Spinal Versus General Anesthesia for Outpatient Total Hip and Knee Arthroplasty in the Ambulatory Surgery Center: A Matched-Cohort Study.

BACKGROUND: Spinal anesthesia is the predominant regimen in outpatient total joint arthroplasty (TJA), but induction often is unsuccessful, unobtainable, or against patient preference. We compared outcomes of same-day discharge (SDD) TJA with spinal versus general anesthesia in a free-standing ambulatory surgery center (ASC). METHODS: We took 105 general anesthesia TJA and one-to-one nearest-neighbor matched them to 105 spinal anesthesia TJA over 7 years at 1 ASC. The rate of successful SDD, minutes to discharge, postoperative pain and nausea, and 90-day complications were compared. Postanesthesia care unit outcomes were additionally stratified by spinal anesthetic (mepivacaine versus bupivacaine). RESULTS: All spinal anesthetic patients underwent SDD compared with 103 (98%) general anesthetic patients (P = .498). Mepivacaine spinal anesthesia patients spent the fewest minutes in postanesthesia care unit prior to discharge from the facility (206), followed by general anesthesia (227), and bupivacaine spinal anesthesia (291; P < .001). General anesthesia patients had the highest levels of pain at 1 hour (5.2 versus 1.5 versus 1.5) and 2 hours (3.2 versus 2.0 versus 1.3) postoperatively, and rates of nausea (48 versus 22 versus 28%) compared with mepivacaine and bupivacaine spinal anesthesia, respectively. The 90-day complications (6 versus 7), admissions (1 versus 3), and reoperations (5 versus 2) were similar among spinal and general anesthesia, respectively (P ≥ .445). CONCLUSIONS: Both spinal and general anesthesia led to reliable SDD with similar 90-day complication rates. General anesthesia facilitated faster discharge from the ASC compared with bupivacaine spinal anesthesia but led to higher levels of pain and incidence of nausea postoperatively. LEVEL OF EVIDENCE: Level 3, Retrospective Cohort Comparison.

Comparison of triamcinolone and methylprednisolone efficacy and steroid flare reaction rates after shoulder corticosteroid injection: a prospective interrupted time series study.

BACKGROUND: A corticosteroid flare reaction is a well-described phenomenon that causes significant pain and dysfunction. The paucity of literature impedes decision making regarding which corticosteroid to use for shoulder injection. The purpose of this study was to compare methylprednisolone acetate (MPA) and triamcinolone acetonide (TA) injections in the glenohumeral joint and/or subacromial space in terms of efficacy and the incidence of steroid flare reactions. METHODS: In this prospective, interrupted time series, parallel study, patients received injections in the glenohumeral joint and/or subacromial space. MPA and TA were used during 2 discrete 3-month periods. The injections consisted of 2 mL of lidocaine, 2 mL of bupivacaine, and 80 mg of either MPA or TA. Visual analog scale (VAS) pain scores were recorded immediately before injection; 1-7 days after injection; and 3, 6, and 12 months after injection. The primary outcome was the incidence of a steroid flare reaction, defined as a post-injection increase in the VAS score by ≥2 points. The secondary outcome was injection failure, defined as a post-injection VAS score greater than the baseline score or the need for another intervention. We used linear mixed models with a patient-level random intercept to identify the mean VAS score change for TA injections in the first week after injection. RESULTS: MPA or TA shoulder injections were administered in 421 patients; of these patients, 15 received bilateral-joint injections whereas 406 received a single-joint injection, for a total of 436 injections (209 MPA and 227 TA injections). Pain scores in the first week after injection were available for 193 MPA and 199 TA injections. Significantly more patients in the MPA cohort reported flare reactions compared with the TA cohort (22.8% vs. 4.0%, P < .001) during the first week after injection. In the first week after injection, the mean VAS score of patients receiving TA injections was 1.05 (95% confidence interval, 0.47-1.63) lower than that of patients receiving MPA injections when adjusted for age, sex, race, pain type, surgeon type, and injection site. At 3 months, surveys for 169 MPA and 172 TA injections were completed, with no significant difference in the rate of injection failure for MPA vs. TA (42.6% vs. 36.1%, P = .224). Treatment failure rates were significantly higher for MPA than for TA at 6 months (78.44% vs. 62.5%, P < .001) but not at 12 months (81.18% vs. 81.42%, P = .531.) CONCLUSION: TA injections resulted in a >5-fold reduction in steroid flare reactions, with statistically superior 6-month efficacy rates, compared with MPA injections. This study supports TA as a more viable corticosteroid option for shoulder injection.

