Bioengineering

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

Minimally invasive endovascular procedures offer reduced surgical trauma, shorter recovery times, and improved outcomes, but rely on 2D fluoroscopic X-ray imaging, which provides limited depth perception and exposes patients and clinicians to ionizing radiation. Here we present an augmented reality (AR) system that fuses intravascular ultrasound (IVUS) and electromagnetic (EM) position tracking with preoperative computed tomography (CT) to produce an anatomically accurate, deformation-corrected navigational reference. A robotic device performs ECG-gated pullback of the IVUS probe, capturing 4D aortic motion across the cardiac cycle. We introduce a deep learning architecture for extracting vascular lumen boundaries and side-branch orifices from artifact-prone IVUS streams, and a semantically driven non-rigid CT-IVUS fusion pipeline robust to false positive landmarks. We evaluate the platform with trained surgeons in benchtop phantom studies and in-vivo ovine models, and demonstrate its application to fenestrated endovascular aneurysm repair (FEVAR). Compared to fluoroscopy alone, AR guidance significantly reduces cannulation time, radiation exposure, and cognitive workload, while improving procedural efficiency and safety. Our IVUS-EM and CT aortic datasets are released open source.

ABSTRACTThe repair of large bone defects remains a major clinical challenge, in part due to inadequate vascularization and poor integration of graft materials. Tissue engineering strategies that recapitulate the developmental process of endochondral ossification, whereby a cartilage template remodels into bone, have shown significant potential in pre-clinical models of large bone defect healing. However, successfully scaling these approaches to clinically relevant sizes will require the development of strategies to support the rapid vascularization of the graft following implantation in vivo. Here, mechanically reinforced templates were first fabricated by integrating hypertrophic cartilage microtissues derived from human mesenchymal stem/stromal cells (MSCs) within an osteoconductive 3D-printed polycaprolactone (PCL) framework coated with nano-hydroxyapatite (nanoHA). In vitro the cartilage microtissues fused and generated an extracellular matrix rich in sulphated glycosaminoglycans and collagen. To prevascularize these constructs, vascular microtissues derived from a co-culture of endothelial cells and MSCs were incorporated into a central channel within the construct, which generated a microvascular network within the graft in vitro. Following subcutaneous implantation, hypertrophic cartilage templates with ( vascular-channel group) and without ( empty-channel group) this central vascularized channel supported endochondral bone formation. Quantitative microCT and histological analyses revealed significantly greater remaining bone in the empty-channel group, whereas the vascular-channel group supported enhanced vascularization and remodeling of the graft in vivo. These findings support the continued development and testing of a modular biofabrication strategy that combine self-organizing hypertrophic cartilage and vascular microtissues with osteoconductive 3D-printed architectures to generate scalable, prevascularised hypertrophic cartilage templates for endochondral bone repair. Key-words: spheroids, microtissues, hypertrophic cartilage, vascularization, endochondral ossification, bone tissue engineering.

Purpose: To assess the feasibility of quantitative fundus autofluorescence (FAF) measurement in early age-related macular degeneration (AMD) using the freely available ImageJ software, to characterize signal intensity across FAF patterns, and to evaluate interobserver reproducibility in pattern classification. Methods: Single-center, non-blinded, retrospective, consecutive-case analytical study. FAF images acquired with Spectralis OCT+HRA (Heidelberg Engineering) from patients with early dry AMD seen at a tertiary referral center between January 2010 and September 2016 were analyzed. A standardized 300x300-pixel region of interest (ROI) centered on the fovea was evaluated in ImageJ v2.0.0-rc54/1.51h (Fiji distribution). Mean, minimum, and maximum autofluorescence (AF) pixel intensity were recorded. Each image was independently classified according to the Bindewald classification system by two graders; a third senior grader adjudicated discordances. Cohen's kappa (k) was used to assess interobserver agreement. Results: Of 423 patients with available FAF studies, 107 had dry AMD; 45 met quality and diagnostic criteria for early AMD and were included in the quantitative analysis. Mean age was 73.47 +/- 8.1 years; 62.2% were female. Mean FAF intensity was 120.26 (range 74.76-160.79); mean minimum was 32.07 (range 3-63) and mean maximum was 205.80 (range 125-255). Seven of eight Bindewald patterns were identified; the stippled pattern was absent. The most frequent pattern was minimal changes (31.1%), followed by increased focal (24.4%) and patchy (15.6%). Reticular pattern showed the highest mean AF (143.8), while lace pattern showed the lowest (88.4). Interobserver agreement for Bindewald pattern classification was almost perfect (k = 0.969; 95% CI, 0.908-1.000; p < 0.001). Agreement for lesion extent was moderate (k = 0.531) and for foveal involvement was substantial (k = 0.622). Conclusions: Quantitative FAF evaluation of early AMD using ImageJ is feasible and reproducible. ImageJ represents a cost-free alternative for multimodal retinal image analysis, with potential for automated screening applications in resource-limited settings. Keywords: age-related macular degeneration; fundus autofluorescence; ImageJ; quantitative autofluorescence; image analysis; Bindewald classification; interobserver agreement

