E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing.

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models.

Corrections to "STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation with Gait Signature Simulation and Deep Learning".

[This corrects the article PMC10399976.].

STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation with Gait Signature Simulation and Deep Learning.

Abnormal gait is a significant non-cognitive biomarker for Alzheimer's disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar, a non-wearable technology, can capture human gait movements for potential early ADRD risk assessment. In this research, we propose to design STRIDE integrating micro-Doppler radar sensors with advanced artificial intelligence (AI) technologies. STRIDE embeds a new deep learning (DL) classification framework. As a proof of concept, we develop a "digital-twin" of STRIDE, consisting of a human walking simulation model and a micro-Doppler radar simulation model, to generate a gait signature dataset. Taking established human walking parameters, the walking model simulates individuals with ADRD under various conditions. The radar model based on electromagnetic scattering and the Doppler frequency shift model is employed to generate micro-Doppler signatures from different moving body parts (e.g., foot, limb, joint, torso, shoulder, etc.). A band-dependent DL framework is developed to predict ADRD risks. The experimental results demonstrate the effectiveness and feasibility of STRIDE for evaluating ADRD risk.

Usage of compromised lung volume in monitoring steroid therapy on severe COVID-19.

BACKGROUND: Quantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of coronavirus disease 2019 (COVID-19) and for monitoring its progress. The present study aimed to assess the association between steroid therapy and quantitative CT parameters in a longitudinal cohort with COVID-19. METHODS: Between February 7 and February 17, 2020, 72 patients with severe COVID-19 were retrospectively enrolled. All 300 chest CT scans from these patients were collected and classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (at admission); Stage 2 (3-7 days); Stage 3 (8-14 days); Stage 4 (15-21 days); and Stage 5 (22-31 days). QCT was performed using a threshold-based quantitative analysis to segment the lung according to different Hounsfield unit (HU) intervals. The primary outcomes were changes in percentage of compromised lung volume (%CL, - 500 to 100 HU) at different stages. Multivariate Generalized Estimating Equations were performed after adjusting for potential confounders. RESULTS: Of 72 patients, 31 patients (43.1%) received steroid therapy. Steroid therapy was associated with a decrease in %CL (- 3.27% [95% CI, - 5.86 to - 0.68, P = 0.01]) after adjusting for duration and baseline %CL. Associations between steroid therapy and changes in %CL varied between different stages or baseline %CL (all interactions, P < 0.01). Steroid therapy was associated with decrease in %CL after stage 3 (all P < 0.05), but not at stage 2. Similarly, steroid therapy was associated with a more significant decrease in %CL in the high CL group (P < 0.05), but not in the low CL group. CONCLUSIONS: Steroid administration was independently associated with a decrease in %CL, with interaction by duration or disease severity in a longitudinal cohort. The quantitative CT parameters, particularly compromised lung volume, may provide a useful tool to monitor COVID-19 progression during the treatment process. Trial registration Clinicaltrials.gov, NCT04953247. Registered July 7, 2021, https://clinicaltrials.gov/ct2/show/NCT04953247.

Deep learning-based pulmonary tuberculosis automated detection on chest radiography: large-scale independent testing.

Background: It is critical to have a deep learning-based system validated on an external dataset before it is used to assist clinical prognoses. The aim of this study was to assess the performance of an artificial intelligence (AI) system to detect tuberculosis (TB) in a large-scale external dataset. Methods: An artificial, deep convolutional neural network (DCNN) was developed to differentiate TB from other common abnormalities of the lung on large-scale chest X-ray radiographs. An internal dataset with 7,025 images was used to develop the AI system, including images were from five sources in the U.S. and China, after which a 6-year dynamic cohort accumulation dataset with 358,169 images was used to conduct an independent external validation of the trained AI system. Results: The developed AI system provided a delineation of the boundaries of the lung region with a Dice coefficient of 0.958. It achieved an AUC of 0.99 and an accuracy of 0.948 on the internal data set, and an AUC of 0.95 and an accuracy of 0.931 on the external data set when it was used to detect TB from normal images. The AI system achieved an AUC of more than 0.9 on the internal data set, and an AUC of over 0.8 on the external data set when it was applied to detect TB, non-TB abnormal and normal images. Conclusions: We conducted a real-world independent validation, which showed that the trained system can be used as a TB screening tool to flag possible cases for rapid radiologic review and guide further examinations for radiologists.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

Longitudinal changes of laboratory measurements after discharged from hospital in 268 COVID-19 pneumonia patients.

