Real-Time Obstructive Sleep Apnea Detection from Raw ECG and SpO2 Signal Using Convolutional Neural Network.

Obstructive sleep apnea is a sleep disorder that is linked with many health complications and severe form of apnea can even be lethal. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Recently, there have been numerous studies demonstrating the application of artificial intelligence to detect apnea in real time. But the majority of these studies apply data pre-processing and feature extraction techniques resulting in a longer inference time that makes the real-time detection system inefficient. This study proposes a single convolutional neural network architecture that can automatically extract spatial features and detect apnea from both electrocardiogram (ECG) and blood-oxygen saturation (SpO2) signals. Using segments of 10s, the network classified apnea with an accuracy of 94.2% and 96% for ECG and SpO2 respectively. Moreover, the overall performance of both models was consistent with an AUC score of 0.99.

ASCC1 structures and bioinformatics reveal a novel helix-clasp-helix RNA-binding motif linked to a two-histidine phosphodiesterase.

Activating signal co-integrator complex 1 (ASCC1) acts with ASCC-ALKBH3 complex in alkylation damage responses. ASCC1 uniquely combines two evolutionarily ancient domains: nucleotide-binding K-Homology (KH) (associated with regulating splicing, transcriptional, and translation) and two-histidine phosphodiesterase (PDE; associated with hydrolysis of cyclic nucleotide phosphate bonds). Germline mutations link loss of ASCC1 function to spinal muscular atrophy with congenital bone fractures 2 (SMABF2). Herein analysis of The Cancer Genome Atlas (TCGA) suggests ASCC1 RNA overexpression in certain tumors correlates with poor survival, Signatures 29 and 3 mutations, and genetic instability markers. We determined crystal structures of Alvinella pompejana (Ap) ASCC1 and Human (Hs) PDE domain revealing high-resolution details and features conserved over 500 million years of evolution. Extending our understanding of the KH domain Gly-X-X-Gly sequence motif, we define a novel structural Helix-Clasp-Helix (HCH) nucleotide binding motif and show ASCC1 sequence-specific binding to CGCG-containing RNA. The V-shaped PDE nucleotide binding channel has two His-Φ-Ser/Thr-Φ (HXT) motifs (Φ being hydrophobic) positioned to initiate cyclic phosphate bond hydrolysis. A conserved atypical active-site histidine torsion angle implies a novel PDE substrate. Flexible active site loop and arginine-rich domain linker appear regulatory. Small-angle X-ray scattering (SAXS) revealed aligned KH-PDE RNA binding sites with limited flexibility in solution. Quantitative evolutionary bioinformatic analyses of disease and cancer-associated mutations support implied functional roles for RNA binding, phosphodiesterase activity, and regulation. Collective results inform ASCC1's roles in transactivation and alkylation damage responses, its targeting by structure-based inhibitors, and how ASCC1 mutations may impact inherited disease and cancer.

Formation and energetics of head-to-head and tail-to-tail domain walls in hafnium zirconium oxide.

180 ∘ domains walls (DWs) of head-to-head/tail-to-tail (H-H/T-T) type in ferroelectric (FE) materials are of immense interest for a comprehensive understanding of the FE attributes as well as harnessing them for new applications. Our first principles calculation suggests that such DW formation in hafnium zirconium oxide (HZO) based FEs depends on the unique attributes of the HZO unit cell, such as polar-spacer segmentation. Cross pattern of the polar and spacer segments in two neighboring domains along the polarization direction (where polar segment of one domain aligns with the spacer segment of another) boosts the stability of such DWs. We further show that low density of oxygen vacancies at the metal-HZO interface and high work function of metal electrodes are conducive for T-T DW formation. On the other hand, high density of oxygen vacancy and low work function of metal electrode favor H-H DW formation. Polarization bound charges at the DW get screened when band bending from depolarization field accumulates holes (electrons) in T-T (H-H) DW. For a comprehensive understanding, we also investigate their FE nature and domain growth mechanism. Our analysis suggests that a minimum thickness criterion of domains has to be satisfied for the stability of H-H/T-T DW and switching of the domains through such DW formation.

Enrichment of a Data Lake to Support Population Health Outcomes Studies Using Social Determinants Linked EHR Data.

