Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study.

BACKGROUND: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION: The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

Integrating Tumor and Nodal Radiomics to Predict the Response to Neoadjuvant Chemotherapy and Recurrence Risk for Locally Advanced Gastric Cancer.

AIMS: To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC). BACKGROUND: Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC. OBJECTIVE: A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65). METHODS: We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram. RESULTS: The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis. CONCLUSION: The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.

INTRODUCTION: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency. METHODS: A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U-Net, ResUNet and U-Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter-observer was also evaluated. RESULTS: The AttSEResUNet with post-processing showed perfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139). CONCLUSION: Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter-observer.

Neurodevelopmental outcomes of preterm with necrotizing enterocolitis: a systematic review and meta-analysis.

While neonatal necrotising enterocolitis (NEC) is associated with high mortality rates in newborns, survivors can face long-term sequelae. However, the relationship between NEC and neurodevelopmental impairment (NDI) in preterm infants remains unclear. To explore the relationship between neonatal NEC and neurodevelopmental outcomes in preterm infants, we searched PubMed, EMBASE, and the Cochrane Library from their inception to February 2024 for relevant studies. Studies included were cohort or case-control studies reporting neurodevelopmental outcomes of NEC in preterm infants. Two independent investigators extracted data regarding brain damage and neurodevelopmental outcomes in these infants at a corrected age exceeding 12 months. Odds ratios (ORs) were pooled using a random effects model. We included 15 cohort studies and 18 case-control studies, encompassing 60,346 infants. Meta-analysis of unadjusted and adjusted ORs demonstrated a significant association between NEC and increased odds of NDI (OR 2.15, 95% CI 1.9-2.44; aOR 1.89, 95% CI 1.46-2.46). Regarding brain injury, pooled crude ORs indicated an association of NEC with severe intraventricular haemorrhage (IVH) (OR 1.42, 95% CI 1.06-1.92) and periventricular leucomalacia (PVL) (OR 2.55, 95% CI 1.76-3.69). When compared with conservatively treated NEC, surgical NEC potentially carries a higher risk of NDI (OR 1.78, 95% CI 1.09-2.93) and severe IVH (OR 1.57, 95% CI 1.20-2.06). However, the risk of PVL did not show a significant difference (OR 1.60, 95% CI 0.47-5.40). CONCLUSIONS:  Our meta-analysis provides evidence suggesting an association between NEC and NDI. Additionally, the severity of intestinal lesions appears to correlate with a higher risk of NDI. Further high-quality studies with comprehensive adjustments for potential confounding factors are required to definitively establish whether the association with NDI is causal. WHAT IS KNOWN: • NEC is a serious intestinal disease in the neonatal period with a high mortality rate, and surviving children may have digestive system sequelae. • Compared with non-NEC preterm infants, the reported incidences of brain injury and neurodevelopmental disorders in NEC preterm infants are not the same. WHAT IS NEW: • The risk of neonatal brain injury and neurodevelopmental disorders in preterm infants with NEC is higher than that in non-NEC infants, and the risk of NDI in surgical NEC infants is higher than that in the conservative treatment group. • NEC may increase the risk of motor, cognitive, language development delays, and attention deficits in children.

Comparative metabolomics reveals complex metabolic shifts associated with nitrogen-induced color development in mature pepper fruit.

Pigments derived from red pepper fruits are widely used in food and cosmetics as natural colorants. Nitrogen (N) is a key nutrient affecting plant growth and metabolism; however, its regulation of color-related metabolites in pepper fruit has not been fully elucidated. This study analyzed the effects of N supply (0, 250, and 400 kg N ha-1) on the growth, fruit skin color, and targeted and non-target secondary metabolites of field-grown pepper fruits at the mature red stage. Overall, 16 carotenoids were detected, of which capsanthin, zeaxanthin, and capsorubin were the dominant ones. N application at 250 kg ha-1 dramatically increased contents of red pigment capsanthin, yellow-orange zeaxanthin and ß-carotene, with optimum fruit yield. A total of 290 secondary metabolites were detected and identified. The relative content of most flavonoids and phenolic acids was decreased with increasing N supply. Correlation analysis showed that color parameters were highly correlated with N application rates, carotenoids, flavonoids, phenolic acids, lignans, and coumarins. Collectively, N promoted carotenoid biosynthesis but downregulated phenylpropanoid and flavonoid biosynthesis, which together determined the spectrum of red color expression in pepper fruit. Our results provide a better understanding of the impact of N nutrition on pepper fruit color formation and related physiology, and identification of target metabolites for enhancement of nutritional quality and consumer appeal.

Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease.

OBJECTIVES: To develop and compare radiomics model and fusion model based on multiple MR parameters for staging liver fibrosis in patients with chronic liver disease. MATERIALS AND METHODS: Patients with chronic liver disease who underwent multiparametric abdominal MRI were included in this retrospective study. Multiparametric MR images were imported into 3D-Slicer software for drawing bounding boxes on MR images. By using a 3D-Slicer extension of SlicerRadiomics, radiomics features were extracted from these MR images. The z-score normalization method was used for post-processing radiomics features. The least absolute shrinkage and selection operator method (LASSO) was performed for selecting significant radiomics features. The logistic regression analysis was used for building the radiomics model. A fusion model was built by integrating serum fibrosis biomarkers of aspartate transaminase-to-platelet ratio index (APRI) and the fibrosis-4 index (FIB-4) with radiomics signatures. RESULTS: In the training cohort, AUCs of radiomics and fusion model were 0.707-0.842 and 0.718-0.854 for differentiating different groups. In the testing cohort, AUCs were 0.514-0.724 and 0.609-0.728. For the training cohort, there was no significant difference of AUCs between radiomics and fusion model (p > 0.05). For the testing cohort, AUCs of fusion model were higher than those of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4 (p = 0.011 & 0.042). CONCLUSIONS: Radiomics model and fusion model based on multiparametric MRI exhibited the feasibility for staging liver fibrosis in patients with CLD, and APRI and FIB-4 could improve the diagnostic performance of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4.

Development and validation of multiparametric models based on computed tomography enterography to determine endoscopic activity and surgical risk in patients with Crohn's disease: A multi-center study.

Objective: To develop novel multiparametric models based on computed tomography enterography (CTE) scores to identify endoscopic activity and surgical risk in patients with Crohn's disease (CD). Methods: We analyzed 171 patients from 3 hospitals. Correlations between CTE outcomes and endoscopic scores were assessed using Spearman's rank correlation analysis. Predictive models for moderate to severe CD were developed, and receiver operating characteristic (ROC) curves were constructed to determine the area under the ROC curve (AUC). A combined nomogram based on CTE scores and clinical variables was also developed for predicting moderate to severe CD and surgery. Results: CTE scores were significantly correlated with endoscopy scores at the segment level. The global CTE score was an independent predictor of severe (HR = 1.231, 95% CI: 1.048-1.446, p = 0.012) and moderate-to-severe Simplified Endoscopic Scores for Crohn's Disease (SES-CD) (HR = 1.202, 95% CI: 1.090-1.325, p < 0.001). The nomogram integrating CTE and clinical data predicted moderate to severe SES-CD and severe SES-CD scores in the validation cohort with AUCs of 0.837 and 0.807, respectively. The CTE score (HR = 1.18; 95% CI: 1.103-1.262; p = 0.001) and SES-CD score (HR = 3.125, 95% CI: 1.542-6.33; p = 0.001) were independent prognostic factors for surgery-free survival. A prognostic nomogram incorporating CTE scores, SES-CD and C-reactive protein (CRP) accurately predicted the risk of surgery in patients with CD. Conclusion: The newly developed CTE score and multiparametric models displayed high accuracy in predicting moderate to severe CD and surgical risk for CD patients.

Pulmonary nodules segmentation based on domain adaptation.

