Blood-based molecular and cellular biomarkers of early response to neoadjuvant PD-1 blockade in patients with non-small cell lung cancer.

BACKGROUND: Despite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response. METHODS: Transcriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model. RESULTS: The gene expression of hundreds of DEGs (FDR p < 0.05, fold change < -2 or > 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p < 0.05) and heightened responsiveness to anti-PD-1 treatment in patient cohort and co-cultured cell lines. CONCLUSION: This study discovered and validated peripheral blood-based biomarkers with evident predictive efficacy for early therapy response and patient stratification before treatment for neoadjuvant PD-1 blockade in NSCLC patients.

Machine learning-enhanced predictive modeling for arbitrary deterministic lateral displacement design and test.

The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and diagnostics. Deterministic Lateral Displacement (DLD) is prominent for sorting particles in microfluidics by size. However, the design, fabrication, and testing of DLDs are complex and time-consuming. Researchers typically rely on finite element analysis to predict particle trajectories, which are crucial in evaluating the performance of DLD. Traditional particle trajectory predictions through finite element analysis often inaccurately reflect experimental results due to manufacturing and experimental variabilities. To address this issue, we introduced a machine learning-enhanced approach, combining past experimental data and advanced modeling techniques. Our method, using a dataset of 132 experiments from 40 DLD chips and integrating finite element simulation with a microfluidic-optimized particle simulation algorithm (MOPSA) and a Random Forest model, improves trajectory prediction and critical size determination without physical tests. This enhanced accuracy in simulation across various DLD chips speeds up development. Our model, validated against three DLD chip designs, showed a high correlation between predicted and experimental particle trajectories, streamlining chip development for clinical applications.

Traffic flow detection method based on improved SSD algorithm for intelligent transportation system.

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

Effect of Pilates combined with pelvic floor muscle training on continence of post-prostatectomy incontinence in patients with different body mass index.

BACKGROUND: Urinary incontinence symptoms severely affect older people with different body mass index (BMI).To compare the efficacy of the pelvic floor muscle training (PFMT) in patients with post-prostatectomy incontinence with different BMI. METHODS: Thirty-seven patients with post-prostatectomy incontinence were included. They were divided into group A (BMI ≤ 25,12), group B (26 ≤ BMI ≤ 30,14), and group C (BMI ≥ 31,11) based on difference BMI. Three groups of patients underwent the same Pilates combined with kegel training. Participants were assessed with 1-hour pad test, the number of incontinence episodes, International Consultation on Incontinence Questionnaire and Oxford Grading Scale. RESULTS: In the 1-hour pad test, the differences before and after training were statistically significant in all three groups of participants. Group A decreased from 81.83 ± 8.79 to 31.08 ± 5.64 g (P < 0.01). Group B decreased from 80.57 ± 8.87 to 35.85 ± 5.66 g (P < 0.01). Group C decreased from 83.55 ± 10.24 to 40.18 ± 7.01 g (P < 0.01). The number of incontinent episodes in group A decreased from 9.33 ± 1.07 to 3.25 ± 0.62 (P < 0.01). Group B decreased from 8.86 ± 1.09 to 3.79 ± 0.80 (P < 0.01). Group C decreased from 9.27 ± 1.10 to 4.09 ± 0.70 (P < 0.01). The correlation between the three groups of participants and the 1-hour pad test, with an R2 of 0.51. The correlation between the three groups of participants and the number of urinary incontinence episodes with a R2 of 0.43. CONCLUSIONS: Pelvic floor muscle training can affect the recovery of urinary continence in patients with different BMI. Maintaining a lower BMI can be beneficial for improving urinary control. TRIAL REGISTRATION: Date of trial registration: November 27, 2023.

A multi-center retrospective study on the efficacy and safety of regorafenib vs. regorafenib combined with PD-1 inhibitors as a second-line therapy in patients with advanced hepatocellular carcinoma.

