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Biology subjects

Jahandideh, F.

Publications and source records attributed to Jahandideh, F..

3 recordsLinked to original sources

A neonatal rat sepsis score captures the time course and severity of disease in a clinically relevant rat peritonitis model.

BackgroundNeonatal sepsis is a major cause of infant morbidity and mortality worldwide, particularly in preterm and very low birthweight babies. Fundamental differences between neonates and adults warrant clinically relevant models of neonatal sepsis. Here, we describe a preclinical fecal-slurry (FS)-induced peritonitis model of polymicrobial sepsis in neonatal rats, along with a novel neonatal rat sepsis score (nRSS) to monitor illness severity. MethodsPeritonitis was induced in 3-day-old Sprague Dawley rats by intraperitoneal injection of various doses (0.3-1.5mg/g body weight) of fecal slurry (FS); control pups received equivalent doses of vehicle. All pups received analgesics (buprenorphine), antibiotics (ampicillin and gentamicin), and fluids (saline) to model clinical standards of sepsis treatment. Time-dependent changes in circulating cytokines (IL-6, IL-1{beta}) and biomarkers of sepsis pathology (hemoglobin, glucose, alanine transaminase [ALT] levels) were assessed and correlated with nRSS scores. ResultsFS administration caused a dose-dependent increase in severity of sepsis over time, as indicated by increases in mortality rates (based on predefined criteria for euthanasia), nRSS scores, as well as time-dependent changes in circulating glucose, hemoglobin, IL-6, IL-1{beta}, and ALT activity levels. nRSS scores correlated with all quantitative measures of sepsis pathology. Notably, females showed higher mortality and higher early NRSS scores than males at moderate to high FS doses, yet biochemical markers and time of death did not differ between sexes, suggesting that the apparent female vulnerability may reflect more conspicuous behavioral manifestations of illness rather than greater underlying physiological severity. ConclusionInduction of peritonitis in rats at postnatal day 3 produced a consistent and reproducible model of polymicrobial neonatal sepsis. Illness severity was monitored using a newly developed nRSS. By minimizing distress and incorporating standards of care, this model and scoring system may serve as a platform for future investigations into the underlying mechanisms and potential therapeutic interventions for neonatal sepsis. ImpactO_LIA clinically relevant rat model of neonatal polymicrobial sepsis was developed, incorporating standards of care (analgesics, antibiotics, and fluid resuscitation) to better reflect the clinical context in which preclinical findings must ultimately translate. C_LIO_LIA novel neonatal rat sepsis scoring system (nRSS) was developed and validated, providing a sensitive, non-invasive measure of disease severity that correlates with biochemical markers and predicts mortality. C_LIO_LIFemale pups showed higher mortality and earlier behavioral signs of illness than males despite equivalent biochemistry, highlighting that clinical scores may capture sex-dependent vulnerability not apparent in standard biochemical measures. C_LIO_LITogether, this model and scoring system offer a refined platform for mechanistic and therapeutic studies of neonatal sepsis while advancing the welfare-conscious 3Rs principles essential to rigorous preclinical research C_LI

physiology↗

Early sex-specific organ transcriptional divergence without physiological differences in a murine model of fecal-induced peritonitis

Sepsis is defined as a dysregulated response to infection, leading to life-threatening organ dysfunction that particularly affects parenchymal organs. Clinical studies remain inconclusive regarding the impact of biological sex on sepsis, and preclinical studies are predominantly performed in male animals. We examined early (8 h) septic responses in male and female mice using a fecal-induced peritonitis (FIP) model. Blood biochemical parameters, body temperature, and murine sepsis scores provided evidence of a septic response in animals randomized to FIP compared to controls, but showed no physiological differences between male and female mice. Transcriptomic analysis of the liver, kidney, and lung showed consistent inflammatory activation in response to sepsis as compared to controls. Notably, in the kidney and lung, female mice exhibited stronger immune activation and a heightened inflammatory response compared to males. Thus, biological sex differences in the septic response can be detected in early acute sepsis without apparent physiological differences.

pathology↗

Artificial Intelligence-enabled Histological Analysis in Preclinical Respiratory Disease Models: A Scoping Review

Histological analysis is a cornerstone of preclinical respiratory disease research, enabling assessment of pathology, therapeutic effects, and mechanisms. However, conventional approaches rely on manual scoring, which is subjective, time-consuming, and difficult to scale due to low throughput and inter-observer variability. Artificial intelligence (AI), particularly deep learning, offers potential to automate histology workflows, but its use and evaluation in preclinical respiratory models have not been synthesized. We conducted a scoping review following Joanna Briggs Institute guidelines, searching MEDLINE and Embase (inception-January 2025) for preclinical studies using AI to analyze histology in respiratory disease models. Screening, full-text review, and data extraction were performed in duplicate. Of 6271 studies screened, 29 met inclusion criteria. Most used murine models (76%) and investigated lung cancer (28%), pulmonary fibrosis (24%), or tuberculosis (17%). Hematoxylin and eosin was the most common stain (48%), with others targeting collagen or immune markers. AI tasks included image classification (n=20), segmentation (n=10), and object detection (n=4), predominantly using convolutional neural networks (69%). Preprocessing methods (e.g., stain normalization) were common, but annotation and training practices were inconsistently reported. Performance was generally high (accuracy [≥]90%; 7 studies) though validation metrics varied, and external validation was absent. Most studies used "black box" models, with minimal application of explainability techniques. Reproducibility measures, such as sharing datasets or code were rarely reported. AI tools are poised to transform histological analysis in preclinical respiratory research. By addressing gaps in validation, transparency, and standardization, the field can harness these technologies to deliver robust, efficient, and scalable workflows. Registration: Open Science Framework https://doi.org/10.17605/OSF.IO/NM94E

cell biology↗