Net Improvement Research Articles

Hardware Removal After Lisfranc ORIF Results in Improved Physical Function.

The purpose of this study was to investigate whether HWR after Lisfranc ORIF resulted in significant impact via PROMIS physical function (PF) and pain intensity (PI) scores. Design: Retrospective cohort. Level-1 trauma center. Adult patients with isolated Lisfranc injuries who were treated via ORIF between 2002-2023 that had PROMIS PF and PI scores through 6 months follow-up were included. Patients were excluded if they received index treatment other than ORIF or underwent secondary surgical intervention prior to HWR. A sub analysis was performed at 1 year follow- up. Primary outcomes were PROMIS PF and PI scores. The Wilcoxon signed-rank test compared differences between PROMIS scores within the HWR group. The Wilcoxon ranked-sum test compared differences between HWR vs no HWR. Distributive MCID was calculated using the 0.5 SD method. There were 482 patients (489 feet) identified with isolated Lisfranc injuries. Seventy-seven feet underwent ORIF followed by HWR. Thirty feet underwent ORIF without HWR. The average age of the no HWR group was 45.8 (18.0-81.3) versus the HWR group which was 38.7 (18.3-74.1) (p=0.053). Nineteen (63.3%) were female in the no HWR group compared to 33 (42.9%) in the HWR group (p=0.084). HWR occurred an average of 4.43 months after ORIF. Patients who underwent HWR had a statistically significant increase in average PF scores (39.7 to 45.9, p<0.001) at their standard 6 week (1.5 month) postoperative visit. HWR patients had a non-significant decrease in average PI scores (56.5 to 53.9, p=0.24). Compared to those with retained hardware, the HWR group demonstrated a statistically significant net improvement in PF and PI scores from surgery, with an average improvement of 5.6 and 1.7, respectively (P=0.002, 0.008). Patients experienced significant improvement in PROMIS PF scores for Lisfranc ORIF at 6 weeks after HWR. Compared to patients with retained hardware, they also experienced significant improvement in PROMIS PF and PI scores. III.

Exoskeletal-Assisted Walking in Veterans With Paralysis: A Randomized Clinical Trial.

Robotic exoskeletons leverage technology that assists people with spinal cord injury (SCI) to walk. The efficacy of home and community exoskeletal use has not been studied in a randomized clinical trial (RCT). To examine whether use of a wheelchair plus an exoskeleton compared with use of only a wheelchair led to clinically meaningful net improvements in patient-reported outcomes for mental and physical health. This RCT of veterans with SCI was conducted at 15 Veterans Affairs medical centers in the US from September 6, 2016, to September 27, 2021. Data analysis was performed from March 10, 2022, to June 20, 2024. Participants were randomized (1:1) to standard of care (SOC) wheelchair use or SOC plus at-will use of a US Food and Drug Administration (FDA)-cleared exoskeletal-assisted walking (EAW) device for 4 months in the home and community. Two primary outcomes were studied: 4.0-point or greater improvement in the mental component summary score on the Veterans RAND 36-Item Health Survey (MCS/VR-36) and 10% improvement in the total T score of the Spinal Cord Injury-Quality of Life (SCI-QOL) physical and medical health domain and reported as the proportion who achieved clinically meaningful changes. The primary outcomes were measured at baseline, post randomization after advanced EAW training sessions, and at 2 months and 4 months (primary end point) in the intervention period. Device usage, reasons for not using, and adverse events were collected. A total of 161 veterans with SCI were randomized to the EAW (n = 78) or SOC (n = 83) group; 151 (94%) were male, the median age was 47 (IQR, 35-56) years, and median time since SCI was 7.3 (IQR, 0.5 to 46.5) years. The difference in proportion of successes between the EAW and SOC groups on the MCS/VR-36 (12 of 78 [15.4%] vs 14 of 83 [16.9%]; relative risk, 0.91; 95% CI, 0.45-1.85) and SCI-QOL physical and medical health domain (10 of 78 [12.8%] vs 11 of 83 [13.3%]; relative risk, 0.97; 95% CI, 0.44-2.15) was not statistically different. Device use was lower than expected (mean [SD] distance, 1.53 [0.02] miles per month), primarily due to the FDA-mandated companion being unavailable 43.9% of the time (177 of 403 instances). Two EAW-related foot fractures and 9 unrelated fractures (mostly during wheelchair transfers) were reported. In this RCT of veterans with SCI, the lack of improved outcomes with EAW device use may have been related to the relatively low device usage. Solutions for companion requirements and user-friendly technological adaptations should be considered for improved personal use of these devices. ClinicalTrials.gov Identifier: NCT02658656.

