Diagnostic and Prognostic Value of Methyltransferase Like 13 in Hepatocellular Carcinoma Based on Bioinformatics Analysis.

BACKGROUND/AIMS:  Hepatocellular carcinoma (HCC) is one of the cancers with the highest incidence and mortality rates. This study aims to explore the diagnostic and prognostic utility of methyltransferase like 13 (METTL13) in patients with HCC via bioinformatics analysis. MATERIALS AND METHODS:  We obtained mRNA data of HCC from the database of the Cancer Genome Atlas (TCGA), drawing survival curve by R 4.2.1 software. Cox regression analysis was conducted based on tumor stage and METTL13 expression. The GSE114564 dataset was chosen from the Gene Expression Omnibus. The differences in serum METTL13 levels between the groups of early HCC (eHCC) and non-cancer controls were evaluated. Using a receiver operating characteristic curve, we calculated the area under the curve (AUC) of serum METTL13 for diagnosing eHCC. RESULTS:  A total of 225 cases with HCC were screened from TCGA, and 29 cases were normal controls. The results showed that the METTL13 expression in the HCC group was higher than that in the normal controls (P <.001). The univariate [hazard ratio (HR) = 1.895, P = .006] and multivariate Cox regression (HR = 1.702, P = .037) analyses showed that high METTL13 expression reduced overall survival in HCC. Serum METTL13 levels were higher in the eHCC group than in the non-cancer controls (P = .008). The optimum AUC for predicting eHCC by serum METTL13 was 0.7091. CONCLUSION:  Serum METTL13 has a moderate diagnostic value for eHCC. High METTL13 expression is correlated with a worse prognosis in patients with HCC. Methyltransferase like 13 possesses the potential to be a novel biomarker for HCC.

Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning.

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.

Identification and Removal of Pollen Spectral Interference in the Classification of Hazardous Substances Based on Excitation Emission Matrix Fluorescence Spectroscopy.

Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky-Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation-emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including Staphylococcus aureus, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols.

Retroperitoneal Laparoscopic Debridement and Drainage for Pancreatic Abscess.

In patients with severe necrotizing pancreatitis, pancreatic necrosis and secondary infection of surrounding tissues can quickly spread to the whole retroperitoneal space. Treatment of pancreatic abscess complicating necrotizing pancreatitis is difficult and has a high mortality rate. The well-accepted treatment strategy is early debridement of necrotic tissues, drainage, and postoperative continuous retroperitoneal lavage. However, traditional open surgery has several disadvantages, such as severe trauma, interference with abdominal organs, a high rate of postoperative infection and adhesion, and hardness with repeated debridement. The retroperitoneal laparoscopic approach has the advantages of minimal invasion, a better drainage route, convenient repeated debridement, and avoidance of the spread of retroperitoneal infection to the abdominal cavity. In addition, retroperitoneal drainage leads to fewer drainage tube problems, including miscounting, displacement, or siphon. The debridement and drainage of pancreatic abscess tissue via the retroperitoneal laparoscopic approach plays an increasingly irreplaceable role in improving patient prognosis and saving healthcare resources and costs. The main procedures described here include laying the patient on the right side, raising the lumbar bridge and then arranging the trocar; establishing the pneumoperitoneum and cleaning the pararenal fat tissues; opening the lateral pyramidal fascia and the perirenal fascia outside the peritoneal reflections; opening the anterior renal fascia and entering the anterior pararenal space from the rear; clearing the necrotic tissue and accumulating fluid; and placing drainage tubes and performing postoperative continuous retroperitoneal lavage.

Advanced lung cancer inflammation index predicts overall survival of hepatocellular carcinoma after hepatectomy.