The Incidence of Posterolateral Tibial Plateau and Central Lateral Femoral Condylar Impaction Fractures in a Pediatric and Young Adult Population.

BACKGROUND: Posterolateral tibial plateau and central lateral femoral condylar impaction fractures are known to occur in the setting of anterior cruciate ligament (ACL) tears. There have been no prior investigations into the incidence and morphology of posterolateral tibial plateau impaction fractures in the setting of ACL injury in a pediatric population. METHODS: Patients between 9 and 22 years of age with knee magnetic resonance imagings (MRIs) performed demonstrating complete or partial ACL tear were included in this study. MRI reports were reviewed to denote the presence of posterior cruciate ligament, medial collateral ligament, or lateral collateral ligament injury, meniscus tears, cartilage lesions. MRIs were reviewed by 2 fellowship-trained orthopaedic surgeons to denote the presence of posterolateral tibial plateau and central lateral femoral condylar impaction fractures and physeal status of femoral and tibial physes. Statistical analysis performed included χ 2 analysis and the Student t testing. RESULTS: A total of 328 patients with a primary ACL tear were identified. The mean age of patients included was 16.5 years (range: 9.0-21.5). The incidence of posterolateral tibial plateau impaction fractures was 83/328 (25.3%) while the incidence of lateral femoral condylar impaction fractures was 119/328 (36.3%). Bipolar impaction fractures occurred in 37/328 (11.3%). Of the 83 tibial impaction fractures identified, 82 were low-grade morphologic subtypes. Patients with lateral tibial plateau impaction fractures were older than those with no fracture (17.2±2.2 vs. 16.3±2.1, P =0.001). Only 3/38 (7.9%) patients with an open tibial physis sustained a tibial plateau impaction fracture compared with 80/290 (27.6%) with a closed tibial physis (χ 2 value: 6.9, P =0.009). There was no difference in proportion of patients with lateral femoral condylar impaction fractures based on femoral physeal status ( P =0.484). CONCLUSION: The incidence of posterolateral tibial plateau impaction fractures in the setting of ACL tear in a pediatric and young adult patient population appears to be lower while lateral femoral condylar impaction fractures occur more frequently when comparing to previously reported incidences found in adult populations in the literature. Furthermore, posterolateral tibial plateau impaction fractures occur less frequently in those with an open proximal tibial physis and high-grade posterolateral tibial plateau bone loss is exceedingly rare in pediatric and young adult patients. Lateral femoral condylar impaction fractures are associated with lateral meniscal tears and medial meniscal ramp lesions. LEVEL OF EVIDENCE: Level IV-cross-sectional study.

Motion control for laser machining via reinforcement learning.

Laser processing techniques such as laser machining, marking, cutting, welding, polishing and sintering have become important tools in modern manufacturing. A key step in these processes is to take the intended design and convert it into coordinates or toolpaths that are useable by the motion control hardware and result in efficient processing with a sufficiently high quality of finish. Toolpath design can require considerable amounts of skilled manual labor even when assisted by proprietary software. In addition, blind execution of predetermined toolpaths is unforgiving, in the sense that there is no compensation for machining errors that may compromise the quality of the final product. In this work, a novel laser machining approach is demonstrated, utilizing reinforcement learning (RL) to control and supervise the laser machining process. This autonomous RL-controlled system can laser machine arbitrary pre-defined patterns whilst simultaneously detecting and compensating for incorrectly executed actions, in real time.

Single-frame 3D lensless microscopic imaging via deep learning.

