Penggunaan Media Gambar Dalam Meningkatkan Kemampuan Membaca Permulaan Siswa Kelas I SDN Uwedaka Kecamatan Pagimana Kabupaten Banggai

Pokok permasalahan dalam penelitian ini adalah rendahnya tingkat kemampuan membaca permulaan siswa kelas I SDN Uwedaka dalam pembelajaran Bahasa Indonesia. Tujuan Penelitian adalah untuk meningkatkan kemampuan membaca permulaan siswa kelas I SDN Uwedaka Kecamatan Pagimana Kabupaten Banggai. Berdasarkan hasil observasi yang didapatkan masih terdapat beberapa siswa yang sama sekali belum bisa membaca. Pembelajaran membaca permulaan di SDN Uwedaka selama ini hanya menggunakan media pembelajaran yang konvensional yaitu dengan menggunakan papan tulis, pembelajaran yang hanya berpusat pada guru, penggunaan media dalam pembelajaran sebagai alat bantu masih sangat terbatas, hal ini menyebabkan kemampuan membaca permulaan yang masih rendah dan terlihat hampir 65% siswa masih mengalami kesulitan membaca dalam proses belajar mengajar. Metode yang digunakan adalah metode deskriptif kualitatif dan kuantitatif. Data kualitatif didapatkan dari hasil tes dan observasi siswa dan guru. data kuantitatif didapatkan dari hasil tes belajar. Desain penelitian ini mengacu pada desain oleh Kemmis dan Mc Taggart yang terdiri dari empat tahapan, yaitu perencanaan, pelaksanaan tindakan, observasi dan refleksi. Data dikumpulkan melalui penilaian proses dan penilaian hasil setiap akhir tindakan. Penelitian ini dilakukan dalam dua siklus. Pada siklus I diperoleh nilai rata-rata siswa yaitu sebesar 67 dengan ketuntasan belajar klasikal sebesar 40% serta daya serap 66,6%. Pada siklus II, nilai rata-rata meningkat menjadi 83 dengan ketuntasan klasikal sebesar 100% serta daya serap klasikal sebesar 83,3%. Bersarkan hasil penelitian maka dapat disimpulkan bahwa penggunaan media gambar dapat meningkatkan kemampuan membaca permulaan terhadap siswa kelas I SDN Uwedaka Kecamatan Pagimana Kabupaten Banggai

Patient characteristics of the three cohorts used in CTC profile discovery and validation.

<p>Patient characteristics of the three cohorts used in CTC profile discovery and validation.</p

Network analysis of CTC-profile genes indicted two main functional networks: a cellular survival/proliferation associated network (colored orange) and a cellular migration/angiogenesis related network (colored purple).

<p>CTC-profile genes are indicated by grey symbols.</p

Independent validation of the CTC-predictive profile on 49 early-stage lymph-node negative breast cancer patients.

<p>(<b>A</b>)Distribution of CTC-profile indexes for CTC-positive and CTC-negative patients. The dashed line indicates the threshold as was determined in the training cohort. (<b>B</b>) Validation ROC curve showing classification performance of the CTC-profile. (<b>C</b>) Kaplan-Meier survival curves of relapse-free survival of patients classified as CTC-positive or CTC-negative using the CTC-profile.</p

Figure 3

<p>Kaplan-Meier survival analysis of a second independent validation patient cohort consisting of 123 early-stage breast cancer patients from the van de Vijver <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032426#pone.0032426-vandeVijver1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032426#pone.0032426-Glas1" target="_blank">[18]</a> dataset classified with the CTC-profile (<b>A</b>), MammaPrint 70-gene profile (<b>B</b>), and both classifications combined (<b>C)</b>.</p

A gene expression profile derived from the primary tumor accurately predicts the presence of CTCs in the peripheral blood in breast cancer patients.

