From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response.

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data.

Geometric deep learning enables 3D kinematic profiling across species and environments.

Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.

Cold-induced vasodilation during sequential immersions of the hand.

A common practice for those operating in cold environments includes repetitive glove doffing and donning to perform specific tasks, which creates a repetitive cycle of hand cooling and rewarming. This study aimed to determine the influence of intraday repeated hand cooling on cold-induced vasodilation (CIVD), sympathetic activation, and finger/hand temperature recovery. Eight males and two females (mean ± SD age: 28 ± 5 year; height: 181 ± 9 cm; weight: 79.9 ± 10.4 kg) performed two 30-min hand immersions in cold (4.3 ± 0.92 °C) water in an indoor environment (18 °C). Both immersions (Imm1; Imm2) were performed on the same day and both allowed for a 10-min recovery. CIVD components were calculated for each finger (index, middle, ring) during each immersion. CIVD onset time (index, p = 0.546; middle, p = 0.727; ring, p = 0.873), minimum finger temperature (index, p = 0.634; middle, p = 0.493; ring, p = 0.575), and mean finger temperature (index, p = 0.986; middle, p = 0.953; ring, p = 0.637) were all similar between immersions. Recovery rates generally demonstrated similar responses as well. Findings suggest that two sequential CIVD tests analyzing the effect of prior cold exposure of the hand does not impair the CIVD response or recovery. Such findings appear promising for those venturing into cold environments where hands are likely to be repeatedly exposed to cold temperatures.

Improved 3D Markerless Mouse Pose Estimation Using Temporal Semi-Supervision.

Three-dimensional markerless pose estimation from multi-view video is emerging as an exciting method for quantifying the behavior of freely moving animals. Nevertheless, scientifically precise 3D animal pose estimation remains challenging, primarily due to a lack of large training and benchmark datasets and the immaturity of algorithms tailored to the demands of animal experiments and body plans. Existing techniques employ fully supervised convolutional neural networks (CNNs) trained to predict body keypoints in individual video frames, but this demands a large collection of labeled training samples to achieve desirable 3D tracking performance. Here, we introduce a semi-supervised learning strategy that incorporates unlabeled video frames via a simple temporal constraint applied during training. In freely moving mice, our new approach improves the current state-of-the-art performance of multi-view volumetric 3D pose estimation and further enhances the temporal stability and skeletal consistency of 3D tracking.

Increasing a microscope's effective field of view via overlapped imaging and machine learning.

This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.

Interindividual variability in average glucose-glycated haemoglobin relationship in type 1 diabetes and implications for clinical practice.

AIM: Glycated haemoglobin (HbA1c) can fail to reflect average glucose levels, potentially compromising management decisions. We analysed variability in the relationship between mean glucose and HbA1c in individuals with diabetes. MATERIALS AND METHODS: Three months of continuous glucose monitoring and HbA1c data were obtained from 216 individuals with type 1 diabetes. Universal red blood cell glucose transporter-1 Michaelis constant KM and individualized apparent glycation ratio (AGR) were calculated and compared across age, racial and gender groups. RESULTS: The mean age (range) was 30 years (8-72) with 94 younger than 19 years, 78 between 19 and 50 years, and 44 were >50 years. The group contained 120 women and 96 men with 106 white and 110 black individuals. The determined KM value was 464 mg/dl and AGR was (mean ± SD) 72.1 ± 7 ml/g. AGR, which correlated with red blood cell lifespan marker, was highest in those aged >50 years at 75.4 ± 6.9 ml/g, decreasing to 73.2 ± 7.8 ml/g in 19-50 years, with a further drop to 71.0 ± 5.8 ml/g in the youngest group (p <0 .05). AGR differed between white and black groups (69.9 ± 5.8 and 74.2 ± 7.1 ml/g, respectively; p < .001). In contrast, AGR values were similar in men and women (71.5 ± 7.5 and 72.5 ± 6.6 ml/g, respectively; p = .27). Interestingly, interindividual AGR variation within each group was at least four-fold higher than average for between-group variation. CONCLUSIONS: In this type 1 diabetes cohort, ethnicity and age, but not gender, alter the HbA1c-glucose relationship with even larger interindividual variations found within each group than between groups. Clinical application of personalized HbA1c-glucose relationships has the potential to optimize glycaemic care in the population with diabetes.

