This improvementin model accuracy suggests that transfer-learning models provide asolution for narrowing the knowledge gaps and can improve the accuracyof mapping long-term near-road air pollution concentrations usingshort-term on-road mobile monitoring data.Mobile measurements are increasingly used to developspatiallyexplicit  air quality maps using land-use regression (LUR)models. The prevailing design of mobile monitoring campaigns resultsin the collection of short-term, on-road air pollution measurementsduring daytime on weekdays. We hypothesize that LUR models trainedwith such mobile measurements are not optimized for estimating long-termaverage residential air pollution concentrations. To bridge the knowledgegaps in space (on-road versus near-road) and time (short- versus long-term),we propose transfer-learning techniques to adapt LUR models by transferringthe mobile knowledge into long-term near-road knowledge in an end-to-endmanner. We trained two transfer-learning LUR models by incorporatingmobile measurements of nitrogen dioxide (NO The proposed transfer-learning LUR modelcan map long-termnear-road air pollution concentrations in fine spatial resolutionby integrating mobile and long-term near-road measurements. With substantial spatialcoverage, mobile measurements are increasingly used to build land-useregression models (LUR) for estimating air pollution concentrationswith high spatial resolutions for large spatial areas.10 argued that the temporal scarcityof mobile measurements poses a challenge of representing long-termconcentrations. They found only a modest correlation between mobileand nearby long-term measurements in a large mobile monitoring campaignin Oakland. In addition to the temporal difference, since all mobilemeasurements are on roads, there is also a spatial difference betweenon-road measurements and near-road concentrations. Kerckhoffs et al.found that nitrogen dioxide (NO2) and ultrafine particle(UFP) predictions made by LUR models based on mobile monitoring areapproximately 15–30% higher than home-outdoor stationary measurements.11 Moreover, potentially differentcollection instruments used in mobile campaigns and long-term monitoringnetworks also contribute to the systematic difference between mobilemeasurements and the target long-term measurements.12Ideally, with multiple repeated measures on roads over a longperiodof time, the mean of mobile measurements should be able to fully representthe on-road long-term concentrations . However,several practical factors introduce additional biases to this representationwhen attempting at mapping residential air pollution using mobilemeasurements. Restricted by the length of campaigns and the numberof collection vehicles, usually only a few seconds can be measuredat each location, especially for large study regions. Chambliss etal.13 (hereafter referred to as the distribution ofdata instances over the sample space as the domain14). If this assumption is violated, the knowledge learnedfrom the training domain  will differfrom the knowledge in the target application domain . The knowledge here refers to the associationbetween covariates and the response (concentrations). This differencein knowledge between the training and the target domain is definedas the knowledge gaps. Such knowledge gaps result in models basedon solely mobile training data not being optimized for predictionaccuracy.16 This problem is widely recognized as domain shiftsin computer science research.These spatiotemporal and instrumentaldifferences between mobiletraining measurements and the target long-term concentrations makethat the conventional empirical LUR model contradicts one of the coreassumptions of supervised learning methods. Namely, the training andpredicting instances must be subject to a similar distribution17We propose one domain adaptationand one boosting-based transfer-learningalgorithm to bridge the knowledge gaps between the mobile and thelong-term near-road domain in an end-to-end manner. These methodscan directly or indirectly incorporate long-term measurements withmobile monitoring data to adapt the training of LUR models. The coreidea is to reweight the training goal from minimizing the loss functionin the mobile domain into optimizing the loss function in the desiredlong-term domain and thus efficiently fit machine learning (ML) modelswith more appropriate parameters.2 andUFP.11 Two transfer-learning LUR modelswere compared to the mobile LUR model based on random forest (RF_LUR)and stepwise linear regression (SLR) trained with mobile measurementsonly. Prediction accuracy was evaluated by external long-term near-roadvalidation data collected by routine monitoring campaigns.This paper describes to what extent the end-to-endtransfer-learningLUR model can bridge the knowledge gaps between the mobile on-roaddomain and the long-term near-road domain in order to boost the accuracyof mapping long-term air pollution near roads. We limited the goalas near-road air pollution because most long-term routine monitoringsites in the study area (Amsterdam) were deployed near roads. We useddata from a 10-month mobile monitoring campaign in Amsterdam, wheretwo Google Street View cars continuously measured NO22.12 and UFP were collected on weekdaysbetween 08:00 and 22:00 measured by the CAP sensor and the MiniDiSCsensor, respectively. Both sensors show a high correlation comparedto the stationary measurements in previous studies.19 A total of 5.9 million measurements foreach pollutant were recorded, along with timestamps and geographiccoordinates, covering almost all streets of Amsterdam. Mobile measurementsof both NO2 and UFP were temporally corrected accordingto one reference site located in a suburban area of Amsterdam, awayfrom traffic sources. Details of the data collection and preprocessingcan be found in our previous work.11Themobile measurements used to train the LUR models were collected bytwo Google Street View (GSV) cars in Amsterdam from 25 May 2019 to15 March 2020 (stopped due to COVID lockdown policy). Briefly, 1-secondmeasurements of NOn = 46,664). The mobile measurement points weresnapped to the nearest road segment . The mean value of the snapped measured points was set asthe mobile measurements of the corresponding road segment. Drive passeswere defined as the number of days that the collection vehicles drovethrough a road segment. On average, each street segment consistedof 3–10 s measurements per drive-pass and eight unique drive-passes.The road network in Amsterdam was divided into 50 m road segments trafficvariables from the national traffic databases in the Netherlands suchas traffic counts and road types;21 and(3) population density data from the Netherlands Environmental AssessmentAgency.22 The specific variables includingevaluated buffer sizes are summarized in Appendix Table S1.The predictors usedto predict air pollution concentrations consist mainly of three components:(1) land use from the Copernicus CORINE 2018 dataset,2.32 measurements collected by the Dutch Municipal HealthService (GGD) were used.23 This data consistsof repeated 4-weekly average passive measurements by Palmes tubeslocated on road-side building façades and lampposts. A totalof 82 monitoring sites were located within 30 m of road segments andhad complete data during the time period of the mobile monitoringcampaign. Their averaged distance to the centerline of the nearestroad is about 7 m. For UFP, we used data from the EXPOsOMICS studyencompassing 17 sites  measured continuouslyfor repeated 24 h on three different days in different seasons (3× 24 h) in Amsterdam from 2014 to 2015.24 The average of the measurements from the 3 days was used. Togetherwith the corresponding environmental predictors, they represent thelong-term near-road knowledge. The data flow and models used are summarizedin Long-termmeasurements refer to stationary measurements that consecutively measureair pollution at a location for a long period of time. These long-termmeasurements provide more temporal coverage but at a smaller numberof locations. We use the term “near-road” here for measurementsnear all roads including minor and major roads. We applied these long-termnear-road measurements to validate the accuracy of LUR models in termsof predicting long-term near-road concentrations and for the developmentof the transfer-learning LUR models. For this purpose, long-term PalmesNO2.411 Next, we implemented two transfer-learning LUR models to mitigatethe knowledge gaps between the mobile and the long-term near-roaddomain by incorporating mobile measurements with information derivedfrom long-term measurements.We implementedand compared four LUR models  TrAdaBoost,a boosting-based transfer-learning algorithm,16 and (2) Prior_RF, a domain adaptation technique.14Transfer-learningmethods can transfer the knowledge learned from a prior task as astarting point to train a new model on a different but related task,as this requires less training data.xs, ys) with the long-term observations(target instances)  to form a single dataset and assign equal initial weightsto each instance. These initial weights will be updated during eachboosting iteration, according to the absolute errors of predictingtarget instances. In this way, the source instances that are similarto the target data are emphasized (larger weights) while the differentinstances are de-emphasized.16 As an improved versionof TrAdaBoost.R2, Two_stage_TraAdaBoost.R2 adjusts instance weightsin two stages. In the first stage, at each boosting step, TrAdaBoostdecreases the relative weights of source instances that are differentfrom the target instances. In the second stage, the weights of allsource instances are frozen while the weights of the target instancesthat are different from the source instances are increasing.16TrAdaBoost is implemented as the Two_stage_TraAdaBoost.R2.TraAdaBoost.R2 is an adapted AdaBoost algorithm that is a popularboosting-based ensemble learning algorithm. Ensemble learning is anML paradigm where multiple models ”are trained to solve the same problem and combined to get better results.As an ensemble learning algorithm, boosting methods are designed totrain these weak learners sequentially in an adaptative way: eachweak learner in the sequence is fitted by giving more weights to instancesin the training dataset that caused higher errors by the previousweak learner in the sequence. Intuitively, each weak learner focuseson fitting the most difficult instances in each boosting iteration.At the end, these weak learners are combined to form a “strong”model that is accurate at predicting all the cases learned from thetraining instances. In our work, TrAdaBoost.R2 directly merges themobile monitoring data (source instances) (Rt(f) by minimizing the approximated riskfunction in the source domain Rs(f). Therefore, the distributions of the covariates and theresponse (concentration measurements) in the sample space betweentraining and prediction are required to be similar. When they aredifferent, the domain adaptation algorithms are used to push the Rs(f) closer to Rt(f) by re-reweighing Rs(f) with the ratio of the prior probabilitydistribution between source and target samples (14 RuLSIF (relativeunconstrained least-squares importance fitting) is then applied toestimate this ratio directly from the discrete observations of itsnumerator and denominator.26 Afterward, the traininginstances reweighted by this ratio are fed into the training of theconventional RF algorithm. Additionally, in this paper, to bettercompare the performance between RF_LUR and Prior_RF, the hyperparametersof Prior_RF are kept the same as in RF_LUR, such as number of trees,number of random splitting variables, and maximum of terminal nodes.Prior_RF is a domain adaptation methodthat reweights the riskfunction  of RFby the ratio that reflects the difference between mobile (source)and