Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data for Advanced Colorimetric E-Nose.

The colorimetric sensor-based electronic nose has been demonstrated to discriminate specific gaseous molecules for various applications, including health or environmental monitoring. However, conventional colorimetric sensor systems rely on RGB sensors, which cannot capture the complete spectral response of the system. This limitation can degrade the performance of machine learning analysis, leading to inaccurate identification of chemicals with similar functional groups. Here, we propose a novel time-resolved hyperspectral (TRH) data set from colorimetric array sensors consisting of 1D spatial, 1D spectral, and 1D temporal axes, which enables hierarchical analysis of multichannel 2D spectrograms via a convolution neural network (CNN). We assessed the outstanding classification performance of the TRH data set compared to an RGB data set by conducting a relative humidity (RH) concentration classification. The time-dependent spectral response of the colorimetric sensor was measured and trained as a CNN model using TRH and RGB sensor systems at different RH levels. While the TRH model shows a high classification accuracy of 97.5% for the RH concentration, the RGB model yields 72.5% under identical conditions. Furthermore, we demonstrated the detection of various functional volatile gases with the TRH system by using experimental and simulation approaches. The results reveal distinct spectral features from the TRH system, corresponding to changes in the concentration of each substance.

3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer.

The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10-6 M (femtomole-(10-17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.

Non-intrusive quality appraisal of differentiation-induced cardiovascular stem cells using E-Nose sensor technology.

Stem cell technology holds immense potential for revolutionizing medicine, particularly in regenerative treatment for heart disease. The unique capacity of stem cells to differentiate into diverse cell types offers promise in repairing damaged tissues and implanting organs. Ensuring the quality of differentiated cells, essential for specific functions, demands in-depth analysis. However, this process consumes time and incurs substantial costs while invasive methods may alter stem cell features during differentiation and deplete cell numbers. To address these challenges, we propose a non-invasive strategy, using cellular respiration, to assess the quality of differentiation-induced stem cells, notably cardiovascular stem cells. This evaluation employs an electronic nose (E-Nose) and neural pattern separation (NPS). Our goal is to assess differentiation-induced cardiac stem cells (DICs) quality through E-Nose data analysis and compare it with standard commercial human cells (SCHCs). Sensitivity and specificity were evaluated by interacting SCHCs and DICs with the E-Nose, achieving over 90% classification accuracy. Employing selective combinations optimized by NPS, E-Nose successfully classified all six cell types. Consequently, the relative similarity among DICs like cardiomyocytes, endothelial cells with SCHCs was established relied on comparing response data from the E-Nose sensor without resorting to complex evaluations.

Dissociation of hydrogen peroxide in water and methanol through a biased molecular dynamics investigation.

The two dissociation channels of HOOH, namely, HOOH and HOOH, in water and methanol are investigated using umbrella-sampling ab initio molecular dynamics. Our potential of mean force calculations reveals the HOOH dissociation to be more favorable in methanol with a free energy barrier of 7.56 kcal/mol, while the HOOH dissociation possesses a free energy barrier of 11.46 kcal/mol. In water, the HOOH dissociation channel is more favorable (8.25 kcal/mol), while the HOOH dissociation process requires a higher free energy (11.28 kcal/mol). Such reaction favorability can be explained by inspecting the formation of secondary radical species during the course of multiple hydrogen donating-accepting processes in each reaction channel. The radical species, that is, H3 O• (observed in water) and CH3 OH2• (observed in methanol), are the first subordinate species upon the HOOH dissociation. For the HOOH dissociation channel in methanol, the secondary species such as water and formaldehyde can be observed, while the re-generation of HOOH in water can be spotted.

Self-selected step length asymmetry is not explained by energy cost minimization in individuals with chronic stroke.

BACKGROUND: Asymmetric gait post-stroke is associated with decreased mobility, yet individuals with chronic stroke often self-select an asymmetric gait despite being capable of walking more symmetrically. The purpose of this study was to test whether self-selected asymmetry could be explained by energy cost minimization. We hypothesized that short-term deviations from self-selected asymmetry would result in increased metabolic energy consumption, despite being associated with long-term rehabilitation benefits. Other studies have found no difference in metabolic rate across different levels of enforced asymmetry among individuals with chronic stroke, but used methods that left some uncertainty to be resolved. METHODS: In this study, ten individuals with chronic stroke walked on a treadmill at participant-specific speeds while voluntarily altering step length asymmetry. We included only participants with clinically relevant self-selected asymmetry who were able to significantly alter asymmetry using visual biofeedback. Conditions included targeting zero asymmetry, self-selected asymmetry, and double the self-selected asymmetry. Participants were trained with the biofeedback system in one session, and data were collected in three subsequent sessions with repeated measures. Self-selected asymmetry was consistent across sessions. A similar protocol was conducted among unimpaired participants. RESULTS: Participants with chronic stroke substantially altered step length asymmetry using biofeedback, but this did not affect metabolic rate (ANOVA, p = 0.68). In unimpaired participants, self-selected step length asymmetry was close to zero and corresponded to the lowest metabolic energy cost (ANOVA, p = 6e-4). While the symmetry of unimpaired gait may be the result of energy cost minimization, self-selected step length asymmetry in individuals with chronic stroke cannot be explained by a similar least-effort drive. CONCLUSIONS: Interventions that encourage changes in step length asymmetry by manipulating metabolic energy consumption may be effective because these therapies would not have to overcome a metabolic penalty for altering asymmetry.

