Waiting for surgery after hip fracture-Health and/or economic risk?

OBJECTIVES: Waiting for surgery is a disconcerting experience. It can have a negative impact on patients' outcomes and length of stay (LOS) as driver for treatment costs. Process-optimisation may be a strategy to improve quality and cost-efficacy. The study investigates the correlation between waiting for hip fracture surgery and patient characteristics, organisational variables, outcomes, LOS, and the distribution of waiting times and LOS over time, including cost estimates. Thereby the study aims to identify the potential for organisational improvements with respect to managing the waiting time. METHODS: Ten-year routine health data (patient characteristics and follow-up information) and process-indicators that is, waiting time and LOS from a Swiss trauma-centre were analysed retrospectively. Cost-estimates were calculated based on Swiss diagnosis related groups and daily costs to evaluate hospital revenues. RESULTS: In total, 2572 patients aged ≥60 years with low-energy hip fractures were included. Waiting times >48 h were associated with sub-optimal outcomes. Over the years long waiting times decreased. This reduction was not reflected by a reduction in LOS which remained stable around 10 days, primarily driven by late discharge to in-patient rehabilitation. Reimbursement persisted at an average revenue in the low 4-5-digit range, depending on implant costs. CONCLUSIONS: While there has been a reduction of waiting times, this has not translated into a reduction of LOS or potential savings in health care costs, due to the various dependencies along the patient journey. Managing waiting times may be an area for improvement, increasing cost-efficacy, especially since long waiting times are still associated with inferior outcomes and LOS.

Optimisation of a Microwave Synthesis of Silver Nanoparticles by a Quality by Design Approach to Improve SERS Analytical Performances.

A major limitation preventing the use of surface-enhanced Raman scattering (SERS) in routine analyses is the signal variability due to the heterogeneity of metallic nanoparticles used as SERS substrates. This study aimed to robustly optimise a synthesis process of silver nanoparticles to improve the measured SERS signal repeatability and the protocol synthesis repeatability. The process is inspired by a chemical reduction method associated with microwave irradiation to guarantee better controlled and uniform heating. The innovative Quality by Design strategy was implemented to optimise the different parameters of the process. A preliminary investigation design was firstly carried out to evaluate the influence of four parameters selected by means of an Ishikawa diagram. The critical quality attributes were to maximise the intensity of the SERS response and minimise its variance. The reaction time, temperature and stirring speed are critical process parameters. These were optimised using an I-optimal design. A robust operating zone covering the optimal reaction conditions (3.36 min-130 °C-600 rpm) associated with a probability of success was modelled. Validation of this point confirmed the prediction with intra- and inter-batch variabilities of less than 15%. In conclusion, this study successfully optimised silver nanoparticles by a rapid, low cost and simple technique enhancing the quantitative perspectives of SERS.

Comprehensive improvements in the emergency laboratory test process based on information technology.

OBJECTIVE: To explore the application effects of information technology (IT) on emergency laboratory testing procedures. METHODS: In this study, IT-based optimisation of the emergency laboratory testing process was implemented between October and December 2021. Thus, the emergency laboratory test reports from January to September 2021 were placed into the pre-optimised group, while those from January to September 2022 were categorised into the post-optimised group. Besides, the emergency laboratory test report time, emergency laboratory test report time limit coincidence rate, error rate, and employee and patient satisfaction levels in individual months and across the whole period were described. Moreover, changes in the above indicators before and after the implementation of IT-based optimisation were explored and the application effects of IT-based optimisation were also evaluated. RESULTS: The emergency laboratory test report times after the implementation of IT-based optimisation were shorter than those before IT-based optimisation (P < 0.05). The total number of laboratory test items before and after information optimization amounted to 222,139 and 259,651, respectively. Also, IT-based optimisation led to an increase in the emergency laboratory test report time limit coincidence rate from 98.77% to 99.03% (P < 0.05), while the emergency laboratory test report error rate fell from 0.77‱ to 0.15‱ (P < 0.05). Additionally, IT-based optimisation resulted in increases in both employee satisfaction, from 80.65% to 93.55% (N = 31, P > 0.05), and patient satisfaction, from 93.06% to 98.44% (P < 0.05). CONCLUSION: The automation and IT-based optimisation of the emergency laboratory testing process significantly reduces the emergency laboratory test report time and error rate. Additionally, IT-driven optimization enhances the alignment of emergency laboratory test report deadlines and enhances the overall quality and safety of emergency laboratory testing.

