bluuebunny/crossref_metadata_2025 huggingface dataset:
Research progress on flexible WO3 based thin film electrodes for supercapacitor applications: a comprehensive review
Authors: Muslu Elif , Eren Esin , Oksuz Aysegul Uygun
Published: June 2024

In recent years, there has been significant research interest in flexible supercapacitors as energy storage devices for enhancing wearable and portable electronics. This is due to their lightweight nature, high power density, excellent cyclic durability, fast charge/discharge rate, and robust mechanical integrity. Flexible supercapacitors offer the potential to revolutionize the field of energy storage by providing efficient and reliable power sources for various portable and wearable applications. Thin film based electrodes, as one of the ingredients of flexible supercapacitors, have a considerable role on the electrochemical performance of flexible supercapacitors. Tungsten oxide (WO3), a transition metal oxide (TMO), is a highly desirable electrode material for flexible supercapacitor applications. It offers several advantages, including low cost, environmental friendliness, inherent conductivity, versatile oxidation states, and high theoretical capacity. These properties make WO3 an excellent choice for developing efficient and sustainable energy storage solutions. Specially, this review provides current developments on the WO3 based thin film electrodes toward flexible supercapacitors. We will present the focus attention on the charge storage mechanisms, fabrication strategies, and characterization methods employed in this field.
May the SSRIs play a role in the onset of peri- and post-menopausal cervical cancer?
Authors: Gargiulo Isacco Ciro , Del Prete Raffaele , Distratis Pietro , Lazzaro Rita , Nguyen Kieu C. D. , Inchingolo Francesco , Pham Van Hung , Tran Toai Cong , Santacroce Luigi , Balzanelli Mario G.
Published: June 2024

The emergence of cancer-causing bacteria and viruses that have become resistant to either antibiotics or viral treatment is often attributed to their overuse in people and livestock. Intriguingly, the class of antidepressant drugs, such as selective serotonin reuptake inhibitors (SSRIs), may also contribute to generating resistant microorganisms with a profound disturbing role in local cervix-vagina homeostasis. Therefore, we proposed the possible involvement of SSRIs in the onset and growth of cervical cancer (CC), which is based on some empirical considerations, (i) the number of women diagnosed with post-menopausal CC is almost equivalent to the number of women diagnosed with depressive problems, (ii) the proven affection of SSRIs on both vaginal and intestinal microbiota, (iii) the essential role of the microbiota in the immune response against oncogenic pathogens, including human papillomavirus and Chlamydia trachomatis (C. trachomatis), (IV) the role of SSRIs in favoring the rise of antibiotic-resistant microorganisms, and (V) the inhibitory effect of SSRIs in the estradiol-serotonin-prolactin axis. These features are important in the overproduction of reactive oxygen species, responsible for different harmful activities such as higher cell membrane permeability and porosity, hyperstimulation of transcription, and translational activities, with overall stress on check-point genes involved in ATP synthesis, DNA breaks, and abnormal mutations. This paper's results suggested reconsidering the efficacy of SSRIs on long-term use since the overall increase in prescriptions is mainly due to long-term treatment rather than the diagnoses per se, secondly little is known about the consequences of long-term use. The evidence that supports the role of antidepressant drugs in increasing the risk of CC was searched and discussed.
Biodegradation of low-density polyethylene by the bacterial strains isolated from the dumping site community
Authors: Khampratueng Pornya , Rice Daniel , Anal Anil Kumar
Published: June 2024

Low-density polyethylene (LDPE) is the predominant single-use plastic and rarely decomposes after disposal. The primary objective of this study was to identify potential bacteria capable of degrading LDPE plastic and investigating the biochemical pathways of this process. Bacteria were isolated from soil samples collected from a local garbage dumping site in Thailand and tested on their capability to degrade LDPE plastic. Two of the bacteria isolated from the dumping site, Bacillus sp. AS3 and Sphingobacterium sp. AS8, demonstrated 3.06% and 2.01% (w/w) LDPE plastic weight loss over four weeks, respectively. Analysis by FTIR showed that both bacterial strains degraded the LDPE in the region of 3200–3400 cm−1, which represents the OH group in a commercial LDPE polymer. Bacillus sp. AS3 caused the formation of a new range in the carbonyl group (C=O stretch) and the alcohol, carboxylic acid, esters, and ethers group (–C–O stretch). GC–MS analysis revealed various depolymerized compounds, such as alkane, alcohol, and carboxylic compounds, during LDPE degradation by Bacillus sp. AS3. Bacillus sp. AS3 illustrated esterase activity as 0.608 ± 0.004 U/mL after incubation. The proposed schematic of the LDPE biodegrading pathway by Bacillus sp. AS3 relies on the identification of depolymerized molecules as evidence. This suggests that Bacillus sp. AS3 possesses extracellular enzymes that break down LDPE into smaller molecules through depolymerization. Moreover, the surface of LDPE degraded by Bacillus sp. AS3 and Spingobacterium sp. AS8 was marked by cavities and a rough texture when observed under SEM analysis. This study provides microbial applications to reduce plastic pollution by utilising microorganisms to assimilate plastic waste as a carbon source.
Non-native flora changes in rural gardens of China: the role of tourism
Authors: Yang Mingyu , Zhang Dezheng , Geng Yupeng
Published: June 2024

