Treating low- and intermediate-risk acute promyelocytic leukemia with and without chemotherapy: A comparison in a tertiary care center.

Background: Acute promyelocytic leukemia (APL) comprises approximately 10% of acute myeloid leukemia (AML) cases. Material and Methods: Both options of treatment (ATRA-ATO and ATRA-chemotherapy) were discussed with patients with low- and intermediate-risk APL, pros and cons explained in details, and treatment regimen selected after getting informed written consent. Results: Total 71 patients were included in the study; among these patients, 3 were negative for both FISH for t (15,17) and RT-PCR for promyelocytic leukemia retinoic acid receptor alpha, and 36 patients with APL had white blood cell count at diagnosis >10 × 109/l. Total 30 patients with newly diagnosed as low- and intermediate-risk-APL fulfilled all inclusion criteria, treated and followed for a minimum period of 2 years up to June, 2016. Fifteen patients liked to be treated with all-trans retinoic acid (ATRA) and arsenic trioxide (ATO), while rest of the 15 patients preferred treatment with ATRA and chemotherapy. Conclusion: Combination of ATRA and ATO is equally effective, less toxic, and more feasible in comparison to ATRA and chemotherapy for patients with low- and intermediate-risk APL and is a viable option for this subset of patients, especially in countries with limited resources.

Tumor-associated alterations in white matter connectivity have prognostic significance in MGMT-unmethylated glioblastoma.

PURPOSE: We investigated the prognostic significance of tumor-associated white matter (TA-WM) tracts in glioblastoma (GBM) using magnetic resonance-diffusion tensor imaging (MR-DTI). We hypothesized that (1) TA-WM tracts harbor microscopic disease not targeted through surgery or radiotherapy (RT), and (2) the greater the extent of TA-WM involvement, the worse the survival outcomes. METHODS: We studied a retrospective cohort of 76 GBM patients. TA-WM tracts were identified by MR-DTI fractional anisotropy (FA) maps. For each patient, 22 TA-WM tracts were analyzed and each tract was graded 1-3 based on FA. A TA-WM score (TA-WMS) was computed based on number of involved tracts and corresponding FA grade of involvement. Kaplan-Meier statistics were utilized to determine survival outcomes, log-rank test was used to compare survival between groups, and Cox regression was utilized to determine prognostic variables. RESULTS: For the MGMT-unmethylated cohort, there was a decrease in OS for increasing TA-WMS (median OS 16.5 months for TA-WMS 0-4; 13.6 months for TA-WMS 5-8; 7.3 months for TA-WMS > 9; p = 0.0002). This trend was not observed in the MGMT-methylated cohort. For MGMT-unmethylated patients with TA-WMS > 6 and involvement of tracts passing through brainstem or contralateral hemisphere, median OS was 8.3 months versus median OS 14.1 months with TA-WMS > 6 but not involving aforementioned critical tracts (p = 0.003 log-rank test). For MGMT-unmethylated patients, TA-WMS was predictive of overall survival in multivariate analysis (HR = 1.14, 95% CI 1.03-1.27, p = 0.012) while age, gender, and largest tumor dimension were non-significant. CONCLUSION: Increased TA-WMS and involvement of critical tracts are associated with decreased overall survival in MGMT-unmethylated GBM.

A review on morphology, nanostructure, chemical composition, and number concentration of diesel particulate emissions.

Particulate matter (PM) emitted from compression ignition (CI) engines (diesel engines) has a detrimental effect on human health and the environment. The physical and chemical characteristics of PM emitted from CI-engines are influenced by engine operating conditions and fuel properties. The morphology, nanostructure, and chemical composition of PM affect its toxicity and interaction with the environment. From automotive industry perspective, these parameters influence the design of diesel particulate filters. This study presents a review of the physical and chemical characteristics of particulate emissions from the CI-engine. The present study commences with a brief description about the composition of PM emitted from CI-engine and the PM formation mechanism in CI-engine. Later on, the detailed review of PM's physical and chemical characteristics and the effect of engine operating parameters and alternative fuels on the particle number concentration, morphology, nano-structure, and oxidative reactivity of PM is presented. Online and offline methods of diesel particulate characterization and emerging chemical characterization techniques such as X-ray photoelectron spectroscopy and X-ray absorption fine structure (EXAFS) are also discussed briefly. Correlation between physical and chemical properties, and oxidative reactivity of PM is also discussed. It was found that engine operating parameters affect the physical and chemical properties of PM. Use of alternative fuels changes the diesel particulate morphology, nanostructure, and chemical composition which enhances the oxidative reactivity of PM.

