Stress-free two-way shape memory polymers have great practical potential in smart biomedical devices without repeated programming. Despite several progresses that have been achieved, an ultimate goal yet to be fulfilled is body-temperature-triggered programmable shape actuation featuring customizable shapes. In this work, we demonstrate the use of the cocrystallizable copolyester phase in the pseudoeutectic point as the actuation phase and the high crystalline temperature of homopolymer chain segments as the shifting-geometry-determining phase to fabricate the copolymer networks via thiol-ene click chemistry. Structural anisotropy arises from the partial melting of double-crystalline phases after thermomechanical programming, which affords the resultant network architecture an actuation strain of up to 16.2% through melting-induced contraction and crystallization-induced elongation under stress-free conditions. Noteworthily, within the same polymer network, the thin film could further be reconfigured into the three-dimensional (3D) structure via dynamic transesterification that allows achieving a synergetic benefit, notably realizing a unique reversible transformation for bestowing the material with peculiar adjustability while not losing its 3D-shaped support upon the destruction of the anisotropic structure. Our study offers useful guidelines in designing shape-shifting materials and could better target their specific applications in biomedical devices.

ABSTRACTAlthough some studies have reported high‐toughness or high‐strength self‐healing materials, the self‐healing process still requires demanding conditions such as high temperatures. It remains challenging to achieve a balance between mechanical properties and self‐healing efficiency under mild conditions. Herein, we design a novel self‐healing polyurea‐urethane (PUU) material. The polymer has crosslinked internal “cores” and well‐designed flexible chains with multiple alicyclic structures, which are crosslinked via tetrahedral boronate‐esters bonds (TBEs) and hydrogen bonds, resulting in a dynamic crosslinked PUU network (denoted as TBE‐PUU). The resulting materials (TBE‐PUU2) not only exhibit outstanding tensile strength (28.5 MPa), stretchability (1217%), toughness (166.1 MJ m−3), etc., but also possess excellent self‐healing ability in response to water or thermal stimuli. It achieves high self‐healing efficiency of 92.0% at room temperature for 48 h with the aid of water and 97.7% at 60°C for only 8 h. The new strategy offers new possibilities to seek a balance between material dynamic properties and mechanical robustness.

Polyureas are widely used in many fields such as civil, industry, and defense due to their excellent performance and structural adjustable properties. The development of self-healing polyurea materials with high strength and toughness, key connotations of their advanced applications, is both fascinating and challenging because these properties are associated with conflicting structural features, making it difficult to optimize these contradictory properties in a single material. In this review, the relationship between polyurea structure and performance is discussed, and the design strategy of self-healing polyurea networks based on dynamic interactions that allow for balancing high mechanical performance and repairability is delineated from a molecular design point of view. Lastly, a summary of the potential applications of polyurea in the fields of sensing, protective coatings, and recycling, as well as possible future challenges, is presented.

The combined biodegradable and stimulus-responsive elastomers are an emerging class of smart materials used for biomedical devices by virtue of their tunable deformable stability and biological mimetic functions. However, the inherent high-triggering temperature and nonprogrammed deformation behavior reduce their advantages. The biodegradable copolyester-based elastomer with one-step stretchable and programmable shape morphing enables the design of functional biomedical devices that would otherwise be impossible to realize with conventional manufacturing techniques. Shown here is that a dynamic random copolyester elastomer with comparable crystallinity and the combined body temperature responsiveness displays excellent shape stretchability, as well as strain-induced crystallization for shape maintaining. We demonstrate that the poly[(epsilon-caprolactone)-ran-(delta-valerolactone)] precursors via one-pot melt-induced transesterification exhibited tunable thermal characteristics with the lowest melting point of 29.3 degrees C. Upon further secondary photoinitiated thiol-ene click of acrylate-terminated precursors in the pseudoeutectic point and dynamic transesterification, the resulting architecture affords the elastomer with shape reconfiguration and responsiveness for programmable shape transformations on demand. Further results on in vitro cytocompatibility demonstrate that the elastomer could be employed as an important biomedical vascular stent, offering insights into the design of smart biomedical devices.

