Accurate quantification of aluminum (Al) uptake is essential for evaluating metal toxicity in experimental models. This study aimed to optimize pretreatment strategies and determine the effects of individual number and dose on Al accumulation and assess the biological relevance of Al exposure in the Caenorhabditis elegans model. The investigation was conducted in two stages. In the first stage, synchronized groups of 10, 25, 50, 100, 200, and 400 worms, along with controls, were exposed to 10, 15, and 25 mM AlCl₃ for seven days After exposure, worms were washed to remove Al from the culture medium, viability was terminated using nitric acid, and Al levels were quantified by ICP-MS without pretreatment. In the second stage, 50 worms exposed to 100 mM AlCl₃ for seven days and subjected to four different pretreatment protocols prior to ICP-MS analysis. Reproductive toxicity was additionally evaluated to determine the biological impact of Al accumulation. Significant dose-dependent increases in Al uptake were observed, particularly in groups containing 10, 25, and 50 worms (first trial). Among pretreatment methods (second trials), the combined sonication and centrifugation supernatant protocol yielded the highest Al levels (78.47 mM), followed by pretreatment A (68.68 mM), C (64.52 mM), and B (59.60 mM). Furthermore, Al exposure significantly reduced reproductive capacity compared with controls These findings demonstrate that both organism number and pretreatment strategy critically influence Al quantification and confirm that accumulated Al is associated with biologically relevant toxic effects. The optimized methodology provides a robust framework for future metal toxicity studies.
Accurate identification of ultra-low-frequency tumor-derived variants is critical for circulating tumor DNA (ctDNA)-based minimal residual disease (MRD) profiling. However, current ctDNA analysis workflows largely operate as predefined, sequential pipelines without explicit mechanisms for continuous monitoring of variant-calling performance or adaptive control, thereby limiting detection stability in genomically heterogeneous regions. To address this limitation, we developed MRDsteer, an autonomous closed-loop agent driven by artificial intelligence (AI). MRDsteer monitors the analytical reliability of variant calling during the analysis process using multidimensional quality metrics, such as filtration ratio and strand bias. When the estimated reliability falls below an actionable threshold, MRDsteer triggers localized re-calling only in high-risk genomic regions, instead of repeating the entire analysis. In this way, MRDsteer provides closed-loop control by continuously assessing variant-calling reliability and applying targeted intervention when needed. Comparative analyses in simulated and real-world datasets showed that MRDsteer improved the stability and sensitivity of ctDNA variant detection. Under challenging conditions, including ultra-low variant allele frequencies, MRDsteer demonstrated improved detection performance compared with representative baseline methods. In clinical cohorts, MRDsteer improved ctDNA-based MRD stratification and strengthened progression-free survival separation in the K438 cohort, including both non-small cell lung cancer (NSCLC) and nasopharyngeal carcinoma (NPC) subgroups. These results suggest that MRDsteer may provide a robust and clinically useful computational strategy for sensitive MRD detection and longitudinal ctDNA monitoring. https://github.com/aAT0047/MRDsteer.git.
Accurate molecular geometries are indispensable for predictive quantum chemistry, yet iterative density functional theory (DFT) optimization remains a major computational bottleneck in large-scale screening. Here, we introduce GeoOpt-Net, a deterministic, SE(3)-equivariant single-step geometry refinement network that maps inexpensive force-field conformers directly to B3LYP/TZVP-quality structures. Trained using a two-stage multifidelity protocol with theory-aware feature modulation, GeoOpt-Net learns transferable geometric priors and calibrates them to target-level quantum accuracy in a single forward pass. Under strictly matched B3LYP/TZVP conditions, GeoOpt-Net achieves structural deviations on the order of 10-4 Å and single-point energy deviations on the order of 10-4 kcal mol-1, outperforming classical, semiempirical, and neural potential-based approaches. Notably, its predicted geometries satisfy all standard DFT convergence criteria for 65.0% (loose) and 33.4% (default) of molecules, whereas baseline methods remain near zero, substantially reducing subsequent optimization effort. By replacing iterative relaxation with deterministic single-step refinement, GeoOpt-Net offers a scalable and physically consistent protocol for accelerating high-throughput quantum-chemical workflows.
