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INTRODUCTION Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODS We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTS Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI‐to‐AD progression within 6 years. DISCUSSION The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy‐to‐administer screening tool for MCI‐to‐AD progression prediction, facilitating development of remote assessment. Highlights Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI‐powered pipeline can lead to fully automated assessment that could enable remote and cost‐effective screening and prognosis for Alzehimer's disease.

Objective Early exercise is a physical adjuvant therapy that begins on day 1 postoperatively. It prevents postoperative stiffness, fatty infiltration, muscle atrophy and loss of range of motion. Usually, use of a brace fixation that immobilizes the shoulder in 30° of abduction during the postoperative rehabilitation period reduces tension on the repaired tendon, which improves tendon-bone healing. To investigate the effect of early exercise and brace fixation on postoperative recovery after arthroscopic rotator cuff repair by systematic review, thereby providing evidence-based evidence for clinical practice. Methods Chinese and English databases (PubMed, Web of Science, Cochrane Library, CNKI, Wanfang database, and VIP database) were searched by keywords until November 15, 2024. Randomized controlled studies comparing early exercise versus brace fixation after arthroscopic rotator cuff repair surgery were included, along with an evaluation of such studies using the Cochrane Collaboration risk assessment tool. Afterward, the effect of the intervention on the visual analogue scale (VAS) for pain, function, shoulder range of motion (forward flexion, abduction, internal rotation, external rotation), and postoperative complications (stiffness, re-tear) was evaluated based on a fixed or random effects model. Results Eleven high-quality randomized controlled studies were included. Compared with brace fixation, early exercise improved the range of motion of the subjects’ shoulders. Compared with brace fixation, shoulder flexion (WMD of 6 weeks = 10.57, 95% CI: 1.30, 19.84, WMD of 3 months = 12.39, 95% CI: 7.51, 17.27, WMD of 6 months = 2.88, 95% CI: 1.02, 4.73, WMD of 1 year = 2.59, 95% CI: 0.40, 4.77) and shoulder abduction (WMD of 6 weeks = 13.17, 95% CI: 9.80, 16.55, respectively). The improvement degree of WMD = 2.28 in 6 months and internal rotation (WMD = 5.08, 95% CI: 3.16, 7.01, in 6 weeks and WMD = 8.23, 95% CI: 4.23, 12.23, in 3 months) was statistically different. Early exercise also reduced the risk of postoperative stiffness (RR = 0.34; 95%CI:0.19, 0.60). However, compared with brace fixation, there was no statistical difference in pain score (WMD = 0.05, 95% CI:0.09, 0.18) and shoulder joint recovery score (SMD = 0.05, 95% CI: 0.12, 0.03). Conclusion Early exercise can improve the range of motion of early shoulder joint and reduce the risk of postoperative stiffness, but the effect of pain and function improvement is not obvious, which can play a positive role in postoperative rehabilitation of patients, but it needs more comprehensive research and improvement to guide clinical practice.

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease. The new coronavirus (now named SARS-CoV-2) causing the disease pandemic in 2019 (COVID-19), has so far infected over 35 million people worldwide and killed more than 1 million. Most people with COVID-19 have no symptoms or only mild symptoms. But some become seriously ill and need hospitalization. The sickest are admitted to an Intensive Care Unit (ICU) and may need mechanical ventilation to help them breath. Being able to predict which patients with COVID-19 will become severely ill could help hospitals around the world manage the huge influx of patients caused by the pandemic and save lives. Now, Hao, Sotudian, Wang, Xu et al. show that computer models using artificial intelligence technology can help predict which COVID-19 patients will be hospitalized, admitted to the ICU, or need mechanical ventilation. Using data of 2,566 COVID-19 patients from five Massachusetts hospitals, Hao et al. created three separate models that can predict hospitalization, ICU admission, and the need for mechanical ventilation with more than 86% accuracy, based on patient characteristics, clinical symptoms, laboratory results and chest x-rays. Hao et al. found that the patients’ vital signs, age, obesity, difficulty breathing, and underlying diseases like diabetes, were the strongest predictors of the need for hospitalization. Being male, having diabetes, cloudy chest x-rays, and certain laboratory results were the most important risk factors for intensive care treatment and mechanical ventilation. Laboratory results suggesting tissue damage, severe inflammation or oxygen deprivation in the body's tissues were important warning signs of severe disease. The results provide a more detailed picture of the patients who are likely to suffer from severe forms of COVID-19. Using the predictive models may help physicians identify patients who appear okay but need closer monitoring and more aggressive treatment. The models may also help policy makers decide who needs workplace accommodations such as being allowed to work from home, which individuals may benefit from more frequent testing, and who should be prioritized for vaccination when a vaccine becomes available.

