Placenta

Preterm birth is the leading cause of neonatal death. Despite sustained efforts to identify high-risk women in the mid-trimester, accurate prediction remains difficult. Quantitative cervical ultrasound texture has been proposed as a predictor of spontaneous preterm birth. However, earlier models were developed in small single-centre samples and were not externally validated. We developed image-texture (Local Binary Patterns with a Random Forest), deep-learning (Vision Transformer), clinical-variable, and multimodal models to predict spontaneous preterm birth on the prospective GARBH-Ini cohort. We then externally validated our best models on an independent cohort scanned on a different ultrasound machine. Our best overall model reached an internal-test area under the receiver-operating-characteristic curve of 0.71 (95% CI 0.60, 0.82), but performed modestly at 0.52 (95% CI 0.38, 0.64) externally. The deep-learning and multimodal models did not perform better. Discrimination appeared higher in a clinically high-risk subgroup at the 34-week threshold. These estimates were imprecise because of few cases and need to be confirmed in future studies. Among the several likely reasons for the modest external performance is the heterogeneity of preterm birth. Predicting distinct preterm-birth subtypes separately, and integrating additional biomarkers and data domains, might improve model performance. Keywords: preterm birth; cervical ultrasound; prediction model; external validation; deep learning

Background: Prospective pregnancy registries and biobanking infrastructures are essential for future translational studies investigating maternal, placental and offspring health. However, circulating nucleic acid analyses are highly sensitive to preanalytical variability, particularly regarding blood-collection tube type and sample processing conditions. We established a prospective pregnancy registry and biobanking workflow at Cruces University Hospital and evaluated the impact of preanalytical variables on circulating cell-free DNA (cfDNA) and cell-free RNA (cfRNA) preservation in maternal plasma collected at delivery. Methods: The Registry of Pregnant Women at Cruces University Hospital was designed as a prospective infrastructure integrating placental sampling, maternal blood collection and ethically controlled future access to maternal and offspring clinical data. Within this framework, peripheral blood samples from 50 women at delivery were simultaneously collected into EDTA, Norgen and Roche tubes. Plasma samples processed within or after 24 hours following collection underwent cfDNA/cfRNA extraction, electrophoretic profiling, fluorometric quantification and RT-qPCR analyses targeting different stress-related genes. Results: By the end of June 2026, 1,127 women had been prospectively recruited into the registry, with 661 plasma samples, 637 serum samples and 858 sets of four placental biopsies collected, processed and stored in the Basque Biobank. In the preanalytical substudy, EDTA tubes yielded higher cfDNA concentrations, likely reflecting reduced cellular preservation and genomic DNA contamination. In contrast, Roche tubes showed superior cfRNA preservation, with higher cfRNA concentrations and more consistent detection of the characteristic 5S rRNA peak compared with EDTA and Norgen tubes. Processing delays beyond 24 hours reduced cfRNA concentration, while associations between circulating transcripts and gestational age were more consistently detectable in preservative-containing tubes. Conclusions: Prospective infrastructures like ours offer strong foundation for large scale, long-term studies in the framework of the Developmental Origins of Health and Disease hypothesis. Technically, Roche tubes provided superior cfRNA preservation and enhanced sensitivity for detecting subtle biological associations, supporting the importance of standardized preanalytical workflows within prospective pregnancy biobanking resource.

