Observational data have become a popular source of evidence for causal effects when no randomized controlled trial exists, or to supplement information provided by those. In practice, a wide range of designs and analytical choices exist, and one recent approach relies on the target trial emulation framework. This framework is particularly well suited to mimic what could be obtained in a specific randomized controlled trial, while avoiding time-related selection biases. In this abstract, we present how this framework could be useful to emulate trials in malignant melanoma, and the challenges faced when planning such a study using longitudinal observational data from a cohort study. More specifically, two questions are envisaged: duration of immune checkpoint inhibitors, and trials comparing treatment strategies for BRAF V600-mutant patients (targeted therapy as 1st line, followed by immunotherapy as 2nd line, vs. immunotherapy as 2nd line followed by targeted therapy as 1st line). Using data from 1027 participants to the MELBASE cohort, we detail the results for the emulation of a trial where immune checkpoint inhibitor would be stopped at 6 months vs. continued, in patients in response or with stable disease.

Identifying biomarkers that predict current and future cognition may improve estimates of Alzheimer’s disease risk among cognitively unimpaired older adults (CU). In vivo measures of amyloid and tau protein burden and task-based functional MRI measures of core memory mechanisms, such as the strength of cortical reinstatement during remembering, have each been linked to individual differences in memory in CU. This study assesses whether combining CSF biomarkers with fMRI indices of cortical reinstatement improves estimation of memory function in CU, assayed using three unique tests of hippocampal-dependent memory. Participants were 158 CU (90F, aged 60-88 years, CDR=0) enrolled in the Stanford Aging and Memory Study (SAMS). Cortical reinstatement was quantified using multivoxel pattern analysis of fMRI data collected during completion of a paired associate cued recall task. Memory was assayed by associative cued recall, a delayed recall composite, and a mnemonic discrimination task that involved discrimination between studied ‘target’ objects, novel ‘foil’ objects, and perceptually similar ‘lure’ objects. CSF Aβ42, Aβ40, and p-tau181 were measured with the automated Lumipulse G system (N=115). Regression analyses examined cross-sectional relationships between memory performance in each task and a) the strength of cortical reinstatement in the Default Network (comprised of posterior medial, medial frontal, and lateral parietal regions) during associative cued recall and b) CSF Aβ42/Aβ40 and p-tau181, controlling for age, sex, and education. For mnemonic discrimination, linear mixed effects models were used to examine the relationship between discrimination (d’) and each predictor as a function of target-lure similarity. Stronger cortical reinstatement was associated with better performance across all three memory assays. Age and higher CSF p-tau181 were each associated with poorer associative memory and a diminished improvement in mnemonic discrimination as target-lure similarity decreased. When combined in a single model, CSF p-tau181 and Default Network reinstatement strength, but not age, explained unique variance in associative memory and mnemonic discrimination performance, outperforming the single-modality models. Combining fMRI measures of core memory functions with protein biomarkers of Alzheimer’s disease significantly improved prediction of individual differences in memory performance in CU. Leveraging multimodal biomarkers may enhance future prediction of risk for cognitive decline.

COVID-19 lockdown measures have caused severe disruptions to work and education and prevented people from engaging in many rewarding activities. Cannabis users may be especially vulnerable, having been previously shown to have higher levels of apathy and anhedonia than non-users. In this survey study, we measured apathy and anhedonia, before and after lockdown measures were implemented, in n = 256 adult and n = 200 adolescent cannabis users and n = 170 adult and n = 172 adolescent controls. Scores on the Apathy Evaluation Scale (AES) and Snaith-Hamilton Pleasure Scale (SHAPS) were investigated with mixed-measures ANCOVA, with factors user group, age group, and time, controlling for depression, anxiety, and other drug use. Adolescent cannabis users had significantly higher SHAPS scores before lockdown, indicative of greater anhedonia, compared with adolescent controls (P = .03, η p2 = .013). Contrastingly, adult users had significantly lower scores on both the SHAPS (P < .001, η p2 = .030) and AES (P < .001, η p2 = .048) after lockdown compared with adult controls. Scores on both scales increased during lockdown across groups, and this increase was significantly smaller for cannabis users (AES: P = .001, η p2 = .014; SHAPS: P = .01, η p2 = .008). Exploratory analyses revealed that dependent cannabis users had significantly higher scores overall (AES: P < .001, η p2 = .037; SHAPS: P < .001, η p2 = .029) and a larger increase in scores (AES: P = .04, η p2 =.010; SHAPS: P = .04, η p2 = .010), compared with non-dependent users. Our results suggest that adolescents and adults have differential associations between cannabis use as well as apathy and anhedonia. Within users, dependence may be associated with higher levels of apathy and anhedonia regardless of age and a greater increase in levels during the COVID-19 lockdown.

