We describe the interpretable and opinionated visualizations in the variant and location focussed reports available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data, and the server infrastructure that enables widespread data dissemination via a high performance API that can be accessed using an R package. We present a case study that illustrates how outbreak.info can be used for genomic surveillance and as a hypothesis generation tool to understand the ongoing pandemic at varying geographic and temporal scales. With an emphasis on scalability, interactivity, interpretability, and reusability, outbreak.info provides a template to enable genomic surveillance at a global and localized scale.The emergence of SARS-CoV-2 variants of concern has prompted the need for near real-time genomic surveillance to inform public health interventions. In response to this need, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present  The first SARS-CoV-2 genome was made publicly available on 10 January 20203. Since then, the global scientific community, through an unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 20225. To keep track of the evolving genetic diversity of SARS-CoV-2, Rambaut et al. developed a dynamic phylogeny-informed nomenclature (PANGO) to classify SARS-CoV-2 lineages6. As of May 2022, over 2,000 lineages have been designated, which has enabled public health agencies such as Public Health England (PHE), the Centers for Disease Control (CDC), and the World Health Organization (WHO) to identify Variants of Concern (VOC), Variants of Interest (VOI/VUI), and Variants Under Monitoring (VUM/VBM) based on the phenotypical characterization of these lineages7. Currently, there are five designated VOCs: B.1.1.7*  lineage resulting in increased transmissibility8, B.1.351* (Beta) lineage exhibiting immune evasion9, the P.1* (Gamma) lineage exhibiting immune evasion10, the B.1.617.2* lineage exhibiting increased transmissibility due to the P681R mutation in the Spike gene11, and more recently, the B.1.1.529* (Omicron) lineage exhibiting very rapid growth and the ability to substantially avoid antibody neutralization13.In December 2019, a series of cases of pneumonia of unknown origin appeared in Wuhan, China, and on 7 January 2020, the virus responsible for the diseases was identified as a novel coronavirus, SARS-CoV-214. This extraordinarily high sampling rate of infecting viruses provides a unique opportunity to track the evolution of the virus in near real-time. For example, in December 2021 alone, over a million new genomes were submitted to GISAID15. Traditionally, phylodynamic approaches have been employed to retrospectively characterize lineage dynamics during outbreaks of viruses such as Zika18, West Nile19 and Ebola viruses21. Existing tools like NextStrain22 and frameworks such as Microreact23 primarily rely on a phylogeny to elucidate transmission chains and monitor the evolution of the virus. However, these tools were not designed to track thousands of new genomes per day, and given that building phylogenies for large sets of genomes is computationally intensive and time consuming, obtaining timely insights from the data is often problematic24. However, the high sampling rate of the virus has opened up the possibility of tracking the pandemic using the available near real-time genomic data without the need for computationally intensive modeling.The emergence of VOCs with fitness advantages has led to global “sweeps” with newly emerged VOCs displacing previously circulating variants. More importantly, the growth of each VOC has led to a renewed surge in infections worldwide. This has prompted the need for near real-time genomic surveillance to inform early public health interventions to control the rise of infections. In response to this need, thousands of academic, non-academic, and public health labs have been depositing sequences predominantly on the sharing platform of the GISAID Initiativeoutbreak.info, a platform that currently tracks over 40 million combinations of PANGO lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials, and the general public. In the following sections, we describe the data pipelines that enable the scalable ingestion and standardization of heterogeneous data on SARS-CoV-2 variants, the server infrastructure that enables the dissemination of the processed data, and the client-side applications that provide intuitive visualizations of the underlying data.Here, we present 25. While initially thought to be more transmissible, it was eventually shown that the increase in prevalence was due to a resurgence in travel and not due to increased transmissibility. In contrast, a few months later, the B.1.1.7 lineage was shown to be 40–60% more transmissible than previously circulating lineages and this intrinsic biological property led to the rapid growth in its prevalence worldwide27. Epidemiological factors such as mobility29, mask usage30, and public health interventions31 vary over time and across geographies worldwide, while biological properties are a function of the mutations found in a given lineage  epidemiological data curated by Johns Hopkins University32, and (2) public literature, clinical trial, protocol, and dataset metadata from sources such as bioRxiv, medRxiv, and LitCovid33. Here, we describe how each of these data sources is used in cohesion to assist in genomic surveillance.The growth rate of a given viral lineage is a function of epidemiology and its intrinsic biological properties . For exa lineage . Hence, The overall workflow of genomic data is shown in The ongoing SARS-CoV-2 pandemic has been punctuated by the emergence of VOCs with fitness advantages over previously circulating variants, resulting in “waves” of infections. 9 and Brazil36 respectively. Similarly, B.1.62137 was only dominant in Columbia, A.2.5 was only dominant in Panama, and B.1.177 exhibited a high growth rate only in European countries due to a resurgence of travel in the summer of 202038. Factors such as the attack rate, population immunity due to previous infection or vaccination, and social mobility vary from one region to the next and have a significant impact on the growth rates exhibited by a given lineage. To account for such localized factors, it is important to have the ability to track the growth of lineages at different geographic scales. We built the Location Tracker on outbreak.info to facilitate the surveillance of SARS-CoV-2 lineages at a country, state/province, or county/city level. The Location Tracker provides a snapshot of circulating lineages with a focus on the last 60 days, and allows users to compare the prevalence of a customizable set of lineages/mutations over time in that location. Furthermore, the tracker also integrates reported cases over time to provide insights on the impact of growth of various lineages on caseloads in the region. As with the Lineage/Mutation Tracker, we designed the user interface to answer a set of specific questions as shown in Some VOCs have only been regionally dominant. For example, Beta and Gamma were dominant in South Africa40 and were subsequently outcompeted globally by the Delta variant that was first detected in December 202041. The Omicron lineage, first detected in November 202112, was able to outcompete Delta and grew much more rapidly relative to previous VOCs during their emergence , Beta (B.1.351*), Gamma (P.1*), Delta (B.1.617.2*), and Omicron (B.1.1.529*). Of these, Alpha, Beta and Gamma were estimated to have emerged between September and December 2020mergence . Whereasmergence , 5c. Add39. There were multiple introductions of the lineage into the United States (U.S.) as early as late November42. The Alpha variant showed a transmission advantage of 40–50% in the U.S.27, in line with observations in the United Kingdom and the Netherlands. In the U.S., Alpha was able to outcompete previously circulating lineages and continued to increase in prevalence until the introduction of the Delta variant around April 2021  elements powered by a high performance API, and interpretability via intuitive visualization of data based on discussions with researchers, epidemiologists, and public health officials.The global community has generated over 11 million genomes of SARS-CoV-2 as of May 2022, shared on platforms such as GISAID package . This apoutbreak.info has been enabled by unprecedented global genomic sequencing efforts, and we developed every element of the application to fully leverage this capacity. However, genomic sampling varies globally with the vast majority of sequences coming from high income countries; even within well-sampled regions, there is geographic and temporal variation14. To communicate the increased uncertainty due to low sampling, we calculate confidence intervals of estimates wherever applicable, provide histograms of sampling density, and mask data when there are very few data points available. Furthermore, sampling can be selective as samples of the Alpha variant and BA.1 lineage (sublineage of Omicron) show S gene target failure on a widely used qPCR assay. Such sampling biases impact the insights that can be derived from quantities such as the prevalence of a lineage/mutation. We communicate these limitations through a dedicated “caveats” page with warnings regarding the interpretation of data interspersed throughout the interface. outbreak.info continues to provide a mechanism for researchers, epidemiologists, and public health officials to easily track the growth of variants, across any number of locations. The platform, backed by robust infrastructure, allows users to quickly access key statistics for known VOCs, emerging variants, and any combination of mutations without having to run any time consuming analyses. This allows researchers to focus on data exploration, hypothesis generation and more complex downstream analyses. Beyond the SARS-CoV-2 pandemic, outbreak.info serves as a model for providing scalable and reusable metrics to track the spread of any pathogen during an outbreak via interactive and interpretable visualizations.Bjorn, to count mutations from a given set of genomes in a scalable manner daily  Convert the alignment into a FASTA file using gofasta (https://github.com/virus-evolution/gofasta); (5) count mutations and deletions from this alignment; (6) standardize and filter the metadata: country, division, location, pangolin lineage, date of collection, and date of submission and (7) combine results from all chunks and convert to a JSON object. We standardized geographic identifiers using shapefiles from GADM60. The final JSON object is loaded into an Elasticsearch index within the BioThings framework61. The code for Bjorn is available at https://github.com/andersen-lab/bjorn.We built a data pipeline, er daily . The pip32. We used shapefiles from Natural Earth62 to standardize geographic identifiers, and obtain populations for countries and states outside the U.S. For the U.S., we used the county level shapefiles and population estimates from the 2019 population estimates by the Census Bureau to standardize geographic identifiers and get population estimates. We standardized reported date formats, and geographic identifiers across the two resources. The code for the EpiData pipeline is available at https://github.com/outbreak-info/biothings_covid19.We built the EpiData pipeline to ingest reported global cases, and deaths from Johns Hopkins Universityoutbreak.info are binomial proportions. We calculate 95% confidence intervals for these estimates using a Jeffrey’s Interval, the 2.5 and 97.5 quantiles of the beta distribution β where x is the number of successes and n is the number of trials.Most estimates of prevalence on 61. The JSON outputs of our data pipelines are ingested by the BioThings framework and the processed data is stored in individual Elasticsearch indices. A Tornado server is used to create API endpoints that leverage the search capabilities of Elasticsearch to perform complex aggregations of the underlying data. These API endpoints allow the client-side applications to query the underlying data within reasonable query times while accounting for the scale of the ingested data. The BioThings Hub maintains historical data by default, allowing