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Abstract Photothermal superhydrophobic coatings are supposed promising to prevent ice accumulation on infrastructures but often experience significant performance degradation in real icing conditions and lack mechanical robustness. Here, we report design of robust photothermal superhydrophobic coatings with three-tier hierarchical micro-/nano-/nanostructures by deposition of nanosized MOFs on natural attapulgite nanorods, fluorination, controlled phase separation of a hydrophobic adhesive and spraying assembly. Phase separation degree and adhesive content significantly influence the coatings’ properties by regulating the structural parameters and morphology. In simulated/real icing environments, the coatings simultaneously show (i) high superhydrophobicity and stable Cassie-Baxter states due to their low-surface-energy, three-tier micro-/nano-/nanostructure, (ii) excellent photothermal effect primarily due to nanosized MOFs, and (iii) good mechanical robustness by the phase-separated adhesive, reinforcement with attapulgite and the coatings’ self-similar structure. Accordingly, combined with low thermal conductivity, the coatings exhibit remarkable anti-icing/frosting (e.g., no freezing in at least 150 min and almost free of frost in 25 min) and de-icing/frosting performances (e.g., fast de-icing in 12.7 min and fast de-frosting in 16.7 min) in such environments. Furthermore, we realize large-scale preparation of the coatings at reasonable costs. The coatings have great application potential for anti-icing and de-icing in the real world by efficiently using natural sunlight.

The fibroblast growth factor receptor (FGFR) is a crucial receptor tyrosine kinase involved in essential biological processes, including growth, development, and tissue repair. However, FGFR gene mutations, including amplification, fusion, and mutation, can disrupt epigenetics, transcriptional regulation, and tumor microenvironment interactions, leading to cancer development. Targeting these kinase mutations with small molecule drugs or antibodies has shown clinical benefits. For example, erdafitinib is approved for treating locally advanced or metastatic urothelial cancer patients with FGFR2/FGFR3 mutations, and pemigatinib is approved for treating cholangiocarcinoma with FGFR2 fusion/rearrangement. Effective screening of FGFR variant patients is crucial for the clinical application of FGFR inhibitors. Various detection methods, such as polymerase chain reaction, next-generation sequencing, fluorescence in situ hybridization, and immunohistochemistry, are available, and their selection should be based on diagnostic and treatment decision-making needs. Our developed expert consensus aims to standardize the diagnosis and treatment process for FGFR gene mutations and facilitate the practical application of FGFR inhibitors in clinical practice.

This study examined the potential correlation between the immoderate intake of sugar-sweetened beverages (SSBs) and the subsequent rate of diabetes remission (DR). 206 individuals who met the eligibility criteria between January 2019 and June 2022 were recruited. Inquiries were conducted to gather information on the participants’ beverage consumption before the onset. Subsequently, the participants were separated into the diabetes remission group (DR group) and nondiabetes remission group (NDR group) depending on whether they met the diagnostic criteria for diabetes remission. Baseline clinical elements within the two groups were juxtaposed, and factors influencing diabetes remission were identified through logistic regression analyses. The cutoff values of each critical factor were determined based on the receiver operating characteristic curve. One hundred and nine patients reported a history of SSB consumption, while the remaining 58 reported no such history. After 1 year, 40 patients achieved remission from diabetes. Compared with the NDR group, a higher SSBs ratio, body mass index (BMI), and blood creatinine (BCr) was observed in the DR group after adjusting for confounders, SSBs (odds ratio [OR] = 3.503; 95% confidence interval [CI] = 1.334–9.202; p = 0.011) and BCr (OR = 1.038; 95% CI = 1.003–1.079; p = 0.042) emerged as independent predictors of DR. The composite index of SSBs and BCr efficaciously predicted DR (area under the ROC curve [AUC] = 0.810, p < 0.001). SSBs and BCr were independent risk factors for DR. The amalgamation of these markers could more accurately predict DR.

