The clinical efficacy and safety of a drug is determined by its molecular targets in the human proteome. However, proteome-wide evaluation of all compounds in human, or even animal models, is challenging. In this study, we present an unsupervised pre-training deep learning framework, termed ImageMol, from 8.5 million unlabeled drug-like molecules to predict molecular targets of candidate compounds. The ImageMol framework is designed to pretrain chemical representations from unlabeled molecular images based on local- and global-structural characteristics of molecules from pixels. We demonstrate high performance of ImageMol in evaluation of molecular properties  and molecular target profiles  across 10 benchmark datasets. ImageMol shows high accuracy in identifying anti-SARS-CoV-2 molecules across 13 high-throughput experimental datasets from the National Center for Advancing Translational Sciences (NCATS) and we re-prioritized candidate clinical 3CL inhibitors for potential treatment of COVID-19. In summary, ImageMol is an active self-supervised image processing-based strategy that offers a powerful toolbox for computational drug discovery in a variety of human diseases, including COVID-19. Despite recent advances of biomedical research and technologies, drug discovery and development remains a challenging multidimensional task requiring optimization of vital properties of candidate compounds, including pharmacokinetics, efficacy and safety , 2. It wThe rise of advanced Artificial Intelligence (AI) technologies , 10, motIn this study, we presented an unsupervised molecular image pretraining framework (termed ImageMol) with chemical awareness for learning the molecular structures from large-scale molecular images. ImageMol combines an image processing framework with comprehensive molecular chemistry knowledge for extracting fine pixel-level molecular features in a visual computing way. Compared with state-of-the-art methods, ImageMol has two significant improvements: (1) It utilizes molecular images as the feature representation of compounds with high accuracy and low computing cost; (2) It exploits an unsupervised pre-trained learning framework to capture the structural information of molecular images from 8.5 million drug-like compounds with diverse biological activities at the human proteome . We democf. Here, we developed a pre-training deep learning framework, ImageMol, for accurate prediction of molecular targets. ImageMol pre-trained 8,506,205 molecular images from two large drug-like databases  under the receiver operating characteristic (ROC) curve, ImageMol achieves high AUC values  across HWe further compared the performance of ImageMol with three state-of-the-art molecular representation models: 1) fingerprint-based, 2) sequence-based, and 3) graph-based models. As shown in The ongoing global COVID-19 pandemic caused by a toxic agent SARS-CoV-2 virus, has led to more than 1.1 billion confirmed cases and over 6 million deaths worldwide as of March 15, 2022. There is a critical, time-sensitive need to develop effective anti-viral treatment strategies for the COVID-19 pandemic . We therAcross 13 SARS-CoV-2 targets, ImageMol achieves high AUC values ranging from 72.0% to 82.6% . To testWe further compared ImageMol with both deep learning and machine learning frameworks: 1) a graph neural network (GNN) with a series of pretraining strategies (termed Jure’s GNN ), and 2. For insIn summary, these comprehensive evaluations reveal high accuracy of ImageMol in identifying anti-SARS-CoV-2 molecules across diverse viral targets and phenotypic assays. Furthermore, ImageMol is more capable on datasets with extreme imbalance of positive and negative samples compared to traditional deep learning pre-trained models  or machiWe next turned to identify potential anti-SARS-CoV-2 inhibitors using 3CL protease as a prototypical example as it has been shown a promising target for therapeutic development in treating of COVID-19 , 38. We 50) less than 10 uM were defined as inhibitors, otherwise they were non-inhibitors. We showed the probability of each drug in DrugBank being inferred as a 3CL protease inhibitor  plot . Moleculnhibitor  and visunhibitor . We founnhibitor , among wnhibitor . To testnhibitor . ImageMonhibitor , suggestin vitro cellular assays and clinical trials) in We further used the HEY293 assay to predict anti-SARS-CoV-2 repurposable drugs. We collected experimental evidence for top 20 drugs as potential SARS-CoV-2 inhibitors . We founMeanwhile, 122 drugs have been identified to block SARS-CoV-2 infection . From thcf. We next turned to use t-SNE to visualize molecular representations from different models to test the biological interpretation of ImageMol. We used the clusters identified by the multi-granularity chemical clusters classification (MG3C) task cf.  to splitcf. Gradient-weighted Class Activation Mapping (Grad-CAM)  is a comIn summary, ImageMol captures the biologically relevant chemical information of molecular images with both local- and global-levels of structural information, outperforming existing state-of-the-art deep learning approaches .2.6The robustness of the model to hyperparameter tuning is important because the initialization of different parameters can affect the performance of the model . Here, wcf. To explore the impact of pre-training with different data scales, we first use 0 million (no pre-training), 0.2 million, 0.6 million, 1 million, and 8.5 million drug-like compounds to pretrain ImageMol respectively and then evaluate their performance. We found that the average ROC-AUC performance of 0 million (75.7%), 0.2 million (76.9%), 0.6 million (81.6%), 1 million (83.8%) and 8.5 million (85.9%) increased from 1.2% to 10.2% as the pre-trained data size increases. Thus, ImageMol can be further improved as the more drug-like molecules cancer be pre-trained. We further investigated the impact of different pretext tasks using multi-granularity chemical clusters classification (MG3C), jigsaw puzzle prediction (JPP), and MASK-based contrastive learning (MCL) cf. , respectWe presented a self-supervised image processing-based pre-training deep learning framework that combines molecular images and unsupervised learning to learn molecular representations. We demonstrated the high accuracy of ImageMol across multiple benchmark biomedical datasets with a variety of drug discovery tasks  and 3. IWe highlighted several improvements of ImageMol compared to other state-of-the-art methods. First, ImageMol achieved high performance across diverse tasks of drug discovery, including drug-like property assessment  and molecular target prediction across diverse targets, such as Alzheimer’s disease  and emerging infectious diseases caused by HIV and SARS-CoV-2 virus. Furthermore, ImageMol outperforms state-of-the-art methods, including traditional deep learning and machine learning models –2c. SecoWe acknowledged several limitations in current study. Although we mitigated the effects of different representations of molecular images through data augmentation, perturbed views  of the input images may still affect the prediction results of ImageMol. We did not optimize for the sparsity of molecular images, which may affect the latent features extracted by the model. It is challenging to explicitly define the chemical properties of atoms and bonds compared to graph-based methods , 34, whiPre-training aims to make the model learn how to extract expressive representations by training on large-scale unlabeled datasets and then apply the well pre-trained model to related downstream tasks and fine-tune to improve their performance. Defining several effective and task related pretext tasks is required for pre-training the model. In this paper, the core of our pretraining strategy is the visual representation of molecules by considering three principles: consistency, relevance, and rationality. These principles lead ImageMol to capture meaningful chemical knowledge and structural information from molecular images. Especially, the consistency means that the semantic information of the same chemical structure in different images is consistent, such as −OH, =O, benzene. The relevance means that different augmentations of the same image  are related in the feature space. For example, the distribution of the image after the mask should be close to the original image. The rationality means that the molecular structure must conform to chemical common sense. The model needs to recognize the rationality of the molecule in order to promote the understanding of the molecular structure. Unlike graph-based and smiles-based pre-training methods (they either only consider consistency or only correlation), ImageMol is the first molecular image-based pre-training framework and considers multiple principles comprehensively by defined five effective pretext tasks.Considering that the semantic information of the same chemical structure in different images is consistent, the Multi-Granularity Chemical Clusters Classification (MG3C) task is proposed , which dMACCS keys as the descriptor of molecular fingerprints, which is a 166-length sequence composed of 0 and 1. These molecular fingerprint sequences can be used as a basis for clustering, and the closer the distance between molecular fingerprints, the more likely it is to be clustered into a cluster. Finally, we use the K-means [K=100,1000,10000 (See K) to cluster molecules to obtain clusters with different granularity from coarse-grained to fine-grained. According to the clustering results, we assigned three pseudo-labels to each molecular image and then applied ResNet18 [Especially, we employed the  K-means  with difResNet18  as molecfθ and θ refer to the mapping function and corresponding parameters of molecular encoder, respectively. w100, w1000 and w10000 represent the parameters of three fully connected classification layers in structural classifier with 100, 1000 and 10000 neurons, respectively. W represents all parameters of w100, w1000 and w10000. ℓ is the multinomial logistic loss or the negative log-softmax function.Where Based on the assumption that different augmentations  of the same image are related in the feature space, we use a pixel-level task to reconstruct molecular images from latent features and use an image-level task to maximize the correlation between the original sample and the mask sample in that space.xn into molecular encoder to obtain the latent feature fθ(xn). To make the model learn the correlation between the molecular structures in the image, we shuffle and rearrange the input image xn  in the hope that the correct image can be reconstructed. After that, we define a generator G and a discriminator D to reconstruct the latent features. G is composed of four layers of 2D deconvolution layers with a batch normalization 2D layer and ReLU activation function, and one layer of deconvolution layer with a Tanh activation function. The discriminator is also composed of four layers of 2D convolutional layers with a batch normalization 2D layer, a LeakyReLU activation function, and one layer of 2D convolutional layer with Sigmoid activation function. For further details of the GAN model see fθ(xn) to a 64 × 64 molecular image xn of 224 × 224 to the molecular image D together with the molecular image generated by G at the same time to obtain D(G fθ(xn)). Finally, we update the parameters of the generator and the discriminator through their cost functions MIR reconstruct the latent features back to the molecular images. We input the original molecular image G(fθ(xn)) and the corresponding real image xn. Finally, the molecular encoder model is updated by using the cost function For xn are simultaneously input into molecular encoder to extract latent features fθ(xn). Finally, the cost function Recently, the performance gap between the unsupervised pre-training and supervised leaning in computer vision has narrowed, notably owing to the achievements of contrastive learning methods , 26. Howfθ(xn).Where Inspired by human understanding of the world, we proposed the rationality principle, which means that the structural information described by molecular images must conform to chemical common sense. We rearranged the original images to construct irrational molecular images and designed two pre-training tasks to predict them , which cxn into 9 patches and number each patch 1 to 9. Then, these patch numbers are randomly shuffled and re-spliced according to the shuffled patch to form an image with the same dimensions as the original image. Finally, these disordered images are viewed as irrational samples xn and the shuffled image fθ(xn) and wMRDfθ(xn) whether the sample is reasonable. Here, we define the cost function of molecular rationality discrimination task The reason why people can easily judge whether things in the image are