A Robust CETSA Data Analysis Automation Workflow for Routine Screening.

The Cellular Thermal Shift Assay (CETSA) enables the study of protein-ligand interactions in a cellular context. It provides valuable information on the binding affinity and specificity of both small and large molecule ligands in a relevant physiological context, hence forming a unique tool in drug discovery. Though high-throughput lab protocols exist for scaling up CETSA, subsequent data analysis and quality control remain laborious and limit experimental throughput. Here, we introduce a scalable and robust data analysis workflow which allows integration of CETSA into routine high throughput screening (HT-CETSA). This new workflow automates data analysis and incorporates quality control (QC), including outlier detection, sample and plate QC, and result triage. We describe the workflow and show its robustness against typical experimental artifacts, show scaling effects, and discuss the impact of data analysis automation by eliminating manual data processing steps.

Benchmarking medical laboratory performance on a global scale.

Background and aims: Laboratory performance as a relative concept needs repetitive benchmarking for continuous improvement of laboratory procedures and medical processes. Benchmarking as such establishes reference levels as a basis for improvements efforts for healthcare institutions along the diagnosis cycle, with the patient at its center. But while this concept seems to be generally acknowledged in laboratory medicine, a lack of practical implementation hinders progress at a global level. The aim of this study was to examine the utility of a specific combination of indicators and survey-based data collection approach, and to establish a global benchmarking dataset of laboratory performance for decision makers in healthcare institutions. Methods: The survey consisted of 44 items relating to laboratory operations in general and three subscales identified in previous studies. A global sample of laboratories was approached by trained professionals. Results were analyzed with standard descriptive statistics and exploratory factor analysis. Dimensional reduction of specific items was performed using confirmatory factor analysis, resulting in individual laboratory scores for the three subscales of "Operational performance," "Integrated clinical care performance," and "Financial sustainability" for the high-level concept of laboratory performance. Results and conclusions: In total, 920 laboratories from 55 countries across the globe participated in the survey, of which 401 were government hospital laboratories, 296 private hospital laboratories, and 223 commercial laboratories. Relevant results include the need for digitalization and automation along the diagnosis cycle. Formal quality management systems (ISO 9001, ISO 15189 etc.) need to be adapted more broadly to increase patient safety. Monitoring of key performance indicators (KPIs) relating to healthcare performance was generally low (in the range of 10-30% of laboratories overall), and as a particularly salient result, only 19% of laboratories monitored KPIs relating to speeding up diagnosis and treatment. Altogether, this benchmark elucidates current practice and has the potential to guide improvement efforts and standardization in quality & safety for patients and employees alike as well as sustainability of healthcare systems around the globe.

Comprehensive wheat coccinellid detection dataset: Essential resource for digital entomology.

Wheat (Triticum aestivum) is a major cereal crop planted in the Southern Great Plains. This crop faces diverse pests that can affect their development and reduce yield productivity. For example, aphids are a significant pest in wheat, and their management relies on pesticides, which affect the sustainability and biodiversity of natural predators that prey on aphids. Coccinellids, commonly named lady beetles, are the most abundant natural predators of wheat. These natural enemies contribute to the natural predation of aphids, which can reduce the use of excessive pesticides for aphid management. Usually, visual observations of these natural enemies are performed during pest sampling; however, it is time-consuming and requires manual labor, which can be expensive. An automation system or detection models based on machine learning approaches that can detect these insects is needed to reduce unnecessary pesticide applications and manual labor costs. However, developing an automation system or computer vision models that automatically detect these natural enemies requires imagery to train and validate this cutting-edge technology. To solve this research problem, we collected this dataset, which includes images and label annotations to help researchers and students develop this technology that can benefit wheat growers and science to understand the capabilities of automation in Entomology. We collected a dataset using mobile devices, which included a diverse range of coccinellids on wheat images. The dataset consists of 2,133 images with a standard size of 640 × 640 pixels, which can be used to train and develop detection models for machine learning purposes. In addition, the dataset includes annotated labels that can be used for training models within the YOLO family or others, which have been proven to detect small insects in crops. Our dataset will increase the understanding of machine learning capabilities in entomology, precision agriculture, education, and crop pest management decisions.

Automated workflows using Quantitative Colour Pattern Analysis (QCPA): a guide to batch processing and downstream data analysis.

