Harnessing the synergistic potential of TiO2-CuSe composites for enhanced photocatalytic and antibacterial activities.

With growing environmental concerns, the removal of toxic industrial dyes from wastewater has become a critical global issue. In this study, TiO2-CuSe composites were synthesized using a cost-effective and simple chemical method to determine the optimal concentration of CuSe for the efficient degradation of methylene blue (MB) under visible light. The TiO2 samples exhibited a mix of rutile and anatase phases, while CuSe formed in a hexagonal phase. Both TiO2 and CuSe were observed to have agglomerated particles with indistinct boundaries. The optical bandgap shifted towards the visible region from 3.25 eV (pure TiO2) to 2.91 eV with increasing the amount of CuSe in the composites. The photocatalytic activity of TiO2, CuSe, and TiO2-CuSe composites was evaluated by monitoring MB degradation, with the composites outperforming the individual components under visible light. Notably, the TiO2-20% CuSe composite (AK-4) demonstrated superior efficiency, removing 98% of MB in just 70 minutes. The photocatalysts also exhibited enhanced antibacterial properties, effectively reducing E. coli colonies from 1.71 × 1012 CFU mL-1 (pure TiO2) to 1 × 1010 CFU mL-1 for the AK-4 composite. This study suggests that visible light-activated TiO2-CuSe composites could be effective for both water purification and bacterial infection control.

Genito Urinary Infection and Urinary Tract Infection in Patients with Type 2 Diabetes Mellitus Receiving SGLT2 Inhibitors: Evidence from a Systematic Literature Review of Landmark Randomized Clinical Trial.

BACKGROUND AND PURPOSE: SGLT2 inhibitors are class of drugs that are used in adults with type 2 diabetes through a novel mechanism of action by reducing renal tubular glucose reabsorption, leading to a reduction in blood glucose without stimulating insulin release. In this systematic review, we report the effects of treatment with SGLT2 inhibitors on urinary tract infection (UTI) and genitourinary infection (GUI). METHOD: The study integrated data from landmark trials of SGLT2 inhibitors (CANVAS, CREDENCE, DECLARE-TIMI 58, and EMPA-REG) to interpret the association of SGLT2 inhibitors with genital infection (GI) and UTI. We reported the review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The primary outcome was a composite of participants reporting UTI and GUI prescribed on SGLT2 inhibitors. RESULTS: The analysis of four studies involving 38,723 participants revealed incidences of both UTIs and GUI. In the SGLT2 inhibitor group, comprising 21,266 participants, 222 (1.04%) experienced UTIs, and 477 (2.24%) reported GUI. In contrast, among the placebo group consisting of 17,457 participants, 201 (1.15%) reported UTIs, and 70 (0.40%) reported genital infections. These findings underscore the elevated risk associated with SGLT2 inhibitor use, particularly regarding GUI, necessitating careful consideration in clinical practice and patient management strategies. CONCLUSION: The incidence of UTIs and particularly more pronounced GUI associated with SGLT2 inhibitors highlights the importance of careful risk assessment and monitoring in clinical decision-making, underscoring the need for patient management strategies.

Isolation of a novel isoprenylated phenolic compound and neuroprotective evaluation of Dodonaea viscosa extract against cerebral ischaemia-reperfusion injury in rats.

Dodonaea viscosa grows widely in Saudi Arabia, but studies evaluating its neuroprotective activity are lacking. Thus, this study aimed to isolate and identify the secondary metabolites and evaluate the neuroprotective effects of D. viscosa leaves. The isolation and identification of phytochemicals were performed using chromatographic and spectroscopic techniques. The neuroprotective potential of the extract was evaluated against focal cerebral ischaemia-reperfusion injury in rat model. Neurobehavioural deficits in the rats were evaluated, and their brains were harvested to measure infarct volume and oxidative biomarkers. Results revealed the presence of three compounds: a novel isoprenylated phenolic derivative that was elucidated as 4-hydroxy-3-(3'-methyl-2'-butenyl) phenyl 1-O-ß-D-apiosyl-(1''' â†’ 6'')- ß-D-glucopyranoside (named Viscomarfadol) and two known compounds (isorhamnetin-3-O-rutinoside and epicatechin (4-8) catechin). Pre-treatment of the rats with the extract improved neurological outcomes. It significantly reduced neurological deficits and infarct volume; significantly reduced lipid peroxidation, as evidenced by decreased malondialdehyde levels; and significantly elevated antioxidant (superoxide dismutase, catalase, and glutathione) activities. These results indicate that D. viscosa is a promising source of bioactive compounds that can improve neurological status, decrease infarct volume, and enhance antioxidant activities in rats with cerebral ischaemic injury. Thus, D. viscosa could be developed into an adjuvant therapy for ischaemic stroke and other oxidative stress-related neurodegenerative disorders. Further investigations are warranted to explore other bioactive compounds in D. viscosa and evaluate their potential neuroprotective activities.

