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.

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.

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.

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..

Identification and dynamic expression profiling of circadian clock genes in Spodoptera litura provide new insights into the regulation of sex pheromone communication.

Spodoptera litura is an important pest that causes significant economic damage to numerous crops worldwide. Sex pheromones (SPs) mediate sexual communication in S. litura and show a characteristic degree of rhythmic activity, occurring mainly during the scotophase; however, the specific regulatory mechanisms remain unclear. Here, we employed a genome-wide analysis to identify eight candidate circadian clock genes in S. litura. Sequence characteristics and expression patterns were analyzed. Our results demonstrated that some circadian clock genes might regulate the biosynthesis and perception of SPs by regulating the rhythmic expression of SP biosynthesis-related genes and SP perception-related genes. Interestingly, all potential genes exhibited peak expression in the scotophase, consistent with the SP could mediate courtship and mating behavior in S. litura. Our findings are helpful in elucidating the molecular mechanism by which circadian clock genes regulate sexual communication in S. litura.

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.

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.

Identification of METTL3 as an Adverse Prognostic Biomarker in Hepatocellular Carcinoma.

INTRODUCTION: N6-methyladenosine (m6A), the most prominent mRNA modification, plays a critical role in many physiological and pathological processes. However, the roles of m6A RNA modification in hepatocellular carcinoma (HCC) remain largely unknown. MATERIALS AND METHODS: We investigated the mRNA expression and clinical significance of m6A-related genes using data from The Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma cohort. Mutation, copy number variation (CNV), methylation, differential expression, and gene ontology analyses, gene set enrichment analysis and the construction of a competing endogenous RNA (ceRNA) regulatory network were performed to investigate the underlying mechanisms of the aberrant expression of m6A-related genes. RESULTS: m6A-related genes were frequently dysregulated in cancers but with a cancer-specific pattern. METTL3, YTHDF2, and ZC3H13 were found to be independent prognostic factors of overall survival (OS); however, only METTL3 was found to be an independent prognostic factor of recurrence-free survival (RFS). Joint effects analysis showed the predictive capacity of combining METTL3, YTHDF2, and ZC3H13 for HCC OS. Then the potential mechanisms of METTL3 were further explored due to its prognostic role in both OS and RFS. CNV and DNA methylation, but not somatic mutations, might contribute to the abnormal upregulation of METTL3 in HCC. Significantly altered genes, microRNAs, and lncRNAs were identified, and a ceRNA regulatory network was constructed to explain the upregulation of METTL3 in HCC. CONCLUSIONS: Our study identified several m6A-related genes, especially METTL3, that could be potential prognostic biomarkers in HCC.

Chemosensory genes in the head of Spodoptera litura larvae.

The tobacco cutworm Spodoptera litura (Lepidoptera: Noctuidae) is a polyphagous pest with a highly selective and sensitive chemosensory system involved in complex physiological behaviors such as searching for food sources, feeding, courtship, and oviposition. However, effective management strategies for controlling the insect pest populations under threshold levels are lacking. Therefore, there is an urgent need to formulate eco-friendly pest control strategies based on the disruption of the insect chemosensory system. In this study, we identified 158 putative chemosensory genes based on transcriptomic and genomic data for S. litura, including 45 odorant-binding proteins (OBPs, nine were new), 23 chemosensory proteins (CSPs), 60 odorant receptors (ORs, three were new), and 30 gustatory receptors (GRs, three were new), a number higher than those reported by previous transcriptome studies. Subsequently, we constructed phylogenetic trees based on these genes in moths and analyzed the dynamic expression of various genes in head capsules across larval instars using quantitative real-time polymerase chain reaction. Nine genes-SlitOBP8, SlitOBP9, SlitOBP25, SlitCSP1, SlitCSP7, SlitCSP18, SlitOR34, SlitGR240, and SlitGR242-were highly expressed in the heads of 3- to 5-day-old S. litura larvae. The genes differentially expressed in olfactory organs during larval development might play crucial roles in the chemosensory system of S. litura larvae. Our findings substantially expand the gene inventory for S. litura and present potential target genes for further studies on larval feeding in S. litura.

Analysis of chemosensory genes in Semiothisa cinerearia reveals sex-specific contributions for type-II sex pheromone chemosensation.

Insects employ a sensitive chemosensory system to accurately recognize external odorants, which help them to make a behavioral response quickly. Semiothisa cinerearia has caused serious damages to Sophora japonica L. in recent years, and there is still a lack of effective strategy to control the pest. Although the two type-II sex pheromones of S. cinerearia, 6Z,9Z-cis-3,4-epoxy-17:H and 3Z,6Z,9Z-17:H, have been identified for 30 years, the molecular mechanisms underlying the chemosensation of the two sex pheromones are still unknown. Here, we found that there are differences in the types of antennae sensilla between sexes, and revealed 146 putative chemosensory genes in the antennal transcriptome. Among these genes, 11 and 40 of them displayed male-biased and female-biased expression, respectively. Our findings greatly improve the chemosensory gene resources for S. cinerearia and provide a foundation for functional studies of these sex-biased genes on the chemosensation of sex pheromones and on other sex-related behaviors.

