Clinical Chemistry

Cervical intraepithelial neoplasia grade 2 (CIN2) lesions show variable outcomes, and accurate prediction of regression remains a major clinical challenge. We developed an interpretable machine learning pipeline that integrates quantitative histological, clinical, and human papillomavirus (HPV) -genotyping data to predict lesion regression within one and two years. Using panoptic segmentation of routine hematoxylin and eosin (H&E) -stained biopsies, we extracted human-interpretable morphological and immune cell infiltration related features that capture the key histopathological characteristics of CIN2 and identified features that predicted lesion regression. Further, integrating these features to predictive clinical features achieved higher predictive accuracy than clinical variables alone. These findings demonstrate that quantitative, interpretable analysis of H&E histology of routine diagnostic biopsies contains relevant information that predicts the natural history of CIN2 lesions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/26344510v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@11735f5org.highwire.dtl.DTLVardef@d76e89org.highwire.dtl.DTLVardef@19a1d39org.highwire.dtl.DTLVardef@f48a01_HPS_FORMAT_FIGEXP M_FIG Created in BioRender. Lehtonen, O. (2026) https://BioRender.com/rlnkbkp C_FIG

BackgroundPrognostic assessment in critically ill patients with cancer remains challenging, as conventional ICU severity scores often perform suboptimally in this population. Machine learning (ML) approaches may improve outcome prediction by integrating acute physiology, organ dysfunction, and oncologic variables. We aimed to develop and validate ML-based models to predict ICU mortality and 30-day survival in critically ill cancer patients. MethodsWe conducted a retrospective cohort study including 997 critically ill cancer patients admitted to the ICU. Forty-eight demographic, oncologic, physiological, laboratory, and therapeutic variables collected at ICU admission were used to train and validate ML models. Eight algorithms were evaluated using stratified cross-validation with feature selection and hyperparameter optimization. Model performance was assessed using discrimination, calibration, and classification metrics. Model interpretability was explored using Shapley additive explanations (SHAP). ResultsCatBoost achieved the best performance for ICU mortality prediction (AUROC 0.96), showing excellent discrimination and calibration, and outperforming other ML models. Prediction of 30-day survival was less accurate (best AUROC 0.75), reflecting the influence of post-ICU factors not captured at admission. Key predictors of ICU mortality included severity of organ dysfunction, therapeutic objectives, vasopressor and methylene blue use, SAPS III score, lactate, platelet count, and blood urea nitrogen. For 30-day survival, baseline physiological status, admission type, SAPS III, lactate, creatinine, age, and body mass index were most relevant. SHAP analysis demonstrated that acute physiology and organ dysfunction, rather than cancer diagnosis alone, primarily drove short-term outcomes. ConclusionsML-based models, particularly CatBoost, outperformed traditional prognostic tools for predicting ICU mortality in critically ill cancer patients. Cancer was not an independent predictor of short-term mortality; outcomes were primarily driven by pre-ICU conditions, acute physiology, and severity of organ dysfunction. External validation is needed to confirm generalizability and support future integration of ML-based prediction tools into clinical decision-making in oncologic critical care.

