Search

Here we present a plasma proteomics dataset that was generated to understand the importance of self-reported race for biomarker discovery in Alzheimer's disease. This dataset is related to the article “Why inclusion matters for Alzheimer's disease biomarker discovery in plasma” [1]. Plasma samples were obtained from clinically diagnosed Alzheimer's disease and cognitively normal adults of African American/Black and non-Hispanic White racial and ethnic backgrounds. Plasma was immunodepleted, digested, and isobarically tagged with commercial reagents. Tagged peptides were fractionated using high pH fractionation and resulting fractions analysed by liquid chromatography – mass spectrometry (LC-MS/MS & MS3) analysis on an Orbitrap Fusion Lumos mass spectrometer. The resulting data was processed using Proteome Discoverer to produce a list of identified proteins with corresponding tandem mass tag (TMT) intensity information.

The fact that neutrinos carry a non-vanishing rest mass is evidence ofphysics beyond the Standard Model of elementary particles. Their absolute massbears important relevance from particle physics to cosmology. In this work, wereport on the search for the effective electron antineutrino mass with theKATRIN experiment. KATRIN performs precision spectroscopy of the tritium$\beta$-decay close to the kinematic endpoint. Based on the first fiveneutrino-mass measurement campaigns, we derive a best-fit value of $m_u^{2} ={-0.14^{+0.13}_{-0.15}}~\mathrm{eV^2}$, resulting in an upper limit of $m_u <{0.45}~\mathrm{eV}$ at 90 % confidence level. With six times the statistics ofprevious data sets, amounting to 36 million electrons collected in 259measurement days, a substantial reduction of the background level and improvedsystematic uncertainties, this result tightens KATRIN's previous bound by afactor of almost two.

Some extensions of the Standard Model of particle physics allow for Lorentz invariance and charge-parity-time invariance violations. In the neutrino sector strong constraints have been set by neutrino-oscillation and time-of-flight experiments. However, some Lorentz-invariance-violating parameters are not accessible via these probes. In this work, we focus on the parameters (aof(3))00, (aof(3))10, and (aof(3))11 which would manifest themselves in a nonisotropic β-decaying source as a sidereal oscillation and an overall shift of the spectral endpoint. Based on the data of the first scientific run of the KATRIN experiment, we set the first 90% confidence-level limit on |(aof(3))11| of

In this work we present a keV-scale sterile-neutrino search with a low-tritium-activity data set of the KATRIN experiment, acquired in a commissioning run in 2018. KATRIN performs a spectroscopic measurement of the tritium β -decay spectrum with the main goal of directly determining the effective electron anti-neutrino mass. During this commissioning phase a lower tritium activity facilitated the measurement of a wider part of the tritium spectrum and thus the search for sterile neutrinos with a mass of up to 1.6keV . We do not find a signal and set an exclusion limit on the sterile-to-active mixing amplitude of sin 2θ< 5 × 10 - 4 (95 % C.L.) at a mass of 0.3 keV. This result improves current laboratory-based bounds in the sterile-neutrino mass range between 0.1 and 1.0 keV.

The Karlsruhe Tritium Neutrino (KATRIN) experiment is designed to measure a high-precision integral spectrum of the endpoint region of T2 β decay, with the primary goal of probing the absolute mass scale of the neutrino. After a first tritium commissioning campaign in 2018, the experiment has been regularly running since 2019, and in its first two measurement campaigns has already achieved a sub-eV sensitivity. After 1000 days of data-taking, KATRIN’s design sensitivity is 0.2 eV at the 90% confidence level. In this white paper we describe the current status of KATRIN; explore prospects for measuring the neutrino mass and other physics observables, including sterile neutrinos and other beyond-Standard-Model hypotheses; and discuss research-and-development projects that may further improve the KATRIN sensitivity.

