Evaluation of the Binding Mechanism of Dietary Phytochemical, Ellagic Acid, with Human Transferrin: Spectroscopic, Calorimetric, and Computational Approaches Targeting Neurodegenerative Diseases.

Human transferrin (Htf) is vital in maintaining iron within the brain cells; any disruption results in the development of neurodegenerative diseases (NDs) and other related pathologies, especially Alzheimer's disease (AD). Ellagic acid (EA), a naturally occurring phenolic antioxidant, possesses neuroprotective potential and is present in a broad variety of fruits and vegetables. The current work explores the binding mechanism of dietary polyphenol, EA, with Htf by a combination of experimental and computational approaches. Molecular docking studies unveiled the binding of EA to Htf with good affinity. Molecular dynamic (MD) simulation further provided atomistic details of the binding process, demonstrating a stable Htf-EA complex formation without causing substantial alterations to the protein's conformation. Furthermore, fluorescence binding measurements indicated that EA forms a high-affinity interaction with Htf. Isothermal titration calorimetric measurements advocated the spontaneous nature of binding and also revealed the binding process to be exothermic. In conclusion, the study deciphered the binding mechanism of EA with Htf. The results demonstrated that EA binds with Htf with an excellent affinity spontaneously, thereby laying the groundwork for potential applications of EA in the realm of therapeutics for NDs in the context of iron homeostasis.

Fabrication of MoS2 restrained magnetic chitosan polysaccharide composite for the photocatalytic degradation of organic dyes.

Chitosan (CS) polysaccharide is expected to exhibit greater ionic conductivity, which can be attributed to its increased amino group content when it is blended with different semiconducting materials. Herein, the work used this conducting ability of chitosan and prepared a heterogeneous MoS2-induced magnetic chitosan (MF@CS) composite via the co-precipitation method, which was used to scrutinize the catalytic performance with Methylene Blue (MB) and Malachite Green (MG) dyes by visible light irradiation. The saturation magnetization value of the MF@CS composite is found to be 7.8 emu/g, which is less when compared to that of pristine Fe3O4 (55.7 emu/g) particles. The bandgap of the MF@CS composite is âˆ¼ 2.17eV, which exceeds the bandgap (Eg) of bare MoS2 of 1.80 eV. The maximum color removal of 96.3 % and 93.4 % for MB and MG dyestuffs is recognized in the exposure of the visible spectrum, respectively. At a starting dye dosage of 30 mg/L, 0.1 g/L of MF@CS, a pH level of 8-11, and 70 min of contact with direct light. The photocatalyst provides extremely good durability for a maximum of five phases. Hence, the MF@CS matrix is a viable and appropriate substance for the efficient treatment of effluents containing dye molecules.

Evaluation of binding mechanism of dietary phytochemical, capsaicin, with human transferrin: targeting neurodegenerative diseases therapeutics.

Human transferrin (htf) plays a crucial role in regulating the balance of iron within brain cells; any disruption directly contributes to the development of Neurodegenerative Diseases (NDs) and other related pathologies, especially Alzheimer's Disease (AD). In recent times, a transition towards natural compounds is evident to treat diseases and this shift is mainly attributed to their broad therapeutic potential along with minimal side effects. Capsaicin, a natural compound abundantly found in red and chili peppers, possess neuroprotective potential. The current work targets to decipher the interaction mechanism of capsaicin with htf using experimental and computational approaches. Molecular docking analysis revealed that capsaicin occupies the iron binding pocket of htf, with good binding affinity. Further, the binding mechanism was investigated atomistically using Molecular dynamic (MD) simulation approach. The results revealed no significant alterations in the structure of htf implying the stability of the complex. In silico observations were validated by fluorescence binding assay. Capsaicin binds to htf with a binding constant (K) of 3.99 × 106 M-1, implying the stability of the htf-capsaicin complex. This study lays a platform for potential applications of capsaicin in treatment of NDs in terms of iron homeostasis.

Molecular and functional insight into focal adhesion kinases: Therapeutic implications for oral malignancies.

Oral carcinoma is the sixth most common cancer globally, with one death occurring every hour. Focal adhesion kinase (FAK) is an intercellular protein tyrosine kinase, a key indicator of the development of oral cancer. FAK overexpression leads to the initiation and significant progression of metastasis in head and neck cancers, indicating its vital role in cancer progression and potential as a biomarker for early oral malignant transformation. The present review elaborates on FAK's function in oral malignancies since it could serve as a biomarker of the initial stages of oral malignant transformation and a possible predictive factor for risk assessment.

