Multi-MXene assisted large-scale manufacturing of electrochemical biosensors based on enzyme-nanoflower enhanced electrodes for the detection of H2O2 secreted from live cancer cells.

In situ monitoring of H2O2 in cellular microenvironments plays a critical role in the early diagnosis and pretreatment of cancer, but is limited by the lack of efficient and low-cost strategies for the large-scale preparation of real-time biosensors. Herein, a universal strategy for MXene-based composite inks combined with a scalable screen-printing process is validated in large-scale manufacturing of electrochemical biosensors for in situ detection of H2O2 secreted from live cells. Compositing biocompatible carboxymethyl cellulose (CMCS) with excellent conductive MXene, a water-based ink electrode (MXene/CMCS) with tunable viscosity is efficiently printed with desirable printing accuracy. Subsequently, the MXene/CMCS@HRP electrochemical biosensor exhibits stable electrochemical performance through HRP nanoflower modification, showing rapid electron transport and high electrocatalytic capacity, and demonstrating a low limit of detection (0.29 µM) with a wide linear detection range (0.5 µM-3 mM), superior sensitivity (56.45 µA mM-1 cm-2), long-term stability and high anti-interference ability. Moreover, this electrochemical biosensor is effectively employed for in situ detection of H2O2 secreted from HeLa cells, revealing good biocompatibility and outstanding biosensing capability. This proposed strategy not only extends the possibility of low-cost biomedical devices, but also provides a promising approach for early diagnosis and treatment of cancer.

DL-Serine Hydrazide Hydrochloride Multiple-site Synergy Induced Effective and Stable Formamidine-Rich Perovskite Solar Cells.

The efficiency and stability of solar cells are two key indicators that determine for the commercial feasibility of photovoltaic devices. Formamidine-cesium perovskite has been extensively investigated since its excellent thermal stability and has great potential in achieving high power conversion efficiency. However, during the aging process, especially under light conditions, formamidine-rich perovskites are prone to produce iodine, and the escape of iodine is one of the important factors leading to device degradation. Here, DL-Serine Hydrazide Hydrochloride containing the reducing group is introduced into the precursor solution of formamidine-cesium perovskite, which achieves multiple-site passivation. Hydrazine reacts with iodine to reduce it to iodine ions, inhibiting the escape of iodine. In addition, carbonyl groups and uncoordinated lead ions form coordination bonds to reduce defects. In the end, the perovskite solar cell with DL-Serine Hydrazide Hydrochloride added achieves a champion efficiency of 22.22%, and maintains 85.88% of the initial efficiency after continuous exposure under 1 sun for 7000 s at a relative humidity of ≈40%. Additionally, DL-Serine Hydrazide Hydrochloride added device shows good stability in air environments with relative humidity of 50%-60%. DL-Serine Hydrazide Hydrochloride improves the stability of formamidine-rich perovskite solar cells and provides a low-cost strategy for commercial development.

(Ba0.55Sr0.45)1-xLaxTi1.01O3-Bi0.5Na0.5TiO3 Positive Temperature Coefficient Resistivity Ceramics with Low Curie Temperature (~-15 °C).

Positive temperature coefficient of electrical resistivity (PTCR) materials with low Curie temperature have been paid increasing attention lately. In this study, PTCR materials with a Curie temperature of approximately -15 °C were investigated by La3+ doping Ba0.55Sr0.45TiO3 ceramics. It could be expected to meet the requirements of thermal management systems for low-temperature control. In addition, a trace amount of Bi0.5Na0.5TiO3 (BNT) was employed to improve the resistivity and the PTCR performance. A significant PTCR effect was achieved with a high resistivity jump of nearly four orders of magnitude, a high temperature coefficient of ~28.76%/°C, and a narrow transition temperature span of 22 °C in the (Ba0.55Sr0.45)0.99875La0.00125Ti1.01O3-0.0025Bi0.5Na0.5TiO3 ceramics. The PTCR enhancement mechanism of BNT is discussed.

Three-Direction Fusion for Accurate Volumetric Liver and Tumor Segmentation.

