Mental Health Issues of the Medical Workforce during COVID-19: A Review

The COVID-19 pandemic is a public health emergency. As we write, the world counts more than 10 million positive cases and more than 500 thousand deaths. The difficult conditions faced by healthcare workers helping with the COVID-19 pandemic are leading to severe adverse mental health consequences. The aim of this review is to summarize and analyze the mental health issues that healthcare workers are experiencing during the COVID-19 outbreak. We conduct a systematic literature review to investigate the healthcare workforce’s mental health disorders. About 145 articles were retrieved for the period between January 1, 2020 and April 30, 2020. After screening, 27 articles were selected for full-text examination, 13 were included in the review. Of the studies included, 69% (9/13) and 61% (8/13) investigated depression and anxiety, respectively, although other mental health disorders such as insomnia, distress, stress, and fear were also assessed. Most of the healthcare workers in the studies reported high levels of stress, anxiety, and severe symptoms of depressions. Caregivers are working under high levels of pressure, in a high-risk environment, and are dealing with many physical and psychological challenges. Appropriate actions and well-timed psychological support to protect medical workers’ mental health should be considered

Some contributions to phase I and II clinical trials: incorporating patient characteristics and potential time trends into designs and analysis

This work summarizes the two years of research that I have conducted at Dana-Farber Cancer Institute(DFCI)/Harvard T.H. Chan School of Public Health, in Boston (MA, USA), where I collaborated with Lorenzo Trippa (Associate Professor at Harvard University and Dana Farber Cancer Institute) and Steffen Ventz (Assistant Professor at University of Rhode Island). The thesis is divided in two main parts. The first part represents the main contribute of my research and on which I spent a dominant portion of my PhD period. In this part, called "Bayesian Uncertainty-Directed Dose Finding Designs", we introduce Bayesian uncertainty directed (BUD) designs for dose finding trials. This class of designs assigns patients to candidate dose levels with the aims of maximizing explicit information metrics at completion of the trial, while also avoiding the treatment of patients with toxic or ineffective dose levels during the trial. Explicit information metrics provide, at completion of the clinical trial, accuracy measures of the final selection of optimal or nearly optimal dose levels. The BUD approach utilizes the decision theoretic framework, and builds on utility functions that rank candidate dose levels. The utility of a dose combines the probabilities of toxicity events and the probability of a positive response to treatment. We discuss the application of BUD designs in three distinct settings; (i) dose finding studies for single agents, (ii) dose optimization for combination therapies of multiple agents, and (iii) precision medicine studies with biomarker measurements that allow dose optimization at the individual level. The proposed approach and the simulation scenarios used in evaluation of BUD designs are motivated by a Stereotactic Body Radiation Therapy (SBRT) study in lung cancer at Dana Farber Cancer Institute. The second part of the thesis, called "Inference in Adaptive Trials under Time Trends in the Patient Population", is a smaller project that we started only a few months ago, and thus many questions about the topic have not been investigated yet. The project addresses the problem of changes in the patient population over time during a clinical trial. Standard analysis methods in clinical trials implicitly assume that the patient characteristics do not change over time, and the treatment effect remains constant during the study period. Since trials run for many years, this hypothesis may not hold and time trends in the patient population can constitute a potential source of bias in both estimation and testing of the treatment effects. This is especially important for trials using adaptive randomization, where the randomization probabilities change as a function of the outcome observed during the trial. Consider a randomized two-arm trial of total sample size N with a binary endpoint. The response probability for the first N/2 patients is 0.2 for the control arm and 0.5 for the experimental arm. Due to changes in patient population, the response probabilities changes to 0.4 and 0.7 for the remaining patients in the two arms respectively. With balanced randomization (BR), where patients are allocated to the arms with equal probabilities, the expectation of the estimated overall response probabilities are 0.3 and 0.6 for the two arms, and the difference is 0.3, which is constant before and after the change. However, if response adaptive randomization is employed and the randomization probability changes to 2:1 for experimental vs control for the last N/2 patients, the expectation of the estimated overall response probabilities are now (0.2N/4 + 0.4N/6)/(N/4 + N/6) = 0.28 and (0.5N/4 + 0.7N/3)/(N/4 + N/3) = 0.61 for the control and experimental arms with a difference of 0.33, which is inflated by 10%. In this work, we propose a procedure which reduces the bias of treatment effect estimates and preserves the frequentist operating characteristics. We account for time trends by using Generalized Additive Models (GAMs) to estimate the treatment effect. We then use a parametric bootstrap to obtain valid inferences for treatment effects. The testing procedure can be implemented for any adaptive design and any estimator of the treatment effect. We apply our procedure to some well-known Response Adaptive Randomization (RAR) designs to evaluate the performance of the proposed method. For each design, we assess the estimation and testing capabilities of the method by simulating different time trends in both standard multi-arm clinical trials and platform trials

