Rapid Way Research Articles

Enzymatization of mouse monoclonal antibodies to the corresponding catalytic antibodies

Catalytic antibodies possess a dual function that enables both antigen recognition and degradation. However, their time-consuming preparation is a significant drawback. This study developed a new method for quickly converting mice monoclonal antibodies into catalytic antibodies using site-directed mutagenesis. Three mice type monoclonal antibodies targeting hemagglutinin molecule of influenza A virus could be transformed into the catalytic antibodies by deleting Pro95 in CDR-3 of the light chain. No catalytic activity was observed for monoclonal antibodies and light chains. In contrast, the Pro95-deleted light chains exhibited a catalytic activity to cleave the antigenic peptide including the portion of conserved region of hemagglutinin molecule. The affinity of the Pro95-deleted light chains to the antigen increased approximately 100-fold compared to the wild-type light chains. In the mutants, three residues (Asp1, Ser92, and His93) come closer to the appropriate position to create the catalytic site and contributing to the enhancement of both catalytic function and immunoreactivity. Notably, the Pro95-deleted catalytic light chains could suppress influenza virus infection in vitro assay, whereas the parent antibody and the light chain did not. This strategy offers a rapid and efficient way to create catalytic antibodies from existing antibodies, accelerating the development for various applications in diagnostic and therapeutic applications.

Insight into the potential of bone turnover biomarkers: integration in the management of osteoporosis and chronic kidney disease-associated osteoporosis.

Disturbances in mineral and bone metabolism occurring in osteoporosis and chronic kidney disease-associated osteoporosis place patients at high risk of fracture making these conditions a major public health concern. Due to the limited use of bone histomorphometry in clinical practice, the gold standard for assessing bone turnover, extensive efforts have been made to identify bone turnover markers (BTMs) as noninvasive surrogates. Since the identification of certain commonly used markers several decades ago, considerable experience has been acquired regarding their clinical utility in such bone disorders. Mounting evidence suggested that BTMs represent a simple, low-risk, rapid and convenient way to obtain data on the skeletal health and that they may be useful in guiding therapeutic choices and monitoring the response to treatment. BTMs could provide clinicians with useful information, independent from, and often complementary to bone mineral density (BMD) measurements. They have proven valuable for monitoring the effectiveness of osteoporosis therapy, as well as promising for discriminating low and high turnover states. Improved performance is observed when BTMs are combined, which may be useful for selecting treatments for chronic kidney disease-bone mineral disorders (CKD-MBD).

Impact of COVID-19 on bus rapid transit system usage in Lagos State, Nigeria: A comparative analysis

Purpose: This study aims to analyse the patronage levels of the Bus Rapid Transit (BRT) system in Lagos State, Nigeria, before, during, and after the COVID-19 pandemic. Methodology: Primary data were collected through an online survey distributed via Google Forms, gathering socio-economic and travel information from respondents. Regression analysis was used to identify factors influencing trip frequency on the BRT system during each phase of the pandemic. Results: The findings revealed significant changes in transport costs and trip frequencies across the three periods. Before the pandemic, 55.64% of respondents spent between N501 and N1000 weekly on transport, while 56.41% spent N1501 and more weekly after the pandemic. The factors influencing trip frequency were seat availability, travel stability, driver attitude, ease of boarding and alighting, safety, travel time, and reliability. Theoretical Contribution: This research contributes to understanding the nuanced effects of COVID-19 on public transportation, particularly within the context of BRT systems in Lagos State. Practical Implications: The study suggests that strategic attention should be given to the lessons learned from COVID-19 to develop appropriate strategies for future unforeseen circumstances.

WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease.

Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG-AMP) have issued a set of guidelines for variant classification. However, given the vast number of variants associated with any disorder, it is impossible to manually apply these guidelines to all known variants. Machine learning methodologies offer a rapid way to classify large numbers of variants, as well as variants of uncertain significance as either pathogenic or benign. Here we classify ATP7B genetic variants by employing ML and AI algorithms trained on our well-annotated WilsonGen dataset. We have trained and validated two algorithms: TabNet and XGBoost on a high-confidence dataset of manually annotated, ACMG & AMP classified variants of the ATP7B gene associated with Wilson's Disease. Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings. We have created a ready to deploy tool, that can classify variants linked with Wilson's disease as pathogenic or benign, which can be utilized by both clinicians and researchers to better understand the disease through the nature of genetic variants associated with it.

