Stretchable strain sensors have gained increasing popularity as wearable devices to convert mechanical deformation of the human body into electrical signals. Two-dimensional transition metal carbides (Ti3C2Tx MXene) are promising candidates to achieve excellent sensitivity. However, MXene films have been limited in operating strain ranges due to rapid crack propagation during stretching. In this regard, this study reports MXene/carbon nanotube bilayer films with tunable sensitivity and working ranges. The device is fabricated using a scalable process involving spray deposition of well-dispersed nanomaterial inks. The bilayer sensor's high sensitivity is attributed to the cracks that form in the MXene film, while the compliant carbon nanotube layer extends the working range by maintaining conductive pathways. Moreover, the response of the sensor is easily controlled by tuning the MXene loading, achieving a gauge factor of 9039 within 15% strain at 1.92 mg/cm2 and a gauge factor of 1443 within 108% strain at 0.55 mg/cm2. These tailored properties can precisely match the operation requirements during the wearable application, providing accurate monitoring of various body movements and physiological activities. Additionally, a smart glove with multiple integrated strain sensors is demonstrated as a human-machine interface for the real-time recognition of hand gestures based on a machine-learning algorithm. The design strategy presented here provides a convenient avenue to modulate strain sensors for targeted applications.
The triplet annihilator is a critical component for triplet-triplet annihilation upconversion (TTA-UC); both the photophysical properties of the annihilator and the intermolecular orientation have pivotal effects on the overall efficiency of TTA-UC. Herein, we synthesized two supramolecular annihilators A-1 and A-2 by grafting 9,10-diphenylanthracene (DPA) fragments, which have been widely used as triplet annihilators for TTA-UC, on a macrocyclic host-pillar[5]arenes. In A-1, the orientation of the two DPA units was random, while, in A-2, the two DPA units were pushed to a parallel arrangement by intramolecular hydrogen-bonding interactions. The two compounds showed very similar photophysical properties and host-guest binding affinities toward electron-deficient guests, but showed totally different TTA-UC emissions. The UC quantum yield of A-2 could be optimized to 13.7% when an alkyl ammonia chain-attaching sensitizer S-2 was used, while, for A-1, only 5.1% was achieved. Destroying the hydrogen-bonding interactions by adding MeOH to A-2 significantly decreased the UC emissions, demonstrating that the parallel orientations of the two DPA units contributed greatly to the TTA-UC emissions. These results should be beneficial for annihilator designs and provide a new promising strategy for enhancing TTA-UC emissions.
Real-world clinical experience of using anti-programmed death-ligand 1 (PD-L1) immune checkpoint inhibitors (ICIs) combined with chemotherapy in the first-line treatment of extensive-stage small-cell lung cancer (SCLC) patients has rarely been reported. In this study, we aimed to perform a retrospective multicenter clinical analysis of extensive-stage SCLC patients receiving first-line therapy with anti-PD-L1 ICIs combined with chemotherapy. Between November 2018 and March 2022, 72 extensive-stage SCLC patients receiving first-line atezolizumab or durvalumab in combination with chemotherapy, according to the cancer center databases of Linkou, Chiayi, and Kaohsiung Chang Gung Memorial Hospitals, were retrospectively included in the analysis. Twenty-one patients (29.2%) received atezolizumab and fifty-one (70.8%) received durvalumab. Objective response (OR) and disease control (DC) rates of 59.7% and 73.6%, respectively, were observed with first-line ICI plus chemotherapy. The median progression-free survival (PFS) was 6.63 months (95% confidence interval (CI), 5.25-8.02), and the median overall survival (OS) was 16.07 months (95% CI, 15.12-17.0) in all study patients. A high neutrophil-to-lymphocyte ratio (NLR; >4) and a high serum lactate dehydrogenase (LDH) concentration (>260 UL) were identified as independent unfavorable factors associated with shorter OS in the multivariate analysis. Regarding safety, neutropenia was the most common grade 3 treatment-related adverse event (AE), but no treatment-related deaths occurred in the study patients. First-line anti-PD-L1 ICIs combined with chemotherapy are effective and safe for male extensive-stage SCLC patients. Further therapeutic strategies may need to be developed for patients with unfavorable outcomes (e.g., baseline high NLR and serum LDH level).
