The objective of this meta-analysis was to compare outcomes between sacubitril/valsartan and enalapril in patients with heart failure. We performed this meta-analysis according to the guidelines reported in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Two independent authors systematically searched online databases including PubMed, Cochrane Library, and Web of Science from inception till September 15, 2023. Outcomes assessed in this meta-analysis included all-cause mortality, cardiovascular mortality, and cardiovascular-related hospitalization. A total of nine studies were included in this meta-analysis. Pooled analysis showed that the risk of all-cause mortality was higher in patients receiving enalapril compared to patients receiving sacubitril/valsartan . Risk of cardiovascular mortality was significantly higher in the enalapril group compared to the sacubitril/valsartan group . The risk of cardiovascular hospitalization was significantly higher in the enalapril group compared to the sacubitril/valsartan group . In conclusion, our meta-analysis of nine studies underscores the superior clinical performance of sacubitril/valsartan compared to enalapril in managing patients with heart failure. Heart failure (HF), characterized by any structural or functional impairment of ventricular filling or ejection of blood, is a leading global cause of morbidity and mortality . In the Sacubitril/valsartan, an innovative angiotensin receptor-neprilysin inhibitor, has been approved for the treatment of heart failure with reduced ejection fraction (HFrEF) due to its demonstrated superiority over the angiotensin-converting enzyme inhibitor (ACEi) enalapril in the pivotal PARADIGM-HF trial . In thisFurthermore, a meta-analysis conducted in 2021, which included five relevant randomized controlled trials (RCTs), concluded that sacubitril/valsartan improved left ventricular ejection fraction in HF patients, as indicated by a standardized mean deviation of 1.1 . Additionally, it significantly reduced the left ventricular volume index and lowered the risk of cardiovascular death  and HF-related rehospitalization  when compared to the control group [Enalapril falls in the category of angiotensin-converting enzyme (ACE) inhibitors. Its mode of action revolves around the inhibition of ACE, an enzyme responsible for constricting blood vessels and elevating blood pressure . EnalaprSurprisingly, there has been a lack of comprehensive reviews addressing various aspects of efficacy and safety parameters on this subject to date. Additionally, existing primary studies have provided somewhat inconclusive evidence. Therefore, we conducted an exhaustive systematic review and meta-analysis to explore the roles of sacubitril/valsartan and enalapril in managing patients with HF. The objective of this meta-analysis was to make a comparison between sacubitril/valsartan and enalapril in patients with HF.MethodologyWe performed this meta-analysis according to the guidelines reported in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.Literature Search StrategyTwo independent authors systematically searched online databases including PubMed, Cochrane Library, and Web of Science from inception till September 15, 2023. The keywords used to search for relevant studies included the following: “Enalapril,” “Sacubitril/valsartan,” and “heart failure.” To carry out the search comprehensively, we merged medical subject headings (MeSH) and free-text headings. We used the appropriate Boolean operators including “AND,” “OR,” “NOT.” Additionally, reference lists of the included studies were manually searched to find additional studies relevant to the study topic. Other data sources were also searched, such as databases of grey/unpublished/unprinted literature, relevant reviews, and editorials.Study Design and Selection CriteriaThe eligibility and article inclusion/exclusion process followed a hierarchical approach, involving a thorough evaluation of the title, abstract, and full text. The selection and critical appraisal of studies adhered to the Joanna Briggs Institute's (JBI) protocol, which offers a more precise and stringent set of criteria for the study selection process. The pre-defined inclusion criteria were observational studies (retrospective cohorts and prospective cohorts) or RCTs comparing sacubitril/valsartan and enalapril in patients with HF and reported one of the required outcomes. The pre-defined exclusion criteria included (1) studies that involved animals, (2) reports, case series, review articles, and editorials, and (3) studies without a control or comparison group.Data ExtractionAll included articles from the systematic search were imported into EndNote X9 Reference Manager , where duplicates from various online databases were removed. Subsequently, two independent researchers conducted a thorough assessment of the remaining articles, selecting only those that met the predetermined inclusion criteria. Initially, titles and abstracts of all studies were screened, followed by a full-text review to assess relevance. Any discrepancies were resolved through discussion with a third researcher. Data were collected on study characteristics  and population characteristics  for two study groups. Outcomes assessed in this meta-analysis included all-cause mortality, cardiovascular mortality, and cardiovascular-related hospitalization.Statistical AnalysisWe conducted all statistical analyses using Review Manager . The outcomes were expressed as risk ratios (RRs) along with their corresponding 95% confidence intervals and were combined utilizing a random-effects model based on inverse variance weighting. To assess heterogeneity, we employed both the chi-square test of heterogeneity and the I-square statistic. A p-value of less than 0.1 indicated significant heterogeneity, while the I-square value quantified the extent of heterogeneity.ResultsFigure All-Cause MortalitySix studies were included in the pooled analysis of all-cause mortality. The risk of all-cause mortality was higher in patients receiving enalapril compared to patients receiving sacubitril/valsartan , as shown in Figure Cardiovascular MortalityFive studies were included in the pooled analysis of comparison of cardiovascular mortality in the sacubitril/valsartan and enalapril groups. As shown in Figure Cardiovascular HospitalizationSeven studies compared the risk of cardiovascular hospitalization between the sacubitril/valsartan and enalapril groups. As shown in Figure DiscussionExtensive research has explored the use of sacubitril/valsartan for treating patients with HF, leading to recommendations for its inclusion as a crucial component in managing such patients. Consequently, when considering both sacubitril/valsartan and enalapril, both medications are used for HF treatment; however, they operate through distinct mechanisms and offer varying potential advantages. Comparative studies serve to elucidate these differences, shedding light on their respective strengths and weaknesses, ultimately enhancing patient care and informing treatment protocols.A total of nine studies were included in this meta-analysis, with eight of them being RCTs. The present meta-analysis reported that the risk of all-cause mortality, cardiovascular mortality, and cardiovascular hospitalization was significantly higher in patients receiving enalapril compared to patients receiving sacubitril/valsartan. This finding aligns with previous reviews comparing the efficacy of sacubitril/valsartan with other drugs regarding hospitalization and mortality outcomes in patients with HF ,20. The The PARADIGM-HF trial  is one oThere is limited research on the use of sacubitril/valsartan in older individuals. The risk of mortality is higher in individuals with HF aged more than 65 years compared to their younger counterparts . In a reOne of the limitations of this meta-analysis is that we were not able to conduct a subgroup analysis due to a lack of individual-level data. Secondly, the follow-up of majority of the studies was less than six months. Therefore, in the future, more prospective trials are required to compare the long-term outcomes between the sacubitril/valsartan and enalapril groups.In conclusion, our meta-analysis of nine studies underscores the superior clinical performance of sacubitril/valsartan compared to enalapril in managing patients with HF. Notably, sacubitril/valsartan demonstrated a significant advantage over enalapril in all-cause mortality, cardiovascular mortality, and cardiovascular hospitalization. These findings, supported by previous reviews, highlight the potential for sacubitril/valsartan to enhance patient outcomes and optimize healthcare resource utilization. However, further research is needed, particularly in elderly and renal-impaired populations, to provide a more comprehensive understanding of these medications' long-term efficacy and safety profiles.


