Studies on Tumour Active Compounds with Multiple Metal Centres

Four tumour active trinuclear complexes: DH4Cl: [{trans-PtCl(NH3)2}2m-{trans-Pd( NH3)2(H2N(CH2)4NH2)2]Cl4, DH5Cl: [{trans-PtCl(NH3)2}2m-{trans-Pd( NH3)2(H2N(CH2)5NH2)2]Cl4, DH6Cl: [{trans-PtCl(NH3)2}2m-{trans-Pd( NH3)2(H2N(CH2)6NH2)2]Cl4, DH7Cl: [{trans-PtCl(NH3)2}2m-{trans-Pd(NH3)2-( H2N(CH2)7NH2)2]Cl4 and one dinuclear complex DHD: [{trans-PtCl(NH3)2}�-{ H2N(CH2)6NH2}{trans-PdCl(NH3)2]Cl(NO3), have been prepared and characterised based on elemental analyses, IR, Raman, mass and 1 H NMR spectral measurements. For the trinuclear complexes, the synthesis has been carried out using a step-up method branching out from the central palladium unit. A purity of about 95% has been obtained by repeated dissolution and precipitation. The activity against human cancer cell lines including ovary cell lines: A2780, A2780 cisR , A2780 ZD0473R , non small lung cell line: NCI-H640 and melanoma: Me-10538 have been determined based on MMT assay. Cell uptakes, DNA-binding have been determined for ovary cell lines: A2780, A2780 cisR . The nature of interaction with pBR322 plasmid DNA and ssDNA has been studied for trinuclear complexes DH4Cl, DH5Cl, DH6Cl and DH7Cl and the dinuclear complex DHD. Interaction of DH6Cl with adenine and guanine has also been studied by HPLC. The compounds are found to exhibit significant anticancer activity against cancer cell lines especially ovarian cancer cell lines: A2780, A2780 cisR and A2780 ZD0473R . DH6Cl in which the linking diamine has six carbon atoms is found to be the most active compound. As the number of carbon atoms in thelinking diamine is changed from the optimum value of six, the activity is found to decrease, illustrating the structure-activity relationship. The increase in uptake of the trinuclear complexes in A2780 cell line with the increase in size of the linking diamine coupled with the low molar conductivity values found for the solutions of the compounds suggest that the compounds would remain in solution as undissociated �molecules� and hence could cross the cell membrane by passive diffusion. Much lower resistance factors for the all the multinuclear compounds including DHD as applied to A2780 cisR cell line, as compared to that for cisplatin, suggest that the compounds are able to overcome multiple mechanisms of resistance operating in the cell line. All of the multinuclear complexes are expected to form long-range interstrand GG adducts with DNA, causing irreversible global changes in the DNA conformation but unlike cisplatin do not cause sufficient DNA bending to be recognized by HMG 1 protein. Increasing prevention of BamH1 digestion with the increase in concentration of the multinuclear compounds also provide support to the idea that the compounds because of the formation of a plethora of interstrand GG adducts are able to cause irreversible changes in DNA conformation. The results of the study show that indeed new trinuclear tumour active compounds can be found by replacing the central platinum unit in BBR3464 with other suitable metal units

Successive Drug Therapy for a Very Rare Autosomal Diseases

It is very rare to find reports concerning a drug therapy successively treating chromosomal abnormalities. In this paper, we are reporting a successive use of nitisinone in treating a fatal and very rare autosomal disease called hereditary tyrosinemia type-1 [HT-1]. HT-1 is affecting about one person in 100,000 to 120,000 births worldwide. It is due to a genetic defect in the enzyme fumarylacetoacetate hydroxylase (FAH), which is responsible for the final degradation of tyrosine. Accumulation of tyrosine metabolites is responsible for tissue damage such as liver, kidney, and neural tissues, finally causing the death of the newborn babies in their early months of life if not treated. Fumarylacetoacetate hydrolase gen has mapped on chromosome 15q23-15q25. Since 1992, the initiation of treating HT-1 with nitisinone (NTBC) has become the medical therapy of choice in combination with diet. NTBC therapy has shown a direct correlation between age of initiation and subsequent clinical course. We are reporting three brothers treated safely and successively with NTBC in combination with diet. All of them are in very good conditions. The elder brother is on NTBC since 27 years ago

trans-Bis(3-hy­droxy­pyridine-κN)diiodidoplatinum(II) dimethyl sulfoxide disolvate

