Predict Disease Severity Research Articles

THE ROLE OF COPEPTIN IN VIRAL LOWER RESPIRATORY TRACT INFECTIONS IN CHILD: A PROSPECTIVE CASE-CONTROL STUDY

BackgroundLower respiratory tract infection (LRTI) is a leading cause of morbidity and mortality among children globally. Copeptin, released by the pituitary gland, serves as a biomarker for various conditions and, as a neuroendocrine stress hormone, is useful in acute conditions. This study aimed to determine the role of copeptin levels in LRTI in children and whether it can reliably predict pneumonia severity. Materials and MethodsThis prospective case-control study was performed between April and October, 2023. The study included four groups: (i) patients diagnosed with bronchiolitis (Group 1, n=25), (ii) patients diagnosed with mild to moderate pneumonia (Group 2, n=25), (iii) patients diagnosed with severe pneumonia (Group 3, n=25), and (iv) a control group (Group 4, n=26). ResultsCopeptin values differed significantly between the groups (p<0.001 for all comparisons). Copeptin demonstrated a sensitivity of 87.4% and specificity of 82.2% for distinguishing between patients with bronchiolitis and pneumonia, using a cut-off value of >0.586 ng/ml. For the identification of patients with severe pneumonia versus those with mild to moderate pneumonia, copeptin exhibited a sensitivity of 97.9% and specificity of 94.7% with a cut-off value of >1.215 ng/ml. The copeptin level exhibited a positive correlation with fibrinogen and FAR levels while demonstrating a negative correlation with albumin levels (r=0.354, ⁎⁎P=0.002; r=0.408, ⁎⁎⁎P<0.001; and r=−0.334, ⁎⁎P=0.003, respectively). ConclusionsCopeptin demonstrates potential as a predictor of disease severity in children with pneumonia. It can also serve as a valuable tool to guide physicians in differentiating between bronchiolitis and pneumonia, as well as in diagnosing severe pneumonia.

Vocal cord dysfunction: Does laryngeal adduction on laryngoscopy predict disease severity and response to laryngeal retraining therapy?

AbstractIntroductionVocal cord dysfunction (VCD) is a complex disorder characterized by episodic adduction of the vocal folds during inspiration and expiration, which can lead to dyspnea, wheezing, cough, and acute‐onset respiratory distress. Currently, there is a lack of standardized criteria among treating physicians across multiple disciplines, including otolaryngologists, pulmonologists, allergists, and speech and language pathologists, for diagnosis and treatment of VCD, although laryngeal‐respiratory retraining therapy (LRT) has emerged as the preferred treatment modality.ObjectiveIn the present study, we examined the efficacy of LRT in patients presenting with a clinical diagnosis of VCD in the presence and absence of laryngeal adduction on laryngoscopy.ResultsOverall, 74.1% of the cohort showed a response to LRT, of which 62.1% were partial and 12.1% were significant responses. When comparing between patients with and without laryngeal adduction on laryngoscopy, there were no significant differences in the number of sessions of LRT undertaken, mean time to response, and overall response rate between the groups.ConclusionOur findings suggest that LRT should be utilized for all patients presenting with symptoms of VCD, even in the absence of laryngeal adduction on laryngoscopy.

Osteopontin as a Novel Biomarker in Distinguishing Chronic Rhinosinusitis with Nasal Polyp Endotypes and Predicting Disease Severity

