Anticipating Tomorrow: Tailoring Parkinson's Symptomatic Therapy Using Predictors of Outcome.

BACKGROUND: Although research into Parkinson's disease (PD) subtypes and outcome predictions has continued to advance, recommendations for using outcome prediction to guide current treatment decisions remain sparse. OBJECTIVES: To provide expert opinion-based recommendations for individually tailored PD symptomatic treatment based on knowledge of risk prediction and subtypes. METHODS: Using a modified Delphi approach, members of the Movement Disorders Society (MDS) Task Force on PD subtypes generated a series of general recommendations around the question: "Using what you know about genetic/biological/clinical subtypes (or any individual-level predictors of outcome), what advice would you give for selecting symptomatic treatments for an individual patient now, based on what their subtype or individual characteristics predict about their future disease course?" After four iterations and revisions, those recommendations with over 75% endorsement were adopted. RESULTS: A total of 19 recommendations were endorsed by a group of 13 panelists. The recommendations primarily centered around two themes: (1) incorporating future risk of cognitive impairment into current treatment plans; and (2) identifying future symptom clusters that might be forestalled with a single medication. CONCLUSIONS: These recommendations provide clinicians with a framework for integrating future outcomes into patient-specific treatment choices. They are not prescriptive guidelines, but adaptable suggestions, which should be tailored to each individual. They are to be considered as a first step of a process that will continue to evolve as additional stakeholders provide new insights and as new information becomes available. As individualized risk prediction advances, the path to better tailored treatment regimens will become clearer.

Simplifying Data Analysis in Biomedical Research: An Automated, User-Friendly Tool.

Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.

Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately-severe hemophilia A using a machine-learning network.

BACKGROUND: Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge. AIM: To construct a predictive model for inhibitor development in HA using a network of clinical variables and biomarkers based on the individual similarity network. METHODS: Previously untreated and minimally treated children with severe/moderately-severe HA, participants of the HEMFIL Cohort Study, were followed-up until reaching 75 exposure days (ED) without inhibitor (INH-) or upon inhibitor development (INH+). Clinical data and biological samples were collected before the start of factor VIII (FVIII) replacement (T0). A predictive model (HemfilNET) was built to compare the networks and potential global topological differences between INH- and INH+ at T0, considering the network robustness. For validation, the "leave-one-out" cross-validation technique was employed. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the machine-learning model. RESULTS: We included 95 children with HA (CHA), of whom 31 (33%) developed inhibitors. The algorithm, featuring 37 variables, identified distinct patterns of networks at T0 for INH+ and INH-. The accuracy of the model was 74.2% for CHA INH+ and 98.4% for INH-. By focusing the analysis on CHA with high-risk F8 mutations for inhibitor development, the accuracy in identifying CHA INH+ increased to 82.1%. CONCLUSION: Our machine-learning algorithm demonstrated an overall accuracy of 90.5% for predicting inhibitor development in CHA, which further improved when restricting the analysis to CHA with a high-risk F8 genotype. However, our model requires validation in other cohorts. Yet, missing data for some variables hindered more precise predictions.

A new methodology for a rapid and high-throughput comparison of molecular profiles and biological activity of phytoextracts.

To robustly discover and explore phytocompounds, it is necessary to evaluate the interrelationships between the plant species, plant tissue, and the extraction process on the extract composition and to predict its cytotoxicity. The present work evaluated how Fourier Transform InfraRed spectroscopy can acquire the molecular profile of aqueous and ethanol-based extracts obtained from leaves, seeds, and flowers of Cynara Cardunculus, and ethanol-based extracts from Matricaria chamomilla flowers, as well the impact of these extracts on the viability of mammalian cells. The extract molecular profile enabled to predict the extraction yield, and how the plant species, plant tissue, and extraction process affected the extract's relative composition. The molecular profile obtained from the culture media of cells exposed to extracts enabled to capture its impact on cells metabolism, at a higher sensitivity than the conventional assay used to determine the cell viability. Furthermore, it was possible to detect specific impacts on the cell's metabolism according to plant species, plant tissue, and extraction process. Since spectra were acquired on small volumes of samples (25 µL), after a simple dehydration step, and based on a plate with 96 wells, the method can be applied in a rapid, simple, high-throughput, and economic mode, consequently promoting the discovery of phytocompounds.