Therapeutic peptides play an essential role in human physiology, treatment paradigms and bio-pharmacy. Several computational methods have been developed to identify the functions of therapeutic peptides based on binary classification and multi-label classification. However, these methods fail to explicitly exploit the relationship information among different functions, preventing the further improvement of the prediction performance. Besides, with the development of peptide detection technology, peptide functions will be more comprehensively discovered. Therefore, it is necessary to explore computational methods for detecting therapeutic peptide functions with limited labeled data.In this study, a novel method called TPpred-LE based on Transformer framework was proposed for predicting therapeutic peptide multiple functions, which can explicitly extract the function correlation information by using label embedding methodology and exploit the specificity information based on function-specific classifiers. Besides, we incorporated the multi-label classifier retraining approach (MCRT) into TPpred-LE to detect the new therapeutic functions with limited labeled data. Experimental results demonstrate that TPpred-LE outperforms the other state-of-the-art methods, and TPpred-LE with MCRT is robust for the limited labeled data.In summary, TPpred-LE is a function-specific classifier for accurate therapeutic peptide function prediction, demonstrating the importance of the relationship information for therapeutic peptide function prediction. MCRT is a simple but effective strategy to detect functions with limited labeled data.The online version contains supplementary material available at 10.1186/s12915-023-01740-w. Therapeutic peptides play an essential role in human physiology, treatment paradigms, and bio-pharmacy –3. Over The data-driven computational methods have been widely used in therapeutic peptide function prediction over the past decade. Those methods can be categorized into two groups in terms of the methodologies: (i) binary classification methods and (ii) multi-label classification methods.The binary classification methods usually utilize conventional machine learning predictors by employing different feature extraction methods. PEPred-Suite  is an efThe multi-label classification methods have attracted more and more attentions in recent years. MLBP  treated As discussed above, the existing methods are suffering from two major disadvantages: (i) the existing methods failed to explicitly and accurately capture the relationship among different therapeutic peptide functions. For example, the methods based on binary classifiers only consider the specificity information of mono-functional therapeutic peptides ignoring their correlation information among different functions. (ii) For the newly sequenced therapeutic peptides, the existing computational predictors cannot accurately detect their comprehensive functions. Therefore, it is desired to recognize their unknown functions with limited labeled data.In this study, we proposed a computational predictor called TPpred-LE for multi-functional therapeutic peptide prediction. TPpred-LE exploits the relationship among different functions based on label embedding and function-specific classifiers, including the correlation information and the specificity information. Furthermore, we proposed a multi-label classifier retraining approach (MCRT) based on the classifier retraining approach (cRT) , which wIn this study, we exploit the prediction ability of TPpred-LE on the benchmark dataset with 15 different therapeutic peptide functions, including AMP , TXP (toxic peptide), ABP , AIP (anti-inflammatory peptide), AVP , ACP (anti-cancer peptide), AFP , DDV (drug delivery vehicle peptide), CPP (cell-penetrating peptide), CCC (cell–cell communication peptide), APP (anti-parasitic peptide), AAP (anti-angiogenic peptide), AHTP (anti-hypertensive peptide), PBP (polystyrene surface-binding peptide), and QSP (quorum sensing peptide). The benchmark dataset is divided into training dataset, validation dataset, and independent test dataset.The framework of TPpred-LE is illustrated in Fig. example (example-based Accuracy) and F1label (label-based F1-score) to evaluate the overall performance of TPpred-LE [We use the multi-label metrics ACCPpred-LE . BesidesPpred-LE , MatthewPpred-LE , the F1 Ppred-LE , and thePpred-LE .We conduct ablation experiments to investigate the importance of the relationship information among different therapeutic peptide functions, including the correlation information and the specificity information. The corresponding results are listed in Table We further visualize Pearson’s correlation coefficient  of the fMost of the existing methods only predict some specific therapeutic peptide functions and treat this task as binary classification problem. In contrast, TPpred-LE is the only method for comprehensively predicting 15 different therapeutic peptide functions. The performance of TPpred-LE is measured by binary classification metrics and compared with the state-of-the-art binary classification methods for therapeutic peptide prediction, including PEPred-Suite , PPTPP , and TPpThe compared methods are based on conventional machine learning, and they have extracted the hand-crafted manual features by integrating different properties. To erase the impact of the input features, we further compare TPpred-LE with one-versus-all RFs trained on one-hot and PSSM-encoded sequences of the training set as TPpred-LE. We trained a RF model for each function with a one-versus-all strategy. Besides, we constructed the input for RFs in two strategies: concatenating or averaging all the input residual vectors. The results are shown in Table To further evaluate the performance of TPpred-LE, we compare TPpred-LE with MLBP  and PrMFmentclass2pt{minimweak label ratio (WL ratio) [In previous therapeutic peptide prediction studies, there is an assumption that all peptide sequences are comprehensively labeled. However, the assumption hardly holds in reality , 23. WitL ratio)  varying L ratio) .The performance of TPpred-LE* (TPpred-LE with MCRT), TPpred-LE, MLBP, and PrMFTP on these datasets with various WL ratios are shown in Fig. To investigate the role of three types of attention in the model, we visualize the learned attention weights in the last layer of the Transformer encoder and decoder. We visualize the overall received attention weights for all functions and residues. The results are illustrated in Fig. Furthermore, we focus on a single peptide sequence to visualize the three types of attentions. We take the peptide “GVAKFGKAAAHFGKGWIKEMLNS” as an example, which has the functions of AMP, TXP, and ABP. The weights of three types of attention are shown in Fig. The aforementioned results reveal limitations in the predictive capabilities of current methods for therapeutic peptide function prediction. On one hand, the binary classification techniques focus on specific peptide functions, while overlooking the relationship information among different peptide functions. These methods frequently yield a high false-positive rate, resulting in lower precision. On the other hand, the existing multi-label classification-based methods still fail to explicitly employ the relationship information, which leads to unsatisfactory accuracy, particularly when dealing with a limited number of training samples.TPpred-LE is an innovative approach designed for the prediction of multifunctional therapeutic peptides, which incorporates the relationship information between different peptide functions effectively. This method utilizes the encoder and decoder to learn the correlation information among residues and functions to improve the prediction ability as shown in the ablation experiment. Furthermore, TPpred-LE benefits from the integration of the attention mechanism, which allows for the straightforward visualization of attention weights for three different types. The three difference weight types improve the performance of TPpred-LE. Finally, we introduced the label missing problem