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Abstract Deep‐blue multi‐resonance (MR) emitters with stable and narrow full‐width‐at‐half‐maximum (FWHM) are of great importance for widening the color gamut of organic light‐emitting diodes (OLEDs). However, most planar MR emitters are vulnerable to intermolecular interactions from both the host and guest, causing spectral broadening and exciton quenching in thin films. Their emission in the solid state is environmentally sensitive, and the color purity is often inferior to that in solutions. Herein, a molecular design strategy is presented that simultaneously narrows the FWHM and suppresses intermolecular interactions by combining intramolecular locking and peripheral shielding within a carbonyl/nitrogen‐based MR core. Intramolecularly locking carbonyl/nitrogen‐based bears narrower emission of 2,10‐dimethyl‐12,12‐diphenyl‐4H‐benzo[9,1]quinolizino[3,4,5,6,7‐defg]acridine‐4,8(12H)‐dione in solution and further with peripheral‐shielding groups, deep‐blue emitter (12,12‐diphenyl‐2,10‐bis(9‐phenyl‐9H‐fluoren‐9‐yl)−4H‐benzo[9,1]quinolizino[3,4,5,6,7‐defg]acridine‐4,8(12H)‐dione, DPQAO‐F) exhibits ultra‐pure emission with narrow FWHM (c.a., 24 nm) with minimal variations (∆FWHM ≤ 3 nm) from solution to thin films over a wide doping range. An OLED based on DPQAO‐F presents a maximum external quantum efficiency (EQEmax) of 19.9% and color index of (0.134, 0.118). Furthermore, the hyper‐device of DPQAO‐F exhibits a record‐high EQEmax of 32.7% in the deep‐blue region, representing the first example of carbonyl/nitrogen‐based OLED that can concurrently achieve narrow bandwidth in the deep‐blue region and a high electroluminescent efficiency surpassing 30%.

Abstract Esophageal and gastric varices (EGV) bleeding is a dangerous side effect of liver cirrhosis. Ascites may affect the effectiveness of carvedilol in preventing EGV rebleeding. A retrospective analysis was done on patients with EGV bleeding who visited our gastroenterology department between January 1, 2015, and October 29, 2020, and were given carvedilol therapy again. Patients were classified based on whether they had ascites. The primary outcome was EGV rebleeding. A total of 286 patients were included, with a median follow‐up of 24.0 (19.0–42.0) months, comprising those without ascites (N = 155) and those with ascites (N = 131). The mean age of the patients was 55.15 ± 12.44 years, and 177 (61.9%) of them were men. There were 162 (56.6%) Child‐Pugh A grades. The etiology of cirrhosis included 135 (47.2%) cases of hepatitis B. After carvedilol therapy, the patient's portal vein diameter (DPV) was widened (p

Temperature stability is a critical factor affecting the performance of the most subsystems in the lithography system, due to the high precision and sensitivity of system components to temperature variations. The temperature control system of the lithography machine is characterized by its large inertial constant, time delay characteristics, as well as susceptibility to multiple disturbances. The temperature control system of the lithography machine chiefly requires response speed, high accuracy, and stable and constant temperature control. The contribution of this study is not only avoiding complex precision modeling processes based on real-time parameter estimation and neural network self-tuning but also improving the performance of temperature control in real time under external disturbances. A novel adaptive algorithm with a cascade structure based on generalized predictive control (GPC) and backpropagation (BP) neural network proportional-integral (PI) control is successfully proposed for high accuracy temperature control of lithography machine with a large inertial constant, time delay, and multiple disturbances. In this study, firstly, the liquid circulating temperature control system is developed based on heat exchanger and heater. Secondly, an adaptive controller composed of GPC and BP neural network PI control is successfully proposed. A BP neural network is employed to enable the parameters of the PI controller to adjust in real time, and the mathematical model parameters of the control system are identified in real time by the least square method. Also, the performance of the proposed controller is evaluated comparing with conventional PI controller and GPC controller in terms of robustness and quantitative study of error analysis. Finally, the temperature stability and robustness of the temperature control system controlled with the proposed adaptive GPC-PI algorithm has been investigated by the simulation results carried out in different working scenarios. The simulation results show that the steady-state error from the proposed algorithm is less than 0.01°C under the action of disturbance input. It can effectively counteract the influence of environmental interference and time-varying system parameters. The results of the simulation experiment indicate that the proposed adaptive GPC and PI control algorithm exhibits significant advantages in terms of control accuracy, anti-interference ability, and robustness compared to the conventional control method.

IntroductionArrhythmia is an important indication of underlying cardiovascular diseases (CVD) and is prevalent worldwide. Accurate diagnosis of arrhythmia is crucial for timely and effective treatment. Electrocardiogram (ECG) plays a key role in the diagnosis of arrhythmia. With the continuous development of deep learning and machine learning processes in the clinical field, ECG processing algorithms have significantly advanced the field with timely and accurate diagnosis of arrhythmia.MethodsIn this study, we combined the wavelet time-frequency maps with the novel Swin Transformer deep learning model for the automatic detection of cardiac arrhythmias. In specific practice, we used the MIT-BIH arrhythmia dataset, and to improve the signal quality, we removed the high-frequency noise, artifacts, electromyographic noise and respiratory motion effects in the ECG signals by the wavelet thresholding method; we used the complex Morlet wavelet for the feature extraction, and plotted wavelet time-frequency maps to visualise the time-frequency information of the ECG; we introduced the Swin Transformer model for classification and achieve high classification accuracy of ECG signals through hierarchical construction and self attention mechanism, and combines windowed multi-head self-attention (W-MSA) and shifted window-based multi-head self-attention (SW-MSA) to comprehensively utilise the local and global information.ResultsTo enhance the confidence of the experimental results, we evaluated the performance using intra-patient and inter-patient paradigm analyses, and the model classification accuracies reached 99.34% and 98.37%, respectively, which are better than the currently available detection methods.DiscussionThe results reveal that our proposed method is superior to currently available methods for detecting arrhythmia ECG. This provides a new idea for ECG based arrhythmia diagnosis.

