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ABSTRACT Background Despite significant advances in lung cancer treatment, cisplatin (DDP)‐based chemotherapy remains a cornerstone for managing the disease. However, the prevalence of chemoresistance presents a major challenge, limiting its effectiveness and contributing to poor outcomes. This underscores the urgent need for novel therapeutic strategies to overcome chemoresistance and improve chemotherapy efficacy in lung cancer patients. Exploring approaches to sensitize tumors to cisplatin could enhance treatment responses and overall survival rates. Methods and Results Our study utilized a variety of lung cancer models, including cell lines, mouse models, and patient‐derived organoids, to validate the synergistic cytotoxic effects of dihydroartemisinin (DHA) and cisplatin (DDP). When combined with DDP, we demonstrate that DHA is a promising therapeutic agent that effectively triggers ferroptosis in lung cancer cells, offering a potential strategy for overcoming chemoresistance. Mechanistically, the combination of DHA and DDP synergistically enhances ZIP14 expression, modulating iron homeostasis and upregulating oxidative stress, leading to both in vitro and in vivo ferroptosis. Notably, our findings revealed that the sequential administration of DDP followed by DHA significantly increases ZIP14 expression and induces superior therapeutic outcomes compared to the simultaneous administration or DHA followed by DDP. This observation underscores the importance of the drug administration order in optimizing treatment efficacy, providing new insights into enhancing chemotherapy response in lung cancer. Conclusion Our findings suggest that combining dihydroartemisinin (DHA) with cisplatin (DDP) presents a promising strategy to overcome chemoresistance in lung cancer patients. Importantly, administering DHA during chemotherapy intervals could further optimize treatment outcomes, enhancing the overall efficacy of lung cancer chemotherapy.

The topology of low-voltage distribution systems changes with the load or the on/off position of the circuit switch. This will affect power flows, losses, and so on. This paper submits a new method to identify the topology of a low-voltage feeder using the injection high-frequency signal. An inductor can block the high-frequency signal. It can change the propagation direction of the injected high-frequency signal to make it propagate unidirectionally along the low-voltage feeder. By injecting a 5 MHz sinusoidal signal from the upstream direction of the low-voltage feeder, all the line segments and devices on the feeder can be identified. The wavelength of the high-frequency signal is short. The wavelength of the 5 MHz signal is 60 meters. Through the delay of different observation points on the feeder, the length of the line section can be roughly calculated. The highfrequency signal has an obvious reflection on the feeder. Using this feature, we can roughly calculate the length of the line segment. The correctness of the method is demonstrated by MATLAB simulation verification.

The structure of the low-voltage distribution network often changes. The change of topology will affect fault detection, fault location, line loss calculation, etc. It leads to fault detection error, inaccurate positioning and abnormal line loss calculation. This paper presents a new method to automatically identify the topology of a low-voltage power grid by using the injection current signal. When the disturbance current signal is injected into the low-voltage line, the current upstream of the injection point will change, and the current downstream of the injection point will not be affected. It is proved theoretically by using the superposition principle. With this method, the disturbance current signal can be injected into the line in turn, and the topology can be identified by observing the change of the current in line. The correctness of the method is proved by Matlab simulation and laboratory verification.

The goal of carbon neutrality is an extensive and profound economic and social change, which will have far-reaching impacts on industrial structure, energy structure, and social consumption structure. Energy sectors will face in-depth adjustment, and it is essential to optimize major structures consequently due to the foresight of talent training. This research first employs Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, Analytic Network Process (ANP), and the weighted fuzzy Technique for Order Performance by Similarity to Ideal Solutions (TOPSIS) to formulate and analyze the structure optimization of academic disciplines, and finally, the universities featuring mining are taken as an example to verify the feasibility of the method. Results reveal that the integration of ANP, SWOT, and the fuzzy TOPSIS evaluation method is able to qualify the assessment for academic discipline optimization. The specialty structure optimization results should focus on clean, intelligent, and sustainable development of the coal industry. The first priority is to increase relevant research on sustainable development of the mining industry, with a priority value of 0.0435. The modern coal chemistry and intelligent coal mining are also highly valued as the options for achieving carbon neutrality. Adding natural gas-related majors is underestimated as the least recognized priority, with a priority value of 0.0133. Suggestions and implications are provided for structure optimization of academic disciplines in universities featuring energy.