Abstract Background. Quantitative optical coherence tomography (OCT) measurements are essential for retinal disease monitoring, yet leading vendors store acquisition data in undocumented proprietary formats or encode measurements exclusively in private DICOM tags inaccessible to open systems. Methods. We present Transducin, an open-source Python library that reverse-engineers the undocumented Optopol Revo FC130 and Revo 60 .OPT binary format and extracts quantitative measurements from Zeiss Cirrus HDOCT private DICOM tags, generating TID 1500 Structured Reports with SNOMEDCT coded findings for both platforms. A novel finding, that OCTPARAMS tag 23 encodes ocular laterality through the arithmetic sign of the foveal horizontal position, enables geometry based laterality inference requiring no operator data entry, validated across 18 files from two device models and four software versions with 100% accuracy. Results. The primary corpus of 452 Optopol .OPT files (73 patients, 7 acquisition types) was parsed with 100% success. Cross-version compatibility was confirmed across SOCT versions 11.5.0 through 21.5.0, spanning approximately eight years of software development. The Zeiss Cirrus pipeline generated TID 1500 SRs for all 41 applicable studies (100%), yielding CMT 203to 630um and RNFL 53 to123 um across a clinically representative range. Conclusions. Transducin provides the first publicly documented specification of the Optopol .OPT format and the first open-source multivendor pipeline generating SNOMEDCT coded DICOM Structured Reports from both Optopol Revo and Zeiss Cirrus devices, closing a gap explicitly confirmed by both manufacturers' own documentation. The code is available at https://github.com/oftalmos-org/transducin (Apache License 2.0).

Distal radius fractures (DRFs) can delay return to activities of daily living and social participation because of postoperative pain, temporary joint immobilization, and limited wrist and forearm range of motion. The Ghost System developed at Saitama Prefectural University, Japan, combines visual action observation with tendon vibration stimulation and has shown potential as an adjunct to conventional rehabilitation. This Study Protocol describes a modified Ghost system intended to improve clinical implementation by replacing the head-mounted virtual reality display with iPad-based action observation and by using a wristband-type vibrator. This single-center, single-arm, open-label feasibility trial will enroll 10 adults after palmar locking plate fixation for DRF. The intervention will be delivered twice weekly during outpatient rehabilitation follow-up sessions from the early postoperative period (postoperative days 2-10 after enrollment) through the approved early postoperative rehabilitation period (generally up to postoperative week 8), in parallel with standard rehabilitation practices. Primary feasibility and preliminary clinical outcomes include device fit and acceptability, pain assessed using a 100-mm Visual Analog Scale, and wrist/forearm range of motion. Secondary implementation and safety outcomes include Disabilities of the Arm, Shoulder and Hand (DASH), Patient-Rated Wrist Evaluation (PRWE), Hand20 Questionnaire (HANDS-20), EuroQol 5 Dimensions 5 Levels (EQ-5D-5L), body ownership and hand-illusion questionnaires, setup time, setup errors, adherence, adverse events, and device incidents. We hypothesize that the modified Ghost system will be feasible and acceptable for early postoperative outpatient rehabilitation and will be delivered without serious device-related adverse events. Clinical outcomes will be summarized descriptively to inform a future controlled study rather than to establish efficacy.

