Ultra-high-Q free-space coupling to microtoroid resonators.

Whispering gallery mode (WGM) microtoroid resonators are one of the most sensitive biochemical sensors in existence, capable of detecting single molecules. The main barrier for translating these devices out of the laboratory is that light is evanescently coupled into these devices though a tapered optical fiber. This hinders translation of these devices as the taper is fragile, suffers from mechanical vibration, and requires precise positioning. Here, we eliminate the need for an optical fiber by coupling light into and out from a toroid via free-space coupling and monitoring the scattered resonant light. A single long working distance objective lens combined with a digital micromirror device (DMD) was used for light injection, scattered light collection, and imaging. We obtain Q-factors as high as 1.6 × 10 8 with this approach. Electromagnetically induced transparency (EIT)-like and Fano resonances were observed in a single cavity due to indirect coupling in free space. This enables improved sensing sensitivity. The large effective coupling area (~10 µm in diameter for numerical aperture = 0.14) removes the need for precise positioning. Sensing performance was verified by combining the system with the frequency locked whispering evanescent resonator (FLOWER) approach to perform temperature sensing experiments. A thermal nonlinear optical effect was examined by tracking the resonance through FLOWER while adjusting the input power. We believe that this work will be a foundation for expanding the implementation of WGM microtoroid resonators to real-world applications.

Low Part-Per-Trillion, Humidity Resistant Detection of Nitric Oxide Using Microtoroid Optical Resonators.

The nitric oxide radical plays pivotal roles in physiological as well as atmospheric contexts. Although the detection of dissolved nitric oxide in vivo has been widely explored, highly sensitive (i.e., low part-per-trillion level), selective, and humidity-resistant detection of gaseous nitric oxide in air remains challenging. In the field, humidity can have dramatic effects on the accuracy and selectivity of gas sensors, confounding data, and leading to overestimation of gas concentration. Highly selective and humidity-resistant gaseous NO sensors based on laser-induced graphene were recently reported, displaying a limit of detection (LOD) of 8.3 ppb. Although highly sensitive (LOD = 590 ppq) single-wall carbon nanotube NO sensors have been reported, these sensors lack selectivity and humidity resistance. In this report, we disclose a highly sensitive (LOD = 2.34 ppt), selective, and humidity-resistant nitric oxide sensor based on a whispering-gallery mode microtoroid optical resonator. Excellent analyte selectivity was enabled via novel ferrocene-containing polymeric coatings synthesized via reversible addition-fragmentation chain-transfer polymerization. Utilizing a frequency locked optical whispering evanescent resonator system, the microtoroid's real-time resonance frequency shift response to nitric oxide was tracked with subfemtometer resolution. The lowest concentration experimentally detected was 6.4 ppt, which is the lowest reported to date. Additionally, the performance of the sensor remained consistent across different humidity environments. Lastly, the impact of the chemical composition and molecular weight of the novel ferrocene-containing polymeric coatings on sensing performance was evaluated. We anticipate that our results will have impact on a wide variety of fields where NO sensing is important such as medical diagnostics through exhaled breath, determination of planetary habitability, climate change, air quality monitoring, and treating cardiovascular and neurological disorders.

