The clinical notes in electronic health records have many possibilities for predictive tasks in text classification. The interpretability of these classification models for the clinical domain is critical for decision making. Using topic models for text classification of electronic health records for a predictive task allows for the use of topics as features, thus making the text classification more interpretable. However, selecting the most effective topic model is not trivial. In this work, we propose considerations for selecting a suitable topic model based on the predictive performance and interpretability measure for text classification. We compare 17 different topic models in terms of both interpretability and predictive performance in an inpatient violence prediction task using clinical notes. We find no correlation between interpretability and predictive performance. In addition, our results show that although no model outperforms the other models on both variables, our proposed fuzzy topic modeling algorithm (FLSA-W) performs best in most settings for interpretability, whereas two state-of-the-art methods (ProdLDA and LSI) achieve the best predictive performance. Inpatient violence at psychiatry departments is a common and severe problem . Then, we analyze how each model's interpretability compares to its predictive value. From this analysis, we make the following contributions:We are the first to analyze both the predictive performance and topic modeling interpretability.We compare 17 topic modeling algorithms based on both criteria.We present considerations that can be used for the selection of a topic model for text classification.The outline of the article is as follows. In Section 2, we describe how topic models work, how they can be used for text classification, how different algorithms relate to one another and which measures are used for evaluation. In Section 3, we describe our comparison methodology and the data set that we used. In Section 4, we provide tables and show graphs to illustrate how different topic modeling algorithms compare to each other. In Section 5, we discuss our findings, its implications and we conclude the work in Section 6.We compare different topic modeling algorithms based on their interpretability and predictive performance for text classification. In this section, we describe the task of text classification, followed by a description of topic models. Then, we discuss the best-known topic modeling algorithms and discuss how these have been used for text classification.Classification models are a set of techniques that map input data (in the feature space) to a fixed set of output labels Flach, . Text clrepresentation step,classification step.In the first step, a text file is transformed from a string into a numeric representation, called an embedding. The classification algorithm in the next step then calculates the most likely label based on the embedding. The choice of the technique depends on various aspects such as the number of features, the size of the data set and whether a technique should be interpretable. Typically, classification models are considered to be interpretable if they can indicate the weights that have been assigned to each input feature. Amongst classification models, the subset of commonly used interpretable models include linear regression, logistic regression, decision trees, fuzzy systems, and association rules  to represent each word as a one-hot-encoding - the probability of word i given topic k,P(Tk|Dj)- the probability of topic k given document j,with:i word index i∈{1, 2, 3, ..., M},j document index j∈{1, 2, 3, ..., N},k topic index k∈{1, 2, 3, ..., C},M the number of unique words in the data set,N the number of documents in the data set,C the number of topics.n words with the highest probability per topic are typically taken to represent a topic. Topic models aim to find topics in which these top-n words in each topic are coherent with each other so that the topic is interpretable and a common theme can be derived. Using topic embeddings for text classification, each input document is transformed into a vector of size C. Each cell indicates the extent to which the document belongs to a topic. After predictions are made for each input text, interpretable classification algorithms can reveal which topics were most important for performing classifications.The top-We compare a set of state-of-the-art topic modeling algorithms as defined in Terragni et al.  supplemeA. This could be a simple bag-of-words representation, but typically a weighting mechanism such as tf-idf is applied. The algorithms based on dimensionality reduction are the following.The algorithms based on dimensionality reduction all start with a document-term matrix A, NMF returns two matrices W & H. Since the vectors of the decomposed representations are non-negative, their coefficients are non-negative as well. W contains the found topics (topics × words) and H contains the coefficients (documents × topics). Then, NMF modifies W and H's initial values so that its product approaches A.One of the oldest methods is non-negative matrix factorization (NMF) , V becomes the term-topic matrix P(Wi|Tk) and Σ contains singular values in its diagonal.Other foundational work on topic modeling is latent semantic indexing (LSI)A and uses singular value decomposition for dimensionality reduction , then performs fuzzy c-means clustering  starts with matrix  Bezdek,  to find U matrix, which gives singular values for each document, the clustering is based on documents. Yet, topics are distributions over words and therefore clustering words seems to make more sense. Therefore, FLSA-W clusters on the V matrix instead of U, hence by clustering on words directly.Since FLSA works with the While FLSA and FLSA-W implicitly assume that the projection to a lower dimensional space occurs in a meaningful way, there is no explicit step guarantying it. FLSA-V uses a projection method similar to multi-dimensional scaling  is proposed  exist in multiple languages in parallel. They cover similar content, but the linguistic differences make it impossible to use traditional, BOW-based topic models. Models have to be either unilingual or suffer from a vast but highly sparse vocabulary. Both issues can be addressed with transfer learning. The cross-lingual contextualized topic model (CTM), a zero-shot cross-lingual topic model, learns topics in one language and predicts them for unseen documents in different languages. CTM extends ProdLDA and is trained with input document representations that account for word-order and contextual information, overcoming one of the main limitations of the BOW models , which is a Bayesian non-parametric model that can be used to model mixed-membership data with a potentially infinite number of components. In contrast to all the algorithms discussed in this section, HDP is the only algorithm that determines the number of topics itself (rather than being set by the user). Given a document collection, posterior inference is used to determine the number of topics needed and to characterize their distributions  Utrecht's psychiatry ward between 2012-08-01 and 2020-03-01 as used in previous studies  for the classification of each document. We found 20 words to be most interpretable in previous research  to quantify this. Secondly, different topics should focus on different themes and be diverse; for this, we use the diversity score (inter-topic assessment). Then, we formulate the interpretability score as the product between coherence and diversity, similar to Dieng et al.  must be semantically related. We use the coherence score  is calculated for the combination of all the top-n words in a topic. For the calculation of the NPMI, a sliding window of size 110 is used to calculate the probabilities. Then, the arithmetic mean is calculated to aggregate the scores for different topics.Amongst the quantitative measures for intra-topic assessment, such as perplexity or held-out likelihood, methods that are based on Normalized Pointwise Mutual Information (NPMI) correlate best with human interpretation  Fawcett,  and the 20 means the top 20 words only and all means the entire topic distribution is considered. The patterns in Therefore, we will focus on the AUC only for the rest of this analysis. From the graphs, it can be seen that there is no correlation between a model's interpretability and predictive power. Also, no model outperforms other models for both indicators, for all parameter settings. When basing the predictions on the top 20 words per topic only, FLSA-W (with fcm clustering) and ProdlDA seem to perform best. ProdLDA has many instances with the highest predictive performance (and average interpretability) and a few instances with the highest interpretability . In contrast, almost all instances of FLSA-W have high predictive performance and high interpretability, but no instance has a maximum for either of the variables. It seems that FLSA-W operates at a different trade-off point between performance and interpretability than ProdLDA.We study the behavior of different topic modeling algorithms based on their interpretability and predictive performance. LDA was used as a topic embedding in earlier text classification approaches with topic embeddings. However, n (20 in our case) words are preferred over the ones based on the entire distribution on two counts. Firstly, we use topic embeddings for interpretable text classification. It is more intuitive to interpret a set of n words, than it is to interpret complete distributions where all distributions contain the same words, but the probabilities per word vary. Secondly, no meaningful coherence score can be calculated on a full distribution as the coherence considers the words of a topic and not the probability. If the full topic distribution is taken, the coherence score would be the same for all topic models. Note that, the best predictive performance of the LSI model, based on all words, performs almost on par (with the AUC slightly below 0.8) with the best predictive performance in earlier work  seems to be the best model for interpretability, whereas ProdLDA seems the best choice for predictive performance. However, this finding is based on one dataset only, and future work should assess the generalizability to other datasets. We found no correlation between a model's interpretability and predictive performance. More specifically, we observed that some topic models' predictive performance correlate positively with interpretability, whereas others show an inverse correlation. Also, we found that some models' predictions are better when the entire topic distribution is used, whereas others score better with the top 20 words only. This work demonstrates that selecting a topic modeling algorithm for text classification is not straightforward and requires careful consideration. Future work will investigate based on which information classifiers make their decisions. This insight could explain why different models' predictive performance correlates differently with interpretability and why for some models, predictions are better when based on the entire distribution and others are better when based on the top-e.f.g.rijcken@tue.nl.The datasets presented in this article are not readily available because this is a dataset with pseudonymized clinical notes from the EHR. Requests to access the datasets should be directed to ER and UK planned the experiment. The experiment was carried out by ER. PM supported with the coding. The manuscript was written by ER, with support from UK, FS, MS, and KZ. The dataset was provided by FS. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Organically-coated nanomaterials are intensively studied and find numerous applications in a wide range of areas from optics to biomedicine. One of the recent trends in material science is the application of bio-mimetic polydopamine coatings that can be produced on a variety of substrates in a cost-efficient way under mild conditions. Such coatings not only modify the biocompatibility of the material but also add functional amino groups to the surface that can be further modified by classic conjugation techniques. Here we show an alternative strategy for substrates modification using dopamine conjugates instead of native dopamine. Compared to the classic scheme, the proposed strategy allows separation of the “organic” and “colloidal” stages, and simplified identification and purification steps. Modification with pre-modified dopamine made it possible to achieve high loading capacities with active components up to 10.5% wt. A series of organo-inorganic hybrids were synthesized and their bioactivity was analyzed. Surface properties and surface functionality play a very important role in the operational characteristics of nanomaterials. These aspects are becoming even more important when it comes to bio-applied materials due to the complexity and versatility of the interactions on the biointerfaces ,2,3,4. IIn 2007 Lee et al. described a universal coating strategy inspired by the way mussels attach to various surfaces . ProteomFurther development of the conception has shifted attention from commercially-available dopamine to other catechol systems and dopamine derivatives, giving rise to polydopamine-like coatings. This approach relies on dopamine derivatives that are made from dopamine or other catecholic compounds by synthetic approaches with a subsequent application for surface modification ,29,30,31Here we highlight the potency of pre-modified dopamine application in the creation of functional nanocomposites. We chose magnetite nanoparticles as a substrate due to their unique magnetic properties and excellent biocompatibility, and successfully modified them to produce model bioactive hybrid materials. We synthesized several dopamine conjugates and compared the effectiveness of modification using these agents on three types of magnetite nanoparticles. The dependencies of the modification results on the synthetic conditions and bioactivity of the synthesized composites on model systems are presented.Initial efforts were aimed at finding an optimal nanoparticle to be used for further modifications. Among the great variety of the known magnetic nanosystems, we decided to consider three types of magnetite nanoparticles in order to define the best and most convenient one. The first system was magnetite hydrosol (Ferria), which has been described in our previous works ,35. ThisTwo more magnetite hydrosols were used to evaluate the effect of surface chemistry on the effectiveness of biomimetic surface modification. The second tested system was the well-known and widely applied citrate-capped magnetite hydrosol (CitMP) nanoparticles. This type of magnetite hydrocolloid is based on the ability of citric anions to coordinate on the surface of magnetite nanoparticles by two carboxylic and one hydroxyl group, resulting in highly charged and stable hydrosols ,39,40,41 surface ,43. All rameters . The mosrameters g. This dThe comparative characteristics of the used systems can be seen in The surface of the synthesized NPs was modified via biomimetic polymerization of dopamine derivatives. In the initial experiments, the applicability of dopamine derivatives for this process was evaluated. For this purpose we firstly compared the effectiveness of nanoparticle modification by dopamine and dopamine conjugate using glycine (GlyDop) at various pH levels. The coating efficiency for both catechols showed nonlinear dependence on pH level, with a local maximum near pH 8, which correlates with previously published data ,45. The The compositions of the resulting materials were additionally evaluated by TGA measurements, which demonstrated the mass fraction of polydopamine coating in GlyDop@Ferria, GlyDop@NitMP, and GlyDop@CitMP at levels of 32%, 28% and 21% wt., respectively a–c, corrTo confirm the successful modification of the organo-inorganic composites, the materials were characterized with FTIR spectroscopy in order to determine the characteristic functional groups. The measurements were carried out for all three types of MNPs and GlyDop@MNPs composites. The results are presented at  groups. . For speVisualization of the composites with SEM and TEM showed that the obtained materials maintained near-spherical morphology . It can E. coli Nova Blue pBad strain which was genetically designed to produce Green Fluorescent Protein (GFP) when arabinose is added to the nutrient media and compared to AraDop conjugate and free arabinose. The concentration of GFP was measured as a fluorescence intensity at 532 nm, thus allowing for evaluation of the bioactivity of the systems. The results are presented in Biomimetic modification of nanoparticles offers a quick and convenient strategy for surface engineering and may be used to alternate the bioactivity of nanoparticle systems. To evaluate this possibility, two systems were prepared and their bioactivity was tested on bacteria models. In the first model, MNPs were modified with the conjugate of dopamine and arabinose (AraDop). The mass fraction of the organic component was estimated at the level of 11% wt according to UV studies, the produced particles had a hydrodynamic radius of 40 nm, and zeta potential valued at +18 mV. The bioavailability of the material was evaluated on E. coli strain ATCC 25922  form . In AraDIn its turn, the mechanism of ampicillin antibacterial activity is mediated through its interaction with penicillin-binding proteins (PBPs). PBPs are transpeptidases or carboxypeptidases involved in the synthesis of peptidoglycan, which is the major component of bacterial cell walls. Ampicillin and other activity . The intE. coli with a varied efficiency depending on various factors. The mechanisms of these processes are unknown. It was shown that the uptake efficiency can be modulated by NPs surface modification or by addition of various additives such as tissue homogenate S9 fraction containing cytosol and microsomes [Taking into account the above-mentioned issues, it is not surprising that AraDop@Ferria and AmpDop@Ferria had even lower activities compared to free molecules. Due to the high stability of the synthesized coatings, the spontaneous release of AraDop and AmpDop or corresponding bioactive residues is almost absent, so implementation of the bioactivity is possible only after the internalization of nanoparticles within bacteria. It is known from the literature that nanoparticles can be taken up by crosomes ,57,58,59Here, we demonstrated the concept of nanoparticle surface modification based on pre-synthesized dopamine conjugates. Magnetic nanoparticles were modified with dopamine conjugates under mild conditions, leading to organo-inorganic composites with a high rate of the organic component. Application of dopamine conjugates enabled achievement of highly active molecules, loading up to 10.5% wt. The synthesized systems demonstrated bioactivity inherent to the used dopamine conjugates. The contingency of the modification process and bioactivity of the synthesized materials proved the potency of the proposed strategy, which will be further evaluated in the upcoming works.Chemicals: All reagents and solvents were obtained or distilled according to standard procedures. The reagents used for experiments were purchased from Sigma-Aldrich Co. and used as received unless otherwise noted. N,N-dimethylformamide (DMF), acetonitrile, 1,4-dioxane, and tetrahydrofuran (THF) were distilled from phosphorus pentoxide under argon, and solvents were stored under argon.Bacterial Strains: E. coli strain ATCC 25922 and E. coli XL1 Blue carrying plasmid pBADcycle3-mutant strains were grown at 37 Ferria):Magnetite hydrosol :Nitric-acid-stabilized magnetite ( Nanoparticles were synthesized according to the procedure from [ure from . BrieflyCitMP):Citric-acid-stabilized magnetite  was added, under nitrogen, 25 mL of 2.0 N sodium hydroxide and then an ethereal solution of chloroacetic anhydride . Vigorous stirring was continued for 0.5 hr. More sodium hydroxide  and 1.71 g of chloroacetic anhydride were added under stirring. The reaction mixture was continuously stirred for 1 hr more. The reaction mixture was neutralized to pH 6 by the addition of hydrochloric acid and extracted with ethyl acetate four times. The combined extracts left an oily residue, 2.4 g, which was chromatographed on a column of 45 g of silica gel. The column was eluted with chloroform containing 4% methanol, and fractions of 7 mL were collected. Fractions 30–50 contained pure N-chloroacetyl-3,4-dihydroxyphenethylamine which was recrystallized from chloroform to yield colorless crystalline granules , mp 108–109 GlyDop):amino)-2-oxoethyl)glycine  in 1,4-dioxane (20 mL) was added, under nitrogen, 25 mL of 2.0 N sodium hydroxide and then p-tosylglycine . Vigorous stirring was continued for 3 hr. The reaction mixture was neutralized to pH 1 by the addition of hydrochloric acid and extracted with ethyl acetate four times. The organic extracts were combined and dried over MgSOAraDop):Conjugate of dopamine with arabinose  and potassium carbonate  in anhydrous acetonitrile (15 mL) was stirred at room temperature under Nv/v) as the eluent. The solvent was removed under reduced pressure and the residue purified by flash silica gel column chromatography using hexane/ethyl acetate 1:2 (v/v) as the eluent to yield conjugate . Mp 112–114 AmpDop):Conjugate of dopamine with ampicillin  and CDI  in anhydrous DMF (15 mL) was stirred at room temperature under Nv/v) as the eluent. The solvent was removed under reduced pressure and the residue was purified by flash silica gel column chromatography using petroleum ether/ethyl acetate 2:8 (v/v) as the eluent to yield conjugate . Mp 99–102 PDA layer formation on MNPs: For PDA layer formation experiments, 100 Stability of PDA layer: 1 mg of the composite was dispersed in 1 mL of PBS and incubated at 20 Antibiotic activity testing: The strain of E. coli ATCC 25922, which is sensitive to ampicillin, was used for the experiment. 5 Bacteria biofluorescence experiments: 5 E. coli XL1 Blue suspension was added to test tubes with 1 mL LB of nutrient medium and cultured for 24 h. After that, the tested substance was added and the culture was grown for another 24 h in 1 mL of LB culture medium for 24 h at 37


