A biocompatible open-surface droplet manipulation platform for detection of multi-nucleotide polymorphism.

We present a novel and simple method to manipulate droplets applicable to an open-surface microfluidic platform. The platform comprised a control module for pneumatic droplets and a superhydrophobic polydimethylsiloxane (PDMS) membrane. With pneumatic suction to cause deflection of the flexible PDMS-based superhydrophobic membrane, the sample and reagent droplets on the membrane become transported and mixed. A facile one-step laser micromachining technique serves to fabricate a superhydrophobic surface; a contact angle of 150° and a hysteresis angle of 4° were achieved without chemical modification. Relative to previous open-surface microfluidic systems, this platform is capable of simultaneous and precise delivery of droplets in two-dimensional (2D) manipulation. Droplets were manipulated with suction, which avoided interference from an external driving energy (e.g. heat, light, electricity) to affect the bio-sample inside the droplets. Two common bio-samples, namely protein and DNA, verified the performance of the platform. Based on the experimental results, operations on protein can be implemented without adsorption on the surface of the platform. Another striking result is the visual screening for multi-nucleotide polymorphism with hybridization-mediated growth of gold-nanoparticle (AuNP) probes. The detection results are observable with the naked eye, without the aid of advanced instruments. The entire procedure only takes 5 min from the addition of the sample and reagent to obtaining the results, which is much quicker than the traditional method. The total sample volume consumed in each operation is only 10 µL, which is significantly less than what is required in a large system. According to this approach, the proposed platform is suitable for biological and chemical applications.

A simple clinical scoring system to improve the sensitivity and standardization of the diagnosis of mycosis fungoides type cutaneous T-cell lymphoma: logistic regression of clinical and laboratory data.

BACKGROUND: The diagnosis of mycosis fungoides (MF) is notoriously difficult to establish because in the early stages, histological features may be nonspecific or merely suggestive. OBJECTIVES: To standardize the diagnosis of MF. METHODS: We studied 138 patients with suspected MF referred over a 7-year period to a university department of a dermatology-based cutaneous lymphoma clinic. Six diagnostic criteria were evaluated: clinical morphology, clinical distribution, skin biopsy T-cell receptor gene rearrangement (TCR-GR), skin biopsy pan T-cell marker loss > or = 2, skin biopsy CD4/CD8 ratio > or = 6, and skin biopsy diffuse epidermal HLA-DR expression. These six clinical and laboratory criteria were compared by logistic regression analysis in patients with histologically diagnosed MF and those with benign disease. RESULTS: Of the 138 patients, 74 had histology of MF, 47 of benign dermatoses and 17 were indeterminate. Close associations were found between a histological diagnosis of MF and TCR-GR (odds ratio 14.4), classical morphology (7.5), classical distribution (2.5) and diffuse epidermal HLA-DR expression (2.8). Logistic regression models were developed depending on the availability of data (either TCR-GR or HLA-DR). Probabilities for correctly diagnosing MF compared with histology as the 'gold standard' were derived from these logistic regression models. A scoring system assigning point values based on these probabilities was then created in order to assist the clinician in making the diagnosis. If using TCR-GR data, a positive TCR-GR = 2.5 points, the presence of classical morphology = 2.0 points, and the presence of classical distribution = 1.5 points. A total score of > or = 3.5 points assigns a high probability (> 85%) of having MF. If using HLA-DR expression, then the presence of classical morphology = 2.5 points, a positive diffuse epidermal HLA-DR expression = 2.0 points, and the presence of classical distribution = 1.5 points. In this case, a total score of > or = 4.0 points assigns a high probability (> 85%) of MF. CONCLUSIONS: The logistic regression models and scoring systems integrate clinical and laboratory assessments, allow rapid probability estimation, and provide a threshold for the diagnosis of MF in an objective, standardized manner.