Chemical properties of emissions from solid residential fuels used for energy in the rural sector of the southern region of India.

In the present study, we have estimated the emission factors (EFs) of particulate matter (PM), organic and elemental carbon (OC and EC), oxide of sulfur and nitrogen, and water-soluble ionic species emitted from residential fuels (fuelwood, crop residue, dung cake) used in the rural sector of five states (Kerala, Karnataka, Andhra Pradesh, Telangana, Tamil Nadu) of the southern region of India. Average EFs of PM, OC, and EC from fuelwood (FW), crop residues (CR), and dung cakes (DC) from southern region of India are estimated as follows: PM: 6.35 ± 5.64 g/kg (FW), 6.99 ± 5.46 g/kg (CR), 9.69 ± 3.73 g/kg (DC); OC: 1.60 ± 1.72 g/kg (FW), 1.50 ± 1.52 g/kg (CR), 3.54 ± 0.75 g/kg (DC); and EC: 0.46 ± 0.53 g/kg (FW), 0.29 ± 0.17 g/kg (CR), 0.21 ± 0.11 g/kg (DC), respectively. Similarly, the average EFs of SO2, NOx from FW, CR, and DC are determined to be as follows: SO2: 0.40 ± 0.37 g/kg (FW), 1.17 ± 0.25 g/kg (CR), and 0.18 ± 0.10 g/kg (DC); NOx: 1.11 ± 1.22 g/kg (FW), 0.69 ± 0.37 g/kg (CR), and 0.91 ± 0.54 g/kg (DC), respectively. PO43- shows the highest EF from FW (646.02 ± 576.35 mg/kg), CR (531.06 ± 678.29 mg/kg) among all anions followed by Cl- (FW: 512.91 ± 700.35 mg/kg, CR: 661.61 ± 865.46 mg/kg and DC: 104.16 ± 54.01 mg/kg); whereas, Na+ shows highest EF from FW (254.05 ± 298.50 mg/kg) and CR (249.36 ± 294.85 mg/kg) among all cations. The total emissions of trace gases, PM, and their chemical composition from FW, CR, and DC have been calculated using laboratory-generated EFs over the southern region of India. CR (1595.58 ± 14.24 Gg) contributes to higher emission of PM as compared to FW (218.78 ± 53.93 Gg), whereas the contribution from DC is negligible.

Patient-specific modeling of the volume of tissue activated (VTA) is associated with clinical outcome of DBS in patients with an obsessive-compulsive disorder.

Deep brain stimulation (DBS) promises to treat an increasing number of neurological and psychiatric disorders. DBS outcome is directly a factor of optimal targeting of the relevant brain structures. Computational models can help to interpret a patient's outcome by predicting the volume of tissue activated (VTA) around DBS electrode contacts. Here we report results of a preliminary study of DBS in two patients with obsessive-compulsive disorder and show that VTA predictions, which are based on patient-specific volume conductor models, correlate with clinical outcome. Our results suggest that patient specific VTA calculation can help inform device programing to maximize therapeutic effects and minimize side effects.Clinical Relevance- Patient-specific modeling of the volume of activated tissue can predict clinical outcomes and thus, can help to optimize DBS device programing to maximize therapeutic effects.

For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth.