<p>(A) The CTC-profile indexes of 72 breast tumor samples are highly correlative with CTC status. Samples are ordered according to CTC-profile index and colored based on CTC-status. The dashed line indicates the classification threshold with optimal sensitivity and specificity. (B) A heatmap shows the level of expression of the 34 CTC profile genes for CTC-negative and CTC-positive patients. (C) The ROC curve of CTC-profile indexes compared to actual CTC status.</p

DTC and VI's prognostic dependency upon the vascular markers.

<p>The figure shows Kaplan-Meier plots of DTC (A–H) and VI (I–P) for the end-points DDFS (A–F and I–L) and BCSS (E–H and M–P) within vascular sub-groups (columns): Small vessels (A, E, I and M), large vessels (B, F, J and N), low complexity vessels (C, G, K, and O) and high complexity vessels (D, H, L and P). Gray curves: DTC or VI negative; black: Positive. HR: Cox regression hazard ratios with 95% confidence interval. p-values are by log-rank test.</p

CD34 section case examples.

<p>A) High MVSμ; low MVPμ, MVA_Σ and MVD; B) Average MVSμ, high MVPμ and MVA_Σ, and low MVD; C) Low MVSμ, high MVPμ and MVA_Σ, and low MVD; D) Low MVSμ, average MVPμ and MVA_Σ, and high MVD. High MVPμ values (large vessels) and low MVSμ values (high complexity shapes) contribute to poor prognosis; as well as high MVA_Σ values (high vascular area) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075954#pone.0075954-Luukkaa1" target="_blank">[29]</a>, but MVD is inconsequential <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075954#pone.0075954-Luukkaa1" target="_blank">[29]</a>.</p

The Clinical Impact of Mean Vessel Size and Solidity in Breast Carcinoma Patients

<div><p>Angiogenesis quantification, through vessel counting or area estimation in the most vascular part of the tumour, has been found to be of prognostic value across a range of carcinomas, breast cancer included. We have applied computer image analysis to quantify vascular properties pertaining to size, shape and spatial distributions in photographed fields of CD34 stained sections. Aided by a pilot (98 cases), seven parameters were selected and validated on a separate set from 293 breast cancer patients.</p><p>Two new prognostic markers were identified through continuous Cox regression with endpoints Breast Cancer Specific Survival and Distant Disease Free Survival: The average size of the vessels as measured by their perimeter (p = 0.003 and 0.004, respectively), and the average complexity of the vessel shapes measured by their solidity (p = 0.004 and 0.004). The Hazard ratios for the corresponding median-dichotomized markers were 2.28 (p = 0.005) and 1.89 (p = 0.016) for the mean perimeter and 1.80 (p = 0.041) and 1.55 (p = 0.095) for the shape complexity. The markers were associated with poor histologic type, high grade, necrosis, HR negativity, inflammation, and p53 expression (vessel size only).</p><p>Both markers were found to strongly influence the prognostic properties of vascular invasion (VI) and disseminated tumour cells in the bone marrow. The latter being prognostic only in cases with large vessels (p = 0.004 and 0.043) or low complexity (p = 0.018 and 0.024), but not in the small or complex vessel groups (p>0.47). VI was significant in all groups, but showed greater hazard ratios for small and low complexity vessels (6.54–11.2) versus large and high complexity vessels (2.64–3.06).</p><p>We find that not only the overall amount of produced vasculature in angiogenic hot-spots is of prognostic significance, but also the morphological appearance of the generated vessels, <i>i.e.</i> the size and shape of vessels in the studied hot spots.</p></div

Continuous Cox regression survival analysis of validation parameters.

*<p>Significant at the Bonferroni corrected 0.05/7 level.</p>#<p>Hazard ratios (HR) are for one standard deviation increase in the parameter value.</p><p>MVA_CV: Coefficient of variation of the vessel areas. MVPμ: Mean vessel perimeter length; MV_luminal: Fraction of vessels with open lumen. MV_scale: Marker for the vascular density's dependency on the field size. ICD: Inter-capillary distance.</p