COVID-19 expanded rapidly throughout the world, with enormous health, social, and economic consequences. Mental health is the most affected by extreme negative emotions and stress, but it has been an underestimated part of human life during the pandemic. We hypothesized that people may have responded to the pandemic spontaneously with increased interest in and creation of funny internet memes. Using Google and Google Trends, we revealed that the number of and interest in funny internet memes related to COVID-19 exploded during the spring 2020 lockdown. The interest in coronavirus memes was positively correlated with interest in mortality due to COVID-19 on a global scale, and positively associated with the real number of deaths and cases reported in different countries. We compared content of a random sample of 200 coronavirus memes with a random sample of 200 non-coronavirus memes found on the Internet. The sentiment analysis showed that coronavirus memes had a similar proportion of positive and negative words compared to non-coronavirus memes. However, an internet questionnaire revealed that coronavirus memes gained higher funniness scores than a random sample of non-coronavirus memes. Our results confirm that societies may have turned to humor to cope with the threat of SARS-CoV-2. The COVID-19 pandemic has had a lasting impact worldwide ,3. EmotiThe most widely known consequence of exposure to prolonged traumatic and stressful events is post-traumatic stress disorder ,12. A stClinical research indicates that the effects of traumatic events and chronic stress can be significantly buffered by humor ,18. HumoHumor may be shared and provided in various ways ,30. Howememe was coined by Dawkins [The term  Dawkins , who pro Dawkins ,40,41. T Dawkins . Specifi Dawkins ,42,43,44 Dawkins  stresses Dawkins . Memes a Dawkins ,40,47. M Dawkins ,48,49. M Dawkins . The ess Dawkins ,49. Some Dawkins . Viral m Dawkins ,52. Meme Dawkins . TherefoAs COVID-19 erupted, memes related to the pandemic also became widespread on social media ,52. COVIcoronavirus memes: Digital images (often containing text) created for the purpose of communication about COVID-19. Furthermore, we tested the hypothesis that interest in coronavirus memes is a response to perceived risk of death from COVID-19. Thus, we expected interest in the coronavirus memes in different countries, measured using Google Trends, to be positively correlated with (1) interest in deaths due to COVID-19 and (2) pandemic statistics, such as the number of cases and deaths reported in a given country. Finally, we compared the image and text contents and valued the funniness of a random sample of coronavirus memes with a sample of internet memes unrelated to the pandemic. We expected that (3) coronavirus memes would be rated as being more humorous than random non-coronavirus memes, to be explained by differences in the image and text content.In this study, we tested the hypothesis that the COVID-19 pandemic, especially during the spring 2020 lockdown when the first worldwide measures for stopping the virus occurred, is associated with the rise in the number and interest in funny internet memes related to COVID-19. We used a definition of https://www.worldometers.info/coronavirus/ (accessed on 13 October 2020)” ” . In our https://www.google.com/support/enterprise/static/gsa/docs/admin/current/gsa_doc_set/xml_reference/index.html (accessed on 10 May 2020)”. The first was the rc = 1 request parameter to request an accurate result count for up to one million documents. The second filter was automatic filtering set to 1, allowing directory filtering (filtering documents coming from the same source folder) and duplicate snippet filtering . Both filters were officially advised by Google  using the search string “(coronavirus OR covid 19 OR covid) meme” . We also constructed a time-series (at a weekly frequency from October 2019 to October 2020) representing the intensity of the meme-generating process. We performed an analogous search for non-coronavirus memes (memes not related to the COVID-19 pandemic). We did this using the search string “meme -coronavirus – covid -‘covid 19′”. We used two filters officially advised by Google Help “gle  and risk of death due to COVID-19 (“coronavirus death”) and the reference words . The time-series obtained from this tool are informative for determining the dynamics of society’s demand for information  and can A search in Google Trends using the terms “covid 19” and “covid” resulted in similar findings; however these terms were much less popular and present in only 20 and 50 countries, respectively. Therefore, we present most of the analyses of these strings in the We selected 200 random coronavirus memes (out of 749) and 200 random non-coronavirus memes (out of 839) found via a Google search using image filtering as described in the second paragraph of The classification of relevant images as memes was visually performed. A graphic file was classified as a meme if it contained a recognizable image and text (excluding copyright watermarks). Some memes contained no text and were excluded from analysis . Then, these memes were scrutinized by writing down the text present in the meme and classifying the meme image content. The latter was classified as containing (a) human object, (b) non-human object , or (c) both. After this, we determined the detailed content of images . There was no specific hypothesis with these categories, and the purpose of this study was to compare the content of coronavirus and non-coronavirus memes.https://tinyurl.com/yczetymo 2. https://tinyurl.com/yatnyfxa 3. https://tinyurl.com/y7h9jk6a.Apart from the analysis of meme content, we prepared an internet questionnaire to compare the funniness  of coronAll statistical analyses were performed using R version 4.0.3 .10 transformed to homogenize variations and remove the effects of detached observations [R2, the proportion of variation in the dependent variable explained by the covariates. The assumptions of the linear models were tested with the “performance” v. 0.7.3 [Associations among the Google Trends interest in coronavirus memes , number of web pages containing coronavirus memes, number of webpages with memes (not coronavirus memes), the number of COVID-19 cases per week, and number of deaths from COVID-19 per week were tested by correlation analysis and visualized in ‘ggplot2′ v. 3.3.5  and ‘resrvations . The skev. 0.7.3  and by av. 0.7.3  implemenv. 0.7.3  R packag10 transformed) due to COVID-19 per one million inhabitants of a given country and the total number of cases (log10 transformed) per one million inhabitants (10 transformed) was used as a covariate to control for possible differences in access to the Internet among countries. We used the package “rcompanion” v. 2.4.1 [R2) in GLMs with binomial error variance. This is one of the most commonly used and straightforward proxies for explained variance in GLMs with binary response variables [The general linear model (GLM) with binomial error variance and logit link-function was used to test the association between the probability of occurrence of interest in coronavirus memes  in different countries and the total number of deaths  in CLMM and ‘ggeffects’ v. 1.1.0 [We used the cumulative link mixed model (CLMM) to compare the funniness  of coronavirus memes  with non-coronavirus memes (coded as 2) and a sample of images . We included age , gender , and interaction between them because perceived humor, including COVID-19 humor, differs between genders ,29,81 anv. 1.1.0  to visuar = 0.871, t = 12.538, df = 50, p < 0.001), “coronavirus deaths” , but only moderately correlated with the interest in simple word: “death”  or with temporal changes in the number of deaths due to COVID-19  and with temporal changes in the number of deaths due to COVID-19  meme”. The relative interest in coronavirus memes (Google Trends index) was highest during the spring lockdown . The temr = 0.446, t = 3.523, df = 50, p < 0.001), “covid 19 meme” , and “covid meme” , and positively correlated with changes in the number of deaths due to COVID-19 , but not with changes in the number of new cases of COVID-19  meme” were moderately positively correlated with the relative interest in Go ogle Trends for “coronavirus meme” (The relative interest in coronavirus memes (search string “coronavirus meme”) in different countries was positively associated with the total number of deaths due to COVID-19 per one million inhabitants of a given country  and the Image content. There was a very similar number of memes containing human objects in coronavirus and non-coronavirus memes . The number of memes containing non-human objects in coronavirus and non-coronavirus memes was also similar . Only five memes contained both human and non-human objects. Overall, the content of images  did not differ between coronavirus and non-coronavirus memes . When we analyzed visual content in detail, we found that both types of memes were dominated by movie actors or cartoon characters . Analysis repeated on 30 the most common words found in both types of memes also showed considerable similarity .pandemic , while npandemic . There wSentiment analysis. In coronavirus memes, 125 words (12%) were identified as emotional. Forty-six words were positive and 79 were negative. In non-coronavirus memes, 101 words (12%) were emotional. Forty-two patients were positive and 56 were negative. The differences in the frequency of emotional words , as well as the proportion of positive versus negative words , were statistically non-significant between coronavirus memes and non-coronavirus memes.Funniness of coronavirus memes. One-hundred-and-thirty-one individuals from 12 countries  filled out the online questionnaire. Sixty-three participants were women, 65 were men, and three participants did not specify their gender  from Google searches was not perfect. For example, it was not possible to fully account for image repetition. Moreover, webpages with coronavirus memes appeared as early as October 2019. We scrutinized several of these webpages and found that they were built before the pandemic, but contained “suggestion links” to current new memes. Thus, the series used in this study reflected certain properties of the Google proprietary algorithms, which are impossible to fully control, introducing one more arbitrary aspect. We cannot exclude the possibility that memes on other coronaviruses  were built before the COVID-19 pandemic and were associated with the search terms. However, the time-series had a logical interpretation: Despite webpages with coronavirus memes being found in October-November 2019, the highest number of webpages was created during the spring 2020 lockdown. These limitations do not apply to Google Trends. Additionally, the webpages found via Google search included some social media e.g., Reddit but not Facebook or Twitter webpages, probably because of privacy setting in these media. This may create a large gap in the meme sharing behavior. However, Google’s search algorithm usually returns the most popular results first, thus perhaps those spread via social media.Humor research suffers from an optimistic bias, meaning researchers focus on the positive aspects of the phenomenon, ignoring its darker, negative aspects . Very liMost of our analyses were correlative. However, studies on a global scale rarely, if at all, are experiments. Our general and generalized linear models explained usually below 40% of variance. This indicates that we did not include some predictors or there is high random variance in the dependent variables. However, in non-experimental studies regression models usually explain less than 20% of variance in dependent variables . NeverthOur online questionnaire had a geographic bias, as 75% of respondents came from Poland. We intended to include more participants from other countries, however, without success, probably because we distributed the questionnaire using the snowball sampling technique: the survey was shared with a small pool of respondents who participate, and each of those participants shares the link with their network or pool of respondents .All our memes were in English. We decided to use them because searches in Polish resulted in English memes (many memes in English are simply published or shared on Polish websites). This is probably because the text in memes is just a graphic and Google search recognizes the origin of the website, but it does not recognize the content of the meme. We believe this is not a serious problem because over 60% of citizens in Poland (aged 20-50 years) possess at least basic proficiency in English, and Poland is ranked 16th among 112 countries in English proficiency . MoreoveOur findings suggest that societies responded to the threat linked to the pandemic by searching for ways of releasing stress. Interest in memes may allow not only a way of coping with anxiety, fear, or uncertainty, but also a way of shaping attitudes to the novel coronavirus and participating and developing social networks during the pandemic. The occurrence of positive emotions associated with memes may provide the necessary psychological rest to help buffer against stress, replenish, and restore further coping efforts. Moreover, the dynamics of the number and interest in coronavirus memes have some properties of the real virus disease trajectory. However, the dynamics do not follow the COVID-19 pattern in new cases or deaths.


