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A 56-year-old man presented following an aborted cardiac arrest. His initial ECGs showed episodes of transient repolarization abnormalities. Coronary vasospasm can be a precipitant for ventricular arrhythmia in these patients, underpinning the importance of continuous ECG for accurate diagnosis and management.

A 50-year-old gentleman with old anterior wall myocardial infarction with implantable cardioverter defibrillator (ICD, Abbott Medical, Fortify ST VR 1235-40) presented with recurrent appropriate ICD shock. The ICD stored EGM indicated a possibility of supraventricular tachycardia (SVT) rather than ventricular tachycardia (VT) when the morphology match was found high. Bundle brunch re-entry (BBR) VT was another differential. An EP study conducted on antiarrhythmic drugs (AAD) induced reproducible but only ill-sustained tachycardia too short to perform any SVT maneuvers during tachycardia. However, critical analysis of the tachycardia electrograms suggested atypical AVNRT as the most likely mechanism. The other differentials were atrial tachycardia (AT) and BBR VT. Manoeuvres during sinus rhythm and ventricular pacing excluded other diagnosis. A single point radiofrequency ablation (RFA) near the SP region cured the arrhythmia. The reason for misclassification of SVT as VT was also sought for. It was found that the shocks were received due to fulfilment of 2/3 criteria (sudden onset and regular tachycardia). Hence, he received therapy despite an appropriate morphology match favouring SVT. This is one of the known limitations of ICDs where regular SVTs (AVNRT/AVRT or AT) may receive inappropriate ICD therapies. After slow pathway modification there was no further recurrence of either SVT or VT; hence , a substrate modification was deferred.

Pancreatic ductal adenocarcinoma (PDAC) has a survival rate of 12%, and multiple clinical trials testing anti-PD-1 therapies against PDAC have failed, suggesting a need for a novel therapeutic strategy. In this study, we evaluated the potential of milbemycin oxime (MBO), an antiparasitic compound, as an immunomodulatory agent in PDAC. Our results show that MBO inhibited the growth of multiple PDAC cell lines by inducing apoptosis. In vivo studies showed that the oral administration of 5 mg/kg MBO inhibited PDAC tumor growth in both subcutaneous and orthotopic models by 49% and 56%, respectively. Additionally, MBO treatment significantly increased the survival of tumor-bearing mice by 27 days as compared to the control group. Interestingly, tumors from MBO-treated mice had increased infiltration of CD8+ T cells. Notably, depletion of CD8+ T cells significantly reduced the anti-tumor efficacy of MBO in mice. Furthermore, MBO significantly augmented the efficacy of anti-PD-1 therapy, and the combination treatment resulted in a greater proportion of active cytotoxic T cells within the tumor microenvironment. MBO was safe and well tolerated in all our preclinical toxicological studies. Overall, our study provides a new direction for the use of MBO against PDAC and highlights the potential of repurposing MBO for enhancing anti-PD-1 immunotherapy.

BACKGROUND: Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions.
OBJECTIVE: This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support.
METHODS: We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model\'s performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support.
RESULTS: We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4.
CONCLUSIONS: When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.

