable devices compares favourably with that of non-resorbable clinical standards. In our experiments, insulated percutaneous wires connect to an externally mounted, miniaturized wireless potentiostat for data transmission. In a separate set-up, we connect a sensor to an implanted (but only partially resorbable) data-communication system, proving the principle that there is no need for any percutaneous wiring. The devices can be adapted to sense fluid flow, motion, pH or thermal characteristics, in formats that are compatible with the body's abdomen and extremities, as well as the deep brain, suggesting that the sensors might meet many needs in clinical medicine.

SIG-DB: Leveraging homomorphic encryption to securely interrogate privately held genomic databases.

Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to biological function. Advances in synthetic biology and the decreased cost of sequencing are increasing the amount of privately held genomic data. As the quantity and value of private genomic data grows, so does the incentive to acquire and protect such data, which creates a need to store and process these data securely. We present an algorithm for the Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm enables databases of genomic sequences to be searched with an encrypted query sequence without revealing the query sequence to the Database Owner or any of the database sequences to the Querier. SIG-DB is the first application of its kind to take advantage of locality-sensitive hashing and homomorphic encryption to allow generalized sequence-to-sequence comparisons of genomic data.

The Relationship Between Multidirectional Jumping and Performance in Change of Direction Tasks.

Bourgeois, FA II, Gamble, P, Gill, ND, and McGuigan, MR. The relationship between multidirectional jumping and performance in change of direction tasks. J Strength Cond Res 32(3): 690-699, 2018-This study investigated the test-retest reliability of 2 change of direction (COD; 180 and 45° COD) sprints and 3 multidirectional jump (MDJ) tests. Variables examined were approach time (sprint before plant-step), exit time (sprint after plant-step), total time (time to completion) and MDJ approach time, and distance, respectively. Second, the ability of MDJ tests to predict performance in COD tests was examined. Twenty men (age: 27.5 ± 5.9 years; height: 1.79 ± 0.1 m; and body mass: 79.1 ± 12.0 kg) performed 5 trials for each assessment, executing left plant-leg (LT) and right plant-leg (RT) steps, on 2 testing occasions separated by 7 days. Between-session and within-session intraclass correlation coefficients (ICCs) and coefficients of variation (CVs) for all measurements were calculated. Usefulness of COD and MDJ tests was assessed using typical error and smallest worthwhile change (SWC) comparison. Results showed only one MDJ measurement generated unstable between-session reliability. Within-session reliability of approach and exit COD times, and MDJ approach times possessed confidence limits (90% CL) that extended below 0.75 ICC. All COD total times and MDJ distances presented high reliability (ICC = 0.87-0.99) with low CV (0.9-4.1%). Right-leg MDJ distances were predictors of RT COD performances (r = 0.50-0.68, p = 0.001-0.024), whereas LT MDJ distances were predictors of LT180 COD performance (r = 0.67-0.71, p = 0.001). All measurements were useful in detecting SWC in performance. These findings suggest the COD tests and MDJ distances are reliable for assessing and monitoring COD performance in similar cohorts.

Serial assessment of functional recovery following nerve injury using implantable thin-film wireless nerve stimulators.

INTRODUCTION: Comprehensive assessment of the time course of functional recovery following peripheral nerve repair is critical for surgical management of peripheral nerve injuries. This study describes the design and implementation of a novel implantable wireless nerve stimulator capable of repeatedly interfacing peripheral nerve tissue and providing serial evaluation of functional recovery postoperatively. METHODS: Thin-film wireless implants were fabricated and subcutaneously implanted into Lewis rats. Wireless implants were used to serially stimulate rat sciatic nerve and assess functional recovery over 3 months following various nerve injuries. RESULTS: Wireless stimulators demonstrated consistent performances over 3 months in vivo and successfully facilitated serial assessment of nerve and muscle function following nerve crush and nerve transection injuries. CONCLUSIONS: This study highlights the ability of implantable wireless nerve stimulators to provide a unique view into the time course of functional recovery in multiple motor targets. Muscle Nerve 54: 1114-1119, 2016.

