Improving high throughput manufacture of laser-inscribed graphene electrodes via hierarchical clustering.

Laser-inscribed graphene (LIG), initially developed for graphene supercapacitors, has found widespread use in sensor research and development, particularly as a platform for low-cost electrochemical sensing. However, batch-to-batch variation in LIG fabrication introduces uncertainty that cannot be adequately tracked during manufacturing process, limiting scalability. Therefore, there is an urgent need for robust quality control (QC) methodologies to identify and select similar and functional LIG electrodes for sensor fabrication. For the first time, we have developed a statistical workflow and an open-source hierarchical clustering tool for QC analysis in LIG electrode fabrication. The QC process was challenged with multi-operator cyclic voltammetry (CV) data for bare and metalized LIG. As a proof of concept, we employed the developed QC process for laboratory-scale manufacturing of LIG-based biosensors. The study demonstrates that our QC process can rapidly identify similar LIG electrodes from large batches (n ≥ 36) of electrodes, leading to a reduction in biosensor measurement variation by approximately 13% compared to the control group without QC. The statistical workflow and open-source code presented here provide a versatile toolkit for clustering analysis, opening a pathway toward scalable manufacturing of LIG electrodes in sensing. In addition, we establish a data repository for further study of LIG variation.

Machine Learning for Improved Detection of Pathogenic E. coli in Hydroponic Irrigation Water Using Impedimetric Aptasensors: A Comparative Study.

Reuse of alternative water sources for irrigation (e.g., untreated surface water) is a sustainable approach that has the potential to reduce water gaps, while increasing food production. However, when growing fresh produce, this practice increases the risk of bacterial contamination. Thus, rapid and accurate identification of pathogenic organisms such as Shiga-toxin producing Escherichia coli (STEC) is crucial for resource management when using alternative water(s). Although many biosensors exist for monitoring pathogens in food systems, there is an urgent need for data analysis methodologies that can be applied to accurately predict bacteria concentrations in complex matrices such as untreated surface water. In this work, we applied an impedimetric electrochemical aptasensor based on gold interdigitated electrodes for measuring E. coliO157:H7 in surface water for hydroponic lettuce irrigation. We developed a statistical machine-learning (SML) framework for assessing different existing SML methods to predict the E. coliO157:H7 concentration. In this study, three classes of statistical models were evaluated for optimizing prediction accuracy. The SML framework developed here facilitates selection of the most appropriate analytical approach for a given application. In the case of E. coliO157:H7 prediction in untreated surface water, selection of the optimum SML technique led to a reduction of test set RMSE by at least 20% when compared with the classic analytical technique. The statistical framework and code (open source) include a portfolio of SML models, an approach which can be used by other researchers using electrochemical biosensors to measure pathogens in hydroponic irrigation water for rapid decision support.

A capacitive laser-induced graphene based aptasensor for SARS-CoV-2 detection in human saliva.

SARS-CoV-2 virus induced CoVID-19 pandemic has accelerated the development of diagnostic tools. Devices integrated with electrochemical biosensors may be an interesting alternative to respond to the high demand for testing, particularly in contexts where access to standard detection technologies is lacking. Aptamers as recognition elements are useful due to their stability, specificity, and sensitivity to binding target molecules. We have developed a non-invasive electrochemical aptamer-based biosensor targeting SARS-CoV-2 in human saliva. The aptamer is expected to detect the Spike protein of SARS-CoV-2 wildtype and its variants. Laser-induced graphene (LIG) electrodes coated with platinum nanoparticles were biofunctionalized with a biotin-tagged aptamer. Electrochemical Impedance Spectroscopy (EIS) for BA.1 sensing was conducted in sodium chloride/sodium bicarbonate solution supplemented with pooled saliva. To estimate sensing performance, the aptasensor was tested with contrived samples of UV-attenuated virions from 10 to 10,000 copies/ml. Selectivity was assessed by exposing the aptasensor to non-targeted viruses (hCoV-OC43, Influenza A, and RSV-A). EIS data outputs were further used to select a suitable response variable and cutoff frequency. Capacitance increases in response to the gradual loading of the attenuated BA.1. The aptasensor was sensitive and specific for BA.1 at a lower viral load (10-100 copies/ml) and was capable of discriminating between negative and positive contrived samples (with strain specificity against other viruses: OC43, Influenza A, and RSV-A). The aptasensor detected SARS-CoV-2 with an estimated LOD of 1790 copies/ml in contrived samples. In human clinical samples, the aptasensor presents an accuracy of 72%, with 75% of positive percent of agreement and 67% of negative percent of agreement. Our results show that the aptasensor is a promising candidate to detect SARS-CoV-2 during early stages of infection when virion concentrations are low, which may be useful for preventing the asymptomatic spread of CoVID-19.