Identifying brain-tissue types holds significant research value in the biomedical field of non-contact brain-tissue measurement applications. In this paper, a layered metastructure is proposed, and the second harmonic generation (SHG) in a multilayer metastructure is derived using the transfer matrix method. With the SHG conversion efficiency (CE) as the measurement signal, the refractive index ranges that can be distinguished are 1.23~1.31 refractive index unit (RIU) and 1.38~1.44 RIU, with sensitivities of 0.8597 RIU-1 and 1.2967 RIU-1, respectively. It can distinguish various brain tissues, including gray matter, white matter, and low-grade glioma, achieving the function of a second harmonic mode sensor (SHMS). Furthermore, temperature has a significant impact on the SHG CE, which can be used to define the switch signal indicating whether the SHMS is functioning properly. When the temperature range is 291.4~307.9 Kelvin (K), the temperature switch is in the "open" state, and the optimal SHG CE is higher than 0.298%, indicating that the SHMS is in the working state. For other temperature ranges, the SHG CE will decrease significantly, indicating that the temperature switch is in the "off" state, and the SHMS is not working. By stimulating temperature and using the response of SHG CE, the temperature-switch function is achieved, providing a new approach for temperature-controlled second harmonic detection.
CMOS image sensor (CIS) semiconductor products are integral to mobile phones and photographic devices, necessitating ongoing enhancements in efficiency and quality for superior photographic outcomes. The presence of white pixels serves as a crucial metric for assessing CIS product performance, primarily arising from metal impurity contamination during the wafer production process or from defects introduced by the grinding blade process. While immediately addressing metal impurity contamination during production presents challenges, refining the handling of defects attributed to grinding blade processing can notably mitigate white pixel issues in CIS products. This study zeroes in on silicon wafer manufacturers in Taiwan, analyzing white pixel defects reported by customers and leveraging machine learning to pinpoint and predict key factors leading to white pixel defects from grinding blade operations. Such pioneering practical studies are rare. The findings reveal that the classification and regression tree (CART) and random forest (RF) models deliver the most accurate predictions (95.18%) of white pixel defects caused by grinding blade operations in a default parameter setting. The analysis further elucidates critical factors like grinding load and torque, vital for the genesis of white pixel defects. The insights garnered from this study aim to arm operators with proactive measures to diminish the potential for customer complaints.
Currently, the surface structure modification of photocatalysts is one of the effective means of enhancing their photocatalytic efficiency. Therefore, it is critically important to gain a deeper understanding of how the surface of α-Fe2O3 photocatalysts influences catalytic activity at the nanoscale. In this work, α-Fe2O3 catalysts were prepared using the solvothermal method, and four distinct morphologies were investigated: hexagonal bipyramid (THB), cube (CB), hexagonal plate (HS), and spherical (RC). The results indicate that the hexagonal bipyramid (THB) exhibits the highest degradation activity towards tetracycline (TC), with a reaction rate constant of k = 0.0969 min-1. The apparent reaction rate constants for the cube (CB), hexagonal plate (HS), and spherical (RC) morphologies are 0.0824, 0.0726, and 0.0585 min-1, respectively. In addition, it has been observed that the enhancement of photocatalytic activity is closely related to the increase in surface area, which provides more opportunities for interactions between Fe2+ and holes. The quenching experiments and electron paramagnetic resonance (EPR) results indicate that the ËO2, ËOH and h+ contribute mainly to the degradation of TC in the system. This research contributes to a more comprehensive understanding of catalyst surface alterations and their impact on catalytic performance.
Background: Physical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, ambulation, illness and loss of weight (FRAIL); Cardiovascular Health Study (CHS); and Study of Osteoporotic Fractures (SOF) indices, have been regularly used in clinical and research studies. However, no previous studies have investigated the predictive ability of machine learning (ML) for physical frailty assessment. The aim was to use two ML algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to predict these three physical frailty assessment models. Materials and methods: Questionnaires regarding demographic characteristics, lifestyle habits, living environment, and physical frailty assessment were answered by 445 participants aged 60 years and above. The RF and XGBoost algorithms were used to assess their scores for the three physical frailty indices. Furthermore, feature importance and Shapley additive explanations (SHAP) were used to determine the important physical frailty factors. Results: The XGBoost algorithm obtained higher accuracy for predicting the three physical frailty indices; the areas under the curve obtained by the XGBoost algorithm for the 5-Item FRAIL, CHS, and SOF indices were 0.84. 0.79, and 0.69, respectively. The feature importance and SHAP of the XGBoost algorithm revealed that systolic blood pressure, diastolic blood pressure, age, and body mass index play important roles in all three physical frailty models. Conclusion: The XGBoost algorithm has a more accurate predictive rate than RF across all three physical frailty assessments. Thus, ML can be a useful tool for the early detection of physical frailty.