in the therapeutic peptide function prediction field and proposed the MCRT algorithm to solve it. The study on the limited training labeled data is promising to predict the function more comprehensively. There still exist some limitations in the TPpred-LE. For example, TPpred-LE’s reliance on deep neural networks demands a substantial volume of training samples to effectively learn patterns. In the future, we are planning to incorporate the pre-trained models to improve the performance on therapeutic peptide prediction.In this paper, we propose a novel method called TPpred-LE for therapeutic peptide function prediction. Compared with the other existing computational methods, TPpred-LE has the following advantages: (i) it accurately and comprehensively predicts the 15 different therapeutic peptide functions; (ii) it incorporates label embedding and function-specific classifiers to measure the correlation relationship and the specificity relationship among peptide functions, respectively; (iii) it is able to stably detect the newly detected therapeutic peptide functions with limited labeled data by introducing the MCRT algorithm; and (iv) its web server is constructed, only requiring the peptide sequences in FASTA format as inputs.In this study, we constructed a comprehensive benchmark dataset with 15 different therapeutic peptide functions, including AMP, TXP, ABP, AIP, AVP, ACP, AFP, DDV, CPP, CCC, APP, AAP, AHTP, PBP, and QSP. They were derived from SATPdb , PEPred- removed –30 by us removed . FinallyAs illustrated in Fig. The embedding modules in TPpred-LE learn the discriminative representations of sequences and the therapeutic peptide functions.Firstly, the input sequences and all the functions are embedded as numerical vectors. One-hot encoding  and posiL, which is fixed as 50 in this study. If the length of the sequence is less than For a given sequence, the length of the input sequence is atenated . We haveatenated  generallC is set as 15.An encoded sequence is represented as K, and V are the query, key, and value matrices, respectively.We adopt Transformer  to learnMulti-head attention mechanism allows the model to attend to information from different perspectives , 39, 41 where the The encoder takes nsformer :5\documeentclass1pt{minimaThe positional encodings  strategy for detecting new functions with limited labeled data.Classifier retraining (cRT) has been confirmed to be an effective approach for long-tailed multi-class classification , which lBenefiting from the scalability of the function-specific classifiers, we treat the model as strategy . The squstrategy , 48 is ufined as :10\documMCRT retrains each classifier with the resampling training dataset for each function. When retraining the classifier In TPpred-LE, each function will be projected into a distinct output space due to the independency of each function-specific classifier, which will adversely affect the label embedding process. Therefore, we utilize two training steps to train TPpred-LE. In the first training step, the single classifier is used to learn the embedding in the same output space so as to extract the correlation information among labels. In the second training step, we replace the single classifier with the function-specific classifiers to train the model with the label embedding module keeping fixed, and each classifier will obtain a distinct decision boundary according to its specificity information. The detailed training process is shown in Algorithm 1. Besides, the training process of TPpred-LE based on MCRT is shown in Algorithm 2. The opy loss  is used opy loss :11\documi. AdamW  algorithFor multi-label classification, the evaluation metrics are generally categorized into two groups : exampleollowing , \documeding AUC , MCC [18ding AUC , and RkCding AUC .Additional file 1:Supplementary Material S1. The calculation of the Pearson’s correlation coefficient.Additional file 2:Supplementary Material S2. The construction of the limited labelled datasets.Additional file 3:Table S1. The precision scores of various methods for predicting eight therapeutic peptide functions on the independent dataset. Table S2. The performance of TPpred-LE for predicting 15 therapeutic peptide functions on the independent dataset. Table S3. The statistical information of the 15 therapeutic peptide functions. Table S4. The performance comparison of two strategies for truncating the sequences with length exceeding 50. Table S5. The search space for hyperparameters and their optimal values used in TPpred-LE.Additional file 4:Fig S1. The distribution of different multi-functions and their relationship. Fig S2. The length distribution of the benchmark dataset.


Dear Editor,Meyerson's nevus is an eczematous reaction on a melanocytic nevus. This reaction can also affect other types of lesions such as congenital nevi, dysplastic nevi, melanoma, and non-melanocytic lesions, in which case it is called Meyerson's phenomenon. It may be restricted to a single melanocytic nevus or affect several melanocytic nevi. In a small percentage of cases, it is accompanied by eczematous lesions in other locations. It usually resolves spontaneously or after treatment with topical corticosteroids. Its pathogenesis is not clear, and no triggers have been identified in most cases, although cases have been reported after sunburn or treatment with interferon-alpha alone or combined with ribavirin.Multiple cutaneous manifestations caused by COVID-19 have been described. However, its pathogenesis remains unclear. They have been attributed to an inflammatory mechanism, due to an immune response against viral nucleotides, and to a vascular mechanism, secondary to vasculitis or thrombosis.The authors present the case of a 34-year-old man with no medical history of interest who presented to the dermatology department with erythematous-squamous lesions and crusting over approximately half of his melanocytic nevi on the trunk A. VisualA biopsy of one of the lesions revealed nevus cells arranged in small thecae with adequate maturation without signs of dysplasia, surrounded by lymphocyte-predominant inflammatory infiltrate  and marked spongiosis .Fig. 2BiIt was diagnosed as Meyerson nevus triggered by COVID-19 infection. The lesions resolved after topical corticosteroid treatment (methylprednisolone acetate once daily for two weeks) and have not recurred to date. The affected nevi show a typical pigmented reticular pattern at present.As for nevocentric dermatoses in relation to SARS-CoV-2, a case of nevocentric erythema multiforme three days after COVID-19 vaccination  has recently been reported.In conclusion, COVID-19 infection, through a mechanism mediated by ICAM-1 and interferon, could trigger Meyerson's nevus. The authors encourage the reporting of this type of skin reaction in order to consider their inclusion among the possible cutaneous manifestations related to COVID-19.None declared.Rubén Linares-Navarro: Approval of the final version of the manuscript; critical literature review; data collection, analysis, and interpretation; effective participation in research orientation; intellectual participation in propaedeutic and/or therapeutic; management of studied cases; manuscript critical review; preparation and writing of the manuscript; study conception and planning.Pedro Sánchez-Sambucety: Approval of the final version of the manuscript; critical literature review; data collection, analysis, and interpretation; effective participation in research orientation; intellectual participation in propaedeutic and/or therapeutic; management of studied cases; manuscript critical review; preparation and writing of the manuscript; study conception and planning.Manuel Ángel Rodríguez-Prieto: Approval of the final version of the manuscript; critical literature review; data collection, analysis, and interpretation; effective participation in research orientation; intellectual participation in propaedeutic and/or therapeutic; management of studied cases; manuscript critical review; preparation and writing of the manuscript; study conception and planning.None declared.


