Microscopies have become pillars of our characterization tools to observe biological systems and assemblies. Correlative and synchronous use of different microscopies relies on the fundamental assumption of non-interference during images acquisitions. In this work, by exploring the correlative use of Atomic Force Microscopy and confocal-Fluorescence-Lifetime Imaging Microscopy (AFM-FLIM), we quantify cross-talk effects occurring during synchronous acquisition. We characterize and minimize optomechanical forces on different AFM cantilevers interfering with normal AFM operation as well as spurious luminescence from the tip and cantilever affecting time-resolved fluorescence detection. By defining non-interfering experimental imaging parameters, we show accurate real-time acquisition and two-dimensional mapping of interaction force, fluorescence lifetime and intensity characterizing morphology (AFM) and local viscosity (FLIM) of gel and fluid phases separation of supported lipid model membranes. Finally, as proof of principle by means of synchronous force and fluorescence spectroscopies, we precisely tune the lifetime of a fluorescent nanodiamond positioned on the AFM tip by controlling its distance from a metallic surface. This opens up a novel pathway of quench sensing to image soft biological samples such as membranes since it does not require tip-sample mechanical contact in contrast with conventional AFM in liquid. For that, a large variety of optical-based microscopies are available, historically less resolved than cryo-EM because of the diffraction limit. However, some recently developed super-resolution techniques such as Stimulated Emission Depletion Microscopy (STED)2, Stochastic Optical Reconstruction Microscopy (STORM)3, Photoactivated Localization Microscopy (PALM)4 and MINFLUX5 have bypassed the diffraction limit and improved the lateral resolution down to a few nanometers. While they do have some drawbacks both in terms of photo-toxicity or acquisition speed, their popularity nowadays reflects their coming of age. When it is to observe bio-interfaces at high resolution in physiological conditions, in a label-free configuration, Atomic Force Microscopy (AFM)6 and its fast video-rate version High-Speed AFM (HS-AFM)8 are outstanding techniques to obtain morphological and mechanical information at nano-scale. While all microscopies provide complementary physical and chemical information at different spatial and temporal resolutions, the challenge is now to get correlative observations of biological processes with two or more techniques that require important technological efforts.Real space observation of biomolecules at high resolution is crucial to address fundamental biological questions. In this frame, several microscopy techniques have been developed during the last decades and are currently well-established and available allowing characterizations at different lengths and timescales. Cryogenic Electron Microscopy (cryo-EM) offers nowadays the highest spatial resolution at the molecular and sub-molecular length scales but cannot image living cells in physiological conditions9, as well as STED10, STORM12 and Super-Resolved Structured Illumination Microscopy (SR-SIM)13 have been successfully coupled with AFM. Combining conventional fluorescence and AFM has become increasingly popular since both techniques can work in biologically relevant conditions and they acquire complementary information: specificity mapping of proteins given by specific fluorescent probes nicely complements the high-resolution AFM images. Confocal Laser Scanning Microscopy (CLSM) and AFM have been coupled at first in 199514 to image Langmuir-Blodgett films of 10,12-pentacosadiynoic acid. In the following decades, a large number of setups following similar operational schemes based on fixed optical axes, fixed tip position, and a scanning sample were developed20. In most setups, AFM and fluorescence images are acquired in a sequential manner, and correlation between images is generated afterwards22. However, synchronicity is of high importance as it permits to follow dynamical biological processes.As examples, cryo-EM and 3D super-resolution fluorescence have been coupled to aid protein localization in EM images24. This simultaneous modality is often not allowed due to interference and cross-talk during simultaneous image acquisition resulting in worse performance and data quality, both effects often ignored in the existing literature and only very rarely discussed25. When coupling fluorescence  with AFM, it is, therefore, of critical importance to eliminate any cross-talking between the two techniques by understanding, quantifying, and reducing the artifacts occurring during synchronous operation. We can divide artifacts into two categories: AFM acquisition being affected by the excitation laser used to excite fluorescent probes. The interaction of the laser confocal spot and the AFM tip/cantilever could give rise to optomechanical/photothermal forces that can deflect the cantilever in a non-linear way which depends upon the distance from the sample. Since in time-resolved fluorescence experiments the excitation laser is continuous or pulsed at operating frequencies in the MHz regime, typically much higher than the most of cantilever resonance frequencies, the measured force (the deflection) is due to the static component of the excitation.Fluorescence acquisition being affected by the presence of the AFM tip/cantilever. AFM tip luminescence gives rise to a spurious signal in the photons detection that might not be fully eliminated if there is an overlap between tip/cantilever fluorescence and the sample emission spectra.In addition, it allows the acquisition of optical images while a force is applied by the AFM tip in pump and probe experiments, for instance, monitoring the response of a living cell to an AFM tip indentation or during single-molecule manipulationet al. have used these deflections as a mean to mechanically synchronize AFM and optical measurements26. A wide-field excitation scheme such as Total Internal Reflection excitation (TIRF) eliminates most artifacts since it limits the interaction of light only to a few hundred nanometers above the glass coverslip, where solely the tip’s end is present, the cantilever being typically few microns axially farther. A large number of simultaneous experiments were reported in the last two decades using this modality31. However, this type of illumination is not suitable for thick samples such as cells, since the tip will be positioned at the apical cellular membrane axial position, out of the evanescent excitation field.In the case of wide-field epifluorescence, the optomechanical interactions between light and the cantilever body can result in large cantilever deflections. Cazaux 32, radiation pressure exerted on the tip33 due to the scattering of the confocal spot and finally tip/cantilever luminescence16. We show that all effects highly depend on the choice of the AFM tip and cantilever. We conclude that tip geometry, material, and metallic coating at the cantilever backside, as well as cantilever stiffness, are the key parameters to be taken into account. We also present how these common pitfalls can be avoided, or highly minimized, so there is no spurious cross-talk in the acquired images. As proof of principle we imaged synchronously the topography, lifetime, and fluorescence intensity of gel and fluid phases in Supported Lipid Bilayer (SLB), labeled with a BODIPY fluorescent probe. Finally, for the first time, to the best of our knowledge, we implemented a simultaneous AFM Force Spectroscopy (AFM-FS) and confocal fluorescence spectroscopy operational scheme. In this way, we probed the force and lifetime variation due to Metal Induced Energy Transfer (MIET) between the Nitrogen-Vacancy (NV) centers in a nanodiamond attached to the apex of an AFM tip and a substrate of thin gold, therefore, enabling a novel pathway of quench sensing and opening up novel non-invasive imaging modes in liquid that can be used to image soft and fragile biological specimens.Confocal microscopy is perfectly suited for such cellular imaging, but the interaction of the AFM tip and the confocal spot can, unfortunately, extend to a greater distance. In this work, we focus on the coupling between a confocal spot and an AFM tip/cantilever with the aim to investigate and quantify photothermal induced deflection resulting from the presence of metal coating at the cantilever backsideThe synchronous imaging acquisition setup based on confocal excitation is described by Fig. 34, usually called optical beam deflection.The AFM is mounted on top of an inverted microscope where the sample and the tip can be displaced independently with two XYZ piezo scanners. The synchronization of the AFM and the TCSPC card is made by routing the events (photons) with a Transistor-Transistor Logic (TTL) signal generated by the AFM electronics controller, therefore, enabling to assign a 16. 2. The coating on the cantilever backside can be luminescent as well, as for example gold that emits in the red region of the spectrum35. It is worth to notice that backscattered light will occur in all possible configurations since the tip will always scatter the incoming light of the confocal spot in all directions and, therefore, for the same reason radiation pressure will always be present. However, most of the backscattered light can be easily eliminated by a notch filter when it is not already sufficiently reflected by the emission filter.As previously mentioned, the interaction of the focused excitation light and the AFM tip can give rise to two different phenomena: optomechanical forces and/or tip/cantilever luminescence, where the former branches into radiation pressure and photothermal effect. Radiation pressure Fig.  will alw36. The confocal spot is focused hover mode of the AFM , the tip scans the glass coverslip in the trace scan and then, in the retrace scan, the tip follows the retrace topography vertically displaced in We characterized and quantified these effects by operating the AFM in the reverse-tip imaging configurationver Fig. , commerc37, silicon nitride16, and the metallic coating on the cantilever backside, in our case gold35, the choice of the emission filter is not crucial if compared to fluorescence imaging of conventional organic fluorophores characterized by narrow emission spectra. In Fig. Given the large emission spectra of siliconGiven the data presented in Fig. While ATEC-CONT and qp-BioAC will affect the measured lifetime of fluorescent probes characterized by a short  with their local viscosity (FLIM). We used a lipid mixture of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) in 1:1 molar ratio supplemented with et al. showed in Giant Unilamellar Vesicles (GUVs) that high viscosity lipids present higher lifetime, hence lifetime can be used to probe directly the lipids viscosity38. Figure et al. 38, can be attributed to the fact that supported bilayers are formed on top of a substrate (glass), inducing different physical behaviors in terms of lipid diffusion and potentially local viscosity when compared to free-standing bilayers in the GUVs case. Indeed, even if the presence of a layer of buffer (2-3 nm thickness) between the lipid polar heads and the substrate generally prevents an important hindrance in lipid diffusion39, it is known that the latter can be reduced as compared to giant unilamellar vesicles40. The tip luminescence, dominated by low lifetime  imaging modes, where the topography is mapped by relying on the quantum exchange of energy between donors (tip) and acceptors (sample) rather than the mechanical contact usual in AFM in liquid.The lifetime/force curve, where the fluorescence decay is recorded for each In summary, our results impose important experimental constraints for synchronous operation and interference-free correlative AFM and confocal time-resolved fluorescence. In this work, we have quantified in pN/Images were acquired using a Nanowizard 4  equipped with a Tip Assisted Optics (TAO) module and a Vortis-SPM control unit. The AFM cantilever detection system employs an infrared low-coherence light source, with emission centered at entclass1pt{minimaDuring simultaneous AFM/FLIM images the AFM tip and confocal spot positions were fixed by co-aligning them. Full alignment is obtained in two steps: 1. The tip is at first roughly aligned using white field illumination and then the alignment is fine-tuned using the increase of tip luminescence. It is worth to notice that when tip and confocal spot are aligned, some AFM tips, such as qp-BioAC (Nanosensors), show colocalized tip luminescence and optical forces maxima, whereas so-called “protruding AFM tips” do not show maxima colocalization. 