[Incidentally discovered ectopic tissue in the central neck region (level VI) of the neck - A case series, retrospective analysis and literature review]. / Incidentálisan felfedezett ektópiás szövetmaradványok elofordulása a nyaki VI. (centrális) régióban ­ Esetsorozat ismertetése, retrospektív feldolgozás és irodalmi áttekintés.

The thymus derives from the third branchial pouch, which migrates to the mediastinum through the central region of the neck. During the migration, particles split off and develop separately. The prevalence of ectopic thymus is 20-40%. The purpose of this retrospective case series study was to investigate the prevalence of embryological tissue remnants in the central region, in patients treated for thyroid lesions. Between January 1 2018 and September 1 2020, 84 patients who underwent central neck dissection were selected. Clinicopathological data as age, gender, histopathological result and TNM stage were analyzed. Ectopic tissue in the central neck region was discovered in 28 cases. The prevalence of ectopic lesions showed increase in Stage I thyroid carcinomas. There was no significant correlation with patients' age, gender, or with the stage. We emphasize the clinicopathological role of ectopic tissues, which can occur in the central region of the neck.

bbSelect - An Open-Source Tool for Performing a 3D Pharmacophore-Driven Diverse Selection of R-Groups.

The design of compounds during hit-to-lead often seeks to explore a vector from a core scaffold to form additional interactions with the target protein. A rational approach to this is to probe the region of a protein accessed by a vector with a systematic placement of pharmacophore features in 3D, particularly when bound structures are not available. Herein, we present bbSelect, an open-source tool built to map the placements of pharmacophore features in 3D Euclidean space from a library of R-groups, employing partitioning to drive a diverse and systematic selection to a user-defined size. An evaluation of bbSelect against established methods exemplified the superiority of bbSelect in its ability to perform diverse selections, achieving high levels of pharmacophore feature placement coverage with selection sizes of a fraction of the total set and without the introduction of excess complexity. bbSelect also reports visualizations and rationale to enable users to understand and interrogate results. This provides a tool for the drug discovery community to guide their hit-to-lead activities.

Utilizing the open-source programming language Python to create interactive Quality Assurance dashboards for diagnostic and screening performance in Cytology.

INTRODUCTION: Effective feedback on cytology performance relies on navigating complex laboratory information system data, which is prone to errors and lacks flexibility. As a comprehensive solution, we used the Python programming language to create a dashboard application for screening and diagnostic quality metrics. MATERIALS AND METHODS: Data from the 5-year period (2018-2022) were accessed. Versatile open-source Python libraries (user developed program code packages) were used from the first step of LIS data cleaning through the creation of the application. To evaluate performance, we selected 3 gynecologic metrics: the ASC/LSIL ratio, the ASC-US/ASC-H ratio, and the proportion of cytologic abnormalities in comparison to the total number of cases (abnormal rate). We also evaluated the referral rate of cytologists/cytotechnologists (CTs) and the ratio of thyroid AUS interpretations by cytopathologists (CPs). These were formed into colored graphs that showcase individual results in established, color-coded laboratory "goal," "borderline," and "attention" zones based on published reference benchmarks. A representation of the results distribution for the entire laboratory was also developed. RESULTS: We successfully created a web-based test application that presents interactive dashboards with different interfaces for the CT, CP, and laboratory management (https://drkvcsstvn-dashboards.hf.space/app). The user can choose to view the desired quality metric, year, and the anonymized CT or CP, with an additional automatically generated written report of results. CONCLUSIONS: Python programming proved to be an effective toolkit to ensure high-level data processing in a modular and reproducible way to create a personalized, laboratory specific cytology dashboard.

Experimental and computational insights into the mechanism of the copper(i)-catalysed sulfonylative Suzuki-Miyaura reaction.

