IntroductionUnderstanding the biology of implant-associated infections is essential in order to provide adequate detection, prevention and therapeutic strategies. Advanced 3D in vitro models offer valuable insights into the complex interactions between cells and bacteria in the presence of implant materials. This review aims to give a comprehensive overview of current 3D in vitro models that mimic implant-associated infections.MethodsThe structured literature search initially identified 258 publications, seven of which fitted the inclusion criteria.ResultsThe included 3D models were established either to mimic the in vivo situation (organotypic model) or to investigate future implant materials. In three studies, organotypic models for dental implants were created and one study described an organotypic model containing immune cells. In the remaining three studies, biomaterials for constructing future orthopedic implants were developed and tested. All authors included specific cells and bacteria suitable for the respective implants. The dental implant models used fibroblasts and keratinocytes; the orthopedic implant models used stem cells and fibroblast-like cells; the model containing immune cells incorporated co-cultivation of fibroblasts and THP-1 derived macrophages. For bacterial challenge, most authors used Gram positive bacteria, but three studies employed Gram negative bacterial species. A wide variety of analytical methods of different complexity were applied after co-culture of cells and bacteria and between one and five different methods were used.DiscussionAll models could be employed to provide answers to specific scientific questions regarding implant-associated infections. Nonetheless, this review reveals the limitations of current 3D models for the investigation of implant-associated infections and highlights the opportunities for further development in this scientific field.

The assembled model was imported into ANSYS considering three regions of the contact for the analyses

This is the challenge design document for the "Towards the Automatization of Cranial Implant Design in Cranioplasty" Challenge, accepted for MICCAI 2020. Cranioplasty is the surgical process where a skull defect, caused in a brain tumor surgery or by trauma, is repaired using a cranial implant, which must fit precisely against the borders of the skull defect as an alternative to the removed cranial bone. The designing of the cranial implant is a challenging task and involves several steps: (1) obtaining the 3D imaging data of the skull with defect from CT or MRI, (2) converting the 3D imaging data into 3D mesh model and (3) creating the 3D model of the implant for 3D printing. The last step usually requires expensive commercial software, which clinical institutions often have limited access to. Researchers have been working on CAD software as alternative to the commercial software for the designing of cranial implant whereas these approaches still involve human interaction, which is time-consuming and requires expertise of the specific medical domain. Therefore, a fast and automatic design of cranial implants is highly desired, which also enables in Operation Room (in OR) manufacturing of the implants for the patient. Centered around the topic, our challenge provides 200 healthy skulls acquired from CT scans in clinical routine and seeks data-driven approaches for the problem. We inject artificial defects into each healthy skull to create training pairs. The datasets are split into a training set and a testing set, each containing 100 healthy skulls and their corresponding skulls with artificial defects. Participants are expected to design algorithms (such as deep learning) based on these training pairs for an automatic cranial defect restoration and implant generation. In this sense, the problem is being formulated as a 3D volumetric shape completion task where a defected skull volume is automatically completed by the algorithm from the participants. The restored defect, which is in fact the implant we want, can be obtained by the subtraction of the defected skull from the completed skull. The implants reconstructed from the skulls with the artificial defects will be quantitatively evaluated using the Dice Similarity Score (DSC) and the Hausdorff Distance (HD).

Dental implants are used to replace teeth lost through trauma or poor dental health, and are used as anchors for crowns, bridges and permanent dentures. Implants are available in tapered or parallel-walled designs. Tapered implants offer improved stability in soft bone such as the posterior maxilla, while parallel-walled implants are versatile and often used in denser, harder bone. Dental implants are available in two materials- titanium and zirconium oxide. Titanium implants are considered more versatile, as they are available as one or two piece systems. Two piece systems include the implant and an abutment. The implant is placed at the level of the bone, while the abutment is placed through the gums and supports the teeth. Read More

This data belongs to the original scientific article that is “Cumulative Inaccuracies in Implementation of Additive Manufacturing Through Medical Imaging, 3D Thresholding, and 3D Modeling: A Case Study for an End-Use Implant” by Jan Sher Akmal, Mika Salmi, Björn Hemming, Linus Teir, Anni Suomalainen, Mika Kortesniemi, Jouni Partanen, and Antti Lassila published with MDPI in Applied Sciences (Switzerland) Special Issue — 3D Printing of Bioactive Medical Device . It contains medical CT-images of a sus domesticus acquired using a Siemens Somatom Definition Edge CT system.