Background: At present, there are no definitive optimal treatment options for patients with hepatocellular carcinoma (HCC) following first-line treatment failure. To maximize the survival benefit of patients, we compared the combination therapy of regorafenib and programmed death-1 (PD-1) inhibitors with regorafenib monotherapy as a second-line treatment for patients with advanced HCC. Methods: Our multicenter retrospective study evaluated consecutive patients with advanced HCC who received regorafenib plus PD-1 inhibitors or regorafenib alone as a later-line therapy from May 2019 to January 2022. The primary endpoint was progression-free survival (PFS), and the secondary endpoints included the objective response rate (ORR), disease control rate (DCR), overall survival (OS), and safety. Efficacy was evaluated using the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 criteria, and safety was assessed by Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Results: A total of 133 patients were included in the study (regardless of first-line treatment), including 94 who received regorafenib plus PD-1 inhibitors and 39 who received regorafenib. The regorafenib plus PD-1 inhibitors group had a significantly higher ORR (25.53% vs. 10.26%, P=0.015), higher DCR (87.23% vs. 66.67%, P=0.006), and longer PFS (median 9.0 vs. 4.0 months, P<0.0001) than the regorafenib group. Meanwhile, the median OS (mOS) did not differ between the regorafenib plus PD-1 and regorafenib monotherapy groups {mOS, 14.0 months [95% confidence interval (CI), 14.0-16.0 months] vs. 12.0 months (95% CI, 10.0-22.0 months)}. There was no notable difference in the total incidence of treatment-related adverse effects (TRAEs) (71.79% vs. 78.72%, P=0.39) and the incidence of grade 3/4 serious adverse effects (5.13% vs. 18.09%, P=0.19) between the regorafenib monotherapy group and PD-1 inhibitors combination group. Conclusions: Compared with regorafenib alone, regorafenib combined with PD-1 inhibitors therapy increased PFS, ORR but did not improve OS, and can be used an option in second-line HCC therapy, regardless of first-line treatments. Regorafenib combined with PD-1 inhibitors is recommended as early as a second-line therapy to benefit patients. The combination regimen was as safe as regorafenib monotherapy for treatment of HCC in patients with compensated liver disease [Child-Turcotte-Pugh (CTP) A/B].

Development and Validation of a nomogram for forecasting survival of alcohol related hepatocellular carcinoma patients.

Background: With the increasing incidence and prevalence of alcoholic liver disease, alcohol-related hepatocellular carcinoma has become a serious public health problem worthy of attention in China. However, there is currently no prognostic prediction model for alcohol-related hepatocellular carcinoma. Methods: The retrospective analysis research of alcohol related hepatocellular carcinoma patients was conducted from January 2010 to December 2014. Independent prognostic factors of alcohol related hepatocellular carcinoma were identified by Lasso regression and multivariate COX proportional model analysis, and the nomogram model was constructed. The reliability and accuracy of the model were assessed using the concordance index(C-Index), receiver operating characteristic (ROC) curve and calibration curve. Evaluate the clinical benefit and application value of the model through clinical decision curve analysis (DCA). The prognosis was assessed by the Kaplan-Meier (KM) survival curve. Results: In sum, 383 patients were included in our study. Patients were stochastically assigned to training cohort (n=271) and validation cohort (n=112) according to 7:3 ratio. The predictors included in the nomogram were splenectomy, platelet count (PLT), creatinine (CRE), Prealbumin (PA), mean erythrocyte hemoglobin concentration (MCHC), red blood cell distribution width (RDW) and TNM. Our nomogram demonstrated excellent discriminatory power (C-index) and good calibration at 1-year, 3-year and 5- year overall survival (OS). Compared to TNM and Child-Pugh model, the nomogram had better discriminative ability and higher accuracy. DCA showed high clinical benefit and application value of the model. Conclusion: The nomogram model we established can precisely forcasting the prognosis of alcohol related hepatocellular carcinoma patients, which would be helpful for the early warning of alcohol related hepatocellular carcinoma and predict prognosis in patients with alcoholic hepatocellular carcinoma.