Brewing by-product valorisation: trub debittered for nutritional and quality improvement of pasta.

Trub, a brewing by-product, can be used as alternative ingredient for foods nutritional enrichment after its bitter compounds extraction. Study presents the optimisation of bitter compounds extraction from trub by Box-Behnken design, and use of debittered trub (DT) as new ingredient to enrich pasta. Bitterness extraction process was evaluated at different pH levels, time and extraction steps, and physical-chemical properties of DT (obtained under optimal conditions) were evaluated. Pasta was enriched with DT (5%, 10% and 15%) and its physical-chemical and quality properties were evaluated. Protein structure and chemical composition of trub were altered after process, also modifying its technological properties. Pasta with 10% DT increased in 33.51% protein content. Interaction of DT and wheat proteins resulted in a more compact structure, and DT water absorption capacity provided pasta texture changes. DT use improved pasta nutritional and quality properties, enabling trub valorisation and its use as vegetable proteins alternative source.

Understanding, controlling and optimising the cooling of waste thermal treatment beds including STARx Hottpads.

STARx (Self-sustaining Treatment for Active Remediation ex situ) is a thermal treatment strategy for contaminated soils and organic wastes. Key to this technology is that organics are embedded in porous matrix beds (e.g. sand). STARx induces a self-sustaining smouldering combustion front that traverses the bed, burning away the embedded contaminants/wastes. The time and cost effectiveness of this technology is largely dictated by the time required for cooling of the hot, clean, porous matrix bed that remains after treatment. This study is the first to explore the cooling of these beds. A suite of novel simulations investigated the influence of key parameters on bed-cooling time. The results reveal that cooling time decreased nearly linearly with decreases of volume-averaged bed temperature and bed bulk density. Increased injection air fluxes led to the non-linear decrease of cooling time. Also, cooling time was negatively impacted by bed temperature inhomogeneity, which influenced preferential air flow through cooler regions of the bed, bypassing hotter regions. From these results, using lower bulk density bed materials, increased air fluxes and enhancing wall insulation to improve bed temperature homogeneity were identified as system optimisations to reduce cooling times. While the aim of this research is to improve the STARx cooling process, the results are also highly applicable to many similar engineering systems that involve hot porous bed cooling.

Genetic and process engineering strategies for enhanced recombinant N-glycoprotein production in bacteria.

BACKGROUND: The production of N-linked glycoproteins in genetically amenable bacterial hosts offers great potential for reduced cost, faster/simpler bioprocesses, greater customisation, and utility for distributed manufacturing of glycoconjugate vaccines and glycoprotein therapeutics. Efforts to optimize production hosts have included heterologous expression of glycosylation enzymes, metabolic engineering, use of alternative secretion pathways, and attenuation of gene expression. However, a major bottleneck to enhance glycosylation efficiency, which limits the utility of the other improvements, is the impact of target protein sequon accessibility during glycosylation. RESULTS: Here, we explore a series of genetic and process engineering strategies to increase recombinant N-linked glycosylation, mediated by the Campylobacter-derived PglB oligosaccharyltransferase in Escherichia coli. Strategies include increasing membrane residency time of the target protein by modifying the cleavage site of its secretion signal, and modulating protein folding in the periplasm by use of oxygen limitation or strains with compromised oxidoreductase or disulphide-bond isomerase activity. These approaches achieve up to twofold improvement in glycosylation efficiency. Furthermore, we also demonstrate that supplementation with the chemical oxidant cystine enhances the titre of glycoprotein in an oxidoreductase knockout strain by improving total protein production and cell fitness, while at the same time maintaining higher levels of glycosylation efficiency. CONCLUSIONS: In this study, we demonstrate that improved protein glycosylation in the heterologous host could be achieved by mimicking the coordination between protein translocation, folding and glycosylation observed in native host such as Campylobacter jejuni and mammalian cells. Furthermore, it provides insight into strain engineering and bioprocess strategies, to improve glycoprotein yield and titre, and to avoid physiological burden of unfolded protein stress upon cell growth. The process and genetic strategies identified herein will inform further optimisation and scale-up of heterologous recombinant N-glycoprotein production.

