96 research outputs found
FAKTOR-FAKTOR YANG MEMPENGARUHI PERILAKU MEROKOK SISWA KELAS VII MTS TPI SILAU DUNIA KECAMATAN SILAU KAHEAN KABUPATEN SIMALUNGUN
Abstract
Smoking behavior is a common phenomenon in Indonesian society. Currently smoking has penetrated into the lives of school children starting from high school, junior high school and even some elementary school children have smoked. The prevalence rate of adolescent smokers in North Sumatra Province at the age of 10-19 years is 27.28% of the total population. There are many factors that influence smoking habits among students, including the influence of the parents' environment, the influence of friends, personality factors and because of advertising. This research was conducted at MTS TPI Silau Dunia, Silau Kahean District, Simalungun Regency. This type of research is a quantitative study using a cross sectional. The population determined in this study were all seventh grade students of MTS TPI Silau Dunia. The number of available samples is 58 samples taken by purposive sampling from the population. The results of the bivariate analysis of the influence of knowledge, attitudes, parental control, and peers on smoking behavior obtained p-values of 0.002, 0.001, 0.001, and 0.002, respectively. From the results of the bivariate analysis of p value < (0.05), it can be concluded that there is an influence of knowledge, attitudes, parental control, and peers on the smoking behavior of seventh grade students of MTS TPI Silau Dunia, Silau Kahean District, Simalungun Regency. The need for education from MTS TPI Silau Dunia about substances in cigarettes and the dangers of smoking to health as well as monitoring student behavior, especially students who already smoke
Π‘ΠΈΠ½ΡΠ΅Π· ΡΠ° Π΄ΡΡΡΠ΅ΡΠΈΡΠ½Π° Π΄ΡΡ 8-Π°ΠΌΡΠ½ΠΎΠ·Π°ΠΌΡΡΠ΅Π½ΠΈΡ 7-(2-Π³ΡΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΡΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Ρ
It has been found that natural xanthines, as well as their synthetic analogs, possess the diuretic effect. Analysis of the literature proves that there is a great opportunity of applying synthetic derivatives of N-methylated xanthines as potential diuretics.Aim. To develop preparative methods of the synthesis of 8-aminosubstituted of 7-(2-hydroxy-3-p-metoxyphenoxypropyl-1)-3-methylxanthine and study their physical, chemical and biological properties. Results. The synthesis of a series of 8-aminosubstituted of 7-(2-hydroxy-3-p-metoxyphenoxypropyl-1)-3-methylxanthine was carried out. According to the results of the biological testing