bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of housing construction material on childhood acute respiratory infection: a hospital based case control study in Bangladesh
Authors: Bakchi Jhantu , Rasel Rosul Ahmed , Shammi Khandokar Farmina , Ferdous Sumaiya , Sultana Shamima , Rabeya Mst. Rokshana 
Published: April 2024

AbstractDespite several studies conducted to investigate housing factors, the effects of housing construction materials on childhood ARI symptoms in Bangladesh remain unclear. Hence, the study aimed to measure such a correlation among children under the age of five. A hospital-based case–control study was conducted, involving 221 cases and 221 controls from January to April 2023. Bivariate and multivariate binary logistic regression was performed to measure the degree of correlation between housing construction materials and childhood ARI symptoms. Households composed of natural floor materials had 2.7 times (95% confidence interval 1.27–5.57) and households composed of natural roof materials had 1.8 times (95% confidence interval 1.01–3.11) higher adjusted odds of having under-five children with ARI symptoms than household composed of the finished floor and finished roof materials respectively. Households with natural wall type were found protective against ARI symptoms with adjusted indoor air pollution determinants. The study indicates that poor housing construction materials are associated with an increased risk of developing ARI symptoms among under-five children in Bangladesh. National policy regarding replacing poor housing materials with concrete, increasing livelihood opportunities, and behavioral strategies programs encouraging to choice of quality housing construction materials could eliminate a fraction of the ARI burden.
Using U-Net convolutional neural network to model pixel-based electrostatic potential distributions in GaN power MIS-HEMTs
Authors: Chen Bang-Ren , Hsiao Yu-Sheng , Lin Wei-Cheng , Lee Wen-Jay , Chen Nan-Yow , Wu Tian-Li 
Published: April 2024

AbstractThis study demonstrates a novel use of the U-Net convolutional neural network (CNN) for modeling pixel-based electrostatic potential distributions in GaN metal–insulator-semiconductor high-electron mobility transistors (MIS-HEMTs) with various gate and source field plate designs and drain voltages. The pixel-based images of the potential distribution are successfully modeled from the developed U-Net CNN with an error of less than 1% error relative to a TCAD simulated reference of a 500-V electrostatic potential distribution in the AlGaN/GaN interface. Furthermore, the modeling time of potential distributions by U-Net takes about 80 ms. Therefore, the U-Net CNN is a promising approach to efficiently model the pixel-based distributions characteristics in GaN power devices.
Fractional epidemic model of coronavirus disease with vaccination and crowding effects
Authors: Saleem Suhail , Rafiq Muhammad , Ahmed Nauman , Arif Muhammad Shoaib , Raza Ali , Iqbal Zafar , Niazai Shafiullah , Khan Ilyas 
Published: April 2024

AbstractMost of the countries in the world are affected by the coronavirus epidemic that put people in danger, with many infected cases and deaths. The crowding factor plays a significant role in the transmission of coronavirus disease. On the other hand, the vaccines of the covid-19 played a decisive role in the control of coronavirus infection. In this paper, a fractional order epidemic model (SIVR) of coronavirus disease is proposed by considering the effects of crowding and vaccination because the transmission of this infection is highly influenced by these two factors. The nonlinear incidence rate with the inclusion of these effects is a better approach to understand and analyse the dynamics of the model. The positivity and boundedness of the fractional order model is ensured by applying some standard results of Mittag Leffler function and Laplace transformation. The equilibrium points are described analytically. The existence and uniqueness of the non-integer order model is also confirmed by using results of the fixed-point theory. Stability analysis is carried out for the system at both the steady states by using Jacobian matrix theory, Routh–Hurwitz criterion and Volterra-type Lyapunov functions. Basic reproductive number is calculated by using next generation matrix. It is verified that disease-free equilibrium is locally asymptotically stable if $${R}_{0}<1$$

R
0

<
1

and endemic equilibrium is locally asymptotically stable if $${R}_{0}>1$$

R
0

>
1

. Moreover, the disease-free equilibrium is globally asymptotically stable if $${R}_{0}<1$$

R
0

<
1

and endemic equilibrium is globally asymptotically stable if $${R}_{0}>1$$

R
0

>
1

. The non-standard finite difference (NSFD) scheme is developed to approximate the solutions of the system. The simulated graphs are presented to show the key features of the NSFD approach. It is proved that non-standard finite difference approach preserves the positivity and boundedness properties of model. The simulated graphs show that the implementation of control strategies reduced the infected population and increase the recovered population. The impact of fractional order parameter $$\alpha$$
α
is described by the graphical templates. The future trends of the virus transmission are predicted under some control measures. The current work will be a value addition in the literature. The article is closed by some useful concluding remarks.
Malaria risk mapping among children under five in Togo
Authors: Kombate Gountante , Kone Issouf , Douti Bili , Soubeiga Kamba André-Marie , Grobbee Diederick E. , van der Sande Marianne A. B. 
Published: April 2024

AbstractMalaria is a major health threat in sub-Sahara Africa, especially for children under five. However, there is considerable heterogeneity between areas in malaria risk reported, associated with environmental and climatic. We used data from Togo to explore spatial patterns of malaria incidence. Geospatial covariate datasets, including climatic and environmental variables from the 2017 Malaria Indicator Survey in Togo, were used for this study. The association between malaria incidence and ecological predictors was assessed using three regression techniques, namely the Ordinary Least Squares (OLS), spatial lag model (SLM), and spatial error model (SEM). A total of 171 clusters were included in the survey and provided data on environmental and climate variables. Spatial autocorrelation showed that the distribution of malaria incidence was not random and revealed significant spatial clustering. Mean temperature, precipitation, aridity and proximity to water bodies showed a significant and direct association with malaria incidence rate in the SLM model, which best fitted the data according to AIC. Five malaria incidence hotspots were identified. Malaria incidence is spatially clustered in Togo associated with climatic and environmental factors. The results can contribute to the development of specific malaria control plans taking geographical variation into consideration and targeting transmission hotspots.
Crowding under scotopic and photopic vision in albino and normal-sighted participants
Authors: Moshkovitz Avital , Lev Maria , Polat Uri 
Published: April 2024

AbstractCrowding is a phenomenon in which the ability to recognize an object in a clutter deteriorates. It is, therefore, a fundamental aspect of object recognition and crucial in deciphering resolution. For visually impaired individuals, deficiency in crowding has a tremendous effect on vision and may reflect and predict the amount of deterioration in vision. It is well established that albinos suffer much more from crowding than normally sighted individuals under daylight luminance conditions. However, to our knowledge, this study is the first to investigate crowding in albino participants under low light conditions. In this study, we explored the crowding effect in a group of albino participants (n = 9) and a control group of normally sighted participants (n = 9). Crowding was conducted under daylight (photopic vision) and low light (scotopic vision). We measured the visual acuity threshold under crowding in three-letter spacing (0.5, 1, and 1.5) and compared it to a single target. Results indicate that albino participants experienced stronger crowding than the control under the photopic condition, while crowding under the scotopic condition was apparent in the albino but abolished for the control group. These findings highlight the importance of considering luminance when discussing the visually impaired population in general. In particular, it suggests that crowding in albinism is based on a peripheral-like mechanism and may indicate a cessation in visual development.
Curcumin regulates autophagy through SIRT3-SOD2-ROS signaling pathway to improve quadriceps femoris muscle atrophy in KOA rat model
Authors: Ye Hua , Long Yi , Yang Jia-Ming , Wu Yan-Lin , Dong Ling-Yan , Zhong Yan-Biao , Luo Yun , Wang Mao-Yuan 
Published: April 2024

AbstractKnee osteoarthritis (KOA) usually leads to quadriceps femoris atrophy, which in turn can further aggravate the progression of KOA. Curcumin (CUR) has anti-inflammatory and antioxidant effects and has been shown to be a protective agent for skeletal muscle. CUR has been shown to have a protective effect on skeletal muscle. However, there are no studies related to whether CUR improves KOA-induced quadriceps femoris muscle atrophy. We established a model of KOA in rats. Rats in the experimental group were fed CUR for 5 weeks. Changes in autophagy levels, reactive oxygen species (ROS) levels, and changes in the expression of the Sirutin3 (SIRT3)-superoxide dismutase 2 (SOD2) pathway were detected in the quadriceps femoris muscle of rats. KOA led to quadriceps femoris muscle atrophy, in which autophagy was induced and ROS levels were increased. CUR increased SIRT3 expression, decreased SOD2 acetylation and ROS levels, inhibited the over-activation of autophagy, thereby alleviating quadriceps femoris muscle atrophy and improving KOA. CUR has a protective effect against quadriceps femoris muscle atrophy, and KOA is alleviated after improvement of quadriceps femoris muscle atrophy, with the possible mechanism being the reduction of ROS-induced autophagy via the SIRT3-SOD2 pathway.
Inferring molecular inhibition potency with AlphaFold predicted structures
Authors: Oliveira Pedro F. , Guedes Rita C. , Falcao Andre O. 
Published: April 2024

AbstractEven though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.
Unprecedented insights into extents of biological responses to physical forcing in an Arctic sub-mesoscale filament by combining high-resolution measurement approaches
Authors: Weiß Josefine Friederike , von Appen Wilken-Jon , Niehoff Barbara , Hildebrand Nicole , Graeve Martin , Neuhaus Stefan , Bracher Astrid , Nöthig Eva-Maria , Metfies Katja 
Published: April 2024

AbstractIn Fram Strait, we combined underway-sampling using the remote-controlled Automated Filtration System for Marine Microbes (AUTOFIM) with CTD-sampling for eDNA analyses, and with high-resolution optical measurements in an unprecedented approach to determine variability in plankton composition in response to physical forcing in a sub-mesoscale filament. We determined plankton composition and biomass near the surface with a horizontal resolution of ~ 2 km, and addressed vertical variability at five selected sites. Inside and near the filament, plankton composition was tightly linked to the hydrological dynamics related to the presence of sea ice. The comprehensive data set indicates that sea-ice melt related stratification near the surface inside the sub-mesoscale filament resulted in increased sequence abundances of sea ice-associated diatoms and zooplankton near the surface. In analogy to the physical data set, the underway eDNA data, complemented with highly sampled phytoplankton pigment data suggest a corridor of 7 km along the filament with enhanced photosynthetic biomass and sequence abundances of sea-ice associated plankton. Thus, based on our data we extrapolated an area of 350 km2 in Fram Strait with enhanced plankton abundances, possibly leading to enhanced POC export in an area that is around a magnitude larger than the visible streak of sea-ice.
Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA
Authors: Gong Binsheng , Lababidi Samir , Kusko Rebecca , Bouri Khaled , Prezek Sarah , Thovarai Vishal , Prasanna Anish , Maier Ezekiel J. , Golkaram Mahdi , Sun Xingqiang , Kyriakidis Konstantinos , Kitajima João Paulo , Ebrahim Sahraeian Sayed Mohammad , Guo Yunfei , Johanson Elaine , Jones Wendell , Tong Weida , Xu Joshua 
Published: April 2024

AbstractAccurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration’s (FDA’s) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.
Development of chitosan lipid nanoparticles to alleviate the pharmacological activity of piperine in the management of cognitive deficit in diabetic rats
Authors: Darwish Asmaa Badawy , Mohsen Amira Mohamed , ElShebiney Shaimaa , Elgohary Rania , Younis Mostafa Mohamed 
Published: April 2024

AbstractThe aim of the present study was to prepare and evaluate Piperine (PP) loaded chitosan lipid nanoparticles (PP-CLNPs) to evaluate its biological activity alone or in combination with the antidiabetic drug Metformin (MET) in the management of cognitive deficit in diabetic rats. Piperine was successfully loaded on CLNPs prepared using chitosan, stearic acid, Tween 80 and Tripolyphosphate (TPP) at different concentrations. The developed CLNPs exhibited high entrapment efficiency that ranged from 85.12 to 97.41%, a particle size in the range of 59.56–414 nm and a negatively charged zeta potential values (− 20.1 to − 43.9 mV). In vitro release study revealed enhanced PP release from CLNPs compared to that from free PP suspensions for up to 24 h. In vivo studies revealed that treatment with the optimized PP-CLNPs formulation (F2) exerted a cognitive enhancing effect and ameliorated the oxidative stress associated with diabetes. PP-CLNPs acted as an effective bio-enhancer which increased the potency of metformin in protecting brain tissue from diabetes-induced neuroinflammation and memory deterioration. These results suggested that CLNPs could be a promising drug delivery system for encapsulating PP and thus can be used as an adjuvant therapy in the management of high-risk diabetic cognitive impairment conditions.
Mesoscale activity drives the habitat suitability of yellowfin tuna in the Gulf of Mexico
Authors: Ramírez-Mendoza Zurisaday , Sosa-Nishizaki Oscar , Pardo Mario A. , Herzka Sharon Z. , Wells R. J. David , Rooker Jay R. , Falterman Brett J. , Dreyfus-León Michel J. 
Published: April 2024

AbstractYellowfin tuna, Thunnus albacares, represents an important component of commercial and recreational fisheries in the Gulf of Mexico (GoM). We investigated the influence of environmental conditions on the spatiotemporal distribution of yellowfin tuna using fisheries’ catch data spanning 2012–2019 within Mexican waters. We implemented hierarchical Bayesian regression models with spatial and temporal random effects and fixed effects of several environmental covariates to predict habitat suitability (HS) for the species. The best model included spatial and interannual anomalies of the absolute dynamic topography of the ocean surface (ADTSA and ADTIA, respectively), bottom depth, and a seasonal cyclical random effect. High catches occurred mainly towards anticyclonic features at bottom depths > 1000 m. The spatial extent of HS was higher in years with positive ADTIA, which implies more anticyclonic activity. The highest values of HS (> 0.7) generally occurred at positive ADTSA in oceanic waters of the central and northern GoM. However, high HS values (> 0.6) were observed in the southern GoM, in waters with cyclonic activity during summer. Our results highlight the importance of mesoscale features for the spatiotemporal distribution of yellowfin tunas and could help to develop dynamic fisheries management strategies in Mexico and the U.S. for this valuable resource.
An AI-based approach for modeling the synergy between radiotherapy and immunotherapy
Authors: Peng Hao , Moore Casey , Zhang Yuanyuan , Saha Debabrata , Jiang Steve , Timmerman Robert 
Published: April 2024

AbstractPersonalized, ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is designed to administer tumoricidal doses in a pulsed mode with extended intervals, spanning weeks or months. This approach leverages longer intervals to adapt the treatment plan based on tumor changes and enhance immune-modulated effects. In this investigation, we seek to elucidate the potential synergy between combined PULSAR and PD-L1 blockade immunotherapy using experimental data from a Lewis Lung Carcinoma (LLC) syngeneic murine cancer model. Employing a long short-term memory (LSTM) recurrent neural network (RNN) model, we simulated the treatment response by treating irradiation and anti-PD-L1 as external stimuli occurring in a temporal sequence. Our findings demonstrate that: (1) The model can simulate tumor growth by integrating various parameters such as timing and dose, and (2) The model provides mechanistic interpretations of a “causal relationship” in combined treatment, offering a completely novel perspective. The model can be utilized for in-silico modeling, facilitating exploration of innovative treatment combinations to optimize therapeutic outcomes. Advanced modeling techniques, coupled with additional efforts in biomarker identification, may deepen our understanding of the biological mechanisms underlying the combined treatment.
The possible correlation between miR-762, Hippo signaling pathway, TWIST1, and SMAD3 in lung cancer and chronic inflammatory diseases
Authors: Hussein Neveen A. , Ebid Samia A. , Ahmad Mohammad A. , Khedr Gamal E. , Saad Dina M. 
Published: April 2024

AbstractMicroRNAs are small RNA molecules that have a significant role in translational repression and gene silencing through binding to downstream target mRNAs. MiR-762 can stimulate the proliferation and metastasis of various types of cancer. Hippo pathway is one of the pathways that regulate tissue development and carcinogenesis. Dysregulation of this pathway plays a vital role in the progression of cancer. This study aimed to evaluate the possible correlation between miR-762, the Hippo signaling pathway, TWIST1, and SMAD3 in patients with lung cancer, as well as patients with chronic inflammatory diseases. The relative expression of miR-762, MST1, LATS2, YAP, TWIST1, and SMAD3 was determined in 50 lung cancer patients, 30 patients with chronic inflammatory diseases, and 20 healthy volunteers by real-time PCR. The levels of YAP protein and neuron-specific enolase were estimated by ELISA and electrochemiluminescence immunoassay, respectively. Compared to the control group, miR-762, YAP, TWIST1, and SMAD3 expression were significantly upregulated in lung cancer patients and chronic inflammatory patients, except SMAD3 was significantly downregulated in chronic inflammatory patients. MST1, LATS2, and YAP protein were significantly downregulated in all patients. MiR-762 has a significant negative correlation with MST1, LATS2, and YAP protein in lung cancer patients and with MST1 and LATS2 in chronic inflammatory patients. MiR-762 may be involved in the induction of malignant behaviors in lung cancer through suppression of the Hippo pathway. MiR-762, MST1, LATS2, YAP mRNA and protein, TWIST1, and SMAD3 may be effective diagnostic biomarkers in both lung cancer patients and chronic inflammatory patients. High YAP, TWIST1, SMA3 expression, and NSE level are associated with a favorable prognosis for lung cancer.
TDF and TAF inhibit liver cancer cell migration, invasion via p7TP3
Authors: Zhao Jing , Zhou Li , Zhang Yang , Cheng Jun , Zeng Yilan , Li Xiuling 
Published: April 2024

AbstractTenofovir disoproxil fumarate (TDF) seems to prevent hepatocellular carcinoma (HCC) in patients with chronic hepatitis B virus (HBV). However, the mechanism is still little known. This study aimed to investigate the the roles and mechanisms of TDF, tenofovir alafenamide fumarate (TAF), and entecavir (ETV) on the malignant characteristics of liver cancer cells. Using the wound-healing assays, transwell assays, matrigel transwell assays, and cell counting kit-8 (CCK-8) assays, it was possible to identify that TDF/TAF, inhibited migration, invasion, and proliferation of HepG2 cells and Huh7 cells. To investigate the mechanisms, we performed TOP/FOP-Flash system, Western blot, and RT-qPCR assays of liver cancer cells cultured with TDF/TAF and found a lower activity of Wnt/β-catenin signaling pathway compared with control cells. Finally, Hepatitis C virus p7 trans-regulated protein 3 (p7TP3), a tumor suppressor in liver cancers, was significantly increased in HepG2 cells and Huh7 cells that treated with TDF/TAF. However, entecavir (ETV)-treated liver cancer cells showed no significant difference in the malignant characteristics of liver cancer cells, activity of Wnt/β-catenin signaling pathway, and expression of p7TP3, compared with the control groups. To conclude, TDF/TAF maybe novel promising therapeutic strategy for liver cancers, including HCC and hepatoblastoma, via Wnt/β-catenin signaling pathway, by up-regulating expression of the tumor suppressor, p7TP3.