Regorafenib, a multi-kinase inhibitor, has been widely used to treat patients with gastrointestinal stromal tumors (GIST) who failed the initial treatment with imatinib and sunitinib. This systematic review aims to demonstrate the efficacy and safety of regorafenib for patients with metastatic and/or unresectable GIST. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform this systematic review. We searched PubMed, Science Direct, and Cochrane databases to identify relevant articles based on predefined selection criteria. The implication of the search strategy results in 776 records from all databases. We excluded conference abstracts, discussion articles, case reports, case series, systematic reviews, and other observational non-intervention studies from the study, along with the articles published in languages other than English. After the screening and quality assessment, 10 studies were selected for final review - two randomized controlled trials and eight non-randomized prospective and retrospective review articles of intervention. Regorafenib improved the survival rates of patients after the failure of imatinib and sunitinib treatment, with an acceptable safety profile. Close monitoring of the patients may be needed to detect and manage the grade 4 or higher adverse events. Gastrointestinal stromal tumors (GIST) are the most common tumors of mesenchymal origin arising in the gastrointestinal (GI) tract. Epidemiological studies showed a variable incidence of GISTs depending on geographical distribution, ranging from 4.3 per million to 22 per million new cases annually. The most common primary site of the tumor is the stomach (55.6%), followed by the small intestines 31.8%) and the colon (6%) [.8% and tThe first-line treatment for metastatic and/or unresectable advanced GISTs is imatinib mesylate (IM). IM is a protein-tyrosine kinase inhibitor (TKI) that competitively inhibits C-KIT CD117), PDGFRA, and other tyrosine kinases. It has been approved for the treatment of advanced GIST with a recommended dose of 400mg/day taken by mouth for up to three years . However, PDGFRA,Most primary TKI resistance is due to a D842V mutation in the PDGFRA gene. A study of patients with this mutation treated with TKIs showed poor outcomes with the lowest rates of survival and no clinical benefit . SecondaAn ongoing trial assessing the alternating regimen of imatinib with regorafenib versus imatinib alone for the initial management of advanced GIST evaluates the possibility of overcoming or delaying the secondary TKI resistance problem. Early analysis of data showed no substantial difference in objective tumor response and progression-free survival rates between the experimental groups .Imatinib-resistant patients can be treated with the second-line multi-kinase inhibitor sunitinib malate (SM) with a recommended dose of 50mg once daily in six-week cycles with four weeks on medication and two weeks off until progression of the disease or unmanageable toxicity . INTRIGUAfter both imatinib and sunitinib fail, the other multi-kinase inhibitor regorafenib is indicated as a third-line treatment option. It targets different angiogenic -1, -2, and -3, and angiopoietin-1 receptor (TIE2)), oncogenic , and stromal (PDGFR and fibroblast growth factor receptor (FGFR)) TKs with noticeable survival benefits in patients with advanced GIST resistant to IM and SM . MultiplAlthough many studies showed the efficacy of regorafenib for managing metastatic and/or unresectable, advanced GIST resistant to first-line and second-line treatments, there is no definitive conclusion to prove the effectiveness and the safety of this medication. Therefore, this systematic review aims to show regorafenib's efficacy and safety for treating metastatic and/or unresectable GIST in patients after a failure of imatinib and sunitinib.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to conduct this study .Eligibility CriteriaWe identified specific inclusion criteria to select the studies relevant to our systematic review. Studies evaluating the role of regorafenib in metastatic and/or unresectable advanced gastrointestinal tumors after the failure of imatinib and sunitinib were selected as the primary target of our research. We selected randomized and non-randomized studies and intervention review articles from different medical centers for a systematic review. We defined the study participants' age to be 18 years and above. Articles published in languages other than English were excluded, along with conference abstracts, discussion articles, case reports, case series, systematic reviews, and other observational non-intervention studies.Databases and Search StrategyWe systematically searched the PubMed, ScienceDirect, and Cochrane Library databases to identify the relevant articles and include them in our systematic review. The last date for the search was May 17, 2022. We used keywords and Medical Subject Headings (MeSH) terms to create the search strategy for all databases, as shown in Table The references found in the databases were organized and alphabetized by Microsoft Excel , and duplicate articles were removed. We initially screened the articles by titles and abstracts to rule out irrelevant articles. Subsequently, we conducted a full-text review of the remaining manuscripts to select the studies for further quality assessment.Risk of Bias AssessmentAll selected articles were evaluated for the risk of bias and quality assessment using tools based on the study designs: Cochrane Collaboration Risk of Bias Tool (CCRBT) for randomized controlled trials (RCTs) and Risk of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tool for non-randomized trials (N-RCT) and review articles ,23. We eData Collection and AnalysisWe developed a standardized template using Microsoft Excel  to extract the following data from the selected studies: first author name, publication year, recruitment time, sample size, follow-up duration, treatment regimen, reported adverse events, and outcomes. We performed a descriptive analysis of data to show the efficacy of regorafenib in advanced GIST after the initial failure of imatinib and sunitinib.Outcome MeasuresThe primary outcome of our systematic review was the evaluation of the efficacy and safety profile of regorafenib in advanced GIST. The outcome measures for this systematic review were survival rates , event-free survival (EFS), progression-free survival (PFS)) as well as response measures, including complete response (CR), partial response (PR), and stable disease (SD) rate if applicable.ResultsLiterature Search and Selection of StudiesWe searched three databases  for all potentially relevant articles for this systematic review. The implication of a predetermined search strategy revealed 776 records. After removing duplicates, all remaining records were initially screened by reading the title and abstract. A secondary review was done by reading the full-text manuscripts to rule out irrelevant articles and studies lacking the required data for this systematic review. We created a PRISMA flowchart of study identification shown in Figure Risk of BiasAfter selecting relevant articles, we evaluated the quality of included manuscripts to assess the risk of bias. Tables Study CharacteristicsThis systematic review revealed two RCTs and eight prospective and retrospective studies of interventions showing the efficacy and safety of regorafenib in treating metastatic and/or unresectable advanced gastrointestinal stromal tumors after the failure of imatinib and sunitinib regimens. Demetri et al. (2013) conducted a phase III randomized controlled trial which became the cornerstone for the approval of the drug by the FDA .The standard treatment regimen in all studies was the same: regorafenib 160 mg/daily in four-week cycles with three weeks on medication and one week off of it. Nannini et al. (2021) assessed the effect of personalized dose reductions and temporary interruptions to control and prevent drug-related adverse events . The secSurvival RatesIn phase III randomized controlled trial conducted by Demetri et al. (2013), the median PFS was 4.8 months compared to the placebo group, where it was 0.9 months (Hazard ratio (HR): 0.27, 95% confidence interval (CI) 0.19-0.39; p<0.0001). There was no statistically significant difference in OS between the two groups  . The secGeorge et al. (2013) showed 10 months of median PFS  in a multicenter Phase II trial . Long-teResponse RatesClinical response to the treatment was assessed by response evaluation criteria in solid tumors (RECIST) 1.1 criteria, including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) . DemetriThere was a 75% clinical benefit (CR+PR+SD) (95% CI: 61%-91%) in a multicenter Phase II trial by George et al. (2013), with an SD of 63.4% and PR of 11.6% . Ben-AmiDiscussionThis study aims to demonstrate the efficacy and safety of regorafenib in patients with advanced metastatic and/or unresectable gastrointestinal tumors after the failure of initial imatinib and sunitinib therapy. We will discuss the survival rates and treatment-related adverse events of regorafenib in selected studies and mention the limitations of our systematic review to highlight potential bias associated with the data.Randomized Controlled TrialsWe identified two randomized controlled trials, one of which compared regorafenib to placebo in a target population. Demetri et al. proved the superiority of regorafenib over placebo in a phase III GRID study of 199 patients. Although there was no complete response to the regorafenib treatment, they showed a clinically significant tumor control rate with a disease control rate (DCR) of 52.6% compared to 9.1% in the placebo group . SubgrouKang et al. investigated the difference between outcomes in patients treated with avapritinib versus regorafenib . There wNon-Randomized StudiesPhase II prospective study showed statistically significant improvement in clinical benefit rates (CBR), the most prominent in patients with C-KIT exon 11 mutations and SDH-deficient subtypes. There is an 83% metabolic response in patients with exon 11 mutations . ConversThe survival rates reported in a prospective single-center study of Taiwanese patients showed similarity with the results of the other studies on the Asian population, including Korean and Japanese patients ,31,35.Overall, survival rates in the randomized studies were comparable. However, non-randomized studies showed higher than expected survival rates, probably because of confounding factors, such as ethnicity, follow-up duration, age of the participants, or study designs. On the other hand, response rates reported in selected studies were diverse, mainly because of the different follow-up periods.ToxicityOne of the main reasons for treatment discontinuation in studies of regorafenib is treatment-related toxicities. The most common adverse event (AE) noted in regorafenib studies was palmar-plantar erythrodysesthesia (PPE), more commonly known as hand-foot skin (HFS) reaction comprising 16%-91% of reported grade 3 and more adverse events in studies included in our systematic review, the lowest noted in a study of continuous low-dose regorafenib treatment . A meta-Overall, regorafenib has a sustainable toxicity profile which can be managed by decreasing the dosage, supportive measures, or temporary interruptions in the treatment.LimitationsThis systematic review has some limitations that are worth mentioning. We excluded articles published in languages other than English in this study. We performed a descriptive analysis of data from included studies. Therefore, there is a need for a comprehensive meta-analysis of survival rates and toxicity. The small number of randomized controlled trials evaluating the efficacy of regorafenib and the small sample size in selected studies are the most important limitations of our systematic review.In conclusion, studies included in our systematic review demonstrated a beneficial impact of regorafenib on survival rates of patients with advanced gastrointestinal stromal tumors after the failure of imatinib and sunitinib treatment. Dose adjustments can be crucial during the therapy to improve the tolerability of the medication. The toxicity profile of the regorafenib is manageable, with hand-foot skin reaction and hypertension being most commonly seen. Educating patients about the possibilities of these reactions and the need to seek medical attention when they appear can favor the continuity of the treatment. We also recommend performing molecular testing of tumors to identify specific mutations that can impact patients' future management.There is an implication for future research to conduct randomized controlled clinical trials comparing regorafenib with other medications to demonstrate the superiority of the drug in treating advanced gastrointestinal stromal tumors. The issue of drug resistance persists, so future studies should investigate the detailed mechanisms of primary and secondary resistance and conduct trials with newly developed medications to overcome the problem of TKI resistance.


MicroRNAs have been shown to be able to modulate the tumor microenvironment and the immune response and hence could be interesting biomarkers and therapeutic targets in immuno-oncology; however, dedicated analysis tools are missing. Here, we present a user-friendly web platform MIO and a Python toolkit miopy integrating various methods for visualization and analysis of provided or custom bulk microRNA and gene expression data. We include regularized regression and survival analysis and provide information of 40 microRNA target prediction tools as well as a collection of curated immune related gene and microRNA signatures and processed TCGA data including estimations of infiltrated immune cells and the immunophenoscore. The integration of several machine learning methods enables the selection of prognostic and predictive microRNAs and gene interaction network biomarkers.https://mio.icbi.at, https://github.com/icbi-lab/mio and https://github.com/icbi-lab/miopy.Bioinformatics online. With the advent of cancer immunotherapies, the identification of modulators of the tumor immune microenvironment and the search for predictive biomarkers came into focus. There is growing evidence that cancer-derived microRNAs may play such a role and are involved in the cancer immune escape . A central component in MIO is the collection of published immune-related gene and microRNA signatures e.g. immune checkpoints and beyond the compendium of a variety of computational tools MIO provides a comprehensive searchable data/knowledge base for immuno-oncology data in the context of microRNA-gene targeting. For example, in cancer immunology and in the study of the tumor microenvironment, cell type-related marker genes or microRNAs as well as estimated immune cell infiltrates into the tumor and related microRNAs are of particular interest.The integration of 40 target prediction tools  includinFour main types of analyses with respective filtering and visualization options are available .We use linear, nonlinear or rank-based correlation analysis methods as well as regularized regression models   effects of a microRNA and its target gene on survival.MIO provides also the possibility to predict synthetic lethal microRNA target genes based on a pre-calculated list of synthetic lethal gene pairs . A Kaplan–Meier survival analysis can be performed by dividing patient groups into high versus low microRNA or gene expression by a selected percentile such as median or optimized dichotomization.MIO allows to identify prognostic biomarker signatures and ranking of features  based on presence in the top candidates during a k-fold cross-validation, ROC curve and principal component analysis with selected features are provided.In order to identify predictive (network) biomarkers between two levels of a selected clinical parameter (e.g. response versus non-response) the feature selection process is very similar as for identifying prognostic features using a number of algorithms and ensemble methods such as adaptive boosting (A specific use case is the identification of microRNAs targeting immune modulators or checkpoints. As most information is available for microRNAs targeting PD-L1 (CD274), we compared these results with those generated using MIO for different cancer entities (Use Case S1). In this context, we identified the hsa-miR-150-5p/CD276 ratio as one of the most prognostic in lung adenocarcinoma (TCGA-LUAD), whereby hsa-miR-150-5p is targeting CD276  and is inversely associated with overall survival (Use Case S2). Deficient or down-regulated genes of the antigen processing and presentation machinery have been shown as predictive marker for response to cancer immunotherapy. Therefore, we studied how these genes are affected by microRNAs (Use Case S3). In order to identify a microRNA signature predictive of the microsatellite instability subtype in colorectal cancer, we performed feature selection and classification analyses in the TCGA cohort and validated the results in the CPTAC-2 cohort (Use Case S4). In Use Case S5, we could identify microRNAs potentially targeting genes, which are synthetic lethal to immune (therapy) essential genes indicating cancer therapeutic vulnerabilities. Finally, in Use Case S6, we could associate microRNAs—e.g. hsa-miR-223-3p—with the IPS and estimated infiltrated immune cells in the ovarian cancer cohort (TCGA-OV).There are some limitations as several approaches that are also relevant to immuno-oncology applications are not currently implemented, for instance, a full microRNA-gene network (including transcription factors)  [P34783], the National Bank of Austria (OeNB) [18279 to H.H.], and the ERASMUS+ program from the University of Valencia to P.M.Conflict of Interest: none declared.btac366_Supplementary_MaterialClick here for additional data file.


