Brillouin-Raman micro-spectroscopy and machine learning techniques to classify osteoarthritic lesions in the human articular cartilage

Raman spectroscopic probe provides optical biomarkers of cartilage composition predictive of tissue function

OBJECTIVE

The diagnosis of early osteoarthritis when therapeutic interventions may be most effective at reversing cartilage degeneration presents a clinical challenge. We describe a Raman arthroscopic probe and spectral analysis that measures biomarkers reflective of the content of predominant cartilage extracellular matrix (ECM) constituents-glycosaminoglycans (GAG), collagen, water-essential to cartilage function. We compare the capability of Raman-probe-derived biomarkers to predict functional properties of cartilage to quantitative MRI and histopathology assessments.

DESIGN

Osteochondral blocks were sectioned from 6 bovine femoral condyles with no macroscopic injury (n=62 blocks) and 6 condyles with a focal chondral lesion (n=32 blocks), but no macroscopic degeneration of surrounding cartilage (n=34 blocks). Blocks from 10 human knees were further analyzed (age 27-75; n=235 blocks). Using a custom arthroscopic Raman spectroscopy probe, spectra of chondral layers were measured and subjected to multivariate linear decomposition to extract ECM biomarker scores, reflecting the contribution of each ECM constituent to the spectra. Blocks were further analyzed for elastic modulus, T2/T2* MRI relaxation times, and OARSI scores.

RESULTS

For bovine tissues, Raman biomarkers revealed depleted GAG and cartilage softening peripheral to the lesion despite no macroscopic degeneration. Raman biomarkers accounted for 78% of GAG content variation and 71% of modulus variation. For human tissues, Raman biomarkers accounted for 71% of modulus variation. Raman biomarkers accounted for a greater variation of modulus (71%-72%) than OARSI (12-54%), T2* (15%-27%), or T2 (25%-30%).

CONCLUSIONS

These data support the application of Raman-probe-derived biomarkers for molecular assessment of key ECM constituents that define cartilage properties in health and disease.

Combining Multiple Spectroscopic Techniques to Reveal the Effects of Staphylococcus aureus Infection on Human Bone Tissues

Osteomyelitis (OM) and periprosthetic joint infections (PJIs) are major public health concerns in Western countries due to increased life expectancy. Infections usually occur due to bacterial spread through fractures, implants, or blood-borne transmission. The pathogens trigger an inflammatory response that hinders bone tissue regeneration. Treatment requires surgical intervention, which involves the precise removal of infected tissue, wound cleansing, and local and systemic antibiotic administration. Staphylococcus aureus (SA) is one of the most common pathogens causing infection-induced OM and PJIs. It forms antimicrobial-resistant biofilms and is frequently found in healthcare settings. In this proof-of-concept, we present an approach based on multiple spectroscopic techniques aimed at investigating the effects of SA infection on bone tissue, as well as identifying specific markers useful to detect early bacterial colonization on the tissue surface. A cross-section of a human femoral diaphysis, with negative-culture results, was divided into three parts, and the cortical and trabecular regions were separated from each other. Two portions of each bone tissue type were infected with SA for one and seven days, respectively. Multiple techniques were used to investigate the impact of the infection on bone tissue, Brillouin-Raman microspectroscopy and attenuated total reflection Fourier transform infrared spectroscopy were employed to assess and develop a new noninvasive diagnostic method to detect SA by targeting the bone of the host. The results indicate that exposure to SA infection significantly alters the bone structure, especially in the case of the trabecular type, even after just one day. Moreover, Raman spectral markers of the tissue damage were identified, indicating that this technique can detect the effect of the pathogens' presence in bone biopsies and pave the way for potential application during surgery, due to its nondestructive and contactless nature.

Brillouin spectroscopy of medically relevant samples of bovine jugular vein and pericardium

There is the rapid growth in application of Brillouin scattering spectroscopy to biomedical objects in order to characterize their mechanoelastic properties in this way. However, the possibilities and limitations of the method when applied to tissues have not yet been clarified. Here, applicability of Brillouin spectroscopy for testing the elastic response of medically relevant tissues of bovine jugular vein and pericardium was considered. Parameters of the Brillouin peak were studied for samples untreated, diepoxide-fixed, and preserved after treatment in alcohol solutions. It was found that diepoxide cross-linking resulted to a slight tendency to increase the Brillouin position for hydrated tissues. The variations in the position and width of the Brillouin peaks, associated with local fluctuations in water concentration, were reduced after diepoxide treatment in the case of the pericardium, but not in the case of the vein wall. To obtain more information about the elastic response of the protein scaffold without the participation of water, dried samples were also studied. Brillouin spectra of the dried pericardium and vein wall revealed a significant increase in the Brillouin peak position (elastic modulus) after conservation in alcohol. In the case of the vein wall, this effect was found for both collagen and elastin-related peaks, which were identified in the Brillouin spectrum. This result corresponds to a denser packing of fibrous proteins after preservation in alcohol solutions. The ability of Brillouin spectroscopy to independently characterize the effect of treatment on the instantaneous elastic modulus of various tissue components is also attractive for its application in the development of new materials for bioimplants. A comparison of the Brillouin longitudinal and Young's elastic moduli determined for the hydrated samples of the vein and pericardium showed that there is no clear correspondence between these material parameters. The usefulness of using both experimental methods to obtain new information about the elastic response of the material is discussed.

