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Background: The functions and related signal pathways of the IFIT3 gene in the skin lesions of patients with psoriasis were explored through bioinformatics methods to determine the potential specific molecular markers of psoriasis. Methods: The "limma" R package was used to analyze three datasets from the Gene Expression Omnibus database (GSE13355, GSE30999 and GSE106992), and the differential genes were screened. The STRING database was used for gene ontology (GO) enrichment analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, and protein-protein interaction network integration. Then, the IFIT3 subnetwork was extracted and analyzed by gene set enrichment analysis (GSEA) using the Metascape database to verify the effectiveness of gene differentiation and disease tissue identification. Results: In this study, 426 differential genes were obtained, of which 322 were significantly upregulated and 104 were significantly downregulated. GO enrichment analysis showed that the differential genes were mainly involved in immunity and metabolism; the KEGG pathway enrichment analysis mainly included the chemokine signal pathway, PPAR signal pathway, and IL-17 signal pathway, among others. Based on the IFIT3 subnetwork analysis, it was found that IFIT3 was mainly involved in the biological processes of viruses, bacteria, and other microorganisms. The pathways obtained by GSEA were mainly related to immunity, metabolism, and antiviral activities. IFIT3 was highly expressed in psoriatic lesions and may thus be helpful in the diagnosis of psoriasis. Conclusion: The differential genes, biological processes, and signal pathways of psoriasis, especially information related to and diagnostic efficiency of the IFIT3 gene, were obtained by bioinformatics analysis. These results are expected to provide the theoretical basis and new directions for exploring the pathogenesis of psoriasis, in addition to helping with finding diagnostic markers and developing drug treatment targets.

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OBJECTIVES: Neuropsychological tests (NPTs) are standard tools for assessing cognitive function. These tools can evaluate the cognitive status of a subject, which can be time-consuming and expensive for interpretation. Therefore, this paper aimed to optimize the systematic NPTs by machine learning and develop new classification models for differentiating healthy controls (HC), mild cognitive impairment, and Alzheimer's disease dementia (ADD) among groups of subjects. PATIENTS AND METHODS: A total dataset of 14,926 subjects was obtained from the formal 46 NPTs based on the Seoul Neuropsychological Screening Battery (SNSB). The statistical values of the dataset included an age of 70.18 ± 7.13 with an education level of 8.18 ± 5.50 and a diagnosis group of three; HC, MCI, and ADD. The dataset was preprocessed and classified in two- and three-way machine-learning classification from scikit-learn (www.scikit-learn.org) to differentiate between HC versus MCI, HC versus ADD, HC versus Cognitive Impairment (CI) (MCI + ADD), and HC versus MCI versus ADD. We compared the performance of seven machine learning algorithms, including Naïve Bayes (NB), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), AdaBoost, and linear discriminant analysis (LDA). The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predictive value (NPV), area under the curve (AUC), confusion matrixes, and receiver operating characteristic (ROC) were obtained from each model based on the test dataset. RESULTS: The trained models based on 29 best-selected NPT features were evaluated, the model with the RF algorithm yielded the best accuracy, sensitivity, specificity, PPV, NPV, and AUC in all four models: HC versus MCI was 98%, 98%, 97%, 98%, 97%, and 99%; HC versus ADD was 98%, 99%, 96%, 97%, 98%, and 99%; HC versus CI was 97%, 99%, 92%, 97%, 97%, and 99% and HC versus MCI versus ADD was 97%, 96%, 98%, 97%, 98%, and 99%, respectively, in predicting of cognitive impairment among subjects. CONCLUSION: According to the results, the RF algorithm was the best classification model for both two- and three-way classification among the seven algorithms trained on an imbalanced NPTs SNSB dataset. The trained models proved useful for diagnosing MCI and ADD in patients with normal NPTs. These models can optimize cognitive evaluation, enhance diagnostic accuracy, and reduce missed diagnoses.