Introduction: The objective of this study was to ascertain the predictive value of osteopontin (OPN), a cytokine with pro-inflammatory properties implicated in inflammatory and allergic conditions, in nasal secretions for the identification of chronic rhinosinusitis with nasal polyp (CRSwNP) endotypes and the assessment of disease severity. Methods: A cohort comprising 81 individuals diagnosed with CRSwNP was enrolled, which included 37 subjects with the non-eosinophilic CRSwNP and 44 subjects with the eosinophilic CRSwNP (eCRSwNP), alongside 32 healthy controls (HCs). Nasal secretions and tissue samples were collected from all participants. The quantification of OPN in these samples was conducted using ELISA and immunohistochemistry. Nasal fractional exhaled nitric oxide levels were determined with the Nano Coulomb Breath Analyzer. The diagnostic efficacy of OPN levels in distinguishing eCRSwNP was assessed using the receiver operating characteristic (ROC) curve, while Pearson correlation analysis was employed to evaluate the correlation between OPN levels and disease severity indicators. Results: Concentrations of OPN in nasal secretions were found to be elevated in CRSwNP patients compared to the HC group and significantly higher in patients with eCRSwNP. A positive correlation was identified between OPN levels in nasal secretions and peripheral blood eosinophil counts and percentages, and tissue eosinophil counts, as well as the Visual Analog Scale (VAS) score, Lund-Mackay score, and Lund-Kennedy score. The ROC analysis demonstrated that the OPN level in nasal secretions possesses a robust discriminatory capacity for eCRSwNP, with a cutoff value of 121.05 ng/mL. Furthermore, the OPN concentration was determined to be a more precise predictor of the VAS score, Lund-Mackay score, and Lund-Kennedy score than the CRSwNP endotypes. Conclusion: The findings of this study indicate that OPN is differentially expressed in the nasal secretions of eCRSwNP patients and correlates with eosinophilic inflammation. The presence of OPN in nasal secretions emerges as a novel and potentially valuable biomarker for the differentiation of CRSwNP endotypes and the prognostication of disease severity.

Thyroid eye disease and ocular myasthenia gravis.

An overview of two ocular diseases, which significantly impact quality of life: thyroid eye disease (TED) and ocular myasthenia gravis (OMG). Additionally, we describe the clinical challenge when they occur simultaneously. We will describe the pathophysiology of both conditions, the currently available diagnostic tools, and the therapies available. Recent literature has described newer diagnostic modalities, predictors of disease severity and co-occurrence of TED and OMG, and novel therapies. There is also critical analysis of current therapeutics and risk factors. The findings from this review suggest a need for heightened clinical awareness and early detection strategies for TED and OMG due to their overlapping clinical presentation. Emerging therapies and diagnostic techniques should be integrated into practice. Further research is warranted to explore the long-term safety and efficacy of novel treatments and the potential genetic links between these conditions.

5. A SCOPING REVIEW: COMPARISON OF COAGULATION PARAMETERS RESULTS IN DECEASED AND RECOVERED COVID-19 PATIENTS

Objective: Compare coagulation markers between deceased and recovered COVID-19 patients. Methods: A search of PubMed was conducted to identify peer-reviewed articles published between January 1, 2022, and April 1, 2024. Articles were selected based on predefined inclusion and exclusion criteria. Data extraction focused on coagulation parameters, including platelet count, fibrinogen (Fib), D-Dimer, prothrombin time (PT), and activated partial thromboplastin time (APTT) across six selected studies involving a total of 7,052 patients. Results: The findings indicated that recovered patients had significantly higher platelet counts compared to deceased patients (p &lt; 0.05). D-Dimer and Fib levels were lower in recovered patients, while PT results were significantly prolonged in deceased individuals (p &lt; 0.05). APTT results showed variability, with some studies reporting higher levels in deceased patients, while others indicated longer APTT in recovered patients, remaining within normal ranges. Conclusion: Coagulation markers exhibit significant differences between deceased and recovered COVID-19 patients, suggesting their potential utility in predicting disease severity and guiding clinical management.

Development of a metabolome-based respiratory infection prognostic during COVID-19 arrival.

In a new respiratory virus pandemic, optimizing allocation of scarce medical resources becomes an urgent challenge. Infection prognosis takes on particular importance when allocating scarce antiviral antibodies and drugs, which are most effective when administered before the onset of severe disease. During arrival of the COVID-19 pandemic to the United States in 2020, we conducted a prognostic biomarker discovery and validation effort based upon metabolomic profiling with a liquid-chromatography-mass spectrometer (LC-MS) type used clinically for rapid toxicology. We obtained urine specimens from 163 patients presenting for evaluation. We obtained LC-MS profiles in the initial cohort and used machine learning methods to define a simplified urine metabolomic signature associated with respiratory failure or death by 90 days. This signature was composed of three metabotypes linked to intestinal microbiome metabolism and anticonvulsant use, with a receiver-operator characteristic area under the curve (ROC AUC) of 89.4%. Blinded application of this signature to the subsequent validation cohort yielded a ROC AUC of 81.2%. A model trained on the two baseline metabotypes present before intubation exhibited similar performance in the validation cohort. This study demonstrates the plausibility and promise of rapid metabolome-based prognostic discovery and validation in the opening wave of a pandemic. The approach used here could be used to inform therapeutic and resource allocation decisions early in a future epidemic.IMPORTANCEIn a new respiratory virus pandemic, the ability to identify patients at greatest risk for severe disease is essential to direct scarce medical resources to those most likely to benefit from them. Tools to predict disease severity are best developed early in a pandemic, but laboratory-based resources to develop these may be limited by available technology and by infection precautions. Here, we show that an accessible metabolic profiling approach could identify a prognostic signature of severe disease in the initial wave of COVID-19, when patients presenting for care often exceeded the available doses of convalescent plasma and remdesivir. In a future pandemic, this approach, alongside efforts to identify clinical disease severity predictors, could improve patient outcomes and facilitate therapeutic trials by identifying individuals at high risk for severe disease.

