INTRODUCTION:

Diabetes is a chronic disease caused due to inefficiency of beta cells to produce enough insulin¹. World health organization global report suggests that 422 million people lived with diabetes in 2014 as compared to 1908 having diabetes only 108 million. This report clarifies diabetes has nearly doubled since 1908, the percentage rising from 4.7 to 8.5 in adults. In 2012 there are 1.5 million deaths were reported. Additional 2.2million deaths are caused because of higher than optimal glucose and also by increasing cardiovascular diseases. Possible complications include kidney failure, stroke, nerve damage, heart attack, and vision loss.

Current medication used for management of diabetes may require it to be taken on life-long basis and may have adverse effects.

Medicinal plants are the sources of active constituents to treat various diseases and disorders². The best-suited extraction techniques are to be used to get maximum extractive yield and a great number of active constituents. For optimization of extraction process and chemical constituents yields, Box Behnken design (BBD) is one of the best tools to calculate the results. In plants, phenolic compounds are one of the largest groups commonly present in plants which can act as anti-diabetics remedies.

Marsilea quadrifolia L. is a semiaquatic plant also called European water clover and sushni belonging to Marsileaceae family. The plant is widely found in the aquatic regions of India. The plant consists of phenolic compounds, which include kaempferol, quercetin, hegoflavone A, Caffeic acid, ethyl caffeate, cyperusphenol A, mesocyperusphenol A, scirpusin A, and ethyl caffeate. Other than diabetes, the extract showed anticonvulsant activity in pentylenetetrazol (PTZ) induced seizures³. The epileptic rats showed decreased glutathione and increased MDA concentration in the hippocampus and prefrontal cortex as compared to normal control rats⁴. The in-vitro cytotoxic activity of ethyl acetate extract of M. quadrifolia has also been reported on MCF-7 (human breast cancer cells)⁵. This plant also showed effective inhibition of gram-negative bacteria such as Pseudomonas aeruginosa, Escherichia coli, and Salmonella typhi⁶. Ethanolic extract of leaves of M. quadrifolia showed significant anti-hyperglycemic activity in albino mice⁷. Methanolic extract of aerial parts of M. quadrifolia showed in-vitro antioxidant and hypoglycemic activity in rats⁸. At a dose of 200mg/kg of M. quadrifolia extract showed anti-diabetic activity in alloxan-induced rats⁹. M. quadrifoliais mentioned in Charka Samhita for management of diabetes in combination with other antihyperglycemic plants. Ethnobotanical survey finds that whole plant of M. quadrifoliais used for diabetes in Birbhum district of west Bengal¹⁰. The current study (Illustration figure 1) focuses on extraction process optimization, total phenolic content, and evaluation of type 2 anti-diabetic activity of hydroalcoholic extract of M. quadrifoliabased on previous reports and ethnobotanical surveys.

Figure 1: Graphical representation of work plan

MATERIALS AND METHODS:

Collection and authentication of Plant:

Whole plant of Marsilea quadrifolia L. was collected in September from the agriculture fields of Banaras Hindu University, Varanasi. Collected specimen was authenticated by Dr. N. K. Dubey, Department of Botany, Institute of Science, Banaras Hindu University, Varanasi (Voucher specimen no. Marsil. 2016/1).

Preparation of extract, process optimization, and total phenolic content:

Freshly collected plant was cleaned with water to remove adhering dust, and foreign matter and shade dried. Dried plant material was then powdered using mixer, Powdered material was stored in a tightly closed container until further use.

For optimization of extraction procedure, maceration technique was used and results were calculated through Box-Behnken design (BBD)¹¹. Three factors that influence the extractive yield during extraction process i.e., extraction time, extraction solvent, and mesh size were used to be optimized with the help of BBD. Design expert software (Version 12.0.0 Trial, Stat-Ease Ink.) was used to determine the boundary of the experiment, for evaluation of the experiment and optimization process. The levels of design factors are shown in table 1. Design factors were fixed in three levels i.e., high, medium, and low. Actual extractive experiments with independent variables and obtained results are listed in table 2.Total 17 extractive cycles were performed. The percentage yield of extraction is indicative of extraction efficiency while the phenolic yield of extract was estimated via folin-coicalteau calorimetric method using a reference compound (gallic acid).Then the crude drug is extracted by using hydro alcohol solvent through the maceration process. The obtained extract was filtered and concentrated by using rotary evaporator to get crude hydroalcoholic extract of Marsilea quadrifolia L. (HMQ).