Since the pollen of different species varies in shape and size, visualizing the 3-dimensional structure of a pollen grain can aid in its characterization. Lensless sensing is useful for reducing both optics footprint and cost, while the capability to image pollen grains in 3-dimensions using such a technique could be truly disruptive in the palynology, bioaerosol sensing, and ecology sectors. Here, we show the ability to employ deep learning to generate 3-dimensional images of pollen grains using a series of 2-dimensional images created from 2-dimensional scattering patterns. Using a microscope to obtain 3D Z-stack images of a pollen grain and a 520 nm laser to obtain scattering patterns from the pollen, a single scattering pattern per 3D image was obtained for each position of the pollen grain within the laser beam. In order to create a neural network to transform a single scattering pattern into different 2D images from the Z-stack, additional Z-axis information is required to be added to the scattering pattern. Information was therefore encoded into the scattering pattern image channels, such that the scattering pattern occupied the red channel, and a value indicating the position in the Z-axis occupied the green and blue channels. Following neural network training, 3D images were formed from collated generated 2D images. The volumes of the pollen grains were generated with a mean accuracy of ∼84%. The development of airborne-pollen sensors based on this technique could enable the collection of rich data that would be invaluable to scientists for understanding mechanisms of pollen production climate change and effects on the wider public health.

A light-guiding urinary catheter for the inhibition of Proteus mirabilis biofilm formation.

Catheter-associated urinary tract infection (CAUTI) is a leading cause of hospital-acquired infections worldwide causing debilitating illness for patients as well as a significant financial and treatment burden on health services. CAUTI is linked with the build-up of biofilms on catheter surfaces which act as a reservoir for infection. Additionally, urease-producing bacteria such as Gram-negative Proteus mirabilis (PM), can form crystalline biofilms which encrust catheter surfaces ultimately leading to blockages which require immediate removal of the catheter. Currently there are limited treatments available to prevent the formation of biofilms by PM as well as other urinary tract infection causing bacteria. A novel concept for a light-guiding urinary catheter is presented where a silicone elastomer waveguide incorporated along the length of the catheter is used to irradiate the catheter surfaces with antimicrobial blue light (405 nm) to prevent biofilm formation in situ. The prototype device is mass producible while also easy to fabricate in a lab setting for research studies. The inhibitory effect of blue light on PM biofilm formation over a range of irradiances is described for the first time showing an LD90 at 192-345 J/cm2 and total inhibition at 1,700 J/cm2 In vitro studies show that the light-guiding catheter (LGC) prototypes exhibit a 98% inhibition in PM biofilm formation inside the catheter lumen at an average estimated irradiance of 30-50 mW/cm2 (324-540 J/cm2 fluence) showing that the concept is highly effective, promising to be a powerful and economical antimicrobial approach to prevent catheter associated biofilm development and blockage.

Opioid-sparing pain management protocol after shoulder arthroplasty results in less opioid consumption and higher satisfaction: a prospective, randomized controlled trial.

BACKGROUND: The opioid epidemic has become a central focus in health care. In an effort to reduce opioid use, orthopedic surgeons use multimodal strategies to control postoperative pain. However, no clear consensus exists on ideal pain management strategies after shoulder arthroplasty, and most protocols are opioid-driven. This study sought to determine if patients undergoing shoulder arthroplasty using a postoperative opioid-sparing pain-control regimen would have equivalent pain scores and satisfaction as patients using a traditional opioid-based regimen. METHODS: Patients undergoing primary anatomic or reverse total shoulder arthroplasty were prospectively enrolled and randomized into an opioid-sparing (OS) or a traditional opioid-based (OB) postoperative pain protocol. Both groups received opioid education, periarticular injection with liposomal bupivacaine, and preoperative and postoperative multimodal management (acetaminophen, celecoxib, and gabapentin). The OB group was discharged with 40 oxycodone tablets and standard icing, whereas the OS group received ketorolac during admission, continuous cryotherapy, and discharged with 10 oxycodone tablets for rescue. Patients were queried regarding levels of pain and opioid consumption at days 1-7 and at 2, 6, and 12 weeks postoperatively. Patient satisfaction was recorded at 1, 2, 6, and 12 weeks. Range of motion (ROM), American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES), and Single Assessment Numerical Evaluation (SANE) scores were assessed preoperatively and at 12 weeks postoperatively. Complications, readmissions, and reoperations were recorded. RESULTS: In 78 patients, no difference in VAS pain scores were seen at any time between groups. The OS group consumed less oral morphine equivalents (OME) from inpatient hospitalization to 12 weeks postoperatively (P < .05). Total OME consumption was reduced by 213% for the OS vs. the OB group (112 vs. 239; P < .0001). The OS group consumed fewer opioid pills at all time points (P < .05). A 395% reduction in number of opioid pills consumed in the first 12 weeks postoperatively was seen in the OS vs. the OB group (4.3 vs. 17.0; P < .0001). Significantly more patients in the OS group discontinued opioids by 2 weeks postoperatively (86.1% vs. 58.5%; P = .011), and 94.4% in the OS group discontinued opioids by 6 weeks postoperatively. The OS group was more satisfied with pain management at 1 and 6 weeks (P = .05). No difference in ROM, ASES or SANE scores, complications, readmissions, or reoperations were seen between groups. CONCLUSIONS: This study demonstrated a nearly 4-fold reduction in opioid pain pill consumption and earlier cessation of opioids with an OS pain management protocol. Patients also reported higher satisfaction with this pain management strategy.

