Prader Willi Syndrome (PWS) is a genetic disorder caused by the absence of expression of the paternal copies of maternally imprinted gene(s) located at 15q11–q13. While the physical and medical characteristics of PWS, including short stature, hyperphagia and endocrine dysfunction are well-characterized, systematic investigation of the long-recognized psychiatric manifestations has been recent.n = 39) using the Penn Computerized Neurocognitive Battery (Penn-CNB).Here, we report on the first remote (web-based) assessment of neurobehavioral traits, including psychosis-risk symptoms  and sleep behaviors , in a cohort of 128 participants with PWS, of whom 48% had a paternal deletion, 36% uniparental disomy, 2.4% an imprinting mutation and 13% unknown mutation . We aimed to identify the most informative variables that contribute to psychosis-risk symptoms. Multiple domains of cognition (accuracy and speed) were also assessed in a subset of PWS participants (p's < 0.029). Sleep disturbance ratings were the strongest predictors of psychosis-risk symptoms. Regarding cognition, individuals with PWS showed the most prominent deficits in accuracy on measures of social cognition involving faces, namely Face Memory, Age Differentiation and Emotion Recognition, and greatest slowing on measures of Attention and Emotion Recognition. However, there were no significant differences in psychosis-risk symptoms or cognitive performance as a function of PWS genetic subtype.Individuals with PWS reported a range of psychosis-risk symptoms, with over half reporting cognitive disorganization (63.1%) and about one third reporting unusual beliefs (38.6%) and/or suspiciousness (33.3%). Subjectively-reported sleep quality, nap frequency, sleep duration, sleep disturbance, and daytime dysfunction were significant predictors of psychosis-risk symptom frequency and severity (all PWS is associated with a high prevalence of distressing psychosis-risk symptoms, which are associated with sleep disturbance. Findings indicate that self/parent-reported neurobehavioral symptoms and cognition can be assessed remotely in individuals with PWS, which has implications for future large-scale investigations of rare neurogenetic disorders. Prader Willi Syndrome (PWS) is a genetic disorder caused by the absence of expression of the paternal copies of maternally imprinted gene(s) genes located at 15q11–q13 . While tLarge-scale genetic association studies have identified novel schizophrenia-associated recurrent risk loci within the 15q region –8, suggeThere is converging evidence of wide-ranging sleep disturbances in PWS from both human and animal studies. Polysomnographic (PSG) studies of sleep in humans report increased instances of sleep disordered breathing (SDB) and narcoleptic-like traits, including daytime sleepiness and REM abnormalities in children, adolescents, and adults with PWS –13. TranWith regard to neurocognition, global cognitive function is typically in the impaired range . DeIn the present study, we sought to remotely evaluate neurobehavioral traits potentially associated with psychosis risk, in order to allow for large-scale data collection; determining the feasibility of such assessments is particularly important for rare disorders, where travel to a research site may not be feasible. Specifically, we assessed dimensionally measured psychosis-risk symptoms, subjective sleep disturbance, and multiple domains of cognition in individuals with PWS. We first characterized psychosis-risk symptoms and sleep disturbance in the whole sample and determined which sleep and demographic variables were the most predictive of overall psychosis-risk symptom frequency and severity. Next, we assessed differences in psychosis-risk symptoms as a function of mutation type. Lastly, in a subset of the sample, we assessed neurocognitive predictors of psychosis-risk symptom frequency and severity.Potential study participants were notified of the online study via the Foundation for Prader-Willi Research (FPWR), and through Facebook pages and other social media. In order to maximize data collection and study participation, our inclusion criteria were broad: (1) Genetic diagnosis of Prader-Willi Syndrome; (2) ages 12–50; (3) able to provide informed consent or assent.Study procedures were described in an online consent form; parents or legal guardians signed consent, and a simplified assent form was provided for participants with PWS. After consent and assent forms were completed, a link was provided to the online clinical and neurocognitive assessment forms described below. Study participants and parents each received a $20 gift card for participation. All study procedures were approved by the UCLA Institutional Review Board. All research was performed in accordance with relevant guidelines and regulations, in accordance with the ethical principles for medical research in the Declaration of Helsinki.Online clinical assessments were completed either by self-report or parent-report, depending on the age and abilities of the participant.Recorded information included age, sex, race/ethnicity, personal education level, parental education level, and type of PWS mutation.A modified parent-report version of the Prodromal Questionnaire [PQ-B ], a screWe measured subjective sleep quality, sleep duration, sleep latency , subjective sleep disturbance, daytime dysfunction due to sleepiness, nap frequency, nap duration, sleep consistency, sleep satisfaction, and sleep timing using the Pittsburgh Sleep Quality Index [PSQI ], and thHigher scores on the PSQI have been associated with increased positive and negative symptom severity in youth at clinical high risk for psychosis , 20, 41,We employed an online neurocognitive battery that has been extensively validated across a wide age range, which assesses multiple domains of cognition, specifically measures of executive function and attention, verbal and non-verbal memory, verbal and non-verbal reasoning, social cognition , and processing speed . ThTo our knowledge, the PQ-B has not yet been validated for use as a parent-report measure in the PWS population. To assess the measure's validity in this population, we first conducted a single-factor Confirmatory Factor Analysis (CFA) on the PQ-B frequency and distress scores. We used the Comparative Fit Index (CFI), Tucker-Lewis index, and root mean-squared error of the approximation (RMSEA) to determine if each model was a good fit, as in Fonseca-Pedrero et al. . A CFI az-scores based on the means and standard deviations from a large normative cohort; the Motor Praxis test assessed motor speed with no accuracy score .q < 0.05), two linear models were conducted to test the effect of the predictor on both PQ-B frequency and distress scores. Age, sex, and reporter (parent vs. self) were added as covariates to these models. The scaled beta coefficients of each predictor were compared to determine the strongest predictors of each outcome (frequency score and distress score). We then conducted a forward stepwise regression in which each predictor was individually added into a model that included the strongest predictor  and covariates. Models with and without the additional predictor were compared using an ANOVA to determine if addition of the predictor explained additional variance. If addition of the predictor explained significantly more variance (p < 0.05), it remained in the model. The stepwise regression procedure was conducted to build models for predicting both PQ-B frequency and distress scores. Since beta coefficients were used to compare prediction ability of each variable, we did not FDR-correct p-values of regression models. A secondary, exploratory analysis utilized the same procedure in the subgroup of participants who completed the neurocognitive battery to investigate neurocognitive predictors of psychosis in PWS. However, we assessed correlations between neurocognitive domains and PQ-B frequency and distress score, instead of each individual PQ-B item, due to the small sample size and reduced statistical power for this analysis.Next, we sought to determine potential sleep, neurocognitive, genetic, and demographic predictors of psychosis-risk symptoms. Primary predictors of interest included sleep variables reported on the PSQI and RU-SATED  as well as demographic variables . We conducted Spearman correlations between the predictors and the distress score for each individual PQ-B item. If the participant did not endorse the item, they were given a distress score of 0. We then assessed the effect of the predictors of interest on PQ-B frequency and distress scores. If a predictor was found to be significantly correlated with a PQ-B item after False Discovery Rate (FDR) correction  completed the neurocognitive assessment battery. A summary of their demographic information is presented in Of the 128 respondents that completed demographic information, 100 additionally completed sleep questionnaires and 84 completed the PQ-B  scored higher than the standardized clinical cut-off score [i.e., >9 points , suspiciousness , and unusual beliefs . Existential fears (Exist) and visual perceptual anomalies  were relatively uncommon, endorsed by <5% of the sample .Individuals with PWS endorsed a range of psychosis-risk symptomatology . 13.1% . For both frequency and distress scores, sleep disturbance was the strongest predictor .Spearman correlations revealed 24 statistically significant associations between PQ-B items and sleep measures that survived FDR correction. The results are presented in p's > 0.122). The final model for PQ-B frequency score, which only included sleep disturbance, had an adjusted R2 = 0.171 and mean squared error (MSE) = 0.790. The final model for PQ-B distress score, which similarly only included sleep disturbance as a predictor, had an adjusted R2 = 0.205 and an MSE = 0.757. In the final models, there was a trend toward a significant effect of age on PQ-B frequency score  and distress scores , but no statistically significant effect of sex .Two forward step-wise regression analyses were performed with sleep disturbance, sleep duration, daytime dysfunction, sleep quality, sleep satisfaction, nap frequency, and nap duration as predictors of PQ-B frequency and distress scores. For both PQ-B frequency and distress scores, we began with a model including sleep disturbance as a predictor and covariates of age, sex, and reporter (parent vs. self). No variable explained significantly more variance in PQ-B frequency or distress scores than sleep disturbance did  or distress  scores as a function of PWS genetic subtype (mUPD vs. other genetic subtype). These results remained similar when excluding subjects with an imprinting mutation , as well as unusual thought content , and suspiciousness . These results are summarized in There were no significant differences in PQ-B frequency (tion see . The mosz = −1.5 ± 0.20) and Emotion Recognition , there were no differences between groups as a function of mutation type that survived multiple comparison correction .Compared to the cohort of typically developing individuals , particir = 0.529, q = 0.021; q < 0.05. In regression analyses controlling for age, sex, and reporter, overall, Face Memory speed was a significant predictor of PQ-B frequency and