Nearshore water-level prediction has a substantial impact on the daily lives of coastal residents, fishing operations, and disaster prevention and mitigation. Compared to the limitations and high costs of traditional empirical forecasts and numerical models for nearshore water-level prediction, data-driven artificial intelligence methods can more efficiently predict water levels. Attention mechanisms have recently shown great potential in natural language processing and video prediction. Convolutional long short-term memory(ConvLSTM) combines the advantages of convolutional neural networks (CNN) and long short-term Memory (LSTM), enabling more effective data feature extraction. Therefore, this study proposes a ConvLSTM nearshore water level prediction model that incorporates an attention mechanism. The ConvLSTM model extracts multiscale information from historical water levels, and the attention mechanism enhances the importance of key features, thereby improving the prediction accuracy and timeliness. The model structure was determined through experiments and relevant previous studies using five years of water level data from the Zhuhai Tide Station and the surrounding wind speed and rainfall data for training and evaluation. The results indicate that this model outperforms the four other baseline models of PCCs, RMSE, and MAE, effectively predicting future water levels at nearshore stations up to 48 h in advance. Compared to the ConvLSTM model, the model with the attention mechanism showed an average improvement of approximately 10% on the test set, with a greater error reduction in short-term forecasts than that in long-term forecasts. During Typhoon Higos, the model reduced the MAE of the best-performing baseline model by approximately 3.2 and 2.4 cm for the 6- and 24-hour forecasts, respectively, decreasing forecast errors by approximately 18% and 11%, effectively enhancing the ability of the model to forecast storm surges. This method provides a new approach for forecasting nearshore tides and storm surges.

Superhydrophobic coatings for umbrella canopies have the potential to solve the issue of rain residue. However, achieving both impalement resistance and mechanical robustness in these coatings poses a significant challenge and limits their practical applications. In this study, we propose a similar “pole erecting” method utilizing perfluorodecyl polysiloxane modified SiO2 nanoparticles (F-silica) as the pole and poly(dimethylsiloxane) (PDMS) as the screw to enhance impalement resistance and mechanical robustness, which is attributed to the three-tier hierarchical micro-/micro-/nanostructure bonded by PDMS, along with a reduction in aspect ratio. The obtained PDMS0.5-F-silica could withstand 150 min of water drop impacting (equivalent to approximately 19,000 water drops) and 60 s of 50 kPa water jetting, also showed exceptional endurance against various mechanical tests, including 200 cycles of reciprocating abrasion, 1000 cycles of Martindale abrasion and 200 cycles of tape peeling. Moreover, when applying the PDMS0.5-F-silica coating to umbrella canopies, we observed an absence of rain residue after subjecting the umbrella to 20 h rain. These findings are promising for the development and practical implementation of superhydrophobic coatings.

Hyperspectral image (HSI) can provide rich spectral information, which can be helpful for accurate classification in many applications. However, the hyperspectral image classification task has challenges, including limited labeled data, data redundancy, data sparsity, and imbalanced class samples. Over time, various methods have been proposed to solve the above-mentioned problems. To mitigate the issue mentioned above in this paper, we present the neighborhood attention transformer with a channel-wise shift technique for hyperspectral image classification. The neighborhood attention transformer leverages the power of the attention mechanism to capture the spatial relationship between neighboring pixels and extract discriminative features. The channel-wise shift techniques empower the model to adjust the spectral characteristics of each channel adaptively, enhancing its ability to handle the spectral variations present in hyperspectral data. To validate the effectiveness of the proposed model, we conduct comprehensive experiments on the publically available dataset. The results demonstrate that our model consistently outperforms other state-of-the-art methods. The overall accuracy of the proposed model reached four datasets.

Abstract Dynamic covalent polymers (DCPs) that strike a balance between high performance and rapid reconfiguration have been a challenging task. For this purpose, a solution is proposed in the form of a new dynamic covalent supramolecular motif—guanidine urea structure (GUAs). GUAs contain complex and diverse chemical structures as well as unique bonding characteristics, allowing guanidine urea supramolecular polymers to demonstrate advanced physical properties. Noncovalent interaction aggregates (NIAs) have been confirmed to form in GUA‐DCPs through multistage H‐bonding and π‐π stacking, resulting in an extremely high Young's modulus of 14 GPa, suggesting remarkable mechanical strength. Additionally, guanamine urea linkages in GUAs, a new type of dynamic covalent bond, provide resins with excellent malleability and reprocessability. Guanamine urea metathesis is validated using small molecule model compounds, and the temperature dependent infrared and rheological behavior of GUA‐DCPs following the dissociative exchange mechanism. Moreover, the inherent photodynamic antibacterial properties are extensively verified by antibacterial experiments. Even after undergoing three reprocessing cycles, the antibacterial rate of GUA‐DCPs remains above 99% after 24 h, highlighting their long‐lasting antibacterial effectiveness. GUA‐DCPs with dynamic nature, tuneable composition, and unique combination of properties make them promising candidates for various technological advancements.

