Multiparameter Persistent Homology for Molecular Property Prediction

In this study, we present a novel molecular fingerprint generation method based on multiparameter persistent homology. This approach reveals the latent structures and relationships within molecular geometry, and detects topological features that exhibit persistence across multiple scales along multiple parameters, such as atomic mass, partial charge, and bond type, and can be further enhanced by incorporating additional parameters like ionization energy, electron affinity, chirality and orbital hybridization. The proposed fingerprinting method provides fresh perspectives on molecular structure that are not easily discernible from single-parameter or single-scale analysis. Besides, in comparison with traditional graph neural networks, multiparameter persistent homology has the advantage of providing a more comprehensive and interpretable characterization of the topology of the molecular data. We have established theoretical stability guarantees for multiparameter persistent homology, and have conducted extensive experiments on the Lipophilicity, FreeSolv, and ESOL datasets to demonstrate its effectiveness in predicting molecular properties.Comment: ICLR 2023-Machine Learning for Drug Discovery. arXiv admin note: text overlap with arXiv:2211.0380

Topology-Aware Focal Loss for 3D Image Segmentation

The efficacy of segmentation algorithms is frequently compromised by topological errors like overlapping regions, disrupted connections, and voids. To tackle this problem, we introduce a novel loss function, namely Topology-Aware Focal Loss (TAFL), that incorporates the conventional Focal Loss with a topological constraint term based on the Wasserstein distance between the ground truth and predicted segmentation masks' persistence diagrams. By enforcing identical topology as the ground truth, the topological constraint can effectively resolve topological errors, while Focal Loss tackles class imbalance. We begin by constructing persistence diagrams from filtered cubical complexes of the ground truth and predicted segmentation masks. We subsequently utilize the Sinkhorn-Knopp algorithm to determine the optimal transport plan between the two persistence diagrams. The resultant transport plan minimizes the cost of transporting mass from one distribution to the other and provides a mapping between the points in the two persistence diagrams. We then compute the Wasserstein distance based on this travel plan to measure the topological dissimilarity between the ground truth and predicted masks. We evaluate our approach by training a 3D U-Net with the MICCAI Brain Tumor Segmentation (BraTS) challenge validation dataset, which requires accurate segmentation of 3D MRI scans that integrate various modalities for the precise identification and tracking of malignant brain tumors. Then, we demonstrate that the quality of segmentation performance is enhanced by regularizing the focal loss through the addition of a topological constraint as a penalty term

EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG

One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities within an end-to-end discriminative setting. We propose a novel end-to-end machine learning pipeline, EEG-NeXt, which facilitates transfer learning by: i) aligning the EEG trials from different subjects in the Euclidean-space, ii) tailoring the techniques of deep learning for the scalograms of EEG signals to capture better frequency localization for low-frequency, longer-duration events, and iii) utilizing pretrained ConvNeXt (a modernized ResNet architecture which supersedes state-of-the-art (SOTA) image classification models) as the backbone network via adaptive finetuning. On publicly available datasets (Physionet Sleep Cassette and BNCI2014001) we benchmark our method against SOTA via cross-subject validation and demonstrate improved accuracy in cognitive activity classification along with better generalizability across cohorts

Successful treatment of long spontaneous coronary dissection with medical management: Not to intervene

AbstractSpontaneous coronary artery dissection (SCAD) is an uncommon cause of acute coronary syndrome (ACS) and optimal therapy has not been well-defined. We present a case of long SCAD with complete healing due to medical management. A 47-year-old woman presented to emergency department because of sudden onset of typical chest pain. Electrocardiogram (ECG) showed minimal ST-segment elevation in leads V1–V4. Coronary angiography showed a long spiral dissection extending from the middle segment to the distal segment of the left anterior descending artery and TIMI flow grade three. We decided to follow-up with medical management and have control angiography unless hemodynamic instability and chest pain emerged. Control angiography displayed complete healing of dissect segment after six months. SCAD should be considered, especially in women who present with an ACS without a history of cardiovascular disease and risk factor. This report offers the idea that medical management can be a choice even if in the long segment SCAD setting

ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery

In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds. Most VS methods to date have focused on using canonical compound representations (e.g., SMILES strings, Morgan fingerprints) or generating alternative fingerprints of the compounds by training progressively more complex variational autoencoders (VAEs) and graph neural networks (GNNs). Although VAEs and GNNs led to significant improvements in VS performance, these methods suffer from reduced performance when scaling to large virtual compound datasets. The performance of these methods has shown only incremental improvements in the past few years. To address this problem, we developed a novel method using multiparameter persistence (MP) homology that produces topological fingerprints of the compounds as multidimensional vectors. Our primary contribution is framing the VS process as a new topology-based graph ranking problem by partitioning a compound into chemical substructures informed by the periodic properties of its atoms and extracting their persistent homology features at multiple resolution levels. We show that the margin loss fine-tuning of pretrained Triplet networks attains highly competitive results in differentiating between compounds in the embedding space and ranking their likelihood of becoming effective drug candidates. We further establish theoretical guarantees for the stability properties of our proposed MP signatures, and demonstrate that our models, enhanced by the MP signatures, outperform state-of-the-art methods on benchmark datasets by a wide and highly statistically significant margin (e.g., 93% gain for Cleves-Jain and 54% gain for DUD-E Diverse dataset).Comment: NeurIPS, 2022 (36th Conference on Neural Information Processing Systems

