The analysis of research paper collections is an interesting topic that can give insights on whether a research area is stalled in the same problems, or there is a great amount of novelty every year. Previous research has addressed similar tasks by the analysis of keywords or reference lists, with different degrees of human intervention. In this paper, we demonstrate how, with the use of Normalized Relative Compression, together with a set of automated data-processing tasks, we can successfully visually compare research articles and document collections. We also achieve very similar results with Normalized Conditional Compression that can be applied with a regular compressor. With our approach, we can group papers of different disciplines, analyze how a conference evolves throughout the different editions, or how the profile of a researcher changes through the time. We provide a set of tests that validate our technique, and show that it behaves better for these tasks than other techniques previously proposed. We believe that the difference measure used throughout this paper (that measures the proportion of a string that cannot be computed with the other), gives an estimate of the innovation, or the degree upon which a text presents novel techniques or addresses problems not dealt with in another paper. We use NRC to compare single papers as well as documents collections, and provide visualizations to communicate the evolution of a full conference edition by comparing it with other editions. Moreover, we also provide fine grain insights by visualizing the measured differences between all the papers of one edition versus all the papers of the other.Visualizing the evolution of a certain researcher or conference, getting insights on whether it is evolving or stalled, is an interesting problem that has been addressed previously through the analysis of co-occurrence of topics. However, to the authors’ knowledge, most of the previous analysis requires a big amount of human effort in identifying, refining, and classifying keywords or topics . MoreoveA method for comparing the relative difference of a paper with respect to another.A fully automatic pipeline to process and evaluate PDF files of research articles.A series of visualizations intended to illustrate conference or author profile evolution.To sum up, the contributions of this paper are:As already stated, we propose the use of NRC for paper comparison, but instead of using it in plain files, we will perform a series of text processing tasks, borrowed from machine learning, to improve the comparison results. These tasks will be applied in a totally automated fashion using a series of scripts and Python programs. To ensure that the measure behaves as expected, we have run a set of tests that show that NRC, with the processed data, seems a good estimator of paper difference. Finally, we propose some visual depictions to communicate this information for full conference editions, author profiles, and the whole set of documents of a conference.The rest of the paper is organized as follows. First, we will analyze previous work. Then, the selected corpus of papers is presented, and an initial analysis is carried out using word frequencies in Text analysis tools and techniques have been previously developed with several objectives such as spam identification, plagiarism detection, authorship attribution, and so on. In visualization, it has been used to visually classify text documents, and to analyze the evolution or research papers . Our papmove from one representation to another. The metric has been used in many applications, such as image similarity comparison [word2vec.Text analysis comes in very different flavors that depend on the tasks to be performed . For examparison ,9. Kusnemparison  designedAnother popular family of methods to compare texts is the use of compression-based measures. Compression-based techniques usually require less effort than deep learning approaches, and sometimes can be parameter-free. Pinho et al. use Normalized Relative Compression for authorship attribution of texts  and repoIn the area of visualization, the problem of visually communicating information relative to the research paper has also been addressed. Isenberg et al. recently analyzed keywords of scientific papers to explore the evolution of IEEE Vis conferences . In thisword2vec distributed representations of words [Some previous techniques are also related to our work here, such as the systems that analyze the texts to perform different tasks such as literature recommendation. Ponsard et al.  developeof words , coupledof words . In our Before delving into the more complex domain of text comparison, we approached the problem using a naive analysis technique: studying words frequencies. The idea is to explore how far this strategy can go, and what are the difficulties with the input data. To begin with the analysis, we need to select a suitable set of papers to analyze, as described next.To demonstrate the different algorithms, we decided to work with visualization papers. Consequently, we use all the full papers of the IEEE VisWeek for the last 10 years. There are several reasons behind this paper selection. First, the total number of papers in a regular VisWeek from the three main conferences is larger than 100, which makes human processing of the data very difficult. Second, we want to keep the input data as homogeneous as possible, so this may be a suitable data source. To preserve homogeneity, we decided to use only the papers that were published by the TVCG journal, which amount a total of 999 for these years. Although VAST as a conference exists since 2006, it was not incorporated to the VisWeek until year 2011. However, the papers did not appear in the TVCG journal until 2012. Using the same format for the input data is also important because it guarantees that the preprocessing we are applying to the data will have the same results. To further keep the consistency of the format, the input data was downloaded as PDFs from the IEEEXplore website.The simplest way to reduce a text to some small meaningful representation is the selection of most repeated words. Though with its own limitations, word frequencies can somewhat communicate the general topics discussed in a document. Most frequent words can be displayed in an intuitive way by using word clouds. As a first attempt, we will use such clouds to illustrate the evolution of the three main VisWeek conferences by comparing three years, namely 2012, 2015, and 2018. In our example, we select the 15 most frequently used words, and depict them in If we inspect these most frequent words, we find some expected results, such as the words “data” or “visualization” among the highest frequencies. A deeper analysis lets us see that SciVis seems to contain a larger word set than the other conferences, i.e., there is more variety in the most mentioned words.Unfortunately, reducing the conference to a small set of words is extremely lossy, and therefore, these reduced samples may underrepresent the big picture. A further step could consist of a more thorough analysis of the most frequent topics per year, or analyzing keywords, as in . While tOn the other hand, this initial analysis sheds light on the need for properly dealing with the input data to ensure its quality and robustness. Therefore, the initial steps before comparing texts will use automated techniques erase stop words, as well as properly cleaning the files of extraneous symbols.We also learnt another property of the data: the same word (or concept) may be written differently, e.g., use or using will mean something similar, but if they are counted as different words, measures based on frequency will not work. To solve this, we will use another well-known technique from natural language processing, word stemming, as will be explained later.Indicates the degree of novelty of a certain paper.Can be used to classify papers by research areas.Is able to further separate within papers of the same area.We had two main goals when addressing this project. We wanted to visually depict the evolution of a conference as a whole, and we also wanted to obtain visual descriptions of the evolution of a certain author profile. To do this, previous methods for research analysis fall short. Therefore, we seek a measure of paper difference with the following properties:degree of novelty of a paper. Consequently, it is not straightforward to find a baseline to use as reference for all these properties. Therefore, as explained below, we created a series of synthetic tests that cover all desired cases. For example, we can compare the degree of novelty by analyzing a paper and a follow-up of the same paper. Or we can easily determine whether visualization papers can be separated from other areas by comparing them with several papers of different fields downloaded from arxiv.org, and so on.These properties are difficult to express formally. Even an expert might find difficult to measure the arxiv.org or from some author web pages. These extra documents are enumerated in the This process includes gathering the files and processing them. For the comparison of full conferences, we downloaded all the papers of the IEEE VisWeek conferences for the last 10 years. In this case, the input data consists of the PDFs downloaded from the IEEEXplore website. For the individual tests for validation, we use a mix of papers obtained from these conferences, as well as other downloaded from either Text extraction.Data cleaning.Word stemming.To perform a systematic analysis of data, the treatment must be the same. With the lessons learnt from the word frequency analysis, we define a processing pipeline that consists of three main steps, applied to each paper:text extraction can be done in different ways, but finally opted for the pdftotext software. We initially tried with different tools, but the results were never perfect, and this one seemed to have the most consistent behavior. In some rare cases, the generated files consisted on binary files or contained a lot of garbage, thus making them unusable. In those cases, we also tested Adobe software for the same articles, but it also had problems for generating the text. Consequently, we had to remove half a dozen of files for the comparison tasks. This is a limitation that can be addressed in the future, as discussed later. The second step is data cleaning. This was carried out by using a series of Python and Perl scripts that perform the following tasks:Stopword removal.Removing spaces and changing text to lowercase.Erasing non-printable characters.Removing extraneous characters.The Stopwords are words that are very common in English, such as ’we’, ’our’, and so on, but that do not change the semantics of a text significantly. Learning approaches remove stopwords as part as the data cleaning. Extra spacings are also non-significant, and are thus also removed. Besides, to facilitate word comparisons, learning techniques also change the letters to lowercase. We also do this by a Python script with the nltk library. The extracted text often contains extraneous characters such as many points or non-printable characters. A couple of Perl scripts take care of those.We considered and tested other potential text processing, such as removing words that are non-existent in English, or removing all words with letters not belonging to the alphabet, but all of them have shortcomings. For example, erasing words not belonging to the English language would eliminate most authors’ names. Since highly repeated names may indicate similarities  we would somewhat hurt the similarity search. Moreover, sometimes authors coin a certain name to dub a technique that is then used repeatedly, and might not appear in a regular English dictionary. Removing letters not belonging to the alphabet has also some issues: for instance, it mutilates, if not completely erases all formulae in the papers, years in citations, and other names that are commonly used, such as 3D. Therefore, we have preferred to work with the resulting data of the previous processing. Learning techniques also transform digits into letters, but we preferred to avoid this to preserve  equations.nltk library.Finally, we further process the text by using a well-known technique named stemming. Stemming is the process that intends to reduce word inflections to their stems. In our case, we used Porter stemming , althougAlthough the original data requires more than one GB, after text extraction, cleaning, stop word removal, and stemming, the result is a set of files that amounts around 50MB.normal  compressor. A compressor is normal if it fulfills the properties of idempotency, monotonicity, symmetry and distributivity up to a logarithm term [f, and x and y. Although the similarity metric has values in As already mentioned, there are several ways in the literature to measure similarity between documents. In our case, the fundamental problem when analyzing the contents of the articles is to be able to compare texts. Some popular measures for text comparison are Normalized Compression Distance  or Normathm term . Normalifections . NCD hasfections , image sfections ,33, or ffections , to namex relative to y. Pinho et al. [y, the compressor builds a representation of it using finite context models of different orders, and then builds a representation of x based on this representation. As a result, the relative compression measures how much of the second file can be built using the reference file.Normalized Relative Compression, on the other hand, is similar to the previous one, but its formulation depends on the use of a conditional compressor. It measures the portion of one string that cannot be computed with the other. It can be calculated using the following expression:o et al.  developex can be built efficiently from (is very similar to) y;where x given y .