Biomedical image segmentation of organs, tissues and lesions has gained increasing attention in clinical treatment planning and navigation, which involves the exploration of two-dimensional (2D) and three-dimensional (3D) contexts in the biomedical image. Compared to 2D methods, 3D methods pay more attention to inter-slice correlations, which offer additional spatial information for image segmentation. An organ or tumor has a 3D structure that can be observed from three directions. Previous studies focus only on the vertical axis, limiting the understanding of the relationship between a tumor and its surrounding tissues. Important information can also be obtained from sagittal and coronal axes. Therefore, spatial information of organs and tumors can be obtained from three directions, i.e. the sagittal, coronal and vertical axes, to understand better the invasion depth of tumor and its relationship with the surrounding tissues. Moreover, the edges of organs and tumors in biomedical image may be blurred. To address these problems, we propose a three-direction fusion volumetric segmentation (TFVS) model for segmenting 3D biomedical images from three perspectives in sagittal, coronal and transverse planes, respectively. We use the dataset of the liver task provided by the Medical Segmentation Decathlon challenge to train our model. The TFVS method demonstrates a competitive performance on the 3D-IRCADB dataset. In addition, the t-test and Wilcoxon signed-rank test are also performed to show the statistical significance of the improvement by the proposed method as compared with the baseline methods. The proposed method is expected to be beneficial in guiding and facilitating clinical diagnosis and treatment.

Demonstration of Germanium Vertical Gate-All-Around Field-Effect Transistors Featured by Self-Aligned High-κ Metal Gates with Record High Performance.

A special Ge nanowire/nanosheet (NW/NS) p-type vertical sandwich gate-all-around (GAA) field-effect transistor (FET) (Ge NW/NS pVSAFET) with self-aligned high-κ metal gates (HKMGs) is proposed. The Ge pVSAFETs were fabricated by high-quality GeSi/Ge epitaxy, an exclusively developed self-limiting isotropic quasi atomic layer etching (qALE) of Ge selective to both GeSi and the (111) plane, top-drain implantation, and ozone postoxidation (OPO) channel passivation. The Ge pVSAFETs, which have hourglass-shaped (111) channels with the smallest size range from 5 to 20 nm formed by qALE, have reached a record high Ion of ∼291 µA/µm and exhibited good short channel effects (SCEs) control. The integration flow is compatible with mainstream CMOS processes, and Ge pVSAFETs with precise control of gate lengths/channel sizes were obtained.

Regression-Based Hyperparameter Learning for Support Vector Machines.

Unification of classification and regression is a major challenge in machine learning and has attracted increasing attentions from researchers. In this article, we present a new idea for this challenge, where we convert the classification problem into a regression problem, and then use the methods in regression to solve the problem in classification. To this end, we leverage the widely used maximum margin classification algorithm and its typical representative, support vector machine (SVM). More specifically, we convert SVM into a piecewise linear regression task and propose a regression-based SVM (RBSVM) hyperparameter learning algorithm, where regression methods are used to solve several key problems in classification, such as learning of hyperparameters, calculation of prediction probabilities, and measurement of model uncertainty. To analyze the uncertainty of the model, we propose a new concept of model entropy, where the leave-one-out prediction probability of each sample is converted into entropy, and then used to quantify the uncertainty of the model. The model entropy is different from the classification margin, in the sense that it considers the distribution of all samples, not just the support vectors. Therefore, it can assess the uncertainty of the model more accurately than the classification margin. In the case of the same classification margin, the farther the sample distribution is from the classification hyperplane, the lower the model entropy. Experiments show that our algorithm (RBSVM) provides higher prediction accuracy and lower model uncertainty, when compared with state-of-the-art algorithms, such as Bayesian hyperparameter search and gradient-based hyperparameter learning algorithms.

Preparation of Hydrophobic Octadecylphosphonic Acid-Coated Magnetite Nanoparticles for the Demulsification of n-Hexane-in-Water Nanoemulsions.