BackgroundSepsis is a major public health challenge, and reliable biomarkers are essential for distinguishing sepsis from other conditions. Neutrophil Gelatinase-Associated Lipocalin (Neutrophil gelatinase-associated lipocalin (NGAL)) has shown promise as a diagnostic marker due to its role in the immune response. This study evaluates plasma NGAL as a diagnostic tool at the time of ICU admission. MethodsWe analysed plasma NGAL and C-reactive protein (CRP) levels in 4732 adult patients admitted to four ICUs between 2015 and 2018. All patients were retrospectively screened for Sepsis-3 criteria at ICU admission. The discriminative performance of NGAL and CRP for sepsis was assessed using receiver operating characteristic (ROC) analysis, with NGAL levels adjusted for Chronic kidney disease (CKD) and age. Patients were stratified by renal function. ResultsPlasma NGAL levels were significantly higher in septic patients (p<0.001). For the whole cohort, NGAL alone yielded an Area under the curve (AUC) of 0.67 (Confidence interval (CI) 0.66-0.69), CRP yielded an AUC of 0.72 (CI 0.71-0.73, p<0.001), and combining NGAL with CRP nominally improved discriminative performance (AUC 0.74 vs 0.72, p<0.001). Stratified analyses indicated that NGAL, together with CRP, significantly outperformed CRP alone in patients with no kidney injury and those with Acute Kidney Injury (AKI) only. In contrast, differences were not significant in patients with CKD only or CKD and AKI. ConclusionIn this large cohort, NGAL showed modest discrimination for sepsis, with a nominal improvement when combined with CRP. These findings do not indicate that NGAL meaningfully improves sepsis diagnosis in the ICU.

BackgroundErythrocyte indices are essential for the diagnosis and monitoring of hematologic diseases, but their determination depends on automated hematology analyzers, which limits access in regions with limited laboratory infrastructure. Although artificial intelligence approaches have been proposed for hematologic analysis, they usually rely on slide scanners or digitization systems. To date, no validated approaches have been identified in the literature that estimate these indices directly from images obtained through the eyepiece of conventional optical microscopes. ObjectiveTo evaluate the feasibility of automated prediction of erythrocyte indices from blood smear images obtained directly through the eyepiece of conventional microscopes using convolutional neural networks. MethodsTwo hundred blood samples stained using the May-Grunwald-Giemsa method were analyzed and photographed using a standard optical microscope. Four architectures, DenseNet-121, EfficientNet-B0, ResNet-18, and ResNet-34, were evaluated at different resolutions using 10-fold K-Fold cross-validation. ResultsFor RBC, HGB, and HCT, ResNet-34 at a resolution of 1024x1024 pixels achieved superior performance, with R2 between 0.90 and 0.92, Pearson correlation r > 0.95, and mean absolute errors of 0.184 x106/{micro}L, 0.524 g/dL and 1.292%, respectively. For RDW-CV, DenseNet-121 achieved R2 = 0.49 and r = 0.71, reflecting the greater complexity of this parameter. Bland-Altman analysis confirmed adequate agreement, with biases close to zero and more than 94% of observations within the limits of agreement. ConclusionArtificial intelligence demonstrated excellent predictive performance in estimating the erythrocyte indices RBC, HGB, and HCT, with R2 > 0.90, from images obtained using a conventional microscope and accessible hardware. This approach has significant potential to democratize access to hematologic analysis in resource-limited settings, although multicenter validation and regulatory evaluation are required before clinical implementation.

ObjectiveTo evaluate the ability of a natural language processing system to automatically reconstruct the SOFA score from unstructured clinical notes in patients with sepsis and validate its applicability in intensive care units. Materials and methodsRetrospective study in the MIMIC-III database that included 284 adults with sepsis. The SOFA calculated with structured data was compared with the SOFA reconstructed by free text extraction. Clinical rules were applied for calculation at 24 h and 48 h. Variable completeness, severity reclassification, and association with hospital mortality were evaluated using logistic regression. ResultsAutomated extraction increased the availability of critical variables (respiratory 33% to 100%, vasopressor 12% to 41%). The reconstructed SOFA increased by 3 points at 24 hours, reclassifying patients with high severity (SOFA [&ge;] 6) from 17% to 48% and SOFA [&ge;] 10 from 5% to 22%. Reconstructed scores remained associated with mortality at 24 h (OR 1.16, 95% CI 1.09-1.24) and at 48 h (OR 1.23, 95% CI 1.15-1.31), comparable to that based on structured data (p < 0.001). DiscussionAutomatic reconstruction of the SOFA from free text recovers information missing from structured fields, reducing underestimation of severity. ConclusionNLP approaches supported by large language models provide a more complete and clinically consistent SOFA score in sepsis when structured data are insufficient.

