Major Indices Research Articles

Influence of Fermentation Time on the Chemical and Functional Composition of Different Cocoa Clones from Southern Colombia

Cocoa bean quality depends mainly on genetic material, edaphoclimatic factors and post-harvest processes such as fermentation. The impact of the fermentation process on the chemical and functional composition of different cocoa clones grown in southern Colombia was analyzed. A factorial design with repeated measures over time was used to analyze the effect of clone and fermentation time on chemical characteristics (bromatology, phenolic compounds and antioxidant activity). The bromatological characteristics showed significant differences between clones and fermentation time. Clones EET-8 and CCN-51 showed higher contents of acidity (0.51%), fat (63.61%), protein (12.85%) and carbohydrates (1.63 mg g−1). Moisture, acidity and sucrose increased their values between day 4 and day 6 of fermentation (p < 0.05). Phenolic compounds and antioxidant activity had significant differences between clones, where clones CCN-51 and ICS-95 had high contents of phenols (64.56 mg g−1), flavonoids (3.30 mg g−1) and DPPH reducing capacity (325.55 µmol g−1). In this sense, we consider the FSV-41 clone as the major grain quality index based on the results of chemical composition at the bromatological level, antioxidant activity and phenolic compounds.

Complex treatment of destructive pulmonary tuberculosis using inhaled tauractant therapy

Introduction. Chemotherapy is the main component used in the treatment of tuberculosis. In addition to chemotherapy, pathogenetic treatment holds a special place in the complex treatment. The inhaled tauractant therapy restores phospholipid content on the surface of the alveolar epithelium, stimulates the involvement of additional lung parenchyma areas in breathing, facilitates elimination of toxic substances together with sputum from the alveolar space and protects the alveolar epithelium against injury caused by chemical and physical agents. Thereby it promotes acceleration of the lung parenchyma recovery processes.Aim. To study the experience of using Surfactant-BL in patients with destructive pulmonary tuberculosis receiving a standard chemotherapy regimen, taking into account the drug susceptibility for Mycobacterium tuberculosis.Materials and methods. A study was conducted involving 10 patients with destructive pulmonary tuberculosis who were diagnosed with it for the first time in their lives. They received treatment in Respiratory Tuberculosis Patients Department No. 4 of the Budgetary Healthcare Institution of Omsk Region – Clinical TB Dispensary in 2022–2023.Results. Tauractant showed a pronounced positive effect on the destructive tuberculosis process in patients who already received a standard chemotherapy regimen. Inhaled therapy promoted acceleration of the cavity closure.Conclusion. The addition of inhaled tauractant therapy to the standard chemotherapy regimen in patients with multidrug resistance (MDR) and pre-extensively drug-resistant (pre-XDR) respiratory TB significantly accelerated the TB cavity closure. The severity of intoxication and respiratory syndrome decreased in terms of clinical symptoms (relief of shortness of breath and cough). Spirography showed an increase in major indices of the pulmonary function test monitored at 2and 4-month follow-up.

Perioperative loss of the psoas major muscle area index in elderly patients with hip fracture: spinal anesthesia versus general anesthesia-a retrospective cohort study.

In hip fracture patients aged ≥ 80years, we investigated whether the perioperative reduction in the psoas major muscle index (PMI) for spinal anesthesia was less than that for general anesthesia. A total of 262 patients surgically treated for intertrochanteric or femoral neck fractures between August 2015 and August 2022 were enrolled. After adjusting for propensity score matching, 50 patients were included in this analysis. After matching, patients were divided into those receiving spinal or general anesthesia. We measured the psoas major muscle area (PMA) by adjusting for the patient's height as PMA (cm2) divided by height (m) squared. The adjusted PMA was defined as the PMI. We calculated the variability in PMI (ΔPMI) before and after surgery. The primary outcome was the proportion of patients with a > 10% reduction in ΔPMI. The secondary outcomes were the mean ΔPMI and estimated factors affecting the postoperative reduction in the PMI. We compared the primary and secondary outcomes between spinal and general anesthesia. The proportion with a > 10% reduction in ΔPMI did not differ between spinal and general anesthesia (36.0% vs. 40.0%, odds ratio:1.19, 95% CI:0.38-3.72, p = 0.31). The ΔPMI did not differ between spinal and general anesthesia (-8.7% ± 7.9% vs. -8.9% ± 8.3%, p = 0.93). The factors affecting the postoperative reduction in the PMI were male sex, preoperative non-sarcopenia, and intramedullary nailing. There was no significant difference in ΔPMI between hip fracture patients ≥ 80years of age receiving spinal versus general anesthesia undergoing surgical treatment.

