BACKGROUND: Lebrikizumab is a novel monoclonal antibody with established efficacy in patients with moderate-to-severe atopic dermatitis (AD) in multiple Phase 3 trials. One of the ultimate treatment goals for patients with moderate-to-severe AD is to achieve stable disease control without concern for planning future life events.
METHODS: In ADvocate1 and ADvocate2, lebrikizumab-treated patients meeting the protocol-defined response criteria at Week 16 were re-randomized 2:2:1 to receive lebrikizumab every 2 weeks (Q2W), lebrikizumab every 4 weeks (Q4W), or placebo Q2W (lebrikizumab withdrawal) for 36 additional weeks. In this post hoc analysis, we evaluated the proportions of patients with no or minimal fluctuations of efficacy during the 36-week maintenance period and plotted individual patient trajectories. We defined no or minimal fluctuations as achieving and maintaining the defined endpoint (≥ 75% improvement in the Eczema Area and Severity Index [EASI 75], ≥ 90% improvement in EASI, Pruritus Numeric Rating Scale [NRS] ≥ 4-point improvement, or Pruritus NRS ≥ 3-point improvement) for ≥ 80% of the study visits. If patients used rescue medication, discontinued treatment, or transferred to the escape arm, data collected at or after the event were imputed as non-response.
RESULTS: The proportions of lebrikizumab responders who maintained EASI 75 with no or minimal fluctuations were 70.8% (lebrikizumab Q2W), 71.2% (lebrikizumab Q4W), and 60.0% (lebrikizumab withdrawal). Of the patients with baseline Pruritus NRS ≥ 4 and who achieved ≥ 4-point improvement at Week 16, 66.1% (lebrikizumab Q2W), 62.7% (lebrikizumab Q4W), and 55.2% (lebrikizumab withdrawal) maintained ≥ 4-point Pruritus NRS improvement with no or minimal fluctuations.
CONCLUSIONS: Patients who met the response criteria at Week 16 and continued treatment with lebrikizumab Q2W or Q4W demonstrated a stable response with no or minimal fluctuations of efficacy in measures of skin and itch up to Week 52.
BACKGROUND: NCT04146363 (ADvocate1) and NCT04178967 (ADvocate2).
Atopic dermatitis, also known as atopic eczema (or just eczema), is a common skin disease that causes itchy, dry skin. Patients with eczema are often unsure of when disease flares will happen, even while receiving treatment. In two global studies, ADvocate1 and ADvocate2, lebrikizumab improved the signs and symptoms of moderate-to-severe eczema after 16 weeks of treatment. Most of these patients also saw improvement up to 52 weeks. We wanted to know if patients continued to feel better between Week 16 and Week 52. Patients who responded to lebrikizumab after 16 weeks were given lebrikizumab every 2 weeks, lebrikizumab every 4 weeks, or placebo every 2 weeks. We tested how many patients experienced stable response to therapy, which we said was maintaining the same level of improvement on skin signs and itch symptoms for at least 80% of study visits from Week 16 to Week 52. In patients treated with lebrikizumab every 2 weeks or every 4 weeks, we saw that about seven of every ten patients maintained a stable response in skin improvement and about six of every ten patients maintained stable response in itch symptoms. In patients who stopped lebrikizumab therapy, six out of every ten patients maintained a stable skin improvement and more than five of every ten patients maintained a stable improvement in itch symptoms. In ADvocate1 and ADvocate2, most lebrikizumab-treated patients showed a stable response over time on skin and itch with dosing every 2 weeks or every 4 weeks.

