Multiparametric Data Research Articles

Multiparametric cloth-based wearable, SimpleSense, estimates blood pressure

Targeted maintenance of blood pressure for hypertensive patients requires accurate monitoring of blood pressure at home. Use of multiparametric vital signs ECG, heart sounds, and thoracic impedance for blood pressure estimation at home has not been reported previously. In an observational multi-site study, 120 subjects (female (N = 61, 52%)) between 18 and 83 years of age were recruited with the following stratification (Normal (20%), prehypertensive (37%), stage 1(26%), and stage 2 (18%). From these subjects, 1686 measurements of blood pressure from a sphygmomanometer were associated with simultaneously acquired signals from the SimpleSense device. An ensemble of tree-based models was trained with inputs as metrics derived from the multiparametric and patient demographics data. A test Mean Absolute Difference (MAD) of ± 6.38 mm of Hg and ± 5.10 mm of Hg were obtained for systolic and diastolic blood pressures (SBP; DBP), respectively. Comparatively, the MAD for wrist-worn blood pressure cuff OMRON BP6350 (GUDID—10073796266353) was ± 8.92 mm of Hg and ± 6.86 mm of Hg, respectively. Machine learning models trained to use multiparametric data can monitor SBP and DBP without the need for calibration, and with accuracy levels comparable to at-home cuff-based blood pressure monitors.

Synthetic MRI for stroke: a qualitative and quantitative pilot study

Synthetic MR provides qualitative and quantitative multi-parametric data about tissue properties in a single acquisition. Its use in stroke imaging is not yet established. We compared synthetic and conventional image quality and studied synthetic relaxometry of acute and chronic ischemic lesions to investigate its interest for stroke imaging. We prospectively acquired synthetic and conventional brain MR of 43 consecutive adult patients with suspected stroke. We studied a total of 136 lesions, of which 46 DWI-positive with restricted ADC (DWI + /rADC), 90 white matter T2/FLAIR hyperintensities (WMH) showing no diffusion restriction, and 430 normal brain regions (NBR). We assessed image quality for lesion definition according to a 3-level score by two readers of different experiences. We compared relaxometry of lesions and regions of interest. Synthetic images were superior to their paired conventional images for lesion definition except for sFLAIR (sT1 or sPSIR vs. cT1 and sT2 vs. cT2 for DWI + /rADC and WMH definition; p values < .001) with substantial to almost perfect inter-rater reliability (κ ranging from 0.711 to 0.932, p values < .001). We found significant differences in relaxometry between lesions and NBR and between acute and chronic lesions (T1, T2, and PD of DWI + /rADC or WMH vs. mirror NBR; p values < .001; T1 and PD of DWI + /rADC vs. WMH; p values of 0.034 and 0.008). Synthetic MR may contribute to stroke imaging by fast generating accessible weighted images for visual inspection derived from rapidly acquired relaxometry data. Moreover, this synthetic relaxometry could differentiate acute and chronic ischemic lesions.

Effects of changing climate on the groundwater potential: A case of Chongwe and Rufunsa Districts along the Chongwe River Catchment, Zambia

This study assesses the effects of changing climate on the groundwater potential along the Chongwe River Catchment of Chongwe and Rufunsa Districts, through the use of remote sensing (RS) and geographic information systems (GIS). The study area has since the year 2012 experienced water supply shortages, partly attributed to climate change, with the local utility considering the prospects of using groundwater for the area's water supply. The area's groundwater characterisation employed multi-parametric data sets that comprised of remotely sensed data and conventional maps, generated using a weighted overlay approach in geographical information system. A total of 12 general circulation models (GCMs) outputs for precipitation were used for the period 1980 to 2080 as one of the thematic layers for the prediction of the groundwater potential (GWP) zones. The GCMs predicted decreases in precipitation, with the EC-EARTH model being the best performing model in restructuring precipitation with NSE of 0.66 compared with the other 11 GCMs. The spatial-temporal GWP zones showed reductions in zones of high and moderate potential under decreasing rainfall amounts. For continued groundwater availability, the study advocates for optimal allocation of groundwater resources based on the spatial variability of groundwater potential and aquifer yields.