Glycaemic measures for 8914 adult FreeStyle Libre users during routine care, segmented by age group and observed changes during the COVID-19 pandemic.

AIM: To evaluate the impact of the stay-at-home policy on different glucose metrics for time in range (%TIR 3.9-10 mmol/L), time below range (%TBR < 3.9 mmol/L) and time above range (%TAR > 10 mmol/L) for UK adult FreeStyle Libre (FSL) users within four defined age groups and on observed changes during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: Data were extracted from 8914 LibreView de-identified user accounts for adult users aged 18 years or older with 5 or more days of sensor readings in each month from January to June 2020. Age-group categories were based on self-reported age on LibreView accounts (18-25, 26-49, 50-64 and ≥65 years). RESULTS: In January, prior to the COVID-19 pandemic, the 65 years or older age group had the highest %TIR (57.9%), while the 18-25 years age group had the lowest (51.2%) (P < .001). Within each age group, TIR increased during the analysed months, by 1.7% (26-49 years) to 3.1% (≥65 years) (P < .001 in all cases). %TBR was significantly reduced only in the 26-49 years age group, whereas %TAR was reduced by 1.5% (26-49 years) to 3.0% (≥65 years) (P < .001 in both cases). The proportion of adults achieving both of the more than 70% TIR and less than 4% TBR targets increased from 11.7% to 15.9% for those aged 65 years or older (P < .001) and from 6.0% to 9.1% for those aged 18-25 years (P < .05). Mean daily glucose-sensor scan rates were at least 12 per day and remained stable across the analysis period. CONCLUSIONS: Our data show the baseline glucose metrics for FSL users in the UK across different age groups under usual care. During lockdown in the UK, the proportion of adults achieving TIR consensus targets increased among FSL users.

Real world hypoglycaemia related to glucose variability and Flash glucose scan frequency assessed from global FreeStyle Libre data.

AIM: Flash glucose monitoring provides a range of glucose metrics. In the current study, we aim to identify those that indicate that glycaemic targets can be consistently met and contrast the total (t-CV) and within-day coefficient of variation (wd-CV) to guide the assessment of glucose variability and hypoglycaemia exposure. METHODS: De-identified data from Flash readers were collected. The readers were sorted into 10 equally sized groups of scan frequency followed by quartiles of estimated A1c (eA1c). A similar grouping was performed for the total coefficient of variation (t-CV) and within-day coefficient of variation (wd-CV). In addition, analysis of the association of time below 54 mg/dl and glucose variability measured by t-CV and wd-CV was performed. RESULTS: The dataset included 1 002 946 readers. Readers sorted by 10 equal groups of scan rate and quartiles by eA1c, t-CV and wd-CV represented 25 074 readers per group. The association of lower eA1c with higher time in range and reduced time above range was clear. The correlation of eA1c quartiles and time below range was not consistent. An association between glucose variability and hypoglycaemia was found. Both wd-CV and t-CV were associated with time below range. For achieving the consensus target of <1% time below 54 mg/dl, the associated wd-CV and t-CV values were 33.5% and 39.5%, respectively. CONCLUSIONS: The type of CV reported by the different continuous glucose monitoring systems should be acknowledged. CV <36% might not be adequate to ensure low hypoglycaemia exposure. To our knowledge, the majority of continuous glucose monitoring reports the t-CV. Appropriate thresholds should be used to identify patients that would probably meet time below range targets (t-CV <40% or wd-CV <34%).

Evaluation of continuous glucose monitoring-derived person-specific HbA1c in the presence and absence of complications in type 1 diabetes.