long-term (target) concentrations. The goal of training a conventionalRF model is to identify the parameters and structures of the modelfor minimizing the risk function in the target domain 2.4.211 The SLR model started with an empty model (intercept only) and thenvariables were added based on adjusted R2.7 Variables were only added when the direction of theassociation was as predefined, e.g., positive for traffic intensity(see the directions for each covariates in Appendix Table S1).The mobile LURmodel refers to the LUR model trained exclusively with mobile measurements.As a linear-regression-based mobile LUR model, the SLR model was implementedfollowing previously described criteria.4 Toavoid overfitting, the mobile training data were divided into a splitof 70% training and 30% test data. RF was trained on the 70% trainingdata based on the fivefold cross-validation. To obtain the best performance,the RF was fine-tuned by the discrete hyperparameter search. The bestmodel was then applied to predict the 30% test data. When the trainingaccuracy is similar to the test accuracy, the model is considerednot overfitting.The RF_LUR model is based on the conventionalRF algorithm that is a popular tree-based ML algorithm and has beenapplied previously in modeling air pollution.2.5nNO2_train = 41; nUFP_train= 9)and used the other half of the long-term measurements as validationdata . However, the random splitting may bring additionaluncertainty to the stability of models, due to the relatively smallsize of the validation data especially for UFP. To incorporate thisuncertainty, we repeated the random splitting 20 times with differentrandom seeds. For each iteration, TrAdaboost was trained repeatedly.TrAdaBoost requires directly involving a number of long-term instancesas inputs. We input mobile and half of the long-term measurements(random split) to train the TrAdaBoost model  calculatedby the “R2” function from the R package “Caret.”27After training,all the abovementioned models were applied to predictconcentrations for all 46,664 road segments. The mean of the predictedroad segments within 30 m of long-term monitoring stations was comparedto the corresponding long-term external measurements (the parts notused in the training stage), for estimating the prediction accuracy.Three common accuracy metrics were used to compare model performance:(1) the normalized mean absolute error (nMAE), which normalizes thestandard MAE by the mean of the validation data. This metric is commonlyused to indicate the averaged errors in the prediction tasks; (2)the normalized root mean square error (nRMSE), which normalizes RMSEby the mean of validation data; and (3) goodness of fit estimatedby squared Pearson correlation  were higher than those of the external long-termvalidation data  and NO2 (R2 = 0.45) when comparing the long-term withmobile measurements within30 m of the external long-term sites. The NO2 value ofmobile measurements distributes on a wider range as compared to thelong-term measurements when plotting the density distribution ofmeasurements at the locations where both mobile and long-term measurementswere available . The test accuracy was similar to the training accuracy .RF_LUR was less accurate than SLR for both NO forNO2 3. The tr3.2.2Thevisual evaluation of the density plots shows that SLR predictionswere generally in a narrower range than the desired long-term range,especially for UFP 3. The pr3.2.3Table S2. Traffic variables were the most important predictors, followedby population and port-related features. Similarly, traffic-relatedvariables were also the main and most important variables for RF_LURand both transfer-learning models . Transfer-learning LUR models considered additionallythe population and other environmental contextual information suchas the water area and the urban green space. All NO2 andUFP maps show that the ring road of Amsterdam is the most pollutedarea as compared to other locations .The selected predictors and corresponding coefficientsof the SLR model are presented in Appendix 2 concentrationsthan SLR and RF_LUR at residential locations, especially in the citycenter .Comparing the spatial differences among the testedmodels, Prior_RFand TrAdaBoost generally predicted lower NOycenter 5.In con3.311 In addition, differentsensors used in mobile campaigns and regular monitoring networks oftenbring a certain number of extra differences, although calibrationsand collocations are performed.When applying mobile measurements to estimatelong-term air pollution concentrations near roads, empirical mobileLUR models are hampered by the spatiotemporal and instrumental differencesbetween the training and the application domains. First, mobile measurementsoften consist of just a few seconds of observations per road segment.In our GSV campaign, two cars collected daytime air pollution on weekdaysfor 10 months and measured on average 8 drive-passes for each 50 mroad segment. In contrast, long-term measurements measure air pollutionover a longer period including all hours and days during the studyperiod. Second, in our mobile monitoring campaign, the on-road airpollution is measured. However, significant differences in (traffic-related)air pollution concentrations by the spatial distance to the road havebeen reported.28 Inour study, RF_LUR showed a decrease from its training accuracy to the application accuracy . This is not an overfitting issue, sincethe training accuracy was similar to the test accuracy when only mobiledata were used . The similar accuracy between the trainingand the test mobile data indicates that the model learned from themobile instances generalized well to other datasets in the same domain(mobile domain).When the application domain shifts into another domain, the predictions accuracy will not necessarilybe equal to the training accuracy.All of these knowledge gapsoriginating from space, time, and instruments are at odds with thecore assumption of supervised learning methods. Consequently, trainedexclusively with mobile measurements, the training accuracy of mobileLUR models (estimated by mobile measurements) is often not equal totheir performance in predicting long-term near-road air pollutionwhen validated by long-term near-road measurements.29 ML models consist of a more complexstructure with a larger number of parameters that need to be optimizedbased on the training samples. The mobile training data made the modelfully delineate the mobile knowledge. However, this mobile knowledgedid not translate to the long-term domain. Consequently, the advantageof ML, namely, accurate fitting, turns out to be the major limitationwhen the prediction domain shifts away from the training domain.Both the RF_LUR model andSLR face the same issues, as both aresupervised learning algorithms trained solely with mobile measurements.However, RF_LUR was less accurate than SLR 4. This s3.4Despite the complex spatiotemporal and instrumentaldifferences between the mobile and long-term concentrations, witha limited amount of target  information, our proposedtransfer-learning LUR models were able to narrow the knowledge gapby transferring learnings from the mobile domain into the long-termdomain in an end-to-end paradigm. This end-to-end paradigm was implementedby assigning smaller weights to the mobile training instances thatwere different from the target long-term near-road instances and emphasizingthe target instances that are different from mobile instances to adjustthe risk function of LUR models. This pushed the LUR model to learnmore from the long-term near-road instances while still optimallyutilizing the mobile instances to capture the detailed hyperlocalvariations at the same time. Compared to mobile LUR models trainedwith mobile instances only, transfer-learning LUR models achievedsmaller errors in predicting long-term near-road air pollution concentrations3 and 4. 2 and UFP from TrAdaBoost and Prior_RF predictionssuggest that transfer-learning LUR models can correct this biasedtrend .The mobile measurements were higher than the long-term near-roadmeasurements 2, due toAlthough the performance of TrAdaBoost was validatedon a halfof the long-term measurements, it is still reasonable to be comparedwith other LUR models that were validated on the full dataset. Inthe sensitivity test, validated on only half of the long-term data,the averaged performances of Prior_RF_half and RF_LUR_half were similarto the mean of Prior_RF and RF_LUR for both air pollutants. Thesesimilar mean performances estimated on the full and half versionsindicate that the mean value of the separated half validation datacan represent the full validation data. Thus, the averaged performanceof TrAdaBoost can be directly compared to the models validated bythe full set of validation data.3.52, 41 long-term measurements could be used in training to approximatethe long-term knowledge. This makes TrAdaBoost stable and accurateto transfer the learned mobile knowledge toward long-term knowledge.In our result, TrAdaBoost achieved better nMAE and nRMSE than Prior_RFfor NO2  to the long-term instances. Thus,TrAdaBoost can better transfer the mobile knowledge toward long-termknowledge in terms of absolute errors. In contrast, Prior_RF reweightsthe risk function by the ratio of the probability distribution betweenmobile and long-term measurements. In this way, Prior_RF predictionscan obtain a better correlation with the long-term data, which isreflected in the higher R2 observed forPrior_RF as compared to the TrAdaBoost model . In contrast, the number of the long-term UFP measurementsused is relatively small (n = 17) and could not temporallycover the entire study period. The number and quality of long-termobservations directly influence the performance of transfer-learningLUR models. However, at this point, it is not clear how many long-termmeasurements are sufficient. It will depend on various factors, suchas the choice of hyperparameters, the feature space, and the sizeof study area. According to the study described in this work, it seemsthat Prior_RF has higher robustness to the number of long-term datathan TrAdaBoost. Transfer learning methods in this work focus on modelinglong-term average concentrations. Future work could evaluate transferlearning methods for shorter-term exposures .Althoughtransfer-learning LUR models outperform the mobile LUR methods, acertain level of long-term knowledge in the study area is required.An advantage of selecting Amsterdam as the study area is the largenumber of long-term NOOur Google Street View campaign extensivelymeasured air pollution at a spatially fine granularity. Together withthe external long-term monitoring data, Amsterdam provides a uniqueopportunity to evaluate empirical LUR methods on the ability of estimatinglong-term average air pollution concentrations using short-term on-roadmobile measurements. Although our collection cars have traveled aroundAmsterdam for about 10 months and collected air pollution at 1.7 millionlocations (an average of 8 drive-passes per road segment), such anintensive mobile dataset still lacks temporal coverage for each locationand inherently only measures on-road air pollution as compared tothe desired long-term concentrations. We emphasized that the spatiotemporaland instrumental differences cause the mobile knowledge learned byempirical mobile LUR models to deviate from the long-term near-roadknowledge. By augmenting the mobile data with temporally rigorouslong-term and near-road measurements, our proposed transfer-learningLUR methods showed promising ability to narrow these knowledge gaps.These spatiotemporal and instrumental differences likely exist inmost long-term air pollution mapping work when using mobile monitoringdata. More attention to these knowledge gaps is needed when applyingempirical LUR models to map long-term residential air pollution withmobile monitoring data.