reasonable based on the knowledge they have learned is because people are very good at summarizing the spatial structure information in the image scene. For example, image of a blue sky under the grass and an image of a blue sky above the grass, we can easily distinguish the former is unreasonable and the latter is reasonable. However, it is difficult for an artificial intelligence model to pay attention to this global-level spatial structure information spontaneously during the learning process. Motivated by these phenomena, we construct a rational and an irrational molecular image pair for each molecular image to guide the model to learn the structural information. Especially, as shown in wMRD represents the parameters of the rationality classifier. Where the first term and the second term represent the binary classification loss of the rational image and the irrational image respectively. xn and the shuffled image fθ(xn) and Compared with MRD, JPP provides a more fine-grained prediction to discover the invariance and regularity of molecular images , which iwjig represents the parameters of the jigsaw classifier.Where the first term and the second term represent the classification loss of the original ordered image and the shuffled image respectively. In pre-training, we used two large-scale datasets (ZINC and ChEMBL) for unsupervised pre-training. ZINC is a dataset containing 8 million unlabeled molecules sampled from the ZINC15 database, and ChEMBL is a smaller dataset containing ~0.43 million unlabeled molecules. The two datasets have been preprocessed and are publicly available online . OverallRandomHorizontalFlip, RandomGrayscale and RandomRotation. Hence, before the original images are input into our pre-training model, each image has a 50% probability of being horizontal flipped, 20% probability of being converted to grayscale, and 100% probability of being rotated between 0°–360°. The augmentations are provided by PyTorch (https://pytorch.org/).Data augmentation is a simple way to effectively augment a limited number of samples and significantly improve the generalization ability and robustness of the model, which has been widely used in supervised and unsupervised representation learning. However, different from ordinary images, the molecular images are relatively sparser as they are filled mostly (>90%) by zeros, resulting in “usable” data being limited to a very small fraction of the image . In viewxn, we forward these molecular images xn to the ResNet18 model to extract latent features fθ(xn). Then, these latent features are used by five pretext tasks to calculate the total cost function Here, we used the ResNet18 as our molecular encoder. After using data augmentations to obtain molecular images Finally, the total loss function gft after the ResNet18, and the output dimension of the full connection layer is equal to the number of classifications of downstream tasks. In fine-tuning, we first input the molecular image gft to obtain the logical value After completing the pre-training, we fine-tune the pre-trained ResNet18 in the downstream task. Clearly, the performance of the model can be further improved by establishing a complex fine-tuning task for the pre-trained model. However, fine-tuning is not the research focus of this paper, so we only use a simple and common fine-tuning method to adapt the model to different downstream tasks. In detail, we only add an additional full connection layer N and K respectively represent the number of samples and the number of categories in downstream tasks. λk represents category weight, which is calculated by Nk is the number of samples of category k). k-th category of the i-th sample. Finally, the loss function Where, To evaluate our proposed pre-training model, we designed three types of downstream tasks related to molecular representation learning for testing, which are molecular property prediction, drug metabolism prediction and antiviral activities prediction, respectively.MoleculeNet  is a popFor a comprehensive comparison, we selected several different types of popular methods, which are the SMILES sequence-based pre-training methods  and PubChem Data Set II  from  to evaluk-NN and NB) and ensemble learning methods  respectively. In this task, the accuracy and ROC-AUC are calculated for comparison.We compare the proposed ImageMol with two latest molecular image-based methods (ADMET-CNN  and QSARFor fairness, we keep the experimental settings consistent with these methods. We use 5-fold cross-validation on PubChem Data Set I to evaluate the performance of our ImageMol, ADMET-CNN and QSAR-CNN. In addition, we also use the model trained in PubChem Data Set I to evaluate the performance of all models mentioned in this task on the external validation set PubChem Data Set II.50 to generate our datasets and set AC50 greater than 10 and less than 10 as non-inhibitors and inhibitors, respectively. The overview of the processed datasets is summarized in Anti-viral activities prediction is vital for the development of new drugs to treat COVID-19. We then use anti-SARS-CoV-2 activities prediction as our task to prioritize compounds when screening in vitro. The experimental datasets are obtained from the COVID-19 portal  in the NWe chose two representative methods for experimental comparison, Jure’GNN  and REDIIn order to compare our ImageMol with Jure’s GNN, we reproduced Jure’s GNN by using the public source code they provided to extract molecular features and added a fully connected layer for fine-tuning on downstream tasks. We uniformly split these datasets into 80% training set and 20% test set, and report the AUC and AUPR results on test set. We also compared our method with REDIAL-2020. To compare fairly with REDIAL-2020, we use the same experimental configuration as REDIAL-2020. See [Supplement 1Supplement 2


With the rapid proliferation of social networking sites (SNS), automatic topic extraction from various text messages posted on SNS are becoming an important source of information for understanding current social trends or needs. Latent Dirichlet Allocation (LDA), a probabilistic generative model, is one of the popular topic models in the area of Natural Language Processing (NLP) and has been widely used in information retrieval, topic extraction, and document analysis. Unlike long texts from formal documents, messages on SNS are generally short. Traditional topic models such as LDA or pLSA  suffer performance degradation for short-text analysis due to a lack of word co-occurrence information in each short text. To cope with this problem, various techniques are evolving for interpretable topic modeling for short texts, pretrained word embedding with an external corpus combined with topic models is one of them. Due to recent developments of deep neural networks (DNN) and deep generative models, neural-topic models (NTM) are emerging to achieve flexibility and high performance in topic modeling. However, there are very few research works on neural-topic models with pretrained word embedding for generating high-quality topics from short texts. In this work, in addition to pretrained word embedding, a fine-tuning stage with an original corpus is proposed for training neural-topic models in order to generate semantically coherent, corpus-specific topics. An extensive study with eight neural-topic models has been completed to check the effectiveness of additional fine-tuning and pretrained word embedding in generating interpretable topics by simulation experiments with several benchmark datasets. The extracted topics are evaluated by different metrics of topic coherence and topic diversity. We have also studied the performance of the models in classification and clustering tasks. Our study concludes that though auxiliary word embedding with a large external corpus improves the topic coherency of short texts, an additional fine-tuning stage is needed for generating more corpus-specific topics from short-text data. Due to the rapid developments of computing and communication technologies and the widespread use of internet, people are gradually becoming accustomed to communicating through various online social platforms, such as microblogs, Twitter, webpages, Facebook, etc. These messages over web and social networking sites contain important information regarding current social situations and trends, people’s opinions on different products and services, advertisements, and announcements of government policies, etc. An efficient text-processing technique is needed to automatically analyze these huge amounts of messages for extracting information. In the area of traditional natural language processing, a topic-modeling algorithm is considered an effective technique for the semantic understanding of text documents. Conventional topic models, such as pLSA  or LDA  and theiThe messages posted on various social network sites are generally short compared to the length of relatively formal documents such as newspapers or scientific articles. The main characteristics of these short texts are: (1) a limited number of words in one document, (2) the use of new and informal words, (3) meanings and usages of words that may change greatly depending on the posting, 4) spam posts, and 5) the restricted length of posts, such as API restrictions on Twitter. The direct application of traditional topic models for short-text analysis results in poor performance due to lack of word co-occurrence information in each short text document, originating from the above characteristics of short texts [ spam pos the restTo cope with the problems of short-text topic-modeling by traditional topic models, three main categories of algorithms are found in the literature . A simplAnother limitation of the above models for short-text analysis is that the context or background information is not used, resulting in the generation of not-so-coherent topics. The statistical information of words in the text document cannot fully capture words that are semantically correlated but that rarely co-occur. Recent advances in word embedding  providesThe increasing complexity of inference processes in conventional topic models on large-text data, along with the recent developments of deep neural networks, has led to the emergence of neural-topic models (NTM). These models combine the performance, efficacy, scalability, and ease of leveraging parallel computing facilities, such as GPU, to probabilistic topic-modeling . Neural-The objective of the present research is to explore computationally easy and efficient techniques for improving the interpretability of generated topics from real-world short texts using neural-topic models. However, learning context information is the most challenging issue of topic-modeling for short texts, and incorporating pretrained word embedding into a topic model seems to be one of the most efficient ways of explicitly enriching the content information. Neural-topic models with pretrained word embedding for short-text analysis has not been extensively explored yet, compared to its long-text counterparts. In , we presA proposal for fine-tuning with the original short-text corpus, along with the pretrained word embedding with the large external corpus, for generating more interpretable and coherent corpus-specific topics from short texts;An extensive evaluation of the performance of several neural-topic models, with and without pretrained word embedding and with an added fine-tuning stage, in terms of topic quality and measured by several metrics of topic coherence and topic diversity;A performance evaluation of the proposed fine-tuned neural-topic models for classification and clustering tasks.In summary, our contributions in this paper are as follows:In the next section, neural-topic models are introduced in brief, followed by a short description of related works on neural-topic models (NTM), especially NTMs for short texts. The following sections contain our proposal, followed by simulation experiments and results. The final section presents the conclusion.The most popular neural-topic models (NTMs) are based on a variational autoencoder (VAE) , a deep In addition to VAE-based NTMs, there are a few other frameworks for NTMs. In , an autoIn order to generate coherent, meaningful, and interpretable topics from short texts by incorporating semantic and contextual information, a few researchers used NTMs in lieu of conventional topic models. In ,44, a coFrom the analysis of present research works on neural-topic models on short-text analysis, it seems that incorporating auxiliary information from an external corpus is one of the most popular and effective techniques for dealing with sparsity in short texts. As mentioned in the introduction, in our previous work , we founIn this work, we propose an additional fine-tuning stage, using the original short-text