2. The sample is imaged and any mismatch between topography and FLIM image is adjusted by correcting the tip position.Keeping the confocal spot and AFM tip aligned during data acquisition has the advantage to maintain constant all spurious effects resulting from synchronous operation. In addition to the tip/cantilever luminescence background and the optomechanical deflection, we mention the mirror-like effect that is due to the reflection of the incoming excitation light by the metallic coating at the cantilever backside: this results in a higher excitation intensity that is constant all over the image acquisition if confocal spot and tip are aligned, whereas it is not in case of wide-field epifluorescence or confocal spinning disk operational schemes.45. Since hundreds or thousands of photons are needed to properly evaluate the lifetime, our acquisition bandwidth must be reduced to few tens/hundreds of Hz. Faster acquisition rates would result in the collection of too few photons (or even none) and the lifetime evaluation would be impossible. For this reason we have fixed our acquisition bandwidth to Acquisition of approach-retract curves with simultaneous collection of force, photon intensity (photon counter) and fluorescent nanodiamond lifetime was performed using 532/10 nm as excitation filter and 709/167 nm as emission filter with a laser excitation power of Lifetime was evaluated by amplitude weighting the decay curve for times greater than qp-BioAC . The fundamental peak was used with a correction of In all AFM experiments, the inverse optical lever sensitivity and lever stiffness of qp-BioAC and ATEC-CONT cantilevers were calibrated using a combination of a SaderMLCT-BIO-DC triangular cantilever optical lever sensitivity was evaluated with the acquisition of an approach-retract curve (deflection versus hover mode (JPK instruments) where in the trace (forward) line the tip is in contact with the sample, whereas in the retrace (backward) line the tip is still following the topography (recorded from the trace) at a fixed distance from the sample defined by the AFM user. In this way, in the retrace image, the deflection can be monitored and can be converted to the force acting on the cantilever at a specific distance. By varying the latter, it is possible to probe the optomechanical interactions acting at different planes of the laser path.Simultaneous intensity (photon counter) and force images presented in Fig. Images were acquired using a 50. 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) were purchased from Avanti Polar Lipids Inc. 2--1-hexadecanoyl-sn-glycero-3-phosphocholine (BODIPY) was purchased from Thermofisher. Chloroform and methanol were purchased from Sigma-Aldrich. Membrane experiments were performed in DPBS buffer solution filtered before use with an inorganic membrane filter (Images were obtained using a 488/10 nm excitation filter and a 535/39 nm emission filter with a laser excitation power of Circular glass coverslips (Marienfeld) were subjected to sonication cycles in acetone (5 minutes) and ethanol (5 minutes), and subsequently dried under nitrogen flow. 5 nm or 36. The use of PLL permanently sets the nanodiamond to be at the tip apex if low forces are used to avoid dislodging events. The modified qp-BioAC, with a circular plateau at the tip apex, ensures higher nanodiamond stability. The absence of the plateau in the ATEC-CONT makes the nanodiamond more prone to dislodgement if higher forces are used.At the AFM tip apex was performed on both ATEC-CONT and qp-BioAC tips. Fluorescence images were acquired without AFM tip to pre-localize nanodiamond candidates on a glass coverslip for the grafting process. Subsequently, an AFM image with low force setpoint (Supplementary Information.


MicroRNAs (miRNA) are small noncoding RNA sequences consisting of about 22 nucleotides that are involved in the regulation of almost 60% of mammalian genes. Presently, there are very limited approaches for the visualization of miRNA locations present inside cells to support the elucidation of pathways and mechanisms behind miRNA function, transport, and biogenesis. MIRLocator, a state-of-the-art tool for the prediction of subcellular localization of miRNAs makes use of a sequence-to-sequence model along with pretrained k-mer embeddings. Existing pretrained k-mer embedding generation methodologies focus on the extraction of semantics of k-mers. However, in RNA sequences, positional information of nucleotides is more important because distinct positions of the four nucleotides define the function of an RNA molecule. Considering the importance of the nucleotide position, we propose a novel approach (kmerPR2vec) which is a fusion of positional information of k-mers with randomly initialized neural k-mer embeddings. In contrast to existing k-mer-based representation, the proposed kmerPR2vec representation is much more rich in terms of semantic information and has more discriminative power. Using novel kmerPR2vec representation, we further present an end-to-end system (MirLocPredictor) which couples the discriminative power of kmerPR2vec with Convolutional Neural Networks (CNNs) for miRNA subcellular location prediction. The effectiveness of the proposed kmerPR2vec approach is evaluated with deep learning-based topologies  and Recurrent Neural Network (RNN)) and by using 9 different evaluation measures. Analysis of the results reveals that MirLocPredictor outperform state-of-the-art methods with a significant margin of 18% and 19% in terms of precision and recall. The biological functions of most coding RNAs and their encoded proteins is well explored ,2, and tCurrently, the classification of noncoding RNAs ,15 and tMediated by posttranscriptional regulation of genes, miRNAs influence many cellular processes in plants and animals including development, differentiation, and proliferation . In thatMus musculus, Homo sapiens, and Saccharomyces cerevisiae), involving 42 subcellular localizations  and 9 RNA classes .As the experimental identification of subcellular localizations of noncoding RNAs is labor-intensive and can be quite complex, the development of appropriate computational prediction methodologies is of high interest. Robust computational methodologies could enable scientists to accelerate research and to get a deeper understanding of ncRNA structure and function as well as their various biomedical implications. The release of RNALocate meta-thesaurus  has playThrough utilizing various sources such as RNALocate meta-thesaurus , ENCODE Up until now, a limited amount of work has been performed regarding in silico prediction of subcellular localization of miRNAs. The most eminent reasons for this are the distinct subcellular localization properties of miRNAs as well as a lack of prior knowledge-based features  and functional annotations in public datasets. To the best of our knowledge, so far, there exists only one methodology for the prediction of subcellular microRNA localizations, namely, MIRLocator presented by Xiao et al. . MIRLocaMost of the existing DNA and RNA sequence analysis approaches generally rely on k-mers of DNA or RNA sequences which are generated by sliding a fixed size window over the sequences with a particular stride size ,35,36. APublicly available approaches for the learning of word embeddings from textual data, or DNA or RNA sequences operate on a basic principle in which a fixed size window is convolved on sequences or textual data ,41. In aFurthermore, the performance of the deep learning model varies at different k-mers due to differences in the distribution of the four nucleotides  . As it iFinally, by using both the proposed Convolutional Neural Network (CNN)-based methodology and the adapted Recurrent Neural Network (RNN) methodology, a fair performance comparison of the proposed kmerPR2vec and 4 other feature representation approaches is conducted using 9 renowned evaluation measures. Our experimental results demonstrate that the proposed feature representation approach significantly improved the performance of both classification methodologies. Overall, the proposed MirLocPredictor clearly outperforms both the adapted TextRNN and the state-of-the-art MIRLocator methodologies.We propose a novel kmerPR2vec feature representation approach that captures the positional information of the k-mers of miRNA sequences. This positional information is injected with randomly initialized neural k-mer embeddings.We performed extensive experimentation with two diverse classification methodologies to prove the effectiveness of the proposed feature representation approach.We present an end-to-end system for the prediction of miRNA subcellular localization.Our contribution can be summarised as follows:This section briefly describes the proposed MirLocPredictor and adapted TextRNN methodologies for the prediction of miRNA subcellular locations. It also discusses the characteristics of the experimental dataset, followed by performance evaluation measures.A statistical representation of DNA and RNA sequences needs to be generated before feeding to deep learning models. In order to generate a statistical representation of corpus sequences, first n-grams of a sequence need to be created. In the domain of biomedical sequence analysis, these n-grams are known as k-mers. Broadly, there are 2 different ways to generate k-mers of sequences. One way is to slide a fixed size window over the sequences with any stride size that is less than the size of window. This approach produces overlapping k-mers for the sequences. In this approach, greater widow size generates more and discriminative k-mers that make the classes distinguishable. Also, overlapping k-mers at a large extent maintain the positional distribution of basis 4 nucleotides. In the second approach, we get nonoverlapping k-mers by sliding a window of a particular size with a stride size equal to the size of widow. As a whole, nonoverlapping k-mers are more discriminative as compared to overlapping k-mers; however, they lose positional distribution of the base 4 nucleotides which is immensely important in biomedical sequence analysis. Building on this, we used overlapping k-mers of the sequences. To generate overlapping k-mers, we