Attachment Of Streptococcus Pneumoniae Capsular Polysaccharide To The Cell

Streptococcus pneumoniae is an important human pathogen. The major virulence factor for S. pneumoniae is the capsular polysaccharide (CPS). Proper expression and localization of the CPS is essential for pathogenesis. Despite the importance of the proper transfer to the cell surface of this virulence factor, no studies have shown the exact structure and attachment to either the cell wall or membrane. Using the S. pneumoniae serotype 2 CPS as a model, which is synthesized by the widespread Wzy mechanism, we found that the CPS attaches to the cell wall β-D-N-acetylglucosamine (GlcNAc) of peptidoglycan (PG) via a direct glycosidic linkage. This type of linkage has also been demonstrated in serotype 8 and serotype 31. While CPS is built on the membrane anchor undecaprenyl-phosphate (Und-P), we demonstrate that the majority of membrane linked CPS is attached via a different lipid, an acylglycerol. Further, we have identified the first reported polysaccharide sulfation in a Gram-positive membrane linked CPS. These results are the first detailed description of a CPS-PG linkage to the cell wall for any organism and to the membrane for any Gram-positive bacteria. Identification of these novel linkages will help ascertain a mechanism for the transfer of CPS and may provide new targets for therapeutic intervention

App Assisted Depression Self-Management in Integrated Primary Care: A Pilot Study Exploring Patient and Provider Experiences

The integrated primary care (IPC) setting is currently the de facto institution for accessing mental healthcare among underserved populations. However, many barriers exist for these populations when seeking regular and adequate mental healthcare, including provider availability and transportation challenges. This study piloted the incorporation of mindLAMP, a mHealth app, into IPC treatment to offer a low-resource intervention designed to improve and expand access to mental healthcare for underserved patients between face-to-face visits. This study gathered data from a sample of both behavioral health providers and patients (N = 6) in IPC settings to pilot the use of the mindLAMP app among traditionally underserved patients with depression. The Patient Activation Measure and Patient Health Questionnaire were used to assess patient outcomes in addition to conducting a directed content analysis on qualitative data obtained with interviews of both patients and providers. Results from the mixed methods study design indicate that implementing this digital intervention in the IPC setting is feasible and positively experienced by both patients and providers. Perceived positive outcomes were reported by both patients and providers after incorporating the mHealth app into their mental healthcare. However, both patients and providers discussed barriers to mHealth app use that need to be addressed in order to accommodate more widespread use. Further, constraints and barriers at the provider, patient, and healthcare system levels were discussed and need to be examined further to increase the implementation of this multilevel digital intervention

MAVEN Deliverable 6.4: Integration Final Report

This document presents the work that has been performed in WP6 after D6.3, and therefore focussing on the integration sprints 3-6. It describes which parts of the system are implemented and how they are put together. To do so, it builds upon the deliverables created so far, esp. D6.3 and all other deliverables of the underlying work packages 3, 4 and 5. Another important aspect for understanding the content of this deliverable is D2.1 [4] for the scenario definition of the whole MAVEN project, and the deliverables D6.1 [5] and D6.2 [6], which give an overview on the existing infrastructure and vehicles used in MAVEN

Noncontact respiration monitoring techniques in young children:A scoping review

Pediatric sleep-related breathing disorders, or sleep-disordered breathing (SDB), cover a range of conditions, including obstructive sleep apnea, central sleep apnea, sleep-related hypoventilation disorders, and sleep-related hypoxemia disorder. Pediatric SDB is often underdiagnosed, potentially due to difficulties associated with performing the gold standard polysomnography in children. This scoping review aims to: (1) provide an overview of the studies reporting on safe, noncontact monitoring of respiration in young children, (2) describe the accuracy of these techniques, and (3) highlight their respective advantages and limitations. PubMed and EMBASE were searched for studies researching techniques in children &lt;12 years old. Both quantitative data and the quality of the studies were analyzed. The evaluation of study quality was conducted using the QUADAS-2 tool. A total of 19 studies were included. Techniques could be grouped into bed-based methods, microwave radar, video, infrared (IR) cameras, and garment-embedded sensors. Most studies either measured respiratory rate (RR) or detected apneas; n = 2 aimed to do both. At present, bed-based approaches are at the forefront of research in noncontact RR monitoring in children, boasting the most sophisticated algorithms in this field. Yet, despite extensive studies, there remains no consensus on a definitive method that outperforms the rest. The accuracies reported by these studies tend to cluster within a similar range, indicating that no single technique has emerged as markedly superior. Notably, all identified methods demonstrate capability in detecting body movements and RR, with reported safety for use in children across the board. Further research into contactless alternatives should focus on cost-effectiveness, ease-of-use, and widespread availability.</p

Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar

Background: Sleep is an important driver of development in infants born preterm. However, continuous unobtrusive sleep monitoring of infants in the neonatal intensive care unit (NICU) is challenging.Objective: To assess the feasibility of ultra-wideband (UWB) radar for sleep stage classification in preterm infants admitted to the NICU.Methods: Active and quiet sleep were visually assessed using video recordings in 10 preterm infants (recorded between 29 and 34 weeks of postmenstrual age) admitted to the NICU. UWB radar recorded all infant's motions during the video recordings. From the baseband data measured with the UWB radar, a total of 48 features were calculated. All features were related to body and breathing movements. Six machine learning classifiers were compared regarding their ability to reliably classify active and quiet sleep using these raw signals.Results: The adaptive boosting (AdaBoost) classifier achieved the highest balanced accuracy (81%) over a 10-fold cross-validation, with an area under the curve of receiver operating characteristics (AUC-ROC) of 0.82.Conclusions: The UWB radar data, using the AdaBoost classifier, is a promising method for non-obtrusive sleep stage assessment in very preterm infants admitted to the NICU