With the development of deep learning, the methods based on transfer learning have promoted the progress of medical image segmentation. However, the domain shift and complex background information of medical images limit the further improvement of the segmentation accuracy. Domain adaptation can compensate for the sample shortage by learning important information from a similar source dataset. Therefore, a segmentation method based on adversarial domain adaptation with background mask (ADAB) is proposed in this paper. Firstly, two ADAB networks are built for the source and target data segmentation, respectively. Next, to extract the foreground features that are the input of the discriminators, the background masks are generated according to the region growth algorithm. Then, to update the parameters in the target network without being affected by the conflict between the distinguishing differences of the discriminator and the domain shift reduction of the adversarial domain adaptation, a gradient reversal layer propagation is embedded in the ADAB model for the target data. Finally, an enhanced boundaries loss is deduced to make the target network sensitive to the edge of the area to be segmented. The performance of the proposed method is evaluated in the segmentation of pulmonary nodules in computed tomography images. Experimental results show that the proposed approach has a potential prospect in medical image processing.

Acousto-optic reconfigurable filter based on vector mode fusion in dispersion-compensating fiber.

An acousto-optic reconfigurable filter (AORF) is proposed and demonstrated based on vector mode fusion in dispersion-compensating fiber (DCF). With multiple acoustic driving frequencies, the resonance peaks of different vector modes in the same scalar mode group can be effectively fused into a single peak, which is utilized to obtain arbitrary reconfiguration of the proposed filter. In the experiment, the bandwidth of the AORF can be electrically tuned from 5 nm to 18 nm with superposition of different driving frequencies. The multi-wavelength filtering is further demonstrated by increasing the interval of the multiple driving frequencies. The bandpass/band-rejection can also be electrically reconfigured by setting the combination of driving frequencies. The proposed AORF gains the feature of reconfigurable filtering types, fast and wide tunability, and zero frequency shift, which is advantageous for high-speed optical communication networks, tunable lasers, fast optical spectrum analyzing and microwave photonics signal processing.

Temperature-Regulated Bidirectional Capillary Force Self-Assembly of Femtosecond Laser Printed Micropillars for Switchable Chiral Microstructures.

Bottom-up self-assembly is regarded as an alternative way to manufacture series of microstructures in many fields, especially chiral microstructures, which attract tremendous attention because of their optical micromanipulations and chiroptical spectroscopies. However, most of the self-assembled microstructures cannot be tuned after processing, which largely hinders their broad applications. Here, we demonstrate a promising manufacturing strategy for switchable microstructures by combining the flexibility of femtosecond laser printing induced capillary force self-assembly and the temperature-responsive characteristics of smart hydrogels. Through designing asymmetric cross-link density, the printed microarchitectures can be deformed in the opposite direction and assembled into switchable ordered microstructures driven by capillary forces under different temperatures. Finally, the assembled chiral microstructures with switchable opposite handedness are realized, which shows tunable vortical dichroism. The proposed strategy holds potential applications in the fields of chiral photonics, chiral sensing, and so on.

Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors.

OBJECTIVES: This study evaluated the ability of a preoperative contrast-enhanced CT (CECT)-based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT). METHODS: Images and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis. RESULTS: The radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model. CONCLUSIONS: The preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning. KEY POINTS: • A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis. • Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone. • For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.

Optical Encryption in the Photonic Orbital Angular Momentum Dimension via Direct-Laser-Writing 3D Chiral Metahelices.

Vortex beams, which intrinsically possess optical orbital angular momentum (OAM), are considered as one of the promising chiral light waves for classical optical communications and quantum information processing. For a long time, it has been an expectation to utilize artificial three-dimensional (3D) chiral metamaterials to manipulate the transmission of vortex beams for practical optical display applications. Here, we demonstrate the concept of selective transmission management of vortex beams with opposite OAM modes assisted by the designed 3D chiral metahelices. Utilizing the integrated array of the metahelices, a series of optical operations, including display, hiding, and even encryption, can be realized by the parallel processing of multiple vortex beams. The results open up an intriguing route for metamaterial-dominated optical OAM processing, which fosters the development of photonic angular momentum engineering and high-security optical encryption.