UNASSIGNED: Intraindividual variability across a battery of neuropsychological tests (IIV-dispersion) can reflect normal variation in scores or arise from cognitive impairment. An alternate interpretation is IIV-dispersion reflects reduced engagement/invalid test data, although extant research addressing this interpretation is significantly limited.
UNASSIGNED: We used a sample of 97 older adult (mean age: 69.92), predominantly White (57%) or Black/African American (34%), and predominantly cis-gender male (87%) veterans. Examinees completed a comprehensive neuropsychological battery, including measures of reduced engagement/invalid test data (a symptom validity test [SVT], multiple performance validity tests [PVTs]), as part of a clinical evaluation. IIV-dispersion was indexed using the coefficient of variance (CoV). We tested 1) the relationships of raw scores and \"failures\" on SVT/PVTs with IIV-dispersion, 2) the relationship between IIV-dispersion and validity/neurocognitive disorder status, and 3) whether IIV-dispersion discriminated the validity/neurocognitive disorder groups using receiver operating characteristic (ROC) curves.
UNASSIGNED: IIV-dispersion was significantly and independently associated with a selection of PVTs, with small to very large effect sizes. Participants with invalid profiles and cognitively impaired participants with valid profiles exhibited medium to large (d = .55-1.09) elevations in IIV-dispersion compared to cognitively unimpaired participants with valid profiles. A non-significant but small to medium (d = .35-.60) elevation in IIV-dispersion was observed for participants with invalid profiles compared to those with a neurocognitive disorder. IIV-dispersion was largely accurate at differentiating participants without a neurocognitive disorder from invalid participants and those with a neurocognitive disorder (areas under the Curve [AUCs]=.69-.83), while accuracy was low for differentiating invalid participants from those with a neurocognitive disorder (AUCs=.58-.65).
UNASSIGNED: These preliminary data suggest IIV-dispersion may be sensitive to both neurocognitive disorders and compromised engagement. Clinicians and researchers should exercise due diligence and consider test validity (e.g. PVTs, behavioral signs of engagement) as an alternate explanation prior to interpretation of intraindividual variability as an indicator of cognitive impairment.

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe \"Onion Clustering\": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

Identifying and validating a biomarker with high specificity in early-stage dementia with Lewy bodies (DLB) using a feasible method is crucial to enhance the current suboptimal diagnostic procedure. Previous research revealed abnormalities, including hypoperfusion in the right anterior insular cortex at group level, in prodromal DLB. Exploring hypoperfusion of the right anterior insula, at an individual-level and assessing its relevance as a potential imaging biomarker in early DLB, has, to our knowledge, not been investigated. Our preliminary study aims to assess the feasibility of the technique and to provide a methodological framework for further investigation. We assessed the feasibility and accuracy of the hypoperfusion of the right anterior insula per arterial spin labelling magnetic resonance imaging (ASL-MRI) as a diagnostic biomarker in early DLB and provided rough estimates of its sensitivity and specificity. Defining the region of interest based on previous research, we established the biomarker as the hypoperfusion of the right anterior insula. Discriminative and analytical performances were assessed in comparison to a control group of treatment-resistant depression patients. Bayesian diagnostic reasoning was employed to assess the biomarker diagnostic usability in early DLB in two scenarios: healthy elderly controls and mild cognitive impairment. Additionally, we updated probabilities by integrating data from the Mayo-clinic cognitive fluctuations scale and real-time quaking-induced conversion (RT-QuIC) α-synuclein data. Lastly, a whole-brain perfusion analysis of DLB patients was conducted to identify further brain regions with discriminative abilities. We successfully replicated the right anterior insular hypoperfusion (RAI-Hypo) in all DLB patients at the individual level. The overall sensitivity of the biomarker was 96%, and the specificity was 92%. Bayesian testing revealed the biomarker\'s highest performance in early-stage DLB with cognitive fluctuations, showcasing a diagnostic potential associated with a high precision and moderate accuracy. In a cognitively non-impaired population, the RAI-Hypo showed a limited usability and lacked in selectivity to qualify as a screening tool. The exploratory whole-brain analysis revealed perfect discriminative capacities in the bilateral anterior insulae and the left inferior parietal lobule. Further studies are needed to confirm our preliminary results. If performance is maintained in subsequent studies and is compared to a more suitable control population, the proposed biomarker may be eventually sufficient to discriminate early-stage DLB from non-DLB.

BACKGROUND: Children\'s cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning. The relative dearth of empirical work on variability, historically limited due to a lack of suitable data and quantitative methodology, has left crucial questions unanswered, which the CODEC (COgnitive Dynamics in Early Childhood) study aims to address.
METHODS: The CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a \'burst\' week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms.
CONCLUSIONS: CODEC\'s unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC\'s longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal \'burst\' design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children.
BACKGROUND: ClinicalTrials.gov - NCT06330090.