Trimethylamine-N-oxide (TMAO) is a gut-derived metabolite formed from dietary choline and l-carnitine, known to impede cholesterol metabolism and is implicated in the pathogenesis of thrombosis and atherosclerosis, contributing to the etiology of cardiovascular diseases. We present a dataset derived from an experimental study designed to elucidate the cardiotoxic effects of TMAO. This dataset encompasses echocardiographic assessments from two cohorts of mice: one subjected to a 6-week regimen of 20 mg/kg/day TMAO injections (n = 16) and a control group (n = 18). Each subject's echocardiographic dataset comprises six high-resolution TIFF images, capturing both B-type and M-mode views in standard echocardiographic planes, along with two additional M-mode images enriched with analysed cardiac functional data. Complementing these images, a CSV-formatted report details critical cardiac parameters, including heart rate, ejection fraction, and fractional shortening, among others. In a novel approach to enhance data integrity and permit tailored analyses, we provide the original output files from the echocardiography apparatus, which researchers can reprocess using dedicated analysis software. This dataset is anticipated to be instrumental in advancing our understanding of the mechanistic links between TMAO exposure and cardiac dysfunction.

For mutual coupling evaluation between antennas on the platform, present an enhanced algorithm that combines the spherical wave expansion (SWE), the uniform theory of diffraction (UTD), and the reaction theorem to accurately calculate the mutual coupling between two antennas mounted on a platform. The algorithm begins by employing SWE to compute the direct electromagnetic field radiated by the antennas. Subsequently, UTD is introduced to capture the field scattered by the platform. Finally, the mutual coupling is evaluated through the reaction integral. Numerical examples highlight the algorithm's promising speed and accuracy, surpassing the performance of the MLFMA and MoM-UTD methods. With a computational complexity of O(LMN3), our approach offers significant improvements for mutual coupling analysis in platform-based antenna systems.

Zn2P2O7, a representative material in the A2P2O7 family, exhibits negative thermal expansion (NTE) and possesses a remarkable negative linear expansion coefficient. Here we employ variable temperature and high-pressure Raman spectroscopy to investigate the phonon anomalies arising from the α- to β-phase transition in Zn2P2O7. Combing with the refinement results of our X-ray diffraction (XRD) experiment, we have identified the specific variations of the bond lengths, bond angles and the polyhedral distortion indexes that result in the phonon anomalies. Some certain phonons, whose energy decreases with increasing pressure, have been demonstrated to give rise to negative Grüneisen parameters that can characterize phonon anharmonicity and lead to lower thermal expansion in the α-phase of Zn2P2O7 compared to the β-phase. These results provide new insight into the relationship between lattice deformation and phonon anomalies during the structural phase transition in the NTE material Zn2P2O7.

Background: Kirsten rat sarcoma virus (KRAS) has evolved from a genotype with predictive value to a therapeutic target recently. The study aimed to establish non-invasive radiomics models based on MRI to discriminate KRAS from epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations in lung cancer patients with brain metastases (BM), then further explore the optimal sequence for prediction. Methods: This retrospective study involved 317 patients (218 patients in training cohort and 99 patients in testing cohort) who had confirmed of KRAS, EGFR or ALK mutations. Radiomics features were separately extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences. The maximal information coefficient and recursive feature elimination method were used to select informative features. Then we built four radiomics models for differentiating KRAS from EGFR or ALK using random forest classifier. ROC curves were used to validate the capability of the models. Results: The four radiomics models for discriminating KRAS from EGFR all worked well, especially DWI and T2WI models (AUCs: 0.942, 0.942 in training cohort, 0.949, 0.954 in testing cohort). When KRAS compared to ALK, DWI and T2-FLAIR models showed excellent performance in two cohorts (AUCs: 0.947, 0.917 in training cohort, 0.850, 0.824 in testing cohort). Conclusions: Radiomics classifiers integrating MRI have potential to discriminate KRAS from EGFR or ALK, which are helpful to guide treatment and facilitate the discovery of new approaches capable of achieving this long-sought goal of cure in lung cancer patients with KRAS.

Bizarre parosteal osteochondromatous proliferation (BPOP), also termed Nora lesion, is a rare, benign tumor most often located in the hands and feet. We herein present the second reported case of BPOP affecting the spine, an uncommon location. One year after surgical excision, the patient was pain-free and showed no evidence of recurrence. We reviewed a total of 323 cases of BPOP among 101 articles, providing the first systematic update on the latest knowledge of BPOP. The age of patients with BPOP ranges from 3 months to 87 years, peaking in the second and third decades of life. The hands are the most common location of BPOP (58.39%), followed by the feet (20.81%). Imaging features play a key role in the diagnosis of BPOP, but histopathologic diagnosis remains the gold standard. Differential diagnosis of BPOP should be based on the epidemiologic and clinical features as well as clinical examination findings. Surgical resection is the most extensively used treatment for BPOP. Recurrence is common (37.44%) and can be treated with re-excision. This article can deepen our understanding of BPOP and will be helpful for the diagnosis and treatment of BPOP in clinical practice.

Abstract The adjustable structures and remarkable physicochemical properties of 2D monoelemental materials, such as silicene and germanene, have attracted significant attention in recent years. They can be transformed into silicane (SiH) and germanane (GeH) through covalent functionalization via hydrogen atom termination. However, synthesizing these materials with a scalable and low‐cost fabrication process to achieve high‐quality 2D SiH and GeH poses challenges. Herein, groundbreaking 2D SiH and GeH materials with varying compositions, specifically Si0.25Ge0.75H, Si0.50Ge0.50H, and Si0.75Ge0.25H, are prepared through a simple and efficient chemical exfoliation of their Zintl phases. These 2D materials offer significant advantages, including their large surface area, high mechanical flexibility, rapid electron mobility, and defect‐rich loose‐layered structures. Among these compositions, the Si0.50Ge0.50H electrode demonstrates the highest discharge capacity, reaching up to 1059 mAh g−1 after 60 cycles at a current density of 75 mA g−1. A comprehensive ex‐situ electrochemical analysis is conducted to investigate the reaction mechanisms of lithiation/delithiation in Si0.50Ge0.50H. Subsequently, an initial assessment of the c‐Li15(SixGe1‐x)4 phase after lithiation and the a‐Si0.50Ge0.50 phase after delithiation is presented. Hence, this study contributes crucial insights into the (de)lithiation reaction mechanisms within germanane‐silicane alloys. Such understanding is pivotal for mastering promising materials that amalgamate the finest properties of silicon and germanium.