used different window sizes ranging from 3 to 10. For all window sizes, we took the same stride size of 1. In this way, 8 different subsets of benchmark dataset are generated with distinct k-mer-based sequences.To better illustrate the process of generating overlapping k-mers, consider a hypothetical sequence ACGUACGUCGU. As is shown by the After k-mer generation, the next step is to create a statistical representation of generated k-mers. In this regard, among many, 3 approaches are predominantly used, namely, one hot vector encoding, randomly initialized embeddings, and pretrained neural k-mer embeddings . While oAfter statistical representation, to extract discriminative features, we use convolutional neural network, the details of which are summarized in In natural language processing, following the success of transformer -based apThe proposed kmerPR2vec feature representation approach consists of three steps. Firstly, we randomly generate k-mer embeddings. Secondly, we capture the positional information of all k-mers in a sequence. The positional information is encoded to a fixed length vector equal to the length of randomly initialize embeddings. Finally we aggregate vectors of randomly initialize embeddings and positional encoded vectors to get the final representation of each k-mer. A pseudo code to generate the positional encoding is given in Algorithm 1.X k-mers present in a sequence. As is illustrated by In the pseudo code, the outer loop shows the total number of positions for unique k-mers in the miRNA subcellular localization dataset and the inner loop represents the dimension of positional encoding. It is important to mention that positional information of k-mers can also be expressed as binary values. However, binary values waste a significant amount of memory. To illustrate this, let us consider a hypothetical example where we have to capture the positional information of In a nutshell, tracking the k-mer position in terms of binary values consumes a significant amount of memory. Hence, sinusoidal representation based on continuous space is a better way to capture the positional information at different time steps.Algorithm 1: Pseudo code to create the positional encoding.Result: Position vector with the dimension of embedding dimension emb_dim = integer value; n_pos = max_len_of_seq +1; Initialize PE matrix of (n_pos * emb_dim) i = 0; n = 0;  These functions represent the alteration in positional bits in such a way that even and odd position values are in the ranges of sin(x) and cosine(x), respectively. This representation is also known as sinusoidal representation, where the range of all real numbers A final statistical representation of the generated k-mers is created using two different settings. In order to fuse positional information in randomly initialized embeddings, we have generated 120 dimensional positional vectors using the proposed algorithm and 120 dimensional randomly initialized vectors for each k-mer of the sequence using a pytorch embedding layer. Then, to generate the final vector for the sequence, we aggregate both vectors of all k-mers present in a sequence. Similarly, to fuse positional information with pretrained k-mer embeddings, we take public pretrained embeddings provided by Xiao et al. . As in tAs miRNA sequence data is one-dimensional, we utilise a one-dimensional convolution layer for the extraction of discriminative features. Suppose a miRNA sequence has n length of k-mers In order to prove the integrity of the proposed kmerPR2vec feature representation approach for recurrent neural networks, we adopted the text classification methodology given by Liu et al. . The adohttp://www.mirbase.org/) byusing extracted IDs. They prepared the benchmark dataset using following steps:They extracted 9456 entries of miRNA using the RNALocate database and combined them through the same gene names. This resulted in 2559 distinct miRNAs.They eliminated miRNAs which did not have sequence information in miRBase, resulting in 1048 human miRNAs.Taking into account that a handful of subcellular locations had very few samples for training the model, they considered the 6 most common subcellular locations from 9 subcellular locations of human miRNAs and eliminated three less common classes.In order to evaluate the integrity of the proposed methodology for the task of miRNA subcellular localization prediction, we use a publicly available benchmark dataset provided by Xiao et al. . They coEventually, they obtained the benchmark dataset containing 1048 human miRNAs in 6 subcellular localities, namely the exosome, cytoplasm, mitochondrion, microvesicle, circulating, and nucleus locations. In benchmark dataset, each sequence is made up of 4 nucleotides, A, C, U, and G, and the overall sequence length lies between 20 to 30 nucleotides. In order to provide an effective overview of label cardinality and density, multi-dimensional analysis of the benchmark dataset was performed. The findings of this analysis are summarized in the forms of a pie chart, bar graph, and multi-label confusion matrices in According to the pie chart shown in In order to visualize the number of miRNA sequences against each class, the bar graph shown in Finally, to provide important information regarding how often different classes have shown up together in the benchmark dataset, only bisubcellular and trisubcellular location-based confusion matrices are created shown in  as very Furthermore, analysis of the trisubcellular location-based confusion matrix developed for the benchmark dataset shows that microvesicle, circulating, and exosome locations have appeared together in most miRNA sequences succeeded by mitochondrion, circulating, and exosome locations, which show up togther in the second highest number of miRNA sequences. Moreover, most trisubcellular location-based miRNA sequences contain the exosome location.http://www.mirbase.org/) and indexed in RNALocate database [Xiao et al.  prepareddatabase . Using tdatabase , we havedatabase  publisheIn order to visualize which subcellular location has appeared the most or least in the benchmark dataset, we created a bar graph illustrating the appearance frequency of each subcellular location in different colors. Among all subcellular locations, exosome has appeared in most miRNA sequences, with a total of 291 instances, followed by circulating locations, which has appeared in 58 miRNA sequences. The remaining classes including nucleus, mitochondrion, and microvesicle show up in very few miRNA sequences.http://www.mirbase.org).Provided by Xiao at al. , originaFurther statistics of the benchmark dataset against each class label are summarized in Among all 421 miRNA sequences, uni-label, bi-label, tri-label, tetra-label, penta-label, and hexa-label have 424, 233, 128, 78, 64, and 120 samples, respectively. Turning towards the sequences of each class, the exosome and circulating classes have the most samples, 106 in total, followed by 39 samples of exosome and microvesicle, 34 samples of exosome and nucleus, and 23 samples of exosome and mitochondrion.Evaluation of multi-label classification methodologies is quite difficult and a way different task than evaluating multi-class classification methodologies ,47. In mLet us suppose that C is a multi-label corpus consisting of Suppose in multi-label classification corpse C has a sample set  whereas each sample set is represented as  . OverallPrecision of a sample sequence is computed by taking the ratio between correctly predicted labels and actual labels of the particular sequence sample . FinallyRecall is a proportion of correctly predicted labels to overall predicted labels of a sample sequence . Recall For every relevant label, it estimates how many relevant labels are actually ranked before it and takes the mean against the set of relevant labels. Mathematically, average precision can be defined as follows:To evaluate the indignity of the proposed methodology, the idea of average precision is borrowed from information retrieval, where it has been extensively used to evaluate the ranking of relevant documents retrieved against certain queries . Averageprecision and recall [d recall . In multThe harmonic mean among micro-precision and micro-recall refers to micro-F1, which cane be defined as follows:Macro-F1 is the harmonic mean among trivial multi-label precision and recall, where first of all, the average is computed for a single sample sequence and, afterward, the mean across all corpuses is taken . ConsideHamming loss estimates the frequency to which a sample label is incorrectly classified, and it mainly focuses on labels which are not predicted at all (missing the prediction of a relevant label) or are wrongly predicted (prediction error). Mathematically hamming loss  is definReceiver Operating Characteristic (ROC) is a probability curve which measures the performance of classification methodologies at different thresholds settings. Area under the curve (AUC) measures the degree of separability; in other words, it infers up to what extent the classifier is capable of discriminating different classes. A higher figure of AUC illustrates that classifiers correctly categorize positive and negative instances. The ROC probability curve is plotted between the true positive rate (TPR) and the false positive rate (FPR), the mathematical expressions of which are given below:This section summarizes the process used to train MirLocPredictor along with optimal values of the most crucial hyperparameters. It discusses the impact of distinct feature representation approaches over the performance of MirLocPredictor and TextRNN. Finally, it compares the performance of MirLocPredictor with the adapted TextRNN and the state-of-the-art MiRNA subcellular location prediction methodology. Finally, it sheds light on significant insights for hands-on tasks.For miRNA subcellular location prediction using a benchmark dataset, we performed 10 fold cross validation. Nine folds are used for training; 10% validation data is taken from 9 folds and the remaining 1 fold is used to test MirLocPredictor and TextRNN. In the proposed MirLocPredictor and adapted TextRNN, ADAM  is used To prove the effectiveness of the proposed kmerPR2vec feature representation approach, we compared its performance with 4 different feature representation approaches using 2 distinct deep learning-based classification models. Firstly, we fed the deep learning methodologies with randomly initialized 120 dimensional word vectors followed by pretrained neural k-mer embeddings provided by Xiao et al. . SecondlAll feature representation approaches are named with appropriate prefixes; for example, pretrained neural k-mer embeddings are referred to as pre-embedding, randomly initialize embeddings are named rand-embedding, position-based embeddings are named pos-encoding, and positional information fused with pretrained neural k-mer embeddings are referred as pre-embedding + pos-encoding.This section illustrates the impact of 4 different feature representation approaches along with the proposed kmerPR2vec and MirLocPredictor. Furthermore, it also shows that gain in performance on the existing approach (TextRNN) when coupled with kmerPR2vec representation.As is depicted by On the other hand, amongst all feature representation approaches, MIRLocPredictor produces the most promising performance using the proposed kmerPR2vec feature representation approach across 7 evaluation metrics. Only for the average precision measure, MIRLocPredictor