Deep learning-based predictive models for pavement patching and manholes evaluation

ABSTRACT Deep Learning (DL) techniques have been applied to the processing and evaluation of pavement conditions. DL-based predictive models are rapid and have higher accuracy compared to traditional methods. In this research, some models were developed to appraise pavement patching by considering the severity and the manhole cover utilising images collected by the Road Surface Profiler (RSP) from Bus Rapid Transit (BRT) lines in Tehran, Iran. Two cases (scenarios) were regarded in this research: 1) Patching without considering the severity, and 2) Patching by considering the severity. YOLOv5, YOLOv6, and YOLOv7 as one-stage object detection algorithms, and Faster R-CNN (ResNet-50) and Faster R-CNN (MobileNet_v3) as two-stage object detection algorithms were evaluated to choose the desired algorithm. The results exhibited that the trained model on YOLOv5 has better performance and is faster than the rest of the algorithms. In addition, the influence of the classes’ combination on model performance was scrutinised. Based on the results of the other classes, the outcomes of the study suggested that the model can detect and train better when the model is trained by a dataset with all classes. Furthermore, the performance of the model was also investigated by examining the effect of image size and YOLOv5's architectures. Based on the obtained results, YOLOv5 and an image size of 640*640 outperformed the rest. In Case 1, the precision, recall, F1-score, and mAP were 75.8%, 67.4%, 71.35%, and 77.4%, respectively. Moreover, the values of these metrics in Case 2, were 70%, 61.2%, 65.3%, and 64.4%, respectively. Hence, the results demonstrated that the developed model can be used with high performance in the detection and evaluation of pavement patching by severity, and manholes in Pavement Management Systems (PMS) and maintenance and rehabilitation plans.

Cellulose nanofiber-mediated manifold dynamic synergy enabling adhesive and photo-detachable hydrogel for self-powered E-skin

Self-powered skin attachable and detachable electronics are under intense development to enable the internet of everything and everyone in new and useful ways. Existing on-demand separation strategies rely on complicated pretreatments and physical properties of the adherends, achieving detachable-on-demand in a facile, rapid, and universal way remains challenging. To overcome this challenge, an ingenious cellulose nanofiber-mediated manifold dynamic synergy strategy is developed to construct a supramolecular hydrogel with both reversible tough adhesion and easy photodetachment. The cellulose nanofiber-reinforced network and the coordination between Fe ions and polymer chains endow the dynamic reconfiguration of supramolecular networks and the adhesion behavior of the hydrogel. This strategy enables the simple and rapid fabrication of strong yet reversible hydrogels with tunable toughness ((Valuemax-Valuemin)/Valuemax of up to 86%), on-demand adhesion energy ((Valuemax-Valuemin)/Valuemax of up to 93%), and stable conductivity up to 12 mS cm−1. We further extend this strategy to fabricate different cellulose nanofiber/Fe3+-based hydrogels from various biomacromolecules and petroleum polymers, and shed light on exploration of fundamental dynamic supramolecular network reconfiguration. Simultaneously, we prepare an adhesive-detachable triboelectric nanogenerator as a human-machine interface for a self-powered wireless monitoring system based on this strategy, which can acquire the real-time, self-powered monitoring, and wireless whole-body movement signal, opening up possibilities for diversifying potential applications in electronic skins and intelligent devices.