corpus, along with the pretrained word embedding and a large external corpus. For pretrained word embedding, we decided to use GloVe  after soHere, pretrained word embedding is denoted as PWE. We have performed three sets of experiments for topic extraction, using only neural-topic models (NTM), neural-topic models with pretrained word embedding (NTM-PWE), and neural-topic models with pretrained embedding and the proposed fine-tuning step (NTM-PWE/fine-tuning). In all the cases, the data corpus is first pre-processed, and in NTM-PWE, word embedding vectors are replaced by PWE after the model parameters are initialized; the weights of PWE are not updated during the training step. In our proposed PWE/fine-tuning or simple fine-tuning (as mentioned in the text), the weights are gradually updated in the training step after replacing the word embedding vectors, as in PWE. In this case, it is possible to update the parameters at the same learning rate that is set to update the entire model, but experiments have shown that updating the PWE values at a large learning rate can easily over-fit the training data. Therefore, in the simulation experiments, we have set the learning rate of the word embedding vectors to a smaller value than the learning rate of the entire model.We have used popular VAE-based neural-topic models with a few similar WAE (Wasserstein autoencoder)-based models, and ten popular benchmark datasets, for our simulation experiments. The performance of each neural-topic model with no word embedding, pretrained word embedding, and additional fine-tuning has been evaluated by the generated topic quality using different evaluation metrics of topic coherence and topic diversity. The neural-topic models, datasets, and evaluation metrics used in this study are described below.In this section, the neural-topic models used in this study are described briefly. h is sampled by Gaussian distribution, and ed below :(1)Ld=EqIt is empirically known that maximizing this ELBO alone will result in smaller (worse) topic diversity. In order to solve this problem, some NTMs use a regularization term to increase the topic diversity :(2)a.1. OCTIS: https://arxiv.org/pdf/1701.00185.pdf (accessed on 14 January 2022).2. STTM: The first two datasets fall in the category of long documents, and the other eight datasets can be considered as the short-text corpus, as the average document length is quite short compared to the long documents.The datasets shown in the table are pre-processed. HTML tags and other symbols have been removed from each dataset, and all words have been lowercased. Then, the stop-words were removed and lemmatized. From these datasets, 80% of the total documents was used as the training data and the rest as the test data. These pre-processed corpora are then converted into a BoW (Bag-of-Words), which basically has word frequency as an element, to be used as input data for the NTM. However, for the NSTM, the vector corresponding to each document in the BoW is divided by the total value of the vectors, as in the original paper.It is quite challenging to evaluate the performance of topic models, including NTMs, according to the quality of the generated topics. Topics generated by topic models can be considered as soft clusters of words. Under the constraints of the topic model, this is a probability distribution that collects the probability of word generation for each topic; the same is true for NTM, but this may not be in the form of a probability distribution for document models that impose even weaker constraints than the topic model. Either way, a topic here is a topic–word distribution, and each distribution has as many dimensions as the number of lexemes that occur in the corpus. It is very difficult to understand the goodness of a topic by directly comparing them with human topics. Therefore, in practice, analysts check a list of N words characteristic of a topic based on the values of the word distributions. In most cases, the list of the top-N words in terms of the large probability values in the word distribution is used.Various metrics have been proposed to evaluate the quality of the top-N words with two main directions. One is to check whether the meaning of words belonging to the top-N words are consistent with each other, defined as topic coherence (TC). The other is to measure the diversity of the top-N words of each pair of topics, defined as topic diversity (TD) or topic uniqueness. Topics with high TC may have low TD. In this case, the top-N words of most topics will be nearly the same, which is not desirable. So, to evaluate the quality of a topic for human-like interpretability, it should have high TC as well as high TD.w is the list of the top-N words for a topic. N is usually set to 10. For K topics, averages of NPMI over all topics are used for evaluation;Normalized Point-Wise Mutual Information (NPMI) : NPMI isk; N is taken as 10.Word Embeddings Topic Coherence (WETC) : WETC reof GloVe  have beeFor computing TC, general coherence between two sets of words are estimated based on word co-occurrence counts in a reference corpus . The choTopic diversity is defined here as the percentage of unique words in the top 25 words of all topics, according to . A diverThe simulation experiments have been performed with several benchmark datasets, and the performance of the topic models are evaluated by topic coherence and topic diversity measures.https://nlp.stanford.edu/projects/glove/ (accessed on 14 January 2022), which cover largest number of vocabularies.For the purpose of comparison and evaluation, the experimental setting should be similar for all the neural-topic models and all the datasets. At the beginning, we completed some trial experiments, and determined that the optimum topic size parameter should be set at The other experimental parameters are set as follows: number of units of the encoder’s hidden layers: On WLDA, we employ Dirichlet as the prior distribution for topic proportion-generation, using MMD for this model’s training. On NSTM, Sinkhorn algorithm’s max number of updates is 2000, and the threshold value for updating termination condition is 0.05, constant value For many datasets, NVLDA-PWE/fine-tuning has the highest TC. One of the challenges of using PWE without fine-tuning is that the high domain gap between the PWE training corpus and the corpus for topic modeling has a negative impact. In many cases, our proposal produces better results, but not for all the datasets or for all the models. The dataset “GoogleNews” often has the best TC with PWE, and does not show better performance with additional fine-tuning. This is probably because this corpus has a similar domain as the training data for PWE. In a few datasets, the best performance is noticed when no pretraining word embedding is used. It is verified that for those datasets, the original corpus contains sufficient word co-occurrence information.However, we noted that the TC value changes significantly depending on the type of word embedding. This result suggests that the quality of the word embeddings may have a significant impact on the training of the topic model. In particular, whether or not the unique words in the training corpus are included in the unique words in the PWE has a significant impact. If the coverage of this word dictionary is large, the PWE can be used for evaluation, but if there are many missing words, the reliability of the evaluation value will be greatly compromised.Furthermore, WLDA and NSTM show similar results without this regularization term, indicating that these models are able to learn without compromising topic diversity in their raw form. To check if this regularization term is working well, we have added TopicCentroidDistance (TCD) in the tables. The larger this metric is, the better, but the values are almost the same for all cases. This metric was evaluated based on two PWEs, and since the values varied, we can infer that the quality of the embedding has a significant impact on the evaluation of the topic model.Although the results of TopicDiversity varied greatly depending on model and dataset, when checked individually, the scores were sufficiently better in many cases. However, as in the case of Biomedical’s NVLDA-pwe/fine-tuning results, there were cases where the TC showed good scores but the TD showed bad scores. In this respect, InvertedRBO also shows a good score, but MSCD, which is an evaluation using the entire topic–word distribution, shows a relatively large value , indicating that the topics are relatively tangled. Metrics such as TopicDiversity and InvertedRBO, which are based on the top-N words, are useful for evaluating topic diversity, but it is also important to evaluate the entire topic–word distribution.For classification, VAE-based models, such as NVDM and GSM, exhibit good performance, while WAE-based models, such as WLDA and NSTM, show relatively poor performance. NSTM shows good performance in TC and TD, especially in TD, without adding any regularization term. However, the application to downstream tasks using WAE variants remains a challenge. Considering the overall trend, for long texts, PWE with fine-tuning improves all the scores, but for short texts, the performance is the best for the cases without embedding. Although, after fine-tuning, the scores got better than those obtained with pretrained embedding only.For clustering results, the large NMI and Purity scores for all models and all datasets for both long and short texts indicate that there is a concentration of documents with the same label around the topic-centroid vector, which proves that the proposal of PWE/fine-tuning improves topic cohesion. Therefore, we can see that our proposal of PWE/fine-tuning contributes to narrowing the domain gap between the training corpus and PWE.Short-text data are now becoming ubiquitous in the real world through various social networking sites. The importance of analysing these short messages is also growing day by day. Unlike long texts or documents, short texts suffer from a lack of word co-occurrence information due to their restricted lengths, posing a difficulty in generating coherent and interpretable topics with popular topic-model techniques.The use of pretrained word embedding in neural-topic models is a good choice to easily increase the generated topic quality as measured by topic coherence and topic diversity. This is effective for both long and short texts, and reduces the number of trainable parameters, thus shortening the training step time. However, to achieve better topic coherence, especially in short texts, or to make the top-N words of a topic more relevant to the real semantic contents of the training corpus, the additional fine-tuning stage proposed in this work is indeed necessary. The extensive study in this work with several neural-topic models and benchmark datasets justifies our proposal.However, the use of pretrained word embedding (PWE) has its inherent limitations, which may affect the quality of the extracted topics from short texts. The short-text corpus to be analyzed may contain words that are not included in the vocabulary covered by the corpus used for pretrained word embedding. In this case, NTM-PWE uses a vector initialized with zero. As the vocabulary coverage increases, the performance is likely to deteriorate. Moreover, in the case of NTM-PWE/fine-tuning, there is a possibility that the number of parameter updates will increase until the loss function converges, resulting in an increase in training time. If the time difference between the corpus used for PWE training and the corpus to be analyzed is too large, the meanings of words may change with time, which may have a negative impact on the production of interpretable topics.It is also seen that the improvement in topic quality after introducing a fine-tuning stage is not the same for all the datasets and all the models. It is difficult to define the correlation between the structure of neural-topic models and the inherent characteristics of the datasets, which poses a challenge to our study. In this work, we limited our study to benchmark datasets available on the internet. Currently, we are collecting data for the evaluation of our proposal with real-world datasets.By incorporating the additional training with the original training corpus, along with pretrained word embedding with the external corpus, we can improve the purity and NMI of the topics evaluated using the class labels of the documents. Thus, we can construct topics that are more suitable for the training corpus. This method can also be expected to improve the performance of downstream tasks, such as classifications for long texts. Even for short texts, the performance of the downstream tasks is better than when using pretrained word embedding without fine-tuning.


