Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation

Decreased myocardial capillary density has been reported as an important histopathological feature associated with various heart disorders. Quantitative assessment of cardiac capillarization typically involves double immunostaining of cardiomyocytes (CMs) and capillaries in myocardial slices. In contrast, single immunostaining of basement membrane components is a straightforward approach to simultaneously label CMs and capillaries, presenting fewer challenges in background staining. However, subsequent image analysis always requires manual work in identifying and segmenting CMs and capillaries. Here, we developed an image analysis tool, AutoQC, to automatically identify and segment CMs and capillaries in immunofluorescence images of collagen type IV, a predominant basement membrane protein within the myocardium. In addition, commonly used capillarization-related measurements can be derived from segmentation masks. AutoQC features a weakly supervised instance segmentation algorithm by leveraging the power of a pre-trained segmentation model via prompt engineering. AutoQC outperformed YOLOv8-Seg, a state-of-the-art instance segmentation model, in both instance segmentation and capillarization assessment. Furthermore, the training of AutoQC required only a small dataset with bounding box annotations instead of pixel-wise annotations, leading to a reduced workload during network training. AutoQC provides an automated solution for quantifying cardiac capillarization in basement-membrane-immunostained myocardial slices, eliminating the need for manual image analysis once it is trained

Trajectory optimization for multi-sensor multi-target search and tracking with bearing-only measurements

This paper proposes a trajectory optimization approach for multi-sensor multi-target search and tracking using bearing-only sensors. Based on the framework of the joint integrated probabilistic data association (JIPDA) filter, the intensity of potential unknown targets is updated according to the trajectories of the UAVs. The performance indices for target search and tracking are constructed based on, respectively, the intensity of unknown targets in the search area and the tracking error covariance. A dimensionless criterion, evaluating the search and tracking performance, is formulated and leveraged as the objective function of the UAV trajectory optimization problem. Simulations were carried out in different search and tracking scenarios to demonstrate the effectiveness of the proposed approach

BNT162b2 or CoronaVac Vaccinations Are Associated With a Lower Risk of Myocardial Infarction and Stroke After SARS‐CoV‐2 Infection Among Patients With Cardiovascular Disease

Background: COVID‐19 vaccines have demonstrated effectiveness against SARS‐CoV‐2 infection, hospitalization, and mortality. The association between vaccination and risk of cardiovascular complications shortly after SARS‐CoV‐2 infection among patients with cardiovascular disease remains unknown. Methods and Results: A case–control study was conducted with cases defined as patients who had myocardial infarction or stroke within 28 days after SARS‐CoV‐2 infection between January 1, 2022 and August 15, 2022. Controls were defined as all other patients who attended any health services and were not cases. Individuals without history of cardiovascular disease were excluded. Each case was randomly matched with 10 controls according to sex, age, Charlson comorbidity index, and date of hospital admission. Adjusted odds ratio with 95% CI was estimated using conditional logistic regression. We identified 808 cases matched with 7771 controls among all patients with cardiovascular disease. Results showed that vaccination with BNT162b2 or CoronaVac was associated with a lower risk of myocardial infarction or stroke after SARS‐CoV‐2 infection with a dose–response relationship. For BNT162b2, risk decreased from 0.49 (95% CI, 0.29–0.84) to 0.30 (95% CI, 0.20–0.44) and 0.17 (95% CI, 0.08–0.34) from 1 to 3 doses, respectively. Similar trends were observed for CoronaVac, with risk decreased from 0.69 (95% CI, 0.57–0.85) to 0.42 (95% CI, 0.34–0.52) and 0.32 (95% CI, 0.21–0.49) from 1 to 3 doses, respectively. Conclusions: Vaccination with BNT162b2 or CoronaVac is associated with a lower risk of myocardial infarction or stroke after SARS‐CoV‐2 infection among patients with cardiovascular disease

Machine Learning for Prediction of Sudden Cardiac Death in Heart Failure Patients With Low Left Ventricular Ejection Fraction: Study Protocol for a Retrospective Multicentre Registry in China