produces better performance when it is fed with the hybrid feature representation (pre-embedding + pos-encoding) approach. In a nutshell, it can be concluded that MIRLocPredictor has better performance with both feature representation approaches pre-embedding + pos-encoding and kmerPR2vec as compared to the performance produced using simple pretrained or randomly initialized k-mer embeddings.Turning towards the other half of It is a tedious task to generate pretrained neural embeddings for different k-mers. As shown by As is shown by On the other hand, as shown by To summarize, across all evaluation metrics, the proposed kmerPR2vec feature representation approach significantly raises the performance of both classification methodologies.This section compares the performance of the proposed MirLocPredictor with the adapted TextRNN and state-of-the-art MirLocator methodologies for the task of miRNA subcellular location prediction.This section compares the performances of the proposed MirLocPredictor and TextRNN in terms of area under the receiver operating characteristic (AUROC). AUROC is widely used to evaluate the performance of classification models.To better illustrate the performance of both approaches, the AUROC produced by MirLocPredictor and TextRNN over the testing data of 5 different folds is presented in Analysis of the graphical illustrations produced for MirLocPredictor and TextRNN across 5 folds indicates that, for most folds, the proposed MirLocPredictor consistently achieves a better degree of seperability using the proposed kmerPR2vec representation approach, whereas TextRNN has decent performance across 5 folds shared among 3 different representation approaches including randomly initialized embeddings, pretrained embeddings with positional encodings, and kmerPR2vec. Taking the top performance figures of both predictors into account, MirLocPredictor attains an average AUROC score of 70% as compared to TextRNN, which achieves an average AUROC score of 68%. Further, after the kmerPR2vec representation approach, the second best average AU-ROC score of 68% is achieved by MirLocPredictor through fusing positional encoding in pretrained k-mer embeddings.To sum up, the proposed kmerPR2vec representation approach marks the best performance among all representation schemes. The ROC curve is far more consistent across 5 folds for MirLocPredictor than TextRNN. In addition, the proposed MirLocPredictor based on kmerPR2vec outperforms TextRNN with a decent margin when evaluated in terms of AUROC scores across 5 folds.A performance evaluation of the proposed methodology over an independent test set (if possible) is widely considered indispensable to validate the effectiveness of the proposed predictor for hands-on tasks. In this study, for miRNA subcellular location prediction, we validated MirLocPredictor on a newly developed independent test set.From 45 miRNA sequences belonging to exactly one subcellular location, 11 are predicted correctly, and from 16 miRNA sequences having bi-subcellular locations, 3 are predicted correctly. Two miRNA sequences of hexasubcellular localities are correctly predicted and only 1 miRNA sequence of trisubcellular localities is correctly classified from a total of 8 miRNA sequences. From 5 miRNA sequences of tetrasubcellular localities and 1 miRNA sequence of pentasubcellular locality, no instances is predicted correctly.For the independent test set, in the paradigm of the one versus all strategy, the performance of MirLocPredictor is illustrated in To sum up, the proposed MirLocPreditor manages to achieve a precision of 51%, a recall of 80%, an F1-score of 55%, a micro-F1 score of 55%, a macro-F1 score of 48%, an accurcay of 44%, a hamming loss of 38%, and an average precision of 59%.This section sheds light on which k-mer embeddings produce optimal performance among 8 different k-mers for both approaches MirLocPredictor and TextRNN. It also discusses the usefulness of the proposed kmerPR2vec assessed using CNN (MirLocPredictor) and LSTM-based (TextRNN) methodologies. Finally, it performs a class-level comparison between the proposed MirLocPredictor and TextRNN methodologies.Among all statistical representation schemes, the proposed kmerPR2vec marks the best performance across all evaluation measures. We have experimented with 8 different k-mers ranging from 3–10 for all 5 statistical representation schemes shown in . To bettOn the other hand, the hypothesis presented in this study is that, unlike NLP, in biomedical sequence analysis, recurrent neural networks  require external assistance to effectively capture the positional information of k-mers entirely proven correct. Experimentation with 5 different representation schemes demonstrates that, among all representation schemes, LSTM achieves the highest performance with the proposed kmerPR2vec shown in Furthermore, to illustrate the effectiveness of MirLocPredictor over TextRNN by comparing the performance at class level, accuracy confusion matrices for both approaches using the proposed kmerPR2vec are created. We consider the one vs all strategy to create confusion matrices where each subcellular location acts as a positive class and other subcellular locations act as negatives. Moreover, for each confusion matrix, true positives, false positives, true negatives, and false negatives are computed by considering the total appearance of one particular subcellular location in the settings of one versus all.Analysis of the performance produced by MirLocPredictor and TextRNN at the class level indicates that MirLocPredictor achieves better true positive and false positive figures for most subcellular locations. For instance, from the 348 appearances of microvesicle, MirLocPredicor correctly identifies 101 appearances as compared to TextRNN, which manages to accurately detect only 85 appearances. Likewise, from 338 appearances of mitochondrion, 349 appearances of nucleus, and 209 appearances of cytoplasm, MirLocPredictor correctly classifies 98, 114, and 48 appearances in constrast to TextRNN, which accurately categorizes 91, 62, and 32 appearances of the respective subcellular locations. Moreover, in the benchmark dataset, subcellular location exosome appeared the most, a total of 869 times either solely or with 5 other subcellular locations, succeeded by circulating subcellular location, which showed up 513 times. In both classification methodologies, most appearances of exosome and circulating subcellular location are perfectly identified.Overall, MirLocPredictor performs far better than TextRNN across all subcellular locations as for each subcellular location, and a higher number of samples are correctly classified as compared to TextRNN, where a significant number of miRNA sequences are missclassified with respect to each subcellular location.Whereas a significant number of negative miRNA sequences are classified as negative when cytoplasm and microvesicle subcellular locations are treated as positive, a total of 779 and 606 miRNA sequences are correctly categorized in the respective classes. Likewise, in TextRNN, most positive miRNA sequences are classified as positive when exosome is taken as a positive class and the rest are taken as a negative class, with a total of 866 mirRNA sequences succeeded by 390 miRNA sequences of circulating class. Like MirLocPredictor, yet again, the highest number of miRNA sequences are correctly classified as negative when cytoplasm is treated as a positive class.This paper proposes a novel kmerPR2vec feature representation approach that fuses positional information of k-mers and randomly initialized k-mer embeddings. Through precisely analyzing the performance of different feature representation approaches with two distinct classification methodologies across 9 evaluation measures, we concluded that pretrained neural k-mer embeddings do not produce promising performances in miRNA sequence analysis tasks similar to what neural word embeddings managed to produce in natural language processing tasks. Primarily, this is because, in DNA or RNA sequences, positions of k-mers are more significant as compared to their semantics. Experimental results on a public benchmark dataset have proved that the proposed feature representation approach significantly improves the performance of convolutional and recurrent neural network-based approaches for the task of miRNA subcellular location prediction. In addition, experimental results prove that sequence-to-sequence models do not perform well for multi-label classification as they highly depend on label order information. Two simple models have significantly outperformed the performance of the state-of-the-art MIRLocator approach based on a sequence-to-sequence model. Considering the effectiveness of the presented kmerPR2vec feature representation approach, we believe that it can also be used to improve the performance of other DNA and RNA classification tasks. In the future, we will assess the performance impact of the proposed feature representation approach in DNA and RNA classification tasks and will also design a robust classification model for miRNA subcellular localization.https://github.com/muas16/MirLocPredictor.Benchmark dataset and source code of MirLocPredictor is available at


Recent findings implicate that immune cells such as T cells, ILCs, dendritic cells, and macrophages and cytokines secreted by these cells are critical in the regeneration of ISCs and intestinal epithelium. Transplantation of ISC to the inflamed mucosa may be a new therapeutic approach to reconstruct the epithelial barrier in IBD. Considering the links between ISC and immune cells, we predict that the integration of biological agents and ISC transplantation will revolutionize the future therapy of IBD patients.The mammalian intestine is the largest immune organ that contains the intestinal stem cells (ISC), differentiated epithelial cells , and gut resident-immune cells . Inflammatory bowel disease (IBD), a chronic inflammatory disease characterized by mucosa damage and inflammation, threatens the integrity of the intestine. The continuous renewal and repair of intestinal mucosal epithelium after injury depend on ISCs. Inflamed mucosa healing could be a new target for the improvement of clinical symptoms, disease recurrence, and resection-free survival in IBD treated patients. The knowledge about the connections between ISC and immune cells is expanding with the development of  The mammalian intestinal tract is the main organ for nutrient digestion and absorption in the body. Moreover, it serves as the first barrier mechanism of the body defense system. In fact, intestinal health is closely related to the host’s health status, nutrition, environment, psychological state, and intestinal flora composition , 2. Anat+ ISC is a highly active ISC necessary for intestinal epithelium renewal  and ulcerative colitis (UC), which are the chronic and immunologically inflammatory disorders in the gastrointestinal tract . Typicalin vitro intestinal organoid culture and single-cell RNA sequencing technology proffer improved techniques to better understand the interaction between ISC and immune cells -mediated GI damage, targets the ISC niche as the primary site in the intestine under three-dimensional imaging examination  cells contributes to ISC function and intestinal epithelial remodeling, as reported in a recent publication  cells interactions. Supplementation with pre-activated TH2 cells or IL-14 and IL-13 resulted in the depletion of