Benzimidazole-oxindole hybrids as multi-kinase inhibitors targeting melanoma.

In the current study, a series of benzimidazole-oxindole conjugates 8a-t were designed and synthesized as type II multi-kinase inhibitors. They exhibited moderate to potent inhibitory activity against BRAFWT up to 99.61 % at 10 µM. Notably, compounds 8e, 8k, 8n and 8s demonstrated the most promising activity, with 99.44 to 99.61 % inhibition. Further evaluation revealed that 8e, 8k, 8n and 8s exhibit moderate to potent inhibitory effects on the kinases BRAFV600E, VEGFR-2, and FGFR-1. Additionally, compounds 8a-t were screened for their cytotoxicity by the NCI, and several compounds showed significant growth inhibition in diverse cancer cell lines. Compound 8e stood out with a GI50 range of 1.23 - 3.38 µM on melanoma cell lines. Encouraged by its efficacy, it was further investigated for its antitumor activity and mechanism of action, using sorafenib as a reference standard. The hybrid compound 8e exhibited potent cellular-level suppression of BRAFWT, VEGFR-2, and FGFR-1 in A375 cell line, surpassing the effects of sorafenib. In vivo studies demonstrate that 8e significantly inhibits the growth of B16F10 tumors in mice, leading to increased survival rates and histopathological tumor regression. Furthermore, 8e reduces angiogenesis markers, mRNA expression levels of VEGFR-2 and FGFR-1, and production of growth factors. It also downregulated Notch1 protein expression and decreased TGF-ß1 production. Molecular docking simulations suggest that 8e binds as a promising type II kinase inhibitor in the target kinases interacting with the key regions in their kinase domain.

Recent Advances in Structural Optimization of Quinazoline-Based Protein Kinase Inhibitors for Cancer Therapy (2021-Present).

Cancer is a complicated, multifaceted disease that can impact any organ in the body. Various chemotherapeutic agents have a low selectivity and are very toxic when used alone or in combination with others. Resistance is one of the most important hurdles that develop due to the use of many anticancer therapeutics. As a result, treating cancer requires a target-specific palliative care strategy. Remarkable scientific discoveries have shed light on several of the molecular mechanisms underlying cancer, resulting in the development of various targeted anticancer agents. One of the most important heterocyclic motifs is quinazoline, which has a wide range of biological uses and chemical reactivities. Newer, more sophisticated medications with quinazoline structures have been found in the last few years, and great strides have been made in creating effective protocols for building these pharmacologically active scaffolds. A new class of chemotherapeutic agents known as quinazoline-based derivatives possessing anticancer properties consists of several well-known compounds that block different protein kinases and other molecular targets. This review highlights recent updates (2021-2024) on various quinazoline-based derivatives acting against different protein kinases as anticancer chemotherapeutics. It also provides guidance for the design and synthesis of novel quinazoline analogues that could serve as lead compounds.

Receptor-based pharmacophore modeling, molecular docking, synthesis and biological evaluation of novel VEGFR-2, FGFR-1, and BRAF multi-kinase inhibitors.

