Unsupervised discovery of family specific vocal usage in the Mongolian gerbil.

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.

Potential utility of ADNP in circulating tumor cells as biomarker for prognostics in non-muscle-invasive bladder cancer.

This study aims to evaluate the prognostic utility of Activity-dependent neuroprotective protein (ADNP) expression in Circulating Tumor Cells (CTCs) inpatients with Non-muscle-invasive Bladder Cancer (NMIBC) undergoing Transurethral Resection of Bladder Tumor (TURBT). A prospective cohort of 74 bladder cancer patients and 22 non-cancer controls were enrolled. The expression of ADNP mRNA was detected by immunomagnetic beads-droplet digital PCR. The ADNP mRNA expression was evaluated in patients with high-risk NMIBC and those with indeterminate invasion depth post 2nd TURBT. Primary cultured bladder cancer cells and PBMCs from healthy donors were immunofluorescence stained. Our findings suggest that baseline ADNP mRNA level in CTCs shows potential as a prognostic marker for NMIBC with a sensitivity of 83.33% and a specificity of 73.58%. In comparison to baseline, ADNP mRNA expression increased post 2nd TURBT in 5 patients, where 2 experienced recurrence. Meanwhile, among the 12 patients with decreased levels, only one patient relapsed. A considerable limitation of this study entails the small sample size. The Immuno-magnetic beads-ddPCR technique provided a viable method for ADNP mRNA detection in CTCs from bladder cancer patients. The preoperative ADNP mRNA level in CTCs was identified as a prognostic indicator for NMIBC. Longitudinal monitoring of ADNP mRNA in CTCs of bladder cancer patients shows promise in evaluating treatment responses and predicting prognosis.

Tandem gait step-width increases more rapidly in more severely affected people with Parkinson's disease.

INTRODUCTION: Tandem gait performance reportedly predicts fall risk in people with Parkinson's disease (PwPD) and help distinguish PwPD from atypical parkinsonism. In a cross-sectional study, we previously showed that tandem gait step-width widens with increasing Hoehn and Yahr (H&Y) staging scores. In this longitudinal study, we aimed to determine if progression in tandem gait deficits is dependent on disease severity in PwPD. METHODS: Participants underwent an instrumented tandem gait measurement every 6 months for at least 2 years. The mean and variability of 4 tandem gait parameters were calculated at each visit: step-width, step-length, step-time, and step-velocity. The change in these parameters over time for 3 H&Y groups (stage 1, 2 and 2.5+) compared to aging controls was determined using a random coefficients regression model. The annual percent change in tandem gait parameters was correlated with initial disease features using Kendall's τB. RESULTS: 66 participants were analyzed (46 PD, 20 controls). Mean step-width increased over time in an H&Y stage-dependent manner, with H&Y 2 and H&Y 2.5+ experiencing increases of 6% and 10% per year (p = 0.001 and 0.024 respectively). Annual percent-change in step-width was correlated with initial motor Unified Parkinson's Disease Rating Scale (UPDRS) scores (Kendall's τB = 0.229), total UPDRS scores (τB = 0.249), H&Y scores (τB = 0.266) and inversely correlated with Montreal Cognitive Assessment (MoCA) scores (τB = -0.209; ps ≤ 0.019). CONCLUSION: Tandem gait step-width widens over time more rapidly in more severely affected PD patients. These results suggest that tandem gait should be routinely clinically evaluated and considered in the management of imbalance in PwPD.

Changes in Physical Fitness, Muscle Damage and Cognitive Function in Elite Rugby Players over a Season.