The advanced development of molecular targeted therapy for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), one of the most common malignant tumors in China, severely threatens the life and health of patients. In recent years, precision medicine, clinical diagnoses, treatments, and innovative research have led to important breakthroughs in HCC care. The discovery of new biomarkers and the promotion of liquid biopsy technologies have greatly facilitated the early diagnosis and treatment of HCC. Progress in targeted therapy and immunotherapy has provided more choices for precise HCC treatment. Multiomics technologies, such as genomics, transcriptomics, and metabolomics, have enabled deeper understanding of the occurrence and development mechanisms, heterogeneity, and genetic mutation characteristics of HCC. The continued promotion and accurate typing of HCC, accurate guidance of treatment, and accurate prognostication have provided more treatment opportunities and prolonged survival timelines for patients with HCC. Innovative HCC research providing an in-depth understanding of the biological characteristics of HCC will be translated into accurate clinical practices for the diagnosis and treatment of HCC.

Multifunctional Chiral 2D Lead Halide Perovskites with Circularly Polarized Photoluminescence and Piezoelectric Energy Harvesting Properties.

Introducing the chiral spacers to two-dimensional (2D) lead halide perovskites (LHPs) enables them to exhibit circularly polarized photoluminescence (CPPL), which could have applications in chiral-optics and spintronics. Despite that a great deal of effort has been made in this field, the reported polarization degree of CPPL at ambient conditions is still very limited, and the integration of multiple functionalities also remains to be explored. Here we report the structures, CPPL, and piezoelectric energy harvesting properties of chiral 2D LHPs, [R-1-(4-bromophenyl)ethylaminium]2PbI4 (R-[BPEA]2PbI4) and [S-1-(4-bromophenyl)ethylaminium]2PbI4 (S-[BPEA]2PbI4). Our results show that these chiral perovskites are direct bandgap semiconductors and exhibit CPPL centered at ∼513 nm with a maximum degree of polarization of up to 11.0% at room temperature. In addition, the unique configurational arrangement of the chiral spacers is found to be able to reduce the interlayer π-π interactions and consequently result in strong electron-phonon coupling. Furthermore, the intrinsic chirality of both R-[BPEA]2PbI4 and S-[BPEA]2PbI4 enables them to be piezoelectric active, and their composite films can be applied to generate voltages and currents up to ∼0.6 V and ∼1.5 µA under periodic impacting with a strength of 2 N, respectively. This work not only reports a high degree of CPPL but also demonstrates piezoelectric energy harvesting behavior for realizing multifunctionalities in chiral 2D LHPs.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy.

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Privacy-Preserving Reversible Data Hiding for Medical Images Employing Local Rotation.

As digitalization becomes more common, patients' concerns about the leakage of private information, such as electronic medical record, are increasing, and those concerns motivated this case study of secure covert communication. Therefore, in this paper, a novel reversible data hiding method based on pixel rotation is proposed for medical images. Using pixel rotation, a state mapping model is constructed to represent the payload. More specifically, many intermediate states are derived from an image block, and each of them is used to form a one-to-one mapping relationship with a specific sequence of payload bits. In addition, to ensure the visual quality of stego-medical-images, the payload bits are only concealed in the regular blocks and the other blocks are unchanged. Moreover, the smoother regular image block will be priority to be used to embed the payload to enhance the visual quality of stego-medical-image. The experimental results showed that the stego-medical-images generated by the proposed reversible data hiding method have better visual quality with an average PSNR of 47.0307 dB, which is higher than that provided by some state-of-the-art methods.

Ferroptosis: a cell death connecting oxidative stress, inflammation and cardiovascular diseases.

