Levels Of Disease Severity Research Articles

In heart failure, self-care behavior is an independent determinant of health-related quality of life, regardless of the severity of this patients

Abstract Background Poor self-care behaviour is associated with a higher risk of readmission and mortality in patients with heart failure (HF). The interplay between self-care and patient-reported outcomes such as health-related quality of life (QoL) has not been fully established. Purpose To describe the association between self-care and QoL in real-world cohort of HF patients. Methods We conducted an observational, prospective, cohort study of 1120 consecutive patients with chronic HF. We used the modified European Heart Failure Self-care Behaviour Scale 9-item version (EHFSCBS-9) to measure self-care. Scores were inverted and standardized (higher scores indicate better selfcare). To assess QoL, we used the Minnesota Living with Heart Failure Questionnaire (MLHFQ) (higher scores indicate worse QoL). Written informed consent, complete clinical and psychosocial information of patients were obtained at baseline. To determine the impact of global self-care and its dimensions (autonomy-based adherence, consulting behaviour and provider-based adherence), multivariate linear regressions models (backwards stepwise methods) to predict QoL were constructed. The multivariate parametric and non-parametric associations between EHFSCBS-9 scoresand the β estimated risk of impairment of QoL (defined as MLHFQ scores ≥ median values) were explored using General Additive Models (GAM). All models were adjusted by age, sex and prognostic factors such as LVEF, NYHA, NT-proBNP levels and recent hospitalization among other well-known determinants of HF severity. Results A total of 484 patients (43%) were women, mean age was 72±11 years, and mean LVEF was 45±17%. Mean MLHFQ scores were 45±24. Mean EHFSCBS-9 scores were 69±28. There was a significant inverse correlation between EHFScBS-9 and MLHFQ scores (r=−0.158; p-value<0.0001). As shown in Table 1, self-care behaviour and its 3 domains were independent predictors of QoL in 7 out of the 16 associations explored. These models confirmed a weak linear relationship between EHFSCBS-9 scores and MLHFQ scores particularly between global selfcare and consulting behaviour dimension with global, emotional and social dimensions of QoL. Interestingly, when QoL was dichotomized as impaired/preserved QoL, GAM showed a significant non-parametric “U-shape” relationship (p-value<0.05) between EHFScBS-9 scores and the risk of impairment in QoL (Figure 1). Conclusions We have shown that self-care behaviour is an independent predictor of health related QoL in HF regardless the level of disease severity. Global self-care and consulting behaviour were particularly and significantly associated with global QoL and emotional and social dimensions of QoL. Interestingly the relationship between self-care scores and the risk of impaired global QoL was non-linear showing a “U-shape” association pointing out that patients with extreme levels of self-care were more likely to experience impairments in QoL. Funding Acknowledgement Type of funding sources: None.

In patients with chronic heart failure, there have been no gender differences in the impact of self behavior on patient-reported outcomes

Abstract Background Poor self-care behaviour is associated with a higher risk in heart failure (HF). Gender is a determinant of patient-reported outcomes (PROMs) such as health-realted quality of life (QoL) in HF. Female gender is consistently associated with worse QoL. Whether gender may influence the effect of self-care on PROMS is not known. Purpose Describe the effect of self-care on QoL according to gender in a real world cohort of HF patients. Methods 1120 patients with chronic HF were analysed. We used the modified European Heart Failure Self-care Behaviour Scale 9-item version (EHFSCBS-9) to measure self-care (inverted and standardised: higher scores indicate better self-care). To assess QoL, we used Minnesota Living with Heart Failure Questionnaire (MLHFQ) (higher scores indicate worse QoL). General linear models (GLM) were constructed to explore the interaction between gender and poor self-care (defined as EHFSCBS-9 ≤lower tertile) on adjusted MLHFQ scores. To determine the impact of global self-care and its dimensions, multivariate linear regressions models to predict QoL were constructed stratified according to gender. All models were adjusted by determinants of HF severity. Results Mean age was 72±11 years, 43% were women and mean LVEF was 45±17%. There was a significant inverse correlation between EHFScBS-9 and MLHFQ scores (r=−0.158; p-value<0.0001). Unadjusted MLHFQ scores were consistenly higher in women compared to men (49±23 vs. 43±24; p-value<0.001). In adjusted GLM, female gender (F=4.8; p-value=0.028) and poor global self-care (F=12.6; p-value<0.001) were independent predictors of higher adjusted scores in MLHFQ, indicating worse global QoL. As shown in Figure 1, the interaction term gender by poor self-care was not significantly associated with MLHFQ scores in adjusted GLM (F=0.018; p-value=0.893): the gap between women and men in terms of QoL was observed in both self-care strata and the effect of self care on QoL was observed in both genders. Multivariate linear regresion models (Table 1) stratified according to female gender (panel A) or male gender (panel B) showed that higher scores in the EHFScBS-9 were associated with lower scores in the MLHFQ in women and men. This effect (Table 1) was equally seen in both genders for global QoL and was driven by global self-care, consulting behaviour, and in a lesser extent by autonomy-baseda dherence. The physical dimension of QoL was not affected by self-care in either women or men except for consulting behaviour. Conclusions Self-care is an independent predictor of health-related QoL in HF, regardless of the level of disease severity. Global self-care, autonomy-based adherence and consulting behaviour were particularly associated with global QoL and emotional and social dimensions of QoL in women and men. In both genders, the effect of self-care on QoL was driven by its effect on emotional and social dimensions. Provider-based adherence did not influence QoL. Funding Acknowledgement Type of funding sources: None.

