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Background Remission duration and treatment response following phototherapy for psoriasis are highly variable and factors influencing these are poorly understood. Objectives Our primary outcome was to investigate whether selected clinical/serum biomarkers were associated with remission duration, and secondly with psoriasis clearance at the end of phototherapy. In addition, we looked at whether early trajectory of UVB clearance was associated with final clearance outcome. Methods We performed a prospective cohort study of 100 psoriasis patients, routinely prescribed Narrowband UVB and measured selected clinical and biochemical biomarkers, including weekly PASI (psoriasis area and severity index) scores. Patients were followed up for 18 months. Results The median time to relapse was 6 months (95% CI 5-18) if PASI90 was achieved, and 4 months (95% CI 3-9) if less than PASI90 was achieved. Achieving PASI100 did not result in prolonged remission. On UVB completion, the median final PASI (n = 96) was 1.0 (IQR 0.5, 1.6) with 78 (81%) achieving PASI75 and 39 (41%) achieving PASI90. Improved PASI90 response was significantly associated with lower BMI, higher baseline PASI, non-smoking status and lower cumulative NbUVB. Serum levels of C-reactive protein (CRP) and vitamin D were not associated with clearance or remission duration. Early treatment response from weeks 2-3 was predictive of final outcome. For example, achieving PASI30 at week 3 was significantly associated with PASI90 at the end of the course [36/77 (51%) vs. 2/24 (8%), P Conclusions Raised BMI and positive smoking status predicted poorer phototherapy response. For the first time, we have shown that PASI clearance trajectory over the first 2-3 weeks of UVB, can predict psoriasis clearance. This is an important new step towards developing psoriasis personalized prescribing, which can now be formally tested in clinical trials. These simple clinical measures can be used to inform patient treatment expectations; allowing treatment modifications and/or switching to alternative therapies.

Despite increased understanding about psoriasis pathophysiology, currently there is a lack of predictive computational models. We developed a personalisable ordinary differential equations model of human epidermis and psoriasis that incorporates immune cells and cytokine stimuli to regulate the transition between two stable steady states of clinically healthy (non-lesional) and disease (lesional psoriasis, plaque) skin. In line with experimental data, an immune stimulus initiated transition from healthy skin to psoriasis and apoptosis of immune and epidermal cells induced by UVB phototherapy returned the epidermis back to the healthy state. Notably, our model was able to distinguish disease flares. The flexibility of our model permitted the development of a patient-specific “UVB sensitivity” parameter that reflected subject-specific sensitivity to apoptosis and enabled simulation of individual patients’ clinical response trajectory. In a prospective clinical study of 94 patients, serial individual UVB doses and clinical response (Psoriasis Area Severity Index) values collected over the first three weeks of UVB therapy informed estimation of the “UVB sensitivity” parameter and the prediction of individual patient outcome at the end of phototherapy. An important advance of our model is its potential for direct clinical application through early assessment of response to UVB therapy, and for individualised optimisation of phototherapy regimes to improve clinical outcome. Additionally by incorporating the complex interaction of immune cells and epidermal keratinocytes, our model provides a basis to study and predict outcomes to biologic therapies in psoriasis.Author SummaryWe present a new computer model for psoriasis, an immune-mediated disabling skin disease which presents with red, raised scaly plaques that can appear over the whole body. Psoriasis affects millions of people in the UK alone and causes significant impairment to quality of life, and currently has no cure. Only a few treatments (including UVB phototherapy) can induce temporary remission. Despite our increased understanding about psoriasis, treatments are still given on a ‘trial and error’ basis and there are no reliable computer models that can a) elucidate the mechanisms behind psoriasis onset or flare and b) predict a patient’s response to a course of treatment (e.g., phototherapy) and the likelihood of inducing a period of remission. Our computer model addresses both these needs. First, it explicitly describes the interaction between the immune system and skin cells. Second, our model captures response to therapy at the individual patient level and enables personalised prediction of clinical outcomes. Notably, our model also supports prediction of amending individual UVB phototherapy regimes based on the patient’s initial response that include for example personalised delivery schedules (i.e., 3x weekly vs. 5x weekly phototherapy). Therefore, our work is a crucial step towards precision medicine for psoriasis treatment.

Memory corruption bugs continue to plague low-level systems software generally written in unsafe programming languages. In order to detect and protect against such exploits, many pre- and post-deployment techniques exist. In this position paper, we propose and motivate the need for a hybrid approach for the protection against memory safety vulnerabilities, combining techniques that can identify the presence (and absence) of vulnerabilities pre-deployment with those that can detect and mitigate such vulnerabilities post-deployment. Our hybrid approach involves three layers: hardware runtime protection provided by capability hardware, software runtime protection provided by compiler instrumentation, and static analysis provided by bounded model checking and symbolic execution. The key aspect of the proposed hybrid approach is that the protection offered is greater than the sum of its parts -- the expense of post-deployment runtime checks is reduced via information obtained during pre-deployment analysis. During pre-deployment analysis, static checking can be guided by runtime information.

Cyber-physical system models often feature stochastic behaviour that itself depends on uncertain parameters (e.g., transition rates). For these systems, verifying reachability amounts to computing a range of probabilities depending on how uncertainty is resolved. In general, this is a hard problem for which rigorous solutions suffer from the well-known curse of dimensionality. In this paper we focus on hybrid systems with random parameters whose distribution is subject to nondeterministic uncertainty. We show that for these systems the reachability probability is a smooth function of the nondeterministic parameters, and thus Gaussian processes can be used to approximate the reachability probability function itself very efficiently over its entire domain. Furthermore, we introduce a novel approach that exploits rigorous probability enclosures for training Gaussian processes. We apply our approaches to non-trivial hybrid systems case studies, and we empirically demonstrate their advantages with respect to standard statistical model checking.

We present a new method for the automated synthesis of digital controllers with formal safety guarantees for systems with nonlinear dynamics, noisy output measurements, and stochastic disturbances. Our method derives digital controllers such that the corresponding closed-loop system, modeled as a sampled-data stochastic control system, satisfies a safety specification with probability above a given threshold. The proposed synthesis method alternates between two steps: generation of a candidate controller pc, and verification of the candidate. pc is found by maximizing a Monte Carlo estimate of the safety probability, and by using a non-validated ODE solver for simulating the system. Such a candidate is therefore sub-optimal but can be generated very rapidly. To rule out unstable candidate controllers, we prove and utilize Lyapunov's indirect method for instability of sampled-data nonlinear systems. In the subsequent verification step, we use a validated solver based on SMT (Satisfiability Modulo Theories) to compute a numerically and statistically valid confidence interval for the safety probability of pc. If the probability so obtained is not above the threshold, we expand the search space for candidates by increasing the controller degree. We evaluate our technique on three case studies: an artificial pancreas model, a powertrain control model, and a quadruple-tank process., Comment: 12 pages, 4 figures, 4 tables