ArticleBiomedical journal2026
Backs under pressure: measuring and estimating lumbar spinal loads.
Article in Biomedical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This issue of the Biomedical Journal examines lumbar spinal loading and current approaches to its assessment using invasive measurements, biomechanical modeling, imaging, wearable technologies, and AI-assisted methods. Other contributions address coumarin derivatives as modulators of autophagy and PI3K/mTOR signaling in cancer and apply multi-omics approaches to uric acid metabolism and cardiometabolic disease. Further studies investigate molecular mechanisms linking mechanical irritation to oral squamous cell carcinoma progression, predictors of HBsAg seroclearance after antiviral treatment withdrawal, and age-dependent effects of exercise and losartan. Additional contributions explore PD-L1 signaling in allergic asthma, the antiviral activity of BPR2-D2 against chikungunya virus replication, and differences in objective tear function between patients with and without mental disorders.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.