DNA polymerase epsilon mutated (POLEmut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients.Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into  DNA polymerase epsilon (POLE) can identify POLE-mutated (POLEmut) EC. Second, mismatch repair-deficient (MMRd) EC is defined by loss of expression of one of the mismatch repair proteins  by immunohistochemistry (IHC). Third, p53 IHC is performed to identify EC with abnormal p53 expression patterns (p53abn)  EC. Studies on EC with more than one molecular alteration, commonly referred to as “multiple-classifiers,” have served to identify the order by which these tests should be performed  classification in the fifth edition of the World Health Organization (WHO) classification of female genital tumors and the 2021 European treatment guidelines , who div(p53abn) . Finallyerformed . It has ally p53   in EC, POLEmut EC, were first described by Church et al.   , 67, andgrade 3) ; second,grade 3) , 68; thigrade 3) –71; finaLEmut EC –74.POLEmut EC, and only few studies have studied specific markers  pathway leaves unrepaired post-DNA replication errors. Thus, the phenotype of MMR-deficient EC (MMRd EC) much like entation Figure 4entation , 73, 76,LEmut EC , 72. FurLEmut EC , 77, 78.LEmut EC , 15, 18.LEmut EC . A smallLEmut EC , 18. Thid tumors , 80, 81.d tumors . FinallyPOLE mutations, immunohistochemical staining of the four MMR proteins is therefore used to identify MMRd EC. Approximately one quarter of all EC show loss of expression of one of the MMR proteins. The most common combination (about 70%) in MMRd EC is loss of MLH1 and PMS2 expression, which is usually caused by promotor hypermethylation of the MLH1 gene. The remaining cases show loss of protein expression in other combinations, namely, single loss of MSH6, single loss of PMS2, or MSH2/MSH6 loss, of which about 10% is Lynch syndrome associated  has a classic serous histology with a (micro-) papillary or (pseudo-) glandular architecture. The papillae or glands are lined by a single layer of tumor cells with strong cytonuclear atypia resulting in a ragged luminal surface Figure 5LEmut EC , 70, 72.POLE wild type and displays one of the well-described mutant-like immunohistochemical p53 staining patterns  EC. The majority shows a predominant glandular proliferation in which the glands have smooth luminal borders and the nuclei have mild to moderate atypia (FIGO grades 1 and 2)  , 18, 78.utations –90. In autations Figure 6utations –93, whicreported , 70, 72.reported –19, 78.Emerging data suggest that NSMP EC may be further stratified into two groups with a distinct prognosis based on hormone receptor expression status . AlthougPOLEmut EC and p53abn EC while carrying distinct genomic alterations and having excellent and poor prognosis, respectively , 0.76 for MSI (N=25 MSI patients), 0.87 for copy-number high (N=20 copy-number high patients), and 0.65 for copy-number low (N=43 copy-number low patients) on the CPTAC external test set. Additionally, they obtained an AUC of 0.77 for the prediction of the TP53 mutational status (N=30 TP53-mutated patients). Although both studies represent first proofs of concept of predicting genetic mutations from H&E slide images that can be expanded to EC, the test sets remained relatively small and did not reflect the heterogeneity of histological subtypes and FIGO grades known to be present in each molecular class. Specifically, with a few non-endometroid samples included in the TCGA  providing a cost-effective alternative to expensive molecular testing without the need of additional tissue and (ii) speeding the diagnosis process up and advancing treatment initiation, which, in a real-world practice, can be delayed by weeks with next-generation sequencing. Until a clinical-grade accuracy of such model has been achieved, alternative and less complex DL tasks can be taken forward to support EC patient management. In fact, a binary predictive model trained toward one single specific molecular alteration may yield a higher sensitivity and specificity than a four-class model and could subsequently serve as a pre-screening tool. In the field of EC, pre-identifying p53abn EC in a population of low-risk EC may potentially be used to identify those few patients with a poor prognosis for confirmatory testing . Similarexplored , 65, 70.An avenue by which DL has currently probably the biggest role is as a research tool combined with gynecopathological expertise. Several studies showed , 37, 44 With increasing availability of digitalization aids such as cloud computing and resources, DL-driven diagnostic tools could be made available worldwide as an additional inexpensive, if not free, resource without the need of local hardware or knowledge . ParticuPOLEmut EC and MMRd EC (In the past four decades, the classification of EC has evolved from a histology-based to a molecular system. The recent integration into guidelines indicates the increasing prognostic value of the four-class molecular classification over morphology in EC , 2, yet  MMRd EC , 70. LasIn the quickly progressing research domain of computer science, DL has demonstrated a well-known capability to work with high-dimensional and multi-modal datasets, up to learn phenotype–genotype correlates from highly complex and extra-large digitized tumor slides , 54, 55.SF wrote sections of the review. All authors contributed to manuscript revision, read, and approved the submitted version.This work was supported by an unrestricted grant by the Hanarth Foundation.VK: Research grants from Swiss Federal Institute of Technology Strategic Focus Area: Personalized Health and Related Technologies PHRT, the Swiss National Science Foundation and Promedica unrelated to the current works. NH: Unrestricted research grants unrelated to the current work from the Dutch Cancer Society and Varian. TB: Research grants from the Dutch Cancer Society unrelated to the current work.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The morphologies and size of the electrospun ultrathin nanofibers are characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atomic force microscopy (AFM). AFM images combined with theoretic calculations show that sub-nanometer fiber of approximate 0.17–0.63 nm only containing one molecular chain was generated via electrospinning from ultra-dilute PAA solutions for the first time. These quite small sub-nanometer fibers would open a new area of electrospinning and provide further explorations on the production and application of electrospun sub-nanometer fibers with single molecular chains.Sub-nanometer materials have received wide attention due to their unique properties in recent years. Most studies focus on the preparation and properties investigation of the inorganic sub-nanometer materials, while there are few reports on organic especially polymeric sub-nanometer materials such as sub-nanometer fiber due to the obstacles with respect to fabricating such small nanofibers. In this work we prepare PAA nanofibers with diameters ranging from hundreds of nanometers down to sub-nanometer  Super-fine nanofibers with diameter below 1 nanometer are prepared by electrospinning from ultra-dilute solutions. Nanocluster, graphene, and sub-nanometer metal or inorganic wires (SNWs) are representative SNMs.2–7 SNWs were usually synthesized by template assisted growth,8 oriented attached growth,9 ligand controlled growth,10 catalyst-guided growth.11 Most of the SNMs are made from carbon materials, metal or inorganic materials. However, there are few reports regarding the one dimensional sub-nanometer polymeric fibers. Generally, sub-nanometer polymeric fibers refer to the one dimensional structure with ultrahigh aspect ratio with the diameter ranging from several angstroms to dozens of angstroms. From the theoretic point of view, such sub-nanometer polymeric fibers can only contain several polymer chains or even single macromolecular chain. These sub-nanometer fibers consisting of only one polymer chain have potential applications for size standards or molecular mass. The behavior of sub-nanometer fibers is expected to be different from bulk polymer materials or even conventional nanomaterials. Direct behavioral information of sub-nanometer fibers will inspire the appearance of new theories in polymer physics and also be very useful for the development of molecular devices based on high performance polymer materials. However, it was difficult to obtain its single molecular level information, e.g. mechanical, electrical and magnetic properties due to the obstacle toward fabricating such small nanofiber. Ultrathin nanofibers with the diameter of less than 100 nm can be produced by phase separation,12 self-assembling,13 sea-island method,14 template synthesis,15 electrospinning,16 and bubble-electrospinning.17 For example, crystalline nanofibers of linear polyethylene with the diameter of ranging from 30 to 50 nm was obtain by freeze-drying the polymeric mass with ultra-high molecular weight polyethylene.18 Fu Renchun et al. fabricated pure PANI nanofibers with high electrical conductivity and a diameter of 40–50 nm using ethyl cellulose as the template.19 Kazuhiro