BackgroundTelehealth has emerged as a popular channel for providing outpatient services in many countries. However, the majority of telehealth systems focus on operational functions and offer only a sectional patient journey at most. Experiences with incorporating longitudinal real-world medical record data into telehealth are valuable but have not been widely shared. The feasibility and usability of such a telehealth platform, with comprehensive, real-world data via a live feed, for cancer patient care are yet to be studied. ObjectiveThe primary purpose of this study is to understand the feasibility and usability of cancer patient care using a telehealth platform with longitudinal, real-world data via a live feed as a supplement to hospital electronic medical record systems specifically from physician’s perspective. MethodsA telehealth platform was constructed and launched for both physicians and patients. Real-world data were collected and curated using a comprehensive data model. Physician activities on the platform were recorded as system logs and analyzed. In February 2023, a survey was conducted among the platform’s registered physicians to assess the specific areas of patient care and to quantify their before and after experiences, including the number of patients managed, time spent, dropout rate, visit rate, and follow-up data. Descriptive and inferential statistical analyses were performed on the data sets. ResultsOver a period of 15 months, 16,035 unique users (13,888 patients, 1539 friends and family members, and 174 physician groups with 608 individuals) registered on the platform. More than 382,000 messages including text, reminders, and pictures were generated by physicians when communicating with patients. The survey was completed by 78 group leaders (45% of the 174 physician groups). Of the participants, 84% (65.6/78; SD 8.7) reported a positive experience, with efficient communication, remote supervision, quicker response to questions, adverse event prevention, more complete follow-up data, patient risk reduction, cross-organization collaboration, and a reduction in in-person visits. The majority of the participants (59/78, 76% to 76/78, 97.4%) estimated improvements in time spent, number of patients managed, the drop-off rate, and access to medical history, with the average ranging from 57% to 105%. When compared with prior platforms, responses from physicians indicated better experiences in terms of time spent, the drop-off rate, and medical history, while the number of patients managed did not significantly change. ConclusionsThis study suggests that a telehealth platform, equipped with comprehensive, real-world data via a live feed, is feasible and effective for cancer patient care. It enhances inpatient management by improving time efficiencies, reducing drop-off rates, and providing easy access to medical history. Moreover, it fosters a positive experience in physician-patient interactions.

BackgroundGowing number of studies have demonstrated the association between gut microbiome and T2DM microvascular complications, however the causal relationship remains unclear. Therefore, we using the Mendelian randomization (MR) approach to investigate this causal relation.MethodsUsing gut microbiome data from the International MiBioGen Consortium genome-wide association study (GWAS) and T2DM microvascular complications data from the FinnGen Consortium GWAS to perform MR analyses. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs), the inverse variance weighting (IVW) method was used as the primary analysis method, and the results were tested for heterogeneity and horizontal pleiotropy.ResultsOur research identified that there are 5 known microbial species and 2 unknown microbial species in the gut microbiome that were causally related to T2DM retinopathy. Besides, three and seven known microbial species causal relationships between the gut microbiome and T2DM neuropathy and T2DM nephropathy, respectively.ConclusionsUsing MR methods, we demonstrated the causal relationship between gut microbiome and microvascular complications in T2DM, providing a new strategy for the prevention and treatment of it.

Graph neural networks (GNNs) offer a potent framework for learning and representing data with graph structures. In the context of hyperspectral images (HSIs) classification tasks, GNNs have been successfully employed. Among the diverse variations of GNNs, graph attention networks (GATs) stand out, demonstrating cutting-edge performance in node prediction. GATs excel by acquiring the capability to assign dense attention coefficients to each neighboring node during the process of feature aggregation. First, the intricate nature of land distribution, coupled with the high dimensionality of hyperspectral imaging (HSI) data, poses a hurdle for the direct application of GATs in effectively distinguishing between various coverage categories. Second, the insufficiency of spectral information further compounds the issue by adversely affecting classification accuracy, contributing to GATs’ susceptibility to overfitting during training. Additionally, prevailing classification approaches commonly derive features either at the pixel or superpixel levels. The utilization of single-scale feature extraction techniques proves inadequate in concurrently addressing both local and global features of land cover. This limitation contributes to less-than-optimal classification outcomes. To address this issue, this article proposes a double-branch multilevel skip sparse GAT for HSI classification. The network comprises two parallel branches of sparse GATs, extracting features from the perspectives of pixels and superpixels. Via the construction of a multilevel skip graph, these two branches adeptly gather both local and global context information. Subsequently, a fully connected network is deployed to classify the integrated features derived from both branches. The effectiveness of the proposed method is validated through experimental results on three benchmark HSI datasets, showcasing its superior performance.