To design more environmentally friendly, economical, and efficient demulsifiers for oily wastewater treatment, hydrophobic octadecylphosphonic acid (ODPA)-modified Fe3O4 nanoparticles (referred to as Fe3O4@ODPA) were prepared by condensation of hydroxyl groups between ODPA and Fe3O4 nanoparticles using the co-precipitation method. The prepared magnetite nanoparticles were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscope (SEM), Fourier transform infrared (FTIR) spectroscopy, and thermogravimetric/differential thermogravimetric (TG/DTG) analysis. The water contact angles (θW) of Fe3O4@ODPA nanoparticles were more than 120°, indicating hydrophobic nature, and the diameter of the obtained spherical-shaped magnetite nanoparticles was 12-15 nm. The ODPA coating amount (AO) (coating weight per gram Fe3O4) and specific surface area (SO) of Fe3O4@ODPA were 0.124-0.144 g·g-1 and 78.65-91.01 m2·g-1, respectively. To evaluate the demulsification ability, stability, and reusability, the magnetite nanoparticles were used to demulsify an n-hexane-in-water nanoemulsion. The effects of the magnetite nanoparticle dosage (CS), pH value of nanoemulsion, and NaCl or CaCl2 electrolytes on the demulsification efficiency (RO) were investigated. The RO of Fe3O4@ODPA samples was found to be higher than that of bare Fe3O4 samples (S0, ST, and SN) under all CS values. With the increase in CS, the RO of Fe3O4@ODPA samples initially increased and then approached equilibrium value at Cs = 80.0 g·L-1. A maximum RO of ~93% was achieved at CS = 100.0 g·L-1 for the Fe3O4@ODPA sample S2. The pH and two electrolytes had a minor effect on RO. The Fe3O4@ODPA nanoparticles maintained high RO even after being reused for demulsification 11 times. This indicates that the hydrophobic Fe3O4@ODPA samples can be used as an effective magnetite demulsifer for oil-in-water nanoemulsions.

Colorimetric sensor array for discriminating and determinating phenolic pollutants basing on different ratio of ligands in Cu/MOFs.

The high toxicity and low biodegradability of the phenolic pollutants destroyed the balance of the environment and influenced human health seriously. Here, we developed a three-dimensional coloremetric sensor array for discriminating and determinating phenolic pollutants basing on the distinct Cu/nucleotides MOFs. Firstly, three laccase-mimic Cu/MOFs (Cu/AMP, Cu/CMP, and Cu/GMP) were obtained by regulating the molar ratio of Cu2+ and nucleotides. Then the Cu/MOFs as the recognition elements of the sensor array catalyzed the pollutants-4-AAP-H2O2 system, obtaining the colored benzoquinone products. Subsequently, the data array obtaining from the combined training matrix (3 Cu/MOFs × 6 pollutants × 5 replicates) was projected into a new dimensional space to obtain the 3D canonical scores, and classified into individual clusters by introducing LDA method. No overlap in their respective LDA plots for the six phenolic pollutants with different concentrations suggested the prominent discriminating performance of the sensor array. Furthermore, the sensor array exhibited high selectivity compared to the "lock-and-key" sensors even other active matrices coexisting in water samples. Importantly, the most influential discrimination factor was used to monitor the levels of the six targets, evidencing the potential application in assessing water pollution and maintaining human health.

Study of Selective Dry Etching Effects of 15-Cycle Si0.7Ge0.3/Si Multilayer Structure in Gate-All-Around Transistor Process.

Gate-all-around (GAA) structures are important for future logic devices and 3D-DRAM. Inner-spacer cavity etching and channel release both require selective etching of Si0.7Ge0.3. Increasing the number of channel-stacking layers is an effective way to improve device current-driving capability and storage density. Previous work investigated ICP selective etching of a three-cycle Si0.7Ge0.3/Si multilayer structure and the related etching effects. This study focuses on the dry etching of a 15-cycle Si0.7Ge0.3/Si multilayer structure and the associated etching effects, using simulation and experimentation. The simulation predicts the random effect of lateral etching depth and the asymmetric effect of silicon nanosheet damage on the edge, both of which are verified by experiments. Furthermore, the study experimentally investigates the influence and mechanism of pressure, power, and other parameters on the etching results. Research on these etching effects and mechanisms will provide important points of reference for the dry selective etching of Si0.7Ge0.3 in GAA structures.

Correction: Li et al. Fringe Projection Profilometry Based on Saturated Fringe Restoration in High Dynamic Range Scenes. Sensors 2023, 23, 3133.

The authors wish to make the following corrections to the original paper [...].

Detection and Prediction of the Early Thermal Runaway and Control of the Li-Ion Battery by the Embedded Temperature Sensor Array.

Sorts of Li-ion batteries (LIB) have been becoming important energy supply and storage devices. As a long-standing obstacle, safety issues are limiting the large-scale adoption of high-energy-density batteries. Strategies covering materials, cell, and package processing have been paid much attention to. Here, we report a flexible sensor array with fast and reversible temperature switching that can be incorporated inside batteries to prevent thermal runaway. This flexible sensor array consists of PTCR ceramic sensors combined with printed PI sheets for electrodes and circuits. Compared to room temperature, the resistance of the sensors soars nonlinearly by more than three orders of magnitude at around 67 °C with a 1 °C/s rate. This temperature aligns with the decomposition temperature of SEI. Subsequently, the resistance returns to normal at room temperature, demonstrating a negative thermal hysteresis effect. This characteristic proves advantageous for the battery, as it enables a lower-temperature restart after an initial warming phase. The batteries with an embedded sensor array could resume their normal function without performance compromise or detrimental thermal runaway.