Mechanistic insights into the beneficial effects of curcumin on insulin resistance: Opportunities and challenges.

The past couple of decades in particular have seen a rapid increase in the prevalence of type 2 diabetes mellitus (T2DM), a debilitating metabolic disorder characterised by insulin resistance. The insufficient efficacy of current management strategies for insulin resistance calls for additional therapeutic options. The preponderance of evidence suggests potential beneficial effects of curcumin on insulin resistance, while modern science provides a scientific basis for its potential applications against the disease. Curcumin combats insulin resistance by increasing the levels of circulating irisin and adiponectin, activating PPARγ, suppressing Notch1 signalling, and regulating SREBP target genes, among others. In this review, we bring together the diverse areas pertaining to our current understanding of the potential benefits of curcumin on insulin resistance, associated mechanistic insights, and new therapeutic possibilities.

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models.

Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120-400 bar, 308-338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E-06, 1.95E-06, and 3.45E-06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E-06, 2.34E-06, and 5.26E-06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e-05).

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent.

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308-338 K, and pressure 120-400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E-04, 7.40E-05, and 9.73E-06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E-01, DTR shows an error rate equal to 1.63E-01, and GPR as the best mode illustrates 5.06E-02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E-03).

Medicinal Plants of Solanum Species: The Promising Sources of Phyto-Insecticidal Compounds.

Several medicinal plants have the potential to be a promising alternative pharmacological therapy for a variety of human illnesses. Many insects, including mosquitoes, are important vectors of deadly pathogens and parasites, which in the world's growing human and animal populations can cause serious epidemics and pandemics. Medicinal plants continue to provide a large library of phytochemicals, which can be used to replace chemically synthesized insecticides, and utilization of herbal product-based insecticides is one of the best and safest alternatives for mosquito control. Identifying new effective phyto-derived insecticides is important to counter increasing insect resistance to synthetic compounds and provide a safer environment. Solanum genus (Solanaceae family or nightshades) comprises more than 2500 species, which are widely used as food and traditional medicine. All research publications on insecticidal properties of Solanaceae plants and their phytoconstituents against mosquitoes and other insects published up to July 2020 were systematically analyzed through PubMed/MEDLINE, Scopus, EBSCO, Europe PMC, and Google Scholar databases, with focus on species containing active phytoconstituents that are biodegradable and environmentally safe. The current state of knowledge on larvicidal plants of Solanum species, type of extracts, target insect species, type of effects, name of inhibiting bioactive compounds, and their lethal doses (LC50 and LC90) were reviewed in this study. These studies provide valuable information about the activity of various species of Solanum and their phytochemical diversity, as well as a roadmap for optimizing select compounds for botanical repellents against a variety of vectors that cause debilitating and life-threatening human diseases.

Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug.

The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents is known as a breakthrough technology towards developing cost-effective therapeutic drugs. Drug solubility is a significant parameter which must be measured before designing the process. Decitabine belongs to the antimetabolite class of chemotherapy agents applied for the treatment of patients with myelodysplastic syndrome (MDS). In recent years, the prediction of drug solubility by applying mathematical models through artificial intelligence (AI) has become known as an interesting topic due to the high cost of experimental investigations. The purpose of this study is to develop various machine-learning-based models to estimate the optimum solubility of the anti-cancer drug decitabine, to evaluate the effects of pressure and temperature on it. To make models on a small dataset in this research, we used three ensemble methods, Random Forest (RFR), Extra Tree (ETR), and Gradient Boosted Regression Trees (GBRT). Different configurations were tested, and optimal hyper-parameters were found. Then, the final models were assessed using standard metrics. RFR, ETR, and GBRT had R2 scores of 0.925, 0.999, and 0.999, respectively. Furthermore, the MAPE metric error rates were 1.423 × 10-1 7.573 × 10-2, and 7.119 × 10-2, respectively. According to these facts, GBRT was considered as the primary model in this paper. Using this method, the optimal amounts are calculated as: P = 380.88 bar, T = 333.01 K, Y = 0.001073.

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization.

