Infrared spectroscopy of synovial fluid as a potential screening approach for the diagnosis of naturally occurring canine osteoarthritis associated with cranial cruciate ligament rupture

Fracture-related infection blood-based biomarkers: Diagnostic strategies

Fracture-related infections are significant postoperative complications that carry substantial patient burden and additional healthcare costs. Despite their impact on outcome, early diagnosis of these infections remains challenging due to current available tests lacking acceptable diagnostic parameters. This review compiles existing information on blood-based biomarkers that have been evaluated as early diagnostic tools and highlights the challenges in their reliability. To begin to overcome these challenges new avenues of biomarker discovery utilizing "omics" technologies and novel analytical methods are being investigated in recent years. It appears that, despite their complexity, these newer approaches may be the future in biomarker discovery for fracture-related infection diagnosis.

Infrared Spectroscopy of Synovial Fluid Shows Accuracy as an Early Biomarker in an Equine Model of Traumatic Osteoarthritis

Osteoarthritis is a leading cause of lameness and joint disease in horses. A simple, economical, and accurate diagnostic test is required for routine screening for OA. This study aimed to evaluate infrared (IR)-based synovial fluid biomarker profiling to detect early changes associated with a traumatically induced model of equine carpal osteoarthritis (OA). Unilateral carpal OA was induced arthroscopically in 9 of 17 healthy thoroughbred fillies; the remainder served as Sham-operated controls. The median age of both groups was 2 years. Synovial fluid (SF) was obtained before surgical induction of OA (Day 0) and weekly until Day 63. IR absorbance spectra were acquired from dried SF films. Following spectral pre-processing, predictive models using random forests were used to differentiate OA, Sham, and Control samples. The accuracy for distinguishing between OA and any other joint group was 80%. The classification accuracy by sampling day was 87%. For paired classification tasks, the accuracies by joint were 75% for OA vs. OA Control and 70% for OA vs. Sham. The accuracy for separating horses by group (OA vs. Sham) was 68%. In conclusion, SF IR spectroscopy accurately discriminates traumatically induced OA joints from controls.

Breast cancer early detection by using Fourier-transform infrared spectroscopy combined with different classification algorithms

Early detection of breast cancer is of great value in improving the prognosis. The current detection methods of breast cancer have their own limitations. In this study, we investigated the feasibility of Fourier Transform Infrared (FT-IR) spectroscopy combined with different classification algorithms for the early detection of breast cancer in a large sample of 526 patients, including 308 invasive breast cancer, 101 ductal carcinoma in situ, and 117 healthy controls. The serum was measured with FT-IR spectroscopy. Kennard-Stone (KS) algorithm was used to divide the data into the training set and testing set. Support vector machine (SVM) model and back propagation neural network (BPNN) model were used to distinguish ductal carcinoma in situ, invasive breast cancer from healthy controls. The accuracies of the SVM model and BPNN model were 92.9% and 94.2%. To determine the effect of different material absorption bands on early detection, the band was divided into four parts including 900-1425 cm^-1, 1475-1710 cm^-1, 2800-3000 cm^-1, and 3090-3700 cm^-1, to be modeled and detected respectively. The final results showed that the ranges 900-1425 cm^-1 and 1475-1710 cm^-1 had superior classification accuracies. The region 900-1425 cm^-1 corresponded to the lipids, proteins, sugar, and nucleic acids, and the region 1475-1710 cm^-1 corresponded to the proteins. The biochemical substances in other bands also contributed some unique potential to the classification, so the classification accuracy was the best in the full band. The study indicates that serum FT-IR spectroscopy combined with SVM and BPNN models is an effective tool for the early detection of breast cancer.

Utility of Plasma Protein Biomarkers and Mid-infrared Spectroscopy for Diagnosing Fracture-related Infections: A Pilot Study

OBJECTIVES

To compare a large panel of plasma protein inflammatory biomarkers and mid-infrared (MIR) spectral patterns in patients with confirmed fracture-related infections (FRIs) with those in controls without infection.

DESIGN

Prospective case-control study.

SETTING

Academic, Level 1 trauma center.

PATIENTS

Thirteen patients meeting confirmatory FRI criteria were matched to 13 controls based on age, time after surgery, and fracture region.

INTERVENTION

Plasma levels of 49 proteins were measured using enzyme-linked immunosorbent assay techniques. Fourier transform infrared spectroscopy of dried films was used to obtain MIR spectra of plasma samples.

MAIN OUTCOME MEASUREMENTS

The main outcome measurements included plasma protein levels and MIR spectra of samples.

RESULTS

Multivariate analysis-based predictive model developed using enzyme-linked immunosorbent assay-based biomarkers had sensitivity, specificity, and accuracy of 69.2% ± 0.0%, 99.9% ± 1.0%, and 84.5% ± 0.6%, respectively, with platelet-derived growth factor-AB/BB, C-reactive protein, and MIG selected as the minimum number of variables explaining group differences ( P < 0.05). Sensitivity, specificity, and accuracy of the predictive model based on MIR spectra were 69.9% ± 6.2%, 71.9% ± 5.9%, and 70.9% ± 4.8%, respectively, with 6 wavenumbers as explanatory variables ( P < 0.05).

CONCLUSIONS

This pilot study demonstrates the feasibility of using a select panel of plasma proteins and Fourier transform infrared spectroscopy to diagnose FRIs. Preliminary data suggest that the measurement of these select proteins and MIR spectra may be potential clinical tools to detect FRIs. Further investigation of these biomarkers in a larger cohort of patients is warranted.

LEVEL OF EVIDENCE

Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.