The biopharmaceutical industry is witnessing a rapid growth, with the therapeutic antibody market expanding significantly due to the increasing demand for targeted therapies and personalized medicine. Traditional fed-batch processes for monoclonal antibody (mAb) production, while effective and well established, necessitate the use of large bioreactors and extensive supporting infrastructure. These requirements translate to high operational costs and complex logistics, which pose significant challenges for small and medium-sized enterprises (SMEs) that may lack the resources and capital to invest in such expansive setups. This study proposes a new single-step concentrated fed-batch process leveraging Alternating Tangential Flow (ATF) perfusion of the inoculum integrated to the production bioreactor to achieve high initial cell densities. A model-based approach was adopted to rationally optimise the process parameters. By integrating high seeding densities and optimised process strategies, this approach enhanced bioreactor efficiency and product yield by 4 to 6-fold (conventional 1.9 g/L to 10 - 12 g/L in intensified process), without compromising protein quality. The intensified process was further validated using different territory CHO cell lines and was found to yield similar results. An analysis of conventional fed-batch process to the new plug-and-play hybrid process suggested a 60 % reduction in costs, improvement in production efficiency, and consistent product quality, including post-translational modifications and glycosylation profiles. This scalable method is adaptable to various cell lines and biopharmaceutical products, offering a promising alternative to conventional methods and enabling faster, more cost-effective biomanufacturing.

INTRODUCTION:

Autografts are the gold standard for ligament replacement but have notable disadvantages. Tissue-engineered implants, particularly electrospun scaffolds, offer a promising alternative by replicating the extracellular matrix's morphology [1]. Machine learning (ML) can optimise the electrospinning process, creating ligament implants with morphology and mechanical properties similar to native tissues [2]. Studying the in vivo hyper-elastic behavior of ligaments through motion capture and musculoskeletal models can further inform biomimetic construct design [3]. This research aims to develop a manufacturing optimisation methodology using ML models and experimental biomechanics to create biomimetic ligament implants.

METHODS:

Polyvinyl alcohol (PVA) scaffolds were produced by systematically modifying the polymer concentration and production parameters. Both 2D and 3D scaffolds were characterised morphologically via scanning electron microscopy and mechanically through tensile testing. Data from 2560 observations informed 20 ML models to predict fibre diameter and inter-fibre separation. Additionally, 28 ML models predicted mechanical properties, including Young's modulus and ultimate tensile strength. A musculoskeletal knee model, combined with kinematic data from 12 young participants, estimated the in vivo biomechanics of the anterior cruciate ligament (ACL).

RESULTS AND DISCUSSION:

Decision Trees and Rule-Based Models generated a visual route to optimise the electrospinning process, achieving a morphology prediction accuracy of 0.868. Cubist models were most accurate for predicting mechanical properties, with an R² of 0.93. Crosslinked triple-twisted/braided filament scaffolds replicated the hyper-elastic behaviour of the native ACL effectively, showing R² values of 0.971 and 0.999 when using Mooney Rivlin and non-linear string-based models, respectively.

CONCLUSIONS:

PVA electrospun scaffolds, optimised using Decision Trees and Rule-Based Models, successfully replicated the morphology and hyper-elastic behaviour of natural ACLs.

REFERENCES:

1. Roldán, E. et. al. Frontiers in Bioengineering and Biotechnology 2023, 11, doi:10.3389/fbioe.2023.1160760.
2. Roldán, E. et. al. Frontiers in Physics 2023, 11, doi:10.3389/fphy.2023.1112218.
3. Roldán, E. et. al. Materialia 2024, 33, 102042, doi:10.1016/j.mtla.2024.102042.

Exopolysaccharides (EPSs) have many potential uses in several fields. However, the cost of production of many exopolysaccharides is disadvantageous and the labour requirement is costly. Apart from the use of exopolysaccharides as a food additive, they also have increasingly important applications in sectors such as medicine and cosmetics.

This study aimed to scale up exopolysaccaride production by Rhizobium radiobacter from 500 mL Erlenmeyer to 2000 mL in a stirred tank bioreactor using constant impeller tip speed methodology. Rhizobium radiobacter, obtained from the American Type Culture Collection (ATCC) in a total of 19358 strains, was used to produce exopolysaccarides. The cells were grown in 30 °C with a pH value of 5.5 for 96 h at 238 rpm in the bioreactor and at 180 rpm in Erlenmeyer. The 3.5-dinitrosalicylic acid (DNS) assay and phenol-sulphuric acid method were used to determine the reducing sugar and total sugar levels, respectively.

The biomass produced in the bioreactor was found to be 5.48±0.02 g/L, which was 13% better than that produced in the flask. On the other hand, an exopolysaccharide yield of 12.38±0.01 g/g was obtained in the bioreactor, and an increase of 7% was recorded when compared with Erlenmeyer production.

