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Background Currently, coronavirus disease-2019 (COVID-19) is continuously and rapidly circulating, resulting in serious and extensive effects on human health. Due to the absence of antiviral medicine for COVID-19 thus far, there is a desperate need to develop effective medicine. Traditional Chinese medicine (TCM) has been widely applied in the treatment of epidemic diseases in China, with the aim of achieving clinical efficacy and decreasing the use of antibiotics and glucocorticoids. The aim of this study was to evaluate the efficacy and safety of Baidu Jieduan granules in treating COVID-19. Methods/design This multicentre, open-label, randomized controlled trial will be conducted in 300 patients with COVID-19. The patients will be randomly (1:1) divided into a treatment group and a control group. All patients will receive standard therapy at the same time. Patients in the experimental group will receive Baidu Jieduan granule treatment twice a day for 14 days. The outcomes will be assessed at baseline and at 3, 5, 7 and 14 days after treatment initiation. The primary outcome will be the rate of symptom (fever, fatigue and coughing) recovery. Adverse events (AEs) will be monitored throughout the trial. Discussion The study will provide high-quality clinical evidence to support the efficacy and safety of Baidu Jieduan granules in the treatment of moderate COVID-19, and enrich the theory and practice of TCM in treating COVID-19. Trial registration Chinese Clinical Trial Registry ChiCTR2000029869. Registered on 15 February 2020

Bisphosphonate-related osteonecrosis of the jaw (BRONJ) presents as a morbid jawbone lesion in patients exposed to a nitrogen-containing bisphosphonate (N-BP). Although it is rare, BRONJ has caused apprehension among patients and healthcare providers and decreased acceptance of this antiresorptive drug class to treat osteoporosis and metastatic osteolysis. We report here a novel method to elucidate the pathological mechanism of BRONJ by the selective removal of legacy N-BP from the jawbone using an intra-oral application of hydroxymethylene diphosphonate (HMDP) formulated in liposome-based deformable nanoscale vesicles (DNV). After maxillary tooth extraction, zoledronate-treated mice developed delayed gingival wound closure, delayed tooth extraction socket healing and increased jawbone osteonecrosis consistent with human BRONJ lesions. Single cell RNA sequencing of mouse gingival cells revealed oral barrier immune dysregulation and unresolved proinflammatory reaction. HMDP-DNV topical applications to nascent mouse BRONJ lesions resulted in accelerated gingival wound closure and bone socket healing as well as attenuation of osteonecrosis development. The gingival single cell RNA sequencing demonstrated resolution of chronic inflammation by increased anti-inflammatory signature gene expression of lymphocytes and myeloid-derived suppressor cells. This study suggests that BRONJ pathology is related to N-BP levels in jawbones and demonstrates the potential of HMDP-DNV as an effective BRONJ therapy.

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

At present, depression has become a main health burden in the world. However, there are many problems with the diagnosis of depression, such as low patient cooperation, subjective bias and low accuracy. Therefore, reliable and objective evaluation method is needed to achieve effective depression detection. Electroencephalogram (EEG) and eye movements (EMs) data have been widely used for depression detection due to their advantages of easy recording and non-invasion. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, both static and dynamic CBEM were discussed. In the proposed model, EEG or EMs dataset was divided into subsets by the context of the experiments, and then a majority vote strategy was used to determine the subjects' label. The validation of the method is testified on two datasets which included free viewing eye tracking and resting-state EEG, and these two datasets have 36,34 subjects respectively. For these two datasets, CBEM achieves accuracies of 82.5% and 92.65% respectively. The results show that CBEM outperforms traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and provide an effective method for identificationof depression, which in the future could be used for the auxiliary diagnosis of depression.

