Detection of high-risk HPV 16 genotypes in cervical cancers using isothermal DNA amplification with electrochemical genosensor.

Cervical cancer emerges as the third most prevalent types of malignancy among women on a global scale. Cervical cancer is significantly associated with the persistent infection of human papillomavirus (HPV) type 16. The process of diagnosing is crucial in order to prevent the progression of a condition into a malignant state. The early detection of cervical cancer through initial stage screening is of the utmost significance in both the prevention and effective management of this disease. The present detection methodology is dependent on quantitative polymerase chain reaction (qPCR), which necessitates the use of a costly heat cycler instrument. In this study, we report the development of an electrochemical DNA biosensor integrated with an isothermal recombinase polymerase amplification (RPA) reaction for the detection and identification of the high-risk HPV-16 genotype. The electrochemical biosensor exhibited a high degree of specificity and sensitivity, as evidenced by its limit of detection (LOD) of 0.23 copies/µL of HPV-16 DNA. The validity of this electrochemical platform was confirmed through the analysis of 40 cervical tissues samples, and the findings were consistent with those obtained through polymerase chain reaction (PCR) testing. Our straightforward electrochemical detection technology and quick turnaround time at 75 min make the assay suitable for point-of-care testing in low-resource settings.

A Multianalyte Electrochemical Genosensor for the Detection of High-Risk HPV Genotypes in Oral and Cervical Cancers.

Infection with high-risk human papillomavirus (HPV) is a major risk factor for oral and cervical cancers. Hence, we developed a multianalyte electrochemical DNA biosensor that could be used for both oral and cervical samples to detect the high-risk HPV genotypes 16 and 18. The assay involves the sandwich hybridization of the HPV target to the silica-redox dye reporter probe and capture probe, followed by electrochemical detection. The sensor was found to be highly specific and sensitive, with a detection limit of 22 fM for HPV-16 and 20 fM for HPV-18, between the range of 1 fM and 1 µM. Evaluation with oral and cervical samples showed that the biosensor result was consistent with the nested PCR/gel electrophoresis detection. The biosensor assay could be completed within 90 min. Due to its simplicity, rapidity, and high sensitivity, this biosensor could be used as an alternative method for HPV detection in clinical laboratories as well as for epidemiological studies.

Detection of Human Cholangiocarcinoma Markers in Serum Using Infrared Spectroscopy.

Cholangiocarcinoma (CCA) is a malignancy of the bile duct epithelium. Opisthorchis viverrini infection is a known high-risk factor for CCA and in found, predominantly, in Northeast Thailand. The silent disease development and ineffective diagnosis have led to late-stage detection and reduction in the survival rate. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) is currently being explored as a diagnostic tool in medicine. In this study, we apply ATR-FTIR to discriminate CCA sera from hepatocellular carcinoma (HCC), biliary disease (BD) and healthy donors using a multivariate analysis. Spectral markers differing from healthy ones are observed in the collagen band at 1284, 1339 and 1035 cm-1, the phosphate band (vsPO2-) at 1073 cm-1, the polysaccharides band at 1152 cm-1 and 1747 cm-1 of lipid ester carbonyl. A Principal Component Analysis (PCA) shows discrimination between CCA and healthy sera using the 1400-1000 cm-1 region and the combined 1800-1700 + 1400-1000 cm-1 region. Partial Least Square-Discriminant Analysis (PLS-DA) scores plots in four of five regions investigated, namely, the 1400-1000 cm-1, 1800-1000 cm-1, 3000-2800 + 1800-1000 cm-1 and 1800-1700 + 1400-1000 cm-1 regions, show discrimination between sera from CCA and healthy volunteers. It was not possible to separate CCA from HCC and BD by PCA and PLS-DA. CCA spectral modelling is established using the PLS-DA, Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN). The best model is the NN, which achieved a sensitivity of 80-100% and a specificity between 83 and 100% for CCA, depending on the spectral window used to model the spectra. This study demonstrates the potential of ATR-FTIR spectroscopy and spectral modelling as an additional tool to discriminate CCA from other conditions.

Monitoring the Progression of Liver Fluke-Induced Cholangiocarcinoma in a Hamster Model Using Synchrotron FTIR Microspectroscopy and Focal Plane Array Infrared Imaging.

