Levels Of Disease Severity Research Articles

Body Awareness in COPD and Its Relation with Patients’ Clinic States

The detection of impairments in body awareness can provide important data for the clinical assessment and treatment planning of patients with COPD. In this study, we aimed to evaluate body awareness in patients with COPD, and to examine its relationship with patients’ clinics. A total of 114 subjects were divided into two groups: the COPD group (n:56) and the control group (n:58). Disease severity (COPD Assessment Test), functional capacity (6 Minute Walk Test), dyspnea (Modified Borg Scale), body awareness (Body Awareness Questionnaire), depression (Beck Depression Inventory) and anxiety (Beck Anxiety Inventory) levels of the groups were evaluated. Compared to the control group, the COPD group had lower levels of body awareness and higher levels of anxiety and depression (p

ADAPTIVE OPTICS IMAGING IN DIABETIC RETINOPATHY: A Prospective Cohort Study.

To investigate the correlation between diabetic retinopathy (DR) severity and microscopic retinal and vascular alterations using adaptive optics imaging. In this single-center, prospective cohort study, adult participants with healthy eyes or DR underwent adaptive optics imaging. Participants were classified into control/mild nonproliferative DR, moderate/severe nonproliferative DR, and proliferative DR. Adaptive optics imaging using the RTX1 camera was obtained from 48 participants (87 eyes) for photoreceptor data and from 36 participants (62 eyes) for vascular data. Photoreceptor parameters significantly differed between DR groups at 2° and 4° of retinal eccentricity. Wall-to-lumen ratio varied significantly at 2° eccentricity, while other vascular parameters remained nonsignificant. Cone density and dispersion were the strongest predictors for DR severity ( P < 0.001) in multivariable generalized estimating equation modeling, while other vascular parameters remained nonsignificant between DR severity groups. All photoreceptor parameters showed significant correlations with visual acuity overall and across most DR severity groups. To date, this is one of the largest studies evaluating the use of adaptive optics imaging in DR. Adaptive optics imaging was demonstrated to differentiate between various levels of disease severity in DR. These results support the potential role in diagnostic and therapeutic microstructural evaluation in research and clinical practice.

Machine learning in the classification of asian rust severity in soybean using hyperspectral sensor

Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can increase efficiency in disease monitoring, enabling faster, more accurate and time- and labor-saving evaluations. The aims of the study were: (i) to identify the spectral signature of different levels of Asian soybean rust severity; (ii) to identify the most accurate machine learning algorithm for classifying disease severity levels; (iii) which spectral input provides the highest classification accuracy for the algorithms; (iv) to determine a sample size of leaves that guarantees the best accuracy for the algorithms. A field experiment was carried out in the 2022/2023 harvest in a randomized block design with a 6x3 factorial scheme (ML algorithms x severity levels) and four replications. Disease severity levels assessed were: healthy leaves, 25 % severity, and 50 % severity. Leaf hyperspectral analysis was carried out over a wide range from 350 to 2500 nm. From this analysis, 28 spectral bands were extracted, seeking to distinguish the spectral signature for each severity level with the least input dataset. Data was subjected to machine learning analysis using Artificial Neural Network (ANN), REPTree (DT) and J48 decision trees, Random Forest (RF), and Support Vector Machine (SVM) algorithms, as well as a traditional classification method (Logistic Regression - LR). Two different input datasets were tested for each algorithm: the full spectrum (ALL) provided by the sensor and the 28 spectral bands (SB). Tests with different sample sizes were also conducted to investigate the algorithms' ability to detect severity levels with a reduced sample size. Our findings indicate differences between the spectral curves for the severity levels assessed, which makes it possible to differentiate between healthy plants with low and high severity using hyperspectral sensing. SVM was the most accurate algorithm for classifying severity levels by using all the spectral information as input. This algorithm also provided high classification accuracy when using smaller leaf samples. This study reveals that hyperspectral sensing and the use of ML algorithms provide an accurate classification of different levels of Asian rust severity, and can be powerful tools for a more efficient disease monitoring process.