Predicting Range of Initial Warfarin Dose Based on Pharmacometabolomic and Genetic Inputs.

Anticoagulation response to warfarin during the initial stage of therapy varies among individuals. In this study, we aimed to combine pharmacometabolomic and pharmacogenetic data to predict interindividual variation in warfarin response, and, on this basis, suggest an initial daily dose range. The baseline metabolic profiles, genotypes, and clinical information of 160 patients with heart valve disease served as the variables of the function of the last international normalized ratio measured before a patient's discharge (INRday7 ) to screen for potential biomarkers. The partial least-squares model showed that two baseline metabolites (uridine and guanosine), one single-nucleotide variation (VKORC1), and four clinical parameters (weight, creatinine level, amiodarone usage, and initial daily dose) had good predictive power for INRday7 (R2  = 0.753 for the training set, 0.643 for the test set). With these biomarkers, a machine learning algorithm (two-dimensional linear discriminant analysis-multinomial logit model) was used to predict the subgroups with extremely warfarin-sensitive or less warfarin-sensitive patients with a prediction accuracy of 91% for the training set and 90% for the test set, indicating that individual responses to warfarin could be effectively predicted. Based on this model, we have successfully designed an algorithm,"IniWarD," for predicting an effective dose range in the initial 7-day warfarin therapy. The results indicate that the daily dose range suggested by the IniWarD system is more appropriate than that of the conventional genotype-based method, and the risk of bleeding or thrombus due to warfarin could thus be avoided.

Quality consistency evaluation of commercial transfer factor injections by chromatographic fingerprint combined with multivariate statistical analysis.

The current quality control methods relying mainly on chromogenic reaction can hardly ensure the quality and safety of the biochemical drug with complex chemical composition. Therefore, a chromatographic fingerprint method was developed for the quality evaluation of a multicomponent biochemical drug, transfer factor injection. High-performance liquid chromatography fingerprint was measured by using a C18 column (250 × 4.6 mm, 5 µm) with a mobile phase composed of 0.1% trifluoroacetic acid-water and 0.085% trifluoroacetic acid-acetonitrile under gradient elution. The developed method was validated and was subsequently applied to 57 batches of commercial products which were sampled by National Drug Assessment Program. High-resolution mass spectrometry analysis was performed on characteristic peaks of fingerprints, and a series of amino acids, nucleosides, and deoxynucleosides were identified. In the fingerprint assessments, principal component analysis and Hotelling T2 analysis yielded the best results. The results generally indicated that there was a significant difference among products of batch-to-batch or from different manufacturers. Abnormal samples and its discriminatory components were also explored. In summary, the established fingerprinting method with multivariate statistical analysis could offer an efficient, reliable, and practical approach for quality consistency evaluation of transfer factor injection, providing a reference for the quality control of other multicomponent biochemical drugs.

The effects of three modified Hank's balanced salt solutions on root resorption of late replanted teeth: A pilot study.