Limitations of Noninvasive Tests-Based Population-Level Risk Stratification Strategy for Nonalcoholic Fatty Liver Disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are highly prevalent but underdiagnosed. AIMS: We used an electronic health record data network to test a population-level risk stratification strategy using noninvasive tests (NITs) of liver fibrosis. METHODS: Data were obtained from PCORnet® sites in the East, Midwest, Southwest, and Southeast United States from patients aged [Formula: see text] 18 with or without ICD-10-CM diagnosis codes for NAFLD, NASH, and NASH-cirrhosis between 9/1/2017 and 8/31/2020. Average and standard deviations (SD) for Fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), and Hepatic Steatosis Index (HSI) were estimated by site for each patient cohort. Sample-wide estimates were calculated as weighted averages across study sites. RESULTS: Of 11,875,959 patients, 0.8% and 0.1% were coded with NAFLD and NASH, respectively. NAFLD diagnosis rates in White, Black, and Hispanic patients were 0.93%, 0.50%, and 1.25%, respectively, and for NASH 0.19%, 0.04%, and 0.16%, respectively. Among undiagnosed patients, insufficient EHR data for estimating NITs ranged from 68% (FIB-4) to 76% (NFS). Predicted prevalence of NAFLD by HSI was 60%, with estimated prevalence of advanced fibrosis of 13% by NFS and 7% by FIB-4. Approximately, 15% and 23% of patients were classified in the intermediate range by FIB-4 and NFS, respectively. Among NAFLD-cirrhosis patients, a third had FIB-4 scores in the low or intermediate range. CONCLUSIONS: We identified several potential barriers to a population-level NIT-based screening strategy. HSI-based NAFLD screening appears unrealistic. Further research is needed to define merits of NFS- versus FIB-4-based strategies, which may identify different high-risk groups.

Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses.

Current commercial air-quality monitoring devices lack a large dynamic range, especially at the small, ultrafine size scale. Furthermore, there is a low density of air-quality monitoring stations, reducing the precision with which local particulate matter hazards can be tracked. Here, we show a low-cost, lensfree, and portable air-quality monitoring device (LPAQD) that can detect and measure micron-sized particles down to 100 nm-sized particles, with the capability to track and measure particles in real time throughout a day and the ability to accurately measure particulate matter densities as low as 3 µg m-3. A vapor-condensed film is deposited onto the coverslip used to collect particles before the LPAQD is deployed at outdoor monitoring sites. The vapor-condensed film increases the scattering cross section of particles smaller than the pixel size, enabling the sub-pixel and sub-diffraction-limit-sized particles to be detected. The high dynamic range, low cost, and portability of this device can enable citizens to monitor their own air quality to hopefully impact user decisions that reduce the risk for particulate matter-related diseases.

Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay.

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 103 copies per mL, with a tested dynamic range of 3 orders of magnitude, without yet reaching the upper limit. This biosensor can be used for fast SARS-CoV-2 diagnosis in low-resource POC settings and has the potential to mitigate the spread of future waves of the pandemic.

Part-per-Trillion Trace Selective Gas Detection Using Frequency Locked Whispering-Gallery Mode Microtoroids.

Rapid detection of toxic and hazardous gases at trace concentrations plays a vital role in industrial, battlefield, and laboratory scenarios. Of interest are both sensitive as well as highly selective sensors. Whispering-gallery mode (WGM) microresonator-based biochemical sensors are among the most sensitive sensors in existence due to their long photon confinement times. One main concern with these devices, however, is their selectivity toward specific classes of target analytes. Here, we employ frequency locked WGM microtoroid optical resonators covalently modified with various polymer coatings to selectively detect the chemical warfare agent surrogate diisopropyl methylphosphonate (DIMP) as well as the toxic industrial chemicals formaldehyde and ammonia at parts-per-trillion concentrations (304, 434, and 117 ppt, respectively). This is 1-2 orders of magnitude better than previously reported, depending on the target, except for pristine graphene and pristine carbon nanotube sensors, which demonstrate similar detection levels but in vacuum and without selectivity. Selective polymer coatings include polyethylene glycol for DIMP sensing, accessed by the modification of commercially available materials, and 3-(triethoxysilyl) propyl-terminated polyvinyl acetate (PVAc) for ammonia sensing. Notably, we developed for the first time an efficient one-pot procedure to access 3-(triethoxysilyl) propyl-terminated PVAc that utilizes cobalt-mediated living radical polymerization and a nitroxyl polymer-terminating agent. Alkaline hydrolysis of PVAc coatings to form polyvinyl alcohol coatings directly bound to the microtoroid proved to be reliable and reproducible, leading to WGM sensors capable of the rapid and selective detection of formaldehyde vapors. The selectivity of these three polymer coatings as sensing media was predicted, in part, based on their functional group content and known reactivity patterns with the target analytes. Furthermore, we demonstrate that microtoroids coated with a mixture of polymers can serve as an all-in-one sensor that can detect multiple agents. We anticipate that our results will facilitate rapid early detection of chemical agents, as well as their surrogates and precursors.

Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017-2018 transient elastography data and application of machine learning.

This cohort analysis investigated the prevalence of nonalcoholic fatty liver disease (NAFLD) and NAFLD with fibrosis at different stages, associated clinical characteristics, and comorbidities in the general United States population and a subpopulation with type 2 diabetes mellitus (T2DM), using the National Health and Nutrition Examination Survey (NHANES) database (2017-2018). Machine learning was explored to predict NAFLD identified by transient elastography (FibroScan® ). Adults ≥20 years of age with valid transient elastography measurements were included; those with high alcohol consumption, viral hepatitis, or human immunodeficiency virus were excluded. Controlled attenuation parameter ≥302 dB/m using Youden's index defined NAFLD; vibration-controlled transient elastography liver stiffness cutoffs were ≤8.2, ≤9.7, ≤13.6, and >13.6 kPa for F0-F1, F2, F3, and F4, respectively. Predictive modeling, using six different machine-learning approaches with demographic and clinical data from NHANES, was applied. Age-adjusted prevalence of NAFLD and of NAFLD with F0-F1 and F2-F4 fibrosis was 25.3%, 18.9%, and 4.4%, respectively, in the overall population and 54.6%, 32.6%, and 18.3% in those with T2DM. The highest prevalence was among Mexican American participants. Test performance for all six machine-learning models was similar (area under the receiver operating characteristic curve, 0.79-0.84). Machine learning using logistic regression identified male sex, hemoglobin A1c, age, and body mass index among significant predictors of NAFLD (P ≤ 0.01). Conclusion: Data show a high prevalence of NAFLD with significant fibrosis (≥F2) in the general United States population, with greater prevalence in participants with T2DM. Using readily available, standard demographic and clinical data, machine-learning models could identify subjects with NAFLD across large data sets.

Individualized HbA1c Goals, and Patient Awareness and Attainment of Goals in Type 2 Diabetes Mellitus: A Real-World Multinational Survey.

AIMS: While glycemic control is key in effective type 2 diabetes mellitus management, many patients fail to reach their individualized glycemic goal. This analysis aimed to describe a real-world picture of diabetes management: individualized hemoglobin A1c (HbA1c) goals, rate of goal attainment, HbA1c at each line of therapy, and patient awareness of their glycemic goal. Secondly, we aimed to understand physician satisfaction with HbA1c amongst patients aware vs. those unaware of HbA1c goal. METHODS: Analysis of physicians and the next ten consulting patients with type 2 diabetes mellitus conducted in Europe and the USA including medical record data abstraction/assessment by physicians, a patient-reported survey and a physician survey. Patients were diagnosed for 3 months or more with a known current and target HbA1c. For the sub-analysis assessment of patient awareness of HbA1c goal, in addition to the above, these patients had to have completed a patient-reported questionnaire and answer the question on awareness of HbA1c goal. RESULTS: A total of 730 physicians provided data on 8794 patients with type 2 diabetes mellitus; 5331 patients were eligible for this analysis. Overall, mean (standard deviation, SD) individualized HbA1c goal was 6.8% (0.68%). Of eligible patients, 39.1% met their HbA1c goal; of 60.9% of patients not reaching their HbA1c goal, the mean distance from individualized HbA1c goal was 0.9% (SD 1.0%). Physicians progressed patients' antihyperglycemic therapy when HbA1c was 8% or higher. Among 2560 patients who were included in the sub-analysis assessing the effect of patient awareness of their HbA1c goal on multiple parameters, 70.5% were aware of their HbA1c goal; mean HbA1c goal was 6.8% (0.7%) and current mean HbA1c value 7.1% (1.2%). A total of 949 patients in the sub-analysis (39.2%) achieved their goal; achieving HbA1c goal was not related to knowledge of goal. Patients aware of their HbA1c goal were slightly more adherent to their antihyperglycemic medication. They also were prescribed more antihyperglycemic agents, more often on a later therapy line receiving a GLP-1 receptor agonist, SGLT2i, or insulin, and more often tested their blood glucose levels than patients who were unaware. Physicians were not satisfied with the current blood glucose level of one third of their patients, believing that more of those who were aware of their HbA1c goal could achieve better glucose control (32.4% of aware vs. 28.2% of unaware patients; p = 0.003). CONCLUSIONS: Our results showed that the proportion of patients with type 2 diabetes mellitus achieving their goals for glycemic control was suboptimal when compared to current guideline criteria, with only about 40% of patients achieving their individualized HbA1c goal. Treatment intensification was often delayed until HbA1c was 8% and higher. Patients aware of their HbA1c goal were slightly more adherent to their antihyperglycemic medication; however, awareness of HbA1c goal did not enhance goal attainment. This highlights the need for a holistic approach to diabetes management, involving patient education, and patient-physician communication and partnership.

