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Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.

Abstract This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987.

BackgroundWrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. ObjectiveThe aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. MethodsParticipants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. ResultsThe best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. ConclusionsAlgorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial RegistrationISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2021-050785

Advanced footwear technology featuring stack heights higher than 30 mm has been proven to improve running economy in elite and recreational runners. While it is understood that the physiological benefit is highly individual, the individual biomechanical response to different stack heights remains unclear. Thirty-one runners performed running trials with three different shoe conditions of 25 mm, 35 mm, and 45 mm stack height on an outdoor running course wearing a STRYD sensor. The STRYD running variables for each participant were normalized to the 25 mm shoe condition and used to cluster participants into three distinct groups. Each cluster showed unique running patterns, with leg spring stiffness and vertical oscillation contributing most to the variance. No significant differences were found between clusters in terms of body height, body weight, leg length, and running speed. This study indicates that runners change running patterns individually when running with footwear featuring different stack heights. Clustering these patterns can help understand subgroups of runners and potentially support running shoe recommendations.

Abstract Background Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed. Methods This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 $$\times$$ × 10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme. Results The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%. Conclusion Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment.

Abstract Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors

IntroductionThe clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings.MethodsHere, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data.Results and discussionThe results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and

Abstract Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual’s mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.

Abstract Background Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. Methods The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants’ strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson’s disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. Results The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. Conclusions The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. Trial registration: ISRCTN—12246987.

Early detection and treatment of cancers can significantly increase patient prognosis and enhance the quality of life of affected patients. The emerging significance of the tumor microenvironment (TME) as a new frontier for cancer diagnosis and therapy may be exploited by radiolabeled tracers for diagnostic imaging techniques such as positron emission tomography (PET). Cancer-associated fibroblasts (CAFs) within the TME are identified by biomarkers such as fibroblast activation protein alpha (FAPα), which are expressed on their surfaces. Targeting FAPα using small-molecule 18F-labeled inhibitors (FAPIs) has recently garnered significant attention for non-invasive tumor visualization using PET. Herein, two potent aryl-fluorosulfate-based FAPIs, 12 and 13, were synthetically prepared, and their inhibition potency was determined using a fluorimetric FAP assay to be IC50 9.63 and 4.17 nM, respectively. Radiofluorination was performed via the sulfur [18F]fluoride exchange ([18F]SuFEx) reaction to furnish [18F]12 and [18F]13 in high activity yields (AY) of 39–56% and molar activities (Am) between 20–55 GBq/µmol. In vitro experiments focused on the stability of the radiolabeled FAPIs after incubation with human serum, liver microsomes and liver cytosol. Preliminary PET studies of the radioligands were performed in healthy mice to investigate the in vivo biodistribution and 18F defluorination rate. Fast pharmacokinetics for the FAP-targeting tracers were retained and considerable bone uptake, caused by either 18F defluorination or radioligand accumulation, was observed. In summary, our findings demonstrate the efficiency of [18F]SuFEx as a radiolabeling method as well as its advantages and limitations with respect to PET tracer development.

Glioblastoma (GBM) is still an incurable tumor that is associated with high recurrence rate and poor survival despite the current treatment regimes. With the urgent need for novel therapeutic strategies, immunotherapies, especially chimeric antigen receptor (CAR)-expressing T cells, represent a promising approach for specific and effective targeting of GBM. However, CAR T cells can be associated with serious side effects. To overcome such limitation, we applied our switchable RevCAR system to target both the epidermal growth factor receptor (EGFR) and the disialoganglioside GD2, which are expressed in GBM. The RevCAR system is a modular platform that enables controllability, improves safety, specificity and flexibility. Briefly, it consists of RevCAR T cells having a peptide epitope as extracellular domain, and a bispecific target module (RevTM). The RevTM acts as a switch key that recognizes the RevCAR epitope and the tumor-associated antigen, and thereby activating the RevCAR T cells to kill the tumor cells. However, in the absence of the RevTM, the RevCAR T cells are switched off. In this study, we show that the novel EGFR/GD2-specific RevTMs can selectively activate RevCAR T cells to kill GBM cells. Moreover, we show that gated targeting of GBM is possible with our Dual-RevCAR T cells, which have their internal activation and co-stimulatory domains separated into two receptors. Therefore, a full activation of Dual-RevCAR T cells can only be achieved when both receptors recognize EGFR and GD2 simultaneously via RevTMs, leading to a significant killing of GBM cells both in vitro and in vivo.