Discriminative analysis dictionary learning with adaptively ordinal locality preserving.

Dictionary learning has found broad applications in signal and image processing. By adding constraints to the traditional dictionary learning model, dictionaries with discriminative capability can be obtained which can deal with the task of image classification. The Discriminative Convolutional Analysis Dictionary Learning (DCADL) algorithm proposed recently has achieved promising results with low computational complexity. However, DCADL is still limited in classification performance because of the lack of constraints on dictionary structures. To solve this problem, this study introduces an adaptively ordinal locality preserving (AOLP) term to the original model of DCADL to further improve the classification performance. With the AOLP term, the distance ranking in the neighborhood of each atom can be preserved, which can improve the discrimination of coding coefficients. In addition, a linear classifier for the classification of coding coefficients is trained along with the dictionary. A new method is designed specifically to solve the optimization problem corresponding to the proposed model. Experiments are performed on several commonly used datasets to show the promising results of the proposed algorithm in classification performance and computational efficiency.

Identification of hub necroptosis-related lncRNAs for prognosis prediction of esophageal carcinoma.

Necroptosis is a newly identified programmed cell death associated with the biological process of various cancers, including esophageal carcinoma (ESCA). Meanwhile, the dysregulation of long non-coding RNAs (lncRNAs) is greatly implicated in ESCA progression and necroptosis regulation. However, the lncRNAs involved in regulating necroptosis in ESCA are still unclear. In this study, we aim to explore the expression profile of necroptosis-related lncRNAs (NRLs), and evaluate their roles in ESCA prognosis and treatment. In the present study, 198 differentially expressed NRLs were identified between the ESCA and adjacent normal tissues through screening the data extracted from the Cancer Genome Atlas (TCGA) database. And, a prognostic panel consisting of 6 NRLs was constructed using the LASSO algorithm and multivariate Cox regression analysis. The ESCA patients with high risks had a markedly reduced survival time and higher mortality prevalence. Moreover, C-index of 6 NRLs-panel was superior to 48 published prognostic models based on lncRNAs or mRNAs for ESCA. There were significant differences between the high-risk and low-risk groups in tumor-related pathways, genetic mutations, and drug sensitivity responses. In vitro analysis revealed that inhibition of PVT1 impeded the proliferation, migration, and colony formation of ESCA cells, increased the expressions of p-RIP1 and p-MLKL and promoted necroptosis. By contrast, PVT1 overexpression resulted in a decrease in necroptotic cell death events, thus promoting tumor progression. Collectively, the established 6-NRLs panel was a promising biomarker for the prognostic prediction of ESCA. Moreover, our current findings provided potential targets for individualized therapy for ESCA patients.

Transcriptome analysis revealed molecular basis of cold response in Prunus mume.

Japanese apricot (Prunus mume Sieb. et Zucc.) is a traditional woody flower and fruit tree restrictedly cultivated in northern area due to its inability to survive harsh winters and early springs. In the current study, RNA-seq and physiological assay were used to study the cold response of P. mume 'Xuemei'. A total of 4705 genes were identified as differentially expressed genes (DEGs) in the 21 pairwise comparisons among seven time points under 0 °C cold treatment, and 3678 of them showed differential levels compared with control at normal temperature. The gene expression profiles indicated that the number of upregulated genes increased with prolongation of treatment time throughout the whole 48 h. Hierarchical clustering suggested three obvious phases of the gene expression profiles. Gene ontology (GO) analysis of the 4705 DEGs resulted in 102 significantly enriched GO items in which the transcription activity was dominant. 225 DEGs were predicted to encode transcription factor (TF) genes. Some important TFs (ERF, CBF, WRKY, NAC, MYB, bHLH) were strongly induced during the whole cold treatment. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that plant signal transduction pathways such as plant hormone and calcium (Ca2+) were notable. Metabolic pathways such as sugar metabolism, especially RFOs (raffinose family oligosaccharides) were activated, which was accompanied by the accumulation of soluble sugars. SOD and POD enzyme activities coupled with reactive oxygen species (ROS)-related gene expression profile implied a gradually induced ROS scavenging system under cold treatment. These results might shed light on the sensitivity to cold stress in Japanese apricot and provide new insights into hardiness studies in P. mume and its related species. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01376-2.