Robust Helical Dichroism on Microadditively Manufactured Copper Helices via Photonic Orbital Angular Momentum.

Three-dimensional chiral metallic metamaterials have already attracted extensive attention in the wide research fields of chiroptical responses. These artificial chiral micronanostructures, possessing strong chiroptical signals, show huge significance in next-generation photonic devices and chiroptical spectroscopy techniques. However, most of the existing chiral metallic metamaterials are designed for generating chiroptical signals dependent on photonic spin angular momentum (SAM). The chiral metallic metamaterials for generating strong chiroptical responses by photonic orbital angular momentum (OAM) remain unseen. In this work, we fabricate copper microhelices with opposite handedness by additively manufacturing and further examine their OAM-dominated chiroptical response: helical dichroism (HD). The chiral copper microhelices exhibit differential reflection to the opposite OAM states, resulting in a significant HD signal (∼50%). The origin of the HD can be theoretically explained by the difference in photocurrent distribution inside copper microhelices under opposite OAM states. Moreover, the additively manufactured copper microhelices possess an excellent microstructural stability under varying annealing temperatures for robust HD responses. Lower material cost and noble-metal-similar optical properties, accompanied with well thermal stability, render the copper microhelices promising metamaterials in advanced chiroptical spectroscopy and photonic OAM engineering.

High-performance Marangoni hydrogel rotors with asymmetric porosity and drag reduction profile.

Miniaturized rotors based on Marangoni effect have attracted great attentions due to their promising applications in propulsion and power generation. Despite intensive studies, the development of Marangoni rotors with high rotation output and fuel economy remains challenging. To address this challenge, we introduce an asymmetric porosity strategy to fabricate Marangoni rotor composed of thermoresponsive hydrogel and low surface tension anesthetic metabolite. Combining enhanced Marangoni propulsion of asymmetric porosity with drag reduction of well-designed profile, our rotor precedes previous studies in rotation output (~15 times) and fuel economy (~34% higher). Utilizing thermoresponsive hydrogel, the rotor realizes rapid refueling within 33 s. Moreover, iron-powder dopant further imparts the rotors with individual-specific locomotion in group under magnetic stimuli. Significantly, diverse functionalities including kinetic energy transmission, mini-generator and environmental remediation are demonstrated, which open new perspectives for designing miniaturized rotating machineries and inspire researchers in robotics, energy, and environment.

An MRI-Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors.

BACKGROUND: Sinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem. PURPOSE: To establish an MRI-based radiomics nomogram for assessing relapse risk in patients with SNMT. STUDY TYPE: Retrospective. POPULATION: A total of 143 patients with 68.5% females (development/validation set, 98/45 patients). FIELD STRENGTH/SEQUENCE: A 1.5-T and 3-T, fat-suppressed fast spin echo (FSE) T2-weighted imaging (FS-T2WI), FSE T1-weighted imaging (T1WI), and FSE contrast-enhanced T1WI (T1WI + C). ASSESSMENT: Three MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS-T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high-dimensional features. The clinical model was built based on clinical and MRI features. The Ki-67-based and tumor-node-metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse-free survival analysis was used among 143 patients. STATISTICAL TESTS: The intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan-Meier curve, log-rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant. RESULTS: The T1 + C-based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS-T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki-67-based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated. DATA CONCLUSION: A radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki-67-based model, and TNM model. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 5.

A review and evaluation of nonroad diesel mobile machinery emission control in China.