Frailty , Geriatric Assessment , Machine Learning , Osteoporotic Fractures , Humans , Female , Aged , Male , Middle Aged , Frailty/diagnosis , Surveys and Questionnaires , Geriatric Assessment/methods , Aged, 80 and over , Frail Elderly/statistics & numerical data , Algorithms
Zero-dimensional metal halides have received wide attention due to their structural diversity, strong quantum confinement, and associated excellent photoluminescence properties. A reversible and tunable luminescence would be desirable for applications such as anti-counterfeiting, information encryption, and artificial intelligence. Yet, these materials are underexplored, with little known about their luminescence tuning mechanisms. Here we report a pyramidal coplanar dimer, (TBA)Sb2Cl7 (TBA = tetrabutylammonium), showing broadband emission wavelength tuning (585-650 nm) by simple thermal treatment. We attribute the broad color change to structural disorder induced by varying the heat treatment temperatures. Increasing the heating temperature transitions the material from long-range ordered crystalline phase to highly disordered glassy phase. The latter exhibits stronger electron-phonon coupling, enhancing the self-trapped exciton emission efficiency. The work provides a new material platform for manifold optical anti-counterfeiting applications and sheds light on the emission color tuning mechanisms for further design of stimuli-responsive materials.
BACKGROUND: Physical education and training are essential ways to improve the physical quality of the nation, and China has incorporated "building a healthy China" and "fitness for all" into its national development strategy, integrating a strong sports nation into the Chinese dream. OBJECTIVE: The study of digital recording and automated training in sports is of profound value. Motion capture technology can digitally record the training process in a digital physical education training system. At the same time, accurate modeling and calculation can analyze the training effects and give appropriate guidance and feedback. This study develops a new and improved hierarchical K-means algorithm by combining the known classification algorithm K-means with a hierarchical algorithm. METHODS: The performance of the old and new algorithms are compared and then applied to physical education training data to produce clustering results and analysis to reduce the model, which is used to reduce the number of parameters in the model and improve the recognition speed. RESULTS: The experimental results demonstrate that the relevant models proposed in this study achieve an average accuracy of 91.27% and 92.26%, respectively, which is better than a single network model and can effectively use big data for health event detection. CONCLUSION: The empirical results show that the improved model algorithm outperforms the single network model and can detect health events using big data.
The abnormal estrogens levels in human body can cause many side effects and diseases, but the quantitative detection of the trace estrogens in complex biological samples still remains great challenge. Here we reported the fabrication of a novel core-shell structured magnetic cyclodextrin microporous organic network (Fe3O4@CD-MON) for rapid magnetic solid phase extraction (MSPE) of four estrogens in human serum and urine samples prior to HPLC-UV determination. The uniform spherical core-shell Fe3O4@CD-MONs was successfully regulated by altering the reactive monomers and solvents. The Fe3O4@CD-MONs owned high specific surface area, good hydrophobicity, large superparamagnetism, and abundant extraction sites for estrogens. Under optimal conditions, the proposed MSPE-HPLC-UV method provided wide linearity range (2.0-400 µg L-1), low limits of detection (0.5-1.0 µg L-1), large enrichment factors (183-198), less adsorbent consumption (3 mg), short extraction time (3 min), and good stability and reusability (at least 8 cycles). The established method had also been successfully applied to the enrichment and detection of four estrogens in serum and urine samples with a recovery of 88.4-105.1 % and a relative standard deviation of 1.0-5.9 %. This work confirmed the feasibility of solvent and monomer regulation synthesis of Fe3O4@CD-MON composites, and revealed the great prospects of magnetic CD-MONs for efficient enrichment of trace estrogens in complex biological samples.
