Tryp Fpv Mac M1
Nickie Koskinen
Pursuant to the terms and conditions of a definitive arrangement agreement entered into by Tryp and Exopharm on December 8, 2023, as amended, all holders of outstanding common shares ("Tryp Shares") of Tryp are entitled to receive 3.616 ordinary shares in the capital of Exopharm (the "Exopharm Shares") for each Tryp Share held immediately prior to the effective time of the Arrangement.
As previously announced, the Arrangement was approved by shareholders of the Company at its annual general and special meeting of shareholders held on March 8, 2024 and by the Supreme Court of British Columbia on March 11, 2024.
All registered shareholders of the Company must complete, sign, date and return the letter of transmittal, which has been previously mailed and is available under the Company's SEDAR+ profile at www.sedarplus.ca, with accompanying Tryp Share certificate(s) or DRS advice-statement(s) (if applicable) to Computershare Investor Services Inc. as soon as possible, if they have not already done so. Non-registered shareholders of the Company should contact their broker or other intermediary for instructions and assistance in receiving the consideration in respect of their Tryp Shares.
With the completion of the Arrangement, the Tryp Shares are expected to be de-listed from the Canadian Securities Exchange and to cease trading on the OTCQB Venture Market on the close of markets on or around May 1, 2024. The Company also anticipates applying to cease to be a reporting issuer under applicable Canadian securities laws.
Tryp Therapeutics is a clinical-stage biotechnology company focused on developing proprietary, novel formulations for the administration of psilocin in combination with psychotherapy to treat diseases with unmet medical needs. Tryp's lead program, TRP-8803, is a proprietary formulation of IV-infused psilocin (the active metabolite of psilocybin) that alleviates numerous shortcomings of oral psilocybin including: significantly reducing the time to onset of the psychedelic state, controlling the depth and duration of the psychedelic experience, and reducing the overall duration of the intervention to a commercially feasible timeframe. The Company has completed a Phase 2a clinical trial for the treatment of binge eating disorder at the University of Florida, which demonstrated an average reduction in binge eating episodes of greater than 80%. The Company also recently announced commencement of patient dosing in a Phase 2a clinical trial for the treatment of fibromyalgia in collaboration with the University of Michigan and is preparing to initiate a Phase 2a clinical trial in collaboration with Massachusetts General Hospital for the treatment of abdominal pain and visceral tenderness in patients suffering from irritable bowel syndrome. Each of the studies is utilizing TRP-8802 (synthetic, oral psilocybin) to demonstrate clinical benefit in these indications. Where a positive clinical response is demonstrated, subsequent studies are expected to utilize TRP-8803 (IV-infused psilocin), which has the potential to further improve efficacy, safety, and patient experience. For more information, please visit www.tryptherapeutics.com.
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Trypanosomiasis, a neglected tropical disease (NTD), challenges communities in sub-Saharan Africa and Latin America. The World Health Organization underscores the need for practical, field-adaptable diagnostics and rapid screening tools to address the negative impact of NTDs. While artificial intelligence has shown promising results in disease screening, the lack of curated datasets impedes progress. In response to this challenge, we developed the Tryp dataset, comprising microscopy images of unstained thick blood smears containing the Trypanosoma brucei brucei parasite. The Tryp dataset provides bounding box annotations for tightly enclosed regions containing the parasite for 3,085 positive images, and 93 images collected from negative blood samples. The Tryp dataset represents the largest of its kind. Furthermore, we provide a benchmark on three leading deep learning-based object detection techniques that demonstrate the feasibility of AI for this task. Overall, the availability of the Tryp dataset is expected to facilitate research advancements in diagnostic screening for this disease, which may lead to improved healthcare outcomes for the communities impacted.
