Although substantive research has devoted increasing attention to variability in human resource practices at the organization, group, and individual levels, the critical role of line managers’ leadership in predicting this variability in the human resource management delivery and implementation process has been overlooked. Drawing from social information processing theory and human resource (HR) attributions theory, this study proposes that authentic leadership moderates the positive relationship between department-level high-performance work systems and employee-perceived high-performance work systems. Moreover, employee-perceived high-performance work systems can enhance employees’ thriving at work through commitment-focused HR attributions (well-being and performance). Analyzing the matched data from 145 departments and 834 employees, we found that the extent to which department-level high-performance work systems are positively related to employee-perceived high-performance work systems depends on authentic leadership within departments. We also found that employee-perceived high-performance work systems will result in commitment-focused HR attributions (well-being and performance), which in turn motivate employees to thrive at work. This study sheds light on whether and how line managers’ leadership influences the HR management process. Since the 1990s, the accumulated evidence has demonstrated the potential benefits of high-performance work systems (HPWS) , which rTo this end, several scholars have provided valuable insights into this issue. For example, To unveil how line managers shape the implementation process of HPWS, the leadership literature may provide a novel lens, as leadership captures a significant effect on employees’ sensemaking process . In geneFurthermore, although the HR process began to be highlighted in strategic human resource management (SHRM) research since Whereas prior research has documented that perceived HPWS are positively associated with favorable attitudes and behaviors, most of these works are based on social exchange theory or the ability, motivation, and opportunity model . HoweverThe present study makes several key contributions to both the SHRM and the leadership literature. First, our study advances the HRM literature by integrating HPWS with the leadership literature and exploring the critical role of line managers, especially authentic leadership, in the implementation of HPWS. Answering the call of High-performance work systems are defined as a series of independent and interconnected HR practices designed to enhance employees’ skills, motivation, and opportunities and ultimately improve organizational performance . In geneAlthough HPWS is seen as organizational politics, HR implementations within teams and groups often require line managers to translate the organizational practices into situation-specific actions . SpecifiFurthermore, line managers can affect employees’ perceptions of HPWS through their leadership behaviors , which dSocial information processing theory provides a theoretical rationale for explicating the moderating role of authentic leadership in the relationship between department-level HPWS and employee-perceived HPWS. The core tenant of social information processing theory is that social information clues help individuals interpret and then construct the context, which subsequently influences their attitudes and behaviors . In lineSpecifically, authentic leadership includes multiple inherent positive capabilities , such asAlso, authentic leaders emphasize relational transparency and balance processing  and respAccordingly, we suggest that authentic leadership facilitates the alignment between department-level HPWS and employee-perceived HPWS.Hypothesis 1: Authentic leadership moderates the positive relationship between department-level high-performance work systems and employee-perceived high-performance work systems.To provide further insight into the implementation process of HPWS, we tend to illuminate how employees perceive and interpret these HR practices, thereby having a consequential effect on their feelings of thriving at work. According to the HR attributions theory , employeWe predict that employee-perceived HPWS are likely to elicit commitment-focused HR attributions (well-being and performance).Commitment-Focused Human Resource Attributions (Well-Being): It is well established that HR practices, particularly HPWS, can function as “signals” or expected intentions of organizations . SpecifiCommitment-Focused Human Resource Attributions (Performance): Along with commitment-focused HR attributions (well-being), we also posit that employee-perceived HPWS are positively related to commitment-focused HR attributions (performance). In line with the SHRM literature , the priAccordingly, we hypothesize the following:Hypothesis 2a: Employee-perceived high-performance work systems are positively related to well-being-focused HR attributions.Hypothesis 2b: Employee-perceived high-performance work systems are positively related to performance-focused HR attributions.Next, we suggest that well-being-focused HR attributions will be linked to thriving at work. Thriving at work is defined as a psychological state that contains joint feelings of vitality and learning . When emIn addition, when employees attribute HPWS to the enhancement of individual performance and ultimately organizational performance, the explicit link between individual work and business strategy fosters their sense of positive meaning, which comes from “the creation of worth or value with work, colleagues, or the organization” . In thesIn sum, we posit the following:Hypothesis 3a: Well-being-focused human resource attributions mediate the positive relationship between employee-perceived high-performance work systems and thriving at work.Hypothesis 3b: Performance-focused human resource attributions mediate the positive relationship between employee-perceived high-performance work systems and thriving at work.As the discussion mentioned earlier noted, authentic leadership enables the alignment between department-level HPWS and employee-perceived HPWS by demonstrating the credibility, integrity, and authenticity of HR practices and better utilizing their abilities in the HR implementation process . BecauseIn line with the process model of HPWS, social information processing theory, and HR attributions theory, we expect that employee-perceived HPWS and commitment-focused HR attributions (well-being-focused and performance-focused HR attributions) mediate the interacting effect of department-level HPWS and authentic leadership on thriving at work. Specifically, authentic leadership and department-level HPWS interact to affect employee-perceived HPWS. Employee-perceived HPWS, in turn, foster commitment-focused HR attributions (well-being and performance), which promote thriving at work. Thus, we hypothesize the following serial mediated moderation effects:Hypothesis 4a: The interaction effect of department-level authentic leadership and department-level high-performance work systems on thriving at work is through employee-perceived high-performance work systems and well-being-focused human resource attributions.Hypothesis 4b: The interaction effect of department-level authentic leadership and department-level high-performance work systems on thriving at work is through employee-perceived high-performance work systems and performance-focused human resource attributions.We collected data from 99 organizations ranging from manufacturing, high-tech, and service industries in six main provinces of China. Through the alumni network, chief executive officers of these organizations were connected and invited to participate in this study. We first contacted these chief executive officers one by one, explained the purpose of the current study, and asked them to request their HR managers support our data collection. Under the support of human resource managers, questionnaires with unique identification codes and blank envelopes were assigned to department managers and their subordinates to pair the data from two sources. To reduce selection bias, we mentioned on the first page of the questionnaires that the present study aims to better understand HRM in practice and did not enclose the true research purpose to respondents. Additionally, we confirmed data confidentiality and recommended that respondents seal their surveys in blank envelopes by themselves after completing their questionnaires.To reduce common method bias, this study collected data from two different sources, department managers and employees. Department managers reported department-level high-performance work systems. Employees completed the measures of perceived HPWS, well-being-focused HR attributions, performance-focused HR attributions thriving at work, and authentic leadership. Initially, our sample consisted of 99 organizations, 186 department managers, and 1,182 employees. After removing unpaired data and void response, the final sample included 145 departments and 834 paired employees. The response rates for department managers and employees were 77.9 and 70.6%, respectively. At the employee level, 53% were male, 47% were female, 23.2% were under 35 years old, 32.9% were 36 to 40 years old, 23.7% were 41–45 years old, and 20.2% were above 46 years old. A total of 87.1% had a college degree. The average tenure of employees was 46.7 months.All the measures of the focal variables were adopted from the leading management journals and extensively used in the management area. Following the translation and back-translation procedures recommended by Department-level High-Performance Work Systems: Department-level HPWS were measured using Employee-Perceived High-Performance Work Systems: Employee-perceived HPWS were rated by employees using Commitment-focused Human Resource Attributions (Well-Being and Performance): Commitment-focused HR attributions contain well-being and performance-focused HR attributions : employeThriving at Work: Thriving at work was measured using 11-item scales adapted by Authentic Leadership: Authentic leadership was rated by employees using a 14-item scale developed by We controlled for two-level variables that may affect employees’ thriving at work. Consistent with previous studies , we cont2 = 127.590, RMSEA = 0.027, SRMR for within = 0.028, CFI = 0.991, and NNFI = 0.987). This provided support for the construct validity of all the variables in this study.In this study, given the nested nature of the data, we used HLM6 to estimate the proposed model. Before testing all the hypotheses, ICC (1) values for individual variables  were calculated to identify whether the multilevel analysis was needed. We found that ICC (1) values for employee-perceived HPWS, well-being-focused HR attributions, performance-focused HR attributions and thriving at work are 0.27, 0.15, 0.13 and 0.17, respectively, which exceed the recommended cutoff of 0.12. Next, individual-level variables were group-mean-centered, and department-level