Synthesis of novel pyrimidine and fused pyrimidine derivatives

4-Amino-2-(benzylthio)-6-(4-methoxyphenyl)pyrimidine-5-carbonitrile (1) was prepared by treatment of s-benzylthiuronium chloride with 2-(4-methoxybenzylidene)malononitrile in ethanolic sodium hydroxide with hydrazine hydrate to afford the hydrazino derivative 2, which was allowed to react with different electrophilic reagents to give the pyrimidine derivatives 3-12. The proclivity of (E)-2-cyano-3-(4-nitrophenyl)acrylamide (13) towards carbon and nitrogen nucleophiles was also investigated. IR, 1H NMR and mass spectra for all the synthesized compounds were discussed. All derived compounds were investigated for anti avian influenza (H5N1) virus activity and compared with zanamivir as control drug. All the synthesized compounds didn’t possess any antiviral activity

Synthesis and reactions of (Z)-2-imino-5-(3,4,5-trimethoxy benzylidene)thiazolidin-4(H)one

5-Arylmethylene-2-imino-4-oxo-2-thiazolidine 3 was obtained as the sole product from the reaction of α-cyano-3,4,5-trimethoxy cinnamonitrile and/or ethyl-α-cyano-3,4,5-trimethoxy cinnamate (1a,b) with 2-imino-4-oxo-2-thiazolidine 2. The reaction of 3 with benzyl amine gave the imidazolidin-4(H)one derivative 4 while with hydrazine hydrate afforded the dimeric product 5. Also, reaction of thiazolidinone derivative 3 with piperidine gave thiazol-4(5H)one derivative 6 which on treatment with Grignard reagent and active methylene compounds afforded thiazolidin-4-one derivatives 7-9, respectively. Compound 6 was converted to the potassium salt 10 which treated with acetic acid, ethyl chloroacetate and furoyl chloride to give the compounds 11-13, respectively. The structures of all new compounds were evidenced by microanalytical data and spectral data

Dehydroepiandrosterone sulfate level and knee osteoarthritis in older adults: preliminary data for the possible link

ABSTRACT Background: Dehydroepiandrosterone (DHEA) is believed to be protective against articular cartilage injury and it is widely used as one of the natural remedies for inflammatory and degenerative arthritis. Yet, information about the association between DHEA level and knee OA is lacking. Objectives: To explore the link between serum dehydroepiandrosterone sulfate (DHEAs) levels and knee OA among elderly patients. Methods: A case control study was conducted on 80 elderly subjects (40 males, 40 females) aged 60 years and older attending the outpatient clinics in Ain Shams University Hospital. Participants underwent a standard clinical examination of the knee, assessment of physical difficulty and pain severity using WOMAC OA index. Weight-bearing anteroposterior radiographs of the knees in the semi-flexed position were performed. Serum levels of DHEAs were examined. Results: The serum level of DHEAs in males with knee OA was 0.29 ± 0.17 μg/mL compared to those without knee OA 0.59 ± 0.51 μg/mL (p=0.006), small but significant difference existed between the serum level of DHEAs in females with knee OA 0.25 ± 0.11 μg/mL compared to those without knee OA 0.39 ± 0.26 μg/mL (p=0.044*). Additionally, the serum DHEAs level negatively correlated with the severity of knee OA in both sexes. Conclusions: There was a sex difference in serum DHEAs level and occurrence of knee OA. Lower levels of DHEAs were reported in elderly with knee OA and the serum DHEAs level negatively correlated with the severity of knee OA

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning

Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities—DXA and retinal images)—to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respectively. The multi-modal model showed an improved accuracy of 78.3% in classifying CVD group and the control group. We used gradient class activation map (GradCAM) to highlight the areas of interest in the retinal images that influenced the decisions of the proposed DL model most. It was observed that the model focused mostly on the centre of the retinal images where signs of CVD such as hemorrhages were present. This indicates that our model can identify and make use of certain prognosis markers for hypertension and ischemic heart disease. From DXA data, we found higher values for bone mineral density, fat content, muscle mass and bone area across majority of the body parts in CVD group compared to the control group indicating better bone health in the Qatari CVD cohort. This seminal method based on DXA scans and retinal images demonstrate major potentials for the early detection of CVD in a fast and relatively non-invasive manner

Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors

Obesity is an emerging public health problem in the Western world as well as in the Gulf region. Qatar, a tiny wealthy county, is among the top-ranked obese countries with a high obesity rate among its population. Compared to Qatar’s severity of this health crisis, only a limited number of studies focused on the systematic identification of potential risk factors using multimodal datasets. This study aims to develop machine learning (ML) models to distinguish healthy from obese individuals and reveal potential risk factors associated with obesity in Qatar. We designed a case-control study focused on 500 Qatari subjects, comprising 250 obese and 250 healthy individuals- the later forming the control group. We obtained the most extensive collection of clinical measurements for the Qatari population from the Qatar Biobank (QBB) repertoire, including (i) Physio-clinical Biomarkers, (ii) Spirometry, (iii) VICORDER, (iv) DXA scan composition, and (v) DXA scan densitometry readings. We developed several machine learning (ML) models to distinguish healthy from obese individuals and applied multiple feature selection techniques to identify potential risk factors associated with obesity. The proposed ML model achieved over 90% accuracy, thereby outperforming the existing state of the art models. The outcome from the ablation study on multimodal clinical datasets revealed physio-clinical measurements as the most influential risk factors in distinguishing healthy versus obese subjects. Furthermore, multiple feature ranking techniques confirmed known obesity risk factors (c-peptide, insulin, albumin, uric acid) and identified potential risk factors linked to obesity-related comorbidities such as diabetes (e.g., HbA1c, glucose), liver function (e.g., alkaline phosphatase, gamma-glutamyl transferase), lipid profile (e.g., triglyceride, low density lipoprotein cholesterol, high density lipoprotein cholesterol), etc. Most of the DXA measurements (e.g., bone area, bone mineral composition, bone mineral density, etc.) were significantly (p-value < 0.05) higher in the obese group. Overall, the net effect of hypothesized protective factors of obesity on bone mass seems to have surpassed the hypothesized harmful factors. All the identified factors warrant further investigation in a clinical setup to understand their role in obesity

Machine Learning Models Reveal The Importance of Clinical Biomarkers for the Diagnosis of Alzheimer\u27s Disease

Alzheimer\u27s Disease (AD) is a neurodegenerative disease that causes complications with thinking capability, memory and behavior. AD is a major public health problem among the elderly in developed and developing countries. With the growth of AD around the world, there is a need to further expand our understanding of the roles different clinical measurements can have in the diagnosis of AD. In this work, we propose a machine learning-based technique to distinguish control subjects with no cognitive impairments, AD subjects, and subjects with mild cognitive impairment (MCI), often seen as precursors of AD. We utilized several machine learning (ML) techniques and found that Gradient Boosting Decision Trees achieved the highest performance above 84% classification accuracy. Also, we determined the importance of the features (clinical biomarkers) contributing to the proposed multi-class classification system. Further investigation on the biomarkers will pave the way to introduce better treatment plan for AD patients

Loss of corneal nerves and brain volume in mild cognitive impairment and dementia

Abstract Introduction This study compared the capability of corneal confocal microscopy (CCM) with magnetic resonance imaging (MRI) brain volumetry for the diagnosis of mild cognitive impairment (MCI) and dementia. Methods In this cross‐sectional study, participants with no cognitive impairment (NCI), MCI, and dementia underwent assessment of Montreal Cognitive Assessment (MoCA), MRI brain volumetry, and CCM. Results Two hundred eight participants with NCI (n = 42), MCI (n = 98), and dementia (n = 68) of comparable age and gender were studied. For MCI, the area under the curve (AUC) of CCM (76% to 81%), was higher than brain volumetry (52% to 70%). For dementia, the AUC of CCM (77% to 85%), was comparable to brain volumetry (69% to 93%). Corneal nerve fiber density, length, branch density, whole brain, hippocampus, cortical gray matter, thalamus, amygdala, and ventricle volumes were associated with cognitive impairment after adjustment for confounders (All P’s < .01). Discussion The diagnostic capability of CCM compared to brain volumetry is higher for identifying MCI and comparable for dementia, and abnormalities in both modalities are associated with cognitive impairment

