Assessment of Upper Extremity Function in Multiple Sclerosis: Feasibility of a Digital Pinching Test.

BACKGROUND: The development of touchscreen-based assessments of upper extremity function could benefit people with multiple sclerosis (MS) by allowing convenient, quantitative assessment of their condition. The Pinching Test forms a part of the Floodlight smartphone app (F. Hoffmann-La Roche Ltd, Basel, Switzerland) for people with MS and was designed to capture upper extremity function. OBJECTIVE: This study aimed to evaluate the Pinching Test as a tool for remotely assessing upper extremity function in people with MS. METHODS: Using data from the 24-week, prospective feasibility study investigating the Floodlight Proof-of-Concept app for remotely assessing MS, we examined 13 pinching, 11 inertial measurement unit (IMU)-based, and 13 fatigability features of the Pinching Test. We assessed the test-retest reliability using intraclass correlation coefficients [second model, first type; ICC(2,1)], age- and sex-adjusted cross-sectional Spearman rank correlation, and known-groups validity (data aggregation: median [all features], SD [fatigability features]). RESULTS: We evaluated data from 67 people with MS (mean Expanded Disability Status Scale [EDSS]: 2.4 [SD 1.4]) and 18 healthy controls. In this cohort of early MS, pinching features were reliable [ICC(2,1)=0.54-0.81]; correlated with standard clinical assessments, including the Nine-Hole Peg Test (9HPT) (|r|=0.26-0.54; 10/13 features), EDSS (|r|=0.25-0.36; 7/13 features), and the arm items of the 29-item Multiple Sclerosis Impact Scale (MSIS-29) (|r|=0.31-0.52; 7/13 features); and differentiated people with MS-Normal from people with MS-Abnormal (area under the curve: 0.68-0.78; 8/13 features). IMU-based features showed similar test-retest reliability [ICC(2,1)=0.47-0.84] but showed little correlations with standard clinical assessments. In contrast, fatigability features (SD aggregation) correlated with 9HPT time (|r|=0.26-0.61; 10/13 features), EDSS (|r|=0.26-0.41; 8/13 features), and MSIS-29 arm items (|r|=0.32-0.46; 7/13 features). CONCLUSIONS: The Pinching Test provides a remote, objective, and granular assessment of upper extremity function in people with MS that can potentially complement standard clinical evaluation. Future studies will validate it in more advanced MS. TRIAL REGISTRATION: ClinicalTrials.gov NCT02952911; https://clinicaltrials.gov/study/NCT02952911.

An observational study to assess validity and reliability of smartphone sensor-based gait and balance assessments in multiple sclerosis: Floodlight GaitLab protocol.

Background: Gait and balance impairments are often present in people with multiple sclerosis (PwMS) and have a significant impact on quality of life and independence. Gold-standard quantitative tools for assessing gait and balance such as motion capture systems and force plates usually require complex technical setups. Wearable sensors, including those integrated into smartphones, offer a more frequent, convenient, and minimally burdensome assessment of functional disability in a home environment. We developed a novel smartphone sensor-based application (Floodlight) that is being used in multiple research and clinical contexts, but a complete validation of this technology is still lacking. Methods: This protocol describes an observational study designed to evaluate the analytical and clinical validity of Floodlight gait and balance tests. Approximately 100 PwMS and 35 healthy controls will perform multiple gait and balance tasks in both laboratory-based and real-world environments in order to explore the following properties: (a) concurrent validity of the Floodlight gait and balance tests against gold-standard assessments; (b) reliability of Floodlight digital measures derived under different controlled gait and balance conditions, and different on-body sensor locations; (c) ecological validity of the tests; and (d) construct validity compared with clinician- and patient-reported assessments. Conclusions: The Floodlight GaitLab study (ISRCTN15993728) represents a critical step in the technical validation of Floodlight technology to measure gait and balance in PwMS, and will also allow the development of new test designs and algorithms.

Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones.

Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using smartphone sensor data that may contain missing values. First, we collect digital measurements related to gait and balance, and upper extremity functions using sensor-based assessments administered on a smartphone. Next, we treat missing data via imputation. We then discover potential markers of MS by employing a generalized estimation equation. Subsequently, parameters learned from multiple training datasets are ensembled to form a simple, unified longitudinal predictive model to forecast MS over time in previously unseen people with MS. To mitigate potential underestimation for individuals with severe disease scores, the final model incorporates additional subject-specific fine-tuning using data from the first day. The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.

Delay in loop ileostomy reversal surgery does not impact upon post-operative clinical outcomes. Complications are associated with an increased loss of microflora in the defunctioned intestine.

Loop ileostomy is a common surgical procedure to allow downstream tissue healing, with the aim of re-joining the bowel approximately 12 months later. The reversal procedure is associated with a substantial morbidity up to 40%. Our previous research demonstrated that defunctioned ileum becomes atrophied, with extensive microbial dysbiosis. This study sought to investigate the potential influence of delaying ileostomy reversal surgery upon both clinical and pathological outcomes. Post-operative clinical data was recorded, including routine blood test results, duration of hospital stay, length of time with stoma and incidence of post-operative complications. We measured ileal fibrosis and atrophy and assessed whether these, or dysbiosis, were impacted by the length of time a stoma was in place, or were linked to clinical outcomes. Associations between clinical data were investigated using scatterplot matrix analysis and t-tests. We found no differences in time between ileostomy formation and reversal in patients experiencing complications vs. individuals with no complications. Furthermore, there were no correlations between days with stoma and pathological measures, such as atrophy or fibrosis, and no ongoing increases in collagen production at the time of reversal surgery. This data suggests that the length of time a stoma is in place does not impact on the likelihood of complications. The incidence of complications is associated with increased loss of microbiota in the defunctioned ileum, but importantly, the decrease in bacteria is not linked to time with stoma. Microbiota diversity in the functional and defunctioned limb correlated within an individual, and was not significantly different between those who experienced complications following surgery vs. those that didn't. Microbiota diversity was also not significantly impacted through delay (>365 days) in stoma reversal. We propose that methods to restore intestinal microbiota numbers, and not necessarily their composition, prior to reversal should be explored to improve the clinical outcomes of ileostomy reversal surgery.

Preliminary validity of the Draw a Shape Test for upper extremity assessment in multiple sclerosis.

OBJECTIVE: To validate the smartphone sensor-based Draw a Shape Test - a part of the Floodlight Proof-of-Concept app for remotely assessing multiple sclerosis-related upper extremity impairment by tracing six different shapes. METHODS: People with multiple sclerosis, classified functionally normal/abnormal via their Nine-Hole Peg Test time, and healthy controls participated in a 24-week, nonrandomized study. Spatial (trace accuracy), temporal (mean and variability in linear, angular, and radial drawing velocities, and dwell time ratio), and spatiotemporal features (trace celerity) were cross-sectionally analyzed for correlation with standard clinical and brain magnetic resonance imaging (normalized brain volume and total lesion volume) disease burden measures, and for capacity to differentiate people with multiple sclerosis from healthy controls. RESULTS: Data from 69 people with multiple sclerosis and 18 healthy controls were analyzed. Trace accuracy (all shapes), linear velocity variability (circle, figure-of-8, spiral shapes), and radial velocity variability (spiral shape) had a mostly fair/moderate-to-good correlation (|r| = 0.14-0.66) with all disease burden measures. Trace celerity also had mostly fair/moderate-to-good correlation (|r| = 0.18-0.41) with Nine-Hole Peg Test performance, cerebellar functional system score, and brain magnetic resonance imaging. Furthermore, partial correlation analysis related these results to motor impairment. People with multiple sclerosis showed greater drawing velocity variability, though slower mean velocity, than healthy controls. Linear velocity (spiral shape) and angular velocity (circle shape) potentially differentiate functionally normal people with multiple sclerosis from healthy controls. INTERPRETATION: The Draw a Shape Test objectively assesses upper extremity impairment and correlates with all disease burden measures, thus aiding multiple sclerosis-related upper extremity impairment characterization.

Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis.

BACKGROUND: A study was undertaken to evaluate remote monitoring via smartphone sensor-based tests in people with multiple sclerosis (PwMS). This analysis aimed to explore regional neural correlates of digital measures derived from these tests. METHODS: In a 24-week, non-randomized, interventional, feasibility study (NCT02952911), sensor-based tests on the Floodlight Proof-of-Concept app were used to assess cognition (smartphone-based electronic Symbol Digit Modalities Test), upper extremity function (Draw a Shape Test, Pinching Test), and gait and balance (Static Balance Test, Two-Minute Walk Test, U-Turn Test). In this post-hoc analysis, digital measures and standard clinical measures (e.g., Nine-Hole Peg Test [9HPT]) were correlated against regional structural magnetic resonance imaging outcomes. Seventy-six PwMS aged 18-55 years with an Expanded Disability Status Scale score of 0.0-5.5 were enrolled from two different sites (USA and Spain). Sixty-two PwMS were included in this analysis. RESULTS: Worse performance on digital and clinical measures was associated with smaller regional brain volumes and larger ventricular volumes. Whereas digital and clinical measures had many neural correlates in common (e.g., putamen, globus pallidus, caudate nucleus, lateral occipital cortex), some were observed only for digital measures. For example, Draw a Shape Test and Pinching Test measures, but not 9HPT score, correlated with volume of the hippocampus (r = 0.37 [drawing accuracy over time on the Draw a Shape Test]/ - 0.45 [touching asynchrony on the Pinching Test]), thalamus (r = 0.38/ - 0.41), and pons (r = 0.35/ - 0.35). CONCLUSIONS: Multiple neural correlates were identified for the digital measures in a cohort of people with early MS. Digital measures showed associations with brain regions that clinical measures were unable to demonstrate, thus providing potential novel information on functional ability compared with standard clinical assessments.

Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones.

Goal: Smartphone and wearable devices may act as powerful tools to remotely monitor physical function in people with neurodegenerative and autoimmune diseases from out-of-clinic environments. Detection of progression onset or worsening of symptoms is especially important in people living with multiple sclerosis (PwMS) in order to enable optimally adapted therapeutic strategies. MS symptoms typically follow subtle and fluctuating disease courses, patient-to-patient, and over time. Current in-clinic assessments are often too infrequently administered to reflect longitudinal changes in MS impairment that impact daily life. This work, therefore, explores how smartphones can administer daily two-minute walking assessments to monitor PwMS physical function at home. Methods: Remotely collected smartphone inertial sensor data was transformed through state-of-the-art Deep Convolutional Neural Networks, to estimate a participant's daily ambulatory-related disease severity, longitudinally over a 24-week study. Results: This study demonstrated that smartphone-based ambulatory severity outcomes could accurately estimate MS level of disability, as measured by the EDSS score ([Formula: see text]: 0.56,[Formula: see text]0.001). Furthermore, longitudinal severity outcomes were shown to accurately reflect individual participants' level of disability over the study duration. Conclusion: Smartphone-based assessments, that can be performed by patients from their home environments, could greatly augment standard in-clinic outcomes for neurodegenerative diseases. The ability to understand the impact of disease on daily-life between clinical visits, through objective digital outcomes, paves the way forward to better measure and identify signs of disease progression that may be occurring out-of-clinic, to monitor how different patients respond to various treatments, and to ultimately enable the development of better, and more personalised care.

Socioeconomic and health factors related to polypharmacy and medication management: analysis of a Household Health Survey in North West Coast England.

