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    ATCC t1 e coli atcc 11303
    ( A ) ROC analysis was applied to Raman spectral fingerprint regions to identify biomarkers distinguishing infected from uninfected bacteria. Raman spectra were collected from three sensitive systems (T1- <t>Escherichia</t> <t>coli</t> ATCC11303, T4- E. coli ATCC11303, T4- E. coli ATCC25922) at six time points (20∼120 min) under MOI = 10. Peaks showing an average area under the ROC curve (AUC) > 0.80 in at least four of six time points across all three systems are considered candidate biomarkers (n = 15). Combined with difference spectrum analysis, only those showing consistent directional changes (either increased or decreased upon infection) across systems are retained, resulting in four universal candidate regions: 658∼663 cm¹ (decreased), 713∼719 cm¹ (decreased), 1430∼1450 cm¹ (increased), and 1589∼1595 cm¹ (increased). ( B ) Temporal dynamics of the four biomarkers are compared between sensitive and resistant systems. In the sensitive T1-ATCC11303 system, 658∼663 cm¹ and 713∼719 cm¹ gradually decrease, whereas 1430∼1450 cm¹ and 1589∼1595 cm¹ continuously increase over time. In contrast, in the resistant T1-DH5α system, the initial decrease of 658∼663 cm¹ and 713∼719 cm¹ (≤ 40 min) is followed by recovery or stabilization, while 1430∼1450 cm¹ and 1589∼1595 cm¹ transiently decrease at early time points (≤ 60 min) and then remain unchanged or slightly decline. ( C ) Temporal dynamics of the four biomarkers in the weakly sensitive system under different MOI conditions. Under MOI = 10, 658∼663 cm¹ and 713∼719 cm¹ consistently decrease, while 1430∼1450 cm¹ and 1589∼1595 cm¹ increase. Under MOI = 1, responses fluctuate: the first two regions decrease at 40 min, increase at 80 min, and sharply drop at 100 min; the latter two increase at 40∼80 min but also decline at 100 min. n.s., not significant; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.
    T1 E Coli Atcc 11303, supplied by ATCC, used in various techniques. Bioz Stars score: 93/100, based on 13 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    1) Product Images from "Rapid and Quantitative Phage Susceptibility Test by Ramanome"

    Article Title: Rapid and Quantitative Phage Susceptibility Test by Ramanome

    Journal: bioRxiv

    doi: 10.64898/2026.02.07.704537

    ( A ) ROC analysis was applied to Raman spectral fingerprint regions to identify biomarkers distinguishing infected from uninfected bacteria. Raman spectra were collected from three sensitive systems (T1- Escherichia coli ATCC11303, T4- E. coli ATCC11303, T4- E. coli ATCC25922) at six time points (20∼120 min) under MOI = 10. Peaks showing an average area under the ROC curve (AUC) > 0.80 in at least four of six time points across all three systems are considered candidate biomarkers (n = 15). Combined with difference spectrum analysis, only those showing consistent directional changes (either increased or decreased upon infection) across systems are retained, resulting in four universal candidate regions: 658∼663 cm¹ (decreased), 713∼719 cm¹ (decreased), 1430∼1450 cm¹ (increased), and 1589∼1595 cm¹ (increased). ( B ) Temporal dynamics of the four biomarkers are compared between sensitive and resistant systems. In the sensitive T1-ATCC11303 system, 658∼663 cm¹ and 713∼719 cm¹ gradually decrease, whereas 1430∼1450 cm¹ and 1589∼1595 cm¹ continuously increase over time. In contrast, in the resistant T1-DH5α system, the initial decrease of 658∼663 cm¹ and 713∼719 cm¹ (≤ 40 min) is followed by recovery or stabilization, while 1430∼1450 cm¹ and 1589∼1595 cm¹ transiently decrease at early time points (≤ 60 min) and then remain unchanged or slightly decline. ( C ) Temporal dynamics of the four biomarkers in the weakly sensitive system under different MOI conditions. Under MOI = 10, 658∼663 cm¹ and 713∼719 cm¹ consistently decrease, while 1430∼1450 cm¹ and 1589∼1595 cm¹ increase. Under MOI = 1, responses fluctuate: the first two regions decrease at 40 min, increase at 80 min, and sharply drop at 100 min; the latter two increase at 40∼80 min but also decline at 100 min. n.s., not significant; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.
    Figure Legend Snippet: ( A ) ROC analysis was applied to Raman spectral fingerprint regions to identify biomarkers distinguishing infected from uninfected bacteria. Raman spectra were collected from three sensitive systems (T1- Escherichia coli ATCC11303, T4- E. coli ATCC11303, T4- E. coli ATCC25922) at six time points (20∼120 min) under MOI = 10. Peaks showing an average area under the ROC curve (AUC) > 0.80 in at least four of six time points across all three systems are considered candidate biomarkers (n = 15). Combined with difference spectrum analysis, only those showing consistent directional changes (either increased or decreased upon infection) across systems are retained, resulting in four universal candidate regions: 658∼663 cm¹ (decreased), 713∼719 cm¹ (decreased), 1430∼1450 cm¹ (increased), and 1589∼1595 cm¹ (increased). ( B ) Temporal dynamics of the four biomarkers are compared between sensitive and resistant systems. In the sensitive T1-ATCC11303 system, 658∼663 cm¹ and 713∼719 cm¹ gradually decrease, whereas 1430∼1450 cm¹ and 1589∼1595 cm¹ continuously increase over time. In contrast, in the resistant T1-DH5α system, the initial decrease of 658∼663 cm¹ and 713∼719 cm¹ (≤ 40 min) is followed by recovery or stabilization, while 1430∼1450 cm¹ and 1589∼1595 cm¹ transiently decrease at early time points (≤ 60 min) and then remain unchanged or slightly decline. ( C ) Temporal dynamics of the four biomarkers in the weakly sensitive system under different MOI conditions. Under MOI = 10, 658∼663 cm¹ and 713∼719 cm¹ consistently decrease, while 1430∼1450 cm¹ and 1589∼1595 cm¹ increase. Under MOI = 1, responses fluctuate: the first two regions decrease at 40 min, increase at 80 min, and sharply drop at 100 min; the latter two increase at 40∼80 min but also decline at 100 min. n.s., not significant; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

