Data Structure Research Articles

In silico studies of the open form of human tissue transglutaminase

Human tissue transglutaminase (tTG) is an intriguing multifunctional enzyme involved in various diseases, including celiac disease and neurological disorders. Although a number of tTG inhibitors have been developed, the molecular determinants governing ligand binding remain incomplete due to the lack of high-resolution structural data in the vicinity of its active site. In this study, we obtained the complete high-resolution model of tTG by in silico methods based on available PDB structures. We discovered significant differences in the active site architecture between our and known tTG models, revealing an additional loop which affects the ligand binding affinity. We assembled a library of new potential tTG inhibitors based on the obtained complete model of the enzyme. Our library substantially expands the spectrum of possible drug candidates targeting tTG and encompasses twelve molecular scaffolds, eleven of which are novel and exhibit higher binding affinity then already known ones, according to our in silico studies. The results of this study open new directions for structure-based drug design of tTG inhibitors, offering the complete protein model and suggesting a wide range of new compounds for further experimental validation.

Syntheses and Patterns of Changes in Structural Parameters of the New Quaternary Tellurides EuRECuTe3 (RE = Ho, Tm, and Sc): Experiment and Theory

The layered orthorhombic quaternary tellurides EuRECuTe3 (RE = Ho, Tm, Sc) with Cmcm symmetry were first synthesized. Single crystals of the compounds up to 500 μm in size were obtained by the halide-flux method at 1120 K from elements taken in a ratio of Eu/RE/Cu/Te = 1:1:1:3. In the series of compounds, the changes in lattice parameters were in the ranges a = 4.3129(3)–4.2341(3) Å, b = 14.3150(9)–14.1562(9) Å, c = 11.2312(7)–10.8698(7) Å, V = 693.40(8)–651.52(7) Å3. In the structures, the cations Eu2+, RE3+ (RE = Ho, Tm, Sc), and Cu+ occupied independent crystallographic positions. The structures were built with distorted copper tetrahedra forming infinite chains [CuTe4]7− and octahedra [RETe6]9− forming two-dimensional layers along the a-axis. These coordination polyhedra formed parallel two-dimensional layers CuRETe32−∞2. Between the layers, along the a-axis, chains of europium trigonal prisms [EuTe6]10− were located. Regularities in the variation of structural parameters and the degree of distortion of coordination polyhedra depending on the ionic radius of the rare-earth metal in the compounds EuRECuCh3 (RE = Ho, Er, Tm, Lu, Sc; Ch = S, Se, Te) were established. It is shown that with a decrease in the ionic radius ri(RE3+) in the compounds EuRECuTe3, the unit-cell volume, bond length d(RE–Te), distortion degree [CuTe4]7−, and crystallographic compression of layers [RECuTe3]2− decreased. The distortion degree of tetrahedral polyhedra [CuCh4]7−, as well as the structural parameters in europium rare-earth copper tellurides EuRECuTe3, were higher than in isostructural quaternary chalcogenides. Ab initio calculations of the crystalline structure, phonon spectrum, and elastic properties of compounds EuRECuTe3 (RE = Ho, Tm, and Sc) ere conducted. The types and wave numbers of fundamental modes were determined, and the involvement of ions in IR and Raman modes was assessed. The calculated data of the crystal structure correlated well with the experimental results.

Syntheses and Patterns of Changes in Structural Parameters of the New Quaternary Tellurides EuRECuTe3 (RE = Ho, Tm, and Sc): Experiment and Theory

The layered orthorhombic quaternary tellurides EuRECuTe3 (RE = Ho, Tm, Sc) with Cmcm symmetry were first synthesized. Single crystals of the compounds up to 500 μm in size were obtained by the halide-flux method at 1120 K from elements taken in a ratio of Eu/RE/Cu/Te = 1:1:1:3. In the series of compounds, the changes in lattice parameters were in the ranges a = 4.3129(3)–4.2341(3) Å, b = 14.3150(9)–14.1562(9) Å, c = 11.2312(7)–10.8698(7) Å, V = 693.40(8)–651.52(7) Å3. In the structures, the cations Eu2+, RE3+ (RE = Ho, Tm, Sc), and Cu+ occupied independent crystallographic positions. The structures were built with distorted copper tetrahedra forming infinite chains [CuTe4]7− and octahedra [RETe6]9− forming two-dimensional layers along the a-axis. These coordination polyhedra formed parallel two-dimensional layers CuRETe32−∞2. Between the layers, along the a-axis, chains of europium trigonal prisms [EuTe6]10− were located. Regularities in the variation of structural parameters and the degree of distortion of coordination polyhedra depending on the ionic radius of the rare-earth metal in the compounds EuRECuCh3 (RE = Ho, Er, Tm, Lu, Sc; Ch = S, Se, Te) were established. It is shown that with a decrease in the ionic radius ri(RE3+) in the compounds EuRECuTe3, the unit-cell volume, bond length d(RE–Te), distortion degree [CuTe4]7−, and crystallographic compression of layers [RECuTe3]2− decreased. The distortion degree of tetrahedral polyhedra [CuCh4]7−, as well as the structural parameters in europium rare-earth copper tellurides EuRECuTe3, were higher than in isostructural quaternary chalcogenides. Ab initio calculations of the crystalline structure, phonon spectrum, and elastic properties of compounds EuRECuTe3 (RE = Ho, Tm, and Sc) ere conducted. The types and wave numbers of fundamental modes were determined, and the involvement of ions in IR and Raman modes was assessed. The calculated data of the crystal structure correlated well with the experimental results.