OBJECTIVES: To examine the socioeconomic and demographic drivers associated with polypharmacy (5-9 medicines), extreme polypharmacy (9-20 medicines) and increased medication count. DESIGN, SETTING AND PARTICIPANTS: A total of 5509 participants, from two waves of the English North West Coast, Household Health Survey were analysed OUTCOME MEASURES: Logistic regression modelling was used to find associations with polypharmacy and extreme polypharmacy. A negative binomial regression identified associations with increased medication count. Descriptive statistics explored associations with medication management. RESULTS: Age and number of health conditions account for the greatest odds of polypharmacy. ORs (95% CI) were greatest for those aged 65+ (3.87, 2.45 to 6.13) and for those with ≥5 health conditions (10.87, 5.94 to 19.88). Smaller odds were seen, for example, in those prescribed cardiovascular medications (3.08, 2.36 to 4.03), or reporting >3 emergency attendances (1.97, 1.23 to 3.17). Extreme polypharmacy was associated with living in a deprived neighbourhood (1.54, 1.06 to 2.26). The greatest risk of increased medication count was associated with age, number of health conditions and use of primary care services. Relative risks (95% CI) were greatest for those aged 65+ (2.51, 2.23 to 2.82), those with ≥5 conditions (10.26, 8.86 to 11.88) or those reporting >18 primary care visits (2.53, 2.18 to 2.93). Smaller risks were seen in, for example, respondents with higher levels of income deprivation (1.35, 1.03 to 1.77). Polypharmic respondents were more likely to report medication management difficulties associated with taking more than one medicine at a time (p<0.001). Furthermore, individuals reporting a mental health condition, were significantly more likely to consistently report difficulties managing their medication (p<0.001). CONCLUSION: Age and number of health conditions are most associated with polypharmacy. Thus, delaying or preventing the onset of long-term conditions may help to reduce polypharmacy. Interventions to reduce income inequalities and health inequalities generally could support a reduction in polypharmacy, however, more research is needed in this area. Furthermore, increased prevention and support, particularly with medication management, for those with mental health conditions may reduce adverse medication effects.

Circadian regulation of protein cargo in extracellular vesicles.

The circadian clock controls many aspects of physiology, but it remains undescribed whether extracellular vesicles (EVs), including exosomes, involved in cell-cell communications between tissues are regulated in a circadian pattern. We demonstrate a 24-hour rhythmic abundance of individual proteins in small EVs using liquid chromatography-mass spectrometry in circadian-synchronized tendon fibroblasts. Furthermore, the release of small EVs enriched in RNA binding proteins was temporally separated from those enriched in cytoskeletal and matrix proteins, which peaked during the end of the light phase. Last, we targeted the protein sorting mechanism in the exosome biogenesis pathway and established (by knockdown of circadian-regulated flotillin-1) that matrix metalloproteinase 14 abundance in tendon fibroblast small EVs is under flotillin-1 regulation. In conclusion, we have identified proteomic time signatures for small EVs released by tendon fibroblasts, which supports the view that the circadian clock regulates protein cargo in EVs involved in cell-cell cross-talk.

Simulating the impact of noise on gait features extracted from smartphone sensor-data for the remote assessment of movement disorders.

Signs and symptoms of movement disorders can be remotely measured at home through sensor-based assessment of gait. However, sensor noise may impact the robustness of such assessments, in particular in a Bring-Your-Own-Device setting where the quality of sensors might vary. Here, we propose a framework to study the impact of inertial measurement unit noise on sensor-based gait features. This framework includes synthesizing realistic acceleration signals from the lower back during a gait cycle in OpenSim, estimating the magnitude of sensor noise from five smartphone models, perturbing the synthesized acceleration signal with the estimated noise in a Monte Carlo simulation, and computing gait features. In addition, we show that realistic levels of sensor noise have only a negligible impact on step power, a measure of gait.

A hierarchical Bayesian approach for detecting global microbiome associations.