Assembly of multicomponent structures from hundreds of micron-scale building blocks using optical tweezers.

The fabrication of three-dimensional (3D) microscale structures is critical for many applications, including strong and lightweight material development, medical device fabrication, microrobotics, and photonic applications. While 3D microfabrication has seen progress over the past decades, complex multicomponent integration with small or hierarchical feature sizes is still a challenge. In this study, an optical positioning and linking (OPAL) platform based on optical tweezers is used to precisely fabricate 3D microstructures from two types of micron-scale building blocks linked by biochemical interactions. A computer-controlled interface with rapid on-the-fly automated recalibration routines maintains accuracy even after placing many building blocks. OPAL achieves a 60-nm positional accuracy by optimizing the molecular functionalization and laser power. A two-component structure consisting of 448 1-µm building blocks is assembled, representing the largest number of building blocks used to date in 3D optical tweezer microassembly. Although optical tweezers have previously been used for microfabrication, those results were generally restricted to single-material structures composed of a relatively small number of larger-sized building blocks, with little discussion of critical process parameters. It is anticipated that OPAL will enable the assembly, augmentation, and repair of microstructures composed of specialty micro/nanomaterial building blocks to be used in new photonic, microfluidic, and biomedical devices.

Accurate and fast modeling of scattering from random arrays of nanoparticles using the discrete dipole approximation and angular spectrum method.

Lens-free microscopes can utilize holographic reconstruction techniques to recover the image of an object from the digitally recorded superposition of an unperturbed plane wave and a wave scattered by the object. Image reconstruction most commonly relies on the scalar angular spectrum method (ASM). While fast, the scalar ASM can be inaccurate for nanoscale objects, either because of the scalar approximation, or more generally, because it only models field propagation and not light-matter interaction, including inter-particle coupling. Here we evaluate the accuracy of the scalar ASM when combined with three different light-matter interaction models for computing the far-field light scattered by random arrays of gold and polystyrene nanoparticles. Among the three models-a dipole-matched transmission model, an optical path length model, and a binary amplitude model-we find that which model is most accurate depends on the nanoparticle material and packing density. For polystyrene particles at any packing density, there is always at least one model with error below 20%, while for gold nanoparticles with 40% or 50% surface coverage, there are no models that can provide errors better than 30%. The ASM error is determined in comparison to a discrete dipole approximation model, which is more computationally efficient than other full-wave modeling techniques. The knowledge of when and how the ASM fails can serve as a first step toward improved resolution in lens-free reconstruction and can also be applied to other random nanoparticle array applications such as lens-based super-resolution imaging, sub-diffraction beam focusing, and biomolecular sensing.

