Early Signals Research Articles

The reduction of the left ventricular long-axis early diastolic peak velocity (e') evident by early-middle age occurs in the absence of aging-related slowing of active relaxation.

A decrease in the left ventricular (LV) long-axis early diastolic peak velocity (e') is evident by early-middle age, but it is unknown to what extent this decrease is due to slowing of the speed of active relaxation versus a reduction in LV long-axis excursion during early diastole (EDExc). Pulsed-wave tissue Doppler imaging (TDI) signals were acquired from the septal and lateral borders of the mitral annulus in 62 healthy adult subjects of age 18-45years. EDExc and LV systolic excursion (SExc) were measured as the integrals of the respective TDI signals. The speed of active relaxation was indirectly assessed using time interval measurements related to the TDI early diastolic signal, including the isovolumic relaxation time (IVRT'), the acceleration time (EDAT), and the duration (EDDur). Multiple linear regression analyses were performed to identify the relationships between e', age, EDExc, SExc, and time intervals. The findings were similar for both LV walls. Age was negatively correlated with e' and EDExc, but was not correlated with SExc, IVRT', EDAT, or EDDur. The closest correlate of EDExc was SExc, and EDExc was independently correlated with both SExc and age. e' was also positively correlated with SExc, but the closest correlate of e' was EDExc, and when combined with EDExc, EDDur became an independent predictor of e'. The aging-related decrease in e' evident by early-middle age occurs in the absence of aging-related slowing of active relaxation and therefore can be largely attributed to the accompanying reduction in EDExc.

Specific network information gain for detecting the critical state of colorectal cancer based on gut microbiome.

There generally exists a critical state or tipping point from a stable state to another in the development of colorectal cancer (CRC) beyond which a significant qualitative transition occurs. Gut microbiome sequencing data can be collected non-invasively from fecal samples, making it more convenient to obtain. Furthermore, intestinal microbiome sequencing data contain phylogenetic information at various levels, which can be used to reliably identify critical states, thereby providing early warning signals more accurately and effectively. Yet, pinpointing the critical states using gut microbiome data presents a formidable challenge due to the high dimension and strong noise of gut microbiome data. To address this challenge, we introduce a novel approach termed the specific network information gain (SNIG) method to detect CRC's critical states at various taxonomic levels via gut microbiome data. The numerical simulation indicates that the SNIG method is robust under different noise levels and that it is also superior to the existing methods on detecting the critical states. Moreover, utilizing SNIG on two real CRC datasets enabled us to discern the critical states preceding deterioration and to successfully identify their associated dynamic network biomarkers at different taxonomic levels. Notably, we discovered certain 'dark species' and pathways intimately linked to CRC progression. In addition, we accurately detected the tipping points on an individual dataset of type I diabetes.

A study and analysis on the role of legal accounting in fraud detection and prevention

Forensic accountants are CPAs that look for evidence of crimes and commonly work for insurance companies, financial institutions, and law enforcement agencies. Forensic accountants analyze financial records and accounts that may be used as legal evidence and often testify in court cases as expert witnesses. Forensic accounting is a specialized area of accounting and a challenging one. A forensic accountant investigates incidents of fraud, bribery, money laundering and embezzlement by analyzing financial records and transactions, tracing assets, and more. This study on forensic accounting services and fraud prevention was empirically investigated. The study is vital as it portrays the extent to which the application of forensic accounting services influence the incidence of fraud in health care firms in Nigeria. Two hypotheses were formulated to guide the investigation and the statistical test of parameter estimates was conducted using Kendall’s Coefficient of Concordance. The study anchored on the White Collar Crime Theory adopted a Survey Design Approach. Hence, data were collected using questionnaire survey administered to the relevant accounting sections of health care firms in Nigeria. The empirical analysis of the study indicates that the application of forensic accounting service has reduced the incidence of frauds and has also led to fraud prevention at 1% significant level. Thus, the study concludes that the application of forensic accounting services prevents fraud in health care firms in Nigeria. In lieu of this, the study recommended that adequate internal control system should also be established to have checks and balances among the staff to reduce fraud to its nearest minimum, thereby, restoring confidence to potential and prospective investors. Also, since staff of health care firms in Nigeria are strategic in prevention of fraud, their remunerations and welfare should be made a priority and taken serious. Also, training and re-training of these staff would encourage them to detect early warning signals for fraud activities in the company.