Industrial-based application of supercritical CO2 (SCCO2) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C15H18O3) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO2, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10-4, 7.814 × 10-5, and 1.664 × 10-4 for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10-4, 6.197 × 10-5, and 8.777 × 10-5errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x1 = 40.0, x2 = 338.0, Y = 1.27 × 10-3) vector.

Strategies to Improvise Organ Donor Pool: A Study on the Knowledge, Attitudes, and Performance of Higher Secondary School Teachers Towards the Organ Donation.

Introduction: This study aimed to assess higher secondary school teachers' knowledge, attitude, and performance levels towards organ transplantation and donation (OTD). Teachers have an essential role in giving knowledge to children and teenagers, and they can influence their views. Organ transplantation offers re-life to many patients, yet organ shortages are a global issue. Teachers who influence students' future attitudes regarding organ donation must have a favorable attitude and genuine knowledge. Materials and Methods: The research method was descriptive and cross-sectional. The sample size was 372 school teachers in Villupuram district of Tamilnadu, India, selected using a convenient sampling method. A survey questionnaire was used to assess the knowledge and attitude about OTD, the reason for donating/not donating organs. Multivariate analysis was performed to identify critical variables affecting intent to practice. Results: The teachers' mean scores with SD on knowledge, attitude, and performance were 7.61 ± 2.74, 8.81 ± 2.08, and 0.38 ± 0.11, respectively. The linear regression analysis showed that the knowledge (p < 0.001) and attitude (p < 0.05) of the participants were positively associated with organ donation performance. A significant relationship was also observed between gender (p < 0.036), age (p < 0.01), and education status (p < 0.001) with the performance of the teachers. Lack of family support was the most spelt reason for unwillingness for organ donation. Conclusion: The positive linear correlations underline that having more information may lead to a more optimistic mindset and, as a result, to better practices. Teachers should be provided with overall health teaching campaigns to increase the number of possible organ donors. Teachers serve as role models for students, families, and society by changing their attitudes.

Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug.

These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO2). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E-09, 9.71E-09, and 3.78E-11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning.

Accurate specification of the drugs' solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

Association of Maternal Observation and Motivation (MOM) Program with m-Health Support on Maternal and Newborn Health.

Maternal and child nutrition has been a critical component of health, sustainable development, and progress in low- and middle-income countries (LMIC). While a decrement in maternal mortality is an important indicator, simply surviving pregnancy and childbirth does not imply better maternal health. One of the fundamental obligations of nations under international human rights law is to enable women to endure pregnancy and delivery as an aspect of their enjoyment of reproductive and sexual health and rights and to live a dignified life. The aim of this study was to discover the correlation between the Maternal Observation and Motivation (MOM) program and m-Health support for maternal and newborn health. A comparative study was done among 196 pregnant mothers (study group-94; control group-102 mothers) with not less than 20 weeks of gestation. Maternal outcomes such as Hb and weight gain and newborn results such as birth weight and crown-heel length were obtained at baseline and at 28 and 36 weeks of gestation. Other secondary data collected were abortion, stillbirth, low birth weight, major congenital malformations, twin or triplet pregnancies, physical activity, and maternal well-being. The MOM intervention included initial face-to-face education, three in-person visits, and eight virtual health coaching sessions via WhatsApp. The baseline data on Hb of the mothers show that 31 (32.98%) vs. 27 (28.72%) mothers in the study and control group, respectively, had anemia, which improved to 27.66% and 14.98% among study group mothers at 28 and 36 weeks of gestation (p < 0.001). The weight gain (p < 0.001), level of physical activity (p < 0.001), and maternal well-being (p < 0.01) also had significant differences after the intervention. Even after controlling for potentially confounding variables, the maternal food practices regression model revealed that birth weight was directly correlated with the consumption of milk (p < 0.001), fruits (p < 0.01), and green vegetables (p < 0.05). As per the physical activity and maternal well-being regression model, the birth weight and crown-heel length were strongly related with the physical activity and maternal well-being of mothers at 36 weeks of gestation (p < 0.05). Combining the MOM intervention with standard antenatal care is a safe and effective way to improve maternal welfare while upholding pregnant mothers' human rights.

School-Based Exercise and Life Style Motivation Intervention (SEAL.MI) on Adolescent's Cardiovascular Risk Factors and Academic Performance: Catch Them Young.