the compounds synthesized belong to the toxicity of class IV. 7-(2-Hydroxy-3-p-methoxyphenoxypropyl-1)-8-(furyl-2-methylamino)-3-methylxanthine xanthine shows the highest diuretic activity, and hence, requires a more in-depth study since it is twice more active than hydrochlorothiazide. It should be emphasized that all compounds synthesized exhibit a marked diuretic effect. Experimental part. 8-Bromo-7-(2-hydroxy-3-p-methoxyphenoxypropyl-1)-3-methylxanthine was obtained by heating 8-bromo-3-methylxanthine with p-methoxyphenoxymethyloxirane in butanol-1 and in the presence of N,N-dimethylbenzylamine. 8-Aminosubstitutied of 7-(2-hydroxy-3-p-metoxyphenoxypropyl-1)-3-methylxanthine was obtained by boiling of bromoalcohol with the primary and secondary amines. The structure of the compounds synthesized was unambiguously confirmed by NMR-spectroscopy. The acute toxicity of the compounds obtained was studied by Kerber method. The study of the diuretic activity of the compounds was carried out using Ye. Berkhin method. Hydrochlorothiazide was used as a reference substance. Conclusions. Simple methods for the synthesis of 8-amino-7- (2-hydroxy-3-p-methoxyphenoxypropyl-1)-3-methylxanthines have been developed. The structure of the compounds synthesized has been confirmed by the method of NMR 1H-spectroscopy. The acute toxicity and the diuretic activity of the compounds obtained have been studied.ΠΠ·Π²Π΅ΡΡΠ½ΠΎ, ΡΡΠΎ ΠΊΠ°ΠΊ ΠΏΡΠΈΡΠΎΠ΄Π½ΡΠ΅ ΠΊΡΠ°Π½ΡΠΈΠ½Ρ, ΡΠ°ΠΊ ΠΈ ΠΈΡ
ΡΠΈΠ½ΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π°Π½Π°Π»ΠΎΠ³ΠΈ ΠΎΠ±Π»Π°Π΄Π°ΡΡ Π΄ΠΈΡΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ΠΌ. ΠΠ½Π°Π»ΠΈΠ· Π΄Π°Π½Π½ΡΡ
Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΠ΅Ρ ΠΎ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ½ΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΡΡ
N-ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΊΡΠ°Π½ΡΠΈΠ½ΠΎΠ² Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΡ
Π΄ΠΈΡΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ΅Π΄ΡΡΠ². Π¦Π΅Π»Ρ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ β ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠΈΠ²Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΈΠ½ΡΠ΅Π·Π° 8-Π°ΠΌΠΈΠ½ΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½Π½ΡΡ
7-(2-Π³ΠΈΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΠΈΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Π° ΠΈ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΈΡ
ΡΠΈΠ·ΠΈΠΊΠΎ-Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΠΎΠΉΡΡΠ². Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈ ΠΈΡ
ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅. Π‘ΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½ ΡΡΠ΄ 8-Π°ΠΌΠΈΠ½ΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½Π½ΡΡ
7-(2-Π³ΠΈΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΠΈΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Π°. ΠΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΡΠΏΡΡΠ°Π½ΠΈΠΉ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΡΡΡΡΡ ΠΊ IV ΠΊΠ»Π°ΡΡΡ ΡΠΎΠΊΡΠΈΡΠ½ΠΎΡΡΠΈ. 7-(2-ΠΠΈΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΠΈΠ»-1)-8-(ΡΡΡΠΈΠ»-2-ΠΌΠ΅ΡΠΈΠ»Π°ΠΌΠΈΠ½ΠΎ)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½ ΠΈΠΌΠ΅Π΅Ρ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΡΡ Π΄ΠΈΡΡΠ΅ΡΠΈΡΠ΅ΡΠΊΡΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ, Π° ΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ Π΄ΠΎΡΠΊΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅, ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΠΎΠ½ Π±ΠΎΠ»Π΅Π΅ ΡΠ΅ΠΌ Π² 2 ΡΠ°Π·Π° Π°ΠΊΡΠΈΠ²Π½Π΅Π΅Β Π³ΠΈΠ΄ΡΠΎΡ
Π»ΠΎΡΡΠΈΠ°Π·ΠΈΠ΄Π°. ΠΠ΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΠΏΠΎΠ΄ΡΠ΅ΡΠΊΠ½ΡΡΡ, ΡΡΠΎ Π²ΡΠ΅ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ ΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΠ΅ Π΄ΠΈΡΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅. ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Π°Ρ ΡΠ°ΡΡΡ. 8-ΠΡΠΎΠΌ-7-(2-Π³ΠΈΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΒΠΏΡΠΎΠΏΠΈΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½ ΠΏΠΎΠ»ΡΡΠ΅Π½ Π½Π°Π³ΡΠ΅Π²Π°Π½ΠΈΠ΅ΠΌ 8-Π±ΡΠΎΠΌ-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Π° Ρ ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΌΠ΅ΡΠΈΠ»ΠΎΠΊΡΠΈΡΠ°Π½ΠΎΠΌ Π² Π±ΡΡΠ°Π½ΠΎΠ»Π΅-1 Π² ΠΏΡΠΈΡΡΡΡΡΠ²ΠΈΠΈ N,N-Π΄ΠΈΠΌΠ΅ΡΠΈΠ»Π±Π΅Π½Π·ΠΈΠ»Π°ΠΌΠΈΠ½Π°. 8-ΠΠΌΠΈΠ½ΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½Π½ΡΠ΅ 7-(2-Π³ΠΈΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΒΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΠΈΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Π° ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Ρ ΠΏΡΡΠ΅ΠΌ ΠΊΠΈΠΏΡΡΠ΅Π½ΠΈΡ Π±ΡΠΎΠΌΠΎΡΠΏΠΈΡΡΠ° Ρ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΡΠΌΠΈ ΠΈ Π²ΡΠΎΡΠΈΡΠ½ΡΠΌΠΈ Π°ΠΌΠΈΠ½Π°ΠΌΠΈ. Π‘ΡΡΡΠΊΡΡΡΠ° ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ ΠΎΠ΄Π½ΠΎΠ·Π½Π°ΡΠ½ΠΎ Π΄ΠΎΠΊΠ°Π·Π°Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΠΠ -ΡΠΏΠ΅ΠΊΡΡΠΎΡΠΊΠΎΠΏΠΈΠΈ. ΠΡΡΡΠ°Ρ ΡΠΎΠΊΡΠΈΡΠ½ΠΎΡΡΡ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ ΠΈΠ·ΡΡΠ°Π»Π°ΡΡ ΠΏΠΎ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΠ΅ΡΠ±Π΅ΡΠ°. ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ Π΄ΠΈΡΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ ΠΏΠΎ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΠ΅ΡΡ
