Clinical Chemistry

IntroductionDespite its promise, accuracy of microbial cell-free DNA (mDNA) in plasma as a diagnostic tool is hindered by its low abundance and process contaminants. We have previously shown that combining size selection with single-stranded DNA (ssDNA) library preparation increased mDNA yield by 200-fold but also decreased sensitivity for pathogen detection due to higher background noise. A recent study showed that pathogen-derived DNA was enriched for CC dinucleotide at 5 ends compared to contaminants. Since ssDNA libraries preserve sequence motifs at both ends (5 and 3), we hypothesized that analysis of nucleotide motifs at microbial fragment ends in size-selected ssDNA libraries could help differentiate pathogen DNA from background noise. MethodsWe performed deep sequencing on size-selected ssDNA libraries (<110 bp) generated from longitudinal plasma samples of 11 critically-ill patients (5 with culture-proven infections, 20 samples; 6 without infections, 18 samples) and 6 no-template controls (NTCs). For each 2-mer and 1-mer motif, we calculated the ratio between its frequency observed at 5 and 3 fragment ends in sequencing data and its expected frequency in the corresponding reference genome (O/E ratio). We compared enrichment of motifs in pathogen DNA and contaminant DNA fragments. ResultsPathogen-derived mDNA fragments were more biased in O/E end motif ratios compared to contaminants across all 3 groups (NTCs, no-infections and culture-proven infections), at both 5 and 3 fragment ends. Notably, the GG dinucleotide was enriched at the 3 end in pathogens compared to contaminants (P < 0.0001). Combining O/E ratios for C and G nucleotides at the 3 end achieved areas under the receiver operating characteristic curve of >0.98 for distinguishing common contaminants from culture-proven pathogens. ConclusionsPathogen-derived mDNA in size-selected ssDNA libraries is biased at 5 and 3 fragment end compared to contaminants. Incorporating microbial fragment end motif analysis can enhance signal-to-noise ratio and improve pathogen detection and identification in plasma metagenomic sequencing.

The COVID-19 pandemic is challenging the global supply chain and equipment needed for mass testing with RT-qPCR, the gold standard for SARS-CoV-2 diagnosis. Here, we propose the RT-LAMP assay as an additional strategy for rapid virus diagnosis. However, its validation as a diagnostic method remains uncertain. In this work, we validated the RT-LAMP assay in 1,266 nasopharyngeal swab samples with confirmed diagnosis by CDC 2019-nCoV RT-qPCR. Our cohort was divided, the first (n=984) was used to evaluate two sets of oligonucleotides (S1 and S3) and the second (n=281) to determine whether RT-LAMP could detect samples with several types of variants. This assay can identify positive samples by color change or fluorescence within 40 minutes and shows high concordance with RT-qPCR in samples with CT [&le;]35. Also, S1 and S3 are able to detect SARS-CoV-2 with a sensitivity of 68.4% and 65.8%, and a specificity of 98.9% and 97.1%, respectively. Furthermore, RT-LAMP assay identified 279 sequenced samples as positive (99.3% sensitivity) corresponding to the Alpha, Beta, Gamma, Delta, Epsilon, Iota, Kappa, Lambda, Mu and Omicron variants. In conclusion, RT-LAMP is able to identify SARS-CoV-2 with good sensitivity and excellent specificity, including all VOC, VOI, VUM and FMV variants.

BackgroundCurrent next generation sequencing (NGS) and microarray based Non-Invasive Prenatal Tests (NIPT), used for the detection of common fetal trisomies, are still expensive, time consuming and need to be performed in centralized laboratories. To improve NIPT in clinical routine practice as universal prenatal screening, we have developed a digital droplet PCR (ddPCR) based assay called iSAFE NIPT using cell free fetal DNA (cffDNA) for detection of fetal trisomies 13, 18 and 21 in a single reaction with advantage of high diagnostic accuracy and reduced cost. Materials and MethodsWe first used artificial DNA samples to evaluate analytical sensitivity and specificity of the iSAFE NIPT. Next, we analysed 269 plasma samples for the clinical validation of iSAFE NIPT. Fifty-eight of these, including five trisomies 21, two trisomies 18 and one trisomy 13 were utilised to establish the assay cut-off values based on ratios between chromosome counts. The remaining 211 plasma samples, including 10 trisomies 21, were analysed to evaluate iSAFE NIPT clinical performance. ResultsiSAFE NIPT achieved a 100% analytical sensitivity (95% CI 94.9-100% trisomy 21; 79.4-100% trisomy 18; 73.5-100% trisomy 13) and 100% specificity (95% CI 96.3-100% trisomy 21; 97.6-100% trisomy 18; 97.6-100% trisomy 13). It also achieved a 100% clinical sensitivity and specificity for trisomy 21 detection in the 211 clinical samples (95% CI for sensitivity is 69.1-100%, and 95% CI for specificity is 98.2-100%). ConclusionsThe iSAFE NIPT is a highly multiplexed ddPCR based assay for detection of fetal trisomies from maternal blood. Based on clinical validation, the iSAFE NIPT has high diagnostic sensitivity and specificity. It can be decentralized in routine clinical laboratories, is fast, easy to use and economical comparing to current NIPT.

