Machine learning algorithms evaluate immune response to novel Mycobacterium tuberculosis antigens for diagnosis of tuberculosis

RationaleTuberculosis diagnosis in children remains challenging. Microbiological confirmation of tuberculosis disease is often lacking, and standard immunodiagnostic including the tuberculin skin test and interferon-gamma release assay for tuberculosis infection has limited sensitivity. Recent research suggests that inclusion of novel Mycobacterium tuberculosis antigens has the potential to improve standard immunodiagnostic tests for tuberculosis.ObjectiveTo identify optimal antigen-cytokine combinations using novel Mycobacterium tuberculosis antigens and cytokine read-outs by machine learning algorithms to improve immunodiagnostic assays for tuberculosis.MethodsA total of 80 children undergoing investigation of tuberculosis were included (15 confirmed tuberculosis disease, five unconfirmed tuberculosis disease, 28 tuberculosis infection and 32 unlikely tuberculosis). Whole blood was stimulated with 10 novel Mycobacterium tuberculosis antigens and a fusion protein of early secretory antigenic target (ESAT)-6 and culture filtrate protein (CFP) 10. Cytokines were measured using xMAP multiplex assays. Machine learning algorithms defined a discriminative classifier with performance measured using area under the receiver operating characteristics.Measurements and main resultsWe found the following four antigen-cytokine pairs had a higher weight in the discriminative classifier compared to the standard ESAT-6/CFP-10-induced interferon-gamma: Rv2346/47c- and Rv3614/15c-induced interferon-gamma inducible protein-10; Rv2031c-induced granulocyte-macrophage colony-stimulating factor and ESAT-6/CFP-10-induced tumor necrosis factor-alpha. A combination of the 10 best antigen-cytokine pairs resulted in area under the curve of 0.92 +/- 0.04.ConclusionWe exploited the use of machine learning algorithms as a key tool to evaluate large immunological datasets. This identified several antigen-cytokine pairs with the potential to improve immunodiagnostic tests for tuberculosis in children.Immunogenetics and cellular immunology of bacterial infectious disease

Dynamics and Adaptive Benefits of Protein Domain Emergence and Arrangements during Plant Genome Evolution

Plant genomes are generally very large, mostly paleopolyploid, and have numerous gene duplicates and complex genomic features such as repeats and transposable elements. Many of these features have been hypothesized to enable plants, which cannot easily escape environmental challenges, to rapidly adapt. Another mechanism, which has recently been well described as a major facilitator of rapid adaptation in bacteria, animals, and fungi but not yet for plants, is modular rearrangement of protein-coding genes. Due to the high precision of profile-based methods, rearrangements can be well captured at the protein level by characterizing the emergence, loss, and rearrangements of protein domains, their structural, functional, and evolutionary building blocks. Here, we study the dynamics of domain rearrangements and explore their adaptive benefit in 27 plant and 3 algal genomes. We use a phylogenomic approach by which we can explain the formation of 88% of all arrangements by single-step events, such as fusion, fission, and terminal loss of domains. We find many domains are lost along every lineage, but at least 500 domains are novel, that is, they are unique to green plants and emerged more or less recently. These novel domains duplicate and rearrange more readily within their genomes than ancient domains and are overproportionally involved in stress response and developmental innovations. Novel domains more often affect regulatory proteins and show a higher degree of structural disorder than ancient domains. Whereas a relatively large and well-conserved core set of single-domain proteins exists, long multi-domain arrangements tend to be species-specific. We find that duplicated genes are more often involved in rearrangements. Although fission events typically impact metabolic proteins, fusion events often create new signaling proteins essential for environmental sensing. Taken together, the high volatility of single domains and complex arrangements in plant genomes demonstrate the importance of modularity for environmental adaptability of plants

