Validation of Inertial-Measurement-Unit-Based Ex Vivo Knee Kinematics during a Loaded Squat before and after Reference-Frame-Orientation Optimisation.

Recently, inertial measurement units have been gaining popularity as a potential alternative to optical motion capture systems in the analysis of joint kinematics. In a previous study, the accuracy of knee joint angles calculated from inertial data and an extended Kalman filter and smoother algorithm was tested using ground truth data originating from a joint simulator guided by fluoroscopy-based signals. Although high levels of accuracy were achieved, the experimental setup leveraged multiple iterations of the same movement pattern and an absence of soft tissue artefacts. Here, the algorithm is tested against an optical marker-based system in a more challenging setting, with single iterations of a loaded squat cycle simulated on seven cadaveric specimens on a force-controlled knee rig. Prior to the optimisation of local coordinate systems using the REference FRame Alignment MEthod (REFRAME) to account for the effect of differences in local reference frame orientation, root-mean-square errors between the kinematic signals of the inertial and optical systems were as high as 3.8° ± 3.5° for flexion/extension, 20.4° ± 10.0° for abduction/adduction and 8.6° ± 5.7° for external/internal rotation. After REFRAME implementation, however, average root-mean-square errors decreased to 0.9° ± 0.4° and to 1.5° ± 0.7° for abduction/adduction and for external/internal rotation, respectively, with a slight increase to 4.2° ± 3.6° for flexion/extension. While these results demonstrate promising potential in the approach's ability to estimate knee joint angles during a single loaded squat cycle, they highlight the limiting effects that a reduced number of iterations and the lack of a reliable consistent reference pose inflicts on the sensor fusion algorithm's performance. They similarly stress the importance of adapting underlying assumptions and correctly tuning filter parameters to ensure satisfactory performance. More importantly, our findings emphasise the notable impact that properly aligning reference-frame orientations before comparing joint kinematics can have on results and the conclusions derived from them.

Empirical assessment of ChatGPT's answering capabilities in natural science and engineering.

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to greatly impact society, research, and education. An essential step to understand ChatGPT's expected impact is to study its domain-specific answering capabilities. Here, we perform a systematic empirical assessment of its abilities to answer questions across the natural science and engineering domains. We collected 594 questions on natural science and engineering topics from 198 faculty members across five faculties at Delft University of Technology. After collecting the answers from ChatGPT, the participants assessed the quality of the answers using a systematic scheme. Our results show that the answers from ChatGPT are, on average, perceived as "mostly correct". Two major trends are that the rating of the ChatGPT answers significantly decreases (i) as the educational level of the question increases and (ii) as we evaluate skills beyond scientific knowledge, e.g., critical attitude.

DNA templated Click Chemistry via 5-vinyl-2'-deoxyuridine and an acridine-tetrazine conjugate induces DNA damage and apoptosis in cancer cells.

AIMS: Click Chemistry is providing valuable tools to biomedical research, but its direct use in therapies remains nearly unexplored. For cancer treatment, nucleoside analogues (NA) such as 5-vinyl-2'-deoxyuridine (VdU) can be metabolically incorporated into cancer cell DNA and subsequently "clicked" to form a toxic product. The inverse electron-demand Diels-Alder (IEDDA) reaction between VdU and an acridine-tetrazine conjugate (PINK) has previously been used to label cell nuclei of cultured cells. Here, we report tandem usage of VdU and PINK to induce cytotoxicity. MAIN METHODS: Cell lines were subsequently treated with VdU and PINK, and cell viability was measured via well confluency and 3D tumor spheroid assays. DNA damage and apoptosis were evaluated using Western Blotting and cell cycle analysis by flow cytometry. Double stranded DNA break (DSB) formation was measured using the comet assay. Apoptosis was assessed by fluorescent detection of externalized phosphatidylserine residues. KEY FINDINGS: We report that the combination of VdU and PINK synergistically induces cytotoxicity in cultured human cells. The combination of VdU and PINK strongly reduced cell viability in 2D and 3D cultured cancer cells. Mechanistically, the compounds induced DNA damage through DSB formation, which leads to S-phase accumulation and apoptosis. SIGNIFICANCE: The combination of VdU and PINK represents a novel and promising DNA-templated "click" approach for cancer treatment via selective induction of DNA damage.

ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology.

The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.

Chemical data intelligence for sustainable chemistry.

