A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms.

Eutrophication happens when water bodies are enriched by minerals and nutrients. Dense blooms of noxious are the most obvious effect of eutrophication that harms water quality, and by increasing toxic substances damage the water ecosystem. Therefore, it is critical to monitor and investigate the development process of eutrophication. The concentration of chlorophyll-a (chl-a) in water bodies is an essential indicator of eutrophication in them. Previous studies in predicting chlorophyll-a concentrations suffered from low spatial resolution and discrepancies between predicted and observed values. In this paper, we used various remote sensing and ground observation data and proposed a novel machine learning-based framework, a random forest inversion model, to provide the spatial distribution of chl-a in 2 m spatial resolution. The results showed our model outperformed other base models, and the goodness of fit improved by over 36.6% while MSE and MAE decreased by over 15.17% and over 21.26% respectively. Moreover, we compared the feasibility of GF-1 and Sentinel-2 remote sensing data in chl-a concentration prediction. We found that better prediction results can be obtained by using GF-1 data, with the goodness of fit reaching 93.1% and MSE only 3.589. The proposed method and findings of this study can be used in future water management studies and as an aid for decision-makers in this field.

Spatiotemporal transmission of infectious particles in environment: A case study of Covid-19.

This study explores the dynamic transmission of infectious particles due to COVID-19 in the environment using a spatiotemporal epidemiological approach. We proposed a novel multi-agent model to simulate the spread of COVID-19 by considering several influencing factors. The model divides the population into susceptible and infected and analyzes the impact of different prevention and control measures, such as limiting the number of people and wearing masks on the spread of COVID-19. The findings suggest that reducing population density and wearing masks can significantly reduce the likelihood of virus transmission. Specifically, the research shows that if the population moves within a fixed range, almost everyone will eventually be infected within 1 h. When the population density is 50%, the infection rate is as high as 96%. If everyone does not wear a mask, nearly 72.33% of the people will be infected after 1 h. However, when people wear masks, the infection rate is consistently lower than when they do not wear masks. Even if only 25% of people wear masks, the infection rate with masks is 27.67% lower than without masks, which is strong evidence of the importance of wearing a mask. As people's daily activities are mostly carried out indoors, and many super-spreading events of the new crown epidemic also originated from indoor gatherings, the research on indoor epidemic prevention and control is essential. This study provides decision-making support for epidemic preventions and controls and the proposed methodology can be used in other regions and future epidemics.

Evaluation of different machine learning approaches and aerosol optical depth in PM2.5 prediction.

Atmospheric Aerosol Optical Depth (AOD), derived from polar-orbiting satellites, has shown potential in PM2.5 predictions. However, this important source of data suffers from low temporal resolution. Recently, geostationary satellites provide AOD data in high temporal and spatial resolution. However, the feasibility of these data in PM2.5 prediction needs further study. In this paper, we analyzed the impact of AOD derived from Himawari-8 in PM2.5 predictions. Moreover, by combining wavelet, machine learning techniques, and minimum redundancy maximum relevance (mRMR), a novel hybrid model was proposed. The results showed that AOD missing rate over Yangtze River Delta region is the highest in Nanjing, Hefei, and Maanshan. In addition, missing rates are the lowest in winter and summer (∼80%). Moreover, we found that considering AOD, as an auxiliary variable in the model, could not improve the accuracy of PM2.5 predictions, and in some cases decreased it slightly. In comparison with other models, our proposed hybrid model showed higher prediction accuracy, R2 is improved by 11.64% on average, and root mean square error, mean absolute error, and mean absolute percentage error is reduced by 26.82%, 27.24%, and 29.88% respectively. This research provides a general overview of the availability of Himawari-8 AOD data and its feasibility in PM2.5 predictions. In addition, it evaluates different machine learning approaches in PM2.5 predictions. Our proposed framework can be used in other regions to predict different air pollutants concentrations and can be used as an aid for air pollution controlling programs.

Mapping the social stock and spatiotemporal distribution of high-tech minerals from wasted mobile phones in China: 2001-2019.