    Techniques Used: Infection, Bacteria

    ( A-B ) Performance comparison of six machine learning models integrating four RPST-derived biomarkers, evaluated by 5×5 cross-validation: direct summation, logistic regression, LDA, naïve Bayes, SVM, and RF. Based on ( A ) ROC and ( B ) precision-recall curves, both AUC and PR-AUC follow RF > LDA > SVM > naïve Bayes > logistic regression > direct summation, with RF achieving the best performance (AUC: 0.995 ± 0.003; PR-AUC: 0.993 ± 0.003). ( C-D ) Comparison between single biomarkers and the RF-based CII. Across repeated cross-validation, CII consistently outperforms all individual features in both ( C ) ROC and ( D ) precision–recall analyses. ( E ) RF-derived feature importance averaged across folds: 1430∼1450 cm¹ (0.516 ± 0.010), 1589∼1595 cm¹ (0.244 ± 0.012), 713∼719 cm¹ (0.151 ± 0.018), and 658∼663 cm¹ (0.089 ± 0.014). ( F ) Dual-cutoff framework for CII-based classification. The optimal cutoff (0.6000) maximizing G-mean defines infection status with 99% confidence bounds (lower: 0.5970; upper: 0.6030), categorizing populations as uninfected (CII < lower), infected (CII > upper), or uncertain (between cutoffs). ( G-H ) Temporal dynamics of CII. In T1- E. coli ATCC11303 (susceptible), CII exceeds the cutoff by 20 min (0.73 ± 0.21), stabilizing by 40 min (0.99 ± 0.02). In T1- E. coli DH5α (resistant), both the infected and control groups remain below the cutoff. ( I ) Validation across 25 phage-host systems ( E. coli , P. aeruginosa , Salmonella , K. pneumoniae ). CII-based classification agrees with plaque assays in 24/25 cases (96.0% accuracy).
    Figure Legend Snippet: ( A-B ) Performance comparison of six machine learning models integrating four RPST-derived biomarkers, evaluated by 5×5 cross-validation: direct summation, logistic regression, LDA, naïve Bayes, SVM, and RF. Based on ( A ) ROC and ( B ) precision-recall curves, both AUC and PR-AUC follow RF > LDA > SVM > naïve Bayes > logistic regression > direct summation, with RF achieving the best performance (AUC: 0.995 ± 0.003; PR-AUC: 0.993 ± 0.003). ( C-D ) Comparison between single biomarkers and the RF-based CII. Across repeated cross-validation, CII consistently outperforms all individual features in both ( C ) ROC and ( D ) precision–recall analyses. ( E ) RF-derived feature importance averaged across folds: 1430∼1450 cm¹ (0.516 ± 0.010), 1589∼1595 cm¹ (0.244 ± 0.012), 713∼719 cm¹ (0.151 ± 0.018), and 658∼663 cm¹ (0.089 ± 0.014). ( F ) Dual-cutoff framework for CII-based classification. The optimal cutoff (0.6000) maximizing G-mean defines infection status with 99% confidence bounds (lower: 0.5970; upper: 0.6030), categorizing populations as uninfected (CII < lower), infected (CII > upper), or uncertain (between cutoffs). ( G-H ) Temporal dynamics of CII. In T1- E. coli ATCC11303 (susceptible), CII exceeds the cutoff by 20 min (0.73 ± 0.21), stabilizing by 40 min (0.99 ± 0.02). In T1- E. coli DH5α (resistant), both the infected and control groups remain below the cutoff. ( I ) Validation across 25 phage-host systems ( E. coli , P. aeruginosa , Salmonella , K. pneumoniae ). CII-based classification agrees with plaque assays in 24/25 cases (96.0% accuracy).

    Techniques Used: Comparison, Derivative Assay, Biomarker Discovery, Infection, Control

    ( A-B ) Effect of multiplicity of infection (MOI) on the composite infection index (CII) in the Ecp54- E. coli DH5α system. ( A ) Boxplots show progressive CII elevation with increasing MOI after 40 min co-incubation. ( B ) Density plots at 40 min reveal a gradual rise in the proportion of infected cells, exceeding uninfected populations at MOI = 0.1 and approaching complete infection at MOI = 10. ( C-D ) Evaluation of phage Pap12 against three P. aeruginosa strains. ( C ) RPST-based CII distributions show MOI-dependent increases for susceptible strains Pae1 and Pae307, surpassing the infection cutoff at MOI = 0.01 and 1, respectively, while the resistant strain Pae5 remains below the cutoff even at MOI = 10. ( D ) Plaque-based relative infection efficiency (RIE) assays yield RIE = 1 (Pae1), 0.01 (Pae307), and 0 (Pae5). ( E-F ) Evaluation of phage Kpap4 against three K. pneumoniae strains. ( E ) RIE assays show values of 1, 0.1, and 0 for Kpn25, Kpn30, and Kpn6, respectively. ( F ) Corresponding RPST-based CII distributions demonstrate cutoff crossing at MOI = 0.1 for Kpn25, persistent sub-cutoff levels for Kpn6 even at MOI = 10, and gradual but incomplete increases for Kpn30 (RIE > 0). ( G ) Comparative analysis of twelve E. coli phages (MOI = 10, 40 min). Nine elevated population CII above the cutoff, with Ecp9 showing the highest CII. Density plots exhibit unimodal distributions for infective phages, while three noninfective ones display bimodal patterns with population CII remaining below the cutoff.
    Figure Legend Snippet: ( A-B ) Effect of multiplicity of infection (MOI) on the composite infection index (CII) in the Ecp54- E. coli DH5α system. ( A ) Boxplots show progressive CII elevation with increasing MOI after 40 min co-incubation. ( B ) Density plots at 40 min reveal a gradual rise in the proportion of infected cells, exceeding uninfected populations at MOI = 0.1 and approaching complete infection at MOI = 10. ( C-D ) Evaluation of phage Pap12 against three P. aeruginosa strains. ( C ) RPST-based CII distributions show MOI-dependent increases for susceptible strains Pae1 and Pae307, surpassing the infection cutoff at MOI = 0.01 and 1, respectively, while the resistant strain Pae5 remains below the cutoff even at MOI = 10. ( D ) Plaque-based relative infection efficiency (RIE) assays yield RIE = 1 (Pae1), 0.01 (Pae307), and 0 (Pae5). ( E-F ) Evaluation of phage Kpap4 against three K. pneumoniae strains. ( E ) RIE assays show values of 1, 0.1, and 0 for Kpn25, Kpn30, and Kpn6, respectively. ( F ) Corresponding RPST-based CII distributions demonstrate cutoff crossing at MOI = 0.1 for Kpn25, persistent sub-cutoff levels for Kpn6 even at MOI = 10, and gradual but incomplete increases for Kpn30 (RIE > 0). ( G ) Comparative analysis of twelve E. coli phages (MOI = 10, 40 min). Nine elevated population CII above the cutoff, with Ecp9 showing the highest CII. Density plots exhibit unimodal distributions for infective phages, while three noninfective ones display bimodal patterns with population CII remaining below the cutoff.

    Techniques Used: Infection, Incubation

    ( A-C ) MOI- and time-resolved trajectories of CII in three phage-host systems. Populations were monitored under five MOIs (0, 0.01, 0.1, 1, and 10) across seven timepoints (0∼120 min, 20-min intervals). ( A ) In the T1- E. coli ATCC11303 system, populations at MOI = 0 remain below the infection cutoff throughout the experiment, whereas those at MOI = 10 cross the threshold within 20 min. Intermediate MOIs (0.01, 0.1, 1) exhibit delayed but complete transitions into the infected state within 60 min. ( B ) In the T4- E. coli ATCC11303 system, populations cross the infection cutoff within ≤ 20 min at MOI = 10, 60 min at MOI = 1, 80 min at MOI = 0.1, and 120 min at MOI = 0.01, while remaining uninfected at MOI = 0. (C) In the T4- E. coli ATCC25922 system, only MOI = 10 leads to a transition to the infected state. At lower MOIs, CII values rise transiently but subsequently decline, and population-level CII never exceeds the infection cutoff.
    Figure Legend Snippet: ( A-C ) MOI- and time-resolved trajectories of CII in three phage-host systems. Populations were monitored under five MOIs (0, 0.01, 0.1, 1, and 10) across seven timepoints (0∼120 min, 20-min intervals). ( A ) In the T1- E. coli ATCC11303 system, populations at MOI = 0 remain below the infection cutoff throughout the experiment, whereas those at MOI = 10 cross the threshold within 20 min. Intermediate MOIs (0.01, 0.1, 1) exhibit delayed but complete transitions into the infected state within 60 min. ( B ) In the T4- E. coli ATCC11303 system, populations cross the infection cutoff within ≤ 20 min at MOI = 10, 60 min at MOI = 1, 80 min at MOI = 0.1, and 120 min at MOI = 0.01, while remaining uninfected at MOI = 0. (C) In the T4- E. coli ATCC25922 system, only MOI = 10 leads to a transition to the infected state. At lower MOIs, CII values rise transiently but subsequently decline, and population-level CII never exceeds the infection cutoff.