A hybrid deep learning model was proposed to effectively detect electricity theft in smart grids.The hybrid model employed a deep learning convolutional neural network (AlexNet) to tackle the curse of dimensionality, significantly enhancing data processing accuracy and efficiency.It further improved classification accuracy by differentiating between normal and abnormal electricity usage using adaptive boosting (AdaBoost).To resolve the issue of class imbalance, undersampling techniques were utilized, ensuring balanced performance across various data classes.Additionally, the artificial bee colony algorithm was used to optimize hyperparameters for both AdaBoost and AlexNet, effectively boosting overall model performance.The effectiveness of this hybrid model was evaluated using real smart meter datasets from an electricity company.Compared to similar models, this hybrid model achieves accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the curve-receiver operating characteristic curve (AUC-ROC) scores of 88%, 86%, 84%, 85%, 78%, and 91%, respectively.The proposed model not only increases the accuracy of electricity usage monitoring, but also offers a new perspective for intelligent analysis in power systems.

Esophageal cancer (EC) is an aggressive malignancy with a poor prognosis. Various factors, including dietary habits, and antacid and antibiotic use, have been shown to influence the esophageal microbiome. Conversely, enrichment and diversity of the esophageal microbiome can also impact its function. Recent studies have revealed prevalent changes in the esophageal microbiome among patients with EC, thus suggesting the potential contribution of the esophageal microbiome to EC development. Additionally, distinct microbiome compositions have been observed in patients with different responses to radiotherapy and chemotherapy, indicating the role of the esophageal microbiome in modulating treatment outcomes. In this review, we have examined previous studies on the esophageal microbiome in healthy individuals and patients with EC or other esophageal diseases, with a focus on identifying microbial communities associated with EC pathogenesis and prognosis. Understanding the role of the microbiome in EC may aid in early detection and optimized treatment strategies, ultimately leading to better outcomes for patients.

Abstract Disulfidptosis a new cell death mode, which can cause the death of Hepatocellular Carcinoma (HCC) cells. However, the significance of disulfidptosis-related Long non-coding RNAs (DRLs) in the prognosis and immunotherapy of HCC remains unclear. Based on The Cancer Genome Atlas (TCGA) database, we used Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression model to construct DRL Prognostic Signature (DRLPS)-based risk scores and performed Gene Expression Omnibus outside validation. Survival analysis was performed and a nomogram was constructed. Moreover, we performed functional enrichment annotation, immune infiltration and drug sensitivity analyses. Five DRLs (AL590705.3, AC072054.1, AC069307.1, AC107959.3 and ZNF232-AS1) were identified to construct prognostic signature. DRLPS-based risk scores exhibited better predictive efficacy of survival than conventional clinical features. The nomogram showed high congruence between the predicted survival and observed survival. Gene set were mainly enriched in cell proliferation, differentiation and growth function related pathways. Immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group. Additionally, the high-risk group exhibited higher sensitivity to Afatinib, Fulvestrant, Gefitinib, Osimertinib, Sapitinib, and Taselisib. In conclusion, our study highlighted the potential utility of the constructed DRLPS in the prognosis prediction of HCC patients, which demonstrated promising clinical application value.

Abstract Cetuximab therapy is the major treatment for colorectal cancer (CRC), but drug resistance limits its effectiveness. Here, we perform longitudinal and deep proteomic profiling of 641 plasma samples originated from 147 CRC patients (CRCs) undergoing cetuximab therapy with multi-course treatment, and 90 healthy controls (HCs). COL12A1, THBS2, S100A8, and S100A9 are screened as potential proteins to distinguish CRCs from HCs both in plasma and tissue validation cohorts. We identify the potential biomarkers (RRAS2, MMP8, FBLN1, RPTOR, and IMPDH2) for the initial response prediction. In a longitudinal setting, we identify two clusters with distinct fluctuations and construct the model with high accuracy to predict the longitudinal response, further validated in the independent cohort. This study reveals the heterogeneity of different biomarkers for tumor diagnosis, the initial and longitudinal response prediction respectively in the first course and multi-course cetuximab treatment, may ultimately be useful in monitoring and intervention strategies for CRC.

Abstract Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.

Due to high-intensity mining and complex geological conditions, frequent occurrence of rock burst disasters in deep coal mines in China is posing serious threats to mine safety and efficiency. The insufficient locating accuracy of the seismic monitoring system in burst-prone coal mines is still presented, which leads to difficulties in accurately identifying and controlling coal burst hazards. To precisely characterize spatial evolution of seismic activities and reduce the impact of location error on the seismic pre-warning, this paper used the current seismic networks in burst-prone coal mines to conduct forward modeling experiments to explore the distribution characteristics of source location error vectors in the mining field. A new method for characterizing seismic fracture connectivity that considers location accuracy impacts was proposed and applied for coal burst pre-warning in a longwall face. The results show that significant vectorial differences of seismic source location accuracy at different area of the mining field is a key factor in false identifying coal burst risks. The proposed Fracture-connectivity-probability Index (\begin{document}$ F{_{{\mathrm{sum}}}} $\end{document}) characterizes the rupture scale by using the near-field radius that relates to the seismic energy level, which considers the locating error impacts on fracture connectivity between seismic sources at different distances. \begin{document}$ F{_{{\mathrm{sum}}}} $\end{document} can restore the distribution law of the fracture extension and connectivity probabilities of the coal-rock mass in the greatest extent, and it also corresponds well to the coal burst risks. \begin{document}$ F{_{{\mathrm{sum}}}} $\end{document} can balance forecast accuracy and recall rate and have better correlation with coal burst, which can be an ideal indicator for periodical coal burst risk assessment. The outcome of this research can provide references for evaluating the monitoring capability of seismic networks and improving the ability and efficiency of coal burst pre-warning and prevention.

