Relationship between amide ratio assessed by Fourier-transform infrared spectroscopy: A biomarker candidate for polycythemia vera disease.

The study utilized Fourier transform infrared (FTIR) spectroscopy coupled with chemometrics to investigate protein composition and structural changes in the blood serum of patients with polycythemia vera (PV). Principal component analysis (PCA) revealed distinct biochemical properties, highlighting elevated absorbance of phospholipids, amides, and lipids in PV patients compared to healthy controls. Ratios of amide I/amide II and amide I/amide III indicated alterations in protein structures. Support vector machine analysis and receiver operating characteristic curves identified amide I as a crucial predictor of PV, achieving 100% accuracy, sensitivity, and specificity, while amide III showed a lower predictive value (70%). PCA analysis demonstrated effective differentiation between PV patients and controls, with key wavenumbers including amide II, amide I, and CH lipid vibrations. These findings underscore the potential of FTIR spectroscopy for diagnosing and monitoring PV.

Investigation of the structural changes in the hippocampus and prefrontal cortex using FTIR spectroscopy in sleep deprived mice.

Sleep is a basic, physiological requirement for living things to survive and is a process that covers one third of our lives. Melatonin is a hormone that plays an important role in the regulation of sleep. Sleep deprivation affect brain structures and functions. Sleep deprivation causes a decrease in brain activity, with particularly negative effects on the hippocampus and prefrontal cortex. Despite the essential role of protein and lipids vibrations, polysaccharides, fatty acid side chains functional groups, and ratios between amides in brain structures and functions, the brain chemical profile exposed to gentle handling sleep deprivation model versus Melatonin exposure remains unexplored. Therefore, the present study, aims to investigate a molecular profile of these regions using FTIR spectroscopy measurement's analysis based on lipidomic approach with chemometrics and multivariate analysis to evaluate changes in lipid composition in the hippocampus, prefrontal regions of the brain. In this study, C57BL/6J mice were randomly assigned to either the control or sleep deprivation group, resulting in four experimental groups: Control (C) (n = 6), Control + Melatonin (C + M) (n = 6), Sleep Deprivation (S) (n = 6), and Sleep Deprivation + Melatonin (S + M) (n = 6). Interventions were administered each morning via intraperitoneal injections of melatonin (10 mg/kg) or vehicle solution (%1 ethanol + saline), while the S and S + M groups underwent 6 h of daily sleep deprivation from using the Gentle Handling method. All mice were individually housed in cages with ad libitum access to food and water within a 12-hour light-dark cycle. Results presented that the brain regions affected by insomnia. The structure of phospholipids, changed. Yet, not only changes in lipids but also in amides were noticed in hippocampus and prefrontal cortex tissues. Additionally, FTIR results showed that melatonin affected the lipids as well as the amides fraction in cortex and hippocampus collected from both control and sleep deprivation groups.

Role of MIC levels and 23S rRNA mutation sites to clarithromycin in 14-day clarithromycin bismuth quadruple therapy for Helicobacter pylori eradication: A prospective trial in Beijing.

Background: Rising clarithromycin resistance undermines Helicobacter pylori (H. pylori) treatment efficacy. We aimed to determine clarithromycin's minimum inhibitory concentration (MIC) levels and identify specific mutation sites in the 23S ribosomal subunit (23S rRNA) that predict treatment outcomes in a 14-day regimen of clarithromycin bismuth quadruple therapy (amoxicillin 1g, clarithromycin 500 mg, rabeprazole 10 mg, and colloidal bismuth pectin 200 mg). Materials and methods: We included adult H. pylori patients who hadn't previously undergone clarithromycin-based treatment, either as initial or rescue therapy. Exclusions were made for penicillin allergy, recent use of related medications, severe illnesses, or inability to cooperate. Patients underwent a 14-day clarithromycin bismuth quadruple therapy. Gastric mucosa specimens were obtained during endoscopy before eradication. MIC against amoxicillin and clarithromycin was determined using the E-test method. The receiver operating characteristic (ROC) curve helped to find the optimal clarithromycin resistance MIC breakpoint. Genetic sequences of H. pylori 23S rRNA were identified through Sanger Sequencing. (ChiCTR2200061476). Results: Out of 196 patients recruited, 92 met the inclusion criteria for the per-protocol (PP) population. The overall intention-to-treat (ITT) eradication rate was 80.00 % (84/105), while the modified intention-to-treat (MITT) and PP eradication rates were 90.32 % (84/93) and 91.30 % (84/92) respectively. No amoxicillin resistance was observed, but clarithromycin resistance rates were 36.19 % (38/105), 35.48 % (33/93), and 34.78 % (33/92) in the ITT, MITT, and PP populations respectively. Compared with the traditional clarithromycin resistance breakpoint of 0.25 µg/mL, a MIC threshold of 12 µg/mL predicted better eradication. Among 173 mutations on 152 sites in the 23S rRNA gene, only the 2143A > G mutation could predict eradication outcomes (p < 0.000). Conclusions: Interpretation of elevated MIC values is crucial in susceptibility testing, rather than a binary "susceptible" or "resistant" classification. The 2143A > G mutation has limited specificity in predicting eradication outcomes, necessitating further investigation into additional mutation sites associated with clarithromycin resistance.

