Metabolomic and transcriptomic analyses of yellow-flowered crocuses to infer alternative sources of saffron metabolites.

BACKGROUND: The increasing demand for saffron metabolites in various commercial industries, including medicine, food, cosmetics, and dyeing, is driven by the discovery of their diverse applications. Saffron, derived from Crocus sativus stigmas, is the most expensive spice, and there is a need to explore additional sources to meet global consumption demands. In this study, we focused on yellow-flowering crocuses and examined their tepals to identify saffron-like compounds. RESULTS: Through metabolomic and transcriptomic approaches, our investigation provides valuable insights into the biosynthesis of compounds in yellow-tepal crocuses that are similar to those found in saffron. The results of our study support the potential use of yellow-tepal crocuses as a source of various crocins (crocetin glycosylated derivatives) and flavonoids. CONCLUSIONS: Our findings suggest that yellow-tepal crocuses have the potential to serve as a viable excessive source of some saffron metabolites. The identification of crocins and flavonoids in these crocuses highlights their suitability for meeting the demands of various industries that utilize saffron compounds. Further exploration and utilization of yellow-tepal crocuses could contribute to addressing the growing global demand for saffron-related products.

Structure and dynamics determine G protein coupling specificity at a class A GPCR.

G protein coupled receptors (GPCRs) exhibit varying degrees of selectivity for different G protein isoforms. Despite the abundant structures of GPCR-G protein complexes, little is known about the mechanism of G protein coupling specificity. The ß2-adrenergic receptor is an example of GPCR with high selectivity for Gαs, the stimulatory G protein for adenylyl cyclase, and much weaker for the Gαi family of G proteins inhibiting adenylyl cyclase. By developing a new Gαi-biased agonist (LM189), we provide structural and biophysical evidence supporting that distinct conformations at ICL2 and TM6 are required for coupling of the different G protein subtypes Gαs and Gαi. These results deepen our understanding of G protein specificity and bias and can accelerate the design of ligands that select for preferred signaling pathways.

Softness matters: effects of compression on the behavior of adsorbed microgels at interfaces.

Deformable colloids and macromolecules adsorb at interfaces as they decrease the interfacial energy between the two media. The deformability, or softness, of these particles plays a pivotal role in the properties of the interface. In this study, we employ a comprehensive in situ approach, combining neutron reflectometry with molecular dynamics simulations, to thoroughly examine the profound influence of softness on the structure of microgel Langmuir monolayers under compression. Lateral compression of both hard and soft microgel particle monolayers induces substantial structural alterations, leading to an amplified protrusion of the microgels into the aqueous phase. However, a critical distinction emerges: hard microgels are pushed away from the interface, in stark contrast to the soft ones, which remain firmly anchored to it. Concurrently, on the air-exposed side of the monolayer, lateral compression induces a flattening of the surface of the hard monolayer. This phenomenon is not observed for the soft particles as the monolayer is already extremely flat even in the absence of compression. These findings significantly advance our understanding of the key role of softness on both the equilibrium phase behavior of the monolayer and its effect when soft colloids are used as stabilizers of responsive interfaces and emulsions.

Cu(dppf) complexes can be synthesized from Cu-exchanged solids and enable a quantification of the Cu-accessibility by 31P MAS NMR spectroscopy.

Herein, we apply three different copper-exchanged materials (Na-[Al]SBA-15, silica, Na-MCM-22) as hosts for a direct synthesis of CuI(1,1'-bis(diphenylphosphino)ferrocene = dppf) complexes in cationic ion exchange position. Using 31P MAS NMR spectroscopy, we show that identical complexes as after ion exchange are generated if the solids are applied as reactants directly. The homogeneity of copper exchanges is evaluated by EDX spectroscopy. Both CuI and CuII result in the formation of complexes, thereby oxidizing dppf. Cu-particles were not reactive. Optimized conditions for a maximized complex formation are identified applying quantitative 31P MAS NMR spectroscopy and ICP-OES. Only accessible copper in cationic position of the solids forms the complexes. This enables a quantification of the amount of copper in mesopores vs. the total copper amount. Thus, besides a new synthesis of the complex a suitable method for quantitative elucidation of the location of copper cations is demonstrated herein.