Click detection rate variability of central North Pacific sperm whales from passive acoustic towed arrays.

Passive acoustic monitoring (PAM) is an optimal method for detecting and monitoring cetaceans as they frequently produce sound while underwater. Cue counting, counting acoustic cues of deep-diving cetaceans instead of animals, is an alternative method for density estimation, but requires an average cue production rate to convert cue density to animal density. Limited information about click rates exists for sperm whales in the central North Pacific Ocean. In the absence of acoustic tag data, we used towed hydrophone array data to calculate the first sperm whale click rates from this region and examined their variability based on click type, location, distance of whales from the array, and group size estimated by visual observers. Our findings show click type to be the most important variable, with groups that include codas yielding the highest click rates. We also found a positive relationship between group size and click detection rates that may be useful for acoustic predictions of group size in future studies. Echolocation clicks detected using PAM methods are often the only indicator of deep-diving cetacean presence. Understanding the factors affecting their click rates provides important information for acoustic density estimation.

Relationship between Femur Mineral Content and Local Muscle Strength and Mass.

Among the stimuli able to prevent early decreases in bone mineralization, exercise has a noticeable role per se as the source of mechanical stimulus or through lean tissue enlargement by its increasing of tensional stimulus. However, prevention strategies, including exercise, generally do not establish the moment in life when attention should begin to be paid to bone integrity, according to age group- and sex-related differences. Thus, this study analyzed the relationship between variables from the diagnosis of total and regional body composition, muscle strength, and bone mineral content (BMC) of femurs in young adult males. Thirty-four young Caucasian men (24.9 ± 8.6 years) had their body composition and bone density assessed by dual X-ray absorptiometry. The subjects performed a one-repetition maximum test (1-RM) in a bench press, front pulley, seated-row, push press, arm curl, triceps pulley, leg flexion, leg extension, and 45° leg press for the assessment of muscle strength in upper and lower limbs in single- and multi-joint exercises. Lean tissue mass in the trunk and upper and lower limbs were related to femoral BMC (Pearson coefficient ranging from 0.55 to 0.72, p < 0.01), and 1-RM values for different exercises involving both upper and lower limbs also correlated with femoral BMC (Pearson coefficients ranging from 0.34 to 0.46, p < 0.05). Taken together, these correlations suggest that muscle mass and strength are positively linked with the magnitude of femoral mass in men, even in early adulthood. Hence, the importance of an enhanced muscle mass and strength to the health of femoral bones in young adults was highlighted.

Comparing Video Analysis to Computerized Detection of Limb Position for the Diagnosis of Movement Control during Back Squat Exercise with Overload.

Incorrect limb position while lifting heavy weights might compromise athlete success during weightlifting performance, similar to the way that it increases the risk of muscle injuries during resistance exercises, regardless of the individual's level of experience. However, practitioners might not have the necessary background knowledge for self-supervision of limb position and adjustment of the lifting position when improper movement occurs. Therefore, the computerized analysis of movement patterns might assist people in detecting changes in limb position during exercises with different loads or enhance the analysis of an observer with expertise in weightlifting exercises. In this study, hidden Markov models (HMMs) were employed to automate the detection of joint position and barbell trajectory during back squat exercises. Ten volunteers performed three lift movements each with a 0, 50, and 75% load based on body weight. A smartphone was used to record the movements in the sagittal plane, providing information for the analysis of variance and identifying significant position changes by video analysis (p < 0.05). Data from individuals performing the same movements with no added weight load were used to train the HMMs to identify changes in the pattern. A comparison of HMMs and human experts revealed between 40% and 90% agreement, indicating the reliability of HMMs for identifying changes in the control of movements with added weight load. In addition, the results highlighted that HMMs can detect changes imperceptible to the human visual analysis.