Arsenic speciation in picromerite K2Mg(SO4)2•6H2O: Electron paramagnetic resonance and synchrotron X-ray absorption spectroscopic characterizations and implications for organic fertilizers

Picromerite K2Mg(SO4)2•6H2O (also known as schoenite, schönite, or sulfate of potash) formed from alkaline lakes by evaporation is an increasingly important chlorine-free fertilizer and has been used to produce other organic fertilizers such as arcanite K2SO4 and boussingaultite (NH4)2Mg(SO4)2•6H2O. Picromerite and boussingaultite, two Tutton’s salts, are also common secondary solid phases in diverse types of mine tailings and play important roles in controlling the mobility and bioavailability of various heavy metal(loid)s, including arsenic. In this study, picromerite crystals containing 64 ppm As have been synthesized at ambient conditions from aqueous solutions by slow evaporation. Arsenic K-edge X-ray absorption near-edge-structure (XANES) and extended X-ray absorption fine structure (EXAFS) data show that the dominant oxidation state is +5 and that As5+ occupies the S site in picromerite. Single-crystal and powder electron paramagnetic resonance (EPR) spectra of gamma-ray-irradiated picromerite reveal three arsenic-associated oxyradicals: AsO42−, AsO32−, and AsO22−. The orientations of the principal 75As hyperfine directions of the AsO32− and AsO22− radicals match the bonding directions of the SO42− groups in the picromerite structure, further supporting substitutions of their respective diamagnetic precursors AsO43- and AsO33- for the sulfate group. These AsO42−, AsO32−, and AsO22− radicals in picromerite are similar but not identical to their counterparts in boussingaultite and gypsum, suggesting that sulfate minerals are capable of sequestrating both arsenate and arsenite, with important implications for understanding the fate and bioavailability of arsenic associated with agricultural applications of organic fertilizers.

Arsenic speciation in picromerite K2Mg(SO4)2•6H2O: Electron paramagnetic resonance and synchrotron X-ray absorption spectroscopic characterizations and implications for organic fertilizers

Picromerite K2Mg(SO4)2•6H2O (also known as schoenite, schönite, or sulfate of potash) formed from alkaline lakes by evaporation is an increasingly important chlorine-free fertilizer and has been used to produce other organic fertilizers such as arcanite K2SO4 and boussingaultite (NH4)2Mg(SO4)2•6H2O. Picromerite and boussingaultite, two Tutton’s salts, are also common secondary solid phases in diverse types of mine tailings and play important roles in controlling the mobility and bioavailability of various heavy metal(loid)s, including arsenic. In this study, picromerite crystals containing 64 ppm As have been synthesized at ambient conditions from aqueous solutions by slow evaporation. Arsenic K-edge X-ray absorption near-edge-structure (XANES) and extended X-ray absorption fine structure (EXAFS) data show that the dominant oxidation state is +5 and that As5+ occupies the S site in picromerite. Single-crystal and powder electron paramagnetic resonance (EPR) spectra of gamma-ray-irradiated picromerite reveal three arsenic-associated oxyradicals: AsO42−, AsO32−, and AsO22−. The orientations of the principal 75As hyperfine directions of the AsO32− and AsO22− radicals match the bonding directions of the SO42− groups in the picromerite structure, further supporting substitutions of their respective diamagnetic precursors AsO43- and AsO33- for the sulfate group. These AsO42−, AsO32−, and AsO22− radicals in picromerite are similar but not identical to their counterparts in boussingaultite and gypsum, suggesting that sulfate minerals are capable of sequestrating both arsenate and arsenite, with important implications for understanding the fate and bioavailability of arsenic associated with agricultural applications of organic fertilizers.

