Pseudomonas aeruginosa (PA) is a leading cause of healthcare-associated infections. A variety of antibiotic classes are used in the treatment of PA infections, including beta-lactams (BLs) and fluoroquinolones (FQs), given either together in combination therapy or alone in monotherapy. A systematic review and meta-analysis were performed to evaluate the therapeutic efficacy of BL agents versus FQ agents as active, definitive monotherapy in PA infections in adults. Methods: Comprehensive literature searches of the Medline and Scopus electronic databases, alongside hand searches of the Cochrane Database of Systematic Reviews, PubMed, and Google Scholar, were performed without a time restriction to identify studies published in English comparing BL and FQ agents given as monotherapy for PA infection in hospitalized adults for which mortality, bacteriological eradication, or clinical response was evaluated. One reviewer screened search results based on pre-defined selection criteria. Two reviewers independently assessed included studies for methodological quality using NIH assessment tools. Two fixed-effects meta-analyses were performed. Results: A total of 368 articles were screened, and six studies involving 338 total patients were included in the meta-analysis. Upon evaluation of methodological quality, two studies were rated good, three fair, and one poor. A meta-analysis of three studies demonstrates FQ monotherapy is associated with significantly improved survival compared to BL monotherapy for patients with PA bacteremia . A meta-analysis of three studies demonstrates FQ monotherapy is associated with equivalent bacteriological eradication compared to BL monotherapy for PA pneumonia or skin and soft tissue infection . Conclusion: The meta-analyses demonstrate FQ monotherapy significantly improves survival in PA bacteremia and is associated with similar rates of bacteriological eradication in pneumonia and skin and soft tissue infection caused by PA compared to BL monotherapy. However, more research is needed to make meaningful clinical recommendations.Introduction:  Pseudomonas aeruginosa (PA) is a pathogenic Gram-negative bacterium and leading cause of healthcare-associated infections (HAIs) around the world [he world ,2,3. In he world ,5. Meanwhe world ,2,3, withe world . PA can he world , puttinghe world ,2,3. In In addition to its relatively high prevalence and potential for serious infection, multidrug-resistant (MDR) PA strains are particularly concerning for human health ,7,8. CliCurrently, a variety of antibiotic classes are used in the treatment of PA infections, including beta-lactams (BLs), fluoroquinolones (FQs), aminoglycosides (AGs), and, rarely, colistin, in combination therapy or alone in monotherapy . HoweverMuch effort has focused on comparing the therapeutic efficacy of monotherapy versus combination therapy in the treatment of PA infection, yet the results are generally considered controversial ,11. On tThis systematic review and meta-analyses were performed and reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol guidelines . This reAs an overarching framework, the PICOT  model was implemented to define the inclusion and exclusion criteria for this systematic review and meta-analysis. Studies with populations of adult inpatients infected with PA were included. Studies with comparisons between definitive BL or FQ monotherapy, active against the infecting PA strain, were eligible for inclusion. Based on precedence in the literature, BL monotherapy was defined as BL ± beta-lactamase inhibitor . StudiesPseudomonas aeruginosa, Pseudomonas infections, Pseudomonas, nosocomial, and hospital acquired. A full, detailed record of the search strategy is included in the With the assistance of a research librarian, comprehensive literature searches of Medline and Scopus electronic databases were performed. Hand searches of the Cochrane Database of Systematic Reviews, PubMed, Google Scholar, and bibliographies of relevant articles and meta-analyses were also performed. The search strategy was formulated by three study investigators  and executed by a research librarian at Creighton University Health Sciences Library (CUHSL). A combination of key terms and MESH terms were formulated into the search, including beta-lactams , fluoroquinolones (ciprofloxacin OR levofloxacin), monotherapy, The titles and abstracts of the literature search results were screened for eligibility and annotated in Microsoft Word by one study investigator (ER) in accordance with the PICOT-based, predefined selection criteria. Eligible studies were selected, and full-text articles were retrieved via available electronic sources, CUHSL stacks, or interlibrary loan. Full-text versions of the selected studies were reviewed by one investigator (ER) for inclusion in the meta-analysis. The final population of studies included in the meta-analysis was reviewed and assessed for methodological quality and risk of bias independently by two reviewers (ER and RW) using the NIH National Heart, Lung, and Blood Institute Study Quality Assessment Tools for Controlled Intervention Studies or Observational Cohort and Cross-Sectional Studies for randomized control or cohort studies, respectively . These aUnadjusted data were independently manually extracted by two study investigators (ER and RW) and compared in duplicate. Mortality was the primary outcome assessed for observational studies, whereas bacteriological eradication and clinical success were the primary outcomes assessed for controlled studies. These outcomes were selected because they are traditionally used in studies comparing antimicrobial efficacy and reflect important clinical variables and/or therapeutic efficacy. For studies in which causative organisms of infection other than PA were reported, only PA-specific data were extracted and used in the analysis. If an intent-to-treat (ITT) analysis was reported, ITT data were utilized in the meta-analysis. For mortality, 28-day and 30-day mortalities were included in the same data analysis. Based on consistency among definitions between studies, the definition of microbiological eradication included confirmed eradication and presumed eradication, and the definition of clinical success included clinical cure and improvement. We planned to stratify the data based on specific agents within a drug class and perform subgroup analyses if possible.2 and I2. Effect sizes for outcomes from the retrospective cohort studies are reported as odds ratios (OR), whereas effect sizes from the randomized controlled trials are reported as risk differences (RD). All effect sizes are reported along with a 95% confidence interval (CI). Individual study and composite effects are displayed in separate forest plots for each outcome. Following a fixed-effects meta-analysis, publication bias was evaluated visually via funnel plots.All extracted data were entered manually into Review Manager 5.3. The decision to use a fixed- or random-effects meta-analysis was based on between-study heterogeneity as indexed by τThe literature search resulted in a total of 397 potentially relevant studies for screening, ranging in year of publication from 1981 to 2019 . Six addUpon evaluation of methodological quality, two studies were rated good, three rated fair, and one rated poor . Of the The characteristics of the studies included in the meta-analysis are shown in p = 0.02; A total of 211 patients with PA bacteremia from the three cohort studies were included into the meta-analysis comparing the effects of BL and FQ monotherapy on mortality. Among the six total treatment arms, mortality ranged from 0% to 32%. Four patients (9.8%) who received FQ monotherapy died compared to 50 (29.4%) who received BL monotherapy. The results of a fixed-effects meta-analysis indicate that FQ monotherapy resulted in significantly higher survival compared to BL monotherapy . Based on the composite risk difference, the number needed to treat (NNT) was 15, indicating that 15 patients would need to be treated with FQ monotherapy for one patient to benefit from BL monotherapy. Visual analysis of the associated funnel plot indicates no apparent publication bias  who received FQ monotherapy were found to have culture-confirmed or presumed PA eradication compared to 21 (35.0%) who received BL monotherapy. The fixed-effects meta-analysis indicates that FQ monotherapy was not associated with increased bacteriological eradication compared to BL monotherapy .Torres et al. was the only study to have PA-specific data comparing the effects of BL and FQ monotherapy on clinical response. Twenty-six patients with PA pneumonia were included in the report. Ten patients (71.4%) who received ciprofloxacin monotherapy were found to be clinically cured or improved compared to 8 (66.7%) who received imipenem-cilastatin  changed the minimum inhibitory concentration (MIC) breakpoints for FQ in 2019 . Large-sand 2008 . Llanes 1 mg/mL.  Low-leveStrengths of this meta-analysis include the use of NIH quality assessment tools and adherence to a well-defined PICOT model and the PRISMA reporting guidelines for systematic reviews and meta-analyses. Additionally, the variations in study year, geography, and patient demographics among the included studies enhance the external validity of this study. However, its limitations include a paucity of literature comparing the efficacy of BL and FQ as monotherapies, small sample sizes for each individual study, differing definitions of terms and outcomes among studies, incomplete data, the lack of gray literature search, and known and unknown discrepancies in patient demographics between treatment arms for infections caused specifically by PA. The latter is explained by the fact that not all studies were designed with the purpose of making direct comparisons between BL and FQ monotherapy or studying PA-specific infection . Additionally, the studies evaluated definitive therapy, and the efficacy of empiric therapy could have impacted the results. Consequently, these results should be interpreted with caution.Overall, little research has focused specifically on comparing the therapeutic efficacy of BL and FQ drug classes as active, definitive monotherapy for PA infection. To our knowledge, this is the first meta-analysis to do so. The vast majority of systematic reviews compares combination therapy and monotherapy but results from existing studies are contradictory and controversial ,11. AlthWhen selecting a preferred drug class for PA infection, no overarching recommendations exist, and our results do not come close to bridging the gap. More likely, no one drug class or antimicrobial agent is the ideal choice. Antimicrobial resistance is in flux and varies by regions, so local antibiogram data are essential to selecting drug therapies. Once PA antimicrobial susceptibility testing is complete, the interplay between patient characteristics and drug features remains critically important in selecting definitive therapy. Furthermore, definitive antimicrobial therapy for PA must minimize the potential of selecting resistance.Overall, these results provide insight into the therapeutic efficacy of BL and FQ drug classes as active, definitive monotherapy for PA infection in adult inpatients but fall short of offering definitive answers. The results suggest FQ monotherapy is associated with significantly higher survival rates compared to BL monotherapy, but more rigorous research is required to make definitive conclusions. Clinicians should continue to weigh the pros and cons of drug classes and individual agents for a particular patient when selecting definitive therapy for PA infection.