This study proposes to monitor the physical, immune and cognitive responses and adaptations of elite rugby players throughout the season based on the loads performed. Anthropometric measurements, physical fitness tests (e.g., muscle strength and power, linear and change-of-direction speed, cardiorespiratory fitness) and analyses of serum concentrations of markers of muscle damage (creatine kinase [CK] and lactate dehydrogenase [LDH]) and brain-derived neurotrophic factor (BDNF) were carried out over a sporting season (24 weeks) for 17 elite rugby players (10 forwards and 7 backs) aged 18.91 ± 0.76 years. The physical fitness test results show improvements in the performance of both forwards and backs over the season (p < 0.05), with an advantage for backs compared with forwards in most tests (p < 0.05). Muscle damage markers decreased at the end of the season compared with the baseline levels for forwards (p < 0.05). CK levels were unchanged for the backs, but there were increased LDH concentrations at the end of the season compared with baseline (p < 0.05). Serum BDNF levels decreased for the total group between the second and third sampling (p < 0.05). The muscular and physical capacities of rugby players differ according to their playing position. Immune responses and adaptations, as well as BDNF levels, vary throughout the season and depend on the physical load performed.

Modelling future bone mineral density: Simplicity or complexity?

BACKGROUND: Osteoporotic fractures are a major global public health issue, leading to patient suffering and death, and considerable healthcare costs. Bone mineral density (BMD) measurement is important to identify those with osteoporosis and assess their risk of fracture. Both the absolute BMD and the change in BMD over time contribute to fracture risk. Predicting future fracture in individual patients is challenging and impacts clinical decisions such as when to intervene or repeat BMD measurement. Although the importance of BMD change is recognised, an effective way to incorporate this marginal effect into clinical algorithms is lacking. METHODS: We compared two methods using longitudinal DXA data generated from subjects with two or more hip DXA scans on the same machine between 2000 and 2018. A simpler statistical method (ZBM) was used to predict an individual's future BMD based on the mean BMD and the standard deviation of the reference group and their BMD measured in the latest scan. A more complex deep learning (DL)-based method was developed to cope with multidimensional longitudinal data, variables extracted from patients' historical DXA scan(s), as well as features drawn from the ZBM method. Sensitivity analyses of several subgroups was conducted to evaluate the performance of the derived models. RESULTS: 2948 white adults aged 40-90 years met our study inclusion: 2652 (90 %) females and 296 (10 %) males. Our DL-based models performed significantly better than the ZBM models in women, particularly our Hybrid-DL model. In contrast, the ZBM-based models performed as well or better than DL-based models in men. CONCLUSIONS: Deep learning-based and statistical models have potential to forecast future BMD using longitudinal clinical data. These methods have the potential to augment clinical decisions regarding when to repeat BMD testing in the assessment of osteoporosis.

Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): a multi-centre prospective cohort study protocol.

BACKGROUND: Eating disorders (EDs) are serious, often chronic, conditions associated with pronounced morbidity, mortality, and dysfunction increasingly affecting young people worldwide. Illness progression, stages and recovery trajectories of EDs are still poorly characterised. The STORY study dynamically and longitudinally assesses young people with different EDs (restricting; bingeing/bulimic presentations) and illness durations (earlier; later stages) compared to healthy controls. Remote measurement technology (RMT) with active and passive sensing is used to advance understanding of the heterogeneity of earlier and more progressed clinical presentations and predictors of recovery or relapse. METHODS: STORY follows 720 young people aged 16-25 with EDs and 120 healthy controls for 12 months. Online self-report questionnaires regularly assess ED symptoms, psychiatric comorbidities, quality of life, and socioeconomic environment. Additional ongoing monitoring using multi-parametric RMT via smartphones and wearable smart rings ('Oura ring') unobtrusively measures individuals' daily behaviour and physiology (e.g., Bluetooth connections, sleep, autonomic arousal). A subgroup of participants completes additional in-person cognitive and neuroimaging assessments at study-baseline and after 12 months. DISCUSSION: By leveraging these large-scale longitudinal data from participants across ED diagnoses and illness durations, the STORY study seeks to elucidate potential biopsychosocial predictors of outcome, their interplay with developmental and socioemotional changes, and barriers and facilitators of recovery. STORY holds the promise of providing actionable findings that can be translated into clinical practice by informing the development of both early intervention and personalised treatment that is tailored to illness stage and individual circumstances, ultimately disrupting the long-term burden of EDs on individuals and their families.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor.

