Title	HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring into the Home
ID	30791344
External ID	110000930
Author
Published Date	March 10, 2026, 6 a.m.
Summary	This is a paid press release. Contact the press release distributor directly with any inquiries. HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring
Tags
URL	https://finance.yahoo.com/news/heartbeam-mount-sinai-announce-strategic-110000930.html
Sentiment	Negative: 0.00765 Neutral: 0.20947 Positive: 0.78287
Relevance	0.00000

Content ID	37926999
Key	14d0086d-a26a-480c-8323-21604f347e7f
Content	This is a paid press release. Contact the press release distributor directly with any inquiries. HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring into the Home Business Wire Tue, March 10, 2026 at 6:00 AM CDT 8 min read BEAT Accelerates development of personalized cardiac AI on the HeartBeam platform for wellness and clinical applications, including assessing heart attack risk Combines Mount Sinai’s world-class AI and clinical expertise with HeartBeam’s groundbreaking 3D ECG signal collection technology HeartBeam is the only platform capable of collecting 12-lead ECG data from patients anytime, anywhere, over time – extending access beyond traditional clinical settings Positions HeartBeam to expand from symptom-based cardiac rhythm monitoring into AI-enabled disease assessment and management SANTA CLARA, Calif., March 10, 2026 --( BUSINESS WIRE )-- HeartBeam, Inc. (NASDAQ: BEAT), a medical technology company focused on transforming cardiac care by providing powerful cardiac insights, today announced a strategic collaboration with the Icahn School of Medicine at Mount Sinai in New York to accelerate the joint development and validation of next-generation AI-ECG algorithms. The collaboration will leverage HeartBeam’s patented ECG platform, which captures the heart’s electrical activity from 3 non-coplanar dimensions. The partnership marks a significant milestone in the Company’s long-term strategy to build an ecosystem around its platform and strengthen its leadership in AI-enabled cardiac monitoring. A core value driver of the collaboration is HeartBeam’s differentiated ability to generate longitudinal, high-fidelity synthesized 12-lead ECG datasets from patients in the home setting—data that has historically been inaccessible to AI development. This creates a foundation for developing increasingly personalized algorithms earlier in the care journey and enabling 12-lead ECG assessments in real-world settings, supporting both wellness use cases and clinically relevant assessments, such as heart attack risk. The combination of HeartBeam’s continuously expanding dataset and Mount Sinai’s clinically annotated 12-lead ECG data can accelerate the training and validation of various AI models. Over time, this collaboration will result in a data engine that is expected to support the development of increasingly personalized algorithms—positioning HeartBeam to expand into new clinical indications and reimbursement pathways. "We believe expanding access to 12-lead ECG data assessment beyond the clinic is one of the biggest opportunities," said Robert Eno, Chief Executive Officer of HeartBeam. "By pairing our ability to gather high-fidelity real-world ECG data with Mount Sinai’s extensive clinical data resources and AI expertise, we are creating a differentiated cardiac intelligence engine that can scale beyond traditional care settings and broaden the reach of predictive cardiology, ultimately expanding our clinical and commercial opportunity." Story Continues Under the collaboration, HeartBeam’s in-house AI team led by Lance Myers, PhD, a leading authority on AI applications in biosensor technologies, will work closely with Mount Sinai researchers to develop, train and validate a suite of advanced AI-ECG algorithms intended for deployment onto the HeartBeam platform. Joshua Lampert, MD, FACC, a pioneer in AI-ECG and cardiovascular deep learning research and clinician recognized for his excellence in patient care, Vivek Reddy, MD, an internationally renowned electrophysiologist and innovator, and Girish N. Nadkarni, MD, MPH, the Chief AI Officer of the Mount Sinai Health System and Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai, will help guide the scientific and clinical development of the partnership. "While AI-ECG has rapidly progressed as a field over recent years, there is room for improvement in the portability and scalability of such algorithms beyond acquisition devices that require complex multi-electrode systems. Additionally, current approaches struggle to leverage deep learning inference opportunities outside of traditional health care settings, which is where dynamic changes to cardiovascular health first start before patients present for care. The collaboration addresses these vital needs. By combining deep learning tools with the ability to record full 3-dimensional cardiac electrical activity without cables, we can provide clinically meaningful and operationally pragmatic models at scale regardless of environment," said Dr. Lampert, Cardiac Electrophysiologist, Medical Director of Machine Learning for Mount Sinai Fuster Heart Hospital and Director of Cardiovascular Artificial Intelligence for the Windreich Department of Artificial Intelligence and Human Health at Mount Sinai. Pairing HeartBeam’s innovative hardware and approach to cardiac waveform engineering with Mount Sinai’s advanced AI and clinical expertise creates a powerful foundation for developing tools that dynamically meet modern patient and clinician needs wherever they are. "Heart disease