Advanced Science

This study evaluates the feasibility of depth-sensitive bedside monitoring of cerebral blood flow (CBF) and low-frequency oscillations (LFOs) using time-domain diffuse correlation spectroscopy (TD-DCS) in healthy controls and patients with disorders of consciousness (DOC). A 1064 nm TD-DCS system equipped with superconducting nanowire single-photon detectors (SNSPDs) was used to collect 10-minute resting-state data from 25 healthy adults and 5 patients with traumatic brain injury (TBI) diagnosed with DOC, including minimally conscious state (MCS) and coma, in the subacute phase. Photon arrival times were temporally gated to distinguish superficial and cortical-weighted tissue contributions. The blood-flow index (BFI) was extracted from gated autocorrelation functions, and LFOs were quantified using power spectral density within the Slow-5 (0.01-0.027 Hz), Slow-4 (0.027-0.073 Hz), and Slow-3 (0.073-0.198 Hz) bands. Compared to healthy controls, DOC patients exhibited altered resting-state LFO amplitude and spectral distribution, suggestive of altered neurovascular dynamics in severe brain injury. An auditory "smile" command was delivered to five healthy subjects, one MCS patient, and one unresponsive wakefulness syndrome (UWS) patient to assess task-evoked hemodynamic responses. During the task, healthy participants showed clear hemodynamic responses, whereas DOC patients demonstrated attenuated and more transient responses. Overall, TD-DCS provides a noninvasive, depth-resolved approach for assessing cerebral hemodynamics and residual cortical responsiveness, supporting its potential for bedside neurocritical-care monitoring.

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas-associated systems have recently emerged as a focal point for developing next-generation molecular diagnosis, particularly for nucleic acid detection. However, the detection of proteins is equally critical across diverse applications in biology, medicine, and the food industry, especially for diagnosing and prognosing diseases like cancer, Alzheimers and cardiovascular conditions. Despite recent efforts to adapt CRISPR/Cas systems for protein detection with immunoassays, these methods typically achieved sensitivity only in the femtomolar to picomolar range, underscoring the need for enhanced detection capabilities. To address this, we developed CRISPR-AMPED, an innovative CRISPR/Cas-based immunoassay enhanced by magnetic proximity extension and detection. This approach combines proximity extension assay (PEA) with magnetic beads that converts protein into DNA barcodes for quantification with effective washing steps to minimize non-specific binding and hybridization, therefore reducing background noise and increasing detection sensitivity. The resulting DNA barcodes are then detected through isothermal nucleic acid amplification testing (NAAT) using recombinase polymerase amplification (RPA) coupled with the CRISPR/Cas12a system, replacing the traditional PCR. This integration eliminates the need for thermocycling and bulky equipment, reduces amplification time, and provides simultaneous target and signal amplification, thereby significantly boosting detection sensitivity. CRISPR-AMPED achieves attomolar level sensitivity, surpassing ELISA by over three orders of magnitude and outperforming existing CRISPR/Cas-based detection systems. Additionally, our smartphone-based detection device demonstrates potential for point-of-care applications, and the digital format extends dynamic range and enhances quantitation precision. We believe CRISPR-AMPED represents a significant advancement in the field of protein detection.

When a colloidal droplet dries on a substrate, a unique pattern results from multi-facet phenomena such as Marangoni convection, capillary flow, mass transport, mechanical stress, colloid-colloid, and colloid-substrate interactions. Even under uniform conditions (surface wettability, humidity, and temperature), slight differences in the initial colloidal composition alter the drying pattern. This paper shows how the evolving patterns during drying in the sessile droplets depend on the initial composition and are crucial for assessing any abnormalities in the blood. To do so, texture statistics are derived from time-lapse images acquired during drying, and different traditional machine learning are applied. In addition, a neural network analysis is performed on both images and their texture statistics. As the drying phenomena are correlated with the varying composition, these methods exhibit excellent performance in distinguishing blood abnormalities with an Fl score of over 97%. This indicates that analysis of time-lapse images during drying and their texture statistics, rather than conventional analysis using images at the final dry state, are crucial for classification. Our results highlight the potential of droplet drying as a low-volume, accurate, and simple screening tool for detecting the type and stage of any disease in bio-fluid samples, such as blood, urine, and saliva.