Classifying hyperspectral images in remote sensing applications is challenging due to limited training samples and high dimensionality of data. Deep-learning-based methods have recently demonstrated promising results in the classification of HSI. This article presents a proposed methodology for extracting local features and high-level semantic features from HSI input data using a light-weighted spectral–spatial transformer. This approach will allow us to comprehensively examine the spatial and spectral characteristics while reducing the computing expenses. The proposed model integrates lightweight multihead self-attention and residual feedforward modules in order to effectively capture long-range dependencies and address the computational challenges associated with this model. In order to assess the efficiency of the proposed model, we conducted experiments on four publicly available datasets. The obtained experimental results were then compared with those of the existing state-of-the-art models. The proposed model obtains the best classification results in terms of classification accuracy and computational complexity under limited training samples. The overall accuracy of the proposed model achieved $99.91\%,\;98.06\%,\;99.43\% $ and $99.01\% $ on four datasets.

The fusion genes NRG1 and NRG2, members of the epidermal growth factor (EGF) receptor family, have emerged as key drivers in cancer. Upon fusion, NRG1 retains its EGF-like active domain, binds to the ERBB ligand family, and triggers intracellular signaling cascades, promoting uncontrolled cell proliferation. The incidence of NRG1 gene fusion varies across cancer types, with lung cancer being the most prevalent at 0.19 to 0.27%. CD74 and SLC3A2 are the most frequently observed fusion partners. RNA-based next-generation sequencing is the primary method for detecting NRG1 and NRG2 gene fusions, whereas pERBB3 immunohistochemistry can serve as a rapid prescreening tool for identifying NRG1-positive patients. Currently, there are no approved targeted drugs for NRG1 and NRG2. Common treatment approaches involve pan-ERBB inhibitors, small molecule inhibitors targeting ERBB2 or ERBB3, and monoclonal antibodies. Given the current landscape of NRG1 and NRG2 in solid tumors, a consensus among diagnostic and treatment experts is proposed, and clinical trials hold promise for benefiting more patients with NRG1 and NRG2 gene fusion solid tumors.

Insulin resistance has been regarded as a hallmark of diabetes heart disease (DHD). Numerous studies have shown that insulin resistance can affect blood circulation and myocardium, which indirectly cause cardiac hypertrophy and ventricular remodeling, participating in the pathogenesis of DHD. Meanwhile, hyperinsulinemia, hyperglycemia, and hyperlipidemia associated with insulin resistance can directly impair the metabolism and function of the heart. Targeting insulin resistance is a potential therapeutic strategy for the prevention of DHD. Currently, the role of insulin resistance in the pathogenic development of DHD is still under active research, as the pathological roles involved are complex and not yet fully understood, and the related therapeutic approaches are not well developed. In this review, we describe insulin resistance and add recent advances in the major pathological and physiological changes and underlying mechanisms by which insulin resistance leads to myocardial remodeling and dysfunction in the diabetic heart, including exosomal dysfunction, ferroptosis, and epigenetic factors. In addition, we discuss potential therapeutic approaches to improve insulin resistance and accelerate the development of cardiovascular protection drugs.

