Our growing aging population and increased prevalence of obesity have become an emerging health problem as these conditions lead to the development of related diseases. Specifically, obesity and aging are associated with cardiovascular disease and a decline in cognitive function underlying the inflammatory mechanisms. However, the literature regarding how both interact and impact these complex physiological processes across the lifespan remains to be elucidated. As such, this review discusses how obesity in aging adults mediates inflammatory, cardiovascular, and neurobiological effects of exercise in this population.Obesity with advancing age leads to increased health complications that are involved in various complex physiological processes. For example, inflammation is a critical cardiovascular disease risk factor that plays a role in the stages of atherosclerosis in both aging and obesity. Obesity can also induce profound changes to the neural circuitry that regulates food intake and energy homeostasis with advancing age. Here we discuss how obesity in older adults impacts inflammatory, cardiovascular, and neurobiological functions with an emphasis on how exercise mediates each topic. Although obesity is a reversible disorder through lifestyle changes, it is important to note that early interventions are crucial to prevent pathological changes seen in the aging obese population. Lifestyle modifications such as physical activity (including aerobic and resistance training) should be considered as a main intervention to minimize the synergistic effect of obesity on age-related conditions, such as cerebrovascular disease. Biological aging is associated with increased visceral adipose tissue  and a sh+ and CD8+ T cell recruitment that consequently leads to insulin resistance within the adipose tissue [+ to CD4+ T-cells have been shown to increase with obesity, limiting the secretion of anti-inflammatory cytokines that inhibit macrophage migration from CD4+ regulatory T-cells [Obese individuals have immune dysfunction leading to higher infection rates and impaired wound healing . Excess e tissue ,12. Withe tissue . This ove tissue . Pro-infe tissue . This pre tissue . Additio T-cells ,18. In f T-cells . Obesity T-cells .Adipocytes release signaling molecules (adipokines), including leptin and adiponectin, that have immunomodulatory actions . While lImportantly, exercise is a main lifestyle modification that is considered a first-line defense to combat age-related obesity by counteracting positive energy balance and also modulate immune cells, adipokines, and inflammatory cytokines . Our preMoreover, resistance exercise can also be used to combat obesity and age-related chronic inflammation. One year of resistance exercise training has shown to significantly decease CRP levels and increase adiponectin concentrations in young overweight women . In factIndeed, a combination of diet, aerobic, and resistance exercise training is likely to be the most effective lifestyle modification aimed at improving visceral adipose tissue, systemic inflammation, blood pressure (BP), lipid profile, and insulin sensitivity in obese older adults . For exaExcess adipose tissue increases cardiovascular (CV) strain which, over time, places profound stress on the CV system. CV late effects, such as stroke, left ventricular hypertrophy, heart failure (HF), and various forms of cardiomyopathies, are highly prevalent with age-related obesity ,57,58,59Exercise capacity and tolerance in obese adults are less than their lean counterparts. In a cohort of obese women, acute submaximal capacity, peak aerobic capacity and recovery rate were reduced compared to lean controls , demonstRepeated bouts of aerobic and resistance exercise, or a combination of both, are crucial to improve exercise tolerance and overall CV health in obese populations. Participating in regular exercise training can enhance functional characteristics of the CV system and begin to reverse CV risk associated with obesity. Chronic exercise training improves vascular function such as endothelial function ,112, exeLifestyle changes (diet and exercise training) are suggested to be the most optimal strategy to improve physical limitations  and systEpidemiological data show marked reductions in 10-year CVD risk in overweight and obese middle-aged adults who perform ≥150 min/week of moderate-to-vigorous exercise . This isWithin the central nervous system, eating behavior is thought to be largely regulated by an intricate interaction between the circuitry that regulates energy intake requirement and reward pathways . In thisMeanwhile, the mesolimbic and mesocortical pathways are both dopaminergic components of the brain’s reward circuitry that are heavily involved in addictive behavior such as substance abuse ,182. In The benefits of aerobic exercise towards brain health and synaptic plasticity are well established in the literature ,196. UnsObesity and aging are involved in a decline in cognitive function underlying the inflammatory mechanisms; however, the literature regarding how both interact and impact this complex process of cognitive impartment across the lifespan still remains elucidated. Although obesity is a reversible disorder through lifestyle changes, it is important to note that early interventions are crucial to prevent accelerated CV aging and reduce the risk of severe CV late effects seen in the aging obese population. Lifestyle modifications such as physical activity should be considered as a main intervention to minimize the synergistic effect of obesity on age-related conditions as summarized in


Antibody measurements play a central role in the diagnosis of many autoimmune and infectious diseases. One antibody detection technology, Luciferase Immunoprecipitation Systems (LIPS), utilizes genetically encoded recombinant luciferase antigen fusion proteins in an immunoglobulin capture format to generate robust antibody measurement with high diagnostic sensitivity and specificity. The LIPS technology has been highly useful in detecting antibodies for research diagnostics and the discovery of new autoantigens. The methodology of the assay requires immunoglobulin binding reagents such as protein A/G beads and washing steps to process the immune complex before antibody levels are measured by light production with a luminometer. Recently, simplified mix and read immunoassays based on split components of the nanoluciferase enzyme in a complementation format have been developed for antibody measurements without requiring immunoglobulin-capturing beads or washing steps. The mix and read immunoassays utilize two or three nanoluciferase fragments which when reconstituted via antigen-specific antibody binding generate a functional enzyme. At present, these split luciferase tests have been developed mainly for detecting SARS-CoV-2 antibodies. Here, we describe the traditional LIPS technology and compare it to the new split luciferase methodologies focusing on their technical features, strengths, limitations, and future opportunities for diagnostic research, and clinical applications. The detection of antibodies represents a well-established approach for the diagnosis of many infectious and autoimmune diseases. Measurements of antibodies directed against infectious agents yield not only diagnostic information for current infection but can inform about past exposure and provide detailed information about vaccine status . The detWhile many different immunoassays are available for measuring antibodies and autoantibodies including immunofluorescence, Western blot, Enzyme-linked immunosorbent assay (ELISA), protein arrays, and a variety of bead-based assays , all these approaches have advantages and disadvantages ,4. By faBased on the evolving nature of multiple immunoassay formats, antibody detection technologies are on the threshold of undergoing major change, which will impact their application in clinical medicine. One first-generation non-ELISA immunoassay that employs genetically encoded luciferase fusion target proteins, Luciferase immunoprecipitation systems (LIPS), provides a highly sensitive diagnostic and discovery platform for many different antibodies . More reRenilla luciferase, Gaussia luciferase, and nanoluciferase, have all been utilized for measuring antibodies, and a summary of their biochemical characteristics and other attributes is shown in Renilla luciferase. This gene