…) are different in terms of the contents, while NRC of two strings attempts to answer on the portion of the second string that cannot be constructed from information of the first one [NRC has been used for applications such as authorship attribution , or studirst one . With thconditional compression of the sequence x given y. Conditional compression can be achieved using a special compressor that takes y as input and builds an internal model of y. In our case, we use the compressor by Pinho et al. [y, x is compressed using these previously defined models and another set of FCMs that learn the statistics of x as it is processed. Each symbol of x is encoded using a probability estimate, resulting from a mixture of the probabilities produced by each of the FCMs (those modeling y and those modeling x). Conditional compression can also be approximated by Another similarity measure that has been used to compare texts is the so-called Normalized Conditional Compression Distance , which io et al. , which bmpressor . Howeverconditional compression of the sequence x given y. NCC is also a semi-distance, like NRC, and we will also evaluate its performance.A similar measure is the Normalized Conditional Compression, which can be calculated as:move from one representation to another. The metric has been used in many applications, such as image similarity comparison [word2vec. Other techniques have been proposed previously, such as the use of Bag-of-Words directly. However, as reported by Kusner et al. [Lately, deep learning techniques have been used for text processing. A popular family of methods are based on word embeddings. One of such techniques is WMD. WMD is a metric by Kusner et al. ,6 whose mparison ,9. Kusnemparison  designedr et al. , it is paccuracy test). Then, we need to discard that NRC is effectively measuring author style instead of the contents of the paper, since it has been used for this goal previously in the literature. Then, we need to test whether NRC can separate papers of different areas. Besides, we also need to check the behavior of the technique against other metrics proposed in the literature, such as NCCD, NCC, NCD, and WMD.To ensure that the measures work as intended, it is of uttermost importance to validate the measure we selected with our goal in mind. Therefore, we run different validation tests intended to ensure that NRC is measuring what we expected, and to demonstrate that it works better than other alternatives. One of the most important elements to ensure is whether the formulation is correctly estimating the difference between two files , and execute a program that fetches 500 words from Y and appends them to X. Then, a second run appends 500 more, and so on. Finally, we measure the differences between the different versions of the files. As expected, the results should show that the difference value decreases softly as the number of words shared by the two papers grows. The results are shown in Estimating the differences between two scientific texts is difficult to describe formally. Previous literature has used citations , documeIn light of the results, we can discard WMD for our problem, and the next tests will show only NRC, NCCD, NCC, and NCD.Previous literature has shown that NRC can be used for text attribution. More concretely, Pinho et al.  successfTo ensure that our system was going beyond author style classification, we performed the following experiment: We took several visualization papers of different authors on volume rendering. The papers belong to the authors’ initial research, since they are published during the PhD thesis development. Therefore, they have lots in common , and most likely, are going to have the author’s own writing style. We process the papers with the aforementioned pipeline, and calculate the NRC among all of them. Then, we evaluate the differences (in both directions) among the papers.If NRC only accounts for authorship attribution after our text processing, it will issue smaller values of difference for the papers of the same author. Otherwise, the distances will correspond more likely to the contents of the paper than their authors. It turns out that comparing the papers using NRC on our dataset does not find smaller differences papers based on its author (or for authors belonging to the same institution), but more in line with its contents. Of course, some papers of the same author have very small differences because they are very closely related, and we cannot discard that the authorship is there, though at a lower proportion. However, other papers score higher than with respect to other authors. Please note that even a manual accurate selection on what two papers are closest to each other is, when papers are on a similar subject, very difficult to achieve. Though the style of an author may change from year to year, this test seems to confirm that NRC is able to approximate the level of novelty (and probably the inherent part of author evolution). Therefore, it seems that NRC, together with the data processing we performed, can effectively help us classify papers based on contents, not on authorship.We also ran the same experiment using NCC and the results are shown in arxiv.org. The complete list of the papers used in this experiment is also in the We performed a second test to see whether NRC was able to classify according to research areas. Here, we selected a small set of visualization papers (randomly chosen from the corpus of IEEE VisWeek), and downloaded a small random set of papers from different areas, mainly from We also performed the same comparison with NCC, shown in contains the papers in the area.The goal of this test is to evaluate whether NRC can be used to estimate the novelty of a paper with respect to the other. In this case, we take advantage of fact that NRC is a semi-distance. Again, finding a baseline is complex, so we opted for the two following experiments: First, we compare what happens if we compare a State-of-the Art paper with respect to a set of papers of the same area, and then we compare a paper with a follow-up of the same authors. The intuition says that the difference from the STAR to another paper will be smaller than the difference from a paper of the same area to the STAR, since, in some sense, the STAR describes a larger amount of concepts, and therefore For this comparison, we downloaded a STAR on molecular visualization, and several papers of the same field. The results can be seen in contain the initial paper. Again, the testing with two papers that can be ensured that one is the follow-up of the other yield the same results, as shown in We did a second test of a related concept: the follow-up paper. It is similar than the previous, but in this case, the two are more closely related. Intuitively, a follow-up paper will also All these tests prove that NRC, combined with our text processing, is a powerful tool for document comparison, and that can be successfully used for the visualization of research evolution of a person or a conference. NCC is close to NRC, as it also passes the containment test, but does not classify papers as clearly as NRC. To sum up, the different tests are depicted in containment benefits from the fact that NRC is a semi-distance. Since NCD, NCCD, and WMD are symmetric, these cannot be used to estimate this property.Please note that the problem we dubbed Now we proceed to point how the NRC can be used to visualize documents collections or author profiles with the goal of visually explaining their evolution. For all the experiments, we used the relative compressor by Pinho et al. .One application of our technique is to evaluate how a conference evolves through the years. We can use it to have some insights on whether the developments are more incremental or there are great differences (that may indicate innovative contents). We can get some idea by analyzing how the different papers in one year compare to another, and how the whole conference edition compares to another one.For comparing the degree of difference between one year and another, we take all the papers of a conference edition (properly processed), and append them in a single file. By doing the same for each edition, we can compare editions of a conference, as well as differences between one conference and another. A second test that can be done is by comparing all the papers against each other, from one edition to the other. This might highlight some paper that is clearly different from the previous ones. We did this in predicting the other: lighter colors indicate the ability of an edition to predict the other. Note how the 2018 vs. 2012 comparison (right) shows lighter colors than the 2012 to 2018, which may give a hint on the fact that the 2018 edition has novel contributions with respect to the 2012 edition.To facilitate the interpretation, we sort the rows and columns so that larger values are moved to the bottom left and bottom right borders, respectively. This facilitates the interpretation by: i) it is easy to see whether one of the conference editions is better at A second advantage of this layout is that we can easily identify papers that seem highly innovative, or, at least different, such as the bottom row of left map. This is the case for the 2018 paper entitled “Hexahedral Mesh Structure Visualization and Evaluation”, which has large distances from any of the papers on the 2012 edition. This may be an indication that the paper has something that places it apart from the others. With this color coding and data sorting, it is very simple to identify such cases. Another clear case is the bright spot in the left heatmap. This corresponds to two different papers (in 2012 and 2018) that deal with Morse–Smale complexes which actually share the same authors. This kind of information would be very difficult to easily detect by other means.i paper of year y. This means that we calculate how different is the new paper with respect to the older one. The indices of the paper happen to be arranged according to download PDF names. The full list of the papers with the ids that appear in the chart are described in the Presently, with Visualization and Computer Graphics evolving so fast, researchers may sometimes change their research focus. Consequently, it may be interesting to see whether an author has a publication profile within a single area, or, on the contrary, she changes the research area along her career. One way to do so is to compare the papers published along several years, and the differences between the initial one(s) and the rest. This is what we did for a certain researcher. In this case, we took the papers published from 2003 and 2007 by Dr Ivan Viola, and they belong to the volume rendering area. We compare them in two different ways: first, we compare all the papers with the first one (in 2003), and second, we compare the papers year to year. Please note that as described in the In If we analyze the resulting chart, the differences seem to indicate that his research field expands a little bit almost every year. Moreover, we can also extract another insight from the chart: it seems that the first paper in 2004  has high relationship with the paper in 2003. Indeed, the paper on 2003 is about hardware-accelerated filtering and segmentation of volume data, and the first paper in 2004 is about GPU-based frequency domain rendering, while the second one in 2004 is about importance-driven volume rendering.per paper. In this case, the evaluate the difference, for each paper i in year y, with all the papers j in year novelty of paper i with respect to the ones in the previous year, not the other way around.We can also visualize the profile of the researcher in a different manner. We can see the year-to-year evolution, as shown in Like in the previous case, the heatmap is colored using white for the minimum difference and a medium-dark blue for the largest difference value among all of them. We can see how some of the papers each year seem to be innovative with respect to the papers in the previous year, while some of them are more incremental. One may be curious about the last paper in 2007, which seems to have a low difference with the second paper of 2006. It turns out that the 2006 paper is about importance-driven focus of attention, and the 2007 paper is about the role of topology in focus+context visualization. Therefore, this seems to indicate that NRC is making a good job at evaluating researcher evolution.This chart will naturally require more horizontal space than the previous one, since the number of papers that an author publishes typically grows with the time. However, this value also saturates around a dozen or less . Therefore, since heatmaps can be very compact, we believe the depiction is valid for a wide range of situations.All the experiments have been performed using the conditional compressor by Pinho et al. , which kTo perform the compression, we used the compressor with context models of order 2 =C7za, a compressor that performs Prediction by Partial Matching and is especially designed for speed, achieves a much faster compression timings that the compressor used here. However, since the one used here was not designed for speed, doing a numerical comparison would be unfair. Other tools for compression could be used [Finally, NCC achieves similar results to NRC, though it seems that it can separate research areas less effectively than NRC. Concerning the cost, in our experiments, NCC required 0.3305 seconds per evaluation on average over several files of different sizes, while NRC requires 0.11275 seconds with the same parameters and same file set. However, as mentioned above, NCC could be computed using the approximation  be used .In this paper, we have presented a data-processing pipeline and a method to use Normalized Relative Compression to estimate the difference between research papers. We have also introduced how to use well-known visualization techniques to express those differences to get insights on how a certain conference evolves and how the profile of an author changes along the time. The developed pipeline has been validated through a set of experiments that demonstrate that NRC can be used to investigate how a certain research area fares, as well as to visually depict author profiles.We used several series of papers, and the largest dataset was from the last 10 years of IEEE VisWeek conference. In the future, we would like to investigate with broader sets of data, such as downloading data from other conferences, as well as other researcher profiles. Currently, our system only uses text, but an obvious extension would be to include images. In this case, not only the image processing, but the image extraction alone, presents important challenges to do it in a fully automatic fashion.


