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The application of brief high voltage electrical pulses to tissue can lead to an irreversible or reversible electroporation effect in a cell-specific manner. In the management of ventricular arrhythmias, the ability to target different tissue types, specifically cardiac conduction tissue (His-Purkinje System) vs. cardiac myocardium would be advantageous. We hypothesize that pulsed electric fields (PEFs) can be applied safely to the beating heart through a catheter-based approach, and we tested whether the superficial Purkinje cells can be targeted with PEFs without injury to underlying myocardial tissue. In an acute (n = 5) and chronic canine model (n = 6), detailed electroanatomical mapping of the left ventricle identified electrical signals from myocardial and overlying Purkinje tissue. Electroporation was effected via percutaneous catheter-based Intracardiac bipolar current delivery in the anesthetized animal. Repeat Intracardiac electrical mapping of the heart was performed at acute and chronic time points; followed by histological analysis to assess effects. PEF demonstrated an acute dose-dependent functional effect on Purkinje, with titration of pulse duration and/or voltage associated with successful acute Purkinje damage. Electrical conduction in the insulated bundle of His (n = 2) and anterior fascicle bundle (n = 2), was not affected. At 30 days repeat cardiac mapping demonstrated resilient, normal electrical conduction throughout the targeted area with no significant change in myocardial amplitude (pre 5.9 ± 1.8 mV, 30 days 5.4 ± 1.2 mV, p = 0.92). Histopathological analysis confirmed acute Purkinje fiber targeting, with chronic studies showing normal Purkinje fibers, with minimal subendocardial myocardial fibrosis. PEF provides a novel, safe method for non-thermal acute modulation of the Purkinje fibers without significant injury to the underlying myocardium. Future optimization of this energy delivery is required to optimize conditions so that selective electroporation can be utilized in humans the treatment of cardiac disease.

Stem cell paracrine activity is implicated in cardiac repair. Linkage between secretome functionality and therapeutic outcome was here interrogated by systems analytics of biobanked human cardiopoietic cells, a regenerative biologic in advanced clinical trials. Protein chip array identified 155 proteins differentially secreted by cardiopoietic cells with clinical benefit, expanded into a 520 node network, collectively revealing inherent vasculogenic properties along with cardiac and smooth muscle differentiation and development. Next generation RNA sequencing, refined by pathway analysis, pinpointed miR‐146 dependent regulation upstream of the decoded secretome. Intracellular and extracellular integration unmasked commonality across cardio‐vasculogenic processes. Mirroring the secretome pattern, infarcted hearts benefiting from cardiopoietic cell therapy restored the disease proteome engaging cardiovascular system functions. The cardiopoietic cell secretome thus confers a therapeutic molecular imprint on recipient hearts, with response informed by predictive systems profiling. Reverse translational decoding characterized the secretome of cardiopoietic cells, a regenerative therapeutic in clinical testing. A cardiovasculogenic systems signature distinguished high from low response profiles, an imprint echoed in the restored diseased proteome. Linkage of realized outcome with secretome identity suggests paracrine centrality in regenerative fitness. Pre‐intervention secretome profiling would thus inform optimized selection of regenerative biologic candidates.

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment 1 – 3 . Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing 4 , 5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7–94.9%, with an improvement of 5.36–14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care. A clinical language model trained on unstructured clinical notes from the electronic health record enhances prediction of clinical and operational events.

Approximately every 40 seconds, someone in the United States has a heart attack. This amounts to about 605,000 new and 200,000 recurrent heart attacks each year. While improvements in detection and timely therapy have led to decreases in acute mortality, improvement in acute survival has paradoxically led to increased incidence of heart failure. Patients that experience ischemic injury are also at an increased risk of sudden cardiac death from ventricular arrhythmias that often do not foreshadow their manifestation. Currently, there is a paucity of therapies to adjust the disease trajectory and risk associated with cardiac ischemia. The objective of this work is to develop and translate cell-free regenerative platforms to help patients with cardiac ischemia. To achieve this goal, the following specific aims were pursued: 1) define the biologic system of injury in myocardial infarction; 2) use targeted immunomodulation and exosome-based regenerative interventions to improve outcomes post-MI; and 3) mitigate arrhythmogenic sequelae of myocardial injury. The findings of this thesis work have demonstrated a TNFα centric inflammatory response that influences outcomes post-STEMI and that targeted immunomodulation with Infliximab improves outcomes post-MI. Also, off-the-shelf exosomes, endowed with anti-oxidant and immunomodulatory effect, when delivered intracoronary upon myocardial reperfusion, improve survival and cardiac function. Lastly, using selective irreversible electroporation of Purkinje fibers reduces the vulnerability of the heart to ventricular fibrillation. The data and conclusions presented here provide insight into novel cell-free regenerative platforms that can be translated into clinical practice.

General-purpose VLMs demonstrate impressive capabilities, but their opaque training on uncurated internet data poses critical limitations for high-stakes decision-making, such as in neurosurgery. We present CNS-Obsidian, a neurosurgical VLM trained on peer-reviewed literature, and demonstrate its clinical utility versus GPT-4o in a real-world setting. We compiled 23,984 articles from Neurosurgery Publications journals, yielding 78,853 figures and captions. Using GPT-4o and Claude Sonnet-3.5, we converted these into 263,064 training samples across three formats: instruction fine-tuning, multiple-choice questions, and differential diagnosis. We trained CNS-Obsidian, a fine-tune of the 34-billion parameter LLaVA-Next model. In a blinded, randomized trial at NYU Langone Health (Aug 30-Nov 30, 2024), neurosurgery consultations were assigned to either CNS-Obsidian or a HIPAA-compliant GPT-4o endpoint as diagnostic co-pilot after consultations. Primary outcomes were diagnostic helpfulness and accuracy, assessed via user ratings and presence of correct diagnosis within the VLM-provided differential. CNS-Obsidian matched GPT-4o on synthetic questions (76.13% vs 77.54%, p=0.235), but only achieved 46.81% accuracy on human-generated questions versus GPT-4o's 65.70% (p<10-15). In the randomized trial, 70 consultations were evaluated (32 CNS-Obsidian, 38 GPT-4o) from 959 total consults (7.3% utilization). CNS-Obsidian received positive ratings in 40.62% of cases versus 57.89% for GPT-4o (p=0.230). Both models included correct diagnosis in approximately 60% of cases (59.38% vs 65.79%, p=0.626). Domain-specific VLMs trained on curated scientific literature can approach frontier model performance despite being orders of magnitude smaller and less expensive to train. This establishes a transparent framework for scientific communities to build specialized AI models.

In 2015, high rates of microcephaly were reported in Northeast Brazil following the first South American Zika virus (ZIKV) outbreak. Reported microcephaly rates in other Zika-affected areas were significantly lower, suggesting alternate causes or the involvement of arboviral cofactors in exacerbating microcephaly rates. We merged data from multiple national reporting databases in Brazil to estimate exposure to 9 known or hypothesized causes of microcephaly for every pregnancy nationwide since the beginning of the ZIKV outbreak; this generated between 3.6 and 5.4 million cases (depending on analysis) over the time period 1 January 2015-23 May 2017. The association between ZIKV and microcephaly was statistically tested against models with alternative causes or with effect modifiers. We found no evidence for alternative non-ZIKV causes of the 2015-2017 microcephaly outbreak, nor that concurrent exposure to arbovirus infection or vaccination modified risk. We estimate an absolute risk of microcephaly of 40.8 (95% CI 34.2-49.3) per 10,000 births and a relative risk of 16.8 (95% CI 3.2-369.1) given ZIKV infection in the first or second trimester of pregnancy; however, because ZIKV infection rates were highly variable, most pregnant women in Brazil during the ZIKV outbreak will have been subject to lower risk levels. Statistically significant associations of ZIKV with other birth defects were also detected, but at lower relative risks than that of microcephaly (relative risk < 1.5). Our analysis was limited by missing data prior to the establishment of nationwide ZIKV surveillance, and its findings may be affected by unmeasured confounding causes of microcephaly not available in routinely collected surveillance data. This study strengthens the evidence that congenital ZIKV infection, particularly in the first 2 trimesters of pregnancy, is associated with microcephaly and less frequently with other birth defects. The finding of no alternative causes for geographic differences in microcephaly rate leads us to hypothesize that the Northeast region was disproportionately affected by this Zika outbreak, with 94% of an estimated 8.5 million total cases occurring in this region, suggesting a need for seroprevalence surveys to determine the underlying reason.

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human–AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes. The CONSORT-AI and SPIRIT-AI extensions improve the transparency of clinical trial design and trial protocol reporting for artificial intelligence interventions.