Solid tumors remain difficult to treat because their therapeutic resistance is shaped not only by malignant-cell heterogeneity but also by stromal barriers that prevent therapeutic agents and immune cells from effectively reaching and eliminating tumor cells. Among these stromal components, cancer-associated fibroblasts (CAFs) are among the most abundant and functionally influential cell populations in the tumor microenvironment. Through extracellular matrix remodeling, paracrine signaling, immune regulation, and metabolic crosstalk, CAFs profoundly influence therapeutic response and prognosis in solid tumors. As central stromal regulators, CAFs determine whether antitumor therapies can achieve effective tumor inhibition. This central regulatory role makes CAFs rational and increasingly important therapeutic targets in solid tumors. However, CAF-targeted therapy is complicated by the pronounced heterogeneity and plasticity of CAF populations. Distinct CAF subsets may exert divergent, or even opposing, effects on tumor progression and therapeutic response. Therefore, effective CAF-targeted therapy should move beyond nonspecific CAF depletion and instead focus on precise stromal modulation. Nanomedicine provides a powerful strategy to strengthen CAF-directed therapy because nanomaterials can be engineered to match the biological and spatial features of CAF-rich tumor stroma. By tuning size, charge, shape, porosity, surface ligands, biomimetic coatings, and stimulus-responsive release, nanocarriers can enhance stromal accumulation, bind CAF-associated targets, and convert CAF biology into actionable therapeutic selectivity. In this review, we summarize the biological origins, activation mechanisms, subtype heterogeneity, dual functions, and biomarker landscape of CAFs in solid tumors, and systematically discuss how CAFs drive therapeutic resistance through physical, biochemical, metabolic, and immune barriers. We then highlight current CAF-targeted nanomedicine strategies, including CAF-selective delivery, extracellular matrix remodeling, CAF reprogramming, immune microenvironment regulation, and combination with chemotherapy, radiotherapy, photothermal/photodynamic therapy, immune checkpoint blockade, and adoptive cell therapy. Finally, we discuss key translational challenges, including target specificity, deep stromal penetration, long-term safety, biomarker-guided patient stratification, scalable manufacturing, and AI-assisted nanocarrier optimization. Overall, CAF-targeted nanomedicine offers a promising route to transform the tumor stroma from a barrier to therapy into a modifiable therapeutic interface, thereby improving the precision and efficacy of solid tumor treatment.
New long field-of-view (FOV) PET scanners using bismuth germanate (BGO) detectors without time-of-flight (TOF) capability are now available. These systems incorporate deep learning-based TOF (DLb-TOF) models to compensate for the absence of TOF. There is a lack of studies systematically investigating the optimal balance between signal to noise ratio and lesion detectability across a broader range of acquisition times and β-values for these DLb-TOF models. This study aims to evaluate the trade-off between acquisition time, signal-to-noise ratio (SNR) and lesion detectability to guide optimization of clinical protocol. Twenty patients referred for a clinical [18F]fluorodeoxyglucose (FDG) PET scan were included. Each patient received 3.5 MBq/kg of [18F]FDG and underwent a whole-body PET acquisition (120 s/bed) on a digital BGO PET/CT (32 cm FOV) 60 min post-injection. Data were reconstructed into images (384 × 384 matrix) representing different acquisition times (120 s, 90 s, 60 s, 45 s, 30 s and 15 s) using BSREM with β-values ranging from 50 to 1100. Three DLb-TOF models (Low, Medium, High) were applied. Volumes of interest were placed in the liver and two avid lesions per patient. SNR were calculated as SUVmeanliver/SDliver and detectability were calculated as SUVpeaktumor/SUVpeakliver. SNR increased with longer acquisition times and higher β-values. DLb-TOF models improved SNR across all settings, with the Low DLb-TOF model producing the largest increase. Lesion detectability depended on the acquisition time and β-value. At longer acquisition times (120 s, 90 s), β100 provided the highest detectability, while shorter times (60-15 s) required higher β-value (β300) for optimal detectability. Among DLb-TOF models, the High model gave the best detectability overall, though the Low model performed better at lower β-values. SNR increased with higher β-values, longer acquisition times, and DLb-TOF application. Lesion detectability, defined as the ratio of SUVpeak in the lesion to SUVpeak in the liver, depended on the β-value, acquisition time, and the DLb-TOF model used. The Low DLb-TOF model had the best SNR but at the expense of detectability. The optimal parameters for the evaluated BGO PET/CT system, balancing SNR and lesion detectability within a clinical reasonable acquisition time, were 60-90 s with β-values of 500-300, in combination with the Medium DLb-TOF model, when 3.5 MBq/kg [18F]FDG was administered.