Concurrent platinum-based chemoradiotherapy (CRT) followed by maintenance treatment with the PD-L1 inhibitor durvalumab is the most effective therapy in unresectable stage III non-small-cell lung cancer (NSCLC). However, severe toxicity of this approach may lead to an unsatisfactory outcome. Cardiovascular diseases (CVD) may justify the use of sequential CRT to avoid severe adverse events and maintain satisfactory effectiveness. Ensuring that patients with CVD do not have a worse prognosis than patients without CVD is one of the goals of cardio-oncology. It is important that, after sequential CRT as personalized for patients with CVD, there is no increased mortality, and this is the first achievement of this study. Patients receiving beta-blockers and statins had lower all-cause mortality over 2, 3, and 4 years. The clear benefit of treatment with beta-blockers (in possible combination with statin) was confirmed in a subgroup of patients with CVD. The patients with CVD and indications for different cardiac therapy could live significantly longer if they received beta-blockers with or without statins during long-term follow-up. It may be a time to consider beta-blockers and statins as prevention strategy in patients undergoing CRT for NSCLC.p < 0.05 for all), there was a statistically nonsignificant trend for lower all-cause mortality in patients with CVD. The lowest all-cause mortality was observed in patients treated with beta-blockers and statins after two , three  and even four  years of follow-up. The benefit in OS remained significant in 101 patients with CVD treated with beta-blockers , and eventually statin, throughout the whole follow-up (log-rank p < 0.05). Further prospective studies are necessary to confirm the role of beta-blockers and statins in reduction of mortality in NSCLC patients undergoing radical CRT.The study was conducted in the era when maintenance immunotherapy with durvalumab was not available in clinical practice after chemoradiotherapy (CRT) in unresectable non-small-cell lung cancer (NSCLC). The main aim of the study was to check whether the presence of cardiovascular diseases (CVD) and their pharmacotherapy affects the overall survival (OS) in such NSCLC patients undergoing sequential CRT. The group of 196 patients were analyzed: 101 patients with CVD (51.53%) and 95 patients with other reasons of qualification for sequential CRT . Although patients with CVD were more often in older age, and they more often experienced cardiac and nephrological complications ( A decrease in mortality due to cardiovascular reasons is the main goal of therapeutic intervention in cardiology. Similarly, in oncology, an array of therapeutic strategies leads to the prolongation of survival time due to the reduction of death caused by cancer disease. However, both specialties regard death for any reason as an important observation point. All-cause mortality seems to be crucial in cardio-oncology. Oncology patient treated with potentially cardiotoxic drugs may benefit with prolongation of progression-free survival or time to cancer disease recurrence, but toxicity of that treatment may also negatively impact the outcome. Damage of the heart due to anticancer therapy may lead to premature cardiac death and, in other cases, to quitting the cancer treatment and shortening of survival time. The biggest cardio-oncology registry, CARDIOTOX, shows a crucial relationship between diagnosis of cardiotoxicity and all-cause mortality (thereby not only in relation to death of cardiovascular cause), which means significantly shorter overall survival (OS) in oncology patients . There aIn locally advanced unresectable lung cancer, radiotherapy is part of radical treatment.Thus, the risk of premature death due to cardiological causes appears to be a disturbing effect of the therapy .An unfavThe PACIFIC study has set a new standard for the treatment of locally advanced unresectable lung cancer. Looking at the results of 5-year survival after concurrent CRT, it can be concluded that the addition of maintenance immunotherapy with durvalumab increases the OS rate after this period from 33.4% to 42.9% . It can So far, the problem of long-term prognosis in patients with non-small-cell lung cancer (NSCLC) after CRT and concomitant CVD is poorly documented. The main hypothesis of the study is that sequential CRT is an adequate treatment option for patients with unresectable locally advanced NSCLC and CVD. The main aim of the study was to check whether the presence of concomitant cardiovascular diseases (CVD) and their pharmacotherapy affects the OS of patients with locally advanced unresectable NSCLC undergoing sequential CRT.This study was a retrospective observation of patients qualified for sequential CRT in the period from July 2010 to October 2014. This means that the study was conducted in the era when maintenance immunotherapy was not available in clinical practice, but the therapeutic role of sequential CRT was assessed. The current available long-term follow-up can define prognostic determinants.The inclusion criteria were defined as diagnosis of unresectable locally advanced NSCLC and the indication for sequential CRT due to significant comorbidities (including CVD) or older age or decreased performance status.The exclusion criteria concerned patients who, nevertheless, qualified for concurrent CRT or received only palliative treatment (radiotherapy or chemotherapy) due to the advancement of NSCLC.All patients were scheduled to receive 2–4 cycles of chemotherapy based on cisplatin or carboplatin. More than half of them received 2 full cycles of chemotherapy , and every third received 3 cycles . The preferred time to start radiation therapy was within the next cycle of chemotherapy, i.e.,3–4 weeks after the end of chemotherapy, or slightly longer in the case of slow bone marrow recovery. Administration of subsequent cycles of chemotherapy during or after radiotherapy was not recommended. The mean heart dose (MHD) during radiotherapy was median 12.36 Gy (interquartile range: 6.55–20.85 Gy).Toxicity was determined according to Common Terminology Criteria for Adverse Events version 5. In the current study, attention was focused on complications that could affect the prognosis and to which the history of CVD could predispose. No complication had an immediate fatal outcome.The primary point of observation was overall survival (OS), which was the time from the start of chemotherapy to the moment of death from any cause. The Kaplan–-Meier survival curves and Cox proportional hazards analysis were used to compare OS in patients with and without CVD. A special focus of this study was to assess the impact of treatment with different cardiac drugs on all-cause mortality. The oncological characteristic of the patients was compared in relationship with the presence of CVD and cardiac therapy by using the chi2 test with a possible Yates’ correction.There was no detailed analysis of the dose and type of cardiac drugs. A special focus was associated with beta-blockers and statins, with which administration affected prognosis. Only the fact of their administration was taken into the account.➢arterial hypertension in 79 patients (40.3%),➢chronic coronary syndrome in 25 patients (12.8%),➢history of arterial thromboembolic events (ATE) in 32 patients (16.3%),➢history of venous thromboembolic events (VTE) in 7 patients (3.6%),➢atrial fibrillation in 4 patients (2.0%).The group of 196 patients were analyzed: 101 patients with CVD (51.53%) and 95 patients with other reasons of qualification for sequential CRT . Among the 101 patients with history of CVD, the following diagnoses were present:In addition to CVD, there were other significant prognostic internal diseases, such as chronic obstructive pulmonary disease (COPD) in 43 patients (21.9%), thyroid disease in 20 patients (10.2%), diabetes in 19 patients (9.7%), and lipid disorders in 12 patients (6.1%).p = 0.0003), significantly more often received chemotherapy without cisplatin (p = 0.009), and significantly more often experienced cardiac (p = 0.04) and nephrological complications (p = 0.03).The detailed demographic and oncological characteristics of all 196 patients with NSCLC are presented in the p = 0.2) after 2 years and 62.38% vs. 69.47% (p = 0.3) after 3 years  and three  years of follow-up. Patients treated with beta-blockers had significantly lower all-cause mortality  after twPatients on beta-blockers had a better temporary outcome; however, they were less likely to have a good prognosis, which is defined as a baseline performance status KPS = 100 . On the p = 0.047), three  and even four  years of follow-up. The detailed results of the analysis of all-cause mortality risk are presented in The lowest all-cause mortality was observed in patients treated with beta-blockers and statins  after twAmong the 47 patients receiving beta-blockers, as many as 46 were diagnosed with CVD, 18 had diagnosis of chronic coronary syndrome, 20 had a history of ATE, 35 had arterial hypertension, and one had atrial fibrillation. Of the 16 patients receiving beta- blockers and statin, all had CVD, 12 had chronic coronary syndrome and a history of ATE, and only 7 had arterial hypertension.p = 0.045). Similarly, among patients with CVD, an improvement in OS was demonstrated for patients treated with a beta-blocker and statin  and reduced baseline WHO fitness ≥ 2 . When the risk in patients with a cardiac history was analyzed , it turned out that 61  patients developed a cardiac complication. The WHO-PS efficiency was assessed as ≥2, and chemotherapy with