AbstractThe field of radiology is currently undergoing revolutionary changes owing to the increasing application of artificial intelligence (AI). This scoping review identifies and summarizes the technical methods and clinical applications of AI applied to magnetic resonance imaging of cerebrovascular diseases (CVDs). Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews was adopted and articles listed in PubMed and Cochrane databases from January 1, 2018 to December 31, 2023, were assessed. In total, 67 articles met the eligibility criteria. We obtained a general overview of the field, including lesion types, sample sizes, data sources, and databases and found that nearly half of the studies used multisequence magnetic resonance as the input. Both classical machine learning and deep learning were widely used. The evaluation metrics varied according to the five main algorithm tasks of classification, detection, segmentation, estimation, and generation. Cross‐validation was primarily used with only one third of the included studies using external validation. We also illustrate the key questions of the CVD research studies and grade the clinical utility of their AI solutions. Although most attention is devoted to improving the performance of AI models, this scoping review provides information on the availability of algorithms, reliability of external validations, and consistency of evaluation metrics and may facilitate improved clinical applicability and acceptance.

2D materials-based broadband photodetectors have extensive applications in security monitoring and remote sensing fields, especially in supersonic aircraft that require reliable performance under extreme high-temperature conditions. However, the integration of large-area heterostructures with 2D materials often involves high-temperature deposition methods, and also limited options and size of substrates. Herein, a liquid-phase spin-coating method is presented based on the interface engineering to prepare larger-area Van der Waals heterojunctions of black phosphorus (BP)/reduced graphene oxide (RGO) films at room temperature on arbitrary substrates of any required size. Importantly, this method avoids the common requirement of high-temperature, and prevents the curling or stacking in 2D materials during the liquid-phase film formation. The BP/RGO films-based devices exhibit a wide spectral photo-response, ranging from the visible of 532 nm to infrared range of 2200 nm. Additionally, due to Van der Waals interface of Schottky junction, the array devices provide infrared detection at temperatures up to 400 K, with an outstanding photoresponsivity (R) of 12 A W-1 and a specific detectivity (D*) of approximate to 2.4 x 109 Jones. This work offers an efficient approach to fabricate large-area 2D Schottky junction films by solution-coating for high-temperature infrared photodetectors. Large-area Schottky junction of BP/RGO films are fabricated by the developed liquid-phase spin-coating method. The devices exhibit a wide spectrum photodetection from visible of 532 to infrared of 2200 nm with large area uniform. The devices show outstanding photo-response under a high temperature of 400 K with R and D* up to 12 A W-1 and 2.4 x 109 Jones. image

Vitrimers are polymers rich in dynamic covalent bonds in cross-link networks. When the dynamic covalent bonds are not activated, the vitrimers show the performance stability of the traditional thermosetting polymer. When the dynamic covalent bonds are activated, the vitrimers can show some novel and unique properties, such as stress relaxation, self-healing and reprocessing. This new type of polymer has attracted wide attention because of its unique properties. As thermoset materials, the degree of cross-link and cross-link density of the materials are very important for the performance of vitrimers. In order to find out the effects of cross-link density on the properties of vitrimers, a series of dynamic polyimine/epoxy cross-link networks with different cross-link densities were designed and prepared, and their properties were characterized. The materials with higher cross-link density show higher thermal properties, mechanical properties and shape fixation ratio. However, due to the increase of cross-link density, the mobility of molecular chain and the exchange of dynamic bonds are limited, so the healing efficiency, shape recovery ratio and shape recovery rate will decrease to a certain extent. This study provides important insights into a deeper understanding of this new type of polymer.

Developing polymers with high self-healing rates as well as robust mechanical properties remains a significant challenge. Herein, a series of novel self-healing polyurethane-urea (PUU) elastomers with high strength and toughness were prepared using 4,4 '-diaminodicyclohexyl methane (HMDA) and N,N '-ditert-butylethylenediamine (DBDA). The HMDA amino groups react with the alicyclic isocyanates to form strong hard segments composed of multiple alicyclic structures and strong urea hydrogen bonds. The strong hard segments play a crucial role in generating strain-induced crystallization (SIC), resulting in improved mechanical properties. The weak hard segments contain dynamic hindered urea bonds and weak hydrogen bonds, which ensures the dynamics of the system and provides the possibility for self-healing. The optimized sample (PUU-2) not only exhibits outstanding tensile strength (49.6 MPa), toughness (385.6 MJ/m3), etc. but also has a healing efficiency of up to 90.5% (at 120 degrees C for 6 h). This study presents a facile strategy for the design and fabrication of self-healable polyurethane-urea elastomers with high performance.

