Machine intelligence accelerated design of conductive MXene aerogels with programmable properties.

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.

Few-Shot Classification with Multiscale Feature Fusion for Clinical Skin Disease Diagnosis.

Introduction: Skin disease is one of the most common diseases and can affect people of all ages and races. However, the diagnosis of skin diseases via observation is a highly challenging task for both doctors and patients, and would benefit from the use of an intelligent system. Building a large benchmark with professional dermatologists is resource-intensive, and we believe that few-shot learning (FSL) methods would be helpful in solving the problem of annotated data scarcity. In this paper, we propose CDD-Net (Context Feature Fusion and Dual Attention Dermatology Net), a plug-in module for FSL clinical skin disease classification. Methods: Current FSL methods used in skin disease classification are limited to nonuniversal approaches and few disease classes. Our CDD-Net has a flexible structure, including a context feature-fusion module and dual-attention module to extract discriminating texture feature and emphasize contributive regions and channels. The context feature-fusion module localizes discriminatory texture details of skin lesions by integrating features from different layers, while the dual-attention module highlights discriminative regions via channel-wise and pixel-wise depictions based on weight vectors and restrains the contributions of irrelevant areas. We also present Derm104, a new clinical skin disease data benchmark that has significant coverage of rare diseases and reliable annotation between primary species and subspecies for better validation of our approach. Results: Our experiments validated the versatility of CDD-Net for different FSL methods and achieved an improvement in accuracy of up to 9.14 percentage points compared with the vanilla network, which can be considered state of the art. The ablation study also showed that the dual-attention module and context feature-fusion module worked efficiently in CDD-Net.

Results from the long-term extension of PRIME: A randomized Phase 1b trial of aducanumab.

INTRODUCTION: Aducanumab selectively targets aggregated forms of amyloid beta (Aß), a neuropathological hallmark of Alzheimer's disease (AD). METHODS: PRIME was a Phase 1b, double-blind, randomized clinical trial of aducanumab. During the 12-month placebo-controlled period, participants with prodromal AD or mild AD dementia were randomized to receive aducanumab or placebo. At week 56, participants could enroll in a long-term extension (LTE), in which all participants received aducanumab. The primary endpoint was safety and tolerability. RESULTS: Amyloid-related imaging abnormalities-edema (ARIA-E) were the most common adverse event. Dose titration was associated with a decrease in the incidence of ARIA-E. Over 48 months, aducanumab decreased brain amyloid levels in a dose- and time-dependent manner. Exploratory endpoints suggested a continued benefit in the reduction of clinical decline over 48 months. DISCUSSION: The safety profile of aducanumab remained unchanged in the LTE of PRIME. Amyloid plaque levels continued to decrease in participants treated with aducanumab. HIGHLIGHTS: PRIME was a Phase 1b, double-blind, randomized clinical trial of aducanumab. We report cumulative safety and 48-month efficacy results from PRIME. Amyloid-related imaging abnormalities-edema (ARIA-E) were the most common adverse event (AE); 61% of participants with ARIA-E were asymptomatic. Dose titration was associated with a decrease in the incidence of ARIA-E. Aducanumab decreased levels of amyloid beta (Aß) in a dose- and time-dependent manner.

Machine intelligence-accelerated discovery of all-natural plastic substitutes.

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

Machine Learning Integrated Workflow for Predicting Schwann Cell Viability on Conductive MXene Biointerfaces.

Severe injuries to the peripheral nervous system (PNS) require Schwann cells to aid in neuronal regeneration. Low-frequency electrical stimulation is known to induce the cogrowth of neurons and Schwann cells in an injured PNS. However, the correlations between electrical stimulation and Schwann cell viability are complex and not well understood. In this work, we develop a machine learning (ML)-integrated workflow that uses conductive hydrogel biointerfaces to evaluate the impacts of fabrication parameters and electrical stimulation on the Schwann cell viability. First, a hydrogel array with varying MXene and peptide loadings is fabricated, which serves as conductive biointerfaces to incubate Schwann cells and introduce various electrical stimulation (at different voltages and frequencies). Upon specific fabrication parameters and stimulation, the cell viability is evaluated and input into an artificial neural network model to train the model. Additionally, a data augmentation method is applied to synthesize 1000-fold virtual data points, enabling the construction of a high-accuracy prediction model (with a testing mean absolute error ≤11%). By harnessing the model's predictive power, we can accurately predict Schwann cell viability based on a given set of fabrication/stimulation parameters. Finally, the SHapley Additive exPlanations model interpretation provides several data-scientific insights that are validated by microscopic cellular observations. Our hybrid approach, involving conductive biointerface fabrication, ML algorithms, and data analysis, offers an unconventional platform to construct a preclinical prediction model at the cellular level.

