Reimagining Day Rehabilitation For Frailty and Neurodegenerative Conditions through the integrated Rehabilitation and EnAblement Program (iREAP).

Background: integrated Rehabilitation and EnAblement Program (iREAP) is an innovative redesign of the traditional day rehabilitation model, providing an anticipatory, early assessment and intervention program that manages care of community-dwelling older people with complex needs. It coordinates access to disciplines across medical, allied health and nursing, with a self-management focus, partnering with primary health in an integrated approach. Objective: This observational study reviews the effectiveness of iREAP on frailty, patient activation, quality of life and physical outcome measures on older people at risk of, or experiencing falls and frailty, or with neurodegenerative conditions, including Parkinson's Disease. Methods: 99 participants completed the eight-week multidisciplinary program. Patient outcome measures included Rockwood Clinical Frailty Scale, quality of life measures, Patient Activation Measure, Timed Up and Go, 6 Minute Walk Test and Berg Balance Scale. Results: On completion of iREAP, participants displayed improvements in their Rockwood Clinical Frailty Scores (mildly frail to vulnerable), 'patient activation' (55.08 to 60.61), quality of life (Parkinson's Disease Questionnaire-39, 49.93 to 47.16; WHO Quality of Life - Bref physical domain, 21 to 22.7) and physical measures including balance (44 to 49/56 Berg Balance scale) and mobility (294 m to 336 m, 6-minute walk test). Falls were not reduced at twelve months post-program (3.40 to 2.01). Conclusion: iREAP is an interdisciplinary, early assessment and intervention program with the potential to reverse frailty and improve quality of life for complex older patients. This paper offers a platform for future research, given the paucity of evidence reviewing the efficacy of integrated anticipatory models of care in older adults with complex needs.

Plasma-derived extracellular vesicles (EVs) as biomarkers of sepsis in burn patients via label-free Raman spectroscopy.

Sepsis following burn trauma is a global complication with high mortality, with ∼60% of burn patient deaths resulting from infectious complications. Diagnosing sepsis is complicated by confounding clinical manifestations of the burn injury, and current biomarkers lack the sensitivity and specificity required for prompt treatment. There is a strong rationale to assess circulating extracellular vesicles (EVs) from patient liquid biopsy as sepsis biomarkers due to their release by pathogens from bacterial biofilms and roles in the subsequent immune response. This study applies Raman spectroscopy to patient plasma-derived EVs for rapid, sensitive, and specific detection of sepsis in burn patients, achieving 97.5% sensitivity and 90.0% specificity. Furthermore, spectral differences between septic and non-septic burn patient EVs could be traced to specific glycoconjugates of bacterial strains associated with sepsis morbidity. This work illustrates the potential application of EVs as biomarkers in clinical burn trauma care and establishes Raman analysis as a fast, label-free method to specifically identify features of bacterial EVs relevant to infection amongst the host background.

Regulation of host metabolism and defense strategies to survive neonatal infection.

Two distinct defense strategies, disease resistance (DR) and disease tolerance (DT), enable a host to survive infectious diseases. Newborns, constrained by limited energy reserves, predominantly rely on DT to cope with infection. However, this approach may fail when pathogen levels surpass a critical threshold, prompting a shift to DR that can lead to dysregulated immune responses and sepsis. The mechanisms governing the interplay between DR and DT in newborns remain poorly understood. Here, we compare metabolic traits and defense strategies between survivors and non-survivors in Staphylococcus epidermidis (S. epidermidis)-infected preterm piglets, mimicking infection in preterm infants. Compared to non-survivors, survivors displayed elevated DR during the initial phase of infection, followed by stronger DT in later stages. In contrast, non-survivors showed clear signs of respiratory and metabolic acidosis and hyperglycemia, together with exaggerated inflammation and organ dysfunctions. Hepatic transcriptomics revealed a strong association between the DT phenotype and heightened oxidative phosphorylation in survivors, coupled with suppressed glycolysis and immune signaling. Plasma metabolomics confirmed the findings of metabolic regulations associated with DT phenotype in survivors. Our study suggests a significant association between the initial DR and subsequent DT, which collectively contributes to improved infection survival. The regulation of metabolic processes that optimize the timing and balance between DR and DT holds significant potential for developing novel therapeutic strategies for neonatal infection.