doesn’t only show up during a brief visit to the clinic. This collaboration gives us an opportunity to bring powerful clinical-grade heart monitoring into patients’ daily lives," said Dr. Nadkarni. "By combining advanced AI with HeartBeam’s ability to capture full 12-lead ECG signals from home over time, we can study the heart in ways that simply haven’t been possible before—helping clinicians detect risk earlier and guide care more precisely." "This collaboration addresses an important need by leveraging deep learning and 3-dimensional waveform data for scalable diagnostic and predictive purposes, allowing insights beyond even expert human ability," added Dr. Reddy, who serves as the Director of Cardiac Arrhythmia Services for Mount Sinai Health System. Together, the two organizations aim to accelerate the development of high-value algorithms that can be deployed broadly across HeartBeam’s platform. These AI models may include patient-relevant wellness insights, condition-focused assessments, and applications for chronic condition management. By enabling AI models to operate on longitudinal, real-world synthesized 12-lead ECG data rather than isolated clinical snapshots, the collaboration has the potential to significantly expand the addressable market for AI-driven cardiac monitoring. The collaboration could unlock new opportunities in preventive cardiology, chronic disease management, and remote patient monitoring—further reinforcing HeartBeam’s position as a leader in cardiac intelligence platforms. About the Mount Sinai Health System Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with 48,000 employees working across seven hospitals, more than 400 outpatient practices, more than 600 research and clinical labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time—discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients’ medical and emotional needs at the center of all treatment. The Health System includes approximately 9,000 primary and specialty care physicians and 10 free-standing joint-venture centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida. Hospitals within the System are consistently ranked by Newsweek’s ® "The World’s Best Smart Hospitals, Best in State Hospitals, World Best Hospitals and Best Specialty Hospitals" and by U.S. News & World Report's ® "Best Hospitals" and "Best Children’s Hospitals." The Mount Sinai Hospital is on the U.S. News & World Report ® "Best Hospitals" Honor Roll for 2025-2026. For more information, visit https://www.mountsinai.org/ or find Mount Sinai on Facebook , Instagram , LinkedIn , X , and YouTube . About HeartBeam, Inc. HeartBeam, Inc. (NASDAQ: BEAT) is a medical technology company dedicated to transforming the detection and monitoring of critical cardiac conditions. The Company is creating the first-ever cable-free device capable of collecting ECG signals in 3D, from three non-coplanar directions, and synthesizing the signals into a 12-lead ECG. This platform technology is designed for portable devices that can be used wherever the patient is to deliver actionable heart intelligence. Physicians will be able to identify cardiac health trends and acute conditions and direct patients to the appropriate care – all outside of a medical facility, thus redefining the future of cardiac health management. HeartBeam’s 3D ECG technology received FDA clearance for arrhythmia assessment in December 2024 and the 12-Lead ECG synthesis software in December 2025 1 . The Company holds over 20 issued patents related to technology enablement. For additional information, visit HeartBeam.com . Forward-Looking Statements All statements in this release that are not based on historical fact are "forward-looking statements." While management has based any forward-looking statements included in this release on its current expectations, the information on which such expectations were based may change. Forward-looking statements involve inherent risks and uncertainties which could cause actual results to differ materially from those in the forward-looking statements, as a result of various factors including those risks and uncertainties described in the Risk Factors and in Management’s Discussion and Analysis of Financial Condition and Results of Operations sections of our Forms 10-K, 10-Q and other reports filed with the SEC and available at www.sec.gov . We urge you to consider those risks and uncertainties in evaluating our forward-looking statements. We caution readers not to place undue reliance upon any such forward-looking statements, which speak only as of the date made. Except as otherwise required by the federal securities laws, we disclaim any obligation or undertaking to publicly release any updates or revisions to any forward-looking statement contained herein (or elsewhere) to reflect any change in our expectations with regard thereto or any change in events, conditions or circumstances on which any such statement is based. 1 Cleared Indications for Use The HeartBeam System with 12-Lead ECG synthesis software for arrhythmia assessment received FDA clearance in December 2025. Refer to the Company’s Cleared Indications for Use at https://www.heartbeam.com/indications for details on the intended use of its technology. View source version on businesswire.com: https://www.businesswire.com/news/home/20260310761343/en/ Contacts Media Contact: media@heartbeam.com Investor Relations Contact: Chris Tyson Executive Vice President MZ North America Direct: 949-491-8235 BEAT@mzgroup.us www.mzgroup.us View Comments Terms and Privacy Policy Your Privacy Choices More Info
Content Reference URL	None