BackgroundTo identify gender differences in factors associated with the health literacy of hospitalized older patients with chronic diseases.MethodsA total of 471 hospitalized older patients with chronic diseases in four hospitals were investigated from May 2019 to June 2020. The self-developed demographic information questionnaire, the “Health Literacy Scale for Patients with Chronic Diseases” and the “Self-Efficacy for Managing Chronic Diseases 6-item Scale” were applied in this study. Multiple linear regression was used to assess the factors influencing health literacy among older patients with chronic diseases by gender.ResultsThe factors influencing health literacy differed by gender. Male health literacy was related to education background, number of children, monthly income, duration of chronic disease and chronic disease self-efficacy. For females, health literacy was associated with age, education background, monthly income, duration of chronic disease and chronic disease treatment.ConclusionHealthcare providers should focus on the above-mentioned factors that could help identify those with low health literacy differ base on gender. Gender-specific strategies should be developed to improve the health literacy of older patients with chronic diseases and strengthen their chronic disease management.

According to the WHO, the number of mental disorder patients, especially depression patients, has overgrown and become a leading contributor to the global burden of disease. With the rising of tools such as artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and recordings of spoken language data from clinically depressed patients and matching normal controls, who were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes data collected using a traditional 128-electrodes mounted elastic cap and a wearable 3-electrode EEG collector for pervasive computing applications. The 128-electrodes EEG signals of 53 participants were recorded as both in resting state and while doing the Dot probe tasks; the 3-electrode EEG signals of 55 participants were recorded in resting-state; the audio data of 52 participants were recorded during interviewing, reading, and picture description.

At present, most brain functional studies are based on traditional frequency bands to explore the abnormal functional connections and topological organization of patients with depression. However, they ignore the characteristic relationship of electroencephalogram (EEG) signals in the time domain. Therefore, this paper proposes a network decomposition model based on Improved Empirical Mode Decomposition (EMD), it is suitable for time-frequency analysis of brain functional network. On the one hand, it solves the problem of mode mixing on original EMD method, especially on high-density EEG data. On the other hand, by building brain function networks on different intrinsic mode function (IMF), we can perform time-frequency analysis of brain function connections. It provides a new insight for brain function connectivity analysis of major depressive disorder (MDD). Experimental results found that the IMFs waveform decomposed by Improved EMD was more stable and the difference between IMFs was obvious, it indicated that the mode mixing can be effectively solved. Besides, the analysis of the brain network, we found that the changes in MDD functional connectivity on different IMFs, it may be related to the pathological changes for MDD. More statistical results on three network metrics proved that there were significant differences between MDD and normal controls (NC) group. In addition, the aberrant brain network structure of MDDs was also confirmed in the hubs characteristic. These findings may provide potential biomarkers for the clinical diagnosis of MDD patients.

Advances in the design of potential bone-selective drugs for the treatment of various bone-related diseases are creating exciting new directions for multiple unmet medical needs. For bone-related cancers, off-target/non-bone toxicities with current drugs represent a significant barrier to the quality of life of affected patients. For bone infections and osteomyelitis, bacterial biofilms on infected bones limit the efficacy of antibiotics because it is hard to access the bacteria with current approaches. Promising new experimental approaches to therapy, based on bone-targeting of drugs, have been used in animal models of these conditions and demonstrate improved efficacy and safety. The success of these drug-design strategies bodes well for the development of therapies with improved efficacy for the treatment of diseases affecting the skeleton. LINKED ARTICLES: This article is part of a themed issue on The molecular pharmacology of bone and cancer-related bone diseases. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.9/issuetoc.

Background Currently, coronavirus disease 2019 (COVID-19) is continuously and rapidly circulating, causing heavy damage on public health. No effective antiviral treatment has been proved thus far. Traditional Chinese medicine (TCM) has been widely applied in the treatment of a variety of infection diseases in China, hoping to produce clinical effects and reduce the use of antibiotics and glucocorticoid. The aim of this study is to evaluate the efficacy and safety of Shenhuang granule in treatment of severe COVID-19. Methods/design This multicenter, open-label randomized controlled trial is conducted in 160 participants with severe COVID-19. The participants will be randomly (1:1) divided into treatment group or control group. All participants are given standard therapy at the same time. The experiment will receive Shenhuang granule treatment twice a day for 14 days. The clinical indicators of patients will be assessed at baseline and at 3, 5, 7, and 14 days after treatment initiation. The primary outcome is 14-day clinical outcome. Adverse events will be monitored throughout the trial. Discussion This will be the first randomized controlled trial, which evaluate the effect of Shenhuang granule in patients with severe COVID-19 in China. The results of this trial may not only provide evidence-based recommendations to clinicians to treat severe COVID-19, but also enrich the theory and practice of TCM in treating infectious diseases. Trial registration Chinese Clinical Trial Registry, ChiCTR2000029777. Registered on 13 February 2020.