Thyroid carcinomas are detected with increasing incidence rates in many countries. This is mainly contributed to more frequent diagnostic procedures around the world.Most of the tumors are papillary and follicular thyroid carcinomas, followed by non-follicular medullary thyroid carcinomas and anaplastic carcinomas. The clinical spectrum shows a wide range from sometimes almost benign disease in patients with papillary microcarcinomas, especially in the elderly and when detected incidentally, to the rare cases of anaplastic thyroid carcinomas and primary squamous cell carcinoma of the thyroid with an unfavorable outcome.Hansen et al. investigated the role of the Leu56Met variant in the RET gene by clinical follow up of a Danish RET Leu56Met cohort. Using predefined criteria none of the patients exhibited evidence of MEN 2. With an allele frequency of 0.59% the Leu56Met variant suggests that it is a common variant in the population with no pathogenetic importance.For some types of thyroid carcinomas genetic causes are best known. 25% of medullary thyroid carcinomas (MTC) arise in the context of multiple endocrine neoplasia type 2 A and B. The knowledge of causative genetic alterations on cancer development has led to a strong improvement in the survival of patients with MEN-2 disease. A genotype-phenotype association showing different outcomes with specific mutations has led to individualized therapy strategies with prophylactic surgery in many cases, detected by family screening. PTEN-Hamartoma-tumor-Syndrome such as Cowden-syndrome. Further entities include Carney-complex, Werner-Syndrome and DICER-1-syndrome. Several single genes are involved or suspected to be to be involved in the pathogenesis of NMTC.Less frequent are genetic causes of non-medullary thyroid carcinomas (NMTC). About 3% of NFTC are estimated to be of hereditary origin. Most of these tumors arise in the context of syndromic diseases such as familiary adenomous polyposis coli and Many studies in recent years gave insight in the important role of genetic alterations in the development and growth progression in papillary thyroid carcinoma (PTC).V600E mutation is the most frequent mutation found in papillary thyroid carcinoma and associated with a poorer prognosis, lymph node involvement, extrathyroidal tumor expansion and distant metastases. Due to its high kinase activity secondary genetic alterations such as mutations in phosphoinositide 3-kinase-Akt serine/threonine kinase (PI3K-AKT) pathway may occur leading to more aggressive tumors and an unfavorable clinical outcome for the patients.Genetic alterations often involve the mitogen-activated protein kinase (MAPK). BRAF as part of the RAF/MAPK pathway is an effector of cell proliferation. Many BRAF mutations have been described in papillary thyroid carcinomas. The BRAFParvathareddy et al. could confirm that BRAFV600E mutations are the most frequent mutations in papillary microcarcinomas. They could detect BRAFV600E mutations in 45.7% of their cases (84 of 184 patients in a middle east population). Beside BRAF V600E mutations telomerase reverse transcriptase (TERT) promoter mutations play a relevant role in the aggressiveness of tumor growth in papillary thyroid carcinoma. TERT promoter mutations were detected in the series of Parvathareddy in 8.7% cases and were significantly associated with distant metastases and shorter metastasis-free survival in multivariate analysis.Gao et al. investigated if dysregulation of miRNA plays a role in PCT wit BRAF mutations. In PTC an upregulation of miR-222-3p has already been demonstrated. The actual study showed that miR-222-3p led to more aggressive clinical manifestations of PTC promoted via the snail transcription factor.Liu et al. used expression profiles of mRNAs and miRNAs in addition to clinical data of PTC patients. They could apply a two-RNA model (IPCEF1 and hsa-mir-486-5p) associated with the prognosis of RAI-therapy. Together with their RNA-based risk score calculated on the Cox coefficient of the individual RNA and clinical parameters (age at diagnosis and tumor stage) the authors were able to demonstrate high precision in predicting the RAI response of PTC patients.With surgery and radioiodine therapy most of the patients with differentiated thyroid carcinoma have a favorable outcome. But in radioiodine refractory carcinomas overall survival and progression-free survival time is limited. Clinical management of patients can be improved if the response to radioactive iodine can be detected early. Rearranged- during transfection (RET) kinase as a protooncogene can also be involved in tumorigenesis of thyroid carcinoma. Besides its role in hereditary forms of medullary thyroid carcinoma, chromosomal RET rearrangements are found almost only in PTC . Ret/PTCV600E and others  data base (2004-2015) to compare differences in characteristics between anaplastic thyroid carcinoma (ATC) and primary squamous cell carcinoma of the thyroid (PSCCTh) and establish prognostic models. For patients with PSCCTh (n=124) prognostic factors influencing the cancer-specific survival were age, radiotherapy, multiple primary tumors, and surgery.For ATC patients (n=1164) these factors were different comprising age, sex, radiotherapy, chemotherapy, surgery, multiple primary tumors, marital status, and distant metastasis. This may lead to the need of different clinical treatment and management in this group of patients.Conclusively, the understanding of genetic alterations in thyroid carcinoma is growing fast, leading to new treatment options for our patients.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Detached off-grids, subject to the generated renewable energy (RE), need to balance and compensate the unstable power supply dependent on local source potential. Power quality (PQ) is a set of EU standards that state acceptable deviations in the parameters of electrical power systems to guarantee their operability without dropout. Optimization of the estimated PQ parameters in a day-horizon is essential in the operational planning of autonomous smart grids, which accommodate the norms for the specific equipment and user demands to avoid malfunctions. PQ data for all system states are not available for dozens of connected / switched on household appliances, defined by their binary load series only, as the number of combinations grows exponentially. The load characteristics and eventual RE contingent supply can result in system instability and unacceptable PQ events. Models, evolved by Artificial Intelligence (AI) methods using self-optimization algorithms, can estimate unknown cases and states in autonomous systems contingent on self-supply of RE power related to chaotic and intermitted local weather sources. A new multilevel extension procedure designed to incrementally improve the applicability and adaptability to training data. The initial AI model starts with binary load series only, which are insufficient to represent complex data patterns. The input vector is progressively extended with correlated PQ parameters at the next estimation level to better represent the active demand of the power consumer. Historical data sets comprise training samples for all PQ parameters, but only the load sequences of the switch-on appliances are available in the next estimation states. The most valuable PQ parameters are selected and estimated in the previous algorithm stages to be used as supplementary series in the next more precise computing. More complex models, using the previous PQ-data approximates, are formed at the secondary processing levels to estimate the target PQ-output in better quality. The new added input parameters allow us to evolve a more convenient model form. The proposed multilevel refinement algorithm can be generally applied in modelling of unknown sequence states of dynamical systems, initially described by binary series or other insufficient limited-data variables, which are inadequate in a problem representation. Most AI computing techniques can adapt this strategy to improve their adaptive learning and model performance. Fully self-producing power units must control the PQ in a mid-time horizon considering the optimal utilization of RE sources to minimize unwanted events and signal spikes in the electrical components. Detached smart houses must stabilize their energy production by using a supporting accumulator storage to compensate for uncertainty and fluctuations in RE power depending on local potential. Stable energy supply determines the functionality of electrical equipment and appliances according to PQ monitoring much more than in conventional grids. Novel AI techniques can be used in the estimation of PQ to optimize the parameters in scheduling active load appliances in the next switching-time states, ensuring PQ acceptable ranges for off-grid systems whose behavior is impossible to define exactly. Complete system states are unknown for varying environments and operating plans, determined by the external RE supply limitations and characteristics of the applied system components. Future states of autonomous systems must be estimated and resolved in unknown cases, which is unusual in standard grid networks. Optimal consumer load (re)scheduling according to the peak RE power supply, using a time-shifting plan, can be practiced in real operation modes using the estimated PQ and RE data. Real-time load planning or PQ monitoring, considering predicted RE supply and backup state storage, is necessary to compensate for power production instabilities in user demands and secure failure-free operation of autonomous grids2. Specific load appliances in various sequenced combinations of switching time modes affect the PQ and subsequent system states3. Automatic detection of PQ events and anomalies with full self-control in user-adaptable power demand scenarios is also important from the point of integration of distributed RE supply in complex grids4. Active management of power demand enables efficient detection and reduction of undesirable PQ events in load planning and RE utilization5. Several publications only attempt to estimate PQ variances and irregularities on the daily or midterm horizon, though their solutions are inevitable in operation and early warning in smart systems. Detection of PQ distortion waves is fundamental in the regulation of RE power consumption and optimization of load scenarios6. A unit based on 2-D dynamic programming can recognize an acceptable