Osseointegration is a well-established concept used in applications including the percutaneous Bone-Anchored Hearing System (BAHS) and auricular rehabilitation. To date, few retrieved implants have been described. A systematic review including cases where percutaneous bone-anchored implants inserted in the temporal bone were retrieved and analyzed was performed. We also present the case of a patient who received a BAHS for mixed hearing loss. After the initial surgery, several episodes of soft tissue inflammation accompanied by pain were observed, leading to elective abutment removal 14 months post-surgery. Two years post-implantation, the implant was removed due to pain and subjected to a multiscale and multimodal analysis: microbial DNA using molecular fingerprinting, gene expression using quantitative real-time polymerase chain reaction (qPCR), X-ray microcomputed tomography (micro-CT), histology, histomorphometry, backscattered scanning electron microscopy (BSE-SEM), Raman spectroscopy, and fluorescence hybridization (FISH). Evidence of osseointegration was provided micro-CT, histology, BSE-SEM, and Raman spectroscopy. Polymicrobial colonization in the periabutment area and on the implant, including that with and , was determined using a molecular analysis a 16S-23S rDNA interspace [IS]-region-based profiling method (IS-Pro). The histology suggested bacterial colonization in the skin and in the peri-implant bone. FISH confirmed the localization of and coagulase-negative staphylococci in the skin. Ten articles (54 implants, 47 patients) met the inclusion criteria for the literature search. The analyzed samples were either BAHS (35 implants) or bone-anchored aural epitheses (19 implants) between 2 weeks and 8 years. The main reasons for elective removal were nonuse/changes in treatment, pain, or skin reactions. Most samples were evaluated using histology, demonstrating osseointegration, but with the absence of bone under the implants' proximal flange. Taken together, the literature and this case report show clear evidence of osseointegration, despite prominent complications. Nevertheless, despite implant osseointegration, chronic pain related to the BAHS may be associated with a chronic bacterial infection and raised inflammatory response in the absence of macroscopic signs of infection. It is suggested that a multimodal analysis of peri-implant health provides possibilities for device improvements and to guide diagnostic and therapeutic strategies to alleviate the impact of complications.
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http://dx.doi.org/10.3389/fcimb.2021.640899 | DOI Listing |
J Mol Neurosci
January 2025
Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Alzheimer's disease (AD) is a neurodegenerative disease with no effective treatment, often preceded by mild cognitive impairment (MCI). Multimodal imaging genetics integrates imaging and genetic data to gain a deeper understanding of disease progression and individual variations. This study focuses on exploring the mechanisms that drive the transition from normal cognition to MCI and ultimately to AD.
View Article and Find Full Text PDFNat Methods
January 2025
Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets.
View Article and Find Full Text PDFNat Commun
January 2025
Institute of Physiology and Pathophysiology, Medical Faculty, Heidelberg University, Heidelberg, Germany.
Complex experimental protocols often require multi-modal data acquisition with precisely aligned timing, as well as state- and behavior-dependent interventions. Tailored solutions are mostly restricted to individual experimental setups and lack flexibility and interoperability. We present an open-source, Linux-based integrated software solution, called 'Syntalos', for simultaneous acquisition and synchronization of data from an arbitrary number of sources, including multi-channel electrophysiological recordings and different live imaging devices, as well as closed-loop, real-time interventions with different actuators.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui, China (Y.Z., Y.D., Q.Z., C.Z.). Electronic address:
Rationale And Objectives: This study aimed to develop a predictive model for peritoneal metastasis (PM) in ovarian cancer using a combination radiomics and clinical biomarkers to improve diagnostic accuracy.
Patients And Methods: This retrospective cohort study of 619 ovarian cancer patients involved demographic data, radiomics, O-RADS standardized description, clinical biomarkers, and histological findings. Radiomics features were extracted using 3D Slicer and Pyradiomics, with selective feature extraction using Least Absolute Shrinkage and Selection Operator regression.
J Biomed Inform
January 2025
Northwest Normal University, College of Computer Science and Engineering, Lanzhou, China. Electronic address:
Background: In the medical context where polypharmacy is increasingly common, accurately predicting drug-drug interactions (DDIs) is necessary for enhancing clinical medication safety and personalized treatment. Despite progress in identifying potential DDIs, a deep understanding of the underlying mechanisms of DDIs remains limited, constraining the rapid development and clinical application of new drugs.
Methods: This study introduces a novel multimodal drug-drug interaction (MMDDI) model based on multi-source drug data and comprehensive feature fusion techniques, aiming to improve the accuracy and depth of DDI prediction.
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