Multistage orbital hybridization induced by multisite exchange interactions in high-entropy perovskites for high oxygen evolution reaction.

The oxygen evolution reaction (OER) has garnered considerable attention because of its promising prospects in electrochemical energy conversion applications, but a significant challenge is faced by the insufficient understanding of sluggish OER kinetics. In fact, the intrinsic "acceptance-donation" process of electrons between active sites and reactants is responsible for improving OER activity. Herein, we suggest a multielement hybridization strategy to rematch spin electron occupation and energy splitting in high-entropy perovskites with multiple orbital coordination. In this concept, electronic hopping between t2g and eg orbitals among particular catalytic sites can be obviously enforced due to introducing more favorable energy levels from neighboring metal sites, which can demonstrate multistage orbital hybridization reaction activity. As a result, our proposed multistage-hybridized high-entropy perovskites display an impressive activity of 199.8 mA cm-2 as an overpotential of âˆ¼0.46 V, which is âˆ¼5.3 times that of pristine perovskite. Different from traditional catalyst designs, this study focuses on multistage orbital hybridization and electron exchange interactions through a multisite coordination mechanism to construct a fast reaction pathway. Our findings provide a new strategy for accelerating OER catalytic kinetics.

Generating Semantic Adversarial Examples via Feature Manipulation in Latent Space.

The susceptibility of deep neural networks (DNNs) to adversarial intrusions, exemplified by adversarial examples, is well-documented. Conventional attacks implement unstructured, pixel-wise perturbations to mislead classifiers, which often results in a noticeable departure from natural samples and lacks human-perceptible interpretability. In this work, we present an adversarial attack strategy that implements fine-granularity, semantic-meaning-oriented structural perturbations. Our proposed methodology manipulates the semantic attributes of images through the use of disentangled latent codes. We engineer adversarial perturbations by manipulating either a single latent code or a combination thereof. To this end, we propose two unsupervised semantic manipulation strategies: one based on vector-disentangled representation and the other on feature map-disentangled representation, taking into consideration the complexity of the latent codes and the smoothness of the reconstructed images. Our empirical evaluations, conducted extensively on real-world image data, showcase the potency of our attacks, particularly against black-box classifiers. Furthermore, we establish the existence of a universal semantic adversarial example that is agnostic to specific images.

Public Attitudes About the Use of Gene Therapy in Mainland China.

Importance: In addition to technical barriers, public attitudes about the use of gene therapy have an important association with the clinical implementation of gene therapy. Objective: To investigate the factors associated with public acceptance of gene therapy among individuals in China. Design, Setting, and Participants: This cross-sectional study used data from a survey conducted among 21 880 individuals in mainland China from June 20 to August 31, 2022. Main Outcomes and Measures: Stepwise linear regression was used to analyze factors associated with public acceptance of gene therapy in 5 key areas: basic personal information (gender, region, age, and educational level), family situation (marital status, children, and cousins), economic status (assets, debts, and insurance coverage), health knowledge (health literacy score and media use), and physical health status (chronic illness, cancer, European Quality of Life 5-Dimension 5-Level version [EQ-5D-5L] score, and Brief Illness Perception Questionnaire [BIPQ] score). Acceptance scores were calculated based on a visual analog scale (range, 0-100, with higher scores indicating higher acceptance of gene therapy). Further subgroup analysis was carried out in different age subgroups and populations with or without chronic diseases. Results: A total of 21 880 participants (mean [SD] age, 39.4 [18.9] years; 10 947 female participants [50.0%]; 10 933 male participants [50.0%]) were analyzed in this study. The mean (SD) acceptance score of gene therapy in the survey was 60.56 (27.60). Compared with people aged 60 years or older, those aged 12 to 18 years had higher acceptance of gene therapy (ß = 1.48 [95% CI, 0.09-2.88]), while groups aged 19 to 30 years (ß = -3.43 [95% CI, -4.80 to -2.07]), 31 to 44 years (ß = -1.44 [95% CI, -2.76 to -0.12]), and 45 to 59 years (ß = -2.05 [95% CI, -3.27 to -0.83]) had lower acceptance. Compared with people living in Eastern China, those in Central China had lower acceptance of gene therapy (ß = -1.58 [95% CI, -2.54 to -0.62]), while those in Western China had higher acceptance (ß = 0.92 [95% CI, 0.09-1.76]). Higher educational level (undergraduate or above vs junior high or below) was associated with higher acceptance of gene therapy (ß = 1.56 [95% CI, 0.49-2.63]). Number of properties owned was also associated with higher acceptance of gene therapy (2 vs 0: ß = 2.38 [95% CI, 1.04-3.72]; ≥3 vs 0: ß = 4.66 [95% CI, 2.92-6.39]). Diagnosis of chronic disease was associated with lower acceptance of gene therapy (ß = -17.86 [95% CI, -20.49 to -15.24]), while diagnosis of cancer was associated with higher acceptance (ß = 6.99 [95% CI, 1.84-12.14]). Higher BIPQ score (ß = 0.40 [95% CI, 0.34-0.45]), higher health literacy score (ß = 0.70 [95% CI, 0.62-0.78]), and media use (ß = 0.49 [95% CI, 0.41-0.57]) were all associated with high acceptance of gene therapy, while a higher EQ-5D-5L score was associated with lower acceptance (ß = -0.29 [95% CI, -0.47 to -0.11]). For older people, being in debt, not having health insurance, and the EQ-5D-5L score were uniquely relevant factors. For people with chronic disease, having an undergraduate degree or higher, a diagnosis of cancer, and the BIPQ score were uniquely relevant factors. Conclusions and Relevance: These results suggest that basic personal information, economic status, health knowledge, and physical health status were the main factors associated with the acceptance of gene therapy. Improving the health literacy of the population and promoting trust in gene therapy may be effective ways to increase the acceptance of gene therapy. Poorer economic levels and worse disease states may reduce the public's willingness to accept gene therapy.

CSF biomarker concordance with amyloid PET in Phase 3 studies of aducanumab.

INTRODUCTION: We assessed the use of cerebrospinal fluid (CSF) biomarkers as an alternative to positron emission tomography (PET) for brain amyloid beta (Aß) pathology confirmation in the EMERGE and ENGAGE clinical trials. METHODS: EMERGE and ENGAGE were randomized, placebo-controlled, Phase 3 trials of aducanumab in participants with early Alzheimer's disease. Concordance between CSF biomarkers (Aß42, Aß40, phosphorylated tau 181, and total tau) and amyloid PET status (visual read) at screening was examined. RESULTS: Robust concordance between CSF biomarkers and amyloid PET visual status was observed (for Aß42/Aß40, AUC: 0.90; 95% CI: 0.83-0.97; p < 0.0001), confirming CSF biomarkers as a reliable alternative to amyloid PET in these studies. Compared with single CSF biomarkers, CSF biomarker ratios showed better agreement with amyloid PET visual reads, demonstrating high diagnostic accuracy. DISCUSSION: These analyses add to the growing body of evidence supporting CSF biomarkers as reliable alternatives to amyloid PET imaging for brain Aß pathology confirmation. HIGHLIGHTS: CSF biomarkers and amyloid PET concordance were assessed in Ph3 aducanumab trials. Robust concordance between CSF biomarkers and amyloid PET was observed. CSF biomarker ratios increased diagnostic accuracy over single CSF biomarkers. CSF Aß42/Aß40 demonstrated high concordance with amyloid PET. Results support CSF biomarker testing as a reliable alternative to amyloid PET.

Discovery of cysteine-targeting covalent histone methyltransferase inhibitors.

Post-translational methylation of histone lysine or arginine residues by histone methyltransferases (HMTs) plays crucial roles in gene regulation and diverse physiological processes and is implicated in a plethora of human diseases, especially cancer. Therefore, histone methyltransferases have been increasingly recognized as potential therapeutic targets. Consequently, the discovery and development of histone methyltransferase inhibitors have been pursued with steadily increasing interest over the past decade. However, the disadvantages of limited clinical efficacy, moderate selectivity, and propensity for acquired resistance have hindered the development of HMTs inhibitors. Targeted covalent modification represents a proven strategy for kinase drug development and has gained increasing attention in HMTs drug discovery. In this review, we focus on the discovery, characterization, and biological applications of covalent inhibitors for HMTs with emphasis on advancements in the field. In addition, we identify the challenges and future directions in this fast-growing research area of drug discovery.

