In single-cell analysis, biological variability can be attributed to individual cells, their specific state, and the ability to respond to external stimuli, which are determined by protein abundance and their relative alterations. Mass spectrometry (MS)-based proteomics  can be used as a non-targeted method to detect molecules across hundreds of individual cells. To achieve high-throughput investigation, novel approaches in Single-Cell Proteomics (SCP) are needed to identify and quantify proteins as accurately as possible. Controlling sample preparation prior to LC-MS analysis is critical, as it influences sensitivity, robustness, and reproducibility. Several nanotechnological approaches have been developed for the removal of cellular debris, salts, and detergents, and to facilitate systematic sample processing at the nano- and microfluidic scale. In addition, nanotechnology has enabled high-throughput proteomics analysis, which have required the improvement of software tools, such as DART-ID or DO-MS, which are also fundamental for addressing key biological questions. Single-cell proteomics has many applications in nanomedicine and biomedical research, including advanced cancer immunotherapies or biomarker characterization, among others; and novel methods allow the quantification of more than a thousand proteins while analyzing hundreds of single cells. Complex biological processes are based on dynamic interactions between individual cells, involving in many cases multiple cell types as well as different states and susceptibilities . TraditiMost single-cell studies are focused on nucleic acids, especially the genes expressed at the cellular level . HoweverCurrently, high-content data sets of single-cell genomic and transcriptomic data can be generated and, as Single-Cell Proteomics (SCP) emerges, researchers will be able to integrate single-cell mRNA and proteomic measurements . The abuHelicobacter pylori, hepatitis C virus, or Kaposi’s sarcoma-associated herpesvirus being some examples [Several human health situations are related to disturbances of the immune system  and other pathologies with different ontogeny  . Recentlexamples ,16. Noveexamples . The undexamples . Tumor gexamples . In thesexamples . Moreoveexamples . Therefoexamples ,3.Single-cell heterogeneity is gaining relevance due to recent advances in single-cell RNA and protein analysis, characterizing processes such as the cell division cycle . TraditiThe sample preparation preceding MS analysis is a critical step, as it greatly impacts sensitivity, robustness, and reproducibility . Sample Focused Acoustic Sonication (FAS) is a cell lysis method with no MS-incompatible chemicals and, therefore, can be used in LC-MS/MS without further cleaning. However, while FAS allows a clean lysis, it requires large volumes (5–10 µL), resulting in low throughput, which limits its potential for SCP . To reliTo make further progress in single-cell MS proteomics, a miniaturized and massively parallel sample preparation method was developed for high-throughput characterization: automated nano-ProteOmic sample Preparation (nPOP) . This noGiven the lack of amplification methods, the wide variety of species, the relatively low abundance, and the large dynamic range of proteins, they have barely been studied in single-cell research compared to other macromolecules . TherefoBiological variations can be attributed—in single-cell analysis—to cells individually, instead of being averaged from a complex tissue . CurrentSome proteomics methods rely on antibodies to detect chosen targets in single cells ,34. HoweA high impact on the biomedical field is expected as soon as an in-depth characterization and an unbiased protein expression profile of individual cells is accomplished, thereby unravelling microenvironmental factors that either enhance or inhibit tumor growth, identifying previously unknown cellular subpopulations, or developing pathways that may be missed in bulk measurements ,34. NoveAs stated above, mass spectrometry has become a central technology in the field of proteomics, although further understanding of biological components by determining their distribution is needed. This anatomical dimension has been added by mass spectrometry imaging, especially by MALDI-MSI . It offeAdditionally, Single-Cell ProtEomics by MS (SCoPE-MS) and the second-generation SCoPE2 are also technologies that allow the quantification and identification of thousands of proteins through hundreds of single-cell samples with LC-MS/MS . They coTo provide MS proteomics data quantitatively accurately from single cells and answer biological questions, True Single-Cell Proteomics is also emerging . The worFurthermore, large-scale single-cell analyses are fundamental to studying biological heterogeneity within complex cell systems such as cancer , but have been limited to technologies based on RNA and overlooking protein levels . Schoof As is well known, thousands of proteins can be identified and quantified by LC-MS/MS in microgram-level samples. However, sometimes—as in the case of mammalian single cell proteomes—they are not enough for a reliable identification of peptides . SCP aimThe IceR is a high identification tool that adapts the concept of match-between-runs for both ultra-low inputs and a possible absence in MS1 spectra of isotopic ions, improving the ability to discriminate among single cells  Table 2Table 2. To identify weak-spectrum peptides, Chen et al. developed Data-driven Alignment of Retention Times for IDentification (DART-ID), which uses an ion retention time as well as its spectra for a more confident characterization . DART-IDHuffman et al. created Data-driven Optimization of MS (DO-MS), an open-source platform for their interactive visualization and analysis, which diagnoses problems and suggests solutions as specifically as possible . To enabA complete immune system and tumor microenvironment screening is essential for an accurate and successful cancer therapy. The immune system is complex and requires long-term follow-up of the patient to ensure successful treatments. Understanding intra- and anti-tumor dynamics and heterogeneity is one of the major challenges that can be assisted by proteomic techniques . SpecifiOne of the main goals of nanomedicine is to find precise and early indicators of the disease; in particular, anti-tumor immunotherapy has shown great efficacy in earlier stages of the disease ,60. For Additionally, there are no high-throughput single-cell techniques capable of capturing both changes in phosphorylation levels and gene expression patterns ,68. RiveRecently, a novel approach for spatial characterization of single cells called Deep Visual Proteomics (DVP) has been developed . DVP offSCP have many applications in biomedical research and nanomedicine. In some cases, they may overlap with single-cell RNA-seq purposes, such as sorting cell states and types, while others may be accomplished only by protein measurement, for example regenerative therapies using rational engineering of directed cell differentiation . HoweverFor the application of MS nanotechnologies to the analysis of single cells, the SCoPE-MS approach introduced the idea of using an isobaric carrier, which fulfills three critical functions: i. to minimize loss of sample; ii. to improve ion detectability during MS1 scans; and iii. to provide fragment ions to identify the peptide sequence. Combining this approach with a cell lysis compatible with MS makes it possible to quantify proteins from single cells . IncreasFurthermore, some multi-omic technologies are able to evaluate complex molecular, cellular or physiological biomarkers, both quantitative and qualitative, in order to gain a closer understanding of the nature of aging  and cardLife is derived from dynamic interactions occurring among individual cells and the state of each one of them, as well as the ability to response to environmental signals monitored by protein abundances. Until recently, researchers have been limited to bulk proteomic analyses with samples derived from millions of cells, providing deep protein coverage while eliminating heterogeneity between individual cells . In multSCP offers a unique perspective that will drive the further development of single-cell biology since it enables the analysis of hundreds of individual cells per day while quantifying thousands of proteins, allowing the characterization of the functional state of cells ,82 Figu. FurtherTo scale up SCP, there are two main requirements: i. make robust and widely available approaches, i.e., accessible, and ii. raise the total number of individual cells analyzed per data project, thus allowing for high throughput to achieve sufficient statistical power ,82. The Therefore, the emerging need for protein analysis at the single cell level has been a major thrust in MS-based SPC, enabling the development of numerous methods and protocols capable of quantifying more than a thousand proteins from each cell, while analyzing hundreds of single cells. To leverage these capabilities, robust and accessible analytical pipelines and procedures are required, thus driving the progress of further analytical and computational SCP tools  . The rol


