Notes

Lighting Sheet Microscopy involving Rapidly Heart failure Dynamics in Zebrafish Embryos.
The human retina and retinal imaging technologies continue to increasingly gain the attention of schizophrenia researchers. With the same embryologic origin as the brain, the retina offers a window into neurovascular changes that may underlie disease. Recently, two technologies that have already revolutionized the field of ophthalmology, optical coherence tomography (OCT), and a functional extension of this, optical coherence tomography angiography (OCTA), have gained traction. Together, these non-invasive technologies allow for microscopic imaging of both structural and vascular features of the retina. With ease of use and no side effects, these devices are likely to prove powerful digital health tools in the study and treatment of schizophrenia. They may also prove key to discovering disease relevant biomarkers that underly neurodevelopmental and neurodegenerative aspects of conditions such as schizophrenia.
Children of parents with mental disorders have a high chance of developing a mental disorder themselves. However, this at-risk group is regularly overlooked and typically not seen by any mental health professionals. Internet- and mobile-based interventions (IMIs) can provide a means of promoting mental health for children of parents with mental disorders.

The introduced study will evaluate the clinical- and cost-effectiveness of the iCHIMPS IMI in promoting mental health for children of parents with mental disorders.

A two-armed multicentered cluster-randomized controlled trial (cRCT) comparing the clinical- and cost-effectiveness of the iCHIMPS IMI in the intervention group (IG) to a treatment-as-usual (TAU) control group will be conducted. Recruitment will be handled at currently 21 adult mental health clinics throughout Germany. Participating families will be randomly divided into the two groups until the final sample size of 306 participating adolescents (age 12-18) has been reached. The adolescentsiveness of the iCHIMPS mental health promotion IMI for children of parents with mental disorders. This provides the opportunity to gain insights into an innovative as well as time- and location-independent form of support for this often-overlooked at-risk group. Additionally, the larger CHIMPS-NET project allows comparisons between internet-based and face-to-face interventions for a similar target group.

www.ClinicalTrials.gov, identifier DRKS00025158.
www.ClinicalTrials.gov, identifier DRKS00025158.
Automated speech recognition (ASR) systems have become increasingly sophisticated, accurate, and deployable on many digital devices, including on a smartphone. This pilot study aims to examine the speech recognition performance of ASR apps using audiological speech tests. In addition, we compare ASR speech recognition performance to normal hearing and hearing impaired listeners and evaluate if standard clinical audiological tests are a meaningful and quick measure of the performance of ASR apps.

Four apps have been tested on a smartphone, respectively AVA, Earfy, Live Transcribe, and Speechy. Selleckchem Panobinostat The Dutch audiological speech tests performed were speech audiometry in quiet (Dutch CNC-test), Digits-in-Noise (DIN)-test with steady-state speech-shaped noise, sentences in quiet and in averaged long-term speech-shaped spectrum noise (Plomp-test). For comparison, the app's ability to transcribe a spoken dialogue (Dutch and English) was tested.

All apps scored at least 50% phonemes correct on the Dutch CNC-test fo apps was limited on audiological tests that provide little linguistic context or use low signal to noise levels. For Dutch audiological speech tests in quiet, ASR apps performed similarly to a person with a moderate hearing loss. In noise, the ASR apps performed more poorly than most profoundly deaf people using a hearing aid or cochlear implant. Adding new performance metrics including the semantic difference as a function of SNR and reverberation time could help to monitor and further improve ASR performance.Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, "big data" approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.Searching the commercial Google Play Store and App Store is one of the most common strategies for discovering mobile applications for digital health, both among consumers and healthcare professionals. However, several studies have suggested a possible mismatch between this strategy and the objective of finding apps in physical and mental health that are both clinically relevant and reliable from a privacy standpoint. This study provides direct evidence of a gap between the five-star user rating system and expert ratings from a curated library of over 1,200 apps that cover both physical and mental health. An objective metric is derived to assess the strength of the user-expert gap for each app, which in turn allows identifying missed opportunities-low user ratings and high expert ratings-and overrated apps-high user ratings and low expert ratings. Implications for practice and care delivery are discussed.
Electronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before circulating for secondary use. Machine learning (ML) based named entity recognition (NER) model has emerged as a popular technique of automatic de-identification.

