Association of level of adherence to Mediterranean diet with cognitive and dementia status in subjects with chronic diseases: a cross-sectional study.

Background: The Mediterranean diet has been linked to brain neuroprotection. Evidence from meta-analyses showed reduced risk of dementia with greater intake of vegetables and fruits, fish, and the Mediterranean diet. The current study raises important questions about the association between low risk dementia and Mediterranean diet.Objective: The objective was to evaluate the association between levels of adherence to the Mediterranean diet and dementia and cognitive status in subjects 50 years of age and older.Method: The Mediterranean Diet Adherence Screener (MEDAS), the modified 30-item 'Diagnostic and Statistical Manual of Mental Disorders Third Edition (DSM-III) risk of dementia, and the Standard Mini-Mental Status Examination (SMMSE) cognitive status scores were used to assess the levels of adherence to the Mediterranean diet'.Results: A total of 150 subjects were enrolled in the study. Forty-one (27.3%) had 'suspected or confirmed dementia, while 48 individuals (32%) were categorized as having moderate to severe cognitive decline. Subjects who reported moderate to high adherence to the Mediterranean diet (55, 36.7%) had significantly lower dementia scores (7.0 3.8 versus 17.6 5.1) and higher cognitive (25.4 3.8 versus 8.6 7.2) scores compared to those (38, 25.3%) who reported low adherence to the Mediterranean diet.Conclusion: Subjects who were highly or moderately adherent to the Mediterranean diet had significantly lower dementia scores and better cognitive status than those with low adherence.

The flavonoid luteolin reduces mutant huntingtin aggregation and cytotoxicity in huntingtin-mutated neuroblastoma cells.

Background: Huntington's disease is an inherited progressive neurodegenerative disorder caused by an expansion of the polyglutamine tract leading to malformation and aggregation of the mutant huntingtin protein in the cell cytoplasm and nucleus of affected brain regions. The development of neuroprotective agents from plants has received considerable research attention. Objective: Our study aims to investigate the neuroprotective effects of luteolin and the mechanisms that underline its potential mediated protection in the mutant htt neuroblastoma cells. Methods: The mutant htt neuroblastoma cells were transfected with 160Q, and the control wild-type neuroblastoma cells were transfected with 20Q htt for 24 h and later treated with luteolin. Cell viability was determined by MTT and PI staining in both groups, while western blotting was used to evaluate caspase 3 protein expression. Aggregation formation was assessed via immunofluorescence microscopy. Also, western blotting was utilized to measure the protein expression of mutant htt aggregated and soluble protein, Nrf2 and HO-1. The impact of Nrf2 on luteolin-treated neuroblastoma cells was assessed using small interfering RNAs. Results: Our study reports that luteolin can protect cultured cells from mutant huntingtin cytotoxicity, evidenced by increased viability and decreased apoptosis. Also, luteolin reduced the accumulation of soluble and insoluble mutant huntingtin aggregates in mutant htt neuroblastoma cells transfected with 160Q compared to the control wild-type. The mutant htt aggregate reduction mediated by luteolin appeared to be independent of the Nrf2 -HO-1 antioxidant pathway. Conclusion: Luteolin presents a new potential therapeutic and protective agent for the treatment and decreasing the cytotoxicity in neurodegenerative diseases such as Huntington's disease.

Using Big Data Analytics to "Back Engineer" Protein Conformational Selection Mechanisms.

In the living cells, proteins bind small molecules (or "ligands") through a "conformational selection" mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine learning approaches to identify, in a very large amount of protein:ligand complexes, what protein properties are associated with their capacity to bind small molecules. In order to do so, we calculate 40 physicochemical properties on about 1.5 millions of protein conformations: ligand and protein conformations. This work describes a machine learning approach to identify the unique physico-chemical descriptors of a protein that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2) and OPRK1 (Opioid Receptor Kappa 1). We find adequate machine learning techniques can increase by an order of magnitude the identification of "binding protein conformations" in an otherwise very large ensemble of protein conformations, compared to random selection of protein conformations. This opens the door to the systematic identification of such "binding conformations" for proteins and provides a big data approach to the conformational selection mechanism.

Novel Big Data-Driven Machine Learning Models for Drug Discovery Application.