The human gut microbiome has been shown to be associated with a variety of human diseases, including cancer, metabolic conditions and inflammatory bowel disease. Current approaches for detecting microbiome associations are limited by relying on specific measures of ecological distance, or only allowing for the detection of associations with individual bacterial species, rather than the whole microbiome. In this work, we develop a novel hierarchical Bayesian model for detecting global microbiome associations. Our method is not dependent on a choice of distance measure, and is able to incorporate phylogenetic information about microbial species. We perform extensive simulation studies and show that our method allows for consistent estimation of global microbiome effects. Additionally, we investigate the performance of the model on two real-world microbiome studies: a study of microbiome-metabolome associations in inflammatory bowel disease, and a study of associations between diet and the gut microbiome in mice. We show that we can use the method to reliably detect associations in real-world datasets with varying numbers of samples and covariates.

The role of chronotype and reward processing in understanding social hierarchies in adolescence.

INTRODUCTION: Circadian rhythms shift toward an evening preference during adolescence, a developmental period marked by greater focus on the social domain and salience of social hierarchies. The circadian system influences maturation of cognitive architecture responsible for motivation and reward, and observation of responses to reward cues has provided insights into neurocognitive processes that underpin adolescent social development. The objective was to investigate whether circadian phase of entrainment (chronotype) predicted both reward-related response inhibition and social status, and to explore whether mediator and moderator relationships existed between chronotype, reward processing, and social status outcomes. METHODS: Participants were 75 adolescents aged 13-14 years old (41 females) who completed an eye tracking paradigm that involved an inhibitory control task (antisaccade task) within a nonsocial reward (Card Guessing Game) and a social reward (Cyberball Game) context. Chronotype was calculated from weekend midsleep and grouped into early, intermediate, and later terciles. Participants indicated subjective social status compared with peers in seven domains. RESULTS: An intermediate and later chronotype predicted improved inhibitory control in the social versus nonsocial reward context. Chronotype also predicted higher perceived social status in two domains (powerful, troublemaker). Intermediate chronotypes reported higher "Powerful" status whereas later chronotypes were higher on "Troublemaker." Improved social reward-related performance predicted only the higher powerful scores and chronotype moderated this relationship. Improved inhibitory control to social reward predicted higher subjective social status in the intermediate and later chronotype group, an effect that was absent in the early group. CONCLUSION: This behavioral study found evidence that changes toward a later phase of entrainment predicts social facilitation effects on inhibitory control and higher perceived power among peers. It is proposed here that circadian delayed phase in adolescence is linked to approach-related motivation, and the social facilitation effects could reflect a social cognitive capacity involved in the drive to achieve social rank.

A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response.

Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response.

Capturing the transcription factor interactome in response to sub-lethal insecticide exposure.

The increasing levels of pesticide resistance in agricultural pests and disease vectors represents a threat to both food security and global health. As insecticide resistance intensity strengthens and spreads, the likelihood of a pest encountering a sub-lethal dose of pesticide dramatically increases. Here, we apply dynamic Bayesian networks to a transcriptome time-course generated using sub-lethal pyrethroid exposure on a highly resistant Anopheles coluzzii population. The model accounts for circadian rhythm and ageing effects allowing high confidence identification of transcription factors with key roles in pesticide response. The associations generated by this model show high concordance with lab-based validation and identifies 44 transcription factors putatively regulating insecticide-responsive transcripts. We identify six key regulators, with each displaying differing enrichment terms, demonstrating the complexity of pesticide response. The considerable overlap of resistance mechanisms in agricultural pests and disease vectors strongly suggests that these findings are relevant in a wide variety of pest species.

A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates.

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens.

High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, we interrogate subpopulations carrying sensitivity biomarkers and consecutively investigate unexpectedly resistant (UNRES) cell lines for unique genetic alterations that may drive resistance. By analyzing the GDSC and CTRP datasets, we find 53 and 35 UNRES cases, respectively. For 24 and 28 of them, we highlight putative resistance biomarkers. We find clinically relevant cases such as EGFRT790M mutation in NCI-H1975 or PTEN loss in NCI-H1650 cells, in lung adenocarcinoma treated with EGFR inhibitors. Interrogating the underpinnings of drug resistance with publicly available CRISPR phenotypic assays assists in prioritizing resistance drivers, offering hypotheses for drug combinations.

Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study.