Genome-Wide Identification and Characterization of CDPK Gene Family in Cultivated Peanut (Arachis hypogaea L.) Reveal Their Potential Roles in Response to Ca Deficiency.

This study identified 45 calcium-dependent protein kinase (CDPK) genes in cultivated peanut (Arachis hypogaea L.), which are integral in plant growth, development, and stress responses. These genes, classified into four subgroups based on phylogenetic relationships, are unevenly distributed across all twenty peanut chromosomes. The analysis of the genetic structure of AhCDPKs revealed significant similarity within subgroups, with their expansion primarily driven by whole-genome duplications. The upstream promoter sequences of AhCDPK genes contained 46 cis-acting regulatory elements, associated with various plant responses. Additionally, 13 microRNAs were identified that target 21 AhCDPK genes, suggesting potential post-transcriptional regulation. AhCDPK proteins interacted with respiratory burst oxidase homologs, suggesting their involvement in redox signaling. Gene ontology and KEGG enrichment analyses affirmed AhCDPK genes' roles in calcium ion binding, protein kinase activity, and environmental adaptation. RNA-seq data revealed diverse expression patterns under different stress conditions. Importantly, 26 AhCDPK genes were significantly induced when exposed to Ca deficiency during the pod stage. During the seedling stage, four AhCDPKs (AhCDPK2/-25/-28/-45) in roots peaked after three hours, suggesting early signaling roles in pod Ca nutrition. These findings provide insights into the roles of CDPK genes in plant development and stress responses, offering potential candidates for predicting calcium levels in peanut seeds.

Ethanol Extract of Radix Asteris Suppresses Osteoclast Differentiation and Alleviates Osteoporosis.

Radix Asteris, the root of Aster tataricus L. f., is historically significant in East Asian medicine for treating respiratory conditions. Yet, its implications on bone health remain uncharted. This research investigated the impact of an aqueous ethanol extract of Radix Asteris (EERA) on osteoclast differentiation and its prospective contribution to osteoporosis management. We discerned that EERA retards osteoclast differentiation by inhibiting receptor activator of nuclear factor kappa-B ligand (RANKL) expression and obstructing RANKL-induced osteoclastogenesis. EERA markedly suppressed RANKL-induced expression of NFATc1, a pivotal osteoclastogenic factor, via modulating early RANK signaling. EERA's therapeutic potential was underscored by its defense against trabecular bone degradation and its counteraction to increased body and perigonadal fat in ovariectomized mice, mirroring postmenopausal physiological changes. In the phytochemical analysis of EERA, we identified several constituents recognized for their roles in regulating bone and fat metabolism. Collectively, our findings emphasize the potential of EERA in osteoclast differentiation modulation and in the management of osteoporosis and associated metabolic changes following estrogen depletion, suggesting its suitability as an alternative therapeutic strategy for postmenopausal osteoporosis intertwined with metabolic imbalances.

Research on early warning model of electric submersible pump wells failure based on the fusion of physical constraints and data-driven approach

At present, there are too few characteristic parameters for early identification of electric submersible pump (ESP) wells failures. In addition, existing predictive models rely only on data-driven techniques, which do not provide a comprehensive understanding of the intrinsic mechanisms behind the occurrence of failures. This article presents the development of a health index calculation approach for evaluating the operational status of the ESP wells. The wellbore energy conservation equation and the health index calculation formula were employed to develop a physical constraints (PC) for the loss function in the network model of Long Short-Term Memory (LSTM). This constraint was then utilized to construct the PC-LSTM ESP health warning model. The study examines the health index trends associated with three types of faults: ESP clogging, sand production in oil wells, and reservoir pressure rise. The results indicate that the PC-LSTM network structure exhibits faster convergence during training. In relation to the accuracy of prediction, PC-LSTM demonstrates a notable enhancement. Specifically, when the ESP clogging fault, the traditional LSTM achieves a mean relative error (MRE) of 211.15%, whereas the PC-LSTM achieves a reduced MRE of 12.74%. Similarly, for sand production in oil wells, the traditional LSTM yields an MRE of 19.07%, while the PC-LSTM achieves a reduced MRE of 9.62%. Lastly, in the scenario of reservoir pressure rise, the traditional LSTM exhibits an MRE of 41.07%, whereas the PC-LSTM achieves a reduced MRE of 12.74%. In the meantime, the model demonstrates a high level of accuracy in capturing the pre- and post-trends of the health status of the ESP wells. This improved prediction accuracy and interpretability of the underlying mechanisms serve as valuable tools for analyzing the lift performance of ESP wells and providing early warning signals for potential failures.