Biochemical Markers of Iron Status Among Iraqi Patients at Different Severity Levels of Celiac Disease

In people with a hereditary predisposition, gluten consumption causes celiac disease, a multifactorial autoimmune condition. The ensuing small intestine inflammatory process develops specific antibodies and causes various gastrointestinal and extra-intestinal symptoms with varying degrees of severity. Evaluation of the blood iron profile at various histological celiac disease severity in Iraqi patients was the goal of the current study. Seventy-five Iraqi patients with celiac disease, with a mean age of 18.68± 11.13 years, were the study's participants. These people tested positive for celiac antibodies and experienced gastrointestinal symptoms. Based on the disease histological severity, they were divided into two groups: marsh (III a, b, c) group and marsh (0, I) group. For comparison purposes, 46 people who appeared to be in good health and matched in age and gender to the patients utilized in the study were also included. The levels of iron, transferrin, hemoglobin, ferritin, total iron binding capacity, unsaturated iron binding capacity, and the percentage of transferrin that is saturated with iron were all measured. In comparison to the other patient groups under study, celiac patients (marsh III) group, and particularly those of group (marsh III c), showed a substantial drop (p&lt;0.05) in iron, the percentage of transferrin saturating with iron, the levels of each ferritin and of hemoglobin. The total iron binding capacity and the unsaturated iron binding capacity showed a significant elevation (p&lt;0.05) in (marsh III c) patients’ group, meanwhile non-significant differences (p&gt; 0.05) were found in transferrin concentration. Moreover, significant differences (p&lt; 0.05) were obtained in the iron profile between celiac patients who followed strict gluten free diet and those patients with gluten contained diet.

Relationships between disease severity, psychological stress, and health-related quality of life among patients with acute coronary syndrome: mediation of illness perception.

Patients with acute coronary syndrome (ACS) often experience reduced health-related quality of life (HRQOL), which may be attributable to the disease severity and psychological stress. While illness perception is speculated to be a potential pathway underlying these relationships, evidence supporting this mechanism remains limited. This study aimed to investigate the relationships between disease severity, psychological stress, and HRQOL and whether these relationships are mediated by illness perception in patients with ACS. Data were collected from June to July 2019 and June to September 2020 in the cardiology departments of four public hospitals in China. Eligible patients completed measures of disease severity, psychological stress, illness perception, HRQOL, and socio-demographic and clinical characteristics. Data were analysed employing hierarchical multiple regression and structural equation modelling. This study included 405 participants (mean age 60.63 years, 67.4% male). After controlling for socio-demographic and clinical covariates, higher levels of disease severity (β = 0.115, P = 0.024) and psychological stress (β = -0.209, P＜0.001) were associated with poorer HRQOL; however, the relationships became non-significant after adding illness perception into the regression model. Structural equation modelling analysis suggested that illness perception played a mediating role between disease severity, psychological stress, and HRQOL, accounting for 45.95% and 65.79% of the total effects, respectively. This study found that illness perception mediated the relationships between disease severity, psychological stress, and HRQOL among patients with ACS. Improving patients' HRQOL should consider its important influencing factors with a focus on promoting positive illness perception.

Age-guided Cardiovascular Disease Diagnosis from Retinal Images: A Multitask Deep Learning Framework