encodes a 36 KDa intracellular enzyme, which produces a light flash signal in the presence of coelenterazine substrate [Renilla luciferase (Ruc) fusion proteins are well suited for tagging human, viral, and other protein antigens, in part because the fusion proteins generally exhibit a low background binding in the LIPS assay format. Gaussia luciferase , codes for a secreted protein of 185 amino acids [Renilla luciferase and produces a flash with the coelenterazine substrate . Producing high levels of luciferase in mammalian cells can be further enhanced by the generation of nanoluciferase-antigen fusions, which generally produce 10–100 times higher light emission than For LIPS antibody testing, the addition of a defined amount of the luciferase-antigen fusion protein, based on the LU of the extract, typically 10 million LU, is used per sample. To set up the assay, serum, buffer, and cell extract are combined and incubated for one hour. These assays require only 1.0 microliter of serum/plasma and between 5–10 microliters of saliva. If specific antibodies are present, they bind to the target antigen-luciferase fusion protein . The reaAnother approach using luciferase-tagged antigens involves microtiter plates coated with Protein A to capture the antigen-antibody complexes. This plate method does not require the more expensive microtiter filter plate and has shown promise as an inexpensive way to measure antibodies for different pathogens including against HIV  and ZikaThe detection of autoantibodies is an important clinical component for the diagnosis and monitoring of many different autoimmune diseases . LIPS isBesides cross-sectional studies in autoimmunity, LIPS has been used to investigate autoantibody responses over time to understand disease progression before disease diagnosis and to monitor treatment. In one study of retrospective biobanked serum samples, LIPS was used to detect autoantibodies against Ro52, Ro60, CENP-A, and other autoantigens in subjects with systemic sclerosis years before clinical diagnosis highlighting detectable autoantibodies in some cases ~25 years before diagnosis . In anotLIPS also has been instrumental in the serology-enabled discovery of novel autoantibodies against proteins with extracellular domains or secreted proteins that directly cause disease including cytokines, secreted molecules, and plasma membrane receptors . In 2010Other examples of LIPS-based autoimmune discovery include the discovery of the first case of autoimmune tumoral calcinosis by autoantibodies against the FGF-23 hormone that controls blood phosphate levels . In addiRobust antibody detection with the LIPS assay has also contributed significantly to the study of diverse infectious agents. As described, LIPS assays can detect antibodies associated with over thirty human and animal infectious diseases . Since tBesides established infectious agents, there remains an increasing need to discover and characterize new human and animal pathogens. LIPS has provided an important tool for the discovery and exploration of several new viral agents. Importantly, LIPS has been used to investigate and characterize several Coronaviruses that cause severe pathology. Prior to the COVID-19 pandemic, Zhou et al. (2018) used several molecular approaches along with LIPS and identified the viral cause of a novel fatal porcine diarrhea outbreak in China . In thisRenilla and Gaussia luciferase fusions, respectively, to evaluate antibody emergence after initial COVID-19 infection in different patient groups including immunocompromised individuals [In late November of 2019, less than 2 years after the porcine SADS-CoV publication, the COVID-19 pandemic emerged. With previous virological and serological studies as a guide, at least six independent groups used LIPS to study humoral responses against SARS-CoV-2 ,60,61,62ividuals . In an iividuals . Lampasoividuals ,64. In aividuals . A thirdividuals . OverallAnother application of LIPS for SARS-CoV-2 research involves the investigation of antibody responses against spike protein vaccines. Several LIPS studies have measured spike antibody levels in patients receiving SARS-CoV-2 vaccines in vulnerable patient populations including inborn errors of immunity , and canWhile LIPS in its standard configuration is a powerful tool for the discovery and diagnosis of infectious and autoimmune diseases, it has not yet been adapted to the rapid point-of-care use in the field or clinic. As an alternative strategy for assay simplification, several groups have recently exploited the unique biomarker potential of luciferases based on specific antibody-mediated reconstitution of luciferase enzyme fragments into an intact catalytically active enzyme. This complementation strategy is based on existing studies showing that all three luciferases, Ruc, GLuc, and Nano, can be split into protein fragments, and when brought into proximity, can reconstitute enzymatic activity. Initially, the applications of these “split systems” were in areas of cell biology . The lucAs shown in By comparison, a truly simple mix and read assay format is based on a strategy exploiting the two Fab arms of an antibody for antigen binding activity. In this assay, the two separate LgBiT and SmBiT components are genetically fused to the same target antigen and must be first purified as recombinant proteins . If an aThe split Nano Fab-binding immunoassay could detect antibodies over a range of serum dilutions with high sensitivity and specificity in about 1 h, in which the amount of antibody present is proportional to the light produced . Of noteIn a follow-up report, the split luciferase Fab-binding immunoassays for spike and nucleocapsid antibody detection were compared to a variety of immunoassays including standard LIPS, ELISA, and Luminex formats and produced extremely promising diagnostic performance equivalent to the other assays . MoreoveAnother strategy shown in Besides detecting SARS-CoV-2 antibodies, a similar tripartite split nanoluciferase strategy has been employed to measure tumor necrosis factor (TNF) therapeutic antibodies . TNF antLIPS was the first technology to exploit luciferase fusion proteins for measuring antibodies for autoimmune and infectious diseases . LIPS haE. coli such a large protein and it would unlikely be folded properly. Nevertheless, these considerations should not impede the development of mix and read assays for other appropriate smaller targets, many of which have substantial clinical importance.Despite the powerful capabilities of LIPS, rapid point-of-care tests developed using the technology are relatively sparse. This is mainly due to the required washing steps of the bead-bound immune complexes. The alternative strategies discussed here have been developed based on the reconstitution of split nanoluciferase protein fragments in complementation assays. The feasibility of these split Nano immunoassays is facilitated by the glow reaction (rather than a brief flash) along with the higher enzymatic activity. Three different split formats have been developed: a hybrid, a two-component, and a three-component assay. As shown in It is likely that the mix and read technologies will continue to evolve in the clinical arena. One major commercial opportunity would be to develop a large panel of antigenic targets in the mix and read format that is already known to have high diagnostic performance in the standard LIPS assay. These include a variety of infectious agents such as HIV, HCV, and HBV, as well as autoantigens highly useful for the diagnosis and monitoring of various autoimmune diseases. In summary, mix and read assays based on the reconstitution of two or three Nano fragments have proven to be a viable immunoassay format for antibody detection. The ability to couple this format to detection by a hand-held luminometer may facilitate POC testing. Based on the compelling potential of these mix and read assays, a large panel of these immunoassays could be used in parallel to provide fast antibody-based information for diagnoses, treatment monitoring, and comprehensive health monitoring. Lastly, one potential future commercial development would be to develop an instrument that could simultaneously process and read multiple individual mix and read tests for different autoimmune and infectious diseases at the same time, analogous to a device used for array-based comprehensive molecular pathogen detection .


