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BACKGROUND: The Patient-Reported Outcomes Measurement Information System (PROMIS) Preference Score (PROPr) is estimated from descriptive health assessments within the PROMIS framework. The underlying item response theory (IRT) allows researchers to measure PROMIS health domains with any subset of items that are calibrated to this domain. Consequently, this should also be true for the PROPr. We aimed to test this assumption using both an empirical and a simulation approach. METHODS: Empirically, we estimated 3 PROMIS Pain inference (PI) scores from 3 different item subsets in a sample of n=199 cancer patients: 4 PROMIS-29 items (estimate: θ4), the 2 original PROPr items (θ2), and 10 different items (θ10). We calculated mean differences and agreement between θ4, and θ2 and θ10, respectively, and between their resulting PROPr4, PROPr2, PROPr10, using intraclass correlation coefficients (ICC) and Bland-Altman (B-A) plots with 95 %-Limits of Agreement (LoA). For the simulation, we used the IRT-model to calculate all item responses of the entire 7 PROPr domain item banks from the empirically observed PROMIS-29+cognition θ. From these simulated item banks, we chose the 2 original PROPr items per domain to calculate PROPrsim and compared it to PROPr4 again using ICC and B-A plots. RESULTS: θ4 vs θ10 showed smaller bias (-0.012, 95 %-LoA -0.88;0.85) than θ4 vs θ2 (0.025, 95 %-LoA -0.95;1.00. ICC>0.85 (p<0.001) in both θ-comparisons. PROPr4 vs PROPr10 showed lower bias (0.0012, 95 %-LoA -0.039;0.042) than PROPr4 vs PROPr2 (-0.0029, 95 %-LoA -0.049;0.044). ICC>0.98 (p<0.0001) on both PROPr-comparisons. Mean PROPrsim was larger than mean PROPr4 (0.0228, 95 %-LoA -0.1103; 0.1558) and ICC was 0.95 (95 %CI 0.93; 0.97). CONCLUSION: Different item subsets can be used to estimate the PROMIS PI for calculation of the PROPr. Reduction to 2 items per domain rather than 4 does not significantly change the PROPr estimate on average. Agreements differ across the spectrum and in individual comparisons.

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As integrated circuits continue to scale toward the atomic limit, bottom-up processes, such as epitaxial growth, have come to feature prominently in their fabrication. At the same time, chemistry has developed highly tunable molecular semiconductors that can perform the functions of ultimately scaled circuit components. Hybrid techniques that integrate programmable structures comprising molecular components into devices however are sorely lacking. Here we demonstrate a wafer-scale process that directs the localization of a conductive polymer, Mw = 20 kg mol-1 polyaniline, from dilute solutions into 50 nm vertical nanogap device architectures using electric-field-driven self-assembly. The resulting metal-polymer-metal junctions were characterized by electron microscopy, Raman spectroscopy and transport measurements demonstrating that our technique is highly selective, assembling conductive polymers only in electrically activated nanogaps. Our results represent a step toward scalable hybrid nanoelectronics that seamlessly integrate established lithographic top-down fabrication with bottom-up synthesized molecular functional circuit components.

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BACKGROUND: Health-related quality of life (HRQL) has become an important outcome parameter in cardiology. The MOS 36-ltem Short-Form Health Survey (SF-36) and the PROMIS-29 are two widely used generic measures providing composite HRQL scores. The domains of the SF-36, a well-established instrument utilized for several decades, can be aggregated to physical (PCS) and mental (MCS) component summary scores. Alternative scoring algorithms for correlated component scores (PCSc and MCSc) have also been suggested. The PROMIS-29 is a newer but increasingly used HRQL measure. Analogous to the SF-36, physical and mental health summary scores can be derived from PROMIS-29 domain scores, based on a correlated factor solution. So far, scores from the PROMIS-29 are not directly comparable to SF-36 results, complicating the aggregation of research findings. Thus, our aim was to provide algorithms to convert PROMIS-29 data to well-established SF-36 component summary scores. METHODS: Data from n = 662 participants of the Berlin Long-term Observation of Vascular Events (BeLOVE) study were used to estimate linear regression models with either PROMIS-29 domain scores or aggregated PROMIS-29 physical/mental health summary scores as predictors and SF-36 physical/mental component summary scores as outcomes. Data from a subsequent assessment point (n = 259) were used to evaluate the agreement between empirical and predicted SF-36 scores. RESULTS: PROMIS-29 domain scores as well as PROMIS-29 health summary scores showed high predictive value for PCS, PCSc, and MCSc (R2 ≥ 70%), and moderate predictive value for MCS (R2 = 57% and R2 = 40%, respectively). After applying the regression coefficients to new data, empirical and predicted SF-36 component summary scores were highly correlated (r > 0.8) for most models. Mean differences between empirical and predicted scores were negligible (|SMD|<0.1). CONCLUSIONS: This study provides easy-to-apply algorithms to convert PROMIS-29 data to well-established SF-36 physical and mental component summary scores in a cardiovascular population. Applied to new data, the agreement between empirical and predicted SF-36 scores was high. However, for SF-36 mental component summary scores, considerably better predictions were found under the correlated (MCSc) than under the original factor model (MCS). Additionally, as a pertinent byproduct, our study confirmed construct validity of the relatively new PROMIS-29 health summary scores in cardiology patients.

