Citations

Abstract Additive manufacturing (AM), commonly referred to as 3D printing, has been broadly used in the field of medical imaging and radiotherapy. In medical imaging applications, patient‐specific anatomic models have been used to aid in preoperative planning, patient communication, imaging technique assessment and optimization, and medical education. Within the field of radiation therapy, 3D printing finds significance in fabricating patient‐specific immobilization devices, custom bolus devices, anthropomorphic phantoms, brachytherapy devices, and electron therapy devices. Consistent with standard medical physics practices there is a need to establish quality assurance and quality control (QA/QC) protocols, tolerances, and testing frequencies. The diversity of 3D printing technologies and products poses challenges for medical adoption and thus requires guidelines in workflow standardization and establishment of testing procedures. This report is centered around providing comprehensive vendor‐neutral implementation guidelines and recommendations for QC and QA processes. The scope of this report encompasses diverse requirements within medical imaging and radiation therapy settings, serving both clinical and research purposes. Brief overview of 3D printing technology, common materials, and potential clinical applications is provided. QA team, infrastructure, measurement techniques and challenges are discussed. QA processes for each step in 3D printing are discussed in great detail and baseline and action limits are recommended. This report also touched on regulatory considerations. Key recommendations are summarized at the end of the report.

Background: Given the faster onset and shorter duration of action of ultra-rapid insulin lispro-aabc (URLi, Lyumjev™ insulin), compared with insulin lispro (a rapid-acting insulin [RAI]), the present study investigated the safety and glycemic outcomes of MiniMed™ 780G advanced hybrid closed-loop (AHCL) use with URLi in youths and adults with T1D. Methods: This 19-site, single-arm, clinical trial enrolled children and adolescents (aged 7–17 years) and adults (aged 18–80 years) with T1D who underwent a run-in period (∼2 weeks) using open loop or automated basal only with an RAI (aspart or lispro), followed by a 3-month study period using URLi with AHCL (AHCL + URLi). End-of-study glycemic metrics and A1c were compared to predefined thresholds and to data from baseline/run-in. Use of recommended optimal settings (ROS, 100 mg/dL glucose target with an active insulin time [AIT] of 2 h) was also assessed. Results: In an intention-to-treat population ( N = 101 pediatric and N = 110 adult participants), percentage of time in range (%TIR) during the last ∼6 weeks of the study period was significantly greater than the predefined thresholds for both the pediatric and adult participants. A1c was unchanged in the pediatric cohort but significantly reduced in the adult cohort (from 7.4 ± 0.9% to 6.9 ± 0.6% [ P < 0.001]). Compared with run-in, study period %TIR and percentage of time in tight target range (%TITR) were significantly increased (pediatric mean %TIRΔ [17.4%] and %TITRΔ [16.0%], respectively, and adult mean %TIRΔ [10.6%] and %TITRΔ [11.6%], respectively; P < 0.001 for all). Additionally, % time above range for both groups was significantly reduced. Use of ROS allowed more participants to achieve recommended glycemic targets. There were no severe hypoglycemia or diabetic ketoacidosis events. Most caregivers agreed that mealtime versus premeal URLi delivery simplified pediatric meal management. Conclusions: Similar to previous studies investigating automated URLi delivery, URLi use in the MiniMed™ 780G system was safe, and glycemic outcomes were favorable for pediatric and adult participants.

ABSTRACT Objective Identifying and addressing pediatric hearing loss is critical to supporting a child's development. School‐based hearing screening is a mainstay of timely identification of hearing loss. The objectives of this study were to characterize the current hearing screening practices in public, charter, and private elementary schools in Minnesota. Methods This was a cross‐sectional survey with data collected between March and June 2023. Surveys assessed the presence of standardized hearing screening processes and compliance with American Academy of Audiology (AAA) or Minnesota Department of Health (MDH) guidelines. Results About 146 public schools, 43 charter schools, and 60 private schools met inclusion criteria and responded to the survey. There was a statistically significant difference in the rate of standardized screening between school types ( χ 2 = 18.06; p < 0.001). Only 10.44% ( n = 26) of schools completed hearing screenings per AAA guidelines, and even fewer, 4.82% ( n = 12), completed hearing screenings according to MDH guidelines. The odds of screening per AAA or MDH guidelines were 3.50 times higher in charter schools compared to public schools (95% CI: 1.14, 10.73) and 4.61 times higher in private schools compared to public schools (95% CI: 1.73, 12.26). Conclusion There is a lack of standardization in hearing screening processes in Minnesota elementary schools. Adherence to screening per AAA or MDH recommendations is low in all types of elementary schools. Charter and private schools were more likely than public schools to screen per AAA or MDH guidelines. Overall, our data demonstrate an opportunity for improvement in school‐based hearing screening. Level of Evidence N/A.

Abstract We present chromosome-level, phased diploid genome assemblies of two widely used human fibroblast cell lines: BJ (46,XY) and IMR-90 (46,XX). Using Oxford Nanopore, PacBio HiFi, and Hi-C sequencing data, we generated assemblies spanning 5.9 and 6.0 Gbp with diploid quality values exceeding QV 60. To validate structural integrity, we developed KaryoScope, an alignment-free tool for generating computational karyograms from k-mer feature databases. We identify &gt;50 000 structural variants relative to T2T-CHM13v2.0, the majority of which are heterozygous and cell-line-specific. Combining reference-based and de novo gene annotation, we uncover a previously unreported 1 Mbp homozygous duplication at the 16p11.2 locus in BJ, demonstrating that even karyotypically normal cell lines can harbor clinically relevant submicroscopic rearrangements. We show that mapping publicly available short-read, RNA-seq, and ChIP-seq data to sample-matched diploid assemblies substantially improves read alignment and enables haplotype phasing of 23%–28% of short reads. The BJ and IMR-90 assemblies and associated variant calls are publicly available as a resource for the research community.

Background Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary barrier to improving its otherwise poor rate of survival. Objective To develop and validate the Radiomics-based Early Detection MODel (REDMOD), an AI framework to identify subvisual radiomic signatures of pre-diagnostic PDA on standard-of-care CT. Designs REDMOD was trained on a multi-institutional cohort (n=969; 156 pre-diagnostic, 813 control) and tested on an independent set (n=493; 63 pre-diagnostic, 430 control), simulating a low prevalence (~1:6) early detection paradigm. The fully automated framework couples AI-driven segmentation with a heterogeneous ensemble architecture trained on a 40-feature radiomic signature derived from Synthetic Minority Over-sampling Technique (SMOTE)-balanced data. A tunable Youden Index-optimised classification threshold enables performance calibration without retraining. Validation included direct comparison with radiologists, longitudinal test–retest analysis and external specificity validation across two independent cohorts (n=539 and n=80). Results On an independent test set (n=493), REDMOD identified occult PDA (AUC 0.82; 73.0% sensitivity) at a median 475-day lead time. This represented nearly twofold higher sensitivity than radiologists (38.9%; p<0.001), which grew to nearly threefold (68.0% vs 23.0%) at >24 months lead time. REDMOD showed strong longitudinal stability (90–92% concordance) and generalisable specificity across multi-institutional (81.3%; n=539) and public (87.5%; n=80) datasets. Mechanistic analyses confirmed predictive power derived principally from multi-scale wavelet-filtered textural features (90% of selected signature), which outperformed unfiltered features (AUC 0.82 vs 0.74; p=0.007) in capturing subvisual architectural disruptions. Conclusions REDMOD is an automated, mechanistically grounded, longitudinally stable, externally validated AI that surpasses radiologists for PDA detection at its visually occult pre-diagnostic stage. These attributes position it for prospective validation in high-risk cohorts, a necessary step towards shifting the paradigm from late-stage symptomatic diagnosis to proactive pre-clinical interception.

Abstract Background Diabetic retinopathy (DR) is the leading cause of preventable blindness among working-age adults worldwide, yet screening coverage remains inadequate, particularly in low-and middle-income countries. Automated deep learning systems offer potential to address the global shortage of expert graders, but most existing models lack lesion-level interpretability and are not aligned with established clinical referral frameworks. We developed and validated DRAGS (Diabetic Retinopathy Automated Grading System), a hybrid deep learning model that grades DR according to the UK Diabetic Eye Screening Programme (DESP) classification and provides lesion-level explainability. Methods We trained and validated a DenseNet-201-based convolutional neural network on 20,281 anonymised fundus images from two tertiary eye care institutions in Bangladesh. Images were graded by fellowship-trained retinal specialists using the UK DESP framework, resulting in 10 clinically interpretable classes that combine retinopathy grade (R0–R3) and maculopathy status (M0/M1). A companion dataset of 2,936 pixel-level lesion masks spanning nine pathological categories was used to train a parallel multi-label lesion-detection head. The dataset was partitioned 70:15:15 (patient-stratified). Performance was evaluated using macro-averaged AUROC (DeLong estimator), sensitivity, specificity, F1 score, quadratically weighted Cohen’s κ, and expected calibration error (ECE), with 95% CIs from 2000 bootstrap resamples. Grad-CAM spatial alignment with ground-truth lesion masks was assessed using Dice and IoU. This study follows the TRIPOD+AI reporting guidelines. Findings On the held-out test set (Component I: n = 3,044; Component II: n ≈ 440), DRAGS achieved class-wise precision, recall, and F1 scores ranging from 0·88 to 0·99 across all ten UK DESP grades, with advanced proliferative stages (R3–M0, R3–M1) consistently exceeding 0·95. Overall accuracy was approximately 91·1% and quadratically weighted Cohen’s κ was approximately 0·90. For referable versus non-referable DR, sensitivity was 90·7% and specificity was 91·9%. The companion lesion-detection head achieved macro-averaged sensitivity of 93·9%, specificity of 99·5%, and AUC of 0·997 across nine lesion classes; seven of nine classes achieved AUC = 1·00. Grad-CAM activations showed progressive spatial shift from diffuse (normal) to lesion-dense peripheral patterns (proliferative DR), with maximal agreement for microaneurysms and exudates. Mean inference time was 110–160 ms per image. Interpretation DRAGS demonstrates high diagnostic accuracy for nine-class UK DESP-aligned DR grading, with clinically interpretable lesion-level explainability on a large real-world LMIC dataset. External validation and prospective clinical evaluation are warranted before deployment. Funding The present study received no funding.

Abstract Importance Decline in olfactory function may be used as a predictor of cognitive decline, to enhance early detection models, improve risk stratification, and enable early intervention. Objective To assess the longitudinal association between olfactory decline, cognitive decline, and postmortem neuropathology. Design, setting and participants Retrospective longitudinal analysis with clinicopathological correlations of a prospective population-based cohort study using data from the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) and its Brain and Body Donation Program. Participants included cognitively unimpaired individuals without parkinsonism that converted to mild cognitive impairment (MCI) and/or dementia or remained cognitively stable. Main Outcomes and Measures longitudinal change in olfaction, neuropsychiatric symptoms, motor function and memory, conversion to MCI/dementia, postmortem neuropathology Results Over a mean follow-up period of 7.7 ± 5.4 years, out of 922 participants who were cognitively unimpaired at the first cognitive conference, 643 remained cognitively unimpaired, 279 converted to MCI, and 82 developed dementia. Of these, 633 individuals had at least 2 olfactory tests. Converters showed reduced olfactory function (t=-12.6, p <0.0001), faster progression in neuropsychiatric symptom burden (t=3.42, p < 0.001), and faster decline in memory (t= -7.33, p <0.0001) prior to conversion while no significant differences were observed in motor scores between converters and non-converters. Using ROC analysis, olfactory decline, increased neuropsychiatric symptom burden, as well as motor and memory decline predicted conversion to MCI with a consistent accuracy of ∼ 70% up to 5 years before conversion, while UPSIT alone had an accuracy of ∼ 60%. Longitudinal decline in olfaction was associated with a higher burden of a-synuclein (t= -8.21, p <0.0005), tau tangle (t= -2.66, p < 0.01) and amyloid plaque burden (t= -2.85, p < 0.005) and a faster decline over time was associated with a higher burden of tau (t=5.66, p<0.0001). Conclusions and Relevance A reduction in olfactory identification ability is observed up to a decade prior to conversion to MCI and is associated with underlying burden of neuropathology markers, underscoring the value of incorporating olfactory testing in cognitively unimpaired individuals to identify those at-risk of future cognitive decline.