Optimal response to tislelizumab plus chemotherapy in metastatic triple-negative breast cancer: a case report and literature review.

Metastatic triple-negative breast cancer (mTNBC) has the worst prognosis among breast cancer subtypes. Immune checkpoint inhibitors (ICIs) plus chemotherapy have promising survival benefits. Herein, we report a 51-year-old woman whose metastatic lesions were diagnosed as triple-negative subtype and who received tislelizumab plus eribulin treatment and achieved excellent efficacy. To our knowledge, this study is the first attempt to present tislelizumab in combination with eribulin for mTNBC treatment. New treatments resulting in prolonged survival and durable clinical responses would benefit mTNBC patients. Then, we summarize the possible influencing factors of the interaction between tislelizumab and eribulin.

Large-area single-crystal TMD growth modulated by sapphire substrates.

Transition metal dichalcogenides (TMDs) have recently attracted extensive attention due to their unique physical and chemical properties; however, the preparation of large-area TMD single crystals is still a great challenge. Chemical vapor deposition (CVD) is an effective method to synthesize large-area and high-quality TMD films, in which sapphires as suitable substrates play a crucial role in anchoring the source material, promoting nucleation and modulating epitaxial growth. In this review, we provide an insightful overview of different epitaxial mechanisms and growth behaviors associated with the atomic structure of sapphire surfaces and the growth parameters. First, we summarize three epitaxial growth mechanisms of TMDs on sapphire substrates, namely, van der Waals epitaxy, step-guided epitaxy, and dual-coupling-guided epitaxy. Second, we introduce the effects of polishing, cutting, and annealing processing of the sapphire surface on the TMD growth. Finally, we discuss the influence of other growth parameters, such as temperature, pressure, carrier gas, and substrate position, on the growth kinetics of TMDs. This review might provide deep insights into the controllable growth of large-area single-crystal TMDs on sapphires, which will propel their practical applications in high-performance nanoelectronics and optoelectronics.

Tapping into Permutation Symmetry for Improved Detection of k-Symmetric Extensions.

Symmetric extensions are essential in quantum mechanics, providing a lens through which to investigate the correlations of entangled quantum systems and to address challenges like the quantum marginal problem. Though semi-definite programming (SDP) is a recognized method for handling symmetric extensions, it struggles with computational constraints, especially due to the large real parameters in generalized qudit systems. In this study, we introduce an approach that adeptly leverages permutation symmetry. By fine-tuning the SDP problem for detecting k-symmetric extensions, our method markedly diminishes the searching space dimensionality and trims the number of parameters essential for positive-definiteness tests. This leads to an algorithmic enhancement, reducing the complexity from O(d2k) to O(kd2) in the qudit k-symmetric extension scenario. Additionally, our approach streamlines the process of verifying the positive definiteness of the results. These advancements pave the way for deeper insights into quantum correlations, highlighting potential avenues for refined research and innovations in quantum information theory.

A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction.

In light of the ongoing COVID-19 pandemic, predicting its trend would significantly impact decision-making. However, this is not a straightforward task due to three main difficulties: temporal autocorrelation, spatial dependency, and concept drift caused by virus mutations and lockdown policies. Although machine learning has been extensively used in related work, no previous research has successfully addressed all three challenges simultaneously. To overcome this challenge, we developed a novel online multi-task regression algorithm that incorporates a chain structure to capture spatial dependency, the ADWIN drift detector to adapt to concept drift, and the lag time series feature to capture temporal autocorrelation. We conducted several comparative experiments based on the number of daily confirmed cases in 20 areas in California and affiliated cities. The results from our experiments demonstrate that our proposed model is superior in adapting to concept drift in COVID-19 data and capturing spatial dependencies across various regions. This leads to a significant improvement in prediction accuracy when compared to existing state-of-the-art batch machine learning methods, such as N-Beats, DeepAR, TCN, and LSTM.

Carbon dots co-doped with nitrogen and chlorine for "off-on" fluorometric determination of the activity of acetylcholinesterase and for quantification of organophosphate pesticides.

Nitrogen and chlorine dually-doped carbon dots (N,Cl-CDs) were hydrothermally prepared starting from 4-chloro-1,2-diaminobenzene and dopamine. The N,Cl-CDs exhibit strong orange fluorescence, with excitation/emission maxima at 420/570 nm and a relative high quantum yield (15%). The N,Cl-CDs were employed to detect acetylcholinesterase (AChE) activity and organophosphate pesticides (OPs) which are enzyme inhibitors. Acetylthiocholine is enzymatically split by AChE to produce thiocholine which triggers the decomposition of Ellmans's reagent to form a yellow colored product (2-nitro-5-thiobenzoate anion). The product causes an inner filter effect (IEF) on the fluorescence of the N,Cl-CDs. Fluorescence decreases linearly in the 0.017 to 5.0 Unit·L-1 AChE activity range, and the detection limit is 2 mUnit·L-1. If organophosphates are present, the activity of AChE becomes increasingly blocked, and this leads to a less expressed IFE and an increasing recovery of fluorescence. This was used for the quantification of OPs. Response is linear in the 0.3-1000 µg·L-1 OP concentration range with a 30 ng·L-1 detection limit. Graphical abstractSchematic representation of the synthesis of nitrogen and chlorine dually-doped carbon dots (N,Cl-CDs) and the recognition of organophosphate pesticides by N,Cl-CDs.

