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A randomised Trial of Autologous Blood products, leukocyte and platelet-rich fibrin (L-PRF), to promote ulcer healing in LEprosy : The TABLE trial
Get PDFIntroduction:
Autologous blood products like Platelet Rich Plasma (PRP) and Leukocyte and Platelets Rich Fibrin (L-PRF) have been used for many years across many types of skin ulcers. However, the effectiveness of autologous blood products on wound healing is not well established.
Methods:
We evaluated the ‘second generation’ autologous product- Leukocyte and Platelet- Rich Fibrin (L-PRF). Our trial was undertaken on patients suffering from neuropathic leprosy ulcers at the Anandaban hospital which serves the entire country of Nepal. We conducted a 1:1 (n = 130) individually randomised trial of L-PRF (intervention) vs. normal saline dressing (control) to compare rate of healing and time to complete healing. Rate of healing was estimated using blind assessments of ulcer areas based on three different measurement methods. Time to complete healing was measured by the local unblinded clinicians and by blind assessment of ulcer images.
Results:
The point estimates for both outcomes were favourable to L-PRF but the effect sizes were small. Unadjusted mean differences (intervention vs control) in mean daily healing rates (cm2) were respectively 0.012 (95% confidence interval 0.001 to 0.023, p = 0.027); 0.016 (0.004 to 0.027, p = 0.008) and 0.005 (-0.005 to 0.016, p = 0.313) across the three measurement methods. Time to complete healing at 42 days yielded Hazard Ratios (unadjusted) of 1.3 (0.8 to 2.1, p = 0.300) assessed by unblinded local clinicians and 1.2 (0.7 to 2.0, p = 0.462) on blind assessment.
Conclusion:
Any benefit from L-PRF appears insufficient to justify routine use in care of neuropathic ulcers in leprosy.
Trial registration:
ISRCTN14933421. Date of trial registration: 16 June 2020
Introspection of DNN-based perception functions in automated driving systems : state-of-the-art and open research challenges
Get PDFAutomated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This data is then processed by a perception subsystem to create semantic knowledge of the world around the vehicle. State-of-the-art ADSs’ perception systems often use deep neural networks for object detection and classification, thanks to their superior performance compared to classical computer vision techniques. However, deep neural network-based perception systems are susceptible to errors, e.g., failing to correctly detect other road users such as pedestrians. For a safety-critical system such as ADS, these errors can result in accidents leading to injury or even death to occupants and road users. Introspection of perception systems in ADS refers to detecting such perception errors to avoid system failures and accidents. Such safety mechanisms are crucial for ensuring the trustworthiness of ADSs. Motivated by the growing importance of the subject in the field of autonomous and automated vehicles, this paper provides a comprehensive review of the techniques that have been proposed in the literature as potential solutions for the introspection of perception errors in ADSs. We classify such techniques based on their main focus, e.g., on object detection, classification and localisation problems. Furthermore, this paper discusses the pros and cons of existing methods while identifying the research gaps and potential future research directions
Organizational cybersecurity systems and sustainable business performance of Small and Medium Enterprises (SMEs) in Saudi Arabia : the mediating and moderating role of cybersecurity resilience and organizational culture
Get PDFCybersecurity challenges in Saudi Arabia’s service and manufacturing sectors are escalating due to increased digital adoption, highlighting the need for robust security measures and awareness in SMEs. Therefore, this research is significant due to the increasing reliance on digital technologies and the unique cybersecurity challenges faced by SMEs in these vital economic sectors. With rapid technological advancements, IT capabilities and cybersecurity have become paramount, particularly in the post-COVID-19 era. The service and manufacturing sectors in Saudi Arabia have seen significant shifts towards digital operations. This study aimed to explore the impact of organizational cybersecurity systems on organizational resilience and sustainable business performance in Saudi Arabia’s service and manufacturing sectors, examining the mediating and moderating effects of organizational resilience and culture. A quantitative research method was employed, combining a thorough literature review with empirical data from a sample of 394 respondents in Saudi Arabia, split evenly between the service and manufacturing sectors. Smart PLS 3.3.3 was used to test the proposed hypotheses. The findings suggested a positive effect of the factors of organizational cybersecurity systems on organizational resilience. Organizational cybersecurity systems also significantly influenced sustainable business performance; however, organizational resilience and culture did not play mediating and moderating roles. This study is one of the first to offer a nuanced analysis of IT capabilities and cybersecurity within Saudi Arabia’s service and manufacturing sectors, especially in a post-COVID-19 context. The insights gleaned contribute to the academic discourse and have pivotal managerial implications for organizations navigating the digital era in Saudi Arabia
In vivo monitoring of glycerolipid metabolism in animal nutrition biomodel-fed smart-farm eggs
Get PDFAlthough many studies have examined the biochemical metabolic pathways by which an egg (egg yolk) lowers blood lipid levels, data on the molecular biological mechanisms that regulate and induce the partitioning of hepatic glycerolipids are missing. The aim of this study was to investigate in vivo monitoring in four study groups using an animal nutrition biomodel fitted with a jugular-vein cannula after egg yolk intake: CON (control group, oral administration of 1.0 g of saline), T1 (oral administration of 1.0 g of pork belly fat), T2 (oral administration of 1.0 g of smart-farm egg yolk), and T3 (oral administration of T1 and T2 alternately every week). The eggs induced significant and reciprocal changes in incorporating 14C lipids into the total glycerolipids and releasing 14CO2, thereby regulating esterification and accelerating oxidation in vivo. The eggs increased phospholipid secretion from the liver into the blood and decreased triacylglycerol secretion by regulating the multiple cleavage of fatty acyl-CoA moieties’ fluxes. In conclusion, the results of the current study reveal the novel fact that eggs can lower blood lipids by lowering triacylglycerol secretion in the biochemical metabolic pathway of hepatic glycerolipid partitioning while simultaneously increasing phospholipid secretion and 14CO2 emission