For EPS production, cost-effective conditions were determined according to this process in order to both increase product efficiency and reduce production costs. In addition, in order to increase product quantity and efficiency, processes with shorter production times can be designed and appropriate environmental conditions can be provided.

Introduction: The relationship between trunk muscles, low back pain, and exercise has been studied for many years. Subjects with low back pain have shown different responses to fatigue compared to healthy subjects, and this particular behaviour has been proposed as a predictor of low back pain. Several experimental studies have been conducted to identify valid and reliable biomarkers that explain the functioning of the trunk muscle complex. The aim of this study was to analyse the behaviour of the lumbar musculature in healthy subjects and a subject with a history of low back pain.

Methods: The participants underwent a squat protocol using a barbell loaded with 60% of their body weight. They performed as many repetitions as possible at controlled eccentric and maximal concentric speeds. The connectivity of the multifidus muscles was analysed using intermuscular coherence (IMC) during the concentric phase of the exercise.

Results and Discussion: The findings indicated that intermuscular coherence (IMC) decreased among control subjects by the end of the squat series, whereas the subject with a history of low back pain exhibited the opposite trend. These results contrast with the existing literature, though the study's limitations preclude definitive comparisons.

Conclusions: Despite the findings, IMC could potentially be a valuable tool for characterising fatigue and low back pain.

The rapid development of biomedical technologies, particularly in the development of new diagnostic devices, has sparked the collaboration of experts from different fields such as biology, materials engineering, and physics. The most important challenge is to create innovative materials and improve biomedical sensors capable of detecting the presence or concentration of specific biological substances.

The possibility of applying poly(4-vinyl pyridine) layers cross-linked with transition metal complexes as active layers in biomedical sensors was tested. The successful modification of the P4VP coating with CuBr2 or ZnBr2 was verified using time of flight--secondary ion mass spectrometry and X-ray photoelectron spectroscopy. The topography of the coatings was examined by using atomic force microscopy. Tests of the biological activity of coatings indicated strong protein adsorption, good biocompatibility, and no antimicrobial activity. The potential of the coatings to be used as active layers of biosensors was verified by systematic impedance-based measurements, which showed the sensitivity of the P4VP:CuBr2 coatings to the presence of proteins and cells in different concentrations and indicated different detection limits for the P4VP:ZnBr2 layers. The high selectivity of the coatings toward the defined analyte was confirmed by the specific antigen--antibody immunoreaction and the possibility of in situ monitoring of protein adsorption and cell adhesion for individual cells. Finally, the conductive response of a bilayer system that mimics an Organic Field Effect Transistor was shown. These results point to a great potential for both coatings to serve as active layers of sensitive and highly selective biosensors.

In recent decades, there has been an increasing interest in developing detection systems at the smallest scale and as easy to use as possible for a wide range of applications, including the food and environmental sectors, but especially for medical diagnostics. Advances in analytical electrochemistry research facilitate expanding the application range, improving repeatability, lowering detection limits, and simplifying the target analyte detection process by leveraging nanotechnology to produce sensors. In this work, we developed a synthesis method without additives for developing hybrid nanostructures obtained by embedding In2O3 nanoparticles in graphene oxide (GO) sheets for electrochemical applications. Using GO obtained by the Hummer method and indium nitrate as a precursor, In2O3-GO nanocomposites were obtained by an in-situ hydrothermal method. The samples' shape, size, structural phase purity, functional groups, and wetting capability were assessed using a range of analytical techniques. The structural characteristics of the oxide, carbon material, and composite were examined by spectroscopic analysis. The surface morphology, particle size, and distribution of In2O3 nanoparticles in the carbon material were examined using a field emission scanning electron microscope. The wetting and percolation threshold of the nanocomposite were observed through goniometric experiments. Cyclic voltammetry was used to evaluate the application potential of In2O3-GO nanocomposites.

Acknowledgements:

This work was supported by the Core Program within the National Research Development and Innovation Plan 2022-2027, carried out with the support of MCID, project no. 2307 (µNanoEl).

One novel approach is the usage of artificial intelligence (AI) and/or machine learning (ML) algorithms to monitor, estimate, and manage the risks in microalgal processes. Artificial intelligence (AI) allows computers to simulate the intelligence of humans and machine learning is an artificial intelligence subfield. The basic idea of machine learning is to use inductive analysis to expand the connections between input and output, which are subsequently used to impact decisions in new scenarios. The input variables are pH, carbon dioxide level, inoculum, illumination, temperature, and nutrient level, whereas the outputs are biomass and bioproduct yields in microalgal processes. Enormous quantities of data generated by sensor monitoring systems may be employed as inputs to optimize parameters in AI/ML models. Therefore, AI/ML algorithms can forecast biomass/bioproduct production.