Objective To investigate the effect of different radiation doses on the accuracy of iodine quantification and CT value of virtual monochromatic image with a muti-spectral CT iodine phantom using second-generation dual-layer detector spectral CT (DLCT). Methods The second-generation DLCT scan abdominal phantom was used, which consisted of six different concentrations of iodine rods at 4, 8 and 12 g/L. The scan parameters were set at 100, 120 and 140 kVp tube voltages, and six radiation exposure sequences were scanned at each tube voltage: 5, 10, 15, 20, 25 and 30 mGy. Measure the iodine concentration value and the CT value at the single level of 50, 70 and 100 keV, and calculate the absolute error value of iodine concentration and CT value under different radiation exposure. SPSS 22.0 software single factor analysis of variance was used to compare the difference between the iodine concentration and the absolute error of CT value under different Radiation exposure, and LSD method was used to compare the two groups. Results The absolute error of CT value of 4 g/L iodine rod with 10~30 mGy scanning at 100 keV energy level was significantly higher than that of 12 g/L and 8 g/L(P＜0.05), and the absolute error of iodine concentration measurement of other iodine rods with different concentrations at different Radiation exposure had no statistical difference. Conclusions The second-generation double-layer detector spectral CT has high measurement stability for iodine concentration quantification and CT value accuracy under conditions of radiation dose greater than 5 mGy.

AbstractContext Si-Miao-Yong-An (SMYA) has been widely used for the clinical treatment of atherosclerosis (AS). Yet, its complete mechanism of action is not fully understood.Objective To investigate the mechanism by which SMYA stabilizes AS plaques from the perspective of inhibiting vasa vasorum (VV) angiogenesis.Materials and methods We used male ApoE-/- mice to establish an AS model. The mice were divided into model, SMYA (11.7 mg/kg/d), and simvastatin (SVTT) (2.6 mg/kg/d) groups. Mice were given SMYA or SVTT by daily gavage for 8 weeks. HE staining, immunofluorescence double-labelling staining, and immunohistochemical staining were used to observe the pathological changes in the plaques. Finally, the protein and mRNA expression levels of the Wnt1/β-catenin signalling pathway were detected by Western blot and qRT-PCR, respectively.Results SMYA significantly attenuated cholesterol crystallization, and lipid accumulation in AS plaques, resulting in smaller plaque size (0.25 mm2 vs. 0.46 mm2), and lowering ratio of plaque to lumen area (20.04% vs. 38.33%) and VV density (50.64/mm2 vs. 98.02/mm2). Meanwhile, SMYA suppressed both the positive area percentage of Wnt1 (2.53 vs. 3.56), β-catenin (3.33 vs. 5.65) and Cyclin D1 (2.10 vs. 3.27) proteins in the aortic root plaques, and mRNA expression of Wnt1 (1.38 vs. 2.09), β-catenin (2.05 vs. 3.25) and Cyclin D1 (1.39 vs. 2.57).Discussion and conclusions SMYA has a protective effect against AS, which may be related to its anti-VV angiogenesis in plaques, suggesting that SMYA has the potential as a novel botanical formulation in the treatment of AS.

Summary: Acidic and basic sites of catalysts are essential for CO2 capture and activation. In this work, Zr, N-ZnO/ZnAl-LDH-IL composites in ionic liquid and methanol systems were fabricated, and applied to catalyze the synthesis of ethylene carbonate (EC) from ethylene glycol (EG) and CO2 with about 4.76 mmolEC gCat.−1 h−1. The composites showed more strong basic sites due to the effective induction of reactive groups on the catalyst surface by Zr doping, resulting in an increase of pyridinic-N groups from 5.48% to 22.25%. More C atoms adjacent to pyridinic-N as strong basic sites was conducive to the activation of CO2 and EG. In addition, the possible catalytic pathway and mechanism of the composites for synthesizing EC as well as the doping of La, Fe, Ce, and Cu were also investigated, which provides an effective strategy for regulating the acid-base centers on the catalyst surface through ionic liquids and methanol solvents.

Acute myeloid leukemia (AML) with t(16;21)(p11;q22)/FUS::ERG is a rare AML subtype associated with poor prognosis. However, its clinical and molecular features remain poorly defined. We determined the clinicopathological, genomic, and transcriptomic characteristics and outcomes of patients with AML harboring FUS::ERG at our center. Thirty-six AML patients harboring FUS::ERG were identified, with an incidence rate of 0.3%. These patients were characterized by high lactate dehydrogenase levels (median: 838.5 U/L), elevated bone marrow blast counts (median: 71.5%), and a CD56-positive immunophenotype (94.3%). Notably, we found that RTK–RAS GTPase (RAS) pathway genes, including NRAS (33%) and PTPN11 (24%), were frequently mutated in this subtype. Transcriptome analysis revealed enrichment of the phosphatidylinositol-3-kinase-Akt (PI3K-Akt), mitogen-activated protein kinase (MAPK), and RAS signaling pathways and upregulation of BCL2, the target of venetoclax, in FUS::ERG AML compared to RUNX1::RUNX1T1 AML, a more common AML subtype with good prognosis. The median event-free survival in patients with FUS::ERG AML was 11.9 (95% confidence interval [CI]: 9.0–not available [NA]) months and the median overall survival was 18.2 (95% CI: 12.4–NA) months. Allogeneic hematopoietic stem cell transplantation failed to improve outcomes. Overall, the high incidence of RTK–RAS pathway mutations and high expression of BCL2 may indicate promising therapeutic targets in this high-risk AML subset.