the ISC pool. Similarly, co-culture with pre-activated TH1 and TH17 cells, or cytokines secreted by these cells, such as IFNγ and IL-17, led to the decrease in the number of ISC but increase the number of transit-amplifying cells. On the contrary, co-culture with regulatory T (Treg) cells or IL-10, however, led to the expansion of the ISC pool , which identified IL-2 as the key factor that induced the maturation via signal transducer and activator of transcription 3 (STAT3) signaling  act as critical regulators of intestinal mucosal immunology and are crucial for maintaining gut homeostasis and epithelium barrier integrity . ILCs arth cells . IL-22 ith cells . This knth cells . The stuth cells . This evLactobacillus reuteri on integrity of intestinal mucosa. We demonstrated that Lactobacillus reuteri metabolite indole-3-aldehyde stimulated LPLs to secret IL-22 and promoted ISC-mediated intestinal epithelial regeneration  also affect ISC fate. For instance, IL-13 produced by ILC2 has been demonstrated to accelerate the differentiation of goblet cells in the intestinal organoid model. In addition, IL-33 secreted by intestinal epithelium was shown to promote IL-13 production by ILC2s . FurtherMacrophages and dendritic cells exist throughout the lamina propria of the gut and contribute to innate and adaptive immunity, thus maintaining gut homeostasis. The co-culture of intestinal organoids with macrophages and dendritic cells has also been successfully performed to study complex networks between immune cells and ISC –64. Macrvia producing multiply cytokines  could produce multiple pro-inflammatory and anti-inflammatory cytokines in response to environmental factors in patients with IBD and mouse models of colitis. Moreover, the development of mucosal inflammation is regulated by the balance between pro-inflammatory and anti-inflammatory cytokines in the intestinal mucosa of patients with IBD  on the treatment of IBD have shown little promise so far –120. HowMulti-cytokine inhibitors that block multiple pro-inflammatory cytokine signals such as JAK-STAT signaling pathways could be used to improve the therapeutic effect in IBD. For example, JAK inhibitor (tofacitinib) has recently been demonstrated to be effective against UC but have no effect on CD in the initial clinical studies , 129. ThOver the last 3 decades, molecular mechanisms of cytokine biology in IBD have been explored mainly in experimental animal models and clinical studies, leading to their successful translation into drug targets. This provides a new possibility for the control of IBD symptoms and the long-term remission of IBD. Despite significant advances, cytokine therapies are only at the early stage, which renders it unsafe for human clinical trials. In other words, more in-depth and systematic researches tailored to specific disease conditions will be required for making informed deductions about cytokine dynamics and mechanisms of actions in order to better understand the potentials of cytokine therapies for clinical translations.New data showed that inflamed mucosa healing could be a new target for the improvement of clinical symptoms, disease recurrence, and resection-free survival in IBD treated patients –136. In in vitro  and the Funding of Young Talent Supporting Program of the College of Animal Science and Technology of the China Agricultural University Education Foundation (2017DKA002).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis revealed that all significantly deregulated miRNAs are involved in the Hippo and Transforming Growth Factor-beta (TGF-beta) signalling pathways. Our study confirmed a different profile of selected circulating miRNAs in the plasma of BC patients with an emphasis on some critical points in the analysis process.MicroRNAs in the circulation of breast cancer (BC) patients have great potential for the early diagnosis, treatment and monitoring of breast cancer. The aim of this preliminary study was to obtain the expression profile of selected miRNAs in the plasma of BC patients that could discriminate BC patients from healthy volunteers and may be useful in early detection of BC. Significantly deregulated miRNAs were evaluated by pathway analysis with the prediction of potential miRNA targets. The study enrolled plasma samples from 65 BC patients and 34 healthy volunteers. Selected miRNAs were screened in pilot testing by the real-time PCR (qPCR) method, and the most appropriate reference genes were selected for normalisation by the geNorm algorithm. In the final testing, we detected miR-99a, miR-130a, miR-484 and miR-1260a ( Breast cancer is the leading gynaecologic cancer disease worldwide with an estimated incidence of 46.3 age-standardised rate (ASR) and estimated mortality of 13.0 ASR per 100,000 women , which aBreast cancer is heterogeneous disease comprising phenotypically diverse tumours categorised into molecular subtypes based on the overexpression of receptors for the hormones oestrogen (ER) or progesterone (PR), or for the human epidermal growth factor receptor 2 (HER2). According to the St. Galen Consensus 2011, the molecular subtypes of BC are identified as luminal A (ER+/PR+/HER2-/low Ki-67), luminal B (ER+/PR+/HER2-/+/high Ki-67), HER2-overexpression (ER-/PR-/HER2+) and triple-negative/TNBC (ER-/PR-/HER2-). Molecular subtypes of BC are important in the definition of therapy indications, as systematic treatment recommendations, risk and predictive factors should be considered .It is known that hereditary breast cancer represents 5–10% of all BC cases ,7. HowevEarly detection of BC and appropriate adjuvant therapy contribute to reducing the number of deaths from BC. Currently, many states have established a national screening program of BC, which includes breast self-examination and clinical examination and a gold standard imaging method such as mammography at age-defined intervals . HoweverMicroRNAs (miRNAs) are a class of short non-coding RNAs 21–23 ribonucleotides in length, with the ability to control protein translation and/or influence the stability of messenger RNA (mRNA). MicroRNAs are key regulators involved in the homeostasis of multiple biological systems such as organ function as well as pathobiological processes, for example cancer and aging . It is sThe aim of this preliminary study was to detect miRNA expression in the plasma of breast cancer patients in order to discriminate BC patients from healthy volunteers; these miRNAs could be useful in the early detection of BC. To control for the role of significantly deregulated miRNAs in the circulation of BC patients, we performed pathway analysis with the prediction of miRNA targets and gene ontology annotations.A total of 99 women of the Caucasian race were recruited in the study, including 65 patients with breast cancer (BC) and 34 normal (control) patients. The patient characteristics are summarised in Based on our literature review  and our MicroRNA biomarkers were chosen based on a panel of 179 miRNAs detectable in plasma in Exiqon’s database built on total RNA extracted from human blood plasma or serum. Pilot testing was performed on 15 BC and 15 control samples, each sample was analysed in triplicate by miRCURY Locked Nucleic Acid (LNA) miRNA assays using a LightCycler 480 (Roche) and the second derivative method for cycle quantification value (Cq) calculation. Secondary data analysis was performed in the GeneGlobe data analysis tool with the geNorm normalisation method  on pre-dProfiling of the selected miRNAs was performed in the remaining samples. In the final testing by the GeneGlobe data analysis tool, all BC (n = 65) and control (n = 34) samples were analysed by the same methodology as in pilot testing. The miRNAs miR-191-5p, miR-520d-5p and miR-1228-3p were excluded from the analysis. The average arithmetic means of miR-16-5p (stability factor 0.1164) and miR-103a-3p (stability factor 0.21) was 25.63 and 25.29 in the control and cancer groups, respectively. The final testing confirmed up- or downregulation in seven miRNAs with no significant differences in the plasma of BC patients .Input data from the GeneGlobe data analysis tool were computed using the standard delta–delta Ct method (ddCt) with miR-16-5p and miR-103a-3p as references. Fold change was calculated, and the homogeneity of data was visualised. Up- or downregulation of miRNAs was confirmed in 100% of analysed samples when compared to the GeneGlobe final experiment . The delp-values and dotplots with overlaid medians in cases with weak significance are listed in The causality of all miRNAs and histopathological parameters was tested by robust ANOVA. However, no significant associations were found. The table with The most differentially expressed miRNAs were analysed by KEGG pathway analysis using the DIANA-mirPath v3.0 online web analysis tool to investigate the mechanism of their function and to analyse their target genes. All four miRNAs were significantly enriched in pathways involving the Hippo signalling pathway (hsa04390) and TGF-beta signalling pathway (hsa04350).MicroRNAs miR-130a-3p, miR-484 and miR-1260a were enriched in the phosphatidylinositol signalling system (hsa04070) and oestrogen signalling pathway (hsa04915). MicroRNAs miR-130a-3p, miR-484 and miR-99a were enriched in the glioma (hsa05214) and mammalian target of rapamycin (mTOR) signalling pathway (hsa04150). The genes affected by the deregulation of miRNAs are presented in The Gene Ontology (GO) analysis was useful to investigate biological function in subcategories such as biological process, cellular component and molecular function corresponding to the target genes regulated by the up-regulated miRNAs. The GO annotation results for the gene union of the target genes of up-regulated miRNAs in breast cancer are listed in The preliminary study targeted differentially expressed miRNAs in the circulation of breast cancer patients compared to healthy volunteers. Breast cancer is a heterogeneous disease with different cell populations within the same tumour , and a bIn the technical part of the study, we wanted to overcome some critical points in the steps such as RNA extraction from plasma, reverse transcription, real-time PCR (qPCR) and data analysis. The process of RNA extraction and reverse transcription was controlled by synthetic spike-ins with different concentrations to monitor the presence of inhibitors and sample quality. In qPCR, we used the second derivative method for Cq calculation that has been described as being more reliable and appropriate when a large number of assays are analysed on multiple plates . This prWe analysed five potentially reference miRNAs that have been described as the most useful in the normalisation of qPCR data. Therefore, the geNorm algorithm in pilot testing was used to find the most stable reference miRNAs with Cq below 30 cycles. Normalisation to more than one reference gene produces more reliable results and reduces the bias due to the differences between samples in the efficiency of miRNA extraction as well as in reverse transcription and amplification. Inadequate choice of reference genes also produces misleading results .In the selection of the most appropriate reference genes, miR-520d-5p and miR-1228-3p were recommended as the most stable and useful in the spectrum of clinical material, such as exosomes, plasma and tissues . HoweverMicroRNA-16 and miR-103a-3p are generally used as references, although some studies have reported a significant downregulation of miR-16-5p in the plasma and/or serum