bluuebunny/crossref_metadata_2025 huggingface dataset:
Testing an mHealth System for Individuals with Mild to Moderate Alcohol Use Disorders: Protocol for a Type 1 Hybrid Effectiveness-Implementation Trial
Authors: Park Linda S , Chih Ming-Yuan , Stephenson Christine , Schumacher Nicholas , Brown Randall , Gustafson David , Barrett Bruce , Quanbeck Andrew
Published: February 2024

Background: The extent of human interaction needed to achieve effective and cost-effective use of mobile health (mHealth) apps for individuals with mild to moderate alcohol use disorder (AUD) remains largely unexamined. This study seeks to understand how varying levels of human interaction affect the ways in which an mHealth intervention for the prevention and treatment of AUDs works or does not work, for whom, and under what circumstances.
Objective: The primary aim is to detect the effectiveness of an mHealth intervention by assessing differences in self-reported risky drinking patterns and quality of life between participants in three study groups (self-monitored, peer-supported, and clinically integrated). The cost-effectiveness of each approach will also be assessed.
Methods: This hybrid type 1 study is an unblinded patient-level randomized clinical trial testing the effects of using an evidence-based mHealth system on participantsâ€™ drinking patterns and quality of life. There are two groups of participants for this study: individuals receiving the intervention and health care professionals practicing in the broader health care environment. The intervention is a smartphone app that encourages users to reduce their alcohol consumption within the context of integrative medicine using techniques to build healthy habits. The primary outcomes for quantitative analysis will be participant data on their risky drinking days and quality of life as well as app use from weekly and quarterly surveys. Cost measures include intervention and implementation costs. The cost per participant will be determined for each study arm, with intervention and implementation costs separated within each group. There will also be a qualitative assessment of health care professionalsâ€™ engagement with the app as well as their thoughts on participant experience with the app.
Results: This protocol was approved by the Health Sciences Minimal Risk Institutional Review Board on November 18, 2019, with subsequent annual reviews. Recruitment began on March 6, 2020, but was suspended on March 13, 2020, due to the COVID-19 pandemic restrictions. Limited recruitment resumed on July 6, 2020. Trial status as of November 17, 2021, is as follows: 357 participants were enrolled in the study for a planned enrollment of 546 participants.
Conclusions: The new knowledge gained from this study could have wide and lasting benefits related to the integration of mHealth systems for individuals with mild to moderate AUDs. The results of this study will guide policy makers and providers toward cost-effective ways to incorporate technology in health care and community settings.
Research on Privacy Security Risk Evaluation of Mobile Commerce Users based on Information Entropy and Markov Theory
Authors: Wang Jia , Gao Tilei
Published: February 2024

To apply for precise personalized services in mobile commerce, the users have to disclose their personal information to the operator, which constitutes a potential threat to their privacy security. In this paper, a mobile commerce privacy security risk evaluation model is established based on information entropy and Markov theory, and effective security risk measurement and evaluation method is put forward. In this way, accurate and quantitative privacy disclosure risk evaluation results are provided, to guide the users’ selection of safe mobile commerce applications and protect their privacy security.
Artificial Intelligence and Its Impact on Educarist's Innovative Behavior: A Survey Exploration Guided by Rogers' Theory of Innovation
Authors: Tang Xuan , Zainal Siti Rohaida
Published: February 2024

This research is located at the exciting juncture of Artificial Intelligence (AI) and education, with the specific aim of understanding how AI technologies, including Machine Learning (ML), Natural Language Processing (NLP), and Robotics affect the innovative behavior of Educarist, guided by Rogers' Theory of Innovation. It uses the theoretical underpinnings of innovation in education and the burgeoning role of AI in shaping pedagogical approaches. The study used a survey-based method to gather and analyze data from 205 Educarists in Guangdong province, China. This data underwent rigorous statistical scrutiny, including structural equation modeling, to discern the relationships between various AI technologies and innovative behaviors in early childhood education. The key findings show that NLP and Robotics play a significant role in stimulating innovative behavior among Educarists. Interestingly, despite the growing application of ML in education, its influence on innovative behavior was found to be statistically insignificant. Additionally, the analysis uncovers intriguing interrelationships among the AI technologies themselves, showing a possible synergistic effect of these technologies on innovative behavior. This research contributes to the expanding literature that explores the intersection of AI and education, supplying valuable insights into how specific AI technologies can mold innovative teaching practices. The novelty of this research lies in its empirical investigation into the impact of three different AI technologies and their interrelationships on the innovative behavior of Educarists. Nevertheless, it acknowledges its scope and generalizability limitations due to the specific sample of Educarists involved. The unexpected finding concerning the role of ML in fostering innovation presents an intriguing avenue for further research. It needs a deeper exploration into the contextual factors that influence this relationship.
Protocol for a systematic review and network meta-analysis of the use of prophylactic antibiotics in hand trauma surgery
Authors: Zhang Chen , Farang Soma , Wade Ryckie George , Wormald Justin Conrad Rosen
Published: February 2024