In Mongolia, an average of 65,000 women become pregnant each year, and about 59,500 babies are born. Although the number of pregnancies is decreasing by 8-12 percent each year, the level of fetal monitor usage remains high. The capital's maternity hospital currently has 27 fetal monitors in use, and an average of 30-35 calls are recorded per month. However, there is a lack of research on the use of fetal monitors, the causes and influencing factors of damage, and the organization of technical services. Therefore, this topic was chosen to determine the usage status of fetal monitors, the causes of malfunctions, and ways to improve them. Purpose To study the causes and factors affecting possible damage and injury during the use of fetal monitors, and to identify ways to reduce them. Materials and methods A one-time study was conducted on 10 MT-610 fetal monitors that were put into operation in 2019 at the Urgo Maternity Hospital in the capital. Data were collected and processed using document analysis methods from the technical passports and call logs of these devices. The factors contributing to common failures were identified using focus group interviews with the engineers and technicians responsible for the equipment. Results This study found that fetal monitor failures are caused by improper use, lack of regular calibration, electrical fluctuations, ambient temperature and humidity, and insufficient medical staff skills, training, and knowledge of how to use the device, all of which contribute to failures and measurement errors. It is also observed that when a replacement part is needed for a monitor that frequently breaks, the monitor is more likely to break again if it is used as a replacement from a previously broken monitor. Therefore, training doctors and nurses who replace spare parts on their use has been observed to significantly reduce future breakdowns. Conclusion According to the study results, the breakdowns and failures of fetal monitoring devices are mainly related to internal system failures, unstable power supply, and wear and tear of accessories and mechanical parts. The highest percentage of device failures indicates the need for special attention to the reliability of the device's basic functions. Additionally, the high percentage of accessory and printer failures indicates the need for proper use and monitoring of the entire device. In addition to technical factors, human misuse, lack of maintenance, and environmental influences also play a significant role in damage. Therefore, it is concluded that to ensure the reliable operation of fetal monitors, it is necessary to perform regular maintenance, stabilize the power supply, improve the quality of accessories, and increase the knowledge and skills of medical staff. Keywords: Fetal Monitoring, Equipment Failure, Risk Factors

Sleep deprivation is associated with impaired endurance performance and an increased risk of running-related injury. Previous research has identified alterations in running biomechanics following a single night of sleep deprivation under laboratory conditions. However, whether these biomechanical changes can be detected using wearable technology remains unknown. Twenty-one recreationally active runners completed submaximal treadmill running under both normal sleep and total sleep deprivation conditions in a randomized crossover design. Biomechanical features were extracted simultaneously using a full-body motion capture system and a trunk-mounted wearable sensor. Five machine learning classifiers were evaluated in two classification tasks: a within-subject task using paired recordings from the same individual, and a between-subject task performed without individual baseline data. Within-subject classification consistently exceeded chance level for both measurement systems, with best accuracies of 85% for the wearable sensor (Logistic Regression) and 83% for the motion capture system (Random Forest). These findings indicate that sleep deprivation produces a systematic and individually consistent biomechanical signature during running. In contrast, between-subject classification failed across nearly all models and systems, with accuracies remaining close to chance level ([~]50%), demonstrating that inter-individual variability obscures the sleep-deprivation signal in the absence of personalized baseline data. Both systems converged on temporal organization, loading-related variables, and stride-to-stride variability as the most discriminative feature domains. Contrary to expectations, the laboratory motion capture system did not outperform the wearable sensor. Together, these findings demonstrate that individualized, baseline-referenced monitoring is essential for detecting sleep-deprivation-related changes in running gait, and suggest that a single trunk-mounted wearable sensor may provide a practical solution for real-world monitoring when paired recordings are available.

Background Menstrual cycle disturbances have been increasingly reported after COVID-19 vaccination, raising questions about their prevalence and clinical significance among women of reproductive age. Objective This study aimed to investigate the incidence and types of menstrual cycle alterations following different doses of COVID-19 vaccines among Iranian women of reproductive age. Methods A cross-sectional survey was conducted among vaccinated women who reported their menstrual cycle status before and after each vaccine dose. Data on cycle regularity, flow characteristics, and specific menstrual disorders were collected and analyzed. Results Menstrual cycle alterations were reported by 28.8%, 25.4%, 30.3%, and 68.4% of participants after the first, second, third, and fourth vaccine doses, respectively. The most common changes were oligomenorrhea after the first and second doses (8.9% and 5.6%), menorrhagia after the third dose (5.3%), and hypomenorrhea after the fourth dose (8.3%). Comparisons with international studies revealed a wide variation in prevalence (ranging from 25% to 78%), which may be explained by differences in methodology, population characteristics, vaccine types, and pre-vaccination health status. Conclusion A considerable proportion of Iranian women experienced menstrual alterations following COVID-19 vaccination, most commonly oligomenorrhea, menorrhagia, and hypomenorrhea. While generally self-limiting, these findings highlight the need to integrate menstrual health into post-vaccination monitoring and patient counseling. Future research should explore the underlying immune-endocrine mechanisms and long-term clinical implications of these changes.