Understanding and measuring mechanical signals in the tumor stroma

The tumor microenvironment (TME) is well known for its immune suppressive role, especially in solid tumors which are characterized by a thick, dense stroma. Apart from cell-cell interactions and biochemical signals, the tumor stroma is also characterized by its distinct mechanical properties, which are dictated by the composition and architecture of its extracellular matrix (ECM). Cancer-associated fibroblasts (CAFs) are the main producers and remodelers of the stromal ECM, and their heterogeneity has recently become a focus of intense research. This review describes recent findings highlighting CAF subtypes and their specific functions, as well as the development of 3D models to study tumor stroma mechanics in vitro. Finally, we discuss the quantitative techniques used to measure tissue mechanical properties at different scales. Given the diagnostic and prognostic value of stroma stiffness and composition, and the recent development of anti-tumor therapeutic strategies targeting the stroma, understanding and measuring tumor stroma mechanical properties has never been more timely or relevant.

Highly Flexible, High-Performance, and Stretchable Piezoelectric Sensor Based on a Hierarchical Droplet-Shaped Ceramics with Enhanced Damage Tolerance

Stretchable self-powered sensors are of significant interest in next-generation wearable electronics. However, current strategies for creating stretchable piezoelectric sensors based on piezoelectric polymers or 0-3 piezoelectric composites face several challenges such as low piezoelectric activity, low sensitivity, and poor durability. In this paper, a biomimetic soft-rigid hybrid strategy is used to construct a new form of highly flexible, high-performance, and stretchable piezoelectric sensor. Inspired by the hinged bivalve Cristaria plicata, hierarchical droplet-shaped ceramics are manufactured and used as rigid components, where computational models indicate that the unique arched curved surface and rounded corners of this bionic structure can alleviate stress concentrations. To ensure electrical connectivity of the piezoelectric phase during stretching, a patterned liquid metal acts as a soft circuit and a silicone polymer with optimized wettability and stretchability serves as a soft component that forms a strong mechanical interlock with the hierarchical ceramics. The novel sensor design exhibits excellent sensitivity and durability, where the open circuit voltage remains stable after 5000 stretching cycles at 60% strain and 5000 twisting cycles at 180°. To demonstrate its potential in heathcare applications, this new stretchable sensor is successfully used for wireless gesture recognition and assessing the progression of knee osteoarthritis.

Temporal variation of water quality parameters in the lacustrine of the Thrace Region, Northwest Türkiye

Thrace is a part of the Marmara Region northwest of Türkiye. This region hosts several lentic ecosystems used for irrigation and drinking water supply. The present study was conducted to analyze the temporal distributions of water quality parameters (WQPs) of lentic ecosystems (lacustrine habitats), including lakes (L1-L2), reservoirs (R1-R12), and ponds (P1-P19) of the Thrace Region. Thirty-three lacustrine habitats were identified in the region. Freshwaters were collected in the wet (end of winter) and dry (end of summer) seasons of 2021-2022 and tested for 12 WQPs. Data was evaluated for the water quality index (WQI) and nutrient pollution index (NPI) and their overall quality level. For the evaluation of non-carcinogenic health risk indices of WQPs, the chronic daily index (CDI), hazard quotient (HQ), and hazard index (HI) were applied. Cluster analysis (CA), Pearson correlation index (PCI), and principal component analysis (PCA) were used to classify the lacustrine habitats and identify the source of WQPs. The average values were as follows: 9.28 mg/L for dissolved oxygen (DO), 94.6% for oxygen (O2) saturation, 9.29 for pH, 613 μS/cm for electrical conductivity (EC), 3.96 NTU for turbidity, 358 mg/L for total dissolved solids (TDS), 3.17 mg/L for nitrate (NO3), 0.05 mg/L for nitrite (NO2), 1.01 mg/L for phosphate (PO4), 78.5 mg/L for sulfate (SO4), and 102 mg/L for chloride (Cl). Results showed a significant increase in WQPs, including NO3, NO2, and PO4, in the wet season, while the salinity decreased from the dry to wet season. Results revealed that HI values of water contaminants in lacustrine habitats were noted to be less than one. Based on determined WQPs, the present study recommends using lacustrine water habitats for irrigation, drinking, and other domestic and industrial purposes.

An Artificial Neural Network to Eliminate the Detrimental Spectral Shift on Mid-Infrared Gas Spectroscopy

We demonstrate the successful implementation of an artificial neural network (ANN) to eliminate detrimental spectral shifts imposed in the measurement of laser absorption spectrometers (LASs). Since LASs rely on the analysis of the spectral characteristics of biological and chemical molecules, their accuracy and precision is especially prone to the presence of unwanted spectral shift in the measured molecular absorption spectrum over the reference spectrum. In this paper, an ANN was applied to a scanning grating-based mid-infrared trace gas sensing system, which suffers from temperature-induced spectral shifts. Using the HITRAN database, we generated synthetic gas absorbance spectra with random spectral shifts for training and validation. The ANN was trained with these synthetic spectra to identify the occurrence of spectral shifts. Our experimental verification unambiguously proves that such an ANN can be an excellent tool to accurately retrieve the gas concentration from imprecise or distorted spectra of gas absorption. Due to the global shift of the measured gas absorption spectrum, the accuracy of the retrieved gas concentration using a typical least-mean-squares fitting algorithm was considerably degraded by 40.3%. However, when the gas concentration of the same measurement dataset was predicted by the proposed multilayer perceptron network, the sensing accuracy significantly improved by reducing the error to less than ±1% while preserving the sensing sensitivity.