Human cultivation facilitates the naturalization and subsequent invasion of non-native plant species through, for example, protection from predators or reducing environmental stochasticity. With the development of tourism, non-native plant species have been increasingly introduced into rural home gardens for landscape greening and amenity planting. However, few studies have examined non-native flora in rural home gardens, and in particular the importance of tourism in determining changes of garden non-native flora has not been scrutinized. In this study, we investigated non-native plant species in 135 home gardens across five rural tourism villages in tropical China. Attributes related to garden or household characteristics were also collected through interviews and questionnaires. A distance-based redundancy analysis was then performed to reveal the relationships between the non-native species composition and garden attributes. A total of 338 non-native plant species were recorded in the surveyed gardens. Among them, the majority (63%) were ornamentals, whereas 19% were for nutritional uses and 12% were spontaneous weeds. Gardening preference and distance to tourist attractions governed the non-native compositions, with gardens preferring cultural (ornamental) cultivation or gardens close to tourist attractions having more non-native species. These results highlight an increasing role of tourism in promoting non-native cultivation in rural gardens. Recommendations for species risk assessment, trade and supply regulation, and proper garden management are provided to help harness plant invasion along tourism development in rural China and other regions around the world.
A binary-domain recurrent-like architecture-based dynamic graph neural network
Authors: Chen Zi-chao , Lin Sui
Published: June 2024

The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments. To address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains, and over-smoothing caused by traditional graph neural networks, a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed: Binary Domain Graph Neural Network (BDGNN). The proposed model begins by utilizing a modified Graph Convolutional Network (GCN) without an activation function to extract meaningful graph topology information, ensuring non-redundant embeddings. In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights, aiming to mitigate the impact of noise within the graph sequence. In the spatial domain, the AdaBoost (Adaptive Boosting) algorithm is applied to replace the traditional approach of stacking layers in a graph neural network. This allows for the utilization of multiple independent graph learners, enabling the extraction of higher-order neighborhood information and alleviating the issue of over-smoothing. The efficacy of BDGNN is evaluated through a series of experiments, with performance metrics including Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) for link prediction tasks, as well as metrics for traffic speed regression tasks across diverse test sets. Compared with other models, the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information, but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN.
Suspected myometrial invasion on MRI is correlated with worse outcomes of fertility-preserving treatment in young women with endometrial cancer
Authors: Ning Chengcheng , Xu Zhiying , Ma Fenghua , Zhu Qin , Tao Xiang , Luo Xuezhen , Zhang Hongwei , Chen Xiaojun
Published: June 2024

Purpose

Myometrial invasion of endometrial cancer (EC) impairs the effectiveness of progestin-based treatment and results in high risks of disease progression and relapse. However, it is difficult to decide whether myometrial invasion exists when suspected endometrial invasion is detected on magnetic resonance imaging (MRI). This study developed an MRI-guided hysteroscopic precise en bloc resection method to actually diagnose myometrial invasion in patients with EC and suspected myometrial invasion on MRI. The fertility-preserving outcomes of patients with EC and suspected myometrial invasion on MRI but negative pathological findings after en bloc resection was also evaluated.

Materials and methods

EC patients with suspected myometrial invasion on MRI (n = 38) were prospectively enrolled and evaluated. Suspected myometrial invasion was identified on MR images and resected via an en bloc resection technique (resecting the endometrial lesion, basal layer, and underneath myometrium en bloc) under hysteroscopy for pathological examination. Patients with negative pathological findings received progestin-based conservative therapy. The clinical characteristics and fertility-preserving outcomes of these patients were compared with those of patients without any evidence of myometrial invasion on MRI (n = 98, reference group).

Results

Among patients with suspected myometrial invasion on MRI, myometrial invasion was pathologically excluded in 29 patients (non-MI group). The cumulative complete response rate was lower in the non-MI group than in the reference group at the 8th month (43.4% vs. 64.9%, P = 0.040) and was not significant at the 18th month (87.5% vs. 96.8%, P = 0.11). The 24-month cumulative relapse rate was 31.2% in the non-MI group, versus 0% in the reference group (P < 0.0001).