Tumor necrosis factor (TNF) is a central regulator of immunity. Due to its dominant pro-inflammatory effects, drugs that neutralize TNF were developed and are clinically used to treat inflammatory and autoimmune diseases, such as rheumatoid arthritis, inflammatory bowel disease and psoriasis. However, despite their clinical success the use of anti-TNF drugs is limited, in part due to unwanted, severe side effects and in some diseases its use even is contraindicative. With gaining knowledge about the signaling mechanisms of TNF and the differential role of the two TNF receptors (TNFR), alternative therapeutic concepts based on receptor selective intervention have led to the development of novel protein therapeutics targeting TNFR1 with antagonists and TNFR2 with agonists. These antibodies and bio-engineered ligands are currently in preclinical and early clinical stages of development. Preclinical data obtained in different disease models show that selective targeting of TNFRs has therapeutic potential and may be superior to global TNF blockade in several disease indications. Tumor necrosis factor (TNF) is a key regulatory component of the immune system that regulates innate and adaptive immunity and contributes to initiation and maintenance of inflammation . The majThe limitations of anti-TNF therapy may depend on TNF’s pleiotropic biological functions via two distinct TNF receptors (TNFR). Synthesized as a transmembrane protein (tmTNF), the tmTNF form can activate both, TNFR1 and TNFR2. After proteolytical processing, the soluble trimers (sTNF) mainly activate TNFR1 . In diffLymphotoxin-α (LTα) is another homotrimeric ligand of the TNF superfamily (TNFSF) that shares 50% homology with TNF  and can Tumor necrosis factor is synthesized as a 26 kDa type II transmembrane protein that assembles into a homotrimeric molecule (tmTNF)  that cand = 1.9 × 10–11 M) than TNFR2 (Kd = 4.2 × 10–10 M)  . This hi10–10 M) . Stoichi10–10 M) . Indeed,10–10 M) . AltogetThe membrane-proximal extracellular stalk regions were identified as a crucial determinant in controlling responsiveness to sTNF . RichterUpon TNF binding, TNF receptor 1 associated protein with death domain (TRADD), the receptor interacting protein kinase 1 (RIP1), TNF receptor associated factor 2 (TRAF2), and the cellular inhibitor of apoptosis proteins (cIAPs) 1 and 2 are recruited to the receptor . The cIAIn contrast to the very well characterized TNFR1 signaling pathways and their physiologic relevance early in TNF research, TNFR2-mediated signaling pathways and in particular their role in TNF biology were uncovered much later . TNFR2 aSimilar to TNFR1 and TNFR2 activation may result in induction of the c-Jun N-terminal kinase (JNK)  and the Tumor necrosis factor plays an important role for regulation of the adaptive and innate immune system and thus, is a key player for both infectious and non-infectious inflammatory disorders. Interestingly, TNF induces opposing effects in the immune system, i.e., it plays a key role for the initiation and orchestration of inflammation, while it also suppresses immune cell activity. These antithetic effects often can be explained by the diverse signaling mediated via TNFR1 and TNFR2 .+FoxP3+ nTregs in vitro and in vivo  after sensitization with D-galactosamine ], whereas they are still sensitive to elevated doses of LPS only treatment   . Howeveris (CIA) . Supportrthritis . Because–/– mice do not develop experimental autoimmune encephalomyelitis (EAE), an animal model of brain inflammation resembling MS. In contrast, TNFR2–/– mice develop an exacerbated form of EAE . Recently, + T cells but not normal T cells isolated from diabetes type I patients  or neurodegenerative diseases . Precline events . Other se events . AltogetSimilar, TNF contributes to neuropathology, i.e., it was shown that genetic overexpression of TNF in the CNS resulted in T cell infiltration, astrocytosis, and microgliosis, and chronic inflammatory demyelination . These s–/– and TNFR2–/– mice were performed to investigate TNFR-selective responses. Interestingly, using the EAE immunization mouse modelseveral independent groups showed that TNFR1–/–-mice do not develop EAE motor disease, whereas TNFR2 deficiency resulted in an exacerbated form of EAE  resulted in reduced infarction, brain oedema and BBB permeability. Mechanistically, the group showed that cannabinoid treatment downregulated expression of TNF and TNFR1, with TNFR1 expression levels being correlated with the infarct volume , b. Simiin vitro studies with primary neurons. –/– or TNFR2–/– neurons. Only neurons from wild type or TNFR1–/– animals were protected, while TNF activation had no protective effect on neurons from TNFR2–/– mice, indicating that presence of TNFR2 was responsible for TNF-mediated neuroprotection. Mechanistically this study showed TNF-mediated neuroprotection was dependent on prolonged activation of NFκB and activation of the PI3K-PKB/Akt pathway , a long-lasting chronic pain that is caused by damage to the somatosensory nervous system and is associated with various diseases/conditions, including neurodegenerative and inflammatory diseases, diabetes, cancer and chemotherapy . Indeed,llodynia  and therllodynia , highlig females , indicat–/– and TNFR2–/– mice developed an alleviated form of mechanical and cold allodynia compared to wild type mice. Another study demonstrated that TNFR1/TNFR2-double knockout mice showed reduced tactile hypersensitivity, while spontaneous pain behavior was transiently increased in a model of bone-cancer related pain. In contrast, TNFR1 or TNFR2 single knockout did not show an effect on pain sensitivity  model, immunohistochemistry analysis demonstrated that both TNFR1 and TNFR2 levels were significantly increased in the red nucleus after SNI, compared to sham-operated and normal rats . A tempo–/– mice . In a mo–/– mice . Interes–/– mice , indicatof Tregs , suggestThe activities mediated by TNFR1 and TNFR2 can be modulated in several ways and adapted to the desired therapeutic effects. Inhibition of the proinflammatory activities induced by TNFR1 can be achieved either at the level of ligand or receptor. Most of the approved therapeutics interfering with the proinflammatory activity are antibodies directed against TNF, including three IgG molecules  and a PEGylated Fab fragment (certolizumab-pegol) . These aNovel therapeutics currently in development aim at a more selective inhibition of TNFR1 or are developed for a selective activation of TNFR2 . Selecti50 values in the low nanomolar range , which acts a competitive antagonist and lacks agonistic activity . This moutic use . Informautic use . Anotherutic use . In one utic use .50 values in the nanomolar range and showed therapeutic activity in ex vivo and in vivo models of inflammation, e.g., in a EAE model in human TNFR1 transgenic in a mouse TNFR1-k/o-background  isolated from an alpaca immunized with recombinant human soluble TNFR1 were genetically linked to an albumin-binding Nb to generate a bispecific half-life extended molecule named “TNF Receptor-One Silencer” (TROS) . TROS cockground . In thesin silico ADMET  prediction  and small-molecule inhibitors identified by screening compounds of the NIH Clinical Collection , 2019. Tediction .The use of TNF muteins represents another approach for selective interference with TNFR1 activity. R1antTNF is a modified TNF with specificity for TNFR1 isolated from a TNF phage display library . This TNin silico method to predict and design homotrimeric TNF variants exhibiting decreased receptor binding and being capable of sequestering native TNF homotrimers into inactive native:variant heterotrimers leading to inhibition of TNF-mediated signaling  to the N-terminus of TNFR2-selective scTNF, resulting in a nonavalent molecule capable of clustering TNFR2 (R2), which was also capable of specifically binding to TNFR2 and inducing efficient receptor activation  carrying 2 mutations, S95C and G148C, which result in disulfide-linked TNF molecules with increased stability and, surprisingly, the capability to activation TNFR2 without further crosslinking, as shown in CD4analyzed .reg cells, capable of correcting type 1 diabetes-associated Treg activation defects  and spinal cord injury (SCI). XPro1595 was evaluated in two parallel studies by the groups of Lesley Probert and John Bethea in the EAE mouse model of MS. Both studies showed that blocking the action of sTNF by XPro1595, but not of sTNF/tmTNF by the non-selective anti-TNF therapeutic etanercept, protected mice against the clinical symptoms of EAE. The laboratory of Malú Tansey has demonstrated that blocking sTNF signaling attenuates loss of dopaminergic neurons in models of Parkinson’s disease. Local administration of the dominant-negative TNF inhibitor XENP345, an earlier version of XPro1595 that works via the same mechanism of action, reduced the retrograde nigral degeneration induced by a striatal injection of the oxidative neurotoxin 6-hydroxydopamine (6-OHDA) by 50%. Similar neuroprotective effects were observed after chronic co-infusion of XENP345 with bacterial lipopolysaccharide (LPS) into the substantia nigra . Anotheramyloid precursor protein (APP) and presenilin-1 (PSEN1) transgenes and recapitulate many AD-related phenotypes, they showed that peripheral injection of XPro1595 alleviated the age-dependent increase in activated immune cells in the brain of transgenic mice, decreased beta-amyloid plaque load, and rescued impaired long-term potentiation (LTP). This indicates that sTNF neutralization may impact brain immune cell infiltration and prevent or delay neuronal dysfunction in AD . Using 5xFAD mice, which express human on in AD . Similaron in AD . Furtheron in AD .Interestingly, genetic ablation of sTNF did not reduce lesion size and improve functional recovery after moderate SCI in mice . In contClausen et al. studied systemic administration of Xpro1595 and etanercept on infarct volume, functional recovery and inflammation after focal cerebral ischemia in mice. They found that systemically administered XPro1595 and etanercept significantly improved functional outcomes, such as brain inflammation and liver acute phase response (APR), but did not affect infarct volumes . In a foGenetic data indicate that TNFR1 plays an essential role for pain development in males . Accordi4)-induced model, R1antTNF administration significantly reduced serum levels of ALT , a marker for liver damage. In a concanavalin A (ConA)-induced T-cell-dependent model, R1antTNF administration reduced serum levels of the inflammatory cytokines IL-2 and IL-6 , a wide-spread disease with increasing prevalence that is associated with the development of liver fibrosis/cirrhosis, a major risk factor of liver-related and all-cause mortality in this disease . Activatex vivo cultured inflamed colon biopsies from patients suffering from active Crohn’s disease. Similar, in liver chimeric humanized mice, TROS antagonized inflammation in a model of acute TNF-induced liver inflammation (tg/wt