The long non-coding RNA (lncRNA)–protein interaction plays an important role in the post-transcriptional gene regulation, such as RNA splicing, translation, signaling, and the development of complex diseases. The related research on the prediction of lncRNA–protein interaction relationship is beneficial in the excavation and the discovery of the mechanism of lncRNA function and action occurrence, which are important. Traditional experimental methods for detecting lncRNA–protein interactions are expensive and time-consuming. Therefore, computational methods provide many effective strategies to deal with this problem. In recent years, most computational methods only use the information of the lncRNA–lncRNA or the protein–protein similarity and cannot fully capture all features to identify their interactions. In this paper, we propose a novel computational model for the lncRNA–protein prediction on the basis of machine learning methods. First, a feature method is proposed for representing the information of the network topological properties of lncRNA and protein interactions. The basic composition feature information and evolutionary information based on protein, the lncRNA sequence feature information, and the lncRNA expression profile information are extracted. Finally, the above feature information is fused, and the optimized feature vector is used with the recursive feature elimination algorithm. The optimized feature vectors are input to the support vector machine (SVM) model. Experimental results show that the proposed method has good effectiveness and accuracy in the lncRNA–protein interaction prediction. Long non-coding RNA (lncRNA)–protein interactions play an important role in the post-transcriptional gene regulation, polyadenylation, splicing, and translation, and predicting lncRNA–protein interactions helps to understand lncRNA-related activities  Construction of prediction models on the basis of lncRNA and protein features. The feature information of lncRNA and protein can be extracted using feature extraction methods based on sequence information, structure, and various physicochemical properties, which are fused to construct feature vectors. Feature vectors are fed into machine learning classification algorithms to construct prediction models for lncRNA–protein interaction relationships. Bellucci et al.  have pro(2) Heterogeneous network-based prediction model. Given the development of related experimental techniques and the accumulation of research results in the field of lncRNA, many lncRNA–protein interaction relationships have been experimentally confirmed, and researchers have successively proposed many network-based prediction algorithms to study the interaction relationships between lncRNAs and proteins. Li et al.  have conBased on the above analysis, this paper proposes a multifeature information fusion method based on lncRNA and protein sequence features and heterogeneous network topological features to predict lncRNA and protein interaction relationships. First, a novel feature extraction method based on the topological feature information of lncRNA and protein heterogeneous networks is proposed to extract the topological network features of lncRNA and protein, lncRNA sequence mutual information, the basic statistical information of lncRNA sequence bases and lncRNA expression profile features, and the evolutionary information and the composition–transition–distribution (CTD) feature information of protein sequences. Then, the above features are fused, and the fused feature information are input into the SVM to train and construct the lncRNA–protein prediction model.In this paper, we propose a multi-information fusion-based lncRNA–protein association prediction model consisting of three main phases, namely, (1) dataset preparation, (2) feature extraction and optimization, and (3) model training and prediction. In the dataset preparation, candidate lncRNA and protein sequences and their interaction data are usually collected from validated databases and related literature. Good training and test sets are usually required to build a high-quality prediction model. The training set is used for model training, and the test set is used to verify the transferability and the reliability of the training model. In the feature extraction and optimization, lncRNA and protein topological network features are proposed, and the protein sequence, Position Specific Scoring Matrix (PSSM), lncRNA sequence, and lncRNA expression spectrum features are extracted. Feature vectors are usually optimized by removing some irrelevant features to improve the performance of the feature information. In the model training and prediction, the SVM is used to train the input training set, and the grid search provides SVM training parameters for the construction of the training model. The prediction is performed on the given set of prediction vectors. The overall framework of the entire lncRNA–protein association prediction model is shown in With the development of high-throughput sequencing technologies, many public databases are available for scientists to study lncRNA–protein interactions. The NPInter database includes experimentally validated information on interactions between non-coding RNAs and other biomolecules . The NONCODE  Candidate data collection, human lncRNA, and its association term data are extracted from the NPInter V2.0 database  is constructed in this paper through the above data processing steps.In this paper, five types of feature information, namely, lncRNA–protein network topology features, protein evolution information  denotes the submatrix features, the calculation of which is shown in Equation (3)Here, λ = 1, …Fφ represents the row sum of amino acid residues in the sub-PSSM matrix. In this study, k = 1; thus, we obtain a total of 400 dimensional features.Here, φ denotes the 20 amino acid residues {A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y}. In this paper, we have used the CTD .x, y, and z denote three bases that are consecutively adjacent to each other, and the equations for the calculation of MI and conditional mutual information MI are as follows.Here H(x|z) and H are calculated as follows:Where p(x) denotes the frequency of occurrence of base x in the lncRNA sequence, p denotes the frequency of occurrence of 2 grams of bases x and y in the lncRNA sequence, and p denotes the frequency of occurrence of 3 grams of bases x, y, and z in the lncRNA sequence. The values of p(x), p, and p can be calculated by Equations (11)–(13) as follows.Where Nx denotes the number of bases x that appear in the pre-miRNA sequence and L is the length of the given lncRNA sequence. The ε in Equations (11–13), denoting a very small positive real number, is used to avoid using 0 as the denominator.Here, For the lncRNA base composition feature information, given any lncRNA sequence, we have calculated the percentage of 4 nucleotide  and 16 dinucleotide  types in each lncRNA sequence separately and obtained 20-dimensional feature vectors. The lncRNA sequence mutual information and the lncRNA base composition feature information have 19 and 16 dimensions, respectively. Thus, the total number of lncRNA sequence feature dimensions is 35; i.e., the dimensionality of the feature vector is 35 dimensions.In this paper, we have obtained the lncRNA expression profile information from the NONCODE4.0 database, which contains 170,601 lncRNA expression profile data. The expression profiles describe the expression of lncRNAs in 24 types of human tissues or cells. Thus, the lncRNA expression profile features contain 24-dimensional feature vectors.By the above analysis, we can extract a total of 1,978  dimensional features obtained.The feature space of lncRNA–protein interactions consists of five features, namely, lncRNA–protein network topology, lncRNA sequence, lncRNA expression profile, protein CTD information, and protein sequence evolution information features. Compared with individual features, the fusion of multiple features can capture increased sequence information, which leads to improved prediction performance. However, the fusion of multiple features produces a high-dimensional redundant feature and may lead to problems, such as excessive training time and bias in performance. Therefore, in this paper, we have used the SVM Recursive Feature Elimination (SVM-RFE) and Correlation Bias Reduction (CBR)  workspace to build predictive models. The specific form of the kernel function has a large impact on the performance of the SVM. The Gaussian radial basis kernel function (RBF) has good results for non-linear classification and is widely used for bioinformatics classification; therefore, we choose RBF as the kernel function for SVM. A grid search based on five-fold cross-validation was applied to optimize the SVM parameters γ and the penalty parameter C. The grid search yielded the optimal C = 256 and γ = 0.002 set as their values.In this paper, we choose SVM as the classifier to build the prediction model. Specifically, the open source Library of Support Vector Machines (LIBSVM) is used for model training and construction. The LIBSVM toolbox can be downloaded for free at Several measures were used to evaluate the performance of the lncRNA–protein interaction prediction method comprehensively ] were obtained. To evaluate the effect and the importance of each feature subset on the prediction results, this paper uses the SVM classifier to train the prediction model, and the grid search algorithm was employed to adjust the parameters of the SVM so that each feature subset achieves the best accuracy in the same threshold range. Five-fold cross-validation tests were conducted on these six feature subsets. Experimental results are shown in The experimental results of the six feature subsets constructed in this paper by five-fold cross-validation tests are shown in The ACC, SE, MCC, F score, AUC, and AUPR values for lRNA + LDNet features were 90.81, 78.69, 0.615, 65.52, 90.99, and 73.75%, respectively, and were the maximum values in these six feature subsets . The valWe selected the following six excellent methods for experimental comparison on the benchmark dataset to compare the performance of our proposed method with existing excellent methods. These six methods included IRWNRLPI  method and 2.21% higher than the third ranked LPI–ETSLP method. These results showed that our method had very good prediction performance. We plotted the curves of AUPR and ROC for the five-fold cross-validation tests to demonstrate the AUPR and the AUC values, respectively , 3.Next, we further analyzed the ACC and the F1 score values of these prediction models. The ACC of our method was 90.96% smaller than those of RWR (95.36%) and LPBNI (95.81) but better than that of IRWNRLPI (90.09%) because of very few experimentally validated lncRNA–protein interactions, which were far less than the unknown lncRNA–protein association relationships in the benchmark dataset. Therefore, the use of F1 score values to evaluate the performance of different methods than the ACC evaluation was reasonable. The F1 score value of our method was 65.91%, which was the highest among all methods and higher than those of the RWR (36.03%) and the LPBNI (38.68%). Therefore, the combined results of all experiments further demonstrated the good performance of our method in predicting lncRNA–protein associations. Notably, the four evaluation metrics  of our method, which constructed prediction models on the basis of lncRNA and protein features, were more remarkable than RPISeq–RF and RPISeq–SVM.lncRNAs are involved in the regulation of gene expression at the transcriptional level, epigenetics, and other life activity processes by interacting with RNA-binding proteins. Therefore, related research on the prediction of lncRNA–protein interaction relationship is beneficial in the excavation and the discovery of the mechanism of lncRNA function and action occurrence.In this paper, a computational model for lncRNA–protein interaction relationship prediction based on the multisource information fusion is proposed. A method for representing the topological feature information of the network of lncRNA–protein interactions is proposed. Subsequently, protein evolutionary information, protein CTD sequence information features, lncRNA sequence mutual information features, and lncRNA expression profile information are extracted, and the recursive feature elimination algorithm is used to optimize feature vectors. The obtained optimized feature vectors are fed into SVM to predict lncRNA–protein interactions. Our proposed method is experimentally compared with six excellent lncRNA–protein prediction algorithms by using five-fold cross-validation tests on benchmark datasets, and experimental results show that our proposed method achieves the best performance values in AUPR and F1 score, illustrating the effectiveness and the accuracy of the proposed method in lncRNA–protein association prediction methods.