Comparing actigraphy and diary to measure daily and average sleep in firefighters: a Bland-Altman analysis.

This study sought to examine the relationship between actigraphy and the Consensus Sleep Diary to contribute information on their concurrent validity in a sample of career firefighters. Sixty firefighters were recruited from a large, urban fire department in the southwest United States that utilizes a fire-based emergency medical services system and a 5/6 shift schedule. A total of 329 differences were recorded during participants' 6-day between-shift recovery period. Data was collected utilizing the two most common forms of sleep analysis in an outpatient setting, wrist actigraphy (Actiwatch-2) and the Consensus Sleep Diary. Nine major sleep indices were computed: wake time after sleep onset, total sleep time, sleep onset latency, sleep offset, in-bed time, lights-off time, out-of-bed time, wake time, and sleep efficiency. Firefighters overestimated sleep efficiency and underestimated wake after sleep onset by values that were greater than the American Academy of Sleep Medicine a priori clinical significance thresholds. All indices showed very broad limits of agreement. For example, the 95% confidence interval for diary and actigraphic total sleep time estimates fell within a 4.7-hour range. Firefighters receiving recovery sleep between tours demonstrated significantly large disagreements between their daily self-reported sleep and measured actigraphic sleep. Sleep findings from actigraphic and Consensus Sleep Diary sleep assessments in this population should be interpreted cautiously until each method is compared against other reliable sleep analysis methods. Currently it is unclear if clinicians are using properly validated tools when diagnosing shift work disorder or other sleep disorders in firefighters. Marmis R, McGoldrick-Ruth L, Kelly MR, Haynes PL. Comparing actigraphy and diary to measure daily and average sleep in firefighters: a Bland-Altman analysis. J Clin Sleep Med. 2024;20(4):497-503.

Major dietary patterns and dietary inflammatory index in relation to dyslipidemia using cross-sectional results from the RaNCD cohort study

Dyslipidemia can increase the risk of heart attack and stroke due to the restriction of blood flow through the blood vessels. Dietary modification is an appropriate approach to reducing this phenomenon. This cross-sectional study aimed to evaluate major dietary patterns and the dietary inflammatory index (DII) in relation to dyslipidemia. 5954 participants in the Ravansar non-communicable diseases (RaNCD) cohort study were eligible for this study. Dyslipidemia was diagnosed based on the lipid profile under consideration of the RaNCD physician. Dietary patterns were assessed by principal component analysis. The three identified dietary patterns included (1) plant-based pattern; (2) high protein and sugar pattern; and (3) energy-dense dense pattern. DII was also calculated based on the dietary information from a validated semi-quantitative food frequency questionnaire (FFQ). We found that higher adherence to DII was significantly associated with increased odds of dyslipidemia after adjusting for age, sex, and physical activity (OR: 1.24; CI 95% 1.09–1.42). Additionally, higher adherence to the high protein and sugar diet and an energy-dense diet was significantly associated with higher odds for dyslipidemia (OR: 1.31; CI 95% 1.16–1.49) and (OR: 1.28; CI 95% 1.12–1.46). Nevertheless, according to our results, following plant-based diet had no association with dyslipidemia in both crude and adjusted models. Our findings revealed that greater adherence to DII, a high-protein, high-sugar diet, and an energy-dense diet can have undesirable effects on dyslipidemia.