As we embark upon the third decade of the 21st Century, the scientific community has witnessed remarkable achievements in recent years, particularly in the domain of non-neuronal cells. These include resident immune cells, glial cells (such as astrocytes and cells of the oligodendrocyte lineage), neurovascular cells , as well as other cell types inhabiting the nervous system. In light of these advancements, Frontiers has orchestrated a series of Research Topics to showcase the latest breakthroughs in various research fields.This special edition of our Research Topic aims to provide insights into the progress achieved over the past decade within the non-neuronal cells field, while also addressing the challenges that lie ahead. Our editorial initiative is centered on presenting new perspectives, emerging developments, ongoing obstacles, recent discoveries, and future outlooks in the realm of non-neuronal cells. We aspire to both inspire and inform researchers, offering them valuable direction and guidance. This special edition comprises six distinct articles that delve into these topics.Ngozi and Bolton they discuss what is known about how neuronal subtype impacts the interactions between microglia and neurons in the developing brain, and in particular, how subpopulations of microglia often treat excitatory and inhibitory neurons differently. The developing hypothalamus is highlighted, and the authors discuss their recent work showing that even the type of neuropeptide produced by a neuron can impact microglial behaviors. This emerging field holds the potential for future therapies to target unique microglia-neuron interactions, which may offer new strategies for personalized medicine.Microglia are critical regulators of brain development, but only more recent work focuses on the importance of neuronal subtype in microglia-neuron interactions. In a Perspective article by de Almeida et al. discuss the involvement of neural stem and precursor cells (NPCs) as regulators of microglial biology, including microglial survival, proliferation, migration, phagocytosis, and reactivity. These immunomodulatory roles are described in the developing, injured, and degenerating central nervous system (CNS), highlighting the importance of these findings to future therapies aimed at targeting CNS regeneration and repair in cases of traumatic brain insults and neurodegeneration.Two Mini Reviews next provide novel insights into the regulation of microglial function by neural stem cells, and the functional relationship between embryonic radial glia and tanycytes during development. Fong and Kurrasch provide a comprehensive review of tanycyte biology in the hypothalamus, beginning with the development of these cells from embryonic radial glia to their emerging roles in the neural stem cell niche postnatally. Similar to hypothalamic radial glia, tanycytes line the third ventricle and extend a long process into the hypothalamic parenchyma. This heterogeneous population of cells contribute to a broad range of physiologies, including energy balance, which places them in a unique position to regulate neural stem cell biology in the adult in response to changes in homeostasis or insults sensed in the periphery.Carrier et al. discuss how non-neuronal cells maintain and regulate structural and functional connectivity, metrics of neuronal network dynamics. Non-neuronal cells regulate neuronal populations, connectivity, energy metabolism, and also the neurovascular unit that is critical for nutrients to enter into the CNS. These functions are critical for structural and functional connectivity, but altered in schizophrenia, major depression disorder, and disorders of consciousness.The Research Topic further presents three Review articles which together shed light onto how non-neuronal cells impact brain networks, synaptic function, and behavior while also examining their heterogeneity in neurodegenerative conditions like Alzheimer's disease (AD). Basilico et al. describe the multitude of genetic and pharmacological strategies currently available for the depletion of microglia allowing a new avenue to investigate microglial biology. These microglial removal strategies enabled new insight into the roles that microglia play in synaptic function, learning, memory, and behavior in health and disease—highlighting the potential therapeutic use of microglial depletion in various brain pathologies involving aberrant microglia behavior.Vu et al. outline the importance of non-neuronal cells in AD, and how single-cell (scRNAseq) and single-nucleus RNA sequencing (snRNAseq) approaches are accelerating our understanding of this disease. Non-neuronal cells were classically overlooked with AD research, but new genome wide association studies highlight several risk factors that are highly expressed in non-neuronal cells. New snRNAseq studies of postmortem tissue evaluate the disease states of non-neuronal cells, identifying new cellular states and functions during AD. Moving forward Vu et al. suggest strategies to appreciate the interconnectedness of non-neuronal cells in the context of AD.M-ÈT is a Tier 2 CRC in Neurobiology of Aging and Cognition. JP is a Tier 2 Canada Research Chair in Glial Neuroimmunology. JR is a Tier 2 Canada Research Chair in Immune Regulation of Developmental Programs.JR: Writing – original draft, Writing – review & editing. M-ÈT: Writing – original draft, Writing – review & editing. JP: Writing – original draft, Writing – review & editing.


Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician’s point of view. In recent years, increasing antimicrobial resistance (AMR) has become a serious global concern. After having been exposed to antibiotics, bacteria can quickly develop resistance due to their short growth cycle and various adaptation mechanisms.Klebsiella pneumoniae and Acinetobacter spp. to third-generation cephalosporins and carbapenems and the rapid dissemination of resistant isolates limit antimicrobial choices for patients infected with these pathogens [Multidrug-resistant, extensively resistant, or even pan-drug-resistant microorganisms are commonly encountered in a hospital environment, especially in the critical care setting ,2. Accorathogens . Failureathogens . Severalathogens . Therefoathogens .To deal with this rapidly growing problem, it is necessary to implement a multidisciplinary intervention known as an antimicrobial stewardship program (ASP) to optimize antibiotic prescription through evidence-based clinical decisions, and limit AMR . ConcernAt the same time, the global increase in the use of electronic health records (EHR) has resulted in massive amounts of routinely available electronic patient and microbiological data that could be used to support individualized antimicrobial stewardship . In prevArtificial intelligence (AI), through its ability to process data and information and turn it into insight and knowledge, facilitates data analysis that exceeds human mind capabilities and solves the problem of limited rational decision-making due to insufficient information and time constraints. AI, when properly designed, can also be free of behavioral constraints, including irrational deviations from guidelines, peer influence on hierarchical cultural norms, and fatigue. Algorithms can learn objectively and are often able to make more accurate predictions than those observed in everyday practice. In view of its potential promise in AMR, ML could greatly improve research efficiency, allowing scientists to focus on more complex scientific matters .In this paper, we reviewed the existing literature on machine learning applications with respect to antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as an adjunct tool in the antibiotic stewardship practice, mainly from the clinician’s perspective. Recently, a systematic review of the literature with meta-analysis was published on the topic of ML-driven prediction of AMR . In the We investigated Medline/Pubmed up to December 2022 for original studies containing the keywords ((machine learning) AND  AND (resistance) AND ) NOT (DNA) NOT (sequencing) NOT (peptides) NOT (molecules) NOT (genome) NOT (discovery)). As seen in the flow diagram , our resSupervised machine learning algorithms are commonly used with linear and logistic regression, k-nearest neighbors (k-NN), support vector machine (SVM), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), the most prevalent algorithms, followed by neural networks and deep learning approaches. The area under receiver operating curve characteristic (AUROC) is the main performance metric used in ML-driven prediction models. Other performance metrics include accuracy, sensitivity, specificity, positive and negative predictive values, precision, recall, and F1 measure, all of which, however, can be derived from a confusion matrix, a simple tabular structure that essentially summarizes the hits and misses of a learning algorithm.There is growing evidence that, despite increased resistance to antibiotics, machine learning can help doctors select proper anti-infective treatment, based on patient demographics and past clinical histories . During More specifically, ML methods have been developed to analyze bacterial genomes, forecast medication susceptibility, recognize epidemic patterns for surveillance purposes, or propose new antibacterial treatments or vaccines . In addiSeveral patient characteristics including comorbidities, demographics, previous infection history, antibiotic treatments, and hospital admissions have been associated with multidrug-resistant (MDR) infections in a number of studies ,19. The Currently, AMR is principally diagnosed using two techniques in clinical microbiology . One is The implementation of ML methods has substantially reduced the time of bacterial susceptibility profiling to less than three hours for the flow-cytometry AST method (FAST)  and onlyAlthough matrix-assisted laser desorption/ionization coupled to time-of-flight mass spectrometry (MALDI-TOF MS) is widely recognized as a reference method for the rapid and inexpensive identification of microorganisms in routine laboratories, little attention has been paid to its ability to determine AMR. Some recent studies have evaluated its potential use in conjunction with machine learning to detect AMR in clinical pathogens. ,24. SomeKirby–Bauer disk-diffusion and microdilution antibiograms are recommended as reference methods by the European committee