After more than 4 million deaths worldwide, the ongoing vaccination to conquer the COVID-19 disease is now competing with the emergence of increasingly contagious mutations, repeatedly supplanting earlier strains. Following the near-absence of historical examples of the long-time evolution of infectious diseases under similar circumstances, models are crucial to exemplify possible scenarios. Accordingly, in the present work we systematically generalize the popular susceptible-infected-recovered model to account for mutations leading to repeatedly occurring new strains, which we coarse grain based on tools from statistical mechanics to derive a model predicting the most likely outcomes. The model predicts that mutations can induce a super exponential growth of infection numbers at early times, which self-amplify to giant infection waves which are caused by a positive feedback loop between infection numbers and mutations and lead to a simultaneous infection of the majority of the population. At later stages -- if vaccination progresses too slowly -- mutations can interrupt an ongoing decrease of infection numbers and can cause infection revivals which occur as single waves or even as whole wave trains featuring alternative periods of decreasing and increasing infection numbers. Our results might be useful for discussions regarding the importance of a release of vaccine-patents to reduce the risk of mutation-induced infection revivals but also to coordinate the release of measures following a downwards trend of infection numbers.

Gestational exposure to environmental toxins, infections, and stressors are epidemiologically linked to neurodevelopmental disorders with strong male-bias, such as autism spectrum disorder. We modeled some of these prenatal risk factors in mice, by co-exposing pregnant dams to an environmental pollutant and limited-resource stress, which robustly dysregulated the maternal immune system. Male but not female offspring displayed long-lasting behavioral abnormalities and alterations in the activity of brain networks encoding social interactions, along with disruptions of gut structure and microbiome composition. Cellularly, prenatal stressors impaired microglial synaptic pruning in males during early postnatal development. Precise inhibition of microglial phagocytosis during the same critical period mimicked the impact of prenatal stressors on the male-specific social deficits. Conversely, modifying the gut microbiome rescued the social and cellular deficits, indicating that environmental stressors alter neural circuit formation in males via impairing microglia function during development, perhaps via a gut-brain disruption.

The main transmission mode of the COVID-19 disease is through virus-laden aerosols and droplets generated by expiratory events, such as breathing and sneezing. Patients with respiratory diseases are typically treated with oxygenation devices in hospitals, homes, and other settings where they increase the risk of spreading the disease to caregivers and first responders. Here, I will discuss a systematic study of aerosol and droplet dispersal through the air and their final deposition on surfaces. Through laser and fluorescent imaging techniques, we measure the volumetric spatial-temporal dynamics of droplet dispersal while varying rheological properties of the mucosaliva. We then demonstrate that a standard nose and mouth mask reduces the amount of mucosaliva dispersed by a factor of at least a hundred. Our ongoing collaborations with doctors and respiratory therapists from the Baystate Medical Hospital are developing new guidelines to help mitigate disease spread in a hospital setting.

ONS figures suggest that at least 10% of individuals suffering COVID -19 Infection continue to experience several weeks after testing positive, and other studies report the proportions as even higher (e.g. Logue et al., 2021). One of the most prevalent reported symptoms among these “Long Covid” sufferers is cognitive dysfunction (Davis et al., 2020). However, to date the cognitive sequelae of COVID -19 are little understood. There are a number of reasons why COVID -19 infection might be associated with cognitive impairment and mental illness (e.g. Bougakov et al., 2020). In particular, increasing evidence indicates inflammation (e.g. Huang et al., 2020) and dysfunctional clotting (e.g. Taquet et al., 2021) as issues of major concern, both of which have been previously linked to a range of cognitive deficits (e.g. Vintimilla et al., 2019; Cumming et al., 2013). Indeed, evidence is beginning to emerge that cognitive issues may be widespread in the post-infection period, particularly among hospitalised and ventilated patients (e.g. Hampshire et al., 2020; Alemanno et al,. 2020). Here I shall present “Hot off the [SPSS]Press” results from a study on memory and cognition following COVID infection in a non-hospitalized cohort.