Analyzing interconnections between national stock exchange India and global markets using data analytics

<span>Financial networks have become increasingly complex and interconnected in the global economy. In this context, the use of data analytics has emerged as a valuable tool for understanding and analyzing the relationships between different financial markets. This paper presents a study using Pearson and partial correlation analysis to explore the interconnections between the National Stock Exchange India (NSEI) and global stock markets. The analysis aims to provide insights into the dynamics and patterns or cross-markets interactions and to identify potential opportunities for investment and risk management that to considering the world’s 43 major indices based on imports and exports. The historical data from 1<sup>st</sup> January 2008 to 30<sup>th</sup> June 2022 was obtained from the public domain. In this study, a Python framework was developed to investigate Pearson and partial correlation-based networks in financial markets. The results showed that these two types of networks are significantly different from each other. Furthermore, the study discovered that the NSEI is closely connected to the Singapore market and is integrated with other markets. These findings emphasize the complexities to financial market relationships and have important implications for investors and policymakers.</span>

Dynamic financial and monetary security risk assessment based on information service security assessment model and blockchain

With the rapid development of blockchain technology, the financial and monetary (FM) blockchain fields also began to collide. Therefore, this study aims to explore the relationship between the two fields and efficiently evaluate the security of financial information. Firstly, this study introduces the theoretical basis of blockchain in the dynamic linkage mechanism of FM and gives the overall framework of digital currency based on blockchain. Meanwhile, the relationship between blockchain and digital finance is empirically analyzed and designed. Secondly, the framework of financial information service security assessment (ISSA) is created using blockchain technology, and the frame of security risk evaluation is verified by taking electronic invoice information as the research object. Finally, the results show that: (1) foreign exchange (forex) events and major stock index decline events have a significant impact on the return of Bitcoin (BTC) in the short term. Moreover, more than 70% of uncertainty events will make BTC’s abnormal return (AR) significantly positive; (2) Under the influence of forex uncertainty events, only one BTC’s AR is remarkably negative in short order, while the other seven times are markedly positive. In the uncertainty events of major stock indexes, only two times are significantly negative, and the other six times are positive. This indicates that uncertain events in the short run make prominent AR of BTC; the proposed blockchain-based ISSA model and assessment index are scientific, feasible, and operable.

How does an innovative decision-making scheme affect the high-quality economic development driven by green finance and higher education?

This research examines the various elements that contribute to the achievement of high-quality economic development (HQED) in China. Specifically, it explores the influence of several major indices, such as the Green Finance Index, Energy Development Index, Higher Education, Technology Market Environment, and Human Capital. In this study, we utilize a Spatial Durbin Model to examine the interdependencies and spatial linkages between various variables, and their combined impact on China's efforts towards achieving HQED. The Green Finance Index serves as an indicator of a nation's dedication to the implementation of financially sustainable practices and investments that align with environmental objectives. The Energy Development Index assesses the extent to which the energy sector contributes to both economic growth and sustainability. The significance of education and skill development in promoting economic advancement is underscored by Higher Education and Human Capital measures. The Technology Market Environment Index examines the impact of the innovation ecosystem on fostering economic growth. The empirical analysis in our study utilizes extensive data sets collected from 30 provinces and regions throughout China. In this study, we analyze the spatial and temporal interactions of the indices, considering the potential spillover effects from adjacent locations. The findings offer significant contributions to understanding the intricate interconnections among green financing, energy development, education, technology, and human capital, and their combined influence on China's continuous endeavor towards achieving high-quality economic growth. Comprehending the intricate interconnections and spatial dynamics holds paramount importance for policymakers and stakeholders who aspire to foster sustainable, inclusive, and ecologically conscientious economic development in China. The results obtained from this study possess the potential to provide valuable insights for evidence-based policy decisions and strategies aimed at promoting the nation's pursuit of HQED objectives.