Objective To evaluate adherence to published American Society for Reproductive Medicine (ASRM) infertility evaluation and treatment recommendations among commercially insured infertility patients who subsequently underwent in vitro fertilization (IVF) and to assess whether observed care gaps support the need for a restorative reproductive medical framework. Methods A retrospective claims-based analysis was performed using MarketScan(R) Commercial Claims and Encounter Data between January 1, 2021, and December 31, 2024. Approximately five million commercially insured members were evaluated. Patients with infertility-related diagnoses who subsequently underwent IVF were identified. Claims were analyzed for evidence of diagnostic testing, medical treatment, or surgical intervention recommended by ASRM or AUA/ASRM guidance before IVF initiation. Cumulative adherence rates were assessed over nine months following initial infertility diagnosis. Results IVF initiation rose early and consistently exceeded completion of nearly all guideline-recommended evaluations and treatments. Observed care gaps ranged from approximately 13% to 78% for most recommended evaluations and treatments, with several measures demonstrating gaps exceeding 50 percentage points, suggesting substantial divergence between guideline recommendations and observed clinical practice. By 3 months, IVF initiation ranged from 28% to 39% across cohorts, while adherence to many recommended interventions remained low. Overall, by 9 months, IVF utilization commonly exceeded 70-85%, while many guideline-supported evaluations and treatments remained below 40% adherence, with several interventions remaining below 15%. These findings suggest substantial divergence between published infertility-care recommendations and observed pre-IVF practice patterns. From an RRM perspective, the gaps are clinically important because many recommended steps are directed toward identifying, correcting, restoring, or preserving reproductive function and anatomy before reproductive barriers are bypassed through IVF. Conclusions Many commercially insured infertility patients appeared to progress to IVF without documented evidence of diagnostic evaluation or therapeutic intervention recommended in ASRM and AUA/ASRM guidance. These findings raise important questions regarding the implementation of infertility guidelines before IVF and the extent to which patients receive meaningful opportunities for diagnosis-directed treatment of potentially reversible causes of infertility. The findings further suggest an important role for restorative reproductive medicine as a quality-of-care framework focused on comprehensive evaluation, correction of underlying dysfunction, preservation of reproductive anatomy and physiology, and optimization of patient-centered fertility care prior to attempts with assisted reproduction.