Pheochromocytomas (PCCs) are rare but potentially lethal tumors that arise from the adrenal medulla. The clinical suspicion and diagnosis of PCC can be challenging due to the non-specific nature of signs and symptoms. In many patients, infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could lead to long-term symptoms including fatigue, headaches, and cognitive dysfunction. Here, we present the case of a patient incidentally diagnosed with an adrenal mass that proved to be a PCC after imaging was performed due to persisting complaints after coronavirus disease 2019 (COVID-19) infection. A 37-year-old male patient was referred to our center because of a right-sided inhomogeneous adrenal mass, incidentally found during a computed tomographic scan of the thorax performed due to cough and dyspnea that persisted after COVID-19 infection. Other complaints that were present prior to COVID-19 infection included profuse sweating, dizziness, exhaustion with chronic fatigue, and concentration difficulties. The patient had no history of hypertension, his blood pressure was normal, and the 24-h ambulatory blood pressure monitoring confirmed normotension but with the absence of nocturnal dipping. Plasma normetanephrine was 5.7-fold above the upper limit (UL) of reference intervals (738 pg/ml, UL = 129 pg/ml), whereas plasma metanephrine and methoxytyramine were normal at 30 pg/ml (UL = 84 pg/ml) and

Abstract Background To objectively assess a patient’s gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. Method To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson’s Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. Results The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts ( $$< 30$$ < 30 strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts ( $$> 50$$ > 50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications.

Prostate specific membrane antigen (PSMA) is an excellent target for imaging and treatment of prostate carcinoma (PCa). Unfortunately, not all PCa cells express PSMA. Therefore, alternative theranostic targets are required. The membrane protein prostate stem cell antigen (PSCA) is highly overexpressed in most primary prostate carcinoma (PCa) cells and in metastatic and hormone refractory tumor cells. Moreover, PSCA expression positively correlates with tumor progression. Therefore, it represents a potential alternative theranostic target suitable for imaging and/or radioimmunotherapy. In order to support this working hypothesis, we conjugated our previously described anti-PSCA monoclonal antibody (mAb) 7F5 with the bifunctional chelator CHX-A″-DTPA and subsequently radiolabeled it with the theranostic radionuclide 177Lu. The resulting radiolabeled mAb ([177Lu]Lu-CHX-A″-DTPA-7F5) was characterized both in vitro and in vivo. It showed a high radiochemical purity (>95%) and stability. The labelling did not affect its binding capability. Biodistribution studies showed a high specific tumor uptake compared to most non-targeted tissues in mice bearing PSCA-positive tumors. Accordingly, SPECT/CT images revealed a high tumor-to-background ratios from 16 h to 7 days after administration of [177Lu]Lu-CHX-A″-DTPA-7F5. Consequently, [177Lu]Lu-CHX-A″-DTPA-7F5 represents a promising candidate for imaging and in the future also for radioimmunotherapy.

Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects’ habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.

Efficient 18F-fluorination procedures for the production of radiopharmaceuticals are urgently needed to satisfy the increasing demand for clinical diagnostics using positron emission tomography (PET). However, the development of PET tracers is often a time-consuming and challenging process. This work examines the applicability of the recently described sulfur [18F]fluoride exchange ([18F]SuFEx) chemistry as a fast screening approach towards a number of clinically relevant PET tracer preparations. The preparation of a number of 18F-labeled compounds commenced with [18F]fluoride loading onto a quarternary methylammonium (QMA) cartridge, which was eluted with a methanolic solution containing a base, followed by solvent removal under reduced pressure. Thereafter, the radiolabeling precursors in MeCN were added to the reaction vessels, and allowed to react via [18F]SuFEx at room temperature for 5 min. The radiofluorination reactions were quenched by water dilution followed by C18 cartridge purification. The 18F-labeled products were isolated by elution from the cartridge with EtOH and the identities of the products were confirmed by radio-ultra high performance liquid chromatography (UHPLC). The optimized preparations of 18F-labeled prostate-specific membrane antigen (PSMA) inhibitor, Programmed death-ligand 1 (PD-L1) ligand, cyclooxygenase-2 inhibitor (COXIB), and Fibroblast activation protein alpha inhibitor (FAPI) were achieved with high non-decay corrected isolated activity yields (AY) of 33–57% (n = 12) and >95% radiochemical purity (RCP) in 25 min. The automated radiosynthesis procedures afforded the radiolabeled products in an unoptimized 8–15% AY (n = 5), with >95% RCP in 40 min. The ultra-fast [18F]SuFEx reaction permitted several structurally diverse 18F-labeled compounds for potential imaging to be rapidly achieved in excellent isolated AYs and high RCP. Presently, optimization of the automated radiosynthesis and biological assessment of the 18F-labeled products is underway.

COX-2 can be considered as a clinically relevant molecular target for adjuvant, in particular radiosensitizing treatments. In this regard, using selective COX-2 inhibitors, e.g., in combination with radiotherapy or endoradiotherapy, represents an interesting treatment option. Based on our own findings that nitric oxide (NO)-releasing and celecoxib-derived COX-2 inhibitors (COXIBs) showed promising radiosensitizing effects in vitro, we herein present the development of a series of eight novel NO-COXIBs differing in the peripheral substitution pattern and their chemical and in vitro characterization. COX-1 and COX-2 inhibition potency was found to be comparable to the lead NO-COXIBs, and NO-releasing properties were demonstrated to be mainly influenced by the substituent in 4-position of the pyrazole (Cl vs. H). Introduction of the N-propionamide at the sulfamoyl residue as a potential prodrug strategy lowered lipophilicity markedly and abolished COX inhibition while NO-releasing properties were not markedly influenced. NO-COXIBs were tested in vitro for a combination with single-dose external X-ray irradiation as well as [177Lu]LuCl3 treatment in HIF2α-positive mouse pheochromocytoma (MPC-HIF2a) tumor spheroids. When applied directly before X-ray irradiation or 177Lu treatment, NO-COXIBs showed radioprotective effects, as did celecoxib, which was used as a control. Radiosensitizing effects were observed when applied shortly after X-ray irradiation. Overall, the NO-COXIBs were found to be more radioprotective compared with celecoxib, which does not warrant further preclinical studies with the NO-COXIBs for the treatment of pheochromocytoma. However, evaluation as radioprotective agents for healthy tissues could be considered for the NO-COXIBs developed here, especially when used directly before irradiation.

Abstract Purpose The widespread use of 68Ga for positron emission tomography (PET) relies on the development of radiopharmaceutical precursors that can be radiolabelled and dispensed in a simple, quick, and convenient manner. The DATA (6-amino-1,4-diazapine-triacetate) scaffold represents a novel hybrid chelator architecture possessing both cyclic and acyclic character that may allow for facile access to 68Ga-labelled tracers in the clinic. We report the first bifunctional DATA chelator conjugated to [Tyr3]octreotide (TOC), a somatostatin subtype 2 receptor (SST2)-targeting vector for imaging and functional characterisation of SSTR2 expressing tumours. Methods The radiopharmaceutical precursor, DATA-TOC, was synthesised as previously described and used to complex natGa(III) and 68Ga(III). Competition binding assays of [natGa]Ga-DATA-TOC or [natGa]Ga-DOTA-TOC against [125I-Tyr25]LTT-SS28 were conducted in membranes of HEK293 cells transfected to stably express one of the hSST2,3,5 receptor subtypes (HEK293-hSST2/3/5 cells). First in vivo studies were performed in female NMRI-nude mice bearing SST2-positive mouse phaeochromocytoma mCherry (MPC-mCherry) tumours to compare the in vivo SST2-specific tumour-targeting of [68Ga]Ga-DATA-TOC and its overall pharmacokinetics versus the [68Ga]Ga-DOTA-TOC reference. A direct comparison of [68Ga]Ga-DATA-TOC with the well-established PET radiotracer [68Ga]Ga-DOTA-TOC was additionally performed in a 46-year-old male patient with a well-differentiated NET (neuroendocrine tumour), representing the first in human administration of [68Ga]Ga-DATA-TOC. Results DATA-TOC was labelled with 68Ga with a radiolabelling efficiency of > 95% in less than 10 min at ambient temperature. A molar activity up to 35 MBq/nmol was achieved. The hSST2-affinities of [natGa]Ga-DATA-TOC and [natGa]Ga-DOTA-TOC were found similar with only sub-nanomolar differences in the respective IC50 values. In mice, [68Ga]Ga-DATA-TOC was able to visualise the tumour lesions, showing standardised uptake values (SUVs) similar to [68Ga]Ga-DOTA-TOC. Direct comparison of the two PET tracers in a NET patient revealed very similar tumour uptake for the two 68Ga-radiotracers, but with a higher tumour-to-liver contrast for [68Ga]Ga-DATA-TOC. Conclusion [68Ga]Ga-DATA-TOC was prepared, to a quality appropriate for in vivo use, following a highly efficient kit type process. Furthermore, the novel radiopharmaceutical was comparable or better than [68Ga]Ga-DOTA-TOC in all preclinical tests, achieving a higher tumour-to-liver contrast in a NET-patient. The results illustrate the potential of the DATA-chelator to facilitate the access to and preparation of 68Ga-radiotracers in a routine clinical radiopharmacy setting.