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.YC, XF, and LZ conceived the concept of the work. YC, XF, and LP performed the experiments. YC, ZL, and LZ wrote the paper. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. Methods: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient’s death, thus making the results easy to interpret. Results. Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples . We constructed a prognostic scale to determine the probability of death associated with each score. Conclusions: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization. Despite substantial efforts to prevent the spread of coronavirus 19 disease (COVID-19), at the end of June 2020 over 14 million people worldwide had tested positive for SARS-CoV-2, and more than 603,000 had died [During March and April 2020, Spain had one of the highest rates of COVID-19 and had experienced one of the most severe outbreaks of the disease worldwide. The rate of infections in the Autonomous Region of Madrid has exceeded that of every other region in Spain, with more than 28% of all confirmed cases and a cumulative total of 42,747 hospitalized patients and 8441 deaths . The excThe wide variation in the symptoms of COVID-19 makes it difficult to predict the clinical course, thus complicating triage. Clinical experience has demonstrated significant heterogeneity in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection: while some patients are asymptomatic or progress with mild symptoms, others develop severe acute respiratory distress syndrome with multiorgan failure and death . In addiEarly detection of cases that are at high risk of progression to severe COVID-19 with an imminent risk of death and identification of contributing factors are now urgent and challenging. Accurate prediction of the mortality of COVID-19 would therefore enable targeted strategies that facilitate appropriate and early supportive care and enable patients to be categorized according to severity and prognosis. This is especially important in outbreaks, where prioritization of patients can reduce unnecessary or inappropriate use of health care resources .We designed a reliable prognostic model using routine, widely available demographic, clinical, and laboratory data to predict mortality in patients hospitalized with COVID-19.We performed a retrospective observational study at Infanta Leonor University Hospital (ILUH), a secondary level hospital with 361 beds (including eight intensive care beds) in Vallecas, an area in the southeast of Madrid that is home to more than 305,000 inhabitants. Vallecas was one of the areas most affected by COVID-19 in the city of Madrid, with 4713 total confirmed cases as of July 7th, 2020 [The study population comprised all patients admitted to hospital with a confirmed diagnosis of COVID-19 based on a positive result in the SARS-CoV-2 reverse transcriptase-polymerase chain reaction assay between 2nd March and 31st May 2020. Samples were obtained via nasopharyngeal swabs.In order to construct our predictive score, we collected demographic data, previous diseases, clinical presentation, and laboratory and radiological data at admission from electronic medical records and managed our findings using REDCap  hosted at the Ideas for Health Association. REDCap is a secure, web-based software platform designed to support data capture for research studies .The study was approved by the Institutional Investigation and Ethics Review Board of Infanta Leonor University Hospital (CEI-ILUH) (Code ILUH R 027-20). Given the retrospective nature of the study, the need for informed consent from patients was waived. Data confidentiality was maintained at all times according to Spanish legislation.The primary cohort was divided into a training, a validation, and a test sample, with 40%, 30%, and 30% of the observations reserved, respectively, for each.In order to fit a logistic regression model, data were previously processed to avoid the effect of outliers, include missing values, and to consider possible non-linear associations between the target and the input variables.During this preprocessing step we discretized the continuous variables into a specific number of categories and then generated a binary or dummy variable for each one. Discretization was performed using classification trees, one for each input. The aim was to identify cut-off points in the observed input values that discriminate as much as possible between the different target categories in order to enhance the predictive capacity of the transformed variables.By definition, missing values and outliers are not present in binary variables. Moreover, the estimation of a value for each of these variables makes it possible to consider non-linear effects. The disadvantage of this approach is that a huge number of dummy variables can be generated. We solved this problem by applying a data transformation method that is widely used in the credit scoring field .The method generates a new continuous variable (WoEx) for each input variable X. The values are obtained by averaging the target in the different categories obtained through the discretization process. This transformation procedure is usually known as the WoE (Weight of Evidence) transformation . WoE meaWith Y as a binary dependent variable, X as an explanatory variable, and Xc as the associated intermediate variable obtained after discretizing X into the XI attributes x1, xI, the value of WX at each category xi is defined as:ix derived from the original input variable.This value is derived by taking the logarithm transformation over the odds values associated with each of the categories p-value when deciding whether or not to keep the variable in the regression model.WoE methodology overcomes previous selection problems and prevents the creation of an elevated number of dummy variables. Indeed, this approach generates only one transformed variable per input and, therefore, only one X, is included in the regression model, the odds ratios cannot be used to explain the effect of the original variable X over the dependent variable, because WX is not measured in the same units as X. Therefore, a linear transformation of the value resulting from the multiplication of X is carried out. The aim of this transformation is to scale this value to a particular range of scores [If a WoE variable, Wf scores .The score points are proportional to the logarithm of the predicted death/non-death odds of the patient. For each attribute α is the intercept of the logistic regression modeln is the number of variables in the logistic regression modelFactors and offset values are scaling parameters that enable the analyst to control the range of the scores and the rate of change in odds for a given increase in the score. The sum of the scores associated with each of the input variables provides a total score for the patient.For purposes of calibration, patients in the test database were split into deciles ordered by their probability of death. For each decile, the mean of the predicted probability of death was calculated and compared with the mean observed probability of death.Moreover, to evaluate predictive capacity, the model was compared with other competitive machine learning methods. Based on the method described by DeLong et al. , these rhttp://www.R-project.org/ version 3.1.1  and SAS Enterprise Miner . All tests were two-tailed, and p-values < 0.05 were considered significant.The results are presented as the median (interquartile range) for continuous variables and as frequencies and percentages for categorical variables. Categorical data and proportions were analyzed using the chi-squared test or Fisher’s exact test, as required. The Mann-Whitney test was used to compare continuous variables. In the case of multiple comparisons, a Bonferroni correction was applied to compare the results between the groups (α = 0.05). The statistical analysis was performed using the statistical package R, foundation for Statistical Computing, Vienna, Austria. URL The study population comprised 1968 patients. The baseline patient characteristics were as follows: median age was 67 (27) years, and 1104 patients (56.4%) were male. A total of 325 patients died during hospitalization (17.7%). Hypertension was the most frequent comorbidity (991 patients (51.8%)), followed by dyslipidemia (36.8%), smoking (28%), and chronic heart disease (21.7%). The most frequent symptoms at admission were fever (75.1%), cough (66.7%), and dyspnea (56.8%).Median oxygen saturation at admission was 94%  .p < 0.001) and mainly male , with more comorbidities, such as hypertension, dyslipidemia, smoking/ex-smoking, and chronic heart disease. Dyspnea, cough, myalgia, headache, and low level of consciousness were more frequent in patients who died. These patients also had lower oxygen saturation (89 (11) vs. 95 (5); p < 0.001).Patients who died during hospitalization were older  to name the score To evaluate predictive capacity, the model was compared with other competitive machine learning methods . The TraOur model showed good predictive capacity in the training, validation, and test samples, with an AUC of 0.865, 0.808, and 0.883, respectively. However, the presence of high AUC values did not mean that the individual probability of each class was well calibrated. Several studies have reported risk factors associated with death in patients with COVID-19, although very few propose reliable prediction models, which should be constructed using an adequate sample size and a standardized methodology to avoid significant bias ,12,13,14www.pandemyc-score.com). This simple model enables us to prioritize patients, especially during a pandemic, when limited healthcare resources have to be allocated. It also makes it possible to select patients for early discharge or to establish more intensive monitoring and treatment [PANDEMYC showed high diagnostic accuracy (AUC = 0.88), as reported elsewhere , revealereatment . For exaAll the factors included were clinically significant, with age, oxygen saturation, and serum creatinine at admission being the most relevant variables according to their weight and significance in the regression model. This observation is consistent with previous reports, thus showing the importance of aging, respiratory distress, and kidney injury as prognostic factors ,14.Analytical variables such as C-reactive protein, lymphopenia, and anemia indicate the association between the degree of inflammation and the risk for severe COVID-19 . ThromboDespite the nature of the target classification variable, two main approaches are commonly distinguished for addressing classification problems, namely, interpretable methods and predictive methods . The forIt is noteworthy that our model assigns a score to the missing values of each variable, thus creating a realistic model for real-life practice. Our model is not influenced by outliers, because they are not affected in the discretization process and therefore play no role in score assignment.The main limitation of this study is that it was performed in a single center with no external validation. Nevertheless, the predictive capacity of the model was assured, because the AUC between the training and test samples, without the participation of the last in the adjustment process of the model, were very similar (0.865 and 0.883 respectively). Another limitation is that the level of saturation could have affected patient outcome. However, as saturation involves many components , we were unable to investigate it.In conclusion, we designed a reliable, easily applicable prognostic score that can be applied in limited-resource settings to optimize management of patients hospitalize with COVID-19.