AIM: To evaluate the accuracy of a novel kinetic model at predicting HbA1c in a real-world setting and to understand and explore the role of diabetes complications in altering the glucose-HbA1c relationship and the mechanisms involved. MATERIALS AND METHODS: Deidentified HbA1c and continuous glucose monitoring values were collected from 93 individuals with type 1 diabetes. Person-specific kinetic variables were used, including red blood cell (RBC) glucose uptake and lifespan, to characterize the relationship between glucose levels and HbA1c. The resulting calculated HbA1c (cHbA1c) was compared with glucose management indicator (GMI) for prospective agreement with laboratory HbA1c. RESULTS: The cohort (42 men and 51 women) had a median age (IQR) of 61 (43, 72) years and a diabetes duration of 21 (10, 33) years. A total of 24 459 days of continuous glucose monitoring (CGM) data were available and 357 laboratory HbA1c were used to assess the average glucose-HbA1c relationship. cHbA1c had a superior correlation with laboratory HbA1c compared with GMI with a mean absolute deviation of 1.7 and 6.7 mmol/mol, r2  = 0.85 and 0.44, respectively. The fraction within 10% of absolute relative deviation from laboratory HbA1c was 93% for cHbA1c and 63% for GMI. Macrovascular disease had no effect on the model's accuracy, whereas microvascular complications resulted in a trend towards higher HbA1c, secondary to increased RBC glucose uptake. CONCLUSIONS: cHbA1c, which takes into account RBC glucose uptake and lifespan, accurately reflects laboratory HbA1c in a real-world setting and can aid in the management of individuals with diabetes.

Diversity and Chemical Library Networks of Large Data Sets.

The quantification of chemical diversity has many applications in drug discovery, organic chemistry, food, and natural product chemistry, to name a few. As the size of the chemical space is expanding rapidly, it is imperative to develop efficient methods to quantify the diversity of large and ultralarge chemical libraries and visualize their mutual relationships in chemical space. Herein, we show an application of our recently introduced extended similarity indices to measure the fingerprint-based diversity of 19 chemical libraries typically used in drug discovery and natural products research with over 18 million compounds. Based on this concept, we introduce the Chemical Library Networks (CLNs) as a general and efficient framework to represent visually the chemical space of large chemical libraries providing a global perspective of the relation between the libraries. For the 19 compound libraries explored in this work, it was found that the (extended) Tanimoto index offers the best description of extended similarity in combination with RDKit fingerprints. CLNs are general and can be explored with any structure representation and similarity coefficient for large chemical libraries.

Extended continuous similarity indices: theory and application for QSAR descriptor selection.

Extended (or n-ary) similarity indices have been recently proposed to extend the comparative analysis of binary strings. Going beyond the traditional notion of pairwise comparisons, these novel indices allow comparing any number of objects at the same time. This results in a remarkable efficiency gain with respect to other approaches, since now we can compare N molecules in O(N) instead of the common quadratic O(N2) timescale. This favorable scaling has motivated the application of these indices to diversity selection, clustering, phylogenetic analysis, chemical space visualization, and post-processing of molecular dynamics simulations. However, the current formulation of the n-ary indices is limited to vectors with binary or categorical inputs. Here, we present the further generalization of this formalism so it can be applied to numerical data, i.e. to vectors with continuous components. We discuss several ways to achieve this extension and present their analytical properties. As a practical example, we apply this formalism to the problem of feature selection in QSAR and prove that the extended continuous similarity indices provide a convenient way to discern between several sets of descriptors.

Construct validity of the Surgical Autonomy Program for the training of neurosurgical residents.

OBJECTIVE: There is no standard way in which physicians teach or evaluate surgical residents intraoperatively, and residents are proving to not be fully competent at core surgical procedures upon graduating. The Surgical Autonomy Program (SAP) is a novel educational model that combines a modified version of the Zwisch scale with Vygotsky's social learning theory. The objective of this study was to establish preliminary validity evidence that SAP is a reliable measure of autonomy and a useful tool for tracking competency over time. METHODS: The SAP breaks each surgical case into 4 parts, or zones of proximal development (ZPDs). Residents are evaluated on a 4-tier autonomy scale (TAGS scale) for each ZPD in every surgical case. Attendings were provided with a teaching session about SAP and identified appropriate ZPDs for surgical cases under their area of expertise. All neurosurgery residents at Duke University Hospital from July 2017 to July 2021 participated in this study. Chi-square tests and ordinal logistic regression were used for the analyses. RESULTS: Between 2017 and 2021, there were 4885 cases logged by 27 residents. There were 30 attendings who evaluated residents using SAP. Faculty completed evaluations on 91% of cases. The ZPD of focus directly correlated with year of residency (postgraduate year) (χ2 = 1221.1, df = 15, p < 0.001). The autonomy level increased with year of residency (χ2 = 3553.5, df = 15, p < 0.001). An ordinal regression analysis showed that for every year increase in postgraduate year, the odds of operating at a higher level of independence was 2.16 times greater (95% CI 2.11-2.21, p < 0.001). The odds of residents performing with greater autonomy was lowest for the most complex portion of the case (ZPD3) (OR 0.18, 95% CI 0.17-0.20, p < 0.001). Residents have less autonomy with increased case complexity (χ2 = 160.28, df = 6, p < 0.001). Compared with average cases, residents were more likely to operate with greater autonomy on easy cases (OR 1.44, 95% CI 1.29-1.61, p < 0.001) and less likely to do so on difficult cases (OR 0.72, 95% CI 0.67-0.77, p < 0.001). CONCLUSIONS: This study demonstrates preliminary evidence supporting the construct validity of the SAP. This tool successfully tracks resident autonomy and progress over time. The authors' smartphone application was widely used among surgical faculty and residents, supporting integration into the perioperative workflow. Wide implementation of SAP across multiple surgical centers will aid in the movement toward a competency-based residency education system.