    Techniques Used: Infection

    Related Articles

    Infection:

    Article Title: Rapid and Quantitative Phage Susceptibility Test by Ramanome
    Article Snippet: .. To assess whether RPST can resolve infection dynamics and determine the minimal effective MOI, we established a multidimensional experimental matrix incorporating five MOIs (0, 0.01, 0.1, 1, and 10) and six time points (0∼120 min at 20-min intervals) across three phage-bacterium systems: T1- E. coli ATCC 11303, T4- E. coli ATCC 11303, and T4- E. coli ATCC 25922. ..



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    ATCC t1 e coli atcc 11303
    ( A ) ROC analysis was applied to Raman spectral fingerprint regions to identify biomarkers distinguishing infected from uninfected bacteria. Raman spectra were collected from three sensitive systems (T1- <t>Escherichia</t> <t>coli</t> ATCC11303, T4- E. coli ATCC11303, T4- E. coli ATCC25922) at six time points (20∼120 min) under MOI = 10. Peaks showing an average area under the ROC curve (AUC) > 0.80 in at least four of six time points across all three systems are considered candidate biomarkers (n = 15). Combined with difference spectrum analysis, only those showing consistent directional changes (either increased or decreased upon infection) across systems are retained, resulting in four universal candidate regions: 658∼663 cm¹ (decreased), 713∼719 cm¹ (decreased), 1430∼1450 cm¹ (increased), and 1589∼1595 cm¹ (increased). ( B ) Temporal dynamics of the four biomarkers are compared between sensitive and resistant systems. In the sensitive T1-ATCC11303 system, 658∼663 cm¹ and 713∼719 cm¹ gradually decrease, whereas 1430∼1450 cm¹ and 1589∼1595 cm¹ continuously increase over time. In contrast, in the resistant T1-DH5α system, the initial decrease of 658∼663 cm¹ and 713∼719 cm¹ (≤ 40 min) is followed by recovery or stabilization, while 1430∼1450 cm¹ and 1589∼1595 cm¹ transiently decrease at early time points (≤ 60 min) and then remain unchanged or slightly decline. ( C ) Temporal dynamics of the four biomarkers in the weakly sensitive system under different MOI conditions. Under MOI = 10, 658∼663 cm¹ and 713∼719 cm¹ consistently decrease, while 1430∼1450 cm¹ and 1589∼1595 cm¹ increase. Under MOI = 1, responses fluctuate: the first two regions decrease at 40 min, increase at 80 min, and sharply drop at 100 min; the latter two increase at 40∼80 min but also decline at 100 min. n.s., not significant; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.
    T1 E Coli Atcc 11303, supplied by ATCC, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    ATCC t4 atcc 11303 b 1 host cell e coli atcc 11303
    Fig. 3 (A) Shapes of volume-scattering functions for four dif- ferent viruses, MS2 <t>(E.</t> <t>coli</t> virus; solid circles), T4 (E. coli virus; triangles), Y1 (marine bacteriophage; squares), and C2 (marine bac- teriophage; open circles). The sizes of these different viruses are 25–30 nm, 100 nm, 50–80 nm, and 110 nm respectively. Note, volume scattering (Y axis) is a log scale, and the VSFs are relatively flat compared to typical VSFs found for larger marine particulate matter (Mobley 1994). (B) Same results as (A), but with expanded ordinate scale to show lack of symmetry for VSFs. Lines represent least-square fits to the data using the following equations: Preisen- dorfer Turbid water data fitted with Beardsley-Zaneveld equation; Y 5 7.45 3 1023{1/([1 2 (7.521 3 1021[cos X])]4 3 [1 1 (0.388[cos X])4])}; r2 5 0.999. MS2 fitted to polynomial: Y 5 3.618 3 105([1.045 3 102 3 X 2] 1 [2.616X] 1 2.5307 3 102); r2 5 0.992. T4 fitted to polynomial: Y 5 4.137 3 105([3.332 3 103 3 X 2] 1 [1.220X] 1 2.273 3 102); r2 5 0.999. Y1 fitted to polyno- mial: Y 5 2.988 3 105([5.805 3 103 3 X 2] 1 [1.698X] 1 2.372 3 102); r2 5 0.988. C2 fitted to polynomial: Y 5 1.254 3 105([7.723 3 103 3 X 2] 1 [2.064X] 1 2.321 3 102); r2 5 0.991. Pure water using Rayleigh fit: Y 5 1.483 3 104{1 1 [(cos X)2]}; r2 5 0.998.
    T4 Atcc 11303 B 1 Host Cell E Coli Atcc 11303, supplied by ATCC, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    ( A ) ROC analysis was applied to Raman spectral fingerprint regions to identify biomarkers distinguishing infected from uninfected bacteria. Raman spectra were collected from three sensitive systems (T1- Escherichia coli ATCC11303, T4- E. coli ATCC11303, T4- E. coli ATCC25922) at six time points (20∼120 min) under MOI = 10. Peaks showing an average area under the ROC curve (AUC) > 0.80 in at least four of six time points across all three systems are considered candidate biomarkers (n = 15). Combined with difference spectrum analysis, only those showing consistent directional changes (either increased or decreased upon infection) across systems are retained, resulting in four universal candidate regions: 658∼663 cm¹ (decreased), 713∼719 cm¹ (decreased), 1430∼1450 cm¹ (increased), and 1589∼1595 cm¹ (increased). ( B ) Temporal dynamics of the four biomarkers are compared between sensitive and resistant systems. In the sensitive T1-ATCC11303 system, 658∼663 cm¹ and 713∼719 cm¹ gradually decrease, whereas 1430∼1450 cm¹ and 1589∼1595 cm¹ continuously increase over time. In contrast, in the resistant T1-DH5α system, the initial decrease of 658∼663 cm¹ and 713∼719 cm¹ (≤ 40 min) is followed by recovery or stabilization, while 1430∼1450 cm¹ and 1589∼1595 cm¹ transiently decrease at early time points (≤ 60 min) and then remain unchanged or slightly decline. ( C ) Temporal dynamics of the four biomarkers in the weakly sensitive system under different MOI conditions. Under MOI = 10, 658∼663 cm¹ and 713∼719 cm¹ consistently decrease, while 1430∼1450 cm¹ and 1589∼1595 cm¹ increase. Under MOI = 1, responses fluctuate: the first two regions decrease at 40 min, increase at 80 min, and sharply drop at 100 min; the latter two increase at 40∼80 min but also decline at 100 min. n.s., not significant; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

