Early differential diagnosis of pancytopenia related diseases based on serum surface-enhanced Raman spectroscopy.

Pancytopenia is a common blood disorder defined as the decrease of red blood cells, white blood cells and platelets in the peripheral blood. Its genesis mechanism is typically complex and a variety of diseases have been found to be capable of causing pancytopenia, some of which are featured by their high mortality rates. Early judgement on the cause of pancytopenia can benefit timely and appropriate treatment to improve patient survival significantly. In this study, a serum surface-enhanced Raman spectroscopy (SERS) method was explored for the early differential diagnosis of three pancytopenia related diseases, i.e., aplastic anemia (AA), myelodysplastic syndrome (MDS) and spontaneous remission of pancytopenia (SRP), in which the patients with those pancytopenia related diseases at initial stage exhibited same pancytopenia symptom but cannot be conclusively diagnosed through conventional clinical examinations. The SERS spectral analysis results suggested that certain amino acids, protein substances and nucleic acids are expected to be potential biomarkers for their early differential diagnosis. In addition, a diagnostic model was established based on the joint use of partial least squares analysis and linear discriminant analysis (PLS-LDA), and an overall accuracy of 86.67 % was achieved to differentiate those pancytopenia related diseases, even at the time that confirmed diagnosis cannot be made by routine clinical examinations. Therefore, the proposed method has demonstrated great potential for the early differential diagnosis of pancytopenia related diseases, thus it has significant clinical importance for the timely and rational guidance on subsequent treatment to improve patient survival.

The prognostic prediction of periodontal non-surgery therapy in periodontitis patients based on surface-enhanced Raman measurements of pre-treatment saliva.

Periodontitis is one of the most prevalent dental diseases, and the patients with periodontitis often suffer from refractory periodontitis or recurrence of disease due to improper or inadequate treatment. In clinical practice, the early and accurate assessment of post-treatment prognosis in periodontitis patients is always very important in order to implement timely interventions. In this study, a pre-treatment saliva SERS based prognostic protocol was explored to predict the prognosis of periodontal non-surgery therapy in periodontitis patients. According to the biomolecular analysis, significant differences in the levels of ascorbic acid, uric acid and glutathione are observed between good prognosis group and poor prognosis group, which are expected to serve as potential prognostic markers. Furthermore, high accuracy, sensitivity and specificity can also be achieved by using the proposed prognostic model. The excellent performance of the proposed method has demonstrated its potential for fast, accurate, and non-invasive prognostic prediction of periodontal non-surgery therapy in periodontitis patients, even at the time before implementing treatment, thus is expected to benefit timely and rational guidance on clinical interventions.

Prediction of the postoperative prognosis in patients with non-muscle-invasive bladder cancer based on preoperative serum surface-enhanced Raman spectroscopy.

Non-muscle-invasive bladder cancer (NMIBC) is a common urinary tumor and has a high recurrence rate due to improper or inadequate conservative treatment. The early and accurate prediction of its recurrence can be helpful to implement timely and rational treatment. In this study, we explored a preoperative serum surface-enhanced Raman spectroscopy based prognostic protocol to predict the postoperative prognosis for NMIBC patients at the time even before treatment. The biochemical analysis results suggested that biomolecules related to DNA/RNA, protein substances, trehalose and collagen are expected to be potential prognostic markers, which further compared with several routine clinically used immunohistochemistry expressions with prognostic values. In addition, high prognostic accuracies of 87.01% and 89.47% were achieved by using the proposed prognostic models to predict the future postoperative recurrence and recurrent type, respectively. Therefore, we believe that the proposed method has great potential in the early and accurate prediction of postoperative prognosis in patients with NMIBC, which is with important clinical significance to guide the treatment and further improve the recurrence rate and survival time.

Early assessment of chemotherapeutic response in hepatocellular carcinoma based on serum surface-enhanced Raman spectroscopy.