Conclusion

Myometrial invasion was pathologically confirmed in approximately one-fourth of patients with suspected myometrial invasion on MRI. However, suspected myometrial invasion on MRI with negative pathological findings was correlated with worse fertility-preserving outcomes in young women with endometrial cancer.
Radioactive Isotopes Trace Hidden Arctic Currents
Authors: Scharping Nathaniel
Published: June 2024

Tracing anthropogenic radionuclides shows researchers how water from the Atlantic flows into and mingles with Arctic currents.
Strain sensor on a chip for quantifying the magnitudes of tensile stress on cells
Authors: Zhang Yuyin , Wang Yue , Yin Hongze , Wang Jiahao , Liu Na , Zhong Songyi , Li Long , Zhang Quan , Yue Tao
Published: June 2024

During cardiac development, mechanotransduction from the in vivo microenvironment modulates cardiomyocyte growth in terms of the number, area, and arrangement heterogeneity. However, the response of cells to different degrees of mechanical stimuli is unclear. Organ-on-a-chip, as a platform for investigating mechanical stress stimuli in cellular mimicry of the in vivo microenvironment, is limited by the lack of ability to accurately quantify externally induced stimuli. However, previous technology lacks the integration of external stimuli and feedback sensors in microfluidic platforms to obtain and apply precise amounts of external stimuli. Here, we designed a cell stretching platform with an in-situ sensor. The in-situ liquid metal sensors can accurately measure the mechanical stimulation caused by the deformation of the vacuum cavity exerted on cells. The platform was applied to human cardiomyocytes (AC16) under cyclic strain (5%, 10%, 15%, 20 and 25%), and we found that cyclic strain promoted cell growth induced the arrangement of cells on the membrane to gradually unify, and stabilized the cells at 15% amplitude, which was even more effective after 3 days of culture. The platform’s precise control and measurement of mechanical forces can be used to establish more accurate in vitro microenvironmental models for disease modeling and therapeutic research.
Fasting-mimicking diet remodels gut microbiota and suppresses colorectal cancer progression
Authors: Luo Man , Wang Qingyi , Sun Yong , Jiang Yao , Wang Qiwen , Gu Yanrou , Hu Zhefang , Chen Qianyi , Xu Jilei , Chen Shujie , Hou Tongyao , Feng Lijun
Published: June 2024

The progression of colorectal cancer is closely associated with diet. Fasting-mimicking diet (FMD) is a promising type of dietary intervention that have beneficial effects in the prevention and treatment of various cancers. We investigated the therapeutic effect of 4-day FMD against colorectal cancer in mice through immune cell analysis, microbiota composition analysis and anti-PD-1 treatment. These FMD cycles effectively suppressed colorectal cancer growth, reduced cell proliferation and angiogenesis, increased tumor-infiltration lymphocytes especially CD8+T cells. FMD stimulated protective gut microbiota, especially Lactobacillus. Supplementation of Lactobacillus johnsonii induced similar results as FMD intervention, which also suppressed tumor growth and increased CD45+ and CD8+ T cells. Additionally, FMD synthesizing with anti-PD-1 therapy effectively inhibited CRC progression. These findings suggest that Lactobacillus. johnsonii is necessary for the anticancer process of FMD in CRC. FMD through its effects on both gut microbiota and immune system, effectively suppressed colorectal cancer progression in mouse model.
Electrochemical behavior and theoretical studies of arylazo (1-naphthyl-2-cyanoacetamide) derivatives as new corrosion inhibitors for Inconel 800 in chloride solution
Authors: Motawea Mariem M.
Published: June 2024

In this study, synthesize and insight the corrosion inhibition properties of two novel derivatives of 1-naphthyl-2-cyanoacetamide (NCDs) [2-cyano-2-((5,6-dimethyl-1H-benzo[d]imidazol-2-yl) diazenyl)-N (naphthalene-1-yl)acetamide] (NCD1) and [2-Cyano-N-(naphthalene-1-yl)-2-[(4,6-dimethyl-1H-pyrazolo [3, 4-b] pyridine-3-yl) hydrazono] acetamide] (NCD2). The characterization of the synthesized NCDs was confirmed through the utilization of Mass fragmentation analysis, 1H-NMR, and IR. The corrosion inhibition performance of NCDs as a novel and environmentally safe corrosion inhibitor has been investigated by electrochemical techniques, a chemical technique, and theoretical studies for its anti-corrosion behavior of Inconel 800 in chloride medium. In addition, the surface morphology and inhibitor adsorption on the Inconel 800 surface were confirmed utilizing SEM, EDX, FTIR, and AFM. The advantages of NCDs include their low toxicity, environmental friendliness, ease of preparation, low odor, contain (N, O, and π-Bonds), and the inhibition efficiency elevated with decreasing solution temperature as well as inhibitor dose increase, yielding increased efficiencies of 91.8% and 95.7% for NCD1 and NCD2, respectively, at the optimum concentration of 21 $$\times$$
×
10–6 mol. L−1 and 298 K temperature. An analysis of Tafel plots reveals that NCDs adhere to a mixed and isothermal Langmuir adsorption mechanism. Density Functional Theory (DFT) and Monte Carlo (MC) simulation manifest the two compounds of NCDs can be adsorbed at the Fe (110) surface in a paralleled way, and can have a smaller energy gap (ΔE) value and exhibit higher efficiency. The experimental and theoretical findings confirm that the synthesized compounds obtained are capable of protecting the Inconel 800 from corrosion by creating an anti-corrosion coating on the surface.
A comparative study of an advanced skin imaging system in diagnosing facial pigmentary and inflammatory conditions
Authors: Huang Yu-Wen , Arkesteijn Walter , Lai Yi-Jing , Ng Chau Yee
Published: June 2024