variation range of power demand and battery state of charge in a longer-term horizon, to secure efficient operation of each module.The characteristics of household appliances must be evaluated in the preparation of load plans for the RE potential and various (re)charge states7. Smart meters allow demand operators to apply PQ indices in residential house distribution networks, based on the computing of probabilistic intervals for models with harmonic data distortion8. Remote microgrids can be represented by clustering techniques, considering the distribution distance and source potential, to be supplied with additional information to improve stability and efficiency in energy utilization and shared power exchange. Hierarchical strategies based on model decentralization provide distributed control at smart levels with power backup and exchange in units through efficient energy management9. The measurement information approach in modeling optimizes and corrects the energy consumption plan in building networks. The voltage index can determine the overall energy efficiency in smart buildings in the analysis and quantification of PQ signals10. Two key types of irregularities can be identified in analyzing the solar production capacity of low-power PV grids: certain and uncertain events; considering deterministic, statistics, or probabilistic signal processing11.Neuro-Fuzzy controllers can determine the operation modes of components in microgrids in a shorter time, by evaluating the real conditions and constraints of the system12. Clustering techniques are usually used to partition PQ data into main groups in cases if information (of the desired output) is available. Hybrid AI approaches fuse various data pre-processing and optimization techniques to identify eventual PQ anomalies13. Finite element control can eliminate wave distortion and noise in harmonic signals to be further applied in conditioned prediction14. Photovoltaic (PV) panels unbalance the harmonics of PQ signals by increasing the temporal variability. Probabilistic intervals can be analyzed to re-assess the uncertainty in PV power supply at different levels of RE production and load schemes15. Neighbor adaptive sorting recognizes block and merge amplitudes in signals to locate potential sources of PQ disturbances. Data-driven modeling can transform and segregate signals into categorical levels to identify errors related to environment conditions16. Downsampling, based on empirical mode decomposition, can be used to calculate the characteristics of the data to better detect PQ-distorted signals17. A visual attention technique can select data feature indices, in the fusion of PQ detection and scale analysis, using processed images of the original signals18. Adaptive resolution based on the S transform can be used to analyze the PQ data in the frequency-time domain. The multi-objective window optimizer improves the performance of PQ models in relation to the maximum concentration energy19. Principal component analysis (PCA), applied together with K-means clustering, can be integrated into a search algorithm for atypical patterns in PQ variances in short times20. Statistical distribution characteristics of data can be computed according to sparse-envelope Kurtosis index maxima. The extracted features are then applied in a kernel classifier that uses a functional link network with random vectors21. Atomic decomposition algorithms refine PQ data to produce physically clear signals. The convolutional neural network model can then be used to select subspaces in each atomic predictor to reduce the search for computational and optimization time22. The combination sequence of power load appliances, active in the off-grid, can be defined as binary data. No additional PQ values are available for the next grid states in load planning to be processed in the prediction. However, the PQ-training data can be measured without problem, but the unseen load scheme input series do not comprise any. The learning process with only input of binary codes ‘1/0’ is generally inefficient in modelling real-world data and does not allow adequate pattern representation for unknown load sequenced combinations (applied as input to the model in the new estimates). The proposed multistage procedure selects and increases progressively PQ inputs, used to develop more complex structural models in training, able to approximate the unknown load PQ data in better detail. Approximate PQ data for selected input parameters, correlated with the load codes, are calculated before the consequent processing in the next estimate stage to obtain better quality in the model target output. This approach enables us to train PQ-data samples, added to the input vector, including initial load codes, to process the approximate PQ series as model input in the next load estimates. This quality improvement algorithm produces more capable models in each subsequent iteration step to obtain a better approximation of the PQ data, determined by the unknown load series in detached systems. It is applicable to improve solutions of the over-viewed publication methods. Multistep PQ prediction was performed and tested on data sets recorded in an experimental off-grid without producing RE, as PV panels were disconnected, so weather conditions are irrelevant in this study. Several types of single-model recent AI machine learning and statistics methods using no data preprocessing, e.g. transformation, decomposition, etc., were compared and evaluated in the modelling of PQ parameters. Overall data measurements for all possible combination load sequences allow adequate training and testing in the proposed scheme.Optimization, identification, and real-time monitoring of PQ can be done by hybrid AI algorithms usually fusing the two basic groups of data preprocessing and machine learning methods to detect/predict the sources of PQ events which are classified into stationary/nonstationary irregularities in general Fig. . The phaStarting with the low-level models, applying the input binary data only, to code active load components, as PQ-data are unknown in the next prediction state of a schedule plan.Binary codes of active load consumers, inadequate in the representation of real data patterns, define the model input in the first stage to be extended with PQ approximates.The progressively expanded model input vector (with selected PQ-parameters) enables to evolve more advanced model structures in each iteration to better estimate the target PQ output.A more complex representation of the problem allows us to train data samples of the added PQ inputs and subsequent processing of the PQ replacement series to compute the target output.The step-by-step expanded AI model vector enables the use of approximate support data, where the real-world series are unknown in the next states.The designed procedure improves step by step the accuracy of AI self-optimizing models evolved in several hierarchical levels. Its key characteristics are listed below.The detached off-grid system has two connected PV panels, each of which can induce 2 kWph in peak power. Photovoltaic power was not supplied at the time of measurement experiments to ensure the same condition (100$ fully recharged battery) in modelling deterministic states for all the simulated load combination sequences. The power load of three combined turn-on electrical appliances of 10 types in all, including: mower, drill, heating, TV, fridge, lighting, kettle, boiler, microwave, switching; is supported from the backup Ni–Cd accumulator bank , Power Factor (PF), Power (P), Frequency (Freq), Short-Term Flicker Perceptibility (Pst) and THDU  data were measured and averaged in 1 min series in the off-grid for all attached experimental equipment load to be automatically stored by a control software in the database. The PQ parameters were after-estimated by AI models for various unlearned 12-min. switching-time power load of 3-attached electrical appliances, not considering the recharge time in 18 min, to simulate real operational conditions in a smart microgrid [H].6. The most important PQ parameters are calculated from the relative amplitude and fundamental frequency waves23. Converters in energy transmission and distribution with non-linear loads can induce sag, swell, impulsive, or transient oscillatory harmonics, and signal interruption24.The PQ standards define the power characteristics to avoid system failures, dropout, and loss in performance. The European standards: EN 50160 and EN 61000 normalize the technical conditions of PQ in power systems, equipment compatibility, and measurement protocols. Classification and detection of PQ events is possible in relation to the data quantity thresholds concerning the system states and nominal time. PQ abnormalities in nominal states can corrupt or limit the desired life of electrical equipment and appliances. They represent unacceptable very short-time fluctuations or ramp-spikes in PQ-parameters in comparison with the standard wave signals. The abnormal magnitudes are largely induced by switching components in a sequenced load, which is obvious in detached off-grid systems dependent on RE. Incompatible control modules can produce variations in harmonics and flickers with unbalanced load and frequency regulationDifferential learning employs self-organization in node-by-node expanded synchronized structure of Polynomial Neural Network (PNN) in evolvement modular components. Each PNN selected module can be added to the output sum Fig. , solvingf(t), f’(t), …, fn)((t) are continuous originals in the interval <0+ , ∝> , p and t are complex and real valued variables.Node-defined sub-PDEs are polynomial-converted into the Laplace form using the adapted functional derivative transformation of Operator Calculus (OC) .1\documeThe inverse L-transform  is appli25.u(x) = f is the searched n-variable separable function; a, b, c, d, … are coefficients and uk are sum series of u function.1, x2—inputs.Complex Neural Networks (CNN) apply the Euler data representation which can be used in solving node sub-PDEs. Differential Polynomial Neural Network (D-PNN) partitions the n variable PDE of the general k-th order of  into a s26.yi are particular sub-PDE solutions of node searched functions, arctg(x2/x1) is the ϕ phase of 2-inputs x1, x2; sig is a sigmoidal transform of square variables; ai, bi, and w – member parameters and term weights.This procedure can be efficiently used