PULSE, consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network, can automatically learn the hidden patterns of pseudouridylation from the local sequence information. Extensive validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy, thus enabling us to further characterize the transcriptome-wide landscape of Ψ sites. We further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation, such as the regulations of RNA secondary structure, codon usage, translation, and RNA stability, and the connection to single nucleotide variants. The source code and final model for PULSE are available at https://github.com/mlcb-thu/PULSE.Pseudouridine (Ψ) is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and mRNAs. However, the functions, mechanisms, and precise distribution of Ψs  still remain largely unclear. The landscape of Ψs across the transcriptome has not yet been fully delineated. Here, we present a highly effective model based on a convolutional neural network (CNN), called PseudoUridyLation Site Estimator (PULSE), to analyze large-scale profiling data of Ψ sites and characterize the contextual sequence features of  Therefore, efficient computational approaches to identify Ψs accurately are especially needed to complement these experimental methods and facilitate the studies of pseudouridylation. The computational prediction of transcriptome-wide Ψ sites and characterization of their sequence contexts may also provide important hints in understanding the functional roles of pseudouridylation in RNA regulation. Although several computational approaches and web servers, such as PPUS i.e., can only predict PUS4-specific sites for human) or need to use the handcrafted features derived from the chemical properties of nucleotides.To characterize the intrinsic properties of Ψs, we need to develop efficient methods to accurately identify Ψ sites at single-base resolution and obtain a transcriptome-wide map of Ψs. Traditional Ψ detection methods are mainly based on the NRecently, deep learning techniques, especially convolutional neural networks (CNNs), have been widely used in genomic data analyses for extracting accurate sequence features In this study, we have developed a computational framework, called PseudoUridyLation Site Estimator (PULSE), to predict novel Ψ sites from large-scale profiling data of Ψs based on the sequence contexts of target sites. To our knowledge, our study is the first deep learning-based attempt to characterize the contextual sequence features of pseudouridylation by fully exploiting the currently available large-scale profiling data of Ψs. PULSE employs a CNN model, which contains two alternately-stacked convolution and pooling layers responsible for local feature extraction from the input contextual sequences and two fully-connected layers responsible for feature integration and estimation of the pseudouridylation potential of a candidate site. Tests on both human and mouse data have demonstrated that PULSE can achieve high prediction accuracy and significantly outperform other state-of-the-art prediction approaches. The new sequence features captured by PULSE are not only consistent with the recognized motifs of known PUSs, but also match the binding patterns of several nucleotide-binding proteins, which may provide useful hints for discovering new potential PUSs or associating proteins. In addition, the underlying sequence contexts of Ψs detected by PULSE offer an effective indicator to investigate the functional effects of single nucleotide variants (SNVs) on pseudouridylation, which may help reveal possible associations between pseudouridylation and complex diseases. Moreover, the trained PULSE model allows us to unveil the transcriptome-level characteristics of pseudouridylation. The prediction results of PULSE provide several new insights about the functional roles of pseudouridylation. For example, pseudouridylation is codon biased and rare codons are more likely to be pseudouridylated to achieve optimal mRNA translation. Also, our integrative analysis of ribosome profiling data demonstrated that pseudouridylation is involved in modulating the translation initiation and elongation processes. These results indicated that the predictions of PULSE may shed light on the underlying mechanisms and functional roles of pseudouridylation in post-transcriptional regulation.vs. 3819 negative samples; mouse: 3057 positive samples vs. 3109 negative samples). The sequence samples were then encoded into binary matrices as the input to our model using the one-hot encoding scheme. For the imbalanced testing datasets with 1:n positive-to-negative ratio (PNR), the positive samples were collected using the same way as we described above, while the negative samples were collected from the nearest n thymine sites that have no overlap with any positive samples.The Ψ modification site data were downloaded from the RMBase database We have designed a computational pipeline to fully characterize pseudouridylation A. To encl, the information flow of PULSE can be abstracted into the following equation:lPPS(l) represents the final prediction score of the target site, iconv, iacti, ipool, and inet stand for the i-th convolution, neuron activation, pooling, and full-connection operations, respectively. In our framework, the length of the input sequence l is set to 101, as we extend the target site both upstream and downstream by 50 nt. We used grid search with a cross-validation procedure Overall, for a given sequence L × 4 (where L stands for the length of the input sequences) are first cross-correlated with several convolution filters and then the convolved outputs are rectified by a Parametric Rectified Linear Unit (PReLU) activation function i.e., hPULSE and mPULSE). In our model, the hyperparameters of hPULSE and mPULSE are almost the same except the number of training epoches.The core of PULSE is a CNN consists of two alternately-stacked convolution and pooling layers and two fully-connected layers. During convolution, the input matrices of dimension https://keras.io) in Python. Back propagation is applied in the training process for efficiently updating the parameters. In addition, several optimization techniques, including stochastic gradient descent (SGD), dropout, batch normalization, early stopping, and momentum, are used to improve the training process .PULSE is implemented based on the Keras library  to scan the flanking regions on both sides of a Ψ site. During this scanning process, those sequence segments (of length 8) with activation values more than half of the maximum score are output. Then these detected sequence segments are converted into the position weight matrix (PWM) form to generate the corresponding motifs representing the sequence contexts of pseudouridylation. To compare these obtained motifs to those known binding patterns of RNA-binding proteins (RBPs) and transcription factors (TFs), we search over the CIS-BP We apply the filters embedded in the first convolution layer of our CNN model to generate the sequence motifs of pseudouridylation, using the same strategy as previously described We further apply PULSE to detect potential Ψ sites on each transcript along the genome. All the RNA sequences of human and mouse were downloaded from Ensembl by Biomart under references hg19 and mm10, respectively. For each transcript, every thymine site and the flanking 50-nt regions on its both sides are extracted as the input sequence profile to PULSE (‘N’s are padded if the flanking windows are out of the transcripts). Then PULSE computes the local pseudouridylation potential score (lPPS) for each thymine, which measures its pseudouridylation probability.i.e., the estimation of the overall pseudouridylation level of a complete transcript, we defined a new metric called the transcript pseudouridylation potential score (tPPS). In particular, for a transcript s, its tPPS value can be defined as follows:num represents a count function, I represents a binary indicator function, klPPS stands for the lPPS of the k-th Ψ site in s, K represents the total number of thymines in s, and L stands for the length of s. In the aforementioned equation, the numerator represents the ratio between Ψs and thymines, which thus measures the relative abundance of possible Ψs in a transcript. However, this value may bias to those uridine-enriched transcripts. In order to eliminate such bias, the ratio in the numerator is further normalized by the abundance of both thymines and Ψs in the transcript.To evaluate the pseudouridylation potential of a particular transcript, k-mers that also uses only sequence information To evaluate the prediction performance of PULSE, we applied a 10-fold cross-validation procedure on both human and mouse data (see Method). In our training process, the Ψ sites identified from high-throughput profiling experiments and the corresponding flanking regions of 50 nt on both sides of individual Ψ sites were considered positive samples, while uridine sites with flanking windows of 50 nt on both sides that are the closest to some Ψ sites and do not have any overlap with the positive samples were considered as the negative samples. We trained PULSE on human and mouse datasets separately, resulting in two trained models called hPULSE and mPULSE, respectively. We also compared the prediction performance of PULSE to that of a baseline approach, called gkm-SVM, which is a widely-used SVM-based classifier based on gapped Previous studies showed that the pseudouridylation profiles of transcriptome across human and mouse were conserved to some extent despite the possible difference in the underlying mechanisms of pseudouridylation i.e., PNR is 1:1). However, in the real world, usually there are much more unmodified or undetected