Animal and plant colouration presents a striking dimension of phenotypic variation, the study of which has driven general advances in ecology, evolution, and animal behaviour. Quantitative Colour Pattern Analysis (QCPA) is a dynamic framework for analysing colour patterns through the eyes of non-human observers. However, its extensive array of user-defined image processing and analysis tools means image analysis is often time-consuming. This hinders the full use of analytical power provided by QCPA and its application to large datasets. Here, we offer a robust and comprehensive batch script, allowing users to automate many QCPA workflows. We also provide a complimentary set of useful R scripts for downstream data extraction and analysis. The presented batch processing extension will empower users to further utilise the analytical power of QCPA and facilitate the development of customised semi-automated workflows. Such quantitatively scaled workflows are crucial for exploring colour pattern spaces and developing ever-richer frameworks for analysing organismal colouration accounting for visual perception in animals other than humans. These advances will, in turn, facilitate testing hypotheses on the function and evolution of vision and signals at quantitative and qualitative scales, which are otherwise computationally unfeasible. Supplementary Information: The online version contains supplementary material available at 10.1007/s10682-024-10291-7.

POStoperative INTELLiVENT-adaptive support VEntilation in cardiac surgery patients (POSITiVE) II-study protocol of a randomized clinical trial.

BACKGROUND: One single-center randomized clinical trial showed that INTELLiVENT-adaptive support ventilation (ASV) is superior to conventional ventilation with respect to the quality of ventilation in post-cardiac surgery patients. Other studies showed that this automated ventilation mode reduces the number of manual interventions at the ventilator in various types of critically ill patients. In this multicenter study in patients post-cardiac surgery, we test the hypothesis that INTELLiVENT-ASV is superior to conventional ventilation with respect to the quality of ventilation. METHODS: "POStoperative INTELLiVENT-adaptive support VEntilation in cardiac surgery patients II (POSITiVE II)" is an international, multicenter, two-group randomized clinical superiority trial. In total, 328 cardiac surgery patients will be randomized. Investigators screen patients aged > 18 years of age, scheduled for elective cardiac surgery, and expected to receive postoperative ventilation in the ICU for longer than 2 h. Patients either receive automated ventilation by means of INTELLiVENT-ASV or ventilation that is not automated by means of a conventional ventilation mode. The primary endpoint is quality of ventilation, defined as the proportion of postoperative ventilation time characterized by exposure to predefined optimal, acceptable, and critical (injurious) ventilatory parameters in the first two postoperative hours. One major secondary endpoint is ICU team staff workload, captured by the ventilator software collecting manual settings on alarms. Patient-centered endpoints include duration of postoperative ventilation and length of stay in ICU. DISCUSSION: POSITiVE II is the first international, multicenter, randomized clinical trial designed to confirm that POStoperative INTELLiVENT-ASV is superior to non-automated conventional ventilation and secondary to determine if this closed-loop ventilation mode reduces ICU team staff workload. The results of POSITiVE II will support intensive care teams in their choices regarding the use of automated ventilation in postoperative care of uncomplicated cardiac surgery patients. TRIAL REGISTRATION: Clinicaltrials.gov NCT06178510 . Registered on December 4, 2023.

A review of robot-assisted ultrasound examination: Systems and technology.

BACKGROUND: At present, the number and overall level of ultrasound (US) doctors cannot meet the medical needs, and the medical ultrasound robots will largely solve the shortage of medical resources. METHODS: According to the degree of automation, the handheld, semi-automatic and automatic ultrasound examination robot systems are summarised. Ultrasound scanning path planning and robot control are the keys to ensure that the robot systems can obtain high-quality images. Therefore, the ultrasound scanning path planning and control methods are summarised. The research progress and future trends are discussed. RESULTS: A variety of ultrasound robot systems have been applied to various medical works. With the continuous improvement of automation, the systems provide high-quality ultrasound images and image guidance for clinicians. CONCLUSION: Although the development of medical ultrasound robot still faces challenges, with the continuous progress of robot technology and communication technology, medical ultrasound robot will have great development potential and broad application space.

Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed.

BACKGROUND: The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice. METHODS: In November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies. RESULTS: From 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs. CONCLUSIONS: Automation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.

Comparative assessment of the diagnostic performances of particle-based multianalyte technology and commercial ELISA for antiphospholipid autoantibody testing.