Background: The use of prophylactic antibiotics in surgery is contentious. With the rise in antimicrobial resistance, evidence based antibiotic use should be followed. This systematic review and network meta-analysis will assess the effectiveness of different antibiotics on the prevention of surgical site infection (SSI) following hand trauma surgery.
Methods and analysis: The databases EMBASE, MEDLINE, CINAHL and CENTRAL, metaRegister of controlled trials, clinicaltrials.gov and the WHO International Clinical Trials Registry Platform will be searched for published and unpublished studies which will then be screened by two persons independently to identify eligible studies.
This systematic review will include both randomised and non-randomised prospective comparative studies in participants with hand and/or wrist injuries requiring surgery. The network meta-analysis will compare the use of different prophylactic antibiotics against each other, placebo and/or no antibiotics on the development of SSI within 30 days of surgery (or 90 days if there is an implanted device).
The Cochrane Risk of Bias tool 2 will be used to assess the risk of methodological bias in randomised controlled trials and the Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I) tool will be used to assess the risk of bias in non-randomised studies. The ROB-MEN tool will be used to evaluate the risk of bias due to missing evidence in network meta-analysis.
A random effects network meta-analysis will be conducted along with subgroup analyses looking at antibiotic timing, operation type and injury type. Sensitivity analyses including only low risk of bias studies and the study location will be conducted and the confidence in the results will be assessed using Confidence in Network Meta‐Analysis (CINEMA).
Discussion: This systematic review and network meta-analysis aims to provide an up to date synthesis of the studies assessing the use of antibiotics following hand and wrist trauma to enable evidence-based peri-operative prescribing.
Registration: Prospero registration number CRD42023429618
Efficacy and safety of D-penicillamine, trientine and zinc in pediatric Wilson disease patients
Authors: Lee Eun Joo , Woo Min Hyung , Moon Jin Soo , Ko Jae Sung
Published: February 2024

Objectives
Wilson disease (WD) is a rare genetic disease affecting copper metabolism and the biliary tract’s copper excretion. Lifelong medication is necessary to prevent liver failure, neurological complications, and death. Although D-penicillamine (DPA), trientine, and zinc are used to treat Wilson disease (WD), there is limited research on the long-term outcomes of these drugs, especially in children. This study aimed to evaluate the effecacy and safety of DPA, trientine, and zinc in patients diagnosed with WD during childhood.
Methods
Ninety out of 92 patients were included in the analysis, excluding two patients who underwent liver transplantation without drug treatment due to an acute liver failure diagnosis. Treatment outcomes and reasons for discontinuation of therapy in 148 treatment blocks (37 DPA, 50 trientine, and 61 zinc) were analyzed using Kaplan-Meier analysis.
Results
The median age at diagnosis was 8.3 years. There was a statistically significant difference in drug changes due to treatment ineffectiveness among the three drugs: trientine (22/50, 44%), zinc (15/61, 25%), and DPA (2/37, 5%) (all p < 0.05). Regarding drug changes due to adverse effects, the rate was the highest for DPA, followed by zinc and trientine. There were significant differences between DPA and zinc, zinc and trientine (all p < 0.05), but no significant difference was observed between DPA and zinc (p = 0.22).
Conclusions
In pediatric WD, DPA, zinc, and trientine have therapeutic effects in that order. However, DPA and zinc were associated with more adverse effects than trientine.
Obesity modifies cell fate plasticity of Pdgfrα-expressing adipocyte progenitors to promote an aberrant mammary microenvironment
Authors: Joshi Purna , Kwende Sharon , Nuthalapati Prashant , Singh Jacqulene Sunder , Maity Subhajit , Ning Dun , Shryock Douglas , Xuan Zhenyu
Published: February 2024

Excess fat gain culminating in obesity is a mounting global pandemic associated with a myriad of diseases including a higher risk of developing multiple cancers such as breast cancer. The underlying biological basis for obesity-linked breast cancer is attributed to alterations in adipose tissue-derived endocrine, metabolic and inflammatory factors. However, the precise cell types responsible for generating a cancer-susceptible mammary gland in obesity are not well understood. Using diet-induced obesity in conjunction with genetic reporter mouse models, we reveal elevated mammary adipocyte progenitors (MAPs) expressing Platelet-Derived Growth Factor Receptor alpha (PDGFRα) in the mammary tissue microenvironment during obesity. Single-cell RNA sequencing analysis demonstrate intensified obesity-driven trajectories of MAPs to immune and epithelial progenitor cells. Further, lineage tracing indicates an invasive cellular state of MAPs that confers greater access of MAP descendants into the mammary epithelium concomitant with their transition into immune and epithelial cell fates. MAPs in obesity exhibit heightened activation of cancer-associated and inflammatory pathways where Egfr is identified as a common target upregulated in MAPs. Mechanistic studies on purified MAPs with a major obesity diet component and EGFR stimulation corroborate dynamic EGFR-mediated MAP responses. Our findings uncover MAPs as key contributors to forging an aberrant mammary gland in obesity, providing insight into the potential utility of targeting this cell lineage for eradicating cancer risk related to augmented adiposity.
A Feasibility Study on the Vaporization of Liquefied Carbon Dioxide Due to Heat Transfer in Submerged Pipeline with Start-up, Shutdown and Abnormal Dynamic Simulation Scenarios Prediction
Authors: Asante Daniel Owusu , Galanido Russel J. , Cho Jungho , Park Ilsu , Hwang Philsung , Cheol Jungki
Published: February 2024