Most contemporary drug discovery projects start with a 'hit discovery' phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate this initial drug discovery process, 'virtual docking calculations' are routinely performed, where computational models of proteins and computational models of small chemicals are evaluated for their capacities to bind together. In cutting-edge, contemporary implementations of this process, several conformations of protein targets are independently assayed in parallel 'ensemble docking' calculations. Some of these protein conformations, a minority of them, will be capable of binding many chemicals, while other protein conformations, the majority of them, will not be able to do so. This fact that only some of the conformations accessible to a protein will be 'selected' by chemicals is known as 'conformational selection' process in biology. This work describes a machine learning approach to characterize and identify the properties of protein conformations that will be selected (i.e., bind to) chemicals, and classified as potential binding drug candidates, unlike the remaining non-binding drug candidate protein conformations. This work also addresses the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor) and OPRK1 (Opioid Receptor Kappa 1), and subsequently reduces the failure rates and hastens the drug discovery process.

Advancements and Challenges in Antiamyloid Therapy for Alzheimer's Disease: A Comprehensive Review.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder caused by the accumulation of amyloid-beta (Aß) proteins and neurofibrillary tangles in the brain. There have been recent advancements in antiamyloid therapy for AD. This narrative review explores the recent advancements and challenges in antiamyloid therapy. In addition, a summary of evidence from antiamyloid therapy trials is presented with a focus on lecanemab. Lecanemab is the most recently approved monoclonal antibody that targets Aß protofibrils for the treatment of patients with early AD and mild cognitive impairment (MCI). Lecanemab was the first drug shown to slow cognitive decline in patients with MCI or early onset AD dementia when administered as an infusion once every two weeks. In the Clarity AD trial, lecanemab was associated with infusion-site reactions (26.4%) and amyloid-related imaging abnormalities (12.6%). The clinical relevance and long-term side effects of lecanemab require further longitudinal observation. However, several challenges must be addressed before the drug can be routinely used in clinical practice. The drug's route of administration, need for imaging and genetic testing, affordability, accessibility, infrastructure, and potential for serious side effects are some of these challenges. Lecanemab's approval has fueled interest in the potential of other antiamyloid therapies, such as donanemab. Future research must focus on developing strategies to prevent AD; identify easy-to-use validated plasma-based assays; and discover newer user-friendly, and cost-effective drugs that target multiple pathways in AD pathology.

Luteolin as potential treatment for Huntington's disease: Insights from a transgenic mouse model.

AIMS: The study aimed to evaluate the potential benefits of luteolin treatment in Huntington's disease (HD), an inherited progressive neurodegenerative disorder. METHODS: HD N171-82Q transgenic and WT mice received luteolin or vehicle for treatment at 6 weeks of age. The mice's body weight changes and survival rates were monitored throughout the study, and a series of motor functional tests were conducted. Serum level of the marker NfL was also determined. Immunohistochemical staining and western blotting were utilized to assess the expression of huntingtin aggregates. RESULTS: Luteolin treatment enhanced survival and prevented weight loss in HD mice compared to the vehicle-treated HD group. Furthermore, the luteolin-treated HD mice exhibited enhanced motor coordination and balance and significantly reduced motor dysfunction. Also, luteolin decreased serum NfL levels in HD mice. Notably, the accumulation of huntingtin aggregates was significantly reduced in the brain's cortex, hippocampus, and striatum of luteolin-treated HD mice compared to the vehicle-treated HD group. CONCLUSION: Luteolin holds promise as a therapeutic agent for improving survival outcomes, managing motor dysfunction, and reducing huntingtin aggregates in HD. The findings are of significance as currently, there are no approved therapeutic interventions that reverse HD pathology or slow down its progression.

Information sharing behaviors in text-based communication during emergency response.

Emergency response teams consist of highly experienced professionals who must effectively communicate to respond to crisis situations characterized by high levels of uncertainty and complexity. Although the existing literatures on team collaboration and information processing are well established, research examining team information sharing through text-based communication technology facing emergency situations is limited. With the increasing complex nature of emergency response situations, emergency response organizations are likely to introduce new technologies to address such events. In this paper, we examine text-based information sharing behaviors related to team situation awareness in a simulated fire-rescue operation of 62 teams. We elaborate on different types of information sharing behaviors and identify a typology and linguistic features as well as differences between high- and low-performing teams in the occurrence of team situational information sharing behaviors. We conclude by discussing implications for emergency response teams and implications for future research avenues.

Big Data analytics for improved prediction of ligand binding and conformational selection.

