bluuebunny/crossref_metadata_2025_split huggingface dataset:
Giardiasis: Report of a Case Refractory to Treatment
Authors: Atnafu Thomas Asfaw , Usman Abdurahim Wereka , Azerefegne Eskedar Ferdu , Shemsu Elham Sany 
Published: February 2025

ABSTRACTGiardiasis, an intestinal infection caused by Giardia duodenalis, remains a significant global health concern. Although standard treatments such as metronidazole are typically effective, there are increasing reports of treatment resistance, highlighting the need for alternative therapeutic strategies for which established guidelines do not exist. This case report illustrates the challenges in diagnosing and managing treatment‐refractory giardiasis. A 32‐year‐old male presented with chronic symptoms of watery diarrhea, abdominal pain, and significant weight loss, despite multiple rounds of standard therapies, including metronidazole and albendazole. The persistent presence of Giardia lamblia in stool samples despite appropriate treatments, underscores the necessity for clinicians to recognize treatment failures and explore alternative strategies in the absence of standard protocols. This instance, prolonged combination therapy with metronidazole and albendazole proved effective after previous treatment failures, resulting in symptom resolution and negative stool tests. Clinicians should consider treatment‐refractory giardiasis as a differential diagnosis in patients with chronic gastrointestinal complaints and a history of giardiasis treatment, enabling earlier diagnosis and intervention. This case emphasizes the need for ongoing monitoring of treatment‐refractory giardiasis and calls for further study of resistant strains. It also provides an effective approach for managing cases of treatment‐resistant giardiasis.
Association Between Anti‐TIF1‐γ Antibody and Triple‐Negative Breast Carcinoma in Dermatomyositis Patients: A Case Report
Authors: Zhang Fan , Guo Hailing , Ma Jia 
Published: February 2025

ABSTRACTDermatomyositis (DM) is always associated with various types of malignancies in previously published reports. Anti‐transcription intermediary factor 1‐γ (anti‐TIF1‐γ) antibody is more prevalent in DM patients with malignancy than in those without malignancy. In this paper, we want to show a possible relation between anti‐TIF1‐γ and triple‐negative breast carcinoma.
Client perceptions of counselor cognitive complexity
Authors: Gantt‐Howrey Alexandra 
Published: February 2025

AbstractThe present study is a quantitative investigation of clients’ (N = 390) perceptions of their counselors’ cognitive complexity (CC) as measured by the Counselor CC (3C)‐Client Perceptions Scale. Results indicate a significant relationship between perceived CC and client satisfaction. Participants’ perceptions that counselors understood their worldview and formed appropriate goals significantly predicted client satisfaction. Implications for how counselor educators and supervisors may use clients’ perspectives to inform the enhancement of counseling students’ CC conclude.
Improving Vapor Pressure Prediction Through Integration of Multiple Molecular Representations: A Super Learner Approach
Authors: Hyun Nam Ji , Lee Seul , Jo Seongil , Kim Jaeoh , Lee Jooyeon , Koo Jahyun , Lee Byounghwak , Jeong Keunhong , Yu Donghyeon 
Published: February 2025

ABSTRACTAccurate prediction of vapor pressure is essential in chemical engineering, environmental science, and pharmaceutical development, impacting the volatility and stability of compounds. Traditional methods often fall short for complex and new molecular structures. This study introduces an advanced machine learning approach, integrating graph neural networks (GNNs), and CHEM‐BERT models to improve prediction accuracy. Utilizing the largest dataset to date, we derived comprehensive chemical descriptors and fingerprints. We evaluated 19 predictive models, including ridge regression, random forest, support vector regression, and feed‐forward neural networks, trained on diverse features like PaDEL and Morgan fingerprints, chemical descriptors, and Chem‐BERT embeddings. Central to our methodology is the super learner architecture, which combines 19 multiple models to enhance accuracy. The super learner achieved a root mean squared error (RMSE) of 0.8200, outperforming individual models and previous reports. These successful results highlight the effectiveness of integrating GNNs and Chem‐BERT for capturing detailed molecular information, setting a new benchmark for vapor pressure prediction. This study underscores the value of advanced machine learning techniques and comprehensive datasets, offering a robust tool for researchers and paving the way for future advancements in chemical property prediction.
Progress of Complex System Process Analysis Based on Modern Spectroscopy Combined With Chemometrics
Authors: Li Maogang , Cai Qi , Zhang Tianlong , Tang Hongsheng , Li Hua 
Published: February 2025

ABSTRACTIn recent years, the role of analytical chemistry has undergone a gradual transformation, evolving from a mere participant to a pivotal decision‐maker in process optimisation. This shift can be attributed to the advent of sophisticated analytical instrumentation, which has ushered in a new era of analytical capabilities. This article presents a review of the developments in the application of intelligent analysis techniques, including infrared (IR) spectroscopy, Raman spectroscopy, and laser‐induced breakdown spectroscopy (LIBS), in the processing of complex systems over the past decade. The review provides an introduction to the fundamental principles of these analytical techniques and examines the evolution of their instrumentation to accommodate online process monitoring. The analysis of spectral data in complex system processes represents a fundamental aspect of the attainment of on‐site quality monitoring, process optimisation and control. Accordingly, the review provides a comprehensive overview of the methodologies employed in process chemometrics, encompassing spectral preprocessing, feature selection, modelling techniques, and optimisation strategies for model performance. Furthermore, this article presents a summary of three intelligent spectral analysis tools, namely infrared spectroscopy, Raman spectroscopy, and LIBS, which are widely employed in process simulation, monitoring, optimisation, and control across multiple disciplines, including the environment, energy, biology, and food. The objective of this review is to provide a valuable reference point and guidance for the further promotion and utilisation of spectral intelligent analysis instruments, with the aim of promoting their in‐depth application and development in a greater number of fields.
Preoxidation Assisted Alkali Activation Constructs Polyacrylonitrile‐Based Porous Carbon for Supercapacitors
Authors: Ma Weijun , Zhu Zejia , Qiu Xuxin , Yan Yaocheng , Zhang Dongdong , Jiang Yongdong 
Published: February 2025

AbstractThe porous structural tailoring of polyacrylonitrile‐based carbon materials is the key to maximize capacitive performance. Herein, hierarchical porous carbons (HPC) with high specific surface area and controllable porosity are synthesized by preoxidation assisted alkali activation strategy using polyacrylonitrile as the precursor. The results show that high preoxidation temperature is beneficial to increase the specific surface area and pore volume of HPC. The optimized HPC‐300 has a high BET specific surface area of 3161 m2 g−1, a micropore volume of 1.45 cm3 g−1, and an AD/AG of 3.56. Electrochemical test shows that the specific capacitances of HPC‐300 in 6 M KOH electrolyte at 1 A g−1 and 10 A g−1 are 314.1 F g−1 and 213.0 F g−1, respectively, with a capacitance retention of 67.8 %. The assembled supercapacitors based on HPC‐300 deliver an energy density of 11.7 Wh kg−1 at a power density of 250.0 W kg−1 in KOH electrolyte and 21.1 Wh kg−1 at 450.0 W kg−1 in Na2SO4 electrolyte and possess a capacitance retention of 98.1 % over 20,000 charge‐discharge cycles. This preoxidation coupled activation method provides a potential strategy for the preparation of PAN‐based porous carbon with high specific surface area and capacitance.
Syntheses of Carbazoles by Photosensitized Electrocyclization of Triarylamines. Effect of Inductive Electron‐Withdrawing Groups on the Photocyclization
Authors: Romero Ivan E. , Vallejos Margarita M. , Barata‐Vallejo Sebastian , Bonesi Sergio M. , Postigo Al 
Published: February 2025

AbstractThe acetone‐sensitized [6π]‐electrocyclization of unsubstituted, mono‐, and disubstituted triarylamines (TAAs: substituents H, CH₃, CH₃O, Cl, CnF₂n+1; n=1, 3, 4, 6, 8, 10) under 310 nm light led to the formation of endo‐ and exo‐carbazoles in high yields; particularly, TAAs substituted with inductive electron‐withdrawing groups (Cl, CnF2n+1) exhibit remarkable endo‐selectivity towards cyclization. For TAAs substituted with both inductive electron‐ withdrawing and donating groups, the photocyclization towards endo carbazoles is dictated by the presence of the electron‐withdrawing groups. Chemical yields (%), quantum yields of conversion (φR), apparent cyclization rate constants (kcyc), and Hammett plots correlations are illustrated for all substituted TAAs in acetone. Photophysical studies reveal that TAAs substituted with resonance electron‐withdrawing groups (OAc, NO2, CHO) do not undergo electrocyclization in acetone at 310 nm due to formation of charge‐transfer states that vastly deactivate the triplet reactive manifold, paralleling the behavior observed in MeCN as solvent. Absorption and emission spectra, Stokes shifts, and singlet excited state energies are illustrated for substituted TAAs and carbazoles. In‐silico studies support the high stereoselectivity observed for the preferred endo‐ photosensitized [6π]‐electrocyclization of Cl‐ and perfluoroalkyl‐substituted TAAs.
Analysis of biomass as a feedstock in a chemical looping‐based polygeneration process for CO2 valorization
Authors: Pankhedkar Nimish , Sunkara Sushma , Dwivedi Abhishek , Gudi Ravindra , Biswas Pratim , Bhargava Suresh 
Published: February 2025

AbstractFrom the viewpoint of circular economy, the utilization of biomass as a preferred feedstock for power generation with carbon capture has become prevalent. Biomass utilization leads to a carbon‐neutral balance by leveraging its inherent carbon that was absorbed from the atmosphere. While chemical looping combustion (CLC) is a promising carbon capture technology for solid fuels such as coal, this paper further explores the feasibility of using biomass in CLC along with valorization of CO2 to valuable chemicals. A polygeneration approach utilizing biomass has been proposed to produce power and value‐added chemicals (methanol and dimethyl ether [DME]), thus yielding an integrated CO2 capture and utilization system. Biomass has a significant amount of oxygen and a higher H:C ratio than conventional fuels like coal. The effect of the same has been analyzed by assessing biomass as a feedstock in the proposed process and evaluating the effect of higher oxygen content towards the objective of CO2 valorization relative to that of coal. Furthermore, a comparative discussion of the suitability of biomass compared to coal from a valorization perspective has been presented. Detailed techno‐feasibility analysis on the schemes and optimization studies to maximize the performance of the system in terms of energetics, CO2 mitigation, and profitability has been conducted. From the analysis, it has been identified that the utilization of biomass as a feedstock in CLC‐based processes results in higher chemical production rates and higher profits with lesser CO2 emissions than coal. The above analysis and evaluation has been carried out through simulations using Aspen Plus® software.
127I Solid‐State NMR Spectroscopy of Organic Periodates Featuring Halogen Bonded IO4− ⋅ ⋅ ⋅ IO4− Adducts
Authors: Beccaria Roberta , Pizzi Andrea , Resnati Giuseppe , Bryce David L. 
Published: February 2025

AbstractHydroxyanions and oxyanions can overcome electrostatic repulsion between like charges to form supramolecular architectures whose formation is driven by non‐covalent interactions such as hydrogen bonds and halogen bonds (HaB). We report here a 127I solid‐state nuclear magnetic resonance (SSNMR) study of a series of six organic periodates including compounds which feature I ⋅ ⋅ ⋅ O HaB between pairs of IO4− anions and control samples which do not feature HaB. 127I SSNMR spectra of powdered samples acquired under stationary conditions at 9.4, 11.7, and 21.1 T are simulated using an exact diagonalization of the Zeeman‐quadrupolar Hamiltonian to provide the isotropic chemical shift, 127I nuclear quadrupolar coupling constant (CQ), and quadrupolar asymmetry parameter for each compound. One of the HaB compounds, 4‐(pyrrolidin‐1‐yl)pyridinium periodate, is characterized by the largest CQ(127I) value measured to date for a periodate anion, 52.70 MHz. Control organic periodates which do not have HaBs have CQ(127I) values that are much lower than those seen in the halogen‐bonded systems, thereby easily differentiating between these two sets of compounds. The CQ(127I) values for those compounds featuring only halogen‐bonded periodate anions correlate with the shear strain of the anion, which may be attributed to the influence of the HaB on the local geometry. More rigorous correlations between structure and the 127I NMR data are confounded by the presence of dynamics in some of the samples.
Investigation of CoB/SBA‐15 Catalyst for Efficient Hydrogen Generation via Sodium Borohydride Hydrolysis
Authors: Yu Hao , Tshimanga Mukendi Archip , Tshimanga Tshimanga Landry Wa , Gao Meng 
Published: February 2025

AbstractHydrolysis of NaBH4 has garnered great concern in the production of hydrogen. Four different cobalt loadings (5 wt.%, 10 wt.%, 12.5 wt.% and 15 wt.%) were impregnated on SBA‐15 molecular sieve. The effect of Co loading on the catalytic performance of the CoB/SBA‐15 catalysts for NaBH4 hydrolysis using the drainage method was studied. Characterizations using XRD, SEM, XPS, and N2 adsorption‐desorption were analytically conducted to examine the physiochemical properties of the CoB/SBA‐15 catalyst. The catalytic activity test revealed that the optimum Co loading was 10 wt.% at which the maximum rate of hydrogen production was 153.0 mL/min⋅gcat, higher than the other samples. In addition, the effect of Co loaded, catalyst amount, and temperature were examined. Furthermore, the stability of the catalyst was maintained after five cycles. Cobalt‐based catalysts are a promising catalytic system for hydrogen generation by hydrolysis of NaBH4.
Phenoxazinone Synthase‐Like Catalytic Activity of Bi‐ and Trinuclear Copper(II) Complexes with 2‐Benzylethanolamine: Experimental and Theoretical Investigations
Authors: Nesterova Oksana V. , Bondarenko Olena E. , Pombeiro Armando J. L. , Nesterov Dmytro S. 
Published: February 2025

AbstractThe self‐assembly reaction of 2‐benzylaminoethanol (Hbae) with CuCl2 or Cu(NO3)2 leads to the formation of binuclear [Cu2(bae)2(Cl)2] (1) and [Cu2(Hbae)2(bae)2](NO3)2 (2) complexes, while the trinuclear [Cu3(Hbae)2(bae)2(dmba)2](NO3)2 (3) compound was obtained using the auxiliar bulky substituted 2,2‐dimethylbutyric acid (Hdmba). Crystallographic studies reveal the molecular structures of 1 and 2 based on the similar {Cu2(μ‐O)2} core, while the structure of 3 features the {Cu3(μ‐O)2} core with consecutive arranement of the metal centres, supported by the additional carboxylate bridges. The strong intermolecular hydrogen bonds join the molecular structures into 1D (for 1 and 3) or 2D (for 2) architectures. All three compounds act as catalysts for the aerobic oxidation of 2‐aminophenol to the phenoxazinone chromophore (phenoxazinone synthase‐like activity) with the maximum reaction rates up to 2.3×10−8 M s−1. The substrate scope involves methyl‐, nitro‐ and chloro‐substituted 2‐aminophenols, disclosing the negligible activity of nitro‐derivatives, while the 6‐amino‐m‐cresol substrate shows the highest activity with the initial reaction rate of 5.8×10−8 M s−1. The mechanism of the rate‐limiting reaction step (copper‐catalysed formation of 2‐aminophenoxyl radicals) was investigated at the DFT level. The combined DFT and CASSCF studies of the copper superoxo CuII‐OO⋅ radical species as possible unconventional reaction intermediates resulted in a rational mechanism of H‐atom abstraction, where the activation energies follow the experimental reactivity of substituted 2‐aminophenols. The TDDFT and STEOM‐DLPNO‐CCSD theoretical calculations of the absorption spectra of substrates, phenoxazinone chromophores and putative polynuclear species containing 2‐aminophenoxo ligand are reported.
Identification of stem rust resistance genes in monogenic lines derived from wheat cultivar Waldron
Authors: Klindworth Daryl L. , Sharma Jyoti Saini , Faris Justin D. , Friesen Timothy L. , Peters Haugrud Amanda R. , Xu Steven S. 
Published: February 2025

AbstractThe wheat (Triticum aestivum L.) cultivar Waldron carries at least six genes for resistance to stem rust caused by Puccinia graminis Pers.:Pers. f.sp. tritici Eriks. and Henn. (Pgt). The identity and map location of some of these genes are known, but there is limited knowledge of SrWld1, an important gene that conditions resistance to all North American Pgt races. Our objective was to identify and map additional stem rust resistance genes in Waldron through aneuploid analysis, allelism tests, and linkage with molecular markers. Four lines, WDR‐A1, WDR‐C2, WDR‐D1, and WDR‐E4, each carrying a single stem rust resistance gene from Waldron, were crossed to appropriate genetic stocks. Telosomic analysis failed to confirm the previously reported location of SrWDR‐C2 to chromosome 7A. Monosomic analysis, allelism tests, and multi‐pathotype seedling tests indicated that the genes in WDR‐A1 and WDR‐D1 were Sr18 and Sr9g, respectively. Telosomic analysis, allelism tests, and marker analysis of WDR‐E4 indicated that SrWld1 should be reassigned from chromosome 2A to 2BL. When families were tested with races LBBLB, TMLKC, and TPPKC, co‐segregation of resistance was not observed. This suggests that inheritance of the SrWld1 phenotype was quantitative and that additional studies are needed on the inheritance of SrWld1. When results from this study were combined with previous studies, the stem rust resistance genes in Waldron are identified as Sr9g, Sr11, Sr18, Sr41, SrWld1, and SrWld2.
Mandated Board Gender Diversity and ESG Performance: Evidence From the European “Women on Boards” Directive
Authors: Fernández‐Méndez Carlos , Pathan Shams 
Published: February 2025

ABSTRACTUsing the 2022 European Union (EU) “women on boards” directive as a backdrop, we examine its wealth impact on firms in reference to their sustainability scores. Our analysis shows that investors respond positively to the EU gender quota, with a stronger effect on firms with low ESG scores. Particularly, we find that the abnormal returns on the announcement day are 0.5% higher on average for firms with weaker ESG performance, suggesting they have more to gain from the EU quota regulation. Our results reveal also that investors' expectations about the impact of the EU gender quota on ESG performance and firm value vary with differences in the institutional context across European countries. We observe more positive response in countries with strong regulatory environment characterized by high regulatory quality and strong legal enforcement and also in countries aligned with gender equality and sustainability values.
Cognitive markers for the distinction between asymptomatic and prodromal Alzheimer's disease in Down syndrome: Correlations with volumetric brain changes
Authors: García‐Alba Javier , Molanes‐López Elisa M. , Zuluaga Pilar , Bell‐Fenellos Cristina , Vaquero Lucía , Alfayate Eva , García Felipe , Mateo Gloria , Modenhauer Fernando , Galván‐Román José M. , Bajo Ricardo , Fernández Alberto 
Published: February 2025

AbstractINTRODUCTIONMotivated by the difficulties in detecting cognitive deterioration in the context of Down syndrome (DS), we aimed to identify markers of prodromal Alzheimer's disease (AD) in this population.METHODSSixty‐two participants with DS (age > 45) distributed in three groups (asymptomatic [ADS], prodromal [PDS], and dementia [DDS]) completed the Cambridge Examination for Mental Disorders of Older People with Down's Syndrome and Others with Intellectual Disabilities, Cambridge Cognitive Examination for older adults with Down's Syndrome, and Barcelona Test for Intellectual Disability tests and a magnetic resonance imaging scan.RESULTSAlthough temporal orientation showed significant differences among groups, only a predictive diagnostic model based on verbal short‐term memory tasks (relying on “cued” recall) allowed the correct classification of 88.5% of ADS, 75.0% of PDS, and 95% of DDS individuals. Cognitive decline strongly correlated with brain volume reductions in orbitofrontal, medial‐temporal, and bilateral thalamus within the DDS group.DISCUSSIONNeuropsychological results showed that PDS cases were characterized by a significant deterioration of verbal memory and temporal orientation, compared to ADS. This pattern might be crucial to support diagnosis in clinical settings.Highlights
Detecting signs of prodromal dementia is a major challenge in Down syndrome.
Such challenge is due to a poor definition of the early cognitive manifestations.
Memory tasks relying on “cued” recall allowed the detection of prodromal cases.
A pattern of temporal disorientation was also evident in the prodromal phase.
These cognitive deficits preceded volumetric brain changes only present in dementia.
Reviewing on AI‐Designed Antibiotic Targeting Drug‐Resistant Superbugs by Emphasizing Mechanisms of Action
Authors: Yönden Zafer , Reshadi Samira , Hayati Ahmad Farrokh , Hooshiar Mohammad Hossein , Ghasemi Sholeh , Yönden Hakan , Daemi Amin 
Published: February 2025

ABSTRACTThe emergence of drug‐resistant bacteria, often referred to as “superbugs,” poses a profound and escalating challenge to global health systems, surpassing the capabilities of traditional antibiotic discovery methods. As resistance mechanisms evolve rapidly, the need for innovative solutions has never been more critical. This review delves into the transformative role of AI‐driven methodologies in antibiotic development, particularly in targeting drug‐resistant bacterial strains (DRSBs), with an emphasis on understanding their mechanisms of action. AI algorithms have revolutionized the antibiotic discovery process by efficiently collecting, analyzing, and modeling complex datasets to predict both the effectiveness of potential antibiotics and the mechanisms of bacterial resistance. These computational advancements enable researchers to identify promising antibiotic candidates with unique mechanisms that effectively bypass conventional resistance pathways. By specifically targeting critical bacterial processes or disrupting essential cellular components, these AI‐designed antibiotics offer robust solutions for combating even the most resilient bacterial strains. The application of AI in antibiotic design represents a paradigm shift, enabling the rapid and precise identification of novel compounds with tailored mechanisms of action. This approach not only accelerates the drug development timeline but also enhances the precision of targeting superbugs, significantly improving therapeutic outcomes. Furthermore, understanding the underlying mechanisms of these AI‐designed antibiotics is crucial for optimizing their clinical efficacy and devising proactive strategies to prevent the emergence of further resistance. AI‐driven antibiotic discovery is poised to play a pivotal role in the global fight against antimicrobial resistance. By leveraging the power of artificial intelligence, researchers are opening new frontiers in the development of effective treatments, ensuring a proactive and sustainable response to the growing threat of drug‐resistant bacteria.