Immune-mediated inflammatory diseases (IMIDs) encompass several entities such as “classic” autoimmune disorders or immune-mediated diseases with autoinflammatory characteristics. Adult stem cells including mesenchymal stem cells (MSCs) are by far the most commonly used type in clinical practice. However, due to the possible side effects of MSC-based treatments, there is an increase in interest in the MSC-secretome  as an alternative therapeutic option in IMIDs. A wide spectrum of MSC-secretome-related biological activities has been proven thus far including anti-inflammatory, anti-apoptotic, and immunomodulatory properties. In comparison with MSCs, the secretome is less immunogenic but exerts similar biological actions, so it can be considered as an ideal cell-free therapeutic alternative. Additionally, since the composition of the MSC-secretome can be engineered, for a future perspective, it could also be viewed as part of a potential delivery system within nanomedicine, allowing us to specifically target dysfunctional cells or tissues. Although many encouraging results from pre-clinical studies have recently been obtained that strongly support the application of the MSC-secretome in IMIDs, human studies with MSC-secretome administration are still in their infancy. This article reviews the immunomodulatory effects of the MSC-secretome in IMIDs and provides insight into the interpretation of its beneficial biological actions. The term immune-mediated inflammatory diseases (IMIDs) encompasses several disease entities including definite, “classic” autoimmune disorders (ADs) ; neurological disorders (such as multiple sclerosis); cutaneous inflammatory conditions (like psoriasis and atopic dermatitis); immune-mediated disorders with autoinflammatory characteristics such as the spondyloarthritis (SpA) disease spectrum, inflammatory bowel disease (IBD), asthma, graft-versus-host disease as well as other conditions in which the immune system is involved, but not the primary element of the pathogenesis  ,2.Approximately 3–5 percent of the world’s population is currently affected by ADs, of which there are over 100 different types, and both rates are still rising ..93].Although animal experiments have been extremely encouraging and no significant adverse effects associated with the use of secretome have been reported, human trials have not begun yet. It should also be borne in mind that some new research ,95 suggeSpondyloarthritis (SpA) is defined by synovial and entheseal involvement, resulting in spinal and oligoarticular peripheral arthritis, most commonly in genetically predisposed (HLA-B27+) individuals. Additionally, extra-musculoskeletal, systemic inflammatory organ manifestations may also occur. SpA may be listed as an autoinflammatory disease, with a predominant involvement of innate immunity. Adult spondyloarthritis can be classified into several well-defined subtypes; (one of the most known is Bechterew’s disease) . Interestingly, the use of MSCs and MSC-secretome in the SpA group can cause not only beneficial but also detrimental effects. In mice with a low expression of A20  in myeloid cells, early enthesitis has been found . In paraMultiple sclerosis (MS) is an inflammatory demyelinating autoimmune disease of the central nervous system in which the protective myelinated axons of the nerve fibers in the brain and spinal cord are targeted, along with parallel disruption of the blood–brain barrier. MS can be present in several forms such as a progressive or a fluctuating, relapsing-remitting type ,104,105.The success of using MSCs in MS has fallen short of the expectations. In patients with the relapsing-remitting type, disease-modifying treatment altered the activity of MSCs . AdditioBy modifying exosomes, receptors may be added to their surface, allowing for targeted drug delivery to cells. Exosome-encapsulated curcumin or JSI124 (a selective STAT3 inhibitor) were delivered to microglia cells via an intranasal route in mice . The resThe therapeutic efficacy of MSC-exosomes is also supported by the fact that MSC-derived exosomes injected into the tail vein of EAE rats exhibited immunoregulatory capacity, indicating that exosomes from MSCs could regulate microglia polarization from the M1 to M2 phenotype . AdditioExosomes loaded selectively and made their membrane cell specific enables the development of additional targeted treatment options. Exosomes isolated from rhesus monkey MSCs were utilized in two mouse models of demyelination  . In the Exosomes derived from neural stem cells were targeted using a lentivirus-armed PDGFR-alpha ligand capable of anchoring the membrane . Exosomewww.clinicaltrials.gov (accessed on 22 May 2022), only one study is planned in relapsing MS, in which, as a sub-target, the analysis of molecular and biochemical parameters including circulating exosomes are planned to perform and compare responder and non-responder patients.Although these procedures may represent a paradigm shift in the treatment of MS, their clinical utility remains unknown. In a recent study, the suppressive activity of hUCMSCEs was investigated in vitro in co-culture with peripheral blood mononuclear cells (PBMCs) . It was Uveitis is an intraocular inflammatory condition, a form of potentially blinding diseases that can affect either one or both eyes. Different kinds of uveitis may affect various areas of the eye . UveitisAnimal models of experimental autoimmune uveitis are frequently used to study the disease . Upon a The post-uveitis macular hole is a potentially severe complication . In a reType 1 (or insulin-dependent) diabetes mellitus (T1DM) is a chronic autoimmune disease that results in the destruction of insulin-producing β-cells in the pancreas . ConsequImmunotherapies and cell replacement, which include islet transplantation and stem cell differentiation into β-cells, are the primary treatment strategies for T1DM ,127. TheIn murine models of T1DM, EVs that originated from bone marrow-derived MSCs resulted in a delayed onset of T1DM, the preservation of islet cells, the reduction in insulitis and T cell infiltration, the decrease in antigen-presenting cell (APC) and T cell activation, lessening the Th1/Th17 population, and a decline in the production of inflammatory cytokines including IL-17, IL-6, IFNγ, TNFα, and IL-12 . It was EVs from human adipose tissue-derived MSCs increased the number of regulatory T cells in the spleen, raised the levels of immunomodulatory cytokines , and decreased the production of inflammatory cytokines such as IFNγ and IL-17 . AccordiThe total exosome fraction isolated from Psh-Fas-anti-miR375 transfected human bone marrow-derived MSCs co-cultured with peripheral blood MSCs was found to reduce islet cell apoptosis and stimulate insulin release. Moreover, exosomes increased the number of regulatory T cells, reduced the levels of inflammatory cytokines (IL-2 and IFNγ), and diminished immune-rejection after islet transplantation .It was found that human bone marrow-derived MSC-EVs induced the immature IL-10-producing phenotype of dendritic cells, increased the IL-10 and IL-6 levels, and the number of regulatory T cells, while it decreased the number of Th17 cells . Last buDespite the encouraging results, additional research is required to optimize the therapeutic administration of the MSC-secretome in T1DM.The term inflammatory bowel disease (IBD) refers to a group of disorders that are characterized by chronic, progressive immune-mediated inflammation of the digestive tract with frequent extra-intestinal, systemic organ involvement. Ulcerative colitis (UC) and Crohn’s disease (CD) are listed as the prototype disorders . The GI-Based on animal models of IBD, the use of MSCs has been shown to have effective anti-inflammatory and tissue repair actions. The efficacy and safety of the MSC-secretome have also been demonstrated in animal studies. It is hypothesized that if MSCs are injected locally into the therapy-refractory fistulas of Crohn’s disease patients, the closure of fistulas is mainly due to the immunomodulatory activity of the secretome, instead of the MSCs per se . The local or systemic administration of MSC-derived EVs in mice IBD models induced by dextran sodium sulfate (DSS) has been shown to ameliorate the clinical severity of colitis, resulting in improved survival rates and reduced gut inflammation in histology ,138,139.In vitro exposure of macrophages to the MSC-derived secretome, followed by their implantation in a 2,4,6-trinitrobenzenesulfonic acid (TNBS)-induced colitis mouse model, demonstrated that preconditioned macrophages enhanced the survival rate by decreasing weight loss and bloody stool, thereby preventing colitis progression and disease recurrence .Although animal and cell culture researches in IBD have indicated the favorable effect of the MSC-secretome, human studies are still needed.Graft versus host disease (GvHD) represents a potentially fatal multiorgan complication of allogeneic bone marrow or peripheral blood HSC transplantation. It is based on the fact that the host appears to non-self to the graft and so it can stimulate antigenically, employing a broad spectrum of immune mechanisms. GvHD is classified into acute and chronic types . The chrIn 2004, it was reported that the immunomodulatory qualities of MSCs were effective in curing GvHD caused by HSCT . Since tUndoubtedly, EVs have demonstrated promising results in the prevention and treatment of GvHD . Using MPsoriasis refers to a chronic, immune-mediated, non-infectious inflammatory skin disease with keratinocyte hyperproliferation in the epidermis that is characterized clinically by the development of reddish, scaly papules and plaques. Several subtypes can be distinguished. Systemic manifestations such as painful (spondylo)arthritis or ocular involvement could also occur . PsoriasUntil recently, there have been no applicable animal models of psoriasis, so the xenotransplantation of psoriatic skin pieces from human to mice with severe combined immunodeficiency was utilized . The occIn a recent and only human study , adiposeThe results so far are encouraging for skin diseases, but further studies are required to determine the route and duration of application for human therapy.Atopic