mice and upon icv AβO injection, outlining the therapeutic potential of TNFR1 antagonists for AD. Similar to Atrosab, TROS was therapeutic in a model of MS. It was shown that prophylactic TROS treatment significantly delayed disease onset and ameliorated EAE symptoms in mice. Treatment initiated early after disease onset prevented further disease development. Altogether, TROS administration reduced neuroinflammation and preserved myelin and neurons (The nanobody-based selective inhibitor of TNFR1 TROS reduced secretion of IL-6, IL-8 and TNF in ammation . The neu neurons . The the neurons . Togethe neurons .ex vivo and in vivo (in vitro. In vivo administration of STAR2 resulted in mild myelopoiesis in naïve mice but did not affect immune cell composition. In mice with chronic inflammation, STAR2 treatment expanded CD4+ Tregs and improved their suppressive function (TNFR2 agonist may work via a dual mode of action, modulation of immunity and direct neuroprotection. Therefore, TNFR2 agonists were evaluated in models of inflammation and neurodegeneration . Indeed, in vivo , 2019a,b in vivo . Anotherfunction .R2, we demonstrated that selective activation of TNFR2 induces anti-inflammatory responses and alleviates experimental arthritis. Interestingly, we observed that TNFR2 agonism expands both CD4+ and CD8+ FoxP3+ Tregs both ex vivo and in CIA mice (+ suppressor cells were shown to be more suppressive in arthritic mice than their CD4+ counterparts (R2 in a prophylactic setting, whereas treatment after onset of arthritis did not impact arthritic disease within the observation period. However, another study using STAR2 in CIA mice showed that TNFR2 agonist treatment ameliorates established collagen-induced arthritis in mice (Using the mouse TNFR2 agonist EHD2-sc-mTNFCIA mice . This miterparts . In the  in mice . Of noteR2 we confirmed the neuroprotective role of TNFR2 and demonstrated that selective activation of TNFR2 rescued dopaminergic neurons (R2 into the magnocellular nucleus basalis of mice protected cholinergic neurons and their cortical projections from excitotoxic cell death induced by glutamate and reverted the injury-associated memory impairment testes by a passive avoidance paradigm (R2-mediated activation of TNFR2 in the spinal cord improved locomotion and cortical neural activity (R2 in mice promoted long-lasting pain recovery after CCI. TNFR2 agonist treatment alleviated peripheral and central inflammation and reduced neuronal injury. Importantly, depletion of Tregs abolished the therapeutic effect of TNFR2 agonist treatment (R2. Similar, we demonstrated that in EAE mice systemic administration of EHD2-sc-mTNFR2 alleviated inflammation resulting in reduced demyelination and neurodegeneration. The behavioral data showed that TNFR2 agonist treatment alleviated motor disease and promoted long-term recovery from CNP. Mechanistically, this study indicated that TNFR2 agonist treatment in EAE mice follows a dual mode of action and promotes suppression of CNS autoimmunity as well as remyelination (Using EHD2-scTNF neurons  and olig neurons  from oxi neurons . In thisparadigm . Similaractivity . Due to reatment , indicatlination .+, but not CD4+, T cells in blood samples from patients with type 1 diabetes was vulnerable to TNFR2 induced death. However, other activated and memory T cell populations were resistant to TNFR2-triggered cell death (The group of Denise Faustman used an agonistic TNFR2-selective antibody to demonstrate that a subpopulation of insulin-specific CD8ll death . This inll death . TherefoNext to its potential use as a therapeutic target in inflammatory and degenerative diseases, TNFR2 was recently identified as a novel drug target for the treatment of cancer . Next toTumor necrosis factor blockers have demonstrated their clinical effectiveness, are successfully used to treat autoimmune diseases and are under the top-selling biologics world-wide. However, despite this success the development of serious side-effects and the failure of clinical trials in specific indications such as heart disease and MS revealed the limitations of anti-TNF therapy. Research of the last two decades has established that TNF mediates inflammation and tissue degeneration via TNFR1 signaling and immunomodulation and tissue regeneration via TNFR2. Accordingly, a novel class of drugs that selectively target TNF signaling at the level of the ligand or receptor has emerged. As outlined in this review, selective blocking of sTNF/TNFR1 signaling, which will preserve functional tmTNF/TNFR2 signaling, seems to be sufficient to interfere with pathological TNF signaling. In contrast to global TNF blockers that neutralize sTNF and tmTNF, this class of therapeutics may induce less severe side-effects and may be therapeutic for other diseases such as MS or neurodegenerative diseases, where complete TNF inhibition is contraindicative. Indeed, preclinical evaluation of DN-TNF muteins and TNFR1 antagonists was promising and often superior to conventional anti-TNF therapeutics. Similar, TNFR2 agonists were developed and first pre-clinical evaluation using prototype molecules was successful. However, development of completely human clinical grade products will be necessary to succeed into clinical trials. Ultimately, combination therapies, using sTNF/TNFR1 antagonists together with TNFR2 agonists, may rebalance pathologically deregulated TNF signaling and induce tissue repair and might be a novel superior therapeutic concept to treat a multitude of inflammatory and degenerative diseases.RF and RK wrote the review and generated the figures. KP reviewed and revised the manuscript.RK and KP are named inventors on patent applications covering TNFR1 specific antagonists. RF, RK, and KP are named inventors on patent applications covering the TNFR2 agonist technology.


In the RV144 trial, vaccine-induced V1V2 IgG correlated with decreased HIV-1 risk. We investigated circulating antibody specificities in two phase 1 poxvirus prime-protein boost clinical trials conducted in South Africa: HVTN 097 (subtype B/E) and HVTN 100 (subtype C). With cross-subtype peptide microarrays and multiplex binding assays, we probed the magnitude and breadth of circulating antibody responses to linear variable loop 2 (V2) and conformational V1V2 specificities. Antibodies targeting the linear V2 epitope, a correlate of decreased HIV-1 risk in RV144, were elicited up to 100% and 61% in HVTN 097 and HVTN 100, respectively. Despite higher magnitude of envelope-specific responses in HVTN 100 compared to HVTN 097 (p’s < 0.001), the magnitude and positivity for V2 linear epitope and V1V2 proteins were significantly lower in HVTN 100 compared to HVTN 097. Meanwhile, responses to other major linear epitopes including the variable 3 (V3) and constant 5 (C5) epitopes were higher in HVTN 100 compared to HVTN 097. Our data reveal substantial differences in the circulating antibody specificities induced by vaccination in these two canarypox prime-protein boost trials. Our findings suggest that the choice of viral sequences in prime-boost vaccine regimens, and potentially adjuvants and immunogen dose, influence the elicitation of V2-specific antibodies. After the RV144 trial was identified as the first preventative HIV vaccine efficacy trial to report decreased HIV-1 acquisition2, advances were made in identifying correlates of risk. The heterologous prime-boost RV144 regimen, tested in Thailand, used a prime vaccine of a canarypox vector (ALVAC-HIV) with a subtype AE envelope (env) glycoprotein 120 (gp120) insert and a boost vaccine of subtype B/E bivalent gp120. The primary correlate of decreased HIV-1 risk was an IgG binding response to the variable loops 1 and 2 (V1V2) of the HIV envelope3. Secondary and exploratory correlates analyses revealed further evidence of V2-specific antibody immune correlates: linear V2 IgG4, V2 IgG breadth5, V1V2 IgG36, and V1V2-specific complement binding antibodies7 correlated with decreased infection risk. A sieve analysis of breakthrough viral sequences from infected vaccine recipients and placebo recipients suggested vaccine-induced immune pressure at amino acid (aa) positions 169 and 181 in V28.Most known correlates of protection for licensed vaccines involve antibody responses13 and SHIV acquisition14, and with viremia control after SIV infection13. Passive transfer of 830 A, a V2-specific monoclonal antibody (mAb), resulted in improved viremia control in nonhuman primates15.Nonhuman primate studies conducted after RV144 supported the importance of non-neutralizing antibodies to V2 for protection from experimental challenge. Vaccine-elicited V2 IgG correlated with delayed SIV22. V2-specific mAbs recognizing aa169, which were isolated from RV144 vaccine recipients, were shown to mediate tier 1 neutralization, bind to infectious virions23, and mediate killing of primary HIV-1 isolate infected cells16. Aa169 is located within the linear epitope sequence bound by RV144 vaccine-elicited antibodies that correlated with decreased HIV-1 risk4. The other known site of RV144-induced immune pressure in V2, aa181, is also part of the leucine-aspartic acid-isoleucine/valine (LDI/V) aa179–181 sequence motif reported to mediate HIV-1 envelope interaction with the gut mucosal homing integrin receptor α4β7 to facilitate cell-to-cell spread20. It has been postulated that V2-specific antibodies may block or interfere with the interaction between α4β7 and the HIV-1 envelope to prevent viral transmission20. In participants from the RV144 trial and the RV305 trial, in which delayed boosters were given to RV144 participants, a group of V2-specific mAbs were shown to be capable of blocking AE.92TH023 V2 peptide binding to α4β724. The α4β7-blocking antibodies included both those targeting the linear V2 hotspot and those targeting the conformational epitopes25. V2-specific antibodies can mediate phagocytosis27 and can synergize with C1-C2 specific IgG for enhanced antibody-dependent cell-mediated cytotoxicity (ADCC)28. Furthermore, quaternary epitopes involving the V2 loop were identified as a target for broadly neutralizing antibodies29.Many studies have reported antiviral functions that include antibody Fc effector functions and direct neutralization mediated by antibodies that recognize V230. As part of this program, two phase 1/2 trials were designed in South Africa: HIV Vaccine Trials Network (HVTN) 09731  determined immune responses of an African population to the RV144 regimen, and HVTN 10032  investigated the immunogenicity of the regimen adapted to subtype C. The selection criteria for the subtype C envelope immunogens included binding and affinity by V2-specific mAbs34. The HVTN 100 primary immunological data were evaluated against prespecified immunological criteria and guided the decision to proceed with the vaccine regimen into an efficacy trial, HVTN 702. Vaccine-elicited responses in HVTN 100 met all four prespecified go/no-go criteria for the continuation of the HVTN 702 trial, including the