In the twenty-first century, mobile phones have become one of the most indispensable electronic products in the international community. The pollution of wasted mobile phones has become an urgent problem worldwide and needs special attention. In this paper, we applied the consumption and usage method to calculate the high-tech mineral elements in China from 2001 to 2019. To analyze the spatial distribution of per capita high-tech minerals in China, we proposed a model (3D GHM) through which a 3D grid of high-tech minerals in wasted mobile phones can be obtained in 1 km resolution. The results showed that the total amount of wasted mobile phones in China from 2001 to 2019 was 8.6 billion, with a growth rate of 1026.7% in 2019 compared with 2001. Moreover, the spatiotemporal distribution of wasted mobile phones is characterized by more in the east and less in the west. The total amount of cobalt, palladium, antimony, beryllium, neodymium, praseodymium, and platinum in wasted mobile phones from 2001 to 2019 reached 42,422.4 tons. Based on our results, we proposed a system for efficient collecting and recycling of wasted mobile phones in China.

The relationship between air quality and MODIS aerosol optical depth in major cities of the Yangtze River Delta.

The AOD derived from the MODIS deep blue(DB) algorithm and AQI were used to investigate the correlation between AOD and AQI in seven major cities of Yangtze River Delta (YRD) from January to December 2019. The accuracy of MODIS AOD was validated by AERONET. Moreover, the AOD and AQI were studied to explore the annual and seasonal distribution characteristics, and the correlation analysis was carried out using five regression models. It was found: Ⅰ) There was a significant correlation between AOD and AERONET data (R2 ˃ 0.80, RMSE = 0.106, and MAE = 0.089). Ⅱ) The highest AQI was observed in winter (83), followed by spring (76), autumn (74), and summer (72). Ⅲ) The monthly average AOD showed noticeable seasonal variations, which reached the highest in summer (0.91) and the lowest in winter (0.69), followed by spring and autumn. Ⅳ) Among the five models, the cubic model obtained the best results with R2 ˃ 0.55. In the sub-seasonal regression model, the cubic model outperformed other models in spring (R2 ˃ 0.57), summer (R2 ˃ 0.76) and autumn (R2 ˃ 0.38). However, in winter the composite model outperformed others (R2 ˃ 0.68). Ⅴ) Considering annual data, the AOD can predict over 70% of the variations in AQI (0.41＜R2 ＜0.81). These results demonstrate the feasibility of AOD derived from the MODIS DB algorithm in AQI prediction. The method used in this study can be applied as an aid for air pollution control programs in different regions.

Landscape ecological risk assessment and driving factor analysis in Dongjiang river watershed.

The ecological and environmental quality of Dongjiang river watershed has great influence on Guangdong, Hong Kong and Macao. The landscape ecological risk assessment model could effectively monitor and assess environmental quality. In this study, spatial autocorrelation and geographic detector methods were used to explore the spatial characteristics of landscape ecological risk and their driving factors in the Dongjiang river watershed for four decades. The results showed that the ecological risks of Dongjiang River Source Watershed are mainly classified as low and intermediate, which are distributed in the hilly regions and the marginal mountainous regions at the junction of the Xunwu and Dingnan counties. From 1980 to 2018, the area of regions with the low ecological risk increased by 587.01 km 2. The size of regions with moderate, high and severe ecological risk decreased by 165.6 km 2, 258.82 km2 and 162.58 km2, respectively. Moreover, landscape ecological risk values exhibited an apparent spatial dependency, and high-risk areas cluster together. Among influencing factors, population density has the most significant impact on the change of landscape ecological risk in the Dongjiang river watershed, followed by elevation (DEM), human interface, vegetation index (NDVI), and urbanization level. However, the interaction of driving factors has a greater impact on the ecological risk of the Dongjiang river watershed than a single driving factor. The research provides good knowledge for environmental quality management, and the proposed methods can be used for other regions.

Spatio-temporal variation of ozone pollution risk and its influencing factors in China based on Geodetector and Geospatial models.

