Background/Objectives: Studies have shown that consistent reductions of 2 mm Hg in systolic blood pressure (SBP) for the general normotensive population can result in significant decreases in mortality from heart disease and stroke. The purpose of this meta-analysis was to determine the optimal dose and duration of treatment for magnesium and potassium supplementation, having previously discovered that both reduce SBP by −2.79 and −2.10 mm Hg, respectively. Methods: Placebo-controlled, randomized clinical trials examining the effects of magnesium and potassium supplementation on SBP were identified. Pairwise meta-analyses with subgroups for dosage and treatment duration were run. Results: Magnesium at dosages of ≤360 mg/day and durations greater than 3 months reduced SBP by −3.03 and −4.31 mm Hg, respectively. Potassium at dosages of ≤60 mmol/day and durations greater than 1 month reduced SBP by −2.34 and −2.80 mm Hg, respectively. Conclusions: Both supplements demonstrated greater reductions in SBP for the general population at lower dosages and longer treatment durations. Future studies are needed to validate these findings and provide tailored recommendations. These studies could investigate varying dosages over long-term follow-up to provide robust data on optimal dosages and treatment durations, as our findings were limited due to reliance on previously published trials.

Myocardial ischemia reperfusion injury (IRI) in acute coronary syndromes is a condition in which ischemic/hypoxic injury to cells subtended by the occluded vessel continues despite successful resolution of the thrombotic obstruction. For decades, most efforts to attenuate IRI have focused on interdicting singular molecular targets or pathways, but none have successfully transitioned to clinical use. In this work, we investigate a nanoparticle-based therapeutic strategy for profound but local thrombin inhibition that may simultaneously mitigate both thrombosis and inflammatory signaling pathways to limit myocardial IRI. Perfluorocarbon nanoparticles (PFC NP) were covalently coupled with an irreversible thrombin inhibitor, PPACK (Phe[D]-Pro-Arg-Chloromethylketone), and delivered intravenously to animals in a single dose prior to ischemia reperfusion injury. Fluorescent microscopy of tissue sections and ¹⁹F magnetic resonance images of whole hearts ex vivo demonstrated abundant delivery of PFC NP to the area at risk. Echocardiography at 24 h after reperfusion demonstrated preserved ventricular structure and improved function. Treatment reduced thrombin deposition, suppressed endothelial activation, inhibited inflammasome signaling pathways, and limited microvascular injury and vascular pruning in infarct border zones. Accordingly, thrombin inhibition with an extraordinarily potent but locally acting agent suggested a critical role for thrombin and a promising therapeutic strategy in cardiac IRI.

The progress of incorporating deep learning in the field of medical image interpretation has been greatly hindered due to the tremendous cost and time associated with generating ground truth for supervised machine learning, alongside concerns about the inconsistent quality of images acquired. Active learning offers a potential solution to these problems of expanding dataset ground truth by algorithmically choosing the most informative samples for ground truth labeling. Still, this effort incurs the costs of human labeling, which needs minimization. Furthermore, automatic labeling approaches employing active learning often exhibit overfitting tendencies while selecting samples closely aligned with the training set distribution and excluding out-of-distribution samples, which could potentially improve the model’s effectiveness. We propose that the majority of out-of-distribution instances can be attributed to inconsistent cross images. Since the FDA approved the first whole-slide image system for medical diagnosis in 2017, whole-slide images have provided enriched critical information to advance the field of automated histopathology. Here, we exemplify the benefits of a novel deep learning strategy that utilizes high-resolution whole-slide microscopic images. We quantitatively assess and visually highlight the inconsistencies within the whole-slide image dataset employed in this study. Accordingly, we introduce a deep learning-based preprocessing algorithm designed to normalize unknown samples to the training set distribution, effectively mitigating the overfitting issue. Consequently, our approach significantly increases the amount of automatic region-of-interest ground truth labeling on high-resolution whole-slide images using active deep learning. We accept 92% of the automatic labels generated for our unlabeled data cohort, expanding the labeled dataset by 845%. Additionally, we demonstrate expert time savings of 96% relative to manual expert ground-truth labeling.

Objective: : To determine if thrombin‐inhibiting nanoparticles preserve renal function when administered after onset of ischemia‐reperfusion acute kidney injury (AKI), and to determine physiologic mechanisms by which this occurs. Hypothesis: AKI therapy remains mainly supportive, as recent clinical trials of promising therapeutic agents have failed to preserve renal function. It has been previously shown that delivery of direct thrombin inhibiting nanoparticles prior to onset of AKI in mice has successfully preserved renal function. We hypothesized that inhibition of thrombin clotting and inflammatory signaling with the use of a sustained locally acting nanoparticle formulation might both preserve renal vasculature and broadly modulate pathophysiologic processes that contribute to renal ischemia reperfusion injury even after its onset. Methods: C57BL/6 mice underwent bilateral warm ischemia for 17 minutes at 37°C followed by reperfusion. Two hours after reperfusion, mice were injected with either direct thrombin‐inhibiting (n=10) or control (n=12) perfluorocarbon nanoparticles (PFC NP). The treatment PFC NP contained ~ 13650 copies per nanoparticle of the direct thrombin inhibitor, PPACK (Proline‐Phenylalanine‐Arginine‐Chloromethyl‐Ketone). After 24 hours, blood and kidneys were collected for evaluation of BUN, inflammatory eicosanoids and cytokines, histological analysis of vascular damage, endothelial activation, and cellular infiltrates. Results: : Mice treated after AKI with direct thrombin‐inhibiting PPACK NP exhibited significantly improved kidney function at 24 hours versus the control group (BUN: 62.10±8.71mg/dL vs 110.96 ±6.21mg/dL, p=0.0002). Vascular integrity was preserved as evidenced by increased expression of CD31 in both cortex (4.28±0.62% vs 1.75±0.34%, p=0.0018) and medulla (3.24±0.38% vs 1.91±0.29%; p=0.0073). Additionally, the PPACK NP group demonstrated significantly less acute tubular necrosis (ATN), further mitigating vascular damage (ATN: 24.44±1.76% vs 37.00±4.96%, p=0.035). Treatment reduced red blood cell trapping and subsequent iron accumulation in both cortex (3.37±0.50% vs 5.34±0.42%, p=0.0083) and medulla (0.50%±0.10% vs 1.01%±0.12%, p=0.006). Inflammatory markers were also reduced in the PPACK NP group, including endothelial VCAM‐1 (positive area in medulla 0.49±0.12% vs 1.41±0.60%, p=0.0073), ICAM‐1 (positive area in medulla 3.07±0.51% vs 4.46±0.35%, p=0.030). and serum M‐CSF and IL‐6. Mast cell infiltration and prostaglandin D2 (PGD2) were also decreased. Conclusion: Nanoparticle delivery of local thrombin inhibitors that have a known minimal effect on systemic clotting parameters successfully protects renal function by preserving renal vasculature, reducing ATN and iron accumulation, and broadly attenuating thrombin‐induced inflammatory mechanisms even when delivered after onset of AKI.