Adiposity, fat depots and the prediction of stroke.

Despite the progress in research, the utility of clinical assessment for the prediction of stroke is limited. The aim herein, was to evaluate the predictive values of major ultrasound indexes of carotid artery and fat depots for stroke in patients with high and very high cardiovascular (CV) risk. The study group included 364 patients (age: 61.3 ± 7.2 years old) with typical CV risk factors scheduled for elective coronary angiography (2012-2013). A comprehensive baseline assessment included the following ultrasound indexes: carotid artery intima-media thickness (IMT), extra-media thickness (EMT), epicardial (EFT) and pericardial fat thickness (PFT), abdominal subcutaneous (ASF) and visceral fat (AVF) and combined periarterial adipose tissue intima-media adventitia (PATIMA) index. Afterwards, all patients were followed for 80.9 ± 7.1 months. There were 23 strokes and 25 cases with new-onset atrial fibrillation during follow-up. Receiver operating characteristics (ROC) analysis showed, that selected clinical parameters (age, waist circumference [WC], waist-hip ratio [WHR]) and ultrasound indexes (EFT: area under curve [AUC] 0.672, p < 0.01 and PATIMA index: AUC 0.658, p < 0.01) were predictive for stroke. However, their predictive values showed no significant differences (p = NS). The baseline body mass index (BMI) was the only parameter, which showed a prediction for new-onset atrial fibrillation (BMI > 33 kg/m2: sensitivity 65%, specificity 76%). It was found that age, WC and echocardiographic EFT revealed significant predictive values for stroke. Both WC and EFT showed a very high NPV suggesting that they should be implemented into the clinical practice as a tool affirming a very low risk of stroke.

Bank-specific factors, market conditions and the riskiness of Islamic and conventional banks: evidence from recent quantile approaches

PurposeThe purpose of this paper is to investigate how bank-specific factors affect the riskiness of conventional and Islamic banks in response to shocks in major financial indices as market conditions change.Design/methodology/approachThe authors use a multivariate quantile model using daily equity returns data to analyze financial risk spillovers in the values at risk that may occur between major financial indices and the equity prices of conventional and Islamic banks worldwide. Then, using both quantile and quantile-on-quantile models, the authors examine the effects of bank-specific variables such as leverage ratio, bank size, return on equity and capital adequacy ratio on the initial impact of shocks in major global financial indices on bank equity price returns at different quantiles of shocks and bank-specific variables.FindingsThe findings reveal that major financial indices can predict bank stock returns. Moreover, the authors find that the effect of bank-specific factors on the riskiness of banks is heterogeneous in that it depends on the bank type (Islamic vs conventional), the level of banking variable (high vs low) and, more importantly, market conditions.Originality/valueTo the best of the authors’ knowledge, this is the first study that compares the dual banking system with stock market performance while considering bank-specific variables as market conditions change. The results of this study reveal that the effect of bank-specific variables on bank performance varies according to different quantiles of shocks and bank-specific variables. Islamic banks may echo or differ from conventional banks depending on the specific factor under investigation.

Data-driven joint multi-objective prediction and optimization for advanced control during tunnel construction