MLAs have recently been used in the research of microalgal processes, although this is still in ithe early stages for industrial applications. Approximately 75% of the energy consumed is wasted during drying step; however, using ML models might dramatically cut costs while improving output. In particular, artificial neural networks (ANNs) are mostly used for predicting microalgal growth, the support vector machine (SVM) algorithm is chosen for microalgal wastewater treatment. On the other hand, the genetic algorithm (GA) is utilized to optimize biomass and bioproduct production and the random forest (RF) algorithm performs better when determining whether microalgal populations are dead or living. In general, the reliability of machine learning models improves as data availability rises.

Using AI/ML models may help achieve microalgal production objectives through a more sustainable, smart, and economical approach. Using AI/ML-powered smart systems, such as 3D-printed, real-time optical density monitoring instruments and an Internet of Things (IoT) enabled by smartphones, could help microalgal processes make better decisions.

The analysis of volatile organic compounds (VOCs) in exhaled breath offers promising insights into metabolic processes and their changes in response to food intake. This study investigates the impact of food intake on exhaled breath VOC profiles using a custom-built electronic nose system. The primary objective was to differentiate the exhaled breath profile before and after food intake at various time intervals. A cohort of 35 healthy, non-smoking individuals was selected for the study. Breath samples were collected and analyzed at four key time points: before food intake, ten minutes post intake, one hour post intake, and two hours post intake. The collected VOC data were subjected to analysis using a Support Vector Machine (SVM) classification model. The SVM model successfully differentiated breath samples taken before food intake from those taken ten minutes after with an accuracy of 71%, and from those taken one hour after with an accuracy of 69%. However, no significant difference was observed in the breath profiles between the baseline (before food intake) and two hours post intake. These findings suggest that the exhaled VOC profile is significantly influenced by recent food intake, particularly within the first hour. The lack of significant differences between the baseline and two hours post intake indicates a potential stabilization of metabolic changes induced by food. This study demonstrates the potential of using electronic nose technology for monitoring dietary impacts on metabolic processes through VOC analysis in exhaled breath.

Introduction: Electrophysiological studies of cortical activity have highlighted the organizational and functional differences in the cortex of blind subjects versus normal subjects. This reorganization is optimized by the nervous system to adapt to the new sensory modalities that subjects use in daily life. A cortical visual prosthesis is capable of restoring visual sensations to blind subjects based on phosphenes, which attempt to provide them with information about their surrounding environment. In this context, our study aims to characterize the cortical alterations resulting from the use of a vision neuroprosthesis.

Methods: In this preliminary approach, the blind subjects under study were temporary users (6 months) of a visual neuroprosthesis, which, through an array of microelectrodes implanted in the primary visual cortex, provided patterns of electrical stimulation that in turn evoked perceptions of phosphenes. To explore the cortical alterations resulting from the use of this neuroprosthesis, the cortical connectivity (spectral coherence, SC) of the users was analyzed in the resting state using electroencephalography techniques.

Results and Discussion: SC between all EEG channels revealed significant changes (p < 0.001) in 60% of the connections in the alpha band as a result of daily use of the neuroprosthesis (before implantation vs. after explantation). In other energy bands, connectivity was altered to a lesser extent (around 30% in the beta band). When comparing resting state activity with that of normal subjects, significant differences were observed in the beta band before the implant, whereas after the implant these differences tended to disappear.

Conclusions: These preliminary results revealed that cortical connectivity in the resting state significantly changes with the use of the vision neuroprosthesis, tending in some cases towards cortical patterns similar to those of non-blind individuals.

Introduction:
Cricket bowling has traditionally required a rigid arm, with no elbow straightening allowed. However, research shows that maintaining a completely rigid arm is impractical, leading to rule changes. The current regulation mandates that the elbow extension angle must not increase by more than 15 degrees. This study aims to determine whether this rule is upheld across various bowlers and styles and to explore if alternative kinematic measures can better distinguish between legal and suspect actions.
Methods:
Sixteen male fast bowlers from NSW grade clubs were analysed using a Cortex 2.0 motion analysis system (200 Hz). The study measured changes in elbow angular kinematics, defined as the angle between the upper arm and forearm relative to the flexion-extension axis of a joint coordinate system.
Results and Discussion:
The mean elbow extension angle was 14.0 ± 8.4°. Surprisingly, six bowlers exceeded the 15° legal limit, even though only one was considered suspect a priori. This may be due to a perspective error of elbow flexion during the critical period when the arm is horizontal and behind the bowler’s body. The mean change in elbow flexion-extension angular velocity at release was -123.5 ± 316.9 °/s, showing significant variability, an indicator of the diversity of elbow mechanics in bowling. This diversity suggests that a one-size-fits-all model for elbow movement during bowling is not valid. Coaches and regulatory bodies need to consider this variability when developing training programs and rules.
Conclusions:
More bowlers exceed the legal limit than visually assessed. Rigorous biomechanical screening programs are essential to ensure fairness for batters and compliant bowlers. High elbow angular velocities make it difficult to detect throw-like actions visually. Educating coaches on perspective errors is crucial. Analysing elbow extension angular velocity at ball release provides a more effective differentiation. Cricket authorities must address bowling legality issues for the game's future equity.