The integration of electronic health records (EHRs) with social determinants of health (SDoH) is crucial for population health outcome research, but it requires the collection of identifiable information and poses security risks. This study presents a framework for facilitating de-identified clinical data with privacy-preserved geocoded linked SDoH data in a Data Lake. A reidentification risk detection algorithm was also developed to evaluate the transmission risk of the data. The utility of this framework was demonstrated through one population health outcomes research analyzing the correlation between socioeconomic status and the risk of having chronic conditions. The results of this study inform the development of evidence-based interventions and support the use of this framework in understanding the complex relationships between SDoH and health outcomes. This framework reduces computational and administrative workload and security risks for researchers and preserves data privacy and enables rapid and reliable research on SDoH-connected clinical data for research institutes.

Ultrafine Mix-Phase SnO-SnO2 Nanoparticles Anchored on Reduced Graphene Oxide Boost Reversible Li-Ion Storage Capacity beyond Theoretical Limit.

Tin-based materials with high specific capacity have been studied as high-performance anodes for Li-ion storage devices. Herein, a mix-phase structure of SnO-SnO2@rGO (rGO = reduced graphene oxide) was designed and prepared via a simple chemical method, which leads to the growth of tiny nanoparticles of a mixture of two different tin oxide phases on the crumbled graphene nanosheets. The three-dimensional structure of graphene forms the conductive framework. The as-prepared mix phase SnO-SnO2@rGO exhibits a large Brunauer-Emmett-Teller surface area of 255 m2 g-1 and an excellent ionic diffusion rate. When the resulting mix-phase material was examined for Li-ion battery anode application, the SnO-SnO2@rGO was noted to deliver an ultrahigh reversible capacity of 2604 mA h g-1 at a current density of 0.1 A g-1. It also exhibited superior rate capabilities and more than 82% retention of capacity after 150 charge-discharge cycles at 0.1 A g-1, lasting until 500 cycles at 1 A g-1 with very good retention of the initial capacity. Owing to the uniform defects on the rGO matrix, the formation of LiOH upon lithiation has been suggested to be the primary cause of this very high reversible capacity, which is beyond the theoretical limit. A Li-ion full cell was assembled using LiNi0.5Mn0.3Co0.2O2 (NMC-532) as a high-capacity cathodic counterpart, which showed a very high reversible capacity of 570 mA h g-1 (based on the anode weight) at an applied current density of 0.1 A g-1 with more than 50% retention of capacity after 100 cycles. This work offers a favorable design of electrode material, namely, mix-phase tin oxide-nanocarbon matrix, exhibiting adequate electrochemical performance for Li storage applications.

A Privacy-Preserved Transfer Learning Concept to Predict Diabetic Kidney Disease at Out-of-Network Siloed Sites Using an In-Network Federated Model on Real-World Data.

Successful implementation of data-driven artificial intelligence (AI) applications requires access to large datasets. Healthcare institutions can establish coordinated data-sharing networks to address the complexity of large clinical data accessibility for scientific advancements. However, persisting challenges from controlled access, safe data transferring, license restrictions from regulatory and legal concerns discourage data sharing among the in-network hospitals. In contrast, out-of-network healthcare institutions are deprived of access to any big EHR database; hence, limiting their research scope. The main objective of this study is to design a privacy-preserved transfer learning architecture that can utilize the knowledge from a federated model developed from in-network hospital-site EHR data for predicting diabetic kidney cases at out-of-network siloed hospital sites. In all our experiments, transfer learning showed improved performance compared to models trained with out-of-network site datasets. Thus, we demonstrate the proof-of-concept of transferring knowledge from established networks to aid data-driven AI discoveries at siloed sites.

ECG and SpO2 Signal-Based Real-Time Sleep Apnea Detection Using Feed-Forward Artificial Neural Network.

Sleep apnea (SA) is a common sleep disorder characterized by respiratory disturbance during sleep. Polysomnography (PSG) is the gold standard for apnea diagnosis, but it is time-consuming, expensive, and requires manual scoring. As an alternative to PSG, we investigated a real-time SA detection system using oxygen saturation level (SpO2) and electrocardiogram (ECG) signals individually as well as a combination of both. A series of R-R intervals were derived from the raw ECG data and a feed-forward deep artificial neural network is employed for the detection of SA. Three different models were built using 1-minute-long sequences of SpO2 and R-R interval signals. The 10-fold cross-validation result showed that the SpO2-based model performed better than the ECG-based model with an accuracy of 90.78 ± 10.12% and 80.04 ± 7.7%, respectively. Once combined, these two signals complemented each other and resulted in a better model with an accuracy of 91.83 ± 1.51%.