Digital mobility assessment using wearable sensor systems has the potential to capture walking performance in a patient's natural environment. It enables monitoring of health status and disease progression and evaluation of interventions in real-world situations. In contrast to laboratory settings, real-world walking occurs in non-conventional environments and under unconstrained and uncontrolled conditions. Despite the general understanding, there is a lack of agreed definitions about what constitutes real-world walking, impeding the comparison and interpretation of the acquired data across systems and studies. The goal of this study was to obtain expert-based consensus on specific aspects of real-world walking and to provide respective definitions in a common terminological framework. An adapted Delphi method was used to obtain agreed definitions related to real-world walking. In an online survey, 162 participants from a panel of academic, clinical and industrial experts with experience in the field of gait analysis were asked for agreement on previously specified definitions. Descriptive statistics was used to evaluate whether consent (> 75% agreement as defined a priori) was reached. Of 162 experts invited to participate, 51 completed all rounds (31.5% response rate). We obtained consensus on all definitions ("Walking" > 90%, "Purposeful" > 75%, "Real-world" > 90%, "Walking bout" > 80%, "Walking speed" > 75%, "Turning" > 90% agreement) after two rounds. The identification of a consented set of real-world walking definitions has important implications for the development of assessment and analysis protocols, as well as for the reporting and comparison of digital mobility outcomes across studies and systems. The definitions will serve as a common framework for implementing digital and mobile technologies for gait assessment and are an important link for the transition from supervised to unsupervised gait assessment.

Abstract Background Novel theranostic options for high-risk non-muscle invasive bladder cancer are urgently needed. This requires a thorough evaluation of experimental approaches in animal models best possibly reflecting human disease before entering clinical studies. Although several bladder cancer xenograft models were used in the literature, the establishment of an orthotopic bladder cancer model in mice remains challenging. Methods Luciferase-transduced UM-UC-3LUCK1 bladder cancer cells were instilled transurethrally via 24G permanent venous catheters into athymic NMRI and BALB/c nude mice as well as into SCID-beige mice. Besides the mouse strain, the pretreatment of the bladder wall (trypsin or poly-L-lysine), tumor cell count (0.5 × 106–5.0 × 106) and tumor cell dwell time in the murine bladder (30 min – 2 h) were varied. Tumors were morphologically and functionally visualized using bioluminescence imaging (BLI), magnetic resonance imaging (MRI), and positron emission tomography (PET). Results Immunodeficiency of the mouse strains was the most important factor influencing cancer cell engraftment, whereas modifying cell count and instillation time allowed fine-tuning of the BLI signal start and duration – both representing the possible treatment period for the evaluation of new therapeutics. Best orthotopic tumor growth was achieved by transurethral instillation of 1.0 × 106 UM-UC-3LUCK1 bladder cancer cells into SCID-beige mice for 2 h after bladder pretreatment with poly-L-lysine. A pilot PET experiment using 68Ga-cetuximab as transurethrally administered radiotracer revealed functional expression of epidermal growth factor receptor as representative molecular characteristic of engrafted cancer cells in the bladder. Conclusions With the optimized protocol in SCID-beige mice an applicable and reliable model of high-risk non-muscle invasive bladder cancer for the development of novel theranostic approaches was established.