The effects of ampicillin trihydrate concentration (4–12%) and addition of PLGA (20–80%) on the spinnability of the solutions, morphology, average nanofiber diameter, encapsulation efficiency, drug release, and mechanical properties of PLA and PLA/PLGA nanofibers were examined. All nanofibers were bead-free and uniform. They had favorable encapsulation efficiency (approx. 90%) and mechanical properties. The increase in the amount of ampicillin trihydrate caused an increase in the diameter and burst effect of the nanofibers. The drug release ended on the 7th and 3rd day with nanofibers containing 4% and 12% of drug, respectively. The prolonged and controlled drug release for ten days was obtained with nanofibers containing 8% of drug. Thus, the ideal drug concentration was determined to be 8%. Nanofibers containing PLA/PLGA had a larger diameter than those including PLA. In addition, both the strength and elongation of nanofibers decreased depending on the increase in nanofiber size with the addition of PLGA, increased amount of drug, and ratios of PLGA. Drug release studies showed that PLA/PLGA nanofibers exhibited a lower burst effect and a decrease in drug release when compared to PLA nanofibers. Finally, PLA/PLGA nanofibers can be produced with enhanced encapsulation efficiency and mechanical properties, resulting in controlled and tailored release of ampicillin trihydrate for at least ten days. In conclusion, it was demonstrated that the addition of PLGA in different ratios and the amount of drug can be manipulated to obtain the desired properties  of PLA nanofibers.The aim of this study was to produce ampicillin trihydrate-loaded poly(lactic acid) (PLA) and PLA/poly(lactic- Polymeric nanofibers have been widely used in many fields such as tissue engineering and drug delivery systems. Electrospinning is the most commonly used polymeric nanofiber preparation method, because it is an easy, single-step, low-cost, and reproducible method. It allows for the production of extracellular matrix-like nanofibers that can be easily scaled up and has different properties with many polymers and solvents –4. Drug-co-glycolic acid) (PLGA), which are frequently preferred polymers in the production of polymeric nanofibers, were used because they are biodegradable, biocompatible, non-toxic, and provide high mechanical strength [In our study, FDA-approved polylactic acid (PLA) and poly and PLGA addition 20–80%) were also investigated. A limited number of studies examining electrospun PLA/PLGA nanofibers have focused on drug-free or hydrophilic drug-loaded PLA/PLGA nanofibers produced with different polymers and solvent systems than those in the current study 0% were a. The cur% and PLGPLA and PLA/PLGA nanofibers prepared in this study, plus the average nanofiber diameters calculated in ImageJ using scanning electron microscopy (SEM) images of nanofibers, are given in Stable jet and continuous nanofiber formation was observed in all PLA nanofibers containing different amounts of drug and in PLA/PLGA nanofibers with different ratios of PLGA. All PLA and PLA/PLGA nanofibers showed randomly aligned, smooth, and bead-free morphology .p < 0.05). As the amount of drug in the nanofiber increased, the nanofiber diameter also increased. This could be attributed to the increase in the amount of drug, resulting in surface loading [The diameters of the PLA nanofibers ranged from 417 to 477 nm. While the diameter of the nanofiber containing 4% of drug was 417 nm, the diameter of the nanofiber increased to 433 and 477 nm when the amount of drug was increased to 8% and 12%, respectively ( loading .p < 0.05). The diameters of the nanofibers showed an increase from 433 to 820 nm with the addition of 20% of PLGA in comparison to nanofibers containing only PLA. However, upon an increase in the percentage of PLGA added (from 40 to 80%), nanofiber diameters were found to be 748 and 447 nm, respectively. These obtained values did not meet the expected nanofiber diameter increase that would have been achieved in correlation with the PLGA percentage increase. The increase in nanofiber diameter with the addition of PLGA can be explained by the higher molecular weight of PLGA than that of PLA since the increase in polymer molecular weight increases the viscosity, causing an increase in nanofiber diameter [In order to examine the effects of PLGA addition on PLA nanofibers, PLA/PLGA nanofibers were produced by replacing 20 to 80% of PLA with PLGA in the F2 coded formulation containing 8% of drug. As seen in diameter –16. In odiameter .The nanofiber diameter is also greatly affected by changes in polymer structure –18 and cIn our previous study, we observed that the addition of PCL (20–80%) to PLA nanofibers caused an increase in fiber diameter . In bothHiep et al. found that an increase in the amount of PCL in PLGA/PCL nanofibers from 10% to 20% causes a decrease in the fiber diameter from 1000 to 500 nm, while an increase in the amount of PCL to 30% causes an increase in the diameter to 2000 nm [p < 0.05). While the encapsulation efficiency of nanofibers containing 4% and 8% of ampicillin trihydrate was approx. 90%, it decreased to 65% when the amount of ampicillin trihydrate was increased to 12%. It is thought that the encapsulation efficiency is reduced due to of the undissolved drug in the solution [It has been shown that all nanofibers exhibited superior encapsulation characteristics. The encapsulation efficiency of PLA nanofibers containing up to 8% of drug was quite high. A significant decrease in encapsulation efficiency was observed with an increase in the drug content to 12% (solution . In addiThe addition of PLGA with different concentrations did not cause any changes in the encapsulation efficiency. All PLA/PLGA nanofibers were shown to have favorable encapsulation efficiency (approx. 90%).In vitro drug release from PLA and PLA/PLGA nanofibers was investigated by using the static method. The in vitro drug release of PLA electrospun nanofibers produced by varying the amount of ampicillin trihydrate is shown in By increasing the amount of drug used in formulations, the burst effect was increased. Cumulative drug release at 24 h was 32.1%, 39.6%, and 69.4% for formulations containing 4% (F1), 8% (F2), and 12% (F3) of ampicillin trihydrate, respectively. While the drug release ended within three days in F3 (containing 12% of drug) and on the 7th day in F1 (containing 4% of drug), drug release was considerably extended up to ten days for the F2 coded formulation (containing 8% if drug) . In otheIn vitro drug release of PLA/PLGA electrospun nanofibers produced by different ratios of PLA/PLGA are shown in PLA/PLGA nanofibers had a lower burst effect and slower drug release compared to PLA nanofibers. In addition, as the amount of added PLGA was increased, the burst effect and release rate were slowed. As seen in In our previous study, we examined the effect of adding another synthetic polymer, PCL, to PLA nanofibers. Higher burst effects were observed and drug release was significantly accelerated by adding increasing amounts of PCL to PLA nanofibers . These rAs shown in The mechanical properties of nanofibers depend on their composition, porosity, average size, distribution, individual nanofiber orientation, interaction between nanofibers, and arrangement and entanglement of the nanofibers –29.p < 0.05)  .p < 0.05). Similarly, the increase in the amount of drug to 12% caused an increase in the nanofiber diameter and a decrease in mechanical properties values (p < 0.05). This was similar to our previous studies with linezolid-loaded PLGA and PCL/PLGA nanofibers [co-ethyl ethylene phosphate) nanofibers [The values of tensile strength and elongation at break of PLA nanofibers containing 4% of drug were 2.62 mPa and 21.59%, respectively. When the amount of drug was increased from 4% to 8%, the aforementioned values decreased to 2.06 mPa and 11.64%, while the nanofiber diameter increased from 417 to 433 nm, respectively , 40% (F5), and 80% (F6), the tensile strength was 2.58, 2.66 and 2.15 mPa, respectively . As PLGAThe improvement in the mechanical properties of nanofibers with the addition of PLGA can be explained as follows: i) The nanofiber size affects the deformation behavior as fibers with a larger diameter tend to display bulk-like properties and have a compact arrangement and stable structure ,32. ii) As a result, it has been proven that the type of polymer added to PLA nanofibers and the mechanical properties of the polymer have a direct effect on the mechanical properties of nanofibers. Enhanced mechanical properties are known to improve cell viability and differentiation . The mecNanofibers can be effectively used in tissue engineering and controlled drug delivery due to their structural properties, which are morphologically similar to those of the extracellular matrix. Ampicillin trihydrate-loaded smooth, bead-free electrospun PLA and PLA/PLGA nanofibers have been successfully developed as an implantable system. All nanofibers have favorable encapsulation efficiency and mechanical properties. The increase in the amount of drug (from 4% to 12%) in the PLA nanofiber led to an increase in the nanofiber diameter and resulted in a higher burst effect and faster drug release. F2 coded nanofiber was chosen as the ideal PLA nanofiber with ideal drug concentration (8%) due to its favorable encapsulation efficiency, nanofiber diameter, morphology and mechanical properties, and its ability to allow for the best controlled drug release for at least ten days.PLA/PLGA nanofibers containing 8% of drug and different proportions of PLGA (20–80%), have a lower burst effect and slower drug release compared to PLA nanofibers. However, as the amount of PLGA used in the production of PLA/PLGA electrospun nanofibers were increased, the initial burst effect and release rate were also increased.The reasons for the reduction in drug release in the presence of PLGA are: i) PLGA has a higher molecular weight than that of PLA, and PLA/PLGA nanofibers have thicker diameters due to the higher viscosity of PLGA/PLA solutions, ii) the difference in structure and hydrophobicity of PLGA causes differences in the intermolecular interactions of the electrospinning solution.The increase in the amount of drug caused an increase in the nanofiber diameter and thus a decrease in the mechanical properties of the PLA nanofibers. PLA/PLGA nanofibers may be advantageous for improving cell viability and differentiation thanks to its advanced mechanical properties compared to those of PLA nanofibers. Nevertheless, the mechanical properties of all nanofibers produced in the study are appropriate for the use in tissue engineering.From our study, it may be concluded that the characteristics of the nanofibers  can be tailored by manipulating the addition of PLGA, a different type of polymer, the amount of added drug, and PLGA. As a result, it is advantageous to produce PLA and PLA/PLGA nanofiber mats via electrospinning, with favorable encapsulation efficiency (approx. 