distress scores , with increased speed indicating worse symptom scores. Further, the addition of Face Memory Speed into the previously determined “best-fit” model explained significantly more variance in both PQ-B frequency and distress scores  = 14.872, p < 0.001; Distress: F = 21.268, p < 0.001) in the subset of the sample who completed both the PQ-B and neurocognitive measures (n = 32). Therefore, the strongest overall predictors for PQ-B frequency and distress scores in our sample of subjects with PWS were sleep disturbance and Face Memory Speed. The model including Face Memory Speed and sleep disturbance had an adjusted R2 = 0.400 and MSE = 0.496 for predicting frequency, and adjusted R2 = 0.501 and MSE = 0.409 for predicting distress score.There was a significant correlation between Face Memory Speed, a measure of episodic memory and social cognition, and PQ-B distress score  and neurocognition in individuals with PWS. These results are important as they show, in a relatively large sample, a substantial proportion of individuals with PWS experience both distressing psychosis-risk symptoms and disrupted sleep. The most frequently endorsed symptoms in individuals with PWS were related to cognitive disorganization, endorsed in about half the sample, and suspiciousness and unusual beliefs, endorsed in about one-third of the sample. Regarding relationships between sleep disturbance, as measured by the PSQI, and psychosis-risk symptoms, as assessed by the PQ-B, both of the methods we employed revealed that questions on sleep behavior/habits and daytime dysfunction are relatively important for explaining distress related to unusual experiences and other psychosis-risk symptomatology. While excessive daytime sleepiness is a common, well-established phenomenon in individuals with PWS, related to hypothalamic dysfunction , 50, ourThis novel web-based approach offers substantial cost savings and efficiency, as no subject travel was required. Further, it allows families in rural locations to participate, who are otherwise unlikely to be able to take part in this research. The development of more scalable methods for research participation is important not only for rare disorders, but for other populations that lack adequate access, such as families with limited financial resources .In help-seeking youth in the general population, the PQ-B shows good concurrent validity with interview-based diagnoses of a psychosis risk syndrome . Our CFAContrary to expectations based on prior reports of higher rates of overt psychotic disorder in PWS resulting from mUPD , 60, we Although our cognitive assessment was only completed by a subset of the larger sample, findings revealed differential impairment in social cognitive domains, particularly tasks involving processing of faces, implicating cortico-limbic dysfunction . This paSeveral limitations of the current study should be noted. First, this may not be a representative sample of people with PWS; for example, those who may have been mentally unwell at the time of the study may not have been able to participate. Further, the fact that some participants self-reported while others did not could have created bias in the sample such that those who self-reported were older or higher functioning. We addressed this concern by including reporter as a covariate in the regression analyses. Secondly, the online format of the study limited the amount of detailed prior history we could obtain on study participants. As such, we were only able to obtain cross-sectional information on current (past month) sleep and psychotic-like experiences and we were not able to collect information on whether there was a previous history of a psychotic illness or sleep disorder that had been treated. We also were unable to collect information of growth hormone therapy (GHT) and body mass index (BMI). Existing data suggests a complex relationship between sleep apnea, GHT, and BMI in PWS –77 with PWS is associated with a high prevalence of distressing psychosis-risk symptoms, which were associated with sleep disturbances. Social cognition, particularly tasks involving faces, showed differential impairment. These results support feasibility of remote assessment of individuals with rare neurogenetic syndromes.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by UCLA Institutional Review Board. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.CB designed the study and drafted the manuscript. KO'H and ZZ conducted statistical analyses and drafted methods and results text. KO'H, ZZ, ZH, and AV contributed to data management, quality control, and analyses. LP-H, LK-W, and AV oversaw recruitment and participant assessment. RG designed the neurocognitive battery and provided critical input on interpretation of results. ER and AH provided critical input on the manuscript text and interpretation of results. All authors participated in manuscript revisions and have given final approval of this version for submission.This work was supported by the Foundation for Prader Willi Research  and NIMH MH117014 (RG).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.


Development of severe immune-related adverse events (irAEs) is a major predicament to stop treatment with immune checkpoint inhibitors, even though tumor progression is suppressed. However, no effective early phase biomarker has been established to predict irAE until now.This study retrospectively used the data of four international, multi-center clinical trials to investigate the application of blood test biomarkers to predict irAEs in atezolizumab-treated advanced non-small cell lung cancer (NSCLC) patients. Seven machine learning methods were exploited to dissect the importance score of 21 blood test biomarkers after 1,000 simulations by the training cohort consisting of 80%, 70%, and 60% of the combined cohort with 1,320 eligible patients.XGBoost and LASSO exhibited the best performance in this study with relatively higher consistency between the training and test cohorts. The best area under the curve (AUC) was obtained by a 10-biomarker panel using the XGBoost method for the 8:2 training:test cohort ratio . This panel could be further narrowed down to a three-biomarker panel consisting of C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), and thyroid-stimulating hormone (TSH) with a small median AUC difference using the XGBoost method .Blood test biomarkers currently do not have sufficient predictive power to predict irAE development in atezolizumab-treated advanced NSCLC patients. Nevertheless, biomarkers related to adaptive immunity and liver or thyroid dysfunction warrant further investigation. Immune checkpoint inhibitor (ICI) therapy has become a widely used first-line therapy for unresectable non-small cell lung cancer (NSCLC) patients. ICIs were developed against programmed cell death ligand 1 (PD-L1) on cancer cells, and the immune suppressive receptors programmed cell death 1 (PD-1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) on cytotoxic T cells –4. Over The real challenge of irAE prediction is finding the right biomarkers to indicate the immune landscape in a spatial and temporal manner . In contBlood test is regularly conducted before and during treatment and is feasible in almost any hospital. The regular blood test consists of two operations: the blood cell count test (BCT) and the blood biochemistry test (BBT). BCT provides a direct overview of the immune landscape based on the prevalence of immune cell populations. Our previous study established a BCTscore model as a valid predictive and prognostic biomarker for the early prediction of atezolizumab treatment outcomes . BecausePseudonymized individual participant data from the single-arm phase II studies FIR NCT01846416)  and BIRC  and BIRirAEs are summarized using the National Cancer Institute (NCI) Common Terminology Criteria for Adverse Event (CTCAE) version 4.0  by clinicreateDataPartition of caret (Classification and Regression Training) package v.6.0.89  thev.6.0.89  was usedThe paradigm of this study is illustrated in Initially, we compared the median performance of all 21 blood test biomarkers after 1,000 simulations on the training cohorts by the seven machine learning methods . AlternaTaken together, we conclude that blood test biomarkers indicating adaptive immunity and liver or thyroid dysfunction are useful for irAE prediction in atezolizumab-treated advanced NSCLC patients.Because the seven machine learning methods are vastly different in terms of mathematical modeling and application, we tested which method is optimal for irAE prediction in atezolizumab-treated advanced NSCLC patients. Therefore, we compared the AUC distribution of our retrospective cohorts using the 21-, 10-, and 3-biomarker panels, respectively   advanced NSCLC patients with no prior chemotherapy, all trials recruited advanced NSCLC patients who underwent platinum-based therapy. Therefore, the failure of certain methods to predict irAE could not stem from accidental sample bias during simulation. Furthermore, it is noteworthy that the difference in median AUC predicted by the seven machine learning methods using the top 3-biomarker panel (Blood test biomarkers do not have sufficient predictive power to predict irAE development in atezolizumab-treated advanced NSCLC patients. Biomarkers related to adaptive immunity and liver or thyroid dysfunction warrant further investigation.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: this publication is based on research using data from data contributors, Roche, that have been made available through Vivli, Inc. . Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication.The studies involving human participants were reviewed and approved by the Institutional Review Board of the Second Affiliated Hospital of Zunyi Medical University [No. YXLL(KY-R)-2021-010]. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.Conceptualization: JGZ, AHW, HM, UG, and SHJ. Methodology: JGZ, AHW, and FT. Validation: MH, HM, UG, and JGZ. Data curation: JGZ, AHW, GS, SSH, HM, SRC, and UG. Writing—original draft preparation: AHW, HW, and JGZ. Writing—review and editing: UG, HM, BF and MH. Visualization: JGZ and HW. Supervision: HM, UG, and JGZ. Project administration: JGZ, HM, and UG. Funding acquisition: JGZ, SHJ, and AHW. All authors contributed to thearticle and approved the submitted version.This research was funded by the National Natural Science Foundation of China (Grant No. 82060475), the National Natural Science Foundation of Guizhou Province , and Lian Yun Gang Shi Hui Lan Public Foundation (Grant No. HL-HS2020-92). AW Medical Company Limited received startup funding from the University Development Fund of University of Macau.AHW is a founder and shareholder of AW Medical Company Limited. MH reports collaborations with Merck Serono , MSD , AstraZeneca (research funding), Novartis (research funding), BMS , and Teva (travel expenses). UG and RF received support for presentation activities for Dr Sennewald Medizintechnik GmbH, have received support for investigator initiated clinical studies (IITs) from MSD and AstraZeneca, and contributed at Advisory Boards Meetings of AstraZeneca and Bristol-Myers Squibb.The remaining 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.