Background. Low-birth-weight (LBW) neonates carry a disproportionate share of newborn morbidity and mortality in sub-Saharan Africa. Distinguishing which maternal and neonatal characteristics mark the highest risk supports bedside risk stratification in resource-limited newborn units. This study examined maternal and neonatal predictors of severe adverse outcomes among LBW neonates admitted to a county referral hospital in Kenya. Methods. A facility-based cross-sectional analysis was conducted on 169 LBW neonate-mother pairs admitted to the newborn unit at Kericho County Referral Hospital. The outcome was a severe adverse outcome, defined as a composite of respiratory distress, sepsis, hypothermia, hypoglycaemia, prolonged admission (>= 7 days), or neonatal death. Maternal and neonatal factors were screened using chi-square, Fisher's exact, and independent-samples t tests, with crude odds ratios (ORs) and 95% confidence intervals (CIs). A restricted multivariable logistic regression, limited to maternal and neonatal predictors, produced adjusted odds ratios (AORs). Results. Severe adverse outcomes occurred in 136 of 169 neonates (80.5%). At the bivariate level, pregnancy-induced hypertension (PIH; OR = 6.69, 95% CI 1.53-29.27, p = 0.004) and preterm birth (OR = 3.79, 95% CI 1.50-9.56, p = 0.003) were associated with higher odds of a severe outcome, and severe outcomes clustered at lower birth weight and gestational age and higher neonatal risk-factor counts (all p < 0.001). In the restricted adjusted model, PIH (AOR = 7.66, 95% CI 1.56-37.55, p = 0.012) and birth weight (AOR = 0.997 per gram, p = 0.002) retained independent significance. Conclusion. Neonatal biological vulnerability-particularly lower birth weight-together with maternal pregnancy-induced hypertension were the predictors most strongly and independently associated with severe adverse outcomes. Risk stratification of LBW neonates should prioritise the smallest infants and those born to mothers with hypertensive disease. Keywords: low birth weight; neonatal outcomes; pregnancy-induced hypertension; preterm birth; risk stratification; Kenya

Background: Stunting affects approximately 32% of children under five years in Benin. While birth intervals shorter than 33 months are a recognized risk factor for childhood malnutrition, the optimal birth interval for preventing stunting in the Beninese context is still unclear. Objective: This study examined the association between preceding birth interval (PBI) and stunting among children aged 6 to 59 months in Benin. Methods: This study used a cross sectional design to analyze data from the 2017 to 2018 Benin Demographic and Health Survey. We included 10,153 children aged 6 to 59 months. Stunting was defined as height for age z score below 2 standard deviations from World Health Organization standards. Preceding birth interval was categorized as <24, 24 to 32, 33 to 44, 45 to 56, and >56 months. Survey adjusted multivariable logistic regression was used to estimate adjusted odds ratios (aORs) and 95% confidence intervals for the association between PBI and stunting, controlling for child, maternal, and household level covariates. Potential effect modification by child age group (6 to 23 vs. 24 to 59 months) was assessed through a multiplicative interaction term and evaluated using information criteria, a likelihood ratio test, and the statistical significance of individual interaction terms. Results: Compared with children born after an interval of <24 months, those born after 45 to 56 months (AOR: 0.63; 95% CI: 0.51 to 0.78) and >56 months (AOR: 0.67; 95% CI: 0.54 to 0.82) had significantly lower odds of stunting (both p<0.001). Intervals of 24 to 32 months and 33 to 44 months were not significantly associated with stunting, nor was firstborn status. No evidence of effect modification by child age group was found (likelihood ratio test p=0.336), and stratified analyses conducted separately for children aged 6 to 23 months and 24 to 59 months yielded results consistent with those from the pooled model. Conclusion: The lack of protective effect for intervals shorter than 45 months indicates a threshold specific to this context above which nutritional benefits become manifest. Integrating family planning messages emphasizing birth intervals longer than 45 months into child nutrition programs, coupled with strengthened access to modern contraception, could contribute to stunting reduction in Benin among other factors. Alongside this, nutritional support for pregnant and breastfeeding women, including adequate dietary supplementation and counselling, may further contribute to improved child growth outcomes. Keywords: Birth interval; stunting; family planning; Benin; childhood morbidity; nutrition

Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultrasound but are limited by availability and prolonged time-to-imaging. Novel artificial intelligence (AI) guidance systems have been designed to enable non-ultrasound-trained operators to acquire proximal lower extremity compression ultrasounds for remote clinician interpretation. Methods: This multicenter, double-blinded, prospective, nonrandomized study evaluated the performance of an AI guidance system (ThinkSono Guidance, ThinkSono, GmbH). Patients underwent AI-guided ultrasound(s) and standard of care ultrasound(s). Primary and secondary endpoints were image quality, sensitivity and specificity for proximal DVT, and prioritization specificity, a measure of specificity in identifying patients requiring standard of care ultrasound after AI-guided scan. Results: Of 634 recruited subjects, 594 were analyzed, with 67 DVTs across 700 scans. 86.83% of AI-guided scans achieved diagnostic image quality. Triage sensitivity was 92.86%, triage specificity 39.12%, prioritization specificity 97.96%. Standard of care ultrasounds could be avoided in 35.32% of patients. Total median AI-guided scan and review time was 7.57 minutes. Conclusions: Clinician-reviewed AI-guided scans were rapid, sensitive for DVT, and specific for prioritizing patients requiring standard of care ultrasounds. These findings suggest AI-guided ultrasound may be a scalable triage strategy to expand DVT evaluation access, particularly in resource-constrained and after-hours settings