BACKGROUND: Diagnosing Antiphospholipid Syndrome (APS) relies heavily on laboratory findings, particularly the detection of specific antibodies like lupus anticoagulant (LA), IgG and/or IgM anti-cardiolipin (aCL), and IgG and/or IgM anti-ß2 glycoprotein 1 (aB2GP1). Although ELISA is widely used in the US for this purpose, standardization between different assay methodologies remains challenging, leading to significant variability across laboratories. Particle-based multi-analyte technology (PMAT) offers a streamlined one-step detection for all six antiphospholipid (aPL) autoantibodies, covering aCL and aB2GP1 of IgA, IgG, and IgM isotypes. METHODS: In this study involving 224 subjects, including 34 clinically diagnosed with APS, alongside 160 non-APS patients and 30 healthy donors, PMAT's performance was evaluated against commercial ELISA in detecting aPL antibodies. RESULTS: At the manufacturer's suggested cutoff, PMAT exhibited sensitivity comparable to ELISA, albeit with a low to moderate decrease in specificity for certain antibodies. With anti-CL IgM alone, PMAT displayed a 17.7% decrease in sensitivity, accompanied by a corresponding 31.1% increase in specificity compared to ELISA. However, applying a stricter cutoff (88-90% specificity), IgA and IgM antibodies yielded 5.9-17.6% higher sensitivities with PMAT, and IgG antibodies displayed similar sensitivity. CONCLUSIONS: In this study cohort, PMAT demonstrated higher or comparable sensitivity to that of commercial ELISA for all six aPL antibodies at a specificity cutoff near 90%. Notably, PMAT demonstrated superior sensitivity and specificity overall in detecting IgA aCL and aB2GP1 antibodies. This study highlights the potential of automated PMAT for detecting aPL antibodies in APS evaluation.

AutoBioTech─A Versatile Biofoundry for Automated Strain Engineering.

The inevitable transition from petrochemical production processes to renewable alternatives has sparked the emergence of biofoundries in recent years. Manual engineering of microbes will not be sufficient to meet the ever-increasing demand for novel producer strains. Here we describe the AutoBioTech platform, a fully automated laboratory system with 14 devices to perform operations for strain construction without human interaction. Using modular workflows, this platform enables automated transformations of Escherichia coli with plasmids assembled via modular cloning. A CRISPR/Cas9 toolbox compatible with existing modular cloning frameworks allows automated and flexible genome editing of E. coli. In addition, novel workflows have been established for the fully automated transformation of the Gram-positive model organism Corynebacterium glutamicum by conjugation and electroporation, with the latter proving to be the more robust technique. Overall, the AutoBioTech platform excels at versatility due to the modularity of workflows and seamless transitions between modules. This will accelerate strain engineering of Gram-negative and Gram-positive bacteria.

Large language models are changing landscape of academic publications. A positive transformation?

The advent of large language models (LLMs) based on neural networks marks a significant shift in academic writing, particularly in medical sciences. These models, including OpenAI's GPT-4, Google's Bard, and Anthropic's Claude, enable more efficient text processing through transformer architecture and attention mechanisms. LLMs can generate coherent texts that are indistinguishable from human-written content. In medicine, they can contribute to the automation of literature reviews, data extraction, and hypothesis formulation. However, ethical concerns arise regarding the quality and integrity of scientific publications and the risk of generating misleading content. This article provides an overview of how LLMs are changing medical writing, the ethical dilemmas they bring, and the possibilities for detecting AI-generated text. It concludes with a focus on the potential future of LLMs in academic publishing and their impact on the medical community.

A practical approach to supported liquid extraction and measurement of 18 steroids in plasma and serum by targeted liquid chromatography tandem mass spectrometry.

Chromatography combined with mass spectrometry is a gold standard technique for steroid measurement, however the type of sample preparation, the dynamic range and reliability of the calibration curve, the chromatographic separation and mass spectrometry settings ultimately determine the success of the method. The steroid biosynthetic pathway is conserved in higher mammals and literature demonstrates that the concentration ranges of different steroid groups are relatively comparable across species. We sought to develop a robust and reliable multi steroid targeted analysis method for blood that would have wide application across higher mammals. The method was developed following bioanalytical method validation guidelines to standards typically applied to human clinical studies, including isotopically labelled internal standards where at all possible. Here we describe the practical approach to a 96-well supported liquid extraction (SLE) method of extraction from plasma (200 µL) using an Extrahera liquid handling robot (Biotage, Sweden), including quality control samples, followed by a comprehensive separation and targeted LC-MS/MS analysis of 18 steroids in plasma (pregnenolone, progesterone, 17α-hydroxyprogesterone, 11-deoxycorticosterone, corticosterone, 11-dehydrocorticosterone, aldosterone, 11-deoxycortisol, 21-deoxycortisol, cortisol, cortisone, androstenedione, testosterone, 5α-dihydrotestosterone, dehydroepiandrosterone, estrone, 17ß-estradiol and estriol). •SLE in a 96-well format of up to 74 biological plasma samples, enriched with multiple isotopically labelled internal standards, a 12-point aqueous calibration curve, and 6 serum quality controls, designed to monitor long-term performance of the method•Chromatographic separation of multiple steroids along the gradient, with ammonium fluoride mobile phase additive to improve sensitivity, followed by electrospray ionisation and constant polarity switching•Aqueous calibration standards that cover physiologically relevant ranges - high nanomolar glucocorticoids, low nanomolar androgens and picomolar ranges for estrogens and steroid intermediates.