Publisher Name	Financial Planning (#6135)
Publisher URL	https://finance.yahoo.com
Description	None
Trust Flow Score	83
Citation Flow Score	57

Data Source Name	Yahoo Finance News (#2)
Data Source Type	news
Data Source URL	https://finance.yahoo.com/
Description	Yahoo! Finance is a media property that is part of the Yahoo! network. It provides financial news, data and commentary including stock quotes, press releases, financial reports, and original content. It also offers some online tools for personal finance management.
Weight	10

DJDT

Versions

Package	Name	Version
Django		5.2.1
Python		3.11.8
coverage	Coverage	7.4.4
debug_toolbar	Debug Toolbar	4.3.0
django_extensions	Django Extensions	5.2.1
grappelli	Grappelli	4.0.2

Time

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Resource	Value
User CPU time	53.170 msec
System CPU time	3.777 msec
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Elapsed time	87.141 msec
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Browser timing

Timing attribute	Timeline	Milliseconds since navigation start (+length)

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Key	Value
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Host	pennyloupe.com
User-Agent	Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)

Response headers

Key	Value
Content-Type	text/html; charset=utf-8

WSGI environ

Since the WSGI environ inherits the environment of the server, only a significant subset is shown below.

Key	Value
CONTENT_LENGTH
CONTENT_TYPE
PATH_INFO	/ticker/BEAT/content/30791344
QUERY_STRING
REMOTE_ADDR	216.73.216.103
REQUEST_METHOD	GET
SCRIPT_NAME
SERVER_NAME	pennyloupe.com
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SERVER_PROTOCOL	HTTP/1.1

Request

View information

View function	Arguments	Keyword arguments	URL name
`app.core.views.app.ticker_content_detail`	`()`	`{'content_id': 30791344, 'ticker': 'BEAT'}`	`ticker_content_detail`

No cookies

No session data

No GET data

No POST data

SQL queries from 1 connection

default 13.71 ms (5 queries )

Query Timeline Time (ms) Action

SELECT "pl_content"."created_dt",
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4.69

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Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
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/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
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/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2353)
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SELECT "pl_content_content"."created_dt",
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5.27

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
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/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2359)
  content_content = ContentContent.objects.filter(details=content).first()

SELECT "pl_content_publisher"."created_dt",
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WHERE "pl_content_publisher"."id" = 6135
LIMIT 21SELECT ••• FROM "pl_content_publisher" WHERE "pl_content_publisher"."id" = 6135 LIMIT 21

1.38

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2362)
  publisher = content.publisher

SELECT "pl_data_source"."created_dt",
       "pl_data_source"."updated_dt",
       "pl_data_source"."status_id",
       "pl_data_source"."id",
       "pl_data_source"."name",
       "pl_data_source"."type",
       "pl_data_source"."url",
       "pl_data_source"."description",
       "pl_data_source"."weight",
       "pl_data_source"."class_name",
       "pl_data_source"."class_params_json"
  FROM "pl_data_source"
WHERE "pl_data_source"."id" = 2
LIMIT 21SELECT ••• FROM "pl_data_source" WHERE "pl_data_source"."id" = 2 LIMIT 21

1.09

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2363)
  data_source = content.data_source

SELECT COUNT(*) AS "__count"
FROM "pl_content"
WHERE "pl_content"."parent_content_id" = 30791344SELECT COUNT(*) AS "__count" FROM "pl_content" WHERE "pl_content"."parent_content_id" = 30791344

