Brain-Computer Interface: Use of Electroencephalogram in Neuro-Rehabilitation

Brain-computer interface is a technology that has been under enormous research in the last few decades. It uses brain signals by converting them into action to control the external environment. The focus of the future is the application of such technology in rehabilitating patients with physical disabilities. This chapter will mainly explore the use of EEG (electroencephalogram), a popular non-invasive method, on which the brain-computer interface is based. The process of signal extraction, selection and classification will be discussed. The challenges and techniques in communication and rehabilitation of people with motor impairment, along with the recent research study in this field, will be mentioned

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Brain-Computer Interface: Use of Electroencephalogram in Neuro-Rehabilitation

Brain-computer interface is a technology that has been under enormous research in the last few decades. It uses brain signals by converting them into action to control the external environment. The focus of the future is the application of such technology in rehabilitating patients with physical disabilities. This chapter will mainly explore the use of EEG (electroencephalogram), a popular non-invasive method, on which the brain-computer interface is based. The process of signal extraction, selection and classification will be discussed. The challenges and techniques in communication and rehabilitation of people with motor impairment, along with the recent research study in this field, will be mentioned.</jats:p

Understanding consumer’s behaviour through past experience, situation and subjective culture.

This study argues that people’s perception of their culture, personal values, past user experiences and situations of consumption will have an impact on their preference of prestigious brand of goods. Additionally, this study proposed that people’s perception of their culture would have a moderation effect on the influence of personal values. Firstly, past user experience was found to have a robust effect for the preference of prestigious brand regardless of consumption situation. Secondly, under the condition of public consumption, there was a clear preference for prestigious good. Thirdly, people’s perception of culture was found to interact with personal value. More specifically, when perceived cultural materialism was low, personal endorsement of materialism will have no effect on the brand preference.Bachelor of Art

The influence of optimal social identities on consumption in public and private contexts.

Optimal distinctiveness theory posits that individuals attempt to strike a balance between assimilation and distinctiveness in maintenance of optimal social identities. Individuals can achieve this by modifying their buying decisions and adopting or avoiding popular choices of products. They may further differentiate their buying decisions when the decisions are private decisions or potentially public decisions. Sixty undergraduates from Nanyang Technological University were recruited for this computerized study and the results showed that the three way interaction between need state, popularity of products and nature of decision was not found. Participants were found to be more likely to choose the popular products over unpopular products in all conditions.Bachelor of Art

Public self-consciousness and group identification on buying preference.

The current literature has established that public self-consciousness, the disposition to think about how the self is perceived by others, is a predictor of buying reference. As compared to low public self-consciousness, high public self-consciousness has been reported to predict a greater preference for products that enhance the public images of the individual. The present study investigated this effect and did not find support for previous findings. The effect of public self-consciousness on buying preference was further examined with respect to group identification. A moderating effect of group identification was found in the study, suggesting possible avenues for future research.Bachelor of Art

Rat Plantar Fascia Stem/Progenitor Cells Showed Lower Expression of Ligament Markers and Higher Pro-Inflammatory Cytokines after Intensive Mechanical Loading or Interleukin-1β Treatment In Vitro

The pathogenesis of plantar fasciitis is unclear, which hampers the development of an effective treatment. The altered fate of plantar fascia stem/progenitor cells (PFSCs) under overuse-induced inflammation might contribute to the pathogenesis. This study aimed to isolate rat PFSCs and compared their stem cell-related properties with bone marrow stromal cells (BMSCs). The effects of inflammation and intensive mechanical loading on PFSCs’ functions were also examined. We showed that plantar fascia-derived cells (PFCs) expressed common MSC surface markers and embryonic stemness markers. They expressed lower Nanog but higher Oct4 and Sox2, proliferated faster and formed more colonies compared to BMSCs. Although PFCs showed higher chondrogenic differentiation potential, they showed low osteogenic and adipogenic differentiation potential upon induction compared to BMSCs. The expression of ligament markers was higher in PFCs than in BMSCs. The isolated PFCs were hence PFSCs. Both IL-1β and intensive mechanical loading suppressed the mRNA expression of ligament markers but increased the expression of inflammatory cytokines and matrix-degrading enzymes in PFSCs. In summary, rat PFSCs were successfully isolated. They had poor multi-lineage differentiation potential compared to BMSCs. Inflammation after overuse altered the fate and inflammatory status of PFSCs, which might lead to poor ligament differentiation of PFSCs and extracellular matrix degeneration. Rat PFSCs can be used as an in vitro model for studying the effects of intensive mechanical loading-induced inflammation on matrix degeneration and erroneous stem/progenitor cell differentiation in plantar fasciitis

Using Electronic Health Records for Personalized Dosing of Intravenous Vancomycin in Critically Ill Neonates: Model and Web-Based Interface Development Study