Human proteases play major roles in various pathological conditions, including dysregulated immune responses in sepsis, making them strong candidates for developing diagnostic markers. Despite this potential, the progress of developing protease-based diagnostic tools has remained slow due to significant technical barriers associated with measuring protease activity, mainly stemming from the vast diversity and the lack of substrate specificity, which complicate the interpretation of protease activity profiles. In this work, we advanced the current state of assay development by designing substrate molecule sensors and implementing an analytical approach based on mass spectrometry. Specifically, we chemically modified protease substrates for human neutrophil elastase (HNE) and matrix metalloproteinases (MMPs) to enhance specificity in mass spectrometry. This approach yields distinct cleavage products with non-overlapping mass-to-charge signatures, allowing precise differentiation of each proteases activity. We then integrated the modified substrates into a mass spectrometry-based multiplexed assay platform, enabling quantification of multiple protease activities in a single run. We applied the assay to plasma samples and demonstrated that the assay detects distinct protease activity profiles. Our study demonstrated that the assay achieved a diagnostic sensitivity of 88% and specificity of 87% for sepsis detection. The combination of low cost, rapidness, and robust diagnostic performance makes this platform well-suited to a wide range of clinical settings. One Sentence SummaryNovel modifications to protease substrates enable a multiplexed activity assay for accurate sepsis diagnosis in a 3-hour timeframe.

Lower Respiratory Tract Infections (LRTIs) represent the leading cause of death due to infectious diseases. Current diagnostic modalities primarily depend on clinical symptoms and lack specificity, especially in light of common colonization without overt infection. To address this, we developed a noninvasive diagnostic approach that employs BreathBiomics, an advanced human breath sampling system, to detect protease activities induced by bacterial infection in the lower respiratory tract. Specifically, we engineered a high-sensitivity and high-specificity molecular sensor for human neutrophil elastase (HNE). The sensor undergoes cleavage in the presence of HNE, an event that is subsequently detected via Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS). Application of this methodology to clinical samples, breath specimens collected from intubated patients with LRTIs, demonstrated the detection of the cleaved sensor by MALDI-TOF MS. Our findings indicate that this novel approach offers a noninvasive and specific diagnostic strategy for people with LRTIs. O_TEXTBOXSignificance The potential for using human breath for noninvasive disease detection and diagnosis has long been recognized, yet the lack of effective biomolecular sampling technologies has hindered progress. To address this limitation, we developed BreathBiomics, an advanced sampling system designed to efficiently capture biomolecules in human exhaled breath. By focusing on protease dysregulation, an established event induced by bacterial infections, we demonstrated that BreathBiomics can capture proteases and facilitate their subsequent activity-based detection for the diagnosis of LRTI. We verified the assays sensitivity and clinical applicability through empirical studies. Our work marks a significant advancement by providing the first viable pathway for the development of in vitro diagnostic assays leveraging human breath for disease detection and diagnosis. C_TEXTBOX