The Effects of Long COVID on Thai Teenagers

The post-COVID-19 condition, also known as the Long COVID syndrome, was first widely acknowledged in the scientific and medical worlds. Since it affects COVID-19 survivors at all disease severity levels, including younger individuals, children, and those who are not in hospitals, this illness is poorly understood. Presently, there is a limitation to the definition of the post-COVID condition. This study would fill in this gap by surveying the most common symptoms among Thai teenagers (10–24). This data is gathered by the online survey: Google Form and statistically analyzed through percentages. Consequently, the majority of symptoms are aches and pains (37.037%), asymptomatic (31.579%), depression (18.324%), and bedridden symptoms (6.324%) as a result of the prolonged quarantine. In terms of drug complications, the majority of respondents had none (89.330%), acid reflux (4.715%), and glycemic instability (1.489%). In terms of physical health effects following illness, the majority of respondents reported symptoms such as shortness of breath (30.615%), fatigue (27.227%), chronic cough (13.802%), insomnia (11.543%), and so on. This study shows that Long COVID has an impact on the respondents' daily lives at levels 1-10 on level 7 (15.960%), level 5 (13.965%), and level (13.965%). Keywords: Long COVID, Thai teenager, Thailand

Soybean rust detection and disease severity classification by remote sensing

AbstractThe detection and monitoring of soybean rust (SBR) through remote sensing is promising because of the importance of the crop and the aspects of the disease. We evaluated the effects of different levels of SBR severity on soybean [Glycine max (L.) Merr.] leaflets reflectance aiming for the construction of a disease classification model. Leaflet reflectance was evaluated on two cultivars (susceptible and partially resistant) at four disease severity levels: healthy, low, moderate, and high. Leaflets were collected in the field and taken to the laboratory for spectral evaluation through the spectrophotometer UV 2700 coupled with Integrating Sphere Attachment ISR‐603, in the range of 270–1000 nm. The feasibility of using a collection of vegetation indices (VIs) and data dimensionality reduction through multiple factor analysis (MFA) was evaluated, and a classification model was constructed. Ten algorithms were assessed based on precision, sensibility, and accuracy parameters, using 80% of the dataset as training data and 20% as testing dataset. The visible range and red edge region contributed more significantly to the disease prediction and classification model. The MFA performed satisfactorily in the dimensionality reduction and unveiled the effect of specific wavelengths on the classification of each class. Most of the VIs studied had high correlation performance across the severity classes. Classification accuracy and precision were >70% for all models. Linear support vector machine with the collection of VIs achieved the best results. This study provides a practical path for developing a detection model to be integrated into SBR management programs.

Reduction of natural killer cells is associated with poor outcomes in patients with hepatitis B virus-related acute-on-chronic liver failure.

Natural killer (NK) cells are critical innate effectors that respond to viral infections and contribute to immunopathology. Here, we aimed to investigate the role of NK cells in hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) and elucidate the underlying mechanism by examining their phenotypic and functional profiles. We included patients with HBV-ACLF (n = 37) and chronic hepatitis B (n = 19), and healthy controls (n = 13) in our study. We examined the phenotype and function of different subsets of peripheral NK cells using flow cytometry and RNA-sequencing analysis, and screened liver NK cells using immunohistochemistry. We detected inflammatory cytokines using a Luminex assay. In addition, we analyzed the relationships between these parameters and disease severity. Peripheral NK cells were decreased and characterized by high expression of caspase-3, Ki67, CXCR3, NKG2D, NKp46, CD107a, and GM-CSF, and typified by higher cell migration and immune response by RNA-sequencing analysis in patients with HBV-ACLF than in those with chronic hepatitis B. Accumulations of CXCL-10 and NK cells were found in the liver, and excessive production of CXCL-10 in the peripheral blood contributed to the apoptosis of NK cells in vitro. The decrease in NK cells was associated with the level of HBV DNA and disease severity and had good prognostic performance in predicting the outcome of patients with HBV-ACLF through AUROC analysis. NK cells were significantly decreased and showed dysfunction of phenotypic and functional profiles across distinct subsets in the peripheral blood of patients with ACLF. Crosstalk between CXCL-10 and NK cells may mediate the unbalanced distribution of NK cells. Understanding the dysfunction and decrease in NK cells may provide new insights into ACLF pathogenesis.