of BC as well as TNBC patients . Ng et aMicroRNA-99a has a tumour suppressive role in the mammalian target of rapamycin (mTOR) signalling pathway . It is dMicroRNA-130a expression is aberrant in various types of cancer, including breast cancer, mirroring its different roles as an oncogene or tumour suppressor. Studies on cell cultures and mice showed that miR-130a-3p acts as an onco-miR targeting Phosphatase and Tensin homolog (PTEN) and driving malignant cell survival and growth in the tumour . Anotherp = 0.0002) in the plasma of BC patients. Similarly, Zaero et al. (2014) found higher expression of miR-484 in the serum  of early breast cancer patients. In accordance with these authors, we did not find any correlation between histopathological parameters and miR-484 expression [We identified miR-484 as significantly upregulated (fold change (FC) = 1.22, pression . In a stpression . High expression . In contet al. 20 found hipression .Little is known about miR-1260a expression. Some studies have identified miR-1260a as an immune-related miRNA that has an overlapping seed sequence with miR-1260b. Robust miR-1260a/1260b expression in maternal breast milk may impart immune protection on the infant gut . ExpressWhile the expression of miR-130a and miR-484 was in harmony with published results, miR-99a and miR-1260a were detected in the opposite pattern in the circulation. This discrepant expression is difficult to explain at this time; it could be due to the multiple origins of circulating miRNAs that do not originate only in the tumour environment . It is aIn our study, we identified some miRNAs  that were significantly up-regulated in the plasma of breast cancer patients. At first glance, the fold change was not distinct from the control group. However, the input data are relatively uniform, as we followed several recommendations ,25,59. TA preliminary study has limitations, e.g., small sample size included in the BC and control cohorts and the small subgroups in the evaluation of histopathological parameters and miRNA expression. In subsequent studies, we plan to analyse the miRNA profile of BC tissues and to examine the entire image of circulating miRNAs in the plasma of BC and control cohort by the microarray method.In subsequent studies, it will be necessary to evaluate circulating miRNA expression in more samples with a standardised and accepted methodology to obtain a broader view on the complex state of BC patients. Accurate information on miRNA expression in the plasma of cancer patients may help with a personalised approach and lead to the development of novel targeted therapy strategies. The level of circulating miRNA expression is further influenced by many factors that are related to the physiological state of the organism, and may even change during the day. The simple idea of early detection of miRNAs in a patient’s circulation then becomes a complex task in which many difficulties have to be overcome.All breast cancer patients were recruited at the Clinic of Obstetrics and Gynaecology, University Hospital Martin, Jessenius Faculty of Medicine, Comenius University in Martin and signed the informed consent form for inclusion into the study. The study was approved by the institutional Ethics Committee in accordance with the Declaration of Helsinki. The cohort included 65 breast cancer patients and 34 healthy volunteers. Breast cancer (BC) patients were selected according to the following criteria: (a) all patients were female; (b) all patients were diagnosed with a defined clinical stage; (c) disease was confirmed by routine histopathology; (d) none of the patients had another cancer or disease that may affect the miRNA plasma profile and e) none of the patients underwent preoperative radiotherapy or adjuvant chemotherapy. The healthy cohort was predominantly composed of female faculty staff and female volunteers who wanted to participate in the study with no history of malignant disease and no inflammatory conditions. All blood samples were collected in EthyleneDiamineTetraAcetic acid (EDTA)-anticoagulant tubes and processed as soon as possible, no later than 4 h after collection. The short-term storage and transport temperature was 4 °C. Plasma was separated from blood using two-step centrifugation . The level of haemolysis in plasma samples was evaluated by visual inspection and the measurement of oxyhaemoglobin absorbance at 414 nm. Clear plasma was stored at −80 °C until analysis.Circulating RNA was extracted from 200 μL of plasma using the miRNeasy Serum/Plasma Advanced kit  according to the manufacturer’s recommended protocol. Total circulating RNA, including the miRNA fraction, was resuspended in 20 μL of RNase-free water. One microliter of total RNA was then reversely transcribed into complementary DNA (cDNA) in a 10 μL reaction using the miRCURY LNA RT kit (Qiagen) according to the manufacturer’s recommendations. The process of RNA extraction and cDNA synthesis was monitored through synthetic spike-ins  added in both steps according to recommendations of the manufacturer.p-value.Based on the literature review, specific miRNAs were selected with potentially stable plasma expression  and target miRNAs  ,52,60 wip-value below 0.05 were considered statistically significant. The data analyses were performed in R [miRNA expression and fold change (FC) were computed using the standard formula . The datmed in R  ver. 3.5med in R , robustbmed in R  and the med in R .Homo sapiens [p-value of <0.05 was considered statistically significant, and the Micro T threshold was set at 0.8. In the KEGG analysis, we prefer the gene union and in GO analysis, the category union and intersection for merging the results with false discovery rate (FDR) correction.The most statistically significant miRNAs were analysed using online software to assess the miRNA regulatory roles and identify controlled pathways (mirPath v.3) from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) based on the functional pathway enrichment analysis, as well as multiple segments of Gene Ontology analysis (GO) in  sapiens . The spe


Associations between community-level risk factors and COVID-19 incidence are used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020.2.5), and institutional facilities.Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods. We examined town-level demographic variables, including z-scores of percent Black, Latinx, over 80 years and undergraduate students, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PMAssociations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage Black residents (IRR=1.12 CI=(1.12–1.13) in spring, IRR=1.01 CI=(1.00–1.01) in fall). The association with number of long-term care facility beds per capita also decreased over time (IRR=1.28 CI=(1.26–1.31) in spring, IRR=1.07 CI=(1.05–1.09)in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidence of COVID-19 throughout the pandemic  in spring, IRR=1.20, CI=(1.17–1.22) in fall). Towns with higher percentages of Latinx residents also had sustained elevated incidence over time  in spring, IRR=1.14 CI=(1.13–1.15) in fall).Town-level COVID-19 risk factors vary with time. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence have decreased over time, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level. To our knowledge, no studies to date have investigated the time-dependent association of COVID-19 incidence rate at a town level with key predictors.Early in the pandemic, multiple community-level factors were associated with higher COVID-19 incidence rates, with disproportionate burdens among communities with more racial and ethnic diversity and workers in essential services.In this study, we present a novel set of models analyzing associations between publicly available town-level data and COVID-19 incidence, with the goal of evaluating changes in key town-level predictors across Massachusetts over time. We ran parallel regression models with the same set of predictors over five different time periods from the first recorded case in Massachusetts in March 2020 to October 2020 to assess the temporal variability of the relative magnitude and significance of these predictors on COVID-19 incidence. Characterization of the time-variant nature of these predictors may assist in future efforts to target interventions to specific subpopulations and assess the effectiveness of mitigation strategies on the community level over time.8 Case data were centrally reported to MA DPH by all laboratories across the state that conducted COVID-19 testing, and cumulative case counts and rates published weekly. We stratified that case data across 5 time periods selected to reflect distinct waves of case incidence in the state, starting with the spring “ first wave” , the summer nadir , and the early months of the “second wave” in the fall . The specific end date was chosen based on COVID-19 case incidence data availability at the time of study.We used publicly available case incidence data for 351 cities and towns published by the Massachusetts Department of Public Health (MA DPH) from April 14, 2020 through October 29, 2020.9 Some rural census tracts contained several smaller towns, thus these had identical ACS percentage values. Estimates for percentage of essential workers were modified from ACS data based on an integrated dataset developed by the American Civil Liberties Union, which restricted ACS data on service workers to those job types considered “essential” during the pandemic including healthcare practitioners, transportation occupation, food preparation, etc.10 Data on town-level imprisoned population were obtained through MassGIS11 and long-term care beds by town were obtained from MA DPH.13 The percentage of people commuting to work was estimated using the SafeGraph Social Distancing Metrics dataset derived from anonymized cell phone mobility data, where a commuter is defined as someone who spends 8 hours a day M-F in a single location away from their home block group.14 Annual PM2.5 concentrations were obtained from Kloog et al. at 1 km grid resolution, averaged by town for the year of 2015, and linked to reflect a general measure of air pollution.15 Population density, as a proxy for local transmission risk, was calculated per town using 100 meter cell size population grids derived from the 2010 Census counts available at the census block level . For each town we averaged the cell values depicting population totals in each cell at different Euclidean distance radii  to capture the effects of urbanicity and population density at different scales.1617 of each town as an offset to account for differences in population per town. To minimize multi-collinearity while maintaining a good fit, we used a backward selection process of covariates where we stepwise excluded covariates with a variance inflation factor higher than 2.5 in the regression of the first time period.18 All numerical predictors were normalized to zero mean and standard deviation of 1. We included the county of each town as a random effect to control for spatial autocorrelation of residuals (351 towns divided over 14 counties). For every period except the first period ending on April 14, we included an ordinal variable of the COVID-19 case count per capita of the previous period, divided into quintiles, as a random effect to adjust for between-period temporal autocorrelation. To assess collinearity structure of the predictor variables, we calculated Pearson correlations between all bivariate predictor combinations and