One of the major global health concerns today is cardiovascular disease. A cardiovascular disease is a disease that involves the heart and blood vessels, its most common causes being smoking, alcohol, high blood pressure and blood cholesterol, etc. Added to the fact that it is a major global health issue, the fact that it is incurable once diagnosed makes the disease significantly more severe. Hence, an early diagnosis is crucial in preventing the disease and, if detected, mitigating it. Traditionally, detection for cardiovascular diseases was usually done by computerized tomography scans; however, these methods are too costly and not easily accessible for the general population. Because of these shortcomings of the traditional diagnosis method, there is a need for a more automated, accessible early diagnosis system, and that is the center of this research. In this study, we propose a novel machine learning-based approach for the detection of cardiovascular diseases using retinal images. The proposed method comprises three modules: a feature extractor, an age prediction network, and a cardiovascular disease diagnosis network. We consider the diagnosis of cardiovascular diseases as a classification task, aiming to assess the severity level of the condition based on the retinal image. The proposed method classifies retinal images into one of four categories, each corresponding to a specific level of disease severity. Through extensive experimentation, the results demonstrate that the proposed method outperforms state-of-the-art approaches. We expect that the proposed method holds the potential to become a novel machine learning-powered biomarker for diagnosing cardiovascular diseases.

Comparison of DASH diet score and total antioxidant capacity of diet on serum levels of TMPRSS-2, inflammatory biomarkers, and disease severity in COVID-19 patients: A case-control study.

There is evidence that healthy diets improve the immune system and lessen the severity of infectious diseases such as COVID-19. We have investigated whether the dietary total antioxidant capacity (TAC) and dietary approach to stop hypertension (DASH) score could be associated with the occurrence and clinical outcomes of COVID-19. This case-control study included 120 adults who were admitted to the hospital. Dietary TAC and DASH diet scores were determined by a 138-item semi-quantitative food frequency questionnaire (FFQ). Inflammation-related markers including C-reactive protein (CRP) and transmembrane protease serine 2 (TMPRSS-2) differential were measured. Also, using chest radiology criteria, the severity of the disease was evaluated. The mean CRP values in the lowest and highest tertiles of either dietary TAC or DASH diet scores were 9.44 ± 11.26 and 3.52 ± 4.83 mg/dL (p = .003) or 9.04 ± 11.23 and 4.40 ± 6.23 mg/dL (p = .013), respectively. Individuals with higher dietary TAC were at a lower risk of COVID-19 (OR: 0.06, p < ·0001). Individuals with greater DASH diet scores were also at decreased odds of COVID-19 (OR: 0.12, p < ·0001). No significant associations were found between dietary TAC and DASH diet scores with severity of COVID-19 disease, CRP, or TMPRSS-2 (p > 0.05). The study found that adherence to a diet with higher dietary TAC and DASH diet scores may be protective against COVID-19 and improve outcomes of the disease. More research is needed to corroborate these findings.

Patient With Parkinson Disease and Care Partner Perceptions of Key Domains Affecting Health-Related Quality of Life: Systematic Review

To effectively customize Parkinson disease (PD) programs, it is important to incorporate the "individual's voice" and have a thorough understanding of the symptom priorities of people with PD (PwP) and care partners (CP). In this convergent integrated mixed-method systematic review, we aimed to analyze qualitative and quantitative evidence of PD motor and nonmotor symptoms affecting health-related quality of life (HRQOL) in PwP and CP, comparing priorities across different levels of disease severity. We searched MEDLINE, PsycINFO, Web of Science, Embase, and Scopus; ProQuest Dissertations and Theses Global; and the Michael J. Fox Foundation Data Resources for studies published up to June 29, 2022. We included qualitative, quantitative, and mixed-method studies investigating PD symptom priorities among PwP and CP. We critically appraised eligible studies for methodological quality using the Mixed-Methods Appraisal Tool. Derived terms were mapped and coded according to thematic attribution. Independent syntheses of qualitative and quantitative evidence and transformation of quantitative data into qualitative formats were performed. Of the 7,716 identified studies, we included 70 that provided qualitative (n = 13), quantitative (n = 53), and mixed (n = 4) evidence. We included 604 mapped terms representing 11 PwP-identified and CP-identified motor and nonmotor symptom categories. Across all PD stages, both PwP and CP considered 5 domains more affecting their HRQOL, namely: "motor functionality," "mood," "cognition," "gait, balance, posture, and falls," and "nighttime sleep disorders." In early disease, PwP and CP considered "mood" the domain that most affected their HRQOL. In advanced PD, PwP considered "pain" the domain that most affects their HRQOL, while CP considered "psychiatric symptoms." The domain "gait, balance, posture, and falls" was equally considered by both PwP and CP as the second domain that most affects their HRQOL in the advanced stage of PD. The ranking of the priority of symptoms is largely shared by PwP and CP, and motor symptom priorities dominate the full disease spectrum. However, the nonmotor symptom priorities shift according to the disease severity stage. Tailored care and research require that providers consider these shifting priorities and incorporate the "individual's voice" into treatment decisions.

