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Boston Medical Center Study Furthers Understanding of Lung Regeneration

Researchers at Boston Medical Center (BMC) and Boston University (BU) today announced findings from a new research study, published in Cell Stem Cell, detailing the development of a method for generating human alveolar epithelial type I cells (AT1s) from pluripotent stem cells (iPSCs). The ability to recreate these cells in an iPSC-based model will allow researchers to analyze the historically difficult to isolate cells in greater detail, helping to further the understanding of human lung regeneration and may ultimately expedite progress in treatment and therapeutic options for people living with pulmonary diseases. The results of this study provide an in vitro model of human AT1 cells, which line the vast majority of the gas exchange barrier of the distal lung, and are a potential source of human AT1s to develop regenerative therapies. This new study also furthers the CReM’s goal… 

Boston Medical Center Study Furthers Understanding of Lung Regeneration

Researchers at Boston Medical Center (BMC) and Boston University (BU) today announced findings from a new research study, published in Cell Stem Cell, detailing the development of a method for generating human alveolar epithelial type I cells (AT1s) from pluripotent stem cells (iPSCs). The ability to recreate these cells in an iPSC-based model will allow researchers to analyze the historically difficult to isolate cells in greater detail, helping to further the understanding of human lung regeneration and may ultimately expedite progress in treatment and therapeutic options for people living with pulmonary diseases. The results of this study provide an in vitro model of human AT1 cells, which line the vast majority of the gas exchange barrier of the distal lung, and are a potential source of human AT1s to develop regenerative therapies. This new study also furthers the CReM’s goal… 

Precision Medicine Collaboration Targeting  Alzheimer’s Disease

NeuroSense Therapeutics Ltd. (NASDAQ: NRSN) („NeuroSense“), a company developing novel treatments for severe neurodegenerative diseases announced today a collaboration in Alzheimer’s Disease (AD) drug development with Genetika+, a leader in precision medicine for psychiatry and neurology. The multi-phase collaboration, which will commence in NeuroSense’s currently ongoing Phase 2 AD clinical trial, leverages Genetika+’s state-of-the-art technology that derives frontal cortex neurons from individual patients‘ blood to quantify drug-induced neuronal plasticity in vitro. Distinguished by its innovative approach, NeuroSense’s PrimeC therapy stands out in the landscape of AD treatments. Unlike conventional methods that predominantly target amyloid-beta (A?), PrimeC adopts a multi-targeted strategy, concurrently addressing A? aggregation, TDP-43, and other key disease-related pathologies. This unique approach not only diversifies the therapeutic targets but also offers the potential for more potent treatment outcomes.

Precision Medicine Collaboration Targeting  Alzheimer’s Disease

NeuroSense Therapeutics Ltd. (NASDAQ: NRSN) („NeuroSense“), a company developing novel treatments for severe neurodegenerative diseases announced today a collaboration in Alzheimer’s Disease (AD) drug development with Genetika+, a leader in precision medicine for psychiatry and neurology. The multi-phase collaboration, which will commence in NeuroSense’s currently ongoing Phase 2 AD clinical trial, leverages Genetika+’s state-of-the-art technology that derives frontal cortex neurons from individual patients‘ blood to quantify drug-induced neuronal plasticity in vitro. Distinguished by its innovative approach, NeuroSense’s PrimeC therapy stands out in the landscape of AD treatments. Unlike conventional methods that predominantly target amyloid-beta (A?), PrimeC adopts a multi-targeted strategy, concurrently addressing A? aggregation, TDP-43, and other key disease-related pathologies. This unique approach not only diversifies the therapeutic targets but also offers the potential for more potent treatment outcomes.

Next-generation pancreatic cancer detection test

Immunovia (NASDAQ Stockholm: IMMNOV), the diagnostics company with the mission to increase pancreatic cancer survival through early detection, today?announces its next-generation pancreatic cancer test achieved both the primary and secondary endpoints in a model-development study.  In the study, Immunovia’s next-generation test demonstrated specificity of 98 percent and sensitivity of 75 percent in detecting early stage (1 and 2) pancreatic ductal adenocarcinoma (PDAC), a very aggressive and the most common form of pancreatic cancer. The Immunovia test was also significantly more accurate than CA19-9, the biomarker commonly used to detect pancreatic cancer.  Importantly, these results confirm the technical advancement of the next-generation test over Immunovia’s first-generation test, IMMray PanCan-d. The next-generation test includes high-performing protein biomarkers, making the test less reliant on CA19-9. This is a major achievement as around 10 percent of patients, including many patients of African ancestry, do… 

 The Forgotten Biochemistry 101 of COVID-19

Legacy coronavirus biochemistry was overlooked that governs spike protein toxicity, key morbidities, risk factors and therapeutic responses. TrialSite News features a paper published today in Viruses (Basel), authored by an international team of researchers, including two fellows of their nations‘ academies of sciences (Colleen Aldous, senior author Wendy Hoy) and others who participated in Nobel prize-honored research (Thomas Borody, Morimasa Yagisawa). The publication reveals how coronavirus biochemistry well-established over past decades governs the morbidities of COVID-19, risk factors and therapeutic approaches. The glycan monomer sialic acid, ubiquitous on eukaryotic cell surfaces, serves as the initial attachment point to host cells for the COVID–19 virus—SARS–CoV–2—as well as for other coronaviruses. The virus can then slide over to ACE2 for cell entry. SARS–CoV–2 spike protein attaches particularly tightly to the dense sialic acid coatings on the trillions of red blood cells (RBCs), platelets and endothelial cells in the human… 

