To prevent heat injury among individuals working in high-temperature environments, monitoring their core temperature is of significant importance. This paper reviewed the current research status of measurement methods and prediction models for core temperature in such conditions. First, it highlighted the ongoing global increase in temperatures and the consequent rise in the risk of heat stroke among workers in high-temperature settings, underscoring the critical role of core temperature monitoring in occupational health protection. Next, it systematically introduced the application of rectal temperature measurement, capsule gastrointestinal temperature measurement, and heat flux method for core temperature assessment. Furthermore, it summarized the application status of widely used core temperature prediction models, such as Gagge model, Fiala model, the Predictive Heat Stress (PHS) model, the ECTemp^TM (the estimated core temperature) model, and the Heat Strain Decision Aid (HSDA) model, while also outlining recent advances in machine learning-based prediction models. Finally, the advantages and disadvantages of various measurement methods and prediction models were comprehensively analyzed, and future directions for the development of core temperature prediction models were proposed. The findings indicate that rectal temperature measurement remains the most direct method for assessing core temperature; prediction models for core temperature in high-temperature environments have achieved considerable progress and have been applied in certain practical scenarios; and future efforts should leverage deep learning algorithms to analyze relevant data and develop more accurate core temperature prediction models.

Leukocyte differentiation antigens (LDAs) play important roles in the immune system, by serving as surface markers and participating in multiple biological activities, such as recognizing pathogens, mediating membrane signals, interacting with other cells or systems, and regulating cell differentiation and activation. Data mining is a powerful tool used to identify novel LDAs from whole genome. LRRC25 (leucine rich repeat-containing 25) was predicted to have a role in the function of myeloid cells by a large-scale "omics" data analysis. Further experimental validation showed that LRRC25 is highly expressed in primary myeloid cells, such as granulocytes and monocytes, and lowly/intermediately expressed in B cells, but not in T cells and almost all NK cells. It was down-regulated in multiple acute myeloid leukemia (AML) cell lines and bone marrow cells of AML patients and up-regulated after all-trans retinoic acid (ATRA)-mediated granulocytic differentiation in AML cell lines and acute promyelocytic leukemia (APL; AML-M3, FAB classification) cells. Localization analysis showed that LRRC25 is a type I transmembrane molecule. Although ectopic LRRC25 did not promote spontaneous differentiation of NB4 cells, knockdown of LRRC25 by siRNA or shRNA and knockout of LRRC25 by the CRISPR-Cas9 system attenuated ATRA-induced terminal granulocytic differentiation, and restoration of LRRC25 in knockout cells could rescue ATRA-induced granulocytic differentiation. Therefore, LRRC25, a potential leukocyte differentiation antigen, is a key regulator of ATRA-induced granulocytic differentiation.
Antigens, Differentiation ; immunology ; metabolism ; Cell Differentiation ; drug effects ; Cell Line, Tumor ; Granulocytes ; cytology ; drug effects ; immunology ; metabolism ; Humans ; Leukocytes ; cytology ; drug effects ; immunology ; metabolism ; Membrane Proteins ; antagonists & inhibitors ; immunology ; metabolism ; RNA, Small Interfering ; pharmacology ; Tretinoin ; pharmacology

OBJECTIVETo study the mathematical model of optimum fertilizer application to Pinellia by field experiments.

METHODThree factors and 5 levels quadratic rotational combination design was applied to study the effects of N, P and K.

RESULT AND CONCLUSIONN, P and K combination application can achieve the maximum yield of 29,921.4 kg x hm(-2), the amount of N, P2O5, K2O were 413.79, 224.79, 164.01 kg x hm(-2), respectively. The effects of the 3 factors were in following orders: P > N > K.

Agriculture ; methods ; Fertilizers ; Linear Models ; Models, Biological ; Pinellia ; drug effects ; growth & development