variables were grand-mean-centered following the recommendations. For Hypotheses 2 and 3, we used the parameter-based resampling method in R to estimate the indirect effects .p < 0.01). Thus, Hypothesis 1 was supported. To clearly demonstrate the moderating role of authentic leadership, we plotted the simple slopes when authentic leadership is at high (+SD) and low (−SD) levels. As P < 0.001). In contrast, when authentic leadership was at a low level, the relationship between department-level HPWS and employee-perceived HPWS was non-significant , which also supported Hypothesis 1.Hypothesis 1 predicted that authentic leadership moderates the positive relationship between department-level HPWS and employee-perceived HPWS. As model 3 in P < 0.001), supporting Hypothesis 2a. Model 6 showed that employee-perceived HPWS have a positive relationship with performance-focused HR attributions , supporting Hypothesis 2b.Hypotheses 2a and 2b posit that employee-perceived HPWS are positively related to commitment-focused HR attributions (well-being and performance). Model 4 demonstrated that employee-perceived HPWS were positively related to well-being-focused HR attributions . Employee-perceived HPWS were positively related to well-being-focused HR attributions  (Hypothesis 2a). The results of model 9 showed that when employee-perceived HPWS and well-being-focused HR attributions were added, the relationship between employee-perceived HPWS and thriving at work decreased , and the effect of well-being-focused HR attributions and thriving at work was also significant . Therefore, Hypothesis 3a was supported. We also used the Monte Carlo method to estimate the 95% confidence interval (CI) for the indirect effect. The results showed that the 95% CI was  and did not include zero, supporting Hypothesis 3a. Similarly, for Hypothesis 3b, model 6 showed that employee-perceived HPWS have a positive relationship with performance-focused HR attributions . As model 11 shows, the relationship between employee-perceived HPWS and thriving at work decreased , and the effect of performance-focused HR attributions and thriving at work was also significant  when employee-perceived HPWS and performance-focused HR attributions were added. The 95% CI for the indirect effect in Hypothesis 3b was , which supported Hypothesis 3b.Hypotheses 3a and 3b proposed that commitment-focused HR attributions (well-being and performance) would mediate the positive relationship between employee-perceived HPWS and thriving at work. For Hypothesis 3a, as model 8 in p < 0.01), the effect of department average HPWS on well-being-focused HR attributions , and the effect of department average well-being-focused HR attributions on thriving at work . To examine the mediated moderation effect, we conducted parametric bootstrapping for 20,000 repetitions to assess the 95% CI by using R software. The results showed that the 95% CI was  and did not include zero, which supports Hypothesis 4a. Similarly, the 95% CI of the indirect effect in Hypothesis 3b was , which supports Hypothesis 4b.Hypotheses 4a and 4b stated that the interaction between department-level HPWS and authentic leadership could affect thriving at work though employee-perceived HPWS and commitment-focused HR attributions (well-being and performance). For Hypothesis 4a, as the indirect effect includes three sequential paths, that is, the interaction effect of department-level HPWS and authentic leadership on employee-perceived HPWS .The present study makes several key contributions to the SHRM and leadership literature.First, our study offers a standpoint to take line managers’ leadership into account to unravel the implementation process of HPWS. It is widely acknowledged that there exists variability between actual HR practices and employees’ perceptions of HR practices . In thisSecond, by integrating authentic leadership and HPWS implementation, this study also extends the leadership literature and suggests that authentic leadership and HPWS may interact to affect individual outcomes. In Third, we contribute to the SHRM literature on the HR process by illuminating the subsequent process through which employee-perceived HPWS affect thriving at work. Although recent studies have begun to shift their focus from HR content to HR process, few studies uncover and examine the whole causal chain of HR implementation process that includes enacting HR practices by line managers, receiving and interpretation of these by employees, and, subsequently, how HR practices influence employees’ attitudes. Accordingly, we addressed this question and developed a theoretical model to test the relationships among department-level HPWS, employee-perceived HPWS, employee-commitment-focused HR attributions, and thriving at work. Thus, a comprehensive understanding of the HR process would not only shed light on the contingency perspective but also enrich the SHRM literature.Finally, this paper adds novel knowledge to the thriving at work literature by identifying HR practices and commitment-focused HR attributions as antecedents of thriving at work. As pointed out by Our study offers several practical implications for managers. As shown by our results, in the HR implementation process, authentic leadership of line managers does matter in the alignment of HPWS across organizational hierarchies. Thus, organizations should not only pay attention to line managers but also provide more opportunities for them to cultivate these specific leadership styles, which can facilitate organizational goal achievement. There have been studies focusing on the antecedents of authentic leadership; for example, For HR managers, on the one hand, considering line managers’ actions would benefit monitoring and controlling the effectiveness of HR practices. In addition, unlocking the influence linkage between formal practices and daily behavior manners of line managers is also necessary for the vertical and horizontal fit of HR systems. On the other hand, the implementation of HR systems is a process, and each part of the causal chain may be affected by various contextual factors. Thus, a process perspective is critical for HR managers to capture the complete picture of organizational systems and better manage HR implementation.Although our data are from multiple sources, department managers and employees, there are several limitations to our research. First, as our research design is cross-sectional, the causal inferences among variables cannot be confirmed in this study. Thus, we encourage future studies to use a longitudinal research design to further test this theoretical model. Second, our study only focuses on one specific leadership of line managers, authentic leadership, because of the similar value underlying it and HPWS and the unique contributions of authentic leadership to the alignment of HR implementations. As njucaoman@126.com.The datasets presented in this article are not readily available because according to the contracts signed with organizations before collecting data, we have to confirm data confidentiality and cannot make it public. Requests to access the datasets should be directed to MC, 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 patients/participants or patients/participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.MC put forward the conceptual model and wrote the manuscript. YZ conducted the language editing and helped to improve the flow of the manuscript. SM collected the data and made valuable suggestions for both the initial draft and subsequent revisions. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This work explores the potential utility of neural network classifiers for real-time classification of field-potential based biomarkers in next-generation responsive neuromodulation systems. Compared to classical filter-based classifiers, neural networks offer an ease of patient-specific parameter tuning, promising to reduce the burden of programming on clinicians. The paper explores a compact, feed-forward neural network architecture of only dozens of units for seizure-state classification in refractory epilepsy. The proposed classifier offers comparable accuracy to filterclassifiers on clinician-labeled data, while reducing detection latency. As a trade-off to classical methods, the paper focuses on keeping the complexity of the architecture minimal, to accommodate the on-board computational constraints of implantable pulse generator systems.Clinical relevance—A neural network-based classifier is presented for responsive neurostimulation, with comparable accuracy to classical methods at reduced latency. IDeep brain stimulation (DBS) first received approval for the symptomatic treatment of Parkinson’s disease in 1997. While similar in design to cardiac pacemakers, the implantable pulse generators (IPG) of the time offered only an open-loop form of therapy, with typically a single stimulation pattern, set by a clinician for each patient. Real-time seizure detection and responsive neurostimulation (RNS) was first attempted using a computer-in-the-loop system by Gotman et al. in 1976 , 3], , .Filter-based spectral-feature detectors have since been used successfully in other conditions, most notably for tremor suppression in Parkinson’s disease, through the discovery of beta oscillations as a correlate of disease state . HoweverPatient-specific filter design, while possible to aid with software, can be a complex problem, likely to limit both clinician-capacity and patient-throughput. Establishing and validating a neural-network (NN) training pipeline based on clinician-labeled data could offer a systematic classifier tuning process. Networks could be pre-trained on aggregate data from multiple patients, and refined based on individual labeled data at the point of deployment . Of cour® Cortex-M4), through model compression and quantization techniques, showcasing several advanced NN topologies.Liu et al.  demonstrThis paper is meant as an initial study to bring focus to the fundamental challenge of NNclassifiers: computational cost. As state-of-the-art deep neural networks reach everincreasing model sizes , 17], w, w17], wIIATo establish a baseline for performance as well as for computational cost, we used a classical band-power estimation filter chain to detect epileptiform discharges , 6], th, th6], tBIn our search for a low-complexity classifier for time series input, we explored two main NN families. (1) Multi-Layer Perceptrons (MLP) are the simplest, and oldest family or of artificial neural networks , where tRecurrent neural networks, an otherwise natural choice for processing time series data, were dismissed from consideration as recurrence necessitates the introduction of dynamic state variables, which significantly increases memory footprint . WithoutCOur raw dataset consisted of LFP signals recorded from two patients, for a combined 24 hours, with 30 professionally labeled events of clinical significance. The recordings were resampled to a 256 Hz sampling frequency for uniformity.As seizures are comparatively rare events scattered among very long periods of normal activity, we decided to introduce class imbalance into our training sets to best prepare the NNs for real-life use. The training set was biased towards negative samples in a 3:1 ratio, based on clinician annotations. The dataset was split in the common 70:30 ratio between a training and a validation set. Network weights and biases were quantized to 8-bit integers.DNeural networks were modeled and trained in Tensorflow Lite version 2.7.0, using an Intel Core i7 CPU with 16 GB of RAM. Embedded performance was tested on an Arduino Nano 33 BLE Sense evaluation board for the nRF52840 ARM Cortex-M4F microprocessor.EFThe performance of the MLP model, when trained on a given dataset, is primarily determined by two hyperparameters: the number of timepoints in the input window, and the hidden layer’s size. We found that varying the number of hidden layers had very modest effects on accuracy (not shown in this paper). IIIThe next step is to compare our best MLP result to the baseline classifier. The ROC of To explore the MLP classifier’s internal representation of a seizure, we present a small interpretation experiment in IVThe example design of the MLP classifier demonstrates that even tiny neural networks can be effective at simple signal processing tasks. As the final step, we should reflect on the embedded resource usage achieved, so we refer the reader to


Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing’s neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability. Motor imaging (MI) is the dynamic cognitive capability of generating mental movements without executing them. This mental process triggers the neurocognitive mechanisms that underlie voluntary movement planning, similar to how the action is performed realistically. MI has been proposed as a reliable tool in acquiring new motor skills to increase sports performance and physical therapy ,2,3,4, iBCI inefficiency) poses a challenge in MI research [i) Developing guidelines in neural testing set-ups, practice, and instructions to ensure better performance of brain responses; and (ii) Promoting evaluation tools to forecast the system performance may help identify the core issue of variability to incorporate compensating actions for the inefficiency when solving BCI-based tasks. In particular, a calibration strategy can be added, working hand in hand within the training stage. Therefore, it is possible to adapt the decoding scheme with an explicit brain pattern [wPLI and phase-locking value PLV) can discriminate between different MI tasks [An enhanced approach to addressing this EEG data complexity is to conduct multiple training sessions to refine the modulation of sensorimotor rhythms (SMR). Nonetheless, the inter-subject variability, together with uncertain long-term effects and the apparent failure of some individuals to achieve self-regulation, makes a non-negligible portion of users , among  pattern . It has MI tasks ,24.Therefore, predicting motor performance from the resting motor-system functional connectivity can be determined as in , showingr-squared) is often applied to assess the prediction shape, while the p-value levels its statistical significance that can be implemented through several test procedures, as developed in [p-values [As regards the prediction model, several regression methods are available for prognosticating MI accuracy from neurophysiological variables like simple and multiple linear regression ,32, steploped in . A commop-values .Here, to increase the prediction performance of the baseline linear regression models, we develop a deep network regression model devoted to prognosticating Motor Imagery Skills using EEG Functional Connectivity Indicators, appraising three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning connecting neighboring runs. Our approach comprises functional connectivity predictors extracted from electroencephalographic signals to favor the data interpretability. To deal with the risk of overfitting prediction assessments because of the deep learning framework, we intend to preserve as much information as possible from the measured scalp potentials. Thus, to reach competitive values of prediction errors achieved by the leave-one-out cross-validation scheme, we introduce the following procedures: (i) Monte Carlo dropout layers to decrease the probability that the learned rules from specific training data cannot be generalized to new observations; (ii) Subject efficiency clustering to adapt the DNR estimator more effectively to complex EEG measurements inherent to BCI inefficiency subjects; (iii) For Prediction of Initial-training Synchronization, transfer learning of the weights inferred at the predecessor run to deal with the few-trial sets. The validation is performed in two MI databases (150 users) acquired in conditions close to real MI applications. Obtained results show how our approach can achieve a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability. We further compared the DRN predictor prediction performance  with the results obtained by linear regression models that are reported, at least for DBI, in the baseline work , presentThe rest of the paper is organized as follows: Initially, we consider predictors based on the following two widely-used FC measures of phase synchronization between every pair of EEG channels PLV):Phase Locking Value ν1(cransform .wPLI):Weighted Phase Locking Index  :–Strengtpredictor) and response) be a couple of random variables for which the mutual dependence regressor) Let Deep Regression Network (DRN) developed in [j-th layer  Predicting capability estimation of the pre-training desynchronization under a conventional linear regression model, testing different scenarios of input arrangements to improve the system performance; (ii) Prediction assessment of the pre-training desynchronization under the data-driven network regression model; (iii) Enhanced network prediction assessment using leave-one-out cross-validation combined with Monte Carlo dropout layers and clustering of subject inefficiency; (iv) Enhanced network regression prediction of initial-training synchronization with an additional transfer learning procedure.The methodology for enhanced prediction of motor imagery skills using functional connectivity indicators is evaluated under a regression model to predict the bi-class accuracy response of subjects, embracing the following stages: , which maximizes the class variance. To accurately extract the subject EEG dynamics over time, the sliding window is adjusted to 2 s, having an overlap of On the other hand, the pre-training desynchronization predictor relies on the fact that the change in neural activity, intentionally evoked by a mental imagery task, shows certain regularities through training runs or sessions. Accordingly, the pre-training indicator of neural desynchronization attempts to anticipate the MI responses evoked within every run’s wakefulness data.Giga-DBI: This MI dataset is publicly available at . It gathers EEG records from fifty subjects , holds Biosig Toolbox that is free available at http://biosig.sourceforge.net, accessde on 30 January 2021). Further, the electrophysiological indicator set, MNE package in Python, while the graph predictors are estimated using the Brain Connectivity Toolbox (brain-connectivity-toolbox.net).Every raw EEG channel of either database was band-pass filtered in the frequency range –IN: We consider two inputs-layer arrangements: multivariate indicator C the electrode and links number when using graph indexes and FC, respectively).–tanh-based activation is employed to reveal non-linear relationships.–CT: A concatenate layer is applied to append the resulting feature maps from the set of patterns extracted in –tanh is used as activation function.–: A one-neuron layer with linear activation is used to predict the MI skill value –DO: This Dropout layer randomly skips neurons according to drop rate. We fix the drop rate at 0.2 empirically.The proposed Deep Regression Network architecture comprises see :–IN: We M subjects. The quantity measures account for the influence to predict the acceptance rate on the electrodes performed by individuals, namely, for computation of value Rp-value is computed from a two-sided t-test whose null hypothesis is that the regression slope is zero [Mean Absolute Error (MAE), and Root Squared Error (RMSE) are also assessed, as carried out in [For measuring the relationship between the response variable and the composite predictor, we build the set  is zero . It is w is zero ,44, becad out in ,52: (7)Ri) Matrix indicator, when computing one individual network vector to reflect the electrode contribution (termed multichannel); (ii) Vector indicator, holding a single scalar value of FC accomplished by each subject. For comparison purposes with similar reported works, we analyze two indicator approaches extracted from each individual: (a) Average that obtains the mean value over the electrode set, and (b) the channel with the best R-squared (best channel).Here, we consider two scenarios of input predictor arrangements: , which are calculated using the Sklearn package in Python. As seen, both indicator approaches, multichannel and average, perform below the procedure for best channel extraction from the resting-state data regardless of the brain graph predictor employed. Thus, selecting the best channel allows achieving higher values of R-squared with lower p-values within the considered frequency rhythms. In the case of the predictors directly extracted from FC measures, PLV and WPLI, the upper triangular matrix is vectorized to feed the regression, yielding a performance similar to the graph indexes. Note that the best channel approach is not reported for PLV and WPLI because of difficulties in their implementation. Consequently, the above prediction results show that DBI achieves a poor performance in predicting the pre-training desynchronization, at least, using the baseline linear regression. Besides, the DBII collection gives a much worse prediction than DBI since the former EEG data contain fewer trials (100 vs. 22 per label), more subjects (105 vs. 50), and was acquired with a much lower sampling frequency (512 vs. 150 Hz). Still, the importance of considering EEG collections with elevated complexity remains an actual problem since they are more close to the requirements of real MI applications.Intending to evaluate the linear regression model, R-squared can be accomplished using either scenario of input arrangement representation.We also consider the case of multiple linear regression, involving both graph index predictors. Wide&Deep neural network with different training sets simultaneously that are learned by the first layer separately. The next layer merges all input predictor sets, exploring common relations among them. In particular, we contrast the network regression fed by the connectivity indicators extracted within all three frequency rhythms against the widely used extracting approach from