A receptor-based pharmacophore model describing the binding features required for the multi-kinase inhibition of the target kinases (VEGFR-2, FGFR-1, and BRAF) were constructed and validated. It showed a good overall quality in discriminating between the active and the inactive in a compiled test set compounds with F1 score of 0.502 and Mathew's correlation coefficient of 0.513. It described the ligand binding to the hinge region Cys or Ala, the glutamate residue of the Glu-Lys αC helix conserved pair, the DFG motif Asp at the activation loop, and the allosteric back pocket next to the ATP binding site. Moreover, excluded volumes were used to define the steric extent of the binding sites. The application of the developed pharmacophore model in virtual screening of an in-house scaffold dataset resulted in the identification of a benzimidazole-based scaffold as a promising hit within the dataset. Compounds 8a-u were designed through structural optimization of the hit benzimidazole-based scaffold through (un)substituted aryl substitution on 2 and 5 positions of the benzimidazole ring. Molecular docking simulations and ADME properties predictions confirmed the promising characteristics of the designed compounds in terms of binding affinity and pharmacokinetic properties, respectively. The designed compounds 8a-u were synthesized, and they demonstrated moderate to potent VEGFR-2 inhibitory activity at 10 µM. Compound 8u exhibited a potent inhibitory activity against the target kinases (VEGFR-2, FGFR-1, and BRAF) with IC50 values of 0.93, 3.74, 0.25 µM, respectively. The benzimidazole derivatives 8a-u were all selected by the NCI (USA) to conduct their anti-proliferation screening. Compounds 8a and 8d resulted in a potent mean growth inhibition % (GI%) of 97.73% and 92.51%, respectively. Whereas compounds 8h, 8j, 8k, 8o, 8q, 8r, and 8u showed a mean GI% > 100% (lethal effect). The most potent compounds on the NCI panel of 60 different cancer cell lines were progressed further to NCI five-dose testing. The benzimidazole derivatives 8a, 8d, 8h, 8j, 8k, 8o, 8q, 8r and 8u exhibited potent anticancer activity on the tested cell lines reaching sub-micromolar range. Moreover, 8u was found to induce cell cycle arrest of MCF-7 cell line at the G2/M phase and accumulating cells at the sub-G1 phase as a result of cell apoptosis.

Rational design and synthesis of novel quinazolinone N-acetohydrazides as type II multi-kinase inhibitors and potential anticancer agents.

In the current investigation, a new class of quinazolinone N-acetohydrazides 9a-v was designed as type II multi-kinase inhibitors. The target quinazolinones were tailored so that the quinazolinone moiety would occupy the front pocket of the binding sites of VEGFR-2, FGFR-1 and BRAF kinases, meanwhile, the phenyl group at position 2 would act as a spacer which was functionalized at position 4 with an N-acetohydrazide linker that could achieve the key interactions with the essential gate area amino acids. The hydrazide moiety was linked to diverse aryl derivatives to occupy the hydrophobic back pocket of the DFG-out conformation of target kinases. The synthesized quinazolinone derivatives 9a-v demonstrated moderate to potent VEGFR-2 inhibitory activity with IC50 spanning from 0.29 to 5.17 µM. Further evaluation of the most potent derivatives on FGFR-1, BRAFWT and BRAFV600E showed that the quinazolinone N-acetohydrazides 9d, 9e, 9f, 9l and 9m have a potent multi-kinase inhibitory activity. Concurrently, 9b, 9d, 9e, 9k, 9l, 9o, 9q demonstrated potent growth inhibitory activity on NCI cancer cell lines with GI50 reaching 0.72 µM. In addition, compound 9e arrested the cell cycle progression in MDA-MB-231 cell line at the G2/M phase and showed the ability to induce apoptosis.

Association of health anxiety, fatalism and medication adherence among geriatric clients: An exploratory study.

OBJECTIVES: This study explored the relationship between health anxiety, fatalistic beliefs, and medication adherence among geriatric clients. Also, it determines the extent to which health anxiety and fatalism can predict the variance in medication adherence among the same population of geriatric clients. DESIGN: A cross-sectional analytical survey on 200 eligible participants using the Arabic Version of the Short Health Anxiety Inventory, Fatalism Scale, and Morisky Medication Adherence Scale-8 items. RESULTS: The study found a statistically significant negative relationship between the studied geriatric clients' fatalism and health anxiety and their medication adherence (r = -0.160, - 0.187, and P = 0.024, 0.008), respectively. CONCLUSION: This study highlights the importance of considering psychological factors such as health anxiety and fatalistic beliefs in addressing medication adherence among geriatric clients. By addressing these factors, healthcare providers can develop more effective strategies to improve medication adherence and ultimately improve the health outcomes of geriatric clients.

Psychometric properties of the arabic version of the maslach burnout inventory-human services survey (MBI-HSS) among lebanese dentists.

