bluuebunny/crossref_metadata_2025_split huggingface dataset:
Acetylcholinesterase enzyme among cancer patients a potential diagnostic and prognostic indicator a multicenter case–control study
Authors: Ahmed Gasmelseed Y. , Osman Ahmed A. , Mukhtar Ahlam 
Published: March 2024

AbstractAcetylcholinesterase enzyme (AChE) activity is impaired by a variety of inhibitors including organophosphorus pesticides, leading to the accumulation of acetylcholine. In this study, we aimed to determine the association between cancer and the blood level of the (AChE). This is a multicenter hospital-based case–control study conducted in the Radiation and Isotopes Center Khartoum, and Institute of Nuclear Medicine and Molecular Biology and Oncology Gezira. One hundred and fifty participants, half of them cancer patients and half cancer free were recruited. All participants were screened for demographic, environmental, occupational, and clinical characteristics. Blood for the (AChE) activity test was drawn from participants in the two groups. The mean age of the participants was 40.6 ± 14.8 years. Geographical distribution showed the Central Region of Sudan had the highest rate of cancer, followed by North State, Khartoum State, West State, and East State. The most common tumor subtype was breast cancer, followed by leukemia, colon, esophageal, and prostate cancer. Inferential analysis revealed significantly impaired (AChE) activity among cancer patients compared to controls (53.4 ± 20.3% vs. 93.8 ± 8.8, p-value 0.001). There was a significant statistical association between impaired (AChE) activity and cancer. (AChE) activity might be applied in the future as a diagnostic biomarker and therapeutic target. Further large sample and molecular studies are recommended.
Polarization and incident angle independent multifunctional tunable terahertz metasurface based on graphene
Authors: Qureshi Ubaid Ur Rahman , Basir Shahid , Mallek Fatma , Hamam Habib 
Published: March 2024

AbstractMotivated by the imperative demand for design integration and miniaturization within the terahertz (THz) spectrum, this study presents an innovative solution to the challenges associated with singular functionality, limited application scope, and intricate structures prevalent in conventional metasurfaces. The proposed multifunctional tunable metasurface leverages a hybridized grapheme–metal structure, addressing critical limitations in existing designs. Comprising three distinct layers, namely a graphene–gold resonance layer, a Topas dielectric layer, and a bottom gold film reflective layer, this terahertz metasurface exhibits multifunctionality that is both polarization and incident-angle independent. The metasurface demonstrates a broadband circular dichroism (CD) function when subjected to incident circularly polarized waves. In contrast, under linear incidence, the proposed design achieves functionalities encompassing linear dichroism (LD) and polarization conversion. Remarkably, graphene's chemical potential and the incident light’s state can be manipulated to tune each functional aspect's intensity finely. The proposed tunable multifaceted metasurface showcases significant referential importance within the terahertz spectrum, mainly contributing to advancing CD metamirrors, chiral photodetectors, polarization digital imaging systems, and intelligent switches.
Ecological and social factors influence interspecific pathogens occurrence among bees
Authors: Tiritelli Rossella , Flaminio Simone , Zavatta Laura , Ranalli Rosa , Giovanetti Manuela , Grasso Donato Antonio , Leonardi Stefano , Bonforte Marta , Boni Chiara Benedetta , Cargnus Elena , Catania Roberto , Coppola Francesca , Di Santo Marco , Pusceddu Michelina , Quaranta Marino , Bortolotti Laura , Nanetti Antonio , Cilia Giovanni 
Published: March 2024

AbstractThe interspecific transmission of pathogens can occur frequently in the environment. Among wild bees, the main spillover cases are caused by pathogens associated with Apis mellifera, whose colonies can act as reservoirs. Due to the limited availability of data in Italy, it is challenging to accurately assess the impact and implications of this phenomenon on the wild bee populations. In this study, a total of 3372 bees were sampled from 11 Italian regions within the BeeNet project, evaluating the prevalence and the abundance of the major honey bee pathogens (DWV, BQCV, ABPV, CBPV, KBV, Nosema ceranae, Ascosphaera apis, Crithidia mellificae, Lotmaria passim, Crithidia bombi). The 68.4% of samples were positive for at least one pathogen. DWV, BQCV, N. ceranae and CBPV showed the highest prevalence and abundance values, confirming them as the most prevalent pathogens spread in the environment. For these pathogens, Andrena, Bombus, Eucera and Seladonia showed the highest mean prevalence and abundance values. Generally, time trends showed a prevalence and abundance decrease from April to July. In order to predict the risk of infection among wild bees, statistical models were developed. A low influence of apiary density on pathogen occurrence was observed, while meteorological conditions and agricultural management showed a greater impact on pathogen persistence in the environment. Social and biological traits of wild bees also contributed to defining a higher risk of infection for bivoltine, communal, mining and oligolectic bees. Out of all the samples tested, 40.5% were co-infected with two or more pathogens. In some cases, individuals were simultaneously infected with up to five different pathogens. It is essential to increase knowledge about the transmission of pathogens among wild bees to understand dynamics, impact and effects on pollinator populations. Implementing concrete plans for the conservation of wild bee species is important to ensure the health of wild and human-managed bees within a One-Health perspective.
Integrating wastewater and randomised prevalence survey data for national COVID surveillance
Authors: Li Guangquan , Diggle Peter , Blangiardo Marta 
Published: March 2024

AbstractDuring the COVID-19 pandemic, studies in a number of countries have shown how wastewater can be used as an efficient surveillance tool to detect outbreaks at much lower cost than traditional prevalence surveys. In this study, we consider the utilisation of wastewater data in the post-pandemic setting, in which collection of health data via national randomised prevalence surveys will likely be run at a reduced scale; hence an affordable ongoing surveillance system will need to combine sparse prevalence data with non-traditional disease metrics such as wastewater measurements in order to estimate disease progression in a cost-effective manner. Here, we use data collected during the pandemic to model the dynamic relationship between spatially granular wastewater viral load and disease prevalence. We then use this relationship to nowcast local disease prevalence under the scenario that (i) spatially granular wastewater data continue to be collected; (ii) direct measurements of prevalence are only available at a coarser spatial resolution, for example at national or regional scale. The results from our cross-validation study demonstrate the added value of wastewater data in improving nowcast accuracy and reducing nowcast uncertainty. Our results also highlight the importance of incorporating prevalence data at a coarser spatial scale when nowcasting prevalence at fine spatial resolution, calling for the need to maintain some form of reduced-scale national prevalence surveys in non-epidemic periods. The model framework is disease-agnostic and could therefore be adapted to different diseases and incorporated into a multiplex surveillance system for early detection of emerging local outbreaks.
Comprehensive investigation of isotherm, RSM, and ANN modeling of CO2 capture by multi-walled carbon nanotube
Authors: Khoshraftar Zohreh , Ghaemi Ahad , Hemmati Alireza 
Published: March 2024

AbstractChemical vapor deposition was used to produce multi-walled carbon nanotubes (MWCNTs), which were modified by Fe–Ni/AC catalysts to enhance CO2 adsorption. In this study, a new realm of possibilities and potential advancements in CO2 capture technology is unveiled through the unique combination of cutting-edge modeling techniques and utilization of the recently synthesized Fe–Ni/AC catalyst adsorbent. SEM, BET, and FTIR were used to analyze their structure and morphology. The surface area of MWCNT was found to be 240 m2/g, but after modification, it was reduced to 11 m2/g. The modified MWCNT showed increased adsorption capacity with higher pressure and lower temperature, due to the introduction of new adsorption sites and favorable interactions at lower temperatures. At 25 °C and 10 bar, it reached a maximum adsorption capacity of 424.08 mg/g. The optimal values of the pressure, time, and temperature parameters were achieved at 7 bar, 2646 S and 313 K. The Freundlich and Hill models had the highest correlation with the experimental data. The Second-Order and Fractional Order kinetic models fit the adsorption results well. The adsorption process was found to be exothermic and spontaneous. The modified MWCNT has the potential for efficient gas adsorption in fields like gas storage or separation. The regenerated M-MWCNT adsorbent demonstrated the ability to be reused multiple times for the CO2 adsorption process, as evidenced by the study. In this study, a feed-forward MLP artificial neural network model was created using a back-propagation training approach to predict CO2 adsorption. The most suitable and efficient MLP network structure, selected for optimization, consisted of two hidden layers with 25 and 10 neurons, respectively. This network was trained using the Levenberg–Marquardt backpropagation algorithm. An MLP artificial neural network model was created, with a minimum MSE performance of 0.0004247 and an R2 value of 0.99904, indicating its accuracy. The experiment also utilized the blank spreadsheet design within the framework of response surface methodology to predict CO2 adsorption. The proximity between the Predicted R2 value of 0.8899 and the Adjusted R2 value of 0.9016, with a difference of less than 0.2, indicates a high level of similarity. This suggests that the model is exceptionally reliable in its ability to predict future observations, highlighting its robustness.
Effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin measurements in liver transplantation recipients
Authors: Yoon Soo Bin , Jung Chul-Woo , Kim Taeyup , Lee Hyung-Chul 
Published: March 2024

AbstractThis study evaluated the effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin (SpHb) measurements in liver transplantation recipients. Overall, 1465 SpHb and laboratory hemoglobin (Hb) measurement pairs (n = 296 patients) were analyzed. Patients were grouped into normal (< 1.2 mg/dL), mild-to-moderate (1.2–3.0 mg/dL), and severe (> 3.0 mg/dL) hyperbilirubinemia groups based on the preoperative serum total bilirubin levels. Bland–Altman analysis showed a bias of 0.20 (95% limit of agreement, LoA: − 2.59 to 3.00) g/dL, 0.98 (95% LoA: − 1.38 to 3.35) g/dL, and 1.23 (95% LoA: − 1.16 to 3.63) g/dL for the normal, mild-to-moderate, and severe groups, respectively. The four-quadrant plot showed reliable trending ability in all groups (concordance rate > 92%). The rates of possible missed transfusion (SpHb > 7.0 g/dL for Hb < 7.0 g/dL) were higher in the hyperbilirubinemia groups (2%, 7%, and 12% for the normal, mild-to-moderate, and severe group, respectively. all P < 0.001). The possible over-transfusion rate was less than 1% in all groups. In conclusion, the use of SpHb in liver transplantation recipients with preoperative hyperbilirubinemia requires caution due to the positive bias and high risk of missed transfusion. However, the reliable trending ability indicated its potential use in clinical settings.
SOCS5-RBMX stimulates SREBP1-mediated lipogenesis to promote metastasis in steatotic HCC with HBV-related cirrhosis
Authors: Wang Youpeng , Zhao Ziyin , Guo Tingting , Wu Tiansong , Zhang Mao , Luo Dingan , Dou Kunpeng , Yang Yeni , Jin Cheng , Zhang Bingyuan , Zhang Bin , Han Bing 
Published: March 2024

AbstractAbnormal lipid metabolism promotes hepatocellular carcinoma (HCC) progression, which engenders therapeutic difficulties owing to unclear mechanisms of the phenomenon. We precisely described a special steatotic HCC subtype with HBV-related cirrhosis and probed its drivers. Hematoxylin-eosin (HE) staining of 245 HCC samples revealed a special HCC subtype (41 cases) characterized by HBV-related cirrhosis and intratumoral steatosis without fatty liver background, defined as steatotic HCC with HBV-related cirrhosis (SBC-HCC). SBC-HCC exhibits a larger tumor volume and worse prognosis than non-SBC-HCC. Screening for driver genes promoting fatty acid (FA) biosynthesis in the Gao’s HBV-related cirrhosis HCC cases and GSE121248’ HBV-related HCC cases revealed that high expression of SOCS5 predicts increased FA synthesis and that SOCS5 is upregulated in SBC-HCC. Through proteomics, metabolomics, and both in vivo and in vitro experiments, we demonstrated that SOCS5 induces lipid accumulation to promote HCC metastasis. Mechanistically, through co-IP and GST-pulldown experiments, we found that the SOCS5-SH2 domain, especially the amino acids Y413 and D443, act as critical binding sites for the RBMX-RRM domain. SOCS5-RBMX costimulates the promoter of SREBP1, inducing de novo lipogenesis, while mutations in the SH2 domain, Y413, and D443 reverse this effect. These findings precisely identified SBC-HCC as a special steatotic HCC subtype and highlighted a new mechanism by which SOCS5 promotes SBC-HCC metastasis.
Why do probabilistic clinical models fail to transport between sites
Authors: Lasko Thomas A. , Strobl Eric V. , Stead William W. 
Published: March 2024

AbstractThe rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites. In this perspective, we argue that we should typically expect this failure to transport, and we present common sources for it, divided into those under the control of the experimenter and those inherent to the clinical data-generating process. Of the inherent sources we look a little deeper into site-specific clinical practices that can affect the data distribution, and propose a potential solution intended to isolate the imprint of those practices on the data from the patterns of disease cause and effect that are the usual target of probabilistic clinical models.
Unraveling the transcriptomic landscape of eye migration and visual adaptations during flatfish metamorphosis
Authors: Guerrero-Peña Laura , Suarez-Bregua Paula , Sánchez-Ruiloba Lucía , Méndez-Martínez Luis , García-Fernández Pablo , Tur Ricardo , Tena Juan J. , Rotllant Josep 
Published: March 2024

AbstractFlatfish undergo a remarkable metamorphosis from symmetrical pelagic larvae to fully asymmetrical benthic juveniles. The most distinctive features of this transformation is the migration of one eye. The molecular role of thyroid hormone in the metamorphosis process in flatfishes is well established. However, the regulatory network that facilitates eye movement remains enigmatic. This paper presents a morphological investigation of the metamorphic process in turbot eyes, using advanced imaging techniques and a global view of gene expression. The study covers migrant and non-migrant eyes and aims to identify the genes that are active during ocular migration. Our transcriptomic analysis shows a significant up-regulation of immune-related genes. The analysis of eye-specific genes reveals distinct patterns during the metamorphic process. Myosin is highlighted in the non-migrant eye, while ependymin is highlighted in the migrant eye, possibly involved in optic nerve regeneration. Furthermore, a potential association between thealx3gene and cranial restructuring has been identified. Additionally, it confirmed simultaneous adaptation to low light in both eyes, as described by changes in opsins expression during the metamorphic process. The study also revealed that ocular migration activates systems asynchronously in both eyes, providing insight into multifaceted reorganization processes during metamorphosis of flatfish.
EGFR signaling and pharmacology in oncology revealed with innovative BRET-based biosensors
Authors: Gross Florence , Mancini Arturo , Breton Billy , Kobayashi Hiroyuki , Pereira Pedro Henrique Scarpelli , Le Gouill Christian , Bouvier Michel , Schann Stephan , Leroy Xavier , Sabbagh Laurent 
Published: March 2024

AbstractMutations of receptor tyrosine kinases (RTKs) are associated with the development of many cancers by modifying receptor signaling and contributing to drug resistance in clinical settings. We present enhanced bystander bioluminescence resonance energy transfer-based biosensors providing new insights into RTK biology and pharmacology critical for the development of more effective RTK-targeting drugs. Distinct SH2-specific effector biosensors allow for real-time and spatiotemporal monitoring of signal transduction pathways engaged upon RTK activation. Using EGFR as a model, we demonstrate the capacity of these biosensors to differentiate unique signaling signatures, with EGF and Epiregulin ligands displaying differences in efficacy, potency, and responses within different cellular compartments. We further demonstrate that EGFR single point mutations found in Glioblastoma or non-small cell lung cancer, impact the constitutive activity of EGFR and response to tyrosine kinase inhibitor. The BRET-based biosensors are compatible with microscopy, and more importantly characterize the next generation of therapeutics directed against RTKs.
History-dependent nano-photoisomerization by optical near-field in photochromic single crystals
Authors: Arakawa Yuji , Uchiyama Kazuharu , Uchida Kingo , Naruse Makoto , Hori Hirokazu 
Published: March 2024

AbstractThe history dependence of physical systems is promising for solving complex problems. To exploit spontaneous nanometre-scale optical memory for computation, the history dependence must be elucidated. Here, we demonstrate history-dependent or dynamic nano-photoisomerization through the sequential formation of multiple memory pathways in photochromic crystals via optical near-field interactions. Incident photons passing through photoisomerization memory pathways are observed using a double-probe optical near-field microscope, with one probe located on the front surface for local excitation and the other on the rear surface for near-field observations. By performing localised near-field excitation twice but at spatially different positions, negatively correlated near-field output patterns are observed between the first and second memory pathways. We also confirm that the first memory pathway is preserved after the formation of the second memory pathway. This result indicates that photoisomerization using an optical near field in diarylethene crystals has a history dependence, leading to brain-like dynamic information memorisation.
A transformerless Z‐source photovoltaic grid‐connected inverter with coupled inductor coil
Authors: Dai Yunzhong , Zhang Huaiyu 
Published: March 2024

AbstractThe quasi‐Z‐source H‐bridge grid‐connected inverter (QHGCI) is well known for its advantages of the void of the shoot‐through problem and the high DC‐voltage utilization. But the existence of the common‐mode leakage current in the power frequency cycle, lower power density, and higher thermal stress make it hard applicable to the grid‐penetrating application. Thus with the purpose to conquer the problem relating to the QHGCI, an innovative transformerless Z‐source photovoltaic grid‐connected inverter with a coupled inductor coil (TZPGCI‐CIC) is proposed. The circuitry topology and an unipolar sine pulse width modulation strategy are first introduced in short. Thereafter, the common‐mode voltage in the whole working process is derived and evaluated through the detailed operating mode analysis, in which a constant value of it has been theoretically revealed. Lastly, a prototype platform of TZPGCI‐CIC is set up and its good performance on leakage current suppression, and lower thermal stress are validated with the experimental results.
Sub‐optimal Internet of Thing devices deployment using branch and bound method
Authors: Kim Haesik 
Published: March 2024

AbstractThe Internet of Thing (IoT) network deployments are widely investigated in 4G and 5G systems and will still be key technical systems to drive massive connectivity of 6G systems. In 6G, IoT systems will operate with 6G new technologies such as Integrated Sensing and Communications. The IoT systems of 6G will be a platform to collect information in real world and create new use cases and business models. As the IoT devices and cellular networks are getting smarter, the IoT ecosystem allows us to bridge between human life and digital life and accelerate the transition towards a hyper‐connected world. Optimal and scalable IoT network design has been investigated in many research groups but key challenges in this topic still remain. An IoT devices deployment problem is investigated to minimise the transmission and computation cost among network nodes. The IoT devices deployment problem is formulated as Mixed‐Integer Nonlinear Programming problem. After relaxing the constraints and transforming the problem to a mixed integer linear programming (MILP) problem, the authors propose a new branch and bound (BB) method with a machine learning function and solve the MILP problem as a sub‐optimal solution. In the numerical analysis, the authors evaluate both conventional BB method and the proposed BB method with weighting factors and compare the objective function values, the number of explored nodes, and computational time. The performances of the proposed BB method are significantly improved under the given simulation configuration. The author finds the optimal mapping of IoT devices to fusion nodes.