in modelling complex and high-dynamic unknown systems. PNN performs feature extraction from dozens of variables. Each added node searches for the optimal 2-inputs to solve a pre-defined sub-PDE in the progressively expanded PDE modular model. The selected node components , i.e. de27. D-PNN gradually forms a binary PNN tree structure. New nodes are added in the following layers to expand the synchronized sum series model with the selected PDE modules and obtain a better approximation of the desired output28.Gradual modular extension and error minimization can lead, according to Goeddel’s theorem of incompleteness, to optimal pattern model in adaptation to input–output data samples. As D-PNN applies the L transformation to PDE derivatives and a self-selection of 2-input nodes, no signal pre-processing or feature extraction is needed29.Blocks group possible PDE-solutions in the nodes to form simple neurons or composite terms (CTs), formed in relation to variables of the previous layer. The best PDE-component is inserted into the network sum output. Active block nodes are processed in the iterative optimization process to select a neuron or CT that best contributes to the D-PNN output and can be included or removed from / from the model Fig. . Optimalforms node-by-node the optimal tree structure to parse the n-variable PDE into low-order sub-PDEs of 2-inputs using the adapted OC procedure.produces sub-PDE modules from selected basic rational, power, or periodic OC substitution functions in selected tree PNN nodes.Expands step-by-step the initial ‘zero’ PDE model using Goedel's theorem, progressively increasing its complexity in minimizing the defined criteria.uses a new designed optimization framework that inserts the most valuable PDE components by removing redundant ones from previous layers to synchronize their active application, reducing modular model complexity.Features of the neuro-inspired designed D-PNN. It:Binary tree—The optimal branches are followed in the tree structure from the root to leaf nodes that define the output variable. The predictor values are checked in each node to select which one of the 2 branches to follow. The final response is assigned by the value of an end leaf node —Support vectors are data points placed outside the output interval determined by the ε-parameter for all training points x as flat as possible. Kernel functions determine the nonlinear transformation applied to input data prior to SVM learning [B].Gaussian Process Regression (GPR)—The model response is calculated as a probability distribution function over the definition space. The GPR basis defines the exact form of a prior mean function. The kernel functions determine the input correlations in the output calculation as a function of distance [D].Ensemble Boosted or Bagged Tree (EBT)—EBT ensemble models combine results from a set of single weak tree-learners by applying least-squares boosting, bootstrap aggregating or bagging.The Matlab Statistics and Machine Learning Toolbox (SMLT) for regression involves AI soft computing AI and standard mathematical methods. The best operating models were recognized by considering the minimal test error criteria at each prediction PQ level [A]. SMLT includes:The “Regression Learner App” allows the import of data from columns in a.TXT file and training all or one of the selected methods to form the model applied in testing [E]. It is possible to select some training or testing parameters, e.g. k-fold cross-validation or percentage of data, but all SMLT methods automatically self-optimize model development [A]. After training, a model with minimal testing error is highlighted in a result list to be exported and applied in PQ estimation with unseen data Fig.  [F]. No The multistage PQ model development uses the initial binary defined codes “0” or “1” of the load combinations of 10 attached household equipment in the first step. PQ data records are not at disposal in this starting phase for the selected appliances of unknown load sequences Fig. . The decAll target PQ outputs of U, THDU P, PF, Pst, and Freq are modelled using 2/3 of the data samples in the training. The resulting final model is tested with the reserved unlearned 1/3 part of the rest data to compute the output PQ series Fig. . These aMeasurement of data for correlated PQ parameters can extend the input vector in their next sampled training. After testing, the last predicted PQ approximates, which are missing in the unknown load processing sequences, can be applied to the new developed extension models. Additional PQ inputs allow us to form the next generation models in a more compact and representative structure to get better PQ approximation quality Fig. . The algCross-validation of K-folds is a well-known approach applied in the effective testing of AI models. Random subsets of K-folds of the same size are partitioned from the training set. Each part of the testing subset, named fold, is used in the k-cross validation of the developed model, opposite to the rest of the k-1 folds, which means subsets, used in training. This verification strategy is repeated in k-cross validation cycles for each particular subset (fold) [C].The 2/3 part of the available data were used to develop the models in learning and test, opposite to the 1/3 rest part, used in the PQ estimation Fig. . All theThe graphs in Figs. Estimates of the D-PNN and SMLT models were obtained by evaluating their test error minima for the untrained reserved part of the data using the described fivefold cross-validation [C]. The most applicable SMLT models were recognized: GPR, EBT, and SVM processing the same input–output data samples as D-PNN Fig. . Their mTree or EBT techniques were recognized as the best SMLT modelling approaches, using only binary input load data and the corresponding decimal code of connected power equipment in the initial primary development level. The evaluated tree-ensemble models of the SMLT methods have the most representative structure for the binary character of input data, applicable in the first starting phase Fig. . Their tVariances in frequency spikes are probably induced by abnormalities in the PQ measurement Fig. . The ano30 can be used to model distorted partial-wave functions at the nodes. This periodic PDE conversion, used to represent corrupted PQ cycles, allows one to obtain a better approximation ability than using a separate polynomial-type model. Limitations in computing node output polynomials are partially eliminated by the proposed and published sigmoidal transformation of squared members in itemized equations applied to rationally converted PDE components27. The proposed multilevel model extension and development refinement algorithm can be improved in several aspects in future research work.Selection of more than one correlated input in the next-stage model development.Time-lagged data inputs to use more detailed training series.Growth in the complexity of code definition for input binary load combinations.Additional or decomposed data used in different computing levels to improve the model convergence.PQ estimate series are possible to express by time-irregular circular functions. The periods represent time-waves related to the load specifics of the attached power equipment. D-PNN employs the standard GMDH computing approach based on 2-variable processing polynomial functions without an activation, calculated to produce node output and compose ratio- or periodic components, inversely transformed and summed in the PDE model. The sine and cosine Laplace images of sub-PDE derivativesThe approximate series of the extra chosen PQ parameters, calculated in the previous data-approaching step, are shared by the next-generation models in the parallel processing to compute better estimates.The presented hierarchical AI modelling approach progressively raises the starting level of processed data, expressed initially in binary sequences only, to improve the modelling basisThe enhanced structure of models is gradually improved along with the quality of approximation on unseen data.Real-valued PQ-data samples, used in model development, are extended with PQ approximate series for unknown load combinations in the estimation stage.The stepwise evolution strategy enables modeling complex dynamical systems at several categorical levels, where real-valued data parameters are unavailable or insufficient in approximation of the next-time states.The advanced data-producing algorithm (Sect. 4) can be applied in modelling of various unseen combinations of load-attached equipment and adapted by most of AI computing techniques (overviewed in Sect. 1) to improve their modelling adaptability for limited or missing input data processed in computing estimates.The presented procedure can be additionally improved from the point of optimal input extraction, that is, added one by one or at all, and data processing based on distribution statistics, data segmentation, etc.The selection of additional input variables would improve the incremental modelling process using more sophisticated AI techniques, as only a few PQ parameters were evaluated in the experiments.The proposed multilevel PQ feature extraction and model extension algorithm was validated using real-world data. Its efficiency was tested and approved for the gradual improvement of AI modelling capability. Model experiments were carried out on real PQ data sets measured in the detached system for all 120 combinations of 3 attached binary-coded load consumers, that is, 10 in all. The main inclusion findings and implications resulting from the data experiments:The most valuable SMLT models were binary trees in the starting 1st level, since the related type of input data was processed by PQ models. The extra inputs enable us to get a better adaptability in the model evolution at the next computation levels by applying more elaborate AI strategies such as D-PNN, GPR, or EBT. (Re)charging of the accumulator backup is to be considered in the system modelling and operational planning of power consumption in addition to RE production depending on weather sources. Extraction and decomposition of the most valuable environmental observation and PQ measurement data into optimal training and testing sets, based on pattern similarity and comparative analysis between historical and forecast data, can help solve some problems in RE power utilization and optimal load scheduling of smart grids.Supplementary Information.