Distribution automation (DA) and Internet of Things (IoT) all need the topology information of power distribution network to support some applications, such as fault diagnosis, network reconfiguration and optimization. IEC 61850 is a general communication model and standard for information exchange between intelligent electronic devices (IEDs). However, it has no mechanism for feeder topology information exchange. This paper solves this problem by developing the corresponding information model. Firstly, a feeder model is established as a container of the equipment along a distribution line. Secondly, logical models, such as terminal and connection nodes, are added to describe the physical connection relationship between the electrical equipment. Taking a circuit breaker as an example, this paper introduces how to add the terminal attribute to an existing logical node (XCBR). The physical connection between the circuit breaker and other electrical equipment is described by adding the logic node LCNN. Then, a new logical node LTPN is added to describe the logical connection between the devices. A new logical node, FTPA, is added to describe the status of the topology analysis and the topology results. Based on these new logical nodes, this paper proposes the mechanism of topology information exchange between IEDs. Three IEDs and the IEEE 13-node model are used to build an experimental environment. The result verifies the effectiveness of this method. More distributed applications can be used to test the validity and interoperability of the proposed model.

Abstract Uterine myomas are the most common pelvic tumors in women, which can lead to abnormal uterine bleeding, abdominal pain, pelvic compression symptoms, infertility, or adverse pregnancy. In this article, we provide a dataset named uterine myoma MRI dataset (UMD), which can be used for clinical research on uterine myoma imaging. The UMD is the largest publicly available uterine MRI dataset to date including 300 cases of uterine myoma T2-weighted imaging (T2WI) sagittal patient images and their corresponding annotation files. The UMD covers 9 types of uterine myomas classified by the International Federation of Obstetrics and Gynecology (FIGO), which were annotated and reviewed by 11 experienced doctors to ensure the authority of the annotated data. The UMD is helpful for uterine myomas classification and uterine 3D reconstruction tasks, which has important implications for clinical research on uterine myomas.

Uterine myomas affect 70% of women of reproductive age, potentially impacting their fertility and health. Manual film reading is commonly used to identify uterine myomas, but it is time-consuming, laborious, and subjective. Clinical treatment requires the consideration of the positional relationship among the uterine wall, uterine cavity, and uterine myomas. However, due to their complex and variable shapes, the low contrast of adjacent tissues or organs, and indistinguishable edges, accurately identifying them in MRI is difficult. Our work addresses these challenges by proposing an instance segmentation network capable of automatically outputting the location, category, and masks of each organ and lesion. Specifically, we designed a new backbone that facilitates learning the shape features of object diversity, and filters out background noise interference. We optimized the anchor box generation strategy to provide better priors in order to enhance the process of bounding box prediction and regression. An adaptive iterative subdivision strategy ensures that the mask boundary details of objects are more realistic and accurate. We conducted extensive experiments to validate our network, which achieved better average precision (AP) results than those of state-of-the-art instance segmentation models. Compared to the baseline network, our model improved AP on the uterine wall, uterine cavity, and myomas by 8.8%, 8.4%, and 3.2%, respectively. Our work is the first to realize multiclass instance segmentation in uterine MRI, providing a convenient and objective reference for the clinical development of appropriate surgical plans, and has significant value in improving diagnostic efficiency and realizing the automatic auxiliary diagnosis of uterine myomas.

Laser spraying technology is a new type of thermal spraying technology， which is based on the principle of using laser as a heat source to heat the powder so that the particles reach a molten or semi-molten state and form a coating on the surface of the substrate.In this work， the nickel coatings were fabricated on the surface of nickel mesh through laser spraying.The surface morphology of the coatings was characterized by scanning electron microscopy (SEM).The catalytic activity of hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) of electrodes was characterized by electrochemical methods， and the effect of nickel particle size on the electrolytic catalytic performance of coatings was studied.The results showed that the particle size affected the degree of melting of the particles when passing through the laser.The number of particles deposited on the surface of the prepared electrodes increased as the particle size decreased， and pore structure particles were found on the electrode surface， while the HER and OER overpotentials of the electrodes decreased， the double-layer capacitance value increased， and the impedance decreased， indicating that the HER and OER activities of the prepared electrodes were enhanced.The electrode prepared with an average particle size of 45 μm powder showed the best catalytic activity in HER and OER， with overpotentials of 416 mV and 371 mV， respectively， at a current density of 10 mA/cm2.This study provided a new method for the efficient and low-cost green preparation of catalytic electrodes for hydrogen production by alkaline water electrolysis.