90%) and mechanical properties and a tailored and controlled release for approx. ten days for the use in tissue engineering. It has also been concluded that local application of drug-loaded nanofibers could reduce the systemic side effects caused by the drug and increase patient compliance and treatment efficacy.Ampicillin trihydrate was obtained from Atabay  as a gift. PLA (MW of 103000 g/mol), ester-terminated PLGA , and HFIP were obtained from Sigma-Aldrich. All the other chemicals used were of analytical grade.Nanofibers were produced via the electrospinning method in a similar manner as described by our previous study . PLA nanDifferential scanning calorimetry analysis thermograms of drug, polymer, and nanofibers were obtained using a differential scanning calorimeter . The samples were heated from 25 to 200 °C at a rate of 10 °C/min under nitrogen atmosphere.n = 3). After evaporation of DCM, the volume of each solution was completed to 10 mL with buffer and the amount of ampicillin trihydrate was analyzed using a UV spectrophotometer (Thermo Scientific Evolution 201). The encapsulation efficiency of the nanofibers was calculated using the following equation.Nanofibers (1 × 1 cm) were weighed and 1 mL of dichloromethane was added to dissolve fiber mats and 1 h later 7 mL of phosphate buffer solution (pH 7.4) was added to dissolve the drug released from the nanofibers  × 100.n = 3). All of the released medium was removed and 5 mL of fresh solution was added at predetermined time intervals. The amount of drug released was assayed using a UV spectrophotometer.The static method was used to evaluate the in vitro drug release of the nanofiber mats. Nanofiber mats (2 × 2 cm) were weighed and incubated in 5 mL of phosphate buffer solution (pH 7.4) at 37 °C .For calibration and validation of ampicillin trihydrate release, solutions were prepared at 2.5–30 µg/mL concentrations by making dilutions from a 200 µg/mL ampicillin trihydrate stock. Absorbance values were measured at a wavelength of 213 nm . The average diameters of the resulting nanofibers were calculated by the measurement of 100 single nanofibers from SEM images using the ImageJ analysis software .n = 3). Tensile strength (mPa) and elongation at break (%) values of the nanofibers were calculated from the strain–stress curves.The mechanical properties, such as tensile strength and elongation at break values of the electrospun nanofibers (2 × 1 cm) were evaluated on a texture analyzer  with an extension rate of 10 mm/s . The significance was evaluated with one-way ANOVA followed by Tukey’s post hoc test (SPSS 20.0). The data were considered significant at


Antheraea pernyi silk fibroin (ASF)-based nanofibers have wide potential for biomaterial applications due to superior biocompatibility. It is not clear whether the ASF-based nanofibers scaffold can be used as an in vitro cancer cell culture platform. In the current study, we fabricated novel ASF-based thermoresponsive hydrogel nanofibers by aqueous electrospinning for colon cancer (LoVo) cells culture. ASF was reacted with allyl glycidyl ether (AGE) for the preparation of allyl silk fibroin (ASF-AGE), which provided the possibility of copolymerization with allyl monomer. The investigation of ASF-AGE structure by 1H NMR revealed that reactive allyl groups were successfully linked with ASF. ASF-based thermoresponsive hydrogel nanofibers (p (ASF-AGE-NIPAAm)) were successfully manufactured by aqueous electrospinning with the polymerization of ASF and N-isopropylacrylamide (NIPAAm). The p (ASF-AGE-NIPAAm) spinning solution showed good spinnability with the increase of polymerization time, and uniform nanofibers were formed at the polymerization time of 360 min. The obtained hydrogel nanofibers exhibited good thermoresponsive that the LCST was similar with PNIPAAm at about 32 °C, and good degradability in protease XIV PBS solution. In addition, the cytocompatibility of colon cancer (LoVo) cells cultured in hydrogel nanofibers was assessed. It was demonstrated that LoVo cells grown on hydrogel nanofibers showed improved cell adhesion, proliferation, and viability than those on hydrogel. The results suggest that the p (ASF-AGE-NIPAAm) hydrogel nanofibers have potential application in LoVo cells culture in vitro. This study demonstrates the feasibility of fabricating ASF-based nanofibers to culture LoVo cancer cells that can potentially be used as an in vitro cancer cell culture platform. Cell culturing is a fundamental and essential process of biological sciences, for a wide range of basic and clinical in vitro research, which has led to outstanding developments in the fields of life science, biomedical, and biomaterial science . The celPoly (N-isopropylacrylamide) (PNIPAAm) is the most intensively and widely investigated thermoresponsive polymer that exhibits a volume phase-transition in response to temperature changes at a lower critical solution temperature (LCST) around 32 °C . PNIPAAmAntheraea pernyi is a wild non-mulberry silkworm species belonging to the Saturniidae family, which is commonly known as Chinese temperate (oak) tussah. A. pernyi silk fibroin (ASF) derived from natural A. pernyi silkworm mainly consists of the primary structure of alanine (43.07%), glycine (27.27%), serine (11.26%), tyrosine (5.26%), and aspartic acid (4.47%) [Numerous natural materials have been used to construct in vitro cancer cell culture models such as gelatin , collage 4.47%) ,36. ASF  (4.47%) . It is r (4.47%) . Therefo (4.47%) . Silk fi (4.47%) . In vitr (4.47%) . The sol (4.47%) . The ste alanine .07%, gly (4.47%) , whereby (4.47%) . Gels ar (4.47%) , trifluo% ,36. AS (4.47%)  with str (4.47%) ,50. To a (4.47%) ,52. It i (4.47%) . It can  (4.47%)  reported1H NMR. ASF-based thermoresponsive hydrogel nanofibers (p (ASF-AGE-NIPAAm)) were successfully manufactured by aqueous electrospinning at the polymerization time of 360 min. The morphological, thermosensitivity of p (ASF-AGE-NIPAAm) hydrogel nanofibers were investigated by scanning electron microscopy (SEM) and differential scanning calorimetry (DSC). And the degradability of p (ASF-AGE-NIPAAm) nanofibers was evaluated in vitro. The obtained hydrogel nanofibers showed good thermoresponsive that the LCST was similar with PNIPAAm at about 32 °C, and good degradability in protease XIV PBS solution. In particular, the growth and adhesion of colon cancer (LoVo) cells cultured with p (ASF-AGE-NIPAAm) hydrogel nanofibers in vitro were assessed. LoVo cells grown on hydrogel nanofibers showed improved cell adhesion, proliferation, and viability. Based on the results, we conclude that p (ASF-AGE-NIPAAm) hydrogel nanofibers are suitable for in vitro culture of the LoVo cells line which demonstrated improved cell proliferation and viability.Many previous researches have demonstrated the application of ASF-based scaffolds for tissue engineering, but few reports dealing with the ASF-based scaffolds for in vitro cancer cell culture can be found in literature. In our previous studies, it has been confirmed that ASF-based hydrogel support normal cell adhesion and growth . Hence, Antheraea pernyi  were used for preparation of regenerated ASF. Ally glycidyl ether (AGE) was purchased from Sigma-Aldrich, St. Louis, MO, USA. N-isopropylacrylamide (NIPAAm)  was purified by dissolution and recrystallization in hexane. All other materials and reagents were purchased from Sinopharm Chemical Reagent Co., Ltd. Shanghai, China, and used as supplied.Raw silk cocoons of A. pernyi were degummed by boiling for 30 min in an aqueous solution of 0.5% Na2CO3 three times, and then rinsed thoroughly with deionized water. The degummed silk was dried at 40 °C for two days prior to use. After drying, the degummed fibers were dissolved in molten Ca(NO3)2 at 105 °C for 4 h. The dissolution process was kept under stirring to guarantee the completely dissolution. The mixed solution was dialyzed against deionized water for 3 days with an 8–14 kDa molecular weight cut-off dialysis tube to remove salts. The solution was finally centrifuged at 8000 rpm for 10 min to remove impurities and frozen at −50 °C for 12 h, followed by freeze-drying for 48 h to obtain regenerated ASF sponge  to attack the carbon center atom of the epoxy group under this alkaline condition. Then nitrogen, as a protective gas (isolate the air), was continuously injected into the flask together with AGE that was dripped into it slowly. This flask was joined with a reflux condenser and reacted in a thermostatically controlled bath with stirring at the desired temperature. At the end of reaction, the mix solution was treated by 1 M HCl solution to adjust the pH to neutral. The solution was dialyzed against deionized water for 3 days with an 8–14 kDa molecular weight cut-off dialysis tube  at 4 °C, the solution was rapidly frozen at −50 °C for 8 h, then the allyl silk fibroin (ASF-AGE) was obtained by freeze-drying.The regenerated ASF sponge sample was accurately weighted