Multitask Learning Based on Improved Uncertainty Weighted Loss for Multi-Parameter Meteorological Data Prediction

With the exponential growth in the amount of available data, traditional meteorological data processing algorithms have become overwhelmed. The application of artificial intelligence in simultaneous prediction of multi-parameter meteorological data has attracted much attention. However, existing single-task network models are generally limited by the data correlation dependence problem. In this paper, we use a priori knowledge for network design and propose a multitask model based on an asymmetric sharing mechanism, which effectively solves the correlation dependence problem in multi-parameter meteorological data prediction and achieves simultaneous prediction of multiple meteorological parameters with complex correlations for the first time. The performance of the multitask model depends largely on the relative weights among the task losses, and manually adjusting these weights is a difficult and expensive process, which makes it difficult for multitask learning to achieve the expected results in practice. In this paper, we propose an improved multitask loss processing method based on the assumptions of homoscedasticity uncertainty and the Laplace loss distribution and validate it using the German Jena dataset. The results show that the method can automatically balance the losses of each subtask and has better performance and robustness.

Abstract 1890: Development of the drug efficacy testing in ex vivo 3D cultures (DETECT) platform and its application to functional precision medicine in ovarian cancer

Abstract Many ovarian cancer (OC) patients receiving chemotherapy relapse with acquired resistance, hence novel treatment allocation strategies are urgently needed. 3D cell culture models and organoid technologies have emerged as exciting tools to study cancer. Despite the advances, there is a need for protocols that can quickly generate patient-relevant 3D models for high-throughput drug screening and for diagnostic drug sensitivity testing. Ideally, such methods should be cost-effective, provide multi-parametric data to reveal distinct phenotypes, have single cell resolution to reveal heterogeneity and cell interactions, meet diagnostic timeframes and be miniaturized to allow the analysis of many drugs and combinations with small numbers of primary tumor cells. Here, we aimed to develop a robust technology for high-throughput ex vivo drug sensitivity testing for functional precision medicine using OC tissue and ascites samples (mostly high-grade serous carcinoma) from patients. We present a scalable drug screening platform: DETECT (Drug Efficacy Testing in Ex-vivo 3D Cultures), where tumor-derived patient cells can be rapidly screened within one week from sampling. Using this information patient-specific drug combinations are subsequently designed and tested with these data available within 9 days. This is much faster than with most screening protocols for organoids and PDC models where many weeks or months are required for establishment of the models and expansion of the required numbers of cells. We have currently tested the efficacy of 58 selected chemo- and targeted drugs in 5 doses using high content imaging and then a complete dose-matrix of 3 combinations at step 2. We report robust drug screening data in 15 out of 18 OC patient samples, which has resulted in a functional taxonomy of patient samples and a taxonomy of drugs based on their efficacy across patient samples. Some of the most common responses were seen for the BCL-XL inhibitor A-1331852 (9/15 patients), Topotecan (7), Dactinomycin (7), Omipalisib (6) and Omacetaxine (6). All but 2 drugs showed efficacy in at least one OC sample, suggesting heterogeneity and opportunities to make use of unique drug response patterns. Combination screening revealed that Carboplatin and A-1331852, a BCL-XL inhibitor, showed increased efficacy in 3 of the 5 tested patient samples. In conclusion, our 3D HT-drug testing assay DETECT with a combination screening capability could in the future be useful for guiding individualized treatment in a clinical setting as well as for identifying existing and emerging drugs and drug combinations for repurposing in OC. Citation Format: Emma Åkerlund, Greta Gudoityte, Elisabeth Moussaud Lamodiere, Joseph Carlson, Emelie Wallin, Josefin Fernebro, Olli Kallioniemi, Päivi Östling, Ulrika Joneborg, Brinton Seashore-Ludlow. Development of the drug efficacy testing in ex vivo 3D cultures (DETECT) platform and its application to functional precision medicine in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1890.

VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients

In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development.

