Due to the continuous rise in global incidence and severity of invasive fungal infections (IFIs), particularly among immunocompromised and immunodeficient patients, there is an urgent demand for swift and accurate fungal pathogen diagnosis. Therefore, the need for fungal-specific positron emission tomography (PET) imaging agents that can detect the infection in the early stages is increasing. Cellobiose, a disaccharide, is readily metabolized by fungal pathogens such as Aspergillus species. Recently, our group reported fluorine-18 labeled cellobiose, 2-deoxy-2-[18F]fluorocellobiose ([18F]FCB), for specific imaging of Aspergillus infection. The positive imaging findings with very low background signal on delayed imaging make this ligand a promising fungal-specific imaging ligand. Inspired by this result, the decision was made to automate the radiolabeling procedure for better reproducibility and to facilitate clinical translation. A Trasis AllInOne (Trasis AIO) automated module was used for this purpose. The reagent vials contain commercially available 2-deoxy-2-[18F]fluoroglucose ([18F]FDG), glucose-1-phosphate, and enzyme (cellobiose phosphorylase). A Sep-Pak cartridge was used to purify the tracer. The overall radiochemical yield was 50%-70% (n = 6, decay corrected) in 75-min synthesis time with a radiochemical purity of > 98%. This is a highly reliable protocol to produce current good manufacturing practice (cGMP)-compliant [18F]FCB for clinical PET imaging.

BACKGROUND: The occurrence of tics is the main basis for the diagnosis of Gilles de la Tourette syndrome (GTS). Video-based tic assessments are time consuming.
OBJECTIVE: The aim was to assess the potential of automated video-based tic detection for discriminating between videos of adults with GTS and healthy control (HC) participants.
METHODS: The quantity and temporal structure of automatically detected tics/extra movements in videos from adults with GTS (107 videos from 42 participants) and matched HCs were used to classify videos using cross-validated logistic regression.
RESULTS: Videos were classified with high accuracy both from the quantity of tics (balanced accuracy of 87.9%) and the number of tic clusters (90.2%). Logistic regression prediction probability provides a graded measure of diagnostic confidence. Expert review of about 25% of lower-confidence predictions could ensure an overall classification accuracy above 95%.
CONCLUSIONS: Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.

Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116 492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.

Human observations can only capture a portion of ongoing classroom social activity, and are not ideal for understanding how children\'s interactions are spatially structured. Here we demonstrate how social interaction can be investigated by modeling automated continuous measurements of children\'s location and movement using a commercial system based on radio frequency identification. Continuous location data were obtained from 16 five-year-olds observed during three 1-h classroom free play observations. Illustrative coordinate mapping indicated that boys and girls tended to cluster in different physical locations in the classroom, but there was no suggestion of gender differences in children\'s velocity (i.e., speed of movement). To detect social interaction, we present the radial distribution function, an index of when children were in social contact at greater than chance levels. Rank-order plots indicated that children were in social contact tens to hundreds of times more with some peers than others. We illustrate the use of social ties (higher than average levels of social contact) to visualize the classroom network. Analysis of the network suggests that transitivity is a potential lens through which to examine male, female, and mixed-sex cliques. The illustrative findings suggest the validity of the new measurement approach by re-examining well-established gender segregation findings from a new perspective.

BACKGROUND: Adoption of 3D imaging systems in humanitarian settings requires accuracy comparable with manual measurement notwithstanding additional constraints associated with austere settings.
OBJECTIVE: This study aimed to evaluate the accuracy of child stature and mid-upper arm circumference (MUAC) measurements produced by the AutoAnthro 3D imaging system (third generation) developed by Body Surface Translations Inc.
METHODS: A study of device accuracy was embedded within a 2-stage cluster survey at the Malakal Protection of Civilians site in South Sudan conducted between September 2021 and October 2021. All children aged 6 to 59 months within selected households were eligible. For each child, manual measurements were obtained by 2 anthropometrists following the protocol used in the 2006 World Health Organization Child Growth Standards study. Scans were then captured by a different enumerator using a Samsung Galaxy 8 phone loaded with a custom software, AutoAnthro, and an Intel RealSense 3D scanner. The scans were processed using a fully automated algorithm. A multivariate logistic regression model was fit to evaluate the adjusted odds of achieving a successful scan. The accuracy of the measurements was visually assessed using Bland-Altman plots and quantified using average bias, limits of agreement (LoAs), and the 95% precision interval for individual differences. Key informant interviews were conducted remotely with survey enumerators and Body Surface Translations Inc developers to understand challenges in beta testing, training, data acquisition and transmission.
RESULTS: Manual measurements were obtained for 539 eligible children, and scan-derived measurements were successfully processed for 234 (43.4%) of them. Caregivers of at least 10.4% (56/539) of the children refused consent for scan capture; additional scans were unsuccessfully transmitted to the server. Neither the demographic characteristics of the children (age and sex), stature, nor MUAC were associated with availability of scan-derived measurements; team was significantly associated (P<.001). The average bias of scan-derived measurements in cm was -0.5 (95% CI -2.0 to 1.0) for stature and 0.7 (95% CI 0.4-1.0) for MUAC. For stature, the 95% LoA was -23.9 cm to 22.9 cm. For MUAC, the 95% LoA was -4.0 cm to 5.4 cm. All accuracy metrics varied considerably by team. The COVID-19 pandemic-related physical distancing and travel policies limited testing to validate the device algorithm and prevented developers from conducting in-person training and field oversight, negatively affecting the quality of scan capture, processing, and transmission.
CONCLUSIONS: Scan-derived measurements were not sufficiently accurate for the widespread adoption of the current technology. Although the software shows promise, further investments in the software algorithms are needed to address issues with scan transmission and extreme field contexts as well as to enable improved field supervision. Differences in accuracy by team provide evidence that investment in training may also improve performance.