Optimization of flow path parameters for enhanced sensitivity lateral flow devices.

Lateral flow devices (LFDs) or lateral flow tests (LFTs) are one of the most widely used biosensor platforms for point-of-care (POC) diagnostics. The basic LFD design has remained largely unchanged since its first appearance, and this has limited LFD use in clinical applications due to a general lack of analytical sensitivity. We report here a comprehensive study of the use of laser-patterned geometric control barriers that influence the flow dynamics within an LFD, with the specific aim of enhancing LFD sensitivity and lowering the limit of detection (LOD). This control of sample flow produces an increase in the time available for optimizing the binding kinetics of the implemented assay. The geometric modification to the flow path is in the form of a constriction that is produced by depositing a photo-sensitive polymer onto the nitrocellulose membrane which when polymerized, creates impermeable barrier walls through the depth of the membrane. Both the position of the constriction within the flow path and the number of constrictions allow for an increase in the sensitivity because of a slower overall flow rate within the test and a larger volume of sample per unit width of the test line. For these high sensitivity LFDs (HS-LFD), through optimization of the constriction position and addition of a second constriction we attained a 62% increase in test line color intensity for the detection of procalcitonin (PCT) and were also able to lower the LOD from 10 ng/mL to 1 ng/mL. In addition, of relevance for future commercial exploitation, this also significantly decreases the antibody consumption per device leading to reduced costs for test production. We have further tested our HS-LFD with contrived human samples, validating its application for future clinical use.

Deep-Learning-Assisted Focused Ion Beam Nanofabrication.

Focused ion beam (FIB) milling is an important rapid prototyping tool for micro- and nanofabrication and device and materials characterization. It allows for the manufacturing of arbitrary structures in a wide variety of materials, but establishing the process parameters for a given task is a multidimensional optimization challenge, usually addressed through time-consuming, iterative trial-and-error. Here, we show that deep learning from prior experience of manufacturing can predict the postfabrication appearance of structures manufactured by focused ion beam (FIB) milling with >96% accuracy over a range of ion beam parameters, taking account of instrument- and target-specific artifacts. With predictions taking only a few milliseconds, the methodology may be deployed in near real time to expedite optimization and improve reproducibility in FIB processing.

Single step phase optimisation for coherent beam combination using deep learning.

Coherent beam combination of multiple fibres can be used to overcome limitations such as the power handling capability of single fibre configurations. In such a scheme, the focal intensity profile is critically dependent upon the relative phase of each fibre and so precise control over the phase of each fibre channel is essential. Determining the required phase compensations from the focal intensity profile alone (as measured via a camera) is extremely challenging with a large number of fibres as the phase information is obfuscated. Whilst iterative methods exist for phase retrieval, in practice, due to phase noise within a fibre laser amplification system, a single step process with computational time on the scale of milliseconds is needed. Here, we show how a neural network can be used to identify the phases of each fibre from the focal intensity profile, in a single step of ~ 10 ms, for a simulated 3-ring hexagonal close-packed arrangement, containing 19 separate fibres and subsequently how this enables bespoke beam shaping. In addition, we show that deep learning can be used to determine whether a desired intensity profile is physically possible within the simulation. This, coupled with the demonstrated resilience against simulated experimental noise, indicates a strong potential for the application of deep learning for coherent beam combination.

A SARS-CoV-2 nucleocapsid ELISA represents a low-cost alternative to lateral flow testing for community screening in LMI countries.