Macrophage migration inhibitory factor (MIF), a multifunctional cytokine, is secreted by various cells and participates in inflammatory reactions, including innate and adaptive immunity. There are some evidences that MIF is involved in many vitreoretinal diseases. For example, MIF can exacerbate many types of uveitis; measurements of MIF levels can be used to monitor the effectiveness of uveitis treatment. MIF also alleviates trauma-induced and glaucoma-induced optic nerve damage. Furthermore, MIF is critical for retinal/choroidal neovascularization, especially complex neovascularization. MIF exacerbates retinal degeneration; thus, anti-MIF therapy may help to mitigate retinal degeneration. MIF protects uveal melanoma from attacks by natural killer cells. The mechanism underlying the effects of MIF in these diseases has been demonstrated: it binds to cluster of differentiation 74, inhibits the c-Jun N-terminal kinase pathway, and triggers mitogen-activated protein kinases, extracellular signal-regulated kinase-1/2, and the phosphoinositide-3-kinase/Akt pathway. MIF also upregulates Toll-like receptor 4 and activates the nuclear factor kappa-B signaling pathway. This review focuses on the structure and function of MIF and its receptors, including the effects of MIF on uveal inflammation, retinal degeneration, optic neuropathy, retinal/choroidal neovascularization, and uveal melanoma.

Abstract Background To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously. Methods A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model. Results We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000 for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively. Conclusions The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases. Relevance statement The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment. Key points • This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype. Graphical Abstract

Abstract Due to the doping of considerable noise and impact components in vibration signals of quay crane gearboxes, some traditional methods have difficulty uncovering degradation patterns. To accurately extract degradation features from vibration monitoring signals, a degradation feature extraction technique based on static divided symbol sequence entropy is proposed. Based on the basic scale entropy technique, considering the uniformity of the symbolization standard, the technique takes the root mean square of the health condition signal as the basis and incorporates the scale coefficient to establish a uniform basic scale. Simultaneously, the symbol set is expanded to enhance the information content and the ability of the approach to characterize the complexity levels of signals in large-value regions. Therefore, the proposed static divided symbol sequence entropy technique can accurately and flexibly characterize performance degradations. The logistic chaotic sequence and the lifetime signal of hoisting mechanism gearbox are separately used for analysis. It shows that the proposed technique can characterize the complexity of the nonlinear time series and sensitively describe the performance degradation exhibited by hoisting mechanism gearbox. This technique is computationally fast, and it can be implemented as a foundation for developing new methods for evaluating the health conditions of quay crane gearboxes.

Abstract The expression level of SLC35A3 is associated with the prognosis of many cancers, but its role in colorectal cancer (CRC) is unclear. The purpose of our study was to elucidate the role of SLC35A3 in CRC. The expression levels of SLC35A3 in CRC were evaluated through tumor immune resource assessment (TIMER), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), Human Protein Atlas (HPA), qRT-PCR, and immunohistochemical evaluation. TCGA, GEO, and ICGC databases were used to analyze the diagnostic and prognostic value of SLC35A3 in CRC. A overall survival (OS) model was constructed and validated based on the expression level of SLC35A3 and multivariable analysis results. The cBioPortal tool was used to analyze SLC35A3 mutation in CRC. The UALCAN tool was used to analyze the promoter methylation level of SLC35A3 in colorectal cancer. In addition, the role of SLC35A3 in CRC was determined through GO analysis, KEGG analysis, gene set enrichment analysis (GSEA), immune infiltration analysis, and immune checkpoint correlation analysis. In vitro experiments validated the function of SLC35A3 in colorectal cancer cells. Compared with adjacent normal tissues and colonic epithelial cells, the expression of SLC35A3 was decreased in CRC tissues and CRC cell lines. Low expression of SLC35A3 was associated with N stage, pathological stage, and lymphatic infiltration, and it was unfavorable for OS, disease-specific survival (DSS), recurrence-free survival (RFS), and post-progression survival (PPS). According to the Receiver Operating Characteristic (ROC) analysis, SLC35A3 is a potential important diagnostic biomarker for CRC patients. The nomograph based on the expression level of SLC35A3 showed a better predictive model for OS than single prognostic factors and TNM staging. SLC35A3 has multiple types of mutations in CRC, and its promoter methylation level is significantly decreased. GO and KEGG analysis indicated that SLC35A3 may be involved in transmembrane transport protein activity, cell communication, and interaction with neurotransmitter receptors. GSEA revealed that SLC35A3 may be involved in energy metabolism, DNA repair, and cancer pathways. In addition, SLC35A3 was closely related to immune cell infiltration and immune checkpoint expression. Immunohistochemistry confirmed the positive correlation between SLC35A3 and helper T cell infiltration. In vitro experiments showed that overexpression of SLC35A3 inhibited the proliferation and invasion capability of colorectal cancer cells and promoted apoptosis. The results of this study indicate that decreased expression of SLC35A3 is closely associated with poor prognosis and immune cell infiltration in colorectal cancer, and it can serve as a promising independent prognostic biomarker and potential therapeutic target.

Abstract Background To explore the application value of hyperthermic intraperitoneal chemotherapy (HIPEC) in patients with incomplete cytoreduction for appendiceal pseudomyxoma peritonei (PMP). Methods We retrospectively analyzed the clinical data of 526 patients with incomplete cytoreduction for appendiceal PMP to discover its prognostic factors, and the therapeutic value of HIPEC. Results The 5-year and 10-year overall survival rates of patients after cytoreductive surgery (CRS) treated with HIPEC were significantly higher than those without HIPEC (5y-OS: 58% vs. 48%, 10y-OS: 37% vs. 16%, P = 0.032). The median progression-free survival (PFS) following CRS was 20 months, with a 20% 3-year PFS. The median PFS following CRS + HIPEC was 33 months, with a 60% 3-year PFS (P = 0.000). Univariate analysis indicated that HIPEC, gender, completeness of cytoreduction (CCR) and pathological grade had statistical difference. Multivariate analysis showed that CRS without HIPEC and high pathological grade were independent risk factors for poor prognosis and rapid tumor progression. Conclusions HIPEC may prolong the survival in patients with incomplete cytoreduction for low-grade appendiceal PMP. High pathological grade indicates poor survival and rapid tumor progression.