We report on the direct search for cosmic relic neutrinos using data acquired during the first two science campaigns of the KATRIN experiment in 2019. Beta-decay electrons from a high-purity molecular tritium gas source are analyzed by a high-resolution MAC-E filter around the end point at 18.57 keV. The analysis is sensitive to a local relic neutrino overdensity ratio of η

We present the results of the light sterile neutrino search from the second Karlsruhe Tritium Neutrino (KATRIN) measurement campaign in 2019. Approaching nominal activity, 3.76×106 tritium β-electrons are analyzed in an energy window extending down to 40 eV below the tritium end point at E0=18.57 keV. We consider the 3ν+1 framework with three active and one sterile neutrino flavors. The analysis is sensitive to a fourth mass eigenstate m42≲1600 eV2 and active-to-sterile mixing |Ue4|2≳6×10-3. As no sterile-neutrino signal was observed, we provide improved exclusion contours on m42 and |Ue4|2 at 95% C.L. Our results supersede the limits from the Mainz and Troitsk experiments. Furthermore, we are able to exclude the large Δm412 solutions of the reactor antineutrino and gallium anomalies to a great extent. The latter has recently been reaffirmed by the BEST Collaboration and could be explained by a sterile neutrino with large mixing. While the remaining solutions at small Δm412 are mostly excluded by short-baseline reactor experiments, KATRIN is the only ongoing laboratory experiment to be sensitive to relevant solutions at large Δm412 through a robust spectral shape analysis.

Since the discovery of neutrino oscillations, we know that neutrinos have non-zero mass. However, the absolute neutrino-mass scale remains unknown. Here we report the upper limits on effective electron anti-neutrino mass, mν, from the second physics run of the Karlsruhe Tritium Neutrino experiment. In this experiment, mν is probed via a high-precision measurement of the tritium β-decay spectrum close to its endpoint. This method is independent of any cosmological model and does not rely on assumptions whether the neutrino is a Dirac or Majorana particle. By increasing the source activity and reducing the background with respect to the first physics campaign, we reached a sensitivity on mν of 0.7 eV c–2 at a 90% confidence level (CL). The best fit to the spectral data yields mν2 = (0.26 ± 0.34) eV2 c–4, resulting in an upper limit of mν < 0.9 eV c–2 at 90% CL. By combining this result with the first neutrino-mass campaign, we find an upper limit of mν < 0.8 eV c–2 at 90% CL.

AbstractThe KATRIN experiment is designed for a direct and model-independent determination of the effective electron anti-neutrino mass via a high-precision measurement of the tritium $$\upbeta $$ β -decay endpoint region with a sensitivity on $$m_\nu $$ m ν of 0.2 $$\hbox {eV}/\hbox {c}^2$$ eV / c 2 (90% CL). For this purpose, the $$\upbeta $$ β -electrons from a high-luminosity windowless gaseous tritium source traversing an electrostatic retarding spectrometer are counted to obtain an integral spectrum around the endpoint energy of 18.6 keV. A dominant systematic effect of the response of the experimental setup is the energy loss of $$\upbeta $$ β -electrons from elastic and inelastic scattering off tritium molecules within the source. We determined the energy-loss function in-situ with a pulsed angular-selective and monoenergetic photoelectron source at various tritium-source densities. The data was recorded in integral and differential modes; the latter was achieved by using a novel time-of-flight technique. We developed a semi-empirical parametrization for the energy-loss function for the scattering of 18.6-keV electrons from hydrogen isotopologs. This model was fit to measurement data with a 95% $$\hbox {T}_2$$ T 2 gas mixture at 30 K, as used in the first KATRIN neutrino-mass analyses, as well as a $$\hbox {D}_2$$ D 2 gas mixture of 96% purity used in KATRIN commissioning runs. The achieved precision on the energy-loss function has abated the corresponding uncertainty of $$\sigma (m_\nu ^2)< {{10}^{-2}}{\hbox {eV}^{2}}$$ σ ( m ν 2 ) < 10 - 2 eV 2 [1] in the KATRIN neutrino-mass measurement to a subdominant level.