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PURPOSE: We applied a previously established common T-score metric for patient-reported and performance-based physical function (PF), offering the unique opportunity to directly compare measurement type-specific patterns of associations with potential laboratory-based, psychosocial, sociodemographic, and health-related determinants in hemodialysis patients. METHODS: We analyzed baseline data from the CONVINCE trial (N = 1,360), a multinational randomized controlled trial comparing high-flux hemodialysis with high-dose hemodiafiltration. To explore the associations of potential determinants with performance-based versus patient-reported PF, we conducted multiple linear regression (backward elimination with cross-validation and Lasso regression). We used standardized T-scores as estimated from the PROMIS PF short-form 4a (patient-reported PF) and the Physical Performance Test (performance-based PF) as dependent variables. RESULTS: Performance-based and patient-reported PF were both significantly associated with a laboratory marker-based indicator of muscle mass (simplified creatinine index), although the effects were relatively small (partial f2 = 0.04). Age was negatively associated with PF; the effect size was larger for performance-based (partial f2 = 0.12) than for patient-reported PF (partial f2 = 0.08). Compared to performance-based PF, patient-reported PF showed a stronger association with self-reported health domains, particularly pain interference and fatigue. When using the individual difference between patient-reported and performance-based T-scores as outcome, we found that younger age and more fatigue were associated with lower patient-reported PF compared to performance-based PF (small effect size). CONCLUSION: Patient-reported and performance-based assessments were similarly associated with an objective marker of physical impairment in hemodialysis patients. Age and fatigue may result in discrepancies when comparing performance-based and patient-reported scores on the common PF scale. Trial Registration CONVINCE is registered in the Dutch Trial Register (Register ID: NL64750.041.18). The registration can be accessed at: https://onderzoekmetmensen.nl/en/trial/52958 .

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Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.

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In the CONVINCE trial, the primary analysis demonstrated a survival benefit for patients receiving high-dose hemodiafiltration (HDF) as compared with high-flux hemodialysis (HD). A secondary objective was to evaluate effects on health-related quality of life (HRQoL); assessed in eight domains (physical function, cognitive function, fatigue, sleep disturbance, anxiety, depression, pain interference, social participation) applying instruments from the Patient-Reported Outcome Measurement Information System (PROMIS) before randomization and every three months thereafter. In total 1360 adults with dialysis-dependent chronic kidney disease, eligible to receive high-flux HDF (23 liters or more), were randomized (1:1); 84% response rate to all questionnaires. Both groups reported a continuous deterioration in all HRQoL domains. Overall, raw score changes from baseline were more favorable in the HDF group, resulting in a significant omnibus test after a median observation period of 30 months. Most relevant single raw score differences were reported for cognitive function. Patients receiving HDF reported a decline of -0.95 units (95% confidence interval - 2.23 to +0.34) whereas HD treated patients declined by -3.90 units (-5.28 to - 2.52). A joint model, adjusted for mortality differences, utilizing all quarterly assessments, identified a significantly slower HRQoL decline in physical function, cognitive function, pain interference, and social participation for the HDF group. Their physical health summary score declined -0.46 units/year slower compared to the HD group. Thus, the CONVINCE trial showed a beneficial effect of high-dose hemodiafiltration for survival as well as a moderate positive effect on patients' quality of life, most pronounced with respect to their cognitive function. REGISTRATION: NTR7138 on the International Clinical Trials Registry Platform.