C-reactive protein and lactate dehydrogenase serum levels potentially predict the response to checkpoint inhibitors in patients with advanced non-small cell lung cancer.

Background: Programmed cell death-ligand 1 (PD-L1) expression and other biomarkers are not completely reliable predictors of the response to checkpoint inhibitors in patients with advanced non-small cell lung cancer (NSCLC). We investigated the value of peripheral serological inflammatory indicators and their combination in predicting the prognosis of patients with advanced NSCLC treated with checkpoint inhibitors. Methods: This study retrospectively analyzed 116 NSCLC patients treated with anti-programmed cell death protein 1 (PD-1)/PD-L1 monoclonal antibodies. Clinical data of the patients were collected before treatment. X-tile plots determined the optimal cut-point for C-reactive protein (CRP) and lactate dehydrogenase (LDH). A survival analysis was performed using the Kaplan-Meier method. Multi-factor Cox regression analysis was used to evaluate the statistically significant factors identified in the univariate analysis. Results: The X-tile plots show the cut-points of CRP and LDH were 8 mg/L and 312 U/L, respectively. Univariate analyses showed high baseline serum LDH and low CRP levels were associated with adverse progression-free survival (PFS). Multivariate analyses indicated that CRP (HR, 0.214, 95% CI: 0.053-0.857, P=0.029) could be a predictive indicator for PFS. In addition, we evaluated the combination of CRP and LDH, and univariate analyses showed that patients with high CRP and low LDH exhibited significantly better PFS than those in the other groups. Conclusions: Baseline levels of serum CRP and LDH have the potential to become a convenient clinical tool to predict response to immunotherapy in advanced non-small cell lung cancer.

A Novel Composite Graph Neural Network.

Graph neural networks (GNNs) have achieved great success in many fields due to their powerful capabilities of processing graph-structured data. However, most GNNs can only be applied to scenarios where graphs are known, but real-world data are often noisy or even do not have available graph structures. Recently, graph learning has attracted increasing attention in dealing with these problems. In this article, we develop a novel approach to improving the robustness of the GNNs, called composite GNN. Different from existing methods, our method uses composite graphs (C-graphs) to characterize both sample and feature relations. The C-graph is a unified graph that unifies these two kinds of relations, where edges between samples represent sample similarities, and each sample has a tree-based feature graph to model feature importance and combination preference. By jointly learning multiaspect C-graphs and neural network parameters, our method improves the performance of semisupervised node classification and ensures robustness. We conduct a series of experiments to evaluate the performance of our method and the variants of our method that only learn sample relations or feature relations. Extensive experimental results on nine benchmark datasets demonstrate that our proposed method achieves the best performance on almost all the datasets and is robust to feature noises.

LINC00470 represses cell autophagy and cisplatin sensitivity of glioma via suppressing PTEN expression.

INTRODUCTION: Glioma is one of primary brain tumours which has the worst clinical prognoses of patients. As an alternative chemotherapeutic drug for malignant glioma, the therapeutic effect of cisplatin (CDDP) is devastatingly affected due to resistance in patients. In this study, we investigated the effect of LINC00470/PTEN on the CDDP sensitivity of glioma cells. MATERIAL AND METHODS: Differentially expressed lncRNAs and the downstream regulators in glioma tissue were obtained via bioinformatics analysis. LINC00470 and PTEN mRNA expression levels were detected using qRT-PCR. IC50 values of glioma cells were examined using Cell Counting Kit-8 (CCK-8). Cell apoptosis was revealed by flow cytometry. The expression level of autophagy-related protein was detected by western blot. Intracellular autophagosome formation was detected by immunofluorescence staining, and the methylation level of PTEN promoter was detected via methylation-specific PCR (MSP). RESULTS: Through the above steps, we found that LINC00470 was highly expressed in glioma cells, and the survival rate of patients was reduced in the presence of high expression of LINC00470. Silenced LINC00470 promoted LC3 II expression and autophagosome formation, and facilitated cell apoptosis to inhibit resistance to CDDP. While silenced PTEN could successfully reverse the previous effects on glioma cells. CONCLUSIONS: Based on the above, LINC00470 repressed cell autophagy by constraining PTEN, thereby enhancing CDDP resistance of glioma cells.