Table 1: Input factors with their ascribed values and coded levels

Factor	Levels used, Actual (coded)
Factor	Low (-1)	Medium (0)	High (+1)
X₁=Mesh Size (sieve number)	10	20	40
X₂=Solvent Blend (ethanol: water)	70	85	100
X₃=Time (h)	6	15	24
Dependent Variables	Constraints
Y₁=Extraction Efficiency (%)	Maximize
Y₂=Phenolic content(mg/g)	Maximize

Table 2: Actual extractive values to obtain best results

Batch	Independent variables			Dependent variables
Batch	A (Mesh Size)	B: Solvent Blend (Ethanol: Water)	C (Time hrs.)	Y₁ (%)	Y₂ (mg/g)
1	20	100:0	24	10.28	103.91
2	20	70:30	24	10.71	109.04
3	40	70:30	15	8.35	101.36
4	40	100:0	15	9.07	104.43
5	20	85:15	15	13.5	119.45
6	10	85:15	6	5.47	99.93
7	10	100:0	15	5.93	99.89
8	20	85:15	15	14.01	112.17
9	20	85:15	15	12.73	109.85
10	20	70:30	6	5.95	99.89
11	20	85:15	15	12.99	110.38
12	10	85:15	24	11.89	109.72
13	10	70:30	15	9.72	108.21
14	20	100:0	6	5.95	99.47
15	40	85:15	6	6.38	101.36
16	20	85:15	15	13.88	110.49
17	40	85:15	24	12.84	110.38

Chemicals:

All chemicals used in the experiments were analytical grade. Streptozotocin (STZ) was procured from Sisco research laboratories Pvt Ltd. Blood glucose level and other biochemical estimation kits were purchased from Spam diagnostics Pvt Ltd.

Experimental animals:

Healthy albino rats, weights between 120-200g were procured from the central animal purchase house (approval no. Dean/2016/CAEC/329), Banaras Hindu University, Varanasi. The animals were placed under standard environmental conditions 22-28^°C, 60 to 70% RH, 12 hrs. dark vs. 12 hrs. light cycle, with standard rat pellet diet and water ad libitum. Before starting the experiment, rats were allowed to acclimatize to the new environment for seven days.

Acute oral toxicity study:

Thetoxicity studies were conducted as per the guidelines of the organization of Economic corporation and development 425(OECD 425). For this study, five female albino rats were selected because females are more sensitive as compared to male rats. Before commencing the experiment, animals were fasted and allowed water ad libitum (12hrs.). A single dose of HMQ at 2000mg/kg body weight was administered orally. After oral administration of HMQ, animals were precisely observed for next three days for any impairment in motor functions, tremors, lacrimation, convulsions, diarrhea, skin colour, loss of lighting reflex, etc.^12,13.

Induction of type 2 diabetes millets:

For induction of diabetes, a freshly prepared solution of nicotinamide (in normal saline) 110mg/kg body weight was administered i.p. 15 minutes post nicotinamide injection, and STZ was administered i.p. route, at the dose of 65mg/kg. The solution of STZ was prepared in the citrate buffer (pH 4.5). The normal control group received ml of citrate buffer¹⁴. STZ can cause fatal hypoglycemia by excessive release of insulin from beta cells in the pancreas. Hence, to avoid hypoglycemia condition, 10% of glucose solution was given for next 6-24hours of STZ injection. After 2 days of STZ administration, the rats were checked for blood glucose levels using glucose kit and it continued to check blood glucose for up to seven days. The overnight fasted rats with greater than 200mg/dl blood glucose levels in stable mannerwereconsidered to be diabetic and were used for further studies¹⁵.

Design of experiment:

For experimental design, rats were divided into six groups and each group consists of six rats. Whereas, n= number of rats. Group1 (NC)- Normal rats (n=6) with vehicle alone (0.5% of CMC), Group2 (DC)- Diabetic rats (n=6) with vehicle alone (0.5% of CMC), Group3 (HMQ 150mg/kg)- Diabetic rats (n=6) with HMQ 150mg/kg., Group4 (HMQ 300mg/kg)- Diabetic rats (n=6) with HMQ 300mg/kg., Group 5 (HMQ 450mg/kg)- Diabetic rats (n=6) with HMQ 450mg/kg., Group6 (Glib 10mg/kg)- Diabetic rats (n=6) with glibenclamide 10mg/kg.