dermatitis (AD) is a common, heterogeneous, chronic, itchy, inflammatory skin disease frequently with increased serum IgE. Defects in terminal keratinocyte differentiation and altered immune responses are cardinal disease determinants. AD can be the initial manifestation of a spectrum of allergic diseases (the “atopic march” theory) .It has been reported that exosomes derived from adipose tissue-derived MSCs (AT-MSCs) inhibit the lipopolysaccharide-induced nitric oxide production of RAW264.7 macrophages . In a muTherefore, it seems that AT-MSC-exosomes are able to favorably influence the outcome of AD, but this hypothesis demands further investigations.Asthma is a heterogeneous chronic inflammatory disease of the lungs’ airways provoked by exogenous  and endogenous factors , and are mainly driven by a “type 2-high” immune signature . Most cuSome years ago, in an ovalbumin-induced murine asthma model (OVA-mice) , it was Not only would it be important to investigate the use of MSC-secretome in ADs and IMIDs because it is a relatively simple, cell-free immunomodulatory method and does not have the same ethical implications as MSC treatments, but the side effects of the MSC-secretome should also be considered. The factors that introduce variability in the secretome composition  have been recently summarized . Noting In addition to the technical limitations above-mentioned, it was reported that epigenetic modifications of MSCs  also affect the composition of the MSC-secretome, and thus both MSCs and the MSC-secretome may contribute to the development, maintenance, and progression of inflammatory diseases ,101,102.However, one of the most worrying of these is the protumor effect . MSCs arThe efficacy of conventional non-specific immunosuppressive medications for autoimmune diseases is limited, especially in severe refractory phenotypes, and could be accompanied by several harmful side effects. Therefore, constant efforts are being made to develop a new generation of highly effective treatment alternatives. In this review, we focused on the major biological, molecular, and pathological effects of administering the MSC-secretome to animal models or in vitro cell cultures of ADs and IMIDs as well as the human results reported to date.A broad spectrum of MSC-secretome-related biological activities has been proven so far including anti-inflammatory, anti-apoptotic, and immunomodulatory properties, making the secretome theoretically capable of restoring aberrant immune regulation . In comparison with MSCs, the highly biocompatible secretome is far less immunogenic but exerts similar biological actions, so it can be considered as an ideal cell-free therapy option. Moreover, since the composition of the MSC secretome can be engineered, from a future perspective, it could also be viewed as part of a potential delivery system within nanomedicine, allowing us to specifically target dysfunctional cells or tissues.www.clinicaltrials.gov (accessed on 22 May 2022), there are currently only five ongoing human studies that deal with the therapeutic potential of the MSC-secretome in IMIDs. These include investigations that have already been completed (uveitis), are in progress (asthma), or are still being planned . Therefore, much effort has to be made to translate the promising experimental data of MSC-secretome utility to daily clinical practice with a special focus on its safety. For the purpose of the challenging human applications, the development of standardized secretome isolation, purification, and storage methods/protocols, and quality assurance parameters is indispensable. Furthermore, the dose and route of secretome administration must also be clearly determined.Although, until recently, many encouraging results from pre-clinical studies using different animal models and cell culture experiments have been obtained that strongly support the application of the cell-free MSC-secretome in various autoimmune and immune-mediated inflammatory conditions, human studies with MSC-secretome administration are still in their infancy. According to In summary, therapy with MSC-secretome as a cell-free alternative biological approach may expand the therapeutic horizons of autoimmune and immune-mediated inflammatory diseases.


In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human–computer interaction), etc. Such emotions could be detected from various means, such as information integration from facial expressions, gestures, speech, etc. Though such physical depictions contribute to emotion detection, EEG  signals have gained significant focus in emotion detection due to their sensitivity to alterations in emotional states. Hence, such signals could explore significant emotional state features. However, manual detection from EEG signals is a time-consuming process. With the evolution of artificial intelligence, researchers have attempted to use different data mining algorithms for emotion detection from EEG signals. Nevertheless, they have shown ineffective accuracy. To resolve this, the present study proposes a DNA-RCNN  to extract the appropriate features based on the discriminative representation of features. The proposed NN also explores alluring features with the proposed attention modules leading to consistent performance. Finally, classification is performed by the proposed M-RF (modified-random forest) with an empirical loss function. In this process, the learning weights on the data subset alleviate loss amongst the predicted value and ground truth, which assists in precise classification. Performance and comparative analysis are considered to explore the better performance of the proposed system in detecting emotions from EEG signals that confirms its effectiveness. Emotions greatly impact an individual’s social skills and view of the world. In recent years, several intelligent systems have begun to utilize emotion recognition frameworks to enhance their interactivity with humans. This process is significant as systems could acclimatize their behavioral patterns and responses under human emotions and make interactions natural. Such emotions could also be identified by additional factors encompassing speech, physiological signals, and expressions. Among several factors, EEG  signals have proven to possess strong associations with emotional states and have maximum accuracy for classifying emotions. It also reacts more quickly to emotional alterations. Hence, evaluating EEG signals have turned efficient and reliable in accomplishing emotion recognition . HoweverExtracting suitable features from the EEG signals is one of the main tasks in correct emotion detection. Considering this, the research  has usedp-value and correlation value. Additionally, DWT (Discrete Wavelet Transform) has been applied for noise removal to extract 20 eigenvalues (as features). Then, optimal features have been selected through p-value and correlation analysis. The suggested system has exposed significant minimization in trainable parameters required for procuring a better detection rate.However, different approaches have been considered for recognizing emotions. Accordingly, LSTM has been used to perform epileptic EEG classification through an optimal wavelet algorithm that relied on p-value feature) have been employed to minimize features for the complexity reduction of LSTM [Furthermore, PCA  and CCP  for extracting relevant features based on the discriminative representation of features.To internally validate the classification performance of the proposed M-RF (Modified-Random Forest) with NB (Naïve Bayes), DT (Decision Tree), and K-NN (K-Nearest Neighbor) for exploring the effective performing model in emotion classification.To validate the proposed system by comparative evaluation with the conventional system for confirming the proposed system’s effective performance in detecting emotions.The main contributions of this study are:The paper is organized as follows: Conventional researchers have considered varied data mining algorithms for emotion detection. The complementary approaches are discussed in this section.Study  used a dIn addition, a method for feature extraction has been suggested that has fused LDA and differential entropy. Five classification models have been considered for classification, namely LR (Logistic Regression), SVM, K-NN, MLP (Multi-Layer Perceptron), and RF (Random Forest). An average accuracy of 68% has been attained. Execution time has exposed that the suggested system has minimum time complexity. Furthermore, signal processing approaches and optimized SVM with genetic algorithm have been employed to develop the smart methodology for enhancing emotion detection. Attained outcomes have explored that, the recommended system has revealed 93.86% accuracy ,14. A muFurthermore, a median filter has been utilized to enhance the classification performance to eliminate false detections that have exposed 86.56% accuracy . MoreoveSimilarly, an automated framework has been considered to identify emotions. The suggested system has relied on three varied methodologies: VMD  and IMF/EMD  for noise elimination from signals and to attain better details from EEG data. In addition, four major classifiers have been used for classification: K-NN, DT (Decision Tree), CNN, and NB (Naïve Bayes). Nevertheless, CNN has performed better than conventional techniques in categorizing EEG signals . BesidesThe study  also intOn the contrary, the conventional emotion recognition model, DE , has been extracted from the individual partitioned segment for generating a feature cube. Multiple GCNNs have been employed for extracting graph-domain features from individual feature cubes. Additionally, LSTM cells have been employed for memorizing the relationship alterations amongst two channels of EEG for extracting temporal features. Accuracy has been exposed to be 90.60% and 90.45% for arousal and valence in experiments dependent on the subject. Similarly, a data-adaptable CNN model has been designed to enhance accuracy while classifying emotions. An efficient spectrogram-feature extraction model has been executed and a multi-model classifier has been suggested which considers dual features that have accomplished 73.4% accuracy . LikewisSimilarly, the research  has consSignificant issues determined through the evaluation of the above conventional works are exposed in this section.