Sociocultural determinants and patterns of healthcare utilization for epilepsy care in Uganda.

OBJECTIVE: Epilepsy is a global public health concern, with the majority of cases occurring in lower- and middle-income countries where the treatment gap remains formidable. In this study, we simultaneously explore how beliefs about epilepsy causation, perceived barriers to care, seizure disorder characteristics, and demographics influence the initial choice of healthcare for epilepsy and its impact on attaining biomedical care (BMC). METHODS: This study utilized the baseline sample (n = 626) from a prospective cohort study of people with epilepsy (PWE) attending three public hospitals in Uganda (Mulago National Referral Hospital, Butabika National Referral Mental Hospital, and Mbarara Regional Referral Hospital) for epilepsy care. Patient and household demographics, clinical seizure disorder characteristics, and sociocultural questionnaires were administered. Logistic regression and principal component analyses (PCA) were conducted to examine associations with the choice of primary seizure treatment. RESULTS: The sample was 49% female, and 24% lived in rural settings. A biomedical health facility was the first point of care for 355 (56.7%) participants, while 229 (36.6%) first sought care from a traditional healer and 42 (6.7%) from a pastoral healer. Preliminary inspection of candidate predictors using relaxed criteria for significance (p < 0.20) identified several factors potentially associated with a greater odds of seeking BMC first. Demographic predictors included older caredriver (decision-maker for the participant) age (odds ratio [OR]: 1.01, 95% confidence interval [CI]: [0.99, 1.02], p-value: 0.09), greater caredriver education level (OR = 1.21, 95% CI: [1.07, 1.37], p-value = 0.003), and lower ratio of sick to healthy family members (OR = 0.77 [0.56, 1.05], P = 0.097). For clinical predictors, none of the proposed predictors associated significantly with seeking BMC first. Self-report causation predictors associated with a greater odds of seeking BMC first included higher belief in biological causes of epilepsy (OR = 1.31 [0.92, 1.88], P = 0.133) and lower belief in socio-spiritual causes of epilepsy (OR = 0.68 [0.56, 0.84], P < 0.001). In the multivariate model, only higher caredriver education (OR = 1.19 [1.04, 1.36], P = 0.009) and lower belief in socio-spiritual causes of epilepsy (OR = 0.69 [0.56, 0.86], P < 0.01) remained as predictors of seeking BMC first. Additionally, PCA revealed a pattern which included high income with low beliefs in nonbiological causes of epilepsy as being associated with seeking BMC first (OR = 1.32 [1.12, 1.55], p = 0.001). Despite reaching some form of care faster, individuals seeking care from traditional or pastoral healers experienced a significant delay to eventual BMC (P < 0.001), with an average delay of more than two years (traditional healer: 2.53 years [1.98, 3.24]; pastoral care: 2.18 [1.21, 3.91]). CONCLUSIONS: Coupled with low economic and educational status, belief in spiritual causation of epilepsy is a dominant determinant of opting for traditional or pastoral healing over BMC, regardless of concurrent belief in biological etiologies. There is a prolonged delay to eventual BMC for PWE who begin their treatment seeking with nonallopathic providers, and although nonallopathic healers provide PWE with benefits not provided by BMC, this notable delay likely prevents earlier administration of evidence-based care with known efficacy. Based on these findings, initiatives to increase public awareness of neurobiological causes of epilepsy and effectiveness of biomedical drug treatments may be effective in preventing delays to care, as would programs designed to facilitate cooperation and referral among traditional, faith-based, and biomedical providers. This article is part of the Special Issue "The Intersection of Culture, Resources, and Disease: Epilepsy Care in Uganda".

