Validation and stability analysis of guanine deaminase assay kit.

Guanine deaminase (GD)plays important roles in the diagnosis of liver function. However, there is no totally rapid and simple for the eatimation of GD activity in clinical application. Herein, we have constructed an enzymatic assay system with highly sensitive and strong stability for quantification of GD activity by highly double enzyme-coupling (xanthine oxidase and uric acid oxidase) and adding compound stabilizer in GD kit. In this study, we validated parameters, including reagent blank, sensitivity, accuracy, inter-batch difference, intra-batch difference, linear range. Furthermore, composite stabilizers, containing gentamicin sulfate, bovine serum albumin, and mannitol, were selected to improve stability of GD kit during long-term storage. The experimental results showed that the absorbance of the reagent blank was <0.2, the mean recovery rate was 103 %, the inter-batch and intra-batch diffeerence were <15 %, The linearity range was 0 U/L-50 U/L (R2 > 0.99). All indicators met the kit requirements for clinical applications. When gentamicin sulfate, bovine serum albumin, and mannitol were used as a stabilizer, the kit remained stable for 12 months without significant loss of enzymatic activity. These results indicated that GD kit possesses high sensitivity and strong stability, which can be used for routine biochemical applications and is of great significance for the diagnosis and differential diagnosis of liver diseases.

Design of Machine Learning Algorithms and Internal Validation of a Kidney Risk Prediction Model for Type 2 Diabetes Mellitus.

Objective: This study aimed to explore specific biochemical indicators and construct a risk prediction model for diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D). Methods: This study included 234 T2D patients, of whom 166 had DKD, at the First Hospital of Jilin University from January 2021 to July 2022. Clinical characteristics, such as age, gender, and typical hematological parameters, were collected and used for modeling. Five machine learning algorithms [Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF)] were used to identify critical clinical and pathological features and to build a risk prediction model for DKD. Additionally, clinical data from 70 patients (nT2D = 20, nDKD = 50) were collected for external validation from the Third Hospital of Jilin University. Results: The RF algorithm demonstrated the best performance in predicting progression to DKD, identifying five major indicators: estimated glomerular filtration rate (eGFR), glycated albumin (GA), Uric acid, HbA1c, and Zinc (Zn). The prediction model showed sufficient predictive accuracy with area under the curve (AUC) values of 0.960 (95% CI: 0.936-0.984) and 0.9326 (95% CI: 0.8747-0.9885) in the internal validation set and external validation set, respectively. The diagnostic efficacy of the RF model (AUC = 0.960) was significantly higher than each of the five features screened with the highest feature importance in the RF model. Conclusion: The online DKD risk prediction model constructed using the RF algorithm was selected based on its strong performance in the internal validation.

Rapid Identification of Peucedanum praeruptorum Dunn and Its Adulterants by Hand-Held Near-Infrared Spectroscopy.

BACKGROUND: Peucedanum praeruptorum Dunn (PPD) is a traditional Chinese medical herb of high medical and economic value. However, PPD is often adulterated by inexpensive plants. OBJECTIVE: In order to establish an integrated and straightforward methodology to identify adulterated PPD products, hand-held near-infrared spectroscopy (NIRS) combined with chemical pattern recognition techniques was employed. METHOD: The standard normal variate (SNV) was used to preprocess the original near-infrared spectra. Principal component analysis (PCA), linear discriminant analysis (LDA), and partial least-squares regression analysis (PLSDA) were used to construct the recognition models. RESULTS: PCA analysis could not correctly distinguish PPD from non-PPD. However, based on absorbance in the spectral region of 1405-2442 nm and SNV pretreatment, the accuracy of the LDA model was above 90% at identifying genuine PPD. Compared with the LDA method, the PLSDA model is more stable and reliable, and its model prediction accuracy was 93.4%. CONCLUSION: The combination of NIRS and chemometric methods based on a hand-held near-infrared spectrometer is an efficient, nondestructive, and reliable method for validating traditional Chinese medicine PPD. HIGHLIGHTS: The advanced method based on a hand-held near-infrared spectrometer can be used for rapid identification and quality evaluation of PPD in the field, medicinal material markets, and points of sale.

Compound Heterozygous Mutations in the F7 Gene in 2 Unrelated Families With Congenital Factor VII Deficiency.

Factor VII (FVII) deficiency is a rare bleeding disorder normally caused by homozygous and compound heterozygous mutations in the F7 gene. Whole-exome sequencing was performed to identify F7 mutations in 3 individuals from 2 unrelated families who were diagnosed with FVII deficiency. Four compound heterozygous mutations were identified and validated in these 3 probands with FVII deficiency. Among the 4 identified mutations, NM_000131.4:c.572-1_581del, NM_000131.4:c.1250A>G (p.Tyr417Cys), and NM_000131.4:c.647G>T (p.Gly216Val) were novel. All 3 novel mutations were predicted to be likely pathogenic by the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines.

Genetic variant rs72613567 of HSD17B13 gene reduces alcohol-related liver disease risk in Chinese Han population.