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.

Chemical constituents from Centaurothamnus maximus and their antimicrobial activity.

A new sesquiterpene lactone, 3ß,10α-dihydroxy-10ß-(hydroxymethyl)-8α-(4-hydroxymethacrylate)-1αH,5αH,6ßH,7αH-guai-4(15), 11(13)-dien-6,12-olide (1), along with twenty-one known compounds, were identified from the aerial parts of Centaurothamnus maximus. The structures of the isolated compounds were elucidated on the basis of spectroscopic evidences and correlated with known compounds. Compounds (2, 3, 5‒13 and 15‒22) were identified from C. maximus for the first time. Antibacterial and antifungal activities of the isolated compounds were tested using the agar disc diffusion method. Compounds that demonstrated promising antimicrobial activity were evaluated for their minimum inhibitory concentration (MIC). The results showed that compounds 3 and 7 were the most effective antibacterial compounds against B. subtilis ATCC 6633, S. aureus ATCC 25923 and S. pyogenes ATCC 27736, with MIC estimates between 8 and 32 mg/mL. In addition, compound 2 exhibited the strongest antifungal activity against C. albicans ATCC 14243 and C. krusei ATCC 14243 with MIC 8 mg/mL.

Twenty-two compounds were first isolated from Centaurothamnus maximusThe structure of the new sesquiterpene lactone, thamnolide (1), was established.Antibacterial and antifungal activities were tested for the isolated compounds.Compounds 3 and 7 were the strongest antibacterial compounds whilst 2 exhibited the strongest antifungal activity.

Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.

RATIONALE AND OBJECTIVES: Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We developed and evaluated the clinical feasibility of a deep learning algorithm to generate digitally reconstructed tomography (DRT) images of the chest from digitally reconstructed frontal and lateral radiographs (DRRs) and use them to detect SPNs. METHODS: This single-institution retrospective study included 637 patients with noncontrast helical CT of the chest (mean age 68 years, median age 69 years, standard deviation 11.7 years; 355 women) between 11/2012 and 12/2020, with SPNs measuring 10-30 mm. A deep learning model was trained on 562 patients, validated on 60 patients, and tested on the remaining 15 patients. Diagnostic performance (SPN detection) from planar radiography (DRRs and CT scanograms, PR) alone or with DRT was evaluated by two radiologists in an independent blinded fashion. The quality of the DRT SPN image in terms of nodule size and location, morphology, and opacity was also evaluated, and compared to the ground-truth CT images RESULTS: Diagnostic performance was higher from DRT plus PR than from PR alone (area under the receiver operating characteristic curve 0.95-0.98 versus 0.80-0.85; p < 0.05). DRT plus PR enabled diagnosis of SPNs in 11 more patients than PR alone. Interobserver agreement was 0.82 for DRT plus PR and 0.89 for PR alone; and interobserver agreement for size and location, morphology, and opacity of the DRT SPN was 0.94, 0.68, and 0.38, respectively. CONCLUSION: For SPN detection, DRT plus PR showed better diagnostic performance than PR alone. Deep learning can be used to generate DRT images and improve detection of SPNs.

Assessment of the knowledge beliefs and associated factors among Saudi adults towards blood donation in Saudi Arabia.

BACKGROUND: Blood transfusion is essential in the treatment of a wide range of illnesses. There are two sorts of donors in the blood donation system voluntary and replacement donors. OBJECTIVES: In this study, we examined Saudi adults' knowledge, beliefs, and associated factors towards blood donation in Saudi Arabia. METHODS: A cross-sectional web-based survey was conducted over three months between November 2019 & January 2020 among the general public, using structured self-administered 18-items online questionnaires. A descriptive analysis was performed, a chi-square test was conducted to determine the relationships between the variables. A p-value <0.05 was considered statistically significant. RESULTS: A total of 364 respondents (93.1%) believed that blood donation is an important responsibility of every individual. When asked about the reason for previous donations 261(66.8%) said voluntary while approximately 130 (33.2%) donated for their families and friends. Fear of needles 91 (23.3%), fear of infection 53 (13.6%) a lack of time 88 (22.5%) were common barriers, and 270 (69.1%) agreed that token gifts should be given to donors. In this study, 71.1% (n = 278) were found to have good knowledge, and 96.7% (n = 378) found positive beliefs towards blood donation. The knowledge is significantly associated with being a male gender (p < 0.049), and the educational level of the participants (p < 0.003). positive beliefs were significantly associated with young donors (p < 0.045). CONCLUSION: These outcomes indicate that the Saudi public has positive beliefs and acceptable knowledge about blood donation and its importance in the society and health care system. Furthermore, educational programs should be done to increase the level of awareness about blood donation and its significance.

