T. cruzi and the derivative 2'-(4-bromophenyl)-1-methyl-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol showed moderate biological activity (IC50 = 23.9 µM) when compared to benznidazole, a standard trypanocidal drug. These compounds did not present cytotoxic effects at concentrations near the trypanocidal IC50, being considered a good starting point for the development of new anti-Chagas drug candidates.Herein, we report the design, synthesis and trypanocidal activity of some novel trisubstituted imidazole derivatives. These heterocyclic derivatives were structurally planned by exploring the concept of molecular hybridisation between two arylhydrazones derived from megazol, which has potent trypanocidal activity. The trypanocidal activity of these triarylimidazole derivatives was evaluated against infective trypomastigote forms of  Trypanosoma cruzi  is a nitroheterocyclic derivative shown to be highly active against nidazole ,7; thus,nidazole ,9. Despi effects ,11,12. T effects ,15,16,17N-hydroxyimidazoles have been reported to possess fungicidal and bacteriostatic activities ) (100%). Anal. Calcd. for C19H15N3O4: C: 65.32; H: 4.33; N: 12.03. Found: C: 65.31; H: 4.33; N: 12.03.In a 250 mL flask, cooled in an ice bath, acetone (34 mL) and 1,2-dimethyl-5-nitroimidazole  were added to a 250 mL flask that was seeded with H2SO4 (56.6 mL) and cooled in an ice bath while stirring. After 30 min, glacial acetic acid (20.6 mL) and then phenylvinyl benzoate 14  were added. The reaction was maintained at 65 °C for 3 h and the temperature was then adjusted to 20 °C. Next, water (166 mL) was added, and this mixture was subsequently filtered in a Buchner funnel. Compound 15 was suspended in ethanol and filtered through a Buchner funnel, resulting in a yellow solid with a yield of 80%. mp 180–182 °C; 1H-NMR  δ 3.78 , 7.58 H and Ar(C5)H), 7.71 H), 7.98 H and Ar(C6)H), 8.21 H), 13.85 : 13C-NMR  δ 34.51 (5-NO2-imidazole-N-CH3), 128.47 (Ar(C3)H and Ar(C5)H), 130.16 (Ar(C2)H and Ar(C6)H), 132.24 (5-NO2-imidazole-(C4)H), 133.53 (Ar(C4)H), 135.79 (5-NO2-imidazole-(C5)NO2), 139.45 (5-NO2-imidazole-(C2)-C=N-OH), 142.46 (Ar(C1)), 146.04 (C=N-OH), 189.07 (C=O): MS (ESI) m/z: 273.1 (100%). Anal. Calcd. for C12H10N4O4: C: 52.56; H: 3.68; N: 20.43. Found: C: 52.54; H: 3.68; N: 20.42.In small portions, NaNO15 , NH4OAc , the corresponding benzaldehyde  and ethanol (12 mL) were added while stirring. The temperature was maintained at reflux for 5 h. The reaction was cooled 20 °C and then placed on crushed ice. The precipitate was filtered on a Buchner funnel and washed with water. All new 2,4,5-triaryl-N-hydroxyimidazole derivatives 3–12 were purified by recrystallization from a 9:1 solution of ethanol/water.In a 100 mL flask, ketoxime 1-Methyl-5-nitro-2',5'-diphenyl-1H,3'H-2,4'-biimidazol-3'-ol (3). Yellow solid; mp 217–218 °C; yield 69%; 1H-NMR  δ 3.72 (5-NO2-imidazole-N-CH3), 7.28 H), 7.34 H and Ar(C5)H), 7.47 H), 7.54 H and Ar(C5’)H), 7.57 H and Ar(C6)H), 8.16 H and Ar(C6’)H), 8.35 H), 12.48 : 13C-NMR  δ 34.57 (5-NO2-imidazole-N-CH3), 115.87 (N-OH-imidazole-C4), 125.89 (Ar(C2)H and Ar(C6)H), 127.39 (Ar(C2’)H and Ar(C6’)H), 127.71 (Ar(C4)H), 128.42 (Ar(C1’)), 128.61 (Ar(C3)H and Ar(C5)H), 128.70 (Ar(C3’)H and Ar(C5’)H), 129.33 (Ar(C4’)H), 132.75 (5-NO2-imidazole-(C4)H), 133.10 (N-OH-imidazole-C2), 137.56 (Ar(C1)), 139.97 (5-NO2-imidazole-(C2)), 141.28 (5-NO2-imidazole-(C5)NO2), 142.25 (N-OH-imidazole-C5): 13C-NMR DEPT δ 34.57 (5-NO2-imidazole-N-CH3), 125.89 (Ar(C2)H and Ar(C6)H), 127.39 (Ar(C2’)H and Ar(C6’)H), 127.71 (Ar(C4)H), 128.61 (Ar(C3)H and Ar(C5)H), 128.70 (Ar(C3’)H and Ar(C5’)H), 129.33 (Ar(C4’)H), 132.75 (5-NO2-imidazole-(C4)H): IR (KBr) νmax cm−1: 3284 (ν N-O-H), 3130–3064 (ν C-H(aromatic), 1526–1470 (ν C=C(aromatic), 1368 (ν N=O2): MS (ESI) m/z: 360.5 (100%). Anal. Calcd. for C19H15N5O3: C: 63.15; H: 4.18; N: 19.38. Found: C: 63.17; H: 4.18; N: 19.40.2'-(4-Fluorophenyl)-1-methyl-5-nitro-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol (4). Yellow solid; mp 148–149 °C; yield 48%; 1H-NMR  δ 3.87 (5-NO2-imidazole-N-CH3), 7.27 H), 7.34 H and Ar(C5’)H), 7.39 H and Ar(C6’)H), 7.56 H and Ar(C6)H), 8.19 H and Ar(C5’)H), 8.34 H), 12.49 : 13C-NMR  δ 34.51 (5-NO2-imidazole-N-CH3), 115.72 H and Ar(C5’)H), 124.95 (N-OH-imidazole-(C4)), 125.85 (Ar(C2)H and Ar(C6)H), 127.70 (Ar(C4)H), 128.57 (Ar(C3)H and Ar(C5)H), 129.63 H and Ar(C6’)H), 132.70 (5-NO2-imidazole-(C4)H), 132.97 (N-OH-imidazole-(C2)), 137.49 (Ar(C1)), 139.95 (5-NO2-imidazole-(C2)), 141.17 (5-NO2-imidazole-(C5)NO2), 141.42 (N-OH-imidazole-(C5)), 162.52 F): IR (KBr) νmax cm−1: 3284 (ν N-O-H), 3130–3066 (ν C-H(aromatic), 1534–1468 (ν C=C(aromatic), 1365 (ν N=O2), 1225 (ν C-F): MS (ESI) m/z: 378.2 (100%). Anal. Calcd. for C19H14FN5O3: C: 60.16; H: 3.72; N: 18.46. Found: C: 60.18; H: 3.72; N: 18.47.2'-(4-Chlorophenyl)-1-methyl-5-nitro-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol (5). green solid; mp 149–250 °C; yield 20%; 1H-NMR  δ 3.71 (5-NO2-imidazole-N-CH3), 7.27 H), 7.33 H and Ar(C5)H), 7.55 H and Ar(C6)H), 7.60 H and Ar(C6’)H), 8.17 H and Ar(C5’)H), 8.33 H), 12.58 : 13C-NMR  δ 34.54 (5-NO2-imidazole-N-CH3), 116.14 (N-OH-imidazole-(C4)), 125.86 (Ar(C2)H and Ar(C6)H), 127.72 (Ar(C4)H), 128.58 (Ar(C3)H and Ar(C5)H), 128.77 (Ar(C2’)H and Ar(C6’)H), 128.86 (Ar(C3’)H and Ar(C5’)H), 132.72 (5-NO2-imidazole-(C4)H), 133.86 (N-OH-imidazole-(C2)), 137.60 (Ar(C1)), 139.94 (5-NO2-imidazole-(C2)), 141.10 (5-NO2-imidazole-(C5)NO2): IR (KBr) νmax cm−1: 3437 (ν N-O-H), 3144–3058 (ν C-H(aromatic), 1533–1470 (ν C=C(aromatic), 1364 (ν N=O2): MS (ESI) m/z: 394.5 (100%). Anal. Calcd. for C19H14ClN5O3: C: 57.66; H: 3.57; N: 17.69. Found: C: 57.68; H: 3.57; N: 17.69.2'-(4-Bromophenyl)-1-methyl-5-nitro-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol (6). Yellow solid; mp 218–219 °C; yield 69%; 1H-NMR  δ 3.71 , 7.28 H), 7.33 H and Ar(C5)H), 7.55 H and Ar(C6)H), 7.75 H and Ar(C6’)H), 8.10 H and Ar(C5’)H), 8.34 H), 12.51 : 13C-NMR  δ 34.54 (5-NO2-imidazole-N-CH3), 116.15 (N-OH-imidazole-(C4)), 122.68 (Ar(C4’)Br), 125.84 (Ar(C2)H and Ar(C6)H), 127.54 (Ar(C1’)), 127.75 (Ar(C4)H), 128.60 (Ar(C3)H and Ar(C5)H), 129.12 (Ar(C2’)H and Ar(C6’)H), 131.73 (Ar(C3’)H and Ar(C5’)H), 132.71 (5-NO2-imidazole-(C4)H), 132.89 (N-OH-imidazole-(C2)), 137.66 (Ar(C1)), 139.97 (5-NO2-imidazole-(C2)), 141.00 (5-NO2-imidazole-(C5)NO2), 141.15 (N-OH-imidazole-(C5)): IR (KBr) νmax cm−1: 3140–3056 (ν C-H(aromatic), 1533–1470 (ν C=C(aromatic), 1364 (ν N=O2), 828 (ν C-N (Ar-NO2),: MS (ESI) m/z: 440.3 (100%). Anal. Calcd. for C19H14BrN5O3: C: 51.83; H: 3.21; N: 15.91. Found: C: 51.85; H: 3.21; N: 15.91.1-Methyl-5-nitro-2'-(4-nitrophenyl)-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol (7). Yellow solid; mp 361–362 °C; yield 62%; 1H-NMR  δ 3.73 , 7.27 H), 7.33 H and Ar(C5)H), 7.57 H and Ar(C6)H), 7.80 H, Ar(C3’)H, Ar(C5’)H, or Ar(C6’)H), 7.92 H, Ar(C3’)H, Ar(C5’)H, or Ar(C6’)H), 7.97 H, Ar(C3’)H, Ar(C5’)H, or Ar(C6’)H), 8.15H, Ar(C3’)H, Ar(C5’)H, or Ar(C6’)H), 8.33 H), 12.40 : 13C-NMR  δ 34.39 (5-NO2-imidazole-N-CH3), 115.50 (N-OH-imidazole-(C4)), 124.63 (Ar(C3’)H and Ar(C5’)H), 125.94 (Ar(C2)H and Ar(C6)H), 127.78 (Ar(C4)H), 128.56 (Ar(C3)H and Ar(C5)H), 131.15 (Ar(C2’)H and Ar(C6’)H), 132.60 (5-NO2-imidazole-(C4)H), 132.79 (N-OH-imidazole-(C2)), 133.64 (Ar(C1)), 139.49 (N-OH-imidazole-(C5)), 140.05 (5-NO2-imidazole-(C2)NO2), 140.72 (5-NO2-imidazole-(C5)NO2), 148.48 (Ar(C4’)NO2): IR (KBr) νmax cm−1: 3120–3033 (ν C-H(aromatic), 1524–1465 (ν C=C(aromatic), 1349–1365 (ν N=O2), 826 (ν C-N (Ar-NO2),: MS (ESI) m/z: 405.3 (100%). Anal. Calcd. for C19H14N6O5: C: 56.16; H: 3.47; N: 20.68. Found: C: 56.15; H: 3.47; N: 20.67.2'-(4-Methoxyphenyl)-1-methyl-5-nitro-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol (8). Yellow solid; mp 218–219 °C; yield 62%; 1H-NMR  δ 3.73 , 3.81 OCH3), 7.08 H, Ar(C3’)H, Ar(C5’)H or Ar(C6’)H), 7.17 H, Ar(C3’)H, Ar(C5’)H or Ar(C6’)H), 7.25 H), 7.32 H, Ar(C3)H, Ar(C5)H or Ar(C6)H), 7.51 H and Ar(C5’)H), 7.56 H and Ar(C6)H), 8.34 H), 11.72 : 13C-NMR  δ 34.43 (5-NO2-imidazole-N-CH3), 55.72 (Ar(C4’)OCH3), 111.77 and 120.18 (Ar(C3’)H and Ar(C5’)H), 114.71 (N-OH-imidazole-(C4)), 117.77 (Ar(C1’)), 125.82 (Ar(C2)H and Ar(C6)H), 127.38 (Ar(C4)H), 128.43 (Ar(C3)H and Ar(C5)H), 131.37 and 131.54 (Ar(C2’)H and Ar(C6’)H), 132.63 (5-NO2-imidazole-(C4)H), 133.35 (N-OH-imidazole-(C2)), 137.48 (Ar(C1)), 139.85 (5-NO2-imidazole-(C2)), 141.58 (5-NO2-imidazole-(C5)NO2), 141.76 (N-OH-imidazole-(C5)), 157.70 (Ar(C4’)OCH3): IR (KBr) νmax cm−1: 3448 (ν N-O-H), 3070–3054 (ν C-H(aromatic), 1471–1488 (ν C=C(aromatic), 1363 (ν N=O2), 825 (ν C-N (Ar-NO2),: MS (ESI) m/z: 390.4 (100%). Anal. Calcd. for C20H17N5O4: C: 56.16; H: 3.47; N: 20.68. Found: C: 56.15; H: 3.47; N: 20.67.2'-(4-Hydroxyphenyl)-1-methyl-5-nitro-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol (9). Yellow solid; mp 145–146 °C; yield 71%; 1H-NMR  δ 3.70 , 6.90 H and Ar(C5’)H), 7.25 H), 7.32 H and Ar(C5)H), 7.53 H and Ar(C6)H), 7.97 H and Ar(C6’)H), 8.36 H), 9.89 OH), 12.21 : 13C-NMR  δ 34.52 (5-NO2-imidazole-N-CH3), 115.09 (N-OH-imidazole-(C4)), 115.42 (Ar(C3’)H and Ar(C5’)H), 119.40 (Ar(C1’)), 125.79 (Ar(C2)H and Ar(C6)H), 127.51 (Ar(C4)H), 128.54 (Ar(C3)H and Ar(C5)H), 129.04 (Ar(C2’)H and Ar(C6’)H), 132.75 (5-NO2-imidazole-(C4)H), 133.26 (N-OH-imidazole-(C2)), 137.13 (Ar(C1)), 139.87 (5-NO2-imidazole-(C2)), 141.56 (5-NO2-imidazole-(C5)NO2), 142.74 (N-OH-imidazole-(C5)), 158.50 (Ar(C4’)OH): IR (KBr) νmax cm−1: 3123–3062 (ν C-H(aromatic), 1527–1465 (ν C=C(aromatic), 1283 (ν N=O2), 827 (ν C-N (Ar-NO2),: MS (ESI) m/z: 376.3 (100%). Anal. Calcd. for C19H15N5O4: C: 60.47; H: 4.01; N: 18.56. Found: C: 60.49; H: 4.01; N: 18.56.4-benzene-1,2-diol (10). Yellow solid; mp 224–225 °C; yield 42%; 1H-NMR  δ 3.70 , 6.85 H), 7.25 H), 7.32 H and Ar(C5)H), 7.49 H), 7.53 H and Ar(C6)H), 7.62 H), 8.32 H), 9.23 OH or Ar(C4’)OH), 9.33 OH or Ar(C4’)OH), 12.16 : 13C-NMR  δ 34.54 (5-NO2-imidazole-N-CH3), 114.87 (Ar(C2’)H), 115.09 (N-OH-imidazole-(C4)), 115.63 (Ar(C5’)H), 119.17 (Ar(C6’)H), 119.69 (Ar(C1’)), 125.80 (Ar(C2)H and Ar(C6)H), 127.49 (Ar(C4)H), 128.54 (Ar(C3)H and Ar(C5)H), 132.77 (5-NO2-imidazole-(C4)H), 133.31 (N-OH-imidazole-(C2)), 137.05 (Ar(C1)), 139.86 (5-NO2-imidazole-(C2)), 141.61 (5-NO2-imidazole-(C5)NO2), 142.69 (N-OH-imidazole-(C5)), 145.20 Ar(C3’)OH), 146.82 Ar(C4’)OH): IR (KBr) νmax cm−1: 3367 (ν O-H), 3124–3066 (ν C-H(aromatic), 1528–1468 (ν C=C(aromatic), 1365 (ν N=O2), 827 (ν C-N (Ar-NO2),: MS (ESI) m/z: 392.2 (100%). Anal. Calcd. for C19H15N5O5: C: 58.01; H: 3.84; N: 17.80. Found: C: 57.09; H: 3.84; N: 17.79.2'-(3-Hydroxy-4-methoxyphenyl)-1-methyl-5-nitro-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol (11). Gray solid; mp 213 °C; yield 45%; 1H-NMR  δ 3.70 , 3.82 OCH3), 7.05 H, Ar(C5)H or Ar(C6)H), 7.26 H), 7.33 H and Ar(C5)H), 7.53 H and Ar(C6)H), 7.61 H, Ar(C5)H or Ar(C6)H), 8.32 H), 9.28 : 13C-NMR  δ 34.46 (5-NO2-imidazole-N-CH3), 56.41 (Ar(C4’)OCH3), 113.63 (Ar(C2’)H, 114.73 (N-OH-imidazole-(C4)), 116.10 (Ar(C6’)H, 118.21 (Ar(C1’), 125.91 (Ar(C2)H and Ar(C6)H), 127.46 (Ar(C4)), 128.48 (Ar(C3)H and Ar(C5)H), 132.70 (5-NO2-imidazole-(C4)H), 137.11 (Ar(C1)), 133.15 (N-OH-imidazole-(C2)), 136.53 (Ar(C5’)NO2, 137.02 (Ar(C1)), 139.74 (5-NO2-imidazole-(C2)), 140.09 (Ar(C4’)OH, 141.59 (5-NO2-imidazole-(C5)NO2), 144.67 (N-OH-imidazole-(C5)), 149.82 (Ar(C3’)OCH3: IR (KBr) νmax cm−1: 3368 (ν N-O-H), 3130–3056 (ν C-H(aromatic), 1533–1503 (ν C=C(aromatic), 1369 (ν N=O2), 827 (ν C-N (Ar-NO2),: MS (ESI) m/z: 406.4 (100%). Anal. Calcd. for C20H17N5O5: C: 58.97; H: 4.21; N: 17.19. Found: C: 58.98; H: 4.21; N: 17.19.2'-(3-Hydroxy-4-methoxy-5-nitrophenyl)-1-methyl-5-nitro-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol (12). Red solid; mp 245–247 °C; yield 43%; 1H-NMR  δ 3.69 , 3.90 OCH3), 7.25 OH), 7.32 H and Ar(C5)H), 7.54 H and Ar(C6)H), 7.89 H), 8.26 H and Ar(C6’)H): 13C-NMR  δ 34.43 (5-NO2-imidazole-N-CH3), 55.41 (Ar(C3’)OCH3), 125.90 (Ar(C2)H and Ar(C6)H), 127.46 (Ar(C4)H), 128.44 (Ar(C3)H and Ar(C5)H), 132.70 (5-NO2-imidazole-(C4)H), 137.15 (Ar(C1)), 139.74 (5-NO2-imidazole-(C2)), 140.09 (5-NO2-imidazole-(C5)NO2), 141.59 (Ar(C1’)), 149.82 (Ar(C3’)OCH3): IR (KBr) νmax cm−1: 3530 (ν O-H), 1555–1542 (ν C=C(aromatic), 1365 (ν N=O2), 827 (ν C-N (Ar-NO2),: MS (ESI) m/z: 451.4 (100%). Anal. Calcd. for C20H16N6O7: C: 53.10; H: 3.56; N: 18.58. Found: C: 52.98; H: 3.57; N: 18.55.T. cruzi (Y strain) were isolated from infected Swiss mice and re-suspended with Dulbecco’s modified Eagle medium plus 10% foetal calf serum (DMES) to a parasite concentration of 10 × 106 cells/mL. This suspension (100 μL) was added to the same volume of each NAH derivative, previously prepared at twice the desired concentrations in DMES. Stock solutions of the NAH derivatives were prepared in dimethylsulfoxide (DMSO), and they were assayed in the range of 0.5 to 2000 μg/mL. The final concentration of DMSO never exceeded 0.5%, thus, it had no deleterious effect on the parasites [Stock solutions of NAH derivatives were prepared in DMSO. Bloodstream trypomastigote forms of arasites .4–6) was determined using Alamar Blue assay (Invitrogen). The macrophage cell line J774 was seeded into black clear flat-bottomed 96-well plates in a density of 2.5 × 106 cells/well. After 1 h of incubation in controlled atmosphere ,cells received fresh medium with or without Tween 20 (3%), DMSO (0.5%) and the TAI compounds (1 to 100 μM) in a quadruplicate assay. After 21 h of incubation in the previous conditions, 20 μL of Alamar Blue solution was added to each well and after 3 h, fluorescence was measured using SpectraMax M5/M5e microplate reader .Cellular viability in the presence and absence of TAI derivatives (in vitro. Among these N-hydroxytriarylimidazole (TAI) derivatives, we were able to identify the derivative 2'-(4-bromophenyl)-1-methyl-5'-phenyl-1H,3'H-2,4'-biimidazol-3'-ol (6), which showed moderate trypanocidal activity (IC50 = 23.9 µM) when compared to benznidazole, which is used as the standard drug. This compound did not present cytotoxic effects at concentrations near the IC50, being considered a good starting point in the development of new anti-Chagas drug candidates.We have described herein a novel structural triarylheterocyclic template capable of displaying a significant antitrypanosomal profile


C-methyl-1′-cyano-7-deaza-adenosine C-nucleotide analog with desirable selectivity and potency for the treatment of hepatitis C virus (HCV) infection.Delivery of pharmacologically active nucleoside triphosphate analogs to sites of viral infection is challenging. In prior work we identified a 2′- C-methyl-1′-cyano-7-deaza-adenosine C-nucleotide analog with desirable selectivity and potency for the treatment of hepatitis C virus (HCV) infection. However, the prodrug selected for clinical development, GS-6620, required a high dose for meaningful efficacy and had unacceptable variability due to poor oral absorption as a result of suboptimal solubility, intestinal metabolism, and efflux transport. While obtaining clinical proof of concept for the nucleotide analog, a more effective prodrug strategy would be necessary for clinical utility. Here, we report an alternative prodrug of the same nucleoside analog identified to address liabilities of GS-6620. A phosphoramidate prodrug containing the nonproteinogenic amino acid methylalanine, an isopropyl ester and phenol in the (S) conformation at phosphorous, GS2, was found to have improved solubility, intestinal stability, and hepatic activation. GS2 is a more selective substrate for hepatically expressed carboxyl esterase 1 (CES1) and is resistant to hydrolysis by more widely expressed hydrolases, including cathepsin A (CatA) and CES2. Unlike GS-6620, GS2 was not cleaved by intestinally expressed CES2 and, as a result, was stable in intestinal extracts. Levels of liver triphosphate following oral administration of GS2 in animals were higher than those of GS-6620, even when administered under optimal conditions for GS-6620 absorption. Combined, these properties suggest that GS2 will have better oral absorption in the clinic when administered in a solid dosage form and the potential to extend the clinical proof of concept obtained with GS-6620.Delivery of pharmacologically active nucleoside triphosphate analogs to sites of viral infection is challenging. In prior work we identified a 2′- Nucleoside analogs are hydrophilic, require sequential intracellular phosphorylation to form pharmacologically active triphosphates, and often distribute suboptimally throughout the body, resulting in reduced activity and unwanted side effects. Nucleotide prodrug strategies have been heavily explored to preferentially load target tissues and bypass the often rate-limiting first phosphorylation step . DespiteC-nucleotide prodrug with potent activity against the hepatitis C virus in vitro (L)-alanine-isopropyl ester and a phenol moiety that mask the two charges of the 5′-monophosphate. A 3′-isobutyryl ester was added to mask the hydrogen bond donor of the 3′-hydroxyl group and improved passive permeability and oral bioavailability  was initially characterized for anti-HCV activity. Fractionation of GS2(R+S) using chiral column chromatography yielded slow- and fast-eluting isomers. As shown in S) configuration at phosphorus (data not shown). Since the data for the mixture was within 2-fold of GS2's activity and showed a similar pattern of relative activity, data obtained with GS2(R+S) in some assays were reported where the single stereoisomer GS2 was not tested.The 1:1 mixture of the two diastereomers at phosphorus GS2. Both GS-6620 and GS2 form the same pharmacologically active triphosphate, and these results led us to suspect that activation by distinct hydrolases differentially expressed in the various cell lines strongly modulates the in vitro antiviral potency.The activities of GS-6620 and GS2 were tested in replicons for GT1a, GT1b, and GT2a generated in two different Huh-derived cell lines: stably transfected Huh-Lunet cells and transiently transfected Huh-1C cells. As shown in The relative expression levels of hydrolases CatA and CES1 were analyzed in human intestinal S9 fraction, human liver S9 fraction, and different Huh-derived replicon cells, including stably transfected 1a, 2a, and 1b Huh-Lunet cells and untransfected Huh-Lunet and Huh-1C cells . CatA exR+S). As shown in R+S) were 16-fold lower in Huh-Lunet cells. These results correlate with the relative potency observed in Huh-1C and Huh-Lunet cells and further suggest the primary role of CES1 in the activation of GS2.In order to correlate triphosphate levels with CES1 expression levels, formation of the pharmacologically active triphosphate metabolite was determined in cells with low (Huh-Lunet) and high (Huh-1C) CES1 expression. These levels were measured following 24 h of incubation with 10 μM either GS-6620 or GS2 profile of GS2 relative to GS-6620 is summarized in The GS2 showed higher stability in intestinal S9 from hamster, rat, dog, monkey, and human. While GS-6620 had a half-life of <30 min in all species except dog, GS2 had a half-life of greater than 1 h in all species and was numerically most stable in human intestinal S9 . The stain vitro. Following a 1-h incubation with primary human hepatocytes, GS2 showed 1.9- to 11-fold higher levels of triphosphate metabolite than GS-6620 in cells from 3 separate donors  yet decreasing to an undetectable concentration by 2 h postdose. While the maximal portal concentration obtained by GS2 was approximately 2-fold lower than that of GS-6620, all other pharmacokinetic parameters were within 2-fold due to its relatively prolonged exposure.The portal vein profile and pharmacokinetic parameters following oral administration to pentagastrin-pretreated and portal vein-cannulated dogs of GS2 were compared to those of GS-6620 . Plasma R+S) or diastereomeric mixtures containing