Potassium (K+) is an essential macronutrient for appropriate plant development and physiology. However, little is known about the mechanisms involved in the regulation of leaf water relations by K under water deficit. A pot experiment with two K supplies of 0.45 and 0 g K2O per pot (3 kg soil per pot) and two watering conditions (well-watered and water-deficit) was conducted to explore the effects of K deficiency on canopy transpiration characteristics, leaf water status, photosynthesis, and hydraulic traits in two rice genotypes with contrasting resistance to drought. The results showed that K deficiency reduced canopy transpiration rate by decreasing stomatal conductance, which led to higher canopy temperatures, resulting in limited water deficit tolerance in rice. In addition, K deficiency led to further substantial reductions in leaf relative water content and water potential under water deficit, which increased the imbalance in leaf water relations under water deficit. Notably, K deficiency limited leaf gas exchange by reducing leaf hydraulic conductance, but decreased the intrinsic water use efficiency under water deficit, especially for the drought-resistant cultivar. Further analysis of the underlying process of leaf hydraulic resistance revealed that the key limiting factor of leaf hydraulic conductance under K deficiency was the outside-xylem hydraulic conductance rather than the xylem hydraulic conductance. Overall, our results provide a comprehensive perspective for assessing leaf water relations under K deficiency, water deficit, and their combined stresses, which will be useful for optimal rice fertilization strategies.
Droughts , Oryza , Plant Leaves , Plant Transpiration , Potassium , Water , Oryza/physiology , Oryza/genetics , Oryza/metabolism , Plant Leaves/physiology , Plant Leaves/metabolism , Water/metabolism , Plant Transpiration/physiology , Potassium/metabolism , Photosynthesis/physiology , Plant Stomata/physiology , Xylem/physiology , Xylem/metabolism
This study involved direct doping of In2O3 into silicon carbide (SiC) powder, resulting in 8.0 at% In-doped SiC powder. Subsequently, heating at 500 °C was performed to form a target, followed by the utilization of electron beam (e-beam) technology to deposit the In-doped SiC thin films with the thickness of approximately 189.8 nm. The first breakthrough of this research was the successful deposition of using e-beam technology. The second breakthrough involved utilizing various tools to analyze the physical and electrical properties of In-doped SiC thin films. Hall effect measurement was used to measure the resistivity, mobility, and carrier concentration and confirm its n-type semiconductor nature. The uniform dispersion of In ions in SiC was as confirmed by electron microscopy energy-dispersive spectroscopy and secondary ion mass spectrometry analyses. The Tauc Plot method was employed to determine the Eg values of pure SiC and In-doped SiC thin films. Semiconductor parameter analyzer was used to measure the conductivity and the I-V characteristics of devices in In-doped SiC thin films. Furthermore, the third finding demonstrated that In2O3-doped SiC thin films exhibited remarkable current density. X-ray photoelectron spectroscopy and Gaussian-resolved spectra further confirmed a significant relationship between conductivity and oxygen vacancy concentration. Lastly, depositing these In-doped SiC thin films onto p-type silicon substrates etched with buffered oxide etchant resulted in the formation of heterojunction p-n junction. This junction exhibited the rectifying characteristics of a diode, with sample current values in the vicinity of 102 mA, breakdown voltage at approximately -5.23 V, and open-circuit voltage around 1.56 V. This underscores the potential of In-doped SiC thin films for various semiconductor devices.
Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.
As a byproduct of manufacturing soybeans, okara is high in dietary fiber, protein, and fats, and it contains all of the essential amino acids. Wheat, the primary ingredient in noodles, will lose nutrients during manufacturing, creating an imbalance in nutrients. This experiment is for the purpose of studying the effects of okara on quality, antioxidant properties, amino acid content, resistant starch (RS) content, and microstructure of noodles. The results indicate that the addition of 9% okara noodles increased hardness and adhesiveness by 107.19% and 132.14%, respectively, and improved ABTS free radical scavenging activity by 60.78%. The addition of 12% okara noodles increased the DPPH free radical scavenging ability by 23.66%, reduced the rapidly digestible starch (RDS) content of the noodles to 21.21%, and the resistant starch content increased to 44.85% (p < .05). Therefore, to address the issue of nutritional imbalance in wheat noodles without compromising the quality of the noodles, it is recommended to add 9% or 12% okara for the preparation of nutritionally fortified noodles.
Numerous myofibroblasts are arisen from endothelial cells (ECs) through endothelial to mesenchymal transition (EndMT) triggered by TGF-ß. However, the mechanism of ECs transforms to a different subtype, or whether there exists an intermediate state of ECs remains unclear. In present study, we demonstrate Midkine (MDK) mainly expressed by CD31 + ACTA2+ECs going through partial EndMT contribute greatly to myofibroblasts by spatial and single-cell transcriptomics. MDK is induced in TGF-ß treated ECs, which upregulates C/EBPß and increases EndMT genes, and these effects could be reversed by siMDK. Mechanistically, MDK promotes the binding ability of C/EBPß with ACTA2 promoter by stabilizing the C/EBPß protein. In vivo, knockout of Mdk or conditional knockout of Mdk in ECs reduces EndMT markers and significantly reverses fibrogenesis. In conclusion, our study provides a mechanistic link between the induction of EndMT by TGF-ß and MDK, which suggests that blocking MDK provides potential therapeutic strategies for renal fibrosis.