Visual assessment, while the primary method for pigmentation and erythema evaluation in clinical practice, is subjective, time-consuming, and may lead to variability in observations among clinicians. Objective and quantitative techniques are required for a precise evaluation of the disease's severity and the treatment's efficacy. This research examines the precision and utility of a newly developed skin imaging system in assessing pigmentation and erythema. Sixty participants were recruited, and their facial images were analyzed with the new OBSERV 520 x skin imaging system, compared to DERMACATCH for regional analysis and VISIA for full-face examination. The degree of skin pigmentation was clinically graded using the MASI scores evaluated by dermatologists. The data revealed positive correlations between the novel skin imaging system and the two conventional instruments in quantifying pigmentation and erythema, whether in regional or full-face analysis. Furthermore, the new skin imaging system positively correlated with the clinical MASI scores (r = 0.4314, P < 0.01). In contrast, our study found no significant correlation between the traditional system and clinical assessment, indicating a more substantial capacity for hyperpigmentation assessment in the new system. Our study validates the innovative skin imaging system's accuracy in evaluating pigmentation and erythema, demonstrating its feasibility for quantitative evaluation in both clinical and research purposes.
Study of internal heat source, rotation, magnetic field, and initial stress influence on p-waves propagation in a photothermal semiconducting medium
Authors: Rashid M. M. , Abd-Alla A. M. , Abo-Dahab S. M. , Alharbi F. M.
Published: June 2024

The purpose of the current study is to establish a novel mathematical model in the p-waves in a photothermal semiconducting medium with an internal heat source. The fundamental equations in the context of isotropic and homogeneous medium have been presented. For the solution of the required problem, the normal mode analysis along with the displacement components, stress components and temperature has been utilized. For graphical representation of different physical quantities such as displacement components, stress components and carrier density as well as the temperature distribution. Using MATLAB R2023a software, a parametric analysis is performed, and the resulting data is represented graphically. A comparison is made to show the effect of the new parameters on the phenomenon. A graphic representation of the relationship between rotation, magnetic field, and initial stress in relation to the fluctuations in non-dimensional field quantities is provided, along with an analysis of the findings.
A new Egyptian Grid Weighted Mean Temperature (EGWMT) model using hourly ERA5 reanalysis data in GNSS PWV retrieval
Authors: Sleem Ragab Elhady , Abdelfatah Mohamed Amin , Mousa Ashraf El-Kutb , El-Fiky Gamal Saber
Published: June 2024

Precise modeling of weighted mean temperature (Tm) is essential for Global Navigation Satellite System (GNSS) meteorology. In retrieving precipitable water vapor (PWV) from GNSS, Tm is a crucial parameter for the conversion of zenith wet delay (ZWD) into PWV. In this study, an improved Tm model, named EGWMT, was developed to accurately estimate Tm at any site in Egypt. This new model was established using hourly ERA5 reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) covering the period from 2008 to 2019 with a spatial resolution of 0.25° × 0.25°. The performance of the proposed model was evaluated using two types of data sources, including hourly ERA5 reanalysis data from 2019 to 2022 and radiosonde profiles over a six-year period from 2017 to 2022. The accuracy of the EGWMT model was compared to that of four other models: Bevis, Elhaty, ANN and GGTm-Ts using two statistical quantities, including mean absolute bias (MAB) and root mean square error (RMSE). The results demonstrated that the EGWMT model outperformed the Bevis, Elhaty, ANN and GGTm-Ts models with RMSE improvements of 32.5%, 30.8%, 39% and 48.2%, respectively in the ERA5 data comparison. In comparison with radiosonde data, the EGWMT model achieved RMSE improvements of 22.5%, 34%, 38% and 19.5% against Bevis, Elhaty, ANN and GGTm-Ts models, respectively. In order to determine the significance of differences in means and variances, statistical tests, including t-test and F-test, were conducted. The results confirmed that there were significant differences between the EGWMT model and the four other models.
Analysis of factors associated with the development of delayed encephalopathy following acute carbon monoxide poisoning
Authors: Liu Zi-bo , Wang Li-chun , Lian Jia-jia , Li Sha , Zhao Long , Li Hong-Ling
Published: June 2024