Ozone (O3) has become the primary pollutant in many cities, and high concentrations of O3 cause significant harm to the ecological environment and human health. This study investigated the spatiotemporal distribution of surface concentrations of ozone over entire China and analyzed the influencing factors based on the geographical detector technique. Moreover, the Pearson correlation analysis was used to analyze the influence of various meteorological factors on ozone concentrations. The results showed that, on the national scale, the daily average O3 concentration in the cities of China in 2019 was 92.441 µg/m3 and the nonattainment rate of daily average ozone was 7.98%. However, the ozone nonattainment rate was 33.33% in heavily polluted regions. The highest O3 concentration was observed in summer, and the lowest was observed in spring. The O3 concentrations in cities across the country showed significant spatial distribution characteristics. Among the five pollutants, the highest correlation was observed between O3 and PM2.5 and the lowest was observed between O3 and SO2. Among the metrological factors, wind speed and solar radiation are the most influencing factors, and showed positive correlation. Moreover, the annual precipitation is negatively correlated with O3-8h concentrations. The methods and findings of this paper can be used as an aid for air pollution control programs in different regions for diminishing the risk of exposure to various air pollutants.

DESA: a novel hybrid decomposing-ensemble and spatiotemporal attention model for PM2.5 forecasting.

Exposure to fine particulate matter can easily lead to health issues. PM2.5 concentrations are associated with various spatiotemporal factors, which makes the prediction of PM2.5 concentrations still a challenging task. One of the reasons that makes the accurate prediction by statistical learning method difficult is severe fluctuations in input data. In addition, the abstraction method of space will also affect the prediction results. To address these important issues, a novel hybrid decomposing-ensemble and spatiotemporal attention (DESA) model is proposed to improve the prediction accuracy by decomposing the mode-mixed time series into single-mode series and automatically assign weights to the spatiotemporal factors. In our proposed framework, raw PM2.5 series are firstly decomposed into simple sub-series via the complete ensemble empirical mode decomposition (CEEMD) method. Then, to keep the results independent of the spatial abstraction method, a data-driven approach called multiscale spatiotemporal attention network is employed to extract spatiotemporal features from the sub-series. Finally, the predictions of each sub-series are processed separately and combined to obtain the final prediction results. The experimental results indicate that the proposed model achieved the better performance with RMSE of 11.15, 17.49, 24.84, and 26.93 for 6-, 12-, 24-, and 36-h forecasting, respectively. The proposed method is expected to be applied in fine prediction of air pollution and controlling programs and therefore provide decision support or useful guidance.

Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China.

In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China's non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = - 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers to control the current and future epidemics.

Reflexin A, a new indole alkaloid from Rauvolfia reflexa induces apoptosis against colon cancer cells.

One new indole alkaloid, reflexin A (1), and two known indoles, macusine B (2) and vinorine (3), were isolated from the bark of Rauvolfia reflexa. Their structures were elucidated by 1D and 2D NMR, UV, IR, and MS spectroscopic analyses. Compound 1 displayed anticancer activity against HCT-116 colon cancer cells with an IC50 value of 30.24 ± 0.75 µM. The results implied that the newly isolated 1 induced apoptosis in HCT-116 cells, suggesting its possible role as an anticancer agent. In vivo acute toxicity study was performed on compound 1 to evaluate its safety profile.

A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory.

Increasing availability of data related to air quality from ground monitoring stations has provided the chance for data mining researchers to propose sophisticated models for predicting the concentrations of different air pollutants. In this paper, we proposed a hybrid model based on deep learning methods that integrates Graph Convolutional networks and Long Short-Term Memory networks (GC-LSTM) to model and forecast the spatiotemporal variation of PM2.5 concentrations. Specifically, historical observations on different stations are constructed as spatiotemporal graph series, and historical air quality variables, meteorological factors, spatial terms and temporal attributes are defined as graph signals. To evaluate the performance of the GC-LSTM, we compared our results with several state-of-the-art methods in different time intervals. Based on the results, our GC-LSTM model achieved the best performance for predictions. Moreover, evaluations of recall rate (68.45%), false alarm rate (4.65%) (both of threshold: 115 µg/m3) and correlation coefficient R2 (0.72) for 72-hour predictions also verify the feasibility of our proposed model. This methodology can be used for concentration forecasting of different air pollutants in future.