on antimicrobial susceptibility testing (EUCAST) and the Clinical and Laboratory Standards Institute (CLSI) for determining antimicrobial resistance . ResultsAlthough these conventional methods are effective, they are cumbersome, time-consuming, and do not enable the rapid choice of an effective targeted anti-infective treatment . As the Whole genome system (WGS)-based diagnostic approaches are being used to overcome these limitations, especially for viral infections and tuberculosis, where culture-based microbiological diagnostics are either not applicable or time-consuming . WGS canThe development of molecular tests has significantly contributed to rapid diagnostic testing and timely identification of pathogens and antibiotic resistance patterns, but their high costs and limited availability prevent them from being widely used. In this context, ML-driven predictive models of antimicrobial resistance may serve as a bridge between specimen collection and results from molecular and genotypic susceptibility analysis, facilitating time-sensitive empirical antibiotic choices.Accurate prediction of resistance against different antibiotics is directly beneficialfrom the patient’s point of view, because it helps avoid treatment failures. Such a prediction could have additional long-term benefits, for example, enabling the use of more targeted antibiotics, decreasing the need to use multiple antibiotics to cure the same infection, and lowering the risk of onward transmission. Machine learning algorithms have the potential to help clinicians predict antimicrobial resistance.Escherichia coli and Klebsiella spp. bacteremia based on patient epidemiological and microbiological data [Besides the detection of antimicrobial resistance phenotypes, different ML modeling tools have been applied by several researchers to predict antibiotic susceptibility patterns of pathogens, allowing for the selection of the most appropriate treatment. Goodman et al. used recursive partitioning to build a decision tree for the prediction of extended-spectrum β-lactamase (ESBL) production in cal data . Sousa ecal data . In contcal data .Moran et al. assessed the accuracy of an open-source machine learning algorithm (XGBoost), trained in predicting antibiotic resistance for three Gram-negative bacterial species isolated from patients’ blood and urine within 48 h of hospital admission . The ML McGuire et al. demonstrated that clinical data retrieved from the EHR of the patients could be used to train an ML algorithm and predict the presence of carbapenem resistance at the time of culture collection . The preRecently, Feretzakis et al. evaluated five ML algorithms to identify predictors of antibiotic susceptibility using simple patient demographics, culture, and antibiotic susceptibility test results of patients being treated in medical wards . The autIt is common for non-infection specialists to treat infections in hospitals. The physicians are encouraged to follow local antimicrobial guidelines and evidence-based policies. However, adherence to the prescribing policies tends to be deficient. Human and behavioral factors influence the doctor’s prescribing decisions. On the other hand, clinicians are frequently urged to overuse antibiotics due to worries about the high mortality linked to delayed prescribing in diseases such as sepsis, the increase in drug-resistant infections, and the lack of accurate diagnostics to enable dynamic decision-making ,8. In a A strategy for combating inappropriate antibiotic prescriptions in community and nursing home-acquired urinary tract infections (UTIs) is described by Yelin et al. . The resKanjilal et al. applied a machine learning approach to hospital electronic health record data to predict the likelihood that first- and second-line antibiotics would be resistant to uncomplicated UTIs . Using tHowever, most of these data-driven predictive models lack generalizability and should be retrained in different healthcare settings due to the dynamic nature of bacterial infections, differences in local susceptibility patterns, and the inconsistency of patient variables in electronic medical records . With inAnother study analyzed the performance of antibiotic selections informed by personalized antibiograms in comparison with antibiotic selections made by clinicians; it also systematically evaluated the trade-off in performance when fewer broad-spectrum antibiotics are selected. By reducing the unnecessary use of broad-spectrum antibiotics that breed resistant organisms, empiric precision antibiotic prescribing with personalized antibiograms could improve patient safety and antibiotic stewardship . In the Another major concern is the emergence of resistant infections despite susceptibility-matched treatment, often caused by a different strain than the original infection, as demonstrated by Stracy et al. . CombiniIn the aforementioned studies, algorithms and models have been developed for predicting antibiotic resistance based on epidemiologic factors. However, it remains largely unstudied whether they will affect antimicrobial prescribing when implemented into clinical practice . RecentlAs antibiotic resistance is a major cause of mortality, it is imperative that researchers develop rapid and efficient methods to guide the rational administration of antibiotics, collectively known as antimicrobial stewardship programs (ASPs) ,7. AntibThe development of ML-CDSS using minimum variables may be beneficial when data are not readily available across certain areas or when resources are limited . ParticuDecision support models for empiric treatment of sepsis can integrate predictors of antibiotic resistance and permit rapid antibiotic de-escalation without endangering timely and sufficient treatment . MoreoveIn a retrospective study carried out in a children’s hospital in Cambodia, Oonsivalai and colleagues, propose a patient-level data-driven decision support system using a variety of machine learning techniques . Mainly In a recent study, Liang et al. developed an ML-driven predictive model for the timely prediction of carbapenem-resistant (CR) GNB carriage among ICU patients within one week . CR-GNB Goodman et al. examined two methodologies for the development of clinical decision support tools: a conventional logistic regression-derived clinical risk score versus an ML-derived decision tree . AlthougAnother group of researchers showed that deep neural networks outperformed multivariable logistic regression in predicting the generation of ESBL in community-onset Enterobacteriaceae bacteraemia. The large number of features used for training the model could be a potential drawback of this study . For thiMultivariate associations rule mining methods—a subset of unsupervised ML techniques originally used in market-basket analysis—may efficiently identify and quantify correlations between resistance patterns, enabling the identification and tracking of clinically relevant MDR through comparisons between relevant subsets of isolates. In the clinical context, the application of association rule mining in the antimicrobial susceptibility dataset could also offer better antibiotic treatment policies ,57. HoweThe problem of AMR is multifactorial and arises from the interaction of bacterial evolution, human behavior, and environmental factors that play a significant role in the transmission of resistant bacteria and pathogen emergence . There iMachine learning and deep learning models have been validated for the prediction of ARGs in various environmental sources, such as in recreational beaches, soil, wastewater, and in several geographical regions ,61. JangEarly detection of AMR remains challenging despite rapid diagnostic advances. A delay in diagnosis can prolong the period of ineffective antibiotic therapy. Statistical models for predicting drug resistance can play an important role, especially in settings where rapid diagnostic tests are unavailable or are difficult to perform due to a lack of resources.While traditional regression techniques (linear/logistic) are long-established for the development of predictive models and risk scores, ML approaches based on complex patient data and medical information are increasingly gaining ground in clinical prediction, given their flexibility, practicality, and the ability to handle a large number of predictors . HoweverThere is often a lack of ML expertise among healthcare professionals that can make it difficult to construct and train a successful model, deploy it in production, and integrate it with the clinical workflow . For heaDespite satisfactory results achieved by most studies using ML methods for the prediction of AMR, several issues exist that limit a wider ML adoption in clinical practice. Firstly, the lack of high-quality data is a prerequisite for successful machine learning modeling with accurate results. Data are usually obtained from electronic health records which are frequently unreliable due to entry errors, duplicate records, or missing observations; additionally, the cost of collecting, storing, curating, and analyzing data has to be taken into account for investing in departmental, institutional or wider-area infrastructures . The secData volume and quality are substantial determinants of machine learning performance. In a recently published systematic review, increased risk of bias due to retrospective methodology, nonhomogeneous data processing, and lack of external validation are suggested as potential reasons for the current limited clinical applicability of ML-based predictive algorithms . In ordeRecently, there has been increased research interest in various ML applications in the field of antimicrobial resistance prediction with promising results. In this review, we examined the existing literature on the topic of AMR prediction using ML algorithms and their potential role in the antimicrobial stewardship practice through accurate prediction of multidrug resistance patterns, a more personalized antibiotic prescription, and the development of accessible clinical decision support systems.It is undoubtedly true that in the future, AI will enhance our ability to support healthcare decision-making, but humans must still interpret information according to the unique circumstances of each patient. Clinical decision-making is complex and the utility of ML-driven approaches in real-world settings has to be proved before they can be integrated into the clinical workflow.