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Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outcomes. This article aims to provide a basic understanding of AI enabled ECG technology. Specific conditions and findings will be discussed, followed by reviewing associated terminology and methodology. In the appendix, definitions of AUC versus accuracy are explained. The application of deep learning models enables detecting diseases from normal electrocardiograms at accuracy not previously achieved by technology or human experts. Results with AI enabled ECG are encouraging as they considerably exceeded current screening models for specific conditions (i.e., atrial fibrillation, left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy). This could potentially lead to a revitalization of the utilization of the ECG in the insurance domain. While we are embracing the findings with this rapidly evolving technology, but cautious optimism is still necessary at this point.

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Cervical malignancy (CC) is the 2nd most prevalent malignancy among females, leading to cancer mortality. Primary detection of CC tumors results in an improved prognosis. CC is a malignant gynecological tumor, with few treatment options. New diagnostic and therapeutic agents are required to expand patient survival and quality of life. If CC tumors can be found at an early stage, the prognosis is much brighter. New diagnostic and therapeutic agents are needed to increase patient survival and quality of life. In this work, we performed whole-exome sequencing utilizing V5 (Illumina platform) 10 samples, 5 control and 5 CC tumour tissue, and we compared the results with transcriptome studies. KMT2C variations were shown to be among the most vicious in this analysis. From an Indian viewpoint, we found a plethora of SNVs and mutations, including those with known, unknown, and possible effects on health. Based on our findings, we know that the KMT2C gene is on chr. Seven and in exon 8, all three recognized variants are missense, synonymous, coding synonymous, non-coding variants, and GnomAD MAF (- 0.05). The variation at position (7:152265091, T > A, SNV 62478356) in KMT2C is unique, potent, and pathogenic. The missense coding transcript CIQTNF maps to chromosome 7 and displays T > C SNV. In addition, we performed single strand conformational polymorphism analysis on 64 samples and further confirmed them using Sanger sequencing to understand and verify the mutations. KMT2C shows a log FC value of - 1.16. Understanding emerging harmful mutations from an Indian viewpoint is facilitated by our bioinformatics-based, extensive correlation studies of WES analysis. Potentially harmful and new mutations were found in our preliminary analysis; among these ten top mutated genes, KMT2C and CIQTNF were altered in ten cases of CC with an Indian phenotype.

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BACKGROUND: Timely and accurate screening of malnutrition at the community level is essential to identifying malnourished children. The World Health Organization (WHO) guidelines classify non-oedematous acute malnutrition among children using mid-upper arm circumference (MUAC) or weight-for-height Z-score (WHZ). Study Design: A cross-sectional study. METHODS: This study was conducted among children aged 6‒60 months. After necessary exclusions, 433 participants were selected using a multi-stage simple random sampling method. Using WHO guidelines for global acute malnutrition (GAM) [WHZ<-2, MUAC<12.5 cm], the sensitivity (Se), specificity (Sp), predictive values, likelihood ratios, Youden index, and receiver operating characteristic (ROC) curve were calculated for MUAC using WHZ as the criterion. RESULTS: Out of 433 participants, 30% were diagnosed with GAM using WHZ, while 17.6% were found malnourished using MUAC measurements. As per WHO cut-offs, the Se, Sp, positive predictive value (PPV), negative predictive value (NPV), Youden index, positive likelihood ratio (LR+), and negative likelihood ratio (LR-) of MUAC were 48%, 96%, 83%, 81%, 0.44, 12, and 0.54, respectively. The ROC curve displayed an area under the curve of 0.86 (95% confidence interval=0.83, 0.90) for MUAC<12.5 cm. Bivariate Pearson correlation also demonstrated a positive linear relationship (R2=0.302) between the WHZ and MUAC variables. CONCLUSION: Based on the findings, 48% of the children were correctly identified by the MUAC with an 83% probability of GAM (PPV=0.83). Moreover, there was 96% Sp in non-malnourished children, with only 4% false positives. Therefore, personnel at the grassroots level can use MUAC for timely and accurate screening of children in Anganwadi centers (AWCs) due to its ease of use and simplicity.