[A Real-time Reconstruction Method of CBCT Short Scan Based on Attenuation Compensation].

Aiming at the problem of timeliness of CBCT reconstruction, a CBCT fast short scan reconstruction method is proposed. At the same time, the image reconstruction process in which a new attenuation compensation algorithm is applied to improve image quality. When performing FDK three-dimensional reconstruction of a single-frame acquisition image, the Parker-weighted image is calculated in real time, and a new attenuation compensation algorithm is applied in the back projection process to complete the short scan Parker-weighted reconstruction. This method simulates the CBCT synchronous acquisition and reconstruction process by establishing collection and reconstruction threads. Under the premise of satisfying the reconstruction quality, the reconstruction can be completed within 1 to 2 seconds after the patient collection is completed, which achieves the purpose of real-time.

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma.

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.

The effect of metformin on lung cancer risk and survival in patients with type 2 diabetes mellitus: A meta-analysis.

WHAT IS KNOWN AND OBJECTIVE: Metformin has received increasing attention owing to its potential protective effect against cancer. We aimed to summarize evidence regarding the association between metformin and the risk or survival in lung cancer patients with type 2 diabetes. METHODS: We selected observational studies examining the association between exposure to metformin and the risk or survival in lung cancer. Available publications were searched in PubMed, Cochrane Library, ScienceDirect, Wiley and SpringerLink databases. Meta-analysis was performed with hazard ratios (HRs) and 95% confidence intervals (95% CIs) as effect measures for risk or survival in lung cancer. RESULTS: Eighteen studies (eight on lung cancer risk and ten on lung cancer survival) were included. Metformin treatment was associated with decreased lung cancer incidence (HR 0.78; 95% CI 0.70-0.86) and increased lung cancer survival (HR 0.65; 95% CI 0.55-0.77). In the subgroup analysis by ethnicity, a significant protective effect of metformin use on lung cancer risk was observed among Asian patients (HR 0.66; 95% CI 0.56-0.76), but not in European patients. On the other hand, the protective effect of metformin use on lung cancer survival was observed in both Asian (HR 0.57; 95% CI 0.49-0.66) and non-Asian (HR 0.79; 95% CI 0.71-0.88) patients. In the subgroup analysis by histology, a protective effect of metformin on lung cancer survival was observed in both non-small-cell lung cancer (HR 0.68; 95% CI 0.54-0.84) and small-cell lung cancer (HR 0.52; 95% CI 0.39-0.69). Funnel plot showed that no significant publication bias existed. CONCLUSIONS: Our findings demonstrate that metformin is significantly associated with a decreased risk and increased survival in lung cancer.

Different binding properties of two general-odorant binding proteins in Athetis lepigone with sex pheromones, host plant volatiles and insecticides.

Athetis lepigone (Alep) is a polyphagous pest native to Europe and Asia that has experienced major outbreaks in the summer maize area of China since 2011 and has shown evidence of resistance to some insecticides. Insect olfaction is crucial for recognition of sex pheromones, host plant volatiles and even insecticides, in which two general-odorant binding proteins (GOBPs) play important roles. To elucidate the functions of GOBPs in A. lepigone, we first expressed the two AlepGOBP proteins in the E. coli expression system. Then, the results of fluorescence competitive binding assays demonstrated that the high binding affinity of AlepGOBP2 with sex pheromones [(Z)-7-dodecenyl acetate (Z7-12:Ac), Ki = 0.65 µM; (Z)-9-tetradecenyl acetate (Z9-14:Ac), Ki = 0.83 µM], two maize plant volatiles [Ocimene, Ki = 9.63 µM; (E)-ß-Farnesene, Ki = 4.76 µM] and two insecticides (Chlorpyrifos Ki =5.61 µM; Phoxim, Ki = 4.38 µM). However, AlepGOBP1 could only bind Ocimene (Ki = 13.0 µM) and two insecticides (Chlorpyrifos Ki =4.46 µM; Phoxim, Ki = 3.27 µM). These results clearly suggest that AlepGOBP1 and AlepGOBP2 differentiate among odorants and other ligands. The molecular docking results further revealed different key residues involved in the ligand binding of AlepGOBPs. In summary, this study provides a foundation for exploring the olfactory mechanism of A. lepigone and identified two potential target genes for the development of highly effective insecticides in the future.

Prognostic utility and clinical significance of lysyl oxidase-like 2 protein expression in digestive system cancers.

