Quantitative analysis of periodontal pathogens by ELISA and real-time polymerase chain reaction

RamanNet: a lightweight convolutional neural network for bacterial identification based on Raman spectra

Raman spectroscopy combined convolutional neural network (CNN) enables rapid and accurate identification of the species of bacteria. However, the existing CNN requires a complex hyperparameters model design. Herein, we propose a new simple network architecture with less hyperparameter design and low computation cost, RamanNet, for rapid and accurate identifying of bacteria at the species level based on its Raman spectra. We verified that compared with the previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset and PKU-bacterial Raman spectral datasets, but using only about 1/45 and 1/297 network parameters, respectively. RamanNet achieved an average isolate-level accuracy of 84.7 ± 0.3%, antibiotic treatment identification accuracy of 97.1 ± 0.3%, and distinguished accuracy of 81.6 ± 0.9% for methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) on the Bacteria-ID dataset, respectively. Moreover, it achieved an average accuracy of 96.04% on the PKU-bacterial dataset. The RamanNet model benefited from fewer model parameters that can be quickly trained even using CPU. Therefore, our method has the potential to rapidly and accurately identify bacterial species based on their Raman spectra and can be easily extended to other classification tasks based on Raman spectra.

We propose a novel CNN model named RamanNet for rapid and accurate identification of bacteria at the species-level based on Raman spectra. Compared to previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset.

One-step immunoassay of C-reactive protein using droplet microfluidics

We present a wash-free high-sensitivity immunoassay of C-reactive proteins with droplet microfluidics. Microbeads are encapsulated within droplets for the immunoassay, and the droplets are scanned by a fluorescence detection platform to quantify the amount of proteins captured on the microbeads. The limit of detection determined by our platform is 0.01 μg mL^-1, which is ten times more sensitive than conventional high-sensitivity C-reactive protein assays. With the decrease in diffusion distance within droplets, the immunoassay requires only half of the time required for similar conventional approaches. This approach for carrying out immunoassays can potentially be applied to other biomarkers beyond C-reactive proteins.

Antimicrobial photodynamic therapy combined with periodontal treatment for metabolic control in patients with type 2 diabetes mellitus: study protocol for a randomized controlled trial

Background

The relationship between diabetes mellitus (DM) and periodontal disease is bidirectional. DM is a predisposing and modifying factor of periodontitis, which, in turn, worsens glycemic control and increases proteins found in the acute phase of inflammation, such as C-reactive protein. The gold standard for the treatment of periodontal disease is oral hygiene orientation, scaling and planing. Moreover, systemic antibiotic therapy may be employed in some cases. In an effort to minimize the prescription of antibiotics, photodynamic therapy (PDT) has been studied as an antimicrobial technique and has demonstrated promising results. The aim of the proposed study is to determine whether PDT as a complement to periodontal therapy (PT) is helpful in the metabolic control of individuals with type 2 diabetes and the reduction of acute-phase inflammatory markers.

Methods/Design

The patients will be randomized using a proper software program into two groups: 1) PT + placebo PDT or 2) PT + active PDT. All patients will first be examined by a specialist, followed by PT performed by two other healthcare professionals. At the end of each session, PDT (active or placebo) will be administered by a fourth healthcare professional. The following will be the PDT parameters: diode laser (660 nm); power output = 110 mW; exposure time = 90 s per point (9 J/point); and energy density = 22 J/cm². The photosensitizer will be methylene blue (50 μg/mL). The patients will be re-evaluated 15, 30, 90 and 180 days after treatment. Serological examinations with complete blood count, fasting glucose, glycated hemoglobin and salivary examinations to screen for tumor necrosis factor alpha, interleukin 1, interleukin 6, ostelocalcin, and osteoprotegerin/RANKL will be performed at each evaluation. The data will be statistically evaluated using the most appropriate tests.

Discussion

The results of this study will determine the efficacy of photodynamic therapy as an adjuvant to periodontal treatment in diabetic patients.

Trial registration

The protocol for this trial was registered with Clinical Trials registration number NCT01964833 on 14 October 2013.

Highly specific protease-based approach for detection of porphyromonas gingivalis in diagnosis of periodontitis

Porphyromonas gingivalis is associated with the development of periodontitis. Here we describe the development of a highly specific protease-based diagnostic method for the detection of P. gingivalis in gingival crevicular fluid. Screening of a proteolytic peptide substrate library, including fluorogenic dipeptides that contain d-amino acids, led to the discovery of five P. gingivalis-specific substrates. Due to the presence of lysine and arginine residues in these substrates, it was hypothesized that the cleavage was mediated by the gingipains, a group of P. gingivalis-specific proteases. This hypothesis was confirmed by the observation that P. gingivalis gingipain knockout strains demonstrated clearly impaired substrate cleavage efficacy. Further, proteolytic activity on the substrates was increased by the addition of the gingipain stimulators dithiothreitol and l-cysteine and decreased by the inhibitors leupeptin and N-ethylmaleimide. Screening of saliva and gingival crevicular fluid of periodontitis patients and healthy controls showed the potential of the substrates to diagnose the presence of P. gingivalis proteases. By using paper points, a sensitivity of approximately 10(5) CFU/ml was achieved. P. gingivalis-reactive substrates fully composed of l-amino acids and Bz-l-Arg-NHPhNO(2) showed a relatively low specificity (44 to 85%). However, the five P. gingivalis-specific substrates that each contained a single d-amino acid showed high specificity (96 to 100%). This observation underlines the importance of the presence of d-amino acids in substrates used for the detection of bacterial proteases. We envisage that these substrates may improve the specificity of the current enzyme-based diagnosis of periodontitis associated with P. gingivalis.