Preeclampsia (PE), new onset hypertension after 20 weeks of gestation, affects 10% of all pregnancies in the U.S. and it is associated with progesterone deficiency, chronic inflammation, elevated angiotensin II type 1 receptor agonistic autoantibody (AT1-AA) and endothelial dysfunction. Progesterone, through its receptors, stimulates an anti- inflammatory protein called Progesterone Induced Blocking Factor (PIBF) which decreases during various pregnancy disorders. Therefore, this study was designed to test the hypothesis that a progestogen, in the form of 17-hydroxyprogesterone caproate, stimulates PIBF, lowers vasoactive mechanisms which reduces maternal blood pressure in women with early-onset preeclampsia (EOPE). PE women received 17-OHPC (250 mg, I.M.) and blood draws were collected before and after 17-OHPC supplementation. Placentas were collected at the delivery. 17-OHPC prolonged time of delivery beyond 72h on average and maternal blood pressure was significantly decreased in PE+17- OHPC. Progesterone and PIBF levels were reduced in PE group vs. NP group. Importantly, 17-OHPC increased PIBF and decreased vasoactive mechanisms and markers of inflammation. In conclusion, 17-OHPC or progesterone supplementation improves maternal outcomes in response to EOPE without causing further harm to the fetus.

Premature ovarian insufficiency (POI) significantly impacts quality of life, yet the immediate cognitive landscape and lived experience of younger women remain under-researched. In 125 young women (aged 19-48; 66 with idiopathic POI, 59 age-matched controls), we examined self-reported cognitive distress and symptom burden within the POI cohort and compared objective global and domain-specific cognitive performance between groups. Objective accuracy scores were derived from six online tasks (Cognitron) and combined into a robust global measure. Within the POI cohort, there were significant differences in symptom burden domains ({chi}(3) = 61.90, p<0.001), with psychological and sexual symptoms reported at a significantly higher intensity than physical and vasomotor symptoms (all p<0.001). Furthermore, the standardised magnitude of perceived cognitive distress (56.20%) was significantly greater than that of overall symptom burden (42.00%, p<0.001). Case-control comparisons revealed no significant differences in global cognitive performance (p=0.615), yet the POI cohort performed significantly less accurate than controls in verbal analogical reasoning (-0.86 SD, 95% CI: -1.52, -0.20, p = 0.011). The findings highlight an urgent need for comprehensive emotional and psychosexual support in POI care. Additionally, the presence of high cognitive distress alongside localised objective deficits demonstrates that cognitive health monitoring must be proactive in early adulthood, especially given their established long-term risks for later-life cognitive decline and dementia.

Background: Anemia during pregnancy remains a major public health concern, particularly in low- and middle-income countries, where it contributes substantially to maternal and neonatal morbidity and mortality. Identifying women at increased risk is essential for timely intervention and improved pregnancy outcomes. Objective: This study aimed to identify the significant predictors of anemia among pregnant women using logistic regression and to evaluate the association between selected clinical and sociodemographic characteristics and anemia. Methods: A cross-sectional study was conducted using secondary data obtained from a community maternity care facility in the Suame Municipality of the Ashanti Region, Ghana. Pregnant women who attended antenatal care during the study period and had complete information on hemoglobin concentration and relevant predictor variables were included. Women with missing hemoglobin measurements at registration or delivery were excluded. Logistic regression analysis was performed to identify independent predictors of anemia. Additional analyses examined the effects of age and weight, as well as the relationship between sickle cell status and blood group. Results: Logistic regression identified diastolic blood pressure, height, hemoglobin concentration at registration, maternal weight and gestational age as significant predictors of anemia during pregnancy (p < 0.05). Although employment status was statistically significant in the model, its direct association with anemia was relatively weak. Maternal age was not significantly associated with anemia. Pregnant women with sickle cell disease had a significantly higher likelihood of anemia. Blood group did not demonstrate a significant relationship with anemia. Effect sizes and confidence intervals were not available in the dataset. Conclusion: Diastolic blood pressure, height, hemoglobin concentration at registration, maternal weight, gestational age, sickle cell status and employment status were identified as important predictors of anemia during pregnancy. These findings highlight the importance of incorporating both clinical and sociodemographic characteristics into antenatal risk assessment and screening programs. Further prospective studies with larger sample sizes and more comprehensive clinical measurements are recommended to validate these findings and strengthen predictive models for anemia during pregnancy.