Effect of the data-informed platform for health intervention on the culture of data use for decision-making among district health office staff in North Shewa Zone, Ethiopia: a cluster-randomised controlled trial

BackgroundSimilar to other low and middle-income countries, Ethiopia faces limitations in using local health data for decision-making.We aimed to assess the effect of an intervention, namely the data-informed platform for health, on the culture of data-based decision making as perceived by district health office staff in Ethiopia’s North Shewa Zone.MethodsBy designating district health offices as ‘clusters’, a cluster-randomised controlled trial was implemented. Out of a total of 24 districts in the zone, 12 districts were allocated to intervention arm and the other 12 in the control group arms. In the intervention arm district health office teams were supported in four-monthly cycles of data-driven decision-making over 20 months. This support included: (a) defining problems using a health system framework; (b) reviewing data; (c) considering possible solutions; (d) value-based prioritizing; and (e) a consultative process to develop, commit to, and follow up on action plans. To measure the culture of data use for decision-making in both intervention and control arms, we interviewed 120 health management staff (5 per district office). Using a Likert scale based standard Performance of Routine Information System Management tool, the information is categorized into six domains:- evidence-based decision making, emphasis on data quality, use of information, problem solving, responsibility and motivation. After converting the Likert scale responses into percentiles, difference-in-difference methods were applied to estimate the net effect of the intervention. In intervention districts, analysis of variance was used to summarize variation by staff designation.ResultsThe overall decision-making culture in health management staff showed a net improvement of 13% points (95% C.I:9, 18) in intervention districts. The net effect of each of the six domains in turn was an 11% point increase (95% C.I:7, 15) on culture of evidence based decision making, a 16% point increase (95% C.I:8, 24) on emphasis on data quality, a 20% point increase (95% C.I:12, 28) on use of information, a 21% point increase (95% C.I:13, 29) on problem solving, and a 10% point increase (95% C.I:4, 16) on responsibility and motivation. In terms of variation by staff designation within intervention districts, statistically significant differences were observed only for problem solving and responsibility.ConclusionThe data-informed platform for health strategy resulted in a measurable improvement in data use and structured decision-making culture by using existing systems, namely the Performance Monitoring Team meetings. The intervention supported district health offices in identifying and solving problems through a structured process. After further research, DIPH intervention could also be applied to other health administration and facility levels.Trial registrationClinicalTrials.gov ID: NCT05310682, Dated 25/03/ 2022.

Plasma Metabolomics Identifies Key Metabolites and Improves Prediction of Diabetic Retinopathy: Development and Validation across Multinational Cohorts

PurposeTo identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and evaluate their utility in predicting DR development and progression. DesignMulticenter, multi-ethnic cohort study. ParticipantsThis study included 17,675 participants with baseline pre-diabetes/diabetes, in accordance with the 2021 American Diabetes Association guideline, and free of baseline DR from the UK Biobank (UKB); and an additional 638 diabetic participants from the Guangzhou Diabetic Eye Study (GDES) for external validation. MethodsLongitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort. Model assessments included the C-statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical utility in both cohorts. Main Outcome MeasuresDR development, progression, and retinal microvascular damage. ResultsOf 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C-statistic: 0.802, 95% CI, 0.760–0.843 vs. 0.751, 95% CI, 0.706–0.796; P = 5.56×10−4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C-statistic: 0.807, 95% CI, 0.711–0.903 vs. 0.617, 95% CI, 0.494, 0.740; P = 1.68×10−4) and progression (C-statistic: 0.797, 95% CI, 0.712–0.882 vs. 0.665, 95% CI, 0.545–0.784; P = 0.003) in the external cohort. Improvements in NRIs, IDIs, and clinical utility were also evident in both cohorts (all P <0.05). In addition, lactate and citrate were associated to microvascular damage across macular and optic disc regions (all P <0.05). ConclusionsMetabolomic profiling has proven effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology.