us to roll back to previous data backups if issues are discovered with new data after they are deployed. The code for the server-side application is available at https://github.com/outbreak-info/outbreak.api.In order to scale with the increasing size of existing data sources and the heterogeneity of newly emerging data sources, we used the BioThings framework63, a model–view–viewmodel JavaScript framework which enables the two-way binding of user interface elements and the underlying data allowing the user interface to reflect any changes in underlying data and vice versa. The client-side application uses the high performance API to interactively perform operations on the database. Customized data visualizations on the client were built using D3.js64, giving us the ability to develop novel, and intuitive visual elements as part of the user interface. We designed these visualizations to answer specific questions of interest to epidemiologists, researchers, and public health officials. We further added functionality to enable the 1-click copy or download of every chart in the interface as a png or svg. The code for the client-side application is available at: https://github.com/outbreak-info/outbreak.infoThe web application was built using Vue.jsoutbreak.info to allow researchers and other individuals to easily access the data via the API for downstream analyses and visualizations. The R package is composed of three parts: functions that allow the user to access genomic data, functions to access the epidemiological data, and functions to access the Research Library metadata. They all consist of a base function that contains arguments for all possible parameters that can be used to query the API. While users can utilize this base function directly to access data, several wrapper functions are available that inherit the arguments from the base function in addition to pre-specified parameters to simplify the process of querying the API. For example, while getGenomicData can be used directly to access data regarding the daily global prevalence of a specified lineage, doing so would require a user to be familiar with the name of the endpoint as specified in the API URL . Instead, the user can access this data with the more intuitively named getPrevalence. Therefore, these wrapper functions allow users to easily and quickly obtain the data they need. The R package also contains an authenticateUser function that allows users to authenticate against their GISAID credentials and access computed statistics from the primary genomic data provided by GISAID.We developed an R package for getISO3Code and getLocationIdGenomic. The latter function uses the genomics API endpoint to obtain the ISO3 code for a given location. The ISO3 code can be obtained with either a full or incomplete location name; in the latter case, the user will be provided a list of matching locations and must specify the location they are interested in. This function is embedded in the parent getGenomicData function, and is therefore inherited in all wrapper functions. Therefore, searching for data by location in the R package replicates the experience on the client-side web application. Documentation is available at: https://outbreak-info.github.io/R-outbreak-info with vignettes located at https://outbreak-info.github.io/R-outbreak-info/articles/index.html. The R package can be downloaded and installed using the remotes package function: install_github(“outbreak-info/R-outbreak-info”).In addition, as the API queries location by ISO3 code, rather than by location name, two functions have been created that allow users to forgo the step of searching for the ISO3 code themselves: Supplement 1


Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient classification model predicting the survival of children undergoing BMT is presented using a public dataset. Several supervised ML methods were investigated in this regard with an 80-20 train-test split ratio. To ensure prediction with minimal time and resources, only the top 11 out of the 59 dataset features were considered using Chi-square feature selection method. Furthermore, hyperparameter optimization (HPO) using the grid search cross-validation (GSCV) technique was adopted to increase the accuracy of prediction. Four experiments were conducted utilizing a combination of default and optimized hyperparameters on the original and reduced datasets. Our investigation revealed that the top 11 features of HPO had the same prediction accuracy (94.73%) as the entire dataset with default parameters, however, requiring minimal time and resources. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records. Cancer kills millions of people, even in its most curable forms. According to the statistics of 2020, the estimated death tolls in the USA from colon, pancreatic, lung, breast, and prostate cancers are 53200, 47050, 135720, 42690, and 33330, respectively . ClinicaHealthy BM can be either extracted from the patient (autologous transplant) or conferred on by a volunteer donor . In the case of autologous transplant, stem cells come from any other healthy organ of the patient . And forK-nearest neighbors (KNN), gradient boosting classifier (GBC), AdaBoost (AdB), XG Boost (XGB), and a dataset obtained from the UCI ML repository [To summarize, BMT is a treatment that saves and risks life at the same time. The success rate of BMT is mostly dependent on earlier examination, data collecting, and analysis. Hence, a lot of clinical data collection and generation are required before therapy. In this regard, a machine learning-based support system can help healthcare professionals in a variety of situations. It has significant predictive ability for this type of problem and has been extensively used in recent years in a variety of sophisticated healthcare systems –20. Morepository . The Chipository . The entThe contributions of this study may be summarized as follows: (1) development of a suitable predictive model from raw data, (2) determination of critical factors influencing post-BMT survival, and (3) improvement of the prediction accuracy by reducing dimensionality problems. To the best of our knowledge, this dataset has never been exploited and analyzed in this way before, and it may appear as a significant contribution in helping the healthcare industry to develop a more trustworthy e-healthcare system and create a new horizon in the medical sector.C-index of 0.670 utilizing the ensemble model [In recent times, ML techniques have been extensively exploited for diagnosis, prognosis, and therapeutics in the healthcare sector. Its applications are not limited to treatment procedures; rather, they are expeditiously gaining traction in a variety of research fields. In a narrative review, Nathan et al. highlighted essential ML concepts for novice readers, discussed the applicability of ML in hematology-related malignancies, and indicated key points for practitioners to consider before evaluating ML studies . Vibhutile model . On the le model . InspireIn this study, an ML stratagem was adopted for eliciting a prediction of the survival rate of patients who had BMT or HSCT. All previous works augmented the prediction study and related investigation through distinctive strategies; however, all have limitations that need to be overcome. The sole purpose of this research is to investigate whether HPO along with a reduced feature set can provide a reliable outcome using an investigative ML approach and to distinguish the most impactful factors on children's survival who have received BMTs. A preprint has previously been published in .Jupiter Notebook and Python to determine the dataset's properties.The dataset used in this study was retrieved from the ML repository at the University of California, Irvine, and the version utilized in this study was extracted from . It coveAs a type of statistical procedure, Chi-square tests are used to determine the level of independence between categorical variables. It is also a widely used nonparametric method for parametric and normal distribution testing of nominal data . This teAfter preprocessing the data, which includes filling in missing values, encoding categorical variables, and normalization, the Chi-squared statistical test is used to determine the attributes' independence. The top attribute in this list is “PLT recovery,” followed by “ANC recovery,” “time_to_acute_GvHD_III_IV,” “survival_time,” and so on. The summary of the test on this preprocessed dataset is shown in Supplementary Materials  and random search cross-validation (RSCV) are two HPO processes that work well for a variety of ML tasks . HPO is Following early data analysis, the dataset underwent multiple preprocessing stages before being used in the machine learning models. First, the dataset underwent multiple preprocessing stages before being used in ML models. The missing values of the dataset were filled with mean values for numerical ones and the most frequent values for categorical ones. Since categorical data cannot be handled by ML models, the categorical variables were encoded into a numerical form. The dummy variable encoding technique was employed for this purpose, and the attributes were turned into Boolean attributes that could readily fit into any ML model . Second,As mentioned earlier, a total of four distinct experiments, A, B, C, and D, were carried out in this study. In experiments A and C, no HPO was performed. However, in experiments B and D, the train dataset was cross-validated using GSCV to determine the optimum hyperparameters of the ML model. After training the ML models, the test dataset was fed to evaluate the performance of various models. Finally, all performance metrics were calculated, and various comparisons and analyses were performed to determine the impact of hyperparameter tuning and the use of the full-feature dataset and the reduced dataset, in which attributes were chosen based on the results of the Chi-squared test. This research was entirely carried out on an Intel Core i5-8300H CPU operating at 2.30 GHz, 8 GB of RAM, and an NVIDIA GTX 1050 Ti graphics unit with 4 GB of GPU memory using Jupyter Notebook v6.1.4 (Python 3 v3.8.5) and Anaconda-v4.10.3.F1, and ROC_AUC values) of the ML models are summarized in F1 score, and DT has the highest recall and ROC_AUC.This experiment was conducted using the processed full-feature dataset with no optimization of model hyperparameters. The dataset for this experiment has 58 attributes and 1 objective attribute. F1 score, whereas LR, GBC, and AdB have the highest recall and ROC_AUC.In this experiment, the full set of features of the dataset was utilized along with HPO. The training dataset was cross-validated 10-folds using GSCV to determine the optimal hyperparameters and using which all other performance metrics were assessed. The performance metrics of the ML models are summarized in F1 score, recall, and the ROC_AUC value. However, in regard to precision, RF, LR, KNN, GNB, and XGB all perform the same.This experiment was conducted considering the top 11 features of the dataset, obtained from the Chi-square test. The ML models were initially trained on the selected training set and subsequently verified on the test set using the default hyperparameters without any sort of optimization. The performance metrics are reported in Similar to experiment C, experiment D is conducted using the reduced feature dataset with HPO. As before, HPO was performed using GSCV with 10-fold cross-validation to determine the optimal hyperparameters. The reduced dataset comprised of 11 attributes, and the performances were evaluated based on them. The performance metrics are reported in F1 score (0.9523), and ROC_AUC (0.953). In terms of accuracy, the best algorithms are DT, LR, GBC, and AdB. However, in experiment B, the maximum accuracy was 94.73%, and the precision, recall, F1 score, and ROC_AUC were 1, 0.9523, 0.9545, and 0.9467, respectively. The well-performing algorithms were the same as in the experiment A, but the overall performance was improved in this experiment since HPO was performed with the 10-fold GSCV method. On the other hand, the experiments C and D are carried out based on the Chi-square test results. The top 11 features from the