The unique palindromic inverted terminal repeats (ITRs) and single-stranded nature of adeno-associated virus (AAV) DNA are major hurdles to current sequencing technologies. Due to these characteristics, sequencing noncanonical AAV genomes present in AAV vector preparations remains challenging. To address this limitation, we developed thorough molecule configuration analysis of noncanonical AAV genomes (TMCA-AAV-seq). TMCA-AAV-seq takes advantage of the documented AAV packaging mechanism in which encapsidation initiates from its 3′ ITR, for AAV-seq library construction. Any AAV genome with a 3′ ITR is converted to a template suitable to adapter addition by a Bst DNA polymerase-mediated extension reaction. This extension reaction helps fix ITR heterogeneity in the AAV population and allows efficient adapter addition to even noncanonical AAV genomes. The resulting library maintains the original AAV genome configurations without introducing undesired changes. Subsequently, long-read sequencing can be performed by the Pacific Biosciences (PacBio) single-molecule, real-time (SMRT) sequencing technology platform. Finally, through comprehensive data analysis, we can recover canonical, noncanonical AAV DNA, and non-AAV vector DNA sequences, along with their molecular configurations. Our method is a robust tool for profiling thorough AAV-population genomes. TMCA-AAVseq can be further extended to all parvoviruses and their derivative vectors.

Self-complementary AAV vectors (scAAV) use a mutant inverted terminal repeat (mITR) for efficient packaging of complementary stranded DNA, enabling rapid transgene expression. However, inefficient resolution at the mITR leads to the packaging of monomeric or subgenomic AAV genomes. These noncanonical particles reduce transgene expression and may affect the safety of gene transfer. To address these issues, we have developed a novel class of scAAV vectors called covalently closed-end double-stranded AAV (cceAAV) that eliminate the mITR resolution step during production. Instead of using a mutant ITR, we used a 56-bp recognition sequence of protelomerase (TelN) to covalently join the top and bottom strands, allowing the vector to be generated with just a single ITR. To produce cceAAV vectors, the vector plasmid is initially digested with TelN, purified, and then subjected to a standard triple-plasmid transfection protocol followed by traditional AAV vector purification procedures. Such cceAAV vectors demonstrate yields comparable to scAAV vectors. Notably, we observed enhanced transgene expression as compared to traditional scAAV vectors. The treatment of mice with hemophilia B with cceAAV-FIX resulted in significantly enhanced long-term FIX expression. The cceAAV vectors hold several advantages over scAAV vectors, potentially leading to the development of improved human gene therapy drugs.

Abstract The rearranged during transfection (RET) gene is one of the receptor tyrosine kinases and cell‐surface molecules responsible for transmitting signals that regulate cell growth and differentiation. In non‐small cell lung cancer (NSCLC), RET fusion is a rare driver gene alteration associated with a poor prognosis. Fortunately, two selective RET inhibitors (sRETi), namely pralsetinib and selpercatinib, have been approved for treating RET fusion NSCLC due to their remarkable efficacy and safety profiles. These inhibitors have shown the ability to overcome resistance to multikinase inhibitors (MKIs). Furthermore, ongoing clinical trials are investigating several second‐generation sRETis that are specifically designed to target solvent front mutations, which pose a challenge for first‐generation sRETis. The effective screening of patients is the first crucial step in the clinical application of RET‐targeted therapy. Currently, four methods are widely used for detecting gene rearrangements: next‐generation sequencing (NGS), reverse transcription‐polymerase chain reaction (RT‐PCR), fluorescence in situ hybridization (FISH), and immunohistochemistry (IHC). Each of these methods has its advantages and limitations. To streamline the clinical workflow and improve diagnostic and treatment strategies for RET fusion NSCLC, our expert group has reached a consensus. Our objective is to maximize the clinical benefit for patients and promote standardized approaches to RET fusion screening and therapy.