On the road toward artificial general intelligence (AGI), two solution paths have been explored: neuroscience-driven neuromorphic computing such as spiking neural networks (SNNs) and computer-science-driven machine learning such as artificial neural networks (ANNs). Owing to availability of data, high-performance processors, effective learning algorithms, and easy-to-use programming tools, ANNs have achieved tremendous breakthroughs in many intelligent applications. Recently, SNNs also attracted a lot of attention due to its biological plausibility and the possibility of achieving energy-efficiency  models. In the first training epoch, the novelty estimations of all samples are obtained through bio-plausible synaptic plasticity; next, the samples whose novelty estimations exceed the threshold are repeatedly learned and the novelty estimations are updated in the next five epochs; then, all samples are learned with one more epoch. The last two steps are periodically taken until convergence. CBSNNs show better accuracy and higher efficiency in processing several small-scale datasets than conventional voltage-driven plasticity-centric SNNs. Daram et al. propose ModNet, an efficient dynamic learning system inspired from the neuromodulatory mechanism in the insect brain. An inbuilt modulatory unit regulates learning based on the context and internal state of the system. The network with modulatory trace achieves 98.8% ± 1.16 on average over the omniglot dataset for five-way one-shot image classification task while using 20x fewer trainable parameters compared to state-of-the-art models. Kaiser, Mostafa et al. introduce deep continuous local learning (DECOLLE), an SNN model equipped with local error functions for online learning. The synaptic plasticity rules are derived from user-defined cost functions and neural dynamics by leveraging existing autodifferentiation methods of machine learning frameworks. The model demonstrates state-of-the-art performance on N-MNIST and DvsGesture datasets. Fang et al. propose a bio-plausible noise structure to optimize the performance of SNNs trained by gradient descent. Through deducing the strict saddle condition for synaptic plasticity, they demonstrate that the noise helps the optimization escape from saddle points on high dimensional domains. The accuracy improvement can reach at least 10% on MNIST and CIFAR10 datasets. Panda et al. modify the SNN configuration with backward residual connections, stochastic softmax, and hybrid artificial-and-spiking neuronal activations. In this way, the previous learning methods are improved with comparable accuracy but large efficiency gains over the ANN counterparts.Next, we briefly summarize the recent progress of neural network  learning presented in our accepted papers. Inspired by the curiosity-based learning mechanism of the brain, Wu et al. explore the first work that uses SNNs for large-vocabulary continuous automatic speech recognition (LVCSR) tasks. Their SNNs demonstrate competitive accuracies on par with their ANN counterparts while consuming only 10 algorithmic timesteps and 0.68× total synaptic operations. They integrate the models into the PyTorch-Kaldi Speech Recognition Toolkit for rapid development. Kugele et al. apply SNNs for processing spatiotemporal event streams . They improve the ANN-to-SNN conversion learning method by introducing connection delays during the pre-training of ANNs to match the propagation delays in converted SNNs. In this way, the resulting SNNs can handle the above tasks accurately and efficiently.Unlike the artificial neuron in ANNs, each spiking neuron in SNNs has intrinsic temporal dynamics, which is appropriate for processing sequence information. In this Research Topic, we accepted two papers that discuss SNN applications. In addition, besides energy-efficiency  inspired by stochastic computing (SC) that uses bitstreams to represent numbers, to improve machine learning hardware. Benefitting from the CNN substrate, SC-CNN can achieve high accuracy; further benefitting from SC, SC-CNN is highly efficient, scalable, and fault-tolerant. Different from the common digital machine learning accelerators, Kaiser, Faria et al. present a clockless autonomous probabilistic circuit, wherein synaptic weights are read out in the form of analog voltages, for fast and efficient learning with no use of digital computing. They demonstrate a circuit built with existing technology to emulate the Boltzmann machine learning algorithm. Muller et al. introduce bias matching, a top-down neural network design approach, to match the inductive biases required in a machine learning system to the hardware constraints of its implementation.Lee et al. propose a spike-train level direct feedback alignment (ST-DFA) algorithm. Compared to the state-of-the-art backpropagation learning algorithm, they demonstrate excellent performance vs. overhead tradeoffs on FPGA for speech and image classification applications. Dutta et al. propose an all ferroelectric field-effect transistors (FeFET)-based SNN hardware that allows low-power spike-based information processing and co-localized memory and computing. They implement a surrogate gradient supervised learning algorithm on their efficient SNN platform, which further accounts for the impacts of device variation and limited bit precision of on-chip synaptic weights on the classification accuracy.To alleviate the high cost training of SNNs using backpropagation, Parsa et al. build a hierarchical pseudo agent-based multi-objective Bayesian hyperparameter optimization framework for both software and hardware. They can not only maximize the performance of the network, but also minimize the energy and area overheads of the corresponding neuromorphic hardware. They validate the proposed framework using both ANN and SNN models, which involves both deep learning accelerators [e.g., PUMA (Ankit et al., Wang et al. further present an end-to-end mapping framework for implementing various hybrid neural networks. By constructing hardware configuration schemes for four typical signal conversions and establishing a global timing adjustment mechanism among different heterogeneous modules, they implement hybrid models with low execution latency and low power consumption.Machine learning and neuromorphic computing are two modeling paradigms on the road toward AGI. ANNs have achieved tremendous breakthroughs in many intelligent applications benefitting from big data, high-performance processors, effective learning algorithms, and easy-to-use programming tools; in contrast, SNNs are still in its infant stage and there is a dire need for more neuromorphic benchmarks. Through cross-discipline research, we expect to understand and bridge the gap between neuromorphic computing and machine learning. This Research Topic is just a small step in this direction, and we look forward to more innovations that can achieve brain-like intelligence.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa  Physical activity behavior has a major influence on public health ,2. HowevDifferent studies have trained and tested their machine learning models on self-recorded datasets, but only a few of these sets are publicly available ,14,15,16Free-living activity data from approximately 35,000 people have been recorded in the fourth round of Norway’s biggest health study, the Trøndelag Health Study (HUNT4) ,32. The https://github.com/ntnu-ai-lab/harth-ml-experiments, accessed on 16 November 2021). Second, we train seven different baseline classification models on HARTH, including (1) the k-nearest neighbors (k-NN), (2) the support vector machine (SVM), (3) the random forest (RF), (4) the extreme gradient boost (XGB), (5) the bidirectional long short-term memory (BiLSTM), (6) the convolutional neural network (CNN), and (7) a CNN with multi-resolution modules.Two contributions are made in this paper. First, we present the Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants performed different activities during their regular working hours while carrying out their everyday activities as naturally as possible. Two experts annotated twelve activities in total. We used two accelerometers placed on the thigh and lower back to collect sensor data. HARTH provides high-quality acceleration measurements with fixed sensor placements and professionally annotated labels. To the best of our knowledge, the combination of these three factors is not yet considered by other accelerometer-based and publicly available free-living HAR datasets. HARTH is publicly available to enable an objective comparison between HAR models for future research  to perform health studies based on physical activity behavior. Previous works ,13 and iThis paper is organized as follows. According to Micucci et al. , and Reihttps://lbd.udc.es/research/real-life-HAR-dataset (accessed on 22 April 2021)). They did not specify the smartphone placement. The participants were free to perform the activities whenever they wanted during their everyday life. They were only asked to annotate the beginning and the end of the activity using an app on their smartphone. The activities were: inactive (not carrying the phone), active, walking/running, and driving. “Active” means that the person carried the phone but did not walk . Driving includes all types of engine-based transportation. The dataset considers two physical activities, namely walking and standing/sitting (included in driving and active).Garcia-Gonzalez et al.  proposedhttp://www.shl-dataset.org (accessed on 22 April 2021)). Annotations were created during the data collection using one smartphone. The labels were validated after the data collection using the camera’s video signal. The data were recorded over several days, and instructions were given on what to perform each day. However, the subjects were able to decide when and where to perform the activities. Furthermore, they were free to perform activities of their everyday life. Four physical activities are considered in the dataset: walking, running, cycling, and sitting/standing. The transportation activity “still” includes both standing and sitting, making it impossible to distinguish them.In the Sussex-Huawei Locomotion (SHL) dataset ,37, threhttp://hub.hasc.jp/corpora (accessed on 22 April 2021)). Eighty-one subjects were recorded in an everyday life setting. They were free to perform six activities whenever they wanted in their daily lives as long as they were performed between landmarks, i.e., user-defined start and end geographical locations. The activities were no activity (standing/sitting/lying), walking, running, skipping, and walking stairs. A smartphone accelerometer was used for data acquisition, and annotations were performed via an app. It was not specified where to wear the smartphone or which manufacturer to use.The HASC-PAC2016  is a colhttps://www.cis.fordham.edu/wisdm/dataset.php#actitracker (accessed on 22 April 2021)). Users recorded data during everyday life while carrying the smartphone. They were free to annotate particular activities by themselves  ( (99.1% fters are ), and [6ters are   was plaWe recorded the dataset in two sessions. In the first session, 15  participants were told to perform their everyday life as normally as possible, during a recording period of 1.5–2 h. They were instructed to perform the activities sitting, standing, lying, walking, and running (including jogging) for at least two to three minutes. During this time, the two sensors recorded acceleration data with a sampling rate of 100 Hz (which we later downsampled to 50 Hz) and a measurement range of ion tool . As a reWe consider seven supervised machine learning approaches for HAR, namely k-NN, SVM, RF, CNN, bidirectional LSTM, extreme gradient boost (XGB), and CNN with multi-resolution modules (multi-resolution CNN). This allows us to provide a good benchmark on how different types of machine learning models perform on HARTH. We cover often-used (former four) and seldom-used (latter three) machine learning approaches for HAR. It also enables the comparison of deep learning and traditional