Mid-Trimester Cervical Length Screening: Effect of Poorly Developed Lower Uterine Segment on Pregnancy Outcome.

OBJECTIVES: To identify whether a poorly developed lower uterine segment (PDLUS) observed during cervical length (CL) screening affects the duration of gestation in women with no prior spontaneous preterm birth (sPTB). MATERIALS AND METHODS: A retrospective cohort study of women with a singleton gestation and no prior sPTB, who underwent transvaginal CL screening at our institution. We excluded women with progesterone exposure, major anomalies, and women delivering elsewhere. Women with PDLUS were compared to those with a measured (normal) CL ≥25 mm. PRIMARY OUTCOME: birth gestational age (GA). SECONDARY OUTCOMES: sPTB <35 and 37 weeks, hospital evaluation for preterm labor without delivery, delivery indication, and mode. A Cox proportional-hazards survival model considered time from CL scan to delivery. We powered the study to detect a one-half week difference in birth GA. RESULTS: We included 270 women with PDLUS and 985 women with normal CL. Mean birth GA was 38.9 ± 2.0 weeks with PDLUS versus 38.7 ± 2.4 weeks with normal CL (p = .10). Women with PDLUS were less likely to experience sPTB <37 weeks (1.1% vs 3.6%; p = 0.04). There was no difference in sPTB <35 weeks (0.8% vs 1.7%; p = .25). Hospital evaluation for preterm labor (17% vs 19%; p = .54), delivery indication, and mode were not different. The hazard ratio for earlier birth in women with PDLUS was 0.67 (95% CI 0.46, 0.98; p = .04). CONCLUSIONS: We observed no difference in mean GA at birth; however, PDLUS was protective against sPTB <37 weeks and was associated with a lower hazard ratio for earlier birth.

Vasa previa: a multicenter retrospective cohort study.

OBJECTIVE: The objective of the study was to describe the characteristics and outcomes of patients with antenatal diagnosis of vasa previa and evaluate the predictive factors of resolution in a contemporary large, multicenter data set. STUDY DESIGN: This was a retrospective multicenter cohort study of all antenatally diagnosed cases of vasa previa, identified via ultrasound and electronic medical record, between January 2011 and July 2018 in 5 US centers. Records were abstracted to obtain variables at diagnosis, throughout pregnancy, and outcomes, including maternal and neonatal variables. Data were reported as median [range] or n (percentage). Descriptive statistics, receiver-operating characteristics, and logistic regression analysis were used as appropriate. RESULTS: One hundred thirty-six cases of vasa previa were identified in 5 centers during the study period, 19 (14%) of which resolved spontaneously at median estimated gestational age of 27 weeks [19-34]. All subjects with unresolved vasa previa underwent cesarean delivery at a median estimated gestational age of 34 weeks [27-39] with the median estimated blood loss of 800 mL [250-2000]. Rates for vaginal bleeding, preterm labor, premature rupture of membrane, and need for blood product transfusion were not different between the resolved and unresolved group (P = NS). The odds ratio for resolution in those with the estimated gestational age of less than 24 weeks at the time of diagnosis was 7.9 (95% confidence interval, 2.1-29.4) after adjustment for confounding variables. CONCLUSION: Our data suggest that outcomes in antenatally diagnosed cases of vasa previa are excellent. Furthermore, our data report a higher chance of resolution when the condition is diagnosed before 24 weeks of gestation.

Anticipating cognitive effort: roles of perceived error-likelihood and time demands.