BACKGROUND: Dentists are at risk of burnout syndrome, which can have negative impacts on their work environment and productivity. Assessing burnout is crucial for maintaining the well-being and effectiveness of dentists in their profession. The present study aims to evaluate the psychometric properties of the Arabic version of the Maslach Burnout Inventory Human Services Survey (MBI-HSS) among dentists. METHODS: The original English version of the MBI-HSS was translated into Arabic, and then back-translated into English by experienced bilingual professionals. Lebanese dentists were asked to participate in the study between February and June 2019. Data collected included demographic information and items from the Arabic version of the MBI-HSS. RESULTS: A total of 441 people participated in the study, of whom 58.3% were men. The mean age of the sample was 39.6 years (SD = 12.8), with a range of 23 to 68 years old. Approximately 60% of dentists were specialists. Cronbach's alphas were as follows: emotional exhaustion (alpha = 0.855), depersonalization (alpha = 0.823), and personal achievement (alpha = 0.667). The results of the test-retest reliability assessment demonstrated the strong reproducibility of the MBI-HSS [EE, ICC = 0.927 (0.845, 0.966), p-value < 0.0001; PA, ICC = 0.963 (0.921-0.983), p-value < 0.001; DP, ICC = 0.764 (0.497-0.889), p-value < 0.0001]. The exploratory factor analysis of the MBI-HSS yielded three psychometrically robust sub-domains representing dimensions of "emotional exhaustion," "depersonalization," and "personal achievement," which explained 57.8% of the scale's total variance. The confirmatory factor analysis revealed that the 15-item model (excluding items 4, 5, 12, 13, 16, 20, and 22) was the most fitting for the data. CONCLUSIONS: The Arabic version of the MBI-HSS scale demonstrated good psychometric properties in Lebanese dentists. However, it would be important to conduct further research to confirm its reliability and validity in other Arab countries.

Mediating role of weight concerns in the relationship between seasonality and eating behavior among community-dwelling older adults: A path analysis.

BACKGROUND: Weight concerns are common among older adults, and it is unclear how they may impact the relationship between seasonality and eating behaviors, which can contribute to various health-related issues. AIM: This study investigated the mediating role of weight concerns in the relationship between seasonality and eating behavior among community-dwelling older adults. METHOD: A descriptive correlational analytical design was used on 200 randomly chosen participants who completed the Personal Inventory for Depression and Seasonal Affective Disorder Self-Assessment Version, the Adult Eating Behavior Questionnaire, and the Weight Concern Subscale. A path analysis was conducted to test the hypothesized model. RESULTS: The study findings indicated that most older adults reported moderate-to-severe seasonal variations, moderate enjoyment of food, emotional overeating, emotional undereating, and food fussiness. Weight concern partially mediated the relationship between seasonality and eating behavior. CONCLUSION: By understanding the complex interplay between these factors, weight concerns may play an essential role in mediating the effects of seasonal changes on eating behavior, while seasonal winter symptoms may directly impact eating behavior. These results have potential implications for nurses' efforts to develop interventions to promote healthy eating behaviors and manage weight concerns during seasonal variations, especially in the winter.

Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment.

A Cyber-Physical System (CPS) is a network of cyber and physical elements that interact with each other. In recent years, there has been a drastic increase in the utilization of CPSs, which makes their security a challenging problem to address. Intrusion Detection Systems (IDSs) have been used for the detection of intrusions in networks. Recent advancements in the fields of Deep Learning (DL) and Artificial Intelligence (AI) have allowed the development of robust IDS models for the CPS environment. On the other hand, metaheuristic algorithms are used as feature selection models to mitigate the curse of dimensionality. In this background, the current study presents a Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-based Intrusion Detection (SCAVO-EAEID) technique to provide cybersecurity in CPS environments. The proposed SCAVO-EAEID algorithm focuses mainly on the identification of intrusions in the CPS platform via Feature Selection (FS) and DL modeling. At the primary level, the SCAVO-EAEID technique employs Z-score normalization as a preprocessing step. In addition, the SCAVO-based Feature Selection (SCAVO-FS) method is derived to elect the optimal feature subsets. An ensemble Deep-Learning-based Long Short-Term Memory-Auto Encoder (LSTM-AE) model is employed for the IDS. Finally, the Root Means Square Propagation (RMSProp) optimizer is used for hyperparameter tuning of the LSTM-AE technique. To demonstrate the remarkable performance of the proposed SCAVO-EAEID technique, the authors used benchmark datasets. The experimental outcomes confirmed the significant performance of the proposed SCAVO-EAEID technique over other approaches with a maximum accuracy of 99.20%.