presented them in a correlation matrix. Mixed-effect models were executed in R 3.6.1 19 using the glmer function from the lme4 package.20We developed a series of mixed-effect Poisson regression models to predict COVID-19 case incidence by town for each of the five distinct time periods of the pandemic. We used the most recent population estimate2.5 in the upper left corner and a cluster of housing crowdedness with percent of the population working in essential services, under the poverty line, or with disabilities. Both clusters show positive correlations with % Black population and Hispanic/Latino (henceforth referred to as Latinx) population. Point estimates and associated 95% confidence intervals are presented for all eight predictors in the model across all time periods . Whereas an increase of one standard deviation in the percentage of town residents identifying as Black was associated with an increase of more than 10% in COVID-19 cases before April 14, the association steadily decreased and became statistically non-significant by September-October. At the start of the pandemic, we observed a nearly 20% increase in COVID-19 cases by town associated with one standard deviation increase in the percentage of town residents identifying as Latinx, but the strength of this positive association varied over time, with a decreasing association from April to September and an uptick in the association in the last phase of our model (September 3rd to October 29th ).We observed a decreasing trend in the association between percent of the town population older than 80 years and the number of long term care facility beds per town with COVID-19 incidence, indicating that the presence of older residents and institutional care facilities in a town had a stronger association with incidence in the early, but not the later, periods of the study. The percent of essential workers by town consistently showed a significant positive association with COVID-19 incidence throughout all time periods.Population density, measured as the total number of people living within 20 km from the boundary of each town, showed notable changes in association with COVID-19 case incidence, with high positive associations in the first and fourth time periods, and non-significant associations during the other time periods. The undergraduate student population, measured as the percentage of the town population enrolled in undergraduate education, was associated with a small positive effect on COVID-19 incidence early in the pandemic and a larger association after September 3rd, when several universities throughout the state resumed with in-person or hybrid classes after shutting down for in-person learning in March/April 2020.7 our study provides novel insight about how both patterns and predictors change over time. Likewise, associations that remain significant over time with consistent coefficients in these adjusted models, such as workers in essential services, may suggest that existing approaches to reduce risk within these subgroups are less effective, perhaps due to structural challenges in reducing certain exposures.Our findings illustrate the time-varying nature of sociodemographic and economic predictors of COVID-19 case incidence at the community level in Massachusetts during an eight-month period. These observations suggest that fixed assumptions regarding community-level COVID-19 vulnerability, such as increased risk among Black communities or continued elevated relative risk among the elderly, may not accurately represent the pandemic at any given point in time, and that these associations should be continually reassessed though time for relevance alongside shifting mitigation efforts, policies and individual behavior. While other studies have identified racial/ethnic disparities in COVID-19 incidence in Massachusetts and the extent to which these patterns are explained by social factors,We note that the community-level predictor variables used here serve as proxies for underlying factors that influence individual SARS-CoV-2 exposure risk. This distinction is important in our analysis, as it remains possible that analyses using individual-level predictors would lead to different findings. However, our analysis provides insight into sociodemographic patterns of COVID-19 and subpopulations who may be at elevated risk, informing community-scale public health interventions, and vaccination strategies by location, as well as a useful and adaptable structure to assess risk alongside public data.21 In our models, Latinx population was positively correlated with greater housing density, suggesting that this ethnicity variable may serve in part as a proxy for crowded housing (> 1 person/room) in our models. Within-household transmission is an established risk factor for COVID-19 transmission,23 and it is possible that our findings here reflect established challenges in reducing transmission within housing environments where residents are unable to meaningfully distance from an infectious individual.The consistently elevated risk observed in communities with increased Latinx populations  may underscore challenges in reaching these communities with successful interventions, or barriers to reduced exposure in these communities. The sustained elevated risk of Latinx populations throughout the first eight months of the pandemic is consistent with recent studies.7 The authors noted racial disparity in disease incidence in the early wave after adjustment for essential workers, immigration, and household size, while the association between COVID-19 cases and Latinx population was attenuated in models adjusted for these factors. Together, our studies support the hypotheses that systemic racism and inequities not otherwise captured in core demographic datasets play a role in driving COVID-19 racial inequities and that distinct analyses focused on systemic inequities are needed to fully understand the specific risks faced by Black individuals and communities.We did not observe a correlation between housing density and Black population in a town, or between this race variable and other sociodemographic covariates in our model. This finding suggests that other factors beyond the scope of our analysis may be responsible for the elevated COVID-19 risks faced by towns with higher percentage of Black residents, especially early in the pandemic. Our findings parallel core conclusions of Figueroa et al., who evaluated cross-sectional COVID-19 incidence alongside demographic data in Massachusetts from March-May 2020.26 Our finding parallels other observations as to the reduced racial disparity in COVID-19 cases over time.27 Likewise, substantial reductions in the association between long-term care beds and percentage of town population over 80 years and COVID-19 incidence may reflect success in interventions to protect the elderly, especially those living in long-term or nursing care, after the initial, devastating impacts on this population early in the pandemic.29 It remains possible that factors not included in our analysis, notably biases associated with testing availability or other unstudied correlates, are responsible for the reductions in risk we observe here, especially by race. Analysis of more severe outcomes, including hospitalization and deaths, would help inform these questions, although requiring less-nuanced temporal resolution, and should be prioritized for future work.Reduced risk of COVID-19 incidence in communities with higher percentages of Black residents, adjusting for other factors, may suggest that the clear racial disparity observed in early months of the pandemic has diminished over time in Massachusetts.30, although the data are highly correlated with true Census population.31 However, it could also indicate that changes in worker mobility (e.g. “return to work” efforts) were not associated with increased cases when these workers were not part of the essential workforce. This would reinforce that communities with more non-essential workers face distinctly lower risk than those with more essential workers, even with resumption of economic activities, highlighting inequities in exposure profiles on the job.The continued positive association between essential workers and COVID-19 incidence may reflect the continued inability of this workforce to adequately protect themselves from infection, despite workplace controls and personal mitigation behaviors, such as masking and maintaining social distance. Essential workers remained at the highest risk of all subgroups studied in our models throughout the pandemic, highlighting key challenges in protecting these populations, even many months into the pandemic. Interestingly, however, a covariate representing people spending more than 3 hours in a location other than their home during office hours showed a null association with COVID-19 cases. The non-significant effect of worker mobility on case incidence may reflect limitations in the underlying data. SafeGraph data includes only 10% of cellphones in the US 32 Targeted interventions that focus on communities with elevated percentages of uninsured persons are important in fully understanding disease dynamics in the state.The significant association between the percentage of town residents without health insurance and case incidence in the spring and summer periods may suggest growing case incidence among immigrants during those times. Due to a state health insurance mandate, the number of non-insured individuals in Massachusetts is low , but some municipalities have uninsured rates up to 25%, such as Chelsea, Everett and Lawrence. These communities have higher proportions of younger adults, non-citizens, and those who are less educated than the insured population.As noted, our study is limited by the use of town-level covariates, which reduce our ability to draw causal inferences on the individual level, though our insights remain relevant for targeted public health strategies. The use of static predictors from the ACS and other databases allowed us to evaluate and interpret changes in coefficient magnitude and significance over time, but gave us limited ability to capture time-dynamic exposure factors (with the exception of SafeGraph data). While most of these predictors are expected to be fairly stable during the study period, notably demographic data, it is possible that real-time changes in these covariates may have altered our findings. It is possible that disparities in testing availability, as noted earlier, contribute to observed trends in case incidence if communities with greater racial diversity had reduced access to case identification, and this is an important area for additional work.Our analyses demonstrate that the relevance and magnitude of risk factors on the community-level for COVID-19 are alterable, suggesting in part the relative effectiveness of intervention and mitigation efforts by population subgroups . Our study highlights the need for local jurisdictions to use up-to-date data on vulnerable and high-risk populations to direct COVID-19 interventions, including vaccinations, rather than data from early in the pandemic. Our models were derived entirely from publicly-available data and could be rapidly refit to newer case incidence data. Community-level analyses can help characterize social inequities embedded in the pandemic and track the evolution of these inequities with time, highlighting successes as well as disproportionate burden experienced by vulnerable populations.