    Journal: bioRxiv

    Article Title: Rapid and Quantitative Phage Susceptibility Test by Ramanome

    doi: 10.64898/2026.02.07.704537

    Figure Lengend Snippet: ( A ) ROC analysis was applied to Raman spectral fingerprint regions to identify biomarkers distinguishing infected from uninfected bacteria. Raman spectra were collected from three sensitive systems (T1- Escherichia coli ATCC11303, T4- E. coli ATCC11303, T4- E. coli ATCC25922) at six time points (20∼120 min) under MOI = 10. Peaks showing an average area under the ROC curve (AUC) > 0.80 in at least four of six time points across all three systems are considered candidate biomarkers (n = 15). Combined with difference spectrum analysis, only those showing consistent directional changes (either increased or decreased upon infection) across systems are retained, resulting in four universal candidate regions: 658∼663 cm¹ (decreased), 713∼719 cm¹ (decreased), 1430∼1450 cm¹ (increased), and 1589∼1595 cm¹ (increased). ( B ) Temporal dynamics of the four biomarkers are compared between sensitive and resistant systems. In the sensitive T1-ATCC11303 system, 658∼663 cm¹ and 713∼719 cm¹ gradually decrease, whereas 1430∼1450 cm¹ and 1589∼1595 cm¹ continuously increase over time. In contrast, in the resistant T1-DH5α system, the initial decrease of 658∼663 cm¹ and 713∼719 cm¹ (≤ 40 min) is followed by recovery or stabilization, while 1430∼1450 cm¹ and 1589∼1595 cm¹ transiently decrease at early time points (≤ 60 min) and then remain unchanged or slightly decline. ( C ) Temporal dynamics of the four biomarkers in the weakly sensitive system under different MOI conditions. Under MOI = 10, 658∼663 cm¹ and 713∼719 cm¹ consistently decrease, while 1430∼1450 cm¹ and 1589∼1595 cm¹ increase. Under MOI = 1, responses fluctuate: the first two regions decrease at 40 min, increase at 80 min, and sharply drop at 100 min; the latter two increase at 40∼80 min but also decline at 100 min. n.s., not significant; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

    Article Snippet: To assess whether RPST can resolve infection dynamics and determine the minimal effective MOI, we established a multidimensional experimental matrix incorporating five MOIs (0, 0.01, 0.1, 1, and 10) and six time points (0∼120 min at 20-min intervals) across three phage-bacterium systems: T1- E. coli ATCC 11303, T4- E. coli ATCC 11303, and T4- E. coli ATCC 25922.