This research introduces new machine learning and deep learning approaches, collectively referred to as Big Data analytics techniques that are unique to address the protein conformational selection mechanism for protein:ligands complexes. The novel Big Data analytics techniques presented in this work enables efficient data processing of a large number of protein:ligand complexes, and provides better identification of specific protein properties that are responsible for a high probability of correct prediction of protein:ligand binding. The GPCR proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2), OPRD1 (Opioid receptor Delta 1) and OPRK1 (Opioid Receptor Kappa 1) are examined in this study using Big Data analytics techniques, which can efficiently process a huge ensemble of protein conformations, and significantly enhance the prediction of binding protein conformation (i.e., the protein conformations that will be selected by the ligands for binding) about 10-38 times better than its random selection counterpart for protein conformation selection. In addition to providing a Big Data approach to the conformational selection mechanism, this also opens the door to the systematic identification of such "binding conformations" for proteins. The physico-chemical features that are useful in predicting the "binding conformations" are largely, but not entirely, shared among the test proteins, indicating that the biophysical properties that drive the conformation selection mechanism may, to an extent, be protein-specific for the protein properties used in this work.

Choice of Antimicrobials in Surgical Prophylaxis - Overuse and Surgical Site Infection Outcomes from a Tertiary-Level Care Hospital.

Background: This study was aimed to describe the choice of Surgical Antimicrobial Prophylaxis at a tertiary-level care hospital in United Arab Emirates. It also associated the choice between two leading antimicrobials for the SAP to the site of surgery. Methods: A descriptive drug use evaluation was performed retrospectively to study choices of antimicrobials in surgical antibiotic prophylaxis. An analytical cross-sectional study design was used to develop a hypothesis regarding the choice of ceftriaxone. Data were collected from the medical records of Hospital from July 2020 to December 2020. Results were presented in numbers and percentages. Results: SAP data were collected from 199 patients, of which 159 were clean or clean-contaminated. Dirty surgeries (18) needed a higher level of antimicrobials as there were infections to be treated. For other surgeries with no infection, overuse of antimicrobials was found regarding the choice of antimicrobials. Surgical antibiotic Prophylaxis was administered within the recommended time prior to surgeries. Ceftriaxone was preferred over cefuroxime in all types of surgeries based on the timing of Surgical Antibiotic Prophylaxis, wound classification, and the surgical site. A statistically significant association for choice of ceftriaxone over cefuroxime was found regarding surgical sites (p-value <0.05). About 99% of the patients were prescribed discharge antimicrobials when 158 (80%) surgeries were clean or clean-contaminated. Conclusion: Overuse of antimicrobials was found in surgical antimicrobial prophylaxis. Ceftriaxone was preferred more than cefuroxime in all types of surgeries. No surgical site infections were reported. A follow-up comparative study is recommended to decrease antimicrobial use without increasing risk of surgical site infection.

Effect of different antidiabetic medications on atherosclerotic cardiovascular disease (ASCVD) risk score among patients with type-2 diabetes mellitus: A multicenter non-interventional observational study.

OBJECTIVE: The aim of this study was to compare the clinical outcomes associated with different combinations of oral diabetic drugs among patients with type 2 diabetes mellitus. METHOD: A prospective multicenter longitudinal, noninterventional observation study design was applied. At baseline (0 month), clinical parameters including glucose profile, renal function, lipid profile and risk assessment for cardiovascular risks were calculated. Mean Weighted difference (MWD) with heterogeneity and effect z was calculated to determine the risk reduction at the end of the study. RESULTS: A total of 1,657 were enrolled to different cohorts with response rate of 75.5%. The distribution of patients was based on prescribed drug. A total of 513 (30.9%) in G1 (metformin alone), 217 (13.09%) in G2 (metformin with Glimepiride), 231 (12.85%) in G3 (Metformin with Gliclazide), 384 (23.17%) in G4 (metformin with Sitagliptin) and 312 (18.89%) in G5 (Metformin with Saxagliptin). There was no significant different in all clinical and social variables at baseline. The Intergroup analysis showed significant differences with all the primary outcome variables except BMI (p = 0.217) and eGFR (p = 0.782) among patients using sulphonylurea (SU) combination (G2 & G3). Findings also showed significant high frequency of emergency visit and hospitalization in G1 (78.16% & 30.8%) as compared to SU (70.1% & 28.3%, p = 0.001) and DPP-4 (56.6% & 20.4%, p = 0.001). The overall reported effect was z = 2.58, p = 0.001 for ASCVD risk reduction assessment. CONCLUSION: The study concluded that significant effect of Dipeptidyl peptidase-4 inhibitor on reduction of hospitalization, lipid profile and also ASCVD risk score of type-II diabetes mellitus patients regardless of clinical comorbidities. Also, sulfonylurea combinations have showed significant reduction in LDL and triglycerides values.

Aducanumab Therapy to Treat Alzheimer's Disease: A Narrative Review.

