bluuebunny/crossref_metadata_2025_split huggingface dataset:
Selectivity analysis of diaminopyrimidine-based inhibitors of MTHFD1, MTHFD2 and MTHFD2L
Authors: Jha Vibhu , Eriksson Leif A. 
Published: May 2024

Abstract

The mitochondrial enzyme methylenetetrahydrofolate dehydrogenase (MTHFD2) is involved in purine and thymidine synthesis via 1C metabolism. MTHFD2 is exclusively overexpressed in cancer cells but absent in most healthy adult human tissues. However, the two close homologs of MTHFD2 known as MTHFD1 and MTHFD2L are expressed in healthy adult human tissues, which makes the development of selective MTHFD2 inhibitors challenging for cancer drug discovery. Tricyclic coumarin-based compounds (substrate site binders) and xanthine derivatives (allosteric site binders) are the only selective inhibitors of MTHFD2 reported till date. Nanomolar potent diaminopyrimidine-based inhibitors of MTHFD2 have been reported recently, however, they also demonstrate significant inhibitory activities against MTHFD1 and MTHFD2L. In this study, we have employed extensive computational modeling involving molecular docking and molecular dynamics simulations in order to investigate the binding modes and key interactions of diaminopyrimidine-based inhibitors at the substrate binding sites of MTHFD1, MTHFD2 and MTHFD2L, and compare with the tricyclic coumarin-based selective MTHFD2 inhibitor. The outcomes of our study provide significant insights into desirable and undesirable structural elements for rational structure-based design of new and selective inhibitors of MTHFD2 against cancer.
Body image and weight gain during pregnancy: A cross-sectional study
Authors: Moudi Asieh , Taheri Narjes Khatoon , Mortezapoor Mohaddese , Nazaralivand Roghayeh 
Published: May 2024

Abstract

Introduction: Maternal weight at the beginning of pregnancy and maternal weight gain during pregnancy affect the fetal health. Overweighting during pregnancy is affected by nutritional, physical, and psychological factors, including body image. Therefore, this study examined the relationship between body image and gestational weight gain.
Methods: This descriptive-analytic study was conducted on 200 women who were 38-40 weeks pregnant in 4 subsets—thin, normal, overweight, and obese. These individuals were selected by random block sampling from health centers in Qaen City. Body image data were collected via a body image questionnaire in 6 dimensions, and BMI before pregnancy, and gestational weight gain were obtained from maternal health records. The data were analyzed by SPSS software version 24 and ANOVA, chi square tests, and linear regression.
Results: There was no significant difference in gestational weight gain between individuals with moderate, good, and excellent body image (P=0.183). Gestational weight gain was significantly different between obese women with intermediate, well, and excellent body image (P=0.046). Adjustment for confounding variables by linear regression showed that BMI was the only variable predicting gestational weight gain (R2=-0.212, 95% CI [-0.244—0.45], P=0.005).
Conclusion: Among the variables of age, gravidity, number of live children, BMI, and body image, only BMI predicted gestational weight gain. However, more research is needed to evaluate other psychosocial factors affecting pregnancy weight gain. In addition, longitudinal studies on body image concerns during pregnancy and gestational weight gain are recommended.
Treatment-train strategy realizes broad-spectrum capture of hundreds of per- and polyfluoroalkyl substances from fluorochemical wastewater
Authors: Lin Hui , Yang Yiyang , Yang Lihui , Tang Caiming , Yang Ying , Liang Shangtao , Wang Anqi , Xu Jiale , Huang Qingguo 
Published: May 2024