Red blood cell (RBC) disorders arose as an advantageous evolutionary response to malaria infections. In a heterozygous condition, such as in Southeast Asian Ovalocytosis (SAO), hosts are protected against severe malaria. In malaria-endemic regions, RBC disorders are presumed to be highly prevalent. Tanjung Leidong, a moderately endemic area in North Sumatra (API 1.13 in 2024), lacks comprehensive data on RBC disorder prevalence beyond G6PD deficiency. Therefore, this study aims to characterize the RBC disorders in this region as well as to characterize the anemia status in children living in Tanjung Leidong. Schoolers attending D. I. Panjaitan elementary to high school were recruited and screened for malaria by microscopy and G6PD deficiency using the STANDARD G6PD Assay. The DNA of the participants was also extracted to be genotyped for SAO, Hemoglobin E (HbE), and -thalassemia. Exclusively, G6PD-deficient DNA samples were genotyped further to determine variants. The proportion of G6PD deficiency, SAO, HbE, -thalassemia one-gene deletion, and two-gene deletion were 0.90%, 0.90%, 2.40%, 6.26%, and 0.30%, respectively. Anemia prevalence was approximately 14%, and RBC disorders were observed across children with normal to obese BMI. No malaria infections were detected by microscopy. The predominance of asymptomatic RBC disorders highlights that they are protective against malaria infection, although their protective role against malaria could not be directly assessed in this study. Both nutritional and genetic factors are found to contribute to anemia in this cohort. These findings underscore the importance of integrated screening strategies for RBC disorders and anemia in malaria-endemic settings.

Purpose: To compare the cross-site generalization of radiomic features and deep learning embeddings for MRI prediction of substantial lymphovascular space invasion (LVSI) in endometrial cancer. Materials and Methods: This retrospective two-center study included 206 women (mean age, 59.8 years) with endometrial cancer who underwent preoperative 3-T MRI from March 2016 to March 2023. Hospital A (n = 130) was used for development and Hospital B (n = 76) for strict external testing. T2-weighted, reduced field-of-view diffusion-weighted, and apparent diffusion coefficient images were manually segmented. Radiomic features and seed-pooled embeddings from 3D ResNet18, DenseNet121, and U-NEXtractor were modeled with elastic-net logistic regression or XGBoost. Out-of-fold Platt calibration and sensitivity-targeted thresholds were estimated using development data only. AUCs were summarized with 95% bootstrap confidence intervals. Results: External radiomics with elastic-net achieved an AUC of 0.609 (95% CI: 0.464, 0.740) and sensitivity of 0 of 12 (0%). DenseNet121 with elastic-net had the highest external AUC (0.685; 95% CI: 0.538, 0.822) but sensitivity of 3 of 12 (25%). U-NEXtractor with elastic-net detected 10 of 12 positive cases (83.3%) with specificity of 32 of 64 (50.0%) and balanced accuracy of 0.667. XGBoost showed higher apparent development performance but weaker external operating behavior. Conclusion: Under real-world cross-site MRI acquisition shift, DenseNet121 and U-NEXtractor embeddings showed better external generalization than handcrafted radiomic features for substantial LVSI prediction.

