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Abstract Achieving achromaticity across the visible light spectrum is crucial for metalenses in imaging systems. Single-layer metalenses struggle with weak focusing power or small aperture sizes due to inadequate group delay control. Multilayer metalenses offer some improvement but come with increased design and fabrication complexity. Here, we demonstrate a strategy using meta-atoms with material layers engineered for matching dispersion, allowing large and fine adjustments of group delay. Our design substantially broadens the group delay range, allowing us to experimentally demonstrate several polarization-independent metalenses operating across the entire visible spectrum (400-700 nm). We design, fabricate, and characterize achromatic metalenses with aperture diameters of 16 μm, 66 μm, 200 μm, and 400 μm, and numerical apertures of 0.27, 0.11, 0.04, and 0.02, respectively. Among them, the 400-μm diameter, 0.02-numerical-aperture metalens is used to demonstrate full-color imaging capabilities. Our results exhibit diffraction-limited performance, high efficiency, and accurate full-color image reproduction.

Abstract Global outcomes have been reported to be associated with cerebrovascular reactivity (CVR) in the acute phase following moderate and severe traumatic brain injury (TBI). The association of CVR in the acute and chronic phase of injury with patient-reported health-related quality of life metrics (HRQOL) metrics has never been explored. The aim of this study is to examine the association of CVR, as measured by the cerebral oxygen indices (COx and COx_a), in the acute and chronic phase following moderate and severe TBI, with patient reported HRQOL. In this prospective cohort study, performed in a Canadian quaternary care center, the association between continuous acute and chronic phase CVR with patient reported HRQOL outcomes following moderate and severe TBI was examined. The main outcomes of interest of this study were validated measures of patient-reported HRQOL over various domains as measured by both the 12-Item Short-Form Health Survey (SF-12) and a Quality of Life after Brain Injury (QOLIBRI) questionnaire. In the 29 subjects of this cohort, acute phase CVR was found to be significantly more active in those with a favorable Mental Component Summary (MCS) scores of the SF-12 at early follow-up when measured by COx (-0.015 [IQR: -0.067 to 0.032] vs 0.040 [IQR: 0.019 to 0.137] for Favorable first MCS vs Unfavorable respectively; Mann–Whitney U test p-value = 0.046) and COx_a (0.038 [IQR: 0.009 to 0.062] vs 0.112 [IQR: 0.065 to 0.167] for Favorable first MCS vs Unfavorable respectively; Mann–Whitney U test p-value = 0.014). Further, multivariable logistic regression analysis found acute phase COx and COx_a to improve model performance when predicting favorable versus unfavorable early MCS scores over established parameters such as age and measures of injury severity. Associations between outcomes and chronic phase CVR were limited, potentially due to short recording periods. This is the first ever pilot study to identify a relationship between acute phase CVR following moderate-to-severe TBI with mental and cognitive outcomes as experienced by patients. Given the small cohort, these findings will need to be confirmed in a larger multicenter study. This highlights the need for additional examination of the role dysfunctional CVR may play in mental and cognitive outcomes, as well as patient-reported outcomes more generally following TBI.

Recent advancements in 3D-bioprinting technology have sparked a growing interest in its application for brain repair, encompassing tissue regeneration, drug delivery, and disease modeling. This literature review examines studies conducted over the past five years to assess the current state of research in this field. Common bioprinting methods and key parameters influencing their selection are explored, alongside an analysis of the diverse types of bioink utilized and their associated parameters. The extrusion-based 3D-bioprinting method emerged as the most widely studied and popular topic, followed by inkjet-based and laser-based bioprinting and stereolithography. Regarding bioinks, fibrin-based and collagen-based bioinks are predominantly utilized. Furthermore, this review elucidates how 3D bioprinting holds promise for neural tissue repair, regeneration, and drug screening, detailing the steps involved and various approaches employed. Neurovascular 3D printing and bioscaffold 3D printing stand out as the top two preferred methods for brain repair. The recent studies’ shortcomings and potential solutions to address them are also examined and discussed. Overall, by synthesizing recent findings, this review provides valuable insights into the potential of 3D bioprinting for advancing brain repairment strategies.