Serum neurofilament light chain predicts disease severity in axonal variants of acute immune neuropathies: A retrospective monocentric cohort study.

The purpose was to explore the prognostic utility of neurofilament light chain (NfL) in patients with immune-mediated polyradiculoneuropathies (IMPs). This retrospective monocentric study analysed serum and cerebrospinal fluid samples from patients diagnosed with IMP collected prior to treatment initiation. NfL concentrations were correlated with clinical outcomes, including F score and hospitalization duration. Amongst 115 IMP patients tested, baseline cerebrospinal fluid and serum NfL (sNfL) concentrations were higher in acute inflammatory axonal polyradiculoneuropathy (AIAP) than other IMP variants. In the AIAP cohort, a positive correlation was observed between baseline sNfL concentrations, F score and hospitalization duration. Multivariate linear regression analysis further supported the predictive relationship between elevated baseline sNfL concentrations and clinical outcomes. Using receiver operating characteristic analysis, a cut-off value for sNfL of 351 pg/mL was found to predict an F score >3 in AIAP with a sensitivity of 40% and specificity of 81.8%. AIAP patients with sNfL concentrations above this threshold required longer hospitalization (extended by 15 days). Our findings highlight the potential of baseline sNfL as an effective marker for distinguishing between IMP variants and predicting the prognosis of AIAP. Further validation may facilitate translation of sNfL into clinical practice, potentially identifying high-risk patients for tailored treatment approaches.

Parkinson's Disease: Biomarkers for Diagnosis and Disease Progression.

Parkinson's disease (PD) is a progressive neurological disease that causes both motor and nonmotor symptoms. While our understanding of putative mechanisms has advanced significantly, it remains challenging to verify biomarkers with sufficient evidence for regular clinical use. Clinical symptoms are the primary basis for diagnosing the disease, which can be mild in the early stages and overlap with other neurological disorders. As a result, clinical testing and medical records are mostly relied upon for diagnosis, posing substantial challenges during both the initial diagnosis and the continuous disease monitoring. Recent biochemical, neuroimaging, and genetic biomarkers have helped us understand the pathophysiology of Parkinson's disease. This comprehensive study focuses on these biomarkers, which were chosen based on their relevance, methodological excellence, and contribution to the field. Biochemical biomarkers, including α-synuclein and glial fibrillary acidic protein (GFAP), can predict disease severity and progression. The dopaminergic system is widely used as a neuroimaging biomarker to diagnose PD. Numerous genes and genome wide association study (GWAS) sites have been related to the development of PD. Recent research on the SNCA gene and leucine-rich repeat protein kinase 2 (LRRK2) has shown promising results. By evaluating current studies, this review intends to uncover gaps in biomarker validation and use, while also highlighting promising improvements. It emphasizes the need for dependable and reproducible indicators in improving PD diagnosis and prognosis. These biomarkers may open up new avenues for early diagnosis, disease progression tracking, and the development of personalized treatment programs.