Lysyl oxidase-like 2 (LOXL2) participates in the occurrence and development of digestive system cancers (DSCs). The aim of this study was to determine whether LOXL2 protein could serve as a prognostic biomarker in patients with DSCs. Relevant studies published before October 1, 2018 were identified from a comprehensive literature review in PubMed, Web of Science, and Embase. This meta-analysis was conducted via STATA/SE 14.1 software. Finally, a total of 12 publications and 6 different kinds of DSCs were identified. Meta-analysis indicated that increased expression of LOXL2 protein was significantly correlated with reduced overall survival (hazard ratios [HR]: 1.52; 95% confidence interval [CI]: 1.32-1.72) and worse progression-free survival/disease-free survival (HR: 2.15; 95% CI: 1.48-2.83) in cases with DSCs. In addition, clinicopathological parameters, including tumor invasion, lymph node metastasis, distant metastasis, and clinical stage were significantly related to LOXL2 protein expression in DSCs. High LOXL2 protein expression is significantly associated with worse clinical outcomes in DSCs and its expression level may represent a candidate prognostic biomarker in these cancers.

Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a major challenge for public health worldwide. Considering the great heterogeneity of HCC, more accurate prognostic models are urgently needed. To identify a robust prognostic gene signature, we conduct this study. MATERIALS AND METHODS: Level 3 mRNA expression profiles and clinicopathological data were obtained in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). GSE14520 dataset from the gene expression omnibus (GEO) database was downloaded to further validate the results in TCGA. Differentially expressed mRNAs between HCC and normal tissue were investigated. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, multivariate Cox regression analysis, nomogram, and decision curve analysis (DCA) were used to assess the prognostic capacity of the six-gene signature. The prognostic value of the gene signature was further validated in independent GSE14520 cohort. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. The performance of the prognostic signature in differentiating between normal liver tissues and HCC were also investigated. RESULTS: A novel six-gene signature (including CSE1L, CSTB, MTHFR, DAGLA, MMP10, and GYS2) was established for HCC prognosis prediction. The ROC curve showed good performance in survival prediction in both the TCGA HCC cohort and the GSE14520 validation cohort. The six-gene signature could stratify patients into a high- and low-risk group which had significantly different survival. Cox regression analysis showed that the six-gene signature could independently predict OS. Nomogram including the six-gene signature was established and shown some clinical net benefit. Furthermore, GSEA revealed several significantly enriched oncological signatures and various metabolic process, which might help explain the underlying molecular mechanisms. Besides, the prognostic signature showed a strong ability for differentiating HCC from normal tissues. CONCLUSIONS: Our study established a novel six-gene signature and nomogram to predict overall survival of HCC, which may help in clinical decision making for individual treatment.

A Δ9 desaturase (SlitDes11) is associated with the biosynthesis of ester sex pheromone components in Spodoptera litura.

Sex pheromone biosynthesis in moths relies on the activity of multiple enzymes, including Δ9 desaturase, which plays an important role in catalyzing desaturation at the Δ9 position of the carbon chain. However, the physiological function of moth Δ9 desaturase has not been elucidated in vivo. In this study, we used the CRISPR/Cas9 system to knockout the Δ9 desaturase gene (SlitDes11) of Spodoptera litura to analyze its role in sex pheromone biosynthesis. First, through the direct injection of SlitDes11-single guide RNA (sgRNA)/Cas9 messenger RNA into newly laid eggs, gene editing was induced in around 30% of eggs 24 h after injection and was induced in 20.8% of the resulting adult moths. Second, using a sibling-crossing strategy, insects with mutant SlitDes11 (bearing a premature stop codon) were selected, and homozygous mutants were obtained in the G5 generation. Third, pheromone gland extracts of adult female homozygous SlitDes11 mutants were analyzed using Gas chromatography (GC). The results showed that titers of all three ester sex pheromone components; Z9, E11-14:Ac, Z9,E12-14:Ac, and Z9-14:Ac; were reduced by 62.40%, 78.50%, and 72.50%, respectively. This study provides the first direct evidence for the role of SlitDes11 in sex pheromone biosynthesis in S. litura, and indicates the gene could be as potential target to disrupt sexual communication in S. litura for developing a new pollution-free insecticide.

A modified method for isolating mouse islets of an adequate quality, quantity, and purity.

Mouse islets are widely used in diabetes research. Thus an adequate quality, quantity, and purity of islets are needed for high-quality investigations. We performed a combination of filtration and density gradient separation and optimized many steps in the islet isolation procedure, including perfusion, digestion, and purification. Our results show that an increased quality, quantity, and purity of isolated islets can be achieved using these modifications. Moreover, this method can guarantee maximal recovery and purity of the isolated islets and is easy to perform with practice.