1.28

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2375)
  return render(request, "ticker_content_detail.html", context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/shortcuts.py in render(25)
  content = loader.render_to_string(template_name, context, request, using=using)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/loader.py in render_to_string(62)
  return template.render(context, request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/backends/django.py in render(107)
  return self.template.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(171)
  return self._render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/test/utils.py in instrumented_test_render(114)
  return self.nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/loader_tags.py in render(159)
  return compiled_parent._render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/test/utils.py in instrumented_test_render(114)
  return self.nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/loader_tags.py in render(65)
  result = block.nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/defaulttags.py in render(320)
  match = condition.eval(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/smartif.py in eval(61)
  return func(context, self.first, self.second)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/smartif.py in <lambda>(107)
  ">": infix(10, lambda context, x, y: x.eval(context) > y.eval(context)),

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/defaulttags.py in eval(886)
  return self.value.resolve(context, ignore_failures=True)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in resolve(722)
  obj = self.var.resolve(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in resolve(854)
  value = self._resolve_lookup(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in _resolve_lookup(925)
  current = current()

27	`<td><a href="{% url 'ticker_content_detail' ticker=ticker content_id=content_detail.content.parent.id %}">{{ content_detail.content.parent.title }}</a></td>`
28	`</tr>`
29	`{% endif %}`
30	`{% if content_detail.content.children.count > 0 %}`
31	`<tr>`
32	`<th>Children</th>`
33	`<td>`
34	`<ul>`

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/templates/ticker_content_detail.html

Static files (720 found, 2 used)

Static file paths

None

Static file apps

grappelli
django.contrib.admin
django_htmx
app.core
django_extensions
debug_toolbar

Static files

css/pennyloupe.css: /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/css/pennyloupe.css
img/penny_loupe_logo.png: /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/penny_loupe_logo.png

django.contrib.staticfiles.finders.AppDirectoriesFinder (720 files)

Templates (6 rendered)