Trypanosomiasis is a debilitating disease caused by pathogenic species of the trypanosome parasite. The World Health Organization (WHO) has categorized two forms of this condition, namely Chagas disease and human African trypanosomiasis (HAT), as neglected tropical diseases (NTDs)1,2. Chagas disease, also known as American trypanosomiasis, is caused by the parasite Trypanosoma cruzi and is primarily transmitted by infected triatomine bugs. This disease is mainly found in Latin America, affecting approximately six million individuals worldwide3. HAT, commonly referred to as sleeping sickness, is caused by two species of the Trypanosoma brucei parasite, namely T. b. gambiense and T. b. rhodesiense. Tsetse flies in sub-Saharan African nations are the primary vector for HAT transmission. If left untreated, HAT is usually chronic and fatal, with infected individuals frequently succumbing within six months4.
Despite its prevalence in the screening and diagnosis of trypanosomiasis, manual microscopy presents notable limitations, including its labor-intensive nature, low sensitivity, and the requirement for skilled personnel5,6,7,8. Firstly, the labor-intensive nature of manual microscopy necessitates a significant commitment of time and resources, potentially causing delays in diagnosis and treatment in settings with high disease prevalence. Secondly, the inherent subjectivity of the approach can lead to inconsistencies in result interpretation, thereby compromising the sensitivity and overall diagnostic accuracy of the technique. Lastly, the necessity for skilled personnel, particularly problematic in resource-constrained environments where the disease is endemic, can significantly impede effective disease screening and diagnostic practices due to limited access to trained professionals5. We posit that integrating Artificial Intelligence (AI) could substantially alleviate the aforementioned challenges inherent to manual microscopy in trypanosomiasis diagnosis. The potential application of AI to screen or diagnose diseases is promising and is receiving increasing research attention9,10,11,12,13,14,15. Researchers have also employed AI to detect or screen NTDs such as trachomatous trichiasis16, leprosy17, helminths and schistosoma18, and trypanosomiasis19,20. While the current body of research on using AI for automated screening of trypanosomiasis from microscopy images of fresh unstained thick blood smears is relatively sparse, the choice to utilize unstained fresh blood samples was a deliberate one, informed by the urgent needs of prominent research laboratories in the field of trypanosomiasis research. This approach, which emphasizes efficiency and innovation, aims to obviate the need for staining techniques, potentially transforming the method by which parasites are identified in practice.
To address the limitations of manual microscopy, we have created a curated dataset for detecting trypanosome parasites in microscopy images of unstained thick blood smears. Our dataset enables the training of deep learning models to detect the trypanosome parasite in these images. We further provide a benchmark on three leading deep learning-based object detection techniques that demonstrate the feasibility of AI for this task. This way, we want to stimulate AI research on trypanosome parasite detection to help facilitate the achievement of the WHO targets.
The Tryp dataset has been curated to facilitate research on developing and assessing object detection models specifically tailored for trypanosomiasis screening. As visually summarized in Fig. 1, this section details the comprehensive procedures and methodologies employed in generating and characterizing this dataset.
Data quality is crucial in developing deep neural network (DNN) models for real-world applications, especially in critical areas such as health care. To ensure that the data acquisition process closely reflects real-world scenarios, we implemented specific measures, such as using thick blood smears that allow parasites to move in and out of visibility within the same microscope field of view (FOV). Additionally, we encouraged the expert and student researchers to (1) freely use the microscope settings that help them to confirm the presence or absence of parasites within the microscope FOV without any restrictions and (2) cover multiple FOVs in a single thick blood smear whenever possible. We provide a small sample of the extracted frames in Fig. 3 to illustrate the diversity in the capturing process.
The models evaluated in this study take images as input, requiring the conversion of captured videos in formats with extensions such as .mov, .avi, and .mpeg4 into a series of JPEG image frames, resulting in 40,931 images. However, the annotation of such a large number of images is cost-prohibitive, and the video capture process introduces limitations, including temporal redundancy and motion blur, which can diminish the effectiveness of certain frames for training DNN models. Temporal redundancy may arise in microscopy video capture of trypanosome parasites due to the fixed position of the microscope eyepiece and the smear slide, resulting in consecutive frames with minimal changes, despite the high motility of the parasites.
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