ABSTRACT Background: Athletes who return to soccer after anterior cruciate ligament reconstruction (ACLR) remain at elevated risk of secondary injury despite meeting conventional discharge criteria, and neuromuscular deficits in the reconstructed limb are known to be exposed by fatigue. Objective: To determine whether match-play fatigue differentially affects muscle stiffness, countermovement jump (CMJ) force symmetry, and rate of force development (RFD) asymmetry between soccer players with a history of ACLR and uninjured teammates. Methods: A prospective, cross-sectional, matched-control study enrolled 128 competitive soccer players (64 ACLR, 6-22 months post-surgery; 64 uninjured controls) across five recruitment waves (February-June 2026). Bilateral CMJ peak vertical force, jump height, RFD, and myotonometric stiffness of the rectus femoris (RF), vastus medialis (VM), and biceps femoris (BF) were recorded immediately before and after a standardized competitive match. Fatigue was quantified from second-half heart rate (percentage of age-predicted maximum) and end-match rating of perceived exertion (RPE). Within-group pre-to-post changes were evaluated with paired t-tests, between-group differences in the magnitude of change with independent-samples t-tests, and associations between fatigue indices and asymmetry changes with Pearson correlations. Results: Match play reduced CMJ limb symmetry index (LSI) in both groups, but the decline was more than three-fold greater in the ACLR group, 92.6% (SD 5.4%) to 85.1% (SD 7.1%), than in control group, 97.3% (SD 3.9%) to 95.0% (SD 4.2%), group-by-time difference, p < 0.001, (d = 0.64). RFD asymmetry approximately doubled in the ACLR group, 10.6% (SD 4.1%) to 17.6% (SD 6.5%), compared with a smaller rise in control group, 4.6% (SD 2.4%) to 6.3% (SD 3.7%); p < 0.001, d = 0.77). Involved-limb stiffness losses in the ACLR group exceeded those of controls for the RF (-21.2 vs. -9.2 N/m, p < 0.001), VM (-17.7 vs. -6.1 N/m, p < 0.001), and BF (-13.3 vs. -6.6 N/m, p < 0.001), whereas uninvolved-limb stiffness losses did not differ between groups (all p > 0.05). Fatigue markers (heart rate, RPE) were not significantly correlated with the magnitude of individual asymmetry change (|r| [&le;] 0.18, p > 0.15). Conclusions: In competitive soccer players 6-22 months after ACLR, match-play fatigue selectively compromises stiffness and explosive force output of the reconstructed limb, widening inter-limb asymmetries beyond what is seen in uninjured teammates, even though global cardiovascular and perceptual fatigue were comparable between groups. These findings suggest that return-to-sport testing performed only in a rested state may underestimate residual neuromuscular deficits, and support fatigue-inclusive assessment protocols before athletes are cleared for unrestricted competition. Abbreviations: ACL: anterior cruciate ligament, ACLR: anterior cruciate ligament reconstruction, BF: biceps femoris, CMJ: countermovement jump, HRmax: maximum heart rate, LSI: limb symmetry index, RF: rectus femoris, RFD: rate of force development, RPE: rating of perceived exertion, RTS: return to sport, VM: vastus medialis, SD: standard deviation. Keywords: Anterior cruciate ligament reconstruction, muscle fatigue, muscle stiffness, countermovement jump, limb symmetry index, rate of force development, soccer, return to sport.

This study presents the use of sub-micron nanobubbles (NBs) as contrast agents for ultrasound localization microscopy (ULM), a super-resolution imaging technique that visualizes microvascular structure and flow beyond the acoustic diffraction limit. While ULM has traditionally relied on micron-sized microbubbles (MBs), the reduced dimensions and prolonged circulation times of NBs make them attractive candidates for localization-based imaging. However, their weaker acoustic responses present significant challenges for reliable detection and tracking. To address this challenge, we developed the ULM Master GUI, an interactive framework for optimization of the complete ULM processing pipeline. Using custom ultrasound-compatible wall-less gelatin flow phantoms containing vessel-mimicking channels and bifurcations ranging from 100 to 500 m, we demonstrate that NB-based ULM achieves velocity reconstruction and flow partitioning measurements comparable to conventional MB-based ULM. Across all investigated geometries, NBs faithfully reproduced the underlying flow patterns and hemodynamic behavior despite their substantially reduced acoustic scattering. These findings establish the feasibility of NB-based ULM, expand the range of contrast agents available for localization microscopy, and provide a foundation for future super-resolution ultrasound imaging using nanoscale acoustic contrast agents. The ULM processing GUI is publicly available at https://github.com/grisha1998/ulm-super-resolution-toolbox.

Brain-computer interfaces (BCIs) have achieved impressive performance by decoding motor and articulatory signals associated with speech production. However, considerably less is known about whether higher-level semantic representations can be decoded from human cortical activity. Demonstrating semantic decoding would advance both our understanding of language organization and the development of BCIs that rely on conceptual rather than purely articulatory information. We recorded intracranial neural activity from patients undergoing stereotactic electroencephalography (sEEG) for clinical epilepsy monitoring while they performed language tasks requiring semantic processing. High-gamma power was extracted from local field potentials and used to generate trial-level features for supervised machine-learning classification. Classification performance was evaluated using cross-validation. Semantic category information was decoded significantly above chance, with mean classification accuracy reaching 29.8% across 15 semantic categories (chance = 6.7%). These findings demonstrate that high-gamma activity contains information about conceptual category membership that can be extracted on individual trials. These results provide evidence that semantic information is accessible from intracranial population recordings and support the feasibility of semantic decoding as a complementary direction for future language BCIs. Beyond neuroprosthetic applications, this work contributes to understanding how conceptual knowledge is represented in the distributed human language network.