There are shreds of evidence of shared biological mechanisms between obesity and hypertension during childhood intoadulthood, and loads of research literature has proven that it will profoundly cost nations' economies and health if neglected. The prevention and early diagnosis of cardiovascular risk factors such as overweight and hypertension is an essential strategy for control, effective treatment and prevention of its' complications. The study aims to assess the effect of school-based Exercise and Lifestyle Motivation Intervention (SEAL-MI) on adolescents' cardiovascular risk factors and academic performance. An experimental study was conducted among 1005 adolescents-520 and 485 were randomly selected for the control and study groups, respectively.A structured interview questionnaire was used to collect demographic details and data related to dietary habits, physical activity, sleep qualityand academic performance. The study group adolescents were given the SEAL-MI for six months, including a school-based rope exercise for 45 min per day for 5 days a week and a motivation intervention related to dietary habits, physical activity, and sleep. Post tests-1 and 2 were done after 3 and 6 months of intervention.The prevalence of overweight among adolescents was 28.73%, and prehypertension was 9.26%. Among overweight adolescents, the prevalence of prehypertension was found to be very high (32.25%). There was a significant weight reduction in post-intervention B.P. (p = 0.000) and improvement in dietary habits, physical activity, sleep (p = 0.000), and academic performance. A significant positive correlation was found between BMI and SBP (p = 0.000) and BMI and academic performance (p = 0.003). The linear regression analyses revealed that the gender (ß: 0.47, 95% CI: 0.39, 0.81), age (ß: 0.39, 95% CI: 0.17, 0.46), family income (ß: 0.2, 95% CI: 0.41, 0.5), residence (ß: 0.19, 95% CI: 0.01, 0.27), and type of family (ß: 0.25, 95% CI: 0.39, 0.02) had the strongest correlation with the BMI of the adolescents. Additionally, Mother's education (ß: 0.35, 95% CI: 0.18, 0.59) had the strongest correlation with the SBP of the adolescents. In contrast, the DBP was negatively persuaded by age (ß: -0.36, 95% CI: 1.54, 0.29) and gender (ß: -0.26, 95% CI: 1.34, 0.12) of the adolescents. Regular practice of rope exercise and lifestyle modification such as diet, physical activity, and quality sleep among adolescents prevent and control childhood CVD risk factors such asoverweight and hypertension. The SEAL-MI may lead to age-appropriate development of adolescents as well as improve their academic performance and quality of life. Giving importance to adolescents from urban habitats, affluent, nuclear families, and catching them young will change the disease burden significantly.

Social Support, Self-Care Behaviour and Self-Efficacy in Patients with Type 2 Diabetes during the COVID-19 Pandemic: A Cross-Sectional Study.

Diabetes mellitus is a major public health issue that considerably impacts mortality, morbidity, and healthcare costs worldwide. The COVID-19 pandemic has created havoc in diabetes management, too, like other spectrums of life. A descriptive, cross-sectional study was adopted to determine the effect of Social Support, Self-Care Behaviour and Self-Efficacy in Type 2 Diabetes Mellitus (T2D) during this COVID-19 pandemic. Two hundred T2D patients who satisfied the inclusion criteria were chosen using a convenient sampling procedure. The tool consists of four sections, including socio-demographic characteristics, Multidimensional Scale of Perceived Social Support (MSPSS), revised Summary of Diabetes Self-Care Activities (SDSCA) Scale and modified Diabetes Management Self-Efficacy Scale (DMS). Descriptive and inferential statistics were used to analyze the obtained data. The mean and SD of diabetic management self-efficacy is 5.74 (1.95) and 4.37 (1.4), respectively, for patients with HbA1c < 6.5% and HbA1c ≥ 6.5%. The self-care activities of the patients who had good glycemic control were 4.31 (2.06) compared to 3.50 (1.73) who did not. The social support received by the patients was 6.13 (2.13) vs. 5.31 (1.67) among patients with glycemic control vs. no control. The results show that social support (p = 0.04), self-efficacy (p =0.01) and self-care activities (p = 0.001) were significantly related to the level of glycemic control of the T2D patients. A significant relationship was also identified between gender (p = 0.036), age (p = 0.001) and education status (p = 0.000) with HbA1c control of the participants. This study demonstrates a significant relationship between social support, self-care behaviours, self-efficacy and glycemic management in T2D patients. During this COVID-19 pandemic, interventions to enhance the self-care activities like exercise and social support to boost their self-efficacy; for better diabetes management, reducing diabetes complications or prolonging their onset are the need of the hour.