Background Widespread access to diagnosis of cognitive decline remains inadequate. Assessment tools rely on neuroimaging, biofluid markers, or lengthy neuropsychological batteries. This reliance limits their use in primary care and low-resource settings and contributes to healthcare disparities. We developed and validated a three-level, resource-stratified machine learning framework to provide scalable dementia screening and prognostic risk stratification across diverse healthcare settings. Methods We used data from 31,081 participants in the National Alzheimer’s Coordinating Center. We trained Gradient Boosted Tree models for multi-class detection (Cognitively Intact/Mild Cognitive Impairment [MCI]/Dementia) and 10-year risk stratification (MCI-to-dementia progression). The framework tiered inputs by resource intensity. The minimal-resource Level 1 included demographic and basic functional data. The moderate-resource Level 2 added standard cognitive tests. The high-resource Level 3 added comprehensive neuropsychological batteries. Results The Level 3 model achieved high classification performance (AUC: 93.98%), and the Level 1 model achieved comparable performance (AUC: 91.53%). For MCI-to-dementia progression, the framework showed strong prognostic performance. The Level 3 and Level 1 models achieved AUCs of 85.90% and 81.90% for predicting progression over a 2-year window, respectively. Key predictors remained consistent across all resource levels, such as difficulty managing finances and self-reported cognitive decline, and sociodemographic factors, including education level and Black race. Conclusions The tiered system offers a scalable and accessible approach for dementia detection and prognostic stratification. It helps non-specialists conduct initial evaluations and identify high-risk individuals with minimal data. High-risk patients may then be triaged for specialized assessment. This resource-stratified framework offers a strategy to expand diagnostic capacity and reduce care inequities, especially in low-resource settings.

In this work, a simple anti‐fingerprint strategy (reduced ≈40–60%) to rearrange fingerprint droplets by patterned micro/nano‐pore/spoons texture, air cushion and low surface energy is reported. Superior to previous precise and complex preparation methods, the micro/nano‐texture are fabricated through conventional process of spray and dip. The pore and spoon can be freely switched by changing the process. The number (N) and size (r) of pores and spoons are controlled intelligently by solvent volatilization and interfacial tension (γFP/PU−H2O ${{\\bm{\\gamma }}_{{\\rm{FP/PU}}{\\bm{ - }}{{\\rm{H}}_2}{\\rm{O}}}$and γFP/PU − Solvent), the key for pore/spoon's forming and expanding. A physical model of interface intelligent controlling the micro/nano‐pore/spoon surface texture induced by “Vacancy‐Capture” is proposed. The description is being reported, for the first time, to the knowledge, expecting to make it possible to prepare anti‐fingerprint engineering materials with newly potential industrial applications on optical device for large‐scale in the future. An anti‐fingerprint polymer composite coating that rearranges fingerprint through the patterned micro/nano‐pore/spoon surface texture, air cushion, and low surface energy is developed. The number (N) and size (r) of the pores and spoons fabricated are controlled through changing solvent volatilization and interfacial tension (γFP/PU‐H2O and γFP/PU‐Solvent) induced by phase separation, the key for pore and spoon forming and expanding. Superior to previous precise and complex preparation methods for surface texture, the freely switching between pore and spoon is realized by conventional spray and dip methods, two engineering painting processes. A physical model of interface intelligent controlling the micro/nano‐pore/spoon surface texture induced by “Vacancy‐Capture” mechanism has been proposed.