Abstract Steam condensers are vital components of thermal power plants, responsible for converting turbine exhaust steam back into water for reuse in the power generation cycle. Effective pressure regulation is crucial to ensure operational efficiency and equipment safety. However, conventional control strategies, such as PI, PI-PDn and FOPID controllers, often struggle to manage the nonlinearities and disturbances inherent in steam condenser systems. This paper introduces a novel multistage controller, TDn(1 + PIDn), optimized using the diligent crow search algorithm (DCSA). The proposed controller is specifically designed to address system nonlinearities, external disturbances, and the complexities of dynamic responses in steam condensers. Key contributions include the development of a flexible multi-stage control framework and its optimization via DCSA to achieve enhanced stability, faster response times, and reduced steady-state errors. Simulation results demonstrate that the TDn(1 + PIDn) controller outperforms conventional control strategies, including those tuned with advanced metaheuristic algorithms, in terms of settling time, overshoot, and integral of time weighted absolute error (ITAE). This study marks a significant advancement in pressure regulation strategies, providing a robust and adaptive solution for nonlinear industrial systems.

Earth observation satellites possess key characteristics such as high temporal and spatial resolution, wide coverage imaging, multispectral capabilities, and access to a rich archive of images through various gateways. This enables the regular and continuous monitoring of phenomena related to various pollutants on a global scale. Presently, weather models leverage satellite imagery to record and report pollutant levels hourly and daily, allowing for the identification of sources and patterns of pollutants. Remote sensing technology, with its ability to measure concentration and intensity, as well as track the movement and changes in pollutant locations, has multiple applications in assessing air quality. The use of this technology facilitates informed decision-making and expert analysis to reduce air pollution, contributing to the goal of achieving clean and healthy air on the planet. In this report, Sentinel 5 satellite data was utilized to monitor NO2 gas emissions in the city of Ankara. The data was analyzed month by month over the course of a year, providing valuable insights into the trends and patterns of NO2 concentrations.at the end of this article we will find that Google Earth Engine and remote sensing may replace ground stations, shifting their role to validation. The study finds high similarity in ground and satellite data, especially in analyzing NO2 concentration and its seasonality. Utilizing advanced satellite tech, like Google Earth Engine, emphasizes language and technology in addressing environmental challenges for a sustainable future

Abstract In this study, we introduce a novel method that can be seamlessly integrated into existing metacognitive algorithms, significantly enhancing their performance during both exploitation and exploration phases. This method offers several advantages, including ease of implementation and simplicity in calculations, which collectively accelerate convergence to the global minimum and enhance the algorithm's robustness. Notably, it effectively avoids local minima, ensuring the algorithm does not become trapped. Furthermore, this method eliminates the need for developing new metacognitive algorithms. To demonstrate its benefits, we apply this method to the crow search optimization algorithm (CSA), which is notably deficient in convergence speed, robustness, stability, and escaping local minima. Consequently, the enhanced algorithm is termed the diligent crow search optimization algorithm (DCSA). Additionally, we utilize the powerful DCSA algorithm to identify the parameters of solar cells, aiming to maximize power output from solar energy—a critical global concern. To evaluate the proposed algorithm, we tested it on various solar cell models, including one-diode, two-diode, and three-diode configurations, as well as several widely used solar panels such as SM55, KC200GT, and SW255. We also examined the impacts of radiation, temperature, and unknown parameters on these solar panels. The simulation results demonstrate that implementing the proposed method on the crow algorithm resulted in a 98% improvement in stability and a sevenfold increase in convergence speed.