Modular Splicing Is Linked to Evolution in the Synapse-Specificity Molecule Kirrel3.

Kirrel3 is a cell-adhesion molecule that instructs the formation of specific synapses during brain development in mouse and Kirrel3 variants may be risk factors for autism and intellectual disabilities in humans. Kirrel3 is predicted to undergo alternative splicing but brain isoforms have not been studied. Here, we present the first in-depth characterization of Kirrel3 isoform diversity in brain using targeted, long-read mRNA sequencing of mouse hippocampus. We identified 19 isoforms with predicted transmembrane and secreted forms and show that even rare isoforms generate detectable protein in the brain. We also analyzed publicly-available long-read mRNA databases from human brain tissue and found 11 Kirrel3 isoforms that, similar to mouse, encode transmembrane and secreted forms. In mice and humans, Kirrel3 diversity arises from alternative, independent use of protein-domain coding exons and alternative early translation-stop signals. Intriguingly, the alternatively spliced exons appear at branch points in the chordate phylogenetic tree, including one exon only found in humans and their closest living relatives, the great apes. Together, these results validate a simple pipeline for analyzing isoform diversity in genes with low expression and suggest that Kirrel3 function is fine-tuned by alternative splicing and may play a role in brain evolution.

A Plasma Exosomal Metabolic Profiling of Nonalcoholic Fatty Liver Disease Patients Complicated with Impaired Fasting Glucose.

Nonalcoholic fatty liver disease is considered as the hepatic manifestation of metabolic syndrome. Detection of circulating exosomes together with metabolomic analysis of their cargo would provide early signals for metabolic derangements and complications associated with nonalcoholic fatty liver disease. Therefore, this study profiled exosomal metabolome of patients with nonalcoholic fatty liver disease and impaired fasting glucose. Plasma exosomes were extracted from nonalcoholic fatty liver disease patients with or without impaired fasting glucose through differential ultracentrifugation. Their metabolite profiles were examined by ultrahigh-performance liquid chrom atography-quadrupole time-of-flight mass spectrometry. Pathway analysis was carried out on platform MetaboAnalyst 4.0. Thirty-nine patients were enrolled, including nonalcoholic fatty liver disease-alone group (n = 26) and age-and gender-comparable nonalcoholic fatty liver disease plus impaired fasting glucose group (n = 13). Although less than and different from their plasma counterparts, a total of 10 significantly differential exosomal metabolites were identified. Nonalcoholic fatty liver disease plus impaired fasting glucose group had higher concentrations of linoleic acid, palmitamide, stearamide, and oleamide, as well as a lower concentration of phosphatidylethanolamine [20:5(5Z,8Z,11Z,14Z,17Z)/20:5(5Z,8Z,11Z,14Z,17Z)]. Pathway analysis showed an obviously changed metabolism of linoleic acid. Metabolomic analysis of plasma exosomes revealed a distinct change in fatty acids and related pathways in nonalcoholic fatty liver disease patients with impaired fasting glucose. These preliminary results provide a metabolomic snapshot and basis for further investigation of exosome biology for these patients.

The Role of the Innate Immune Response in Oral Mucositis Pathogenesis.