Haplotype-resolved assembly of a tetraploid potato genome using long reads and low-depth offspring data.

Potato is one of the world's major staple crops, and like many important crop plants, it has a polyploid genome. Polyploid haplotype assembly poses a major computational challenge. We introduce a novel strategy for the assembly of polyploid genomes and present an assembly of the autotetraploid potato cultivar Altus. Our method uses low-depth sequencing data from an offspring population to achieve chromosomal clustering and haplotype phasing on the assembly graph. Our approach generates high-quality assemblies of individual chromosomes with haplotype-specific sequence resolution of whole chromosome arms and can be applied in common breeding scenarios where collections of offspring are available.

An empirical approach and practical framework for a decentralized Ethereum Ecosystem Index (EEI).

Stock market indices are pivotal tools for establishing market benchmarks, enabling investors to navigate risk and volatility while capitalizing on the stock market's prospects through index funds. For participants in decentralized finance (DeFi), the formulation of a token index emerges as a vital resource. Nevertheless, this endeavor is complex, encompassing challenges such as transaction fees and the variable availability of tokens, attributed to their brief history or limited liquidity. This research introduces an index tailored for the Ethereum ecosystem, the leading smart contract platform, and conducts a comparative analysis of capitalization-weighted (CW) and equal-weighted (EW) index performances. The article delineates exhaustive criteria for token eligibility, intending to serve as a comprehensive guide for fellow researchers. The results indicate a consistent superior performance of CW indices over EW indices in terms of return and risk metrics, with a 30-constituent CW index outshining its counterparts with varied constituent numbers. The recommended CW30 index demonstrates substantial advantages in comparison to established benchmarks, including prominent indices like DeFi Pulse Index (DPI) and CRypto IndeX (CRIX). Additionally, the article explores the practicality of implementing the CW30 in Layer 2 networks of the Ethereum Ecosystem, advocating for the Arbitrum infrastructure as the optimal choice for the decentralized crypto index protocol herein referred to as the Ethereum Ecosystem Index (EEI). The study's insights aspire to enrich the DeFi ecosystem, offering a nuanced understanding of network selection and a strategic framework for implementation. This research significantly enhances the existing literature on index construction and performance within the Ethereum ecosystem. To our knowledge, it represents a pioneering comprehensive analysis of an index that accurately mirrors the Ethereum market, advancing our comprehension of its intricacies and wider ramifications. Moreover, this study stands as one of the initial thorough examinations of index construction methodologies within the nascent asset class of crypto. The insights gleaned provide a pragmatic approach to index construction and introduce an index poised to serve as a benchmark for index products. In illuminating the unique facets of the Ethereum ecosystem, this research makes a substantial contribution to the current discourse on crypto, offering valuable perspectives for investors, market stakeholders, and the ongoing exploration of digital assets.

Deep learning based histological classification of adnex tumors.

BACKGROUND: Cutaneous adnexal tumors are a diverse group of tumors arising from structures of the hair appendages. Although often benign, malignant entities occur which can metastasize and lead to patients´ death. Correct diagnosis is critical to ensure optimal treatment and best possible patient outcome. Artificial intelligence (AI) in the form of deep neural networks has recently shown enormous potential in the field of medicine including pathology, where we and others have found common cutaneous tumors can be detected with high sensitivity and specificity. To become a widely applied tool, AI approaches will also need to reliably detect and distinguish less common tumor entities including the diverse group of cutaneous adnexal tumors. METHODS: To assess the potential of AI to recognize cutaneous adnexal tumors, we selected a diverse set of these entities from five German centers. The algorithm was trained with samples from four centers and then tested on slides from the fifth center. RESULTS: The neural network was able to differentiate 14 different cutaneous adnexal tumors and distinguish them from more common cutaneous tumors (i.e. basal cell carcinoma and seborrheic keratosis). The total accuracy on the test set for classifying 248 samples into these 16 diagnoses was 89.92 %. Our findings support AI can distinguish rare tumors, for morphologically distinct entities even with very limited case numbers (< 50) for training. CONCLUSION: This study further underlines the enormous potential of AI in pathology which could become a standard tool to aid pathologists in routine diagnostics in the foreseeable future. The final diagnostic responsibility will remain with the pathologist.

