Antimicrobial effects of folk medicinal plants from the North of Iran against Mycobacterium tuberculosis

Background: Medicinal plants have been used traditionally in Golestan province (north of Iran), against Mycobacterium tuberculosis or the clinical signs of tuberculosis (TB). Objectives: This study aimed to define the inhibitory effects of ethanolic extracts of six of these medicinal plants against Mycobacterium tuberculosis. Materials and Methods: Peganum harmala (seed extract), Punica granatum (peel extract), Digitalis sp. (leaf extract), fruit extract of Citrus lemon, Rosa canina and Berberis vulgaris were extracted in ethanol and their activity against M. tuberculosis isolates were determined by the agar diffusion method. The zone of inhibition (at 200 to 1.6 mg/mL) was measured and the results were compared with isoniazid and rifampin as standard positive controls. Also the concentration of vitamin C of each the extracts was evaluated. Results: The ethanolic extract of Peganum harmala seed and Punica granatum peel exhibited potential activity against all M. tuberculosis isolates with mean inhibitory zone of 18.7 and 18.8 mm, at 200 mg/mL concentration. The mean inhibitory zone around isoniazid and rifampinwere 19.2 and 18.8 mm. Ethanolic extract of Citrus lemon showed moderate inhibitory activity only against sensitive (non MDR; non multi drug resistant) strains of M. tuberculosis, and Digitalis sp. showed inhibitory effects on five isolates. Ascorbic acid content was 43.3 mg/dL in Punica granatum and Digitalis sp. and only 9.1 mg/dL in ethanolic extract of Peganum harmala. Conclusions: The highest content of vitamin C was observed in the extract of Punica granatum, which was observed to be highly active against Mycobacterium tuberculosis, while the P. harmala must have contained other phytochemical constituents that contributed to the anti-tuberculosis effects of this plant. Our findings showed that ethanolic extracts of P. granatum and P. harmala had anti-TB effects comparable to isoniazid and rifampin and can be good candidates for novel and safe natural products against tuberculosis. © 2015, Pediatric Infections Research Center

Cognitive fatigue assessment in operational settings: a review and UAS implications

Recent technological improvements allow UAS (Unmanned Aircraft System) operators to carry out increasingly long missions. Shift work was introduced during long-endurance missions to reduce the risk of fatigue. However, despite these short work periods and the creation of a fatigue risk management system (FRMS), the occurrence of intense and monotonous phases remains a factor of cognitive fatigue. This fatigue can have an impact on vigilance, attention, and operator performance, leading to reduce mission safety. This paper aims at presenting different ways to characterize the cognitive fatigue of UAS operators. The use of machine learning to estimate cognitive fatigue based on physiological measures is also presented as a promising venue to mitigate these issues

Predicting inpatient length of stay in Iranian Hospital: Conceptualization and validation

Objective: The length of stay is an important indicator of hospital performance and efficiency. Regarding the importance of the length of stay, this study aimed to design a structural model of the inpatients' length of stay in the educational and therapeutic health care facilities of Iran in order to identify the influencing dimensions. Methods: The present study was an analytical and applied study. The face validity of the data gathering tool was investigated by the expert judgment and the construct validity was examined by using the exploratory factor analysis. In order to verify the reliability of the tool, the internal consistency was also trialed by using the Cronbach's alpha. For ranking the influencing dimensions and factors and also in order to examine the causal relationships between the variables in a coherent manner and presenting the final model, the structural equation modeling technique was used in AMOS software at a significant level of 0.05. Results: The mentioned structural model consists of 4 dimensions and 29 factors influencing the length of stay of hospitalized patients. The independent variables are based on priority and importance as follows: patients' conditions, the underlying factors, the clinical staff performance, and hospitals' service delivery, which were examined by second-order factor analysis in order to study the relationship between them and the inpatients' length of stay. Conclusion: Considering the importance of each one of the proposed dimensions from the point of view of service providers in some therapeutic centers of the country by paying attention to the role of each one of them in preventing prolonged hospitalization can be essential in the effectiveness of the treatment and cost reduction. © 2020 Asian Pacific Organization for Cancer Prevention

A neuroergonomic approach to performance estimation in a psychomotor vigilance task