BackgroundLiquid biopsy based on cell-free DNA (cfDNA) is an established approach in clinical diagnostics. In recent years, a fraction of cfDNA comprising short fragments has been discovered, that is enriched at gene promoters and binding sites of DNA-binding proteins. However, the diagnostic potential of such short double-stranded cell-free DNA (footprint DNA) remains to be fully explored. Therefore, we characterized the clinical utility of footprint DNA in septic patients. MethodsWe enriched for footprint DNA based on size selection and subsequent high-throughput DNA sequencing to receive an unbiased, genome-wide picture of the host response to the infection. Footprint DNA occupancies were analyzed for correlation with clinical metrics including urea, hemoglobin, or alanine aminotransferase (ALT). Additionally, footprint DNA markers were benchmarked by read and receiver operating curve (ROC) analysis against procalcitonin (PCT) as an established marker for infection status as well as against clinical parameters for early death prediction. FindingsWe found that levels of occupancy of footprint DNA at defined genomic loci semi-quantitatively correlated with physiological markers like ALT or urea from major organ systems including liver or kidney. In a small proof-of-concept cohort, differential signatures of DNA footprints distinguished between patient groups with bacterial and viral infections with an area under the ROC (AUROC) of 1.0, which is considerably better than PCT with an AUROC of 0.75. Likewise, footprint DNA could also predict early death in septic patients with an AUROC of 0.983, compared to the SOFA (Sepsis-related organ failure assessment) score with an AUROC of 0.76. InterpretationOur findings show that footprint DNA delivers quantitative information on physiology at the DNA level, demonstrating its diagnostic and prognostic potential. Identified footprint biomarker regions could be helpful in the clinical assessment of septic patients and other complex diseases outperforming current state-of-the-art clinical diagnostics. FundingThis study was financed with internal funds from Fraunhofer society.

IntroductionAccess to accurate, non-invasive diagnostics remains a critical unmet need in womens health. Menstrual effluence, containing endometrial tissue, immune cells, and microbial communities, represents a clinically relevant specimen for genomic and molecular pathology applications, yet has historically been underutilized due to concerns about sample integrity and variability. MethodsWe developed and validated a standardized, at-home tampon-based collection system designed to preserve nucleic acids at ambient temperature for clinical-grade analyses. 1,067 tampon samples from 328 participants underwent, RNA sequencing and metatranscriptomic profiling to assess specimen transcript integrity, diagnostic fidelity, and microbial composition over time. 12 patients were exome sequenced using matched menstrual effluence and whole blood to assess assay concordance between sample types. ResultsRNA extracted from menstrual effluence maintained stability for up to 14 days without refrigeration, achieving sufficient yield and quality for sequencing in >97% of samples. Variant detection via exome sequencing demonstrated 100% concordance among overlapping single nucleotide variants between menstrual fluid and matched venous blood, confirming clinical equivalency for genetic testing. Transcriptomic analyses revealed cycle-dependent variation in key reproductive and immune markers, while metatranscriptomic profiling identified shifts in microbial communities consistent with known reproductive tract dysbiosis. ConclusionsStandardized at-home collection of menstrual effluence provides a clinically actionable platform that supports remote specimen acquisition without compromising molecular assay fidelity, offering a scalable solution to improve access to carrier screening, reproductive health assessment, and infectious disease monitoring in clinical practice.

Sepsis is the bodys dysfunctional response to infection associated with organ failure. Delays in diagnosis have a substantial impact on survival. Herein, samples from 586 in-house patients were used in conjunction with machine learning and cross-validation to narrow a gene expression signature of immune cell reprogramming to predict clinical deterioration in patients with suspected sepsis within the first 24 hours (h) of clinical presentation using just six genes (Sepset). The accuracy of the test ([~]90% in early intensive care unit (ICU) and 70% in emergency room patients) was validated in 3,178 patients from existing independent cohorts. A real-time reverse transcriptase polymerase chain reaction (RT-PCR)-based test was shown to have a 98% sensitivity in >230 patients to predict worsening of the sequential organ failure scores or admission to the ICU within the first 24 h following Sepset detection. A stand-alone centrifugal microfluidic instrument that integrates the entire automated workflow for detection of the Sepset classifier in whole blood using digital droplet PCR was developed and tested. This PREcision meDIcine for CriTical care (PREDICT) system had a high sensitivity of 92%, specificity of 89%, and an overall accuracy of 88% in identifying the risk of imminent clinical deterioration in patients with suspected sepsis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/24314844v2_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@82f577org.highwire.dtl.DTLVardef@1c18921org.highwire.dtl.DTLVardef@111f119org.highwire.dtl.DTLVardef@ebbb87_HPS_FORMAT_FIGEXP M_FIG Description of Graphic AbstractFeature reduction and development of a gene classifier that predicts deterioration-risk-groups in patients starts with in-house RNA sequencing data from patient collected from a heterogenous cohort of patients with suspected sepsis (top left) to reduce our original published gene signature down to 6-genes (Sepset), for which expression could be related to 2 housekeeping genes. Feature selection was performed using machine learning (ML) and AI and the classifier validated in samples from published transcriptomic studies. Molecular assay is then developed by designing and testing primer/probe sequences specific to the target genes using digital droplet PCR. In parallel, sample-to-answer microfluidic platform and cartridges are developed (bottom right) and analytical performance of multiplex quantitative assay is tested. Prognostic enrichment is obtained by analyzing the results using ML algorithm to determine the percent likelihood of significant clinical deterioration within the immediate next 24 h. The deployment of PREDICT platform (center) at the point-of-care is anticipated to aid in triage and management of prospective sepsis within the first 3 h of clinical presentation. C_FIG