The equity implications of TOD in Curitiba

Transit Oriented Development (TOD) projects are being increasingly adopted worldwide as a way to promote the coordination between transport and land-use planning. However, little attention has been given to how TOD projects are associated with socioeconomic and spatial inequalities and its effects on people’s access to economic activities and public services, particularly in the Global South. In this paper we analyze how socio-spatial inequalities have been shaped by transport and land-use planning in Curitiba (Brazil), a city internationally recognized for its TOD planning based on Bus Rapid Transit (BRT). We examine how the spatial organization of the BRT system is associated with the distribution of population densities, socioeconomic groups, and real-estate values and its implications in terms of inequalities of access to employment opportunities and health services. The results show that Curitiba's TOD has had limited influence on population densities, but contributed for the unequal distribution of its benefits, privileging high-income classes and premium real-estate along its main BRT corridors. These effects contribute to push low-income communities to peripheral urban areas with limited accessibility benefits from the transit system. Our findings suggest that Curitiba’s success story should be seen as a cautionary tale about the consequences of TOD planning, which perpetuate the spatial concentration of resources and reinforce inequalities of access to opportunities. The broader lesson of this study is that TOD planning must be constantly evaluated by its socials and environmental impacts, and be guided by mixed housing, and social inclusion to avoid potential consequences in terms of segregation and peripheralization of poorer communities.

An Assessment of Accessibility from a Socially Sustainable Urban Mobility Approach in Mass Transit Projects: Contributions from the Northern Central American Triangle

This article aims to address the lack of research on the social dimension of sustainability, also known as social sustainability, in urban mobility projects, primarily in cities of the Global South. It proposes a strategy to partially assess social sustainability, focusing on accessibility, which is one of the key dimensions for conducting such an evaluation. To this end, a comparative analysis of three study cases is conducted in the capital cities of the Northern Central American Triangle (NCAT) before and after the construction of bus rapid transit (BRT) projects between 2000 and 2020. Accessibility is evaluated through equity and spatial efficiency indicators obtained through geographical information system (GIS) modeling, including layers representing transportation networks, populated areas, and locations of basic urban facilities. The result is an unprecedented assessment of accessibility in the NCAT capitals, which shows how the Guatemala City BRT project has improved the city’s social sustainability by reducing access times to basic urban facilities, mainly public health clinics and educational facilities, and narrowing the inequality gap as compared to projects in San Salvador and Tegucigalpa, the other capital cities in the NCAT. Additionally, it is emphasized that this methodology can be replicated in the Global South while considering the scarcity of information and the use of open-source software in the process.

Safety performance of dedicated and preferential bus lanes using multivariate negative binomial models for Bogotá, Colombia

Public transport priority systems such as Bus Rapid Transit (BRT) and Buses with High Level of Service (BHLS) are top-rated solutions to mobility in low-income and middle-income cities. There is scientific agreement that the safety performance level of these systems depends on their functional, operational, and infrastructure characteristics. However, there needs to be more evidence on how the different characteristics of bus corridors might influence safety. This paper aims to shed some light on this area by structuring a multivariate negative binomial model comparing crash risk on arterial roads, BRT, and BHLS corridors in Bogotá, Colombia. The analyzed infrastructure includes 712.1 km of arterial roads with standard bus service, 194.1 km of BRT network, and 135.6 km of BHLS network. The study considered crashes from 2015 to 2018 –fatalities, injuries, and property damage only– and 30 operational and infrastructure variables grouped into six classes –exposure, road design, infrastructure, public means of transport, and land use. A multicriteria process was applied for model selection, including the structure and predictive power based on [i] Akaike information criteria, [ii] K-fold cross-validation, and [iii] model parsimony. Relevant findings suggest that in terms of observed and expected accident rates and their relationship with the magnitude of exposure –logarithm of average annual traffic volumes at the peak hour (LOG_AAPHT) and the percentage of motorcycles, cars, buses, and trucks– the greatest risk of fatalities, injuries, and property damage occurs in the BHLS network. BRT network provides lower crash rates in less severe collisions while increasing injuries and fatalities. When comparing the BHLS network and the standard design of arterial roads, BHLS infrastructure, despite increasing mobility benefits, provides the lowest safety performance among the three analyzed networks. Individual factors of the study could also contribute to designing safer roads related to signalized intersection density and curvature. These findings support the unique characteristics and traffic dynamics present in the context of Bogotá that could inform and guide decisions of corresponding authorities in other highly dense urban areas from developing countries.