Ischemic stroke is a highly morbid disease, with over 50% of large vessel stroke  patients suffering disability despite maximal acute reperfusion therapy with thrombolysis and thrombectomy. The discovery of the ischemic penumbra in the 1980s laid the foundation for a salvageable territory in ischemic stroke. Since then, the concept of neuroprotection has been a focus of post-stroke care to (1) minimize the conversion from penumbra to core irreversible infarct, (2) limit secondary damage from ischemia-reperfusion injury, inflammation, and excitotoxicity and (3) to encourage tissue repair. However, despite multiple studies, the preclinical–clinical research enterprise has not yet created an agent that mitigates post-stroke outcomes beyond thrombolysis and mechanical clot retrieval. These translational gaps have not deterred the scientific community as agents are under continuous investigation. The NIH has recently promoted the concept of cerebroprotection to consider the whole brain post-stroke rather than just the neurons. This review will briefly outline the translational science of past, current, and emerging breakthroughs in cerebroprotection and use of these foundational ideas to develop a novel paradigm for optimizing stroke outcomes. Stroke is a highly morbid, heterogenous disease in which a focal neurologic deficit occurs due to ischemia or hemorrhage . IschemiNotably, altering coagulation profiles, augmenting hemodynamic failure, and modulating elements in secondary cerebral damage to facilitate cerebroprotection have been extensively investigated and reviewed. To our group, clot lysis using thrombolytics and mechanical removal fall under indirect neuroprotectants and direct vasculoprotectants as their primary function is the “maintenance of circulatory patency or the reversal of vascular occlusion” . These cBlood clots are currently purported to be aggregations of “fibrin, platelets, RBCs, white blood cells, VWF (von Willebrand Factor), and extracellular DNA” . Their rReduced efficacy for thrombolytics in large vessel occlusion led to the development of sophisticated mechanical retrieval devices and even direct clot aspiration as visualized in angiography. Original trials likely failed to show superiority due to suboptimal patient selection and the device’s need to retrieve the clot and leave the target vessel undamaged. However, the release of trials from 2015—REVASCAT , MR CLEASince stroke is a failure of blood flow and oxygenation delivery to ischemic tissue, hemodynamic manipulation, oxygen supplementation, and temperature regulation have been explored in various studies. Flat head of bed positioning, despite early evidence showing improvement in the blood velocities of affected vessels without a concomitant increase in intracranial pressure ,52, demo2O2 and superoxide O2.− being the principal mediators for ROS-mediated damage, especially in the brain i, thereby activating neuronal nitric oxide synthase (nNOS) and NADPH oxidase 2 (NOX2). This activation generates ROS and (RNS). These toxic byproducts are associated with Zinc (Zn++) overload following Ca++ influx;Induction of NMDA-Receptor 2B (NR2B) receptors to raise [Ca(2)++ influx signal by Ca++/Zn++ activation of other channels and feed-forward Glut release due to astrocytic swelling and ROS;Amplification of the Ca(3)++/Zn++ overload, potassium (K+) efflux, hydrogen (H+) influx and oxidative stress to promote calpain-mediated death with severe necrosis in the core and enhancement of oxidative damage if reperfusion does occur;Expression of multiple pathways mediating cell-death activated by Ca(4)Promotion of inflammation and subsequent leukocyte infiltration.++, Mg++, and H+, various issues have led to translational failures. The first preclinical trials of drugs [Despite efforts to target glutamate-mediated excitotoxicity by employing glutamate competitive inhibitors, noncompetitive inhibitors, and modulation of Znof drugs ,95 showeof drugs , likely of drugs . In factof drugs .++—given that it is an NMDA antagonist by blocking the ion pore to limit Ca++ influx [To counter the timeliness issue, a clinical trial known as FAST-MAG attempted pre-hospital administration of Mg+ influx . Unfortu+ influx . Given t+ influx  have conInflammation, the process by which white blood cells are recruited to damaged tissue, has long been sought for therapeutic intervention ,100. BegThe contention between spatial location of entry of leukocytes and whether it matters has been difficult to navigate. Some preclinical work has shown that neutrophils enter the brain , others Intercellular adhesion molecule-1 (ICAM-1) is a homodimeric ligand between leukocytes and endothelial vessels . BlockinGiven the ambiguity between endothelial adhesion molecules, incomplete blockade of leukocyte extravasation had been identified as a significant limitation of targeting distinct adhesion molecules and may have partially contributed to the lack of clinical success observed with ICAM blocking strategies. Consequently, upstream integrins involved in leukocyte transendothelial migration have been posited as potentially attractive targets to induce a more substantial blockade of cell influx. One therapeutic strategy is natalizumab, an anti-VLA4 antibody that blocks VLA4-Vascular Cell Adhesion Molecule (VCAM)-1 interaction-mediated T-cell invasion . VCAM-1 A second trial, ACTION II, was conducted with a plan to further investigate functional status as a primary outcome rather than infarct size using MRI . This trMaraviroc, a drug originally developed against the human immunodeficiency virus (HIV), is the latest example of a repurposed drug making headway in the stroke literature. As a C-C chemokine receptor 5 (CCR-5) inhibitor, this drug was found in a Pfizer drug screen to inhibit the fusion of the gp120 HIV envelope protein to CCR-5 . By doinIL-1, a prominent pleiotropic cytokine released by the ischemic brain ,122,123 To foster rehabilitation, stem cell therapy has emerged as a promising approach to elevate functional recovery following ischemic stroke. This potential mechanism includes establishing a novel neuronal network through transplanted stem cells and preserving the penumbra zone before it transitions into the core infarct. This protective effect is believed to be mediated by stem cell secretion of paracrine factors that promote anti-inflammatory and immunomodulatory effects, induce anti-apoptotic effects, and mobilization of endogenous neural stem cells.7 to 109, mainly within a single amount. Despite the wide range, no toxicity or severe adverse effects have been reported.Various types of stem cells have been used in clinical trials for ischemic stroke. Mesenchymal stem cells (MSCs) are widely used based on their self-renewable, differentiation, and secretory and immunomodulatory properties that promote the regeneration of damaged tissues and ease inflammation . Bone maStem cells are transplanted through different routes. Intravenous injection is the most accessible and widely used, but studies show only small quantities of cells can be found in the damaged brain tissue with IV injection, and most cells are trapped in the lungs . Therefo® (invimestorcel) cells—which do not require immune suppression nor tissue matching—at doses of either 400 million cells, 1200 million cells or a placebo towards moderate non-lacunar stroke 24–48 h post ictus. showed no improvement in NIHSS after three months. However, according to their post-hoc analysis, patients who received cells between 24 and 36 h showed a significant improvement in motor recovery one year following treatment [We define acute phase infusion as SC transplantation within 14 days of stroke onset. Cells used at this time include autologous BMMNCs, which can easily be obtained from bone marrow ,132,133,reatment . MASTERSIn