Visual working memory (VWM) plays an important role during visual search, with some theories suggesting an equivalence between working memory representations and guidance from attentional templates. However, recent work has shown that participants can also use \'negative templates\', the foreknowledge of distractor-features stored in VWM, to guide attention away from distractors during visual search. These negative templates must also be represented in working memory, but the question remains whether the quality of the working memory representations underlying negative and positive templates are similar, in spite of their opposite impacts on attention. In this study, participants (N = 33) engaged in a visual search task for a shape-defined target after receiving a positive cue (target color), negative cue (distractor color) or neutral cue (non-informative). In 20% of the trials, a color-wheel probe was presented instead of a search array to measure the quality of the cue representation stored in VWM. Our results revealed that participants were more likely to guess in response to neutral cues than negative cues. Yet, the comparison between positive and negative cues showed no significant differences. However, we found no difference in memory precision for the three cue types. More interestingly, the more the VWM quality is boosted by the negative cue, the greater the ability to guide attention away from distractors. Such a pattern of results might map to recent evidence of between-individuals differences in utilization of negative cues. These findings highlight the distinction between attentional templates and simple maintenance in working memory.

BACKGROUND: Parkinson\'s disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, primarily because of the impairment of dopaminergic neurons. Long-term use of levodopa, the standard PD treatment, often results in fluctuating therapeutic effects and dyskinesia, necessitating alternative therapies.
OBJECTIVE: This review aims to synthesize current insights and clinical experiences with foslevodopa-foscarbidopa, focusing on its pharmacokinetics, efficacy, and safety profile, to evaluate its potential in transforming PD therapy.
METHODS: A systematic literature search was conducted up to November 2023 using databases PubMed, Web of Science, and Cochrane Library. The search yielded eight eligible articles, including pharmacological studies, case reports, observational studies, and controlled trials. No language restrictions were applied.
RESULTS: Foslevodopa and foscarbidopa, as prodrugs of levodopa and carbidopa, exhibited excellent chemical stability and solubility, facilitating continuous subcutaneous infusion. Clinical trials demonstrated that these prodrugs maintain stable levodopa levels, thereby addressing the limitations of oral levodopa therapy. Phase 1 and 3 studies indicated significant improvements in motor function and quality of life in advanced PD patients. However, a higher incidence of treatment-emergent adverse events, mainly infusion site reactions, was observed compared to oral therapies.
CONCLUSIONS: Foslevodopa-foscarbidopa emerges as a promising alternative for advanced PD treatment, offering sustained symptom control. Its efficacy in managing motor fluctuations and dyskinesia makes it a viable option in the PD therapeutic spectrum. Future research should focus on long-term safety, economic impact, and broader accessibility. Foslevodopa-foscarbidopa is now commercially distributed in many countries in Europe and in Japan.

Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice.

Controlling the time evolution of a probability distribution that describes the dynamics of a given complex system is a challenging problem. Achieving success in this endeavour will benefit multiple practical scenarios, e.g., controlling mesoscopic systems. Here, we propose a control approach blending the model predictive control technique with insights from information geometry theory. Focusing on linear Langevin systems, we use model predictive control online optimisation capabilities to determine the system inputs that minimise deviations from the geodesic of the information length over time, ensuring dynamics with minimum \"geometric information variability\". We validate our methodology through numerical experimentation on the Ornstein-Uhlenbeck process and Kramers equation, demonstrating its feasibility. Furthermore, in the context of the Ornstein-Uhlenbeck process, we analyse the impact on the entropy production and entropy rate, providing a physical understanding of the effects of minimum information variability control.

This study aims to analyze fluctuations and disparity in broiler and carcass prices before, during, and after COVID-19 pandemic in five broiler-producing regions in Indonesia, including North Sumatra, West Java, Central Java, East Java, and South Sulawesi. Weekly data series were used to analyze fluctuations and disparity before (2017-February 2020), during (March 2020-2022), and after (January-June 2023) COVID-19 pandemic. Furthermore, Coefficient of variation (CV) and convergence models were also used during the analysis. The results showed that broiler price fluctuations in the five study areas were categorized as \"medium\" before and during pandemic. After pandemic, the majority of the areas were in the \"low\" category, except for South Sulawesi province, where CV was 10.02%. Carcass price fluctuations were categorized as \"low\" and \"moderate\" before and during COVID-19 pandemic. After the viral outbreak, all the provinces investigated in this study were classified as \"low\". This indicated that the variability in carcass price decreased across all provinces after pandemic. The coefficient value of β1 was less than 1 indicating that broiler and carcass price did not show significant disparity between regions at all periods. The primary drivers of fluctuations and disparity were related to production and availability factors, and the government played a role in maintaining stability in producing areas.