The Nutri-Score is a candidate for the harmonized mandatory front-of-pack nutrition label enabling consumers in the European Union to make healthier food choices. Nutri-Score classifies foods (including beverages) from A  to E  based on the foods’ qualifying and disqualifying components. We aimed to evaluate the updated Nutri-Score for foods (2022) and beverages (2023) in a Norwegian setting by exploring its ability to discriminate the nutritional quality of foods within categories. Additionally, we assessed Nutri-Scores’ ability to classify foods in accordance with the Norwegian food-based dietary guidelines (FBDGs).The updated Nutri-Score was calculated for 1,782 foods in a Norwegian food database. The discriminatory ability of the updated Nutri-Score was considered by exploring the distribution of Nutri-Score within categories of foods using boxplots and frequency tables, and by examining which qualifying and disqualifying components that contributed most to the Nutri-Score class. Accordance with the Norwegian FBDGs was assessed by exploring Nutri-Score for foods specifically mentioned in the guidelines.Overall, the updated Nutri-Score seemed to discriminate the nutritional quality of foods within categories, in a Norwegian setting. The foods’ content of salt and the beverages’ content of sugar were components contributing the most to Nutri-Scores’ discriminatory ability. Furthermore, in most cases the updated Nutri-Score classified foods in accordance with the Norwegian FBDGs. However, there were minor inconsistencies in how Nutri-Score classified certain foods, such as the inabilities to discriminate between full-fat and low-fat/leaner cheeses, cremes and processed meats (sausages), and between whole grain and refined pasta/rice.We observed an overall acceptable discriminatory performance of the updated Nutri-Score in a Norwegian setting and in most cases the updated Nutri-Score classified foods in accordance with the Norwegian FBDGs. However, minor inconsistencies were observed. Together with the FBDGs, the updated Nutri-Score could be a useful tool in guiding consumers towards healthier food choices in Norway, but consumer evaluations are warranted to fully assess the performance of the updated Nutri-Score in a Norwegian context.The online version contains supplementary material available at 10.1186/s12966-023-01525-y. Diet is a major contributor to health . Low intFront-of-pack nutrition labels (FoPNLs) are of interest because they aim to help consumers make healthier food choices and to stimulate food reformulation , therebyNutri-Score is a debated contender for the harmonized FoPNLs in the European Union –13. NutrTo our knowledge, there is currently no published scientific paper presenting an evaluation of the updated Nutri-Score for foods and beverages. More knowledge about how the updated Nutri-Score performs in different countries with different food patterns and dietary guidelines is important for understanding the potential for Nutri-Score to function as a harmonized FoPNL across European countries. As part of the NewTools-project , aiming We used the food composition database KBS, version 7.4, AE-22 at the Department of Nutrition, University of Oslo, Norway, in the evaluation of the updated Nutri-Score. The database mainly contains generic foods consumed in Norway and the necessary nutrients for calculation of Nutri-Score, except the foods’ proportion of fruit, vegetables and legumes and presence of non-nutritive sweeteners. The two latter components were estimated as described under Nutri-Score calculation. Of the 4,199 foods in the database, 2,404 were excluded as products were not eligible for the Nutri-Score, such as alcoholic beverages; herbs and spices; meal replacement bars; special dietary foods; baby foods; and home-cooked foods and dishes not representative of products sold in Norwegian stores. Due to missing on total sugar, 13 foods were additionally excluded. We calculated Nutri-Score for whole foods  and composite foods. The final sample for analyses consisted of 1,782 products.The updated Nutri-Score comprises three algorithms: one for general foods , one forTo estimate the fruit, vegetables and legumes proportion, the component was initially set to 100% for all pure fruit, vegetables and legumes as defined by Nutri-Score . For comIn general, the Nutri-Score was calculated by allocating 0 to 20 points for each unfavorable component in a food, while each favorable component provided 0 to 7 points . By subtThe included foods were categorized by the authors into main- and subcategories for evaluating the updated Nutri-Score in a Norwegian setting, based on how others categorize foods , existineach component in the Nutri-Score algorithms. These statistics were calculated overall for each algorithm and for categories of foods to investigate the relative contribution of each component to the total points within each food category.This study used descriptive statistics and boxplots to explore the updated Nutri-Scores’ ability to discriminate the nutritional quality of foods within food categories. Boxplots and dots indicating individual foods were used to display the distribution of Nutri-Score within categories. We present the distribution (n and percent) of Nutri-Score classes within each food category in additional files. The discriminatory ability was pragmatically assessed by considering the most frequent Nutri-Score class and the number of available classes within each food category, for both main- and subcategories of foods. Having products in three or more classes within a category was considered acceptable to be able to discriminate between foods, similar to previous studies , 25–27. The Norwegian FBDGs were developed from a review of systematic reviews and evaluation of the quality of evidence of the association of foods and nutrients with obesity and chronic diet-related diseases, with the aim to prevent these diseases in the Norwegian population . A briefTo structure the assessment, we created a table with the Norwegian FBDGs where we commented upon Nutri-Scores’ classification of foods in accordance with each FBDG based on all results for each relevant food category or specific foods. The FBDGs recommend limiting intake of red and processed meat but also specify to choose lean meat and lean meat products . We inteWe also investigated agreement between Nutri-Score and the Norwegian Bread Scale label for breads, using information available in the food database. The Bread Scale label is a voluntary label for breads helping consumers to choose breads with more whole grains, by indicating the coarseness based on the percentage of whole grains, wholewheat meal flour and bran from the total amount of flour in the bread . The labEthical approval was not required as no human or animal subjects were involved in this study.Of the 1,782 foods and beverages in the sample, 27% were classified with Nutri-Score A, 12% with B, 23% with C, 21% with D, and 17% with E ; grains, pasta, rice and noodles (84%); flour and flour mixes (71%); and eggs (80%) were classified with Nutri-Score A or B ; crisps (81%); chocolate/candy (98%); and sweet biscuits/pastries (89%) were classified with Nutri-Score D or E.There were foods in at least three out of the five Nutri-Score classes in all subcategories of general foods, except for Oils and soft/liquid margarines and salted/coated nuts had foods in more than three of the Nutri-Score classes. All butter, butterblends and hard margarine and oils were classified with either Nutri-Score D or E, while most (87%) oils and soft/liquid margarine were classified with B or C .other beverages category were classified with Nutri-Score B, 95% of artificially sweetened beverages were classified with Nutri-Score C and most (93%) of sugar-sweetened beverages were classified with D or E ), saturated fat/total fat proportion (2 (8)), salt (0 (4)), protein (0 (6)) and fiber (0 (5)). For beverages, sugar (4 (7)) and energy (3 (3)) components contributed to the most points and variation in total points, while protein provided variation in favorable points (IQR: 6). More details in Additional file fruit, vegetables, and legumes category were classified with Nutri-Score A or B  association was found between Nutri-Score and the Bread Scale. More than 90% of breads with 75–100% coarseness were classified with Nutri-Score A, while 77% of breads with 0–25% coarseness were classified with Nutri-Score C or D , ready meals and pasta/rice, the updated Nutri-Score did not capture differences consistent with the Norwegian FBDGs. For example, regular semi-hard cheese and cremes and their low-fat equivalent, sausages and their leaner versions, and ready meals with red meat versus white meat/fish, in many cases get the same Nutri-Score class  foods the Norwegian