Ferroptosis, a recently identified and iron-dependent cell death, differs from other cell death such as apoptosis, necroptosis, pyroptosis, and autophagy-dependent cell death. This form of cell death does not exhibit typical morphological and biochemical characteristics, including cell shrinkage, mitochondrial fragmentation, nuclear condensation. The dysfunction of lipid peroxide clearance, the presence of redox-active iron as well as oxidation of polyunsaturated fatty acid (PUFA)-containing phospholipids are three essential features of ferroptosis. Iron metabolism and lipid peroxidation signaling are increasingly recognized as central mediators of ferroptosis. Ferroptosis plays an important role in the regulation of oxidative stress and inflammatory responses. Accumulating evidence suggests that ferroptosis is implicated in a variety of cardiovascular diseases such as atherosclerosis, stroke, ischemia-reperfusion injury, and heart failure, indicating that targeting ferroptosis will present a novel therapeutic approach against cardiovascular diseases. Here, we provide an overview of the features, process, function, and mechanisms of ferroptosis, and its increasingly connected relevance to oxidative stress, inflammation, and cardiovascular diseases.

Predicting the fluid behavior of random microfluidic mixers using convolutional neural networks.

With the various applications of microfluidics, numerical simulation is highly recommended to verify its performance and reveal potential defects before fabrication. Among all the simulation parameters and simulation tools, the velocity field and concentration profile are the key parts and are generally simulated using finite element analysis (FEA). In our previous work [Wang et al., Lab Chip, 2016, 21, 4212-4219], automated design of microfluidic mixers by pre-generating a random library with the FEA was proposed. However, the duration of the simulation process is time-consuming, while the matching consistency between limited pre-generated designs and user desire is not stable. To address these issues, we inventively transformed the fluid mechanics problem into an image recognition problem and presented a convolutional neural network (CNN)-based technique to predict the fluid behavior of random microfluidic mixers. The pre-generated 10 513 candidate designs in the random library were used in the training process of the CNN, and then 30 757 brand new microfluidic mixer designs were randomly generated, whose performance was predicted by the CNN. Experimental results showed that the CNN method could complete all the predictions in just 10 seconds, which was around 51 600× faster than the previous FEA method. The CNN library was extended to contain 41 270 candidate designs, which has filled up those empty spaces in the fluid velocity versus solute concentration map of the random library, and able to provide more choices and possibilities for user desire. Besides, the quantitative analysis has confirmed the increased compatibility of the CNN library with user desire. In summary, our CNN method not only presents a much faster way of generating a more complete library with candidate mixer designs but also provides a solution for predicting fluid behavior using a machine learning technique.

Synthesis of Magnetic Ni0.3Mg0.3Zn0.4Fe2O4 Nanoparticles and Their Adsorption Performances of Congo Red.

Magnetic Ni0.3Mg0.3Zn0.4Fe2O4 nanoparticles were synthesized by the absolute alcohol combustion method. The morphology, microstructure, and composition of as-prepared Ni0.3Mg0.3Zn0.4Fe2O4 nanoparticles were characterized by several techniques: the vibrating sample magnetometer (VSM), the scanning electron microscopy (SEM), the X-ray diffraction (XRD), and the energy dispersive spectroscopy (EDS). The experimental results showed that the calcination temperature and the solvent volume were the crucial factors for the synthesis of the magnetic Ni0.3Mg0.3Zn0.4Fe2O4 nanoparticles. The adsorption performance of Ni0.3Mg0.3Zn0.4Fe2O4 nanoparticles for congo red (CR) was investigated. The model of pseudo-second-order kinetic was optimal matching for obtaining the parameters of adsorption CR in the initial range of 100-400 mg/L-1, while, the isotherm data of CR onto Ni0.3Mg0.3Zn0.4Fe2O4 nanoparticles could conform to the Temkin model owing to the values of the square deviations, which revealed that the adsorption of CR onto Ni0.3Mg0.3Zn0.4Fe2O4 nanoparticles at room temperature was the monolayer and multilayer adsorption mechanism.

A Vision toward Ultimate Optical Out-Coupling for Organic Light-Emitting Diode Displays: 3D Pixel Configuration.