Abstract Background Cardiac masses can encompass a variety of conditions, such as tumors, thrombi, vegetations, calcific lesions, and other rare diseases. Treatment and management of these types of cardiac masses differ considerably. Thus, accurately distinguishing among thrombi, benign tumors, and malignant tumors in the heart is of great importance. Contrast echocardiography (CE) has emerged as a promising technology. Although published guidelines suggest that CE can enhance image quality and assist in differentiating between benign and malignant lesions, most studies on CE diagnosis of cardiac masses are limited to case reports or retrospective/small-sample-sized prospective cohorts. This study aims to evaluate the diagnostic accuracy of CE in patients with suspected cardiac masses and address the insufficient evidence for differential diagnosis using CE. Methods Between April 2018 and July 2022, a prospective multicenter study was conducted, which included 145 consecutive patients suspected to have cardiac masses based on transthoracic echocardiography. All patients underwent CE examinations. The echocardiographic diagnosis relied on qualitative factors such as echogenicity, boundary, morphology of the base, mass perfusion, pericardial effusion, and motility as well as quantitative factors such as the area of the masses and the peak intensity ratio of the masses to adjacent myocardium (A1/A2). Results The final confirmed diagnoses were as follows: 2 patients had no cardiac mass, 4 patients had pseudomass, 43 patients had thrombus, 66 patients had benign tumors, and 30 patients had malignant tumors. The receiver operating characteristic (ROC) analysis indicated that an optimal A1/A2 cutoff value of 0.499 distinguished a cardiac tumor from a thrombus, with AUC, sensitivity, specificity, PPV, and NPV of 0.977, 97.9%, 90.7%, 95.9%, and 95.1%, respectively. The optimal A1/A2 cutoff value of 1.583 distinguished a cardiac tumor from a thrombus, with AUC, sensitivity, specificity, PPV, and NPV of 0.950, 93.3%, 93.9%, 87.5%, and 96.9%, respectively. Conclusions Combined with qualitative and quantitative analyses, CE has the potential to accurately differentiate among different types of cardiac masses.

Abstract Radiopharmaceuticals play a vital role in cancer therapy. The carrier of radiopharmaceuticals can precisely locate and guide radionuclides to the target, where radionuclides kill surrounding tumor cells. Effective application of radiopharmaceuticals depends on the selection of an appropriate carrier. Herein, different types of carriers of radiopharmaceuticals and the characteristics are briefly described. Subsequently, we review radiolabeled monoclonal antibodies (mAbs) and their derivatives, and novel strategies of radiolabeled mAbs and their derivatives in the treatment of lymphoma and colorectal cancer. Furthermore, this review outlines radiolabeled peptides, and novel strategies of radiolabeled peptides in the treatment of neuroendocrine neoplasms, prostate cancer, and gliomas. The emphasis is given to heterodimers, bicyclic peptides, and peptide-modified nanoparticles. Last, the latest developments and applications of radiolabeled nucleic acids and small molecules in cancer therapy are discussed. Thus, this review will contribute to a better understanding of the carrier of radiopharmaceuticals and the application in cancer therapy.

Abstract Background Hepcidin is the master regulator of iron homeostasis. Hepcidin downregulation has been demonstrated in the brains of Alzheimer’s disease (AD) patients. However, the mechanism underlying the role of hepcidin downregulation in cognitive impairment has not been elucidated. Methods In the present study, we generated GFAP-Cre-mediated hepcidin conditional knockout mice (Hamp GFAP cKO) to explore the effect of hepcidin deficiency on hippocampal structure and neurocognition. Results We found that the Hamp GFAP cKO mice developed AD-like brain atrophy and memory deficits. In particular, the weight of the hippocampus and the number of granule neurons in the dentate gyrus were significantly reduced. Further investigation demonstrated that the morphological change in the hippocampus of Hamp GFAP cKO mice was attributed to impaired neurogenesis caused by decreased proliferation of neural stem cells. Regarding the molecular mechanism, increased iron content after depletion of hepcidin followed by an elevated level of the inflammatory factor tumor necrosis factor-α accounted for the impairment of hippocampal neurogenesis in Hamp GFAP cKO mice. These observations were further verified in GFAP promoter-driven hepcidin knockdown mice and in Nestin-Cre-mediated hepcidin conditional knockout mice. Conclusions The present findings demonstrated a critical role for hepcidin in hippocampal neurogenesis and validated the importance of iron and associated inflammatory cytokines as key modulators of neurodevelopment, providing insights into the potential pathogenesis of cognitive dysfunction and related treatments. Graphical Abstract

BiFeO3-BaTiO3 (BF-BT) ceramics are important multiferroic materials, which are attracting significant attention for potential applications in high temperature lead-free piezoelectric transducers. In the present study, the effects of Sr2+ as an acceptor dopant for Bi3+, in the range from 0 to 1.0% (in mole), on the structure and ferroelectric/piezoelectric properties of 0.7BiFeO3-0.3BaTiO3 ceramics were evaluated. The use of a post-sintering Ar annealing process was found to be an effective approach to reduce electrical conductivity induced by the presence of electron holes associated with reoxidation during cooling. A low Sr dopant concentration (0.3%, in mole) yielded enhanced ferroelectric (Pmax ∼ 0.37 C/m2, Pr ∼ 0.30 C/m2) and piezoelectric (d33 ∼ 178 pC/N, kp ∼ 0.27) properties, whereas higher levels led to chemically heterogeneous core-shell structures and secondary phases with an associated decline in performance. The electric field-induced strain of the Sr-doped BF-BT ceramics was investigated using a combination of digital image correlation macroscopic strain measurements and in-situ synchrotron X-ray diffraction. Quantification of the intrinsic (lattice strain) and extrinsic (domain switching) contributions to the electric field induced strain indicated that the intrinsic contribution dominated during the poling process.

