BackgroundRapid diagnostic tests (RDTs) have revolutionized the diagnosis of malaria. Among the various factors affecting RDTs sensitivity is genetic variation of the antigen used. The genetic variation in PfHRP2 and PfHRP3 proteins was studied among the Indian Plasmodium falciparum isolates.MethodsOne hundred and forty isolates of P. falciparum were collected from six geographical regions of India. Target genes encoding PfHRP2 and PfHRP3 antigens were sequenced to study genetic polymorphism. Minimum detection limit giving a positive rapid diagnostic test was also determined.ResultsExtensive variations were observed in amino acid repeat types of PfHRP2 and PfHRP3. PfHRP2 exhibited more polymorphism than PfHRP3. Significant relation was observed between type 2 and type 7 repeats and RDT detection rate as higher number of these repeats showed better sensitivity with RDTs.ConclusionThe results provide insights into the genetic diversity of Pfhrp2 and Pfhrp3 genes among Indian P. falciparum population and its relation to RDT sensitivity.

BACKGROUND: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or ECG signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes. METHODS: Consecutive patients who underwent catheter ablation between 2015 and 2017 with panoramic left atrial electrogram before ablation and clinical follow-up for at least 1 year following ablation were included. Convolutional neural network and a novel multimodal fusion framework were developed for predicting 1-year atrial fibrillation recurrence after catheter ablation from electrogram, ECG signals, and clinical features. The models were trained and validated using 10-fold cross-validation on patient-level splits. RESULTS: One hundred fifty-six patients (64.5±10.5 years, 74% male, 42% paroxysmal) were analyzed. Using electrogram signals alone, the convolutional neural network achieved an area under the receiver operating characteristics curve of 0.731, outperforming the existing APPLE scores (area under the receiver operating characteristics curve=0.644) and CHA2DS2-VASc scores (area under the receiver operating characteristics curve=0.650). Similarly using 12-lead ECG alone, the convolutional neural network achieved an AUROC of 0.767. Combining electrogram, ECG, and clinical features, the fusion model achieved an AUROC of 0.859, outperforming single and dual modality models. CONCLUSIONS: Deep neural networks trained on electrogram or ECG signals improved the prediction of catheter ablation outcome compared with existing clinical scores, and fusion of electrogram, ECG, and clinical features further improved the prediction. This suggests the promise of using machine learning to help treatment planning for patients after catheter ablation.

A 62-year-old man presented with 2 weeks of progressive dyspnea and chest pain. He was previously diagnosed with high-grade invasive urothelial carcinoma (UC) of the bladder and underwent neoadjuvant chemotherapy followed by radical cystectomy 10 months earlier, resulting in pathologic complete remission. Clinical evaluation and echocardiographic imaging was consistent with a diagnosis of cardiac tamponade. Due to a history of malignancy, the patient was referred for a surgical pericardial window, to include biopsy of the pericardium. Pericardial fluid and pericardial biopsy specimens were consistent with metastatic UC. Cardiac tamponade due to metastatic UC is a rare presentation, and, to our knowledge, there have been only 5 cases reported in the English literature. We report a rare case of cardiac tamponade due to isolated pericardial metastases from high grade UC of the bladder and discuss the symptoms, treatment, and prognosis of this pathologic condition. We also present a brief review of previously published literature. Through this discussion, we would like to emphasize the (1) consideration of cardiac metastases in the differential diagnosis for patients with a history of UC presenting with cardiac or pulmonary symptoms and (2) improved diagnostics with pericardial biopsy and pericardiocentesis over pericardiocentesis alone.

The ankle-brachial index (ABI) is a predictor of cardiovascular events, mortality and functional status. Some studies have noted a higher prevalence of peripheral artery disease in females compared to males. Differences in height might account for these observed sex differences, but findings are conflicting. The 2003-2004 National Health and Nutrition Examination Survey (NHANES) cohort includes participants from 15 geographic locations, selected annually to represent the general population. Sample-weighted multivariable linear and logistic regression modeling was performed with ABI as the dependent variable and height and sex as primary exposure variables of interest. There were 3052 participants with ABI data (mean age 57 years, 51% female). The mean (±SE) ABI was 1.09 (±0.006) and 1.13 (±0.005) for females and males, respectively ( p < 0.0001). Shorter height was associated with a low ABI (OR 0.91 per 4 cm, 95% CI: 0.86-0.96; p=0.001). In a fully adjusted model, female sex was associated with a low ABI (OR 1.34, 95% CI: 1.04-1.72; p=0.025) independent of height and traditional cardiovascular disease (CVD) risk factors. Age, diabetes, tobacco use, known CVD, hypertension and race were associated with a low ABI (all p < 0.001). The ABI was 0.03 lower in females than in males in the general population and in a healthy cohort. Lower ABI values in healthy females do not appear to be due to occult vascular disease but rather a normal phenomenon with some contribution from height. Therefore, population sex-specific ABI thresholds should be utilized in the diagnosis of peripheral artery disease to account for these intrinsic differences.

The epidemiological necessity for distancing during the COVID-19 pandemic has resulted in postponement of non-emergent hospitalizations and increase use of telemedicine. The feasibility of virtual antiarrhythmic drug (AAD) loading specifically with digital QTc electrocardiographic monitoring (EM) in conjunction with telemedicine video visits is not well established. We tested the hypothesis that existing digital health technologies and virtual communication platforms could provide EM and support medically guided AAD loading for patients with symptomatic tachyarrhythmia in the ambulatory setting, while reducing physical contact between patient and healthcare system. A prospective pilot, case series approved by the institutional ethics committee, entailing three subjects with symptomatic arrhythmia during the COVID-19 pandemic who were enrolled for virtual AAD loading at home. Clinicians met with participants twice daily via video visits conducted after QTc analysis (Kardia 6L mobile sensor) and telemetry review (Mobile Cardiac Outpatient Telemetry of silent arrhythmias). Participants received direct instruction to either terminate the study or proceed with the next single dose of AAD. All participants completed contactless loading of 5 AAD doses, without untoward event. Scheduled video visits allowed dialogue and participant counseling where decision making was guided by remote review of EM. Participant adherence with transmissions and scheduled visits was 98.3%; a single electrocardiogram was delayed beyond the two-hours-post-dose schedule. This virtual approach reduced overall expenditures based on retrospective comparison with previous AAD load hospitalizations. We found that a ‘virtual hospitalization’ for AAD loading with remote electrocardiographic monitoring and twice daily virtual rounding is feasible using existing digital health technologies.