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Single-walled carbon nanotube biosensor for real-time monitoring of nitric oxide in inflammatory responses
No full textOsteoarthritis (OA) is a degenerative inflammatory joint disease affecting millions of people worldwide. The early detection of OA and the continuous monitoring of its progression are essential for managing the disease. In this study, we develop an optical system for monitoring OA-related inflammation by detecting nitric oxide (NO), a molecule that is overproduced in joints during OA. The NO sensor is based on fluorescent single-walled carbon nanotubes (SWCNTs) coated with single-stranded DNA (ssDNA). The sensor fluorescence was characterized in the presence of cells and biological tissue using a custom-built optical shortwave infrared (SWIR) reader with LED excitation centered at 657 nm and 726 nm and emissions collected above 1000 nm. The ssDNA-SWCNTs were embedded in gelatin methacryloyl (GelMA) hydrogels to monitor the release of NO in inflamed (1 ng/mL IL1β) bovine chondrocytes over 48 h. The sensors show a concentration-dependent mechanical stability, maintaining a stable Young's modulus for at least 30 days at 1:10 ssDNA-SWCNT:GelMA mixing ratios (17.8 mg/L SWCNTs). The sensor was incorporated into a custom microfabricated sensor tag that was surgically inserted ex vivo into bovine and human knees. The reader measurements confirm measurable SWIR signal depths of up to 6 mm under the skin and 6 mm under muscle tissue. The measurements further confirm no significant sensor tag displacement after 2500 flexion knee cycles. The custom ssDNA-SWCNT sensor tag and reader thus demonstrate a potential pathway for integrating SWIR technologies into clinical and orthopedic applications
Blood transfusions and hemoglobin behavior in major upper gastrointestinal tract cancer surgery
No full textThe passive stretching response of the human biceps femoris long head muscle varies regionally
No full textThe in vivo passive behavior of biceps femoris long head (BFlh) across different regions remains poorly understood despite its relevance for improving localized muscle force estimation in musculoskeletal models and understanding the mechanisms of hamstring strain injury. We investigated the region-specific passive stretch response of BFlh during passive knee extension in 20 healthy participants (17 males, 3 females). Using shear wave elastography, the shear modulus of BFlh was assessed at proximal, middle, and distal regions at 5° increments from 90° to 0° of knee flexion. A piecewise exponential model was fitted to the shear modulus-knee joint angle relationship to determine the slack angle (i.e., the knee joint angle at which the shear modulus began to increase), slack shear modulus (i.e., shear modulus before slack angle), and the exponential increase beyond slack angle (α). Slack angle differed significantly across regions (p = 0.040), occurring at a higher knee flexion angle in the distal (63.8 ± 14.1°) compared to proximal (52.8 ± 10.6°, p = 0.031) region. The distal region (0.0123 ± 0.0069) had a larger α than the proximal region (0.0080 ± 0.0048, p = 0.039), but this effect was not observed when assessing only males (p = 0.135). No significant regional differences were observed for slack shear modulus. Overall, the passive stretch response of BFlh to knee extension varies across regions, with slack angle at more flexed knee angles and steeper increase in shear modulus in the distal than the proximal region, though the latter was evident only when including female participants. However, given the low number of female participants, this finding should be interpreted with caution, and future studies including larger female cohorts are warranted. These results have important implications for BFlh muscle function, injury risk, and validity of musculoskeletal modeling estimates
Audiovisual-cognition-inspired network with explainability for oil price forecasting
No full textAccurate forecasting of the oil price is crucial for stabilizing the energy economy and improving investment and government decision-making. However, due to the complex nonlinear fluctuations in oil price series, this task is extremely challenging. Furthermore, most deep forecasting networks struggle to handle the impact of emergencies on oil prices and lack model explainability, which reduces their credibility and applicability. Inspired by biomimetic principles, this paper develops an audiovisual-cognition-inspired network (AC-Net) for oil price forecasting during the COVID-19 pandemic and the Russia-Ukraine conflict. Specifically, the audiovisual cognition offers the useful framework to design AC-Net with audiovisual extraction, brain analysis, and forecasting components, further enhancing the model structure rationality. Imitating the process by which light and sound stimulus are transmitted into electricity signals by neurons, the audiovisual extraction employs multi-kernel convolution operations and parallel gated mechanisms to extract features at different scales and identify key features, aiding in the detection of nonlinear changes. Simulating the independent processing of electrical signals by the left and right hemispheres, the brain analysis utilizes two structures activated by self-attention and gated mechanisms to capture time dependencies, increasing the feature completeness. Simulating the process of transmitting processed information to the higher cerebral cortices for environmental comprehension, the forecasting component compares three fusion strategies with attention, gated, and concatenate, and the best one generates forecasts. Moreover, the kernel loss function reveals nonlinear errors in a higher-dimensional space and trains the proposed network, and two post-hoc explainability technologies analyses model global and local explainability. Overall, the combination of the above components not only ensures that the proposed method can effectively address emergency oil price shocks but also increases safety and trustworthiness. Finally, the experiments and discussions present that the proposed method significantly outperforms benchmark models, achieving mean absolute percent error values of 2.8768%, 1.1796%, and 2.0625% on datasets from the COVID-19 shock period, the COVID-19 stabilization period, and the Russia-Ukraine conflict period, respectively, showcasing high-performance oil price forecasting
Differential Associations of PFAS in Follicular Fluid and Serum with Reproductive Hormones: Insights from a Chinese IVF Cohort GIVES
No full textPer- and polyfluroroalkyl substances (PFAS) are potential endocrine disruptors, but their associations with reproductive hormones remain inconsistent, and evidence linking PFAS in ovarian follicular fluid (FF) to hormones is limited. In this study, we investigated these associations among 501 women undergoing in vitro fertilization (IVF) treatment from the GIVES cohort, with PFAS levels measured in follicular fluid from all participants and in serum from a subset of 410 women. Serum samples were collected on menstrual cycle days 2–5 and the ovulation trigger day for hormone measurement, and PFAS levels were assessed in FF and serum. We found distinct associations between PFAS in FF and serum with reproductive hormones. For example, the highest quartile of FF 6:2 Cl-PFESA was negatively associated with basal luteinizing hormone (LH) (β = −0.13, 95 %CI: 0.23, −0.02), while that of serum 6:2 Cl-PFESA was positively associated with basal LH (β = 0.13, 95 %CI: 0.01, 0.25), compared to the lowest quartile. Additionally, significant associations were identified between certain PFAS and hormones following controlled ovarian stimulation. Bayesian Kernel Machine Regression models indicated a negative association between a mixture of nine FF PFAS and basal follicle stimulating hormone. These findings provide new insights into PFAS exposure and female reproductive hormones, highlighting the importance of measuring PFAS in multiple biological matrices to comprehensively assess their effects on ovarian health
Active commuting for a healthier workforce : Associations of active commute with self-rated health, work ability, and sickness absence
No full textOppimiseen vaikuttavat tekijät etä- ja monimuoto-opetuksen aikakaudella : Läsnäolo-opetus, suoratoisto, vodcast ja podcast terveydenhuollon opetuksessa
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