In this study, we analyzed the factors influencing the development of delayed encephalopathy in patients with acute carbon monoxide poisoning (ACOP) (DEACMP) following conventional treatment such as hyperbaric oxygen therapy (HBOT). Between January 2012 and January 2022, we retrospectively analyzed 775 patients with ACOP, who were admitted to the Second Department of Rehabilitation Medicine and received HBOT in the Second Hospital of Hebei Medical University. These patients were divided into the non-DEACMP and DEACMP groups based on their follow-up; we then compared the general data, clinical characteristics, admission examination, and treatment between the two groups to identify risk factors for the development of DEACMP. The DEACMP group comprised of 168 cases, while the non-DEACMP group consisted of 607 cases. Univariate analysis showed that there were 20 possible prognostic factors in the non-DEACMP and DEACMP groups. The results of multivariable regression analyses suggested that the occurrence of DEACMP was significantly correlated with advanced age, the combination of multiple medical histories, the duration of CO exposure, the duration of coma, poisoning degree, the Interval between ACOP and the first HBOT, the total number of HBOTs, and the combination with rehabilitation treatment. DEACMP patients who are older, have more comorbidities, prolonged CO exposure, prolonged coma, severe intoxication, long intervals between ACOP and the first HBOT, fewer HBOT treatments, and who are not treated with a combination of rehabilitative therapies have a poor prognosis.
Belowground plant allocation regulates rice methane emissions from degraded peat soils
Authors: Sriskandarajah Nijanthini , Wüst-Galley Chloé , Heller Sandra , Leifeld Jens , Määttä Tiia , Ouyang Zutao , Runkle Benjamin R. K. , Schiedung Marcus , Schmidt Michael W. I. , Tumber-Dávila Shersingh Joseph , Malhotra Avni
Published: June 2024

Carbon-rich peat soils have been drained and used extensively for agriculture throughout human history, leading to significant losses of their soil carbon. One solution for rewetting degraded peat is wet crop cultivation. Crops such as rice, which can grow in water-saturated conditions, could enable agricultural production to be maintained whilst reducing CO2 and N2O emissions from peat. However, wet rice cultivation can release considerable methane (CH4). Water table and soil management strategies may enhance rice yield and minimize CH4 emissions, but they also influence plant biomass allocation strategies. It remains unclear how water and soil management influences rice allocation strategies and how changing plant allocation and associated traits, particularly belowground, influence CH4-related processes. We examined belowground biomass (BGB), aboveground biomass (AGB), belowground:aboveground ratio (BGB:ABG), and a range of root traits (root length, root diameter, root volume, root area, and specific root length) under different soil and water treatments; and evaluated plant trait linkages to CH4. Rice (Oryza sativa L.) was grown for six months in field mesocosms under high (saturated) or low water table treatments, and in either degraded peat soil or degraded peat covered with mineral soil. We found that BGB and BGB:AGB were lowest in water saturated conditions where mineral soil had been added to the peat, and highest in low-water table peat soils. Furthermore, CH4 and BGB were positively related, with BGB explaining 60% of the variation in CH4 but only under low water table conditions. Our results suggest that a mix of low water table and mineral soil addition could minimize belowground plant allocation in rice, which could further lower CH4 likely because root-derived carbon is a key substrate for methanogenesis. Minimizing root allocation, in conjunction with water and soil management, could be explored as a strategy for lowering CH4 emissions from wet rice cultivation in degraded peatlands.
Hybrid YOLOv3 and ReID intelligent identification statistical model for people flow in public places
Authors: Zheng Yao
Published: June 2024

The statistical model for automatic flow recognition is significant for public place management. However, the current model suffers from insufficient statistical accuracy and low lightweight. Therefore, in this study, the structure of the lightweight object detection model "You Only Live Once v3" is optimized, and the "Deep Simple Online Real-Time Tracking" algorithm with the "Person Re-Identification" module is designed, so as to construct a statistical model for people flow recognition. The results showed that the median PersonAP of the designed model was 94.2%, the total detection time was 216 ms, the Rank-1 and Rank-10 were 87.2% and 98.6%, respectively, and the maximum occupied memory of the whole test set was 2.57 MB, which was better than all comparison models. The results indicate that the intelligent identification statistical model for public crowd flow obtained through this design and training has higher statistical accuracy, less computational resource consumption, and faster computing speed. This has certain application space in the management and guidance of crowd flow in public places.
Application of citric acid can enhance the accuracy for 13C-urea breath tests in the diagnosis of Helicobacter pylori infection in Chinese patients
Authors: Chen Gang , Zhang Weiping , Wu Qiaoling , Yu Qin , Cai Yongping , Luo Wenwu , Xu Jianming , Zhang Lei , Hong Rutao
Published: June 2024

