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  • Even some of the best AI can’t beat this new benchmark Kyle Wiggers
    The nonprofit Center for AI Safety (CAIS) and Scale AI, a company that provides a number of data labeling and AI development services, have released a challenging new benchmark for frontier AI systems. The benchmark, called Humanity’s Last Exam, includes thousands of crowdsourced questions touching on subjects like mathematics, humanities, and the natural sciences. To make […] © 2024 TechCrunch. All rights reserved. For personal use only.
     

Even some of the best AI can’t beat this new benchmark

24 January 2025 at 07:29

The nonprofit Center for AI Safety (CAIS) and Scale AI, a company that provides a number of data labeling and AI development services, have released a challenging new benchmark for frontier AI systems. The benchmark, called Humanity’s Last Exam, includes thousands of crowdsourced questions touching on subjects like mathematics, humanities, and the natural sciences. To make […]

© 2024 TechCrunch. All rights reserved. For personal use only.

Multiple time points for detecting circulating tumor DNA to monitor the response to neoadjuvant therapy in breast cancer: a meta-analysis

23 January 2025 at 19:00

BMC Cancer. 2025 Jan 22;25(1):115. doi: 10.1186/s12885-025-13526-0.

ABSTRACT

BACKGROUND: Not all breast cancer (BC) patients can benefit from neoadjuvant therapy (NAT). A poor response may result in patients missing the best opportunity for treatment, ultimately leading to a poor prognosis. Thus, to identify an effective predictor that can assess and predict patient response at early time points, we focused on circulating tumor DNA (ctDNA), which is a vital noninvasive liquid biopsy biomarker. We performed a meta-analysis to explore the predictive value of response by monitoring ctDNA at four time points of NAT using pathologic complete response (pCR) and residual cancer burden (RCB).

METHODS: By searching Embase, PubMed, the Cochrane Library, and the Web of Science until December 24, 2023, we selected studies concerning the relationship between ctDNA and response or prognosis. We analysed the results at the following various time points: baseline (T0), first cycle of NAT (T1), mid-treatment (MT), and end of NAT (EOT). pCR and RCB were used to evaluate the response as the primary endpoint. The secondary endpoint was to investigate the relationship between ctDNA and prognosis. Odds ratios (ORs) and hazard ratios (HRs) were used as effect indicators.

RESULTS: Thirteen reports from twelve studies were eligible for inclusion in this meta-analysis. The results demonstrated that ctDNA negativity was associated with pCR at T1 (OR = 0.34; 95% CI: 0.21-0.57), MT (OR = 0.35; 95% CI: 0.20-0.60), and EOT (OR = 0.38; 95% CI: 0.22-0.66). When RCB was used to evaluate responses, ctDNA negativity was associated with RCB-0/I at the MT (OR = 0.34; 95% CI: 0.21-0.55) and EOT (OR = 0.26; 95% CI: 0.15-0.46). Furthermore, ctDNA positivity at T1 predicted a worse prognosis for patients (HR = 2.73; 95% CI: 1.29-5.75). We also performed a subgroup analysis to more accurately assess the predictive value of ctDNA for triple-negative breast cancer.

CONCLUSIONS: Our meta-analysis suggested that the ctDNA status at the early stage of NAT can predict patient response, which provides evidence for adjusting personalized treatment strategies and improving patient survival.

PROSPERO REGISTRATION NUMBER: CRD42024496465.

PMID:39844103 | PMC:PMC11752932 | DOI:10.1186/s12885-025-13526-0

A commonly inherited human PCSK9 germline variant drives breast cancer metastasis via LRP1 receptor

Characterization of a common germline variant in PCSK9 unveils a hereditary basis underlying breast cancer metastasis and suggests that PCSK9 inhibition therapy could be a promising strategy for breast cancer metastasis prevention.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations

Complex disorders require precise strategies for their characterization. AI-based digital phenotypes from biosensors can be used to predict psychiatric disorders and identify GWAS loci.

Artificial intelligence in biomedicine: A key to analyzing millions of individual cells

24 January 2025 at 02:18
Our bodies are made up of around 75 billion cells. But what function does each individual cell perform and how greatly do a healthy person's cells differ from those of someone with a disease? To draw conclusions, enormous quantities of data must be analyzed and interpreted. For this purpose, machine learning methods are applied. Researchers have now tested self-supervised learning as a promising approach for testing 20 million cells or more.

Researchers develop biomarker algorithm for noninvasive detection of Barrett's esophagus and esophageal cancer

23 January 2025 at 03:58
By studying biomarkers known to be involved in gastrointestinal cancers, researchers have developed a biomarker algorithm that, when combined with a noninvasive method to collect esophageal cells for study, could give clinicians insight into which patients have esophageal cancer or precancerous conditions such as Barrett's esophagus (BE) or high-grade dysplasia.
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