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Small-scale robots offer significant potential in minimally invasive medical procedures. Due to the nature of soft biological tissues, however, robots are exposed to complex environments with various challenges in locomotion, which is essential to overcome for useful medical tasks. A single mini-robot often provides insufficient force on slippery biological surfaces to carry medical instruments, such as a fluid catheter or an electrical wire. Here, for the first time, a team of millirobots (TrainBot) is reported to generate around two times higher actuating force than a TrainBot unit by forming a convoy to collaboratively carry long and heavy cargos. The feet of each unit are optimized to increase the propulsive force around three times so that it can effectively crawl on slippery biological surfaces. A human-scale permanent magnetic set-up is developed to wirelessly actuate and control the TrainBot to transport heavy and lengthy loads through narrow biological lumens, such as the intestine and the bile duct. The first electrocauterization performed by the TrainBot is demonstrated to relieve a biliary obstruction and open a tunnel for fluid drainage and drug delivery. The developed technology sheds light on the collaborative strategy of small-scale robots for future minimally invasive surgical procedures.

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The covalent interaction of N-heterocyclic carbenes (NHCs) with transition metal atoms gives rise to distinctive frontier molecular orbitals (FMOs). These emergent electronic states have spurred the widespread adoption of NHC ligands in chemical catalysis and functional materials. Although formation of carbene-metal complexes in self-assembled monolayers on surfaces has been explored, design and electronic structure characterization of extended low-dimensional NHC-metal lattices remains elusive. Here we demonstrate a modular approach to engineering one-dimensional (1D) metal-organic chains and two-dimensional (2D) Kagome lattices using the FMOs of NHC-Au-NHC junctions to create low-dimensional molecular networks exhibiting intrinsic metallicity. Scanning tunneling spectroscopy and first-principles density functional theory reveal the contribution of C-Au-C π-bonding states to dispersive bands that imbue 1D- and 2D-NHC lattices with exceptionally small work functions.

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The integration of low-energy states into bottom-up engineered graphene nanoribbons (GNRs) is a robust strategy for realizing materials with tailored electronic band structure for nanoelectronics. Low-energy zero-modes (ZMs) can be introduced into nanographenes (NGs) by creating an imbalance between the two sublattices of graphene. This phenomenon is exemplified by the family of [n]triangulenes (n ∈ N). Here, we demonstrate the synthesis of [3]triangulene-GNRs, a regioregular one-dimensional (1D) chain of [3]triangulenes linked by five-membered rings. Hybridization between ZMs on adjacent [3]triangulenes leads to the emergence of a narrow band gap, Eg,exp ∼ 0.7 eV, and topological end states that are experimentally verified using scanning tunneling spectroscopy. Tight-binding and first-principles density functional theory calculations within the local density approximation corroborate our experimental observations. Our synthetic design takes advantage of a selective on-surface head-to-tail coupling of monomer building blocks enabling the regioselective synthesis of [3]triangulene-GNRs. Detailed ab initio theory provides insights into the mechanism of on-surface radical polymerization, revealing the pivotal role of Au-C bond formation/breakage in driving selectivity.

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Topological phases in laterally confined low-dimensional nanographenes have emerged as versatile design tools that can imbue otherwise unremarkable materials with exotic band structures ranging from topological semiconductors and quantum dots to intrinsically metallic bands. The periodic boundary conditions that define the topology of a given lattice have thus far prevented the translation of this technology to the quasi-zero-dimensional (0D) domain of small molecular structures. Here, we describe the synthesis of a polycyclic aromatic hydrocarbon (PAH) featuring two localized zero modes (ZMs) formed by the topological junction interface between a trivial and nontrivial phase within a single molecule. First-principles density functional theory calculations predict a strong hybridization between adjacent ZMs that gives rise to an exceptionally small HOMO-LUMO gap. Scanning tunneling microscopy and spectroscopy corroborate the molecular structure of 9/7/9-double quantum dots and reveal an experimental quasiparticle gap of 0.16 eV, corresponding to a carbon-based small molecule long-wavelength infrared (LWIR) absorber.