Despite stringent power consumption requirements in many applications, over years organic light-emitting diode (OLED) displays still suffer unsatisfactory energy efficiency due to poor light extraction. Approaches have been reported for OLED light out-coupling, but they in general are not applicable for OLED displays due to difficulties in display image quality and fabrication complexity and compatibility. Thus to date, an effective and feasible light extraction technique that can boost efficiencies and yet keep image quality is still lacking and remains a great challenge. Here, a highly effective and scalable extraction-enhancing OLED display pixel structure is proposed based on embedding the OLED inside a three-dimensional reflective concave structure covered with a patterned high-index filler. It can couple as much internal emission as possible into the filler region and then redirect otherwise confined light for out-coupling. Comprehensive multi-scale optical simulation validates that ultimately high light extraction efficiency approaching ≈80% and excellent viewing characteristics are simultaneously achievable with optimized structures using highly transparent top electrodes. This scheme is scalable and wavelength insensitive, and generally applicable to all red, green, and blue pixels in high-resolution full-color displays. Results of this work are believed to shed light on the development of future generations of advanced OLED displays.

Simultaneous Recognition and Assessment of Post-Stroke Hemiparetic Gait by Fusing Kinematic, Kinetic, and Electrophysiological Data.

Gait analysis for the patients with lower limb motor dysfunction is a useful tool in assisting clinicians for diagnosis, assessment, and rehabilitation strategy making. Implementing accurate automatic gait analysis for the hemiparetic patients after stroke is a great challenge in clinical practice. This study is to develop a new automatic gait analysis system for qualitatively recognizing and quantitatively assessing the gait abnormality of the post-stroke hemiparetic patients. Twenty-one post-stroke patients and twenty-one healthy volunteers participated in the walking trials. Three of the most representative gait data, i.e., marker trajectory (MT), ground reaction force (GRF), and electromyogram, were simultaneously acquired from these subjects during their walking. A multimodal fusion architecture is established by using these different modal data to qualitatively distinguish the hemiparetic gait from normal gait by different pattern recognition techniques and to quantitatively estimate the patient's lower limb motor function by a novel probability-based gait score. Seven decision fusion algorithms have been tested in this architecture, and extensive data analysis experiments have been conducted. The results indicate that the recognition performance and estimation performance of the system become better when more modal gait data are fused. For the recognition performance, the random forest classifier based on the GRF data achieves an accuracy of 92.26% outperformed other single-modal schemes. When combining two modal data, the accuracy can be enhanced to 95.83% by using the support vector machine (SVM) fusion algorithm to fuse the MT and GRF data. When integrating all the three modal data, the accuracy can be further improved to 98.21% by using the SVM fusion algorithm. For the estimation performance, the absolute values of the correlation coefficients between the estimation results of the above three schemes and the Wisconsin gait scale scores for the post-stroke patients are 0.63, 0.75, and 0.84, respectively, which means the clinical relevance becomes more obvious when using more modalities. These promising results demonstrate that the proposed method has considerable potential to promote the future design of automatic gait analysis systems for clinical practice.

Adsorption Kinetics and Adsorption Isotherm of Methylene Blue on Magnetic 0.9NiFe2O4/0.1SiO2 Nanocomposites Prepared via a Rapid Combustion Process.

Magnetic 0.9NiFe2O4/0.1SiO2 nanocomposites were successfully prepared via a rapid combustion process, and the morphology and magnetic properties of them were investigated by TEM, XRD, VSM, and BET techniques. The magnetic 0.9NiFe2O4/0.1SiO2 nanocomposites calcined at 400 °C for 2 h with absolute alcohol of 20 mL were characterized with the average particle size of about 28 nm and the specific magnetization of 192.0 Am2/kg. The magnetic 0.9NiFe2O4/0.1SiO2 nanocomposites were employed to remove methylene blue (MB) from aqueous solutions, the adsorption kinetics and adsorption isotherm of MB onto 0.9NiFe2O4/0.1SiO2 nanocomposites at room temperature were investigated, and the regression equations were found in good agreement with the pseudo-second-order model in the initial MB concentrations of 100-600 mg/L; the adsorption equilibrium data of MB onto 0.9NiFe2O4/0.1SiO2 nanocomposites at room temperature were analyzed with Langmuir, Freundlich and Redlich-Peterson models, and the adsorption isotherm was most effectively described by the Redlich-Peterson model based on the value of the correlation coefficient (0.9810).