Estimation of Prostate Cancer Cost in Egypt From a Societal Perspective.

Introduction: The main objective of this study was to assess the cost of prostate cancer over a 1-year period from a societal perspective. Methods: We constructed a cost-of-illness model to assess the cost of different health states of prostate cancer, metastatic or nonmetastatic, among Egyptian men. Population data and clinical parameters were extracted from the published literature. We relied on different clinical trials to extract clinical data. We considered all direct medical costs, including the costs of treatment and required monitoring, in addition to the indirect costs. The unit costs were captured from Nasr City Cancer Center and Egyptian Authority for Unified Procurement, Medical Supply, and Management of Medical Technology, and resource utilization were collected from clinical trials and validated by the Expert Panel. One-way sensitivity analysis was conducted to ensure model robustness. Results: The number of targeted patients with nonmetastatic hormone-sensitive prostate cancer, hormone-sensitive prostate cancer, and metastatic castration-resistant prostate cancer was 215,207, 263,032, and 116,732, respectively. The total costs, in Egyptian pounds (EGP) and US dollars (USD), for the targeted patients, including drug costs and nondrug costs over a time horizon of 1 year, were EGP 41.44 billion (USD 9.010 billion) for localized prostate cancer; for metastatic prostate cancer, they doubled to EGP 85.14 billion (USD 18.510 billion), which reflects a huge burden on the Egyptian healthcare system. The drug costs for localized and metastatic prostate cancer are EGP 41,155,038,137 (USD 8.946 billion) and EGP 81,384,796,471 (USD 17.692 billion), respectively. A significant difference in nondrug costs between localized and metastatic prostate cancer was demonstrated. Nondrug costs were estimated at EGP 293,187,203 (USD 0.063 billion) for localized prostate cancer and EGP 3,762,286,092 (USD 0.817 billion) for metastatic prostate cancer. This significant difference in nondrug costs highlights the importance of early treatment due to the increased costs of progression and the burden of follow-up and productivity loss associated with metastatic prostate cancer. Conclusion: Metastatic prostate cancer has a huge economic burden on the Egyptian healthcare system compared with localized prostate cancer owing to the increased costs of progression, follow-up, and productivity loss. This highlights the necessity of early treatment of these patients to save costs and lighten the burden of the disease on the patient, society, and economy.

Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques.

The outbreak of the COVID-19 pandemic has also triggered a tsunami of news, instructions, and precautionary measures related to the disease on social media platforms. Despite the considerable support on social media, a large number of fake propaganda and conspiracies are also circulated. People also reacted to COVID-19 vaccination on social media and expressed their opinions, perceptions, and conceptions. The present research work aims to explore the opinion dynamics of the general public about COVID-19 vaccination to help the administration authorities to devise policies to increase vaccination acceptance. For this purpose, a framework is proposed to perform sentiment analysis of COVID-19 vaccination-related tweets. The influence of term frequency-inverse document frequency, bag of words (BoW), Word2Vec, and combination of TF-IDF and BoW are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (ETC), logistic regression, Naïve Bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and recurrent neural network (RNN). Results reveal that ETC outperforms using BoW with a 92% of accuracy and is the most suitable approach for sentiment analysis of COVID-19-related tweets. Opinion dynamics show that sentiments in favor of vaccination have increased over time.

Binary Chimp Optimization Algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor Networks.

