Non-neotissue constituents as underestimated confounders in the assessment of tissue engineered constructs by near-infrared spectroscopy.

Non-destructive assessments are required for the quality control of tissue-engineered constructs and the optimization of the tissue culture process. Near-infrared (NIR) spectroscopy coupled with machine learning (ML) provides a promising approach for such assessment. However, due to its nonspecific nature, each spectrum incorporates information on both neotissue and non-neotissue constituents of the construct; the effect of these constituents on the NIR-based assessments of tissue-engineered constructs has been overlooked in previous studies. This study investigates the effect of scaffolds, growth factors, and buffers on NIR-based assessments of tissue-engineered constructs. To determine if these non-neotissue constituents have a measurable effect on the NIR spectra of the constructs that can introduce bias in their assessment, nine ML algorithms were evaluated in classifying the NIR spectra of engineered cartilage according to the scaffold used to prepare the constructs, the growth factors added to the culture media, and the buffers used for storing the constructs. The effect of controlling for these constituents was also evaluated using controlled and uncontrolled NIR-based ML models for predicting tissue maturity as an example of neotissue-related properties of interest. Samples used in this study were prepared using norbornene-modified hyaluronic acid scaffolds with or without the conjugation of an N-cadherin mimetic peptide. Selected samples were supplemented with transforming growth factor-beta1 or bone morphogenetic protein-9 growth factor. Some samples were frozen in cell lysis buffer, while the remaining samples were frozen in PBS until required for NIR analysis. The ML models for classifying the spectra of the constructs according to the four constituents exhibited high to fair performances, with F1 scores ranging from 0.9 to 0.52. Moreover, controlling for the four constituents significantly improved the performance of the models for predicting tissue maturity, with improvement in F1 scores ranging from 0.09 to 0.77. In conclusion, non-neotissue constituents have measurable effects on the NIR spectra of tissue-engineered constructs that can be detected by ML algorithms and introduce bias in the assessment of the constructs by NIR spectroscopy. Therefore, controlling for these constituents is necessary for reliable NIR-based assessments of tissue-engineered constructs.

Identification of Aspergillus species in human blood plasma by infrared spectroscopy and machine learning.

Invasive Aspergillosis is a challenging infection that requires convenient, efficient, and cost-effective diagnostics. This study addresses the potential of infrared spectroscopy to satisfy this clinical need with the aid of machine learning. Two models, based on Partial Least Squares-Discriminant Analysis (PLS-DA), have been trained by a set of infrared spectral data of 9 Aspergillus-spiked and 7 Aspergillus-free plasma samples, and a set of 200 spectral data simulated by oversampling these 16 samples. Two further models have also been trained by the same sets but with auto-scaling performed prior to PLS-DA. These models were assessed using 45 mock samples, simulating the challenging samples of patients at risk of Invasive Aspergillosis, including the presence of drugs (9 tested) and other common pathogens (5 tested) as potential confounders. The simple model shows good prediction performance, yielding a total accuracy of 84.4%, while oversampling and autoscaling improved this accuracy to 93.3%. The results of this study have shown that infrared spectroscopy can identify Aspergillus species in blood plasma even in presence of potential confounders commonly present in blood of patients at risk of Invasive Aspergillosis.

Diagnosis of cancer multidrug resistance by bacterium-mediated imaging.

Multidrug resistance (MDR) is a phenomenon expressed by many tumors affecting the chemotherapy efficacy, treatment decision, and the disease prognosis. Considering its great implication, non-invasive approaches are needed to identify this phenomenon in early stages of the disease. This article discusses the potential of the emerging non-invasive bacterium-mediated imaging of cancer in diagnosis of MDR. This potential is derived from the effect of cancer MDR on the pharmacokinetics of certain antibiotics, which are substrates of the MDR proteins. Since MDR proteins actively pump their substrates outside the resistant cancer cells, the elimination of the employed reporter bacteria, proliferating within MDR cancer cells, would require a larger dose of these antibiotics compared to those inside non-MDR cancer cells. These bacteria bear reporter genes that produce specific signals such as bioluminescent, fluorescent, magnetic, or radioactive signals that can be detected by non-invasive imaging modalities. Therefore, the presence, degree, and mechanism of MDR can be estimated by comparing the concentration of the employed antibiotic, required to cease these signals (reflecting the elimination of the bacteria), to a pre-determined reference. The real time imaging of MDR cancer and the early diagnosis of MDR, offered by this approach, would provide a better tool for preclinical studies of MDR, and allow a prompt choice of the most appropriate therapy.

In-vivo evaluation of clindamycin release from glyceryl monooleate-alginate microspheres by NIR spectroscopy.

The purpose of this study was to use near-infrared (NIR) transmission spectroscopic technique to determine clindamycin plasma concentration after oral administration of clindamycin loaded GMO-alginate microspheres using rabbits as animal models. Lyophilized clindamycin-plasma standard samples at a concentration range of 0.001-10 µg/ml were prepared and analyzed by NIR and HPLC as a reference method. NIR calibration model was developed with partial least square (PLS) regression analysis. Then, a single dose in-vivo evaluation was carried out and clindamycin-plasma concentration was estimated by NIR. Over 24 h time period, the pharmacokinetic parameters of clindamycin were calculated for the clindamycin loaded GMO-alginate microspheres (F3) and alginate microspheres (F2), and compared with the plain drug (F1). PLS calibration model with 7-principal components (PC), and 8000-9200 cm(-1) spectral range shows a good correlation between HPLC and NIR values with root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP), and calibration coefficient (R(2)) values of 0.245, 1.164, and 0.9753, respectively, which suggests that NIR transmission technique can be used for drug-plasma analysis without any extraction procedure. F3 microspheres exhibited controlled and prolonged absorption Tmax of 4.0 vs. 1.0 and 0.5 h; Cmax of 2.37±0.3 vs. 3.81±0.8 and 5.43±0.7 µg/ml for F2 and F1, respectively. These results suggest that the combination of GMO and alginate (1:4 w/w) could be successfully employed for once daily clindamycin microspheres formulation which confirmed by low Cmax and high Tmax values.

MDR-selective microbial-based therapy: a novel approach to cancer treatment.

Microbial-based therapy of cancer is one of the earliest non-surgical anticancer therapies. The main limitation of such therapies is the toxicity of the therapeutic dose. This article discusses a novel approach that exploits cancer multidrug resistance (MDR) to provide a safer microbial-based therapy. As multidrug resistant cells can only contain limited amounts of a variety of susceptible drugs including certain antibiotics, we can take advantage of MDR to create a micro-environment (antibiotic free) that favors growth of intracellular bacteria within cancer cells. Thus, this approach targets cancer cells and spares normal cells (shielded by antibiotic): providing a more selective thus safer anticancer treatment. This article also explores the potentials of Chlamydia pneumoniae as an anti-cancer agent in this MDR-selective microbial-based therapy: its unique life cycle and the immune response to its infection suggest that it could be used directly, in the proposed approach, without any pre-requirements.