We present a streamlined, solid-phase workflow for Oxford Nanopore sequencing that integrates DNA extraction, purification, and library preparation within a single microfluidic cartridge. By eliminating tube transfers and performing all enzymatic steps directly on captured DNA, the method minimizes sample loss, reduces hands-on time, and simplifies library generation for long-read sequencing. Starting from volumes as small as a single drop of blood, this integrated approach produces high-quality sequencing libraries from cell lines, whole blood, and tissue. The workflow achieves robust recovery of high-molecular-weight DNA and high pore occupancy, enabling rapid, low-complexity sample preparation suitable for clinical, field, and decentralized sequencing applications.

BackgroundAnemia remains a major health concern, particularly during pregnancy. However, blood draws, laboratory capacity, processing time, and equipment cost required for hemoglobin testing to diagnose anemia can be prohibitive. The Total Hemoglobin SpHb Rad-67 Pulse CO-Oximeter offers a non-invasive, portable, and affordable point-of-care alternative. We aimed to validate SpHb against complete blood count (CBC) in a pregnant and postpartum population. MethodsThis was a substudy of the Pregnancy Risk, Infant Surveillance, and Measurement Alliance (PRISMA) Maternal and Newborn Health Study. A total of 2,700 participants in Zambia, Kenya, and Pakistan provided hemoglobin measurements both by CBC and the Rad-67 Masimo SpHb device at four visits during pregnancy and at six-weeks postpartum. We assessed agreement between SpHb and CBC and used mixed models to identify factors that explained or influenced differences between the two methods. ResultsWe found the mean hemoglobin measurement by SpHb (12.8{+/-}1.6 g/dL) was higher than by CBC (11.0{+/-}1.6 g/dL). However, this overall positive bias masked systematic misclassification at the extremes: SpHb overestimated values among women with very low hemoglobin (Mean Difference (MD) =-3.95; 95%CI-4.28,-3.62) and underestimated values at very high hemoglobin levels (MD = 2.44; 95%CI 2.14, 2.74), even after adjustment. Using CBC, 48% of observations were classified as anemic (<11 g/dL), compared to 9% by SpHb; conversely, just 7% of CBC readings fell within 13-15 g/dL, compared to 38% by SpHb. Agreement metrics consistently showed poor concordance between the two methods. ConclusionsSpHb systematically overestimated hemoglobin on average and showed poor agreement with CBC, particularly at clinically relevant extremes. Until greater accuracy of SpHb is demonstrated in this population, hemoglobin testing with laboratory-based methods is recommended to inform clinical decision-making in pregnancy. AUTHOR SUMMARYAnemia is a major global health challenge linked with maternal morbidity, adverse birth outcomes, and impaired infant development. Accurate and accessible hemoglobin testing is critical for timely anemia diagnosis and treatment. The gold standard for hemoglobin testing is complete blood count using venous blood; however, this method requires laboratory infrastructure, blood sample collection, and trained personnel, limiting feasibility in low-resource and rural settings where anemia burden is highest. We evaluated the accuracy of the Masimo Total Hemoglobin SpHb(R) measured by Rad-67(R) Pulse CO-Oximeter(R): a device that measures hemoglobin via an optical sensor placed on the patients finger. We found the mean hemoglobin measurement by SpHb (12.8{+/-}1.6 g/dL) was higher than by CBC (11.0{+/-}1.6 g/dL).We found that the Masimo device consistently overestimated hemoglobin at very low levels and underestimated at very high levels. Masimo SpHb classified 9% of women as anemic (i.e. hemoglobin <11 g/dL), compared to 48% using the gold standard method. The Masimo SpHb was even less accurate for women later in gestation, living with HIV, and who reported using betelnut, tobacco, or smoking. Device improvements or software-based correction factors are needed to improve performance of the Total Hemoglobin SpHb Rad-67 Pulse CO-Oximeter before it can be used to measure hemoglobin in pregnancy.

