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Help Creating an Excel File to Calculate Student Commutes to Clinical Sites and Filter Site Details
Hello, I’m hoping someone can help me create an Excel document for a fairly complex need. I oversee a large number of students across my state and am trying to ensure fairness in the clinical rotations they are assigned to. I would like to set up an Excel spreadsheet that can: House student names along with their home addresses. List multiple clinical site addresses that students may rotate to. Calculate and display the commute time and distance (in miles) from each student’s home address to each potential clinical site. Additionally (if possible), I would love to be able to filter the clinical sites based on certain characteristics, such as: Types of MRI scans performed at the site Patient volume (high volume vs slower paced) Type of location (small town hospital, large city hospital, or mobile MRI unit) If the filtering features are too complicated, I would at least like help setting up the commute calculations between home addresses and multiple site addresses. I appreciate any guidance or ideas. Thank you so much in advance for your help!
24 hour time slots from a specific time point
Hi! Is there a formula to make 24 hour time slots from a specific time? For example, 3/3/25 @ 0810. The 1st 24 hour box would be (3/3/25 @ 0810 - 3/4/25 @ 0810), 2nd 24 hour box (3/4/25 @ 0810 - 3/5/25 @ 0810), etc. Also, once those 24 hour prefilled dates and times are created for 15 days, is it possible to take a shreadsheet with dates and time entries and place them into the correlating 24 hour time slots from a specific time? For example, if an entry was dated and timed 3/3/25 @ 0935, and 3/4/25 @ 0700, both of those would fall into the 1st 24 hour box and so on. Thank you in advance for saving me hundreds of hours doing this by hand!Enhancing Healthcare AI with Model Context Protocol and Semantic Kernel
AI in healthcare isn’t just about chatbots or summarizing clinical notes anymore. We’re entering an era where AI must act—connecting to enterprise systems, pulling live data, and executing workflows—all while respecting the complex and high-stakes environment of healthcare. That’s where Microsoft’s Model Context Protocol (MCP) and the Semantic Kernel SDK come in. The full article is here: https://pauljswider.substack.com/p/enhancing-healthcare-ai-with-model Not trying to spam. I was receiving errors when I attempted to copy here. Feedback is appreciated.
How Social Support Programs Impact Healthcare Accessibility in South Africa
How have https://onlinesassastatuscheck.co.za/ Grants impacted poverty alleviation and social welfare in South Africa? Join the conversation to share your insights, experiences, and analysis of the role SASSA plays in supporting vulnerable populations across the country.
Excel Template
Hi, I would like a suggestion on how to create an excel sheet to monitor sample reception monthly and yearly. I would like to include variables such as total samples received monthly, number of samples tested, number of positive and the district the samples were received from. I am not really a tech guru but I follow instructions. ThanksAI-powered tool predicts gene activity in cancer cells from biopsy images
To determine the type and severity of a cancer, pathologists typically analyze thin slices of a tumor biopsy under a microscope. But to figure out what genomic changes are driving the tumor's growth -; information that can guide how it is treated -; scientists must perform genetic sequencing of the RNA isolated from the tumor, a process that can take weeks and costs thousands of dollars. Now, Stanford Medicine researchers have developed an artificial intelligence-powered computational program that can predict the activity of thousands of genes within tumor cells based only on standard microscopy images of the biopsy. The tool, described online in Nature Communications Nov. 14, was created using data from more than 7,000 diverse tumor samples. The team showed that it could use routinely collected biopsy images to predict genetic variations in breast cancers and to predict patient outcomes. This kind of software could be used to quickly identify gene signatures in patients' tumors, speeding up clinical decision-making and saving the health care system thousands of dollars." Olivier Gevaert, PhD, professor of biomedical data science and senior author of the paper The work was also led by Stanford graduate student Marija Pizuria and postdoctoral fellows Yuanning Zheng, PhD, and Francisco Perez, PhD. Driven by genomics Clinicians have increasingly guided the selection of which cancer treatments -; including chemotherapies, immunotherapies and hormone-based therapies -; to recommend to their patients based on not only which organ a patient's cancer affects, but which genes a tumor is using to fuel its growth and spread. Turning on or off certain genes could make a tumor more aggressive, more likely to metastasize, or more or less likely to respond to certain drugs. However, accessing this information often requires costly and time-consuming genomic sequencing. Gevaert and his colleagues knew that the gene activity within individual cells can alter the appearance of those cells in ways that are often imperceptible to a human eye. They turned to artificial intelligence to find these patterns. The researchers began with 7,584 cancer biopsies from 16 different of cancer types. Each biopsy had been sliced into thin sections and prepared using a method known as hematoxylin and eosin staining, which is standard for visualizing the overall appearance of cancer cells. Information on the cancers' transcriptomes -; which genes the cells are actively using -; was also available. A working model After the researchers integrated their new cancer biopsies as well as other datasets, including transcriptomic data and images from thousands of healthy cells, the AI program -; which they named SEQUOIA (slide-based expression quantification using linearized attention) -; was able to predict the expression patterns of more than 15,000 different genes from the stained images. For some cancer types, the AI-predicted gene activity had a more than 80% correlation with the real gene activity data. In general, the more samples of any given cancer type that were included in the initial data, the better the model performed on that cancer type. "It took a number of iterations of the model for it to get to the point where we were happy with the performance," Gevaert said. "But ultimately for some tumor types, it got to a level that it can be useful in the clinic." Gevaert pointed out that doctors are often not looking at genes one at a time to make clinical decisions, but at gene signatures that include hundreds of different