A new possibilistic-based clustering method for probability density functions and its application to detecting abnormal elements.

Cluster analysis can also detect abnormalities besides building a basis for identifying elements into clusters. Detecting abnormalities is a highly developed feature in the field of unsupervised learning. However, existing studies have mainly focused on discrete data, not probability density functions. This paper enables a possibilistic approach to solving the clustering for probability density functions dealing with abnormal elements. First, the data are extracted using the density function. Then, they are passed through the proposed algorithm to produce a possibilistic partition. Finally, a decision rule is established to recognize which function is abnormal. We compare the proposed algorithm with baseline algorithms in clustering PDFs, such as k-means, FCF, and Self-Updated Clustering. The results of three numerical examples applied to the image are typical for this new method. Furthermore, The proposed algorithm reaches accuracy at 100% over simulated benchmark data and outperforms baseline methods. Additionally, two last examples apply to image data reaching G-mean up from 96 to 100% (Sensitivity: 92-100% and Specificity: 100%). The proposed method can be researched and used to understand the internal structures of big data in the digital age through the probability density functions.

Aortic Sarcoma Patients with Bone Metastasis.

BACKGROUND: Metastases to the bone of aortic sarcoma include osteolytic and nonosteolytic lesions. This study aims to review the clinical symptoms, the sites, and diagnostic methods of bone metastases and to compare the osteolytic and nonosteolytic metastases of patients with aortic sarcoma. METHODS: A systematic search was conducted in PubMed and scientific journals published from 1972 to 2022. Database included reports of aortic sarcomas with bone metastasis published in English and in French. Characteristics of patients were analyzed with chi-square test or Fisher's exact test between lytic and sclerotic bone metastases. RESULTS: In 29 patients with bone destruction, the symptoms of low back pain and claudication were observed in 10 (34.5%) and 9 cases (31%), respectively. Acute ischemia of the legs and arms accounted for 7 cases (24.1%). There were 4 cases with hypertension (13.8%) and 5 cases with chest pain or abdominal pain or epigastric pain (17.2%). Metastases to the vertebrae, pelvis, and femur were observed in 14 (48.3%), 12 (41.4%), and 11 cases (37.9%), respectively. Osteolytic lesions were detected at the time of diagnosis in 16/29 (55.2%) cases. In 27 aortic sarcoma patients with sclerotic bone metastases, symptoms of hypertension were observed in 10 (37.0%), of back pain in 7 (25.9%), of chest pain or abdominal pain in 5 cases (18.5%). Acute ischemia of the leg occurred in 6 cases (22.2%). Metastases to the vertebrae, bone, pelvis, and femur were observed in 10 (37.0%), 9 (33.3%), 7 (25.9%), and 6 cases (22.2%), respectively. The sign of claudication and methods for detected bone destruction by X-rays were the difference between osteolytic and nonosteolytic metastases of aortic sarcoma (P = 0.019; P = 0.001), respectively. CONCLUSIONS: Back pain is a common symptom of aortic sarcoma with bone metastasis. The sign of intermittent claudication is the difference between osteolytic and nonosteolytic metastases of aortic sarcoma. Bone destruction occurred in all bones, but mainly in vertebrae, pelvis, and femur. Methods for detection of bone destruction are mainly by X-rays or computed tomography (CT). Bone destruction was an important sign to detect aortic sarcoma. Sclerotic bone metastases occurred mainly in vertebrae, pelvis, bone, and femur. The detection of sclerotic bone metastases is based on magnetic resonance imaging, positron emission tomography/CT, and autopsy.