Acute kinematic and kinetic augmentation in horizontal jump performance using haltere type handheld loading.

The purposes of this study were to investigate the effects of haltere/handheld loading on the kinematics and kinetics of horizontal jumping and to determine if an optimum relative load (% body mass [BM]) exists to maximize jump distance. A repeated measures analysis of variance with post hoc contrasts was used to determine the effects of haltere loading (no external loading, 6, 8, 12, and 16 kg) on the horizontal jump performance of 16 sportsmen as quantified by an in-ground force plate. The haltere loads of 6 and 8 kg elicited significant (p < 0.05) increases in jump distance (effect size [ES] = 0.22-0.37). The incremental loads also resulted in significant increases in jump duration (ES = 1.22-1.83), peak vertical ground reaction force (GRF) (ES = 0.20-0.37), and vertical (ES = 0.69-1.22) and horizontal (ES = 0.70-0.88) impulse. There was a significant reduction in jump distance with the 16 kg load (ES = 0.45). Significant decreases in mean horizontal GRF were likewise observed with the 12 and 16 kg loads. The optimum relative load for enhancing jump distance was 9.2 ± 3.4% of BM, which resulted in a predicted augmented horizontal jump of 13.6 ± 7.7 cm (ES = 0.56). The findings clearly indicate that haltere/handheld loading augments vertical and horizontal force and impulses. This could have a number of interesting training implications if the strength profiling of athletes identify horizontal and/or vertical deficits in force production. Further longitudinal investigation is warranted to establish what chronic adaptations result with repeated application of this type of training.

Development and Characterization of a low intensity vibrational system for microgravity studies.

The advent of extended-duration human spaceflight demands a better comprehension of the physiological impacts of microgravity. One primary concern is the adverse impact on the musculoskeletal system, including muscle atrophy and bone density reduction. Ground-based microgravity simulations have provided insights, with vibrational bioreactors emerging as potential mitigators of these negative effects. Despite the potential they have, the adaptation of vibrational bioreactors for space remains unfulfilled, resulting in a significant gap in microgravity research. This paper introduces the first automated low-intensity vibrational (LIV) bioreactor designed specifically for the International Space Station (ISS) environment. Our research covers the bioreactor's design and characterization, the selection of an optimal linear guide for consistent 1-axis acceleration, a thorough analysis of its thermal and diffusion dynamics, and the pioneering use of BioMed Clear resin for enhanced scaffold design. This advancement sets the stage for more authentic space-based biological studies, vital for ensuring the safety of future space explorations.

User-centred design for machine learning in health care: a case study from care management.

OBJECTIVES: Few machine learning (ML) models are successfully deployed in clinical practice. One of the common pitfalls across the field is inappropriate problem formulation: designing ML to fit the data rather than to address a real-world clinical pain point. METHODS: We introduce a practical toolkit for user-centred design consisting of four questions covering: (1) solvable pain points, (2) the unique value of ML (eg, automation and augmentation), (3) the actionability pathway and (4) the model's reward function. This toolkit was implemented in a series of six participatory design workshops with care managers in an academic medical centre. RESULTS: Pain points amenable to ML solutions included outpatient risk stratification and risk factor identification. The endpoint definitions, triggering frequency and evaluation metrics of the proposed risk scoring model were directly influenced by care manager workflows and real-world constraints. CONCLUSIONS: Integrating user-centred design early in the ML life cycle is key for configuring models in a clinically actionable way. This toolkit can guide problem selection and influence choices about the technical setup of the ML problem.

Canada's health human resource challenges: what is the fate of our healthcare heroes?

Each year, the Canadian health education system graduates thousands of health professionals who have the best intentions of practising to their full scope of knowledge and skills to help improve the patient care experience in this country. However, a recent research study points to the fact that members of the healthcare team may be practising in a challenging environment in which only a limited number of their skills are actually being used. The Michener Institute for Applied Health Sciences believes that these issues, which include increased role specialization, limited scopes of practice, rapidly advancing technology, and challenges transitioning from hospital to community settings, have broader health education and health system implications that need to be addressed by policy makers, educators, and healthcare system leaders in order to enhance health professional education as well as patient care.