This research develops a hybrid approach that integrates light gradient boosting machine (LightGBM) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the tunnel boring machine (TBM) performance during excavation. The TBM operational data are first extracted and meta-models are established to estimate the key TBM performance, including the penetration rate, over/under excavation ratio, and energy consumption. An optimization process is proposed by adopting NSGA-II and the technique for order preference by similarity to an ideal solution (TOPSIS) analysis to determine ideal operational parameters. The developed approach acts as a useful tool that assists tunnel construction automation and improves TBM performance under different in-situ conditions. Real data from a tunnel project in Singapore is utilized as a case study to examine the applicability and efficiency of the proposed approach. The results indicate that (1) The proposed meta-model provides reliable estimation with an average RMSE and MAE of 2.604mm/min and 3.402mm/min for TBM’s penetration rate(O1), 0.0211 and 0.0324 for over/under excavation ratio (O2), and 15.512kwh and 23.088kwh for energy consumption (O3), respectively. The prediction accuracy is better than the state-of-the-art methods; (2) The TBM’s performance can be optimized by the proposed approach with an average improvement of 33.14 %, 1.32 %, and 17.95 % for O1 to O3, respectively, and an overall improvement of 39.60 %; (3) The overall reliability of TBM operation improved after optimization with a significant reduction in data variance by 91.16 %, 76.92 %, and 97.35 % for O1 to O3, respectively. This paper contributes to proposing a novel method that integrates LightGBM with NSGA-II in resolving the complex TBM operation problem by considering the major performance indexes including excavation efficiency, safety, and energy consumption during TBM operation.

Covid-19 on stock market performance: evidence from Italy

This paper has explored the impact of the Covid-19 pandemic on the Italian stock market at an industry level, analysing companies listed in the two major stock indexes: MIB 30 and STAR. Using daily firm-level stock prices (from December 2019, until October 31, 2020), we employed an event-study approach to analyse short-term stock market reactions, considering different pandemic windows period. Results showed that stocks reacted negatively to the announcement of the first case in the country, with deep reversal effects when the country was locked down. Monetary policy measures showed potential to ease stock markets: the announcement of Next Generation Agreement highlights the reversed role of Market Capitalization. Firm-specific variables were included in order to make inferences about firm characteristics that emerged as value drivers during the pandemic: in the first lockdown period, a greater company’s capitalization ensured a greater resilience to the Covid-19 shock. Reversals at both an industry and a company level are observed. Results allow to understand how an outbreak of contagious disease affects stock returns in various sectors, helping investors to develop trading strategies to protect their wealth from future epidemics and providing inputs into the assessment of economic vulnerability to pandemic crises.

Dynamic connectedness between investors’ sentiment and asset prices: A comparison between major markets in Europe and USA

In this study, we use the GARCH-MIDAS model and its extensions to provide new insights on the impact of investor sentiment on asset prices focusing on major market indices in Europe and that of USA. Specifically, we account for leverage, thresholds, and structural heterogeneity in the volatility behaviour of the indices. Furthermore, we decompose the total conditional volatility of the indices into short- and long-term components. Our findings indicate that volatility of the sampled indices, at any given period, is notably characterized by the type of news (good/bad), extreme events, and more importantly, investors’ sentiments. We also find that volatility in the United States conveys significant information to the UK and the Euro area. Although the volatility in the UK has little effect on the Euro area, the volatility from the latter however cascades to the UK significantly. Our findings are robust having passed through a battery of diagnostic tests.

Influence of BDNF Val66Met genetic polymorphism in Major Depressive Disorder and Body Mass Index: Evidence from a meta-analysis of 6481 individuals

BackgroundMajor depressive disorder (MDD) and obesity are global health problems that frequently co-occur. Among shared etiological factors, genetic variation at the brain-derived neurotrophic factor (BDNF) gene is interesting since its implication in energy balance regulation, food intake and synaptic function. Thus, the aim of this study was to investigate the influence of the BDNF Val66Met polymorphism in relation to MDD and body mass index (BMI) in two large independent cohorts. MethodsThe sample consisted of 2646 individuals with MDD and 3835 controls from the PISMA-ep and Radiant studies. Linear regressions were performed to test the association between the polymorphism and BMI and the interaction between the polymorphism and MDD on BMI. A meta-analysis across cohorts was conducted. ResultsNo association was found between the polymorphism and BMI. However, we found an association with MDD, showing these individuals higher BMI than controls in both cohorts. No differences were found in BMI depending on Val66Met genotype and no interaction between this polymorphism and MDD in relation to BMI was found. Although a tendency towards an interaction was found in the Radiant sample, the results of the meta-analysis did not support this finding. LimitationsThe use of self-reported height and weight measures to calculate BMI values. ConclusionsWe provide evidence for an association between BMI and MDD confirming previous results. Our meta-analysis including two large cohorts showed no interaction between BDNF, BMI and MDD. Future studies will be needed to confirm the role of this polymorphism in the relationship between BMI and MDD.