Factors affecting acceptance of organ donation in mainland China: A national cross-sectional study.

AIMS AND OBJECTIVES: To explore the acceptances and associated influences of organ donation in mainland China. BACKGROUND: The shortage of organ donors has limited the development of organ transplantation in China. It is important to recognise the target population who has high intention to donate their organs may change the status. DESIGN: We conducted a cross-sectional, multi-stage sampling study collected demographic data and individuals' willingness to accept organ donation. METHODS: A stepwise linear regression analysis was adopted to evaluate the factors related to the attitudes toward organ donation. RESULTS: We collected 11,031 valid samples for the survey. The willingness to donate organs among Chinese residents averaged 56.93 points. To be specific, males (ß = -.03), religious believers (ß = -.01) and parents with a different number of children (all: ß = -.04) are less willing to donate their organs. Respondents who live in an urban area (ß = .03), have higher education level (High school or junior college ß = .04, Bachelor degree or above ß = .09), feel anxious (mild, moderate ß = .02), feel pressured (moderate, severe ß = .08), have higher scores of the Short-Form Health Literacy Instrument (HLS-SF12) (ß = .31), The Self-Management Scale (SHMS) (ß = .16), EuroQol Five Dimensions Questionnaire (EQ-5D) (ß = .04) and EuroQol Visual Analogue Scale (EQ-VAS) (ß = .24), are more positive to donate. CONCLUSIONS: This study firstly discusses the public acceptance of organ donation through a nationwide sample around China. In this study, we discovered that Chinese residents' acceptance level of organ donation and that gender, house, anxiety, pressure, social support and health literacy were the main influencing factors on residents' attitudes. RELEVANCE TO CLINICAL PRACTICE: To figure out the Chinese public acceptance and its influencing factors of organ donation can help nurse transplant coordinators to recognise the target population and the obstacles of organ donation. PATIENT OR PUBLIC CONTRIBUTION: At the phase of collecting data, participants were recruited to fill the questionnaires.

What are the acceptances and associated influences of hospice care in Mainland China? A national cross-sectional study.

Background: China ranks 53rd out of 81 countries in the Quality of Death Index for 2021. Although hospice care demand is increasing, the progress remains slow. It is of great significance to explore the acceptances and associated influencing factors of hospice care. Methods: A cross-sectional survey by quota sampling was conducted in China from July 10th to September 15th, 2021. We collected demographic data and hospice care acceptance. A stepwise linear regression analysis was used. Results: This survey contained 11,031 valid questionnaire results to investigate the hospice care acceptance. It was found that individuals with undergraduate or above (ß = 0.04), more properties [2 (ß = 0.02), 3 (ß = 0.01)], and higher reimbursement types of medical insurance [employee health insurance and commercial health (ß = 0.03), government insurance (ß = 0.04)] had higher hospice acceptance willingness, while males (ß = -0.02) were less willing to accept than females. Psychological conditions [mild anxiety (ß = 0.03), moderate anxiety (ß = 0.01), moderate stress (ß = 0.05), and severe stress (ß = 0.06)] also played an important role. The Self-Management Scale (SHMS) (ß = 0.12), EuroQol Five Dimensions Questionnaire (EQ-5D) (ß = 0.05), EuroQol Visual Analog Scale (EQ-VAS) (ß = 0.21), Short-Form Family Health Scale (FHS-SF) (ß = 0.12), higher scores of the Short-Form Health Literacy Instrument (HLS-SF12) (ß = 0.16), and Perceived Social Support Scale (PSSS) (ß = 0.10) also contributed. Gender subgroup showed that in the male group, age, highest educational level, marital status, number of properties, whether having children, psychological conditions, the SHMS, EQ-5D, EQ-VAS, HLS-SF12, and PSSS showed significant difference. Urban and rural subgroups showed that age, highest educational level, number of properties, whether having chronic disease or psychological conditions, the SHMS, EQ-VAS, HLS-SF12, and PSSS were contributing factors in rural areas. Conclusion: The average score of acceptance of hospice care was 65.02 points. Gender, house, anxiety, pressure, social support, and health literacy were the main influencing factors on residents' attitudes.