Previous published data have confirmed that the addition of a citric acid meal improves the accuracy of the 13C-urea breath test (13C-UBT). However, some studies have suggested that a citric acid test meal may not be necessary. Thus, the aim of this study was to evaluate the combination of a 13C-UBT with a citric acid meal for the diagnosis of Helicobacter pylori (Hp) infection in a Chinese population, particularly for patients with results in the gray zone. In this paired self-controlled study, all subjects had previously undergone 13C-UBTs without citric acid meals and were randomly divided into two groups based on different doses of citric acid (a low-dose citric acid group and a high-dose citric acid group, comprising meals with 0.68 g and 3.84 g citric acid powder, respectively). Positive rapid urease test (CLO) test and histology results were considered the 'gold standard'. The mean delta over baseline (DOB) value, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were compared between the two groups, particularly for patients with results in the gray zone. In total, 285 patients were tested. Of these patients, 189 were included in the low-dose citric acid group, and 96 were included in the high-dose citric acid group. Among patients with a positive 13C-UBT result without citric acid [delta over baseline (DOB) value ≥ 4‰, n = 174] and a negative 13C-UBT result without citric acid (DOB value < 4‰, n = 111), 8.0% (14/174) were false positive, and 0.9% (1/111) was false negative as determined by gold standard. Of 14 patients with false positive, 78.6% (11/14) false positive were in the gray zone of 4–10‰. However, there were no false positive 13C-UBT results with citric acid in the the gray zone of 4–10‰. In the comparison of the commercial 13C-UBT with the 13C-UBT in the low-dose citric acid group, the sensitivity, specificity, PPV, NPV and accuracy at 15 min were as follows: 99.1% vs. 99.1%, 97.5% vs. 88.9%, 98.2% vs. 92.2%, 98.8% vs. 98.6% and 98.4% vs. 94.7%, respectively. In the the gray zone of 4.0–10.0‰, the comparison of the commercial 13C-UBT with the 13C-UBT in the low-dose citric acid group, the sensitivity, specificity, PPV, and accuracy at 15 min were as follows: 94.4% vs. 100.0%, 100.0% vs. 0%, 100.0% vs. 75.0% and 95.8% vs. 75.0%, respectively. No significant difference was observed between the 15-min and 30-min measurement intervals in the low- and high-dose citric acid groups, including patients with results in the gray zone. The low-dose citric acid test, with an optimal measurement interval of 15 min, was highly accurate in the diagnosis of Hp infection in the Chinese population, especially for individuals with results in the gray zone.
Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand
Authors: Javed Muhammad Faisal , Khan Majid , Fawad Muhammad , Alabduljabbar Hisham , Najeh Taoufik , Gamil Yaser
Published: June 2024

The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.
Effect of 2 weeks rest-pause on oxidative stress and inflammation in female basketball players
Authors: Cichoń-Woźniak Justyna , Ostapiuk-Karolczuk Joanna , Cieślicka Mirosława , Dziewiecka Hanna , Basta Piotr , Maciejewski Dariusz , Skarpańska-Stejnborn Anna
Published: June 2024

Intense exercise leads to increased production of free radicals, resulting in an inflammatory response in athletes. For this reason, it was decided to investigate whether a single intensive exercise until exhaustion applied after a 2-week rest period would result in a violation of the pro-oxidant-antioxidant balance. Twenty-seven trained female basketball players (age: 16.55 ± 0.96 years, body mass: 66.40 ± 13.68 kg, height: 173.45 ± 5.14 cm) were enrolled to the study following the application of inclusion and exclusion criteria. Study was conducted at the end of the competitive training phase. Participants underwent incremental treadmill exercise, with blood samples collected before the test, immediately post-exercise, and after a 3-h restitution period. Total antioxidant capacity (TAC) levels increased significantly after exercise and remained unchanged after 3 h. Concentration of interleukin-10 (IL-10) and creatine kinase (CK) significantly increased after exercise and then decreased. Concentration of interleukin-2 (IL-2) was significantly reduced immediately and 3 h after exercise, while interleukin-13 (IL-13), interleukin-1α (IL-1α), and tryptophan (TRP) decreased 3 h after exercise. No significant changes were observed in other biochemical parameters. Obtained results show an increased antioxidant capacity which reduced oxidative stress and inflammation in response to intense exercise indicating that rested athletes have a high adaptation and elevated tolerance to effort.
Caffeic acid phenethyl ester promotes oxaliplatin sensitization in colon cancer by inhibiting autophagy
Authors: Xing Fei , Liu Ning , Wang Can , Wang Xu-Dong
Published: June 2024

Colon cancer ranks as the third most prevalent form of cancer globally, with chemotherapy remaining the primary treatment modality. To mitigate drug resistance and minimize adverse effects associated with chemotherapy, selection of appropriate adjuvants assumes paramount importance. Caffeic acid phenethyl ester (CAPE), a naturally occurring compound derived from propolis, exhibits a diverse array of biological activities. We observed that the addition of CAPE significantly augmented the drug sensitivity of colon cancer cells to oxaliplatin. In SW480 and HCT116 cells, oxaliplatin combined with 10 µM CAPE reduced the IC50 of oxaliplatin from 14.24 ± 1.03 and 84.16 ± 3.02 µM to 2.11 ± 0.15 and 3.92 ± 0.17 µM, respectively. We then used proteomics to detect differentially expressed proteins in CAPE-treated SW480 cells and found that the main proteins showing changes in expression after CAPE treatment were p62 (SQSTM1) and LC3B (MAP1LC3B). Gene ontology analysis revealed that CAPE exerted antitumor and chemotherapy-sensitization effects through the autophagy pathway. We subsequently verified the differentially expressed proteins using immunoblotting. Simultaneously, the autophagy inhibitor bafilomycin A1 and the mCherry-EGFP-LC3 reporter gene were used as controls to detect the effect of CAPE on autophagy levels. Collectively, the results indicate that CAPE may exert antitumor and chemotherapy-sensitizing effects by inhibiting autophagy, offering novel insights for the development of potential chemosensitizing agents.
Establishing the distribution of cerebrovascular resistance using computational fluid dynamics and 4D flow MRI
Authors: Vikström Axel , Holmlund Petter , Holmgren Madelene , Wåhlin Anders , Zarrinkoob Laleh , Malm Jan , Eklund Anders
Published: June 2024

Cerebrovascular resistance (CVR) regulates blood flow in the brain, but little is known about the vascular resistances of the individual cerebral territories. We present a method to calculate these resistances and investigate how CVR varies in the hemodynamically disturbed brain. We included 48 patients with stroke/TIA (29 with symptomatic carotid stenosis). By combining flow rate (4D flow MRI) and structural computed tomography angiography (CTA) data with computational fluid dynamics (CFD) we computed the perfusion pressures out from the circle of Willis, with which CVR of the MCA, ACA, and PCA territories was estimated. 56 controls were included for comparison of total CVR (tCVR). CVR were 33.8 ± 10.5, 59.0 ± 30.6, and 77.8 ± 21.3 mmHg s/ml for the MCA, ACA, and PCA territories. We found no differences in tCVR between patients, 9.3 ± 1.9 mmHg s/ml, and controls, 9.3 ± 2.0 mmHg s/ml (p = 0.88), nor in territorial CVR in the carotid stenosis patients between ipsilateral and contralateral hemispheres. Territorial resistance associated inversely to territorial brain volume (p < 0.001). These resistances may work as reference values when modelling blood flow in the circle of Willis, and the method can be used when there is need for subject-specific analysis.
A novel signature constructed by mitochondrial function and cell death-related gene for the prediction of prognosis in bladder cancer
Authors: Yan Zhiwei , Liu Yunxun , Wang Minghui , Wang Lei , Chen Zhiyuan , Liu Xiuheng
Published: June 2024

Bladder urothelial carcinoma (BLCA) presents a persistent challenge in clinical management. Despite recent advancements demonstrating the BLCA efficacy of immune checkpoint inhibitors (ICI) in BLCA patients, there remains a critical need to identify and expand the subset of individuals who benefit from this treatment. Mitochondria, as pivotal regulators of various cell death pathways in eukaryotic cells, exert significant influence over tumor cell fate and survival. In this study, our objective was to investigate biomarkers centered around mitochondrial function and cell death mechanisms to facilitate prognostic prediction and guide therapeutic decision-making in BLCA. Utilizing ssGSEA and LASSO regression, we developed a prognostic signature termed mitochondrial function and cell death (mtPCD). Subsequently, we evaluated the associations between mtPCD score and diverse clinical outcomes, including prognosis, functional pathway enrichment, immune cell infiltration, immunotherapy response analysis and drug sensitivity, within high- and low-risk subgroups. Additionally, we employed single-cell level functional assays, RT-qPCR, and immunohistochemistry to validate the differential expression of genes comprising the mtPCD signature. The mtPCD signature comprises a panel of 10 highly influential genes, strongly correlated with survival outcomes in BLCA patients and exhibiting robust predictive capabilities. Importantly, individuals classified as high-risk according to mtPCD score displayed a subdued overall immune response, characterized by diminished immunotherapeutic efficacy. In summary, our findings highlight the development of a novel prognostic signature, which not only holds promise as a biomarker for BLCA prognosis but also offers insights into the immune landscape of BLCA. This paradigm may pave the way for personalized treatment strategies in BLCA management.
Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences
Authors: Capone Cristiano , Paolucci Pier Stanislao
Published: June 2024

Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing the number of necessary interactions with the environment to learn a desirable policy. However, these methods require biological implausible ingredients, such as the detailed storage of older experiences, and long periods of offline learning. The optimal way to learn and exploit world-models is still an open question. Taking inspiration from biology, we suggest that dreaming might be an efficient expedient to use an inner model. We propose a two-module (agent and model) spiking neural network in which “dreaming” (living new experiences in a model-based simulated environment) significantly boosts learning. Importantly, our model does not require the detailed storage of experiences, and learns online the world-model and the policy. Moreover, we stress that our network is composed of spiking neurons, further increasing the biological plausibility and implementability in neuromorphic hardware.
Machine learning enables electrical resistivity modeling of printed lines in aerosol jet 3D printing
Authors: Li Mingdong , Yin Shuai , Liu Zhixin , Zhang Haining
Published: June 2024

