Quantum Interference at the Recombination Junction of Perovskite-Si Tandem Solar Cells Improves Efficiency.

We show quantum interference effects can enhance coherent electron transmission in perovskite tandem solar cells using ultrathin indium tin oxide (ITO) layers. We develop a model for the behavior of the power conversion efficiency based on a finite difference time domain solution of the time-dependent Schrödinger equation. The modeled potential includes an imaginary part to simulate probability loss by incoherent scattering. The results agree with observations of efficiency as a function of ITO thickness, suggesting an optimized design.

Development and validation of a nomogram for predicting overall survival in patients with early-onset endometrial cancer.

BACKGROUND: This study aimed to investigate the differences in the clinicopathological characteristics of younger and older patients with endometrial cancer (EC) and develop a nomogram to assess the prognosis of early onset EC in terms of overall survival. METHODS: Patients diagnosed with EC from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were selected. Clinicopathological characteristics were compared between younger and older patients, and survival analysis was performed for both groups. Prognostic factors affecting overall survival in young patients with EC were identified using Cox regression. A nomogram was created and internal validation was performed using the consistency index, decision curve analysis, receiver operating characteristic curves, and calibration curves. External validation used data from 70 patients with early onset EC. Finally, Kaplan-Meier curves were plotted to compare survival outcomes across the risk subgroups. RESULTS: A total of 1042 young patients and 12,991 older patients were included in this study. Younger patients were divided into training (732) and validation (310) cohorts in a 7:3 ratio. Cox regression analysis identified age, tumorsize, grade, FIGO stage(International Federation of Gynecology and Obstetrics) and surgery as independent risk factors for overall survival, and a nomogram was constructed based on these factors. Internal and external validations demonstrated the good predictive power of the nomogram. In particular, the C-index for the overall survival nomogram was 0.832 [95% confidence interval (0.797-0.844)] in the training cohort and 0.839 (0.810-0.868) in the internal validation cohort. The differences in the Kaplan-Meier curves between the different risk subgroups were statistically significant. CONCLUSIONS: In this study, a nomogram for predicting overall survival of patients with early onset endometrial cancer based on the SEER database was developed to help assess the prognosis of patients and guide clinical treatment.

A prognostic risk model for ovarian cancer based on gene expression profiles from gene expression omnibus database.

This study explored prognostic genes of ovarian cancer and built a prognostic model based on these genes to predict patient's survival, which is of great significance for improving treatment of ovarian cancer. GSE26712 dataset was downloaded from Gene Expression Omnibus database as training set, while OV-AU dataset was downloaded from ICGC website as validation set. All genes in GSE26712 were analyzed by univariate Cox regression, Lasso regression, and multivariate Cox regression analyses. Then prognosis-related feature genes were screened to construct a multivariate risk model. Meanwhile, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed on samples in the high/low-risk groups using Gene Set Enrichment Analysis (GSEA) software. Finally, survival curve and receiver operating characteristic curve were drawn to verify the validity of the model. Ten feature genes related to prognosis of ovarian cancer were obtained: CMTM6, COLGALT1, F2R, GPR39, IGFBP3, RNF121, MTMR9, ORAI2, SNAI2, ZBTB16. GSEA enrichment analysis showed that there were notable differences in biological pathways such as gap junctions and homologous recombination between the high/low-risk groups. Through further verification of training set and validation set, the 10-gene prognostic model was found to be effective for the prognosis of ovarian cancer patients. In this study, we constructed a 10-gene prognostic model which predicted the prognosis of ovarian cancer patients well by integrating clinical prognostic parameters. It may have certain reference value for subsequent clinical treatment research of ovarian cancer patients and help in clinical treatment decision-making.

Fabrication and Performance of a Ta2O5 Thin Film pH Sensor Manufactured Using MEMS Processes.

In this work, a capacitive pH sensor consisting of Ta2O5 functional film is designed and fabricated by employing MEMS-based procedures. The Ta2O5 thin film has an amorphous microstructure, and its surface roughness is less than 3.17 nm. A signal processing circuit and a software filtering algorithm are also designed to measure the pH value, thus improving the detection accuracy and anti-interference ability. Good linearity (R2 = 0.99904) and sensitivity (63.12 mV/pH) are recorded for the proposed sensing element in the range of pH 2~12. In addition, the sensor's drift and hysteresis are equal to 5.1 mV and 5.8 mV, respectively. The enhanced sensing performance in combination with the facile miniaturization process, low fabrication cost, and suitability for mass production render the fabricated sensor attractive for applications where pH change measurements in a water environment are required.