Current and future applications of artificial intelligence in pathology: a clinical perspective.

During the last decade, a dramatic rise in the development and application of artificial intelligence (AI) tools for use in pathology services has occurred. This trend is often expected to continue and reshape the field of pathology in the coming years. The deployment of computational pathology and applications of AI tools can be considered as a paradigm shift that will change pathology services, making them more efficient and capable of meeting the needs of this era of precision medicine. Despite the success of AI models, the translational process from discovery to clinical applications has been slow. The gap between self-contained research and clinical environment may be too wide and has been largely neglected. In this review, we cover the current and prospective applications of AI in pathology. We examine its applications in diagnosis and prognosis, and we offer insights for considerations that could improve clinical applicability of these tools. Then, we discuss its potential to improve workflow efficiency, and its benefits in pathologist education. Finally, we review the factors that could influence adoption in clinical practices and the associated regulatory processes.

Determining breast cancer biomarker status and associated morphological features using deep learning.

Background: Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and well-validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker findings can prompt repeat testing to address biological, interpretative, or technical reasons for unexpected results. Methods: We developed three independent deep learning systems (DLS) to directly predict ER/PR/HER2 status for both focal tissue regions (patches) and slides using hematoxylin-and-eosin-stained (H&E) images as input. Models were trained and evaluated using pathologist annotated slides from three data sources. Areas under the receiver operator characteristic curve (AUCs) were calculated for test sets at both a patch-level (>135 million patches, 181 slides) and slide-level (n = 3274 slides, 1249 cases, 37 sites). Interpretability analyses were performed using Testing with Concept Activation Vectors (TCAV), saliency analysis, and pathologist review of clustered patches. Results: The patch-level AUCs are 0.939 (95%CI 0.936-0.941), 0.938 (0.936-0.940), and 0.808 (0.802-0.813) for ER/PR/HER2, respectively. At the slide level, AUCs are 0.86 (95%CI 0.84-0.87), 0.75 (0.73-0.77), and 0.60 (0.56-0.64) for ER/PR/HER2, respectively. Interpretability analyses show known biomarker-histomorphology associations including associations of low-grade and lobular histology with ER/PR positivity, and increased inflammatory infiltrates with triple-negative staining. Conclusions: This study presents rapid breast cancer biomarker estimation from routine H&E slides and builds on prior advances by prioritizing interpretability of computationally learned features in the context of existing pathological knowledge.

WU-NEAT: A clinically validated, open-source MATLAB toolbox for limited-channel neonatal EEG analysis.

BACKGROUND: Limited-channel EEG research in neonates is hindered by lack of open, accessible analytic tools. To overcome this limitation, we have created the Washington University-Neonatal EEG Analysis Toolbox (WU-NEAT), containing two of the most commonly used tools, provided in an open-source, clinically-validated package running within MATLAB. METHODS: The first algorithm is the amplitude-integrated EEG (aEEG), which is generated by filtering, rectifying and time-compressing the original EEG recording, with subsequent semi-logarithmic display. The second algorithm is the spectral edge frequency (SEF), calculated as the critical frequency below which a user-defined proportion of the EEG spectral power is located. The aEEG algorithm was validated by three experienced reviewers. Reviewers evaluated aEEG recordings of fourteen preterm/term infants, displayed twice in random order, once using a reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard methodology, reviewers assigned a background pattern classification. Inter/intra-rater reliability was assessed. For the SEF, calculations were made using the same fourteen recordings, first with the reference and then with the WU-NEAT algorithm. Results were compared using Pearson's correlation coefficient. RESULTS: For the aEEG algorithm, intra- and inter-rater reliability was 100% and 98%, respectively. For the SEF, the mean±SD Pearson correlation coefficient between algorithms was 0.96±0.04. CONCLUSION: We have demonstrated a clinically-validated toolbox for generating the aEEG as well as calculating the SEF from EEG data. Open-source access will enable widespread use of common analytic algorithms which are device-independent and unlikely to become outdated as technology changes, thereby facilitating future collaborative research in neonatal EEG.