    Techniques: Infection, Bacteria

    ( A-B ) Performance comparison of six machine learning models integrating four RPST-derived biomarkers, evaluated by 5×5 cross-validation: direct summation, logistic regression, LDA, naïve Bayes, SVM, and RF. Based on ( A ) ROC and ( B ) precision-recall curves, both AUC and PR-AUC follow RF > LDA > SVM > naïve Bayes > logistic regression > direct summation, with RF achieving the best performance (AUC: 0.995 ± 0.003; PR-AUC: 0.993 ± 0.003). ( C-D ) Comparison between single biomarkers and the RF-based CII. Across repeated cross-validation, CII consistently outperforms all individual features in both ( C ) ROC and ( D ) precision–recall analyses. ( E ) RF-derived feature importance averaged across folds: 1430∼1450 cm¹ (0.516 ± 0.010), 1589∼1595 cm¹ (0.244 ± 0.012), 713∼719 cm¹ (0.151 ± 0.018), and 658∼663 cm¹ (0.089 ± 0.014). ( F ) Dual-cutoff framework for CII-based classification. The optimal cutoff (0.6000) maximizing G-mean defines infection status with 99% confidence bounds (lower: 0.5970; upper: 0.6030), categorizing populations as uninfected (CII < lower), infected (CII > upper), or uncertain (between cutoffs). ( G-H ) Temporal dynamics of CII. In T1- E. coli ATCC11303 (susceptible), CII exceeds the cutoff by 20 min (0.73 ± 0.21), stabilizing by 40 min (0.99 ± 0.02). In T1- E. coli DH5α (resistant), both the infected and control groups remain below the cutoff. ( I ) Validation across 25 phage-host systems ( E. coli , P. aeruginosa , Salmonella , K. pneumoniae ). CII-based classification agrees with plaque assays in 24/25 cases (96.0% accuracy).

    Journal: bioRxiv

    Article Title: Rapid and Quantitative Phage Susceptibility Test by Ramanome

    doi: 10.64898/2026.02.07.704537

    Figure Lengend Snippet: ( A-B ) Performance comparison of six machine learning models integrating four RPST-derived biomarkers, evaluated by 5×5 cross-validation: direct summation, logistic regression, LDA, naïve Bayes, SVM, and RF. Based on ( A ) ROC and ( B ) precision-recall curves, both AUC and PR-AUC follow RF > LDA > SVM > naïve Bayes > logistic regression > direct summation, with RF achieving the best performance (AUC: 0.995 ± 0.003; PR-AUC: 0.993 ± 0.003). ( C-D ) Comparison between single biomarkers and the RF-based CII. Across repeated cross-validation, CII consistently outperforms all individual features in both ( C ) ROC and ( D ) precision–recall analyses. ( E ) RF-derived feature importance averaged across folds: 1430∼1450 cm¹ (0.516 ± 0.010), 1589∼1595 cm¹ (0.244 ± 0.012), 713∼719 cm¹ (0.151 ± 0.018), and 658∼663 cm¹ (0.089 ± 0.014). ( F ) Dual-cutoff framework for CII-based classification. The optimal cutoff (0.6000) maximizing G-mean defines infection status with 99% confidence bounds (lower: 0.5970; upper: 0.6030), categorizing populations as uninfected (CII < lower), infected (CII > upper), or uncertain (between cutoffs). ( G-H ) Temporal dynamics of CII. In T1- E. coli ATCC11303 (susceptible), CII exceeds the cutoff by 20 min (0.73 ± 0.21), stabilizing by 40 min (0.99 ± 0.02). In T1- E. coli DH5α (resistant), both the infected and control groups remain below the cutoff. ( I ) Validation across 25 phage-host systems ( E. coli , P. aeruginosa , Salmonella , K. pneumoniae ). CII-based classification agrees with plaque assays in 24/25 cases (96.0% accuracy).

    Article Snippet: To assess whether RPST can resolve infection dynamics and determine the minimal effective MOI, we established a multidimensional experimental matrix incorporating five MOIs (0, 0.01, 0.1, 1, and 10) and six time points (0∼120 min at 20-min intervals) across three phage-bacterium systems: T1- E. coli ATCC 11303, T4- E. coli ATCC 11303, and T4- E. coli ATCC 25922.

