An Electronic Medical Record-Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation.

Background: Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategies to patients at high risk. However, fall risk assessment tools have typically been inaccurate for prediction, ineffective in prevention, and time-consuming to complete.

Evaluating automated machine learning platforms for use in healthcare.

Objective: To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models.

Materials And Methods: Evaluation criteria for selecting an Auto ML platform suited to ML needs of a local health district were developed in 3 steps: (1) identification of key requirements, (2) a market scan, and (3) an assessment process with desired outcomes.

Results: The final checklist comprising 21 functional and 6 non-functional criteria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case.

Some pyrimidohexahydroquinoline candidates: synthesis, DFT, cytotoxic activity evaluation, molecular docking, and studies.

Some hexahydroquinoline candidates were prepared by reacting 2-amino-3-cyano-1-cyclohexylhexahydroquinoline with oxalyl chloride and triethyl orthoformate. The computational chemical approach agreed with the product-testing results. The produced substances were examined for their antiproliferative activity against liver carcinoma (HepG2), breast adenocarcinoma (MCF7), prostate cancer (PC3), and colon cancer (HCT116) cell lines.

The adverse inpatient medication event and frailty (AIME-frail) risk prediction model.

Background: Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable.

Aim: To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients.

Removal of Toxic Lead from Wastewater by Seed Hull.

In this work, we address two concerns at once: waste reduction and the development of a lead removal adsorbent. The potential of seed hull (LSH) powder as an efficient, innovative, and economical adsorbent for Pb(II) absorption was examined in this study. Fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy, and scanning electron microscopy investigations were used to determine the structural and morphological properties of the LSH adsorbent.

Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.

The clinical prioritisation criteria (CPC) are a clinical decision support tool that ensures patients referred for public specialist outpatient services to Queensland Health are assessed according to their clinical urgency. Medical referrals are manually triaged and prioritised into three categories by the associated health service before appointments are booked. We have developed a method using artificial intelligence to automate the process of categorizing medical referrals based on clinical prioritization criteria (CPC) guidelines.

Design and synthesis of 2-amino-4,6-diarylpyrimidine derivatives as potent α-glucosidase and α-amylase inhibitors: structure-activity relationship, , QSAR, molecular docking, MD simulations and drug-likeness studies.

In the present study, a series of 2-amino-4,6-diarylpyrimidine derivatives was designed, synthesized, characterized and evaluated for their α-glucosidase and α-amylase enzyme inhibition assays. The outcomes proved that this class of compounds exhibit considerable inhibitory activity against both enzymes. Among the target compounds, compounds and demonstrated the most potent dual inhibition with IC = 0.

Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach.

Background: Unfractionated heparin (UFH) is an anticoagulant drug that is considered a high-risk medication because an excessive dose can cause bleeding, whereas an insufficient dose can lead to a recurrent embolic event. Therapeutic response to the initiation of intravenous UFH is monitored using activated partial thromboplastin time (aPTT) as a measure of blood clotting time. Clinicians iteratively adjust the dose of UFH toward a target, indication-defined therapeutic aPTT range using nomograms, but this process can be imprecise and can take ≥36 hours to achieve the target range.

Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment.

The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-driven tool developed using real-time EMR data for identifying patients at high risk of reaching critical conditions that may demand immediate interventions.

Efficacy of Sofosbuvir plus Ledipasvir in Egyptian patients with COVID-19 compared to standard treatment: a randomized controlled trial.

COVID-19 is a pandemic disease caused by SARS-CoV-2, which is an RNA virus similar to the hepatitis C virus (HCV) in the replication process. Sofosbuvir/ledipasvir is an approved drug to treat HCV infection. This study investigates the efficacy of Sofosbuvir/ledipasvir as a treatment for patients with moderate COVID-19 infection.

Theatre Performance Dashboard: Development and Challenges.

Manual theatre performance measurement is resource yearning and inaccurate. To automate the process, we built a dashboard which provides interactive visualisation of key performance metrics related to operating theatres. The aim is to assist in the efficient management of surgical services and provide visibility on metrics trending over time for health service facilities.

The Clinical Nursing and Midwifery Dashboard (CNMD): A State-Wide Implementation.

The clinical nursing and midwifery dashboard (CNMD) was built to provide a near real-time information and data visualisations for nurse unit managers (NUMs) and maternity unit managers (MUMs) within only a 5-15 minutes delay from when they enter data to the integrated electronic medical records (ieMR) system. The dashboard displays metrics and information about current adult inpatients in overnight wards. The aim is to support NUMs and MUMs to manage their daily workload and have continuous visibility of patients nursing risk and safety assessment documentation.

The Anti-Inflammatory Effect of a γ-Lactone Isolated from Ostrich Oil of (Ratite) and Its Formulated Nano-Emulsion in Formalin-Induced Paw Edema.

The ostrich oil of (Ratite) found uses in folk medicine as an anti-inflammatory in eczema and contact dermatitis. The anti-inflammatory effect of a γ-lactone (5-hexyl-3H-furan-2-one) isolated from ostrich oil and its formulated nano-emulsion in formalin-induced paw edema was investigated in this study. Ostrich oil was saponified using a standard procedure; the aqueous residue was fractionated, purified, and characterized as γ-lactone (5-hexyl-3H-furan-2-one) through the interpretation of IR, NMR, and MS analyses.

What is needed to mainstream artificial intelligence in health care?

Artificial intelligence (AI) has become a mainstream technology in many industries, but not yet in health care. Although basic research and commercial investment are burgeoning across various clinical disciplines, AI remains relatively non-existent in most healthcare organisations. This is despite hundreds of AI applications having passed proof-of-concept phase, and scores receiving regulatory approval overseas.

Systematic review of machine learning models for personalised dosing of heparin.

Aim: To identify and critically appraise studies of prediction models, developed using machine learning (ML) methods, for determining the optimal dosing of unfractionated heparin (UFH).

Methods: Embase, PubMed, CINAHL, Web of Science, International Pharmaceutical Abstracts and IEEE Xplore databases were searched from inception to 31 January 2020 to identify relevant studies using key search terms synonymous with artificial intelligence or ML, 'prediction', 'dose', 'activated partial thromboplastin time (aPTT)' and 'UFH.' Studies had to have used ML methods for developing models that predicted optimal dose of UFH or target therapeutic aPTT levels in the hospital setting.

Phytochemical Profiling, In Vitro and In Silico Anti-Microbial and Anti-Cancer Activity Evaluations and Staph GyraseB and -TOP-IIβ Receptor-Docking Studies of Major Constituents of L. Aqueous-Ethanolic Extract and Its Subsequent Fractions: An Approach to Validate Traditional Phytomedicinal Knowledge.

, an edible halophytic plant, is part of the traditional medicine chest in the Mediterranean region for symptomatic relief of diabetes, hypertension, wound healing, burns, infections, and rheumatoid arthritis pain. The current study aimed to characterize phytoconstituents, and the evaluations of the anti-microbial-biofilm, and anti-cancers bioactivities of the plant's mother liquor, i.e.

Development and validation of the Adverse Inpatient Medication Event model (AIME).

Aims: Medication harm has negative clinical and economic consequences, contributing to hospitalisation, morbidity and mortality. The incidence ranges from 4 to 14%, of which up to 50% of events may be preventable. A predictive model for identifying high-risk inpatients can guide a timely and systematic approach to prioritisation.

Genetic Variation at 15 Autosomal STR Loci Among Seven Egyptian Populations.

Egypt is a transcontinental country containing substantial ethnic, cultural, and linguistic diversity among its people. This study was conducted to investigate the genetic variation at 15 AmpFlSTR Identifiler short tandem repeat (STR) loci, D8S1179, D21S11, D7S820, CSF1PO, D3S1358, TH01, D13S317, D16S539, D2S1338, D19S433, vWA, TPOX, D18S51, D5S818, and FGA, within and between seven Egyptian populations. Samples of 814 unrelated individuals from Northern Coast, Delta, Greater Cairo, Canal governorates, Northern Upper Egypt, Southern Upper Egypt, and Sinai were investigated.

Rat hippocampal CA3 neuronal injury induced by limb ischemia/reperfusion: A possible restorative effect of alpha lipoic acid.

Limb ischemia reperfusion (I/R) injury is associated with serious local and systemic effects. Reperfusion may augment tissue injury in excess of that produced by ischemia alone. The hippocampus has been reported to be vulnerable to I/R injury.

Vulnerability of glia and vessels of rat substantia nigra in rotenone Parkinson model.

Background: Astrocytes have been implicated as potentially exerting both neurotoxic and neuroprotective activities in Parkinson's disease (PD). Whether glial cells negatively impact the neuron integrity remains to be determined. We aimed to assess the vulnerability of glia and vessels in rat substantia nigra in a rotenone PD model.

Design, Synthesis, and Cytotoxicity Evaluation of Novel Griseofulvin Analogues with Improved Water Solubility.

Griseofulvin is an important antifungal agent that has recently received attention due to its antiproliferative activity in mammalian cancer cells. Study of SAR of some griseofulvin analogues has led to the identification of 2'-benzyloxy griseofulvin , a more potent analogue which retards tumor growth through inhibition of centrosomal clustering. However, similar to griseofulvin , compound exhibited poor aqueous solubility.

Phenylthiomethyl Ketone-Based Fragments Show Selective and Irreversible Inhibition of Enteroviral 3C Proteases.

Lead structure discovery mainly focuses on the identification of noncovalently binding ligands. Covalent linkage, however, is an essential binding mechanism for a multitude of successfully marketed drugs, although discovered by serendipity in most cases. We present a concept for the design of fragments covalently binding to proteases.

Detection of Occult Acute Kidney Injury in Glucose-6-Phosphate Dehydrogenase Deficiency Anemia.

Background: Glucose-6-phosphate dehydrogenase (G6PD) deficiency anemia is associated with intravascular hemolysis. The freely filtered hemoglobin can damage the kidney. We aimed to assess any subclinical renal injury in G6PD children.

Nicotine impact on the structure of adult male guinea pig auditory cortex.

Background: A growing body of evidence suggests that chronic cigarette smoking causes detrimental effects on brain morphology.

Aim Of Work: To study the structural changes in auditory cortex region (Layer V), under the influence of nicotine.

Material And Methods: Three animal groups (10 each) were used; group I (control) and groups IIa and IIb received 3 and 6mg/kg nicotine respectively.