Nakata et al. prepared continuous PET nanofibers with the diameter of 39 nm by sea-island-type conjugated meltspinning and laser-heated flow drawing method.20 Yang et al. successfully produced ultrafine PVA nanofibers with diameter of 20 nm or less through bubble-electrospinning method.21 In our previous work, continuous nylon-4,6 nanofiber with diameter of 1.6 nm had been successfully yielded by electrospinning, which only contains tens of polymer chains in theory.22 To the best of our knowledge, it is the smallest nanofiber which has been reported until now. Electrospinning is a straightforward and simple method to produce polymer nanofibers with diameters ranging from several micrometers to several nanometers, by forcing polymer liquids (solution or melt) through a spinneret with an electric field.23–25 Electrospinning occurs when the electrical forces form the electrostatic repulsion at the surface of a polymer solution or melt overcome the surface tension and creates an electrically charged jet out of the pipette.26 In traditional electrospinning, researchers are always restricted by the mechanism of electrospun fiber formation requiring highly entanglements of macromolecules in a certain viscous polymer solution.27–29 Although electrospinning is a facile technology to produce ultrathin nanofibers, till now, to the best of our knowledge, it is still a great challenge to prepare sub-nanometer fibers by electrospinning.In the past several decades, huge progress has been made in nanomaterials, and sub-nanometer materials (SNMs) is a burgeoning multi-disciplinary topic in nanoscience. The term, sub-nanometer, is used to delimit the size below one nanometer.Via electrospinning an ultra-dilute solution (0.03–0.01 wt%) from a polyamic acid (PAA) with ultra-high molecular weight (Mn > 106 g mol−1), the sub-nanometer fibers with diameter approximate 0.17–0.63 nm were achieved. Further molecular simulation suggests that such small nanofiber contains even only one molecular chain. This is the first time to produce single molecular chain-based sub-nanometer fibers from electrospinning, and this discovery is expected to open a new insight to the preparation of single molecular chain-based sub-nanometer fibers and future investigations on their properties and applications.In this work, we break through the constraint of the formation mechanism of traditional electrospun fibers and proposed the concept to produce sub-nanometer fibers by electrospinning ultra-dilute polymer solution with weak macromolecular entanglement. 2.2.130,31 The macromolecular structure of PAA was shown in Fig. S1.η]) and the average molecular weight (Mw) were 5.4 dL g−1 and 1.09 × 106 g mol−1, respectively.The PAA solution (8.74 wt%) for electrospinning, was synthesized from equimolar ratio of dianhydride BPDA and BPA by a polycondensation in DMAc at −5 °C with intense mechanical stirring for 24 h.2.2−1, by applying a positive 30 kV and a negative 10 kV electrical potential between a spinneret and a collector. The electrospun nanofibers were collected with 20 cm collecting distance by copper grids with holy carbon films for SEM and high-resolution TEM measurement and freshly cleaved mica for AFM characterization respectively. All the samples were dried at 80 °C for 8 h in vacuum to remove the residual solvent.The solutions for electrospinning were prepared by diluting the as-prepared PAA solution (8.74 wt%) with different concentrations from 6 wt% to 0.01 wt%. 0.1 wt% of DTAC with respect to DMAc was added to increase the electrical conductivity of the solutions when the PAA solution concentration was below 2 wt%. Polymer solution in DMAc was held in a syringe with an internal diameter of about 12.5 mm with a needle . The electric fields were 200 kV m2.3The intrinsic viscosity measurements were measured at 25 °C using an ubbelohde capillary viscometer. The morphologies of polymer nanofibers were examined by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atomic force microscopy (AFM). SEM measurements were performed with a JSM-6701F microscope  at an acceleration voltage of 5 kV. TEM images were obtained using a JEM 2100 microscope  and a Technai-12 microscope  with an operation voltage of 200 kV and 120 kV, respectively. AFM measurement was carried out with a Multimode V8 instrument equipped with a NanoScope V controller  and a standard silicon cantilever (OTESPA model) with a length ranging from 140 to 180 μm and typically resonance frequency of about 300 kHz. The tip radius is about 7 nm. All AFM images (512 × 512 pixels) were obtained with a scan rate of 1.0 Hz and scan angle of 0° in tapping mode at room temperature under ambient conditions. Offline images were only simply flattened by Nanoscope III 5.12r2 software without any further process.3.3.132–34 The formation of macromolecular chain entanglements has been acknowledged as the primary effect for the fiber formation. According to the concentration dependence of viscosity of linear polymers in good solvents, Colby et al. identified four different concentration regimes including dilute, semidilute unentangled, semidilute entangled, and concentrated regimes.35 The critical chain overlap concentration (c*), is the boundary between the dilute and the semidilute concentration regimes. The entanglement concentration (ce) is the crossover concentration between the semidilute unentangled and semidilute entangled regimes. Physically, ce is defined as the point at which a significant overlap of polymer chains topologically constrains chain motion, causing entanglement couplings. Guopta et al. defined the value of c/c* for the different solution regimes, dilute regime (c/c* < 1), semidilute unentangled regime (1 < c/c* < 3), semidilute entangled solutions (3 < c/c* < 6), relatively concentrated solutions (c/c* > 6), and the entanglement concentration (ce) (c/c* ∼ 3).36Diameters of electrospun fibers can be tuned by changing the concentrations of polymer solutions.c*) is frequently defined by the reciprocal of the intrinsic viscosity,36,37 1/[η]. Hence, according to Guopta's study, c* of the PAA solution is about 0.19 wt%, and the concentrations used to electrospinning in this study can be divided into four categories: dilute regime (0.01–0.1 wt%), semidilute unentangled regime (0.3 wt% and 0.5 wt%), semidilute entangled (1 wt%), and concentrated regime (2–6 wt%). The absolute viscosity and electrical conductivity of the above polymer solutions with different concentrations were summarized in Table S1Electrospinning of dilute, semidilute unentangled, semidilute entangled and concentrated polymer solutions resulted in the formation of only polymer droplets or beads, a mixture of polymer droplets and beaded fibers, beaded fibers, uniform and beaded free fibers, respectively. In general, the critical chain overlap concentration  and c/c* (X) in this study can be fitted by cubic model  and the fiber diameter Y increased dramatically. In the semidilute entangled regime (III) and semidilute unentangled regime (II), the effect of solution concentration on the fiber diameter became smaller and smaller. When the solution concentration shifted into dilute regime (I), it had a tiny effect on the fiber diameters. In this regime (I), the fiber diameter decreased in the range of sub-nanometer, where fibers composed of single molecular chain or several molecular chains could be obtained.By correlation the fiber diameters to the solution concentrations, the  Table S2, respect3.4ab initio simulation package (VASP),44–46 and the Perdew–Burke–Ernzerhof (PBE) type exchange-correlation.47 The kinetic energy cutoff for the plane-wave expansion was set to 500 eV, and the effects of spin polarization were considered. The repeat unit of single PAA chain contains 28 C atoms, 2 N atoms, 6 O atoms and 18 H atoms in the systems with the dimension of 30 × 30 × 21.86 Å. The minimum distance between the sidewall of the individual macromolecules of PAA and its periodic images is greater than 24.0 Å, which is large enough to avoid the interaction between the neighboring single PAA chains. And a (1 × 1 × 3) Monkhorst–Pack was used for the k-point sampling. The role of the van der Waals (vdW) interaction was investigated using DFT-D2 method including the pairwise force field implemented by Grimme.48To investigate the molecular conformation in such sub-nanometer fibers, theoretical calculations were performed. The structural optimizations for macromolecules of PAA were performed using the projector-augmented wave (PAW) formalism of density functional theory (DFT), as implemented in the Vienna 3.5Theoretical simulation was performed to investigate the stack of PAA macromolecules in the sub-nanometer fibers. 3.626,33,49,50 Polymer chain entanglements are one of many parameters that can significantly affect nanofiber formation during the electrospinning process. At high concentration, chain entanglement behaves in a similar manner as chemical cross links, significant overlap of neighboring chains occurs, so uniform nanofibers can be produced via electrospinning. In ultra-dilute polymer solutions, the randomly distributed chains are separated and there is no topological constraint or polymer chains entanglement in initial electrospun solution. From the perspective of the fiber formation by traditional electrospinning, in dilute regime, application of voltage results in electrospraying or bead formation primarily due to Rayleigh instability.28 However, in this paper, we successfully fabricated sub-nanometer PAA fibers via electrospinning of ultra-dilute PAA solutions (concentrations below 0.1 wt%). The formation mechanism of traditional electrospinning had been investigated in detail by Reneker group51,52 and other researchers.53,54 Reneker et al. found that the detail motion path of the continuous jet is complex and the draw ratio of the jet under the applied electrical forces can reach up to 105 accounting for evaporation of the solvent.51 When longitudinal strain rate multiplied by the conformational relaxation time of the molecule is greater than 0.5, the random coil begins to transform to a stretched macromolecule and the macromolecular coils are likely to be stretched along the axis of the jet, which led to the decrease of cross-sectional area of the jet as much as 105. Presumably, in the process of electrospinning of ultra-dilute solution, when the ejected jet flows far away from the tip, the solvent evaporates rapidly and the concentration of jet increases considerable. Subsequently, weak topological constraint between polymer chains and gradient viscosity occurs. It gives the macromolecular chains a chance to entangle with each other. When the chain entanglements can sufficiently stabilize the electrospinning jet, sub-nanometer fibers can be yielded, otherwise the jet break up into little droplets.It is well known that nanofiber properties can be effect by numerous parameters such as applied voltage, needle to collector distance, initial jet radius, relaxation time, and solution properties like viscosity, polymer concentration, conductivity and solvent.4.