RATIONALE, AIMS AND OBJECTIVES: Artificial intelligence and big data are more and more used in medicine, either in prevention, diagnosis or treatment, and are clearly modifying the way medicine is thought and practiced. Some authors argue that the use of artificial intelligence techniques to analyze big data would even constitute a scientific revolution, in medicine as much as in other scientific disciplines. Moreover, artificial intelligence techniques, coupled with mobile health technologies, could furnish a personalized medicine, adapted to the individuality of each patient. In this paper we argue that this conception is largely a myth: what health professionals and patients need is not more data, but data that are critically appraised, especially to avoid bias. METHODS: In this historical and conceptual article, we focus on two main problems: first, the data and the problem of its validity; second, the inference drawn from the data by AI, and the establishment of correlations through the use of algorithms. We use examples from the contemporary use of mobile health (mHealth), i.e. the practice of medicine and public health supported by mobile or wearable devices such as mobile phones or smart watches. RESULTS: We show that the validity of the data and of the inferences drawn from these mHealth data are likely to be biased. As biases are insensitive to the size of the sample, even if the sample is the whole population, artificial intelligence and big data cannot avoid biases and even tend to increase them. CONCLUSIONS: The large amount of data thus appears rather as a problem than a solution. What contemporary medicine needs is not more data or more algorithms, but a critical appraisal of the data and of the analysis of the data. Considering the history of epidemiology, we propose three research priorities concerning the use of artificial intelligence and big data in medicine.

A comparative evaluation of bone regeneration using mesenchymal stem cells versus blood coagulum in sinus augmentation procedures.

OBJECTIVE: The present study evaluated the quality and quantity of new bone formation in the maxillary sinus lift procedures and stability of implants in posterior atrophic maxilla. MATERIALS AND METHODS: This prospective randomized controlled split-mouth study included 20 patients (16 males and 4 females having a mean age of 36.7 years) having atrophic maxilla. They were divided randomly into two groups: Group A using mesenchymal stem cells and Group B into blood coagulum. They were radiographically evaluated using cone-beam computed tomography (CBCT) for residual bone height preoperatively and availability of new bone formation around implants, density, and stability of implants 6 months postoperatively. RESULTS: The placement of dental implants in posterior maxilla is challenging due to rapid resorption of alveolar bone after extraction of teeth due to pneumatization of maxillary sinuses. In both the groups, more pain and swelling were observed in the 2nd postoperative day which gradually decreased over a period of 7 days. Membrane perforation occurs in only four cases (20%). A significant gain in alveolar bone height was observed in Group A (7.69 mm ± 2.5 mm) and Group B (9.32 mm ± 2 mm) after 6 months. On comparing both the groups, there is a similar significant increase in bone density in Hounsfield units postoperatively at various levels buccally and palatally. Total 40 sinuses were lifted and 42 implants were placed, respectively. All implants showed primary stability. CONCLUSIONS: Such findings provide a significant contribution in future perspective studies that the use of stem cells had the same success rate as blood coagulum.

Source Apportionment of PM10 Over Three Tropical Urban Atmospheres at Indo-Gangetic Plain of India: An Approach Using Different Receptor Models.

The present work is the ensuing part of the study on spatial and temporal variations in chemical characteristics of PM10 (particulate matter with aerodynamic diameter ≤ 10 µm) over Indo Gangetic Plain (IGP) of India. It focuses on the apportionment of PM10 sources with the application of different receptor models, i.e., principal component analysis with absolute principal component scores (PCA-APCS), UNMIX, and positive matrix factorization (PMF) on the same chemical species of PM10. The main objective of this study is to perform the comparative analysis of the models, obtained mutually validated outputs and more robust results. The average PM10 concentration during January 2011 to December 2011 at Delhi, Varanasi, and Kolkata were 202.3 ± 74.3, 206.2 ± 77.4, and 171.5 ± 38.5 µg m-3, respectively. The results provided by the three models revealed quite similar source profile for all the sampling regions, with some disaccords in number of sources as well as their percent contributions. The PMF analysis resolved seven individual sources in Delhi [soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), biomass burning (BB), sodium and magnesium salt (SMS), fossil fuel combustion, and industrial emissions (IE)], Varanasi [SD, VE, SA, BB, SMS, coal combustion, and IE], and Kolkata [secondary sulfate (Ssulf), secondary nitrate, SD, VE, BB, SMS, IE]. However, PCA-APCS and UNMIX models identified less number of sources (besides mixed type sources) than PMF for all the sampling sites. All models identified that VE, SA, BB, and SD were the dominant contributors of PM10 mass concentration over the IGP region of India.