Introduction: Passive brain-computer interfaces (pBCI; tools that enable an implicit mental state estimation) have gained attention in a wide range of applications, including performance and vigilance monitoring in high-risk work settings (Lotte & Roy, 2019). Vigilance can be defined as the ability to maintain sustained attention to a stimulus for an extended period of time (Al-Shargie et al., 2019), and is influenced by the time of day and fatigue (Lim and Dinges, 2008). A vigilance decrement impacts performance over time (called time-on-task -TOT) during tedious monitoring tasks, resulting in slower reaction times or increased errors (Pattyn et al., 2008). This effect is experienced in all kinds of activities such as in aeronautics where pilots can experience a performance drop during the flight (Wiggins, 2011). Hence, vigilance and performance estimation is a crucial step towards the implementation of safer work settings. Machine learning applied to physiological measures, such as cerebral activity (via electroencephalogram -EEG- recordings), is a promising way to estimate performance. EEG’s spectral activity is impacted by fluctuations in vigilance (Matousek & Petersén, 1983) and the power in both theta and alpha bands can be considered as robust biomarkers of mental fatigue (Tran et al., 2020). Numerous studies have attempted to estimate performance during a vigilance task based on EEG measures (Tian et al. 2018), or electrocardiographic (ECG) measures (e.g., heart rate -HR-, and its variability -HRV; Chua et al., 2012). To our knowledge, performance estimation during monotonous tasks has not reached a high accuracy, and pBCI pipelines could be improved. Hence, the objective of this study is to employ a comprehensive neuroergonomic approach to vigilance and performance characterization for a typical vigilance task: the Psychomotor Vigilance Task (PVT; Dinges & Powell, 1985) encompassing statistical analyses, as well as EEG-based performance classification using pre-stimulus signal. Methods: Ten volunteers (3 females; Mage=25, sd=3; ethical number from Univ. Toulouse: 2021-342) performed a 10-minute PVT (i.e., 90 stimuli) from a task battery. They had to complete a fatigue questionnaire (Karolinska Sleepiness Scale; Åkerstedt & Gillberg, 1990) and their response time (RT) was measured. In addition, EEG and ECG activities were recorded using an ActiCHamp system (63+1 electrodes). Data were processed in two ways: i) TOT analysis: data were split into ten 1-minute windows; and ii) Performance analysis: response-based epoching (-2:0s for EEG; -10:0s for ECG), only the 30 best and 30 worst trials were kept (labeled according to RT), and also used for performance classification. Considering statistical analyses, one-way repeated measures ANOVAs and paired samples t-tests (Student and Wilcoxon), were performed separately on each dataset. To perform TOT analysis on RT, the signal was cut into 10 periods of 9 simultaneous stimuli and the reciprocal RT (mean 1/RT) was calculated on each period. EEG data were filtered (1-40 Hz) and ocular artifacts were automatically removed (SOBI method). The theta, alpha, and beta power were extracted for three electrode clusters (frontal, central, posterior). The Task Load Index (TLI: thetaFz/alphaPz) and the engagement ratio (beta/[theta+alpha]; average on all electrodes) were also calculated. ECG data were filtered (1-40 Hz) and normalized using a 1-minute eyes-open resting state period. HR and HRV (as SDNN) were then extracted. For EEG-based estimation, a dimensionality reduction method based on Laplacian was applied (Xu et al., 2021), and a minimum distance to mean with geodesic filtering classifier (FgMDM) was trained and tested using a 10-fold cross-validation procedure. Results: There was no significant difference in subjective fatigue (regardless of PVT order in battery). There was a significant linear downward trend on reciprocal RT (p<.05; other contrasts n.s.; Fig.1.A). There was a significant effect of TOT on alpha power at frontal (p<.05, ηp²=.26) and posterior (p<.05, ηp²=.27) sites, as well as on the engagement ratio (p<.05, ηp²=.31; Fig.1.B). TOT also significantly impacted HR (p<.001, ηp²=.31; Fig.1.C) and HRV (p<.05, ηp²=.27). Regarding performance (best/worst trials), alpha power was significantly lower for the best than worst trials at frontal (p<.01, rB=-.93), and posterior (p<.01, rB=-.89) sites, while the TLI was higher for the best trials (p<.05, d=.87). In terms of performance classification, the FgMDM classifier achieved a mean accuracy of 58.2% without dimensionality reduction. By selecting the number of dimensions that gives the best accuracy for each subject, the mean accuracy with dimensionality reduction was 70.5% (mean number of dimensions = 27.3, Fig.2.A). By comparison, the principal component analysis (PCA) achieved a mean accuracy of 71.1% (mean number of dimensions = 23). Discussion: The results showed that the neuroergonomic approach employed in this study enabled us to assess physiological modulations due to vigilance fluctuations, such as alpha power and engagement ratio decreased over time. However, the HR drop observed in the first minutes may be due to the initial presentation novelty (Kelsey et al., 1999). Also, analyses of the best and worst trials showed that vigilance decreased during the bad trials. However, some results were not as strong as those obtained in the literature. This may be due to either the short task duration (10 minutes) compared to the several sessions performed over several hours or days in the literature, or because our participants were not sleep deprived. Besides, these analyses were performed on a small number of participants (n=10). Regarding performance estimation, the classification accuracy was low for several reasons. Firstly, there is little difference in terms of vigilance during the 10-minute PVT task, even though we have chosen the 30 best and worst trials. Secondly, the number of training samples is quite limited (i.e., 60 per subject). In this case, dimensionality reduction becomes essential as shown by the results. Both dimensionality reduction methods (non-supervised) significantly improve accuracy while reducing the computation time. Moreover, on average the Laplacian-based method performs better in lower dimensions than PCA (Fig.2.B). In summary, this study shows that even with a short task and a normal level of fatigue, it is possible to observe an impact of a monotonous task on behavioral and physiological measures at the group level. Yet, it remains difficult to implement an accurate performance estimation pipeline using a single short session at the individual level