To develop a pipeline for evaluating large language models (LLMs) on the task of capturing symptoms from clinical encounters. We created a gold standard dataset of symptom annotations from simulated doctor-patient encounter excerpts (264 encounters; 16 symptoms; double-coded and adjudicated). Nine different LLMs from 4 vendors (OpenAI, Meta, DeepSeek, Moonshot AI) were used as examples to test our evaluation pipeline; outputs were assessed for correct structure and symptom information. Of 3085 excerpts, 2087 (68%) contained symptoms. Pain, cough, and shortness of breath were most common; LLMs achieved F1 scores ranging 0.66-0.88 for these symptoms with minimal prompt engineering. Of tested models, GPT-4.1 demonstrated the best overall performance. Our evaluation pipeline and benchmarking dataset are publicly available and applicable to various LLMs, including open-source models. This work supports the development and optimization of models that seek to improve patient symptom understanding.
Adeno-associated virus (AAV) vector-based liver gene therapy for inherited diseases has demonstrated efficacy in clinical trials in adults. However, its application to pediatric patients is limited by loss of AAV genomes during hepatocyte proliferation, compromising long-term benefits. Additionally, the anti-AAV immune responses induced after the initial AAV-administration preclude vector re-dosing. One key driver of this immune response is mTOR-dependent activation of dendritic cells. Inhibition of this pathway with rapamycin can promote immune tolerance. We assessed the safety and efficacy of rapamycin-loaded synthetic nanoparticles (ImmTOR) in juvenile OTCSpf-Ash mice, a model of ornithine transcarbamylase (OTC) deficiency (OTCD). Treatment of postnatal day 30 mice with ImmTOR alone transiently activated autophagy and reduced urinary orotic acid levels. Treatment with a liver-specific AAV8 vector encoding codon-optimized human OTC cDNA normalized urinary orotic acid levels and significantly increased ureagenesis, OTC protein expression, and enzyme activity compared with untreated controls. AAV-vector co-administration with ImmTOR prevented IgM and IgG formation and induced a dose-dependent reduction of anti-AAV neutralizing antibodies, consistent with modulation of the humoral immune response. These findings suggest that ImmTOR can mitigate humoral immune responses to AAV vectors in OTCSpf-Ash mice and may enable AAV vector re-administration, although further optimization is required for clinical translation.
With a rapidly aging American population, elective shoulder procedures are increasingly performed in older adults. Prior studies have largely focused on total shoulder arthroplasty, leaving limited data on outcomes across a broader range of elective shoulder procedures. This study aimed to compare postoperative outcomes between septuagenarians and octogenarians undergoing elective shoulder surgery. The American College of Surgeons National Surgical Quality Improvement Program database (2011-2022) was queried for patients aged 70-89 years undergoing elective shoulder procedures, and patients were stratified into septuagenarians (70-79 years) and octogenarians (80-89 years). Multivariable logistic regression analyses adjusted for demographic and clinical covariates were used to assess postoperative outcomes. A total of 12,099 patients were included, comprising 10,810 septuagenarians (89%) and 1289 octogenarians (11%). After adjustment, octogenarians had higher odds of unplanned readmission (OR 1.57, 95% CI 1.04-2.29; p = 0.026) and non-home discharge (OR 2.44, 95% CI 1.57-3.72; p < 0.001). No significant differences were observed in overall complications, reoperation, or 30-day mortality. Octogenarians undergoing elective shoulder procedures are at increased risk for readmission and non-home discharge despite similar short-term complication rates. These findings underscore the importance of perioperative planning and discharge optimization in this population.