Discriminating Parkinson’s disease patients from healthy controls using nasal respiratory airflow

BackgroundBreathing patterns may inform on health. We note that the sites of earliest brain damage in Parkinson’s disease (PD) house the neural pace-makers of respiration. We therefore hypothesized that ongoing long-term temporal dynamics of respiration may be altered in PD.MethodsWe applied a wearable device that precisely logs nasal airflow over time in 28 PD patients (mostly H&Y stage-II) and 33 matched healthy controls. Each participant wore the device for 24 h of otherwise routine daily living.ResultsWe observe significantly altered temporal patterns of nasal airflow in PD, where inhalations are longer and less variable than in matched controls (mean PD = −1.22 ± 1.9 (combined respiratory features score), Control = 1.04 ± 2.16, Wilcoxon rank-sum test, z = −4.1, effect size Cliff’s δ = −0.61, 95% confidence interval = −0.79 – (−0.34), P = 4.3 × 10−5). The extent of alteration is such that using only 30 min of recording we detect PD at 87% accuracy (AUC = 0.85, 79% sensitivity (22 of 28), 94% specificity (31 of 33), z = 5.7, p = 3.5 × 10−9), and also predict disease severity (correlation with UPDRS-Total score: r = 0.49; P = 0.008).ConclusionsWe conclude that breathing patterns are altered by H&Y stage-II in the disease cascade, and our methods may be further refined in the future to provide an indication with diagnostic and prognostic value.

A Comparative Evaluation of the Genetic Variant Spectrum in the USH2A Gene in Russian Patients with Isolated and Syndromic Forms of Retinitis Pigmentosa.

Pathogenic variants in the USH2A gene are the primary cause of both non-syndromic autosomal recessive inherited retinitis pigmentosa (RP) and the syndromic form, characterized by retinal degeneration and sensorineural hearing loss. This study presents a comparative assessment of the genetic variant spectrum in the USH2A gene among Russian patients in two clinical groups. A retrospective analysis was conducted on massive parallel panel sequencing data from 2415 blood samples of unrelated patients suspected of having hereditary retinal diseases. The copy number of USH2A exons was determined using the quantitative MLPA method with the MRC-Holland SALSA MLPA kit. Biallelic pathogenic and likely pathogenic variants in the USH2A gene were identified in 69 patients (8.7%). In the group of patients with isolated hereditary RP (55 patients), the most frequent pathogenic variants were p.(Glu4445_Ser4449delinsAspLeu) (20.9%), p.(Trp3955*) (15.5%), and p.(Cys934Trp) (5.5%). In patients with the syndromic form (14 patients), the most frequent variants were p.(Trp3955*) (35.7%) and c.8682-9A>G (17.9%). It was found that patients with isolated vision impairment rarely had two "null" variants (17.8%), whereas this was common among patients with both hearing and vision impairment (71.4%) (p ≤ 0.05), explaining the severity of the disease and the earlier onset of clinical symptoms in the syndromic form of RP. Ten previously undescribed loss-of-function variants were identified. The estimated prevalence of USH2A-associated retinal dystrophy in Russia was 1.9 per 100,000 individuals. The obtained data on the differences in the spectra of genetic variants in the USH2A gene in the two studied groups highlight the importance of establishing genotype-phenotype correlations and predicting disease severity, aiming at potential early cochlear implantation and selection of target therapy.

DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation

Plant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation (HDMR) to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems.

Abstract 4117883: Long noncoding RNAs and machine learning to improve cardiovascular outcomes of COVID-19

Introduction/Background: Cardiovascular symptoms appear in a high proportion of patients in the few months following a severe SARS-CoV-2 infection. Non-invasive methods to predict disease severity could help personalizing healthcare and reducing the occurrence of these symptoms. Research Questions/Hypothesis: We hypothesized that blood long noncoding RNAs (lncRNAs) and machine learning (ML) could help predict COVID-19 severity. Goals/Aims: To develop a model based on lncRNAs and ML for predicting COVID-19 severity. Methods/Approach: Expression data of 2906 lncRNAs were obtained by targeted sequencing in plasma samples collected at baseline from four independent cohorts, totaling 564 COVID-19 patients. Patients were aged 18+ and were recruited from 2020 to 2023 in the PrediCOVID cohort (n=162; Luxembourg), the COVID19_OMICS-COVIRNA cohort (n=100, Italy), the TOCOVID cohort (n=233, Spain), and the MiRCOVID cohort (n=69, Germany). The study complied with the Declaration of Helsinki. Cohorts were approved by ethics committees and patients signed an informed consent. Results/Data: After data curation and pre-processing, 463 complete datasets were included in further analysis, representing 101 severe patients (in-hospital death or ICU admission) and 362 stable patients (no hospital admission or hospital admission but not ICU). Feature selection with Boruta, a random forest-based method, identified age and five lncRNAs (LINC01088-201, FGDP-AS1, LINC01088-209, AKAP13, and a novel lncRNA) associated with disease severity, which were used to build predictive models using six ML algorithms. A naïve Bayes model based on age and five lncRNAs predicted disease severity with an AUC of 0.875 [0.868-0.881] and an accuracy of 0.783 [0.775-0.791]. Conclusion: We developed a ML model including age and five lncRNAs predicting COVID-19 severity. This model could help improve patients’ management and cardiovascular outcomes.

