1 Pengaruh Mengkonsumsi Rebusan Daun Sirsak Terhadap Penurunan Nyeri Pada Penderita Gout Artritis Di Wilayah Kerja Puskesmas Pineleng

Gout artritis merupakan penyakit yang ditandai dengan nyeri yang terjadi berulang-ulang yang disebabkan adanya endapan kristal monosodium urat yang tertumpuk di dalam sendi sebagai akibat tingginya kadar asam urat di dalam darah. Mengkonsumsi rebusan daun sirsak (Anonna muricata) adalah salah satu jenis terapi nonfamakologi yang bertujuan untuk menurunkan tingkat nyeri pada penderita gout artritis karena senyawa yang terkandung dalam daun sirsak berfungsi sebagai analgetik yang mempu mengurangi nyeri gout.Tujuan penelitan ini adalah untuk menganalisis pengaruh mengkonsumsi rebusan daun sirsak terhadap penurunan nyeri pada penderita gout artritis di wilayah kerja Puskesmas Pineleng.Sampel diambil dengan menggunakan total sampling yaitu 34 orang yang memenuhi kriteria inklusi.Desain penelitian yang digunakan adalah Time Series Design dan data yang dikumpulkan dari responden menggunakan lembar observasi.Hasil penelitian uji Wilcoxon sign rank test pada hasil akhir didapatkan nilai p = 0,004 < α = 0,005 sehingga dapat diambil Kesimpulan bahwa hipotesis penelitian diterima, hal ini menunjukan bahwa ada pengaruh mengkonsumsi rebusan daun sirsak terhadap penurunan nyeri pada penderita gout artritis di wilayah kerja Puskesmas Pineleng.Saran untuk penelitian selanjutnya dapat menggunakan populasi yang lebih besar untuk hasil yang lebih akurat serta dapat mengembangkan penelitian tentang pengaruh mengkonsumsi rebusan daun sirsak terhadap variabel yang lain seperti penurunan tekanan darah pada penderita hipertensi

Implementasi Program Wirausaha Baru Oleh Dinas Tenaga Kerja Dan Transmigrasi Dalam Mendukung Gerdu Kempling Kota Semarang Tahun 2014

The Government of Semarang through Local Regulation Number 4 of 2008 about poverty reduction in Semarang City which is an acceleration in poverty reduction efforts. The strategy called Gerdu Kempling (Integrated Health, Economy, Education, Infrastructure, and Environment ) and one of the program that is New Entrepreneur Program by Dinas Tenaga Kerja dan Transmigrasi Kota Semarang. This research was meant to find out how the implementation of New Entrepreneur Program by Dinas Tenaga Kerja Dan Transmigrasi that supports Gerdu Kempling Kota Semarang in 2014 and knowing the influence factors of this implementation. New Entrepreneur Program has been part of Gerdu Kempling starting in 2011. There are three locations in this research: Village of Bulusan, Ngadirgo and Padangsari. This research using qualitative descriptive research methods. The subject in this study consisted of eight (8) informants. The results showed that the implementation of New Entrepreneur Program are still less effective that is seen from the precision implementation aspects. The factors that influence the implementation such as the goals and basic of policy, resource policy, communication and implementation activities, the implementing agency characteristics, external conditions as well as the disposition of the implementor are still less optimal too. Based on these conclusions, the researcher recommend to the implementation agency and target of this program need high commitment and take maximal advantages for sustainable in order to achieve the purpose of this program

Statistical properties of cell cycle progression.

<p>Experimental observations for wild-type cells [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153738#pone.0153738.ref038" target="_blank">38</a>] (blue bars) are compared to simulations from four different cases of our model: the full stochastic SCM (red bars), the stochastic SCM without the mRNA-inherited noise term (green bars), the deterministic SCM with “extrinsic” noise only (magenta bars), and the completely deterministic SCM (black bars). <i>T</i>_c is cell cycle duration (min) = <i>T</i>_G1 + <i>T</i>_b = <i>T</i>₁ + <i>T</i>₂ + <i>T</i>_b. <i>T</i>₁ is the duration from cell birth to Start (min), which we identify with SBF reaching 50% of its maximum value. <i>T</i>₂ is the period from Start to bud emergence (min), which we identify with [BUD]_n = 1. <i>T</i>_G1 = <i>T</i>₁ + <i>T</i>₂ = duration of the “unbudded phase” (min). <i>T</i>_b is the duration of the “budded phase,” from bud emergence to the next cell division (min). <i>V</i>_birth is cell size at birth (fL). Asterisks indicate unreported data.</p