The potential of wastewater-based epidemiology (WBE) as a surveillance and early warning tool for the COVID-19 outbreak has been demonstrated. For areas with limited testing capacity, wastewater surveillance can provide information on the disease dynamic at a community level. A predictive model is a key to generating quantitative estimates of the infected population. Modeling longitudinal wastewater data can be challenging as biomarkers in wastewater are susceptible to variations caused by multiple factors associated with the wastewater matrix and the sewersheds characteristics. As WBE is an emerging trend, the model should be able to address the uncertainties of wastewater from different sewersheds. We proposed exploiting machine learning and deep learning techniques, which are supported by the growing WBE data. In this article, we reviewed the existing predictive models, among which the emerging machine learning/deep learning models showed great potential. However, most models are built for individual sewersheds with few features extracted from the wastewater. To fulfill the research gap, we compared different time-series and non-time-series models for their short-term predictive performance of COVID-19 cases in 9 diverse sewersheds. The time-series models, long short-term memory (LSTM) and Prophet, outcompeted the non-time-series models. Besides viral (SARS-CoV-2) loads and location identity, domain-specific features like biochemical parameters of wastewater, geographical parameters of the sewersheds, and some socioeconomic parameters of the communities can contribute to the models. With proper feature engineering and hyperparameter tuning, we believe machine learning models like LSTM can be a feasible solution for the COVID-19 trend prediction via WBE. Overall, this is a proof-of-concept study on the application of machine learning in COVID-19 WBE. Future studies are needed to deploy and maintain the model in more real-world applications. Wastewater-based epidemiology (WBE) has been employed as a complementary tool for COVID-19 monitoring worldwide since the beginning of the pandemic . One of A recent study summarized that reasonable lead time for WBE and clinical report range from 0–4 and 0–6 days, respectively, depending on various factors, such as the clinical testing lag, result reporting delay, WBE result turnaround time, and the viral shedding dynamic in the stool . When clAnother advantage of wastewater surveillance is that community-level data is collected instead of individual testing data, which enhances data privacy. Moreover, signals from both pre-symptomatic and asymptomatic carriers can be captured in wastewater . GloballHowever, few challenges exist with tracking COVID-19 via wastewater surveillance. A major criticism of WBE is associated with the uncertainties of making quantitative predictions on infected cases from the viral genetic marker concentration in wastewater . For COVConventional statistical models for predicting COVID-19 infection cases from wastewater were proposed in the early stage of the global pandemic. Most of them are regression-based epidemiological models with very few wastewater parameters included. More recent studies implemented various models for case prediction and forecasting from wastewater, including but not limited to the susceptible-exposed-infectious-recovered (SEIR) model, vector autoregression, and machine learning/deep learning models . Among aSince the beginning of the COVID-19 pandemic, machine learning techniques have been applied in contact tracing, outbreak forecasting, diagnosis, and recommending control strategies . OutbreaExisting machine learning or deep learning models in COVID-19 WBE research have two major limitations. They are non-time series models and hardly mine any time-related features. In this long-term pandemic, time is an intrinsic and important factor that can impact the evolution of the outbreak. Seasonal trends and holiday surges have been widely observed . MoreoveIn this study, we propose applying predictive time-series machine learning and deep learning models to track COVID-19 outbreaks in multiple communities . Thus, pFive supervised machine learning models were implemented in this study to predict COVID-19 cases from the community in a sewershed using SARS-COV-2 viral loads in wastewater and other sewershed/community-related features. Firstly, three widely used non-time series machine learning models were developed, including multiple/univariate linear regression (MLR/LR), gradient boosting decision tree (GBDT), and feed-forward deep neural network (DNN) , 24. As Six hundred twenty wastewater samples were obtained twice a week from nine sewersheds in central Ohio in the United States between September 2020 and June 2021. The sewersheds represent a diversity of communities in urban and rural areas, serving a population ranging from 14,000 to 900,000. More details about the sewersheds were explained in our previous study . The sewTime and seasonality are important drivers of COVID-cases, which are the key components of various non-WBE based COVID-19 trend predicting models . This stIt is expected that the SARS-CoV-2 gene concentration is one of the most powerful features, while other features may enhance the performance of the model in handling uncertainties in the wastewater data. Despite that non-time series models cannot learn from the short/long-term dependency of sequential data, wastewater surveillance features can be fed to the non-time series models for COVID-19 case prediction in the community represented by the sewershed. These features can also be uptaken by the time-series model like LSTM and Prophet to provide a more comprehensive wastewater monitoring of COVID-19 in the sewersheds.scikit-learn and scipy.stats package in Python 3. Models were further built with the Tensorflow framework (version 2.7.0). All models were trained and tested using a train/validate split ratio of 70% and 30%. A univariant linear regression model was built first to serve as a baseline using the SARS-CoV-2 viral loads only. Multivariate non-time series models were then trained for next-time-step COVID-19 case prediction in different communities. Data from all communities in different sewersheds are trained jointly. 5-fold cross-validation and grid search were performed for parameter tuning and model evaluation.Data preprocessing was conducted using To prevent data leakage, for time series models, the COVID-19 case in time step A is predicted from data from the past 16 time steps (sliding window of 8 weeks) in different communities . FeatureThe performance of the models was evaluated with the root mean square error (RMSE) metrics of the test set . Compare2 = 0.83, test set R2 = 0.63), LSTM model  derived higher R2s. Moreover, according to Pearson’s correlation coefficient between the predicted value and observed value, LSTM yielded higher predictive accuracy than Prophet for the Athens’ data, which is the ‘bad case’ among all sewersheds (Overall, time-series models outperform the non-time series models, with LSTM being the best. Prophet and LSTM are powerful models in jointly making long-term predictions for all communities/sewersheds. Compared to Prophet (training set Rwersheds . Our preIn other words, compared to Prophet, LSTM showed better generalizing capability among different communities, probably owing to the fully connected layer after concatenating the LSTM layers. This also indicates that the LSTM model is preferred when the communities are diverse in wastewater biochemical attributes, and geographical, and socioeconomic parameters, which is a typical case in real-world COVID-19 WBE projects. LSTM is known for its ability to handle high-dimensional data, while the capability of Prophet in that regard remained understudied. Owing to the bad case and the variations among the sewersheds, the performance of both models in the test set is not very satisfying. This performance can also result from underestimating the case number , a relatCollectively, we recommend time-series machine learning models to deal with multi-site WBE data, especially LSTM. With proper feature selection, feature engineering, and hyperparameter tuning, we believe machine learning can be a powerful tool for predicting COVID-19 trends from wastewater surveillance.The feasibility of applying machine learning in the COVID-19 wastewater surveillance system is discussed in this article. However, further attention is needed in many other realms. First, WBE systems or networks on a larger scale are preferred due to the lack of standardization in wastewater sampling and processing methods. The resulting dataset can cover communities of different types. As mentioned above, one of the challenges for multi-community prediction is overfitting. For LSTM, we found L1 and L2 regularization and dropout to be effective methods. Removing redundant features and correct feature engineering can further reduce the risk of overfitting. As a pilot study, the dataset in this study is relatively small. Larger datasets can enhance the generalization of the models on various communities.Due to the small dataset size, this study only focused on the next-time-point prediction with a sliding window of 16-time points. Future research is welcomed for developing WBE-based machine learning models to forecast the COVID-19 trends in a longer future time period. A longer or shorter sliding window is also worth examining. Other LSTM variants, such as bidirectional LSTM and encoder and decoder LSTM models, have been successfully applied to forecast the spread of COVID-19 in India . ApplicaMoreover, deployed models and wastewater testing approaches need to be updated constantly using upcoming WBE data. For example, the lead time of WBE results to clinical reports might vary among the communities and change over time. The reported COVID-19 case number can become less reliable due to the increased self-testing and vaccination rate . When neIn this prolonged pandemic, wastewater surveillance is an effective complementary tool for COVID-19 monitoring. The development of predictive models is necessary but challenging due to the intrinsic complexity of the wastewater matrix and the variations in the sewersheds. Machine learning/deep learning are emerging techniques for WBE that have great potential to resolve the uncertainties in wastewater data. As the scale of wastewater surveillance is continuously expanding, it is crucial to adapt models for multi-community prediction. We proved that this adaption can be achieved with time-series deep learning models like LSTM and Prophet. The wastewater data and deployed machine learning models will need proper maintenance. To enable the automatic model deployment, updating, and interpretation, a machine learning system can be built in the future. Studies on machine learning-based long-term forecasting of COVID via WBE are also recommended.