Next, we employ the multichannel matrix indicator extracted from the whole subject set to predict the pre-training desynchronization. We also analyze the joint characterization of both graph indexes (strength and clustering coefficient), concatenating their vector representations of each subject into a single supervector. Besides, we take advantage of the wide path to feed the R-squared value, the estimates of MAE and RMSE are also computed to evaluate the DRN performance, accounting for the influence of the adopted leave-one-out cross-validation strategy on the DRN performance. Concerning the examined graph indexes, Besides the R-squared value as high as MAE and RMSE are still high. Even worse, the corresponding error values for DBI are lower than those estimated for DBII due to its greater variability.Overall, the network regression reaches an p ≤ 0.1. Secondly, the leave-one-out cross-validation is further refined by incorporating the Monte Carlo dropout layers, containing neurons with a probability of being ignored during training and validation. Therefore, both assessments in Equation  are robust and spread all over the scalp, as happens with the latter FC measure (third column). DBII faces a similar situation (fifth and seventh columns), though providing fewer estimates. This result can be explained because of the higher complexity of DBII. Next, the use of leave-one-out cross-validation with Monte Carlo dropout  regardless of the subject group under consideration. Moreover, the improvement becomes higher for DBII, which means that the regression analysis using partitions can be also effective in databases with more complex EEG measurements.For these cases, we calculate the DRN prediction with the performance improved. That is, we test the functional connectivity indicators of all three frequency rhythms extracted from resting-state data together with the leave-one-out cross-validation, including Monte Carlo dropout layers. ⋆), showing that the channel selection strategy also improves the performance of every subject partition compared to the corresponding values in The next aspect refers to the assessment of resting-state activation on a reduced number of electrodes. To this end, we evaluate the DRN performance for the predictor sets extracted over the sensorimotor area, selecting the following electrodes, as suggested in :  to build a predictive regression model of BCI control. From the obtained results for the linear regression model . As suggested in , the leaRegression assessments using subject clustering of BCI inefficiency. One more issue impacting the regression prediction is the user’s categorization depending on their SMR activity and classifier performance during the MI runs. The obtained results show that the prediction performance improves  regardless of the subject group under consideration. Therefore, we hypothesize that the regression analysis using partitions may be more effective in databases with complex EEG measurements. Consequently, clustering combined with DNR models enhances understanding of the factors influencing subjects’ accuracy performance with significant BCI inefficiency.DNR prediction with transfer learning. We also assess the DNR performance of predicting the MI accuracy at each run using the single-trial PLV predictor of wakefulness data. However, we associate the values learned from each run’s MI data with the weights inferred at the predecessor run to deal with the few-trials sets. Thus, compared to the all set performance, the initial-training synchronization prediction increases in each group of individuals.For future work, the authors plan to enhance FC predictors’ feature extraction, providing a better understanding of their impact and interaction on BCI-related tasks to identify potential non-learners.Profiting from MI-based BCI learning progression, dynamic network regression models must be developed to capture the sequence regression’s latent trends. In this line of analysis, the cluster-based enhancing procedure and the vector accuracy response should also account for FC predictors’ dynamic behavior. Intending to improve the DNR prediction, an extended panel of standardized and validated psychological questionnaires are to be included within the network estimator, accounting for user’s specific characteristics like daily motor activity and age.One more aspect to explore is to adjust the DNR pipeline to learn the weights for supporting prediction in a broader clinical application class, relying on the ability of deep learning architectures to extract complex random structures from EEG data.Julian Caicedo-Acosta received his undergraduate degree in electronic engineering (2018) and his M.Sc. degree in engineering industrial automation (2019) from the Universidad Nacional de Colombia. Currently, he is a PhD student at the same university. His research interests include machine learning, signal processing and bioengineering.German A. Castaño received his undergraduate degree in economics (1986) and business administration (1987), and his specialization of informatics administration (1993) from the Universidad Nacional de Colombia. Currently, he is a Professor in the Department of Administration at the Universidad Nacional de Colombia – Manizales. In addition, he is Chairman of the “Grupo de investigación Cultura de la Calidad en la Educación”at the same university. His research interests include quality of education, peace, and post-conflict.Carlos Acosta-Medina received a B.S. degree in Mathematics from Universidad de Sucre in Colombia in 1996. In 2000, he received a M.Sc. degree in mathematics, and in 2008 a Ph.D. in mathematics, both of them from Universidad Nacional de Colombia—Sede Medellín. Currently he is Associated Professor at Universidad Nacional de Colombia—Sede Manizales. His research interests are Regularization, Conservation Laws, and Discrete Mollification.Andres Alvarez-Meza received his undergraduate degree in electronic engineering (2009), his M.Sc. degree in engineering industrial automation (2011), and his Ph.D. in engineering—automatics (2015) from the Universidad Nacional de Colombia. Currently, he is a Professor in the Department of Electrical, Electronic, and Computation Engineering at the Universidad Nacional de Colombia – Manizales. His research interests include machine learning and signal processing.German Castellanos-Dominguez received his undergraduate degree in radiotechnical systems and his Ph.D. in processing devices and systems from the Moscow Technical University of communications and Informatics, in 1985 and 1990 respectively. Currently, he is a Professor in the Department of Electrical, Electronic, and Computation Engineering at the Universidad Nacional de Colombia, Manizales. In addition, he is Chairman of the GCPDS at the same university. His teaching and research interests include information and signal theory, digital signal processing, and bioengineering.


KoRV is unique among retroviruses in that one strain (KoRV-A) is undergoing endogenization, whereas the other main subtype (KoRV-B) and another subtype (KoRV-C) are reportedly exogenous strains. Its transmission and pathogenesis are of interest in the study of retroviruses and are crucial for any conservation strategy geared toward koala health. This study provides new evidence on the modes of KoRV transmission from parent koalas to their joey. We found vertical transmission of KoRV-A, confirming its endogenization, but with closer conservation between the joey and its sire than its dam . This is also the first report of a KoRV-B-negative joey from KoRV-B-positive parents, contrasting with the few previous reports of 100% transmission of KoRV-B from dams to joeys. Thus, the results in this study give some novel insights for the transmission mode of KoRV. Phascolarctos cinereus) genome. Although KoRV has frequently been isolated in wild and captive koala populations, its pathogenesis and transmission remain to be fully characterized, and most previous research has concentrated on adult koalas rather than on joeys. Here, we characterized KoRV isolates obtained from a deceased male joey and its parents  to investigate KoRV transmission mode and pathogenesis. We sequenced the KoRV long terminal repeat (LTR) and envelope genes isolated from the joey and its parents and found KoRV-A and KoRV-C in genomic DNA from both the parents and the joey. Notably, both parents were also positive for KoRV-B, whereas the joey was KoRV-B negative, further confirming that KoRV-B is an exogenous strain. The KoRV LTR sequence of the joey was considerably closer to that of its sire than its dam. For further characterization, total KoRV, KoRV-A, KoRV-B, and KoRV-C proviral loads were quantified in peripheral blood mononuclear cells from the parents and in blood samples from the joey. Total KoRV, KoRV-A, and KoRV-C proviral loads were also quantified for different tissues  from the joey, revealing differences suggestive of a distinct tissue tropism . The amount of KoRV-C in the parents was less than that in the joey. Our findings contribute to an improved understanding of KoRV pathogenesis and transmission mode and highlight useful areas for future research.Koala retrovirus (KoRV) is of an interest to virologists due to its currently active endogenization into the koala (IMPORTANCE KoRV is unique among retroviruses in that one strain (KoRV-A) is undergoing endogenization, whereas the other main subtype (KoRV-B) and another subtype (KoRV-C) are reportedly exogenous strains. Its transmission and pathogenesis are of interest in the study of retroviruses and are crucial for any conservation strategy geared toward koala health. This study provides new evidence on the modes of KoRV transmission from parent koalas to their joey. We found vertical transmission of KoRV-A, confirming its endogenization, but with closer conservation between the joey and its sire than its dam . This is also the first report of a KoRV-B-negative joey from KoRV-B-positive parents, contrasting with the few previous reports of 100% transmission of KoRV-B from dams to joeys. Thus, the results in this study give some novel insights for the transmission mode of KoRV. Gammaretrovirus in the family Retroviridae. KoRV is endemic in both wild and captive populations, and it represents a major threat to koala health  is from the genus a health –6. KoRV  health –, 8.A total of nine KoRV subtypes (KoRV-A to -I) have been identified up to the present , among wThe mode of transmission has been investigated for both subtypes. As with other endogenous retroviruses, KoRV-A is integrated into the germ line of the host and transmitted vertically to offspring. Reportedly, this vertical transmission apparently occurs between dams and their offspring , 10. In We previously reported on KoRV infection dynamics , and in The koala population at Hirakawa Zoo in Kagoshima, Japan, was among those targeted in our previous KoRV research. Staff at the zoo recently found a 6-month-old joey which had died and been ejected from its dam’s pouch. The KoRV statuses