Metabolome and Proteome Profiling of Complex I Deficiency Induced by Rotenone

Complex I (CI; NADH dehydrogenase) deficiency causes mitochondrial diseases, including Leigh syndrome. A variety of clinical symptoms of CI deficiency are known, including neurodegeneration. Here, we report an integrative study combining liquid chromatography–mass spectrometry (LC–MS)-based metabolome and proteome profiling in CI deficient HeLa cells. We report a rapid LC–MS-based method for the relative quantification of targeted metabolome profiling with an additional layer of confidence by applying multiple reaction monitoring (MRM) ion ratios for further identity confirmation and robustness. The proteome was analyzed by label-free quantification (LFQ). More than 6000 protein groups were identified. Pathway and network analyses revealed that the respiratory chain was highly deregulated, with metabolites such as FMN, FAD, NAD⁺, and ADP, direct players of the OXPHOS system, and metabolites of the TCA cycle decreased up to 100-fold. Synthesis of functional iron–sulfur clusters, which are of central importance for the electron transfer chain, and degradation products like bilirubin were also significantly reduced. Glutathione metabolism on the pathway level, as well as individual metabolite components such as NADPH, glutathione (GSH), and oxidized glutathione (GSSG), was downregulated. Overall, metabolome and proteome profiles in CI deficient cells correlated well, supporting our integrated approach

Conceptual Planning of Micro-Assembly for a Better Utilization of Reconfigurable Manufacturing Systems

Reconfigurable manufacturing systems (RMS) can be used to produce micro-assembled products that are too complex for assembly on flat substrates like printed circuit boards. The greatest advantage of RMS is their capability to reuse machine parts for different products, which enhances the economical efficiency of quickly changing or highly individualized products. However, often, process engineers struggle to achieve the full potential of RMS due to product designs not being suited for their given system. Guaranteeing a better fit cannot be done by static guidelines because the higher degree of freedom would make them too complex. Therefore, a new method for generating dynamic guidelines is proposed. The method consists of a model, with which designers can create a simplified assembly sequence of their product idea, and another model, with which process engineers can describe the RMS and the procedures and operations that it can offer. By combining both, a list of possible machine configurations for an RMS can be generated as an automated response for a modeled assembly sequence. With the planning tool for micro-assembly, an implementation of this method as a modern web application is shown, which uses a real existent RMS for micro-assembly

A Product Development Approach in The Field of Micro-Assembly with Emphasis on Conceptual Design

Faster product lifecycles make long-term investments in machines for micro assembly riskier. Therefore, reconfigurable manufacturing systems gain more and more attention. But most companies are uncertain if a reconfigurable manufacturing system can fulfill their needs and justify the initial investment. New and improved techniques for product development have the potential to foster the utilization and decrease the investment risk for such systems. In this paper, four different methods for product development are reviewed. A set of criteria regarding micro assembly on reconfigurable manufacturing systems RMS is established. Based on those criteria and the assessment, a novel approach for a product development method is provided, which tries to combine the strengths of the beforehand presented approaches. It focuses on the conceptual design phase to overcome the customers’ uncertainty in the development process. For this, an abstract representation of a micro-assembly product idea as well as a decision tree for joining processes are established and validated by real product ideas using expert interviews. The validation shows that the conceptual design phase can be used as a useful tool in the product development process in the field of micro assembly

The impact of the COVID-19 pandemic on the dental-maxillofacial emergency service of a German university hospital in the year 2020

Objectives!#!COVID-19 is considered one of the most serious pandemic in history and has posed major challenges to the world's health care. Dentistry and oral and maxillofacial surgery (CMFS) are particularly affected due to direct exposure to the respiratory tract, as the reservoir of SARS-CoV-2. In this study, the impact of the COVID-19-pandemic on a dental and CMFS emergency services in Germany in 2020 was first time investigated and correlated with governmental restriction measures in public life.!##!Materials and methods!#!Epidemiological data of a German University Hospital were analysed from a total of 8386 patients in 2019 and 2020. Parameters included information on demographics, time, weekday and reason for presentation, as well as diagnosis and therapy performed. Data from 2020 were compared with those from 2019, taking into account the nationwide periods of public life restrictions.!##!Results!#!In 2020, 22% fewer patients presented via dental and CMFS emergency service. In a monthly comparison, there were negative peaks of up to - 41% in November, but also a plus of 26% in July. The largest decreases were recorded during the lockdown periods in spring (- 33%) and winter (- 39%). Further, a threefold increase in actual emergencies and inpatient admissions revealed during these time periods (p < 0.001).!##!Conclusions!#!COVID-19 pandemic had a significant impact on the dental and CMFS emergency service in 2020 resulting in more severe cases.!##!Clinical relevance!#!This study underlines the importance of maintaining an emergency service system and basic outpatient care in these specialities, which requires uniform recommendations from the medical-dental societies and politics

Hyperspectral imaging and artificial intelligence to detect oral malignancy – part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network

Background!#!Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library.!##!Methods!#!A total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN).!##!Results!#!The reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each.!##!Conclusion!#!Oral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies