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In industrial processes, different operating conditions and ratios of ingredients are used to produce multi-grade products in the same production line. Yet, the production grade changes so quickly as the demand from customers varies from time to time. As a result, the process data collected in certain operating regions are often scarce. Process dynamics, nonlinearity, and process uncertainty increase the hardship in developing a reliable model to monitor the process status. In this paper, the source-aided variational state–space autoencoder (SA-VSSAE) is proposed. It integrates variational state–space autoencoder with the Gaussian mixture. With the additional information from the source grades, SA-VSSAE can be used for monitoring processes with sparse target data by performing information sharing to enhance the reliability of the target model. Unlike the past works which perform information sharing and modeling in a two-step procedure, the proposed model is designed for information sharing and modeling in a one-step procedure without causing information loss. In contrast to the traditional state–space model, which is linear and deterministic, the variational state–space autoencoder (VSSAE) extracts the dynamic and nonlinear features in the process variables using neural networks. Also, by taking process uncertainty into consideration, VSSAE describes the features in a probabilistic form. Probability density estimates of the residual and latent variables are given to design the monitoring indices for fault detection. A numerical example and an industrial polyvinyl chloride drying process are presented to show the advantages of the proposed method over the comparative methods. [ABSTRACT FROM AUTHOR]

• Common features are extracted from the source domain for the target-scare system. • The extracted feature knowledge in transfer learning avoids information loss. • Semi-supervised mechanism is designed to use the information of unlabeled data. • The designed objective functions enhance step-ahead prediction for target grades. • Dynamic characteristics of processes are represented by latent state-space models. In response to the dynamic demands of the market, this study addresses the challenge of frequent operational adjustments in a production line to accommodate diverse product grades. The resulting scarcity of data in the new operating conditions (target grade) impedes the development of reliable soft-sensor prediction models. A two-step learning method, termed S2-LGMNSSM-TS-T, is proposed. This method employs a semi-supervised latent Gaussian mixture nonlinear state space model (S2-LGMNSSM-TS) trained on both target and source data, providing a dynamic, one-step-ahead predictive soft sensor. To overcome data scarcity in the target grade, insights from the source are utilized. With a Gaussian mixture prior distribution, S2-LGMNSSM-TS identifies dynamic behaviors in both target and source grades. The enhanced S2-LGMNSSM-TS-T configuration focuses on target-grade predictions, leveraging source knowledge and mitigating data scarcity issues. A numerical example and an industrial case study demonstrate the model's effectiveness in improving target-grade predictions through source knowledge utilization. [ABSTRACT FROM AUTHOR]

The pivotal factor to regulate and enhance within an operating batch is the quality process, a challenging variable to monitor online. Soft sensors offer an immediate alternative for providing real-time insights into process quality, yet persisting issues include the imbalance between process and quality measurements, noisy measurements, and the intricate 3-dimensional dynamic batch data structure. This paper introduces the Robust Semi-Supervised Dual-Attention Latent Dynamic Conditional State-Space Model (RS2DA-LDCSSM) to address these challenges for within-batch quality prediction. Given the frequent absence of quality data due to measurement inconveniences, an imputation network is embedded within the RS2DA-LDCSSM to facilitate the prediction of future quality. To prevent information distortion during data unfolding, the Attentional Sequence-to-Sequence RNN Encoder-Decoder (AS2S-RNNED) is employed to process the 3-dimensional batch data. The proposed method integrates AS2S-RNNED with a probability state-space model to filter out noisy process data and stabilize the probability prediction of quality data, extracting spatial and temporal latent data from past process and quality data while minimizing the loss in multi-step prediction. This work represents a novel approach to dynamic nonlinear batch processes through probabilistic semi-supervised learning. The RS2DA-LDCSSM is adaptable to any batch process for within-batch quality prediction, as evidenced by numerical cases demonstrating its reliability, achieving an R2 value of 0.87, surpassing comparison methods. In an industrial penicillin fermentation batch process, the RS2DA-LDCSSM exhibits remarkable robustness with a high R2 index value of 0.99 in practical quality prediction scenarios, showcasing its efficacy. • Semi-supervised model training utilizes unlabeled process data. • Separate networks are crafted for process and quality feature extraction. • The state-space model with the encoder-decoder is used to extract latent features. • A multi-step-ahead prediction learning scheme yields favorable prediction results. [ABSTRACT FROM AUTHOR]

The pivotal factor to regulate and enhance within an operating batch is the quality process, a challenging variable to monitor online. Soft sensors offer an immediate alternative for providing real-time insights into process quality, yet persisting issues include the imbalance between process and quality measurements, noisy measurements, and the intricate 3-dimensional dynamic batch data structure. This paper introduces the Robust Semi-Supervised Dual-Attention Latent Dynamic Conditional State-Space Model (RS2DA-LDCSSM) to address these challenges for within-batch quality prediction. Given the frequent absence of quality data due to measurement inconveniences, an imputation network is embedded within the RS2DA-LDCSSM to facilitate the prediction of future quality. To prevent information distortion during data unfolding, the Attentional Sequence-to-Sequence RNN Encoder-Decoder (AS2S-RNNED) is employed to process the 3-dimensional batch data. The proposed method integrates AS2S-RNNED with a probability state-space model to filter out noisy process data and stabilize the probability prediction of quality data, extracting spatial and temporal latent data from past process and quality data while minimizing the loss in multi-step prediction. This work represents a novel approach to dynamic nonlinear batch processes through probabilistic semi-supervised learning. The RS2DA-LDCSSM is adaptable to any batch process for within-batch quality prediction, as evidenced by numerical cases demonstrating its reliability, achieving an R2 value of 0.87, surpassing comparison methods. In an industrial penicillin fermentation batch process, the RS2DA-LDCSSM exhibits remarkable robustness with a high R2 index value of 0.99 in practical quality prediction scenarios, showcasing its efficacy. • Semi-supervised model training utilizes unlabeled process data. • Separate networks are crafted for process and quality feature extraction. • The state-space model with the encoder-decoder is used to extract latent features. • A multi-step-ahead prediction learning scheme yields favorable prediction results. [ABSTRACT FROM AUTHOR]

The authors describe the impact of telephone calls to older people on later visits with a physician. They mention the effect of the COVID-19 pandemic in increasing the isolation of older people, the program of volunteers calling patients, and how the calls increased the likelihood of outpatient visits.

Cell-of-origin determination has emerged as an important prognostic factor for patients initially diagnosed with diffuse large B cell lymphoma (DLBCL). Specifically, the nongerminal center B cell–like (non-GCB) subtype, composed predominantly of the activated B cell–like (ABC) molecular subtype, has been shown to portend poor prognosis because of its more aggressive nature and resistance to standard cyclophosphamide, hydroxydaunorubicin, oncovin, prednisone (CHOP)-like chemotherapy compared with the GCB subtype. The recurrent MyD88 L265P mutation, present in 29% of ABC DLBCL, was reported as an independent poor prognostic factor for patients with newly diagnosed DLBCL. For patients whose disease relapses or is refractory to first-line chemotherapy, high-dose chemotherapy with autologous stem cell transplantation (ASCT) is frequently offered as salvage therapy. However, the impact of MyD88 mutation status on post-ASCT outcome has not been reported. Here, we retrospectively analyzed, with up to 20 years of follow-up, 165 patients who underwent ASCT for relapsed/refractory DLBCL at our institution. We found that MyD88 mutation status did not correlate with overall survival (OS), post-ASCT OS, or progression-free survival (PFS). Patients with non-GCB subtype had significantly worse OS from initial diagnosis and after ASCT. Notably, high International Prognostic Index score was predictive of poor pre- and post-transplant PFS and post-transplant OS. [ABSTRACT FROM AUTHOR]