Correlation of Hemoglobin Level With New Inflammatory Markers in the Emergency Department: A Retrospective Study Exploring Neutrophil-to-Lymphocyte, Monocyte-to-Lymphocyte, Platelet-to-Lymphocyte, and Mean Platelet Volume-to-Platelet Count Ratios.

Background Anemia of chronic disease is known to be associated with inflammation. However, the relationship between hemoglobin (Hb) levels and potential inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume-to-platelet count ratio (MPV/PC) has not been extensively studied. The primary objective of this retrospective analytical study conducted at Al Zahraa Hospital University Medical Center (ZHUMC), Beirut, was to investigate the correlation between Hb levels and potential inflammatory markers (NLR, MLR, PLR, MPV/PC) in patients visiting the emergency department (ED), across different genders and age groups. The secondary objectives were to compare Hb levels and inflammatory markers values between the referred medical ward group (the hospitalized patients who were admitted to the medical ward), and the non-referred to medical ward group (the patients who were discharged home from the ED), and to evaluate the predictability of inflammatory markers and Hb levels for referral to the medical ward, including the determination of optimal cutoff values for hospital admission to the medical ward. Methods We analyzed the blood parameters of 379 adult patients who presented to the ED with various medical complaints between September 1, 2022, and November 30, 2022 (three months). These patients were included in the study after we checked their eligibility regarding the verification of all our inclusion and exclusion criteria. Results Our findings revealed a significant negative correlation between Hb levels and PLR (r = -0.24) in both genders and across different age groups. The group referred to the medical ward exhibited lower Hb levels and higher NLR, MLR, and PLR values (P < 0.001). NLR/Hb ratio emerged as a predictive factor for admission in genitourinary (R² = 0.158; OR = 5.62) and respiratory groups (R² = 0.206; OR = 5.89), with specific cutoff values of 0.533 (Sensitivity = 57.1% & Specificity = 84.2%) and 0.276 (Sensitivity = 85% & Specificity = 51.1%), respectively. Conclusions Our study demonstrates that hemoglobin level negatively correlates with PLR. NLR, MLR, and PLR stand as important inflammatory markers. Moreover, we present the first study in the literature to show that NLR/Hb ratio can serve as a predictor for referral to the medical ward, particularly in the genitourinary and respiratory patient groups, underscoring its value in risk assessment as a prognostic marker reflecting the need for admission when the case is more serious.

Comparing multi-class classifier performance by multi-class ROC analysis: A nonparametric approach.

The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. Real world applications often require classification into multiple (more than two) classes. For multi-class classifiers that produce class membership scores, a popular multi-class AUC (MAUC) variant is to average the pairwise AUC values [1]. Due to the complicated correlation patterns, the variance of MAUC is often estimated numerically using resampling techniques. This work is a generalization of DeLong's nonparameteric approach for binary AUC analysis [2] to MAUC. We first derive the closed-form expression of the covariance matrix of the pairwise AUCs within a single MAUC. Then by dropping higher order terms, we obtain an approximate covariance matrix with a compact, matrix factorization form, which then serves as the basis for variance estimation of a single MAUC. We further extend this approach to estimate the covariance of correlated MAUCs that arise from multiple competing classifiers. For the special case of binary correlated AUCs, our results coincide with that of DeLong. Our numerical studies confirm the accuracy of the variance and covariance estimates. We provide the source code of the proposed covariance estimation of correlated MAUCs on GitHub (https://tinyurl.com/euj6wvsz) for its easy adoption by machine learning and statistical analysis packages to quantify and compare multi-class classifiers.

