pwc-archive/papers-with-abstracts huggingface dataset:
Inhibiting Alzheimer's Disease by Targeting Aggregation of Beta-Amyloid
Authors: Ananya Joshi, George Khoury, Christodoulas Floudas
Date: 2025-06-16 00:00:00

Alzheimer's disease is characterized by dangerous amyloid plaques formed by deposits of the protein Beta-Amyloid aggregates in the brain. The specific amino acid sequence that is responsible for the aggregates of Beta-Amyloid is lys-leu-val-phe-phe (KLVFF). KLVFF aggregation inhibitors, which we design in this paper, prevent KLVFF from binding with itself to form oligomers or fibrils (and eventually plaques) that cause neuronal death. Our binder-blocker peptides are designed such that, on one side, they bind strongly to KLVFF, and on the other side, they disrupt critical interactions, thus preventing aggregation. Our methods use optimization techniques and molecular simulations and identify 10 candidate sequences for trial of the 3.2 million possible sequences. This approach for inhibitor identification can be generalized to other diseases characterized by protein aggregation, such as Parkinson's, Huntington's, and prion diseases.
An 11,000-Study Open-Access Dataset of Longitudinal Magnetic Resonance Images of Brain Metastases
Authors: Saahil Chadha, David Weiss, Anastasia Janas, Divya Ramakrishnan, Thomas Hager, Klara Osenberg, Klara Willms, Joshua Zhu, Veronica Chiang, Spyridon Bakas, Nazanin Maleki, Durga V. Sritharan, Sven Schoenherr, Malte Westerhoff, Matthew Zawalich, Melissa Davis, Ajay Malhotra, Khaled Bousabarah, Cornelius Deuschl, MingDe Lin, Sanjay Aneja, Mariam S. Aboian
Date: 2025-06-16 00:00:00
Tasks: Management

Brain metastases are a common complication of systemic cancer, affecting over 20% of patients with primary malignancies. Longitudinal magnetic resonance imaging (MRI) is essential for diagnosing patients, tracking disease progression, assessing therapeutic response, and guiding treatment selection. However, the manual review of longitudinal imaging is time-intensive, especially for patients with multifocal disease. Artificial intelligence (AI) offers opportunities to streamline image evaluation, but developing robust AI models requires comprehensive training data representative of real-world imaging studies. Thus, there is an urgent necessity for a large dataset with heterogeneity in imaging protocols and disease presentation. To address this, we present an open-access dataset of 11,884 longitudinal brain MRI studies from 1,430 patients with clinically confirmed brain metastases, paired with clinical and image metadata. The provided dataset will facilitate the development of AI models to assist in the long-term management of patients with brain metastasis.
Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data
Authors: Laurence Freeman, Philip Shamash, Vinam Arora, Caswell Barry, Tiago Branco, Eva Dyer
Date: 2025-06-16 00:00:00
Tasks: Decision Making

Transformer models have become state-of-the-art in decoding stimuli and behavior from neural activity, significantly advancing neuroscience research. Yet greater transparency in their decision-making processes would substantially enhance their utility in scientific and clinical contexts. Sparse autoencoders offer a promising solution by producing hidden units that respond selectively to specific variables, enhancing interpretability. Here, we introduce SAEs into a neural decoding framework by augmenting a transformer trained to predict visual stimuli from calcium imaging in the mouse visual cortex. The enhancement of the transformer model with an SAE preserved its original performance while yielding hidden units that selectively responded to interpretable features, such as stimulus orientation and genetic background. Furthermore, ablating units associated with a given variable impaired the model's ability to process that variable, revealing how specific internal representations support downstream computations. Together, these results demonstrate that integrating SAEs with transformers combines the power of modern deep learning with the interpretability essential for scientific understanding and clinical translation.
BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization
Authors: Alejandro Golfe, Natalia P. García-de-la-puente, Adrián Colomer, Valery Naranjo
Date: 2025-06-16 00:00:00
Tasks: Data Augmentation, Diagnostic, SSIM