Gene Editing Cattle for Enhancing Heat Tolerance: A Welfare Review of the “PRLR-SLICK Cattle” Case

In March 2022 the US Food and Drug Administration (FDA) published a risk assessment of a recent animal gene editing proposal submitted by Acceligen™. The proposal concerned the possibility of changing the cattle genome to obtain a slicker, shorter hair coat. Using CRISPR-Cas9 it was possible to introduce an intentional genomic alteration (IGA) to the prolactin receptor gene (PRLR), thereby producing PRLR-SLICK cattle. The goal was to diminish heat stress in the cattle by enhancing their heat-tolerance. With regard to unintended alterations (i.e., off-target effects), the FDA stated that the IGA posed a low, but still present, risk to animal safety. The aim of this article is to present some initial insights into the welfare issues raised by PRLR-SLICK cattle by addressing the question: Do SLICK cattle have better welfare than non-SLICK cattle when exposed to heat stress? Two potential welfare concerns are examined. The first is pleiotropy, an issue that arises when one gene affects multiple traits. Given the pleiotropic nature of prolactin, it has been suggested that the IGA for SLICK cattle may also affect their hepatic and other functions. The second concern relates not primarily to direct effects on cattle health, but rather to the indirect risk that this more heat-tolerant animal would just be used in the livestock sector under farming conditions that are such that the net welfare improvement would be non-existent.

Digital voice-based biomarker for monitoring respiratory quality of life: findings from the colive voice study

Regular monitoring of respiratory quality of life (RQoL) is essential in respiratory healthcare, facilitating prompt diagnosis and tailored treatment for chronic respiratory diseases. Voice alterations resulting from respiratory conditions create unique audio signatures that can potentially be utilized for disease screening or monitoring. Analyzing data from 1908 participants from the Colive Voice study, which collects standardized voice recordings alongside comprehensive demographic, epidemiological, and patient-reported outcome data, we evaluated various strategies to estimate RQoL from voice, including handcrafted acoustic features, standard acoustic feature sets, and advanced deep audio embeddings derived from pretrained convolutional neural networks. We compared models using clinical features alone, voice features alone, and a combination of both. The multimodal model combining clinical and voice features demonstrated the best performance, achieving an accuracy of 70.8% and an area under the receiver operating characteristic curve (AUROC) of 0.77; an improvement of over 5% in terms of accuracy and 7% in terms of AUROC compared to model utilizing voice features alone. Incorporating vocal biomarkers significantly enhanced the predictive capacity of clinical variables across all acoustic feature types, with a net classification improvement (NRI) of up to 0.19. Our digital voice-based biomarker is capable of accurately predicting RQoL, either as an alternative to or in conjunction with clinical measures, and could be used to facilitate rapid screening and remote monitoring of respiratory health status.

Simultaneous dose and dose rate optimization via dose modifying factor modeling for FLASH effective dose.