Technocratic totalitarianism : Gunnar Kaiser and dissident discourse in pandemic-era Germany
Get PDFThis article examines the ideas, reception and social role of Germany’s most prominent cultural critic of lockdown politics, Gunnar Kaiser. In contrast to the majority of European intellectuals, Kaiser took an early public stand against the naive adoption of science as a social authority and the unprecedented overturning of core democratic principles. He argued that without vigorous, open debate, the worldwide state of emergency threatened to usher in a fundamentally new (bio)political era, in which liberal democracy would be replaced by an increasingly totalitarian technocracy. Yet despite his voluminous philosophical output, which included a highly creative and increasingly professionalised YouTube channel as well as two best-selling books, Kaiser found himself largely excluded from mainstream German discourse. Crew analyses the full range of Kaiser’s interventions while also situating him within the broader landscape of European dissent
Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification
Get PDFProton magnetic resonance spectroscopy (1H‐MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H‐MRS. Eighty‐three/forty‐two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short‐echo‐time point‐resolved spectroscopy. The acquired raw 1H‐MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post‐noise‐suppression 1H‐MRS showed significantly elevated signal‐to‐noise ratios (P .05, Wilcoxon signed‐rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed‐rank test). Specifically, the cross‐validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H‐MRS. The study shows that fitting‐based signal‐to‐noise ratios of clinical 1H‐MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post‐noise‐suppression 1H‐MRS may have better diagnostic performance for paediatric brain tumours
Working the edge : the emotional experiences of commissioning and funding arrangements for service leaders in the sexual violence voluntary sector
Get PDFThe specialist voluntary sector plays a crucial role in supporting survivors of sexual violence. However, in England, short-term funding underpins the sector's financial stability. This article examines sector leaders’ ways of coping, resisting and being affected by funding practices. Using the concept of edgework, we show how funding and commissioning dynamics push individuals to the edge of service sustainability, job satisfaction, and emotional well-being. We examine how these edges are “worked,” for example, by circumventing and remolding the edge. We offer an original way to theorize participants, make visible the emotional toll of service precarity and offer suggestions for support
Predicting the hydraulic response of critical transport infrastructures during extreme flood events
Get PDFUnderstanding the effects of extreme floods on critical infrastructures such as bridges is paramount for ensuring safety and resilient design in the face of climate change and extreme events. This study develops robust computational and predictive modeling tools for assessing the impacts of extreme floods on the hydraulic response and structural resilience of bridges. A computational fluid dynamic (CFD) model utilizing RANS equations and k-ω Shear Stress Transport (SST) for simulating supercritical flows is adopted to compute hydrodynamic pressures and the water levels on bridge piers of cylindrical and rectangular shapes during a flood event. The CFD model is validated based on the case study data obtained from the Haj Omran Bridge, built on the Khorramabad River in Iran. The numerical simulations consider hydrological conditions and exclude geotechnical parameters and abutment damages. The numerical results are evaluated based on well-established design guidelines. Machine learning techniques, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized with Grid Search Cross-Validation (GSCV), are adopted to enhance the accuracy of hydrodynamic pressure forecasting at bridge piers. The XGBoost model exhibits superior performance (R2 = 0.908, RMSE = 0.0279, and E = 3.41%) compared to the RF and SVR models. All the estimated pressure data by XGBoost falls within ±6 percent error lines, highlighting the model's robustness for out-of-range hydrodynamic pressure prediction. Additionally, an optimized Long Short-Term Memory (LSTM) model is adopted to effectively predict free surface flow profiles (i.e. flood depth) over the bridge (R2 = 0.937 and RMSE = 0.083), demonstrating its potential for practical applications of flood depth predictions over bridge infrastructures. The proposed methodological framework outlined in this study can facilitate modeling the impacts of extreme floods on bridges, enabling robust climate resilience assessment of critical infrastructures
A distinct, high-affinity, alkaline phosphatase facilitates occupation of P-depleted environments by marine picocyanobacteria
Get PDFMarine picocyanobacteria are globally important primary producers, a facet facilitated via their ability to proliferate in nutrient impoverished regions of the sunlit ocean including oligotrophic gyres that are expected to expand due to climate change. Phosphorus is a major macronutrient potentially limiting growth and CO2 fixation capacity in such systems. Here, we identify a unique high-affinity phosphatase which in picocyanobacteria is present only in populations that occupy these P-deplete systems. This phosphatase is abundant and highly expressed in these regions, suggesting that genetic capacity exists within these populations to provide resilience to long-term P depletion. Moreover, this phosphatase is widely distributed in both heterotrophic bacteria and eukaryotic algae hinting that such a trait is broadly utilized to access such environments