Accurately identifying oocytes that progress to the blastocyst stage is crucial in reproductive medicine, but the limited availability of annotated high-quality embryo images presents challenges for developing automated diagnostic tools. To address this, we propose BlastDiffusion, a generative model based on Latent Diffusion Models (LDMs) that synthesizes realistic oocyte images conditioned on developmental outcomes. Our approach utilizes a pretrained Variational Autoencoder (VAE) for latent space representation, combined with a diffusion process to generate images that distinguish between oocytes that reach the blastocyst stage and those that do not. When compared to Blastocyst-GAN, a GAN-based model we trained for this task, BlastDiffusion achieves superior performance, with a global Frechet Inception Distance (FID) of 94.32, significantly better than Blastocyst-GAN's FID of 232.73. Additionally, our model shows improvements in perceptual (LPIPS) and structural (SSIM) similarity to real oocyte images. Qualitative analysis further demonstrates that BlastDiffusion captures key morphological differences linked to developmental outcomes. These results highlight the potential of diffusion models in reproductive medicine, offering an effective tool for data augmentation and automated embryo assessment.
Seq2Bind Webserver for Decoding Binding Hotspots directly from Sequences using Fine-Tuned Protein Language Models
Authors: Xiang Ma, Supantha Dey, Vaishnavey SR, Casey Zelinski, Qi Li, Ratul Chowdhury
Date: 2025-06-16 00:00:00
Tasks: Blind Docking

Decoding protein-protein interactions (PPIs) at the residue level is crucial for understanding cellular mechanisms and developing targeted therapeutics. We present Seq2Bind Webserver, a computational framework that leverages fine-tuned protein language models (PLMs) to determine binding affinity between proteins and identify critical binding residues directly from sequences, eliminating the structural requirements that limit most affinity prediction tools. We fine-tuned four architectures including ProtBERT, ProtT5, ESM2, and BiLSTM on the SKEMPI 2.0 dataset containing 5,387 protein pairs with experimental binding affinities. Through systematic alanine mutagenesis on each residue for 14 therapeutically relevant protein complexes, we evaluated each model's ability to identify interface residues. Performance was assessed using N-factor metrics, where N-factor=3 evaluates whether true residues appear within 3n top predictions for n interface residues. ESM2 achieved 49.5% accuracy at N-factor=3, with both ESM2 (37.2%) and ProtBERT (35.1%) outperforming structural docking method HADDOCK3 (32.1%) at N-factor=2. Our sequence-based approach enables rapid screening (minutes versus hours for docking), handles disordered proteins, and provides comparable accuracy, making Seq2Bind a valuable prior to steer blind docking protocols to identify putative binding residues from each protein for therapeutic targets. Seq2Bind Webserver is accessible at https://agrivax.onrender.com under StructF suite.
Evaluating Cell Type Inference in Vision Language Models Under Varying Visual Context
Authors: Samarth Singhal, Sandeep Singhal
Date: 2025-06-15 00:00:00
Tasks: image-classification, Image Classification, In-Context Learning

Vision-Language Models (VLMs) have rapidly advanced alongside Large Language Models (LLMs). This study evaluates the capabilities of prominent generative VLMs, such as GPT-4.1 and Gemini 2.5 Pro, accessed via APIs, for histopathology image classification tasks, including cell typing. Using diverse datasets from public and private sources, we apply zero-shot and one-shot prompting methods to assess VLM performance, comparing them against custom-trained Convolutional Neural Networks (CNNs). Our findings demonstrate that while one-shot prompting significantly improves VLM performance over zero-shot ($p \approx 1.005 \times 10^{-5}$ based on Kappa scores), these general-purpose VLMs currently underperform supervised CNNs on most tasks. This work underscores both the promise and limitations of applying current VLMs to specialized domains like pathology via in-context learning. All code and instructions for reproducing the study can be accessed from the repository https://www.github.com/a12dongithub/VLMCCE.
Maximal Speed of Glucose Change Significantly Distinguishes Prediabetes from Diabetes
Authors: Dandan Wang, Xiaoyan Chen, Jingxiang Lin, Teng Zhang, Lianyi Huang, Dongliang Leng, Xiaohua Douglas Zhang, Gang Li
Date: 2025-06-14 00:00:00

