bluuebunny/crossref_metadata_2025_split huggingface dataset:
The association of early life factors with depression and anxiety in adults aged 40–69 years: a population-based cohort study
Authors: Wang Ruirui , Shi Mengyao , Zhang Qilu , Zhang Jing , Sun Lulu , Jia Yiming , Zhu Zhengbao , Xu Tan , Zhang Yonghong 
Published: July 2024

AbstractThis study was aimed to explore the longitudinal association of five early life factors (breastfeeding, maternal smoking around birth, birth weight, being born in a multiple birth, and adoption) during the in-utero, perinatal, and early childhood development stages with incidence of depression and anxiety in adults aged 40–69 years. We used data from the UK biobank, 5,02,394 participants aged 40–69 years were recruited between 2006 and 2010. Participants provided information on early life exposures through touchscreen questionnaires or verbal interviews at baseline. The primary outcomes, depression, and anxiety, were defined according to the International Classification of Diseases, 10th Revision. Hazard ratios (HR) and 95% confidence intervals (CI) for each factor were reported. During a median follow-up of 13.6 years, 16,502 (3.55%) participants developed depression, and 15,507 (3.33%) developed anxiety. After adjusting for potential confounders, increased risk of depression was found to be significantly associated with non-breastfeeding (HR, 1.08; 95% CI, 1.04–1.13), maternal smoking around birth (HR, 1.19; 95% CI, 1.14–1.23), being born in multiple births (HR, 1.16; 95% CI, 1.05–1.27), low birth weight (HR, 1.14; 95% CI, 1.07–1.22), and being an adoptee (HR, 1.42; 95% CI, 1.28–1.58). Increased risk of anxiety was associated with non-breastfeeding (HR, 1.09; 95% CI, 1.04–1.13), maternal smoking around birth (HR, 1.11; 95% CI, 1.07–1.16), being born in a multiple births (HR, 1.05; 95% CI, 0.95–1.17), low birth weight (HR, 1.12; 95% CI, 1.05–1.20), and being an adoptee (HR, 1.25; 95% CI, 1.10–1.41). Each of these five early life factors can be considered as early life risk factors for incident depression and anxiety in adulthood independently. The dose-response relationship was also observed, suggesting that with an increase in the number of early life risk factors, the likelihood of experiencing depression and anxiety also increased. These findings highlighted the imperative consideration of early life factors in comprehending the susceptibility to mental health disorders later in life, including non-breastfeeding, maternal smoking around birth, being born in multiple births, low birth weight, and being an adoptee.
Differential effects of purified low molecular weight Poly(I:C) in the maternal immune activation model depend on the laboratory environment
Authors: Tillmann Katharina E. , Schaer Ron , Mueller Flavia S. , Mueller Karin , Voelkl Bernhard , Weber-Stadlbauer Ulrike , Pollak Daniela D. 
Published: July 2024

AbstractThe Poly (I:C) (polyriboinosinic-polyribocytidilic acid) paradigm of maternal immune activation (MIA) is most widely used as experimental model for the evaluation of the effects of gestational infection on the brain and behavior of the progeny. We have previously reported significant batch-to-batch variability in the effects of Poly (I:C), purchased from the same supplier (Sigma–Aldrich), on maternal and fetal immune responses and found these differences to be dependent on the relative amount of synthetic double-stranded RNA fragments in the high versus low molecular weight (LMW) range contained in the compound. We here resorted to Poly (I:C) purified for LMW dsRNA fragments to establish a MIA paradigm with increased reproducibility and enhanced standardization in an effort to refine the MIA paradigm and characterize its effect on offspring behavior. We found that the parallel application of LMW Poly (I:C) in two different MIA-experienced laboratories (Vienna and Zurich) yielded differential outcomes in terms of maternal immune responses and behavioral phenotypes in the offspring generation. In both experimental sites, administration of LMW Poly (I:C) induced a significant sickness response and cytokine induction in the pregnant dam and fetal brains, while the expected deficit in sociability as one main behavioral outcome parameter in the MIA progeny, was only present in the Zurich, but not the Vienna cohort. We conclude that although using Poly (I:C) purified for a defined molecular weight range reduces batch-to-batch variability, it does not make the MIA model more reliable and robust. The differential response in behavioral phenotypes of the MIA offspring between the two laboratories illustrates the highly complex interaction between prenatal and postnatal milieus - including the laboratory environment - that determine offspring phenotypic outcomes after MIA. Consequently, establishing a new MIA protocol or implementing the MIA model firstly under new or changed environmental conditions must include the assessment of offspring behavior to ensure solid and reproducible experimental outcomes.
Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS
Authors: Zhang Xinyi , Venkatachalapathy Saradha , Paysan Daniel , Schaerer Paulina , Tripodo Claudio , Uhler Caroline , Shivashankar G. V. 
Published: July 2024