Zirconia Phase Transformation in Zirconia-Toughened Alumina Ceramic Femoral Heads: An Implant Retrieval Analysis.

BACKGROUND: Zirconia-toughened alumina ceramic was introduced as a femoral head material for total hip arthroplasty. The material combines the stability of alumina with the toughness of zirconia. Despite inherent benefits for bearing surfaces, concern exists in the medical field that phase transformation of the zirconia grains could worsen wear resistance and lower the strength of the head. We examined these concerns in retrieved and artificially aged ceramic heads. METHODS: Twenty-eight ceramic composite heads retrieved at revision surgery were combined with 5 pristine heads (as negative controls for phase transformation) and 5 artificially aged pristine heads (as positive controls). The extent of zirconia phase transformation at the bearing surfaces was examined through confocal Raman spectroscopy and X-ray diffraction. Burst testing was conducted on all pristine and aged heads and the 4 retrieved implants with the longest lengths of implantation. RESULTS: Retrieved heads had higher maximum average volume fractions of the monoclinic phase compared to pristine or aged heads. Length of implantation was not correlated to the volume fraction of the monoclinic phase. All the heads achieved a burst load far above the 46 kN Food and Drug Administration acceptance criterion; 3 of the 4 retrieved heads had burst strengths exceeding 100kN. CONCLUSION: Our results showed that phase transformation occurs in vivo in ceramic composite femoral heads, but the amount transformed did not increase with the length of time the head had been implanted. The negligible effect upon burst strength of the retrieved and artificially aged heads is reassuring. These results support continued clinical use of this alumina-zirconia composite material as a head material.

Mechanically induced bone formation is not sensitive to local osteocyte density in rat vertebral cancellous bone.

Osteocytes play an integral role in bone by sensing mechanical stimuli and releasing signaling factors that direct bone formation. The importance of osteocytes in mechanotransduction suggests that regions of bone tissue with greater osteocyte populations are more responsive to mechanical stimuli. To determine the effects of osteocyte population on bone functional adaptation we applied mechanical loads to the 8th caudal vertebra of skeletally mature female Sprague Dawley rats (6 months of age, n = 8 loaded, n = 8 sham controls). The distribution of tissue stress/strain within cancellous bone was determined using high-resolution finite element models, osteocyte distribution was determined using nano-computed tomography, and locations of bone formation were determined using three-dimensional images of fluorescent bone formation markers. Loading increased bone formation (3D MS/BS 10.82 ± 2.09% in loaded v. 3.17 ± 2.05% in sham control, mean ± SD). Bone formation occurred at regions of cancellous bone experiencing greater tissue stress/strain, however stress/strain was only a modest predictor of bone formation; even at locations of greatest stress/strain the probability of observing bone formation did not exceed 41%. The local osteocyte population was not correlated with locations of new bone formation. The findings support the idea that local tissue stress/strain influence the locations of bone formation in cancellous bone, but suggest that the size of the osteocyte population itself is not influential. We conclude that other aspects of osteocytes such as osteocyte connectivity, lacunocanilicular nano-geometry, and/or fluid pressure/shear distributions within the marrow space may be more influential in regulating bone mechanotransduction than the number of osteocytes. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:672-681, 2018.

Retrieval Analysis of Neck-Stem Coupling in Modular Hip Prostheses.

BACKGROUND: Dual-taper modular stems have suffered from high revision rates caused by adverse local tissue reactions secondary to fretting and corrosion. We compared the fretting and corrosion behavior of a group of modular neck designs to that of a design that had been recalled for risks associated with fretting and corrosion at the modular neck junction. METHODS: We previously analyzed fretting and corrosion on 60 retrieved Rejuvenate modular neck-stem implants. Here we compare those results to results from 26 retrieved implants from 7 other modular neck designs. For the 26 additional cases, histology slides of tissue collected at revision were reviewed and graded for aseptic lymphocyte-dominated vasculitis-associated lesion (ALVAL). Multivariate analyses were performed to assess differences in fretting and corrosion, adjusting for confounding factors (eg, length of implantation). RESULTS: The Rejuvenate design had higher damage and corrosion scores than the other 7 designs (P < .01). Histologic samples from the recalled design were 20 times more likely to show ALVAL than samples from the other designs (P < .01). Mixed metal couples had higher fretting (P < .01) and corrosion (P = .02) scores than non-mixed metal couples. CONCLUSION: Fretting and corrosion occurred on all modular neck-stem retrievals regardless of design. However, mixed metal couples suffered more corrosion than homogenous couples. This may be due to the lower modulus of the titanium alloy used for the stem, allowing for increased metal transfer and surface damage when loaded against a cobalt alloy modular neck, which in turn could account for the higher ALVAL and corrosion scores. Due to increased corrosion risk with mixed metals and increased neck fracture risk with non-mixed metal stem and necks, we suggest that clinicians avoid implantation of modular neck-stem systems.