Background Intravenous (IV) vancomycin is used in the treatment of severe infection in neonates. However, its efficacy is compromised by elevated risks of acute kidney injury. The risk is even higher among neonates admitted to the neonatal intensive care unit (NICU), in whom the pharmacokinetics of vancomycin vary widely. Therapeutic drug monitoring is an integral part of vancomycin treatment to balance efficacy against toxicity. It involves individual dose adjustments based on the observed serum vancomycin concentration (VCs). However, the existing trough-based approach shows poor evidence for clinical benefits. The updated clinical practice guideline recommends population pharmacokinetic (popPK) model–based approaches, targeting area under curve, preferably through the Bayesian approach. Since Bayesian methods cannot be performed manually and require specialized computer programs, there is a need to provide clinicians with a user-friendly interface to facilitate accurate personalized dosing recommendations for vancomycin in critically ill neonates. Objective We used medical data from electronic health records (EHRs) to develop a popPK model and subsequently build a web-based interface to perform model-based individual dose optimization of IV vancomycin for NICU patients in local medical institutions. Methods Medical data of subjects prescribed IV vancomycin in the NICUs of Prince of Wales Hospital and Queen Elizabeth Hospital in Hong Kong were extracted from EHRs, namely the Clinical Information System, In-Patient Medication Order Entry, and electronic Patient Record. Patient demographics, such as body weight and postmenstrual age (PMA), serum creatinine (SCr), vancomycin administration records, and VCs were collected. The popPK model employed a 2-compartment infusion model. Various covariate models were tested against body weight, PMA, and SCr, and were evaluated for the best goodness of fit. A previously published web-based dosing interface was adapted to develop the interface in this study. Results The final data set included EHR data extracted from 207 subjects, with a total of 689 VCs measurements. The final model chosen explained 82% of the variability in vancomycin clearance. All parameter estimates were within the bootstrapping CIs. Predictive plots, residual plots, and visual predictive checks demonstrated good model predictability. Model approximations showed that the model-based Bayesian approach consistently promoted a probability of target attainment (PTA) above 75% for all subjects, while only half of the subjects could achieve a PTA over 50% with the trough-based approach. The dosing interface was developed with the capability to optimize individual doses with the model-based empirical or Bayesian approach. Conclusions Using EHRs, a satisfactory popPK model was verified and adopted to develop a web-based individual dose optimization interface. The interface is expected to improve treatment outcomes of IV vancomycin for severe infections among critically ill neonates. This study provides the foundation for a cohort study to demonstrate the utility of the new approach compared with previous dosing methods. </jats:sec

Using Electronic Health Records for Personalized Dosing of Intravenous Vancomycin in Critically Ill Neonates: Model and Web-Based Interface Development Study (Preprint)

BACKGROUND Intravenous (IV) vancomycin is used in the treatment of severe infection in neonates. However, its efficacy is compromised by elevated risks of acute kidney injury. The risk is even higher among neonates admitted to the neonatal intensive care unit (NICU), in whom the pharmacokinetics of vancomycin vary widely. Therapeutic drug monitoring is an integral part of vancomycin treatment to balance efficacy against toxicity. It involves individual dose adjustments based on the observed serum vancomycin concentration (VC&lt;sub&gt;s&lt;/sub&gt;). However, the existing trough-based approach shows poor evidence for clinical benefits. The updated clinical practice guideline recommends population pharmacokinetic (popPK) model–based approaches, targeting area under curve, preferably through the Bayesian approach. Since Bayesian methods cannot be performed manually and require specialized computer programs, there is a need to provide clinicians with a user-friendly interface to facilitate accurate personalized dosing recommendations for vancomycin in critically ill neonates. OBJECTIVE We used medical data from electronic health records (EHRs) to develop a popPK model and subsequently build a web-based interface to perform model-based individual dose optimization of IV vancomycin for NICU patients in local medical institutions. METHODS Medical data of subjects prescribed IV vancomycin in the NICUs of Prince of Wales Hospital and Queen Elizabeth Hospital in Hong Kong were extracted from EHRs, namely the Clinical Information System, In-Patient Medication Order Entry, and electronic Patient Record. Patient demographics, such as body weight and postmenstrual age (PMA), serum creatinine (SCr), vancomycin administration records, and VC&lt;sub&gt;s&lt;/sub&gt; were collected. The popPK model employed a 2-compartment infusion model. Various covariate models were tested against body weight, PMA, and SCr, and were evaluated for the best goodness of fit. A previously published web-based dosing interface was adapted to develop the interface in this study. RESULTS The final data set included EHR data extracted from 207 subjects, with a total of 689 VC&lt;sub&gt;s&lt;/sub&gt; measurements. The final model chosen explained 82% of the variability in vancomycin clearance. All parameter estimates were within the bootstrapping CIs. Predictive plots, residual plots, and visual predictive checks demonstrated good model predictability. Model approximations showed that the model-based Bayesian approach consistently promoted a probability of target attainment (PTA) above 75% for all subjects, while only half of the subjects could achieve a PTA over 50% with the trough-based approach. The dosing interface was developed with the capability to optimize individual doses with the model-based empirical or Bayesian approach. CONCLUSIONS Using EHRs, a satisfactory popPK model was verified and adopted to develop a web-based individual dose optimization interface. The interface is expected to improve treatment outcomes of IV vancomycin for severe infections among critically ill neonates. This study provides the foundation for a cohort study to demonstrate the utility of the new approach compared with previous dosing methods. </sec