uridines in RNA transcripts than the modified ones, which means that the previous balanced tests may overestimate the precision of the models. To solve this problem, we retrained our models with the same hyperparameters as we searched before and then tested several imbalanced testing sets with different PNRs . The tests on these imbalanced datasets also showed competitive performances of our models  which did not overlap with the training data. Expectedly, these comparison results also showed that our models performed much better than the others , to estimate the overall pseudouridylation level of this transcript. Based on the lPPS and tPPS profiles derived from PULSE, we are able to study the nucleotide- and transcript-level landscapes of pseudouridylation, respectively.To examine the transcriptome-wide distribution of pseudouridylation (see Method for transcriptome-wide detection of Ψ sites) across different genomic regions, we compared the percentages of pseudouridylation predicted by PULSE among different types of regions, including 5′ UTRs, CDS regions, and 3′ UTRs. Our comparison showed that Ψs appear primarily in the CDS regions (∼ 50%) and the 3′ UTRs (∼ 40%) of both human and mouse mRNAs A, which P < 1 × 10−200, rank-sum test; n = 20,918, n = 37,852, n = 3302, n = 7597, n = 16,717, n = 34,357, n = 28,260, n = 48,402, n = 3355, n = 7194, n = 18,193, n = 32,270]. In addition, we applied our model to predict the profiles of tRNAs annotated by tRNAscan-SE P = 4.30 × 10−31, rank-sum test; n = 6357, n = 4513]. For mouse tRNAs, we did not observe the same trend , which was probably due to the lack of Ψ sites on small RNAs in the mouse training data  may pair with nearby nucleotides in space to help stabilize the loop structure, while Ψs in external or flanking SS regions may contribute to supporting and immobilizing their proximal inner loops. Although we cannot rule out that this phenomenon may be caused by the bias of the antibodies used in Ψ identification experiments, which may have different binding affinities to the Ψ sites between RNA SS and DS regions, our new analysis results covered pre-miRNAs, lincRNAs, and mRNAs, and previous known distributions in tRNAs and rRNAs strongly supported our hypothesis.To explain the discrepancy in the distributions of Ψs in different types of RNA secondary structures, we hypothesize that Ψs may play an important role in stabilizing or rigidifying RNA secondary structures D. Such aMoreover, the Gene Ontology (GO) enrichment analyses for the top 500 genes with the highest tPPS values for both human and mouse showed that genes with high tPPS values predicted by PULSE are mainly distributed in the nucleus and contribute to DNA or RNA binding . This phP = 4.71 × 10−7, rank-sum test; n(High tPPS) = 3215, n(Low tPPS) = 4260]. We also performed a similar analysis on the relationships between the tPPS values of CDS regions and mRNA half-lives, but did not observe any significant effect of Ψs in the CDS regions on mRNA half-lives . These results indicated that Ψs in the 3′ UTRs of mRNAs may improve their stability, which was also supported by the previous study P = 3.07 × 10−53 for the length of 3ʹ UTR, P = 1.71 × 10−267 for the GC-content of 3ʹ UTR, rank-sum test; n(High tPPS) = 3215, n(Low tPPS) = 4260], which implied that Ψs in 3′ UTRs can compensate for the down-regulation effects of RNA stability caused by their short lengths and high GC-contents. To further verify this relationship, we performed additional analyses on several other curated RNA half-life datasets. The additional analysis results suggested that the relationship mentioned above is probably robust to different cell lines . These results implied that pseudouridylation prefers to occur in relatively rare codons or codons with lower supply of their corresponding tRNAs. Furthermore, for codon rareness, we considered the %MinMax metric that has been developed to evaluate the relative rareness of codons in a coding sequence P < 1 × 10−200, rank-sum test), which was consistent with our previous results. In summary, our analysis results showed that pseudouridylation prefers to occur in rare codons, which suggested that pseudouridylation may be involved in controlling the rhythm of translation and perhaps the co-translational folding of the nascent peptide chains.It has been reported that Ψs in mRNAs may affect their translation fidelity It has been revealed that pseudouridylation in mRNAs can enhance the translational capacity i.e., within the range of +/−1 codon). After that, we ran the PULSE model to calculate the lPPS values of the flanking sequences centered at the uridines that were closest to the selected initiation codon sites. Next, we extracted two groups of codons from these selected initiation positions, which had lower than 25% quantile (termed by ‘< 1st Qu.’) and greater than 75% quantile (termed by ‘> 3rd Qu.’) of lPPS values, respectively . This result suggested that Ψs in the translation initiation regions may help reduce the accumulation of ribosomes. We speculated that this may be realized by attracting the rRNAs in the small subunits (SSUs) of ribosomes through the formation of extra hydrogen bonds, which can thus accelerate the installing process of initiating ribosomes . This result implied that Ψs in mRNAs may help modulate the translation elongation rates. We speculated that Ψs in mRNAs may serve to affect the elongation process by dragging the aminoacyl tRNAs to the A sites of ribosomes during elongation through the force of extra hydrogen bonds , the corresponding lPPS values dropped significantly. On the other hand, when the other alleles were replaced by the T allele, the corresponding lPPS values increased significantly  and validated by 1000Genomes . Next, wal AFCP; . Interesficantly A. This ri.e., with relatively high MFE, which generally correspond to flexible RNA regions, e.g., SS regions . From this observation, we speculate that SNVs may act on RNA structures through affecting the pseudouridylation profiles of their corresponding sequences. To study this issue, we first compared the MAF values between two groups of SNVs, which had AFCP values greater than 1.0 and less than −1.0, respectively. This comparison showed that SNVs with larger MAF values are more likely to be pseudouridylated . In addition, we compared the ΔMFE values between two groups of SNVs with AFCP values greater than 0.0 and less than 0.0, respectively. As a result, we found that alleles with larger predicted pseudouridylation potentials are associated with relatively higher MFE values . Note that here, although the energy gap (ΔMFE) between major and minor alleles was small , it can lead to a dramatic transformation of RNA secondary structure  over different types of genomic regions. The enrichment analysis results showed that SNVs with high AFCP values are significantly enriched in the splice donor regions  and depleted in the 3′ UTRs , when compared to the background . These results implied that pseudouridylation may be relatively more sensitive to SNVs in the contextual sequences that are related to RNA splicing and regulatory functions of 3′ UTRs. We also combined the traits associated to the SNVs with the predicted lPPS values to illustrate latent relations between pseudouridylation and important phenotypes, such as complex diseases. In particular, we first selected the top 10 variants with the highest absolute AFCP values, including rs7190997, rs11073328, rs4846923, rs10007186, rs11635553, rs72807343, rs12150660, rs7286917, rs7089227, and rs1934179. We then investigated the disease traits associated with these 10 selected SNVs. Interestingly, we found that these 10 variants were mainly associated with immune system lesions  and the limitations of current experimental profiling techniques, it is generally difficult to explore the biological functions of pseudouridylation through conventional experimental methods . This challenge can also partially explain why pseudouridylation is relatively less studied compared to other RNA modifications. Here, our proposed model provides a natural method to unify all current available Ψ profiling data and fully exploit the underlying contextual sequence features of pseudouridylation. Such a strategy can take advantage of the learning and predictive power of the CNN model to reveal the characteristics and potential functional roles of pseudouridylation by connecting the prediction results to the profiles of other biological factors or processes , which can provide useful hints into understanding the underlying mechanisms of pseudouridylation.RNA pseudouridylation is obviously crucial to RNA regulation simply by its prevalence in transcriptome and its high conservation across different species. Therefore, a comprehensive understanding of RNA pseudouridylation will be conducive to the consummate studies of RNA modifications and RNA epigenetics. The studies of RNA pseudouridylation especially in mRNAs may help understand its functional roles in post-transcriptional regulation. Given the complication of the underlying pseudouridylation mechanisms  and Alzheimer’s disease (associate trait of the rs72807343 SNV) https://github.com/mlcb-thu/PULSE.The source code, the data files used in the analyses, and the final PULSE model can be downloaded from Xuan He: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization. Sai Zhang: Methodology, Supervision. Yanqing Zhang: Data curation. Zhixin Lei: Validation. Tao Jiang: Supervision, Writing - review & editing, Funding acquisition. Jianyang Zeng: Conceptualization, Methodology, Writing - review & editing, Supervision, Project administration, Funding acquisition. All authors have read and approved the final manuscript.The authors have declared no competing interests.


