Continuous sensing and quantification of body motion in infants:A systematic review

Abnormal body motion in infants may be associated with neurodevelopmental delay or critical illness. In contrast to continuous patient monitoring of the basic vitals, the body motion of infants is only determined by discrete periodic clinical observations of caregivers, leaving the infants unattended for observation for a longer time. One step to fill this gap is to introduce and compare different sensing technologies that are suitable for continuous infant body motion quantification. Therefore, we conducted this systematic review for infant body motion quantification based on the PRISMA method (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this systematic review, we introduce and compare several sensing technologies with motion quantification in different clinical applications. We discuss the pros and cons of each sensing technology for motion quantification. Additionally, we highlight the clinical value and prospects of infant motion monitoring. Finally, we provide suggestions with specific needs in clinical practice, which can be referred by clinical users for their implementation. Our findings suggest that motion quantification can improve the performance of vital sign monitoring, and can provide clinical value to the diagnosis of complications in infants.</p

Targeted DNA Damage at Individual Telomeres Disrupts Their Integrity and Triggers Cell Death

Cellular DNA is organized into chromosomes and capped by a unique nucleoprotein structure, the telomere. Both oxidative stress and telomere shortening/dysfunction cause aging-related degenerative pathologies and increase cancer risk. However, a direct connection between oxidative damage to telomeric DNA, comprising \u3c1% of the genome, and telomere dysfunction has not been established. By fusing the KillerRed chromophore with the telomere repeat binding factor 1, TRF1, we developed a novel approach to generate localized damage to telomere DNA and to monitor the real time damage response at the single telomere level. We found that DNA damage at long telomeres in U2OS cells is not repaired efficiently compared to DNA damage in non-telomeric regions of the same length in heterochromatin. Telomeric DNA damage shortens the average length of telomeres and leads to cell senescence in HeLa cells and cell death in HeLa, U2OS and IMR90 cells, when DNA damage at non-telomeric regions is undetectable. Telomere-specific damage induces chromosomal aberrations, including chromatid telomere loss and telomere associations, distinct from the damage induced by ionizing irradiation. Taken together, our results demonstrate that oxidative damage induces telomere dysfunction and underline the importance of maintaining telomere integrity upon oxidative damage

Mutations in EDA and EDAR Genes in a Large Mexican Hispanic Cohort with Hypohidrotic Ectodermal Dysplasia

Ectodermal dysplasias (ED) encompass nearly 200 different genetic conditions identified by the lack, or dysgenesis, of at least two ectodermal derivatives, such as hair, nails, teeth, and sweat glands. Hypohidrotic/anhidrotic ED (HED) is the most frequent form of ED and it can be inherited as an X-linked (XL)-HED (MIM 305100), autosomal recessive (AR)-HED (MIM 224900), or autosomal dominant (AD)-HED (MIM 229490) condition. HED is caused by mutations in any of the three ectodisplasin pathway genes: ectodisplasin (EDA), which encodes a ligand for the second gene, the EDA receptor (ectodysplasin A-receptor, EDAR), and EDARADD, an intracellular signaling for this pathway. HED is characterized by a triad of clinical features including absent or diminished eccrine sweat glands, missing and/or malformed teeth, and thin, sparse hair. It also includes dryness of the skin, eyes, airways, and mucous membranes, as well as other ectodermal defects and, in some cases, fever, seizures, and rarely, death. XL-HED is caused by mutations in the EDA gene, located on chromosome Xq12-q13.1, which encodes a signaling molecule of the tumor necrosis factor (TNF) superfamily. AR- and AD-HED are caused by mutations in the EDAR gene, located on chromosome 2q11.q13 or the EDARAssociated Death Domain encoding gene, EDARADD, located on chromosome 1q42-q431. Several mutations in the EDA, EDAR, and EDARADD genes have been described as causing HED in different populations. The XL-HED form is the most common and is responsible for 90% of all HED cases2-6. The three forms of HED are clinically indistinguishable. To date, a comprehensive evaluation of HED in the Mexican Hispanic population has not been undertaken. In the present study, we aimed to characterize the mutations in EDA, EDAR, and EDARADD genes present in Mexican Hispanic patients with HED. Male and female patients (35 families) from different geographical regions of Mexico with features suggestive of HED were enrolled in the study (Fig. 1). Index cases and their parents were screened for missing or malformed teeth, thin or sparse hair, and nail changes; all subjects answered questions about sweating, heat intolerance, fever, seizures, and family history of siblings deceased due to unknown feve