Attitudes and Associated Demographic Factors Contributing towards the Abuse of Illicit Drugs: A Cross-Sectional Study from Health Care Students in Saudi Arabia.

Background and objective: The purpose of this study is to compare the attitudes, views, and factors that influence drug abuse among pharmacy and nursing students at a Saudi Arabian university. Materials and Methods: A cross-sectional study, was conducted among pharmacy and nursing students who are currently enrolled in the respective courses at the study site. The data were collected over 4 months from August to November 2019 using structured self-administered paper-based questionnaires. Results: Among the participants, pharmacy students accounted for 184 (58.2%) while 132 (41.8%) of the students were from nursing. More than a third of the students 129, (40.8%) smoked cigarettes. The majority of pharmacy (80.4%) and nursing students (67.4%) reported having undertaken a drug misuse course in college. Among the participants, 132 (41.7%) stated that an offer from friends, followed by joy seeking 129 (40.8%), parents' divorce 126 (39.8%), having access to drugs 125 (39.5%), family issues 110 (34.8%), 66 (20.8%) having a family member who is addicted, and 101 (31.9%) reported curiosity to be the factors regarding the use of abusive drugs. Transient euphoria (75.9%) followed by depression 197 (62.3%) was the most prevalent physical or psychological change that occurred following drug use. The family size and father's education have significantly affected the attitudes scores of the students (F = 5.188; p = 0.0001). Conclusion: In this study, joy-seeking, access to drugs, and family issues were found to be the major factors listed as reasons for drug abuse, with some of them being controllable or reversible. Educating about the adverse outcomes of abused drugs is warranted.

PI3K-AKT Pathway Modulation by Thymoquinone Limits Tumor Growth and Glycolytic Metabolism in Colorectal Cancer.

Colorectal cancer (CRC) is the third leading cause of death in men and the fourth in women worldwide and is characterized by deranged cellular energetics. Thymoquinone, an active component from Nigella sativa, has been extensively studied against cancer, however, its role in affecting deregulated cancer metabolism is largely unknown. Further, the phosphoinositide 3-kinase (PI3K) pathway is one of the most activated pathways in cancer and its activation is central to most deregulated metabolic pathways for supporting the anabolic needs of growing cancer cells. Herein, we provide evidence that thymoquinone inhibits glycolytic metabolism (Warburg effect) in colorectal cancer cell lines. Further, we show that such an abrogation of deranged cell metabolism was due, at least in part, to the inhibition of the rate-limiting glycolytic enzyme, Hexokinase 2 (HK2), via modulating the PI3/AKT axis. While overexpression of HK2 showed that it is essential for fueling glycolytic metabolism as well as sustaining tumorigenicity, its pharmacologic and/or genetic inhibition led to a reduction in the observed effects. The results decipher HK2 mediated inhibitory effects of thymoquinone in modulating its glycolytic metabolism and antitumor effects. In conclusion, we provide evidence of metabolic perturbation by thymoquinone in CRC cells, highlighting its potential to be used/repurposed as an antimetabolite drug, though the latter needs further validation utilizing other suitable cell and/or preclinical animal models.

Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography.