Pericardial effusion, defined as the abnormal accumulation of fluid within the pericardial sac, has a broad differential diagnosis, and identifying the underlying etiology can be particularly challenging when multiple potential causes coexist. Mixed connective tissue disease (MCTD) is a rare systemic autoimmune disorder. MCTD is characterized by overlapping features of systemic lupus erythematosus (SLE), systemic sclerosis (SSc), and polymyositis in association with anti-U1 ribonucleoprotein antibodies (anti-U1-RNP). Although pericarditis is the most common cardiac manifestation of MCTD, large pericardial effusion as the initial presenting feature has been described in only a small number of case reports. We describe the case of a 62-year-old malnourished female (BMI 17.7 kg/m2) with a history of hypertension, hypothyroidism, and chronic dysphagia who presented with persistent nausea and vomiting. Laboratory evaluation revealed leukopenia, anemia, hypoalbuminemia (2.8 g/dL), subclinical hypothyroidism, and normal inflammatory markers. CT obtained for gastrointestinal symptoms incidentally revealed a large pericardial effusion. Transthoracic echocardiography confirmed a large circumferential effusion with an end-diastolic diameter of 4.87 cm and early tamponade physiology, including right atrial systolic collapse and significant respiratory variation in tricuspid inflow. Ultrasound-guided pericardiocentesis yielded approximately 1000 mL of clear yellow fluid; cultures and cytology were negative. The sausage-shaped swollen fingers seen clinically prompted serologic tests, which were positive for antinuclear antibodies (ANA), anti-U1-RNP, and anti-Sjögren's-syndrome-related antigen A (anti-SS-A) antibodies. Anti-double-stranded DNA (anti-dsDNA), anti-Smith, anti-Scl-70, and anti-centromere antibodies were negative. Esophagogastroduodenoscopy showed a lower esophageal stricture consistent with chronic reflux. The patient's findings were consistent with the Kasukawa classification criteria for MCTD, with the patient demonstrating anti-U1-RNP antibodies, swollen digits (a common symptom), pericarditis/serositis (an SLE-like feature), and esophageal involvement (an SSc-like feature). The patient improved after pericardiocentesis and nutritional optimization and was discharged with multidisciplinary follow-up. This case illustrates the diagnostic challenge of determining the etiology of a large pericardial effusion in a patient with multiple possible confounding factors, including malnutrition, subclinical hypothyroidism, and unrecognized autoimmune disease. Normal CRP and absence of fever were suggestive of a noninflammatory process, which may be seen in autoimmune pericardial disease, though a normal CRP is not specific for autoimmune pericarditis. While prior case reports have described large pericardial effusion as an initial manifestation of MCTD, the unique contribution of this case lies in the specific diagnostic challenge of disentangling MCTD from concurrent malnutrition and subclinical hypothyroidism in an elderly patient, a clinical scenario that underscores the importance of maintaining a high index of suspicion for autoimmune disease in patients with idiopathic pericardial effusions, even when nutritional or metabolic causes seem more likely.
Integrating artificial intelligence with computational methods has emerged as a transformative force in materials research, exemplified by breakthroughs such as DeepH and DeepMD. However, a critical gap persists in multiscale intelligent design: the lack of mesoscopic models capable of bridging microstructural features with macroscopic properties. Here, we develop DeepMeso, a generative deep learning-enabled mesoscopic model, for the intelligent design of complex heterogeneous materials. To exemplify the framework, we instantiate it in ferroelectrics (DeepFerro), where our workflow first integrates a data-driven surrogate model to predict key ferroelectric properties with an accuracy exceeding 99.6%. Then, we implement a 3D generative network to achieve inverse design across both composition and microstructure spaces, directly targeting predefined polarization objectives. When tasked with multi-objective on-demand generation, DeepFerro yields a mean squared error of 0.0497 and an R 2 value of 95.44% against simulation benchmarks. Critically, it also exhibits robust transferability and extrapolation capability across 63 distinct ferroelectric systems, demonstrating its generalizability in end-to-end optimization. In parallel, model interpretability translates the learned relations into explicit, hierarchical guidelines. This generalizable intelligent design framework DeepMeso could be further extended to diverse heterogeneous materials, which will deepen the understanding of composition-microstructure-property relationships and facilitate the on-demand inverse design at the mesoscopic scale.