BackgroundWe previously proposed two cfDNA-based scores (genome-wide z-score and nucleosome score) as candidate non-invasive biomarkers to further improve pre-surgical diagnosis of ovarian malignancy. We aimed to investigate the added value of these cfDNA-based scores to the predictors of the ADNEX model (Assessment of Different NEoplasias in the adnexa) to estimate the risk of ovarian malignancy. Methods526 patients with an adnexal mass scheduled for surgery were consecutively recruited in three oncology referral centers. cfDNA-based scores were calculated in pre-operative plasma samples. Logistic regression models were fitted for ADNEX predictors alone and after adding cfDNA scores. We reported likelihood ratio tests, the area under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, and Net Benefit for thresholds between 5% and 40%. ResultsThe study included 272 benign, 86 borderline, 36 stage I invasive, 113 stage II-IV invasive, and 19 secondary metastatic tumors. The likelihood ratio tests for adding the cfDNA variables to the ADNEX model were statistically significant (p<0.001 for ADNEX without CA125, p=0.001 for ADNEX with CA125). The accompanying increases in AUC were 0.013 and 0.003. Net Benefit, sensitivity and specificity were similar for all models. The increase in Net Benefit at the recommended 10% threshold estimated risk of malignancy was 0.0017 and 0.0020, respectively. According to these results, adding cfDNA markers required at least 453 patients per additional true positive. ConclusionAlthough statistically significant, the addition of the cfDNA scores to the ADNEX model do not improve the ADNEX model in a clinically meaningful way.

Neoantigens have emerged as promising targets for personalized cancer immunotherapy. However, accurate identification of immunogenic neoantigens remains a challenge due to limitations in existing predictive models. Here, we present CNNeo, a novel deep learning-based neoantigen prediction model, and CNNeoPP, an integrated computational pipeline for neoantigen discovery. CNNeo employs natural language processing-based sequence encoding and multi-modal feature integration, demonstrating superior predictive performance compared to existing tools. CNNeoPP was rigorously validated using independent datasets, including the TESLA dataset, and experimental validation via ELISpot T-cell assays. Additionally, we conducted a proof-of-concept study utilizing plasma cell-free DNA to explore the feasibility of non-invasive neoantigen prediction. We found that increased sequencing depth enhances neoantigen detectability, further amplified by the prioritization strategy of CNNeoPP. CNNeoDB, a publicly accessible database was developed compiling neoantigen data from multiple sources. This study establishes robust tools for neoantigen prediction, with implications for optimizing cancer immunotherapy and liquid biopsy-based tumor monitoring.

Sensitive detection of IgG antibodies against SARS-CoV-2 is important to assessing immune responses to viral infection or vaccination and immunity duration. Antibody assays using non-invasive body fluids such as saliva could facilitate mass testing including young children, elderly and those who resist blood draws, and easily allowing longitudinal testing/monitoring of antibodies over time. Here, we developed a new lateral flow (nLF) assay that sensitively detects SARS-CoV-2 IgG antibodies in the saliva samples of vaccinated individuals and previous COVID-19 patients. The 25-minute nLF assay detected anti-spike protein (anti-S1) IgG in saliva samples with 100% specificity and high sensitivity from both vaccinated (99.51% for samples [&ge;] 19 days post 1st Pfizer/BioNTech or Moderna mRNA vaccine dose) and infected individuals. Antibodies against nucleocapsid protein (anti-NCP) was detected only in the saliva samples of COVID-19 patients and not in vaccinated samples, allowing facile differentiation of vaccination from infection. SARS-CoV-2 anti-S1 IgG antibody in saliva measured by nLF demonstrated similar evolution trends post vaccination to that in matching dried blood spot (DBS) samples measured by a quantitative pGOLD lab-test, enabling the nLF to be a valid tool for non-invasive personalized monitoring of SARS-CoV-2 antibody persistence. The new salivary rapid test platform can be applied for non-invasive detection of antibodies against infection and vaccination in a wide range of diseases.