envelope-binding and V1V2-binding IgG response32.The Pox-Protein Public-Private Partnership (P5) program was established to develop a subtype C-directed vaccine based on the RV144 regimenOur study outlines the similarities and differences in the V2-directed responses elicited by the vaccines used in the HVTN 097  and HVTN 100 trials. We thereby demonstrate the importance of envelope sequence selection in optimizing epitope-specific antibody responses critical for vaccine protection.31 and HVTN 100 (phase 1–2)32 clinical trials both evaluated vaccine regimens consisting of a canary pox virus vector prime and a bivalent envelope gp120 protein boost in South Africa. Differences between the two regimens include: (1) the envelope vaccine strain was 92TH023 for the ALVAC prime and A244 and MN for the gp120 boost in HVTN 097, whereas HVTN 100 contained the ZM96 sequence for the ALVAC prime and 1086 and TV1 for the gp120 boost; (2) the adjuvant was aluminum hydroxide for HVTN 097 compared to MF59 for HVTN 100; (3) the protein dose in HVTN 097 was 3 times higher than that in HVTN 100; (4) the actual dose of ALVAC-HIV in HVTN 097 was 2.7 times more than that in HVTN 100 (ref. 35 and unpublished data); and (5) a 11-amino-acid (aa) sequence at the gp120 N terminus was removed and replaced with a herpes simplex virus (HSV) gD sequence for the gp120 proteins used as boosts in HVTN 097, whereas similar modifications were not made to the HVTN 100 gp120 constructs33.The HVTN 097 (phase 1)For the comparison in our analysis, all immunological evaluations were performed on samples obtained 2 weeks after the second protein boost (6.5 months after the first ALVAC prime) in both trials.35. Serum samples collected at 6.5 months  were measured for binding response to gp120 antigens that match to the five subtype C and E HIV-1 envelope vaccine strains in HVTN 097 and HVTN 100 at the same 1:50 serum dilution . Binding to linear V2 peptides was observed in both studies, and was predominantly to subtype C 1086, subtype AE A244, and subtype AE 92TH023 peptides in both studies. Binding to the subtype B MN V2 peptides was also observed but only in HVTN 097 097 Fig. . The pea097 Fig. . This pe100 Fig. , Table 1100 Fig. . Differe100 Fig.  for the In addition to the antibody binding to the V2 hotspot against the vaccine strains C.1086, AE.A244, AE.92TH023, and B.MN, there was low magnitude V2 hotspot binding for several consensus strains  in HVTN 097, but this activity was absent for HVTN 100 Table . These rLinear gp120 epitope specificities of antibodies elicited in HVTN 100 and HVTN 097 were compared using data obtained from the peptide microarray described above, for serum samples at 2 weeks after the second protein boost. In both trials, antibody responses were directed to linear epitopes in C1, V2, C2, V3, and C5 regions across gp120 Fig. . While m−16 for either magnitude or positivity rate) seen for the V2 hotspot , C1-V1 , C2.3 , V3 , C5.2 , and C5.3    Table .We further examined the relative magnitude and breadth of IgG binding to major epitopes. The major epitopes are defined as having a positivity rate >75% for any of the vaccine strains in either trial. As visualized by spider plots in Fig. 34. Immunogenicity evaluation two weeks after the 4th vaccination  timepoint in HVTN 100 revealed robust humoral responses by the subtype C ALVAC/bivalent gp120 vaccine32. HVTN 100 vaccine recipients developed IgG responses against gp120 antigens with sequences matched to the vaccine at a higher response rate and magnitude among positive responders compared to that in RV144, and a higher response rate for CD4+ T-cell response against the ALVAC insert strain compared to that in RV144. However, the response rate for the V1V2 region of 1086.C (one of the envelope strains in the protein boost of HVTN 100) was lower than that seen in RV144 against its vaccine sequence-matched AE.A244 V1V2. This raised questions as to whether the lower V1V2 responses observed in HVTN 100 compared to RV144 reflected a difference in participant populations and whether the different regimen subtype C ALVAC/gp120 immunogen in HVTN 100 elicited the same V2 specificities as did the subtype B/E regimen of RV144. In this analysis, we interrogated the specificity, magnitude, and breadth of HIV-1 envelope antibody responses in HVTN 097 and HVTN 100 to inform decisions on bridging of HIV vaccine candidates across subtypes with similar prime-boost strategies.The promising results of the RV144 Thai vaccine efficacy trial prompted interest in optimizing the vaccine immunogen strategy to address the HIV epidemic in the most affected region of the world. Two major considerations guided further exploration of the poxvirus-protein prime-boost strategy in South Africa. First, it was not known whether the vaccine had to be modified to encode antigens from subtype C, the subtype of HIV most prevalent in sub-Saharan Africa where the epidemic is most marked. Second, it was not known whether the immune correlates identified in RV144 could be confirmed with another vaccine trial in a different population using a similar prime-boost regimen. Both conducted in South Africa, the HVTN 097 and HVTN 100 trials used the same strategy of a poxvirus vector-prime/protein-boost, but tested different HIV-1 envelope antigens expressed by the vector prime and administered as the gp120 protein boosts. In particular, the boosting antigens for HVTN 100 were derived from southern African subtype C HIV strains and selected based on their antigenicity, manufacturability, and preclinical immunogenicity with the goal to enhance immune coverage of genetically related strains in South Africa32, we found significantly higher levels of gp120-binding responses to respective vaccine-matched antigens in HVTN 100 compared to HVTN 097. These data indicate that the overall immunogenicity of the subtype C HVTN 100 regimen was not inferior to that of the subtype B/E HVTN 097 regimen. However, binding to V1V2 scaffold protein antigens, including subtype C V1V2 antigens, were lower in HVTN 100 compared to HVTN 097. This is consistent with findings from previous comparisons of V1V2 responses between HVTN 100 versus HVTN 097 and RV144 where higher magnitude of binding to V1V2 proteins in HVTN 097 and RV144 compared to HVTN 100 was reported .Consistent with previous findings of similar or stronger gp120-binding response in HVTN 100 compared to RV144These results demonstrated that a V2-specific IgG response was elicited in both HVTN 100 and HVTN 097, and the same V2 linear epitope was targeted by both vaccines. However, both the magnitude and positivity of the linear V2 hotspot responses were lower in HVTN 100 compared to HVTN 097, even for subtype C strains. Positivity for linear V2 binding responses was highest for AE.A244 and AE.92TH023 in both studies, with positivity rates of 61% and 100% in HVTN 100 and HVTN 097 respectively. V2 hotspot antibodies elicited in HVTN 100 preferentially bound to AE.A244 V2 sequence over subtype C V2 linear sequences. Minimal binding responses were observed to the TV1 V2 linear epitope in HVTN 100 (16% positivity). The response rate to TV1 V1V2 gp70 scaffold was 58.5%. However, that included responses directed against V1, to which a relatively more frequent binding response was seen for TV1 strain compared to other strains in HVTN 100. Sequence alignment of the vaccine strains showed that C.1086 differs from AE.92TH023 by two amino acids in the V2 hotspot region and is closer to AE.A244 than any of the other strains tested in the array library for this epitope. This relatively high sequence similarity between the V2 hotspot sequences of C.1086 and AE.A244, the focusing of V2 hotspot binding response on these two strains in both trials, and the poor binding response to the C.TV1 V2 hotspot, suggest that of the three envelope immunogens in the HVTN 100 vaccine regimen, the C.1086 envelope contributed the most to the linear V2 responses. The higher magnitude of binding response to the AE.A244 V2 hotspot compared to the binding response to C.1086 V2 hotspot in HVTN 100 suggests that the AE.A244 V2 region is a better ligand for the V2 IgG elicited by either AE.A244 or C.1086 Env. Meanwhile, the higher binding responses to both the C.1086 and AE.A244 V2 hotspot in HVTN 097 compared to HVTN 100 indicate that AE.A244 Env is also a better immunogen than C.1086 Env for V2 specificity. The low antibody response to the C.TV1 V2 region in HVTN 100 suggests other subtype C vaccine candidates could improve upon the subtype C or cross-subtype V2 antibody responses observed in HVTN 100 by including antigens with more immunogenic V2 regions.25, and a more recent study22 showed that a group of V2 linear epitope binding antibodies (V2p)26 that can block α4β7 binding recognized helical V2 conformations involving a potential α4β7 binding secondary determinant at aa170-17237, which the V2 hotspot overlaps. Therefore, even though the binding response that directly targets the LDI/V motif was minimal in this study, other V2 antibodies including those targeting the linear V2 hotspot may have the potential to interfere with V2- α4β7 interactions.In addition to responses to the V2 hotspot, another potentially important anti-V2 response would be one that could interfere with α4β7 integrin binding. We hence looked for evidence of antibodies binding to peptides covering the LDI/V α4β7 binding site in V2. Peptide #57 in the microarray library is the peptide that centers around the LDI/V motif. Our data showed limited binding to this peptide in both HVTN 100 and HVTN 097 in only a very small subset of subjects, with positivity rates lower than 7.5% for any given strain by either trial. Therefore, antibodies directly targeting the linear LDI/V α4β7 binding site, as measured by the peptide microarray, are not likely to contribute significantly to potential protection mechanisms involving gp120-α4β7 interactions with these two vaccine regimens. Nevertheless, V2 hotspot binding mAbs isolated from RV144 and RV305 subjects were shown to be capable of blocking α4β7 binding39); the protein doses in HVTN 100 were 1/3 that of the protein doses in HVTN 097; and, the ALVAC dose in HVTN 097 was 2.7 times the ALVAC dose in HVTN 100. However, the stronger or similar envelope gp120 responses in HVTN 100 compared to RV144 and HVTN 097 indicate that the lower V2 binding response in HVTN 100 is likely due to vaccine strain sequence differences rather than dose and adjuvant differences that tend to affect overall binding antibody responses. A previous study40 compared an ALVAC vCP1521 (AE.92TH023) prime, followed by different vaccine sequences as part of their bivalent B/E boosts. It included similar adjuvant and protein dose differences  as in our study. In contrast to our study, both Env gp120 and V1V2 IgG responses were higher in the alum adjuvanted study compared to the MF59 adjuvanted study. Therefore, the contrasting higher gp120 response and substantially lower V1V2 response reported in our study for HVTN 