Aim: Limited data are available regarding ALI's clinical relevance and prognostic value in patients with hepatocellular carcinoma (HCC) after hepatectomy. Materials and methods: HCC patients who received hepatectomy at the Meizhou People's Hospital from May 2011 to February 2022 were enrolled in the study cohort. The ALI was calculated as follows: ALI = BMI (kg/m2) × ALB (g/dL)/(absolute neutrophil count/absolute lymphocyte count). The primary outcome was overall survival (OS). The secondary outcome was cancer-specific survival (CSS). Univariate and multivariate Cox regression analyses were performed, followed by nomogram construction and decision curve analysis (DCA). Results: 425 HCC patients were enrolled for analyses. Lower preoperative ALI was significantly correlated with incomplete tumor capsule and advanced tumor stage. Lower preoperative ALI was an adverse independent prognostic factor for OS (HR: 1.512, 95% CI: 1.122-2.039, P 0.007) and CSS (HR: 1.754, 95% CI: 1.262-2.438, P <0.001) in HCC patients. The nomogram plot was built based on three (including age, TNM stage, and ALI) and two (including TNM stage and ALI) independent prognostic factors for OS and CSS, respectively. Further analyses indicated that the nomogram had better predictive value and some net benefit than the traditional TNM stage alone, especially in long-term OS. Conclusions: Our study further indicated that ALI could be a prognostic marker for OS and CSS in HCC patients after hepatectomy, especially in long-term OS.

Investigating the impact of attenuated fluorescence spectra on protein discrimination.

The optical remote sensing techniques are promising for the real-time detection, and identification of different types of hazardous biological materials. However, the received fluorescent spectra from a remote distance suffer from the atmospheric attenuation effect upon the spectral shape. To investigate the influence of atmospheric attenuation on characterizing, and classifying biological agents, the laboratory-measured fluorescence data of fourteen proteins combined with the atmospheric transmission factors of the MODTRAN model were conducted with different detection ranges. The multivariate analysis techniques of principal component analysis (PCA) and linear discriminant analysis (LDA), and the predictors of Random Forest and XGBoost were employed to assess the separability and distinguishability of different spectra recorded. The results showed that the spectral-shift effect on attenuated spectra varied as a function of the detection range, the atmospheric visibility, and the spectral distribution. According to the PCA and LDA analysis, the distribution of decomposed factors changed in the spectral explanatory power with the increasing attenuation effect, which was consistent with the hierarchical clustering results. Random Forest exhibited higher performance in classifying protein samples than that of XGBoost, while the two methods performed similarly in identifying harmful and harmless subgroups of proteins. Fewer subgroups decreased the sensitivity of the classification accuracy to the attenuation effect. Our analysis demonstrated that combining atmospheric transport models to build a fluorescence spectral database is essential for fast identification between spectra, and reduced classification criteria could facilitate the compatibility of spectral database and classification algorithms.

Integrating street view images and deep learning to explore the association between human perceptions of the built environment and cardiovascular disease in older adults.

Understanding how built environment attributes affect health remains important. While many studies have explored the objective characteristics of built environments that affect health outcomes, few have examined the role of human perceptions of built environments on physical health. Baidu Street View images and computer vision technological advances have helped researchers overcome the constraints of traditional methods of measuring human perceptions (e.g., these methods are laborious, time-consuming, and costly), allowing for large-scale measurements of human perceptions. This study estimated human perceptions of the built environment (e.g., beauty, boredom, depression, safety, vitality, and wealth) by adopting Baidu Street View images and deep learning algorithms. Negative binomial regression models were employed to analyze the relationship between human perceptions and cardiovascular disease in older adults (e.g., ischemic heart disease and cerebrovascular disease). The results indicated that wealth perception is negatively related to the risk of cardiovascular disease. However, depression and vitality perceptions are positively associated with the risk of cardiovascular disease. Furthermore, we found no relationship between beauty, boredom, safety perceptions, and the risk of cardiovascular disease. Our findings highlight the importance of human perceptions in the development of healthy city planning and facilitate a comprehensive understanding of the relationship between built environment characteristics and health outcomes in older adults. They also demonstrate that street view images have the potential to provide insights into this complicated issue, assisting in the formulation of refined interventions and health policies.