machine learning approaches.X and an unlabeled data sample x, the k-nearest neighbors (k-NN) [x by computing the (Euclidean) distance to all Given a previously observed training set s (k-NN)  algorithediction .kernel trick to avoid this problem. Instead of projecting the data points directly into a higher-dimensional space, a kernel function is used that describes the dot-product of data points in that N-dimensional space, which is enough to find an optimal decision boundary.The support vector machine (SVM)  algorithThe random forest (RF) ,68 algorAlthough the extreme gradient boost (XGB) is seldom used in HAR, it achieves state-of-the-art performance in many other research fields . XGB is ediction . The finThe bidirectional long short-term memory (BiLSTM) ,72 is anA major difference between convolutional neural networks (CNNs) and standard neural networks such as like multilayer perceptrons is the weight sharing between successive layers . InsteadNafea et al.  showed pWe performed five preprocessing steps before training the machine learning models, as illustrated in First, we synchronized the two sensors and the video labels with the help of the aforementioned heel drops to get annotated acceleration signals. Second, we used a 20 Hz low-pass fourth-order Butterworth filter on our dataset since human body movements are below 20 Hz . Third, mean, the median, the standard deviation, the coefficient of variation, the 25th, and 75th percentile, as well as the minimum and maximum for each frame of the gravity components, to get orientation information. For the movement components, we computed the skew, kurtosis, and signal energy, as well as the frequency-domain features frequency-domain magnitudes’ mean, frequency-domain magnitudes’ standard deviation, dominant frequency, dominant frequency’s magnitude, spectral centroid, and total signal power. Narayanan et al. [axis correlation between all six axes and between the two vector magnitude signals. In addition, we computed the mean across the two sensors’ gravity components. In total, we generated 161 features for each window. As a fifth and last preprocessing step, we scaled the features through min–max scaling to the range 0–1 in order to avoid large range differences between features. The target of each machine learning model is to learn the twelve labels of our dataset.Fourth, for the traditional machine learning models, we extracted time- and frequency-domain features out of each window. We consider eight signals for feature computation, the six axes (three for each sensor), and each sensor’s vector magnitude t et al. , featuren et al.  showed tThe experiments are examined in two stages. First, we performed hyperparameter optimization combined with cross-validation to find reasonable hyperparameters for each machine learning model. Afterward, a leave-one-subject-out cross-validation was carried out to compare the performance metrics between the different machine learning models.The hyperparameter optimization with cross-validation was carried out by using two randomly chosen subjects of each of the mentioned dataset’s two sessions for testing. The remaining 18 subjects were used for training. By consistently using test subjects of both sessions, we avoid a possible bias towards the larger one. This cross-validation technique results in three iterations, each having different subjects in the test set. We trained each hyperparameter assignment on these three iterations and averaged the results for comparison. We focus here on the average F1-score  as a performance metric since it is more robust to class imbalance than the accuracy .We utilized 1D convolutional kernels in the first layers of the two CNN models to enable a single kernel to learn to extract useful information of each axis. The bidirectional LSTM, on the other hand, uses a fully connected input layer for the whole C, with larger values causing a more substantial penalty on wrongly classified samples (best: 10). Furthermore, we utilized different X and the number of features For the k-NN, different numbers of neighbors For each of the seven machine learning approaches, we choose the hyperparameters with the highest F1-score to perform a leave-one-subject-out cross-validation (LOSO). Hence, we train each model on 21 subjects of our dataset and test them on the remaining subject. We repeat this 22 times with a different test subject each time. LOSO shows less subject-based bias than other cross-validation methods , which iWe are mainly interested in physical activity classification. Some of our labels involve a similar physical activity even though they have a different label. Therefore, we can merge certain labels. In particular, shuffling, transport (standing), and standing are fused to the same physical activity standing. Sitting and transport (sitting) are merged into sitting. This merging is achieved by summing up the corresponding columns and rows in the summed confusion matrix, respectively, resulting in nine activity labels. Our results show that the SVM is the best model. However, all trained methods have similar high performance, indicating well-chosen hyperparameter assignments. Additionally, all seven models seem to struggle with the same issues. First, when the activities are not merged see , a high Our dataset shows a strong resemblance to the HUNT4 data regarding used sensors, sensor positions, and recordings in free living. With its professionally annotated activities, it serves as a qualified training dataset to train HAR machine learning models that can be used for physical activity-based public health studies using the HUNT4 data.An accelerometer-based HAR dataset needs two essential properties for physical activity behavior-based public health research. First, accurate acceleration measurements are required, including fixed sensor positions, noise robustness, and professionally annotated physical activities. Second, the data need to be recorded under free-living conditions. To the best of our knowledge, there is currently no benchmark accelerometer-based HAR dataset publicly available that has both properties. We make two contributions in this work. First, we fill this gap in existing benchmarks by introducing the human activity recognition Trondheim dataset (HARTH), a professionally annotated dataset, recorded under free-living conditions using two accelerometers attached to the participants’ back and thigh. Our second contribution is the training of seven baseline machine learning models. The HARTH dataset and the source code of our models are publicly available. Thus, they can be used as a reference for further development in future research.The window size plays a crucial role in the HAR performance. Future work can investigate the usage of dynamic windows as well as the temporal relation between windows. Another approach is to extract features of different window sizes at once, similar to the work of Herrera-Alcántara et al. . A challDue to the high-quality recordings and annotations of our dataset, as well as its challenging nature, we provide a promising basis for different research directions such as data augmentation, class balancing, and single data sample prediction. Our results show that there is still room for improvement for researchers to develop innovative machine learning approaches to facilitate a more precise human activity recognition in free-living environments.


Although individual antiretroviral drugs have been shown to be associated with elevated cardiovascular disease (CVD) risk, data are limited on the role of antiretroviral drug combinations. Therefore, we sought to investigate CVD risk associated with antiretroviral drug combinations.Using an administrative health-plan dataset, risk of acute myocardial infarction (AMI) associated with current exposure to antiretroviral drug combinations was assessed among persons living with HIV receiving antiretroviral therapy (ART) across the U.S. from October 2009 through December 2014. To account for confounding-by-indication and for factors simultaneously acting as causal mediators and confounders, we applied inverse probability of treatment weighted marginal structural models to longitudinal data of patients.Over 114,417 person-years  of ART exposure, 602 cases of AMI occurred at an event rate of 5.26 /1000 person-years. Of the 14 antiretroviral drug combinations studied, persons taking abacavir-lamivudine-darunavir had the highest incidence rate  of AMI. Risk  of AMI was elevated for current exposure to abacavir-lamivudine-darunavir , abacavir-lamivudine-atazanavir , and tenofovir-emtricitabine-raltegravir . Tenofovir-emtricitabine-efavirenz was associated with reduced risk . Abacavir-lamivudine-darunavir was associated with increased risk of AMI beyond that expected of abacavir alone, likely attributable to darunavir co-administration. We did not find an elevated risk of AMI when abacavir-lamivudine was combined with efavirenz or raltegravir.The antiretroviral drug combinations abacavir-lamivudine-darunavir, abacavir-lamivudine-atazanavir and tenofovir-emtricitabine-raltegravir were found to be associated with elevated risk of AMI, while tenofovir-emtricitabine-efavirenz was associated with a lower risk. The AMI risk associated with abacavir-lamivudine-darunavir was greater than what was previously described for abacavir, which could suggest an added risk from darunavir. The results should be confirmed in additional studies.The online version contains supplementary material available at 10.1186/s12981-021-00383-7. Over the last decade, there has been an extraordinary increase in the number of people living with HIV (PLHIV) receiving antiretroviral therapy (ART), with that number increasing from 8 million in 2010 to 25.4 million by 2019 [There is ongoing debate on the association of several contemporary antiretroviral agents with increased risk of cardiovascular disease, including abacavir –8, lamivIn this study, we investigated the relationship between receipts of various antiretroviral drug combinations containing agents from four major drug classes, including nucleos(t)ide reverse transcriptase inhibitors (NRTI), non-nucleoside reverse transcriptase inhibitors (NNRTI), protease inhibitors (PI), and integrase strand transfer inhibitors (InSTI), used in the treatment of HIV infection and the subsequent risk of acute myocardial infarction (AMI).Risk of AMI associated with exposure to combinations of antiretroviral drugs was assessed among PLHIV ≥ 18 years of age started on ART between October 1, 2009 and December 31, 2014 and enrolled in the IMS’ PharMetrics Plus database in the United States. QuintilesIMS PharMetrics Plus is one of the largest health plan insurance claims databases in the U.S., containing adjudicated claims for more than 100 million unique enrollees from four regions of the U.S. . In the Individual antiretroviral drugs in the database were identified by their unique generic product identifier (gpi) codes. Any two prescriptions for a given antiretroviral agent separated by < 30 days were combined to represent a single continuous exposure; gaps ≥ 30 days were treated as separate ART exposures and not considered as a single continuous exposure. Start and stop dates are available for each antiretroviral drug; this enables determination of whether a specific antiretroviral drug(s) was switched to another drug. Exposure time for a drug combination was generated based on whether an individual was receiving the component drugs simultaneously, determined by examining the start and stop dates of the individual drugs. Of 23 drug combinations defined a priori, 14 combinations were assessed based on availability of adequate exposure-time (~ 1500 person-years), ascertained by examining the duration of exposure given by the dates of initiation and stopping of the antiretroviral agent in the database. Each drug combination consisted of three (or four if boosted) drugs: two NRTIs and either a boosted PI, an NNRTI, an InSTI or another NRTI. A separate analytical dataset was generated for each exposure. The data were longitudinal and each