ΠΈΠ½Π° Π. Π. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΡΡΠ°Π»ΠΎΠ½Π° ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈ Π³ΠΈΠ΄ΡΠΎΡ
Π»ΠΎΡΡΠΈΠ°Π·ΠΈΠ΄. ΠΡΠ²ΠΎΠ΄Ρ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Ρ ΠΏΡΠΎΡΡΡΠ΅ Π² Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΡΠΈΠ½ΡΠ΅Π·Π° 8-Π°ΠΌΠΈΠ½ΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½Π½ΡΡ
7-(2-Π³ΠΈΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΠΈΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Π°. Π‘ΡΡΡΠΊΡΡΡΠ° ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ Π΄ΠΎΠΊΠ°Π·Π°Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΠΠ -ΡΠΏΠ΅ΠΊΡΡΠΎΡΠΊΠΎΠΏΠΈΠΈ. ΠΠ·ΡΡΠ΅Π½Π° ΠΎΡΡΡΠ°Ρ ΡΠΎΠΊΡΠΈΡΠ½ΠΎΡΡΡ ΠΈ Π΄ΠΈΡΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π²Π΅ΡΠ΅ΡΡΠ².ΠΡΠ΄ΠΎΠΌΠΎ, ΡΠΎ ΡΠΊ ΠΏΡΠΈΡΠΎΠ΄Π½Ρ ΠΊΡΠ°Π½ΡΠΈΠ½ΠΈ, ΡΠ°ΠΊ Ρ ΡΡ
ΡΠΈΠ½ΡΠ΅ΡΠΈΡΠ½Ρ Π°Π½Π°Π»ΠΎΠ³ΠΈ Π²ΠΈΡΠ²Π»ΡΡΡΡ Π΄ΡΡΡΠ΅ΡΠΈΡΠ½Ρ Π΄ΡΡ. ΠΠ½Π°Π»ΡΠ· Π΄Π°Π½ΠΈΡ
Π»ΡΡΠ΅ΡΠ°ΡΡΡΠΈ ΡΠ²ΡΠ΄ΡΠΈΡΡ ΠΏΡΠΎ Π·Π½Π°ΡΠ½Ρ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ ΡΠΈΠ½ΡΠ΅ΡΠΈΡΠ½ΠΈΡ
ΠΏΠΎΡ
ΡΠ΄Π½ΠΈΡ
N-ΠΌΠ΅ΡΠΈΠ»ΡΠΎΠ²Π°Π½ΠΈΡ
ΠΊΡΠ°Π½ΡΠΈΠ½ΡΠ² Π² ΡΠΊΠΎΡΡΡ ΠΏΠΎΡΠ΅Π½ΡΡΠΉΠ½ΠΈΡ
Π΄ΡΡΡΠ΅ΡΠΈΡΠ½ΠΈΡ
Π·Π°ΡΠΎΠ±ΡΠ². ΠΠ΅ΡΠΎΡ Π΄Π°Π½ΠΎΡ ΡΠΎΠ±ΠΎΡΠΈ Ρ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠΈΠ²Π½ΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΡΠ² ΡΠΈΠ½ΡΠ΅Π·Ρ 8-Π°ΠΌΡΠ½ΠΎΠ·Π°ΠΌΡΡΠ΅Π½ΠΈΡ
7-(2-Π³ΡΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΡΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Ρ ΡΠ° Π²ΠΈΠ²ΡΠ΅Π½Π½Ρ ΡΡ
ΡΡΠ·ΠΈΠΊΠΎ-Ρ
ΡΠΌΡΡΠ½ΠΈΡ
ΡΠ° Π±ΡΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΈΡ
Π²Π»Π°ΡΡΠΈΠ²ΠΎΡΡΠ΅ΠΉ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΠΈ ΡΠ° ΡΡ
ΠΎΠ±Π³ΠΎΠ²ΠΎΡΠ΅Π½Π½Ρ. ΠΡΠ² ΡΠΈΠ½ΡΠ΅Π·ΠΎΠ²Π°Π½ΠΈΠΉ ΡΡΠ΄ 8-Π°ΠΌΡΠ½ΠΎΠ·Π°ΠΌΡΡΠ΅Π½ΠΈΡ
7-(2-Π³ΡΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΡΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Ρ. ΠΠ° ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ Π±ΡΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΈΡ
Π²ΠΈΠΏΡΠΎΠ±ΡΠ²Π°Π½Ρ ΡΠΈΠ½ΡΠ΅Π·ΠΎΠ²Π°Π½Ρ ΡΠΏΠΎΠ»ΡΠΊΠΈ Π²ΡΠ΄Π½ΠΎΡΡΡΡΡΡ Π΄ΠΎ IV ΠΊΠ»Π°ΡΡ ΡΠΎΠΊΡΠΈΡΠ½ΠΎΡΡΡ. 7-(2-ΠΡΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΡΠ»-1)-8-(ΡΡΡΠΈΠ»-2-ΠΌΠ΅ΡΠΈΠ»Π°ΠΌΡΠ½ΠΎ)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½ Π²ΠΈΡΠ²Π»ΡΡ Π½Π°ΠΉΠ²ΠΈΡΡ Π΄ΡΡΡΠ΅ΡΠΈΡΠ½Ρ Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ, Π° ΠΎΡΠΆΠ΅ ΠΏΠΎΡΡΠ΅Π±ΡΡ Π±ΡΠ»ΡΡ Π΄ΠΎΡΠΊΠΎΠ½Π°Π»ΠΎΠ³ΠΎ Π²ΠΈΠ²ΡΠ΅Π½Π½Ρ, ΠΎΡΠΊΡΠ»ΡΠΊΠΈ Π²ΡΠ½ Π±ΡΠ»ΡΡ Π½ΡΠΆ Ρ 2 ΡΠ°Π·ΠΈ Π°ΠΊΡΠΈΠ²Π½ΡΡΠΈΠΉ Π·Π° Π³ΡΠ΄ΡΠΎΡ