Cerebrovascular pressure reactivity plays a key role in maintaining constant cerebral blood flow. Unfortunately, this mechanism is often impaired in acute traumatic neural injury states, exposing the already injured brain to further pressure-passive insults. While there has been much work on the association between impaired cerebrovascular reactivity following moderate/severe traumatic brain injury (TBI) and worse long-term outcomes, there is yet to be a comprehensive review on the association between cerebrovascular pressure reactivity and intracranial pressure (ICP) extremes. Therefore, we conducted a systematic review of the literature for all studies presenting a quantifiable statistical association between a continuous measure of cerebrovascular pressure reactivity and ICP in a human TBI cohort. The methodology described in the Cochrane Handbook for Systematic Reviews was used. BIOSIS, Cochrane Library, EMBASE, Global Health, MEDLINE, and SCOPUS were all searched from their inceptions to March of 2023 for relevant articles. Full-length original works with a sample size of ?10 patients with moderate/severe TBI were included in this review. Data were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A total of 16 articles were included in this review. Studies varied in population characteristics and statistical tests used. Five studies looked at transcranial Doppler-based indices and 13 looked at ICP-based indices. All but two studies were able to present a statistically significant association between cerebrovascular pressure reactivity and ICP. Based on the findings of this review, impaired reactivity seems to be associated with elevated ICP and reduced ICP waveform complexity. This relationship may allow for the calculation of patient-specific ICP thresholds, past which cerebrovascular reactivity becomes persistently deranged. However, further work is required to better understand this relationship and improve algorithmic derivation of such individualized ICP thresholds.

Abstract Background Optimal cerebral perfusion pressure (CPPopt) has emerged as a promising personalized medicine approach to the management of moderate-to-severe traumatic brain injury (TBI). Though literature demonstrating its association with poor outcomes exists, there is yet to be work done on its association with outcome transition due to a lack of serial outcome data analysis. In this study we investigate the association between various metrics of CPPopt and failure to improve in outcome over time. Methods CPPopt was derived using three different cerebrovascular reactivity indices; the pressure reactivity index (PRx), the pulse amplitude index (PAx), and the RAC index. For each index, % times spent with cerebral perfusion pressure (CPP) above and below its CPPopt and upper and lower limits of reactivity were calculated. Patients were dichotomized based on improvement in Glasgow Outcome Scale-Extended (GOSE) scores into Improved vs. Not Improved between 1 and 3 months, 3 and 6 months, and 1- and 6-month post-TBI. Logistic regression analyses were then conducted, adjusting for the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) variables. Results This study included a total of 103 patients from the Winnipeg Acute TBI Database. Through Mann–Whitney U testing and logistic regression analysis, it was found that % time spent with CPP below CPPopt was associated with failure to improve in outcome, while % time spent with CPP above CPPopt was generally associated with improvement in outcome. Conclusions Our study supports the existing narrative that time spent with CPP below CPPopt results in poorer outcomes. However, it also suggests that time spent above CPPopt may not be associated with worse outcomes and is possibly even associated with improvement in outcome.

Artifacts induced during patient monitoring are a main limitation for near-infrared spectroscopy (NIRS) as a non-invasive method of cerebral hemodynamic monitoring. There currently does not exist a robust “gold-standard” method for artifact management for these signals. The objective of this review is to comprehensively examine the literature on existing artifact management methods for cerebral NIRS signals recorded in animals and humans. A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search yielded 806 unique results. There were 19 articles from these results that were included in this review based on the inclusion/exclusion criteria. There were an additional 36 articles identified in the references of select articles that were also included. The methods outlined in these articles were grouped under two major categories: (1) motion and other disconnection artifact removal methods; (2) data quality improvement and physiological/other noise artifact filtering methods. These were sub-categorized by method type. It proved difficult to quantitatively compare the methods due to the heterogeneity of the effectiveness metrics and definitions of artifacts. The limitations evident in the existing literature justify the need for more comprehensive comparisons of artifact management. This review provides insights into the available methods for artifact management in cerebral NIRS and justification for a homogenous method to quantify the effectiveness of artifact management methods. This builds upon the work of two existing reviews that have been conducted on this topic; however, the scope is extended to all artifact types and all NIRS recording types. Future work by our lab in cerebral NIRS artifact management will lie in a layered artifact management method that will employ different techniques covered in this review (including dynamic thresholding, autoregressive-based methods, and wavelet-based methods) amongst others to remove varying artifact types.