Investigating the human fallopian tube (FT) microbiota has significant implications for understanding the pathogenesis of ovarian cancer (OC). In this large prospective study, we collected swabs intraoperatively from the FT and other surgical sites as controls to profile the microbiota in the FT and to assess its relationship with OC. 81 OC and 106 non-cancer patients were enrolled and 1001 swabs were processed for 16S rRNA gene PCR and sequencing. We identified 84 bacterial species that may represent the FT microbiota and found a clear shift in the microbiota of the OC patients when compared to the non-cancer patients. Of the top 20 species that were most prevalent in the FT of OC patients, 60% were bacteria that predominantly reside in the gastrointestinal tract, while 30% normally reside in the mouth. Serous carcinoma had higher prevalence of almost all 84 FT bacterial species compared to the other OC subtypes. The clear shift in the FT microbiota in OC patients establishes the scientific foundation for future investigation into the role of these bacteria in the pathogenesis of ovarian cancer. SUMMARYO_ST_ABSIntroductionC_ST_ABSInvestigating the human fallopian tube (FT) microbiota has significant implications for understanding the pathogenesis of ovarian cancer (OC), pelvic inflammatory disease, and tubal ectopic pregnancy, as well as normal fertilization. Several studies have provided evidence that the FT may not be sterile, but rigorous controls are needed to assess the microbiota in low biomass samples. In this large prospective study, we collected swabs intraoperatively from the FT and other surgical sites as controls to profile the microbiota in the FT and to assess its relationship with OC. MethodsWe collected swabs from the cervix, FT, ovarian surfaces, and paracolic gutters of patients, and from laparoscopic ports and air in the operating room. Surgical indications included known or suspected ovarian cancers, risk-reducing salpingo-oophorectomies due to genetic risk, and benign gynecological disorders. DNA was extracted from the swabs and the bacterial concentrations were quantified using broad-range bacterial quantitative PCR. Bacterial composition was characterized using amplicon PCR targeting the V3-V4 hypervariable region of the 16S rRNA gene combined with next generation sequencing. Multiple negative controls and filtering approaches were used to differentiate FT microbiota from likely contaminant sequences. Presence of the bacterial taxa in both the cervical and FT sample set was required to identify ascending genital tract bacteria. ResultsA total of 81 ovarian cancer patients and 106 non-cancer patients were enrolled and 1001 swabs were processed. The bacterial concentrations of FT and ovarian surfaces averaged 2.5 copies of 16S rRNA genes/l of DNA (standard deviation, SD 4.6), similar to the paracolic gutter and higher than the controls (p-value < 0.001). We identified 84 bacterial species that may represent the FT microbiota. After ranking the FT bacteria based on the prevalence difference, we found a clear shift in the microbiota of the OC patients when compared to the non-cancer patients. Of the top 20 species that were most prevalent in the FT of OC patients, 60% were bacteria that predominantly reside in the gastrointestinal tract, such as Klebsiella, Faecalibacterium prausnitzii, Ruminiclostridium, and Roseburia, while 30% normally reside in the mouth, such as Streptococcus mitis, Corynebacterium simulans/striatum, and Dialister invisus. On the contrary, vaginal bacterial species are more prevalent in the FT from non-cancer patients, representing 75% of the top 20 bacterial species that are most prevalent in non-cancer patients. Serous carcinoma had higher prevalence of almost all 84 FT bacterial species compared to the other OC subtypes. ConclusionIn this large low biomass microbiota study using intraoperatively collected swabs, we identified a group of bacterial species that appear to reside in the FT across multiple participants. A higher prevalence of some of these bacterial species, especially those that normally reside outside the female genital tract, was noted in the FT from patients with OC, laying the scientific foundation to explore whether these bacteria may have a role in enhancing ovarian cancer risk.