Rapid changes in blood glucose levels can have severe and immediate health consequences, leading to the need to develop indices for assessing these rapid changes based on continuous glucose monitoring (CGM) data. We proposed a CGM index, maxSpeed, that represents the maximum of speed of glucose change (SGC) in a subject, respectively, and conducted a clinical study to investigate this index along with SGC mean (meanSpeed) and SGC standard deviation (sdSpeed), coefficient of variation (CV), standard deviation (SD), glycemic variability percentage (GVP), mean amplitude of glycemic excursions (MAG), mean absolute glucose excursion (MAGE), mean of daily differences (MODD) and continuous overlapping net glycemic action (CONGA). Our study revealed that, there exist multiple patterns in distinguishing non-diabetes, prediabetes, type 1 diabetes (T1D) and type 2 diabetes (T2D). First, maxSpeed significantly distinguishes between either of non-diabetes and prediabetes and either of T1D and T2D. Second, meanSpeed, sdSpeed, GVP and MAG significantly distinguish between non-diabetes and either of T1D and T2D. Third, MODD and CONGA of 24 hours significantly distinguish between non-diabetes and either of T1D and T2D, between T1D and either of prediabetes and T2D. Fourth, SD, MAGE and CONGA of 12 hours significantly distinguish between non-diabetes and either of T1D and T2D, between T1D and pre-diabetes. Fifth, CV significantly distinguishes between T1D and either of Non-diabetes and T2D. maxSpeed assesses the rapid change of glucose in a short term, which is important both biologically and clinicially because our human body may not tolerate too rapid change in a short term.
Interpretable Causal Representation Learning for Biological Data in the Pathway Space
Authors: Jesus de la Fuente, Robert Lehmann, Carlos Ruiz-Arenas, Jan Voges, Irene Marin-Goñi, Xabier Martinez-de-Morentin, David Gomez-Cabrero, Idoia Ochoa, Jesper Tegner, Vincenzo Lagani, Mikel Hernaez
Date: 2025-06-14 00:00:00
Tasks: Representation Learning

Predicting the impact of genomic and drug perturbations in cellular function is crucial for understanding gene functions and drug effects, ultimately leading to improved therapies. To this end, Causal Representation Learning (CRL) constitutes one of the most promising approaches, as it aims to identify the latent factors that causally govern biological systems, thus facilitating the prediction of the effect of unseen perturbations. Yet, current CRL methods fail in reconciling their principled latent representations with known biological processes, leading to models that are not interpretable. To address this major issue, we present SENA-discrepancy-VAE, a model based on the recently proposed CRL method discrepancy-VAE, that produces representations where each latent factor can be interpreted as the (linear) combination of the activity of a (learned) set of biological processes. To this extent, we present an encoder, SENA-{\delta}, that efficiently compute and map biological processes' activity levels to the latent causal factors. We show that SENA-discrepancy-VAE achieves predictive performances on unseen combinations of interventions that are comparable with its original, non-interpretable counterpart, while inferring causal latent factors that are biologically meaningful.
Evaluation of machine-learning models to measure individualized treatment effects from randomized clinical trial data with time-to-event outcomes
Authors: Elvire Roblin, Paul-Henry Cournède, Stefan Michiels
Date: 2025-06-13 00:00:00

In randomized clinical trials, regression models can be used to explore the relationships between patients' variables (e.g., clinical, pathological or lifestyle variables, and also biomarker or genomics data) and the magnitude of treatment effect. Our aim is to evaluate the value of flexible machine learning models that can incorporate interactions and nonlinear effects of high-dimensional data to estimate individualized treatment recommendations in the setting of such trials with time-to-event outcomes. We compare survival models based on neural networks (CoxCC and CoxTime) and random survival forests (Interaction Forests). A Cox model, including an adaptive LASSO penalty, is used as a benchmark. Specific metrics for individualized treatment recommendations are used: the C-for-Benefit, the E50-for-Benefit, and RMSE for treatment benefit. We conduct an extensive simulation study using 2 different data generation processes incorporating nonlinearity and interactions up to the third order. The models are applied to gene expression and clinical data from 2 breast cancer studies. The machine learning-based methods show reasonable performances on the simulation data sets, especially in terms of discrimination for Interaction Forests and calibration for the neural networks. They can be used to evaluate individualized treatment effects from randomized trials when nonlinear and interaction effects are expected to be present.
Optimal experiment design for practical parameter identifiability and model discrimination
Authors: Yue Liu, Philip K. Maini, Ruth E. Baker
Date: 2025-06-12 00:00:00
Tasks: Experimental Design