In clinical practice, the transcatheter arterial chemoembolization (TACE) has been widely accepted as the first option for non-surgical hepatocellular carcinoma (HCC) treatment. However, patients with HCC often suffer from poor response to TACE therapy. This can be prevented if the chemotherapeutic response can be early and accurately assessed, which is essential to guide timely and rational management. In this study, the serum SERS technique was for the first time investigated as a potential prognostic tool for early assessment of HCC chemotherapeutic response. According to the SERS spectral analysis results, it is newly found that not only the absolute circulating nucleic acids and collagen levels in pre-therapeutic serum but also the changes in circulating nucleic acids and amino acids between pre-therapeutic and post-therapeutic serum are expected to be potential serum markers for HCC prognosis. By further applying chemometrics methods to establish prognostic models, excellent prognostic accuracies were achieved within only 3 days after TACE therapy. Thus, the proposed method is expected to provide guidance on timely and rational management of HCC to improve its survival rate.

Predicting prognosis in acute myeloid leukemia patients by surface-enhanced Raman spectroscopy.

Aim: To develop a timely and accurate method for predicting acute myeloid leukemia (AML) prognosis after chemotherapy treatment by surface-enhanced Raman spectroscopy (SERS). Methods: Biomolecular differences between AML patients with good and poor prognosis and individuals without AML were investigated based on SERS measurements of bone marrow supernatant fluid samples. Multivariate analysis was implemented on the SERS measurements to establish an AML prognostic model. Results: Significant differences in amino acid, saccharide and lipid levels were observed between AML patients with good and poor prognoses. The AML prognostic model achieved a prediction accuracy of 84.78%. Conclusion: The proposed method could be a potential diagnostic tool for timely and precise prediction of AML prognosis.

Lay abstract Acute myeloid leukemia (AML) is a disease in which too many immature white blood cells are found in the blood and bone marrow. Prognosis (the chance of recovery) for this disease is not favorable. However, if the disease can be quickly and precisely assessed, a personalized chemotherapy plan can be used. This could significantly improve the cure rate. In this study, a technique known as surface-enhanced Raman spectroscopy was used to analyze bone marrow samples from patients with and without AML. The samples were looked at to find biological molecules that could act as indicators for the disease and for disease prognosis. Using the collected data, models were established to predict whether a patient had AML and, if so, whether they had good or poor prognosis.

Visible-Light-Responsive Photocatalyst of Graphitic Carbon Nitride for Pathogenic Biofilm Control.

Pathogenic biofilms raise significant health and economic concerns, because these bacteria are persistent and can lead to long-term infections in vivo and surface contamination in healthcare and industrial facilities or devices. Compared with conventional antimicrobial strategies, photocatalysis holds promise for biofilm control because of its broad-spectrum effectiveness under ambient conditions, low cost, easy operation, and reduced maintenance. In this study, we investigated the performance and mechanism of Staphylococcus epidermidis biofilm control and eradication on the surface of an innovative photocatalyst, graphitic carbon nitride (g-C3N4), under visible-light irradiation, which overcame the need for ultraviolet light for many current photocatalysts (e.g., titanium dioxide (TiO2)). Optical coherence tomography and confocal laser scanning microscopy (CLSM) suggested that g-C3N4 coupons inhibited biofilm development and eradicated mature biofilms under the irradiation of white light-emitting diodes. Biofilm inactivation was observed occurring from the surface toward the center of the biofilms, suggesting that the diffusion of reactive species into the biofilms played a key role. By taking advantage of scanning electron microscopy, CLSM, and atomic force microscopy for biofilm morphology, composition, and mechanical property characterization, we demonstrated that photocatalysis destroyed the integrated and cohesive structure of biofilms and facilitated biofilm eradication by removing the extracellular polymeric substances. Moreover, reactive oxygen species generated during g-C3N4 photocatalysis were quantified via reactions with radical probes and 1O2 was believed to be responsible for biofilm control and removal. Our work highlights the promise of using g-C3N4 for a broad range of antimicrobial applications, especially for the eradication of persistent biofilms under visible-light irradiation, including photodynamic therapy, environmental remediation, food-industry applications, and self-cleaning surface development.