High-Speed Lens-Free Holographic Sensing of Protein Molecules Using Quantitative Agglutination Assays.

Accurate, cost-effective, easy-to-use, and point-of-care sensors for protein biomarker levels are important for disease diagnostics. A cost-effective and compact readout approach that has been used for several diagnostic applications is lens-free holographic microscopy, which provides an ultralarge field of view and submicron resolution when it is coupled with pixel super-resolution techniques. Despite its potential as a diagnostic technique, lens-free microscopy has not previously been applied to quantitative protein molecule sensing in solution, which can simplify sensing protocols and ultimately enable measurements of binding kinetics in physiological conditions. Here, we sense interferon-γ (an immune system biomarker) and NeutrAvidin molecules in solution by combining lens-free microscopy with a one-step bead-based agglutination assay, enabled by a custom high-speed light-emitting diode (LED) array and automated image processing routines. We call this a quantitative large-area binding (QLAB) sensor. The high-speed light source provides, for the first time, pixel super-resolved imaging of >104 2 µm beads in solution undergoing Brownian motion, without significant motion blur. The automated image processing routines enable the counting of individual beads and clusters, providing a quantitative sensor readout that depends on both bead and analyte concentrations. Fits to the chemical binding theory are provided. For NeutrAvidin, we find a limit of detection (LOD) of <27 ng/mL (450 pM) and a dynamic range of 2-4 orders of magnitude. For mouse interferon-γ, the LOD is <3 ng/mL (200 pM) and the dynamic range is at least 4 orders of magnitude. The QLAB sensor holds promise for point-of-care applications in low-resource communities and where protocol simplicity is important.

Comparative Efficacy of Dual and Single Initiation of Add-On Oral Antihyperglycemic Agents in Type 2 Diabetes Uncontrolled on Metformin Alone: A Systematic Literature Review and Network Meta-Analysis.

INTRODUCTION: Current guidelines recommend adding an oral antihyperglycemic agent (AHA) to metformin in patients with type 2 diabetes mellitus (T2DM) uncontrolled on metformin. Recent randomized clinical trials (RCTs) have demonstrated that adding dual AHAs instead of a single AHA provided more effective glycemic control. However, the comparative efficacy of approved single and dual initiation strategies is unknown. Therefore, we conducted a Bayesian network meta-analysis to compare the efficacy of dual and single add-on oral AHAs in patients uncontrolled on metformin. METHODS: A systematic literature review of RCTs was conducted following Cochrane and ISPOR guidelines. MEDLINE, Embase, and CENTRAL were searched from inception to November 19, 2019. Approved oral doses of sodium-glucose co-transporter-2 (SGLT-2) inhibitors, dipeptidyl peptidase-4 (DPP-4) inhibitors, and glucagon-like peptide-1 (GLP-1) receptor agonists in single or dual initiation therapies were indirectly compared. Outcomes focused on efficacy and included mean change from baseline in hemoglobin A1c (HbA1c), weight, systolic blood pressure (SBP), diastolic blood pressure, and achieving HbA1c target < 7% at 24-26 weeks. Fixed and random effects models with Markov chain Monte Carlo simulations were used. RESULTS: Of 1955 unique records screened, 25 RCTs (14,264 participants) were included. In patients uncontrolled on metformin, dual AHA added to metformin had statistically significant or a trend of greater reduction in HbA1c compared to single AHAs, with ertugliflozin + sitagliptin showing the greatest improvement. Statistically significant reductions in weight and SBP were observed with ertugliflozin + sitagliptin, ertugliflozin, or canagliflozin compared to single initiation DPP-4 inhibitors. CONCLUSION: For reduction of HbA1c, weight, and SBP in patients uncontrolled on metformin, add-on dual AHAs showed greater improvement compared to single AHAs. These findings can further inform the treatment of T2DM patients uncontrolled on metformin.