Collection of blood and estimation of blood glucose:

Before collection of blood, the rats fasted overnight and blood samples were collected through puncturing retro-orbital flexesusing capillary tubes on the 0^th, 7^th, and 14^th day. First day of the treatment was considered to be 0^th day. After collection of blood, serum and plasma was separated by usingcentrifuge (1200rpm for 20min.) Followed by estimation of serum blood glucose levels by using glucose kit.

Estimation of body weight:

The body weight was taken for all groups of rats on the 0^th, 7^th, and 14^th day by using a laboratory animal weighing balance.

Evaluation of lipid profile:

On the last day of the experiment (14^th day), lipid profile estimations were performed such as triglycerides, high-density lipoproteins (HDL), low-density lipoproteins (LDL), and total cholesterol levels by using a span diagnostic kit for lipid profile.

Estimation of liver glycogen:

Liver glycogen was estimated in rats on the 14^th day of the experiment. For that, liver was weighed 200gm and triturated with 5% TCA in a homogenizer. The liver glycogen levels were estimated by using the iodine reagent method.

In vivo antioxidant activity:

For the preparation of tissue homogenate, animals were sacrificed on 14^th day by cervical dislocation. Then immediately liver was isolated and washed in cold saline to remove blood. The isolated liver was triturated with tris HCL buffer and transferred to a centrifuge to obtain 10% homogenate. The clear supernatant obtained was used to evaluate lipid peroxidation (LPO)¹⁶, evaluation of catalase activity (CAT)¹⁷, and estimation of superoxide dismutase activity (SOD)¹⁸.

Histopathological study:

On the last (14^th) day of the experiment, pancreatic tissue was isolated and sections (2mm) were taken using a microtome. The tissue sections were fixed on glass slide to observe under a microscope.

Statistical analysis:

All the data were analyzed statistically using graph pad prism software (version 4). The values were recorded as mean±standard error of the mean in two-way ANOVA, followed by Bonferroni posttest was performed for blood glucose data. For all other parameters, one-way ANOVA followed by Tukey’s multiple comparison tests was applied.

RESULTS:

HMQ was obtained through maceration technique and process was optimized using BBD to increase extractive yield and phenolic content. The optimum conditions were adopted to be, powder screened through sieve no. 20, and extracted with hydroalcoholic (85:15) solvent for 15 hours to achieve highest extraction efficiency and total phenolic content. The obtained extrusive yield and phenolic content were found to be 14.01% and 119.45 mg/g gallic acid of dried extract respectively. The final quadratic equations produced by the software with coded variables are as follows:

Extraction efficiency = 13.86+(-0.2768*A)+2.77*B+ 0.4538*C+(-0.1075*AB)+0.9639*AC+ 0.1591*BC + (-3.04*A²) + (-2.16*B²) + (-2.55*C²).

Total phenolic content = 112.88 + (-0.8984) + 4.05*B + (-0.0275*C) + (-1.18*AB) + 2.71*AC + 0.0237*BC + (-5.63*A²) + (-3.76*B²) + (-3.77*C²).

Selection of response model from amongst linear, 2FI, quadratic and cubic models to delineate obtained and expected outcomes. Fitting the data for observed responses to selected models, and determination of significant factors are explained in Table 3. The 3D response surface plots of DoE for extraction efficiency and phenolic yield were shown in figure 2.

Table 3: Model Summary Statistics and selection of significant factors

Extraction Efficiency
Model Selection	Std. Dev.	R²	Adj. R²	Pred. R²	PRESS	Remark
Linear	2.73	0.3900	0.2492	0.1053	141.94
2FI	3.04	0.4157	0.0651	-0.429	226.80
Quadratic	0.8409	0.9688	0.9287	0.6237	59.71	S
Cubic	0.5539	0.9923	0.9691	0.1053	*	AL
	S.S.	df	M.S.	F-value	p-value
Model	153.70	9	17.08	24.15	0.0002	S
A	0.5825	1	0.5825	0.8237	0.3943
B	58.43	1	58.43	82.63	< 0.0001
C	1.65	1	1.65	2.33	0.1708
AB	0.0462	1	0.0462	0.0654	0.8056
AC	3.92	1	3.92	5.55	0.0507
BC	0.1068	1	0.1068	0.1511	0.7090
A²	38.87	1	38.87	54.97	0.0001
B²	19.66	1	19.66	27.81	0.0012
C²	20.45	1	20.45	28.92	0.0010
Residual	4.95	7	0.7072
Lack of Fit	3.72	3	1.24	4.05	0.1052	NS
Pure Error	1.23	4	0.3068
Cor Total	158.65	16

S=Selected; AL= Aliased; SG= Significant; NS= Not significant

Table 3: Cont……..