GS-6620 in hamsters and rat, alongside data from the oral administration of GS2 and GS-6620 in dogs  for liver triphosphate for GS2 was 1.5-, 1.8-, and 1.4-fold higher than that of GS-6620 in hamster, rat, and dog, respectively  are more likely to be maintained in the clinic by GS2 . While GS2 showed a remarkable level of cell background-dependent variability in replicon activity, it was found to be, on average, 5-fold more efficient at loading primary human hepatocytes in vitro, and (ii) in dogs, where GS2 was not able to match the intestinal absorption of GS-6620 and had similar activation in isolated hepatocytes in vitro. In particular, liver levels of pharmacologically active triphosphate of GS2 compared favorably to those reported for sofosbuvir in rats and were within 2-fold in dogs . Notably, the advantage in liver levels for GS2 was observed in two species: (i) in hamsters, where GS-6620 was more efficiently metabolized in isolated hepatocytes  in dogs .In conclusion, GS2 was found to have markedly improved solubility, intestinal stability, and hepatic activation relative to those of GS-6620. While still less intrinsically permeable than GS-6620, as reflected in a lower fraction absorbed in dogs, these properties should result in better performance in humans administered a solid dosage form due to the improved solubility and stability for CES2, a more highly expressed isoform in human than dog. Improved hepatic activation should serve to further reduce the required dose needed to obtain targeted antiviral activity. Taken together, these properties should allow for GS2 to achieve sufficient and consistent liver loading of the pharmacologically active triphosphate to drive the antiviral effects that were only inconsistently observed with GS-6620 when given at high doses.In Vitro Technologies .GS-6620, GS2, metabolite X, nucleoside, and its 5′-triphosphate active metabolite were synthesized at Gilead Sciences, Inc. . The hydrolase cathepsin A (CatA) was purified from peripheral blood mononuclear cells (PBMCs) according to a previously published procedure . CES1 an6 cells/well). Huh-Lunet cells were obtained from ReBlikon GmbH   . Huh-1C Germany) –22. CacoR+S).GS2 is a single diastereomeric prodrug, as shown in GT1a, GT1b, and GT2a are authentic subgenomic replicons of the indicated strain. Creation of these HCV replicons was reported previously .in vitro using a MEGAscript T4 kit  and transfected into Huh-Lunet and −1C cells using published methods .Biochemical assays studying prodrug cleavage have been described previously . In brieBlood plasma and S9 stability was studied as described previously . In briet1/2) was determined assuming first-order kinetics.The data  were plotted on a semilog scale and fitted using an exponential fit. The half-life (6 cells/well), and primary human hepatocytes from three different donors and animal hepatocytes from hamster, rat, and dog were seeded in 12-well plates at confluence (0.88 × 106 cells/well). Ten μM GS2 or GS-6620 was incubated for 24 h in Huh-Lunet and Huh-1C cells and for 1 h in primary hepatocytes. The intracellular metabolites were extracted and analyzed by LC-MS/MS.Methods for determining intracellular activation in cell lines and primary hepatocytes has been described in detail . In brieCaco-2 cells between passage 43 and 71 were grown to confluence for ≥21 days on 12- or 24-well polyethylene terephthalate plates (BD Biosciences). The bidirectional permeability of compounds was tested as described previously . In brieAs described previously , kineticR+S) in solution formulations at between 5 and 50 mg/kg of body weight. Similarly, studies were completed by administering GS-6620 or its 1:1 diastereomeric mixture in solution or micronized suspension formulations at between 5 and 21.1 mg/kg. Animal doses were chosen to correspond to surface area-adjusted human doses targeted for clinical development (150 to 500 mg). In order to understand hepatic extraction (HE), fraction absorbed (aF), and oral bioavailability (F), intravenous (i.v.) and oral (p.o.) studies were conducted with GS2 and GS-6620 in intact and portal vein-cannulated dogs. Intravenous dosing used solution formulations at 0.5 mg/kg of the respective prodrugs. Animals were housed and handled in accordance with the Guide for the Care and Use of Laboratory Animals  HST column . A multistage linear gradient from 10% (mobile phase A) to 50% acetonitrile (mobile phase B) in a mobile phase containing 3 mM ammonium formate (pH 5.0) with 10 mM dimethylhexylamine at a flow rate of 0.15 ml/min was used to elute the analytes.Plasma samples were prepared through protein precipitation extraction by adding acetonitrile to a final concentration of 70% containing internal standard (5-iodotubericidin). Samples were dried completely under a stream of nitrogen at 40°C and reconstituted with 0.2% formic acid in water to 3 times the original volume of plasma for LC-MS/MS analysis. Liver samples were prepared by sectioning into smaller pieces and collecting into preweighed 15-ml conical tubes kept on dry ice. Ice-cold extraction buffer  was added to ∼0.5 g of each liver sample. The mixtures were promptly homogenized using an Omni-Tip TH with disposable, hard tissue homogenizer probes . Aliquots of the homogenate were filtered by using 0.2-μm 96-well polypropylene filter plates (Varian Captiva). The filtrates were evaporated to dryness and reconstituted with an equal volume of 1 mM ammonium phosphate buffer (pH 7) prior to LC-MS/MS analysis. Plasma and liver samples were analyzed on a Sciex API-4000  LC-MS/MS instrument. Analytes in plasma samples were separated on a 4-μm, 150- by 2-mm Synergi Max-RP column  using a mobile phase containing 0.2% formic acid and a linear gradient from 2% to 100% acetonitrile at a flow rate of 250 μl/min over 7 min. Analytes in liver samples were separated using a 50- by 2-mm by 2.5-μm Luna C50s, defined as the concentration at which there was a measured 50% decrease in HCV replicon level, were determined in replicon assay as previously described :The ECescribed . BrieflyX represents log of compound concentration and Y represents replicon replication as the percentage of DMSO control. EC50s were calculated as an average from at least three independent experiments.6 cells in 200 μl of 1× lysis buffer with 1× protease/phosphatase inhibitor cocktail  and incubating on ice for 20 min. Extracts were then centrifuged at 1,000 × g for 5 min to remove insoluble components. The supernatants were collected and normalized to 2 mg/ml protein after quantitation using the bicinchoninic acid protein assay kit . For Western analysis, cell extract , human liver S9 (0.5 μg for CES1 and 10 μg for CatA), human intestinal S9 (10 μg), recombinant CatA , and recombinant CES1  were resolved by SDS-PAGE  and transferred to nitrocellulose membranes using an iBlot gel transfer device . Nitrocellulose membranes were blocked with 10% nonfat dry milk in Tris-buffered saline–0.1% Tween 20 solution (TTBS) overnight at 4°C and incubated with either anti-CatA (0.2 μg/ml) or anti-CES1 (2 μg/ml) primary antibodies, followed by appropriately matched horseradish peroxidase-conjugated secondary antibodies  diluted in 5% milk–TTBS. Immunoreactive protein bands were visualized by enhanced chemiluminescence  on a VersaDoc Imaging System and quantitated by densitometry under subsaturating exposure conditions using Quantity One v4.3.0 software . Cellular expression levels of CatA and CES1 were determined from a dilution series of recombinant CatA and CES1 that were fit by linear regression and resolved on the same polyvinylidene difluoride (PVDF) membrane(s).Protein extract from the different cells were prepared by resuspending 2 × 10HE; aF; F; Noncompartmental pharmacokinetic parameters were calculated using WinNonlin 5.01 . Equations for calculating hepatic extraction EH; , fractioP values were calculated using GraphPad Prism (version 7.0).Two-way analysis of variance (ANOVA) was conducted to evaluate if the difference between two compared groups is more than that expected by chance. The


Urinary tract infections (UTIs) are one of the most common infections in older people and are associated with increased morbidity and mortality. UTIs are also associated with increased risk of antimicrobial resistance (AR). This study examined changes in AR among older inpatients with a primary diagnosis of UTIs in the United States over an 8-year period and the impact of AR on clinical outcomes and hospital costs. Data were obtained from the longitudinal hospital HCUP-NIS database from 2009 to 2016 for inpatient episodes that involved those aged 65+ years. The ICD-9 and ICD-10 codes were used to identify episodes with a primary diagnosis of UTIs, comorbidities, AR status and age-adjusted Deyo-Charlson comorbidity index (ACCI) for the patient concerned. Weighted multivariable regression was used to examine the impact of AR on all-cause inpatient mortality, discharge destination, length of stay and hospital expenditures, adjusted for socio-demographic and clinical covariates. The proportion of admissions with AR increased, from 3.64% in 2009 to 6.88% in 2016 (p<0.001), with distinct patterns for different types of resistance. The likelihood of AR was higher in admissions with high ACCI scores and admissions to hospitals in urban areas. Admissions with AR were more likely to be discharged to healthcare facilities (e.g. care homes) compared to routine discharge , had increased length of stay  and hospital costs . Resistance due to MRSA was specifically associated with increased hospital mortality . Our findings suggest that the prevalence of AR has increased among older inpatients with UTIs in the USA. The study highlights the impact of AR among older inpatients with a primary diagnosis of UTIs on clinical outcomes and hospital costs. These relationships and their implications for the care homes to which patients are frequently discharged warrant further research. Antimicrobial resistance (AR) is recognized as a global threat to health and healthcare systems. AR could lead to negative impacts on patient outcomes such as increased length of hospital stay, functional decline, increased healthcare expenditure and all-cause mortality . In the Older people are more susceptible to AR due to physiological changes and comorbidity . This poClostridium difficile infection if treated inappropriately [Urinary tract infections (UTIs) are one of the most common infections in older people and are associated with increased morbidity and mortality ,8. Diffipriately . High rapriately –18. ThisThere is a paucity of evidence in respect of the impact of AR on the treatment and outcomes of older patients with UTIs in an inpatient setting. This study aimed to examine changes in AR over time and its impact on clinical outcomes and hospital costs among older people hospitalized with UTIs in the USA.We conducted a retrospective observational study using the National (Nationwide) Inpatient Sample—Healthcare Cost and Utilization Project (HCUP-NIS), Agency for Healthcare Research and Quality (AHRQ). The HCUP-NIS is the largest available all-payer inpatient healthcare administrative dataset in the USA corresponding to an approximately 20 percent stratified sample of all discharges from community hospitals but excluding rehabilitation and long-term acute care hospitals . Each reWe obtained HCUP-NIS data from 2009 to 2016. The study cohort was restricted to episodes of care that involved those aged 65 years and over, the age commonly used to define older persons in the USA . The IntStaphylococcus aureus (MRSA) have been associated with high rates of mortality and prolonged hospital stay [Infections due to Methicillin-resistant tal stay , and MRStal stay . Therefotal stay  and havetal stay ,5. AR watal stay ,26. Epistal stay ,26.C. difficile infection, a common complication of long-term use of broad-spectrum antibiotics potentially due to UTIs treatment, using ICD codes (see HCUP-NIS provided a range of socio-demographic variables comprising insurance type (Medicare/Medicaid/Private/Others), ethnicity, sex , age at admission, median household income for patients’ ZIP code and location . In order to control for hospital characteristics, we used the unique HCUP hospital number to link the core data to the hospital data. These variables comprised a hospital's ownership/control category (government/private owned), location , and its teaching status . Clinical variables included the comorbidity index ACCI , mode of presentation (elective/non-elective) and total number of procedures performed in the hospital. We also identified cases with sepsis, a severe complication of UTIs, and We first described the characteristics of admissions with UTIs, proportion of admissions with AR in general and with specific types of resistance . The four main outcomes associated with AR, comprising all-cause inpatient mortality, discharge destination, length of stay in the hospital, and hospital incurred costs, were measured to estimate the impact of AR . DischarSample characteristics were described using median and interquartile range (IQR) for continuous variables with skewed distribution, mean and standard deviation (SD) for other continuous variables and proportions for categorical variables. In order to generate national estimates using HCUP-NIS data that span multiple years, all analyses were weighted by trend weight for data years prior to 2012 and by the discharge-level weight for data from 2012 and later as recommended by the AHRQ  unless sThe incidence of UTIs as a primary diagnosis and proportion of admissions with AR, BR, resistance due to MRSA, MR, or QR were described on a monthly basis and weighted to provide national estimates. Multivariable logistic regression was used to examine factors affecting the likelihood of having AR, BR, MR, and QR. Explanatory variables used in these analyses comprised socio-demographic variables , and clinical variables  as suggested by the literature –31.C. difficile infection and sepsis status). Year was added as a predictor to control for variability over time in all regression models.We estimated the impact of AR by examining the four outcomes: all-cause inpatient mortality, discharge to another healthcare facility, length of stay in the hospital, and hospital incurred costs. We examined factors associated with the likelihood of inpatient mortality and discharge to a healthcare facility among those admitted with a primary diagnosis of UTIs using weighted multivariable logistic regression. Models were adjusted for socio-demographic variables , and clinical variables  were used to accommodate the continuous, positive and skewed nature of hospital cost data in the cost analysis. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the fit of the GLM model . The linThe UTI definition of AHRQ Prevention Quality Indicator 12 for Urinary Tract Infections includes acute pyelonephritis, renal/perirenal abscess, pyeloureteritis cystica, pyelonephritis or pyonephrosis not specified as acute or chronic, and infection of kidney . Such coAll analyses and data manipulations were performed using STATA version 15 .Over the 8-year period, a total of 546,305 eligible admissions with a primary diagnosis of UTIs were included in the study. Characteristics of episodes with primary UTIs are presented in As shown in C. difficile infection, having sepsis, being Black American, older age and higher ACCI. Moreover, having any types of insurance (Medicaid/Medicare/private insurance), being admitted to private hospital or hospital in an urban area reduced risk of inpatient mortality. Risk of inpatient mortality also fell over time.Over the 8-year period, the all-cause inpatient mortality rate was 1.42% for UTI admissions with associated AR, compared to 1.09% in the non-AR group (p < 0.001). In the multivariable logistic model, we found non-significant difference in the odds of death between the two groups during the observed inpatient episode see . HoweverAmong survivors, 22.8% of patients with AR were routinely discharged, while 75.4% were transferred to other healthcare facilities , compared to 35.8% and 62.8% in the non-AR group, respectively. Multivariable regression showed that admissions with any types of AR were more likely to be discharged to a healthcare facility than those without resistance. Among different types of AR, admissions with resistance due to MRSA showed the highest odds of being discharged to a health facility, which was over two times higher than those without resistance  see .Compared to those without AR, the unadjusted additional length of stay for admissions with AR was 1. 77 days (p<0.001). After being adjusted for other covariates, admissions with AR were 1. 12 days  longer in hospital compared to non-AR. The predicted length of stay of those with BR, resistance due to MRSA, and MR were 0.74, 1.36, 0.83 days longer than those without AR, respectively see . There wHospital discharges were converted to costs using cost-to-charge ratio provided by the HCUP and were inflated to the 2016 value. The unadjusted costs associated with hospitalization with AR were 2730 USD  higher than non-AR group (p<0.001). After estimating a series of GLM models, the AIC and BIC statistics supported the use of log link function and distribution family gamma. In the multivariable regression, admissions with AR, on average, consumed 1259 USD (95%:1178–1340) more than those without AR, though distinct patterns were observed in different types of AR see .Results of the sensitivity analyses on a refined sample without upper UTIs diagnosis confirmed our findings see . The genWhile the prevalence of AR has been shown to increase over time , especiaWithin our study, the number of admissions with UTIs did not significantly change during the 8-year period of observation; however, there was a distinct seasonal pattern. Summer peaks among episodes with UTIs has been reported in a previous study . EpisodeRisk factors associated with the likelihood of AR among inpatients with UTIs in our study are consistent with findings in previous studies. High ACCI scores indicated multiple comorbidities which are considered as a risk factor for antimicrobial resistance ,41. MaleWe found that increased age was associated with a decreased likelihood of AR, BR and MR; nevertheless, the AR and MR group had higher ACCI and lower age compared to the non-AR group . This coEscherichia coli [With regard to the burden of AR, episodes with recorded resistance due to MRSA were associated with an increased likelihood in inpatient mortality and an increase in length of hospital stay, which is consistent with findings of previous studies ,47. Intehia coli ,47. The hia coli ,50. GiveThe current study used a population-based data which represented 20% of national inpatient data in the USA. By pooling data in 8 years, the large number of episodes supported the study power. However, our study had several limitations. Chronological age (e.g. 65 years and over) is commonly used as cut-off to define older populations but its usefulness as an indicator for physiological and functional status of the older population has been disputed . FrailtyMoreover, HCUP-NIS data are collected mainly for administrative purposes; thus, important clinical data such as medication use or microbiological results were not available. The cross-sectional nature of this study also limited our ability to make further suggestions regarding our findings. In addition, primary and secondary diagnoses and antimicrobial resistance were identified using ICD codes which have been reported with potentially missing cases . NeverthOur findings suggest that AR is increasing among older inpatients with UTIs. We also found distinct patterns in the relationship between specific types of AR and the likelihood of all-cause inpatient mortality, hospital discharge destination, length of stay, and hospital costs. These relationships and their implications for the care homes to which patients are likely discharged warrant further research.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S3 Appendix(DOCX)Click here for additional data file.