Introduction: Volatile organic compounds (VOCs) are hazardous toxic pollutants in the air, which are released from various industrial sources. Due to the adverse effects of xylene on health, the effective removal of VOCs from the air by nano sorbents is crucial. In this study, nanographene (NG) and nanographene oxide (NGO) were used as adsorbents to investigate the efficiency of xylene removal. Material and Methods: In this study, in order to investigate the absorption efficiency of nanographene and nanographene oxide after the synthesis of nano absorbents in a dynamic system, xylene vapor was produced in a chamber in pure air and stored in a Tedlar sampling bag and then transferred to the adsorbent. Subsequently, the effect of various parameters such as xylene concentration, inlet air flow rate, and absorbent mass values at 32% humidity and 25°C temperature on the absorption rate and performance of the desired absorbents was investigated. Finally, the gas chromatographic flame ion detector (GC-FID) determined the concentration of xylene in air after the adsorption-desorption process. Results: The average adsorption efficiencies for NG and NGO were found to be 96.8% and 17.5%, respectively. The characteristics of the NG and NGO adsorbents indicated that the particle size range was less than 100 nanometers. Conclusion: The results demonstrated that the adsorption efficiency of NG for the removal of xylene from the air is higher than that of NGO. The GC-MS method validated the proposed approach in real air samples.

Abstract The imbalance between generated power and load demand often causes unwanted fluctuations in the frequency and tie-line power changes within a power system. To address this issue, a control process known as load frequency control (LFC) is essential. This study aims to optimize the parameters of the LFC controller for a two-area power system that includes a reheat thermal generator and a photovoltaic (PV) power plant. An innovative multi-stage TDn(1 + PI) controller is introduced to reduce the oscillations in frequency and tie-line power changes. This controller combines a tilt-derivative with an N filter (TDn) with a proportional-integral (PI) controller, which improves the system’s response by correcting both steady-state errors and the rate of change. This design enhances the stability and speed of dynamic control systems. A new meta-heuristic optimization technique called bio-dynamic grasshopper optimization algorithm (BDGOA) is used for the first time to fine-tune the parameters of the proposed controller and improve its performance. The effectiveness of the controller is evaluated under various load demands, parameter variations, and nonlinearities. Comparisons with other controllers and optimization algorithms show that the BDGOA-TDn(1 + PI) controller significantly reduces overshoot in system frequency and tie-line power changes and achieves faster settling times for these oscillations. Simulation results demonstrate that the BDGOA-TDn(1 + PI) controller significantly outperforms conventional controllers, achieving a reduction in overshoot by 75%, faster settling times by 60%, and a lower integral of time-weighted absolute error by 50% under diverse operating conditions, including parameter variations and nonlinearities such as time delays and governor deadband effects.

Abstract The escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.

Purpose: Many available questionnaires fail to distinguish between the severity of symptoms and functional limitations caused by low back pain (LBP) in different areas. Therefore, it is difficult to determine which symptoms and functional limitations are associated with existing low back pain. The lack of a specific questionnaire for functional symptoms and constraints related to back pain in the Persian language in Iran has made it necessary to develop new and reliable tools in this field. Therefore, this research aims to localize and validate the Persian version of this questionnaire. Methods: The questionnaire was finalized after necessary corrections using the translation-re-translation method. Two methods, content validity index (CVI) and content validity ratio (CVR), were used to ensure content validity. The internal consistency test (Cronbach’s α) reliability and test re-test reliability were evaluated. Results: The CVI results indicated that all questions scored above 0.79 in communication, clarity, simplicity, and ambiguity. The questionnaire demonstrated a high level of content validity with an average CVI (S-CUI/Ave) of 0.93. Statistical analysis revealed high internal consistency for the symptoms (26 questions, Cronbach’s α=0.91) and functional limitations (29 questions, Cronbach’s α=0.95) section of the questionnaire. Conclusion: After conducting the questionnaire, it is possible to identify people with varying levels of pain and functional limitations associated with chronic low back pain. It can help determine the progression of symptoms and limitations.