Molecular Tumour Boards (MTBs) rely on different bioinformatics tools and knowledgebases for variant annotation, oncogenicity classification, and estimation of complex biomarkers to identify actionable alterations. However, the typical bioinformatics workflow to process raw next-generation sequencing (NGS) data into clinically meaningful variants involves multiple steps and is inherently complex, thus requiring repeated manual intervention and causing delays in providing molecularly informed precision oncology. Here, we aimed at overcoming these limitations by developing a fully-automated integrative workflow to support NGS-based analyses within MTBs. Our workflow was established at the Institute of Pathology, University Hospital Erlangen (Germany), and adapted to the fully digitized Pathology department at Gravina Hospital in Caltagirone (Italy), using the Illumina TruSight Oncology 500 HRD assay as case study. A trigger event initiates all the downstream bioinformatics analyses to support variant interpretation. In Erlangen, the trigger event is the automatic detection of new NGS data on the Illumina Connected Analytics cloud-based platform. In Caltagirone, the analyses are manually triggered from the anatomic pathology laboratory information system (AP-LIS). The workflow automatically: (i) generates an intuitive overview of sequencing quality metrics, (ii) performs variant annotation, (iii) classifies variant oncogenicity through a fully-automated implementation of the ClinGen/CGC/VICC guidelines, and (iv) generates homologous recombination deficiency scores with genomic instability plots. In the digitized pathology department, results can be readily opened from the AP-LIS and visualized in the patient gallery. Taken together, our end-to-end fully-automated workflow streamlines NGS-based analyses within MTBs by integrating variant interpretation, oncogenicity classification, and estimation of clinically relevant biomarkers.

We evaluated the BOSCH Vivalytic MPXV assay using serial dilutions of monkeypox virus clade Ib, a new offshoot of clade I identified in 2023. The assay detected viral DNA down to [~]100 copies/mL demonstrating comparable analytical sensitivity to our in-house reference PCR and to other commercial platform-based mpox molecular assays.

Blood-based biomarkers have emerged as a promising tool for the detection and monitoring of neurodegenerative diseases such as Alzheimers disease (AD), yet broad implementation of ultrasensitive protein quantification remains constrained by reliance on specialized instrumentation and centralized laboratory infrastructure. Here we present SPLASH (Solid Phase Ligation Assay with Single wasH), an ultrasensitive proximity ligation assay platform that achieves sub-pg/mL sensitivity using only standard benchtop qPCR equipment. We developed five assays targeting Alzheimers disease biomarkers - pTau-217, A{beta}1-40, A{beta}1-42, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) - with limits of detection ranging from 0.0005 to 0.119 pg/mL. Direct comparison with Simoa demonstrated high concordance (R2 = 0.95) for plasma pTau-217 quantification across AD-positive and AD-negative samples. We further established compatibility with dried plasma spot samples, enabling decentralized collection and quantitation without cold-chain storage. A multiplexed five-analyte panel was applied to 69 plasma samples, revealing heterogeneous biomarker profiles consistent with AD-associated patterns. By eliminating dependencies on proprietary instrumentation, SPLASH facilitates broad implementation of ultrasensitive protein quantification for neurodegenerative disease research and diagnostics.

Influenza A, Influenza B, SARS-CoV-2, Respiratory Syncytial Virus and other respiratory pathogens are an ongoing public health concern. The ability to rapidly identify these viruses early in infection is essential for effective treatment and outbreak control. The AMDI Fast PCR Mini Respiratory Panel (MRP) incorporates sample preparation and real-time RT-PCR for detection of Flu A, Flu B, SARS-CoV-2 and RSV from anterior nasal swab (ANS) specimens in less than 10 minutes at the point of care. We established the analytical performance characteristics of the Fast PCR MRP and determined that the limit of detection (LoD) is 250 copies/mL for Flu A and RSV, and 500 copies/mL for Flu B and SARS-CoV-2. In a reproducibility study at 3 clinical sites, there was at least 98.2% positivity for each target for a weak positive (2x LoD) sample, at least 99.6% positivity for a moderate positive (5x LoD) sample and the negative sample returned a negative result for at least 99.3% of the tests. Fast PCR MRP had 100% analytical reactivity to all strains tested (23 Flu A, 6 Flu B, 8 SARS-CoV-2 and 6 RSV) and at least 98% predicted inclusivity from in silico analysis. There was no cross-reactivity to 40 viruses, bacteria and fungi, nor interference for 15 endogenous and exogenous substances in ANS matrix. The Fast PCR MRP delivers excellent analytical performance comparable to high complexity laboratory assays, at the point of care.