A mechanistic study into the copper(i)-catalysed sulfonylative Suzuki-Miyaura reaction, incorporating sulfur dioxide, is described. Utilising spectroscopic and computational techniques, an exploration into the individual components of the competing catalytic cycles is delineated, including identification of the resting state catalyst, transmetalation of arylboronic acid onto copper(i), the sulfur dioxide insertion process, and the oxidative addition of aryl halide to CuI. Studies also investigated prominent side-reactions which were uncovered, including a competing copper(ii)-catalysed mechanism. This led to an additional proposed and connected CuI/CuII/CuIII catalytic cycle to account for by-product formation.

Profiling Sulfur(VI) Fluorides as Reactive Functionalities for Chemical Biology Tools and Expansion of the Ligandable Proteome.

Here, we report a comprehensive profiling of sulfur(VI) fluorides (SVI-Fs) as reactive groups for chemical biology applications. SVI-Fs are reactive functionalities that modify lysine, tyrosine, histidine, and serine sidechains. A panel of SVI-Fs were studied with respect to hydrolytic stability and reactivity with nucleophilic amino acid sidechains. The use of SVI-Fs to covalently modify carbonic anhydrase II (CAII) and a range of kinases was then investigated. Finally, the SVI-F panel was used in live cell chemoproteomic workflows, identifying novel protein targets based on the type of SVI-F used. This work highlights how SVI-F reactivity can be used as a tool to expand the liganded proteome.

Application of deep metric learning to molecular graph similarity.

Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular graph similarity based on distance between learned embeddings separate from any endpoint. Using a minimal definition of similarity, and data from the ZINC database of public compounds, this work demonstrate the properties of the embedding and its suitability for a range of applications, among them a novel reconstruction loss method for training deep molecular auto-encoders. Finally, we compare the applications of the embedding to standard practices, with a focus on known failure points and edge cases; concluding that our approach can be used in conjunction to existing methods.

Molecule Ideation Using Matched Molecular Pairs.

Matched Molecular Pair Analysis (MMP) is a very important tool during the lead optimization stage in drug discovery. The usefulness of this tool in the lead optimization stage has been discussed in several peer-reviewed articles. The application of MMP in Molecule generation is relatively new. This brings several challenges one of them being the need to encode contextual information into the transforms. In this chapter, we discuss how we use MMPs as a molecule generation method and how does it compare with other molecular generators.

A direct-to-biology high-throughput chemistry approach to reactive fragment screening.

Methods for rapid identification of chemical tools are essential for the validation of emerging targets and to provide medicinal chemistry starting points for the development of new medicines. Here, we report a screening platform that combines 'direct-to-biology' high-throughput chemistry (D2B-HTC) with photoreactive fragments. The platform enabled the rapid synthesis of >1000 PhotoAffinity Bits (HTC-PhABits) in 384-well plates in 24 h and their subsequent screening as crude reaction products with a protein target without purification. Screening the HTC-PhABit library with carbonic anhydrase I (CAI) afforded 7 hits (0.7% hit rate), which were found to covalently crosslink in the Zn2+ binding pocket. A powerful advantage of the D2B-HTC screening platform is the ability to rapidly perform iterative design-make-test cycles, accelerating the development and optimisation of chemical tools and medicinal chemistry starting points with little investment of resource.

A Turing Test for Molecular Generators.

Machine learning approaches promise to accelerate and improve success rates in medicinal chemistry programs by more effectively leveraging available data to guide a molecular design. A key step of an automated computational design algorithm is molecule generation, where the machine is required to design high-quality, drug-like molecules within the appropriate chemical space. Many algorithms have been proposed for molecular generation; however, a challenge is how to assess the validity of the resulting molecules. Here, we report three Turing-inspired tests designed to evaluate the performance of molecular generators. Profound differences were observed between the performance of molecule generators in these tests, highlighting the importance of selection of the appropriate design algorithms for specific circumstances. One molecule generator, based on match molecular pairs, performed excellently against all tests and thus provides a valuable component for machine-driven medicinal chemistry design workflows.