Genome-wide association and polygenic risk score estimation of type 2 diabetes mellitus in Kinh Vietnamese-A pilot study.

A genome-wide association study (GWAS) is a powerful tool in investigating genetic contribution, which is a crucial factor in the development of complex multifactorial diseases, such as type 2 diabetes mellitus. Type 2 diabetes mellitus is a major healthcare burden in the Western Pacific region; however, there is limited availability of genetic-associated data for type 2 diabetes in Southeast Asia, especially among the Kinh Vietnamese population. This lack of information exacerbates global healthcare disparities. In this study, 997 Kinh Vietnamese individuals (503 with type 2 diabetes and 494 controls) were prospectively recruited and their clinical and paraclinical information was recorded. DNA samples were collected and whole genome genotyping was performed. Standard quality control and genetic imputation using the 1000 Genomes database were executed. A polygenic risk score for type 2 diabetes was generated in different models using East Asian, European, and mix ancestry GWAS summary statistics as training datasets. After quality control and genetic imputation, 107 polymorphisms reached suggestive statistical significance for GWAS (≤5 × 10-6) and rs11079784 was one of the potential markers strongly associated with type 2 diabetes in the studied population. The best polygenic risk score model predicting type 2 diabetes mellitus had AUC = 0.70 (95% confidence interval = 0.62-0.77) based on a mix of ancestral GWAS summary statistics. These data show promising results for genetic association with a polygenic risk score estimation in the Kinh Vietnamese population; the results also highlight the essential role of population diversity in a GWAS of type 2 diabetes mellitus.

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

Time-course cross-species transcriptomics reveals conserved hepatotoxicity pathways induced by repeated administration of cyclosporine A.

Cyclosporine A (CsA) has shown efficacy against immunity-related diseases despite its toxicity in various organs, including the liver, emphasizing the need to elucidate its underlying hepatotoxicity mechanism. This study aimed to capture the alterations in genome-wide expression over time and the subsequent perturbations of corresponding pathways across species. Six data from humans, mice, and rats, including animal liver tissue, human liver microtissues, and two liver cell lines exposed to CsA toxic dose, were used. The microtissue exposed to CsA for 10 d was analyzed to obtain dynamically differentially expressed genes (DEGs). Single-time points data at 1, 3, 5, 7, and 28 d of different species were used to provide additional evidence. Using liver microtissue-based longitudinal design, DEGs that were consistently up- or down-regulated over time were captured, and the well-known mechanism involved in CsA toxicity was elucidated. Thirty DEGs that consistently changed in longitudinal data were also altered in 28-d rat in-house data with concordant expression. Some genes (e.g. TUBB2A, PLIN2, APOB) showed good concordance with identified DEGs in 1-d and 7-d mouse data. Pathway analysis revealed up-regulations of protein processing, asparagine N-linked glycosylation, and cargo concentration in the endoplasmic reticulum. Furthermore, the down-regulations of pathways related to biological oxidations and metabolite and lipid metabolism were elucidated. These pathways were also enriched in single-time-point data and conserved across species, implying their biological significance and generalizability. Overall, the human organoids-based longitudinal design coupled with cross-species validation provides temporal molecular change tracking, aiding mechanistic elucidation and biologically relevant biomarker discovery.

Plasma-derived Extracellular Vesicles (EVs) as Biomarkers of Sepsis in Burn Patients via Label-free Raman Spectroscopy.

Sepsis following burn trauma is a global complication with high mortality, with ~60% of burn patient deaths resulting from infectious complications. Sepsis diagnosis is complicated by confounding clinical manifestations of the burn injury, and current biomarkers markers lack the sensitivity and specificity required for prompt treatment. Circulating extracellular vesicles (EVs) from patient liquid biopsy as biomarkers of sepsis due to their release by pathogens from bacterial biofilms and roles in subsequent immune response. This study applies Raman spectroscopy to patient plasma derived EVs for rapid, sensitive, and specific detection of sepsis in burn patients, achieving 97.5% sensitivity and 90.0% specificity. Furthermore, spectral differences between septic and non-septic burn patient EVs could be traced to specific glycoconjugates of bacterial strains associated with sepsis morbidity. This work illustrates the potential application of EVs as biomarkers in clinical burn trauma care, and establishes Raman analysis as a fast, label-free method to specifically identify features of bacterial EVs relevant to infection amongst the host background.