Interleukin-28B Plays a Therapeutic Role on Mouse U14 Cervical Cancer Cells by Down-Regulating CD4+CD25+FoxP3+Regulatory T Cells In Vivo.

AIM: To investigate the immunotherapeutic effectiveness of adenoviral vector expressing mouse interleukin (IL)-28B (Ad-mIL-28B) against cervical cancer and its mechanism. METHOD: U14 cervical cancer cell-bearing mice were treated with Ad-mIL-28B. Meanwhile, whole cell vaccine was prepared by repeated freezing and thawing U14 cells. Then CD4⁺CD25⁺FoxP3⁺regulatory T (Treg) cells were evaluated by flow cytometry. Tumor volume and metastasis in BALB/c and C57BL/6j mice were detected. RESULTS: Ad-mIL-28B treatment significantly decreased the number of CD4⁺CD25⁺FoxP3⁺Treg cells. Subsequently, there was a significant decrease in the size of tumor tissue and the numbers of heteromorphic tumor cells. The tumor metastasis in the lung and liver of the Ad-mIL-28B group also decreased. However, there was no therapeutic effect observed for whole cell vaccine on U14 tumor-bearing mice. CONCLUSION: Interleukin-28B can inhibit the growth and metastasis of cervical cancer in U14 tumor-bearing mice by down-regulating Treg cells.

SPC25 Functions as a Prognostic-Related Biomarker, and Its High Expression Correlates with Tumor Immune Infiltration and UCEC Progression.

BACKGROUND: Most tumor tissues expressed spindle pole body component 25 (SPC25), one of the four subunits of the NDC80 complex, at greater levels compared to surrounding normal tissues. According to earlier researches, this subunit strongly encouraged tumor cell proliferation and tumor growth, which resulted in worse prognoses in patients with hepatocellular, breast, lung, and prostate cancer. Precisely because SPC25's role in uterine corpus endometrial carcinoma (UCEC) is understudied, we chose to concentrate on UCEC for gaining a more scientific and thorough understanding of SPC25. METHODS: Along with examining SPC25's differential expression, prognostic significance, and biological function in UCEC, our research sought to clarify the underlying mechanism by which SPC25 influences the course of UCEC and patient prognosis from the viewpoints of methylation and immune infiltration. RESULTS: We observed differential expression of SPC25 gene in different clinicopathological features of UCEC and identified SPC25 as a hazard factor for poorer overall survival (OS), disease-specific survival (DSS), and progress free interval (PFI) in UCEC, particularly in its multiple clinical subtypes. In addition, we also discovered that SPC25 and its co-expressed genes mostly engaged in biological processes and signal transduction routes linked to cell cycle and cell division in UCEC. After investigating SPC25's methylation status, we discovered that patients with UCEC had elevated SPC25 expression and a poor prognosis due to hypomethylation of CpG sites in the SPC25 gene sequence. Finally, we investigated SPC25's potential role in immunotherapy and discovered that SPC25 might alter the major immune cell infiltration levels in the tumor microenvironment (TME) by regulating the expression of immunoregulatory molecules and chemokines, which would be beneficial for SPC25 to control the progression of UCEC. CONCLUSIONS: In conclusion, SPC25 was a useful predictive biomarker as well as a possible therapeutic target for UCEC.

Leveraging behavioral science and artificial intelligence to support mental health in the workplace: a pilot study.

Introduction: Many American employers seek to alleviate employee mental health symptoms through resources like employee assistance programs (EAPs), yet these programs are often underutilized. This pilot study explores the design of a behavioral science-based email campaign targeting engagement with stress management and mental health resources via an EAP, among employees of a large home builder in the Southeastern US. Methods: Behavioral designers created a behavioral science intervention using a multi-step design approach and evidence based behavioral strategies. For this pilot intervention, employees received either a treatment message [i.e., behavioral science message assembled and delivered via the behavioral reinforcement learning (BRL) agent] or a control message (i.e., a single generic, supportive message with a stock photo) with a call to action to utilize their EAP. Results: A total of 773 employees received emails over the course of 1 year. Engagement was high, with an 80% email open rate. Over 170 employees (22%, 159 treatment and 14 control) clicked the CTA and logged into the EAP site at least once. Discussion: This pilot study suggests that using behavioral science and artificial intelligence can improve employee usage of EAP, specifically with the intention of exploring mental health and stress management resources, compared to benchmark rates of 5% per year.

Correlation of several forms of folic acid with endometrial cancer: cross-sectional data from the National Health and Nutrition Examination Surveys (NHANES) 2011-2018.

OBJECTIVE: Endometrial cancer (EC) is a common malignancy of the female reproductive system and although most patients have a good prognosis, 20-30% of patients with advanced disease have a poor prognosis. There are currently no reliable biomarkers for early diagnosis and effective prognostic improvement of the disease. The purpose of this study was to explore the correlation between different forms of folic acid and endometrial cancer. METHODS: This study included 8809 female subjects aged ≥ 20 years in the NHANES database from 2011 to 2018, including 8738 non-oncology patients and 71 EC patients. Selection bias was reduced using 1:1 propensity score matching (PSM) method. Restricted cubic spline (RCS) was plotted to explore the non-linear relationship between different forms of folic acid and EC. RESULT: Using data from the NHANES database from 2011 to 2018, the association between folic acid and the risk of developing EC was assessed. The results of the 1:1 ratio propensity score matching (PSM) showed 68 each for EC patients and non-oncology participants. Total serum folate, 5-methyltetrahydrofolate (5-methylTHF), 5-formyltetrahydrofolate (5-formylTHF), tetrahydrofolate (THF) and 5,10-methylenetetrahydrofolate (5,10-methenylTHF) were significantly correlated with EC (p < 0.05). In addition, the RCS showed a significant non-linear correlation between THF and 5,10-formyl THF and the risk of developing EC. CONCLUSION: The results of this study showed that changes in serum total folate, 5-methylTHF, 5-formylTHF, THF and 5,10-methenylTHF were related to EC.

Comparison and Verification of Three Algorithms for Accuracy Improvement of Quartz Resonant Pressure Sensors.

Pressure measurement is of great importance due to its wide range of applications in many fields. AT-cut quartz, with its exceptional precision and durability, stands out as an excellent pressure transducer due to its superior accuracy and stable performance over time. However, its intrinsic temperature dependence significantly hinders its potential application in varying temperature environments. Herein, three different learning algorithms (i.e., multivariate polynomial regression, multilayer perceptron networks, and support vector regression) are elaborated in detail and applied to establish the prediction models for compensating the temperature effect of the resonant pressure sensor, respectively. The AC-cut quartz, which is sensitive to temperature variations, is paired with the AT-cut quartz, providing the essential temperature information. The output frequencies derived from the AT-cut and AC-cut quartzes are selected as input data for these learning algorithms. Through experimental validation, all three methods are effective, and a remarkable improvement in accuracy can be achieved. Among the three methods, the MPR model has exceptionally high accuracy in predicting pressure. The calculated residual error over the temperature range of -10-40 °C is less than 0.008% of 40 MPa full scale (FS). An intelligent automatic compensation and real-time processing system for the resonant pressure sensor is developed as well, which may contribute to improving the efficiency in online calibration and large-scale industrialization. This paper paves a promising way for the temperature compensation of resonant pressure sensors.

Analysis of prognostic factors of metastatic endometrial cancer based on surveillance, epidemiology, and end results database.

Objective: To explore the risk factors for survival and prognosis of patients with metastatic endometrial cancer and to build and verify a reliable prediction model. Methods: We retrospectively analyzed patients diagnosed with metastatic endometrial cancer in the US Surveillance, Epidemiology, and End Results (SEER) database between January 2010 and December 2015. Univariate and multivariate Cox regression analyses were used to assess clinical variables impact on survival and to construct nomograms. The results of the consistency index (C-index), subject operating characteristic (ROC) curve, and calibration curve were used to evaluate the predictive ability of the nomogram. Results: This study included 3,878 patients with metastatic endometrial cancer. In the univariate analysis, variables associated with overall survival (OS) and cancer-specific survival (CSS) included age, race, marital status, pathological type, pathological grade, T-stage, N-stage, surgery, radiotherapy, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. In the multivariate analysis, age, race, pathological type, pathological grade, T-stage, N-stage, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, and lung metastasis were independent risk factors for OS and CSS (all P < 0.05). Combined with the results of the multiple factors, the 1-, 3-, 5-, and 8-year nomograms were constructed. For OS and CSS, T-stage had the greatest impact on the adverse prognosis of patients with metastatic endometrial cancer. The C-indexes of the OS and CSS nomograms in the training cohort were 0.749 (95% CI, 0.739-0.760) and 0.746 (95% CI, 0.736-0.756), respectively. The C-indices of OS and CSS in the validation cohort were 0.730 (95% CI, 0.714-0.746) and 0.728 (95% CI, 0.712-0.744), respectively. The ROC curve revealed our model's good prediction accuracy and clinical practicability. The calibration curve also confirmed the consistency between the model and actual existence. The Kaplan-Meier curves revealed statistically significant differences between the risk subgroups (P < 0.05). Conclusion: Our SEER-based nomograms for predicting survival in patients with metastatic endometrial cancer were helpful for the clinical evaluation of patient prognosis.

A Hardware System for Synchronous Processing of Multiple Marine Dynamics MEMS Sensors.

Temperature, depth, conductivity, and turbulence are fundamental parameters of marine dynamics in the field of ocean science. These closely correlated parameters require time-synchronized observations to provide feedback on marine environmental problems, which requires using sensors with synchronized power supply, multi-path data solving, recording, and storage performances. To address this challenge, this work proposes a hardware system capable of synchronously processing temperature, depth, conductivity, and turbulence data on marine dynamics collected by sensors. The proposed system uses constant voltage sources to excite temperature and turbulence sensors, a constant current source to drive a depth sensor, and an alternating current (AC) constant voltage source to drive a conductivity sensor. In addition, the proposed system uses a high-precision analog-digital converter to acquire the direct current (DC) signals from temperature, depth, and turbulence sensors, as well as the AC signals from conductivity sensors. Since the sampling frequency of turbulence sensors is different from that of the other sensors, the proposed system stores the generated data at different storage rates as multiple-files. Further, the proposed hardware system manages these files through a file system (file allocation tab) to reduce the data parsing difficulty. The proposed sensing and hardware logic system is verified and compared with the standard conductivity-temperature-depth measurement system in the National Center of Ocean Standards and Metrology. The results indicate that the proposed system achieved National Verification Level II Standard. In addition, the proposed system has a temperature indication error smaller than 0.02 °C, a conductivity error less than 0.073 mS/cm, and a pressure error lower than 0.8‱ FS. The turbulence sensor shows good response and consistency. Therefore, for observation methods based on a single point, single line, and single profile, it is necessary to study multi-parameter data synchronous acquisition and processing in the time and spatial domains to collect fundamental physical quantities of temperature, salt, depth, and turbulence. The four basic physical parameters collected by the proposed system are beneficial to the in-depth research on physical ocean motion, heat transfer, energy transfer, mass transfer, and heat-energy-mass coupling and can help to realize accurate simulation, inversion, and prediction of ocean phenomena.

A Direct-Reading MEMS Conductivity Sensor with a Parallel-Symmetric Four-Electrode Configuration.

This work proposes a design for a direct-reading conductivity sensor with a parallel symmetrical four-electrode structure, which integrates a silicon-based platinum thin-film strip electrode and a serpentine temperature compensation electrode. The optimal structural parameters of the electrode were determined by finite element simulations performed via COMSOL Multiphysics. Next, the designed conductivity sensor chip was fabricated using MEMS technology, and subsequently, the conductivity measurement circuit was designed to test the fabricated sensor's performance. In laboratory tests, the optimal AC excitation frequency was observed to be 1.067 kHz, while the maximum measurement range was 0-107.41 mS/cm and the measurement precision in low concentration range (0-76.422 mS/cm) was ±0.1 mS/cm. Furthermore, the maximum measurement error of the sensor evaluated using the National Center of Ocean Standards and Metrology was ±0.073 mS/cm. The designed sensor possesses the characteristics of high accuracy, high range, and miniaturization, and enables real-time reading of conductivity value and temperature compensation, which is of great significance for the on-site observation of the physical parameters of marine environment.

Design, Fabrication, and Dynamic Environmental Test of a Piezoresistive Pressure Sensor.