The role of metabolism in normal and malignant B-cell biology is an area of rapid evolution and interest. While previous research has focused on glycolysis, glutaminolysis and oxidative phosphorylation, recent advances have demonstrated the importance of additional metabolic pathways and how these interact with oncogenic drivers and cellular signaling. This article reviews the current understanding of normal germinal center metabolism and how this relates to the metabolism of germinal center-derived lymphomas. There is increasing interest in the potential of targeting metabolic pathways for anti-cancer therapy, with some new agents already entering clinical trials. This article will also review novel enzymatic targets and pathways, and how existing agents modulate metabolism.There have been significant recent advances in the understanding of the role of metabolism in normal and malignant B-cell biology. Previous research has focused on the role of MYC and mammalian target of rapamycin (mTOR) and how these interact with B-cell receptor signaling and hypoxia to regulate glycolysis, glutaminolysis, oxidative phosphorylation (OXPHOS) and related metabolic pathways in germinal centers. Many of the commonest forms of lymphoma arise from germinal center B-cells, reflecting the physiological attenuation of normal DNA damage checkpoints to facilitate somatic hypermutation of the immunoglobulin genes. As a result, these lymphomas can inherit the metabolic state of their cell-of-origin. There is increasing interest in the potential of targeting metabolic pathways for anti-cancer therapy. Some metabolic inhibitors such as methotrexate have been used to treat lymphoma for decades, with several new agents being recently licensed such as inhibitors of phosphoinositide-3-kinase. Several other inhibitors are in development including those blocking mTOR, glutaminase, OXPHOS and monocarboxylate transporters. In addition, recent work has highlighted the importance of the interaction between diet and cancer, with particular focus on dietary modifications that restrict carbohydrates and specific amino acids. This article will review the current state of this field and discuss future developments. During development and differentiation, B-cells undergo significant variations in metabolic activity. In the periphery, naive B-cells are metabolically quiescent, which is thought to contribute to their long-term viability . During This review will summarize the current understanding of normal GC metabolism, how this is reflected in observations regarding the metabolism of GC-derived B-cell lymphomas and how this is shaped by the microenvironment. It will then discuss how advances in lymphoma metabolism could be translated into novel therapies for B-cell lymphomas in the clinic.Resting normal naïve B-cells show little metabolic activity and do not proliferate . On encoThe role that metabolism plays in GC biology is complex, with some observations that appear conflicting at first sight. BCR stimulation primes B-cells for both glycolysis and OXPHOS via PI3K/Akt and PKC beta signaling ,16,17,18The importance of OXPHOS has been highlighted by a recent study which demonstrated that GC B-cells rely on oxidation of long-chain fatty acids . This isOverall, the concept of GC B-cells having a single metabolic program is likely to be a gross oversimplification. Instead, it is likely that B-cells modulate their metabolism depending on where they are within the GC reaction, along with the relative affinity of their BCRs. Fatty acids alone, for example, cannot fully support the tricarboxylic acid (TCA) cycle without the contribution of glucose- or glutamine-derived carbons needed for net-oxaloacetate synthesis . Further18F-fluorodeoxyglucose-positron emission tomography (FDG-PET). Patients are injected with 18F-fluorodeoxyglucose, which is taken up by tumor cells and phosphorylated by hexokinase but not metabolized further, leading to its accumulation within the lymphoma cells underpinning its clinical utility. The increase in glucose uptake is paralleled by increased flux through glycolysis and increased lactate production. The significance of this is reflected in clinical observations of patients with advanced stage high-grade lymphoma having a resting metabolic acidosis and hyperlactatemia . MY. MY40]. CL cases ,42. Furtia (CLL) . An intectivated .MYC oncogene placing it under the regulation of immunoglobulin loci, is present in all cases [MYC-induced expression of the transcription factor AP4 is able to maintain the expression of MYC target genes in the DZ even after MYC itself is downregulated [BL is the prototypical example of a MYC-driven cancer. Translocation of the ll cases ,45. BL sll cases . DZ GC Bll cases . Howeverll cases ,47. One egulated ,48.TCF3 or ID3, which results in tonic BCR signaling via PI3K [Eµ-Myc lymphoma mouse model, highlighting the therapeutic potential of inhibiting de novo serine synthesis [Unsurprisingly, MYC overexpression in BL cells results in many metabolic features known to be driven by MYC including increased glucose consumption and lactate production, but also increased contribution of glucose-derived carbon to the TCA cycle . MYC alsvia PI3K . Interesvia PI3K . From a via PI3K . Furtherynthesis .DLBCL is the commonest form of high-grade lymphoma. DLBCL cases can be classified by their cell-of-origin as GCB-DLBCL or ABC-DLBCL . GCB-DLBlow lymphomas also required mTORC1 signaling and glutaminolysis, with combined inhibition of all three pathways inhibiting lymphoma growth in murine models and inducing clinical responses in human DLBCL patients [A further classification of DLBCL, independent of the cell-of-origin subtypes, relates to their metabolic signature: OXPHOS-DLBCL with upregulated mitochondrial metabolism; BCR-DLBCL with increased expression of cell-cycle regulatory genes; with a third DLBCL subset characterized by markers of an ongoing host inflammatory/immune response—HR-DLBCL . The OXPpatients . Interespatients .MYC-translocations and is associated with poor prognosis [MYC translocations depends on presence of another hit, usually the anti-apoptotic gene BCL2, less commonly BCL6. These are known as double-hit lymphomas  if all three genes are rearranged) and have unfavorable progression-free survival and overall survival [Expression of MYC is observed in almost half of DLBCL cases including those without rognosis ,43. The survival . In contsurvival . In addisurvival . In accosurvival . The impsurvival . Interessurvival . In addisurvival . Notablysurvival . SIRT3 wsurvival .TP53 gene), which is known marker of worse prognosis in CLL [There have also been several recent advances in the understanding of the metabolism of CLL. Initial observations noted an increase in the mitochondrial mass of CLL cells when compared with healthy B-cells, with accompanying increases in mitochondrial respiration, mitochondrial reactive oxygen species and oxidative stress ,68,69,70s in CLL . The Bcls in CLL ,83.The emerging importance of metabolism in lymphoma has highlighted the potential for metabolic enzymes and pathways to be therapeutic targets . This isThe effector of PI3K signaling and cellular metabolic sensor, mTORC1, is dysregulated in several lymphomas (including DLBCL and FL) and has been studied extensively as a therapeutic target ,89. Its Another major class of antimetabolic agent that has been undergoing clinical testing for lymphoma are inhibitors of the monocarboxylate transporter (MCT) family, typically MCT1. These are transmembrane proteins that mediate the bidirectional transport of lactate  in and out of cells . As MCTsSeveral recent reviews have summarized new developments in targeting cancer metabolism and the range of metabolic inhibitors undergoing preclinical and clinical testing ,95. AmonMYC-driven lymphoma, and that these mice also respond to inhibition of the serine synthesis pathway [Another area of potential future therapeutic innovation regards the impact of dietary modulation on lymphoma. Numerous studies spanning over a century of research have shown that caloric restriction can slow growth of a variety of tumors in rodent models, and in patients who have reduced intake due to anorexia or gastric bypass surgery . More re pathway ,102. Ind pathway . With th pathway ,104. Thi pathway . The com pathway . Other e pathway ,106,107. pathway ,94. Whil pathway ,59,108.Research advances over the last couple of decades have made a huge impact on therapy and improved patient outcome in some of the B-cell malignancies, particularly CLL, while comparable changes in treatment for high-grade lymphomas such as DLBCL and BL have lagged behind. There is no doubt that advances in immunotherapy, including chimeric antigen receptor-modified T-cells and bispecific antibodies are making an impact and will have an expanding role in the future. Inhibitors of kinases involved in BCR signaling such as ibrutinib and idelalisib, along with BCL2 inhibitors such as venetoclax have underpinned the paradigm shift in CLL treatment. However, resistance mutations have been identified that either affect the ability of the drug to bind to the target kinase or activate downstream signaling to bypass the pharmacological blockade. One of biggest advantages of anti-metabolic therapy is that it blocks a process reliant on a physical metabolite rather than a signal, making it harder or even impossible for cancer cells to bypass. However, several challenges remain. One of the major hurdles in translation of metabolic inhibitors into the clinic is the disparity between in vitro and in vivo observations. This can be documented by conflicting results on mTORC1 activation in B-cell lymphoma cell lines versus primary human lymphoma samples, along with differential effects observed with mTORC1 inhibitors in cell culture and in clinical trials . This ma