Gastroprotective activity of a novel Schiff base derived dibromo substituted compound against ethanol-induced acute gastric lesions in rats.

BACKGROUND: Basic function of bromine in body is to activate pepsin production in gastritis with low acidity. The present study encompasses a broad in vivo study to evaluate gastroprotective activity of a novel dibromo substituted Schiff base complex against Sprague Dawley (SD) rats. METHODS: 2, 2'-[1, 2-cyclohexanediylbis (nitriloethylidyne)]bis(4-bromophenol) (CNBP) is synthesized via a Schiff base reaction, using the related ketone and diamine as the starting materials. SD rats are divided as normal, ulcer control (5 ml/kg of 10% Tween 20), testing (10 and 20 mg/kg of CNBP) and reference groups (omeprazole 20 mg/kg). Except for the normal group, the rest of the groups are induced gastric ulcer by ethanol 1 h after the pre-treatment. Ulcer area, gastric wall mucus, and acidity of gastric content of the animal stomachs are measured after euthanization. Antioxidant activity of the compound is tested by Ferric reducing antioxidant power (FRAP) test and safety of the compound is identified through acute toxicity by [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Moreover, activities of superoxide dismutase (SOD), catalase (CAT), levels of prostaglandins E2 (PGE2) and also malondialdehyde (MDA) are determined. RESULTS: Antioxidant activity of CNBP was approved via FRAP assay. Vast shallow hemorrhagic injury of gastric glandular mucosa was observed in the ulcer group compared to the CNBP-treated animals. Histological evaluations confirmed stomach epithelial defense effect of CNBP with drastic decrease of gastric ulceration, edema and leucocytes penetration of submucosal stratum. Immunostaining exhibited over-expression in HSP70 protein in CNBP-treated groups compared to that of the ulcer group. Also, gastric protein analysis showed low levels of MDA, PGE2 and high activity of SOD and CAT. CONCLUSIONS: CNBP with noticeable antioxidant property showed gastroprotective activity in the testing rodents via alteration of HSP70 protein expression. Also, antioxidant enzyme activities which were changed after treatment with CNBP in the animals could be elucidated as its gastroprotective properties.

Polyethylene glycol-coated porous magnetic nanoparticles for targeted delivery of chemotherapeutics under magnetic hyperthermia condition.

PURPOSE: Although magnetite nanoparticles (MNPs) are promising agents for hyperthermia therapy, insufficient drug encapsulation efficacies inhibit their application as nanocarriers in the targeted drug delivery systems. In this study, porous magnetite nanoparticles (PMNPs) were synthesized and coated with a thermosensitive polymeric shell to obtain a synergistic effect of hyperthermia and chemotherapy. MATERIALS AND METHODS: PMNPs were produced using cetyltrimethyl ammonium bromide template and then coated by a polyethylene glycol layer with molecular weight of 1500 Da (PEG1500) and phase transition temperature of 48 ± 2 °C to endow a thermosensitive behavior. The profile of drug release from the nanostructure was studied at various hyperthermia conditions generated by waterbath, magnetic resonance-guided focused ultrasound (MRgFUS), and alternating magnetic field (AMF). The in vitro cytotoxicity and hyperthermia efficacy of the doxorubicin-loaded nanoparticles (DOX-PEG1500-PMNPs) were assessed using human lung adenocarcinoma (A549) cells. RESULTS: Heat treatment of DOX-PEG1500-PMNPs containing 235 ± 26 mg·g-1 DOX at 48 °C by waterbath, MRgFUS, and AMF, respectively led to 71 ± 4%, 48 ± 3%, and 74 ± 5% drug release. Hyperthermia treatment of the A549 cells using DOX-PEG1500-PMNPs led to 77% decrease in the cell viability due to the synergistic effects of magnetic hyperthermia and chemotherapy. CONCLUSION: The large pores generated in the PMNPs structure could provide a sufficient space for encapsulation of the chemotherapeutics as well as fast drug encapsulation and release kinetics, which together with thermosensitive characteristics of the PEG1500 shell, make DOX-PEG1500-PMNPs promising adjuvants to the magnetic hyperthermia modality.