This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data. The chemical industry requires a transformation towards more sustainable practices, eliminating its dependencies on fossil fuels and limiting its impact on the environment. However, identifying more sustainable process alternatives is, at present, a cumbersome, manual, iterative process, based on chemical intuition and modelling. We give a perspective on methods for automated discovery and assessment of competitive sustainable reaction routes based on renewable or waste feedstocks. Three key areas of transition are outlined and reviewed based on their state-of-the-art as well as bottlenecks: (i) data, (ii) evaluation metrics, and (iii) decision-making. We elucidate their synergies and interfaces since only together these areas can bring about the most benefit. The field of chemical data intelligence offers the opportunity to identify the inherently more sustainable reaction pathways and to identify opportunities for a circular chemical economy. Our review shows that at present the field of data brings about most bottlenecks, such as data completion and data linkage, but also offers the principal opportunity for advancement.

Sulfur mustard induced nuclear translocation of glyceraldehyde-3-phosphate-dehydrogenase (GAPDH).

Sulfur Mustard (SM) is a vesicant chemical warfare agent, which is acutely toxic to a variety of organ systems including skin, eyes, respiratory system and bone marrow. The underlying molecular pathomechanism was mainly attributed to the alkylating properties of SM. However, recent studies have revealed that cellular responses to SM exposure are of more complex nature and include increased protein expression and protein modifications that can be used as biomarkers. In order to confirm already known biomarkers, to detect potential new ones and to further elucidate the pathomechanism of SM, we conducted large-scale proteomic experiments based on a human keratinocyte cell line (HaCaT) exposed to SM. Surprisingly, our analysis identified glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) as one of the up-regulated proteins after exposure of HaCaT cells to SM. In this paper we demonstrate the sulfur mustard induced nuclear translocation of GAPDH in HaCaT cells by 2D gel-electrophoresis (2D GE), immunocytochemistry (ICC), Western Blot (WB) and a combination thereof. 2D GE in combination with MALDI-TOF MS/MS analysis identified GAPDH as an up-regulated protein after SM exposure. Immunocytochemistry revealed a distinct nuclear translocation of GAPDH after exposure to 300µM SM. This finding was confirmed by fractionated WB analysis. 2D GE and subsequent immunoblot staining of GAPDH demonstrated two different spot locations of GAPH (pI 7.0 and pI 8.5) that are related to cytosolic or nuclear GAPDH respectively. After exposure to 300µM SM a significant increase of nuclear GAPDH at pI 8.5 occurred. Nuclear GAPDH has been associated with apoptosis, detection of structural DNA alterations, DNA repair and regulation of genomic integrity and telomere structure. The results of our study add new aspects to the pathophysiology of sulfur mustard toxicity, yet further studies will be necessary to reveal the specific function of nuclear GAPDH in the pathomechanism of sulfur mustard.

Decrease in circulating myeloid dendritic cell precursors in coronary artery disease.

OBJECTIVES: We analyzed the frequency of myeloid dendritic cell (mDC) and plasmacytoid dendritic cell (pDC) precursors in blood of patients with coronary artery disease (CAD) and in atherosclerotic carotid plaques of patients with cerebrovascular disease (CVD). BACKGROUND: Circulating DC precursors are reduced in several autoimmune diseases. Atherosclerosis has features of an autoimmune disease, such as the presence of autoantibodies or autoreactive T cells. Tissue-resident DCs were previously described in atheromata, and it is assumed that they are important for the activation of T cells against autoantigens there. METHODS: Circulating mDC and pDC precursors were flow cytometrically detected in healthy controls (n = 19), CAD patients with stable (n = 20) and unstable angina pectoris (n = 19), and acute myocardial infarction (n = 17). In human carotid plaques (n = 65), mDC and pDC precursors were identified immunohistochemically. RESULTS: Circulating mDC precursors were significantly reduced in patients with stable angina pectoris (0.19%, p = 0.04), unstable angina pectoris (0.16%, p = 0.004), and acute myocardial infarction (0.08%, p < 0.001) compared with control patients (0.22% of peripheral blood mononuclear cells). In contrast, pDC numbers were not significantly altered. Circulating mDC precursors inversely correlated with high-sensitivity C-reactive protein (r = -0.38, p = 0.001) or interleukin-6 (r = -0.42, p < 0.001). In contrast to pDC, significantly more mDC precursors were observed in vulnerable carotid plaques (24, 0.25 mm2; n = 31; p = 0.003) than in stable ones (6.4, 0.25 mm2; n = 34). CONCLUSIONS: Similar to autoimmune diseases, circulating mDC precursors were significantly reduced in patients with CAD. The emergence of mDC precursors in vulnerable plaques suggests their recruitment into atheromata as a possible reason for their decrease in blood. In contrast, no significant association of circulating pDC precursors with atherosclerosis was observed.