Comparative assessment of early mortality risk upon immune checkpoint inhibitors alone or in combination with other agents across solid malignancies: a systematic review and meta-analysis.

BACKGROUND: The early crossing of survival curves in randomised clinical trials (RCTs) with immune checkpoint blockers suggests an excess of mortality in the first months of treatment. However, the exact estimation of the early death (ED) rate, the comparison between ED upon immune checkpoint inhibitors (ICI) alone or in combination with other agents and the impact of tumour type, and PD-L1 expression on ED are unknown. METHODS: RCTs comparing ICI alone (ICI-only group) or in combination with other non-ICI therapies (ICI-OT group) (experimental arms) versus non-ICI treatments (control arm) were included. ED was defined as death within the first 3 months of treatment. The primary outcome was the comparison of ED between experimental and control arms, and the secondary outcome was the comparison of ED risk between ICI-only and ICI-OT. ED rates estimated by risk ratio (RR) were pooled by random effect model. RESULTS: A total of 56 RCTs (40,215 participants, 14 cancer types) were included. ED occurred in 14.2% and 6.7% of patients in ICI-only and ICI-OT groups, respectively. ED risk significantly increased with ICI-only (RR: 1.29, 95% CI 1.05-1.57) versus non-ICI therapies, while it was lower with ICI-OT versus non-ICI treatments (RR: 0.81, 95% CI 0.73-0.90). ED risk was significantly higher upon ICI-only compared to ICI-OT (RR: 1.57, 95% CI 1.26-1.95). Gastric and urothelial carcinoma were at higher risk of ED. PD-L1 expression and ICI drug classes were not associated with ED. CONCLUSIONS: ED upon first-line ICI is a clinically relevant phenomenon across solid malignancies, not predictable by PD-L1 expression but preventable through the addition of other treatments to ICI.

Transcranial electric stimulation as a neural interface to gain insight on human brain functions: current knowledge and future perspective.

The use of brain stimulation approaches in social and affective science has greatly increased over the last two decades. The interest in social factors has grown along with technological advances in brain research. Transcranial electric stimulation (tES) is a research tool that allows scientists to establish contributory causality between brain functioning and social behaviour, therefore deepening our understanding of the social mind. Preliminary evidence is also starting to demonstrate that tES, either alone or in combination with pharmacological or behavioural interventions, can alleviate the symptomatology of individuals with affective or social cognition disorders. This review offers an overview of the application of tES in the field of social and affective neuroscience. We discuss the issues and challenges related to this application and suggest an avenue for future basic and translational research.

Spatial Patterns of Light-Harvesting Antenna Complex Arrangements Tune the Transfer-to-Trap Efficiency of Excitons in Purple Bacteria.

In photosynthesis, the efficiency with which a photogenerated exciton reaches the reaction center is dictated by chromophore energies and the arrangement of chromophores in the supercomplex. Here, we explore the interplay between the arrangement of light-harvesting antennae and the efficiency of exciton transport in purple bacterial photosynthesis. Using a Miller-Abrahams-based exciton hopping model, we compare different arrangements of light-harvesting proteins on the intracytoplasmic membrane. We find that arrangements with aggregated LH1s have a higher efficiency than arrangements with randomly distributed LH1s in a wide range of physiological light fluences. This effect is robust to the introduction of defects on the intracytoplasmic membrane. Our result explains the absence of species with aggregated LH1 arrangements in low-light niches and the large increase seen in the expression of LH1 dimer complexes in high fluences. We suggest that the effect seen in our study is an adaptive strategy toward solar light fluence across different purple bacterial species.

What Matters Most to Lung Cancer Patients? A Qualitative Study in Italy and Belgium to Investigate Patient Preferences.

Background: The potential value of patient preference studies has been recognized in clinical individual treatment decision-making between clinicians and patients, as well as in upstream drug decision-making. Drug developers, regulators, reimbursement and Health Technology Assessment (HTA) bodies are exploring how the use of patient preference studies could inform drug development, regulatory benefit risk-assessment and reimbursement decisions respectively. Understanding patient preferences may be especially valuable in decisions regarding Non-Small Cell Lung Cancer (NSCLC) treatment options, where a variety of treatment options with different characteristics raise uncertainty about which features are most important to NSCLC patients. As part of the Innovative Medicines Initiative PREFER project, this qualitative study aimed to identify patient-relevant lung cancer treatment characteristics. Methods: This study consisted of a scoping literature review and four focus group discussions, 2 in Italy and 2 in Belgium, with a total of 24 NSCLC patients (Stages III-IV). The focus group discussions sought to identify which treatment characteristics patients find most relevant. The discussions were analyzed thematically using a thematic inductive analysis. Results: Patients highlighted themes reflecting: 1) positive effects or expected gains from treatment such as greater life expectancy and maintenance of daily functioning, 2) negative effects or adverse events related to therapy that negatively impact patients' daily functioning such as fatigue and 3) uncertainty regarding the duration and type of treatment effects. These overarching themes were consistent among patients from Belgium and Italy, suggesting that treatment aspects related to efficacy and safety as well as the psychological impact of lung cancer treatment are common areas of concern for patients, regardless of cultural background or country. Discussion: Our findings illustrate the value of using qualitative methods with patients to identify preferred treatment characteristics for advanced lung cancer. These could inform a subsequent quantitative preference survey that assesses patient trade-offs regarding treatment options.