−1 can lead to the electrospun fibers in sub-nanometer. SEM and TEM are difficult to observe such small fibers due to the sensitivity of polymer molecules to electron beam and the spatial resolution of electron microscopy. AFM with tapping mode are useful to observe the morphology and measure the fiber diameter of such sub-nanometer PAA fibers. From AFM measurements, the smallest sub-nanometer PAA fibers possessed diameters in the range of 0.17–0.63 nm. Further theoretic simulations suggest that such sub-nanometer fibers contain only one or two PAA molecular chains. Future developments on such electrospun sub-nanometer fibers could be their physical properties and applications.In this work, sub-nanometer fibers containing only one or two polymer molecular chains were successfully prepared for the first time by electrospinning from ultra-dilute PAA solution. The fiber diameters are greatly influenced by the solution concentration and conductivity. The PAA solutions in dilute regime (0.01–0.1 wt%) with electrical conductivity ∼120 μS cmThere are no conflicts to declare.RA-008-C7RA13444D-s001


Considering the metal-based nanocrystal (NC) hierarchical structure requirements in many real applications, starting from basic synthesis principles of electrostatic spinning technology, the formation of functionalized fibrous materials with inorganic metallic and semiconductor nanocrystalline materials by electrostatic spinning synthesis technology in recent years was reviewed. Several typical electrostatic spinning synthesis methods for nanocrystalline materials in polymers are presented. Finally, the specific applications and perspectives of such electrostatic spun nanofibers in the biomedical field are reviewed in terms of antimicrobial fibers, biosensing and so on. In recent decades, electrostatic spinning has received more and more attention as an important technology for the preparation of micro/nanomaterials due to the rapid demand for fiber-based applications. Electrostatic spinning is a process in which a polymer solution is stretched into nanofibers by applying a high-voltage electric field. The concept of electrostatic spinning was conceived as early as 1600 by Gilbert, who observed in a study that polymer solutions could form conical droplets under an electric field   nanofibers enabled by the dispersion of PVA and Au NCs in distilled water by controlling the corresponding electrospun conditions . Electron fibers . They foa et al.  70]. Ce. Ce3O4-m3-soluble PVA solution at 105 °C for 1 h, resulting in Ag NCs being generated in this process, and then the nanofibers were obtained by the electrospinning process. The second method was to dissolve PVA in deionized water to obtain a viscous solution and then add AgNO3 as an electrospinning solution. After the end of electrospinning, Ag NCs could be formed inside the nanofibers under full ultraviolet (UV) lamp illumination. Among them, the size and yield of doped Ag NCs can be adjusted by controlling preheating treatment or UV irradiation [2 acetone solution to load Pt. After heat treatment in an inert atmosphere, the nanofibers loaded with Pt NPs were carbonized at high temperatures. In another representative study by Dakota et al., Ag NP-modified polycaprolactone (PCL) nanofibers were fabricated by in situ plasma treatment [3 were dissolved in acetone followed by electrospinning under appropriate conditions. The electrospun fibers were then treated with air plasma, which could be a simple and effective method to generate Ag NPs on fiber membranes.In situ growth of NCs on electrospun fibers is another efficient strategy to achieve NC-modified nanofibers. Generally, the precursor solution of the metal salt is dissolved in the electrospinning solution followed by using light, electricity, heat, chemical reduction and other methods to trigger the reduction in and oxidation of metal ions in the electrospun solution or electrospinning fiber. As shown in adiation . Soon etadiation . The PANreatment . First, 2S NPs in a N-N dimethylformamide/tetrahydrofuran (DMF/THF) solvent mixture forming Cu2S NC-mixed polymer fibers, as shown in Similarly, Wang et al. dissolved polylactic acid (PLA)/PCL and Cu4 (TIP), ethanol and hydrochloric acid was used for electrospraying, as a precursor to Ti. The obtained coating fibers were sufficiently dried under vacuum and then transferred to an autoclave for hydrothermal treatment and the Ti precursors were finally converted into TiO2 NPs [As for the synthesis of semiconductor NC-modified nanofibers, as shown in TiO2 NPs .By virtue of the aforementioned advantages of electrospun nanofibers, such as nanoscale size, high porosity and large specific surface area, their potential applications have been widely studied in many fields ,9,10,11.3, which indicated that samples with a concentration of AgNO3 at 0.066 mol/L had the highest antimicrobial rates against E. coli and S. aureus, at 98% and 99%, respectively. By direct mixing, Erick et al. incorporated Ag NPs into PCL electrospun nanofibers to study their antimicrobial properties. They demonstrated the antibacterial activity of fiber scaffolds by agar diffusion and the results indicated that the antibacterial activity of fiber scaffolds on S. aureus, E. coli, K. pneumoniae and P. aeruginosa was directly proportional to the concentration of Ag NPs. Compared with Gram-negative bacteria , Gram-positive strains  were more sensitive to PLA-Ag NPs nanofibers [p < 0.05) and bacterial fluorescence was reduced on the Ag-modified scaffolds after 24 h inoculation against Staphylococcus aureus and Streptococcus mutans. In a mouse periodontal disease model, the PP-pDA-Ag-COL scaffold enhanced alveolar bone regeneration (31.8%) and was effective for periodontitis treatment. These results demonstrate that this novel PP-pDA-Ag-COL scaffold enhanced biocompatibility and osteogenic and antibacterial properties.Many kinds of noble metals and oxides of some metals can exhibit certain antimicrobial properties ,80,81. Anofibers . Reza etnofibers . As shownofibers . The sca2 NCs, as an inorganic antibacterial agent, especially under light irradiation, have also gradually received attention as a promising nano-antibacterial material by virtue of their advantages of high stability, nontoxicity and easily manipulated properties. Pant et al. prepared TiO2-containing nylon-6 nanofibers using electrospinning technology and experimentally demonstrated that the nanofibers had good antibacterial properties [2-modified PLA by direct mixing [TiOoperties . Toniattt mixing . The preIn addition to the above materials being widely used in the field of antibacterial biology, nanomaterials, such as Au NPs , Se NPs Biosensing is an important branch of chemical sensing, which has been applied for the detection of small biological molecules, enzymes, nucleic acids, disease markers, cells, bacteria, etc. As for different biological reactions, designing and constructing suitable NC/electrospun composite fiber membranes are important for biosensing applications.3 quantums (CPBQD) by one-step electrospinning [In a recent study reported by Beak et al., Cu nanoflower-modified Au NP-graphene oxide (GO) nanofibers were synthesized as electrochemical biosensors for glucose detection using a novel electrospinning method . Electrospinning . The fib2O2 [2O2 could be an order of magnitude higher than other previously reported electrochemical sensors.Hybrid materials composed of polymer nanofibers and plasmonic noble metal NCs have also developed significantly in recent years for biosensing. For example, as shown in 2O2 . By virtIn addition to antimicrobial applications and biosensing, NC-loaded electrospun fiber membranes were also applied in other biomedical fields. For example, Ming et al. synthesized an electrospun fiber membrane loaded with Au NRs for photothermal treatment of cancer . This stConsidering the huge requirements in applications of flexible electronic devices, biomedicine and energy harvesting and conversion, etc., the organization of inorganic NCs as building blocks coordinated into hierarchical cross-dimensional and cross-size micro/nano matrix with maintained high performances would be attractive. In this review, taking advantage of electrostatic spinning technologies induced polymer fibers, the research progress of inorganic NCs/polymer fibers synthesis and biomedical applications were reviewed. As mentioned above, electrostatic spinning, as a kind of simple but effective fiber production technology, has been widely used in medical releasing, biosensing and other fields. However, inorganic NC/polymer fiber composites still have many challenges to explore, even in biomedical fields, as illustrated in (1) Though NC-modified nanofibers have exhibited high potential in many applications, how to modulate the depositing position of NCs is still challenging. Based on the reported strategies, a portion of NCs could be located inside the nanofibers, which could restrain their access to reactive molecules for efficient catalysis or biomedical applications. Hence, efficient strategies to precisely modulate the depositing sites of NCs on nanofibers could be helpful to further explore their applications ,100.(2) Compared to single-component modified nanofibers, composites consisting of an electrospun fiber membrane with multicomponent NCs could be developed for more biomedical applications, such as wound dressing applications . Though (3) Functional NC-modified electrospun fibers, incorporated with sensing and therapeutic capabilities, could be a potential approach in the development of personalized healthcare . For exa(4) Hybrid NCs, such as Au/semiconductors hetero-NCs with plasmon enhancement, could also exhibit unique biomedical applications when coupled with nanofibers, such as multi-level enhanced chemodynamic, sonodynamic and therapy applications, based on their sonosensitizers and photodynamic functionalities ,108,109.