Background: Neonatal meningitis and pneumonia are important causes of morbidity and mortality among hospitalized newborns in low-resource settings. Numerous single-centre studies have been conducted in Ethiopian public hospitals, yet no quantitative synthesis of neonatal meningitis prevalence previously existed, and the depth of the pneumonia-specific evidence base was undocumented. This review aimed to estimate the pooled prevalence of neonatal meningitis, and to establish the state of the evidence base for neonatal pneumonia, among neonates treated at public hospitals in Ethiopia. Methods: A systematic search of PubMed/MEDLINE, PubMed Central, PLOS, BioMed Central, Frontiers, Taylor & Francis Online, and institutional repositories was conducted without date restriction. Cross-sectional and retrospective studies reporting a standalone prevalence of neonatal meningitis or neonatal pneumonia among neonates treated at Ethiopian public hospitals were eligible. Methodological quality was appraised using the JBI Critical Appraisal Checklist for prevalence studies. A random-effects meta-analysis pooled meningitis prevalence; heterogeneity was quantified with I2 and Cochran's Q. Results: Of 26 full-text articles assessed for eligibility, four studies (N = 3,522 neonates) met inclusion criteria for neonatal meningitis; none met inclusion criteria for a standalone neonatal pneumonia prevalence outcome. The pooled prevalence of neonatal meningitis was 6.26% (95% CI: 2.81-13.35%; I2 = 96.6%), with individual study estimates ranging from 1.73% to 19.30%. A sensitivity analysis restricted to the three studies using a "suspected-meningitis" denominator yielded a pooled prevalence of 4.23% (95% CI: 1.92-9.05%). No eligible primary study reported neonatal pneumonia prevalence as a standalone, separately ascertained outcome across the accessible literature, pneumonia is consistently subsumed within composite "neonatal sepsis" case definitions. Conclusion: An estimated 1 in 16 to 1 in 24 neonates tested for suspected meningitis at Ethiopian public hospitals had a culture-confirmed or clinically diagnosed case, with wide variation across settings and case-ascertainment methods. A previously undocumented evidence gap exists for standalone neonatal pneumonia prevalence in Ethiopia. Future primary studies should report pneumonia as a distinct, separately ascertained neonatal outcome to enable future quantitative synthesis.

Prenatal alcohol exposure (PAE) is a major cause of neurodevelopmental disorders, yet most children are diagnosed late or misdiagnosed. Neuroplacentology suggest that placental factors released into maternal and/or umbilical cord blood contribute to fetal brain development. Consistently, a preclinical inter-organ transcriptomic database revealed that PAE disrupts the expression ratio of angiogenic and inflammatory factors suggesting an angio-inflammatory response. This study aimed i) to assay, by multiplex immunoassay, angiogenic and inflammatory factors in maternal and umbilical cord blood from alcohol-consuming women and ii) to perform a maternofetal analysis according to neonatal sex. Afterwards, dysregulated factors from mothers who gave birth to females or males were submitted to STRING and ShinyGO analyses. Results showed that PAE differently altered the distribution profiles of dysregulated angiogenic and inflammatory factors in maternal and umbilical cord blood. Moreover, sex-specific differences were observed, with 36% of dysregulated proteins specific to males, 48% to females, and 16% common to both. STRING analysis revealed robust functional protein-protein interactions linking together inflammatory and angiogenic clusters while the ShinyGO analysis identified enriched pathways related to vascular shear stress. These findings provide the first maternofetal analysis of combined angiogenic and inflammatory factors from alcohol-consuming mothers.

Introduction Adolescent idiopathic scoliosis (AIS) requires frequent x-rays for management, exposing young patients to cumulative radiation risks. While radiation-sparing imaging modalities exist, access across the National Health Service (NHS) remains uneven and information given to patients is variable. This qualitative study investigated the systemic, geographic, and interpersonal dynamics of AIS imaging in England. Design This qualitative study employed in-depth semi-structured interviews with healthcare professionals (HCPs) from NHS paediatric spinal centres, patients aged 13 to 25 years old with AIS and parents/carers of young people with AIS. Setting England. Participants A total of 22 HCPs from 13/24 NHS paediatric spinal centres in England, 19 10-25 years with AIS and 11 parents/carers. Results Conventional x-ray remains the main imaging modality. Significant geographic inequality exists. The most commonly available radiation-sparing imaging modality available is the EOS system, which uses slot-scanning technology, is available at 7 centres in England, primarily in London imaging networks. Acquisition of EOS systems is currently driven by local charitable funding rather than a centralised strategy, with high capital and installation costs cited as primary barriers. Inconsistent knowledge of imaging within primary care and a lack of specialist expertise in local secondary care services led to diagnostic redundancy, gatekeeping, and low value inconsistent imaging. These systemic delays frequently closed the window for conservative treatments like bracing. A professional balancing act exists between the duty to inform and the desire to minimise patient anxiety. HCPs often use selective communication regarding radiation risks. Conversely, families demonstrate high relational trust with HCPs and low baseline knowledge of cumulative exposure, often viewing frequent imaging as a reassuring marker of clinical progress. In centres with EOS systems, clinicians felt empowered to lead proactive, transparent risk discussions. In standard X-ray settings, dialogue remains reactive and infrequent, leading to a reliance on implied rather than truly informed consent. Conclusions AIS imaging in England is variable. Geographic location dictates access to low-dose radiation technology and the quality of informed consent. Systemic inefficiencies and fragmented referral pathways contribute to diagnostic redundancy and delayed specialist care. National standardisation of clinical pathways, information provision and a centralised strategy for low-dose technology procurement are essential to eliminate structural inequalities and ensure equitable, transparent care for all patients.