OBJECTIVE: To characterise the clinical features of patients admitted to hospital with coronavirus disease 2019 (covid-19) in the United Kingdom during the growth phase of the first wave of this outbreak who were enrolled in the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study, and to explore risk factors associated with mortality in hospital. DESIGN: Prospective observational cohort study with rapid data gathering and near real time analysis. SETTING: 208 acute care hospitals in England, Wales, and Scotland between 6 February and 19 April 2020. A case report form developed by ISARIC and WHO was used to collect clinical data. A minimal follow-up time of two weeks (to 3 May 2020) allowed most patients to complete their hospital admission. PARTICIPANTS: 20 133 hospital inpatients with covid-19. MAIN OUTCOME MEASURES: Admission to critical care (high dependency unit or intensive care unit) and mortality in hospital. RESULTS: The median age of patients admitted to hospital with covid-19, or with a diagnosis of covid-19 made in hospital, was 73 years (interquartile range 58-82, range 0-104). More men were admitted than women (men 60%, n=12 068; women 40%, n=8065). The median duration of symptoms before admission was 4 days (interquartile range 1-8). The commonest comorbidities were chronic cardiac disease (31%, 5469/17 702), uncomplicated diabetes (21%, 3650/17 599), non-asthmatic chronic pulmonary disease (18%, 3128/17 634), and chronic kidney disease (16%, 2830/17 506); 23% (4161/18 525) had no reported major comorbidity. Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the reporting date. 17% (3001/18 183) required admission to high dependency or intensive care units; of these, 28% (826/3001) were discharged alive, 32% (958/3001) died, and 41% (1217/3001) continued to receive care at the reporting date. Of those receiving mechanical ventilation, 17% (276/1658) were discharged alive, 37% (618/1658) died, and 46% (764/1658) remained in hospital. Increasing age, male sex, and comorbidities including chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, liver disease and obesity were associated with higher mortality in hospital. CONCLUSIONS: ISARIC WHO CCP-UK is a large prospective cohort study of patients in hospital with covid-19. The study continues to enrol at the time of this report. In study participants, mortality was high, independent risk factors were increasing age, male sex, and chronic comorbidity, including obesity. This study has shown the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks. STUDY REGISTRATION: ISRCTN66726260.

The joint lasso: high-dimensional regression for group structured data.

We consider high-dimensional regression over subgroups of observations. Our work is motivated by biomedical problems, where subsets of samples, representing for example disease subtypes, may differ with respect to underlying regression models. In the high-dimensional setting, estimating a different model for each subgroup is challenging due to limited sample sizes. Focusing on the case in which subgroup-specific models may be expected to be similar but not necessarily identical, we treat subgroups as related problem instances and jointly estimate subgroup-specific regression coefficients. This is done in a penalized framework, combining an $\ell_1$ term with an additional term that penalizes differences between subgroup-specific coefficients. This gives solutions that are globally sparse but that allow information-sharing between the subgroups. We present algorithms for estimation and empirical results on simulated data and using Alzheimer's disease, amyotrophic lateral sclerosis, and cancer datasets. These examples demonstrate the gains joint estimation can offer in prediction as well as in providing subgroup-specific sparsity patterns.

Looking beyond the hype: Applied AI and machine learning in translational medicine.

Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. However, ML technologies do not come without their limitations and shortcomings. Current technical limitations and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.

Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks.

In this chapter, we review the problem of network inference from time-course data, focusing on a class of graphical models known as dynamic Bayesian networks (DBNs). We discuss the relationship of DBNs to models based on ordinary differential equations, and consider extensions to nonlinear time dynamics. We provide an introduction to time-varying DBN models, which allow for changes to the network structure and parameters over time. We also discuss causal perspectives on network inference, including issues around model semantics that can arise due to missing variables. We present a case study of applying time-varying DBNs to gene expression measurements over the life cycle of Drosophila melanogaster. We finish with a discussion of future perspectives, including possible applications of time-varying network inference to single-cell gene expression data.