BackgroundCandidemia is a rare but life-threatening bloodstream infection that remains difficult to predict using conventional risk stratification approaches, highlighting the need for improved predictive strategies. As a result, empiric antifungal therapy is often delayed even in high-risk patients. MethodsWe developed a deep learning model (PyTorch_EHR) to predict 7-day candidemia risk by using electronic health record data from two large cohorts (Houston Methodist Hospital System [HMHS] and MIMIC-IV), including adult inpatients who underwent at least one blood culture. Model performance was compared with logistic regression (LR), LightGBM, and established intensive care unit candidemia scores. We further implemented a two-step prediction framework integrating candidemia and 30-day mortality risk models to inform empiric antifungal decision-making. ResultsAmong 213,404 and 107,507 patients in the HMHS and MIMIC-IV cohorts, candidemia occurred in fewer than 1% (851 [0.4%] and 634 [0.6%], respectively). PyTorch_EHR outperformed LR, LightGBM, and existing candidemia scores, particularly in terms of area under the precision-recall curve (AUPRC) in HMHS and MIMIC-IV. By integrating 30-day mortality risk, the two-step framework identified an additional 20 and 28 candidemia cases beyond the one-step model, increasing coverage to 61% (121/199) and 46% (68/147) in HMHS and MIMIC-IV, respectively. Many patients identified by the two-step framework had high mortality yet did not receive empiric antifungal therapy (61.1% HMHS; 82.6% MIMIC-IV). ConclusionA two-step deep-learning framework integrating candidemia and mortality risk may support early identification of high-risk patients and facilitate timely empiric antifungal therapy. Prospective studies are warranted to confirm the findings.

PurposeVentilator-associated pneumonia (VAP) remains one of the most serious hospital-acquired infections in the intensive care unit (ICU), with high morbidity and mortality. Early identification of patients at risk for developing VAP could enable timely diagnostics and intervention. However, current clinical tools are limited in their ability to detect early physiologic signals preceding VAP onset. We aimed to build supervised machine learning models to predict short term onset of VAP. MethodsWe analyzed electronic health record data from a prospective observational cohort of ICU patients, where VAP was adjudicated using a standardized published protocol by a panel of critical care physicians. Clinical features (including vital signs, ventilator settings, laboratory values, and support devices) were extracted for each patient-ICU-day. We explored unsupervised clustering to characterize feature dynamics associated with VAP onset. We built multiple machine learning models across different prediction windows (3, 5, 7 days before VAP). We examined model performance in two external cohorts, MIMIC-IV and secondary analysis of the AMIKINHAL trial. Results were evaluated with discrimination metrics such as AUROC. ResultsThe internal cohort included 507 patients with BAL-confirmed diagnoses: 261 developed VAP and 246 did not have VAP. Visualization using clustering identified distinct physiologic states enriched for VAP-labeled days. The best-performing model achieved an AUROC of 0.866 in predicting VAP up to seven days before clinical diagnosis. Temporal model probability trajectories showed rising model confidence in the days leading up to VAP. On external validation in MIMIC-IV, the best model achieved an AUROC of 0.817 for forecasting VAP within five days. There was low feature overlap with the AMIKINHAL trial data, leading to poor model performance. Feature analysis revealed that platelet count, positive end-expiratory pressure (PEEP), ventilator duration, and inflammatory markers were key drivers of model predictions. ConclusionsMachine learning models trained on routinely collected ICU data with careful labeling can anticipate VAP onset up to a week in advance with strong predictive performance. Model performance generalized to data from an entirely different hospital system despite differences in practice and labeling patterns, but did not perform well when there was poor feature overlap. Future work should focus on real-time prospective evaluation.