The subjective indicator of health self-rated health (SRH) and the chronic inflammation biomarker soluble urokinase plasminogen activator receptor (suPAR) are both robust predictors of healthcare use and mortality. However, the possible relationship between SRH and suPAR in the assessment of hospitalization and mortality risk is unknown. We used data from the Danish population-based Inter99 cohort to examine the association between SRH and suPAR and test their individual and combined associations with 2-year risk of acute hospitalization and 5- and 15-year mortality. SRH and serum suPAR levels were measured in 5490 participants . Poorer SRH was associated with elevated suPAR. In unadjusted analyses, SRH and suPAR were individually associated with higher risks of acute hospitalization and mortality, and both measures remained independently associated with higher risks of hospitalization and 15-year mortality after mutual adjustments. The association of suPAR with mortality was stronger in poorer SRH categories, and when combined, SRH and suPAR could identify different groups of individuals with increased risk of acute hospitalization and mortality. Both SRH and suPAR were independently associated with risk of acute hospitalization and mortality, and different combinations of the two measures could identify different groups of individuals at increased risk. Self-rated health (SRH) is a simple subjective measure capturing a condensed summary of experienced somatic information, and is thought to have a biological foundation, however, these processes are not yet well identified7.There is solid evidence that a person’s self-evaluation of their overall health status is a strong predictor of morbidity, use of health services, and mortality independently of objective measures of health and other risk factors such as socioeconomic status, age, chronic illness, gender, and physical examination10. In a recent study, researchers found a significant association between SRH and 57 out of 150 investigated blood and urine biomarkers encompassing a wide range of biological domains, and 26 of the biomarkers (including the inflammation marker C-reactive protein [CRP]) remained significantly associated with SRH after adjusting for chronic diseases and physical functioning10. Several other studies have reported associations between SRH and biomarkers of systemic inflammation, including cytokines13, CRP12, white blood cell counts9. A small number of studies further examined whether biomarker levels explained the strong association between SRH and mortality, and all reported that after adjusting for biomarkers, the association was only slightly weakened and SRH remained independently associated with mortality16. Only two of these studies have compared the performance of SRH and biomarkers in the prediction of mortality and found that while SRH was a better predictor of mortality than biomarkers15, the combination of SRH with biomarkers improved the predictive ability of the models14.Studies have suggested that “sickness signals” such as changes in biomarker levels influence how the body or brain interpret general health, and that a deviation from normal values might negatively impact a person’s SRH17, and a strong, unspecific, prognostic biomarker for morbidity and mortality19. suPAR levels are elevated with many chronic diseases21, and shown to positively correlate with other inflammatory biomarkers and remain a strong predictor of adverse health outcomes even after adjustments for inflammatory biomarkers such as IL-6 and CRP26. suPAR levels are elevated by unhealthy lifestyle such as smoking and an unhealthy diet19. However, change of lifestyle habits towards a healthier lifestyle results in lowering of suPAR, and the resultant suPAR is predictive of mortality27. Furthermore, a recent study demonstrated that suPAR is a causal factor in the development of cardiovascular disease28. This indicates that suPAR is an early warning biomarker and that interventions modifying suPAR can lower the risk of adverse outcomes.The soluble form of the urokinase plasminogen activator receptor (suPAR) is a novel biomarker of systemic chronic inflammation29, but the relationship between the two biomarkers has not been thoroughly investigated, and it is unclear whether they reflect common or independent processes related to increased risk of illness and mortality. The present study aimed to (i) examine the association between SRH and suPAR, (ii) test whether SRH and suPAR are independently associated with the risk of acute hospitalization and mortality, as well as (iii) test the interaction between SRH and suPAR in their association with the risk of acute hospitalization and mortality.Both SRH and suPAR are unspecific measures of general health and strong prognostic markers of outcome. Poor SRH is correlated with higher suPAROf the 6784 individuals who participated in the baseline examination, 6717 had answered the SRH question, and 5540 had a serum suPAR measurement available. A total of 5490 individuals had data available for both SRH and suPAR levels and were included in this study , 3.4 ng/mL (IQR: 2.8–4.3) for those reporting good SRH, and 3.8 ng/mL (IQR: 3.1–4.9) for those reporting fair/bad SRH (Supplementary Table The participants had a median suPAR level of 3.4 ng/mL IQR: 2.7–4.3), and suPAR levels increased with worse SRH  and adjusted  analyses. The interaction between suPAR and sex was not statistically significant (P = 0.46). In individual analyses of SRH and suPAR as 3-level categories, participants reporting good or fair/bad SRH, as well as those with intermediate or high suPAR, were at significantly higher risk of hospitalization compared to participants with excellent/very good SRH or low suPAR, respectively .Within 2 years after baseline examination, 437 (8.0%) participants were acutely hospitalized, 8 (0.2%) had died, and 10 (0.2%) were lost to follow-up. When suPAR levels were analyzed as a continuous variable, a doubling in suPAR levels was associated with a significantly higher risk of acute hospitalization both in unadjusted  analyses, but not in analyses adjusted for age, sex, smoking status, BMI, and comorbidities . The interaction between suPAR levels and sex was not statistically significant (P = 0.79). In unadjusted analysis of SRH categories, participants reporting fair/bad SRH, but not those reporting good SRH, had a significantly higher risk of 5-year mortality compared to participants with excellent/very good SRH  participants had died and 19 0.4%) were lost to follow-up. A doubling in suPAR levels was associated with a significantly higher risk of 5-year mortality in unadjusted  and adjusted  analyses. When the interaction term between suPAR levels and sex was included (P = 0.03), higher suPAR levels were significantly associated with a higher risk of 15-year mortality for men , but not for women . In univariate analyses of SRH or suPAR categories, participants with good and fair/bad SRH as well as those with intermediate and high suPAR had a significantly higher risk of 15-year mortality compared to participants with excellent/very good SRH or low suPAR, respectively . Figure low suPAR in the excellent/very good SRH category as the reference. Increasing suPAR levels had a larger effect on the risk of 15-year mortality for participants reporting good and fair/bad SRH than for those reporting excellent/very good SRH  participants had died and 47 (0.9%) were lost to follow-up. A doubling in suPAR levels was significantly associated with a higher risk of 15-year mortality both in unadjusted  and an objective biomarker (suPAR) of health with the risk of acute hospitalization, short- and long-term mortality. The main finding of our study is that both SRH and suPAR were strongly and independently associated with 2-year risk of acute hospitalization and long-term mortality after adjustments for age, sex, smoking, BMI, and comorbidities, and that the strength of these associations was unchanged by mutual adjustments for suPAR and SRH, respectively. Furthermore, we found that overall, different combinations of poorer SRH and/or elevated suPAR were associated with higher risks of acute hospitalization and mortality compared to the combination of the best category of SRH and the lowest suPAR levels.32, and previous reports on suPAR29, we found that suPAR levels increased with poorer SRH. Kananen et al. proposed three pathways by which biomarkers could influence SRH10. First, individuals may be aware of their levels of certain biomarkers related to specific diagnoses such as cholesterol or glucose levels and may take these biomarkers-or the associated diagnoses-into account in their assessment of health10. Although participants would not have been aware of their suPAR levels at the time of SRH assessment, it is possible that they would take into account other diagnoses or biomarker levels that are known to be associated with elevated suPAR, such as diabetes, cardiovascular disease, elevated cholesterol35. Second, individuals