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Context: Steatotic liver disease is common but overlooked in childhood obesity; diagnostic methods are invasive or expensive. Objective: We sought to determine the diagnostic accuracy of vibration-controlled transient elastography (VCTE) compared with magnetic resonance imaging (MRI) in adolescents with obesity and high risk for hepatosteatosis. Methods: Baseline data in 3 clinical trials enrolling adolescents with obesity were included (NCT03919929, NCT03717935, NCT04342390). Liver fat was assessed using MRI fat fraction and VCTE-based controlled attenuation parameter (CAP). Hepatosteatosis was defined as MRI fat fraction ≥5.0%. The area under the receiver-operating characteristic curves (AUROCs) for CAP against MRI was calculated, and optimal CAP using the Youden index for hepatosteatosis diagnosis was determined. Results: Data from 82 adolescents (age 15.6 ± 1.4 years, body mass index 36.5 ± 5.9 kg/m2, 81% female) were included. Fifty youth had hepatosteatosis by MRI (fat fraction 9.3% ; 95% CI 6.7, 14.0), and 32 participants did not have hepatosteatosis (fat fraction 3.1%; 95% CI 2.2, 3.9; P < .001). The hepatosteatosis group had higher mean CAP compared with no hepatosteatosis (293 dB/m; 95% CI 267, 325 vs 267 dB/m; 95% CI 248, 282; P = .0120). A CAP of 281 dB/m had the highest sensitivity (60%) and specificity (74%) with AUROC of 0.649 (95% CI 0.51-0.79; P = .04) in the entire cohort. In a subset of participants with polycystic ovary syndrome (PCOS), a CAP of 306 dB/m had the highest sensitivity (78%) and specificity (52%) and AUROC of 0.678 (95% CI 0.45-0.90; P = .108). Conclusion: CAP of 281 dB/m has modest diagnostic performance for hepatosteatosis compared with MRI in youth with significant obesity. A higher CAP in youth with PCOS suggests that comorbidities might affect optimal CAP in hepatosteatosis diagnosis.

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Very little is known about the diagnostic performance of the American Diabetes Association glycated haemoglobin (HbA1c) cut-off of 6.5% in resource-limited settings. This study, conducted between February 2023 and May 2023, aimed to determine the optimal HbA1c cut-off for the diagnosis of diabetes mellitus by measuring HbA1c and fasting plasma glucose levels in 120 adults attending care at a tertiary hospital in Harare, Zimbabwe. The optimal HbA1c cut-off was 6.1% and glucose levels were strongly correlated with HbA1c values. The prevalence of diabetes mellitus was higher (28.3%) at our derived HbA1c cut-off than with the American Diabetes Association criterion (21.6%). What this study adds: This study highlights the need for population-specific cut-off HbA1c values in the diagnosis of diabetes mellitus.

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INTRODUCTION: Manic depressive psychosis (MDP) or bipolar disorder, a prevalent psychiatric condition globally and in the Indian population, has been attributed to various pathological mechanisms. Hydrogen sulphide (H2S), a member of the gasotransmitter family, may be linked to the development of bipolar disorder because it plays a crucial role in maintaining proper neuronal function in terms of excitability, plasticity, and homeostatic functions. There is very little data regarding the role of the gasotransmitter H2S in MDP in terms of its association, diagnostic ability, and severity prediction, which led us to conduct this study among MDP patients in the Sub-Himalayan region of West Bengal. METHODS: This was an observational case-control study performed in the Department of Biochemistry, North Bengal Medical College and Hospital, Siliguri, West Bengal, India, from January 2022 to December 2022. Fifty diagnosed MDP patients and 50 healthy age- and sex-matched control subjects satisfying the inclusion and exclusion criteria were studied. The H2S level in the blood was assayed using the standardised spectrophotometric methylene blue method. The severity of depression was assessed by Hamilton Depression Rating Scale (HAM-D) scoring. RESULTS: Of the 50 MDP patients, 45 (90%) were in the depressive phase, and five (10%) were in the manic phase. Of the 45 depressive patients, eight (17.8%) had mild depression, 12 (26.7%) had moderate depression, 19 (42.2%) had severe depression, and six (13.3%) had very severe depression. The mean H2S level in MDP patients (41.98±18.88 µmol/l) was significantly (P<0.05) lower than that in control subjects (99.20± 15.20 µmol/l). It was also observed that the mean H2S level in MDP patients decreased with the duration of the disease but was not statistically significant. The mean H2S levels in the different depression severity groups were found to be significantly different (P<0.001). Receiver operating characteristic (ROC) curve analysis revealed that a cut-off value of H2S <78.5 µmol/l was associated with MDP, with a sensitivity of 96% and a specificity of 88%, and a cut-off value of H2S < 53 µmol/l predicted the severity of depression with a sensitivity of 89.3% and a specificity of 76.5%. CONCLUSION: The significant association of the gasotransmitter H2S in MDP patients and its role as a diagnostic and severity predictive marker can help us to employ proper measures for better management of MDP and improving quality of life.