Therapeutic value of tafamidis in patients with wild-type transthyretin amyloidosis (ATTRwt) with cardiomyopathy based on cardiovascular magnetic resonance (CMR) imaging

ObjectivesThe purpose of this study was to carefully analyse the therapeutic benefit of tafamidis in patients with wild-type transthyretin amyloidosis (ATTRwt) and cardiomyopathy (ATTRwt-CM) after one year of therapy based on serial multi-parametric cardiovascular magnetic resonance (CMR) imaging.BackgroundNon-sponsored data based on multi-parametric CMR regarding the effect of tafamidis on the cardiac phenotype of patients with ATTRwt-CM are not available so far.MethodsThe present study comprised N = 40 patients with ATTRwt-CM who underwent two serial multi-parametric CMR studies within a follow-up period of 12 ± 3 months. Baseline (BL) clinical parameters, serum biomarkers and CMR findings were compared to follow-up (FU) values in patients treated “with” tafamidis 61 mg daily (n = 20, group A) and those “without” tafamidis therapy (n = 20, group B). CMR studies were performed on a 1.5-T system and comprised cine-imaging, pre- and post-contrast T1-mapping and additional calculation of extracellular volume fraction (ECV) values.ResultsWhile left ventricular ejection fraction (LV-EF), left ventricular mass index (LVMi), left ventricular wall thickness (LVWT), native T1- and ECV values remained unchanged in the tafamidis group A, a slight reduction in LV-EF (p = 0.003) as well as a subtle increase in LVMi (p = 0.034), in LVWT (p = 0.001), in native T1- (p = 0.038) and ECV-values (p = 0.017) were observed in the untreated group B. Serum NT-proBNP levels showed an overall increase in both groups, however, with the untreated group B showing a relatively higher increase compared to the treated group A. Assessment of NYHA class did not result in significant intra-group differences when BL were compared with FU, but a trend to improvement in the treated group A compared to a worsening trend in the untreated group B (∆p = 0.005).ConclusionAs expected, tafamidis does not improve cardiac phenotype in patients with ATTRwt-CM after one year of therapy. However, tafamidis seems to slow down cardiac disease progression in patients with ATTRwt-CM compared to those without tafamidis therapy based on multi-parametric CMR data already after one year of therapy.

Amide Proton Transfer-Weighted Magnetic Resonance Imaging for Detecting Severity and Predicting Outcome after Traumatic Brain Injury in Rats.

After traumatic brain injury (TBI), early assessment of secondary injury severity is critically important for estimating prognosis and treatment stratification. Currently, secondary injury severity is difficult to estimate. The objective of this study was to investigate the capacity of non-invasive amide proton transfer-weighted (APTw) magnetic resonance imaging (MRI) techniques to assess TBI injury in different brain regions and predict long-term neurobehavior outcomes. Fifty-five male and female rats were subjected to a controlled cortical impact with one of three different impactor depths to produce different degrees of TBI. Multi-parameter MRI data were acquired on a 4.7-Tesla scanner at 1 h, 1 day, and 3 days. Immunofluorescence staining was used to detect activated microglia at 3 days, and neurobehavioral tests were performed to assess long-term outcomes after 28 days. The APTw signal in the injury core at 1 day correlated with deficits in sensorimotor function, the sucrose preference test (a test for anhedonia), and spatial memory function on the Barnes maze. The APTw signal in the perilesion ipsilateral cortex gradually increased after TBI, and the value at 3 days correlated with microglia density at 3 days and with spatial memory decline and anhedonia at 28 days. The correlation between APTw and activated microglia was also observed in the ipsilateral thalamus, and its correlation to memory deficit and depression was evident in other ipsilateral sites. These results suggest that APTw imaging can be used for detecting secondary injury and as a potential predictor of long-term outcomes from TBI.