Template path

templates

Templates

ticker_content_detail.html: /mnt/disks/pennyloupe_disk/pennyloupe/app/core/templates/ticker_content_detail.html; Toggle context
{'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7f68d6c0ea20>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/BEAT/content/30791344'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7f68d6a3cf40>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7f68d6a3cf40>>'} {'content_detail': {'content': <Content: HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring into the Home - >, 'content_content': <ContentContent: Content ID: 37926999>, 'data_source': <DataSource: Yahoo Finance News>, 'publisher': <ContentPublisher: Financial Planning>}, 'ticker': 'BEAT'}
base.html: /mnt/disks/pennyloupe_disk/pennyloupe/templates/base.html; Toggle context
{'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7f68d6c0ea20>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/BEAT/content/30791344'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7f68d6a3cf40>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7f68d6a3cf40>>'} {'content_detail': {'content': <Content: HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring into the Home - >, 'content_content': <ContentContent: Content ID: 37926999>, 'data_source': <DataSource: Yahoo Finance News>, 'publisher': <ContentPublisher: Financial Planning>}, 'ticker': 'BEAT'}
components/leftnav.html: /mnt/disks/pennyloupe_disk/pennyloupe/templates/components/leftnav.html; Toggle context
{'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7f68d6c0ea20>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/BEAT/content/30791344'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7f68d6a3cf40>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7f68d6a3cf40>>'} {'content_detail': {'content': <Content: HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring into the Home - >, 'content_content': <ContentContent: Content ID: 37926999>, 'data_source': <DataSource: Yahoo Finance News>, 'publisher': <ContentPublisher: Financial Planning>}, 'ticker': 'BEAT'} {'block': <Block Node: leftnav. Contents: [<TextNode: '\n <div'>, <IncludeNode: template=<FilterExpression '"components/leftnav.html"'>>, <TextNode: '\n </di'>]>}
components/header.html: /mnt/disks/pennyloupe_disk/pennyloupe/templates/components/header.html; Toggle context
{'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7f68d6c0ea20>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/BEAT/content/30791344'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7f68d6a3cf40>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7f68d6a3cf40>>'} {'content_detail': {'content': <Content: HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring into the Home - >, 'content_content': <ContentContent: Content ID: 37926999>, 'data_source': <DataSource: Yahoo Finance News>, 'publisher': <ContentPublisher: Financial Planning>}, 'ticker': 'BEAT'} {'block': <Block Node: header. Contents: [<TextNode: '\n\n '>, <IncludeNode: template=<FilterExpression '"components/header.html"'>>, <TextNode: '\n\n '>]>}
components/ticker_subnav.html: /mnt/disks/pennyloupe_disk/pennyloupe/app/core/templates/components/ticker_subnav.html; Toggle context
{'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7f68d6c0ea20>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/BEAT/content/30791344'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7f68d6a3cf40>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7f68d6a3cf40>>'} {'content_detail': {'content': <Content: HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring into the Home - >, 'content_content': <ContentContent: Content ID: 37926999>, 'data_source': <DataSource: Yahoo Finance News>, 'publisher': <ContentPublisher: Financial Planning>}, 'ticker': 'BEAT'} {'block': <Block Node: content. Contents: [<TextNode: '\n\n <div class="uk-flex'>, <IncludeNode: template=<FilterExpression '"components/ticker_subnav.html"'>>, <TextNode: '\n <!-- /SUB NAV--'>, <Variable Node: content_detail.content.title>, <TextNode: '</td>\n '>, <IfNode>, <TextNode: '\n '>, <IfNode>, <TextNode: '\n '>, <Variable Node: content_detail.content.id>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.external_id>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.author>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.publish_dt>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.summary>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.tags>, <TextNode: '</td>\n '>, <IfNode>, <TextNode: '\n '>, <Variable Node: content_detail.content.sentiment_negative>, <TextNode: '   \n '>, <Variable Node: content_detail.content.sentiment_neutral>, <TextNode: '   \n '>, <Variable Node: content_detail.content.sentiment_positive>, <TextNode: '<br/>\n '>, <django.template.defaulttags.WidthRatioNode object at 0x7f68d71fa2d0>, <TextNode: '%;"></div>\n '>, <Variable Node: content_detail.content.sentiment_neutral>, <TextNode: '" style="width:'>, <django.template.defaulttags.WidthRatioNode object at 0x7f68d7338dd0>, <TextNode: '%;"></div>\n '>, <Variable Node: content_detail.content.sentiment_positive>, <TextNode: '" style="width:'>, <django.template.defaulttags.WidthRatioNode object at 0x7f68d7376610>, <TextNode: '%;"></div>\n '>, <Variable Node: content_detail.content.relevance_score>, <TextNode: '</td>\n '>, <IfNode>, <TextNode: '\n '>, <IfNode>, <TextNode: '\n <li clas'>, <Variable Node: content_detail.data_source.name>, <TextNode: '\n '>, <Variable Node: content_detail.data_source.id>, <TextNode: ')\n '>, <Variable Node: content_detail.data_source.type>, <TextNode: '</td>\n '>, <Variable Node: content_detail.data_source.url>, <TextNode: '" target="_blank">'>, <Variable Node: content_detail.data_source.url>, <TextNode: '</a></td>\n '>, <Variable Node: content_detail.data_source.description>, <TextNode: '</td>\n '>, <Variable Node: content_detail.data_source.weight>, <TextNode: '</td>\n '>]>} {'ticker': 'BEAT'}
components/footer.html: /mnt/disks/pennyloupe_disk/pennyloupe/templates/components/footer.html; Toggle context
{'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7f68d6c0ea20>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/BEAT/content/30791344'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7f68d6a3cf40>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7f68d6a3cf40>>'} {'content_detail': {'content': <Content: HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring into the Home - >, 'content_content': <ContentContent: Content ID: 37926999>, 'data_source': <DataSource: Yahoo Finance News>, 'publisher': <ContentPublisher: Financial Planning>}, 'ticker': 'BEAT'} {'block': <Block Node: footer. Contents: [<TextNode: '\n '>, <IncludeNode: template=<FilterExpression '"components/footer.html"'>>, <TextNode: '\n '>]>}

Context processors

django.template.context_processors.csrf: Toggle context
{'csrf_token': <SimpleLazyObject: <function csrf.<locals>._get_val at 0x7f68d6c0ea20>>}
django.template.context_processors.debug: Toggle context
{}
django.template.context_processors.request: Toggle context
{'request': <WSGIRequest: GET '/ticker/BEAT/content/30791344'>}
django.contrib.auth.context_processors.auth: Toggle context
{'user': <SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7f68d6a3cf40>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7f68d6a3cf40>>)}
django.contrib.messages.context_processors.messages: Toggle context
{'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/BEAT/content/30791344'>>, 'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30, 'ERROR': 40}}

Cache calls from 1 backend

Summary

Total calls	Total time	Cache hits	Cache misses
0	0 ms	0	0

Commands

add	get	set	get_or_set	touch	delete	clear	get_many	set_many	delete_many	has_key	incr	decr	incr_version	decr_version
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