• Dynamic features of process and quality data are learned for quality prediction. • Bi-directional RNN is trained by past and future data to prevent over-fitting. • The unlabeled process data are used to enhance the quality prediction performance. • Improved loss functions for different data types enables parallel parameter update. The online quality variables of soft sensors contribute greatly to obtaining immediate process information. The complex correlations between a large number of process variables inherited from the dynamic and nonlinear characteristics of chemical processes put more challenges on constructing soft-sensor models. Past developed steady-state soft sensors are not reliable for dynamic operating systems. Unequal sampling rates for the process and quality data cause missing values of quality data at some time points. This paper proposes a semi-supervised latent dynamic variational autoencoder to learn features between the process and quality data. A prediction network is constructed to generate artificial quality values for model training. Then the process and quality data are compressed into the latent space and the temporal relation is modeled in the clean latent space. The proposed method is compared with the conventional method for quality prediction in a numerical case and an industrial case. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Breast implant-associated anaplastic large-cell lymphoma: long-term follow-up of 60 patients" by R. N. Miranda in the 2014 issue.

• VEW-MWPLS is proposed to enhance the prediction performance of NOx emission. • With adjustable forgetting factor, VEW-MWPLS can change modeling data window. • An efficient updated scheme with detailed derivations is provided. • Computational efficiency of VEW-MWPLS and past methods are analyzed and compared. The efficiency of selective catalytic reduction denitrification heavily depends on the amount of ammonia injection, which is determined by the emission of nitrogen oxides (NOx) in flue gas. To accurately predict NOx emission of the coal-fired boiler with strong variable correlations, nonlinear and time-varying characteristics for determining the amount of injected ammonia, a soft sensor modeling method based on the moving window partial least squares (MWPLS) and locally weighted regression, referred to as variable exponentially weighted MWPLS (VEW-MWPLS), is proposed. It adjusts the window size in disguised form by automatically strengthen or weaken the information in the selected window according to the prediction errors of the current model. In this work, with the added new sample and the removed old sample, a series of rank-1 modification formulations are used to calculate both, the means and standard deviations among the input and output variables and the covariance and cross-covariance matrices so that a quick and adaptive update can be done for the PLS model. The numerical results have demonstrated that VEW-MWPLS algorithm can reduce the mean and standard deviation of prediction errors by one order of magnitude, and reduce the computational complexity by two orders of magnitude when being compared with other algorithms. Thus the proposed VEW-MWPLS is more suitable for predicting NOx emission online. [ABSTRACT FROM AUTHOR]

To explore the effects of customized 3D-printed assistive technology (AT) on functional performance and feasibility in patients with neurological impairment. Patients with neurological impairment were recruited and randomized into customized 3D-printed assistive device group (group 1; n = 17) or standard device group (group 2; n = 14). The device was designed to assist their writing, spoon using, and typing. Each patient underwent 4-week intervention with the device (30 min per session, twice a week). We observed significant differences in shoulder abduction (p =.00), external rotation (p =.01), and internal rotation (p =.02) in group 1. And significant differences in abduction (p =.05) and external rotation (p =.05) between the 2 groups. Group 1 achieved significant improvements in writing without AT (p =.04) and with AT (p =.02), spoon use without AT (p =.02) and with AT (p =.03), and hemiplegia-side typing with AT (p =.00). Group 2 achieved significant improvements in writing without AT (p =.01), hemiplegia-side typing without AT (p =.01), and bil-side typing with AT (P =.05). Moreover, no significant differences were noted in other outcome measures. This study demonstrated that customized 3D-printed AT can improve shoulder active motion for patients with neurological impairment. A positive effect in functional hand tasks after AT intervention. Offering customized AT with specific training could enhance the efficacy of interventions. The feasibility of using 3D printing technology to produce customized AT, which has the potential to be cost-effective and efficient. 3D-printed assistive device incorporating a splint can improve shoulder active motion compared to conventional assistive device for patients with neurological impairment. A positive effect in functional hand tasks after assistive device intervention. [ABSTRACT FROM AUTHOR]

