Enhancing Bone Infection Diagnosis with Raman Handheld Spectroscopy: Pathogen Discrimination and Diagnostic Potential.

Osteomyelitis is a bone disease caused by bacteria that can damage bone. Raman handheld spectroscopy has emerged as a promising diagnostic tool for detecting bone infection and can be used intraoperatively during surgical procedures. This study involved 120 bone samples from 40 patients, with 80 samples infected with either Staphylococcus aureus or Staphylococcus epidermidis. Raman handheld spectroscopy demonstrated successful differentiation between healthy and infected bone samples and between the two types of bacterial pathogens. Raman handheld spectroscopy appears to be a promising diagnostic tool in bone infection and holds the potential to overcome many of the shortcomings of traditional diagnostic procedures. Further research, however, is required to confirm its diagnostic capabilities and consider other factors, such as the limit of pathogen detection and optimal calibration standards.

Determination of the clean-up efficiency of the solid-phase extraction of rosemary extracts: Application of full-factorial design in hyphenation with Gaussian peak fit function.

We present a novel method for the quantitative determination of the clean-up efficiency to provide a calculated parameter for peak purity through iterative fitting in conjunction with design of experiments. Rosemary extracts were used and analyzed before and after solid-phase extraction using a self-fabricated mixed-mode sorbent based on poly(N-vinylimidazole/ethylene glycol dimethacrylate). Optimization was performed by variation of washing steps using a full three-level factorial design and response surface methodology. Separation efficiency of rosmarinic acid from interfering compounds was calculated using an iterative fit of Gaussian-like signals and quantifications were performed by the separate integration of the two interfering peak areas. Results and recoveries were analyzed using Design-Expert® software and revealed significant differences between the washing steps. Optimized parameters were considered and used for all further experiments. Furthermore, the solid-phase extraction procedure was tested and compared with commercial available sorbents. In contrast to generic protocols of the manufacturers, the optimized procedure showed excellent recoveries and clean-up rates for the polymer with ion exchange properties. Finally, rosemary extracts from different manufacturing areas and application types were studied to verify the developed method for its applicability. The cleaned-up extracts were analyzed by liquid chromatography with tandem mass spectrometry for detailed compound evaluation to exclude any interference from coeluting molecules.

Handheld hyperspectral imaging as a tool for the post-mortem interval estimation of human skeletal remains.

In forensic medicine, estimating human skeletal remains' post-mortem interval (PMI) can be challenging. Following death, bones undergo a series of chemical and physical transformations due to their interactions with the surrounding environment. Post-mortem changes have been assessed using various methods, but estimating the PMI of skeletal remains could still be improved. We propose a new methodology with handheld hyperspectral imaging (HSI) system based on the first results from 104 human skeletal remains with PMIs ranging between 1 day and 2000 years. To differentiate between forensic and archaeological bone material, the Convolutional Neural Network analyzed 65.000 distinct diagnostic spectra: the classification accuracy was 0.58, 0.62, 0.73, 0.81, and 0.98 for PMIs of 0 week-2 weeks, 2 weeks-6 months, 6 months-1 year, 1 year-10 years, and >100 years, respectively. In conclusion, HSI can be used in forensic medicine to distinguish bone materials >100 years old from those <10 years old with an accuracy of 98%. The model has adequate predictive performance, and handheld HSI could serve as a novel approach to objectively and accurately determine the PMI of human skeletal remains.

Comparison of Mid-Infrared Handheld and Benchtop Spectrometers to Detect Staphylococcus epidermidis in Bone Grafts.

Bone analyses using mid-infrared spectroscopy are gaining popularity, especially with handheld spectrometers that enable on-site testing as long as the data quality meets standards. In order to diagnose Staphylococcus epidermidis in human bone grafts, this study was carried out to compare the effectiveness of the Agilent 4300 Handheld Fourier-transform infrared with the Perkin Elmer Spectrum 100 attenuated-total-reflectance infrared spectroscopy benchtop instrument. The study analyzed 40 non-infected and 10 infected human bone samples with Staphylococcus epidermidis, collecting reflectance data between 650 cm-1 and 4000 cm-1, with a spectral resolution of 2 cm-1 (Agilent 4300 Handheld) and 0.5 cm-1 (Perkin Elmer Spectrum 100). The acquired spectral information was used for spectral and unsupervised classification, such as a principal component analysis. Both methods yielded significant results when using the recommended settings and data analysis strategies, detecting a loss in bone quality due to the infection. MIR spectroscopy provides a valuable diagnostic tool when there is a tissue shortage and time is of the essence. However, it is essential to conduct further research with larger sample sizes to verify its pros and cons thoroughly.

Post-Mortem Interval of Human Skeletal Remains Estimated with Handheld NIR Spectrometry.

Estimating the post-mortem interval (PMI) of human skeletal remains is a critical issue of forensic analysis, with important limitations such as sample preparation and practicability. In this work, NIR spectroscopy (NIRONE® Sensor X; Spectral Engines, 61449, Germany) was applied to estimate the PMI of 104 human bone samples between 1 day and 2000 years. Reflectance data were repeatedly collected from eight independent spectrometers between 1950 and 1550 nm with a spectral resolution of 14 nm and a step size of 2 nm, each from the external and internal bone. An Artificial Neural Network was used to analyze the 66,560 distinct diagnostic spectra, and clearly distinguished between forensic and archaeological bone material: the classification accuracies for PMIs of 0−2 weeks, 2 weeks−6 months, 6 months−1 year, 1 year−10 years, and >100 years were 0.90, 0.94, 0.94, 0.93, and 1.00, respectively. PMI of archaeological bones could be determined with an accuracy of 100%, demonstrating the adequate predictive performance of the model. Applying a handheld NIR spectrometer to estimate the PMI of human skeletal remains is rapid and extends the repertoire of forensic analyses as a distinct, novel approach.