Abstract

Hundreds of per- and polyfluoroalkyl substances (PFAS) are found in fluorochemical production effluents, and existing adsorption devices are inadequate to address this PFAS challenge given their extreme structural diversity. Here, we achieve the efficient and broad-spectrum capture of 107 PFASs from fluorochemical effluents using a treatment-train strategy that combines Zn-based electrocoagulation (EC) with anion-exchange resin (AER) beds. The “zero-carbon” adsorbent, zinc hydroxide flocs, generated in-situ by Zn-based EC bulk removes PFAS with logKow>4 through a semi-micellar adsorption mechanism similar to mineral flotation, resulting in the highest adsorption capacities among all reported adsorbents. Technical-economic analysis and life-cycle environmental impact showed that coupling Zn-based EC reduces the cost by an order-of-magnitude and the carbon-footprint by 70% compared to AER beds alone. It was also observed that iodinated PFAS, in which the fluorine atom is replaced by an iodine atom, had significantly improved adsorption selectivity, which may shed light on designing environmentally-friendly fluorochemicals.
Green synthesized silver nanoparticles using Sempervivum tectorum extract have an antibacterial effect against Staphylococcus pseudintermedius isolated from a dog's otitis externa
Authors: Dégi Diana-Maria , Lányi Katalin , Beteg Florin , Herman Viorel , Dégi János , Morariu Sorin , Muselin Florin 
Published: May 2024

Abstract

Despite developing a broad spectrum of antibiotics, pathogenic bacteria remain the primary health concern. The emergence of drug-resistant strains increased therapeutic failure and mortality. The researchers are currently focusing on developing nanotechnology-based medicines to combat resistance. Our study focuses on the antimicrobial characteristics of plant-mediated silver nanoparticles (Ag NPs). The AgNPs were green synthesized (using thermal treatment) to reduce silver nitrate solution (AgNO3) using Sempervivum tectorum L. plant ethanolic aqueous extract. They were found to be broad-spectrum antimicrobial agents against two Staphylococcus pseudintermedius strains. We investigated AgNPs' antibacterial activity with time-kill curves, MIC, MBC, and well diffusion. The tests also showed a high activity of the particles against Staphylococcus pseudintermedius, with the minimum inhibitory concentrations ranging between 18.6–90.13 µg/ml. The prepared AgNPs were then characterized using scanning UV-visible spectroscopy, X-ray diffraction, and transmission electronic microscopy (TEM) to study their morphology and crystallinity. Two distinctive absorption peaks of AgNP samples were observed at 474 nm and 487 nm for leaf extract samples, with a spherical shape with face-centered cubic (FCC) crystalline structure and diameter range between 10–60 nm, respectively. The impact was significantly greater when the NPs were less than 30 nm in size. The study revealed that an ethanolic aqueous extract of Sempervivum tectorum L. may effectively reduce and stabilize silver nanoparticles (AgNPs) of various sizes, depending on the solvent concentration. The AgNPs have shown efficacy in both suppressing and eradicating microorganisms. The activity of AgNPs may be facilitated by a dual approach involving several processes of nanoparticles, such as the formation of reactive oxygen species and the presence of phytocompounds such as phenolics and proanthocyanidins bound to the nanoparticle surface. Nanotechnology-based therapies play a crucial role in treating microbial illnesses in veterinary medicine. Significant areas of research on nanoparticles (NPs) could include studying the synergistic interactions between Ag NPs and antibiotics, doing toxicity profiling, examining stability, and investigating bioavailability.
Intelligent Sample Allocation via Q-Value Inter-Episode Propagation
Authors: Che Guoliang , Zhou Ziyun , Kong Yan 
Published: May 2024

Abstract

Reinforcement learning (RL) algorithms face the challenge of limited data efficiency, especially when dealing with high-dimensional state spaces and large-scale problems. Typically, in updating the Critic of the agent, most reinforcement learning methods rely solely on state transition information within the same transition. To significantly enhance the data efficiency of reinforcement learning algorithms, we introduce a novel Q-value Inter-Episode propagation mechanism called the "Q Propagator (QP)." The QP is designed to enable information generated from a single sample to effectively propagate across transitions or episodes to different states, rather than being confined to propagation within the same episode. This capability strengthens the model's understanding of interdependencies among states, facilitating more effective learning from limited sample information. To promote versatility, we extend the QP as a plug-and-play module that can seamlessly integrate into other widely adopted RL algorithms. Our experiments demonstrate that the QP consistently improves the performance of several mainstream RL algorithms such as SAC and TD3 in certain tasks, achieving notable performance gains across various tasks in MuJoCo.
Treating Chronic Kidney Disease in Danish Primary Care: Results from the Observational ATLAS Study
Authors: Lindhardt Morten , Knudsen Søren Tang , Saxild Thomas , Charles Morten , Borg Rikke 
Published: May 2024

Abstract

Objectives: To describe the clinical characteristics, comorbidity, and medical treatment in a primary care population with chronic kidney disease (CKD). Additionally, to investigate how primary care physicians (PCPs) diagnose, manage and treat impaired kidney function, including uptake of cardio-renoprotective renin-angiotensin-aldosterone system inhibitors (RAASis) and sodium glucose co-transporter 2 inhibitors (SGLT2is).
Design: An observational study of CKD prevalence, treatment patterns and comorbidities in primary care based on patient record data combined with a questionnaire on diagnosis, management and treatment of impaired kidney function in a real-world, primary care setting.
Setting: 128/211 randomly invited primary care clinics throughout Denmark and a questionnaire completed by 125/128 participating PCPs.
Methods: A computerized selection identified 12 random individuals with CKD per clinic with ≥2 measurements of eGFR <60 mL/min/1.73 m2 or UACR >30 mg/g within two years (N=1 497). Pre-specified data collected from individual electronic health records included demographics, clinical variables, comorbidities, and relevant prescribed medications.
Results: Of the CKD study population (N=1 497), 80% had hypertension, 32% diabetes (DM), 13% heart failure (HF), 59% no DM/HF. ACEis/ARBs were prescribed to 65%, statins to 56%, SGTL2is to 14%, and MRAs to 8% of all individuals. Treatment patterns differed between individuals with varying comorbidities, e.g., ACEis/ARBs usage was higher in DM (76%) or HF (74%) vs. no DM/HF (58%), as was statin usage (76% in DM vs. 45% in no DM/HF). SGTL2i usage in no DM/HF was low. Most PCPs identified CKD using eGFR <60 mL/min/1.73 m2 (62%) or UACR >30 mg/g (58%) and 62% reported initiating treatment to retard kidney function decline.
Conclusions: Despite good PCP awareness and wish to use relevant guidelines, a gap exists in implementation of cardio-renoprotective treatment, especially in individuals without DM/HF. This offers an opportunity for clear recommendations to PCPs to optimize early cardio-renal protection in individuals with CKD.
Exploring Machine Learning Utilization on Influenza Pandemic Dataset
Authors: Hussain Shahid , Fatima Ubaida 
Published: May 2024

Abstract

There must be an exact system for monitoring the influenza outbreaks to have an optimum solution for the recovery of infected people’s health. For reducing the spread of future outbreaks of influenza virus, forecasting plays an important role. Influenza a is type of disease which is transferred to human beings through pigs, found in animals. It became pandemic in Spain, approximately, 1/3rd of human population died and 1/4th of pig population. Again in 2009, influenza "A" caused millions of deaths, and spread like a pandemic rapidly. Variety of researches inspected data obtained from World Health Organization and local hospitals at country level. This research work is based on mathematical biology using data science techniques in the domain of machine learning. This research suggests a modeling scheme for influenza pandemic predictions, its different classifications and types such as H1N1, B-Victoria etc. via machine learning prediction and regression as well as classification algorithms such as Logistic Regression (LR), Support Vector Machines (SVM) using Linear, Polynomial and RBF kernels; Naïve Bayes (NB) and Random Forest (RF) method for the prediction of influenza disease and its outbreak, the influenza kind became pandemic with the infected populated area. After using various kernels in SVM algorithm, it is observed that Polynomial and Linear kernels have approximately the same accuracy scores, while RBF kernel was not best-fitted for the considered influenza datasets. As far as the overall performance is concerned, at average, RF has the highest accuracy score as 74% while the LR had also the better average score as 72% after RF. After applying the considered ML algorithms, Random Forest algorithm performed in well-effective manner and comparatively it was analyzed as the best-fitted algorithm for the considered datasets.
Pillar-Pore-Structured Nanophotonic Coating with Superior Solar and Thermal Modulation Abilities
Authors: Yanfeng Gao 
Published: May 2024

Abstract

There is a growing focus on the development of advanced materials to enhance energy efficiency in space heating and cooling, which accounts for 13% of the annual consumption of global energy. Wavelength-selective dynamic regulation is undisputedly requisite to solve this problem. Radiative cooling materials work in the wavelength span of 8-14 µm, which is effective in heat dissipation. However, their fixed emissivity results in unpleasant undercooling. Adaptive cooling materials can change emissivity in response to temperature shifts, solving the problem of overcooling, however they absorb too much solar heat in daytime, especially in summer. Hence, a material is requisite to have high reflectance in solar wavelengths (0.25 – 2.5 µm). To address this issue, we propose a temperature-adaptive radiative cooling coating with high solar reflectance that efficiently reflects solar radiation and automatically switches its sky-window emissivity from 0.19 to 0.74, delivering an 85.92% solar reflectance for optimal energy efficiency and all-year-round thermal comfort within a specific climate zone. The simulations demonstrate that our sample surpasses existing roofing coatings in terms of energy efficiency across different climate zones, particularly those with substantial seasonal variations.
Species and Site Contributions to Odonata adult assemblage uniqueness in Brazilian Amazon streams
Authors: Nascimento Ana Caroline Leal , Martins André Ribeiro , Dias-Silva Karina , Juen Leandro , Mortati Amanda Frederico , Oliveira-Junior José Max Barbosa 
Published: May 2024

Abstract

Studies that assess the contribution of sites (LCBD) and species (SCBD) to total beta diversity (BDtotal) along environmental/spatial gradients are of fundamental importance for biodiversity knowledge, species conservation and for environmental restoration. We analyzed which spatial, environmental and environmental heterogeneity (LCEH) predictors were responsible for uniqueness species composition (LCBD). We analyzed the relationship of LCBD with species richness and dominance, and SCBD with abundance and occupancy. Habitat integrity (HII) can act as an environmental filter in the selection of species sensitive or tolerant to environmental changes and can reflect in the LCBD. We verified the differences in the compositional uniqueness (LCBD) of Anisoptera and Zygoptera along the habitat integrity gradient (HII). We expect that streams with greater habitat integrity (HII) will show greater compositional uniqueness (LCBD) in relation to Zygoptera species, because Zygoptera species are exclusive to streams with high HII values. In our results LCBD is mainly determined by the distance between sites, HII and depth stream. The streams with the highest LCBD had the lowest species richness and the highest dominance. Species with highest SCBD had the highest number of individuals and occupancy. Our results indicate that Odonata uniqueness depends on the distance and specific environmental conditions. The high compositional uniqueness (LCBD) of Odonata adults is related to priority sites for environmental restoration.
A Robust Parkinson’s Disease Detection Model Based on Time-varying Synaptic Efficacy Function in Spiking Neural Network
Authors: Das Priya , Nanda Sarita , Panda Ganapati , Dash Sujata , Mallik Saurav , Ksibi Amel , Alsenan Shrooq , Bouchelligua Wided 
Published: May 2024

Abstract

Parkinson’s disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function-based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD. SEFRON explores the advantages of Spiking Neural Network (SNN) which is suitable for neuromorphic devices consuming less energy and higher computational efficiency. To evaluate the performance of SEFRON, a publicly available standard UCI: Oxford Parkinson's Disease Detection Dataset is used. The performance is compared with other well-known neural network models: Multilayer Perceptron Neural Network (MLP-NN) and Radial Basis Function Neural Network (RBF-NN) as well as contemporary SNN models. The experimental results show that SEFRON classifier achieves highest accuracy of 100% and average accuracy of 99.49% which is the highest in comparison to other two classifiers. From the performance, it is proved that the presented model can help to develop a robust, less complex, and energy-efficient automated PD detection device that can assist the physicians to diagnose the disease at its early stage.
A cross-sectional study investigating malaria prevalence and associated predictors of infection among migrants to a newly established gold mining settlement in the Gambella Region of Ethiopia
Authors: Glendening Natasha , Haileselassie Werissaw , Lee Ming-Chieh , Yan Guiyun , Parker Daniel M 
Published: May 2024

Abstract

Background
Malaria is a major disease burden in Ethiopia. Migration can influence malaria transmission dynamics, with individuals relocating from malaria-free highland regions to malarious lowlands potentially facing elevated risks of contracting malaria. Migrants may find it difficult to protect themselves against malaria and have limited access to diagnosis or treatment. Settlers in goldmining sites are one type of migrant and are often neglected in malaria research yet may have particularly high malaria risk. We conducted a malaria prevalence survey in a new goldmining settlement in the highly malarious Gambella Region, Ethiopia.
Methods
We interviewed participants for demographic information and their knowledge and practices of malaria. We also tested participants for malaria using rapid diagnostic tests and blood samples. Using logistic regressions, we analyzed the influence of demographic characteristics on malaria infections and bednet access. Interviews were also conducted among community members to comprehend the community living conditions and healthcare accessibility.
Results
The overall prevalence of P. falciparum was 39.7% (CI: 34.7%-44.4%). Young children were most likely to have malaria, with individuals aged 15–24 having 67% lower odds (aOR: 0.33; CI: 0.13–0.86) of infection compared to those aged 0–4 years old. Meanwhile, those age 25plus had 75% decreased odds of malaria infection (aOR 0.25; CI: 0.10–0.65). Individuals with bednets had approximately 50% decreased odds of testing positive for falciparum malaria than those reporting having no bednet (aOR: 0.47; CI: 0.22–0.97). Individuals who relocated from low elevation with high malaria test positivity rate areas were more prone to testing positive for malaria, as were those residing in densely populated households with multiple malaria cases. Conversely, individuals from higher elevations with low malaria test positivity rates, and those living in households with 5–10 occupants and < 2 malaria infections, were more likely to possess bednets.
Conclusions
This goldmining settlement provides an example of an oft-neglected atypical community where malaria is a significant, but under-addressed, health problem. Within this community, future interventions focused on distributing bednets, particularly to larger households and those with children, have great potential to alleviate the malaria burden. Efforts should also be made to provide affordable, and accessible, early diagnosis and treatment.
Caregiving responsibility and psychological distress among community-dwelling cancer survivors in the United States
Authors: Mahmood Asos , Kim Hyunmin , Kedia Satish , Boykins Alexandria , Goldsmith Joy V. 
Published: May 2024

Abstract

Purpose. There are more than 18 million cancer survivors in the US, with a projected increase of 24.4% over the next decade. Currently, little is known about the relationship between a cancer survivor’s caregiving responsibility and psychological distress. This study examines whether cancer-survivor caregivers experience increased psychological distress compared to cancer survivors without caregiving responsibilities.
Methods. Data were drawn from the National Cancer Institute’s Health Information National Trends Survey (HINTS5, Cycles 1 through 4, 2017–2020). The analytical sample includes 2,579 US cancer survivors (aged ≥65 years=50.5%, females=57.1%). Caregiving responsibility was self-reported, and psychological distress was assessed through the Patient Health Questionnaire-4 (PHQ-4). Accounting for the complex design features of HINTS and jackknife replicate weights, a multivariable multinomial logistic regression model was fit to compute adjusted odds ratios (aORs) and their associated 95% confidence intervals (CIs).
Results. About 19.1% (n=377) of cancer survivors self-reported caregiving responsibilities. Overall, 19.3% (n=438) of cancer survivors had mild, and 10.9% (n=252) had moderate to severe psychological distress. Compared to cancer survivors with no caregiving responsibilities, caregivers had more than two-fold higher odds of experiencing mild (aOR=2.25; 95% CI: 1.17, 4.29), and moderate to severe (aOR=2.18; 95% CI: 1.07, 4.46) psychological distress.
Conclusions. Our findings indicate that caregiving responsibilities among cancer survivors have a substantial adverse impact on their mental and emotional wellbeing.
Implications for Cancer Survivors.Cancer-survivor caregivers are a distinct subgroup that navigates both survivorship and caregiving burdens at the same time. There is a need to identify and develop tailored interventions, programs, and resources for this vulnerable group of caregivers.