Interpretation of the odds ratio from logistic regression after a transformation of the covariate vector

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

The Role of Healthcare Worker-Mediated Contact Networks in the Transmission of Vancomycin-Resistant Enterococci

BACKGROUND

User- and time-stamped data from hospital electronic health records (EHRs) present opportunities to evaluate how healthcare worker (HCW)-mediated contact networks impact transmission of multidrug-resistant pathogens, such as vancomycin-resistant enterococci (VRE).

METHODS

This is a retrospective analysis of incident acquisitions of VRE between July 1, 2016 and June 30, 2018. Clinical and demographic patient data were extracted from the hospital EHR system, including all recorded HCW contacts with patients. Contacts by an HCW with 2 different patients within 1 hour was considered a "connection". Incident VRE acquisition was determined by positive clinical or surveillance cultures collected ≥72 hours after a negative surveillance culture.

RESULTS

There were 2952 hospitalizations by 2364 patients who had ≥2 VRE surveillance swabs, 112 (4.7%) patients of which had incident nosocomial acquisitions. Patients had a median of 24 (interquartile range [IQR], 18-33) recorded HCW contacts per day, 9 (IQR, 5-16) of which, or approximately 40%, were connections that occurred <1 hour after another patient contact. Patients that acquired VRE had a higher average number of daily connections to VRE-positive patients (3.1 [standard deviation {SD}, 2.4] versus 2.0 [SD, 2.1]). Controlling for other risk factors, connection to a VRE-positive patient was associated with increased odds of acquiring VRE (odds ratio, 1.64; 95% confidence interval, 1.39-1.92).

CONCLUSIONS

We demonstrated that EHR data can be used to quantify the impact of HCW-mediated patient connections on transmission of VRE in the hospital. Defining incident acquisition risk of multidrug-resistant organisms through HCWs connections from EHR data in real-time may aid implementation and evaluation of interventions to contain their spread.

Standardizing effect size from linear regression models with log-transformed variables for meta-analysis

BACKGROUND

Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized.

METHODS

We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or asymmetrically distributed.

RESULTS

In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be used with caution in the case of independent variables with asymmetric distributions that significantly differ from the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the potential effects on neurodevelopment in children exposed to arsenic and manganese.

CONCLUSIONS

The procedure proposed has been shown to be valid and capable of expressing the effect size of a linear regression model based on different change criteria in the variables. Homogenizing the results from different studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations had been performed on the dependent and/or independent variables.

New risk factors for adult-onset incident asthma. A nested case-control study of host antioxidant defense

RATIONALE

Host antioxidant defense, consisting of enzymatic antioxidant activity and nonenzymatic antioxidant micronutrients, is implicated in asthma pathogenesis. Studies of antioxidant defense and adult incident asthma have either used measures of antioxidants estimated from questionnaires or not considered enzymatic aspects of host defense.

OBJECTIVES

We conducted the first study designed and powered to investigate the association of antioxidant defenses on adult incident asthma.

METHODS

In a nested case-control study, we followed Shanghai women (aged 40-70 years) without prevalent asthma at baseline, over 8 years. Subjects with incident asthma were ascertained prospectively by gold standard testing of symptomatic women and matched to two asymptomatic control subjects.

MEASUREMENTS AND MAIN RESULTS

Baseline urinary F2-isoprostanes, plasma concentrations of antioxidant micronutrients (tocopherols, xanthines, carotenes, and lycopene), and antioxidant enzyme activity (platelet-activating factor acetylhydrolase [PAF-AH] and superoxide dismutase) were measured from samples collected before disease onset. Among 65,372 women, 150 (0.24%) developed asthma. F2-isoprostane levels before asthma onset were not different between cases and control subjects. Doubling of α-tocopherol concentrations and PAF-AH activity was associated with 50 and 37% decreased risk of incident asthma (α-tocopherol: adjusted odds ratio = 0.52; 95% confidence interval, 0.32-0.84; PAF-AH: adjusted odds ratio = 0.63; 95% confidence interval, 0.42-0.93).