BACKGROUND: Adolescence is a critical developmental period to prevent and treat the emergence of mental health problems. Smartphone-based conversational agents can deliver psychologically driven intervention and support, thus increasing psychological well-being over time.
OBJECTIVE: The objective of the study was to test the potential of an automated conversational agent named Kai.ai to deliver a self-help program based on Acceptance Commitment Therapy tools for adolescents, aimed to increase their well-being.
METHODS: Participants were 10,387 adolescents, aged 14-18 years, who used Kai.ai on one of the top messaging apps (eg, iMessage and WhatsApp). Users\' well-being levels were assessed between 2 and 5 times using the 5-item World Health Organization Well-being Index questionnaire over their engagement with the service.
RESULTS: Users engaged with the conversational agent an average of 45.39 (SD 46.77) days. The average well-being score at time point 1 was 39.28 (SD 18.17), indicating that, on average, users experienced reduced well-being. Latent growth curve modeling indicated that participants\' well-being significantly increased over time (β=2.49; P<.001) and reached a clinically acceptable well-being average score (above 50).
CONCLUSIONS: Mobile-based conversational agents have the potential to deliver engaging and effective Acceptance Commitment Therapy interventions.

UNASSIGNED: To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models.
UNASSIGNED: Evaluation criteria for selecting an Auto ML platform suited to ML needs of a local health district were developed in 3 steps: (1) identification of key requirements, (2) a market scan, and (3) an assessment process with desired outcomes.
UNASSIGNED: The final checklist comprising 21 functional and 6 non-functional criteria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case.
UNASSIGNED: A team of clinicians, data scientists, and key stakeholders developed a checklist which can be adapted to ML needs of healthcare organizations, the use case providing a relevant example.
UNASSIGNED: An evaluative checklist was developed for selecting Auto ML platforms which requires validation in larger multi-site studies.

OBJECTIVE: Cognitive impairment is now recognized as an impending public health crisis. About one-third of adults are concerned about their cognition, and the prevalence of objective cognitive impairment is much higher among those with neurological disorders. Existing screening tools are narrowly focused on detecting dementia in older adults and must be clinician-administered and scored, making them impractical for many neurology practices. This study examined the utility of a brief, self-administered, computerized cognitive screening tool, the Brief Assessment of Cognitive Health (BACH), in identifying cognitive impairment in adults.
METHODS: 912 adults (ages 18-84) completed BACH and a neuropsychological battery. Multivariable models were developed to provide a BACH index score reflecting the probability of cognitive impairment for individual patients. Predictive accuracy was compared to that of the Montreal Cognitive Assessment (MoCA) in a subset of 160 older adults from a Memory Disorders clinic.
RESULTS: The final multivariable model showed good accuracy in identifying cognitively impaired individuals (c = 0·77). Compared to MoCA, BACH had superior predictive accuracy in identifying older patients with cognitive impairment (c = 0·79 vs. 0·67) as well as differentiating those with MCI or dementia from those without cognitive impairment (c = 0·86 vs. c = 0·67).
CONCLUSIONS: Results suggest that cognitive impairment can be identified in adults using a brief, self-administered, automated cognitive screening tool, and BACH provides several advantages over existing screeners: self-administered; automatic scoring; immediate results in health record; easily interpretable score; utility in wide range of patients; and flags for treatable factors that may contribute to cognitive complaints (i.e., depression, sleep problems, and stress).

BACKGROUND: Thailand\'s HIV epidemic is heavily concentrated among men who have sex with men (MSM), and surveillance efforts are mostly based on case surveillance and local biobehavioral surveys.
OBJECTIVE: We piloted Kai Noi, a web-based respondent-driven sampling (RDS) survey among MSM.
METHODS: We developed an application coded in PHP that facilitated all procedures and events typically used in an RDS office for use on the web, including e-coupon validation, eligibility screening, consent, interview, peer recruitment, e-coupon issuance, and compensation. All procedures were automated and e-coupon ID numbers were randomly generated. Participants\' phone numbers were the principal means to detect and prevent duplicate enrollment. Sampling took place across Thailand; residents of Bangkok were also invited to attend 1 of 10 clinics for an HIV-related blood draw with additional compensation.
RESULTS: Sampling took place from February to June 2022; seeds (21 at the start, 14 added later) were identified through banner ads, micromessaging, and in online chat rooms. Sampling reached all 6 regions and almost all provinces. Fraudulent (duplicate) enrollment using \"borrowed\" phone numbers was identified and led to the detection and invalidation of 318 survey records. A further 106 participants did not pass an attention filter question (asking recruits to select a specific categorical response) and were excluded from data analysis, leading to a final data set of 1643 valid participants. Only one record showed signs of straightlining (identical adjacent responses). None of the Bangkok respondents presented for a blood draw.
CONCLUSIONS: We successfully developed an application to implement web-based RDS among MSM across Thailand. Measures to minimize, detect, and eliminate fraudulent survey enrollment are imperative in web-based surveys offering compensation. Efforts to improve biomarker uptake are needed to fully tap the potential of web-based sampling and data collection.

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole\'s anatomic localization. Here, we introduce a novel tool, the \"Magnetoencephalography Atlas Viewer\" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient\'s Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles\' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.