Design of flexible partitions for physical distancing in buses in Colombia during COVID-19

The scientific community recognizes that the transport of cargo and passengers is a key factor in the spread of infectious disease pandemics. In particular, during the COVID-19 pandemic, public transport systems have been considered high-risk environments for the transmission of the virus; therefore, in many places around the world, restrictions have been imposed on passenger transport. Although different aspects of transport have been studied during the pandemic, there are still gaps in knowledge about the effect of different means of transport and specific interventions for vehicle design strategies to reduce transmission rates. In this context, this article presents the process of co-designing a flexible partition to divide seats on articulated, standard and complementary vehicles of a bus rapid transit system in a Colombian capital city as a vehicle design strategy to contain the spread of the virus and generate a physical barrier when the physical distance of one meter, as required by national regulations, was unable to be maintained. The design methodology followed an incremental and iterative process of 6 stages until reaching the final design. The process began with the identification of the need, the establishment of the design requirements and determinants, the generation of proposals framed within the requirements, the performance of functionality and cost feasibility tests and the building of the prototype with the chosen design. Additionally, a participatory evaluation was carried out based on the identification of relevant aspects, doubts, criticisms and new contributions to the prioritized prototype. Finally, the generated prototype met the design requirements in addition to a significant cost reduction of 70% compared with the initial proposal by the bus rapid transit. The low cost allows the implementation of the partition throughout the bus fleet, but controlled observational and clinical studies on the effectiveness of the partition in the prevention of COVID-19 are required.

Influence of pH and lipid membrane on the liquid–liquid phase separation of wheat γ-gliadin in aqueous conditions

Protein body (PB) formation in wheat seeds is a critical process influencing seed content and nutritional quality. In this study, we investigate the potential mechanisms governing PB formation through an in vitro approach, focusing on γ-gliadin, a key wheat storage protein. We used a microfluidic technique to encapsulate γ-gliadin within giant unilamellar vesicles (GUVs) and tune the physicochemical conditions in a controlled and rapid way. We examined the influence of pH and protein concentration on LLPS and protein-membrane interactions using various microscopy and spectroscopy techniques. We showed that γ-gliadin encapsulated in GUVs can undergo a pH-triggered liquid–liquid phase separation (LLPS) by two distinct mechanisms depending on the γ-gliadin concentration. At low protein concentrations, γ-gliadins phase separate by a nucleation and growth-like process, while, at higher protein concentration and pH above 6.0, γ-gliadin formed a bi-continuous phase suggesting a spinodal decomposition-like mechanism. Fluorescence and microscopy data suggested that γ-gliadin dense phase exhibited affinity for the GUV membrane, forming a layer at the interface and affecting the reversibility of the phase separation.

Assessment of energy consumption, environmental effects and fuel costs of the bus rapid transit system in Bogotá (Colombia)

Abstract Colombia aims to boost the utilization of mass transportation systems in its major cities while simultaneously reducing greenhouse gas emissions by 20%, in alignment with the commitments of the COP21 agreement. In 2020, the transport sector in Colombia accounted for 34.4% of the country’s energy demand and was responsible for ~49% of its total CO2 emissions. This article presents an assessment of energy consumption, environmental effects and the fuel costs of Bogotá’s bus rapid transit system based on the Activity, Share, Intensity, Fuel methodology. A long-term analysis spanning from 2021 to 2040 was developed using the long-range energy alternatives planning platform. To conduct this assessment, the tool was calibrated using data from 2019 and 2020. Four distinct scenarios based on energy policies implemented in Bogotá were examined: Business as Usual, Fast Transition, High Growth and Low Growth. Regarding energy consumption and environmental effects, the results underscore the pivotal role of diversifying energy sources and reducing reliance on fossil fuels such as oil. Consequently, the analysis emphasizes the urgent need to accelerate the transition to alternative energy sources such as natural gas and electricity.

