Background: Lower respiratory tract infections (LRTIs) remain a major cause of morbidity and mortality among hospitalized patients.1 However, isolating organisms from respiratory samples often leads to diagnostic uncertainty due to the coexistence of colonizers, commensals, and contaminants.2 To address this challenge, this study employed a structured, stepwise exploratory model to differentiate true pathogens from non-pathogens in aerobic respiratory cultures and Multiplex PCR (Biofire(R) FilmArray) results. Methods: This prospective, longitudinal time-bound study was conducted over three months (August-October 2024) at a tertiary care center in northern India. Adult patients ([≥] 18 years) with positive lower respiratory tract samples (aerobic culture or Multiplex PCR (Biofire(R) FilmArray) were enrolled. Each isolate was independently classified by the treating clinician, microbiologist, and study investigator using a six-step clinical-pathological algorithm that incorporated clinical signs, Sequential Organ Failure Assessment (SOFA) score trends, alternative infection sources, host factors, and outcome data. The final classification was determined by the investigator. Outcomes, including treatment response and mortality at 28 days, were compared across pathogen and non-pathogen groups. Findings: Of the 145 included cases, 131 (903%) were classified as pathogens and 14 (97%) as non-pathogens. Cohen’s Kappa between investigator and microbiologist classifications was 028, indicating fair agreement. Among pathogen cases, 68 (519%) responded to treatment, while 63 (481%) did not respond to treatment in the pathogenic group. In contrast, 12 of 14 non-pathogen cases (857%) were not treated, with favourable outcomes in most, and only one unrelated death (71%). Interpretation: The structured clinico-microbiological model strongly correlates with treatment outcomes, making it useful for differentiating infection from colonization. Crucially, microbiological detection alone does not determine pathogenicity. Integrating clinical, laboratory, and outcome data is essential for rational antibiotic use and effective antimicrobial stewardship. Funding: None
The Hidden Power of Normalization: Exponential Capacity Control in Deep Neural Networks
arXiv:2511.00958v1 Announce Type: cross Abstract: Normalization methods are fundamental components of modern deep neural networks (DNNs). Empirically, they are known to stabilize optimization dynamics and