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the ‘curse of dimensionality’, leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Structure analysis of the telomere resolvase from the Lyme disease spirochete Borrelia garinii reveals functional divergence of its C-terminal domain.

Borrelia spirochetes are the causative agents of Lyme disease and relapsing fever, two of the most common tick-borne illnesses. A characteristic feature of these spirochetes is their highly segmented genomes which consists of a linear chromosome and a mixture of up to approximately 24 linear and circular extrachromosomal plasmids. The complexity of this genomic arrangement requires multiple strategies for efficient replication and partitioning during cell division, including the generation of hairpin ends found on linear replicons mediated by the essential enzyme ResT, a telomere resolvase. Using an integrative structural biology approach employing advanced modelling, circular dichroism, X-ray crystallography and small-angle X-ray scattering, we have generated high resolution structural data on ResT from B. garinii. Our data provides the first high-resolution structures of ResT from Borrelia spirochetes and revealed active site positioning in the catalytic domain. We also demonstrate that the C-terminal domain of ResT is required for both transesterification steps of telomere resolution, and is a requirement for DNA binding, distinguishing ResT from other telomere resolvases from phage and bacteria. These results advance our understanding of the molecular function of this essential enzyme involved in genome maintenance in Borrelia pathogens.

Systematic Analysis of Deep Learning Models vs. Machine Learning

Deep learning (DL) and classical machine learning (ML) models are compared and contrasted in this study, which offers a complete overview of the differences and technological improvements between the two types of models. Through an analysis of a diverse range of research publications, the study draws attention to the distinct advantages and uses of both techniques. Deep learning, which is characterized by its use of neural networks with several layers, is particularly effective at managing massive datasets that are not organized. It has also made great progress in the areas of image and audio recognition, natural language processing, and complicated pattern identification exercises. On the other hand, classic machine learning models, which are based on the extraction of features and simpler methods, continue to be quite successful in structured data situations such as classification, regression, and clustering challenges. By concentrating on aspects such as data quantity, computing resources, and unique application needs, the survey sheds light on the parameters that should be considered when selecting between deep learning and machine learning. In addition to this, it addresses the ever-changing environment of hybrid models, which combine methods from both deep learning and machine learning in order to capitalize on the advantages of both approaches. This study highlights the significance of contextual awareness in the fast-developing area of artificial intelligence by providing researchers and practitioners with useful insights that can be used to deploy the AI models that are the most appropriate for their particular requirements.

Systematic Analysis of Deep Learning Models vs. Machine Learning

Deep learning (DL) and classical machine learning (ML) models are compared and contrasted in this study, which offers a complete overview of the differences and technological improvements between the two types of models. Through an analysis of a diverse range of research publications, the study draws attention to the distinct advantages and uses of both techniques. Deep learning, which is characterized by its use of neural networks with several layers, is particularly effective at managing massive datasets that are not organized. It has also made great progress in the areas of image and audio recognition, natural language processing, and complicated pattern identification exercises. On the other hand, classic machine learning models, which are based on the extraction of features and simpler methods, continue to be quite successful in structured data situations such as classification, regression, and clustering challenges. By concentrating on aspects such as data quantity, computing resources, and unique application needs, the survey sheds light on the parameters that should be considered when selecting between deep learning and machine learning. In addition to this, it addresses the ever-changing environment of hybrid models, which combine methods from both deep learning and machine learning in order to capitalize on the advantages of both approaches. This study highlights the significance of contextual awareness in the fast-developing area of artificial intelligence by providing researchers and practitioners with useful insights that can be used to deploy the AI models that are the most appropriate for their particular requirements.

A Strep-tag Imprinted Polymer Platform for Heterogenous Bio(electro)catalysis.

Molecularly imprinted polymers (MIPs) are artificial receptors equipped with selective recognition sites for target molecules. One of the most promising-strategies for protein MIPs relies on the exploitation of short surface-exposed protein fragments, termed epitopes, as templates to imprint binding sites in a polymer scaffold for a desired protein. However, the lack of high-resolution structural data of flexible surface-exposed regions challenges the selection of suitable epitopes. Here, we addressed this drawback by developing a polyscopoletin-based MIP that recognizes recombinant proteins via the widely used Strep-tag II affinity peptide. Electrochemistry, surface-sensitive spectroscopy, and molecular dynamics simulations were employed to ensure an utmost control of the Strep-MIP electrosynthesis. The functionality of this novel platform was verified with two Strep-tag labeled enzymes: an O2-tolerant [NiFe]-hydrogenase, and an alkaline phosphatase. The enzymes preserved their biocatalytic activities after multiple utilization confirming the efficiency of Strep-MIP as a general biocompatible platform to confine recombinant proteins for exploitation in biotechnology.