Physiological Functions of Carbon Dots and Their Applications in Agriculture: A Review.

Carbon dots are carbon-based nanoparticles, which have the characteristics of a simple preparation process, photoluminescence, biocompatibility, an adjustable surface function, water solubility, and low-level toxicity. They are widely used in biological applications, such as imaging, biosensing, photocatalysis, and molecular transfer. They have also aroused great interest among researchers in agriculture, and there has been significant progress in improving crop growth and production. This review presents the physiological functions of carbon dots for crop growth and development, photosynthesis, water and nutrient absorption, and abiotic stress resistance and their applications in improving the ecological environment and agriculture as biosensors, and future application prospects and research directions of carbon dots in agriculture.

Spatial scale analysis for the relationships between the built environment and cardiovascular disease based on multi-source data.

To examine what built environment characteristics improve the health outcomes of human beings is always a hot issue. While a growing literature has analyzed the link between the built environment and health, few studies have investigated this relationship across different spatial scales. In this study, eighteen variables were selected from multi-source data and reduced to eight built environment attributes using principal component analysis. These attributes included socioeconomic deprivation, urban density, street walkability, land-use diversity, blue-green space, transportation convenience, ageing, and street insecurity. Multiscale geographically weighted regression was then employed to clarify how these attributes relate to cardiovascular disease at different scales. The results indicated that: (1) multiscale geographically weighted regression showed a better fit of the association between the built environment and cardiovascular diseases than other models (e.g., ordinary least squares and geographically weighted regression), and is thus an effective approach for multiscale analysis of the built environment and health associations; (2) built environment variables related to cardiovascular diseases can be divided into global variables with large scales (e.g., socioeconomic deprivation, street walkability, land-use diversity, blue-green space, transportation convenience, and ageing) and local variables with small scales (e.g., urban density and street insecurity); and (3) at specific spatial scales, global variables had trivial spatial variation across the area, while local variables showed significant gradients. These findings provide greater insight into the association between the built environment and lifestyle-related diseases in densely populated cities, emphasizing the significance of hierarchical and place-specific policy formation in health interventions.

Impact of different classification schemes on discrimination of proteins with noise-contaminated spectra using laboratory-measured fluorescence data.

Biological agents are important to detect and identify with respect to environmental contamination and public health. Noise contamination in fluorescent spectra is one of the contributors to the uncertainties of identification. In order to investigate the noise-tolerant capability provided by laboratory-measured excitation-emission matrix (EEM) fluorescence spectra that are used as a database, fluorescence properties of four proteinaceous biotoxin samples and ten harmless protein samples were characterized by EEM fluorescence spectra, and the predicting performance of models trained by laboratory-measured fluorescence data was tested and verified from validation data with noise-contaminated spectra. By means of peak signal of noise (PSNR) as an indicator of noise levels, the potential impact of noise contaminations on the characterization and discrimination of these samples was evaluated quantitatively. Different classification schemes utilizing multivariate analysis techniques of Principal Component Analysis (PCA), Random Forest (RF), and Multi-layer Perceptron (MPL) coupled with feature descriptors of differential transform (DT), Fourier transform (FT) and wavelet transform (WT) were conducted under different PSNR values. We systematically analyzed the performance of classification schemes by the case study at 20 PSNR and by statistical analysis from 1-100 PSNR. The results show that the spectral features with EEM-WT decreased the demanding number of input variables while retaining high performances in sample classification. The spectral features with EEM-FT presented the worst performance although having the largest number of features. The distributions of feature importance and contribution were found sensitive to noise contaminations. The classification scheme of PCA prior to MPL with EEM-WT as input presented an improvement in lower PSNR. These results indicate that robust features extracted by corresponding techniques are critical to enhancing the spectral differentiation capabilities among these samples and play an important role in eliminating the noise effect. The study of classification schemes for discriminating protein samples with noise-contaminated spectra presents tremendous potential for future developments in the rapid detection and identification of proteinaceous biotoxins based on three-dimensional fluorescence spectrometry.