MXenes are emerging as promising electrode materials for energy storage applications owing to their high conductivity and redox-active surfaces. However, their inherent propensity to undergo oxidation is frequently regarded as a limitation. Here, we demonstrate that the routine washing step following molten-salt etching of Mo2CTx can be exploited as a controlled strategy to engineer Mo2CTx/MoOx hybrid interfaces. By systematically varying the washing duration in HCl/CuCl2 solution (30, 90, and 240 minutes), we precisely modulate the degree of Mo oxidation, with an additional H2O2 treatment serving as a reference for deliberate chemical oxidation. In-depth structural and spectroscopic analyses reveal two distinct oxidation mechanisms: the washing process induces selective Mo-centered oxidation that maintains the integrity of the carbide framework, resulting in conductive Mo2CTx/MoOx domains, whereas H2O2 oxidation also involves both Mo and C sites, yielding a hydroxylated surface that compromises long-term stability. The electrochemical performance in acidic electrolyte highlights the advantages of the washing protocol: Mo2C-90 delivers the highest capacitance (∼150 F g-1 at 10 mV s-1) with robust cycling stability (90% retention after 10 000 cycles). This study establishes washing-induced oxidation as a simple method for tailoring Mo2CTx MXenes in supercapacitor applications, transforming a standard synthesis into a deliberate tool for performance optimization.
The most conventional atomic layer processing method, atomic layer deposition (ALD), delivers ultrathin blanket coatings with sub-nanometer thickness precision. Lateral confinement underpins the direct atomic layer processing (DALP®) family of deposition techniques. ALD chemistry applied to DALP® is a 3D printing method called atomic-layer additive manufacturing (ALAM). Here, we demonstrate the applicability of ALAM to the additive buildup of the crucial ZnS / Sb2S3 / V2O5 semiconductor stack which constitutes an functional inorganic solar cell. To this goal, ALAM processes are first optimized and evaluated for the individual materials vanadium(V) oxide, zinc sulfide, and antimony(III) sulfide. We establish the layer-by-layer growth mode controlled by self-limiting surface chemistry and characterize the materials' structure and the smooth surface morphology of ALAM-coated areas. Finally, all three materials are 3D-printed in ALAM mode in combination with electrodes and the electron acceptor titania (TiO2) to form functional solar cells with a 120 nm thick Sb2S3 absorber layer. This novel fabrication of solar cells highlights the advantages of using direct patterning in the prototyping and optimization of photovoltaics in research and development.
Todd's paralysis is a transient focal neurological deficit following epileptic seizures that may closely mimic an acute ischemic stroke, posing a diagnostic challenge in the emergency department. We report the case of a 32-year-old woman with a history of epilepsy who presented with persistent expressive aphasia and right hemiparesis after experiencing multiple generalized tonic-clonic seizures. On admission, her National Institutes of Health Stroke Scale (NIHSS) score was 6, initially raising strong suspicion for an acute ischemic cerebrovascular event. Noncontrast cranial computed tomography (CT) showed no intracranial hemorrhage or early ischemic changes. Due to the persistence of focal deficits, brain magnetic resonance imaging (MRI) was performed, which demonstrated no evidence of acute cerebral infarction or structural abnormalities. Electroencephalography (EEG) demonstrated focal epileptiform activity with secondary generalization. Following optimization of her antiseizure regimen with the addition of valproate, the patient achieved complete seizure control and full resolution of all neurological deficits, establishing the diagnosis of Todd's paralysis with postictal aphasia. This case underscores the importance of considering Todd's paralysis in the differential diagnosis of acute focal neurological deficits, particularly in patients with a history of epilepsy and prolonged postictal symptoms. In resource-limited settings where advanced perfusion imaging may be unavailable, integrating clinical findings, conventional neuroimaging, and electroencephalographic evaluation can provide sufficient diagnostic confidence to distinguish severe postictal deficits from acute ischemic stroke and avoid unnecessary or potentially hazardous interventions.
Postoperative infections (POIs) significantly contribute to morbidity and mortality following partial hepatectomy for hepatocellular carcinoma (HCC). While the Systemic Immune-Inflammation Index (SII) and Prognostic Nutritional Index (PNI) are recognized biomarkers for immune-inflammatory and nutritional status, their combined predictive value for POIs in liver surgery requires further investigation. This study evaluates SII and PNI as preoperative predictors for infections in this patient population. A retrospective observational study was conducted on 300 patients undergoing partial hepatectomy between 2022 and 2024. Preoperative laboratory data were used to calculate SII and PNI, with POIs identified within 30 days based on CDC guidelines. Statistical analyses, including multivariate logistic regression, were performed to compare infected and non-infected cohorts and identify independent predictors of infection. Of the 300 patients, 96 (32%) developed POIs. The infected group exhibited significantly higher SII (1142 ± 618 vs 792 ± 450) and lower PNI (40.1 ± 5.8 vs 46.4 ± 5.9) than the non-infected group. Multivariate analysis confirmed high SII (OR 2.85) and low PNI (OR 3.26; 95% CI, 2.01-5.12) as independent predictors. Furthermore, infections were associated with prolonged hospitalization, increased ICU admissions, and higher 30-day mortality. Preoperative SII and PNI are effective, independent predictors of POIs in patients undergoing hepatectomy for liver cancer. Integrating these biomarkers into routine evaluation enhances risk stratification and guides perioperative optimization. Early identification through these indices allows for targeted interventions, such as nursing-led nutritional support and intensified surveillance, to reduce complications and improve surgical outcomes.