BACKGROUND AND OBJECTIVE: Monitoring recovery process of coronavirus disease 2019 (COVID-19) patients released from hospital is crucial for exploring residual effects of COVID-19 and beneficial for clinical care. In this study, a comprehensive analysis was carried out to clarify residual effects of COVID-19 on hospital discharged patients. METHODS: Two hundred sixty-eight cases with laboratory measured data at hospital discharge record and five follow-up visits were retrospectively collected to carry out statistical data analysis comprehensively, which includes multiple statistical methods (e.g., chi-square, T-test and regression) used in this study. RESULTS: Study found that 13 of 21 hematologic parameters in laboratory measured dataset and volume ratio of right lung lesions on CT images highly associated with COVID-19. Moderate patients had statistically significant lower neutrophils than mild and severe patients after hospital discharge, which is probably caused by more efforts on severe patients and slightly neglection of moderate patients. COVID-19 has residual effects on neutrophil-to-lymphocyte ratio (NLR) of patients who have hypertension or chronic obstructive pulmonary disease (COPD). After released from hospital, female showed better performance in T lymphocytes subset cells, especially T helper lymphocyte% (16% higher than male). According to this sex-based differentiation of COVID-19, male should be recommended to take clinical test more frequently to monitor recovery of immune system. Patients over 60 years old showed unstable recovery process of immune cells (e.g., CD45 + lymphocyte) within 75 days after discharge requiring longer clinical care. Additionally, right lung was vulnerable to COVID-19 and required more time to recover than left lung. CONCLUSIONS: Criterion of hospital discharge and strategy of clinical care should be flexible in different cases due to residual effects of COVID-19, which depend on several impact factors. Revealing remaining effects of COVID-19 is an effective way to eliminate disorder of mental health caused by COVID-19 infection.

Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids.

Purpose: Given the recent COVID-19 pandemic and its stress on global medical resources, presented here is the development of a machine intelligent method for thoracic computed tomography (CT) to inform management of patients on steroid treatment. Approach: Transfer learning has demonstrated strong performance when applied to medical imaging, particularly when only limited data are available. A cascaded transfer learning approach extracted quantitative features from thoracic CT sections using a fine-tuned VGG19 network. The extracted slice features were axially pooled to provide a CT-scan-level representation of thoracic characteristics and a support vector machine was trained to distinguish between patients who required steroid administration and those who did not, with performance evaluated through receiver operating characteristic (ROC) curve analysis. Least-squares fitting was used to assess temporal trends using the transfer learning approach, providing a preliminary method for monitoring disease progression. Results: In the task of identifying patients who should receive steroid treatments, this approach yielded an area under the ROC curve of 0.85 ± 0.10 and demonstrated significant separation between patients who received steroids and those who did not. Furthermore, temporal trend analysis of the prediction score matched expected progression during hospitalization for both groups, with separation at early timepoints prior to convergence near the end of the duration of hospitalization. Conclusions: The proposed cascade deep learning method has strong clinical potential for informing clinical decision-making and monitoring patient treatment.

Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.

BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment. PURPOSE: In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans. METHODS: For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infection cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance. RESULTS: Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists' performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance. CONCLUSION: A deep learning algorithm-based AI model developed in this study successfully improved radiologists' performance in distinguishing COVID-19 from other pulmonary infections using chest CT images.

Developing and verifying automatic detection of active pulmonary tuberculosis from multi-slice spiral CT images based on deep learning.

OBJECTIVE: Diagnosis of tuberculosis (TB) in multi-slice spiral computed tomography (CT) images is a difficult task in many TB prevalent locations in which experienced radiologists are lacking. To address this difficulty, we develop an automated detection system based on artificial intelligence (AI) in this study to simplify the diagnostic process of active tuberculosis (ATB) and improve the diagnostic accuracy using CT images. DATA: A CT image dataset of 846 patients is retrospectively collected from a large teaching hospital. The gold standard for ATB patients is sputum smear, and the gold standard for normal and pneumonia patients is the CT report result. The dataset is divided into independent training and testing data subsets. The training data contains 337 ATB, 110 pneumonia, and 120 normal cases, while the testing data contains 139 ATB, 40 pneumonia, and 100 normal cases, respectively. METHODS: A U-Net deep learning algorithm was applied for automatic detection and segmentation of ATB lesions. Image processing methods are then applied to CT layers diagnosed as ATB lesions by U-Net, which can detect potentially misdiagnosed layers, and can turn 2D ATB lesions into 3D lesions based on consecutive U-Net annotations. Finally, independent test data is used to evaluate the performance of the developed AI tool. RESULTS: For an independent test, the AI tool yields an AUC value of 0.980. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are 0.968, 0.964, 0.971, 0.971, and 0.964, respectively, which shows that the AI tool performs well for detection of ATB and differential diagnosis of non-ATB (i.e. pneumonia and normal cases). CONCLUSION: An AI tool for automatic detection of ATB in chest CT is successfully developed in this study. The AI tool can accurately detect ATB patients, and distinguish between ATB and non- ATB cases, which simplifies the diagnosis process and lays a solid foundation for the next step of AI in CT diagnosis of ATB in clinical application.

Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: three case reports.

In this article, we analyze and report cases of three patients who were admitted to Renmin Hospital, Wuhan University, China, for treating COVID-19 pneumonia in February 2020 and were unresponsive to initial treatment of steroids. They were then received titrated steroids treatment based on the assessment of computed tomography (CT) images augmented and analyzed with the artificial intelligence (AI) tool and output. Three patients were finally recovered and discharged. The result indicated that sufficient steroids may be effective in treating the COVID-19 patients after frequent evaluation and timely adjustment according to the disease severity assessed based on the quantitative analysis of the images of serial CT scans.

Mortality under early access to antiretroviral therapy vs. Eswatini's national standard of care: the MaxART clustered randomized stepped-wedge trial.

OBJECTIVES: Current WHO guidelines recommend the treatment of all HIV-infected individuals with antiretroviral therapy (ART) to improve survival and quality of life, and decrease infection of others. MaxART is the first implementation trial of this strategy embedded within a government-managed health system, and assesses mortality as a secondary outcome. Because primary findings strongly supported scale-up of the 'treat all' strategy (hereafter Treat All), this analysis examines mortality as an additional indicator of its impact. METHODS: MaxART was conducted in 14 Eswatinian health clinics through a clinic-based stepped-wedge design, by transitioning clinics from then-national standard of care (SoC) to the Treat All intervention. All-cause, disease-related, and HIV-related mortality were analysed using the Cox proportional hazards model, censoring SoC participants at clinic transition. Median follow-up time among study participants was 292 days. There were 36/2034 deaths in SoC (1.77%) and 49/1371 deaths in Treat All (3.57%). RESULTS: Between September 2014 and August 2017, 3405 participants were enrolled. In SoC and Treat All interventions, respectively, the multivariable-adjusted 12-month all-cause mortality rates were 1.42% [95% confidence interval (CI): 0.66-2.17] and 1.60% (95% CI: 0.78-2.40), disease-related mortality rates were 1.02% (95% CI: 0.40-1.64) and 1.10% (95% CI: 0.46-1.73), and HIV-related mortality rates were 1.03% (95% CI: 0.40-1.65) and 0.99% (95% CI: 0.40-1.58). Treat All had no impact on all-cause [hazard ratio (HR) = 1.12, 95% CI: 0.58-2.18, P = 0.73], disease-related (HR = 1.04, 95% CI: 0.52-2.11, P = 0.90), or HIV-related mortality (HR = 0.93, 95% CI: 0.46-1.87, P = 0.83). CONCLUSION: There was no immediate benefit of the Treat All strategy on mortality, nor evidence of harm. Longer follow-up of participants is needed to establish long-term consequences.

TGF-beta-mediated activation of RhoA signalling is required for efficient (V12)HaRas and (V600E)BRAF transformation.

Transforming growth factor beta-1 (TGF-beta) acts as both a tumour suppressor and a tumour promoter in a context-dependent manner. The tumour-promoting activities of TGF-beta are likely to result from a combination of Smad and non-Smad signalling pathways but remain poorly understood. Here we show that TGF-beta-mediated activation of RhoA is dependent on the kinase activity of ALK5 and that continuous ALK5 activity maintains basal RhoA-ROCK signalling, cell morphology and actin dynamics in serum-starved rodent fibroblasts independently of Smad2, Smad3 and Smad4. In immortalized human diploid fibroblasts, we show that oncogenic rewiring by transduction of (V12)HaRas instigates regulation of RhoA-ROCK signalling through an autocrine TGF-beta1-ALK5 pathway. Furthermore, we show that ALK5-mediated activation of RhoA is required for efficient (V12)HaRas, V-Raf and (V600E)BRAF transformation and (V12)HaRas-mediated anchorage-independent growth. These findings identify a new pro-oncogenic activity of TGF-beta and indicate that tumours harbouring (V12)HaRas and (V600E)BRAF mutations may be susceptible to TGF-beta signalling inhibitors.