CONCLUSIONS

In this prospective study, α-tocopherol, within normal reference ranges, and PAF-AH enzymatic activity were associated with decreased asthma development. These modifiable risk factors may be an effective strategy to test for primary asthma prevention.

Risk of using logistic regression to illustrate exposure-response relationship of infectious diseases

Background

In most biological experiments, especially infectious disease, the exposure-response relationship is interrelated by a multitude of factors rather than many independent factors. Little is known about the suitability of ordinary, categorical exposures, and logarithmic transformation which have been presented in logistic regression models to assess the likelihood of an infectious disease as a function of a risk or exposure. This study aims to examine and compare the current approaches.

Methods

A simulated human immunodeficiency virus (HIV) population, dynamic infection data for 100,000 individuals with 1% initial prevalence and 2% infectivity, was created. Using the Monte Carlo method (computational algorithm) to repeat random sampling to obtain numerical results, linearity between log odds and exposure, and suitability in practice were examined in the three model approaches.

Results

Despite diverse population prevalence, the linearity was not satisfied between log odds and raw exposures. Logarithmic transformation of exposures improved the linearity to a certain extent, and categorical exposures satisfied the linear assumption (which was important for modelling). When the population prevalence was low (assumed < 10%), performances of the three models were significantly different. Comparing to ordinary logistic regression, the logarithmic transformation approach demonstrated better accuracy of estimation except that at the two inflection points: likelihood of infection increased from slowly to sharply, then slowly again. The approach using categorical exposures had better estimations around the real values, but the measurement was coarse due to categorization.

Conclusions

It is not suitable to directly use ordinary logistic regression to explore the exposure-response relationship of HIV as an infectious disease. This study provides some recommendations for practical implementations including: 1) utilize categorical exposure if a large sample size and low population prevalence are provided; 2) utilize a logarithmic transformed exposure if the sample size is insufficient or the population prevalence is too high (such as 30%).

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2334-14-540) contains supplementary material, which is available to authorized users.

Imbalanced estrogen metabolism in the brain: possible relevance to the etiology of Parkinson's disease

Damage to DNA by dopamine quinone and/or catechol estrogen quinones may play a significant role in the initiation of Parkinson's disease (PD). Depurinating estrogen-DNA adducts are shed from cells and excreted in urine. The aim of this study was to discover whether higher levels of estrogen-DNA adducts are associated with PD. Forty estrogen metabolites, conjugates, and DNA adducts were analyzed in urine samples from 20 PD cases and 40 matched controls by using ultra performance liquid chromatography/tandem mass spectrometry. The levels of adducts in cases versus controls (P < 0.005) suggest that unbalanced estrogen metabolism could play a causal role in the initiation of PD.

Sexual partner selection and HIV risk reduction among Black and White men who have sex with men

OBJECTIVES

We examined differences in sexual partner selection between Black and White men who have sex with men (MSM) to better understand how HIV status of participants' sexual partners and related psychosocial measures influence risk taking among these men.

METHODS

We collected cross-sectional surveys from self-reported HIV-negative Black MSM and White MSM attending a gay pride festival in Atlanta, Georgia.

RESULTS

HIV-negative White MSM were more likely than were HIV-negative Black MSM to report having unprotected anal intercourse with HIV-negative men, and HIV-negative Black MSM were more likely than were HIV-negative White MSM to report having unprotected anal intercourse with HIV status unknown partners. Furthermore, White MSM were more likely to endorse serosorting (limiting unprotected partners to those who have the same HIV status) beliefs and favorable HIV disclosure beliefs than were Black MSM.

CONCLUSIONS

White MSM appear to use sexual partner-related risk reduction strategies to reduce the likelihood of HIV infection more than do Black MSM. Partner selection strategies have serious limitations; however, they may explain in part the disproportionate number of HIV infections among Black MSM.