100 compared to HVTN 097 is likely due to virus sequence differences among the regimens instead of dose or adjuvant differences. Supporting this conclusion is that differences in antibody binding to other major HIV-1 envelope epitopes were in opposite directions from the V2 response .Other than the difference in the sequences of the vaccine strains in the vector and protein immunogens for HVTN 100 and HVTN 097, the adjuvants and the protein doses differed as well: HVTN 097 used an inorganic adjuvant  while HVTN 100 used an organic adjuvant . Furthermore, the stronger V2 response in HVTN 097 compared to HVTN 100, even against subtype C strains, suggests the potential of including subtype E antigens in vaccines for a subtype C epidemic for an enhanced V2 antibody response. Heterologous boosts incorporating a subtype E Env with demonstrated V2 immunogenicity (such as A244) and carefully selecting subtype C antigens for optimal V2 antibody responses may be worth further exploration for the subtype C epidemic in southern Africa. In addition, the V2 hotspot sequence is identical between the poxvirus prime strain AE.92TH023 and the protein boost strain AE.A244, making it a homologous boost for V2 in HVTN 097. In HVTN 100, such homology is absent between prime and boost strains, which could have contributed to the lower V2 response in HVTN 100. Interestingly, recent data examining the effect of delayed boosts of RV144 vaccine recipients in RV305 revealed preferential boosting of certain specificities including V2 by ALVAC 92TH0232 Fold) of IgG linear epitope responses in HVTN 100 was to V3 , C5.2 (8.1 for C.TV1), and C2.3 (8.8 for C.TV1). In contrast, for HVTN 097, the highest mean magnitude of IgG linear epitope responses was to V2 (7.9 for AE.A244) and V3 (6.4 for AG.con). Notably, this is consistent with our findings in nonhuman primate vaccine studies using HIV-1 envelope immunogens where V3 responses dominated in all studies tested except for one with AE.A244 gp120 protein as the immunogen boost36. Additionally, in both HVTN 100 and HVTN 097, antibodies targeting the linear V3 epitope showed cross-subtype breadth, with >65% positivity rate against all 6 vaccine strains in both studies, and >65% for the majority of the consensus strains. In contrast, antibodies targeting the linear V2 hotspot showed limited breadth, with positivity rates for AE.A244, AE.TH023, and C.1086 ranging from 40% to 61% for HVTN 100 and 88% to 100% for HVTN 097. Linear V2 response rates for the consensus sequences tested were all <10% for HVTN 100, and <30% for HVTN 097. The limited breadth of the V2 linear response highlights the challenge for vaccine coverage of diverse V2 sequences in regions with high HIV-1 envelope sequence diversity especially in the V2 region43.In addition to the differences in elicitation of V2-specific antibodies, HVTN 100 and HVTN 097 also differ in the elicitation of antibodies to other linear epitope specificities. Even though the two vaccine trials targeted the same major regions of the HIV envelope (defined as epitopes with >75% positivity rate for any strain tested in either study), the magnitude and breadth of the antibody responses to specific sequences differed between trials. HVTN 097 elicited higher magnitude of C1.2 linear epitope binding and higher magnitude and breadth of binding to V2 hotspot whereas HVTN 100 elicited higher magnitude and breadth of binding to C5.1 and C5.3, and higher magnitude for subtype C strains for binding to C2.3 and C5.2. Overall, the highest mean magnitude (Sanofi Pasteur) at months 0 and 1, and ALVAC-HIV vCP1521 plus aluminum-hydroxide-adjuvanted bivalent B/E gp120 protein (A244 of the subtype E strain and MN from the subtype B strain)  at months 3 and 62. Both MN and A244 gp120 constructs were made with an 11– amino acid deletion at the gp120 N-terminus and an HSV gD peptide tag inserted in place of the mutation46. The actual dose for ALVAC-HIV vCP1521 in HVTN 097 is 5.2 × 107 CCID5035; and dose for gp120 proteins was 300 µg each protein, similar to that in RV1442.The HVTN 097 products and vaccine schedule were the same as that for RV144, and included ALVAC-HIV vCP1521  (Sanofi Pasteur) at months 0 and 1, and ALVAC-HIV vCP2438 plus MF59-adjuvanted bivalent subtype C gp120 (of the C.1086 and C.TV1 subtype C strains)33 (GSK Vaccines) at months 3, 6, and 1232. The actual dose for ALVAC-HIV vCP2438 in HVTN 100 was 1.9 × 107 CCID50 (unpublished data); and the dose for the gp120 proteins was 100 µg each protein.The HVTN 100 regimen included ALVAC-HIV vCP2438  and the South African National Clinical Trials Registry (SANCTR number: DOH-27-0313-4201). The HVTN 100 trial was approved by the research ethics committees of the University of the Witwatersrand, the University of Cape Town, the University of KwaZulu-Natal, and the Medical Research Council32. Informed consent was obtained from all subjects in HVTN 097 and HVTN 100. All experimental methods were carried out in accordance with relevant guidelines and regulations.The HVTN 097 trial was approved by the University of the Witwatersrand Human Research Ethics Committee for Klerksdorp and Soweto sites and by the University of Cape Town Ethics Committee for the Cape Town site47. Briefly, carboxylated fluorescent beads  were covalently coupled to Env gp120 proteins and gp70-V1V2 scaffolds and were incubated with diluted serum samples. Antigen-specific IgG were detected with biotinylated goat anti-human IgG, followed by an incubation with streptavidin PE. Beads were then washed and acquired on a Bio-Plex instrument to measure florescence intensity. BAMA was run under GCLP compliant conditions, including tracking of positive controls by Levy-Jennings charts using 21CFR Part 11 compliant software. BAMA analysis for serum samples was performed on all HVTN 100 and HVTN 097 per-protocol (PP) vaccine recipients that have samples available for both 6.5 month and 13 month . PP refers to receipt of the first four scheduled vaccinations and excludes those who had an HIV-1 positive test by month 6.5. HVTN 100 samples have been previously tested at a serum dilution of 1:200 and data were reported by Bekker et al.32. For a direct head-to-head comparison with HVTN 097, the HVTN 100 samples were tested again at 1:50 serum dilution. Differences among binding responses to antigens tested are consistent between the 1:50 and 1:200 dilution tests.Binding antibody multiplex assay was performed by a custom HIV BAMA as previously described47. Microarray slides were provided by JPT Peptide Technologies GmbH (Germany) by printing a library designed by Dr. B. Korber, Los Alamos National Laboratory, onto Epoxy glass slides . The library contains overlapping peptides (15-mers overlapping by 12) covering full length envelope sequences for seven consensus gp160 sequences and six gp120 virus strains. A full list of peptides in the microarray library is in Supplementary Table th percentile intensity of all pre-immunization samples for the peptide; and (2) signal for the peptide >1.58, which represents a 3-fold difference in fluorescence intensity between sample and its baseline. For positivity determination, pepStat process included the “smooth” function to reduce false positivity. Since linear epitope mapping for RV144 was performed over 10 years ago and the assay and analytical methods have been updated, the positivity rates cannot be directly compared to RV144. Tests for HVTN 100 and HVTN 097 were performed contemporaneously for the immunogenicity comparison in order to ensure the robustness of the comparison between the two trials.Linear epitope mapping of the purified IgG against HIV Env was performed as previously described with minor modificationsFor the detailed epitope mapping by peptide microarray, we focused on vaccine recipients with detectable antibody responses to the V1V2 region. Plasma samples were down-selected based on having sufficient levels of antibody binding while maintaining gender balance. The details for each protocol based on pre-specified binding criteria are described here. For HVTN 100, a subset of 53 PP vaccinees and 5 placebos were selected for linear epitope mapping based on binding plasma IgG against V1V2 antigens as measured in BAMA as follows: 1) Select the n = 32 PP vaccinees with the highest IgG binding antibody titers (log10 net MFI > 2.75) to three V1V2 antigens . 2) Select an additional n = 6 of the highest PP vaccinee responders to C.1086C_V1_V2_Tags whowere not high responders to gp70_B.CaseA_V1_V2 and/or subtype C gp70-TV1.21 V1V2 . 3) Select a random sample of n = 15 from the remaining PP vaccinees, stratified by gender to balance the gender ratio observed in the n = 38 high responders . 4) Randomly select n = 5 PP placebos, stratified by gender.For HVTN 097, a subset of 45 PP vaccinees and 5 placebos were selected for linear epitope mapping based on binding plasma IgG against V1V2 antigens as measured in BAMA as follows: 1) Randomly select n = 30 PP vaccinees with the highest visit 14 IgG binding antibody titers (log10 net MFI > 2.75) to all three primary antigens  based on the two (of three) available antigens used in the HVTN 100 selection and the V1V2 antigen with the strongest response, while maintaining gender balance across the entire selection and treatment group (T1/T2) balance between step 1 and step 2. 2) Select a random sample of n = 15 from the remaining PP vaccinees not among the high responders as defined in step 1, stratified by gender to balance the gender across the entire selection and treatment group (T1/T2) balance between step 1 and step 2. 3) Randomly select n = 5 PP placebos, stratified by gender. These pre-specified selection criteria were met before initiation of the epitope mapping by peptide microarray.Comparisons of magnitude and positivity of antibody responses for V1V2 protein binding in BAMA were performed using a two-sided Wilcoxon rank-sum test (for magnitude of positive responders) and a two-sided Fisher’s exact test (for positive response rates) respectively, using SAS  and R statistical software . P-values less than 0.001 were considered significant; no adjustment was made for multiple testing.48 based on the corresponding selection criteria was performed for estimation and hypothesis testing about response rates, mean magnitudes, and any other population-level parameters that were estimated for data visualization, using R statistical software . This method yields estimates of means, mean differences, 95% confidence intervals for mean differences, and 2-sided Wald-based p-values for whether a mean difference departs from zero. Analysis was adjusted for potential confounding by gender.For linear epitope mapping data, the subset of subjects included in the analysis were randomly selected with sampling probabilities depending on participant variables (see “Selection of subjects for linear epitope mapping” under Methods). To account for this subset analysis, augmented inverse probability weightingSupplementary Table 1.Supplementary Table 2.