dataset were extracted, and this reduced dataset was employed in these tests. In experiment C, the maximum accuracy of 92.1% was obtained for KNN, with precision (0.9047), recall (0.95), F1 score (0.9268), and ROC_AUC (0.9229). In experiment D, HPO was performed on the same reduced dataset as in experiment C. This time, the performance of all ML algorithms improved significantly, DT having the best performance measures. The performance measures for DT in experiment D are as follows: accuracy (0.9473), precision (0.9523), recall (0.9523), F1 score (0.9523), and ROC_AUC (0.9467). The graphical presentation of the comparative analysis of experiments A, B, C, and D is illustrated in Figures 9As can be seen, the overall study included four experiments with four different approaches. The whole feature dataset was employed in the experiment A without HPO, and the maximum accuracy was found to be 94.73%, as were the precision (0.9523), recall (1), F1, and ROC_AUC. The top five critical attributes established in this study are “PLT recovery,” “ANC recovery,” “duration of acute GvHD III IV,” “survival time,” and “recipient body mass.”Based on the above four experiments, it is evident that HPO is critical to enhancing the performance of the ML algorithm, and the Chi-square test plays a significant role in determining the most important feature. The computation time of GSCV using complete and reduced feature datasets is shown in Previously, researchers employed a prediction method to predict the survivability of patients receiving BMTs. For instance, Gudys et al. employed a rule-based predictive model in this dataset and produced a tool named RuleKit for predicting BMT survival rates in children . LikewisF1 (0.9545), AUC (0.9523), and the top five attributes that influence the survivability rate are “PLT recovery,” “ANC recovery,” “duration of acute GvHD III IV,” “survival time,” and “recipient body mass.” Historically, this dataset has not been evaluated in such a manner before, and it could provide the health sector with a unique perspective. Therefore, this study can make a noteworthy contribution to the development of ML-based healthcare prediction systems in environments where resources are scarce and healthcare practitioners lack more data. The current algorithm performs admirably with our tested dataset and appears to be effective in the clinical phase. This model might be deployed in the clinical phase in the future, and a clinical trial could be done to evaluate and improve the model to make it more robust and trustworthy. To take full advantage of this type of support system, healthcare professionals and patients need be trained on how to use the technology.The bone marrow transplant is a crucial life-saving treatment for a certain type of malignancy. For this reason, early detection of survivability after BMT can play a vital role in the patient's treatment process. Moreover, if healthcare providers have a prior prediction, they can make more informed decisions about treatment options. In this regard, technologies like ML can be useful, since they can be used in situations requiring prediction and can uncover hidden patterns in previous data to create an accurate prediction. Nowadays, it is increasingly being employed in every situation that requires prediction. In this study, we developed a Chi-square feature selection method and an HPO-based efficient model for predicting the survival of children who received BMT and identified the most significant parameters for survival after BMT. All four experiments that were conducted yielded satisfactory predictions. The models operate well on a synthetic dataset that has been constructed from the raw dataset via a series of preprocessing phases that reduce the dataset's dimensionality. On the entire feature synthetic dataset, the experiment A achieves an accuracy of 94.73%. However, as experiment B optimizes the hyperparameters using the same dataset as experiment A, it achieves the highest overall performance of all models. On the other hand, experiments C and D use the 11 most correlated feature dataset based on the Chi-squared test, and experiment D outperforms all performance measures when combined with HPO, achieving high accuracy (94.73%) with less time, data, and resource consumption. In this study, we obtained the maximum accuracy (0.9473), precision (1), recall (1),


Mycobacterium tuberculosis (MTB). Most infection lacks traditional signs. Latent TB is the name given to this ailment. Of these latent infections, 10% become active and cause illness. Fever, night sweats, a prolonged cough with blood-containing mucus, and weight loss are common signs of active TB infection. Diabetes, on the other hand, is a group of metabolic illnesses characterized by elevated serum glucose levels. It is a chronic metabolic condition brought on by a deficiency in insulin secretion or resistance. It is of two types, that is, type 1 and type 2. Among all the cases of diabetes, the occurrence of type 2 is more common and less fatal than type 1. The prevalence of diabetes is currently increasing in low- and middle-income nations. As both diabetes and TB come under the most widespread chronic condition; therefore, their combined effect is evaluated. In recent years, the higher occurrence of TB in patients with hyperglycemia has come to light. People with elevated blood glucose levels exhibit several risk factors that make them more vulnerable to contracting TB. This review provides information on epidemiological data about the prevalence of TB in patients with hyperglycemia. In addition, this paper discusses the immunological underpinnings of TB development in patients with diabetes mellitus and how glycemic management reduces the risk of TB infection. It illustrates how the clinical signs and radiographic evidence of TB differ between people with diabetes and healthy people and mentions diabetes and TB combined management.Tuberculosis (TB) is one of the most widespread and infectious diseases in the world, which is brought on by  Mycobacterium tuberculosis (MTB) is the microorganism that causes the highly contagious disease tuberculosis (TB), which mostly affects the lungs. It may also impact the colon, meninges, bones, joints, lymph nodes, skin, and other body tissues. Two types of TB are known. One is bovine TB, in which the cattle are affected, which can sometimes be transmitted to humans. Another is human TB affecting human beings. In human TB, pulmonary TB is a major and serious health issue affecting the population worldwide despite the availability of efficient and compelling drugs and vaccines. Geographically, the highest number of cases taken down was from Southeast Asia (44%), Africa (25%), and Western Pacific (18%). India accounts for 26% of cases and is the utmost burden country, followed by Indonesia (8.5%) and China (8.4%). In recent years, the occurrence of TB has seen a downfall in developed countries, probably due to proper management. Still, the prevalence is high in countries having an increased number of HIV cases, a high number of malnourished children, poor living conditions, and poor TB control and management [ Pacific 8%. India, Africa 5%, and Wnagement . Type 2 ast Asia 4%, Afric Pacific 8%. IndiaEpidemiologyT2DM, a hyperglycemic disorder, has started affecting low- and middle-income countries due to rapid development and changes in food habits, with its strong prevalence in countries with high-socioeconomic status . AccordiDM is a risk factor for a rise in the reactivation of TBDM, a metabolic disorder, is a significant risk factor for relapsing latent TB infection among all the identified risk factors . Many unImmunological basis of development of TB in diabeticsA study conducted by Ayelign et al.  showed tImpairment of Innate ImmunityThe metabolic changes brought on by DM cause neutrophils, macrophages, natural killer cells, and other innate immunity-related cells to function less effectively . The alvImpairment of Acquired ImmunityT helper cells 1 (Th1) are crucial to the host's defensive system because of acquired immunity. The growth and multiplication of Th1 need cytokine IL-2, and Th1 is responsible for the generation of interferon-gamma, which worsens the nitric oxide (NO)-dependent death of macrophages . In indiA diagrammatic representation of the correlation between diabetes and TB is given in Figure Is prediabetes also a risk factor of rise for relapse of TB?Prediabetes, also called impaired glucose tolerance, is a condition reflecting high blood sugar levels, but not high enough to be considered in the type 2 diabetes category. A fasting plasma glucose level below 99 mg/dL is normal, between 100 and 125 mg/dL indicates a prediabetic condition, and above 126 mg/dL indicates diabetes. A correlation between prediabetes and increased risk of TB was tried to be established, although due to limited evidence, this fact is not proved yet. A cross-sectional survey and research on refugees in the United States having latent TB showed that 39.1% of the refugees were prediabetics, which raised a question among epidemiologists about the association of prediabetes with an increased risk of TB infection [Obesity and DM-TB2, is a significant risk factor for TB infection because obesity simultaneously increases the risk for diabetes, but overweight, that is, BMI between 25 and 30 kg/m2 without diabetes, reduces the risk of TB infection [Obesity, that is, a BMI of more than 30 kg/mnfection . Reducednfection .How glycemic control affects TBFor a long, people with DM and TB have been linked . NumerouHow to reduce the incidence of DM-TB: Is DM prevention worth it?Failure of DM prevention has focused the concept on its ways of prevention and treatment to reduce the incidence of TB. A program named Diabetes Prevention Program was a lifestyle change program initiated to prevent diabetes by changing the lifestyle of people and by using metformin. This program showed a fall in several cases of diabetes but for only a short period. After a long-term follow-up, it was disclosed that this program only delayed the onset of diabetes but could not prevent it . This prRadiographic findings in DM-TB patientsThe radiographic finding varies with the duration of illness and host immunity. In a study by Dooley and Chaisson, lower lung involvement was seen in people with diabetes with TB, while the nondiabetics showed infiltration in the upper lobe . AnotherAmalgamated management of diabetes and TBIndia is the highest-burden country with coexisting cases of diabetes and TB worldwide . Hence, However, more studies are required to determine the exact role of metformin in cavitary TB patients as, until now, it cannot be used as general adjuvant therapy for diabetic patients with coexisting TB infection. Apart from the glycemic levels, other things are also present in DM-TB patients, which should be considered, and proper treatment and management should be provided. For example, an increased mortality rate was found in DM-TB patients from cardiovascular disease, as mentioned in studies held in Taiwan and Tanzania .This review focuses on the increasing prevalence of pulmonary TB in diabetics, thus highlighting the importance of early screening and amalgamated management of diabetes and TB. It also highlights the immunological impairment seen in diabetics, which makes the individual vulnerable to TB infection. More epidemiological studies should be carried out to establish a more strong correlation between these two diseases and to determine the exact pathophysiology responsible for it. However, no adequate guidelines for the amalgamated management of DM and TB are there, but controlling blood glucose levels reduces the risk. It is one of the major steps taken to reduce the incidence of pulmonary TB in diabetics. Other associated factors in DM-TB patients should also be considered for proper management, giving equal weightage to all. Overall, still many studies are required and should be carried out to verify the use of escalated and prolonged ATT in TB patients with associated diabetes. All possible investigations and interventions should be used to check and reduce DM in DM-TB patients, like glycemic control, management of cardiovascular risk, and periodic screening for DM.