Electroencephalography (EEG)-based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infancy, especially in effective monitoring and quantitative measure aspects. Aiming at mild depression recognition, this study proposed a computer-aided detection (CAD) system using convolutional neural network (ConvNet). However, the architecture of ConvNet derived by trial and error and the CAD system used in clinical practice should be built on the basis of the local database; we therefore applied transfer learning when constructing ConvNet architecture. We also focused on the role of different aspects of EEG, i.e., spectral, spatial, and temporal information, in the recognition of mild depression and found that the spectral information of EEG played a major role and the temporal information of EEG provided a statistically significant improvement to accuracy. The proposed system provided the accuracy of 85.62% for recognition of mild depression and normal controls with 24-fold cross-validation (the training and test sets are divided based on the subjects). Thus, the system can be clinically used for the objective, accurate, and rapid diagnosis of mild depression. Graphical abstract The EEG power of theta, alpha, and beta bands is calculated separately under trial-wise and frame-wise strategies and is organized into three input forms of deep neural networks: feature vector, images without electrode location (spatial information), and images with electrode location. The role of EEG's spectral and spatial information in mild depression recognition is investigated through ConvNet, and the role of EEG's temporal information is investigated using different architectures to aggregate temporal features from multiple frames. The ConvNet and models for aggregating temporal features are transferred from the state-of-the-art model in mental load classification.

The bisphosphonates ((HO)

The research detailed in this paper focuses on the processing of Electroencephalography (EEG) data to identify attention during the learning process. The identification of affect using our procedures is integrated into a simulated distance learning system that provides feedback to the user with respect to attention and concentration. The authors propose a classification procedure that combines correlation-based feature selection (CFS) and a k-nearest-neighbor (KNN) data mining algorithm. To evaluate the CFS+KNN algorithm, it was test against CFS+C4.5 algorithm and other classification algorithms. The classification performance was measured 10 times with different 3-fold cross validation data. The data was derived from 10 subjects while they were attempting to learn material in a simulated distance learning environment. A self-assessment model of self-report was used with a single valence to evaluate attention on 3 levels (high, neutral, low). It was found that CFS+KNN had a much better performance, giving the highest correct classification rate (CCR) of $80.84 \pm 3.0$ % for the valence dimension divided into three classes.

Background Coronavirus disease 2019 (COVID-19) is still a pandemic, with a high mortality rate in severe/critical cases. Therapies based on the Shenghuang Granule have proved helpful in viral infection and septic shock. Hypothesis/Purpose The objective of the current study was to compare the efficacy and safety of the traditional Chinese medicine, Shenhuang Granule, with standard care in hospitalized patients with severe/critical COVID-19. Study Design and Methods This was an open-label, multicenter, randomized, controlled clinical trial. At 4 medical centers, a total of 111 severe/critical patients were randomly assigned to receive Shenhuang Granule (SHG group) twice a day for 14 days, in addition to standard care, or to receive standard care alone (Control group). The maximal follow up time was 75 days. The clinical endpoint was clinical improvement and mortality. Results 54 patients were assigned to the control group and 57 to the SHG group. The overall mortality was 75.9% (41/54) in the control group, and 38.6% (22/57) in the SHG group (p, Graphical abstract Image, graphical abstract