In the title compound, [PtI2(C5H5NO)2]·2(CH3)2SO, the PtII ion lies on an inversion center and is coordinated in a slightly distorted square-planar environment by two trans iodide ligands and two pyridine N atoms. In the crystal, complex mol­ecules and solvent dimethyl sulfoxide mol­ecules are linked by inter­molecular O—H⋯O hydrogen bonds

Electroencephalogram Signals for Detecting Confused Students in Online Education Platforms with Probability-Based Features

Article discusses how despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models

The impact of oral health on diet among the ageing population in Saudi Arabia

BACKGROUND: In older adults, there are many factors that determine dietary intake, including an individual's socio-economic status, physical well-being, and general state of health. Another crucial factor is dental status. The aim of this study is to explore how dental status impacts the perceived ability to eat particular foods and the nutrient intake of older adults in Saudi Arabia. METHODS: The study conducted an analysis of the sample gathered from the Saudi Demographic and Health Survey. The data were collected from an online food frequency questionnaire. Data related to health behaviour, general and oral health information, and socio-economic data were collected using an online questionnaire. The oral health status was assessed clinically. Participants in the 60 years and above age category (n = 326) attended clinical examinations, and (n =275) of them completed all elements of the study. To analyse the cross-sectional link between nutrient intake, food selection, and dental status, multiple regression methods were performed. RESULTS: The participants’ mean age was 70.29 years (range 60-104) with an SD of 8.71. 62.6% had 20 or more teeth, 25.2% had less than 20 teeth, and 9.5% were edentulous. Participants with no dentures constituted the largest group (78.8.6%). which means the most participants had natural teeth or were edentulous without dentures. 70.8% only visited the dentist when there is a problem. The mean DMFT for older adults was 15.5 with an SD of 9.4. The edentulous participants were more likely to report having difficulty eating all 15 examined foods listed compared to the dentate participants. There were significant differences in having difficulty eating food, with 95% CI between all 15 foods and all number of teeth groups except for cheese; there was no significant difference between people with 1-19 teeth and 20 or more teeth. Also, the findings demonstrate that edentate, denture-wearing seniors consumed lower levels of important nutrients, which are protein, carbohydrates, fibre, and fat, also calories, compared to dentate people. However, edentulous people not wearing any dentures consumed more nutrients than denture-wearing older adults. The subjects with natural teeth and no dentures generally had an energy intake greater than that recommended by the US government: 2513.6 Kcal compared to 2000 Kcal. This was mirrored in terms of major dietary constituents, with a protein intake of 111.9g compared to 57g and a carbohydrate intake of 341.5g compared to 130g. This population was relatively unusual in achieving the recommended intake of dietary fibre of 29.2g compared to 28g. In contrast, the other participants with compromised oral function (teeth and partial dentures, teeth and complete dentures in one jaw, edentulous with complete dentures, and edentulous and with no denture) had lower than the recommended energy intake at 1244.3–1628.2 Kcal and lower dietary fibre intake (15.5–21.3 g). Their protein intake was close to the dietary recommendation, with participants with full dentures consuming on average 51.9g, and the edentulous without dentures and people wearing partial dentures consuming 69.6g and 67.5g, respectively. In addition, for carbohydrates all participants were above the recommended goal. There were no observed differences between groups in relation to the perceived ability to eat and consume nutrition after socio-demographic and health behavioural had been adjusted, except for age groups. CONCLUSION: In this older adult sample, it can be concluded that weakened dental status has a possible influence on the foods that the individuals choose to eat, and consequently, their consumption of crucial nutrients. Therefore, future studies could concentrate on the development of dental interventions together with dietary counselling. This is likely to encourage individuals in this high-risk population to embrace healthy eating habits

Modeling Behavior and Vaccine Hesitancy for Predicting Daily Vaccination Inoculations Using Trends, Case, Death, and Twitter Sentiment Data

