Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis ...have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.
Abstract To better understand how sleep/wake dysregulation affects Alzheimer’s disease (AD) we compared the cerebrospinal fluid (CSF) orexin and histamine levels from early to late phases of 82 ...patients with Alzheimer’s process including 41 probable AD with high level of evidence and 41 MCI due to AD (i.e 27 MCI due to AD-high likelihood and 14 MCI due to AD-intermediate likelihood) according to the NIA diagnosis criteria guidelines, 24 other neurological disorders (OND) and 24 controls. We determined the relationships between these biomarkers, the CSF amyloid-β42 , total-tau, phosphorylated-tau (p-tau) concentrations and the sleep profile based on clinical interview and sleep validated questionnaires (n=86). CSF orexin-A, but not histamine/tele-methylhistamine (HA/t-MHA) levels were higher in MCI due to AD and AD than OND and controls (p=0.0003). CSF orexin-A correlated to CSF amyloid-β42 within the Alzheimer process (p=0.03). This relation is not explained by age, gender, MMSE, total-tau, p-tau, histamine nor sleep parameters. Nighttime sleep duration was longer in MCI due to AD and AD patients than controls (p=0.004). In MCI due to AD, nighttime sleep duration was negatively correlated with CSF amyloid-β42 level and MMSE. To conclude, CSF orexin-A was upregulated in AD and correlated with amyloid-β42 level. No CSF HA and t-HMA level differences were observed within the Alzheimer’s process. Lower levels of CSF amyloid-β42 were associated with longer night-sleep duration in MCI due to AD patients. Our data suggested a change in the sleep-wake pattern at an early stage of the disease that needs further investigation to better understand the mechanistic interplay between sleep and Alzheimer.
This study investigates the possibility of adopting motor and cognitive dual-task (MCDT) approaches to identify subjects with mild cognitive impairment (MCI) and subjective cognitive impairment ...(SCI).
The upper and lower motor performances of 44 older adults were assessed using the SensHand and SensFoot wearable system during three MCDTs: forefinger tapping (FTAP), toe-tapping heel pin (TTHP), and walking 10 m (GAIT). We developed five pooled indices (PIs) based on these MCDTs, and we included them, along with demographic data (age) and clinical scores (Frontal Assessment Battery (FAB) scores), in five logistic regression models.
Models which consider cognitively normal adult (CNA) vs MCI subjects have accuracies that range from 67% to 78%. The addition of clinical scores stabilised the accuracies, which ranged from 85% to 89%. For models which consider CNA vs SCI vs MCI subjects, there are great benefits to considering all three regressors (age, FAB score, and PIs); the overall accuracies of the three-class models range between 50% and 59% when just PIs and age are considered, whereas the overall accuracy increases by 18% when all three regressors are utilised.
Logistic regression models that consider MCDT PIs and age have been effective in distinguishing between CNA and MCI subjects. The inclusion of clinical scores increased the models' accuracy. Particularly high performances in distinguishing among CNA, SCI, and MCI subjects were obtained by the TTHP PI. This study suggests that a broader framework for MCDTs, which should encompass a greater selection of motor tasks, could provide clinicians with new appropriate tools.
Certain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely ...diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders.
Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.
Different speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality-and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores.
Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.
Disease-modifying drug use necessitates better Alzheimer disease (AD) detection. Mild cognitive impairment (MCI) leverages cognitive decline to identify the risk group; similarly, mild behavioral ...impairment (MBI) leverages behavioral change. Adding MBI to MCI improves dementia prognostication over conventional approaches of incorporating neuropsychiatric symptoms (NPS). Here, to determine if adding MBI would better identify AD, we interrogated associations between MBI in MCI, and cerebrospinal fluid biomarkers β-amyloid (Aβ), phosphorylated-tau (p-tau), and total-tau (tau)-ATN, cross-sectionally and longitudinally.
Data were from two independent referral-based cohorts, ADNI (meanSD follow-up 3.141.07 years) and MEMENTO (4.251.40 years), collected 2003-2021. Exposure was based on three-group stratification: 1) NPS meeting MBI criteria; 2) conventionally measured NPS (NPSnotMBI); and 3) noNPS. Cohorts were analyzed separately for: 1) cross-sectional associations between NPS status and ATN biomarkers (linear regressions); 2) 4-year longitudinal repeated-measures associations of MBI and NPSnotMBI with ATN biomarkers (hierarchical linear mixed-effects models-LMEs); and 3) rates of incident dementia (Cox proportional hazards regressions).
Of 510 MCI participants, 352 were from ADNI (43.5% females; mean SD age, 71.68 7.40 years), and 158 from MEMENTO (46.2% females; 68.98 8.18 years). In ADNI, MBI was associated with lower Aβ42 (standardized β 95%CI, -5.52% -10.48-(-0.29)%; p = 0.039), and Aβ42/40 (p = 0.01); higher p-tau (9.67% 3.96-15.70%; p = 0.001), t-tau (7.71% 2.70-12.97%; p = 0.002), p-tau/Aβ42 (p < 0.001), and t-tau/Aβ42 (p = 0.001). NPSnotMBI was associated only with lower Aβ42/40 (p = 0.045). LMEs revealed a similar 4-year AD-specific biomarker profile for MBI, with NPSnotMBI associated only with higher t-tau. MBI had a greater rate of incident dementia (HR 95%CI, 3.50 1.99-6.17; p < 0.001). NPSnotMBI did not differ from noNPS (HR 0.96 0.49-1.89; p = 0.916). In MEMENTO, MBI demonstrated a similar magnitude and direction of effect for all biomarkers, but with a greater reduction in Aβ40. HR for incident dementia was 3.93 (p = 0.004) in MBI, and 1.83 (p = 0.266) in NPSnotMBI. Of MBI progressors to dementia, 81% developed AD dementia.