subject’s follow-up time was divided into consecutive one-month periods during which treatment status was allowed to vary. Current exposure to a drug combination was defined as exposure (yes/no) during each one-month observation period. The exposure time of a specific drug combination was compared to exposure-time of all other antiretroviral agents in assessing the outcome. The outcome was defined as the first occurrence of acute myocardial infarction after baseline, represented in the database by International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) code 410. xx.  marginal structural models . To consThe final marginal structural hazard model was adjusted for the sIPTW and baseline values of the following covariates: baseline age, sex, year of treatment start in the database, smoking, use of alcohol, and substance, prior heart disease or receipt of medications for heart disease, lipodystrophy, hypertension, chronic kidney disease, diabetes mellitus, and dyslipidemia. Standard errors for the treatment and marginal models were calculated robustly to account for repeated measures within an individual. We performed sensitivity analyses to determine variables to include into the treatment model and assess if the models yield qualitatively different results, as described by Cole and Hernan .In secondary analyses, conventional Cox proportional hazard models were run for each exposure, adjusting for baseline and time-dependent variables that included: age, treatment year, tobacco use (ever), substance use (ever) and the following conditions at baseline: stroke, hepatitis B or C virus infections, any cancer, prior heart disease or receipt of cardiovascular medications, lipodystrophy, categorical BMI status, chronic kidney disease, diabetes mellitus, and dyslipidemia. We did not adjust for multiple comparisons, as we had a priori hypotheses based on findings in the published literature and a separate dataset was meticulously generated to test each exposure; hence, such adjustments would be unnecessarily conservative. The data were extracted from the claims databases using TERADATA , and SAS version 9.1 . The marginal structural models were implemented in STATA version 13.1  following methods described by Fewell et al. . We haveThe database contained 114,417 person-years of exposure-time to ART contributed by 73,071 study participants. AMI occurred in 602 individuals at an event rate of 5.26  per 1000 person-years. The median age of the study population was 45 years and 81.5% were male. The median follow-up time ranged from 2.67 years for individuals receiving tenofovir-emtricitabine-darunavir to 3.58 years for those tenofovir-lamivudine-zidovudine/efavirenz. The demographic and clinical characteristics of the participants overall and stratified by AMI outcome are shown in Table Using sIPTW marginal structural models, we found significantly elevated risk of AMI for current exposure to abacavir-lamivudine-darunavir , abacavir-lamivudine-atazanavir , and tenofovir-emtricitabine-raltegravir . Forest plot and Kaplan Meier Survival curves for the risk of acute myocardial infarction associated with the antiretroviral drug combinations are shown in Figs. Using a large nationally representative real-world health plan dataset containing longitudinal information of more than 70,000 PLHIV receiving care across the United States, we have described the results of AMI risk for various antiretroviral drug combinations, several of which were recommended initial regimens. We found an elevated risk of AMI associated with current exposure to three antiretroviral drug combinations: abacavir-lamivudine-darunavir, abacavir-lamivudine-atazanavir, and tenofovir-emtricitabine-raltegravir. The incidence rate and hazard ratio were highest for abacavir-lamivudine-darunavir  and lowest for tenofovir-emtricitabine-efavirenz . The population level estimate for the annual incidence of myocardial infarction in the U.S. is 1.86/1000 people , whereasThere is an accumulating body of evidence suggesting an elevated risk of CVD associated with use of abacavir –8, 15. PWe did not find any significantly increased risk of AMI for most of the tenofovir-containing combinations, including tenofovir-lamivudine-efavirenz, tenofovir-emtricitabine-atazanavir, and tenofovir-emtricitabine-fosamprenavir, tenofovir-lamivudine-atazanavir, tenofovir-lamivudine-darunavir, and tenofovir-emtricitabine-darunavir. Our findings on tenofovir-based regimens largely agree with that of the VA study. We are not aware of a prior study that has implicated tenofovir/emtricitabine in the development of AMI. Tenofovir has been shown to have an intrinsic lipid lowering effect and to decrease the carotid intima media thickness, suggesting possible cardiovascular and cerebrovascular protective effects , 27. We Our findings on the drug combinations have implications for clinical practice, as they inform the thinking about risk of CVD in the setting of combination ART—the only way in which ART is currently used. The combination of abacavir-lamivudine and dolutegravir, an InSTI, is a recommended initial ART regimen for PLHIV. Our finding of no increased risk when abacavir-lamivudine was combined with raltegravir suggests value in further study of other InSTI-based combinations including abacavir-lamivudine-dolutegravir. However, we note that raltegravir and dolutegravir, although both being InSTIs, can be quite different in terms of their side-effect profiles . We encoThe claims database we used has limitations that should be noted. First, there is no information on possible risk modifiers, such as CD4 cell-count, HIV viral load and race/ethnicity. We are unsure how these factors may have influenced the prescription of a tenofovir or abacavir based antiretroviral regimen. Also, the majority of the study population are male and therefore, the results may not be readily generalizable to women living with HIV. Although, the ICD-9-CM code used to ascertain AMI outcome has been previously validated,  some misThere are a number of strengths to this work. Most studies on CVD risk from ART exposure so far have been in clinical cohorts; validating risk in a different setting is important. Compared to prior studies, we used more recent data. Because of the possibility of confounding-by-indication where an individual receives (or not) a drug based on specific clinical indications, such as chronic kidney disease or dyslipidemia, we used inverse-probability weighted marginal structural models to account for confounding. We have reported our results from marginal structural and conventional Cox models. Although the estimates from marginal structural models and Cox models did not differ substantially, the former seeks to capture the CVD risk had all PLHIV received the given antiretroviral drug combination vs had they all not received the ART combination. Hence, the estimates from the marginal models may be more representative of the true population estimates , 32.The antiretroviral drug combinations of abacavir-lamivudine-darunavir, abacavir-lamivudine-atazanavir, and tenofovir-emtricitabine-raltegravir were found to be associated with a greater risk of AMI, whereas tenofovir-emtricitabine-efavirenz was found to be associated with a lower risk of AMI. The AMI risk for abacavir-lamivudine-darunavir was greater than what has been previously described for abacavir, suggesting an added risk from darunavir. The results should be confirmed in additional studies. Greater awareness of the effects of specific antiretroviral drug combinations can benefit patients with specific risk profiles, ART history, or antiretroviral drug contraindications.Additional file 1: Table S1. ICD-9-CM, and CPT codes for defining various covariates and outcomes. Table S2. Risk of myocardial infarction associated with current exposure to various combinations of antiretroviral agents among people living with HIV in the United States


Recent years, the immunosuppressive properties of mesenchymal stem cells (MSCs) have been demonstrated in preclinical studies and trials of inflammatory and autoimmune diseases. Emerging evidence indicates that the immunomodulatory effect of MSCs is primarily attributed to the paracrine pathway. As one of the key paracrine effectors, mesenchymal stem cell-derived exosomes (MSC-EXOs) are small vesicles 30-200 nm in diameter that play an important role in cell-to-cell communication by carrying bioactive substances from parental cells. Recent studies support the finding that MSC-EXOs have an obvious inhibitory effect toward different effector cells involved in the innate and adaptive immune response. Moreover, substantial progress has been made in the treatment of autoimmune diseases, including multiple sclerosis (MS), systemic lupus erythematosus (SLE), type-1 diabetes (T1DM), uveitis, rheumatoid arthritis (RA), and inflammatory bowel disease (IBD). MSC-EXOs are capable of reproducing MSC function and overcoming the limitations of traditional cell therapy. Therefore, using MSC-EXOs instead of MSCs to treat autoimmune diseases appears to be a promising cell-free treatment strategy. In this review, we review the current understanding of MSC-EXOs and discuss the regulatory role of MSC-EXOs on immune cells and its potential application in autoimmune diseases. MSCsin vitro . In addiin vitro . Abundanor cells –9. Folloor cells . At presexosomes , 11.Extracellular vesicles(EVs) are a family of particles/vesicles found in blood and body fluids. They are composed of phospholipid bilayer and carry a variety of molecules that serve as mediators for intercellular communication . In 1946Exosomes are spherical vesicles composed of lipid bilayer membranes with a diameter of 40-200 nm, which contain complex and abundant active substances such as proteins and nucleic acids. Expression of exosome markers, proteins, nucleic acids, and other bioactive molecules is related to the cell of origin . In the Both eukaryotic and prokaryotic cells release EVs, which are regarded as a part of their normal physiology and acquired abnormalities . EVs repvia hypercentrifugation, which cannot differentiate exosomes from other EVs or biological macromolecules  , 41. Altmay form . Also, Mmay form . Using Mmay form . In addimay form . Howeverexposure . Moreovevia exosomal miR-146a, which ultimately resulted in prolonging the survival of sepsis mice , which can ingest, process, and present antigens to T and B lymphocytes . Most DCferation . Researcferation . Moreovenclusion . In brienclusion Figure 2in vitro. In the CpG-stimulated PBMC co-culture system, MSC-EVs can completely replicate the inhibitory effect of MSCs on immunoglobulin secretion, B cell proliferation and differentiation in a dose-dependent manner , Systemic lupus erythematosus (SLE), Type-1 diabetes (T1DM), uveitis, Rheumatoid arthritis (RA), and Inflammatory bowel disease (IBD)  Table 1.MS is the most common inflammatory disease of the central nervous system (CNS), which is characterized by demyelination, neuronal damage and loss, and ultimately neurological dysfunction . Experimin vitro, there have been no studies on the use of MSC-EVs for the treatment of SLE in mice or humans. In the future, MSCs are expected to provide a novel and safe treatment for SLE patients.SLE is a chronic autoimmune disease characterized by immune inflammation and multiple organ damage. In addition, SLE pathogenesis is extremely complex, primarily due to the comprehensive influence of genetic, environmental, hormonal, epigenetic, and other factors . With thin vivo, particularly MSC-EXOs  and IL-17+CD4+ cells (Th17) in EAU mice treated with MSC-EXOs was significantly lower than that in the PBS-treated mice  infectious uveitis; 2) non-infectious uveitis; and 3) masquerade uveitis, of which non-infectious uveitis is believed to be caused by autoimmunity, and EAU is used as a mouse model . Traditie retina . In addited mice . The aboe manner . In anote manner . AlthougRA is a chronic inflammatory disease characterized by synovial hyperplasia and immune cell infiltration, leading to joint destruction , 106. Thvia expanding Bregs  and Crohn’s disease (CD), has become a global disease with an increasing incidence . StudiesThe treatment of autoimmune diseases is challenging and there is currently no effective cure. In this review, we discussed the regulatory role of MSC-EXOs on immune cells and the new progress of MSC-EXOs in the treatment of autoimmune diseases, suggesting that MSC-EXOs may be a new cell-free drug for the treatment of autoimmune diseases.http://www.clinicaltrials.gov/). Although encouraging results have been achieved with MSC-EXOs, several uncharted territories remain to be explored before MSC-EXOs can be used as drug vectors for clinical use. First, although it has been reported that clinical-grade exosomes can be produced using good manufacturing techniques and standard operating procedures, large-scale production of exosomes for clinical use remains to be explored , Summit of the Six Top Talents Program of Jiangsu Province (Grant No. 2017-YY-006).