Why are some actions evaluated as effortful? In the present set of experiments we address this question by examining individuals' perception of effort when faced with a trade-off between two putative cognitive costs: how much time a task takes vs. how error-prone it is. Specifically, we were interested in whether individuals anticipate engaging in a small amount of hard work (i.e., low time requirement, but high error-likelihood) vs. a large amount of easy work (i.e., high time requirement, but low error-likelihood) as being more effortful. In between-subject designs, Experiments 1 through 3 demonstrated that individuals anticipate options that are high in perceived error-likelihood (yet less time consuming) as more effortful than options that are perceived to be more time consuming (yet low in error-likelihood). Further, when asked to evaluate which of the two tasks was (a) more effortful, (b) more error-prone, and (c) more time consuming, effort-based and error-based choices closely tracked one another, but this was not the case for time-based choices. Utilizing a within-subject design, Experiment 4 demonstrated overall similar pattern of judgments as Experiments 1 through 3. However, both judgments of error-likelihood and time demand similarly predicted effort judgments. Results are discussed within the context of extant accounts of cognitive control, with considerations of how error-likelihood and time demands may independently and conjunctively factor into judgments of cognitive effort.

BioTIME: A database of biodiversity time series for the Anthropocene.

MOTIVATION: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. MAIN TYPES OF VARIABLES INCLUDED: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. SPATIAL LOCATION AND GRAIN: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2). TIME PERIOD AND GRAIN: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. MAJOR TAXA AND LEVEL OF MEASUREMENT: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. SOFTWARE FORMAT: .csv and .SQL.

Ambulatory glucose profile analysis of the juvenile diabetes research foundation continuous glucose monitoring dataset-Applications to the pediatric diabetes population.

BACKGROUND: Increased continuous glucose monitor (CGM) use presents both the benefit and burden of increased data for clinicians to rapidly analyze. The ambulatory glucose profile (AGP) is an evolving a universal software report for CGM data analysis. OBJECTIVES/HYPOTHESES: We utilized the Juvenile Diabetes Research Foundation-CGM dataset to evaluate the AGP across a broad spectrum of patients to show how AGP can be used clinically to assist with CGM-related decision making. We hypothesized that AGP metrics would be different across age and HbA1c strata. SUBJECTS: AGPs were generated from the JDRF-CGM trial dataset for all periods during which there were ≥10 days of CGM coverage in the 2 weeks adjacent to an HbA1c measurement yielding 1101 AGPs for 393 unique subjects. METHODS: AGPs were stratified by age group (8-14, 15-24, and ≥25 years) and HbA1c (within or above target for age) and compared for between group differences in AGP metrics via two-factor ANOVA. Glycemic differences between time periods were analyzed via segmented regression analysis. RESULTS: Glucose exposure (average and estimated A1c) and variability (standard deviation and interquartile range) were different between the low and high HbA1c levels. Within a given HbA1c level all age groups were significantly different from each other with older patients having lower averages with less variability than younger patients. CONCLUSIONS: AGP analysis of the JDRF-CGM data highlights significant differences in glycemic profiles between pediatric and adult age groups and between well and less well-controlled patient populations.

Lower urinary tract obstruction: fetal intervention based on prenatal staging.

The authors present an overview of lower urinary tract obstruction (LUTO) in the fetus with a particular focus on the insult to the developing renal system. Diagnostic criteria along with the challenges in estimating long-term prognosis are reviewed. A proposed prenatal LUTO disease severity classification to guide management decisions with fetal intervention to maintain or salvage in utero and neonatal pulmonary and renal function is also discussed. Stage I LUTO (mild form) is characterized by normal amniotic fluid index after 18 weeks, normal kidney echogenicity, no renal cortical cysts, no evidence of renal dysplasia, and favorable urinary biochemistries when sampled between 18 and 30 weeks; prenatal surveillance is recommended. Stage II LUTO is characterized by oligohydramnios/anhydramnios, hyperechogenic kidneys but absent renal cortical cysts or apparent signs of renal dysplasia and favorable fetal urinary biochemistry; fetal vesicoamniotic shunting (VAS) or fetal cystoscopy is indicated to prevent pulmonary hypoplasia and renal failure. Stage III LUTO is oligohydramnios/anhydramnios, hyperechogenic kidneys with cortical cysts and renal dysplasia and unfavorable fetal urinary biochemistry after serial evaluation; fetal vesicoamniotic shunt may prevent severe pulmonary hypoplasia but not renal failure. Stage IV is characterized by intrauterine fetal renal failure, defined by anhydramnios and ultrasound (US) findings suggestive of severe renal dysplasia, and is associated with death in 24 h of life or end-stage renal disease (ESRD) within the first week of life; fetal vesicoamniotic shunt and fetal cystoscopy are not indicated.