China's emission control for nonroad diesel mobile machinery (NDMM) must deal with a fast increase in stock as well as regulations that are two decades behind those for on-road vehicles. This study provides the first large-scale review and evaluation of China's NDMM policies, along with emission measurements and an investigation on diesel fuel quality. The sulfur contents of the investigated diesel declined from 430 ppm (median value) in 2011 to 6-8 ppm during the 2017-2018 period. The emission control of NOx and PM greatly improved with the shift from the China II to China IV standards, as demonstrated by engine tests and field NOx measurements. However, the NOx emission factors for non-type-approved engines were approximately twice the limits of the China II standards. Emission compliance based on bench tests was not sufficient to control actual emissions because the field-measured NOx emission factors of all machinery ranged from 24% to 225% greater than the respective emission limits for the engines. These circumstances adversely affected the effectiveness of the regulations and policies for China's emission control of NDMM. Nevertheless, the policies on new and in-use NDMM, as well as diesel fuel quality, prevented NOx and PM emissions amounting to 4.4 Tg and 297.8 Gg during the period 2008-2017, respectively. The emission management strategy contributed to enhancing the international competitiveness of China's NDMM industries by promoting advanced technologies. For effective NDMM emission control in the future, portable testing and noncontact remote supervision should be strengthened; also, the issue of noncompliant diesel should be addressed through rigorous control measures and financial penalties.

Direct Observation of Spin-Orbit Interaction of Light via Chiroptical Responses.

The spin-orbit interaction of light is a fundamental manifestation of controlling its angular momenta with numerous applications in photonic spin Hall effects and chiral quantum optics. However, observation of an optical spin Hall effect, which is normally very weak with subwavelength displacements, needs quantum weak measurements or sophisticated metasurfaces. Here, we theoretically and experimentally demonstrate the spin-orbit interaction of light in the form of strong chiroptical responses by breaking the in-plane inversion symmetry of a dielectric substrate. The chiroptical signal is observed at the boundary of a microdisk illuminated by circularly polarized vortex beams at normal incidence. The generated chiroptical spectra are tunable for different photonic orbital angular momenta and microdisk diameters. Our findings, correlating photonic spin-orbit interaction with chiroptical responses, may provide a route for exploiting optical information processing, enantioselective sensing, and chiral metrology.

Magnetically encoded 3D mesostructure with high-order shape morphing and high-frequency actuation.

Inspired by origami/kirigami, three-dimensional (3D) mesostructures assembled via a mechanics-guided approach, with reversible and maneuverable shape-morphing capabilities, have attracted great interest with regard to a broad range of applications. Despite intensive studies, the development of morphable 3D mesostructures with high-order (multi-degree-of-freedom) deformation and untethered high-frequency actuation remains challenging. This work introduces a scheme for a magnetically encoded transferable 3D mesostructure, with polyethylene terephthalate (PET) film as the skeleton and discrete magnetic domains as actuation units, to address this challenge. The high-order deformation, including hierarchical, multidirectional and blending shape morphing, is realized by encoding 3D discrete magnetization profiles on the architecture through ultraviolet curing. Reconfigurable 3D mesostructures with a modest structural modulus (∼3 GPa) enable both high-frequency (∼55 Hz) and large-deformation (∼66.8%) actuation under an alternating magnetic field. Additionally, combined with the shape-retention and adhesion property of PET, these 3D mesostructures can be readily transferred and attached to many solid substrates. On this basis, diverse functional devices, including a switchable colour letter display, liquid mixer, sequential flashlight and biomimetic sliding robot, are demonstrated to offer new perspectives for robotics and microelectronics.

Accelerating Neurite Growth and Directing Neuronal Connections Constrained by 3D Porous Microtubes.

Investigation of neural growth and connection is crucial in the field of neural tissue engineering. Here, using a femtosecond laser direct writing (fs-DLW) technique, we propose a directionally aligned porous microtube array as a culture system for accelerating the growth of neurons and directing the connection of neurites. These microtubes exhibited an unprecedented guidance effect toward the outgrowth of primary embryonic rat hippocampal neurons, with a wrap resembling the myelin sheaths of neurons. The speed of neurite growth inside these microtubes was significantly faster than that outside these microtubes. We also achieved selective/directing connection of neural networks inside the magnetic microtubes via precise microtube delivery to a gap between two neural clusters. This work not only proposes a powerful microtube platform for accelerated growth of neurons but also offers a new idea for constructing biological neural circuits by arranging the size, location, and pattern of microtubes.