Near-infrared spectroscopy (NIRS) regional cerebral oxygen saturation (rSO2)-based cerebrovascular reactivity (CVR) monitoring has enabled entirely non-invasive, continuous monitoring during both acute and long-term phases of care. To date, long-term post-injury CVR has not been properly characterized after acute traumatic neural injury, also known as traumatic brain injury (TBI). This study aims to compare CVR in those recovering from moderate-to-severe TBI with a healthy control group. A total of 101 heathy subjects were recruited for this study, along with 29 TBI patients. In the healthy cohort, the arterial blood pressure variant of the cerebral oxygen index (COx_a) was not statistically different between males and females or in the dominant and non-dominant hemispheres. In the TBI cohort, COx_a was not statistically different between the first and last available follow-up or by the side of cranial surgery. Surprisingly, CVR, as measured by COx_a, was statistically better in those recovering from TBI than those in the healthy cohort. In this prospective cohort study, CVR, as measured by NIRS-based methods, was found to be more active in those recovering from TBI than in the healthy cohort. This study may indicate that in individuals that survive TBI, CVR may be enhanced as a neuroprotective measure.

The modeling and forecasting of cerebral pressure–flow dynamics in the time–frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure–flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications.

The contemporary monitoring of cerebrovascular reactivity (CVR) relies on invasive intracranial pressure (ICP) monitoring which limits its application. Interest is shifting towards near-infrared spectroscopic regional cerebral oxygen saturation (rSO2)-based indices of CVR which are less invasive and have improved spatial resolution. This study aims to examine and model the relationship between ICP and rSO2-based indices of CVR. Through a retrospective cohort study of prospectively collected physiologic data in moderate to severe traumatic brain injury (TBI) patients, linear mixed effects modeling techniques, augmented with time-series analysis, were utilized to evaluate the ability of rSO2-based indices of CVR to model ICP-based indices. It was found that rSO2-based indices of CVR had a statistically significant linear relationship with ICP-based indices, even when the hierarchical and autocorrelative nature of the data was accounted for. This strengthens the body of literature indicating the validity of rSO2-based indices of CVR and potential greatly expands the scope of CVR monitoring.

Regional cerebral oxygen saturation (rSO2), a method of cerebral tissue oxygenation measurement, is recorded using non-invasive near-infrared Spectroscopy (NIRS) devices. A major limitation is that recorded signals often contain artifacts. Manually removing these artifacts is both resource and time consuming. The objective was to evaluate the applicability of using wavelet analysis as an automated method for simple signal loss artifact clearance of rSO2 signals obtained from commercially available devices. A retrospective observational study using existing populations (healthy control (HC), elective spinal surgery patients (SP), and traumatic brain injury patients (TBI)) was conducted. Arterial blood pressure (ABP) and rSO2 data were collected in all patients. Wavelet analysis was determined to be successful in removing simple signal loss artifacts using wavelet coefficients and coherence to detect signal loss artifacts in rSO2 signals. The removal success rates in HC, SP, and TBI populations were 100%, 99.8%, and 99.7%, respectively (though it had limited precision in determining the exact point in time). Thus, wavelet analysis may prove to be useful in a layered approach NIRS signal artifact tool utilizing higher-frequency data; however, future work is needed.