Stable carbon and nitrogen isotopic composition of PM10 over Indo-Gangetic Plains (IGP), adjoining regions and Indo-Himalayan Range (IHR) during a winter 2014 campaign.

For source identification, a field campaign involving simultaneous sampling of particulate matter (PM10) was conducted at eight sampling sites in the Indian mainland during winter 2014. The sampling sites include Delhi (upper IGP), Lucknow (middle IGP), and Kolkata (lower IGP) in the Indo-Gangetic Plains (IGP); Mohal-Kullu and Darjeeling in the Indo-Himalayan Range (IHR). In addition, Ajmer, located upwind of the IGP in NW-India and Giridih and Bhubaneswar, in the downwind to the IGP has also been chosen. To characterize the sources of the ambient PM10, stable isotope ratios of carbon (δ13CTC) and nitrogen (δ15NTN) for the total carbon (TC) and total nitrogen (TN) fractions have been considered. Ancillary chemical parameters, such as organic carbon (OC), elemental carbon (EC), and water-soluble ionic components (WSIC) mass concentrations are also presented in this paper. There was very small variation in the daily average δ13CTC ratios (- 24.8 to - 25.9‰) among the sites. Comparison with end-member stable C isotopic signatures of major typical sources suggests that the PM10 at the sites was mainly from fossil fuel and biofuel and biomass combustion. Daily average δ15NTN ratios were not observed to vary much between sites either (8.3 to 11.0‰), and the low δ15NTN levels also indicate substantial contributions from biofuel and biomass burning of primarily C3 andC4 plant matter. Graphical abstract Scatter plot of the average (± 1 standard deviation (SD)) δ13CTC (‰) compared to δ15NTN (‰) at the sampling sites.

Effect of compression ratio, nozzle opening pressure, engine load, and butanol addition on nanoparticle emissions from a non-road diesel engine.

Currently, diesel engines are more preferred over gasoline engines due to their higher torque output and fuel economy. However, diesel engines confront major challenge of meeting the future stringent emission norms (especially soot particle emissions) while maintaining the same fuel economy. In this study, nanosize range soot particle emission characteristics of a stationary (non-road) diesel engine have been experimentally investigated. Experiments are conducted at a constant speed of 1500 rpm for three compression ratios and nozzle opening pressures at different engine loads. In-cylinder pressure history for 2000 consecutive engine cycles is recorded and averaged data is used for analysis of combustion characteristics. An electrical mobility-based fast particle sizer is used for analyzing particle size and mass distributions of engine exhaust particles at different test conditions. Soot particle distribution from 5 to 1000 nm was recorded. Results show that total particle concentration decreases with an increase in engine operating loads. Moreover, the addition of butanol in the diesel fuel leads to the reduction in soot particle concentration. Regression analysis was also conducted to derive a correlation between combustion parameters and particle number emissions for different compression ratios. Regression analysis shows a strong correlation between cylinder pressure-based combustion parameters and particle number emission.

Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India.

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM2.5 was recorded as 121.9 ± 93.2 µg m-3 (range 25.1-429.8 µg m-3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ∼17% of PM2.5. Strong seasonal variation was observed in PM2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM2.5 from the north-west India and Pakistan were mostly pertinent.

Inter-annual Variation of Ambient Ammonia and Related Trace Gases in Delhi, India.