Clinicians' Perspectives in Using Patient-Generated Health Data to Improve Ischemic Heart Disease Management.

Patients suffering from ischemic heart disease (IHD) should be monitored closely after being discharged. With recent advances in digital health tools, collecting, using, and sharing patient-generated health data (PGHD) has become more achievable. PGHD can complement the existing clinical data and provide a comprehensive picture of the patient's health status. Despite the potential value of PGHD in healthcare, its implementation currently remains limited due to the clinicians' concern with the reliability and accuracy of the gathered data to support decision-making and concerns regarding the added workload that PGHD might cause to clinical workflow. The main objective of the study was to investigate the clinicians' perspectives towards the use of PGHD for IHD management, focusing on data sharing, interpretation, and efficiency in decision-making. The study consists of semi-structured interviews with seven clinicians. Study results identified four main themes: data generation, data integration, data presentation, data interpretation and utilization in clinical decision-making.

A study on Ti-doped Fe3O4 anode for Li ion battery using machine learning, electrochemical and distribution function of relaxation times (DFRTs) analyses.

Among many transition-metal oxides, Fe3O4 anode based lithium ion batteries (LIBs) have been well-investigated because of their high energy and high capacity. Iron is known for elemental abundance and is relatively environmentally friendly as well contains with low toxicity. However, LIBs based on Fe3O4 suffer from particle aggregation during charge-discharge processes that affects the cycling performance. This study conjectures that iron agglomeration and material performance could be affected by dopant choice, and improvements are sought with Fe3O4 nanoparticles doped with 0.2% Ti. The electrochemical measurements show a stable specific capacity of 450 mAh g-1 at 0.1 C rate for at least 100 cycles in Ti doped Fe3O4. The stability in discharge capacity for Ti doped Fe3O4 is achieved, arising from good electronic conductivity and stability in microstructure and crystal structure, which has been further confirmed by density functional theory (DFT) calculation. Detailed distribution function of relaxation times (DFRTs) analyses based on the impedance spectra reveal two different types of Li ion transport phenomena, which are closely related with the electron density difference near the two Fe-sites. Detailed analyses on EIS measurements using DFRTs for Ti doped Fe3O4 indicate that improvement in interfacial charge transfer processes between electrode and Li metal along with an intermediate lithiated phase helps to enhance the electrochemical performance.

Investigation of the Utility of Features in a Clinical De-identification Model: A Demonstration Using EHR Pathology Reports for Advanced NSCLC Patients.

BACKGROUND: Electronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before circulating for secondary use. Machine learning (ML) based named entity recognition (NER) model has emerged as a popular technique of automatic de-identification. OBJECTIVE: The performance of a machine learning model highly depends on the selection of appropriate features. The objective of this study was to investigate the usability of multiple features in building a conditional random field (CRF) based clinical de-identification NER model. METHODS: Using open-source natural language processing (NLP) toolkits, we annotated protected health information (PHI) in 1,500 pathology reports and built supervised NER models using multiple features and their combinations. We further investigated the dependency of a model's performance on the size of training data. RESULTS: Among the 10 feature extractors explored in this study, n-gram, prefix-suffix, word embedding, and word shape performed the best. A model using combination of these four feature sets yielded precision, recall, and F1-score for each PHI as follows: NAME (0.80; 0.79; 0.80), LOCATION (0.85; 0.83; 0.84), DATE (0.86; 0.79; 0.82), HOSPITAL (0.96; 0.93; 0.95), ID (0.99; 0.82; 0.90), and INITIALS (0.97; 0.49; 0.65). We also found that the model's performance becomes saturated when the training data size is beyond 200. CONCLUSION: Manual de-identification of large-scale data is an impractical procedure since it is time-consuming and subject to human errors. Analysis of the NER model's performance in this study sheds light on a semi-automatic clinical de-identification pipeline for enterprise-wide data warehousing.

Nonlinear Reaction Coordinate of an Enzyme Catalyzed Proton Transfer Reaction.