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Chronic drug-induced liver injury (DILI) occurs in up to 20% of all DILI patients. It presents a chronic pattern with persistent or relapsed episodes and may even progress to cirrhosis. However, its underlying development mechanism is poorly understood.Aims: To find serum metabolite signatures of chronic DILI with or without cirrhosis, and to elucidate the underlying mechanism.Methods: Untargeted metabolomics coupled with pattern recognition approaches were used to profile and extract metabolite signatures from 83 chronic DILI patients, including 58 non-cirrhosis (NC) cases, 14 compensated cirrhosis (CC) cases, and 11 decompensated cirrhosis (DC) cases.Results: Of the 269 annotated metabolites associated with chronic DILI, metabolic fingerprints associated with cirrhosis (including 30 metabolites) and decompensation (including 25 metabolites), were identified. There was a significantly positive correlation between cirrhosis-associated fingerprint (eigenmetabolite) and the aspartate aminotransferase-to-platelet ratio index (APRI) . The efficacy of cirrhosis-associated eigenmetabolite coupled with APRI to identify cirrhosis from non-cirrhosis patients was significantly better than APRI alone [area under the curve (AUC) value 0.914 vs. 0.573]. The decompensation-associated fingerprint (eigenmetabolite) can effectively identify the compensation and decompensation periods . The results of the metabolic fingerprint pathway analysis suggest that the blocked tricarboxylic acid cycle (TCA cycle) and intermediary metabolism, excessive accumulation of bile acids, and perturbed amino acid metabolism are potential mechanisms in the occurrence and development of chronic DILI-associated cirrhosis.Conclusions: The metabolomic fingerprints characterize different stages of chronic DILI progression and deepen the understanding of the metabolic reprogramming mechanism of chronic DILI progression to cirrhosis. DILI is one of the most common and serious adverse reactions to drugs –5, and aMetabolomics is the study of small molecular metabolites. It improves our understanding of the pathophysiological evolution of diseases and greatly promotes the diagnosis, treatment, and prognosis of diseases . MetabolThus, we conducted untargeted metabolomic analysis of 83 chronic DILI patients  and compared three groups of serum metabolomics characteristics, to establish the metabolic fingerprint of chronic DILI-related cirrhosis, including its potential biomarkers and mechanism of occurrence and development.n = 58), compensated cirrhosis , and decompensated cirrhosis  groups were enrolled. In addition, 10 serum samples of healthy subjects (HS) were collected. This research scheme was approved by the Ethics Committee of the hospital based on the ethical principles of the Declaration of Helsinki. Also, written informed consent was obtained from all patients. Based on the utilization of untargeted metabolomics, we obtained metabolic entities (metabolome profile) with significant difference (P < 0.05) between cirrhosis (CC + DC) and NC groups. The differentially abundant metabolites from metabolome profiles were deemed to be cirrhosis-associated metabolome features. Similarly, the differentially abundant metabolites for comparisons between DC and CC groups were considered as decompensation-associated metabolome features. The metabolic fingerprint (a cluster of metabolites) was screened using a hierarchical cluster and heat map analysis. We mainly used unsupervised principal component analysis (PCA) for fingerprint metabolites and formed one eigenmetabolite, which is defined as the first principal component  evaluations were conducted on an Agilent 6550 iFunnel Q-TOF LC/MS equipped with electron spray ionization (ESI). MS data in both positive and negative modes were acquired based on optimal parameters, including a mass range from m/z 50–1,200; capillary voltage of 2.2 kV (negative) and 2.5 kV (positive); cone voltage of 40 V; ion source temperature of 130°C; desolvation temperature of 350°C; cone airflow of 50 L/h; and desolvation airflow of 800 L/h.https://www.metaboanalyst.ca/) was used for data filtering and normalization. The clinical data of patients was expressed as a median  or numerical value. Differences between groups were analyzed by the non-parametric test in SPSS software program  and reported as statistically significant if the P < 0.05. PCA and orthogonal partial least square discrimination analysis (OPLS-DA) were carried out using the SIMCA-P software . The MetaboAnalyst website was used to acquire AUC and the associated P-values of differential metabolites. The human metabolome database  and Kyoto encyclopedia of genes and genomes (https://www.kegg.jp) were used to construct an interactive network for the metabolic fingerprint and associated metabolic pathway.Mass hunter Profinder software was used for raw data pre-processing. The online analysis tool MetaboAnalyst 4.0  were included in this study. Comparisons between CC and NC groups were conducted and the levels of cholinesterase, platelet, albumin/globulin ratio, and pre-albumin were found to be significantly lower in CC than NC, and the reverse was true for total bile acid. Patients in the DC group showed significantly higher values of direct bilirubin, international normalized ratio, and immunoglobulin M, but significantly lower values of albumin, albumin/globulin ratio, and pre-albumin, compared to the CC group. These results suggest that a gradual disturbance of liver functions occurred during anabolism and metabolism in chronic DILI advancement.The demographic and clinical parameters of the three groups of study population are listed in + and ESI− modes (P < 0.05) between cirrhosis (CC + DC) and NC, as well as DC vs. CC. Of the 1,324 metabolites (including positive and negative ion modes) differentiating between cirrhosis and NC groups, 195 metabolites in the database were annotated  in patients with chronic DILI-related cirrhosis . Citric As main synthesis and metabolism in liver, bile acid is seriously affected after liver disease. We found a significant increase in conjugated bile acids  in patients with chronic DILI-associated cirrhosis. The excessive accumulation of chenodeoxycholic acid glycine conjugate and taurochenodesoxycholic acid promote apoptosis of liver cells and liver failure , 40. TheOur analysis provides novel information to further our understanding of the pathological changes of several metabolic pathways, based on serum metabolites, during the progression of chronic DILI. However, there are still a few limitations in the study. As all know, due to the abundant metabolites and biological process, the metabolomic study needs to further verify the results using animal models. And multi-center, large-sample studies could improve the application of these metabolic fingerprints to clinical DILI management in the future. Nevertheless, the study is expected to offer references for clinical diagnosis and monitoring of the occurrence and development of chronic DILI-associated cirrhosis, and to serve as a resource of metabolic adaptation and reprogramming to guide future investigations on clinical prognosis, mechanisms and novel therapeutics.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Medical Ethics Committee of Fifth Medical Center of Chinese PLA General Hospital. The patients/participants provided their written informed consent to participate in this study.J-bW, X-hX, and Z-sZ were responsible for the study concept and design. S-sC, YH, and Y-mG performed sample collection, conducted LC-MS data analysis, and manuscript preparation. S-sC and J-bW drafted the manuscript. S-sL and ZS reviewed and modified the manuscript. MN helped with data analysis. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


N = 1220 Ohio-residing boys of ages 11–16 were recruited into a cohort in 2015 and 2016. Participants completed surveys every six months for four years. Surveys assessed cognitive and affective responses to tobacco advertisements  and tobacco use after an advertisement viewing activity. We used mixed-effects Poisson regression models with robust standard errors to estimate risk of initiating use of each tobacco product according to participants’ cognitive  and affective  responses to advertisements for that product. No associations between affective responses to advertisements and tobacco use outcomes were detected in adjusted models. However, finding health risks memorable was associated with reduced risk of ever smoking initiation  and a reduced risk of ever SLT initiation that approached statistical significance . Measures to increase saliency of health risks on cigarette and SLT advertisements might reduce use among youth.Exposure to tobacco advertisements is associated with initiation of tobacco use among youth. The mechanisms underlying this association are less clear. We estimated longitudinal associations between youths’ cognitive and affective responses to advertisements for cigarettes, e-cigarettes, and smokeless tobacco (SLT) and initiation of these products.  Over the past decade, the prevalence of cigarette smoking and smokeless tobacco (SLT) use has declined among youth, reaching an estimated 3.3% and 2.3%, respectively, of youth in 2020 ,2. ConveAccording to the McGuire communication–persuasion matrix, receptivity to an advertisement is based on a hierarchy of items, including exposure, cognitive response, and affective response ,13,14. CAlthough this theoretical framework exists, our understanding of the associations between cognitive and affective responses to tobacco advertisements and tobacco use outcomes among youth is limited. A large body of research has investigated the associations between advertisement exposures, advertisement receptivity, and tobacco use ,9,10,11.Our goal was to assess the associations between cognitive and affective responses to tobacco advertisements, operationalized as memorability of health risks and likeability of the advertisement, respectively, and prospective initiation of cigarette smoking, e-cigarette use, and SLT use. We hypothesized that youth who found health risks less memorable and advertisements more likeable would have increased risk of becoming ever or current tobacco users over the four-year study.N = 1220) were recruited into a longitudinal study between January 2015 and June 2016. Girls were not recruited because one of our aims was to evaluate predictors of SLT use, which is rare among girls [N = 991) and convenience sampling (N = 229). Convenience sampling methods were used due to slow recruitment and less income and racial/ethnic diversity using probability sample alone. Additional details about sampling and recruitment are provided elsewhere [Male youth N = 220 were Trained interviewers administered a questionnaire to male youth participants during an in-person interview. Sensitive items  were administered via audio computer-assisted self-interviewing (ACASI). Remaining items  were interviewer-administered. Parents or guardians of the male youth completed a self-administered survey to report indicators of family