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Background: Paraneoplastic ischemic stroke has a poor prognosis. We have recently reported an algorithm based on the number of ischemic territories, C-reactive protein (CRP), lactate dehydrogenase (LDH), and granulocytosis to predict the underlying active cancer in a case-control setting. However, co-occurrence of cancer and stroke might also be merely incidental. Objective: To detect cancer-associated ischemic stroke in a large, unselected cohort of consecutive stroke patients by detailed analysis of ischemic stroke associated with specific cancer subtypes and comparison to patients with bacterial endocarditis. Methods: Retrospective single-center cohort study of consecutive 1612 ischemic strokes with magnetic resonance imaging, CRP, LDH, and relative granulocytosis data was performed, including identification of active cancers, history of now inactive cancers, and the diagnosis of endocarditis. The previously developed algorithm to detect paraneoplastic cancer was applied. Tumor types associated with paraneoplastic stroke were used to optimize the diagnostic algorithm. Results: Ischemic strokes associated with active cancer, but also endocarditis, were associated with more ischemic territories as well as higher CRP and LDH levels. Our previous algorithm identified active cancer-associated strokes with a specificity of 83% and sensitivity of 52%. Ischemic strokes associated with lung, pancreatic, and colorectal (LPC) cancers but not with breast and prostate cancers showed more frequent and prominent characteristics of paraneoplastic stroke. A multiple logistic regression model optimized to identify LPC cancers detected active cancer with a sensitivity of 77.8% and specificity of 81.4%. The positive predictive value (PPV) for all active cancers was 13.1%. Conclusion: Standard clinical examinations can be employed to identify suspect paraneoplastic stroke with an adequate sensitivity, specificity, and PPV when it is considered that the association of ischemic stroke with breast and prostate cancers in the stroke-prone elderly population might be largely incidental.

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The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

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BACKGROUND: Sirtuin 7 (SIRT7), as a nicotinamide adenine dinucleotide-dependent protein/histone deacetylase, has been implicated in the pathogenesis of cardiovascular diseases. However, whether SIRT7 is related to hypertension remains largely unclear. Thus, this study aims to explore the effects and correlation between SIRT7 and hypertension. METHODS: A total of 72 patients with essential hypertension and 82 controls with non-hypertension were recruited at Beijing Tongren Hospital Affiliated with Capital Medical University from July 2022 to June 2023. Plasma SIRT7 expression was measured using enzyme-linked immunosorbent assay analysis. Clinical baseline characteristics, laboratory measurements, echocardiographic data, and medical therapy were collected. RESULTS: Plasma levels of SIRT7 were lower in hypertensive patients compared with non-hypertensive patients [0.97 (0.58-1.30) vs. 1.24 (0.99-1.46) ng/mL, P < 0.001, respectively]. Furthermore, compared with the low SIRT7 group, there were lower levels of systolic blood pressure, hyperlipidemia, and the ultrasonic electrocardiogram parameters left ventricular end-diastolic diameter and left atrial in diastole in the high SIRT7 group (P < 0.05, respectively). More importantly, multivariate logistic regression analyses indicated that plasma SIRT7 was a predictor of hypertension [OR: 0.06, 95 % CI (0.02-0.19), P < 0.001]. Receiver operating characteristics curve analysis revealed that the optimal cutoff value for plasma SIRT7 levels in detecting hypertension was determined as 0.85 ng/mL with a sensitivity of 73.6 % and a specificity of 89.0 %. The area under the curve for SIRT7 was 0.821 (95 % CI, 0.751-0.878; P < 0.001). CONCLUSION: Plasma levels of SIRT7 are decreased in patients with essential hypertension, implying its potential as a biomarker for diagnosing essential hypertension..