Background: There is an increasing trend for researchers to combine mirror therapy with another rehabilitation therapy when treating the upper extremity of patients with stroke.Objective: To evaluate the synergistic effect of combined mirror therapy (MT) on the upper extremity in patients with stroke and to judge efficacies of four combined mirror therapy subgroups .Methods: CNKI, Wan Fang, VIP, Web of Science, ScienceDirect, PubMed, OVID LWW, and Cochrane were used. We searched these databases for randomized controlled trials published from January 2013 to August 2019, which presented results of combining mirror therapy with other rehabilitation therapies. Quality assessments were performed using the Cochrane Handbook criteria in order to accurately review interventions. The primary outcomes were measured by the Fugl–Meyer Assessment—upper extremity (FMA-UE).Results: Ten trials, with a total of 444 patients whose upper limb functions were damaged after stroke, were included in the meta-analysis. Compared with the control group, a remarkable effect of combined mirror therapy  on functional recovery of the upper limb was detected. However, a high value of heterogeneity  was found. The subgroup analysis  showed moderate improvement in functional recovery of the upper extremity in patients with stroke when mirror therapy was combined with conventional therapy. Furthermore, no difference in efficacy on upper extremity in patients with stroke was observed between the EMGBF group and the ES group.Conclusion: Despite the heterogeneity, the results indicate that combining mirror therapy with another rehabilitation therapy on the upper extremity in patients with stroke is better than single rehabilitation therapy. However, more randomized controlled clinical trials and larger sample sizes are required for an in-depth meta-analysis. Stroke is one of the primary causes of disability not only in middle-aged but also elderly people worldwide . Stroke Several current interventions are used to improve upper limb function, including mirror therapy (MT) , 5, consEMG-BF has been established as a significant treatment for all kinds of peripheral nerve injuries (PNI) , 21. It However, data is still not completely accurate, and further studies are still necessary. The aim of this meta-analysis is to investigate the synergistic effect of mirror therapy combined with other rehabilitation therapies on the upper extremity in patients with stroke, to screen for more effective rehabilitation methods for patients.According to the guidelines for randomized controlled trials provided by the Cochrane systematic evaluation of interventions, we systematically searched for studies published from January 2013 to August 2019 in the following databases: CNKI, VIP, Wan Fang, Web of Science, ScienceDirect, PubMed, OVID LWW, and Cochrane library.To ensure the reliability of the included studies, two independent authors screened each study to assess quality using the criteria of the Cochrane Handbook (update 15.1.0) and the PEDro scale for reviewing interventions. The risk assessment criteria in the Cochrane Handbook are as follows : random Randomized controlled clinical trials (RCTs) that combined mirror therapy with another rehabilitation therapy on the upper extremity in patients with stroke were examined.The enrolled patients were not restricted by age, gender, or area of limb hemiplegia , 2. PatiCombined mirror therapy was compared with single rehabilitation therapy, and all the patients received conventional therapy. There are four combined therapies such as EMGBF + MT, AT + MT, ES + MT, and MG + MT. Since the experimental scheme of each combined method is different, the strategies of classifying it into a class of the same methods are (i) the same principle of experiment; (ii) target group consistency; (iii) using an identical single-blind method; (iv) had initiative in moving their impaired upper extremity or moved assisted by therapist in order to be in line with unaffected extremity.FMA-UE, as a professional assessment, was used to measure the outcome in the upper limb's functional recovery in terms of reflex ability, synergic movement, wrist stability, and hand grip strength.All the searches were performed in electronic databases published in English or Chinese, specifically CNKI , PubMed ; Wan Fang , Web of Science (time span: 2013–2019), ScienceDirect , SpringerLink (show documents published: between 2013 and 2019), OVID LWW (publication year: 2013.2018), Cochrane library . There were three key words used to search the literature, namely  AND (“stroke” or “cerebrovascular stroke” or “cerebrovascular accident”) AND (“mirror therapy”).The results of the literature search were brought into the CNKI E-study, and duplicate records were removed. One author reviewed and assessed the title, abstract, and purpose of the document to remove irrelevant studies. After this preliminary screening, two independent authors filtered the remaining results according to (i) clear outcome; (ii) combined therapy; (iii) completed data; (iv) outcome assessment of FMA-UE; (v) randomized controlled trial; and (vi) single blind or double blind. After discussion and negotiation, 10 studies were included in the quantitative synthesis .Blinded to the journal, we made a detailed form , 2 basedI2 tests  and chi-square . Subgroup analysis (1) combined therapy subgroup: EMGBF group, ES group, AT group, and MG group, and (2) the subgroup's control method: (i) adding mirror therapy to rehabilitation therapy in the experimental group. (ii) adding rehabilitation therapy to mirror therapy in the experimental group) was performed using Review Manager 5.3. In order to investigate the sources of heterogeneity, we rigorously applied moderator analyses using Stata12.0   with a confidence interval of 95% (95% CI). Given the continuity of the data, the best methods random effects model and the statistical method of inverse variance were, respectively, used to compare combined therapy with single rehabilitation therapy. The weight mean difference (WMD) and 95% confidence intervals (95% CI) were used to assess the mean effect size of therapy. Heterogeneity among studies was assessed using on bias) . DiffereNine hundred sixty-one records were identified through database searching, and 761 records were retained after removal of duplicates. In the end, 10 studies  were included in the quantitative synthesis . The detailed process for selecting studies is demonstrated in 2 = 20.09, p = 0.02; I2 = 55%) was significant. The value for overall effect is 7.20 (p < 0.00001) in random mode due to the existence of substantial heterogeneity, and the total weight mean difference were 8.07 . Meanwhile, a subgroup analysis (I2 = 70%) was the important factors. p = 0.82). The difference between the subgroup analysis in 2 = 15.42, I2 = 74%) and a subgroup difference  between the AT group and MG group. Meta-analysis regression (p > 0.352) and during treatment (p > 0.782) showed significant correlation with high heterogeneity. Finally, an Egger test  showed no sign of publication bias among the 10 studies  between the chronic group and subacute group.analysis  was applgression  was used studies . The sub studies  was appl2 = 20.09, p < 0.00001; I2 = 55%) was high, and one study [Lee (10)] did not draw a precise conclusion about whether combined mirror therapy (mirror therapy with MG therapy) was better than pure mirror therapy in promoting upper limb motor function. The difference between Lee's study and the other studies is that the stimulation intensity—other studies , it is clear that the high heterogeneity came from the AT group as expected. A further subgroup analysis separated interventional methods: adding mirror therapy to rehabilitation therapy in the experimental group (p > 0.186) nor duration of treatment (p > 0.787) could be regarded as the cause of high heterogeneity. The result was discussed in correspondence with Wen Zeng (15) whose meta-analysis mainly explored mirror therapy on motor function of the upper extremity in patients with stroke. From this discussion, the conclusion that two factors (sample size and duration of treatment) were regarded as the cause of high heterogeneity can be reached. This is the first meta-analysis probing the synergistic effect of combined mirror therapy on the upper extremity in patients with stroke. Some preliminary conclusions can be drawn from this meta-analysis. First and foremost, this meta-analysis of 10 RCTs including 444 patients showed that combined mirror therapy (mirror therapy mixed with other rehabilitation therapies) was superior to single rehabilitation therapy to promote upper limb motor function of stroke patients  in terms of muscle reflex ability, coordinated movement, and accurate operation in the Fugl–Meyer Assessment (FMA). However, heterogeneity , age, and sex, resulting in incomplete data in There are several limitations of this study that should be taken into account. First, the number of studies included in meta-analysis was limited, reducing the representativeness of the article. This was unavoidable due to the particularity of topic selection, the limitation of resources, and the rigor of the article. Second, the high heterogeneity of the studies partly limits the impact of this paper. The objective of this meta-analysis is to study combined therapy, focusing on mirror therapy mixed with other therapies such as AT, ES, EMBGF, and MG, so the high heterogeneity is unavoidable. Third, studies published in English and Chinese were included in the analysis, but studies in other languages were not included. Fourth, all articles were randomized controlled trials, but there is a belief that non-randomized controlled trials should also be taken into account when RCTs are unfeasible or unethical.From a patient's perspective, we must take expense and time spent on combined mirror therapy into consideration. If there is a directly proportional relationship between expense and efficacy on recovery, we might as well take combined therapy as first choice for patients after stroke. In summary, combining mirror therapy with another rehabilitation therapy  is better than single rehabilitation therapy on upper extremity in patients