To investigate spatially distinctive features in fundus photographs of highly myopic glaucoma (HMG) by integrating radiomics and deep learning. Cross-sectional study. Semi-automated optic disc segmentation was performed on 2000 images sourced from the Retinal Fundus Glaucoma Challenge Edition and Pathologic Myopia Challenge public data sets. We trained models with 628 images, including 217 cases of high myopia (HM), 221 cases of primary open-angle glaucoma (POAG), and 190 cases of HMG. An external validation set of 106 images was collected including 31, 36, and 39 fundus photographs from patients with HM, HMG, and POAG, respectively. Semi-automated optic disc segmentation was performed by U-Net combined with manual delineation. Additionally, 5 regions of interest (ROIs) covering the optic disc and its surrounding region were explored. Fundus photography-based radiomics feature selection and optimization were conducted using random forest and support vector machine algorithms, which underwent fivefold cross-validation. Model performance was evaluated for radiomics, clinical, and combined models. An external validation set was used to evaluate the models performance, and we also examined radiomics features varied across glaucoma stages. A total of 414 radiomics features were extracted from 5 regions of interest. We evaluated the model performance using accuracy, recall, precision, F1 score, and area under the receiver operating characteristic curve (AUROC). The Youden index was used to determine the cut-off value, and sensitivity and specificity were calculated. The U-Net-based optic disc segmentation reached a Dice Similarity Coefficient of 0.95. The radiomics model detected HMG from HM and POAG achieved the accuracies of 0.97 and 0.85, respectively. Both results exceeded the performance of the clinical model, which achieved 0.90 and 0.71. The top radiomics features achieved AUROC of 0.984 and 0.855 for HMG versus HM and HMG versus POAG in the external validation, respectively, including intensity features in the extra-optic nerve and textural features in the optic nerve. These features showed strong diagnostic capability when stratified by Youden values and exhibited independence from glaucoma progression. The study combined U-Net and radiomics to delineate the spatial distribution biomarkers of HMG, establishing a quantitative classification model correlated with anatomical features. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
The nasal epithelium is the first respiratory epithelium that is exposed to inhaled airborne pathogens. As a result, it is crucial to model host-pathogen interactions occurring in this tissue. To facilitate the efficient modeling of these interactions, we have developed a method to generate de novo apical-out nasal organoids from nasal epithelial cell aggregates. Optimization of this method revealed a stark tissue-specific effect of the culture temperature, as organoids were generated in much higher efficiency at 32.5°C compared to more widely used temperatures of 37°C. These organoids recapitulate the native tissue cellular composition of ciliated, basal, and goblet cells, while maintaining high homogeneity in size. Functionally, the system demonstrates susceptibility to viral infection and provides a robust platform for modeling antiviral drug responses. This standardized approach offers a reproducible system with high potential to be utilized in host-pathogen interaction studies and personalized medicine.
Directed self-assembly (DSA) of block copolymer (BCP) thin films is a promising strategy for fabricating nanoscale patterns. However, defects and limited controllability during processing remain significant barriers to practical implementation. In this study, we introduce solvent immersion annealing (SIA) as a facile and effective approach to improving alignment quality and suppressing defect formation in shear-aligned polystyrene-b-poly(2-vinylpyridine) (PS-b-P2VP) thin films. By immersing shear-aligned films in solvent mixtures, SIA provides enhanced control over polymer swelling and chain mobility, enabling rapid annihilation of defects and long-range ordering within minutes. Systematic analysis of immersion time, solvent composition, and swelling behavior reveals that the optimal SIA process is achieved by preserving a delicate balance between chain rearrangement and film stability during solvent immersion. Compared to solvent vapor annealing (SVA), SIA offers superior swelling controllability and reduced sensitivity to external factors such as ambient humidity. Furthermore, large-area SEM and GISAXS analyses confirm that SIA enables scalable fabrication of well-aligned nanopatterns across extended surfaces. Taken together, this work highlights SIA as a robust and efficient strategy for achieving BCP nanopatterns with substantially reduced defect density, therefore offering potential for future lithography applications.