CCAAT-Enhancer-Binding Protein-beta , Fibrosis , Midkine , Midkine/metabolism , Midkine/genetics , Animals , Mice , Humans , CCAAT-Enhancer-Binding Protein-beta/metabolism , CCAAT-Enhancer-Binding Protein-beta/genetics , Epithelial-Mesenchymal Transition , Endothelial Cells/metabolism , Endothelial Cells/pathology , Kidney Diseases/metabolism , Kidney Diseases/pathology , Kidney Diseases/genetics , Myofibroblasts/metabolism , Myofibroblasts/pathology , Transforming Growth Factor beta/metabolism , Mice, Inbred C57BL , Male , Kidney/metabolism , Kidney/pathology , Mice, Knockout , Endothelial-Mesenchymal Transition
AIM: To evaluate the effectiveness and safety of early lens extraction during pars plana vitrectomy (PPV) for proliferative diabetic retinopathy (PDR) compared to those of PPV with subsequent cataract surgery. METHODS: This multicenter randomized controlled trial was conducted in three Chinese hospitals on patients with PDR, aged >45y, with mild cataracts. The participants were randomly assigned to the combined (PPV combined with simultaneously cataract surgery, i.e., phacovitrectomy) or subsequent (PPV with subsequent cataract surgery 6mo later) group and followed up for 12mo. The primary outcome was the change in best-corrected visual acuity (BCVA) from baseline to 6mo, and the secondary outcomes included complication rates and medical expenses. RESULTS: In total, 129 patients with PDR were recruited and equally randomized (66 and 63 in the combined and subsequent groups respectively). The change in BCVA in the combined group [mean, 36.90 letters; 95% confidence interval (CI), 30.35-43.45] was significantly better (adjusted difference, 16.43; 95%CI, 8.77-24.08; P<0.001) than in the subsequent group (mean, 22.40 letters; 95%CI, 15.55-29.24) 6mo after the PPV, with no significant difference between the two groups at 12mo. The overall surgical risk of two sequential surgeries was significantly higher than that of the combined surgery for neovascular glaucoma (17.65% vs 3.77%, P=0.005). No significant differences were found in the photocoagulation spots, surgical time, and economic expenses between two groups. In the subsequent group, the duration of work incapacity (22.54±9.11d) was significantly longer (P<0.001) than that of the combined group (12.44±6.48d). CONCLUSION: PDR patients aged over 45y with mild cataract can also benefit from early lens extraction during PPV with gratifying effectiveness, safety and convenience, compared to sequential surgeries.
AIM: To evaluate the role of semaphorin 7A (Sema7A) and its associated regulatory mechanisms in modulating the barrier function of cultured human corneal epithelial cells (HCEs). METHODS: Barrier models of HCEs were treated with recombinant human Sema7A at concentrations of 0, 125, 250, or 500 ng/mL for 24, 48, or 72h in vitro. Transepithelial electrical resistance (TEER) as well as Dextran-fluorescein isothiocyanate (FITC) permeability assays were conducted to assess barrier function. To quantify tight junctions (TJs) such as occludin and zonula occludens-1 (ZO-1) at the mRNA level, reverse transcription-polymerase chain reaction (RT-PCR) analysis was performed. Immunoblotting was used to examine the activity of the nuclear factor-kappa B (NF-κB) signaling pathway and the production of TJs proteins. Immunofluorescence analyses were employed to localize the TJs. Enzyme-linked immunosorbent assay (ELISA) and RT-PCR were utilized to observe changes in interleukin (IL)-1ß levels. To investigate the role of NF-κB signaling activation and IL-1ß in Sema7A's anti-barrier mechanism, we employed 0.1 µmol/L IκB kinase 2 (IKK2) inhibitor IV or 500 ng/mL IL-1 receptor (IL-1R) antagonist. RESULTS: Treatment with Sema7A resulted in decreased TEER and increased permeability of Dextran-FITC in HCEs through down-regulating mRNA and protein levels of TJs in a time- and dose-dependent manner, as well as altering the localization of TJs. Furthermore, Sema7A stimulated the activation of inhibitor of kappa B alpha (IκBα) and expression of IL-1ß. The anti-barrier function of Sema7A was significantly suppressed by treatment with IKK2 inhibitor IV or IL-1R antagonists. CONCLUSION: Sema7A disrupts barrier function through its influence on NF-κB-mediated expression of TJ proteins, as well as the expression of IL-1ß. These findings suggest that Sema7A could be a potential therapeutic target for the diseases in corneal epithelium.