Signals

Signal	Receivers
class_prepared
connection_created	register_type_handlers
got_request_exception
m2m_changed
post_delete
post_init
post_migrate	create_permissions, create_contenttypes
post_save	create_initial_trade_position
pre_delete
pre_init
pre_migrate	inject_rename_contenttypes_operations
pre_save
request_finished	close_old_connections, close_caches, reset_urlconf
request_started	reset_queries, close_old_connections
setting_changed	reset_cache, clear_cache_handlers, update_installed_apps, update_connections_time_zone, clear_routers_cache, reset_template_engines, storages_changed, clear_serializers_cache, language_changed, localize_settings_changed, complex_setting_changed, root_urlconf_changed, static_storage_changed, static_finders_changed, form_renderer_changed, auth_password_validators_changed, user_model_swapped, update_toolbar_config, reset_hashers, Options.setting_changed, update_level_tags, uninstall_if_needed, clear_caches, StaticFilesStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties

Path	Location
.DS_Store	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/.DS_Store
grappelli/.DS_Store	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/.DS_Store
grappelli/js/grappelli.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/grappelli.js
grappelli/js/jquery.grp_related_m2m.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_related_m2m.js
grappelli/js/jquery.grp_autocomplete_fk.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_autocomplete_fk.js
grappelli/js/jquery.grp_autocomplete_generic.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_autocomplete_generic.js
grappelli/js/jquery.grp_timepicker.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_timepicker.js
grappelli/js/jquery.grp_collapsible_group.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_collapsible_group.js
grappelli/js/jquery.grp_related_generic.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_related_generic.js
grappelli/js/jquery.grp_related_fk.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_related_fk.js
grappelli/js/jquery.grp_collapsible.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_collapsible.js
grappelli/js/grappelli.min.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/grappelli.min.js
grappelli/js/jquery.grp_autocomplete_m2m.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_autocomplete_m2m.js
grappelli/js/jquery.grp_inline.js	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_inline.js
grappelli/images/spritesheet-1694777276.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/spritesheet-1694777276.png
grappelli/images/backgrounds/loading-small.gif	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/loading-small.gif
grappelli/images/backgrounds/changelist-results.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/changelist-results.png
grappelli/images/backgrounds/ui-sortable-placeholder.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/ui-sortable-placeholder.png
grappelli/images/backgrounds/nav-grabber.gif	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/nav-grabber.gif
grappelli/images/backgrounds/messagelist.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/messagelist.png
grappelli/images/icons/ui-datepicker-next.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/ui-datepicker-next.png
grappelli/images/icons/link-external-small_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/link-external-small_hover.png
grappelli/images/icons/tools-arrow-up-handler_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-arrow-up-handler_hover.png
grappelli/images/icons/date-hierarchy-back.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/date-hierarchy-back.png
grappelli/images/icons/tools-delete-handler-predelete.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-delete-handler-predelete.png
grappelli/images/icons/breadcrumbs-rtl.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/breadcrumbs-rtl.png
grappelli/images/icons/datepicker_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/datepicker_hover.png
grappelli/images/icons/tools-trash-handler_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-trash-handler_hover.png
grappelli/images/icons/selector-add-m2m-vertical_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/selector-add-m2m-vertical_hover.png
grappelli/images/icons/tools-related-remove-handler-small_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-related-remove-handler-small_hover.png
grappelli/images/icons/sorted-descending.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/sorted-descending.png
grappelli/images/icons/breadcrumbs_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/breadcrumbs_hover.png
grappelli/images/icons/tools-viewsite-link_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-viewsite-link_hover.png
grappelli/images/icons/object-tools-add-link.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/object-tools-add-link.png
grappelli/images/icons/tools-related-edit-handler-small-disabled.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-related-edit-handler-small-disabled.png
grappelli/images/icons/tools-related-remove-handler-small-disabled.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-related-remove-handler-small-disabled.png
grappelli/images/icons/sorted-ascending.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/sorted-ascending.png
grappelli/images/icons/link-internal-rtl-small_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/link-internal-rtl-small_hover.png
grappelli/images/icons/pulldown-handler_selected.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/pulldown-handler_selected.png
grappelli/images/icons/tools-delete-handler_hover.png	/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-delete-handler_hover.png

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