The comparison of the effect of poetry therapy on anxiety and post-traumatic stress disorders in patients with myocardial infarction

This study evaluates the effect of poetry therapy on anxiety and Post-traumatic stress disorders treatment in patients with myocardial infarction. The intervention has not been done in the control group. Poetry therapy was performed during four 45-minute sessions in a week for the case group. The anxiety and post-traumatic stress have been reduced during three periods time: before the intervention, after intervention and follow-up in the case group (p =.0001). In contrast, anxiety has been reduced in the control group (p =.0001). There was a significant difference in anxiety and post-traumatic stress between case and control groups before the intervention, after the intervention and in follow-up (p =.0001). Due to the side effects of medicinal therapy and the effect of poetry therapy on anxiety and post-traumatic stress disorders in myocardial infarction patients, this method is recommended in remedial programs and nurse training

Retrospective on the first passive brain-computer interface competition on cross-session workload estimation

As is the case in several research domains, data sharing is still scarce in the field of Brain-Computer Interfaces (BCI), and particularly in that of passive BCIs— i.e ., systems that enable implicit interaction or task adaptation based on a user's mental state(s) estimated from brain measures. Moreover, research in this field is currently hindered by a major challenge, which is tackling brain signal variability such as cross-session variability. Hence, with a view to develop good research practices in this field and to enable the whole community to join forces in working on cross-session estimation, we created the first passive brain-computer interface competition on cross-session workload estimation. This competition was part of the 3rd International Neuroergonomics conference. The data were electroencephalographic recordings acquired from 15 volunteers (6 females; average 25 y.o.) who performed 3 sessions—separated by 7 days—of the Multi-Attribute Task Battery-II (MATB-II) with 3 levels of difficulty per session (pseudo-randomized order). The data -training and testing sets—were made publicly available on Zenodo along with Matlab and Python toy code ( https://doi.org/10.5281/zenodo.5055046 ). To this day, the database was downloaded more than 900 times (unique downloads of all version on the 10th of December 2021: 911). Eleven teams from 3 continents (31 participants) submitted their work. The best achieving processing pipelines included a Riemannian geometry-based method. Although better than the adjusted chance level (38% with an α at 0.05 for a 3-class classification problem), the results still remained under 60% of accuracy. These results clearly underline the real challenge that is cross-session estimation. Moreover, they confirmed once more the robustness and effectiveness of Riemannian methods for BCI. On the contrary, chance level results were obtained by one third of the methods—4 teams- based on Deep Learning. These methods have not demonstrated superior results in this contest compared to traditional methods, which may be due to severe overfitting. Yet this competition is the first step toward a joint effort to tackle BCI variability and to promote good research practices including reproducibility