We made a low-cost centrifuge that can be useful for carrying out low-cost LAMP based detection of SARS-Cov2 virus in saliva. The 3D printed centrifuge (Mobilefuge) is portable, robust, stable, safe, easy to build and operate. The Mobilefuge doesn’t require soldering or programming skills and can be built without any specialised equipment, yet practical enough for high throughput use. More importantly, Mobilefuge can be powered from widely available USB ports, including mobile phones and associated power supplies. This allows the Mobilefuge to be used even in off-grid and resource limited settings. Website: https://www.cappa.ie/chinna-devarapu/

The risks of in utero and early exposure to environmental contaminants, such as heavy metals and persistent organic pollutants, on child neurodevelopment is now established, however our understanding of how these contaminants alter the human brain is very limited. To address this issue, more effort must be made to integrate brain imaging tools with epidemiological studies. In this seminar, I will be presenting EEG and MRI data collected in birth-cohort studies where impairments of cognitive and sensory functions were observed in association with prenatal exposure to mercury, lead, PCB or organophosphate insecticides. Results obtained in children and adolescents suggest that each pollutant might affect different levels of brain processing and that frontal regions are particularly vulnerable.

The SARS-CoV-2 pandemic is awash in data, including daily, spatially-resolved COVID case data, virus sequence data, patients `omics data, and mobility data. Journals are now also awash in studies that make use of quantitative modeling approaches to gain insight into the geographic spread of SARS-CoV-2 and its temporal dynamics, as well as studies that predict the impact of control strategies on SARS-CoV-2 circulation. Some, but by no means all, of these studies are informed by the massive amounts of available data. Some, but by no means all, of these studies have been useful — in that their predictions revealed something beyond simple back of the envelope calculations. To summarize some of these findings, in this symposium, we will address questions such as: What do we want from models of disease spread? What can and should be predicted? Which data are the most useful for predictions? When do we need mechanistic models? What have we learned about how to model disease spread from unmet and/or conflicting predictions? The workshop speakers will explore these questions from different perspectives on what data need to be considered and how models can be evaluated. As at other TMLS workshops, each speaker will deliver a 10-minute talk with ample time set aside for moderated questions/discussion. We expect the talks to be provocative and bold, while respecting different perspectives.

Adolescence is a period of life characterised by heightened sensitivity to social stimuli, an increased need for peer interaction and peer acceptance, and development of the social brain. Lockdown and social distancing measures intended to mitigate the spread of COVID-19 are reducing the opportunity to engage in face-to-face social interaction with peers. The consequences of social distancing on human social brain and social cognitive development are unknown, but animal research has shown that social deprivation and isolation have unique effects on brain and behaviour in adolescence compared with other stages of life. It is possible that social distancing might have a disproportionate effect on an age group for whom peer interaction is a vital aspect of development.

COVID -19 has affected social interaction and healthcare worldwide. This talk will focus on the impact of the pandemic and “lockdown” on mental health services, community physical health services, and patient mortality in Cambridgeshire and Peterborough, based on the analysis of de-identified data from the primary NHS provider of secondary care mental health services to this population (~0.86 million)

Meg was awarded funding to look into how the coronavirus pandemic has affected children's mental health and wellbeing.

The SARS-CoV-2 virus has rapidly evolved into a pandemic that is threatening public health, economics, and quality of life worldwide. The gold-standard for testing individuals for COVID-19 is using traditional RT-qPCR, which is expensive and can take up to several hours. Expanding surveillance across a global scale will call for new strategies and tests that are inexpensive, require minimal reagents, decrease assay time, and allow for simple point-of-care (POC) monitoring without need of trained personnel and with quick turnaround time. To expand the speed of COVID-19 surveillance, we are working on a point-of-care microfluidic chip to enable significantly faster and easier testing. This is based upon digital drop loop-mediated isothermal amplification that will allow for rapid testing of large populations at a reasonable cost. The device will employ a nucleic-acid based test called reverse transcriptase LAMP (RT- LAMP) that operates at a temperature of 60-65°C. RT-LAMP removes the bottleneck of thermal cycling and high temperatures required by traditional RT-qPCR thermocycling. The simplicity, speed, and sensitivity will enable early treatment and response to infection.

Recently a powerful example of a replicating nano-machine entered our society. In principle, it’s just a normal disease, that one attempts to model with 3 or 4 simple coupled equations with 2 important parameters: a timescale, and a replication factor (the famous R0). Then one tries to guess how changes in society change R0 and perhaps adopt some more or less strong lock-down measures. However, this virus has more “personality” than that. It behaves differently in different persons, and persons behave differently. Presumably, only a few of us infect a lot, while most do not infect so much. This assumption is supported by the observation that couples living together only infect each other with about 15 percent probability, indicating that most infected people are not really infectious. I will discuss this and other aspects of Covid-19 in the perspective of models that describe heterogeneous individuals in a society. In particular, we suggest that limiting superspreading opportunities is a cost-effective strategy to mitigate Covid-19.