    Techniques: Comparison, Derivative Assay, Biomarker Discovery, Infection, Control

    ( A-B ) Effect of multiplicity of infection (MOI) on the composite infection index (CII) in the Ecp54- E. coli DH5α system. ( A ) Boxplots show progressive CII elevation with increasing MOI after 40 min co-incubation. ( B ) Density plots at 40 min reveal a gradual rise in the proportion of infected cells, exceeding uninfected populations at MOI = 0.1 and approaching complete infection at MOI = 10. ( C-D ) Evaluation of phage Pap12 against three P. aeruginosa strains. ( C ) RPST-based CII distributions show MOI-dependent increases for susceptible strains Pae1 and Pae307, surpassing the infection cutoff at MOI = 0.01 and 1, respectively, while the resistant strain Pae5 remains below the cutoff even at MOI = 10. ( D ) Plaque-based relative infection efficiency (RIE) assays yield RIE = 1 (Pae1), 0.01 (Pae307), and 0 (Pae5). ( E-F ) Evaluation of phage Kpap4 against three K. pneumoniae strains. ( E ) RIE assays show values of 1, 0.1, and 0 for Kpn25, Kpn30, and Kpn6, respectively. ( F ) Corresponding RPST-based CII distributions demonstrate cutoff crossing at MOI = 0.1 for Kpn25, persistent sub-cutoff levels for Kpn6 even at MOI = 10, and gradual but incomplete increases for Kpn30 (RIE > 0). ( G ) Comparative analysis of twelve E. coli phages (MOI = 10, 40 min). Nine elevated population CII above the cutoff, with Ecp9 showing the highest CII. Density plots exhibit unimodal distributions for infective phages, while three noninfective ones display bimodal patterns with population CII remaining below the cutoff.

    Journal: bioRxiv

    Article Title: Rapid and Quantitative Phage Susceptibility Test by Ramanome

    doi: 10.64898/2026.02.07.704537

    Figure Lengend Snippet: ( A-B ) Effect of multiplicity of infection (MOI) on the composite infection index (CII) in the Ecp54- E. coli DH5α system. ( A ) Boxplots show progressive CII elevation with increasing MOI after 40 min co-incubation. ( B ) Density plots at 40 min reveal a gradual rise in the proportion of infected cells, exceeding uninfected populations at MOI = 0.1 and approaching complete infection at MOI = 10. ( C-D ) Evaluation of phage Pap12 against three P. aeruginosa strains. ( C ) RPST-based CII distributions show MOI-dependent increases for susceptible strains Pae1 and Pae307, surpassing the infection cutoff at MOI = 0.01 and 1, respectively, while the resistant strain Pae5 remains below the cutoff even at MOI = 10. ( D ) Plaque-based relative infection efficiency (RIE) assays yield RIE = 1 (Pae1), 0.01 (Pae307), and 0 (Pae5). ( E-F ) Evaluation of phage Kpap4 against three K. pneumoniae strains. ( E ) RIE assays show values of 1, 0.1, and 0 for Kpn25, Kpn30, and Kpn6, respectively. ( F ) Corresponding RPST-based CII distributions demonstrate cutoff crossing at MOI = 0.1 for Kpn25, persistent sub-cutoff levels for Kpn6 even at MOI = 10, and gradual but incomplete increases for Kpn30 (RIE > 0). ( G ) Comparative analysis of twelve E. coli phages (MOI = 10, 40 min). Nine elevated population CII above the cutoff, with Ecp9 showing the highest CII. Density plots exhibit unimodal distributions for infective phages, while three noninfective ones display bimodal patterns with population CII remaining below the cutoff.

    Article Snippet: To assess whether RPST can resolve infection dynamics and determine the minimal effective MOI, we established a multidimensional experimental matrix incorporating five MOIs (0, 0.01, 0.1, 1, and 10) and six time points (0∼120 min at 20-min intervals) across three phage-bacterium systems: T1- E. coli ATCC 11303, T4- E. coli ATCC 11303, and T4- E. coli ATCC 25922.

    Techniques: Infection, Incubation

    ( A-C ) MOI- and time-resolved trajectories of CII in three phage-host systems. Populations were monitored under five MOIs (0, 0.01, 0.1, 1, and 10) across seven timepoints (0∼120 min, 20-min intervals). ( A ) In the T1- E. coli ATCC11303 system, populations at MOI = 0 remain below the infection cutoff throughout the experiment, whereas those at MOI = 10 cross the threshold within 20 min. Intermediate MOIs (0.01, 0.1, 1) exhibit delayed but complete transitions into the infected state within 60 min. ( B ) In the T4- E. coli ATCC11303 system, populations cross the infection cutoff within ≤ 20 min at MOI = 10, 60 min at MOI = 1, 80 min at MOI = 0.1, and 120 min at MOI = 0.01, while remaining uninfected at MOI = 0. (C) In the T4- E. coli ATCC25922 system, only MOI = 10 leads to a transition to the infected state. At lower MOIs, CII values rise transiently but subsequently decline, and population-level CII never exceeds the infection cutoff.