Performance control study of interleaved meltblown non-woven materials based on statistical analysis and predictive modeling.

Meltblown nonwoven materials have gained attention due to their excellent filtration performance. The research on the performance of the intercalation meltblown preparation process is complex and a current research focus in the field of chemical production. Based on data related to intercalated and unintercalated meltblown materials under given process conditions, a product performance prediction model of intercalated meltblown materials was established under different process parameters (receiving distance, hot air velocity). The structural variables (thickness, porosity, and compressive resilience), the change in product performance, and the relationship between structural variables and product performance (filtration resistance, efficiency, air permeability) after intercalation were studied. Multiple regression analysis was used to analyze the structural variables, and evaluation of the regression results were made using R2, MSE, SSR, and SST. A BP neural network prediction model for product performance was established. The BP neural network model was used to find the maximum filtration efficiency. The study provides theoretical support for regulating product performance by solving the maximum filtration efficiency using BP neural network model.

Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry.

Three-dimensional excitation emission matrix (EEM) fluorescence spectroscopy was employed to discriminate protein samples comprising bovine serum albumin, neurotensin, ovalbumin, ricin, trypsin from bovine pancreas and trypsin from porcine pancreas. Two methods of feature extraction with and without parameterization were applied to the spectral data in order to evaluate their performance of discrimination between protein samples. The discrimination of protein samples was conducted by k-means clustering algorithm and eigenvalue extracting procedure based on principal component analysis (PCA). It was found that the method of feature extraction without parameterization performed best, correctly attributing 100% of the spectral data in the condition of two principal components (PCs) captured. Features extracted with spectral parameterization failed to separate ricin and trypsin from bovine pancreas in same condition. Without spectral parameterization, less dimensionality and unique principal components captured by PCA indicates the spectrally-resolved features of corresponding protein samples. By clustering using each spectrum at fixed excitation wavelength, excitation wavelengths matched with common intrinsic fluorophores were found to be more sensitive to the classification accuracy. Contributions of spectral features extracted from EEM to the principal components were discussed and demonstrated their feature differentiation capabilities among six protein samples. These results reveal that appropriate extraction approach of features in combination with PCA analysis could be used in discrimination of protein samples at species level as a spectroscopic diagnostic tool. Our study provides fundamental references about computational strategies when EEM are used to explore proteins in ambient environment.

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms.

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

A robust molecularly imprinted electrochemiluminescence sensor based on a Ni-Co nanoarray for the sensitive detection of spiramycin.

To prepare portable and robust sensors for the sensitive and selective detection of small molecules is still a challenge for the study of electroanalytical sensors. Here, we developed a molecularly imprinted electrochemiluminescence sensor (MIECL) for the detection of spiramycin (SPI), a type of multi-component macrolide antibiotic. First, Ni-Co LDH nanoarrays were synthesized by a one-step hydrothermal method and then directly used as a sensing platform. Then, as-synthesized N-Ti3C2 was modified on the nanoarrays. Due to the functional nanomaterial N-Ti3C2 not only serving as a substrate material to enable loading a large amount of perylene tetracarboxylic acid (PTCA) but also acting as a co-reaction promoter to accelerate the decomposition of S2O82- to generate more SO4˙-, the modified nanoarrays displayed a significantly enhanced electrochemiluminescence (ECL) signal. Finally, the molecularly imprinted polymer (MIP) and ECL techniques were combined to greatly improve the selectivity and sensitivity of the sensor. Under the optimal conditions, the easily constructed MIECL sensor showed good selectivity, reproducibility, and stability, and a detection limit of up to 3.14 × 10-13 M. The as-fabricated sensor was further evaluated by applying it to detect SPI in milk samples.

