Expression and Clinical Significance of Antiapoptotic Gene (Survivin) in NB4 and Acute Promyelocytic Leukemia Cells

To study survivin gene expression in APL cells and to explore its correlation with clinical manifestations. PML/RARα and survivin mRNA expression were analysed using RT-PCR. By treatment of ATRA, the survivin mRNA expression in NB4 cells gradually decreased with time and was almost undetectable in the 72th hour. Survivin was expressed in 67% of the 36 APL cases (de novo and relapse patients) with PML/RARα fusion gene expression. However, in 22 cases of remission stage patients without PML/RARα fusion gene expression, survivin was expressed in 36%. The survivin mRNA expression positive rate in de novo and relapse groups, and PML/RARα fusion gene L-type positive groups, was obviously higher than those in remission period groups and was significantly lower than those in acute leukemia groups. In 36 cases of de novo and relapse APL patients, all cases could obtain complete remission, irrespective of the survivin expression. APL patients expressed with survivin mRNA had DIC and serious infection (one patient died). The clinical symptom included slight skin or mucosa bleeding, fever, and asthenic for patients without the survivin mRNA expression. Later, two cases of APL patients with the survivin mRNA expression were treated by ATRA, induction differentiation sign in their peripheral blood and bone marrow figure was not obvious. It was concluded that the survive gene expression was lower in APL than those in any other types of leukemia, thus closely associated with clinical manifestation

Trojan Horse nanotheranostics with dual transformability and multifunctionality for highly effective cancer treatment.

Nanotheranostics with integrated diagnostic and therapeutic functions show exciting potentials towards precision nanomedicine. However, targeted delivery of nanotheranostics is hindered by several biological barriers. Here, we report the development of a dual size/charge- transformable, Trojan-Horse nanoparticle (pPhD NP) for delivery of ultra-small, full active pharmaceutical ingredients (API) nanotheranostics with integrated dual-modal imaging and trimodal therapeutic functions. pPhD NPs exhibit ideal size and charge for drug transportation. In tumour microenvironment, pPhD NPs responsively transform to full API nanotheranostics with ultra-small size and higher surface charge, which dramatically facilitate the tumour penetration and cell internalisation. pPhD NPs enable visualisation of biodistribution by near-infrared fluorescence imaging, tumour accumulation and therapeutic effect by magnetic resonance imaging. Moreover, the synergistic photothermal-, photodynamic- and chemo-therapies achieve a 100% complete cure rate on both subcutaneous and orthotopic oral cancer models. This nanoplatform with powerful delivery efficiency and versatile theranostic functions shows enormous potentials to improve cancer treatment

Research on secondary colour optical dot gain model in electrostatic digital colour printings

Rasterski element je osnovni tiskarski element u reprodukciji kontinuirane slike i stvaranju otisnute slike. Kvaliteta tiskanja ovisi o kvaliteti prijenosa rasterskog elementa. Važno je pratiti prirast rasterskog elementa, izoštravanje, deformaciju, udvostručenje i pojavu mrlja. U praksi se koriste neke specifične tehnike nadgledanja kvalitete u praćenju promjena rasterskih elemenata. Kao metoda praćenja prirasta rasterskih elemenata često se koriste multi kolorimetrijske skale za istraživanje prirasta rasterskih elemenata ili promjene tona. Cilj je ovoga rada ispitati prirast rasterskog elementa sekundarne boje kod elektrostatičkog digitalnog tiska, razmatrajući prirast rasterskog elementa kolorimetrijskih skala ciana, magente, žute, plave, crvene i zelene. Proučavaju se odnosi između sekundarnog kolorimetrijskog prirasta rasterskih elemenata i primarnog kolorimetrijskog prirasta rasterskih elemenata. Tri primarne kolorimetrijske skale, cian, magenta, žuta, te tri sekundarne kolorimetrijske skale, plava, crvena i zelena, dizajnirane su od 2 % do 90 %. Kolorimetrijske su skale postavljene elektrostatičkim tiskom u boji te mjerene spektrofotometrom. Prirast rasterskog elementa sekundarnih kolorimetrijskih skala modeliran je u skladu s pravilima o prirastu rasterskih elemenata kod primarnih kolorimetrijskih skala primjenom metode višestruke linearne regresije. Rezultati pokazuju da se prirast rasterskog elementa sekundarne boje mijenja istodobno s prirastom rasterskog elementa primarnih kolorimetrijskih skala, i da je potrebno daljnje istraživanje za kompenzaciju prirasta rasterskog elementa i kontrolu postupka tiskanja.Screening dot is the basic printing element to reproduce continuous image and to form the printing image. Printing quality depends on the transfer quality of the screening dot. It is important to control the dot gain, sharpening, deformation, doubling and slur. In practice, some specific quality control techniques are used to monitor the screening dot variation. For dot gain control technique, multi-colour scales are often used to investigate dot gain or tone change. This article aims to investigate the secondary colour dot gain in electrostatic digital printing, by considering dot gain of the colour scales, Cyan, Magenta, Yellow, Blue, Red and Green. The relations between secondary colour dot gain and primary colour dot gain are studied. Three primary colour scales, Cyan, Magenta, Yellow, and three secondary colour scales, Blue, Red and Green, are designed from 2 % to 90 %. The colour scales are output by electrostatic colour press and measured by spectrophotometer. The dot gain of the secondary colour scales is modelled according to the dot gain rules of the primary colour scales using multiple linear regression method. The results illustrate that the dot gain of secondary colour changes with the dot gain of primary colour scales synchronously, and that further research is needed to make for dot gain compensation and printing process control