A comparison of alternative ranking methods in two-stage clinical trials with multiple interventions: An application to the anxiolysis for laceration repair in children trial.

BACKGROUND/AIMS: Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care. METHODS: We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (Pbest) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either Pbest or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either Pbest or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial. RESULTS: As the number of interventions increases, the SUCRA results in a higher predictive power compared with Pbest. Using Pbest results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using Pbest and the SUCRA, respectively. Thus, we chose Pbest as the ranking method for the ALICE trial. CONCLUSION: Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than Pbest. Future work should consider whether other ranking methods may also be relevant for clinical trial design.

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

Sexually Transmitted Infection Co-testing in a Large Urban Emergency Department.

Introduction: The incidence of sexually transmitted infections (STI) increased in the United States between 2017-2021. There is limited data describing STI co-testing practices and the prevalence of STI co-infections in emergency departments (ED). In this study, we aimed to describe the prevalence of co-testing and co-infection of HIV, hepatitis C virus (HCV), syphilis, gonorrhea, and chlamydia, in a large, academic ED. Methods: This was a single-center, retrospective cross-sectional study of ED patients tested for HIV, HCV, syphilis, gonorrhea or chlamydia between November 27, 2018-May 26, 2019. In 2018, the study institution implemented an ED-based infectious diseases screening program in which any patient being tested for gonorrhea/chlamydia was eligible for opt-out syphilis screening, and any patient 18-64 years who was having blood drawn for any clinical purpose was eligible for opt-out HIV and HCV screening. We analyzed data from all ED patients ≥13 years who had undergone STI testing. The outcomes of interest included prevalence of STI testing/co-testing and the prevalence of STI infection/co-infection. We describe data with simple descriptive statistics. Results: During the study period there were 30,767 ED encounters for patients ≥13 years (mean age: 43 ± 14 years, 52% female), and 7,866 (26%) were tested for at least one of HIV, HCV, syphilis, gonorrhea, or chlamydia. We observed the following testing frequencies (and prevalence of infection): HCV, 7,539 (5.0%); HIV, 7,359 (0.9%); gonorrhea, 574 (6.1%); chlamydia, 574 (9.8%); and syphilis, 420 (10.5%). Infectious etiologies with universal testing protocols (HIV and HCV) made up the majority of STI testing. In patients with syphilis, co-infection with chlamydia (21%, 9/44) and HIV (9%, 4/44) was high. In patients with gonorrhea, co-infection with chlamydia (23%, 8/35) and syphilis (9%, 3/35) was high, and in patients with chlamydia, co-infection with syphilis (16%, 9/56) and gonorrhea (14%, 8/56) was high. Patients with HCV had low co-infection proportions (<2%). Conclusion: Prevalence of STI co-testing was low among patients with clinical suspicion for STIs; however, co-infection prevalence was high in several co-infection pairings. Future efforts are needed to improve STI co-testing rates among high-risk individuals.

Association between poor sleep and mental health issues in Indigenous communities across the globe: a systematic review.