Π»ΠΎΡΡΡΠ°Π·ΠΈΠ΄. ΠΠ΅ΠΎΠ±Ρ
ΡΠ΄Π½ΠΎ ΠΏΡΠ΄ΠΊΡΠ΅ΡΠ»ΠΈΡΠΈ, ΡΠΎ Π²ΡΡ ΡΠΈΠ½ΡΠ΅Π·ΠΎΠ²Π°Π½Ρ ΡΠΏΠΎΠ»ΡΠΊΠΈ Π²ΠΈΡΠ²Π»ΡΡΡΡ Π²ΠΈΡΠ°Π·Π½Ρ Π΄ΡΡΡΠ΅ΡΠΈΡΠ½Ρ Π΄ΡΡ.ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Π° ΡΠ°ΡΡΠΈΠ½Π°. 8-ΠΡΠΎΠΌΠΎ-7-(2-Π³ΡΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΡΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½ ΠΎΡΡΠΈΠΌΠ°Π»ΠΈ Π½Π°Π³ΡΡΠ²Π°Π½Π½ΡΠΌ 8-Π±ΡΠΎΠΌΠΎ-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Ρ Π· ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΌΠ΅ΡΠΈΠ»ΠΎΠΊΡΠΈΡΠ°Π½ΠΎΠΌ Ρ Π±ΡΡΠ°Π½ΠΎΠ»Ρ-1 Π² ΠΏΡΠΈΡΡΡΠ½ΠΎΡΡΡ N,N-Π΄ΠΈΠΌΠ΅ΡΠΈΠ»Π±Π΅Π½Π·ΠΈΠ»Π°ΠΌΡΠ½Ρ. 8-ΠΠΌΡΠ½ΠΎΠ·Π°ΠΌΡΡΠ΅Π½Ρ 7-(2-Π³ΡΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΡΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Ρ ΡΠΈΠ½ΡΠ΅Π·ΠΎΠ²Π°Π½Ρ ΡΠ»ΡΡ
ΠΎΠΌ ΠΊΠΈΠΏβΡΡΡΠ½Π½Ρ Π±ΡΠΎΠΌΠΎΡΠΏΠΈΡΡΡ Π· ΠΏΠ΅ΡΠ²ΠΈΠ½Π½ΠΈΠΌΠΈ ΡΠ° Π²ΡΠΎΡΠΈΠ½Π½ΠΈΠΌΠΈ Π°ΠΌΡΠ½Π°ΠΌΠΈ. Π‘ΡΡΡΠΊΡΡΡΠ° ΡΠΈΠ½ΡΠ΅Π·ΠΎΠ²Π°Π½ΠΈΡ
ΡΠΏΠΎΠ»ΡΠΊ Π±ΡΠ»Π° ΠΎΠ΄Π½ΠΎΠ·Π½Π°ΡΠ½ΠΎ Π΄ΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΠΠ -ΡΠΏΠ΅ΠΊΡΡΠΎΡΠΊΠΎΠΏΡΡ. ΠΠΎΡΡΡΠ° ΡΠΎΠΊΡΠΈΡΠ½ΡΡΡΡ ΡΠΈΠ½ΡΠ΅Π·ΠΎΠ²Π°Π½ΠΈΡ
ΡΠΏΠΎΠ»ΡΠΊ Π±ΡΠ»Π° Π²ΠΈΠ²ΡΠ΅Π½Π° Π·Π° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΠ΅ΡΠ±Π΅ΡΠ°. ΠΠΈΠ²ΡΠ΅Π½Π½Ρ Π΄ΡΡΡΠ΅ΡΠΈΡΠ½ΠΎΡ Π΄ΡΡ ΠΎΡΡΠΈΠΌΠ°Π½ΠΈΡ
ΡΠΏΠΎΠ»ΡΠΊ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π·Π° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΠ΅ΡΡ
ΡΠ½Π° Π. Π. Π ΡΠΊΠΎΡΡΡ Π΅ΡΠ°Π»ΠΎΠ½Ρ ΠΏΠΎΡΡΠ²Π½ΡΠ½Π½Ρ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΠ²Π°Π»ΠΈ Π³ΡΠ΄ΡΠΎΡ
Π»ΠΎΡΡΡΠ°Π·ΠΈΠ΄. ΠΠΈΡΠ½ΠΎΠ²ΠΊΠΈ. ΠΡΠ»ΠΈ ΡΠΎΠ·ΡΠΎΠ±Π»Π΅Π½Ρ ΠΏΡΠΎΡΡΡ Ρ Π²ΠΈΠΊΠΎΠ½Π°Π½Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΡΠΈΠ½ΡΠ΅Π·Ρ 8-Π°ΠΌΡΠ½ΠΎΠ·Π°ΠΌΡΡΠ΅Π½ΠΈΡ
7-(2-Π³ΡΠ΄ΡΠΎΠΊΡΠΈ-3-ΠΏ-ΠΌΠ΅ΡΠΎΠΊΡΠΈΡΠ΅Π½ΠΎΠΊΡΠΈΠΏΡΠΎΠΏΡΠ»-1)-3-ΠΌΠ΅ΡΠΈΠ»ΠΊΡΠ°Π½ΡΠΈΠ½Ρ. Π‘ΡΡΡΠΊΡΡΡΠ° ΡΠΈΠ½ΡΠ΅Π·ΠΎΠ²Π°Π½ΠΈΡ
ΡΠΏΠΎΠ»ΡΠΊ Π±ΡΠ»Π° Π΄ΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΠΠ -ΡΠΏΠ΅ΠΊΡΡΠΎΡΠΊΠΎΠΏΡΡ. ΠΠΈΠ²ΡΠ΅Π½Π° Π³ΠΎΡΡΡΠ° ΡΠΎΠΊΡΠΈΡΠ½ΡΡΡΡ ΡΠ° Π΄ΡΡΡΠ΅ΡΠΈΡΠ½Π° Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ ΠΎΡΡΠΈΠΌΠ°Π½ΠΈΡ
ΡΠ΅ΡΠΎΠ²ΠΈΠ½
A Modular Network Architecture Resolving Memory Interference through Inhibition
International audienceIn real learning paradigms like pavlovian conditioning, several modes of learning are associated, including generalization from cues and integration of specific cases in context. Associative memories have been shown to be interesting neuronal models to learn quickly specific cases but they are hardly used in realistic applications because of their limited storage capacities