Activation of mitochondrial function and heat production in adipose tissue by the modification of dietary fat is a promising strategy against obesity. However, as an important source of lipids for ketogenic and daily diets, the function of fats extracted from different adipose tissue sites was largely unknown. In this study, we illustrated the function of fats extracted from adipose tissues with different “beigeing” properties in the ketogenic diet and identified lipid profiles of fats that facilitate energy expenditure. We found that the anti-obesity effect of ketogenic diets was potentiated by using “beigeing” fat [porcine subcutaneous adipose tissue (SAT)] as a major energy-providing ingredient. Through lipidomic analyses, phosphatidylserine (PS) was identified as a functional lipid activating thermogenesis in adipose tissue. Moreover, in vivo studies showed that PS induces adipose tissue thermogenesis and alleviates diet-induced obesity in mice. In vitro studies showed that PS promotes UCP1 expression and lipolysis of adipocytes. Mechanistically, PS promoted mitochondrial function in adipocytes via the ADCY3-cAMP-PKA-PGC1α pathway. In addition, PS-PGC1a binding may affect the stability of the PGC1α protein, which further augments PS-induced thermogenesis. These results demonstrated the efficacy of dietary SAT fats in diminishing lipid accumulation and the underlying molecular mechanism of PS in enhancing UCP1 expression and mitochondrial function. Thus, our findings suggest that as dietary fat, “beigeing” fat provides more beneficial lipids that contribute to the improvement of mitochondrial function, including PS, which may become a novel, nonpharmacological therapy to increase energy expenditure and counteract obesity and its related diseases.

Hyperproliferative keratinocytes and subcutaneous inflammation contribute to the characteristic symptoms of psoriasis, including erythema, scales, or scaly plaques on the skin. These symptoms significantly affect patients’ quality of life and cause severe physical and psychological distress. However, current treatment strategies have limited therapeutic effect and may lead to adverse side effects. In this study, we present the novel organic photosensitizer TBTDC [5-(((5-(7-(4-(diphenylamino)phenyl)benzo[c][1,2,5]thiadiazol-4-yl)thiophen-2-yl)methylene)amino)-3-methylthiophene-2,4-dicarbonitrile] nanoparticles (NPs) with aggregation-induced emission (AIE) characteristics to mediate photodynamic therapy (TBTDC NP-PDT) for psoriasis treatment. We demonstrate that TBTDC NPs effectively generate reactive oxygen species upon light irradiation and lead to significant apoptosis of psoriatic keratinocytes. Furthermore, TBTDC NPs exhibit high cellular uptake in diseased keratinocytes and induce endoplasmic reticulum stress (ERS)-mediated autophagy, which can also enhance apoptosis. Importantly, TBTDC NPs show no cytotoxicity toward keratinocytes. These unique properties of TBTDC NPs enable remarkable therapeutic effects against psoriasis-like skin lesions and related inflammation in vivo. Overall, our AIE-active TBTDC NP-PDT represents a promising strategy for treating psoriasis in clinical settings.

The emergence of multi-petawatt laser facilities is expected to push forward the maximum energy gain that can be achieved in a single stage of a laser wakefield acceleration (LWFA) to tens of giga-electron volts, which begs the question—is it likely to impact particle physics by providing a truly compact particle collider? Colliders have very stringent requirements on beam energy, acceleration efficiency, and beam quality. In this article, we propose an LWFA scheme that can for the first time simultaneously achieve hitherto unrealized acceleration efficiency from the laser to the electron beam of >20% and a sub-1% energy spread using a stepwise plasma structure and a nonlinearly chirped laser pulse. Three-dimensional high-fidelity simulations show that the nonlinear chirp can effectively mitigate the laser waveform distortion and lengthen the acceleration distance. This, combined with an interstage rephasing process in the stepwise plasma, can triple the beam energy gain compared to that in a uniform plasma for a fixed laser energy, thereby dramatically increasing the efficiency. A dynamic beam loading effect can almost perfectly cancel the energy chirp that arises during the acceleration, leading to the sub-percent energy spread. This scheme is highly scalable and can be applied to petawatt LWFA scenarios. Scaling laws are obtained, which suggest that electron beams with parameters relevant for a Higgs factory could be reached with the proposed high-efficiency, low-energy-spread scheme.

