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BACKGROUND: Alzheimer's disease (AD) is a progressive, neurodegenerative disease presenting along a continuum ranging from asymptomatic disease to mild cognitive impairment (MCI), followed by dementia characterized as mild, moderate, or severe. OBJECTIVES: To better understand the medical journey of patients with all-cause MCI or mild AD dementia from the perspective of patients, care partners, and physicians. DESIGN: Cross-sectional study. SETTING: Online surveys in the United States between February 4, 2021, and March 1, 2021. PARTICIPANTS: 103 patients with all-cause MCI or mild AD dementia and 150 care partners participated in this survey. 301 physicians (75 of whom were neurologists) completed a survey. MEASUREMENTS: The surveys included questions regarding attitudes, experiences, and behaviors related to diagnosis and management of MCI and mild AD dementia. For the patient and care partner surveys, questions regarding healthcare received for MCI and mild AD dementia were only asked of care partners. RESULTS: Most patients (73%) had a similar medical journey. The majority (64%) initially consulted a primary care physician on average 15 months after symptom onset, with symptoms primarily consisting of forgetfulness and short-term memory loss. About half (51%) of patients in the typical medical journey were diagnosed by a neurologist. Upon diagnosis, most neurologists reported having discussions with patients and care partners about the potential causes of MCI or mild AD dementia (83%); of these physicians, 83% explained the effect other conditions have on the risk of the diagnoses and symptom progression. Neurologists (52%) consider themselves the coordinator of care for patients with MCI or mild AD dementia. Amongst patients and care partners, about one-third (35%) perceive the neurologists to be the coordinating physician. CONCLUSIONS: Neurologists commonly diagnose MCI and mild AD dementia but are typically not the first point of contact in the medical journey, and patients do not consult with a physician for over a year after symptom onset. Neurologists play a key role in the medical journey for patients and care partners, and could help ensure earlier diagnosis and treatment, and improve clinical outcomes by coordinating MCI and mild AD dementia care and collaborating with primary care physicians.

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Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. In this work, we leverage the statistical approach of bootstrap aggregating (bagging) to robustify the sparse identification of the nonlinear dynamics (SINDy) algorithm. First, an ensemble of SINDy models is identified from subsets of limited and noisy data. The aggregate model statistics are then used to produce inclusion probabilities of the candidate functions, which enables uncertainty quantification and probabilistic forecasts. We apply this ensemble-SINDy (E-SINDy) algorithm to several synthetic and real-world datasets and demonstrate substantial improvements to the accuracy and robustness of model discovery from extremely noisy and limited data. For example, E-SINDy uncovers partial differential equations models from data with more than twice as much measurement noise as has been previously reported. Similarly, E-SINDy learns the Lotka Volterra dynamics from remarkably limited data of yearly lynx and hare pelts collected from 1900 to 1920. E-SINDy is computationally efficient, with similar scaling as standard SINDy. Finally, we show that ensemble statistics from E-SINDy can be exploited for active learning and improved model predictive control.

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Type 2 diabetes and its associated comorbidities are growing more prevalent, and the complexity of optimising glycaemic control is increasing, especially on the frontlines of patient care. In many countries, most patients with type 2 diabetes are managed in a primary care setting. However, primary healthcare professionals face the challenge of the growing plethora of available treatment options for managing hyperglycaemia, leading to difficultly in making treatment decisions and contributing to treatment and therapeutic inertia. This position statement offers a simple and patient-centred clinical decision-making model with practical treatment recommendations that can be widely implemented by primary care clinicians worldwide through shared-decision conversations with their patients. It highlights the importance of managing cardiovascular disease and elevated cardiovascular risk in people with type 2 diabetes and aims to provide innovative risk stratification and treatment strategies that connect patients with the most effective care.