Automatic stroke lesion segmentation has become a research hotspot in recent years.In order to comprehensively review current progress of deep learning-based MRI stroke lesion segmentation methods, start with the clinical problems of stroke treatment, we further elaborate the research background and challenges of deep learning-based lesion segmentation, and introduce common public datasets (ISLES and ATLAS) for stroke lesion segmentation.Then, we focus on the innovation and progress of deep learning-based stroke lesion segmentation methods, and summarize the research progress from three perspectives: network structure, training strategy, and loss function, and compare the advantages and disadvantages of various methods.Finally, we discusse the difficulties and challenges in this research and its future development trend.

Abstract Malignant pleural mesothelioma (MPM) is a malignant tumor originating from the pleura, and its incidence has been increasing in recent years. Due to the insidious onset and strong local invasiveness of MPM, most patients are diagnosed in the late stage and early screening and treatment for high‐risk populations are crucial. The treatment of MPM mainly includes surgery, chemotherapy, and radiotherapy. Immunotherapy and electric field therapy have also been applied, leading to further improvements in patient survival. The Mesothelioma Group of the Yangtze River Delta Lung Cancer Cooperation Group (East China LUng caNcer Group, ECLUNG; Youth Committee) developed a national consensus on the clinical diagnosis and treatment of MPM based on existing clinical research evidence and the opinions of national experts. This consensus aims to promote the homogenization and standardization of MPM diagnosis and treatment in China, covering epidemiology, diagnosis, treatment, and follow‐up.

Currently, target detection on unmanned aerial vehicle (UAV) images is a research hotspot. Due to the significant scale variability of targets and the interference of complex backgrounds, current target detection models face challenges when applied to UAV images. To address these issues, we designed an effective and lightweight full-scale target detection network, FSTD-Net. The design of FSTD-Net is based on three principal aspects. Firstly, to optimize the extracted target features at different scales while minimizing background noise and sparse feature representations, a multi-scale contextual information extraction module (MSCIEM) is developed. The multi-scale information extraction module (MSIEM) in MSCIEM can better capture multi-scale features, and the contextual information extraction module (CIEM) in MSCIEM is designed to capture long-range contextual information. Secondly, to better adapt to various target shapes at different scales in UAV images, we propose the feature extraction module fitting different shapes (FEMFDS), based on deformable convolutions. Finally, considering low-level features contain rich details, a low-level feature enhancement branch (LLFEB) is designed. The experiments demonstrate that, compared to the second-best model, the proposed FSTD-Net achieves improvements of 3.8%, 2.4%, and 2.0% in AP50, AP, and AP75 on the VisDrone2019, respectively. Additionally, FSTD-Net achieves enhancements of 3.4%, 1.7%, and 1% on the UAVDT dataset. Our proposed FSTD-Net has better detection performance compared to state-of-the-art detection models. The experimental results indicate the effectiveness of the FSTD-Net for target detection in UAV images.

Electrochemical biosensors have emerged as powerful tools for the ultrasensitive detection of lung cancer biomarkers like carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and alpha fetoprotein (AFP). This review comprehensively discusses the progress and potential of nanocomposite-based electrochemical biosensors for early lung cancer diagnosis and prognosis. By integrating nanomaterials like graphene, metal nanoparticles, and conducting polymers, these sensors have achieved clinically relevant detection limits in the fg/mL to pg/mL range. We highlight the key role of nanomaterial functionalization in enhancing sensitivity, specificity, and antifouling properties. This review also examines challenges related to reproducibility and clinical translation, emphasizing the need for standardization of fabrication protocols and robust validation studies. With the rapid growth in understanding lung cancer biomarkers and innovations in sensor design, nanocomposite electrochemical biosensors hold immense potential for point-of-care lung cancer screening and personalized therapy guidance. Realizing this goal will require strategic collaboration among material scientists, engineers, and clinicians to address technical and practical hurdles. Overall, this work provides valuable insight for developing next-generation smart diagnostic devices to combat the high mortality of lung cancer.