Direct Asymmetric α-C-H Addition of N-unprotected Propargylic Amines to Trifluoromethyl Ketones by Carbonyl Catalysis.

Direct asymmetric functionalization of the inert α C-H bonds of N-unprotected propargylic amines is a big challenge in organic chemistry, due to the low acidity (pKa ≈42.6) of the α C-H bonds and interruption of the nucleophilic NH2 group. By using a chiral pyridoxal as carbonyl catalyst, we have successfully realized direct asymmetric α-C-H addition of N-unprotected propargylic amines to trifluoromethyl ketones, producing a broad range of chiral alkynyl ß-aminoalcohols in 54-84 % yields with excellent stereoselectivities (up to 20 : 1 dr and 99 % ee). The α C-H bonds of propargylic amines are greatly activated by the pyridoxal catalyst via the formation of an imine intermediate, resulting in the increase of acidity by up to 1022  times (from pKa  42.6 to pKa  20.1), which become acidic enough to be deprotonated under mild conditions for the asymmetric addition. This work presented an impressive example for asymmetric functionalization of inert C-H bonds enabled by an organocatalyst.

Two Odorant-Binding Proteins Involved in the Recognition of Sex Pheromones in Spodoptera litura Larvae.

Usually, the recognition of sex pheromone signals is restricted to adult moths. Here, our behavioral assay showed that fourth-instar Spodoptera litura larvae are attracted to cabbage laced with minor sex pheromones Z9,E12-tetradecadienyl acetate (Z9,E12-14:Ac) or Z9-tetradecenyl acetate (Z9-14:Ac). Seven odorant-binding proteins (OBPs) were upregulated after exposure to Z9,E12-14:Ac, and one OBP was upregulated after exposure to Z9-14:Ac. Fluorescence competitive binding assays showed that GOBP2 and OBP7 bound to sex pheromones. RNAi treatment significantly downregulated GOBP2 and OBP7 mRNA expression by 70.37 and 63.27%, respectively. The siOBP-treated larvae were not attracted to Z9,E12-14:Ac or Z9-14:Ac, and the corresponding preference indices were significantly lower than those in siGFP-treated larvae. Therefore, we concluded that GOBP2 and OBP7 are involved in the attraction of S. litura larvae to food containing Z9,E12-14:Ac and Z9-14:Ac. These results provide an important basis for exploring the olfactory mechanisms underlying sex pheromone attraction in moth larvae.

Facile Ionization of the Nanochannels of Lamellar Membranes for Stable Ionic Liquid Immobilization and Efficient CO2 Separation.

Two-dimensional (2D) lamellar membranes, with highly ordered nanochannels between the adjacent layers, have revealed potential application prospects in various fields. To separate gases with similar kinetic diameters, intercalation of a functional liquid, especially an ionic liquid (IL), into 2D lamellar membranes is proved to be an efficient method due to the capacity of imparting solubility-based separation and sealing undesired defects. Stable immobilization of a high content of liquid is challenging but extremely required to achieve and maintain high separation performance. Herein, we describe the intercalation of a typical IL, 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]), into the ionized nanochannels of sulfonated MXene lamellar membranes, where the sulfonate groups are anchored onto MXene nanosheets through a facile method based on metal-catechol chelating chemistry. Thanks to the intrinsic benefits of MXene as building blocks and the decorated sulfonate groups, the optimal membrane possesses adequate interlayer spacing (∼1.8 nm) and high IL uptake (∼47 wt %) and therefore presents a CO2 permeance of 519 GPU and a CO2/N2 selectivity of 210, outperforming the previously reported liquid-immobilized lamellar membranes. Moreover, the IL loss rate of the membrane within 7 days at elevated pressure (5 bar) is measured to be significantly decreased (from 43.2 to 9.0 wt %) after growing sulfonate groups on the nanochannel walls, demonstrating the excellent IL storage stability.