Correlations between Sagittal Parameters and Functional Scores in 65-Year-Old Osteoporotic Females with Vertebral Body Fracture under Low-Energy Mechanism.

Background: Vertebral compression fractures (VCFs) are the most common fragility fractures associated with low-energy injury mechanisms in postmenopausal women with osteoporosis. No clear consensus is currently available on the optimal timing for surgical intervention in specific cases. Methods: This study examined the correlations between sagittal parameters, functional scores, and the appropriate timing for surgical intervention during the recovery stage in patients with osteoporosis with thoracolumbar (TL) vertebral body fractures. A total of 161 women aged ≥ 65 years with osteoporosis were included in the study. Spinal sagittal parameters from standing plain films and functional outcomes as the Oswestry disability index (ODI) and the visual analogue scale (VAS) were collected. Results: We found that TL junction Cobb angle was significantly correlated with ODI > 30 (p < 0.001) and VAS > 6 (p < 0.001) and the discriminative values for predicting ODI > 30 and VAS > 6 were a TL kyphotic angle of 14.5° and 13.5°, respectively. Among women aged ≥ 65 years with osteoporosis, the back pain and functional impairment observed within 6 months following a compression fracture are associated with a greater TL kyphosis angle. Conclusions: This suggests that a more proactive approach may be necessary when addressing the conditions of these patients.

Validating the accuracy of the Hendrich II Fall Risk Model for hospitalized patients using the ROC curve analysis.

This retrospective study was conducted at a medical center in southern Taiwan to assess the accuracy of the Hendrich II Fall Risk Model (HIIFRM) in predicting falls. Sensitivity, specificity, accuracy, and optimal cutoff points were analyzed using receiver operating characteristic (ROC) curves. Data analysis was conducted using information from the electronic medical record and patient safety reporting systems, capturing 303 fall events and 47,146 non-fall events. Results revealed that at the standard threshold of HIIFRM score ≥5, the median score in the fall group was significantly higher than in the non-fall group. The top three units with HIIFRM scores exceeding 5 were the internal medicine (50.6%), surgical (26.5%), and oncology wards (14.1%), indicating a higher risk of falls in these areas. ROC analysis showed an HIIFRM sensitivity of 29.5% and specificity of 86.3%. The area under the curve (AUC) was 0.57, indicating limited discriminative ability in predicting falls. At a lower cutoff score (≥2), the AUC was 0.75 (95% confidence interval: 0.666-0.706; p < 0.0001), suggesting acceptable discriminative ability in predicting falls, with an additional identification of 101 fall events. This study emphasizes the importance of selecting an appropriate cutoff score when using the HIIFRM as a fall risk assessment tool. The findings have implications for fall prevention strategies and patient care in clinical settings, potentially leading to improved outcomes and patient safety.

On the bias in the AUC variance estimate.

The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. A popular approach to estimating the AUCs and the associated variabilities - the variance of the AUC or the full covariance matrix of multiple correlated AUCs - is the one proposed by DeLong et al [1], which is based on the Mann Whitney two-sample U-statistics. The bias of a variance estimator is an important factor in applications such as hypothesis testing and construction of confidence intervals - a negatively biased variance estimator may lead to incorrect conclusions, and a positive bias is conservative hence preferable. In this work, we show that the (co-)variance estimate in DeLong's approach is always positively biased. More specifically, the difference matrix between the expectation of the estimated covariance and the true covariance is a positive semi-definite matrix. This bias is non-negligible when the sample size is small, and quickly diminishes as the sample size increases. Our method relies on constructing, from the AUC kernel, a random variable whose (co-)variance matrix coincides with the bias, thereby establishing the claim. We also discuss alternative approaches to AUC variance estimation that may potentially reduce the bias.

A novel Bayesian Latent Class Model (BLCM) evaluates multiple continuous and binary tests: A case study for Brucella abortus in dairy cattle.

Bovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests. In this study, we developed a novel Bayesian Latent Class Model that integrates both binary and continuous testing outcomes, incorporating additional fixed (parity) and random (farm) effects, to calibrate optimal cut-off values by maximizing Youden Index. We tested 651 serum samples collected from six dairy farms in two regions of Henan Province, China with four serological tests: Rose Bengal Test, Serum Agglutination Test, Fluorescence Polarization Assay, and Competitive Enzyme-Linked Immunosorbent Assay. Our analysis revealed that the optimal cut-off values for FPA and C-ELISA were 94.2 mP and 0.403 PI, respectively. Sensitivity estimates for the four tests ranged from 69.7% to 89.9%, while specificity estimates varied between 97.1% and 99.6%. The true prevalences in the two study regions in Henan province were 4.7% and 30.3%. Parity-specific odds ratios for positive serological status ranged from 1.2 to 2.2 for different parity groups compared to primiparous cows. This approach provides a robust framework for validating diagnostic tests for both continuous and discrete tests in the absence of a gold standard test. Our findings can enhance our ability to design targeted disease detection strategies and implement effective control measures for brucellosis in Chinese dairy farms.

Role of metabolomic profile as a potential marker to discriminate membranous nephropathy from IgA nephropathy.

BACKGROUND: Membranous nephropathy (MN) and IgA nephropathy (IgAN) are the most common primary glomerulopathies worldwide. The systemic metabolic changes in the progression of MN and IgAN are not fully understood. METHODS: A total of 87 and 70 patients with MN and IgAN, respectively, and 30 healthy controls were enrolled in this study. Untargeted metabolomics was performed to explore the differential metabolites and metabolic pathways in the early stage of MN and IgAN. To judge the diagnostic ability of biomarkers, receiver operating characteristic curve analysis (ROC) were performed. RESULTS: Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) suggested that patients with MN and IgAN showed an obvious separation trend from the healthy controls. In addition, 155 and 148 metabolites were identified to be significantly altered in the MN and IgAN groups, respectively. Of these, 70 metabolites were markedly altered in both disease groups; six metabolites, including L-tryptophan, L-kynurenine, gamma-aminobutyric acid (GABA), indoleacetaldehyde, 5-hydroxyindoleacetylglycine, and N-alpha-acetyllysine, showed the opposite tendency. The most affected metabolic pathways included the amino acid metabolic pathways, citrate cycle, pantothenate and CoA biosynthesis, and hormone signaling pathways. CONCLUSIONS: Substantial metabolic disorders occurred during the progression of MN and IgAN. L-tryptophan, L-kynurenine, GABA, indoleacetaldehyde, 5-hydroxyindoleacetylglycine, and N-alpha-acetyllysine may show potential as biomarkers for the identification of MN and IgAN.

Deciphering Intermediate Nugent scores: Utility of Real-time PCR for Bacterial Vaginosis diagnosis.

PURPOSE: To optimize real-time PCR assays for diagnosis of Bacterial Vaginosis (BV) and determine cut-off loads by ROC analysis for Gardnerella vaginalis, Atopobium vaginae and Lactobacillus spp. as compared to Nugent scoring (Gold standard) in clinical samples. RESULTS: Out of 125 women, 34 were positive, 26 intermediate and 65 negative for BV by Nugent scoring. All three real-time PCR assays were found to be highly sensitive & specific and AUC suggested excellent diagnostic accuracy. An optimal cut-off was >9.45 × 103 copies/ ml, >3.34 × 103 copies/ ml & ≤ 18.63 × 103 copies/ ml for G. vaginalis, A. vaginae and Lactobacillus spp. respectively, in BV positives. Gram staining and qPCR were discordant only in patients with intermediate scores (n = 26) where qPCR identified 15 (57.69%) as positive and 11 (42.3%) as negative. CONCLUSION: PCR-based molecular BV diagnosis is more accurate and can be used for deciphering intermediate Nugent scores.