genes. For instance, many cancer cells activate the same groups of hundreds of genes related to https://www.news-medical.net/health/What-Does-Inflammation-Do-to-the-Body.aspx, or hundreds of genes related to cell growth. Compared with its performance at predicting individual gene expression, SEQUOIA was even more accurate at predicting whether such large genomic programs were activated. To make the data accessible and easy to interpret, the researchers programmed SEQUOIA to display the genetic findings as a visual map of the tumor biopsy, letting scientists and clinicians see how genetic variations might be distinct in different areas of a tumor. Predicting patient outcomes To test the utility of SEQUOIA for clinical decision making, Gevaert and his colleagues identified breast cancer genes that the model could accurately predict the expression of and that are already used in commercial breast cancer genomic tests. (The Food and Drug Administration-approved MammaPrint test, for instance, analyzes the levels of 70 breast-cancer-related genes to provide patients with a score of the risk their cancer is likely to recur.) "Breast cancer has a number of very well-studied gene signatures that have been shown over the past decade to be highly correlated with treatment responses and patient outcomes," Gevaert said. "This made it an ideal test case for our model." SEQUOIA, the team showed, could provide the same type of genomic risk score as MammaPrint using only stained images of tumor biopsies. The results were repeated on multiple different groups of breast cancer patients. In each case, patients identified as high risk by SEQUOIA had worse outcomes, with higher rates of cancer recurrence and a shorter time before their cancer recurred. The AI model can't yet be used in a clinical setting -; it needs to be tested in clinical trials and be approved by the FDA before it's used in guiding treatment decisions -; but Gevaert said his team is improving the algorithm and studying its potential applications. In the future, he said, SEQUOIA could reduce the need for expensive gene expression tests. "We've shown how useful this could be for breast cancer, and we can now use it for all cancers and look at any gene signature that is out there," he said. "It's a whole new source of data that we didn't have before." Scientists from Roche Diagnostics were also authors of the paper. Funding for this research was provided by the National Cancer Institute (grant R01 CA260271), a fellowship of the Belgian American Educational Foundation, a grant from Fonds Wetenschappelijk Onderzoek-Vlaanderen, the Fulbright Spanish Commission and Ghent University
Understanding the Role of SASSA Grants: A Discussion
How have https://onlinesassastatuscheck.co.za/ Grants impacted poverty alleviation and social welfare in South Africa? Join the conversation to share your insights, experiences, and analysis of the role SASSA plays in supporting vulnerable populations across the country.Did Microsoft make an effort to lift poverty in South Africa?
What specific initiatives has Microsoft undertaken to address poverty in South Africa, and how do these efforts compare with government programs like https://srd-sassa-gov.co.za/ and other social assistance initiatives aimed at alleviating poverty and promoting economic empowerment?
SASSA and Health: A Brief Overview
SASSA (South African Social Security Agency) plays a crucial role in supporting the health and well-being of vulnerable South Africans. By providing social grants to those in need, SASSA contributes to improved access to healthcare, nutrition, and basic necessities. 1. SASSA Services Portal services.sassa.gov.za Key areas of intersection between SASSA and health include: Disability Grants: Supporting individuals with disabilities to access necessary medical care and rehabilitation services. 1. SASSA Disability Grants: Support and Benefits for Individuals with Disabilities statuscheck.co.za Child Support Grants: Contributing to the health and development of children through nutrition, immunization, and healthcare access. Older Persons Grants: Assisting elderly individuals in affording essential healthcare, medication, and medical aids. Care Dependency Grants: Supporting caregivers of people with severe disabilities, indirectly impacting the health of both the caregiver and the dependent. 1. SASSA Care Dependency Grant Eligibility Requirements - Status Check statuscheck.co.za While SASSA grants provide financial relief, challenges such as grant delays, insufficient amounts, and access to healthcare facilities still persist. Addressing these issues is crucial for maximizing the positive impact of SASSA on the health of beneficiaries. SASSA Status Check : visit https://sassa-status.co.zais a process to determine the progress or outcome of a grant application submitted to the South African Social Security Agency (SASSA). It involves verifying the application status using various methods such as online portals, SMS, WhatsApp, or in-person visits to SASSA offices.How does Azure ensure the security and privacy of sensitive patient data in the cloud?
In the healthcare industry, where privacy and security are paramount, storing sensitive patient data in the cloud can feel like a gamble. But Microsoft Azure employs a multi-layered approach to ensure your information stays safe. Here's how: Encryption at Rest and In Transit: Imagine your data wrapped in multiple layers of security. Azure encrypts patient data at rest (when stored) using industry-standard 256-bit AES encryption, which is practically uncrackable. And when data travels between Azure datacenters, it's encrypted again using secure protocols for additional protection. Compartmentalization: Azure uses a multi-tenant model, meaning various customers share the physical infrastructure. But worry not! Logical isolation keeps your data segregated from others, like placing your files in a separate folder on a shared server. Customer Control: You hold the reins! Azure Key Vault empowers you to manage the encryption keys that unlock your data. This ensures only authorized personnel can access sensitive information. Confidentiality Through Confidential Computing: For an extra layer of security, Azure offers confidential computing environments. These are like secure fortresses within the cloud that encrypt data even while it's being processed. This makes it virtually impossible for unauthorized users, even within Microsoft, to access your data. Compliance with Regulations: Azure adheres to a wide range of healthcare data privacy regulations, including HIPAA and HITRUST. This gives you peace of mind knowing your data security meets industry standards. By implementing these robust security measures, Azure ensures your patient data remains confidential, compliant, and protected in the cloud.
