Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

BACKGROUND: Wrist-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. OBJECTIVE: The 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. METHODS: Participants 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. RESULTS: The 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. CONCLUSIONS: Algorithms 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 REGISTRATION: ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-050785.

Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking.

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.

Improving susceptibility of neuroendocrine tumors to radionuclide therapies: personalized approaches towards complementary treatments.

Radionuclide therapies are an important tool for the management of patients with neuroendocrine neoplasms (NENs). Especially [131I]MIBG and [177Lu]Lu-DOTA-TATE are routinely used for the treatment of a subset of NENs, including pheochromocytomas, paragangliomas and gastroenteropancreatic tumors. Some patients suffering from other forms of NENs, such as medullary thyroid carcinoma or neuroblastoma, were shown to respond to radionuclide therapy; however, no general recommendations exist. Although [131I]MIBG and [177Lu]Lu-DOTA-TATE can delay disease progression and improve quality of life, complete remissions are achieved rarely. Hence, better individually tailored combination regimes are required. This review summarizes currently applied radionuclide therapies in the context of NENs and informs about recent advances in the development of theranostic agents that might enable targeting subgroups of NENs that previously did not respond to [131I]MIBG or [177Lu]Lu-DOTA-TATE. Moreover, molecular pathways involved in NEN tumorigenesis and progression that mediate features of radioresistance and are particularly related to the stemness of cancer cells are discussed. Pharmacological inhibition of such pathways might result in radiosensitization or general complementary antitumor effects in patients with certain genetic, transcriptomic, or metabolic characteristics. Finally, we provide an overview of approved targeted agents that might be beneficial in combination with radionuclide therapies in the context of a personalized molecular profiling approach.

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device.

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.

Design, Synthesis, and Biological Evaluation of Small-Molecule-Based Radioligands with Improved Pharmacokinetic Properties for Imaging of Programmed Death Ligand 1.

Small molecules offer some advantages for developing positron emission tomography (PET) tracers and are therefore a promising approach for imaging and therapy monitoring of programmed death ligand 1 (PD-L1) positive tumors. Here, we report six biphenyl PD-L1 radioligands using the NODA-GA-chelator for efficient copper-64 complexation. These radioligands contain varying numbers of sulfonic and/or phosphonic acid groups, serving as hydrophilizing units to lower the log D7.4 value down to -4.28. The binding affinities of compounds were evaluated using saturation binding and a real-time binding assay, with a highest binding affinity of 21 nM. Small-animal PET imaging revealed vastly different pharmacokinetic profiles depending on the quantity and type of hydrophilizing units. Of the investigated radioligands, [64Cu]Cu-3 showed the most favorable kinetics in vitro. This was also found in vivo, with a predominantly renal clearance and a specific uptake in the PD-L1-overexpressing tumor. With further modifications, this compound could be a promising candidate for the imaging of PD-L1 in the clinical setting.

Preparation of 18F-Labeled Tracers Targeting Fibroblast Activation Protein via Sulfur [18F]Fluoride Exchange Reaction.

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.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases.

Introduction: The 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. Methods: Here, 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 discussion: The 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 <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning.

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 [Formula: see text] 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.

Comparative Saturation Binding Analysis of 64Cu-Labeled Somatostatin Analogues Using Cell Homogenates and Intact Cells.

The development of novel ligands for G-protein-coupled receptors (GPCRs) typically entails the characterization of their binding affinity, which is often performed with radioligands in a competition or saturation binding assay format. Since GPCRs are transmembrane proteins, receptor samples for binding assays are prepared from tissue sections, cell membranes, cell homogenates, or intact cells. As part of our investigations on modulating the pharmacokinetics of radiolabeled peptides for improved theranostic targeting of neuroendocrine tumors with a high abundance of the somatostatin receptor sub-type 2 (SST2), we characterized a series of 64Cu-labeled [Tyr3]octreotate (TATE) derivatives in vitro in saturation binding assays. Herein, we report on the SST2 binding parameters measured toward intact mouse pheochromocytoma cells and corresponding cell homogenates and discuss the observed differences taking the physiology of SST2 and GPCRs in general into account. Furthermore, we point out method-specific advantages and limitations.

Preclinical Characterization of the 177Lu-Labeled Prostate Stem Cell Antigen (PSCA)-Specific Monoclonal Antibody 7F5.

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.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

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 < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. CONCLUSIONS: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

Design and Biological Evaluation of Small-Molecule PET-Tracers for Imaging of Programmed Death Ligand 1.

Noninvasive molecular imaging of the PD-1/PD-L1 immune checkpoint is of high clinical relevance for patient stratification and therapy monitoring in cancer patients. Here we report nine small-molecule PD-L1 radiotracers with solubilizing sulfonic acids and a linker-chelator system, designed by molecular docking experiments and synthesized according to a new, convergent synthetic strategy. Binding affinities were determined both in cellular saturation and real-time binding assay (LigandTracer), revealing dissociation constants in the single digit nanomolar range. Incubation in human serum and liver microsomes proved in vitro stability of these compounds. Small animal PET/CT imaging, in mice bearing PD-L1 overexpressing and PD-L1 negative tumors, showed moderate to low uptake. All compounds were cleared primarily through the hepatobiliary excretion route and showed a long circulation time. The latter was attributed to strong blood albumin binding effects, discovered during our binding experiments. Taken together, these compounds are a promising starting point for further development of a new class of PD-L1 targeting radiotracers.

Specific and safe targeting of glioblastoma using switchable and logic-gated RevCAR T cells.

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.

Metastatic Pheochromocytoma and Paraganglioma: Somatostatin Receptor 2 Expression, Genetics, and Therapeutic Responses.

CONTEXT: Pheochromocytomas and paragangliomas (PPGLs) with pathogenic mutations in the succinate dehydrogenase subunit B (SDHB) are associated with a high metastatic risk. Somatostatin receptor 2 (SSTR2)-dependent imaging is the most sensitive imaging modality for SDHB-related PPGLs, suggesting that SSTR2 expression is a significant cell surface therapeutic biomarker of such tumors. OBJECTIVE: Exploration of the relationship between SSTR2 immunoreactivity and SDHB immunoreactivity, mutational status, and clinical behavior of PPGLs. Evaluation of SSTR-based therapies in metastatic PPGLs. METHODS: Retrospective analysis of a multicenter cohort of PPGLs at 6 specialized Endocrine Tumor Centers in Germany, The Netherlands, and Switzerland. Patients with PPGLs participating in the ENSAT registry were included. Clinical data were extracted from medical records, and immunohistochemistry (IHC) for SDHB and SSTR2 was performed in patients with available tumor tissue. Immunoreactivity of SSTR2 was investigated using Volante scores. The main outcome measure was the association of SSTR2 IHC positivity with genetic and clinical-pathological features of PPGLs. RESULTS: Of 202 patients with PPGLs, 50% were SSTR2 positive. SSTR2 positivity was significantly associated with SDHB- and SDHx-related PPGLs, with the strongest SSTR2 staining intensity in SDHB-related PPGLs (P = .01). Moreover, SSTR2 expression was significantly associated with metastatic disease independent of SDHB/SDHx mutation status (P < .001). In metastatic PPGLs, the disease control rate with first-line SSTR-based radionuclide therapy was 67% (n = 22, n = 11 SDHx), and with first-line "cold" somatostatin analogs 100% (n = 6, n = 3 SDHx). CONCLUSION: SSTR2 expression was independently associated with SDHB/SDHx mutations and metastatic disease. We confirm a high disease control rate of somatostatin receptor-based therapies in metastatic PPGLs.

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization.

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.

Enzymological Characterization of 64Cu-Labeled Neprilysin Substrates and Their Application for Modulating the Renal Clearance of Targeted Radiopharmaceuticals.

