Objective

The authors report methods developed for the implantation of micro-wire bundles into mesial temporal lobe structures and subsequent single neuron recording in epileptic patients undergoing in-patient diagnostic monitoring. This is done with the intention of lowering the perceived barriers to routine single neuron recording from deep brain structures in the clinical setting.

Approach

Over a 15 month period, 11 patients were implanted with platinum micro-wire bundles into mesial temporal structures. Protocols were developed for (A) monitoring electrode integrity through impedance testing, (B) ensuring continuous 24-7 recording, (C) localizing micro-wire position and 'splay' pattern and (D) monitoring grounding and referencing to maintain the quality of recordings.

Main results

Five common modes of failure were identified: (1) broken micro-wires from acute tensile force, (2) broken micro-wires from cyclic fatigue at stress points, (3) poor in vivo micro-electrode separation, (4) motion artifact and (5) deteriorating ground connection and subsequent drop in common mode noise rejection. Single neurons have been observed up to 14 days post-implantation and on 40% of micro-wires.

Significance

Long-term success requires detailed review of each implant by both the clinical and research teams to identify failure modes, and appropriate refinement of techniques while moving forward. This approach leads to reliable unit recordings without prolonging operative times, which will help increase the availability and clinical viability of human single neuron data.

More efficacious treatment regimens are needed for tuberculosis, however, drug development is impeded by a lack of reliable biomarkers of disease severity and of treatment effect. We conducted a directed screen of host biomarkers in participants enrolled in a tuberculosis clinical trial to address this need. Serum samples from 319 protocol-correct, culture-confirmed pulmonary tuberculosis patients treated under direct observation as part of an international, phase 2 trial were screened for 70 markers of infection, inflammation, and metabolism. Biomarker assays were specifically developed for this study and quantified using a novel, multiplexed electrochemiluminescence assay. We evaluated the association of biomarkers with baseline characteristics, as well as with detailed microbiologic data, using Bonferroni-adjusted, linear regression models. Across numerous analyses, seven proteins, SAA1, PCT, IL-1β, IL-6, CRP, PTX-3 and MMP-8, showed recurring strong associations with markers of baseline disease severity, smear grade and cavitation; were strongly modulated by tuberculosis treatment; and had responses that were greater for patients who culture-converted at 8weeks. With treatment, all proteins decreased, except for osteocalcin, MCP-1 and MCP-4, which significantly increased. Several previously reported putative tuberculosis-associated biomarkers (HOMX1, neopterin, and cathelicidin) were not significantly associated with treatment response. In conclusion, across a geographically diverse and large population of tuberculosis patients enrolled in a clinical trial, several previously reported putative biomarkers were not significantly associated with treatment response, however, seven proteins had recurring strong associations with baseline radiographic and microbiologic measures of disease severity, as well as with early treatment response, deserving additional study.

Background

Lanadelumab demonstrated efficacy in preventing hereditary angioedema (HAE) attacks in the phase 3 HELP Study.

Objective

To assess time to onset of effect and long-term efficacy of lanadelumab, based on exploratory findings from the HELP Study.

Methods

Eligible patients with HAE type I/II received lanadelumab 150 mg every 4 weeks (q4wks), 300 mg q4wks, 300 mg q2wks, or placebo. Ad hoc analyses evaluated day 0-69 findings using a Poisson regression model accounting for overdispersion. Least-squares mean monthly HAE attack rate for lanadelumab was compared with placebo. Intrapatient comparisons for days 0-69 versus steady state (days 70-182) used a paired t test for continuous endpoints or Kappa statistics for categorical endpoints.

Results

One hundred twenty-five patients were randomized and treated. During days 0-69, mean monthly attack rate was significantly lower with lanadelumab (0.41-0.76) vs placebo (2.04), including attacks requiring acute treatment (0.33-0.61 vs 1.66) and moderate/severe attacks (0.31-0.48 vs 1.33, all P ≤ .001). More patients receiving lanadelumab vs placebo were attack free (37.9%-48.1% vs 7.3%) and responders (85.7%-100% vs 26.8%). During steady state, the efficacy of lanadelumab vs placebo was similar or improved vs days 0-69. Intrapatient differences were significant with lanadelumab 300 mg q4wks for select outcomes. Lanadelumab efficacy was durable-HAE attack rate was consistently lower vs placebo, from the first 2 weeks of treatment through study end. Treatment emergent adverse events were comparable during days 0-69 and 70-182.

Conclusion

Protection with lanadelumab started from the first dose and continued throughout the entire study period.

Instructor Talk-noncontent language used by instructors in classrooms-is a recently defined and promising variable for better understanding classroom dynamics. Having previously characterized the Instructor Talk framework within the context of a single course, we present here our results surrounding the applicability of the Instructor Talk framework to noncontent language used by instructors in novel course contexts. We analyzed Instructor Talk in eight additional biology courses in their entirety and in 61 biology courses using an emergent sampling strategy. We observed widespread use of Instructor Talk with variation in the amount and category type used. The vast majority of Instructor Talk could be characterized using the originally published Instructor Talk framework, suggesting the robustness of this framework. Additionally, a new form of Instructor Talk-Negatively Phrased Instructor Talk, language that may discourage students or distract from the learning process-was detected in these novel course contexts. Finally, the emergent sampling strategy described here may allow investigation of Instructor Talk in even larger numbers of courses across institutions and disciplines. Given its widespread use, potential influence on students in learning environments, and ability to be sampled, Instructor Talk may be a key variable to consider in future research on teaching and learning in higher education.

Active-learning pedagogies have been repeatedly demonstrated to produce superior learning gains with large effect sizes compared with lecture-based pedagogies. Shifting large numbers of college science, technology, engineering, and mathematics (STEM) faculty to include any active learning in their teaching may retain and more effectively educate far more students than having a few faculty completely transform their teaching, but the extent to which STEM faculty are changing their teaching methods is unclear. Here, we describe the development and application of the machine-learning-derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze thousands of hours of STEM course audio recordings quickly, with minimal costs, and without need for human observers. DART analyzes the volume and variance of classroom recordings to predict the quantity of time spent on single voice (e.g., lecture), multiple voice (e.g., pair discussion), and no voice (e.g., clicker question thinking) activities. Applying DART to 1,486 recordings of class sessions from 67 courses, a total of 1,720 h of audio, revealed varied patterns of lecture (single voice) and nonlecture activity (multiple and no voice) use. We also found that there was significantly more use of multiple and no voice strategies in courses for STEM majors compared with courses for non-STEM majors, indicating that DART can be used to compare teaching strategies in different types of courses. Therefore, DART has the potential to systematically inventory the presence of active learning with ∼90% accuracy across thousands of courses in diverse settings with minimal effort.