Purpose The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions. Methods In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report. Results A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08). Conclusion The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)–based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques. We conducted a multicenter, retrospective diagnostic study (March 2013–May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications. We included 790 patients (median age 72, IQR [61–80], 42

BACKGROUND AND PURPOSE: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient?s age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve. RESULTS: Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth. CONCLUSIONS: To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.

Thermoset epoxy resin materials can be endowed with unique dynamic properties by introducing dynamic covalent bonds (DCB) into the cross-link network of the epoxy resin. However, it remains a challenge to obtain epoxy materials with high mechanical, thermodynamic, and dynamic properties. In this work, a series of epoxy vitrimers with double DCBs were prepared by using a curing agent with imine bonds (PAMD) and an epoxy resin with disulfide bonds (BGPDS). PAMD and BGPDS are cross-linked and form a uniform aromatic framework that ensures the mechanical and thermal properties of the material. High-density DCBs ensure the dynamic performance of epoxy vitrimer. In addition, tetra-needle-like ZnO whiskers (T-ZnOw) were added to the epoxy vitrimer for further reinforcement. The results demonstrated that epoxy vitrimer composites with T-ZnOw content of 10 wt.% exhibit high tensile strength, high Tg, high healing efficiency, shape reconfiguration, reprocessing and rapid degradation. The degradation products can recycle and produce new epoxy vitrimer, leading to a closed-loop recycling which reduced the environmental pollution and resource waste. image

Background Aneurysmal subarachnoid hemorrhage results in significant mortality and disability, which is worsened by the development of delayed cerebral ischemia. Tests to identify patients with delayed cerebral ischemia prospectively are of high interest. Objective We created a machine learning system based on clinical variables to predict delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage patients. We also determined which variables have the most impact on delayed cerebral ischemia prediction using SHapley Additive exPlanations method. Methods 500 aneurysmal subarachnoid hemorrhage patients were identified and 369 met inclusion criteria: 70 patients developed delayed cerebral ischemia (delayed cerebral ischemia+) and 299 did not (delayed cerebral ischemia-). The algorithm was trained based upon age, sex, hypertension (HTN), diabetes, hyperlipidemia, congestive heart failure, coronary artery disease, smoking history, family history of aneurysm, Fisher Grade, Hunt and Hess score, and external ventricular drain placement. Random Forest was selected for this project, and prediction outcome of the algorithm was delayed cerebral ischemia+. SHapley Additive exPlanations was used to visualize each feature's contribution to the model prediction. Results The Random Forest machine learning algorithm predicted delayed cerebral ischemia: accuracy 80.65% (95% CI: 72.62-88.68), area under the curve 0.780 (95% CI: 0.696-0.864), sensitivity 12.5% (95% CI: -3.7 to 28.7), specificity 94.81% (95% CI: 89.85-99.77), PPV 33.3% (95% CI: -4.39 to 71.05), and NPV 84.1% (95% CI: 76.38-91.82). SHapley Additive exPlanations value demonstrated Age, external ventricular drain placement, Fisher Grade, and Hunt and Hess score, and HTN had the highest predictive values for delayed cerebral ischemia. Lower age, absence of hypertension, higher Hunt and Hess score, higher Fisher Grade, and external ventricular drain placement increased risk of delayed cerebral ischemia. Conclusion Machine learning models based upon clinical variables predict delayed cerebral ischemia with high specificity and good accuracy.

Purpose: We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients. Methods: In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables. Results: Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3's hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis. Conclusion: Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.

