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"Addiction is best described as a disorder of maladaptive neuroplasticity involving the simultaneous strengthening of reward circuitry that drives compulsive drug seeking and weakening of circuits involved in executive control over harmful behaviors. Psychedelics have shown great promise for treating addiction, with many people attributing their therapeutic effects to insights gained while under the influence of the drug. However, psychedelics are also potent psychoplastogens-molecules capable of rapidly re-wiring the adult brain. The advent of non-hallucinogenic psychoplastogens with anti-addictive properties raises the intriguing possibility that hallucinations might not be necessary for all therapeutic effects of psychedelic-based medicines, so long as the underlying pathological neural circuitry can be remedied. One of these non-hallucinogenic psychoplastogens, tabernanthalog (TBG), appears to have long-lasting therapeutic effects in preclinical models relevant to alcohol and opioid addiction. Here, we discuss the implications of these results for the development of addiction treatments, as well as the next steps for advancing TBG and related non-hallucinogenic psychoplastogens as addiction therapeutics.Late-onset Alzheimer's disease (LOAD) is the most common age-related dementia, and its etiology remains unclear. Recent studies have linked abnormal neuronal aging to LOAD. Neurons are non-proliferative, and thus, majority of aged neurons must be rejuvenated through repairing or eliminating damaged molecules to regain their healthy status and functionalities. We discovered a surge of oxidative stress in neurons at middle age in mice. A rapid upregulation of neuronal rejuvenation is vital, while astrocyte-expressed interleukin33 (IL33), an IL1-like cytokine, is critical for this process. Thus, IL33-deficiency cripples the neuronal rejuvenation mechanisms, such as repairing DNA double strand breaks, eliminating damaged molecules by autophagy or by glymphatic drainage. IL33-deficient mice develop tau deposition and age-related dementia following a path similar to LOAD. We hypothesize that any interferences on IL33-initiated rejuvenation process for aged neurons after middle life is a potential risk for LOAD development.

To (1) examine the potentiality of using the robot PARO to mediate care provided by the family and (2) identify problems when utilizing PARO in the home context.

Family members of 7 households were asked to use PARO for at least three times per week, over 1 to 3 months. Research data, including standardized assessments, interviews, and observations, were collected at initial and subsequent monthly visits. Collected data were analyzed through descriptive statistics and inductive thematic content analysis.

Out of the seven participants, five responded positively to PARO, thereby achieving their goals of improving activity engagement, relaxation, a respite from supervision, and improved mood. A positive initial interaction with PARO showed continued interest to it. Participants were observed to communicate with caregivers and relate to PARO.

The application of PARO at home is possibly influenced by the persons' initial level of interest toward PARO. It is crucial to perform careful observation and assessment before deciding to use PARO within the home context to support the life of older persons with dementia.

The application of PARO at home is possibly influenced by the persons' initial level of interest toward PARO. It is crucial to perform careful observation and assessment before deciding to use PARO within the home context to support the life of older persons with dementia.

There are reports describing the relationship between baseline impedance level and esophageal mucosal integrity at endoscopy, such as erosive and nonerosive reflux esophagitis. However, many children with symptoms of gastroesophageal reflux disease have normal findings or minor changes on esophagogastroduodenoscopy. We aimed to examine whether modest changes at esophagogastroduodenoscopy can be evaluated and correlated with esophageal multichannel intraluminal impedance monitoring.

Patients (ages 0-17 years) with upper gastrointestinal symptoms who underwent combined esophagogastroduodenoscopy and multichannel intraluminal impedance monitoring at the Women's and Children's Hospital, Adelaide, Australia, between 2014 and 2016 were retrospectively studied and the following data were collected and used for analysis demographics, multichannel intraluminal impedance data, included baseline impedance. Endoscopic findings were classified by modified Los Angeles grading, Los Angeles

as normal, Los Angeles M ashanges in the lower esophagus. A higher frequency of acid and nonacid reflux episodes was also predictive of minimal endoscopic change in the lower esophagus.Age-related changes cause more fall-related injuries and impede the recoveries by older adults compared to younger adults. This study assessed the lower limb joint moments and muscle responses to split-belt treadmill perturbations in two groups (14 healthy young group [23.36 ± 2.90 years] and 14 healthy older group [70.93 ± 4.36 years]) who performed two trials of unexpected split-belt treadmill perturbations while walking on a programmable split-belt treadmill. A motion capture system quantified the lower limb joint moments, and a wireless electromyography system recorded the lower limb muscle responses. The compensatory limb's (i.e., the tripped limb's contralateral side) joint moments and muscle responses were computed during the pre-perturbation period (the five gait cycles before the onset of a split-belt treadmill perturbation) and the recovery period (from the split-belt treadmill perturbation to the baseline gait relying on the ground reaction forces' profile). Joint moments were assessed by maximum jadults who train on fall-inducing systems could improve therapeutic regimens.