with stroke. In the future, there should be considerable work applied by researchers to more deeply probe the optimal specific combination therapy.All datasets generated for this study are included in the article/supplementary material.JZ and QZ: conception and design, drafting the article. HH, SL, and RZ: acquisition of data. ZL and YZ: analysis and interpretation of data, editing the article. JL, XL, and SC: study supervision and revising the article. ZL and YZ contributed equally to this work and should be considered co-first authors. All authors proofed and approved the submitted version of the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The content of total carotenoids, phenolic compounds and glucosinolates were higher in autumn compared with spring, showing increases of 2.8-fold, 2-fold and 1.2-fold, respectively. Moreover, a double application of MeJA increased the contents of total carotenoids, phenolic compounds and glucosinolates by 22%, 32% and 39%, respectively, relative to the untreated samples. Considering our results, the controlled and timely application of 250 µM MeJA to the aerial parts of the plants four days before harvest, on two consecutive days, seems to be a valid agronomic strategy to improve the health-promoting capacity of Parthenon broccoli, without compromising its overall quality.Broccoli is a source of bioactive compounds that provide an important nutritional value. The content of these compounds can vary depending on agronomic and environmental conditions, as well as on elicitation. In this study, three crop trials were carried out to evaluate the effects of the cultivation season, the application of different dosages of methyl-jasmonate (MeJA) on the overall quality and on the total content of bioactive compounds of ‘Parthenon’ broccoli cultivated under the field conditions of southeastern Spain. Color parameters, chlorophyll content, total phenolic compounds, total flavonoids and antioxidant activity were measured to evaluate the overall quality. Moreover, individual carotenoids, phenolic compounds and glucosinolates were evaluated by high performance liquid chromatography with diode array detection (HPLC-DAD) and high performance liquid chromatography equipped with diode array detector coupled to mass spectrometer using electro spray ionization (HPLC-DAD-ESI/MS Brassica family is a group of vegetables widely consumed around the world, including cabbages, cauliflower, Brussels sprouts, radishes and broccoli  among others − fragmentation ions, UV-visible spectra and order of elution, as described previously for this method [v/v), in an ultrasonic bath, for 10 min. After that, the samples were heated at 70 °C for 30 min, shaking every 5 min, then centrifuged at 12,000× g for 10 min. The supernatant was dried under vacuum in a Laborota-4002 rotatory evaporator , re-dissolved in 1 mL of Milli-Q water and filtered through a 0.22-µm polyvinylidene fluoride membrane (PVDF) filter to prepare the analytical sample. The contents of total and individual GLS and phenolic compounds are expressed as mg/kg f.w.To analyze the GLS and phenolic compounds, the multipurpose LC method described by Francisco et al.  was folls method ,39. On ts method ,38. Boths method . Chromatn = 10), chlorophyll content (n = 5) and antioxidant activity (n = 6), and the results are expressed as the means and standard deviation (SD). An analysis of variance (ANOVA) was performed, considering the different treatments, when the data were in accordance with the assumptions of normality and homogeneity of variance. To determine differences among the mean values, a post-hoc Tukey´s test was conducted. If the data were not in accordance with normality assumptions, a Kruskal–Wallis test was selected and a Nemenyi´s test was used for post-hoc analyses. Differences were considered significant for a p-value < 0.05. The statistical analyses were carried out using R studio, version 3.4.3 .All analyses were performed in triplicate, with the exception of those of the color parameters, TPCs and TFs , 5 mL of excipient (E) and 5 mL of 250 µM MeJA (M)—with the only differences between the two studies being the weather conditions, since they were carried out in different seasons.Chlorophyll content determines the intensity of the green color of broccoli, which is important to overall quality and consumer acceptability, and hence likelihood of purchase. Camellia sinensis leaves (a negative correlation with sunlight exposure and precipitation and a positive one with daily lowest temperatures and relative humidity). For our study, we can state that the content of chlorophyll was higher in broccoli plants cultivated in autumn than in those cultivated in spring, due to the lower precipitation and shorter time of exposure to sunlight in autumn . On the other hand, it had a negative relationship with parameter b*  in the autumn assay, a trend that was also observed in the spring, but without significance.It is important to highlight the relationship between the color parameters and chlorophyll content. In this sense, the yellowing process that reduces the quality of the broccoli was correlated with higher values of parameter b* and lower values of h*, which also correlated with low values of chlorophyll. These results are in agreement with the chlorophyll content, which was higher in the C samples. Hence, the MeJA treatment promoted chlorophyll degradation and, consequently, the yellowing process, with differences between treated and untreated samples; however, it was only observed a decrease in L* parameter without significant change in b* and h* values. Although no changes were observed in greenness in this study, different authors ,43 have TPCs and TF are parameters related to the radical-scavenging capacity of the plant material  . The resThere was a significant seasonal effect on these three parameters, the values of TPC, TF and ORAC being significantly higher in the broccoli cultivated in spring than in the autumn-grown plants . These results are in agreement with those obtained by Vallejo et al. , who repBrotrytis cymosa broccoli sprouts does not differ after the application of MeJA. The treatment with MeJA only had a significant effect on the content of TFs in the autumn broccoli. In the samples from plants cultivated in spring, no differences were observed among the treatments for TPCs and TFs, and no differences were found for ORAC between groups C and M; meanwhile, the application of the excipient led to a significant decrease in ORAC. These results agree with those of Kang and John , who didBroccoli florets cultivated in autumn showed differences in TFs and ORAC, with the samples of plants treated with the excipient having higher values than those of the C and M groups. As pointed out earlier for the spring assay, in the autumn assay the TPC also did not show differences between untreated and MeJA-treated samples . These rThe color parameters and chlorophyll content in the samples of broccoli plants treated with different concentrations of MeJA are shown in The excipient (ethanol and polyglycol solution) and MeJA had a catabolic effect on the total chlorophyll content in this assay, as has been described by other authors ,42. It sAccording to a two-way ANOVA, the treatments influenced the parameters under analysis, with the exception of TFs . After tFurthermore, the ORAC values were affected by the MeJA treatment, with the samples of the treated broccoli plants (M② and M①) showing the highest values. The inverse relationship observed between the TPCs and the ORAC indicates that other antioxidant compounds such as vitamin C  could contribute to the antioxidant properties of broccoli.Carotenoids were identified via HPLC-DAD according to their ultraviolet (UV) spectra and retention times, using chromatographic comparisons with authentic standards, as well as the carotenoids’ spectral characteristics, based on data previously reported ,52,53. TThe contents of carotenoids are shown in Brassica carotenoids is not clear [In general, the content of carotenoids was higher in broccoli cultivated in autumn than in spring-grown plants a,b, withot clear , in florot clear ,55. Moreot clear . Due to ❄ group), whereas it did not show any effect on plants cultivated in spring (M✹ group and M① group), and in some cases even led to an increase in carotenoid content (M②) (As can be seen in ent (M②) b,c. It ient (M②) c.p-value < 0.01); this trend was also observed in the spring assay, but without significance  was glucoiberin (GIB), followed by glucoraphanin (GRA). By contrast, the order was reversed in the broccoli cultivated in autumn, with GRA being the main compound, followed by GIB, for all treatments. Temperature is an important factor that may affect the qualitative profile of GLSs in broccoli plants. Molman et al.  reportedOf the two classes of GLSs present in broccoli, aliphatic GLSs were predominant in our Parthenon samples, representing on average 76%, 86% and 83%, of the total GLSs in the first, second and third assays, respectively. Chiu et al.  have repIn relation to the effect of MeJA on the content of GLSs, Chiu et al.  have repConsidering that broccoli is a rich source of bioactive compounds, our results are of great interest to growers and food-trading industries, particularly in Murcia (Spain), the major broccoli-growing region in Europe. New strategies need to be implemented in the field to seek out the best agronomic practices and conditions by which to improve the health-promoting capacity of broccoli, without compromising its overall quality, although the effects of MeJA could be modified by seasonal conditions and other agronomic parameters. From this study we can conclude that the controlled and timely (four days before harvest) application of 250 µM MeJA as an elicitor to the aerial parts of the plants, on two consecutive days, yielded florets of Parthenon broccoli with higher contents of bioactive compounds, without changing its overall quality.