cisplatin were significant risk factors in both the univariate and multivariate analyses. The appearance of a cardiac complication did not significantly increase mortality, but it is worth noting that 5% of the patients died of cardiac causes . Analysis of the prognosis after CRT  shows thp < 0.001). Patients receiving a cardiac dose ≥ 20 Gy had a significantly higher risk of cardiac complications than those receiving <10 Gy  or a dose in the range of 10–20 Gy . Therefore, radiotherapy planning is crucial for oncological and cardiological prognosis. Our study confirmed that sequential CRT may be a personalized treatment option for patients with CVD, as we obtained a prognosis not worse than found in patients without CVD.There are more and more studies showing that cardiovascular complications are quite common in patients after lung cancer treatment. The available studies showed that within 2 years after lung cancer treatment, the incidence of serious cardiac adverse events was 11–23% ,21,22. CRadiotherapy can lead to various cardiovascular complications, especially in long-term observation . CardiacRadiotherapy can also cause vascular complications, and in the treatment of symptomatic ischemic heart disease, beta-blockers have an established position. Therefore, it can be assumed that, also in the aspect of preventing cardiac ischemic events after radiotherapy, beta-blockers may play a beneficial role and thus reduce mortality in our population, especially in the period when the effect of radiotherapy on accelerating the heart rate and possibly inducing cardiac ischemia was observed.The cardioprotective role of beta-blockers against heart damage caused by various oncological drugs is also being intensively analyzed. In the case of cardioprotection against anthracyclines, their importance is best documented . HoweverIn a large retrospective follow-up of NSCLC patients treated with radical radiotherapy, it was found that patients receiving beta-blockers had significantly longer distance metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival . InteresPropranolol may prolong survival when using first line TKI-EGFR for lung adenocarcinoma . PropranStatins not only lower blood cholesterol levels and reduce the risk of cardiovascular disease but may also play an important role in the prevention and treatment of lung cancer . They hap < 0.001) was obtained in patients with lung cancer treated with TKI-EGFR  [Several observational studies have investigated associations between statin use and lung cancer risk and lung cancer prognosis. In a follow-up of patients who received statins either before or after a diagnosis of lung cancer , it was noted that patients taking statins  had lower lung cancer-related mortality . A large of PFS) . Among p of PFS) .In a meta-analysis of nearly 100,000 lung cancer patients, statin use was associated with a significant improvement in OS (HR = 0.79) and cancer-specific survival (HR = 0.83) . Patientp = 0.022), while no such association was observed among patients receiving statins. Patients with locally advanced non-small-cell lung cancer have a high incidence of co-occurring coronary atherosclerosis and are at an additional cardiac risk after thoracic radiotherapy.Single studies showed the possible influence of statins on the prognosis of patients with locally advanced lung cancer. The retrospective analysis included 748 patients treated with chest radiotherapy, including 433 patients who underwent chemoradiotherapy . In patiAnother study showed a relationship between statin therapy and better regional control of stage III NSCLC in a 2-year follow-up after chemoradiation, but the result was not statistically significant . AnotherThe presented results indicate the need for further studies evaluating the impact of statin treatment on the survival of patients with locally advanced lung cancer undergoing chemoradiotherapy. It seems important to consider cardiovascular risk factors or the dose of radiotherapy to the heart, as well as the duration of therapy, drug compliance, and lipid assessment. Future research should also consider consolidation immunotherapy, as statins have been reported to have anticancer effects also through anti-inflammatory effects and immune regulation. In the literature, there are studies on the beneficial effects of statins in patients undergoing immunotherapy, which have been shown to be associated with a higher response rate and a longer time to treatment failure . The present study has a number of significant limitations. This is a retrospective study, based on the experience of one center. Data were collected when maintenance immunotherapy after concurrent CRT had not yet been used. In addition, only patients undergoing sequential CRT as the treatment of choice for patients with CVD were included in the study. The effects of different beta-blockers and different statins have been evaluated, but dose-dependent effects and efficacy have not been confirmed. We have no data on what heart rate was achieved with a beta-blocker or how much cholesterol was lowered with a statin. It should be taken into account that the relationship between the development of cancer disease and heart failure can be very complicated ,51. The During sequential radical chemoradiotherapy, the prognosis of patients with concomitant cardiovascular diseases seems to not be less favorable. Patients taking beta-blockers for classic cardiological indications have a chance of significantly reduced all-cause mortality. When a beta-blocker was combined with a statin, all-cause mortality was reduced in all NSCLC patients with CVD during long-term follow-up. The results seem particularly relevant in terms of adding the possible maintenance immunotherapy with durvalumab.


Background and Objectives: Quality of life (QoL) and chronic pain are important outcomes following hernia surgery. The long-term effects of Transcutaneous Electric Nerve Stimulation (TENS) on postoperative recovery are not well known. In this trial we investigated the role of TENS on QoL and on the incidence of chronic pain following inguinal hernia repair with mesh. Materials and Methods: A total of 80 male patients with elective primary unilateral hernia Lichtenstein repair were randomly allocated to receive TENS or a placebo-TENS procedure. The TENS group received conventional TENS twice a day on the first and second postoperative days. The intensity was set at 0–0.5 mA in the placebo-TENS group. General and hernia-specific QoL, as well as the incidence of chronic pain were assessed using SF-36v2 and the Carolinas comfort scale. Results: Less sensation of mesh was reported by the TENS group patients one week after surgery. At this time point, the mean sensation score was 6.07 ± 8.88 in the TENS group and 14.08 ± 16.67 in the placebo-TENS group (p = 0.029). Although at two days and one week postoperatively, TENS group patients tended to have less pain, less movement restrictions and better overall hernia-specific QoL, the differences were not statistically significant. At 6 months postoperatively, no incidence of chronic pain was found in either the placebo-TENS or TENS group. Conclusions: Conventional TENS applied in the early postoperative period following inguinal hernia repair with mesh was found to reduce mesh-related foreign body sensation one week after surgery. Promising results were also found for other QoL domains. Inguinal hernia repair is one of the most common surgical procedures performed today. The lifetime occurrence of inguinal hernia is 27–43% in men and 3–6% in women . Open, tPain is the most common complaint reported by patients following inguinal hernia surgery . One of Transcutaneous electrical nerve stimulation (TENS) is a noninvasive, inexpensive and easy to learn technique that is used to manage acute and chronic pain of nociceptive and neuropathic origin ,23. TENSWe present here the follow-up results of a randomized, double blind and placebo-controlled trial in which TENS was incorporated into the multimodal treatment of acute postoperative pain following open inguinal hernia repair with mesh. We evaluated the impact of TENS on early and late QoL and on the development of chronic pain.Details regarding the study design, sample size calculation, randomization and blinding, surgery, postoperative treatment, TENS and placebo-TENS application were described in our previous open access report . BrieflyInclusion criteria for the trial were: male gender; primary hernia; no prior TENS procedure; American Society of Anesthesiologists physical status I to III; no cognitive, language, hearing or visual disorders; no movement disorders. Exclusion criteria were: surgical complications; nerves in the inguinal region could not be identified or were damaged; known allergy to a patch glue; chronic use of non-steroidal anti-inflammatory drugs or opioids; neuropathic disease; general contraindication for TENS procedure (such as skin disorder at site of electrode application or pacemaker); non-Lichtenstein modification for hernia repair.The final sample size was calculated using data on pain ratings when walking at the interim analysis, a two-group design, α = 0.05, and power = 0.80. The sample size for QoL analysis was not calculated separately because this was planned as a secondary outcome of the trial.On the first postoperative day, participants were randomized to receive active TENS procedures (TENS-group) or inactive TENS procedures (placebo-TENS group) according to a computer-generated random assignment list. Participants and the investigators responsible for outcome