(i)Conventional works have attained a better accuracy rate in recognizing emotions from EEG signs using different data mining approaches. Accordingly, an entropy-weighted clustering approach has been integrated with sparse learning has exposed 68.35% , while R(ii)Varied DL-based models could be utilized for dynamic modeling and feature extraction to enhance performance .(iii)Further research must examine alterations to the emotion analysis model by integrating multiple NNs. Another strategy is enhancing the concatenation of convolution layers. There might be a possibility for constructing convolutional layers for individual data features to enhance classification performance through several convolution layers .The research aims to achieve emotion detection through suitable data mining algorithms. Though traditional works have endeavoured to perform this, they could have been more effective concerning accuracy rate. To mitigate this pitfall, the study proposes different algorithms for pre-processing the data, feature extraction, dimensionality reduction, and classification, as shown in At first, the EEG dataset is taken, and pre-processing is performed. In this context, feature scaling is performed in the pre-processing stage. This approach is used for normalizing the independent variable range or data features. Following this, feature extraction is undertaken using DNA-RCNN. This process assists in minimizing redundant data. Such data reduction supports constructing the model with minimum efforts of the machine that also enhances the learning speed as well as the generalization stages in the ML process. Following this, dimensionality reduction is undertaken using PCA , which determines the minimum or eliminates the least significant variables from the model. This process will alleviate the complexity of the model and remove data noise. By this, dimensionality reduction supports mitigating overfitting. Finally, classification is accomplished by M-RF based on the empirical loss function. Furthermore, through the use of the proposed classifier with an empirical loss function on RF (Random Forest) is capable of affording reliable and accurate outcomes in comparison with conventional algorithms due to the optimized loss-variations undertaken with the proposed system. In this process, other ML classifiers  are also considered for classification to assess the performance of proposed M-RF internally and considered ML classifiers. The overall performance of the model is assessed through performance metrics.Generally, CNN possesses the feature of considering significant and clear features at the sight line, so it is extensively utilized in feature extraction. Weight sharing and local perception of CNN could greatly minimize the parameters, thereby enhancing the efficacy of the learning models. Moreover, CNN comprises three main parts: convolution, pooling, and FC (Fully Connected) layer. The individual convolution layer encompasses the plurality of the convolution kernel. When the convolution function of the convolution layer succeeds, data features are extracted. This study additionally proposes Attention Residual CNN for training deep attention networks that could be easily scaled to hundred layers. In addition to several discriminative feature indications created by the attention mechanism, the proposed model also explores appealing features such as enhancing the attention modules resulting in consistent enhancement. Stacking the attention modules might have a direct negative impact on performance. Thus, this study proposes DNA-RCNN with CNN for optimizing deep network with several layers wherein the output of attention module PCA is typically a renowned statistical strategy and an orthogonal-linear transformation that is considered for minimizing dataset dimensionality. This algorithm performs dataset transformation into a new system. Maximum data point variance in a new system through any data object projection lies on the initial coordinate, termed an ‘initial principal component’. Subsequent variance is termed the second-principal component, and this process persists. By this process, PCA transforms the data matrix ,The formula is standardized as per the Equation (4),The overall standardized matrix is given by Equation (5),2.Compute the correlation-coefficient matrix as shown in the Equation (6),where 3.Compute eigenvalues and vectors of the correlation-coefficient matrix. Eigenvalues ,The cumulative contribution of principal components ,6.Find the comprehensive-evaluation function given by Equation (10),Algorithm 1: Modified-Random ForestStep-1: Step-2: Step-3: Step-4: A loss function Step-5: s(f)Step-6: Split on the best Feat in the Step-7: Random Forests are generally an ensemble learning approach. The fundamental idea behind this algorithm is that the construction of small DT with minimum features seems computationally cheap. Moreover, when constructing several small and weak DTs in parallel, these trees can be integrated to attain strong learners by considering a majority vote. This work enhances the use of RFs for SSL (Semi-Supervised Learning) issues. From this insight, a multi-class marginal definition is developed for unlabeled data with an iterative deterministic annealing style training approach enhancing multiple class margins of unlabeled and labeled samples. Particularly, this permits using predicted labels corresponding to unlabeled data with additional variables for optimization. The main intention of this algorithm lies in learning weights on the data subset, which alleviates loss or error amongst predicted value and ground truth for precise classification of input to related label. In addition, the empirical loss function considered with RF computes the variation between predicted class and ground truth labels, termed cross-entropy. The loss value seems less when the prediction is near the ground-truth label. Contrarily, the resultant log loss seems to be high when the prediction seems to be beyond the true label. The overall process is shown in Algorithm 1.Algorithm 2: Naïve BayesStep-1:Step-2:Step-3:Step-4:Naïve Bayes is a simple probabilistic and easy classifier that employs the Bayes theorem. This algorithm regards individual attribute variables as independent variables. Such a classifier could be trained efficiently in supervised learning. It could also be used in complex real-time circumstances. The main merit of NB is that it needs less training data for characterization. Due to these inherent advantages of NB, this study considers NB. Typically, classification is accomplished through the Bayes principle, and its overall process is shown in Algorithm 2.A Decision Tree remains a robust and renowned tool for prediction and classification. It is a flow chart-like structure wherein an individual internal node indicates the test on an attribute, an individual branch denotes the test outcome and an individual leaf node represents the class label. Moreover, DT classifies the instances by sorting from root to certain leaf nodes that afford instance classification. Constructing DT does not need any parameter setting or domain-oriented knowledge. Hence, it is suitable for exploratory knowledge discovery.Algorithm 3: Decision TreeStep-1:Step-2:Step-3:Step-4:Furthermore, DTs could also handle data with high dimensions. The DT induction is a general inductive strategy for knowledge learning on classification. Due to such merits, this study considers DT for classification. Its overall process is shown in Algorithm 3.Algorithm 4: K-Nearest NeighborStep-1:Step-2:Step-3:Step-4:Step-5:K-Nearest Neighbor is a supervised simple ML algorithm that could be utilized for solving classification problems. It is easy to execute and comprehend. It attempts to determine the correct test data class by computing the distance between training and testing data. Subsequently, K-points are selected that are near to test data. K-NN computes the test data probability of K-training data classes and holds the maximum probability for selection. Due to these advantages, this study considers K-NN. The overall process of this algorithm is shown in Algorithm 4.Outcomes attained after the execution of the proposed system are discussed in this section with dataset description, performance metrics, outcomes for performance, and comparative analysis.https://www.kaggle.com/datasets/birdy654/eeg-brainwave-dataset-feeling-emotions (accessed on 1 September 2022).This dataset comprises EEG brainwave data administered with the original statistical extraction approach. Data was gathered from two individuals  for three minutes/state: positive, negative, and neutral. Muse EEG-headband that recorded AF8, TP10-EEG, AF7, and TP9 placements through dry-electrodes is utilized. Moreover, resting ‘neutral’ data is recorded for 6 minutes. The dataset is taken from, This section presents the main metrics regarded for this research and their corresponding mathematical representation.