FBDGs recommend to consume more of  , were inThe updated Nutri-Score has so far been scarcely applied in the scientific literature , 34, andNutri-Score should help consumers in choosing healthier foods by discriminating between foods within categories . We obseNutri-Score’s ability to discriminate between nutritional quality of foods is linked to the algorithm’s components. In our study, we explored which components that contributed to variation in the Nutri-Score. We found that salt allocated many points, and variation in points, for several food categories  representing common products, including several brand-specific products. However, this number of products does not reflect all salty snack products found in Norwegian stores. Our salty snacks data contain approximately one product from each producer, but in stores each producer offer several similar products with small differences in nutritional content . Hence, we might not show the real distribution of Nutri-Score within food categories which further could limit capturing inconsistencies with the updated Nutri-Score and its accordance with the Norwegian FBDGs. Nevertheless, the database contains a representative number of foods likely sufficient to evaluate Nutri-Scores’ discriminatory ability of the nutritional quality of foods in a Norwegian setting, as well as its ability to classify foods in accordance with the Norwegian FBDGs, as this database is used in national dietary surveys representative of the Norwegian population. Furthermore, the currently used food database provides information on fiber and many recipes for foods which are needed when calculating Nutri-Score, thus limiting errors and misclassification.The main limitation in the current study is the use of a food database containing mostly generic foods, lacking the markets share of products and many brand-specific foods currently sold in Norwegian stores. For example, there were 16 products in the An inherent problem when evaluating nutrient profiling models, such as Nutri-Score, is the lack of a gold standard for defining a healthy food  and no cThis study is the first to evaluate the complete updated Nutri-Score. We observed an overall acceptable discriminatory performance of the Nutri-Score in a Norwegian setting. In most cases the updated Nutri-Score was in accordance with the Norwegian FBDGs, however, we observed inconsistencies related to inabilities to discriminate between whole versus refined grain products and between cheeses, cremes and processed red meats (sausages) with different fat content. Our evaluation suggests that together with the FBDGs, the updated Nutri-Score could be a useful tool in guiding consumers toward healthier food choices in a Norwegian setting, but the use and effect of the updated Nutri-Score among Norwegian consumers remains to be evaluated.Additional file 1. The updated Nutri-Score algorithms.Additional file 2. Main food categories.Additional file 3. Subcategories of foods.Additional file 4. The development of the Norwegian food-based dietary guidelines.Additional file 5. Distribution of Nutri-Score classes for main food categories.Additional file 6. Distribution of Nutri-Score classes for subcategories of general foods.Additional file 7. Distribution of Nutri-Score for main categories of foods.Additional file 8. Detailed description of the updated Nutri-Score for subcategories of foods.Additional file 9. Central and dispersion of points from each component in the Nutri-Score algorithms within food categories.Additional file 10. Breads with the bread scale and Nutri-Score.


Machine learning (ML) classification tools are known to accurately predict many cardiac surgical outcomes. A novel approach, ML-based survival analysis, remains unstudied for predicting mortality after cardiac surgery. We aimed to benchmark performance, as measured by the concordance index (C-index), of tree-based survival models against Cox proportional hazards (CPH) modeling and explore risk factors using the best-performing model.144,536 patients with 147,301 surgery events from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) national database were used to train and validate models. Univariate analysis was performed using Student's T-test for continuous variables, Chi-squared test for categorical variables, and stratified Kaplan-Meier estimation of the survival function. Three ML models were tested, a decision tree (DT), random forest (RF), and gradient boosting machine (GBM). Hyperparameter tuning was performed using a Bayesian search strategy. Performance was assessed using 2-fold cross-validation repeated 5 times.The highest performing model was the GBM with a C-index of 0.803 (0.002), followed by RF with 0.791 (0.003), DT with 0.729 (0.014), and finally CPH with 0.596 (0.042). The 5 most predictive features were age, type of procedure, length of hospital stay, drain output in the first 4 h (ml), and inotrope use greater than 4 h postoperatively.Tree-based learning for survival analysis is a non-parametric and performant alternative to CPH modeling. GBMs offer interpretable modeling of non-linear relationships, promising to expose the most relevant risk factors and uncover new questions to guide future research. The extraordinary physiologically stress of cardiac surgery carries a high risk of adverse postoperative outcomes . An impoPrevious ML research modeling survival converts time-to-event to a binary outcome such as 5-year mortality, which sacrifices potentially useful information . SurvivaMany important ML concepts have been translated into a survival analysis domain including neural networks, support vector machines, gradient boosting machines (GBM), and random forests (RF) , 10. In Tree-based learners are a class of models which expand on the decision tree, whereby strong models are constructed from ensembles of weak decision trees such as the random forest , 12. AddFirstly, we aimed to compare model fit of tree-based machine learning to Cox proportional hazards modeling. Secondly, we aimed to use the best-performing model to determine the key predictor variables for long-term mortality. We hypothesized that tree-based ML provides better model fit and explanations compared with CPH modeling.The Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) Database registry recorded 153,944 cardiac surgery events in 151,089 unique patients from April 2001 to December 2019, captured at 42 centers in Australia . The datLong-term mortality was defined as any death that was recorded after a procedure. Patients were followed from the date of surgery to their death as recorded in either the database or the NDI. The date of the last linkage with the NDI, the “follow-up time”, was the 1st of August, 2019.T-test for continuous variables and a Chi-Squared test for categorical variables. For fitting models, non-binary categorical variables were converted to a set of dummy variables.All perioperative variables in the database were considered for inclusion. Any variables which were missing 90% or more data were excluded from the analysis. Preliminary data analysis was conducted by comparing the univariate distribution of each variable between survivors and non-survivors at the last follow-up. Hypothesis testing for distributional differences were performed using a student's A Kaplan-Meier (KM) estimator was fitted to plot the survival function in order to visualize the univariate effects of sex, indigenous status, and type of operation on long-term survival.Time-to-event regression differs from standard regression as for a subset of cases the time of an event occurring has not been observed, and is thus censored. Rather than learning a direct relationship between input variables and the time of an event, survival analysis seeks to estimate the survival and cumulative hazard functions based on input covariates.One of the oldest and most widely used methods for regressing censored data is the CPH model , 13. It Tree-based machine learning incorporates decision tree and ensemble decision tree methods. These models are non-parametric and make no proportional hazards assumptions.Survival trees are simple models that learn decision rules derived from input features, that is often represented as an expanding set of branching paths . These mRandom survival forests are ensembles of decision trees that are trained on bootstrapped samples of training data and a random subset of input variables , 13. UnlSimilar to random forests, gradient boosting machines are an ensemble of decision trees, however, the model fits an additional term to minimize the residual error of the weak learner using the gradient of the error . SurvivaFor each candidate model, a hyper-parameter search was conducted using a Bayesian search strategy . The parPerformance was measured using the