measurements and data collection were blinded to the group assignment. Group affiliation was revealed after discharge of the participant and when the investigators could not impact the participant’s future responses. During the first two postoperative days, participants received regular pharmacological analgesia to treat their pain and reach a comfortable level. They could ask for additional doses in case of insufficient analgesia. On the first and second postoperative days, the TENS group received conventional TENS using a constant stimulation frequency of 100 Hz and pulse duration of 200 μs. Segmental and local stimulation was used. Two adhesive electrodes (10 × 5 cm) were placed on the skin at the inguinal region on both sides of the surgical incision. Two adhesive electrodes (5 × 5 cm) were also placed on the skin and parallel to the spine 2 cm from the adjacent spinal processes of the 12th thoracic to 3rd lumbar vertebrae on the surgical side, according to segmental innervation of the inguinal region. The intensity was increased until the participant was able to feel a maximal tolerable tingling sensation without causing discomfort, pain, or muscle contraction. In the placebo-TENS group the whole procedure was the same as in TENS group, but intensity was set at 0–0.5 mA, and participants could not feel any sensations. TENS and placebo-TENS procedures were applied twice a day on the first and second postoperative days, giving a total of 4 procedures for each participant. On the day of discharge from hospital, Ketorolac was prescribed as 10 mg pills and participants were instructed to keep the pain at a comfortable level during daily activities and during rest. The study design is shown in The outcome measures were general and specific hernia-related QoL in the early and late postoperative period, as well as the incidence of chronic pain and the proportion of symptomatic patients in the TENS and placebo-TENS groups.General QoL was evaluated using the well-known QoL questionnaire SF-36 v2 short form. This is a reliable and validated measuring instrument that has already been used in the Lithuanian population for different clinical conditions ,40. SF-3Specific (hernia-related) QoL was evaluated using the Lithuanian version of the Carolinas Comfort Scale (CCS) . This haGeneral QoL and specific QoL were assessed before surgery on the admission day, then on the second postoperative day and 1-week, 4-weeks and 6-months postoperatively. At each time point the participants were asked to complete SF-36 and CCS questionnaires. Participants were briefly instructed on how to complete the questionnaires. On the second postoperative day, participants completed the questionnaires before discharge from the hospital. The questionnaires for other time points were mailed to participants, together with stamped, self-addressed envelopes. Participants completed the questionnaires and mailed them back to investigators. This method has been validated against in-person responses or replies obtained by telephone and used successfully in previous trials ,43.The incidence of chronic pain was determined from the CCS questionnaire completed 6 months after surgery. The criteria for chronic pain were: question b, any activity rated > 2; and question c, any activity rated > 0. Symptomatic patients were identified from the CCS questionnaire completed 6 months after surgery and according to the scoring algorithm. Cases with late postoperative complications were recorded in the follow-up period.p < 0.05. Continuous variables were represented as the mean ± Standard Deviation (SD), while categorical variables were represented as percentages.SPSS version 22 software  was used for statistical analysis. Categorical variables were analyzed using Pearson’s chi-square test. The Mann–Whitney test for independent samples was used to compare QoL subscales and total scores between the placebo-TENS and TENS groups at different time points. CCS scores were expressed as percentages of the maximum possible score. Pearson’s chi-square test was used to compare the incidence of chronic pain between the groups. Results for all tests were considered significant at n = 40) group and the placebo-TENS (n = 40) group. At the end of the 6-month follow-up period, QoL was analyzed for 33 and 31 participants in the placebo-TENS and TENS groups, respectively. The flow chart for this study is shown in A total of 80 patients were enrolled in the trial and divided equally into the TENS (p > 0.05)  .p = 0.029)  . At the = 0.029) , howeverFewer responders in the TENS group scored ≥ 1 in the sensation scale compared to the placebo-TENS group (35.5% vs. 54.5%), but the difference was not significant .There were no differences in total SF-36 scores or scores for the different SF-36 domains between the placebo-TENS and TENS groups for general QoL at any of the time points. Results for the SF-36 total score, pain and physical functioning scores are shown in No incidence of chronic pain was found in any of the placebo-TENS (0/33) or TENS (0/31) patients at 6-months postoperatively.A seroma occurred one-week postoperatively in one case from the TENS group and was treated using several punctions. No other surgery or TENS-related complication was registered.p = 0.125).More participants were symptomatic 6 months after surgery in the placebo-TENS group  than in the TENS group , but this did not reach statistical significance  one week after open unilateral inguinal hernia repair surgery. However, the impact of an early postoperative TENS course on late general and hernia-specific QoL at different time points remains to be determined, as does the role of TENS in reducing chronic pain following open inguinal hernia repair.Earlier results from this trial showed that TENS reduces acute postoperative pain and analgesic use after open inguinal hernia repair with mesh . HoweverTo date, the TENS procedure is known to produce a short-term analgesic effect . TENS haMany patients report a feeling of stiffness or foreign body after implantation of a mesh in the inguinal region following hernia surgery . The patIn a meta-analysis of open pre-peritoneal versus Lichtenstein repair, chronic postoperative pain of greater than 6 months duration was found in 7.1% and 12.3% of patients, respectively. In another meta-analysis of totally extra peritoneal (TEP) hernia repair versus Lichtenstein, chronic postoperative pain of greater than 3 months duration was reported in 12.5% and 16.8% of patients, respectively . In our Another reason for the development of chronic pain is the presence and poor control of risk factors , such asWe believe that good postoperative pain control and the absence of other risk factors can reduce the incidence of chronic pain.We also evaluated the impact of TENS on specific hernia-related QoL following open hernia surgery with mesh. Total CCS scores and results for other domains at the two-day and 1-week postoperative time points were promising. The TENS group had a lower total CCS score than the placebo-TENS group, but this did not reach statistical significance. After one month and 6 months postoperatively, no differences were apparent between the two groups for all CCS domains and for the CCS total score. At 6-months postoperatively, fewer patients in the TENS group tended to respond positively to the mesh perception question compared to the placebo-TENS group, with less symptomatic patients identified in the TENS group. A statistically significant difference was not apparent between the two groups. The main reason for this was likely to be the smaller number of patients analyzed at these time points. Indeed, this was a major limitation of the follow-up part of this trial.Another reason why TENS did not impact the long term QoL may have been the short course of TENS therapy. Extending the course is likely to result in a greater benefit from TENS. Observational studies on the efficacy of long-term TENS for the treatment of chronic pain from various causes have shown that it reduces pain interference with work, home and social activities. Long-term TENS also increased activity levels and pain management, decreased the use of other therapies , and decreased the use of narcotics, tranquilizers, muscle relaxants, nonsteroidal anti-inflammatory drugs and steroids ,61. In mThe main limitation of the study was that sample size calculation was based on acute pain outcomes in specific on pain ratings when walking. Therefore, secondary outcome analysis was not enough powered. Perhaps this is the reason why the difference in general and specific hernia-related quality of life outcomes were not found. Other limitations of this study were the selection of participants according to narrow criteria  and a single-center study site.In conclusion, conventional TENS applied in the postoperative period following inguinal hernia repair with mesh has an analgesic effect. It also reduces mesh-related foreign body sensation one week after surgery and shows promising results for other QoL domains. We suggest that physicians involved in hernia surgery should consider integrating conventional TENS into multimodal postoperative pain treatment. Additional good quality trials with a larger sample size are needed to confirm the potential benefit of TENS for reducing the incidence of chronic pain and for improving late postoperative QoL following hernia repair.Conventional TENS applied in the postoperative period following inguinal hernia re-pair with mesh has not only analgesic effect but also reduces mesh-related foreign body sensation one week after surgery.