Hexavalent chromium elimination from wastewater by integrated micro-electrolysis composites synthesized from red mud and rice straw via a facile one-pot method.

The widely spread chromium (Cr) contamination is rising environmental concerns, while the reutilization of agro-industrial by-products are also urgently demanded due to their potential risks. In this study, we prepared the integrated micro-electrolysis composites (IMC) through a facile one-pot method with red mud and rice straw. The effects of components relatively mass ratios as well as pyrolysis temperature were analyzed. The XRD, XPS, SEM, FTIR, and various techniques proved the IMC was successfully synthesized, which was also used to analyze the reaction mechanisms. In this study, the dosage of IMC, pH, adsorption time, and temperature of adsorption processes were explored, in the adsorption experiment of Cr(VI), dosage of IMC was 2 g/L (pH 6, 25 °C, and 200 rpm) for isothermal, while the concentration and contact time were also varied. According to the batch experiments, IMC exhibited acceptable removal capacity (190.6 mg/g) on Cr(VI) and the efficiency reached 97.74%. The removal mechanisms of adsorbed Cr(VI) were mainly elaborated as chemical reduction, complexation, co-precipitation, and physical adherence. All these results shed light on the facile preparation and agro-industrial by-products recycled as engineering materials for the heavy metals decontamination in wastewater.

Design, synthesis, and bioactivity evaluation of macrocyclic benzo[b]pyrido[4,3-e][1,4]oxazine derivatives as novel Pim-1 kinase inhibitors.

Pim-1 kinase is a serine/threonine kinase which is vital in many tumors. The Pim-1 inhibitor 10-DEBC and its derivatives discovered in our previous work were modified through macrocyclization strategy. A series of benzo[b]pyridine[4,3-e][1,4]oxazine macrocyclic compounds were designed, synthesized, and evaluated as novel Pim-1 kinase inhibitors. Among these compounds, compound H5 exhibited the highest activity with an IC50 value of 35 nM. In addition, the crystal complex structure of Pim-1 kinase bound with compound H3 was determined, and the structure-activity relationship of these macrocyclic compounds was analyzed, which provides the structural basis of further optimization of novel macrocyclic Pim-1 kinase inhibitors..

AlepPBP2, but not AlepPBP3, may involve in the recognition of sex pheromones and maize volatiles in Athetis lepigone.

Athetis lepigone Möschler (Lepidoptera, Noctuidae) is a common maize pest in Europe and Asia. However, there is no long-term effective management strategy is available yet to suppress its population. Adults rely heavily on olfactory cues to locate their optimal host plants and oviposition sites. Pheromone-binding proteins (PBPs) are believed to be responsible for recognizing and transporting different odorant molecules to interact with receptor membrane proteins. In this study, the ligand-binding specificities of two AlepPBPs (AlepPBP2 and AlepPBP3) for sex pheromone components and host plant (maize) volatiles were measured by fluorescence ligand-binding assay. The results demonstrated that AlepPBP2 had a high affinity with two pheromones [(Z)-7-dodecenyl acetate, Ki = 1.11 ± 0.1 µM, (Z)-9-tetradecenyl acetate, Ki = 1.32 ± 0.15 µM] and ten plant volatiles, including (-)-limonene, α-pinene, myrcene, linalool, benzaldehyde, nonanal, 2-hexanone, 3-hexanone, 2-heptanone and 6-methyl-5-hepten-2-one. In contrast, we found that none of these chemicals could bind to AlepPBP3. Our results clearly show no significant differences in the functional characterization of the binding properties between AlepPBP2 and AlepPBP3 to sex pheromones and host plant volatiles. Furthermore, molecular docking was employed for further detail on some crucial amino acid residues involved in the ligand-binding of AlepPBP2. These findings will provide valuable information about the potential protein binding sites necessary for protein-ligand interactions which appear as attractive targets for the development of novel technologies and management strategies for insect pests.