In the chemical industry, manufacturers produce multiple products with different grades constantly by changing the operating conditions and raw materials in the same production line to meet the customers' needs and market demands. Thus, establishing a model for monitoring such a process is inadequate because the data available in an operating region is often scarce. To deal with the hardship in monitoring the process that produces various products with multi-grades, the data sparsity problem in the target grade is addressed by simultaneously extracting and sharing common knowledge of the available grades with the target grade. In fact, multi-grade processes have similar operating conditions but they may have different ratios of ingredients. In this paper, the Gaussian mixture prior variational autoencoder (GMPVAE) is proposed to perform information sharing to enhance the target model. Complementary encoder and decoder networks represent the mapping of the source and target input data to extract the common features among the grades from the same production line. In contrast with an ordinary variational autoencoder, a Gaussian mixture model instead of a unit Gaussian distribution is set as the prior distribution of GMPVAE so that the unique characteristics of the target and source grades can be obtained. In addition, the objective function is designed on the basis of the goals to increase the reliability of the target model and to do information sharing and modeling in one step to reduce information loss during the information transferring procedure. Accordingly, the probability density estimates of latent variables and residuals rather than point estimates are then given so that distribution-based monitoring indices of the target grade can be designed and the fault detection decisions can be made opportunely. A numerical example and an industrial ammonia synthesis example are presented to show the effectiveness of the proposed method. • Useful information of target data-scarce systems is extracted from the source data. • The designed objective function enhances monitoring to build good target models. • Data features are extracted and integrated in one step to avoid information loss. • Abundant monitoring indices instead of point estimates are set for fault detection. [ABSTRACT FROM AUTHOR]

We demonstrate a novel avalanche photodiode (APD) design which fundamentally relaxes the trade-off between responsivity and saturation-current performance at receiver end in coherent system. Our triple In0.52Al0.48As based multiplication (M-) layers with a stepped electric (E-) field inside has more pronounced avalanche process with significantly less effective critical-field than the dual M-layer. Reduced E-field in active M-layers ensures stronger E-field allocation to the thick absorption-layer with a smaller breakdown voltage (Vbr) resulting in less serious space-charge screening effect, less device heating at high output photocurrent. Compared to the dual M-layer reference sample, the demonstrated APD exhibits lower punch-through (− 9 vs. − 24 V)/breakdown voltages (− 43 vs. − 51 V), higher responsivity (19.6 vs. 13.5 A/W), higher maximum gain (230 vs. 130), and higher 1-dB saturation-current (> 5.6 vs. 2.5 mA) under 0.95 Vbr operation. Extremely high saturation-current (> 14.6 mA), high responsivity (7.3 A/W), and decent O-E bandwidth (1.4 GHz) can be simultaneously achieved using the demonstrated APD with a 200 µm active window diameter. In coherent FMCW LiDAR test bed, this novel APD exhibits a larger signal-to-noise ratio and high-quality 3-D images than the reference dual M-layer and high-performance commercial p-i-n PD modules, while requiring significantly less optical local-oscillator (LO) power (0.5 vs 4 mW). [ABSTRACT FROM AUTHOR]

Alterations to the epigenetic landscape of diffuse large B-cell lymphoma (DLBCL) play a fundamental role in deregulating genes involved in normal lymphocyte differentiation. To determine whether targeted epigenetic therapy could reverse these pathogenic chromatin changes and suppress the expression of a lymphoma oncogene, we focused onBCL6, a transcriptional repressor whose aberrant expression is tightly linked to DLBCL proliferation and survival. We fused zinc-finger (ZF) domains specific for regulatory regions in theBCL6locus to a repressive epigenetic modifier, the Kruppel-associated box (KRAB) repressor domain. Distinct ZF–KRAB fusions repressed the local chromatin landscape, suppressedBCL6expression, significantly impaired DLBCL growth, and caused widespread cell death in aBCL6-dependent manner. Importantly, expression of ectopicBCL6protein rescued ZF–KRAB-induced cell death, demonstrating the modifiers’ specificity. We show that sequence-specific epigenetic modifiers can alter oncogene expression and induce apoptosis in cancer cells, underscoring their potential for future development as targeted epigenetic protein therapies. [ABSTRACT FROM AUTHOR]

Infection is a common cause of morbidity and mortality in chronic lymphocytic leukemia and should be considered when examining bone marrow specimens to identify a potentially treatable pathogen. [ABSTRACT FROM AUTHOR]