The vaporization of liquefied carbon dioxide (LCO2) resulting from heat transfer in undersea pipeline flow was examined in this feasibility study. The purpose of the study was to assess how several factors, including flow velocity, temperature and height, affected the rate at which CO2 evaporated in a submerged pipeline. Ansys Fluent and Aspen Plus were two of the simulation tools used for the analysis. For the assessment, the SRK thermodynamic model was chosen. Start-up, shutdown, and abnormal dynamic studies were further conducted to ascertain the safe operation of the pipeline using Aspen Plus and Aspen Dynamics. The study emphasizes the need to take heat transfer into account in the design and operation of these pipelines and offers insightful information about the behavior of CO2 in undersea pipelines. Reliable seabed and oceanographic data were obtained with the corresponding temperatures for the prediction of pipeline landfall. The maximum pressure of 76.61 barg was established at Node 11 but further drop at the last 200m pipe segment (N11 to N13) to achieve an outlet pressure of 59.72 barg. The loss of pressure was due to gravity since the fluid was directed upward to the sea platform. There was a gradual drop in temperature along the pipeline. The temperature at the pipe outlet was calculated to be 3.33oC. The results of this study can be applied to improve efficiency and lower the risk of accidents associated with the design and operation of underwater pipelines for the transportation and storage of CO2. The results in this work is significant since it offers a thorough grasp of how CO2 behaves in submerged pipes, knowledge that may be utilized to guarantee the effective and safe transit and storage of this material.
Benefits of multiple remotely sensed datasets and machine learning models to predict the Chlorophyll-a concentration in the Nakdong River, South Korea
Authors: Lee Byeongwon , Im Jong Kwon , Han Ji Woo , Kang Taegu , Kim Wonkook , Lee Sangchul
Published: February 2024

Machine learning models (MLMs) are increasingly used with remotely sensed data to monitor chlorophyll-a (Chl-a). MLMs require large amounts of remotely sensed data to monitor Chl-a effectively. However, weather conditions, satellite revisit cycles, and coverage constraints can impede the collection of adequate remotely sensed data. To address this, we tested whether MLMs effectively improved the predictions of Chl-a concentrations within the 16 lakes of the Nakdong River in South Korea using two remotely sensed datasets (Sentinel-2 and Landsat-8). This study evaluated four MLMs: Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and eXetreme Gradient Boosting (XGB) using three different input datasets: 1) two remotely sensed datasets (Sentinel-2 and Landsat-8), 2) Sentinel-2, and 3) Landsat-8. The MLP model with multiple remotely sensed datasets outperformed other MLMs affected by data imbalance. The predictive map of the spatial distribution of Chl-a generated by the MLP model highlighted areas with high and low Chl-a concentrations. In conclusion, this study emphasizes the value of multiple remotely sensed datasets in enhancing the accuracy and reliability of Chl-a estimation, mainly when using the MLP model. This also highlights the need to address data imbalances when predicting Chl-a concentrations using MLMs. These findings provide valuable insights into utilizing MLMs effectively for Chl-a monitoring.
Development and Validation of Prognostic Prediction Model for Submandibular Gland Cancer Based on the SEER Database.
Authors: He Junkun , Zhao Feng , Li Jiangmiao , Wei Fangyu , Li Qiyun , Su Jiping
Published: February 2024

Objective
Accurately predicting the prognosis of submandibular gland carcinoma (SGC) patients remains a challenging task. The purpose of this study was to develop a columnar graph prognostic prediction model for submandibular gland cancer based on the SEER database, using feature selection with lasso regression and modeling with Cox regression.
Methods
This study utilized data from the SEER database, focusing on 1362 cases of SGC. Various clinical and demographic factors, including age, tumor size, histology, and lymph node metastasis, were considered as potential prognostic factors. Feature selection was performed using lasso regression, and a Cox proportional hazards model was constructed, taking into account the complex interactions between variables and their impact on survival outcomes.
Results
The established prognostic prediction model demonstrated good accuracy and reliability. The model effectively identified several important prognostic factors, including age, tumor size, histology, and lymph node metastasis, which strongly influenced the prognosis of SGC. The model showed good discrimination and calibration with c-indexes of 0.802 (0.784–0.821) in the training set and 0.756 (0.725–0.787) in the validation set. The decision curve analysis (DCA) curve reflected clinical utility.
Conclusion
This study suggests that the prognostic prediction model based on Cox regression is a valuable tool for predicting the prognosis of patients with SGC. This approach has the potential to improve patient outcomes by facilitating personalized treatment plans and identifying high-risk patients who may benefit from more aggressive interventions.
Assessing the ductile fracture characteristics of CuZn30 brass under various stress conditions
Authors: Ganjiani Mehdi , Ghobadi Sina
Published: February 2024