Utilisation of Non-Edible Source (Pongamia pinnata Seeds Shells) for Producing Methyl Esters as Cleaner Fuel in the Presence of a Novel Heterogeneous Catalyst Synthesized from Waste Eggshells.

Waste eggshells were considered for synthesising a precursor (CaO) for a heterogeneous catalyst, further impregnated by alkali caesium oxide (Cs2O). The following techniques were used to characterise the synthesised catalysts: X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and Temperature Programmed Desorption (CO2-TPD). The synthesised catalyst revealed its suitability for transesterification to produce biodiesel. The biodiesel production process was optimised, and it showed that the optimal biodiesel yield is 93.59%. The optimal set of process parameters is process temperature 80 °C, process time 90 min, methanol-to-oil molar ratio 8 and catalyst loading 3 wt.%. It has been found that the high basicity of the catalyst tends to give a high biodiesel yield at low methanol-to-oil ratio 8 when the reaction time is also less (90 min). The fuel properties of biodiesel also satisfied the standard limits defined by ASTM and the EN standards. Thus, the synthesised catalyst from waste eggshells is highly active, improved the biodiesel production conditions and PPSS oil is a potential nonedible source.

Production and secretion dynamics of prokaryotic Penicillin G acylase in Pichia pastoris.

To take full advantage of recombinant Pichia pastoris (Komagataella phaffii) as a production system for heterologous proteins, the complex protein secretory process should be understood and optimised by circumventing bottlenecks. Typically, little or no attention has been paid to the fate of newly synthesised protein inside the cell, or its passage through the secretory pathway, and only the secreted product is measured. However, the system's productivity (i.e. specific production rate qp), includes productivity of secreted (qp,extra) plus intracellularly accumulated (qp,intra) protein. In bioreactor cultivations with P. pastoris producing penicillin G acylase, we studied the dynamics of product formation, i.e. both the specific product secretion (qp,extra) and product retention (qp,intra) as functions of time, as well as the kinetics, i.e. productivity in relation to specific growth rate (µ). Within the time course, we distinguished (I) an initial phase with constant productivities, where the majority of product accumulated inside the cells, and qp,extra, which depended on µ in a bell-shaped manner; (II) a transition phase, in which intracellular product accumulation reached a maximum and productivities (intracellular, extracellular, overall) were changing; (III) a new phase with constant productivities, where secretion prevailed over intracellular accumulation, qp,extra was linearly related to µ and was up to three times higher than in initial phase (I), while qp,intra decreased 4-6-fold. We show that stress caused by heterologous protein production induces cellular imbalance leading to a secretory bottleneck that ultimately reaches equilibrium. This understanding may help to develop cultivation strategies for improving protein secretion from P. pastoris.Key Points• A novel concept for industrial bioprocess development.• A Relationship between biomass growth and product formation in P. pastoris.• A Three (3) phases of protein production/secretion controlled by the AOX1-promoter.• A Proof of concept in production of industrially relevant penicillin G acylase.

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression.

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.

Potato Protein Fining of Phenolic Compounds in Red Wine: A Study of the Kinetics and the Impact of Wine Matrix Components and Physical Factors.

Producing wines within an acceptable range of astringency is important for quality and consumer acceptance. Astringency can be modified by fining during the winemaking process and the use of vegetable proteins (especially potato proteins) as fining agents has gained increasing interest due to consumers' requirements. The research presented was the first to investigate the effect of a potato protein dose on the kinetics of tannin and phenolic removal compared to gelatin for two unfined Cabernet Sauvignon wines. To further understand the results, the influence of the wine matrix and fining parameters (including pH, ethanol concentration, sugar concentration, temperature, and agitation) were tested according to a fractional 25-1 factorial design on one of the Cabernet Sauvignon wines using potato proteins. The results from the factorial design indicate that potato protein fining was significantly influenced by wine pH, ethanol concentration, fining temperature as well as an interaction (pH × ethanol) but not by sugar content or agitation. Insights into the steps required for the optimisation of fining were gained from the study, revealing that potato protein fining efficiency could be increased by treating wines at higher temperatures (20 °C, rather than the conventional 10-15 °C), and at both a lower pH and/or alcohol concentration.