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The classification of personality disorder (PD) is undergoing a paradigm shift in which categorically defined specific PDs are being replaced by dimensionally defined maladaptive trait domains. To bridge the classificatory approaches, this study attempts to use items from the categorical PD model in DSM-IV to measure the maladaptive trait domains described in DSM-5 Section III/ICD-11. A general population sample comprising 1228 participants completed the Screening Questionnaire of the Structured Clinical Interview for DSM-IV Axis II (SCID-II-SQ), the Personality Inventory for DSM-5 (PID-5), and the anankastia scale of the Personality Inventory for ICD-11 (PiCD). Using item response theory models and a psychometric linking technique, SCID-II-SQ items were evaluated for their contribution to measuring maladaptive trait domains. The best discriminating items were then selected to derive proxy scales. We found that convergent validity of these proxy scales was in a similar range to that of other self-report measures for PD, except for the proxy scale for PiCD anankastia. However, only the proxy scale for negative affectivity showed acceptable reliability that would allow its application in research settings. Future studies should seek to establish a common metric between specific PDs and maladaptive trait domains using self-report measures with higher specificity or semi-structured interviews.

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BACKGROUND: The interpretation of patient-reported outcomes requires appropriate comparison data. Currently, no patient-specific reference data exist for the Patient-Reported Outcome Measurement Information System (PROMIS) Physical Function (PF), Upper Extremity (UE), and Pain Interference (PI) scales for individuals 50 years and older. QUESTIONS/PURPOSES: (1) Can all PROMIS PF, UE, and PI items be used for valid cross-country comparisons in these domains among the United States, the United Kingdom, and Germany? (2) How are age, gender, and country related to PROMIS PF, PROMIS UE, and PROMIS PI scores? (3) What is the relationship of age, gender, and country across individuals with PROMIS PF, PROMIS UE, and PROMIS PI scores ranging from very low to very high? METHODS: We conducted telephone interviews to collect custom PROMIS PF (22 items), UE (eight items), and PI (eight items) short forms, as well as sociodemographic data (age, gender, work status, and education level), with participants randomly selected from the general population older than 50 years in the United States (n = 900), United Kingdom (n = 905), and Germany (n = 921). We focused on these individuals because of their higher prevalence of surgeries and lower physical functioning. Although response rates varied across countries (14% for the United Kingdom, 22% for Germany, and 12% for the United States), we used existing normative data to ensure demographic alignment with the overall populations of these countries. This helped mitigate potential nonresponder bias and enhance the representativeness and validity of our findings. We investigated differential item functioning to determine whether all items can be used for valid crosscultural comparisons. To answer our second research question, we compared age groups, gender, and countries using median regressions. Using imputation of plausible values and quantile regression, we modeled age-, gender-, and country-specific distributions of PROMIS scores to obtain patient-specific reference values and answer our third research question. RESULTS: All items from the PROMIS PF, UE, and PI measures were valid for across-country comparisons. We found clinically meaningful associations of age, gender, and country with PROMIS PF, UE, and PI scores. With age, PROMIS PF scores decreased (age ß Median = -0.35 [95% CI -0.40 to -0.31]), and PROMIS UE scores followed a similar trend (age ß Median = -0.38 [95% CI -0.45 to -0.32]). This means that a 10-year increase in age corresponded to a decline in approximately 3.5 points for the PROMIS PF score-a value that is approximately the minimum clinically important difference (MCID). Concurrently, we observed a modest increase in PROMIS PI scores with age, reaching half the MCID after 20 years. Women in all countries scored higher than men on the PROMIS PI and 1 MCID lower on the PROMIS PF and UE. Additionally, there were higher T-scores for the United States than for the United Kingdom across all domains. The difference in scores ranged from 1.21 points for the PROMIS PF to a more pronounced 3.83 points for the PROMIS UE. Participants from the United States exhibited up to half an MCID lower T-scores than their German counterparts for the PROMIS PF and PROMIS PI. In individuals with high levels of physical function, with each 10-year increase in age, there could be a decrease of up to 4 points in PROMIS PF scores. Across all levels of upper extremity function, women reported lower PROMIS UE scores than men by an average of 5 points. CONCLUSION: Our study provides age-, gender-, and country-specific reference values for PROMIS PF, UE, and PI scores, which can be used by clinicians, researchers, and healthcare policymakers to better interpret patient-reported outcomes and provide more personalized care. These findings are particularly relevant for those collecting patient-reported outcomes in their clinical routine and researchers conducting multinational studies. We provide an internet application ( www.common-metrics.org/PROMIS_PF_and_PI_Reference_scores.php ) for user-friendly accessibility in order to perform age, gender, and country conversions of PROMIS scores. Population reference values can also serve as comparators to data collected with other PROMIS short forms or computerized adaptive tests. LEVEL OF EVIDENCE: Level II, diagnostic study.