of the deceased joey’s sire and dam (both KoRV-A and KoRV-B positive) were known from our previous studies. Further examination of the parents and the deceased joey represented an opportunity to expand our knowledge on the disease and its transmission between parents and offspring. Accordingly, in this study, we aimed to improve the understanding of KoRV transmission mode and pathogenesis in joeys by investigating the KoRV status of the parents and their joey and by characterizing KoRV in blood and tissues from the joey.The hematology results for the parents are shown in 3; total protein [TP], 3.4 g/dl; fibrin deposit, +) and peritoneal  cavities  isolated from peripheral blood mononuclear cells (PBMCs) for the determination of KoRV proviral copy numbers in the parents and joey, with normalization against koala β-actin. Various proviral copy numbers were found in these koalas . We measTo investigate the tissue tropism of KoRV in the joey, the following organs/tissues were collected at necropsy : heart , lung F, spleen Reverse transcription-quantitative PCR (RT-qPCR) was performed to determine viral load in plasma. The viral copy numbers were 1,070,526/ml of plasma in the sire and 1,147,045/ml of plasma in the dam  and coulPCR amplification of sequences with primers U3F and U5R  resultedenv gene sequences (GenBank accession numbers MK605477 and MK605481)  from the joey and performed multiple-sequence alignment with the parents’ KoRV-A K605481) . MultiplK605481) . One poiK605481) , we alsoK605481) .In the present study, we investigated the subtype status, tissue tropism, and transmission mode of KoRV in a deceased joey koala from parents previously established to be KoRV-A and KoRV-B positive in our previous studies , 11.For KoRV-A, we speculated that vertical parent-to-offspring transmission had occurred in this case, which is in line with previous findings of 100% vertical KoRV-A transmission in regional koala populations in Australia and the inheritance of KoRV after its endogenization into the genome . Notablyde novo) infection from their dams. Such transmission could occur through uterine fluids, milk, or pap, which probably originates in the dam’s cecum. KoRV-B-negative joeys with KoRV-B-positive sires have been featured in these reports; however, to our knowledge, this is the first report of a KoRV-B-negative joey from a KoRV-positive dam. As an understanding of transmission is key to any strategy for species conservation, further research on KoRV prevalence and transmission in joeys would appear to be necessary.Our findings for KoRV-B are of particular interest. The joey was found to KoRV-B negative, although both of its parents were found KoRV-B positive (in both this study and our previous study). Although adult-to-adult transmission of KoRV reportedly occurs at a low rate, the previous (limited) reports on parent-to-offspring transmission suggested that 100% of joeys contract this subtype through horizontal   , 17, 18.Overall, our study confirms that KoRV-B is an exogenous variant of KoRV, and that KoRV-A is inherited by vertical transmission, as has been previously widely reported. However, we suggest that previous assumptions about the 100% dam-to-joey transmission rate for KoRV-B may need to be reexamined. A fuller understanding of transmission and pathogenesis is required to develop any conservation strategies, such as vaccination programs for wild animals, and will be also be of interest to virologists studying the exogenous and endogenous subtypes of this disease. In future research on the koala populations in Japanese zoos, we hope to further expand our knowledge of KoRV and provide answers to some of the questions raised by recent research.The joey was a 6-month-old male found dead and ejected from its dam’s pouch by staff at the Hirakawa Zoo in Japan ; its cadEDTA-treated whole-blood samples were collected by venipuncture from two the adult koalas (sire and dam) housed at the Hirakawa Zoological Park in Kagoshima, Japan, in accordance with the protocols of the Institutional Animal Care and Use Committee of the Joint Faculty of Veterinary Medicine, Kagoshima University. Tissue samples  were collected at necropsy from the dead joey at Hirakawa Zoological Park and stored at −80°C for further characterization. We collected whole-blood samples with heparin or EDTA by venipuncture from 2 koalas (parents) and tissue samples  from their dead joey at Hirakawa Zoological Park, in accordance with the Institutional Animal Care and Use Committee protocols.To determine the health status of the parent koalas, a hematological panel was obtained with standard protocols, and this panel included white blood cell (WBC) count, red blood cell (RBC) count, hemoglobin (HGB), packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC).Whole-blood samples (EDTA treated) were centrifuged at 3,000 rpm and 20°C for 5 min to obtain plasma samples, which were then stored at −80°C. To measure viral plasma load, viral RNA was isolated from plasma using an Isogen-LS kit , according to the manufacturer’s instructions. To remove any contaminating DNA, extracted RNA was treated with RQ1 RNase-free DNase (Promega), according to the manufacturer’s instructions.Genomic DNA (gDNA) was isolated from EDTA-treated whole-blood samples using a Wizard genomic DNA purification kit (Promega), according to the manufacturer’s instructions. gDNA was isolated from frozen tissue samples using a phenol-chloroform extraction method. The extracted gDNA was used for further characterization.Taq polymerase. The resulting target PCR fragments were subcloned into the pCR2.1 TOPO vector (Invitrogen) and sequenced.PCR was performed using gDNA as a template to amplify the envelope genes of KoRV-A, -B, and -C with genotype-specific primer sets , as descenv region of KoRV-C, using gDNA as the template. The sequence data of KoRV-C env were submitted to GenBank (accession no. MT134110). Tissue was isolated from koala blood or tissue samples using the primer sets in A nested PCR was performed to determine the nucleotide sequence of the long terminal repeat (LTR) for KoRV-A and the env gene nucleotides and LTR nucleotides was performed using the MEGA7 software .The sequence data for the KoRV-A envelope gene and KoRV LTRs are available in the GenBank database (accession numbers


Substance use disorders (SUDs) lead to tens-of-thousands of overdose deaths and other forms of preventable deaths in the USA each year. This results in over $500 billion per year in societal and economic costs as well as a considerable amount of grief for loved ones of affected individuals. Despite these health and societal consequences, only a small percentage of people seek treatment for SUDs, and the majority of those that seek help fail to achieve long-term sobriety. E-health applications in healthcare have proven to be effective at sustaining treatment and reaching patients traditional treatment pathways would have missed. However, e-health adoption and sustainment rates in healthcare are poor, especially in the SUD treatment sector. Implementation engineering can address this gap in the e-health field by augmenting existing implementation models, which explain organizational and individual e-health behaviors retrospectively, with prospective resources that can guide implementation.. This project, based in Iowa, will compare a control condition  to software implementation utilizing NIATx-TI, which includes change management training, followed by coaching on how to implement and use the mobile application. While e-health spans many modalities and health disciplines, this project will focus on implementing the Addiction Comprehensive Health Enhancement Support System (A-CHESS), an evidence-based SUD treatment recovery app framework. This trial will be conducted in Iowa at 46 organizational sites within 12 SUD treatment agencies. The control arm consists of 23 individual treatment sites based at five organizations, and the intervention arm consists of 23 individual SUD treatment sites based at seven organizationsThis cluster randomized control trial is designed to test two implementation strategies at adopting an evidence-based mobile e-health technology for SUD treatment. The proposed e-health implementation model is the Network for the Improvement of Addiction Treatment–Technology Implementation (NIATx-TI) FrameworkThis study addresses an issue of substantial public health significance: enhancing the uptake of the growing inventory of patient-centered evidence-based addiction treatment e-health technologies.ClinicalTrials.gov, NCT03954184. Posted 17 May 2019 Contributions to the literatureResearch suggests that e-health technologies in substance use treatment are promising, yet only recently have agencies started to use them.This study suggests a technology implementation model for incorporating e-health technologies in substance use disorder treatment agencies.This randomized control trial addresses a gap in the current literature by comparing the effectiveness of an evidence-based implementation strategy (NIATx-TI) with a widely used implementation strategy (Product Training). It will also be the first to do so in implementing A-CHESS, an evidence-based e-health framework.The outcomes of this study are extremely relevant given the uptick in interest for e-health technologies post COVID-19.Every day on average 96 Americans die due to opioid overdoses, and another six die due to alcohol poisoning, making substance use disorders (SUDs) the third-leading cause of preventable death in the USA . Many inPatient-centered mobile e-health technologies offer innovative ways to improve treatment and recovery supports for SUDs . In FebrDespite the promise of e-health technologies, the benefits are far from being realized –17. A prExtensive research supports building models to speed diffusion and sustain technology adoption . Technoldeliberate  planning for organizational strategy and change was necessary, but not sufficient. Simply too much occurred after planning to create a single “plan”. They instead developed an emergent  planning approach, which used data that emerged from the implementation process to improve the plan throughout implementation. This approach moved planning from a purely centralized (top-down) process to include a more emergent (staff engaged) approach [deliberate) and post-implementation (emergent), along with our authors’ field experiences [The organizational planning field has used two relatively stable theoretical planning approaches for the past two decades: deliberate/prescriptive planning and emergent/descriptive planning . Porter’approach –41. Theseriences –45, leaddeliberate component includes using an organizational change management assessment (OCM) [emergent component includes using a project team to uncover and prioritize implementation barriers as they arise, develop changes to address identified barriers, and monitor selected adoption measures while receiving monthly coaching.NIATx-TI was piloted in the Iowa Rural Health Information Technology Initiative (IRHIT) with 14 of Iowa’s 105 SUD treatment sites and resulted in a twofold increase in patients receiving virtual treatment. The framework’s nt (OCM) . This toThis research will test the technology adoption framework (NIATX-TI) in increasing the use of the Addiction Comprehensive Health Enhancement Support System (A-CHESS), an evidence-based technology platform developed for treating SUD , 47–51. Product training and on-line support have been the customary approach to technology adoption , 64. In Reach or Participation (Primary Outcome): (a) percent and representativeness, based on eligible patients who create an account on the RISE-Iowa app, and (b) average RISE-Iowa use, by days of use; Effectiveness: treatment retention rates of sites between the two arms; Adoption: percent and representativeness, based on age, gender, and education level of counselors using RISE-Iowa; Implementation: (a) organizational readiness for, and (b) fidelity of RISE-Iowa implementation and (c) qualitative analyses to develop a better understanding of the implementation processes for delivering NIATx-TI compared to product training/on-line support alone; Maintenance: RISE-Iowa use  during the sustainability phase. All objectives will be compared at the cluster level.The primary aim of this cluster randomized controlled trial (RCT) is to compare the effectiveness of NIATx-TI (arm 2 only) and product training in implementing an evidence-based platform (A-CHESS), via RISE-Iowa. Impact will be measured using the RE-AIM framework : Reach ocontrol: product training alone (arm 1) and intervention: product training + NIATx-TI (arm 2) to increase RISE-Iowa use among adults (> 18 years) receiving outpatient SUD care. Both the intervention and control arms will complete all the same data collection instruments used in this study. A cluster is a pair of organizations that have similar criteria and will be randomly placed in opposite arms of the study.This research extends over 30 months (18M test period and12M sustainability period). We are using a mixed-methods approach to compare the effectiveness of the We selected Iowa as a partner because of its mix of urban and rural settings, the presence of minority populations, the strength of its data collection and reporting systems, and the limited use of A-CHESS platforms.Recruitment focused on organizations funded by the Iowa Department of Public Health (IDPH) and licensed to provide outpatient treatment services. We assessed eligible organizations for their readiness for implementation, number of outpatient sites, and mix of urban and rural populations. Each Iowa SUD organization consists of one to seven outpatient sites. The aim was to enroll 40 addiction treatment sites with 20 in each arm and estimated this would require 16 organizations. Of the 20 eligible organizations, 11 Iowa organizations consisting of 43 addiction treatment centers were initially enrolled in this study. Using cluster randomization, we randomized the 11 organizations into two groups: five organizations with 23 addiction treatment centers into the control arm and six organizations with 20 addiction treatment centers into the intervention arm. Post-randomization, one organization in the intervention arm dropped out of the study, and two organizations enrolled post-randomization. The two new organizations were assigned to the intervention arm to achieve a roughly equal number of control and experimental sites. Figure n = 11) created one matched pair with two intervention organizations and one control organization. Blinding or concealment of the participants and researchers was not logistically feasible. Post-randomization, a second matched pair was created that paired two intervention organizations to one control organization, to account for the late enrollment of one of the organizations. The second late enrollment replaced the organization that had dropped out of the intervention that was a part of the first 2:1 matched pair. These non-randomized additions were made to keep the total number of SUD treatment centers in each arm even and the pairs as equal as possible in terms of the original randomization matching criteria.The first step of the stratified randomization (or blocking with matched pairs) was to determine sample matchings for the 11 organizations that originally agreed to participate. The matching of the organizations depended on (a) readiness for technology adoption score , (b) the number of rural sites, and (c) the number of outpatient sites per organization. We applied urn randomization to achieve arm balance, using these three traits. Additionally, the odd number of organizations in the study  describes the features of RISE-Iowa , (b) explains the study design and stipend information, (c) informs leadership how to choose an appropriate RISE-Iowa site coordinator for each site location, and (d) brings understanding to our team about how the organization operates and if special considerations are needed.RISE-Iowa demo for site leaders with the organization’s IT, RISE-Iowa site coordinator, and conducted by the RISE-Iowa product trainer takes place during the project setup phase, after the initial RISE-Iowa demo. This secondary RISE-Iowa demo (a) describes the features of RISE-Iowa , (b) provides RISE-Iowa organizational case examples of how it is implemented, and (c) provides examples of how to recruit and prepare clinicians and patients for RISE-Iowa.RISE-Iowa product training is conducted by the RISE-Iowa product trainer and an IT assistant. The product training occurs at the organizational sites. The training serves as the start date of that organizations’ 18-month intervention period. All interested counselors and staff at the organization are invited to participate in the product training, which consists of (a) meeting with organizational leadership to discuss RISE-Iowa implementation plan after the on-site visit, (b) demonstrating RISE-Iowa features and how RISE-Iowa allows patients to self-manage their recovery in conjunction with their counselors, (c) demonstrating how to use the RISE-Iowa clinician dashboard, and (d) providing promotional brochures, posters, flyers.On-line technical support will occur for the 18 months following the product training. Patients and counselors can call or e-mail questions regarding the RISE-Iowa mobile application. Some examples include password/account recovery, account deletion, bug reporting, notification of inappropriate app use, and suggestions for improving the app.The Deliberate (planning) phase: step 1. Define aims: Executive briefing on project setup and discussion of strategic and implementation aims for the organization. This occurs at the initial RISE-Iowa demo. Step 2. Assessment of assets and barriers via organizational change management survey (OCM). This is discussed in detail with the change leader for the intervention arm during the secondary RISE-Iowa demo.Emergent phase: step 3. Change leader and change team training and planning on how to modify organizational processes to address barriers to RISE-Iowa implementation (combined with secondary RISE-Iowa demo and RISE-Iowa product training visit). Step 4. Monitor RISE-Iowa implementation reviews progress on aims and gains real-time user feedback. The study team will provide weekly enrollment reports to the site coordinator on the number of patients enrolled in RISE-Iowa. Step 5. Change cycles (or pilot tests) to overcome barriers to implementing RISE-Iowa. Step 6. Sustain changes: Implement a plan to institutionalize gains. Additionally, approximately a year after RISE-Iowa is implemented, the coaches will conduct the sustainability survey verbally with the change team. The survey will help identify the sustainability of the implementation and lead to more productive change team meetings.In addition to the above product training and on-line support, the second study arm has a NIATx-TI framework and NIATx-TI structural components. These components include deliberate and emergent planning features suggested in the planning literature, which will allow us to develop an implementation plan that incorporates the organization’s barriers/facilitators to adopting the technology and uses a validated organizational change model. Figure NIATx-TI structural components involve:NIATx coach: an expert versed in technology adoption and organizational change who helps treatment organizations make, sustain, and spread RISE-Iowa adoption efforts. A NIATx-TI coach assists the organizations with applying the NIATx-TI framework, provides the training for this intervention, and provides feedback on the assessments during the session . This yields a Cohen’s  = 0.352 . Additio = 0.352 . An esti = 0.352 . With a Table α = .93) [Within the two study conditions, we have the additional secondary measures of effectiveness, adoption, and mediation. Effectiveness: Retention rates will be measured using the Simpson measure of admission date to last therapeutic visit , becauseα = .93) . This int tests (or Mann-Whitney tests) will be used to test for statistically significant baseline differences.A preliminary analysis of organizational data will compare baseline characteristics  between organizations in each of the two intervention arms. The analysis will use the National Survey of Substance Abuse Treatment Services (N-SSATS)  and TreaInitial exploratory analyses of RISE-Iowa use will assess standard summary statistics and graphical presentations at each level and across levels . The datWe will examine the mediating effects of organizational factors of percentage of counselors using RISE-Iowa, organizational readiness (via the OCM), and financial resource availability (via Klein’s inventory), through a causal mediational analysis, using data from mid-test, end of test, and end of sustainability (post-test) periods. These factors will be analyzed using mixed-effects models. Through a mediation analysis , 77, we n = 12) will occur at months 40 to 45 (at the end of the test phase) and months 53 to 57 (at the end of the sustainability phase). A randomly selected site from each organization will be used for the interviews in each phase to gain a retrospective understanding of what occurred during implementation. The interviews will include closed- and open-ended questions designed to explore participants’ experiences within each arm and the perceived effectiveness of each approach in assisting with implementing RISE-Iowa. The closed-ended questions will be based on elements of the Consolidated Framework for Implementation Research, developed by Damschroder et al. [n = 48 interviews per phase. The qualitative analysis will produce a description of the technology implementation process. It will encompass grounded explications of the theoretical concepts that influenced the development of the NIATx-TI framework. This description will enhance our understanding of how the NIATx-TI framework works, promoting valuable insights that can be applied to future dissemination of mobile e-health applications.The qualitative portion of the study will support the specific aims by exploring how NIATx-TI and product training are implemented, how implementation affects RISE-Iowa use, and how a staff user reacts to NIATx-TI, product training, and RISE-Iowa. During the study, interviews from each of the organizations .Currently, the trial’s recruitment phase is complete, much of the baseline data has been collected, and the implementation period has started.More than 20 million adults aged 18 or older struggle with a SUD each year in the USA. Yet, only around a tenth seek SUD treatment , 80, 81,