resulting in interferences when too many examples are considered. Inspired by biological considerations, we propose a modular model of associative memory including mechanisms to manipulate properly multimodal inputs and to detect and manage interferences. This paper reports experiments that demonstrate the good behavior of the model in a wide series of simulations and discusses its impact both in machine learning and in biological modeling
Development of an autonomous IoT-based drone for campus security
In recent years, drone technology has gained popularity across the world because of its numerous applications, particularly in security and surveillance. This technology can be further revolutionized with the deployment of Industrial Revolution 4.0 Technology. This paper discusses the development of an IoT-based autonomous drone for more comprehensive campus security and surveillance system. The drone is featured with the capability of conducting a fully autonomous aerial surveillance, being the first responder in emergencies, streaming video while flying, avoiding obstacles, following a target and communicating with the current IoT based UTMβs security patrolling system for data transfer and drone control. This has been accomplished by using the open source ArduPilot software, Pixhawk flight controller along with Dronekit python library installed on a Raspberry Pi 4. The findings show that the actual performance of the designed drone is fairly similar to the simulation results. The drone has successfully performed autonomous navigation to incident location with 1 to 2 meter accuracy as well as follow-me mode. The cellular technology utilized for drone communication also is more robust and provides promising solution to overcome short operation range and interference
Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.
The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain
Causes of death among infants and children in the Child Health and Mortality Prevention Surveillance (CHAMPS) network
Importance The number of deaths of children younger than 5 years has been steadily decreasing worldwide, from more than 17 million annual deaths in the 1970s to an estimated 5.3 million in 2019 (with 2.8 million deaths occurring in those aged 1-59 months [53% of all deaths in children aged <5 years]). More detailed characterization of childhood deaths could inform interventions to improve child survival.
Objective To describe causes of postneonatal child deaths across 7 mortality surveillance sentinel sites in Africa and Asia.