Oral mucositis (OM) is a significant complication of cancer therapy with limited management strategies. Whilst inflammation is a central feature of destructive and ultimately ulcerative pathology, to date, attempts to mitigate damage via this mechanism have proven limited. A relatively underexamined aspect of OM development is the contribution of elements of the innate immune system. In particular, the role played by barriers, pattern recognition systems, and microbial composition in early damage signaling requires further investigation. As such, this review highlights the innate immune response as a potential focus for research to better understand OM pathogenesis and development of interventions for patients treated with radiotherapy and chemotherapy. Future areas of evaluation include manipulation of microbial-mucosal interactions to alter cytotoxic sensitivity, use of germ-free models, and translation of innate immune-targeted agents interrogated for mucosal injury in other regions of the alimentary canal into OM-based clinical trials.

Early Source Characterization of Large Earthquakes Using W Phase and Prompt Elastogravity Signals

Abstract In the minutes following a large earthquake, robust characterization of the seismic rupture can be obtained from full wavefield records at local distances or from early signals recorded by regional broadband seismometers. We focus here on the latter configuration, and evaluate the individual and joint performances of the early low-frequency elastic phases (W phase) and the recently discovered prompt elastogravity signals (PEGS). The 2011 Mw 9.1 Tohoku–Oki earthquake is a natural target for this evaluation, because the high quality of global and regional networks enabled to gather the best PEGS data set so far. We first confirm that the well-established W-phase method, using records from global seismological networks, is able to provide a reliable centroid moment tensor solution 22 min after the earthquake origin time. Using regional stations, an accurate W-phase solution can be obtained more rapidly, down to 10 min after origin time. On the other hand, a PEGS-based source inversion can provide even earlier, starting 3 min after origin time, a lower bound of the seismic moment (Mw 8.6) and constraints on the focal mechanism type. However, relying solely on PEGS introduces uncertainties caused by the hindering seismic noise and trade-offs between source parameters that limit the accuracy of source determination. We show that incorporating even a few early W phase signals to the PEGS data set reduces these uncertainties. Using more complete W phase and PEGS data sets available 5 min after origin time enables to converge towards a result close to the Global Centroid Moment Tensor solution.

Search-engine-based surveillance using artificial intelligence for early detection of coronavirus disease outbreak

The search-engine-based surveillance methods for the early warning and prediction of infectious diseases cannot achieve search engine keywords automatic filtering and real-time updating, lead to powerless for the early warning of emerging infectious diseases. The aim of this study is to develop an artificial intelligence (AI) method for search-engine-based surveillance to improve the early warning ability for emerging infectious diseases. The 32 keywords (444 million search queries) that may be related to the coronavirus disease (COVID-19) outbreak was collected from December 18, 2019 to February 11, 2020 from Baidu’s search engine database. The graph convolution network (GCN) model was used to select search engine keywords automatically, and then, multiple linear regression was performed to explore the relationship between the daily query frequencies of keywords and daily new cases. The GCN model was used to automatically select keywords. The prediction trend of the GCN model was highly consistent with the true curve with a mean absolute error of 81.65. Three keywords including “epidemic”, “mask” and “coronavirus” were selected. The selection keywords in the search queries were highly correlated with the daily number of confirmed cases (r = 0.96, 0.94, and 0.89; P < 0.01). An abnormal initial peak (3.05 times the normal volume) in queries appeared on December 31, 2019, which could have served as an early warning signal for an outbreak. Of particular concern, 17.5% of query volume originated from the Hubei Province, 51.15% of which was from Wuhan City. The coefficients of determination (R2) of our constructed model were 0.88, 0.88, 0.84, 0.77, 0.77, 0.75, 0.73, and 0.73 for a time lag of 0–7 days, respectively, using selection keywords. The model we constructed was used in the Beijing Xinfadi outbreak as an independent test dataset, which successfully predicted the daily numbers of cases for the following days and detected an early signal during the Beijing Xinfadi outbreak (R2 = 0.79). In this paper search-engine-based surveillance based on the AI method was established for the early detection of the COVID-19 epidemic for the first time. The model achieves automatic filtering and real-time updating of search engine keywords and can effectively detect the early signals of emerging infectious diseases.