Mechanism involved in insulin resistance via accumulation of ß-amyloid and neurofibrillary tangles: link between type 2 diabetes and Alzheimer's disease.

The pathophysiological link between type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) has been suggested in several reports. Few findings suggest that T2DM has strong link in the development process of AD, and the complete mechanism is yet to be revealed. Formation of amyloid plaques (APs) and neurofibrillary tangles (NFTs) are two central hallmarks in the AD. APs are the dense composites of ß-amyloid protein (Aß) which accumulates around the nerve cells. Moreover, NFTs are the twisted fibers containing hyperphosphorylated tau proteins present in certain residues of Aß that build up inside the brain cells. Certain factors contribute to the aetiogenesis of AD by regulating insulin signaling pathway in the brain and accelerating the formation of neurotoxic Aß and NFTs via various mechanisms, including GSK3ß, JNK, CamKII, CDK5, CK1, MARK4, PLK2, Syk, DYRK1A, PPP, and P70S6K. Progression to AD could be influenced by insulin signaling pathway that is affected due to T2DM. Interestingly, NFTs and APs lead to the impairment of several crucial cascades, such as synaptogenesis, neurotrophy, and apoptosis, which are regulated by insulin, cholesterol, and glucose metabolism. The investigation of the molecular cascades through insulin functions in brain contributes to probe and perceive progressions of diabetes to AD. This review elaborates the molecular insights that would help to further understand the potential mechanisms linking T2DM and AD.

Action of Phytochemicals on Insulin Signaling Pathways Accelerating Glucose Transporter (GLUT4) Protein Translocation.

Diabetes is associated with obesity, generally accompanied by a chronic state of oxidative stress and redox imbalances which are implicated in the progression of micro- and macro-complications like heart disease, stroke, dementia, cancer, kidney failure and blindness. All these complications rise primarily due to consistent high blood glucose levels. Insulin and glucagon help to maintain the homeostasis of glucose and lipids through signaling cascades. Pancreatic hormones stimulate translocation of the glucose transporter isoform 4 (GLUT4) from an intracellular location to the cell surface and facilitate the rapid insulin-dependent storage of glucose in muscle and fat cells. Malfunction in glucose uptake mechanisms, primarily contribute to insulin resistance in type 2 diabetes. Plant secondary metabolites, commonly known as phytochemicals, are reported to have great benefits in the management of type 2 diabetes. The role of phytochemicals and their action on insulin signaling pathways through stimulation of GLUT4 translocation is crucial to understand the pathogenesis of this disease in the management process. This review will summarize the effects of phytochemicals and their action on insulin signaling pathways accelerating GLUT4 translocation based on the current literature.

Beta-mangostin from Cratoxylum arborescens activates the intrinsic apoptosis pathway through reactive oxygen species with downregulation of the HSP70 gene in the HL60 cells associated with a G0/G1 cell-cycle arrest.

Xanthones are phytochemical compounds found in a number of fruits and vegetables. Characteristically, they are noted to be made of diverse properties based on their biological, biochemical, and pharmacological actions. Accordingly, the apoptosis mechanisms induced by beta-mangostin, a xanthone compound isolated from Cratoxylum arborescens in the human promyelocytic leukemia cell line (HL60) in vitro, were examined in this study. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay was done to estimate the cytotoxicity effect of ß-mangostin on the HL60 cell line. Acridine orange/propidium iodide and Hoechst 33342 dyes and Annexin V tests were conducted to detect the apoptosis features. Caspase-3 and caspase-9 activities; reactive oxygen species; real-time polymerase chain reaction for Bcl-2, Bax, caspase-3, and caspase-9 Hsp70 genes; and western blot for p53, cytochrome c, and pro- and cleavage-caspase-3 and caspase-9 were assessed to examine the apoptosis mechanism. Cell-cycle analysis conducted revealed that ß-mangostin inhibited the growth of HL60 at 58 µM in 24 h. The administration of ß-mangostin with HL60 caused cell morphological changes related to apoptosis which increased the number of early and late apoptotic cells. The ß-mangostin-catalyzed apoptosis action through caspase-3, caspase-7, and caspase-9 activation overproduced reactive oxygen species which downregulated the expression of antiapoptotic genes Bcl-2 and HSP70. Conversely, the expression of the apoptotic genes Bax, caspase-3, and caspase-9 were upregulated. Meanwhile, at the protein level, ß-mangostin activated the formation of cleaved caspase-3 and caspase-9 and also upregulated the p53. ß-mangostin arrested the cell cycle at the G0/G1 phase. Overall, the results for ß-mangostin showed an antiproliferative effect in HL60 via stopping the cell cycle at the G0/G1 phase and prompted the intrinsic apoptosis pathway.

Mitochondrial outer membrane permeabilization: a focus on the role of mitochondrial membrane structural organization.

Apoptosis is important in regulating cell death turnover and is mediated by the intrinsic and death receptor-based extrinsic pathways which converge at the mitochondrial outer membrane (MOM) leading to mitochondrial outer membrane permeabilization (MOMP). MOMP results in the release of apoptotic proteins that further activate the downstream pathway of apoptosis. Thus, tight regulation of MOMP is crucial in controlling apoptosis, and a lack of control may lead to tissue and organ malformation and the development of cancers. Despite a growing number of studies focusing on the structure and activity of the proteins involved in mediating MOMP, such as the Bcl-2 family proteins, the mechanism of MOMP is not well understood. In particular, the crucial role of the various structural properties and changes in lipid components of the MOM in mediating the recruitment and activation of different Bcl-2 proteins remains poorly understood. Furthermore, the factors that control the changes in mitochondrial membrane integrity from the initiation to the final disruption of MOM have yet to be clearly defined. In this review, we provide an overview of studies that focus on the mitochondrial membrane with a biophysical analysis of the interactions of the Bcl-2 proteins with the mitochondrial membrane.

Beta-mangostin demonstrates apoptogenesis in murine leukaemia (WEHI-3) cells in vitro and in vivo.

BACKGROUND: Beta-mangostin (BM) is a xanthone-type of natural compound isolated from Cratoxylum arborescens. This study aimed to examine the apoptosis mechanisms induced by BM in a murine monomyelocytic cell line (WEHI-3) in vitro and in vivo. METHODS: A WEHI-3 cell line was used to evaluate the cytotoxicity of BM by MTT. AO/PI and Hoechst 33342 dyes, Annexin V, multiparametric cytotoxicity 3 by high content screening (HCS); cell cycle tests were used to estimate the features of apoptosis and BM effects. Caspase 3 and 9 activities, ROS, western blot for Bcl2, and Bax were detected to study the mechanism of apoptosis. BALB/c mice injected with WEHI-3 cells were used to assess the apoptotic effect of BM in vivo. RESULTS: BM suppressed the growth of WEHI-3 cells at an IC50value of 14 ± 3 µg/mL in 24 h. The ROS production was increased inside the cells in the treated doses. Both caspases (9 and 3) were activated in treating WEHI-3 cells at 24, 48 and 72 h. Different signs of apoptosis were detected, such as cell membrane blebbing, DNA segmentation and changes in the asymmetry of the cell membrane. Another action by which BM could inhibit WEHI-3 cells is to restrain the cell cycle at the G1/G0 phase. In the in vivo study, BM reduced the destructive effects of leukaemia on the spleen and liver by inducing apoptosis in leukaemic cells. CONCLUSION: BM exerts anti-leukaemic properties in vitro and in vivo.