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Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.

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OBJECTIVE: ST-segment myocardial infarction (STEMI) is a time-sensitive emergency. This study screened the favorable factors for the survival of STEMI patients with medium- and high-risk thrombolysis in myocardial infarction (TIMI) scores. METHODS: According to the TIMI scores at admission, 433 STEMI patients were retrospectively and consecutively selected and allocated into low-/medium-/high-risk groups, with their general information/blood routine/biochemical indicators/coagulation indicators documented. The factors influencing the in-hospital survival of STEMI patients were analyzed using univariate and multivariate logistic regression analyses. Moreover, the predictive value of favorable factors was analyzed by receiver operating characteristics (ROC) curve, and patients were assigned into high/low level groups based on the cut-off value of these factors, with their in-hospital survival rates compared. RESULTS: The in-hospital survival rate of the medium-/high-risk groups was lower than that of the low-risk group. Emergency percutaneous coronary intervention (PCI), lymphocyte (LYM), total protein (TP), albumin (ALB), and sodium (Na) were independent favorable factors for in-hospital survival in the medium-/high-risk groups. Besides, LYM > 1.275 × 109/L, TP > 60.25 g/L, ALB > 34.55 g/L, and Na > 137.9 mmo1/L had auxiliary predictive value for the survival of STEMI patients with medium-/high-risk TIMI scores. Patients with high levels of LYM, TP, ALB, and Na exhibited higher in-hospital survival rates than patients with low levels. CONCLUSION: For STEMI patients with medium- and high-risk TIMI scores, accepting emergency PCI and normal levels of LYM, TP, ALB, and Na were more conducive to in-hospital survival.

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[This corrects the article DOI: 10.3389/fped.2022.801220.].

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Evaluating the prognostic performance of candidate markers for future disease onset or progression is one of the major goals in medical research. A marker's prognostic performance refers to how well it separates patients at the high or low risk of a future disease state. Often the discriminative performance of a marker is affected by the patient characteristics (covariates). Time-dependent receiver operating characteristic (ROC) curves that ignore the informativeness of the covariates will lead to biased estimates of the accuracy parameters. We propose a time-dependent ROC curve that accounts for the informativeness of the covariates in the case of censored data. We propose inverse probability weighted (IPW) estimators for estimating the proposed accuracy parameters. We investigate the performance of the IPW estimators through simulation studies and real-life data analysis.

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Introduction: Acute respiratory distress syndrome (ARDS) is a common complication of sepsis, which significantly increases the mortality rate. This work explored the diagnostic value of serum NOD-like receptor family pyrin domain containing 3 (NLRP3) concentration in patients with sepsis for ARDS, and the predictive value of serum NLRP3 concentration at the time of diagnosis for death 28 days after treatment. Methods: A total of 150 sepsis patients were included in this study, including age-matched two groups of patients, 75 patients with ARDS and 75 patients without ARDS. In addition, 60 age-matched healthy patients with physical examination were recruited in this study. Serum NLRP3 concentration was determined by enzyme-linked immunosorbent assay (ELISA). The diagnostic values of serum NLRP3 concentration for ARDS in sepsis patients were evaluated by receiver operating characteristics (ROC) analysis. Correlation of serum NLRP3 with APACHE II score and SOFA were performed by Spearman correlation analysis. Results: Pulmonary infection, APACHE II score and serum NLRP3 concentration were risk factors for patients with sepsis complicated with ARDS. ROC curve results showed that the specificity of serum NLRP3 concentration was 74.67%, the sensitivity was 76.00%, and the area under the curve (AUC) was 0.82 (p<0.001). APACHE II score and SOFA were significantly positively correlated with serum NLRP3 concentration. Baseline serum NLRP3 levels had significant predictive value for 28-day mortality in sepsis patients complicated with ARDS. Conclusion: Serum NLRP3 concentration has clinical value in the diagnosis of sepsis complicated with ARDS.

