📌 TOPINDIATOURS Update ai: Zinc-infused gel could help soldiers recover from battl

For soldiers caught in explosions, survival often comes at a steep physical cost. Blast injuries can tear through muscle and bone, leaving survivors with severe tissue loss and long, painful recoveries.

While modern battlefield medicine saves more lives than ever, doctors continue to struggle with the damage that follows once bleeding is controlled.

Researchers at The University of Texas at Arlington are now investigating whether zinc can help speed recovery from such devastating injuries.

Their 20-month study aims to develop a safe and effective way to use zinc to protect and regenerate muscle tissue damaged by trauma.

The study, led by Zui Pan, professor of graduate nursing at UT Arlington, focuses on reducing secondary muscle damage that occurs after the initial blast.

This kind of damage, caused by restricted blood flow, swelling, and infection, can destroy more tissue than the original wound.

Pan explained that saving a soldier’s life often requires using a tourniquet or bandage to stop bleeding, but cutting off circulation can trigger severe ischemia, or loss of blood flow.

When blood rushes back, the sudden surge of oxygen can cause further harm to the tissue. She said the team’s goal is to find ways to limit this secondary destruction while promoting healing.

The research team also includes bioengineering professors Jun Liao and Yi Hong, and assistant research professor Yingjie Liu from UTA’s Bone-Muscle Research Center. Liu is part of the university’s RISE 100 initiative, which supports interdisciplinary scientific work.

Testing zinc’s healing role

Zinc is known to play a role in muscle repair, but Pan said it must be carefully dosed to avoid toxicity. The team plans to test a zinc-infused gel called gelatin methacryloyl, an FDA-approved material, to study how it promotes muscle regeneration following blast injuries.

The research is part of the UT System’s Trauma Research and Combat Casualty Care Collaborative (TRC4), which aims to improve trauma treatment in both military and civilian settings.

Beyond the battlefield

Blast injuries are among the most common wounds in combat. A 2016 report by the Department of Veterans Affairs found that explosions caused 74 percent of all combat injuries between 2001 and 2011.

Many soldiers face long-term disabilities and extended rehabilitation as a result.

The researchers believe their findings could eventually help not only soldiers but also civilians suffering from severe muscle trauma due to car accidents, sports injuries, or natural disasters.

Pan said the team’s long-term goal is to find a safe and convenient way to apply zinc directly to muscle tissue, protecting it from ischemia-reperfusion injury and encouraging regeneration.

If successful, the project could lead to better outcomes for trauma survivors and represent an important step forward in both battlefield and emergency medicine.

🔗 Sumber: interestingengineering.com

📌 TOPINDIATOURS Hot ai: Chronosphere takes on Datadog with AI that explains itself

Chronosphere, a New York-based observability startup valued at $1.6 billion, announced Monday it will launch AI-Guided Troubleshooting capabilities designed to help engineers diagnose and fix production software failures — a problem that has intensified as artificial intelligence tools accelerate code creation while making systems harder to debug.

The new features combine AI-driven analysis with what Chronosphere calls a Temporal Knowledge Graph, a continuously updated map of an organization's services, infrastructure dependencies, and system changes over time. The technology aims to address a mounting challenge in enterprise software: developers are writing code faster than ever with AI assistance, but troubleshooting remains largely manual, creating bottlenecks when applications fail.

"For AI to be effective in observability, it needs more than pattern recognition and summarization," said Martin Mao, Chronosphere's CEO and co-founder, in an exclusive interview with VentureBeat. "Chronosphere has spent years building the data foundation and analytical depth needed for AI to actually help engineers. With our Temporal Knowledge Graph and advanced analytics capabilities, we're giving AI the understanding it needs to make observability truly intelligent — and giving engineers the confidence to trust its guidance."

The announcement comes as the observability market — software that monitors complex cloud applications— faces mounting pressure to justify escalating costs. Enterprise log data volumes have grown 250% year-over-year, according to Chronosphere's own research, while a study from MIT and the University of Pennsylvania found that generative AI has spurred a 13.5% increase in weekly code commits, signifying faster development velocity but also greater system complexity.

AI writes code 13% faster, but debugging stays stubbornly manual

Despite advances in automated code generation, debugging production failures remains stubbornly manual. When a major e-commerce site slows during checkout or a banking app fails to process transactions, engineers must sift through millions of data points — server logs, application traces, infrastructure metrics, recent code deployments — to identify root causes.

Chronosphere's answer is what it calls AI-Guided Troubleshooting, built on four core capabilities: automated "Suggestions" that propose investigation paths backed by data; the Temporal Knowledge Graph that maps system relationships and changes; Investigation Notebooks that document each troubleshooting step for future reference; and natural language query building.

Mao explained the Temporal Knowledge Graph in practical terms: "It's a living, time-aware model of your system. It stitches together telemetry—metrics, traces, logs—infrastructure context, change events like deploys and feature flags, and even human input like notes and runbooks into a single, queryable map that updates as your system evolves."

This differs fundamentally from the service dependency maps offered by competitors like Datadog, Dynatrace, and Splunk, Mao argued. "It adds time, not just topology," he said. "It tracks how services and dependencies change over time and connects those changes to incidents—what changed and why. Many tools rely on standardized integrations; our graph goes a step further to normalize custom, non-standard telemetry so application-specific signals aren't a blind spot."

Why Chronosphere shows its work instead of making automatic decisions

Unlike purely automated systems, Chronosphere designed its AI features to keep engineers in the driver's seat—a deliberate choice meant to address what Mao calls the "confident-but-wrong guidance" problem plaguing early AI observability tools.

"'Keeping engineers in control' means the AI shows its work, proposes next steps, and lets engineers verify or override — never auto-deciding behind the scenes," Mao explained. "Every Suggestion includes the evidence—timing, dependencies, error patterns — and a 'Why was this suggested?' view, so they can inspect what was checked and ruled out before acting."

He walked through a concrete example: "An SLO [service level objective] alert fires on Checkout. Chronosphere immediately surfaces a ranked Suggestion: errors appear to have started in the dependent Payment service. An engineer can click Investigate to see the charts and reasoning and, if it holds up, choose to dig deeper. As they steer into Payment, the system adapts with new Suggestions scoped to that service—all from one view, no tab-hopping."

In this scenario, the engineer asks "what changed?" and the system pulls in change events. "Our Notebook capability makes the causal chain plain: a feature-flag update preceded pod memory exhaustion in Payment; Checkout's spike is a downstream symptom," Mao said. "They can decide to roll back the flag. That whole path — suggestions followed, evidence viewed, conclusions—is captured automatically in an Investigation Notebook, and the outcome feeds the Temporal Knowledge Graph so similar future incidents are faster to resolve."

How a $1.6 billion startup takes on Datadog, Dynatrace, and Splunk

Chronosphere enters an increasingly crowded field. Datadog, the publicly traded observability leader valued at over $40 billion, has introduced its own AI-powered troubleshooting features. So have Dynatrace and Splunk. All three offer comprehensive "all-in-one" platforms that promise single-pane-of-glass visibility.

Mao distinguished Chronosphere's approach on technical grounds. "Early 'AI for observability' leaned heavily on pattern-spotting and summarization, which tends to break down during real incidents," he said. "These approaches often stop at correlating anomalies or producing fluent explanations without the deeper analysis and causal reasoning observability leaders need. They can feel impressive in demos but disappoint in production—they summarize signals rather than explain cause and effect."

A specific technical gap, he argued, involves custom application telemetry. "Most platforms reason over standardized integrations—Kubernetes, common cloud services, popular databases—ignoring the most telling clues that …

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🔗 Sumber: venturebeat.com