The visualization of potentially injured anatomical structures is crucial. Lately the anterolateral ligament (ALL) and the deep structures of the iliotibial tract (ITT) have been of increased clinical interest because of their role as important lateral stabilizers of the knee. The aim of this study was to assess the visibility of the ALL and the deep structures of the ITT using MRI. Good intra- and inter-observer reproducibility was hypothesized.Knee MRI data from patients without ligamentous lesions were retrospectively analyzed by two radiologists at two time points using axial and coronal sequences. The visibility of the different parts of the ALL  and of the deep ITT, namely the deep attachments of the ITT to the distal femur and capsulo-osseous layer of the ITT, were determined on a binary (yes/no) basis.Seventy-one cases  were studied. Inter-observer agreement was high. Cohen’s kappa was 0.97 for the tibial part of the ALL and 0.76 for the femoral part. For the deep attachments of the ITT to the distal femur Cohen’s kappa was 0.94. For each of the investigated parameters absolute agreement between the observers was at least 88%. Regarding intra-observer agreement Cohen’s kappa was 0.62 for the femoral part of the ALL and 0.85 for the tibial part of the ALL. For the deep attachments of the ITT to the distal femur Cohen’s kappa was 0.94. For each investigated parameter absolute agreement between the two time points was at least 83%.The presence of the anterolateral structures of the knee can be determined with substantial inter- and intra-observer agreement using MRI examination. This is applicable for both the ALL and the deep ITT.Diagnostic study – Level III. Since 2013 there has been an increased interest in the anterolateral extra-articular soft-tissue structures of the knee and their importance in controlling rotatory knee stability. In particular, the anterolateral ligament (ALL) was popularized as an important ligamentous stabilizer of the anterolateral knee . This trWhile the above-mentioned authors promote the ALL as a main anterolateral knee stabilizer, others refuted the relevance of the ALL in providing anterolateral knee stability  and put This polarity between the deep structures of the ITT and the ALL is also evident in the field of ‘anterior cruciate ligament reconstruction with concomitant anterolateral extraarticular reconstruction’. While some authors have advocated reconstruction of the ACL in combination with anatomical ALL reconstruction , 34, othThe aim of the present study was to assess the visibility and reproducibility of both the ALL and the deep structures of the ITT using magnetic resonance imaging. It was hypothesized that Cohen’s kappa values for intra- and inter-observer reproducibility would be seen to be above 0.70, indicating substantial agreement between the ratings .Ethics approval was obtained from the Ethics Committee of the Medical University of Innsbruck. The retrospective analysis was conducted in accordance with the ethical standards of the Declaration of Helsinki.MRI data from patients aged 18 to 40 obtained between January 2017 and December 2018 were retrospectively reviewed. Only healthy knees without any clinically or radiologically diagnosed intra- or extraarticular ligamentous pathology were included. Further exclusion criteria were lesions to the joint capsule, fractures, bone edemas as well as foreign material and motion artifacts. Moreover, medical records were checked for past knee injuries or operative interventions.All MRIs were obtained using the identical protocol. Patients were examined in supine position using a dedicated 15-channel knee coil. The following sequences were used for our 1.5 T / 3.0 T Scanner : coronal T1-weighted images ; coronal PD-weighted images with fat saturation ; sagittal PD-weighted images with fat saturation  and axial PD-weighted images with fat saturation .Two trained and experienced musculoskeletal radiologists  analyzed the pictures using the imaging viewer Impax EE . Before commencement, a specialist in the field of anterolateral knee anatomy lectured and briefed both radiologists in a private cadaver dissection class. All relevant lateral and anterolateral structures of the knee were dissected and studied.Thereafter, each observer performed all the below-mentioned analyses separately. To allow calculation of intra-observer agreement, all measurements were performed twice at an interval greater than 2 weeks between evaluations.The analysis to identify the ALL was performed in a manner similar to previous research , 37. TheThe deep attachments of the ITT at the distal femur were also evaluated on a binary (yes/no) basis and further subcategorized according to the literature in three sub-structures: insertion near the septum, supracondylar insertion and retrograde insertion , 17, 23.After data analysis, a consensus meeting of the two radiologists was conducted to discuss discrepant findings.Descriptive statistics for visibility of the above-mentioned structures are given for the consensus rating. Cohen’s kappa and 95% confidence intervals (95% CI) were determined as a measure of inter- and intra-observer reproducibility. In addition, we calculated the agreement separately for the categories “visible” and “not visible” as suggested by Cicchetti and Feinstein . A valueSeventy-one patients  with an average age of 44.7 ± 14.1 years met the inclusion criteria.In 62.0% of the cases the femoral part of the ALL was visible, whereas visibility of the meniscal and the tibial part of the ALL was 0% and 73.2%, respectively. The deep attachments of the ITT to the distal femur were detected in 60.6% of the cases. However, in none of the patients was it possible to further distinguish these fibers as ‘insertion near septum’, ‘supracondylar insertion’ or ‘retrograde insertion’. Visibility of the capsulo-osseous layer of the ITT was 0%.Inter-observer agreement was high. Cohen’s kappa was 0.97 (95%CI: 0.9–1.00) for the tibial part of the ALL and 0.76 (95%CI: 0.61–0.92) for the femoral part of the ALL. As previously mentioned, neither of the two observers was able to detect the meniscal part of the ALL in any of the patients (100% absolute agreement). Therefore, it was not possible to calculate Cohen’s kappa for this variable. For the deep attachments of the ITT to the distal femur Cohen’s kappa was 0.94 (95%CI: 0.86–1.00). As the capsulo-osseous layer of the ITT was not visible to either of the observers in 100% of the cases, it was also not possible to calculate Cohen’s kappa for that parameter. For each of the investigated parameters absolute agreement between the observers was at least 88%. For further details, see Table Regarding intra-observer agreement, Cohen’s kappa was 0.62 (95%CI: 0.43–0.81) for the femoral part of the ALL, 0.85 (95%CI: 0.70–0.91) for the tibial part of the ALL and not quantifiable for the meniscal ALL part. For the deep attachments of the ITT to the distal femur Cohen’s kappa was 0.94 (95%CI: 0.86–1.00). As the capsulo-osseous layer of the ITT was not visible in 100% of the cases, it was also not possible to calculate Cohen’s kappa for that parameter. For each of the investigated parameters absolute agreement between the two time points was at least 83% (Table The primary finding of the present study is good MRI visibility of the ALL and the deep fibers of the ITT with substantial and inter- and intra-observer agreement. The femoral and tibial parts of the ALL were visible in 62% and 73.2% of the cases, respectively, whereas the deep attachment of the ITT to the distal femur was detected in 60.6% of the cases. The capsulo-osseous layer of the ITT could not be assessed in any of the cases.Several previous studies have dealt with MRI visibility of the ALL , 30, 37.To the best of our knowledge, there exist no previous studies that deal with MRI visibility of the deep structures of the ITT. This is somewhat surprising because these structures were described from an anatomic and a biomechanical point of view many years ago and were considered important for anterolateral knee stability , 28, 38.This work has some limitations. This was a retrospective investigation with the weaknesses typically associated with such studies. Although strict inclusion and exclusion criteria were applied, it would have been even better to include subjects 100% free of knee complaints. The subjects included in the present retrospective analysis were free of major knee lesions, but must have had some knee complaints as a prerequisite for MRI examination.The major strength of the present study is that MRI visibility was investigated for the first time for both the ALL and the deep structures of the ITT in one single study, including a large cohort of patients and using a 3 Tesla MRI. Moreover, inter- and intra-observer reproducibility, which have not been reported in previous studies, were assessed. Compared to other studies, only healthy participants with no ligamentous lesions where included and examined.Since the assessment and subsequently the treatment of an injured structure is preferably done after correct radiological representation, the clinical value of the present data is high. This is especially true, since injuries of the anterolateral structures are often seen with concomitant ACL ruptures . WhetherIt can be concluded that the presence of the anterolateral structures of the knee can be determined using MRI with substantial inter- and intra-observer agreement. This is true for both the ALL and the deep structures of the ITT, but not for the capsule-osseous layer.