by an electronic balance  and padded into a 250 mL three-necked round-bottomed flask with distilled water dissolved to a concentration of 10 mg/mL. The ASF solution was treated by 2 M NaNIPAAm was dissolved in a mixture of toluene and n-hexane  solution and a concentration of 15 wt% of NIPAAm solution was prepared. The NIPAAm solution was heated to 60 °C in a thermostatically controlled bath and cooled at room temperature, then placed in a −5 °C fridge until crystal was completely precipitated and the purified NIPAAm was obtained. The purification of NIPAAm was for the sake of removing the inhibitor (MEHQ). ASF-AGE was dissolved in deionized water to a concentration of 5%, then mixing with APS and TEMED in an ice bath. NIPAAm solution (75 mg/mL) was slowly dripped into the mixed solution with nitrogen injected continuously at 20 °C. The formulation of p (ASF-AGE-NIPAAm) spinning solutions was listed in The primary p (ASF-AGE-NIPAAm) spinning solutions were loaded into a 10 mL syringe equipped with a 0.5 mm diameter stainless steel needle. The electrostatic spinning machine  was purchased from Beijing Future Material Sci-tech Co., Ltd. which had a high voltage direct current  with a roller collector (100–2000 rpm). The syringe pump , injector (0.5~200 mL) and stainless steel needle were purchased from Janus New-Materials Co., Ltd. Then, the p (ASF-AGE-NIPAAm) hydrogel nanofibers were produced by the electrostatic spinning system at a stepping rate of 1.5 µL/s under 20 kV voltage field and 12 cm distance between tip and receptor for 15 min at 24 °C and 60% relative humidity. Finally, spun electrospun p (ASF-AGE-NIPAAm) nanofibers were treated with water vapor and immersed in 75% ethanol aqueous to insolubilize it by inducing β-sheet structure formation.1H-NMR spectra of each sample were recorded on a Bruker AV400 NMR spectrometer  with an operating frequency at 400 MHz.The 5 mg products of ASF and ASF-AGE were dissolved in 1 mL of deuterium oxide. The PNIPAAm is a smart synthetic polymer that presents a hydrophilic-hydrophobic thermoresponsive phase transition around 32 °C, known as its lower critical solution temperature (LCST). The DSC technique was employed in order to obtain thermal (endothermic or exothermic) curves and determine the LCST for the sample . The LCSThe morphology of p (ASF-AGE-NIPAAm) hydrogel nanofibers were observed using a scanning electron microscope  along with sputtering a thin layer of gold at 20 kV.Static water contact angle measurements were accomplished by using a drop shape analyzer  with a needle method at a flow rate of 1.0 µL/s in 36% relative humidity, and the test temperatures were 25 and 45 °C, respectively. The Young Laplace drop profile fitting method was applied to evaluate the static contact angle results. The needle specifications were as follows: Diameter—0.5 mm and the water droplet volume—~5 µL. The standard deviation of the measurement series might create the error bars. The measurement was repeated three times.The p (ASF-AGE-NIPAAm) hydrogel nanofibers sample (Nano-6) was cut in a 1 ± 0.2 cm × 1 ± 0.2 cm film. Then, the sample was weighted and incubated in the same volume (30 mL) of PBS solution (pH = 7.4) and 5.0 U/mL protease XIV PBS solution (bath ratio 1:80), respectively. The sample was placed in a test tube in a constant temperature incubator  at 37 °C for 14 days under slow shaking. At regular intervals, the sample was lyophilized and weighed and each sample was evaluated in triplicate. The measurement was repeated three times for final results. Degradation rate was expressed as the percentage of weight loss relative to the initial dry weight. The weight loss ratio (WLR) was calculated using Equation (1):2 in air. In the logarithmic growth phase, the cells were trypsinized using 0.25% trypsin  and resuspended at 1.0 × 105 cells/mL in fresh DMEM with FBS and antibiotics. A 100 μL aliquot of cells suspension was inoculated in each well of the 96-well cell culture plates to cover the p (ASF-AGE-NIPAAm) nanofibers (n = 6); a 100 μL aliquot of cells without samples was the control group and the blank group was not inoculated with cells. The 96-well cell culture plates were kept in a humidified atmosphere of 95% air and 5% CO2 at 37 °C for 1, 3, 5 and 7 days. After culturing, a portion of cells were washed three times with DMEM. The medium was replaced with 100 μL DMEM and mixed with 10 μL MTT agent for 4 h for the cell viability test. After incubation, the reaction was carefully removed from each well, and 100 μL dimethylsulfoxide (DMSO) was added. The plates were gently agitated until the blue-purple crystal (formazan precipitate) completely dissolved, and the absorbance at 490 nm was measured using a Synergy H1 microplate reader . The other cells were dipped into 4% paraformaldehyde solution for 15 min, and washed with PBS solution three times. These cell samples were blocked with PBS containing 1% Triton X-100 by staining with DAPI . After incubation in the dark for 40 min at room temperature, a confocal laser scanning microscope (CLSM)  was used to observe the cells adhesion and growth to the sample.Colon cancer (LoVo) cells (frozen in −80 °C ultra-cold storage freezer) were cultured in 4 mL of Dulbecco’s modified Eagles medium  which was mixed with 10% fetal bovine serum , 100 U/mL penicillin and 100 μg/mL streptomycin, at 37 °C in an atmosphere of 5% CO2S2O3 catalyst solution to adjust the pH to 10 ± 0.2. In this condition, the AGE solution was gradually dripped into it; the nucleophilic substitution occurred with the amino group in ASF and the epoxy group in AGE. The aggregated structure of ASF in solution shows a random coil structure, and numerous terminal amino groups are exposed. A nucleophilic substitution reaction occurred by amine groups of ASF with epoxide groups of the diglycidyl ether  and δ = 5.89 ppm d.d.t (CH=). In this study, for the 1H NMR spectra of ASF and ASF-AGE (shown in 2= and CH=) was 2:1. This result has also been confirmed in literature which matches with the 1H NMR spectra for AGE [2-) protons  resonances, which coincided with the chemical shifts of the AGE in a previous report [1H NMR spectrum, it was found that nucleophilic substitution occurred with the ASF amino group and the AGE epoxy group, and reactive allyl double bonds were obtained on ASF.In the previous literature , Dmitrieshown in , the chashown in b appeare for AGE . The ches report . Whereass report . Meanwhis report . Hence, 4− radicals were decomposed by APS under the catalysis of TEMED, which attacked the allyl C=C of ASF-AGE and NIPAAm, and copolymerization occurred between ASF-AGE and NIPAAm. Then, it was covalently connected with the allyl C=C in ASF-AGE together with the formation of the PNIPAAm polymer long chain. Finally, the solution system gradually became sticky, with the formation of a hydrogen bond between ASF peptide chains and PNIPAAm, with the extension of polymerization time.At present, most electrospun ASF-based nanofibers are fabricated from organic solvents, such as HFIP, TFA and formic acid. One expected application of the electrospun SF non-woven mats is for cellular scaffolds in regenerative medicine ,69. For To demonstrate the spinnability of p (ASF-AGE-NIPAAm) solution, p (ASF-AGE-NIPAAm) spinning solution with different polymerization times was researched. Polymerization time determined the spinnability of the solution through effects on solution viscosity and surface tension. The surface tension and viscosity of spinning solution is shown in The SEM images of p (ASF-AGE-NIPAAm) hydrogel nanofibers with different polymerization times at 30 °C are shown in The thermoresponsive of the p (ASF-AGE-NIPAAm) hydrogel nanofibers sample (Nano-6) was examined by DSC . The onsThe wettability of p (ASF-AGE-NIPAAm) hydrogel nanofibers (Nano-6) with temperature changes are shown in The development of a biodegradable nanofibers scaffold is nowadays of increasing interest and deserves the attention of academic and biomedical research. In this study, in vitro degradation of p (ASF-AGE-NIPAAm) hydrogel nanofibers (Nano-6 sample) was monitored, by examining the weight loss ratio with incubation time in the PBS solution and protease XIV PBS solution at 37 °C, respectively, as presented in p < 0.05). It was shown that more cells grew on p (ASF-AGE-NIPAAm) hydrogel nanofibers than on hydrogel (* p < 0.05). This was due to the difference in surface area between the nanofibers and normal hydrogel. The nanofibers sheet had a larger area and more space for cell adhesion and growth than the normal hydrogel because of its porosity [To show the effectiveness of cancer cell adhesion and growth on p (ASF-AGE-NIPAAm) hydrogel nanofibers, cell culture was performed on the Nano-6 sample with LoVo cells. The yellow MTT was reduced to a purple formazan dye by mitochondrial dehydrogenase in living cells, and can be used to assess cell viability . After cporosity .The CLSM images of the LoVo cells cultured on different substrates for one, three, five and seven days are shown in Novel ASF-based thermoresponsive hydrogel nanofibers were successfully fabricated by aqueous electrospinning for colon cancer (LoVo) cells culture. The p (ASF-AGE-NIPAAm) spinning solution showed good spinnability with increased polymerization, and uniform nanofibers were formed with a diameter of 452 ± 120 nm at the polymerization time of 360 min. The hydrogel nanofibers revealed obvious thermoresponsive behavior, of which the LCST was about 32 °C. The p (ASF-AGE-NIPAAm) hydrogel nanofibers can be easily degraded in the protease XIV PBS solution. Furthermore, by culturing colon cancer (LoVo) cells in vitro, the LoVo cells grown on hydrogel nanofibers showed improved cell adhesion, proliferation, and viability than those on hydrogel. This is a convenient and feasible approach to fabricate ASF-based functional nanofibers in the application of cancer cell culture. We aim to present a valuable route for developing an ASF-based cancer cell culture platform.