may consider lifestyle and behavioral factors in their health assessment10. We have not tested whether lifestyle factors influenced the relationship between suPAR and SRH. But this is a possibility as suPAR levels are known to be dependent on lifestyle36 and can be affected by lifestyle changes19. Third, changes in biomarker levels can influence the perception of health, and in particular, changes in biomarkers of inflammation including cytokines are thought to be involved in symptoms of sickness such as fatigue, loss of appetite, and weakness39. This mechanism has been suggested to explain part of the relationship between SRH and mortality10.Similar to findings with other inflammation biomarkers42, but the two measures have not previously been compared or combined. In the present study, poor SRH and high suPAR were associated with a higher risk of acute hospitalization independently of each other, though the effect of poor SRH was greater than that of high suPAR. Furthermore, poor SRH was significantly associated with a higher risk of acute hospitalization when combined with either high or low suPAR, whereas high suPAR did not increase risk for individuals with good SRH. Although both SRH and suPAR reflect disease processes, the stronger association of SRH with hospitalization risk may not be surprising as individuals faring badly, for example perceiving a greater illness burden and impaired function, may be more likely to actively seek care and be acutely hospitalized, while elevated chronic inflammation might more strongly reflect slowly developing pathological processes leading to increased biological aging and long-term adverse outcomes. In older adults, a composite score of inflammation  was a better predictor of mortality than of hospitalization over a 4-year follow-up43. A large survey investigating the association of SRH with healthcare use concluded that the feeling of poor health was more strongly associated with risk of hospitalization than diagnosed chronic conditions or age44. Thus, the choice to actively seek medical care may be more guided by interoceptive signals or worries, and other behaviors, that are not directly informed by objective health indicators, including chronic inflammation. Nevertheless, poor SRH was also strongly associated with 5- and 15-year mortality, indicating that the feeling of poor health is also grounded in real risk of mortality.The individual relationships of poor SRH and elevated suPAR levels with risk of acute hospitalization have been described18. A small number of studies investigated whether the SRH-mortality association was explained by biomarkers such as CRP or panels that included CRP; in all these studies, adjustment for biomarkers only slightly weakened the associations, and SRH remained statistically associated with mortality16. In the present study, the HRs for SRH and mortality risk were largely unaffected when controlling for suPAR. Similarly, the relationship of suPAR with mortality was unaffected after adjusting for SRH. Thus, our—and previous—findings suggest that the association of SRH with mortality cannot be explained by biomarkers, such as suPAR, alone; and the association between biomarkers such as suPAR and mortality cannot be explained by SRH alone15. Furthermore, in contrast to the reports that SRH is more strongly associated with mortality than biomarker panels15, in our study, the association of high suPAR with mortality was larger than that of fair/bad SRH. Recent evidence suggests that in contrast to traditional inflammation biomarkers such as CRP and cytokines that reflect acute inflammation, suPAR levels increase and decrease at a slower speed, are less affected by acute changes, and are thought to capture other aspects of chronic inflammation17. It is also still unknown whether suPAR is involved in sickness behavior to the same extent as cytokines and acute-phase proteins. Finally, we found that a combination of both suPAR and SRH provided greater information about the risk of mortality than either measure alone; with suPAR adding information about the risk of mortality for participants with excellent to good SRH, and likewise, poorer SRH could identify participants with low suPAR who were at a higher risk of mortality. Furthermore, higher suPAR and poor SRH appeared to impact the risk of 15-year mortality in a synergistic fashion-for participants with good to bad SRH, the risk of mortality further increased with increasing suPAR. Therefore, even though SRH and suPAR levels were related, in this study, the two measures appear to provide unique information regarding health status and may reflect independent pathways to mortality.We observed strong associations of poor SRH and high suPAR with mortality, which have also been repeatedly reported in previous studies19. A recent clinical trial used elevated suPAR levels as a guide for identifying patients with increased risk of progression of COVID-19 and showed a decreased length of hospitalization and mortality for these patients after treatment with an interleukin-1 inhibitor45. This indicates that early identification and intervention of high-risk individuals can lead to lowering of risk of unplanned hospitalization and mortality. By combining suPAR and SRH data in the present study, we have significantly enhanced risk identification, possibly leading to a smaller, but more targeted, group of individuals that may be offered intervention  in future general population intervention studies.Despite being a validated research tool and simple to administer, SRH is not widely used in clinical settings. Incorporating assessment of patient-reported outcomes such as SRH in routine care may help identify at-risk individuals that may otherwise be overlooked by routine biochemical evaluation alone, and those whose complaints during examination are incorrectly interpreted by healthcare professionals. In our study, poor SRH alone, or high suPAR alone, identified different groups of participants, with different risks of hospitalization and mortality, and the combination of both poor SRH and suPAR was consistently associated with an increased risk for all outcomes. Furthermore, in interaction analyses, we found that suPAR was more strongly associated with mortality for participants reporting poor SRH. Thus, the combination of the two biomarkers provided synergistic information on the health status, compared to each measure alone. This may have clinical implications in future intervention studies in the general population, allowing for intervention in this particularly high-risk group of individuals. It has previously been shown that a lifestyle change in the Inter99 cohort, i.e., smoking cessation resulted in lowering of suPAR levels (from baseline to 5-year samples) and that the resultant 5-year suPAR value was associated with future mortality17. Third, due to the cross-sectional design of this study, we could not determine whether there was a causal relationship between high suPAR levels and poorer SRH, nor the direction of this relationship, or whether the association between the two could be related to sickness behavior.This study has limitations. First, despite a large sample size, this general population cohort was relatively young and healthy, and only 60 (1.1%) participants died during the first 5 years after data collection. Second, other biomarkers of inflammation, such as CRP and cytokines, could have been included in the study for comparison with suPAR as it has been suggested that suPAR and CRP or cytokines may reflect different aspects of inflammationOur results show that individuals with poor SRH, high suPAR, or a combination of both poor SRH and high suPAR had increased risk for acute hospitalization and long-term mortality. These findings contribute to the body of knowledge promoting the combined use of subjective and objective indicators of health to assess the risk of healthcare use and mortality.46. The examination included a clinical examination, extensive questionnaires assessing lifestyle, socioeconomic status, mental health, and SRH, as well as blood sampling. Participants were linked to the Danish health and social registers containing information on hospitalizations, ICD-10 codes, and death47. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Scientific Ethics Committee of the Capital Region of Denmark (KA 98 155) and the Danish Data Protection Agency, and was registered as a clinical trial . All participants provided written informed consent before taking part in the study. No effect of the lifestyle intervention was observed in the original study47, and we included all participants who had data available for both SRH and suPAR levels in the present study  were excluded (n = 13), and samples with values above the detectable range were assigned a value corresponding to the upper limit of quantification .Serum levels of suPAR (ng/mL) at baseline were measured using the suPARnostic ELISA assay  as previously reported47. Smoking was categorized as: never smoker, former smoker, occasional, or daily smoker36; alcohol consumption as abstinent (0 units per week), within recommendations , or overuse 36, according to recommendations on alcohol consumption at the time of assessment; and physical activity as low (mainly sedentary), light , moderate (moderate activity at least 3 times per week), or high 19. Body mass index  was calculated from weight and height measurements without shoes. Data on comorbidities included self-reported diagnoses of hypertension, diabetes and cardiovascular disease other than myocardial infarction. A variable for comorbidities including diagnoses of diabetes and cardiovascular disease was generated.The participants’ socioeconomic status, smoking, alcohol consumption, and physical activity habits were assessed at baseline via self-administered questionnaires. The socioeconomic status of the participants was based on the duration of their education after primary school and categorized as low (< 2 years), medium (2–4 years), or high (> 4 years)Injury, poisoning, and certain other consequences of external causes (ICD10 codes: S00-T98) as well as Pregnancy, childbirth, and the puerperium (ICD10 codes: O00-O99) were excluded. Survival status was obtained from the vital status and date of change in vital status variables from the Danish Civil Registration System (CPR). Survival time for participant with vital status coded as “dead” was calculated as the time elapsed between the baseline examination date and the date of death recorded in the register.Participants were followed from the time of baseline examination (March 1999 to January 2001) until 31 December 2016, for the outcomes of acute hospitalization within 2 years as well as 5- and 15-year mortality. Acute hospitalizations within 2 years following baseline examination were identified from the Danish National Patient Register (NPR) as “admission type” coded as acute admissions, and “patient type” coded as 24 h patient, and the time elapsed between the acute hospitalization dates and the date of baseline examination was calculated. If participants had several acute hospitalizations, the acute hospitalization date with the shortest time elapsed after the baseline examination date was selected. Hospitalizations for Descriptive data of participants are presented as the median and interquartile range (IQR) for continuous variables or the count and percentage for categorical variables.We explored the relationship between SRH and suPAR using a one-way ANOVA for comparison of suPAR levels between the SRH category “excellent” and the other four categories. suPAR levels were log-transformed using log2, and back-transformed estimates are reported.We used Cox regression analyses to assess the independent and combined associations of SRH and suPAR with the risk of acute hospitalization within 2-year after baseline, and of 5- and 15-year mortality. Loss to follow-up was considered a censored event, and for the analysis of acute hospitalization risk, death was considered a competing event. For the analyses of acute hospitalization risk, we report sub-distribution hazard ratio (SDHR) and 95% confidence intervals (CI) obtained using the Fine and Gray competing risk regression models. For mortality risk analyses, we report hazard ratios (HRs) with 95% confidence intervals (CIs).excellent/very good , good , and fair/bad . Clinical cut-offs for suPAR have not yet been established. Therefore, and to ease comparison between SRH and suPAR, we created a categorical variable for suPAR where participants were stratified into three categories of the same size as the three SRH categories: low suPAR , intermediate suPAR , and high suPAR . The associations of 3-level SRH and suPAR were tested individually in unadjusted models and models adjusted for age, sex, smoking, BMI and comorbidities, as well as in a mutually adjusted model that included both the 3-level SRH and suPAR, age, sex, smoking, BMI and comorbidities. In addition, we tested the associations of suPAR categories with the outcomes within each of the SRH categories by adding an interaction term for SRH and suPAR in the mutually adjusted models.To assess the independent associations of SRH and suPAR with the outcomes, suPAR was log2-transformed for analysis as a continuous variable and tested in unadjusted models and models adjusted for age, sex, smoking, BMI, and comorbidities; the interaction between suPAR levels and sex was also tested in the adjusted models. The SRH measure was reduced to 3 categories: excellent”, “very good”, or “good” , and the poor SRH group consisted of participants who answered “fair” or “bad”  to the questionnaire. The low suPAR group consisted of participants from the previously defined low and intermediate suPAR categories , and the high suPAR group consisted of participants in the previously defined high suPAR category . Furthermore, we stratified the participants into nine groups (based on the 3 levels of SRH and the 3 levels of suPAR). We tested the associations of the four and the nine combined SRH and suPAR groups with the outcomes in unadjusted and adjusted  models.Finally, to assess the individual and combined associations of SRH and suPAR with the outcomes, we stratified the participants into four groups: (i) good SRH and low suPAR, n = 4434 (80.8%), (ii) good SRH and high suPAR, n = 483 (8.8%); (iii) poor SRH and low suPAR, n = 483 (8.8%); and (iv) poor SRH and high suPAR, n = 90 (1.6%). The good SRH group consisted of participants who answered “For statistical analysis, we used SAS Enterprise Guide version 7.15 . Graphs were made in GraphPad Prism version 9 . Two-sided p-values are reported, and statistical significance was defined as a p-value < 0.05.Supplementary Information.


Background: Pancreatic injury  is a rare adverse event of immune checkpoint inhibitors (ICIs). With the high number of clinical studies on ICIs, the incidence and characteristics of associated pancreatic injury (PI) need to be reevaluated.Methods: A systematic review and meta-analysis was conducted to assess the incidence of PI in cancer patients who received ICIs in randomized controlled trials (RCTs). PubMed, Embase, the ASCO, ESMO, and AACR conference proceedings before 1 April 2022, were investigated for relevant research.Results: 50 RCTs involving 35,223 patients were included. The incidence of ICIs-PI was 2.22% (95% CI = 1.94%–2.53%). The incidence of PI was 3.76% (95% CI = 1.84–7.67%) when combining two ICIs, which was higher than single ICIs [2.25% (95% CI = 1.91–2.65%)]. The ICIs were ranked from high to low based on PI incidence: PD-L1 inhibitors 3.01% (95% CI = 1.86–4.87%), CTLA-4 inhibitors 2.92% (95% CI = 0.99–8.65%) and PD-1 Inhibitor 2% (95% CI = 1.67–2.39%). The ICI with the highest rate of PI was pembrolizumab 7.23.% (95% CI = 1.69–30.89%). In addition, the incidence of severe ICIs-PI was 2.08% (95% CI = 1.76–2.46%); and the incidence of severe PI was 2.32% (95% CI = 1.76–3.06%) when combining two ICIs, which was higher than single ICI [1.95% (95% CI = 1.58–2.41%)]. The ICIs were ranked from high to low according to the incidence of severe PI: PD-L1 inhibitors 3.1% (95% CI = 1.7–5.64%), CTLA-4 inhibitors 2.69% (95% CI = 0.76–9.49%), PD-1 inhibitors 1.80% (95% CI = 1.41–2.29%).Conclusion: Treatment with multiple ICIs result in a higher incidence of PI compared to single ICIs, irrespective of the grade of pancreatic injury. The incidence of PI caused by PD-L1 inhibitors is higher than that of CTLA-4 inhibitors and PD-1 Inhibitor, and Pembrolizumab has the highest rate of ICIs-PI. Although the incidence of ICIs-PI is not high, they are usually severe (≥ grade 3 events). Since the Food and Drug Administration (FDA) approved Ipilimumab for melanoma treatment in 2011, ICIs have revolutionized the oncology treatment landscape . CytotoxAlthough ICIs-PI is uncommon, a growing number of clinical studies related to ICIs have been performed, and its impact on patients’ quality of life and treatment process cannot be ignored. Therefore, it is necessary to update the understanding of the incidence of ICIs-PI, paying particular attention to its mechanism, immunotherapy toxicity, and its clinical management.This systematic review and meta-analysis focus on the incidence of PI caused by ICIs in the treatment of solid malignancies. The article is based on the latest prospective clinical trials and reports the incidence of PI induced by various ICIs as single agents or in combination with other drugs.This meta-analysis was based entirely on the preferred reporting items for systematic reviews and meta-analysis (PRISMA) , and wasA comprehensive search of PubMed and Embase was conducted. Proceedings of the American Society of Clinical Oncology (ASCO), European Society of Medical Oncology (ESMO), and American Association for Cancer Research (AACR) were searched to supplement this study. Literature published up to 1 April 2022 were screened. Three researchers  independently reviewed the titles and abstracts of the studies identified in the search and excluded