Background: Despite significant progress in the research of fibrosis in various organs, fibrosis remains a poorly understood complication of Crohn's disease (CD). We analyzed pathologic features of fibrosis and inflammation in CD and compared them with the normal bowel, aiming to clarify whether fibrosis in CD pathogenetically resembles fibrosis in other organs.Methods: Resection specimens from 30 patients with CD were included. Normal bowel from resection specimens of colorectal carcinoma was used for comparison. Trichrome Masson staining, immunohistochemistry for α-smooth muscle actin, fibroblast activation protein, CD34 and erg, in situ hybridization for TGF-β1 and analysis of selected fibrosis-related microRNAs were performed.Results: In normal bowel, CD34-positive fibroblasts/pericytes were detected in the submucosa and subserosa, particularly around blood vessels. In CD, fibrosis prevailed in the submucosa and subserosa, together with proliferation of myofibroblasts and disappearance of CD34-positive fibroblasts/pericytes. TGF-β1 was present in the lamina propria in normal bowel and CD, and in deeper parts of the bowel wall in CD. MicroRNAs miR-29c, miR-155 miR-150, and miR-155, which have been demonstrated to contribute to fibrosis in various organs, showed significant deregulation in CD.Conclusions: Distribution of fibroblasts/pericytes in the submucosa and subserosa of normal bowel, their disappearance in fibrosis in CD, together with the appearance of myofibroblasts, suggest that fibroblasts/pericytes are the most likely source of myofibroblasts in CD. Furthemore, fibrosis-related microRNAs showed deregulation in fibrotic areas. Pathogenesis of fibrosis in CD is thus comparable to fibrosis in other organs, in which myofibroblasts are the key effector cells, and pericytes have emerged as the main origin of myofibroblasts. Fibrosis in CD should be regarded as a result of (over)response of the bowel wall to the presence of inflammation in deep structures of the bowel wall, presenting another example of a common pathogenetic pathway of fibrosis development. Extensive research in recent decades has provided new insights into the pathogenesis of inflammatory bowel diseases, including Crohn's disease (CD) and has enabled the development of new treatment modalities. Despite significant progress, fibrosis remains a poorly understood complication of CD, in terms of pathogenesis, diagnosis and treatment –4. The iFibrosis is the final common pathway in organ failure in diseases of various organs, including the liver, kidney, lung and heart –13. It iIn contrast to recent progress in our understanding of fibrosis development in the liver, kidney and heart, there has been little progress in our understanding of fibrosis in the bowel, including CD. In particular, pathologic studies of fibrosis in CD are relatively rare . We beliThe study was approved by the National Medical Ethics Committee, Ministry of Health, Republic of Slovenia (No 0120-139/2019/4). It included 30 patients with CD who had undergone bowel resection. The control group consisted of normal bowel (colon and ileum) from 10 patients who had undergone resection for colorectal carcinoma. Diagnosis of CD was made on the basis of clinical, radiological, endoscopic and histologic findings , 24. ForResection specimens that had been fixed in 10% buffered formaldehyde were handled according to standard procedures. Samples were taken from the most stenotic regions, from inflamed mucosa and ulcers as well as from macroscopically normal mucosa. All samples were embedded in paraffin and cut at 4 μm, stained with haematoxylin and eosin and analyzed according to standard criteria .All slides were reviewed for the purpose of this study. Two slides with corresponding paraffin blocks were chosen from each patient for further analysis: one with the most severe fibrosis and one with the most severe inflammation (without fibrosis) on histologic examination. Samples with fistula and samples from anastomosis were not included.All slides were stained with Masson's trichrome with aniline blue  for detection of fibrosis. With this stain, cytoplasm stains red, nuclei black and collagen fibrous tissue blue.For immunohistochemistry, we used antibodies against α-smooth muscle actin (α-SMA) , fibroblast activation protein  and for double immunostaining, antibodies against CD34  and erg . Sections were treated with biotinylated secondary antibody, followed by incubation with peroxidase conjugated streptavidin . Visualization of the immunoreaction was carried out with 3.3' diaminobenzidine. Finally, sections were counterstained with hematoxylin.Normal structures of the bowel wall served as a positive control. α-SMA positivity was found in pericryptal myofibroblasts, muscularis mucosae, muscularis propria and smooth muscles in blood vessel walls. There was no staining of the normal bowel wall for FAP. Double immunostaining exhibited a red cytoplasmic reaction for CD34 and brown nuclear reaction for erg. Both brown and red stainings were found in endothelial cells, whereas red cytoplasmic staining was found in fibroblasts and pericytes. Negative controls omitting the primary antibodies were included in every run of samples.in situ hybridization for TGF-β1, mRNA transcripts were detected using a commercially available in situ hybridization RNAscope 2.0 kit  following the manufacturer's instruction. Briefly, tissue sections were first incubated for 1 h at 60°C, followed by deparaffinization, incubation with Pretreatment Reagent 1 for 10 min at room temperature, Pretreatment Reagent 2 for 15 min at 100°C and Pretreatment Reagent 3 for 30 min at 40°C. Tissue sections were then incubated with probe for 2 h at 40°C. Diaminobenzidine was used as chromogen. Sections were counterstained with hematoxylin. Positive reaction appeared as dot-like signals in the cytoplasm and nuclei.For ., miR-29c, miR-150, and miR-155 was analyzed relatively to miR-16, miR-103a-3p, miR-191, and let-7a using miRCURY LNA miRNA assay and detection system (Qiagen) according to the manufacturer instruction.For microRNA analysis, RNA was isolated and quantified from formalin-fixed paraffin-embedded tissue sections as previously described . Twelve The most important demographic and clinical data at the time of surgery are presented in Histological examination showed features of CD in varying proportions: erosions and ulcers, architectural abnormalities, chronic and active inflammation, granulomas, transmural lymphoid aggregates, fibrosis, and neural hyperplasia. The muscularis mucosae was often distorted or rarely duplicated, particularly in healing ulcers, and muscularis propria was occasionally hypertrophic. The intensity of inflammation in fibrotic regions varied, but inflammation was present in all cases. No significant differences were observed between the ileum and colon samples regarding features related to fibrosis.In normal colon, the blue staining of collagenous fibrous tissue was present mainly in the submucosa . Some naIn CD, collagenous fibrous tissue was increased in the submucosa . There wThe blue staining of collagen fibrous tissue was mostly not present in the lamina propria, in either normal mucosa or CD . The onlIn normal colon, a positive reaction for α-SMA was found in pericryptal myofibroblasts, in smooth muscle cells of the muscularis mucosae and muscularis propria, and in blood vessel walls (not shown).In CD, α-SMA-positive myofibroblasts were identified as spindle-shaped cells with strong cytoplasmic positivity. In CD with inflammation, they were very few in the submucosa  and few In some cases of CD, the muscularis mucosae disappeared or was markedly distorted, admixed with inflammatory cells and fibrosis , making In normal colon, there was no staining for FAP (not shown). In CD, there was no staining for FAP in areas of inflammation without fibrosis . FAP staIn normal colon, double immunostaining for CD34 and erg was found in endothelial cells, while a red cytoplasmic reaction was observed in spindle-shaped cells, which were abundant in the submucosa  and in tIn CD, double immunostaining for CD34 and erg was found in endothelial cells in the submucosa  and in tIn normal bowel, positive reaction with dot-like signals was present in lymphocytes and lymphatic follicles in the lamina propria and submucosa , while nmiR-29c and miR-155 in samples from fibrostenotic areas , as well as statistically significant deregulation of miR-150 and miR-155 in samples from inflamed areas of CD compared to normal mucosa   .We analyzed the pathologic features of fibrosis in resection specimens of CD, with hypothesis that fibrosis in CD pathogenetically resembles fibrosis in other organs. Masson's trichrome staining demonstrated fibrosis in CD mostly in the submucosa and subserosa, but not in the lamina propria. Comparison between samples from fibrostenotic and inflamed areas in CD showed little differences regarding submucosal fibrosis. Important differences were however observed in the subserosa. In inflamed areas, fibrosis was mostly absent or present only focally, close to the muscularis propria. In samples from fibrostenotic areas, dense fibrosis was seen, extending deep into the subserosal fatty tissue, often reaching the serosal surface. Our first conclusion is that the ≫ hot spots ≪ for fibrosis in CD are the submucosa and subserosa.Trichrome staining is a simple, widely used method in pathology to illustrate fibrous tissue containing collagens . It is gThe interpretation of α-SMA as an immunohistochemical marker of activated myofibroblasts is demanding in the bowel wall because α-SMA is also expressed by normal structures, such as smooth muscles in the muscularis mucosae and muscularis propria. The situation is complicated further by another feature of CD, that is, smooth muscle hyperplasia and hypertrophy, known to contribute to stenosis . NeverthDespite a generally accepted central role of myofibroblasts in various physiologic and pathologic conditions, the origin of these important cells has not been entirely established and remains controversial . It has We therefore analyzed the distribution of pericytes/fibroblasts in normal bowel and in CD. There is no ideal marker for pericytes and fibroblasts but, on the basis of available literature, we chose CD34, which has proven useful in many previous studies . Since CIn CD, a consistent pattern was observed in fibrosis: fibroblasts/pericytes disappeared and myofibroblasts appeared in areas of fibrosis. Milia et al.  found a The next conclusion of this study is that the distribution of fibroblasts/pericytes in normal colon and their disappearance in fibrosis, in parallel with the appearance of myofibroblasts, suggests that pericytes/fibroblasts are the source of myofibroblasts in CD.in situ hybridization and observed positive reaction in mononuclear cells in CD. They were particularly abundant in lymphoid agreggates in areas of deep fibrosis in CD, whereas TGF-β1 was not expressed in deeper layers of the normal intestinal wall. The expression patterns of TGF-β1, the most potent profibrogenic cytokine, thus corresponded well to the distribution of inflammation and fibrosis in CD. Similar expression patterns of TGF-β1 were described by Lawrance et al. , an extensive scar formation may protect from perforation . FibrosiThe datasets generated for this study are available on request to the corresponding author.This manuscript is part of a study which was approved by the State Ethical Committee of the Republic of Slovenia (No. 0120-595/2017/5) on 6th Dec, 2017. As stated in the approval document, the study is retrospective, observational, performed on tissue samples that were obtained during routine diagnostic/therapeutic procedures, consisted of either excision or resection. Therefore, enough tissue was available for routine analysis and research. Moreover, tissue is still available for any additional routine, therapy-related or research analyses in the future. Our State Ethical Committee does not require informed consent from patients in such studies. However, the written informed consent was obtained before the routine procedure.NZ and CL contributed conception and design of the study. MJ, DD, and AT organized the database. MJ performed the statistical analysis. NZ wrote the first draft of the manuscript. CL and EB wrote sections of the manuscript. All authors contributed to manuscript revision, read, approved the submitted version, and made substantial contributions to the submitted work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Background: Recent studies suggest the importance of distinguishing the severity levels of disability in the older adult population. However, there is still no consensus regarding an optimal classification. Few studies have estimated the prevalence of severe disability, and the results have been confined to high-income countries. There is no evidence for low- and middle-income countries (LMICs). Therefore, the aim of this study was to provide estimates of the levels of severity associated with disability in older adult populations in LMICs and to examine their relationship with health and socioeconomic factors.Methods: We used data from the Study on global AGEing and adult health (SAGE), wave 1 (2007–2010). Nationally representative samples of adults over 50 years