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The low-energy electronic structure of nanographenes can be tuned through zero-energy π-electron states, typically referred to as zero-modes. Customizable electronic and magnetic structures have been engineered by coupling zero-modes through exchange and hybridization interactions. Manipulation of the energy of such states, however, has not yet received significant attention. We find that attaching a five-membered ring to a zigzag edge hosting a zero-mode perturbs the energy of that mode and turns it into an off-zero mode: a localized state with a distinctive electron-accepting character. Whereas the end states of typical 7-atom-wide armchair graphene nanoribbons (7-AGNRs) lose their electrons when physisorbed on Au(111) (due to its high work function), converting them into off-zero modes by introducing cyclopentadienyl five-membered rings allows them to retain their single-electron occupation. This approach enables the magnetic properties of 7-AGNR end states to be explored using scanning tunneling microscopy (STM) on a gold substrate. We find a gradual decrease of the magnetic coupling between off-zero mode end states as a function of GNR length, and evolution from a more closed-shell to a more open-shell ground state.

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Aims: To map the Oxford Knee Score (OKS) and High Activity Arthroplasty Score (HAAS) items to a common scale, and to investigate the psychometric properties of this new scale for the measurement of knee health. Methods: Patient-reported outcome measure (PROM) data measuring knee health were obtained from the NHS PROMs dataset and Total or Partial Knee Arthroplasty Trial (TOPKAT). Assumptions for common scale modelling were tested. A graded response model (fitted to OKS item responses in the NHS PROMs dataset) was used as an anchor to calibrate paired HAAS items from the TOPKAT dataset. Information curves for the combined OKS-HAAS model were plotted. Bland-Altman analysis was used to compare common scale scores derived from OKS and HAAS items. A conversion table was developed to map between HAAS, OKS, and the common scale. Results: We included 3,329 response sets from 528 patients undergoing knee arthroplasty. These generally met the assumptions of unidimensionality, monotonicity, local independence, and measurement invariance. The HAAS items provided more information than OKS items at high levels of knee health. Combining both instruments resulted in higher test-level information than either instrument alone. The mean error between common scale scores derived from the OKS and HAAS was 0.29 logits. Conclusion: The common scale allowed more precise measurement of knee health than use of either the OKS or HAAS individually. These techniques for mapping PROM instruments may be useful for the standardization of outcome reporting, and pooling results across studies that use either PROM in individual-patient meta-analysis.

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Identifying cellular identities (both novel and well-studied) is one of the key use cases in single-cell transcriptomics. While supervised machine learning has been leveraged to automate cell annotation predictions for some time, there has been relatively little progress both in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues and biological contexts up to whole organisms. Here, we propose scTab, an automated, feature-attention-based cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million human cells in total). In addition, scTab leverages deep ensembles for uncertainty quantification. Moreover, we account for ontological relationships between labels in the model evaluation to accommodate for differences in annotation granularity across datasets. On this large-scale corpus, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales in terms of training dataset size as well as model size - demonstrating the advantage of scTab over current state-of-the-art linear models in this context. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets from a diverse selection of human tissues and demonstrate the benefits of using deep learning methods in this paradigm. Our codebase, training data, and model checkpoints are publicly available at https://github.com/theislab/scTab to further enable rigorous benchmarks of foundation models for single-cell RNA-seq data.