Surface display of active lipase in Pichia pastoris using Sed1 as an anchor protein.

A Pichia pastoris cell-surface display system was constructed using the Sed1 anchor system that has been developed in Saccharomyces cerevisiae. Candida antarctica lipase B (CALB) was used as the model protein and was fused to an anchor that consisted of 338 amino acids of Sed1. The resulting fusion protein CALBSed1 was expressed under the control of the alcohol oxidase 1 promoter (pAOX1). Immunofluorescence microscopy of immunolabeled Pichia pastoris revealed that CALB was displayed on the cell surface. Western blot analysis showed that the fusion protein CALBSed1 was attached covalently to the cell wall and was highly glycosylated. The hydrolytic activity of the displayed CALB was more than 220 U/g dry cells after 120 h of culture. The displayed protein also exhibited a higher degree of thermostability than free CALB.

Display of Candida antarctica lipase B on Pichia pastoris and its application to flavor ester synthesis.

Two alternative cell-surface display systems were developed in Pichia pastoris using the alpha-agglutinin and Flo1p (FS) anchor systems, respectively. Both the anchor cell wall proteins were obtained originally from Saccharomyces cerevisiae. Candida antarctica lipase B (CALB) was displayed functionally on the cell surface of P. pastoris using the anchor proteins alpha-agglutinin and FS. The activity of CALB displayed on P. pastoris was tenfold higher than that of S. cerevisiae. The hydrolytic and synthetic activities of CALB fused with alpha-agglutinin and FS anchored on P. pastoris were investigated. The hydrolytic activities of both lipases displayed on yeast cells surface were more than 200 U/g dry cell after 120 h of culture (200 and 270 U/g dry cell, respectively). However, the synthetic activity of CALB fused with alpha-agglutinin on P. pastoris was threefold higher than that of the FS fusion protein when applied to the synthesis of ethyl caproate. Similarly, the CALB displayed on P. pastoris using alpha-agglutinin had a higher catalytic efficiency with respect to the synthesis of other short-chain flavor esters than that displayed using the FS anchor. Interestingly, for some short-chain esters, the synthetic activity of displaying CALB fused with alpha-agglutinin on P. pastoris was even higher than that of the commercial CALB Novozyme 435.

[Synthesis of flavor esters catalyzed by CALB-displaying Pichia pastoris whole-cells in non-aqueous phase].

An enzyme-displaying yeast as a whole-cell biocatalyst seemed an alternative to immobilized enzyme, due to its low-cost preparation and simple recycle course. Here, we tried to use a recombinant Pichia pastoris displaying Candida antarctica lipase B (CALB) to catalyze the synthesis of short chain flavor esters in n-heptane. We studied some major influential factors of esterification reactions, such as carbon chain length of the substrates, alcohol structure, enzyme concentration, substrates concentration, molar ratio of the substrates. The acid conversions were determined by titration and gas chromatography analysis. About ten kinds of esters were synthesized successfully, and the acid conversions of eight esters reached as high as 90% after reaction for 6 h. The result also indicated that ethanol and hexanoic acid were the most suitable substrates for this whole-cell catalyst. Under the optimal reaction conditions (the amount of lipase 20 g/L (306.0 U/g-dry cell), hexanoic acid concentration 0.8 mol/L, the molar ratio of hexanoic acid to ethanol 1:1.1), hexanoic acid conversion reached 97.3% after reaction for 1.5 h. To our knowledge, the CALB-displaying P. pastoris whole-cell biocatalyst showed good tolerance for high substrates concentration and exhibited high reaction rate on esterification of short chain flavor esters among the present enzyme/cell reported. Thus, CALB-displaying P pastoris whole-cell biocatalyst was promising in commercial application for flavor esters synthesis in non-aqueous phase.