Insight into over Repair of Hot Carrier Degradation by GIDL Current in Si p-FinFETs Using Ultra-Fast Measurement Technique.

In this article, an experimental study on the gate-induced drain leakage (GIDL) current repairing worst hot carrier degradation (HCD) in Si p-FinFETs is investigated with the aid of an ultra-fast measurement (UFM) technique (~30 µs). It is found that increasing GIDL bias from 3 V to 4 V achieves a 114.7% VT recovery ratio from HCD. This over-repair phenomenon of HCD by UFM GIDL is deeply discussed through oxide trap behaviors. When the applied gate-to-drain GIDL bias reaches 4 V, a significant electron trapping and interface trap generation of the fresh device with GIDL repair is observed, which greatly contributes to the approximate 114.7% over-repair VT ratio of the device under worst HCD stress (-2.0 V, 200 s). Based on the TCAD simulation results, the increase in the vertical electric field on the surface of the channel oxide layer is the direct cause of an extraordinary electron trapping effect accompanied by the over-repair phenomenon. Under a high positive electric field, a part of channel electrons is captured by oxide traps in the gate dielectric, leading to further VT recovery. Through the discharge-based multi-pulse (DMP) technique, the energy distribution of oxide traps after GIDL recovery is obtained. It is found that over-repair results in a 34% increment in oxide traps around the conduction energy band (Ec) of silicon, which corresponds to a higher stabilized VT shift under multi-cycle HCD-GIDL tests. The results provide a trap-based understanding of the transistor repairing technique, which could provide guidance for the reliable long-term operation of ICs.

Improved Carrier Management via a Multifunctional Modifier for High-Quality Low-Bandgap Sn-Pb Perovskites and Efficient All-Perovskite Tandem Solar Cells.

All-perovskite tandem solar cells (TSCs) hold great promise in terms of ultrahigh efficiency, low manufacturing cost, and flexibility, stepping forward to the next-generation photovoltaics. However, their further development is hampered by the relatively low performance of low-bandgap (LBG) tin (Sn)-lead (Pb) perovskite solar cells (PSCs). Improving the carrier management, including suppressing trap-assisted non-radiative recombination and promoting carrier transfer, is of great significance to enhance the performance of Sn-Pb PSCs. Herein, a carrier management strategy is reported for using cysteine hydrochloride (CysHCl) simultaneously as a bulky passivator and a surface anchoring agent for Sn-Pb perovskite. CysHCl processing effectively reduces trap density and suppresses non-radiative recombination, enabling the growth of high-quality Sn-Pb perovskite with greatly improved carrier diffusion length of >8 µm. Furthermore, the electron transfer at the perovskite/C60 interface is accelerated due to the formation of surface dipoles and favorable energy band bending. As a result, these advances enable the demonstration of champion efficiency of 22.15% for CysHCl-processed LBG Sn-Pb PSCs with remarkable enhancement in both open-circuit voltage and fill factor. When paired with a wide-bandgap (WBG) perovskite subcell, a certified 25.7%-efficient all-perovskite monolithic tandem device is further demonstrated.

Fringe Projection Profilometry Based on Saturated Fringe Restoration in High Dynamic Range Scenes.

In high dynamic scenes, fringe projection profilometry (FPP) may encounter fringe saturation, and the phase calculated will also be affected to produce errors. This paper proposes a saturated fringe restoration method to solve this problem, taking the four-step phase shift as an example. Firstly, according to the saturation of the fringe group, the concepts of reliable area, shallow saturated area, and deep saturated area are proposed. Then, the parameter A related to the reflectivity of the object in the reliable area is calculated to interpolate A in the shallow and deep saturated areas. The theoretically shallow and deep saturated areas are not known in actual experiments. However, morphological operations can be used to dilate and erode reliable areas to produce cubic spline interpolation areas (CSI) and biharmonic spline interpolation (BSI) areas, which roughly correspond to shallow and deep saturated areas. After A is restored, it can be used as a known quantity to restore the saturated fringe using the unsaturated fringe in the same position, the remaining unrecoverable part of the fringe can be completed using CSI, and then the same part of the symmetrical fringe can be further restored. To further reduce the influence of nonlinear error, the Hilbert transform is also used in the phase calculation process of the actual experiment. The simulation and experimental results validate that the proposed method can still obtain correct results without adding additional equipment or increasing projection number, which proves the feasibility and robustness of the method.