Background: Acute antibody-mediated rejection (AMR) is an uncommon complication after ABO-compatible liver transplantation (LT). This case series investigated the clinicopathologic characteristics and outcomes of acute AMR in LT recipients with autoimmune liver disease (ALD). Patients and Methods: Among 809 patients who underwent LT from January 2014 to December 2020, four ALD patients developed AMR, which was confirmed based on clinical features, histopathology of liver biopsy, donor-specific antibodies (DSA) or panel reactive antibody (PRA) level. Therapies were individualized based on clinical manifestations. Results: The incidence of acute AMR was 0.49%, and the incidence of acute AMR with ALD and non-ALD recipients was 11.1% and 0%, respectively. Three patients had strongly positive HLA class II DSA, and one patient was with the PRA class I and II sensitivities, which were >80%; complement component 4d (C4d) staining was negative in all patients. The first patient underwent re-LT, and the other three patients had good prognoses with treatments. Conclusions: ALD patients are prone to acute AMR after LT, thus should be kept vigilant against the occurrence of acute AMR. Autoimmune liver disease (ALD) is a rare disease with a reported incidence of less than 50 per 100,000 . SeveralThe incidence of acute T cell-mediated rejection (TCMR) after LT is between 10% and 30% ,5. The iStudies have shown that LT recipients with ALD have a higher incidence of acute TCMR than other LT recipients . PreviouA total of 809 patients underwent primary liver transplantation from January 2014 to December 2020 performed by our medical team, including 36 patients with ALD (4 cases had postoperative acute AMR) and 773 patients without ALD and postoperative acute AMR (these data are only for the adult recipients). Four patients with end-stage liver disease caused by ALD  developed acute AMR within 30 days after ABO-compatible LT. Acute AMR was diagnosed based on the following four criteria: (i) clinical signs of graft dysfunction; (ii) histopathology indicative of acute injury ± positive C4d stain; (iii) presence of PRA or human leukocyte antigen (HLA) DSA; (iv) reasonable exclusion of other lesions that might cause a similar pattern of injury. Biochemical indicators of liver function , aspartate aminotransferase (AST), total bilirubin (TBIL), and direct bilirubin (DBIL)), hepatic histopathology, PRA, HLA DSA, immunosuppressive regimen, and prognosis were evaluated for each patient.Percutaneous liver biopsy guided by ultrasound was performed when graft dysfunction was clinically indicated. All liver allograft biopsies were analyzed by an experienced liver pathologist and reviewed by a second pathologist for the purpose of this study, according to Banff Schema on liver allograft rejection. In addition, all liver biopsies were completed with C4d staining in our center. The C4d positive staining criteria were vascular endothelial cells (>50% lumen) positive staining, including hepatic sinusoidal endothelial cells and portal interstitial microvessels, interlobular vein, and central vein endothelial cells staining ,16.HLA antibody detection was performed using Luminex-bead-based panel reactive screening immunoassays. Detection of HLA antibodies in patients, donor-recipient mismatched HLA comparisons, and DSA positivity with potential clinical significance is preliminarily defined as mean fluorescence intensity (MFI) ≥5000; yet, the positive cutoff values vary from laboratory to laboratory and need to be standardized .2), and plasmapheresis .Four LT recipients received an immunosuppressive regimen with steroids, mycophenolate mofetil (MMF), and tacrolimus in the early post-LT period. Basiliximab  was used for immune induction. The valley of tacrolimus troughs levels varies based on each patient’s clinical condition, with an initial target valley level of 6–7 ng/mL (during the first two weeks of treatment); the actual tacrolimus level was 4–6 ng/mL at the time that AMR was diagnosed. Acute AMR therapies were individualized, and included steroids, ATG , high-dose IVIG , rituximab or bortezomib  and AST (118 U/L) began to increase, while bilirubin levels were normal a. The DoThe second liver biopsy showed the lymphocyte infiltrates in the portal area, cholestasis of the capillary bile ducts, and hepatocyte necrosis around the central vein was more severe than before c. C4d stThe third liver biopsy showed that the inflammatory infiltration in the portal area was relieved, but focal necrosis of hepatocytes around the central vein, further aggravation of central perivenulitis and bile duct cholestasis could be seen e. The liA 34-year-old male with end-stage liver disease caused by ALD received ABO-identical orthotopic LT on May 2017. The AST and ALT began to rise on PTD 20 a. The DoA 48-year-old female with end-stage liver disease caused by ALD underwent ABO-compatible piggyback LT in December 2017. The patient was admitted on PTD 27 due to an elevated AST (147 U/L) and ALT (129 U/L) a. DoppleA liver biopsy revealed mild central perivenulitis and inflammatory portal infiltration b. C4d stA 51-year-old woman with end-stage liver disease caused by ALD underwent orthotopic LT in December 2019. The patient was admitted on PTD 27 due to an elevated AST (257 U/L) and ALT (422 U/L) a. DoppleOn PTD 39, the TBIL and DBIL were 176.2 µmol/L and 157.7 µmol/L, respectively. Besides lymphocytic infiltration in the portal tract, the second liver biopsy showed further aggravated central perivenulitis and hepatocyte necrosis around the central vein c. C4d stIn the next half month, the patient was given plasmapheresis, combined with rituximab , ATG, and IVIG treatment a. DuringFrom January 2014 to December 2020, in our medical group, the incidence of acute AMR was 0.49% (4/809), and the incidence rates of acute AMR after LT in ALD and non-ALD recipients were 11.1% (4/36) and 0% (0/773), respectively. The incidence of AMR was significantly higher in patients with ALD than in those without ALD.Four LT recipients were diagnosed with acute AMR based on the clinical manifestations, histopathological and immunological criteria described above. Simultaneously, the recurrence of ALD was ruled out in all the patients. A summary of their clinical characteristics, histopathology, and outcomes is shown in While treatment modality varied for each patient, all patients were treated with plasmapheresis, which was well tolerated. Because of the delay in diagnosis, patient 1 had acute refractory AMR and received re-LT. The other three patients showed improvement in graft function after therapy. All patients were followed up for 1 year after discharge, and the liver functions were normal. The incidence of AMR in ABO-compatible LT is relatively low, and AMR was found in the case reports and reviews to lead to liver allograft dysfunction, and the failure of transplantation is relatively rare . Herein,According to the 2016 Banff Working Group on Liver Allograft Pathology, the “signature” acute AMR microvascular pathology lesions include endothelial cell enlargement/hypertrophy with dilatation and edema, and endothelitis with intraepithelial and marginating eosinophils, macrophages, lymphocytes, and neutrophils within the portal veins, capillaries, and inlet venules . In addiC4d staining is a key component in diagnosing acute AMR . MeanwhiClass II DSA is more associated with graft dysfunction of all organ types. AMR can occur in prefabricated or de novo class I and/or class II DSA ,27,28. IThe mainstay of treatment is plasmapheresis and IVIG that theoretically remove and neutralize the antibodies mediating the rejection process . The oth7. Simultaneously, a too-low tacrolimus blood concentration should be avoided and maintained at about 7–10 ng/mL. In order to avoid insufficient immunosuppression, post-transplant maintenance immunosuppression regimens should be tacrolimus combined with MMF and steroids.Our data suggest that ALD patients are prone to acute AMR after LT. In order to prevent the occurrence of acute AMR, ATG or basiliximab immune induction is recommended; ATG or basiliximab induction strategies are often used to delay the introduction of calcineurin inhibitor (CNI) therapy in patients with renal dysfunction. Induction therapy is also used for those at higher immunological risk The present study has several limitations. This is a retrospective study, and the number of cases with AMR was small. All four recipients with AMR had ALD, and two had autoimmune hepatitis, while the specific type of the other two cases was primary biliary cholangitis.Herein, we reported four cases of AMR in ALD patients who underwent LT. AMR was not observed in LT patients without ALD. The main characteristic manifestations of AMR were central perivenulitis, hepatocyte hemorrhage, and necrosis around the central vein in liver biopsy, even if the C4d staining was negative. Combined with DSA or PRA, acute AMR should be timely examined. To sum up, our data suggest that ALD patients are prone to acute AMR after LT.