Guidelines for Recurrent Neural Network Transfer Learning-Based Molecular Generation of Focused Libraries.

Deep learning approaches have become popular in recent years in the field of de novo molecular design. While a variety of different methods are available, it is still a challenge to assess and compare their performance. A particularly promising approach for automated drug design is to use recurrent neural networks (RNNs) as SMILES generators and train them with the learning procedure called "transfer learning". This involves first training the initial model on a large generic data set of molecules to learn the general syntax of SMILES, followed by fine-tuning on a smaller set of molecules, coming from, e.g., a lead optimization program. To create a well-performing transfer learning application which can be automated, it is important to understand how the size of the second data set affects the training process. In addition, extensive postfiltering using similarity metrics of the molecules generated after transfer learning should be avoided, as it can introduce new biases toward the selection of drug candidates. Here, we present results from the application of a gated recurrent unit cell (GRU)-RNN to transfer learning on data sets of varying sizes and complexity. Analysis of the results has allowed us to provide some general guidelines for transfer learning. In particular, we show that data set sizes containing at least 190 molecules are needed for effective GRU-RNN-based molecular generation using transfer learning. The methods presented here should be applicable generally to the benchmarking of other deep learning methodologies for molecule generation.

Profile of a Highly Selective Quaternized Pyrrolidine Betaine αvß6 Integrin Inhibitor-(3S)-3-(3-(3,5-Dimethyl-1H-pyrazol-1-yl)phenyl)-4-((1S and 1R,3R)-1-methyl-3-(2-(5,6,7,8-tetrahydro-1,8-naphthyridin-2-yl)ethyl)pyrrolidin-1-ium-1-yl)butanoate Synthesized by Stereoselective Methylation.

A quaternary ammonium betaine 7 is described which shows exceptional potency and selectivity (1.4 to >3 logs) for the αvß6 integrin receptor over the other αv integrins as determined in cell adhesion assays. 7 is prepared by remarkably stereoselective methylation, the origins of which are discussed. The chemical, biological, physicochemical, and pharmacokinetic properties of 7 and its docking into αvß6 are described along with related analogues.

A Qualified Success: Discovery of a New Series of ATAD2 Bromodomain Inhibitors with a Novel Binding Mode Using High-Throughput Screening and Hit Qualification.

The bromodomain of ATAD2 has proved to be one of the least-tractable proteins within this target class. Here, we describe the discovery of a new class of inhibitors by high-throughput screening and show how the difficulties encountered in establishing a screening triage capable of finding progressible hits were overcome by data-driven optimization. Despite the prevalence of nonspecific hits and an exceptionally low progressible hit rate (0.001%), our optimized hit qualification strategy employing orthogonal biophysical methods enabled us to identify a single active series. The compounds have a novel ATAD2 binding mode with noncanonical features including the displacement of all conserved water molecules within the active site and a halogen-bonding interaction. In addition to reporting this new series and preliminary structure-activity relationship, we demonstrate the value of diversity screening to complement the knowledge-based approach used in our previous ATAD2 work. We also exemplify tactics that can increase the chance of success when seeking new chemical starting points for novel and less-tractable targets.

The Design of Potent, Selective and Drug-Like RGD αvß1 Small-Molecule Inhibitors Derived from non-RGD α4ß1 Antagonists.

Up to 45 % of deaths in developed nations can be attributed to chronic fibroproliferative diseases, highlighting the need for effective therapies. The RGD (Arg-Gly-Asp) integrin αvß1 was recently investigated for its role in fibrotic disease, and thus warrants therapeutic targeting. Herein we describe the identification of non-RGD hit small-molecule αvß1 inhibitors. We show that αvß1 activity is embedded in a range of published α4ß1 (VLA-4) ligands; we also demonstrate how a non-RGD integrin inhibitor (of α4ß1 in this case) was converted into a potent non-zwitterionic RGD integrin inhibitor (of αvß1 in this case). We designed urea ligands with excellent selectivity over α4ß1 and the other αv integrins (αvß3, αvß5, αvß6, αvß8). In silico docking models and density functional theory (DFT) calculations aided the discovery of the lead urea series.