Aphasia diagnosis in Indonesia remains challenging due to limited culturally and linguistically appropriate instruments. Widely used tools such as the Boston Diagnostic Aphasia Examination (BDAE) and Western Aphasia Battery (WAB) are not adapted to the Indonesian context, while Tes Afasia untuk Diagnosis, Informasi, dan Rehabilitasi (TADIR) provides screening but lacks diagnostic accuracy. To address this gap, we developed the Instrumen Diagnosis dan Evaluasi Afasia (IDEA) for native Indonesian speakers and evaluated its validity, reliability, and normative cutoff values in cognitively healthy Indonesian adults. Eighty-three cognitively normal adults (screened using MoCA-Ina) with no history of neurological disease were assessed using IDEA, which evaluates six language domains. Items were adapted from existing tools and reviewed by experts. Content validity, internal consistency (Cronbachs alpha), and construct validity (Exploratory Factor Analysis) were analyzed using SPSS v25. A total of 83 participants were included (median age = 55.81 years, 54% secondary education). IDEA demonstrated good feasibility, with an average completion time of 45-60 minutes depending on participant engagement. Content validity was established by unanimous expert consensus. Construct validity showed meritorious sampling adequacy (KMO = .872) and significant sphericity (Bartletts test {chi}^2 (15) = 278.523, p<.001), supporting factor analysis. Internal consistency showed good reliability across six domains (Cronbachs = 0.896). IDEA is a valid and reliable tool for assessing aphasia in Indonesian natives. It is a culturally appropriate assessment tool which offers structured, domain-based evaluation and supports differential diagnosis of both classical and progressive aphasia syndromes. Keywords: Aphasia, Language Assessment, Indonesian, IDEA, Validity

Background & Methods: The multifaceted physical nature of heritable cognitive impairment in dementia presents significant challenges for traditional linear frameworks attempting to model synergistic risk. While various loci are identified as contributing to neurocognitive disparities, the emergent phenotypic expression and associated predictive value relative to standard clinical baselines require further investigation. To facilitate dimensional reduction of complex genetic data into identifiable phenotypes, Generalized Low Rank Modeling (GLRM) and K-means clustering are applied to participant data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The utility of these derived archetypes and clusters is assessed, stratifying variance for Mini-Mental State Examination (MMSE) performance. Utilizing generalized additive modeling (GAM) and partial eta squared (p2) effect size, the derived genetic features are compared with other predictors including age, educational attainment, gender, and raw, genetic variant carriage dimensions. Results & Conclusion: In accordance with the hypothesized empirical regularity, age and education persist as primary predictors of MMSE performance. The unsupervised machine learning pipeline successfully identified a composite genetic cluster that emerged as an influential predictor in terms of relative magnitude (p2). Centroid analysis of the GLRM subspace indicated that a particular sub-population (Cluster 2) - defined by a substantial weighting on the EPHA1 target - demonstrated a statistically significant association with MMSE scores, relative to cluster 3. These results suggest that data-driven genetic feature engineering provides an interpretable basis for inference regarding variance in global cognition. By discovering multivariate genetic architecture, this modeling approach captures complexity often missed by individual clinical variable modeling. Such findings implicate the utility of interpretable machine learning for translational dementia research and predictive clinical stratification.

Intracranial neurophysiology studies have typically ignored signals from electrodes located in white matter (WM), assuming that their information content is artifactual or related to nearby gray matter (GM). Here, we tested the electrophysiological and functional features of signals recorded from different WM locations. Signals were recorded from 19 patients undergoing intracranial monitoring for drug-resistant epilepsy by means of stereo-electroencephalography (sEEG). Each sEEG electrode was classified into WM or GM based on the surrounding tissue. We obtained recordings from a total of 1,717 sEEG electrode contacts, 36% in WM, while the patients were in awake resting state (5 minutes). For each sEEG electrode, we employed a model-based spectral decomposition to separate periodic and aperiodic components, and we computed signal complexity metrics. For a subset of participants, we computed WM structural information from diffusion-weighted magnetic resonance imaging and we evaluated functional signals during a cognitive control task. Our results show that signals recorded from WM have different spectral features and higher complexity than GM. Complexity correlates positively with fractional anisotropy, and modulations related to behavior during the task were detected in WM. Overall, this indicates that WM signals carry information that may reflect signal propagation across WM fiber tracts.