This paper focuses on the investigation of ductile fracture in CuZn30 brass under various stress triaxialities and Lode angles. To achieve this, five distinct specimen geometries were fabricated, including dog-bone, notched, two types of in-plane shear, and deep drawing specimens. Every specimen was subjected to testing in three orientations: rolling, diagonal, and transverse, with the aim of investigating the potential presence of anisotropic properties in plastic deformation. A non-linear damage parameter was incorporated into the constitutive equations in two forms: coupled and uncoupled from the hardening law. It is assumed that damage progresses as the Ganjiani ductile fracture model, used as a damage initiation criterion, is established. The von-Mises yield criterion characterizes the plastic behavior of the material. In order to authenticate the experimental results, a combination of experimental and numerical simulations was carried out. In this context, all tests were simulated using ABAQUS/Standard through the implementation of USDFLD and UHARD codes. The outcomes of the simulation demonstrated good concordance with the results obtained through experiment, particularly in terms of force and the softening (necking) zone for all specimen types. It was also observed that when damage is considered as a coupled parameter to the hardening law, the softening (necking) phenomenon could be effectively simulated.
Experimental optical properties explained by Density functional theory of the natural Red Algae for dye-sensitized solar cells application
Authors: Anoua Rania , Touhtouh Samira , Jouad Mohamed El , Hajjaji Abdelowahed , Bakasse Mina , Sahraoui Bouchta , Płóciennik Przemysław , Zawadzka Anna
Published: February 2024

The present study investigates the usage of a novel natural dye derived from Red algae of Morocco in dye-sensitized solar cells (DSSCs) for the first time. The main pigments responsible for sensitizing the semiconductor TiO2 coatings in the red algae were identified as Phycoerythrin, carotenoid, and chlorophyll. The efficiency of a DSSC made from red algae was compared to that of a solar cell made from chlorophyll alone. The photovoltaic performance of the DSSC was evaluated through photocurrent density to photovoltage (J-V) characteristic analysis, and the efficiency was found to be 0.93%. To gain insights into its behavior, the absorbance and photoluminescence in a broad range were studied. Both absorbance and photoluminescence exhibited a broad-spectrum range. Additionally, electronic properties, such as HOMO, LUMO, energy gap, and chemical reactivity parameters, were studied using DFT (density functional theory) calculations.
Multinational Enterprises in India and Climate Change; Opportunities and Challenges
Authors: Anjanappa Janardhana
Published: February 2024

Multinational enterprises (MNEs) play a significant role in the global economy and their operations have a substantial impact on the environment. Climate change is one of the most pressing environmental challenges facing the world today, and MNEs are increasingly being called upon to play a role in addressing it. While there is a growing body of literature on the general role of MNEs in addressing climate change, there is a need for more nuanced research that considers the unique context of India. This paper explores the complex and nuanced relationship between MNEs and climate change in the Indian context. The findings suggest that the Indian government has implemented a number of policies to promote sustainable development and address environmental challenges. However, these policies have had a mixed impact, and there is still much work to be done to address environmental challenges in India. Many MNEs are committed to reducing their environmental impact, but they face a number of challenges in doing so. These challenges include a lack of awareness, a lack of data and metrics, a short-term focus, uncertainty and complexity, a lack of government support, and internal organizational barriers. Governments can play a crucial role in creating an enabling environment by supporting MNEs while also ensuring that their operations are sustainable and responsible. Some effective policy measures include establishing clear and consistent regulations, providing incentives for sustainable practices, promoting transparency and accountability, and encouraging collaboration and knowledge sharing.
Effect of different infusion modes of pirarubicin on recurrence rate and adverse reactions in patients with postoperative chemotherapy for bladder cancer: A Meta-Analysis
Authors: Li Xiaojun , Li Chong , Niu Linyan , Bao Yinan
Published: February 2024

Objective This meta-analysis aims to compare the effect of postoperative pyrazobicin bladder thermal perfusion and plain perfusion in reducing the bladder cancer recurrence rate and the incidence of adverse effects in bladder cancer patients. Methods Computer searches were undertaken on the Cochrane Library, PubMed, the Wanfang database, China Knowledge Network (CNKI) and Wipu databases, all with a search time frame from inception to March 2023. Meta-analysis was performed using the RevMan 5.4.1 software.
Results 18 studies were included, and the results showed that compared to normal perfusion, postoperative pirarubicin bladder thermal perfusion was more effective in reducing the 2-year recurrence rate, the 1-year recurrence rate, and the incidence of urinary pain in bladder cancer patients.
Conclusion Compared to conventional infusion chemotherapy, postoperative pyrazobicin bladder heat infusion reduced the tumour recurrence rate and the incidence of adverse effects in bladder cancer patients more effectively.
Relationships between fixed-site ambient measurements of nitrogen dioxide, ozone, and particulate matter and personal exposures in Grand Paris, France: the MobiliSense study
Authors: Bista Sanjeev , Fancello Giovanna , Zeitouni Karine , Annesi-Maesano Isabella , Chaix Basile
Published: February 2024

Past epidemiological studies using fixed-site outdoor air pollution measurements as a proxy for participants’ exposure might have suffered from exposure misclassification. In the MobiliSense study, personal exposures to ozone (O3), nitrogen dioxide (NO2), and particles with aerodynamic diameters below 2.5 µm (PM2.5) were monitored with a personal air quality monitor. All the spatial location points collected with a personal GPS receiver and mobility survey were used to retrieve background hourly concentrations of air pollutants from the nearest Airparif monitoring station. We modeled 851343 minute-level observations from 246 participants. Visited places including the residence contributed the majority of the minute-level observations, 93.0%, followed by active transport (3.4%), and the rest were from on-road and rail transport, 2.4% and 1.1%, respectively. Comparison of personal exposures and station-measured concentrations for each individual indicated low Spearman correlations for NO2 (median across participants: 0.23), O3 (median: 0.21), and PM2.5 (median: 0.27), with varying levels of correlation by microenvironments. Generally, a large degree of individual variability in the correlation between personal and fixed-site measurements was found for all air pollutants. Results from mixed-effect models indicated that personal exposure was very weakly explained by station-measured concentrations (R2 < 0.07) for all air pollutants. The fit of the model was relatively high for O3 in the active transport microenvironment (R2: 0.25) and for PM2.5 in active transport (R2: 0.16) and in the separated rail transport microenvironment (R2: 0.20). Model fit slightly increased with decreasing distance between participants’ location and the nearest monitoring station. Our results demonstrate a relatively low correlation between personal exposure and station-measured air pollutants, confirming that station-measured concentrations as proxies of personal exposures can lead to exposure misclassification. However, distance and the type of microenvironment are shown to affect the extent of misclassification.
Water Stress Management Based on Groundwater Depletion Estimation in Saudi Arabia: A Hybrid ARIMA-GLS Approach
Authors: Abba Sani , Shah Syed Muzzamil Hussain , Yassin Mohamed A. , Mati Sagiru , Usman Abdullahi G. , Pradipta Arya , Baalousha Husam Musa , Khan Nadeem A , Aljundi Isam H.
Published: February 2024

Water scarcity is a pressing global challenge, and arid regions like Saudi Arabia face the urgent need for effective water stress management. The current study proposes an innovative method to tackle this issue by utilizing a hybrid time series analysis model, comprising of Autoregressive Integrated Moving Average (ARIMA) and Generalized Least Squares (GLS) techniques to estimate groundwater depletion trends in Saudi Arabia. The research employs historical groundwater data, climatic variables, and socioeconomic indicators to formulate comprehensive insight of the factors influencing groundwater depletion. The ARIMA component of the hybrid model captures the temporal dynamics of groundwater levels, while GLS considers the spatial and cross-correlation dependencies among observation points, enhancing the accuracy of depletion estimates. The study also demonstrates the significance of climatic variability and socioeconomic factors in exacerbating water stress in the region. Furthermore, the hybrid ARIMA-GLS model offers a robust tool for forecasting future groundwater depletion trends, aiding proactive decision-making in mitigating water stress. The numerical results for different wells proved to be essential in assessing the Mean Absolute Percent Error (MAPE). For instance, the MAPE values were found to be as (i) hybrid ARIMA-CLS (MAPE = 0.1507), (ii) ARIMA-CLS (MAPE = 0.429834), (iii) ARIMA-CLS (MAPE = 0.109115) for 4-H-84-N, 4-H-86-U, 4-S-316-U, respectively with the expectation of (iv) ARI (MAPE = 6.0285) for DA-45-U well. It is therefore believed that this research contributes to the broader discussion on managing the water resource in arid regions and highlights the significance of integrated approaches that consider both temporal and spatial dimensions. Further, it offers valuable insights and a practical framework for addressing water stress challenges in Saudi Arabia and serves as a model for water management in other arid regions grappling with similar issues.
Energy metabolism, oxidative stress and immunological status in adult hip replacement patients and healthy individuals
Authors: Andruszkiewicz Beata , Flis Damian , Samson Lucjan , Olek Robert , Zrodowski Maciej , Siekierzycka Anna , Kalinowski Leszek , Borkowski Tomasz , Antosiewicz Jędrzej , Ziółkowski Wiesław , Szalewska Dominika
Published: February 2024

The work aimed to compare (1) the activity of energy metabolism enzymes and oxidative stress indicators in homogenates of the tensor fascia lata muscle with peripheral blood mononuclear cell (PBMC) isolates in patients with hip replacement (HRP) and (2) PBMCs energy and stress metabolism, and indicators of inflammation and immunity of HRP and healthy individuals. The study involved 16 HRP patients. Skeletal muscle and blood samples were taken during HR. Moreover, blood samples of 12 control subjects were used to isolate PBMCs. The activity of aerobic (malate dehydrogenase; MDH) and anaerobic (lactate dehydrogenase; LDH) metabolism enzymes and the level of carbonyl groups were measured in skeletal muscle homogenates and PBMC isolates. The indicators of inflammation and immunity were also assessed. The purity level of PBMCs isolates were determined using platelets to PBMC ratio. There was no relationship between MDH and LDH activities and carbonyl groups measured in skeletal muscle homogenate and PBMC isolate. Significantly higher inflammatory indexes (NLR and the number of neutrophils) and the level of protein carbonyl groups were also noted in the HRP group compared to the control group.
Preparation of Sugarcane Based Activated Carbon by One-step Method and Its Adsorption-desorption Performance of Toluene
Authors: sun wenshan , wang yongqiang , Yin Lihong , Ma Xiubiao , Qian Yuxian
Published: February 2024

The sugarcane based activated carbon was prepared by one-step method using sugarcane bagasse as raw material and H3PO4 as activator, meanwhile in order to get the better preparation condition of the materials the dose of phosphoric acid addition, material liquid ratio, activation temperature, and activation time on the adsorption performance of activated carbon were discussed. This study investigated the effects of different preparation conditions on the adsorption and desorption performance of activated carbon through characterization such as BET, FT-IR, XRD, dynamic adsorption, and static adsorption, which has shown that when the phosphoric acid content was 30%, the solid-liquid ratio was 1:2, the activation temperature was 400 ℃, and the activation time was 30 minutes by one-step method, the carbon content reached 55.73%, but its specific surface area can reach 1037.7m2 /g, the saturated adsorption capacity can reach 487.3mg/g, and the adsorption capacity was still above 80% after three regenerated, and the adsorption and desorption performance was the best.
Biological activities of derived pigments and polyphenols from newly recorded alga; Phyllymenia gibbesii
Authors: El-Din Nihal G. Shams , Hafez Mohamed S.M. Abd El , El-Wahab Miral G. Abd , Ibrahim Hassan A.H.
Published: February 2024

The newly recorded Phyllymenia gibesii in the Mediterranean Sea at Alexandria coast of Egypt is regarded a significant source of bioactive substances and applied as an antioxidant, anti-inflammatory, and antimicrobial agents. According to the HPLC chromatograms, the acetone extract of P. gibesii comprised ten photosynthetic pigments (chlorophyll-a, chlorophyll-d, α-carotene, β-carotene, phycocyanin, allophycocyanin, antheraxanthin, β-cryptoxanthin, lutein, and violaxanthin). Total carotenoids were the dominant class in the pigments' profile, achieving a concentration of 257 g/g. The P. gibbesii extract had a total content of phenols as; 146.67 mg/g and a total content of flavonoid as; 104.40 mg/g. The capacity of all the investigated biological activities augmented with the concentration of the algal extract. The maximal DPPH scavenging capacity was 81.44%, with an inhibitory concentration value (IC50); 9.88 μg/mL. Additionally, the highest ABTS scavenging capacity was 89.62%, recording an IC50 value of 21.77 μg/mL. The hemolytic activity of P. gibbesii attained a maximum capacity of 49.88%, exhibiting an IC50 value of 100.25 μg/mL. Data also showed the maximum anti-inflammatory effectiveness as; 81.25%, with an IC50 value of 99.75 μg/mL. Furthermore, the extract exhibited antimicrobial capacity against all reference strains, particularly at high concentration (0.1 mg/mL). The extract had the greatest effect on C. albicans and E. coli.
Identification of endoplasmic reticulum stress-related gene signature reveals KRT8 as a target in ovarian cancer ï¿¿
Authors: Li Xiaoyu , Xia Bairong , Zhu Qi , Huang Xu , Chen Yao , Xiao Lan , Peng Wenju
Published: February 2024

Purpose: Ovarian cancer (OC) is an invasive gynecological cancer with an overall 5-year survival rate less than 45%. Endoplasmic reticulum (ER) stress influence oncogenic events and immune-regulatory programmers to dictate malignant progression, antitumor immunity and response to treatment. However, endoplasmic reticulumstress (ERS) in ovarian cancer is not fully understood and needs further study.
Methods: The RNA-sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). We screened ER stress-related genes associated with ovarian tumor prognosis and developed an ER stress risk score model by LASSO regression. We used the ER stress risk score as the entry point to explore differences in the infiltration rate of immune cells. Subsequently, biological role and expression of risk gene KRT8 was validated base on molecular biology experiment.
Results: We identified 573 genes related to ER stress that were differentially expressed genes (DEGs) between normal and tumor tissues. ER stress-related risk signature (ERRS) constructed using the TCGA dataset was regarded as an independent and significant prognostic model for predicting cancer progression and instructing clinical decisions. Additional KRT8 was overexpressed in ovarian cancer cells and tissues and downregulation of the KRT8 gene inhibited ovarian cancer cell proliferation and migration (in both SKOV3 and OVCAR8 cells) in vitro.
Conclusion:The model constructed with ER stress-related genes could be used to evaluate the prognostic risk for OC patients. Notably, we discovered that KRT8 acts as a risk gene in ovarian cancer, promoting tumor progression, and is expected to be a novel therapeutic target.
Exploring the impacts of institutional quality, globalization, and urbanization on environmental pollution in Somalia: a disaggregate analysis of globalization
Authors: Hussein Hassan Abdikadir , Warsame Abdimalik Ali , Abdi Abdikafi Hassan
Published: February 2024

Environmental pollution and its implications are widespread issues that require a comprehensive understanding of effective strategies that mitigate emissions. Given the unique challenges faced by Somalia, including social, political, and environmental challenges, it is crucial to assess the effects of social and political globalization, urbanization, and institutional quality on greenhouse gas (GHG) emissions. Hence, the study aims to examine the relationship between these variables and the environmental deterioration in Somalia. The study utilizes the autoregressive distributed lag (ARDL) bound test, fully modified ordinary least squares (FMOLS) method, and causality tests. The empirical results of the bound test indicate that institutional quality and social globalization have a significant negative impact on environmental pollution in Somalia in the long run. On the contrary, economic growth impedes environmental quality in Somalia in the long run. However, the remaining explanatory variables are inconsequential in the long run. To find robust results, we perform the fully modified ordinary least squares (FMOLS) as a robust analysis. The findings revealed that social globalization and institutional quality improve environmental quality by reducing GHG emissions whereas urbanization significantly hampers it. Based on the empirical evidence, the study offers several policy implications.
Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures
Authors: Hameed Mohammed Majeed , Masood Adil , Srivast Aman , Rahman Norinah Abd , Razalid Siti Fatin Mohd , Salem Ali , Elbeltagi Ahmed
Published: February 2024

Liquefaction is a devastating consequence of earthquakes that occur in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameters is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines the Extreme Learning Machine (ELM) with the Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.9%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored to the ELM-DOA model, to aid engineers and researchers in effectively utilizing the predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.
Transformer-based Learning Models of Dynamical Systems for Robotic State Prediction
Authors: Reed Alec , Albin Doncey , Pasricha Anuh , Roncone Alessandro , Heckman Christoffer
Published: February 2024

In this paper, we propose a novel approach to data-driven dynamical forecasting using transformerbased learning methods. We explore and evaluate the effectiveness of this approach by developing new benchmarks for generalizable, data-driven dynamical forecasting on a robotic arm and a 4-wheeled ground vehicle. The transformer-based approach utilizes a spatio-temporal forecasting model called the Spacetimeformer (STF), which was originally designed for long-horizon fields such as weather and economics. We demonstrate that with key innovations, the STF model can also be a powerful tool for short-range dynamical forecasting. The results show that our approach can even outperform popular analytical dynamics models, such as the bicycle model for four-wheeled vehicles and the robot dynamics model for serial manipulator arms.
From RNA to DNA: CRISPR/LbuCas13a Demonstrates Exceptional Single-Nucleotide Specificity
Authors: Liu Yizhen , Wu Xiaolong , Luo Siyuan , Guo Chuanghao , Zhao Yi , Zhong Jialing , Hu Ronghuan , Yang Xinyao , Liu Conghui , Zhang Qianling , Chen Yong , Zhang Xueji
Published: February 2024

Traditionally perceived as an RNA-specific nuclease, Cas13a has garnered extensive utilization in RNA detection. This paradigm is challenged by our discovery of LbuCas13a's ability to directly target DNA without the restrictions of (Protospacer Flanking Sequence) FPS and (Protospacer Adjacent Motif) PAM sequences, coupled with robust trans-cleavage activity, a breakthrough in CRISPR-based diagnostics. Contrary to conventional understanding, LbuCas13a does not degrade DNA targets, thereby enabling retesting. Remarkably, our study reveals a striking enhancement in LbuCas13a's single nucleotide specificity against DNA (a 98-fold increase compared to RNA). This heightened specificity is attributed to the lower affinity of crRNA towards DNA, raising the crRNA-DNA binding energy barrier. Leveraging this discovery, we introduce a pioneering molecular diagnostic platform: Advanced LbuCas13a-Strong-Specificity DNA Universal Rapid Enhanced Detection (ASSURED), which achieves high-resolution genotyping, exemplified by the accurate discrimination of the CYP2C19*3 gene variant. ASSURED exhibits exceptional sensitivity, capable of detecting DNA concentrations as minute as 0.3 aM (0.18 cps/µL). ASSURED represents a significant advancement in real-time nucleic acid detection, with its unparalleled specificity and sensitivity, making it an ideal tool for pathogen identification and mutation analysis in clinical diagnostics.
Heavy metal poisoning caused by Chinese folk remedies in psoriasis patients：a retrospective analysis
Authors: Xue Changjiang , Du Xuqin , Zhu Xiaoli , Wu Na , Ye Qiao
Published: February 2024

Psoriasis, characterized as a chronic relapsing disease with a protracted course, often drives patients to seek relief through Chinese folk remedies (CFR). Nonetheless, the complex compositions of these remedies frequently result in unintended adverse effects, notably various types of heavy metal poisoning. Our study involved an exhaustive collection and analysis of clinical data from psoriasis patients who developed heavy metal poisoning due to CFR usage, admitted to Beijing Chao-Yang Hospital from January 2011 to October 2023. Our analysis identified 44 cases of mercury poisoning, 17 of lead poisoning, 21 of arsenic poisoning, and 4 instances of mixed heavy metal poisoning. The folk remedies used ranged from fumigation and inhalation to skin application and oral administration. Distinct pathogenic characteristics were observed in each poisoning type. After treatment with metal chelating agents, all patients experienced a reduction in heavy metal levels in their bodies, accompanied by varying degrees of symptom alleviation. This study underscores the vital necessity of opting for formal, medically approved treatments for psoriasis, thereby avoiding the hazardous consequences of unregulated folk remedies that may lead to severe heavy metal poisoning.
Pushing the redox potentials of deelectronators to highly positive values using solvent effects and weakly coordinating anions
Authors: Krossing Ingo , Armbruster Christian , Sellin Malte , Seiler Matthis , Würz Tanja , Oesten Friederike , Schmucker Maximilian , Sterbak Tabea , Fischer Julia , Radtke Valentin , Hunger Johannes
Published: February 2024

While the development of weakly coordinating anions (WCAs) received much attention, the progress on new weakly coordinating and inert solvents almost stagnated. Here we study the effect of strategic F-substitution on the solvent properties of fluorobenzenes C6FxH6−x (xFB, x = 1–5). Asymmetric fluorination leads to dielectric constants as high as 22.1 for 3FB that exceeds acetone (20.7). Combined with the WCAs [Al(ORF)4]– or [(FRO)3Al-F-Al(ORF)3]– (RF = C(CF3)3), the xFB solvents push the potentials of Ag+ and NO+ ions to +1.50 / +1.52 V vs. Fc+/Fc. This is by +0.85 / +0.40 V higher than any published value. The xFB/WCA-system has electrochemical xFB stability windows that exceed 5 V for all xFBs with positive upper limits between +1.82 V (1FB) and +2.67 V (5FB) vs. Fc+/Fc. High level ab initio calculations with inclusion of solvation energies show that these high potentials result from weak interactions of the ions with solvent and counterion. To access the available positive xFB potential range with stable reagents, the innocent deelectronator salts [anthraceneF]+∙[WCA]– and [phenanthreneF]+∙[WCA]– with potentials of +1.47 and +1.89 V vs. Fc+/Fc are introduced.
The association between testosterone, estradiol, estrogen sulfotransferase and idiopathic pulmonary fibrosis: a bidirectional Mendelian randomization study
Authors: Xu Qingying , Hu Guangwang , Lin Qunying , Wu Menghang , Tang Kenan , Zhang Yuyu , Chen Feng
Published: February 2024

Background
The causal relationships between testosterone, estradiol, estrogen sulfotransferase, and idiopathic pulmonary fibrosis (IPF) are not well understood. This study employs a bidirectional two-sample Mendelian Randomization (MR) approach to explore these associations.
Methods
We extracted significant genetic loci associated with testosterone, estradiol, and estrogen sulfotransferase from GWAS summary data as instrumental variables, with IPF as the outcome variable for a two-sample Mendelian randomization analysis. Instrumental variables and outcome variables were then swapped for a bidirectional two-sample Mendelian randomization analysis. The inverse variance weighted (IVW), MR-Egger, and weighted median methods were used to evaluate causal relationships. Cochran's Q test, MR-Egger regression, MR-PRESSO global test, and leave-one-out method were used for sensitivity analyses.
Results
Genetically predicted increases in serum testosterone levels by one standard deviation were associated with a 58.7% decrease in the risk of developing IPF (OR = 0.413, PIVW=0.029, 95% CI = 0.187 ~ 0.912), while an increase in serum estrogen sulfotransferase by one standard deviation was associated with a 32.4% increase in risk (OR = 1.324, PIVW=0.006, 95% CI = 1.083 ~ 1.618). No causal relationship was found between estradiol (OR = 1.094, PIVW=0.735, 95% CI = 0.650 ~ 1.841) and the risk of IPF. Reverse MR analysis did not reveal any causal relationship between IPF and testosterone (OR = 1.001, PIVW=0.51, 95% CI = 0.998 ~ 1.004), estradiol (OR = 1.001, PIVW=0.958, 95% CI = 0.982 ~ 1.019), or estrogen sulfotransferase (OR = 0.975, PIVW=0.251, 95% CI = 0.933 ~ 1.018).
Conclusions
Increased serum levels of testosterone are associated with a reduced risk of IPF, while increased levels of serum estrogen sulfotransferase are associated with an increased risk. No causal relationship was found between estradiol and the development of IPF. No causal relationship was identified between IPF and testosterone, estradiol, or estrogen sulfotransferase.