Design, Setting, and Participants The Child Health and Mortality Prevention Surveillance (CHAMPS) Network conducts childhood mortality surveillance in sub-Saharan Africa and South Asia using innovative postmortem minimally invasive tissue sampling (MITS). In this cross-sectional study, MITS was conducted in deceased children aged 1 to 59 months at 7 sites in sub-Saharan Africa and South Asia from December 3, 2016, to December 3, 2020. Data analysis was conducted between October and November 2021.
Main Outcomes and Measures The expert panel attributed underlying, intermediate, and immediate conditions in the chain of events leading to death, based on histopathologic analysis, microbiological diagnostics, clinical data, and verbal autopsies.
Results In this study, MITS was performed in 632 deceased children (mean [SD] age at death, 1.3 [0.3] years; 342 [54.1%] male). The 6 most common underlying causes of death were malnutrition (104 [16.5%]), HIV (75 [11.9%]), malaria (71 [11.2%]), congenital birth defects (64 [10.1%]), lower respiratory tract infections (LRTIs; 53 [8.4%]), and diarrheal diseases (46 [7.2%]). When considering immediate causes only, sepsis (191 [36.7%]) and LRTI (129 [24.8%]) were the 2 dominant causes. An infection was present in the causal chain in 549 of 632 deaths (86.9%); pathogens most frequently contributing to infectious deaths included Klebsiella pneumoniae (155 of 549 infectious deaths [28.2%]; 127 [81.9%] considered nosocomial), Plasmodium falciparum (122 of 549 [22.2%]), and Streptococcus pneumoniae (109 of 549 [19.9%]). Other organisms, such as cytomegalovirus (57 [10.4%]) and Acinetobacter baumannii (39 [7.1%]; 35 of 39 [89.7%] considered nosocomial), also played important roles. For the top underlying causes of death, the median number of conditions in the chain of events leading to death was 3 for malnutrition, 3 for HIV, 1 for malaria, 3 for congenital birth defects, and 1 for LRTI. Expert panels considered 494 of 632 deaths (78.2%) preventable and 26 of 632 deaths (4.1%) preventable under certain conditions.
Conclusions and Relevance In this cross-sectional study investigating causes of child mortality in the CHAMPS Network, results indicate that, in these high-mortality settings, infectious diseases continue to cause most deaths in infants and children, often in conjunction with malnutrition. These results also highlight opportunities for action to prevent deaths and reveal common interaction of various causes in the path toward death
Noninvasive Prenatal Diagnosis of Fetal Trisomy 18 and Trisomy 13 by Maternal Plasma DNA Sequencing
Massively parallel sequencing of DNA molecules in the plasma of pregnant women has been shown to allow accurate and noninvasive prenatal detection of fetal trisomy 21. However, whether the sequencing approach is as accurate for the noninvasive prenatal diagnosis of trisomy 13 and 18 is unclear due to the lack of data from a large sample set. We studied 392 pregnancies, among which 25 involved a trisomy 13 fetus and 37 involved a trisomy 18 fetus, by massively parallel sequencing. By using our previously reported standard z-score approach, we demonstrated that this approach could identify 36.0% and 73.0% of trisomy 13 and 18 at specificities of 92.4% and 97.2%, respectively. We aimed to improve the detection of trisomy 13 and 18 by using a non-repeat-masked reference human genome instead of a repeat-masked one to increase the number of aligned sequence reads for each sample. We then applied a bioinformatics approach to correct GC content bias in the sequencing data. With these measures, we detected all (25 out of 25) trisomy 13 fetuses at a specificity of 98.9% (261 out of 264 non-trisomy 13 cases), and 91.9% (34 out of 37) of the trisomy 18 fetuses at 98.0% specificity (247 out of 252 non-trisomy 18 cases). These data indicate that with appropriate bioinformatics analysis, noninvasive prenatal diagnosis of trisomy 13 and trisomy 18 by maternal plasma DNA sequencing is achievable
Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network