The emergence of Omicron and Delta variants of SARS-CoV-2 has begun a number of discussions regarding breakthrough infection, waning immunity, need and timing for vaccine boosters and whether existing mRNA vaccines for the wildtype strain are adequate. Our work leverages a biosensor-based technique to evaluate the binding efficacy of SARS-CoV-2 S1 specific salivary antibodies to the Omicron and Delta variants using a cohort of mRNA vaccinated (n=109) and convalescent (n=19) subjects. We discovered a wide range of binding efficacies to the variant strains, with a mean reduction of 60.5%, 26.7%, and 14.7% in measurable signal to the Omicron strain and 13.4%, 2.4%, and -6.4% percent mean reduction to the Delta Variant for convalescent, Pfizer, and Moderna vaccinated groups respectively. This assay may be an important tool in determining susceptibility to infection or need for booster immunization as the pandemic evolves. Key PointsO_LIAMPERIAL assay developed to quantify salivary SARS-CoV-2 S1 IgG antibodies to Omicron and Delta variants C_LIO_LIThere was a reduction in affinity to both Delta and Omicron Variants C_LIO_LIThe reduction in affinity was more pronounced to Omicron than for Delta Variants C_LIO_LIThere was a significant difference between IgG affinities in Individuals vaccinated with Pfizer versus Moderna Vaccines C_LI

BackgroundThe ADNEX model (Assessment of Different NEoplasias in the adnexa) is the best performing model to predict the risk of malignancy (binary) and type of malignancy (multiclass) in ovarian tumors. The immune system plays a role in the onset and progression of ovarian cancer. Preliminary research has suggested that immune-related biomarkers can help in the discrimination of ovarian tumors. We aimed to assess which proteins have the most additional diagnostic value in addition to ADNEX clinical and ultrasound predictors. Materials and methodsIn this exploratory diagnostic study, 1086 patients with an adnexal mass scheduled for surgery were consecutively enrolled at five oncology centers and one non- oncology center in Belgium, Italy, Czech Republic and United Kingdom between 2015 and 2019. The quantification of 33 serum proteins was carried out preoperatively, using multiplex high throughput immunoassays (Luminex) and electrochemiluminescence immuno-assay (ECLIA). Logistic regression analysis was performed for ADNEX clinical and ultrasound predictors alone (age, maximum diameter of lesion, proportion of solid tissue, presence of >10 cyst locules, number of papillary projections, acoustic shadows and ascites) and after adding proteins. We reported the AUC for benign vs malignant, Polytomous Discrimination Index (PDI; a multiclass AUC) and pairwise AUCs for pairs of tumor types. AUCs were corrected for optimism using bootstrapping. ResultsAfter applying exclusion criteria, 932/1086 patients were eligible for analysis (474 benign, 135 borderline, 84 stage I primary invasive cancer, 208 stage II-IV primary invasive cancer, 31 secondary metastatic invasive tumors). ADNEX predictors alone had an AUC of 0.909 (95% CI 0.894-0.929) to discriminate benign from malignant tumors, and a PDI of 0.532 (0.510-0.589). HE4 yielded the highest increase in AUC (+0.026), followed by CA125 (+0.017). CA125 yielded the highest increase in PDI (+0.049), followed by HE4 (+0.036). Whereas CA125 mainly improved pairwise AUCs between different types of invasive tumors (increases between 0.020-0.165 over ADNEX alone), HE4 mainly improved pairwise AUCs for benign tumors versus stage I (+0.022) and benign tumors versus stage II-IV ovarian cancers (+0.028). CA72.4 might be useful to distinguishing secondary metastatic tumors from benign, borderline, and stage I tumors. CA15.3 might be useful to discriminate borderline tumors from stage I and stage II-IV tumors. Distinguishing stage I and borderline tumors (AUCs [&le;] 0.72) and stage I and secondary metastatic tumors (AUCs [&le;] 0.76) remained difficult after adding proteins. ConclusionsCA125 had the highest added value over clinical and ultrasound predictors to distinguish between the five tumor types, followed by HE4. In addition, CA72.4 and CA15.3 may further improve discrimination but findings for these proteins should be confirmed. The immune-related proteins were in general not able to discriminate the groups.

Mass testing has been proposed as a strategy to address and contain the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic1,2. We have developed MassMark, a novel and highly scalable multiplex method that employs next generation sequencing for high-throughput, accurate and sensitive detection of SARS-CoV-2, while minimizing handling complexity and resources by utilizing a serial pooling strategy to accommodate over 9,000 samples per workflow. Analytical validation showed that MassMark was able to detect SARS-CoV-2 RNA down to a level of 100 copies per reaction. We evaluated the clinical performance of MassMark in a simulated screening testing with 22 characterized samples from three different sources (nasopharyngeal swabs, nasal swabs and saliva), comprising of 12 SARS-CoV-2 positive samples with mid to late Ct values (range: 22.98-32.72) and 10 negative samples. There was one false negative and no false positives, giving an overall sensitivity and specificity of 91.67% and 100% respectively, when compared against an optimized RT-PCR test with a target size within 70 bp (CDC 2019-nCoV Real-Time RT-PCR Diagnostic Panel3).