The ESSENCE Questionnaire (ESSENCE-Q) helps detect early neurodevelopmental disorder signs across multiple developmental domains for timely intervention. We evaluated the feasibility, reliability, accuracy and reproducibility of ESSENCE-Q in 18- and 36-month Japanese infants and toddlers. Longitudinal data from 1373 children who participated in 18- and/or 36-month health check-up were analysed. ESSENCE-Q was completed independently by mothers (ESSENCE-Q-M), public health nurses (ESSENCE-Q-N) and psychologists (ESSENCE-Q-P). Its effectiveness was evaluated using receiver operating characteristic curves, and optimal cut-off values were established. The overall neurodevelopmental disorder prevalence was 11.2%. The internal consistency of ESSENCE-Q was good. ESSENCE-Q-P demonstrated the highest performance at 18 months. Both ESSENCE-Q-N and ESSENCE-Q-P demonstrated excellent performance at 36 months. ESSENCE-Q-M is not sufficient for standalone use. ESSENCE-Q is a highly useful and reproducible screening tool for professionals for 18- and 36-month check-ups. ESSENCE-Q completed by mothers should be supplemented with professional assessments. We assessed the utility of the ESSENCE Questionnaire (ESSENCE‐Q) at 18‐ and 36‐month check‐ups in Japanese children. ESSENCE‐Q assessed by psychologists showed the best performance at 18 months, while assessments by public health nurses and psychologists were best at 36 months. ESSENCE‐Q is highly useful and reproducible when used by professionals; assessments by mothers should be done in conjunction with professionals.
The design of materials with functionalities optimally suited for quantum information applications is a critical need in the field of quantum science and engineering. This perspective focuses on a specific class of systems, spin defects in semiconductors and insulators, and on the manipulation of their electron spins, which can provide controllable qubits with long relaxation and coherence times, and they can be coupled to nuclear spins for long-lived quantum memories. We summarize our recent contributions to the development of integrated theoretical frameworks and high-performance codes to predict and design spin defects and present examples of validated predictions and interpretations of experimental results. Starting from a brief description of the structural and charge stability at zero temperature using density functional theory, we present simulations to understand the mechanism of spin defect formation with first-principles molecular dynamics and machine-learned potentials. We then discuss two classes of properties that are essential for the prediction of spin defects' functionalities: electronic and coherence properties. The discussion of computational frameworks is followed by that of results for specific systems illustrating successes, open problems, and future applications, with examples for heterogeneous solids, inclusive of surfaces and mesoscopic defects, and with a focus on quantum sensing and communication applications.
Adverse childhood experiences (ACEs) and childhood social determinants of health (SDOH) are associated with poorer health outcomes in adults, but their impact on rehabilitation and functional outcomes post-traumatic brain injury (TBI) is unclear. To examine the potential impact of ACEs/SDOH on symptom and functional outcomes following TBI-related rehabilitation in veterans. Participants included 140 veterans enrolled in Veterans Affairs (VA) polytrauma rehabilitation programs between 2010-2024 who completed supplemental assessment of ACEs, SDOH, and other childhood trauma. Participants with more ACEs had significantly lower Functional Independence Measure (FIM) Total and Motor score improvements from admission to discharge. SDOH and other childhood trauma were also significantly correlated with lower FIM Motor score improvements. When controlling for hospitalization length, more ACEs were associated with lower FIM Motor score improvements (β = -.245, p = 0.02) and marginally associated with lower FIM Total score improvements (β = -.205, p = 0.06) following TBI-related rehabilitation. Higher ACEs were associated with less FIM Motor function improvement following TBI-related rehabilitation in veterans. ACEs may be associated with differences in functional motor outcomes in veterans following TBI, contributing to heterogeneous rehabilitation outcomes. Systematic integration of potential childhood adversities into rehabilitation may optimize outcomes for veterans who have been disproportionately affected.