BACKGROUND: The current burden of >5 million deaths yearly is the focus of the Sustainable Development Goal (SDG) to end preventable deaths of newborns and children under 5 years old by 2030. To accelerate progression toward this goal, data are needed that accurately quantify the leading causes of death, so that interventions can target the common causes. By adding postmortem pathology and microbiology studies to other available data, the Child Health and Mortality Prevention Surveillance (CHAMPS) network provides comprehensive evaluations of conditions leading to death, in contrast to standard methods that rely on data from medical records and verbal autopsy and report only a single underlying condition. We analyzed CHAMPS data to characterize the value of considering multiple causes of death. METHODS AND FINDINGS: We examined deaths identified from December 2016 through November 2020 from 7 CHAMPS sites (in Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa), including 741 neonatal, 278 infant, and 241 child <5 years deaths for which results from Determination of Cause of Death (DeCoDe) panels were complete. DeCoDe panelists included all conditions in the causal chain according to the ICD-10 guidelines and assessed if prevention or effective management of the condition would have prevented the death. We analyzed the distribution of all conditions listed as causal, including underlying, antecedent, and immediate causes of death. Among 1,232 deaths with an underlying condition determined, we found a range of 0 to 6 (mean 1.5, IQR 0 to 2) additional conditions in the causal chain leading to death. While pathology provides very helpful clues, we cannot always be certain that conditions identified led to death or occurred in an agonal stage of death. For neonates, preterm birth complications (most commonly respiratory distress syndrome) were the most common underlying condition (n = 282, 38%); among those with preterm birth complications, 256 (91%) had additional conditions in causal chains, including 184 (65%) with a different preterm birth complication, 128 (45%) with neonatal sepsis, 69 (24%) with lower respiratory infection (LRI), 60 (21%) with meningitis, and 25 (9%) with perinatal asphyxia/hypoxia. Of the 278 infant deaths, 212 (79%) had β₯1 additional cause of death (CoD) beyond the underlying cause. The 2 most common underlying conditions in infants were malnutrition and congenital birth defects; LRI and sepsis were the most common additional conditions in causal chains, each accounting for approximately half of deaths with either underlying condition. Of the 241 child deaths, 178 (75%) had β₯1 additional condition. Among 46 child deaths with malnutrition as the underlying condition, all had β₯1 other condition in the causal chain, most commonly sepsis, followed by LRI, malaria, and diarrheal disease. Including all positions in the causal chain for neonatal deaths resulted in 19-fold and 11-fold increases in attributable roles for meningitis and LRI, respectively. For infant deaths, the proportion caused by meningitis and sepsis increased by 16-fold and 11-fold, respectively; for child deaths, sepsis and LRI are increased 12-fold and 10-fold, respectively. While comprehensive CoD determinations were done for a substantial number of deaths, there is potential for bias regarding which deaths in surveillance areas underwent minimally invasive tissue sampling (MITS), potentially reducing representativeness of findings. CONCLUSIONS: Including conditions that appear anywhere in the causal chain, rather than considering underlying condition alone, markedly changed the proportion of deaths attributed to various diagnoses, especially LRI, sepsis, and meningitis. While CHAMPS methods cannot determine when 2 conditions cause death independently or may be synergistic, our findings suggest that considering the chain of events leading to death can better guide research and prevention priorities aimed at reducing child deaths
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Healthcare Professional Training Regarding Gestational Weight Gain: Recommendations and Future Directions
Purpose of review
The aim of this review was to summarise recent evaluations of healthcare professional training regarding gestational weight gain and provide recommendations for future training.
Recent findings
A number of evaluated healthcare professional training sessions regarding gestational weight gain show promising results in terms of increased participant confidence and knowledge and impact on womenβs outcomes. It is clear that the interventions which have also implemented resources in the practice environment to support training are the ones most likely to influence gestational weight gain.
Summary
Support from healthcare professionals are key to influence pregnant womenβs weight gain and should be offered within the standard curriculum and through mandatory training. Factors influencing this support include womenβs and healthcare professional characteristics, interpersonal and healthcare system and policy factors. All of these need to be considered when developing healthcare professional training to support women with their gestational weight gain
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