Cholangiocarcinoma (CCA) is a bile duct cancer that originates in the bile duct epithelium. Northeastern Thailand has the highest incidence of CCA, and there is a direct correlation with liver fluke (Opisthorchis viverrini) infection. The high mortality rate of CCA is a consequence of delayed diagnosis. Fourier transform infrared (FTIR) spectroscopy is a powerful technique that detects the absorbance of molecular vibrations and is perfectly suited for the interrogation of biological samples. In this study, we applied synchrotron radiation-FTIR (SR-FTIR) microspectroscopy and focal plane array (FPA-FTIR) microspectroscopy to characterize periductal fibrosis and bile duct cells progressing to CCA induced by inoculating O. viverrini metacercariae into hamsters. SR-FTIR and FPA-FTIR measurements were performed in liver sections harvested from 1-, 2-, 3-, and 6-month post-infected hamsters compared to uninfected liver tissues. Principal component analysis (PCA) of the tissue samples showed a clear discrimination among uninfected and early-stage (1 and 2 months) and cancerous-stage (3 and 6 months) tissues. The discrimination is based on intensity changes in the phosphodiester band (1081 cm-1), amino acid residue (∼1396 cm-1), and C═O stretching carboxylic esters (1745 cm-1). Infected tissues also show definitive bands at ∼1280, 1234, and 1201 cm-1 characteristic of the collagen triplet and indicative of fibrosis. Hierarchical cluster analysis (HCA) was performed on the FPA data and showed a classification into specific cell types. Hepatocyte, fibrotic lesion, and bile duct (cancer) were classified and HCA mapping showed similar cellular distribution pattern compared to Sirius red staining. This study was also extended to less invasive sample analysis using attenuated total reflectance-FTIR (ATR-FTIR) spectroscopy. Sera from O. viverrini-infected and uninfected hamsters were analyzed using multivariate analysis, including principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA). PCA was able to classify spectra of normal, early-stage CCA, and CCA, while the PLS-DA gave 100% accuracy for the validation. The model was established from 17 samples (11 normal, 6 cancer) in the calibration set and 9 samples in the validation set (4 normal, 2 cancer, 3 precancerous). These results indicate that FTIR-based technology is a potential tool to detect the progression of CCA, especially in the early stages of the disease.

Infrared spectroscopy coupled to cloud-based data management as a tool to diagnose malaria: a pilot study in a malaria-endemic country.

BACKGROUND: Widespread elimination of malaria requires an ultra-sensitive detection method that can detect low parasitaemia levels seen in asymptomatic carriers who act as reservoirs for further transmission of the disease, but is inexpensive and easy to deploy in the field in low income settings. It was hypothesized that a new method of malaria detection based on infrared spectroscopy, shown in the laboratory to have similar sensitivity to PCR based detection, could prove effective in detecting malaria in a field setting using cheap portable units with data management systems allowing them to be used by users inexpert in spectroscopy. This study was designed to determine whether the methodology developed in the laboratory could be translated to the field to diagnose the presence of Plasmodium in the blood of patients presenting at hospital with symptoms of malaria, as a precursor to trials testing the sensitivity of to detect asymptomatic carriers. METHODS: The field study tested 318 patients presenting with suspected malaria at four regional clinics in Thailand. Two portable infrared spectrometers were employed, operated from a laptop computer or a mobile telephone with in-built software that guided the user through the simple measurement steps. Diagnostic modelling and validation testing using linear and machine learning approaches was performed against the gold standard qPCR. Sample spectra from 318 patients were used for building calibration models (112 positive and 110 negative samples according to PCR testing) and independent validation testing (39 positive and 57 negatives samples by PCR). RESULTS: The machine learning classification (support vector machines; SVM) performed with 92% sensitivity (3 false negatives) and 97% specificity (2 false positives). The Area Under the Receiver Operation Curve (AUROC) for the SVM classification was 0.98. These results may be better than as stated as one of the spectroscopy false positives was infected by a Plasmodium species other than Plasmodium falciparum or Plasmodium vivax, not detected by the PCR primers employed. CONCLUSIONS: In conclusion, it was demonstrated that ATR-FTIR spectroscopy could be used as an efficient and reliable malaria diagnostic tool and has the potential to be developed for use at point of care under tropical field conditions with spectra able to be analysed via a Cloud-based system, and the diagnostic results returned to the user's mobile telephone or computer. The combination of accessibility to mass screening, high sensitivity and selectivity, low logistics requirements and portability, makes this new approach a potentially outstanding tool in the context of malaria elimination programmes. The next step in the experimental programme now underway is to reduce the sample requirements to fingerprick volumes.