Quantitative Analysis of Radiation Therapy-Induced Cardiac and Aortic Sequelae in Patients With Lung Cancer via Magnetic Resonance Imaging: A Pilot Study

PurposeTo quantify early radiotherapy (RT)-induced cardiac and aortic changes in patients with lung cancer using cardiac MRI. Methods and MaterialsNine patients with lung cancer treated with RT completed MR scans at baseline (before RT), 3 months, and 6 months following RT. Cine, T1/T2, late gadolinium enhancement (LGE), and 4D-flow MRIs were acquired to assess biological and mechanical cardiovascular changes globally (i.e., over the entire left ventricle (LV) or aorta) and regionally (according to an American Heart Association model). ResultsRegional metrics demonstrated multiple significant changes and dose-dependent responses. Notably, LGE showed changes at 3 and 6 months over septal and high-dose regions (P<0.0458). Longitudinal strain changes were notable at septal and high-dose regions at 3 months and septal regions at 6 months (P<0.0469). Elevated T1/T2 signals (P<0.0391) and changes in radial/circumferential strain at the septum (P<0.0391) were observed at 3 months. Both T1/T2 signal and LGE were correlated with dose at 6 months (T1 signal also at 3 months), with significantly greater changes in regions receiving >50 Gy (P<0.0331). LV dose was not correlated with LV strain changes (P>0.1), but ascending aortic dose was correlated with strain changes at segments 1 and 2 of the LV (P<0.0362). Global metrics identified only two significant responses: increase in LGE volume at 6 months and a reduction in ascending aortic circumferential strain at 3 months (P<0.0356). ConclusionEarly MR-based changes following RT occurred primarily in high-dose regions and the LV septal wall. While several early signals resolved by 6 months, LGE and longitudinal strain changes persisted for at least 6 months. Dose-dependent responses/correlations were observed for T1/T2/LGE changes at 6 months, with the greatest effect in regions exposed to >50 Gy. Further investigations with larger cohorts and longer follow-up are warranted to confirm regional dose dependence and the association between aortic dose and LV strain observed in this pilot study.

Development of lake macroinvertebrate-based multimetric index for monitoring ecological health in North Central Nigeria.

The use of organisms like macroinvertebrates in developing bioassessment tools, such as multimetric indices (MMIs), is gaining global recognition in monitoring the health status of lakes. The transition from traditional methods of physico-chemical parameters is due to the financial and time costs involved in their analysis while failing to provide accurate early warning signals on ecosystem conditions. Currently, there is scanty information on the use of MMIs in the conservation and management of lakes in Nigeria. This study aimed at developing a macroinvertebrate-based MMI to assess the ecological status of lakes in North Central Nigeria. The study was conducted on Tagwai and Old Gawu Lakes, from April to October 2022. Sampling sites were clustered based on organic pollution and categorized into reference (four) and impaired (four) sites. Out of 54 macroinvertebrate-based candidate metrics, only five were selected after discriminatory, stability, and redundancy tests (performed using R software). The final metrics were abundance of Ephemeroptera + Trichoptera; abundance of Coleoptera + Ephemeroptera; Gastropoda richness; Shannon Wiener index; and percentages of shredders + predators + scrapers, hereafter referred to as North Central Nigeria-Lakes Multimetric Index (NCN-LMMI). The NCN-LMMI values ranged as follows: 21-25, 16-20, 11-15, and 5-10 corresponding to categories I, II, III, and IV for water quality, as indications of good, fair, poor, and very poor ecological status of the lake, respectively. The developed NCN-LMMI will be a useful tool for aquatic resource managers and environmentalists to assess the ecological condition of lakes, mainly the North Central Nigeria municipal lakes.