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses.

525 - Real-world effectiveness of upadacitinib in moderate-to-severe atopic dermatitis (AD): Results from cross-sectional analyses of the CorEvitas AD Registry

Abstract Introduction/Background Upadacitinib is an oral Janus kinase inhibitor approved to treat moderate-to-severe atopic dermatitis (AD). While clinical trials have demonstrated its efficacy in treating the signs and symptoms of AD, less is known about its effectiveness in a real-world setting. Objectives This cross-sectional study describes the real-world clinical and patient-reported outcomes of adults with AD enrolled in the CorEvitas AD Registry and treated with upadacitinib for at least four or more weeks. Methods The CorEvitas AD Registry is a prospective, non-interventional registry of adults diagnosed with AD by a dermatologist or qualified dermatology practitioner. The current analysis included participants enrolled between July 21, 2020 through August 7, 2023 who were treated with upadacitinib for at least 4 weeks at the time of evaluation by their provider. Outcome measures included assessments of: skin clearance (Validated Investigators Global Assessment Scale for Atopic Dermatitis [vIGA-AD™], Eczema Area and Severity Index [EASI]); worst itch in the past 24 hours (Peak Pruritus Numeric Rating Scale [PP-NRS]); symptom frequency (Patient-Oriented Eczema Measure [POEM]); quality of life (Dermatologic Life Quality Index [DLQI]); and disease control (Atopic Dermatitis Control Tool [ADCT]). Outcomes were cross-sectionally summarized using data collected at the latest registry visit (Overall cohort), as well as 1 to less than 5 months (1-5 months) and 5-9 months following upadacitinib initiation (Month 1-5 cohort and Month 5-9 cohort, respectively). Subgroup analyses were also conducted based on prior use of biologics indicated for AD (bio-naïve, bio-experienced). Data were analyzed as observed with no imputation. Safety events were not assessed in this ­analysis. Results A total of 335 patients (mean age 45.6 years; 51.6% female; 64.2% White) were treated with upadacitinib (15 mg: n=221 [70.8%]; 30 mg: n=91 [29.2%]; median treatment duration: 6.9 months). Some patients reported concomitant use of topical corticosteroids (28.1%) and prior use of biologics indicated for AD (dupilumab: 45.4%; tralokinumab: 6.0%). For the Overall cohort, 57.3% had clear or almost clear skin (vIGA-AD 0/1). An EASI ≤3 was observed in 74.8% of patients and 45.3% reported no/minimal itch (PP-NRS 0/1); 40.9% had both EASI ≤3 and PP-NRS 0/1. Most patients (69.3%) reported that their AD was controlled (ADCT &amp;lt;7), 36.4% reported clear or almost clear disease (POEM 0–2), and 39.8% reported no impact of AD on their quality of life (DLQI 0/1). Similar results were observed for the Month 1-5 (n=144) and Month 5-9 (n=194) cohorts; results in bio-naïve and bio-experienced patients were comparable. Conclusions More than half of patients with ≥4 weeks of upadacitinib treatment had clear or almost clear skin and greater than two-thirds reported controlled disease, with similar results observed in both bio-naïve and bio-experienced patients. Over a third reported no/minimal symptom and disease burden. These findings suggest that low levels of disease severity are observed with real-world use of upadacitinib. Future analyses will aim to report changes in disease severity and burden due to persistent upadacitinib treatment.

New insights of propolis nanoformulation and its therapeutic potential in human diseases.