Recessive variants of the SLC26A4 gene are an important cause of hereditary hearing impairment. Several transgenic mice with different Slc26a4 variants have been generated. However, none have recapitulated the auditory phenotypes in humans. Of the SLC26A4 variants identified thus far, the p.T721M variant is of interest, as it appears to confer a more severe pathogenicity than most of the other missense variants, but milder pathogenicity than non-sense and frameshift variants. Using a genotype-driven approach, we established a knock-in mouse model homozygous for p.T721M. To verify the pathogenicity of p.T721M, we generated mice with compound heterozygous variants by intercrossing Slc26a4+/T721M mice with Slc26a4919-2A>G/919-2A>G mice, which segregated the c.919-2A > G variant with abolished Slc26a4 function. We then performed serial audiological assessments, vestibular evaluations, and inner ear morphological studies. Surprisingly, both Slc26a4T721M/T721M and Slc26a4919-2A>G/T721M showed normal audiovestibular functions and inner ear morphology, indicating that p.T721M is non-pathogenic in mice and a single p.T721M allele is sufficient to maintain normal inner ear physiology. The evidence together with previous reports on mouse models with Slc26a4 p.C565Y and p.H723R variants, support our speculation that the absence of audiovestibular phenotypes in these mouse models could be attributed to different protein structures at the C-terminus of human and mouse pendrin. [ABSTRACT FROM AUTHOR]

The ongoing COVID‐19 pandemic has had a profound worldwide impact on the laboratory hematology community. Nevertheless, the pace of COVID‐19 hematology‐related research has continued to accelerate and has established the role of laboratory hematology data for many purposes including disease prognosis and outcome. The purpose of this scoping review was to assess the current state of COVID‐19 laboratory hematology research. A comprehensive search of the literature published between December 1, 2019, and July 3, 2020, was performed, and we analyzed the sources, publication dates, study types, and topics of the retrieved studies. Overall, 402 studies were included in this scoping review. Approximately half of these studies (n = 202, 50.37%) originated in China. Retrospective cohort studies comprised the largest study type (n = 176, 43.89%). Prognosis/ risk factors, epidemiology, and coagulation were the most common topics. The number of studies published per day has increased through the end of May. The studies were heavily biased in favor of papers originating in China and on retrospective clinical studies with limited use of and reporting of laboratory data. Despite the major improvements in our understanding of the role of coagulation, automated hematology, and cell morphology in COVID‐19, there are gaps in the literature, including biosafety and the laboratory role in screening and prevention of COVID‐19. There is a gap in the publication of papers focused on guidelines for the laboratory. Our findings suggest that, despite the large number of publications related to laboratory data and their use in COVID‐19 disease, many areas remain unexplored or under‐reported. [ABSTRACT FROM AUTHOR]

Composite lymphoma (CL) occurs when two lymphomas with distinct WHO subtypes arise in one patient. Here, we present two cases of CL. Case 1 presented with diffuse large B-cell lymphoma (DLBCL), Hodgkin lymphoma (HL), and follicular lymphoma (FL). Case 2 presented with FL and HL in two distinct areas of one lymph node. We performed exome sequencing on matched tumor/normal samples (mean depth per sample >70x) from each lymphoma, excluding the FL in case 1. SNVs and indels were filtered and manually reviewed. A subset of variants were validated using Ampliseq. In case 1, following Ampliseq, 44 DLBCL variants were confirmed and 64 HL variants were confirmed. We observed a KMT2D frameshift in DLBCL and a B2M stop gain in HL, both of which are recurrently mutated in DLBCL and HL, respectively. We report 17 shared variants including a stop gain variant in TNFRSF14. In case 2, the validated data showed 94 sites shared between the HL and FL. These shared variants include missense variants in PCLO , and a stop gain variant in TNFRSF14. We sequenced the B-cell receptor locus (BCR) in all samples. In case 1, a dominant BCR clone representing 98.7%, 74.2%, and 33.2% of rearrangements from the DLBCL, FL, and HL was detected. For case 2, a shared rearrangement was detected comprising 1.4% and 1.3% of the rearrangements in the FL and HL. In summary, the cases presented here provide evidence of a common clonal origin for two cases of Hodgkin and non-Hodgkin lymphoma CL. [ABSTRACT FROM AUTHOR]

