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Wednesday, 31 May 2023

Alto Neuroscience uses AI to create biomarkers for personalised mental health drugs

 Alto Neuroscience is a clinical-stage biopharmaceutical startup that uses its artificial intelligence (AI)-enabled platform to measure brain biomarkers, including electroencephalogram (EEG) activity and behavioural patterns, wearable data, genetics and other factors, to drive targeted drug development in mental health.

Using GlobalData’s Drugs Database and a series of searches relating to AI, more than 490 unique drugs from over 140 different companies have been detected that have been discovered using AI-based technologies. These companies include specialist AI vendors that provide services to the pharma industry, as well as small biotechs that have developed drug pipelines using in-house AI technology, such as Alto Neuroscience. Figure 1 shows the top companies by number of drugs developed using AI-based technologies, with AI vendor Recursion Pharmaceuticals leading with 82 drugs in various stages of early development, with many of these candidates inactive.

However, as shown in Figure 2, Alto Neuroscience has the most drugs in clinical development, including three in Phase II studies for major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) (ALTO-100, ALTO-202 and ALTO-300), and eight in Phase I for unspecified psychiatric disorders.In December 2021, Cerebral, a mental health startup, and Alto Neuroscience announced a partnership to launch a decentralised clinical study in precision psychiatry aiming to boost drug development and treatments for patients with mental disorders. The companies will conduct Phase II studies to evaluate Alto Neuroscience’s drug candidates for psychiatric conditions and enrol around 300 participants from Cerebral’s member network. Participants will then undergo in-home evaluations measuring their brain activity, sleep, activity patterns and genetics, in addition to clinical outcomes such as depression and PTSD. Using Alto Neuroscience’s analytical approach to predicting patient outcomes, it will assess whether certain biomarkers are the best way to identify patients most likely to benefit from a given drug candidate. The Phase II study is expected to complete in 2022. This collaboration will tackle the disruption challenges and traditional treatment options that the psychiatric sector has faced for decades and aims to provide patients with easier access to care.

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AI:Create a frameworks using various humanoid robotics with the help of various neural networks and LLMs on the occasiun of Diwali night from late night to peak hours to earley morning detection of fireworks reside, the pollution noice, voice and various traces of scraps found there along with fire broken and taming calming the fire broken in real time in the various neural networks of remote backward areas tow, cities, states countries and continents of the US,UK, Canada, Midle East, Africa, Korea, China, Europe, Asia and Oceania in real time. IoT-Based Automated Fire Detection and Alert System Overview This project automates fire detection and alert systems using IoT technology. It replaces traditional methods of fire reporting, ensuring quick and reliable emergency responses even in the absence of human intervention. The system leverages NodeMCU microcontrollers, environmental sensors, and the MQTT protocol to monitor conditions and trigger alarms in case of abnormalities. Features Automated Fire Detection: Monitors temperature and gas levels using DHT11 and gas sensors. Real-Time Alerts: Sends location-based notifications to receivers for immediate action. Scalable MQTT Communication: Efficient communication using a single MQTT topic for all nodes. Localized Alarm System: Receiver nodes trigger buzzers for the corresponding locations. System Architecture Source Nodes: NodeMCUs (Source_Beleghata, Source_London) equipped with: DHT11 Sensor for temperature monitoring. Gas Sensor for detecting smoke or hazardous gases. Publishes location-based messages upon detecting abnormal conditions. Receiver Nodes: NodeMCUs (Receiver_Beleghata, Receiver_London) equipped with buzzers. Subscribes to a shared MQTT topic and triggers the buzzer if the location matches incoming data. Communication Protocol MQTT Protocol: All nodes are connected to the internet for real-time communication. A shared MQTT topic ensures scalability and simplicity. Demonstration Setup Nodes: Source_Beleghata (with sensors) Receiver_Beleghata (with buzzer) Source_London (with sensors) Receiver_London (with buzzer) Workflow: Source nodes detect abnormal conditions and publish the affected location ("Beleghata" or "London") to the MQTT topic. Receiver nodes subscribe to the topic and trigger an alarm if the message matches their location. Requirements Hardware: 4 NodeMCU boards DHT11 sensors (2 units) Gas sensors (2 units) Buzzers (2 units) Software: Arduino IDE MQTT broker (e.g., Mosquitto) Internet connection for NodeMCU devices Installation and Usage Hardware Setup: Connect sensors to the Source NodeMCUs. Connect buzzers to the Receiver NodeMCUs. Software Configuration: Flash the provided code onto the NodeMCU devices using the Arduino IDE. Set up an MQTT broker and configure the nodes to publish/subscribe to the shared topic. Testing: Simulate abnormal conditions to test the system. Verify that alarms are triggered at the appropriate receiver nodes. Future Enhancements Integration with mobile applications for remote monitoring and alerts. Expansion to support additional locations and sensors. Advanced machine learning algorithms for anomaly detection.

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