We present an in-depth study on the theoretical calculation of an optimum reaction coordinate as a linear or nonlinear combination of important collective variables (CVs) sampled from an ensemble of reactive transition paths for an intramolecular proton transfer reaction catalyzed by the enzyme human carbonic anhydrase (HCA) II. The linear models are optimized by likelihood maximization for a given number of CVs. The nonlinear models are based on an artificial neural network with the same number of CVs and optimized by minimizing the root-mean-square error in comparison to a training set of committor estimators generated for the given transition. The nonlinear reaction coordinate thus obtained yields the free energy of activation and rate constant as 9.46 kcal mol-1 and 1.25 × 106 s-1, respectively. These estimates are found to be in quantitative agreement with the known experimental results. We have also used an extended autoencoder model to show that a similar analysis can be carried out using a single CV only. The resultant free energies and kinetics of the reaction slightly overestimate the experimental data. The implications of these results are discussed using a detailed microkinetic scheme of the proton transfer reaction catalyzed by HCA II.

Vibrational and electrochemical studies of pectin-a candidate towards environmental friendly lithium-ion battery development.

Pectin polymers are considered for lithium-ion battery electrodes. To understand the performance of pectin as an applied buffer layer, the electrical, magnetic, and optical properties of pectin films are investigated. This work describes a methodology for creating pectin films, including both pristine pectin and Fe-doped pectin, which are optically translucent, and explores their potential for lithium-ion battery application. The transmission response is found extended in optimally Fe-doped pectin, and prominent modes for cation bonding are identified. Fe doping enhances the conductivity observed in electrochemical impedance spectroscopy, and from the magnetic response of pectin evidence for Fe3+ is identified. The Li-ion half-cell prepared with pectin as binder for anode materials such as graphite shows stable charge capacity over long cycle life, and with slightly higher specific capacity compare with the cell prepared using polyvinylidene fluoride (PVDF) as binder. A novel enhanced charging specific capacity at a high C-rate is observed in cells with pectin binder, suggesting that within a certain rate (∼5 C), pectin has higher capacity at faster charge rates. The pectin system is found as a viable base material for organic-inorganic synthesis studies.

Electrochemical Performance of Orthorhombic CsPbI3 Perovskite in Li-Ion Batteries.

A facile solution process was employed to prepare CsPbI3 as an anode material for Li-ion batteries. Rietveld refinement of the X-ray data confirms the orthorhombic phase of CsPbI3 at room temperature. As obtained from bond valence calculations, strained bonds between Pb and I are identified within PbI6 octahedral units. Morphological study shows that the as-prepared δ-CsPbI3 forms a nanorod-like structure. The XPS analysis confirm the presence of Cs (3d, 4d), Pb (4d, 4f, 5d) and I (3p, 3d, 4d). The lithiation process involves both intercalation and conversion reactions, as confirmed by cyclic voltammetry (CV) and first-principles calculations. Impedance spectroscopy coupled with the distribution function of relaxation times identifies charge transfer processes due to Li metal foil and anode/electrolyte interfaces. An initial discharge capacity of 151 mAhg-1 is found to continuously increase to reach a maximum of ~275 mAhg-1 at 65 cycles, while it drops to ~240 mAhg-1 at 75 cycles and then slowly decreases to 235 mAhg-1 at 100 cycles. Considering the performance and structural integrity during electrochemical performance, δ-CsPbI3 is a promising material for future Li-ion battery (LIB) application.

Arsenic level in bladder tumor of patients from an exposed population: association with progression and prognosis.

Aims: To elucidate the impact of arsenic on progression and prognosis of bladder cancer. Patients & methods: Total arsenic in 145 tumors (80 non-muscle-invasive [NMIBC] and 65 muscle-invasive bladder cancer [MIBC]) was measured and associated with Ki67 expression, tumor-clinicopathological parameters and patient outcome. Results: Tumor arsenic concentration was higher in exposed than unexposed patients (256 µg/kg vs 77 µg/kg; p < 0.0001) and positively correlated (r = 0.65; p < 0.0001) with arsenic content of patient's drinking water. Arsenic concentration showed significant association with Ki67-overexpression (p = 0.001) and advanced tumor stages (NMIBC vs MIBC; p = 0.0009). In NMIBC, high tumor arsenic (>100 µg/kg) and Ki67 overexpression was established as predictors for recurrence (hazard ratio [HR]: 4.68; p = 0.005 and HR: 3.91; p = 0.018) and progression (HR: 6.04; p = 0.023 and HR: 6.87; p = 0.013). In MIBC, association of high arsenic remained significant with increased risk of recurrence (HR: 4.58; p = 0.04). Conclusion: In NMIBC, high arsenic and Ki67 overexpression and in MIBC, only high arsenic showed prognostic importance in predicting poor patient outcome.