Species discrimination from hair using ATR-FTIR spectroscopy: Application in wildlife forensics

Hair is a commonly encountered trace evidence in wildlife crimes involving mammals and can be used for species identification which is essential for subsequent judicial proceedings. This proof of concept study aims, to distinguish the black guard hair of three wild cat species belonging to the genus Panthera i.e. Royal Bengal Tiger (Panthera tigris tigris), Indian Leopard (Panthera pardus fusca), and Snow Leopard (Panthera uncia) using a rapid and non-destructive ATR-FTIR spectroscopic technique in combination with chemometrics. A training dataset including 72 black guard hair samples of three species (24 samples from each species) was used to construct chemometric models. A PLS2-DA model successfully classified these three species into distinct classes with R-Square values of 0.9985 (calibration) and 0.8989 (validation). VIP score was also computed, and a new PLS2DA-V model was constructed using variables with a VIP score ≥ 1. External validation was performed using a validation dataset including 18 black guard hair samples (6 samples per species) to validate the constructed PLS2-DA model. It was observed that PLS2-DA model provides greater accuracy and precision compared to the PLS2DA-V model during cross-validation and external validation. The developed PLS2-DA model was also successful in differentiating human and non-human hair with R-Square values of 0.99 and 0.91 for calibration and validation, respectively. Apart from this, a blind test was also carried out using 10 unknown hair samples which were correctly classified into their respective classes providing 100 % accuracy. This study highlights the advantages of ATR-FTIR spectroscopy associated with PLS-DA for differentiation and identification of the Royal Bengal Tiger, Indian Leopard, and Snow Leopard hairs in a rapid, accurate, eco-friendly, and non-destructive way.

Virtual reconstruction of archaeological pottery using visual programming language

Working on an archaeological site a large part of the time is invested in the recreation of the pieces from the fragments found. This recreation can be a simple 2D drawing or lately, in order to obtain a greater amount of information, virtual 3D models. In this study we show the process carried out to obtain the virtual model of a hand-made pottery vessel from a point cloud obtained by photogrammetry. Orientation and reconstruction are based on geometry features using visual programming. The workflow generated allows us to reconstruct the complete piece from fragments in a rapid and accurate way. From fragments the complete piece has been reconstructed and texturize with more than 90 % the points adjusted to the model in a distance better than 0.5 mm.

An image segmentation of adhesive droplets based approach to assess the quality of pesticide spray

Pesticide spray is a widely used chemical control method to minimize biological disasters in the agriculture industry. It is important to evaluate the efficacy and the quality of a pesticide sprayer, however, it cannot be conveniently achieved due to the lack of accessibility to the sprayed droplets and the intensity of the associated work. This paper proposes a novel method, based on an image processing-based approach, to assess the spray quality. The proposed method uses a combination of algorithms and criteria functions; such as, the maximum between-cluster variance algorithm, area threshold criteria, roundness factor, mathematical morphology operations, and the optimized watershed algorithm, to segment and assess the dark blue adhesive droplet images on a water-sensitive paper, placed in the crop field. In this work, three kinds of evaluation experiments are considered: (i) the manual analysis via droplet counting and processing, (ii) the automatic analysis by the commercially available droplet analyzer by Shenzhen DJI Co. Ltd., and (iii) the novel image processing based method introduced in this paper. The experimental results show a better consistency between the introduced novel method and the manual method. The former, however, provides a convenient and rapid way to assess the spray quality. It comes with an assessment algorithm along with an embedded device that provides a hardware view of the spray quality.

The Significance of Communication in Resolving Disputes: Insights into the Negotiation Process in BRT Project, Peshawar

Negotiation means reaching a mutual consensus with the aim of resolving the disputes with acceptable solutions for all the parties. Negotiation is common in day-to-day affairs, but is of more significance in the areas, like contracts, business deals and domestic problems. The literature shows that negotiation is a communication process and provides a medium for the opposite sides to sit across the table and resolve the disputes and conflicts. Contrary to this, the available literature emphasis more on the decision-making aspect of the negotiation, but lacks discussion on the communication processes, which can in fact make or break a deal, project, or a contract. To address this issue, the aim of this study was to investigate the communication processes in an actual negotiation scenario. For this purpose, the communication processes in Bus Rapid Transit (BRT) were studied and 30 interviews were conducted from the project team and the affected persons. Data was analyzed using applications of constructivist grounded theory which leads to the formation of Communication as a major theme of study. The results exhibit that communication serves as a medium for exchange of information and has an integral part in negotiation processes. Meetings, interactions, discussions, and networking emerged as major themes of communication in this study, and it mediates the negotiation process and leads to the resolution of disputes. Therefore, effective communication plays a significant role in negotiation, reaching agreements and resolving disputes. This research studied negotiation and communication in BRT project only. Further studies could analyze the negotiation processes in other projects and scenarios to further elaborate mediation of communication in negotiations.