(i)Accuracy—It is the computation of total-correct classification. It is given by Equation (11),(ii)Precision—It is described as the overall correct classification, which is managed by misclassification. It is given by Equation (12),In Equations (11) and (12), (iii)Recall—It is stated as the proportion of relevant text and retrieved text to the proportion of the relevant text. It is given by Equation (13),In Equation (13), (iv)F-Measure—The F1-score is determined as the harmonic mean of recall and precision. It is given by Equation (14),In Equation (14), The performance of the proposed system is an evaluation of metrics such as accuracy, F1-score, recall, and precision. The corresponding outcomes are shown in this section. Initially, the proposed system is assessed, and the attained outcomes are shown in From From From From From From The proposed system has been comparatively evaluated with three conventional studies, and the corresponding outcomes are exposed in this section. Initially, analysis has been undertaken with conventional methods, namely DNN + Sparse Autoencoder , DCNN , Multi Column CNN, RNN , LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Obtained results are shown in From As shown in From In addition, the proposed M-RF with empirical loss function could alleviate loss or error that have led the proposed system to explore effective classification, confirmed by the results. Further, an internal comparison has been undertaken by comparing the proposed M-RF classifier with ML classifiers namely NB, K-NN, and DT. The results are exposed in From Typically, NB algorithm has the ability to work fast and easily detect the test dataset class. It is also capable of solving multi-class prediction issues as it seems to be valuable. This algorithm exposes better classification performance when the assumption of independent features hold. In spite of its better performance, NB makes an assumption that all the features are unrelated or independent, so it is not capable of learning relationships amongst the features. In addition, K-NN might seem to be easy for execution, however, with increase in dataset, speed, and efficacy of the algorithm declines fast. Furthermore, K-NN functions better with less input variables; however, as the variables increase, K-NN encounters greater difficulty in predicting the outcomes of new data-point.Furthermore, the DT algorithm averts the need for feature transformation when handling non-linear data as DTs do not consider multiple weighted integrations concurrently. DT is typically used for solving regression and classification issues. However, the main pitfall of this algorithm is that it typically results in data overfitting. Finally, in the present study, the proposed Deep Normalized Attention-based Residual CNN extracts significant and relevant features with minimum variance within it. These features are later passed into PCA to reduce dimensionality with less information loss. This process will also alleviate the complexity of the model and remove data noise. Dimensionality reduction assists in mitigating overfitting. As the RF typically seems to be biased while handling categorical variables, the proposed system employs an empirical loss function on RF (Random Forest) which makes it capable of accomplishing reliable and accurate outcomes when compared with conventional algorithms due to the optimized loss-variations undertaken with the proposed system. Based on these advantages, the performance gets enhanced in classifying emotions. The stepwise advantages of the proposed system are discussed below,In this study, the proposed Deep Normalized Attention-based Residual CNN encompasses of three dense blocks, after which an attention block and an improved extraction layer with an activation function exist. Initially, present structure of dense connection is simplified. Later, an RCNN is proposed for the system to emphasize the local semantics which are related to emotion features. This enhanced framework is considered for refining the extracted convolutional-features and emphasizes that dense blocks possess fewer parameters while managing discriminative convolutional-feature indication ability. Furthermore, the proposed Modified Random Forest with Empirical loss function assigns attention weights along with parameters for DTs in a particular manner. Weights rely on distance amongst instance that falls into respective tree leaf. The proposed loss function specifically assists in computing weights by resolving the linear optimization issues. By these mechanisms, the proposed model gains the ability to learn and perform accurate prediction in comparison to traditional studies.This study intended to classify emotions from the EEG dataset. To perform this, the study proposed DNA-RCNN for feature extraction, PCA for dimensionality reduction, and M-RF with empirical loss function for classification. Additionally, the study considered three ML classifiers—NB, DT, and K-NN—for internal comparison with M-RF. The confusion matrix was also explored to show the correct classification and misclassification rates. Performance was also assessed about performance metrics. From the analytical outcomes, M-RF exposed better performance than NB, DT, and K-NN, with a high rate of correct classification and a low misclassification rate. To further confirm the better performance of the proposed system, a comparative analysis was also undertaken with three traditional researchers. Outcomes confirmed that the proposed system explored superior performance in classifying emotions than conventional research with a 98% accuracy rate. In the future, advanced fusion methodologies could be utilized by varied feature kinds. For further enhancement, numerous features could be integrated into the system.


Channa striatus (CS), or snakehead murrel, is an obligate air-breathing freshwater fish. Besides its wound healing properties, CS has also been reported to exhibit anti-inflammatory effects in multiple studies. While there are anti-inflammatory medications such as nonsteroidal anti-inflammatory drugs (NSAIDs), their long-term use is associated with an increased risk of peptic ulcers, acute renal failure, stroke, and myocardial infarction. Thus, it is essential to look at natural methods such as CS extract. While there is an abundant number of investigative studies on the inflammatory properties of CS, the quality of these studies has not been evaluated effectively. Thus, this review aims to summarise, evaluate, and critically appraise currently available literature regarding the anti-inflammatory properties of CS extract. This is done by performing a search using four databases, namely Google Scholar, Embase via Elsevier, Scopus, and Web of Science, with the following terms: Channa striatus AND inflammation. From our review, CS has been experimentally shown to positively affect inflammatory conditions such as gastric ulcers, dermatitis, osteoarthritis, and allergic rhinitis. Beneficial effects were also found on inflammation in the presence of tuberculosis and in situations that involve inflammation, such as wound healing. While CS clearly has potential for treating inflammatory conditions, much work needs to be done on identifying and isolating the active constituents before exact mechanisms of action can be worked out to develop future anti-inflammatory medications. Channa striatus (CS), or snakehead murrel, is an obligate air-breathing freshwater fish that inhabits all types of water bodies from small ditches to rice fields, rivers, and lakes , and a combination of CSP and NPLE showed significantly different results (p < 0.05) compared with the negative controls. Interestingly, the combination treatment consisting of CSP and NPLE extract did not show a stronger anti-inflammatory effect than the single extract treatment.  patients to study the effects of CS capsules on cytokine conversion in pulmonary TB patients when given in conjunction with standard anti-TB drugs. The levels of the inflammatory cytokines, TNF-α, IFN-γ, and IL-10, were significantly reduced in the CS group compared to the baseline. This was seen in the control group, except for the IL-10 level, which was not significant. The conclusion was that adjunctive supplementation of CS capsules accelerated the beneficial therapeutic effect of TB chemotherapy, possibly by improving cytokine response .G-omega-Nitro-L-arginine methyl ester (L-NAME).The antiulcer profile of CS extract, given orally at the doses of 50, 350, and 500 mg/kg, was assessed using ethanol- and indomethacin-induced gastric ulcer models. The antiulcer mechanisms of CS extract were determined as follows: 1) anti-secretory activity of CS extract measured using the pyloric ligation rat model, and 2) the role of nitric oxide and sulfhydryl compounds in the modulation of CS extract antiulcer activity. It was found that CS extract exerted significant antiulcer activity in both models of gastric ulcer. CS extract did not change the volume and pH of gastric juice but reduced the total acidity of gastric juice at lower doses. The antiulcer activity by CS extract was reversed by N-methylamide (NEM) but not by Nvia its cytoprotective, but not anti-secretory effect, and in the presence of sulfhydryl compounds, but not nitric oxide (NO)  .Chronic-like dermatitis on the ears was induced in male ICR mice using 12-0-tetradecanoylphorbol 13-acetate (TPA). Treatment with CS cream was carried out by applying 1%, 5%, and 10% cream two times a day for three consecutive days after TPA application. A significant reduction of mouse ear thickness by the CS creams was observed compared to TPA alone group (negative control). All concentrations of CS creams produced comparable effects to hydrocortisone (H-cort), the positive control.Histological analysis revealed suppression of inflammatory cell infiltration and epidermal