Simultaneous rapid screening of multiple drugs of abuse in environmental water facilitates effective monitoring and trend assessments. Herein, a novel porphyrin-based metal organic frameworks modified Ti3C2Tx nanosheets (Cu-TCPP/Ti3C2Tx) composite was prepared and utilized as solid-phase microextraction (SPME) coating for the simultaneous analysis of 21 drugs from water samples. The composite was embedded with matrix-compatible polyacrylonitrile binder to prepare a coated blade with thin and uniform coating layer. Ambient mass spectrometry (MS) technique was used to create a coated blade spray-MS (CBS-MS) method for the quantitative determination of drugs in water samples. High throughput and automated sample preparation were achieved with the use of a Concept 96-well plate system, enabling analysis of 21 drugs of abuse within 1 min per sample, while using only 8 µL of organic solvent for desorption and CBS-MS detection. The developed method showed favorable linearity (R2 ≥ 0.9983) in the range of 0.05 to 10 ng mL-1, low limits of detection (1.5-9.0 ng L-1), sufficient recovery (67.6-133.2%), as well as satisfactory precision (RSDs≤13.5%). This study not only delivers a novel and efficient SPME coating composite, but also demonstrates the excellent performance of a high-throughput, efficient, and green analytical method for determination of drugs in environmental water.
Mass Spectrometry , Metal-Organic Frameworks , Solid Phase Microextraction , Water Pollutants, Chemical , Water Pollutants, Chemical/analysis , Solid Phase Microextraction/methods , Metal-Organic Frameworks/chemistry , Mass Spectrometry/methods , Titanium/chemistry , Limit of Detection , Illicit Drugs/analysis , Environmental Monitoring/methods , Pharmaceutical Preparations/analysis , Pharmaceutical Preparations/chemistry
PURPOSE: This study aimed to construct an m6A and cuproptosis-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using the information acquired from The Cancer Genome Atlas (TCGA) database. METHODS: First, the co-expression analysis was performed to identify lncRNAs linked with N6-methyladenosine (m6A) and cuproptosis in ccRCC. Then, a model encompassing four candidate lncRNAs was constructed via univariate, least absolute shrinkage together with selection operator (LASSO), and multivariate regression analyses. Furthermore, Kaplan-Meier, principal component, functional enrichment annotation, and nomogram analyses were performed to develop a risk model that could effectively assess medical outcomes for ccRCC cases. Moreover, the cellular function of NFE4 in Caki-1/OS-RC-2 cultures was elucidated through CCK-8/EdU assessments and Transwell experiments. Dataset outcomes indicated that NFE4 can have possible implications in m6A and cuproptosis, and may promote ccRCC progression. RESULTS: We constructed a panel of m6A and cuproptosis-related lncRNAs to construct a prognostic prediction model. The Kaplan-Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. Furthermore, the m6A and cuproptosis-related lncRNA model indicated higher diagnostic efficiency than other clinical features. Moreover, the NFE4 function analysis indicated a gene associated with m6A and cuproptosis-related lncRNAs in ccRCC. It was also revealed that the proliferation and migration of Caki-1 /OS-RC-2 cells were inhibited in the NFE4 knockdown group. CONCLUSION: Overall, this study indicated that NFE4 and our constructed risk signature could predict outcomes and have potential clinical value.
BACKGROUND: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. METHODS: This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. RESULTS: A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. CONCLUSIONS: In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.
Brain Ischemia , Machine Learning , Subarachnoid Hemorrhage , Humans , Subarachnoid Hemorrhage/epidemiology , Subarachnoid Hemorrhage/diagnosis , Subarachnoid Hemorrhage/complications , Male , Retrospective Studies , Female , Middle Aged , Brain Ischemia/epidemiology , Brain Ischemia/etiology , Brain Ischemia/diagnosis , Adult , Aged , Cohort Studies , Prognosis , China/epidemiology