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The critical role of primary care clinicians (PCCs) in Alzheimer's disease (AD) prevention, diagnosis and management must evolve as new treatment paradigms and disease-modifying therapies (DMTs) emerge. Our understanding of AD has grown substantially: no longer conceptualized as a late-in-life syndrome of cognitive and functional impairments, we now recognize that AD pathology builds silently for decades before cognitive impairment is detectable. Clinically, AD first manifests subtly as mild cognitive impairment (MCI) due to AD before progressing to dementia. Emerging optimism for improved outcomes in AD stems from a focus on preventive interventions in midlife and timely, biomarker-confirmed diagnosis at early signs of cognitive deficits (i.e. MCI due to AD and mild AD dementia). A timely AD diagnosis is particularly important for optimizing patient care and enabling the appropriate use of anticipated DMTs. An accelerating challenge for PCCs and AD specialists will be to respond to innovations in diagnostics and therapy for AD in a system that is not currently well positioned to do so. To overcome these challenges, PCCs and AD specialists must collaborate closely to navigate and optimize dynamically evolving AD care in the face of new opportunities. In the spirit of this collaboration, we summarize here some prominent and influential models that inform our current understanding of AD. We also advocate for timely and accurate (i.e. biomarker-defined) diagnosis of early AD. In doing so, we consider evolving issues related to prevention, detecting emerging cognitive impairment and the role of biomarkers in the clinic.

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Many physical systems characterized by nonlinear multiscale interactions can be modelled by treating unresolved degrees of freedom as random fluctuations. However, even when the microscopic governing equations and qualitative macroscopic behaviour are known, it is often difficult to derive a stochastic model that is consistent with observations. This is especially true for systems such as turbulence where the perturbations do not behave like Gaussian white noise, introducing non-Markovian behaviour to the dynamics. We address these challenges with a framework for identifying interpretable stochastic nonlinear dynamics from experimental data, using forward and adjoint Fokker-Planck equations to enforce statistical consistency. If the form of the Langevin equation is unknown, a simple sparsifying procedure can provide an appropriate functional form. We demonstrate that this method can learn stochastic models in two artificial examples: recovering a nonlinear Langevin equation forced by coloured noise and approximating the second-order dynamics of a particle in a double-well potential with the corresponding first-order bifurcation normal form. Finally, we apply Langevin regression to experimental measurements of a turbulent bluff body wake and show that the statistical behaviour of the centre of pressure can be described by the dynamics of the corresponding laminar flow driven by nonlinear state-dependent noise.

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Type 2 diabetes and its associated comorbidities are growing more prevalent, and the complexity of optimising glycaemic control is increasing, especially on the frontlines of patient care. In many countries, most patients with type 2 diabetes are managed in a primary care setting. However, primary healthcare professionals face the challenge of the growing plethora of available treatment options for managing hyperglycaemia, leading to difficultly in making treatment decisions and contributing to therapeutic inertia. This position statement offers a simple and patient-centred clinical decision-making model with practical treatment recommendations that can be widely implemented by primary care clinicians worldwide through shared-decision conversations with their patients. It highlights the importance of managing cardiovascular disease and elevated cardiovascular risk in people with type 2 diabetes and aims to provide innovative risk stratification and treatment strategies that connect patients with the most effective care.

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Accurate and efficient aeroelastic models are critically important for enabling the optimization and control of highly flexible aerospace structures, which are expected to become pervasive in future transportation and energy systems. Advanced materials and morphing wing technologies are resulting in next-generation aeroelastic systems that are characterized by highly coupled and nonlinear interactions between the aerodynamic and structural dynamics. In this work, we leverage emerging data-driven modelling techniques to develop highly accurate and tractable reduced-order aeroelastic models that are valid over a wide range of operating conditions and are suitable for control. In particular, we develop two extensions to the recent dynamic mode decomposition with control (DMDc) algorithm to make it suitable for flexible aeroelastic systems: (1) we introduce a formulation to handle algebraic equations, and (2) we develop an interpolation scheme to smoothly connect several linear DMDc models developed in different operating regimes. Thus, the innovation lies in accurately modelling the nonlinearities of the coupled aerostructural dynamics over multiple operating regimes, not restricting the validity of the model to a narrow region around a linearization point. We demonstrate this approach on a high-fidelity, three-dimensional numerical model of an airborne wind energy system, although the methods are generally applicable to any highly coupled aeroelastic system or dynamical system operating over multiple operating regimes. Our proposed modelling framework results in real-time prediction of nonlinear unsteady aeroelastic responses of flexible aerospace structures, and we demonstrate the enhanced model performance for model predictive control. Thus, the proposed architecture may help enable the widespread adoption of next-generation morphing wing technologies.