Abstract A new concept of dynamic phase change material (PCM) wall structure in building envelope is proposed to enhance the passive solar energy utilization, in which PCM wall position could be exchanged with insulation layer. The thermal performance of phase change materials in buildings is related to many factors. To ensure that the novel dynamic PCM wall structure could reach its full potential, the thermal performance of the structure was numerically simulated and optimized based on different climatic conditions, room sizes and the position of PCM layer in the wall. The results indicated that the suitable PCM was different when using the novel dynamic PCM wall in different climates. Among the four cities of Harbin, Tianjin, Jinan and Guangzhou, northern cities, like Tianjin, were more suitable for using PCM A16 than southern cities. In terms of room size design, when the volume of the room remained identical, the room size design formed a certain correlation with the indoor temperature. In addition, results showed that the location of the novel PCM wall system in the wall would cause a great difference in indoor temperature, which was related to heat dissipation at different locations.

Abstract Background The prognostic performance of soluble CD40L (sCD40L) for illness severity in infectious diseases is rarely reported. We investigated the ability of sCD40L combined with Acute Physiology and Chronic Health Evaluation II (APACHE II) score to evaluate mortality in septic patients in the emergency department(ED). Methods We enrolled 222 septic patients in the ED of Beijing Chao-Yang Hospital from October 2020 to April 2021. Their serum sCD40L, PCT, lactate (Lac), Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation II (APACHE II) score were used to predict the prognosis of septic patients in terms of 28-day mortality. Serum sCD40L was detected by Human XL Cytokine Luminex. Logistic regression analysis and receiver operating characteristic (ROC) curves were used to assess the prognostic value of the variables. Results One hundred ninety-five patients met the inclusion criteria, divided into survival group (55 cases) and non-survival group (140 cases). sCD40L, PCT, Lac, SOFA and APACHE II score were found to independently predict 28-day mortality (P

Abstract Required for meiotic nuclear division 5 homolog A (RMND5A), a novel ubiquitin E3 Ligase, has been reported to correlate with poor prognosis of several cancers. However, its role in endothelial cells has not been reported. In this study, overexpression of RMND5A in human umbilical vein endothelial cells (HUVECs) was performed via lentiviral infection, followed by MTT, would healing and tube formation assay as well as signaling analysis. Moreover, crosstalk between HUVECs and oral squamous cell carcinoma (OSCC) cells was investigated by indirect co-culture with condition medium or tumor cell derived exosomes. Our results showed that overexpression of RMND5A reduced the proliferation, migration and tube formation ability of HUVECs by inhibiting the activation of ERK and NF-κB pathway. Interestingly, OSCC cells can inhibit RMND5A expression of endothelial cells via exosomal miR-21. In summary, our present study unveils that OSCC cells can activate endothelial cells via exosomal miR-21/RMND5A pathway to promote angiogenesis, which may provide novel therapeutic targets for the treatment of OSCC.

Landslide inventory is significant for landslide disaster reduction. To construct the landslide inventory, deep learning has received growing attention to detect landslides from satellite images. Among various deep learning algorithms, you-only-look-once (YOLO) has a strong ability to detect objects efficiently and has been widely used in landslide extraction. Despite its efficiency, there is no general rule to select the backbone and attention mechanism for YOLO. The selection of these two modules depends on specific application needs. Meanwhile, YOLO output is a series of anchor boxes, not accurate landslide boundaries. A single bounding box may contain many landslides and cannot extract individual landslides, limiting the YOLO applications in constructing landslide inventory. To address these issues, this article presents a lightweight attention-guided YOLO with level set layer (LA-YOLO-LLL) for landslide detection from optical satellite images. First, we introduced the MobileNetv3 to replace the original backbone of YOLO to simultaneously reduce the parameter complexity and improve the model transferability. Then, we presented a light pyramid features reuse fusion attention mechanism to improve landslide detection performance. Finally, we integrated the level set layer into YOLO head to produce accurate landslide boundaries. This article validated the accuracy and transferability of the presented method in two study areas (Bijie and Taiwan) with similar geo-environmental conditions. Experimental results show that the presented LA-YOLO-LLL model outperformed traditional YOLO in landslide detection. Findings in this article are valuable for landslide inventory construction, land use planning and risk control.

The sediment accumulation in drainage pipes has long been recognized as a significant concern in the environmental field. This study addresses sediment accumulation in drainage pipes by introducing an innovative bioinspired approach using various shapes and angles of plates for long-term sediment reduction. Through experiments and numerical simulations, the velocity field, the turbulent kinetic energy, the head loss, and the dynamic pressure distribution in the pipeline with plates are analyzed. Results demonstrate significant increases in local velocity, dynamic pressure, and turbulence energy due to the presence of plates. The sediment reduction performance shows a positive correlation with the angle for folded plates and a non-linear relation with curvature for curved plates. Notably, the superior performance of folded plates is attributed to their exceptional ability to induce vortex formation. The head loss due to sediment reduction measures increases linearly as the angle and the curvature increase. Furthermore, the intentional induction of strong eddies and high shear flow using the undulating topography created by the locally installed folding plates in the pipeline was the main cause of sediment reduction. This novel approach holds promise for more efficient and sustainable sediment reduction in drainage systems. HIGHLIGHTS A novel measure for long-term sediment reduction inspired by dragonfly wings has been proposed.; A 25° folded plate is considered a favorable choice for this sediment reduction measure.; The main reason for sediment reduction is to induce strong eddies and high shear flow intentionally by using undulating topography.;

Goal: Cervical cancer is one of the most common cancers in women worldwide, ranking among the top four. Unfortunately, it is also the fourth leading cause of cancer-related deaths among women, particularly in developing countries where incidence and mortality rates are higher compared to developed nations. Colposcopy can aid in the early detection of cervical lesions, but its effectiveness is limited in areas with limited medical resources and a lack of specialized physicians. Consequently, many cases are diagnosed at later stages, putting patients at significant risk. Methods: This paper proposes an automated colposcopic image analysis framework to address these challenges. The framework aims to reduce the labor costs associated with cervical precancer screening in undeserved regions and assist doctors in diagnosing patients. The core of the framework is the MFEM-CIN hybrid model, which combines Convolutional Neural Networks (CNN) and Transformer to aggregate the correlation between local and global features. This combined analysis of local and global information is scientifically useful in clinical diagnosis. In the model, MSFE and MSFF are utilized to extract and fuse multi-scale semantics. This preserves important shallow feature information and allows it to interact with the deep feature, enriching the semantics to some extent. Conclusions: The experimental results demonstrate an accuracy rate of 89.2% in identifying cervical intraepithelial neoplasia while maintaining a lightweight model. This performance exceeds the average accuracy achieved by professional physicians, indicating promising potential for practical application. Utilizing automated colposcopic image analysis and the MFEM-CIN model, this research offers a practical solution to reduce the burden on healthcare providers and improve the efficiency and accuracy of cervical cancer diagnosis in resource-constrained areas.