Mechanistic mathematical models of biological systems usually contain a number of unknown parameters whose values need to be estimated from available experimental data in order for the models to be validated and used to make quantitative predictions. This requires that the models are practically identifiable, that is, the values of the parameters can be confidently determined, given available data. A well-designed experiment can produce data that are much more informative for the purpose of inferring parameter values than a poorly designed experiment. It is, therefore, of great interest to optimally design experiments such that the resulting data maximise the practical identifiability of a chosen model. Experimental design is also useful for model discrimination, where we seek to distinguish between multiple distinct, competing models of the same biological system in order to determine which model better reveals insight into the underlying biological mechanisms. In many cases, an external stimulus can be used as a control input to probe the behaviour of the system. In this paper, we will explore techniques for optimally designing such a control for a given experiment, in order to maximise parameter identifiability and model discrimination, and demonstrate these techniques in the context of commonly applied ordinary differential equation models. We use a profile likelihood-based approach to assess parameter identifiability. We then show how the problem of optimal experimental design for model discrimination can be formulated as an optimal control problem, which can be solved efficiently by applying Pontryagin's Maximum Principle.
AZT1D: A Real-World Dataset for Type 1 Diabetes
Authors: Saman Khamesian, Asiful Arefeen, Bithika M. Thompson, Maria Adela Grando, Hassan Ghasemzadeh
Date: 2025-05-27 00:00:00
Tasks: Decision Making

High quality real world datasets are essential for advancing data driven approaches in type 1 diabetes (T1D) management, including personalized therapy design, digital twin systems, and glucose prediction models. However, progress in this area has been limited by the scarcity of publicly available datasets that offer detailed and comprehensive patient data. To address this gap, we present AZT1D, a dataset containing data collected from 25 individuals with T1D on automated insulin delivery (AID) systems. AZT1D includes continuous glucose monitoring (CGM) data, insulin pump and insulin administration data, carbohydrate intake, and device mode (regular, sleep, and exercise) obtained over 6 to 8 weeks for each patient. Notably, the dataset provides granular details on bolus insulin delivery (i.e., total dose, bolus type, correction specific amounts) features that are rarely found in existing datasets. By offering rich, naturalistic data, AZT1D supports a wide range of artificial intelligence and machine learning applications aimed at improving clinical decision making and individualized care in T1D.
[Beat-to-beat AV nodal assessment] ECG-based beat-to-beat assessment of AV node conduction properties during AF
Authors: Mattias Karlsson, Felix Plappert, Pyotr G Platonov, Sten Östenson, Mikael Wallman, Frida Sandberg
Date: 2025-06-24 00:00:00
Tasks: Prognosis

The refractory period and conduction delay of the atrioventricular (AV) node play a crucial role in regulating the heart rate during atrial fibrillation (AF). Beat-to-beat variations in these properties are known to be induced by the autonomic nervous system (ANS) but have previously not been assessable during AF. Assessing these could provide novel information for improved diagnosis, prognosis, and treatment on an individual basis. To estimate AV nodal conduction properties with beat-to-beat resolution, we propose a methodology comprising a network model of the AV node, a particle filter, and a smoothing algorithm. The methodology was evaluated using simulated data and using synchronized electrogram (EGM) and ECG recordings from five patients in the intracardiac atrial fibrillation database. The methodology's ability to quantify ANS-induced changes in AV node conduction properties was evaluated by analyzing ECG data from 21 patients in AF undergoing a tilt test protocol. The estimated refractory period and conduction delay matched the simulated ground truth based on ECG recordings with a mean absolute error ($\pm$ std) of 169$\pm$14 ms for the refractory period in the fast pathway; 131$\pm$13 ms for the conduction delay in the fast pathway; 67$\pm$10 ms for the refractory period in the slow pathway; and 178$\pm$28 ms for the conduction delay in the slow pathway. These errors decreased when using simulated ground truth based on EGM recordings. Moreover, a decrease in conduction delay and refractory period in response to head-up tilt was seen during the tilt test protocol, as expected under sympathetic activation. These results suggest that beat-to-beat estimation of AV nodal conduction properties during AF from ECG is feasible, with different levels of uncertainty, and that the estimated properties agree with expected AV nodal modulation.
Stumbling around uncharted regulatory structures: NAcrins, or the perspective of specialized sources of modulatory non-coding RNAs
Authors: Marouane Benzaki
Date: 2025-06-18 00:00:00