In recent years, silicon photomultiplier (SiPM) is replacing the photomultiplier tube (PMT) in positron emission tomography (PET) systems due to its superior properties, such as fast single-photon timing response, small gap between adjacent photosensitive pixels in the array, and insensitivity to magnetic fields. One of the technical challenges when developing SiPM-based PET systems or other position-sensitive radiation detectors is the large number of output channels coming from the SiPM array. Therefore, various signal multiplexing methods have been proposed to reduce the number of output channels and the load on the subsequent data acquisition (DAQ) system. However, the large PN-junction capacitance and quenching resistance of the SiPM yield undesirable resistance–capacitance delay when multiple SiPMs are combined, which subsequently causes the accumulation of dark counts and signal fluctuation of SiPMs. Therefore, without proper SiPM signal handling and processing, the SiPMs may yield worse timing characteristics than the PMTs. This article reviews the evolution of signal readout and multiplexing methods for the SiPM. In this review, we focus primarily on analog electronics for SiPM signal multiplexing, which allows for the reduction of DAQ channels required for the SiPM-based position-sensitive detectors used in PET and other radiation detector systems. Although the applications of most technologies described in the article are not limited to PET systems, the review highlights efforts to improve the physical performance  of PET detectors and systems. Positron emission tomography (PET) is a biomedical imaging technique that allows for the quantitative evaluation of the metabolic processes and physiological activities of living bodies by visualizing the distribution of radiolabeled tracers –4. To efConventionally, photomultiplier tubes (PMTs) have been widely used for scintillation detectors for radiation measurement and imaging owing to their robustness, high quantum efficiency, and high intrinsic gain –14. HoweWith the advancement of semiconductor technology, solid-state photosensors have been actively investigated to achieve high photon detection efficiency and signal amplification gain with good noise characteristics suitable for various applications –26. InseSiPMs are now replacing PMT in PET systems because of its superior properties, such as fast single-photon timing response, the small gap between adjacent photosensitive pixels in the array, and insensitivity to magnetic fields , 39–47 . TherefoOne of the technical challenges when developing SiPM-based PET systems or other position-sensitive radiation detectors is the large number of output channels coming from the SiPM array. Therefore, various signal multiplexing methods have been proposed to reduce the number of output channels and the load on the subsequent data acquisition (DAQ) system. However, the large PN-junction capacitance and quenching resistance of SiPMs cause undesirable resistance–capacitance (RC) delay when multiple SiPM’s are combined . The comThis article reviews the evolution of signal readout and multiplexing methods for SiPM-based detectors. We focus primarily on analog electronics for SiPM signal multiplexing, which allows for the reduction of DAQ channels that are required for SiPM-based position-sensitive detectors used in PET and other radiation detector systems. The first section reviews the basic principles of PET detector and crystal-photosensor coupling schemes. Next, various SiPM signal multiplexing methods and other technologies to improve the SiPM-based detector performance are introduced. It is worth noting that the signal readout and multiplexing techniques introduced in this review article can also be applied in other systems that utilize SiPM for detecting visible and invisible photons. Although the applications of most technologies described in the article are not limited to PET systems, the review highlights efforts to improve the physical performance  of PET detectors and systems.A basic element of PET scanners is a detector module assembled with monolithic or pixelated scintillation crystal(s) and photosensor array Fig. . In PET,Fast and bright inorganic scintillation crystals with high density and effective atomic number are used to achieve good detector performance while effectively detecting annihilation photons with a relatively high energy of 511 keV. A sensitivity of PET system can be improved by extending the crystal thickness. However, the longer the crystal, the lower is the CTR performance. The collection efficiency and arrival time jitter of traveling scintillation photons are changed by the crystal surface treatment conditions (i.e. polished or unpolished), significantly affecting PET detector performance.4 powder). In the light-sharing configuration, scintillation photons are dispersed in a monolithic crystal block or pixelated crystal array and then measured by multiple SiPMs. Using the light-sharing configuration, the number of DAQ channels in the PET system can be effectively reduced, and the fine intrinsic spatial resolution of the PET detector can reach a size smaller than that of the SiPM pixel. Light guides are typically used in the light-sharing configuration for pixelated crystal arrays to improve crystal positioning accuracy [There are two different schemes for crystal and SiPM coupling: (1) one-to-one coupling and (2) light-sharing configuration. In the one-to-one crystal and SiPM coupling scheme, the dimensions of the crystal and SiPM elements are usually identical, and each crystal element is optically isolated by specular (e.g. enhanced specular reflector or ESR films) or diffusive reflectors  ends of the crystal block. This method is called a dual-ended readout configuration Fig. b. At theIn the lateral side-readout configuration, a lateral side of each crystal element is covered by SiPMs Fig. c. The laUtilizing sparsely arranged SiPM arrays coupled to scintillation crystal arrays allows the detector manufacturing cost to be reduced, at the expense of light collection efficiency degradation  Fig. 4aa4a. OptiRecently, there has been a growing interest in sparsely arranged detector configurations for total-body PET applications. The total-body PET scanner provides dynamic PET data for whole-body kinetic analysis and parametric imaging. However, the high material cost for a large number of many scintillation detectors still hinders the widespread use of this new technology. Therefore, various cost-effective long axial field-of-view PET scanner designs based on the sparse arrangement of PET detectors in axial –88 or trAs mentioned above, SiPM is contributing to improved imaging performance and replacing PMT in modern PET system , 90–94. A simple way to achieve superior SiPM-based PET detector performance is to manage each SiPM signal from the PET detector with an individual readout scheme. In terms of circuitry, the individual readout scheme is simply implemented by connecting the SiPM signals  from the PET detector to the subsequent DAQ system. In principle, the individual readout of SiPM signals may lead to the best achievable PET system performance. This is because individual readout allows us to avoid the accumulation of detector capacitance and the baseline fluctuation caused by interference between SiPM channels (e.g. dark noises).However, the individual readout scheme requires a large number of high-speed DAQ channels. This results in a lack of scalability, which is required to configure a full-ring PET system. The lack of scalability greatly increases the design complexity and manufacturing costs of PET systems. A practical solution for the individual SiPM signal readout is to use application-specific integrated circuits (ASICs) that can individually handle large numbers of SiPM signals with a small footprint and low power consumption . RecentlSignal multiplexing techniques are widely used to reduce the number of DAQ channels required in PET systems. Reducing the readout channel number while minimizing performance degradation is an important research topic in PET hardware development. Multiplexing methods described below were summarized in Table A useful way to reduce the number of readout channels from the PET detector is to modulate the input charge collected from the SiPM arrays based on charge division (or charge sharing) multiplexing networks. The charge division multiplexing network steers the input charge toward output channels, and the amount of input charge is divided by the impedance between the input channel and each of the multiplexed output channels. This allows the interacted crystal position and photon energy information from the PET detectors to be encoded. The charge division multiplexing method can be used not only for one-to-one coupled crystal arrays but also for light-sharing crystal arrays. Typically, the charge division multiplexing network is implemented based on resistive chains, followed by signal shaping and amplification stages at the front-end electronics module.An early version of the charge division multiplexing network was developed by Hal Anger and used in his gamma cameras. In 1958, Anger  proposedA more commonly used charge division multiplexing network in PET detectors is the discretized positioning circuit (DPC) –130. The. [To further improve the performance of the DPC, Park et al.  investigA row-column summing readout circuit is also widely utilized in various PET detectors –137. ThiThe symmetric charge division (SCD) network corresponds to a hybrid approach that combines the row-column summing readout circuit and a one-dimensional (1D) resistive chain. Row and column signals from the row-column summing circuit are further multiplexed into four position-encoded signals using 1D resistive chains Fig. a or weig. [. [Various studies have been conducted to improve the performance of the charge division multiplexing network by using capacitors instead of resistors. The capacitor-based multiplexing methods have a better high-frequency response. As such, they can achieve a faster and more accurate timing performance of PET systems than resistor-based methods . Sun et .  improved. [.  proposed. [. , 146. InSignal multiplexing can also be achieved by modulating the time intervals of propagating signals; this is called the time modulation-based multiplexing method. Compared to the charge modulation-based methods, the time modulation-based method typically features a simpler multiplexing network design and yields higher quality multiplexed output signals that are not significantly affected by RC delays as the multiplexing ratio increases. Therefore, time modulation-based multiplexing could be useful for TOF PET detectors because it overcomes the limitation of resistor-based charge division multiplexing methods. In addition, the time modulation-based multiplexing method can be further combined with a time-over-threshold (TOT) technique, which allows for simple energy measurement by recording the time duration above certain thresholds of SiPM signals, thanks to good signal integrity with minimal shape distortion.. [. [Several useful time modulation-based multiplexing approaches have been proposed. Kim et al.  proposed. [.  proposed. [Vinke et al.  proposed. , 84, 150. [Frequency modulation techniques are widely used in various applications, including computing, telecommunication, radio broadcasting, and radiation detecting fields . The fre.  introduc. [There are multiplexing approaches that modulate PET data based on the polarity of SiPM signals. An analog bipolar multiplexing method was proposed by Yoon et al.  to furth. , 35. The. . In thisEarly digitization of SiPM signals may allow for a better performance of the SiPM-based PET system by effectively preventing the accumulation of undesirable noises  and interference between the multiplexed SiPM elements –157. In . [The usefulness of digital SiPM multiplexing for PET detectors has been demonstrated in several studies. A scalable multiplexing method based on digital pulse sequence generation was proposed by Cates et al.  with theThe digital delay encoding technique was also useful in the development of a PET detector with ICS event identification capability . The proThe pulse-tagging method proposed for a single transmission-line readout is a kind of semi-digital multiplexing approach that allows for readout channel reduction without compromising detector performance . In this. These sensors provide output voltage linearly proportional to the temperature, allowing effective gain drift compensation with simple circuitry. It was also shown that off-the-shelf temperature sensors can be used for the same purpose [One of the technical challenges of maintaining the performance of SiPM-based PET detectors and systems is the temperature-dependent gain variation of SiPM. Various temperature compensation methods have been proposed because the breakdown voltage and intrinsic gain of the SiPM drift with the changes in the operating temperature . Bias vo purpose  . If the DC blocking capacitor CC is sufficiently larger than the detector capacitance CD, the feedback factor is approximately equal to CD. Then according to the Miller effect, the input capacitance Cin can be reformed as CD(1−A) which will be zero if a unity gain amplifier is used as the “bootstrapping” amplifier. So, the effective detector capacitance seen in the following front-end circuit was then reduced by the Miller effect while preventing any undesirable voltage signal or noise signal from entering the following circuits.Miller effect was exploited to implement an active bootstrapping technique , 181. A Recent efforts to detect fast prompt photons, such as Cherenkov photons, have pushed the limits of timing performance in SiPM-based TOF PET detectors , 182–186This paper provides a systematic review of the signal readout schemes for PET detectors based on SiPMs. Several different crystal-to-SiPM coupling and visible light photon readout schemes were introduced, and various signal multiplexing techniques that reduce the readout complexity of high-performance PET systems were reviewed. Technologies to improve the stability and timing performance of SiPM-based PET detectors were also discussed.Most readout schemes for SiPM-based PET detectors have trade-offs between various factors . As described throughout this review, recent research interest is not to focus on one factor, but rather to overcome the trade-offs and improve the imaging quality and overall performance of PET detectors.


