Metabolic control in type 2 diabetes correlates weakly with patient adherence to oral hypoglycaemic treatment.

Introduction: Patient adherence to treatment is viewed as essential to good metabolic control in diabetes. Our primary objective was to determine if self-reported patient adherence correlated strongly with metabolic control. Our secondary objective was to determine the natural grouping of factors which influence adherence. Materials and Methods: Data were collected using a questionnaire set with 5-point Likert scales. Primary analysis was done using Spearman's correlation coefficient between self-reported composite adherence scores and HbA1c. Secondary analysis was done using exploratory factor analysis. Results: The primary analysis suggests that patient adherence to the treatment regime is weakly correlated to metabolic control. Calculated Spearman's rho was 0.197, with a two-tailed P value of 0.027. The secondary analysis demonstrates the natural clustering of factors that influence patient adherence to treatment. A 6-factor solution was found to account for most of the variance in the data. We also found that feelings of frustration, anxiety, and depression were associated with a lack of knowledge about diabetes treatment. In addition, belief in traditional medicine correlated strongly with ethnicity. Conclusion: A good treatment regime for type 2 diabetes mellitus influences metabolic outcome far more than patient adherence

Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems

This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning. We draw on an understanding of ADM as a socio-technical process involving both human and technical elements, beginning before a decision is made and extending beyond the decision itself. While explanations and other model-centric mechanisms may assist some accountability concerns, they often provide insufficient information of these broader ADM processes for regulatory oversight and assessments of legal compliance. Reviewability involves breaking down the ADM process into technical and organisational elements to provide a systematic framework for determining the contextually appropriate record-keeping mechanisms to facilitate meaningful review - both of individual decisions and of the process as a whole. We argue that a reviewability framework, drawing on administrative law's approach to reviewing human decision-making, offers a practical way forward towards more a more holistic and legally-relevant form of accountability for ADM

Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs

AbstractTo address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an example use case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker, as well as forcing a more explicit representation of which values and objectives are prioritised.</jats:p

Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs

AbstractTo address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an example use case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker, as well as forcing a more explicit representation of which values and objectives are prioritised.</jats:p

Innovating with confidence: embedding AI governance and fairness in a financial services risk management framework

An increasing number of financial services (FS) companies are adopting solutions driven by artificial intelligence (AI) to gain operational efficiencies, derive strategic insights, and improve customer engagement. However, the rate of adoption has been low, in part due to the apprehension around its complexity and self-learning capability, which makes auditability a challenge in a highly regulated industry. There is limited literature on how FS companies can implement the governance and controls specific to AI-driven solutions. AI auditing cannot be performed in a vacuum; the risks are not confined to the algorithm itself, but rather permeates the entire organization. Using the risk of unfairness as an example, this paper will introduce the overarching governance strategy and control framework to address the practical challenges in mitigating risks AI introduces. With regulatory implications and industry use cases, this framework will enable leaders to innovate with confidence

Formalising trade-offs beyond algorithmic fairness: lessons from ethical philosophy and welfare economics

Abstract: There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented within narrow and targeted fairness toolkits for algorithm assessments that are difficult to integrate into an algorithm’s broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around the acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker’s ethical values

PRELIMINARY STUDY ON ANTIFUNGAL ACTIVITY OF NEOLAMARCKIA MACROPHYLLA LEAVES EXTRACT

The genus Neolamarckia includes two species which are N. macrophylla and N. cadamba. Many research have been done on various aspect of N. cadamba including the antimicrobial and the phytochemical compounds in the extract of different part of the tree (Qureshi et al., 2021; Islam et al., 2015). In contrary with N. cadamba, there are very limited research study on N. macrophylla. The aims of this research were to extract the antifungal properties of N. macrophylla leaf using aqueous and methanol extraction followed by the evaluation of antifungal activity of the leaf extracts against Aspergillus flavus, Aspergillus niger and Fusarium solani. Extracts of different concentration (12.5 mg/mL, 25 mg/mL, 50 mg/mL and 100 mg/mL) were spread on the potato dextrose agar to evaluate the fungi growth using the poisoned food technique. The results were compared with the normal growth of the fungi in potato dextrose agar. The results on fungi growth showed inconsistency of inhibitory effect on the test fungi exhibiting inhibition percentage within the range of 4-80%. Further improvement on the evaluation of antifungal activity needed in future research to obtained consistent results as to ensure validity of the data

SCREENING OF ANTIBACTERIAL ACTIVITY OF NEOLAMARCKIA MACROPHYLLA LEAF CRUDE EXTRACT

Neolamarckia macrophylla (Roxb.) Bosser belongs to genus Neolamarckia of the family Rubiaceae which commonly known as the red kelampayan by the locals. It is an important timber tree that is used for reforestation, the woods used as raw materials of paper and plywood, while its bark used to relive fever in traditional medicine (Shi et al. 2020; Qalbi et al., 2019). The aim of this study was to determine the antimicrobial potential of N. macrophylla leaf crude extract. The crude extract was obtained using maceration technique using ethanol and distilled water as solvent for the extraction process. The antibacterial activities were screened against Escherichia coli, Pseudomonas aeruginosa, Salmonella typhimurium, Listeria monocytogenes, Staphylococcus aureus and Bacillus cereus with different concentration of the crude extract (12.5 mg/mL, 25 mg/mL, 50 mg/mL and 100 mg/mL) by using disc diffusion method. The test bacteria demonstrated inhibition zone between 7 to 10 mm with the linear increase of inhibition zone with concentration of extract. This findings provide preliminary results on the antibacterial potential of N. Macrophylla to be used in various application. Further purification of compounds might be necessary to explore more on its optimum potential

Molecular Mechanisms of Antiviral Agents against Dengue Virus

Dengue is a major global health threat causing 390 million dengue infections and 25,000 deaths annually. The lack of efficacy of the licensed Dengvaxia vaccine and the absence of a clinically approved antiviral against dengue virus (DENV) drive the urgent demand for the development of novel anti-DENV therapeutics. Various antiviral agents have been developed and investigated for their anti-DENV activities. This review discusses the mechanisms of action employed by various antiviral agents against DENV. The development of host-directed antivirals targeting host receptors and direct-acting antivirals targeting DENV structural and non-structural proteins are reviewed. In addition, the development of antivirals that target different stages during post-infection such as viral replication, viral maturation, and viral assembly are reviewed. Antiviral agents designed based on these molecular mechanisms of action could lead to the discovery and development of novel anti-DENV therapeutics for the treatment of dengue infections. Evaluations of combinations of antiviral drugs with different mechanisms of action could also lead to the development of synergistic drug combinations for the treatment of dengue at any stage of the infection