Synergistic activation of stress-activated protein kinase 1/c-Jun N-terminal kinase (SAPK1/JNK) isoforms by mitogen-activated protein kinase kinase 4 (MKK4) and MKK7.

Stress-activated protein kinase 1 (SAPK1), also called c-Jun N-terminal kinase (JNK), becomes activated in vivo in response to pro-inflammatory cytokines or cellular stresses. Its full activation requires the phosphorylation of a threonine and a tyrosine residue in a Thr-Pro-Tyr motif, which can be catalysed by the protein kinases mitogen-activated protein kinase kinase (MKK)4 and MKK7. Here we report that MKK4 shows a striking preference for the tyrosine residue (Tyr-185), and MKK7 a striking preference for the threonine residue (Thr-183) in three SAPK1/JNK1 isoforms tested (JNK1 alpha 1, JNK2 alpha 2 and JNK3 alpha 1). For this reason, MKK4 and MKK7 together produce a synergistic increase in the activity of each SAPK1/JNK isoform in vitro. The MKK7 beta variant, which is several hundred-fold more efficient in activating all three SAPK1/JNK isoforms than is MKK7 alpha', is equally specific for Thr-183. MKK7 also phosphorylates JNK2 alpha 2 at Thr-404 and Ser-407 in vitro, Ser-407 being phosphorylated much more rapidly than Thr-183 in vitro. Thr-404/Ser-407 are phosphorylated in unstimulated human KB cells and HEK-293 cells, and phosphorylation is increased in response to an osmotic stress (0.5 M sorbitol). However, in contrast with Thr-183 and Tyr-185, the phosphorylation of Thr-404 and Ser-407 is not increased in response to other agonists that activate MKK7 and SAPK1/JNK, suggesting that phosphorylation of these residues is catalysed by another protein kinase, such as CK2, which also phosphorylates Thr-404 and Ser-407 in vitro. MKK3, MKK4 and MKK6 all show a strong preference for phosphorylation of the tyrosine residue of the Thr-Gly-Tyr motifs in their known substrates SAPK2a/p38, SAPK3/p38 gamma and SAPK4/p38 delta. MKK7 also phosphorylates SAPK2a/p38 at a low rate (but not SAPK3/p38 gamma or SAPK4/p38 delta), and phosphorylation occurs exclusively at the tyrosine residue, demonstrating that MKK7 is intrinsically a 'dual-specific' protein kinase.

Characterization of different commercial soybean peroxidase preparations and use of the enzyme for N-demethylation of methyl N-methylanthranilate To produce the food flavor methylanthranilate.

The potential of different peroxidase preparations for the N-demethylation of methyl N-methylanthranilate to produce the food flavor methylanthranilate (MA) was investigated. All tested peroxidase preparations were able to catalyze the N-dealkylation. The tested soybean preparations vary widely with respect to their heme content. Furthermore, the operational stability of purified soybean peroxidase (SP) is at least 25-fold lower than that of horseradish peroxidase and only 5-fold higher than that of microperoxidase 8. Thus, the presence of a large protein chain around a porphyrin cofactor in a peroxidase is, by itself, insufficient to explain the observed differences in operational stability. Despite its relatively low operational stability, SP proved to be the most efficient biocatalyst for the production of MA with high yield and purity, especially observed at the high temperature and low pH values at which SP appeared to be optimally active.

No induction of beta-oxidation in leaves of Arabidopsis that over-produce lauric acid.

Leaves from transgenic Brassica napus L. plants engineered to produce lauric acid show increased levels of enzyme activities of the pathways associated with fatty acid catabolism (V.A. Eccleston and J.B. Ohlrogge, 1998, Plant Cell 10: 613-621). In order to determine if the increases in enzyme activity are mirrored by increases in the expression of genes encoding enzymes of beta-oxidation, which is the major pathway of fatty acid catabolism in plants, the medium-chain acyl-acyl carrier protein (ACP) thioesterase MCTE from California bay (Umbellularia california) was over-expressed under the control of the cauliflower mosaic virus 35S promoter in Arabidopsis thaliana (L.) Heynh. Arabidopsis was the most suitable choice for these studies since gene expression could be analyzed in a large number of independent MCTE-expressing lines using already well-characterized beta-oxidation genes. Levels of MCTE transcripts in leaves varied widely over the population of plants analyzed. Furthermore, active MCTE was produced as determined by enzymatic analysis of leaf extracts of MCTE-expressing plants. These plants incorporated laurate into triacylglycerol of seeds, but not into lipids of leaves as shown by gaschromatographic analysis of total fatty acid extracts. The expression levels of the beta-oxidation and other genes that are highly expressed during developmental stages involving rapid fatty acid degradation were measured. No significant difference in gene expression was observed among MCTE-expressing plants and transgenic and non-transgenic controls. To eliminate the possibility that post-translational mechanisms are responsible for the observed increases in enzyme activity acyl-CoA oxidase activity was also measured in leaves of MCTE-expressing plants using medium and long chain acyl-CoA substrates. No significant increases in either medium- or long-chain acyl-CoA oxidase activities were detected. We conclude that endogenous beta-oxidation is sufficient to account for the complete degradation of laurate produced in rosette leaves of Arabidopsis expressing MCTE.

