The ability for wearable devices to collect high-fidelity biosignals continuously over weeks and months at a time has become an increasingly sought-after characteristic to provide advanced diagnostic and therapeutic capabilities. Wearable devices for this purpose face a multitude of challenges such as formfactors with long-term user acceptance and power supplies that enable continuous operation without requiring extensive user interaction. This review summarizes design considerations associated with these attributes and summarizes recent advances toward continuous operation with high-fidelity biosignal recording abilities. The review also provides insight into systematic barriers for these device archetypes and outlines most promising technological approaches to expand capabilities. We conclude with a summary of current developments of hardware and approaches for embedded artificial intelligence in this wearable device class, which is pivotal for next generation autonomous diagnostic, therapeutic, and assistive health tools. Wearable devices have seen an acute increase in popularity over the last 10 years.The US market for wearable devices reached $24.57 billion in 2018 with a projected growth rate of 24.7% annually to $139.35 billion by 2026Despite widespread adoption among several subpopulations, conventional wearable devices do not meet many standards set out by acceptance models, as many devices fail to drive long-term user engagement. In addition to these factors, there are additional engineering design challenges, including communication security and privacy, power consumption, and transmission reliability ,2) but are available with a wide range of capacities that can handle continuous device operation of 8–10 h. In an effort to render battery powered systems more mechanically compliant, development of stretchable cells that are able to deform and integrate seamlessly into textilesWearable systems for noninvasive monitoring of physiological processes and biomarkers for disease detection, coupled with advances in electronic systems, materials science,Radio frequency (RF) power transfer, which is a popular modality for wearable systems with functional operation distances of 60 cm and low tissue absorption (<20 mW/kg), makes it appealing for wearable applications directly on the skin.−2 with dedicated light sources and 14.6 μW cm−2 from ambient light sources.Another power transfer technique uses photovoltaic cells (PVCs) for converting optical energy into electrical power.−2 , and respiratory rate (RR) along with central-skin and peripheral-skin temperature to monitor patients in the neonatal intensive care (NICU) and pediatric intensive care unit [2, skin-temperature, and the accelerometry data. The system demonstrates reliable and accurate measurements, comparable with the gold standard, hard-wired techniques, for continuous monitoring periods of up to 24 h. Motion artifacts that occur during clinical treatment, feeding, and medical imaging are reduced in scale and prevalence because of the wireless operation, mechanical stability, and wearability of the device increasing recording fidelity. The core benefit of this embodiment is the extended wearability and chronic operation without the need of device interaction that can have substantial effects on preterm infants due to very fragile skin where instruments can leave lifelong scars and the ability to improve clinical outcomes with additional skin to skin interaction with parents, which are otherwise interrupted or reduced by wired connections.Multimodal wireless and battery-free devices for vital sign analysis based on epidermal sensors are described in Refs. are unit . The devare unit . The NICIn addition to expanding sensing capabilities and broadening the use case as well as user compliance of biophysical sensor systems, epidermal form factors of wireless and battery-free devices enable noninvasive investigation into biochemical makers, such as lactate,Among other metabolites of interest, metabolic byproducts, such as glucose and lactate, are important biomarkers in many disease paradigms and human performance analysis and are essential in understanding underlying metabolic processes. While many of these biomarkers are present in biofluids such as blood, they traditionally require transdermal probing for accurate recording, increasing the barrier of usage significantly and making noninvasive alternatives compelling to develop. One device that provides exemplary monitoring capabilities with multimodal sensing capabilities is featured in in vivo testing is shown in Integration of battery-free electronics into contact lenses offers direct access to tear fluid, which is rich in biomarkers that can be harnessed for diagnostic purposes.−2 in untreated human sweat samples and good long-term stability over 2000 cycles. Collection of urea and NH4+ concentrations were carried out using electrochemical patches based on ion-selective electrodes with enzymatic layers and can measure concentrations of 2.5–40 mM for both metabolites. Validation of device operation was carried out during a stationary biking exercise with data presented in 4+ concentrations is observed during the exercise period, which then stabilized after conclusion of cycling.NFC-based communication and powering for wearable systems, while having robust pre-existing infrastructure and development, are unfavorable for many long-term wearable systems as they require close proximity to a transmission device for powering and data readout.Wireless and battery-free biochemical sensors highlight an area of development with potentially high impact if device architectures allow for chronic recording capability that would otherwise require blood draw and laboratory testing, which is often a barrier to preventative measures that results in late diagnosisThe devices discussed in this review feature advances in materials science,Battery-powered wearable systems, such as smartwatches or fitness trackers, offer a low barrier of entry to the consumer market, however, they incur penalties in sensing quality, useability, and user compliance.Current behavioral research suggests that ease of use, among other factors, played a role in low user acceptance of wearable systems over time.Epidermal devices that provide skin-like mechanics, low dimensional profiles, and wireless power transfer functionalities enable high-fidelity biosignal acquisition by removal of weight associated with large battery-powered supplies with the ability to be deployed virtually anywhere on the body. Devices of these form factors, however, are insufficient to meet the demands of chronic data recording due to their inconsistency of power delivery to the system and their use of adhesive-based application for epidermal contact. The shortcomings of these devices are summarized in This physiology confines the use of adhesive based devices to acute applications or requires frequent renewal of the adhesive, which, for example, in a hospital setting is completely acceptable; however, it is less appealing for at-home diagnostic applications. Additionally in some cases, the utilization of strong adhesives may bar users from utilizing the device due to skin sensitivity,To overcome user compliance challenges while providing long-term uninterrupted streams of high-fidelity data, a new class of wearable systems is needed. These devices must provide a robust platform that features body-like mechanical properties with high-fidelity sensing capabilities, long-range power transfer capabilities that enable reliable device operation without constraints of short-distance power transfer techniques, and miniaturized electronics that provide the uncompromised system function with low-profile and small footprint, in a package that enhances the user experience to the point that perceived value from the data far outweighs the burden of wearing a device. An example of such a device class called a biosymbiotic device is featured in The conformality and mechanical profile of the device enables multimodal sensing capabilities matching or exceeding and expanding the current gold standard in battery-powered wearable systems. This has been demonstrated with several sensing capabilities including sub-millikelvin thermography, muscular deformation, and high-precision inertial measurements. The circumferential fit and use of 3D printing also enables unique sensing capabilities that are currently not possible with conventional devices. One such modality is presented in Advances of biosymbiotic electronics enable continuous streams of high-fidelity biosignals due to the ability for the system to wirelessly recharge without user interaction with charging infrastructure. This allows for previously untapped insight into several physiological processes that require continuous examination over extended periods demonstrated in Wearable sensors can provide continuous, noninvasive, real-time monitoring of a wide range of physiological parameters. However, most of the available sensors are considered data collectors and/or alerting units with limited processing capabilities. Artificial intelligence (AI) offers a new path to turn biosensors into smart computational units, capable of making autonomous decisions and on-site detection/diagnosis, which is especially relevant to continuously recording devices such as biosymbiotic electronics. Local computation presents many opportunities in terms of data safety, latency,A wearable AIsystem usually consists of wearable sensors, analog front-end circuit responsible for amplifying, filtering, and signal conditioning the collected raw data from the sensors and a microcontroller used as the central processor, as shown in To train and test the