Abstract Power electronic converters are widely used in various fields of electrical equipment. Due to their fast dynamics and non-linear nature, controlling them requires dealing with various complexities. Therefore, having a well-designed, high-speed, and robust controller is critical to ensure the effective operation of these devices. In a DC-DC converter, steady-state performance with minimum error and fast dynamic response relies on controller design. This paper presents the design of a multi-stage PID controller with an N-filter combined with a one plus proportional derivative (1+PD) controller. This controller illustrates fast tracking reference voltage; additionally, it shows incredible results when the DC-DC converter operates in different modes. The parameters of the proposed controller are effectively determined using the golden eagle optimization (GEO) algorithm. Furthermore, a comprehensive comparison between the proposed controller, proportional–integral–derivative (PID), and fractional order PID (FOPID) controllers, as well as different metaheuristic optimization methods in various conditions, has been conducted to demonstrate the effectiveness of the proposed controller. The behavior of the closed-loop system under different conditions has been thoroughly investigated. The superior time and frequency domain characteristics of the closed-loop system with the PIDn(1+PD) controller highlight its superiority over other controllers. The demonstrated enhancements in settling time, voltage regulation accuracy, and transient response emphasize the potential applicability of the proposed control strategy in real-world power electronics systems, particularly in scenarios requiring high efficiency, stability, and dynamic performance.

Presents a multilevel model of the information filtering process that permits document classification. Evaluates a document classification approach based on a supervised learning algorithm, measures the accuracy of the algorithm in a neural network that was trained to classify medical documents on cell biology, and discusses filtering effectiveness. (LRW)

Abstract This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors.

AbstractIntroduction: The purpose of this research is to present a model for empowering women in the judiciary Methods: Qualitative research method is grounded theory type. After interviewing 21 women who met the research criteria, the interviews were reviewed, organized and coded in three stages, open, central and selective, and the results of the research were divided into four general categories: "causal conditions", "contextual conditions". ", "intervening conditions" and "individual, social and organizational consequences" were divided, and finally, the model of empowering women in the judiciary was drawn. Reliability was done with the triangulation technique and validity was done using the member confirmation technique and discrepant items, which indicated the validity of the research results.Findings: The findings of the research show that Empowerment of women is influenced by individual, organizational and extra-organizational factors, and by strengthening these factors, it is possible to improve their self-confidence and self-esteem, their job satisfaction, and the trust of organizational managers in using the capacity and abilities of women and the belief of other women in the existence of a meritocracy system in The organization created a sense of belonging and organizational commitment, and the effective use of the capacity and capabilities of half of the country's population changed the society's attitude towards women's capabilities.The results: The most important component in all components of women's empowerment is the component of acquiring job knowledge. It seems that by increasing women's job knowledge, they can be empowered to take on management jobs

Background: Milk bank is one of the most important emerging issues in neonatal health. The present study aimed toimprove the attitude and inclination of mothers with premature neonates to feed their suckling with milk donatedfrom the milk bank by comparing the effect of face-to-face training and an educational package. Methods: In this randomized clinical trial, 66 mothers of premature neonates hospitalized in the neonatal intensivecare unit were included and assigned to two groups of 33 mothers. The samples completed the demographicinformation questionnaire and questionnaire of attitude and inclination toward feeding with milk donated from themilk bank. The first group received direct face-to-face training, and the second received an educational package. Thedata was statistically analyzed with SPSS 22.Results: The attitude and inclination scores increased significantly in face-to-face and educational package groups. Theattitude score in the face-to-face group was higher after the intervention compared to the educational package group(P=0.003). Besides, the inclination score in the face-to-face group was higher than that in the educational packagegroup, which was statistically significant (P