Background: Aducanumab, a new monoclonal antibody that targets ß-amyloid aggregates, has been granted conditional approval by the U.S. FDA for treatment of mild Alzheimer's disease (AD). The approval of this drug without a confirmed significant clinical impact has resulted in several debates. Objective: In this narrative review, aducanumab approval-related controversy, the drug's pharmacokinetics and pharmacodynamic characteristics, evidence from the efficacy and safety trials of aducanumab, implications of the drug approval, and the future directions in the management of patients with AD are summarized. Methods: Using relevant keywords, Google Scholar, Web of Science, and MEDLINE databases and manufacturer's website were searched. Results: Infusion of aducanumab at a higher dose resulted in a modest slowing of cognitive decline among patients with mild cognitive impairment or early-onset AD dementia. The drug however can cause amyloid-related imaging abnormalities. Due to modest impact on cognition, the use of this drug by patients with AD will most likely be limited. The manufacturer is required to run an extended phase IIIb trial to verify the benefit of this drug. Access to therapy requires a careful selection of patients and periodic monitoring to ensure the optimal use of the drug. Conclusion: Despite the limitations, aducanumab is the first disease-modifying therapy approved for the treatment of AD. Aducanumab addresses a part of the pathogenesis of AD; therefore, drugs that can act on multiple targets are needed. In addition, the search for preventive strategies, validated plasma-based assays, and newer drugs for AD, which are effective, safe, convenient, and affordable, is vital.

Mandibular Canine Calcification Stages as an Indicator of Skeletal Maturity.

AIMS: The aim of this study was to verify whether there is significant relationship between stages of mandibular canine calcification and skeletal maturation and to determine whether the same can be used as a reliable diagnostic aid to assess skeletal maturity. MATERIALS AND METHODS: The sample consisted of 50 males and 50 females aged between 8 and 15 years. Two radiographs namely orthopantomograph and handwrist radiograph were taken for this study. Skeletal age was ascertained from handwrist radiographs according to the method introduced by Fishman L.S. The developmental stages of mandibular canine were assessed according to Demirjian's stages of dental calcification. RESULTS: The relationship between canine stage calcification and skeletal maturity index was statistically significant in all stages. INTERPRETATION AND CONCLUSION: Mandibular canine calcification stages can be recommended as a supplemental diagnostic aid. Similar studies may be conducted in different population to rule out any possible racial and regional influences on growth characteristics.

Evaluation of Long-term Stability of Bilateral Sagittal Split Osteotomy in Individuals with Different Growth Patterns.

OBJECTIVE: Bilateral sagittal split osteotomy (BSSO) is a routinely used surgical step for the correction of a class III with mandibular prognathism. Many factors influence the stability of the surgical correction achieved. This study was designed to access the role of growth pattern in the surgical stability after a BSSO correction. MATERIALS AND METHODS: A total of 18 individuals (6 vertical growers, 6 horizontal growers, and 6 normal growing individuals) were considered for the study. Five parameters, horizontal plane (HP)-pogonion (POG) angle, HP-occlusal plane angle, POG height, POG depth, and Point B depth, were measured and compared postsurgically and in the follow-up phase. RESULT: Vertical growing individuals showed greater tendency for relapse and clockwise rotation of mandible postsurgically. No major difference was observed in the normal and horizontal growing individuals. When planning BSSO on vertical growing patient, utmost care should be taken to prevent posttreatment relapse to ensure better surgical stability.

Comparative Assessment of Force Decay of the Elastomeric Chain With the Use of Various Mouth Rinses in Simulated Oral Environment: An In Vitro Study.

AIM: This study is aimed to assess the effect of different mouth rinses and its active components on the force decay of elastomeric chains. Listerine, Colgate Phos-Flur, Clohex Plus mouth rinses, 26.9% alcohol, 0.04% sodium fluoride (NaF), and 0.2% chlorhexidine were used for this study to determine its effects on force decay of elastomeric chains. MATERIALS AND METHODS: Seven custom-made jigs were constructed on which 120 short module elastomeric chains were stretched to predetermined lengths between the pins. Using calibrated digital force tester, measurements of force exerted by the elastomeric chains while stretched on the framework were recorded at the time of attachment to the frame and after 24 hours, 7 days, 14 days, 21 days, and 28 days. The custom-made jigs and the elastomeric chains were allowed for complete submersion in artificial saliva throughout the test period, as well as in respective control solution and mouth rinses for 60 seconds, twice daily. RESULTS: All test groups showed significantly more force decay than the control group. Statistically significant differences were observed when comparing force decay among the test groups except between a few. INTERPRETATION AND CONCLUSION: Mouth rinses cause an increase in force decay of elastomeric chain over the time. Listerine and 26.9% alcohol solution caused maximum force decay by the end of 28 days. Least force degradation of elastomeric chain was seen with the use of 0.2% chlorhexidine.