from China, Ghana, India, Mexico, Russian Federation, and South Africa were analyzed . We measured disability using the World Health Organization Disability Assessment Instrument version 2.0 (WHODAS 2.0). Disability levels according to severity were identified through the use of latent class analysis. Socioeconomic and health factors associated with severe disability were estimated using ordinal logistic regression models.Results: We identified four groups of older adult: (1) without disability, 43.4%; (2) mild disability, 33.3%; (3) moderate disability, 15.3%; and (4) severe disability, 8.0%. These results were heterogeneous for the six countries analyzed. Education and socioeconomic status were significantly associated with severe disability along with the following chronic conditions: angina, arthritis, asthma, cataracts, chronic obstructive pulmonary disease, depression, diabetes, and stroke. Severe disability was also associated with the frailty status, sarcopenia, and mild cognitive impairment.Conclusions: In this study, we estimated severity levels of disability for the older adult population in LMICs. Our results show that severe disability affects 8% of older adult, and that there are important socioeconomic and health factors associated with this condition. Measuring the severity of disability is a critical element to study the causes and consequences of aging. Moreover, the identification of older adult with severe disability is vital to design prevention programs, modify interventions, or develop enabling environments. Functional impairment and disability are important health-related conditions in the older adult population , 2. BurdDisability has many conceptual and empirical definitions , 12. OneSeveral studies have estimated the prevalence of disability using the recommended 90th percentile as a threshold for modeling WHODAS 2.0 scores as a dichotomized outcome –18. ThisRegarding ordinal classifications, some studies have utilized the ICF categories  to estimStudies in LMICs have already analyzed disability in older adults. Most of them use scales that focus on the physical component of disability, particularly the limitations for activities  of daily life –25. OtheTherefore, the aims of this study were to provide estimates of the levels of severity associated with disability in older adult populations in LMICs and to assess disability's association with health and socioeconomic factors. Our hypothesis is that the heterogeneity of disability levels could be described in distinct patterns based on the observed responses to the 12 categorical items of WHODAS 2.0. Specifically, we used LCA) to identify homogeneous classes, or groups, related to the severity of disability. To our knowledge, this study is the first attempt to analyze the severity levels of disability among older adults in a group of LMICs.Cross-sectional data from the WHO longitudinal multi-country Study on global AGEing and adult health (SAGE) Wave 1, conducted in China, Ghana, India, Mexico, Russian Federation, and South Africa during 2007–2010, was used for the analyses. At the time of data collection for Wave 1, SAGE included a mix of one low-income (Ghana), two lower middle-income (China and India), and three upper middle-income countries . These countries vary in stages for demographic and epidemiological transitions but are experiencing a rapid rise in their older adult populations .n = 12,969; Ghana, n = 4,302; India, n = 6,559; Mexico, n = 2,211; Russia, n = 3,919; and South Africa, n = 3,681.SAGE employed a multistage cluster design with samples drawn from an updated national sampling frame. Face-to-face interviews were used to capture respondent information. A detailed description of the study, sample design, and weighting is provided elsewhere , 30. SAGWe measured disability using the cross-culturally validated 12-item version of the WHODAS Version 2.0 (WHODAS 2.0). The WHODAS 2.0 scale is widely used to measure last-month limitations in activity and daily-life participation. It covers six domains explored through a total of 12 items (two per domain): [1] cognition and communication, [2] self-care, [3] mobility, [4] interpersonal relations, [5] life activities, and [6] participation. The results of the 12 items are added up to obtain a global score expressed on a continuous scale from 0 (no disability) to 100 (full disability) .Covariates were categorized as follows: sex , age group , marital status (1 = currently married/cohabiting or other), years of education . Socioeconomic status (SES) of the household was derived using the WHO standard approach to estimate permanent income from household ownership of durable goods, dwelling characteristics , and access to services such as water, sanitation, and electricity , 33. SESWe used the list of nine chronic diseases included in the SAGE study. The three following conditions were measured according to self-reported medical diagnoses: diabetes, stroke, and cataracts. Another five conditions were estimated through algorithms for symptomatology and self-reported treatment: angina, arthritis, chronic obstructive pulmonary disease, asthma, and depression. Finally, hypertension was determined by either blood pressure measurement and/or self-reported treatment. A detailed description of definition and operationalization of these diseases has been published elsewhere .We included three variables related to geriatric syndromes. Frailty status was determined using a modified frailty phenotype based on the criteria proposed by Fried et al.  that covWe also used sarcopenia as an additional covariate. According to previous publications, we defined the presence of sarcopenia as having low skeletal muscle mass (SMM) as reflected by lower skeletal muscle mass index (SMI) and either a slow gait speed or weak handgrip strength. Details of this variable have been published using the SAGE study sample as well as the description of the specific algorithms used to define sarcopenia status in the older adult population . BrieflyFinally, mild cognitive impairment (MCI) was also included. Based on recommendations from the National Institute of Aging-Alzheimer's Association in previous work with the SAGE database, an algorithm was used to generate the MCI variable . In sum,We used pooled data from all six countries to conduct the data analysis and summarized sample descriptive characteristics by country. Given that our aims were to provide severity level estimates associated with disability and to examine their associated factors, we conducted the analysis in the two phases described below.We used LCA to identify potential groups of older adults according to severity levels of disability. LCA is similar to cluster analysis in that both techniques aim to identify groups of individuals who have a similar response pattern to a set of observed variables. The main difference between these two methodologies is that LCA is a modeling-based approach .The aim of LCA is to estimate the number of classes of an underlying categorical latent variable that accounts for the association patterns between categorical observed variables. In our case, LCA creates subgroups of older adults who respond in a similar way to the observed variables of WHODAS 2.0. In this model, there are two parameters of interest: (a) class membership probabilities and (b) the probability of response to each observed variable given the membership of the latent class. For LCA models, the posterior probabilities indicate the probability that an individual will be assigned a given class, and individuals are classified into their highest-class probability.k class solution to k-1 class solution where k is a given number of latent classes , we used a multivariate ordinal logistic regression model to identify the correlates of severity levels of disability.p < 0.05 and 95% confidence intervals (CIs) were given.Statistical analyses accounting for the complex survey design, including verification of the proportional odds assumption, were carried out using Stata 16.0 software . Differences were considered statistically significant if The sociodemographic and health characteristics (weighted percentages using the complex sampling design) of respondents are presented in In general, most of the older adults were women, belonged to the youngest age group (50–59 years) and had a partner or were married at the time of the survey. In terms of chronic conditions, the most prevalent were hypertension (54%), arthritis (23.4%), and angina (14%). As for geriatric syndromes, frailty was observed in 10.7% of the sample, sarcopenia in 15%, mild cognitive impairment in 18%, and multimorbidity in 38.4%.We also observed some heterogeneity for the six countries analyzed. The highest prevalence of chronic diseases was observed in Russia, and those of frailty and sarcopenia in India and South Africa. Meanwhile, the percentage of rural population was highest in India, Ghana, and China, and the lowest was in Russia and Mexico. Finally, the highest level of schooling was observed in Russia and the lowest was in Mexico.We adjusted models from one to six classes selecting the four-class model based on indices of fit as well as a graphical inspection of the WHODAS 2.0 score along the classes observed. The results of the estimation of disability severity levels are shown in p < 0.01). Similar heterogeneity patterns were observed for the rest of the severity levels.However, we also observed significant differences between countries regarding the prevalence of severe disability. China had the lowest prevalence (2.1%), followed by Mexico (5.6%) and Russia (6.4%). Meanwhile, India (15.0%), Ghana (12.1%), and South Africa (11.6%) showed the highest prevalence , sarcopenia (OR = 1.25), and cognitive impairment (OR = 1.85) were associated with higher severity levels of disability. Regarding chronic conditions, angina (OR = 1.58), arthritis (OR = 1.72), asthma (OR = 1.48), cataract (OR = 1.26), lung disease (OR = 1.89), depression (OR = 2.00), diabetes (OR = 1.23), and stroke (OR = 2.91) were also significantly associated .Finally, and using China as the reference category, we observed significant differences between countries related to severity of disability, except for Mexico .In this study, we found four groups of older adults according to the severity levels of disability. Almost half of the respondents were classified as no disability and one third with mild disabilities. Meanwhile, moderate or severe disability levels affected a quarter of the elderly population in the LMICs analyzed.However, we also observed significant heterogeneity among the countries. The prevalence of severe disability was lower in China, Mexico, and Russia and between three to five times higher for South Africa, Ghana, and India. Notably, almost 60% of older adults from China and Mexico were disability-free.Higher levels of schooling and SES were associated with lower severity levels of disability. Frailty was the condition that resulted in a stronger association with severe disability, and chronic conditions strongly associated with disability were stroke, depression, and chronic obstructive pulmonary disease.Some studies have estimated the severity levels of disability using WHODAS 2.0 (either 12- or 36-item version), following two general approaches. First, using the ICF-recommended classification, a population is classified as: no disability [0–4], mild disability [5–24], moderate disability [25–49], severe disability [50–95], and extreme disability [96–100]. Second, using some multivariate statistical method, individuals are classified according to the association pattern observed among