The University of Kansas Alzheimer's Disease Center (KU ADC) maintains several large databases to track participant recruitment, enrollment, and capture various research-related activities. It is challenging to manage and coordinate all the research-related activities. One of the crucial activities involves generating a consensus diagnosis and communicating with participants and their primary care providers.

To effectively manage the cohort, the KU ADC utilizes a combination of open-source electronic data capture (EDC) (i.e. REDCap), along with other homegrown data management and analytic systems developed using R-studio and Shiny.

In this article, we describe the method and utility of the user-friendly dashboard that was developed for the rapid reporting of dementia evaluations which allows clinical researchers to summarize recruitment metrics, automatically generate letters to both participants and healthcare providers, which ultimately help optimize workflows.

We believe this general framework would be beneficial to any institution that build reports and summarizing key metrics of their research from longitudinal databases.

We believe this general framework would be beneficial to any institution that build reports and summarizing key metrics of their research from longitudinal databases.Eye tracking is used widely to investigate attention and cognitive processes while performing tasks in electronic medical record (EMR) systems. We explored a novel application of eye tracking to collect training data for a machine learning-based clinical decision support tool that predicts which patient data are likely to be relevant for a clinical task. Specifically, we investigated in a laboratory setting the accuracy of eye tracking compared to manual annotation for inferring which patient data in the EMR are judged to be relevant by physicians. We evaluated several methods for processing gaze points that were recorded using a low-cost eye-tracking device. Our results show that eye tracking achieves accuracy and precision of 69% and 53%, respectively compared to manual annotation and are promising for machine learning. The methods for processing gaze points and scripts that we developed offer a first step in developing novel uses for eye tracking for clinical decision support.During infectious disease outbreaks, health agencies often share text-based information about cases and deaths. This information is rarely machine-readable, thus creating challenges for outbreak researchers. Here, we introduce a generalizable data assembly algorithm that automatically curates text-based, outbreak-related information and demonstrate its performance across 3 outbreaks. After developing an algorithm with regular expressions, we automatically curated data from health agencies via 3 information sources formal reports, email newsletters, and Twitter. A validation data set was also curated manually for each outbreak, and an implementation process was presented for application to future outbreaks. When compared against the validation data sets, the overall cumulative missingness and misidentification of the algorithmically curated data were ≤2% and ≤1%, respectively, for all 3 outbreaks. Within the context of outbreak research, our work successfully addresses the need for generalizable tools that can transform text-based information into machine-readable data across varied information sources and infectious diseases.Physiological data, such as heart rate and blood pressure, are critical to clinical decision-making in the intensive care unit (ICU). Vital signs data, which are available from electronic health records, can be used to diagnose and predict important clinical outcomes; While there have been some reports on the data quality of nurse-verified vital sign data, little has been reported on the data quality of higher frequency time-series vital signs acquired in ICUs, that would enable such predictive modeling. In this study, we assessed the data quality issues, defined as the completeness, accuracy, and timeliness, of minute-by-minute time series vital signs data within the MIMIC-III data set, captured from 16009 patient-ICU stays and corresponding to 9410 unique adult patients. We measured data quality of four time-series vital signs data streams in the MIMIC-III data set heart rate (HR), respiratory rate (RR), blood oxygen saturation (SpO2), and arterial blood pressure (ABP). Approximately, 30% of patient-ICU stays did not have at least 1 min of data during the time-frame of the ICU stay for HR, RR, and SpO2. The percentage of patient-ICU stays that did not have at least 1 min of ABP data was ∼56%. We observed ∼80% coverage of the total duration of the ICU stay for HR, RR, and SpO2. Finally, only 12.5%%, 9.9%, 7.5%, and 4.4% of ICU lengths of stay had ≥ 99% data available for HR, RR, SpO2, and ABP, respectively, that would meet the three data quality requirements we looked into in this study. Our findings on data completeness, accuracy, and timeliness have important implications for data scientists and informatics researchers who use time series vital signs data to develop predictive models of ICU outcomes.

Ensuring an efficient response to COVID-19 requires a degree of inter-system coordination and capacity management coupled with an accurate assessment of hospital utilization including length of stay (LOS). We aimed to establish optimal practices in inter-system data sharing and LOS modeling to support patient care and regional hospital operations.

We completed a retrospective observational study of patients admitted with COVID-19 followed by 12-week prospective validation, involving 36 hospitals covering the upper Midwest. We developed a method for sharing de-identified patient data across systems for analysis. From this, we compared 3 approaches, generalized linear model (GLM) and random forest (RF), and aggregated system level averages to identify features associated with LOS. We compared model performance by area under the ROC curve (AUROC).

A total of 2068 patients were included and used for model derivation and 597 patients for validation. LOS overall had a median of 5.0 days and mean of 8.2 days. Bcl-2 pathway Consistent predictors of LOS included age, critical illness, oxygen requirement, weight loss, and nursing home admission.