hyperplasia, noticeable under ×400 magnification. The reduction of dermal edema can also be observed, which was more visible under ×100 magnification. Gene expression analysis showed that all concentrations of CS cream downregulated the expression of TNF-α, with a significant reduction in a dose-dependent manner .In another study by The chondroprotective activity of CS was evaluated in an osteoarthritis (OA) rabbit model. OA was induced by performing anterior cruciate ligament transection in male New Zealand white rabbits. The articular cartilage was evaluated macroscopically and histologically using semiquantitative and quantitative methods. The levels of serum cartilage oligomeric matrix protein (COMP), cyclooxygenase 2 (COX-2) enzyme, and prostaglandin E2 (PGE2) were also determined. Macroscopic analysis revealed that CS administration significantly lowered the severity grade of the total macroscopic score compared to the control and glucosamine (positive control) groups.p < 0.05) and glucosamine (p < 0.05) groups compared to the control group  stiffness and function scores were achieved. However, no significant differences were found between the groups regarding analgesic scores, serum cartilage oligomeric matrix protein (COMP) levels, and biochemical parameters. In conclusion, CS extract treatment was more effective than placebo in treating the symptoms of knee OA .The wound healing property of CS extract was studied on laparotomy wound healing in malnourished male Wistar Kyoto rats. The study evaluated the effects of CS on tensile strength, epithelialization, and fibroblastic proliferation. CS extract treatment groups demonstrated better tensile strength and significantly higher epithelial and fibroblast cell counts than placebo control . A similIn a separate study, the wound healing effect of CS extract spray was studied in Sprague Dawley rats. Here, CS extract was formulated in an aerosol system, producing a film for wound dressing. CS spray increased the tensile strength of the incision wound and sped up the wound contraction process. This showed that the CS water spray is effective and safe for application to incision and burn injuries .Another study by In a randomized, double-blinded study amongst post-Lower Segment Caesarean Section (LSCS) women, CS extract was found to have significant effects on IL-6, Vascular Endothelial Growth Factor (VEGF), and Matrix metallopeptidase 9 (MMP-9) levels between the CS treatment group and placebo group. These factors are involved in different wound healing phases, which suggests that CS extract has potential wound healing properties . Ab WahaIn a separate randomized, double-blinded, placebo-controlled study among post LSCS women, however, there were no significant differences in the inflammatory markers during wound healing between the CS extract group and the placebo group .A study conducted by In a study by Channa striatus derived treatments in inflammatory conditions, beneficial effects were found for several conditions. These conditions were gastric ulcers, dermatitis, osteoarthritis, and allergic rhinitis. Beneficial effects of CS were also found in wound healing as well as on inflammation in the presence of tuberculosis. All the studies in this review used some form of CS extract rather than working with isolated extract constituents. Nevertheless, the studies in the review pointed out several constituents which could be responsible for the beneficial effect of CS extract. These include albumin  and c-Jun N-terminal kinases (JNKs) . In the From our review, CS has been experimentally shown to positively affect inflammatory conditions such as gastric ulcers, dermatitis, osteoarthritis, and allergic rhinitis. Beneficial effects were also found on inflammation in the presence of tuberculosis and in situations that involve inflammation in some capacity, such as wound healing. While we were able to suggest several anti-inflammatory pathways that CS might act upon, the use of CS extract by all the studies in this review rather than isolated constituents somewhat clouded the exact mechanisms of action. CS extract is anti-inflammatory overall, even though it contains arachidonic acid, which is pro-inflammatory. While CS clearly has potential for treating inflammatory conditions, more work needs to be done on identifying and isolating the active constituents before exact mechanisms of action can be worked out. Proirities should lie in studying the active compounds which contributes to the pharmacological activities of CS extract. Future pre-clinical and clinical studies can be done using these compounds instead of the crude extract. The candidates for active constituents pointed out in this review will hopefully help in the search for future anti-inflammatory medications.


We urgently need answers to basic epidemiological questions regarding SARS-CoV-2 infection in pregnant and postpartum women and its effect on their newborns. While many national registries, health facilities, and research groups are collecting relevant data, we need a collaborative and methodologically rigorous approach to better combine these data and address knowledge gaps, especially those related to rare outcomes. We propose that using a sequential, prospective meta-analysis (PMA) is the best approach to generate data for policy- and practice-oriented guidelines. As the pandemic evolves, additional studies identified retrospectively by the steering committee or through living systematic reviews will be invited to participate in this PMA. Investigators can contribute to the PMA by either submitting individual patient data or running standardized code to generate aggregate data estimates. For the primary analysis, we will pool data using two-stage meta-analysis methods. The meta-analyses will be updated as additional data accrue in each contributing study and as additional studies meet study-specific time or data accrual thresholds for sharing. At the time of publication, investigators of 25 studies, including more than 76,000 pregnancies, in 41 countries had agreed to share data for this analysis. Among the included studies, 12 have a contemporaneous comparison group of pregnancies without COVID-19, and four studies include a comparison group of non-pregnant women of reproductive age with COVID-19. Protocols and updates will be maintained publicly. Results will be shared with key stakeholders, including the World Health Organization (WHO) Maternal, Newborn, Child, and Adolescent Health (MNCAH) Research Working Group. Data contributors will share results with local stakeholders. Scientific publications will be published in open-access journals on an ongoing basis. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to over 244 million confirmed cases of coronavirus disease 2019 (COVID-19) and claimed more than 4.9 million lives globally, as of October 2021 , 4. The Pregnant women with COVID-19, and their offspring, may be at increased risk for complications due to severe illness and adverse pregnancy outcomes. A living systematic review suggests that pregnant women with COVID-19, compared to non-pregnant women with COVID-19, are more than twice as likely to be admitted to an intensive care unit (ICU) or require invasive ventilation . In an oDespite the urgent need to accurately document the number of cases, severe illness, and deaths in pregnant women, available data on maternal and infant complications related to COVID-19 remain heterogenous . The higCOVID-19 data in pregnancy and infancy was scarce at first, and now it is heterogeneous. These shortcomings exist in part because data collection protocols have differed, and the underlying population risks for severe illnesses vary globally. It is therefore necessary to generate high-quality information with a unified data harmonization plan and analytic strategy. This approach will permit us to make evidence-driven decisions and to create guidelines for the prevention and treatment of COVID-19 in pregnant women. Because little of the existing data can be pooled, our ability to make strong recommendations related to COVID-19’s risks in pregnancy and the postpartum period is limited. We are similarly constrained in our capacity to offer guidance as to the potential benefits of new interventions, like vaccines. So we propose to fill this gap by conducting a sequential prospective meta-analysis (PMA). This PMA will be a robust, collaborative, global approach to accrue harmonized data, with the objective of generating policy and practice-relevant data about the epidemiology of COVID-19 in the pregnancy and postnatal periods.The protocol for this serial prospective meta-analysis was registered with PROSPERO (ID: 188955) on May 28, 2020. A prospective meta-analysis identifies studies that will contribute data to the meta-analysis, as well as establishes the analysis plans, before the results of the individual studies are known . This apAll individual study sites will obtain appropriate ethical approval for their independent data collection activities. As this project is a meta-analysis using aggregate study estimates or analyses of secondary, de-identified individual patient data, it is not classified as human research and thus is exempt from IRB approval.Not all those who are pregnant or give birth identify as women. We use the terms “pregnant women” and “pregnant people” throughout. This accurately reflects both study designs (e.g. a comparison group including those who self-report as women between the ages to 15 and 45) and the language used by the studies contributing data.The study aims to answer basic epidemiological questions about COVID-19 and its impact on maternal and newborn health by pooling data from independent studies using harmonized data