Microelectromechanical system (MEMS) pressure sensors have a wide range of applications based on the advantages of mature technology and easy integration. Among them, piezoresistive sensors have attracted great attention with the advantage of simple back-end processing circuits. However, less research has been reported on the performance of piezoresistive pressure sensors in dynamic environments, especially considering the vibrations and shocks frequently encountered during the application of the sensors. To address these issues, this paper proposes a design method for a MEMS piezoresistive pressure sensor, and the fabricated sensor is evaluated in a series of systematic dynamic environmental adaptability tests. After testing, the output sensitivity of the sensor chip was 9.21 mV∙bar-1, while the nonlinearity was 0.069% FSS. The sensor overreacts to rapidly changing pressure environments and can withstand acceleration shocks of up to 20× g. In addition, the sensor is capable of providing normal output over the vibration frequency range of 0-5000 Hz with a temperature coefficient sensitivity of -0.30% FSS °C-1 over the temperature range of 0-80 °C. Our proposed sensor can play a key role in applications with wide pressure ranges, high-frequency vibrations, and high acceleration shocks, as well as guide MEMS-based pressure sensors in high pressure ranges and complex environmental adaptability in their design.

Clinical significance of serum miR-487b in HBV-related hepatocellular carcinoma and its potential mechanism.

BACKGROUND: Hepatocellular carcinoma (HCC) is a malignant tumour with high mortality. In recent years, microRNA (miRNA) has been recognized in the diagnosis and prognosis of cancer. miR-487b has been found to play a role in a variety of cancers. The purpose of this study is to explore the role of miR-487b in the diagnosis and prognosis of hepatitis B virus (HBV)-related HCC, and its influence on the biological behaviour of HCC cells. METHODS: Quantitative real-time polymerase chain reaction (qRT-PCR) method was used to detect the expression level of miR-487b in the serum of HCC patients, HBV patients, and healthy people. The ROC curve was used to evaluate the role of miR-487b in the diagnosis of HCC. The prognostic significance of miR-487b in HCC was analyzed by the Kaplan-Meier survival curve and Cox regression model. The effects of miR-487b on cell proliferation, migration, and invasion were explored through MTT assay and transwell assays. RESULTS: The expression level of miR-487b in the serum of HBV-related HCC patients was significantly higher than that of CHB patients and normal healthy people. The expression level of miR-487b can distinguish HCC patients from CHB patients or normal healthy people. High expression of miR-487b was associated with poor prognosis, which could be used as an independent prognostic factor for HCC. The upregulation of miR-487b promoted cell proliferation, migration, and invasion. CONCLUSION: miR-487b can be used as a biomarker for the diagnosis and prognosis of HCC.

Differential Expression of miR-136 in Gestational Diabetes Mellitus Mediates the High-Glucose-Induced Trophoblast Cell Injury through Targeting E2F1.

BACKGROUND: Gestational diabetes mellitus (GDM) seriously affects the health of mothers and infants. The high-glucose-induced inhibition in trophoblast cell viability is an important event in GDM pathogenesis. This study evaluated the expression and clinical significance of miR-136 in GDM patients, and the biological function and related mechanisms of miR-136 in the regulation of trophoblast cell proliferation were explored. METHODS: The expression of miR-136 in serum and placenta of GDM patients was measured using quantitative Real-Time PCR. Trophoblast cells were stimulated with high-glucose medium to mimic the pathological changes of GDM, and the effect of miR-136 was examined by CCK-8 assay. A luciferase reporter assay was used to confirm the target gene of miR-136, and the relationship of E2F transcription factor 1 (E2F1) with miR-136 in GDM was further analyzed. RESULTS: miR-136 expression was significantly elevated in GDM serum and tissue samples. By high-glucose treatment, trophoblast cell proliferation was inhibited and miR-136 expression was promoted. The knockdown of miR-136 could promote the proliferation of trophoblast cells exposed to high glucose, whereas the overexpression of miR-136 could suppress it. In addition, E2F1 was identified as a target gene of miR-136, which could mediate the regulatory effect of miR-136 on trophoblast cell proliferation. CONCLUSION: Collectively, miR-136 expression is increased in both serum and placental tissues in GDM patients, and miR-136 mediates the inhibiting effect of high glucose on trophoblast cell viability by targeting E2F1.

High expression of survivin predicts poor prognosis in cervical squamous cell carcinoma treated with paclitaxel and carboplatin.