Hearing loss is the most prevalent hereditary sensory disorder in children. Approximately 2 in 1000 infants are affected by genetic hearing loss. The PJVK gene, which encodes the pejvakin protein, has been linked to autosomal recessive non-syndromic hearing loss DFNB59. Previous clinical studies have revealed that PJVK mutations might be associated with a wide spectrum of auditory manifestations, ranging from hearing loss of pure cochlear origin to that involving the retrocochlear central auditory pathway. The phenotypic variety makes the pathogenesis of this disease difficult to determine. Similarly, mouse models carrying different Pjvk defects show phenotypic variability and inconsistency. In this study, we generated a knockin mouse model carrying the c.874G > A (p.G292R) variant to model and investigate the auditory and vestibular phenotypes of DFNB59. • A mouse model of DFNB59 with the Pjvk c.874G > A mutation was established by CRISPR/Cas9 approach. • Pjvk c.874G > A mutant mice exhibited progressive hearing loss, with cochlear hair cell and spiral ganglion degeneration. • Vestibular dysfunction with progressive loss of vestibular ganglion neurons was observed in Pjvk c.874G > A mutant mice. [ABSTRACT FROM AUTHOR]

Abstract Background Endoplasmic reticulum (ER) stress plays a crucial role in cell death decisions in context of various diseases. Although it is known that ER stress occurs in livers subjected to hemorrhagic shock and reperfusion (HS/R), there is no understanding about the influence of the liver architecture on ER stress and the activation of the unfolded protein response (UPR). Materials and methods Mice were subjected to a pressure-controlled HS (30 ± 5 mmHg) for 90 min. Mice were sacrificed 2, 4, 6, 8, 10, 14, 18, and 24 h after shock induction. Plasma levels of inflammatory cytokines (IL-6, CXCL1, CXCL9, CXCL10, CCL2, CCL3) and transaminases were measured. Hematoxylin and eosin stains of paraffin-embedded liver tissue sections were evaluated for liver damage. Immunohistochemistry was used to analyze the hepatic topography of ER stress marker binding immunoglobulin protein and the activation of the three major pathways of the UPR. Results Compared with sham-operated mice, HS/R led to profound liver damage and an elevation of inflammatory cytokines. We found time-dependent topographical changes of ER stress in the livers. Furthermore, the three major pathways of the UPR represented by protein kinase RNA-like ER kinase, activating transcription factor 6, and inositol-requiring enzyme 1 were activated in differing ways dependent on the zonation within the liver acinus. Conclusions These findings show that the liver architecture must be taken into account when investigating the role of ER stress and the UPR in ischemia-reperfusion injury after HS/R. [ABSTRACT FROM AUTHOR]

PABLO is a virtual reality game where a motion sensor system is used. Few studies have investigated the effects of the PABLO system in stroke rehabilitation. We investigated the effects of upper-extremity virtual reality training with the PABLO system in patients with stroke. Stroke patients were randomly assigned to the virtual reality (n = 19) or standard rehabilitation groups (n = 18). Total of 18 sessions were conducted twice per week. The primary outcome measure was the Fugl–Meyer Assessment-Upper Extremity subscale. Secondary outcome measures included the active ranges of motion of the shoulder and elbow, the box and block test, hand grip strength, and the Stroke Impact Scale. Enjoyment of activities and side effects were also recorded. No difference was observed between two groups in primary outcome. Virtual reality group exhibited greater improvements in the hand dexterity between groups (p =.05). In active motion, virtual reality group showed greater improvement in shoulder flexion between groups (p =.03). Virtual reality group also showed greater improvements in elbow pronation between groups (p =.03). The groups differed in their assessments of how enjoyment the rehabilitation activities were found (p =.01). No significant differences between groups were observed in any other tests. Interventions based on the PABLO virtual reality system improved upper extremity hand function, shoulder and elbow movements, and elicited a higher degree of enjoyment from study participants, than did traditional treatment. Trials registration: The study protocol was registered at Clinical Trials.gov PRS (No.NCT04296032). • Virtual reality training improves upper extremity motion and dexterity in chronic stroke patients. • Virtual reality system elicited a higher degree of enjoyment from chronic stroke patients. • Sensor-based virtual reality games did not show positive effect on quality of life. [ABSTRACT FROM AUTHOR]