Scientific research is crucial to develop therapies for various disease severity levels, as modern drugs cause side effects and are difficult to predict. Researchers are exploring herbal alternatives with fewer side effects, particularly propolis, which has been validated through in vitro, in vivo, and clinical studies. This will focus on scientific evidence and its supporting technology for developing new bioactive compounds for chronic diseases. Nanotechnology can improve the delivery and absorption of herbal medicines, which often have poor bioavailability due to their high molecular weight and solubility in water, particularly in oral medicines. This technology can enhance propolis's effects through multi-target therapy and reduce side effects. All publications related to each section of this review were discovered using the search engines Google Scholar, Scopus, and Pubmed. This was only available for publication between 2013 and 2023. The selected publications were used as references in this review after being thoroughly studied. Evaluation of propolis active compounds, the classification of propolis nano formulations, design concepts, and mechanisms of action of propolis nano formulation. Additionally, the challenges and prospects for how these insights can be translated into clinical benefits are discussed. In the last ten years, a list of nanoformulation propolis has been reported. This review concludes the difficulties encountered in developing large-scale nanoformulations. To commercialize them, improvements in nano carrier synthesis, standardized evaluation methodology within the framework of strategy process improvement, and Good Manufacturing Practices would be required.

Haematological Profile and ACE2 Levels of COVID-19 Patients in a Metropolis in Ghana

Background: Several studies have linked coronavirus disease 2019 (COVID-19) risk to age and ABO blood groups. Variations in plasma angiotensin-converting enzyme 2 (ACE2) levels and blood counts have been reported, suggesting an association between disease severity and low lymphocyte levels. Aim: this study aimed to understand how these factors relate to COVID-19 in Ghanaian patients, considering geographical and demographic differences. Methods: Participants were recruited from six hospitals in Kumasi, Ghana, between June 2020 and July 2021. Nasopharyngeal swabs were taken to test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and blood samples were collected for complete blood count testing, ABO/Rhesus typing, and assessment of plasma ACE2 levels. Demographic and COVID-19 severity data were gathered, and IBM SPSS version 25.0 was used for analysis. Results: Overall, 515 patients were enrolled, out of which 55.9% (n = 288/515) were males and 50.3% (n = 259/515) tested positive for SARS-CoV-2. The median age was 37 years (IQR = 26–53). Age was significantly associated with SARS-CoV-2 infection (p = 0.002). The severe COVID-19 group was the oldest (70 years, IQR = 35–80) and presented with anaemia (haemoglobin, g/dL: 9.55, IQR = 7.85–11.93), leukocytosis (WBC × 103/μL: 15.87, IQR = 6.68–19.80), neutrophilia (NEUT × 106/μL: 14.69, IQR = 5.70–18.96) and lymphocytopenia (LYMPH × 106/μL: 0.47, IQR = 0.22–0.66). No association was found between SARS-CoV-2 positivity and ABO (p = 0.711) or Rh (p = 0.805) blood groups; no association was also found between plasma ACE2 levels and SARS-CoV-2 status (p = 0.079). However, among COVID-19 participants, plasma ACE2 levels were significantly reduced in the moderate illness group (40.68 ng/mL, IQR = 34.09–48.10) compared with the asymptomatic group (50.61 ng/mL, IQR = 43.90–58.61, p = 0.015). Conclusions: While there may be no real association between the ABO blood group, as well as plasma ACE2 levels, and SARS-CoV-2 infection in Ghanaian patients, older individuals are at a higher risk of severe disease. Anaemia, and leukocytosis with lymphocytopenia may be indicators of poor disease progression.

Secondary attack rate of COVID-19 in shared households in Slovakia.