Study Objectives: Evidence from studies among non-Indigenous populations has established the association of poor sleep to mental health issues and supported how improving sleep could reduce the risk of mental ill health. In contrast, for Indigenous people, who experience disproportionate rates of mental ill health, the association between sleep and mental health and the potential of sleep health in reducing the risk and severity of mental health issues have never been fully reviewed. Considering the literature gap, this review assesses the association between sleep and mental health in Indigenous people. Methods: Following PRISMA guidelines, a study was submitted to the PROSPERO database for registration (293798) prior to commencing the review. Then academic databases were searched for relevant studies published up till 19 February 2023. Studies with quantitative data on sleep and mental health association in Indigenous people were included and a narrative review/synthesis was conducted. Results: Seven studies, using carer/self-reports (six cross-sectional, one longitudinal) among three Indigenous groups (N = 3066) met the inclusion criteria. In Indigenous Australian children, arousal problems were associated with aggression, and withdrawn behavior, while early bedtime was associated with a lower risk of behavioral problems. In Native American young people, insomnia symptoms were associated with depressive symptoms in adults, short sleep was associated with affective disorders. Clinical sleep issues, i.e. restless leg and apnea, were associated with depression. In Amerindian/Mestizo adults, restless leg syndrome was associated with depression and anxiety. Overall, findings report the prevalence of poor sleep and mental health issues among Indigenous communities across the globe. Six studies scored "moderate quality" and one study scored "high quality" in quality assessment. Conclusions: While there is limited research available, our finding suggests an association between poor sleep and mental health issues in Indigenous people. Further investigation of the potential role of, and investing in, sleep health could help support mental health.

A novel multi-stage enrichment workflow and comprehensive characterization for HEK293F-derived extracellular vesicles.

Extracellular vesicles (EVs) are emerging as a promising drug delivery vehicle as they are biocompatible and capable of targeted delivery. However, clinical translation of EVs remains challenging due to the lack of standardized and scalable manufacturing protocols to consistently isolate small EVs (sEVs) with both high yield and high purity. The heterogenous nature of sEVs leading to unknown composition of biocargos causes further pushback due to safety concerns. In order to address these issues, we developed a robust quality-controlled multi-stage process to produce and isolate sEVs from human embryonic kidney HEK293F cells. We then compared different 2-step and 3-step workflows for eliminating protein impurities and cell-free nucleic acids to meet acceptable limits of regulatory authorities. Our results showed that sEV production was maximized when HEK293F cells were grown at high-density stationary phase in semi-continuous culture. The novel 3-step workflow combining tangential flow filtration, sucrose-cushion ultracentrifugation and bind-elute size-exclusion chromatography outperformed other methods in sEV purity while still preserved high yield and particle integrity. The purified HEK293F-derived sEVs were thoroughly characterized for identity including sub-population analysis, content profiling including proteomics and miRNA sequencing, and demonstrated excellent preclinical safety profile in both in-vitro and in-vivo testing. Our rigorous enrichment workflow and comprehensive characterization will help advance the development of EVs, particularly HEK293F-derived sEVs, to be safe and reliable drug carriers for therapeutic applications.

Intramedullary spinal cord abscess as postoperative complication: A case report.

Background: Intramedullary spinal cord abscesses (ISCA) can result in high morbidity and mortality if not treated in a timely manner. The incidence and outcomes of postsurgical ISCA are unknown. We present a case of a 52-year-old male patient with neurofibromatosis type 1 who developed an intramedullary spinal cord abscess after a previous resection of a cervical intradural, extramedullary neurofibroma. Case Description: A 52-year-old male with a history of neurofibromatosis type 1 had previously undergone multiple resections of cervical intradural, extramedullary neurofibromas with internal stabilization. Sixteen months after his initial surgery, he developed acute-onset interscapular pain with bilateral lower extremity pain and left hemi-body weakness. Magnetic resonance imaging (MRI) of the cervical spine demonstrated an enlarging contrast-enhancing intramedullary lesion. Surgical exploration and evacuation of the lesion were completed. Intramedullary cultures confirmed a Serratia marcescens abscess. After abscess evacuation and intravenous antibiotics, the patient's symptoms resolved. Conclusion: Given the potential for permanent neurologic damage and loss of independence with intramedullary spinal cord abscess, we advocate that clinicians maintain a high index of suspicion in the postsurgical patient. Diagnostic imaging through contrasted MRI or computed tomography myelogram should be obtained, and prompt intervention, including evacuation and/or antibiotics, should be implemented for the best chance of a favorable outcome.