R2 = 0.781, s.d. = 0.078) and FDFS  for LNC estimation by using the artificial neural network is not obvious. The proposed FS + FDFS can improved the accuracy of LNC estimation to some extent . Then, principal component analysis was used in FS and FDFS, and extracted the main fluorescence characteristics. The results indicated that the proposed FS + FDFS exhibited higher robustness and stability for LNC estimation  than that only using FS  or FDFS .Leaf nitrogen concentration (LNC) is a major indicator in the estimation of the crop growth status which has been diffusely applied in remote sensing. Thus, it is important to accurately obtain LNC by using passive or active technology. Laser-induced fluorescence can be applied to monitor LNC in crops through analysing the changing of fluorescence spectral information. Thus, the performance of fluorescence spectrum (FS) and first-derivative fluorescence spectrum (FDFS) for paddy rice (Yangliangyou 6 and Manly Indica) LNC estimation was discussed, and then the proposed FS + FDFS was used to monitor LNC by multivariate analysis. The results showed that the difference between FS ( Leaf biochemical parameters served as the vital indictors for estimating crops' growth status both locally and globally, and have been widely used in many fields. Leaf nitrogen concentration (LNC) is a key parameter of vegetation photosynthetic efficiency and yields, which can be applied for scientific guidance in nitrogen (N) fertilization management –3. HenceBased on the passive hyperspectral remote sensing technology, studies have been conducted by some scholars to build different models to obtain more accurate biochemical parameter estimation. They proposed different vegetation indices based on statistical analysis models for estimating biochemical parameters or crops yields –13. HoweCompared with the reflectance spectrum, chlorophyll fluorescence serving as an alternative means has been diffusely used in the estimation of vegetation growth status with the properties of being non-destructive, rapid and highly sensitive –21. Chloet al. [et al. found that F685/F730 is only correlated to the chlorophyll content [et al. [et al. [McMurtrey et al.  compared content . Subhash [et al.  used the [et al. ,33 analy [et al. –38. In o [et al. .Nevertheless, comparative studies on the abilities of FS and FDFS to monitor LNC are still limited. What is more, there is no relative literature conducted on studying the performance of the combination of FS and FDFS for LNC estimation. Thus, the main purpose of this study is to: (i) compare the abilities of FS and FDFS to estimate LNC based on multivariate analysis, and (ii) discuss whether FS combined with FDFS can improve the accuracy of paddy rice LNC estimation.2.2.1.−1) and 2016 (0 and 150 kg ha−1). According to the suggestion of the local farm extension service, 180 and 150 kg ha−1 were the optimal N fertilization in 2015 and 2016, respectively. Then, an absolute block design, in which ridges of the field were enclosed in plastic films to avoid water leakage, was used for each experimental area to maintain the same cultivation surroundings. In this study, the samples in this study were selected from the second leaves above. Foliar samples were fully unfolded and gathered by randomly collecting nine samples with three replicates for each experimental field on 26 July 2015 and 23 July 2016, respectively. Thus, the total number of foliar samples is 486. The gathering period of rice is tillering stage.Yangliangyou 6 was cultivated in 2015, and Manly Indica was cultivated in 2016. The experimental area is sited in the Jianghan China Plain and the latitude and longitude ranges from 29°58′ N to 31°22′ N and 113°41′ E to 115°05′ E, respectively. In order to obtain different LNCs, different fertilization levels were used in the experimental areas. Different N fertilization levels of urea were used in 2015 ) at iλ is the difference between the fluorescence intensity at each band, minus and plus one wavelength, divided by the range of wavelength, as written:λex is the wavelength of excitation light, I and I denote the excited fluorescence signals at iλ+1 and iλ−1, respectively; i is the corresponding wavelength.Based on the previous studies and concepts of first-derivative ,43, FDFS2.4.Back-propagation neural network (BPNN) can be used in analysing multifarious nonlinear problems by establishing the relationship between the inner neural units based on a succession of trials . This alPrincipal component analysis (PCA) can efficiently reduce the number of original parameters by extracting the key characteristics variables and deleting lower-level components. However, the major spectral information will not be lost . Thus, PR2) and standard deviation (s.d.) were used to evaluate the capability of the BPNN by using different feature variables . The s.d. was calculated as follows:iX denotes the value of each R2 based on BPNN model; and n represents the repeated times. Low s.d. and high R2 denote an excellent capability of training model. To analyse the stability of the fluorescence characteristics for LNC estimation, each feature setting was repeated 1000 times by using BPNN model. Then, the average and s.d. of R2 can be obtained. In this paper, Matlab R2015b , where a lot of regression toolboxes are available, was used to analyse all data.The fluorescence features were stochastically divided into two datasets: 70% as the training dataset and 30% as the validation set. In this study, coefficient of determination  are shown in 3.2.R2-values between the measured and predicted LNC is shown in To discuss the performance of the spectral information for LNC estimation, then BPNN model was used to inverse LNC according to the different fluorescence spectral characteristics in this study. LNC and different fluorescence spectral features corresponded, respectively, to the output and input parameters in the BPNN. In order to analyse the robustness of fluorescence features, each characteristic setting was repeated 1000 times by using BPNN. The distribution of R2-values based on BPNN model for different fluorescence spectral features. It is clear that the performance of FS + FDFS is superior to that of only FS or FDFS for LNC estimation. The results demonstrated that the FDFS can provide more fluorescence features to improve the accuracy of LNC estimation. In addition, the variation range of R2-values is listed in R2 is not obvious for different fluorescence spectral features. However, the robustness of FS + FDFS (s.d. = 0.051) is superior to that using only FS (s.d. = 0.078) or FDFS (s.d. = 0.097) for LNC estimation. Thus, the proposed FS features combined with FDFS can be applied to improve the accuracy of the monitoring of crops' growth status to a certain extent without additional measurements. In addition, we can find that this improvement is not very obvious. The main reason is that the FS and FDFS are information redundant and highly autocorrelated between the adjacent wavelengths with 0.5 nm sample interval, which may impact on the its ability for the LNC monitoring. Thus, PCA was applied in this study to reduce dimensionality FS and FDFS, and then extract main fluorescence characteristics for LNC estimation.From 3.3.PCA was used to analyse FS and FDFS, then the extracted main fluorescence features served as input variables in this model to train BPNN to improve accuracy of LNC estimation. In order to understand the extracted characteristics variables by applying PCA, the loading weight of the first three principal components (PCs) is given in R2-values can be obtained by using the BPNN model with 1000 repeats for each setting data  is superior to FDFS  for LNC monitoring by using the PCA combined with BPNN algorithm. The possible reason is that FDFS is sensitive to oscillations and will increase the risk of possible scattering of the data. Although the smoothing of data has been processed, it will still influence the accuracy of LNC monitoring. The detailed analysis needs to be further analysed in the following work. Compared to using only FS or FDFS, the FS + FDFS displayed better stability for LNC monitoring with higher R2 mean values and lower s.d. The satisfactory results based on the FS + FDFS demonstrated its application potential for the LNC monitoring.The performance of FD and FDFS for LNC monitoring by using PCA combined with BPNN was discussed in this study, and then FS + FDFS was proposed to estimate LNC. The combination with FS and FDFS displayed application potential for LNC estimation. However, the effect of the oscillation of FDFS on the accuracy of LNC monitoring still needs to be further discussed. In addition, the effect of multivariate analysis on accuracy also needs be considered in the following work.4.R2 = 0.781, s.d. = 0.078) and FDFS  for LNC estimation by applying the BPNN is not obvious. The proposed FS + FDFS can improve the accuracy and stability of LNC estimation to some extent . Then, PCA was used to analyse FS and FDFS and to reduce the verbose information. The proposed FS + FDFS exhibited higher robustness for LNC estimation  than that using only FS  or FDFS  based on the PCA combined with BPNN model. Therefore, the study results demonstrated that FS combined with FDFS can improve the accuracy and robustness of LNC monitoring based on multivariate analysis without any additional measurements.In this study, the predictive ability of FS and FDFS for LNC estimation was discussed, and then the combination with FS and FDFS was proposed to monitor LNC by using the PCA and BPNN algorithms. The experimental results indicated that the difference between FS (


In team sports training scenes, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the most active player who performs the given handball technique. This is a very challenging task, for which, apart from an accurate object detector, which is able to deal with complex cluttered scenes, additional information is needed to determine the active player. We propose an active player detection method that combines the Yolo object detector, activity measures, and tracking methods to detect and track active players in time. Different ways of computing player activity were considered and three activity measures are proposed based on optical flow, spatiotemporal interest points, and convolutional neural networks. For tracking, we consider the use of the Hungarian assignment algorithm and the more complex Deep SORT tracker that uses additional visual appearance features to assist the assignment process. We have proposed the evaluation measure to evaluate the performance of the proposed active player detection method. The method is successfully tested on a custom handball video dataset that was acquired in the wild and on basketball video sequences. The results are commented on and some of the typical cases and issues are shown. Many applications of computer vision, such as action recognition, security, surveillance, or image retrieval, depend on object detection in images or videos. The task of object detection is to find the instances of real-world objects, such as people, cars, or faces. Detecting an object includes determining the location of that object and predicting the class to which it belongs. Object location and object classification problems both pose individual challenges, and the choice of the right object detection method can depend on the problem that needs to be solved. In our case, object detection, or specifically, leading player detection, can be very helpful for the recognition of actions in scenes from the handball domain. Handball is a team sport played in the hall on a handball court. Two teams consisting of a goalkeeper and six players participate in the game. The goal of the game is to score more points than the opposing team by throwing the ball into the goal of the opposing team, or by defending the goal, so that the opposing team does not score. The game is very fast and dynamic, with each team trying to arrange the action to defeat the defense and score a goal or to prevent the attack and defend the goal. All of the players can move all over the court except in the space 6 m in front of both goals where the goalkeepers are. The players move fast, change positions, and combine different techniques and handball actions, depending on their current role, which might be attack or defense. A player can shoot the ball toward the goal, dribble it, or pass it to a teammate during the attack, or block the ball or a player when playing defense. Different techniques and actions that players perform, as well as the frequent changes of position and occlusions, cause the shapes and appearances of players to change significantly during the game, which makes the detection and tracking process more challenging. The indoor court environment with relatively reflective floors is often illuminated with harsh lighting so that the shadows and reflections of players are usually present on the recordings and, due to the speed of action performance, motion blur occurs, which further complicates the tracking and detection problems. Additionally, the positions of the player and the distance to the fixed-mounted camera are constantly changing, so that players can be close to the camera, covering most of the image, or at a distance from the camera and occupy just a few pixels. The ball