Finding a vaccine or specific antiviral treatment for a global pandemic of virus diseases (such as the ongoing COVID-19) requires rapid analysis, annotation and evaluation of metagenomic libraries to enable a quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable and there is a need for fast alignment-free techniques for sequence analysis. Information theory and data compression algorithms provide a rich set of mathematical and computational tools to capture essential patterns in biological sequences. In this study, we investigate the use of compression-complexity (Effort-to-Compress or ETC and Lempel-Ziv or LZ complexity) based distance measures for analyzing genomic sequences. The proposed distance measure is used to successfully reproduce the phylogenetic trees for a mammalian dataset consisting of eight species clusters, a set of coronaviruses belonging to group I, group II, group III, and SARS-CoV-1 coronaviruses, and a set of coronaviruses causing COVID-19 (SARS-CoV-2), and those not causing COVID-19. Having demonstrated the usefulness of these compression complexity measures, we employ them for the automatic classification of COVID-19-causing genome sequences using machine learning techniques. Two flavors of SVM (linear and quadratic) along with linear discriminant and fine K Nearest Neighbors classifer are used for classification. Using a data set comprising 1001 coronavirus sequences (causing COVID-19 and those not causing COVID-19), a classification accuracy of 98% is achieved with a sensitivity of 95% and a specificity of 99.8%. This work could be extended further to enable medical practitioners to automatically identify and characterize coronavirus strains and their rapidly growing mutants in a fast and efficient fashion. Pandemics such as the ongoing COVID-19 (caused by SARS-CoV-2) pandemic that leads to enormous loss of life globally can only be controlled by vaccines or a very effective antiviral treatment. Finding a vaccine or specific antiviral treatment for such a global pandemic of virus diseases requires rapid analysis, annotation, and evaluation of metagenomic libraries to enable quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable since they are computationally intensive and cannot be easily scaled up as the number of sequences increases. Thus, there is a need for fast alignment-free techniques for sequence analysis [Identifying various genes that constitute the genome.Identifying the origin of the genome sequence.Understanding the information content present in the coding and non-coding regions.Reconstructing the phylogenetic tree to study evolutionary patterns.Automatic classification and identification of unknown genome sequences.Genome sequences are inherently described by character strings and are hence amenable to mathematical and computational techniques for extracting information. Exactly what information is being sought from such character strings depends on the string itself and the domain as well as the kind of application. Some targets of interest for analyzing genome sequences include:An important objective is to automate the above tasks so that a large number of sequences can be quickly, robustly, and efficiently analyzed (as one of the steps in the endeavor for finding a vaccine).A cursory glance at these character strings does not tell us much about how they can be used for these applications. However, a harmonious blending of complexity analysis with the field of information theory provides deep insight in this regard. Application of complexity measures on these information-bearing character strings may reveal many surprising features that generally cannot be discerned by intuition or visual inspection of the data alone.In this study, we propose compression-complexity based distance measures for the analysis of genomic sequences. To validate the efficacy of this distance measure, we first apply it to the mitochondrial DNA sequences of primates belonging to eight different species clusters and recreate a phylogenetic tree showing these clusters accurately. Subsequently, the distance measure is applied to a group of Severe Acute Respiratory Syndrome (SARS-CoV-1) coronaviruses and also to a group of SARS-CoV-2 viruses to successfully reconstruct their phylogenetic trees, grouping the viruses correctly.Having demonstrated the usefulness of these compression-complexity measures, we employ them for the automatic classification of SARS-CoV-2 viruses using machine learning techniques. The compression-complexity measures extracted from the sequences are passed as features to machine learning algorithms  for classification.The paper is organized as follows. In The total DNA content (RNA for viruses) of an organism is known as the genome, thus representing the entire information coded in a cell, while a gene represents a section of the DNA that codes for RNA or protein. A genome consists of a sequence of multiple genes interspersed with non-coding sequences of nucleic bases .® (http://www.ncbi.nlm.nih.gov/genbank (accessed on 4 October 2022)), a genetic sequence database containing an annotated collection of all publicly available DNA sequences. Ever since its inception in 1982, there has been an exponential rise in the number of sequences in Genbank. This has provided the required resources for researchers and industry people alike for delving into the field of bioinformatics.Genome data classification comes under the broad field of bioinformatics, an established multidisciplinary field for over three decades, encompassing physical and life sciences, computer science, and engineering. Many fundamental problems in the fields of medicine and biology are being tackled using the tools of bioinformatics. The main requirement for accomplishing such tasks is the availability of sequenced genome data. This has been the focus of researchers for the past few decades and efforts have been made by the National Institutes of Health (NIH) to establish GenbankAmong the various aspects involved in bioinformatics, one key element is sequence comparison or analysis of sequence similarity . This isAlignment-based methods: These involve either shifting or insertion of gaps in sequences for the optimal alignment of two or more sequences [equences ,7. The aAlignment-free methods: These are computationally less intensive methods that consider the genome sequences as character strings and use distance-based methods involving frequency and distribution of bases [of bases ,10,11,12There are two kinds of sequence comparison methods:Sequence comparison and genome data classification got a boost in the early 1990s with the use of data compression algorithms that can identify regularities in sequences . They prS using a sequence Q, where the degree of compression obtained by doing so would be an indicator of the distance between them. Although their definition was not mathematically valid, it set a platform for researchers to explore this area. Varre et al. [Q is transformed to sequence S by various mutations like segment-copy, segment-reverse copy, and segment-insertion. Li et al. [S and Q. However, Kolmogorov complexity, [S and Q, sequences Q is appended at the end of the sequence S to yield the new sequence The first attempt at using data compression for phylogenetic tree construction was by Grumbach et al. in . They exe et al.  defined i et al.  define ai et al.  that is plexity,  being anplexity, . Hence iplexity,  proposedplexity,  complexiTo account for the effect of the length of the sequence, a normalized measure is defined as follows:sum distance is defined as follows:A third distance metric based on Finally, the normalized version of the sum distance is defined as: Using these distance measures on mtDNA  samples of a wide range of eutherans , they have successfully re-created phylogenetic trees showing the evolutionary patterns. Other researchers have used these and slight variants of these measures to identify families of coronaviruses, mammals, vertebrates, and salmons. Interested readers are referred to ,26,27,28In this section, we provide details of the datasets as well as the methods used in this study.Genome data from mammals and various types of coronaviruses, obtained from Genbank database are used for the analysis. These are described below.The first dataset we considered was mitochondrial genomes (mtDNA) of 41 mammals grouped into 8 species clusters as shown in 15 SARS-CoV-1 coronaviruses15 Non-SARS-CoV-1 coronaviruses belonging to Groups I, II and III coronavirusesFor our analysis, we use genome sequences of the following viruses:For complete details of the above sequences with accession numbers, please refer to For the third set of analyses, we consider 30 COVID-1 causing coronavirus sequences and 30 coronavirus sequences not causing COVID-19 . For complete details of the above sequences with accession numbers, please refer to For measuring the complexity of the nucleotide sequences, we have used Lempel-Ziv (LZ ) and EffB at the end of sequence A). We then compute the complexity measures We use a distance measure which is computed using a compression-complexity measure (LZ or ETC). Let us say that we have genome sequences of two viruses d Sayood , the disNote that the above distances will always be non-negative and symmetric (https://www.gisaid.org) (accessed on 4 October 2022)) belonging to alpha (131 sequences), beta (130 sequences), gamma (130 sequences) and delta (147 sequences) coronaviruses and SARS-CoV-2 (436 sequences).Various AI techniques are being currently used for COVID-19 related data analytics ,44,45,46TM from MATLAB® ver. R2021a was used for the analysis. To evaluate the system, analysis was done with a 10-fold cross-validation scheme, using a 90–10% dataset splitting (90% of the dataset for training and 10% for testing).The ML algorithms used for classification were support vector machines (SVM), linear (LSVM) and quadratic kernels (QSVM), fine K-nearest neighbors classifier (FKNN) using Euclidean distance with five nearest neighbors and linear discriminant algorithm (LD). A brief description of these methods can be found in For the mammalian mitochondrial genomes, the phylogenetic tree that was obtained with the distance measure using the ETC measure (Equation ) is depiThe pairwise distance values were fed to MEGA  to obtaiHaving validated the distance measure on the mammalian dataset, we applied it to SARS-CoV-1 and non-SARS-CoV-1 coronaviruses and SARS-CoV-2 and non-SARS-CoV-2 coronaviruses. The phylogenetic trees thus obtained (using ETC) are shown in It can be seen in In this section, we analyze the performance of various machine learning algorithms for the classification of SARS-CoV-2 (COVID-19) sequences. Fine KNN outperforms LSVM, QSVM and LD algorithms in terms of F1-score and accuracy. It can be seen that, using ETC or LZC alone as a feature gives detection accuracies in the range of 80–90% while, using a combination of both gives an accuracy of more than 90%. This shows that combining features from multiple compression-complexity measures can significantly boost the classification accuracies of SARS-CoV-2 sequences.Compression-complexity measures such as LZ and ETC which are based on lossless compression algorithms are good candidates for developing fast alignment-free methods for genome sequence analysis, comparison, and identification. The main reason for this is their ability to characterize and analyze information in biological sequences with contiguous segments. The distance measure based on LZ and ETC showed excellent recreation of phylogenetic trees for mammalian mtDNA sequences, SARS-CoV-1 coronaviruses, and SARS-CoV-2 viruses. Compression-complexity measures also serve as excellent features for the automatic classification of COVID-19-causing coronaviruses using machine learning algorithms. Our results demonstrate that combining ETC and LZC yields very high classification accuracies:


