Search

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage.

This research work presents a computational investigation of hafnium(IV) oxide and its crystals doped by Si, Ge and Sn atoms, replacing the oxygen atom in HfO2. Hafnium(IV) oxide, which has a wide band gap, has been used in power electronics applications as insulator for resistive random access memory (RRAM) in metal–oxide–semiconductor field effect transistors (MOSFETs). However, this gives rise to the serious issue of high resistance in optoelectronic devices. In this work, hafnium(IV) oxide was selected to investigate the change in band gap after large-area-surface doping with Si, Ge, and Sn. The lattice parameters and band gap values were calculated using the Perdew–Burke–Ernzerhof (PBE), revised Perdew–Burke–Ernzerhof (RPBE), Perdew–Wang (PW91), Wu–Cohen (WC), and Perdew–Burke–Ernzerhof for solids (PBEsol) nonlocal functionals of the generalized gradient approximation (GGA). This first-principles method based on density functional theory (DFT) was employed to investigate the structural geometry, electronic structure, and optical properties using conventional calculations for HfO2 executed with the computational tools of the CASTEP code from the Materials Studio 8.0 software program. The band gap was recorded at 4.340 eV, 2.033 eV, 1.686 eV, and 3.210 eV for HfO2, Hf0.88Si0.12O2, Hf0.88Ge0.12O2, and Hf0.88Sn0.12O2 crystals, respectively, through GGA with the PBE functional. Further investigations were conducted for GGA with RPBE, GGA with PW91, GGA with WC, and GGA with PBEsol. The results revealed band gap values for HfO2, Hf0.88Si0.12O2, Hf0.88Ge0.12O2, and Hf0.88Sn0.12O2 of 4.427 eV, 2.093 eV, 1.744 eV, and 3.262 eV, respectively, using GGA with WC; 4.333 eV, 2.032 eV, 1.675 eV, and 3.172 eV, respectively, using GGA with PW91; 4.252 eV, 2.002 eV, 1.632 eV, and 3.086 eV, respectively, using GGA with WC; and 4.245 eV, 2.001 eV, 1.629 eV, and 3.076 eV, respectively, using GGA with PBEsol. However, the PBE functional of GGA showed the best electronic band gap, wh

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

Injuries to the ligaments of the knee commonly impact vulnerable and physically active individuals. These injuries can lead to the development of degenerative diseases such as post-traumatic osteoarthritis (PTOA). Non-invasive optical modalities, such as infrared and Raman spectroscopy, provide means for quantitative evaluation of knee joint tissues and have been proposed as potential quantitative diagnostic tools for arthroscopy. In this study, we evaluate Raman spectroscopy as a viable tool for estimating functional properties of collateral ligaments. Artificial trauma was induced by anterior cruciate ligament transection (ACLT) in the left or right knee joint of skeletally mature New Zealand rabbits. The corresponding contralateral (CL) samples were extracted from healthy unoperated joints along with a separate group of control (CNTRL) animals. The rabbits were sacrificed at 8 weeks after ACLT. The ligaments were then harvested and measured using Raman spectroscopy. A uniaxial tensile stress-relaxation testing protocol was adopted for determining several biomechanical properties of the samples. Partial least squares (PLS) regression models were then employed to correlate the spectral data with the biomechanical properties. Results show that the capacity of Raman spectroscopy for estimating the biomechanical properties of the ligament samples varies depending on the target property, with prediction error ranging from 15.78% for tissue cross-sectional area to 30.39% for stiffness. The hysteresis under cyclic loading at 2 Hz (RMSE = 6.22%, Normalized RMSE = 22.24%) can be accurately estimated from the Raman data which describes the viscous damping properties of the tissue. We conclude that Raman spectroscopy has the potential for non-destructively estimating ligament biomechanical properties in health and disease, thus enhancing the diagnostic value of optical arthroscopic evaluations of ligament integrity.

Objective: To evaluate the feasibility of Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy to detect cartilage degradation due to osteoarthritis and to validate the methodology with osteochondral human cartilage samples for future development towards clinical use. Design: Cylindrical (d ​= ​4 ​mm) osteochondral samples (n ​= ​349) were prepared from nine human cadavers and measured with FTIR-ATR spectroscopy. Afterwards, the samples were assessed with Osteoarthritis Research Society International (OARSI) osteoarthritis cartilage histopathology assessment system and divided into two groups: 1) healthy (OARSI 0–2) and 2) osteoarthritic (OARSI 2.5–6). The classification was done with partial least squares discriminant analysis model utilizing cross-model validation. Receiver operating characteristics curve analysis was performed and the area under curve (AUC) was calculated. Results: For all samples combined, classification accuracy was 73% with AUC of 0.79. Femoral samples had accuracy of 74% and AUC of 0.77, while tibial samples had accuracy of 66%, and AUC of 0.74. Patellar samples had accuracy of 84% and AUC of 0.91. Conclusions: The results indicate that FTIR-ATR spectroscopy can differentiate between healthy and osteoarthritic femoral, tibial and patellar human tissue. If combined with a fiber optic probe, FTIR-ATR spectroscopy could provide additional objective intraoperative information during arthroscopic surgeries, which could improve clinical outcomes.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC.

Raman spectroscopy is promising in vivo tool in various biomedical applications; moreover, in recent years, its use for characterizing articular cartilage degeneration has been developing. It has also shown potential for scoring the severity of cartilage lesions, which could be useful in determining the optimal treatment strategy during cartilage repair surgery. However, the effect of different cartilage injury types on Raman spectra is unknown. This study aims to investigate the potential of Raman spectroscopy for detecting changes in cartilage due to different injury types. Artificial injuries were induced in cartilage samples using established mechanical and enzymatic approaches to mimic trauma‐induced and natural degeneration. Mechanical damage was induced using surface abrasion (ABR, n = 12) or impact loading (IMP, n = 12), while enzymatic damage was induced using three different treatments: 30 min trypsin digestion (T30, n = 12), 90 min collagenase digestion (C90, n = 12), and 24 h collagenase digestion (C24, n = 12). Raman spectra were obtained from all specimens, and partial least squares discriminant analysis (PLS‐DA) was used to distinguish cartilage injury types from their respective controls. PLS‐DA cross‐validation accuracies were higher for C24 (88%) and IMP (79%) than for C90 (67%), T30 (63%), and ABR (58%) groups. This study indicates that Raman spectroscopy, combined with multivariate analysis, can discern different cartilage injury types. This knowledge could be useful in clinical decision‐making, for example, selecting the optimal treatment remedy during cartilage repair surgery.

The Triangular Theory of Love (measured with Sternberg’s Triangular Love Scale–STLS) is a prominent theoretical concept in empirical research on love. To expand the culturally homogeneous body of previous psychometric research regarding the STLS, we conducted a large-scale cross-cultural study with the use of this scale. In total, we examined more than 11,000 respondents, but as a result of applied exclusion criteria, the final analyses were based on a sample of 7332 participants from 25 countries (from all inhabited continents). We tested configural invariance, metric invariance, and scalar invariance, all of which confirmed the cultural universality of the theoretical construct of love analyzed in our study. We also observed that levels of love components differ depending on relationship duration, following the dynamics suggested in the Triangular Theory of Love. Supplementary files with all our data, including results on love intensity across different countries along with STLS versions adapted in a few dozen languages, will further enable more extensive research on the Triangular Theory of Love.

Interpersonal touch behavior differs across cultures, yet no study to date has systematically tested for cultural variation in affective touch, nor examined the factors that might account for this variability. Here, over 14,000 individuals from 45 countries were asked whether they embraced, stroked, kissed, or hugged their partner, friends, and youngest child during the week preceding the study. We then examined a range of hypothesized individual-level factors (sex, age, parasitic history, conservatism, religiosity, and preferred interpersonal distance) and cultural-level factors (regional temperature, parasite stress, regional conservatism, collectivism, and religiosity) in predicting these affective-touching behaviors. Our results indicate that affective touch was most prevalent in relationships with partners and children, and its diversity was relatively higher in warmer, less conservative, and religious countries, and among younger, female, and liberal people. This research allows for a broad and integrated view of the bases of cross-cultural variability in affective touch.

This study evaluates the feasibility of near infrared (NIR) spectroscopy to distinguish between different cartilage injury types associated with post-traumatic osteoarthritis and idiopathic osteoarthritis (OA) induced by mechanical and enzymatic damages. Bovine osteochondral samples (n = 72) were subjected to mechanical (n = 24) and enzymatic (n = 36) damage; NIR spectral measurements were acquired from each sample before and after damage, and from a separate control group (n = 12). Biomechanical measurements were then conducted to determine the functional integrity of the samples. NIR spectral variations resulting from different damage types were investigated and the samples classified using partial least squares discriminant analysis (PLS-DA). Partial least squares regression (PLSR) was then employed to investigate the relationship between the NIR spectra and biomechanical properties of the samples. Results of the study demonstrate that substantial spectral changes occur in the region of 1700–2200 nm due to tissue damages, while differences between enzymatically and mechanically induced damages can be observed mainly in the region of 1780–1810 nm. We conclude that NIR spectroscopy, combined with multivariate analysis, is capable of discriminating between cartilage injuries that mimic idiopathic OA and traumatic injuries based on specific spectral features. This information could be useful in determining the optimal treatment strategy during cartilage repair in arthroscopy.