Background: North Carolina Medicaid implemented the Pregnancy Medical Home program to improve access to high-quality maternity care and reduce the risk of adverse perinatal outcomes. Program recipients receive a prenatal risk screening form, originally intended to identify those at high risk of preterm birth and low birth weight, that includes an assessment of social and clinical factors. While prior studies have evaluated whether risk screening can identify pregnancies with higher risk of adverse neonatal outcomes, less is known about the relationship between programmatic risk-stratification and adverse maternal outcomes. Objective: To assess the use of a prenatal risk screen among pregnant Medicaid beneficiaries to identify those at risk of an adverse maternal event. Study design: Linked Medicaid hospital claims, live birth records, and risk screen data from the Pregnancy Medical Home program were used to identify risk factors for adverse maternal events among individuals who gave birth to a liveborn infant in North Carolina between 2014 and 2019. Only those with completed risk screens (75%) were included in the analysis. We used random forest classification to select variables for a multivariable prediction model. We used Poisson regression to model the association between adverse maternal events and selected demographic, psychosocial, clinical, and historical pregnancy characteristics. Adverse maternal events occurring at birth and up to six weeks postpartum included severe maternal morbidity, maternal intensive care unit admission, prolonged birth hospitalization, and postpartum readmissions. Results: A total of 205,916 births met inclusion criteria for this analysis. During the study period, 3.0% of Medicaid beneficiaries had an adverse maternal event occurring between birth and up to six weeks postpartum, including, 0.6% with severe maternal morbidity, 0.9% with an intensive care unit admission at birth, and 1.5% with a prolonged birth hospitalization or postpartum readmission. Maternal age greater than 25 years, Black race, being overweight or obese, smoking, chronic diseases (diabetes, hypertension, mental illness), and pregnancy history characteristics (nulliparity, history of preterm birth, history of hypertensive disorders of pregnancy or gestational diabetes) were associated with an increased risk of adverse maternal events. Modeled together, however, risk factors from the risk form were poorly predictive of the composite outcome. The final model had an Area Under the Curve (AUC) of 0.63 with an optimal sensitivity of 56% and specificity of 63%. Conclusion: Care management during pregnancy is an increasingly relevant topic in public health and prenatal care in the United States. The North Carolina Pregnancy Medical Home is a long-standing and robust Medicaid program that can serve as a model for design and implementation. While this program has effectively designed risk-stratification to identify pregnant people at risk of poor neonatal outcomes who benefit from care management, the risk screen poorly identifies pregnant people at risk of adverse maternal outcomes. Care coordination programs are often designed to optimize neonatal outcomes, and this study highlights the need to center and balance maternal health along with neonatal outcomes to address the needs of a very vulnerable population.