BackgroundBiological aging begins decades before the onset of age-related clinical conditions and is mediated by both cellular senescence and declining adaptive immune function. These processes are functionally related with the rate of senescent cell accumulation dependent upon a balance between induction and immune clearance. We previously showed that biomarkers in these domains can identify patients at-risk of surgery-related adverse events. Here, we describe evidence of clinical relevance in early aging and metabolic phenotypes in a general adult population. MethodsWe enrolled a total of 482 participants (ages 25-90) into two prospective, cross-sectional healthy aging cohorts. Expression of biomarkers of adaptive immune function and cellular senescence (SapereX) was measured in CD3+ T cells isolated from peripheral blood. FindingsWe established a network of biomarkers of adaptive immune function that correlate with cellular senescence and associate with early aging phenotypes. SapereX immune components associated with a decrease in CD4+ T cells, an increase in cytotoxic CD8+ T cells, and a loss of CD8+ naive T cells (Pearson correlation 0.3-0.6). These components also associated with a metric of immune resilience, an ability to withstand antigen challenge and inflammation. In contrast, SapereX components were only weakly associated with GlycanAge (Pearson correlation 0.03-0.15) and commonly used DNA methylation clocks (Pearson correlation 0-0.25). Finally, SapereX biomarkers, in particular p16, were associated with chronic inflammation and metabolic dysregulation. InterpretationMeasurement of SapereX biomarkers may capture essential elements of the relationship between cellular senescence and dysregulated adaptive immune function and may provide a benchmark for clinically relevant health decisions.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self-report, questionnaires, and subjective measures to receive a diagnosis, and many never receiving a clear diagnosis at all. In this study, we utilized a single-cell Raman platform and artificial intelligence to analyze blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. Our results demonstrate that Raman profiles of blood cells can distinguish between healthy individuals, disease controls, and ME/CFS patients with high accuracy (91%), and can further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, we identified specific Raman peaks that correlate with ME/CFS phenotypes and have the potential to provide insights into biological changes and support the development of new therapeutics. This study presents a promising approach for aiding in the diagnosis and management of ME/CFS, and could be extended to other unexplained chronic diseases such as long COVID and post-treatment Lyme disease syndrome, which share many of the same symptoms as ME/CFS.

BackgroundInflammatory changes underly many diseases and therapeutic interventions, making accurate tracking of inflammation critical for clinical evaluation of disease course and therapy response. Traditional methods like fever detection and serum biomarkers are limited by imprecision and invasiveness. Collection of self-reported symptoms after vaccination is a common vaccine trial endpoint, but prone to bias. Wearable sensors offer a promising alternative by detecting subtle physiological changes over time. Prior studies show they identify transient post-vaccine inflammation but lack validation relative to serum biomarkers. MethodsThis study included 61 volunteers who were administered one of four mRNA vaccines (1 or 2 doses) for a total of 80 doses. Participants wore a torso sensor patch for 14 days beginning seven days before vaccination, whose data was used to derive an individualized digital biomarker of inflammation - inflammatory multivariate change index (iMCI). The reference outcome was a serum biomarker panel collected at baseline and five post-vaccination timepoints. Self-reported reactogenicity symptoms were tracked daily for 7 days starting the day of vaccination. The correlation between total iMCI response within 48 hours following vaccination and maximal change in select serum biomarkers was determined, along with their relationship to reactogenicity. FindingsThere was a moderate to strong positive Spearman correlation between total iMCI and change in C-reactive protein (CRP) (0.59, p < 0.01) and interferon gamma (IFN-Y)(0.56, p < 0.01) across vaccine types and vaccine doses, similar to the correlation between CRP and IFN-Y (0.60, p < 0.01). The associations with self-reported systemic reactogenicity was only moderate for all: 0.48, p < 0.01 for iMCI, 0.34, p = 0.01 for interferon gamma, 0.36, p <. 0.01 for C-reactive protein. InterpretationThe personalized multivariate inflammatory digital biomarker derived from wearable sensor data can quantify an individuals inflammatory response to vaccination as an alternative to serial serum biomarker testing. This scalable, non-invasive approach can enable real-time monitoring of the onset, duration, and severity of inflammation. FundingModerna, Inc