Lack of effective biomarkers is one of the challenges in current neoadjuvant chemotherapy to predict drug response and sensitivity of cervical squamous cell carcinoma (CSCC). The present study was designed to investigate the correlation of the expression of survivin, an inhibitor of apoptosis with the prognosis of CSCC patients undergoing neoadjuvant chemotherapy.A total of 117 CSCC patients treated with paclitaxel and carboplatin between May 2015 and April 2017 in the Second Hospital of Lanzhou University were retrospectively analyzed. The pathologic diagnosis and classification of CSCC were based on the Guidelines of the International Federation of Gynaecology and Obstetrics (FIGO). The efficacy was defined as complete remission (CR), partial remission (PR), and stability disease (SD). The expressions of survivin, vascular endothelial growth factor (VEGF), and Ki67 were determined with immunohistochemistry. Data were analyzed with SPSS software.Univariate analysis showed that survivin expression had no correlation with ages, FIGO stage, macroscopic type, lymphovascular invasion, depth of lymphovascular invasion, lymph node metastasis, and tumor size among 117 CSCC patients. However, survivin expression was positively correlated with pathological grade (R = 0.691, P < .001). Multivariate analysis revealed that survivin expression was independently correlated with grades (P < .001). In addition, the analysis of correlation indicated that survivin expression is positively correlated with VEGF expression (R = 0.820, P < .001) and Ki67 expression (R = 0.673, P < .001). The numbers (percentages) of complete remission (CR), partial remission (PR), and stability disease (SD) were 11 (9.4%), 91 (77.8%), and 15 (12.8%) respectively after the treatment of paclitaxel and carboplatin. Univariate analysis showed that efficacy of treatment was negatively correlated with pathological grade (R = 0.513, P < .001), Ki67 expression (R = 0.586, P < .001), VEGF expression (R = 0.476, P < .001) and survivin expression (R = 0.519, P < .001). Multivariate analysis revealed that efficacy of treatment was independently correlated with grades (P = .028), Ki67 (P < .001), and survivin expression (P = .015).The results suggested that survivin expression is negatively correlated with the prognosis of CSCC patients treated with paclitaxel and carboplatin. Therefore, survivin expression might be a marker for prognosis in CSCC following neoadjuvant chemotherapy.

Optimal HP configurations of proteins by combining local search with elastic net algorithm.

The prediction of protein conformation from its amino-acid sequence is one of the most prominent problems in computational biology. But it is NP-hard. Here, we focus on an abstraction widely studied of this problem, the two-dimensional hydrophobic-polar protein folding problem (2D HP PFP). Mathematical optimal model of free energy of protein is established. Native conformations are often sought using stochastic sampling methods, but which are slow. The elastic net (EN) algorithm is one of fast deterministic methods as travelling salesman problem (TSP) strategies. However, it cannot be applied directly to protein folding problem, because of fundamental differences in the two types of problems. In this paper, how the 2D HP protein folding problem can be framed in terms of TSP is shown. Combination of the modified elastic net algorithm and novel local search method is adopted to solve this problem. To our knowledge, this is the first application of EN algorithm to 2D HP model. The results indicate that our approach can find more optimal conformations and is simple to implement, computationally efficient and fast.

Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations.

BACKGROUND: Predicting protein structure from amino acid sequence is a prominent problem in computational biology. The long range interactions (or non-local interactions) are known as the main source of complexity for protein folding and dynamics and play the dominant role in the compact architecture. Some simple but exact model, such as HP model, captures the pain point for this difficult problem and has important implications to understand the mapping between protein sequence and structure. RESULTS: In this paper, we formulate the biological problem into optimization model to study the hydrophobic-hydrophilic model on 3D square lattice. This is a combinatorial optimization problem and known as NP-hard. Particle swarm optimization is utilized as the heuristic framework to solve the hard problem. To avoid premature in computation, we incorporated the Tabu search strategy. In addition, a pulling strategy was designed to accelerate the convergence of algorithm based on the characteristic of native protein structure. Together a novel hybrid method combining particle swarm optimization, Tabu strategy, and pulling strategy can fold the amino acid sequences on 3D square lattice efficiently. Promising results are reported in several examples by comparing with existing methods. This allows us to use this tool to study the protein stability upon amino acid mutation on 3D lattice. In particular, we evaluate the effect of single amino acid mutation and double amino acids mutation via 3D HP lattice model and some useful insights are derived. CONCLUSION: We propose a novel hybrid method to combine several heuristic strategies to study HP model on 3D lattice. The results indicate that our hybrid method can predict protein structure more accurately and efficiently. Furthermore, it serves as a useful tools to probe the protein stability on 3D lattice and provides some biological insights.