The emergence of COVID-19 has resulted in health, socio-economic, and political crises. The overall health impact of this disease can be measured by disability-adjusted life years (DALYs) which is the sum of the life years lost due to disability (YLDs) and the years life lost due to premature death (YLLs). The overarching objective of this systematic review was to identify the health burdens of COVID-19 and summarise the literature that can aid health regulators to make evidence-based decisions on COVID-19 mitigation strategies.This systematic review was conducted using the PRISMA 2020 guidelines. DALYs-based primary studies were collected from databases, manual searches, and included studies’ references. The primary studies published in English language, conducted since the emergence of COVID-19, and using DALYs or its subsets (years life lost due to disability and/or years life lost due to premature death) as health impact metrics, were the inclusion criteria. The combined disability and mortality health impact of COVID-19 was measured in DALYs. The risk of bias due to literature selection, identification, and reporting processes was assessed using the Joanna Bridges Institute critical appraisal tool for cross-sectional studies, and the certainty of evidence was assessed using the GRADE Pro tool.Of the 1459 identified studies, twelve of them were eligible for inclusion in the review. The years life lost due to COVID-19 related mortality was dominant over the years life lost due to COVID-19 related disability  in all included studies. The long-term consequence disability time and the pre-death disability time were not assessed by most of the reviewed articles.The impact of COVID-19 on both the length and quality of life has been substantial and has been causing considerable health crises worldwide. The health burden of COVID-19 was greater than other infectious diseases. Further studies focussing on issues examining increasing preparedness for future pandemics, public sensitization, and multi-sectorial integration are recommended. Coronavirus disease 2019 (COVID-19) is a respiratory infectious disease caused by severe acute respiratory syndrome coronavirus two (SARS-COV-2) and it was named “COVID-19” by the World Health Organization (WHO) on 11 February 2020. COVID-19 was identified as a global public health threat and nominated as a pandemic on 30 January and 11 March 2020 respectively . Nearly Disability-adjusted life years (DALYs) is one of the leading quantitative health burden assessment techniques . One DALThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were used to prepare this systematic review. Before beginning this systematic review, a review protocol was prepared and registered in International Prospective Register of Systematic Reviews (PROSPERO) with a registration number of CRD42022324931. The protocol is published at PLOS ONE and available online at 10.1371/journal.pone.0274468.Primary research articles on the impact of COVID-19 on health were the focus of this systematic review. Primary studies published from the date of COVID-19’s emergence (31 December 2019) to the end of literature search (25 April 2022) were included in the initial screen process, with any studies conducted using qualitative or quantitative techniques other than DALYs metrics then excluded. Primary research articles published in English language only were considered for review. The studies were included regardless of their consideration of long-term COVID-19 consequence (long COVID-19). The focus was on general population health, with any studies concentrating on a specific sub-group of the population  or on the specific health category  not included.(Impact) OR (Burden) OR (Effect) AND (“COVID-19”) OR (“COVID 19″) OR (“SARS-COV-2″) OR (“SARS COV 2”) OR (“Coronavirus disease 2019”) OR (“Coronavirus disease-19″) OR (“Coronavirus diseases 19”) OR (“Severe acute respiratory syndrome coronavirus 2”) OR (“Severe acute respiratory syndrome coronavirus-2”) OR (“Wuhan coronavirus”) OR (Novel coronavirus 2019) AND  AND (DALY) OR (DALYs) OR (“Disability-adjusted life years”)” was used for the search. A snowball searching method was also used to access literature from Google Scholar, with any studies from the first 20 pages considered. The title  was also used to identify studies from Google Scholar. Following the initial identification of eligible papers, each of their references were then explicitly reviewed to attempt to recognise any further studies which were missed through the database searching processes.Three databases  were searched during a period from 20 March to 25 April 2022. The publication dates (from 31 December 2019 to 25 April 2022), the publication language (English), and article type (primary studies) were used as filtering method to identify target articles. A combination of terms was used to identify eligible studies from databases. Since COVID-19 is known by different names, the search strategy was complex. As mentioned in the published protocol , the seaThe first-round selection and screening processes were done by DTG. All the studies identified from databases, manual searches, and references of included studies were imported to EndNote X9 for further screening and de-duplication. Following the removal of duplicates, the non-relevant articles were manually removed using the title and abstract screening technique. Studies that were deemed eligible on that initial process were then further investigated using a full-text review and the articles that did not satisfy the eligibility criteria were removed. To ensure the quality and eligibility of articles and avoid missing valuable studies, the selection and data collection processes done by the lead author, DTG, was independently repeated by the three co-authors . There were no disagreements among the authors.All papers regarding the negative health outcomes   due to CYes, No, Unclear or Not Applicable answers.The Joanna Briggs Institute (JBI) risk of bias assessment tool for cross-sectional studies was used. The JBI tool is formulated for assessing the risk of biases in reviewing cross-sectional primary studies with The effects of COVID-19 on the quality and length of life were measured using the disability-adjusted life years. DALYs due to COVID-19 were analysed using descriptive statistics . The contribution of YLLs and YLDs to the total health burden (DALYs) was expressed using percentages in each study. From the DALYs subsets (YLDs or YLLs), the higher contributed to DALYs was identified for health intervention.A.How much the quality of life was affected by COVID-19? This question was answered using the YLDs arising from illness, distress, suffering and pain specifically associated with COVID-19.B.How much the length of life was affected by COVID-19? This question was answered by calculating the shortened life expectancy (YLLs) due to COVID-19.Even if COVID-19 is causing multi-dimensional crises, this systematic review is focused only on its health impacts. The synthesis of this review was concentrated on answering the following question:high, moderate, low, and very low.” The certainty assessment was independently done by all the review authors and no disagreement occurred.The GRADE tool was used to assess the certainty of evidence from the included studies. This tool is designed to summarise the findings of the studies in the form of tables, with directness of findings, presence of publication biases, consistency of findings, study limitations, and inclusion of health outcomes  considered as the factors for certainty of evidence. Based on the pre-set criteria  for GRADE modalities, the quality of evidence was judged as “n = 786), manual Google scholar searches (n = 64), and from the references of included studies (n = 609)  [Four studies solely collected mortality data , 14–16, Based on the JBI critical appraisal tool for cross-sectional studies, only two studies , 23 measAll the studies used incidence rate as an epidemiological case rate measure and the data were collected using daily COVID-19 morbidity and/or mortality reports. In each study, the years life lost due to COVID-19 related mortality was higher than years life lost due to COVID-19 related disabilities . Four of the studies , 19, 21 Wyper et al.  calculatYLLs due to COVID-19 were assessed using different life expectancy standard tables among the reviewed studies. The difference of life expectancy in different life expectancy standard tables can be a source of bias in calculating the actual burden of diseases. The YLDs estimation mechanisms in the reviewed studies were not uniform (heterogeneous) among the studies. Except for the studies , 14–16 cExcept for two studies , 21, thehigh, moderate, low, and very low). The study with one “very low” and three “high” scores were graded as “moderate” and the studies that has two “very low” and two “high” scores were graded as “low,” whereas the study with three “very lows” and one high were graded as “very low.” The study with four “highs” was graded as “high” ;the number of healthy years of life lost from recovery/discharge from hospital to complete recovery of sequelae (long-term consequences disability time); andthe number of healthy years of life lost from the onset of the diseases to death  , 21.The impact of diseases on the quality of life is measured using the number of years life lost due to disability (YLDs) , 22, 23.To effectively assess the impact of COVID-19, all these disability times should be considered for YLDs estimation .The morbidity time of COVID-19 is noticeably shorter when compared to the years lost due to mortality from COVID-19. For instance, the acute phase of COVID-19 was estimated as 14 days (0.038 years) , 5 days The pre-mortality times , 19, 22 From all the included studies, four , 14–16 dDue to these reasons, the share percentage of YLDs in DALYs was less than YLLs in each study. Conversely, the YLDs due to communicable diseases other than COVID-19 was 71% of the total DALYs, with the remaining 29% contributed by YLLs . In addiThe life length impact of a disease is measured using the years of life lost due to premature death (YLLs) . AccordiThe number of healthy years of life lost due to mortality has a substantial contribution to the general negative health impact of COVID-19. YLL covers 95.44% , 97.99% The impact of COVID-19 on both the quality and length of life is measured using DALYs, which is the sum of YLD and YLL , 28, 29.According to Fan et al. , the 202Based on the finding of this systematic review, the following recommendations and implications for health regulators, policy