the sub-acute phase (within six months of stroke onset), autologous MSCs and autologous BMMNCs have been widely used. Intravenous injection is still the most popular route, and intra-artery is also used through MCA or carotid. One study with a five-year extended follow-up period showed a decreased mRS score and an increased number of patients with an mRS of 0–3 in the MSC group . AnotherVarious cell types have been used during the chronic phase (over six months of stroke) due to the relative limitation on timing. Intra-cerebral infusion of cells is widely used in this phase. The post-transplantation impact through the IV or IA route primarily relies on their secretory and anti-inflammatory functions. However, during the chronic phase, inflammation is typically already established. Hence, the usage of IV or IA routes needs to be extensively employed. Transplantation via intra-cerebral injection has been demonstrated to be safe in all studies. Although randomized control studies are lacking, some studies show improved neuronal function following stem cell infusion compared with baseline, including ESS, NIHSS, Fugl-Meyer scores, F-M motor scale scores, BI, mBI Berg balance scale and Fugl-Meyer modified sensation ,141. SinBased on the above outcomes of the completed trials, improvement of neuronal function, especially the motor function, has been seen in the trials for sub-acute and chronic phases. However, many studies showed no efficacy of stem cell treatment, which is different from the preclinical outcomes. The insights into the failures observed during the completed trials could unlock the pathway to the future success of stem cell treatments for ischemic stroke.There are multiple reasons for the lack of success in stem cell therapeutics. First, low patient enrollment may be a primary reason that no efficacy was observed. Some studies enrolled no more than twenty patients. Even with an appropriate control group, reaching statistical significance for one or more clinical outcomes would be difficult. Second, in many trials, the criteria for stroke type were only “ischemic stroke” within the specific time window. This is understandable due to the difficulties of recruiting enough patients. However, more specific stroke types may lead to positive results. A difference in stem cell therapy effectiveness between white matter and gray matter predominant strokes seems likely. Third, the evaluation of neuronal function, including the parameters utilized, constitutes another crucial element that significantly influences the outcome. NIHSS and mRS are widely used. However, based on the current studies, motor function may benefit evidently from stem cell therapy. Therefore, more granular scales for motor recovery specifically may be necessary to demonstrate efficacy. Fourth, stem cell infusion timing may prove to be critical, especially for the acute phase. Therefore, the accurate windowing for stem cell infusion needs further investigation. Inherited limitations of the stem cells, such as the large size of certain types of stem cells, can lead to the trapping of stem cells in the lung via intravenous application and in the brain vessels via intra-arterial application, which can not only prevent the cells from reaching the brain but also cause secondary injury of the tissues. Although many preclinical studies show promising outcomes of stem cells in treating ischemic strokes, the clinical work was not as good. The discrepancy between preclinical and clinical studies could be attributed, at least in part, to the contrasting regenerative capabilities observed in typically young and healthy animals versus aged patients, often dealing with a range of chronic conditions. Studies show that the proliferation and angiogenic capacity of endothelial progenitor cells and MSCs were impaired in patients with coronary artery disease and metabolic disorders . AdditioGiven the multitude of failures of targeted drugs, generally exploring the field of pharmacologic agents for post-stroke cerebroprotection has also been a recourse for investigation. Several clinical trials have aimed to generate cerebroprotective agents by targeting multiple mechanisms in the hopes of having various effects.2, limiting platelet aggregation [Nelonemdaz, a combination of aspirin and sulfasalazine , was created in 2007 with the hopes that the novel drug can act synergistically better than either drug separately . Aspirinregation . Howeverregation . Consequregation . Though regation . AlthougNimodipine, a second-generation dihydropyridine calcium channel blocker, is most commonly used in subarachnoid hemorrhage to promote clinical neurological improvement per AHA guidelines . It was Statins specifically work by competitive, reversible sterol pathway inhibition of 3-hydroxy-methylglutaryl coenzyme (HMG-CoA reductase), leading to a reduction in mevalonate . BlockinThe role of non-pharmacologic approaches in promoting stroke recovery has also been an area of active investigation. Perhaps the most well-known modality is remote ischemic conditioning (RIC) for stroke patients. RIC utilizes repetitive inflation and deflation of a blood pressure cuff to elevated blood pressures above systolic measurement, with the hope of invoking a low dose I/RI. By doing so, this mechanism is hypothesized to promote an adaptive response to a larger I/RI (such as stroke). As described by others , RIC mayIn brief, other types of non-invasive and invasive neuromodulation have been explored during both the acute and rehabilitation phases after stroke. The advantages of neuromodulation include the following: the stimulus is highly reversible, highly targeted, easily optimized, and very specifically timed. Non-invasive brain stimulation has been found to be effective in promoting recovery after stroke . CorticaDue to the translational gaps between preclinical success and relative clinical failure, the NINDS, industry and leaders in the stroke field sought to develop a multi-arm, multi-site translational platform to encourage rigor, reproducibility, and quality control in efforts to develop a novel stroke drug . By dataOne could easily surmise that the stroke field has false starts, contradictions, methodological concerns, and unforeseen challenges. However, this must be counterbalanced with high levels of promise and potential with shifting outcomes. In less than ten years, EVT transformed malignant stroke syndromes that invariably resulted in increased morbidity and mortality for conditions where patients can leave the hospital unscathed. With an NNT of 3, mechanical thrombectomy + thrombolytic is now one of the most effective treatments in modern medicine. Developing treatment regimens for those patients who do not benefit from EVT, as well as the high proportion of patients who do not have the option for EVT, is the focus of further cerebroprotective management. However, the road to future improvement will be complicated. Understanding the basic biology of stroke has proven difficult, as multiple mechanisms seem to be at play with a balance of benefits and untoward effects or limitations  Though mTo achieve this aspirational goal, the stroke enterprise must not only focus on the development of new drugs but must also understand how to distinguish subsets of stroke patients that, as of 2023, currently seem to share a similar phenotype. This relies on the creation of more sensitive diagnostic and non-invasive tools. For example, although two patients with a significant stroke may seem to have similar deficits, the outcomes may be a sum of different factors for each patient. To complicate matters, there is clear preclinical evidence that spatiotemporal patterns constrain possibilities but also change throughout the hours, days, weeks, and months after stroke. This heterogeneity