Continuous, Ultra-lightweight, and Multipurpose Super-aligned Carbon Nanotube Tapes Viable over a Wide Range of Temperatures.

In extreme environments, such as at ultrahigh or ultralow temperatures, the amount of tape used should be minimal so as to reduce system contamination and unwanted residues. However, tapes made from conventional materials typically lose their adhesiveness or leave residues difficult to remove under such conditions. Thus, the development of more versatile, lightweight, and easily removable tapes for applications in such extreme environments has received considerable attention. Here, we report that horizontally superaligned carbon nanotube (SACNT) tapes can be used to provide perfect van der Waals (vdW) interface contacts over a wide range of temperatures (from -196 to 1000 °C), yielding outstanding adhesiveness with specific adhesion strengths up to ∼1.1 N/µg. With a surface density of only 0.5-5 µg/cm2, hundreds of times lighter than the vertically aligned CNT adhesives, the SACNT tapes can be cost-effectively provided in hundreds of meters. They have multipurpose adhesive abilities for versatile materials and are also easily separated from samples even after exposure to extreme temperature regimes. First-principles calculations confirm the mechanism of vdW adhesion and reveal that ultraflat and nanometer-thick SACNT tapes may yield far greater adhesive abilities. These SACNT tapes show great potential for use in mechanical bonding, electrical bonding, and thermal dissipation in electronic devices.

Bio-Inspired NanoVilli Chips for Enhanced Capture of Tumor-Derived Extracellular Vesicles: Toward Non-Invasive Detection of Gene Alterations in Non-Small Cell Lung Cancer.

Tumor-derived extracellular vesicles (EVs) present in bodily fluids are emerging liquid biopsy markers for non-invasive cancer diagnosis and treatment monitoring. Because the majority of EVs in circulation are not of tumor origin, it is critical to develop new platforms capable of enriching tumor-derived EVs from the blood. Herein, we introduce a biostructure-inspired NanoVilli Chip, capable of highly efficient and reproducible immunoaffinity capture of tumor-derived EVs from blood plasma samples. Anti-EpCAM-grafted silicon nanowire arrays were engineered to mimic the distinctive structures of intestinal microvilli, dramatically increasing surface area and enhancing tumor-derived EV capture. RNA in the captured EVs can be recovered for downstream molecular analyses by reverse transcription Droplet Digital PCR. We demonstrate that this assay can be applied to monitor the dynamic changes of ROS1 rearrangements and epidermal growth factor receptor T790M mutations that predict treatment responses and disease progression in non-small cell lung cancer patients.

Wi-Fi/MARG Integration for Indoor Pedestrian Localization.

With the wide deployment of Wi-Fi networks, Wi-Fi based indoor localization systems that are deployed without any special hardware have caught significant attention and have become a currently practical technology. At the same time, the Magnetic, Angular Rate, and Gravity (MARG) sensors installed in commercial mobile devices can achieve highly-accurate localization in short time. Based on this, we design a novel indoor localization system by using built-in MARG sensors and a Wi-Fi module. The innovative contributions of this paper include the enhanced Pedestrian Dead Reckoning (PDR) and Wi-Fi localization approaches, and an Extended Kalman Particle Filter (EKPF) based fusion algorithm. A new Wi-Fi/MARG indoor localization system, including an Android based mobile client, a Web page for remote control, and a location server, is developed for real-time indoor pedestrian localization. The extensive experimental results show that the proposed system is featured with better localization performance, with the average error 0.85 m, than the one achieved by using the Wi-Fi module or MARG sensors solely.

PILA: Sub-Meter Localization Using CSI from Commodity Wi-Fi Devices.

The aim of this paper is to present a new indoor localization approach by employing the Angle-of-arrival (AOA) and Received Signal Strength (RSS) measurements in Wi-Fi network. To achieve this goal, we first collect the Channel State Information (CSI) by using the commodity Wi-Fi devices with our designed three antennas to estimate the AOA of Wi-Fi signal. Second, we propose a direct path identification algorithm to obtain the direct signal path for the sake of reducing the interference of multipath effect on the AOA estimation. Third, we construct a new objective function to solve the localization problem by integrating the AOA and RSS information. Although the localization problem is non-convex, we use the Second-order Cone Programming (SOCP) relaxation approach to transform it into a convex problem. Finally, the effectiveness of our approach is verified based on the prototype implementation by using the commodity Wi-Fi devices. The experimental results show that our approach can achieve the median error 0.7 m in the actual indoor environment.