regression/classification model, the following workflow, shown in et al.Observing, classifying, and assessing human gestures/movements,Wearable medical devices for the extraction of high-fidelity biosignals are pivotal to the realization of digital medicine, where technology integrates with the human body and utilizes continuous data collection and advances in AI for treatment, diagnosis, and ultimately prevention of disease. Requirements for this paradigm include wearable devices that can be worn continuously to provide uninterrupted steams of clinical grade biosignals, while AI is deployed to identify trends and markers, which may indicate disease onset or changes in underlying physiology. The development of AI has greatly outpaced wearable technology for the realization of this goal. Current wrist band type wearable devices are inadequate due to their low-fidelity sensing capabilities and challenges for long-term wearability, which include both aspects of formfactors and issues with long-term user acceptance and compliance.The emergence of chronically wearable wireless and battery-free devices provides an appealing alternative to standard wearable devices, as they have improved sensing fidelity and expand capabilities towards biosignals that previously required clinic-based wired setups. Additionally, materials and concepts used for this device class improve long-term wearability through reduction of bulk and introduction of overall soft mechanics. Despite this, many demonstrations have been limited to short-term and specific applications with day long duration due to natural physiological processes such as epidermal turnover. Further work geared toward demonstrations of chronically wearable devices in dynamic settings with high-fidelity sensing capabilities is needed. Successful implementation of on-board AI capabilities on these devices will unlock the full potential with broad applications among various disease archetypes. Specifically, these capabilities can improve diagnosis and monitoring of chronic diseases such as aging related frailty,


Soil is acknowledged as a limited resource, which determines crop productivity and ecosystem sustainability. Microorganisms form an integral part of soil, contributing to soil fertility and plant health. The soil, diverse microbial communities and plants are involved in direct and indirect interactions, which are often difficult to predict, depending on environmental factors . A comprSoil sickness is a widespread problem associated with replanting fruit crops and monocropping, with a complex etiology, and influenced by soil and climatic conditions . In addiTo advance beyond a simplified view of individual plant-microbe or soil-plant interactions, the plants-soil biota should be considered as a unique “meta-organism”, influencing complex interactions that impact plant development and productivity. To facilitate efficient exploitation of beneficial microbial strains in practice, a complex set of data needs to be generated to assess their impact on resident soil microbiome (and its functions), soil physio-chemical properties and plant performance under the influence of agronomical practices . This ovZhang et al.), as also found in other crops. Lignocellulose-decomposing enzymes produced and released by the studied microorganisms were shown to have played a crucial role in the assembly of endophytic bacterial communities and their successful colonization of wheat roots. Stimulation of plant metabolic activities by biofertilizers was reviewed by Chaudhary et al., highlighting the multifunctional potential of such bioinocula in terms of promoting growth and activating plant defense in the presence of biotic and abiotic, particularly climate related, stresses. Inoculation of cuttings of the medicinal plant Bacopa monnieri with Bacillus subtilis and Klebsiella aerogenes individually or in combination induced a significant improvement in the accumulation of bacoside A, a triterpenoid saponin with preventive activity against Alzheimer’s disease , demonstrating the potential of the in planta production of secondary metabolites for medical applications.Large-scale production and formulation of microbial strains, alone or in consortia, are the critical steps in the development of commercial products . ApplyinLi et al. provided a novel insight into the relationship between lime amendment of acidified soil, composition of citrus root-associated microbiota and citrus tolerance to Huanglongbing (a systemic soil-borne disease caused by nonculturable bacteria Candidatus Liberibacter spp.). Liming, increased the diversity of root bacterial communities and enriched beneficial microorganisms in roots, two features associated with a lower Huanglongbing disease severity.Among environmental stresses, acidification is considered to limit soil fertility and health. Trinchera et al. studied five cropping systems of organic vegetables and showed that multi-cropping increased the total soil microbial mass, promoting specific bacterial and fungal phyla and mycorrhizal colonization. These changes were associated with positive effects on C and P nutrient cycles and pathogen reduction. Multicropping was successfully applied in greenhouse production of Capsicum annum intercropped with Foeniculum vulgare . The release of antimicrobial terpene compounds by the fennel roots suppressed Phytophthora disease through the induced accumulation of reactive oxygen species in the pathogen.Soil fertility and crop productivity are closely related and dependent on soil biodiversity. Alternative strategies to increase crop diversification can supply several ecosystem services related to nutrient cycling, crop productivity and health, all mediated through soil microbiota. El-Saadony et al. reviewed literatures providing evidence of biocontrol activity of plant growth promoting microorganisms. Exploiting this multifunctional capacity can foster alternative strategies of crop protection, currently still relying on synthetic fungicides, thus responding to the challenge of ensuring healthy and fresh products within a net-zero horticulture ], identified possible mechanisms of action against several soil-borne pathogens, and optimized fermentation process for the large-scale production. Detailed studies about the effect of this strain on plant root development, soil-borne pathogens and soil microbial community led them to propose a framework for managing ARD. In another study , Streptomyces violaceoruber UAE1 isolated from rhizospheric soil of healthy date palms, able to produce 1-aminocyclopropane-1-carboxylic acid deaminase, showed antifungal activities against Fusarium solani, the causal agent of the sudden decline syndrome of date palm, highlighting the potential of this strain in disease management.Biocontrol with specific microbial strains is an efficient tool for achieving sustainable pest control in an environmentally friendly manner. iculture . Duan etBelleís-Sancho et al. studied the relationship between gene expression induced by a beta-rhizobial nifA mutant  and auxin synthesis during the symbiosis with Phaseolus vulgaris. In general, the establishment of this symbiotic interaction relies on a sophisticated molecular and chemical cross-talk between Rhizobium and the plant. The gene regulating nitrogenase expression, nifA, was shown to control expression of two bacterial genes involved in auxin synthesis, leading to their increased production in nodules occupied by the nifA mutant compared to wild-type nodules. It was concluded that the increased abundance of rhizobial auxin in the non-fixing nifA mutant strain may have played a role in root infection and early-stage symbiotic interactions.Vassileva et al. reviewed the risks of contamination of microorganism-bearing fertilizers during different production stages, storage, and application in soil. Direct and indirect spread of zooptic and plant pathogens associated with these microbial-based products were discussed with examples of different products, including compost, manure, biosolids, and bioformulated inoculants with plant growth promoting or biocontrol functions.Safety and quality are important issues for commercialization of biofertilisers and biopesticides. The work published in this issue confirms the urgent need for studies on soil microorganism-mediated processes responsible for different soil functions and ecosystem services, with the emphasis on their regulating mechanisms. This knowledge can be used to inform advisors and farmers and improve their decision-making in crop management to improve food security and quality .All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