BackgroundThere is a need for synthetic peptide-based serologic assays that exploit avidity to replace whole antigens while enabling low-cost diagnostics in resource-limited settings. ObjectiveTo evaluate the diagnostic accuracy of a polymeric peptide-based ELISA leveraging avidity to enhance signal. MethodA 15-member SARS-CoV-2 peptide library corresponding to multiple epitope clusters and proteins was screened by indirect ELISA using pooled sera from RT-PCR-confirmed COVID-19 patients to identify peptides with possible diagnostic utility. The identified lead candidate, S559, possessed terminal cysteine-substitution to allow disulfide polymerization, and the resulting avidity gain was evaluated by comparing the apparent dissociation constant (KDapp) before and after depolymerization with N-acetylcysteine. The performance of an optimized ELISA using S559 was evaluated on 1,222 prospectively collected COVID-19 serum samples and 218 biobanked pre-COVID control serum samples. ResultsPolymeric S559 with a KDapp of 29.26 nM-1was demonstrated to have a 218% avidity gain relative to the completely depolymerized form. At pre-defined thresholds, the optimized S559 ELISA has a sensitivity and specificity of 83.39% (95%CI: 81.18% and 85.43%) and 96.79% (95%CI: 93.50% and 98.70%), respectively. At post hoc thresholds determined by Youden index, sensitivity and specificity reached 95.01 (95% CI: 93.63% - 96.16%) and 100.00% (95% CI: 98.32% - 100.00%), respectively. ConclusionHomomultivalent epitope presentation using polymeric S559 allows a highly specific immunoassay using human sera that may have important value in detecting antibodies, whether for diagnosing infection, confirming vaccination status or conducting surveillance.

BackgroundEarly lactate is widely used to risk-stratify septic shock, yet clinically actionable cut-offs for 28-day mortality remain uncertain. MethodsIn a single-centre study conducted across two intensive care units, we analysed 84 adults with septic shock identified within 24 hours of intensive care unit admission. The primary endpoint was 28-day mortality. Four lactate metrics obtained during the first 24 hours were evaluated: first (admission) lactate, last lactate, peak lactate, and lactate clearance from first to last. Associations were tested using logistic regression with and without adjustment for the Simplified Acute Physiology Score 3; discrimination was assessed by area under the receiver-operating characteristic curve (AUROC), and optimal cut-offs were defined by the Youden index. ResultsThirty-nine of 84 patients (46.4%) died by day 28. Higher absolute lactate values were independently associated with death (adjusted odds ratio (OR) per 1 mmol/L increase: First 1.47, p<0.001; Last 1.41, p=0.002; Peak 1.39, p<0.001), whereas Lactate clearance was not (OR 0.65, p=0.202). Discrimination was moderate to good for peak (AUROC 0.817), first (0.791), and last (0.757) lactate, and poor for clearance (0.577). Youden-derived thresholds provided pragmatic trade-offs: First 3.55 mmol/L (sensitivity 0.821, specificity 0.689), Last 3.15 mmol/L (0.567, 0.864), and Peak 3.55 mmol/L (0.973, 0.556). Kaplan-Meier curves using these cut-offs showed early and sustained separation. ConclusionsIn adults with septic shock, simple early lactate thresholds around 3.3- 3.6 mmol/L (first/peak) and approximately 3.15 mmol/L (last) identify 28-day mortality risk and outperform lactate clearance.