Recognition and Treatment of Cognitive Dysfunction in Major Depressive Disorder

Major Depressive Disorder (MDD) is a prevalent, chronic, disabling, and multidimensional mental disorder. Cognitive dysfunction represents a core diagnostic and symptomatic criterion of MDD, and is a principal determinant of functional non-recovery. Cognitive impairment has been observed to persist despite remission of mood symptoms, suggesting dissociability of mood and cognitive symptoms in MDD. Recurrent impairments in several domains including, but not limited to, executive function, learning and memory, processing speed, and attention and concentration, are associated with poor psychosocial and occupational outcomes. Attempts to restore premorbid functioning in individuals with MDD requires regular screenings and assessment of objective and subjective measures of cognition by clinicians. Easily accessible and cost-effective tools such as the THINC-integrated tool (THINC-it) are suitable for use in a busy clinical environment and appear to be promising for routine usage in clinical settings. However, antidepressant treatments targeting specific cognitive domains in MDD have been insufficiently studied. While select antidepressants, e.g., vortioxetine, have been demonstrated to have direct and independent pro-cognitive effects in adults with MDD, research on additional agents remains nascent. A comprehensive clinical approach to cognitive impairments in MDD is required. The current narrative review aims to delineate the importance and relevance of cognitive dysfunction as a symptomatic target for prevention and treatment in the phenomenology of MDD

Pharmacodynamic and pharmacokinetic evaluation of buprenorphine + samidorphan for the treatment of major depressive disorder

10.1080/17425255.2018.1459564Expert Opinion on Drug Metabolism & Toxicology144475-48

DeepLOS: Deep learning for late-onset sepsis prediction in preterm infants using heart rate variability

Late onset sepsis (LOS) is one of the main causes of death in preterm infants in a neonatal intensive care unit (NICU). LOS can be better treated with early detection, reducing its morbidity and mortality. In this study, an end-to-end deep learning model called DeepLOS was developed to predict LOS in preterm infants in a NICU. The model is based on a residual convolutional neural network (ResNet) with feature map (or channel) attention and uses RR intervals (i.e., interbeat intervals) as input. The model was trained and tested on a dataset composed of 128 preterm infants (60 blood-culture-proven LOS patients and 68 control patients). To minimize the possible age effect on modeling, we also considered an age-matched dataset including 32 LOS and 32 control patients from the full dataset. Prediction was done with a one-hour (non-overlapping) sliding window from 24 h before LOS to onset of LOS. We used 5-fold patient-independent cross validation and F-score to evaluate the model performance. The DeepLOS achieves an F-score of 0.72 for the full dataset and 0.73 for the matched dataset in LOS prediction for all one-hour segments, outperforming the baseline ResNet model without channel attention. F-score is generally higher (>0.75) when coming closer to the onset of LOS. Our study demonstrates the feasibility of deep learning for end-to-end LOS prediction in preterm infants. Furthermore, the model uses readily available RR intervals as input only; and is therefore vendor-processing independent and has the potential to be easily deployed in different NICUs

Towards sustainability of health information systems : how can we define, measure and achieve it?

Health information systems (HIS) in their current form arerarely sustainable. In order to sustain our health information systems and with it our health systems, we need to focus on defining and maintaining sustainable Health Information System building blocks or components. These components need to be easily updatable when clinical knowledge (or anything else) changes, easily adaptable when business requirements or processes change, and easily exchangeable when technology advances. One major prerequisite for this is that we need to be able to define and measure sustainability, so that it can become one of the major business drivers in HIS development. Therefore, this paper analyses general definitions and indicators for sustainability, and analyses their applicability to HIS. We find that general ‘Emergy analysis’ is one possibility to measure sustainability for HIS. Based on this, we investigate major enablers and inhibitors to sustainability in a high level framework consisting of four pillars: clinical, technical, socio-technical, and political/business

A continuous late-onset sepsis prediction algorithm for preterm infants using multi-channel physiological signals from a patient monitor

The aim of this study is to develop an explainable late-onset sepsis (LOS) prediction algorithm based on continuously measured multi-channel physiological signals that can be applied to a bedside patient monitor for preterm infants in a neonatal intensive care unit (NICU). The study highlights the complementary predictive value of motion information for LOS prediction when combined with cardiorespiratory information. The algorithm uses features that contain information on heart rate variability (HRV), respiration, and motion, based on continuously measured physiological waveforms including electrocardiogram (ECG) and chest impedance (CI). In this study, 127 preterm infants were included, of whom 59 were blood-culture-proven LOS patients and 68 were control patients. Features in 24 hours before the onset of sepsis (for the LOS group), and an age-matched onset time point (for the control group) were extracted and fed into machine learning classifiers together with gestational age (GA) and birth weight. We compared the prediction performance of several well-known classifiers using features extracted from different signal channels (HRV, respiration, and motion) individually as well as their combinations. The prediction performance was evaluated using the area under the receiver-operating-characteristics curve (AUC). The best performance for LOS prediction was achieved by an XGB classifier combining features from all signal channels, with an AUC of 0.88, a positive predictive value of 0.80, and a negative predictive value of 0.83 during the 6 hours preceding LOS onset. This feasibility study demonstrates the complementary predictive value of motion information in addition to cardiorespiratory information for LOS prediction. Furthermore, visualization of how each feature measured in the individual patient impacts the algorithm decision can strengthen its interpretability. In clinical practice, it is important that clinical interventions are motivated and this visualization method can help to support the clinical decision