A decision support system to recommend appropriate therapy protocol for AML patients.

Introduction: Acute Myeloid Leukemia (AML) is one of the most aggressive hematological neoplasms, emphasizing the critical need for early detection and strategic treatment planning. The association between prompt intervention and enhanced patient survival rates underscores the pivotal role of therapy decisions. To determine the treatment protocol, specialists heavily rely on prognostic predictions that consider the response to treatment and clinical outcomes. The existing risk classification system categorizes patients into favorable, intermediate, and adverse groups, forming the basis for personalized therapeutic choices. However, accurately assessing the intermediate-risk group poses significant challenges, potentially resulting in treatment delays and deterioration of patient conditions. Methods: This study introduces a decision support system leveraging cutting-edge machine learning techniques to address these issues. The system automatically recommends tailored oncology therapy protocols based on outcome predictions. Results: The proposed approach achieved a high performance close to 0.9 in F1-Score and AUC. The model generated with gene expression data exhibited superior performance. Discussion: Our system can effectively support specialists in making well-informed decisions regarding the most suitable and safe therapy for individual patients. The proposed decision support system has the potential to not only streamline treatment initiation but also contribute to prolonged survival and improved quality of life for individuals diagnosed with AML. This marks a significant stride toward optimizing therapeutic interventions and patient outcomes.

Developmental changes in school burnout: The importance of sleep and problematic Internet use.

INTRODUCTION: School burnout remains a prevalent problem among adolescents; it is associated with low academic achievement and school dropout risk, in turn linked to a whole host of deleterious developmental outcomes. The current longitudinal study sought to better understand the developmental course of school burnout by testing whether poor sleep and problematic internet use each uniquely and additively explained the variance in school burnout over time. METHOD: Data were collected four times over 18 months, 6 months apart from N = 405 adolescents, grades 9 to 11. RESULTS: Sleep quality, but not quantity, was significantly associated with the school burnout intercept (ß = -0.29); no effects were found for the slope. Problematic internet use was also significantly associated with the intercept (ß = .44), but not the slope. In a combined model, both sleep quality and problematic internet use significantly predicted the school burnout intercept. The slope was only predicted by age (ß = -0.21). CONCLUSIONS: The study found partial support for the hypotheses that both poor sleep quality and problematic internet use predicted school burnout, intercept only, not the rate of change. The evidence suggests that school burnout increased across high school; however, the rate of increase slowed with age. In contrast to some previous work, study findings highlight the importance of separately considering both poor sleep and problematic internet use in understanding the development of school burnout during adolescence. N = 229.

COVID-19 and pregnancy: A European study on pre- and post-infection medication use.

PURPOSE: The COVID-19 pandemic has impacted medication needs and prescribing practices, including those affecting pregnant women. Our goal was to investigate patterns of medication use among pregnant women with COVID-19, focusing on variations by trimester of infection and location. METHODS: We conducted an observational study using six electronic healthcare databases from six European regions (Aragon/Spain; France; Norway; Tuscany, Italy; Valencia/Spain; and Wales/UK). The prevalence of primary care prescribing or dispensing was compared in the 30-day periods before and after a positive COVID-19 test or diagnosis. RESULTS: The study included 294,126 pregnant women, of whom 8943 (3.0%) tested positive for, or were diagnosed with, COVID-19 during their pregnancy. A significantly higher use of antithrombotic medications was observed particularly after COVID-19 infection in the second and third trimesters. The highest increase was observed in the Valencia region where use of antithrombotic medications in the third trimester increased from 3.8% before COVID-19 to 61.9% after the infection. Increases in other countries were lower; for example, in Norway, the prevalence of antithrombotic medication use changed from around 1-2% before to around 6% after COVID-19 in the third trimester. Smaller and less consistent increases were observed in the use of other drug classes, such as antimicrobials and systemic corticosteroids. CONCLUSION: Our findings highlight the substantial impact of COVID-19 on primary care medication use among pregnant women, with a marked increase in the use of antithrombotic medications post-COVID-19. These results underscore the need for further research to understand the broader implications of these patterns on maternal and neonatal/fetal health outcomes.