Automation in canine science: enhancing human capabilities and overcoming adoption barriers.

The emerging field of canine science has been slow in adopting automated approaches for data analysis. However, with the dramatic increase in the volume and complexity of the collected behavioral data, this is now beginning to change. This paper aims to systematize the field of automation in canine science. We provide an examination of current automation processes and pipelines by providing a literature review of state-of-the-art studies applying automation in this field. In addition, via an empirical study with researchers in animal behavior, we explore their perceptions and attitudes toward automated approaches for better understanding barriers for a wider adoption of automation. The insights derived from this research could facilitate more effective and widespread utilization of automation within canine science, addressing current challenges and enhancing the analysis of increasingly complex and voluminous behavioral data. This could potentially revolutionize the field, allowing for more objective and quantifiable assessments of dog behavior, which would ultimately contribute to our understanding of dog-human interactions and canine welfare.

Rapid Drug Susceptibility Testing to Preserve Antibiotics.

Antibiotic resistance is a global challenge likely to cost trillions of dollars in excess costs in the health system and more importantly, millions of lives every year. A major driver of resistance is the absence of susceptibility testing at the time a healthcare worker needs to prescribe an antimicrobial. The effect is that many prescriptions are unintentionally wasted and expose mutable organisms to antibiotics increasing the risk of resistance emerging. Often simplistic solutions are applied to this growing issue, such as a naïve drive to increase the speed of drug susceptibility testing. This puts a spotlight on a technological solution and there is a multiplicity of such candidate DST tests in development. Yet, if we do not define the necessary information and the speed at which it needs to be available in the clinical decision-making progress as well as the necessary integration into clinical pathways, then little progress will be made. In this chapter, we place the technological challenge in a clinical and systems context. Further, we will review the landscape of some promising technologies that are emerging and attempt to place them in the clinic where they will have to succeed.

An Update on Current Trend in Sample Preparation Automation in Bioanalysis: strategies, Challenges and Future Direction.

Automation in sample preparation improves accuracy, productivity, and precision in bioanalysis. Moreover, it reduces resource consumption for repetitive procedures. Automated sample analysis allows uninterrupted handling of large volumes of biological samples originating from preclinical and clinical studies. Automation significantly helps in management of complex testing methods where generation of large volumes of data is required for process monitoring. Compared to traditional sample preparation processes, automated procedures reduce associated expenses and manual error, facilitate laboratory transfers, enhance data quality, and better protect the health of analysts. Automated sample preparation techniques based on robotics potentially increase the throughput of bioanalytical laboratories. Robotic liquid handler, an automated sample preparation system built on a robotic technique ensures optimal laboratory output while saving expensive solvents, manpower, and time. Nowadays, most of the traditional extraction processes are being automated using several formats of online techniques. This review covered most of the automated sample preparation techniques reported till date, which accelerated and simplified the sample preparation procedure for bioanalytical sample analysis. This article critically analyzed different developmental aspects of automated sample preparation techniques based on robotics as well as conventional sample preparation methods that are accelerated using automated technologies.

cGMP compliant one-step, one-pot automated [18F]FBnTP production for clinical imaging of mitochondrial activity.