Please cite the original scientific article if you use this data as follows: Akmal, J.S.; Salmi, M.; Hemming, B.; Teir, L.; Suomalainen, A.; Kortesniemi, M.; Partanen, J.; Lassila, A. Cumulative Inaccuracies in Implementation of Additive Manufacturing Through Medical Imaging, 3D Thresholding, and 3D Modeling: A Case Study for an End-Use Implant. Appl. Sci. 2020, 10, 2968. https://doi.org/10.3390/app10082968

Data waveforms from simulation of the behaviour of MICS-based RF Wireless Power Transfer Systems in Matlab

This data belongs to an original scientific article that is “Cumulative inaccuracies in implementation of additive manufacturing through medical imaging, 3D thresholding, and 3D modeling: A case study for an end-use implant” by Jan Sher Akmal, Mika Salmi, Björn Hemming, Linus Teir, Anni Suomalainen, Mika Kortesniemi, Jouni Partanen, and Antti Lassila. It contains medical CT-images of a sus domesticus acquired using a Siemens Somatom Definition Edge CT system.

Please cite the original scientific article if you use this data.

This dataset contains certain models of hearing aids and cochlear implants sold in the USA from 2005 through 2024, the colors the models were sold in, and comparisons between each model color and measures of human skin color.

description: Commercially available regional economic data for Alaska fisheries [such as IMpact analysis for PLANning (IMPLAN)] are unreliable. Therefore, these data need to be either collected or estimated based on more reliable information. These data have been collected or estimated for important economic variables such as cost, employment, and factor income (labor income and capital) for Alaska fisheries. The data thus collected or estimated have been used to develop regional economic models for Alaska fisheries in order to estimate the economic impacts of Alaska fisheries.; abstract: Commercially available regional economic data for Alaska fisheries [such as IMpact analysis for PLANning (IMPLAN)] are unreliable. Therefore, these data need to be either collected or estimated based on more reliable information. These data have been collected or estimated for important economic variables such as cost, employment, and factor income (labor income and capital) for Alaska fisheries. The data thus collected or estimated have been used to develop regional economic models for Alaska fisheries in order to estimate the economic impacts of Alaska fisheries.

Arthroscopy Implants (Orthopedic Devices) – Global Market Analysis and Forecast Model (COVID-19 Market Impact) is built to visualize quantitative market trends within Orthopedic Devices therapeutic area. Read More

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SI-BONE (SIBN) Forecast: Surgical Implant Maker Poised for Growth, Analysts Say.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Quantifying the economic impacts of invasive species is an essential step in developing and prioritizing invasive species management. In particular, kudzu, Pueraria montana (Lour.) Merr. is an aggressive and non-native vine that not only causes ecological damage and reduces biodiversity, but can have multiple economic consequences such as loss of timber value and volume. Using current infestation locations in Oklahoma, southcentral USA, a Monte Carlo simulation was run to estimate the natural as well as anthropogenic spread rate of kudzu in the next five years. Simulations were supplemented with an economic impact analysis within the Impact Analysis for PLANing (IMPLAN) platform. To account for economic loss in the forest product industry, a replacement cost approach with a sensitivity analysis was conducted. Occurrence data collections revealed that current kudzu populations are already established in Oklahoma forests. The results demonstrate that by year five, total industry output could be reduced by $167.9 million, which will influence 780 jobs in the most extreme case scenario. The predicted economic loss due to kudzu expansion could act as an incentive for appropriate management practices and plans to be implemented.

Aesthetic Implants (General Surgery) – Global Market Analysis and Forecast Model (COVID-19 Market Impact) is built to visualize quantitative market trends within General Surgery therapeutic area. Read More

Comparison of the flexible models using different knots.

Abstract The aim of this study was to evaluate the effect of varying the diameter, connection type and loading on stress distribution in the cortical bone for implants with a high crown-implant ratio. Six 3D models were simulated with the InVesalius, Rhinoceros 3D 4.0 and SolidWorks 2011 software programs. Models were composed of bone from the posterior mandibular region; they included an implant of 8.5 mm length, diameter Ø 3.75 mm or Ø 5.00 mm and connection types such as external hexagon (EH), internal hexagon (IH) and Morse taper (MT). Models were processed using the Femap 11.2 and NeiNastran 11.0 programs and by using an axial force of 200 N and oblique force of 100 N. Results were recorded in terms of the maximum principal stress. Oblique loading showed high stress in the cortical bone compared to that shown by axial loading. The results showed that implants with a wide diameter showed more favorable stress distribution in the cortical bone region than regular diameter, regardless of the connection type. Morse taper implants showed better stress distribution compared to other connection types, especially in the oblique loading. Thus, oblique loading showed higher stress concentration in cortical bone tissue when compared with axial loading. Wide diameter implant was favorable for improved stress distribution in the cortical bone region, while Morse taper implants showed lower stress concentration than other connections.

Anthropometric data of the ten patients with the instrumented Hip III implant of the OrthoLoad database.

In the first Sheet each combination of input variables is given in rows with parameters defined in the column headings. Each of the other sheets lists data for an output variable related to the input set in the same row number. (XLSX)

Comparing each simple ResNet model and ABN model.

Number of parameters for simple ResNet model and ResNet with ABN model.

Stress peaks per region and microstrain in bone tissues for each evaluated model.