Narwhal (Monodon monoceros) echolocation click rates to support cue counting passive acoustic density estimation.

Estimating animal abundance is fundamental for effective management and conservation. It is increasingly done by combining passive acoustics with knowledge about rates at which animals produce cues (cue rates). Narwhals (Monodon monoceros) are elusive marine mammals for which passive acoustic density estimation might be plausible, but for which cue rates are lacking. Clicking rates in narwhals were investigated using a dataset from sound and movement tag records collected in August 2013-2016 and 2019 in East Greenland. Clicking rates were quantified for ∼1200 one-second-long systematic random samples from 8 different whales. Generalized additive models were used to model (1) the probability of being in a clicking state versus depth and (2) the clicking rate while in a clicking state, versus time and depth. The probability of being in a clicking state increased with depth, reaching ∼1.0 at ∼500 m, while the number of clicks per second (while in a clicking state) increased with depth. The mean cue production rate, weighted by tag duration, was 1.28 clicks per second (se = 0.13, CV = 0.10). This first cue rate for narwhals may be used for cue counting density estimation, but care should be taken if applying it to other geographical areas or seasons, given sample size, geographical, and temporal limitations.

Rural communities experience higher radon exposure versus urban areas, potentially due to drilled groundwater well annuli acting as unintended radon gas migration conduits.

Repetitive, long-term inhalation of radioactive radon gas is one of the leading causes of lung cancer, with exposure differences being a function of geographic location, built environment, personal demographics, activity patterns, and decision-making. Here, we examine radon exposure disparities across the urban-to-rural landscape, based on 42,051 Canadian residential properties in 2034 distinct communities. People living in rural, lower population density communities experience as much as 31.2% greater average residential radon levels relative to urban equivalents, equating to an additional 26.7 Bq/m3 excess in geometric mean indoor air radon, and an additional 1 mSv/year in excess alpha radiation exposure dose rate to the lungs for occupants. Pairwise and multivariate analyses indicate that community-based radon exposure disparities are, in part, explained by increased prevalence of larger floorplan bungalows in rural areas, but that a majority of the effect is attributed to proximity to, but not water use from, drilled groundwater wells. We propose that unintended radon gas migration in the annulus of drilled groundwater wells provides radon migration pathways from the deeper subsurface into near-surface materials. Our findings highlight a previously under-appreciated determinant of radon-induced lung cancer risk, and support a need for targeted radon testing and reduction in rural communities.

Transitioning from Subtyping to Precision Medicine in Parkinson's Disease: A Purpose-Driven Approach.

The International Parkinson and Movement Disorder Society (MDS) created a task force (TF) to provide a critical overview of the Parkinson's disease (PD) subtyping field and develop a guidance on future research in PD subtypes. Based on a literature review, we previously concluded that PD subtyping requires an ultimate alignment with principles of precision medicine, and consequently novel approaches were needed to describe heterogeneity at the individual patient level. In this manuscript, we present a novel purpose-driven framework for subtype research as a guidance to clinicians and researchers when proposing to develop, evaluate, or use PD subtypes. Using a formal consensus methodology, we determined that the key purposes of PD subtyping are: (1) to predict disease progression, for both the development of therapies (use in clinical trials) and prognosis counseling, (2) to predict response to treatments, and (3) to identify therapeutic targets for disease modification. For each purpose, we describe the desired product and the research required for its development. Given the current state of knowledge and data resources, we see purpose-driven subtyping as a pragmatic and necessary step on the way to precision medicine. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Fin whale song evolution in the North Atlantic.