Artificial intelligence in digital breast pathology: Techniques and applications.

Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.

Comparison of step-by-step kinematics of resisted, assisted and unloaded 20-m sprint runs.

This investigation examined step-by-step kinematics of sprint running acceleration. Using a randomised counterbalanced approach, 37 female team handball players (age 17.8 ± 1.6 years, body mass 69.6 ± 9.1 kg, height 1.74 ± 0.06 m) performed resisted, assisted and unloaded 20-m sprints within a single session. 20-m sprint times and step velocity, as well as step length, step frequency, contact and flight times of each step were evaluated for each condition with a laser gun and an infrared mat. Almost all measured parameters were altered for each step under the resisted and assisted sprint conditions (η2 ≥ 0.28). The exception was step frequency, which did not differ between assisted and normal sprints. Contact time, flight time and step frequency at almost each step were different between 'fast' vs. 'slow' sub-groups (η2 ≥ 0.22). Nevertheless overall both groups responded similarly to the respective sprint conditions. No significant differences in step length were observed between groups for the respective condition. It is possible that continued exposure to assisted sprinting might allow the female team-sports players studied to adapt their coordination to the 'over-speed' condition and increase step frequency. It is notable that step-by-step kinematics in these sprints were easy to obtain using relatively inexpensive equipment with possibilities of direct feedback.

Bioresorbable pressure sensors protected with thermally grown silicon dioxide for the monitoring of chronic diseases and healing processes.

Pressures in the intracranial, intraocular and intravascular spaces are clinically useful for the diagnosis and management of traumatic brain injury, glaucoma and hypertension, respectively. Conventional devices for measuring these pressures require surgical extraction after a relevant operational time frame. Bioresorbable sensors, by contrast, eliminate this requirement, thereby minimizing the risk of infection, decreasing the costs of care and reducing distress and pain for the patient. However, the operational lifetimes of bioresorbable pressure sensors available at present fall short of many clinical needs. Here, we present materials, device structures and fabrication procedures for bioresorbable pressure sensors with lifetimes exceeding those of previous reports by at least tenfold. We demonstrate measurement accuracies that compare favourably to those of the most sophisticated clinical standards for non-resorbable devices by monitoring intracranial pressures in rats for 25 days. Assessments of the biodistribution of the constituent materials, complete blood counts, blood chemistry and magnetic resonance imaging compatibility confirm the biodegradability and clinical utility of the device. Our findings establish routes for the design and fabrication of bioresorbable pressure monitors that meet requirements for clinical use.

Therapeutic electrical stimulation of injured peripheral nerve tissue using implantable thin-film wireless nerve stimulators.

OBJECTIVE Electrical stimulation of peripheral nerve tissue has been shown to accelerate axonal regeneration. Yet existing methods of applying electrical stimulation to injured peripheral nerves have presented significant barriers to clinical translation. In this study, the authors examined the use of a novel implantable wireless nerve stimulator capable of simultaneously delivering therapeutic electrical stimulation of injured peripheral nerve tissue and providing postoperative serial assessment of functional recovery. METHODS Flexible wireless stimulators were fabricated and implanted into Lewis rats. Thin-film implants were used to deliver brief electrical stimulation (1 hour, 20 Hz) to sciatic nerves after nerve crush or nerve transection-and-repair injuries. RESULTS Electrical stimulation of injured nerves via implanted wireless stimulators significantly improved functional recovery. Brief electrical stimulation was observed to increase the rate of functional recovery after both nerve crush and nerve transection-and-repair injuries. Wireless stimulators successfully facilitated therapeutic stimulation of peripheral nerve tissue and serial assessment of nerve recovery. CONCLUSIONS Implantable wireless stimulators can deliver therapeutic electrical stimulation to injured peripheral nerve tissue. Implantable wireless nerve stimulators might represent a novel means of facilitating therapeutic electrical stimulation in both intraoperative and postoperative settings.