Concordance index (C-index) which is measured on a scale between 0.5 and 1.0, where 1.0 indicated perfect model fit and 0.5 indicates performance no better than random chance. In order to benchmark the algorithms' performance, a 2-fold cross-validation scheme repeated 5 times was used . ImbalanThe assessment of machine learning bias was achieved by stratifying the test set of each cross-validation fold by important characteristics and measuring the performance on the stratified subsets. Bias was assessed for the characteristics of sex and indigenous status.After benchmarking performance, the best-performing model was used to assess which features were the most important for predicting the hazard function for long-term mortality .This project was conducted in concordance with the National Health and Medical Research Council (NHMRC) National Statement on Ethical Conduct in Human Research, with approval from the Monash University Human Research Ethics Committee (HREC) approval number 2020-24850-45439.144,536 patients were included in the final analysis with 147,301 surgery events . A summaStratified survival functions as estimated by the Kaplan-Meier estimator are shown in The best performing model was the GBM, with a C-index of 0.803 (0.002), compared with CPH modeling with 0.596 (0.042). The performance of the GBM on female patients was 0.800 (0.004) and for indigenous patients was 0.773 (0.012). All performance scores for each benchmarked model, including their bias assessment are reported in As gradient boosting machines had the highest performance, feature importance was extracted from its weights. This is the first study to investigate tree-based time-to-event models to predict survival. It is also the first to study machine learning bias in this context. Our results show that machine learning outperforms the current gold standard in medical research, the Cox proportional hazards model. Additionally, we have shown that high performing machine learning approaches can be used to determine the most important features for predicting long-term mortality.The initial data exploration included a univariate analysis with a Kaplan-Meier estimator, which indicated sex differences in long-term mortality. The finding that there is an association between female sex and higher long-term mortality has been previously identified . Women wIndigenous mortality is less studied, however, a recent analysis of patients undergoing coronary artery bypass grafting found they were younger but suffering from a higher prevalence of comorbidities . As age Similar to the pre-existing literature of tree-based methods in cardiac surgery, GBM are often as good or better than pre-existing methods , 26. TheAn important feature of any predictive model in healthcare is explainability . We haveThe impact of ML in the clinical domain has been slow, however, tree-based risk stratification tools and apps, such as predictive optimal trees in emergency surgery (POTTER), have introduced clinicians to their routine use . FurtherThere are a number of limitations to our study. Firstly, we made the assumption that data was either missing completely at random, or missing at random. While we believe this is a safe assumption, there may be instances where data is missing not at random that biases our imputation approach. Additionally, compared to CPH and decision tree methods, RF and GBM were significantly more computationally expensive. In settings with very large datasets and limited computational resources they may not be appropriate choices. The use of less computationally intensive algorithms, such as light gradient boosting machine, could improve computational performance however the gain in this analysis would be marginal . FurtherTree-based learning for survival analysis is a non-parametric and performant alternative to Cox proportional hazards modeling. Within the tree-based learning methods, gradient boosting machines perform the best as measured by the C-index. These models can provide risk profiles to guide clinical reasoning and uncover new questions for future research.


Sentiment Analysis (SA) of text reviews is an emerging concern in Natural Language Processing (NLP). It is a broadly active method for analyzing and extracting opinions from text using individual or ensemble learning techniques. This field has unquestionable potential in the digital world and social media platforms. Therefore, we present a systematic survey that organizes and describes the current scenario of the SA and provides a structured overview of proposed approaches from traditional to advance. This work also discusses the SA-related challenges, feature engineering techniques, benchmark datasets, popular publication platforms, and best algorithms to advance the automatic SA. Furthermore, a comparative study has been conducted to assess the performance of bagging and boosting-based ensemble techniques for social network SA. Bagging and Boosting are two major approaches of ensemble learning that contain various ensemble algorithms to classify sentiment polarity. Recent studies recommend that ensemble learning techniques have the potential of applicability for sentiment classification. This analytical study examines the bagging and boosting-based ensemble techniques on four benchmark datasets to provide extensive knowledge regarding ensemble techniques for SA. The efficiency and accuracy of these techniques have been measured in terms of TPR, FPR, Weighted F-Score, Weighted Precision, Weighted Recall, Accuracy, ROC-AUC curve, and Run-Time. Moreover, comparative results reveal that bagging-based ensemble techniques outperformed boosting-based techniques for text classification. This extensive review aims to present benchmark information regarding social network SA that will be helpful for future research in this field. With the incremental growth of information technology and social platforms, user-generated information can easily be posted online, and this information contains people's sentiments and emotions toward a particular issue. While government, companies, and individuals are interested in retrieving the sentiments behind that reviews. Miserably, with the massive amount of data, it is challenging to polarize these comments and reviews. Where human experts are overpriced for labeling these reviews manually. Accordingly, SA is gaining a lot of popularity in research topics  Feature Extraction and (2) Feature Selection. Feature extraction is a process of selecting required or essential features from the original set. Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) are the two popular techniques of feature extraction  Eq. .LSA is the latest dimensionality reduction technique and feature extraction in text classification. LSA works on the procedure of analyzing concepts, terms, and relationships between unstructured texts. It can correlate semantically related terms of latent text. LSA is used for text clustering and page retrieval system. LSA resolves the problem of words with more than one meaning and various words containing similar meanings  GA is an optimal random search-based feature selection method that works on the propaganda of biological science mechanisms. It follows the procedure of genetic evolution in biology that starts from the initial feasible population and after that, applies crossover and mutation  and affective computing. Therefore, emotion extraction and classification became prime aspects in the research field of SA. Several researchers have been working on a distinctive set of emotions and expressions.Gunesa et al.  present This section presents the methodology used for ensemble classification of the text reviews for sentence-level sentiment classification. The ensemble approach of machine learning has been used in various applications and has produced outstanding results. Ensemble learning is also approachable in the SA task. Therefore, we have presented a comparative analysis of diverse ensemble methods that are divided into two main categories: bagging and boosting. This study compares eight popular ensemble learners , Ada-Boost, Gradient-Boosting, XGB, Cat-Boost, and Light-GBM) to choose the best model for SA. The experiments have been conducted on four different domain reviews: Uber reviews, Restaurant reviews, Amazon reviews, and Food reviews. Figure Dataset collection is the initial step of every research, and it plays a crucial role in authentic experiments. Four leading review  resources have been chosen to verify the authenticity of the experiments. Uber reviews dataset contains 1344 customer ride reviews, the Food category reviews dataset holds 25,000 records, Amazon product and Restaurant reviews dataset holds 1000 records for the experiment. Here, both large and small size of the dataset is