PURPOSE: The aim of this study was to assess racial/ethnic and socioeconomic disparities in the difference between atherosclerotic vascular disease prevalence measured by a multitask convolutional neural network (CNN) deep learning model using frontal chest radiographs (CXRs) and the prevalence reflected by administrative hierarchical condition category codes in two cohorts of patients with coronavirus disease 2019 (COVID-19). METHODS: A CNN model, previously published, was trained to predict atherosclerotic disease from ambulatory frontal CXRs. The model was then validated on two cohorts of patients with COVID-19: 814 ambulatory patients from a suburban location (presenting from March 14, 2020, to October 24, 2020, the internal ambulatory cohort) and 485 hospitalized patients from an inner-city location (hospitalized from March 14, 2020, to August 12, 2020, the external hospitalized cohort). The CNN model predictions were validated against electronic health record administrative codes in both cohorts and assessed using the area under the receiver operating characteristic curve (AUC). The CXRs from the ambulatory cohort were also reviewed by two board-certified radiologists and compared with the CNN-predicted values for the same cohort to produce a receiver operating characteristic curve and the AUC. The atherosclerosis diagnosis discrepancy, Δvasc, referring to the difference between the predicted value and presence or absence of the vascular disease HCC categorical code, was calculated. Linear regression was performed to determine the association of Δvasc with the covariates of age, sex, race/ethnicity, language preference, and social deprivation index. Logistic regression was used to look for an association between the presence of any hierarchical condition category codes with Δvasc and other covariates. RESULTS: The CNN prediction for vascular disease from frontal CXRs in the ambulatory cohort had an AUC of 0.85 (95% confidence interval, 0.82-0.89) and in the hospitalized cohort had an AUC of 0.69 (95% confidence interval, 0.64-0.75) against the electronic health record data. In the ambulatory cohort, the consensus radiologists' reading had an AUC of 0.89 (95% confidence interval, 0.86-0.92) relative to the CNN. Multivariate linear regression of Δvasc in the ambulatory cohort demonstrated significant negative associations with non-English-language preference (ß = -0.083, P < .05) and Black or Hispanic race/ethnicity (ß = -0.048, P < .05) and positive associations with age (ß = 0.005, P < .001) and sex (ß = 0.044, P < .05). For the hospitalized cohort, age was also significant (ß = 0.003, P < .01), as was social deprivation index (ß = 0.002, P < .05). The Δvasc variable (odds ratio [OR], 0.34), Black or Hispanic race/ethnicity (OR, 1.58), non-English-language preference (OR, 1.74), and site (OR, 0.22) were independent predictors of having one or more hierarchical condition category codes (P < .01 for all) in the combined patient cohort. CONCLUSIONS: A CNN model was predictive of aortic atherosclerosis in two cohorts (one ambulatory and one hospitalized) with COVID-19. The discrepancy between the CNN model and the administrative code, Δvasc, was associated with language preference in the ambulatory cohort; in the hospitalized cohort, this discrepancy was associated with social deprivation index. The absence of administrative code(s) was associated with Δvasc in the combined cohorts, suggesting that Δvasc is an independent predictor of health disparities. This may suggest that biomarkers extracted from routine imaging studies and compared with electronic health record data could play a role in enhancing value-based health care for traditionally underserved or disadvantaged patients for whom barriers to care exist.

Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19.

We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model's performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85-0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79-0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making.

Diabetes Mellitus during the Pandemic Covid-19: Prevalence, Pathophysiology, Mechanism, and Management: An updated overview.

BACKGROUND: Diabetes mellitus (DM) is among the most frequently reported comorbidities in patients tainted with the pandemic coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With a high pervasiveness of diabetes mellitus, there is an urgency to understand the special aspects of COVID-19 in hyperglycemic patients. Diabetic patients are at higher risk than the general population of viral or bacterial infections, thus require special attention since diabetes is linked with severe, critical, and lethal modes of COVID-19. OBJECTIVE: The objective of this study was to focus on epidemiology, pathophysiology, mechanism, and management of DM with COVID-19. METHODS: The search was carried out on databases portals such as Pubmed, EMBASE, Google Scholar, and CINAHL with the keywords, i.e., COVID-19, coronavirus, SARS-CoV-2, diabetes, covid-19, etc. Result: DM and COVID-19 disease conditions can impact each other in terms of clinical progression and outcome. Available laboratory/clinical observations suggest that hyperglycemia-induced immune dysfunction, inflated lactate grades, and cytokines storm may play critical roles in the seriousness of COVID-19 in patients with diabetes; however, the exact mechanisms linking diabetes and COVID-19 remain to be further clarified. CONCLUSION: Standards to constrain the disease spread at the individual and community level are the key to extenuate the speedily rising pandemic, while definitive treatment, like plasma therapy, chemoprophylaxis, or vaccine for COVID-19, has yet to be discovered.

Amelioration of Experimental Hepatotoxicity in Rats by Portulaca oleracea Linn. from Kashmir Himalaya.