Precision Phenotyping of Dilated Cardiomyopathy Using Multidimensional Data

BackgroundDilated cardiomyopathy (DCM) is a final common manifestation of heterogenous etiologies. Adverse outcomes highlight the need for disease stratification beyond ejection fraction. ObjectivesThe purpose of this study was to identify novel, reproducible subphenotypes of DCM using multiparametric data for improved patient stratification. MethodsLongitudinal, observational UK-derivation (n = 426; median age 54 years; 67% men) and Dutch-validation (n = 239; median age 56 years; 64% men) cohorts of DCM patients (enrolled 2009-2016) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments. Machine learning with profile regression identified novel disease subtypes. Penalized multinomial logistic regression was used for validation. Nested Cox models compared novel groupings to conventional risk measures. Primary composite outcome was cardiovascular death, heart failure, or arrhythmia events (median follow-up 4 years). ResultsIn total, 3 novel DCM subtypes were identified: profibrotic metabolic, mild nonfibrotic, and biventricular impairment. Prognosis differed between subtypes in both the derivation (P < 0.0001) and validation cohorts. The novel profibrotic metabolic subtype had more diabetes, universal myocardial fibrosis, preserved right ventricular function, and elevated creatinine. For clinical application, 5 variables were sufficient for classification (left and right ventricular end-systolic volumes, left atrial volume, myocardial fibrosis, and creatinine). Adding the novel DCM subtype improved the C-statistic from 0.60 to 0.76. Interleukin-4 receptor-alpha was identified as a novel prognostic biomarker in derivation (HR: 3.6; 95% CI: 1.9-6.5; P = 0.00002) and validation cohorts (HR: 1.94; 95% CI: 1.3-2.8; P = 0.00005). ConclusionsThree reproducible, mechanistically distinct DCM subtypes were identified using widely available clinical and biological data, adding prognostic value to traditional risk models. They may improve patient selection for novel interventions, thereby enabling precision medicine.

PD54-03 ARTIFICIAL INTELLIGENCE IN ROBOTIC-ASSISTED LAPAROSCOPIC PROSTATECTOMY: DEVELOPING PREDICTIVE MODELS FOR POSITIVE SURGICAL MARGINS

You have accessJournal of UrologyCME1 May 2022PD54-03 ARTIFICIAL INTELLIGENCE IN ROBOTIC-ASSISTED LAPAROSCOPIC PROSTATECTOMY: DEVELOPING PREDICTIVE MODELS FOR POSITIVE SURGICAL MARGINS Stephen Schmit, Rebecca Ortiz, Philip Caffery, Christopher Tucci, Carsten Eickhoff, Elias Hyams, and Gyan Pareek Stephen SchmitStephen Schmit More articles by this author , Rebecca OrtizRebecca Ortiz More articles by this author , Philip CafferyPhilip Caffery More articles by this author , Christopher TucciChristopher Tucci More articles by this author , Carsten EickhoffCarsten Eickhoff More articles by this author , Elias HyamsElias Hyams More articles by this author , and Gyan PareekGyan Pareek More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002631.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Patients with localized prostate cancer increasingly elect for robotic-assisted laparoscopic prostatectomy (RALP), but this approach may be less preferred when adjuvant therapy is indicated. Adjuvant therapy improves survival but is associated with significant psychological distress and additional treatment complications. The presence of positive surgical margins after RALP is an important indication for adjuvant therapy. The goal of this study was to explore deep learning models for the prediction of positive surgical margins with preoperative data. METHODS: Patient data from a single academic institution was pre-processed to train two artificial intelligence models: a feedforward multilayer perceptron and a random forest model containing 100 trees. A total of 9 preoperative variables were used for model training (prostate specific antigen value, positive biopsy cores, Gleason score on biopsy, perineural invasion on biopsy, body mass index, tobacco use history, family history of cancer, preoperative prostate volume, and age at diagnosis). Model construction was performed using the Python programming language. RESULTS: Data preprocessing yielded a final sample of 461 patients. The optimized neural network demonstrated successful learning with loss minimization over 100 epochs (Figure 1) and a maximum area under the curve (AUC) of 0.60. The random forest model demonstrated an AUC of 0.55 (Figure 2). CONCLUSIONS: Neither model demonstrated significant accuracy in predicting positive surgical margins with preoperative structured data. The integration of structured data may be insufficient for predicting margin status. Future models that utilize raw imaging information should be explored. For example, a convolutional neural network that is trained on preoperative multiparametric resonance imaging data may be a superior approach. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e916 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Stephen Schmit More articles by this author Rebecca Ortiz More articles by this author Philip Caffery More articles by this author Christopher Tucci More articles by this author Carsten Eickhoff More articles by this author Elias Hyams More articles by this author Gyan Pareek More articles by this author Expand All Advertisement PDF DownloadLoading ...

Real-time control for multi-parametric data fusion and dynamic offset optimization in sensor-based variable rate nitrogen application

Real-time sensor systems for variable rate nitrogen (N) application (VRNA) are an established technology nowadays but they have some shortcomings in terms of their capability to consider multiple parameters relevant for plant growth. Further, the abundantly lacking section control in centrifugal spreaders limits the accuracy of a sensor-based VRNA, especially in combination with the temporal and spatial offsets between sensing and fertilizer placement. Fuzzy inference systems were incorporated into a real-time control to numerically fuse the crop N uptake sensed by a real-time sensor system, as well as mapped soil electrical conductivity (ECa) data for the calculation of site-specific N dose rates (DR). A distinction of two subsections within the working width of a sensor-spreader system was made based on the ECa data. Further, by implementing a generic model, the control system agronomically optimized the rate control of a centrifugal spreader in order to compensate positional lags and technical latencies and minimize the spatial offset between DR determination and application in a dynamic manner. With field tests at different driving speed scenarios going partly beyond the usual operation conditions, the real-time control was verified. The differentiation of the sections has resulted in slight DR differences, whereas the control system has shown a high consistency in calculating the DRs and sending commands to the spreader in a coordinated manner. The level of spatial concordance between DR determination and application had a highly stochastic character. However, the deviation was never beyond 1.5 m and the percentage of deviations beyond 1 m reached a maximum of 2.3% among the different recorded datasets, which can be considered as a sufficient performance for practical needs.

Explainable AI for CNN-based prostate tumor segmentation in multi-parametric MRI correlated to whole mount histopathology

Automatic prostate tumor segmentation is often unable to identify the lesion even if multi-parametric MRI data is used as input, and the segmentation output is difficult to verify due to the lack of clinically established ground truth images. In this work we use an explainable deep learning model to interpret the predictions of a convolutional neural network (CNN) for prostate tumor segmentation. The CNN uses a U-Net architecture which was trained on multi-parametric MRI data from 122 patients to automatically segment the prostate gland and prostate tumor lesions. In addition, co-registered ground truth data from whole mount histopathology images were available in 15 patients that were used as a test set during CNN testing. To be able to interpret the segmentation results of the CNN, heat maps were generated using the Gradient Weighted Class Activation Map (Grad-CAM) method. The CNN achieved a mean Dice Sorensen Coefficient 0.62 and 0.31 for the prostate gland and the tumor lesions -with the radiologist drawn ground truth and 0.32 with whole-mount histology ground truth for tumor lesions. Dice Sorensen Coefficient between CNN predictions and manual segmentations from MRI and histology data were not significantly different. In the prostate the Grad-CAM heat maps could differentiate between tumor and healthy prostate tissue, which indicates that the image information in the tumor was essential for the CNN segmentation.

Multiparametric quantitative renal MRI in children and young adults: comparison between healthy individuals and patients with chronic kidney disease