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Live Organoid Cyclic Imaging.

Organoids are becoming increasingly relevant in biology and medicine for their physiological complexity and accuracy in modeling human disease. To fully assess their biological profile while preserving their spatial information, spatiotemporal imaging tools are warranted. While previously developed imaging techniques, such as four-dimensional (4D) live imaging and light-sheet imaging have yielded important clinical insights, these technologies lack the combination of cyclic and multiplexed analysis. To address these challenges, bioorthogonal click chemistry is applied to display the first demonstration of multiplexed cyclic imaging of live and fixed patient-derived glioblastoma tumor organoids. This technology exploits bioorthogonal click chemistry to quench fluorescent signals from the surface and intracellular of labeled cells across multiple cycles, allowing for more accurate and efficient molecular profiling of their complex phenotypes. Herein, the versatility of this technology is demonstrated for the screening of glioblastoma markers in patient-derived human glioblastoma organoids while conserving their viability. It is anticipated that the findings and applications of this work can be broadly translated into investigating physiological developments in other organoid systems.

Small extracellular vesicles from surviving cancer cells as multiparametric monitoring tools of measurable residual disease and therapeutic efficiency.

Although conventional anti-cancer therapies remove most cells of the tumor mass, small surviving populations may evolve adaptive resistance strategies, which lead to treatment failure. The size of the resistant population initially may not reach the threshold of clinical detection (designated as measurable residual disease/MRD) thus, its investigation requires highly sensitive and specific methods. Here, we discuss that the specific molecular fingerprint of tumor-derived small extracellular vesicles (sEVs) is suitable for longitudinal monitoring of MRD. Furthermore, we present a concept that exploiting the multiparametric nature of sEVs may help early detection of recurrence and the design of dynamic, evolution-adjusted treatments.

Yearly intrasubject variability of hematological biomarkers in elite athletes for the Athlete Biological Passport.

Confounding factors including exercise and environments challenge the interpretation of individual Athlete Biological Passports (ABPs). This study aimed to investigate the natural variability of hematological ABP parameters over 1 year in elite athletes compared with healthy control subjects and the validity of a multiparametric model estimating plasma volume (PV) shifts to correct individual ABP thresholds. Blood samples were collected monthly with full blood counts performed by flow cytometry (Sysmex XN analyzers) in 20 elite xc-skiers (ELITE) and 20 moderately trained controls. Individual ABP profiles were generated through Anti-Doping Administration & Management System Training, a standalone version of the ABP's adaptive model developed by the World Anti-Doping Agency. Additionally, eight serum parameters were computed as volume-sensitive biomarkers to run a multiparametric model to estimate PV. Variability in ELITE compared with controls was significantly higher for the Abnormal Blood Profile Scores (P = 0.003). Among 12 Atypical Passport Findings (ATPF) initially reported, six could be removed after correction of PV shifts with the multiparametric modeling. However, several ATPF were additionally generated (n = 19). Our study outlines a larger intraindividual variability in elite athletes, likely explained by more frequent exposure to extrinsic factors altering hematological biomarkers. PV correction for individual ABP thresholds allowed to explain most of the atypical findings while generating multiple new ATPF occurrences in the elite population. Overall, accounting for PV shifts in elite athletes was shown to be paramount in this study outlining the opportunity to consider PV variations with novel approaches when interpreting individual ABP profiles.

Freshwater salinization syndrome limits management efforts to improve water quality.