The objective of this study was to estimate the secondary attack rate in unvaccinated members of households of two regions in the Slovak Republic for the period November 2020 ‒ April 2021. SARS-CoV-2 infection poses ahigh risk of transmission in close-contact indoor settings, such as households. The household transmissibility of SARS-CoV-2 varies widely across countries. We included 278 households for SARS-CoV-2 transmission analysis. We calculated the secondary attack rate (SAR). We assessed sex, level of disease severity and means of isolation during infection of index cases as determinants of disease transmissibilityRESULTS: The secondary attack rate in 278 households was estimated at 63.7% (95%CI: 58.2‒66.7%). The SARs were different by sex (60.2% in females, 67.5% in males). The highest SAR was observed in the households of asymptomatic cases (77.8%), followed by moderate severity (66.5%), hospital admissions (63.2%) and mild disease (58.2%). We found ahigh household secondary attack rate in two regions of Slovakia in the period when Alpha variant (B.1.1.7) of SARS-CoV-2 was dominant in the country. The results highlight the importance of monitoring transmission dynamics (Tab. 1, Fig. 1, Ref. 12). Text in PDF www.elis.sk Keywords: COVID-19, secondary attack rate, Slovak Republic, households.

Characterizing Disease Progression in Parkinson's Disease from Videos of the Finger Tapping Test.

Parkinson's disease (PD) is characterized by motor symptoms whose progression is typically assessed using clinical scales, namely the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Despite its reliability, the scale is bounded by a 5-point scale that limits its ability to track subtle changes in disease progression and is prone to subjective interpretations. We aimed to develop an automated system to objectively quantify motor symptoms in PD using Machine Learning (ML) algorithms to analyze videos and capture nuanced features of disease progression. We analyzed videos of the Finger Tapping test, a component of the MDS-UPDRS, from 24 healthy controls and 66 PD patients using ML algorithms for hand pose estimation. We computed multiple movement features related to bradykinesia from videos and employed a novel tiered classification approach to predict disease severity that employed different features according to severity. We compared our video-based disease severity prediction approach against other approaches recently introduced in the literature. Traditional kinematics features such as amplitude and velocity changed linearly with disease severity, while other non-traditional features displayed non-linear trends. The proposed disease severity prediction approach demonstrated superior accuracy in detecting PD and distinguishing between different levels of disease severity when compared to existing approaches.

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images.

Diagnosing lung diseases accurately and promptly is essential for effectively managing this significant public health challenge on a global scale. This paper introduces a new framework called Modified Segnet-based Lung Disease Segmentation and Severity Classification (MSLDSSC). The MSLDSSC model comprises four phases: "preprocessing, segmentation, feature extraction, and classification." Initially, the input image undergoes preprocessing using an improved Wiener filter technique. This technique estimates the power spectral density of the noisy and original images and computes the SNR assisted by PSNR to evaluate image quality. Next, the preprocessed image undergoes Segmentation to identify and separate the RoI from the background objects in the lung image. We employ a Modified Segnet mechanism that utilizes a proposed hard tanh-Softplus activation function for effective Segmentation. Following Segmentation, features such as MLDN, entropy with MRELBP, shape features, and deep features are extracted. Following the feature extraction phase, the retrieved feature set is input into a hybrid severity classification model. This hybrid model comprises two classifiers: SDPA-Squeezenet and DCNN. These classifiers train on the retrieved feature set and effectively classify the severity level of lung diseases.

Reduced HDL-cholesterol in long COVID-19: A key metabolic risk factor tied to disease severity

This controlled study investigated metabolic changes in non-vaccinated individuals with Long-COVID-19, along with their connection to the severity of the disease. The study involved 88 patients who experienced varying levels of initial disease severity (mild, moderate, and severe), and a control group of 29 healthy individuals. Metabolic risk markers from fasting blood samples were analyzed, and data regarding disease severity indicators were collected. Findings indicated significant metabolic shifts in severe Long-COVID-19 cases, mainly a marked drop in HDL-C levels and a doubled increase in ferritin levels and insulin resistance compared to the mild cases and controls. HDL-C and ferritin were identified as the leading factors predicted by disease severity. In conclusion, the decline in HDL-C levels and rise in ferritin levels seen in Long-COVID-19 individuals, largely influenced by the severity of the initial infection, could potentially play a role in the persistence and progression of Long-COVID-19. Hence, these markers could be considered as possible therapeutic targets, and help shape preventive strategies to reduce the long-term impacts of the disease.