definitions and an individual patient data meta-analytic framework to minimize data variability.Analyses will be performed in three groups. First, we will provide descriptive statistics among pregnant women with COVID-19. To understand the relative risks associated with COVID-19 in pregnancy, we will compare pregnant people with COVID-19 to i) other pregnant people without COVID-19 and ii) non-pregnant women of reproductive age (15–45 years) with confirmed or suspected COVID-19.Specific objectives regarding COVID-19 among pregnant or postpartum women include: i) describe the natural history of COVID-19; ii) estimate the incidence of and risk factors for hospitalization, admission to intensive care unit, receipt of critical care, and use of invasive ventilation (for COVID-19); and iii) estimate the infection and case fatality rate. For each of these outcomes, we will also compare the risk for pregnant women with COVID-19 to non-pregnant women of reproductive age with confirmed or suspected COVID-19.Specific objectives regarding maternal health among pregnant or postpartum women with COVID-19 are to estimate the incidence or prevalence of: i) maternal morbidities including hypertensive disorders of pregnancy, abnormal placentation, preterm prelabor rupture of membranes, hemorrhage, embolic disease, etc.; ii) maternal and pregnancy-related death; iii) adverse pregnancy outcomes including stillbirth, preterm birth, small-for-gestational age birth, and low birthweight. For each of these outcomes, we will also compare the risk for pregnant women with COVID-19 to other pregnant women without COVID-19.Specific objectives regarding newborn health among newborns born to patients with COVID-19 are to estimate the incidence and timing of and risk factors for: i) congenital anomalies; ii) perinatal and (early) neonatal mortality; and iii) intrauterine, intrapartum, or early peripartum transmission of SARS-CoV-2 from mother to child. When appropriate, we will compare these risks to those of newborns born to women without COVID-19 during pregnancy.Specific objectives regarding SARS-CoV-2 in biospecimens  include estimating: i) the proportion of biospecimens with detectable SARS-CoV-2 virus and median viral load; ii) the association between virus or viral load in biospecimen and key outcomes described above.a priori sample size limitations due to the dynamic epidemiology of the pandemic. Study investigators confirmed their intent to contribute to the PMA via a letter of intent or signing a collaboration agreement.We originally recruited study sites to join the proposed prospective meta-analysis first via professional research networks, and subsequently via key stakeholder networks. Stakeholders at the National Institute of Child Health and Human Development (NICHD) at the U.S. National Institutes of Health (NIH) supported recruitment of NIH-funded maternal and child health network groups and other U.S. government funded projects. Stakeholders at the World Health Organization (WHO) in the Departments of Maternal, Newborn, Child, and Adolescent Health (MNCAH) and of Sexual and Reproductive Health and Research (SRH) supported recruitment of researchers engaged in the COVID-19 MNCAH research network and the SRH pregnancy cohorts working group . StakehoBuilding on concepts laid out in the Framework for Adaptive Meta-analyses (FAME) , we willConfirmed cases of COVID-19 will be defined as those with laboratory-confirmed SARS-CoV-2 infection via a nucleic acid amplification test, regardless of clinical signs or symptoms. The original protocol was expanded to include COVID-19 cases confirmed via antigen tests as they became validated and widely used over the course of the pandemic. Suspected cases will be defined according to the WHO August 7, 2020 case definition based on either a) clinical  and epidemiological criteria  or the severe acute respiratory illness (SARI) case definition . ProbablOutcomes related to women’s health will include:Mortality: all-cause mortality, COVID-19 specific mortality, and pregnancy-related and maternal mortality;COVID-19 related clinical signs and symptoms: fever, cough, shortness of breath, dizziness or fainting, body aches, runny nose, sore throat, loss of sense of smell, loss of sense of taste, sneezing, fatigue, nausea, vomiting, diarrhea, and headache;Morbidity and healthcare utilization related to COVID-19 severity: pneumonia diagnosis, hospitalization, admittance to an intensive care unit (ICU), or use of critical care interventions;Pregnancy-related morbidities: hypertensive disorders of pregnancy including gestational hypertension, preeclampsia, eclampsia, hyperemesis gravidarum, fetal growth restriction, abnormal placentation (placenta previa and placenta accreta spectrum), placental abruption, preterm labor, preterm prelabor rupture of membranes, hemorrhage , embolic disease, or anesthetic complications. We also collected data on cesarean delivery.st newborn reference values) , maternal morbidities , maternal vaccination status , calendar time or time since first COVID-19 diagnosis in the study area, parity, maternal age, race or ethnicity, and maternal education. Some of these risk factors may be considered both outcomes of having COVID-19 in pregnancy or factors that exacerbate SARS-CoV-2 infection in pregnancy; we will attempt to disambiguate between the two based on the timing of diagnoses whenever possible.Where appropriate and as sample size allows, we will consider meta-regression or subgroup analyses by the following study level characteristics: study design and sampling strategy, proportion of confirmed COVID-19 cases (out of suspected and confirmed cases), national maternal mortality ratio, national neonatal mortality rate, and geographic region. We will also consider subgroup analysis by the following individual patient characteristics to identify risk factors or effect modifiers for specific outcomes: confirmed versus suspected COVID-19 case status; gestational age at COVID-19 onset (by week or by trimester), COVID-19 severity, pre-pregnancy health conditions or comorbidities [diabetes, hypertension, cardiovascular disease, obesity  were actively participating in the ongoing PMA . Among tOur sequential, prospective meta-analysis aims to pool individual patient data from ongoing or completed studies, registries, and surveillance programs that identify SARS-CoV-2 infections in pregnancy and postpartum.—such as pregnant patients and newborns—who may not be specifically considered in national surveillance programs and may be actively excluded from other clinical research.There are several benefits to our approach, which is guided by the principles of flexibility, robustness, collaboration, and inclusion. Early efforts to plan for pooling can reduce research waste  and improve the collective value of data . ThroughOur approach uses statistical methods for individual patient data meta-analysis, which is generally considered to be the gold standard in pooling data given the many steps used to ensure quality and consistency of the data and analyses. All data will be reviewed with the same data quality assessments, and outliers will be assessed consistently across studies. Analysis definitions will be standardized across studies. This is especially important given that for many outcomes, study definitions vary between countries . Furthermore, we will avoid duplicate case counting by working with and reanalyzing data from studies that include overlapping health facilities and centers that contribute to two or more studies. This has been a major issue in COVID-19-related meta-analyses because much scientific literature is based on case reports or case series; the same cases sometimes appear in multiple published papers. Traditional meta-analyses based on the published literature cannot resolve these methodological issues.Importantly, this approach is robust, flexible and collaborative. Each set of investigators can implement their own protocol, offering more flexibility to design a study specific to a given setting. Investigators can also proceed with their own study publications, thus ensuring an opportunity to be explicit about the local context in which the data collection took place and clearly take the credit associated with authorship. Our basic analyses will be done as a two-stage meta-analysis, allowing us flexibility in how data is shared. Studies that are willing and able can share their individual patient data directly with the data coordinating team for central analysis. Alternatively, studies that are unwilling or unable to share individual patient data can appoint their own study-specific data analyst to join the PMA data coordinating team. This approach to a “distributed” individual patient data meta-analysis meets data sharing needs for different groups, and hopefully ensures that anyone can participate. Finally, our regular steering committee meeting allows for joint decision-making about which analyses should be prioritized and when to move forward with publications. We hope to be responsive to emerging, policy-relevant questions, such as the safety and efficacy of vaccination in pregnancy.Prospective meta-analyses offer a rigorous way to generate definitive answers to emerging questions. In the current context of the COVID-19 pandemic, there is limited high-quality data that can be pooled in order to inform public health guidance and healthcare strategies, specifically for pregnant women and their newborns. The proposed study will contribute necessary information for evidence-based decision-making related to COVID-19 and maternal and neonatal health.S1 Checklist(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.S3 File(DOCX)Click here for additional data file.