Background Controlling the spread of SARS-CoV-2 is problematic because of transmission driven by asymptomatic and pre-symptomatic individuals. Community screening can help identify these individuals but is often too expensive for countries with limited health care resources. Low-cost ELISA assays may address this problem, but their use has not yet been widely reported. Methods We developed a SARS-CoV-2 nucleocapsid ELISA and assessed its diagnostic performance on nose and throat swab samples from UK hospitalised patients and sputum samples from patients in Ghana. Results The ELISA had a limit of detection of 8.4 pg/ml antigen and 16 pfu/ml virus. When tested on UK samples (128 positive and 10 negative patients), sensitivity was 58.6% (49.6-67.2) rising to 78.3% (66.7-87.3) if real-time PCR Ct values > 30 were excluded, while specificity was 100% (69.2-100). In a second trial using the Ghanaian samples (121 positive, 96 negative), sensitivity was 52% (42.8-61.2) rising to 72.6% (61.8-81.2) when a > 30 Ct cut-off was applied, while specificity was 100% (96.2-100). Conclusions: Our data show that nucleocapsid ELISAs can test a variety of patient sample types while achieving levels of sensitivity and specificity required for effective community screening. Further investigations into the opportunities that this provides are warranted.

Semi-quantitative detection of inflammatory biomarkers using a laser-patterned multiplexed lateral flow device.

Inflammatory markers including C-reactive protein (CRP) and procalcitonin (PCT) have been shown to be useful biomarkers to improve triage speed and prevent the inappropriate use of antibiotics for infections such as pneumonia. Here, we present a novel and exciting solution to guide the administration of antibiotic treatment via rapid, semi-quantitative and multiplexed detection of CRP and PCT using an advanced lateral flow device (LFD) designed to have multiple parallel flow-paths, produced via the precise laser-based partitioning of the single flow-path of a standard LFD. Each flow-path within this multiplexed LFD has a unique detection capability which permits tailored detection of CRP within a predefined cut-off range (20 µg/mL - 100 µg/mL) and PCT above a pre-defined threshold (0.5 ng/mL). We demonstrate the use of this LFD in the successful detection of CRP and PCT semi-quantitatively within spiked human serum samples. This multiplexed near-patient assay has potential for development into a rapid triage and treatment of patients with suspected pneumonia.

Reliability Testing of the Mayo Elbow Performance Score in Post-operative Patients.

This study aimed to determine intra-observer and inter-observer reliability of the Mayo Elbow Performance Score (MEPS). Patients undergoing elbow surgery completed a MEPS questionnaire initially and another 2-3 weeks later. During the second interview, patients completed the Oxford Elbow Score (OES) for comparison. Intraclass correlation coefficients (ICC) and Pearson correlation coefficients (PCC) > 0.80 indicated substantial agreement. In 42 patients who had elbow surgery, the average MEPS score initially was 78 (range, 5-100, SD 22.4) and 77 (range, 5-100, SD 21.5) at second interview. The average normalized OES score was 79 (range, 17-100, SD 23.6). The ICC for MEPS scores at the two time points was 0.90, and the PCC between the MEPS and OES scores was 0.87, indicating substantial agreement. The MEPS has strong intra-observer reliability at different time points and strong inter-observer reliability when compared with the OES, validating the MEPS as an outcome measure of elbow surgery. (Journal of Surgical Orthopaedic Advances 31(4):229-232, 2022).

Capillary-based reverse transcriptase loop-mediated isothermal amplification for cost-effective and rapid point-of-care COVID-19 testing.

As the SARS-CoV-2 pandemic continues to spread, the necessity for rapid, easy diagnostic capabilities could never have been more crucial. With this aim in mind, we have developed a cost-effective and time-saving testing methodology/strategy that implements a sensitive reverse transcriptase loop-mediated amplification (RT-LAMP) assay within narrow, commercially available and cheap, glass capillaries for detection of the SARS-CoV-2 viral RNA. The methodology is compatible with widely used laboratory-based molecular testing protocols and currently available infrastructure. It employs a simple rapid extraction protocol that lyses the virus, releasing sufficient genetic material for amplification. This extracted viral RNA is then amplified using a SARS-CoV-2 RT-LAMP kit, at a constant temperature and the resulting amplified product produces a colour change which can be visually interpreted. This testing protocol, in conjunction with the RT-LAMP assay, has a sensitivity of ∼100 viral copies per reaction of a sample and provides results in a little over 30 min. As the assay is carried out in a water bath, commonly available within most testing laboratories, it eliminates the need for specialised instruments and associated skills. In addition, our testing pathway requires a significantly reduced quantity of reagents per test while providing comparable sensitivity and specificity to the RT-LAMP kit used in this study. While the conventional technique requires 25 µl of reagent, our test only utilises less than half the quantity (10 µl). Thus, with its minimalistic approach, this capillary-based assay could be a promising alternative to the conventional testing, owing to the fact that it can be performed in resource-limited settings, using readily available apparatus, and has the potential of increasing the overall testing capacity, while also reducing the burden on supply chains for mass testing.