MicroRNAs (miRNAs) are important regulators of gene expression and define part of the epigenetic signature. Their influence on human health is established and interest in them is progressively increasing. Environmental and occupational risk factors affecting human health include chemical agents. Benzene represents a pollutant of concern due to its ubiquity and because it may alter gene expression by epigenetic mechanisms, including miRNA expression changes. This review summarizes recent findings on miRNAs associated with benzene exposure considering in vivo, in vitro and human findings in order to better understand the molecular mechanisms through which benzene induces toxic effects and to evaluate whether selected miRNAs may be used as biomarkers associated with benzene exposure. Original research has been included and the study selection, data extraction and assessments agreed with PRISMA criteria. Both in vitro studies and human results showed a variation in miRNAs’ expression after exposure to benzene. In vivo surveys also exhibited this trend, but they cannot be regarded as conclusive because of their small number. However, this review confirms the potential role of miRNAs as “early warning” signals in the biological response induced by exposure to benzene. The importance of identifying miRNAs’ expression, which, once validated, might work as sentinel molecules to better understand the extent of the exposure to xenobiotics, is clear. The identification of miRNAs as a molecular signature associated with specific exposure would be advantageous for disease prevention and health promotion in the workplace. Benzene is a ubiquitous environmental pollutant produced by natural  and anthropogenic processes . . Occupat3, which refers to the annual average concentration in urban outdoors. Regarding occupational exposure, the American Conference of Governmental Industrial Hygienists (ACGIH) recommended a time-weighted average-threshold limit value (TLV-TWA) of 0.5 ppm (1.60 mg/m3). The Italian occupational exposure limit (OEL) value reported by Legislative Decree 81/08 [3 of air), but the Directive 2022/431 that will enter into force on 5 April 2024 will reduce it to 0.2 ppm . However, the Committee for Risk Assessment (RAC), which supports the European Commission by giving scientific opinions on OEL, proposed a value of 0.05 ppm to protect workers from leukemia, as well as other adverse health effects. The RAC’s opinion is based on the fact that a threshold based on the indirect genotoxic effects of benzene in workers can be used to derive a new OEL [In spite of these efforts to control benzene pollution, it remains one of the most dangerous contaminants in urban air. The maximum desirable value under the Directive 2008/50/EC on air quality in Europe [.5 ppm 1.0 mg/m3. ee 81/08 ) is 1 ppBenzene was identified as a Group 1 carcinogen and recognized by the International Agency for Research on Cancer (IARC) as a human carcinogen , with evExposure to benzene can lead not only to genetic mutations, associated with DNA and RNA damage, but also to inappropriate gene expression . MoreoveCaenorhabditis elegans in the early 1990s, where the miRNA (LIN 4) negatively regulated the gene LIN14. Afterwards, they were identified to play a major role in the post-transcriptional regulation of human genes [MiRNAs are single-stranded non-coding RNAs, which play important roles in a variety of cell processes  . Their ran genes . MiRNAs an genes . MiRNAs’an genes . They fian genes .Growing evidence that the expression of miRNAs is affected by several known toxicants suggests an important role of miRNAs in toxicology, which could provide a link between environmental influences and gene expression . MiRNAs’The aims of this review are as follows: (a) to understand the potential biological implications of miRNA expression/profile changes induced by benzene exposure in order to better understand the molecular mechanisms through which benzene induces toxic effects, particularly in the bone marrow; (b) to compare the results from in vitro, in vivo and human studies; and (c) to evaluate whether selected miRNAs may be used as biomarkers of exposure and/or effect associated with benzene exposure.A systematic search was performed in accordance with the PRISMA guidelines ,37. A liOnly studies with original data on benzene exposure and miRNA profile were acceptable for this review. Original papers published in English, Italian or Spanish until 30 August 2022 were included. For more than one article on the same study, the most recent or comprehensive one was chosen. The selection was made by first reading the abstract and then the full article. Studies were excluded in accordance with the following criteria: (1) case reports, meta-analysis, reviews and letters; (2) duplicated data and incomplete information studies; and (3) papers not written in English, Italian or Spanish. Two evaluators carried out electronic searches independently using the inclusion and exclusion criteria. Discrepancies in interpretation and disagreements were resolved by a panel discussion and, if necessary, by consultation with the third author. Seven in vitro studies were considered and were selected on the basis of the exposure of different cell lines to increasing concentrations of a typical toxic metabolite of benzene, 1,4-BQ and HQ, the phenolic metabolite of benzene with a potential risk for hematological disorders and hematotoxicity in humans. Regarding studies on animals, only three papers were identified. They were conducted on mice using different routes  and timing of exposure (acute to chronic). The selected human exposure studies considered the following: (1) benzene exposure in the workplace; (2) retrospective studies; and (3) exposure to environmental pollution or smoking habits.All selected articles were compared on the basis of the results and various analytical methods were not taken into consideration. The bias assessment in the studies was performed using the Cochrane bias risk tool . ConsideRegarding ”other bias”, for in vitro studies, the toxic metabolites of benzene and different cell lines, dose and time of exposure were considered. For in vivo studies, the exposure conditions  were considered. For human exposure, the sample size, the number of controls (if present), the entity of exposure and the data source were considered.All selected in vitro studies were carried out by exposing cell lines to the highly reactive and bioactive metabolites of benzene responsible for its toxicity, specifically 1,4-BQ and HQ. 1,4-BQ is believed to be responsible for the myelotoxicity/myeloid neoplasms observed in the bone marrow of subjects who have been exposed to high levels of benzene, even if the molecular mechanisms are not yet completely defined . HQ, in In 2016, Chen et al.  exposed The next year, they demonstrated the upregulation of miR-34a in the U937 cell line following the same exposure conditions . 1,4-BQ exposure causes a gradual decrease in cell viability inducing apoptosis and dose-dependent cytotoxicity, as well as the upregulation of miR-34a . The role of miR-34a was assessed using lentiviral vector transfection. Inhibiting miR-34a increased the Bcl-2 protein which reduced apoptosis induced by 1,4-BQ, demonstrating that miR-34a is involved in benzene-induced hematotoxicity through Bcl-2 .Regarding HQ, to identify the miRNAs specifically involved in benzene exposure and their regulatory role in erythroid differentiation, Liang et al.  performeMiRNA analysis revealed that miRNA-451a, miRNA-486-5p and miRNA-126-3p expressions were significantly lower in HQ-treated CD34+ hematopoietic progenitor cells from a concentration of 5.0 μM with respect to the untreated cells. Jiang et al. showed that the expression levels of miR-221 were significantly increased in HQ-transformed malignant cells (16HBE-T), relative to the normal controls, and that the exposure of the control cells to exosomes, which were derived from HQ-transformed malignant cells, increased miR-221 levels and promoted their proliferation . SimilarFew in vivo studies have been conducted to identify the expression of miRNA after benzene exposure. Based on the selection criteria, three articles were identified where mice were exposed to either benzene alone ,47 or inLiang et al.  evaluateFinally, Wang et al.  evaluate3) and 58 unexposed controls  were recruited for miRNA validation, as well as genotoxicity and DNA repair capacity (DRC) studies. MiR-222 expression was upregulated among both benzene-poisoned and benzene-exposed workers, together with an inverse association with DRC [p < 0.01), while miR-122-5p and miR-638 were significantly downregulated  in the CBP patients. Compared with the non-exposures, the plasma level of miR-638 was significantly upregulated , while the plasma levels of miR-122-5p and miR-221-3p were significantly downregulated  in the exposures [p ≤ 0.05) [3 and 0.05 ± 0.01 mg/m3, respectively. MiRNA-34a and benzene metabolites were determined, and positive correlations were found. Moreover, miR-34a was correlated with peripheral blood counts, alanine transaminase (ALT) and oxidative stress. In the multivariate canonical correlation analysis (CCA), miR-34a and oxidative stress indicators were significantly correlated with peripheral blood counts and ALT. In addition, miR-34a showed the greatest contribution of variables and was significantly associated with the other set of variables, particularly with WBC [3 and 0.06 ± 0.01 mg/m3, respectively. The MiRNAs and apoptotic pathway were examined. Caspase-9 and Caspase-3 were upregulated, while miR-133a expression decreased in benzene-exposed workers. Pearson’s correlation analysis showed that miR-133a was reversely correlated with pro-apoptotic gene Caspase-9 [Epidemiological studies that have been carried out to evaluate the association of benzene exposures with aberrantly expressed miRNAs are summarized in with DRC . In anotxposures . To gain ≤ 0.05) . To explwith WBC . In anotaspase-9 .p < 0.05). The levels of benzene and miR-221 in the exposure group were both significantly higher than in the control group (p < 0.05) and there was a significant positive correlation between the two indexes . An association between benzene levels and ΔCt values  for miR-221 was identified by univariate and multivariate logistic analysis  [However, another study