Assessment of Performance of the Industrial Process of Bulk Vacuum Packaging of Raw Meat with Nondestructive Optical Oxygen Sensing Systems.

The commercially-available optical oxygen-sensing system Optech-O2 Platinum was applied to nondestructively assess the in situ performance of bulk, vacuum-packaged raw beef in three ~300 kg containers. Twenty sensors were attached to the inner surface of the standard bin-contained laminate bag (10 on the front and back sides), such that after filling with meat and sealing under vacuum, the sensors were accessible for optical interrogation with the external reader device. After filling and sealing each bag, the sensors were measured repetitively and nondestructively over a 15-day storage period at 1 °C, thus tracking residual oxygen distribution in the bag and changes during storage. The sensors revealed a number of unidentified meat quality and processing issues, and helped to improve the packaging process by pouring flakes of dry ice into the bag. Sensor utility in mapping the distribution of residual O2 in sealed bulk containers and optimising and improving the packaging process, including handling and storage of bulk vacuum-packaged meat bins, was evident.

Application and optimisation of air-steam cooking on selected vegetables: impact on physical and antioxidant properties.

BACKGROUND: Several studies investigated the impact of different cooking techniques on the quality of vegetables. However, the use of the combined air-steam cooking is still scarcely debated, despite the advantages informally referred by professional catering workers. In this study, its optimisation was studied on Brussels sprouts and pumpkin cubes to obtain the best physical (texture, colour) and antioxidant (FRAP, total phenols) response, in comparison to a conventional steaming treatment. RESULTS: Increasing the strength of the air-steam treatment, Brussels sprouts resulted to be softer, less green (higher a* value), richer in phenols and exhibited lower FRAP values than the steamed ones. The air-steamed pumpkin cubes exhibited an equivalent softening degree to that of steamed ones and, under the strongest cooking conditions, a higher antioxidant quality and a yellow darkening (lower b* value). Varying the cooking time and/or temperature, a linear change of force/compression hardness and a* (negative a*: greenness) for Brussels sprouts, b* (yellowness) and total phenol content for pumpkin cubes was observed. A predictive model for these variables was obtained by response surface methodology. The best process conditions to achieve the optimal desirability were also identified. CONCLUSION: The application of air-steam cooking under suitable time/temperature conditions could be proposed as an alternative method to a traditional steam cooking on Brussels sprouts and pumpkin cubes, being able to preserve or improve their quality. The best air-steam cooking conditions were 25 min at 90 °C for Brussels sprouts and 10 min at 110 °C for pumpkin. © 2017 Society of Chemical Industry.

Preparation of antinutrients-reduced dhokla using response surface process optimisation.

Dhokla, a popular indigenous savoury dish of India, is prepared by soaking bengalgram dal and rice, grinding separately, mixing the batters, and spontaneously fermenting and steaming of mixed batter. Central composite rotatable response surface designs for soaking, fermentation and steaming at five-level combinations were used for optimising preparation of dhokla to achieve reduced contents of antinutrients. Optimum soaking of bengalgram dal (dal-water ratio of 1:5 w/w, pH 7.0, 23 °C, 20 h) and rice (rice-water ratio of 1:5 w/w, pH 5.6, 16 °C, 18 h) resulted in reduced levels of all the antinutrients, except total biogenic amines in rice. Fermentation of dal-rice (3:1 v/v) mixed batter under optimum condition (added NaCl of 8 g/kg, 32 °C, 18 h) further reduced their levels, but total biogenic amines content was enhanced. However, optimum steaming of dal-rice mixed fermented batter for 20 min was effective in reducing all the tested antinutrients. In dhokla, the content of tannins, phytic acid and total biogenic amines reduced by 100, 94 and 20%, respectively; trypsin inhibitor and haemagglutinating activities reduced by 92 and 100%, respectively, over raw ingredients. The optimally prepared product (dhokla) ranked "excellent" in terms of overall sensory quality.

Bioprocess enhancement of feather degradation using alkaliphilic microbial mixture.