VEGFA, PTGS2, NOS3, TNF, and NOS2 genes as risk factors to CZS. Forty children with CZS and forty-eight children who were in utero exposed to ZIKV infection, but born without congenital anomalies, were evaluated. Children with CZS were predominantly infected by ZIKV in the first trimester (p < 0.001) and had mothers with lower educational level (p < 0.001) and family income (p < 0.001). We found higher risk of CZS due the allele rs2297518[A] of NOS2 . T allele and TT/CT genotypes of the TNF rs1799724 and haplotypes associated with higher expression of TNF were more prevalent in children with CZS and severe microcephaly . Our findings showed higher risk of CZS due ZIKV infection in the first trimester and suggested that polymorphisms in NOS2 and TNF genes affect the risk of CZS and severe microcephaly.Zika virus (ZIKV) causes Congenital Zika Syndrome (CZS) in individuals exposed prenatally. Here, we investigated polymorphisms in  Zika virus (ZIKV) is a human teratogen capable of causing neurological and ocular malformations in fetuses with in utero exposure to ZIKV infection ,2. CongeIn order to understand the mechanisms associated with the development of congenital anomalies due to ZIKV infection, environmental and molecular variables have been investigated in humans, in vivo and in vitro; however, risk and protection factors still need to be better elucidated. The timing of ZIKV infection during pregnancy, for example, is a variable that has been discussed in studies as relevant to the occurrence and severity of congenital anomalies ,6. The iTNF, NOS2, PTGS2, and VEGFA, as well as their proteins, are overexpressed during ZIKV infection, acting on the inflammatory response mechanism  was higher in children with a severe microcephaly. This allele has been related to the higher expression of the TNF gene , rs1799724[C], and rs361525[G] of TNF related to the low expression of the gene [TNF, combined with the natural increase of TNF expression to combat the ZIKV infection could be associated to this most severe phenotype in these individuals due to an exacerbated immunological and inflammatory response in the neural cells. On the other hand, the higher frequency of the TCG haplotype of TNF in individuals with moderate microcephaly, which is associated with the lower expression of TNF, could also explain this less severe phenotype in these individuals.Focusing on a specific phenotype of the CZS, the severity of the microcephaly, we found an association of it and the TNF gene ,16. The the gene . TNF is the gene . The immthe gene . In thisIt is important to highlight that the polymorphisms evaluated in this study affect the expression of their genes or function of the proteins, which act on the fetus’ brain, both in the context of development and inflammatory response ,37,38,39In addition, although these genes and polymorphisms have an important role in neurogenesis or neuroinflammation, they have not been described as capable of causing the phenotypes seen in CZS, such as microcephaly, among others outside the context of the ZIKV infection ,39,40,41Despite the interesting results of the present study, some limitations of should be considered for the results to be interpreted in a clear and unbiased manner. Firstly, the sample size of our sample is a limiting factor in the study, as it restricts our power to make strong associations and prevents us from performing more robust statistical analyses. Moreover, it is important to highlight that the number of children with CZS coming from a given region was not matched with the number of children without CZS coming from the same region. These two aspects of the sample may have been responsible for the fact that some genotypic frequencies found in this work were not consistent with the Hardy–Weinberg equilibrium. In this sense, the associations observed in the present study need to be confirmed by other studies in order better understand their real impact on CZS. Although the sample was obtained in partnership with several Brazilian institutions, the recruitment of new and well documented cases is difficult, taking into account that the ZIKV outbreak in Brazil has decreased considerably since 2017. In addition, there are a great number of cases where the confirmation of infection has been lost and, therefore, they were not included in this study. Regarding the clinical description of the individuals in the case group, the data were not available to all individuals due to the resources of the institution where they were attended and their possibility of carrying out exams.NOS2, VEGFA, and TNF genes with the CZS development and severity of the microcephaly in the CZS. Moreover, our results corroborate that the exposure to ZIKV in the first trimester of pregnancy is associated with the CZS as well as with the severity of the microcephaly in the affected individuals. Similarly, we found the socioeconomic level as a possible environmental risk factor to CZS. Future studies must explore such variables in larger samples as well as explore other possible risk factors to the ZIKV teratogenesis.The investigation for risk factors for the ZIKV teratogenesis and for the understanding of its molecular mechanisms still has much to advance, since little is known about this. Teratogenesis is a complex event and, therefore, there is not only one factor that can explain the susceptibility to damage caused by a teratogen exposure. In this study, we reported an interesting association of alleles and haplotypes of


Gordonia rubripertincta NRRL B-16540. Jellybones has a direct terminal repeat and was assigned to the CS2 subcluster with a length of 77,514 bp. NHagos is circularly permuted and was assigned to the DR cluster with a length of 59,580 bp.Jellybones and NHagos are bacteriophages that were identified in the host bacterium  Gordonia rubripertincta NRRL B-16540. Jellybones has a direct terminal repeat and was assigned to the CS2 subcluster with a length of 77,514 bp. NHagos is circularly permuted and was assigned to the DR cluster with a length of 59,580 bp.Jellybones and NHagos are bacteriophages that were identified in the host bacterium  Gordonia rubripertincta NRRL B-16540 , Starterator v1.2 (https://github.com/SEA-PHAGES/starterator), Phamerator (https://phamerator.org) (phagesdb.org/blast) (https://pecaan.kbrinsgd.org/), GeneMark v2.5p (–Annotation of Jellybones and NHagos was completed using the following programs: DNA Master v5.23.2 (tor.org) , PhagesDg/blast) , PECAAN rk v2.5p , Glimmerrk v2.5p , ARAGORNrk v2.5p , HHpred rk v2.5p , tRNAscark v2.5p , TMHMM vrk v2.5p , and SOSrk v2.5p –17.Bacteriophages were placed into clusters as described previously . JellyboMN444874 and SRR11178667, respectively. The GenBank and SRA accession numbers for NHagos are MN369758 and SRR11178666, respectively.The GenBank and SRA accession numbers for Jellybones are


Heart failure (HF) with preserved ejection fraction (HFpEF) is a clinicalsyndrome in which patients have symptoms and signs of HF with normal ornear-normal left ventricular ejection fraction (LVEF ≥50%). Roughly half of allpatients with HF worldwide have an LVEF ≥50% and nearly half have an LVEF<50%. Thanks to the increased scientific attention about the condition andimproved characterization and diagnostic tools, the incidence of HF with reducedejection fraction (HFrEF) dropped while that of HFpEF has increased by 45%.HFpEF has no single guideline for diagnosis or treatment, the patient populationis heterogeneously and inconsistently described, and longitudinal studies arelacking. To better understand and overcome the disease, in this review, weupdated the latest knowledge of HFpEF pathophysiology, introduced the existingpromising diagnostic methods and treatments, and summarized its prognosis byreviewing the most recent cohort studies. Heart failure (HF) is when the heart is unable to pump sufficient blood to maintainblood flow to meet the body's needs. HF is a common, costly, and potentially fatalcondition . In 2015In the past, heart failure with reduced left ventricular ejection fraction (HFrEF)was the most commonly diagnosed clinical entity in HF patients. However, with theimprovement of diagnostic tools, especially with the introduction of newechocardiography modalities, HF has recently been classified into three subtypes,namely HFrEF, heart failure with preserved ejection fraction (HFpEF), and HFmid-range ejection fraction (HFmrEF), according to the ejection fraction,natriuretic peptide levels, and the presence of structural heart disease anddiastolic dysfunction .HFpEF is a vital component of HF. Patients with HFpEF have significant morbidity andmortality but, unlike HFrEF,there are currently no effective validated therapies. Inaddition, HFpEF is poorly investigated .To bettThe structural remodeling that often occurs in HFpEF differs dramatically fromthat in HFrEF. Early descriptive studies in HFpEF suggested that concentric leftventricular (LV) hypertrophy with normal chamber size was typical . HoweverDiastolic dysfunction is defined as the inability to fill the ventricle to anadequate preload volume  at acceptably low pressures. DiastolMost, although not all, studies have demonstrated that the rate of LV pressuredecay during isovolumic relaxation (time constant τ) is prolonged in HFpEF. The minDelayed relaxation is, however, only part of the problem in early diastole inHFpEF. The healthy left ventricle functions as a ‘vacuum cleaner’ that preventsLA hypertension by enhancing suction in response to increases in venous return. Studies2+ pump and augmenting SERCA2aactivity  inhibition may increasecontractility and facilitate active relaxation, improving systolic as well asdiastolic heart function, both of which would be beneficial effects in thetreatment of chronic HF O2])increases in response to exercise (≅2.5×) (2 to augment oxygen uptake(VO2)   is much  (≅2.5×) . Several (≅2.5×) , which ike(VO2) . One stuin HFpEF . Theessrvention . How to rvention .Lower extremity skeletal muscle has been found to display increased intramuscularfat content in patients with HFpEF compared with age-matched controlindividuals, the extent of which was inversely correlated with exercisecapacity. Also, morphological and histochemical changes in skeletal muscle havealso been described in HFrEF, including marked abnormalities in skeletal musclemass, composition, capillary density, fiber type, oxidative metabolism,mitochondrial mass, and mitochondrial function . Some ofAs described above, peripheral endothelial dysfunction has been reported in HFpEF and mighObviously, HFpEF is not merely caused by one pathophysiological factor, but in factis a complex, highly integrated, multisystem loss of cardiac and vascular reservecapacity affecting the left and right ventricles, diastolic and systolic function,atrial reserve, heart rate and rhythm, autonomic control, the vasculature andmicrocirculation, and the