Three-Dimensional Simulation of Particle-Induced Mode Splitting in Large Toroidal Microresonators.

Whispering gallery mode resonators such as silica microtoroids can be used as sensitive biochemical sensors. One sensing modality is mode-splitting, where the binding of individual targets to the resonator breaks the degeneracy between clockwise and counter-clockwise resonant modes. Compared to other sensing modalities, mode-splitting is attractive because the signal shift is theoretically insensitive to the polar coordinate where the target binds. However, this theory relies on several assumptions, and previous experimental and numerical results have shown some discrepancies with analytical theory. More accurate numerical modeling techniques could help to elucidate the underlying physics, but efficient 3D electromagnetic finite-element method simulations of large microtoroid (diameter ~90 µm) and their resonance features have previously been intractable. In addition, applications of mode-splitting often involve bacteria or viruses, which are too large to be accurately described by the existing analytical dipole approximation theory. A numerical simulation approach could accurately explain mode splitting induced by these larger particles. Here, we simulate mode-splitting in a large microtoroid using a beam envelope method with periodic boundary conditions in a wedge-shaped domain. We show that particle sizing is accurate to within 11% for radii a<λ/7, where the dipole approximation is valid. Polarizability calculations need only be based on the background media and need not consider the microtoroid material. This modeling approach can be applied to other sizes and shapes of microresonators in the future.

Tunable terahertz hyperbolic metamaterial slabs and super-resolving hyperlenses.

Terahertz (THz) optics offer the potential to image through objects that are opaque for visible wavelengths and provide unique spectroscopic signatures for a variety of materials and quantum processes. However, the resolution of THz images suffers from the long wavelength of THz light compared to visible. Hyperbolic metamaterials provide a possible solution through the creation of super-resolving lenses and offer greater flexibility in effective refractive index than can be provided by natural materials. Most hyperbolic metamaterials function in a narrow bandwidth due to their resonant nature. In search of a broadband material, we simulate a temperature-tunable hyperbolic metamaterial composed of a multilayer stack of alternating layers of high-density polyethylene (HDPE) and indium antimonide (InSb). At a single temperature, negative effective medium permittivity is found over a small bandwidth of 0.09 THz, but by tuning over a 40°C temperature range the bandwidth is increased dramatically to 1.0 THz. Furthermore, we compute the transmission and negative refraction through the multilayer stack and simulate the imaging properties of curved hyperlens stacks using slits as test objects, achieving resolutions as small as 20 µm at 130 µm wavelength, far below the half-wavelength diffraction limit.

Ertugliflozin Compared to Other Anti-hyperglycemic Agents as Monotherapy and Add-on Therapy in Type 2 Diabetes: A Systematic Literature Review and Network Meta-Analysis.