AbstractDuctal carcinoma in situ (DCIS) is a pre-invasive tumor that can progress to invasive breast cancer, a leading cause of cancer death. We generate a large-scale tissue microarray dataset of chromatin images, from 560 samples from 122 female patients in 3 disease stages and 11 phenotypic categories. Using representation learning on chromatin images alone, without multiplexed staining or high-throughput sequencing, we identify eight morphological cell states and tissue features marking DCIS. All cell states are observed in all disease stages with different proportions, indicating that cell states enriched in invasive cancer exist in small fractions in normal breast tissue. Tissue-level analysis reveals significant changes in the spatial organization of cell states across disease stages, which is predictive of disease stage and phenotypic category. Taken together, we show that chromatin imaging represents a powerful measure of cell state and disease stage of DCIS, providing a simple and effective tumor biomarker.
Discovery of a Ni2+-dependent heterohexameric metformin hydrolase
Authors: Li Tao , Xu Zhi-Jing , Zhang Shu-Ting , Xu Jia , Pan Piaopiao , Zhou Ning-Yi 
Published: July 2024

AbstractThe biguanide drug metformin is a first-line blood glucose-lowering medication for type 2 diabetes, leading to its presence in the global environment. However, little is known about the fate of metformin by microbial catabolism. Here, we characterize a Ni2+-dependent heterohexameric enzyme (MetCaCb) from the ureohydrolase superfamily, catalyzing the hydrolysis of metformin into guanylurea and dimethylamine. Either subunit alone is catalytically inactive, but together they work as an active enzyme highly specific for metformin. The crystal structure of the MetCaCb complex shows the coordination of the binuclear metal cluster only in MetCa, with MetCb as a protein binder of its active cognate. An in-silico search and functional assay discover a group of MetCaCb-like protein pairs exhibiting metformin hydrolase activity in the environment. Our findings not only establish the genetic and biochemical foundation for metformin catabolism but also provide additional insights into the adaption of the ancient enzymes toward newly occurred substrate.
Modelling chemical processes in explicit solvents with machine learning potentials
Authors: Zhang Hanwen , Juraskova Veronika , Duarte Fernanda 
Published: July 2024

AbstractSolvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We apply this strategy to investigate a Diels-Alder reaction in water and methanol. The generated machine learning potentials enable us to obtain reaction rates that are in agreement with experimental data and analyse the influence of these solvents on the reaction mechanism. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner.
Enhanced disulphide bond stability contributes to the once-weekly profile of insulin icodec
Authors: Hubálek František , Cramer Christian N. , Helleberg Hans , Johansson Eva , Nishimura Erica , Schluckebier Gerd , Steensgaard Dorte Bjerre , Sturis Jeppe , Kjeldsen Thomas B. 
Published: July 2024

AbstractInsulin icodec is a once-weekly insulin analogue that has a long half-life of approximately 7 days, making it suitable for once weekly dosing. The Insulin icodec molecule was developed based on the hypothesis that lowering insulin receptor affinity and introducing a strong albumin-binding moiety would result in a long insulin half-life, provided that non-receptor-mediated clearance is diminished. Here, we report an insulin clearance mechanism, resulting in the splitting of insulin molecules into its A-chain and B-chain by a thiol–disulphide exchange reaction. Even though the substitutions in insulin icodec significantly stabilise insulin against such degradation, some free B-chain is observed in plasma samples from minipigs and people with type 2 diabetes. In summary, we identify thiol–disulphide exchange reactions to be an important insulin clearance mechanism and find that stabilising insulin icodec towards this reaction significantly contributes to its long pharmacokinetic/pharmacodynamic profile.
Iron photocatalysis via Brønsted acid-unlocked ligand-to-metal charge transfer
Authors: Jiang Xiaoyu , Lan Yu , Hao Yudong , Jiang Kui , He Jing , Zhu Jiali , Jia Shiqi , Song Jinshuai , Li Shi-Jun , Niu Linbin 
Published: July 2024

AbstractReforming sustainable 3d-metal-based visible light catalytic platforms for inert bulk chemical activation is highly desirable. Herein, we demonstrate the use of a Brønsted acid to unlock robust and practical iron ligand-to-metal charge transfer (LMCT) photocatalysis for the activation of multifarious inert haloalkylcarboxylates (CnXmCOO−, X = F or Cl) to produce CnXm radicals. This process enables the fluoro-polyhaloalkylation of non-activated alkenes by combining easily available Selectfluor as a fluorine source. Valuable alkyl fluorides including potential drug molecules can be easily obtained through this protocol. Mechanistic studies indicate that the real light-harvesting species may derive from the in situ-assembly of Fe3+, CnXmCOO−, H+, and acetonitrile solvent, in which the Brønsted acid indeed increases the efficiency of LMCT between the iron center and CnXmCOO− via hydrogen-bond interactions. We anticipate that this Brønsted acid-unlocked iron LMCT platform would be an intriguing sustainable option to execute the activation of inert compounds.
Utilizing cost-effective pyrocarbon for highly efficient gold retrieval from e-waste leachate
Authors: Fu Kaixing , Liu Xia , Zhang Xiaolin , Zhou Shiqing , Zhu Nanwen , Pei Yong , Luo Jinming 
Published: July 2024

AbstractAddressing burdens of electronic waste (E-waste) leachate while achieving sustainable and selective recovery of noble metals, such as gold, is highly demanded due to its limited supply and escalating prices. Here we demonstrate an environmentally-benign and practical approach for gold recovery from E-waste leachate using alginate-derived pyrocarbon sorbent. The sorbent demonstrates potent gold recovery performance compared to most previously reported advanced sorbents, showcasing high recovery capacity of 2829.7 mg g−1, high efficiency (>99.5%), remarkable selectivity (Kd ~ 3.1 × 108 mL g−1), and robust anti-interference capabilities within environmentally relevant contexts. The aromatic structures of pyrocarbon serve as crucial electrons sources, enabling a hydroxylation process that simultaneously generates electrons and phenolic hydroxyls for the reduction of gold ions. Our investigations further uncover a “stepwise” nucleation mechanism, in which gold ions are reduced as intermediate gold-chlorine clusters, facilitating rapid reduction process by lowering energy barriers from 1.08 to −21.84 eV. Technoeconomic analysis demonstrates its economic viability with an input-output ratio as high as 1370%. Our protocol obviates the necessity for organic reagents whilst obtaining 23.96 karats gold product from real-world central processing units (CPUs) leachates. This work introduces a green sorption technique for gold recovery, emphasizing its role in promoting a circular economy and environmental sustainability.
Anomalous upper critical field in the quasicrystal superconductor Ta1.6Te
Authors: Terashima Taichi , Tokumoto Yuki , Hamano Kotaro , Konoike Takako , Kikugawa Naoki , Edagawa Keiichi 
Published: July 2024

AbstractSuperconductivity in quasicrystals poses a new challenge in condensed matter physics. We measured the resistance and ac magnetic susceptibility of a Ta1.6Te dodecagonal quasicrystal, which is superconducting belowTc ~ 1 K. We show that the upper critical field increases linearly with a large slope of − 4.4 T/K with decreasing temperature down to 0.04 K, with no tendency to level off. The extrapolated zero-temperature critical field exceeds the Pauli limit by a factor of 2.3. We also observed flux-flow resistance with thermally activated behavior and an irreversibility field that is distinct from the upper critical field. We discuss these peculiarities in terms of the nonuniform superconducting gap and spin-orbit interaction in quasicrystal structures.
A scoping review of global COVID-19 vaccine hesitancy among pregnant persons
Authors: Casubhoy Imaima , Kretz Alyssa , Tan Heang-Lee , St Clair Laura A. , Parish Maclaine , Golding Hana , Bersoff-Matcha Susan J. , Pilgrim-Grayson Catherine , Berhane Leah , Pekosz Andrew , Mostafa Heba H. , Cox Andrea L. , Burd Irina , Klein Sabra L. , Morgan Rosemary 
Published: July 2024

AbstractUptake of the COVID-19 vaccine among pregnant persons is lower than the general population. This scoping review explored pregnant people’s attitudes towards the COVID-19 vaccine, reasons for vaccine hesitancy, and whether attitudes about COVID-19 vaccines differ by country of origin. A scoping review was conducted across PubMed, Embase, CINHAL, and Scopus. Inclusion criteria were articles published in English from 2019–2022 focused on attitudes towards COVID-19 vaccination among pregnant persons. Data analysis was done via the 5Cs framework for vaccine hesitancy: Constraints, Complacency, Calculation, Confidence, and Collective Responsibility. 44 articles were extracted. A lack of confidence in vaccine safety was the most prevalent theme of hesitancy among pregnant persons. This was largely driven by a lack of access to information about the vaccine as well as mistrust of the vaccine and medical professionals. Meanwhile, vaccine acceptance was mostly driven by a desire to protect themselves and their loved ones. Overall, COVID-19 vaccine hesitancy among pregnant persons continues to be high. Vaccine hesitancy is primarily driven by fear of the unknown side effects of the vaccine on pregnant persons and their fetuses along with a lack of information and medical mistrust. Some differences can be seen between high income and low- and middle-income countries regarding vaccine hesitancy, showing that a single solution cannot be applied to all who are vaccine hesitant. General strategies, however, can be utilized to reduce vaccine hesitancy, including advocating for inclusion of pregnant persons in clinical trials and incorporating consistent COVID-19 vaccine counseling during prenatal appointments.
The 30 m vegetation maps from 1990 to 2020 in the Tibetan Plateau
Authors: Wu Fan , Ren Hongrui , Zhou Guangsheng 
Published: July 2024

AbstractThe Tibetan Plateau (TP) is crucial for global climate change and China’s ecological security. Given recent drastic changes in vegetation from climate change and human activities, long-term vegetation monitoring is urgently required. This study produced the vegetation maps of the TP from 1990 to 2020 every ten years using random forest classifier and Landsat imagery. We selected the same stable samples and features for mapping to reduce errors between years and proposed spatial filtering to further improve the accuracy. The overall accuracy surpassed 95.00%, with all Kappa coefficients exceeding 0.95. A further assessment based on sampling sites from literature and field survey was higher than 80%. The importance ranking results indicated that in the TP, climate factors and terrain factors are the most important factors in the vegetation mapping. This study provides a method for mapping vegetation in alpine areas and data support for researching the dynamic change of vegetation on the TP and evaluating its response to climate change.
Evaluation of antibacterial, antioxidant, and anti-inflammatory properties of GC/MS analysis of extracts of Ajuga. integrifolia Buch.-Ham. leaves
Authors: Singh Harsha , Kumar Suresh , Arya Atul 
Published: July 2024

AbstractThe purpose of the current study was to examine chemical composition, antioxidant, anti-inflammatory and antibacterial properties of leaves extract of Ajuga integrifolia Buch.-Ham. The antibacterial and antioxidant properties of three different solvents i.e. methanol (AIM), hexane (AIH), and water (AIW) were tested against two bacterial strains Staphylococcus aureus and Escherichia coli. The presence of antioxidant and antibacterial chemicals, such as hexanedioic acid, hexadecanoic acid, nonadecadiene, hexadecen-1-ol, octadecadienoic acid, nonane, phytol, henicosanal, stearyl aldehyde, and neophytadiene, were depicted in the GCMS chromatograms of three extracts. After the extracts' FTIR peaks were examined, it was discovered that phenols, amines, hydroxy groups, and components linked to amino acids were present. Compared to the Hexane and Water extracts, the Methanol extract showed a greater phenolic (196.16 ± 0.0083 mg gallic acid equivalent/gram), flavonoid (222.77 ± 0.002 mg rutin equivalents/g) and phosphomolybdate assay for total antioxidant activity (557.62 ± 0.0023 mg AAE/g). Methanol extract showed the highest scavenging activity with a minimum IC50 value was observed in DPPH assay. AIM showed its maximum anti-denaturation activity i.e. 3.75 ± 0.28%. For antibacterial activities, best zone of inhibition (ZOI) and minimum inhibitory concentration (MIC) was observed in case of the methanol extract as compared to other extracts against methicillin-resistant Staphylococcus aureus and β-lactam-resistant Escherichia coli.
Exploring the therapeutic potential of Aloin: unraveling neuroprotective and anticancer mechanisms, and strategies for enhanced stability and delivery
Authors: Zimbone Stefania , Romanucci Valeria , Zarrelli Armando , Giuffrida Maria Laura , Sciacca Michele F. M. , Lanza Valeria , Campagna Tiziana , Maugeri Ludovica , Petralia Salvatore , Consoli Grazia Maria Letizia , Di Fabio Giovanni , Milardi Danilo 
Published: July 2024

AbstractWe investigate the therapeutic potential of Aloin A and Aloin B, two natural compounds derived from Aloe vera leaves, focusing on their neuroprotective and anticancer properties. The structural differences between these two epimers suggest that they may exhibit distinct pharmacological properties. Our investigations revealed that both epimers are not stable in aqueous solution and tend to degrade rapidly, with their concentration decreasing by over 50% within approximately 12 h. These results underscore the importance of addressing issues such as the need for encapsulation into effective drug delivery systems to enhance stability. ThT fluorescence experiments showed that neither compound was able to inhibit Aβ amyloid aggregation, indicating that other mechanisms may be responsible for their neuroprotective effects. Next, an equimolar mixture of Aloin A and Aloin B demonstrated an ability to inhibit proteasome in tube tests, which is suggestive of potential anticancer properties, in accordance with antiproliferative effects observed in neuroblastoma SH-SY5Y and HeLa cell lines. Higher water stability and increased antiproliferative activity were observed by encapsulation in carbon dot nanoparticles, suggesting a promising potential for further in vivo studies.
Influence of environmental, geographic, socio-demographic, and epidemiological factors on presence of malaria at the community level in two continents
Authors: Villena Oswaldo C. , Arab Ali , Lippi Catherine A. , Ryan Sadie J. , Johnson Leah R. 
Published: July 2024

AbstractThe interactions of environmental, geographic, socio-demographic, and epidemiological factors in shaping mosquito-borne disease transmission dynamics are complex and changeable, influencing the abundance and distribution of vectors and the pathogens they transmit. In this study, 27 years of cross-sectional malaria survey data (1990–2017) were used to examine the effects of these factors on Plasmodium falciparum and Plasmodium vivax malaria presence at the community level in Africa and Asia. Monthly long-term, open-source data for each factor were compiled and analyzed using generalized linear models and classification and regression trees. Both temperature and precipitation exhibited unimodal relationships with malaria, with a positive effect up to a point after which a negative effect was observed as temperature and precipitation increased. Overall decline in malaria from 2000 to 2012 was well captured by the models, as was the resurgence after that. The models also indicated higher malaria in regions with lower economic and development indicators. Malaria is driven by a combination of environmental, geographic, socioeconomic, and epidemiological factors, and in this study, we demonstrated two approaches to capturing this complexity of drivers within models. Identifying these key drivers, and describing their associations with malaria, provides key information to inform planning and prevention strategies and interventions to reduce malaria burden.