Studies of the potential role of bisphosphonates in dentistry date back to physical chemical research in the 1960s, and the genesis of the discovery of bisphosphonate pharmacology in part can be linked to some of this work. Since that time, parallel research on the effects of bisphosphonates on bone metabolism continued, while efforts in the dental field included studies of bisphosphonate effects on dental calculus, caries, and alveolar bone loss. While some utility of this drug class in the dental field was identified, leading to their experimental use in various dentrifice formulations and in some dental applications clinically, adverse effects of bisphosphonates in the jaws have also received attention. Most recently, certain bisphosphonates, particularly those with strong bone targeting properties, but limited biochemical effects (low potency bisphosphonates), are being studied as a local remedy for the concerns of adverse effects associated with other more potent members of this drug class. Additionally, low potency bisphosphonate analogs are under study as vectors to target active drugs to the mineral surfaces of the jawbones. These latter efforts have been devised for the prevention and treatment of oral problems, such as infections associated with oral surgery and implants. Advances in the utility and mechanistic understanding of the bisphosphonate class may enable additional oral therapeutic options for the management of multiple aspects of dental health.

Recent clinical trials have shown that bisphosphonate drugs improve breast cancer patient survival independent of their antiresorptive effects on the skeleton. However, because bisphosphonates bind rapidly to bone mineral, the exact mechanisms of their antitumor action, particularly on cells outside of bone, remain unknown. Here, we used real-time intravital two-photon microscopy to show extensive leakage of fluorescent bisphosphonate from the vasculature in 4T1 mouse mammary tumors, where it initially binds to areas of small, granular microcalcifications that are engulfed by tumor-associated macrophages (TAM), but not tumor cells. Importantly, we also observed uptake of radiolabeled bisphosphonate in the primary breast tumor of a patient and showed the resected tumor to be infiltrated with TAMs and to contain similar granular microcalcifications. These data represent the first compelling in vivo evidence that bisphosphonates can target cells in tumors outside the skeleton and that their antitumor activity is likely to be mediated via TAMs. Significance: Bisphosphonates are assumed to act solely in bone. However, mouse models and clinical trials show that they have surprising antitumor effects outside bone. We provide unequivocal evidence that bisphosphonates target TAMs, but not tumor cells, to exert their extraskeletal effects, offering a rationale for use in patients with early disease. Cancer Discov; 5(1); 35–42. ©2014 AACR. See related commentary by Sterling, p. 14 This article is highlighted in the In This Issue feature, p. 1

The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such as patient denial, poor sensitivity, subjective biases and inaccuracy. An objective, automated method that predicts clinical outcomes in depression is essential for increasing the accuracy of depression recognition and treatments. This paper aims at better recognizing depression using the transformation of EEG features and machine learning methods. An experiment based on emotional face stimuli task was conducted, and twenty-eight subjects' EEG data were recorded from 128-channel HydroCel Geodesic Sensor Net (HCGSN) by Net Station software. The Mini International Neuropsychiatric Interview (MINI) was used by psychiatrists as the criterion for diagnosis of depression patients. The power spectral density and activity were respectively extracted as original features using Auto-regress model and Hjorth algorithm with different time windows. Two separate approaches processed the features: ensemble learning and deep learning. For the ensemble learning, a deep forest transformed the original features to new features that potentially improve feature engineering and a support vector machine (SVM) that was applied as classifier. For deep learning method, we added spatial information of EEG caps to both features by image conversion and adopted convolutional neural network (CNN) to recognize them. The performance of both methods was evaluated for separated and total frequency bands. As a result, the best accuracy obtained was 89.02% when we used the ensemble model and power spectral density. The best accuracy of deep learning method was 84.75% using the activity. These experimental results prove the efficiency of the proposed methods and show that EEG could be used as a reliable indicator for depression recognition, which makes it possible for EEG-based portable system design and application in auxiliary depression recognition in the future.