The revelation of the supreme authority of nucleic acids in the cellular landscape has precipitated the recognition of the versatility of RNAs in cells. The subsequent discovery of non-coding RNAs was a major breakthrough that revealed their extensive involvement in virtually all physiological processes within the cell. Beyond the barriers of the cell, the current perception seems to support the idea of their participation in intercellular regulation and cross-kingdom communication. However, the presence of non-coding RNAs in the extracellular environment remains essentially a mystery, and the understanding of the significance and the processes governing this presence faces several constraints. This has led us to forge an original and predictive idea that seems to allow an emancipation from the various constraints posed in the current perception of the cited phenomena. In this paper, we will attempt to explore the extent of the probable existence of cellular organizations specializing in the production and management of non-coding RNAs. We will try, through the development of this hypothesis, to draw a picture explaining the significance and logistics of extracellular non-coding RNAs, with an emphasis on microRNAs. This exercise will be realized while relying on and confronting purely theoretical points of view, as well as relevant experimental results. In this manuscript, we will address the presumed morphology, intracellular organization, selective export, transport, transfer, distribution, reception and intracellular function of non-coding RNAs, in the perspective of a regulation cycle orchestrated by NAcrins under normal or disturbed physiological contexts.
New tissue engineered scaffolds for rotator cuff tendon-bone interface regeneration
Authors: Ting Zhang, Jianying Zhang
Date: 2025-06-13 00:00:00

Healing of Tendon-bone interface(TBI) injuries is slow and is often repaired with scar tissue formation that compromises normal function. Despite the increasing maturity of surgical techniques, re-tearing of the rotator cuff after surgery remains common. The main reason for this issue is that the original structure of the rotator cuff at the TBI area is difficult to fully restore after surgery, and anatomical healing of the rotator cuff TBI is challenging to achieve solely through surgery. With the advancement of tissue engineering technology, more and more basic researchers and clinical surgeons are recognizing the enormous potential of tissue engineering in promoting TBI healing. Growing research evidence indicates that tissue engineering technology not only effectively promotes repairing and remodeling of the TBI but also reduces the formation of fibrous vascular scar tissue, leading to more orderly tissue reconstruction. The core of tissue engineering technology approaches lies in combining the use of various scaffolds, cells and bioactive molecules to simulate the natural environment of TBI healing, achieving optimal therapeutic outcomes. In this review, we will systematically summarize and highlight recent progress in the application of tissue engineering on TBI regeneration, particularly focusing on advancements in novel scaffolds and their role and potential in promoting healing of the TBI of rotator cuff, providing valuable references for clinical application and research.
American options valuation in time-dependent jump-diffusion models via integral equations and characteristic functions
Authors: Andrey Itkin
Date: 2025-06-23 00:00:00

Despite significant advancements in machine learning for derivative pricing, the efficient and accurate valuation of American options remains a persistent challenge due to complex exercise boundaries, near-expiry behavior, and intricate contractual features. This paper extends a semi-analytical approach for pricing American options in time-inhomogeneous models, including pure diffusions, jump-diffusions, and Levy processes. Building on prior work, we derive and solve Volterra integral equations of the second kind to determine the exercise boundary explicitly, offering a computationally superior alternative to traditional finite-difference and Monte Carlo methods. We address key open problems: (1) extending the decomposition method, i.e. splitting the American option price into its European counterpart and an early exercise premium, to general jump-diffusion and Levy models; (2) handling cases where closed-form transition densities are unavailable by leveraging characteristic functions via, e.g., the COS method; and (3) generalizing the framework to multidimensional diffusions. Numerical examples demonstrate the method's efficiency and robustness. Our results underscore the advantages of the integral equation approach for large-scale industrial applications, while resolving some limitations of existing techniques.