The applicability of radioligands for targeted endoradionuclide therapy is limited due to radiation-induced toxicity to healthy tissues, in particular to the kidneys as primary organs of elimination. The targeting of enzymes of the renal brush border membrane by cleavable linkers that permit the formation of fast eliminating radionuclide-carrying cleavage fragments gains increasing interest. Herein, we synthesized a small library of 64Cu-labeled cleavable linkers and quantified their substrate potentials toward neprilysin (NEP), a highly abundant peptidase at the renal brush border membrane. This allowed for the derivation of structure-activity relationships, and selected cleavable linkers were attached to the somatostatin receptor subtype 2 ligand [Tyr3]octreotate. Radiopharmacological characterization revealed that a substrate-based targeting of NEP in the kidneys with small peptides entails their premature cleavage in the blood circulation by soluble and endothelium-derived NEP. However, for a kidney-specific targeting of NEP, the additional targeting of albumin in the blood is highlighted.

Epigenetic drugs in somatostatin type 2 receptor radionuclide theranostics and radiation transcriptomics in mouse pheochromocytoma models.

Pheochromocytomas and paragangliomas (PCCs/PGLs) are catecholamine-producing tumors. In inoperable and metastatic cases, somatostatin type 2 receptor (SSTR2) expression allows for peptide receptor radionuclide therapy with [177Lu]Lu-DOTA-TATE. Insufficient receptor levels, however, limit treatment efficacy. This study evaluates whether the epigenetic drugs valproic acid (VPA) and 5-Aza-2'-deoxycytidine (DAC) modulate SSTR2 levels and sensitivity to [177Lu]Lu-DOTA-TATE in two mouse PCC models (MPC and MTT). Methods: Drug-effects on Sstr2/SSTR2 were investigated in terms of promoter methylation, mRNA and protein levels, and radiotracer binding. Radiotracer uptake was measured in subcutaneous allografts in mice using PET and SPECT imaging. Tumor growth and gene expression (RNAseq) were characterized after drug treatments. Results: DAC alone and in combination with VPA increased SSTR2 levels along with radiotracer uptake in vitro in MPC (high-SSTR2) and MTT cells (low-SSTR2). MTT but not MPC allografts responded to DAC and VPA combination with significantly elevated radiotracer uptake, although activity concentrations remained far below those in MPC tumors. In both models, combination of DAC, VPA and [177Lu]Lu-DOTA-TATE was associated with additive effects on tumor growth delay and specific transcriptional responses in gene sets involved in cancer and treatment resistance. Effects of epigenetic drugs were unrelated to CpG island methylation of the Sstr2 promoter. Conclusion: This study demonstrates that SSTR2 induction in mouse pheochromocytoma models has some therapeutic benefit that occurs via yet unknown mechanisms. Transcriptional changes in tumor allografts associated with epigenetic treatment and [177Lu]Lu-DOTA-TATE provide first insights into genetic responses of PCCs/PGLs, potentially useful for developing additional strategies to prevent tumor recurrence.

Fall Risk Prediction in Parkinson's Disease Using Real-World Inertial Sensor Gait Data.

Falls are an eminent risk for older adults and especially patients with neurodegenerative disorders, such as Parkinson's disease (PD). Recent advancements in wearable sensor technology and machine learning may provide a possibility for an individualized prediction of fall risk based on gait recordings from standardized gait tests or from unconstrained real-world scenarios. However, the most effective aggregation of continuous real-world data as well as the potential of unsupervised gait tests recorded over multiple days for fall risk prediction still need to be investigated. Therefore, we present a data set containing real-world gait and unsupervised 4x10-Meter-Walking-Tests of 40 PD patients, continuously recorded with foot-worn inertial sensors over a period of two weeks. In this prospective study, falls were self-reported during a three-month follow-up phase, serving as ground truth for fall risk prediction. The purpose of this study was to compare different data aggregation approaches and machine learning models for the prospective prediction of fall risk using gait parameters derived either from continuous real-world recordings or from unsupervised gait tests. The highest balanced accuracy of 74.0% (sensitivity: 60.0%, specificity: 88.0%) was achieved with a Random Forest Classifier applied to the real-world gait data when aggregating all walking bouts and days of each participant. Our findings suggest that fall risk can be predicted best by merging the entire two-week real-world gait data of a patient, outperforming the prediction using unsupervised gait tests (68.0% balanced accuracy) and contribute to an improved understanding of fall risk prediction.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation.

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.