1. The main aim of this work is to develop a robust method to generate a microbial mixture which can successfully degrade poultry feathers to overcome environmental problems. 2. Four different alkaliphilic microbes were isolated and shown to degrade poultry feathers. 3. Two of the isolates were phylogenetically identified as Lysinibacillus and the others were identified as Nocardiopsis and Micrococcus. 4. The best microbial co-culture for white and black feather degradation was optimised for pH, temperature and relative population of the isolates to achieve almost 96% of degradation compared with a maximum of 31% when applying each isolate individually. 5. The maximum activity of keratinase was estimated to be 1.5 U/ml after 3 d for white feathers and 0.6 U/ml after 4 d for black feathers in a basal medium containing feather as the main carbon source. Additionally, non-denaturing polyacrylamide gel electrophoresis showed 4 and 3 protease activity bands for white and black feather, respectively. 6. This study provides a robust method to develop potential new mixtures of microorganisms that are able to degrade both white and black feathers by applying a Central Composite Design.

Analysis of green algal growth via dynamic model simulation and process optimization.

Chlamydomonas reinhardtii is a green microalga with the potential to generate sustainable biofuels for the future. Process simulation models are required to predict the impact of laboratory-scale growth experiments on future scaled-up system operation. Two dynamic models were constructed to simulate C. reinhardtii photo-autotrophic and photo-mixotrophic growth. A novel parameter estimation methodology was applied to determine the values of key parameters in both models, which were then verified using experimental results. The photo-mixotrophic model was used to accurately predict C. reinhardtii growth under different light intensities and in different photobioreactor configurations. The optimal dissolved CO2 concentration for C. reinhardtii photo-autotrophic growth was determined to be 0.0643 g·L(-1) , and the optimal light intensity for algal growth was 47 W·m(-2) . Sensitivity analysis revealed that the primary factor limiting C. reinhardtii growth was its intrinsic cell decay rate rather than light attenuation, regardless of the growth mode. The photo-mixotrophic growth model was also applied to predict the maximum biomass concentration at different flat-plate photobioreactors scales. A double-exposure-surface photobioreactor with a lower light intensity (less than 50 W·m(-2) ) was the best configuration for scaled-up C. reinhardtii cultivation. Three different short-term (30-day) C. reinhardtii photo-mixotrophic cultivation processes were simulated and optimised. The maximum biomass productivity was 0.053 g·L(-1) ·hr(-1) , achieved under continuous photobioreactor operation. The continuous stirred-tank reactor was the best operating mode, as it provides both the highest biomass productivity and lowest electricity cost of pump operation.

Study of different reaction schemes for the enzymatic synthesis of polyglycerol polyricinoleate.

BACKGROUND: Different strategies for the solvent-free enzymatic production of polyglycerol polyricinoleate (PGPR) were explored in an attempt to simplify and improve the process. Besides the conventional procedure (obtaining polyricinoleic acid, followed by its esterification with polyglycerol), two alternative methods are proposed: (1) reversing the synthesis order, i.e. esterification of polyglycerol with ricinoleic acid and then the condensation of ricinoleic acid with the previously obtained polyglycerol ester; and (2) the enzymatic synthesis of PGPR in a single-step process. RESULTS: The reaction sequences were carried out in an open-air reactor with free and immobilised lipases (triacylglycerol acylhydrolases, E.C. 3.1.1.3): Candida rugosa lipase to obtain polyricinoleic acid and Rhizopus oryzae lipase for the esterification of polyglycerol with the carboxyl group of ricinoleic or polyricinoleic acid. A co-immobilised derivative containing both lipases was used to catalyse the single-stage scheme. The three processes were carried out in a vacuum reactor, obtaining in every case PGPR that complied with the legal specifications of the European Community and recommendations provided in the Food Chemical Codex. CONCLUSION: The results demonstrate that all three protocols are viable for the enzymatic synthesis of PGPR and require similar reaction times. The single-stage scheme is easier to carry out.

Preparation, optimisation, and properties of O-carboxymethyl chitosan-g-cholesterol succinic acid monoester polymer nanomicelles.