is also an important object for recognizing the handball scene, but it is even more complex to detect and track than the player, because it is even smaller, moves quickly, in different directions, and it is often covered by the player’s body or not visible because it is in the player’s hands .Handball rules are well-defined and prescribe permitted actions and techniques, but during training, when adopting and practicing different techniques or game elements, the coach changes the rules to maintain a high level of player activity and a lot of repetition. Players then preform several sets of exercises in a row to train and improve certain techniques or actions, and to learn to position themselves properly in situations that imitate real game situations. In training sessions, each player also usually has his own ball, to reduce the waiting time to practice techniques and to train handball skills as quickly as possible. For example, a shooting technique involves a player that shoots the ball towards the goal and the goalkeeper that moves to defend the goal. Other players are waiting their turn to perform this activity or they gather their balls on the playground and then run to their position in the queue, as shown in Although all of the players move and interact in a certain moment, not all players contribute to the action, such as passing the ball or shooting at the goal, which is the most relevant for some exercise or for the interpretation of a handball situation. Thus, the focus should be on the players that are responsible for the currently most relevant handball action, and whose label can then be used to mark the whole relevant part on the video. Here, these players are referred to as active players.The goal of the proposed method is to determine which of the players that are present in the video sequences are active at a certain time of the game or at the training sessions, and who of them performs the requested action at a given moment.Detecting the player who performs an action of interest is particularly demanding during training sessions, because there are more players on the field than allowed during a normal game, there is more than one ball, because each player has their own ball and certain actions take place in parallel to make the players get the most out of the time to adopt or perfect the technique. The given goal is too complex for simple methods for people segmentation, such as background subtraction or Chroma keying, so, for this purpose, we suggest an active player detection method that combines player detection and tracking with an activity measure to determine the leading player(s). For the player detection, we suggest the use of deep convolution neural networks (CNNs) that have proven to be successful in classification and detection tasks in real-world images. In this paper, we will use Yolo V3  which acWe propose three activity measures to determine the level of activity of a particular player. The one, named DT+STIP, is based on density of spatiotemporal interest points (STIPs) that are detected within the area of a player, the second, DT+OF, is computationally simpler and based on optical flow (OF) motion field between consecutive video frames, and the third, DT+Y, uses convolutional neural network (CNN) for determining player activity and classifying active and inactive players.We consider the use of Hungarian assignment algorithm based on the bounding box positions of detected players, and the more complex Deep SORT tracker  that useWe have defined the evaluation measure for evaluate the performance of the proposed active player detection method. The proposed method is tested on videos that were taken in the gym during several training sessions, in real, uncontrolled indoor environment with cluttered background, complex light conditions and shadows, and with multiple players, on average 12, who practice and repeat different handball actions and dynamically change positions and occlude each other.The rest of the paper is organized, as follows: in The goal of detecting the active or leading player in a sports video is to find the sequence of bounding boxes that correspond to the player currently performing the most relevant action. The task can be separated into player detection and player tracking, which produces sequences of bounding boxes that correspond to individual players, combined with saliency detection to determine which of the sequences belong to the active player. Alternatively, the first part of the problem can be viewed as a task of action localization in general, where the goal is to find the sequences of bounding boxes in videos that likely contain actions, not necessarily by explicitly trying to detect players, but possibly using other cues, such as only the foreground motion. Visual object tracking, including player tracking, is a very active research area that attracts dozens of papers to computer vision conferences annually , and numRecently, deep learning-based approaches to player detection, e.g. , are becDetermining who is the leading player among the detected players is related to the problem of salient object detection, where the aim is to detect and segment the object in an image that visually stands out from its surroundings, which would be in the center of attention in the human system. In addition to finding naturally  distinct regions , the criteria for saliency may be based on a specific task, expectations, prior knowledge, etc.  . In thisIn the area of action localization, the goal is to find the sequence of bounding boxes in a sequence of video frames that correspond to an action. Generating a set of candidate spatiotemporal regions, followed by the classification of the candidate region into an action class or the non-action class, is commonly utilized to perform this. Jain et al.  first seIn , basketbThe goal of the proposed method is to automatically determine the player or players in the scene that are responsible for the current action, among all of the players present in the scene. First, the players are detected in the input video while using a CNN-based object detector. Here, the YOLOv3 detector pre-trained on the MS-COCO dataset was used to mark the players on each frame of the video sequence with their bounding boxes A, using Each of the steps is described in more detail below.The Hungarian assignment algorithm was used to track the players, with a distance function based on properties of bounding boxes as the baseline method. As an alternative, the Deep SORT  method, Different ways of computing player activity were considered and three activity measures are proposed. The first measure, named DT+OF, is based on the optical flow calculated within a player’s bounding box and considers the velocity component of the motion, while the second measure, named DT+STIP, uses the density of the spatiotemporal interest points (STIPs) within the bounding boxes to determine the activity measure. The third method, called DT+Y, uses the YOLOv3 network to classify a player into an active or inactive class.The input videos used as input data were recorded during the handball training involving 15 and more players. Each video sequence was selected and trimmed, so that it contains all the steps of the given action.The goal of object detectors is to locate and classify objects on the scene. The detected objects are typically marked with a bounding box and labeled with corresponding class labels.The current focus in object detection is on convolutional neural networks (CNNs) that were, at first, used for image classification, but have later been extended to be able to both detect and localize individual objects on the scene. This can be achieved by the independent processing of image parts that are isolated while using a sliding window that is moved over the image . MultiplHere, the role of the object is to detect the players in each frame of the handball videos. Ideally, the detector should be as precise as possible, yet less computationally demanding, so that in can detect objects in real time. It has been previously shown ,29 that YOLOv3 is the third iteration of the YOLO object detector , which pThe architecture of the YOLOv3 detector is a convolutional network that consists of 53 convolutional layers of 3 × 3 and 1 × 1 filters with shortcut connections between layers  is used for feature extraction. The last convolutional layer of the network predicts the bounding boxes, the confidence scores, and the prediction class. YoloV3 predicts candidate bounding boxes at three different scales using a structure that is similar to feature pyramid networks, so three sets of boxes are predicted at each feature map cell for each scale, to improve the detection of objects that may appear at different sizes.For the task of active player detection, only the bounding boxes and confidence values for the objects of the class "person" were used. An experimentally determined confidence threshold value of 0.55 was used to filter out the unwanted detections and obtain good balance of high detection and low false positives rates. The detections that were obtained with the Yolo detector are independent of each processed frame and, thus, contain no information about which bounding boxes correspond to the same objects in consecutive frames. Finding this correspondence between bounding boxes and player identities across frames is required to obtain the player trajectories. An additional post-processing step for player tracking is used for this purpose. Two methods are considered for this task. The first is a simpler approach utilizing the Hungarian algorithm, which assigns player bounding boxes detected in different frames into the player track while taking the dimensions and positions of the bounding box into account. In the second approach, the Deep SORT method  is used,In the first video frame, the player tracks are initialized, so that a track ID is assigned to each detected player bounding box. In the next frames, the individual detected bounding boxes are assigned to previously initialized tracks while using the Munkres’ version of the Hungarian algorithm , whose oMore precisely, a new bounding box The number of tracks can also change throughout the video, since players can enter or exit the camera field of view at any time. Additionally, the detection of players is not perfect, so some tracks should resume after a period where no detection was assigned. The distance threshold T is used to control the maximum allowed distance between a detected bounding box and a last bounding box assigned to the track. Any box whose distance to a track is greater than this threshold cannot be assigned to that track, even if it is the closest one to the track. If for The DeepSORT  is a traLike in the previous case, in the first frame, a unique track ID is assigned to each bounding box that represents a player and has a confidence value higher than a set threshold and the Hungarian algorithm is used to assign the new detections to existing tracks, so that the assignment cost function reaches the global minimum. The cost function entails the spatial  distance The distance The visual distance The goal of the  be used .When a detection cannot be assigned to any track because it is too far from any track according to the distance The object detector provides the location of all the players present in the scene in bounding boxes, but it has no information regarding the movements or activities of the players. Some information about movement can be obtained by first tracking the players and then by calculating the shift of the bounding box centroid across frames. However, it is expected that strong and sudden changes in both velocity and appearance that cannot be described by the shift of the bounding box centroid alone will characterize various actions in sports videos. Three activity measures are proposed  to better capture the information about the activity of the players, as noted earlier.Both the optical flow estimation method and STIPs detection by themselves only consider sections of video frames without taking the object identities into account. Thus, the information about player locations obtained with Yolo is combined with the activity measure obtained from either optical flow or STIPs within the player’s bounding box to measure the individual player’s activity.In the proposed DT+OF activity measure, the optical flow estimate from time-varying image intensity is used to capture the information regarding speed and direction of movement of image patches within players bounding boxes activity . MovemenThe optical flow motion field in consecutive video frames is estimated while using the Lucas–Kanade method . In the The activity measure . STIPs are an extension of the idea of local points of interests in the spatial domain, i.e. of points with a significant local variation of image intensities into both spatial and temporal domains, by requiring that image values have large variation in both spatial and temporal directions around a point.It is expected that the higher density of STIPs in a certain area will point to a region of higher movement, which might correspond to the higher level of player’s activity, since STIPs capture the spatiotemporal “interestingness" at different points in the image, and most actions in sports videos are characterized by strong variations in velocity