Mechanisms involved in chronic neuropathic pain after avulsion injury

PhDMotor vehicle accidents are the most common cause of injuries involving avulsion of spinal roots from the brachial or lumbosacral plexuses. This results in chronic intractable pain that is refractory to pharmacotherapy. This is largely due to lack of information on underlying mechanisms, and lack of an established animal model to test drug treatments. This thesis has: 1) compared the neuroanatomical effects of dorsal root rhizotomy (DRR) and avulsion (DRA) in the spinal cord. DRR is commonly used to model avulsion injury but unlike avulsion it does not damage the spinal cord, as often happens clinically. 2) Developed a behavioural model of spinal root avulsion injury (SRA). 3) Evaluated the behavioural effects of drugs prescribed to treat neuropathic pain or those used clinically to treat other conditions like motoneuron disease or spinal cord injury. DRA produced a greater and prolonged glial, inflammatory, vascular response and cell loss than DRR. SRA produced thermal and mechanical hypersensitivity in the affected hind-paw. Neurodegenerative and neuroinflammatory responses were observed in both the avulsed and adjacent spinal segments, but produced no changes in the neuronal phenotype adjacent dorsal root ganglion neurons, suggesting that the evoked behaviour is mediated by central mechanisms. Administration of amitriptyline or carbamazepine reduced behavioural hypersensitivity in SRA, confirming their limited clinical efficacy in treatment of avulsion injury. Minocycline and riluzole produced therapeutic efficacy. Both compounds prevented the establishment of behavioural hypersensitivity, which correlated histologically with microglial inhibition, although riluzole was transiently effective. Additionally, minocycline reversed the hypersensitivity, an effect that persisted beyond drug washout, whereas riluzole had a limited effect that only lasted whilst the drug was administered. This thesis provides insight into the mechanisms of avulsion-induced neuropathic pain. The establishment of a behaviourally reproducible avulsion model provides a platform to test new pharmacological candidates for treatment, such as minocycline

Accelerated modelling of moisture diffusion controlled drying using coupled physics informed neural network.

A coupled physics informed neural network (CPINN) was used to simulate liquid diffusion controlled drying, an energy intensive process in the food industry. The architecture of the CPINN was designed to permit the prediction of thermo-physical properties and key source and sink terms at the solution boundaries which cause the solution to be highly coupled. The CPINN structure improves upon limitations of using PINNs in low-temperature food drying simulations, most notably allowing multiple and highly coupled variables to be simulated in additional to ensuring dynamic thermo-physical properties updates. The CPINN successfully solved a system 1-D partial differential equations (PDEs), capturing phenomena such as transient moisture diffusion and heat conduction, evaporative and convective heat transfer at the drying surface and moisture loss to the drying air. A benchmark simulation was used to compare the CPINN predicted product temperature, Tˆ p, and predicted moisture content, Xˆ p, against a numeric solution. The mean absolute error for the respective comparisons was 0.12 °C and 0.0035 kg m kg s −1. Training the CPINN for the first time was the rate limiting step, requiring the greatest time to solve when compared to the numeric solution, with solution times of t cpinn = 321 min and t rk = 82.7 min, respectively, or a time reduction fraction of t r=3.9, due to generalised initialisation of the CPINN parameters. By utilising a staged transfer learning approach, t r was reduced to a range of 0.28–0.027 whilst maintaining solution accuracy, representing a 3 to 37 times faster solution. By saving a library of CPINN models, solutions at key drying conditions of interest can be rapidly evaluated at run time, meaning the saved CPINN effectively acted as a method to compress solutions of PDEs. The techniques used here show how CPINNs can be applied to coupled and multi-scale PDEs using a physics-based approach to problems in the food processing and other sectors.</p

Towards Next Generation “Smart” Tandem Catalysts with Sandwiched Mussel-inspired Layer Switch

In this paper, we prepared a novel reactor with switchable ability to address present challenges in tandem catalyst. By introducing mussel-inspired moiety, this goal was achieved via preparing a “smart” polymer reactor which can open or closes the entry tunnel of the targeted substrate in cascade reactions. The catalyst consisted of two functional layers acting as tandem catalytic parts and one smart layer with mussel-inspired moieties as a controlled middle switch. The top and the bottom layer were made of molecularly imprinted polymers and catalytic components, like acidic parts and metal nanoparticles, respectively. The middle layer made of polymeric dopamine (PDPA) and acrylamide with self-healing ability will allow or inhibit the intermediate product for the reaction, thus controlling the process of the tandem catalysis. As a result, the novel catalyst exhibited self-controlled tandem catalysis, which provides new opportunities to design smart tandem catalysts, showing a promising prospect in this area.</p

LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding

Deductive coding is a widely used qualitative research method for determining the prevalence of themes across documents. While useful, deductive coding is often burdensome and time consuming since it requires researchers to read, interpret, and reliably categorize a large body of unstructured text documents. Large language models (LLMs), like ChatGPT, are a class of quickly evolving AI tools that can perform a range of natural language processing and reasoning tasks. In this study, we explore the use of LLMs to reduce the time it takes for deductive coding while retaining the flexibility of a traditional content analysis. We outline the proposed approach, called LLM-assisted content analysis (LACA), along with an in-depth case study using GPT-3.5 for LACA on a publicly available deductive coding data set. Additionally, we conduct an empirical benchmark using LACA on 4 publicly available data sets to assess the broader question of how well GPT-3.5 performs across a range of deductive coding tasks. Overall, we find that GPT-3.5 can often perform deductive coding at levels of agreement comparable to human coders. Additionally, we demonstrate that LACA can help refine prompts for deductive coding, identify codes for which an LLM is randomly guessing, and help assess when to use LLMs vs. human coders for deductive coding. We conclude with several implications for future practice of deductive coding and related research methods

Bactericidal – Bacteriostatic Foam Filters for Air Treatment

A highly loaded porous polyimide (PI) foam type air filter has been fabricated by incorporating antimicrobial active metals to prevent microbial growth and kill microbes, and so to provide health benefits for people in enclosed spaces. PI foams containing antibacterial agents, such as PCu80 (PI (20 wt %)/copper (80 wt %)), PNi80 (PI (20 wt %)/nickel (80 wt %)), and copper-nickel composites, were synthesized and tested against model bacterium, Erwinia carotovora (Gram negative) to determine the antibacterial efficacy of the air filter. Scanning electron microscope-energy dispersive X-ray spectroscopy (SEM-EDX) confirmed the distribution of copper and nickel throughout PCu80 and PNi80, where concentrations between 70% and 75% were detected. The copper: nickel ratio was consistent throughout the foam for PCu64Ni16 (PI (20 wt %)/copper (64 wt %)/nickel (16 wt %)). PCu80 displayed a high log reduction value (LRV) of 99.996% and, thus, exhibited a bactericidal effect. PNi80 displayed a lower LRV of 99.4%. However, a higher LRV value was observed compared to the control, 95.5% (PI foam without antibacterial agent), and thus, demonstrated a bacteriostatic effect. PCu64Ni16 exhibited and sustained exceptional microbe removal efficiencies of 99.9997% for 24 h at high humidity levels and demonstrated the highest zone of inhibition (ZOI) value of 33.90 ± 0.16 mm compared to PCu80 (27.5 ± 1.1 mm). Nickel strongly inhibited the proliferation of bacteria, while copper destroyed the bacteria on the foam filters. Therefore, such functionalized filters can potentially overcome the inherent limitation in conventional filters and imply their superiority for controlling indoor air quality.</p

3D printed nanofiltration composite membranes with reduced concentration polarisation

3D printed nanofiltration (NF) composite membranes with surface patterns minimising the impact of concentration polarisation (CP) are presented here for the first time. The membranes consist of a NF polydopamine‐coated polyvinylidene fluoride (PVDF/PDA) selective layer on a 3D printed asymmetric wavy (patterned) support. The result is a wavy composite membrane with pure water permeance of 14 ± 2 LMH bar−1 and molecular weight cut-off of ∼550 Da, measured using a crossflow NF setup at a transmembrane pressure of 2 bar for Reynold number (Re) of 700, using a range of dyes (mass balance &gt;97% for all tests). The CP behaviour of the composite membranes was assessed by filtration of Congo red (CR) dye solution (0.01 g L−1), showing that the wavy pattern significantly reduced the impact of CP compared to the flat membranes, with a nearly tripling of the mass transfer coefficient and a 57% decline of the CP factor. Computational fluid dynamics showed that these significant performance improvements were due to improved hydrodynamics, with the maximum surface shear stress induced by the wavy structure (1.35 Pa) an order of magnitude higher than that of the flat membranes (0.18 Pa) at Re = 700. These results demonstrate that 3D printing is a viable technology route to reducing concentration polarisation in membrane nanofiltration applications.</p

3D printed porous contactors for enhanced oil droplet coalescence

The fabrication of 3D printed porous contactors based on triply periodic minimal surfaces (TPMS) is reported here for the first time. The structures, based on the Schwarz-P and Gyroid TPMS, were tested for oil-in-water demulsification via oil droplet coalescence and compared to a contactor with cylindrical pores and natural separation. The contactors were characterized in terms of intrinsic permeability, resistance and oil separation efficiency, for different oil concentrations (0.3, 0.4, 0.5 vol%) in the oil-in-water emulsion, vacuum pressures (10 and 20 mbar) and thickness of the contactors (4.68 and 9.36 mm). Results show that while the Gyroid contactor has the highest resistance and lowest intrinsic permeability of all three structures, it has 18% and 5% higher separation efficiency than the cylindrical and Schwarz-P structures, respectively. These characteristics reflect the higher tortuosity and surface area of the Gyroid structure compared to the other two. At 90%, the Gyroid structure also has a 22% higher separation efficiency and a two order of magnitude higher separation rate for the permeate compared to natural coalescence, attributed to an 8-fold increase in oil droplet diameter of the permeate compared to the feed, as a result of passage through the contactor. Higher vacuum pressure and higher contactor thickness further increase the separation efficiency of all structures, but the effect is more pronounced for the Gyroid structure due to its higher tortuosity. These results show that 3D printing is an effective tool for the design of porous contactors where a high surface area of interaction is key to their success, paving their way to extended use in a variety of industrial applications.</p