    Journal: bioRxiv

    Article Title: Rapid and Quantitative Phage Susceptibility Test by Ramanome

    doi: 10.64898/2026.02.07.704537

    Figure Lengend Snippet: ( A-C ) MOI- and time-resolved trajectories of CII in three phage-host systems. Populations were monitored under five MOIs (0, 0.01, 0.1, 1, and 10) across seven timepoints (0∼120 min, 20-min intervals). ( A ) In the T1- E. coli ATCC11303 system, populations at MOI = 0 remain below the infection cutoff throughout the experiment, whereas those at MOI = 10 cross the threshold within 20 min. Intermediate MOIs (0.01, 0.1, 1) exhibit delayed but complete transitions into the infected state within 60 min. ( B ) In the T4- E. coli ATCC11303 system, populations cross the infection cutoff within ≤ 20 min at MOI = 10, 60 min at MOI = 1, 80 min at MOI = 0.1, and 120 min at MOI = 0.01, while remaining uninfected at MOI = 0. (C) In the T4- E. coli ATCC25922 system, only MOI = 10 leads to a transition to the infected state. At lower MOIs, CII values rise transiently but subsequently decline, and population-level CII never exceeds the infection cutoff.

    Article Snippet: To assess whether RPST can resolve infection dynamics and determine the minimal effective MOI, we established a multidimensional experimental matrix incorporating five MOIs (0, 0.01, 0.1, 1, and 10) and six time points (0∼120 min at 20-min intervals) across three phage-bacterium systems: T1- E. coli ATCC 11303, T4- E. coli ATCC 11303, and T4- E. coli ATCC 25922.

    Techniques: Infection

    Fig. 3 (A) Shapes of volume-scattering functions for four dif- ferent viruses, MS2 (E. coli virus; solid circles), T4 (E. coli virus; triangles), Y1 (marine bacteriophage; squares), and C2 (marine bac- teriophage; open circles). The sizes of these different viruses are 25–30 nm, 100 nm, 50–80 nm, and 110 nm respectively. Note, volume scattering (Y axis) is a log scale, and the VSFs are relatively flat compared to typical VSFs found for larger marine particulate matter (Mobley 1994). (B) Same results as (A), but with expanded ordinate scale to show lack of symmetry for VSFs. Lines represent least-square fits to the data using the following equations: Preisen- dorfer Turbid water data fitted with Beardsley-Zaneveld equation; Y 5 7.45 3 1023{1/([1 2 (7.521 3 1021[cos X])]4 3 [1 1 (0.388[cos X])4])}; r2 5 0.999. MS2 fitted to polynomial: Y 5 3.618 3 105([1.045 3 102 3 X 2] 1 [2.616X] 1 2.5307 3 102); r2 5 0.992. T4 fitted to polynomial: Y 5 4.137 3 105([3.332 3 103 3 X 2] 1 [1.220X] 1 2.273 3 102); r2 5 0.999. Y1 fitted to polyno- mial: Y 5 2.988 3 105([5.805 3 103 3 X 2] 1 [1.698X] 1 2.372 3 102); r2 5 0.988. C2 fitted to polynomial: Y 5 1.254 3 105([7.723 3 103 3 X 2] 1 [2.064X] 1 2.321 3 102); r2 5 0.991. Pure water using Rayleigh fit: Y 5 1.483 3 104{1 1 [(cos X)2]}; r2 5 0.998.

    Journal: Limnology and Oceanography

    Article Title: Light scattering by viral suspensions

    doi: 10.4319/lo.2000.45.2.0492

    Figure Lengend Snippet: Fig. 3 (A) Shapes of volume-scattering functions for four dif- ferent viruses, MS2 (E. coli virus; solid circles), T4 (E. coli virus; triangles), Y1 (marine bacteriophage; squares), and C2 (marine bac- teriophage; open circles). The sizes of these different viruses are 25–30 nm, 100 nm, 50–80 nm, and 110 nm respectively. Note, volume scattering (Y axis) is a log scale, and the VSFs are relatively flat compared to typical VSFs found for larger marine particulate matter (Mobley 1994). (B) Same results as (A), but with expanded ordinate scale to show lack of symmetry for VSFs. Lines represent least-square fits to the data using the following equations: Preisen- dorfer Turbid water data fitted with Beardsley-Zaneveld equation; Y 5 7.45 3 1023{1/([1 2 (7.521 3 1021[cos X])]4 3 [1 1 (0.388[cos X])4])}; r2 5 0.999. MS2 fitted to polynomial: Y 5 3.618 3 105([1.045 3 102 3 X 2] 1 [2.616X] 1 2.5307 3 102); r2 5 0.992. T4 fitted to polynomial: Y 5 4.137 3 105([3.332 3 103 3 X 2] 1 [1.220X] 1 2.273 3 102); r2 5 0.999. Y1 fitted to polyno- mial: Y 5 2.988 3 105([5.805 3 103 3 X 2] 1 [1.698X] 1 2.372 3 102); r2 5 0.988. C2 fitted to polynomial: Y 5 1.254 3 105([7.723 3 103 3 X 2] 1 [2.064X] 1 2.321 3 102); r2 5 0.991. Pure water using Rayleigh fit: Y 5 1.483 3 104{1 1 [(cos X)2]}; r2 5 0.998.

    Article Snippet: The second phage studied was T4 (ATCC 11303 B-1, host cell, E. coli ATCC 11303), a 100-nm double-stranded DNA phage (including an elongated capsid and tail structure).

    Techniques: Virus