Although the FLASH radiotherapy (FLASH) can improve the sparing of organs-at-risk (OAR) via the FLASH effect, it is generally a tradeoff between the physical dose coverage and the biological FLASH coverage, for which the concept of FLASH effective dose (FED) is needed to quantify the net improvement of FLASH, compared to the conventional radiotherapy (CONV). This work will develop the first-of-its-kind treatment planning method called simultaneous dose and dose rate optimization via dose modifying factor modeling (SDDRO-DMF) for proton FLASH that directly optimizes FED. SDDRO-DMF models and optimizes FED using FLASH dose modifying factor (DMF) models, which can be classified into two categories: (1) the phenomenological model of the FLASH effect, such as the FLASH effectiveness model (FEM); (2) the mechanistic model of the FLASH radiobiology, such as the radiolytic oxygen depletion (ROD) model. The general framework of SDDRO-DMF will be developed, with specific DMF models using FEM and ROD, as a demonstration of general applicability of SDDRO-DMF for proton FLASH via transmission beams (TB) or Bragg peaks (BP) with single-field or multi-field irradiation. The FLASH dose rate is modeled as pencil beam scanning dose rate. The solution algorithm for solving the inverse optimization problem of SDDRO-DMF is based on iterative convex relaxation method. SDDRO-DMF is validated in comparison with IMPT and a state-of-the-art method called SDDRO, with demonstrated efficacy and improvement for reducing the high dose and the high-dose volume for OAR in terms of FED. For example, in a SBRT lung case of the dose-limiting factor that the max dose of brachial plexus should be no more than 26Gy, only SDDRO-DMF met this max dose constraint; moreover, SDDRO-DMF completely eliminated the high-dose (V70%) volume to zero for CTV10mm (a high-dose region as a 10mm ring expansion of CTV). We have proposed a new proton FLASH optimization method called SDDRO-DMF that directly optimizes FED using phenomenological or mechanistic models of DMF, and have demonstrated the efficacy of SDDO-DMF in reducing the high-dose volume or/and the high-dose value for OAR, compared to IMPT and a state-of-the-art method SDDRO.

An intelligent model for classifying supraventricular tachycardia mechanisms based on 12-lead wearable electrocardiogram devices

To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia (AVNRT) and atrioventricular re-entrant tachycardia (AVRT) using 12-lead wearable electrocardiogram devices. A total of 356 samples of 12-lead supraventricular tachycardia (SVT) electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model, and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October, 2021 to March, 2023 were selected as the testing set. The changes in electrocardiogram parameters before and during induced tachycardia were compared. Based on multiscale deep neural network, an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated. The 3-lead electrocardiogram signals from Ⅱ, Ⅲ, and Ⅴ1 were extracted to build new classification models, whose diagnostic efficacy was compared with that of the 12-lead model. Of the 101 patients with SVT in the testing set, 68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study. The pre-trained model achieved a high area under the precision-recall curve (0.9492) and F1 score (0.8195) for identifying AVNRT in the validation set. The total F1 scores of the lead Ⅱ, Ⅲ, Ⅴ1, 3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597, 0.6061, 0.3419, 0.6003 and 0.6136, respectively. Compared with the 12-lead classification model, the lead-Ⅲ model had a net reclassification index improvement of -0.029 (P=0.878) and an integrated discrimination index improvement of -0.005 (P=0.965). The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Merging Photorefractive Keratectomy and Collagen Crosslinking: An Analysis of Literature and a Guide to Prevalent Protocols.

The purpose of this review was to summarize the different surgical approaches combining photorefractive keratectomy (PRK) and corneal crosslinking (CXL), present each protocol template in a simple format, and provide an overview of the primary outcomes and adverse events. A literature review was conducted as outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Eight different databases were searched. Papers were included if PRK was immediately followed by CXL. Thirty-seven papers met the inclusion criteria of a total yield of 823. The latest research into simultaneous PRK and CXL has been shown to not only stabilize the cornea and prevent keratoconus progression but also improve the visual acuity of the patient. Improvements in uncorrected distance visual acuity and (spectacle) corrected distance visual acuity were found to be significant when considering all protocols. There were also significant reductions in K1, K2, mean K, Kmax, sphere, cylinder, and spherical equivalent. Random-effects analysis confirmed these trends. Corrected distance visual acuity was found to improve by an average of 0.18 ± 1.49 logMAR (Cohen's D [CD] 0.12; P <0.02). There was also a significant reduction of 2.57 ± 0.45 D (CD 5.74; P <0.001) in Kmax. Cylinder and spherical equivalent were also reduced by 1.36 ± 0.26 D (CD 5.25; P <0.001) and 2.61 ± 0.38 D (CD 6.73; P <0.001), respectively. Combining the 2 procedures appears to be of net benefit, showing stabilization and improvement of ectatic disease, while also providing modest gains in visual acuity. Since customized PRK and CXL approaches appear superior, a combination of these would likely be best for patients.

Identifying the deficiencies of currently available CGM to improve uptake and benefit.