Silicon nanowire field effect transistor (SiNW-FET) biosensors are capable of label-free, real-time and biological detection with high sensitivity and specificity. However, direct observation on protein-protein interaction in blood or serum is still very difficult because of the complex physiological environment and Debye-screening effect. In order to overcome the detection obstacles, we used dialysis desalination method to purify the detection fluid and overcome Debye-screening effect. In our research, a top-down approach was proposed to fabricate the SiNW-FET, APTES-Glu chemical chain was used to link antibody to the SiNWs. And after verified the detection ability of silicon nanometer biosensors, the dynamic detection process of HbA1c was successfully realized. This could be helpful for accurate diagnosis of diabetes for clinical application. And it makes it possible to dynamic research of small biomolecules without markers.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by the deposition of misfolded proteins leading to the death of motor neurons. Several ALS-associated proteins, including TAR DNA-binding protein 43 (TDP-43) and Superoxide dismutase-1 (SOD-1), have been linked to small extracellular vesicles (EVs). However, the role of these EVs and their cargo in ALS patients, prior to treatment intervention, has not been investigated. This study aims to identify the earliest protein changes facilitated by EVs in ALS by examining the serum of recently diagnosed ALS patients. EVs were isolated from the serum of ALS (n = 25) and healthy control (HC, n = 9) patients before undergoing proteomics analysis. This resulted in the identification of a panel of 9 significantly up-regulated proteins and included haptoglobin and hemoglobin subunits, complement, and afamin, which are involved in pathways including heme homeostasis and autophagy. The identification of haptoglobin in ALS serum EVs suggests it has potential as an early diagnostic biomarker whilst activation of autophagy pathways suggests early recruitment of clearance pathways in ALS. This study uncovers the processes and proteins facilitated through small EVs in the initial stages of ALS. Proteomics data are available via ProteomeXchange with identifier PXD036652. Statement of significance of the studyThe role of small EVs, which are involved in cell-to-cell communication, and their cargo in the initiation of ALS has not been investigated. This study is the first to identify the earliest protein changes occurring in ALS through small EV facilitation. This study examined serum from newly diagnosed ALS patients, prior to treatment intervention. Therefore, the EVs, isolated from ALS and healthy control patients, captured novel ALS associated changes without confoundment from medication, which could mask early changes. A panel of 9 statistically up-regulated proteins was identified after mass spectrometry analysis. These included: haptoglobin and hemoglobin subunits, complement, and afamin. The identification of up-regulated levels of these proteins in the ALS serum EVs suggests they have potential as diagnostic biomarkers whilst identifying pathways including chaperone mediated autophagy (CMA) and microautophagy suggests early recruitment of clearance pathways in ALS. Therefore, this study uncovered the proteins being facilitated through small EVs in the initial stages of ALS.

The field of early biomarker discovery is characterized by a lack of consensus on the choice of statistical methodology, which may impede later progress towards clinically useful biomarkers. The Receiver-Operator Characteristic (ROC) is a de facto standard for determining the accuracy of In Vitro Diagnostic (IVD) devices. We demonstrate a biomarker discovery study design that achieves endpoint-consistency through use of ROC analysis from study objectives through sample procurement plan, sample size determination, to data analysis. Through simulations, the investigator can be informed on suitable study size to demonstrate an effect superior to the current best clinically used biomarker for the purpose. The study design is illustrated using proteomic data of newly diagnosed cancer cases and concurrent external controls, and statistically significant composite biomarkers are validated using independent data generated using the same proteomic analysis method. Intriguingly, commonly used feature selection methods do not identify the same composite biomarkers from the same data, and their selections show limited overlap with the ROC-based analysis. The proposed approach can facilitate translation of scientific discoveries into regulatory approved biomarker tests fit for use in clinical medicine.

Current machine learning-based Alzheimers disease (AD) diagnosis methods fail to explore the distinctive brain patterns across different AD stages, lacking the ability to trace the trajectory of AD progression. This limitation can lead to an oversight of the pathological mechanisms of AD and suboptimal performance in AD diagnosis. To overcome this challenge, this paper proposes a novel stage-aware brain graph learning model. Particularly, we analyze the different brain patterns of each AD stage in terms of stage-specific brain graphs. We design a Stage Feature-enhanced Graph Contrastive Learning method, named SF-GCL, utilizing specific features within each AD stage to perform graph augmentation, thereby effectively capturing differences between stages. Significantly, this study unveils the specific brain patterns corresponding to each AD stage, showing great potential in tracing the trajectory of brain degeneration. Experimental results on a real-world dataset demonstrate the superiority of our model.