Posttranslational modifications optimize the ability of SARS-CoV-2 spike for effective interaction with host cell receptors.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein is the prime target for vaccines, diagnostics, and therapeutic antibodies against the virus. While anchored in the viral envelope, for effective virulence, the spike needs to maintain structural flexibility to recognize the host cell surface receptors and bind to them, a property that can heavily depend upon the dynamics of the unresolved domains, most prominently the stalk. Construction of the complete, membrane-bound spike model and the description of its dynamics are critical steps in understanding the inner working of this key element of the viral infection by SARS-CoV-2. Combining homology modeling, protein-protein docking, and molecular dynamics (MD) simulations, we have developed a full spike structure in a native membrane. Multimicrosecond MD simulations of this model, the longest known single trajectory of the full spike, reveal conformational dynamics employed by the protein to explore the surface of the host cell. In agreement with cryogenic electron microscopy (cryo-EM), three flexible hinges in the stalk allow for global conformational heterogeneity of spike in the fully glycosylated system mediated by glycan-glycan and glycan-lipid interactions. The dynamical range of the spike is considerably reduced in its nonglycosylated form, confining the area explored by the spike on the host cell surface. Furthermore, palmitoylation of the membrane domain amplifies the local curvature that may prime the fusion. We show that the identified hinge regions are highly conserved in SARS coronaviruses, highlighting their functional importance in enhancing viral infection, and thereby, provide points for discovery of alternative therapeutics against the virus.

Construction and external validation of a 5-gene random forest model to diagnose non-obstructive azoospermia based on the single-cell RNA sequencing of testicular tissue.

Non-obstructive azoospermia (NOA) is among the most severe factors for male infertility, but our understandings of the latent biological mechanisms remain insufficient. The single-cell RNA sequencing (scRNA-seq) data of 432 testicular cells isolated from the patient with NOA was analyzed, and the cell samples were grouped into 5 cell clusters. A sum of 455 cell markers was identified and then included in the protein-protein interaction network. The Top 5 most critical genes in the network, including CCT8, CDC6, PSMD1, RPS4X, RPL36A, were selected for the diagnosis model construction through the random forest (RF). The RF model was a strong classifier for NOA and obstructive azoospermia (OA), which was validated in the training cohort (n = 58, AUC = 1) and external validation cohort (n = 20, AUC = 0.9). We collected the seminal plasma samples and testicular biopsy samples from 20 OA and 20 NOA cases from the local hospital, and the gene expression was detected via Real-Time quantitative Polymerase Chain Reaction (RT-qPCR) and Immunohistochemistry. The RF model also exhibited high accuracy (AUC = 0.725) in the local cohort. In summary, a novel gene signature was developed and externally validated based on scRNA-seq analysis, providing some new biomarkers to uncover the underlying mechanisms and a promising clinical tool for diagnosis in NOA.

Impact of Reference and Target Region Selection on Amyloid PET SUV Ratios in the Phase 1b PRIME Study of Aducanumab.

SUV ratios (SUVRs) are commonly used to quantify tracer uptake in amyloid-ß PET. Here, we explore the impact of target and reference region-of-interest (ROI) selection on SUVR effect sizes using interventional data from the ongoing phase 1b PRIME study (NCT01677572) of aducanumab (BIIB037) in patients with prodromal or mild Alzheimer disease. Methods: The florbetapir PET SUVR was calculated at baseline (screening) and at weeks 26 and 54 for patients randomized to receive placebo and each of 4 aducanumab doses (1, 3, 6, and 10 mg/kg) using the whole cerebellum, cerebellar gray matter, cerebellar white matter, pons, and subcortical white matter as reference regions. In addition to the prespecified composite cortex target ROI, individual cerebral cortical ROIs were assessed as targets. Results: Of the reference regions used, subcortical white matter, cerebellar white matter, and the pons, alone or in combination, generated the largest effect sizes. The use of the anterior cingulate cortex as a target ROI resulted in larger effect sizes than the use of the composite cortex. SUVR calculations were not affected by correction for brain volume changes over time. Conclusion: Dose- and time-dependent reductions in the amyloid PET SUVR were consistently observed with aducanumab only in cortical regions prone to amyloid plaque deposition, regardless of the reference region used. These data support the hypothesis that florbetapir SUVR responses associated with aducanumab treatment are a result of specific dose- and time-dependent reductions in the amyloid burden in patients with Alzheimer disease.