Phenolic Content
Model Selection	Std. Dev.	R²	Adj. R²	Pred. R²	Press	Remark
Linear	5.34	0.2874	0.1230	-0.051	547.17
2FI	5.78	0.3576	-0.027	-0.518	790.51
Quadratic	3.19	0.8629	0.6866	0.5647	226.64	S
Cubic	4.00	0.8771	0.5086		*	AL
	S.S.	df	M.S.	F-value	p-value
Model	449.25	9	49.92	4.89	0.0240	S
A	6.13	1	6.13	0.6016	0.4634
B	124.90	1	124.90	12.25	0.0100
C	0.0060	1	0.0060	0.0006	0.9812
AB	5.55	1	5.55	0.5439	0.4848
AC	31.00	1	31.00	3.04	0.1248
BC	0.0024	1	0.0024	0.0002	0.9883
A²	133.59	1	133.59	13.10	0.0085
B²	59.46	1	59.46	5.83	0.0465
C²	44.74	1	44.74	4.39	0.0745
Residual	71.38	7	10.20
Lack of Fit	7.42	3	2.47	0.1547	0.9214	NS
Pure Error	63.96	4	15.99
Cor Total	520.63	16

S=Selected; AL= Aliased; SG= Significant; NS= Not significant

Std. Dev.: Standard deviation of obtained values over 17 runs; R2: Regression coefficient (the model maximizing R2 is selected); Adj. R2: Adjusted R2; Pred. R2: Predicted R2 (The selected model should maximize summation of Adj. R2 and Pred. R2); PRESS: Predicted residual sum of squares (PRESS statistic with the lowest value indicates the best model); 2FI:2 Factor Interaction Model;+: PRESS statistic can’t be estimated; Aliased(Implies the number of experimental runs is not adequate to fit cubic model in the response curve); S.S.: Sum of Squares; M.S.: Mean Square; df: Degree(s) of freedom. After adopting the quadratic model, the influence of each factor (A, B, C…C2) was verified by computing F values. The factor with a large F value explains the variance more. The probability of obtaining this F value, if the investigated independent variable did not have any effect on the response is measured by p values. Small probabilities, p<0.05, indicate a significant factor. The insignificant lack of fit for the quadratic model counter-intuitively proves that the model is a good fit and can be pursued for optimization of maceration extraction.

Figure 2: 3D- response surface plots by Design Expert explaining the extractive process.

Table 4: Effect of Marsilea quadrifolia L. on Lipid profile

Group	Treatment	TG (mg/dl)	TC (mg/dl)	HDL (mg/dl)	LDL (mg/dl)
1	Normal control	85.3±4.8	76.41±5	43.9±0.8	15.4±5.1
2	Diabetic control	149.1±4.8⁺	107.3±4.6⁺	25±1.1⁺	52.4±5⁺
3	MQE 150mg/kg	139.4±4.8	101.8±3.7	30.5±1.1	43.3±4.8
4	MQE 300mg/kg	112.2±6.1^*	84.6±4.1^*	39±1.4^*	23.1±3.8^*
5	MQE 450mg/kg	93.9±4.9^**	79.5±5.4^**	41.8±1.4^**	18.8±6.4^**
6	Glib 10mg/kg	90.8±4.2^**	78.1±6.1^**	42±0.7^**	18.01±6.2^**

+p<0.01 compared to Normal control rats (Group 1), *p<0.01 compared to Diabetic control rats (Group 2)

Table 5: Effect of Marsilea quadrifolia L. extract on body weight and liver glycogen of diabetic rats