BackgroundRecent global outbreaks of Mpox have posed significant diagnostic challenges, particularly in resource-limited settings. Conventional diagnostic methods are often inaccessible due to cost, logistical constraints, or lack of trained personnel. These limitations highlight the urgent need for alternative, scalable diagnostic strategies. This study explored the application of machine learning (ML) classifiers trained on clinical symptom data as a rapid, cost-effective tool for Mpox detection. MethodsAn open-access dataset of clinical symptoms from suspected Mpox cases was used to train and evaluate five supervised ML algorithms: Extra Trees, Quadratic Discriminant Analysis (QDA), Decision Trees, Perceptron, and Support Vector Classifier (SVC). Prior to training, data preprocessing steps, including normalization and handling of missing values, were performed after which model training was carried out using a stratified 80:20 train-test split. Performance was assessed using accuracy, recall, area under the receiver operating characteristic curve (ROC-AUC), and F1-score metrics. Subsequently, feature importance was analyzed using permutation-based techniques to determine the contribution of each clinical symptom to model predictions. ResultsAmong the five evaluated models, SVC, QDA, and Perceptron achieved superior and identical performance metrics, with accuracy, ROC-AUC, and F1-score values of 97.7%, and a recall of 95.5%. Each of these models correctly identified 44 true positive cases with zero false positives. In addition, QDA and SVC produced the lowest number of false negatives (2) and the highest number of true negatives (42), indicating robust discriminatory power. Feature importance analysis identified skin rash as the most predictive clinical feature, with a permutation importance score of 0.12. ConclusionsThese findings demonstrate the strong potential of machine learning classifiers for detecting Mpox based on clinical features. Incorporating these models into healthcare systems could significantly enhance early case detection, improve clinical decision-making, and bolster disease surveillance. Future research should focus on prospective validation of these ML classifiers in real-world clinical environments.

RationaleAutonomic dysfunction is a hallmark of sepsis pathophysiology, yet its quantification remains challenging. Multiscale entropy (MSE) derived from heart rate variability (HRV) offers a dynamic measure of physiological complexity and may serve as a biomarker of early deterioration associated with subsequent organ failure, vasopressor escalation, or mortality. ObjectiveTo determine whether MSE computed across multiple temporal scales during the first 24 hours of Intensive Care Unit (ICU) admission is associated with short-term mortality and longer-term organ dysfunction in patients with sepsis, and whether these relationships vary across vasopressor exposure. Unlike prior studies that focused on short-term HRV metrics, we applied MSE across multiple temporal scales and incorporated these features into machine learning models to evaluate their prognostic utility in septic shock. MethodsThis retrospective cohort study included adult ICU sepsis patients at Emory University Hospital from January 2016 to December 2019. Of 2,076 eligible patients, 958 were propensity matched into two cohorts: fluids-only and fluids-plus-vasopressor, with norepinephrine as the primary vasopressor. High-resolution electrocardiogram (ECG) waveforms were analyzed to compute MSE across 20 temporal scales. Machine learning models using (1) MSE features alone and (2) MSE combined with demographic and vital sign data (MSE-DV) were compared against traditional HRV measures based model and severity of illness scores for predicting outcomes. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), with a primary outcome of mortality at day 7 and secondary outcome of persistent organ dysfunction at day 28. ResultsIn the fluids-plus-vasopressor cohort, MSE-based models demonstrated superior predictive performance for 7-day mortality (AUROC 0.84) compared to severity of illness scores (AUROC 0.64). MSE-DV models also predicted organ dysfunction including 28-day renal (AUROC 0.75), neurological (AUROC 0.79), and respiratory (AUROC 0.71) dysfunction. Patients receiving second-line and third-line vasopressors and corticosteroids exhibited progressively lower MSE values, particularly at mid-range and long-range scales. ConclusionMSE features in the first 24 hours of ICU stay predict mortality and organ dysfunction with higher discrimination than traditional severity of illness scores. Future work should validate these findings, assess longitudinal MSE trends, and race-specific autonomic patterns to refine predictive models.