was carried out over the period from May 2011 to October 2014 in southern China with 97 petrol station attendants as the exposure group and 103 general residents as the control group. Plasma benzene was analyzed by using GC/MS. miR-221 in peripheral blood lymphocytes were measured by qRT-PCR. The results showed that the air concentrations of benzene were significantly higher in petrol stations than in the control sites  .Benzene is a pervasive occupational and environmental contaminant that affects human health ,2,3. It The results of the in vitro studies demonstrate and reinforce the use of this model, together with the bioinformatics approach, to understand the molecular mechanisms of benzene toxicity. In this perspective, it is important to determine the role of miRNAs in the activation process to understand which miRNAs are involved and activated, as well as how, to study them in the following animal model.The toxic effects of benzene on the human body are mainly mediated by its bioactive metabolites, among which HQs and BQs are the major representative. The study of Yu et al. 2022  reinforcIn vitro studies, in addition to clarifying molecular mechanisms, may also be used to support evidence from epidemiological studies by which, in addition to quantifying the incidence of pathological conditions, biomarkers of exposure/effect can be identified. This is the case in Chen’s studies ,40 whereOther studies have only considered an epidemiological approach with no in vitro model. The study of Liu et al.  exploredMany articles have studied the effects of in vivo benzene exposure, but very few take the role of miRNA into consideration, and only do so in mice. The study of Liang et al.  showed tEven if the profile of miRNA expression following benzene exposure varies from cultured cells to animal models and to adults, it is clear that their alteration is associated with chemical exposure, as has already been reported by other authors ,64.In light of various studies, how should miRNAs be considered: biomarkers of “dose” or “effect”? A biomarker of “dose” indicates the dose of xenobiotic absorbed by an organism and represents the contribution of all possible sources, but not the existence of an adverse effect. If the biomarker of “dose” is associated with a biomarker of “effect”, the relationship between exposure and biological modification (observed response) can be established ,66. IndeHowever, there are also limitations in considering miRNAs as biomarkers of effect. Firstly, by their nature, miRNAs are non-specific biomarkers ,69, and Another factor to consider is that miRNAs may change over time and during a lifespan depending on different physiological and life conditions  ,72. ConsDespite this, by integrating the expression data of miRNA and mRNA, it was shown that the different expressions of miRNAs are implicated in the molecular pathway of benzene toxicity. This opens new paths of prevention against chronic benzene poisoning through miRNA pharmacological interventions.Another aspect that should be studied is the long-term effects of benzene exposure . It willMiRNAs are biomarkers also able to offer great potential in the occupational medicine field, and they could also be used to understand the mechanism of benzene carcinogenicity. In fact, they are present in all human biological fluids, and they are easily measured by means of commercial kits. Therefore, the use of miRNAs may help to identify the effects of a specific exposure with benefits for disease prevention and health promotion.This study confirms the potential role of miRNAs in the biological response induced by environmental/occupational carcinogens. It also underlines the importance of identifying miRNA signatures specific to benzene exposures, which, once validated, will help to improve workplace safety conditions.


The aim of this study is to evaluate the safety and efficacy of dexmedetomidine in bronchoscopy through a systematic review. (2) Methods: PubMed, Embase, Google Scholar, and Cochrane Library electronic databases were searched for a randomized controlled study of dexmedetomidine (Group D) or other sedative drugs (Group C) for bronchoscopy. Data extraction, quality assessment, and risk of bias analysis were performed in accordance with the preferred reporting items for systematic review and meta-analysis requirements. Meta-analysis was performed using RevMan 5.2. (3) Results: Nine studies were included, with a total of 765 cases. Compared to Group C, the incidence of hypoxemia   Bronchoscopy is often used in the diagnosis and treatment of multiple lung diseases; flexible bronchoscopy is the most direct and effective method to check the airway . An invaDexmedetomidine is a drug which is actually US-FDA approved for procedural sedation, which also included bronchoscopy . DexmedeIn conclusion, compared to other anesthetic drugs, whether dexmedetomidine is more suitable for flexible bronchoscopy is inconclusive. We performed a systematic review and meta-analysis on the safety and efficacy of dexmedetomidine in patients undergoing bronchoscopy.https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020210380 (accessed on 22 October 2021)).According to the recommendations of the Cochrane Collaboration, a quantitative systematic review was conducted. The first step in this process is to define the research question by the PICO strategy . The metEmploying the PubMed, Embase, Google Scholar, and Cochrane Library electronic databases, we retrieved all randomized controlled studies on dexmedetomidine for bronchoscopy from the library establishment to 31 December 2021. Relevant studies and references of the included studies were manually searched. If the full text, relevant data, and study results are incomplete, then various means of communication are used, such as e-mail addresses, to contact the authors of the studies and request the detailed raw data. The search terms were subject and associated words for dexmedetomidine, bronchoscopy, and RCTs, and the search range was obtained without language limits.Studies were included in the present meta-analysis if they met the following inclusion criteria: ① patients undergoing bronchoscopy in outpatient clinics; ② a randomized controlled study comparing dexmedetomidine with other anesthetic drugs for bronchoscopy and without language limitation; ③ bronchoscopy is flexible bronchoscopy rather than rigid bronchoscopy or other types of bronchoscopy; ④ the outcomes of studies must include specific data on respiratory and cardiovascular complications and patient and bronchoscopist satisfaction. Studies with the following conditions were excluded: ① non-bronchoscopy, including bronchial ultrasound, bronchial needle aspiration, and bronchoscopic foreign body removal; ② different doses or dosing methods of dexmedetomidine; ③ review, newsletters, and abstracts; ④ studies with incomplete data; ⑤ repeated publication of the studies.The retrieved studies were screened by two authors (Qianqian Guo and Qi An) according to the inclusion and exclusion criteria and included in the analysis. If the two researchers disagreed about the inclusion of the study, another experienced researcher (Zhenggang Guo) was asked to evaluate it by understanding the point of debate and examining it according to the inclusion and exclusion criteria to decide whether it should be included in the analysis. Primary outcomes were hypoxemia, hypertension, hypotension, tachycardia, and bradycardia. Secondary outcomes were patient satisfaction, cough, laryngospasm, bronchospasm, and arrhythmia.Two authors (Qianqian Guo and Qi An) independently extracted data from the final included studies, and then reviewed by the senior author (Zhenggang Guo). To reduce subjective bias, the name of the author(s), publication year, and country were temporarily withheld, and the other data were finally extracted. The content was uniformly extracted according to the form made in advance in an Excel worksheet, including general information: the name of the author(s), date of publication, and the basic characteristics of the included studies: number of patients, age, weight, ASA grade, operation type, intervention characteristics and inclusion indicators.The methodological quality of the included studies was independently evaluated by two authors (Qianqian Guo and Qi An) according to the RCT evaluation criteria recommended by the Cochrane Intervention System Evaluation Manual (Cochrane Handbook for Systematic Reviews of Interventions) . If the p < 0.05 was considered statistically significant. The χ2 and I2 tests were performed for clinical heterogeneity in the included studies, and p < 0.10 and I2 > 50% showed that χ2 had statistical differences. If the outcomes of the studies had low heterogeneity , a fixed-effects model was selected and OR and 95% CI were represented as the results. Otherwise, the random effects model was selected for meta-analysis and RR and 95% CI were represented as the results. Subgroup analysis and sensitivity analysis excluding literature one by one were used to explore the causes of high heterogeneity.Meta-analysis was performed using RevMan 5.2. For binary variables, odds ratio (OR), relative risk (RR) and 95% confidence interval (CI) was used. Forest plots, OR or RR and 95% CI were represented as the results, and The electronic databases were explored according to the search strategy, resulting in 2040 studies, with 93 duplicate studies removed. A total of 1924 studies were excluded after reading literature titles and abstracts. Fourteen studies did not meet the inclusion criteria. Finally, nine studies were included with 765 patients see .All studies were randomized controlled studies of flexible bronchoscopy. Dexmedetomidine loading dose was 1 µg/kg in five studies ,27,28,29The risk of bias assessment for included studies is shown in p = 0.0001, I2 = 8%), and the difference was statistically significant, meanwhile the heterogeneity between groups was low (p = 0.26) and dexmedetomidine vs. lidocaine (p = 0.49) were not statistically significant. Dexmedetomidine vs. propofol (p = 0.02) and dexmedetomidine vs. opioids (p = 0.03) were statistically significant   .p = 0.62, I2 = 0%). The heterogeneity between the included studies was low, and there was no statistical difference , which was statistically significant, and the heterogeneity between the included