Deep learning classifiers applied to structural MRI (sMRI) have achieved high performance in detecting Alzheimer's Disease (AD), yet systematic investigation of their failure modes remains limited. In this study, we trained two deep learning architectures to classify AD from cognitively normal (CN) participants using sMRI data from the ADNI dataset, and examined whether misclassifications persist across models and training configurations. We identified a subgroup of subjects who were persistently misclassified across 100 model instances, and found that these subjects exhibited a markedly different atrophy subtype distribution compared to correctly classified AD cases, with substantial enrichment of hippocampal-sparing and minimal atrophy subtypes. To disentangle whether persistent false negatives (FN) reflect earlier disease stage or atypically presenting disease, we analyzed longitudinal follow-up scans and tested whether model predictions changed as neurodegeneration progressed. A change in prediction (from FN to true positive (TP)) was observed in only a subgroup of subjects and required intervals of up to five years, suggesting that persistent misclassification may not always be explained by disease staging alone. Although the sample size is small, these findings underscore the importance of accounting for disease heterogeneity in the development and evaluation of clinical AI models for AD detection.

Cognitive impairment is a prevalent symptom extending from physiological ageing to disease. It commonly manifests itself in initial memory problems, progressing and co-occurring in more severe conditions such as Mild Cognitive Impairment, Alzheimer's Disease and Major Depressive Disorder. However, current non-invasive screening assessments either lack biological information or are invasive and restricted to specialized centers with complex and cost-intensive set-ups. Here, we conducted an initial validation of mobile pupillometry with Virtual Reality (VR) under experimental conditions as a digital biomarker for cognitive impairment by testing required biomarker-specific properties. For this purpose, we first assessed its construct validity by testing healthy participants (n=43) on an n-back task in VR while pupil size was measured. Mixed effects models revealed that similar to lab-based eye-tracking systems, pupil size increased in a sensible and distinguishable fashion as a function of working memory load. Second, to test the signal's reliability, the same participants were tested on the identical set-up two to three months after their first visit. We observed that the pupil response profile was highly stable over this period. Third, for its clinical validity, we examined patients (n=89) from three different cohorts with varying degrees of cognitive impairment and compared them to healthy control participants (n=81). Mixed-effects models indicated that pupil size was reduced as a function of cognitive impairment levels at higher cognitive load and that this effect was stronger pronounced with increasing age. In conclusion, we provide initial evidence for mobile pupillometry being a sensitive, reliable and clinically valid digital biomarker for cognitive functioning and impairment, which offers desirable properties due to its quick, automatized and location-independent set-up. Keywords: digital biomarker, mobile pupillometry, Virtual Reality, cognition, , Major Depressive Disorder, Mild Cognitive Impairment, Alzheimer's Disease

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.

Background: aneurysmal subarachnoid hemorrhage (aSAH) is neurological emergency associated with substantial mortality and disability. Current grading systems such as the modified Fisher Scale (mFS) and World Federation of Neurological Societies (WFNS) score, rely on semiquantitative and examination based assessments. Hence, they demonstrate limited predictive precision. The enhanced subarachnoid hemorrhage (eSAH) score is a simplified quantitative model integrating age, Glasgow Coma Scale (GCS), and cisternal subarachnoid hemorrhage volume (SAHV) to predict clinical outcomes after aSAH. Methods: We performed a retrospective multicenter cohort study that included 1088 patients across three tertiary-care centers the United States. Predictive performance for unfavorable functional outcome, in-hospital mortality and delayed cerebral ischemia (DCI) was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). Comparative analyses were performed and compared to the WFNS and mFS grading systems. Results: the eSAH score demonstrated excellent discrimination for unfavorable functional outcome at discharge ( AUC 0.89 ) and in-hospital mortality (AUC 0.87). The DCI subscore demonstrated good discriminatory performance for predicting DCI (AUC 0.77). Compared with conventional grading systems, this was superior to both the WFNS (AUC 0.75) and the mFS ( AUC 0.70). increasing eSAH scores were additionally associated with progressively higher rates of mortality and unfavorable functional outcomes. Conclusion: the eSAH score demonstrates strong external validity, reproducibility and superior predictive performance compared with conventional grading systems in a large multicenter cohort. These findings support the clinical utility of quantitative hemorrhage burden integration for early risk stratification in patients with aSAH.