Extravascular diffusion of tirapazamine: effect of metabolic consumption assessed using the multicellular layer model.

PURPOSE: Hypoxia-selective cytotoxic agents, like tirapazamine (TPZ), must diffuse considerable distances in tumors to reach their target cell population. This study uses a new three-dimensional tissue culture model, in which cells are grown as multicellular layers (MCL), to investigate whether metabolic consumption of TPZ is sufficiently rapid to compromise its extravascular diffusion in tumors. METHODS AND MATERIALS: V79-171b and MGH-U1 cells were grown as MCL to thicknesses of approximately 120 and 360 microm respectively. The extent of hypoxia in MCL, as assessed by EF5 binding, was modulated by altering gas-phase O2 content, and flux of TPZ through MCL was investigated by high-performance liquid chromatography (HPLC). Data were fitted to a diffusion-reaction mathematical model to determine the diffusion coefficient of TPZ in the MCL (DM) and the rate of its metabolic consumption under anoxia. These parameters were used to simulate TPZ transport in tumors. RESULTS: The flux of TPZ through well-oxygenated MCL (equilibrated with 95% O2) was well fitted as Fickian diffusion without reaction, with a D(M) of 7.4 x 10(-7) cm2s(-1) (12-fold lower than in culture medium) for V79 and 1.3 x 10(-6) cm2s(-1) for MGH-U1 MCL. Flux of TPZ was suppressed under anoxia, and fitting the data required inclusion of a reaction term with a rate constant for metabolic consumption of TPZ of 0.52 min(-1) for V79 and 0.31 min(-1) for MGH-U1 MCL. These transport parameters would translate into a 43% or 30% decrease respectively in TPZ exposure, as a result of drug metabolism, in the center of a slab of anoxic tissue 100 microm in thickness. CONCLUSIONS: MCL cultures provide an in vitro model for investigating the interaction between metabolic consumption and diffusion of bioreductive drugs. If rates of diffusion and metabolism similar to those measured in V79 and MGH-U1 MCL apply in tumors, then cells in large confluent regions of hypoxia would be partially protected by failure of TPZ penetration. Simulation of extravascular transport of TPZ-like bioreductive drugs demonstrates that the optimum metabolic rate constant is determined by two competing requirements: it should be high enough to ensure potent cytotoxicity under hypoxia, yet low enough that penetration is not severely compromised.

Synergistic activation of SAPK1/JNK1 by two MAP kinase kinases in vitro.

Mitogen-activated protein kinases (MAPKs) mediate many of the cellular effects of growth factors, cytokines and stress stimuli. Their activation requires the phosphorylation of a threonine and a tyrosine residue located in a Thr-X-Tyr motif (where X is any amino acid) [1]. This phosphorylation is catalysed by MAPK kinases (MKKs), which are all thought to be 'dual specificity' enzymes that phosphorylate both the threonine and the tyrosine residue of the Thr-X-Tyr motif [2]. Here, we report that the MAPK family member known as stress-activated protein kinase-1c (SAPK1c, also known as JNK1) [3] is activated synergistically in vitro by MKK4 ([4] [5] [6]; also called SKK1 and JNKK1) and MKK7 ([7] [8] [9]; also called SKK4 and JNKK2). We found that MKK4 had a preference for the tyrosine residue, and MKK7 for the threonine residue, within the Thr-X-Tyr motif. These observations suggest that the full activation of SAPK1c in vivo may sometimes require phosphorylation by two different MKKs, providing the potential for integrating the effects of different extracellular signals. They also raise the possibility that other MAPK family members may be activated by two or more MKKs and that some MKKs may have gone undetected because they phosphorylate the tyrosine residue only, and therefore do not induce any activation unless the threonine has first been phosphorylated by another MKK.