collected for investigating the ensemble models that can provide better comparative analysis. The experimental dataset contains positive and negative reviews where positive sentiments are denoted by 1 and negative sentiments are denoted by 0. Table It is required to convert raw data into a machine-understandable form. First, we organized the datasets by rectifying the spelling errors, antonyms, and missing fields. After that, basic steps such as punctuation removal, whitespace removal, URL removal, number removal, and hash-tag removal have been made to clean the reviews. These preprocessing steps are needed to get an accurate score for SA because machine learning cannot work effectively on raw and grubby datasets.Tokenization is a fundamental splitting phase in SA that partition the sentence, phrase, or paragraph into single words called tokens. Here tokens can be either character or word that is individually counted. Tokenization is the building block of NLP that is enforced by the n-gram approach. N-gram is a series of n items available in the text or speech. These can be categorized into unigrams, bigrams, or trigrams Eq. .12\documVectorization is the process of converting text into meaningful, informative numbers. It measures the frequency of a word in a document and generates a number accordingly. TF is calculated by the number of times an individual word occurs in a document divided by the total number of words in a document.tfij is the total number of IDF is used to assign the weights to rare words in the documents. TF-IDF is calculated in Eq. . Where \Machine learning supports two types of ensemble techniques bagging and boosting. Bagging selects the random samples from the training set and trains multiple learners Parallelly. In contrast, boosting collect the samples from the output of the previous learner and trains them sequentially. This section describes all the experimented ensemble techniques implemented for comparison. These algorithms are divided into two parts bagging and boosting. Wherefrom the bagging concept, we have selected a Random-Forest (RF), Extra-Tree (ET), and Meta-Estimator (Linear SVC) (M-SVC) for the implementation of SA, and from boosting approach, Ada-Boost (AB), Cat-Boost (CB), Gradient-Boosting (GB), XG-Boost (XGB), and Light-GBM (LGBM) were implemented.Bagging combines homogeneous classifiers and trains them parallelly with random samples. First, multiple bootstrap samples have been created that act individually. After that, base learners are fitted on them, and finally, their outputs are aggregated. Bagging is a popular ensemble approach that helps to reduce the variance of classifiers. Table It is a powerful technique to handle large datasets quickly. Various applications have used it for accurate and effective results. Random-Forest constructs the multiple decision trees that classify the new instance by majority voting. Each node of the DT uses a randomly selected sample from the whole original sample set. We can say that every tree uses a different bootstrap sample, the same as the bagging concept. It follows a few steps:Equation Highly Randomized Trees Classifier is an ensemble method that aggregates the output of multiple decision trees. It is highly similar to the random forest but only differs in DT construction in a forest. Table Every DT of the Extra-Tree forest is formulated from the attributes of the original sample set. Then each individual node of the tree uses the random k feature of the sample, and each DT selects the best split for the creation of multiple de-correlated decision trees. Every DT calculates the entropy Eq.  and infoBagging ensemble meta-estimator provides an option to select own base learner for the bagging process to reduce the base estimator's variance, e.g., a decision tree. Here, we have chosen Linear-SVC instead of DT as a base classifier for the bagging process. Linear SVC finds the hyper-plane space between two classes. It provides faster execution of large datasets and minimizes squared hinge loss. First, we built several substances of Linear SVC on random subsets of the original training set. After that, it aggregates the individual classified results of all substances to form a final classification.Boosting is an ensemble learning approach that boosts the performance of weak learners by sequentially running on multiple subsets of the dataset. Boosting constructs a sequence of models, and each model trains by considering the ambiguity of the previous model  from the training dataset is drawn for base learning, which improves the execution speed and approximation accuracy of gradient boosting substantially Friedman .21\documFirst, a constant model has initialized with lues Eq. . It begition Eq. . Where, aset Eq. . The foublem Eq. . The fif}Fmx Eq. .It is the latest ensemble technique that can incorporate deep learning techniques and work with discriminant data types to solve a wide range of problems. Cat-Boost is made with the combination of two words, "Category" and "Boosting," where category means it can work with varieties of data such as text, image, audio, or video, and boost means that it is a variant of gradient boosting ensemble. Cat-Boost resolves the exponential expansion of the feature combination generated by the greedy method at each split. Cat-Boost first divides the dataset into random subsets, then converts the labels into numerals, and finally transforms the category features into numbers Eq. .26\documHere, CountInclass represents a number of ones in the target for given categorical features; totalCount presents previous objects, and prior shows starting parameters  that can efficiently work with various interfaces. XG-Boost constructs different ensemble trees sequentially for ensemble learning and assigns weights to each value of the database, which decides the probability of getting selected for the next decision tree. The initial weight of each data value is the same, and it updates according to the further analysis of decision trees. The result obtained by the first DT helps to construct a new classifying model Eq. , and thiHere, ions Eq.  to prediHere, F in Eq.  is a defEq.  minimizeEq.  presentsi in Eq.  is seconThe constant function can also be removed for obtaining the following procedure presented by Eq. . This meIt supports the Gradient-Boosting framework, which increases the efficiency of the model with light-weighted decision trees. It includes Exclusive Feature Bundling (EFB) and Gradient-based One Side Sampling (GOSS) techniques to overcome the limitation of the histogram that is primarily used by all Gradient-Boosting-based algorithms. Light-GBM is a variant of Gradient-Boosting, which inherits predictivity and resolves its scalability problem and long computational time using a leaf-wise growth scheme  = TP/TP + FN on the x-axis and False Positive Rate (FPR) = FP/TN + FP on the y-axis , and Extra-Tree) on four popular reviews  datasets. The experiments were conducted on a PC with Intel(R) Core (TM) i5-8265U processor, 4 GB RAM, 64bit operating system, and Windows-10 using Jupyter Notebook. All the datasets are partitioned into two parts, 80% for training purposes and 20% for the testing set. The standard measures, namely TPR, FPR, accuracy, weighted precision, weighted recall, weighted f1-score, AUC-score, and run-time, were adopted to check the performance of each ensemble model. The definition of all the employed measures is initiated with a confusion matrix, as presented in Table Table Figure Figure Figure In testing accuracy, Random-Forest achieves a higher score of 91.82% for Uber reviews, Extra-Tree achieves 71.50% for Restaurant Reviews, Random-Forest and Extra-Tree achieve a higher and equal score of 77.50% for Amazon reviews, and Meta-Estimator (Linear SVC) obtains 86.94% score for Food reviews. In addition, from the boosting concept, XGB receives a higher training accuracy score of 87.62%, 89.50%, and 95.14% for Restaurant, Amazon, and Food reviews datasets. The Cat-Boost ensemble obtains the highest testing accuracy score of 91.07%, 71.00%, 76.00%, and 86.40% for Uber, Restaurant, Amazon, and Food reviews datasets. For Uber reviews, Light-GBM obtains the highest and equal training accuracy of 100% with Random-Forest and Extra-Tree. Conclusively, Cat-Boost achieves better training and testing accuracy than all the boosting techniques but cannot beat the bagging approach's performance as Random-Forest and Extra-Tree outperform over boosting ensemble techniques.Gradient-Boosting generates the minimum difference  between training and testing accuracy scores for  both large and small kinds of datasets, which means it overcomes the problem of overfitting and underfitting and reduces the bias and variance for training the model.Cat-Boost obtains state-of-the-art results