Multiparametric quantitative renal MRI may provide noninvasive radiologic biomarkers of chronic kidney disease (CKD) based on investigations in animal models and adults. We aimed to (1) obtain normative multiparametric quantitative MRI data from the kidneys of healthy children and young adults, (2) compare MRI measurements between healthy control participants and patients with CKD, and (3) determine if MRI measurements correlate with clinical and laboratory data as well as histology. This was a prospective, case-control study of 20 healthy controls and 12 CKD patients who underwent percutaneous renal biopsy ranging from 12 to 23years of age between October 2018 and March 2020. Kidney function was documented and pathology assessed for fibrosis/inflammation. Utilizing a field strength of 1.5T, we examined renal T1, T2, and T2* relaxation mapping, MR elastography (MRE), and diffusion-weighted imaging (DWI). A single analyst made all manual measurements for quantitative MRI pulse sequences. Independent measurements from cortex, medulla, and whole kidney were obtained by drawing regions of interest on single slices from the upper, mid, and lower kidney. A weighted average was calculated for each kidney; if two kidneys, the right and left were averaged. Continuous variables were compared with Mann-Whitney U test; bivariate relationships were assessed using Spearman rank-order correlation. Median estimated glomerular filtration rate (eGFR) was 112.3ml/min/1.73 m2 in controls (n = 20, 10 females) and 55.0ml/min/m2 in CKD patients (n = 12, 2 females) (p < 0.0001). Whole kidney (1333 vs. 1291ms; p = 0.018) and cortical (1212 vs 1137ms; p < 0.0001) T1 values were higher in CKD patients. Cortical T1 values correlated with eGFR (rho = -0.62; p = 0.0003) and cystatin C (rho = 0.58; p = 0.0007). Whole kidney (1.87 vs. 2.02 10-3 mm2/s; p = 0.007), cortical (1.89 vs. 2.04 10-3 mm2/s; p = 0.008), and medullary (1.87 vs. 1.98 10-3 mm2/s; p = 0.0095) DWI apparent diffusion coefficients (ADC) were lower in CKD patients. Whole kidney ADC correlated with eGFR (rho = 0.45; p = 0.012) and cystatin C (rho = -0.46; p = 0.009). Cortical histologic inflammation correlated with DWI ADC (rho = -0.71; p = 0.011). Renal T1 relaxation and DWI ADC measurements differ between pediatric healthy controls and CKD patients, correlate with laboratory markers of CKD, and may have histologic correlates.

Artificial intelligence trained with integration of multiparametric MR-US imaging data and fusion biopsy trajectory-proven pathology data for 3D prediction of prostate cancer: A proof-of-concept study.

We aimed to develop an artificial intelligence (AI) algorithm that predicts the volume and location of clinically significant cancer (CSCa) using convolutional neural network (CNN) trained with integration of multiparametric MR-US image data and MRI-US fusion prostate biopsy (MRI-US PBx) trajectory-proven pathology data. Twenty consecutive patients prospectively underwent MRI-US PBx, followed by robot-assisted radical prostatectomy (RARP). The AI algorithm was trained with the integration of MR-US image data with a MRI-US PBx trajectory-proven pathology. The relationship with the 3D-cancer-mapping of RARP specimens was compared between AI system-suggested 3D-CSCa mapping and an experienced radiologist's suggested 3D-CSCa mapping on MRI alone according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2. The characteristics of detected and undetected tumors at AI were compared in 22,968 image data. The relationships between CSCa volumes and volumes predicted by AI as well as the radiologist's reading based on PI-RADS were analyzed. The concordance of the CSCa center with that in RARP specimens was significantly higher in the AI prediction than the radiologist' reading (83% vs. 54%, p = 0.036). CSCa volumes predicted with AI were more accurate (r = 0.90, p < 0.001) than the radiologist's reading. The limitations include that the elastic fusion technology has its own registration error. We presented a novel pilot AI algorithm for 3D prediction of PCa. AI was trained by integration of multiparametric MR-US image data and fusion biopsy trajectory-proven pathology data. This deep learning AI model may more precisely predict the 3D mapping of CSCa in its volume and center location than a radiologist's reading based on PI-RADS version 2, and has potential in the planning of focal therapy.

Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models.

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.

Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease.

Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.

A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas.