periphery .Patient2+ regulation, decreased β-adrenergic reserve, and physicaldeconditioning, have been identified as critical contributing causes for HFpEF, and evidenceof cardiac dysfunction as a cause of symptoms  .Major HThe latest report from Reddy et al. reveals Clinical manifestations of HFpEF are the same as those for general HF, includingHFrEF. By far, dyspnea  and fatigue are the most common symptoms. Physical signsof HF  may or may not be present .Echocardiography is a key component of the diagnosis and evaluation of patientswith suspected HF. Evaluation of patients with HF includes Doppler transthoracicechocardiography to evaluate LVEF, estimate pulmonary artery systolic pressure,assess left ventricular filling pressure, and assess cause of HF . HoweverCauses of the clinical syndrome of HF with an LVEF ≥50% other than HFpEF includea cardiomyopathy , cardiacamyloidosis, significant valve disease (severe stenosis or regurgitation or atleast moderate mixed stenosis and regurgitation), pericardial disease , and high-output HF . ClinicaFrom the above, HFpEF should be suspected in individuals with all three of thefollowing features: 1) one or more symptoms of HF such as dyspnea or fatigue;physical signs of HF may or may not be present; 2) an LVEF ≥50%; and 3) noapparent cause of HF symptoms other than HFpEF.2 (two points); 2) Hypertensive: two or moreantihypertensive medicines (one point); 3) Atrial fibrillation (AF): paroxysmalor persistent (three points); 4) Pulmonary hypertension (PH): pulmonary arterysystolic pressure >35 mmHg using Doppler echocardiography (one point); 5)Elder: age >60 years (one point); 6) Filling pressure: Dopplerechocardiographic E/e' >9 (one point)  Heave point) .The probability that HFpEF is the cause of symptoms increases with increasingtotal H2FPEF score (range 0 to 9). A low H2FPEF score of 0 or 1 is associatedwith a low (<25%) probability of HFpEF. An intermediate H2FPEF score of 2 to5 is associated with an intermediate (40 to 80%) probability of HFpEF. A H2FPEFscore of 6 or greater is associated with a greater than 90% probability of HFpEFand is thus considered diagnostic for HFpEF.A low score of 0 or 1 suggests that symptoms are most likely due to a non-cardiaccause, and such reasons should be investigated . HoweverWith an intermediate H2FPEF score of 2 to 5, we should take further steps toassess the following: 1) Is the natriuretic peptide level high (BNP >100pg/mL or NT-proBNP >300 pg/mL)?; 2) Is there an absence of significant lungdisease? If the above criteria are satisfied, then HFpEF can be diagnosed. Ifany of the above or both are not met, we suggest cardiology consultation andright heart catheterization .Right heart catheterization is not universally required for diagnosis andevaluation of HFpEF . HoweverThe H2FPEF score was derived from data of 414 patients with an LVEF ≥50% (267with HFpEF confirmed by pulmonary capillary wedge pressure and 147 withnon-cardiac dyspnea) and validated in a test cohort of 100 patients (61 withHFpEF) . The oddClinical trials in HFpEF have produced neutral results to date and treatment islargely directed toward associated conditions  and symptoms. This approach is consistent with recommendations for treatment ofpatients with HFpEF included in the 2013 American College of CardiologyFoundation/American Heart Association (ACC/AHA) HF guidelines . The folThe management of HFpEF differs from the management of HFrEF . The resDiastolic function worsens as part of aging even in individuals without other formsof cardiovascular disease . AsymptoIn this section, we will discuss the HFpEF treatment around the management ofassociated conditions and pharmacologic therapy. As for the prognosis of HFpEF, wewill discuss it in detail using the latest clinical trials.A key component in the treatment of HFpEF patients is treating the contributingfactors and the comorbidities of the disease . This meHypertension remains one of the major modifiable risk factors in HFpEFdevelopment and progression. Of nearly 400 cases of new HF in the Framinghamstudy, 91% were preceded by the development of hypertension . TreatmeAtrial fibrillation (AF) and HF often co-exist. The presence of one increases thelikelihood of the other and each can cause the other . AF is cMyocardial ischemia in HFpEF can result from epicardial coronary artery disease(CAD), high wall stress, or microvascular dysfunction . CAD is Hyperlipidemia is the abnormally elevated levels of any or all lipids orlipoproteins in the blood. Treatment of lipid levels is recommended for theprimary and secondary prevention of cardiovascular disease . Two larTreatment of HFpEF is mostly governed by the management of associated conditionsand symptoms since there is limited direct evidence to support a specific drugregimen. Based on the available evidence, we suggest treatment with amineralocorticoid receptor antagonist in patients with HFpEF who can beappropriately monitored. Diuretics are used to treat volume overload, but asnoted above, care must be taken to avoid volume depletion . Other m−1·(1.73m2)−1. Evidence to support this approach comes fromthe Treatment of Preserved Cardiac Function HF with an Aldosterone Antagonist(TOPCAT) trial (For patients with clear evidence of HFpEF (including increased brain natriureticpeptide [BNP] or NT-proBNP) who can be carefully monitored for changes in serumpotassium and renal function, we suggest treatment with a mineralocorticoidreceptor antagonist. The serum potassium should be <5.0 mEq/L and estimatedglomerular filtration rate should be ≥30 mL·minT) trial ,53.Diuretics improve symptoms of HF patients and are widely used irrespective ofLVEF . Loop diEvidence of efficacy of beta blocker therapy in patients with HFpEF is lacking.An individual patient-level meta-analysis of 11 randomized controlled trials ofbeta blockers in patients with HF found no evidence of benefit in the smallsubgroup of patients in sinus rhythm with LVEF ≥50% . There wHypertension is one of the leading causes of HFpEF in older adults and calciumchannel blockers (CCB) is one of the commonly prescribed anti-hypertensive drugs. Becauseposthoc analysis at one year found that treatment with perindopril wasassociated with an almost significant trend toward reduction in the primaryendpoint of combined all-cause mortality and unexpected hospitalization for HF;this effect was entirely due to fewer unexpected hospitalizations for HF. Thepatients treated with perindopril also had significant improvements infunctional class and six-minute walk distance . In a cohortof 2802 patients discharged from 103 hospitals in Ontario with a diagnosis ofdecompensated HF, one-year mortality was 22% in patients with HFpEFvs 26% in patients with HFrEF. This difference was notstatistically significant  class, lowerLVEF, the extent of coronary artery disease, peripheral artery disease,diabetes, impaired renal function, the degree of diastolic dysfunction asassessed by Doppler echocardiography, elevated plasma natriuretic peptidelevels, pulmonary hypertension, RV dysfunction, and increased red celldistribution width .The proportions of cardiovascular and non-cardiovascular deaths among patientswith HFpEF have varied among trials and epidemiologic studies, with higherproportions of non-cardiovascular deaths in population-based studies . The modMorbidity outcomes in HFrEF and HFpEF are similar. These include the rate andfrequency of hospitalization for HF, symptomatic status as measured byabnormalities in myocardial oxygen consumption, six-minute walk distance,Minnesota Living with HF Questionnaire scores, and other quality-of-lifeindicators. Therefore, patients with HFpEF have a morbidity burden equivalent tothat of patients with HFrEF .In particular, we should note that HFpEF and diastolic dysfunction are notsynonymous. LVEF is an essential clinical indicator. Patients with HFpEF have LVEF≥50%, usually with standard heart size and typically exhibit concentric remodelingor hypertrophy. The most apparent and common abnormality in HFpEF patients isassociated with diastolic dysfunction. Diastolic dysfunction may coexist with slackdamage or increased chamber stiffness or both. These abnormalities, in turn, lead todifficult breathing. In addition to diastolic dysfunction, patients with HFpEF alsoexhibit limitations such as systolic dysfunction , contracAbout half of all HF patients worldwide have LVEF ≥50%. The main contributors toHFpEF are systemic hypertension, aging, coronary artery disease, diabetes, metabolicsyndrome, obesity, and kidney disease. Occult coronary heart disease is a common andpotentially reversible cause of HFpEF. In the diagnosis of HFpEF, it is vital toexclude mimics including non-HF conditions and other causes of HF with LVEF ≥50%,such as valvular heart disease, pericardial disease, cardiomyopathy, cardiacamyloidosis, and high output HF. Regarding the diagnostic approach, we highlyrecommend H2FPEF score diagnostic method from Barry A Borlaug's latest study , which iAn essential component in the treatment of HFpE is the treatment of contributingfactors and comorbidities that often occur and significantly affect clinicalprocesses. The most common include high blood pressure, lung disease, coronaryartery disease, obesity, anemia, diabetes, kidney disease, and sleep-disorderedbreathing. The general principles for the treatment of HFpEF are to controlpulmonary congestion and peripheral edema, treat systolic hypertension, preventrapid heart rate , and establishcoronary revascularization in patients with coronary heart disease. Diuretics orintravenous dilators must be used with caution in patients with left ventriculardiastolic dysfunction with a small, stiff left ventricle. When atrial fibrillationoccurs in patients with HFpEF, restoration and maintenance of sinus rhythm ispreferred, followed by rate control. We do not recommend the use of beta-blockers totreat HFpEF without alternative indications such as angina. For patients with clearHFpEF evidence (including increased BNP), we recommend monitoring changes in serumpotassium and renal function and treatment with mineralocorticoid antagonists. We donot recommend the use of organic nitrates, phosphodiesterase-5 inhibitors, ordigoxin  for thetreatment of patients with HFpEF. Exercise training is the only intervention thatcan continuously improve HFpEF's functional capacity and quality of life. Themorbidities of HFpEF patients are almost identical to that of HFrEF patients.Mortality rates for both HFpEF and HFrEF are high; so far, published data onmortality differences are contradictory.HFpEF is a complex disorder caused by multifactorial stresses secondary tocomorbidities. The current challenge is finding new multidirectional strategies toabrogate cardiac remodeling. Exploring detail pathophysiological mechanisms, seekingeasy and popular diagnostic approaches, and finding a precise treatment are the mainobjectives to overcome HFpEF.