Integrative medicine is used frequently alongside chemotherapy treatment in pediatric oncology, but little is known about the influence on toxicity. This German, multi-center, open-label, randomized controlled trial assessed the effects of complementary treatments on toxicity related to intensive-phase chemotherapy treatment in children aged 1–18 with the primary outcome of the toxicity sum score. Secondary outcomes were chemotherapy-related toxicity, overall and event-free survival after 5 years in study patients.Intervention and control were given standard chemotherapy according to malignancy & tumor type. The intervention arm was provided with anthroposophic supportive treatment (AST); given as anthroposophic base medication (AMP), as a base medication for all patients and additional on-demand treatment tailored to the intervention malignancy groups. The control was given no AMP. The toxicity sum score (TSS) was assessed using NCI-CTC scales.Data of 288 patients could be analyzed. Analysis did not reveal any statistically significant differences between the AST and the control group for the primary endpoint or the toxicity measures (secondary endpoints). Furthermore, groups did not differ significantly in the five-year overall and event-free survival follow up.In this trial findings showed that AST was able to be safely administered in a clinical setting, although no beneficial effects of AST between group toxicity scores, overall or event-free survival were shown.The online version contains supplementary material available at 10.1186/s12885-022-09703-0. Integrative medicine is used in up to 70% of adult oncology cases  and up tThis study investigated the influence of the AST concept on the chemotherapy-associated toxicity in a randomized clinical trial undertaken at 12 German pediatric oncology clinics. The trial, consisting of an AST intervention, compared an application of AMP in pediatric oncology patients aged 1–18 undergoing standard chemotherapy to the control without AMP. A base of 8 AMP plus 11 on-demand, indication-related on-demand AMP were administered in the intervention group during the intensive chemotherapy phase. The trial tested the hypothesis that the AST would reduce the toxicity sum score for pediatric oncology patients undergoing standard treatment. The study documented long term changes with 5-year follow up data. The trial’s primary objective investigated the influence of the AST on the chemotherapy-associated toxicity measured by means of a toxicity sum score for hematology, mucositis, general condition and infection using NCI-CTC scales and the overall safety in terms of overall survival (OS) and event-free survival (EFS) until the end of the 5-year follow up. The secondary objective of the study was to investigate any decrease in chemotherapy-associated toxicity.This clinical trial investigated the effect of the AST for children undergoing chemotherapy combined with standard clinical care from 2005 to 2013 at 12 tertiary-level pediatric care units throughout Germany in a prospective, open-label, individually-randomized, controlled, national clinical study with parallel group design  by trial administrators  into intervention and control group by a computer supported standard operating procedure that used a combination of unique code identifier and abbreviation for the chemotherapy treatment.A total of 556 patients were screened for eligibility at the 12 trial sites between September 2005 and November 2013  by far made up for the largest group with approximately 54% (154 / 288 ITT and 117 / 216 PP patients). Only a minor fraction had previous illnesses (< 15%).p = 0.257 and p = 0.716 for the ITT and PP analysis, respectively  was available for 279 of the 288 ITT (96.9%) and for 208 of the 216 PP patients 96.3%). In the ITT analysis, the mean TSS was slightly higher in the intervention group  than in the control group , but slightly lower in the PP analysis . Neither of these differences became statistically significant in the Mann-Whitney-U-Test, with %. In thep = 0.054; Mann-Whitney-U-test and PP- intervention group with p = 0.044, Mann-Whitney-U-test). However, there was no statistical significance for any other secondary outcome toxicity parameter in either the ITT or PP population.Analysis of 43 additional toxicity criteria on the effect of AMP on other chemotherapy-related toxicity found a statistically significant reduction in number of diarrhea episodes in the intervention group (ITT n = 163). All in all, 607 AE were documented in 123 patients, among them 532 (87.6%) with secured (n = 472), probable (n = 7), possible (n = 49) or undecidable (n = 4) causal relationship to the treatments.Adverse events were only recorded, and thus only analyzed, for the patients in the intervention group  who had self-administered at least one of the AMPs during the observational period  showed that all SAE were related to chemotherapy-associated toxicities and not to the additional AST and were thus assessed as mislabeled records in the sense of the study protocol. Three of the misrepresented SAE had a lethal outcome with sepsis. Two were in the intervention group: one with fulminant sepsis with lethal outcome and the other with fulminant sepsis with absolute neutropenia and presumption diagnosis of intestinal perforation; these had no determined causal relationship with the intervention study medication. The third SAE occurred in the control group. According to the assessment of the study management, the cause for these SAE stemmed from the chemotherapy with which these patients were treated.Overall survival rates (OS) and events-free survival rates (EFS) were based on the intention-to-treat population (288 patients). For the analysis, data of patients in 12 groups with comparable chemotherapy were evaluated  did not reveal any statistically significant differences between the two groups  given in oncology trials with adults , marked However, studies that systematically research the effects of combined and comprehensive AST have been markedly absent. This study contributes significantly to this data gap whereby integrative treatments such as AST are in demand  especialThis study investigated an anthroposophic supportive therapy concept consisting of 19 medications as add-on therapy to standard treatment in children with cancer. For both the primary target criterion toxicity sum score and secondary target parameters, no advantage of using anthroposophic supportive therapy could be shown in this setting. Considering the previous clinical experience in the study population, it can be assumed that the anthroposophic supportive therapy can be applied safely. A key result is that there was no delay in therapy or reduction in therapy in the intervention group nor a statistical difference in 5-year survival as a result of the use of anthroposophic supportive therapy; an essential precondition for effective application of chemotherapy. Notable methodological and logistical lessons were demonstrated about the application of a supportive therapy concept in pediatric oncology that has important transferability for future research.Additional file 1.