Abstract Asthma is a polygenic disease that may onset during childhood. Inhaled corticosteroids (ICS) are the main therapy in asthma, although their efficacy varies among individuals. Nuclear factor κB (NF-κB) is an important target of ICS treatment of asthma. Recent research has reported that GRB2 associated binding protein 1 (GAB1) gene may participate in the pathogenesis of asthma by regulating the NF-κB pathway. Therefore, we used the technique of an improved multiplex ligation detection reaction to sequence GAB1 gene and investigated the involvement of Single-nucleotide variants (SNVs) in GAB1 gene in asthma and ICS efficacy in asthmatic children. We found no differences between asthma cases and controls in allele or genotype frequencies of GAB1. Haplotype analysis showed an increased tendency for AGGAGC frequency in asthma patients compared with controls (OR = 2.69, p = 0.018). The percentage of EOS and genotype distribution of rs1397527 were associated (p = 0.007). The EOS percentage was higher in GT genotype when compared to the GG genotype (5.50 vs 3.00, Bonferroni adjusted p = 0.005). After 12-weeks ICS treatment, GAB1 rs1397527 TT and GT genotype carriers had a smaller change in forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC) than GG carriers (p = 0.009), and rs3805236 GG and AG genotype carriers also had a smaller change in FEV1/FVC than AA carriers (p = 0.025). For ICS response, the frequency of GG genotype of rs1397527 was significantly higher in good responders (p = 0.038). The generalized multifactor dimensionality reduction (GMDR) analysis showed a best significant four-order model (rs1397527, allergen exposure, environmental tobacco smoke exposure, and pet exposure) involving gene-environment interactions (p = 0.001). In summary, we found that GAB1 SNVs were not associated with asthma susceptibility. Haplotype AGGAGC was a risk factor for asthma. GAB1 variants were associated with eosinophils and ICS response in asthmatics. Furthermore, gene-environment interaction was observed.

Abstract This paper develops an improved structural health assessment method for cable-stayed bridge to address the issue of neglecting component correlations in existing assessment standards. Firstly, the directed graph of fault transmission between components in the cable-stayed bridge system was constructed. The Pagerank algorithm was used to analyze the degree of correlation between these components, and then the influencing degree of and the influenced degree of each component were determined. Secondly, considering the failure rate of individual components and the influenced degree of other component faults, a condition evaluation method with component correlation for cable-stayed bridge was proposed. Finally, the improved assessment method was applied to a super large-span steel cable-stayed bridge as a case study and compared with the relevant assessment specifications. The results show that main girder alignment, cable force and main tower alignment have a greater degree of correlation with other components and are important indicators for bridge health monitoring. Visual inspection of main girder and bridge bearing are the fault appearance components and should be paid attention to in preventive maintenance. The drainage system and electromechanical facilities are the fault source components and must be kept in good condition in daily inspections. The proposed method considers the interrelationships among components more comprehensively and can provide more reliable bridge health assessment results to support bridge maintenance decisions.

Abstract The widespread organic pollutants in wastewater are one of the global environmental problems. Advanced oxidation processes (AOPs) are widely used because of their characteristics of high efficiency and strong oxidation. However, AOPs may have some defects, such as incomplete mineralization of organic pollutants and the generation of toxic by-products during the degradation process, thus it is essential to seek efficient and green wastewater treatment technologies. Coupling different AOPs or other processes is beneficial for the mineralization of pollutants and reduces ecological risks to the environment. It is worth noting that carbonaceous materials (CMs) have received widespread attention and application in the degradation of organic pollutants in water by advanced oxidation coupling processes (C-AOPs) due to their excellent physicochemical properties in recent years. However, the behaviors and mechanisms of C-AOPs based on CMs on the degradation of organic pollutants are still unknown. Therefore, it is essential to comprehensively summarize the recent research progress. In this review, the applications of different CMs in C-AOPs were reviewed first. Secondly, the synergistic mechanisms of the C-AOPs based on different CMs were discussed. Then, toxic intermediates were explored and important toxicity assessment methods were proposed. Finally, the application potential of the C-AOPs in the future and the challenges were proposed. This review provides an important reference for the application and optimization of the C-AOPs in organic wastewater treatment in the future. Graphical Abstract

Abstract Cepharanthine (CEP), a bioactive compound derived from Stephania Cephalantha Hayata, is cytotoxic to various malignancies. However, the underlying mechanism of gastric cancer is unknown. CEP inhibited the cellular activity of gastric cancer AGS, HGC27 and MFC cell lines in this study. CEP-induced apoptosis reduced Bcl-2 expression and increased cleaved caspase 3, cleaved caspase 9, Bax, and Bad expression. CEP caused a G2 cell cycle arrest and reduced cyclin D1 and cyclin-dependent kinases 2 (CDK2) expression. Meanwhile, it increased oxidative stress, decreased mitochondrial membrane potential, and enhanced reactive oxygen species (ROS) accumulation in gastric cancer cell lines. Mechanistically, CEP inhibited Kelch-like ECH-associated protein (Keap1) expression while activating NF-E2 related factor 2 (Nrf2) nuclear translocations, increasing transcription of Nrf2 target genes quinone oxidoreductase 1 (NQO1), heme oxygenase 1 (HMOX1), and glutamate-cysteine ligase modifier subunit (GCLM). Furthermore, a combined analysis of targeted energy metabolism and RNA sequencing revealed that CEP could alter the levels of metabolic substances such as D (+) - Glucose, D-Fructose 6-phosphate, citric acid, succinic acid, and pyruvic acid, thereby altering energy metabolism in AGS cells. In addition, CEP significantly inhibited tumor growth in MFC BALB/c nude mice in vivo, consistent with the in vitro findings. Overall, CEP can induce oxidative stress by regulating Nrf2/Keap1 and alter energy metabolism, resulting in anti-gastric cancer effects. Our findings suggest a potential application of CEP in gastric cancer treatment.

Abstract Background This study aimed to evaluate the perioperative safety and efficacy of the Mini-open and trans-tubular approach in patients with spinal metastases who underwent decompression surgery. Methods 37 consecutive patients with spinal metastases who underwent decompression surgery through a Mini-open or trans-tubular approach were retrospectively reviewed between June 2017 and June 2022. Thirty-four patients were included in this study. 19 underwent decompression surgery through the Mini-open approach, and 15 underwent the Trans-tubular approach. T-test and chi-square test were used to evaluate the difference between baseline data and primary and secondary outcomes. Results Baseline characteristics did not differ significantly between Trans-tubular and Mini-open groups except for the Ambulatory status (P 0.05). The trans-tubular group had significantly less amount of postoperative drainage (133.5 ± 30.9 ml vs. 364.5 ± 64.2 ml, p = 0.003), and the time of drainage (3.1 ± 0.2 days vs. 4.6 ± 0.5 days, p = 0.019) compared with Mini-open group (P

Production prediction is crucial for the recovery of hydrocarbon resources. However, accurate and rapid production forecasting remains challenging for unconventional reservoirs due to the complexity of the percolation process and the scarcity of available data. To address this problem, a novel model combining a long short-term memory network (LSTM) and support vector regression (SVR) was proposed to forecast tight oil production. Three variables, the tubing head pressure, nozzle size, and water rate were utilized as the inputs of the presented machine-learning workflow to account for the influence of operational parameters. The time-series response of tight oil production was the output and was predicted by the optimized LSTM model. An SVR-based residual correction model was constructed and embedded with LSTM to increase the prediction accuracy. Case studies were carried out to verify the feasibility of the proposed method using data from two wells in the Ma-18 block of the Xinjiang oilfield. Decline curve analysis (DCA) methods, LSTM and artificial neural network (ANN) models were also applied in this study and compared with the LSTM-SVR model to prove its superiority. It was demonstrated that introducing residual correction with the newly proposed LSTM-SVR model can effectively improve prediction performance. The LSTM-SVR model of Well A produced the lowest prediction root mean square error (RMSE) of 5.42, while the RMSE of Arps, PLE Duong, ANN, and LSTM were 5.84, 6.65, 5.85, 8.16, and 7.70, respectively. The RMSE of Well B of LSTM-SVR model is 0.94, while the RMSE of ANN, and LSTM were 1.48, and 2.32.

Abstract Background To develop a clinical model for predicting high axillary nodal burden in patients with early breast cancer by integrating ultrasound (US) and clinicopathological features. Methods and materials Patients with breast cancer who underwent preoperative US examination and breast surgery at the Affiliated Hospital of Nantong University (centre 1, n = 250) and at the Affiliated Hospital of Jiangsu University (centre 2, n = 97) between January 2012 and December 2016 and between January 2020 and March 2022, respectively, were deemed eligible for this study (n = 347). According to the number of lymph node (LN) metastasis based on pathology, patients were divided into two groups: limited nodal burden (0–2 metastatic LNs) and heavy nodal burden (≥ 3 metastatic LNs). In addition, US features combined with clinicopathological variables were compared between these two groups. Univariate and multivariate logistic regression analysis were conducted to identify the most valuable variables for predicting ≥ 3 LNs in breast cancer. A nomogram was then developed based on these independent factors. Results Univariate logistic regression analysis revealed that the cortical thickness (p

Abstract Background The competing interests of the government, smart senior care technology service providers, and older adults have led to a serious fragmentation of governance in China. This study aims to identify the collaboration mechanisms and evolutionary stabilization strategies for these agents. Methods An evolutionary game model is developed to analyze the strategic decisions made by the government, smart senior care technology service providers, and older adults. A sensitivity analysis is conducted using data from Anhui Province, China, to verify the effects of relevant parameters on the strategy decisions of each agent. Results The results of the simulation and sensitivity analysis indicated that, first, despite changes in the initial willingness values of the tripartite agents, the system eventually converges on 1. Second, the collaboration mechanism of the tripartite agents in the smart senior care system is related to government incentives, penalties, and subsidies, smart senior care service costs, and the additional benefits provided to smart senior care technology service providers. Conclusion The strategy decisions of the government, providers, and older adults interact with each other. To promote collaboration among the tripartite agents and improve governance effectiveness, the government should strengthen the regulations for providers, increase penalties for providers that engage in a breach of trust, provide moderate incentives and subsidies, and control smart senior care service costs.

Abstract Backgroud Multiple studies have identified pathogenic germline variants in cancer susceptibility genes (CSGs) in Chinese lung cancer patients; however, accurate assessment of these variants' contributions to cancer predisposition is always hampered by the absence of data on the prevalence of these variants in the general population. It is necessary to conduct a large‐scale case–control study to identify CSGs that significantly increase the risk of lung cancer. Materials and methods We performed targeted sequencing of a CSGs panel in 1117 lung cancer patients and 16,327 controls from the general Chinese population. Results In comparison to controls, lung cancer patients had a considerably higher prevalence of pathogenic and likely pathogenic (P/LP) variations. Among lung cancer patients, 72% of P/LP variants carriers did not have a family cancer history, who would be ignored if germline testing was only provided for patients meeting family history‐based criteria. Furthermore, compared to individuals with late‐onset lung cancer, patients with early‐onset lung cancer had a considerably higher prevalence of P/LP variations. With odds ratios (ORs) ranging from 4‐fold (BRCA1: OR, 4.193; 95%CI, 1.382–10.768) to 29‐fold (TP53: OR, 29.281; 95%CI, 1.523–1705.506), P/LP variants in the BRCA1 and TP53 genes were discovered to be strongly related to increased lung cancer risk. Additionally, with ORs ranging from 7.322‐fold to infinity, we discovered 23 variations previously categorized as non‐P/LP variants were highly enriched in lung cancer patients. Conclusion Our findings indicated that P/LP variants in BRCA1 and TP53 conferred increased risk of lung cancer in Chinese.