An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims to enhance the effectiveness and efficiency of data analysis and collection, resulting in automation and improved decision-making. Security in WSN-assisted IoT can be referred to as the measures initiated for protecting WSN linked to the IoT. This article presents a Binary Chimp Optimization Algorithm with Machine Learning based Intrusion Detection (BCOA-MLID) technique for secure IoT-WSN. The presented BCOA-MLID technique intends to effectively discriminate different types of attacks to secure the IoT-WSN. In the presented BCOA-MLID technique, data normalization is initially carried out. The BCOA is designed for the optimal selection of features to improve intrusion detection efficacy. To detect intrusions in the IoT-WSN, the BCOA-MLID technique employs a class-specific cost regulation extreme learning machine classification model with a sine cosine algorithm as a parameter optimization approach. The experimental result of the BCOA-MLID technique is tested on the Kaggle intrusion dataset, and the results showcase the significant outcomes of the BCOA-MLID technique with a maximum accuracy of 99.36%, whereas the XGBoost and KNN-AOA models obtained a reduced accuracy of 96.83% and 97.20%, respectively.

Impact of Emotions on Test of Variables of Attention(TOVA) Performance in a Pediatric Clinical Population: A Retrospective Study.

BACKGROUND: Continuous Performance Tests, like the Test of Variables of Attention (TOVA), are commonly used to assess attention processes in clinical settings. Although a few previous studies have explored the effects of emotions on the outcome of such tests, the results are scarce and contradictory at times. OBJECTIVE: Through this retrospective study, we  aimed to explore the correlation between performance on the TOVA and parent-reported emotional symptoms in youth. METHODS: We used preexisting datasets of Mood and Feelings Questionnaire, Screen for Child Anxiety Related Disorders, and Vanderbilt Attention-Deficit/Hyperactivity Disorder Diagnostic Rating Scale as well as preexisting results from the TOVA test from 216 patients aged between 8 and 18 years. Pearson's correlation coefficients, as well as linear regression models, were computed to examine the association between depressive and anxiety symptoms and the four indices of TOVA (response time variability, response time, commission errors, and omission errors). Additionally, we used generalized estimating equations to determine whether the reported emotional symptoms affect the TOVA outcome differently as the test progresses. RESULTS: Our results showed no significant effect of the reported emotional symptoms on the TOVA results even when controlling for sex or reported inattention and hyperactivity. CONCLUSION: TOVA results do not seem to be affected by emotional symptoms in youth. This being said, future studies should also explore other factors that can affect the performance on the TOVA, like motor disability, sleepiness, or neurodevelopmental disorders affecting cognitive abilities.

Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis.

Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be helpful in establishing maximal image classification performance in numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, this comparative study states that the AOADL-HBCC technique displays better performance than other recent methodologies, with a maximum accuracy of 96.77%.

Clinical Decision Support Systems to Predict Drug-Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder.

Big Data analytics is a technique for researching huge and varied datasets and it is designed to uncover hidden patterns, trends, and correlations, and therefore, it can be applied for making superior decisions in healthcare. Drug-drug interactions (DDIs) are a main concern in drug discovery. The main role of precise forecasting of DDIs is to increase safety potential, particularly, in drug research when multiple drugs are co-prescribed. Prevailing conventional method machine learning (ML) approaches mainly depend on handcraft features and lack generalization. Today, deep learning (DL) techniques that automatically study drug features from drug-related networks or molecular graphs have enhanced the capability of computing approaches for forecasting unknown DDIs. Therefore, in this study, we develop a sparrow search optimization with deep learning-based DDI prediction (SSODL-DDIP) technique for healthcare decision making in big data environments. The presented SSODL-DDIP technique identifies the relationship and properties of the drugs from various sources to make predictions. In addition, a multilabel long short-term memory with an autoencoder (MLSTM-AE) model is employed for the DDI prediction process. Moreover, a lexicon-based approach is involved in determining the severity of interactions among the DDIs. To improve the prediction outcomes of the MLSTM-AE model, the SSO algorithm is adopted in this work. To assure better performance of the SSODL-DDIP technique, a wide range of simulations are performed. The experimental results show the promising performance of the SSODL-DDIP technique over recent state-of-the-art algorithms.