The association of systemic immune-inflammation index with lung function, risk of COPD and COPD severity: A population-based study.

The relationship between the levels of Systemic Immune-inflammation Index (SII) and chronic obstructive pulmonary disease (COPD), lung function, and COPD severity were not fully understood. We conducted this cross-sectional, population-based study to investigate the complex association between SII and COPD, lung function, and COPD severity among the US adults. Overall, 18,349 participants were included in the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018. The exposure variable was SII, calculated from platelet counts, neutrophil counts, and lymphocyte counts. Weighted univariable and multivariable logistic regression, subgroup analysis, and restricted cubic spline (RCS) regression were performed to assess the relationship between COPD, lung function, COPD severity and SII. Last, we used a propensity score matching (PSM) analysis to reduce selective bias and validate these relationships. Approximately 1,094 (5.96%) of the participants were diagnosed as COPD. The multivariable-adjusted odds ratio (OR) (95% confidence interval, CI) for the Q2 group (Log-SII > 2.740) was 1.39 (1.16 to 1.68). Before and after matching, multivariable logistic regression models revealed that increased Log-SII levels (SII Logarithmic transformation) associated positively with the risk of COPD. The subgroup analysis showed no interaction between Log-SII and a variety of variables (P for interaction > 0.05). RCS showed a reversed L-shaped relationship between Log-SII with COPD (P for nonlinear = 0.001) in individuals. In addition, we observed negative significant correlations between forced expiratory volume in one second (FEV1) / forced vital capacity (FVC) %, FEV1/FVC% predicted and SII, and reversed U-shaped curve relationships between FEV1, FEV1% predicted and SII. High SII level is associated with severity of COPD, especially at Global Initiative on Obstructive Lung Disease (GOLD) 1 and GOLD 3. In summary, the Log-SII level is associated with COPD risk, lung function, and COPD severity.

The investigation of relationship between serum melatonin levels with Beck Depression Inventory and Beck Scale for Suicidal Ideation in suicide patients.

Melatonin plays a role in many biological and physiological events. There are studies in the literature relating melatonin levels to many psychiatric disorders such as schizophrenia, bipolar disorder, and major depressive disorder. We aimed to investigate the relationship between serum melatonin levels with the Beck Depression Inventory and the Beck Scale for Suicidal Ideation in suicide patients. The study was conducted prospectively with volunteer patients aged 20-50 years who were admitted to the emergency department after a suicide attempt. The social and occupational status, educational levels, marital status, and stressor factors of patients were questioned. Beck Depression Inventory and Beck Scale for Suicidal Ideation were applied to each patient included in the study. Blood melatonin levels were evaluated using the enzyme-linked immunosorbent assay method. The data were analyzed with the SPSS 23.00 statistical program. Descriptive values were expressed by the number of cases (n), percentage (%), median (interquartile range), and mean±standard deviation. The Kolmogorov-Smirnov test was used to assess the distribution of continuous variables, and the Pearson or Spearman correlation test was used to assess the relationship between disease severity and melatonin level. A value of p<0.05 was considered statistically significant. No statistically significant correlation was found between melatonin level and the Beck Depression Inventory score (r=-0.098, p=0.44). However, a statistically weak, inverse, and significant correlation was discovered between melatonin levels and the Beck Scale for Suicidal Ideation score (r=-0.465, p=0.00). According to our results, it was determined that there was a significant negative relationship between melatonin level and the Beck Scale for Suicidal Ideation scoring.