Corneal nerve loss predicts dementia in patients with mild cognitive impairment

Abstract Objectives This study compared the utility of corneal nerve measures with brain volumetry for predicting progression to dementia in individuals with mild cognitive impairment (MCI). Methods Participants with no cognitive impairment (NCI) and MCI underwent assessment of cognitive function, brain volumetry of thirteen brain structures, including the hippocampus and corneal confocal microscopy (CCM). Participants with MCI were followed up in the clinic to identify progression to dementia. Results Of 107 participants with MCI aged 68.4 ± 7.7 years, 33 (30.8%) progressed to dementia over 2.6‐years of follow‐up. Compared to participants with NCI (n = 12), participants who remained with MCI (n = 74) or progressed to dementia had lower corneal nerve measures (p < 0.0001). Progressors had lower corneal nerve measures, hippocampal, and whole brain volume (all p < 0.0001). However, CCM had a higher prognostic accuracy (72%–75% vs 68%–69%) for identifying individuals who progressed to dementia compared to hippocampus and whole brain volume. The adjusted odds ratio for progression to dementia was 6.1 (95% CI: 1.6–23.8) and 4.1 (95% CI: 1.2–14.2) higher with abnormal CCM measures, but was not significant for abnormal brain volume. Interpretation Abnormal CCM measures have a higher prognostic accuracy than brain volumetry for predicting progression from MCI to dementia. Further work is required to validate the predictive ability of CCM compared to other established biomarkers of dementia

Impact of the COVID-19 pandemic on patients with paediatric cancer in low-income, middle-income and high-income countries: a multicentre, international, observational cohort study

OBJECTIVES: Paediatric cancer is a leading cause of death for children. Children in low-income and middle-income countries (LMICs) were four times more likely to die than children in high-income countries (HICs). This study aimed to test the hypothesis that the COVID-19 pandemic had affected the delivery of healthcare services worldwide, and exacerbated the disparity in paediatric cancer outcomes between LMICs and HICs. DESIGN: A multicentre, international, collaborative cohort study. SETTING: 91 hospitals and cancer centres in 39 countries providing cancer treatment to paediatric patients between March and December 2020. PARTICIPANTS: Patients were included if they were under the age of 18 years, and newly diagnosed with or undergoing active cancer treatment for Acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, Wilms' tumour, sarcoma, retinoblastoma, gliomas, medulloblastomas or neuroblastomas, in keeping with the WHO Global Initiative for Childhood Cancer. MAIN OUTCOME MEASURE: All-cause mortality at 30 days and 90 days. RESULTS: 1660 patients were recruited. 219 children had changes to their treatment due to the pandemic. Patients in LMICs were primarily affected (n=182/219, 83.1%). Relative to patients with paediatric cancer in HICs, patients with paediatric cancer in LMICs had 12.1 (95% CI 2.93 to 50.3) and 7.9 (95% CI 3.2 to 19.7) times the odds of death at 30 days and 90 days, respectively, after presentation during the COVID-19 pandemic (p<0.001). After adjusting for confounders, patients with paediatric cancer in LMICs had 15.6 (95% CI 3.7 to 65.8) times the odds of death at 30 days (p<0.001). CONCLUSIONS: The COVID-19 pandemic has affected paediatric oncology service provision. It has disproportionately affected patients in LMICs, highlighting and compounding existing disparities in healthcare systems globally that need addressing urgently. However, many patients with paediatric cancer continued to receive their normal standard of care. This speaks to the adaptability and resilience of healthcare systems and healthcare workers globally