CYSTATIN C AS A MARKER OF EARLY KIDNEY DAMAGE UNDER ARTERIAL HYPERTENSION (LITERATURE REVIEW)

The purpose of this study is to determine kidney functional activity in relation to cystatin C levels, as reported in recent literature. Materials and Methods. A bibliographic-semantic method was employed to assess the current state of research on this topic, analyzing findings from previous scientific studies using both literature sources and electronic resources. Results. Chronic kidney disease (CKD) is as prevalent as hypertension. It is well-known that arterial hypertension exacerbates kidney function, making the assessment of renal functional activity critical. Glomerular filtration rate (GFR) is commonly used to evaluate kidney function, relying on established indicators as well as newer, more objective markers. Among these, cystatin C has emerged as one of the most accurate and sensitive indicators for assessing kidney function. Its concentration in serum negatively correlates with GFR and is particularly valuable for detecting renal pathology even when creatinine levels remain unchanged. This insight has led to the development of an estimated GFR (eGFR) formula that incorporates cystatin C levels. Research suggests that cystatin C levels increase with hypertension, although studies on the specific relationship between cystatin C and hypertension remain limited. Some findings propose that serum cystatin C could serve as a predictor of disease severity, particularly in elderly hypertensive patients with coronary heart disease. The literature supports the utility of cystatin C as a reliable marker for assessing GFR, which facilitates early detection of CKD even when albumin excretion is normal, thus identifying kidney damage at an early stage. Ppotential mechanisms by which elevated cystatin C may contribute to cardiovascular damage are under investigation. As a highly informative endogenous marker of GFR, serum cystatin C not only aids in staging CKD but is especially valuable in identifying early renal dysfunction. Conclusion. The measurement of cystatin C enables a highly accurate assessment of kidney function and aids in evaluating cardiovascular risk, especially when hypertension coexists with chronic kidney disease. Further research is needed to improve predictions of chronic kidney progression at various stages and in the presence of comorbidities. Such insights will support more effective preventive strategies to slow the progression of this pathology.

Prolactin Role in COVID-19 and Its Association with the Underlying Inflammatory Response.

The COVID-19 pandemic has prompted interest in identifying reliable biomarkers to predict disease severity and guide clinical decisions. Prolactin (PRL), a hormone traditionally associated with lactation, has gained attention for its role in immune modulation. This study aimed to assess PRL as a biomarker for disease severity in COVID-19. A prospective cohort of 142 patients with moderate to severe COVID-19, defined as a WHO-CPS 5 or 6, was recruited from the University General Hospital of Patras. Baseline PRL levels were measured using an electrochemiluminescence immunoassay, and serum cytokines, including IL-1β, IL-6, IL-8, IL-10, IL-12p70, and TNF-α, were quantified through flow cytometry. Clinical outcomes, including mortality and the need for invasive mechanical ventilation (IMV), were recorded. Results indicated that PRL levels were significantly higher in female patients (12.95 ng/mL vs. 9.40 ng/mL, p < 0.001) but they did not correlate with key severity indices such as CCI, SOFA score upon admission or inflammatory markers. No significant associations between baseline PRL levels, cytokine concentrations, and clinical outcomes in COVID-19 were noted. Our findings suggest that PRL may lack prognostic reliability for disease severity compared to more established predictive markers and that its role in the immune response remains uncertain.