Group	Treatment (mg/kg body weight)	0^th day	7^th day	14^th day	Liver glycogen (mg/g)
1	Normal control	175±13.8	175.8±11.7	180.8±12.7	13.5±0.69
2	Diabetic control	159.1±13.6⁺	134.1±10.5⁺	120.8±8.4⁺	4.96±1.2⁺
3	MQE 150mg/kg	160.8±9.3	147.5±10.7	142.5±12.2	7.3±0.34
4	MQE 300mg/kg	160±13.7^*	150.8±14.2^*	150±13.1^*	9.2±1.9^*
5	MQE 450mg/kg	160.8±8.3^**	151.6±6.7^**	170.8±6.7^**	10.9±1.8^**
6	Glib 10mg/kg	159.1±11.7^**	152.5±12.5^**	172.5±11.8^**	11.4±0.68^**

+p<0.01 compared to Normal control rats (Group 1), *p<0.01 compared to Diabetic control rats (Group 2)

Table 6: Effect of Marsilea quadrifolia L. extract on LPO, SOD, TBARS

Group	Treatment (mg/kg body weight)	LPO (µ mol/mg protein)	SOD (µ mol/mg protein)	CAT (µ mol/mg protein)
1	Normal control	22.1±1.1	1.94±0.5	28.4±0.7
2	Diabetic control	56.5±6.9⁺	0.311±0.10⁺	18.8±1.2⁺
3	MQE 150mg/kg	46.7±3.4	0.877±0.27	21.2±1.1
4	MQE 300mg/kg	39.9±1.8^*	0.97±0.28^*	23.4±1.7^*
5	MQE 450mg/kg	29.5±2.5^**	1.79±0.33^**	24.6±0.7^**
6	Glib 10mg/kg	31.6±3.9^**	1.85±022^**	26.3±1^**

+p<0.01 compared to Normal control rats (Group 1), *p<0.01 compared to Diabetic control rats (Group 2)

Figure 4: Histopathological views of pancreatic beta cells at 10X.

A. Normal control, B. diabetic control, C. diabetic rats treated with 150 mg/kg, D. Diabetic rats treated with 300 mg/kg, E. Diabetic rats treated with 450 mg/kg, F. Diabetic rats treated with 10 mg/kg glibenclamide.

The effect of HMQ on the bodyweight of rats and liver glycogen results are shown in Table 5. Bodyweight of the rats on 0^th day was very less, and it gets improved on 7^th day. On 14^th day, the rats gained weight which was significant to the standard drug glibenclamide (glib). The treatment with higher doses of HMQ in rats showed more improvement in body weight compared to the low dose received in rats. A liver glycogen test was performed at the end of the experiment where 300mg/kg and 450mg/kg HMQ dose received by rats gave significant (p<0.01) results, compared to the standard drug glib.

In vivo antioxidant study results, at a dose of HMQ 300mg/kg and 450mg/kg; p.o. was comparable with that of standard drug glib. There was an increment noted in the SOD, CAT, and LPO levels shown in table 6.

In Figure 4, Histopathological views of rat pancreatic beta cell observations revealed the destruction of beta cells in diabetic control and dose HMQ 300mg/kg, 450mg/kg given significant beta cell regeneration compared to glib.

DISCUSSION:

The powdered crude drug was used for extractive value, those different solvents were used such as hexane, dichloromethane, ethyl acetate, butanol, ethanol, and hydro alcohol. It was observed that maximum extractive yield was obtained from hydroalcoholic solvent and it was selected as an extractive solvent for further studies. The crude drug passed through sieve no 20, hydroalcoholic solvent (85:15), and 15hrs. of time combination showed a better extractive yield and a high amount of phenolic content.

In the current study, diabetes was induced by injecting (i.p.) streptozotocin in albino rats. STZ selectively destroys beta cells of pancreatic islets. Along with STZ, nicotinamide acts as a protective agent that can decrease beta cell damage¹⁹. Giving HMQ seems to be attributed to its Anti-hyperglycemic effect on rats by decreasing blood glucose levels. Increased glucose uptake by the tissue but decreased glucose output from hepatic tissue; in this way, it produces anti-hyperglycemic and not hypoglycemic effects²⁰. Phytochemical investigation of M. quadrifolia exhibited the presence of different chemical categories such as alkaloids, steroids, terpenoids, tannins, flavonoids, etc.²¹. Alkaloids, flavonoids, terpenoids, tannins, phenolics, as reported in several papers, are known to be major bioactive constituents for diabetes²². The present antidiabetic effect of HMQ may be due to the synergistic effect of different classes of compounds.