PURPOSE: The purpose of this study was to evaluate the effects of replanted rats' teeth that had been soaked in one of three modified Hank's balanced salt solutions (HBSSs) before replantation and after extended extra-oral dry time. MATERIALS AND METHODS: Maxillary right incisors were extracted from 55 Wistar rats and kept dry for 30 or 60 min (n = 5 each). Afterwards, the pulp was extirpated and both the papilla and enamel organ were removed with a scalpel. Each group of teeth was soaked in one of three modified HBSSs or HBSS alone. After 30 min of immersion in solutions, the root canals were dried and filled with calcium hydroxide paste, and the teeth were replanted. After 8 weeks, animals were euthanized; then, specimens were processed as 5 µm-thick serial sections for histological examination and morphometric assessments. RESULTS: The percentages of root resorption for the groups were found to be in the following order: HBSS3 (the bFGF group) > the HBSS only group > HBSS2 (the GSH group) > no soaking (the positive control group) > HBSS1 (the ALN group) for 30 min and the positive control group > the HBSS only group > HBSS2 > HBSS3 > HBSS1 for 60 min. The lowest incidence of resorption was observed in immediately replanted teeth (negative control). CONCLUSIONS: The findings of this study suggest that soaking for 30 min in HBSS containing 1 mM alendronate can significantly inhibit root resorption for avulsed teeth that have been dried for 60 min.

Microbial Lipid Production from Enzymatic Hydrolysate of Pecan Nutshell Pretreated by Combined Pretreatment.

Biodiesel is a fuel composed of monoalkyl esters of long-chain fatty acids derived from renewable biomass sources. In this study, biomass waste pecan nutshell (PS) was attempted to be converted into microbial oil. For effective utilization of PS, sequential pretreatment with ethylene glycol-H2SO4-water (78:2:20, wt:wt:wt) at 130 °C for 30 min and aqueous ammonia (25 wt%) at 50 °C for 24 h was used to enhance its enzymatic saccharification. Significant linear correlation was obtained about delignification-saccharification (R 2 = 0.9507). SEM and FTIR results indicated that combination pretreatment could effectively remove lignin and xylan in PS for promoting its enzymatic saccharification. After 72 h, the reducing sugars from the hydrolysis of 50 g/L pretreated PS by combination pretreatment could be obtained at 73.6% yield. Using the recovered PS hydrolysates containing 20 g/L glucose as carbon source, microbial lipids produced from the PS hydrolysates by Rhodococcus opacus ACCC41043. Four fatty acids including palmitic acid (C16:0; 23.1%), palmitoleic acid (C16:1; 22.4%), stearic acid (C18:0; 15.3%), and oleic acid (C18:1; 23.9%) were distributed in total fatty acids. In conclusion, this strategy has potential application in the future.

Antitumor effects of WNT2B silencing in GLUT1 overexpressing cisplatin resistant head and neck squamous cell carcinoma.

OBJECTIVES: The increased rate of glucose uptake necessary to support the growth of tumor cells is mediated by glucose transporters, and glucose transporter 1 (GLUT1) is overexpressed in several types of cancer in correlation with poor prognosis. And WNT2B overexpression is thought to be involved in tumor progression. Here, we investigated the effects of WNT2B in GLUT1 overexpressing cisplatin resistant head and neck squamous cell carcinoma (HNSCC) in vitro and in vivo. MATERIALS AND METHODS: We generated GLUT1 overexpressing cisplatin resistant CAL27 and SCC25 oral cancer cells. Lentiviral mediated knock-down of WNT2B was performed in CAL27 and SCC25. QRT-PCR and Western blot analysis were used to detect the mRNA and protein expression of GLUT1, WNT2B, Cyclin D1 and ß-catenin. Cell viability was assessed by MTT analysis. Colony formation assay was performed by staining with 0.5% crystal violet. The role of WNT2B in HNSCC was examined in vivo through the generation of a CAL27 (or cisplatin resistant CAL27 or cisplatin resistant CAL27 with WNT2B knock-down) nude mice xenograft model of HNSCC. RESULTS: Knock-down of WNT2B in decreased cell viability and colony formation in cisplatin resistant CAL27 and SCC25 in association with the downregulation of GLUT1, cyclin D1 and ß-catenin. In a cisplatin resistant CAL27 mouse xenograft model, shRNA mediated silencing of WNT2B increased survival and decreased tumor growth in correlation with the downregulation of GLUT1, cyclin D1 and ß-catenin. CONCLUSION: WNT2B plays a role in tumorigenesis and chemotherapy resistance in oral cancer and provide a potential therapeutic target for the treatment of patients with HNSCC.