The integration of sensing and communication can achieve ubiquitous sensing while enabling ubiquitous communication. Within the gradually improving global communication, the integrated sensing and communication system based on optical fibers can accomplish various functionalities, such as urban structure imaging, seismic wave detection, and pipeline safety monitoring. With the development of quantum communication, quantum networks based on optical fiber are gradually being established. In this paper, we propose an integrated sensing and quantum network (ISAQN) scheme, which can achieve secure key distribution among multiple nodes and distributed sensing under the standard quantum limit. The continuous variables quantum key distribution protocol and the round-trip multiband structure are adopted to achieve the multinode secure key distribution. Meanwhile, the spectrum phase monitoring protocol is proposed to realize distributed sensing. It determines which node is vibrating by monitoring the frequency spectrum and restores the vibration waveform by monitoring the phase change. The scheme is experimentally demonstrated by simulating the vibration in a star structure network. Experimental results indicate that this multiuser quantum network can achieve a secret key rate of approximately 0.7 Mbits/s for each user under 10-km standard fiber transmission, and its network capacity is 8. In terms of distributed sensing, it can achieve a vibration response bandwidth ranging from 1 Hz to 2 kHz, a strain resolution of 0.50 \mathrm{n}\epsilon /\sqrt{\mathrm{Hz}}, and a spatial resolution of 0.20 m under shot-noise-limited detection. The proposed ISAQN scheme enables simultaneous quantum communication and distributed sensing in a multipoint network, laying a foundation for future large-scale quantum networks and high-precision sensing networks.

Thrombosis and infection are 2 major complications associated with central venous catheters (CVCs), resulting in substantial mortality and morbidity. The concurrent long-term administration of antibiotics and anticoagulants to address these complications have been demonstrated to cause severe side effects such as antibiotic resistance and bleeding. To mitigate these complications with minimal or no drug utilization, we developed a bioinspired zwitterionic block polymer-armored nitric oxide (NO)-generating functional coating for surface modification of CVCs. This armor was fabricated by precoating with a Cu-dopamine (DA)/selenocysteamine (SeCA) (Cu-DA/SeCA) network film capable of catalytically generating NO on the CVCs surface, followed by grafting of a zwitterionic p(DMA-b-MPC-b-DMA) polymer brush. The synergistic effects of active attack by NO and copper ions provided by Cu-DA/SeCA network and passive defense by zwitterionic polymer brush imparted the CVCs surface with durable antimicrobial properties and marked inhibition of platelets and fibrinogen. The in vivo studies confirmed that the surface-armored CVCs could effectively reduce inflammation and inhibit thrombosis, indicating a promising potential for clinical applications.

Research on the flexible hybrid epidermal electronic system (FHEES) has attracted considerable attention due to its potential applications in human–machine interaction and healthcare. Through material and structural innovations, FHEES combines the advantages of traditional stiff electronic devices and flexible electronic technology, enabling it to be worn conformally on the skin while retaining complex system functionality. FHEESs use multimodal sensing to enhance the identification accuracy of the wearer’s motion modes, intentions, or health status, thus realizing more comprehensive physiological signal acquisition. However, the heterogeneous integration of soft and stiff components makes balancing comfort and performance in designing and implementing multimodal FHEESs challenging. Herein, multimodal FHEESs are first introduced in 2 types based on their different system structure: all-in-one and assembled, reflecting totally different heterogeneous integration strategies. Characteristics and the key design issues (such as interconnect design, interface strategy, substrate selection, etc.) of the 2 multimodal FHEESs are emphasized. Besides, the applications and advantages of the 2 multimodal FHEESs in recent research have been presented, with a focus on the control and medical fields. Finally, the prospects and challenges of the multimodal FHEES are discussed.