In this study, ambient NH3, NO, NO2, CO and SO2 were measured continuously from February, 2008, to December, 2016. The annual average mixing ratios (mole/mole) of NH3, NO, NO2, CO and SO2 were 17.8 ± 3.4 ppb; 21.2 ± 2.3 ppb, 18.1 ± 3.2 ppb; 1.7 ± 0.3 ppm and 2.0 ± 0.3 ppb, respectively. All the trace gases (NH3, NO, NO2, CO and SO2) showed significant annual variation during the study. A significant increasing trend in mixing ratios of ambient NH3 and NO2 were observed at the observational site of Delhi, whereas, increasing trend were recorded in case of NO, CO and NO2 mixing ratios. The results emphasized that traffic could be one of the significant sources of ambient NH3 at the urban site of Delhi, as illustrated by positive correlations of NH3 with traffic related co-pollutants (NO and CO).

Seasonal characteristics of water-soluble inorganic ions and carbonaceous aerosols in total suspended particulate matter at a rural semi-arid site, Kadapa (India).

To better understand the sources as well as characterization of regional aerosols at a rural semi-arid region Kadapa (India), size-resolved composition of atmospheric particulate matter (PM) mass concentrations was sampled and analysed. This was carried out by using the Anderson low-pressure impactor for a period of 2 years during March 2013-February 2015. Also, the variations of organic carbon (OC), elemental carbon (EC) and water-soluble inorganic ion components (WSICs) present in total suspended particulate matter (TSPM) were studied over the measurement site. From the statistical analysis, the PM mass concentration showed a higher abundance of coarse mode particles than the fine mode during pre-monsoon season. In contrast, fine mode particles in the PM concentration showed dominance over coarse mode particle contribution during the winter. During the post-monsoon season, the percentage contributions of coarse and fine fractions were equal, whereas during the monsoon, coarse mode fraction was approximately 26 % higher than the fine mode. This distinct feature in the case of fine mode particles during the studied period is mainly attributed to large-scale anthropogenic activities and regional prevailing meteorological conditions. Further, the potential sources of PM have been identified qualitatively by using the ratios of certain ions. A high sulphate (SO4) concentration at the measurement site was observed during the studied period which is caused by the nearby/surrounding mining activity. Carbon fractions (OC and EC) were also analysed from the TSPM, and the results indicated (OC/EC ratio of ~4.2) the formation of a secondary organic aerosol. At last, the cluster backward trajectory analyses were also performed at Kadapa for different seasons to reveal the origin of sources from long-range transport during the study period.

Source Apportionment of PM2.5 in Delhi, India Using PMF Model.

Chemical characterization of PM2.5 [organic carbon, elemental carbon, water soluble inorganic ionic components, and major and trace elements] was carried out for a source apportionment study of PM2.5 at an urban site of Delhi, India from January, 2013, to December, 2014. The annual average mass concentration of PM2.5 was 122 ± 94.1 µg m(-3). Strong seasonal variation was observed in PM2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon. A receptor model, positive matrix factorization (PMF) was applied for source apportionment of PM2.5 mass concentration. The PMF model resolved the major sources of PM2.5 as secondary aerosols (21.3 %), followed by soil dust (20.5 %), vehicle emissions (19.7 %), biomass burning (14.3 %), fossil fuel combustion (13.7 %), industrial emissions (6.2 %) and sea salt (4.3 %).

Variation of Stable Carbon and Nitrogen Isotopic Composition of PM10 at Urban Sites of Indo Gangetic Plain (IGP) of India.

This paper presents the variation of elemental concentrations of total carbon (TC), total nitrogen (TN) and isotopic ratios of δ13C and δ15N along with δ13OC and OC of PM10 mass over Delhi, Varanasi and Kolkata of the Indo Gangetic Plain (IGP), India. For Delhi, the average concentrations of TC and TN of PM10 were 53.0±33.6 and 14.9±10.8 µg m(-3), whereas δ13C and δ15N of PM10 were -25.5±0.5 and 9.6±2.8‰, respectively. For Varanasi, the average values of δ13C and δ15N of PM10 were -25.4±0.8 and 6.8±2.4‰, respectively. For Kolkata, TC and TN values for PM10 ranged from 9.1-98.2 to 1.4-25.9 µg m(-3), respectively with average values of 32.6±24.9 and 9.3±8.2 µg m(-3), respectively. The average concentrations of δ13C and δ15N were -26.0±0.4 and 7.4±2.7‰, respectively over Kolkata with ranges of -26.6 to -24.9‰ and 2.8±11.5‰, respectively. The isotopic analysis revealed that biomass burning, vehicular emission and secondary inorganic aerosols were likely sources of PM10 mass over IGP, India.