Among various non-contact direct ink writing techniques, aerosol jet printing (AJP) stands out due to its distinct advantages, including a more adaptable working distance (2–5 mm) and higher resolution (~ 10 μm). These characteristics make AJP a promising technology for the precise customization of intricate electrical functional devices. However, complex interactions among the machine, process, and materials result in low controllability over the electrical performance of printed lines. This significantly affects the functionality of printed components, thereby limiting the broad applications of AJP. Therefore, a systematic machine learning approach that integrates experimental design, geometrical features extraction, and non-parametric modeling is proposed to achieve printing quality optimization and electrical resistivity prediction for the printed lines in AJP. Specifically, three classical convolutional neural networks (CNNs) architectures are compared for extracting representative features of printed lines, and an optimal operating window is identified to effectively discriminate better line morphology from inferior printed line patterns within the design space. Subsequently, three representative non-parametric machine learning techniques are employed for resistivity modeling. Following that, the modeling performances of the adopted machine learning methods were systematically compared based on four conventional evaluation metrics. Together, these aspects contribute to optimizing the printed line morphology, while simultaneously identifying the optimal resistivity model for accurate predictions in AJP.
Enhanced osteogenesis of mesenchymal stem cells encapsulated in injectable microporous hydrogel
Authors: Edwards Seth D. , Ganash Mrinal , Guan Ziqiang , Lee Jeil , Kim Young Jo , Jeong Kyung Jae
Published: June 2024

Delivery of therapeutic stem cells to treat bone tissue damage is a promising strategy that faces many hurdles to clinical translation. Among them is the design of a delivery vehicle which promotes desired cell behavior for new bone formation. In this work, we describe the use of an injectable microporous hydrogel, made of crosslinked gelatin microgels, for the encapsulation and delivery of human mesenchymal stem cells (MSCs) and compared it to a traditional nonporous injectable hydrogel. MSCs encapsulated in the microporous hydrogel showed rapid cell spreading with direct cell–cell connections whereas the MSCs in the nonporous hydrogel were entrapped by the surrounding polymer mesh and isolated from each other. On a per-cell basis, encapsulation in microporous hydrogel induced a 4 × increase in alkaline phosphatase (ALP) activity and calcium mineral deposition in comparison to nonporous hydrogel, as measured by ALP and calcium assays, which indicates more robust osteogenic differentiation. RNA-seq confirmed the upregulation of the genes and pathways that are associated with cell spreading and cell–cell connections, as well as the osteogenesis in the microporous hydrogel. These results demonstrate that microgel-based injectable hydrogels can be useful tools for therapeutic cell delivery for bone tissue repair.
Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease
Authors: Pastika Libor , Sau Arunashis , Patlatzoglou Konstantinos , Sieliwonczyk Ewa , Ribeiro Antônio H. , McGurk Kathryn A. , Khan Sadia , Mandic Danilo , Scott William R. , Ware James S. , Peters Nicholas S. , Ribeiro Antonio Luiz P. , Kramer Daniel B. , Waks Jonathan W. , Ng Fu Siong
Published: June 2024

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.
Topological order from measurements and feed-forward on a trapped ion quantum computer
Authors: Iqbal Mohsin , Tantivasadakarn Nathanan , Gatterman Thomas M. , Gerber Justin A. , Gilmore Kevin , Gresh Dan , Hankin Aaron , Hewitt Nathan , Horst Chandler V. , Matheny Mitchell , Mengle Tanner , Neyenhuis Brian , Vishwanath Ashvin , Foss-Feig Michael , Verresen Ruben , Dreyer Henrik
Published: June 2024

Quantum systems evolve in time in one of two ways: through the Schrödinger equation or wavefunction collapse. So far, deterministic control of quantum many-body systems in the lab has focused on the former, due to the probabilistic nature of measurements. This imposes serious limitations: preparing long-range entangled states, for example, requires extensive circuit depth if restricted to unitary dynamics. In this work, we use mid-circuit measurement and feed-forward to implement deterministic non-unitary dynamics on Quantinuum’s H1 programmable ion-trap quantum computer. Enabled by these capabilities, we demonstrate a constant-depth procedure for creating a toric code ground state in real-time. In addition to reaching high stabilizer fidelities, we create a non-Abelian defect whose presence is confirmed by transmuting anyons via braiding. This work clears the way towards creating complex topological orders in the lab and exploring deterministic non-unitary dynamics via measurement and feed-forward.
Characterization of mercury in atmospheric particulate matter in the state of Rio de Janeiro, Brazil
Authors: Silva Luis Fhernando Mendonça da , Felix Caio Silva Assis , Nascimento Madson Moreira , Andrade Jailson Bittencourt de , Canela Maria Cristina , Almeida Cibele Maria Stivanin de , Silveira Carla Semiramis , Carreira Renato da Silva , Gioda Adriana
Published: June 2024

The study investigates mercury levels in PM2.5 in regions of Rio de Janeiro: urban areas, areas affected by sugar cane burning, and an environmental reserve. It highlights seasonal variations and pollution contributions, both local and transboundary.