BACKGROUND: Traditionally, Portulaca oleracea Linn. is used for treating abscesses, dysentery and liver diseases. In addition, recent studies have reported its efficacy as an analgesic, as neuroprotective, anti-inflammatory, bronchodilatory, and anticancer agent, besides antioxidant, wound healing and other important pharmacological actions. AIMS AND OBJECTIVES: For curing liver diseases, Chinese use juice from the fresh leaves of Portulaca oleracea Linn. Hence, to prove this claim, an in-vivo hepatoprotective study of extracts from Portulaca oleracea Linn. against carbon tetrachloride hepatotoxic rats was carried out. METHODS: The in-vitro antioxidant activity by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay and hepatoprotective activity of extracts of Portulaca oleracea Linn. was assessed against the carbon tetrachloride-induced toxicity rat model. RESULTS: The extracts of Portulaca oleracea Linn produced concentration-dependent percentage inhibition of 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical and most significantly, by methanol extract. Among the extracts, methanol extract showed a significant decrease in Serum glutamic oxaloacetic transaminase (SGOT), Serum glutamic pyruvic transaminase (SGPT), alkaline phosphatase (ALP) levels and a significant increase in total protein (TP) levels against carbon tetrachloride-induced liver toxicity. CONCLUSION: From the results obtained, it was concluded that methanol extract of Portulaca oleracea Linn. possesses significant DPPH free radical scavenging and hepatoprotective potential.

Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning.

RATIONALE AND OBJECTIVES: The clinical prognosis of outpatients with coronavirus disease 2019 (COVID-19) remains difficult to predict, with outcomes including asymptomatic, hospitalization, intubation, and death. Here we determined the prognostic value of an outpatient chest radiograph, together with an ensemble of deep learning algorithms predicting comorbidities and airspace disease to identify patients at a higher risk of hospitalization from COVID-19 infection. MATERIALS AND METHODS: This retrospective study included outpatients with COVID-19 confirmed by reverse transcription-polymerase chain reaction testing who received an ambulatory chest radiography between March 17, 2020 and October 24, 2020. In this study, full admission was defined as hospitalization within 14 days of the COVID-19 test for > 2 days with supplemental oxygen. Univariate analysis and machine learning algorithms were used to evaluate the relationship between the deep learning model predictions and hospitalization for > 2 days. RESULTS: The study included 413 patients, 222 men (54%), with a median age of 51 years (interquartile range, 39-62 years). Fifty-one patients (12.3%) required full admission. A boosted decision tree model produced the best prediction. Variables included patient age, frontal chest radiograph predictions of morbid obesity, congestive heart failure and cardiac arrhythmias, and radiographic opacity, with an internally validated area under the curve (AUC) of 0.837 (95% CI: 0.791-0.883) on a test cohort. CONCLUSION: Deep learning analysis of single frontal chest radiographs was used to generate combined comorbidity and pneumonia scores that predict the need for supplemental oxygen and hospitalization for > 2 days in patients with COVID-19 infection with an AUC of 0.837 (95% confidence interval: 0.791-0.883). Comorbidity scoring may prove useful in other clinical scenarios.

Box-Behnken Design (BBD)-Based Optimization of Microwave-Assisted Extraction of Parthenolide from the Stems of Tarconanthus camphoratus and Cytotoxic Analysis.

Parthenolide, a strong cytotoxic compound found in different parts of Tarchonanthus camphoratus which motivated the authors to develop an optimized microwave-assisted extraction (MEA) method using Box-Behnken design (BBD) for efficient extraction of parthenolide from the stem of T. camphoratus and its validation by high-performance thin-layer chromatography (HPTLC) and cytotoxic analysis. The optimized parameters for microwave extraction were determined as: 51.5 °C extraction temperature, 50.8 min extraction time, and 211 W microwave power. A quadratic polynomial model was found the most suitable model with R2 of 0.9989 and coefficient of variation (CV) of 0.2898%. The high values of adjusted R2 (0.9974), predicted R2 (0.9945), and signal-to-noise ratio (74.23) indicated a good correlation and adequate signal, respectively. HPTLC analyzed the parthenolide (Rf = 0.16) content in T. camphoratus methanol extract (TCME) at λmax = 575 nm and found it as 0.9273% ± 0.0487% w/w, which was a higher than expected yield (0.9157% w/w). The TCME exhibited good cytotoxicity against HepG2 and MCF-7 cell lines (IC50 = 30.87 and 35.41 µg/mL, respectively), which further supported our findings of high parthenolide content in TCME. This optimized MAE method can be further applied to efficiently extract parthenolide from marketed herbal supplements containing different Tarconanthus species.