Adaptive Grey Wolf Optimization Technique for Stock Index Price Prediction on Recurring Neural Network Variants

In this paper, we propose a Long short-term memory (LSTM) and Adaptive Grey Wolf Optimization (GWO)--based hybrid model for predicting the stock prices of the Major Indian stock indices, i.e., Sensex. The LSTM is an advanced neural network that handles uncertain, nonlinear, and sequential data. The challenges are its weight and bias optimization. The classical backpropagation has issues of dangling on local minima or overfitting the dataset. Thus, we propose a GWO-based hybrid approach to evolve the weights and biases of the LSTM and the dense layers. We have made the GWO more robust by introducing an approach to improve the best possible solution by using the optimal ranking of the wolves. The proposed model combines the GWO with Adam Optimizer to train the LSTM. Apart from the LSTM, we have also implemented the Adaptive GWO on other variants of Recurring Neural Networks (RNN) like LSTM, Bi-Directional LSTM, Gated Recurrent Units (GRU), and Bi-Directional GRU and computed the corresponding results. The Adaptive GWO here evolves the initial weights and biases of the above-discussed neural networks. In this research, we have also compared the forecasting efficiency of our proposed work with a particle-warm optimization (PSO) based hybrid LSTM model, simple Grey-wolf Optimization (GWO), and Adaptive PSO. According to the experimental findings, the suggested model has effectively used the best initial weights, and its results are the best overall.

Global, glocal or local?

Abstract Local (peripheral) social sciences and humanities journals are underrepresented in major indexes due to linguistic, ideological, and disciplinary bias. To seek international visibility without sacrificing their local identity, they are adopting translation-mediated bilingual publishing to construct a new identity. Since bilingual publishing is a new trend, what identity is being constructed is rarely investigated. This article aims to explore the linguistic, content, and communicated identity of those journals. Content analysis was used to review the language policies (websites, article abstracts, and full articles), the composition of editorial teams, pools of contributors, instructions to authors, journal overviews, and website logos or journal covers of sixty-eight peripheral social sciences and humanities journals. The results indicate that the majority are attempting to construct a glocal identity, a hybrid identity to maintain their unique status as a local journal and simultaneously strive for better recognition in the international community. Another finding is that the journals are divided in terms of communicated identity, causing obstacles to the successful construction of a new identity. This study provides evidence on the construction of a glocal identity by bilingually published peripheral journals and has implications for the strategic use of linguistic and non-linguistic resources in identity construction.

Building and testing a digital model for effective investment decisions to form strategies for development of economic entities

Subject. Sanction restrictions sharply decreased possibilities to attract external borrowings and substantiated the expediency of internal investments. This necessitated software tools enabling calculations and investment decisions. The developed a digital model of ISPI (Information System Portfolio Investor) will help make smart investment decisions, including at the government level. Objectives. The study aims to build a digital model to identify the most attractive investment areas at the regional, country, and cross-country level. Methods. The ISPI model is based on the Markowitz portfolio theory, the Profitability-Risk Model (PRM), and optimization methods. For our calculations, we used yields on Major and Sector Indices of the UK, India, China, USA, France, South Africa for 2014–2021, in one-month increments. Results. Using the developed ISPI model, we constructed scatter plots of leading stock market indices and identified the leading sectors of national economies of the studied countries and the most attractive investment areas. We solved the problem of finding a global optimum for the studied countries, differentiated the leading economic sectors by the level of investment risk, determined that an international portfolio is the most preferable for investment. Conclusions. Our ISPI model helps investors identify a region or country for smart investments. The model enables to determine industries in which investing is most justified within the nearest investment horizon. The model also helps identify the most appropriate financial instruments for investing in individual countries.