studies was low , which was statistically significant, and the heterogeneity between included studies was low , and the heterogeneity between the included studies was high . The difference was not statistically significant, and the heterogeneity between the included studies was low . However, the difference was not statistically significant, and the heterogeneity was high . By subgroup analysis of control drugs, propofol and opioids increased the incidence of hypoxemia during flexible bronchoscopy, but midazolam and lidocaine had no significant difference in the incidence of hypoxemia compared with dexmedetomidine. This means that dexmedetomidine has less effect on breathing during flexible bronchoscopy, and more attention should be paid to patients’ oxygen intake when using propofol and opioids. Of note, dexmedetomidine was more likely to cause bradycardia than control drugs, and the difference was statistically significant (p = 0.0002), which may be related to its sympathetic inhibition. Although it can be corrected by giving atropine, it should be selected using caution for elderly patients, or patients with a high risk of cardiovascular events such as conduction block and arrhythmia.The results of this meta-analysis showed that dexmedetomidine had a lower incidence of hypoxemia compared with the control group (p = 0.62), and the heterogeneity was low. Similarly, there were five studies that counted the number of patients with hypertension [p = 0.06), and the heterogeneity was low. The results showed that there was no statistical difference between dexmedetomidine and control drug on hemodynamics in patients undergoing flexible bronchoscopy.Among the included studies, five studies recorded the number of patients with hypotension ,24,28,29rtension ,29,32. Tp = 0.2), but the heterogeneity of the literature was high, which may be inconsistent with the evaluation criteria of satisfaction. The subjectivity of the patients’ satisfaction expression was strong. So more clinical evidence is required to prove it.Because the evaluation criteria for patient satisfaction in the included studies were inconsistent, we formulated unified rules for the different evaluation criteria. Patients who clearly expressed satisfaction in the study and those who expressed willingness to accept the bronchial examination again were deemed to be satisfied with this examination; otherwise, they were deemed to be dissatisfied. The results show that there was no difference in satisfaction between Group D and Group C (In two studies ,27, therIn one study, there was one case of laryngospasm in Group C . Two stuThere are several limitations in this meta-analysis. First, a total of nine studies were included in this meta-analysis. Although all studies included hypoxemia, only a few studies included other outcome indicators, which may affect the reliability of the results. Secondly, due to limited studies on other indicators, subgroup analysis of these outcome indicators for different drugs could not be conducted. Thirdly, there is a study on children, who are different from adults in physiology and sensitivity to drugs, so meta-analysis may be biased. Finally, it is hoped that more randomized controlled studies with large samples and multi-centers can verify our conclusions in the future.During bronchoscopy, the use of dexmedetomidine can significantly reduce the incidence of hypoxemia in patients; however, it is necessary to be alert to the occurrence of bradycardia.


New Work Values Scale , a brief, 28-item, rating scale that covers a broad set of both, instrumental and symbolic, values, relevant for the appraisal of an employers’ attractiveness. We also applied the scale to a sample representative to the German online population, to explore the controversially discussed generational differences in work values . Results revealed that work values associated with sustainable organizational development or basic needs were highly similar across generations. Younger and older generations only differed significantly with regard to how much they valued clarity, money, career, development, stimulation, and relating, all of which are highly plausible from a lifecycle perspective.The “battle for talent” requires organizations to more strongly focus on employer branding strategies, and, thus, on work values or work orientations of potential candidates. We therefore developed and validated the  This represents a sharp increase compared to the last 16 years. The destabilization of the labor market, caused by the pandemic situation due to COVID-19, increases organizational difficulties to recruit well-matching personnel – or even personnel at all. Several sectors currently suffer from high vacancy rates, and new jobs seem to pop up faster than they can be filled . Instrumental attributes are suggested to be the primary constituents of employer branding that serve for the discrimination between a better and a less good employer . ResearcInstruments measuring instrumental and symbolic attributes within the employer branding framework traditionally focus on the one or the other , suggestNew Work Values Scale, Study 1). Referring to propositions indicating generational differences in employer branding strategies  Power, (3) Benevolence, (4) Universalism, (5) Security, (6) Tradition, (7) Conformity, (8) Self-Direction, (9) Stimulation, and (10) Hedonism  Career, and (3) Calling  Busyness. Social-embeddedness-oriented individuals are expected to work mainly for being part of a group or organization, whereas busyness-oriented individuals work to occupy their time (cf. Job orientation), but also fulfills basic psychological needs by providing status and identity (cf. Career orientation), collective purpose , social contacts , as well as activity and time structure (cf. Busyness orientation).In line with tain job . Work or Calling . Each on Calling . Individugh work . Willnereir time . Interesentreployee. Entreployees are suggested to work with increased organizational flexibility, self-organization and self-control, reduced hierarchy levels and with high amount of team and project work. Work orientations regarding the entreployee-concept were classified by nine different orientations: the need for (1) Efficiency, (2) Challenge, (3) Role Clarity, (4) Opportunity Optimizing Career Development, (5) Autonomy, (6) Security, (7) Spatial Flexibility, (8) Temporal Flexibility, and (9) Segmentation of the Work-Life-Domain  research has shown that poor person-job and/or person-organization fit is a major predictor of employee withdrawal e.g., . Thus, iStandardized and validated measurements are available for all reported concepts. However, to our knowledge, no measure exists that integrates the different approaches. Therefore, the first aim of the present research was to provide a brief measurement that combines all above reviewed aspects into one brief rating scale that can be economically administered in the context of employer branding.generation. In its roots, the idea of a generation was the reference to individuals born within the same historical and socio-cultural context who made comparable formative experiences within a set of historical events they experienced with more or less the same age. As a result, a generation was suggested as a set of individuals who hold to some extent “collective memories” (Baby Boomers (born between 1950 and mid-1960s), (2) Generation X (born between the early 60s and early or mid-80s), (3) Generation Y / Millennials (born between mid-80s and late 90s), and (4) Generation Z . Based on this, 15 distinct work values were defined  online access panel members and received bonus points for their participation that they could eventually swap for products. Excluding respondents with unreasonable response times led to a final sample of n = 316 individuals , aged 18 to 65 years  with levels of education ranging from compulsory (14.9%) to university (23.4%) levels . The majority (66.8%) was employed, 9.2% were in education, and the remaining participants were unemployed/out of the labor force.Three hundred and thirty individuals completed an online questionnaire including questions regarding demographic and employment-related characteristics, the newly developed items, and three well-established scales for the construct validity analyses. They were randomly selected from the German population of Job, Career, Calling, Social-Embeddedness, and Busyness with five items each on a 7-point response scale . Second, the Entreployee Work Orientation Scales .The 97 newly developed work values items  that were included in this study are reported in n Scales  assess tire PVQ;  was usedComfort and its associated items were excluded due to substantial overlap with other work values. Variance explained by the 14 factors was 62.74%. The items loaded on the expected factor, and most items demonstrated negligible cross-loadings. Reliability estimates were mostly acceptable to good  and Inclusion correlated most strongly with Universalism (PVQ); Job Security related most strongly to the value Security (PVQ) and the Need for Security (EWOS). Clarity and Flexibility demonstrated the highest correlations with the Need for Clarity (EWOS) and the Needs for Spatial or Temporal Flexibility and Segmentation (EWOS), respectively. Money related most strongly with Power (PVQ) and Job Orientation (WOQ). Career revealed the highest relationships with Achievement and Power (PVQ), Career Orientation (WOQ) and the Need for Career Development (EWOS). Development most strongly related to Career Orientation (WOQ) as well as the Needs for Efficiency (EWOS) and Achievement (PVQ). Stimulation/Autonomy had their highest correlations with the respective values  and needs . Also, in line with expectations, Meaning correlated highly with the Calling Orientation (WOQ), while Relating correlated most strongly with Social-Embeddedness Orientation (WOQ). Additionally, there were significant, but not as high correlations between Participation and the values Self-Direction, Universalism, and Benevolence (PVQ). Readiness for Change was expected to most strongly relate to low levels of the value of Tradition (PVQ), which, however, could not be confirmed by the results. Thus, this work value seems to capture aspects other than traditional values.Correlations of the 14 work values provided by the NWVS with the