Congenital Portal Vein Aneurysm Associated with Peliosis Hepatis and Intestinal Lymphangiectasia

Portal vein aneurisym (PVA), peliosis hepatis (PH) and intestinal lymphangiectasia (IL) all are very uncommon entities. Herein, we presented a unique patient with these three rare entities who was admitted to our hospital because of portal hypertensive ascites rich in protein and lymphocyte. PVA was extrahepatic and associated with coronary vein aneurysm. Peliosis hepatis was of microscopic form. Lymphangiectasia was present in peritoneum and small intestine. Diagnoses of these rare entities were made by imaging techniques and histopathological findings. Patient also had hydronephrosis caused by ureteropelvic junction narrowing. Best of our knowledge, there is no such a case reported previously with the association of PVA, PH and IL. Therefore, we propose PVAPHIL syndrome to define this novel association

Real-world efficacy and safety of Ledipasvir + Sofosbuvir and Ombitasvir/Paritaprevir/Ritonavir ± Dasabuvir combination therapies for chronic hepatitis C: A Turkish experience

Background/Aims: This study aimed to evaluate the real-life efficacy and tolerability of direct-acting antiviral treatments for patients with chronic hepatitis C (CHC) with/without cirrhosis in the Turkish population. Material and Methods: A total of 4,352 patients with CHC from 36 different institutions in Turkey were enrolled. They received ledipasvir (LDV) and sofosbuvir (SOF)+/- ribavirin (RBV) ombitasvir/paritaprevir/ritonavir +/- dasabuvir (PrOD)+/- RBV for 12 or 24 weeks. Sustained virologic response (SVR) rates, factors affecting SVR, safety profile, and hepatocellular cancer (HCC) occurrence were analyzed. Results: SVR12 was achieved in 92.8% of the patients (4,040/4,352) according to intention-to-treat and in 98.3% of the patients (4,040/4,108) according to per-protocol analysis. The SVR12 rates were similar between the treatment regimens (97.2%-100%) and genotypes (95.6%-100%). Patients achieving SVR showed a significant decrease in the mean serum alanine transaminase (ALT) levels (50.90 +/- 54.60 U/L to 17.00 +/- 14.50 U/L) and model for end-stage liver disease (MELD) scores (7.51 +/- 4.54 to 7.32 +/- 3.40) (p<0.05). Of the patients, 2 were diagnosed with HCC during the treatment and 14 were diagnosed with HCC 37.0 +/- 16.0 weeks post-treatment. Higher initial MELD score (odds ratio [OR]: 1.92, 95% confidence interval [CI]: 1.22-2.38; p=0.023]), higher hepatitis C virus (HCV) RNA levels (OR: 1.44, 95% CI: 1.31-2.28; p=0.038), and higher serum ALT levels (OR: 1.38, 95% CI: 1.21-1.83; p=0.042) were associated with poor SVR12. The most common adverse events were fatigue (12.6%), pruritis (7.3%), increased serum ALT (4.7%) and bilirubin (3.8%) levels, and anemia (3.1%). Conclusion: LDV/SOF or PrOD +/- RBV were effective and tolerable treatments for patients with CHC and with or without advanced liver disease before and after liver transplantation. Although HCV eradication improves the liver function, there is a risk of developing HCC.Turkish Association for the Study of The Liver (TASL)The present study was supported by The Turkish Association for the Study of The Liver (TASL)

Mean platelet volume is elevated in patients with patent foramen ovale

INTRODUCTION: Platelets play a major role in thromboembolic events. Increased mean platelet volume (MPV) indicates higher platelet reactivity and also a tendency to thrombosis. Patent foramen ovale (PFO), persistence of the fetal anatomic shunt between right and left atria, is strongly associated with cryptogenic stroke. The aim of this study is to determine the relationship between MPV and PFO and if such an association exists, whether higher MPV levels may require antiplatelet therapy before a thromboembolic event happens, together with a literature review. MATERIAL AND METHODS: Thirty patients (15 women, 15 men), free of any cerebrovascular events, were diagnosed with PFO by transesophageal echocardiography (TEE), enrolled as the study group. Thirty consecutive patients (16 women and 14 men), who were diagnosed as normal in TEE, were enrolled as the control group. These two groups were compared according to MPV and anatomical features of the right atrium. RESULTS: There was no significant difference between study and control groups in clinical features and also no difference was observed in platelet counts; however, MPV in the PFO group was significantly higher than the control group (8.38 ±0.93 fl and 7.45 ±0.68 fl respectively). CONCLUSIONS: Our results indicate that elevated MPV may be detected in patients with PFO. This might be one of the explanations for the relationship between PFO and cryptogenic stroke; however, larger cohorts are warranted in order to define further mechanisms