Animal songs can change within and between populations as the result of different evolutionary processes. When these processes include cultural transmission, the social learning of information or behaviours from conspecifics, songs can undergo rapid evolutions because cultural novelties can emerge more frequently than genetic mutations. Understanding these song variations over large temporal and spatial scales can provide insights into the patterns, drivers and limits of song evolution that can ultimately inform on the species' capacity to adapt to rapidly changing acoustic environments. Here, we analysed changes in fin whale (Balaenoptera physalus) songs recorded over two decades across the central and eastern North Atlantic Ocean. We document a rapid replacement of song INIs (inter-note intervals) over just four singing seasons, that co-occurred with hybrid songs (with both INIs), and a clear geographic gradient in the occurrence of different song INIs during the transition period. We also found gradual changes in INIs and note frequencies over more than a decade with fin whales adopting song changes. These results provide evidence of vocal learning in fin whales and reveal patterns of song evolution that raise questions on the limits of song variation in this species.

Unveiling Assessment Gaps in Parkinson's Disease Psychosis: A Scoping Review.

BACKGROUND: Parkinson's disease psychosis (PDP) is a multidimensional construct that is challenging to measure. Accurate assessment of PDP requires comprehensive and reliable clinical outcome assessment (COA) measures. OBJECTIVE: To identify PDP measurement gaps in available COAs currently used in clinical and research settings. METHODS: We conducted a scoping review using Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. We implemented a three-step search strategy in international databases with keywords related to Parkinson's disease (PD), psychosis, and COA. We analyzed studies using COA to assess PDP, classifying their items according to domains and subdomains. RESULTS: From 5673 identified studies, we included 628 containing 432 PDP core items from 32 COAs. Among the 32 COAs, 19 were PD-specific, containing 266 items, constructed as clinician-reported outcomes (ClinRO) (148 items), patient-reported outcomes (PRO) (112 items), and observer-reported outcomes (ObsRO) (six items). Across all PD-specific COAs, regardless of structure, 89.4% of the items from 27 COAs focused primarily on assessing PDP symptoms' severity, and only 9.7% of items probed the impact of PDP on a person's daily functioning. CONCLUSIONS: Symptom-based domains are currently prioritized for measuring the severity of PDP, with limited coverage of the functional impact of PDP on patients' lives. Whereas the International Parkinson and Movement Disorder Society has traditionally developed a "Unified" COA that culls items from prior COAs to form a new one, a new COA will largely need newly developed items if the functional impact of PDP is prioritized. © 2024 International Parkinson and Movement Disorder Society.

In silico evaluation of pharmacokinetics properties of withanolides and simulation of their biological activities against Alzheimer's disease.

The withanolides are naturally occurring steroidal lactones found mainly in plants of the Solanaceae family. The subtribe Withaninae includes species like Withania sominifera, which are a source of many bioactive withanolides. In this work, we selected and evaluate the ADMET-related properties of 91 withanolides found in species of the subtribe Withaninae computationally, to predict the relationship between their structures and their pharmacokinetic profiles. We also evaluated the interaction of these withanolides with known targets of Alzheimer's disease (AD) through molecular docking and molecular dynamics. Withanolides presented favorable pharmacokinetic properties, like high gastrointestinal absorption, lipophilicity (logP ≤ 5), good distribution and excretion parameters, and a favorable toxicity profile. The specie Withania aristata stood out as an interesting source of the promising withanolides classified as 5-ene with 16-ene or 17-ene. These withanolides presented a favourable pharmacokinetic profile and were also highlighted as the best candidates for inhibition of AD-related targets. Our results also suggest that withanolides are likely to act as cholinesterase inhibitors by interacting with the catalytic pocket in an energy favorable and stable way.Communicated by Ramaswamy H. Sarma.