The use of diabetes technologies is increasing worldwide, with health systems facilitating improved access to devices. Continuous glucose monitoring is a complex intervention that provides information on glucose concentration, rate and direction of change, historical data and alerts and alarms for extremes of glucose. These data do not themselves change glycaemia and require translation to a meaningful action for impact. It is, therefore, crucial that such systems advance to better meet the needs of individuals using them. Narrative review of the use of, engagement with, limitations and unmet needs of continuous glucose monitoring systems. CGM devices have made a significant contribution to the self-management of diabetes; however, challenges with access and user experience persist, with multiple limitations to uptake and benefit. These limitations include physical size and implementation, with associated stigma, alarm fatigue, sleep disturbance and the challenge of addressing large volumes of real-time data. Greater personalisation throughout the continuous glucose monitoring journey, with a focus on usability, may improve the benefits derived from the device and reduce the burden of self-management. Healthcare professionals may have unconscious biases that affect the provision of continuous glucose monitors due to deprivation, education, age, ethnicity and other characteristics. Continuous glucose monitoring exerts a dose-dependent response; the more it is used, the more effective it is. For optimal use, continuous glucose monitors must not just reduce the burden of management in one dimension but facilitate net improvement in all domains of self-management for all users.

Effects of distortion on a BLI fan

Abstract The BLI (boundary layer ingestion) concept for propulsion seeks to improve the energy efficiency of aircraft propulsion. This is achieved by accelerating low momentum flow ingested from boundary layers and wakes developed over the fuselage through the fan. A major challenge that needs to be overcome to realise the benefits is that the fan needs to work efficiently in distorted flow. Understanding the effects of distortion on the aerodynamic performance and the distortion transfer through the fan is therefore essential to future designs. A BLI fan, designed at reduced scale, is used for analytic modelling and experiments in a rig designed for this purpose. The test rig replicates BLI conditions for a fan installed at the aircraft tail cone. An unsteady model that includes all blades and vanes of the fan, as well as the nacelle and the by-pass duct of the test rig is used for CFD (computational fluid dynamics) simulations. Test results are used to confirm that the CFD model is representative of the aerodynamics of the fan. The tests are conducted using varying fan operating conditions but also tests with an added distortion screen. Analysis results are then used to investigate the effects of distortion on the fan efficiency, as well as on the overall efficiency. The fan efficiency is found to be moderately decreased depending on the level of and extent of inlet circumferential distortion. In terms of overall energy efficiency, a net improvement over a similar fan in clean inlet flow is found.

Defect evolution and mitigation in metal extrusion additive manufacturing: From deposition to sintering

Material extrusion additive manufacturing of metals has found widespread application and is typically thought of as a facile technique to create metal components. This can be undertaken without the need for energy sources typically used in direct energy deposition and powder bed fusion processes. Despite recent advances in achieving net shape and improvements to material/component integrity, these processes are characterized by performance limiting defects which often emerge as a result of deposition strategy and are retained post sintering. Although the debinding and sintering steps have been investigated for metal injection molding technology, quantitative studies of defect evolution are lacking. This work proposes an approach to study the pore evolution behavior from deposition to sintering through X-ray Computed Tomography (XCT) image processing. Quantitative results of pore shrinking, partial and full closure through backtracking of individual pores from the sintered to the green state are presented. It is shown that where pores are larger than 4 × 10-6 mm3, equals to the D90 of the feedstock metal particles, these cannot be fully closed upon sintering and will survive to limit the performance of the final part. The average pore shrinkage is by a factor of 5.6 and partial closure happens in pores with minimum Feret diameter of 33 μm, equals to 160% of the D90 powder particle, or less. The utilization of overfill as a proposed technique in the literature for the elimination of macro pores is investigated, yielding disparate and unsatisfactory results. This discrepancy can be attributed to the insufficient formation of micro-channels within the pore structure during the deposition process of the aqueous feedstock, leading to regions of weak grain bonding. To evaluate the contribution of these to undermining material integrity, tensile tests of linear and circular deposition strategies are undertaken. Experiments show that track interface length plays a major role in determining elongation to failure and tensile strength. It is concluded that the limitation to the mechanical properties of metal paste extrusion components originates from the emergence of defects and their characteristics after the completion of the processing, as opposed to being influenced by the metallurgy prior or post sintering.