Farnesyl pyrophosphate synthase (FPPS, also known as farnesyl diphosphate synthase (FDPS)) is one of the key enzymes involved in the mevalonate pathway and as such is widely expressed. FPPS modulators, specifically FPPS inhibitors, are useful in treating a number of diseases, including bone-related disorders characterized by excessive bone resorption, for example, osteoporosis, cancer metathesis to bone and infectious diseases caused by certain parasites.This review covers structures and applications of novel FPPS modulators described in the patent literature from 2006 to 2010. Patents disclosing new formulations and uses of existing FPPS inhibitors are also reviewed. Thirty-three patents retrieved from the USPTO, EP and WIPO databases are examined with the goal of defining current trends in drug discovery related to FPPS inhibition, and its therapeutic effects.Bisphosphonates (BPs) continue to dominate in this area, although other types of modulators are making their appearance. Remarkable for their high bone mineral affinity, BPs are structural mimics of the dimethylallyl pyrophosphate substrate of FPPS, and constitute the major type of FPPS inhibitor currently used in the clinic for treatment of bone-related diseases. Lipophilic BPs and new classes of non-BP FPPS inhibitors (salicylic acid and quinoline derivatives) have been introduced as possible alternatives for treatment of soft tissue diseases, such as some cancers. Novel formulations, fluorescent diagnostic probes and new therapeutic applications of existing FPPS inhibitors are also areas of significant patent activity, demonstrating growing recognition of the versatility and underdeveloped potential of these drugs.

A novel method for the determination of proteins was developed, based on the enhancement of fluorescence with 4-chloro-(2'-hydroxylophenylazo)rhodanine-Ti(IV) [ClHARP-Ti(IV)] complex as a fluorescence probe. The excitation and emission wavelengths of the system were 335 nm and 376 nm, respectively. The presence of bis(2-ethylhexyl)sulphosuccinate sodium salt (AOT) microemulsion greatly increased the sensitivity of the system. Under optimal conditions, four kinds of proteins, including bovine serum albumin (BSA), human serum albumin (HSA), egg albumin (Ova), and gamma-globin (gamma-G) were studied. The detection limits were 0.182 microg/mL for BSA, 0.0788 microg/mL for HSA, 0.216 microg/mL for Ova and 0.484 microg/mL for gamma-G. The linear ranges of the calibration were 0-12.0, 0-10.0, 0-18.0 and 0-18.0 microg/mL, respectively. The method possessed high sensitivity, good selectivity and was applied to the analysis of protein in milk powder and cornmeal with satisfactory results.

A bone imaging toolkit of 21 fluorescent probes with variable spectroscopic properties, bone mineral binding affinities, and antiprenylation activities has been created, including a novel linking strategy. The linking chemistry allows attachment of a diverse selection of dyes fluorescent in the visible to near-infrared range to any of the three clinically important heterocyclic bisphosphonate bone drugs (risedronate, zoledronate, and minodronate or their analogues). The resultant suite of conjugates offers multiple options to “mix and match” parent drug structure, fluorescence emission wavelength, relative bone affinity, and presence or absence of antiprenylation activity, for bone-related imaging applications.

Feature selection method GSW based on CFS combine with KNN can achieve the optimal performance for mild depression detection.FP1, FP2, F3, O2, T3 with linear features may be a good choice for portable device and auxiliary diagnosis of mild depression.With classification accuracy above 91% and AUC above 0.950, these results are better than some existing studies. Background and objectiveDepression has become a major health burden worldwide, and effectively detection of such disorder is a great challenge which requires latest technological tool, such as Electroencephalography (EEG). This EEG-based research seeks to find prominent frequency band and brain regions that are most related to mild depression, as well as an optimal combination of classification algorithms and feature selection methods which can be used in future mild depression detection. MethodsAn experiment based on facial expression viewing task (Emo_block and Neu_block) was conducted, and EEG data of 37 university students were collected using a 128 channel HydroCel Geodesic Sensor Net (HCGSN). For discriminating mild depressive patients and normal controls, BayesNet (BN), Support Vector Machine (SVM), Logistic Regression (LR), k-nearest neighbor (KNN) and RandomForest (RF) classifiers were used. And BestFirst (BF), GreedyStepwise (GSW), GeneticSearch (GS), LinearForwordSelection (LFS) and RankSearch (RS) based on Correlation Features Selection (CFS) were applied for linear and non-linear EEG features selection. Independent Samples T-test with Bonferroni correction was used to find the significantly discriminant electrodes and features. ResultsData mining results indicate that optimal performance is achieved using a combination of feature selection method GSW based on CFS and classifier KNN for beta frequency band. Accuracies achieved 92.00% and 98.00%, and AUC achieved 0.957 and 0.997, for Emo_block and Neu_block beta band data respectively. T-test results validate the effectiveness of selected features by search method GSW. Simplified EEG system with only FP1, FP2, F3, O2, T3 electrodes was also explored with linear features, which yielded accuracies of 91.70% and 96.00%, AUC of 0.952 and 0.972, for Emo_block and Neu_block respectively. ConclusionsClassification results obtained by GSWź+źKNN are encouraging and better than previously published results. In the spatial distribution of features, we find that left parietotemporal lobe in beta EEG frequency band has greater effect on mild depression detection. And fewer EEG channels (FP1, FP2, F3, O2 and T3) combined with linear features may be good candidates for usage in portable systems for mild depression detection.