Barium-131 is a single photon emission computed tomography (SPECT)-compatible radionuclide for nuclear medicine and a promising diagnostic match for radium-223/-224. Herein, we report on the sufficient production route 133Cs(p,3n)131Ba by using 27.5 MeV proton beams. An average of 190 MBq barium-131 per irradiation was obtained. The SR Resin-based purification process led to barium-131 in high radiochemical purity. An isotopic impurity of 0.01% barium-133 was detectable. For the first time, radiolabeling of the ligand macropa with barium-131 was performed. Radiolabeling methods under mild conditions and reaction controls based on TLC systems were successfully applied. Small animal SPECT/ computed tomography (CT) measurements and biodistribution studies were performed using [131Ba]Ba(NO3)2 as reference and 131Ba-labeled macropa in healthy mice for the first time. Biodistribution studies revealed the expected rapid bone uptake of [131Ba]Ba2+, whereas 131Ba-labeled macropa showed a fast clearance from the blood, thereby showing a significantly (p < 0.001) lower accumulation in the bone. We conclude that barium-131 is a promising SPECT radionuclide and delivers appropriate imaging qualities in small animals. Furthermore, the relative stability of the 131Ba-labeled macropa complex in vivo forms the basis for the development of sufficient new chelators, especially for radium isotopes. Thereby, barium-131 will attain its goal as a diagnostic match to the alpha emitters radium-223 and radium-224.

To wavelet transform the electromyograms of the vastii muscles and generate wavelet intensity patterns (WIP) of runners. Test the hypotheses: 1) The WIP of the vastus medialis (VM) and vastus lateralis (VL) of one step are more similar than the WIPs of these two muscles, offset by one step. 2) The WIPs within one muscle differ by having maximal intensities in specific frequency bands and these intensities are not always occurring at the same time after heel strike. 3) The WIPs that were recorded form one muscle for all steps while running can be grouped into clusters with similar WIPs. It is expected that clusters might have distinctly different, cluster specific mean WIPs.The EMG of the vastii muscles from at least 1000 steps from twelve runners were recorded using a bipolar current amplifier and yielded WIPs. Based on the weights obtained after a principal component analysis the dissimilarities (1-correlation) between the WIPs were computed. The dissimilarities were submitted to a hierarchical cluster analysis to search for groups of steps with similar WIPs. The clusters formed by random surrogate WIPs were used to determine whether the groups were likely to be created in a non-random manner.The steps were grouped in clusters showing similar WIPs. The grouping was based on the frequency bands and their timing showing that they represented defining parts of the WIPs. The correlations between the WIPs of the vastii muscles that were recorded during the same step were higher than the correlations of WPIs that were recorded during consecutive steps, indicating the non-randomness of the WIPs.The spectral power of EMGs while running varies during the stance phase in time and frequency, therefore a time averaged power spectrum cannot reflect the timing of events that occur while running. It seems likely that there might be a set of predefined patterns that are used upon demand to stabilize the movement.

Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy of four algorithms, which calculate the stride velocity and stride length during running using data of an inertial measurement unit (IMU) placed in the midsole of a running shoe. The four algorithms are based on stride time, foot acceleration, foot trajectory estimation, and deep learning, respectively. They are compared using two studies: a laboratory-based study comprising 2377 strides from 27 subjects with 3D motion tracking as a reference and a field study comprising 12 subjects performing a 3.2-km run in a real-world setup. The results show that the foot trajectory estimation algorithm performs best, achieving a mean error of 0.032 ± 0.274 m/s for the velocity estimation and 0.022 ± 0.157 m for the stride length. An interesting alternative for systems with a low energy budget is the acceleration-based approach. Our results support the implementation decision for running velocity and distance tracking using IMUs embedded in the sole of a running shoe.