A blockchain integration to support failures prediction from log files in multi-agent systems technology

Computer system failures can cause significant financial and reputational damage, making it critical to detect and prevent them before they occur. In this article, we propose a novel approach for predicting system failures based on log file analysis, called MABFailPred. Our approach leverages a Blockchain-based Multi-Agent architecture to analyze execution traces and detect early warning signals for system failure prediction during execution. Moreover, we introduce a new method for extracting log keys called HCLPars. The MABFailPred approach comprises four agents that work together to gather log keys, categorize data, develop a failure model, and make predictions. The use of blockchain ensures that agent and administrator predictions are synchronized, and agent collaboration is coordinated. We validate our approach by applying it to a real-world distributed system and accurately predicting problems before they occur. Our proposed approach represents a significant advancement in the field of system failure prediction, with the potential to enhance the reliability and security of computer systems. By employing a machine learning algorithm, our approach can predict anomalies in real-time and places a strong emphasis on the continuous monitoring of system behavior. The paper includes a validation of the approach using the Hadoop Distributed File System (HDFS) log dataset. According to our findings, the proposed approach achieves superior performance compared to other methods on the same dataset, with a precision of 0.998. This result establishes a new state-of-the-art performance level for the dataset.

Network percolation provides early warnings of abrupt changes in coupled oscillatory systems: An explanatory analysis.

Functional networks are powerful tools to study statistical interdependency structures in spatially extended or multivariable systems. They have been used to get insights into the dynamics of complex systems in various areas of science. In particular, percolation properties of correlation networks have been employed to identify early warning signals of critical transitions. In this work, we further investigate the corresponding potential of percolation measures for the anticipation of different types of sudden shifts in the state of coupled irregularly oscillating systems. As a paradigmatic model system, we study the dynamics of a ring of diffusively coupled noisy FitzHugh-Nagumo oscillators and show that, when the oscillators are nearly completely synchronized, the percolation-based precursors successfully provide very early warnings of the rapid switches between the two states of the system. We clarify the mechanisms behind the percolation transition by separating global trends given by the mean-field behavior from the synchronization of individual stochastic fluctuations. We then apply the same methodology to real-world data of sea surface temperature anomalies during different phases of the El Niño-Southern Oscillation. This leads to a better understanding of the factors that make percolation precursors effective as early warning indicators of incipient El Niño and La Niña events.

Why topological data analysis detects financial bubbles?

We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with oscillations increasing in frequency and decreasing in amplitude when approaching a critical transition (tipping point). We show that whenever the LPPLS model is fitting with the data, TDA generates early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin historical price.

Measuring fiscal stress via fiscal vulnerability index: a case of Turkish economy

PurposeThis paper aims to develop a compound measure, which is fiscal vulnerability index, provides early warning signals of fiscal sustainability problems for Türkiye's economy.Design/methodology/approachThe index is constructed using twelve distinct fiscal indicators and applying the portfolio method, which considers the time-varying cross-correlation structure between the subindices.FindingsDynamics of the fiscal vulnerability index indicate that it accurately predicts to the well-known fiscal crisis occurring in Türkiye's recent history. As a result, such a compound measure should be used in the early identification of fiscal vulnerability in Türkiye.Originality/valueThe main contribution of this paper, relative to existing papers, is that a fiscal vulnerability index was constructed by employing the most contemporaneous method and evaluating its performance in terms of capturing historical stress periods.

Plasma essential fatty acid on hospital admission is a marker of COVID-19 disease severity

It is important for allocation of resources to predict those COVID patients at high risk of dying or organ failure. Early signals to initiate cellular events of host immunity can be derived from essential fatty acid metabolites preceding the cascade of proinflammatory signals. Much research has focused on understanding later proinflammatory responses. We assessed if remodelling of plasma phospholipid content of essential fatty acids by the COVID-19 virus provides early markers for potential death and disease severity. Here we show that, at hospital admission, COVID-19 infected subjects who survive exhibit higher proportions of C20:4n-6 in plasma phospholipids concurrent with marked proinflammatory cytokine elevation in plasma compared to healthy subjects. In contrast, more than half of subjects who die of this virus exhibit very low C18:2n-6 and C20:4n-6 content in plasma phospholipids on hospital admission compared with healthy control subjects. Moreover, in these subjects who die, the low level of primary inflammatory signals indicates limited or aberrant stimulation of host immunity. We conclude that COVID-19 infection results in early fundamental remodelling of essential fatty acid metabolism. In subjects with high mortality, it appears that plasma n-6 fatty acid content is too low to stimulate cellular events of host immunity.