瀬戸内島嶼部の柑橘類栽培農家中高年女性における骨密度および生活習慣病関連指標

本研究では柑橘類農家中高年女性の生活習慣病に関連する身体的・代謝的特徴を知るため，瀬戸内海島嶼部の健康な女性107名（平均年齢59.9±8.3歳，S・Y町グループ）について骨密度と生活習慣病関連指標を調べ，地方都市居住の女性86名（平均年齢60.2±7.4歳，M市グループ）の結果と比較した。二重X線吸収法（DXA）で測定したS・Y町グループ女性の骨密度はM市グループ女性の測定値よりも高かった。一方，S・Y町グループ女性の生活習慣病関連指標（中性脂肪，HDLコレステロール，LDLコレステロール，HbA1cなどの血中濃度，血圧および上腕―足首間脈波伝播速度）はM市グループ女性よりもリスクが高い値を示した。しかし，S・Y町グループ女性の本研究での測定値はいずれも健常の範囲内であった。S・Y町グループ女性の生活習慣病予防のためには，食生活改善や身体活動推進など，地域の実情に応じた健康のためのライフスタイル改善の推進策が必要と考えられる。To determine the physical and metabolic features related to metabolic syndrome in middle-aged and elderly women in the citrus fruit farmers, we examined the bone mineral density and risk factors for metabolic syndrome in 107 healthy women （mean age 59.9 ± 8.3） living on a Japan Inland Sea island （S ・Y town group） and compared them with 86 healthy women （mean age 60.2 ± 7.4） living in a local city （M city group） . Bone mineral density in the women of the S ・ Y town group was higher than that of the M city group. On the contrary, the women in the S ・Y town group were at a higher risk for a metabolic syndrome （values of serum lipid, LDL-cholesterol, HDL-cholesterol and HbA1c, blood pressure and brachial-ankle pulse wave velocity） . These risk factors for metabolic syndrome in the S ・Y town group were still within normal limits for healthy adults. In order to prevent metabolic syndrome in the S ・Y town group, a health policy based on a community-based strategy should be implemented to encourage the people to change over to a healthy lifestyle, including good eating habits and physical activity.報告Report

President Brand

https://digitalarchives.apu.edu/citrus-crates/1273/thumbnail.jp

Institusionalisasi Sistem INA-CBGs (Indonesia Case Based Groups), Strategi Efektivitas dan Efisiensi Organisasi: Studi Kasus Pada Rumah Sakit Umum Daerah Dr. Saiful Anwar.

Studi kasus interpretatif dalam penelitian ini digunakan untuk mengetahui tentang institusionalisasi sistem INA-CBGs serta strategi efektivitas dan efisiensi di Rumah Sakit Umum Daerah Dr. Safiul Anwar Malang (RSSA). Sejak tahun 2014 pemerintah telah memperkenalkan sistem INA-CBGs sebagai metode pembayaran klaim pada fasilitas kesehatan tingkat lanjut. Teori Institusional yang dikenalkan oleh Dimaggio dan Powell (1991) digunakan untuk memahami bagaimana sistem INA-CBGs telah dilembagakan dalam RSSA dengan secara teknik maupun kebijakan. Hasil penelitian menunjukkan bahwa penerapan sistem INA-CBGs secara cepat dalam jangka waktu yang relatif singkat hanya berhasil melembagakan sistem yang baru melalui persetujuan dan kompromi. Institusionalisasi sistem INA-CBGs di RSSA lebih ke arah peningkatan legitimasi sebagai rumah sakit pemerintah. Namun demikian, peningkatan pendapatan dan jumlah pasien JKN, tampaknya menjadi faktor yang signifikan untuk melembagakan sistem baru dalam strategi pelayanan mereka daripada untuk peningkatan mutu pelayanan dan efisiensi biaya. Strategi efektivitas dan efisiensi manajemen RSSA yang belum optimal membuat sistem INA-CBGs belum mampu membuat rumah sakit menjadi efektif dan efisien dalam melayani pasien. Pada akhirnya sistem INA-CBGs justru menjadi alat bagi rumah sakit pemerintah dalam memenuhi target pendapatan pemerintah daerah maupun pemerintah pusat serta mengurangi pelayanan pada pasien

South Mountain Brand

https://digitalarchives.apu.edu/citrus-crates/1252/thumbnail.jp

Poinsettia Brand

https://digitalarchives.apu.edu/citrus-crates/1079/thumbnail.jp