Rapid classification and detection of SARS-CoV-2 variants have been critical in comprehending the viruss transmission dynamics. Clinical manifestation of the infection is influenced by comorbidities such as age, immune status, diabetes, and the infecting variant. Thus, clinical management may differ for new variants. For example, some monoclonal antibody treatments are variant-specific. Yet, an FDA-approved test for detecting the SARS-CoV-2 variant is unavailable. A laboratory-developed test (LDT) remains a viable option for reporting the infecting variant for clinical intervention or epidemiological purposes. Accordingly, we have validated the Illumina COVID-Seq assay as an LDT according to the guidelines prescribed by the College of American Pathologists (CAP) and Clinical Laboratory Improvement Amendments (CLIA). The limit of detection (LOD) of this test is Ct<30 ([~]15 viral copies) and >200X genomic coverage, and the test is 100% specific in the detection of existing variants. The test demonstrated 100% precision in inter-day, intra-day, and intra-laboratory reproducibility studies. It is also 100% accurate, defined by reference strain testing and split sample testing with other CLIA laboratories. Advanta Genetics LDT COVID Seq has been reviewed by CAP inspectors and is under review by FDA for Emergency Use Authorization.

ObjectiveWe aimed to evaluate the performance of a cell free DNA (cfDNA) assay that uses next generation sequencing (NGS) with quantitative counting templates (QCT) for the clinical detection of the fetal RHD genotype in a diverse RhD-negative pregnant population in the United States (US). Study DesignThis retrospective cohort study was conducted in four US healthcare centers. The same NGS QCT cfDNA fetal RhD assay was offered to non-alloimmunized, RhD-negative pregnant individuals as part of clinical care. Rh immune globulin (RhIG) was administered at the discretion of the provider. The assays sensitivity, specificity, and accuracy were calculated considering the neonatal RhD serology results. ResultsA total of 401 non-alloimunized RhD-negative pregnancies who received clinical care in the period from August 2020 to November 2023 were included in the analysis. The D antigen cfDNA result was 100% concordant with the neonatal serology, resulting in 100% sensitivity, 100% positive predictive value (both 95% CI: 98.6%-100%), 100% specificity, and 100% negative predictive value (both 95% CI: 97.4%-100%). There were 10 pregnancies where the cfDNA analysis identified a non-RHD gene deletion, including RhD{Psi} (n=5) and RHD-CE-D hybrid variants (n=5). RhIG was administered to 93% of pregnant individuals with cfDNA results indicating an RhD-positive fetus compared to 75% of pregnant individuals with cfDNA results indicating an RhD-negative fetus, signifying providers were using the results to guide pregnancy management. ConclusionThis cfDNA analysis via NGS for detecting fetal RhD status is highly accurate with no false-positive or false-negative results in 401 racially and ethnically diverse pregnancies with 100% follow up of all live births. This study and prior studies of this assay support a recommendation to offer cfDNA screening for fetal Rh status as an alternative option to prophylactic RhIG for all non-alloimmunized RhD-negative individuals, which will result in more efficient and targeted prenatal care with administration of RhIG only when medically indicated.

IntroductionSepsis diagnosis remains clinical and heterogeneous. We hypothesized that a proteomics-informed machine-learning approach could identify a small, easy-to-use, and optimized set of clinical variables to complement or potentially outperform SOFA. MethodsWe conducted a prospective, single-center, observational study in an academic intensive care unit. Plasma from critically ill patients with and without sepsis was analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS). Data were acquired with data-independent acquisition parallel accumulation- serial fragmentation (diaPASEF) and processed using DIA-NN software. Differentially expressed proteins informed model development. Random Forest models were trained in a Discovery cohort (n=55) to select clinical variables linked to the proteome, then tested in an independent Validation cohort (n=59). Recursive feature elimination (RFE) identified a minimal feature set that was predictive of sepsis. The performance was assessed using repeated cross-validation and external validation. ResultsTwelve plasma proteins differed between sepsis and non-sepsis patients at FDR < 0.1, corresponding to 26 proteome-enriched clinical variables. The classifier achieved mean AUCs of 0.73 and 0.76 in Discovery and Validation cohorts, respectively. RFE performance plateaued with [&ge;]9 variables, peaked at an accuracy of 0.78, and deteriorated below seven; the final three features before collapse were plasma BUN, chemokine ligand 3 (CCL3), and creatinine. Proteome-to-clinical regression highlighted creatinine as having the strongest correlation (R{superscript 2} = 0.558). Discussion: A concise set of routinely obtainable variables anchored by renal markers and CCL3 captured proteomic signals and discriminated sepsis across cohorts, supporting a "proteomics-informed, clinic-first" strategy for pragmatic EHR deployment. While larger multicenter studies are warranted, these findings suggest that renal dysfunction exerts a disproportionate influence on sepsis and that increased emphasis on kidney-related markers may improve both recognition and risk assessment.