A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks

<div><p>To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a “standard component” modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the <i>CLB2</i>-<i>db</i>Δ <i>clb5</i>Δ mutant strain. Our simulations show that mathematical modeling with “standard components” can capture in quantitative detail many essential properties of cell cycle control in budding yeast.</p></div

Simulations of wild-type cells.

<p>(A) Start: As the cell grows (increasing <i>V</i>_n), Cln3 accumulates and phosphorylates Whi5. At a critical cell size, SBF is abruptly released from the inactive SBF:Whi5 complex and initiates a positive feedback loop between the accumulation of Cln2 and the phosphorylation of Whi5. (B) G₁/S/G₂/M: SBF also promotes the synthesis of Clb5. Once Clb5 titrates out CKI, then Clb5 and Cln2 together inactivate Cdh1, resulting in the accumulation of Clb2. Exit: Clb2 triggers many mitotic events, eventually leading to the release of Cdc14 during mitotic exit. When Clb2 drops below a normalized concentration of 0.4, the cell divides asymmetrically between daughter and mother cells. The daughter cell receives 42% of the cell size at division, and the mother cell (not shown here) receives the remaining 58%. (C and D) The stochastic model shows the typical fluctuations of protein concentrations around the average dynamics predicted by the corresponding deterministic model. For easier comparison to the deterministic simulation (A and B), we converted the numbers of molecules reported by the stochastic simulation to normalized concentrations. Start and division events are indicated by up-pointing and down-pointing black triangles, respectively.</p

Stochastic simulations of <i>T</i>₁ and <i>T</i>_G1 for the Start SCM with explicit account of fluctuating mRNA species.

<p>As in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153738#pone.0153738.g006" target="_blank">Fig 6</a>, except now we have added synthesis and degradation of mRNA species explicitly to the SCM. The remaining discrepancies are attributable in part to differences between the models and in part to simulating mRNA noise by CLE rather than SSA.</p

Parameter values for the standard component model of the Start transition.

<p>Parameter values for the standard component model of the Start transition.</p

Equations for the standard component model of the full cell cycle system.

<p>Equations for the standard component model of the full cell cycle system.</p

The Start transition.

<p>(A) Schematic diagram of the Start transition in budding yeast. In early G₁, SBF is inactivated by its stoichiometric inhibitor, Whi5. As cell size increases, Cln3 accumulates and begins to phosphorylate Whi5. Phosphorylated Whi5 loses its ability to bind to SBF. As a result, SBF is free and promotes the production of ClbS (Cln2 and Clb5). ClbS exerts positive feedback on its own accumulation by further phosphorylating Whi5. The activation of SBF correlates with the onset of the Start transition. Subsequent accumulation of ClbS promotes both bud emergence and the G₁/S transition. (B) Wiring diagram of the MultiP model. The first three forms of Whi5 (Whi5, Whi5P₁, and Whi5P₂) bind to SBF and inhibit its ability to activate the synthesis of ClbS. The higher phosphorylated forms are inactive and do not bind to SBF. The model also includes mRNA species for each protein component. (C) Wiring diagram of the standard component model. The 10 distinct forms of Whi5 in the MultiP model are replaced by two forms of Whi5 (active and inactive). For panels B and C, solid lines indicate chemical reactions (synthesis and degradation, phosphorylation and dephosphorylation, association and dissociation) and dashed lines indicate activatory or inhibitory influences of components on chemical reactions.</p

Deterministic simulations of the relation between initial cell size (<i>V</i>₀) and cell age at the Start transition (<i>T</i>₁) (upper panel) and cell age at the G₁/S transition (<i>T</i>_G1) (lower panel) for the Start models.

<p>Red bars: MultiP model; green bars: SCM. The left-most bars of the figure correspond to the time-series simulations in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153738#pone.0153738.g003" target="_blank">Fig 3</a>.</p