How do sectoral Islamic equity markets react to geopolitical risk, economic policy uncertainty, and oil price shocks?

This study explores the impact of three major risk and uncertainty indices (Geopolitical Risk (GPR), Economic Policy Uncertainty (EPU), and Oil Market Volatility (OVX)) on the Dow Jones Islamic Market (DJIM) World and the ten major sectoral Islamic equity indices. The investigation further evaluates whether the impact of these uncertainties varies between Islamic sectoral indices and the global benchmark. Our time-varying analysis shows that most Islamic equity indices are more resilient in hedging against GPR and EPU shocks compared to OVX shocks. Furthermore, the Quantile-on-Quantile results establish a positive correlation between DJIM World and most Islamic equity indices with GPR, demonstrating their robust capabilities to hedge against GPR shocks. These hedging abilities are more pronounced in the consumer goods, oil & gas, and financial sectors. The dependence structures between extreme EPU shocks and the DJIM world and consumer goods, financials, healthcare, and industrial sectors are positive only in bearish conditions, suggesting hedging benefits are predominantly limited to lower quantiles. Yet, the basic materials and oil & gas sectors can hedge EPU shocks better than the DJIM world and other sectors, depending on market states. Notably, no Islamic equity indices show resilience against OVX shocks. Our findings have prominent portfolio risk management and policy implications for investors and policymakers. These findings hold significant implications for portfolio risk management and policy strategies for investors and policymakers.

Preparation of Herbal Extracts for Intestinal Immune Modulation Activity Based on In Vitro Screening and In Vivo Evaluation of Zingiber officinale Rosc. Extracts.

Ten traditional herbal extracts effective against diarrhea, infectious diseases, and bacterial activity were selected and analyzed for Peyer's patch cell-mediated intestinal immunomodulatory activity in vitro and in vivo. Among the 10 herbal extracts, Zingiber officinale Rosc. (ZO) extract induced the highest secretion of immunoglobulin A (IgA) and granulocyte macrophage colony-stimulating factor (GM-CSF) in the cells of Peyer's patches. Furthermore, animal experiments showed that IA production was enhanced with the oral administration of ZO extract (100 mg/kg and 300 mg/kg) for 10 days. In addition, 6-, 8-, 10-gingerol, and 6-, 8-, 10-shogaol, the six major index compounds of ZO extract, were analyzed using HPLC. Our study findings confirm the intestinal immunomodulatory activity of ZO extract and lay a strong foundation for future analytical studies aimed at determining the active components of ZO extracts.

Assessment of the Decontamination and Disinfecting Potentials of C. Papaya Synthesized Silver Nanoparticles on Water and Wastewater Samples

The presence of organic, inorganic and biological pollutants in natural water supply are the major indices of water pollution. The application of green nanoparticles in water treatment is one of the promising ways of eradicating these pollutants and providing safe and quality water for domestic, agricultural and industrial purposes. Our research was intended to evaluate the decontamination and disinfecting potentials of C. papaya silver nanoparticles on water and wastewater samples. The green silver nanoparticles were formed using aqueous C. papaya leaf extract and characterized using standard nanotechnological techniques while decontamination and disinfecting potentials of the green silver nanoparticles were examined through physicochemical, heavy metal and bacteriological analysis. The results revealed the spectral and morphological profiles were in conformity with the characteristics of silver nanocrystals. There were significant reductions (P &lt; 0.05) in physicochemical, heavy metal and bacterial qualities of the four water samples at higher (0.5 mg/L) and lower (0.25 mg/L) doses of the green silver nanoparticles and their decontamination and disinfection efficiencies were comparable to the positive control calcium hypochlorite. These findings suggest that C. papaya silver nanoparticles could be exploited in restoring the quality of water and wastewater.