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Primary care physicians are uniquely placed to offer holistic, patient-centred care to patients with T2DM. While the recent FDA-mandated cardiovascular outcome trials offer a wealth of data to inform treatment discussions, they have also contributed to increasing complexity in treatment decisions, and in the guidelines that seek to assist in making these decisions. To assist physicians in avoiding treatment inertia, Primary Care Diabetes Europe has formulated a position statement that summarises our current understanding of the available T2DM treatment options in various patient populations. New data from recent outcomes trials is contextualised and summarised for the primary care physician. This consensus paper also proposes a unique and simple tool to stratify patients into 'very high' and 'high' cardiovascular risk categories and outlines treatment recommendations for patients with atherosclerotic cardiovascular disease, heart failure and chronic kidney disease. Special consideration is given to elderly/frail patients and those with obesity. A visual patient assessment tool is provided, and a comprehensive set of prescribing tips is presented for all available classes of glucose-lowering therapies. This position statement will complement the already available, often specialist-focused, T2DM treatment guidelines and provide greater direction in how the wealth of outcome trial data can be applied to everyday practice.

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Hybrid systems are traditionally difficult to identify and analyse using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely focus on identifying a single set of governing equations solely from measurement data. In this article, we develop a new methodology, Hybrid-Sparse Identification of Nonlinear Dynamics, which identifies separate nonlinear dynamical regimes, employs information theory to manage uncertainty and characterizes switching behaviour. Specifically, we use the nonlinear geometry of data collected from a complex system to construct a set of coordinates based on measurement data and augmented variables. Clustering the data in these measurement-based coordinates enables the identification of nonlinear hybrid systems. This methodology broadly empowers nonlinear system identification without constraining the data locally in time and has direct connections to hybrid systems theory. We demonstrate the success of this method on numerical examples including a mass-spring hopping model and an infectious disease model. Characterizing complex systems that switch between dynamic behaviours is integral to overcoming modern challenges such as eradication of infectious diseases, the design of efficient legged robots and the protection of cyber infrastructures.

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Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling and control efforts, providing a tremendous opportunity to extend the reach of model predictive control (MPC). However, many leading methods in machine learning, such as neural networks (NN), require large volumes of training data, may not be interpretable, do not easily include known constraints and symmetries, and may not generalize beyond the attractor where models are trained. These factors limit their use for the online identification of a model in the low-data limit, for example following an abrupt change to the system dynamics. In this work, we extend the recent sparse identification of nonlinear dynamics (SINDY) modelling procedure to include the effects of actuation and demonstrate the ability of these models to enhance the performance of MPC, based on limited, noisy data. SINDY models are parsimonious, identifying the fewest terms in the model needed to explain the data, making them interpretable and generalizable. We show that the resulting SINDY-MPC framework has higher performance, requires significantly less data, and is more computationally efficient and robust to noise than NN models, making it viable for online training and execution in response to rapid system changes. SINDY-MPC also shows improved performance over linear data-driven models, although linear models may provide a stopgap until enough data is available for SINDY. SINDY-MPC is demonstrated on a variety of dynamical systems with different challenges, including the chaotic Lorenz system, a simple model for flight control of an F8 aircraft, and an HIV model incorporating drug treatment.

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We develop an algorithm for model selection which allows for the consideration of a combinatorially large number of candidate models governing a dynamical system. The innovation circumvents a disadvantage of standard model selection which typically limits the number of candidate models considered due to the intractability of computing information criteria. Using a recently developed sparse identification of nonlinear dynamics algorithm, the sub-selection of candidate models near the Pareto frontier allows feasible computation of Akaike information criteria (AIC) or Bayes information criteria scores for the remaining candidate models. The information criteria hierarchically ranks the most informative models, enabling the automatic and principled selection of the model with the strongest support in relation to the time-series data. Specifically, we show that AIC scores place each candidate model in the strong support, weak support or no support category. The method correctly recovers several canonical dynamical systems, including a susceptible-exposed-infectious-recovered disease model, Burgers' equation and the Lorenz equations, identifying the correct dynamical system as the only candidate model with strong support.