Multistep power consumption forecasting is smart grid electricity management’s most decisive problem. Moreover, it is vital to develop operational strategies for electricity management systems in smart cities for commercial and residential users. However, an efficient electricity load forecasting model is required for accurate electric power management in an intelligent grid, leading to customer financial benefits. In this article, we develop an innovative framework for short-term electricity load forecasting, which includes two significant phases: data cleaning and a Residual Convolutional Neural Network (R-CNN) with multilayered Long Short-Term Memory (ML-LSTM) architecture. Data preprocessing strategies are applied in the first phase over raw data. A deep R-CNN architecture is developed in the second phase to extract essential features from the refined electricity consumption data. The output of R-CNN layers is fed into the ML-LSTM network to learn the sequence information, and finally, fully connected layers are used for the forecasting. The proposed model is evaluated over residential IHEPC and commercial PJM datasets and extensively decreases the error rates compared to baseline models. Electricity load forecasting predicts future load based on single or multiple features or parameters. Features could be of multiple types, such as an ongoing month, hour, weather situation, electricity costs, economic conditions, geographical circumstances, etc. . ElectriThe United States Energy Information Administration stated that from 2015–40 the increase in power consumption would be boosted up to 28% , while tThe following few important points could establish accurate forecasting models of consumption. First, it is crucial to precisely recognize the parameters that contain robust effects on a state’s consumption and add these indicators to the prediction model. Furthermore, choosing an appropriate modeling procedure is also a significant point. The input and output variables contain such a nonlinear relationship that it is very challenging to express them mathematically. Principles to advance the prediction performance develop conspicuously nowadays, instead of hypothetical principles in the model selection. In the end, the vital point is that the methodology must be capable of producing effective forecasting results.Reviewing the literature in recent years, machine learning models are increasingly used by scholars to forecast the short-term energy consumption of buildings. Additionally, most scholars are adopting hybrid models due to their effective performance compared to single models. However, energy consumption in buildings is exaggerated by various factors and is extremely nonlinear. Thus, this area is still worthy and commendable to be studied and explored further. This research work contributes the following points to the current literature.The collected benchmark datasets contain a lot of missing values and outliers, which occur due to defaulted meters, weather conditions, and abnormal customer consumption. These abnormalities and redundancies in datasets lead the forecasting network to ambiguous predictions. To resolve this problem, we performed data preprocessing strategies, including outlier removal via the three sigma rules of thumb algorithm, missing value via NAN interpolation method, and the normalization of the data using the MinMax scaler.We present a deep R-CNN integrated with ML-LSTM for power forecasting using real power consumption data. The motivation behind R-CNN with ML-LSTM is to extract patterns and time-varied information from the input data for effective forecasting.2 compared to recent literature. For the IHEPC dataset, the proposed model achieved 0.0447, 0.0132, 0.002, 0.9759, and 1.024 for RMSE, MAE, MSE, R2, and MAPE, respectively, over the hourly IHEPC dataset while these values are 0.0447, 0.0132, 0.002, 0.9759, and 1.024 over the IHEPC daily dataset. For the PJM dataset, the proposed model achieved 0.0223, 0.0163, 0.0005, 0.9907, 0.5504 for RMSE, MAE, MSE, R2, and MAPE, respectively. The lowest error metrics indicated the supremacy of the proposed model over state-of-the-art methods.The proposed model results in the lowest error rates of MAE, MSE, RMSE, and MAPE and the highest RShort-term load forecasting is a current research area, and numerous studies have been conducted in the literature. These studies are mainly divided into four categories based on the learning algorithm: physical, persistence, artificial intelligence (AI), and statistical. The persistence model can predict future time series data behavior like electricity consumption or forecasting but failed for several hour ahead predictions . TherefoThe AI methods are further categorized into machine learning and deep learning. In machine learning, several studies have been conducted in the literature for electricity load forecasting. Chen et al. used SVR for power consumption forecasting using electricity consumption and temperature data . SimilarFeatures extraction methods include different regular patterns, spectral analysis, and noise. However, these methods reduce the accuracy for meter level load  and thesSimilarly, CNN networks cannot learn temporal features of power consumption. Therefore, hybrid models are developed to forecast power consumption effectively. T. Y. Kim et al. combine CNN with LSTM for power consumption prediction , while aThe overall architecture of R-CNN with ML-LSTM is shown in Smart meter sensors-based data generation contains outliers and missing values for several reasons, such as meter faults, weather conditions, unmanageable supply, storage issues, etc. , and musIf After data cleaning, we apply the data transformation technique to transform the cleaned data into a specific format more suitable for effective training. First, we use the power transformation technique to remove shifts and transform the data into more Gaussian-like. The power transformation includes Box–Cox  and Yeo–Finally, the min–max data normalization technique converts the data into a specific range because deep learning networks are sensitive to diverse data. The equation of min–max normalization is shown in Equation (4).oth convolution filter in the lth layer and The proposed architecture integrates R-CNN with ML-LSTM for power load forecasting. R-CNN and ML-LSTM can store the complex fluctuating trends and extract complicated features for electricity load forecasting. First, the R-CNN layers extract patterns, which are then passed to ML-LSTM as input for learning. CNN is a well know deep learning architecture consisting of four types of layers: convolutional, pooling, fully connected, and regression ,44. The regation ,48. The Due to the strong feature extraction ability, CNN architectures are extensively applied for image classification, video classification, time series, etc. Similarly, in time-series forecasting, these models are used for traffic ,50, reneThe ResNet solves the degradation problem and performs satisfactory results over image recognition data, but electricity consumption is time series sequential data. The CNN architecture cannot learn the sequential features of power consumption data. Therefore, the R-CNN with ML-LSTM architecture is developed in this research study for future electricity load forecasting. The R-CNN layers extract spatial information from electricity consumption data. The extracted features of R-CNN are then fed to ML-LSTM as input for temporal learning.The output of R-CNN is then forwarded to ML-LSTM architecture, that is responsible for storing time information. The ML-LSTM maintains long-term memory by merging its units to update the earlier hidden state, aiming to understand temporal relationships in the sequence. The three gates unit’s mechanism is incorporated to determine each memory unit state through multiplication operations. The input gate, output gate, and forget gate represent each gate unit in the LSTM. The memory cells are updated with an activation. The operation of each gate in the LSTM can be shown in Equations (7)–(9), and the output of each gate is represented by The proposed R-CNN with ML-LSTM is based on three types of layers: R-CNN, ML-LSTM, and fully connected layers. The kernel size, filter numbers, and strides are adjustable in R-CNN layers according to the model’s performance. Many learning speeds, changes, and performance can happen by adjusting these parameters varying on the input data . We can The experimental setup, evaluation metrics, dataset, performance assessment over hourly data, and performance assessment over daily data of R-CNN with the ML-LSTM model are briefly discussed in the following section.To validate the effectiveness of the proposed approach, the IHEPC dataset is used to implement comprehensive experiments. The R-CNN with ML-LSTM is trained over an Intel-Core-i7 CPU having 32GB RAM and GEFORCE-GTX-2060-GPU in Windows 10. The implementation was performed in Python 3.5 using the Keras framework.The model performance is evaluated on mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (The R-CNN with ML-LSTM model is evaluated over the UCI repository’s IHEPC and PJM datasets. The IHEPC comprises nine attributes: date and time variables, active and reactive power, voltage, intensity, and three submetering variables. Description of IHEPC attributes and their unit are shown in The performance of R-CNN with the ML-LSTM model is compared to other models over residential electricity consumption IHEPC and regional electricity forecasting PJM datasets. The performance of the proposed model over these datasets and its comparison with other models are clarified in the subsequent sections.2, and MAPE, respectively.The supremacy of the proposed model over the IHEPC dataset is evaluated on hourly and daily load forecasting, whereas the R-CNN with ML-LSTM achieved satisfactory results. The performance of the test dataset for daily and hourly load forecasting is shown in 2, and MAPE, respectively, for daily load forecasting.The performance of the proposed model over the hourly resolution of data also secured the lowest error rate compared to the baseline model. For a precedent, the performance over the daily resolution of data is compared with regression , CNN 3737, LSTM The superiority of R-CNN with ML-LSTM is also evaluated over several PJM datasets for daily load forecasting. The PJM benchmark includes 14 datasets of electricity load forecasting, while for the experimentation, we chose 10 datasets from the PJM as selected by . In the A two-phase framework is proposed in this work for power load forecasting. Data cleaning is the first phase of our framework where data preprocessing strategies are applied over raw data to make it clean for effective training. Secondly, a deep R-CNN with ML-LSTM architecture is developed where the R-CNN learns patterns from electricity data, and the outputs are fed to ML-LSTM layers. Electricity consumption comprises time series data that include spatial and temporal features. The R-CNN layers extract spatial features in this work, while the ML-LSTM architecture is incorporated for sequence learning. The proposed model was tested over residential and commercial benchmark datasets and conducted with satisfactory results. For residential power consumption forecasting, IHEPC data was used, while the PJM dataset was used for commercial evaluation. The experiments are performed for daily and hourly power consumption forecasting and extensively decrease the error rates. In the future, the proposed model will be tested over medium-term and long-term electricity load forecasting. In addition, we will integrate environmental sensor data that help to predict future electricity consumption. Furthermore, we also intend to investigate the performance of the R-CNN and ML-LSTM in other prediction domains such as fault prediction, renewable power generation prediction, and traffic flow prediction.