Population-based approaches to treatment and readmission after spinal cord injury.

BACKGROUND: Recent studies in surgical and non-surgical specialties have suggested that patients admitted on the weekend may have worse outcomes. In particular, patients with stroke and acute cardiovascular events have shown worse outcomes with weekend treatment. It is unclear whether this extends to patients with spinal cord injury (SCI). This study was designed to evaluate factors for readmission after index hospitalization for spinal cord injury. METHODS: This cohort was constructed from the State Inpatient Databases of California, New York, and Florida. For this study 14,396 patients with SCI were identified. The primary outcome measure evaluated was 30-day readmission. Secondary measures include in-hospital complications. Univariate and multivariate analysis were utilized to evaluate covariates. c2, Fisher's exact, and linear, logistic, and modified Poisson regression methods were utilized for statistical analysis. Propensity score methods were used with matched pairs analysis performed by the McNemar's Test. RESULTS: Weekend admission was not associated with increased 30- day readmission rates in multivariate analysis. Race and discharge to a facility (RR 1.60 [1.43-1.79]) or home with home care (RR 1.23 [1.07-1.42]), were statistically significant risk factors for readmission. Payor status did not affect rates of readmission. In propensity score matched pairs analysis, weekend admission was not associated with increased odds of 30-day readmission (OR 1.04 [0.89-1.21]). Patients admitted to high volume centers had significantly lower risk of readmission when compared with patients admitted to low volume centers. CONCLUSIONS: Our results suggest that the weekend effect, described previously in other patient populations, may not play as important a role in patients with SCI.

Readmission after spinal cord injury: analysis of an institutional cohort of 795 patients.

BACKGROUND: Recent studies in other fields have suggested that healthcare on the weekend may have worse outcomes. In particular, patients with stroke and acute cardiovascular events have shown worse outcomes with weekend treatment. It is unclear whether this extends to patients with spinal cord injury. This study was designed to evaluate factors for readmission after index hospitalization for spinal cord injury. METHODS: A total of 795 consecutive patients over an 11-year period were analyzed. After excluding patients with chronic spinal cord injury and surgical care at an outside hospital, 745 patients remained. The primary outcome measure evaluated was 30-day readmission. Secondary measures include perioperative complications, readmission rate when discharged on the weekend, and the effect of race and insurance status on readmission rate. Univariate and multivariate analysis were utilized to evaluate the covariates collected. The χ2 test, Fisher's exact test, and linear and logistic regression methods were utilized for statistical analysis. RESULTS: A total of 745 patients were analyzed after exclusions. Payer status did not affect length of stay, ICU length of stay, or perioperative complications. Neither weekend admission nor weekend operation affected length of stay, ICU length of stay, or readmission by 30 days. Patients undergoing weekend surgical treatment had lower perioperative complication rates (2.2% vs. 6.5% on weekday, P<0.01). Discharge on the weekend was associated with a significantly lower rate of readmission by 30 days (OR=0.07, 95% CI: 0.009-0.525, P<0.005). Payer status was associated with 30-day readmission (P<0.005). Patients with Medicare (20.8%) and Medicaid (20.1%) showed higher rates of readmission than patients with other payers. 21.1% of African-American patients were readmitted, versus 10.2% of other patients (Odds ratio: 2.2, 95% confidence interval 1.36-3.27, P<0.001). Correcting for payer status lessened but did not eliminate the effect of race on readmission. CONCLUSIONS: Weekend admission did not increase perioperative complications or hospital length of stay. After discharge, patients with Medicaid and Medicare show higher rates of 30-day readmission, as do African-American patients. The effect of race on readmission is multifactorial, and may partially explained by the increased rate of Medicaid coverage in African-Americans in our institutions catchment area.