Combined effects of fungicides formulations and potato varieties on late blight management, yield and net farm income in Kenya

Management of late blight relies on the use of fungicides and potato varieties which have been associated with loss of efficacy over time. Baseline survey and field studies were conducted to evaluate the effects of fungicide formulations and potato varieties in managing late blight and subsequently on net farm income improvement. Four fungicides formulations (Infinito® (Fluopicolide 62.5g L−1 + propamocarb 625 g L−1), Milraz® (Propineb 700 g kg−1 + cymoxanil 60 g kg−1), Mistress 72® (Mancozeb 640 g kg−1 + cymoxanil 80 g kg−1) and Ridomil® (Metalaxyl 40 g kg−1 + mancozeb 640 g kg−1)), and varieties (Kenya Mpya, Shangi and Dutch Robijn) were evaluated. Survey results revealed that the majority (78%) of the farmers selected varieties to grow based on market dynamics as compared to seed availability (18%), disease resistance (12%) and other factors (8%). The most widely and least used fungicides were Ridomil® (28.8%) and Infinito® (1.7%) respectively. Late blight significantly reduced crop growth (height and stem count) and yield in the unprotected plots in Shangi and Dutch Robijn, but no effect was observed in K. Mpya. Mistress® suppressed blight symptoms by 54%, while Milraz® (lowest effects) reduced the disease by (43%). In unprotected plots, disease severity on K. Mpya was below 1%, while in Shangi and Dutch Robijn varieties, it was 60 and 78% respectively. Reduction in disease by Mistress® resulted in higher yield (30.70 t ha−1) as compared to the unprotected plots (9.25 t ha−1). Maximum yield (28.56 t ha−1) was obtained from K. Mpya, while Dutch Robijn, had the lowest observed yield (20.70 t ha−1) in protected plots. Yet, the highest cost benefit ratio (CBR) was recorded in unprotected plots with K. Mpya. The smallest CBR (1.55) was observed on plots where Milraz® was applied on Dutch Robjn. Results of this study show that, the use of fungicides reduced late blight and improved yield of potato substantially, but the efficacy varied with fungicide formulation and potato variety. However, the use of resistant variety was effective in managing late blight hence a useful tool for minimizing fungicide applications. Therefore, use of resistant variety could enhance a healthy biodiversity and at the same time maximize food production.

Improving the efficiency of solar photovoltaics by means of geothermal cooling: A year-long test campaign

Heating up of photovoltaic modules during operation results in a significant power output reduction. This effect is especially relevant in regions with the highest photovoltaic potential, where irradiance and ambient temperature are particularly high. In this paper, a novel low enthalpy geothermal cooling system that improves the efficiency of a commercial solar module is described and experimentally validated. Two heat exchangers, one attached to the backside of the module, and another introduced inside a 15 m deep borehole, are connected in a closed circuit. A water-based coolant is pumped through the system, evacuating the excess heat from the module, and dissipating it underground in a clean and efficient manner, without additional water consumption after filling the system.The performance of the cooling system was experimentally tested by implementing it, for the first time, in a single-axis solar tracking isolated photovoltaic facility. Extensive tests were carried out in Spain, between September 2021 and August 2022 under a wide variety of environmental conditions. The efficiency improvement system has demonstrated its technical feasibility over a one-year period, showing a peak net efficiency improvement up to 13.4 %. Moreover, a positive net efficiency improvement has been demonstrated all year long, even during the winter months.

A New Approach to Structural Damage Identification Based on Power Spectral Density and Convolutional Neural Network

In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is [Formula: see text] in the case of no noise addition; the optimal training MSE is [Formula: see text] in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage.