socioeconomic status and household tobacco use.People Magazine, Sports Illustrated, Popular Science, ESPN Magazine, and Rolling Stone, magazines with relatively high youth readership. The size of the stimulus set ranged from 13 to 30 advertisements for each product at each time point. All advertisements were one page. Advertisements that were primarily sponsorships for concerts or sporting events were excluded, as were advertisements that were overtly sexual. After the interview, interviewers debriefed participants about the advertisement viewing activity. They told participants that advertisements do not show the negative consequences of using the advertised product, including cancer, heart disease, and motor vehicle crashes.An advertisement viewing activity was completed during the interviewer-administered portion of the visit ,21,22. DFollow-up interviews were conducted every six months for four years. The two-year follow-up was conducted in-person, but all other follow-ups were conducted via telephone. The two-year follow-up used the same procedures as baseline, including for the advertisement viewing activity. The follow-up surveys conducted via telephone were shorter, predominantly assessed tobacco use, and did not include the advertisement viewing activity. The two-year follow-up in-person surveys were completed between January 2017 and August 2018 (73.7% retention) and the four-year follow-up phone surveys were completed between January 2019 and June 2020 (62.3% retention).Cognitive and affective responses to tobacco advertisements were the predictors of interest. Responses were assessed at baseline and the two-year follow-up, and thus were modeled as time-varying predictors.Cognitive responses were measured with an open-ended item: “Name one thing about the ad that you particularly noticed or remembered.” Participants spoke their response aloud to the interviewer, who transcribed their responses verbatim. Participants were permitted to list multiple memorable components of the advertisement if they wanted, and in these cases each component was coded. Responses were coded by independent coders with good interrater reliability following procedures reported previously  ,22. BrieAffective response was derived from three close-ended items asking participants to rate (0–10) how enjoyable, likeable, and appealing they found the advertisement . Values Initiation of cigarettes, e-cigarettes, and SLT by the two-year and four-year follow-ups were the outcomes of interest. At the two-year follow-up, participants were classified as having initiated ever use of each product if they were never users at baseline and reported having tried the product at least once at or before their two-year follow-up interview. At the four-year follow-up, participants were classified as having initiated ever use of each product if they had never used the product at the two-year follow-up but reported using the product at least once at or before their four-year follow-up survey. Similar coding was used to establish initiation of current use of each product, although past 30 day use of each product was used to determine initiation at the two- and four-year follow-ups.Several covariates were conceptually identified as confounders of the association between cognitive and affective responses to tobacco advertisements and initiation of tobacco use. Confounding variables included age in years, race and ethnicity , region , parental education (<Bachelor’s degree vs. Bachelor’s degree or higher), household tobacco use (does not live with vs. lives with an adult who uses tobacco), peer tobacco use (none vs. a few or more friends use tobacco), recalled exposure to magazine advertisements in general (never or rarely vs. sometimes or more often), reported grade point average (GPA), sensation seeking mean , ever alcohol use, and ever use of other tobacco products. For time-varying confounders , values from baseline and the two-year follow-up were included in models assessing two-year and four-year tobacco initiation, respectively.We used all available follow-up data to assess initiation of cigarettes, e-cigarettes, and SLT. Baseline advertisement responses were used to predict initiation at any time during the first two years of follow-up, and two-year follow-up advertisement responses were used to predict initiation at any time during the last two years of follow-up. In other words, tobacco use outcomes of interest in longitudinal models occurred at the two- and four-year follow-up surveys. When covariate values changed over time, they were treated as time-varying covariates in models as described above.Descriptive statistics were calculated for all variables to characterize the sample at baseline, the two-year follow-up, and the four-year follow-up. Next, separate mixed-effects Poisson regression models with robust standard errors were run to estimate associations between predictor variables and outcome variables; Poisson regression was used to facilitate estimation of risk ratios . CognitiPrior to running fully adjusted analyses, we conducted model assumption checks for linearity and multicollinearity. This testing resulted in decisions to standardize GPA and sensation seeking scores in all models, include a quadratic term for GPA in cigarette ever use incidence models, and include quadratic terms for sensation seeking in e-cigarette and SLT current use incidence models. Finally, separate fully adjusted mixed-effects Poisson regression models with robust standard errors were used to estimate risk of ever/current initiation of cigarettes, e-cigarettes, and SLT associated with cognitive and affective responses to advertisements for each respective product. Predictor variables, confounders, and an indicator for wave were included as fixed effects, and random intercepts were also included. Analyses were completed using Stata/SE version 17 .At baseline, male youth participants were 14.1 years old on average, 75.8% were White non-Hispanic, 42.0% lived in Ohio Appalachia, and 31.8% lived with an adult tobacco user. Approximately half of male youth participants had a parent or guardian with a Bachelor’s degree or higher . On averFor cognitive responses, the proportion of male youth participants who found health risks memorable after viewing a cigarette advertisement was stable from baseline to the two-year follow-up . No other associations were statistically significant.In models that only adjusted for wave, having a mean affective response >0 was associated with increased risk of initiating ever and current cigarette smoking and e-cigarette use among our participants . Findingp = 0.08).Adjusting for confounders attenuated the associations between affective responses to tobacco advertisements and initiation of cigarette smoking and e-cigarette use to non-significance among our participants. However, the association between finding health risks memorable after viewing cigarette advertisements and reduced risk of cigarette smoking initiation remained statistically significant. The association between finding health risks memorable on SLT advertisements and reduced risk of SLT initiation again approached statistical significance  would provide information about cognitive and affective processing in real time that are not directly accessible through self-report ,32,33. AWe evaluated associations between cognitive and affective responses to tobacco advertisements and incident tobacco use over four years of follow-up among male youth. Overall, we identified that associations between affective responses to cigarette and e-cigarette advertisements and initiation of those products were attenuated after controlling for confounding factors. However, the association between thinking about health risks after viewing a cigarette advertisement and reduced risk of cigarette smoking initiation remained statistically significant. In fact, our youth participants who thought about health risks after viewing a cigarette advertisement were over 40% less likely to have tried smoking a cigarette during follow-up. Our findings provide direction for policies and interventions aimed at reducing cigarette smoking among youth, as well as direction for future research to more comprehensively assess how youth process tobacco advertisements and how this processing is associated with initiation of tobacco use.


The liver is a target organ of life-threatening pathogens and prominently contributes to the variation in drug responses and drug-induced liver injury among patients. Currently available drugs significantly decrease the morbidity and mortality of liver-dwelling pathogens worldwide; however, emerging clinical evidence reveals the importance of host factors in the design of safe and effective therapies for individuals, known as personalized medicine. Given the primary adherence of cells in conventional two-dimensional culture, the use of these one-size-fit-to-all models in preclinical drug development can lead to substantial failures in assessing therapeutic safety and efficacy. Advances in stem cell biology, bioengineering and material sciences allow us to develop a more physiologically relevant model that is capable of recapitulating the human liver. This report reviews the current use of liver-on-a-chip models of hepatotropic infectious diseases in the context of precision medicine including hepatitis virus and malaria parasites, assesses patient-specific responses to antiviral drugs, and designs personalized therapeutic treatments to address the need for a personalized liver-like model. Second, most organs-on-chips lack a monitoring system for cell functions in real time; thus, the review discusses recent advances and challenges in combining liver-on-a-chip technology with biosensors for assessing hepatocyte viability and functions. Prospectively, the biosensor-integrated liver-on-a-chip device would provide novel biological insights that could accelerate the development of novel therapeutic compounds. Individualized genetic factors contribute to disease susceptibility and severity ,2, drug Knowledge in stem cell biology, bioengineering and material sciences has advanced In Vitro models capable of recapitulating human tissue and organs. Examples include organoids and organ-on-chip assays. Organoids are three-dimensional (3D) structures of cells derived from stem cells, including pluripotent stem cells or adult stem cells. The organoid generally grows in a semisolid medium and consists of different cell types that self-organize in a hierarchical manner similar to that of a tissue . Thus, oPlasmodium spp., the causative agents of hepatitis and malaria, respectively. Although drugs are available for these devastating infections, treatment responsiveness, drug resistance and, to a lesser extent, DILI reduce therapeutic efficacy. Therefore, more effective and safer drugs are needed. Here, we review evidence of individual responses to drugs against hepatotropic infectious diseases and advances in current liver-on-a-chip models of those diseases and drug-induced toxicity. Regarding the variation in drug response among individuals, methods to design personalized liver-on-a-chip systems are discussed with a focus on the source of patient-specific cells. Moreover, a combination of liver-on-a-chip technology with biosensors is reviewed for the assessment of hepatocyte viability and functions, and some novel biological insights are needed for the development of therapy.The liver is a target organ of pathogenic microorganisms, including hepatitis viruses and Plasmodium spp., which are causative agents of hepatitis and malaria, respectively. Viral hepatitis and malaria remain global life-threatening infectious diseases. In 2019, over 3 million people were newly infected with chronic hepatitis B and C virus [Hepatocytes are targets of pathogenic microorganisms such as hepatitis virus and  C virus . Viral h C virus . The WHO C virus . Relativ C virus . For mal C virus . Advancedenovo formation of extrachromosomal DNA , resulting in persistent infection in host hepatocytes and subsequent reactivation. Moreover, none