The future of bone regeneration: integrating AI into tissue engineering.

Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.

Modeling adult skeletal stem cell response to laser-machined topographies through deep learning.

The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.

Laser-patterned paper-based sensors for rapid point-of-care detection and antibiotic-resistance testing of bacterial infections.

Antimicrobial resistance (AMR) has been identified by the World Health Organisation as a global threat that currently claims at least 25,000 deaths each year in Europe and 700,000 globally; the number is projected to reach 10 million per year between 2015 and 2050. Therefore, there is an urgent need for low-cost but reliable point-of-care diagnostics for early screening of infections especially in developing countries lacking in basic infrastructure and trained personnel. This work is aimed at developing such a device, a paper-based microfluidic device for infection testing by an unskilled user in a low resource setting. Here, we present our work relating to the use of our laser-patterned paper-based devices for detection and susceptibility testing of Escherichia coli, via a simple visually observable colour change. The results indicate the suitability of our integrated paper devices for timely identification of bacterial infections at the point-of-care and their usefulness in providing a hugely beneficial pathway for accurate antibiotic prescribing and thus a novel route to tackling the global challenge of AMR.

Real-time particle pollution sensing using machine learning.

Particle pollution is a global health challenge that is linked to around three million premature deaths per year. There is therefore great interest in the development of sensors capable of precisely quantifying both the number and type of particles. Here, we demonstrate an approach that leverages machine learning in order to identify particulates directly from their scattering patterns. We show the capability for producing a 2D sample map of spherical particles present on a coverslip, and also demonstrate real-time identification of a range of particles including those from diesel combustion.

Rapid Multiplexed Detection on Lateral-Flow Devices Using a Laser Direct-Write Technique.

Paper-based lateral flow devices (LFDs) are regarded as ideal low-cost diagnostic solutions for point-of-care (POC) scenarios that allow rapid detection of a single analyte within a fluidic sample, and have been in common use for a decade. In recent years, there has been an increasing need for rapid and simultaneous detection of multiple analytes present within a single sample and to facilitate this, we report here a novel solution-detection using a multi-path LFD created via the precise partitioning of the single flow-path of a standard LFD using our previously reported laser direct-write (LDW) technique. The multiple flow-paths allow the simultaneous detection of the different analytes individually within each of the parallel channels without any cross-reactivity. The appearance of coloured test lines in individual channels indicates the presence of the different analytes within a sample. We successfully present the use of a LDW-patterned multi-path LFD for multiplexed detection of a biomarker panel comprising C-reactive protein (CRP) and Serum amyloid A-1 (SAA1), used for the diagnosis of bacterial infections. Overall, we demonstrate the use of our LDW technique in the creation of a novel LFD that enables multiplexed detection of two inflammation markers within a single LFD providing a detection protocol that is comparatively more efficient than the standard sequential multiplexing procedure.

Predictive capabilities for laser machining via a neural network.

The interaction between light and matter during laser machining is particularly challenging to model via analytical approaches. Here, we show the application of a statistical approach that constructs a model of the machining process directly from experimental images of the laser machined sample, and hence negating the need for understanding the underlying physical processes. Specifically, we use a neural network to transform a laser spatial intensity profile into an equivalent scanning electron microscope image of the laser-machined target. This approach enables the simulated visualization of the result of laser machining with any laser spatial intensity profile, and hence demonstrates predictive capabilities for laser machining. The trained neural network was found to have encoded functionality that was consistent with the laws of diffraction, hence showing the potential of this approach for discovering physical laws directly from experimental data.