Bio-Inspired Robust Membranes Nanoengineered from Interpenetrating Polymer Networks of Polybenzimidazole/Polydopamine.

Marine mussel inspired polydopamine (PDA) has received increased attention due to its good thermal and chemical stability as well as strong adhesion on most materials. In this work, high-performance nanofiltration membranes based on interpenetrating polymer networks (IPN) incorporating PDA and polybenzimidazole (PBI) were developed for organic solvent nanofiltration (OSN). Generally, in order to obtain solvent stability, polymers need to be covalently cross-linked under harsh conditions, which inevitably leads to losses in permeability and mechanical flexibility. Surprisingly, by in situ polymerization of dopamine within a PBI support, excellent solvent resistance and permeance of polar aprotic solvents were obtained without covalent cross-linking of the PBI backbone due to the formation of an IPN. The molecular weight cutoff and permeance of the membranes can be fine-tuned by changing the polymerization time. Robust membrane performance was achieved in conventional and emerging green polar aprotic solvents (PAS) in a wide temperature range covering -10 °C to +100 °C. It was successfully demonstrated that the in situ polymerization of PDA-creating an IPN-can provide a simple and green alternative to covalent cross-linking of membranes. To elucidate the nature of the solvent stability, a detailed analysis was performed that revealed that physical entanglement along with strong secondary interaction synergistically enable solvent resistance with as low as 1-3% PDA content.

De Novo Molecule Design by Translating from Reduced Graphs to SMILES.

A key component of automated molecular design is the generation of compound ideas for subsequent filtering and assessment. Recently deep learning approaches have been explored as alternatives to traditional de novo molecular design techniques. Deep learning algorithms rely on learning from large pools of molecules represented as molecular graphs (generally SMILES), and several approaches can be used to tailor the generated molecules to defined regions of chemical space. Cheminformatics has developed alternative higher-level representations that capture the key properties of a set of molecules, and it would be of interest to understand whether such representations can be used to constrain the output of molecule generation algorithms. In this work we explore the use of one such representation, the Reduced Graph, as a definition of target chemical space for a deep learning molecule generator. The Reduced Graph replaces functional groups with superatoms representing the pharmacophoric features. Assigning these superatoms to specific nonorganic element types allows the Reduced Graph to be represented as a valid SMILES string. The mapping from standard SMILES to Reduced Graph SMILES is well-defined, however, the inverse is not true, and this presents a particular challenge. Here we present the results of a novel seq-to-seq approach to molecule generation, where the one to many mapping of Reduced Graph to SMILES is learned on a large training set. This training needs to be performed only once. In a subsequent step, this model can be used to generate arbitrary numbers of compounds that have the same Reduced Graph as any input molecule. Through analysis of data sets in ChEMBL we show that the approach generates valid molecules and can extrapolate to Reduced Graphs unseen in the training set. The method offers an alternative deep learning approach to molecule generation that does not rely on transfer learning, latent space generation, or adversarial networks and is applicable to scaffold hopping and other cheminformatics applications in drug discovery.

[Combined presentation of autoimmune thyroiditis, bilateral papillary thyroid carcinoma, cervical thyrolipoma and diabetes mellitus]. / Az autoimmun thyreoiditis, a kétoldali papillaris pajzsmirigy-carcinoma, a nyaki thyreolipoma és a diabetes mellitus együttes elofordulása.

Thyrolipoma or thyroid adenolipoma is an extremely rare form of thyroid adenoma, which also contains mature adipose tissue and follicles covered with fibrous capsule. We present the case of the growing cervical lesion of a 52-year-old female with diabetes, which was removed during total thyreoidectomy. Autoimmune thyroiditis, bilateral papillary carcinoma and cervical thyrolipoma have been identified by the histopathological examination of the thyroid gland. Orv Hetil. 2018; 159(25): 1024-1032.