Novel benzenesulfonamide-thiouracil conjugates with a flexible N-ethyl acetamide linker as selective CA IX and CA XII inhibitors.

Novel benzenesulfonamide derivatives linked to diverse functionalized thiouracils through a flexible N-ethyl acetamide linker were designed and synthesized as carbonic anhydrase (CA) inhibitors. The synthesized candidates demonstrated a potent inhibitory activity against four different CA isoforms in the nanomolar range. Compound 10d showed more than twofold higher potency than the reference AAZ against CA II with Ki of 5.65 and 12 nM, respectively. Moreover, compounds 10d and 20 revealed potent activity against CA IX with Ki of 18.1 and 14.2 nM, respectively. In addition, 10c, 10d, 11b, 11c, and 20 demonstrated high potency against the CA XII isozyme with a Ki range of 4.18-4.8 nM. Most of the synthesized derivatives displayed preferential selectivity toward the CA IX and CA XII isoforms over CA I and CA II. Compounds 11a and 20 exhibited favorable selectivity toward CA IX over CA II with a selectivity index (SI) of 14.36 and 16.62, respectively, and toward CA XII over CA II with SI of 71.01 and 51.19, respectively. Molecular docking simulations showed that the synthesized conjugates adopted comparable binding modes in the CA I, CA II, CA IX, and CA XII isoforms, involving the deep fitting of the sulfonamide moiety in the base of the CA active site via chelation of the Zn2+ ion and H-bond interaction with the key amino acids Thr199 and/or Thr200. Moreover, the N-ethyl acetamide flexible linker enables the substituted thiouracils and fused thiouracil tail to achieve multiple interactions with the surrounding hydrophobic and hydrophilic regions.

Discovery of novel thioquinazoline-N-aryl-acetamide/N-arylacetohydrazide hybrids as anti-SARS-CoV-2 agents: Synthesis, in vitro biological evaluation, and molecular docking studies.

In the current investigation, two novel series of (tetrahydro)thioquinazoline-N-arylacetamides and (tetrahydro)thioquinazoline-N-arylacetohydrazides were designed, synthesized and investigated for their antiviral activity against SARS-CoV-2. The thioquinazoline-N-arylacetamide 17g as well as the tetrahydrothioquinazoline-N-arylacetohydrazides 18c and 18f showed potent antiviral activity with IC50 of 21.4, 38.45 and 26.4 µM, respectively. In addition, 18c and 18f demonstrated potential selectivity toward the SARS-CoV-2 over the host cells with SI of 10.67 and 16.04, respectively. Further evaluation of the mechanism of action of the three derivatives 17g, 18c, and 18f displayed that they can inhibit the virus at the adsorption as well as at the replication stages, in addition to their virucidal properties. In addition, 17g, 18c, and 18f demonstrated satisfactory physicochemical properties as well as drug-likeness properties to be further optimized for the discovery of novel antiviral agents. The docking simulation on Mpro binding site predicted the binding pattern of the target compounds rationalizing their differential activity based on their hydrophobic interaction and fitting in the hydrophobic S2 subsite of the binding site.

Effect of psychological first aid program on stress level and psychological well-being among caregivers of older adults with alzheimer's disease.

INTRODUCTION: Older adults with Alzheimer's disease (AD) experience drastic changes in their physical and mental abilities. AD patients became heavily dependent on their caregivers for everyday functions, which have significant implications not only for them but also for their caregivers. So, many AD caregivers experienced an increased level of depression and anxiety symptoms, lower perceived control, and higher burden compared to non-AD caregivers. Therefore, psychological first aid (PFA) and educational interventions are designed to enable those caregivers to meet the daily requirements of their patient care and to cope with its challenges. AIM: Determine the effect of psychological first aid program on stress level and psychological well-being among caregivers of older adults with Alzheimer's disease. DESIGN: One group pre-test post-test was followed. SUBJECTS: A convenience sample of one hundred (100) caregivers of older adults with AD. SETTING: All online groups concerned with the care of Alzheimer's disease patients on Facebook. TOOLS: Socio-demographic and clinical data of older adults with Alzheimer's disease and their caregivers' questionnaire, Alzheimer's disease knowledge scale, Kingston caregiver stress scale, and authentic identity measures (AIM) scale of psychological well-being RESULTS: The psychological first aid program has highly statistically significant effect on the AD caregivers' knowledge, stress level and psychological well-being as (t=-30.707, P = 0.000, t = 8.500, P = 0.000 & t= -4.763, P = 0.000 respectively). CONCLUSION: Psychological first aid program is considered an effective intervention in decreasing the AD caregivers' stress and increasing their psychological wellbeing and knowledge regarding delivering care for AD patients.