Aspiration Thrombectomy System Receives FDA Clearance

Expanse ICE announced today the ICE Aspiration System has received 510(k) clearance from the U.S. Food and Drug Administration. This announcement introduces a new and exciting player in the peripheral thrombectomy market.  The ICE System is specifically designed to address the complex challenges associated with peripheral thrombectomies. Blood clots are the third most common vascular disease. Almost one million patients suffer from peripheral blood clots that must be treated each year, and up to 33% of them suffer long-term complications, according to the Center for Disease Control. Expanse ICE is a company born from Expanse Medical, a medical device incubator founded by Dr. Konstantino. The team responsible for the Expanse ICE system also built AngioSculpt, Chocolate and a variety of neurovascular products. Expanse Medical is focused on developing solutions for vascular diseases. The company is dedicated to enhancing the quality… 

Neuer Bisphenol A-Grenzwert für Trinkwasser

Eine im vorigen Jahr neu gefasste Trinkwasserverordnung legt jetzt zusätzlich einen Grenzwert für die verbreitete Substanz Bisphenol A fest. Sie wird bei der Herstellung von Kunststoffen und -harzen verwendet. TÜV SÜD informiert, welche neuen Regelungen jetzt für Bisphenol A gelten und warum auch Lebensmittelunternehmer und Food-Start-ups sich damit beschäftigen sollten. Mit der Aktualisierung der Trinkwasserverordnung gilt seit dem 12. Januar 2024 ein 20-mal geringerer Grenzwert für Bisphenol A, das als Industriechemikalie eingesetzt wird, als vorher. Bisphenol A ist ein chemischer Stoff, der als Rohstoff für Epoxidharze und Kunststoffe verarbeitet wird. Da dieser Stoff nach aktuellen Forschungsergebnissen schädliche Auswirkungen auf das Immun- und Hormonsystem haben kann, wurde seine zulässige Menge im Trinkwasser vom Gesetzgeber deutlich eingeschränkt. Bisphenol A ist in Konsumgütern wie Smartphones, Trinkflaschen, Plastikgeschirr, Farben, Beschichtungen sowie Klebstoffen von Lebensmittelverpackungen enthalten. Die Aufnahme von Bisphenol A in den menschlichen Körper… 

Cognivia: Medikamentenentwicklung mit KI-ML-Lösungen

Cognivia ist das erste und einzige Unternehmen, das die Quantifizierung der Patientenpsychologie mit künstlicher Intelligenz (AI) und maschinellem Lernen (ML) kombiniert, um die Messung der therapeutischen Wirksamkeit in klinischen Studien zu verbessern – und darüber hinaus. Cognivia-Technologien sagen das Patientenverhalten und das Ansprechen auf die Behandlung in klinischen Studien mithilfe von prädiktiven, ML-gestützten Algorithmen voraus, die auf einem quantitativen Verständnis der psychologischen Merkmale, Erwartungen und Überzeugungen der Patienten beruhen, die mit unseren eigenen und speziell zu diesem Zweck entwickelten Fragebögen erhoben wurden. Cognivia hat sich zum Ziel gesetzt, die „Kraft des Geistes“ zu nutzen und dieses einzigartige Phänomen zu quantifizieren, um die Erfolgsquoten klinischer Studien zu verbessern, das Risiko bei der Entwicklung von Arzneimitteln zu senken und letztlich die Gesundheitsversorgung zu verbessern.

ML für Diagnostik und Therapie

Ein internationales Team um Stefan Feuerriegel, Leiter des Instituts für AI in Management an der LMU, hat das Potenzial eines vergleichsweise neuen Zweigs von KI für Diagnostik und Therapie aus. Lassen sich mit sogenanntem Kausalen Maschinellen Lernen (ML) Behandlungsergebnisse abschätzen – besser als mit bisher gängigen Machine-Learning-Verfahren? Ja, heißt es in einer programmatischen Arbeit der Gruppe im angesehenen Fachblatt Nature Medicine, es werde Wirksamkeit und Sicherheit von Behandlungen verbessern können. Insbesondere biete die neue Machine-Learning-Variante „eine Fülle von Möglichkeiten, Behandlungsstrategien zu personalisieren und damit die Gesundheit der Patienten individuell zu verbessern“, schreiben die Forscherinnen und Forscher aus München, Cambridge (Großbritannien) und Boston (USA), zu denen auch Stefan Bauer und Niki Kilbertus, Informatikprofessoren an der Technischen Universität München (TUM) und Arbeitsgruppenleiter bei Helmholtz AI, gehören. Die Autoren führen dafür das Beispiel Diabetes an: Klassisches ML würde darauf abzielen vorherzusagen, wie wahrscheinlich…