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BACKGROUND: Sodium glucose co-transporter 2 (SGLT2) inhibitors are a new class of pharmacologic agents developed for the treatment of type 2 diabetes mellitus (T2DM). Their unique mechanism of action is independent of pancreatic beta-cell function or the degree of insulin resistance, giving these agents the potential for use in combination with any of the existing classes of glucose-lowering agents, including insulin. This makes SGLT2 inhibitors an option for patients with long-standing T2DM, but they also have a promising role for early intervention in T2DM, and that role is explored in this review. METHODS: A literature search was performed to identify relevant English language articles relating to SGLT2 inhibitors, particularly dapagliflozin, canagliflozin and empagliflozin. RESULTS: Clinical trials of dapagliflozin, canagliflozin and empagliflozin, given as monotherapy or in combination with other glucose-lowering agents, reported clinically significant improvements in glycaemic control, body weight and systolic blood pressure. SGLT2 inhibitors were well tolerated and had a generally favourable safety profile. Few serious adverse events have been reported to date. The frequency of hypoglycaemic events was low, similar to that of placebo, and the choice of co-administered glucose-lowering agent was the major determinant of hypoglycaemic risk. Increased genital and urinary tract infections were consistently reported with SGLT2 inhibitors. CONCLUSIONS: SGLT2 inhibitors, with their unique insulin-independent mode of action, could have a significant impact on the early management of T2DM, by addressing some of the specific risk factors associated with this disease. SGLT2 inhibitors induce beneficial changes in a number of cardiovascular risk factors, such as lowering blood pressure and body weight, in addition to improved glycaemic control, although information on clinical cardiovascular outcomes is currently limited.

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BACKGROUND: In patients with type 2 diabetes (T2D), incretin-based therapies improve glycaemic control with low incidence of hypoglycaemia and without weight gain, both advantages over traditional add-ons to metformin. Dipeptidyl peptidase-4 (DPP-4) inhibitors are administered orally and provide a physiological increase in glucagon-like peptide-1 (GLP-1) levels, while GLP-1 receptor agonists (GLP-1RAs) are injectable and deliver pharmacological levels of GLP-1RA. This review aims to distinguish between GLP-1RAs and DPP-4 inhibitors, and discuss when each may be favoured in clinical practice. METHODS: A MEDLINE search, limited to human clinical trials and using the search criteria 'GLP-1RA' or 'DPP-4 inhibitor', identified seven head-to-head studies and one relevant post hoc analysis (all a GLP-1RA vs. the DPP-4 inhibitor sitagliptin). In combination with treatment algorithms, product prescribing information and personal clinical experience, these studies were used to compare the efficacy and suitability of GLP-1RAs and DPP-4 inhibitors in patients with T2D. RESULTS: In head-to-head clinical trials, GLP-1RAs provided greater glycaemic control, weight loss and overall treatment satisfaction vs. the DPP-4 inhibitor sitagliptin. Transient nausea was more frequent with GLP-1RAs and should be addressed through patient education and an incremental dosing approach. Current treatment algorithms recommend incretin-based therapy use after metformin failure, but local guidance may restrict their use. CONCLUSION: GLP-1RAs provide superior glycaemic control and weight loss vs. DPP-4 inhibitors in patients with T2D. DPP-4 inhibitors may sometimes be preferred to a GLP-1RA if weight is not a concern, oral administration is a desirable feature or when a GLP-1RA cannot be tolerated.

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BACKGROUND: The homogeneous genotype and stereotyped phenotype of a unique familial form of amyotrophic lateral sclerosis (ALS) (patients homozygous for aspartate-to-alanine mutations in codon 90 (homD90A) superoxide dismutase 1) provides an ideal model for studying genotype/phenotype interactions and pathological features compared with heterogeneous apparently sporadic ALS. The authors aimed to use diffusion tensor tractography to quantify and compare changes in the intracerebral corticospinal tracts of patients with both forms of ALS, building on previous work using whole-brain voxelwise group analysis. METHOD: 21 sporadic ALS patients, seven homD90A patients and 20 healthy controls underwent 1.5 T diffusion tensor MRI. Patients were assessed using 'upper motor neuron burden,' El Escorial and ALSFR-R scales. The intracranial corticospinal tract was assessed using diffusion tensor tractography measures of fractional anisotropy (FA), mean diffusivity, and radial and axial diffusivity obtained from its entire length. RESULTS: Corticospinal tract FA was reduced in sporadic ALS patients compared with both homD90A ALS patients and controls. The diffusion measures in sporadic ALS patients were consistent with anterograde (Wallerian) degeneration of the corticospinal tracts. In sporadic ALS, corticospinal tract FA was related to clinical measures. Despite a similar degree of clinical upper motor neuron dysfunction and disability in homD90A ALS patients compared with sporadic ALS, there were no abnormalities in corticospinal tract diffusion measures compared with controls. CONCLUSIONS: Diffusion tensor tractography has shown axonal degeneration within the intracerebral portion of the corticospinal tract in sporadic ALS patients, but not those with a homogeneous form of familial ALS. This suggests significant genotypic influences on the phenotype of ALS and may provide clues to slower progression of disease in homD90A patients.