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

BACKGROUND: Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. PATIENTS AND METHODS: In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network. RESULTS: In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established. CONCLUSIONS: AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.

Deep learning detection of melanoma metastases in lymph nodes.

BACKGROUND: In melanoma patients, surgical excision of the first draining lymph node, the sentinel lymph node (SLN), is a routine procedure to evaluate lymphogenic metastases. Metastasis detection by histopathological analysis assesses multiple tissue levels with hematoxylin and eosin and immunohistochemically stained glass slides. Considering the amount of tissue to analyze, the detection of metastasis can be highly time-consuming for pathologists. The application of artificial intelligence in the clinical routine has constantly increased over the past few years. METHODS: In this multi-center study, a deep learning method was established on histological tissue sections of sentinel lymph nodes collected from the clinical routine. The algorithm was trained to highlight potential melanoma metastases for further review by pathologists, without relying on supplementary immunohistochemical stainings (e.g. anti-S100, anti-MelanA). RESULTS: The established method was able to detect the existence of metastasis on individual tissue cuts with an area under the curve of 0.9630 and 0.9856 respectively on two test cohorts from different laboratories. The method was able to accurately identify tumour deposits>0.1 mm and, by automatic tumour diameter measurement, classify these into 0.1 mm to -1.0 mm and>1.0 mm groups, thus identifying and classifying metastasis currently relevant for assessing prognosis and stratifying treatment. CONCLUSIONS: Our results demonstrate that AI-based SLN melanoma metastasis detection has great potential and could become a routinely applied aid for pathologists. Our current study focused on assessing established parameters; however, larger future AI-based studies could identify novel biomarkers potentially further improving SLN-based prognostic and therapeutic predictions for affected patients.

Self-Healing of Charged Microgels in Neutral and Charged Environments.

The softness of microgels depends on many aspects, such as particle characteristic lengths, sample concentration, chemical composition of the sample, and elastic moduli of the particle. Here, the response to crowding of ionic microgels is studied. Charged and uncharged ionic microgels are studied in concentrated suspensions of both neutral and ionic microgels with the same swollen size. The combination of small-angle X-ray and neutron scattering with contrast variation allows us to probe both the particle-to-particle arrangement and the response of individual ionic microgels to crowding. When the ionic microgels are uncharged, initial isotropic deswelling followed by faceting is observed. Therefore, the ionizable groups in the polymeric network do not affect the response of the ionic microgel to crowding, which is similar to what has been reported for neutral microgels. In contrast, the kind of microgels composing the matrix plays a key role once the ionic microgels are charged. If the matrix is composed of neutral microgels, a pronounced faceting and negligible deswelling is observed. When only charged ionic microgels are present in the suspension, isotropic deswelling without faceting is dominant.

Endoscopic Removal of Intrauterine Contraceptive Device Perforating Into the Bladder: A Case Report and Review of Literature.

Intrauterine contraceptive device (IUCD) is a commonly used contraceptive method with the advantage of being a long-acting and reversible contraceptive method. However, its insertion can be rarely associated with serious complications such as uterine perforation, which can more rarely result in injury of the nearby viscus. In this report, we document a rare case of IUCD perforation of the uterus and bladder, its diagnosis using transvaginal ultrasonography and hysteroscopy, and management using a minimally invasive approach with a satisfactory patient outcome.

Genomic, transcriptomic, and metabolomic analysis of Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites.