Freshwater Salinization Syndrome (FSS) refers to groups of biological, physical, and chemical impacts which commonly occur together in response to salinization. FSS can be assessed by the mobilization of chemical mixtures, termed "chemical cocktails", in watersheds. Currently, we do not know if salinization and mobilization of chemical cocktails along streams can be mitigated or reversed using restoration and conservation strategies. We investigated 1) the formation of chemical cocktails temporally and spatially along streams experiencing different levels of restoration and riparian forest conservation and 2) the potential for attenuation of chemical cocktails and salt ions along flowpaths through conservation and restoration areas. We monitored high-frequency temporal and longitudinal changes in streamwater chemistry in response to different pollution events (i.e., road salt, stormwater runoff, wastewater effluent, and baseflow conditions) and several types of watershed management or conservation efforts in six urban watersheds in the Chesapeake Bay watershed. Principal component analysis (PCA) indicates that chemical cocktails which formed along flowpaths (i.e., permanent reaches of a stream) varied due to pollution events. In response to winter road salt applications, the chemical cocktails were enriched in salts and metals (e.g., Na+, Mn, and Cu). During most baseflow and stormflow conditions, chemical cocktails were less enriched in salt ions and trace metals. Downstream attenuation of salt ions occurred during baseflow and stormflow conditions along flowpaths through regional parks, stream-floodplain restorations, and a national park. Conversely, chemical mixtures of salt ions and metals, which formed in response to multiple road salt applications or prolonged road salt exposure, did not show patterns of rapid attenuation downstream. Multiple linear regression was used to investigate variables that influence changes in chemical cocktails along flowpaths. Attenuation and dilution of salt ions and chemical cocktails along stream flowpaths was significantly related to riparian forest buffer width, types of salt pollution, and distance downstream. Although salt ions and chemical cocktails can be attenuated and diluted in response to conservation and restoration efforts at lower concentration ranges, there can be limitations in attenuation during road salt events, particularly if storm drains bypass riparian buffers.

Multifactorial assessment of Parkinson's disease course and outcomes using trajectory modeling in a multiethnic, multisite cohort - extension of the LONG-PD study.

Background: The severity, progression, and outcomes of motor and non-motor symptoms in Parkinson's disease (PD) are quite variable. Following PD cohorts holds promise for identifying predictors of disease severity and progression. Methods: PD patients (N = 871) were enrolled at five sites. Enrollment occurred within 5 years of initial motor symptom onset. Disease progression was assessed annually for 2-to-10 years after onset. Group-based trajectory modeling was used to identify groups differing in disease progression. Models were developed for UPDRS-III scores, UPDRS-III tremor and bradykinesia-rigidity subscores, Hoehn & Yahr (H&Y) stage, Mini-Mental Status Exam (MMSE) scores, and UPDRS-III, H&Y and MMSE scores considered together. Predictors of trajectory-group membership were modeled simultaneously with the trajectories. Kaplan-Meier survival analysis evaluated survival free of PD outcomes. Results: The best fitting models identified three groups. One showed a relatively benign, slowly progressing trajectory (Group 1), a second showed a moderate, intermediately progressing trajectory (Group 2), and a third showed a more severe, rapidly progressing trajectory (Group 3). Stable trajectory-group membership occurred relatively early in the disease course, 5 years after initial motor symptom. Predictors of intermediate and more severe trajectory-group membership varied across the single variable models and the multivariable model jointly considering UPDRS-III, H&Y and MMSE scores. In the multivariable model, membership in Group 2 (28.4% of patients), relative to Group 1 (50.5%), was associated with male sex, younger age-at-onset, fewer education-years, pesticide exposure, absence of reported head injury, and akinetic/rigid subtype at initial presentation. Membership in Group 3 (21.3%), relative to Group 1, was associated with older age-at-onset, fewer education-years, pesticide exposure, and the absence of a tremor-predominant subtype at initial presentation. Persistent freezing, persistent falls, and cognitive impairment occurred earliest and more frequently in Group 3, later and less frequently in Group 2, and latest and least frequently in Group 1. Furthermore, autonomic complications, dysphagia, and psychosis occurred more frequently in Groups 2 and 3 than in Group 1. Conclusion: Modeling disease course using multiple objective assessments over an extended follow-up duration identified groups that more accurately reflect differences in PD course, prognosis, and outcomes than assessing single parameters over shorter intervals.