Novel EM based ML Kalman estimation framework for superresolution of stochastic three-states microtubule signal.

BACKGROUND: Recent research has found that abnormal functioning of Microtubules (MTs) could be linked to fatal diseases such as Alzheimer's. Hence, there is an imminent need to understand the implications of MTs for disease- diagnosis. However, studies of cellular processes like MTs are often constrained by physical limitations of their data acquisition systems such as optical microscopes and are vulnerable to either destruction of the specimen or the probe. In addition, study of MTs is challenged with non-uniform sampling of the MT dynamic instability phenomenon relative to its time-lapse observation of the cellular processes. Thus, the above caveats limit the overall period of time that the MT data can be collected, thereby causing limited data availability scenario. RESULTS: In this work, two novel superresolution frameworks based on Expectation Maximization (EM) based Maximum Likelihood (ML) estimation using Kalman filters (MLK) technique are proposed to address the issues of non-uniform sampling and limited data availability of MT signals. The proposed MLK methods optimizes prediction of missing observations in the MT signal through information extraction using correlation-based patch processing and principal component analysis -based mutual information. Experimental results prove that the proposed MLK-based superresolution methods outperformed nonlinear interpolation and compressed sensing methods. CONCLUSIONS: This work aims to address limited data availability and data/observation loss incurred due to non-uniform sampling of biological signals such as MTs. For this purpose, statistical modelling of stochastic MT signals using EM based ML driven Kalman estimation (MLK) is considered as a fundamental framework for prediction of missing MT observations. It was experimentally validated that the proposed superresolution methods provided superior overall performance, better MT signal estimation using fewer samples, high SNR, low errors, and better MT parameter estimation than other methods.

The Study of Gonadal Hormonal Abnormalities and Sexual Dysfunction in HIV Positive Females: An Exploratory Study.

INTRODUCTION: Every endocrine gland has been reported to be affected at varying rates in HIV. HIV is a highly stigmatized chronic disease with a substantial co-occurrence of mental and sexual health problems; however the sexual health problems in women have not been extensively studied. AIM: To study the gonadal hormonal abnormalities and sexual dysfunction in HIV positive female patients and its possible association. MATERIALS AND METHODS: This descriptive/exploratory study was conducted in the Department of General Medicine at a tertiary care hospital from September 2013 to August 2015. The study group included 50 diagnosed HIV-positive patients. They were also subjected to specific questions regarding sexual dysfunction by female counselors using female sexual function index. Visits of the subjects were scheduled independent of the menstrual cycle. Hormonal levels (free testosterone, FSH, LH) were measured. RESULTS: Out of 50 patients, 26 patients in our study had sexual dysfunction (52%). Patients with age group between 30-39 years had the maximum sexual dysfunction compared to the other groups (<0.001). Patients with a CD4 count between 200 and 499 had the maximum sexual dysfunction (<0.02). Mean duration of HIV in the study was 30 months in sexual dysfunction group which was significant (p<0.005). Hormonal levels were found to be in normal range. All the study patients reported desire, arousal and lubrication problems whereas orgasm and satisfaction problems were noted in 60% patients with pain reported in 52%. CONCLUSION: We identified that although the hormonal levels were in the normal range, they were comparatively in the lower range in the dysfunction group than the non-dysfunctional group. Both free testosterone and FSH levels were low indicating involvement of the pituitary rather than the gonads. We also conclude that duration of HIV and also level of CD4 count is related to sexual dysfunction.

The use of compressive sensing and peak detection in the reconstruction of microtubules length time series in the process of dynamic instability.

Microtubules (MTs) are intra-cellular cylindrical protein filaments. They exhibit a unique phenomenon of stochastic growth and shrinkage, called dynamic instability. In this paper, we introduce a theoretical framework for applying Compressive Sensing (CS) to the sampled data of the microtubule length in the process of dynamic instability. To reduce data density and reconstruct the original signal with relatively low sampling rates, we have applied CS to experimental MT lament length time series modeled as a Dichotomous Markov Noise (DMN). The results show that using CS along with the wavelet transform significantly reduces the recovery errors comparing in the absence of wavelet transform, especially in the low and the medium sampling rates. In a sampling rate ranging from 0.2 to 0.5, the Root-Mean-Squared Error (RMSE) decreases by approximately 3 times and between 0.5 and 1, RMSE is small. We also apply a peak detection technique to the wavelet coefficients to detect and closely approximate the growth and shrinkage of MTs for computing the essential dynamic instability parameters, i.e., transition frequencies and specially growth and shrinkage rates. The results show that using compressed sensing along with the peak detection technique and wavelet transform in sampling rates reduces the recovery errors for the parameters.