Guizhi Fuling capsule relieves memory deficits by inhibition of microglial neuroinflammation through blocking JAK2/STAT3 pathway in presenilin1/2 conditional double knockout mice.

Chronic neuroinflammation has been regarded as an important part of the pathological initiation of Alzheimer's disease (AD), which is associated with the regulation of microglial activation. Preventing microglial activation to inhibit neuroinflammation may become a potential target for the treatment of neurodegenerative diseases. Guizhi Fuling capsule (GZFL) has a strong repression on inflammatory responses.

The impact of natural fibers' characteristics on mechanical properties of the cement composites.

The paper reviews the properties of cement composites reinforced with short fibres. The effect of natural fibres was investigated: cotton, sisal, jute, ramie, bamboo, and synthetic fibres: polymer and polypropylene. It was noticed that the fibres change the consistency of the mixture up to 15%.

Peony seed oil ameliorates neuroinflammation-mediated cognitive deficits by suppressing microglial activation through inhibition of NF-κB pathway in presenilin 1/2 conditional double knockout mice.

Chronic neuroinflammation has been shown to exert adverse influences on the pathology of Alzheimer's disease (AD), associated with the release of abundant proinflammatory mediators by excessively activated microglia, causing synaptic dysfunction, neuronal degeneration, and memory deficits. Thus, the prevention of microglial activation-associated neuroinflammation is important target for deterring neurodegenerative disorders. Peony seed oil (PSO) is a new food resource, rich in α-linolenic acid, the precursor of long chain omega-3 polyunsaturated fatty acids, including docosahexaenoic acid and eicosapentaenoic acid, which exhibit anti-inflammatory properties by altering cell membrane phospholipid fatty acid compositions, disrupting lipid rafts, and inhibiting the activation of the proinflammatory transcription factor NF-κB.

Climate change may interact with nitrogen fertilizer management leading to different ammonia loss in China's croplands.

Despite research into the response of ammonia (NH ) volatilization in farmland to various meteorological factors, the potential impact of future climate change on NH volatilization is not fully understood. Based on a database consisting of 1063 observations across China, nonlinear NH  models considering crop type, meteorological, soil and management variables were established via four machine learning methods, including support vector machine, multi-layer perceptron, gradient boosting machine and random forest (RF). The RF model had the highest R of 0.