The aim of this study is to establish osteoclasts as key immune effectors capable of activating the function of Natural Killer (NK) cells, and expanding their numbers, and to determine in vivo and in vitro effect of bisphosphonates (BPs) during NK cell interaction with osteoclasts and on systemic and local immune function. The profiles of 27 cytokines, chemokines and growth factors released from osteoclasts were found to be different from dendritic cells and M1 macrophages but resembling to untreated monocytes and M2 macrophages. Nitrogen-containing BPs Zoledronate (ZOL) and Alendronate (ALN), but not non-nitrogen-containing BPs Etidronate (ETI), triggered increased release of pro-inflammatory mediators from osteoclasts while all three BPs decreased pit formation by osteoclasts. ZOL and ALN mediated significant release of IL-6, TNF-` and IL-1β, whereas they inhibited IL-10 secretion by osteoclasts. Treatment of osteoclasts with ZOL inhibited NK cell mediated cytotoxicity whereas it induced significant secretion of cytokines and chemokines. NK cells lysed osteoclasts much more than their precursor cells monocytes, and this correlated with the decreased expression of MHC class I expression on osteoclasts. Intravenous injection of ZOL in mice induced pro-inflammatory microenvironment in bone marrow and demonstrated significant immune activation. By contrast, tooth extraction wound of gingival tissues exhibited profound immune suppressive microenvironment associated with dysregulated wound healing due to the effect of ZOL which could potentially be responsible for the pathogenesis of Osteonecrosis of the Jaw (ONJ). Finally, based on the data obtained in this paper we demonstrate that osteoclasts can be used as targets for the expansion of NK cells with superior function for immunotherapy of cancer.

Zoledronic acid, a highly potent nitrogen-containing bisphosphonate used for the treatment of pathological bone loss, is excreted unmetabolized via the kidney if not bound to the bone. In cancer patients receiving high doses of the compound renal excretion may be associated with acute tubular necrosis. The question of how zoledronic acid is internalized by renal tubular cells has not been answered until now. In the current work, using a primary human tubular cell culture system, the pathway of cellular uptake of zoledronic acid (fluorescently/radiolabeled) and its cytotoxicity were investigated. Previous studies in our laboratory have shown that this primary cell culture model consistently mimics the physiological characteristics of molecular uptake/transport of the epithelium in vivo. Zoledronic acid was found to be taken up by tubular cells via fluid-phase-endocytosis (from apical and basolateral side) as evidenced by its co-localization with dextran. Cellular uptake and the resulting intracellular level was twice as high from the apical side compared to the basolateral side. Furthermore, the intracellular zoledronic acid level was found to be dependent on the administered concentration and not saturable. Cytotoxic effects however, were only seen at higher administration doses and/or after longer incubation times. Although zoledronic acid is taken up by tubular cells, no net tubular transport could be measured. It is concluded that fluid-phase-endocytosis of zoledronic acid and cellular accumulation at high doses may be responsible for the acute tubular necrosis observed in some cancer patients receiving high doses of the compound.