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Drug recalls may impact treatment plans or access to suitable therapies. Thus, they inadvertently affect treatment outcomes. We aimed to examine the impact of recalls on patients' safety using pantoprazole-containing products recall as a case study in terms of the occurrence of potential drug-drug interactions (pDDIs). This retrospective study used de-identified electronic health records of adult patients who had a prescription for oral proton pump inhibitors (PPIs) including pantoprazole, esomeprazole, lansoprazole, or omeprazole from April 2020 through September 2021 from a large tertiary care hospital. The study outcome definition was the prevalence of pDDIs in PPIs users before and after the recall date (March 2021). Changes in the prevalence of pDDIs were modeled using interrupted time-series. The rate ratio of pDDIs in the 12 months before and 6 months after the recall was modeled using negative binomial regression. A total of 1,826 pDDIs were identified, and the median monthly prevalence of pDDI before the recall was 102.5 which increased to 115.5 after the recall. A change in the level of pDDIs occurred immediately after the recall date, followed by a gradual decrease over time. The rate of pDDIs was 69% higher after the recall compared to the baseline (rate ratio 1.69; 95% confidence interval, 0.75–1.91). Recall of pantoprazole-containing products was associated with a higher rate of pDDIs. However, the prevalence of pDDIs gradually decreased over time. We highlight the importance of planning of recall process and coordinating all potential stakeholders to avoid potential harms. Word count: 1450. [ABSTRACT FROM AUTHOR]

Object recognition memory (ORM) allows identification of previously encountered items and is therefore crucial for remembering episodic information. In rodents, reactivation during recall in the presence of a novel object destabilizes ORM and initiates a Zif268 and protein synthesis-dependent reconsolidation process in the hippocampus that links the memory of this object to the reactivated recognition trace. Hippocampal NMDA receptors (NMDARs) modulate Zif268 expression and protein synthesis and regulate memory stability but their possible involvement in the ORM destabilization/reconsolidation cycle has yet to be analyzed in detail. We found that, in adult male Wistar rats, intra dorsal-CA1 administration of the non-subunit selective NMDAR antagonist AP5, or of the GluN2A subunit-containing NMDAR antagonist TCN201, 5 min after an ORM reactivation session in the presence of a novel object carried out 24 h post-training impaired retention 24 h later. In contrast, pre-reactivation administration of the GluN2B subunit-containing NMDAR antagonist RO25–6981 had no effect on ORM recall or retention but impeded the amnesia caused by Zif268 silencing and protein synthesis inhibition in dorsal CA1. Our results indicate that GluN2B-containing hippocampal NMDARs are necessary for ORM destabilization whereas GluN2A-containing NMDARs are involved in ORM reconsolidation, and suggest that modulation of the relative activity of these receptor subtypes during recall regulates ORM persistence. [ABSTRACT FROM AUTHOR]

Connected consciousness, assessed by response to command, occurs in at least 5% of general anaesthetic procedures and perhaps more often in young people. Our primary objective was to establish the incidence of connected consciousness after tracheal intubation in young people aged 18–40 yr. The secondary objectives were to assess the nature of these responses, identify relevant risk factors, and determine their relationship to postoperative outcomes. This was an international, multicentre prospective cohort study using the isolated forearm technique to assess connected consciousness shortly after tracheal intubation. Of 344 enrolled subjects, 338 completed the study (mean age, 30 [standard deviation, 6.3] yr; 232 [69%] female). Responses after intubation occurred in 37/338 subjects (11%). Females (13%, 31/232) responded more often than males (6%, 6/106). In logistic regression, the risk of responsiveness was increased with female sex (odds ratio [OR adjusted ]=2.7; 95% confidence interval [CI], 1.1–7.6; P =0.022) and was decreased with continuous anaesthesia before laryngoscopy (OR adjusted =0.43; 95% CI, 0.20–0.96; P =0.041). Responses were more likely to occur after a command to respond (and not to nonsense, 13 subjects) than after a nonsense statement (and not to command, four subjects, P =0.049). Connected consciousness occured after intubation in 11% of young adults, with females at increased risk. Continuous exposure to anaesthesia between induction of anaesthesia and tracheal intubation should be considered to reduce the incidence of connected consciousness. Further research is required to understand sex-related differences in the risk of connected consciousness. [ABSTRACT FROM AUTHOR]