of the antiviral therapies achieve a radical cure, as indicated by a complete loss of hepatitis B surface antigen (HBsAg) in blood [Chronic HBV infection is a leading cause of morbidity and mortality worldwide. More than 250 million people were affected by chronic HBV infection in 2015 . Given tin blood . Thus, ain blood . Thus, HHost genetic factors play a crucial role in the severity and drug response of HBV infection. Many single nucleotide polymorphisms (SNPs) are associated with diverse clinical outcomes of hepatitis B infection among individuals, varying from asymptomaticity, chronicity, and immunologic unresponsiveness to vaccine or the risk of HCC. These genetic variants are mainly identified in human leukocyte antigen loci, which are relevant to host immune responses. For more details of those SNP loci, the review by Akcay et al.  is highlwww.who.int/news-room/fact-sheets/detail/hepatitis-c, accessed on 11 June 2021).Hepatitis C virus causes acute and chronic hepatitis, an inflammation of the liver. The World Health Organization estimated that 71 million people worldwide live with chronic hepatitis C virus infection. However, the chronicity of HCV infection significantly leads to the development of liver fibrosis, cirrhosis or hepatocellular carcinoma. The majority of liver cancer cases is caused by hepatitis C. In 2016, approximately 400,000 people died from cirrhosis and hepatocellular carcinoma. Given the more than 95% efficacy of antiviral therapy, the risk of death caused by cirrhosis and liver cancer can be reduced (Treatments for HCV infection rely on interferon (IFN)-based regimens. Given the life-threatening side effects of IFN, HCV treatments are ineffective in at least half of all patients . The addIL28B gene and sustained virological response, the standard indicator of successful treatment, has been documented among African Americans and those of European ancestry [Responses to HCV therapy vary among populations. Following treatment with the combination of IFN plus ribavirin, the sustained virological response of East Asians was reportedly greater than that of patients of European ancestry ,43. Afriancestry .Although pegylated interferon-alpha combined with ribavirin is the most effective treatment, many patients with chronic HCV infection do not respond to it . Gene trABCB1 (ATP binding cassette subfamily B member 1) and HNFα  variants, encoding sofosbuvir transporters. Drug interactions among the DAAs were also observed [For DAAs, limited data are available on pharmacogenomic-based treatment outcomes compared to the abovementioned combination therapy. In cohort studies of specific populations, DAAs were effective  and safe for patients with chronic HCV infection ,53,54. Nobserved . HCV infPlasmodium falciparum, P. vivax, P. ovale, P. malariae, and zoonotic P. knowlesi. Among Plasmodium species, P. falciparum is the most virulent, while P. vivax has the widest geographical distribution. In 2019, an estimated 229 million cases of malaria were reported, with more than 90% of cases in Africa and 409,000 deaths worldwide. In addition to Africa, Southeast Asia and the eastern Mediterranean zone are malaria endemic areas .Malaria is a major global infectious disease that poses a risk to hundreds of millions of people, especially in Africa. The causative organisms are intracellular protozoan parasites, including Anopheles species bites a host, sporozoites circulate in the bloodstream. In the liver, sporozoites invade hepatocytes via Kupffer cells or sinusoid endothelial cells. In hepatocytes, sporozoites undergo several rounds of cell proliferation, generating thousands of merozoites. Intrahepatic development of Plasmodium parasites causes no clinical manifestations. After a week, rupture of infected hepatocytes, known as liver schizonts, releases merozoites into blood circulation, where they invade erythrocytes to start the intraerythrocytic life cycles: ring-form trophozoites, trophozoites, and schizonts. Merozoites are released from erythrocytic schizonts and infect other erythrocytes. The release of merozoites from erythrocytic schizonts causes periodic fever. In contrast to intraerythrocytic development, some P. vivax or P. ovale sporozoites undergo a non-proliferative, metabolically inactive stage known as hypnozoites, which may be reactivated months or years after sporozoite infection [P. vivax cases are hypothetically from liver-dwelling hypnozoites [Plasmodium liver stage represents a target for blocking progression to clinical malaria and for preventing relapse in subjects with P. vivax and P. ovale malaria. Current prophylactic regimens are atovaquone and proguanil, targeting liver-stage schizonts. Only primaquine and tafenoquine possess anti-hypnozoite activity [Malaria parasites require multiple niches in the human host, including the liver and erythrocytes, to grow and survive. Liver-stage malaria is asymptomatic; however, it is a bottleneck of the symptomatic phase of intraerythrocytic malaria. Briefly, when a female nfection . Thus, hnozoites . Thus, tactivity .P. vivax malaria in a cohort of Australian Defense Force personnel deployed to Papua New Guinea and East Timor [P. vivax malaria, a G6PD deficiency or CYP2D6 impairment causes primaquine ineligibility, representing approximately 35% of patients [Plasmodium parasites in the liver [Despite radical cure and relapse prevention, host genetic factors influence clinical outcomes. In phase 3 clinical trials, primaquine and tafenoquine caused hemolysis in G6PD-deficient patients ,62. Morest Timor . Thus, tpatients . Collecthe liver .Drugs and drug metabolites are capable of damaging the liver, resulting in a spectrum of symptoms ranging from mild to acute liver failure. The most common drugs inducing liver injury are antimicrobials ,73,74. DEvidence of drug-induced liver injury in clinical trials of new drugs for chronic liver disease  warrants screening for potential adverse side effects, especially for patients with advanced chronic liver diseases with a higher risk of mortality ,77,78. CThe mechanism underlying direct and indirect DILI is well understood; however, the pathogenesis of idiosyncratic injury remains unknown . Genome-The microarchitecture of the smallest unit of the liver is important for the design to recapitulate the liver-on-a-chip device . As the Cell polarization is important for the directional transport of molecules across the basolateral and canalicular membranes. In liver lobules, liver cells are grouped into parenchymal and nonparenchymal cells. Hepatocytes are the major parenchymal epithelial cells, constituting 60% of the total number of liver cells . AbsorptSodunke et al. first generated a single microchannel for the culture of primary rat hepatocytes or HepG2 hepatocellular carcinoma cell lines , withoutOrtega-Prieto et al., developed a microfluidic platform for the 3D culture of PHHs . With suBiosensors have advanced disease diagnosis and bedside monitoring and are also known as lab-on-chip sensors. This section revisits current biosensors designed to measure liver functions. Briefly, the three main components of biosensors are recognition, transduction and readout. In terms of recognition, biological molecular targets bind to receptors on transmissible surfaces. Following a biochemical interaction, the target-bound receptor causes a change on the surface, which is translated into a quantifiable, dose-dependent physical signal., Many forms of receptors have been immobilized on the transducer surface, such as antibodies, enzymes, peptides, nucleic acids, aptamers or cells, collectively called biomolecular probes. Biochemical changes on the surface may be mechanical, i.e., molecular interactions, or electrochemical, i.e., electron transfer. Thus, the transducer translates those changes into readable and interpretable signals.In a clinical laboratory, liver function testing is a common routine assay for evaluating liver dysfunction or damage caused by infections (hepatitis viruses), cirrhosis, steatosis and drug-induced hepatitis. Biomarkers of liver function include albumin, bilirubin, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) in blood. Biosensors equipped with microchips are grouped into impedimetric and ampero-metric biosensors. The impedimetric biosensor immobilizes recognition molecules onto an electrode surface. An electrical impedance signal is proportional to analyte activity. The ampero-metric biosensor relies on biochemical oxidation or reduction, generating electron transfer as a signal., 2O2 is oxidized to generate two electrons, which are then transferred to OH on the electrode to transduce an electrochemical signal. Due to the stability of enzymes, the device should be stored at 4 °C when not in use. The maximum period of enzymatic stability is 120 days [Song et al. developed a three-in-one biosensor device to quantitatively measure the concentrations of cholesterol, bilirubin, ALT and AST. The device consists of immobilized enzymes on nano-porous silicon attached to the working electrode to increase the surface area for target recognition. As a byproduct of the enzymatic reaction, H120 days .−4 mg/mL [Human serum albumin (HSA), the most abundant protein circulating in plasma , is synthesized by hepatocytes and secreted into the blood at a concentration of 35–50 mg/mL in healthy individuals . Albumin−4 mg/mL . Huang e−4 mg/mL . The devIn addition to antibody- and electrode-based biosensors, a fluorescence probe was recently developed . The proThe key features of biosensor-integrated organs-on-chip are the real-time monitoring of cell functions and viability. Kinetic data allow us to dissect the mode of action of cytotoxic substances. In this section, current organs-on-chips integrated with biosensing microdevices are highlighted, especially those applicable for assessing liver integrity and injury.Selective transportation of substances and biomolecules via apical-basolateral compartments of many cells and tissues relies on the integrity of the epithelial or endothelial cell layer. Maoz et al. combined a dual biosensing system consisting of multielectrode arrays (MEAs) and electrodes for transepithelial electrical resistance (TEER) to measure the electrical activity of cardiomyocytes and ionic conductance between the paracellular space, respectively  Figure B. CardioAlcohol causes liver fibrosis, which is characterized by an excess amount of collagen and other matrix proteins produced in a repair process after injury or inflammation; however, the underlying mechanism remains unclear. TGF-β is a pleiotropic cytokine that reportedly induces fibrous tissue formation during the recovery phase of liver injury . Zhou etCell survival in a microchamber of a microfluidic chip is critical for assessing biological responses to stimuli. Oxygen represents mitochondrial respiration in a metabolically active cell. Thus, its amount indicates viable cells. The use of oxygen microsensors can lead to the discovery of the mode of action of drugs against hepatocytes. Prill et al. developed a microchip device for long-term maintenance of spheroids of HepG2/C3A cells, while phosphorescent microprobes were embedded in the spheroid to detect cell viability and assess the hepatotoxicity of drugs in the liver-on-a-chip after repeated dosing . Real-tiA personalized liver-on-a-chip requires patient-specific cells. In this section, sources of patient-specific cells and their limitations in individualizing the liver-on-a-chip are reviewed. Since the microchamber requires a very small number of cells, the scalability of cells is less problematic. However, in the case of medium- or high-throughput screening, expansion of the original source of cells prior to hepatocyte induction is necessary .Biopsied tissue could be obtained during a pathological examination or biobank analysis. Given the small size of the cell culture chamber in a chip, a sufficient number of cells can be obtained. However, the number of donors is limited, preventing accessibility to some rare genetic variants. Additionally, performing liver biopsy in healthy individuals