Mitigation of chronic unpredictable stress–induced cognitive deficits in mice by Lycium barbarum L (Solanaceae) polysaccharides

Purpose: To investigate the neuroprotective effects of Lycium barbarum polysaccharide (LBP) against concomitant cognitive dysfunction and changes in hippocampal CREB-BDNF signaling pathway in chronically unpredictable stressed mice.Methods: The mice were subjected to different unpredictable stressors for a period of 4 weeks. Behavioral tests, including open field (OFT) and Morris water maze (MWMT) tests were used to evaluate pharmacological effects. Serum corticosterone levels, protein expression level of BDNF and pCREB/CREB in hippocampus were assessed by ELISA, Western blot and immunohistochemistry methods, respectively. Morphological changes in pyramidal neurons in the hippocampus were studied by Nissl staining.Results: LBP improved mice performance in MWMT, indicating that it reversed chronic unpredictable stress (CUS)-induced cognitive deficits. LBP treatment reduced serum corticosterone levels and prevented neuron loss in the hippocampus. It maintained expression levels of BDNF and phosphorylation of CREB in hippocampus during CUS procedure.Conclusion: Lycium barbarum polysaccharide protects CREB-BDNF signaling pathway in hippocampus and relieves CUS-induced cognitive deficits. These results suggest that Lycium barbarum polysaccharides is potentially an alternative neuro-protective agent against stress-induced psychopathological dysfunction.Keywords: Lycium barbarum, Polysaccharide, Chronic unpredictable stress, Cognitive deficits, Brainderived neurotrophic factor, Calcium/cyclic-AMP responsive binding protei

Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search

Search query classification, as an effective way to understand user intents, is of great importance in real-world online ads systems. To ensure a lower latency, a shallow model (e.g. FastText) is widely used for efficient online inference. However, the representation ability of the FastText model is insufficient, resulting in poor classification performance, especially on some low-frequency queries and tailed categories. Using a deeper and more complex model (e.g. BERT) is an effective solution, but it will cause a higher online inference latency and more expensive computing costs. Thus, how to juggle both inference efficiency and classification performance is obviously of great practical importance. To overcome this challenge, in this paper, we propose knowledge condensation (KC), a simple yet effective knowledge distillation framework to boost the classification performance of the online FastText model under strict low latency constraints. Specifically, we propose to train an offline BERT model to retrieve more potentially relevant data. Benefiting from its powerful semantic representation, more relevant labels not exposed in the historical data will be added into the training set for better FastText model training. Moreover, a novel distribution-diverse multi-expert learning strategy is proposed to further improve the mining ability of relevant data. By training multiple BERT models from different data distributions, it can respectively perform better at high, middle, and low-frequency search queries. The model ensemble from multi-distribution makes its retrieval ability more powerful. We have deployed two versions of this framework in JD search, and both offline experiments and online A/B testing from multiple datasets have validated the effectiveness of the proposed approach