Subclinical anxiety, depressive and somatic symptoms appear closely related. However, it remains unclear whether somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and what the underlying neuroimaging mechanisms are for the mediating effect.n = 466) and participants in remission of major depressive disorder (n = 53) were obtained from the Human Connectome Project. The Achenbach Adult Self-Report was adopted to assess anxiety, depressive and somatic symptoms. All participants completed four runs of resting-state functional magnetic resonance imaging. Mediation analyses were utilized to explore the interactions among these symptoms and their neuroimaging mechanisms.Data of healthy participants (p < 0.01).Somatic symptoms partially mediated the association between subclinical anxiety and depressive symptoms in healthy participants  and participants in remission of MDD . Resting-state functional connectivity (FC) between the right medial superior frontal gyrus and the left thalamus and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in healthy participants . The mean strength of common FCs of subclinical depressive and somatic symptoms, somatic symptoms, and the mean strength of common FCs of subclinical anxiety and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in remission of MDD . These common FCs mainly involved the insula, precentral gyri, postcentral gyri and cingulate gyri. Furthermore, FC between the triangular part of the left inferior frontal gyrus and the left postcentral gyrus was positively associated with subclinical anxiety, depressive and somatic symptoms in remission of MDD (FDR-corrected Somatic symptoms partially mediate the interaction between subclinical anxiety and depressive symptoms. FCs involving the right medial superior frontal gyrus, left thalamus, triangular part of left inferior frontal gyrus, bilateral insula, precentral gyri, postcentral gyri and cingulate gyri maybe underlie the mediating effect of somatic symptoms.The online version contains supplementary material available at 10.1186/s12888-022-04488-9. Subclinical anxiety/depression occurs when individuals experience anxiety/depressive symptoms that do not meet the diagnostic criteria for anxiety disorders (ANX)/major depressive disorder (MDD) –6. SubclThe global age-standardized prevalence of depressive disorder (MDD and dysthymia) was 3.44%, which caused disability-adjusted life years accounting for 37.3% of that due to mental disorders, ranking first among all mental disorders . 85% of Somatic symptoms are closely related to anxiety and depressive symptoms. Patients with ANX or MDD often experienced somatic symptoms , 23, whiBased on the above, we aimed to assess the mediating effect of somatic symptoms on the association between subclinical anxiety and depressive symptoms with simple mediation models and to explore resting-state functional connectivities (FCs) underlying the mediating effect with chain mediation models , 31. IdeOur data were obtained from the Human Connectome Project (HCP)-Young Adult database . HCP datPrevious studies defined a scale item score ≥ 1 as one symptom –36 and dBeyond the inclusion and exclusion criteria of the HCP , particiAll participants underwent a comprehensive assessment of their psychiatric and substance use history over the telephone using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which assessed a range of diagnostic categories  using both the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) and either the Research Diagnostic Criteria or International Classification of Diseases and provided information about both current and lifetime experiences. The SSAGA is a well-validated diagnostic instrument and has been used in numerous previous large-scale studies , 39.The anxiety, depressive and somatic symptoms were assessed by the DSM-IV Anxiety Problems subscale, DSM-IV Depressive Problems subscale and Somatic Complaints subscale, based on the Achenbach Adult Self-Report (ASR) for ages 18–59, and all the items in these subscales are listed in Table SThe rs-fMRI data were acquired with a gradient-echo echo-planar imaging sequence on a customized Siemens Skyra 3 T scanner with a 32-channel head coil . Each run of rs-fMRI lasted approximately 15 minutes, and each session consisted of a run with a phase encoding in a right-to-left direction and another run with a phase encoding in a left-to-right direction. Two sessions were completed during a two-day visit. The MRI data were preprocessed by the HCP consortium with its unified methods with the FMRIB Software Library (FSL), FreeSurfer, and Connectome Workbench software .The gray matter of the whole brain was parcellated into 250 functional regions of interest based on the Shen atlas following preprocessing of the raw imaging data, and their anatomical names were found in Anatomical Automatic Labeling-2 atlas , 45, 46.Spearman partial correlation analysis was used to analyze the correlations among subclinical anxiety, depressive and somatic symptoms after controlling for covariates including age, gender, race, handedness , educatip values corrected by the False Discovery Rate (FDR) less than 0.05 were considered significant [To identify FCs associated with subclinical anxiety symptoms, a general linear regression model was established using the network-based statistic toolkit. In this model, subclinical anxiety symptoms were the dependent variable, FC strength was the independent variable, and the aforementioned control variables were covariates. To address the issue of multiple comparisons, nificant . The samA chain mediation model was established by writing a program in PROCESS  to exploA total of 466 subjects  and 53 subjects  were included in the HP and MDP, respectively. Demographic data, including age, gender, race, handedness, education, parental history of neuropsychiatric disorders, times smoked, times used illicit drug and times used marijuana and history of panic disorder/agoraphobia, are shown in Table p < 0.001; Additional file 1: Table SSubclinical anxiety, depressive and somatic symptoms were significantly interrelated with each other , the somatic complaints scores, and the mean strength of the remaining 12 common FCs associated with both subclinical depressive and somatic symptoms (DS-FC) as mediators into the chain mediation model (Fig. In the present study, we found that somatic symptoms partially mediated the association between subclinical anxiety and depressive symptoms bidirectionally in the healthy participants and participants in remission of MDD, which implies that subclinical anxiety and depressive symptoms interact to some extent by affecting somatic symptoms. Furthermore, in the healthy participants, the FC between the right medial superior frontal gyrus and the left thalamus and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms. In remission of MDD, the mean strength of common FCs of subclinical depressive and somatic symptoms, somatic symptoms, and the mean strength of common FCs of subclinical anxiety and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms; the mean strength of common FCs of subclinical anxiety and somatic symptoms and that of subclinical depressive and somatic symptoms as chain mediators partially mediated the interactions between subclinical anxiety and depressive symptoms. These common FCs mainly involved the insula, precentral gyri, postcentral gyri and cingulate gyri. In addition, the FC between the triangular part of the left inferior frontal gyrus and the left postcentral gyrus was significantly associated with subclinical anxiety, depressive and somatic symptoms in remission of MDD. These FCs mentioned above may explain the relationships among subclinical anxiety, depressive and somatic symptoms. Our findings may improve the understanding of these symptoms.Subclinical anxiety, depressive and somatic symptoms were found to be correlated with each other in our study, as well as in previous studies , 18, whiThe common FC between the right medial superior frontal gyrus and the left thalamus and somatic symptoms as chain mediators partially mediated the effects of subclinical depressive symptoms on subclinical anxiety symptoms in the healthy participants, which reveals that subclinical depressive symptoms may influence the FC and somatic symptoms in sequence and then affect subclinical anxiety symptoms. The superior frontal gyrus was related to emotional regulation  and cognThe FC between the triangular part in the left inferior frontal gyrus and the left postcentral gyrus was significantly associated with subclinical anxiety, depressive and somatic symptoms in remission of MDD. The functional activity of the triangular part in the left inferior frontal gyrus increased in MDD , and theNotably, the mediating effect of somatic symptoms was significant in the simple mediation model, whereas it was insignificant after controlling for the other indirect paths in the chain mediation model in remission of MDD. Furthermore, we found that the mean strength of the common FCs of subclinical anxiety and somatic symptoms and that of subclinical depressive and somatic symptoms as chain mediators independently mediated the interaction between subclinical anxiety and depressive symptoms, and that the mean strength of the common FCs of subclinical depressive and somatic symptoms, somatic symptoms, and the mean strength of the common FCs of subclinical anxiety and somatic symptoms as chain mediators independently mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in remission of MDD. These common FCs mainly involved the insula, precentral gyri, postcentral gyri and cingulate gyri, which were related to anxiety, depressive and somatic symptoms , 76–82. We found a positive correlation between FC strength and the severity of subclinical depressive symptoms in remission of MDD, which was similar to the results of another study with subjects from the HCP . HoweverOur study presents several limitations. First, three items, including feeling tired, heart pounding and sleep problems, overlap across the three subscales , which mSomatic symptoms partially mediate the interaction between subclinical anxiety and depressive symptoms. The FC between the right medial superior frontal gyrus and the left thalamus in healthy people and the common FCs mainly involving the triangular part in the left inferior frontal gyrus, bilateral postcentral gyri, bilateral insula, bilateral precentral gyri and bilateral cingulate gyri in remission of MDD maybe underlie the mediating effect of somatic symptoms on the association between subclinical anxiety and depressive symptoms. Our findings may contribute to a better understanding of these symptoms, and the common FCs may be potential targets for intervention to prevent the onset/relapse of ANX/MDD.Additional file 1.