Problem: Chest radiography is a crucial tool for diagnosing thoracic disorders, but interpretation errors and a lack of qualified practitioners can cause delays in treatment. Aim: This study aimed to develop a reliable multi-classification artificial intelligence (AI) tool to improve the accuracy and efficiency of chest radiograph diagnosis. Methods: We developed a convolutional neural network (CNN) capable of distinguishing among 26 thoracic diagnoses. The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries. Results: The CNN model achieved an average area under the curve (AUC) of 0.961 across all 26 diagnoses in the testing set. COVID-19 detection achieved perfect accuracy (AUC 1.000, [95% confidence interval {CI}, 1.000 to 1.000]), while effusion or pleural effusion detection showed the lowest accuracy (AUC 0.8453, [95% CI, 0.8417 to 0.8489]). In external validation, the model demonstrated strong reproducibility and generalizability within the local dataset, achieving an AUC of 0.9634 for lung opacity detection (95% CI, 0.9423 to 0.9702). The CNN outperformed both radiologists and nonradiological physicians, particularly in trans-device image recognition. Even for diseases not specifically trained on, such as aortic dissection, the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels (all P < 0.05). Additionally, our model exhibited no gender bias (P > 0.05). Conclusion: The developed AI algorithm, now available as professional web-based software, substantively improves chest radiograph interpretation. This research advances medical imaging and offers substantial diagnostic support in clinical settings.

The rapid development of neuromorphic computing has led to widespread investigation of artificial synapses. These synapses can perform parallel in-memory computing functions while transmitting signals, enabling low-energy and fast artificial intelligence. Robots are the most ideal endpoint for the application of artificial intelligence. In the human nervous system, there are different types of synapses for sensory input, allowing for signal preprocessing at the receiving end. Therefore, the development of anthropomorphic intelligent robots requires not only an artificial intelligence system as the brain but also the combination of multimodal artificial synapses for multisensory sensing, including visual, tactile, olfactory, auditory, and taste. This article reviews the working mechanisms of artificial synapses with different stimulation and response modalities, and presents their use in various neuromorphic tasks. We aim to provide researchers in this frontier field with a comprehensive understanding of multimodal artificial synapses.

In recent years, the unique mechanism of sliding ferroelectricity with ultralow switching barriers has been experimentally verified in a series of 2-dimensional (2D) materials. However, its practical applications are hindered by the low polarizations, the challenges in synthesis of ferroelectric phases limited in specific stacking configurations, and the low density for data storage since the switching process involves large-area simultaneous sliding of a whole layer. Herein, through first-principles calculations, we propose a type of semi-sliding ferroelectricity in the single metal porphyrin molecule intercalated in 2D bilayers. An enhanced vertical polarization can be formed independent on stacking configurations and switched via sliding of the molecule accompanied by the vertical displacements of its metal ion anchored from the upper layer to the lower layer. Such semi-sliding ferroelectricity enables each molecule to store 1 bit data independently, and the density for data storage can be greatly enhanced. When the bilayer exhibits intralayer ferromagnetism and interlayer antiferromagnetic coupling, a considerable difference in Curie temperature between 2 layers and a switchable net magnetization can be formed due to the vertical polarization. At a certain range of temperature, the exchange of paramagnetic–ferromagnetic phases between 2 layers is accompanied by ferroelectric switching, leading to a hitherto unreported type of multiferroic coupling that is long-sought for efficient “magnetic reading + electric writing”.

Due to the absence of definitive diagnostic criteria, there remains a lack of consensus regarding the risk assessment of central lymph node metastasis (CLNM) and the necessity for prophylactic lymph node surgery in ultrasound-diagnosed thyroid cancer. The localization of thyroid nodules is a recognized predictor of CLNM; however, quantifying this relationship is challenging due to variable measurements. In this study, we developed a differential isomorphism-based alignment method combined with a graph transformer to accurately extract localization and morphological information of thyroid nodules, thereby predicting CLNM. We collected 88,796 ultrasound images from 48,969 patients who underwent central lymph node (CLN) surgery and utilized these images to train our predictive model, ACE-Net. Furthermore, we employed an interpretable methodology to explore the factors influencing CLNM and generated a risk heatmap to visually represent the distribution of CLNM risk across different thyroid regions. ACE-Net demonstrated superior performance in 6 external multicenter tests (AUC = 0.826), surpassing the predictive accuracy of human experts (accuracy = 0.561). The risk heatmap enabled the identification of high-risk areas for CLNM, likely correlating with lymphatic metastatic pathways. Additionally, it was observed that the likelihood of metastasis exceeded 80% when the nodal margin’s minimum distance from the thyroid capsule was less than 1.25 mm. ACE-Net’s capacity to effectively predict CLNM and provide interpretable disease-related insights can importantly reduce unnecessary lymph node dissections by 37.9%, without missing positive cases, thus offering a valuable tool for clinical decision-making.