In the publication of this article (Liu et al. 2019), there was an error in the method and ethics declarations sections which were published with incorrect animal experiment approval number. The error: 'These animal experimental protocols have been reviewed and approved by the Institutional Animal Care and Use Committee of Taipei Medical University (LAC-99-0142).' Should instead read: These animal experimental protocols have been reviewed and approved by the Institutional Animal Care and Use Committee of Taipei Medical University (LAC-2016-0340). [ABSTRACT FROM AUTHOR]

Background: There were few report concerning anti-glycation and antioxidant activities of the minor amounts of components in curcuminoids, demethylcurcumin and tetrahydroxycurcumin, in vitro and in vivo. Results: The bovine serum albumin/galactose of non-enzymatic glycation models, radical-induced hemolysis, and oxygen radical absorbance capacity (ORAC) were studied in vitro, and the d-galactose-induced oxidative stress in BALB/c mice and then demethylcurcumin or tetrahydroxycurcumin interventions in vivo. The parameters of oxidative stress in plasma and brain extracts were determined among animal groups with or without both curcuminoids interventions. The demethylcurcumin and tetrahydroxycurcumin exhibited anti-glycation, anti-hemolysis, and ORAC activities, and showed much better and significant difference (P < 0.05) compared to those of curcumin in vitro. In animal experiments, the intervened two curcuminoids at both concentrations showed to lower serum malondialdehyde (MDA), brain MDA levels and iNOS protein expressions, and elevate serum ORAC activities, and showed difference (P < 0.05) compared to the galactose-induced control. Conclusion: The demethylcurcumin and tetrahydroxycurcumin showed potentials in developing functional foods for antioxidant-related purposes. [ABSTRACT FROM AUTHOR]

Background: Severe injury and associated hemorrhagic shock lead to an inflammatory response and subsequent increased tissue damage. Numerous reports have shown that injury-induced inflammation and the associated end-organ damage is driven by Toll-like receptor 4 (TLR4) activation via damage-associated molecular patterns. We examined the effectiveness of Eritoran tetrasodium (E5564), an inhibitor of TLR4 function, in reducing inflammation induced during hemorrhagic shock with resuscitation (HS/R) or after peripheral tissue injury (bilateral femur fracture, BFF). Material and methods: Mice underwent HS/R or BFF with or without injection of Eritoran (5 mg/kg body weight) or vehicle control given before, both before and after, or only after HS/R or BFF. Mice were sacrificed after 6 h and plasma and tissue cytokines, liver damage (histology; aspartate aminotransferase/alanine aminotransferase), and inflammation (NF-κB) and gut permeability were assessed. Results: In HS/R Eritoran significantly reduced liver damage (values ± SEM: alanine aminotransferase 9910 ± 3680 U/L versus 1239 ± 327 U/L and aspartate aminotransferase 5863 ± 2000 U/L versus 1246 ± 243 U/L, P < 0.01) at 6 h compared with control when given just before HS and again just prior to resuscitation. Eritoran administration also led to lower IL-6 levels in plasma and liver and less NF-κB activation in liver. Increases in gut barrier permeability induced by HS/R were also prevented with Eritoran. Eritoran similarly diminished BFF-mediated systemic inflammatory responses. Conclusion: These data suggest Eritoran can inhibit tissue damage and inflammation induced via TLR4/myeloid differentiation factor 2 signaling from damage-associated molecular patterns released during HS/R or BFF. Eritoran may represent a promising therapeutic for trauma patients to prevent multiple organ failure. [ABSTRACT FROM AUTHOR]