PTEN and p53, and their associated downstream effects are known to influence seizure likelihood. Similarly, various intratumoral mechanisms, such as GBM-induced blood-brain barrier breakdown and glioma-immune cell interactions within the tumor microenvironment are also cited as contributing to network hyperexcitability. Substantial alterations to peri-tumoral glutamate and chloride transporter expressions, as well as widespread dysregulation of GABAergic signaling are known to confer increased epileptogenicity and excitotoxicity. The abnormal characteristics of GBM alter neuronal network function to result in metabolically vulnerable and hyperexcitable peri-tumoral tissue, properties the tumor then exploits to favor its own growth even post-resection. It is evident that there is a complex, dynamic interplay between GBM and epilepsy that promotes the progression of both pathologies. This interaction is only more complicated by the concomitant presence of spreading depolarization (SD). The spontaneous, high-frequency nature of GBM-associated epileptiform activity and SD-associated direct current (DC) shifts require technologies capable of recording brain signals over a wide bandwidth, presenting major challenges for comprehensive electrophysiological investigations. This review will initially provide a detailed examination of the underlying mechanisms that promote network hyperexcitability in GBM. We will then discuss how an investigation of these pathologies from a network level, and utilization of novel electrophysiological tools, will yield a more-effective, clinically-relevant understanding of GBM-related epileptogenesis. Further to this, we will evaluate the clinical relevance of current preclinical research and consider how future therapeutic advancements may impact the bidirectional relationship between GBM, SDs, and seizures.Glioblastoma (GBM) is the most common and advanced form of primary malignant tumor occurring in the adult central nervous system, and it is frequently associated with epilepsy, a debilitating comorbidity. Seizures are observed both pre- and post-surgical resection, indicating that several pathophysiological mechanisms are shared but also prompting questions about how the process of epileptogenesis evolves throughout GBM progression. Molecular mutations commonly seen in primary GBM, i.e., in  This breakdown allows infiltration of serum proteins into the brain parenchyma, encourages vasogenic oedema, and increases intracranial pressure  receptors induces changes in the expression of critical inwardly rectifying potassium channels . SD is characterized by a near-complete regional depolarization of neurons and glial cells that is accompanied by the collapse of transmembrane ion gradients and followed by a temporary suppression of neuronal activity , tumor-associated neutrophils (TANs), and regulatory T cells (Tregs). These mediators are crucial in maintaining GBM’s extreme immunosuppressive phenotype and are also directly linked to cancer cell proliferation and the recruitment of surrounding non-neoplastic cells  membrane expression and trafficking, enabling greater Ca2+ influx with cytotoxic effects. It also acts to increase the internalization of GABAA receptors (GABAARs), subsequently reducing the influence of GABAergic inhibitory neurotransmission . Chronic MMP-9 production is associated with BBB disruption, independent of the insult, and mediates its pathological effects by decreasing the efficiency of BBB tight junctions , also known by its active subunit xCT , respectively. They are primarily responsible for the pathological reversal of the Cl− gradient that occurs in GBM ; a residue responsible for transporter function and stability in the membrane  or human primary cultures to generate intracranial GBM tumors. Wherein a tumor cell suspension is intracranially injected into brain areas associated with seizure vulnerability and/or propagation . Geneticogenesis . The majogenesis , with maogenesis , 2015. TThe paucity of the literature including seizures as a measured variable in oncological studies is a substantial obstacle to collating information about mechanisms underlying GBM-related epileptogenesis. Where the experimental focus is oncogenesis or novel treatment development, the presence of seizures is often overlooked or only mentioned as a general indicator of a humane endpoint. Experimental animals typically express complex or simple focal seizures characterized by subtle behavioral changes such as freezing periods, facial automatisms, head tremors, or exaggerated startle responses to audiogenic stimuli  with concomitant TMZ chemotherapy and maintained by 20 weeks of adjuvant TMZ  inhibitor and potentiation of the cytotoxicity induced by temozolomide  as the constant presence of the tumor increases the likelihood of seizure recurrence. GBM-associated seizures present unique challenges as they are often poorly controlled, requiring poly-therapy. However, there are several potential contraindications related to the use of ASDs in GBM, including increased risk of enhanced toxicity and undesirable interactions with anti-cancer treatments. Generally, non-enzyme inducing agents are first-line ASD options, i.e., levetiracetam (LEV), as they do not interfere with anti-tumor treatment strategies  in GBM cell lines, conferring reduced resistance to TMZ , recently gained attention as it was shown to significantly reduce the seizure frequency of previously drug-refractory GBM-related seizures , chimeric antigen receptor T cell (CAR-T) therapy, myeloid-targeted therapies, and tumor vaccines, have been the focus of GBM literature in recent years. GBM mediates an immunosuppressive TME by upregulating the expression of immune checkpoint molecules to reduce T cell activation and proliferation. This confers an almost intrinsic resistance to traditional anti-cancer treatments, making immune checkpoint molecules attractive therapeutic targets. In particular, increased expression of CTLA-4 is seen on Tregs and cytotoxic T lymphocytes, and PD-L1 on tumor cells and TAMs  that recognize an identified tumor antigen, e.g., EGFRvIII, before being re-transplanted  that infiltrate the brain through the BBB and they account for up to 50% of the tumor mass . Generally, surgical treatment of GBM can range from a minimally invasive surgical biopsy to a craniotomy involving varying degrees of “total” tumor resection  level. Negative potential shift is an important identifier of SD but is not measured using standard AC-coupled (>0.5 Hz) highpass filtered electrophysiological methods. This can be partially overcome by the use of amplifiers capable of recording at lower high-pass filter limits (>0.02 Hz), or by using a DC-coupled system. However, substantial attenuation of the amplitude and distortion of the SD waveform is evident when using passive metal-based electrodes, as well as issues with drift and amplifier saturation. The presence of SD has not yet been investigated intra- or post-operatively in GBM patients, epileptogenic or otherwise, and only very few experimental studies acknowledge its presence . gSGFETs have the potential to fill this technology gap as they offer the means to record SDs, either independently or concurrently with high-frequency activity, both epi- and intra-cortically with high spatiotemporal fidelity . The remaining authors declare 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.


Emerging evidence suggests transcranial direct current stimulation (tDCS) can improve cognitive performance in older adults. Similarly, music listening may improve arousal and stimulate subsequent performance on memory-related tasks. We examined the synergistic effects of tDCS paired with music listening on auditory neurobehavioral measures to investigate causal evidence of short-term plasticity in speech processing among older adults. In a randomized sham-controlled crossover study, we measured how combined anodal tDCS over dorsolateral prefrontal cortex (DLPFC) paired with listening to autobiographically salient music alters neural speech processing in older adults compared to either music listening (sham stimulation) or tDCS alone. EEG assays included both frequency-following responses (FFRs) and auditory event-related potentials (ERPs) to trace neuromodulation-related changes at brainstem and cortical levels. Relative to music without tDCS (sham), we found tDCS alone (without music) modulates the early cortical neural encoding of speech in the time frame of ∼100–150 ms. Whereas tDCS by itself appeared to largely produce suppressive effects , concurrent music with tDCS restored responses to those of the music+sham levels. However, the interpretation of this effect is somewhat ambiguous as this neural modulation could be attributable to a true effect of tDCS or presence/absence music. Still, the combined benefit of tDCS+music  was correlated with listeners’ education level suggesting the benefit of neurostimulation paired with music might depend on listener demographics. tDCS changes in speech-FFRs were not observed with DLPFC stimulation. Improvements in working memory pre to post session were also associated with better speech-in-noise listening skills. Our findings provide new causal evidence that combined tDCS+music relative to tDCS-alone (i) modulates the early (100–150 ms) cortical encoding of speech and (ii) improves working memory, a cognitive skill which may indirectly bolster noise-degraded speech perception in older listeners. Transcranial direct current stimulation (tDCS) is a type of neuromodulation technique that exerts effects over a specified cortical target region of the brain. Anodal and cathodal electrodes are placed on the scalp and low intensity direct current is applied resulting in polarity-dependent changes in cortical activity . Though Transcranial direct current stimulation studies have overwhelmingly focused on improving memory functions  tDCS and autobiographically salient music listening on cognitive skills in healthy older adults . EEGs reHere, we report on new EEG data from the sample of older adults reported in To this end, we measured frequency-following responses (FFRs) and auditory ERPs to trace tDCS-related changes at both brainstem (FFRs) and cortical (ERPs) levels of the auditory system. We also focused on auditory behavioral measures including hearing thresholds, speech-in-noise processing (QuickSIN) , and WM The sample is identical to that reported in our companion paper on the effects of neurostimulation on cognitive processing in older adults . ResultsN=14; 72.6 ± 5.03 years; 11 females) who reported normal or corrected-to-normal vision and hearing, with no history of neuropsychiatric illness or disorder. This sample size was similar to those of past tDCS and music listening studies . Sessions were counterbalanced across participants.We used a counterbalanced, crossover (repeated-measures) design in which participants underwent three testing sessions each featuring a different neurostimulation condition. The entire protocol, including measures reported previously in During the first session, participants completed demographic questionnaires, audiometric testing, and the QuickSIN test  to ensurtDCS-only, tDCS+Music, or Sham+Music conditions. In the conditions involving music listening, each song from the personalized playlist was played in its entirety. The order of songs was randomized for each participant’s playlist between the tDCS+Music and Sham+Music sessions. Autobiographically salient music was played through soundbooth speakers at an individual comfortable listening level that was kept constant for each participant’s two music listening conditions. Immediately following tDCS, participants completed another measure of emotional affect and WM.The stimulation phase followed, involving one of After behavioral assays and a word recall EEG task (25 min)  . We focused on auditory measures including hearing thresholds, speech-in-noise processing (QuickSIN), and working memory (digit span) performance, as these factors are important determinants of everyday speech recognition abilities in older adults . Similarly, a linear model incorporating the combination of variables shown in We assessed the behavioral relevance of the ERP changes due to tDCS as well as relations between behavioral and demographic variables via bivariate linear regressions (“fitlm” in MATLAB). Shapiro–Wilk tests confirmed normality of the variables , we did find that the change in WM1 performance pre- to post-tDCS (averaged across sessions) was associated with QuickSIN scores. Namely, larger behavioral gains in backward digit span pre-to-post tDCS were associated with better speech-in-noise recognition  , concurrent music with tDCS restored ERP amplitude to baseline levels. However, we note this neural effect could be attributable to a true effect of tDCS or presence/absence music. Changes in WM were also observed from pre- to post intervention session, and were related to better speech-in-noise listening skills. Our findings provide new causal evidence that tDCS stimulation modulates the early neural encoding of speech as indexed by the auditory ERPs. Moreover, we show that tDCS-related improvements in WM [previously reported in In the same sample of listeners, we have previously shown that tDCS while listening to autobiographically salient music amplifies gains in WM relative to either tDCS or music listening alone . Severalunderestimate the true neuroplastic effects had FFRs/ERPs been conducted immediately after tDCS intervention. Still, even after 25 min, tDCS effects on the brain appear to persist long enough to observe modulations in the cortical ERPs (but not brainstem FFRs) post-stimulation.Literature on the effects of tDCS on the auditory ERPs have been equivocal  of the N1 are thought to lie near the auditory cortices within the Sylvian fissure . FunctioTranscranial direct current stimulation did not have an appreciable effect on speech-evoked FFRs. We offer several explanations for these null findings. First, FFRs are dominantly of brainstem origin when recorded via EEG . As suchwithout music listening, which would have allowed for more robust inference regarding the independent effects of tDCS and music on the auditory ERPs. There is a limitation of our study which warrants future investigation. Longitudinal and larger sample studies are also needed to identify the proper dosage of neurostimulation that might produce optimal neuro-behavioral plasticity at the individual level.Our correlational findings offer preliminary evidence for tDCS-induced modulation of cognitive skills in the form of better WM post-intervention [as also reported in We should also emphasize that the present data did not show a link between neural and behavioral outcomes. That is, we did not observe correlations between tDCS+music benefits and auditory measures including QuickSIN, hearing thresholds, and WM performance. Thus, like our previous report examining tDCS and recognition memory , the behRegardless of underlying mechanism(s), our study affirms that listening to music, particularly to songs that evoke autobiographical memories, may heighten responsivity to the effects of tDCS for healthy older adults. The larger effect of music+tDCS than music or tDCS alone supports general findings in the literature of an advantage of combining tDCS with other cognitive tasks to maximize neuroplastic effects e.g., . MoreoveThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Research Ethics Board of the Rotman Research Institute at Baycrest Centre. The participants provided their written informed consent to participate in this study.AN-G, JR, and CA contributed to the conception and design. AN-G and RC conducted the data acquisition. RC, KB, and GB conducted the data processing and analysis. All authors contributed to interpreting the results and writing the manuscript.The authors declare 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.