Diabetes Mellitus is also associated with serum lipid and lipoprotein and it can increase the risk of coronary artery disease²³. Hyperlipidemia is one of the major complications in diabetes; it can be estimated by levels of lipids (cholesterol, triglycerides, phospholipids) and also changes in lipoprotein composition²⁴. The abnormal levels of serum lipids occur due to impaired action of lipid-breaking hormones on the fat depots, mainly because of abnormal secretion of insulin under diseased conditions. Under healthy conditions, lipoprotein lipase is activated by insulin, which hydrolyzes triglycerides²⁵. But in a diabetic state, lipoprotein such as lipase remains unactivated because of low levels of insulin, which in turn causes hypertriglyceridemia, in association with hypercholesterolemia because of disturbances in metabolism²⁶. The HMQ at higher doses significantly reduces the lipid levels indicative of its anti-hyperlipidemic potential and may also reduce the risk of vascular difficulties and related secondary complications²⁷.

Severe loss of body weight is the characteristic feature of streptozotocin-induced diabetes which was noted in the current study too. HMQ at the doses 300mg/kg and 450mg/kg, and glibenclamide 10mg/kg significantly (p<0.01) improved the body weight (on 14^th day) of diabetic rats when compared with diabetic control. Decreased body weight observed in group 2, i.e., diabetic control, maybe due to wastage of protein because of the unavailability of carbohydrates as the source of energy²⁸. The treated groups increased glucose metabolism evidenced by improved body weight, and the blood glucose level is maintained by the liver, which regulates its metabolism. In the liver, the amount of insulin and glucose concentration determines the conversion from glycogen to glucose, which can stimulate glycogen synthesis in the presence of glycogen phosphorylase and glycogen synthase enzyme²⁹. In diabetes, the activity of glycogen phosphorylase is decreased while increased the effect of glycogen phosphorylase is evidenced by the decreased glycogen content in the blood of diabetic rats³⁰. After treatment with HMQ and glibenclamide, the rats significantly restored the liver glycogen due to increased secretion of insulin from beta cells of the pancreas by inhibiting glycogen phosphorylase and activating glycogen synthase.

In hyperglycemia, lipid peroxidation is increased in rats which increases hydroxyl and peroxyl radical concentration causing oxidative damage to the hepatic tissues³¹. An increase in the levels of thiobarbituric acid reactive substance (TBARS) and malondialdehyde (MDA) in plasma and liver tissues of diabetic control rats was indicative of the higher lipid peroxidation³². Treatment with HMQ decreased the TBARS levels in the treatment group significantly as compared to the untreated rats. In diabetes, oxidative stress with antioxidative molecules increases due to a decrease in antioxidant status and an increase in the harmful effects of free radicals. The two major free radical scavenging enzymes which neutralize free radicles in vivo are SOD and CAT. The decreased antioxidant activity can result in excess hydrogen peroxide and superoxide ions in animals, causing the generation of free radicles which initiates lipid peroxidation³³. Increased activities of CAT and SOD in the treatment group are indicative of free radicle scavenging activity of HMQ. Dismutation of o^-₂to hydrogen peroxide is accelerated due to enhanced activity of SOD, which is further removed by catalase³⁴.

In the histopathological study, observations clearly show that HMQ effectively protects and decreases the damage in comparison to diabetic control (group 2) rats. The protection of beta cells at higher doses of HMQ and glib. was observed in histopathological images. The activity might occur either due to antioxidant compounds like flavonoids or due to bioactive compounds acting synergistically.

ABBREVIATIONS:

i.p: intra peritoneal; HMQ: hydroalcoholic extract of Marsilea quadrifolia L.; RH: relative humidity; CMC: carboxy methyl cellulose; ANOVA: analysis of variance.

CONCLUSION:

The study indicates that optimization of extractive value, total phenolic content, and Marsilea quadrifolia L. hydroalcoholic extract has antidiabetic activity in streptozotocin-nicotinamide induced type2 diabetic rats. The antidiabetic activity due to improvement in glucose tolerance or restoration of liver glycogen, HMQ can reduce the effect of secondary complications associated with diabetes. Further studies are also needed to know the exact mechanism of action by which HMQ brings back blood glucose levels and bioactive guided fractions, as well as chromatographic techniques, to unravel the constituents which are responsible for antidiabetic activity.

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Received on 16.03.2022 Modified on 22.09.2022

Research J. Pharm. and Tech 2023; 16(9):4239-4246.

DOI: 10.52711/0974-360X.2023.00694