To construct a radiomics nomogram based on multiparametric MRI data for predicting isocitrate dehydrogenase 1 mutation (IDH +) and loss of nuclear alpha thalassemia/mental retardation syndrome X-linked expression (ATRX -) in patients with lower-grade gliomas (LrGG; World Health Organization [WHO] 2016 grades II and III). A total of 111 LrGG patients (76 mutated IDH and 35 wild-type IDH) were enrolled, divided into a training set (n = 78) and a validation set (n = 33) for predicting IDH mutation. IDH + LrGG patients were further stratified into the ATRX - (n = 38) and ATRX + (n = 38) subtypes. A total of 250 radiomics features were extracted from the region of interest of each tumor, including that from T2 fluid-attenuated inversion recovery (T2 FLAIR), contrast-enhanced T1 WI, ASL-derived cerebral blood flow (CBF), DWI-derived ADC, and exponential ADC (eADC). A radiomics signature was selected using the Elastic Net regression model, and a radiomics nomogram was finally constructed using the age, gender information, and above features. The radiomics nomogram identified LrGG patients for IDH mutation (C-index: training sets = 0.881, validation sets = 0.900) and ATRX loss (C-index: training sets = 0.863, validation sets = 0.840) with good calibration. Decision curve analysis further confirmed the clinical usefulness of the two nomograms for predicting IDH and ATRX status. The nomogram incorporating age, gender, and the radiomics signature provided a clinically useful approach in noninvasively predicting IDH and ATRX mutation status for LrGG patients. The proposed method could facilitate MRI-based clinical decision-making for the LrGG patients. • Non-invasive determination of IDH and ATRX gene status of LrGG patients can be obtained with a radiomics nomogram. • The proposed nomogram is constructed by radiomics signature selected from 250 radiomics features, combined with age and gender. • The proposed radiomics nomogram exhibited good calibration and discrimination for IDH and ATRX gene mutation stratification of LrGG patients in both training and validation sets.

Registers and biobanks in ICU and anesthesia

Anesthesia, perioperative and critical care medicine are specific areas where registries, biobanks and big data are gaining a leading role in increasing knowledge and improving patients' care. The adoption of these robust data infrastructures - aimed at bridling, manipulating, aggregating, and linking patients' multiparametric data - supports anesthesiologists and intensive care physicians in several aspects of bedside practice and clinical research. Indeed, registries-integrated calculators may promote the concept of personalized medicine acting as "sniffers," electronic alarm systems, or decision support systems. Artificial intelligence applied to large databases or meta-registries may further increase dramatically this functionality, identify associations among thousands of different and only apparently uncorrelated variables. From a research perspective, large datasets are increasingly mined to create observations about medical care beyond prospective randomized clinical trials enrolling thousands of patients, often only presumably homogeneous ore well-balanced. Registries in this context may effectively explore the association between patients' management and patients' outcomes with a negligible impact on ethical issues, limited costs, and easy management. Finally, registries may promote self-evaluation and continuous quality improvement in the field of perioperative and critical care medicine. In a different way, the role of biobanks primarily relies on translational medical research. These allow rapidly creating pools of biological samples available for epidemiological description, pathophysiological definition, and treatment effectiveness verification, basically acting as an accelerator of knowledge production in critical care and perioperative medicine. Nowadays, registries and biobanks are thus routine tools for anesthesiologists and critical care physicians.

Predictive Maintenance and Engineered Processes in Mechatronic Industry: An Italian Case Study

The paper proposes the results of a research industry project concerning predictive maintenance process optimization, applied to a machine cutting polyurethane. A company producing cutting machines, has been provided with an online control system able to detect blade status of a machine supplied to a customer producing polyurethane components. A software platform has been developed for the real time monitoring of the blade status and for the prediction of the break up conditions adopting a multi-parametric data analysis approach, based on the simultaneous use of unsupervised and supervised machine learning algorithms. Specifically, the proposed method adopts a k-Means algorithm to classify bidimensional risk maps, and a Long Short Term Memory (LSTM) one to predict the alerting levels based on the analysis of the last values for some process variables. The analysed algorithms are applied to an experimental dataset.

Tidypaleo: Visualizing Paleoenvironmental Archives Using ggplot2

This paper presents the tidypaleo package for R, which enables high-quality reproducible visualizations of time-stratigraphic multivariate data that is common to several disciplines of the natural sciences. Rather than introduce new plotting functions, the tidypaleo package defines several orthogonal components of the ggplot2 package that, when combined, enable most types of stratigraphic diagrams to be created. We do so by conceptualizing multi-parameter data as a series of measurements (rows) with attributes (columns), enabling the use of the ggplot2 facet mechanism to display multi-parameter data. The orthogonal components include (1) scales that represent relative abundance and concentration values, (2) geometries that are commonly used in paleoenvironmental diagrams created elsewhere, (3) facets that correctly assign scales and sizes to panels representing multiple parameters, and (4) theme elements that enable tidypaleo to create elegant graphics. Collectively, this approach demonstrates the efficacy of a minimal ggplot2 wrapper to create domain-specific plots.