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INTRODUCTION: Angiogenesis is considered important in the pathogenesis of multiple myeloma (MM), as well as in the targeted treatment of the disease. Leucine-rich α2-glycoprotein 1 (LRG1) is a protein that participates in angiogenesis and its effect on solid organ tumors has been investigated recently. This study aimed to investigate the relationship between MM and LRG1. METHODS: The MM patients who admitted to Hatay Mustafa Kemal University Hematology Clinic between September 2021 and October 2022 were included in the study. The study consists of a total of 4 groups: newly diagnosed MM (NDMM), relapsed refractory MM (RRMM), MM in remission (Rem-MM), and control group. Demographic data were retrieved from hospital records. Blood samples of our study groups were centrifuged at 1,500 × g for 10 min and serum was collected. LRG1, IL-6, IL-8, TGF-ß1, HIF-1α, FGF-2, and VEGF levels were analyzed in all groups by ELISA method, and statistical analysis was performed. RESULTS: A total of 112 individuals, including NDMM (n: 27), RRMM (n: 18), Rem-MM (n: 42), and control group (n: 25), were enrolled in the study. Based on the analyses, the NDMM group exhibited significantly elevated levels of LRG1 (p < 0.001), TGF-1 (p < 0.001), and HIF-1α (p = 0.046, p < 0.001, and p = 0.003 compared to the RRMM, Rem-MM, and control groups, respectively) compared to the other groups. LRG1 levels were positively correlated with creatinine (r: 0.363, p = 0.001), calcium (r: 0.344, p = 0.001), total protein (r: 0.473, p < 0.001), erythrocyte sedimentation rate (r: 0.547, p < 0.001), lactate dehydrogenase (r: 0.321, p = 0.003), beta-2-microglobulin (r: 0.312, p = 0.017), IL-6 (r: 0.478, p < 0.001), IL-8 (r: 0.240, p = 0.03), TGF-ß1 (r: 0.521, p < 0.001), and HIF-1α (r: 0.321, p = 0.003) levels and were negatively correlated with hemoglobin (r: -0.512, p < 0.001) and albumin (r: -0.549, p < 0.001) levels. Receiver operating characteristics (ROC) analysis revealed the association of LRG1 with the highest AUC value of 0.959 (95% CI: 0.904-1, p < 0.001) and the optimal cut-off value of 534.95 ng/mL (sensitivity: 93% and specificity: 99%) in the NDMM group compared to the control group. CONCLUSION: In this study, providing data for the first time on LRG1 levels in the setting of MM. LRG1 levels were found to be significantly higher in NDMM patients and in our study discriminate this patient population from RRMM, Rem-MM, and normal controls. Therefore, LRG1 seems to a potential biomarker that should be evaluated in future studies addressing the diagnosis, staging, follow-up, prognosis, and treatment target of MM.

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Background Hepatitis B virus DNA (HBV-DNA) assessment is recommended for diagnosing and monitoring chronic hepatitis B (CHB) patients. Quantitative hepatitis B surface antigen (qHBsAg) estimation adjunct to HBV-DNA is vital for assessing HBV chronicity and therapeutic prognosis. This study aimed to estimate the qHBsAg and compare its diagnostic performance with that of the HBV-DNA levels in CHB patients from Bangladesh. Methodology A total of 148 CHB patients were enrolled in this study. qHBsAg and hepatitis B e-antigen (HBeAg) were estimated using chemiluminescent and enzyme immunoassays, respectively, and HBV-DNA was quantified using real-time polymerase chain reaction. The parameters and diagnostic performances were analyzed by receiver operating characteristic (ROC) curve analysis. Results The overall levels (mean ± SD) of qHBsAg, HBV-DNA, and alanine aminotransferase (ALT) among the total population (n = 148) were 3.45 ± 0.80 log10 IU/mL, 4.40 ± 2.44 log10 IU/mL, and 86.17 ± 39.06 IU/L, respectively. Significant differences were observed in the levels of both qHBsAg (p = 0.004) and HBV-DNA (p < 0.0001) in cases with HBeAg positivity. qHBsAg levels showed a weak positive correlation with the levels of HBV-DNA and ALT in HBeAg-positive CHB patients, but no such relationship was observed in HBeAg-negative CHB patients. ROC curve analysis showed that the area under the curve for the qHBsAg level to distinguish high HBV-DNA levels (>5 log10 IU/mL) was 0.653 (p = 0.002), which indicated an acceptable diagnostic performance. The best cut-off of qHBsAg for predicting high HBV-DNA levels was 3.469 log10 IU/mL. Conclusions Our results indicated that qHBsAg might be a useful marker for monitoring HBV-DNA in CHB patients throughout treatment and follow-up.