ABSTRACT Background: Metallic implants such as orthopedic screws, prostheses, and dental hardware produce beam-hardening, photon-starvation, and streak artifacts that degrade computed tomography (CT) image quality, and the metal artifact reduction (MAR) methods developed to mitigate them require objective, reproducible benchmarking. Purpose: Objective evaluation of MAR algorithms in CT is hindered by the absence of phantoms that simultaneously provide anatomically realistic backgrounds, embedded implants of known geometry, and controllable, ground-truth--referenced artifact intensity. We present a dual-filament, voxel-level three-dimensional (3D) printing method that fulfills these requirements and demonstrate its capabilities on a clinically representative cervical spine case with embedded orthopedic spinal screws. Methods: The proposed method extends the PixelPrint framework, a fused-deposition-modeling (FDM) workflow that converts clinical Digital Imaging and Communications in Medicine (DICOM) data directly into 3D-printer Geometric code (G-code) without intermediate segmentation or surface meshing, to interleaved, voxel-level deposition of two filaments: a calcium-doped polylactic acid (PLA) for soft tissue and bone, and a higher-attenuation metal-doped PLA for metallic implants. For demonstration, anonymized DICOM data of a healthy cervical spine were used to design and fabricate three matched phantoms, each with six embedded spinal screws at C4--C6: a 0% metal-infill ground-truth phantom, a 50% medium-metal-infill phantom, and an 85% high-metal-infill phantom. All phantoms were scanned on a clinical spectral CT system at 120 kVp and 1000 mAs, reconstructed at 0.67 mm slice thickness with virtual monoenergetic imaging (VMI) across 50--190 keV. Method performance was characterized by region of interest (ROI)-based Hounsfield Unit (HU) agreement with the source patient data and by the noise-independent Gumbel-distribution p-index metric. Results: The dual-filament method reproduced patient anatomy, soft-tissue contrast, and screw geometry with high fidelity. ROI HU values agreed with patient data within {+/-}25 HU for soft tissue and trabecular bone; cortical regions were underestimated owing to the current ceiling of the calcium-doped PLA used in this study. The tunable-artifact behavior was quantified as follows: the Gumbel location parameter scaled monotonically from 46.7 HU (no-metal background) to 57.1 HU (50% infill) to 90.5 HU (85% infill) for the VMI 70 keV with standard filter. High-keV VMI reconstructions substantially reduced streak and beam-hardening artifacts while preserving anatomic detail. Conclusions: The proposed dual-filament, voxel-level PixelPrint method enables the fabrication of patient-specific, multi-material CT phantoms with embedded metallic implants and controllable, ground-truth--referenced artifact intensity. Although demonstrated here in a single cervical-spine case, the workflow is anatomy- and implant-agnostic by construction and could in principle be adapted to other musculoskeletal sites (e.g., knee, hip, dental) and implant materials, providing a reproducible methodological foundation for benchmarking MAR algorithms, characterizing spectral CT performance, and validating emerging photon-counting detector systems. Keywords: 3D printing methodology; fused deposition modeling; voxel-level multi-material printing; spectral computed tomography; metal artifact reduction; phantom design; orthopedic implants; dual filament; PixelPrint.

Background: Cerebral vasospasm is a frequent complication after aneurysmal subarachnoid hemorrhage (aSAH) and is associated with delayed cerebral ischemia (DCI) and unfavorable outcome. While CTA-based vasospasm grading is frequently used, its relationship with actual cerebral perfusion remains incompletely understood. This study investigates the association between vasospasm severity and distribution and territorial perfusion deficits. Methods: In this retrospective single-center study, 513 CT examinations (CTA and CT perfusion) from 194 patients with aSAH were analyzed. Vasospasm was graded per vessel segment using the CTA Vasospasm Score, and perfusion deficits were assigned to corresponding vascular territories (left/right anterior circulation, posterior circulation). Vasospasm distribution was further classified by severity and multifocality. Associations between vasospasm score and perfusion deficits were assessed using a generalized linear mixed model with binomial distribution, adjusting for Hunt & Hess grade, modified Fisher score, and days since hemorrhage. Results: Vasospasm was detected in 79.3% of examinations, and a perfusion deficit in at least one territory was present in 62.6%. The proportion of perfusion deficits increased progressively with both vasospasm severity and multifocality, ranging from 21.7-25.0% in the absence of vasospasm to 81.2-82.2% in severe multifocal vasospasm. The CTA Vasospasm Score was significantly associated with perfusion deficits in all territories (OR 1.36-1.50), with stronger associations in the anterior than posterior circulation. Conclusion: Vasospasm severity and distribution are strongly associated with perfusion deficits, supporting a continuum model of ischemic risk. However, the substantial proportion of perfusion deficits occurring independent of vasospasm suggests additional microcirculatory mechanisms not captured by CTA. CT perfusion should be considered complementary to CTA, particularly in clinically deteriorating or non-assessable patients.