may seem daunting, but understanding these differences may lead to the development of more refined diagnostics and more precise treatments. Ultimately, we envision a successful therapeutic regimen involving continuous diagnostic assessments of the principal components and sum of each patient’s features, with recursive adjustments contingent upon real-time data.Based on the outcomes of concluded clinical trials for therapies focusing on diverse stroke treatment modalities, further exploration through clinical and preclinical studies is imperative to unravel the intricate mechanisms and optimal interventions tailored to various stroke types and individual patient profiles. Given the time-critical nature of strokes, the dynamic nature of patients’ conditions fluctuates with timing. Therefore, developing and implementing therapies intricately attuned to the temporal context holds the potential to markedly enhance patients’ prognosis. Meanwhile, the combinatory therapeutic approaches hold promise in benefitting patients exhibiting diverse attributes. Like other diseases, stroke therapy necessitates a precision medicine framework considering the intricate interplay between patients’ genetic predispositions and environmental contexts.


Despite the advancements made in the diagnosis and treatment of cancer, the stages associated with metastasis remain largely incurable and represent the primary cause of cancer-related deaths. The dissemination of cancer is facilitated by circulating tumor cells (CTCs), which originate from the primary tumor or metastatic sites and enter the bloodstream, subsequently spreading to distant parts of the body. CTCs have garnered significant attention in research due to their accessibility in peripheral blood, despite their low abundance. They are being extensively studied to gain a deeper understanding of the mechanisms underlying cancer dissemination and to identify effective therapeutic strategies for advanced stages of the disease. Therefore, substantial efforts have been directed towards establishing and characterizing relevant experimental models derived from CTCs, aiming to provide relevant tools for research. In this review, we provide an overview of recent progress in the establishment of preclinical CTC-derived models, such as CTC-derived xenografts (CDX) and cell cultures, which show promise for the study of CTCs. We discuss the advantages and limitations of these models and conclude by summarizing the potential future use of CTCs and CTC-derived models in cancer treatment decisions and their utility as precision medicine tools. Circulating tumor cells play a crucial role in cancer dissemination.Circulating tumor cells are rare in the bloodstream.In vivo/in vitro models are needed to expand circulating tumor cells for research purposes.How accurately do in vitro/in vivo circulating tumor cell-derived models reflect the biology of the original tumor?What are the prerequisites and conditions for the successful implementation of these models?How can these preclinical models be effectively utilized in the management of patient treatment?Cancer dissemination is a dynamic process where circulating tumor cells (CTCs) are released from the primary tumors or metastatic lesions into the bloodstream, allowing the cancer to spread. They can also support their tumors of origin, contributing to tumor growth, angiogenesis, and stromal recruitment through a process known as “tumor self-seeding” . While nDynamic changes in CTCs are not only manifested at the level of changes in their phenotypes, but also in their fluctuating numbers. CTCs are typically present in small quantities, in the order of units to tens per 7.5 ml of whole blood, with significant variations depending on disease stage, cancer type , 26, treThe correlation between the mass of CTCs and disease progression has become evident with advancements in CTCs detection and counting methods. The number of CTCs has been linked to progression-free survival (PFS) and overall survival (OS) in patients with various types of cancer, serving as a prognostic marker for disease progression and as a predictive marker for treatment response (reviewed in ). AlthouPatient-derived xenografts (PDXs), established by engrafting pieces of human tumor tissue into immunocompromised mice, represent a very useful preclinical model for more faithful disease modeling and a step towards precision medicine. Obtaining biopsy material from both the primary tumor and metastases is an invasive and, in some types of tumors, challenging procedure. CTCs, on the contrary, are accessible through less invasive routine blood draw, which also allows and facilitates repeated sampling for easier disease monitoring. CTC-derived xenograft (CDX) models offer a representative molecular snapshot of the disease as they serve as surrogates of otherwise unobtainable metastatic tissues. Due to the significant differences in individual cancer types, the following section is organized according to the types of tumors in which CDX derivation has been described (see Table The first CDX model was established and described by Baccelli and colleagues 10 years ago using CTCs from mBCa patients implanted into the bone marrow of mice . FurtherFaugeroux and colleagues were the first who developed a prostate cancer (PCa) CDX model . For sucSeveral studies describe CDX models of small cell lung carcinoma (SCLC), as this type of primary tumor is known to shed high numbers of CTCs . HodgkinTP53 and RB1, well-known mutations for SCLC. Lastly, the CDX models derived from serial sampling of a patient in the progressive phase, treated with several drugs prior to CDX generation and not responding to the therapy anymore, also reflected the development of resistance to the treatment in vivo.Human SCLC xenograft models derived from primary tumor biopsy (PDXs) and CTCs (CDXs) were compared . Whole-e+/NEUROD1−, ASCL+/NEUROD1+, ASCL1−/NEUROD1+).In another study of SCLC, CDXs from various stages of the disease and therapy phases were generated with a success rate of about 17% after subcutaneous injection . MultiplAnother CDXs from CTCs from SCLC were derived by Stewart and colleagues . Compari+/CK+ CTCs in patient blood before therapy, but no EpCAM+/CK+ CTCs post-therapy. There were more than 150 CTCs in post-therapy blood sample with epithelial CK+/vimentin– (23%), mesenchymal CK–/vimentin+ (30%), and mixed phenotype CK+/vimentin+ (47%) CTCs. In vivo propagated CDX was resistant to treatment with cisplatin and pemetrexed, the similar combination of therapeutics used in patient. Next, Tayoun and colleagues established 4 CDXs from patients with NSCLC after implantation of CTC-enriched fraction in interscapular fat pad [Together five CDXs were so far established from non-small cell lung cancer (NSCLC) models. Morrow and colleagues established CDX from CTCs obtained from blood of patient with metastatic NSCLC after brain radiotherapy, but not from CTCs obtained from the same patient before therapy . Interes fat pad . InteresOnly a few studies have demonstrated the generation and utilization of CDX from melanoma CTCs. A CDX developed from a patient with highly aggressive BRAF-V600E melanoma unresponsive to targeted therapy resembled the patient’s histological features . In miceOne significant advantage of CDX models is their ability to propagate CTCs and generate a significantly higher number of cells compared to the original CTCs obtained from a patient’s blood. This expansion can be performed repeatedly as the disease progresses in a single patient. Recently, the use of CDX models has been described for the identification of genes associated with various steps in the metastatic cascade . In one On the other hand, there are several limitations to keep in mind when working with CDX models. The latency can vary