makers, and health input suppliers were provided.The health regulators’ enforcement of the COVID-19 prevention and control measures is naturally going to change over time. Public awareness of the importance of COVID-19 prevention measures and follow-up of its compliance is recommended to minimise the future impact on DALYs. Further, it is likely that there will be additional benefits from the application of COVID-19 prevention and control measures in the prevention of other communicable diseases.Since COVID-19 is a common issue across all nations, it is necessary to support each other and share resources; the control of COVID-19 in one area of the world does not ensure the eradication of the disease permanently if it is still common in other countries. If we are seeking to improve DALYs, it is suggested that supporting vulnerable cohorts, both within and across countries, is vital if we are to achieve the common goal of either eradication or minimisation of COVID-19. Vaccination and applying preventive and control measures can aid in reducing the impact of the COVID-19 pandemic of DALYs , 35.Obviously, the ongoing nature of the pandemic means that suggestions for definitive research foci are not yet appropriate as the data remains incomplete. However, the results from this initial systematic review on COVID-19 has highlighted its multi-faceted impact, and future research focussing on the disease’s progress and health impact assessments that specifically analyse the years lost due to disabilities and mortality are recommended.The selected studies were affected by publication, selective outcome, and selective analyses biases. Many of the studies did not report the comorbidity and the demographic effect of COVID-19. Most of the studies did not consider the pre-death and long-term consequence disability times for calculating YLDs. Studies used the old GBD guidelines instead of the revised and simplified GHE standard for DALYs estimation. In the contrary, the DALYs, YLLs and YLDs numerical figures indicate in this review are based on the reported cases and deaths of COVID-19 at the time of each study and did not include unreported cases and deaths from the same study area or other parts of the world. As a result, the COVID-19 health impact assessments shown by the reviewed studies did not indicate the actual global burden of COVID-19. Since the studies did not estimate how many the unreported cases are in each study area, we cannot discus on the unreported morbidity and mortality cases of COVID-19.We used the title and abstract screening technique. A recent study confirmed that abstract screening misses around 13% of valuable studies . Due to The systematic review was registered in PROSPERO under the registrations number CRD42022324931. Before the start of the systematic review, a review protocol was prepared, agreed upon by all review authors and published in a reputable journal .The protocol of this systematic review  was publ


Previous studies have indicated elevated levels of impulsivity, hyperactivity, and inattention (IHI) among opioid-dependent patients seeking outpatient treatment with extended-release naltrexone (XR-NTX). This led us to hypothesize that IHI may be associated with a higher discontinuation rate for XR-NTX treatment. In a group of 162 patients with opioid dependence, discontinuation prior to the full 24 weeks of the study period (six injections and attending the study visit at 24 weeks) occurred in 49% of the patients, primarily in the early stage of treatment. IHI above the clinical cut-off on the adult ADHD self-report scale (ASRS) was not associated with a risk of premature discontinuation. This finding was not altered when controlling for socio-demographics, substance, use and mental health severity. Conclusively, high levels of IHI per se is not contradictive for XR-NTX treatment in regard to concern for premature discontinuation. Opioid use disorder (OUD) is considered a chronic condition, and the risk of relapse and overdose following detoxification and reversal of opioid tolerance is high . The staIn the Norwegian NaltRec study, an extended-release formulation of the opioid antagonist naltrexone (NTX) was given as an alternative treatment option in OMT. In contrast to agonist medication, which covers the body’s need for opioids to the extent where physical withdrawal is kept in check, opioid antagonist treatment blocks opioid receptors in the brain, thereby eliminating the effect of exogenous opioids . PatientThere are several known risk factors for the discontinuation of treatment in the field of SUDs, including cognitive dysfunction . PreviouWe investigated the treatment discontinuation rate in a population of opioid-dependent patients qualifying for OMT medication, with a preference for a 24-week treatment period with XR-NTX. We aimed to uncover whether impulsive patients  were more likely to end treatment than patients scoring below the cut-off. For this analysis, we also controlled for other factors relevant to discontinuation.Data in this article were collected in the Norwegian NaltRec study  . Five No®) releasing therapeutic dosages over a period of approximately 28 days. The study was construed with a 24-week study period, including 6 injections of XR-NTX and attending the study visit at 24 weeks, where patients could opt to participate in an additional 28-week (7 injection) treatment. Here, we focus on the first 24-week study period. At each study visit, data were collected in the form of study interviews and assessments of mental distress.Following a complete tapering of opioids, patients were inducted on an intramuscular suspension of 380 mg of extended-release naltrexone hydrochloride  following the ethical principles originating from the Declaration of Helsinki  and consPrior to the start of the study, all patients provided informed consent and were either enrolled or continued in an OMT program at inclusion, ensuring immediate follow-up, counselling and availability of opioid agonist therapy if needed due to the potential discontinuation of XR-NTX. Representatives from user organizations were involved in developing the study.n = 171) and completed the full ASRS at baseline (n = 162).Study enrollment occurred in Norway during the period between September 2018 and September 2020, and patients were mainly recruited from addiction clinics and detoxification units at the participating hospitals. Other recruitment channels were municipal health services and through the network of already recruited patients. Inclusion criteria were a current diagnosis of opioid dependence  and capaAt each study visit, data were collected in the form of study interviews as described below. Trained staff at the participating hospitals carried out all screening procedures. The European version of the Addiction Severity Index (Europ-ASI) was used to collect demographic data and The Mini International Neuropsychiatric Interview, version 6.0, was used to confirm ongoing opioid dependence .Discontinuation was the main outcome and defined as receiving <6 injections of XR-NTX due to failed attendance at a study visit and XR-NTX injection, unwillingness to comply with study procedures, or not wanting to proceed without giving further reason. Per protocol, patients were also required to meet at the 24-week study visit to avoid being considered a non-completer. We used the number of cumulated injections for each patient as a measure for time in the study (recommended time between each injection was 28 days).IHI was measured by the 18-item ASRS (ASRS-18). This scale has questions that target the patients’ own perception and evaluation of impulsivity/hyperactivity and inattention in everyday situations. With nine questions aimed at each area, patients were asked to rate the occurrence of one’s own symptoms of IHI during the past 6 months on a 5-point Likert scale . Each question was dichotomized according to the description of Kessler et al. . For 7 oThe 25-item Hopkin’s Symptom Checklist (H-SCL-25) was used to measure mental distress . Self-reWe used the Europ-ASI to assess the severity of alcohol and drug use . Severitp < 0.05. All statistical analyses were performed using IBM SPSS, version 26 .Descriptive statistics were used to characterize the sample. We used the chi-squared test and the log rank test to compare discontinuation between groups. A Cox regression analysis was used to control for relevant covariates (socio-demographic characteristics and severity variables). Patients who completed the treatment (6 injections and study visit at 24 weeks) or who developed a health condition incompatible with XR-NTX treatment were considered censored in the statistical analysis. The results of this analysis are presented as hazard ratios (HRs) with 95% confidence intervals (CIs). The threshold for significance was set at A total of 162 patients  were included in this study . At base2 = 0.07, p = 0.794). The discontinuation rate for the two groups is shown in the Kaplan–Meier curve in 2 = 0.03, df = 1, p = 0.859).Among those who expressed a desire for XR-NTX, 27 (17%) never initiated the medication, with no significant difference between the ASRS+ and ASRS- groups. Of the patients inducted on XR-NTX, the share who discontinued treatment after the first injection was 15% (20 of 135), with no significant difference between the groups. Cumulatively, this means that 29% (47 of 162) were not retained in the medical treatment after 28 days. At the end of the 24-week study, 79 patients (49%) had discontinued treatment, with 50% in the ASRS+ group and 48% in the ASRS- group  were associated with the discontinuation of XR-NTX. Other studies have found that young age correlates with discontinuation ,23, but Apparently, the reasons for discontinuing treatment with XR-NTX are individual. An outpatient study carried out in the US revealed that a frequent reason for discontinuing XR-NTX was a feeling of “having been cured” or a wish to remain abstinent without medications . DiscontThe measures for both IHI and mental distress rely on patients’ own perceptions of symptom load and, therefore, may suffer the risk of being influenced by their current emotional state. However, trained staff bear this in mind during screening procedures in order to eliminate the influence of ongoing withdrawal or substance use.The situation with the global pandemic made it extra challenging to collect data and carry out a longitudinal medical study such as the present analysis. During periods of lock-down, patient access to hospital areas were limited to injection procedures only, leaving the study interviews to be carried out by phone. This may have influenced the experience of follow-up for some patients and affected the retention time in the study. Future studies should examine the reasons why patients discontinue XR-NTX to provide greater insight into treatment obstacles.The discontinuation rate for XR-NTX treatment among opioid-dependent patients during a 24-week period was 49% in our study. Levels of IHI above the clinical cut-off did not imply a higher discontinuation rate. The scope of follow-up offered in addition to medical treatment may affect adherence; therefore, clinicians should intensify this parameter if patients are offered XR-NTX treatment.