Early and Consistent CRS Detection Using Wearable Device for Remote Patient Monitoring Following CAR-T Therapy in Relapsed/Refractory Multiple Myeloma (RRMM): Early Results of an Investigator-Initiated Trial

The administration of Chimeric Antigen Receptor T-cell (CAR-T) therapy for relapsed refractory multiple myeloma (RRMM) predominantly takes place in the inpatient setting to monitor for adverse effects like cytokine release syndrome (CRS) and neurotoxicity leading to increased healthcare utilization, infection risk and patient discomfort. Shifting the administration model to the outpatient setting may improve patient experience, optimize hospital bed utilization and decrease costs, but requires an objective and reliable method for CRS detection to monitor patients remotely and safely. The feasibility of using a wearable device for detecting CRS following CAR-T therapy in RRMM was compared to standard of care (SoC) nursing-recorded vital signs in this investigator initiated clinical trial (IIT). Patients wore a wearable device that collected continuous measures of temperature, pulse, respiratory rate, and O2 saturation in addition to SoC while receiving CAR-T therapy from the day infusion to discharge. Changes in vital signs captured by the device were analyzed based on 2 thresholds: (i) CRS timestamps were tagged in wearable data when pt's temperature breached a fixed threshold of 38 0C ( Tf) defined as fever by the American Society of Transplantation and Cellular Therapy (ASTCT), and (ii) when pt's temperature breached an individualized threshold of 2 standard deviations above their baseline temperature ( Ti) [see Figure 1]. Outcomes were time to first detection of CRS in wearable data vs. SoC. Wearable adherence was the duration pts wore the device over the total monitoring period. To date, 34 pts were screened, 28 enrolled (82.3% uptake), 1 was excluded from the dataset because of concurrent COVID-19, and 2 pts with CRS were excluded from time to detection comparison because of wearable adherence &amp;lt;50% during high-risk windows (defined as the time period where CRS is expected per known data for the CAR-T product). Max CRS grades were 1 (17 pts), 2 (1 pt), 3 (2 pts), and 4 (1 pt); 5 pts did not have clinical CRS. Patients wore the device for a median of 13 (12-15) days out of a median of 15.5 (14-17) days of inpatient admission for CAR-T infusion. Median individualized temperature threshold was 37.4 (37-37.6) C. Wearable adherence was 64 (51 - 77) % and 72 (57 - 87) % for the overall monitoring and high-risk periods, respectively. The wearable detected the initial CRS events at a median of 22 (-43 - 113.5) min earlier than SoC with Tf and a median of 184 (53 - 312) mins earlier than SoC with Ti method. In the no CRS subgroup, false CRS detection occurred in 1 pt with the Tf and 2 pts with the Ti method. Preliminary results suggest that time to CRS detection by the wearable device preceded SoC detection by a median of 103 minutes corroborated by both threshold methods. Temperature thresholds calibrated to the baseline temperature of individual patients facilitate earlier detection of CRS than fixed thresholds per clinical/ASTCT criteria. While the low false detection rate in is encouraging, these may also potentially be attributed to subclinical events. Both pts who had “false positive” triggers breached their individualized temperature thresholds via device readings while only recording low-grade temperatures that did not meet clinical CRS criteria per ASTCT, indicating possible capture of subclinical events. The study showcases the utility of continuous remote patient vital signs monitoring by wearable devices in efficiently capturing CRS, including subclinical events, earlier than SoC. Objective and reliable CRS monitoring may help safely transition workflows to outpatient CAR-T administration models. Analysis of trends in cytokine biomarkers for early CRS detection signal(s) is currently underway. Cytokine signals, alone and in combination with device data, are planned to be incorporated into machine learning models to build a CRS prediction index in future analyses. Figure 1: Snapshot in time of the continuous temperature data generated by the wearable device after CAR-T infusion showing capture of cytokine release syndrome. – Ti (solid line) represents the median baseline temperature of the patient over the entire duration of the data capture by the wearable device – Tf (dotted line) represents the temperature threshold of 38 0C, the clinical definition of fever as per ASTCT – Black diamond: clinical CRS event, when temperature &amp;gt;38 0C as recorded by nursing