Design, synthesis and antimicrobial activity of novel 2-aminothiophene containing cyclic and heterocyclic moieties.

One of the major challenges in the community and healthcare was an impedance of pathogenic bacteria to antibiotics. This work developed 2-aminothiophene derivatives as novel antimicrobial agents. Various 2-aminothiophene derivatives (3a-f, 5a-c, 6a, b, 7, 8a, b and 9) with cyclic and heterocyclic moieties at 5-position were synthesized, and characterized using NMR, IR, and mass spectroscopic techniques. The newly synthesized compounds were evaluated for their antimicrobial activity against bacteria S. pneumoniae, B. subtilis, P. aeruginosa, E. coli, and fungi A. fumigatus, S. mracemosum, G. candidum, C. albicans. Compound 3a with OH group at para position of phenyl ring exhibited significant antibacterial activity stronger than that of the drug standards Ampicillin and Gentamicin. Compound 6b possess pyrazole ring and compound 9 bearing pyridine ring showed promising antifungal activity compare to the standard drug Amphotericin B. The remaining compounds exhibited good to moderate inhibitory activities. In summary, the results suggest that the compounds from 2-aminothiophene derivatives can be used as antimicrobial agents.

A Subset of Primary Polydipsia, "Dipsogneic Diabetes Insipidus", in Apparently Healthy People Due to Excessive Water Intake: Not Enough Light to Illuminate the Dark Tunnel.

Dipsogenic diabetes insipidus (DDI) is a subtype of primary polydipsia (PP), which occurs mostly in healthy people without psychiatric disease. In contrast, PP is characterized by a polyuria polydipsia syndrome (PPS) associated with psychiatric illness. However, the pathogenesis of DDI is not well established and remains unexplored. In order to diagnose DDI, the patient should exhibit excessive thirst as the main symptom, in addition to no history of psychiatric illness, polyuria with low urine osmolality, and intact urine concentrating ability. Treatment options for DDI remain scarce. On this front, there have been two published case reports with successful attempts at treating DDI patients. The noteworthy commonalities in these reports are that the patient was diagnosed with frequent excessive intake of water due to a belief that drinking excess water would have pathologic benefits. It could therefore be hypothesized that the increasing trend of excessive fluid intake in people who are health conscious could also contribute to DDI. Hence, this review provides an overview of the pathophysiology, diagnosis, and treatment, with a special emphasis on habitual polydipsia and DDI.

Analgesic, Anti-Inflammatory, Cytotoxic Activity Screening and UPLC-PDA-ESI-MS Metabolites Determination of Bioactive Fractions of Kleinia pendula.

Kleinia pendula (Forssk.) DC. is a prostrate or pendent dark green succulent herb found in the southwestern mountain regions of Saudi Arabia. The literature survey of the plant reveals a lack of phytochemical and pharmacological studies, although traditional uses have been noted. The objective of the present work was to assess the in vivo analgesic and anti-inflammatory activities, as well as, the in vitro cytotoxic potential of the fractions of Kleinia pendula, and correlate these activities to the plant metabolites. The methanolic extract of Kleinia pendula was subjected to fractionation with n-hexane, ethyl acetate, chloroform, n-butanol, and water. The fractions were screened for their analgesic and anti-inflammatory activities, as well as cytotoxic activity against breast, liver, and colon cancer cell lines. The n-hexane and chloroform fractions of Kleinia pendula showed significant cytotoxic activity against all three cancer cell lines tested. The ethyl acetate and chloroform fractions showed significant analgesic and anti-inflammatory activities. The metabolites in these three active fractions were determined using UPLC-PDA-ESI-MS. Thus, the analgesic and anti-inflammatory activities of the plant were attributed to its phenolic acids (caffeoylquinic acid derivatives, protocatechuic, and chlorogenic acids). While fatty acids and triterpenoids such as (tormentic acid) in the hexane fraction are responsible for the cytotoxic activity; thus, these fractions of Kleinia pendula may be a novel source for the development of new plant-based analgesic, anti-inflammatory, and anticancer drugs.