Experimental and theoretical investigation of the complexation of methacrylic acid and diisopropyl urea.

The present paper explores the complexation ability of methacrylic acid which is one of the most abundant functional monomer for the preparation of molecularly imprinted polymers. Host-guest interactions and the mechanism of complex formation between methacrylic acid and potentially genotoxic 1,3-diisopropylurea were investigated in the pre-polymerization solution featuring both experimental (NMR, IR) and in silico density functional theory (DFT) tools. The continuous variation method revealed the presence of higher-order complexes and the appearance of self-association which were both taken into account during the determination of the association constants. The quantum chemical calculations - performed at B3LYP 6-311++G(d,p) level with basis set superposition error (BSSE) corrections - are in agreement with the experimental observations, reaffirming the association constants and justifying the validity of computational investigation of such systems. Furthermore, natural bond orbital analysis was carried out to appraise the binding properties of the complexes.

Theoretical study of actinide monocarbides (ThC, UC, PuC, and AmC).

A study of four representative actinide monocarbides, ThC, UC, PuC, and AmC, has been performed with relativistic quantum chemical calculations. The two applied methods were multireference complete active space second-order perturbation theory (CASPT2) including the Douglas-Kroll-Hess Hamiltonian with all-electron basis sets and density functional theory with the B3LYP exchange-correlation functional in conjunction with relativistic pseudopotentials. Beside the ground electronic states, the excited states up to 17 000 cm-1 have been determined. The molecular properties explored included the ground-state geometries, bonding properties, and the electronic absorption spectra. According to the occupation of the bonding orbitals, the calculated electronic states were classified into three groups, each leading to a characteristic bond distance range for the equilibrium geometry. The ground states of ThC, UC, and PuC have two doubly occupied π orbitals resulting in short bond distances between 1.8 and 2.0 Å, whereas the ground state of AmC has significant occupation of the antibonding orbitals, causing a bond distance of 2.15 Å.

Theoretical study of bond distances and dissociation energies of actinide oxides AnO and AnO2.

In the present study we evaluated trends in the bond distances and dissociation enthalpies of actinide oxides AnO and AnO(2) (An = Th-Lr) on the basis of consistent computed data obtained by using density functional theory in conjunction with relativistic small-core pseudopotentials. Computations were carried out on AnO (An = Th-Lr) and AnO(2) (An = Np, Pu, Bk-Lr) species, while for the remaining AnO(2) species recent literature data (Theor. Chem. Acc. 2011, 129, 657) were utilized. The most important computed properties include the geometries, vibrational frequencies, dissociation enthalpies, and several excited electronic states. These molecular properties of the late actinide oxides (An = Bk-No) are reported here for the first time. We present detailed analyses of the bond distances, covalent bonding properties, and dissociation enthalpies.

Theoretical study of the structure and bonding in ThC2 and UC2.

The electronic structure and various molecular properties of the actinide (An) dicarbides ThC(2) and UC(2) were investigated by relativistic quantum chemical calculations. We probe five possible geometrical arrangements: two triangular structures including an acetylide (C(2)) moiety, as well as the linear AnCC, CAnC, and bent CAnC geometries. Our calculations at various levels of theory indicate that the triangular species are energetically more favorable, while the latter three arrangements proved to be higher-energy structures. Our SO-CASPT2 calculations give the ground-state molecular geometry for both ThC(2) and UC(2) as the symmetric (C(2v)) triangular structure. The similar and, also very close in energy, asymmetric (C(s)) triangular geometry belongs to a different electronic state. DFT and single-determinant ab initio methods failed to distinguish between these two similar electronic states demonstrating the power of multiconfiguration ab initio methods to deal with such subtle and delicate problems. We report detailed data on the electronic structure and bonding properties of the most relevant structures.