The communication of corporate social responsibility (CSR) highlights the behavior of the business toward CSR and their framework of sustainable development (SD), thus helping policymakers understand the role businesses play with respect to the 2030 Agenda. Despite its importance, this is still a relatively underexamined and emerging topic. In our paper, we focus on what businesses communicate about CSR through social media and how this relates to the Sustainable Development Goals (SDGs). We identified the topics discussed on Twitter, their evolution over time, and the differences across sectors. We applied the structural topic model (STM) algorithm, which allowed us to estimate the model, including document-level metadata (time and sector). This model proved to be a powerful tool for topic detection and the estimation of the effects of time and sector on the discussion proportion of the topics. Indeed, we found that the topics were well identified overall, and the model allowed catching signals from the data. We derived CSR communication indexes directly from the topic model (TM) results and propose the use of dissimilarity and homogeneity indexes to describe the communication mix and highlight differences and identify clusters. Sustainable development (SD) was formally defined in the Brundtland Report as the “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” WCED, . In the Although the SDGs are not legally binding, they are the basic framework by which all United Nations’ (UN) member states can form future regulations and incentives. Incorporating SDGs into corporate strategies and goals will be crucial in the future. The UN is calling for active participation from companies to reach the SDGs. Activities such as donations and charitable events are no longer sufficient. Companies should help address the key issues highlighted by the SDGs by developing new and innovative ideas and business models. Now, including SDGs in the strategy has become an essential choice for companies to achieve sustainable success and competitive advantage. Since the 2030 Agenda is a plan of action, categories of information and initiatives emerging on social media (SM) seem particularly interesting and can help us understand how and to what extent people, businesses, and organizations are playing a more active role in contributing to SD.SD is linked to the commitment of all the agents in society toward different aspects of progress, which clearly cannot be limited to the study and measurement of economic growth and performance based on the Gross Domestic Product (GDP) indicator. However, it should include different environmental and social dimensions  in the context of SD. Various perspectives, methods, and data sources can be used to study and measure CSR. In the following sections the perspective adopted in the paper and the general context are explained. The paper is organized in six sections. Section In this paper, we focus on businesses with respect to SD, with particular regard to their impact on society. Businesses are economic agents that can and should play a role in the complex scenario of SD. For this reason, for example, the performance of businesses should not be measured only with respect to their economic and financial results. It is necessary to go beyond them. There are several aspects impacting society on the path toward a more sustainable world. Businesses are engaging in new initiatives and behaviors. Moreover, the idea of environmentally friendly products and cleaner production processes has become central in the business world. With respect to the social function of firms, they are transforming their role by focusing not only on philanthropic activities but also on initiatives that meet communities’ needs. SD and socially responsible behaviors are linked to the concept of CSR, which refers to the implementation of activities aiming to improve firms’ reputation and positively impact society.This concept of CSR has a long history in the academic literature, which highlights its multi-dimensional nature. In this respect, the definition of CSR has been widely discussed. The dimensions of CSR and their measurement have been a main issue, and here are some examples of the dimensions. As Caroll , 2016 reIn addition to the discussion of the number of dimensions, the traditional literature is about the measurement—that is, the evaluation of the perspective considering the aggregated indicators of business strategies and actions. A direction to take in the literature on CSR is the study and development of rating scores, indexes, and rankings in order to measure the sustainability efforts and performance of firms  source of information and a powerful communication tool for companies. Indeed, the diffusion of SM led to new opportunities, the first of which was the possibility of reaching a broader public base. Also, with respect to other mass and new media channels, communication on SM is different; it is amplified, and the public can actively participate in discussions. In particular, the communication of CSR activities, which plays a fundamental role in enhancing firms’ reputation, can enjoy the new opportunities derived from their use , UN Global Compact, World Business Council for Sustainable Development (WBCSD) from 2015, and WBCSD from 2021. The contribution of businesses to SD and CSR is exploited according to a complex system of behaviors, and this can be investigated through different perspectives. This paper focuses on CSR communication from a new perspective that aims to obtain insights and measures from Twitter data to understand CSR communication with respect to SDG dimensions.How many CSR topics are identified based on our data?Which of the three CSR dimensions  does each topic fall under?Which of the SDG dimensions does each topic fall under?Q2)Are there differences in the relevance of topics across sectors and over time?Q3)Are there differences in the CSR communication mix across sectors?The set of research questions presented below concerns the topics discussed in the new data source, the CSR Twitter accounts of businesses:Q3, we consider the following hypothesis: CSR dimension weight differs across sectors. Sector differences exist between sectors, which differ by target audience/users and by the characteristics of their products. In particular, these differences are between businesses belonging to the services and businesses providing products, especially final goods.With respect to let the data speak. To elaborate, we studied the underlying properties of the text in order to define the topics composing the text and obtain signals from the data. To complete this task, we applied an unsupervised learning technique named topic modeling. Specifically, we applied the structural topic model (STM) in order to discover the topics discussed and estimate the differences among sectors and those appearing over time.The innovative contribution of this paper is threefold. First, standing apart from other studies based on survey data, we consider Twitter as our data source and focus on a specific group of companies, namely those included in the Dow Jones Industrial Average index, in order to study their CSR communication and construct indexes. We do not only focus on CSR but also study how the communication relates, in an indirect way, to the SDGs. Second, from the previous discussion Sect. , it is eThen, starting from the TM results, we propose to compute an innovative index for CSR communication considering different dimensions and use indexes of dissimilarity and homogeneity to identify clusters and study the differences among sectors and firms.To carry out our analysis, we selected firms included in the Dow Jones Industrial Average index, a stock market index that measures the performance of the 30 largest listed US companies as of the composition in August 2020. We retrieved the full list of firms, jointly with the activity sector from Bloomberg.We organized the accounts into three categories: (1) Multipurpose business, (2) News-type, and (3) CSR communication accounts . For the purpose of our analysis, we primarily considered CSR-related accounts in the US. If a CSR account was not available for a company, we selected the news-type accounts, through which firms shared information about company activities. If such an account was not available, we considered the multipurpose one. The underlying reason is that if a CSR-dedicated account is not available, CSR communications are shared through other accounts. We acknowledge that from a statistical point of view, the analysis and selection of Twitter data has some issues. Salvatore et al.  discuss The full set of original tweets (including retweets without a comment) posted on the firm’s timeline were collected using the Academic Twitter API.data cleaning, and it is an important step of the analysis, aiming at keeping only relevant words, reducing the complexity of the model, and speeding up the estimation process. Data cleaning involves different operations: the elimination of punctuation, symbols, numbers, stop words, and URLs; conversion of text to lowercase; and stemming. In addition, we removed currencies  and words with less than two characters from the text. We split words with hyphenation and hyphenation-like characters, and we remove company identifiers, including names and Twitter handles (usernames).Before applying the TM, the text needed to be pre-processed. This phase is also called rule of thumb is to remove the terms that appear in less than 0.5–1% of the documents  representation, which means that each document is represented as a vector of words without giving importance to the order in which they appear.STM is a probabilistic mixed membership model that allows researchers to estimate a model that includes document-level metadata and, thus, to study the relationship between topics and metadata. In this section, we briefly describe the model; for further technical details, please refer to Roberts et al.  study, wLet us consider a set of mentclass2pt{minimtopical prevalence, which is defined as the proportion of the document associated with a topic, and topical content, which refers to the usage rate of the word in a topic. Thus, topical prevalence covariates affect the discussion proportion of the topic (θ), while topical content covariates affect the rate of word usage within a topic (β). The matrices of the K, observed words X and topic content Y, and a number of parameters. Covariate information is introduced into the model using generalized linear models. Essentially, prior distributions with globally shared mean parameters in the LDA model are replaced with means parameterized by a linear function of observed covariates. For topic prevalence, a logistic normal distribution is used, with a mean vector parameterized as a function of the covariates. For topical content, an exponential family model is adopted and then parameterized as a function of the marginal frequency of occurrence deviations for each term and of deviations from it that are specific to topics, covariates, and their interactions.Document metadata are allowed to influence two components of the model: the evidence lower bound on the marginal likelihood. The model converges when the relative change in the approximate variational lower bound is below a defined tolerance level. The model is developed in the R package stm . In the E-step, the variational posterior for each document’s topic proportions is updated, and in the M-step, the topical prevalence and content coefficients are estimated, maximizing the approximated Spectral initialization, a deterministic algorithm based on the method of moments, is suggested due to its stability . This is because the twitter communication is characterized by audience involvement and a specific wording which makes difficult to assign some topics only to one dimension. It is described in more detail in Sect. To build the index, first, we assigned topics to one of the CSR dimensions—namely economic (Eco), environment (Env), and social (Soc). Given the specific characteristics of communication on Twitter, when addressing soft assignment). These are also frequency distributions and, in order to distinguish between firms or