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BACKGROUND: The impact of microRNAs (miRNAs) on the differentiation and function of inflammatory cells is well-established. MiRNAs play a crucial role in modulating the expression of pro-inflammatory genes in neuronal cells as well. With this knowledge in mind, our study aimed to explore the relationship between the expression of miRNAs and inflammatory markers in the cerebrospinal fluid (CSF) of patients diagnosed with multiple sclerosis (MS). By investigating this relationship, we aimed to gain insights into the potential involvement of miRNAs in the regulation of inflammation in the context of MS. MATERIALS AND METHODS: The expression levels of miRNA-21, miRNA-155, and miRNA-182 in cerebrospinal fluid (CSF) samples from multiple sclerosis (MS) patients and controls were determined by RT-PCR. CSF levels of the inflammatory cytokines IL-1ß, IL-6, and TNF-α were measured by enzyme-linked immunosorbent assay (ELISA). In addition, high-sensitivity C-reactive protein (hs-CRP) levels were measured by quantitative turbidimetry. RESULTS: The expression levels of microRNAs and inflammatory factors were found to be significantly higher in the CSF of MS patients compared to controls (P < 0.05). Receiver operating characteristics (ROC) analysis revealed that miRNA-21, miRNA-182, and miRNA-155 had a high area under the curve (AUC) in discriminating MS patients, with AUC values of 0.97 (P < 0.0001) for miRNA-21, 0.97 (P < 0.0001) for miRNA-182, and 0.96 (P < 0.0001) for miRNA-155. Notably, CSF miRNA-155 showed the highest accuracy in correctly diagnosing MS. Furthermore, a statistically significant relationship was observed between inflammatory cytokines and miRNA-21, miRNA-155 and miRNA-182. CONCLUSION: Our results demonstrated that cerebrospinal fluid (CSF) levels of IL-1ß, IL-6, TNF-α, hs-CRP and specific miRNAs serve as biomarkers for assessing central nervous system (CNS) inflammation and neurodegenerative processes in patients with multiple sclerosis (MS).

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To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information theory to detect marine mammal vocalizations. The proposed detection algorithm has five main steps: sampling, energy characteristics analysis, marginal frequency distribution, feature extraction, and detection, which involve four signal feature extraction and analysis algorithms: energy ratio distribution (ERD), energy spectrum distribution (ESD), energy spectrum entropy distribution (ESED), and concentrated energy spectrum entropy distribution (CESED). In an experiment on 500 sampled signals (blue whale vocalizations), in the competent intrinsic mode function (IMF2) signal feature extraction function distribution of ERD, ESD, ESED, and CESED, the areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were 0.4621, 0.6162, 0.3894, and 0.8979, respectively; the Accuracy scores were 49.90%, 60.40%, 47.50%, and 80.84%, respectively; the Precision scores were 31.19%, 44.89%, 29.44%, and 68.20%, respectively; the Recall scores were 42.83%, 57.71%, 36.00%, and 84.57%, respectively; and the F1 scores were 37.41%, 50.50%, 32.39%, and 75.51%, respectively, based on the threshold of the optimal estimated results. It is clear that the CESED detector outperforms the other three detectors in signal detection and achieves efficient sound detection of marine mammals.

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