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A study in BMC Infectious Diseases finds that children with severe COVID-19 share similar immune response patterns seen in adults and identifies gene signatures that distinguish severe illness from mild infection, highlighting potential targets for new therapies.

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In a study published in Stem Cell Research & Therapy, researchers develop stem cell-derived pituitary cells that produce ACTH and function in both mouse and primate models, advancing the quest for physiologically responsive treatments for hypopituitarism.

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A study published in Parasites & Vectors establishes the first comprehensive distribution models for 16 tick species in Japan for anticipating the occurrence of tick-borne diseases in this region.

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A study published in the Journal of Translational Medicine highlights the therapeutic potential of rapamycin to enhance energy metabolism in patients with chronic fatigue syndrome.

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A study in BMC Ecology and Evolution presents the first comprehensive genomic analysis of the endangered Russian desman and finds that the long-term decline in its population is primarily an ancient process driven by natural causes that began millennia before human impact.

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A study in BMC Veterinary Research explores veterinarians’ perceptions of infectious disease risk in European livestock. Results show variation by country and sector, and highlight awareness gaps for several zoonotic diseases.

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Targeted senolytic immunotherapy enhances the efficacy of regenerative interventions and represents a promising strategy for chronic disease management and modifying biological aging, finds a study in the Journal of Translational Medicine.

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A study in the Journal of Translational Medicine suggests that persistent fatigue in lupus may be linked to ME/CFS, proposing a framework where immune, metabolic, vascular, and nervous system changes interact to cause long-term symptoms in some patients.

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An article in Genome Biology presents dicast: a machine learning method for accurate structural variant detection from short-read sequencing data, with potential benefits for clinical diagnostics and genome-wide association studies.

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A study in Biomarker Research highlights the clinical utility of multigene assay qMIDSV3 as a triage test, sparing most of the oral potentially malignant disorder patients from unnecessary invasive tissue biopsies while accurately identifying oral squamous cell carcinoma cases.

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A study in BMC Cancer suggests that Lip-TDM is a non-infectious, NK-activating, anti-tumor agent which can be administered intraperitoneally or systemically in bladder cancer as a potential alternative to BCG. 🧬

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Low-proteinuric diabetic kidney disease with sustained rapid kidney function decline is associated with endothelial and tubular transcriptional alterations without uniform advanced glomerular filtration-barrier lesions, finds a study in Cardiovascular Diabetology.

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Low telomere length and low skeletal muscle mass are jointly associated with greater risk of all-cause mortality, especially among younger individuals and women, finds a study in Nutrition & Metabolism.

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A study in BMC Primary Care explores the influencing factors towards job satisfaction among medical assistants in Germany.

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When a blood smear tells the story: diagnosing fatal secondary hemophagocytic lymphohistiocytosis in pregnancy This Behind the Paper post from Journal of Medical Case Reports, the authors emphasise communication between the lab and the bedside for effective clinical care.

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Resistance training can reduce body fat percentage while simultaneously improving grip strength, representing an effective management strategy for sarcopenic obesity, reports a systematic review and meta-analysis in BMC Geriatrics.
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A study in BMC Neurology reveals that Alice in Wonderland syndrome, a neuropsychiatric disorder marked by sensory perception distortions and altered body image, is prevalent in younger adults with persistent headaches post-COVID-19, particularly those with a migraine history.
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A study published in BMC Biotechnology shows how AI can reliably spot poisonous mushrooms using an explainable deep learning model with a 95.67% accuracy, thus preventing misidentifications and promoting food safety.

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