1073 Prone Transpsoas (PTP)-LLIF Approach Versus Standard LLIF in Patients Undergoing Surgical Revision Following Adult Spinal Deformity Correction Surgery

INTRODUCTION: Both the prone transpsoas (PTP) and standard LLIF have been developed to facilitate minimally invasive (MIS) indirect decompression, alignment correction, and posterior instrumentation. METHODS: A multi-center cohort of all patients was identified and all relevant data were retrospectively collected. Preoperative standing 36” scoliosis radiographs were retrospectively reviewed for pelvic incidence (PI), lumbar lordosis (LL), and PI minus LL (PI-LL mismatch). Postoperative regional sagittal alignment parameters were measured using the first postoperative standing 36” scoliosis radiographs. Finally, multivariable analysis was performed to assess the primary outcome measure: postoperative change in regional sagittal alignment (PI-LL mismatch). RESULTS: Two-hundred and two (n = 202) patients were retrospectively identified and included for formal analysis. One-hundred and fifty one (n = 151, 74.8%) of the patients underwent PTP revision while 51 (25.2%) underwent standard LLIF revision. Univariate analysis demonstrated a greater value for baseline mean pre-operative PI-LL mismatch in the PTP versus standard LLIF cohort (21.4 vs. 10.45°; p = 0.0001). Furthermore, multiple linear regression demonstrated that the PTP approach enabled significantly greater PI-LL mismatch change (p = 0.0017) and that this change was associated with the number of levels fused (p = 0.0481) as well as percutaneous (vs. open) screw placement (p = 0.0193). Finally, multiple logistic regression identified standard LLIF (p = 0.0378), female sex (p &lt; 0.0001), non-osteoporotic bone density (p &lt; 0.0001), and younger age (p &lt; 0.0001) as predictive of postoperative PI-LL mismatch &lt; 10° (the threshold under which such mismatch is no longer regarded as clinically significant deformity). CONCLUSIONS: Ultimately, PTP-LLIF was associated with greater net improvement in postoperative PI-LL mismatch, the primary outcome measure of this study. The present retrospective analysis therefore corroborated results of previous studies in suggesting that the utility of PTP-LLIF is translatable to ASD revision surgery.

Prognostic nomogram for predicting the overall survival rate of patients with uterine clear-cell carcinoma: Based on SEER database.

To evaluate the risk factors for uterine clear-cell carcinoma (UCCC) and construct nomograms predicting 1-, 3-, and 5-year overall survival rates of patients with UCCC. The demographic and clinical information of 1674 patients diagnosed with UCCC between 2004 and 2015, including age, race, marital status, tumor size, American Joint Committee on Cancer (AJCC) stage, and details of surgery and radiotherapy/chemotherapy, was collected from the Surveillance, Epidemiology, and End Results (SEER) database. After excluding patients with unknown AJCC stage, race, marital status, or lymph node information, 1469 patients remained. Risk factors were determined using univariate and multivariate analyses, and nomograms were developed to predict 1-, 3-, and 5-year overall survival of UCCC. Various indicators were used to evaluate the performance of the nomogram, such as the C-index, net classification improvement (NRI) and decision curve analysis (DCA). Age, log odds of positive lymph nodes, AJCC stage, surgery status, and chemotherapy status were independent risk factors for UCCC. The C-indexes of the training group and AJCC stage groups were 0.771 and 0.697, respectively. The results for the area under the receiver operating characteristics curve, NRI, and calibration curves indicated that the nomogram had good predictive ability. DCA revealed that the nomogram had greater clinical applicability than AJCC stage alone. Internal validation using the validation cohort also demonstrated that this nomogram had good predictive performance. A new nomogram comprising a combination of demographic and clinical characteristics provided better survival predictions than the AJCC staging system alone, which will facilitate prognostic assessments and clinical decision-making.