Dynamic human exposure to airborne bacteria-associated antibiotic resistomes revealed by longitudinal personal monitoring data.

Airborne antibiotic-resistant bacteria (ARB) can critically impact human health. We performed resistome profiling of 283 personal airborne exposure samples from 15 participants spanning 890 days and 66 locations. We found a greater diversity and abundance of airborne bacteria community and antibiotic resistomes in spring than in winter, and temperature contributed largely to the difference. A total of 1123 bacterial genera were detected, with 16 genera dominating. Of which, 7/16 were annotated as major antibiotic resistance gene (ARG) hosts. The participants were exposed to a highly dynamic collection of ARGs, including 322 subtypes conferring resistance to 18 antibiotic classes dominated by multidrug, macrolide-lincosamide-streptogramin, ß-lactam, and fosfomycin. Unlike the overall community-level bacteria exposure, an extremely high abundance of specific ARG subtypes, including lunA and qacG, were found in some samples. Staphylococcus was the predominant genus in the bacterial community, serving as a primary bacterial host for the ARGs. The annotation of ARG-carrying contigs indicated that humans and companion animals were major reservoirs for ARG-carrying Staphylococcus. This study contextualized airborne antibiotic resistomes in the precision medicine framework through longitudinal personal monitoring, which can have broad implications for human health.

A fully automated home cage for long-term continuous phenotyping of mouse cognition and behavior.

Automated home-cage monitoring systems present a valuable tool for comprehensive phenotyping of natural behaviors. However, current systems often involve complex training routines, water or food restriction, and probe a limited range of behaviors. Here, we present a fully automated home-cage monitoring system for cognitive and behavioral phenotyping in mice. The system incorporates T-maze alternation, novel object recognition, and object-in-place recognition tests combined with monitoring of locomotion, drinking, and quiescence patterns, all carried out over long periods. Mice learn the tasks rapidly without any need for water or food restrictions. Behavioral characterization employs a deep convolutional neural network image analysis. We show that combined statistical properties of multiple behaviors can be used to discriminate between mice with hippocampal, medial entorhinal, and sham lesions and predict the genotype of an Alzheimer's disease mouse model with high accuracy. This technology may enable large-scale behavioral screening for genes and neural circuits underlying spatial memory and other cognitive processes.

Preterm Infants off Positive Pressure Respiratory Support Have a Higher Incidence of Occult Cerebral Hypoxia.

OBJECTIVE: To use cerebral near-infrared spectroscopy (NIRS) to quantify occult cerebral hypoxia across respiratory support modes in preterm infants. STUDY DESIGN: In this prospective, longitudinal, observational study, infants ≤32 weeks gestation underwent serial pulse oximetry (oxygen saturation [SpO2]) and cerebral NIRS monitoring (4-6 hours per session) following a standardized recording schedule (daily for 2 weeks, every other day for 2 weeks, then weekly until 35 weeks corrected gestational age). Four calculations were made: median cerebral saturation, median cerebral hypoxia burden (proportion of NIRS samples below the hypoxia threshold [<67%]), median systemic saturation, and median systemic hypoxia burden (proportion of SpO2 samples below the desaturation threshold [<85%]). During each recording session, respiratory support mode was noted (room air, low-flow nasal cannula, high-flow nasal cannula, noninvasive positive pressure ventilation, continuous positive airway pressure, and invasive ventilation). RESULTS: There were 1013 recording sessions made from 174 infants with a median length of 6.9 hours. Although the systemic (SpO2) hypoxia burden was significantly greater for infants on the highest respiratory support (invasive and noninvasive positive pressure ventilation), the cerebral hypoxia burden was significantly greater during recording sessions made on the lowest respiratory support (8% for room air; 29% for low-flow nasal cannula). CONCLUSIONS: Premature infants on the highest levels of respiratory support have less cerebral hypoxia than those on lower respiratory support. These results raise concern about unrecognized cerebral hypoxia during lower acuity periods of neonatal intensive care unit hospitalization and adverse outcomes.