Experimental design for determining the factors influencing the filtration efficiency of solution-blown spun polylactic acid filters

Millions of people are wearing face masks because of the COVID-19 epidemic, and these masks are indiscriminately releasing pollutants into the atmosphere. One of the most appealing and promising bio-based polymers for face masks is polylactic acid (PLA). Solution-blown spinning (SBS) is a rapid and easy way to produce micro and nanofibers that may be quickly deposited on any substrate. This study aims to produce and evaluate solution-blown spun PLA fibers used as filter media. PLA nanowebs were observed for their morphology, weight, thickness, air permeability, water contact angle, and filtration properties. The average diameter of the fibers ranged between ∼163 and ∼282 nm. NaCI aerosol filtration test results showed that the filtration efficiency of these nanowebs was ≥ 94% and the pressure drop was ≤ 240 Pa, meeting requirements was assigned to the class EN 149: FFP2 particle-filtering half-maks. Filtration performance was significantly influenced by the flow rate and polymer concentration. The resulting equations for the design of experiments (DoE) regression model were examined and found to be appropriate for predicting the parameter combinations required for the desired quality factor of PLA filters. The fast and economical features of the SBS process, as well as the biodegradable nature of the PLA polymer, may considerably contribute to the development of environmentally friendly filters.

Rapid synthesis of Cu2O/CeO2 p–n heterojunctions with enhanced photocatalytic properties by a sonochemical method

AbstractThe construction of p–n heterojunction is critical to reduce the electron–hole recombination rate and broaden the light absorption edge of photocatalysts. In this study, we successfully synthesize the Cu2O/CeO2 p–n heterojunctions through a rapid, simple, and environmentally friendly sonochemical method without any additional oxidants or elevation of the temperature and pressure. The prepared Cu2O/CeO2 heterojunction shows excellent adsorption of light, low electronic transfer resistance, and higher electron transfer efficiency. Under the irradiation of visible light, the as‐prepared Cu2O/CeO2 heterojunction exhibits exceptional capability for the degradation of methylene blue as well as outstanding reusability. The photo‐generated h+ is found to have a significant influence on the photocatalytic antibacterial of the as‐prepared Cu2O/CuO2 heterojunction under visible‐light irradiation and the sterilization rate toward Staphylococcus aureus and Escherichia coli of our samples can achieve up to 100%. Our work provides a green and rapid way to produce Cu2O/CeO2 p–n heterojunctions with enhanced photocatalysis.

Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy.

Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.

Seabream Quality Monitoring Throughout the Supply Chain Using a Portable Multispectral Imaging Device

Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision−making, reducing food waste, and increasing sustainability. A portable multispectral imaging sensor was used for the rapid prediction of microbiological quality of fish fillets. Seabream fillets, packaged either in aerobic or vacuum conditions, were collected from both aquaculture and retail stores, while images were also acquired both from the skin and the flesh side of the fish fillets. In parallel to image acquisition, the microbial quality was also estimated for each fish fillet. The data were used for the training of predictive artificial neural network (ANN) models for the estimation of total aerobic counts (TACs). Models were built separately for fish parts (i.e., skin, flesh) and packaging conditions and were validated using two approaches (i.e., validation with data partitioning and external validation using samples from retail stores). The performance of the ANN models for the validation set with data partitioning was similar for the data collected from the flesh (RMSE = 0.402–0.547) and the skin side (RMSE = 0.500–0.533) of the fish fillets. Similar performance also was obtained from validation of the models of the different packaging conditions (i.e., aerobic, vacuum). The prediction capability of the models combining both air and vacuum packaged samples (RMSE = 0.531) was slightly lower compared to the models trained and validated per packaging condition, individually (RMSE = 0.510, 0.516 in air and vacuum, respectively). The models tested with unknown samples (i.e., fish fillets from retail stores-external validation) showed poorer performance (RMSE = 1.061–1.414) compared to the models validated with data partitioning (RMSE = 0.402–0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated. Hence, these outcomes could be beneficial not only for the industry and food operators but also for the authorities and consumers.