Are evolutionary debunking arguments really self-defeating?

Evolutionary Debunking Arguments (EDAs) are defined as arguments that appeal to the evolutionary genealogy of our beliefs to undermine their justification. Recently, Helen De Cruz and her coauthors supported the view that EDAs are selfdefeating: if EDAs claim that human arguments are not justified, because the evolutionary origin of the beliefs which figure in such arguments undermines those beliefs, and EDAs themselves are human arguments, then EDAs are not justified, and we should not accept their conclusions about the fact that human arguments are unjustified. De Cruz's objection to EDAs is similar to the objection raised by Reuben Hersh against the claim that, since by Gödel's second incompleteness theorem the purpose of mathematical logic to give a secure foundation for mathematics cannot be achieved, mathematics cannot be said to be absolutely certain. The response given by Carlo Cellucci to Hersh's objection shows that the claim that by Gödel's results mathematics cannot be said to be absolutely certain is not self-defeating, and can be adopted to show that EDAs are not selfdefeating as well in a twofold sense: an argument analogous to Cellucci's one may be developed to face De Cruz's objection, and such argument may be further refined incorporating Cellucci's response itself in it, to make it stronger. This paper aims at showing that the accusation of being self-defeating moved against EDAs is inadequate by elaborating an argument which can be considered an EDA and which can also be shown not to be self-defeating

Scientific realism, adaptationism and the problem of the criterion

Scientific Realism (SR) has three crucial aspects: 1) the centrality of the concept of truth, 2) the idea that success is a reliable indicator of truth, and 3) the idea that the Inference to the Best Explanation is a reliable inference rule. It will be outlined how some realists try to overcome the difficulties which arise in justifying such crucial aspects relying on an adaptationist view of evolutionism, and why such attempts are inadequate. Finally, we will briefly sketch some of the main difficulties the realist has to face in defending those crucial aspects, and how such difficulties are deeply related: they derive from the inability of SR to satisfyingly avoid the sceptical challenge of the criterion of truth. Indeed, SR seems not to be able to fill the so-called ‘epistemic gap’ (Sankey 2008). In fact, the epistemic gap cannot be filled in no way other than obtaining a criterion of truth, but such a criterion cannot be obtained if the epistemic gap obtains

On Mizrahi's argument against Stanford's instrumentalism

Mizrahi’s argument against Stanford’s challenge to scientific realism is analyzed. Mizrahi’s argument is worth of attention for at least two reasons: (1) unlike other criticisms that have been made to Stanford’s view so far, Mizrahi’s argument does not question any specific claim of Stanford’s argument, rather it puts into question the very coherence of Stanford’s position, because it argues that since Stanford’s argument rests on the problem of the unconceived alternatives, Stanford’s argument is self-defeating. Thus, if Mizrahi’s argument is effective in countering Stanford’s view, it may be able to question the validity of other philosophical positions which similarly rest on the problem of the unconceived alternatives; (2) Mizrahi’s argument against Stanford’s view is in part based on the development of a Stanford-like argument for the field of philosophy. This makes Mizrahi’s argument potentially relevant to the metaphilosophical debate. After careful examination, Mizrahi’s argument against Stanford’s instrumentalism is found wanting. Moreover, a Stanford-like argument is developed, which aims at challenging the metaphilosophical stance implied by Mizrahi’s argument against Stanford’s instrumentalism

Mathematical proofs and scientific discovery

The idea that science can be automated is so deeply related to the view that the method of mathematics is the axiomatic method, that confuting the claim that mathematical knowledge can be extended by means of the axiomatic method is almost equivalent to confuting the claim that science can be automated. I argue that the axiomatic view is inadequate as a view of the method of mathematics and that the analytic view is to be preferred. But, if the method of mathematics and natural sciences is the analytic method, then the advancement of knowledge cannot be mechanized, since non-deductive reasoning plays a crucial role in the analytic method, and non-deductive reasoning cannot be fully mechanized

The pursuit of knowledge and the problem of the unconceived alternatives

In the process of scientific discovery, knowledge ampliation is pursued by means of non-deductive inferences. When ampliative reasoning is performed, probabilities cannot be assigned objectively. One of the reasons is that we face the problem of the unconceived alternatives: we are unable to explore the space of all the possible alternatives to a given hypothesis, because we do not know how this space is shaped. So, if we want to adequately account for the process of knowledge ampliation, we need to develop an account of the process of scientific discovery which is not exclusively based on probability calculus. We argue that the analytic view of the method of science advocated by Cellucci is interestingly suited to this goal, since it rests on the concept of plausibility. In this perspective, in order to account for how probabilities are in fact assigned in uncertain contexts and knowledge ampliation is really pursued, we have to take into account plausibility-based considerations

Acute deep vein thrombosis in COVID 19 hospitalized patients. Risk factors and clinical outcomes

Nello studio vengono analizzati i fattori di rischio in pazienti con infezione da COVID 19 e trombosi venosa profond

Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study

Aim: To assess the effect of the coronavirus disease 2019 (COVID-19) lockdown on glycaemic control in subjects with type 2 diabetes (T2D). Materials and Methods: In this observational, multicentre, retrospective study conducted in the Lazio region, Italy, we compared the differences in the HbA1c levels of 141 subjects with T2D exposed to lockdown with 123 matched controls with T2D who attended the study centres 1 year before. Basal data were collected from 9 December to 9 March and follow-up data from 3 June to 10 July in 2020 for the lockdown group, and during the same timeframes in 2019 for the control groups. Changes in HbA1c (ΔHbA1c) and body mass index (ΔBMI) during lockdown were compared among patients with different psychological well-being, as evaluated by tertiles of the Psychological General Well-Being Index (PGWBS). Results: No difference in ΔHbA1c was found between the lockdown and control groups (lockdown group −0.1% [−0.5%−0.3%] vs. control group −0.1% [−0.4%−0.2%]; p =.482). Also, no difference was found in ΔBMI (p =.316) or ΔGlucose (p =.538). In the lockdown group, subjects with worse PGWBS showed a worsening of HbA1c (p =.041 for the trend among PGWBS tertiles) and BMI (p =.022). Conclusions: The COVID-19 lockdown did not significantly impact glycaemic control in people with T2D. People with poor psychological well-being may experience a worsening a glycaemic control because of restrictions resulting from lockdown. These findings may aid healthcare providers in diabetes management once the second wave of COVID-19 has ended

Clinical features of patients with type 2 diabetes with and without Covid-19: a case control study (CoViDiab I)

Aims: To evaluate whether subjects with diabetes hospitalized for Coronavirus disease-19 (Covid-19) represent a subgroup of patients with high-risk clinical features compared to patients with diabetes without Covid-19. Methods: In this case-control study 79 patients with type 2 diabetes out of 354 adults hospitalized for Covid-19 and 158 controls with type 2 diabetes but without Covid-19, matched for age and gender, were enrolled. Medical history and concomitant therapies were retrieved from medical charts and compared between cases and controls, controlling for confounders. Results: Fully-adjusted multivariate logistic regression model showed that previous CVD history did not differ between patients with and without Covid-19 (odds ratio 1.40, 95% confidence interval [CI]: 0.59–3.32, p = 0.45). A higher prevalence of chronic obstructive pulmonary disease (COPD) (OR 3.72, 95%CI: 1.42–9.72, p = 0.007) and of chronic kidney disease (CKD) (OR 3.08, 95%CI: 1.18–8.06, p = 0.022) and a lower prevalence of ever smokers (OR 0.30, 95%CI: 0.13–0.67, p = 0.003), of users of lipid lowering agents (OR 0.26, 95%CI: 0.12–0.54, p < 0.001), and of anti-hypertensive drugs (OR 0.39, 95%CI: 0.16–0.93, p = 0.033) were found among cases. Conclusions: CVD prevalence does not differ between people with diabetes with and without Covid-19 requiring hospitalization. An increased prevalence of COPD and of CKD in Covid-19 patients with type 2 diabetes is suggested. These findings aid to clarify the relationship between underlying conditions and SARS-CoV-2 infection in the high-risk group of patients with diabetes

Cardiometabolic multimorbidity is associated with a worse Covid-19 prognosis than individual cardiometabolic risk factors. A multicentre retrospective study (CoViDiab II)

Background: Cardiometabolic disorders may worsen Covid-19 outcomes. We investigated features and Covid-19 outcomes for patients with or without diabetes, and with or without cardiometabolic multimorbidity. Methods: We collected and compared data retrospectively from patients hospitalized for Covid-19 with and without diabetes, and with and without cardiometabolic multimorbidity (defined as ≥ two of three risk factors of diabetes, hypertension or dyslipidaemia). Multivariate logistic regression was used to assess the risk of the primary composite outcome (any of mechanical ventilation, admission to an intensive care unit [ICU] or death) in patients with diabetes and in those with cardiometabolic multimorbidity, adjusting for confounders. Results: Of 354 patients enrolled, those with diabetes (n = 81), compared with those without diabetes (n = 273), had characteristics associated with the primary composite outcome that included older age, higher prevalence of hypertension and chronic obstructive pulmonary disease (COPD), higher levels of inflammatory markers and a lower PaO2/FIO2 ratio. The risk of the primary composite outcome in the 277 patients who completed the study as of May 15th, 2020, was higher in those with diabetes (Adjusted Odds Ratio (adjOR) 2.04, 95%CI 1.12-3.73, p = 0.020), hypertension (adjOR 2.31, 95%CI: 1.37-3.92, p = 0.002) and COPD (adjOR 2.67, 95%CI 1.23-5.80, p = 0.013). Patients with cardiometabolic multimorbidity were at higher risk compared to patients with no cardiometabolic conditions (adjOR 3.19 95%CI 1.61-6.34, p = 0.001). The risk for patients with a single cardiometabolic risk factor did not differ with that for patients with no cardiometabolic risk factors (adjOR 1.66, 0.90-3.06, adjp = 0.10). Conclusions: Patients with diabetes hospitalized for Covid-19 present with high-risk features. They are at increased risk of adverse outcomes, likely because diabetes clusters with other cardiometabolic conditions

Bayesian profiling of molecular signatures to predict event times

BACKGROUND: It is of particular interest to identify cancer-specific molecular signatures for early diagnosis, monitoring effects of treatment and predicting patient survival time. Molecular information about patients is usually generated from high throughput technologies such as microarray and mass spectrometry. Statistically, we are challenged by the large number of candidates but only a small number of patients in the study, and the right-censored clinical data further complicate the analysis. RESULTS: We present a two-stage procedure to profile molecular signatures for survival outcomes. Firstly, we group closely-related molecular features into linkage clusters, each portraying either similar or opposite functions and playing similar roles in prognosis; secondly, a Bayesian approach is developed to rank the centroids of these linkage clusters and provide a list of the main molecular features closely related to the outcome of interest. A simulation study showed the superior performance of our approach. When it was applied to data on diffuse large B-cell lymphoma (DLBCL), we were able to identify some new candidate signatures for disease prognosis. CONCLUSION: This multivariate approach provides researchers with a more reliable list of molecular features profiled in terms of their prognostic relationship to the event times, and generates dependable information for subsequent identification of prognostic molecular signatures through either biological procedures or further data analysis