from several months to almost one year Table . This tiBased on the information provided, it becomes evident that determining the number of CTCs in each sample prior to the generation of CDX models is crucial. It is important to note that the quantification of CTCs relies heavily on the methodology employed. The CellSearch® system, for instance, detects CTCs by targeting the epithelial marker EpCAM, thereby excluding EpCAM-negative CTCs from the analysis. Remarkably, a CDX model was successfully established from a patient whose CTCs were undetectable using the CellSearch® method . The resAnother factor influencing the development of CDX is the site of injection/implantation of isolated CTCs. Subcutaneous implantation is a widely used method Table . The impIn summary, CTC-derived xenografts are a valuable model for disease monitoring, searching for candidate genes and/or mutations associated with disease progression or response to treatment, and studying intratumoral heterogeneity. However, the low efficiency and long latency of tumor development in mice currently disqualifies this method for precision medicine, which requires flexible management of each patient’s treatment strategy. This would require greater robustness, higher CDX establishment efficiency, and shorter time to xenograft generation.The CDX generation shows several limitations, as described in the previous chapter. Therefore, the research is focusing also on the seemingly less challenging derivation of in vitro cultures from CTCs. In this chapter, we discuss various aspects important for CTCs cultivation, e.g., short- vs. long-term in vitro expansion, normoxic vs. hypoxic conditions, 2D vs. 3D conditions, or adherent vs. non-adherent cultures. Due to specific conditions in individual cancer types, the following section is organized according to the types of tumors from which CTCs cultivation has been described . CTCs s+ cells [Another variable factor in establishing CTC-derived in vitro cultures is the choice of 2D or 3D conditions. Setting up of adherent 2D conditions is without debate technically easier. However, CTCs under 2D conditions may undergo senescence after several divisions . Still, + cells , 73, can+ cells ) were alIn summary, CTC-derived cultures are another valuable source of material for the study of primary tumors and metastases. Comparison of BCa patient-derived CTCs cultivated in vitro, and matched primary tumors revealed that a large proportion of mutations identified in cultured CTCs were detected also in matched primary tumors . MoreoveAs discussed in previous chapters, introduction of both in vivo and in vitro models of CTCs is still challenging, requires large time and financial costs, and has relatively low efficiency. Therefore, alternative models to those mentioned above are being developed.TP53 was found in the original patient’s tumor, in isolated cell-free DNA, fresh CTCs, and in two in ovo tumors generated by CTCs engraftment in CAM.One alternative model is the in ovo assay using chorioallantoic membrane (CAM). The CAM assay is already established in cancer research and is used to assess many aspects of cancer, such as tumor growth, vasculature, invasion, metastatic potential, genomic instability, mutations, or epigenetic reprogramming (summarized in ). In 202The CAM assay is a rapid model suitable for studying metastatic cascade and drug treatments . MoreoveNumerous studies described clinical validity of CTCs counts in cancer screening, as a predictor in localized tumors and independent prognostic factor in metastatic cancers (summarized in ). MolecuSimilarly, CTC-derived xenografts (CDXs) may serve as a robust model for drug screening and molecular characterization. Several studies described similarity between CDX and patient’s original tumor—grown CDXs have usually comparable histological characteristics and key genetic characteristics and importantly, display comparable response to drugs as patient’s original tumor (e.g. , 55, 61.CTCs play a significant role in the process of cancer dissemination and their analysis provides valuable insights into the metastatic cascade. Numerous efforts have been made to enhance the detection and capture of CTCs and to improve the yield of isolated CTCs. For instance, the CellCollector®, a sterile steel wire coated with EpCAM antibody, has been introduced to collect CTCs in vivo from the patients’ cubital vein, overcoming the limitations associated with the small blood volumes typically used for CTCs isolation and resulting in increased CTCs yield . FurtherAs detection and isolation techniques advance, along with the establishment of preclinical in vivo and in vitro models derived from CTCs, there is growing interest in utilizing CTCs as a source of information for disease status, therapeutic response, and treatment decision-making. However, important considerations need to be carefully addressed for both CDXs and CTC-derived cell cultures. These include understanding how well they represent the patient’s original tumor, their robustness and expandability, and the speed at which these models can be generated to bring the benefit for patients. These fundamental questions require further exploration and answers before these preclinical models can be effectively utilized to predict treatment responses and guide individualized treatment decisions in clinical practice.


Despite the rapid increase in disability and death due to Parkinson’s disease and associated psychiatric comorbidities , the quest for a clear pathophysiological mechanism and treatment remains elusive. Numerous studies aim to identify a metabolomic fingerprint for PD and new, promising biomarkers are discovered with implications beyond neurodegenerative diseases, such as novel markers as predictors of bipolar type in depressed patients.Changes in neuronal microenvironment employ electrochemical techniques, such as cyclic voltammetry, used in both animal and human models of PD to monitor dopamine (DA) alterations in vivo, with high spatial and temporal resolution.Our aim is to investigate the latest scientific literature on PD and associated neuropsychiatric disorders and review the applications cyclic voltammetry has in recent technological advances in the field.To gain a broad understanding of the subject, we have consulted multiple scientific literature databases  using the keywords “cyclic voltammetry, Parkinson’s disease, psychiatric disorders, dopamine” and included original research articles published in the last 10 yearsin situ measurement of DA release in the human brain has been demonstrated in a sequential investment task, with implications for future research in decision-making behavior.The first One study combines cyclic voltammetry and wireless telemetry for in vivo recording of changes in extracellular levels of DA, with high temporal and spatial resolution.Disulfide nanorod-graphene-β-cyclodextrin nanocomposites biosensors have been succesfully used in detecting DA in rodent brain and human blood serum samples, with implications for minimally invasive measuring techniques.Animal studies use cyclic voltammetry to monitor changes in DA levels in cerebrospinal fluid and plasma of mouse models of PD and investigate DA metabolism, release, uptake and receptor sensitivity in Knock-out mice, with implications for the diurnal variation of extracellular DA tone and release.Furthermore, a human alpha-synuclein-expressing mouse model of PD exhibited increased extracellular DA concentrations, decreased DA uptake and relieved paired-stimulus depression.Cyclic voltammetry is a powerful tool in the expansion to humans of electrochemical recording techniques in PD. The final aim is to investigate DA neuron physiology before neurodegeneration onset and to measure neurotransmitter release in real time.None Declared