Background: Cardiac sympathetic response (CSR) and malnutrition-inflammation syndrome (MIS) score are validated assessment tools for patients' health condition. We aim to evaluate the joint effect of CSR and MIS on all-cause and cardiovascular (CV) mortality in patients with hemodialysis (HD).Methods: Changes in normalized low frequency (ΔnLF) during HD were utilized for quantification of CSR. Unadjusted and adjusted hazard ratios (aHRs) of mortality risks were analyzed in different groups of ΔnLF and MIS score.Results: In multivariate analysis, higher ΔnLF was related to all-cause, CV and sudden cardiac deaths . Higher MIS score was associated with incremental risks of all-cause, CV and sudden cardiac deaths . Patients with combined lower ΔnLF (≤6.8 nu) and higher MIS score were at the greatest risk of all-cause and CV mortality .Conclusion: Our data indicate a joint evaluation of CSR and MIS score to identify patients at high risk of death is more comprehensive and convincing. Considering the extremely high prevalence of cardiac autonomic neuropathy and malnutrition-inflammation cachexia in HD population, a non-invasive monitoring system composed of CSR analyzer and MIS score calculator should be developed in the artificial intelligence-based prediction of clinical events. Interest is increasing rapidly in the application of non-invasive monitoring systems and artificial intelligence (AI)-based decision support technologies to predict clinical events in patients undergoing maintenance hemodialysis (HD), such as sudden death, emergency visit, muscle spasm, and hospitalization , 2. CardFrom another point of view, the severity of malnutrition-inflammation cachexia (MIC) is in parallel with risks of all-cause and CV mortality . MalnutrThe study protocol was approved by the Institutional Review Board of the En Chu Kong Hospital . All clinical investigations were conducted according to the principles of the Declaration of Helsinki. The prospective cohort was conducted in HD patients at blood purification centers from March 2016 until February 2020. A written informed consent was obtained from the participants of this study. Patients undergoing HD treatment for at least 3 months were eligible for inclusion. All patients had to be older than 18 years of age and receive thrice-weekly HD. Patients were excluded from the study if they had terminal illness, active infections, active malignancy, or personal reasons. HD vintage was defined as the duration of time between the first day of HD treatment and the first day that the patient entered the study cohort. Each HD session was performed for 3.5–4.5 h with a blood flow rate of 200–300 mL/min and dialysate flow rate of 500 mL/min. Blood pressure was recorded in the horizontal recumbent position before dialysis session. Pre-dialysis blood samples were obtained from the existing vascular access.*100. Thus changes in nLF (ΔnLF) before and after HD represent CSR in HD patients . MIS score includes seven conventional components of the subjective global assessment  and three new elements . Each MIS component has four levels of severity from 0  to 3 (very severe).The following bio-demographic and laboratory parameters of each patient were recorded at baseline: age, gender, hypertension, DM, coronary artery disease (CAD), HD vintage, blood pressure, potassium, calcium, phosphorus, creatinine, pre-dialysis blood urea nitrogen, fasting glucose, alanine aminotransferase, albumin, C-reactive protein (CRP), uric acid, Kt/V , total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, iron, ferritin, total iron-binding capacity (TIBC), hemoglobin, and platelet count. We adjusted serum calcium according to the following equation: adjusted calcium = measured calcium+ . Nonetheless, the p-value for the interaction product term was insignificant (P = 0.182), suggesting the association between ΔnLF and CV mortality was not limited to high levels of MIS score.The services to improve patient safety are delivery of non-invasive early detection and sustained monitoring of high-risk subjects. In this prospective cohort study we present the brand new idea that the joint evaluation of ΔnLF and MIS score provides a more robust predictive value for not only all-cause but also CV mortality with the application of a non-invasive monitoring system. Our data indicate that higher ΔnLF was related to all-cause, CV and SCD . Higher MIS score was associated with incremental risks of all-cause, CV and sudden cardiac deaths . Patients with combined lower ΔnLF (≤6.8 nu) and higher MIS score were at the greatest risk of all-cause and CV mortality . Apparently, there is an intricate relationship between impaired CSR, MIC and fatal events. Several important findings in this work deserve further discussion.It is of prime importance to understand the interplay between the sympathetic and parasympathetic regulatory coordination, which serves to speed up and slow down the heart rate, respectively . HRV is DAN, a serious and common complication of DM, consists of composite nerve damaging disorders . As the P = 0.062 and 0.182 for the interaction term, respectively), our data provided a preliminary data bank for future AI-based prediction models. The majority of maintenance HD patients have CV diseases, and their CV mortality is 20 times higher than in the general population . The patients/participants provided their written informed consent to participate in this study.J-FC and J-CL were responsible for study concept and design, interpretation of data, writing of the manuscript, study supervision, and drafting of the manuscript. C-YH assisted with methodology, investigation, biochemical, and digital analysis. T-MW, Y-YL, and C-CW were responsible for the manuscript revision.The authors declare that this study received funding from Pharmofoods Medical Editing Co., Ltd. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


Immune checkpoint inhibitors (ICIs) have been used to treat many cancers, but ICIs are rarely administered for malignant tumours coexisting with inflammatory bowel disease.We report a 77-year-old man experiencing an ulcerative colitis (UC) flare-up after receiving nivolumab as third-line therapy for multiple metastases of renal cell carcinoma. Mild UC (proctitis form) had been diagnosed at age 59 years and remission was maintained for 17 years with only a low dose of 5-ASA. After nivolumab treatment, the patient developed diarrhoea, bloody stools and was hospitalised. Computed tomography revealed inflammation involving the entire colon and endoscopy revealed severe UC exacerbation. Histological analysis showed UC findings and also increased crypt apoptosis which is unusual for inflammatory bowel diseases, while being typical of ICI-induced colitis. As with ICI-induced colitis, this exacerbation was strongly suggested to have been caused by nivolumab, although remission was achieved by increasing the 5-ASA dose to 4000 mg without prednisolone.The administration of ICI for UC is not as yet sufficiently safe and further research is required. Nivolumab and other immune checkpoint inhibitors (ICIs), which have shown high efficacy against a variety of cancers in recent years, promise long-term survival and even recovery. ICIs are also associated with unique adverse events that are different from those associated with conventional chemotherapy , 2. ImmuRecently, the number of elderly-onset UC patients has been rising . In eldeThe patient was a 77-year-old man. At 59 years of age, he developed bloody faeces and was diagnosed with mild UC (proctitis form). UC was maintained at a Mayo score of 0 by treatment with 5-aminosalicylic acid (5-ASA) administered as a suppository and orally Fig.  15]. At. At15]. Histological analysis revealed erosion, reduced goblet cells, irregular duct layout, cryptitis, crypt abscesses and chronic inflammatory cell infiltrate in the stroma. These findings were consistent with UC flare-up, but after recognising increasing apoptosis, we considered the possibility of PD-1 inhibitor-induced enterocolitis to also be high Fig. . AlthougWe have presented a patient with a worsening UC flare-up due to nivolumab treatment. In this patient, the pre-existing UC was of the mild proctitis type, and remission had been maintained for 17 years with only a low dose of 5-ASA. We had difficulty in differentiating this flare-up from ICI-induced colitis; however, as the patient was elderly with pre-existing UC, prednisolone was not administered, and remission was once again achieved by stopping nivolumab and increasing the dose of 5-ASA.In the few years since the development and approval of ICIs, they have brought about substantial changes in cancer treatment, and we expect that ICI indications will be expanded to include a wide range of malignancies , 2. HoweGiven the mechanism of action of ICIs, there are concerns over an increasing incidence of irAEs and exacerbation of IBD and other autoimmune diseases. This concern has led to very few UC patients being treated with ICIs, and thus very few reports describing such cases.Including the present case, ICI administration has been confirmed in 10 non-postoperative UC patients. Outcomes after treatment were flare-up in 5 patients (50%) and perforation in one (Table Currently, the number of elderly UC patients is increasing globally. Numbers of comorbidities, rates of comorbid infection and perioperative mortality from emergency surgery are also reportedly rising among elderly UC patients, and case fatality rates are correspondingly increasing , 22, 23.When enterocolitis occurs after administering drugs to elderly UC patients for comorbid recurrent progressive cancer, rapid therapeutic intervention is first required to prevent UC from flaring up or becoming more severe , 22, 23.Treatment guidelines for ICIs strongly recommend steroids or early administration of infliximab or vedolizumab for ICI-induced colitis. However, this information conflicts with treatment guidelines for elderly IBD patients, for whom steroids or infliximab may increase mortality and the incidence of infection , 26, 27.By contrast, 5-ASA produces a dose-dependent effect, is very safe and is used throughout the world in many IBD patients , 29. KubThe administration of ICI for UC is not as yet sufficiently safe and further research is required.Elderly UC patients who have a recurrent, progressive cancer need flexible treatment strategies that maintain remission of UC and aim for improving the long-term prognosis, which is the primary goal of cancer treatment.