Novel role of peptidoglycan recognition protein 2 in activating NOD2-NFκB inflammatory axis in coronary artery disease

Backgrounds and aimsThe role of inflammation in driving atherosclerosis is well-established. It exerts systemic effects beyond the local site of plaque formation. In the context of coronary artery disease (CAD), the proteins that show altered levels in the plasma, are potentially important for understanding the key regulatory mechanism in the pathogenesis of atherosclerosis. A case-control study revealed that plasma soluble Peptidoglycan Recognition Protein 2 (PGLYRP2) primarily produced by the liver, is increased in subjects with CAD. Furthermore, the concentration of PGLYRP2 in the blood correlates with the severity of coronary artery disease. Thus, it raises interest in understanding the exact role of the protein in aortic inflammation and plaque progression. MethodsWe evaluated the plasma concentration of PGLYRP2 in three distinct groups: patients with CAD (N = 68), asymptomatic individuals (N = 34), and healthy volunteers (N = 20). Furthermore, we investigated the correlation between disease severity and PGLYRP2 levels in CAD patients. To identify potential binding partners of PGLYRP2, we employed computational analysis. We verified the PGLYRP2-NOD2 interaction in macrophage cells and elucidated the inflammatory pathways activated by PGLYRP2 within these cells. To assess the impact of PGLYRP2, we examined its effects in the atherosclerotic mice model (ApoE−/−). ResultsIn this study, we report for the first time that Nucleotide-binding Oligomerization domain 2 (NOD2) which is expressed on the surface of macrophages, is a receptor of PGLYRP2. The N-terminal domain of PGLYRP2 directly binds to NOD2 and activates the NOD2-RIP2-NFκB cascade that promotes the secretion of proinflammatory cytokines like TNFα, IL1β, and IL-8. In the atherosclerotic mice model (ApoE−/−) we demonstrate that elevated PGLYRP2 level is parallel with increased proinflammatory cytokines in the plasma when fed a High Cholesterol Diet (HCD). Immunohistochemical analysis reveals that PGLYRP2 is co-localized with NOD2 on the macrophages at the site of the lesion. ConclusionsTaken together, our data demonstrate that NOD2 acts as a receptor of PGLYRP2 on macrophages, which mediates the activation of the NOD2-RIP2-NFκB pathway and promotes inflammation, thus significantly contributing to the development and progression of atherosclerosis.

Classification of wheat powdery mildew based on hyperspectral: From leaves to canopy

Powdery mildew is considered to be one of the important diseases that threaten wheat production. It can cause serious economic losses and damage food security. Therefore, early detection of powdery mildew infection is essential to limit its amplification in reproduction. The purpose of this study is to use the hyperspectral image (HSI) to detect wheat powdery mildew (WPM) in an objective and accurate way. A method is proposed to classify the infection degree of WPM by combining cascade interval selection sensitive band with support vector machine (SVM). Firstly, HSI of leaf and canopy scale are obtained, and the image was segmented based on HSV (hue, saturation, value) color space and OTSU algorithm (HSV + OTSU). The spectral reflectance data were extracted after removing the background region of interest (ROI), and then the multiple scattering correction (MSC) was performed. The spectral data after MSC was called RAW, and then the first derivative (D1) and second derivative (D2) operations were performed respectively. Subsequently, the method of backward interval partial least squares (BiPLS) was used to screen out the characteristic bands of the sensitive interval. Finally, the classification models of WPM disease degree at the leaf scale and the canopy scale (RAW-BiPLS-SVM, D1-BiPLS-SVM and D2-BiPLS-SVM) were established respectively. The results showed that the spectral data after derivative operation was enhanced, and the classification effect of D1-BiPLS-SVM model had advantages at both leaf and canopy scales, in which the overall accuracy (OA) at the leaf scale was 95.2 %, and the OA at canopy scale was 74.4 %. In order to increase the difference between diseases, the canopy-scale disease severity levels were integrated. The correct rates of health and disease were 96.9 % and 99.2 %, respectively, and the OA of disease grade recombination was 87.9 %. Therefore, the method proposed in this paper can not only accurately identified healthy and infected wheat, but also realized the classification of different degrees of WPM, and provide technical support for the prevention and control of WPM.