Association of Longitudinal B-Type Natriuretic Peptide Monitoring With Kidney Failure in Patients With CKD: A Cohort Study.

RATIONALE & OBJECTIVE: Both hypervolemia and hypovolemia are associated with chronic kidney disease (CKD) progression. Although longitudinal monitoring of B-type natriuretic peptide (BNP) may aid physicians' decision making about the optimization of volume status, its clinical benefit remains uncertain in CKD. This study assessed the association between BNP monitoring and the risk of incident kidney replacement therapy (KRT). STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: A total of 2,998 outpatients with stages 3-5 of nondialyzed CKD referred to the department of nephrology at an academic hospital. EXPOSURE: BNP monitoring. OUTCOME: KRT, acute kidney injury (AKI), and heart failure hospitalization. ANALYTICAL APPROACH: Marginal structural models, which create a balanced pseudo population at each time point, were applied to account for potential time-dependent confounders. Inverse probability weighted pooled logistic regression models were employed to estimate hazard ratios. RESULTS: At baseline, the median age and estimated glomerular filtration rate were 66 years and 38.1mL/min/1.73m2, respectively. During the follow-up period (median, 5.9 [IQR, 2.8-9.9] years), 449 patients required KRT, 765 had AKI, and 236 were hospitalized for heart failure. After adjustment for time-updated clinical characteristics and physician-specific practice styles, BNP monitoring was associated with lower risks of KRT (HR, 0.44 [95% CI, 0.21-0.92]), AKI (HR, 0.36 [95% CI, 0.18-0.72]), and heart failure hospitalization (HR, 0.37 [95% CI, 0.14-0.95]). The association between BNP monitoring and KRT was attenuated after additional adjustment for AKI or heart failure hospitalization as a time-varying covariate. LIMITATIONS: Residual confounding by measured and unmeasured variables or indications for BNP measurements. CONCLUSIONS: BNP monitoring was associated with a lower risk of KRT among patients with CKD that did not require dialysis. This association is potentially mediated through a reduced risk of AKI or heart failure hospitalization. PLAIN-LANGUAGE SUMMARY: Both volume overload and volume depletion are deleterious to kidney function. B-type natriuretic peptide (BNP) is a biomarker that reflects volume status not only in heart failure but also in nondialysis chronic kidney disease (CKD). Although longitudinal BNP monitoring may aid physicians' decision making about the optimization of volume status, its clinical benefit remains uncertain in CKD. In this cohort study analyzing 2,998 patients with nondialyzed CKD, BNP monitoring was associated with a lower risk of kidney replacement therapy, acute kidney injury, and heart failure hospitalization over the follow-up period. The association with kidney replacement therapy may be mediated through a reduced risk of acute kidney injury or heart failure hospitalization. BNP monitoring may aid physicians in optimal fluid management, potentially conferring better kidney outcomes.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions.

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

A new multimodal paradigm for biomarkers longitudinal monitoring: a clinical application to women steroid profiles in urine and blood.