BACKGROUNDThe temporal clustering of a cancer diagnosis with dermatomyositis (DM) onset is strikingly associated with autoantibodies against transcriptional intermediary factor 1-γ (TIF1-γ). Nevertheless, many patients with anti-TIF1-γ antibodies never develop cancer. We investigated whether additional autoantibodies are found in anti-TIF1-γ-positive patients without cancer.METHODSUsing a proteomic approach, we defined 10 previously undescribed autoantibody specificities in 5 index anti-TIF1-γ-positive DM patients without cancer. These were subsequently examined in discovery (n = 110) and validation (n = 142) cohorts of DM patients with anti-TIF1-γ autoantibodies.RESULTSWe identified 10 potentially novel autoantibodies in anti-TIF1-γ-positive DM patients, 6 with frequencies ranging from 3% to 32% in 2 independent DM cohorts. Autoantibodies recognizing cell division cycle and apoptosis regulator protein 1 (CCAR1) were the most frequent, and were significantly negatively associated with contemporaneous cancer (discovery cohort OR 0.27 [95% CI 0.7-1.00], P = 0.050; validation cohort OR 0.13 [95% CI 0.03-0.59], P = 0.008). When cancer did emerge, it occurred significantly later in anti-CCAR1-positive compared with anti-CCAR1-negative patients (median time from DM onset 4.3 vs. 0.85 years, respectively; P = 0.006). Cancers that emerged were more likely to be localized (89% of anti-CCAR1-positive cancers presenting at stage 0 or 1 compared with 42% of patients without anti-CCAR1 antibodies, P = 0.02). As the number of additional autoantibody specificities increased in anti-TIF1-γ-positive DM patients, the frequency of cancer decreased (P < 0.001).CONCLUSIONAs the diversity of immune responses in anti-TIF1-γ DM patients increases, the likelihood of cancer emerging decreases. Our findings have important relevance for cancer risk stratification in DM patients and for understanding natural immune regulation of cancer in humans.TRIAL REGISTRATIONNot applicable.FUNDING SOURCESThe NIH, the Donald B. and Dorothy L. Stabler Foundation, and the Huayi and Siuling Zhang Discovery Fund.

While comorbidity between coronary heart disease (CHD) and depression is evident, it is unclear whether the two diseases have shared underlying mechanisms. We performed a range of analyses in 367,703 unrelated middle-aged participants of European ancestry from UK Biobank, a population-based cohort study, to assess whether comorbidity is primarily due to genetic or environmental factors, and to test whether cardiovascular risk factors and CHD are likely to be causally related to depression using Mendelian randomization. We showed family history of heart disease was associated with a 20% increase in depression risk (95% confidence interval [CI] 16–24%, p < 0.0001), but a genetic risk score that is strongly associated with CHD risk was not associated with depression. An increase of 1 standard deviation in the CHD genetic risk score was associated with 71% higher CHD risk, but 1% higher depression risk (95% CI 0–3%; p = 0.11). Mendelian randomization analyses suggested that triglycerides, interleukin-6 (IL-6), and C-reactive protein (CRP) are likely causal risk factors for depression. The odds ratio for depression per standard deviation increase in genetically-predicted triglycerides was 1.18 (95% CI 1.09–1.27; p = 2 × 10−5); per unit increase in genetically-predicted log-transformed IL-6 was 1.35 (95% CI 1.12–1.62; p = 0.0012); and per unit increase in genetically-predicted log-transformed CRP was 1.18 (95% CI 1.07–1.29; p = 0.0009). Our analyses suggest that comorbidity between depression and CHD arises largely from shared environmental factors. IL-6, CRP and triglycerides are likely to be causally linked with depression, so could be targets for treatment and prevention of depression.

Systemic lupus erythematosus (SLE or lupus for short) is an autoimmune disease in which the immune system attacks healthy tissue in organs across the body. The cause is unknown, but people with the illness make antibodies that stick to proteins that are normally found inside the cell nucleus, where DNA is stored. To make these antibodies, the immune system must first ‘see’ these proteins and mistakenly recognise them as a threat. But how does the immune system recognise proteins that are normally hidden inside cells? During infection, a type of immune cell called a neutrophil releases DNA from its nucleus to form structures called neutrophil extracellular traps, or NETs for short. The role of these NETs is to capture and kill pathogens, but they also expose the neutrophil’s DNA and the proteins attached to it to other immune cells. It is therefore possible that other immune cells interacting with NETs during infection may contribute to the development of lupus. Two proteins of interest are AIM2 and IFI16. These proteins form large, shield-like structures around strands of DNA, and previous work has shown that some people with lupus make antibodies against IFI16. Antiochos et al. wondered whether IFI16 and AIM2 might stick to NETs, exposing themselves to the immune system. Examining the blood of people with lupus revealed that one in three of them made antibodies that could stick to AIM2. Those people were also more likely to have antibodies that could stick to IFI16 and to strands of DNA. Using microscopy, Antiochos et al. also found AIM2 and IFI16 on NETs in the kidneys of some people with lupus. Further investigation showed that the presence of AIM2 and IFI16 prevents NETs from breaking down. If proteins like AIM2 and IFI16 can stop NETs from breaking down, they could allow the immune system more time to develop antibodies against them. Further investigation could reveal whether this is one of the causes of lupus. A clearer understanding of the antibodies could also boost research into diagnosis and treatment.