Assessing speech dysfunction using BOLD and acoustic analysis in parkinsonism.

INTRODUCTION: Speech dysfunction is often associated with parkinsonism (Parkinson's disease (PD), Multiple System Atrophy (MSA), and Progressive Supranuclear Palsy (PSP)), along with characteristic motor features. Any or all of the following i.e. respiratory, phonatory, resonatory, or articulatory components of speech production may be affected. Articulatory imprecision, repetition of syllables (tachyphrenia), and tremor of oropharyngeal structures add to speech unintelligibility. We studied acoustics using spectrogram and its correlation with BOLD activation during voice/speech production across these subjects. METHODS: BOLD studies were conducted on 108 subjects (29 PD, 20 MSA and 19 PSP and 40 controls) on 1.5 T MR scanner using 130 dynamics. Active phase involved acquisition (10 volumes each) of audible reading of visually presented bi-syllabic meaningful Hindi simple words (5 types of non-nasal stop consonant categories, i.e. namely velars, palatals, retroflexes, dentals, bilabials and one nasal stop consonant) with interleaved silence during baseline. The subjects' voice samples were analyzed for acoustic parameters, namely formant frequencies of the adjoining vowels, voice onset time (VOT), and intensities using spectrogram. Correlation of BOLD activation in different brain areas with acoustic parameters was evaluated. RESULTS: Voice intensity was significantly lowered, while VOTs were delayed in these patients as compared to healthy controls. All acoustic parameters were significantly affected for nasal consonants. BOLD activation correlated positively in primary motor cortex to VOTs, while F2 formants to activation of supplementary motor area. CONCLUSION: The differences in the acoustic quality of various stop consonants in patients may be helpful in differentiating these three parkinsonian disorders.

Cytotoxic evaluation of semisynthetic ester and amide derivatives of oleanolic acid.

Although a number of chemicals have been isolated from Lantana camara, only a few have been evaluated for their biological significance. As part of our drug discovery program for cytotoxic agents from Indian medicinal plants, roots of L. camara L. were chemically investigated, which resulted in the isolation and identification of a cytotoxic agent, oleanolic acid (1b) as a major constituent. Oleanolic acid was converted into six semi-synthetic ester (2-7) and seven amide (8-14) derivatives. The ester derivatives (2-7) showed 3-6 times more selective activity than 1b against the human ovarian cancer cell line (IGR-OV-1), while amide derivatives 8-14 showed 16-53 times more selective activity against the human lung cancer cell line (HOP-62). Structure activity relationship within the ester (2-7) and amide (8-14) derivatives are discussed.

Cytotoxic agents from Terminalia arjuna.

Although a number of chemicals have been isolated from Terminalia arjuna, only a few have been evaluated for their biological significance. As a part of our drug discovery programme for cytotoxic agents from Indian medicinal plants, four novel cytotoxic agents arjunic acid (1), arjungenin (2), arjunetin (3) and arjunoglucoside I (4) were isolated from the bark of T. ARJUNA. Out of the four compounds, arjunic acid (1) was significantly active against the human oral (KB), ovarian (PA 1) and liver (HepG-2 & WRL-68) cancer cell lines. Further, the most active compound arjunic acid was converted into seven semi-synthetic ester derivatives 5 - 11. 2-O-Palmitoyl arjunic acid (6) showed two times more activity, while 2, 3-di-O-acetyl-, 2-O-p-anisoyl-, 2, 3-di-O-benzoyl- and 2, 3-di-O-p-nitrobenzoyl arjunic acid (7 - 10) showed 1.7 - 2.3 times less activity than the cytotoxic drug vinblastine against the liver cancer cell lines HepG-2 and WRL-68 respectively.