BACKGROUND: Most current state-of-the-art strategies to generate individual adaptive reference ranges are designed to monitor one clinical parameter at a time. An innovative methodology is proposed for the simultaneous longitudinal monitoring of multiple biomarkers. The estimation of individual thresholds is performed by applying a Bayesian modeling strategy to a multivariate score integrating several biomarkers (compound concentration and/or ratio). This multimodal monitoring was applied to data from a clinical study involving 14 female volunteers with normal menstrual cycles receiving testosterone via transdermal route, as to test its ability to detect testosterone administration. The study samples consisted of urine and blood collected during 4 weeks of a control phase and 4 weeks with a daily testosterone gel application. RESULTS: Integrating multiple biomarkers improved the detection of testosterone gel administration with substantially higher sensitivity compared with the distinct follow-up of each biomarker, when applied to selected urine and serum steroid biomarkers, as well as the combination of both. Among the 175 known positive samples, 38% were identified by the multimodal approach using urine biomarkers, 79% using serum biomarkers and 83% by combining biomarkers from both biological matrices, whereas 10%, 67% and 64% were respectively detected using standard unimodal monitoring. SIGNIFICANCE AND NOVELTY: The detection of abnormal patterns can be improved using multimodal approaches. The combination of urine and serum biomarkers reduced the overall number of false-negatives, thus evidencing promising complementarity between urine and blood sampling for doping control, as highlighted in the case of the use of transdermal testosterone preparations. The generation in a multimodal setting of adaptive and personalized reference ranges opens up new opportunities in clinical and anti-doping profiling. The integration of multiple parameters in a longitudinal monitoring is expected to provide a more complete evaluation of individual profiles generating actionable intelligence to further guide sample collection, analysis protocols and decision-making in clinics and anti-doping.

Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application.

Dysgraphia is a neurodevelopmental disorder specific to handwriting. Classical diagnosis is based on the evaluation of speed and quality of the final handwritten text: it is therefore delayed as it is conducted only when handwriting is mastered, in addition to being highly language-dependent and not always easily accessible. This work presents a solution able to anticipate dysgraphia screening when handwriting has not been learned yet, in order to prevent negative consequences on the individuals' academic and daily life. To quantitatively measure handwriting-related characteristics and monitor their evolution over time, we leveraged the Play-Draw-Write iPad application to collect data produced by children from the last year of kindergarten through the second year of elementary school. We developed a meta-model based on deep learning techniques (ensemble techniques and Quasi-SVM) which receives as input raw signals collected after a processing phase based on dimensionality reduction techniques (autoencoder and Time2Vec) and mathematical tools for high-level feature extraction (Procrustes Analysis). The final dysgraphia classifier can identify "at-risk" children with 84.62% Accuracy and 100% Precision more than two years earlier than current diagnostic techniques.

Investigation and Management of an Outbreak of Lead Intoxication in an Extensively Managed Beef Herd.

Fifteen hundred 12−15-month-old tropically adapted heifers inadvertently grazed a paddock which had a refuse dump in it containing burnt out vehicle batteries. The cattle grazed this paddock for approximately seven days. Subsequently these cattle were managed as two cohorts (cull and potential replacement breeding animals). Deaths commenced in the cull heifer group approximately 18 days after initial exposure to the refuse dump during relocation to a feedlot. Mortalities continued for 12 days, with other heifers showing clinical signs of marked central nervous system dysfunction requiring euthanasia. Necropsy of several clinically affected cattle plus blood sampling for lead analysis confirmed a diagnosis of lead intoxication. The crude mortality rate in the cull heifers was 6.6% (n = 685). Following confirmation of the diagnosis most of the potential replacement heifers (second cohort) were also relocated to the feedlot. The estimated crude mortality rate in this cohort was 5.8% (n = 815). All possible lead intoxication deaths occurred within 34 days of initial exposure, and apparently after day 16 at the feedlot no further heifers showed any clinical signs which could be attributed to lead intoxication. Longitudinal monitoring of blood lead concentrations was used to identify cattle suitable for slaughter. Overall, 70% of heifers initially blood sampled (n = 1408) had no detectable lead in their blood, however 16% had markedly elevated blood lead concentrations (> 0.7µmol/L) which persisted, and 2% had above the maximum normal threshold 1.5 years later. These latter cattle were subsequently euthanized, and necropsy revealed that visible pieces of lead were still present in the reticulum of several animals. At no time did any of these heifers with persistently high blood lead concentrations show clinical signs of lead intoxication.