These findings support a biological basis for NPS that meet MBI criteria, the continued inclusion of MBI in NIA-AA ATN clinical staging, and the utility of MBI criteria to improve identification of patients for enrollment in disease-modifying drug trials or for clinical care.
The effects of coronavirus disease 2019 (COVID‐19) have been well documented across the world with an appreciation that older people and in particular those with dementia have been disproportionately ...and negatively affected by the pandemic. This is both in terms of their health outcomes (mortality and morbidity), care decisions made by health systems and the longer‐term effects such as neurological damage. The International Dementia Alliance is a group of dementia specialists from six European countries and this paper is a summary of our experience of the effects of COVID‐19 on our populations. Experience from England, France, Germany, the Netherlands, Spain and Switzerland highlight the differential response from health and social care systems and the measures taken to maximise support for older people and those with dementia. The common themes include recognition of the atypical presentation of COVID‐19 in older people (and those with dementia) need to pay particular attention to the care of people with dementia in care homes; the recognition of the toll that isolation can bring on older people and the complexity of the response by health and social services to minimise the negative impact of the pandemic. Potential new ways of working identified during the pandemic could serve as a positive legacy from the crisis.
Abstract
Detailed whole-cell modeling requires an integration of heterogeneous cell processes having different modeling formalisms, for which whole-cell simulation could remain tractable. Here, we ...introduce BiPSim, an open-source stochastic simulator of template-based polymerization processes, such as replication, transcription and translation. BiPSim combines an efficient abstract representation of reactions and a constant-time implementation of the Gillespie’s Stochastic Simulation Algorithm (SSA) with respect to reactions, which makes it highly efficient to simulate large-scale polymerization processes stochastically. Moreover, multi-level descriptions of polymerization processes can be handled simultaneously, allowing the user to tune a trade-off between simulation speed and model granularity. We evaluated the performance of BiPSim by simulating genome-wide gene expression in bacteria for multiple levels of granularity. Finally, since no cell-type specific information is hard-coded in the simulator, models can easily be adapted to other organismal species. We expect that BiPSim should open new perspectives for the genome-wide simulation of stochastic phenomena in biology.
Purpose To compare the long-term brain elimination kinetics and gadolinium species in healthy rats after repeated injections of the contrast agents gadodiamide (a linear contrast agent) or gadoterate ...(a macrocyclic contrast agent). Materials and Methods Nine-week-old rats received five doses of 2.4 mmol gadolinium per kilogram of body weight over 5 weeks and were followed for 12 months with T1-weighted MRI (n = 140 rats, corresponding to seven time points, two contrast agents, and 10 rats per group). Animals were sacrificed at 1 week, 1 month, and 2, 3, 4, 5, and 12 months after the last injection. Brain and plasma were sampled to determine the total gadolinium concentration by using inductively coupled plasma mass spectrometry (ICP-MS). For the cerebellum, gadolinium speciation analysis was performed after mild extraction at four time points (1 month and 3, 5, and 12 months after the last injection) by using size exclusion chromatography and hydrophilic interaction liquid chromatography, both coupled to ICP-MS. Tissue gadolinium kinetics were fitted to estimate the area under the curves and tissue elimination half-lives over the 12-month injection-free period. Results T1 hyperintensity of the deep cerebellar nuclei was observed only in gadodiamide-treated rats and remained stable from the 1st month after the last injection (the ratio of the signal intensity of the deep cerebellar nuclei to the signal intensity of the brain stem at 1 year: 1.101 ± 0.023 vs 1.037 ± 0.022 before injection, P < .001). Seventy-five percent of the total gadolinium detected after the last injection of gadodiamide (3.25 nmol/g ± 0.30) was retained in the cerebellum at 1 year (2.45 nmol/g ± 0.35), with binding of soluble gadolinium to macromolecules. No T1 hyperintensity was observed with gadoterate, consistent with a rapid, time-dependent washout of the intact gadolinium chelate down to background levels (0.07 nmol/g ± 0.03). Conclusion After repeated administration of gadodiamide, a large portion of gadolinium was retained in the brain, with binding of soluble gadolinium to macromolecules. After repeated injection of gadoterate, only traces of the intact chelated gadolinium were observed with time-dependent clearance. Online supplemental material is available for this article.
CTLA4 is an essential negative regulator of T-cell immune responses and a key checkpoint regulating autoimmunity and antitumor responses. Genetic mutations resulting in quantitative defects in the ...CTLA4 pathway are also associated with the development of immune dysregulation syndromes in humans. It has been proposed that CTLA4 functions to remove its ligands CD80 and CD86 from opposing cells by a process known as transendocytosis. A quantitative characterization of CTLA4 synthesis, endocytosis, degradation, and recycling and how these affect its function is currently lacking. In a combined in vitro and in silico study, we developed a mathematical model and identified these trafficking parameters. Our model predicts optimal ligand removal in an intermediate affinity range. The intracellular CTLA4 pool as well as fast internalization, recovery of free CTLA4 from internalized complexes, and recycling is critical for sustained functionality. CD80-CTLA4 interactions are predicted to dominate over CD86-CTLA4. Implications of these findings in the context of control of antigen-presenting cells by regulatory T cells and of pathologic genetic deficiencies are discussed. The presented mathematical model can be reused in the community beyond these questions to better understand other trafficking receptors and study the impact of CTLA4 targeting drugs.
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