for SA on discriminant datasets. It achieves higher testing accuracy and AUC score for all the experimented datasets. Cat-Boost is very easy to implement and generates competitive results with the help of one-hot encoding.As we know that Light-GBM is a robust algorithm and capable of handling large datasets but according to our experiments, Light-GBM provides less accuracy and AUC score for text classification than other experimented ensemble techniques. Although Light-GBM produces higher results as 91.88% training accuracy, 85.58% testing accuracy, and 85 AUC score for the large Food reviews dataset, still unable to beat the performance of Cat-Boost, and XGB.Cat-Boost and Gradient-Boosting are two main approaches with discriminant frameworks. Apart from it, XGB, Light-GBM, and Cat-Boost follow the framework of Gradient-Boosting. Experiments show Ada-Boost performs better than Gradient-Boosting in training, testing, and AUC scores for all the datasets but is unable to solve overfitting and underfitting problem, generating a higher difference between training and testing accuracy than Gradient-Boosting.Random-Forest and Extra-Tree are bagging-based approaches, where Random-Forest chooses the optimum split and Extra-Tree chooses random division for selecting the nodes. According to our experiments, both algorithms obtain equal training  accuracy on all the experimented datasets. For testing and AUC score, they also generate similar approximate values. Therefore, it can declare that Random-Forest and Extra-Tree algorithms are equally capable of sentiment classification.Meta-Estimator with Linear SVC is a bagging-based approach that uses Linear SVC for bagging procedures instead of decision trees. The demonstration shows that Meta-Estimator (Linear-SVC) obtains good results in terms of TPR, FPR, and run-time than all the experimented ensemble techniques, which means it can generate lower false positive and false negative rates and faster execution.After analyzing the results of all the experimented ensemble techniques according to the different measures, we discovered some important facts regarding the high and low performance of bagging and boosting-based ensemble models for SA using multiple datasets. We conclude the different types and lengths of datasets influence the performance of SA distinctly.As discussed above, our primary motive was to compare the bagging-based ensemble with the boosting-based ensemble to perform SA. After analyzing the results presented in Table SA has gained popularity in various fields, including medicine, politics, industries, and finance. Therefore, researchers are developing various intelligent models for SA. Figure SA is gaining popularity in healthcare industries and improving the quality of healthcare services. The opinion and reviews of the patients help healthcare providers to diagnose a particular disease , product analysis, market research, and competitive research. These SA-based applications help industries with decision-making. A novel LSTM-CNN-based model has been developed using a grid search optimization method to find out the opinion of customers for a restaurant (Priyadarshini and Cotton SA is used to evaluate the financial sector news and helps investors to choose beneficial schemes to invest in. The excessive growth of SA in finance has been seen with the increasing popularity of cryptocurrency. Several cryptocurrencies like Bitcoin, Ethereum, Binance Coin, Quant, Solana, and ZCash are available in the digital finance platform. There is no legislated background available for these cryptocurrencies by which users can faith on them to invest. SA is the only solution that can provide the opinion of different people towards a particular cryptocurrency and helps in decision-making. Machine learning techniques have been used to predict the price movement of Bitcoin, Ethereum, Ripple, and Litecoin cryptocurrencies (Valencia et al. SA is widely adopted in different kinds of tasks initiated, from extracting customer opinion (Kumar et al. Machine learning is the most promising approach for SA. Usually, machine learning-based SA provides a high accuracy score than the lexicon-based approach.. It offers various feature engineering techniques that extract the critical features from the dataset and improve the efficiency of SA. A graph-based approach connects interrelated words in text reviews to calculate the sentiment and opinion of people where vertices and nodes conform to features available in reviews. Various graph-based methods and algorithms have been applied in the last decades to solve the problem of SA (Tiwari and Kumar The journals "Elsevier" and "Springer" are two more common venues for SA publications. Where Elsevier, Springer, and ACM are three popular publishers that are chosen by researchers for SA-related authentic research. A benchmark dataset plays a vital role in sound research. It has been seen in the graph that researchers more frequently use product reviews for their experiments. Secondly, Twitter gained more popularity among researchers for SA-related experiments. Few researchers also generate their datasets for sentiment classification. Whereas the researchers also consider movie reviews, medical reviews, and hotel reviews for SA-related experiments. SA is an emerging field, but it has various challenges, making it process-critical and decreasing the efficiency of related models. Although researchers are working to solve these issues using discriminant techniques, there is still a lack of accuracy. These challenges generate obstacles to extracting the correct meaning of sentiments and classifying the correct polarity. The number of N features increases the domain dimensionality of the datasets. Feature engineering is a very important step in SA applications and opinion mining. Feature selection and feature extraction should be intractable with final processing in optimal feature engineering. Table This article presents an immense literature survey of 92 reputed articles, which includes lexicon-based, graph-based, machine learning-based, ensemble-based, and hybrid-based techniques for SA. It is observed that ensemble-based and hybrid-based techniques gained more popularity for text classification. In addition, essential aspects such as frequently used SA datasets, publishing platforms, proposed techniques, SA challenges, SA feature-engineering techniques, and various emotion theories are also discussed in this study. With the rapid demand for SA, several challenges are also occurred in processing the text reviews. So, we discussed several SA-related challenges, namely stance-detection, sarcasm-detection, negation-handling, domain-dependence, huge-lexicon, word sense disambiguation, and anaphora resolution, with their proposed solutions. The feature-engineering is a prime factor for effective text classification. Here we convey an extensive taxonomy of feature-engineering techniques used for text processing. The emotion theory of five admired researchers has also been discussed. The essence of their idea represents disgust, fear, anger, happiness, and sadness, mainly included in basic emotion classification from the text information.Our primary objective is to provide great relevance to the companies for selecting a better sentiment model for their brand monitoring and product reviews. This article also implemented numerous ensemble-based techniques on different domain reviews datasets, providing a systematic comparative analysis of bagging and boosting-based ensemble for SA. We have also illustrated the core of ensemble-based techniques for SA. Five boosting-based ensembles and three bagging-based ensemble techniques have been implemented on four text review datasets to conduct extensive experiments. The previously discussed ensemble-based research, incorporated with experimented results, provides a broad perspective to apply ensemble-based techniques for SA. Finally, experimental results demonstrate that bagging-based ensemble techniques outperform in terms of TPR, FPR, accuracy, weighted precision, weighted recall, weighted f1-score, AUC-score, and run-time for SA. However, XGB and Cat-Boost from boosting approach produced effective results but were unable to beat the performance of bagging-based ensembles. This survey with an analytical study will help in determining the best technique for preparing SA-related applications. For future contributions, we will explore the hybrid approaches, where discriminant techniques and models are combined to develop a better model for SA with reduced computational cost. The goal is to develop a hybrid model for SA application with a combination of different approaches. Therefore, we will assess the effectiveness and reliability of the hybrid methods with different types of parameters.