In the present study, we tested whether processing information in the context of an ancestral survival scenario enhances episodic memory performance in older adults and in stroke patients. In an online study (Experiment 1), healthy young and older adults rated words according to their relevance to an ancestral survival scenario, and subsequent free recall performance was compared to a pleasantness judgment task and a moving scenario task in a within-subject design. The typical survival processing effect was replicated: Recall rates were highest in the survival task, followed by the moving and the pleasantness judgment task. Although older adults showed overall lower recall rates, there was no evidence for differences between the age groups in the condition effects. Experiment 2 was conducted in a neurological rehabilitation clinic with a sample of patients who had suffered from a stroke within the past 5 months. On the group level, Experiment 2 revealed no significant difference in recall rates between the three conditions. However, when accounting for overall memory abilities and executive function, independently measured in standardized neuropsychological tests, patients showed a significant survival processing effect. Furthermore, only patients with high executive function scores benefitted from the scenario tasks, suggesting that intact executive function may be necessary for a mnemonic benefit. Taken together, our results support the idea that the survival processing task – a well-studied task in the field of experimental psychology – may be incorporated into a strategy to compensate for memory dysfunction. [ABSTRACT FROM AUTHOR]

Predicting students' academic performance is of paramount importance to educational institutions. If students' academic performance is predicted course-wise, semester-wise and year-wise then it will be helpful for students to a great extent. In this research article, we generate twenty nine dataset with the help of students' result analysis. Datasets 1 to 9 is the datasets of students' result in first attempt while datasets 10 to 19 represent datasets of students after passing all courses of semesters and datasets 20 to 29 is overall dataset of students' result analysis. To predict the course-wise, semester-wise and year-wise performance of students, we developed a framework titled as E CSY APPS (E ducational C ourse S emester Y ear-wise A cademic P erformance Prediction S ystem) based on classification techniques and designed algorithm for analyzing students' performance in education sector. This E CSY APPS predicts the course-wise, semester-wise and year-wise grade of students. Fifteen classification algorithms such as Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVM), Linear Support Vector Classification (LSVC), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), Bagging, Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Linear Discriminant Analysis (LDA), and Stochastic Gradient Descent Classifier (SGDC) are selected and applied on 29 datasets and compared on basis of performance parameters such as Accuracy, Precision, Recall, F1-score and Mean Absolute Error (MAE). These fifteen classification algorithms are under ten classifiers such as Linear Model, Naive Bayes, Tree, Support Vector Machine, Nearest Neighbors, Ensemble, xgboost, lightgbm, Discriminant Analysis and catboost. If the accuracy of two more classification algorithms for a dataset is same then in that case Precision, Recall, F1-score and Mean Absolute Error (MAE) are compared for deciding the best classification algorithm for the dataset. In this way, best classification algorithm is selected for each of 29 datasets. It is found that classifiers xgboost works best for eleven datasets while ensemble techniques Gradient Boosting and AdaBoost work best for two datasets and six datasets respectively among 29 datasets. Other classification algorithms such as Decision Tree, LightGBM, LDA, and KNN are noted to be best classification algorithm for two, four, three, and two datasets respectively. This framework is tested on new eight datasets related to students' result two methods such as K-Fold Cross-validation and Train-Validation-Test method. The results of this framework on new datasets shows that accuracy obtained on test dataset or validation dataset as compared to the accuracy obtained on old dataset is less than 6%. This framework will be helpful for students as well instructor. For students, it will help them to improve the performance of difficult courses from students'point of view in the examination while faculty can use this framework to improve pedagogical practices. [ABSTRACT FROM AUTHOR]