INTRODUCTION: Ertugliflozin is a new sodium-glucose co-transporter-2 inhibitor (SGLT2i) for the treatment of type 2 diabetes mellitus. As there are no head-to-head trials comparing the efficacy of SGLT2is, the primary objective of this analysis was to indirectly compare ertugliflozin to other SGLT2i in patient populations with inadequately controlled glycated hemoglobin (HbA1c > 7.0%) and previously treated with either diet/exercise, metformin alone or metformin plus a dipeptidyl peptidase-4 inhibitor (DPP4i). METHODS: A systematic literature review (SLR) identified randomized controlled trials (RCTs) reporting outcomes at 24-26 weeks of treatment. Comparators to ertugliflozin were the SGLT2is canagliflozin, dapagliflozin and empagliflozin, with non-SGLT2i comparators also evaluated third-line [insulin and glucagon-like peptide-1 receptor agonists (GLP-1 RAs)]. Outcomes were change from baseline in HbA1c, weight and systolic blood pressure (SBP) as well as HbA1c < 7% and key safety events. Bayesian network meta-analysis was used to synthesize evidence. Results are presented as the median of the mean difference (MD) or as odds ratios with 95% credible intervals (CrI). RESULTS: In patients uncontrolled on diet/exercise, the efficacy of ertugliflozin 5 mg monotherapy was not significantly different from that of other low-dose SGLT2is in terms of HbA1c reduction, while ertugliflozin 15 mg was more effective than dapagliflozin 10 mg (MD - 0.36%, CrI - 0.65, - 0.08) and empagliflozin 25 mg (MD - 0.31%, CrI - 0.58, - 0.04). As add-on therapy to metformin, ertugliflozin 5 mg was more effective in lowering HbA1c than dapagliflozin 5 mg (MD - 0.22%, CrI - 0.42, - 0.02), and ertugliflozin 15 mg was more effective than dapagliflozin 10 mg (MD - 0.26%, CrI - 0.46, - 0.06) and empagliflozin 25 mg (MD - 0.23%, CrI - 0.44, - 0.03). Among patients uncontrolled on combination therapy metformin plus a DPP4i, no relevant RCTs with insulin were identified from the SLR. One study with a GLP-1 RA was included in a sensitivity analysis due to limited data. There were no differences between ertugliflozin 5 or 15 mg and other SGLT2is, with the exception of dapagliflozin 10 mg, which was significantly less effective when added to sitagliptin and metformin. Overall, there were no other significant differences for remaining efficacy and safety outcomes except for a lower SBP for canagliflozin 300 mg compared to ertugliflozin 15 mg in the diet/exercise population. CONCLUSIONS: Indirect comparisons for HbA1c reduction found that ertugliflozin 5 mg was more effective than dapagliflozin 5 mg when added to metformin monotherapy, whereas ertugliflozin 15 mg was more effective than dapagliflozin 10 mg and empagliflozin 25 mg when added to diet/exercise and to metformin monotherapy. The HbA1c reduction associated with ertugliflozin was no different than that associated with canagliflozin across all populations. FUNDING: Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, and Pfizer Inc., New York, NY, USA.

Optimized sensing of sparse and small targets using lens-free holographic microscopy.

Lens-free holographic microscopy offers sub-micron resolution over an ultra-large field-of-view >20 mm2, making it suitable for bio-sensing applications that require the detection of small targets at low concentrations. Various pixel super-resolution techniques have been shown to enhance resolution and boost signal-to-noise ratio (SNR) by combining multiple partially-redundant low-resolution frames. However, it has been unclear which technique performs best for small-target sensing. Here, we quantitatively compare SNR and resolution in experiments using no regularization, cardinal-neighbor regularization, and a novel implementation of sparsity-promoting regularization that uses analytically-calculated gradients from Bayer-pattern image sensors. We find that sparsity-promoting regularization enhances the SNR by ~8 dB compared to the other methods when imaging micron-scale beads with surface coverages up to ~4%.

Fundamental Limits of Optical Tweezer Nanoparticle Manipulation Speeds.

Optical tweezers are a noncontact method of 3D positioning applicable to the fields of micro- and nanomanipulation and assembly, among others. In these applications, the ability to manipulate particles over relatively long distances at high speed is essential in determining overall process efficiency and throughput. In order to maximize manipulation speeds, it is necessary to increase the trapping laser power, which is often accompanied by undesirable heating effects due to material absorption. As such, the majority of previous studies focus primarily on trapping large dielectric microspheres using slow movement speeds at low laser powers, over relatively short translation distances. In contrast, we push nanoparticle manipulation beyond the region in which maximum lateral movement speed is linearly proportional to laser power, and investigate the fundamental limits imposed by material absorption, thus quantifying maximum possible speeds attainable with optical tweezers. We find that gold and silver nanospheres of diameter 100 nm are limited to manipulation speeds of ∼0.15 mm/s, while polystyrene spheres of diameter 160 nm can reach speeds up to ∼0.17 mm/s, over distances ranging from 0.1 to 1 mm. When the laser power is increased beyond the values used for these maximum manipulation speeds, the nanoparticles are no longer stably trapped in 3D due to weak confinement as a result of material absorption, heating, microbubble formation, and enhanced Brownian motion. We compared this result to our theoretical model, incorporating optical forces in the Rayleigh regime, Stokes' drag, and absorption effects, and found good agreement. These results show that optical tweezers can be fast enough to compete with other common, serial rapid prototyping and nanofabrication approaches.