Addendum: The antibody aducanumab reduces Aß plaques in Alzheimer's disease.

This corrects the article DOI: 10.1038/nature21361.

Purification, structural features, antioxidant and moisture-preserving activities of an exopolysaccharide from Lachnum YM262.

A water-soluble exopolysaccharide, designated as LEP-2a, was isolated from Lachnum YM262 and purified by DEAE-Cellulose 52 and Sepharose CL-6B chromatographic columns. LEP-2a was a homogeneous polysaccharide, with a molecular weight of 1.52×105 Da. It was composed of mannose and galactose in a molar ratio of 20.6:1.0. Its structural features were investigated and elucidated by methylation analysis, periodate oxidation and Smith degradation, FT-IR and NMR spectroscopy. Based on obtained data, the backbone of LEP-2a consisted of 1,2-linked-α-d-mannose, 1,3-linked-α-d-mannose, 1,2,6-linked-α-d-mannose and 1,3-linked-ß-d-galactose and the side chains were attached to the backbone at O-6 position of 1,2,6-linked-α-d-mannose. In vitro antioxidant activity assay proved that LEP-2a possessed significant scavenging activities on superoxide, hydroxyl and DPPH radical. Furthermore, LEP-2a had strong in vitro moisture-absorption and -retention capacities as compared to chitosan and glycerol. These results suggested that LEP-2a might have a good potential to be applied as a multifunctional cosmetic additive in cosmetics.

The antibody aducanumab reduces Aß plaques in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by deposition of amyloid-ß (Aß) plaques and neurofibrillary tangles in the brain, accompanied by synaptic dysfunction and neurodegeneration. Antibody-based immunotherapy against Aß to trigger its clearance or mitigate its neurotoxicity has so far been unsuccessful. Here we report the generation of aducanumab, a human monoclonal antibody that selectively targets aggregated Aß. In a transgenic mouse model of AD, aducanumab is shown to enter the brain, bind parenchymal Aß, and reduce soluble and insoluble Aß in a dose-dependent manner. In patients with prodromal or mild AD, one year of monthly intravenous infusions of aducanumab reduces brain Aß in a dose- and time-dependent manner. This is accompanied by a slowing of clinical decline measured by Clinical Dementia Rating-Sum of Boxes and Mini Mental State Examination scores. The main safety and tolerability findings are amyloid-related imaging abnormalities. These results justify further development of aducanumab for the treatment of AD. Should the slowing of clinical decline be confirmed in ongoing phase 3 clinical trials, it would provide compelling support for the amyloid hypothesis.

Amyloid PET Screening for Enrichment of Early-Stage Alzheimer Disease Clinical Trials: Experience in a Phase 1b Clinical Trial.

Amyloid positron emission tomography (PET) imaging is being investigated as a screening tool to identify amyloid-positive patients as an enrichment strategy for Alzheimer disease (AD) clinical trial enrollment. In a multicenter, phase 1b trial, patients meeting clinical criteria for prodromal or mild AD underwent florbetapir PET scanning at screening. PET, magnetic resonance imaging, and coregistered PET/magnetic resonance imaging scans were reviewed by 2 independent readers and binary visual readings tabulated. Semiquantitative values of cortical to whole cerebellar standard uptake value ratios were computed (threshold 1.10). Of 278 patients with an evaluable PET scan, 170 (61%) and 185 (67%) were amyloid-positive by visual reading and quantitative analysis, respectively; 39% were excluded from the study due to an amyloid-negative scan based on visual readings. More ApoE ε4 carriers than noncarriers were amyloid-positive (80% vs. 43%). Comparison of visual readings with quantitative results identified 21 discordant cases (92% agreement). Interreader and intrareader agreements from visual readings were 98% and 100%, respectively. Amyloid PET imaging is an effective and feasible screening tool for enrollment of amyloid-positive patients with early stages of AD into clinical trials.

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.