Correlation between locally versus centrally processed serum procalcitonin during emergency department research evaluation of febrile infants aged 0-60 days.

Introduction: Procalcitonin (PCT) is a useful biomarker in the initial evaluation of febrile infants for serious bacterial infections (SBIs). However, PCT is not always available locally and must at times be frozen and shipped to a reference laboratory for research studies. We sought to compare PCT measured locally versus centrally at a reference laboratory during a research study. Materials and methods: This was a secondary analysis of a multicenter study of febrile infants ≤60 days evaluated for SBIs from June 2016 to April 2019. A PCT cutoff value of 0.5 ng/mL was used to stratify infants at low-versus high-risk of SBIs. Statistical analyses consisted of Spearman's correlation, Bland-Altman difference plotting, Passing-Bablok regression, Deming regression, and Fisher's exact testing at the 0.5 ng/mL threshold. Results: 241 febrile infants had PCT levels measured both locally and at the reference laboratory. PCT levels measured locally on 5 different platforms and from the frozen research samples demonstrated strong Spearman's correlation (ρ = 0.83) and had similar mean PCT values with an average relative difference of 0.02%. Eleven infants with SBIs had PCT values < 0.5 ng/mL in both the clinical and research samples. Six other infants had differences in SBI prediction based on PCT values at the 0.5 ng/mL threshold between the clinical and research platforms. Conclusions: We found no significant differences in detection of febrile infants at high risk for SBI based on locally (on multiple platforms) versus centrally processed PCT. Testing at a central reference laboratory after freezing and shipping is an accurate and reliable alternative for research studies or when rapid turnaround is not required.

Commutability and Uncertainty of Blood Hemoglobin A1c Testing Materials.

BACKGROUND: Hemoglobin A1C (HbA1C) is used to evaluate glycemic control over a three-month period. Blood matrix-based HbA1C materials are needed for quality control and evaluation of HbA1C measurements. This study investigated the commutability of blood materials (BMs) and aimed to upgrade BMs for HbA1C testing for use as proficiency test (PT) material. METHODS: We measured BMs from a DM blood donor (n = 1), an in vitro glycation procedure (n = 3), and from commercial sources (n = 2) for HbA1C in parallel with fresh unprocessed BMs (n = 3) and clinical blood samples (n = 25). Two NGSP-certified methods, including a turbidimetric and an enzymatic immunoassay, were used for HbA1C determinations. Commutability as investigated according to CLSI EP14-Ed4 guidelines. RESULTS: The commutable BMs exhibited a mean paired difference of 0% to 9% when compared to reference methods, whereas the non-commutable BMs represented a mean paired difference of 3% to 11%. Fresh, unprocessed BMs with a low HbA1C of 6.0% were more commutable than BMs with a high HbA1C. The values of HbA1C in BMs (mean and uncertainty following ISO Guide 35 for RM production) were applied to upgrade the PT material to be used as a reference material. The relative uncertainty of BM-Ndm-1 and BM-Gcb-3 were 1 and 0.4%, respectively. CONCLUSIONS: The commutability of hemoglobin in BMs is dependent on the preparation process. Blood materials with a high HbA1C content are usually less commutable versus materials with low HbA1C content when prepared by the same process. Our study showed BMs can be successfully used as quality control or PT materials.

STAT6-targeting antisense oligonucleotides against solitary fibrous tumor.