Over the past 100 years, epidemiological models have evolved to incorporate greater facets of the process. With the advent of social networking, massive computational power, population sentiment analysis can now be added to the epidemiological modeling process. Sentiment analysis is greater understanding of the fears, uncertainties, motivation, and trends of the public with respect to vaccination and associated events. Lack of public confidence in the efficacy of models, safety of vaccines, and appropriateness of policies confounds vaccine inoculation prediction. Sentiment analysis of social media is a seminal technique that accesses shared users\u27 contents and tweets on the Twitter platform for daily fast and accurate modeling of public sentiment. As an applied contribution to this science, we present sentiment-based models for predicting United States daily COVID-19 vaccine inoculations. The research methodology encompasses predictive regression models spanning three phases of the U.S. pandemic including a baseline COVID-19 phase, a Delta variant phase, and Omicron variant phase that when combined span the period June 1, 2021, to March 31, 2022. Additionally, the models incorporate U.S. population behavior responses during the CDC recommended first dose interval, second dose interval, and booster intervals. Investigation of variables influencing daily inoculations identified CDC VOC phase, daily cases, daily deaths, and positive and negative Twitter Tweets as statistically significant for first dose and booster dose intervals exceeding a predictive R square of 77% and 84% respectively. The best regression model for the second dose interval proved to be a three variable- phases, cases, and negative tweets - inoculation model that exceeded a predictive R square of 53%. Limiting tweets collection to geolocated tweets does not encompass the entire U.S. Twitter population. However, Kaiser Family Foundation (KFF) surveys results appear to generally support the regression factors common to the First Dose and Booster Dose regression models and their results

Quantifying the Effects of Social Distancing on the Spread of COVID-19

This paper studies the interplay between social distancing and the spread of the COVID-19 disease—a global pandemic that has affected most of the world’s population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system’s dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%

Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration

With social networking enabling the expressions of billions of people to be posted online, sentiment analysis and massive computational power enables systematic mining of information about populations including their affective states with respect to epidemiological concerns during a pandemic. Gleaning rationale for behavioral choices, such as vaccine hesitancy, from public commentary expressed through social media channels may provide quantifiable and articulated sources of feedback that are useful for rapidly modifying or refining pandemic spread predictions, health protocols, vaccination offerings, and policy approaches. Additional potential gains of sentiment analysis may include lessening of vaccine hesitancy, reduction in civil disobedience, and most importantly, better healthcare outcomes for individuals and their communities. In this article, we highlight the evolution of select epidemiological models; conduct a critical review of models in terms of the level and depth of modeling of social media, social network factors, and sentiment analysis; and finally, partially illustrate sentiment analysis using COVID-19 Twitter data

Electroencephalogram Signals for Detecting Confused Students in Online Education Platforms with Probability-Based Features

Online education has emerged as an important educational medium during the COVID-19 pandemic. Despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study makes use of electroencephalogram (EEG) data for student confusion detection for the massive open online course (MOOC) platform. Existing approaches for confusion detection predominantly focus on model optimization and feature engineering is not very well studied. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models. The PBF approach utilizes the probabilistic output from the random forest (RF) and gradient-boosting machine (GBM) as a feature vector to train machine learning models. Extensive experiments are performed by using the original features and PBF approach through several machine learning models with EEG data. Experimental results suggest that by using the PBF approach on EEG data, a 100% accuracy can be obtained for detecting confused students. K-fold cross-validation and performance comparison with existing approaches further corroborates the results

When procrastination pays off: Role of knowledge sharing ability, autonomous motivation, and task involvement for employee creativity

The prime objective of this research was to investigate procrastination as a prospectively constructive element of the creative process among employees working at different hierarchical levels in a Chinese organization. Building on self-determination theory, this research postulates a connection between procrastination and creativity through the incubation of knowledge absorption, autonomous motivation and task engagement as boundary conditions. Data was collected from 213 individuals from the workforce and their immediate managers belonging to a Chinese furniture company; then analyzed with Mplus for simple regression analysis, mediated moderated analyses, and coefficient estimates of all the study variables. The outcomes of this investigation showed an inverse relationship between procrastination with creativity, while creativity being strongest in the medium levels of procrastination; however, when autonomous motivation and/or task engagement are strong, procrastination depicts an inverted-U-shaped association; however, in scenarios where both autonomous motivation and the task engagement are low, procrastination has a negative linear relationship. With the results of this research, we have shown that moderate procrastination has a causal effect on the generation of creative ideas. This research demonstrated that as long as employees had strong autonomous drive or high task engagement, their supervisors awarded them better ratings when they procrastinated moderately on their assignments. Limitations and future research directions were also discussed