Patient Preferences for Lung Cancer Treatment: A Qualitative Study Protocol Among Advanced Lung Cancer Patients.

Introduction: Lung cancer is the deadliest and most prevalent cancer worldwide. Lung cancer treatments have different characteristics and are associated with a range of benefits and side effects for patients. Such differences may raise uncertainty among drug developers, regulators, payers, and clinicians regarding the value of these treatment effects to patients. The value of conducting patient preference studies (using qualitative and/or quantitative methods) for benefits and side effects of different treatment options has been recognized by healthcare stakeholders, such as drug developers, regulators, health technology assessment bodies, and clinicians. However, evidence-based guidelines on how and when to conduct and use these studies in drug decision-making are lacking. As part of the Innovative Medicines Initiative PREFER project, we developed a protocol for a qualitative study that aims to understand which treatment characteristics are most important to lung cancer patients and to develop attributes and levels for inclusion in a subsequent quantitative preference survey. Methods: The study protocol specifies a four-phased approach: (i) a scoping literature review of published literature, (ii) four focus group discussions with stage III and IV Non-Small Cell Lung Cancer patients, (iii) two nominal group discussions with stage III and IV Non-Small Cell Lung Cancer patients, and (iv) multi-stakeholder discussions involving clinicians and preference experts. Discussion: This protocol outlines methodological and practical steps as to how qualitative research can be applied to identify and develop attributes and levels for inclusion in patient preference studies aiming to inform decisions across the drug life cycle. The results of this study are intended to inform a subsequent quantitative preference survey that assesses patient trade-offs regarding lung cancer treatment options. This protocol may assist researchers, drug developers, and decision-makers in designing qualitative studies to understand which treatment aspects are most valued by patients in drug development, regulation, and reimbursement.

Dielectric response of periodic systems from quantum Monte Carlo calculations.

We present a novel approach that allows us to calculate the dielectric response of periodic systems in the quantum Monte Carlo formalism. We employ a many-body generalization for the electric-enthalpy functional, where the coupling with the field is expressed via the Berry-phase formulation for the macroscopic polarization. A self-consistent local Hamiltonian then determines the ground-state wave function, allowing for accurate diffusion quantum Monte Carlo calculations where the polarization's fixed point is estimated from the average on an iterative sequence, sampled via forward walking. This approach has been validated for the case of an isolated hydrogen atom and then applied to a periodic system, to calculate the dielectric susceptibility of molecular-hydrogen chains. The results found are in excellent agreement with the best estimates obtained from the extrapolation of quantum-chemistry calculations.

Towards an assessment of the accuracy of density functional theory for first principles simulations of water. II.

A series of 20 ps ab initio molecular dynamics simulations of water at ambient density and temperatures ranging from 300 to 450 K are presented. Car-Parrinello (CP) and Born-Oppenheimer (BO) molecular dynamics techniques are compared for systems containing 54 and 64 water molecules. At 300 K, an excellent agreement is found between radial distribution functions (RDFs) obtained with BO and CP dynamics, provided an appropriately small value of the fictitious mass parameter is used in the CP simulation. However, we find that the diffusion coefficients computed from CP dynamics are approximately two times larger than those obtained with BO simulations for T>400 K, where statistically meaningful comparisons can be made. Overall, both BO and CP dynamics at 300 K yield overstructured RDFs and slow diffusion as compared to experiment. In order to understand these discrepancies, the effect of proton quantum motion is investigated with the use of empirical interaction potentials. We find that proton quantum effects may have a larger impact than previously thought on structure and diffusion of the liquid.

Towards an assessment of the accuracy of density functional theory for first principles simulations of water.

A series of Car-Parrinello (CP) molecular dynamics simulations of water are presented, aimed at assessing the accuracy of density functional theory in describing the structural and dynamical properties of water at ambient conditions. We found negligible differences in structural properties obtained using the Perdew-Burke-Ernzerhof or the Becke-Lee-Yang-Parr exchange and correlation energy functionals; we also found that size effects, although not fully negligible when using 32 molecule cells, are rather small. In addition, we identified a wide range of values of the fictitious electronic mass (micro) entering the CP Lagrangian for which the electronic ground state is accurately described, yielding trajectories and average properties that are independent of the value chosen. However, care must be exercised not to carry out simulations outside this range, where structural properties may artificially depend on micro. In the case of an accurate description of the electronic ground state, and in the absence of proton quantum effects, we obtained an oxygen-oxygen correlation function that is overstructured compared to experiment, and a diffusion coefficient which is approximately ten times smaller.

Quantum Monte Carlo calculations of nanostructure optical gaps: application to silicon quantum dots.

Quantum Monte Carlo (QMC) calculations of the optical gaps of silicon quantum dots ranging in size from 0 to 1.5 nm are presented. These QMC results are used to examine the accuracy of density functional (DFT) and empirical pseudopotential based calculations. The GW approximation combined with a solution of the Bethe-Salpeter equation performs well but is limited by its scaling with system size. Optical gaps predicted by DFT vary by 1-2 eV depending on choice of functional. Corrections introduced by the time dependent formalism are found to be minimal in these systems.