BackgroundArtificial intelligence applications for preventive stress monitoring remain limited by dependence on expensive continuous biosensors. We developed and validated an AI-based framework for automated phenotyping of stress-energy responses from accessible smartphone-based circadian temperature monitoring and cognitive-autonomic assessments, enabling scalable population health monitoring without wearable devices. MethodsThis 15-day prospective observational study collected 239 daily observations from 16 adults (age 58.35{+/-}7.8 years; 100% adherence). Daily axillary temperature oscillation ({Delta}T = night-minus-morning), a 6-item cognitive-autonomic index (MiSBIE Brief-6), morning light exposure, and screen time were analyzed using unsupervised K-means clustering. A Composite Stress Load (CSL) index integrating subjective stress (40%), thermal variance (30%), and pain (30%) was computed. Cluster validation employed silhouette analysis, Gap statistics, and Hopkins test. ResultsUnsupervised machine learning identified three distinct stress-energy phenotypes (k=3; silhouette=0.75; Gap p<0.001): Cluster 1 (Low {Delta}T/High Recovery; n=87; {Delta}T=-0.19{+/-}0.09{degrees}C; MiSBIE-delta=+1.84{+/-}0.62), Cluster 2 (Neutral/Intermediate; n=98; {Delta}T=+0.00{+/-}0.07{degrees}C; MiSBIE-delta=+1.12{+/-}0.51), and Cluster 3 (High {Delta}T/Minimal Recovery; n=54; {Delta}T=+0.21{+/-}0.10{degrees}C; MiSBIE-delta=+0.41{+/-}0.68). Elevated {Delta}T strongly correlated with CSL (r=0.52; p<0.001). AI-derived phenotypes predicted 78% of thermal stability variance (R2=0.78; p<0.001). Morning light >15 minutes reduced {Delta}T ({beta}=-0.24{degrees}C; p=0.002). ConclusionsThis validated AI framework achieves automated stress phenotyping at <$5 per participant versus $200-500 for wearables, supporting early identification of elevated allostatic load aligned with the Energy Resistance Principle. Longitudinal phenotype tracking enables predictive early warning and individualized exercise optimization in real-world settings, advancing health equity in preventive monitoring for resource-limited contexts. Integration into public health systems serving millions (e.g., Brazils SUS) could enable anticipatory care delivery, improving quality of life through early intervention before clinical deterioration HighlightsO_LIK-means clustering identified 3 autonomic phenotypes (silhouette=0.75) C_LIO_LI{Delta}T>0{degrees}C predicts stress load elevation (r=0.52, p<0.001) C_LIO_LIMiSBIE-6 explains 78% of thermal variance (R2=0.78, p<0.001) C_LIO_LIMorning light reduces {Delta}T by 0.24{degrees}C; screens increase it 0.19{degrees}C/hour C_LIO_LISmartphone-based framework enables scalable stress phenotyping[AA1] C_LI

Suspected colorectal cancer (CRC) referrals based on non-specific symptoms currently lead to large numbers of patients being referred for invasive investigations and poor yield in cancer detection. Secondary care diagnostics, particularly endoscopy, struggle to meet the ever-increasing demand and patients face lengthy waits from the point of referral. Here we propose a blood test utilising high-throughput Raman spectroscopy and machine learning as an accurate triage tool. We present results from the first mixed methods clinical validation study of its kind, evaluating the ability of the test to perform in its target population of primary care patients, and its acceptability to those administering and receiving the test. The test was able to accurately rule out cancer with a negative predictive value of 98.0%. This performance could reduce the number of invasive diagnostic procedures in the cohort by at least 47%. Collectively, our findings promote a novel, non-invasive solution to triage CRC referrals with potential to reduce patient anxiety, accelerate access to treatment and improve outcomes.

Growth and aging are fundamental features of animal life. The march from fertilization to oblivion comes in enormous variety: days and hundreds of cells for nematodes, decades and trillions of cells for humans.1-4 Since Verhulst (18385) proposed the Logistic Equation - exponential growth with a countervailing linear decline in rate - biologists have searched for ever better density-dependent growth equations,6-12 none of which accurately capture the relationship between size and time for real animals.13-15 Furthermore, while growth and aging run in parallel, whether the relationship is causal has yet to be determined. Similarly unknown has been the reason behind the exponential Force of Mortality, described by Gompertz in 1825 for all-cause mortality16 and reported by Levin et al. in 2020 for COVID-19.17 Here we report that examination in units of numbers of cells, N, Cellular Phylodynamic Analysis,6 reveals that growth, lifespan, and mortality, are linked to the reduction in the fraction of cells dividing, occurring by a simple expression, the Universal Mitotic Fraction Equation. Lifespan is correlated with an age when fewer than one-in-a-thousand cells are dividing, quantifying the long-appreciated mechanism of aging, the failure of cells to be rejuvenated by dilution with new materials made and DNA repaired at mitosis.29-31 These observations provide practical mathematical tools for comprehending and managing the challenges of growth and aging, for such tasks as deciphering COVID-19 lethality and its amelioration by vaccination.