Fast Emitting Nanocomposites for High‐Resolution ToF‐PET Imaging Based on Multicomponent Scintillators

AbstractTime‐of‐Flight Positron Emission Tomography (ToF‐PET) is a medical imaging technique, based on the detection of two back‐to‐back γ‐photons generated from radiotracers injected into the body. Its limit is the ability of employed scintillation detectors to discriminate in time the arrival of γ‐pairs, that is, the coincidence time resolution (CTR). A CTR &lt; 50 ps will enable fast imaging with ultralow radiotracer dose. Monolithic materials do not have simultaneously the required high light output and fast emission characteristics, thus the concept of scintillating heterostructure is proposed, where the device is made of a dense scintillator coupled to a fast‐emitting light material. Here a composite polymeric scintillator loaded with hafnium oxide nanoparticles is presented. This enhanced by +300% its scintillation yield, by surpassing commercial plastic scintillators. The nanocomposite is coupled to bismuth germanate oxide (BGO) realizing a multilayer metascintillator. The energy sharing between its components is observed, which activates the nanocomposite's fast emission enabling a net CTR improvement of 25% with respect to monolithic BGO. These results demonstrate that a controlled loading with dense nanomaterials is an excellent strategy to enhance the performance of polymeric scintillators for their use in advanced radiation detection and imaging technologies.

Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method

Abstract. Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale. Our approach is based on the combination of ensemble model output statistics (EMOS) with a spatio-temporal interpolation process performed through the stochastic partial differential equation–integrated nested laplace approximation (SPDE-INLA). Our interpolation approach includes several spatial and spatio-temporal predictors, including meteorological variables. A use case is provided that scales down the CAMS forecasts on the Italian peninsula. The calibration is focused on the concentrations of several air quality pollutants (PM10, PM2.5, NO2, and O3) at a daily resolution from a set of 750 monitoring sites, distributed throughout the Italian country. Our results show the key role that conditioning variables play in improving the forecast capabilities of ensemble predictions, thus allowing for a net improvement in the calibration with respect to ordinary EMOS strategies. From a deterministic point of view, the performance of the predictive model shows a significant improvement in the performance of the raw ensemble forecast, with an almost-zero bias, significantly reduced root mean square errors, and correlations that are almost always higher than 0.9 for each pollutant; moreover, the post-processing approach is able to significantly improve the prediction of exceedances, even for very low thresholds, such as those recently recommended by the World Health Organisation. This is particularly significant if a forecasting approach is used to predict air quality conditions and plan adequate human health protection measures, even for low alert thresholds. From a probabilistic point of view, the quality of the forecast was verified in terms of reliability and credible intervals. After post-processing, the predictive probability density functions were sharp and much better calibrated than the raw ensemble forecast. Finally, we present some additional results based on a set of gridded (4 km × 4 km) maps covering the entire Italian country for the detection of areas where pollution peaks occur (exceedances of the current and/or proposed regulatory thresholds).

A novel model for predicting prognosis and response to immunotherapy in nasopharyngeal carcinoma patients

Blood-based biomarkers of immune checkpoint inhibitors (ICIs) response in patients with nasopharyngeal carcinoma (NPC) are lacking, so it is necessary to identify biomarkers to select NPC patients who will benefit most or least from ICIs. The absolute values of lymphocyte subpopulations, biochemical indexes, and blood routine tests were determined before ICIs-based treatments in the training cohort (n = 130). Then, the least absolute shrinkage and selection operator (Lasso) Cox regression analysis was developed to construct a prediction model. The performances of the prediction model were compared to TNM stage, treatment, and Epstein–Barr virus (EBV) DNA using the concordance index (C-index). Progression-free survival (PFS) was estimated by Kaplan–Meier (K–M) survival curve. Other 63 patients were used for validation cohort. The novel model composed of histologic subtypes, CD19+ B cells, natural killer (NK) cells, regulatory T cells, red blood cells (RBC), AST/ALT ratio (SLR), apolipoprotein B (Apo B), and lactic dehydrogenase (LDH). The C-index of this model was 0.784 in the training cohort and 0.735 in the validation cohort. K–M survival curve showed patients with high-risk scores had shorter PFS compared to the low-risk groups. For predicting immune therapy responses, the receiver operating characteristic (ROC), decision curve analysis (DCA), net reclassifcation improvement index (NRI) and integrated discrimination improvement index (IDI) of this model showed better predictive ability compared to EBV DNA. In this study, we constructed a novel model for prognostic prediction and immunotherapeutic response prediction in NPC patients, which may provide clinical assistance in selecting those patients who are likely to gain long-lasting clinical benefits to anti-PD-1 therapy.