Effect of intense pulsed-light therapy on hair regrowth in C57BL/6J mice mediated by WNT/β-catenin signaling pathway

Purpose: To evaluate the effect of low-fluence intense pulsed light (IPL) on hair  growth in C57BL/6 mice, and to explore the potential molecular mechanisms of IPL actions on hair growth.Methods: After low-fluence IPL irradiation was applied to depilated dorsal skin of C57BL/6 mice in the telogen, or resting hair cycle phase, tissue samples were obtained and used for histopathological analysis. Hair growth was analyzed by measuring hair length. In addition, protein expression levels of WNT3A and β-catenin were assayed by western blot.Results: Low-fluence IPL irradiation promoted hair growth by inducing the anagen, or growth, phase in telogenic C57BL/6J mice. In particular, hair growth analysis suggested that application of low-fluence IPL induced an earlier transition from telogen to anagen phase and prolonged the duration of anagen phase compared to the control group (p < 0.05). Moreover, western blotting assay revealed that WNT3A and β-catenin protein levels were up-regulated compared to the control group (p < 0.05).Conclusion: These findings suggest that low-fluence IPL irradiation may be effective for promoting hair regrowth via activation of the WNT/β-catenin pathway, and may, therefore, be a potential novel therapeutic treatment to stimulate hair regrowth.Keywords: Intense pulsed light, Hair follicles, Hair growth, WNT3a/β-catenin  pathwa

Synthesis and Anticancer Activity of 4β-Triazole-podophyllotoxin Glycosides

AbstractThe objective of present study was to investigate the effect of various sounds on the green mustard’s (Brassica Juncea) morphology characteristic and productivity. The plant has been subjected to three various sound, namely classical music (rhythmic violin music), machine and traffic noise, and mixed sound (classical music and traffic noise) with 70-75 dB sound pressure level, from germination to harvest for three hours (7-10 am.) each day. Six parameters, i.e. germination, plant height, leaf width, leaf lenght, total plant lenght, and fresh weight, related with growth and productivity of plant were been monitored on regular basis.The results showed classical music improves germination up to 15% for 36 hours, plant height 13,5%, leaf width 14,8%, leaf length 14,2%, and wet weight 57,1%. In general, exposure to classical music gives thebest results on the morphological characteristics and productivity of green mustard.Keywords: Sound exposure, plant morphology , productivity, green mustardAbstrakPenelitian ini bertujuan untuk menginvestigasi efek paparan variasi suara terhadap karakteristik morfologi dan produktivitas tanaman sawi hijau. suara yang dipaparkan antara lain musik klasik (suara biola), bising lalu lintas dan mesin industri (noise) dan campuran antara musik klasik dan noise. Level suara yang digunakan berkisar antara 70-75 dB dimulai sejak masa perkecambahan hingga panen selama 3 jam tiap harinya dimulai pukul 07.00-10.00. Enam parameter yang diamati dan diambil datanya meliputi, daya berkecambah, tinggi tanaman, lebar daun, panjang daun, panjang tanaman total dan berat basah. Hasil penelitian menunjukkan bahwa musik klasik meningkatkan daya berkecambah sebesar 15%, tinggi tanaman sebesar 13,5%, lebar daun sebesar 14,8%, panjang daun sebesar 14,2%, dan berat basah sebesar 57,1%. Secara umum paparan musik klasik memberikan hasil terbaik terhadap karakteristik morfologi dan produktivitas sawi hijau.Kata kunci: Paparan suara, morfologi, produktivitas, sawi hijauDiterima: 21 Oktober 2013;Disetujui: 28 Januari 201

DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN

The optimal multicast tree problem in the Software-Defined Networking (SDN) multicast routing is an NP-hard combinatorial optimization problem. Although existing SDN intelligent solution methods, which are based on deep reinforcement learning, can dynamically adapt to complex network link state changes, these methods are plagued by problems such as redundant branches, large action space, and slow agent convergence. In this paper, an SDN intelligent multicast routing algorithm based on deep hierarchical reinforcement learning is proposed to circumvent the aforementioned problems. First, the multicast tree construction problem is decomposed into two sub-problems: the fork node selection problem and the construction of the optimal path from the fork node to the destination node. Second, based on the information characteristics of SDN global network perception, the multicast tree state matrix, link bandwidth matrix, link delay matrix, link packet loss rate matrix, and sub-goal matrix are designed as the state space of intrinsic and meta controllers. Then, in order to mitigate the excessive action space, our approach constructs different action spaces at the upper and lower levels. The meta-controller generates an action space using network nodes to select the fork node, and the intrinsic controller uses the adjacent edges of the current node as its action space, thus implementing four different action selection strategies in the construction of the multicast tree. To facilitate the intelligent agent in constructing the optimal multicast tree with greater speed, we developed alternative reward strategies that distinguish between single-step node actions and multi-step actions towards multiple destination nodes