Polymer nanomicelles have the advantages of small particle size, improved drug solubility, retention effect and enhanced permeability, so they can be used in the treatment of tumour diseases. The aim of this study was to prepare and optimise a nanomicelle which can improve the solubility of insoluble drugs. Firstly, the carboxyl group of cholesterol succinic acid monoester was grafted with the side chain amino group of O-carboxymethyl chitosan-g-cholesterol succinic acid monoester (CCMC), and its structure was characterized by fourier transform infrared spectroscopy (FTIR) and proton nuclear magnetic resonance (1H-NMR). Particle size has an important impact on tissue distribution, cell uptake, permeability and inhibition of tumour tissue. In this study, particle size and polydispersity index (PDI) were selected as indexes to optimise the preparation process of CCMC nanomicelles through single factor experiment, Plackett-Burman experiment, the steepest climbing experiment and response surface design experiment. The optimised CCMC nanomicelles showed an average particle size of 173.9 ± 2.3 nm and a PDI of 0.170 ± 0.053. The Cell Counting Kit-8 assay showed no significant effect on cell viability in the range of 0-1000 µg ml-1concentration. Coumarin-6 (C6) was used as a fluorescent probe to investigate the drug-carrying ability of CCMC nanomicelles. C6-CCMC showed 86.35 ± 0.56% encapsulation efficiency with a drug loading of 9.18 ± 0.32%. Both CCMC and C6-CCMC demonstrated excellent stability in different media. Moreover, under the same conditions, the absorption effect of C6 in C6-CCMC nanomicelles was significantly higher than that of free C6 while also exhibiting good sustained-release properties. Therefore, this study demonstrates CCMC nanomicelles as a promising new drug carrier that can significantly improve insoluble drug absorption.

Optimised Degradation of Lignocelluloses by Edible Filamentous Fungi for the Efficient Biorefinery of Sugar Beet Pulp.

The degradation of the complex structure of lignocellulosic biomass is important for its further biorefinery to value-added bioproducts. The use of effective fungal species for the optimised degradation of biomass can promote the effectiveness of the biorefinery of such raw material. In this study, the optimisation of processing parameters (temperature, time, and s/w ratio) for cellulase activity and reducing sugar (RS) production through the hydrolysis of sugar beet pulp (SBP) by edible filamentous fungi of Aspergillus, Fusarium, Botrytis, Penicillium, Rhizopus, and Verticillium spp. was performed. The production of RS was analysed at various solid/water (s/w) ratios (1:10-1:20), different incubation temperatures (20-35 °C), and processing times (60-168 h). The Aspergillus niger CCF 3264 and Penicillium oxalicum CCF 3438 strains showed the most effective carboxymethyl cellulose (CMC) degrading activity and also sugar recovery (15.9-44.8%) from SBP biomass in the one-factor experiments. Mathematical data evaluation indicated that the highest RS concentration (39.15 g/100 g d.w.) and cellulolytic activity (6.67 U/g d.w.) could be achieved using A. niger CCF 3264 for the degradation of SBP at 26 °C temperature with 136 h of processing time and a 1:15 solid/water ratio. This study demonstrates the potential of fungal degradation to be used for SBP biorefining.

Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach.

Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamethoxazole (SMX) and total organic carbon (TOC) under varying levels of H2O2, Fe2+ concentration, pH, and temperature using statistical and artificial intelligence techniques including Multiple Regression Analysis (MRA), Support Vector Regression (SVR) and Artificial Neural Network (ANN). In statistical metrics, the ANN model demonstrated superior predictive accuracy compared to its counterparts, with lowest RMSE values of 0.986 and 1.173 for SMX and TOC removal, respectively. Sensitivity showcased H2O2/Fe2+ ratio, time and pH as pivotal for SMX degradation, while in simultaneous SMX and TOC reduction, fine tuning the time, pH, and temperature was essential. Leveraging a Hybrid Genetic Algorithm-Desirability Optimization approach, the trained ANN model revealed an optimal desirability of 0.941 out of 1000 solutions which yielded a 91.18% SMX degradation and 87.90% TOC removal under following specific conditions: treatment time of 48.5 min, Fe2+: 7.05 mg L-1, H2O2: 128.82 mg L-1, pH: 5.1, initial SMX: 97.6 mg L-1, and a temperature: 29.8 °C. LC/MS analysis reveals multiple intermediates with higher m/z (242, 270 and 288) and lower m/z (98, 108, 156 and 173) values identified, however no aliphatic hydrocarbon was isolated, because of the low mineralization performance of Fenton process. Furthermore, some inorganic fragments like NH4+ and NO3- were also determined in solution. This comprehensive research enriches AI modeling for intricate Fenton-based contaminant degradation, advancing sustainable antibiotic removal strategies.

Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation.

Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of "Industry 4.0" concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads.