Structural Diversity Research Articles

Induced lactation in a transgender woman: case report

BackgroundBreastfeeding offers significant health benefits, but its practice and success can vary. While research on induced lactation in cisgender women has been documented, there is limited research on lactation induction in transgender women.Case presentationA 50-year-old transgender woman undergoing hormone therapy and living with a pregnant partner sought to co-feed using induced lactation. After approval by the hospital ethics committee, a regimen of estradiol, progesterone, and domperidone was initiated, accompanied by nipple stimulation. Lactation was successfully induced and maintained, with milk composition analysis indicating high levels of protein and other key nutrients. This case, the seventh reported, highlights the complexity of lactation induction in transgender women, considering factors such as age, obesity, and insulin resistance. The nutrient profile of the milk suggests its suitability for infant feeding, despite some differences from typical human milk.ConclusionsInduced lactation is feasible in transgender women, expanding the understanding of non-puerperal lactation and its potential in diverse family structures. Further research is warranted to optimize lactation induction protocols in transgender women.

New Quinoline Derivatives and their Antimicrobial Potential Against Candida Albicans and Staphylococcus Aureus

AbstractAntimicrobial resistance is a pressing global health concern, demanding the development of novel antimicrobial agents. Quinoline derivatives emerge as promising candidates with their diverse chemical structures and recognized bioactive properties. This study aimed to investigate the antimicrobial potency of newly synthesized quinoline derivatives against Candida albicans and five bacterial strains, which are responsible for various infections and have exhibited varying degrees of antibiotic resistance. A series of quinoline derivatives were synthesized through simple structural modifications, such as the inclusion of nitro, amino, and hydrazino groups, or using the molecular hybridization strategy, involving the combination of quinoline and 1,2,3‐triazoles. None of the compounds showed promising activity against the bacterial strains. However, a promising fungistatic effect was observed against C. albicans. In this test, two of the compounds showed lower MIC values than fluconazole (25 μg/mL). In addition, the most promising compounds showed high affinity for the enzyme lanosterol 14 alpha demethylase, due to the low energy values found (−7.6 and −8.2 kcal/mol). Our study offers new avenues for future drug development in the fight against antimicrobial resistance.

Recent Advances in Anti-Inflammatory Compounds from Marine Microorganisms

Marine microbial secondary metabolites with diversified structures have been found as promising sources of anti-inflammatory lead compounds. This review summarizes the sources, chemical structures, and pharmacological properties of anti-inflammatory natural products reported from marine microorganisms in the past three years (2021–2023). Approximately 252 anti-inflammatory compounds, including 129 new ones, were predominantly obtained from marine fungi and they are structurally divided into polyketides (51.2%), terpenoids (21.0%), alkaloids (18.7%), amides or peptides (4.8%), and steroids (4.3%). This review will shed light on the development of marine microbial secondary metabolites as potential anti-inflammatory lead compounds with promising clinical applications in human health.

The GFAP proteoform puzzle: How to advance GFAP as a fluid biomarker in neurological diseases.

Glial fibrillary acidic protein (GFAP) is a well-established biomarker of reactive astrogliosis in the central nervous system because of its elevated levels following brain injury and various neurological disorders. The advent of ultra-sensitive methods for measuring low-abundant proteins has significantly enhanced our understanding of GFAP levels in the serum or plasma of patients with diverse neurological diseases. Clinical studies have demonstrated that GFAP holds promise both as a diagnostic and prognostic biomarker, including but not limited to individuals with Alzheimer's disease. GFAP exhibits diverse forms and structures, herein referred to as its proteoform complexity, encompassing conformational dynamics, isoforms and post-translational modifications (PTMs). In this review, we explore how the proteoform complexity of GFAP influences its detection, which may affect the differential diagnostic performance of GFAP in different biological fluids and can provide valuable insights into underlying biological processes. Additionally, proteoforms are often disease-specific, and our review provides suggestions and highlights areas to focus on for the development of new assays for measuring GFAP, including isoforms, PTMs, discharge mechanisms, breakdown products, higher-order species and interacting partners. By addressing the knowledge gaps highlighted in this review, we aim to support the clinical translation and interpretation of GFAP in both CSF and blood and the development of reliable, reproducible and specific prognostic and diagnostic tests. To enhance disease pathology comprehension and optimise GFAP as a biomarker, a thorough understanding of detected proteoforms in biofluids is essential.

Synthesis and catalytic application of nanostructured metal oxides and phosphates.

The design and development of new high-performance catalysts is one of the most important and challenging issues to achieve sustainable chemical and energy production. This Feature Article describes the synthesis of nanostructured metal oxides and phosphates mainly based on earth-abundant metals and their thermocatalytic application to selective oxidation and acid-base reactions. A simple and versatile methodology for the control of nanostructures based on crystalline complex oxides and phosphates with diverse structures and compositions is proposed as another approach to catalyst design. Herein, two unique and verstile methods for the synthesis of metal oxide and phosphate nanostructures are introduced; an amino acid-aided method for metal oxides and phosphates and a precursor crystallization method for porous manganese oxides. Nanomaterials based on perovskite oxides, manganese oxides, and metal phosphates can function as effective heterogeneous catalysts for selective aerobic oxidation, biomass conversion, direct methane conversion, one-pot synthesis, acid-base reactions, and water electrolysis. Furthermore, the structure-activity relationship is clarified based on experimental and computational approaches, and the influence of oxygen vacancy formation, concerted activation of molecules, and the redox/acid-base properties of the outermost surface are discussed. The proposed methodology for nanostructure control would be useful not only for the design and understanding of the complexity of metal oxide catalysts, but also for the development of innovative catalysts.

A unified moment tensor potential for silicon, oxygen, and silica

Si and its oxides have been extensively explored in theoretical research due to their technological importance. Simultaneously describing interatomic interactions within both Si and SiO2 without the use of ab initio methods is considered challenging, given the charge transfers involved. Herein, this challenge is overcome by developing a unified machine learning interatomic potentials describing the Si/SiO2/O system, based on the moment tensor potential (MTP) framework. This MTP is trained using a comprehensive database generated using density functional theory simulations, encompassing diverse crystal structures, point defects, extended defects, and disordered structure. Extensive testing of the MTP is performed, indicating it can describe static and dynamic features of very diverse Si, O, and SiO2 atomic structures with a degree of fidelity approaching that of DFT.

Molecular characterization of a novel putative pathogen, Streptococcus nakanoensis sp. nov., isolated from sputum culture.

The genus Streptococcus encompasses a wide range of bacteria with more than 60 species. Recently, there has been a notable increase in reports of novel species of α-Streptococcus based on genomic analysis data. However, limited information exists regarding the pathogenicity of these species. In this study, a quinolone-resistant α-hemolytic Streptococcus strain, MTG105, was isolated from a patient with pneumonia. Genetic analysis revealed that this species was a novel species closely related to S. pseudopneumoniae. In an infection assay using organotypic lung tissue models, MTG105 induced epithelial damage comparable to that caused by S. pneumoniae and S. pseudopneumoniae, strongly suggesting its potential as a pathogenic α-Streptococcus. The natural transformation abilities of Streptococcus species facilitate gene exchange within the same genus, leading to the emergence of species with increasingly diverse genome structures. Therefore, the identification of this species underscores the importance of monitoring the emergence of novel species exhibiting virulence and/or multidrug resistance.

Rapid Targeted Screening and Identification of Active Ingredients in Herbal Extracts through Ligand-Detected NMR and Database Matching.

Herbal extracts are rich sources of active compounds that can be used for drug screening due to their diverse and unique chemical structures. However, traditional methods for screening these compounds are notably laborious and time-consuming. In this manuscript, we introduce a new high-throughput approach that combines nuclear magnetic resonance (NMR) spectroscopy with a tailored database and algorithm to rapidly identify bioactive components in herbal extracts. This method distinguishes characteristic signals and structural motifs of active constituents in the raw extracts through a relaxation-weighted technique, particularly utilizing the perfect echo Carr-Purcell-Meiboom-Gill (peCPMG) sequence, complemented by precise 2D spectroscopic strategies. The cornerstone of our approach is a customized database designed to filter potential compounds based on defined parameters, such as the presence of CHn segments and unique chemical shifts, thereby expediting the identification of promising compounds. This innovative technique was applied to identifying substances interacting with choline kinase α (ChoKα1), resulting in the discovery of four new inhibitors. Our findings demonstrate a powerful tool for unraveling the complex chemical landscape of herbal extracts, considerably facilitating the search for new pharmaceutical candidates. This approach offers an efficient alternative to traditional methods in the quest for drug discovery from natural sources.

Iran’s health insurance ecosystem: challenges and strategies

IntroductionIdentifying and exploiting stewardship and financing challenges in Iran’s health insurance system as an ecosystem is essential to achieving predetermined goals. This study aimed to determine the challenges and strategies in the Iranian health insurance ecosystem to provide relevant evidence to healthcare managers and policymakers to improve its functions and perform necessary reforms.MethodThis qualitative study was conducted at the national level in Iran. Data were collected using semi-structured interviews and analyzed using the directed content analysis method. The study participants included managers and experts in health insurance and faculty of universities of medical sciences, who were selected by purposive sampling.ResultsThe challenges and strategies expressed by participants were categorized into two functions: stewardship and financing. Four main themes, ten subthemes, 22 challenges, and 24 strategies were identified in the stewardship function, along with three main themes, 12 subthemes, 17 challenges, and 16 strategies in the financing function. The major challenge in the Iranian health insurance ecosystem was the complexity and conflict of interests between multiple actors with different roles, which led to fragmentation, diverse structures, and a gap between other functions and objectives, hindering the effective functioning of the ecosystem.ConclusionIn order to deal with the challenges of the health insurance ecosystem, it is suggested to create a coherent insurance system through a single utility system, and by paying more attention to health-oriented services, the health insurance ecosystem becomes a health-oriented system instead of being treatment-oriented. In addition, in order to strengthen the governance of the country’s health insurance ecosystem, the number of actors with multiple roles should be reduced and the roles of the actors should be clarified and separated in order to prevent conflicts of interest and structural corruption in this ecosystem.

A novel approach to crack modeling using extended finite element and substructures methods

In the present work, a combination of Extended Finite Element Method (XFEM) and substructures methods has been utilized for crack modeling. Structures are divided into several substructures, and XFEM is employed to model the presence of cracks within each substructure. In another method, crack modeling has been carried out without using the Extended Finite Element Method. Instead, cracks are modeled by eliminating boundary constraints from two substructures. The Krylov-Schur algorithm is utilized to accelerate the computation of structural modes by preparing stiffness and mass matrices for eigenvalue and eigenvector calculations related to the superstructure. For evaluating this method, four examples have been considered: In the first example, an experimental L-shaped steel structure comprising a beam and a column was examined. The second example involved an experimental steel beam with a box-shaped cross-section. In the third example, a steel beam with simple supports at both ends and a rectangular cross-section was proposed. In the fourth example, a real two-span bridge (Turtle Mountain Bridge) is also presented to evaluate the proposed method. The method was applied for modeling, and the obtained results were validated using experimental and analytical results. In this work, the Structural Similarity Index Method (SSIM) for mode shape similarity assessment has been employed for crack identification. Based on the results obtained from the modeling and crack identification evaluations, it can be concluded that the current method can model multiple cracks in structures with variable cross-sections, real concrete bridges, frame-shaped structures, and diverse structures by assembling substructures. Despite simplifying the problems, it maintains an acceptable level of accuracy in validation. This advantage enables the identification of cracks through a straightforward comparison of mode shapes.

A Comparative Study of Maze Generation Algorithms in a Game-Based Mobile Learning Application for Learning Basic Programming Concepts

This study evaluates several maze generation algorithms applied to generate mazes in a game-based Android mobile application designed to support children in learning basic programming concepts and computational thinking. Each algorithm is assessed for its ability to generate solvable and educationally effective mazes, varying in complexity and size. Key findings indicate that Wilson’s and Aldous–Broder algorithms were identified as the most time inefficient. In comparison, Sidewinder and Binary Tree algorithms perform best for smaller mazes due to their straightforward traversal methods. The Hunt-and-Kill and Recursive backtracker algorithms maintain higher ratios of longest paths, making them suitable for the more complex maze generation required for advanced game levels. Additionally, the study explores various maze-solving algorithms, highlighting the efficiency of the recursive algorithm for simpler mazes and the reliability of Dijkstra’s algorithm across diverse maze structures. This research underscores the importance of selecting appropriate maze generation and solving algorithms to balance generation speed, path complexity, and navigational characteristics. While the study demonstrates the practical applicability of these algorithms in a mobile educational application, it also identifies limitations and suggests directions for future research.

There is no limitation for CO2 excretion across the lung in exercising American alligators (Alligator mississippiensis).

Vertebrates utilize various respiratory organs like gills, lungs, and skin in combination with diverse cardiovascular structures, including single, three, and four-chambered hearts, to enable oxygen delivery and carbon dioxide removal. They also exhibit differences in aerobic and anaerobic metabolism during exertion, but the cardiorespiratory gas transport of all vertebrates follow is a similar a four-step process governed by Fick's Principle and Fick's Law of Diffusion over the entire range of metabolic rates. Hillman et al. (2013) suggested that previous exercise studies have focused too narrowly on mammals and proposed that the cardiorespiratory system's excess capacity serves an evolutionary role in enhancing CO2 excretion in non-mammalian vertebrates. In contrast, an analysis by Hicks and Wang (2021) concluded that vertebrates maintain effective gas exchange even at peak activity, finding no evidence of arterial hypercapnia at maximal oxygen consumption and thus challenging the proposal of significant limitations to pulmonary or branchial CO2 efflux. In the present study, we investigate the limits for CO2 exchange in exercising American alligators (Alligator mississippiensis) and provide evidence that the cardiorespiratory system is adequately built to sustain CO2 excretion during strenuous exercise and maintain arterial PCO2 , with no evidence of diffusion limitation for pulmonary CO2 excretion.

Recent Advances in the Application of 2-Aminobenzothiazole to the Multicomponent Synthesis of Heterocycles.

Heterocycles are a vital class of compounds in numerous fields, including drug discovery, agriculture, and materials science. Efficient methods for the synthesis of heterocycles remain critical for meeting the demands of these industries. Recent advances in multicomponent reactions (MCRs) utilizing 2-aminobenzothiazole (ABT) have shown promising results for the formation of heterocycles. The versatility of 2-aminobenzothiazole in this context has enabled the rapid and efficient construction of diverse heterocyclic structures. Various synthetic methodologies and reactions involving 2-aminobenzothiazole are discussed, highlighting its importance as a valuable building block in the synthesis of complex heterocycles. The potential applications of these heterocycles in drug discovery and material science are also explored. Overall, this review provides a comprehensive overview of the current state of research in the field and offers insights into the future directions of this promising area of study. We highlight the potential of ABT as a versatile and sustainable starting material in heterocyclic synthesis via MCRs, with significant implications for the chemical industry.

Advanced Organic–Inorganic Hybrid Materials for Optoelectronic Applications

AbstractResearch on organic–inorganic hybrid materials (OIHMs) has experienced explosive growth in the past decades. The diversity of organic components allows for the introduction of various spatial scales, functional groups, and polarities, while inorganic components provide higher hardness, heat resistance, and stability, their flexible combination facilitates the formation of diverse structures. Furthermore, simple and cost‐effective synthesis methods, such as room temperature solution processes and mechanochemical techniques, enable precise control over the materials' properties at different scales, thus achieving adjustable structure–performance relationships. This review will discuss the recent research progress of OIHMs within the field of optoelectronics and related optoelectronic device applications. According to the dimension of inorganic components and the nature of organic–inorganic interface, this review divides OIHMs into four structural categories. The ongoing research has revealed diverse applications for OIHMs in the fields of solar cells, light‐emitting devices, detectors, and memristors. As an outlook, the potential of perovskite and 0D metal halide materials, which are currently the most studied, for enhancing optoelectronic performance and stability is discussed.

A review on the development of metals-doped Vanadium oxides for zinc-ion battery

Rechargeable zinc-ion batteries (ZIBs) are emerging as promising energy storage devices for various applications, including large-scale energy storage, due to their environmental friendliness, enhanced safety, and low cost. A key challenge in ZIB development is creating cathode materials that reduce the solubility of active materials in aqueous electrolytes, increase electrical conductivity, and extend life cycles for high performance. Vanadium-based compounds, with their diverse structures and multiple oxidation states (+2, +3, +4, and +5), have been extensively studied as effective cathodes for ZIBs. This mini review highlights recent research on doping transition metals into vanadium oxide materials to achieve superior electrochemical performance compared to electrodes prepared via solid-phase synthesis and hydrothermal methods. Additionally, it offers guidance for the future development of vanadium-based materials.

Sn4+ Alloying and Chiral Incorporation into Tellurium Halides Triggering Tunable Luminescence Emission and Second-Harmonic Generation.

Recently, chiral organic-inorganic hybrid metal halides have attracted considerable interest as promising multifunctional materials, benefiting from their diverse structures and tunable photophysical properties. Herein, by introducing the chiral ligand methylbenzylamine (R-/S-MBA) and alloying Sn4+ cation, a series of tellurium-based halides R-/S-MBA2SnxTe1-xCl6 (x = 0, 0.125, 0.2, 0.365 and 0.54) with second-harmonic generation (SHG) effect and photoluminescence (PL) properties are successfully synthesized. Their optical bandgaps are determined to be 2.48-2.6 eV. Specifically, the introduction of chiral organic cations could break the structural symmetry and cause the tellurium halide to crystallize in the chiral space group. The incorporation of isovalent Sn4+ into the chiral host tellurium halides results in the increase in octahedral distortion, thereby promoting host intrinsic self-trapped emission that originates from the interconfigurational 3P0,1 → 1S0 transitions of Te4+. Consequently, the as-prepared Sn4+ doped halides, R-/S-MBA2SnxTe1-xCl6 (x = 0.365, 0.54), exhibit not only SHG response but also bright orange fluorescence. This study provides an effective strategy for designing chiral multifunctional materials.

Tuning singlet oxygen generation with caged organic photosensitizers

Controlling the succession of chemical processes with high specificity in complex systems is advantageous for widespread applications, from biomedical research to drug manufacturing. Despite synthetic advances in bioorthogonal and photochemical methodologies, there is a need for generic chemical approaches that can universally modulate photodynamic reactivity in organic photosensitizers. Herein we present a strategy to fine-tune the production of singlet oxygen in multiple photosensitive scaffolds under the activation of bioresponsive and bioorthogonal stimuli. We demonstrate that the photocatalytic activity of nitrobenzoselenadiazoles can be fully blocked by site-selective incorporation of electron-withdrawing carbamate moieties and restored on demand upon uncaging with a wide range of molecular triggers, including abiotic transition-metal catalysts. We also prove that this strategy can be expanded to most photosensitizers, including diverse structures and spectral properties. Finally, we show that such advanced control of singlet oxygen generation can be broadly applied to the photodynamic ablation of human cells as well as to regulate the release of singlet oxygen in the semi-synthesis of natural product drugs.

Perturbation approaches to achieving diverse and competitive designs in topology optimisation

Utilising topology optimisation to generate diverse and competitive structures enables designers to create elegant and efficient designs according to their aesthetic intuition and functional needs. This study proposes load and support perturbation approaches based on the bi-directional evolutionary structural optimisation (BESO) method to achieve diverse designs. During the optimisation process, noise is introduced to the sensitivity calculations by randomly perturbing the load angles or material properties near the supports, leading to a variety of local optima. Numerical results demonstrate that the proposed approaches are capable of creating designs with similar stiffness but distinct geometries. In addition, the diversity of the obtained solutions can be controlled by utilising different perturbation functions. This work is of significant practical importance in both architecture and engineering, where multiple design options of high structural performance are in demand.

ConvMambaSR: Leveraging State-Space Models and CNNs in a Dual-Branch Architecture for Remote Sensing Imagery Super-Resolution

Deep learning-based super-resolution (SR) techniques play a crucial role in enhancing the spatial resolution of images. However, remote sensing images present substantial challenges due to their diverse features, complex structures, and significant size variations in ground objects. Moreover, recovering lost details from low-resolution remote sensing images with complex and unknown degradations, such as downsampling, noise, and compression, remains a critical issue. To address these challenges, we propose ConvMambaSR, a novel super-resolution framework that integrates state-space models (SSMs) and Convolutional Neural Networks (CNNs). This framework is specifically designed to handle heterogeneous and complex ground features, as well as unknown degradations in remote sensing imagery. ConvMambaSR leverages SSMs to model global dependencies, activating more pixels in the super-resolution task. Concurrently, it employs CNNs to extract local detail features, enhancing the model’s ability to capture image textures and edges. Furthermore, we have developed a global–detail reconstruction module (GDRM) to integrate diverse levels of global and local information efficiently. We rigorously validated the proposed method on two distinct datasets, RSSCN7 and RSSRD-KQ, and benchmarked its performance against state-of-the-art SR models. Experiments show that our method achieves SOTA PSNR values of 26.06 and 24.29 on these datasets, respectively, and is visually superior, effectively addressing a variety of scenarios and significantly outperforming existing methods.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review.

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement. This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research. Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed. The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them. In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.