We do not memorize items in our surroundings with equal priority. Previous literature has widely shown that emotional stimuli are better remembered than neutral stimuli. However, given emotional stimuli and neutral stimuli often differ in both valence and arousal dimensions, it remains unclear whether the enhancement effects can be attributed to valence, or just to arousal. Importantly, most prior studies relied on a relatively small number of stimuli and non-emotional factors such as word length, imageability and other confounds were hard to control. To address these challenges, we analyzed multiple large databases of recognition memory and free recall tasks from previous research by items with many lexical and semantic covariates included, examining the effects of valence or arousal when controlling for each other. Our results showed a U-shaped relationship between valence and memory performance for both recognition and free recall, and a linear relationship between arousal and memory performance for both tasks. These findings showed that the memory enhancement effects can be attributed to both valence and arousal. We demonstrated these effects with generalizability across many stimuli and controlled for non-emotional factors. Together, these findings disentangle the contribution of valence and arousal in emotional memory enhancement effects and provide insights for current major theories of emotional memory. • Emotional memory enhancement can be attributed to both valence and arousal. • Valence influenced memory quadratically. • Arousal influenced memory linearly. • These effects are generalizable across stimuli and not caused by non-emotional factors. [ABSTRACT FROM AUTHOR]

Although process model discovery has been largely investigated over the past two decades, existing process discovery methods are not yet considered fully satisfactory. A particular issue is the difficulty of discovering process models with a good balance among recall (or fitness), precision, generalization, and simplicity, notably for real-world event logs. This article revisits the genetic algorithms-based X-Processes process discovery method, discussing the results of using a fitness function calculated through a harmonic mean that considers the four main metrics of process model quality used in process mining — recall, precision, generalization and simplicity. Although genetic algorithms have been used to discover process models, such methods have limitations, as do other non-genetic algorithm-based methods. X-Processes was tested with two harmonic mean options — one based on recall and precision only and another based on the four quality metrics. X-Processes showed superior effectiveness when compared to state-of-the-art process discovery methods, including one also based on genetic algorithms. The experimental results obtained for both harmonic mean options, using 12 real-world event logs, show that the overall quality of all process models discovered by X-Processes is superior to that of the baseline methods. While its execution time is longer than the other compared process discovery methods, X-Processes emerges as a solution when the need for a higher quality process model outweighs the hunger for agility. [ABSTRACT FROM AUTHOR]

Process mining studies ways to improve real-world processes using historical event data generated by IT systems that support business processes of organisations. Given an event log of an IT system, process discovery algorithms construct a process model representing the processes recorded in the log, while conformance checking techniques quantify how well the discovered model achieves this objective. State-of-the-art discovery and conformance techniques either completely ignore or consider but hide from the users information about the likelihood of process behaviour. That is, the vast majority of the existing process discovery algorithms construct non-stochastic aware process models. Consequently, few conformance checking techniques can assess how well such discovered models describe the relative likelihoods of traces recorded in the log or how well they represent the likelihood of future traces generated by the same system. Note that this is necessary to support process simulation, prediction and recommendation. Furthermore, stochastic information can provide business analysts with further actionable insights on frequent and rare conformance issues. This article presents precision and recall measures based on the notion of entropy of stochastic automata, which are capable of quantifying and, hence, differentiating, between frequent and rare deviations of an event log and a process model that is enriched with the information on the relative likelihoods of traces it describes. An evaluation over several real-world datasets that uses our open-source implementation of the measures demonstrates the feasibility of using our precision and recall measures in industrial settings. Finally, we propose a range of intuitive desired properties that stochastic precision and recall measures should possess, and study our and other existing stochastic-aware conformance measures with respect to these properties. • It matters how often behaviour occurs in processes, e.g. for performance, simulation, prediction, etc. • Often, this stochastic perspective is not considered by conformance checking techniques. • In this paper, we introduce two sets of stochastic recall+precision measures. • The techniques are practically feasible, though are limited to deterministic stochastic behaviour. • 14 properties for stochastic conformance measures are proposed. • We analyse existing measures using these properties. [ABSTRACT FROM AUTHOR]