While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics’ potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs. KnowingOVID-19)  include The WHO’s End TB Strategy encompasses three pillars centralizing the patients, encouraging government-communities cooperation, and intensifying innovative research to eventually hit the goals of an 80% drop in TB incidence, a 90% drop in TB mortality, and catastrophic cost elimination for TB-affected households by 2030 in comparison to 2015. Reaching these milestones would have required a constant incidence decline of 4-5% per year by 2020 and a further drop of 10% by 2025 . HoweverCulture conversion or sputum smear microscopy is still widely used regardless of their limitations for diagnosing and monitoring treatment responses in adults with pulmonary TB (PTB) . These mMtb gene, represents a significant advancement in diagnosing TB (The polymerase chain reaction (PCR)-based technique, namely GeneXpert MTB/RIF, recently developed to amplify the osing TB  and deteosing TB  within tosing TB . Neverthosing TB . TherefoPlasmodium . By directly targeting expressive proteins, which are immune molecules, the approach bypasses the concern of relatively low transcriptional correlation   in genomIn this article, we summarize the current discovered immune markers and immune-centric approaches for TB investigation in the omics era. Furthermore, we propose a novel TB-management strategy through semi-automated therapeutic drug monitoring (TDM) utilizing model-informed precision dosing (MIPD).2Mtb-infected individuals, and vaccine efficacy prediction in case of patients suffering from other active diseases  and effector memory (TEM), which activate and modulate phagocytosis and produce molecules that can directly influence cytotoxic microbial infection. Based on these understandings, several attempts have been undertaken to characterize the T cell-functional signature correlated with different TB stages. In particular, CD69 or CD137, a co-stimulatory receptor, is responsible for T cell activation and proliferation, and the frequency of CD69+ and CD137+ has been reported to link with active TB  and proliferation (Ki-67)   under phion time . The amoe of Mtb .2.3Mtb-specific antigens encounter and stimulate the immune responses of an Mtb-infected individual  cells and B cells were higher in non-active TB, suggesting that adaptive immune response is lower in active TB patients. However, as it appears more challenging, the immune expression profiles of those with LTBI largely overlapped with HC, making it difficult to distinguish separate clusters based on the 1,000 most variable genes  and multi-functional IL-27 that blocks IL-17 and IRF1 signaling , dendritic cell development (FLT3LG), alternative promoters of inflammation (CD248 and EDAR), and inhibition of neutrophil degranulation by ADORA3.Special or edge populations usually appear with physiological changes or system immaturities that may produce different immune cell markers . Pediatrignaling . In addiMtb will target the monocytes for their growth, while lymphocytes are principally responsible for Mtb clearance. Monocyte to lymphocyte ratio (ML ratio) has gained interest as a potential biomarker that can classify TB diagnosis even in special populations. A previous report investigating infants born to HIV-positive mothers revealed that the increased ML ratio in peripheral blood was associated with definite TB disease. Although the ML ratio could be a good predictor for TB disease, the ratio showed a modest role in predicting TB disease-free survival (Mtb infection can interfere with the development of hematopoietic stem cells (HSCs), the ML ratio might be affected differently among people. For example, Mtb hinders HSCs engraftment and uniquely reorganizes HSCs . It has been widely reported that DM and TB often appear together, especially in the aging population . DM is kMtb-specific immune responses are probably not homogenous in all populations and might be influenced by the immunity alterations of each specific population. Hence, using generalized potential immune biomarkers to monitor and diagnose TB in these populations may not be appropriate.Furthermore, DM also may potentially influence and/or decrease the frequencies of Th1 and Th17 cells due to the increased frequencies of Th2 cells which produce IL-4 . Given a4max), challenging the finding of optimal dose levels for patients  metabolite, detoxification (ABCB1 (encodes P-gp), which is responsible for the low systemic exposure of RIF. The low exposure of RIF as one of key drugs in TB treatment may contribute to the risk of obtaining drug-resistant TB. In addition, the reduced oral clearance of drugs with intermediate to high hepatic extraction ratios, which may affect low binding drugs like INH more, was observed in the patients with elevated proinflammatory cytokines  has been recognized as one of the most important factors causing slow treatment response, treatment failure, and the development of drug resistance in TB treatment . Anti-TBpatients . IIV affpatients . It is wowledged . Like otowledged . Cytokinowledged . Given towledged . Cytochrowledged . Most anowledged . IFN-γ, d CYP3A4 . Rifampification . The dowytokines . A summa+ T cells and impairs the production of Mtb-specific IFN-γ and anti-CFP-10 antibody in latent TB patients  and insertion/deletion (InDels) to large-scale mutations that trigger polymorphism in the individual immune system . PopularUninvolved in the changes in the nucleotide sequences, epigenomics evaluates chromosome accessibility, structure, and any modification of chromatin, DNA, or histone such as methylation and acetylation. The most commonly used techniques to capture chromatin phenomena are assayed for Transposase-Accessible Chromatin using sequencing (ATAC-seq), DNase-seq, and FAIRE-seq. By using the high throughput sequencing technique, both genomics and epigenomics facilitate huge reference genome databases for shotgun analysis with a static link to the organism. However, besides the need for high-performance machine installation, genome reconstruction by bioinformatic software also requires advanced training and troubleshooting, disregarding the fact that many sequenced elements’ activity cannot be fully identified .Transcriptomics depicts RNA profiles under coding and non-coding transcripts of either a single cell or a bulk level. Unlike traditional PCR, which only amplifies a limited number of genes simultaneously, currently used transcriptomic techniques such as RNA-sequencing (RNA-seq) and microarray can quantify the entire transcriptome or the large-scale targeted analysis of most well-known genes. However, aside from handling errors during the long process of RNA extraction and sequencing, there is an undeniable disadvantage: RNA polymorphism is not always reflected in protein translation and functional expression .While the genome is generally constant, proteome differs within cells and with time. Therefore, the large-scale study of proteins, proteomics, is more predictably complicated than other omics studies. In the immune system, immune molecules are often detected by either traditional immunofluorescent staining, 2DGE, or modern immunoassays such as ELISA, EMIT (enzyme multiplied immunoassay technique), and mass spectrometry-based approaches: LC-MS and MALDI-TOF (Matrix-Assisted Laser Desorption/Ionization-Time of Flight). Proteomics describes a direct link to organism phenotypes by surpassing the genomics-based approach regarding the correlation to functional expression. Furthermore, as the direct readout of the phenotype, metabolomics is suitable for quantifying metabolic responses to pathophysiological stimuli or genetic modification of a living organism. Metabolomics and its sub-field, lipidomics, can deal with complex metabolites at a high resolution to provide a reliable link between phenotypic characteristics and metabolic profiles. This technology is also helpful for discovering metabolism-centric biomarkers for TB . HoweverSOC1 and DDIT4 also were found to be associated with the immuno-metabolic pathways of TB progression.It has been highlighted that a single omics approach does not decently capture the whole biological process of TB and is unable to realize the true potential of omics for clinical research and implementation . The comConsidering the drawbacks of previous omics studies and the need for direct references for TB clinical practice, immune profiling emerges as a promising solution that directly measures the immune response to pathogens, including all immune-related molecules and their regulators or targets. High-throughput technology, e.g., xMAP, is a game changer for immunologists Figure 3However, immunomics creates a complicated scenario because it studies and manipulates the genes and proteins involved in immunologic functioning and the complex cellular microenvironment. The future of immunomics lies in the continued pursuit of validation of immunologic markers correlated with clinical outcomes, which is now limited, and many technologies are still in their infancy . In othe6The concept of personalized medicine highlights the importance of a patient-tailored regimen by considering individual physiological and pathological factors. As has been stated earlier, one of the cores of personalized medicine is the translational process from the bench to bedside and vice versa . As multThe COVID-19 pandemic has jeopardized TB treatment and further substantially changed the conventional research and development (R&D) approach in many aspects . VirtualOur recent review paper has proposed a novel strategy by incorporating semi-automated TDM using MIPD as a pillar of TB treatment . MIPD-bamax or distribution time of the drugs would be preferable (We have developed an informatics framework for the MIPD-based TDM algorithm. The algorithm allows us to monitor anti-TB drug concentrations in patients and suggests the optimal dose initiation and adjustment following the model-based estimation PK profile. Established collaboration with other high-burden TB countries allows the transfer of knowledge and technology to attain TB eradication through personalized medicine implementation as the end goal of the partnership. Our current models have been developed explicitly following specific ethnicities , to ensueferable . Our cureferable .In conclusion, TB management is not a new issue, but it could take a long time to accomplish the End TB Strategy’s mission, especially in developing countries and due to the pandemic. COVID-19 has suspended the global progress of TB control, highlighting the lack of accessibility and adaptability of current TB diagnosis and therapies. Immune profiling is emphasized in this paper as both a self-sufficient and an additional instrument to improve the existing TB control strategies in an unbiased manner. Our reviews summarized the relevance of immune biomarkers in TB management and characterized the integration of immunomics into the current TB roadmap of research and development. High-throughput immune profiling benefits not only the validation of new immune biomarkers for TB diagnosis, but also the outcome prediction, treatment monitoring, and anti-TB drug dose optimization, which will speed up TB control globally, and ensure “the world without TB” will not be a far off future.Conceptualization, Y-SC, NL, and J-GS; Writing—Original Draft Preparation, VT, LD, RJ, HT, TH, NL, and J-GS; Writing—Review & Editing, VT, LD, RJ, HT, NL, Y-SC, and J-GS; Visualization, VT, LD, and RJ; Project Administration, NL; Supervision, Y-SC and J-GS; Funding Acquisition, J-GS. All authors have read and agreed to the published version of the manuscript.