During the novel coronavirus disease 2019 (COVID-19) outbreak, traditional face-to-face psychological interventions have been suspended due to high risks of rapid transmission. Developing an effective online model of psychological intervention is deemed necessary to deal with the mental health challenges brought up by this disease. An integrated psychological intervention model coined 'COVID-19 Psychological Resilience Model' was developed in Chengdu, China including live media, 24-hour hotline consultations, online video intervention and on-site crisis intervention sessions to provide services to those in need. A total of 45 episodes of live media programs on COVID-19 outbreak-related psychological problems were broadcasted with over 10 million views. A total of 4,236 hotline consultations were completed. More than 50% of the clients had positive feedback about the hotline consultations. A total of 223 cases received online video intervention, of which 84.97% were redirected from the hotline consultation and 15.03% from COVID-19-designated hospital and community-based observation spots. Seventy one-on-one psychological interventions were conducted with 39 COVID-19 patients, and one-third were treated with medication. Additionally, 5 training sessions were conducted to 98 frontline medical staff. This 'COVID-19 Psychological Resilience Model' is proven effective to the general population during the COVID-19 pandemic. We have greatly improved the overall mental health of our target population during the COVID-19 pandemic. This model could provide valuable experiences and serve as a reference guide for other countries to offer effective psychological intervention, and reduce detrimental negative mental health outcomes in public health emergency. The novel coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis. After it was first reported in Wuhan, Hubei province in China at the end of December, 2019, COVID-19 was rapidly transmitted to all provinces across China and were also reported in more than 200 countries worldwide. This rapid transmission has posed a serious threat to human life. A range of unprecedented measures were adopted to contain the virus, including isolation of suspected and confirmed cases, social distancing, tracing and monitoring of close contacts and limiting the number of people in public gathering Figure 1). Thus, timely and strategic psychological intervention in different stage is extremely critical to reduce undesirable mental health outcomes.When confronted with crisis events, individuals may experience three psychological states. The focus and approach of interventions vary at different stages China has made tremendous progress in applying psychological interventions to successfully address the public psychological crisis after several disasters, for example, the 2008 Wenchuan earthquake This study was conducted ethically in accordance with the World Medical Association Declaration of Helsinki and the study protocol was approved by the research ethical committee of the Mental Health Center of Chengdu, China.th of January 2020, this leading group developed working programs and manuals for psychological intervention based on various channels people were affected by the COVID-19 pandemic in China. The entire work flow was implemented in the following steps.Under the leadership of the government, the Mental Health Center of Chengdu, China, established a leading group which comprised of authoritative experts in crisis intervention, and responsible for the overall planning. According to the “Guideline for the Emergent Psychological Crisis Intervention during the Novel Coronavirus Pneumonia Pandemic” released by the National Health Commission of China on the 25Initially, the leading group formulated work manuals for psychological intervention and conducted a series of pre-training workshops for the back-up team involved in the psychological intervention.Figure 2). Each group had a leader responsible for regularly reporting of their daily work progress to the leading group.A psychological intervention team was established supported by mental health professionals in the Mental Health Center of Chengdu, China. This team consisted of 26 multidisciplinary members, including psychiatrists, psychiatric nurses, psychotherapists and psychologists. Subsequently, they were divided into four groups: 1) live media group; 2) hotline consultation group; 3) online video intervention group and; 4) on-site crisis intervention group . At the bottom of the framework (1st tier) was mental health public awareness, arguably the most widespread layer covering a large number of lay persons in the general population. The 1st tier mainly provided mental health education and helped people to mentally prepare themselves to deal with the crisis. The 2nd tier of our psychological intervention  mainly aimed to identify the target groups which were in need of intervention. Through our telephone hotlines and video psychotherapy platforms, we could quickly start the online consultations and establish problem feedback mechanisms in the 3rd tier. In addition, the psychological rescue team conducted on-site crisis interventions for confirmed COVID-19 patients and/frontline medical staff in hospitals. The leading group positioned at the 4th tier of the framework provided training and supervision during the entire process. Each team functioned as an independent unit to conduct psychological intervention through joint collaboration.We established a 4-tiered structured framework of psychological intervention , our programs were broadcasted on four airwaves in Chengdu, with 11.72 million audience. Short audio/video programs and tweets were promoted by social media platforms including Weibo (mini blog posts), WeChat (chatting software) and TikTok (short video We Media) with nearly 30 million clicks and views.Between 8th of January to the 26th of March, there were 4,236 hotline consultations in total, with an average duration of 11.30 (± 8.48) minutes. Since the outbreak of the COVID-19 epidemic, the number of hotline consultations has risen rapidly compared to the corresponding period of last year . By analyzing 3,704 valid hotline consultation data, we found that among the callers, women constituted the majority . Regarding the type of complaints in consultations, 1039 cases were related to practical difficulties (28.05%), 1862 cases were associated with emotional and behavioral disturbances (50.27%), 304 cases involved the diagnosis and treatment of mental diseases (8.21%), 437 cases concerned their sleeping problems (12.80%), and 62 cases suffered from psychological crisis (1.67%) . Regarding the feedback on our consultations, 27.05% of the clients stated that their problems were solved, 56.26% felt emotionally relieved, though practical difficulties still existed, and 16.69% had more complicated issues needing further intervention . Our findings indicated that our COVID-19 model of psychological intervention achieved very satisfactory results and resolved most psychological crisis emerged during the COVID-19 pandemic.Spanning across two months starting from the 26Between the 26th of January and the 26th of March, 233 patients received online video psychotherapy. A total of 84.97% of patients were redirected from the hotline consultation, and 15.03% were referred from COVID-19-designated hospitals and community-based observation spots.Between 26th of January to the 26th of March, we offered 70 one-on-one psychological interventions to 39 infected patients, one-third of which were treated with medication under the guidance of psychiatrists. We also conducted 5 psychological training sessions with frontline medical staff and received 98 visits from our target populations.To the best of our knowledge, this is the first project in which “the integrated psychological intervention” was developed and implemented. The core idea is to integrate social media and internet technology into the process of intervention while incorporating the elements of preventive approach. Findings emerged have provided solid evidence that this type of intervention can achieve effective results in Chengdu, China.Notably, the number of hotline consultations increased rapidly after the live media program was launched during the COVID-19 pandemic, possibly due to two main reasons: First, the public has increasing willingness to seek psychological assistance via a user-friendly media  According to the analysis of our hotline consultation data, emotional disturbance seemed to be the top priority problem, accounting for half of the consultations. Feedback from clients demonstrated that more than 50% of the callers felt their negative emotions, such as anxiety and depression, were relieved. Although we could not solve their practical problems in a timely fashion during the pandemic, we were able to help ease clients' emotional disturbances and avoid the flooding of negative emotions. Of particular note was that 16.69% of the clients reported more complicated issues due to early childhood trauma or excessive stress. Therefore, hotline consultation could not offer much help. Nonetheless, the hotline consultation was able to screen out the high-risk population and develop an effective intervention mechanism, which we considered very critical and important in the field of psychiatry and psychology. Through the referral mechanism, these clients could receive further assistance, such as online video intervention or on-site crisis intervention, to reduce the risk of psychological crisis events. When people realized their psychological difficulties, fear of exposure to the virus and the perceived stigma attached with their psychological problems might hinder their intention to seek help in mental health facilities Overall, three key highlights with regard to our integrated psychological intervention should be addressed. First, we paid more emphasis on individual's psychological status well before the crisis happened. Our intervention embedded with the use of multimedia enabled us to educate the public about mental health knowledge and offer them self-adjustment skills to improve their self-efficacy. Second, we offered hotline consultations to address the psychological issues that the public mostly concerned with, by using online video psychotherapy sessions to reduce their negative emotions to the minimal extent. Third, we provided timely on-site crisis intervention to the most at-risk group  to prevent any psychological crisis events. With concerted effort, we had improved the overall public and mental health during the COVID-19 pandemic. We believed that this model can offer guidance for a systematic and effective psychological intervention program when there is any sudden outbreak of public health events in the near future.However, there are some limitations on this model. Our methods of intervention, such as the hotline and online videos, were relatively dull, and the signal transmission might be delayed. We were unable to provide high-quality psychological interaction compared with face-to-face situations, which might reduce the effects of our intervention. Unfortunately, effective utilization of internet resources remained a major challenge in China This newly developed COVID-19 Psychological Resilience Model has proven effective in this project. Our integrated psychological intervention program could be used as an expert reference guide to other regions in China or to other countries in the provision of effective psychological intervention which largely reduce negative mental health outcomes in a sudden outbreak of a public health emergency.