Characterization of TREM-1 Signaling in Human Neutrophils By Kinome Array and RNA-Seq

The Triggering Receptor Expressed on Myeloid Cells (TREM)-1 is a member of the Immunoglobulin superfamily and an activating receptor mainly expressed on myeloid cells. TREM-1 ligation leads to immediate activation of polymorphonuclear neutrophils (PMN) resulting in degranulation and release of reactive oxygen species as well as various effector molecules. Beyond its role in acute and chronic inflammatory processes, TREM-1 is also involved in cancer emergence and progression probably by alteration of the tumor associated neutrophils (TAN) and macrophages (TAM). Advanced information about the TREM-1 signaling cascade may reveal novel therapeutic approaches, as various kinases can specifically be targeted with kinase inhibitors. Therefore, we investigated the protein tyrosine kinome (PTK) and serine threonine kinome (STK) of human PMN after TREM-1 activation. To gain further insights beyond the signaling cascade, we performed RNAseq to validate the TREM-1 mediated activation and to reveal the TREM-1 mediated transcriptome. Purified PMN from healthy donors were stimulated with a monoclonal TREM-1 antibody compared to an isotype matched control for 20 minutes (kinome activity) or 60 minutes (RNAseq). For kinomics, PamChip arrays (PamGene) were used to quantify the kinome activity and upstream kinase analysis (UKA) was used to predict the kinase activity. The final data analysis and the UKA was performed by PamGene. A median final score (depending on the significance and the specificity of the kinase activity compared to the control group) &amp;gt; 1.2 was defined as specific kinase activity according to the manufacturer's recommendations. For transcriptomics, Novogene performed library preparation and sequencing, and data analysis was performed with CLC. Differentially expressed genes were defined as FDR adjusted p value ≤ 0.05 and fold change ≥2. Gene set enrichment analysis was performed with Enrichr using the libraries Reactome 2022, WikiPathway 2021 Human, and GO Biological Process 2023 and significance was defined as adjusted p value ≤ 0.05. We identified the activation of 31 PTKs and 38 STKs upon TREM-1 ligation. The kinase statistic revealed changes in the activation state of the kinases present in the TREM-1 treated cells compared to the untreated control group ranging from 2.4 to 5.5 (PTK) and from 1.2 to 1.6 (STK) suggesting more TREM-1 mediated activity alteration of the PTKs. Interestingly, there were no downregulated kinase activities after receptor ligation. Out of the PTK family, we identified ten Src family kinases as well as the Syk family member ZAP70 and other kinases such as BTK, already previously shown to abrogate TREM-1 mediated PMN activation. The RNAseq after TREM-1 ligation revealed 542 differentially expressed genes from which 102 genes were upregulated and 440 genes were downregulated. Gene set enrichment analysis revealed upregulation of genes related to nuclear events (kinase and transcription factor activaton, R-HSA-198725) and cell chemotaxis (GO:0060326) validating the activation of several kinases after TREM-1 ligation. We found increased expressions of genes involved in adipogenesis (WP236) and lung fibrosis (WP3624) suitably to the observed increased activity of Src family kinases that are described to be involved in lung fibrosis. Moreover, some upregulated genes related to NGF-stimulated transcription (R-HSA-9031628) or regulation of transcription in response to stress (GO:0043618) in which the MAPK are known to be involved. However, we did not observe clearly increased MAPK activity. Besides upregulation, TREM-1 ligation led to decreased expression of several genes involved in (positive) regulation of cytokine production involved in inflammatory response (GO:1900017 and GO:1900015) and signaling by interleukins (R-HSA-449147). Taken together, the results contribute to the knowledge about TREM-1 in inflammatory disorders and reveal approaches of its role in cancer. This study revealed various kinases possibly involved in early TREM-1 signaling of neutrophils that may be suitable targets to inhibit TREM-1 mediated cell activation. Moreover, we linked the TREM-1 mediated kinase activity to the resulting gene expression. Further studies are needed to validate the results in vitro and in vivo and and to identify suitable pharmacological inhibitors to modulate TREM-1 mediated activities in inflammatory conditions and cancer.