Patients and caregivers seek informational and emotional support throughout medical care, especially when interpreting unfamiliar laboratory test results. Although resources such as patient portals and online health communities (OHCs) help address questions, gaps remain. The emergence of large language models (LLMs) offers the potential to be a complementary source of support to assist patients and caregivers in understanding and using their test results. The objective of our study is to empirically compare LLM responses to patients online questions containing their laboratory test results to responses written by peers in an OHC. We compared the 519 peer replies to 122 laboratory test-related posts from an OHC to 488 responses generated from four LLMs using mixed computational and qualitative methods. LLMs frequently provided clear explanations of medical terminology and structured interpretations of numeric results but were longer and less readable. Peers offered more personalized, context-specific emotional support. Overall, LLMs have the potential to complement peer responses in OHCs, but require greater emotional depth, reasoning transparency, and alignment with community norms.

We present FootNet, a 453-image multi-view smartphone foot dataset for binary foot segmentation, with expertannotated masks across six anatomical views (dorsal, medial, and plantar, both left and right). We benchmark four segmentation models under a controlled protocol: U-Net with a MobileNetV2 encoder achieves the best performance (IoU 0.9268, Dice 0.9608, 95 % CI [0.9209, 0.9320]); DeepLabV3 with MobileNetV3-Large scores IoU 0.8984 (Dice 0.9449); UNet++ with MobileNetV2 scores IoU 0.8913 (Dice 0.9391); and SAM ViT-B with oracle boundingbox prompt scores IoU 0.9219 on the matched 191-image subset. Bonferroni-corrected Wilcoxon signed-rank tests (k = 6 comparisons) show U-Net significantly outperforms DeepLab (p < 0.001, r = 0.638) and SAM ViT-B with oracle boundingbox (p = 0.005, r = 0.202); UNet++ does not significantly differ from DeepLab (p = 0.062). Connected-component postprocessing yields negligible benefit (mean {triangleup}IoU = +0.0003, 12 of 453 images improved). The extended dataset is available upon request

Purpose: Nonresponse to routinely collected patient-reported outcome measures (PROMs) threatens the representativeness of aggregated data. We characterized patient-, provider-, and clinic-level factors associated with PROMIS Global-10 nonresponse in routine radiation oncology care. Methods: In this retrospective cohort study, all adults seen at five Mass General Brigham radiation oncology clinics over one year were included. The primary outcome was patient-level nonresponse, defined as never completing the portal-administered Global-10 versus completing it at least once. Using iterative mixed-effects logistic regression, we modeled patient-, provider-, and clinic-level factors. Results: Among 12,214 patients, 71 providers, and five clinics, patient- and appointment-level response rates were 35.4% and 10.9%, with patient-level response ranging nearly fivefold across clinics (12.8% to 66.2%). In Model 1, male sex, lower education, not working, and recent surgery had higher odds of nonresponse, and longer time since diagnosis lower odds. After provider- and clinic-level factors were added, patient sex, education, and employment became nonsignificant, whereas recent surgery (adjusted odds ratio [aOR] 1.97) and longer time since diagnosis (aOR 0.46 for >12 months) persisted. A provider's historical collection rate was protective but attenuated at the clinic level. There, a later program launch (aOR 0.29) and higher historical collection rate (aOR 0.79) correlated with lower nonresponse, whereas academic versus community setting did not. Conclusions: Nonresponse to routinely collected PROMs is a multilevel phenomenon driven substantially by clinic-level implementation factors, not patient characteristics alone. Because response rate is only a proxy for representativeness, PROMs programs and PRO-based performance measures should prioritize representative collection over volume.

Estimating dengue force of infection (FOI) is essential for understanding transmission dynamics and targeting intervention programmes, yet surveillance data in endemic settings required for estimations are often incomplete, with varying formats. We developed a Bayesian hierarchical catalytic model that jointly fits age-stratified case data, aggregate case data, and seroprevalence surveys within a single framework, incorporating external covariates to improve parameter identifiability. Synthetic validation showed that covariates alone recovered accurate FOI point estimates even when most districts contributed only aggregate data, but did so with poorly calibrated uncertainty; anchoring the model with a single seroprevalence survey was necessary to bring credible interval coverage close to nominal. Applied to 128 districts across Java and Bali, Indonesia (2016-2024), the model revealed substantial spatial heterogeneity in FOI and reporting rates. Many districts in Java exceeded the WHO-suggested seroprevalence threshold for vaccine introduction, yet were classified as low-priority when using reported incidence as prioritisation criterion, particularly in areas with weak surveillance. Model-based seroprevalence estimation, integrating multiple data sources, offers a more consistent basis for identifying high-priority districts for vaccine introduction, and is less susceptible to surveillance bias than reported incidence.