Biology subjects

Kamaleswaran, R.

Publications and source records attributed to Kamaleswaran, R..

6 recordsLinked to original sources

bioRxiv2026

Microenvironment-informed inference of transcriptional progression geometry

We present BIOCURRENT, a causal inference framework that reconstructs donor-specific pseudotime geometry in transcriptomic data. By modeling gene expression as a function of baseline characteristics, microenvironmental context, and latent pseudotime, BIOCURRENT enables comparison of compressed or expanded progression intervals across transcriptional state transitions. We introduce $\Delta\Delta T$, a geometry-based estimator that quantifies differences in pseudotime intervals across conditions, enabling evaluation of changes in pseudotime intervals under hypothetical modulation of microenvironmental programs. Applications to thymic T-cell developmental lineages and to COVID-19 immune dysregulation reveal condition- and donor-specific distortions of progression intervals. Counterfactual simulation links microenvironmental context to changes in specific intracellular state transition intervals. By localizing deviations in pseudotime geometry, BIOCURRENT identifies whether shifts in transcriptomic programs emerge early or later along transcriptomic coordinates and reveals upstream programs associated with these distortions. Such localization supports transcriptional stage-aware mechanistic hypotheses and suggests candidate intervention checkpoints in complex biological systems.

bioinformatics↗

bioRxiv2026

Methodological assessment of PDMS passive sampling for skin VOC collection across body sites

Background/ObjectivesPolydimethylsiloxane (PDMS) is a non-invasive and versatile material often used for non-invasive collection of skin-emitted volatile organic compounds (VOCs), with potential applicability in acute and pre-critical care settings. However, most existing PDMS-based methodologies rely on extensive sample preparation and environmental control, limiting their feasibility in time-sensitive clinical contexts. MethodsWe conducted a proof-of-concept pilot study in four healthy volunteers to evaluate whether a simplified skin-contact PDMS sampling procedure can capture detectable VOCs and preserve individual-level variation. PDMS strips were applied directly to the skin with minimal preparation, and collected VOCs were analyzed using gas chromatography-mass spectrometry. Donor-associated variability was assessed using Bray-Curtis dissimilarity, and variability in VOC detection was evaluated across body sites. ResultsSkin-contact PDMS sampling detected 160 VOCs across four participants. The mean within-donor Bray-Curtis dissimilarity was 0.308, compared with a mean between-donor dissimilarity of 0.347. Preliminary permutation testing showed distinguishable donor profiles (p-value = 0.004). VOC detection variability differed across body sites, with lower coefficients of variation at the forehead, neck, and wrist than at the ankle. ConclusionsUnder simplified sampling conditions, skin-contact PDMS captured individual-associated VOC profiles with lower within-donor variability than between-donor variability. These findings support the feasibility of PDMS-based skin VOC sampling in minimally controlled settings. Further validation in larger and clinically relevant cohorts is warranted to assess the utility of PDMS-sampled skin VOCs as potential biomarkers for early disease detection.

molecular biology↗

bioRxiv2020

Machine Learning Identifies Complicated Sepsis Trajectory and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission

A complicated clinical course for critically ill patients admitted to the ICU usually includes multiorgan dysfunction and subsequent death. Owning to the heterogeneity, complexity, and unpredictability of the disease progression, patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 hours of PICU admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes that were predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with ten iterations, the overall mean area under the curve reached 0.82. Using the same set of genes, we further achieved an overall area under the curve of 0.72 when tested on an external validation set. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1 and NLRP1 that help to predict sepsis severity or mortality. The discovery of eight novel genetic biomarkers related to the overactive innate immune system and neutrophils functions, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSTranscriptomic biomarkers have long been explored as potential means of earlier disease endotyping. Much of the existing literature has however focused on mortality and discrete outcomes. Additionally, much of prior work in this area has been developed on statistical methods, while recent means of selecting features have not been sufficiently explored. Added value of this studyIn this study, we developed a robust machine learning based model for identifying novel biomarkers of complicated disease courses. We found 20 highly stable genes that predict disease complexity with an average derivation AUROC of 0.82 and validation AUROC of 0.72 within critically ill children, using peripheral blood collected within 24 hrs of ICU admission. Implications of all the available evidenceEarlier identification of disease complexity can inform care management and targeted therapy. Therefore, the 20 gene candidates identified by our rigorous approach, can be used to identify, early in their ICU stay, patients who may ultimately develop significant organ dysfunction and complex care management.