Relação entre estresse oxidativo e COVID-19: uma breve revisão sistemática

OBJECTIVE: Analyse the possible relationship between Oxidative Stress (OS) and the pathogenesis of COVID-19. METHODS: This brief systematic literature review was conducted based on the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The search resulted in the selection of 10 papers that met the research theme and were published between January 2020 and March 2021. RESULTS: The results revealed that in the course of the disease, there is a decrease in thiol levels and an increase in disulfide and EO, with a probable depletion of antioxidant elements. They found that the accumulation of Reactive Oxygen Species (ROS), added to the damage to albumin, were related to a worse prognosis and that neutrophils may be responsible for the increased level of oxidative stress associated with the worsening of the clinical condition. In addition, cells transfected with the SARS-CoV-2 spike protein showed around three times the concentration of ROS compared to non-transfected control cells. As for serum O2- levels, they were significantly elevated in critically ill patients, constituting a probable predictor of disease severity. CONCLUSIONS: Thus, from the findings of this study, it is possible to infer that both patients with severe and moderate forms of COVID-19 have high EO index values. However, further studies need to be carried out to confirm these preliminary results.

Assessment of sestrin 2 as a sepsis marker and predictor of disease severity

Introduction Sestrin 2 is considered a stress-inducible antioxidant protein. This study was aimed to evaluate sestrin 2 in subjects with sepsis, and its correlation with clinical severity and mortality. Methods Sepsis and control group patients were followed from admission to discharge. A blood sample was taken at admission for determination of serum sestrin 2 level. Results Of the total 42 patients with sepsis, there were 25 females and the mean age was 74.9 years. The sestrin 2 levels were significantly higher in the sepsis group. The optimum sestrin 2 cut-off point of ≥3.13 ng/mL had 95.2% sensitivity and 71.4% specificity for sepsis (p < .001). Sestrin 2 levels were higher in patients who needed renal replacement therapy (p = .018), patients who needed vasopressor therapy (p = .001) and patients with organ dysfunction (p = .002). The sestrin 2 level was significantly correlated with Acute Physiology and Chronic Health Evaluation (APACHE) II score, Nutrition Risk in the Critically Ill (NUTRIC) Score, C-reactive protein and albumin. Sestrin 2 levels were not associated with 30 d mortality in sepsis patients. Conclusions Sestrin 2 was significantly higher in the sepsis patients and associated with sepsis related adverse clinical outcomes. These results provided information concerning the clinical utility of sestrin 2.

High inflammatory indices are significant predictors of disease severity in maintenance hemodialysis patients with COVID-19: A cross-sectional study

BackgroundThe patients with kidney failure who undergo maintenance hemodialysis (MHD) at high risk of morbidity and mortality from COVID-19. Previous studies showed that inflammation plays an important role in the progression of COVID-19. This study aimed to evaluate the prognostic value of the inflammatory indices in MHD patients with COVID-19. MethodsWe included 141 patients receive MHD in this single-center. SARS-CoV-2 infection was confirmed by a positive result in RT-PCR analysis of nasal and pharyngeal swab samples, and the demographic, clinical, and laboratory data from December 1, 2022, to January 31, 2023 were reviewed. Inflammatory indices including PLR, NLR and SII were calculated. Binary logistics regression was used to examine the association between inflammatory indices and SARS-CoV-2 infection in MHD patients. The ROC curves were used to detect the sensitivity and specificity of these inflammatory indices in prediction of SARS-CoV-2 infection status in MHD patients. ResultsSARS-CoV-2 infection was detected in 76.43 % of the 141 MHD patients. Lymphocyte (LY), aspartate transaminase (AST), blood urea nitrogen (BUN), uric acid (UA), PLR, NLR and SII were significant predictors of no SARS-CoV-2 infection and symptomatic SARS-CoV-2 infection. In addition, LY, serum ferritin (SF), AST, BUN, UA, PLR and NLR were significant predictors of asymptomatic SARS-CoV-2 infection and symptomatic SARS-CoV-2 infection. The ROC curves showed the best sensitivity and specificity of PLR (66.7 % sensitivity; 68.8 % specificity) and NLR (51.9 % sensitivity; 86.3 % specificity) in predicting symptomatic SARS-CoV-2 infection. ConclusionPLR and NLR can be used as simple and inexpensive biomarkers in predicting the prognosis of COVID-19 in MHD patients.