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Gastro-oesophageal reflux disease (GERD), manifesting with symptoms including heartburn and regurgitation, affects people during both daytime and nocturnal hours. Night-time GERD has been reported to have a greater impact on a patient's life than daytime GERD due to prolonged oesophageal acid exposure time per reflux episode. To further understand this issue, it is important to implement quality of life (QOL) measures. QOL studies are becoming increasingly important to physicians in making clinical decisions, and generic and disease-specific health-related QOL (HRQL) tools have been developed to measure a wide variety of topics. There are currently no universally accepted guidelines on how to best measure HRQL in GERD patients. It is important to note that these surveys may not yield accurate results because many GERD sufferers may feel that their symptoms are not serious enough to seek treatment. Some surveys include the GERD-HRQL assessment, the Short Form Health Survey (SF-36), and the Gallup survey. When compared with daytime GERD patients, night-time GERD patients may suffer from sleep deprivation, which in turn leads to physical and emotional problems and a poor overall QOL. Studies indicate that the prevalence and impact of night-time heartburn have been underestimated and that adequate treatment of symptoms is often not achieved. In addition, GERD greatly affects work productivity and leads to a significant economic burden on society. Although limited studies are available on the impact of pharmacological treatment on GERD QOL, recent findings indicate that proton pump inhibitors are more effective than H(2)-receptor antagonists for the improvement of overall QOL.

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BACKGROUND: A case of avascular necrosis (AN) of the navicular bone, in a 24-year-old woman with Type 1 diabetes with peripheral neuropathy, in the absence of any history of direct trauma is presented. The clinical and radiological features at presentation suggested an evolving Charcot arthropathy (CA), but subsequent serial X-rays clearly confirmed AN. CONCLUSIONS: Swelling and foot deformity in association with long-standing diabetic peripheral neuropathy is suggestive of CA, although AN, a less common condition, may show the same clinical features. It is therefore important to undertake further confirmatory radiological investigations if there is any doubt about the diagnosis.

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BACKGROUND AND OBJECTIVES: This study evaluated use of the patient-administered Personal Pain Tracker in ambulatory primary care practice and tested the hypothesis that use of the instrument is associated with greater patient satisfaction with physician-patient communication regarding pain. METHODS: This randomized clinical trial was conducted in the offices of 12 family physicians practicing in Los Angeles and Orange Counties in Southern California. Study subjects consisted of 79 patients who presented with pain complaints during the study's enrollment period and who conformed to the inclusion and exclusion criteria. Patients randomized to the experimental arm of the study received and completed the Pain Tracker, and both these patients and control group patients participated in a telephone interview with an investigator 1 week subsequent to their visit. RESULTS: Regardless of the specific nature or cause of their pain, patients randomized to the Pain Tracker group were substantially more likely to report that their physicians felt that their pain was genuine and were concerned about it, asked enough questions about the pain and listened carefully, gave them a chance to provide a complete explanation, and performed a thorough examination. Patients in the Pain Tracker group also reported a higher level of overall satisfaction with their visit. CONCLUSIONS: Use of the Pain Tracker instrument improves patients' perceptions of the quality of physician-patient communication regarding pain complaints and results in greater overall satisfaction with medical visits.

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Dust mite proteins have been implicated as a predominant cause of the current increase in asthma prevalence. Recent advances in the understanding of these allergens have vastly increased the potential for more effective treatment and prophylaxis. In the absence of vaccines, environmental controls aimed at allergen avoidance are currently the best defense against the disease.

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Despite an increased understanding of risk factors for cardiovascular disease and the development of new programs, procedures, and medications to reduce risk, effective large-scale primary and secondary prevention have remained difficult to achieve. Large-scale, primarily educational, community-based programs aimed at detecting and reducing risk can be effective, particularly when they are aimed at specific populations at increased risk. However, the long-term benefits of these programs have been modest. Recent studies have demonstrated that lipid-lowering therapy is a critical adjunct to dietary and lifestyle changes. The 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors in particular are highly effective in reducing cardiovascular risk in both primary and secondary prevention programs that combine lifestyle modification and aggressive medical therapy. The ability of these drugs to produce rapid improvements in endothelial function and increase coronary perfusion also supports their use in the medical rather than surgical management of at least some patients with cardiovascular disease who are candidates for percutaneous transluminal coronary angioplasty.

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