Immune-related adverse events (irAEs) are a range of complications associated with the use of immune-checkpoint inhibitors (ICIs). Two major classes of ICIs widely used are Cytotoxic T-Lymphocyte Antigen 4 (CTLA4) and Programmed Cell death-1 (PD-1)/Programmed death-ligand 1 (PD-L1) inhibitors. High-grade irAEs are life-threatening and often cause a severe decline in performance status in such that patients do not qualify for any further anticancer treatments. It is difficult to generalize the evidence in the current literature on risk factors or biomarkers for the entire class of ICIs as the studies so far are either disease-specific  or ICI agent-specific  or irAE-specific . In this review, risk factors and biomarkers to consider before initiating or monitoring ICI are listed with a practical purpose in day-to-day practice. Risk factors are grouped into demographics and social history, medical history, and medication history, tumor-specific and agent-specific risk factors. A higher risk of irAE is associated with age <60 years, high body mass index, women on CTLA4 and men on PD-1/PD-L1 agents, and chronic smokers. Patients with significant kidney (Stage IV-V), cardiac , and lung  are at a higher risk of respective organ-specific irAEs. Pre-existing autoimmune disease and chronic use of certain drugs  also increase the irAE-risk. Biomarkers are categorized into circulating blood counts, cytokines, autoantibodies, HLA genotypes, microRNA, gene expression profiling, and serum proteins. The blood counts and certain protein markers  are readily accessible in current practice. High neutrophil-lymphocyte ratio, eosinophil/monocyte/lymphocyte counts; TSH and troponins at diagnosis and drop in the white count and lymphocyte count can predict irAE. Other biomarkers with limited evidence are cytokines, autoantibodies, HLA genotypes, microRNA, and gene expression profiling. With fast-expanding approvals for ICIs in various cancer types, knowledge on risk factors and biomarkers can help providers assess the irAE-risk of their patients. Prospective disease and agent-specific studies are needed to provide further insight on this essential aspect of ICI therapy. Immune checkpoint inhibitor (ICI) therapy has changed the landscape of managing multiple cancer types in the last decade. They work by altering the immune regulatory pathways and thereby promoting cell-mediated destruction of the tumor cells. Several agents targeting Cytotoxic T-Lymphocyte Antigen 4 (CTLA4), programmed cell death-1 (PD-1), and Programmed death-ligand 1 (PD-L1) have been approved by the FDA (Food and Drug Administration) for multiple cancer types, and the indications for utilization is constantly expanding –10.T cells and cancer cells have a complex regulatory mechanism. Typically, presenting major histocompatibility complex (MHC) peptide to the T cell results in cytoskeletal reorganization, downstream pathways resulting in genetic alteration, CD28 mediated co-stimulation, and an immunological synapse . AlternaImmune-related adverse events (irAE) refer to a set of side-effects in the patients receiving ICIs similar to autoimmune responses . The irAThe irAE profile in an individual depends on the organ that exhibits autoimmune-like activity. It may vary with the class of the ICI agent used (PD-1/PD-L1 vs. CTLA4). Compared to PD-1/PD-L1 inhibitors, CTLA4-inhibitors frequently cause colitis (irAE-GI), hypophysitis, and dermatitis (irAE-skin), and less frequently, cause pneumonitis (irAE-lung), hypothyroidism, and skeletal symptoms such as myalgias and arthralgias , 22. A mIn this review, the available evidence on the risk factors contributing to irAE and biomarkers that predict the irAE occurrence was reviewed in a pattern that can be adopted in daily clinical practice. The patient’s baseline characteristics that increase the possibility of irAE before administering the first dose of ICI were classified into patient-specific risk factors and measurable entities as biomarkers. The basic history-taking sequence steps for risk factors were followed, starting with demographics , past medical history, and medication history. Few physical exam findings and social habits were covered in demographics. The tumor-specific and agent-specific risk factors were included to personalize it for a patient and the choice of the agent. The biomarkers with predictive value incorporated range from simple blood counts to complex microbiome studies. The idea is to provide helpful information for a treating physician to predict the incidence of irAEs.The published studies that evaluated risk factors or biomarkers for irAE were historically conducted in populations that are specific to a primary tumor ; ICI-agent ; organ system involving irAE ; one primary and one agent ; combinations  –28. TherRisk factors refer to a variable associated with an increased risk of disease or infection . In the This section discusses the risk factors for irAE in the patient even before initiating ICI and further divides them into three more sets, demographics and social history, medical history, and medication history.Early studies could not establish any causal relationships with demographic and social factors such as age, gender, race, smoking, or diabetes , 70. EveHowever, other studies had different conclusions. A retrospective review of an insurance company’s database reported an increased risk of severe irAE in the younger population . Though Gender and irAE risk may be dependent on the class of ICI used. Men were at high risk of pulmonary toxicity with PD-1/PD-L1 inhibitors (in multiple cancers including MM and lung cancer), while women are at high risk with CTLA-4 inhibitors (in MM) , 34. The2 had a significantly increased risk of irAEs  , ≥ 25kg/m01-1.16) . This wa01-1.16) . High-BM01-1.16) . In a Ja01-1.16) . The poo01-1.16) . In ipil01-1.16) .Pre-existing autoimmune disease (AD) and even a family history of it seems to increase the risk of irAE in patients treated with ICI –43. ContAllergies  by virtue of Type I hypersensitivity reactions significantly increase the risk of irAE in solid tumors , according to a retrospective study . There iPre-existing lung diseases such as interstitial lung diseases (ILD), pulmonary fibrosis, asthma, COPD increase the risk of irAE-pneumonitis , 45–49. Medications such as PPIs and nonsteroidal anti-inflammatory drugs (NSAID) are associated with kidney injury with chronic usage –53. MultThe debate over immune modulation associated with vaccinations (such as influenza) and its influence on ICI’s response and the risk for irAE has been ongoing for more than a decade. The majority of studies that reviewed the effect of vaccination status on increasing the irAE incidence risk found no causal relation between them –87. A SwThe severity and profiles of irAEs seem to depend on histology for the primary tumor , 64. HeaA meta-analysis published in 2020 with 11 trials reported the fatal adverse events (FAE), not necessarily irAE, associated with pembrolizumab, were highest among breast cancer patients (3.2-1%), followed by NSCLC (2%), UC (0.8), and melanoma (0.2) . CombiniR = 0.704; P <.001)). The metastatic liver disease does not predict high-grade liver-irAE (hepatitis), but the risk is significantly higher in the patients who receive combination therapy (compared to monotherapy)  .The incidence and profile of irAE may differ with the type of agent, PD-1, PDL1, CTLA4, or combination. Studies after studies proved the higher incidence and severity of irAE with CTLA4 inhibitors when used alone or combined with PD-1 or PD-L1 agents such as ipilimumab and nivolumab irrespective of the primary tumor treated , 65–67. A biomarker is defined as a characteristic measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention . The bioThe blood counts such as absolute lymphocyte count (ALC), absolute monocyte count (AMC), absolute eosinophilic count (AEC), platelet count, absolute basophil count (ABC), neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR) have been a constant interest among clinicians and researchers as they would provide a simplest objective way to determine the probability of experiencing irAE. Multiple studies looked at various such blood counts individually or collectively. Some studies used derived NLR, which is the ratio of ANC and total white cell count – neutrophil count , 128. ThAfter initiating ICI, a rise in WBC count (by 59%) and a drop in relative lymphocytic count (by 32%) from baseline was seen in patients having irAE-lung/GI in melanoma patients treated with nivolumab . One stuAs irAE is a product of over-activating the immune system, cytokines were extensively studied to predict them , 97–100.In an Italian study, higher pre-treatment soluble CTLA4 (>200 pg/ml) correlated with worse irAE, specifically GI-irAE in melanoma patients treated with ipilimumab . AutoantHigher baseline albumin (≥3.6 g/dl) in multiple cancers  with PD-1 or PD-L1 inhibitors . Lower pHuman leukocyte antigen (HLA) refers to a set of immunogenic peptides (as receptors) on the T cells that help in distinguishing self and foreign antigens . In HLA Preclinical data shows mice lacking MicroRNA-146a (miR-146a) had significant irAE compared to wild-type mice, along with a marked increase in neutrophils on the inflamed spleen and intestine . When thBacteroidaceae, Rikenellaceae, and Barnesiellaceae is associated with reduced risk of irAE-GI (colitis)   was analyzed in 34 MM patients treated with Ipilumimab, an abundance of microbes from the Bacteroidetes phylum, specifically colitis) . Deficiecolitis) . Tan et colitis) . Samplesing days .Rechallenging patients with ICIs after an irAE is another aspect of the treatment that is understudied. Various cancer societies proposed guidelines for re-initiating ICIs . After sInterestingly, the recurrent irAE was not as severe as the first one. Strategies advised for rechallenging and prevention are included in In the current