Novel 2-substituted thioquinazoline-benzenesulfonamide derivatives as carbonic anhydrase inhibitors with potential anticancer activity.

A novel series of 2-thioquinazoline-benzenesulfonamide hybrids were designed as carbonic anhydrase (CA) inhibitors. The design approach relies on molecular hybridization between the benzenesulfonamide scaffold as a Zn2+ binding group and 2-substituted thioquinazolines as a tail. Assaying the thioquinazoline-benzenesulfonamide conjugates against four different CA isoforms revealed that compounds 12f and 12p are the most potent derivatives. They exhibit Ki = 0.09 and 0.05 µM on CA II, 0.32 and 0.47 µM on CA IX, and 0.58 and 0.46 µM on CA XII, respectively. In addition, 12p demonstrated high selectivity for CA II over CA I with selectivity index (SI) = 92, and slightly higher specificity for CA II over CA IX and CA XII with SI = 9.40 and 9.20, respectively. The synthesized compounds were screened for their cytotoxic activity at 10 µM concentration and derivatives 12o, 12n, and 12f turned out to be the most potent ones from the synthesized series; they exhibit mean growth inhibition % values of 89.38%, 58.75%, and 54.71%, respectively, while 12p demonstrated moderate activity against the NCI cancer cell lines, with mean growth inhibition % = 29.62%. The analysis of the MCF-7 cell cycle after treatment with 5.0 µM of 12f displayed that it arrests the cell cycle at the G2/M phase. Molecular docking simulation of the thioquinazoline-benzenesulfonamide hybrids in the CA II active site rationalized the potent activity to the settlement of the sulfonamide moiety at the depth of the CA II active site and its stabilization by performing the important interactions with the Zn2+ ion as well as with the key amino acids Thr199 and/or Thr200, while the thioquinazoline moiety with different (un)substituted phenyl tails is stabilized by the formation of various hydrogen bonding and hydrophobic interactions with the surrounding amino acids in the binding site.

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI.

Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.

Investigation of the carbonic anhydrase inhibitory activity of benzenesulfonamides incorporating substituted fused-pyrimidine tails.

Two new series of 2-thiocyclopenta[d]pyrimidine-benzenesulfonamides 12a-l and 2-thiotetrahydroquinazoline-benzenesulfonamides 13a-j were synthesized and evaluated for their carbonic anhydrase (CA, EC 4.2.1.1) inhibitory acivity and cytotoxic activity. The derivatives 12a and 12i exerted effective inhibition against CA II with Ki = 0.11 and 0.15 µM, while 12a, 12e, 12i, and 13d (Ki = 0.083-0.087 µM) were found to be the most potent against CA XII. In addition, higher selectivity toward CA II and CA XII over CA I and CA IX was observed for the majority of the synthesized conjugates. Analysis of the effect of the synthesized compounds on NCI cancer cell lines revealed that compounds 12b and 13d showed mean growth inhibitory effects of 53.59% and 49.25%, respectively. Docking of the synthesized hybrids in the CA II and CA XII binding pockets displayed the capability of the benzenesulfonamide derivatives to form, through their SO2 NH2 moiety, the characteristic interactions of the traditional CA inhibitors, besides additional interactions achieved by the tail with isoform-specific residues in the peripheral part of the CA binding sites.