Plants accumulate a vast array of secondary metabolites, which constitute a natural resource for pharmaceuticals. Oldenlandia corymbosa belongs to the Rubiaceae family, and has been used in traditional medicine to treat different diseases, including cancer. However, the active metabolites of the plant, their biosynthetic pathway and mode of action in cancer are unknown. To fill these gaps, we exposed this plant to eight different stress conditions and combined different omics data capturing gene expression, metabolic profiles, and anti-cancer activity. Our results show that O. corymbosa extracts are active against breast cancer cell lines and that ursolic acid is responsible for this activity. Moreover, we assembled a high-quality genome and uncovered two genes involved in the biosynthesis of ursolic acid. Finally, we also revealed that ursolic acid causes mitotic catastrophe in cancer cells and identified three high-confidence protein binding targets by Cellular Thermal Shift Assay (CETSA) and reverse docking. Altogether, these results constitute a valuable resource to further characterize the biosynthesis of active metabolites in the Oldenlandia group, while the mode of action of ursolic acid will allow us to further develop this valuable compound.

Antiaromatic Covalent Organic Frameworks Based on Dibenzopentalenes.

Despite their inherent instability, 4n π systems have recently received significant attention due to their unique optical and electronic properties. In dibenzopentalene (DBP), benzanellation stabilizes the highly antiaromatic pentalene core, without compromising its amphoteric redox behavior or small HOMO-LUMO energy gap. However, incorporating such molecules in organic devices as discrete small molecules or amorphous polymers can limit the performance (e.g., due to solubility in the battery electrolyte solution or low internal surface area). Covalent organic frameworks (COFs), on the contrary, are highly ordered, porous, and crystalline materials that can provide a platform to align molecules with specific properties in a well-defined, ordered environment. We synthesized the first antiaromatic framework materials and obtained a series of three highly crystalline and porous COFs based on DBP. Potential applications of such antiaromatic bulk materials were explored: COF films show a conductivity of 4 × 10-8 S cm-1 upon doping and exhibit photoconductivity upon irradiation with visible light. Application as positive electrode materials in Li-organic batteries demonstrates a significant enhancement of performance when the antiaromaticity of the DBP unit in the COF is exploited in its redox activity with a discharge capacity of 26 mA h g-1 at a potential of 3.9 V vs. Li/Li+. This work showcases antiaromaticity as a new design principle for functional framework materials.

Small Molecule Tools to Study Cellular Target Engagement for the Intracellular Allosteric Binding Site of GPCRs.

A conserved intracellular allosteric binding site (IABS) has recently been identified at several G protein-coupled receptors (GPCRs). Ligands targeting the IABS, so-called intracellular allosteric antagonists, are highly promising compounds for pharmaceutical intervention and currently evaluated in several clinical trials. Beside co-crystal structures that laid the foundation for the structure-based development of intracellular allosteric GPCR antagonists, small molecule tools that enable an unambiguous identification and characterization of intracellular allosteric GPCR ligands are of utmost importance for drug discovery campaigns in this field. Herein, we discuss recent approaches that leverage cellular target engagement studies for the IABS and thus play a critical role in the evaluation of IABS-targeted ligands as potential therapeutic agents.

Interfacial Fluid Rheology of Soft Particles.

In situ interfacial rheology and numerical simulations are used to investigate microgel monolayers in a wide range of packing fractions, ζ_{2D}. The heterogeneous particle compressibility determines two flow regimes characterized by distinct master curves. To mimic the microgel architecture and reproduce experiments, an interaction potential combining a soft shoulder with the Hertzian model is introduced. In contrast to bulk conditions, the elastic moduli vary nonmonotonically with ζ_{2D} at the interface, confirming long-sought predictions of reentrant behavior for Hertzian-like systems.

Structure-based discovery of nonopioid analgesics acting through the α2A-adrenergic receptor.