clinical practice, ICI use is growing exponentially. Limited prospective studies explored the factors contributing to the irAE. Retrospective studies were done in very specific populations, and it is difficult to draw solid conclusions and apply them to all cancers or agents or irAE types. An insight into the risk factors and biomarkers with significant predictive value for irAE incidence will enable the treating clinician to take the necessary steps to protect the patients from irAE and continue benefiting with ICI. In real-world practice, knowledge on organ-specific risk, gender-specific, comorbidity or medication-specific, primary tumor-specific, and ICI-agent-specific risk factors and biomarkers will allow physicians to personalize the necessary monitoring strategies to prevent irAE and restart ICIs post-irAE.Patients must be trained to identify the irAE and alert the treating team early. High-risk individuals  must be monitored closely for irAE. Kidney injury (AKI) associated with ICI is frequent in patients with CKD. It is advisable to hold PPIs and NSAIDs or switch them to alternatives before initiating ICIs. The irAE- pneumonitis is frequent in patients with pulmonary fibrosis, asthma, and COPD. Patients with significant cardiac disease history  and uncontrolled hypertension may have more cardiac adverse events such as myocarditis or pericarditis. Patients receiving HER2 inhibitors, VEGF TKIs, and anthracyclines in prior lines of therapy should be monitored for cardiac irAEs.Circulating blood counts and TSH/T4 are the only biomarkers accessible in the current clinical practice and must be used when possible. Absolute counts such as ALC, AEC, platelet count, or ratios such as NLR and PLR can predict irAE incidence. Following WBC and ALC might help monitor patients on ICI for irAEs. Prospective studies are needed to shed further light on other biomarkers such as cytokines, HLA genes, microRNAs, gene expression profiling, and intestinal microbiome need more studies before they can be adopted in daily practice.Patients having an excellent response to ICI can be rechallenged based on the initial irAE grade and tolerability with careful assessment of risks and benefits. Resumption of the same agent after full recovery is popular, but class switching can be tried. Dropping CTLA4 inhibitors is also advised in patients with irAE on combination therapy. Close monitoring of liver chemistries (every week) for grade 1 hepatitis on ICI therapy and holding the therapy for grade 2 hepatitis is advised. Checking inflammatory markers  for patients with diarrhea might help in the early diagnosis of colitis.The majority of ongoing trials with ICIs are doing correlative studies by collecting patients’ blood, tumor, and microbiome. The focus of research is shifting towards incorporating genomic data (mutations or methylation) and tumor microenvironment (T-cells) from accessible sources such as blood, stool, and urine to study the factors affecting toxicity and efficacy of ICIs. Acknowledging the need to do more prospective studies in this unexplored facet of ICI therapy, these risk factors and biomarkers list can be a good starting point in clinical practice.AC and AM contributed to the conception and design of the study. AC and AM organized the database, selected relevant articles, and wrote the first draft of the manuscript. Multiple revisions and additions to the final manuscript are contributed by LA, NJ, and CP. All authors contributed to manuscript revision, read, 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.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.


The features of preoperative systemic anticancer therapy associated with best outcomes after resection of initially-irresectable liver metastases from colorectal cancer are yet to be identified. We harnessed data from a prospective international surgical database (LiverMetSurvey) to explore the duration and modalities of preoperative systemic anticancer therapy associated with longer overall survival in this clinical setting. Our study included 2793 patients having undergone liver surgery after preoperative systemic anticancer treatment for initially irresectable disease. We found that short  duration was associated with longer survival outcomes, independently from other prognostic factors. Conversely, all the comparisons between different conventional active regimens displayed similar results. Our findings support the recommended onco-surgical approach of aiming at performing liver surgery as soon as technically feasible after response to preoperative systemic anticancer therapy in hepatic metastases from colorectal cancer, initially not amenable to surgery. The results of this study also suggest that, provided the systemic anticancer therapy regimen is active, the choice of the drugs used bears overall little if any impact on the outcomes. Background: Prognostic factors have been extensively reported after resection of colorectal liver metastases (CLM); however, specific analyses of the impact of preoperative systemic anticancer therapy (PO-SACT) features on outcomes is lacking. Methods: For this real-world evidence study, we used prospectively collected data within the international surgical LiverMetSurvey database from all patients with initially-irresectable CLM. The main outcome was Overall Survival (OS) after surgery. Disease-free (DFS) and hepatic-specific relapse-free survival (HS-RFS) were secondary outcomes. PO-SACT features included duration (cumulative number of cycles), choice of the cytotoxic backbone , fluoropyrimidine  and addition or not of targeted monoclonal antibodies (anti-EGFR or anti-VEGF). Results: A total of 2793 patients in the database had received PO-SACT for initially irresectable diseases. Short (<7 or <13 cycles in 1st or 2nd line) PO-SACT duration was independently associated with longer OS (HR: 0.85 p = 0.046), DFS  and HS-RFS . All other PO-SACT features yielded basically comparable results. Conclusions: In this international cohort, provided that PO-SACT allowed conversion to resectability in initially irresectable CLM, surgery performed as soon as technically feasible resulted in the best outcomes. When resection was achieved, our findings indicate that the choice of PO-SACT regimen had a marginal if any, impact on outcomes. Colorectal cancer remains the third cancer in terms of incidence and mortality in both sexes . Liver iIn this context of uncertainty regarding the impact of the type and modalities of pre-operative systemic anticancer therapy (PO-SACT) on patients’ outcomes and aware of the clinical relevance of real-world data , we harnLiverMetSurvey is an international, internet-based registry designed to assess the efficacy of multimodality treatment options for colorectal liver metastases (CLM) ,17. FromIrresectability was defined as the technical inability to completely remove all metastases while leaving at least 30% of normal liver parenchyma. Thus, in these patients, PO-SACT was administered with downsizing conversion intent.The oncologist in charge selected the most appropriate chemotherapy regimen after discussion in a multidisciplinary staff meeting according to local experience, tumor genotype, medical and biological condition and individual clinical treatment history, abiding by the most up-to-date international recommendations. Basically, all patients received fluoropyrimidines (leucovorin-modulated 5-fluorouracil or capecitabine), in association with irinotecan, oxaliplatin or both (triplet combination), and with or without targeted therapies .The response to chemotherapy was evaluated every four to six cycles of treatment with computed tomography of the thorax, abdomen and pelvis according to the Response Evaluation Criteria in Solid Tumours Criteria . PreoperDuring surgery, the consensual policy was to resect all the lesions detected at diagnosis, including remnant calcifications or scar lesions, with the aim to completely clear the liver of palpable and visible tumoral tissue, sparing the highest amount of liver parenchyma possible, as recommended ,19.The indication of the regimen choice and the duration of postoperative therapy were decided by the lead oncologist following a multidisciplinary team discussion after surgery. In the absence of standardized guidelines , the mosPatients were followed at 1 month postoperatively and then every 3 to 4 months with serum tumor markers (carcinoembryonic antigen [CEA] and CA 19.9), clinical examination and diagnostic imaging, as per local practice. Repeat resection of intra- and/or extrahepatic disease recurrence was performed when curative resection could be achieved. Further chemotherapy was administered if deemed indicated.The primary endpoint was overall survival (OS), calculated from the time of surgical resection till death or last follow-up visit. Secondary endpoints included: disease-free survival (DFS), calculated from the time of surgical resection till recurrence in any site or death, and hepatic-specific relapse-free survival (HS-RFS), calculated till the occurrence of liver recurrence only or death.The main aim of the study was to identify the features of preoperative therapy associated with the longest OS after liver resection. In particular, we focused on the duration and type of PO-SACT. For the first aim, we categorized chemotherapy duration into short (6 cycles or less) or long (7 cycles or more) if the first-line regimen . In the We first determined OS probabilities as a function of different features of PO-SACT with the Kaplan-Meier method and compared them using the log-rank test. Then we identified the features of PO-SACT independently predicting for longer OS using multivariable Cox proportional hazard models, accounting for other relevant known potential clinical prognostic factors, including period of surgery (cutoff year: 2005) and Institution (continent-wise).For the secondary endpoints (DFS and HS-RFS), we proceeded with the same approach.2 test for categorical data and the independent-samples t-test or analyses of variance for continuous data.Comparisons of baseline clinical-demographic characteristics between subgroups of patients defined by PO-SACT features were performed using the Chip value ≤ 0.05. All statistical analyses were performed using SAS software version 9.1.3 .The threshold for statistical significance was set for a At the time of LiverMetSurvey database analysis (December 2020), our study population included 2793 patients having received conversion PO-SACT for initially non-resectable disease . The maiAltogether, the most frequent PO-SACT regimen used was FOLFOX: in 28.3% of patients, it was used alone, and with bevacizumab or cetuximab/panitumumab in further 14.2% and 10.4% of patients, respectively. In comparison, FOLFIRI was used alone, in combination with anti-VEGF or with anti-EGFR monoclonal antibodies (mAb) in 10.8%, 10.7% and 8.6%, respectively. Over a third of the patients received less than 7 pre-operative cycles, and resection after 3rd or more line therapy was rare.Postoperative chemotherapy was administered to slightly less than half of the patients in this setting.p = 0.021; p < 0.05).Shorter (as defined above) PO-SACT duration was associated with significantly better OS  and the addition or not of targeted agents , whereas all the other comparisons yielded similar outcomes (not shown).DFS, however, was marginally longer with doublet (median: 15.2) than with triplet regimen . Additionally, HS-RFS was significantly longer without mAbs (median unattained) than with anti-EGFR (33.0) or anti-VEGF ; this secondary outcome, too, did not seem to be influenced by choice of the backbone agent or of the fluoropyrimidine (not shown).For HS-RFS, too, there was a trend towards better outcomes on doublet therapy .p = 0.046); moreover, at multivariable analysis, the use of no targeted agent or of anti-VEGF mAb was retained as a predictor of shorter OS . The other independent negative prognostic factors are shown in Longer  PO-SACT duration was independently confirmed as a prognostic factor of poorer OS  and HS-RFS .Longer PO-SACT was also an independent predictor of shorter DFS  pre-operative systemic anticancer therapy duration as associated with longer OS, DFS and HS-RFS after surgery of initially-irresectable hepatic metastases ; Table 2Thus, in initially unresectable disease, a longer duration of PO-SACT could be assumed as a surrogate of insufficient down-sizing capability due to an extensive tumor load or to a chemoresistant biological tumoral phenotype. Additionally, prolonged pre-operative treatment could increase the risk of disappearing metastases, which might not have been completely sterilized by the PO-SACT ,29. ThusIn accordance with observed outcomes in metastatic disease independently of any surgical strategy ,33,34, oIn accordance with expected antitumor activity being highly comparable between oxaliplatin- and irinotecan-based chemotherapy . We did Somewhat unexpectedly, in our surgical database we only observed independent survival prolongation with intensified conversion PO-SACT, from the adjunction of either cetuximab or panitumumab c, whereaAltogether, since intensive conversion PO-SACT, including triplet with biologicals or hepatic artery infusion, is associated with high response and secondary resection rates ,47, evenThis introduces what we believe to be the main limitation of the current study: the intrinsic nature of our heterogeneous surgical database does not consider the patients referred to the oncologist for optimal chemotherapy for metastatic colorectal with hepatic involvement and never resected. Hence, we are unable to gauge with this “surgical” registry to which extent each type of conversion PO-SACT regimen was able to downsize the hepatic disease enough to allow secondary liver resection. Additionally, we did not routinely collect information about molecular subtypes classification, individual mutational genotyping, or immune-microenvironment, all of which play a role in prognosis and response to medical therapy ,53,54,55Nevertheless, all the patients in this cohort were discussed in multidisciplinary team meetings involving both oncological and surgical expertise, as recommended ; moreoveIn summary, our real-world data in a large international prospective registry revealed that best OS, DFS and HS-RFS are achieved when resection is performed after 6 or fewer cycles of pre-operative first-line treatment (12 for second-line) in patients with the non-resectable disease. Furthermore, our study did not identify the best pre-operative regimens, but suggested that, whenever indicated, the addition of anti-EGFR mAbs to the cytotoxic backbone ought to be preferred. Therefore, optimally active chemotherapy for the individual patient and a limited number of cycles should be aimed towards in as much as the main objective of the strategy. We recommend that reviewing each patient every 2 months at a multidisciplinary meeting as recently proposed by an expert panel is the best means to achieve this objective.


ERTAR-ERTAR faster than ertar-ERTAR; words: ALTAR-ALTAR produces similar response times as altar-ALTAR). This dissociation is thought to result from lexical feedback influencing orthographic representations in word processing. As nonwords do not receive this feedback, bottom-up processing of prime–target integration leads to matched-case effects. However, the underlying mechanism of this effect in nonwords remains unclear. In this study, we added a color congruency manipulation across the prime and target in the matched-case identity-priming design. We aimed to determine whether the case effects originate at the early stages of prime–target perceptual integration or due to bottom-up activation of case-specific letter detectors. Results replicated the previous dissociation between words and nonwords regarding the matched-case identity effect. Additionally, we did not find any modulation of these effects by prime–target color congruency. These findings suggest that the locus of the matched-case identity effect is at an orthographic level of representation that encodes case information.In recent studies with the masked priming lexical decision task, matched-case identity-priming effects occur for nonwords but not for words (e.g., nonwords:  This TABLE were comparable regardless of whether it was briefly preceded by a matched-case identity prime (TABLE) or a mismatched-case identity prime (table). Critically, they also found that responses to a nonword such as SUGLE were faster when preceded by a matched-case identity prime (SUGLE) than when preceded by a mismatched-case identity prime (sugle). This dissociation has been replicated in other languages and tasks ). For comparison purposes, we employed the same set of stimuli as Vergara-Martínez et al. ). Furthermore, as in previous research, we found the expected interaction between lexicality and case . This interaction reflected that, for nonword targets, responses were faster in the matched-case than in the mismatched-case condition , whereas for word targets, there were no signs of an effect . There was some evidence of a small effect of color mismatch , but its credible interval crossed zero. Critically, no other estimates showed evidence of an effect  had an impact on identity priming in a similar manner as case-match between prime and target  [A masked identity-priming experiment utilizing the lexical decision task was conducted to determine whether prime–target color congruency  ,4,5. TheThus, the results of the present experiment revealed a matched-case congruency advantage for nonword targets but not for word targets. This outcome, which replicates previous findings, suggests that the retrieval of abstract letter representations in the lexical decision task depends on activating lexical entries. In the absence of lexical feedback, such as nonword stimuli, matched-case identity priming persists. As stated in the Introduction, we aimed to examine the underlying explanation for this effect in nonword stimuli. Previous research has suggested that in the absence of lexical feedback, nonword prime processing might integrate case-specific letter detectors ,5. If thTo differentiate between these two explanations, we included a manipulation of color congruity across the prime and target. Perceptual features, such as shape and color, would aid in forming an object file at a perceptual level ,21. Moresnow, SNOW, or the nonword nigelo or NIGELO activates different shape-fragment detectors, which are also sensitive to letter-case . Higher in the hierarchy, at later stages of processing, different neural populations (abstract letter detectors) map letter shapes into abstract units free from size, font, case, and orientation information [SUGLE–SUGLE faster than sugle–SUGLE) but also for words (TABLE–TABLE faster than table–TABLE).The present pattern of results is partially consistent with Dehaene et al.’s  LCD modeormation . While tormation ,5,6,7. Tormation , where tormation  found faWe acknowledge that, as in any behavioral study, the findings of the present paper do not provide information regarding the temporal dynamics of prime–target processing. Future research should be conducted to provide more insight on this matter. Based on previous research using event-related potentials ,25,27, iIn conclusion, our findings suggest that an explanation of matched-case identity priming on nonwords based on perceptual contiguity in the lexical decision task can be disregarded. The color match between the prime and target did not affect the typical pattern of effects observed in previous lexical decision experiments, which revealed a dissociation of matched-case identity-priming effects for words and nonwords. Therefore, the effects observed here reflect the interactive dynamics of bottom-up and top-down activity in visual word recognition (see ,28 for s