Phytochemical screening and anti-oxidant activity of Sargassum wightii enhances the anti-bacterial activity against Pseudomonas aeruginosa.

In this study, the phytochemical, phenolic, flavonoid and bioactive compounds were successfully screened from crude extract of Sargassum wightii by LC-MS analysis after NIST interpretation. Bacterial growth inhibition study result was shown with 24 mm zone inhibition at 200 µg/mL concentration against P. aeruginosa. The increased phenolic content was much closed to gallic acid and the range was observed at 250 µg/mL concentration. In addition, flavonoid contents of the algae extract was indicated more significant with rutin at 200 µg/mL. In result, both the phenolic and flavonoid contents of the extract were more correlated with gallic acid and rutin. Further, the total anti-oxidant and DPPH radical scavenging activities were shown increased activity at 200 µg/mL concentrations. Furthermore, the excellent anti-bacterial alteration result was observed at 200 µg/mL concentration by minimum inhibition concentration. Therefore, the result was revealed that the marine algae Sargassum wightii has excellent phytochemical and anti-oxidant activities, and it has improved anti-bacterial activity against P. aeruginosa.

Design, Synthesis, Molecular Modelling, and Biological Evaluation of Oleanolic Acid-Arylidene Derivatives as Potential Anti-Inflammatory Agents.

INTRODUCTION: Oleanolic acid, a pentacyclic triterpenic acid, is widely distributed in medicinal plants and is the most commonly studied triterpene for various biological activities, including anti-allergic, anti-cancer, and anti-inflammatory. METHODS: The present study was carried out to synthesize arylidene derivatives of oleanolic acid at the C-2 position by Claisen Schmidt condensation to develop more effective anti-inflammatory agents. The derivatives were screened for anti-inflammatory activity by scrutinizing NO production inhibition in RAW 264.7 cells induced by LPS and their cytotoxicity. The potential candidates were further screened for inhibition of LPS-induced interleukin (IL-6) and tumour necrosis factor-alpha (TNF-α) production in RAW 264.7 cells. RESULTS: The results of in vitro studies revealed that derivatives 3d, 3e, 3L, and 3o are comparable to that of the oleanolic acid on the inhibition of TNF-α and IL-6 release. However, derivative 3L was identified as the most potent inhibitor of IL-6 (77.2%) and TNF-α (75.4%) when compared to parent compound, and compounds 3a (77.18%), 3d (71.5%), and 3e (68.8%) showed potent inhibition of NO than oleanolic acid (65.22%) at 10µM. Besides, from docking score and Cyscore analysis analogs (3e, 3L, 3n) showed greater affinity towards TNF-α and IL-1ß than dexamethasone. CONCLUSION: Herein, we report a series of 15 new arylidene derivatives of oleanolic acid by Claisen Schmidt condensation reaction. All the compounds synthesized were screened for their anti-inflammatory activity against NO, TNF-α and IL-6. From the data, it was evident that most of the compounds exhibited better anti-inflammatory activity.

The Reproductive Toxicity Associated with Dodonaea viscosa, a Folk Medicinal Plant in Saudi Arabia.

Dodonaea viscosa is a medicinal plant which is being used to treat various diseases in humans. The available safety data suggest that the plant does not produce any side effects, or toxicity, in tested adult experimental animals. However, the influence of D. viscosa on fetus or embryonic development is largely not known. This study was conducted in order to find out the reproductive toxicity of D. viscosa in experimental animals. Zebrafish embryos were used as the in vivo developmental toxicity animal model. Methanolic crude extract, hexane, chloroform, and butanol fractions were prepared from the leaves of D. viscosa. Zebrafish embryos were exposed to serial dilution of crude extract and other fractions. The crude extract and hexane fraction induced higher level of toxicity in zebrafish embryos as compared to chloroform and butanol fractions. The phenol and flavonoid estimation revealed that crude leaves extract and hexane fractions had lower content of phenol and flavonoid. Two major compounds, phytol and methyl ester, of hexadecanoic acid were identified by gas chromatography and mass spectrophotometry (GC-MS) analysis. More detailed studies are needed to check the toxicity of D. viscosa in pregnant experimental animals; however, the results from this study have shown that D. viscosa possesses reproductive toxicity and its use and doses must be carefully monitored in pregnant patients.