Because nonopioid analgesics are much sought after, we computationally docked more than 301 million virtual molecules against a validated pain target, the α2A-adrenergic receptor (α2AAR), seeking new α2AAR agonists chemotypes that lack the sedation conferred by known α2AAR drugs, such as dexmedetomidine. We identified 17 ligands with potencies as low as 12 nanomolar, many with partial agonism and preferential Gi and Go signaling. Experimental structures of α2AAR complexed with two of these agonists confirmed the docking predictions and templated further optimization. Several compounds, including the initial docking hit '9087 [mean effective concentration (EC50) of 52 nanomolar] and two analogs, '7075 and PS75 (EC50 4.1 and 4.8 nanomolar), exerted on-target analgesic activity in multiple in vivo pain models without sedation. These newly discovered agonists are interesting as therapeutic leads that lack the liabilities of opioids and the sedation of dexmedetomidine.

Evaluation of a Deep Learning Approach to Differentiate Bowen's Disease and Seborrheic Keratosis.

Background: Some of the most common cutaneous neoplasms are Bowen's disease and seborrheic keratosis, a malignant and a benign proliferation, respectively. These entities represent a significant fraction of a dermatopathologists' workload, and in some cases, histological differentiation may be challenging. The potential of deep learning networks to distinguish these diseases is assessed. Methods: In total, 1935 whole-slide images from three institutions were scanned on two different slide scanners. A U-Net-based segmentation deep learning algorithm was trained on data from one of the centers to differentiate Bowen's disease, seborrheic keratosis, and normal tissue, learning from annotations performed by dermatopathologists. Optimal thresholds for the class distinction of diagnoses were extracted and assessed on a test set with data from all three institutions. Results: We aimed to diagnose Bowen's diseases with the highest sensitivity. A good performance was observed across all three centers, underlining the model's robustness. In one of the centers, the distinction between Bowen's disease and all other diagnoses was achieved with an AUC of 0.9858 and a sensitivity of 0.9511. Seborrheic keratosis was detected with an AUC of 0.9764 and a sensitivity of 0.9394. Nevertheless, distinguishing irritated seborrheic keratosis from Bowen's disease remained challenging. Conclusions: Bowen's disease and seborrheic keratosis could be correctly identified by the evaluated deep learning model on test sets from three different centers, two of which were not involved in training, and AUC scores > 0.97 were obtained. The method proved robust to changes in the staining solution and scanner model. We believe this demonstrates that deep learning algorithms can aid in clinical routine; however, the results should be confirmed by qualified histopathologists.

Photochromic Fentanyl Derivatives for Controlled µ-Opioid Receptor Activation.

Photoswitchable ligands as biological tools provide an opportunity to explore the kinetics and dynamics of the clinically relevant µ-opioid receptor. These ligands can potentially activate or deactivate the receptor when desired by using light. Spatial and temporal control of biological activity allows for application in a diverse range of biological investigations. Photoswitchable ligands have been developed in this work, modelled on the known agonist fentanyl, with the aim of expanding the current "toolbox" of fentanyl photoswitchable ligands. In doing so, ligands have been developed that change geometry (isomerize) upon exposure to light, with varying photophysical and biochemical properties. This variation in properties could be valuable in further studying the functional significance of the µ-opioid receptor.

Fluorescent Ligands Targeting the Intracellular Allosteric Binding Site of the Chemokine Receptor CCR2.

Fluorescently labeled ligands are versatile molecular tools to study G protein-coupled receptors (GPCRs) and can be used for a range of different applications, including bioluminescence resonance energy transfer (BRET) assays. Here, we report the structure-based development of fluorescent ligands targeting the intracellular allosteric binding site (IABS) of the CC chemokine receptor 2 (CCR2), a class A GPCR that has been pursued as a drug target in oncology and inflammation. Starting from previously reported intracellular CCR2 antagonists, several tetramethylrhodamine (TAMRA)-labeled CCR2 ligands were designed, synthesized, and tested for their suitability as fluorescent reporters to probe binding to the IABS of CCR2. By means of these studies, we developed 14 as a fluorescent CCR2 ligand, enabling cell-free as well as cellular NanoBRET-based binding studies in a non-isotopic and high-throughput manner. Further, we show that 14 can be used as a tool for fragment-based screening approaches. Thus, our small-molecule-based fluorescent CCR2 ligand 14 represents a promising tool for future studies of CCR2 pharmacology.