and appearance over time . Thus, aA number of methods have been proposed to detect the STIPs. The method that is proposed in  is basedIn the Harris3D STIPs detector, a linear scale-space representation ariances :(7)L has the highest activity measure, and track 1 has the second largest activity measure, the track score at frame 1 for track 6 will be 1, and for the track 2 it will be 2. Finally, the active player’s track is the one that is most commonly ranked first by the activity level in the whole sequence, i.e. whose track score vector contains the most ones among all track score vectors. The result of the proposed method for determining the most active player is a series of active player bounding box representations low line . The active player track can ultimately be represented by a collage of thumbnails that correspond to the bounding boxes of the most active player, showing the stages of an action .The proposed method was tested on a custom dataset that consists of scenes that were recorded during a handball training session. The handball training was organized in a sports hall or sometimes in an outdoor terrain, with a variable number of players without uniform jerseys, with challenging artificial lighting and strong sunshine, with cluttered background and other adverse conditions.The dataset consists of 751 videos, each containing one of the handball actions, such as passing, shooting, jump-shot, or dribbling. Multiple players, on average, 12 of them, appear in each video, as shown in Each player can move in any direction, but mostly one player performs an action of interest, so he is considered as the active player. Therefore, each file is only marked with one action of interest. The scenes were shot with stationary GoPro cameras that were mounted on the left or right side of the playground, from different angles and in different lighting conditions (indoor and outdoor). In the internal scenes, the camera is mounted at a height of 3.5 m and in the outside scenes at 1.5 m. The videos were recorded in full HD (1920 × 1080) at 30 frames per second. The total duration of the recordings used for the experiment was 1990 s.In the experiment, the individual steps of the proposed method and the method as a whole were tested, from person detection to selecting the trajectory of an active player.For the player detection task, the Yolo detector was tested on full-resolution videos and without frame skipping. The performance of the Yolo detector was evaluated in terms of recall, precision, and F1 score . The detThe results of the detector greatly depend on the complexity of the scene, which in the used dataset depends on the number of players on the scene and their size, as well as on the distance from the camera and the number of occlusions. For example, if there are up to eight players on the scene, close to the camera, the results are significantly better than in the case when there are nine or more players on scene that are far from the camera and occlusions exist, as shown in The obtained results confirm that player detection, which is the first step of the active player detection method, provides a good starting point for the next phases of the algorithm.In the next step, we tested the performance of each activity measure in terms of precision, recall, and F1. Activity measures for each detected player in a frame are computed based on motion features that are extracted within each player’s bounding box in that video frame in the case of DT+OF and DT+STIP and according to the confidence value of active player class detection in the case of DT+Y. The performance of all measures is evaluated on each frame by comparing the bounding box with the highest activity measure with the ground truth active player bounding box, as the DT+OF and DT+STIP measures are used in combination with the bounding boxes of detected players proposed by Yolo and DT+Y measure, as one of the outputs gives the bounding box of the active player. A detection was considered to be true positive if it was correctly labeled as active and had the largest IoU with the ground truth active player bounding box among the detected boxes. If the goal is to evaluate the performance of an activity measure, that is, how many times the measure has selected the right player as the most active, the exact match of the detected box with the ground truth is not crucial, but it is important to match GT as closely as possible (large IoU) if one wants to monitor player postures while performing an action. The minimum IoU overlap between the detected bounding box and ground truth was set to 10%, since, in this part, the focus was on the ability of the measure to distinguish between activity or inactivity of the player and not on the correct localization.It is necessary to know their activity measure in each frame, but also to uniquely identify that player throughout the period of interest, i.e., to track them through the sequence, to determine a player’s overall activity in a time period. The track of the active player A true positive rate was calculated as a number of true positives divided by the number of tested sequences to quantify the performance. An active player track is considered to be true positive if the IoU of the detected active player is equal to or greater than a threshold value Let be The This means that the measure is strictly defined, because only those results that have a true positive detection of the player in The best overall score was achieved with the DT+Y activity measure that was used in conjunction with the Deep Sort tracker with 67% correct active player sequences, while the second-best result was achieved using the DT+STIP activity measure and Deep Sort tracker, with 53% correct sequences. The gap in performance between the DT+STIP and DT+OF methods from the DT+Y is smaller here, where whole sequences are taken into account, than when being examined for their performance of activity detection in isolation. When considering whole sequences, if player detection and tracking performs well, some mislabeling of players as inactive in certain frames will not affect the label of the whole sequence, so the result will not degrade either. This is important to note, because even though the DT+Y activity measure performs the best for determining the activity of the player, to use it is necessary to have annotated training data, which, if not available, can be a time consuming and tedious job to acquire, and this is not required for the DT+OF and DT+STIP measures. An even larger set of learning data and data balancing is needed to achieve even better results of DT+Y measure, because the number of inactive athletes on the scene is much higher than the inactive ones.With all activity measures, better results were obtained while using the Deep Sort tracker than with Hungarian algorithm-based tracking, so it seems a clear choice, since it does not require much more effort to use.We tested the proposed methods with different Similar rankings hold for most values of the minimum required IoU , primarily because the video sequences in basketball dataset are event-driven rather than determined by the action for which the method was designed. This is most noticeable on actions, such as three-point throw, where it happens that the player who threw the ball is not even present in most of the frames as the camera zooms in on the ball’s trajectory and the surrounding players and basket to track the outcome of the throw. Contrary to the method of detecting events in the basketball game, the proposed methods are defined from the perspective of the player and they correspond to some handball techniques, for example, a shot jump takes from the moment the player receives the ball, takes up to three steps, and throws the ball towards the goal, regardless of the outcome of the action .The results are significant and promising, because they are comparable and even slightly better than the results that were reported in , althougThe track results exhibit similar properties as on the handball dataset, with problems stemming from the same sources—player detection, tracking, or activity measure. The difference is that basketball has fewer players on the field, smaller terrain, bigger ball, and the player’s occlusion in basketball is even greater than in handball scenes because of the speed and difference in the rules of the game. Additionally, in our handball dataset, the actions are defined from the player’s perspective and they correspond to individual handball techniques. Finally, For this reason, for the final track selection, our method assumes that the player should be tracked throughout the whole video sequence, so it is, by design, biased towards longer tracks. It often misses choosing the shooter in cases, such as three-point shot, where the player who throws the ball might not even be present in the majority of the clips d. Track There is great interest in the automatic interpretation of sports videos, for the purposes of analysis of game tactics and individual athlete’s performance, for retrieval, sports analysis and monitoring and refinement of player action techniques and playing style. To facilitate automatic analysis in team sports, such as handball, the detection and tracking of players who are currently performing a relevant action is of particular interest, as the interpretation of the whole scene often depends on that player’s action. Additionally, during training sessions, the time should be efficiently utilized, so that all players are active in sufficient proportion. In this paper, an active player detection method is proposed, which entails frame-by-frame object detection, player activity measures, and tracking methods to detect and track active players in video sequences. The CNN-based Yolo object detector was used for player detection, Hungarian assignment algorithm based method, and Deep SORT were considered for tracking the detected players along the time dimension, and three different measures are proposed for the task of player activity detection in a certain frame, DT+OF, DT+STIP, and DT+Y. The DT+STIP measure is calculated from the spatiotemporal interest points (STIPs) that were detected within the area of a player, the simpler DT+OF method depends on the computed optical flow (OF) motion field and the DT+Y method uses convolutional neural network (CNN) for determining player activity and classifying active and inactive players.The proposed method and its components were tested on the test set of handball videos from several training sessions that were acquired in the wild. A sequence-based evaluation measure was proposed for overall evaluation, and the best result was achieved with the Deep Sort tracking method with the CNN-based DT+Y activity measure, achieving the rate of 67% correct sequences on the test set. The DT+Y method requires training data, and if it is not available, the DT+STIP or DT+OF method can be used, which achieved 53% TPR and 50% TPR on the test set, respectively.To test how general the proposed activity measures are, we have chosen completely different sport scenes from basketball sports domain and evaluated the performance of DT+OF and DT+STIPS measures, since they do not require any leaning. The obtained results are lower than in the handball scenes (up to 8%), primarily because the video sequences are event-driven rather than determined by the action, as in our case, and there is an even greater occlusion of the players, as basketball is played faster and on a smaller field. The results are promising, because they are comparable to the results of a method that was trained on that basketball dataset to detect an active player in a video sequence.The achieved results confirm that active player detection is a very challenging task and it could be further improved by improving all three components . Particularly, player detection should be improved first, because both other components directly depend on its performance. Activity measure should be calculated inside the detected bounding box, so imprecise detection will negatively affect this measure, since parts of player body may be outside the detected box and irrelevant parts of background within. Similarly, in addition to the inability to track objects that are not detected at all, the Deep Sort tracking method uses visual history of previous appearances of players, so their imprecise detection impacts the ability of the tracker to correctly re-identify them after occlusion. For application in match setting, where a single ball exists in play, ball detection could be incorporated as an additional significant clue for detecting the leading player. It is indisputable that the information that the ball has in the game is important for determining the action and the player, but it is a small object that is hidden in the player’s hand most of the time, and, when thrown quickly, moves and changes direction, so it is often blurred and tracking is still a challenge that is being intensively tried to tackle.In the future, the method should be extended to better deal with multi-player actions, such as crossing or defense, and the player activity could be monitored in relation to the type of action the player is performing. Additionally, the method should be extended to handle long-term video clips, where the leading player changes in different time intervals.