Computational sensing of herpes simplex virus using a cost-effective on-chip microscope.

Caused by the herpes simplex virus (HSV), herpes is a viral infection that is one of the most widespread diseases worldwide. Here we present a computational sensing technique for specific detection of HSV using both viral immuno-specificity and the physical size range of the viruses. This label-free approach involves a compact and cost-effective holographic on-chip microscope and a surface-functionalized glass substrate prepared to specifically capture the target viruses. To enhance the optical signatures of individual viruses and increase their signal-to-noise ratio, self-assembled polyethylene glycol based nanolenses are rapidly formed around each virus particle captured on the substrate using a portable interface. Holographic shadows of specifically captured viruses that are surrounded by these self-assembled nanolenses are then reconstructed, and the phase image is used for automated quantification of the size of each particle within our large field-of-view, ~30 mm2. The combination of viral immuno-specificity due to surface functionalization and the physical size measurements enabled by holographic imaging is used to sensitively detect and enumerate HSV particles using our compact and cost-effective platform. This computational sensing technique can find numerous uses in global health related applications in resource-limited environments.

Computational On-Chip Imaging of Nanoparticles and Biomolecules using Ultraviolet Light.

Significant progress in characterization of nanoparticles and biomolecules was enabled by the development of advanced imaging equipment with extreme spatial-resolution and sensitivity. To perform some of these analyses outside of well-resourced laboratories, it is necessary to create robust and cost-effective alternatives to existing high-end laboratory-bound imaging and sensing equipment. Towards this aim, we have designed a holographic on-chip microscope operating at an ultraviolet illumination wavelength (UV) of 266 nm. The increased forward scattering from nanoscale objects at this short wavelength has enabled us to detect individual sub-30 nm nanoparticles over a large field-of-view of >16 mm2 using an on-chip imaging platform, where the sample is placed at ≤0.5 mm away from the active area of an opto-electronic sensor-array, without any lenses in between. The strong absorption of this UV wavelength by biomolecules including nucleic acids and proteins has further enabled high-contrast imaging of nanoscopic aggregates of biomolecules, e.g., of enzyme Cu/Zn-superoxide dismutase, abnormal aggregation of which is linked to amyotrophic lateral sclerosis (ALS) - a fatal neurodegenerative disease. This UV-based wide-field computational imaging platform could be valuable for numerous applications in biomedical sciences and environmental monitoring, including disease diagnostics, viral load measurements as well as air- and water-quality assessment.

Lensless Imaging and Sensing.

High-resolution optical microscopy has traditionally relied on high-magnification and high-numerical aperture objective lenses. In contrast, lensless microscopy can provide high-resolution images without the use of any focusing lenses, offering the advantages of a large field of view, high resolution, cost-effectiveness, portability, and depth-resolved three-dimensional (3D) imaging. Here we review various approaches to lensless imaging, as well as its applications in biosensing, diagnostics, and cytometry. These approaches include shadow imaging, fluorescence, holography, superresolution 3D imaging, iterative phase recovery, and color imaging. These approaches share a reliance on computational techniques, which are typically necessary to reconstruct meaningful images from the raw data captured by digital image sensors. When these approaches are combined with physical innovations in sample preparation and fabrication, lensless imaging can be used to image and sense cells, viruses, nanoparticles, and biomolecules. We conclude by discussing several ways in which lensless imaging and sensing might develop in the near future.