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BACKGROUND: The PROMIS Preference score (PROPr) is a new health state utility (HSU) score that aims to comprehensively incorporate the biopsychosocial model of health and apply favorable psychometric properties from the descriptive PROMIS system to HSU measurements. However, minimal evidence concerning comparisons to the EQ-5D-3L and the PROPr's capability to differentiate clinical severity are available. Therefore, the aim of this study was to compare the PROPr to the EQ-5D-3L in terms of scale agreement, ceiling/floor effects, distribution, construct validity, discriminatory power, and relative efficiency (RE) in terms of the Oswestry Disability Index (ODI) for patients with low back pain (LBP). METHODS: We used intra-class correlation coefficients (ICC) and Bland-Altman plots to compare the PROPr and EQ-5D-3L with regared to scale agreement in a cross-sectional routine sample of LBP patients. For distribution, we used the Pearson's coefficient for skewness and for ceiling/floor effects, a 15%-top/bottom threshold. For convergent validity, we used Pearson's correlation coefficients. For known-groups validity, we applied a linear regression with interaction terms (predictors sex, age, and ODI level) and an analysis of variance (ANOVA). For discriminatory power, we calculated the effect size (ES) using Cohen's d and the ratio of the area under the receiver-operating characteristics curves (AUROC-ratio = AUROCPROPr/AUROCEQ-5D-3L). RE was measured using the ratio of F-values (RE = FPROPr/FEQ-5D-3L). RESULTS: Of 218 LBP patients, 50.0% were female and the mean age was 61.8 years. The mean PROPr (0.20, 95%CI: 0.18; 0.22) and EQ-5D-3L scores (0.55, 95%CI: 0.51; 0.58) showed low agreement (d = 0.35, p < 0.001; ICC 0.27, 95%CI: -0.09; 0.59). The PROPr's distribution was positively skewed, whereas the EQ-5D-3L's was negative. Neither tool showed ceiling/floor effects, but all EQ-5D-3L dimensions did. Pearson correlation was r = 0.66 (95%CI: 0.58; 0.73). Differences were invariant to sex and age but not to ODI severity: ESEQ-5D-3L > ESPROPr and RE < 1 in higher ODI severity; ESEQ-5D-3L < ESPROPr and RE > 1 in lower ODI severity. AUROC-ratios did not show significant differences in terms of ODI severity. CONCLUSIONS: All PROPr and EQ-5D-3L biopsychosocial dimensions of health showed impairment in LPB patients. The capability of EQ-5D-3L and PROPr to differentiate ODI levels depends on ODI severity. Joint application of both tools may provide additional information.

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Substitutional heteroatom doping of bottom-up engineered 1D graphene nanoribbons (GNRs) is a versatile tool for realizing low-dimensional functional materials for nanoelectronics and sensing. Previous efforts have largely relied on replacing C-H groups lining the edges of GNRs with trigonal planar N atoms. This type of atomically precise doping, however, only results in a modest realignment of the valence band (VB) and conduction band (CB) energies. Here, we report the design, bottom-up synthesis, and spectroscopic characterization of nitrogen core-doped 5-atom-wide armchair GNRs (N2-5-AGNRs) that yield much greater energy-level shifting of the GNR electronic structure. Here, the substitution of C atoms with N atoms along the backbone of the GNR introduces a single surplus π-electron per dopant that populates the electronic states associated with previously unoccupied bands. First-principles DFT-LDA calculations confirm that a sizable shift in Fermi energy (∼1.0 eV) is accompanied by a broad reconfiguration of the band structure, including the opening of a new band gap and the transition from a direct to an indirect semiconducting band gap. Scanning tunneling spectroscopy (STS) lift-off charge transport experiments corroborate the theoretical results and reveal the relationship among substitutional heteroatom doping, Fermi-level shifting, electronic band structure, and topological engineering for this new N-doped GNR.

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Metallic graphene nanoribbons (GNRs) represent a critical component in the toolbox of low-dimensional functional materials technology serving as 1D interconnects capable of both electronic and quantum information transport. The structural constraints imposed by on-surface bottom-up GNR synthesis protocols along with the limited control over orientation and sequence of asymmetric monomer building blocks during the radical step-growth polymerization have plagued the design and assembly of metallic GNRs. Here, we report the regioregular synthesis of GNRs hosting robust metallic states by embedding a symmetric zero-mode (ZM) superlattice along the backbone of a GNR. Tight-binding electronic structure models predict a strong nearest-neighbor electron hopping interaction between adjacent ZM states, resulting in a dispersive metallic band. First-principles density functional theory-local density approximation calculations confirm this prediction, and the robust, metallic ZM band of olympicene GNRs is experimentally corroborated by scanning tunneling spectroscopy.

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Small-scale robots hold great potential for targeted cargo delivery in minimally invasive medicine. However, current robots often face challenges in locomoting efficiently on slippery biological tissue surfaces, especially when loaded with heavy cargo. Here, we report a magnetic millirobot that can walk on rough and slippery biological tissues by anchoring itself on the soft tissue surface alternatingly with two feet and reciprocally rotating the body to move forward. We experimentally studied the locomotion, validated it with numerical simulations, and optimized the actuation parameters to fit various terrains and loading conditions. Furthermore, we developed a permanent magnet set-up to enable wireless actuation within a human-scale volume that allows precise control of the millirobot to follow complex trajectories, climb vertical walls, and carry cargo up to four times its own weight. Upon reaching the target location, it performs a deployment sequence to release the liquid drug into tissues. The robust gait of our millirobot on rough biological terrains, combined with its heavy load capacity, makes it a versatile and effective miniaturized vehicle for targeted cargo delivery.