OBJECTIVE: Pathogenic desmoplakin (DSP) gene variants are associated with the development of a distinct form of arrhythmogenic cardiomyopathy known as DSP cardiomyopathy. Patients harbouring these variants are at high risk for sustained ventricular arrhythmia (VA), but existing tools for individualized arrhythmic risk assessment have proven unreliable in this population.
METHODS: Patients from the multi-national DSP-ERADOS (Desmoplakin SPecific Effort for a RAre Disease Outcome Study) Network patient registry who had pathogenic or likely pathogenic DSP variants and no sustained VA prior to enrolment were followed longitudinally for the development of first sustained VA event. Clinically guided, step-wise Cox regression analysis was used to develop a novel clinical tool predicting the development of incident VA. Model performance was assessed by c-statistic in both the model development cohort (n = 385) and in an external validation cohort (n = 86).
RESULTS: In total, 471 DSP patients [mean age 37.8 years, 65.6% women, 38.6% probands, 26% with left ventricular ejection fraction (LVEF) < 50%] were followed for a median of 4.0 (interquartile range: 1.6-7.3) years; 71 experienced first sustained VA events {2.6% [95% confidence interval (CI): 2.0, 3.5] events/year}. Within the development cohort, five readily available clinical parameters were identified as independent predictors of VA and included in a novel DSP risk score: female sex [hazard ratio (HR) 1.9 (95% CI: 1.1-3.4)], history of non-sustained ventricular tachycardia [HR 1.7 (95% CI: 1.1-2.8)], natural logarithm of 24-h premature ventricular contraction burden [HR 1.3 (95% CI: 1.1-1.4)], LVEF < 50% [HR 1.5 (95% CI: .95-2.5)], and presence of moderate to severe right ventricular systolic dysfunction [HR 6.0 (95% CI: 2.9-12.5)]. The model demonstrated good risk discrimination within both the development [c-statistic .782 (95% CI: .77-.80)] and external validation [c-statistic .791 (95% CI: .75-.83)] cohorts. The negative predictive value for DSP patients in the external validation cohort deemed to be at low risk for VA (<5% at 5 years; n = 26) was 100%.
CONCLUSIONS: The DSP risk score is a novel model that leverages readily available clinical parameters to provide individualized VA risk assessment for DSP patients. This tool may help guide decision-making for primary prevention implantable cardioverter-defibrillator placement in this high-risk population and supports a gene-first risk stratification approach.

Sodium-glucose transporter 2 (SGLT2) inhibitors such as empagliflozin are one of the main treatments for type 2 diabetes mellitus (DM2) and heart failure (HF). They have also demonstrated anti-arrhythmic effects in some preclinical and clinical studies. The purpose of this study was to assess the effects of empagliflozin on ventricular arrhythmias in HF patients with an implantable cardioverter-defibrillator (ICD). In a prospective double-blinded, randomized controlled trial of Iran County, Mashhad (72 patients 1:1), we compared the frequency and proportion of ventricular arrhythmias and ICD therapies during the 24 weeks to the prior 24 weeks. Results revealed that empagliflozin significantly reduced the frequency and proportion of ventricular tachycardia (VT)/fibrillation (VF) episodes (P = 0.019 and 0.039, respectively). Moreover, it tended to reduce the frequency and proportion of ICD therapies, including anti-tachycardia pacing (ATP) and shock. Subgroup analysis of patients with or without any antiarrhythmic drugs (digoxin, mexiletine, amiodarone, or sotalol) revealed that only patients who were previously on the antiarrhythmic drugs benefit from empagliflozin antiarrhythmic effects. In conclusion, empagliflozin exhibits anti-arrhythmic effects in HF patients with an ICD. Larger and long-term clinical studies are still needed to investigate and confirm all positive effects of SGLT2 inhibitors in this regard. Trial registration number: IRCT20120520009801N7 (Approval date: June 11, 2022).

The synergistic effect of apoptosis and cuproptosis, along with the activation of the immune system, presents a promising approach to enhance the efficacy against triple-negative breast cancer (TNBC). Here, two prodrugs are synthesized: a reactive oxygen species (ROS)-responsive prodrug PEG-TK-DOX and a glutathione (GSH)-responsive prodrug PEG-DTPA-SS-CPT. These prodrugs are self-assembled and chelated Cu2+ to prepare nanoparticle PCD@Cu that simultaneously loaded doxorubicin (DOX), camptothecin (CPT), and Cu2+. The elevated levels of ROS and GSH in TNBC cells disrupted the PCD@Cu structure, leading to the release of Cu+, DOX, and CPT and the depletion of GSH. DOX and CPT triggered apoptosis with immunogenic cell death (ICD) in TNBC cells. Simultaneously, PCD@Cu downregulated the expression of copper transporting ATPase 2 (ATP7B), causing a significant accumulation of copper ions in TNBC cells. This further induced the aggregation of lipoylated dihydrolipoamide S-acetyltransferase (DLAT) and downregulation of iron-sulfur (Fe-S) cluster proteins, ultimately leading to cuproptosis and ICD in TNBC. In vitro and in vivo experiments confirmed that PCD@Cu induced apoptosis and cuproptosis in TNBC and activated the immune system, demonstrating strong anti-tumor capabilities. Moreover, PCD@Cu exhibited an excellent biosafety profile. Overall, this study provides a promising strategy for effective TNBC therapy.

UNASSIGNED: Achieving a high biventricular pacing percentage (BiV%) is crucial for optimizing outcomes in cardiac resynchronization therapy (CRT). The HeartLogic index, a multiparametric heart failure (HF) risk score, incorporates implantable cardioverter-defibrillator (ICD)-measured variables and has demonstrated its predictive ability for impending HF decompensation.
UNASSIGNED: This study aimed to investigate the relationship between daily BiV% in CRT ICD patients and their HF status, assessed using the HeartLogic algorithm.
UNASSIGNED: The HeartLogic algorithm was activated in 306 patients across 26 centers, with a median follow-up of 26 months (25th-75th percentile: 15-37).
UNASSIGNED: During the follow-up period, 619 HeartLogic alerts were recorded in 186 patients. Overall, daily values associated with the best clinical status (highest first heart sound, intrathoracic impedance, patient activity; lowest combined index, third heart sound, respiration rate, night heart rate) were associated with a BiV% exceeding 99%. We identified 455 instances of BiV% dropping below 98% after consistent pacing periods. Longer episodes of reduced BiV% (hazard ratio: 2.68; 95% CI: 1.02-9.72; P = .045) and lower BiV% (hazard ratio: 3.97; 95% CI: 1.74-9.06; P=.001) were linked to a higher risk of HeartLogic alerts. BiV% drops exceeding 7 days predicted alerts with 90% sensitivity (95% CI [74%-98%]) and 55% specificity (95% CI [51%-60%]), while BiV% ≤96% predicted alerts with 74% sensitivity (95% CI [55%-88%]) and 81% specificity (95% CI [77%-85%]).
UNASSIGNED: A clear correlation was observed between reduced daily BiV% and worsening clinical conditions, as indicated by the HeartLogic index. Importantly, even minor reductions in pacing percentage and duration were associated with an increased risk of HF alerts.

OBJECTIVE: Hospitalizations are common in patients with heart failure and are associated with high mortality, readmission and economic burden. Detecting early signs of worsening heart failure may enable earlier intervention and reduce hospitalizations. The HeartLogic algorithm is designed to predict worsening heart failure using diagnostic data from multiple device sensors. The main objective of this analysis was to evaluate the sensitivity of the HeartLogic alert calculation in predicting worsening heart failure events (HFEs). We also evaluated the false positive alert rate (FPR) and compared the incidence of HFEs occurring in a HeartLogic alert state to those occurring out of an alert state.
METHODS: The HINODE study enrolled 144 patients (81 ICD and 63 CRT-D) with device sensor data transmitted via a remote monitoring system. HeartLogic alerts were then retrospectively simulated using relevant sensor data. Clinicians and patients were blinded to calculated alerts. Reported adverse events with HF symptoms were adjudicated and classified by an independent HFE committee. Sensitivity was defined as the ratio of the number of detected usable HFEs (true positives) to the total number of usable HFEs. A false positive alert was defined as an alert with no usable HFE between the alert onset date and the alert recovery date plus 30 days. The patient follow-up period was categorized as in alert state or out of alert state. The event rate ratio was the HFE rate calculated in alert to out of alert.
RESULTS: The patient cohort was 79% male and had an average age of 68 ± 12 years. This analysis yielded 244 years of follow-up data with 73 HFEs from 37 patients. A total of 311 HeartLogic alerts at the nominal threshold (16) occurred across 106 patients providing an alert rate of 1.27 alerts per patient-year. The HFE rate was 8.4 times greater while in alert compared with out of alert (1.09 vs. 0.13 events per patient-year; P < 0.001). At the nominal alert threshold, 80.8% of HFEs were detected by a HeartLogic alert [95% confidence interval (CI): 69.9%-89.1%]. The median time from first true positive alert to an adjudicated clinical HFE was 53 days. The FPR was 1.16 (95% CI: 0.98-1.38) alerts per patient-year.
CONCLUSIONS: Results suggest that signs of worsening HF can be detected successfully with remote patient follow-up. The use of HeartLogic may predict periods of increased risk for HF or clinically significant events, allowing for early intervention and reduction of hospitalization in a vulnerable patient population.