The importance of WPEs is not only related to their impact on walking recovery, but also to the possibility of using them as assistive technology; however, WPE-assisted community ambulation has rarely been studied in terms of walking performance in real-life scenarios. (2) Methods: This study proposes the integration of an Inertial Measurement Unit (IMU) system to analyze gait kinematics during real-life outdoor scenarios  by comparing the ecological gait (no-WPE condition) and WPE-assisted gait in five able-bodied volunteers. The temporal parameters of gait and joint angles were calculated from data collected by a network of seven IMUs. (3) Results: The results showed that the WPE-assisted gait had less knee flexion in the stance phase and greater hip flexion in the swing phase. The different scenarios did not change the human–exoskeleton interaction: only the low-speed WPE-assisted gait was characterized by a longer double support phase. (4) Conclusions: The proposed IMU-based gait assessment protocol enabled quantification of the human–exoskeleton interaction in terms of gait kinematics and paved the way for the study of WPE-assisted community ambulation in stroke patients. To achirtation” ,6. Althortation” , walkingrtation” . The effrtation” ,16,17,18rtation” ,20,21,22rtation” . Indeed,rtation” ,25. Althrtation” ,26. In trtation” ,28.In thThis article aims to characterize the use of WPE outdoors in terms of walking performance. Gait kinematics in real-life outdoor scenarios  are analyzed by comparing ecological gait (no-WPE condition) and WPE-assisted gait in able-bodied volunteers. This was an observational single-session-assessment pilot study to evaluate lower limb kinematics during the performance of gait tasks in various real-life outdoor scenarios. The study was conducted at the Neurorehabilitation Research Laboratory and Rehabilitation Bioengineering Laboratory of IRCCS San Raffaele Roma .Inclusion criteria:○age between 18–80 years;○ability to fit into the WPE .Exclusion criteria:○persistent contractures of the joints;○orthopedic injuries;○pain;○severe osteoporosis;○skin breakdown;○pregnancy.Adult able-bodied subjects who met the following criteria were recruited:® , an FDA-approved lower-limb powered exoskeleton marketed as a gait trainer and assistive device for use in clinical practice [® hardware consists of four motors for powered movement of bilateral hip and knee joints in the sagittal plane and built-in ankle-foot-orthoses at both ankle joints to provide stability and transfer the weight of the exoskeleton to the ground. Built-in electronic sensors include encoders at each joint that provide the respective joint angles and angular velocities and a six-axis inertial measurement unit in each thigh limb that provides the left and right thigh angles with respect to the vertical. The total mass of the exoskeleton, including the battery, is 17.7 kg. Indego Therapy® can be disassembled and transported in a suitable external case: Each component is adjustable in length according to the anthropometric measurements of the user. The Motion+ software modality (100% assistance) was applied in the experimental protocol: The subject leans forward to initiate motion and Indego Therapy® responds accordingly. The technology readiness level of Indego Therapy® is 9 according to the European Union regulations. The WPE tested in the study was the Indego Therapypractice . The IndThe MOVIT G1 Inertial Measurement Units (IMUs) system  was used for motion capture . Data weWalking over regular terrain: walking through an indoor regular 10 m terrain (marble) at a self-selected normal pace.Walking over irregular terrain: walking through an irregular 10 m outdoor terrain (cobblestones) at self-selected normal speed, self-selected high speed, and self-selected low speed.Ascending slopes: walking on an 8 m outdoor ascending ramp  at self-selected normal speed.Descending slopes: walking on an 8 m outdoor descending ramp  at a self-selected normal speed.Experiments were conducted in the following benchmarking scenarios defined by the European EUROBENCH FSTP-1 subprojects: For each scenario, two walking conditions are considered: (1) ecological gait (no-WPE); (2) WPE-assisted gait. Four walking trials were performed under each condition to ensure the consistency of data. A 10-min rest was allowed between conditions to minimize fatigue. A schematic of the scenarios is depicted in Cadence (step/min)—number of steps taken in one-minute time;Gait cycle (s)—the mean duration of the gait cycle starting with the first heel contact and ending with the next heel contact of the same limb;Double support (s)—time during which the feet are on the ground;Stance (s)—time starting with the first contact and ending at the toe-off of the same limb;Swing (s)—time beginning with toe-off and ending with the first contact of the same limb.The Captiks Motion Analyzer software provided the joint angles in the sagittal, frontal, and transverse planes and the calibrated quaternions. The angles were analyzed using in-house software developed with MATLAB R2020a , the heel strike and toe-off were extracted, and the following temporal gait parameters were calculated: Joint kinematics were normalized as a percentage of the gait cycle, producing kinematic plots of the following parameters: hip flexion/extension; hip abduction/adduction; knee flexion/extension; knee rotation; ankle dorsiflexion/plantar flexion; foot progression.All calculated parameters were averaged within subjects for each scenario. Considering the observational pilot nature of the study, descriptive statistics were performed on gait parameters and joint angles.This study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee . Participants were enrolled in the study after signing an informed consent form.Five able-bodied subjects were enrolled and participated in all experimental scenarios described in the previous paragraph. All participants performed the gait tasks without difficulty. The IMU sensors network recorded the movement during the gait with and without the WPE. The temporal parameters of gait and joint angles were calculated.Joint kinematics  were depicted during the ecological gait and the WPE-assisted gait in the regular indoor terrain, descending, and ascending slope scenarios in When comparing the gait states, it is noticeable that the WPE performed a significant hip flexion during the swing phase. In addition, the knee flexion and extension angle showed an atypical trend when the subjects wore the WPE. Specifically, a large difference in the amount of flexion during the loading response phase occurred. Normally, the knee should flex rapidly during the loading phase after the onset of the stance phase: flexion during phase flexion should be around 15°/18° and corresponds to the phase in which the knee is loaded maximally. In the WPE condition, on the other hand, the knee angle in the loading phase is approximately zero. It is important to note that such an atypical trend is highly visible in all scenarios. The ankle joint was limited in its flexion and extension movements during the WPE-assisted gait compared to the ecological gait. A similar restriction was registered in the abduction and adduction movements of the hip under the same gait conditions.This study was conducted as part of the European EUROBENCH FSTP-2 subproject ESCALATE  as the WPE-assisted walking performance has rarely been evaluated in real-life scenarios with a standardized benchmarking framework . Other sSimilar to Swank et al. , the WPE® WPE.The inter-scenario analysis did not reveal any relevant differences in joint kinematics during WPE-assisted gait: the able-bodied subjects wearing the WPE walked with a similar gait pattern. This outcome is to be expected since the Motion+ software modality (100% support) was applied in the experimental protocol with the Indego Therapy® settings were not modified in the study (only gait speed was changed in accordance with the scenario’s description). A gait kinematics-based fine-tuning procedure for the automatic setting of WPE parameters could be on the future research agenda to tailor the use of the WPE in community ambulation. Some aspects of the study limited the generalizability of our conclusions. First, the sample size was small and consisted of young, healthy, and active individuals. Future studies with elderly and/or stroke patients may help to better understand the performance of WPE-induced gait. Second, the study did not estimate the 3D trajectories of the lower limbs. Analyzing the movement of each body segment during walking in each scenario could help characterize locomotion, especially in complex terrain or perturbations. Third, Indego therapyThe use of WPE for gait in real-life outdoor scenarios is feasible, safe, and allows the user to walk overground with a typical smooth gait pattern. Compared to the ecological walking, the WPE-assisted one showed less knee flexion and greater hip flexion in the stance and swing phases, respectively. The different scenarios did not change the human–exoskeleton interaction. The WPE increased the double support phase during the low-speed gait tasks. Future studies of the use of a WPE in community ambulation of both healthy and stroke subjects are needed to quantify the human–exoskeleton interaction from the robotic, biomechanical, and physiological perspectives and to assess gait kinematics with and without WPE in stroke patients. Furthermore, it would be of strong interest the analysis of kinematics during dual-task walking with and without using a WPE.Regarding the scenarios, further studies are also needed to explore different real-life scenarios, such as outdoor streets, sidewalks, and different irregular terrains.