those not relevant. The full and supplementary texts of the remaining studies were reviewed to determine whether they contained the necessary information. Conflicts in study selection were resolved by referencing the original article and reaching a consensus with the Senior Investigator (WHL). The search strategy can be found at Inclusion criteria: 1) Patients pathologically diagnosed with solid malignant tumors; 2) Randomized controlled trials (RCTs); 3) The experimental group was treated with PD-1/PD-L1/CTLA-4 inhibitors , with or without other treatments; 4) Control groups were given non- ICI treatments or placebos; 5) Adverse events related to pancreatic injury were fully described; 6) More than 50 patients were involved in each arm.Exclusion criteria: 1) Hematologic malignancies; 2)Single-arm trials; 3) Phase I clinical trials; 4) Lack of crucial data from trials; 5) Non-English articles; 6) Publication as letters, conference reports, editorials, case reports, animal studies, basic studies, or systematic reviews.For each eligible study, the following variables were independently extracted by 3 different investigators : first author, year of publication, study name, tumor type, trial phase, tumor staging, drug class, drug name, drug dose, implementation plan, control group implementation content, and plan, version of the  number op-value <0.10 was considered significant bias and further corrected by trim-and-fill method.The quality of the individual studies was evaluated according to The The primary objective was to assess the relationship between ICIs and PI compared to other treatments or placebo. The secondary objectives were to compare the PI risks associated with single-ICI versus dual-ICIs (immunotherapy combinations). The PI risks of different categories of ICIs and the PI risks related to various drugs were also compared. Therefore, subgroup analyses were performed on ICI or dual ICIs, ICIs categories, and ICI drugs. The inconsistency index (I2) was used to assess the heterogeneity between studies, where < 30%, 30%–59%, 60%–75%, and > 75% indicated low, medium, high, and considerable heterogeneity, respectively. A fixed-effects model was used if the heterogeneity was <60%; otherwise, a random-effects model was used. The 95% confidence interval (95% CI) and the summation ratios (ORs) for ICIs-PI incidence were calculated using a generalized linear mixed model (GLMM) . All staThe initial search yielded a total of 4580 relevant publications. After screening and eligibility assessment, 50 clinical trials, including 35,223 patients with malignant tumors, were selected for inclusion in this meta-analysis  Baas et. The chan = 18; 36%), melanoma , breast cancer , and urothelial carcinoma . The studies involved 10 immune checkpoint inhibitors . 46 studies included patients with stage III-IV cancer, and 4 RCTs assessed the effects of ICIs on early and medium-stage cancer. Three RCTs were multi-armed trials , and the incidence of PI with dual-ICIs was 3.76% (95%CI = 1.84–7.67%), which was higher than single ICI [2.25% (95%CI = 1.91–2.65%)]. Among the different types of ICIs, the incidence of PI caused by PD-1 inhibitors was 2% (95% CI = 1.67–2.39%), while PD-L1 inhibitors and CTLA-4 inhibitors were similar at 3.01% (95% CI = 1.86–4.87%) and 2.92% (95%CI = 0.99–8.65%), respectively. The incidence of drug-specific PIs were ranked from high to low: 7.23% (95%CI = 1.69–30.89%) for Pembrolizumab, 3.10% (95%CI = 1.43–6.71%) for Durvalumab, 2.95% (95%CI = 1.59–5.46%) for Avelumab, 2.92% (95%CI = 0.99–8.65%) for Tremelimumab, 2.22% (95%CI = 1.72–2.87%) for Nivolumab, 1.62% (95%CI = 1.21–2.17%) for Atezolizumab and, 1.02% (95%CI = 0.27–3.90%) for Sintilimab. Incidence results are summarized in p = 0.02 (p < 0.05), indicating a particular deviation in the original data, and 16 studies were missing from the right side of the funnel (k = 69 at this time). The trim-and-fill method was used to test whether publication bias affected the combined effect size. After automatic filling through the algorithm, a new comprehensive effect size of 1.7 (1.2–2.2%) was obtained, which showed no significant difference. Hence, it can be concluded that the combined effect was not significantly affected by publication bias.Funnel plots were generated to estimate the intervention effects for each study, and Egger’s test was performed . The linCompared with non-ICIs treatment, the incidence of severe PI events caused by ICIs was 2.08% (95% CI = 1.76–2.46%). The incidence of severe pancreatic injury events when using a combination of two ICIs was 2.32% (95% CI = 1.76–3.06%), which was higher than single ICIs [1.95% (95% CI = 1.58–2.41%)]. In addition, the incidence of severe PIs caused by PD-1 was 1.80% (95% CI = 1.41–2.29%), PD-L1 was 3.1% (95% CI = 1.7–5.64%), and CTLA-4 was 2.69% (95% CI = 0.76–9.49%). Incidence results are summarized in Immunotherapy is now a pillar of anti-tumor therapy . This study investigated the incidence and characteristics of ICIs-PI by comparing ICIs and non-immunotherapy. In 35,223 patients from 50 RCTs, the all-grades PI incidence was 2.22% (95% CI = 1.94–2.53%), which was less than previous similar studies . This stThe incidence of severe ICIs-PI was 2.08% (95% CI = 1.76–2.46%). As a less common irAE, the incidence of severe PI is slightly lower than the incidence of all-grade PI. It can be assumed that the risk of PI due to immunotherapy is small but severe. Furthermore, the incidence of all-grade PI and severe PI were higher with combined ICIs than with single ICI. Moreover, PD-L1 inhibitors showed a higher incidence of severe PI compared to CTLA-4 inhibitors and PD-1 inhibitors. No cases of death due to ICIs-PI were reported in the included studies.In the case of patients with NSCLC, combining ICIs with chemotherapy inevitably increases the incidence of serious adverse events . A studyThe difference between the results of this study and similar studies can be attributed to two aspects. First, it is believed that the incidence of ICIs-PI is a sparse binary classification variable, and to avoid “continuous correction” of zero events and bias in the results of the meta-analysis of sparse data, a generalized linear model for statistics was chosen. Second, many studies using CTLA-4 inhibitors were not included because they did not meet the inclusion criteria for this study. Small samples and non-RCT studies were also excluded, reducing the heterogeneity of this study to some extent, but also affecting the results.The infiltration of the pancreas by CD3+T lymphocytes, CD8+T lymphocytes, cytotoxic particle-associated RNA binding protein (TIA1+), granulase B+ and neutrophils is believed to be involved in the mechanism of ICIs-PI. This reaction leads to pancreatic cell damage, pancreatic acinar-duct metaplasia, decreased pancreatic function, and pancreatic atrophy . The stuIn-depth research and clinical management of ICIs-PI is challenging due to its rarity and the other potential causes of elevated serum lipase and amylase . AccordiSome researchers believe that irAEs are positively correlated with the development of objective response rate, progression-free survival, and overall survival in tumor patients . In a stMoreover, the study had some shortcomings. The trial group included ICIs combined with other treatments. Hence, this study could not demonstrate the exact effect of ICIs on PI. Furthermore, the median follow-up time for each RCT was different, which affects the frequency of ICIs-PI and increases confounding factors. There was no comparison of the relationship between drug dose, cancer type, and PI. In addition, studies investigating the effects of Torlipalimab, Dostalimab, Tislelizumab, and Lambrolizumab did not meet the inclusion criteria and were excluded. Finally, in March this year, Lymphocyte activation gene-3 (LAG-3) was approved by the FDA as the third immune checkpoint for clinical use after PD-1/PD-L1 and CTLA-4 . HoweverCurrently, guidelines do not recommend pancreatin monitoring in cancer patients receiving ICIs. Based on the results discussed earlier, the general public should be educated on pancreatic injury caused by ICIs, especially in severe pancreatic injury. Asymptomatic patients with ICIs-PI also require medical intervention to reduce the likelihood of adverse long-term outcomes. Clinicians should be aware of the challenges related to ICIs-PIs and, if necessary, conduct rational interventions to prevent other complications.This study reevaluates the incidence of ICIs-PI. Combined ICIs lead to a significantly higher incidence of PI than single ICI. The incidence of ICIs-PI caused by PD-L1 inhibitors was higher than those of CTLA-4 inhibitors and PD-1 inhibitors, regardless of the adverse event grade. Pembrolizumab was most likely agent to cause ICIs-PI. Furthermore, the incidence of severe PI caused by ICIs is slightly lower than that of all-grade pancreatic injury. It can be concluded that the risk of pancreatic damage with immunotherapy is not high but is usually severe (≥ grade 3).