Analyzing chemicals and their effects on the environmentfrom alife cycle viewpoint can produce a thorough analysis that takes end-of-life(EoL) activities into account. Chemical risk assessment, predictingenvironmental discharges, and finding EoL paths and exposure scenariosall depend on chemical flow data availability. However, it is challengingto gain access to such data and systematically determine EoL activitiesand potential chemical exposure scenarios. As a result, this workcreates quantitative structure-transfer relationship (QSTR) modelsfor aiding environmental managment decision-making based on chemicalstructure-based machine learning (ML) models to predict potentialindustrial EoL activities, chemical flow allocation, environmentalreleases, and exposure routes. Further multi-label classificationmethods may improve the predictability of QSTR models according tothe ML experiment tracking. The developed QSTR models will assiststakeholders in predicting and comprehending potential EoL managementactivities and recycling loops, enabling environmental decision-makingand EoL exposure assessment for new or existing chemicals in the globalmarketplace. This contribution aims at chemicalend-of-life scenariosidentification, thereby understanding the complete chemical life cycleand assessing environmental and human health impacts. Someof them need to be properly managed to avoid harmful effects on humanhealth and the environment.11 offering the transfer factorsfor all EoL activities when multiple entities are involved. Then,those transfer factors provided by the material flow analysis canbe used either for life cycle impact assessment or exposure scenarioidentification.13 In fact, material flow analysishas been used by practitioners to obtain chemical flow inventoriesfor a large number of chemicals.16 For instance, as partof the program run by the Organization for Economic Cooperation andDevelopment (OECD)’s Working Group on Environmental Informationand Outlooks, a material flow analysis was used to determine environmentalreleases and exposure routes of mercury and quantitatively assessrisk to human health endpoints like digestive and immune system.17 Hernandez-Betancur et al.18 developed a data engineering framework that uses facility-levelinformation from US siloed and publicly accessible databases to performchemical flow analysis (CFA) of EoL chemical flows transferred tooff-site facilities for further EoL management. Hernandez-Betancuret al.19 used data at the pollution abatementunit technology level to perform CFA and allocate chemical flows downstreamof the pollution abatement units. Hernandez-Betancur et al.20 combined the above two frameworks19 and extended them beyond the EoL management chain boundaries toidentify and track recycled chemical flows. Using a Markov randomfield model, Hernandez-Betancur et al.20 outlined the relationship between the entities through the EoL managementchain and recycling loops. Due to the legal, economic, market, andpolicy implications, the behavior of the EoL management chain mayvary from country to country and year to year.21 In addition, to create a database called PRTR_transfersthat contains global inventory data of EoL chemical off-site transfersreported by industrial facilities and businesses, Hernandez-Betancuret al.22 expanded the EoL data engineeringframework suggested by Hernandez-Betancur et al.18 by incorporating data from siloed  andpublicly accessible international database systems. Thus, the dataengineering frameworks can supply the EoL CFA and life cycle inventory(LCI) data of existing chemicals reported in regulatory primary datasources.Material flow analysis can help map EoL flow movements andEoLactivities,23 Meyer et al.24 used a tree-based ML regressorfor estimating chemical releases from manufacturing processes. Ringet al.25 developed predictive algorithmsto obtain the probabilities that a chemical might be associated witha near-field exposure pathway . Based onexisting data, Franzosa et al.26 gainedvaluable information on key events or pathways that may result inadverse effects on human tissues. Isaacs et al.27 used ML classifiers for obtaining chemical functions andweight fractions in consumer products. Cha et al.28 used a random forest regressor to predict the oxidant exposureduring ozonation reactions. Fan and Xu29 implemented deep learning models for predicting the health risksof occupational exposure to toxic chemicals in coal mine workplaces.ML approaches and techniques have been used for the modeling, design,and prediction of energy systems, including the use of support vectormachines to estimate carbon dioxide emissions from combined cyclegas turbine plants.30 Huang et al.31 explored the use of deep learning algorithmsfor air pollutant emission estimation. Tu et al.32 presented a hybrid modeling approach, named “cluster-basedvalidated emission recalculation,” for predicting the emissionsof greenhouse gases from the Great Toronto Area.Machine learning (ML) has been widely used by researchersand practitionersfor chemical releases and exposure analysis.33 Quantitativestructure–activity relationship (QSAR) modeling has been widelyused in medicinal chemistry and computational toxicology. QSAR modelingoffers an in silico tool for the development of predictive modelsthat relate various activity and property endpoints  of chemicals and molecular structure information.34 Beyond the computational toxicology field, MLQSAR models have been implemented in a wide range of topics. Phillipset al.35 used ML QSAR based on random forestfor screening and identifying chemicals that can provide a specificfunction within products so that hazardous chemicals can be substitutedfor safer candidates. Song et al.36 developedan ML QSAR approach via artificial neural networks for performinga rapid life cycle impact assessment for chemicals. Holmquist et al.37 developed an ML QSAR for obtaining characterizationfactors for simplified life cycle impact assessment. Holmquist etal.37 developed a framework that can beused to rapidly assess per- and polyfluoroalkyl substances.38An ML modelcan use different type of predictors or features thatare related to an output or response variable in a particular applicationdomain .22 this work utilizes the PRTR_transfersdatabase to develop ML QSAR models for predicting potential EoL activitiesand identifying EoL exposure scenarios for chemicals found in industrialmaterial transfers to off-site locations for further EoL management.Thus, allocating EoL chemicals flows by the predicted EoL activitiesand their operating conditions establish potential environmental releasesand exposure pathways at the EoL stage.39 In addition to chemical descriptors from the PRTR database, theML QSAR models, hereinafter called quantitative structure-transferrelationship (QSTR) models, use the generator industry sector, theEoL flow transfer amount, the environmental stringency by countryand year, the chemical unit price, and the industry gross value addedby country and year as predictors to predict the potential EoL activities for chemicals outside thePRTR_transfers database. The QSTR models can be incorporated intothe Markov random field described by Hernandez-Betancur et al.20 to predict the model factors or “probabilities”that relate to the strength of the relationship between generatorindustry sectors and off-site EoL activities. Moreover, these workoutcomes can be complemented by estimating environmental releases,EoL CFA fractions, and incorporating sustainability indicators forEoL exposure assessment.40The extensive number of techniques andstrategies for ML QSAR modelingmakes it a viable alternative for extending the CFA described aboveto chemicals that are either not reported to regulatory primary datasources or are new to the market. Based on the work developed by Hernandez-Betancuret al.,A series of steps that are a part of thelife cycle of an ML modeland system were necessary for the developing QSTR models to predictpotential EoL activities and material allocation for industrial chemicaltransfers to off-site locations for further EoL management. The methodologyconsists of five main steps. The first step collects and enrichesthe data to describe crucial factors that will influence decision-makersto determine potential off-site EoL activities for the chemicals ofconcern. The second step focuses on processing the data before feedingthem into the QSTR models. The third step builds the QSTR models.The fourth step uses multi-criteria decision-making to select thedata preprocessing pipeline needed to obtain high QSTR model performance.The final step is the hyperparameter  optimization and tuning to obtain the best possible performancefor the QSTR models.22 Data from siloed and publicly accessible database systems belongingto OECD member nations were standardized and harmonized and storedin this database.41 This database’sinformation is chemical-centric, focusing on individual chemicalsrather than the total quantity of hazardous or non-hazardous wastes.The aforementioned is appropriate for evaluating chemical exposureand risk, as toxicologists usually evaluate chemicals separately insteadof mixtures. This database provides LCI facility-level data that includeoff-site transfers from facilities and businesses manufacturing thechemical, processing it as a reactant or intermediate, incorporatingit into formulations, mixtures, or reaction products, incorporatingit into articles, and using it industrially. The PRTR_transfers databasehas the information to describe the generator industry sectors, EoLactivities or off-site transfer scenarios, the quantity of chemicalsin the off-site transferred flows (in kg/year), and the chemical substances.The database contains 72 industry sectors, 643 chemical substances,and a total of 3,116,211 records. The PRTR_transfers