immunology↗

bioRxiv2019

Machine learning predicts early-onset acute organ failure in critically ill patients with sickle cell disease

BackgroundSickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may progress to organ failure during disease exacerbations. Early detection of acute physiological deterioration leading to organ failure is not always attainable. Machine learning techniques that allow for prediction of organ failure may enable earlier identification and treatment, and potentially reduce mortality. We tested the hypothesis that machine learning physiomarkers could predict the development of organ dysfunction in an adult sample of patients with SCD admitted to intensive care units.\n\nMethods and FindingsWe studied 63 sequential SCD patients with 163 patient encounters, mean age 33.0{+/-}11.0 years, admitted to intensive care units, some of whom (6.7%) had pre-existing cardiovascular or kidney disease. A subset of these patient encounters (37; 23%) met sequential organ failure assessment (SOFA) criteria. The site of organ failure included: central nervous system (32), cardiovascular (11), renal (10), liver (7), respiratory (5) and coagulation (2) systems. Most (81.5%) of the patient encounters who experienced organ failure had single organ failure. The other 126 SCD patient encounters served as controls. A set of signal processing features (such as fast fourier transform, energy, continuous wavelet transform, etc.) derived from heart rate, blood pressure, and respiratory rate were identified to distinguish patients with SCD who developed acute physiological deterioration leading to organ failure, from SCD patients who did not meet the criteria. A random forest model accurately predicted organ failure up to six hours prior to onset, with a five-fold cross-validation accuracy of 94.57% (average sensitivity and specificity of 90.24% and 98.9% respectively).\n\nConclusionsThis study demonstrates the viability of using machine learning to predict acute physiological deterioration heralded organ failure among hospitalized adults with SCD. The discovery of salient physiomarkers through machine learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.

bioinformatics↗

bioRxiv2019

Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients

Septic shock is a severe health condition caused by uncontrolled sepsis. Advancements in the high-throughput sequencing techniques have risen the number of potential genetic biomarkers under review. Multiple genetic markers and functional pathways play a part in the development and progression of pediatric septic shock. Fifty-four differentially expressed pediatric septic shock gene biomarkers were identified using gene expression data from 181 pediatric intensive care unit (PICU) patients within the first 24 hours of admission. The gene expression signatures discovered showed discriminatory power between pediatric septic shock survivors and nonsurvivors types. Using functional enrichment analysis of differentially expressed genes (DEGs), the known genes and pathways in septic shock were validated, and unexplored septic shock-related genes and functional groups were identified. Septic shock survivors were distinguished from septic shock non-survivors by differential expression of genes involved in the immune response, chemokine-mediated signaling, neutrophil chemotaxis, and chemokine activity. The identification of the septic shock gene biomarkers may facilitate in septic shock diagnosis, treatment, and prognosis.

bioinformatics↗

bioRxiv2018

Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice

Rationale: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage.\n\nObjective: Our primary goal was to develop machine learning models capable of predicting sepsis using streaming physiological data in real-time.\n\nMethods: A dataset consisting of high-frequency physiological data from 1,161 critically ill patients admitted to the intensive care unit (ICU) was analyzed in this IRB-approved retrospective observational cohort study. Of that total, 634 patients were identified to have developed sepsis. In this paper, we define sepsis as meeting the Systemic Inflammatory Response Syndrome (SIRS) criteria in the presence of the suspicion of infection. In addition to the physiological data, we include white blood cell count (WBC) to develop a model that can signal the future occurrence of sepsis. A random forest classifier was trained to discriminate between sepsis and non-sepsis patients using a total of 108 features extracted from 2-hour moving time-windows. The models were trained on 80% of the patients and were tested on the remaining 20% of the patients, for two observational periods of lengths 3 and 6 hours.\n\nResults: The models, respectively, resulted in F1 scores of 75% and 69% half-hour before sepsis onset and 79% and 76% ten minutes before sepsis onset. On average, the models were able to predict sepsis 210 minutes (3.5 hours) before the onset.\n\nConclusions: The use of robust machine learning algorithms, continuous streams of physiological data, and WBC, allows for early identification of at-risk patients in real-time with high accuracy.

bioinformatics↗