In this paper, a Hall plate readout with a randomized four-phase spinning-current scheme is proposed. The goal is to remove the maximum number of offset components, including the offset associated with spike demodulation. The outcome is that only the smallest possible offset remains, corresponding to the residual offset of the Hall plate which cannot be distinguished from the Hall signal. An additional innovation is to operate various offset-reduction loops in spread-spectrum mode, allowing the removal of error components without notching out any in-band signals. The resulting approach delivers a very large notch-free bandwidth while simultaneously reducing the Hall plate residual offset, making it an enabler for high-bandwidth Hall-based current sensors. To demonstrate the proposed techniques, we have realized a mixed-mode experimental circuit, where the analog part is implemented in a custom integrated circuit, and the digital control system in an FPGA is connected to the analog chip. Measurement results feature a Hall readout system with a notch-free bandwidth up to 820 kHz and a 47  Hall plates are widely used as magnetic field sensors because they can be easily co-integrated with on-chip readout circuitry in nearly all standard Complementary Metal Oxide Semiconductor (CMOS) technologies. They form a cheap solution for various applications requiring contactless position sensing or galvanic isolated sensing of large electric currents ,4,5,6,7.A popular technique to combat the Hall plate offset is current spinning . Here, tRecently, a readout system has been presented in the literature that comes close to the maximum achievable bandwidth . HoweverThe purpose of the present paper is three-fold. First, we want to demonstrate that by adding randomization to the spinning scheme, in line with what is proposed in , all offThe rest of this paper is organized as follows. In Integrated Hall plates come in two distinct flavors: horizontal and vertical. Our test chip and drawings mostly hint at horizontal Hall plates. These typically have a  readout . Therefo readout . So, theThe application of current spinning to a four-terminal device implies that the function of the Hall plate contacts is periodically swapped from biasing to sensing and vice versa. The main readout configurations thus arising are detailed in These are grouped in two rows, where each row is a representative scheme for four-phase spinning. In the upper scheme, the bias contacts are rotated clockwise . For thN signifies the configuration number. We number the configurations from 0 to 3 for reasons that will become clear later on. As these offsets remain present for as long as the biasing is applied, we refer to them as static offsets.In each configuration, or “phase”, a specific Hall plate voltage Since the different readout phases are equivalent , it makes sense to take the average of the different offset values:We will refer to This is a measure of the “residual offset” that remains after four-phase spinning. For a horizontal Hall plate, according to literature the residual offset is typically two orders of magnitude smaller than the raw offset ,29,30, wWe now have two offset parameters, We will refer to these as the two “offset mismatch” parameters because they encode information on how the offset varies depending on the configuration being used. It is now straightforward to show that the four offset quantities  in the following way:In this, the coefficients c is c relates to the main axis of the Hall plate bias current flow, e.g., horizontal versus vertical current flow in d relates to the direction along this current axis, with Note that the coefficient c. Bringing in . The goal of such a spinning scheme is to make the error components vanish or at least make them harmless. By inspection of  value of see also . Therefoction of  it is, hThe offset components discussed so far are all expected to be proportional to the bias current (or bias voltage) of the Hall plate. In practice, there are additional mechanisms, mostly linked to the readout chain, that introduce similar offset components which are independent of the Hall plate bias current. We already discussed that the offset and 1/f noise of the readout chain can be incorporated into c, d and n indicating the time slot being addressed. These three modulation sequences are plotted in In a classical four-phase spinning scheme, the Hall plate phases change in a regular pattern, going from left-to-right and then back to the beginning. Each phase occupies a fixed time slot. The duration of a spin period is denoted as d c·d in  then chad c·d in  are ±1, d and It is clear that  c·Vo in  represend·Vm2 in  explain c only depends on the LSB of the binary phase number, while the coefficient d is fully determined by the MSB of the phase number. This means that there is a one-to-one correspondence between a set of arbitrary c and d, the c and d.It is clear that the modulating functions ation of . To makeWe now focus our attention on what would be interesting modulation functions. An excellent consequence of the classical four-phase spinning is that the largest offset value, i.e., the raw offset band see . This isd. The product modulation function Turning now to the modulation function fore. In ,32, bandfore. In ,34,35,36d then means that, when in an even phase, we randomly choose between phase 0  associated with the readout chain. There are also, however, dynamic mechanisms that translate into offset.It is known that, especially at higher spinning frequencies, dynamic errors can also act as a source of offset. The origin of these errors lies in the parasitic capacitances that exist at each of the Hall plate nodes. At any time, two of these capacitances are charged in accordance with the bias voltage. Before these nodes can be used as readout nodes , the charge associated with the bias-voltage needs to be evacuated. Due to the finite resistivity of the plate, this happens with a finite time constant. In principle, the dynamic error can be reduced to a large extent by zero-banding (ZB) the Hall signal during these transients. However, zero-banding also deteriorates the noise performance of the readout . Moreovedulation . Here wen is odd, the spikes’ origin is indicated in red, while for n even, it is in blue. Based on the figure, it can be understood that at any instant in time n, the sign of the dynamic error depends on two factors: (i) the sign of the bias voltage as used in the previous readout configuration, i.e., in time slot n. The bias sign is fully determined by the value The mechanism is illustrated in k an odd integer larger than 1, and model of the relevant offsets that appear, after which the techniques described further on can be applied to remove them.The electrical RC effect as discussed above is expected to fully die out within one spin interval. Nevertheless, we have detected in our measurements relatively small offset mechanisms that extend over a few spin intervals. It is actually because we use randomized spinning that we can differentiate these offset sources from those which were already expected a priori. These offsets lead to terms At this point, we have discussed the main offset sources and have built a model that shows how these different offsets are modulated by a classical or a randomized spinning scheme. We can now add the dynamic error sources to our previous expression  for the For the situation with traditional four-phase spinning, we already showed the modulation functions that up-modulate d is a half-rate square wave, while in the latter case it is a random sequence. We limit ourselves here to random sequences with a flat spectral density. An example of the modulation functions appearing in the case of randomized Hall plate spinning is shown in d generated by a maximum-length pseudo-random sequence. The result is shown in pseudo-random signals that appear white. The only offset remaining at DC is the residual offset The main difference between traditional and randomized spinning is that in the former case the modulation function D,k from  are now The middle part of the figure (with red background color) represents the Hall readout chain. Note that in our initial analysis we demodulated given by . In realT denotes the response of the readout chain. The T can be modeled as a simple static gain A. For a more accurate model, however, the dynamic filter effects that occur in the readout chain should be included as well. In the prototype Hall sensor described in In this, On the top-right side, an optional post-readout ORL that operates at DC has been added. As mentioned before, the raw offset of the Hall plates l ORL of , as alsoAt the far-right side, the demodulation with To derive our system equations, we will now elaborate fined in , as:VRO=T to behave linearly:Furthermore, assuming the readout chain and therefore also the operator The first term corresponds to the up-modulated input signal and the unavoidable residual offset. This term will be amplified and then leads, after demodulation, to the signal-related component in the output signal. The second term in the equation represents the reaction of the readout chain to the raw offset To start our explanation for the actual offset reduction loops , statistical averaging needs to be replaced by averaging over time. This averaging is automatically realized by the integrators that adapt the estimates to the right value. However, even though the estimates rts from , they doWhile due to the presence of the possibly large input signal we have little control over the variance of the correlation signals, the variance of the offset parameter estimates does depend on the adaptation parameter Let us first study how the variance of the offset parameter estimates depend on A of the readout chain. This approximation is certainly reasonable for the readout chain we implemented in the prototype σm12d sequence having unit power, the random error on the corrected readout value The approximation on the right side is valid because in practice we always have een from . BecauseUntil now, we considered only the SS-ORL that estimates T can be approximated as a uniform gain A. Then we have that We now calculate the start-up time to reveal its dependency on mined by , which fIf we now define the start-up time as the time needed to reach 90% of the final value, the above expression leads to:It is clear from  that theicted by  is well In any case, the enced by . HoweverT is needed as a characteristic feature of the readout dynamics. The goal of the rest of the paper is to fully demonstrate the enabling capabilities of these concepts to obtain a high-bandwidth low-offset Hall readout system that provides a 2 MS/s digital output. For this, a prototype has been built consisting of a custom test chip and digital logic to control the readout process. Most of the digital functions are implemented in an FPGA to provide maximum development flexibility. The custom test chip is identical to what has been described in [In the previous sections, we provided a basis for the randomized spinning concept and the SS-ORLs that allow us to minimize the system offset. These offset compensation techniques are to a large extent orthogonal to the actual readout chain, which is why in ribed in , with anribed in  to be efD then directly forms the input to our system of SS-ORLs, i.e., D = D [A simplified block diagram of the implemented readout chain is shown in follower , exhibitoutput D . The lato≈0 from  absoluteFirst of all, in the situation where traditional spinning is applied at 2 MHz, the input-referred noise of the complete sensor system has been measured to be 47 Next, randomized spinning is enabled with the SS-ORLs disabled. A 64 kHz magnetic signal is generated by a PCB coil underneath the chip with amplitude 780 We now enable the SS-ORLs to remove the randomized offset terms. We have five loops in total to cover the 2. Using , the reqUsing a magnet, a DC magnetic field can be applied to the chip up to full scale. When the input frequency is increased to 810 kHz, i.e., near the end of the bandwidth, the spectrum shown in n is stepwise increased from its initial value 10 to 18. If we plug the noise-constrained ove into , a startA. Using , an α vaThe randomized spinning scheme allows the identification of many more offset components compared to what is possible with a traditional spinning scheme. We already discussed k. k. Clearly some slow settling effect is present in the Hall plates of the chip, but as stated before, at the time of writing this paper, we do not have a full understanding of where it stems from.To perform statistics on the Hall plate offset terms, 10 samples of the Hall readout chip were measured with the ORLs disabled. A normal distribution was fitted over each offset tone’s measured magnitude. The resulting mean An overview of some important results is showcased in While traditional four-phase spinning provides great potential to minimize the residual offset of a Hall sensor in practice through the process of spike demodulation, the actual residual offset can increase significantly when employing a high spin frequency. Furthermore, there are offset-related spurious tones at half the chopping frequency which limit the available “tone-free” bandwidth or necessitate calibration techniques for removal thereof. We have shown that randomized spinning circumvents these challenges, as such acting as an enabler for high frequency Hall plate spinning and therefore higher bandwidth Hall sensors without in-band spurious tones while at the same time reducing the residual offset. This conclusion is fully supported by the presented Hall readout system featuring a 2 MS/s digital output rate, a spurious-free bandwidth from 820 kHz  to 980 kHz (maximum bandwidth) and an input-referred Hall plate offset as low as