constructs of the three other well-established measurement instruments . As in Study 1, respondents (N = 1.115) received bonus points for completing the short online questionnaire that included socio-demographics and employment-related characteristics as well as the newly developed work values scale. Participants with very short response times and who were no longer part of the working population  were excluded, resulting in a final sample of n = 956 , aged 16 to 65 years  and with different levels of education . Three quarters (75.2%) were employed , 11.0% were in education, and 13.8% unemployed/out of the labor force.The representative German online sample was recruited with the help of the panel provider do not agree at all, 3 = part-part, and 5 = fully agree. A Confirmatory Factor Analysis (CFA) with the 14 work values modelled as latent constructs indicated by the respective two items provided good support for the proposed factor structure, Χ2(259) = 734.18, p < 0.001, Comparative Fit Index (CFI) = 0.957, Tucker Lewis Index (TLI) = 0.932, and Root Mean Square Error of Approximation (RSMEA) = 0.044. Factor loadings ranged from 0.63 to 0.89, and Spearman Brown reliability estimates from 0.67 to 0.87 , Generation X the cohorts 1965 to 1979 (30.2%), Generation Y the cohorts 1980 to 1994 (32.8%), and Generation Z the cohorts 1995 to 2009 (17.2%).Generations were defined based on respondents’ age, relying on conventional classifications cf. of Babybfemale and divers), education , and employment status  were included as covariates  was conducted with the 14 work values as dependent variables and generation as the independent variable. To control for potential confounding, gender  = 3.66, p < 0.001, part. η2 = 0.052. Significant univariate effects were found for six out of the 14 work values: clarity, money, career, development, stimulation, and relating (cf.cf.Clarity) and the two younger cohorts . Generation Z also hold significantly stronger values towards Development and Relating than Generation X. Results showed a significant multivariate effect of generation, revealing that, overall, younger cohorts hold stronger work values, ting cf.. Post-ho cf.cf. revealedThe present research was conducted within a context of dramatic deterioration in talent shortage and the resulting need for organizations to optimize their branding strategies, summarized by the term “employer branding.” Traditionally, employer branding has focused either on so called instrumental  or symbolic  attributes associated with the attractiveness of an organization as place to work. Consequently, scales measuring attributes in the context of employer branding rather focused on the one or the other . With StThe theoretical ground for our selection of the respective set of work values are well established need and value theories, namely the theory of basic personal work values , the semReliability analyses and exploratory as well as confirmatory factor analyses provide strong support for the 14-factor structure of the NWVS. All 28 items loaded on the expected factors and showed negligible cross-loadings as well as acceptable to good reliability estimates. Construct validity was also supported by correlational analyses between the NWVS and well-established measures. Taken together, the NWVS can be considered a sound instrument to briefly assess a wide range of different work values.During the last decade there has been an increase in literature discussing generational differences in work values, mainly with the focus on managerial concerns. Currently, the entrance of post-millennials, also called Generation Z, into the labor market, seems to create some tension, assuming that this young work generation will differ significantly from precedent generations. For instance, it is assumed that generation Z will behave more narcissistic, mentally instable, achievement-oriented, and socially interconnected than their predecessors . GeneratClarity: having a strong need for structure, rules, and guidelines that provide stability, consistency, and orientationMoney: being motivated by monetary rewardsCareer: considering career development opportunities as very importantDevelopment: always striving to be at the best and to therefore develop knowledge, skills, and competenciesStimulation: wanting variety, challenges, and much going onRelating: placing great emphasis on good social relationshipsReadiness for Change, CSR, and Inclusion), basic needs  as well as highly intrinsic aspects  were pretty similar across different generations. The main generational differences became apparent in individual motivators only and here between the two older compared to the two younger cohorts. Baby boomers and Generation X valued Clarity more than did Generation Y and Z, whereas Generation Y and Z reported stronger values towards Money, Career, and Stimulation compared to their predecessors. Additionally, results showed a significant difference between Generation Z and Generation X in regard to Development and Relating, with Generation Z putting more emphasis on these two work values.Thus, work values associated with sustainable organizational development (i.e., Development, considering that they are in the early beginning of their careers. Similarly, the high emphasis of Generation Z towards Relating might represent an age-related preference for intense social exchange, that might decrease as soon as people feel settled in their family and social lives. In general, our results can be considered as supportive for the theory of life-stages or life-cycles rather than the assumption of significantly distinct generational work identities (cf.It seems not surprising that the youngest work generation holds the strongest emphasis on ties cf..Interestingly, overall, all generations put a relatively low emphasis on money as the key motivator to work. Although this might appear surprising at a first glance, it is in line with the idea of Jahoda’s latent benefits of work. According to As the NWVS is a newly developed scale, of course more research is needed to further prove its validity. Another limitation might be represented by the fact that data collection took place in times of COVID-19 pandemic, which affected the labor market at a great pace. It can be hypothesized that this radical change also influenced participant’s work values that might have been rated differently before the labor market shock. Consequently, we consider our results representative for the current work situation, which is a post pandemic situation. Future research will be needed to explore situational differences vs. stability in employees’ work values based on the NWVS. We further want to mention that the NWVS solely considers the perspective of an employee or future job candidate by neglecting the perspective of the organization. It might represent a promising future project to work on a complementary tool, which addresses those attributes that an organization considers as important and essential for creating the organizational brand and attract the ideal job candidates.However, we would like to highlight that the NWVS is unique in its conception and should provide high practical value in the context of employer branding. Major advantages are the short assessment time and the broad coverage of work values that all have been shown to significantly contribute to the attractiveness of an employer’s brand. The NWVS can therefore be used by companies who consider to reevaluate their strategies to attract talents and/or improve the retention of already hired personnel, for example by implementing benefits and conveniences suited to the desires of their members. Furthermore, it can be used as convenient research instrument in the context of employer branding and employer attractiveness. In addition to this, the NWVS is also suitable for questions relating to person-organization fit. Having applied the NWVS to different generations, we agree with several other empirical examinations that failed to find considerable generational differences in regard to work attributes, preferences, and values (reviews, e.g., Babyboomers, Generation X, Generation Y, and Generation Z do not differ as much in their work values as postulated by popular media. Particularly, as long as work values refer to sustainable organizational development, basic needs, and highly intrinsic aspects, the generations seem to be highly similar. And the fact that younger as compared to older generations are more strongly motivated by Money, Career, and Development, or also by Stimulation and Relating, is highly plausible from a lifecycle perspective. For organizations, we therefore highly recommend to adopt a lifecycle perspective to attract and retain employees.Overall, our research supports the notion that The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.BS and BB contributed to conception and design of the study and organized the database. BS performed the statistical analyses. BS and MP wrote the first draft and sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The Johannes Kepler University Linz offered funding for open access publication fees.