通过紫外加速老化试验研究交联聚苯乙烯（CLPS）和CLPS/云母复合材料的抗紫外老化性能。从力学、分子结构及耐热性能等方面探讨云母对CLPS抗紫外老化性能的影响。结果表明,随着辐照时间的延长,CLPS和CLPS/云母复合材料的拉伸强度、冲击强度和弯曲强度均呈下降趋势,CLPS/云母复合材料力学性能的保持率基本都高于CLPS,并且云母含量越大力学性能保持率越高。与CLPS/云母复合材料相比,CLPS在紫外辐照18 d后红外谱图出现明显的C=O吸收峰。热重分析表明,紫外老化后CLPS/云母复合材料的热稳定性能优于CLPS。研究结果证明掺杂云母对交联聚苯乙烯抗紫外老化性能有明显的提高。

This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI). This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen’s kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland–Altman plots were used to compare measurements. One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model’s assistance, trainees’ agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0–2 or 3–4. Trainees and attendings had the highest accuracy (0.95). The DL model’s performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95

The synthesis of polymeric materials that simultaneouslypossessmultiple outstanding mechanical properties and self-healability isalways a great challenge. Herein, we report the synthesis of a robustand self-healing polyurea material, which contains a dual metal coordinationstructure of Zn(II)-urea and Zn(II)-diamidepyridine as well as densehydrogen bonds. Due to the synergistic enhancement of dynamic coordinationbonds and hydrogen bonds in the supramolecular network, the obtainedpolyurea materials possess a combination of excellent mechanical propertiesand high-efficiency self-healing properties, which are adjustableby varying the content of Zn(II) ions. The optimal PU-L/Zn-3 samplehas a tensile strength of & SIM;22.82 MPa, while the elongationat break is & SIM;1312% and the toughness is & SIM;222.68 MJ/m(3). In addition, the fully cut sample exhibits a high healingefficiency of 95.7% based on toughness after treatment at 50 & DEG;Cfor 4 h. Multi-dynamic cross-linked polymers might provide a new wayto obtain robust self-healing materials for protection materials andwearable electronics.

Owing to their excellent physical and chemical properties, the carbon fibre reinforced poly(ether-ether-ketone) composites (CF/PEEK) are widely used in aerospace applications such as rockets, missiles, and high-speed vehicles. However, both carbon fibre (CF) and poly(ether-ether-ketone) (PEEK) have inert molecular chain structures, which seriously affect the interfacial properties of CF/PEEK composites. In this study, to improve the properties of CF/PEEK composites, carboxylated PEEK (PEEK-COOH) with different carboxylation degrees is synthesized as the sizing agent by a "two-step" method. Then, the activated CF surface is coated by PEEK-COOH sizing layers with different functionalization degrees to prepare the CF/PEEK composites. The results show that the interfacial properties of CF/PEEK composites are improved after applying the sizing agent. When the carboxylation degree of PEEK-COOH is 19.61%, the flexural strength, flexural modulus, and interlaminar shear strength (ILSS) of CF/PEEK composites reach 489.34 MPa, 25.387 GPa, and 81.3 MPa, respectively. In addition, the use of PEEK-COOH sizing agents can form an excellent transition layer between CF and PEEK, creating an efficient stress transfer system and facilitating an even stress distribution between CF and PEEK. Furthermore, the main mechanism of material fracture changes from CF debonding to CF and resin fracture.

Broadband Photodetectors In article number 2206590, Zemin Zhang, Junru Wang, Qingliang Feng, and co-workers present large-area Schottky junction of BP/PtSe2 films-based nano-device arrays towards high operating temperature broadband photodetection from 532 to 2200 nm at 470 K via a depletion enhanced photocarrier dynamics strategy. The photodetectors show a state-of-the-art operating temperature up to 470 K with the photo-responsivity and specific detectivity of 25 A W−1 and 6.4 × 1011 Jones under 1850 nm illumination.

Yearly multiple sclerosis MRI follow-ups require the tedious and time-consuming manual comparing and counting of multiple demyelinating lesions, which can reach hundreds in some cases. We evaluated a semi-automatic software for the efficient assessment of such images, and found that a significant increase in the number of reported new multiple sclerosis lesions can be achieved in two-and-a-half minutes reading on average per case. In contrast, current standard reports missed 80% of the new lesions. In addition, The Jazz software permits the efficient reporting of slowly evolving lesions, an entity growing in clinical relevance and typically overlooked in current reporting.