BACKGROUND & AIMS: Recently, the variant rs72613567:TA in the 17-beta-hydroxysteroid dehydrogenase 13 (HSD17B13) has been associated with reduced levels of ALT and AST and a reduced risk of alcohol-related liver disease (ALD) in the European population. Therefore, the aim of this study was to investigate the association between the polymorphisms of HSD17B13 and ALD, liver serum markers and patatin-like phospholipase domain-containing protein 3 (PNPLA3) p.I148M in the Chinese Han population. METHODS: A case-control study was performed from five centres and included 769 ALD patients and 767 healthy controls. Two SNPs (rs72613567 and rs6834314) in HSD17B13 were genotyped using the Sequenom MassArray system and allele association analysis was performed using PLINK 1.90 software. RESULTS: HSD17B13 rs72613567:TA allele was associated with a reduced risk of ALD by 19% (95% confidence interval [CI]: 0.05-0.31, P = .01), uniformly, the G allele in the rs6834314 reduced the risk of ALD by 19% (95% CI: 0.05-0.31, P = 8.28 × 10-3). And the genotypes of two SNPs were associated with reducing the risk of ALD in three genetic model analysis. In addition, we found that TA allele was associated with lower levels of serum ALT, AST and GGT (P = .005, .007 and .02, respectively), higher level of serum ALB (P = .02), but not associated with ALP. In this cohort, the interaction between HSD17B13 rs72613567 and the steatogenic allele PNPLA3 rs738409 was not validated. CONCLUSION: The present study revealed that HSD17B13 rs72613567 was significantly associated with a reduced risk of ALD in Chinese Han population.

Characteristic synergistic cytotoxic effects toward cells in graphene oxide dressing with cadmium and copper ions.

The increasing applications of graphene oxide (GO) in bio-medicine, environment and other fields enhance the exposure possibility of human beings to GO. Studies have been performed to address the in vitro toxicity of GO; however, little information on the in vivo biological consequence of GO with other common disasters is available, especially when cells are co-exposed to GO and common metal ions. To explore the influence and possible mechanisms of such co-exposure scenarios, a series of tests of cell viability, membrane integrity, reactive oxygen species (ROS), cell morphology, and Cd2+ distribution, were conducted. The results showed that the synergistic toxic mechanisms of GO and Cd2+, initiated from the adhesion of GO on HeLa cells, and followed by the recruitment of Cd2+ ions around the cell membrane, impaired the membrane integrity, morphology and adhesion capability, and triggered cell toxicity. The synergistic toxic mechanism of GO and Cu2+ mainly correlated to ROS, while no obvious relationship with membrane integrity was observed. The findings are envisaged to facilitate the application of GO in biology and related fields.

Systematic Toxicity Evaluations of High-Performance Carbon "Quantum" Dots.

Carbon dots (CDots) in a general structure of small carbon nanoparticles with various surface passivation schemes have emerged to represent a new class of carbon nanomaterials in now a rapidly advancing and expanding research field. Among various synthesis methods, the use of pre-processed and selected small carbon nanoparticles for deliberate chemical functionalization of the particle surface with organic molecules have produced high-performance and structurally better defined CDots. Specifically, small organic molecules 2,2'-(ethylenedioxy)bis(ethylamine) and 3-ethoxypropylamine were used for the effective surface passivation of the carbon nanoparticles via chemical functionalization to yield CDots that are brightly fluorescent and also structurally ultra-compact, amenable to various desired cell imaging applications. Thus, a systematic evaluation of these CDots on their cytotoxicity profiles is necessary, and performed in this study by using a diverse selection of cell lines. Also for fluorescence imaging, CDots were modified with their encapsulating selected organic dyes for much enhanced red/near-IR fluorescence emissions. These modified CDots with the dyes as guest were also evaluated for their cytotoxicity profiles. The results suggest that the CDots without and with the guest dyes are generally nontoxic to the selected cell lines, further supporting the notion that CDots can be used as high-performance yet nontoxic bioimaging agents.

Arginine inhibits the malignant transformation induced by interferon-gamma through the NF-κB-GCN2/eIF2α signaling pathway in mammary epithelial cells in vitro and in vivo.

Breast cancer is the most common female malignant tumors in the world. It seriously affects women's physical and mental health and the leading cause of cancer death among women. Our previous study demonstrated that diet-derived IFN-γ promoted the malignant transformation of primary bovine mammary epithelial cells by accelerating arginine depletion. The current study aimed to explore whether arginine addition could inhibit the degree of malignant transformation and its molecular mechanism. The results indicate that arginine addition could alleviate the malignant transformation of mammary epithelial cells induced by IFN-γ, including reducing cell proliferation, cell migration and colony formation, through the NF-κB-GCN2/eIF2α pathway. The in vivo experiments also consistently confirmed that arginine supplementation could significantly inhibit tumor growth in tumor-bearing mice. Furthermore, the investigation of the clinical data also revealed that the plasma or tissue from human breast cancer patients owned lower arginine level and higher IFN-γ level than that from patients with benign breast disease, showing IFN-γ may be a potential control target. Our findings demonstrate that arginine supplement could antagonize the malignant transformation of mammary epithelial cells induced by IFN-γ (nutritionally induced) both in vitro and in vivo, and IFN-γ was higher in breast cancer women. This might provide a novel strategy for the prevention and treatment of breast cancer regarding to nutrition.