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Multiple myeloma (MM) is characterized by clonal plasma cell proliferation in the bone marrow, challenging prognosis prediction. We developed a gene-pairing prognostic risk model using m6A regulatory genes and a nested LASSO method. A cutoff of − 0.133 categorized MM samples into high-risk and low-risk groups. The model showed strong prognostic performance in 2088 newly diagnosed MM samples and predicted response to combination therapy (daratumumab, carfilzomib, lenalidomide, and dexamethasone) in patients who failed or relapsed from bortezomib-containing regimens, with an AUC of 0.9. It distinguished between smoldering MM and MM (cutoff: − 0.45) and between MM and plasma cell leukemia (cutoff: 0.0857). Single-cell analysis revealed higher risk scores at relapse. Combining MM cell lines and sample data, we identified potential drugs and targets (ADAT2 and NUP153) effective against high-risk MM. Integrating the m6A risk model with the International Staging System (ISS) enhanced stratification accuracy. These insights support precision treatment of MM. [ABSTRACT FROM AUTHOR]

An artificial intelligence (AI) model was designed to assist pathologists in diagnosing and quantifying structural changes in tongue lesions induced by chemical carcinogens. Using a tongue cancer model induced by 4‐nitroquinoline‐N‐oxide and treated with β‐elemene, a total of 183 digital pathology slides were processed. The Segment Anything Model (SAM) was employed for initial segmentation, followed by conventional algorithms for more detailed segmentation. The epithelial contour area was computed using OpenCV's findcontour method, and the skeletonize method was used to calculate the distance map and skeletonized representation. The AI model demonstrated high accuracy in measuring tongue epithelial thickness and the number of papilla‐like protrusions. Results indicated that the model group had significantly higher epithelial thickness and fewer papillae compared with the blank group. Furthermore, the treatment group exhibited reduced epithelial thickness and fewer papilla‐like protrusions compared with the model group, though these differences were less pronounced. Overall, the SAM framework algorithm proved effective in quantifying tongue epithelial thickness and the number of papilla‐like protrusions, thereby assisting healthcare professionals in understanding pathological changes and assessing treatment outcomes. [ABSTRACT FROM AUTHOR]

Forest fires are a primary driver of biogeochemical processes in ecosystems and affect the soil nutrient balance by altering the distribution of organic matter and associated nutrients as well as the composition and availability of elemental nutrients. We investigated the changes in soils' phosphorus (P) fractions and the adsorption characteristics of potassium dihydrogen orthophosphate (orthoP) and myo‐inositol hexakisphosphate (IHP) at two depths (0–5 and 5–10 cm) in soils from two sites differing in fire severity: long‐unburned and heavily burned. Five years after the fire, there were increases in the total P and extractable inorganic P (H2O‐P, NaHCO3‐Pi, NaOH‐Pi, D.HCl‐Pi and C.HCl‐Pi) contents. Conversely, there was a decrease in organic P (NaHCO3‐Po, NaOH‐Po and C.HCl‐Po). The adsorption of soil for orthoP at a depth of 0–5 cm was dependent on the amount of P supplied by the soil; at low concentrations, heavily burned soil showed a lower adsorption than long‐unburned soil. However, at high concentrations, the adsorption of heavily burned soil was significantly larger than that of long‐unburned soil. With respect to IHP, the adsorption of long‐unburned soil was also more significant at depths of 0–5 cm. The adsorption of both orthoP and IHP of heavily burned soil was significantly higher than that of long‐unburned soil at a depth of 5–10 cm. Fire also decreased the binding degree (k1) of soil‐P and the solid phase at depths of 0–5 and 5–10 cm. The increase in P adsorption capacity outweighed the addition of P from ash, resulting in less leaching in heavily burned areas and minimizing P loss. Overall, forest fires significantly affect the adsorption characteristics and P fractions of soil, specifically orthoP and IHP, during the initial stage of postfire recovery. These effects are closely related to the direct influences of iron oxide, soil organic matter and aluminium oxide in the soil and may even produce relatively long‐term effects on the P recycling process of the entire ecosystem. [ABSTRACT FROM AUTHOR]