ROLE OF USG IN EARLY DETECTION AND PREDICTING SEVERITY OF DENGUE INFECTION

Background: Early detection and severity assessment are crucial for managing dengue fever. Ultrasound (USG) imaging can help identify key features such as capillary leakage and organ involvement, aiding in diagnosis and prognosis. Aims: To correlate ultrasound findings with the severity of dengue infection. To evaluate USGs sensitivity in detecting early capillary leakage and its prognostic value. Methods: The study involved 66 patients (40 males, 26 females aged 12-70) with serologically confirmed dengue, observed from August 2022 to October 2023. USG was performed within 3 days of fever onset and again after 1 week. Results: Day 1-3: Gallbladder wall thickening (60.6%) and pericholecystic fluid were common. Pleural effusion (34.8%) was observed in milder cases. Day 7: Gallbladder wall thickening persisted in 89.4%. New findings included ascites (24.2%), bilateral pleural effusion (31.8%), and pericardial effusion (9.1%). Severe cases showed hepatomegaly and splenomegaly. Conclusion: USG is a valuable tool for early detection and severity assessment of dengue fever. Key findings like gallbladder wall thickening and pleural effusion can help predict disease severity and guide clinical management.

Novel Insights Into The Role Of Serum Interleukin-39 In Patients With Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that has a genetic predisposition and a complex pathogenesis in which the involvement of various cytokines has been shown. In the current study, serum interleukin-39 (IL-39) levels from 99 patients with SLE and 33 healthy control subjects who attended the Baghdad Teaching Hospital Rheumatology Unit were examined. Patients were divided into 3 subgroups according to disease status: inactive (n = 33), active moderate (n = 33), and active severe (n = 33). This topic has not yet been explored, so the significance of IL-39 as a biomarker for SLE was evaluated. Cytokine levels were measured using enzyme-linked immunosorbent assay kits. Full medical histories, body mass index, gender, and clinical disease activity, the latter evaluated using the SLE disease activity index, were documented. Laboratory disease parameters, including anti-dsDNA antibodies, C3 and C4 levels, erythrocyte sedimentation rate, and C-reactive protein titres, were measured. The mean age of patients was 33.92 ± 0.91 years. The IL-39 level was higher in patients (13.70 ± 0.35 ng/l) than in controls (10.67 ± 0.19 ng/l, p &lt; 0.01). The mean of IL-39 levels was highest in patients with active severe SLE (17.42 ± 0.48 ng/l) and then became incrementally lower with reducing disease severity, i.e., active moderate, 13.34 ± 0.23 ng/l; inactive, 10.93 ± 0.24 ng/l. Serum IL-39 showed good validity for the diagnosis of SLE. With a cut-off value ≥ 10.25 ng/l and an area under the curve of 0.79, diagnostic sensitivity, accuracy, and specificity were 79.98%, 51.5%, and 71.97%, respectively. In conclusion, serum IL-39 levels were significantly higher in patients with SLE than in healthy controls and were correlated with disease activity. This interleukin may be useful in predicting disease severity.

Machine Learning to Predict Disease Severity and Progression in Hospitalized COVID-19 Patients Using Laboratory Data on Admission

Background: Herein, we aimed to develop and test machine learning (ML) models to predict disease severity and/or progression in hospitalized COVID-19 patients through baseline laboratory features. Methods: In this retrospective study of hospitalized COVID-19 patients admitted to a tertiary care center, we evaluated routine admission data to determine the accuracy rates of different ML algorithms: k-nearest neighbor classifier, bagging classifier, random forest (RF), and decision tree. These models were compared over three outcomes: those who needed oxygen supplementation vs. who did not on admission (Analysis 1, n: 180), those who later developed oxygen requirement vs. those who did not (Analysis 2, n: 112), and those who needed invasive mechanical ventilation vs. those who did not during hospitalization (Analysis 3, n: 164). Results: The median age of the patients was 55 (44-68) years, with males constituting 47.2% of the subjects. At admission, 37.8% of the patients required oxygen supplementation. During hospitalization, 17.5% needed mechanical ventilation, and 8.3% died. For all analyses, RF had the highest accuracy in classifying the need for oxygen supplementation on admission (89.4%) or during hospitalization (91.1%) and for invasive mechanical ventilation (92.2%). These were followed by a bagging classifier for Analysis 1 (88.3%) and Analysis 3 (91.0%) and by a decision tree for Analysis 2 (88.4%). C-reactive protein, monocyte distribution width, and high-sensitive troponin-T were the most crucial laboratory contributors to Analysis 1, Analysis 2, and Analysis 3, respectively. Conclusion: Our study showed that ML algorithms could predict the need for oxygen supplementation and mechanical ventilation during hospitalization using baseline laboratory data, suggesting a slight superiority of RF, among others.