BackgroundStorage time influence concentration levels of blood biomarker. This study aimed to assess the effect of water intake prior sampling and storage time on protein concentration, enzyme AChE activity, inhibitor efficacy and to build an efficient inhibitor calibration curve in healthy individuals. MethodsData analysis was performed on 11 participants. Study utilizes substrate acetylcholine chloride and inhibitors BW284c51 (0.01mM) and GUK-987 (0.1mM). Calibration curve ranging from 10-1 to 10-38 mM was build for inhibitor GUK-987 and GDK-510. Data analysis is carried out with Microsoft Excel 2007. Data analysis was performed via IBM SPSS Statistical Software v23.0. Descriptive statistics and parametric statistical tests were assessed for 0, 90, 91 and 92 days of storage in Plasma and Erythrocyte samples. Workflow of building calibration curve and the most efficient inhibition concentration is assessed. ResultsWater intake and storage time have effect on in vitro protein concentration, activation/inhibition of enzyme AChE activity in Plasma and Erythrocyte samples. However, 100% inhibitor efficacy is maintained for inhibitor GUK-987 in Plasma samples and inhibitor BW284c51 in Erythrocyte samples. The most efficient inhibitor concentration is determined. ConclutionSignificant changes and variable association have been estimated between protein concentration, activation/inhibition of enzyme AChE activity, as a cause of water intake and storage time. Taking all these factors into account for further research is important for disease prevention and human wellbeing.

Background & AimsThe current shortage of livers for transplantation has increased the use of organs sourced from donation after circulatory death (DCD). These organs are prone to higher incidence of graft failure, but the underlying mechanisms are largely unknown. Here we aimed to find biomarkers of liver function before transplantation to better inform clinical evaluation. MethodsMatched pre- and post-transplant liver biopsies from DCD (n=24) and donation after brain death (DBD, n=70) were collected. Liver biopsies were analysed using mass spectroscopy molecular phenotyping. First, a discrimination analysis DCD vs DBD was used to parse metabolites associated to DCD. Then a data-driven approach was used to predict Immediate Graft Function (IGF). The metabolites were tested in models to predict survival. ResultsFive metabolites in the purine pathway were selected and investigated. The ratios of: adenine monophosphate (AMP), adenine, adenosine and hypoxanthine to urate, differed between DBD and DCD biopsies at pre-transplantation stage (q<0.05). The ratios of AMP and adenine to urate also differed in biopsies from recipients undergoing IGF (q<0.05). Using random forest a panel composed by alanine aminotransferase (ALT) and AMP, adenine, hypoxanthine ratio to urate predicted IGF with AUC 0.84 (95% CI [0.71, 0.97]). In comparison AUC 0.71 (95%CI [0.52, 0.90]) was achieved by clinical measures. Survival analysis revealed that the metabolite classifier could stratify 6-year survival outcomes (p = 0.0073) while clinical data and donor class could not. ConclusionsAt liver pre-transplantation stage, a panel composed of purine metabolites and ALT in tissue could improve prediction of IGF and survival. Lay summaryNew liver function biomarkers could help clinicians assess livers before transplantation. Purines are small molecules that are found in healthy livers, and in this work we found that their levels changed critically in livers from cardiac death donors. Measuring them before transplantation improved the prediction of the livers immediate graft function. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=65 SRC="FIGDIR/small/19005629v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1e3c207org.highwire.dtl.DTLVardef@1d760b0org.highwire.dtl.DTLVardef@10cf605org.highwire.dtl.DTLVardef@1ebebd5_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThe ratios of purine metabolites to urate differ between DCD and DBD in liver tissue at pre-transplantation. C_LIO_LIThe ratios of purine metabolites to urate and ALT pre-transplantation can improve prediction of IGF after transplantation. C_LIO_LIPurine metabolites ratios to urate stratified 6-year survival outcome better than clinical data and donor class. C_LI