6-Fluoro-1,3,4-triphenyl-1H-pyrazolo[3,4-b]quinoline benzene hemisolvate

In the title compound, C28H18FN3·0.5C6H6, the 1H-pyrazolo[3,4-b]quinoline core is almost planar (r.m.s = 0.0371 Å, maximum deviation = 0.0571 Å) and aromatic. The solvent benzene mol­ecules are located around inversion centres. In the crystal, mol­ecules related by centres of symmetry form dimers, with distances of 3.932 (3) Å between best planes through the fused core due to π⋯π stacking. The phenyl substituents at positions 1, 3 and 4, are twisted away from the core, making dihedral angles of 29.66 (7), 44.59 (7) and 67.94 (6)°, respectively

Machine learning from real data: A mental health registry case study

Imbalanced datasets can impair the learning performance of many Machine Learning techniques. Nevertheless, many real-world datasets, especially in the healthcare field, are inherently imbalanced. For instance, in the medical domain, the classes representing a specific disease are typically the minority of the total cases. This challenge justifies the substantial research effort spent in the past decades to tackle data imbalance at the data and algorithm levels. In this paper, we describe the strategies we used to deal with an imbalanced classification task on data extracted from a database generated from the Electronic Health Records of the Mental Health Service of the Ferrara Province, Italy. In particular, we applied balancing techniques to the original data, such as random undersampling and oversampling, and Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC). In order to assess the effectiveness of the balancing techniques on the classification task at hand, we applied different Machine Learning algorithms. We employed cost-sensitive learning as well and compared its results with those of the balancing methods. Furthermore, a feature selection analysis was conducted to investigate the relevance of each feature. Results show that balancing can help find the best setting to accomplish classification tasks. Since real-world imbalanced datasets are increasingly becoming the core of scientific research, further studies are needed to improve already existing techniqu

The experimental gas-phase structures of 1,3,5-trisilylbenzene and hexasilylbenzene and the theoretical structures of all benzenes with three or more silyl substituents

The structures of 1,3,5-trisilylbenzene and hexasilylbenzene in the gas phase have been determined by electron diffraction, and that of 1,3,5-trisilylbenzene by X-ray crystallography. The structures of three trisilylbenzene isomers, three tetrasilylbenzenes, pentasilylbenzene and hexasilylbenzene have been computed, ab initio and using Density Functional Theory, at levels up to MP2/6-31G*. The primary effect of silyl substituents is to narrow the ring angle at the substituted carbon atoms. Steric interactions between silyl groups on neighbouring carbon atoms lead first to displacement of these groups away from one another, and then to displacement out of the ring plane, with alternate groups moving to opposite sides of the ring. In the extreme example, hexasilylbenzene, the SiCCSi dihedral angle is 17.8(8)°

Nitrogen forms affect root structure and water uptake in the hybrid poplar

The study analyses the effects of two different forms of nitrogen fertilisation (nitrate and ammonium) on root structure and water uptake of two hybrid poplar (Populus maximowiczii x P. balsamifera) clones in a field experiment. Water uptake was studied using sap flow gauges on individual proximal roots and coarse root structure was examined by excavating 18 whole-root systems. Finer roots were scanned and analyzed for architecture. Nitrogen forms did not affect coarse-root system development, but had a significant effect on fine-root development. Nitrate-treated trees presented higher fine:coarse root ratios and higher specific root lengths than control or ammonium treated trees. These allocation differences affected the water uptake capacity of the plants as reflected by the higher sapflow rate in the nitrate treatment. The diameter of proximal roots at the tree base predicted well the total root biomass and length. The diameter of smaller lateral roots also predicted the lateral root mass, length, surface area and the number of tips. The effect of nitrogen fertilisation on the fine root structure translated into an effect on the functioning of the fine roots forming a link between form (architecture) and function (water uptake)

Environment influences on the aromatic character of nucleobases and amino acids

Geometric (HOMA) and magnetic (NICS) indices of aromaticity were estimated for aromatic rings of amino acids and nucleobases. Cartesian coordinates were taken directly either from PDB files deposited in public databases at the finest resolution available (≤1.5 Å), or from structures resulting from full gradient geometry optimization in a hybrid QM/MM approach. Significant environmental effects imposing alterations of HOMA values were noted for all aromatic rings analysed. Furthermore, even extra fine resolution (≤1.0 Å) is not sufficient for direct estimation of HOMA values based on Cartesian coordinates provided by PDB files. The values of mean bond errors seem to be much higher than the 0.05 Å often reported for PDB files. The use of quantum chemistry geometry optimization is strongly advised; even a simple QM/MM model comprising only the aromatic substructure within the QM region and the rest of biomolecule treated classically within the MM framework proved to be a promising means of describing aromaticity inside native environments. According to the results presented, three consequences of the interaction with the environment can be observed that induce changes in structural and magnetic indices of aromaticity. First, broad ranges of HOMA or NICS values are usually obtained for different conformations of nearest neighborhood. Next, these values and their means can differ significantly from those characterising isolated monomers. The most significant increase in aromaticities is expected for the six-membered rings of guanine, thymine and cytosine. The same trend was also noticed for all amino acids inside proteins but this effect was much smaller, reaching the highest value for the five-membered ring of tryptophan. Explicit water solutions impose similar changes on HOMA and NICS distributions. Thus, environment effects of protein, DNA and even explicit water molecules are non-negligible sources of aromaticity changes appearing in the rings of nucleobases and aromatic amino acids residues