Introduction: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF. Methods and analysis: We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study. Ethics and dissemination: The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences

Safety of BNT162b2 or CoronaVac COVID-19 vaccines in patients with heart failure: A self-controlled case series study

BACKGROUND: COVID-19 vaccines are important for patients with heart failure (HF) to prevent severe outcomes but the safety concerns could lead to vaccine hesitancy. This study aimed to investigate the safety of two COVID-19 vaccines, BNT162b2 and CoronaVac, in patients with HF. METHODS: We conducted a self-controlled case series analysis using the data from the Hong Kong Hospital Authority and the Department of Health. The primary outcome was hospitalization for HF and the secondary outcomes were major adverse cardiovascular events (MACE) and all hospitalization. We identified patients with a history of HF before February 23, 2021 and developed the outcome event between February 23, 2021 and March 31, 2022 in Hong Kong. Incidence rate ratios (IRR) were estimated using conditional Poisson regression to evaluate the risks following the first three doses of BNT162b2 or CoronaVac. FINDINGS: We identified 32,490 patients with HF, of which 3035 were vaccinated and had a hospitalization for HF during the observation period (BNT162b2 = 755; CoronaVac = 2280). There were no increased risks during the 0–13 days (IRR 0.64 [95% confidence interval 0.33–1.26]; 0.94 [0.50–1.78]; 0.82 [0.17–3.98]) and 14–27 days (0.73 [0.35–1.52]; 0.95 [0.49–1.84]; 0.60 [0.06–5.76]) after the first, second and third doses of BNT162b2. No increased risks were observed for CoronaVac during the 0–13 days (IRR 0.60 [0.41–0.88]; 0.71 [0.45–1.12]; 1.64 [0.40–6.77]) and 14–27 days (0.91 [0.63–1.32]; 0.79 [0.46–1.35]; 1.71 [0.44–6.62]) after the first, second and third doses. We also found no increased risk of MACE or all hospitalization after vaccination. INTERPRETATION: Our results showed no increased risk of hospitalization for HF, MACE or all hospitalization after receiving BNT162b2 or CoronaVac vaccines in patients with HF. FUNDING: The project was funded by a Research Grant from the Food and Health Bureau, The Government of the Hong Kong Special Administrative Region (Ref. No. COVID19F01). F.T.T.L. (Francisco T.T. Lai) and I.C.K.W. (Ian C.K. Wong)'s posts were partly funded by the D24H; hence this work was partly supported by AIR@InnoHK administered by Innovation and Technology Commission

Identification of the ADPR binding pocket in the NUDT9 homology domain of TRPM2

Activation of the transient receptor potential melastatin 2 (TRPM2) channel occurs during the response to oxidative stress under physiological conditions as well as in pathological processes such as ischemia and diabetes. Accumulating evidence indicates that adenosine diphosphate ribose (ADPR) is the most important endogenous ligand of TRPM2. However, although it is known that ADPR binds to the NUDT9 homology (NUDT9-H) domain in the intracellular C-terminal region, the molecular mechanism underlying ADPR binding and activation of TRPM2 remains unknown. In this study, we generate a structural model of the NUDT9-H domain and identify the binding pocket for ADPR using induced docking and molecular dynamics simulation. We find a subset of 11 residues—H1346, T1347, T1349, L1379, G1389, S1391, E1409, D1431, R1433, L1484, and H1488—that are most likely to directly interact with ADPR. Results from mutagenesis and electrophysiology approaches support the predicted binding mechanism, indicating that ADPR binds tightly to the NUDT9-H domain, and suggest that the most significant interactions are the van der Waals forces with S1391 and L1484, polar solvation interaction with E1409, and electronic interactions (including π–π interactions) with H1346, T1347, Y1349, D1431, and H1488. These findings not only clarify the roles of a range of newly identified residues involved in ADPR binding in the TRPM2 channel, but also reveal the binding pocket for ADPR in the NUDT9-H domain, which should facilitate structure-based drug design for the TRPM2 channel