sectors, which give the same importance to all dimensions or which are specialized in one of them, it is important to study their heterogeneity. For this purpose, given a frequency distribution over CSR dimensions, i = 1, 2, 3, 4 denoting CSR dimensions and j = 1, …, J denoting sectors, we proposed to compute the Frosini heterogeneity index for each sector as follows , while maximum heterogeneity where the communication is equally split over all dimensions . On the contrary, when maximum dissimilarity. By constructing a dissimilarity matrix, it is possible to compare the communication composition among all sectors and identify clusters. This allows us to highlight differences among sectors in the communication of CSR activities. Similar indexes can be defined at the firm level.This relative dissimilarity index is bounded between 0 and 1. When After the cleaning phase, the resulting dataset is composed of 22,716 documents and 2029 vocabulary terms; these data are ready to be analyzed. At first, the model must be specified, and the number of topics identified following the methodology presented in Sect. true number of topics. Thus, in order to select an appropriate number, it is necessary to manually inspect the results. Given the indication from the comparison of the five metrics, we focused on a number of topics between 45 and 50, and after having manually inspected the results (topic composition), we concluded that the model with 47 topics seemed to be the most appropriate, providing a good number of topic details. Table K between 45 and 50.Figure Topic discovery is performed by looking at the most probable words for each topic and tweets with a highs topic proportion and then by labeling them accordingly. The complete results are reported in Appendix Next, economic aspects are discussed (six topics), such as announcements about partnerships, information on financial and economic performances, and CEO communications. Two topics concern environmental issues, such as clean water, waste management, pollution, and clean energy. Interestingly, we also identified eight topics that are not about a specific dimension but show mixed communication on specific and general CSR activities. Thus, we considered an additional fourth dimension. These topics, reported in Table Looking at the topic composition, we can relate SM communication to SDGs. We do that only for the CSR topics that are not mixed. Table While primary SDG goals have been identified and linked to topics, it is clear that the goals are related to each other. For example, SDG 10  is naturally linked with the theme of gender equality (SDG 5) and the reduction of inequalities within and between countries (SDG 1: no poverty). Similarly, when gender equality relates to initiatives improving workplace and working conditions, then it is also linked to SDG 8 (decent work and economic growth). Additionally, SDG 4  can be linked to SDG 11 (sustainable cities and communities). Topic 5, which is about the promotion of grants for schools and students, also relates to grants released and competitions held for the preservation of historical buildings and community cultures.The STM model allows topics to be correlated. Figure Q1, the analysis of the STM model allowed to understand the online communication about CSR and SDGs. In particular, a fourth CSR dimension, namely Mixed-General CSR, was triggered in order to fully characterize the online communication through Twitter. The relationship between CSR and SDGs have been highlighted, showing that they are two interrelated phenomena.With respect to the first research question The characterizing feature of this model is the possibility of estimating the effects of topical prevalence covariates on the discussion proportion of a topic.As expected, some sectors talk more about certain topics. Indeed, looking at the estimated topic proportion and the 95% confidence interval, it is evident that firms in the health care sector tweet more about well-being and health issues (Topics 28 and 38). Topics about human–machine interactions, the social impact of technology, and the closure of the digital divide are discussed mainly by businesses in the technology and communication sectors . Then, firms in the financial, energy and materials, and industrial sectors talk more about economic topics. Firms in the energy and materials sector also talk about environmental issues, especially with reference to clean energy and pollution. Finally, there are mixed and general CSR topics. The majority of these topics is linked to the sponsorship of events, and for that reason, we see peaks in the estimated topic proportion over time.Hereafter, we discuss the results in detail for only one topic by dimension Fig. . The fulTopic 35 relates to business reports or CEO talks about sustainability initiatives that promote clean energy and reduce emissions and pollution, and it is quite stable over time. This is specific to the energy and materials sector. It should be noted that the climate question is also part of Topic 46 as a global issue, in addition to poverty. The second topic about the environment needs clarification. It is Topic 42 about clean water, World Water Day, ocean and marine conservation, and water pollution. However, due to the wording, the subjects of insurance services against weather conditions and household problems (burst pipes) are also prevalent in tweets. Finally, for the mixed and general CSR category, we considered Topic 13, which is about the sponsorship of events—particularly the Super Bowl that took place in February. Indeed, we can see that the topic proportion peaks during this period. It was sponsored by several companies, and for that reason, there is no clear distinction between sectors.Q2. Indeed, the STM proved to be useful in order to study differences in topical prevalence across sectors and over time. It allowed to get signal from the data and distinguish between sector and time specific topics.This analysis allowed us to answer The aim of this study is to get insight into the communication mix of firms and sectors. To this purpose, we analyse the twitter CSR dimension indexes together with those proposed in Sect. It is evident that some sectors are specific to dimensions. The health care sector mainly focuses on the social dimension with a percentage equal to about 74%. The quote of social communication is higher than 50% for all other sectors, excluding financial and industrial ones. Economic communication is specific to the financial sector, which has the highest proportion, equal to 31%. The economic dimension has a proportion of around 20% in all other sectors, except communications, technology, and health care (10% on average). Environmental communication is more important in the energy and materials, finance, and consumer discretionary sectors, while in the other sectors, it is marginal (lower than 5%). Mixed and general CSR communication is the prevalent dimension in the industrial sectors with a share of about 34%, followed by the communication sector with a percentage of 27%.In order to identify clusters and compare sectors, we computed the Frosini heterogeneity index and the dissimilarity index between each couple of frequency distributions, as discussed in Sect. Considering the Frosini indexes together with the frequency distributions in Fig. Looking at the frequency distributions Fig. , the FroQ3 about differences in the communication mix and especially the hypothesis that the communication mix differs between businesses providing services and products, we obtain evidence of this difference. Indeed, firms in the communication, health care, and technology sectors focus more on the social dimension with respect to others.With reference to SDGs require global actions and efforts from the national and local governments, civil society, and corporations to guarantee the protection of the planet and the progress of society. The role of companies is especially important to address key issues through the development of new business models that positively impact local and global communities. This is related to the contemporary view of CSR activities, according to which CSR is part of daily business life. To fully implement CSR firms should engage in activities to promote social good beyond economic interests. Moreover, the importance of stakeholders and their influence are now recognized. Thus, the management of the relationship between companies and stakeholders is crucial to improve the path of society toward sustainability. SM could be an effective tool for CSR communication. The information emerging is particularly interesting and helps one understand the behavior of firms with respect to the 2030 Agenda and the characteristics of their online communication. However, to our knowledge, there are no extensive studies about the characteristics of CSR communication on SM, especially in relation to SDGs. This paper explores CSR communication from this new perspective which refers to the theoretical framework described above. In particular, we addressed three research questions: (1) How the topics discussed in the Social Media Twitter relate to CSR and SDGs, (2) How the topics vary across sectors and over time, and (3) Whether sector behavior with respect to the CSR communication mix is different. From the methodological point of view, the paper is innovative since the construction of CSR indexes is based on TM results.With reference to the first and second research questions, TM analysis has been applied to identify the emerging topics in terms of CSR dimensions and SDGs and the effect of sector and time on topic proportion. Moreover, original sectoral indexes based on TM results and heterogeneity indexes are proposed and computed.From the substantive point of view, we found evidence for, besides classic economic and financial reporting, firms being committed to the social dimension of CSR, mainly to support communities with programs to reduce inequalities and discrimination, support local firms, and organize educational programs. Furthermore, environmental issues are relevant and prevalent in the energy and materials sector. Online communication also entails the promotion of online or real events. Then, we identified a fourth CSR dimension—namely, mixed, and general CSR communication—characterized by specific wordings aiming to boost audience involvement. Using the STM model, it was also possible to identify time- and sector-specific topics. Indeed, the model proved to be useful for detecting events and obtaining insights from the data. The analysis also demonstrated that in their sustainability path, businesses align themselves to SDGs and actively communicate about their initiatives online to involve stakeholders.Based on topic proportions, we studied the communication mix, providing indexes of heterogeneity and dissimilarity to address the third research question. This analysis allowed us to identify clusters and differences among sectors. In particular, our initial hypothesis about the difference in the communication mix between firms that provide services and products has been verified. The social dimension was found to be prevalent in firms providing services  rather than in firms providing final products.This study also has some limitations. First, the construction of indexes requires one to assume that the topic is truly linked to that CSR dimension. Second, many choices in topic modeling are left to the analyst’s judgement, including the pre-processing steps and the choice of the number of topics. The impact of text pre-processing techniques on the output of the final analysis has been investigated with reference to supervised machine-learning classification algorithms and unsupervised learning (LDA), but not with reference to the STM that also includes document-level metadata in the estimation. We will address this issue in a forthcoming study. Moreover, when analyzing Twitter data, practitioners should be aware of data quality aspects and the errors they may encounter  and can help policymakers understand the role of businesses with respect to the 2030 Agenda The use of TM has shown interesting performances, this contributes to follow up a promising research line based on text analysis.