Lay abstract Research work suggests arsenic as risk factor for bladder cancer. In developing countries such as India, arsenic contamination of underground drinking water is a major health problem. The present study aimed to evaluate impact of arsenic on parameters of bladder cancer aggressiveness and clinical outcome of patients from West Bengal, India. Our data showed accumulation of arsenic in bladder tumor of patients exposed mainly through contaminated drinking water. Arsenic content in tumor favored aggressive phenotypes in bladder cancer (higher cell proliferation and advanced tumor stages) and was found to be a potential predictor for cases of death and disease recurrence in patients after receiving primary treatment measures. Therefore, arsenic content in bladder tumor may be used to improve existing protocols for better prediction of patient outcomes in populations with a similar type of exposure.

Electrochemical Activation of Li2MnO3 Electrodes at 0 °C and Its Impact on the Subsequent Performance at Higher Temperatures.

This work continues our systematic study of Li- and Mn- rich cathodes for lithium-ion batteries. We chose Li2MnO3 as a model electrode material with the aim of correlating the improved electrochemical characteristics of these cathodes initially activated at 0 °C with the sstructural evolution of Li2MnO3, oxygen loss, formation of per-oxo like species (O22-) and the surface chemistry. It was established that performing a few initial charge/discharge (activation) cycles of Li2MnO3 at 0 °C resulted in increased discharge capacity and higher capacity retention, and decreased and substantially stabilized the voltage hysteresis upon subsequent cycling at 30 °C or at 45 °C. In contrast to the activation of Li2MnO3 at these higher temperatures, Li2MnO3 underwent step-by-step activation at 0 °C, providing a stepwise traversing of the voltage plateau at >4.5 V during initial cycling. Importantly, these findings agree well with our previous studies on the activation at 0 °C of 0.35Li2MnO3·0.65Li[Mn0.45Ni0.35Co0.20]O2 materials. The stability of the interface developed at 0 °C can be ascribed to the reduced interactions of the per-oxo-like species formed and the oxygen released from Li2MnO3 with solvents in ethylene carbonate-methyl-ethyl carbonate/LiPF6 solutions. Our TEM studies revealed that typically, upon initial cycling both at 0 °C and 30 °C, Li2MnO3 underwent partial structural layered-to-spinel (Li2Mn2O4) transition.

Coordination Dynamics of Zinc Triggers the Rate Determining Proton Transfer in Human Carbonic Anhydrase II.

We present, for the first time, how transient changes in the coordination number of zinc ion affects the rate determining step in the enzyme human carbonic anhydrase (HCA) II. The latter involves an intramolecular proton transfer between a zinc-bound water and a distant histidine residue (His-64). In the absence of time-resolved experiments, results from classical and QM-MM molecular dynamics and transition path sampling simulations are presented. The catalytic zinc ion is found to be present in two possible coordination states; viz. a stable tetra-coordinated state, T and a less stable penta-coordinated state, P with tetrahedral and trigonal bipyramidal coordination geometries, respectively. A fast dynamical inter-conversion occurs between T and P due to reorganization of active site water molecules making the zinc ion more positively charged in state P. When initiated from different coordination environments, the most probable mechanism of proton transfer is found to be deprotonation of the equatorial water molecule from state P and transfer of the excess proton via a short path formed by hydrogen bonded network of active site water molecules. We estimate the rate constant of proton transfer as kP=1.29×106s-1 from P and kT=4.37×104s-1 from T. A quantitative match of estimated kP with the experimental value, ( kexp∼0.8×106s-1 ) suggests that dynamics of Zn coordination triggers the rate determining proton transfer step in HCA II.

Orthogonal order parameters to model the reaction coordinate of an enzyme catalyzed reaction.