database contains10 EoL activities or off-site transfer classes: surface impoundment,sewerage, destruction, energy recovery, landfill, other disposal,other treatment, recycling, storage, and underground injection.The data used to create the QSTR modelsinclude predictors that have an impact on the likelihood that an EoLactivity will occur for a chemical, as illustrated in 21 These economic considerations are crucial becausea company can be willing to recycle a high-value chemical but destroya low-value one. Moreover, a business with high income or sales, whichare related to the gross value added,42 may afford to operate an EoL activity to handle or abate hazardouschemicals and/or wastes.43 Using the CASnumbers for the chemicals, their unit prices (in USD/g) are retrievedautomatically from e-commerce sources like SciFinder, Amazon, Alibaba,and Fisher Scientific. Due to the massive inventory of chemical suppliersaround the world, these systems, particularly SciFinder, are utilizedas search engines, making it easier to find chemical prices.Data associated with the chemical unit price andthe gross valueadded by an industry sector are collected to represent the economicbottom line of sustainability since the economic sustainability dimensionmay affect the occurrence of an EoL activity for a chemical.44 considering the yearand country of the reporting record. If the gross value added by industryand country is not found for a particular year, it is imputed by usinglinear extrapolation and country data. In addition, around 400 moleculardescriptors are obtained from RDKit cheminformatics software via itsPython API.45 However, before obtainingthe descriptors, each chemical’s SMILES is required. SMILESis a notation to encode information on the molecular structure tobe understood by a computer.46 Throughautomation, the SMILESs are obtained from the APIs of the US NationalLibrary of Medicine and PubChem.The gross value added by the industry sector is obtained from theOECD statistics,44 This index is a country-specific and internationally comparablemeasure of the stringency of environmental policy.47 In cases where the index is not found for a country, linearextrapolation, and the country data are used for item imputation.Linear extrapolation has drawbacks, including the inability to takecausal elements into account in the calculus-based observations andthe failure to take into consideration qualitative values that canaffect future values.48 Gross value addedand environmental stringency indexes reported over time can help toexpand the QSTR model application domain, particularly for countriesthat are not part of the PRTR_transfers database, but their industrysector economy and environmental regulation could be represented byany reporting country and/or sector in previous years.The environmental context thatmay affect the probability of anEoL activity occurrence is represented by the OECD environmental policystringency index obtained from OECD statistics.Supporting Information (SI), which goes intogreater detail regarding the methodology, is available to readers.Instead of being based on anyenvironmental decision-making foundation, some components of the methodologyare mathematical operations linked to transforming the data into amachine-readable structure to be understood by the models or performinghyperparameter optimization. The crucial steps for environmental sciencepractitioners to make a more informed and appropriate decision willbe highlighted to maintain the focus on the advantages in the contextof sustainable chemistry and engineering. The manuscript’s 49 The above would be troublesome because for EoL activities with fewsamples, the QSTR might suggest that an EoL activity would not occurwhen it does . The contrary may happen for activitieshaving majority of the samples, indicating that the activity wouldoccur when it does not .50 Despite the fact that both scenarios are unpleasant, the false negativescenario would be the worst since no proper managerial techniquesor equipment would be employed to control the exposure to dangerouschemicals that would damage human health and the environment.51 As a result, this contribution applies ML techniquesto address the data imbalance; for more information on those techniques,see the SI.The information in the PRTR_transfers database is imbalanced, i.e.,sample data has more entries linked with an EoL activity than others.For instance, there are more records for landfills than for storageoperations in the PRTR_transfers. QSTRs are classification modelsthat scan EoL operations for industrial chemical and support specialistsin order to quickly identify potential and high-priority activitiesthat need additional analysis. If the PRTR_transfers imbalance isnot addressed, the QSTRs may classify EoL activities incorrectly forthe parameters specified by model users.52 Thus, this research employs a random forest classifier (RFC), whichhas been demonstrated to be a high-performance algorithm in the fieldof QSAR,54 and it can supply information about thevariable’s importance.55 In additionto the ML models, this contribution explores three modeling strategies:one-vs-all, multi-class classification, and multi-label classification.EoL activities cannot take place simultaneously according to the multi-classclassification.56 Contrary to multi-classclassification, multi-label classification allows for the considerationof one or more activities occurring simultaneously for a chemical,which is possible given that the PRTR_transfers contains annuallyreported LCI data.57 One-vs-all or binaryclassification creates a QSTR for each of the 10 EoL activities.In the context of QSARmodeling, the OECD suggests using, whenit is possible, models that enable getting a mechanistic explanationof the model results. It means that it is better to use a model whosecomplexity enables us to determine the predictors/variable that aremore related to a response or target variable  in an environmental context.58 The models are optimizedto improve their performance as needed, considering factors such asa minimum f1-score value of 70%, the possibilityof improvement across optimization iterations, and an optimizationtime budget (see the SI). For further informationon the methodology, refer to the SI.In the QSTR experiment tracking, this contribution aims to investigatenot only models that can deliver precise and high-performance resultsat a model low-complexity level but also QSTRs that can provide predictionsin a reasonable time and without demanding high costs of storage incase of future use by environmental policy-makers and businesses.Twenty percent of the 30% records from the PRTR transfers databasewere used in the external QSTR validation. Experiments are carriedout to track QSTR performance using baseline values for the RFC, anddimensionality reduction techniques for the three modeling strategiesare selected.59 A public GitHubrepository contains the scripts and notebooks developed for the QSTRmodel development presented in this manuscript (see the SI). For more details on the outcomes and experimenttracking, see the SI.This section presents the resultsobtained following the methodology.The ML predicting model developed in this work should be consideredacknowledging its assumptions and limitations, especially since only30% of the PRTR_transfers database records are used. A data-centricparadigm is needed for developing robust ML models to work in thereal world, i.e., keeping the model structure and workflow, whilethe data are enhanced.The results for theQSTR models following hyperparameter optimization for the three modelingapproaches are shown and discussed in this section. 60 Environmental government agencies typically use annualdata to evaluate chemical exposure and risk; therefore, applying andintegrating the QSTR models into these types of evaluations will notpresent any hindrance.61 Except for thelandfill QSTR, all of the QSTR models created using the one-vs-alltechnique have f1-scores that are higherthan the targeted cutoff of 70%. The aforementioned indicates thatif the QSTR models are used as is, landfill QSTR may provide issueswith accurately predicting if landfill would occur for some chemicals.Nevertheless, the landfill QSTR’s hyperparameter tuning resultsindicate that this model might still keep becoming better even ifa longer tuning time is given . Furthermore, it is important to remember that not allof the PRTR_transfers data were used for running ML experiments dueto computer power limitations.The difference in classification scores between multi-labelandmulti-class highlights an interesting conclusion that the LCI datafrom the PRTR transfer database would make it possible to constructQSTR models that predict potential outcomes for an industrial andhazardous chemical over an annual time horizon. Due to assumptionsand uncertainty propagation, it would not be a good idea to attemptto adjust the time-scale for the PRTR transfer data.y axis represents the true label for a test sample, andthe x axis represents the label that is predictedby the multi-class classifier. The numbers inside the matrix are therelationship between the number of samples in the row predicted withthe label on the x axis and the number of total sampleswith the label on the y axis. For example, 54% ofthe energy recovery samples . In addition,after identifying the potential off-site EoL activity for a chemical,it would be possible to connect this result with the proposal describedby Hernandez-Betancur et al.19 and performthe CFA, including aspects of the pollution abatement technologies.Finally, the QSTR models can provide the factors for establishinga relationship between the generator industry sector and the EoL activityin the Markov random field developed by Hernandez-Betancur et al.20 to outline the relationship between