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Death due to cardiovascular diseases (CVD) increased significantly in China. One possible way to reduce CVD is to identify people at risk and provide targeted intervention. We aim to develop and validate a CVD risk prediction model for Chinese males (CVDMCM) to help clinicians identify those males at risk of CVD and provide targeted intervention.We conducted a retrospective cohort study of 2,331 Chinese males without CVD at baseline to develop and internally validate the CVDMCM. These participants had a baseline physical examination record (2008–2016) and at least one revisit record by September 2019. With the full cohort, we conducted three models: A model with Framingham CVD risk model predictors; a model with predictors selected by univariate cox proportional hazard model adjusted for age; and a model with predictors selected by LASSO algorithm. Among them, the optimal model, CVDMCM, was obtained based on the Akaike information criterion, the Brier's score, and Harrell's C statistic. Then, CVDMCM, the Framingham CVD risk model, and the Wu's simplified model were all validated and compared. All the validation was carried out by bootstrap resampling strategy (TRIPOD statement type 1b) with the full cohort with 1,000 repetitions.CVDMCM's Harrell's C statistic was 0.769 (95% CI: 0.738–0.799), and D statistic was 4.738 (95% CI: 3.270–6.864). The results of Harrell's C statistic, D statistic and calibration plot demonstrated that CVDMCM outperformed the Framingham CVD model and Wu's simplified model for 4-year CVD risk prediction.We developed and internally validated CVDMCM, which predicted 4-year CVD risk for Chinese males with a better performance than Framingham CVD model and Wu's simplified model. In addition, we developed a web calculator–calCVDrisk for physicians to conveniently generate CVD risk scores and identify those males with a higher risk of CVD. According to the World Health Organization, cardiovascular diseases (CVD) are the leading cause of global mortality, accounting for an estimated 17.9 million deaths  each year worldwide with an estimated 523 million prevalent CVD cases in 2019 .To reduce the CVD burden, one possible solution is to identify those at higher risk of CVD and offer them appropriate advice for a healthier lifestyle. Thus, numerous prediction models have been developed globally to estimate the risk of CVD, including the Framingham –7, SCOREIn this study, we conducted a retrospective cohort study using physical examination data from Tongji Hospital in Wuhan, Hubei Province, China. With the cohort, CVDMCM was developed following the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement . CVDMCM We conducted a retrospective cohort study to develop CVDMCM. The participants were Chinese males from the general population going through annual physical examination at Tongji Hospital in Wuhan, Hubei Province in central China. The physical examination department of Tongji Hospital is the biggest physical examination center in Wuhan.Inclusion and exclusion criteria: we included Chinese males aged 30–74 years old who took physical examinations at Tongji Hospital between 2008 and 2016 and had at least one revisit record by September 2019. The baseline record was defined as the first time a participant took the examination between 2008 and 2016. At baseline, participants with the following conditions were excluded: those with a congenital heart disease and ischemic stroke, other heart diseases , a malignant tumor, or a history of liver or kidney failure. If the participants had more than one revisit record and no outcome event occurred by September 2019, we used their last revisit date and their free-of-CVD status. If there was an event by September 2019, we used their CVD event reporting date and CVD event status.The study was approved by the Medical Ethics Committee at Tongji Medical College, Huazhong University of Science and Technology who waived the written informed consents (TJ-IRB20191215). Participants identified with CVD would be recommended to go to see the doctor. No intervention was provided in the physical examination center.Based on prior knowledge, we included 17 potential predictors, including lifestyle characteristics, clinical measurements, and medical history indicators collected by the trained doctors at the physical examination center of Tongji Hospital through standardized in-person interviews . The potThe CVD outcomes included coronary heart disease (CHD) and ischemic stroke. CHD was mainly diagnosed according to symptoms (mainly angina) and electrocardiography or coronary angiography. Echocardiogram, exercise stress test, coronary artery calcium scan, cardiac catheterization or chest x-ray were also used in some patients to assist the diagnosis of CHD. Patients who self-reported having coronary heart disease, coronary artery bypass grafting, coronary stent implantation, percutaneous coronary intervention, or percutaneous transluminal coronary angioplasty in the follow-up visits were also considered to have CHD. Ischemic stroke was diagnosed according to symptoms and cerebral infarction and confirmed by computed tomography or magnetic resonance imaging.2. Based on the three criteria, sample size in the cohort larger than the calculated minimum sample size required was considered as sufficient. The details are shown below:We calculated the minimum sample size required for the prediction model according to Riley's guidance . By trea2 value was assumed to be 0.25, according to existing CVD risk prediction models shown in Siontis et al., with up to 20 parameters  . We compvia calculation of Harrell's C statistic and D statistic. For the model to be acceptable, its Harrell's C statistic should be at least 0.7. If its Harrell's C statistic is over 0.9, it shows that the model has excellent predictive power  were associated with CVD in the age-adjusted univariate Cox regressions, thus were selected. The results of age-adjusted univariable statistics were provided in All the CVDMCMs were internally evaluated in the whole dataset with 1,000 times bootstrap. The results were shown in We calculated the age-standardized CVD prevalence rate of Chinese males according to Liu et al., which was 3.14 . An indivia Harrell's C statistic and D statistic. As shown in P-values smaller than 0.001.We then compared the CVDMCM model performance with Framingham and Wu's model by bootstrap resampling the full cohort with 1,000 replicates. We first compared the predictors of CVDMCM, the Framingham CVD risk model, and Wu's model. As shown in For an easier application of CVDMCM among the clinicians, we provided one example of an individual Mr. X's 4-year CVD risk. A 54-year-old Chinese man, Mr. X, who is a non-smoker with a total bilirubin of 12.30 μmol/L, ABI of 3.39, and SBP of 130mmHg, has a 4-year CVD risk of 2.39%. The calculation is shown below:So, the 4-year CVD risk ishttps://ctan2020.github.io/-calCVDrisk-/) for the physicians to simply enter the parameters obtained from the physical examinations, and the calculator would report the score for the physicians to assess the CVD risks.Since his CVD risk is lower than 3.14%, he would be classified in the low-risk group. If someone has a CVD risk calculated higher than 3.14%, he would be classified in the high-risk group, and the physicians taking the physical examination could advise him for a healthier lifestyle. To make the CVDMCM more convenient for the physicians, we have developed an online calculator calCVDrisk  and demonstrated a better performance than that of the Framingham CVD risk model and Wu's simplified model in the internal validation by bootstrap resampling the full cohort as found in Harrell's C statistic, D statistic, and calibration plot , 18.2 indices, which was more objective. Fourth, for an easier application of our finding, we have developed an online calculator calCVDrisk for physicians to assess the CVD risk scores among those taking physical examinations.There were several strengths of this study. First, we targeted a comparatively high-risk population for CVD, namely Chinese males. Given that CVD is the leading cause of death in China accounting for over 43.6% of all deaths in China, which was an increase from 12.8% in 1957 and 35.8% in 1990, immediate action is needed to change the increasing trend . Thus, wCompared with the classic CVD models, such as the Framingham –7, SCOREIn our study, individuals in CVD group had older age, higher baPWV, lower eGFR, higher hemoglobin A1c, higher SBP, lower total bilirubin than those in Non-CVD group. Higher baPWV indicates arteriosclerosis, and higher hemoglobin A1c indicates abnormal glucose metabolism. These two indicators have been proven to be associated with higher risk of CVDs , 33. LowTotal cholesterol and diabetes mellitus were not included in the model. Zhu et al. indicated that aging is considered as a major risk factor for development of type 2 diabetes . Nunes eTo better utilize CVDMCM, we developed an online calculator calCVDrisk for physicians to calculate the CVD risk of a Chinese male when taking the physical examination. We expect that CVDMCM will be promising in the application as it is a simple model with only five predictors. A previous study has shown that physicians were more willing to use simpler models in clinical practice than more complex ones when they had comparable performance . The estWe believe that the CVDMCM could be helpful for medical doctors in the physical examination departments in China to identify those at higher risks of CVD in 4 years. For health care providers, the integration of a better CVD risk prediction model in their workflow would enable them to track the health statuses of those with high-risk scores so that doctors could provide advice for lifestyle changes and potentially help reduce CVD risks in those patients.We admit that there are some limitations of this study. First, our model was targeting Chinese males only. As people of different ethnical background would have different risk factors, it is better to have tailor-made models for different populations. No females were included as our aim was to construct the CVD prediction model of Chinese male, given their higher risk of developing CVD . Second,We conducted a retrospective cohort study for developing and validating a novel CVD prediction model, CVDMCM. CVDMCM predicted 4-year CVD risk with a better performance than that of the Framingham CVD model and with fewer predictors than those involved in the Wu's simplified model. Thus, this model could be helpful in clinical practices to detect patients at higher risk of CVD, providing appropriate feedback for patients' health.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Medical Ethics Committee at Tongji Medical College, Huazhong University of Science and Technology. The patients/participants provided their written informed consent to participate in this study.YSh, YaZ, CZ, and TL conceived the project and designed the research study. LR, YuZ, and YSa collected the data of the cohort. CT developed the website. YSh, YL, BC, and LY analyzed data. YSh, YuZ, YaZ, YL, BC, YB, and JL wrote the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Key Research and Development Program of China (Grant Number 2020YFC2008002) and Major Technology Innovation of Hubei Province (Program No. 2019ACA141).YSh, YaZ, YL, BC, LY, CT, YB, JL, MJ, and TL were employed by BGI-Shenzhen. The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.The contents do not reflect official views from the Hubei provincial government.