carries greater risks than benefits.Since biopsy of the human liver is too invasive, an alternative method is to generate patient hepatocytes from other cell types obtained through noninvasive or less invasive procedures. Generally, cell differentiation is a hierarchical process in which pluripotent stem cells or multipotent stem cells differentiate into somatic cells with specific functions (mature somatic cells). In contrast, cell trans-differentiation is the process in which mature somatic cells undergo transformation into distinct types of mature somatic cells without passing through pluripotency  or multipotency . Thus, the process to generate personalized hepatocytes for In Vitro assays is shorter.MSCs are a type of adult stem cell with the ability to differentiate into several cell types, including hepatocytes. Several methods have been developed to induce MSCs to undergo trans-differentiation . In addiFibroblasts are nonparenchymal cells located in connective tissue, including skin. Unlike MSCs, trans-differentiation of fibroblasts into hepatocytes requires the ectopic expression of a set of three transcription factors: endoderm-specific FOXA2, hepatocyte-specific HNF4α, and C/EBPβ together with the cell proliferation-enhancing factor cMyc . FibroblEvidence suggests that hematopoietic cells are capable of differentiating into hepatocytes. Khurana et al. documented that the sera of mice with chemical-induced liver injury induce the differentiation of mouse bone marrow (BM)-derived hematopoietic stem cells into hepatocyte-like cells, implying the requirement for soluble host factors relevant to liver recovery . Later, Monocytes are mononuclear phagocytic cells circulating in blood. Human monocytes reportedly differentiate into hepatocyte-like cells capable of metabolizing phase I and phase II drugs. Thus, monocyte-derived hepatocyte-like cells were deployed to assess drug-induced liver injury. Due to the less invasive nature of their collection from peripheral blood, monocytes are suitable for personalizing the liver-on-a-chip of individuals ,111.Due to the invasiveness of cell harvesting procedures and a limited proliferation capability outside the body of primary cells, pluripotent stem cells are an alternative source to overcome these limitations. Following the ectopic expression of four transcription factors, somatic cells are reprogrammed into pluripotent stem cells capable of proliferating and differentiating into various cell types . Thus, pVarious designs of the liver-on-a-chip have allowed researchers to model noninfectious pathological conditions, such as the accumulation of lipids and DILI, as well as to assess drug metabolism. This section highlights key features of those designs, which are potentially applicable for infectious diseases.Lee et al. developed a microchamber bordered by a parallel microfluidic channel A. Primar−4 dyne cm−2), which was lower than that in the diffusion channels (0.01 dyne cm−2). Similar to the central vein of the liver lobule, the outlet of the upper layer allows collecting culture medium to pass through hepatocytes. Due to the high optical transparency of polydimethylsiloxane (PDMS), the device enables an assessment of cell viability, particularly serving as a microscope-based biosensor of oxygen. Each compartment of the hexagonal chamber provides space for hepatocytes to grow in three dimensions, enhancing cell–cell interactions in all directions [Banaeiyan et al. developed a bilayer device consisting of a hexagon-like chamber located in the lower part and a tunnel network for seeding cells and feeding culture medium located at the top. Hepatocytes were perfused in the top layer and grew in the lower chamber. Nutrients and drugs entered the system through the top layer and spread radially through hepatocytes in the lower chamber. Small channels (2 μm wide and 2 μm high) were placed between the flowing medium and hepatocytes in the lower chamber to mimic the fenestration of endothelial cells. Thus, nutrients and oxygen diffused to hepatocytes in a gradient manner, and the device reduced shear stress to hepatocytes by approximately 80 times inside the chamber  cell culture. Multiple factors appear to contribute to the physiological relevance of the microfluidic chip, such as the microfluidic system and cellular interactions with other cells or matrix proteins. Given the continuous flow of culture medium, a liver sinusoid-on-a-chip enhances human hepatocyte secretion of albumin and urea to levels higher than those of a static device . As a 3DIn this section, we briefly provide a basis and discuss in-depth the use of TEER and MEA as assessments of barrier integrity and cellular characteristics. Some examples of electric biosensor-integrated organs-on-a-chip are provided.Transportation of substances and biomolecules plays a role in maintaining organ functions, including the brain  and liveBriefly, a classical method to measure electrical resistance between two compartments is initially based on the use of two electrodes for sensing voltage and current. A well is placed inside in a larger chamber to sense the resistance of the two compartments. This inner well, called the trans-well here, has a permeable filter or membrane at the bottom to which a layer of endothelial cells attaches. Thus, the culture medium inside the trans-well is separated from the medium outside the inner well. Electrodes are placed inside and outside the trans-well, a configuration similar to apical and basolateral sides, respectively. Upon applying a direct current voltage to the electrode, the electrical resistance is then calculated based on Ohm’s law. The lack of resistance of the cell-free inner well and presence of resistance across the upper and lower chambers of the cells growing in the inner well yield a cell-specific resistance. Given damage to the cell and electrode, an alternating current can be used instead of a direct current.At present, TEER measurement devices are commercially available. An Epithelial Volt-ohmmeter (EVOM) applies an alternating current and is capable of measuring electrical resistance ranging from 1–9999 Ω. The EVOM deploys a pair of electrodes (4 mm wide and 1 mm thick), also known as a chopstick electrode. Off electrodes, a silver/silver chloride pellet and a silver electrode are used to measure the voltage and the passing current, respectively. In Vitro BBB models deployed EVOM to examine the permeability of the BBB among different human brain capillary endothelial cell lines . On the In addition to the electrical resistance, electrical impedance, which uses the formula of ohms = voltage (V)/current (I), is deployed as an indicator of TEER. The electrical impedance is in opposition to a circuit providing direct or alternating electric current. If the endothelial layer is equivalent to an electric circuit, flow of electric current through cell junction proteins contributes to TEER. The Electric Cell-Substrate Impedance Sensing (ECIS) from Applied BioPhysics consists of an insulating film containing a gold electrode and a gold counter electrode located on the opposite side. Cells grow on the insulating film and eventually cover the electrode surface. When an alternative current (I) is applied at the bottom of the ECIS arrays, the cell-covered electrode surface generates potential (voltage) across the two electrodes. Thus, based on Ohm’s law (ohms = V/I), it increases impedance. In the case of a cell junction interruption, the voltage changes, leading to an alteration in the impedance. Based on the use of an ECIS-based device, Bernas et al. showed that human brain microvascular endothelial cells form a blood–brain barrier (BBB)-like structure with a high TEER. Exposure to lysophosphatidic acid decreases TEER, indicating the disruption of barrier integrity in real-time .Here, we provide examples of BBB-modeling microfluidic devices integrated with TEER as a biosensor of barrier integrity. Devices connecting the TEER electrode to the inlet and outlet of a microfluidic chip are not within the scope of this review, but these applications are comprehensively reviewed by Srinivasan et al. . The BBBDue to the relatively large size of the chopstick electrode, it is difficult to integrate into a microfluidic chip. Thus, immobilization of microscale electrodes within the microfluidic chip allows proximal contact with cells and reduces electrical resistance caused by the cell culture medium. Booth & Kim cultured a murine endothelial cell line with an astrocytic cell line on porous polycarbonate membranes located between two layers of PDMS to fabricate the BBB on a microfluidic chip. Voltage and current electrodes were inserted between the glass and PDMS layers. The transparency of glass allows morphological observation and cell viability measurements. Both electrodes were connected to an EVNOM epithelial volt-ohmeter to assess the integrity of the BBB. Histamine exposure decreased TEER, followed by recovery to the initial level. Thus, this system is suitable for measuring the transient response of the BBB to substances in real time . MoreoveCell characteristics are surrogate markers of the cellular response to stimuli. The use of fluorescence probes or antibodies for labeling cells in a microfluidic chip has been hampered by technical difficulties and cell damage. These cell-labeling tools allow qualitative analyses at specific times; therefore, many chips are needed to examine drug toxicity in a dose- and time-dependent manner. Thus, a label-free, real-time assay would enable quantitative and continuous monitoring of cells in a chip. The MEA was used as a tool for characterizing cells and primarily relies on electric impedance, which is a measurement of electrical resistance to alternating current. Electrical impedance spectrometry has been deployed as a method for characterizing cells based on their electric properties ,143. ThuMeissner et al. developed a microfluidic chip for liver toxicity assays based on morphological changes in cultured HepG2/C3A cells without cell labeling. Exposure to acetaminophen resulted in a change in the cytoskeleton, consistent with a decrease in electrical impedance. Importantly, a decrease in electrical impedance was detected at 2 h after drug exposure, a time earlier than the detection of cellular damage at 24 h after drug exposure. Moreover, the device was capable of distinguishing recovering cells from nonexposed cells using two different frequencies (10 kHz and 3 MHz). Four triangle-shaped chambers surrounded by PDMS-based micropillars, which decreased shear stress and trapped cells, were used to grow cells in the device. In each chamber, a microelectrode was designed as an interdigitated configuration to cover a large surface area. The assay detected the loss of cell–cell contact before cell death, showing greater sensitivity than the endpoint cell viability assay .An increase in the electrode-covering area possibly enables the sensitive detection of electric impedance; however, attempts to precisely measure a single cell remain technically challenging. Asphahani et al. developed planar microelectrodes in which each human glial cell was patterned on gold microelectrodes via cell-peptide ligand interactions to enable real-time monitoring of single cells . Cells wGiven the emerging evidence of the need for precision medicine in infectious diseases, the assessment of patient-specific responses to antiviral or anti-plasmodial treatments remains a challenge. Currently, the use of the liver-on-a-chip to model hepatitis virus infection provides a useful platform for individualizing treatments; however, liver-stage malaria is still under investigation. Integration with biosensors to enhance the utility of the liver-on-a-chip allows real-time or kinetic monitoring of cell functions, leading to novel biological and mechanistic insights that might be a target for new therapeutic approaches. For instance, a combination of a dual biosensor with the liver-on-a-chip is proposed for assessing the effects of biological barriers on hepatocyte survival and functions . Briefly