Simple Preparation of Carbon Dots and Application in Cephalosporin Detection

Carbon dots have good biocompatibility, low toxicity, excellent photoluminescence properties, and good light stability, endowing them good application prospects in drug detection, chemical analysis, drug delivery, and other fields. In this study, p-phenylenediamine was used as the carbon source, and carbon dots were synthesized in hydrochloric acid medium using microwave method. When the excitation wavelength is about 300 nm, a strong emission peak of 689 nm is detected for the synthesized carbon dots. Carbon dots’ size is about 4.0±0.2 nm, and the carbon dots with spherical shape are uniformly distributed. The quantum yield of carbon dots is 8.07%. In addition, cephalosporins. were detected and analyzed using synthetic carbon dots. The results show that the presence of cephalosporins reduced the fluorescence intensity of carbon dots, and the reduced fluorescence intensity of the synthesized carbon dots showed a linear correlation with the cephalosporins’ concentration. Cephalosporins’ detection scope is 0.2 μmol/L to 80 μ mol/L, and the detection limit is 0.084 μ mol/L. A mechanism study shows that the effect of cephalosporins on carbon dot’s fluorescence intensity can be attributed to the inner filter effect of cephalosporins. On this basis, a sensitive and 0selective cephalosporins detection method was established. Furthermore, this established method for cephalosporins detection was applied to real samples, resulting in a low relative standard deviation (RSD) and good recoveries.

Data cleaning for image-based profiling enhancement

With the advent of high-throughput assays, a large number of biological experiments can be carried out. Image-based assays are among the most accessible and inexpensive technologies for this purpose. Indeed, these assays have proved to be effective in characterizing unknown functions of genes and small molecules. Image analysis pipelines have a pivotal role in translating raw images that are captured in such assays into useful and compact representation, also known as measurements. CellProfiler is a popular and commonly used tool for this purpose through providing readily available modules for the cell/nuclei segmentation, and making various measurements, or features, for each cell/nuclei. Single cell features are then aggregated for each treatment replica to form treatment “profiles.” However, there may be several sources of error in the CellProfiler quantification pipeline that affects the downstream analysis that is performed on the profiles. In this work, we examined various preprocessing approaches to improve the profiles. We consider identification of drug mechanisms of action as the downstream task to evaluate such preprocessing approaches. Our enhancement steps mainly consist of data cleaning, cell level outlier detection, toxic drug detection, and regressing out the cell area from all other features, as many of them are widely affected by the cell area. We also examined unsupervised and weakly-supervised deep learning based methods to reduce the feature dimensionality, and finally suggest possible avenues for future research.

The Application of Nanosensors in Illicit Drugs Determination: A Review

The utilization of nanomaterials (NMs) to produce nanosensors for detecting drugs in a wide range of materials has attracted global attention. Various categories of NMs have been synthesized and applied for the qualitative determination of some additives, contaminants, and illicit materials owing to their unique physicochemical properties at the nanoscale to impact desired effects. Rapid and facile detection techniques employed for on-site analysis of illicit drugs using NMs are reviewed. It is noted that NMs are good candidates in the fabrication of nanosensors for the sensitive detections and determinations of illicit drugs. Thus, this review is focused on the application of these sensors for illicit drug detection. Hence, the application of plasmonic/optical properties of NMs to enhance illicit drug detection in biological samples has been discussed. The fabricated sensors have been shown to possess enhanced selectivity, sensitivity, cost-effectiveness as well as improved automation. As highlighted in the in-depth review, the sensors are designed to utilize biological receptors with a transducer component to detect the analyte-biorecognition element interaction which resulted in producing an optimum signal.

Physicochemical and Pharmacokinetic Properties Determining Drug Detection in Skin

Chemicals, including some systemically administered xenobiotics and their biotransformations, can be detected noninvasively using skin swabs and untargeted metabolomics analysis. We sought to understand the principal drivers that determine whether a drug taken orally or systemically is likely to be observed on the epidermis by using a random forest classifier to predict which drugs would be detected on the skin. A variety of molecular descriptors describing calculated properties of drugs, such as measures of volume, electronegativity, bond energy, and electrotopology, were used to train the classifier. The mean area under the ROC curve was 0.71 for predicting drug detection on the epidermis, and the SHapley Additive exPlanations model interpretation technique was used to determine the most relevant molecular descriptors. Based on the analysis of 2,561 FDA approved drugs, we predict that therapeutic drug classes such as nervous system drugs are more likely to be detected on the skin. Detecting drugs and other chemicals noninvasively on the skin using untargeted metabolomics could be a useful clinical advancement in therapeutic drug monitoring, adherence, and health status.