Relationship between influencing factors of operation time and postoperative complications in hand-assisted laparoscopic living donor nephrectomy / 器官移植

Objective To identify the influencing factors of operation time of hand-assisted laparoscopic living donor nephrectomy, and to analyze the relationship between influencing factors and the severity of postoperative complications. Methods Clinical data of 91 donors who underwent hand-assisted laparoscopic nephrectomy were retrospectively analyzed. The correlation between preoperative baseline data of donors and operation time was analyzed. The relationship between operation time and postoperative complications was assessed and the threshold of operation time was determined. Results Multiple donor renal arteries, thick perirenal and posterior renal fat, metabolic syndrome, high Mayo adhesive probability (MAP) score and Clavien-Dindo score prolonged the operation time. By analyzing the receiver operating characteristic (ROC) curve, we found that when the operation time was ≥138 min, the incidence of postoperative complications of donors was significantly increased (P<0.05). Conclusions For donors with multiple renal arteries, thick perirenal and posterior renal fat, metabolic syndrome and high MAP score and Clavien-Dindo score, experienced surgeons should be selected to make adequate preoperative preparation and pay close attention after surgery, so as to timely detect postoperative complications and reduce the severity of complications, enhance clinical prognosis of the donors.

Comprehensive analysis of immune cell landscapes revealed that immune cell ratio eosinophil/B.cell.memory is predictive of survival in sepsis.

BACKGROUND: Immune dysregulation is a feature of sepsis. However, a comprehensive analysis of the immune landscapes in septic patients has not been conducted. OBJECTIVES: This study aims to explore the abundance ratios of immune cells in sepsis and investigate their clinical value. METHODS: Sepsis transcriptome data sets were downloaded from the NCBI GEO database. The immunedeconv R package was employed to analyze the abundance of immune cells in sepsis patients and calculate the ratios of different immune cell types. Differential analysis of immune cell ratios was performed using the t test. The Spearman rank correlation coefficient was utilized to find the relationships between immune cell abundance and pathways. The prognostic significance of immune cell ratios for patient survival probability was assessed using the log-rank test. In addition, differential gene expression was performed using the limma package, and gene co-expression analysis was executed using the WGCNA package. RESULTS: We found significant changes in immune cell ratios between sepsis patients and healthy controls. Some of these ratios were associated with 28-day survival. Certain pathways showed significant correlations with immune cell ratios. Notably, six immune cell ratios demonstrated discriminative ability for patients with systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis, with an Area Under the Curve (AUC) larger than 0.84. Patients with a high eosinophil/B.cell.memory ratio exhibited poor survival outcomes. A total of 774 differential genes were identified in sepsis patients with a high eosinophil/B.cell.memory ratio compared to those with a low ratio. These genes were organized into seven co-expression modules associated with relevant pathways, including interferon signaling, T-cell receptor signaling, and specific granule pathways. CONCLUSIONS: Immune cell ratios eosinophil/B.cell.memory and NK.cell.activated/NK.cell.resting in sepsis patients can be utilized for disease subtyping, prognosis, and diagnosis. The proposed cell ratios may have higher prognostic values than the neutrophil-to-lymphocyte ratio (NLR).

Nonparametric bootstrap methods for interval estimation of the area under the ROC curve with correlated diagnostic test data: application to whole-virus ELISA testing in swine.

Developing and evaluating novel diagnostic assays are crucial components of contemporary diagnostic research. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are frequently used to evaluate diagnostic assays' performance. The variation in AUC estimation can be quantified nonparametrically using resampling methods, such as bootstrapping, and then used to construct interval estimation for the AUC. When multiple observations are observed from the same subject, which is very common in veterinary diagnostic tests evaluation experiments, a traditional bootstrap-based method can fail to provide valid interval estimations of AUC. In particular, the traditional method does not account for the correlation among data observations and could result in interval estimation that fails to cover the true AUC adequately at the desired confidence level. In this paper, we proposed two novel methods to calculate the confidence interval of the AUC for correlated diagnostic test data based on cluster bootstrapping and hierarchical bootstrapping, respectively. Our simulation studies showed that both proposed methods had adequate coverage probabilities which were higher than the existing traditional method when there were intra-subject correlations. We also discussed applying the proposed methods to evaluate a novel whole-virus ELISA (wv-ELISA) diagnostic assay in detecting porcine parainfluenza virus type-1 antibodies in swine serum.

Elevated Troponin I as a Marker for Unfavorable Outcomes in Acute Ischemic Stroke.