Background: An optimal sequential systemic therapy for advanced hepatocellular carcinoma (HCC) has not been discovered. We developed a decision model based on available clinical trials to identify an optimal risk/benefit strategy for sequences of novel systemic agents. Methods: A Markov model was built to simulate overall survival (OS) among patients with advanced HCC. Three first-line  followed by five second-line treatments  were compared in fifteen sequential strategies. The likelihood of transition between states  was derived from clinical trials. Life-year gained (LYG) was the main outcome. Rates of severe adverse events (SAEs) (≥grade 3) were calculated. The innovative measure, called incremental safety-effectiveness ratio (ISER), of the two best sequential treatments was calculated as the difference in probability of SAEs divided by LYG. Results: Lenvatinib followed by Nivolumab  was the most effective sequence, producing a LYG of 0.75, while Atezolizumab plus Bevacizumab followed by Nivolumab was the safest sequence (SAEs 40%). Accordingly, the net health benefit assessed by ISER favored Lenvatinib followed by Nivolumab, compared to Atezolizumab plus Bevacizumab, followed by Nivolumab in 52% of cases. Conclusion: Further sequential clinical trials or large-scale real-world studies may prove useful to evaluate the net health benefit of the best sequential treatment for advanced HCC. Hepatocellular carcinoma (HCC) is characterized by clinical and biological heterogeneity ,2. In adUntil 2018, Sorafenib was the only systemic treatment which was found to significantly improve overall survival (OS) as a first-line therapy for advanced HCC . SubsequAmong patients who have progressed and are tolerant to Sorafenib, treatment with Regorafenib has resulted in longer OS than placebo . Thus, RTo date, the best sequential first- and second-line systemic treatment remains elusive. In particular, what is the best sequential systemic strategy in terms of OS benefit and safety? Due to the lack of sequential appropriately designed RCTs, these issues remain unanswered.Therefore, we have developed a Markov model based on data from available RCTs, and we describe its use to evaluate the risks and benefits of currently available sequential systemic treatments for patients with advanced HCC.p = 0.002) and Atezolizumab plus Bevacizumab followed by Nivolumab (p = 0.035)  . SurvivaAmong the 15 treatment sequences analyzed, the treatment sequence with the lowest rate of SAEs was first-line Atezolizumab plus Bevacizumab followed by second-line Nivolumab (40.2%). Conversely, the treatment sequences exhibiting the highest toxicity (77.2% SAEs) were first-line Lenvatinib followed by second-line Cabozantinib or Regorafenib. .Next, the most effective sequence (Lenvatinib followed by Nivolumab) was compared with the safest sequence (Atezolizumab plus Bevacizumab followed by Nivolumab). A one-way sensitivity analysis for LYG was performed for these two sequences in comparison with Sorafenib followed by Nivolumab. LYG ranged from 0.25 to 0.50 for Lenvatinib followed by Nivolumab, and from 0 to 0.25 for Atezolizumab plus Bevacizumab followed by Nivolumab. The most influential factors identified for both strategies were: HBV infection, HCV infection, MaVI and/or EHD, ECOG-PS 1, and BCLC stage C . VariatiCompared to Atezolizumab plus Bevacizumab followed by Nivolumab, the ISER of Lenvatinib followed by Nivolumab was 32% of SAEs for LYG. One-way sensitivity analysis of the ISER also showed that MaVI and/or EHD was the most influential factor, producing an ISER ranging from 32–94% of SAEs for LYG. .A two-way sensitivity analysis was performed to demonstrate which therapy would be preferred for varying the difference in SAE and LYG values . At a wiThe results of a probabilistic analysis for LYG are presented in It is well known that sequential trials in oncology are difficult to perform, and in most settings, such as advanced HCC, they are practically unfeasible. Moreover, real-world data of sequential therapies in advanced HCC are time consuming and they are lacking to date. Therefore, comparative evidence among different treatment sequences could be obtained only by simulation models. In the field of HCC, more innovative treatments are found to confer significant survival benefits, even when used in the advanced stages of disease. To our knowledge, this is the first model to date which provides a comprehensive comparison of different sequential strategies for patients with advanced or intermediate HCC, who are unfit for, or who do not respond to, loco-regional therapies.Our model suggests that first-line Lenvatinib followed by second-line Nivolumab or Pembrolizumab may be an attractive sequential systemic strategy in terms of OS for patients with advanced HCC. Meanwhile, first-line Atezolizumab plus Bevacizumab followed by second-line Nivolumab was identified as the safest sequential strategy. Strategies involving first-line MKI followed by second-line MKI appear to be associated with worse survival in all scenarios. Using a willingness-to-risk threshold of 30% of SAEs for LYG, the net health benefit assessed by ISER favored Lenvatinib followed by Nivolumab compared to Atezolizumab plus Bevacizumab, followed by Nivolumab in 52% of cases, giving a wide range of uncertainty about the net health benefit between the two compared sequences.RCTs of systemic therapies for advanced HCC have often showed small, albeit significant, differences in OS between active drug and placebo. If the OS gain is relatively small (on the order of a few months), the quality of life spent on or off active treatment is crucial, and it should be considered in the clinical decision-making process. Hence, our model provides evidence that first- and second-line ICIs are the safest sequential strategies. Moreover, sequential first-line ICIs should be reserved for the most fragile patients, considering the relatively improved safety profile they exhibit compared to MKIs. However, ICIs are associated with immune-mediated hepatotoxicity whose severity is unpredictable. The hepatotoxicity can range from mild to life-threatening, and it is not completely known in patients with cirrhosis ,30.According to ASCO statements , net heaThe results obtained in the present study appear to be plausible and are supported by other evidence. A study of tumor microenvironment and immune setting in patients treated with Sorafenib has revealed a possible role for MKIs also in immunomodulation . FurtherThere are several limitations associated with our model.(1)The biggest and most important issue of our decision model is that it did not consider the carry-over effect. Further sequential trials with a crossover design, properly designed to assess the carryover effect, could solve this intriguing issue.(2)The lack of clinical data concerning the majority of sequential strategies assessed by our model. All the five available second-line therapies were approved in trials, including Sorafenib-experienced patients, while no second-line therapy was evaluated after Lenvatinib or Atezolizumab plus Bevacizumab. As Atezolizumab plus Bevacizumab will become the new standard first-line treatment of advanced HCC, the identification of the optimal treatment sequential strategy represents a burning unsolved medical need. Therefore, our decision model, with all the limitations of a simulation, should be considered a useful tool to evaluate the risk-benefit ratio of treatment sequences not evaluable in the trials performed to date. In the absence of sequential RCTs, large-scale real-world studies are urgently needed.(3)Trials of second-line treatment included in the model had some differences in inclusion and exclusion criteria, particularly regarding MKIs. However, we are confident that the patients enrolled in second-line RCTs that we examined could be roughly comparable, because all of the patients were Sorafenib-experienced.(4)Assessment of the benefit of ICIs raises several issues considering the unconventional pattern of radiological response or progression. Novel radiological response assessment tools  and innovative methodologies  may be able to capture the long-term benefit observed with ICIs. In addition, the use of multivariate meta-analysis, which combines surrogate endpoints  with OS, may prove useful for substantiating the benefit of different sequential treatments on OS.(5)Lack of data regarding all-cause SAEs for Nivolumab may have affected our analyses. However, reported data for Pembrolizumab led to a more accurate assessment of overall tolerability of ICIs in patients with liver disease .(6)Unfortunately, we were unable to calculate quality-adjusted life-years (QALYs), since utilities were not available in the majority of the RCTs examined, particularly regarding ICI trials. Accordingly, we assessed LYG as the primary measure of efficacy. However, utilities may vary widely across different patient subgroups, and they critically depend on quality of life assumptions.(7)Lack of data on potentially relevant prognostic factors , and liThe main strengths of our study are that: (1) this is the first model to estimate survival in sequential therapies by using PFS for first-line therapy and OS for second-line therapy. Simulation remains today the only method to provide quantitative estimates for physicians and policymakers in the setting of advanced HCC; (2) it provides a novel method to estimate the net health benefit by the use of the innovative measure, so-called ISER.p-values ≤ 0.05 were considered statistically significant. As a safety measure, the number of SAEs (grade ≥ 3) from each included trial was extracted. The model evaluated 15 sequential therapy scenarios  advanced HCC eligible to first-line systemic therapy; (2) first progression; and (3) death . The heaLife-year gained (LYG) was the main health outcome. Median OS obtained from the Markov model at different simulated sample sizes was compared by using the Wilcoxon test, in order to identify the minimum sample size needed to assess a statistically significant difference. To provide long-term survival information, a milestone survival analysis at 36 months was performed. Accordingly, the number needed to treat (NNT) was calculated for each sequential strategy.The rate of SAEs (≥grade 3) was calculated for each sequential treatment, by taking into account transition probabilities from first- to second-line treatments, by using a weighted mean of the number of simulated patients transited in each disease state . To obtain a net health benefit measure , clinicaTwo different deterministic sensitivity analyses were performed for LYG and ISER. The following variables were evaluated: male gender, Asian ethnicity, hepatitis C virus (HCV) and hepatitis B virus (HBV) as etiology of liver disease, macrovascular invasion (MaVI) and/or extrahepatic disease (EHD), Eastern Cooperative Oncology Group (ECOG) Performance Status 1, and Barcelona Clinic Liver Cancer (BCLC) stage C. see Table A2In the one-way deterministic sensitivity analysis for LYG, Lenvatinib followed by Nivolumab and Atezolizumab plus Bevacizumab followed by Nivolumab were indirectly compared to Sorafenib followed by Nivolumab.In the one-way deterministic sensitivity analysis for ISER, Lenvatinib followed by Nivolumab versus Atezolizumab plus Bevacizumab followed by Nivolumab was compared, because the latter dominates Sorafenib followed by Nivolumab, both in terms of median survival and SAEs.Two-way sensitivity analysis for ISER was performed by varying the numerator (percentage difference in SAEs) and the denominator (LYG). The variation range for the denominator was chosen to be as large as the maximal value observed, while a ±4% variation was chosen for the numerator. Analogous to the willingness-to-pay aspect of a cost-effectiveness analysis, a willingness-to-risk threshold value must also be established for ISER. Several possible willingness-to-risk thresholds for ISER, and their visual effect in terms of estimated areas, when SAEs and LYG are varied according to their ranges, have been calculated.To examine the extent to which differences in LYG could be explained by differences in patient characteristics, a two-step probabilistic sensitivity analysis was performed. In the first step, four variables  were included in a meta-regression model. The dependent variable was median PFS time. The meta-regression model employed was a weighted multiple linear regression model, where the weight was the study sample sizes. By assuming a uniform distribution for the values of the four variables considered, 1000 pseudo-random values were generated from each variable, in order to obtain corresponding predictions from the model. In the second step, the predicted median times were then proportionally translated into Weibull scale parameters by assuming, in percentage, a proportional shift of the scale parameters for the base case . Then, in turns, these parameters were used as inputs in the Markov model, to obtain 1000 simulated median times.Single-agent first-line Lenvatinib, followed by second-line Nivolumab or Pembrolizumab, seemed to be an attractive option for advanced HCC. Meanwhile, the first-line combination of Atezolizumab plus Bevacizumab, followed by second-line Nivolumab, was the safest sequential strategy. Congruent with the above data, a wide range of uncertainty remains about what is the optimal sequential strategy in terms of net health benefit. Ongoing and future RCTs and data obtained in field practice studies will confirm whether combination or sequential systemic therapies are more beneficial. In the meantime, clinicians, patients and policymakers can use this decision model for the collaborative decision-making process.