BACKGROUND: 4-[18F]fluorobenzyl-triphenylphosphonium ([18F]FBnTP) is a lipophilic cation PET tracer. The cellular uptake of [18F]FBnTP is correlated with oxidative phosphorylation by mitochondria, which has been associated with multiple critical diseases. To date, [18F]FBnTP has been successfully applied for imaging myocardial perfusion, assessment of severity of coronary artery stenosis, delineation of the ischemic area after transient coronary occlusion, and detection/quantification of apoptosis in various animal models. Recent preclinical and clinical studies have also expanded the possibilities of using [18F]FBnTP in oncological diagnosis and therapeutic monitoring. However, [18F]FBnTP is typically prepared through a tediously lengthy four-step, three-pot reaction and required multiple synthesizer modules; Thus, such an approach remains a challenge for this promising radiopharmaceutical to be implemented for routine clinical studies. Herein, we report an optimized one-step, one-pot automated approach to produce [18F]FBnTP through a single standard commercially-available radiosynthesizer that enables centralized production for clinical use. RESULTS: The fully automated production of [18F]FBnTP took less than 55 min with radiochemical yields ranging from 28.33 ± 13.92% (non-decay corrected), apparent molar activity of 69.23 ± 45.62 GBq/µmol, and radiochemical purities of 99.79 ± 0.41%. The formulated [18F]FBnTP solution was determined to be sterile and colorless with a pH of 4.0-6.0. Our data has indicated no observable radiolysis after 8 h from the time of final product formulation and maximum assay of 7.88 GBq. CONCLUSIONS: A simplified and cGMP-compliant radiosynthesis of [18F]FBnTP has been established on the commercially available synthesizer in high activity concentration and radiochemical purity. While the preclinical and clinical studies using [18F]FBnTP PET are currently underway, the automated approaches reported herein facilitate clinical adoption of this radiotracer and warrant centralized production of [18F]FBnTP for imaging multiple patients.

An Automated Versatile Diagnostic Workflow for Infectious Disease Detection in Low-Resource Settings.

Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays.

An engineering biology approach to automated workflow and biodesign.

The paper addresses the application of engineering biology strategies and techniques to the automation of laboratory workflow-primarily in the context of biofoundries and biodesign applications based on the Design, Build, Test and Learn paradigm. The trend toward greater automation comes with its own set of challenges. On the one hand, automation is associated with higher throughput and higher replicability. On the other hand, the implementation of an automated workflow requires an instruction set that is far more extensive than that required for a manual workflow. Automated tasks must also be conducted in the order specified in the workflow, with the right logic, utilizing suitable biofoundry resources, and at scale-while simultaneously collecting measurements and associated data. The paper describes an approach to an automated workflow that is being trialed at the London Biofoundry at SynbiCITE. The solution represents workflows with directed graphs, uses orchestrators for their execution, and relies on existing standards. The approach is highly flexible and applies to not only workflow automation in single locations but also distributed workflows (e.g. for biomanufacturing). The final section presents an overview of the implementation-using the simple example of an assay based on a dilution, measurement, and data analysis workflow.

Blood self-sampling devices: innovation, interpretation and implementation in total lab automation.

The introduction of the vacuum tube in 1949 revolutionized blood collection, significantly improving sample quality and patient comfort. Over the past 75 years, laboratory diagnostics have evolved drastically, from manual to automated processes, reducing required test volumes by over 1,000 times. Despite these advancements, venous blood collection presents logistical challenges, including centralized scheduling and a large volume of biological waste due to the imbalance between the needed blood volume (often very little) and the collected volume (often in excess). The COVID-19 pandemic further emphasized the need for decentralized healthcare solutions and patient empowerment. Capillary blood collection, widely used in point-of-care testing, offers a promising alternative, particularly for patients facing frequently, or difficulties with, venous sampling. The Leiden University Medical Center in the Netherlands experienced a 15 % reduction in volume of laboratory tests during and after the pandemic, attributed to patient preference for local blood collection and testing. To address these challenges, self-sampling devices are emerging, empowering patients and streamlining sample logistics. However, challenges such as cost, transportation regulations, and sample volume adequacy persists. Robust devices tailored for total lab automation and sustainable practices are crucial for widespread adoption. Despite hurdles, the integration of self-sampling into diagnostic processes is inevitable, heralding a shift towards patient-centered, proactive healthcare. Practical recommendations include robust device design, ease of use, affordability, sustainability, sufficient quality and acceptability by seamless integration into laboratory workflows. Although obstacles remain, self-sampling represents the future of laboratory diagnostics, offering convenience, cost-effectiveness, interoperability and patient empowerment.

Optical Genome Mapping: A Machine-Based Platform in Cytogenomics.

Optical genome mapping (OGM) has generated excitement following decades of research and development. Now, commercially available technical platforms have been used to compare various other cytogenetic and cytogenomic technologies, including karyotype, microarrays, and DNA sequencing, with impressive results. In this chapter, using OGM as a case study, we advocate for a new trend in future cytogenomics, emphasizing the power of machine automation to deliver higher-quality cytogenomic data. By briefly discussing OGM, along with its major advantages and limitations, we underscore the importance of karyotype-based genomic research, from both a theoretical framework and a new technology perspective. We also call for the encouragement of further technological platform development for the future of cytogenetics and cytogenomics.

Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives.

Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.