Transformer-based deep neural networks have achieved great success in biomedical research and practice. Leveraging the strength of the self-attention mechanism and self-supervised learning schemes such as Masked Language Modeling (MLM), transformer-based Language Models (LMs) like Clinical BERT have shown impressive performance on clinical Natural Language Processing (NLP) tasks, while Vision Transformers (ViTs) also improve the traditional Convolutional Neural Network (CNN) models on clinical image analysis. These successes indicate the general sequence modeling power of transformers, which can be potentially extended to effectively analyze the biomedical sequential data in other modalities. On the other hand, ViTs trained under Empirical Risk Minimization (ERM) are known to be vulnerable to perturbations in the image, which limits their applications in biomedicine.This dissertation focuses on developing effective and robust transformer models and learning methods for a variety of biomedical applications. We propose a pre-training method to introduce prior knowledge from a medical knowledge base to transformer-based LMs, which can be implemented jointly with traditional MLM and improve the LM's ability on clinical text understanding. Inspired by the strength of MLM, we pre-train and fine-tune transformer-based protein LMs to achieve state-of-the-art MHC-peptide binding predictions. We also propose a causal Electronic Health Record (EHR) modeling scheme for general structured EHR modeling, which is implemented to train a Generative Pre-trained Transformer (GPT) and support real-world, unsupervised novel disease detection. To improve the robustness of transformers, we develop a distributionally robust deep learning framework, which significantly reduces the ViT classifier's error rate under image perturbations, and improves the stroke diagnosis accuracy under accelerated MRI settings. Our work not only practically improves the applicability and reliability of Artificial Intelligence (AI) in healthcare, but also provides general effective and robust sequence data processing frameworks for other research domains.

The cleavage of the oxazine ring of benzoxazine resins generally requires high energy during the ring-opening polymerization, which leads them to be processed at a much higher temperature than the traditional thermoplastics. In this paper, a bio-based substance, L-histidine, has been used as the catalyst to lower the ring-opening polymerization of the well-commercialized benzoxazine resin (BA-a) based on aniline and bisphenol-A. The catalytic effect of L-histidine on the polymerization behaviors of benzoxazine resin (BA-a) has been monitored by differential scanning calorimetry and in situ Fourier transform infrared spectroscopy. In addition, L-histidine has also been found to be a good property modifier for BA-a-derived thermosets. The thermal properties of resulting polybenzoxazines have been investigated by thermogravimetric analysis and dynamic thermomechanical analysis. Notably, the thermoset derived from BA-a with adding only 5 mol% L-histidine exhibits excellent thermal stability in consideration of high T g (206 °C), high T d10 temperature (319 °C) and high chair yield value (40%), making this thermosetting resin a promising material for high-performance applications.

Over the past thirty years, X-Ray Computed Tomography (CT) has been widely used in security checking due to its high resolution and fully 3-d construction. Designing object segmentation and classification algorithms based on reconstructed CT intensity data will help accurately locate and classify the potential hazardous articles in luggage. Proposal-based deep networks have been successful recently in segmentation and recognition tasks. However, they require large amount of labeled training images, which are hard to obtain in CT research. This thesis develops a non-proposal 3-d instance segmentation and classification structure based on smoothed fully convolutional networks (FCNs), graph-based spatial clustering and ensembling kernel SVMs using volumetric texture features, which can be trained on limited and highly unbalanced CT intensity data. Our structure will not only significantly accelerate the training convergence in FCN, but also efficiently detect and remove the outlier voxels in training data and guarantee the high and stable material classification performance. We demonstrate the performance of our approach on experimental volumetric images of containers obtained using a medical CT scanner.

Major histocompatibility complex (MHC) molecules bind to peptides from exogenous antigens, and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system [1]. Predicting whether a given peptide will bind to the MHC is an important step in the above process, motivating the introduction of many computational approaches. NetMHCPan [2], a pan-specific model predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using a shallow neural network. The successful results of AI methods, especially Natural Language Processing (NLP-based) pretrained models in various applications including protein structure determination, motivated us to explore their use in this problem as well. Specifically, we considered fine-tuning these large deep learning models using as dataset the peptide-MHC sequences. Using standard metrics in this area, and the same training and test sets, we show that our model outperforms NetMHCpan4.1 which has been shown to outperform all other earlier methods [2].

We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers. The DRO framework uses a probabilistic ambiguity set defined as a ball of distributions that are close to the empirical distribution of the training set in the sense of the Wasserstein metric. We relax the DRO formulation into a regularized learning problem whose regularizer is a norm of the coefficient matrix. We establish out-of-sample performance guarantees for the solutions to our model, offering insights on the role of the regularizer in controlling the prediction error. We apply the proposed method in rendering deep Vision Transformer (ViT)-based image classifiers robust to random and adversarial attacks. Specifically, using the MNIST and CIFAR-10 datasets, we demonstrate reductions in test error rate by up to 83.5% and loss by up to 91.3% compared with baseline methods, by adopting a novel random training method.