A new method for surge arrester placement in high‐voltage substations considering environmental effects

AbstractConventional approaches for determining the optimal locations of surge arresters (SAs) can result in an unnecessary increase in the number of these devices in high‐voltage substations. This study presents an effective technique for determining the optimal locations of SAs. The lightning back flashover (BF) and switching over‐voltages are predicted with the accurate modeling of the transient behavior of all elements of high‐voltage substations and transmission lines, considering the effect of environmental conditions. Also, the proposed limiting parameter Monte Carlo (MC‐LP) method is utilized to correctly select the probability distribution of the possible strokes for predicting the insulation risk (IR) of a transformer based on transient over‐voltage on the transformer end. Therefore, the most appropriate location to install an SA can be determined with a minimum number of calculations using the structural data of the substation, lines connected to it, and the transformer. Simulations are based on experimental results, and the number of calculations significantly decreases using the proposed algorithm. Simulations of the sample network and implementation of the proposed algorithm with MATLAB and EMTP‐RV prove the efficiency of the proposed method for optimum SA placement.

A Strep‐tag Imprinted Polymer Platform for Heterogenous Bio(electro)catalysis

Molecularly imprinted polymers (MIPs) are artificial receptors equipped with selective recognition sites for target molecules. One of the most promising‐strategies for protein MIPs relies on the exploitation of short surface‐exposed protein fragments, termed epitopes, as templates to imprint binding sites in a polymer scaffold for a desired protein. However, the lack of high‐resolution structural data of flexible surface‐exposed regions challenges the selection of suitable epitopes. Here, we addressed this drawback by developing a polyscopoletin‐based MIP that recognizes recombinant proteins via the widely used Strep‐tag II affinity peptide. Electrochemistry, surface‐sensitive spectroscopy, and molecular dynamics simulations were employed to ensure an utmost control of the Strep‐MIP electrosynthesis. The functionality of this novel platform was verified with two Strep‐tag labeled enzymes: an O2‐tolerant [NiFe]‐hydrogenase, and an alkaline phosphatase. The enzymes preserved their biocatalytic activities after multiple utilization confirming the efficiency of Strep‐MIP as a general biocompatible platform to confine recombinant proteins for exploitation in biotechnology.

A Strep‐tag Imprinted Polymer Platform for Heterogenous Bio(electro)catalysis

Molecularly imprinted polymers (MIPs) are artificial receptors equipped with selective recognition sites for target molecules. One of the most promising‐strategies for protein MIPs relies on the exploitation of short surface‐exposed protein fragments, termed epitopes, as templates to imprint binding sites in a polymer scaffold for a desired protein. However, the lack of high‐resolution structural data of flexible surface‐exposed regions challenges the selection of suitable epitopes. Here, we addressed this drawback by developing a polyscopoletin‐based MIP that recognizes recombinant proteins via the widely used Strep‐tag II affinity peptide. Electrochemistry, surface‐sensitive spectroscopy, and molecular dynamics simulations were employed to ensure an utmost control of the Strep‐MIP electrosynthesis. The functionality of this novel platform was verified with two Strep‐tag labeled enzymes: an O2‐tolerant [NiFe]‐hydrogenase, and an alkaline phosphatase. The enzymes preserved their biocatalytic activities after multiple utilization confirming the efficiency of Strep‐MIP as a general biocompatible platform to confine recombinant proteins for exploitation in biotechnology.

Improve Parallel Resistance of Hashcash Tree

Denial of Service (DoS) attacks remain a persistent threat to online systems, necessitating continual innovation in defense mechanisms. In this work, we present an improved algorithm for mitigating DoS attacks through the augmentation of client puzzle protocols. Building upon the foundation of hashcash trees, a recently proposed data structure combining hashcash and Merkle trees, we introduce a new version of the data structure that enhances resistance against parallel computation (a common tactic employed by attackers). By incorporating the labels of children and the next node in a breadth-first traversal into the hash function, we establish a sequential processing order that inhibits parallel node evaluation. The added dependency on the next node significantly elevates the complexity of constructing hashcash trees, introducing a linear number of synchronization points and fortifying resilience against potential attacks. Empirical evaluation demonstrates the efficacy of our approach, showcasing its ability to accurately control puzzle difficulty while bolstering system security against DoS threats.

Improve Parallel Resistance of Hashcash Tree

Denial of Service (DoS) attacks remain a persistent threat to online systems, necessitating continual innovation in defense mechanisms. In this work, we present an improved algorithm for mitigating DoS attacks through the augmentation of client puzzle protocols. Building upon the foundation of hashcash trees, a recently proposed data structure combining hashcash and Merkle trees, we introduce a new version of the data structure that enhances resistance against parallel computation (a common tactic employed by attackers). By incorporating the labels of children and the next node in a breadth-first traversal into the hash function, we establish a sequential processing order that inhibits parallel node evaluation. The added dependency on the next node significantly elevates the complexity of constructing hashcash trees, introducing a linear number of synchronization points and fortifying resilience against potential attacks. Empirical evaluation demonstrates the efficacy of our approach, showcasing its ability to accurately control puzzle difficulty while bolstering system security against DoS threats.

DNA-PK: A synopsis beyond synapsis

Given its central role in life, DNA is remarkably easy to damage. Double strand breaks (DSBs) are the most toxic form of DNA damage, and DSBs pose the greatest danger to genomic integrity. In higher vertebrates, the non-homologous end joining pathway (NHEJ) is the predominate pathway that repairs DSBs. NHEJ has three steps: 1) DNA end recognition by the DNA dependent protein kinase [DNA-PK], 2) DNA end-processing by numerous NHEJ accessory factors, and 3) DNA end ligation by the DNA ligase IV complex (LX4). Although this would appear to be a relatively simple mechanism, it has become increasingly apparent that it is not.Recently, much insight has been derived regarding the mechanism of non-homologous end joining through a proliferation of cryo-EM studies, structure-function mutational experiments informed by these new structural data, and novel single-molecule imaging approaches. An emerging consensus in the field is that NHEJ progresses from initial DSB end recognition by DNA-PK to synapsis of the two DNA ends in a long-range synaptic complex where ends are held too far apart (115 Å) for ligation, and then progress to a short-range synaptic complex where ends are positioned close enough for ligation. What was surprising from these structural studies was the observation of two distinct types of DNA-PK dimers that represent NHEJ long-range complexes. In this review, we summarize current knowledge about the function of the distinct NHEJ synaptic complexes and align this new information with emerging cellular single-molecule microscopy studies as well as with previous studies of DNA-PK’s function in repair.

Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions.

Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection.

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions.

The German cochlear implant registry: one year experience and first results on demographic data.

Clinical registries have great potential for quality control of medical procedures regarding the indications, therapeutic processes and results, including their possible complications. This is particularly true when providing patients with severe hearing loss or deafness with a cochlear implant (CI). This treatment represents a lifelong care process that requires continuous quality control over time. On the initiative of the Executive Committee of the German Society of Otorhinolaryngology (Deutsche Gesellschaft für Hals-Nasen-Ohren-Heilkunde, Kopf- und Hals-Chirurgie e.V., DGHNO-KHC), a national German CI registry (Deutsches Cochlear Implant Register, DCIR) was established in January 2022. This article focuses on the first demographic and baseline data of the DCIR. The DCIR covers the complete therapeutic process from indication, surgery, fitting and lifelong aftercare in CI therapy. By the end of 2022, 75 hospitals in Germany had agreed to contribute to the DCIR. During the year 2022, 63 hospitals actively contributed data to the DCIR. Pseudonymized data from 2,292 CI implantations (2,176 primary implantations, 99 explantations with immediate re-implantations and 17 re-implantations following an earlier explantation) in 2,108 patients were documented. Cochlear implantation was accomplished in 1,807 adults (≥ 18 years) and 301 children (< 18 years). Fourty patients (1,9%) were children < 1 year of age and 55 (2,6%) were patients>85 years. From the total of 2,292 implantations, 226 (9.9%) were performed as simultaneous bilateral implantations (CI implantation in both ears of 113 patients on the same day of surgery) and 412 implantations (19.1% of 2,162 implantations with data provided on the contralateral ear's hearing status) were in patients with single sided deafness (normal hearing in the contralateral ear). In addition, the reported complications in 2022 were also evaluated. Seven reports (0.4%) of mild to moderate severe facial nerve dysfunctions were documented. No reports of severe or total facial nerve dysfunction (House-Brackmann grade V/VI), meningitis or death related to CI therapy were documented. Although still in the start-up phase, these initial DCIR data already provide an interesting first insight into the demographic structure and baseline data of CI therapy in Germany. The successful implementation of the DCIR represents an important step towards continuous quality control of CI care.