BackgroundProgression of chronic lung disease often leads to the requirement for a lung transplant (LTX). Despite improvements in short-term survival after LTX, chronic lung allograft dysfunction (CLAD) remains a critical challenge for long-term survival. This study investigates the relationship between the metabolome of bronchoalveolar lavage fluid (BALF) from subjects post-LTX with underlying lung disease and CLAD severity. MethodsUntargeted LC-MS/MS metabolomics was performed on 960 BALF samples collected over 10 years from LTX recipients with alpha-1-antitrypsin disease (AATD, n=22), cystic fibrosis (CF, n=46), chronic obstructive pulmonary disease (COPD, n = 79) or pulmonary fibrosis (PF, n=47). Datasets were analyzed using machine learning and multivariate statistics for associations with underlying disease and final CLAD severity. ResultsBALF metabolomes varied by underlying disease state, with AATD LT recipients being particularly distinctive (PERMANOVA, p=0.001). We also found a significant association with the final CLAD severity score (PERMANOVA, p=0.001), especially those with underlying CF. Association with CLAD severity was driven by changes in phosphoethanolamine (PE) and phosphocholine lipids that increased and decreased, respectively, and metabolites from the bacterial pathogen Pseudomonas aeruginosa. P. aeruginosa siderophores, quorum-sensing quinolones, and phenazines were detected in BALF, and 4-hydroxy-2-heptylquinoline (HHQ) was predictive of the final CLAD stage in samples from CF patients (R=0.34; p[&le;]0.01). Relationships between CLAD stage and P. aeruginosa metabolites were especially strong in those with CF, where 61% of subjects had at least one of these metabolites in their first BALF sample after transplant. ConclusionsBALF metabolomes after LTX are distinctive based on the underlying disease and reflect final CLAD stage. In those with more severe outcomes, there is a lipid transition from PC to predominantly PE phospholipids. The association of P. aeruginosa metabolites with CLAD stages in LTX recipients with CF indicates this bacterium and its metabolites may be drivers of allograft dysfunction. Key messagesDespite the high prevalence of CLAD among LTX recipients, its pathology is not well understood, and no single molecular indicator is known to predict disease onset. Our machine learning metabolomic-based approach allowed us to identify patterns associated with a shift in the lipid metabolism and bacterial metabolites predicting CLAD onset in CF. This study provides a better understanding about the progression of allograft dysfunction through the molecular transitions within the transplanted lung from the host and bacterial pathogens.

There have been concerning reports about people experiencing new onset persistent complications (greater than 30 days) following approved SARS-CoV-2 vaccines (BNT162b2 (Pfizer), mRNA-1273 (Moderna), Janssen (Johnson and Johnson), and ChAdOx1 nCoV-19 (AstraZeneca)). We sought to determine the immunologic abnormalities in these patients and to investigate whether the potential etiology was similar to Post-Acute Sequalae of COVID (PASC), or long COVID. We studied 50 individuals who received one of the approved COVID-19 vaccines and who experienced new onset PASC-like symptoms along with 45 individuals post-vaccination without symptoms as controls. We performed multiplex cytokine/chemokine profiling with machine learning as well as SARS-CoV-2 S1 protein detection on CD16+ monocyte subsets using flow cytometry and mass spectrometry. We determined that post-vaccination individuals with PASC- like symptoms had similar symptoms to PASC patients. When analyzing their immune profile, Post-vaccination individuals had statistically significant elevations of sCD40L (p<0.001), CCL5 (p=0.017), IL-6 (p=0.043), and IL-8 (p=0.022). Machine learning characterized these individuals as PASC using previously developed algorithms. Of the S1 positive post-vaccination patients, we demonstrated by liquid chromatography/ mass spectrometry that these CD16+ cells from post-vaccination patients from all 4 vaccine manufacturers contained S1, S1 mutant and S2 peptide sequences. Post-COVID vaccination individuals with PASC-like symptoms exhibit markers of platelet activation and pro-inflammatory cytokine production, which may be driven by the persistence of SARS-CoV-2 S1 proteins in intermediate and non-classical monocytes. The data from this study also cannot make any inferences on epidemiology and prevalence for persistent post-COVID vaccine symptoms. Thus, further studies and research need to be done to understand the risk factors, likelihood and prevalence of these symptoms. SummarySARS CoV-2 S1 Protein in CD16+ Monocytes Post-Vaccination