The performance of a machine learning model highly depends on the selection of appropriate features. The objective of this study was to investigate the usability of multiple features in building a conditional random field (CRF) based clinical de-identification NER model.

Using open-source natural language processing (NLP) toolkits, we annotated protected health information (PHI) in 1,500 pathology reports and built supervised NER models using multiple features and their combinations. We further investigated the dependency of a model's performance on the size of training data.

Among the 10 feature extractorse for enterprise-wide data warehousing.
Transmission of the novel coronavirus strain, SARS-CoV-2, caused many health services to reach crisis points worldwide. As infection rates rose, many countries implemented 'lockdown' periods where only essential services could remain open; this included community pharmacies in the UK. This study retrospectively explores the experiences of community pharmacists during the first lockdown period of the pandemic in the UK, helping us to learn for the future if another pandemic were to arise.

The objectives of this study were to explore the main professional and personal worries and concerns that pharmacists experienced during the first UK lockdown. Key practical issues specific to COVID-19 were investigated staffing levels, precautionary measures undertaken, workload issues, medicines shortages, support, and guidance. In addition, the perceived effectiveness of support available to community pharmacists was examined.

Community pharmacists in the Stoke-on-Trent and Staffordshire regions of the UK were inviteort, recognition, and funding to enable them to remain sustainable and continue to provide a critical service in the face of possible future pandemics.
Overall, this study highlights the crucial role that community pharmacists in the UK played in ensuring patients still received their medication and health advice, despite the challenging circumstances in which they found themselves. Community pharmacies need appropriate support, recognition, and funding to enable them to remain sustainable and continue to provide a critical service in the face of possible future pandemics.
The unique situation related to the COVID-19 outbreak and the consequent worldwide lockdown can have a psychological impact on specific populations. Community pharmacists and pharmacy technicians, as essential healthcare workers on the front line who continue to do their jobs during this pandemic, can also experience psychological distress. Few data are available on the mental health impact of the COVID-19 pandemics on this population.

This study aimed to assess the psychological impact of COVID-19 on Spanish community pharmacists and pharmacy technicians during lockdown, and to identify factors contributing to psychological distress.

A cross-sectional, quantitative, correlational study was designed including 1162 pharmacy team members. The Impact of Event Scale revised was used to assess the psychological impact. Data collection was performed by emailing the instrument to individuals or by using social networks.

Overall participants, almost 70% revealed severe levels of psychological impact. The outbreak of COVID-19 has significantly affected community pharmacy workers, the degree of which is related to gender, age, and feelings of fear/stress.

In the initial phase of the lockdown associated with the COVID-19 outbreak, the majority of the respondents rated the psychological impact as severe. Our findings allow for the identification of factors associated with a greater psychological impact.
In the initial phase of the lockdown associated with the COVID-19 outbreak, the majority of the respondents rated the psychological impact as severe. Our findings allow for the identification of factors associated with a greater psychological impact.
There are regional disparities in pediatric traumatic brain injury (TBI) mortality across the United States, but the factors underlying these differences are unclear.

We performed a retrospective cross-sectional analysis of the Pediatric Health Information System database including inpatient hospital encounters for children less than 18 years old with a primary diagnosis of TBI between 2010-2019.

Lower median family income was associated with pediatric TBI mortality. Encounters from zip-codes with a median family income of <$20,000 had a 3.1% (29/950) mortality, as opposed to 1.3% (29/2,267) mortality for zip-codes with a median family income of >$80,000 (p = 0.00096). In multivariable logistic regression, every $10,000 of income was associated with an odds ratio of mortality of 0.94 (95% confidence interval 0.90 - 0.98). 82.5% (397/481) of ballistic TBI injuries were caused by a firearm. Lower income was associated with a higher proportion of ballistic TBI injuries (2.5% [24/950] for <$20,000 versus 0.
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