Solitary fibrous tumor (SFT) is a rare, non-hereditary soft tissue sarcoma thought to originate from fibroblastic mesenchymal stem cells. The etiology of SFT is thought to be due to an environmental intrachromosomal gene fusion between NGFI-A-binding protein 2 (NAB2) and signal transducer and activator protein 6 (STAT6) genes on chromosome 12, wherein the activation domain of STAT6 is fused with the DNA-binding domain of NAB2 resulting in the oncogenesis of SFT. All NAB2-STAT6 fusion variations discovered in SFTs contain the C-terminal of STAT6 transcript, and thus can serve as target site for antisense oligonucleotides (ASOs)-based therapies. Indeed, our in vitro studies show the STAT6 3' untranslated region (UTR)-targeting ASO (ASO 993523) was able to reduce expression of NAB2-STAT6 fusion transcripts in multiple SFT cell models with high efficiency (half-maximal inhibitory concentration: 116-300 nM). Encouragingly, in vivo treatment of SFT patient-derived xenograft mouse models with ASO 993523 resulted in acceptable tolerability profiles, reduced expression of NAB2-STAT6 fusion transcripts in xenograft tissues (21.9%), and, importantly, reduced tumor growth (32.4% decrease in tumor volume compared with the untreated control). Taken together, our study established ASO 993523 as a potential agent for the treatment of SFTs.

Cyclosporine A-induced systemic metabolic perturbations in rats: A comprehensive metabolome analysis.

A better understanding of cyclosporine A (CsA)-induced nephro- and hepatotoxicity at the molecular level is necessary for safe and effective use. Utilizing a sophisticated study design, this study explored metabolic alterations after long-term CsA treatment in vivo. Rats were exposed to CsA with 4, 10, and 25 mg/kg for 4 weeks and then sacrificed to obtain liver, kidney, urine, and serum for untargeted metabolomics analysis. Differential network analysis was conducted to explore the biological relevance of metabolites significantly altered by toxicity-induced disturbance. Dose-dependent toxicity was observed in all biospecimens. The toxic effects were characterized by alterations of metabolites related to energy metabolism and cellular membrane composition, which could lead to the cholestasis-induced accumulation of bile acids in the tissues. The unfavorable impacts were also demonstrated in the serum and urine. Intriguingly, phenylacetylglycine was increased in the kidney, urine, and serum treated with high doses versus controls. Differential correlation network analysis revealed the strong correlations of deoxycytidine and guanosine with other metabolites in the network, which highlighted the influence of repeated CsA exposure on DNA synthesis. Overall, prolonged CsA administration had system-level dose-dependent effects on the metabolome in treated rats, suggesting the need for careful usage and dose adjustment.

Metabolic phenotyping and global functional analysis facilitate metabolic signature discovery for tuberculosis treatment monitoring.

Tracking alterations in polar metabolite and lipid levels during anti-tuberculosis (TB) interventions is an emerging biomarker discovery and validation approach due to its sensitivity in capturing changes and reflecting on the host status. Here, we employed deep plasma metabolic phenotyping to explore the TB patient metabolome during three phases of treatment: at baseline, during intensive phase treatment, and upon treatment completion. Differential metabolites (DMs) in each period were determined, and the pathway-level biological alterations were explored by untargeted metabolomics-guided functional interpretations that bypassed identification. We identified 41 DMs and 39 pathways that changed during intensive phase completion. Notably, levels of certain amino acids including histidine, bile acids, and metabolites of purine metabolism were dramatically increased. The altered pathways included those involved in the metabolism of amino acids, glycerophospholipids, and purine. At the end of treatment, 44 DMs were discovered. The levels of glutamine, bile acids, and lysophosphatidylinositol significantly increased compared to baseline; the levels of carboxylates and hypotaurine declined. In addition, 37 pathways principally associated with the metabolism of amino acids, carbohydrates, and glycan altered at treatment completion. The potential of each DM for diagnosing TB was examined using a cohort consisting of TB patients, those with latent infections, and controls. Logistic regression revealed four biomarkers (taurine, methionine, glutamine, and acetyl-carnitine) that exhibited excellent performance in differential diagnosis. In conclusion, we identified metabolites that could serve as useful metabolic signatures for TB management and elucidated underlying biological processes affected by the crosstalk between host and TB pathogen during treatment.