The choice of suitable collective variables in formulating an optimal reaction coordinate is a challenging task for activated transitions between a pair of stable states especially when dealing with biochemical changes such as enzyme catalyzed reactions. A detailed benchmarking study is carried out on the choice of collective variables that can distinguish between the stable states unambiguously. We specifically address the issue if these variables may be directly used to model the optimal reaction coordinate, or if it would be better to use their orthogonalized counterparts. The proposed computational scheme is applied to the rate determining intramolecular proton transfer step in the enzyme human carbonic anhydrase II. The optimum reaction coordinate is determined with and without orthogonalization of the collective variables pertinent to a key conformational fluctuation and the actual proton transfer step at the active site of the enzyme. Suitability of the predicted reaction coordinates in different processes is examined in terms of the free energy profile projected along the reaction coordinate, the rate constant of transition and the underlying molecular mechanism of barrier crossing. Our results indicate that a better agreement with earlier simulation and experimental data is obtained when the orthogonalized collective variables are used to model the reaction coordinate.

Data for new protocol to detect the monoclinic phase of La2Mo2O9 and related oxide ion conductors.

In this data article we have presented the Rietveld refinement results of the X-ray diffraction data of both bulk and nanocrystalline La2Mo2O9 and bulk La2MoWO9 oxide ion conductors at different temperatures. Bulk and nanocrystalline La2Mo2O9 samples were prepared by conventional solid state reaction and glycine auto ignition method, respectively. Transmission electron microscopy is also employed to determine the grain size of the sample prepared via glycine auto ignition method.

Reaction Coordinate, Free Energy, and Rate of Intramolecular Proton Transfer in Human Carbonic Anhydrase II.

The role of structure and dynamics of an enzyme has been investigated at three different stages of its function including the chemical event it catalyzes. A one-pot computational method has been designed for each of these stages on the basis of classical and/or quantum mechanical-molecular mechanical molecular dynamics and transition path sampling simulations. For a pair of initial and final states A and B separated by a high free-energy barrier, using a two-stage selection process, several collective variables (CVs) are identified that can delineate A and B. However, these CVs are found to exhibit strong cross-coupling over the transition paths. A set of mutually orthogonal order parameters is then derived from these CVs and an optimal reaction coordinate, r, determined applying half-trajectory likelihood maximization along with a Bayesian information criterion. The transition paths are also used to project the multidimensional free energy surface and barrier crossing dynamics along r. The proposed scheme has been applied to the rate-determining intramolecular proton transfer reaction of the well-known enzyme human carbonic anhydrase II. The potential of mean force, F( r), in the absence of the chemical step is found to reproduce earlier results on the equilibrium population of two side-chain orientations of key residue His-64. Estimation of rate constants, k, from mean first passage times for the three different stages of catalysis shows that the rate-determining step of intramolecular proton transfer occurs with k ≃ 1.0 × 106 s-1, in close agreement with known experimental results.

Neem leaf glycoprotein is superior than cisplatin and sunitinib malate in restricting melanoma growth by normalization of tumor microenvironment.

We have observed earlier that therapeutic treatment with neem leaf glycoprotein (NLGP) inhibits murine B16-melanoma growth in vivo and improves survivability of treated mice. Anti-tumor effect of NLGP is directly associated with enhanced CD8(+) T cell activity and downregulation of suppressive cellular functions. Objective of this present study is to know the efficacy of NLGP in comparison to two popular drugs, Cisplatin and Sunitinib malate (Sutent) in relation to the modulation of tumor microenvironment (TME). Analysis of cytokine milieu within TME revealed IL-10, TGFß, IL-6 rich type 2 characters was significantly switched to type 1 microenvironment with dominance of IFNγ and IL-2 within NLGP-TME, which was not found in other cases; however Cisplatin-TME appeared better in type 2 to type 1 conversion than Sutent-TME as evidenced by RT-PCR, ELISA and immunohistochemical analysis. NLGP-TME educated CD8(+) T cells exhibited greater cytotoxicity to B16 Melanoma cells in vitro and these cells showed comparatively higher expression of cytotoxicity related molecules, perforin and granzyme B than Cisplatin-TME and Sutent-TME educated T cells. Adoptive transfer of NLGP-TME exposed T cells, but not PBS-TME exposed cells in mice, is able to significantly inhibit the growth of melanoma in vivo. Such tumor growth inhibition was in significantly lower extent when therapeutic CD8(+) T cells were exposed to either Cisplatin-TME or Sutent-TME or control-TME. Accumulated evidences strongly suggest that non toxic NLGP normalized TME allows T cells to perform optimally than other TMEs under study to inhibit the melanoma growth.