the items of WHODAS 2.0.Regarding the first approach, one study with older adults aged 60–70 in Poland found a prevalence of severe or extreme disability of 6.3% . Two addFor the second approach, we found three studies that used WHODAS 2.0 and applied LCA to identify groups of individuals with different profiles of disability. A first study conducted in Italy with adults aged ≥65 identified four groups of individuals: without disability (60.8%), with difficulties in movements 21.2%), with difficulties in movements and daily tasks (11.4%), and with very low functioning level (6.6%), this last group being equivalent to our estimation to severe disability . A secon.2%, withRegarding evidence in LMICs, several studies have looked at disability among older adults. However, it is difficult to benchmark our results because no other study had a similar scope. Even so, we chose four studies that are different but of relevance for triangulation. Stewart Williams et al.  analyzedOne more study that also uses the SAGE study's data estimated the prevalence and associated factors of disability but used limitations in basic activities of daily living . The resAs for variables associated with severe disability, we found that two indicators of better SES (schooling and permanent income) were protective factors for the presence of higher levels of severe disability. In that sense, it is believed that SES and disability reinforce each other, operating in a pernicious cycle . A recenConsistent with previous studies, three geriatric syndromes  were associated with severe levels of disability . AlthougThe associations between the nine chronic conditions included in this study and severe disability levels were significant  and corroborate what has been reported in the geriatric literature about the role of chronic conditions and multimorbidity on the disablement process , 51. HowLast but not least, women in our study showed higher severity levels of disability than men. Studies have shown that older women are more likely than men to become and remain disabled , 53. TheEven though the severity levels of disability have been estimated in HICs, there are no cross-country comparisons using harmonized instruments in LMICs. In that vein, our study is the first attempt to fill this gap. Besides this, the WHO SAGE study employed standardized instruments that were used in all six countries that increased the external validity of our findings and ensured comparability of the results across the SAGE countries.Nevertheless, some limitations must be considered in this study. First, recall and survivor bias can be limitations for epidemiological studies with older adult participants. It could be, for example, that older adults with worse disability conditions have died at younger ages. If this was the case, our prevalence of severe disability would be underestimated since the “healthiest” older people were interviewed. Second, most of the variables in the SAGE study, including the disability questions, are self-reported. This may have led to overestimation or underestimation of the true prevalence of severe disability among older adults. In spite of this, consistent and similar prevalence rates have been reported in HICs. Third, it is possible that the pooling of country data to some extent masks observed patterns of severe disability and its associated factors within individual countries. However, the inclusion of a country dummy variable in our analysis highlights some contrasts between countries that could inform to policy makers. For example, compared with the other countries, India has the highest prevalence of severe disability, which adds evidence about a recognized public health concern in that country .Disability in older adults is a complex process that goes beyond the physical limitations. According to the WHO conceptual framework reflected in the ICF, disability involves biological and disease conditions that are integrated into a social and environmental context. Also, disability is not a stable condition—individuals can progress to a more critical state related to its level of severity. In that sense, future aging research could focus in older adults with severe disability since these people usually live with worse socioeconomic and health conditions as corroborated by the results of this study. Additionally, the measurement and study of disability severity could be a critical element to understand the causes and consequences of aging, as well as to plan health programs and services. This is an even more urgent action in LMICs due to the rapid growth of their aging populations and the limited resources that they will face in the short and mid-term , 62.Kaigo Hoken) has set a radical change from the traditional family-based care toward the socialization of older adults' care. This program includes medical care and welfare services with the aim to reduce the care burden of caregivers and maintain the functional status of older adults (A consequence of severe disability in older adults is that their independence in daily activities, mobility, and social participation is reduced, as the results of our study show. With the current projections of rapid aging process in LMICs, the number of severely disabled older adults living in the community will increase, and in turn, the demand for long-term care services will rise. This could represent a threat to the financial sustainability of health systems and social services . Therefor adults . Given tr adults , Japaneshttp://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/sage/about.The datasets analyzed during the current study are available in the WHO repository, All procedures performed in this study were reviewed and approved by the following Ethics Committes: the WHO Ethical Review Committee; Ethics Committee, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, China; Ethical Committee, University of Ghana Medical School, Accra, Ghana; Institutional Review Board, International Institute of Population Sciences, Mumbai, India; Ethics Committee, National Institute of Public Health, Cuernavaca, Mexico; Ethics Committee, School of Preventive and Social Medicine, Russian Academy of Medical Sciences, Moscow, Russia; and the Research Ethics Committee, Human Sciences Research Council, Pretoria, South Africa. All procedures were performed according to the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.AS-R, BM-E, and AR-A contributed to the conception and design of this research. AS-R conducted the data analyses, interpreted the data, and wrote the first and subsequent drafts of the paper. AS-R, BM-E, AR-A, and AS contributed to the interpretation of findings and substantially revised the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Staphylococcus, Lactococcus, Enterococcus, Microbacterium, Kocuria, Acinetobacter, Pseudomonas, Aeromonas, Klebsiella and Enterobacter were identified by the MALDI-TOF MS and biochemical methods. The in vitro produced quantity of eight biogenic amines (BAs) was detected by the HPLC/UV–Vis method. All the isolated bacteria were able to produce four to eight BAs. Tyramine, putrescine and cadaverine belonged to the most frequently produced BAs. Of the isolated bacteria, 41% were able to produce BAs in amounts >100 mg L−1. Therefore, wastewater embodies a potential vector of transmission of decarboxylase positive microorganisms, which should be taken into consideration in hazard analyses within foodstuff safety control. The parameters of this wastewater  were also monitored.Out of six samples of wastewater produced in the dairy industry, taken in 2017 at various places of dairy operations, 86 bacterial strains showing decarboxylase activity were isolated. From the wastewater samples, the species of genera  Lactobacillus, Pediococcus, Streptococcus) or, for instance, bacteria genera: Bacillus, Clostridium, Pseudomonas, Staphylococcus, Micrococcus [Milk and dairy products represent a suitable environment for the growth of microorganisms, which may influence, through their metabolic activity (positively or negatively), the product quality. Both Gram-positive and Gram-negative bacteria having decarboxylase activity may occur in milk and dairy products. These include some lactic acid bacteria . BAs are naturally occurring, low molecular weight organic nitrogen compounds; based on their chemical structures, they can be divided into several groups: aliphatic , aromatic  or heterocyclic . Sometimes, putrescine, cadaverine, spermine, spermidine and agmatine are classified as polyamines . These sThe production of BAs by bacteria can be influenced by several external factors through affecting the kinetics of decarboxylase reactions, the growth of the aminogenic microorganisms and also the expression of their decarboxylase potential. BA synthesis in bacteria is related to energy gain or resistance to an acidic pH.The production of BAs is dependent on intrinsic and extrinsic factors of the environment. In other words, the factors influencing the production of BAs not only include the presence of microorganisms with decarboxylase activity, but also the presence of precursors (amino acids), available sources of carbon  and suitable external conditions for the growth of microorganisms  ,13,14. IThe presence of microorganisms in wastewater produced in the dairy industry is caused on the one hand by flushing some process-important microorganisms out into the wastewater during processing, but also—on the other hand—due to the contamination occurring in these manufacturing processes. As shown by some pilot studies, BA producers are present even in these waters. However, it is not quite clear from the bibliography what importance this vector (wastewater) has for the transfer of decarboxylase-positive microorganisms, which may influence the level of food safety controls. Therefore, the objective of this study was to isolate and identify bacteria possessing decarboxylase activity  not from milk or dairy products, but just from the dairy industry wastewater. Therefore, in order to support this microbiological study in terms of the occurrence of nutrients for contaminating microorganisms with decarboxylase activity, the parameters of this wastewater were also monitored . The presence of nutrient substances (containing nitrogen and phosphorus) has recently been connected with the increase in the eutrophication of surface waters. Moreover, the occurrence of nitrates in foods and in water is a serious threat to human health . Finallyp > 0.05). We used non-parametric Wilcoxon test independently for each sampling point. Therefore, the data were merged and presented as the results obtained in year 2017. Using the MALDI-TOF MS and biochemical methods, 77 bacterial strains belonging to 19 bacterial genera , Staphylococcus (21%), Microbacterium (12%), Enterococcus (9%), Kocuria (9%) and 43 Gram-negative mostly species of the genera Acinetobacter (35% of identified Gram-negative bacteria), Pseudomonas (19%), Aeromonas (14%), Chryseobacterium (9%), Enterobacter (7%) and Klebsiella (7%).In 2017, in two seasons (summer and autumn), wastewater samples  were obtained from six offtake points (the same sampling points in summer and autumn of one dairy) that are linked to the processing of various dairy products and are crucial for drainage of wastewater from the latter mentioned lines . Firstlynera see  out of 8 strains , 34 wereIn the case of individual isolated bacteria , their decarboxylase activity was tested, i.e., the ability to produce BAs. Using the HPLC/UV–Vis method, the production of eight BAs was monitored, namely histamine (HIM), tyramine (TYM), phenylethylamine (PHM), tryptamine (TRM), putrescine (PUT), spermine (SPE), spermidine (SPD) and cadaverine (CAD).In vitro production of the monitored BAs was noted in all tested bacterial strains. Individually isolated bacteria produced four to eight BAs in various combinations and quantities. Apart from the samples, the used decarboxylation medium  was also analyzed and none of the monitored BAs were detected there.