A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability. It is very restrictive for an external molecule to penetrate the blood–brain barrier (BBB) that separates the central nervous system (CNS) from the systemic circulation. Therefore, the ability of BBB passage is a key characteristic for evaluating the suitability of molecules as a CNS drug candidate . BBB perMolecular BBB permeability is usually quantified with the logarithm of blood–brain partitioning (LogBB), the argument of which refers to the ratio of concentrations in brain and plasma. Various computational modeling methods have been proposed so far to predict the LogBB values of organic molecules. Among them, the development of a quantitative structure-activity relationship (QSAR) model has been most actively pursued due to the straightforwardness of the relation between the molecular descriptors and LogBB .Since the development of the comparative molecular field analysis (CoMFA) method , three-dA precise alignment of 3D molecular structures is the most important prerequisite for the high performance of 3D-QSAR models. Because even a slight deviation from the perfect molecular overlay may cause large errors in predicting biological activity , 3D moleIn recent years, the manifestation of sophisticated computational protocols including machine learning and artificial intelligence (AI) algorithms has shed new light on the development of an efficient QSAR model for drug discovery ,16. For In previous work, we established an efficient 3D-QSAR prediction model termed AlphaQ by applying the artificial neural network (ANN) algorithm. As a consequence of combining a rigorous structural alignment method and the quantum mechanical molecular descriptors, the optimized AlphaQ model exhibited a high performance in predicting some biochemical and pharmacological properties . The preMolecular LogBB values have often served as a yardstick to assess permeability across the BBB. Therefore, experimental LogBB data for molecules with varying size, shape, and atomic composition were collected from the literature to construct a dataset with which the 3D-QSAR prediction model could be derived and validated appropriately ,28. AmonAlthough the pattern for structural alignments among the molecules has a critical impact on the performance of the resulting 3D-QSAR prediction model, it is very difficult to obtain an accurate molecular alignment, especially in the case that the dataset involves a broad MW range . This isn electrons was obtained from its determinantal wavefunction, consisting of n molecular orbitals, which were calculated with the ab initio quantum chemical method at the RHF/6-31G** level of theory. Using the individual molecular wavefunctions, the charge density (ρ) values were calculated at 3D grid points placed with uniform spacing of 0.212 Å in the common box. The ESP (φ) values were then determined at all the grid points, embedding the molecule by solving the Poisson’s equation.Three-dimensional distribution of the electrostatic potential (ESP) in a molecule containing 2Based on the 3D distribution of ESP in each molecule, the numerical molecular descriptor was constructed preliminarily in the form of a K-dimensional vector comprising the ESP values at the predefined K grid points in the common box. Due to a huge number of grid points, it was necessary to reduce the dimensionality of the initial ESP descriptors to the extent adequate for QSAR modeling. This was performed by principal component analysis (PCA), which has been widely used to project the high-dimensional numerical data in the low-dimensional space by extracting the principal components only ,33. The Three-dimensional atomic coordinates for calculating the molecular ESP descriptors were obtained by conducting structural alignments among the molecules in the common rectangular box. The length, width, and height of the common grid box for a molecular subset were determined according to the respective maximum distances along the coordinate axes to encompass the van der Waals volumes of all the molecular elements. To provide a sufficient space for translational and rotational movements during the 3D structural alignments, a marginal distance of 2.7 Å was added to the length, width, and height of the common grid box. Grid points were then placed with uniform spacing of 0.106 Å along the three coordinate axes.The starting structures for the 3D molecular alignment were prepared from the quantum chemical geometry optimizations at the RHF/6-31G** level of theory. The pairwise structural alignment proceeded by translating and rotating each molecule (target) in a molecular subset so as to maximize the overlap with the representative one (template). The position of the template molecule was fixed in the common grid box. A total of 2000 rotamers along the three axes were taken into account for each target molecule to find the optimal atomic coordinates with respect to the template. These rotamers were prepared by uniformly incremental sampling in the SO(3) rotation group using the Hopf fibration method . To reduj) and the corresponding charge density data to reflect the intramolecular electronic redistributions, the optimal structural alignment with the stationary template molecule (i) was explored by translating each rotamer. The translational movements were iterated by changing the displacement vectors in such a way as to maximize the quantum mechanical cross correlation (ijE), which was defined between the ESP of i ) and the charge density of j ) as follows.Using 2000 rotamers of a target molecule  values for all molecular pairs. More specifically, the ijd value between the two molecules was given by the difference between the quantum mechanical cross correlation (ijE) and the average of the self-correlations (iiE and jjE) associated with the superposition of two identical molecules.To accomplish the multiple pairwise molecular alignments, it was necessary to determine a representative molecule appropriate for the template with respect to all the other molecules in a subset. The template molecule of each subset was identified with the aid of the distance matrix comprising the distance  was quantified by the number of the shortest paths involving the vertex i out of the total number of the shortest paths for all vertex pairs (pathsN).In each molecular subset, the distance matrix was constructed as a yardstick to choose the central molecule that served as a structural template in the pairwise 3D molecular alignments. The determination of the template molecule in a subset began by representing all the molecules with the vertices placed in a network according to the molecule . Of the molecule . The betif is 1 and 0 if the vertex i is included and excluded in the shortest path between the vertices A and B, respectively. To identify the node with the highest iC value in each molecular subset, Monte Carlo simulations were carried out with the random walk algorithm. The central molecules were determined in the same way for all eight molecular subsets, and then served as the structural template in the multiple pairwise molecular alignments.Here, kiw’s) to generate the intermediate neurons (jO’s) represented the predicted LogBB values of N molecules in the training set.Following the structural alignments and the calculations of molecular ESP descriptors, a 3D-QSAR model for LogBB prediction was derived for each molecular subset using the advanced computational protocols. Among a variety of machine learning and AI tools available for public use, the ANN algorithm was applied in this work in a feed forward fashion with the backpropagation of the error network . The whosgm(x) denotes the sigmoidal function given by (1 + −xe)−1. The output neuron can thus be expressed with the input vectors as follows.Here, F), which was given by the sum of the square differences between the experimental (jD) and the estimated (jO) LogBB values of N molecules in the training set.For simplicity, the number of neurons in the hidden layer (M) was limited to 3 in the optimization of the 3D-QSAR model for LogBB prediction. All experimental data were transformed to range from 0 to 1 to be processed with the sigmoidal function. These normalized experimental LogBB values functioned as the baseline for optimizing the weighting factors to complete a 3D-QSAR prediction model. The parameterization proceeded with a gradient-based minimization on the error hypersurface . Male ICR mice of 7–8 weeks old weighing 30–32 g were purchased from DooYeol Biotech . All these mice were housed 5 per cage under standard conditions (at 23–25 °C and 55–60% relative humidity) on a 12 h light/dark cycle (light on at 7:00 a.m.) with food and water ad libitum . Subjects receiving oral injection were food-deprived overnight.n = 5) at a dose of 5 mg/kg. The mice were then euthanized by carbon-dioxide asphyxiation at 5, 30, 60, 120, and 360 min after the oral dose. Blood samples were collected via cardiac puncture using heparinized plastic syringes, and centrifuged for 1 min at 9000 rpm. Subsequently, the whole brain tissue was collected and homogenized  with a phosphate buffer. Two 50 µL aliquots of the supernatant and plasma samples were collected and stored at −70 °C.LogBB measurements began by the oral administration of each drug candidate to five mice  containing 20 ng/mL carbamazepine  was added to 50 μL of aliquot for each biological sample. Carbamazepine served as an internal standard in the mixture. After vortex mixing and centrifuging at 12000 rpm for 10 min, 10 µL of supernatant was analyzed using LC-MS/MS . Finally, LogBB values of the ten drug candidate molecules were calculated by the logarithm of a ratio of the area under the time-versus-concentration curve (AUC) in brain and plasma.With respect to the whole molecular dataset including 406 organic compounds, the MWs and LogBB values ranged from 200 to 600 amu and from −2.69 to 1.64, respectively. Prior to constructing a 3D-QSAR prediction model, molecules with varying shapes and sizes were categorized into eight subgroups according to MW so that the 3D structural alignments could be performed with accuracy. ijE values with respect to the template molecule. All the molecules were assumed to be neutral in calculating the ijE values to retain the original electronic and structural features. ijE values were calculated on a fully quantum mechanical basis, the AlphaQ program is meritorious over the conventional 3D-QSAR packages in that it can be used to conduct 3D molecular alignments systematically, even in coping with structurally diverse molecules with no identical chemical moiety. Unlike the conventional atom-by-atom matching protocol, however, it is difficult to score the accuracy of 3D structural alignments in the quantitative manner. In the absence of a common chemical group among the molecules, it would be desirable to assess the new structural alignment method by the predictive capability of the resulting 3D-QSAR model.The derivation of a LogBB prediction model termed AlphaQ was initiated by preparing the optimal atomic coordinates of individual molecules in the training and the test set. For this purpose, the pairwise 3D structural alignments were performed in such a way as to maximize the 2train) and that for the test set (R2test) served as yardsticks to measure the accuracy of the LogBB prediction models. These two statistical parameters can be expressed in the following mathematical forms.The reliability of the 3D-QSAR