the entitiesthrough the EoL management chain and recycling loop.The QSTR shows that is it possible to extend the framework developedby Hernandez-Betancur et al.SI). Thechemicals , the sectors, and the annualchemical flows in 19 The chemical price correspondsto the mean values found in SciFinder, and the SMILES are retrievedfrom PubChem. Moreover, the environmental policy stringency indexand industry gross value added for Spain are obtained from the OECDstatistics. Those values correspond to hypothetical cases of potentialvalues that could be fed into the QSTR models to obtain predictions.Using the above values, the probability that an EoL activity willoccur is predicted, as shown in the second and third columns in For morein-depth detail, 20 Using theMonte Carlo method, the local relationship between the two nodes canbe described, providing the annual mean mass flows and, subsequently,the annual mean mass fractions, as shown in the fourth and fifth columnsin 19 to perform CFA and allocate downstream of the pollutionabatement unit technologies.The probabilities in 64 Additionally,stakeholders can use the QSTR models to pinpoint potential EoL activitiesto consider the monetary and environmental costs of pollution controlsystems related to the management of chemical production releases.Instead, sustainable chemistry and engineering solutions can be evaluatedto improve the chemical production sustainability and its impact whenextending the analysis outside the chemical production facility gates.65Considering chemical exposure assessmentand environmental andhuman health impact reduction as upfront criteria to develop moresustainable processes, the proposed QSTR models could assist stakeholdersin predicting potential EoL pathways of material flows generated duringthe chemical manufacturing life cycle stage. Therefore, at the earlystages of process development, decision-makers can check if a newsynthesis of chemical process may have undesirable EoL pathways andimpacts based on chemical structure consideration, business location,and industrial activity. Thus, they will be able to select appropriatepollution abatement technologies, establish risk management procedures,and use source reduction techniques.This work contributes to the developmentof QSTR models, whichare ML models using a QSAR modeling approach. The QSTR models canpredict the probability of an EoL activity for a chemical of industrialrelevance that is transferred to an off-site location, thereby helpingidentify the EoL exposure scenario for a chemical. By leveraging data,the work can incorporate features into the QSTR models associatedwith the generator industry sector, the amount of the chemical involvedin an off-site transfer, molecular descriptors, the gross value addedby an industry sector, environmental policy stringency, and the chemicalunit price. These features help the QSTR models gain insights intothe context related to the decision-making of an EoL activity alternative.Environmental decision-makers can use the predictions from the QSTRmodel to give particular attention to those high-priority scenariosfor chemical exposure and risk reduction.This work shows thatRFC can be used to develop the QSTR modelsand make high-performance predictions of the potential EoL activities.Future work should explore the application of the multi-label classificationstrategy with the implementation of any special technique for multi-labelproblems such as label powersets. A considerable risk of misclassificationexists between EoL activities like destruction via incineration andenergy recovery; hence, aspects related to their similarity shouldbe thoroughly examined in future development. To better understandthe decision-making context and distinguish between these differenttypes of EoL activities, more features could be incorporated intoQSTR models. These models can be used to estimate the mass fractionof a chemical that might be moved to an off-site location for EoLmanagement. To accurately characterize the behavior of the EoL managementchain and recycling loop, further work should go beyond just incorporatingthe QSTR models suggested here into a statistical graphical modellike the Markov random field. As a result, it is possible to addressand improve CFA and EoL exposure assessment, offering valuable implicationsfor environmental regulatory decision-making as well as business decision-makingin the potential life cycle of chemicals circulating in workplaces,homes, neighborhoods, and, generally, the global marketplace.


Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of “virtual hearts” using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically  Sudden cardiac death is an unexpected death from a cardiac cause within a short period  from symptom onset or, if unwitnessed, within 24 h of last being seen alive , 2. WhilEfforts to improve risk stratification for SCD, or even to reclassify risk assigned by EF, have been undertaken across all cardiac imaging modalities. Herein, we focus on the role of advanced imaging, namely cardiac magnetic resonance imaging (CMR), single-photon emission computer tomography (SPECT), and positron emission tomography (PET), and its application to risk stratification for SCD . We highChamber geometry has long been known to reflect the role of remodeling in CVD and, in turn, is associated with outcomes including VA , 8. CardCMR with LGE has developed as a means of characterizing myocardial tissue, and its value in prognosticating cardiovascular outcomes, including SCD, has been widely reviewed and increasingly well-established , 18. ThiIn NICM, the role of LGE for SCD risk stratification has been frequently studied in patients with dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) –33.LGE has been used to identify higher-risk patients who fall outside the EF criterion for primary prevention ICD . In a muProposed diagnostic guidelines for arrhythmogenic cardiomyopathies, which carry profound and inherent risk for VA and SCD, have included LGE patterns among other criteria . In arrhn = 5) reaching this endpoint  with magnetic field inhomogeneity to detect field distortions from the presence of materials such as iron  in SPECT though several tracers have also been studied for use with PET, most notably 11C-hydroxyephedrine (HED). Globally decreased uptake of these tracers is thought to reflect the increased sympathetic tone, which can trigger malignant arrhythmias . HoweverConceptually, hibernating myocardium describes regions of viable tissue with chronically reduced function and resting perfusion caused by chronic ischemia or recurrent . This isIt is worth briefly noting that hybrid PET/MRI scanners have become commercially available relatively recently (since 2010) and brought with them a unique set of technical challenges as well as clinical possibilities . Thus faCT is presently a more accessible and generally lower cost modality than those previously mentioned. It provides higher spatial resolution and for certain purposes better temporal resolution as well. CT is perhaps most prominently used in cardiology for non-invasive assessment of the coronary arteries, the presence (or absence) of which is diagnostically significant and certainly a significant predictor of both VA and SCD . In termCT has been used to detect fibrosis, such as by iodinated contrast enhancement . Scar quIn recent years, machine learning techniques have seen increasing integration within medicine, particularly as applied in imaging . This haPerhaps more widely reported are clinician-facing advances in image analysis and interpretation using ML, described by the overarching term “computer vision” to include any automated interpretation of images . ML-baseNonetheless, manual segmentation has previously been considered the gold standard for accuracy, especially when compared with techniques based on older ML algorithms . More reSequential to obtaining imaging measures is its interpretation and application to predicting cardiovascular endpoints. Techniques such as texture analysis may be applied to pre-existing LGE segmentation to obtain features beyond quantitative scar burden, such as measures of heterogeneity and shape, which have been proven valuable in conventional workflows , 133. NuIn the future, machine learning may be integrated into the risk stratification workflow from the point of image acquisition to patient-facing risk prediction models, and there are studies demonstrating this in principle. One example is incorporating manual measurements and segmentation with using ML for feature extraction and statistical model-building . SophistAs described in this review, there are numerous promising applications of advanced imaging to identify patients at risk of SCD. Some of the above techniques and sequences may likely be incorporated into clinical guidelines and ultimately into regular practice in the coming years. Machine learning may enable advances democratizing advanced imaging for at-risk patients, as has been achieved with CT screening in smoking. Physicians and cardiologists of the future will likely have a wide variety of complementary imaging modalities and analytic tools to identify SCD risk amongst different patient populations optimally.EX and JC contributed to the conception, tables, and design of the manuscript. EX, ESu, ESa, and JC contributed to creation and editing of figures. All authors agree to be accountable for the content of the work. All authors contributed to the drafting, editing, and review of the manuscript. All authors contributed to the article and approved the submitted version.The authors acknowledge funding from NIH (NIH/NHLBI R01HL103812 and R01HL132181 to KW and R01HL142496 and R01HL124893 to NT), Leducq Foundation (NT), and AHA .The authors declare 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.


Lymphocytes in tumor tissue are called tumor-infiltrating lymphocytes (TILs), and they play a key role in the control and treatment of tumor diseases. Since the discovery in 1987 that cultured TILs can kill tumor cells more than 100 times more effectively than T-cells cultured from peripheral blood in melanoma, it has been confirmed that cultured TILs can successfully cure clinical patients with melanoma. Since 1989, after we investigated TIL isolation performance from solid tumors, we modified some procedures to increase efficacy, and thus successfully established new TIL isolation and culture methods in 1994. Moreover, our laboratory and clinicians using our cultured TILs have published more than 30 papers. To improve the efficacy of TILs, we have been carrying out studies of TIL efficacy to treat solid tumor diseases for approximately 30 years. The three main questions of TIL study have been “How do TILs remain silent in solid tumor tissue?”, “How do TILs attack homologous and heterologous antigens from tumor cells of solid tumors?”, and “How do TILs infiltrate solid tumor tissue from a distance into tumor sites to kill tumor cells?”. Research on these three issues has increasingly answered these questions. In this review I summarize the main issues surrounding TILs in treating solid tumors. This review aims to study the killing function of TILs from solid tumor tissues, thereby ultimately introducing the optimal strategy for patients suffering from solid tumors through personalized immunotherapy in the near future. Tumor-infiltrating lymphocyte (TIL) therapy, one of the best adoptive immunotherapies (AITs) or adoptive cell therapies (ACTs), was discovered about 35 years ago, when Dr Steven Rosenberg used TILs to treat melanoma in 1988 –10. AfteAlthough TIL immunotherapy has been broadly accepted to treat melanoma, variable responses mean that the efficacy of TILS in treating solid tumor diseases is still questioned , 20. DesBecause TILs actively killing human tumor cells from solid tumors remains controversial, and to address the auguring, this review presents three issues regarding the efficacy of TILs from solid tumors. Finally, I briefly present the future of adoptive TIL immunotherapy as strategies of personalized immunotherapy .in vitro, TILs isolated from tumor samples were studied in the earliest trials of ACT conducted at the surgical branch of the National Cancer Institute (NCI) in 1988 (After Dr Rosenberg discovered that TILs can be cultured with the aid of the cytokine interleukin (IL)-2 and induced TIL-exhibited cytotoxic activity against melanoma cells  in 1988 , 30. At  in 1988 . After m in 1988 . Now, pa in 1988 .Through studying TIL cytotoxicity mechanisms to autologous tumor cells from solid tumors during that early period, we discovered that TILs had variable efficacy in the treatment of patients with solid tumors , 34. To In 1995, after analyzing TIL proliferation, cytotoxicity, and phenotype from 83 solid tumor cases, we reported that TILs have quiescent status if the TIL is located in solid tumor tissues . AlthougSilencing T-lymphocytes (quiescent lymphocytes) are a group of T-cells that display no spontaneous proliferation and a low metabolic rate . In sileAs shown in in vivo, in vitro, and ex vivo. All ECMs have been shown to produce TGF-β, which blocks TIL function, including the inhibition of TIL growth and cytotoxicity  are present for some time during the spermatogenesis and placental stages, and they are also more highly expressed in different types of neoplastic diseases . NormallNeo-antigens caused by genetic instability during carcinogenesis appear in non-coding and coding regions, whereas amino acid sequence changes caused by mutations in coding regions can generate antigens that do not exist in normal cells . Neo-antThere are two types of neo-antigens: shared neo-antigens and personalized neo-antigens , 76. ShaDifferent types of neo-antigens (TSAs) and different amounts of TAAs can exist in the same tumor in different individuals, resulting in individual immune responses, so TIL administration should be developed into an individualized design for immunotherapy. Based on personalized information such as individual genomic data, personalized TIL therapy can increase the intensity and durability of antitumor effects, improve survival and quality of life, and ultimately improve cancer treatment outcomes for patients. For most patients with solid tumors, personalized TIL therapy is expected to be more feasible and safe.To investigate individualized immune responses in different patients, we have studied TIL responses with distinct networks, even if their TILs were derived from patients with nearly similar clinical and pathological outcomes. Under analysis of their pathways and networks, through achieving gene expression patterns by single-cell gene expressions and analyzing molecular expressions from the heterogeneous responses of TIL CD8 cells, we found that small gene expression changes between two TILs can reveal individual immune responses in the network. A network study  of two Tex vivo-cultured TILs specifically enter tumor tissue from circulating blood after reinfusion (The infiltration of TILs into tumor tissue to kill tumor cells has been studied in two areas (1): TIL CD8 cells should have intact signaling molecules after harvesting TILs from tumor tissue –6, 80, ainfusion . The firEarlier, we found changeable results based on Dr Rosenberg’s tumor disaggregation  to harvest and culture TILs for clinical treatment if we used the disaggregation procedures from solid tumors. Considering the TIL results in experimental and clinical work to treat solid tumors, we studied the TIL functions such as TIL proliferation, cytotoxicity, and phenotype with three enzymes under their conditions 37°C) . Finally7°C . Finin vivo. The mobilization of TILs into tumor tissue has become increasingly accepted after two experiments: Dr Torcellan’s in vivo TIL light-labeling technique in 2017 and Bai’s use of on-site antigen presentation for clonal expansion in 2001 , CCR4 , and others have been modified to enhance T-cell trafficking to the tumor site –92. ThesAs mentioned above, we discovered in our early research that TILs have variable efficacy in the treatment of solid tumors: we used some strategies to resolve some questions, as shown in As mentioned above, the results of silencing TILs showed that the supernatant of TILs’ medium and mixed autologous tumor cells contained inhibitory factors. To overcome these inhibitory factors, we investigated some cleaning methods, such as removing autologous tumor cells during TIL culture to obtain a good response to increasing cytotoxicity against autologous tumor cells derived from solid tumors . After aIn our early research, to increase immune responses to kill tumor cells, we studied transduced retroviral vectors with TNF-α genes in TILs to increase TIL cytotoxicity function –96. To iex vivo-activated autologous TIL intravenous infusion is considered one of the promising approaches, early adoptive T-cell immunization is considered to be effective only in some clinical patients with solid tumors. To increase TIL for efficient contact with tumor cells, two studies were performed on TIL-contacted tumor cells. As mentioned above, we have found that early triple enzymes  disaggregating solid tumors will influence TIL function; we then modified formulations such as collagenase IV only under mild digestion conditions to keep T-cell intactness from solid tumors  or chemo-sensitivity assay (CSA) from a patient’s autogenous tumor cell , 113, asAs noted above, there are at least three main reasons for the variable efficacy of TILs in the treatment of solid tumor diseases. Now, more and more methods and issues of T-lymphocyte treatment will be discovered for the treatment of solid tumors, as shown in A good immunotherapy should have the characteristics of high efficacy, few side effects and low economic cost for every tumor patient. Based on those considerations, these developments in future immunotherapy require the development of affordable treatments, tolerable side effects and responsible effects for each patient in optimal cell culture and correct clinical treatment. To be sure, individualized T-cells are required for optimal culture in the laboratory and the correct treatment conditions in the clinic. Finally, all efforts in adoptive T-cell immunotherapy focus on establishing optimal T-cell cultures in the laboratory, choosing the correct mode of administration in the clinic, considering affordability, and improving availability to each patient , 121.The author confirms being the sole contributor of this work and has approved it for publication.Study TIL and primary tumor cell more than 30 years so that the works were funded as different funds such as the National Natural Science Foundation of China(No.39370706), USA IRG-91-022-09 for single cell TIL genomic analysis and tumor cell genomics analysis (PO-1 75606-3) co-work with Dr. Preisler and RO1 CA60086 and so on.I have studied and set up TIL cultures from solid tumors forclinical application for more than 30 years. All acknowledgements are for my colleagues. 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