BACKGROUNDPatients with endometriosis experience chronic, inflammatory symptoms including severe pelvic pain and infertility, caused by endometrial-like tissue growth outside the uterus. Endometriosis remains poorly understood, largely attributed to disease heterogeneity and dependence on qualitative technologies for research. There is an unmet clinical need for quantitative, biomarker-based methods for improved endometriosis diagnosis, prognosis, and treatment. We describe the biomarker analysis of a novel biofluid for endometriosis research, sonoPODography (SPG) fluid, which is a fluid collected using culdocentesis from the rectouterine pouch upon saline infusion through the uterus and fallopian tubes. METHODSSingle molecule array (Simoa) assays were used to quantify TNF, IL-1{beta}, VEGF, and CA125 in SPG fluid of 33 endometriosis patients. Simoa assays were validated using dilution-linearity and spike-and-recovery experiments in pooled and individual SPG fluid samples from endometriosis patients. The agreement of CA125 measurements by Simoa and enzyme-linked immunosorbent assay (ELISA) was assessed using Spearman correlation and Bland-Altman analyses. RESULTSSimoa assays were validated in SPG fluid and dilution factors 2x (TNF), 4x (IL-1{beta}), 8x (VEGF), 600x (CA125) were selected for biomarker quantification. Median biomarker concentrations in SPG fluid from endometriosis patients were 0.276 pg/mL (TNF), 0.21 pg/mL (IL-1{beta}), 30.55 pg/mL (VEGF), and 1,810 pg/mL (CA125). Simoa and ELISA CA125 measurements were highly correlated ({rho} = 0.80, p < 0.0001). CONCLUSIONWe present the feasibility and potential of SPG fluid as a novel source of biomarkers for endometriosis, which can enable quantitative clinical technologies for endometriosis diagnosis, prognosis, and treatment.

BackgroundA major challenge in controlling the COVID-19 pandemic is the high false-negative rate of the commonly used standard RT-PCR methods for SARS-CoV-2 detection in clinical samples. Accurate detection is particularly challenging in samples with low viral loads that are below the limit of detection (LoD) of standard one- or two-step RT-PCR methods. MethodsWe implement a three-step approach for SARS-CoV-2 detection and quantification that employs reverse transcription, targeted cDNA preamplification and nano-scale qPCR based on the Fluidigm 192.24 microfluidic chip. We validate the method using both positive controls and nasopharyngeal swab samples. ResultsUsing SARS-CoV-2 synthetic RNA and plasmid controls, we demonstrate that the addition of a preamplification step enhances the LoD of the Fluidigm method by 1,000-fold, enabling detection below 1 copy/l. We applied this method to analyze 182 clinical NP swab samples previously diagnosed using a standard RT-qPCR protocol (91 positive, 91 negative) and demonstrate reproducible detection of SARS-CoV-2 over five orders of magnitude (< 1 to 106 viral copies/l). Crucially, we detect SARS-CoV-2 with relatively low viral load estimates (<1 to 40 viral copies/l) in 17 samples with negative clinical diagnosis, indicating a potential false negative rate of 18.7% by clinical diagnostic procedures. ConclusionThe three-step nano-scale RT-qPCR method can robustly detect SARS-CoV-2 in samples with relatively low viral loads (< 1 viral copy/l) and has the potential to reduce the false negative rate of standard RT-PCR-based diagnostic tests for SARS-CoV-2 and other viral infections. SummaryWe test, implement and report the results of a microfluidic RT-qPCR assay system involving sequential RT, preamplification and nano-scale qPCR that can robustly detect SARS-CoV-2 in clinical samples with viral loads less than 1 copy/ul.