Company: APM
Filing Date: 2025-12-05
Form Type: 424B5
Source: 0001213900-25-118752
Chunk: 284

Company: Aptorum Group Ltd
Filing Date: 2025-12-05
Form: 424B5
Chunk 284
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. screening and/or monitoring in clinical trials). These customized projects
offer the potential, when appropriate, for new joint IP to be generated and for companion diagnostic test development. Additionally, they
will provide revenue to support the expansion in operational capacity of DiamiR.

Optimize blood collection methodology.Currently, DiamiR uses plasma, separated from whole blood at the clinical site of collection,
as DiamiR’s primary source of liquid biopsy material to extract miRNA from subjects and for clinical testing. While suitable for
clinical trial support and biopharma services, this methodology can be further improved for physician offices setting to improve customer
adoption and DiamiR is currently exploring options that would allow shipment of whole blood from collection sites to its lab, where DiamiR
laboratory staff would spin down the plasma for use in CogniMIRpanel. DiamiR is testing a number of collection tubes,
preservatives and extraction methods to identify the optimal collection and shipping conditions for the clinical commercial version of
its test.

Classifier and algorithm development.DiamiR is in the process of developing a second generation software to support its LDTs in development.
DiamiR is expanding the capabilities of its current proprietary analytical software by adding Machine Learning and AI capabilities to
it and training its classifier to optimize its performance.

DiamiR began this initiative
in July 2023 through a service agreement with JADBio (jadbio.com), pursuant to which DiamiR purchased a nonexclusive, non-assignable,
non-sublicensable, license to use its Automated Machine Learning (AutoML) Platform and Services (the “JB License”) for development
of predictive models based on DiamiR microRNA panels and other factors for risk assessment in Alzheimer’s disease and Rett syndrome.
Their AutoML tools allows for supervised learning, developing methods falling in the class of feature selection (a.k.a., variable selection
or attribute selection), which in turn can be coupled with predictive modeling algorithms to identify (bio) signatures, defined as minimal-size
subsets of molecular and other biological measurements that collectively lead to optimal predictions. Initially DiamiR’s focus will
be on training and optimizing a miRNA panel and demographic risk factors, such as sex and age. Next, DiamiR will introduce protein biomarkers
to the classifier, re-training it to identify additional risk of progression information. There are no guarantees that DiamiR’s
AI/ML work will result in a working algorithm(s) or that the algorithms Diami