Objective: Head and neck squamous cell carcinoma (HNSCC) with a high mortality rate is among the most commontypes of cancer in the world. Human epidermal growth factor receptor 2 (HER2) is expressed higher than normal levelin the most HNSCC tumors, making them resistant to chemotherapy and radiotherapy. Therefore, HER2 has beenintroduced as a suitable target for anticancer drugs. The aim of this study is to examine the efficacy of a treatmentprotocol involving targeted delivery of idarubicin encapsulated in trastuzumab-decorated liposomes to HNSCC cells.Materials and Methods: In the current experimental study, efficacies of idarubicin, prepared liposomal idarubicin,and constructed immunoliposomal idarubicin (trastuzumab-decorated) were investigated in killing HN5 cells, a HER2-overexpressing HNSCC-originating cell line. Liposomal content of idarubicin and trastuzumab were qualified by UVVisiblespectroscopy and preparations were characterized for shape and size by atomic force microscopy (AFM) anddynamic light scattering (DLS). To clarify role of the missing parts of the available crystal structure (PDB ID: 1n8z)within trastuzumab-HER2 interactions, we used a 40 ns molecular dynamic simulation approach.Results: Based on the obtained results, liposomal idarubicin showed higher toxicity of the encapsulated drug on HN5cells compared to the traditional free drug formulations. The immunoliposomal form of idarubicin was more effectivethan the liposomal formulation, in killing of HN5 cells. In addition, simulation of interactions between trastuzumab andHER2 revealed that the missing parts (in the crystal structure) of HER2 have critical interaction with trastuzumab,through salt-bridges and hydrogen bonds.Conclusion: It seems that the prepared immunoliposomes could attach more efficiently to HER2 overexpressingcells, which consequently leads to increasing cellular uptake of idarubicin through a receptor-mediated endocytosismechanism. Moreover, simulation of the interaction between HER2 and trastuzumab suggested considerablepossibilities for increasing trastuzumab affinity to HER2.

Abstract Understanding tree transpiration variability is vital for assessing ecosystem water‐use efficiency and forest health amid climate change, yet most landscape‐level measurements do not differentiate individual trees. Using canopy temperature data from thermal cameras, we estimated the transpiration rates of individual trees at Harvard Forest and Niwot Ridge. PT‐JPL model was used to derive latent heat flux from thermal images at the canopy‐level, showing strong agreement with tower measurements (R2 = 0.70–0.96 at Niwot, 0.59–0.78 at Harvard at half‐hourly to monthly scales) and daily RMSE of 33.5 W/m2 (Niwot) and 52.8 W/m2 (Harvard). Tree‐level analysis revealed species‐specific responses to drought, with lodgepole pine exhibiting greater tolerance than Engelmann spruce at Niwot and red oak showing heightened resistance than red maple at Harvard. These findings show how ecophysiological differences between species result in varying responses to drought and demonstrate that these responses can be characterized by deriving transpiration from crown temperature measurements.

Abstract Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p

Background and purpose: Carbon nanotubes (CNTs) are a significant discovery in nanotechnology, with widespread applications in modern technology. However, there are concerns about their potential toxicity, particularly in skin cells. This study aimed to investigate the mechanisms by which CNTs induced cytotoxicity and apoptosis in mouse skin fibroblasts. Experimental approach: The mice skin fibroblasts were isolated and exposed to two types of CNTs at various concentrations and then analyzed for changes in viability, reactive oxygen species (ROS) production, the levels of Bcl-2-associated X protein (Bax), and lactate production. Findings/Results: The results demonstrated that CNTs reduced cell viability and increased ROS production in a dose-dependent manner. Additionally, the current study found that CNTs increased the protein levels of Bax, a pro-apoptotic protein, in mouse skin fibroblasts. Furthermore, it was observed a significant decrease in lactate production in cells exposed to CNTs. Conclusion and implications: The findings concluded that CNTs have the potential to be toxic substances for skin fibroblasts, which serve as the body's first line of defense. This is evidenced by their ability to increase the production of ROS and the protein levels of Bax, as well as reduce lactic acid levels. As lactic acid has been reported to have beneficial effects on skin collagen production, further studies are needed to fully understand the impact of carbon nanotube exposure on human skin health.