−1, respectively. TYM in an amount >100 mg L−1 was produced by bacteria, among which the following representatives of the genera Pseudomonas, Acinetobacter, Lactococcus, Kocuria, Enterococcus, Microbacterium, Staphylococcus and Exiguobacterium were identified. The significant isolated PUT producers included representatives of the genera Aeromonas, Enterobacter, Acinetobacter, Klebsiella, Lactococcus, Staphylococcus and Microbacterium. CAD was produced in an amount >100 mg L−1 by the representatives of the genera Klebsiella, Acinetobacter, Kocuria and Lactococcus  in an amount >100 mg L−1  could not be identified by the available methods.From the C sample (rinsing water from the production of Gervais-type cream cheese), we isolated  L−1 see . In addiIn addition to microbiological investigations, other parameters of wastewater, such as nitrite, nitrate, phosphate concentrations and the protein contents, were monitored in all the obtained samples see .−1. The highest concentration of nitrates was found in the E sample (54.1 mg L−1), in other samples the nitrate concentration was below 50.0 mg L−1. The phosphate concentrations measured with the samples A to F fluctuated in the range from 0.8 to 662.4 mg L−1, where the highest concentration was found in the E sample (rinse water from a cheese-making unit with a share of whey). In the tested samples (A to F), concentrations of proteins were found in the E sample at 127.3 ± 1.6 mg L−1, in the A sample (processed cheese plant rinse water) at 97.9 ± 0.8 mg L−1 and the F sample (curd unit rinse water) at 37.0 ± 0.5 mg L−1.The nitrite concentrations in the wastewater samples (A–F) ranged from 0.1 to 1.5 mg LLactobacillus, Pseudomonas, Arthrobacter, Microbacterium, Staphylococcus, Enterococcus and others were isolated from dairy industry wastewater. Nevertheless, some other studies dealt with the bacteria isolation and identification from dairy industry wastewater [Alcaligenes, Leuconostoc, Lactobacillus, Staphylococcus or Lactococcus. In the aforementioned studies, the isolated and identified bacteria were of the same genera that we managed to isolate and identify. However, these studies were not concerned with the decarboxylase activity of these isolated bacteria. In the studies published by Shivsharan et al. ,18, bactstewater ,20, wherEnterobacteriaceae , and bacteria of the genus Pseudomonas present in milk are capable of producing HIM, PUT and CAD [Enterobacteriaceae isolated from curd and cheese samples are described in the study by Torracca et al. [Enterococcus, Lactobacillus, Leuconostoc, Lactococcus and Streptococcus [Enterobacteriaceae species nor LAB were missing. These species are important producers of PUT, TYM, CAD and TRM in particular. The polyamines SPE and SPD frequently occur in raw cow milk [in vitro production of SPD by bacteria isolated from cheese; however, even they did not confirm the ability of the isolated enterobacteria  to produce this type of BA. Apart from this, they did not confirm the SPE production, either. In our study, SPE was produced in vitro by isolated bacteria in quantities from 20.7 to 79.9 mg L−1, almost by all isolated strains. In milk and dairy products, BAs, or their producers, are generally present. According to the bibliography, the Gram-negative bacteria, mostly  and CAD . The proa et al. . Howevertococcus ,24,25. Ttococcus . In the tococcus . The rescow milk , and SPDcow milk  monitore−1. Similar quantities of produced HIM were measured by the authors of studies monitoring the decarboxylase activity of bacteria isolated from cheese [In a comparison with the production of other BAs , the production of HIM by the identified bacteria was significantly lower, it fluctuated within the range from not detected (ND—BA under detection limit) to 28.5 mg Lm cheese .−1. It was evident from the results that the rinsing water contains both starter and non-starter lactic acid bacteria but also contaminating microorganisms. A not negligible share of isolated microorganisms produce BAs in abundant quantities. Therefore, wastewater represents a potential vector through which the decarboxylase-positive microorganisms may penetrate into the products as secondary contamination and thus influence the final product safety. Therefore, it is necessary to include this vector of contamination in the hazard analysis within the Hazard Analysis and Critical Control Points (HACCP) system, to determine its risk and adopt adequate control measures, probably at the level of an operational program of necessary preconditions.The submitted study results show that the bacteria that normally occur in milk or dairy products  are also present in the dairy industry wastewater and their decarboxylase activity  is not negligible—a lot of these bacteria were able to produce in vitro BAs in quantities of hundreds of mg LIn separate wastewater samples (samples A to F), the presence of monitored BAs was analyzed. They were not present here or they were under the detection threshold of the used analytical method. Gubartallah et al.  determin−1. In two of the six offtake points (samples A to F), the nitrite concentrations were determined at lower levels than 0.50 mg L−1, which even met the requirements for drinking water. In the C sample, this concentration was 0.5 ± 0.1 mg L−1, while higher concentrations (<1.60 mg L−1) were determined in the D, E and F samples.Milk processing is usually considered to be the largest source of industrial wastewater, particularly in Europe . The lar−1, but in water for infants the maximum permissible concentration is only 10 mg L−1. The highest concentration of nitrates was found in the E sample (rinse water from a cheese-making unit); however, in all other samples , the nitrate concentrations were rather low, even in compliance with the drinking water limits.Similarly, nitrates are present in all water types. In Decree No. 252/2004 Coll. (Czech Republic), on hygienic requirements for drinking water, the maximum permissible concentration of nitrates is 50 mg L−1 . Substantially higher concentrations of phosphates were found in the tested samples from the dairy industry wastewater, which could have been expected due to the use of some processed cheese salts  [−1 of phosphorus) [The maximum permissible concentration of phosphates for drinking water is stipulated at 3.5 mg Lsphates)  or due tsphorus) . −1. It is well known that proteins are among the contaminating organic substances and the fact that their concentrations in wastewater fluctuate, by orders, in tens of mg L−1, as documented by the study of Westgate and Park [As milk and whey contain a number of proteins , the proand Park .Samples from the dairy industry wastewater (A–F) were taken in one dairy in Czech Republic . In 2017, in two seasons (summer and autumn), wastewater samples were obtained from six offtake points (the same sampling points in summer and autumn) that are linked to the processing of various dairy products . SamplesLactobacillus, identifying medium M17 (HiMedia) for the milk coccobacilli collection, BHI (HiMedia) for the collection of more nutrient intensive bacteria, Endo’s medium (HiMedia) for the collection of enterobacteria, Sabouraud agar for the collection of yeasts and fungi and Slanetz–Bartley cultivating medium for the collection of enterococci.The following cultivating media were used for the isolation of individual bacteria: meat peptone agar  as a universal medium, identifying medium MRS (HiMedia) for the detection of the bacteria presence of the genus For A and B wastewater samples, 1000 µL of each sample was always inoculated (due to a lower number of microorganisms there), while for C, D, E and F samples, 100 µL of decimal-diluted solutions of each was always inoculated for all seven medium types. The cultivation took place for 48 h at 30 °C, or 37 °C in case of enterobacteria and 25 °C on the Sabouraud agar for a period of five days.The bacterial colonies of various phenotypes selected at random from Petri plates with countable numbers of colonies were purified by re-inoculation (three times) to individual colonies. This yielded pure colonies that were consequently identified by the use of commercially available sets of micro-tests  according to the manufacturer’s instructions and by the use of other conventional phenotype tests . The results of the ENTEROtest 24, extended with regular tests, were evaluated by the use of the TNW software application (Pliva-Lachema Diagnostika) and the API results were evaluated by the tool for apiweb identification (bioMérieux).The isolated bacteria were also identified by the MALDI-TOF MS method. Individual colonies were suspended in 150 µL of distilled water and 450 µL of ethanol. The samples prepared in this way were frozen at minus 25 °C and then analyzed using the MALDI-TOF MS instrument , by the earlier described method . The obtv/v) with 0.6 M perchloric acid . Three independent extractions were performed on each culture sample. Subsequently, mixtures were derivatized using dansyl chloride  with 1,7-heptanediamine  as an internal standard. The preparation of decarboxylation medium for the BAs production determination by the monitored bacteria was identical to the methodology described in the paper of Lorencová et al. [This study was focused on the monitoring of the production of the BAs and polyamines  by the bacteria isolated from the samples of dairy industry wastewater. Production of the above BAs was monitored in the cultivation broth after precolumn derivatization with dansyl chloride. Supernatant was diluted 1:1 , the content of proteins was determined in individual samples as well.The nitrite concentration was determined spectrophotometrically (by the Greiss technique) . The nitThe combined nitrate ion selective electrode  was usedThe phosphate concentration in the wastewater samples was determined by colorimetric analysis. In the presence of antimony ions, phosphates react in a medium of sulfuric acid with ammonium molybdate, yielding molybdophosphate acid. Consequently, the yellow complex is transferred by a reduction with ascorbic acid to a solution of phosphomolybdate blue ,42. The In order to determine the protein content in the dairy industry wastewater samples, the spectrophotometric method according to Bradford was used . The metin vitro production of eight BAs  by the bacteria isolated from samples of dairy industry wastewater in Central Europe taken in 2017 in various areas of dairy operations was monitored. Gram-positive bacteria, particularly lactococci, staphylococci and enterococci, were isolated from the wastewater samples. In the case of Gram-negative bacteria, representatives of the genera Pseudomonas, Acinetobacter, Enterobacter, Aeromonas and Klebsiella predominated. These bacteria were significant producers of TYM, PUT, CAD and TRM (>100 mg L−1).In this study, the The results of the study confirm the presence of BA producers (bacteria with significant decarboxylase activity) in the dairy industry wastewater. Therefore, this is a significant contaminating vector for intermediate and final products, which influences their safety. Due to this, it is necessary to include this vector of contamination in the implementation and maintenance of the hazard analysis within the HACCP system .