Aging manifests as progressive deterioration in cellular and systemic homeostasis, requiring systems-level perspectives to understand the gradual molecular dysregulation of underlying biological processes. Here, we report systems-level changes in the molecular regulation of biological processes under multiple lifespan-extending interventions in mice and across age in humans. In mouse cohorts, Differential Rank Conservation (DIRAC) analyses of liver proteomics and transcriptomics show that mechanistically distinct prolongevity interventions tighten the regulation of aging-related biological modules, including fatty acid metabolism and inflammation processes. An integrated analysis of liver transcriptomics with mouse genome-scale metabolic model supports the shifts in fatty acid metabolism. Additionally, the difference in DIRAC patterns between proteins and transcripts suggests biological modules which may be tightly regulated via cap-independent translation. In a human cohort spanning the majority of the adult lifespan, DIRAC analyses of blood proteomics and metabolomics demonstrate that regulation of biological modules does not monotonically loosen with age; instead, the regulatory patterns shift according to both chronological and biological ages. Our findings highlight the power of systems-level approaches to identifying and characterizing the biological processes involved in aging and longevity.

Two control mechanisms are relevant to perform an internal quality assurance: a permissible limit LSMC applied to single measures of control samples and a retrospective statistical analysis to detect increased imprecision and baseline drifts. A common statistical metric is the root mean square (total) deviation (RMSD/RMSTD). To focus on recent changes under low-frequent sampling conditions, the monitored amount of retrospective data is usually very small. Unfortunately, the calculated RMSTD of a small data set with n<50 samples has a significant statistical uncertainty that needs to be considered in adequate limit definitions. In particular, the minimum reasonable limit LRMSTD(n), applied to the RMSTD of a series of n samples, decreases from LSMC (e.g., 2.33*standard_deviation+bias) for n=1 towards Ltrue_RMSTD for n[-&gt;]{infty} (long-term statistics). Two mathematical approaches were derived to reliably estimate an optimal function to adjust LRMSTD(n) to small sample sizes. This knowledge led to the development of a new quality-control method: the Statistical Monitoring by Adaptive RMSTD Tests (SMART). SMART requires just one mandatory limit (either LSMC or Ltrue_RMSTD) per analyte. By definition of up to 7 possible alert levels, SMART can early recognize and evaluate both the significance of a single outlier and establishing critical trends or shifts in recent SMC data. SMART is intended to efficiently monitor and evaluate small amounts of control data.

BackgroundDue to its antioxidative, anti-inflammatory, anti-apoptosis, and antifatigue properties, molecular hydrogen (H2) is potentially a novel therapeutic gas for acute coronavirus disease 2019 (COVID-19) patients. AimTo determine the efficacy and safety profile of hydrogen rich water (HRW) to reduce the risk of progression of COVID-19. Design and settingsWe conducted a phase 3, triple-blind, randomized, placebo-controlled trial to evaluate treatment with HRW started within 5 days after the onset of signs or symptoms in primary care patients with mild-to-moderate, laboratory-confirmed COVID-19 and at least one risk factor for severe COVID-19 illness. MethodParticipants were randomly assigned to receive HRW or placebo twice daily for 21 days. The composite primary endpoint was the incidence of clinical worsening (dyspnea, fatigue) associated with a need for oxygen therapy, hospitalization or death at day-14; the incidence of adverse events was the primary safety end point. ResultsA total of 675 participants were followed up until day-30. 337 in the HRW group and 338 in the placebo group. Baseline characteristics were similar in the two groups. HRW was not superior to placebo in preventing clinical worsening at day-14: in H2 group, 46.1% met a clinical deterioration, 43.5% in the placebo group, Hazard Ratio 1.09, 90% confidence interval [0.90-1.31]. One death was reported in the H2 group and 2 in the placebo group at day-30. Adverse events were reported in 91 (27%) and 89 (26.2%) participants respectively. ConclusionTwice-daily ingestion of HRW from the onset of COVID-19 symptoms for 21 days did not reduce clinical worsening. How this fits inO_LIOnly a few molecules specially developed against SARS-CoV-2 can limit impact of COVID-19 (vaccines, monoclonal antibodies or antiviral drugs) C_LIO_LIUsing their multiple properties, H2 may play a key role in preventing the severe and post-acute forms of COVID-19 C_LIO_LITaking twice daily Hydrogen Rich Water (HRW) was not efficacious to prevent severe COVID-19 in at risk COVID-19 patients. C_LIO_LIHRW confirmed a very safe profil C_LI