Ultra-wideband radar for simultaneous and unobtrusive monitoring of respiratory and heart rates in early childhood:A Deep Transfer Learning Approach

Unobtrusive monitoring of children’s heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children’s RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM &lt; 6.5% of average HR) and 2.63 breaths per minute (BPM &lt; 7% of average RR). We also achieved a significant Pearson’s correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s.</p

Ethnic disparities, clinical and pathways to care characteristics associated with the offer, uptake, and type of psychological therapy during first-episode psychosis: examining the role of early intervention for psychosis

Background: Psychological therapy (PT) along with antipsychotic medication is the recommended first line of treatment for first-episode psychosis (FEP). We investigated whether ethnicity, clinical, pathways to care (PtC) characteristics, and access to early intervention service (EIS) influenced the offer, uptake, and type of PT in an FEP sample. Methods: We used data from the Clinical Record Interactive Search-First Episode Psychosis study. Inferential statistics determined associations between ethnicity, clinical, PtC, and PT offer/uptake. Multivariable logistic regression estimated the odds of being offered a PT and type of PT by ethnicity, clinical and PtC characteristics adjusting for confounders. Results: Of the 558 patients included, 195 (34.6%) were offered a PT, and 193 accepted. Cognitive behavioral therapy (CBT) (n = 165 of 195; 84.1%) was commonly offered than group therapy (n = 30 of 195; 13.3%). Patients who presented via an EIS (adj. OR = 2.24; 95%CI 1.39–3.59) were more likely to be offered a PT compared with those in non-EIS. Among the patients eligible for an EIS, Black African (adj. OR = 0.49; 95%CI = 0.25–0.94), Black Caribbean (adj. OR = 0.45; 95%CI = 0.21–0.97) patients were less likely to be offered CBT compared with their White British counterparts. Patients with a moderate onset of psychosis (adj. OR = 0.34; 95%CI = 0.15–0.73) had a reduced likelihood of receiving CBT compared with an acute onset. Conclusions: Accessing EIS during FEP increased the likelihood of being offered a PT. However, treatment inequalities remain by ethnicity and clinical characteristics

Noncontact respiration monitoring techniques in young children: A scoping review

Pediatric sleep-related breathing disorders, or sleep-disordered breathing (SDB), cover a range of conditions, including obstructive sleep apnea, central sleep apnea, sleep-related hypoventilation disorders, and sleep-related hypoxemia disorder. Pediatric SDB is often underdiagnosed, potentially due to difficulties associated with performing the gold standard polysomnography in children. This scoping review aims to: (1) provide an overview of the studies reporting on safe, noncontact monitoring of respiration in young children, (2) describe the accuracy of these techniques, and (3) highlight their respective advantages and limitations. PubMed and EMBASE were searched for studies researching techniques in children <12 years old. Both quantitative data and the quality of the studies were analyzed. The evaluation of study quality was conducted using the QUADAS-2 tool. A total of 19 studies were included. Techniques could be grouped into bed-based methods, microwave radar, video, infrared (IR) cameras, and garment-embedded sensors. Most studies either measured respiratory rate (RR) or detected apneas; n = 2 aimed to do both. At present, bed-based approaches are at the forefront of research in noncontact RR monitoring in children, boasting the most sophisticated algorithms in this field. Yet, despite extensive studies, there remains no consensus on a definitive method that outperforms the rest. The accuracies reported by these studies tend to cluster within a similar range, indicating that no single technique has emerged as markedly superior. Notably, all identified methods demonstrate capability in detecting body movements and RR, with reported safety for use in children across the board. Further research into contactless alternatives should focus on cost-effectiveness, ease-of-use, and widespread availability

Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar

Background: Sleep is an important driver of development in infants born preterm. However, continuous unobtrusive sleep monitoring of infants in the neonatal intensive care unit (NICU) is challenging. Objective: To assess the feasibility of ultra-wideband (UWB) radar for sleep stage classification in preterm infants admitted to the NICU. Methods: Active and quiet sleep were visually assessed using video recordings in 10 preterm infants (recorded between 29 and 34 weeks of postmenstrual age) admitted to the NICU. UWB radar recorded all infant's motions during the video recordings. From the baseband data measured with the UWB radar, a total of 48 features were calculated. All features were related to body and breathing movements. Six machine learning classifiers were compared regarding their ability to reliably classify active and quiet sleep using these raw signals. Results: The adaptive boosting (AdaBoost) classifier achieved the highest balanced accuracy (81%) over a 10-fold cross-validation, with an area under the curve of receiver operating characteristics (AUC-ROC) of 0.82. Conclusions: The UWB radar data, using the AdaBoost classifier, is a promising method for non-obtrusive sleep stage assessment in very preterm infants admitted to the NICU