OBJECTIVE: To assess if elevated cardiac troponin I (cTnI) serves as a sign of unfavorable functional outcomes in ischemic stroke. METHODS: In this single-center prospective cohort study, 100 consecutive patients admitted with acute ischemic stroke (normal troponin I group n = 52, raised troponin I group n = 48) were included. Hospital mortality was documented in both groups; the remaining patients were followed up to 90 days. Then two groups were compared in terms of unfavorable short-term outcomes (Modified Rankin Scale > 3) and mortality. Multivariate logistic regression was conducted to determine the predictive value of elevated cTnI. The Kaplan-Meier curve was drawn and compared to determine the difference in survival between the two groups. To find out the most probable cut-off level for an unfavorable outcome, a receiver operating characteristic (ROC) analysis was conducted. RESULT:  A higher frequency of coronary artery disease (p=0.030), higher National Institutes of Health Stroke Scale (NIHSS) (p=0.008) score, and lower Glasgow Coma Scale (GCS) (p=0.002) was observed in raised troponin I group. Even after the exclusion of confounding elevated troponin I was found to be an independent predictor of unfavorable outcomes (adjusted odds ratio, OR 8.25 {95% confidence interval, CI: 2.65-25.75}; p<0.001). The patients with raised troponin I had a significantly lower rate of survival after 90 days (p=0.022). The elevated troponin I was observed to have a significantly high accuracy (p<0.001; area under curve, AUC: 0.768 {moderate accuracy}, 95% CI: 0 .676 to 0.861) in predicting unfavorable outcomes. CONCLUSION: Elevated cTnI is independently associated with unfavorable short-term outcomes. It is also associated with a lower rate of survival.

Prevalence of Malnutrition and the Value of Predicting Pancreatic Fistula in Patients with Laparoscopic Pancreatoduodenectomy.

Background: Pancreatoduodenectomy is a standard surgical procedure for periampullary tumors. With recent improvements in perioperative management, postoperative mortality has decreased significantly in recent years; however, postoperative pancreatic fistula (POPF) is still one of the most prevalent and dangerous complications. The purpose of this study was to analyze the prevalence of malnutrition and the value of predicting POPF in patients with laparoscopic pancreatoduodenectomy (LPD). Methods: We retrospectively analyzed the perioperative data of 747 patients undergoing LPD in the Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, China. Simultaneously, we analyzed the prevalence rate of malnutrition with three different nutritional assessment scores and explored the independent risk variables for POPF to identify potential predictive value. Results: Malnutrition was observed in 20.1% of patients with the prognostic nutritional index (PNI), 85.0% of patients with the controlling nutritional status (CONUT) score, and 73.1% of patients with the NRI score. Univariate and multivariate analyses all showed that the risk factors for POPF were pancreatic texture, pancreatic duct diameter, abdominal infection, body mass index (BMI), nomogram-revised risk index (NRI), and PNI. The receiver operating characteristic curve indicated that the BMI/PNI ratio was capable of predicting the occurrence of clinical POPF following LPD, with an area under the curve of 0.708. Conclusions: Compared with no malnourished patients, malnutrition is associated with a higher risk of POPF among patients with LPD. In addition, the BMI/PNI ratio has some predictive value in the development of POPF following LPD.

FTIR- based serum structure analysis in molecular diagnostics of essential thrombocythemia disease.

Essential thrombocythemia (ET) reflects the transformation of a multipotent hematopoietic stem cell, but its molecular pathogenesis remains obscure. Nevertheless, tyrosine kinase, especially Janus kinase 2 (JAK2), has been implicated in myeloproliferative disorders other than chronic myeloid leukaemia. FTIR analysis was performed on the blood serum of 86 patients and 45 healthy volunteers as control with FTIR spectra-based machine learning methods and chemometrics. Thus, the study aimed to determine biomolecular changes and separation of ET and healthy control groups illustration by applying chemometrics and ML techniques to spectral data. The FTIR-based results showed that in ET disease with JAK2 mutation, there are alterations in functional groups associated with lipids, proteins and nucleic acids significantly. Moreover, in ET patients the lower amount of proteins with simultaneously higher amount of lipids was noted in comparison with the control one. Furthermore, the SVM-DA model showed 100% accuracy in calibration sets in both spectral regions and 100.0% and 96.43% accuracy in prediction sets for the 800-1800 cm-1 and 2700-3000 cm-1 spectral regions, respectively. While changes in the dynamic spectra showed that CH2 bending, amide II and CO vibrations could be used as a spectroscopy marker of ET. Finally, it was found a positive correlation between FTIR peaks and first bone marrow fibrosis degree, as well as the absence of JAK2 V617F mutation. The findings of this study contribute to a better understanding of the molecular pathogenesis of ET and identifying biomolecular changes and may have implications for early diagnosis and treatment of this disease.

Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis.

Background: Sepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance. Methods: Gene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was used to identify differentially expressed pathways. Tumor IMmune Estimation Resource (TIMER) was applied to estimate immune cell abundance. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed to identify important pathway genes. Log-rank test was performed to test the prognostic significance of pathways for patient survival probability. DSigDB was used to mine candidate drugs based on pathways. PyMol was used for 3-D structure visualization. LigPlot was used to plot the 2-D pose view for receptor-ligand interaction. Results: Eighty-four KEGG pathways were differentially expressed in sepsis patients compared to healthy controls. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis with Area Under the Curve (AUC) above 0.80. Seven related drugs were screened using survival-related pathways. Conclusion: Sepsis-related pathways can be utilized for disease subtyping, diagnosis, prognosis, and drug screening.

Estimating meaningful thresholds for multi-item questionnaires using item response theory.

PURPOSE: Meaningful thresholds are needed to interpret patient-reported outcome measure (PROM) results. This paper introduces a new method, based on item response theory (IRT), to estimate such thresholds. The performance of the method is examined in simulated datasets and two real datasets, and compared with other methods. METHODS: The IRT method involves fitting an IRT model to the PROM items and an anchor item indicating the criterion state of interest. The difficulty parameter of the anchor item represents the meaningful threshold on the latent trait. The latent threshold is then linked to the corresponding expected PROM score. We simulated 4500 item response datasets to a 10-item PROM, and an anchor item. The datasets varied with respect to the mean and standard deviation of the latent trait, and the reliability of the anchor item. The real datasets consisted of a depression scale with a clinical depression diagnosis as anchor variable and a pain scale with a patient acceptable symptom state (PASS) question as anchor variable. RESULTS: The new IRT method recovered the true thresholds accurately across the simulated datasets. The other methods, except one, produced biased threshold estimates if the state prevalence was smaller or greater than 0.5. The adjusted predictive modeling method matched the new IRT method (also in the real datasets) but showed some residual bias if the prevalence was smaller than 0.3 or greater than 0.7. CONCLUSIONS: The new IRT method perfectly recovers meaningful (interpretational) thresholds for multi-item questionnaires, provided that the data satisfy the assumptions for IRT analysis.

Two-phase stratified sampling and analysis for predicting binary outcomes.

The two-phase study design is a cost-efficient sampling strategy when certain data elements are expensive and, thus, can only be collected on a sub-sample of subjects. To date guidance on how best to allocate resources within the design has assumed that primary interest lies in estimating association parameters. When primary interest lies in the development and evaluation of a risk prediction tool, however, such guidance may, in fact, be detrimental. To resolve this, we propose a novel strategy for resource allocation based on oversampling cases and subjects who have more extreme risk estimates according to a preliminary model developed using fully observed predictors. Key to the proposed strategy is that it focuses on enhancing efficiency regarding estimation of measures of predictive accuracy, rather than on efficiency regarding association parameters which is the standard paradigm. Towards valid estimation and inference for accuracy measures using the resultant data, we extend an existing semiparametric maximum likelihood ethod for estimating odds ratio association parameters to accommodate the biased sampling scheme and data incompleteness. Motivated by our sampling design, we additionally propose a general post-stratification scheme for analyzing general two-phase data for estimating predictive accuracy measures. Through theoretical calculations and simulation studies, we show that the proposed sampling strategy and post-stratification scheme achieve the promised efficiency improvement. Finally, we apply the proposed methods to develop and evaluate a preliminary model for predicting the risk of hospital readmission after cardiac surgery using data from the Pennsylvania Health Care Cost Containment Council.