prediction models derived in this work was validated in terms of the correlation between the experimental and the calculated LogBB data. Basically, the squared Pearson correlation coefficient for the training set  in terms of the R2test values [With respect to the performance of the AlphaQ prediction model for LogBB, it is noteworthy that the Rcriptors , and witcriptors . The acct values . This in2pred) that has been widely used to quantify the accuracy of statistical prediction methods [To further address the performance of AlphaQ, we also calculated the external predictivity parameter  values given by the molecular descriptors. In general, a molecule is considered as an outlier unless the absolute value of its standardized residual is less than three times the standard deviation unit. A molecule also falls outside the applicability domain if the h value of the molecule exceeds the warning leverage (h*) defined as follows.To estimate the applicability domain of the AlphaQ prediction model for LogBB, the outliers and the high-leverage molecules were determined with the leverage approach  using thp and n are the number of ESP descriptors and the number of molecules in the training set, respectively. Predictions with an h value higher than h* may not be reliable because the results can be regarded as a consequence of extrapolation instead of an exact fit.Here, h values substantially lower than h*. Similarly, the standard residuals of the molecules in Subsets 3 and 4 also appeared to reside between the bordering lines. Judging from the William plots for Subsets 3 and 4, the AlphaQ prediction model for LogBB seems to be reliable, at least for molecules with MWs ranging from 276 and 323, on the grounds that all the associated data points reside in the satisfactory realm. On the other hand, the data points of one or two molecules in Subsets 1, 2, and 4–6 turned out to be outliers as they exhibited standardized residuals beyond the boundaries. Overall, 97.3% of the data points were located in the applicability domain. This indicates that LogBB predictions with AlphaQ may involve a high degree of extrapolation for a small number of molecules.The William plots of AlphaQ LogBB prediction models for the eight molecular subsets are displayed in 2train, R2test, and r2pred parameters obtained after 10% of the experimental LogBB data were permutated at random and regressed with the unchanged molecular descriptors. Although all eight randomized models were optimized well with the high R2train values, they became less efficient in LogBB prediction as both R2test and r2pred parameters decreased significantly when compared to those of the original prediction models  between the experimental and calculated LogBB values amounted to 0.843, which is between the R2test values for Subset 1 and Subset 6 (To further validate the performance of the AlphaQ LogBB prediction model on the experimental basis, we calculated the LogBB values of ten molecules that have been under development as drug candidates in the pharmaceutical industry. Compared in Subset 6 . These e2test value, AlphaQ outperforms quantum mechanical solvation models [To the best of our knowledge, AlphaQ may be viewed as the first reliable 3D-QSAR model for predicting the LogBB data of structurally diverse molecules with no common scaffold. In terms of the predictive capability given by the Rn models  as well n models . Furthern models . The AlpAlthough ab initio quantum chemical calculations were applied both in 3D structural alignments and in ESP descriptor calculations, the AlphaQ model for LogBB prediction has much room for further improvement. Most importantly, a large error may arise because the minor tautomers and conformers of each molecule are excluded in model building, as mentioned above. This problem becomes severe in particular when the dataset contains a number of molecules with many torsional degrees of freedom. Hence, the predictive capability of AlphaQ would be enhanced further upon the implementation of 4D-QSAR formalism to calculate the molecular descriptors under consideration of the conformational diversity in molecules . Since aIn the attempt to obtain a reliable computational tool for predicting molecular LogBB data, a 3D-QSAR model termed AlphaQ was derived with an ANN algorithm using the distribution of quantum mechanical ESPs as the numerical descriptor for individual molecules. To raise its predictive capability, pairwise 3D structural alignments were carried out in such a way as to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has an advantage over the conventional atom-by-atom matching protocol in that structurally diverse molecules can be analyzed as rigorously as the chemical derivatives of the same scaffold. As in other 3D-QSAR prediction models, the performance of AlphaQ was limited by the difficulty in finding the optimal structural alignment between large and small molecules. This alignment problem was alleviated in a large part by dividing the molecules in the dataset into eight subsets according to MW. As a consequence, a highly predictive QSAR model was derived for each molecular subset, implying the adequacy of the new 3D structural alignment method and the quantum mechanical ESP descriptors in the development of LogBB prediction models. Due to its high predictability and simplicity in model building, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.


Kaposi sarcoma (KS) is derived from endothelial cell lineage; it is caused by Human Herpes Virus-8 (HHV-8) facilitated by immune suppression. KS remains one of the commonest sarcoma seen in Tanzania. The paucity of recent data makes monitoring the disease a challenge. This study describes the Histopathological Patterns and Topographical distribution of Kaposi Sarcoma at Muhimbili National Hospital, a tertiary care hospital in Tanzania.A hospital-based retrospective cross-sectional study was done to review biopsies sent to the Central Pathology Laboratory (CPL), Muhimbili National Hospital from 2010 to 2014.A total of 818 cases representing 1.8 % of all malignancies during the study period were enrolled in the study. The age of patients at diagnosis ranged from 6 months to 94 years old, with the median age being 37 years. Male to female ratio was 1.4:1.0. Females were younger than males (p < 0.001). The majority of the lesions were in the lower limbs, 352 (64.1 %). Nodular KS accounted for 74.5% of all cases.Kaposi's sarcoma remains a common malignancy. The patients present late at diagnosis. Early diagnosis and improved treatment protocols remain to be key steps towards reducing the burden of KS. Kaposi sarcoma (KS) was first described by Moritz Kaposi, a Hungarian dermatologist in 1872; he described it as “idiopathic multiple pigmented sarcoma of the skin” The epidemiological classification of Kaposi's sarcoma has four displays: the classic KS, the endemic or African KS, the iatrogenic KS and the epidemic (AIDS-associated) KS. Classic KS is a slow progressing neoplasm which affected mainly elderly men of Mediterranean and Eastern European region, especially Ashkenazi Jews Endemic (AIDS-associated) KS experienced an explosive growth in sub-Saharan Africa with the emergence of human immunodeficiency virus (HIV) infection in the region in early the 1990s For unknown reasons, KS is more common in males than in females The histology of KS lesions from the skin, lymph nodes and viscera is very similar. The histological hallmark of KS is the presence of spindle-shaped cells of endothelial origin with slit-like blood vessels and peripheral inflammatory cells 4. KS develops from patch (earliest focus of KS lesions), plaque , or nodular (visible mass dominated by spindle cells) stages  22–24; tKS remains to be the leading malignant vascular tumour and second most prevalent cancer seen in TanzaniaThis was a retrospective study whereby histologically confirmed cases of Kaposi's sarcoma from 1st January 2010 to 31st December 2014 were retrieved from the archives at Central Pathology Laboratory at Muhimbili National Hospital (MNH). MNH receives referred patients from all over the country, and it also serves as a national consultant hospital, a zonal referral hospital for East and Southern Tanzania as well as a university teaching hospital for Muhimbili University of Health and Allied Sciences (MUHAS). It has a 1,500-bed capacity, attending approximately 1,200 inpatients per week and approximately 1,200 outpatients per day.Inclusion criteria were patients of both genders with histologically confirmed Kaposi's sarcoma.Data concerning age, gender, histological type and topographic site of Kaposi's sarcoma were recorded. Patients' identifiers such as names were not recorded.The statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) version 20.0 for Windows . The student t test was used to test for significance of associations between the predictor and outcome variables in the continuous variables. The level of significance was considered as P < 0.05.Ethical clearance was sought from the MUHAS Institutional Review Board and permission to conduct the study at the national hospital was granted by administration.During the study period, a total of 45,250 malignancies were registered at Central Pathology Laboratory at MNH. Of these, 818(1.8 %) were histologically confirmed Kaposi's sarcoma giving an average of 164 cases annually. The age of patients at diagnosis ranged from 6 months to 94 years with a median of 37 years (IQR = 31–45 years). The modal age group was 31–40 years. Of all cases, 473 (57.8 %) were males and 345 (42.2 %) females; this gave male to female ratio of 1.4:1.0. Females were younger than males , The most common pathological stage of KS observed in this study was the Nodular stage, this accounted for 461 cases (74.5 %) of all the KS lesions seen. Patchy and plaque stages accounted for the remaining 120 cases (19.4 %) and 38 cases (6.1%) respectively.In this study, Kaposi's sarcoma accounted for 1.8 % of all malignancies recorded at the Central Pathology Laboratory in Muhimbili National Hospital in the 5-year study period from 2010 to 2014. This figure is lower than the figure of 2.4% reported Chayla et al in the Lake Zone of the country around the same time as this study Kaposi's sarcoma was more prevalent in males than in females, with a male to female ratio of 1.4:1.0. Male predominance has been observed in many other studiesKaposi's sarcoma has shown to affect all age groups and in this study, a 6 months old was diagnosed to have KS, the youngest ever patient diagnosed to have KS was 6 days old In this study, the commonest sites of the disease were the skin, 77.6%. Skin presentation is the commonest mode of presentation, other sites especially the oral cavity and gastrointestinal tract are also commonNodular KS accounted for 461(74.5%) of all KS biopsies in this study. Similar findings were seen in other studies in East Africa and the globe 33, 38, 39. Many studies have attributed this high prevalence of Nodular KS pattern to late presentation of the patients to healthcare facilities; many patients present in late stages for cancer diagnosis This study has shown Kaposi's sarcoma remains a common malignant tumour in the country. Majority of the patients presented with the disease in the fourth decade of their lives. The patients present late with the disease, they present with Nodular KS. Early diagnosis and improved treatment protocols remain to be key steps towards reducing the burden caused by this disease.