BackgroundCervical cancer is one of the most common malignant tumors of the female reproductive system. Existing treatments provide limited benefit for patients with advanced, recurrent or metastatic disease, and reliable prognostic markers are lacking. In this study we integrated multi-omic data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. Protein-coding genes meeting the criteria of an adjusted P value < 0.05 and |log2 fold-change| > 5 were screened; 693 genes were identified. We further focused on three genes related to the tumor microenvironment--interleukin 21 (IL21), C-X-C motif chemokine ligand 9 (CXCL9) and cluster of differentiation 1A (CD1A)--and performed differential expression analysis, survival analysis, clinical stage analysis and immune infiltration correlation analysis to clarify their prognostic value and potential mechanisms in cervical cancer. Results(1) CXCL9 and CD1A were highly expressed in cervical cancer tissues, and all three genes showed high expression across different pathological stages without stage-dependent differences; (2) high expression of IL21, CXCL9 and CD1A improved patient prognosis and was positively associated with overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI); (3) expression of IL21, CXCL9 and CD1A was closely correlated with infiltration of multiple immune cells: IL21 correlated with total T cells, helper T cells and B cells, CXCL9 correlated with T cells and activated dendritic cells, and CD1A correlated with immature dendritic cells. ConclusionIL21, CXCL9 and CD1A are potential prognostic biomarkers and key immunomodulatory factors in cervical cancer. This study provides a new direction for immunotherapy and individualized precision treatment of cervical cancer.

Serological surveillance is fundamental to infectious disease research and informed public-health decision making. Immunoassays used in the study of pathogen-specific immunity have historically relied on the collection of venous blood. While critical for many public-health applications, this sample collection method is invasive and resource intensive. The costs and logistical barriers associated with venous blood collection are exacerbated in resource-limited regions, and the shift to less invasive sampling methods would increase sample availability for pathogen surveillance and study of pathogen-specific immunity. To this end, we have developed and optimized a self-collected, saliva-based immunoassay capable of quantifying pathogen-specific antibody binding in saliva samples. Using samples collected from geographically and epidemiologically diverse regions of the world, we compared antigen-specific IgG levels in paired plasma and saliva samples. We observed that levels of IgG against multiple pathogens of public health concern - including SARS-CoV-2 and dengue virus (DENV) - were highly correlated in plasma and swab-collected saliva. In addition, the decay of maternally derived antibodies in saliva samples collected from infants was readily observed using this immunoassay, demonstrating the assay's sensitivity and potential for use in measuring antibody kinetics. We posit that this assay represents a climate stable, non-invasive tool that can aid in the surveillance and study of pathogen-specific immunity across a broad range of public-health indications.

Over-the-counter (OTC) lateral flow tests for respiratory viruses have newly emerged since the beginning of the coronavirus disease 2019 (COVID-19) pandemic and are increasingly available to consumers. While all marketed tests have met standards for Emergency Use Authorization (EUA), De Novo classification or 510(k) clearance, limited data are available to inform consumers of their relative performance. We performed a head-to-head benchtop analytical assessment of OTC tests available for purchase from retailers in the United States in the fall of 2025. Contrived specimen dilution panels were prepared from propagated viral stocks of influenza A H1N1pdm09, influenza A H3N2, influenza B (Victoria lineage), and SARS-CoV-2 JN.1 (Omicron variant) in clinical matrix and applied to the swab provided with each kit. Tests were subsequently performed according to the manufacturers instructions for use and results were interpreted by two readers blinded to the starting test material. Lower limits at which 3 of 3 replicates of test material were detected were within a 4-fold dilution for all four viruses and all eight OTC tests evaluated. We found that at low viral concentrations many OTC tests were interpreted as negative at the start of the stated interpretation window but converted to a positive result by the end of the interpretation window. We conclude that eight OTC tests that are currently readily available to consumers perform similarly in a contrived specimen analytical study, but we recommend that users of OTC lateral flow tests allow for the full incubation time before concluding that a test is truly negative.