Comparison of 10-2 and 24-2 Perimetry to Diagnose Glaucoma Using OCT as an Independent Reference Standard

To compare the performance of the 10-2 test versus 24-2 standard automated perimetry (SAP) test for the diagnosis of glaucoma using OCT as an independent standard for glaucomatous damage. Cross-sectional study. A total of 1375 pairs of 10-2 and 24-2 SAP tests from 569 eyes of 339 subjects were used for the analysis. A total of 440 (77%) eyes had a diagnosis of glaucoma, and 129 (23%) eyes were normal. All participants underwent 10-2 and 24-2 SAP tests within 30 days. Glaucomatous severity was quantified based on OCT macula ganglion cell layer (mGCL) and circumpapillary retinal nerve fiber layer. The area under the receiver operating characteristic (ROC) curve (AUC) was used to compare 10-2 and 24-2 metrics for discriminating healthy eyes from those of glaucoma, at different levels of disease severity. Areas under the ROC curves and sensitivities at fixed specificities of 80% and 95%. The overall AUC for mean deviation (MD) for the 24-2 test (0.808) was significantly higher than that of the 10-2 test (0.742; P < 0.001). When compared at different stages of the disease, the 24-2 test performed generally better than the 10-2 test, notably in the earlier stages of the disease. For early damage (first quartile), the 24-2 MD had an AUC of 0.658 versus 0.590 for 10-2 MD (P= 0.018). For advanced damage (fourth quartile), corresponding values were 0.954 vs. 0.903 (P= 0.013). Similar trends were observed when glaucoma severity was defined based on structural macular damage with mGCL thickness. The 24-2 SAP test had better diagnostic accuracy compared with that of the 10-2 test for detecting equivalent levels of glaucomatous damage, as measured by quantitative assessment of retinal nerve fiber layer and macula by OCT. Proprietary or commercial disclosure may be found after the references.

How Accurate Are Virtual Assessments Of Function In Persons With Parkinson’s Disease?

The COVID-19 pandemic has been associated with a deterioration of symptoms for those with Parkinson’s Disease (PD), which may be worsened with social isolation, apathy, and anxiety1. The pandemic necessitated the transition to utilizing online delivery of health and recreational activities1. PURPOSE: To compare the reliability of virtual and in-person physical assessments of function commonly utilized in the PD population. METHODS: 11 persons with PD were assessed virtually using the 360° turn test, 5 times sit to stand (5XSTS), Timed Up and Go (TUG) test, forward gait speed and backwards gait speed. Participants were sent a measuring tape with various markings on it to determine standard distances for testing. All assessments were recorded on Zoom video platform. Reference data was collected through literature reviews of these tests done in-person for persons with PD. A one sample T-Test was run to compare the data from virtual assessments (V) to the reference data (R). RESULTS: 360° turn time was not significantly different from the reference time (A: 5.2 ± 1.5 s, R: 6 s p = 0.119)2. 5XSTS and TUG time were found to be significantly less than the reference times (5XSTS V: 13.6 ± 2.5 s, R: 20.3 s, p < 0.0013 ; TUG V: 11.9 ± 2.7 s, R: 14.8 s, p = 0.0054). Forward and backward gait speed were significantly slower than the reference speed (Forward V: 0.9 ± 0.3 m/s, R: 1.2 m/s, p = 0.008; Backward V: 0.5 ± 0.2 m/s, R: 0.7 m/s, p = 0.021)5. CONCLUSIONS: Various factors could have contributed to these results, including inherent variability associated with disease severity levels in persons with PD. Testing at home in a familiar environment has the potential to alter results. Every assessment except the 360° turn test required outside equipment, including either a chair or tape measure, which adds variability to the test depending on chair height and tautness of the tape measure. The reduced variability in the 360° turn test allowed for more standardization in environment and allowed for a more focused camera angle to look only at the participants feet. Sending equipment or markers to participants that identify standard heights for testing could potentially improve consistency of results. The differences in virtual execution of these tests should be noted when using them for patient care for those with PD.

A Novel Approach to Grade Cotton Aphid Damage Severity with Hyperspectral Index Reconstruction

As a kind of important insect pest of cotton crops, aphids cause serious damage in cotton yields and quality worldwide, posing a significant risk to economic losses. Automatic detection of the pest damage level plays an important role in cotton field management. However, it is usually regarded as a classification problem in machine learning, where the disease severity levels are taken as independent categories and the inter-level relationship has not fully been considered. To utilize the inherited relations among different severity levels caused by cotton aphids, a novel approach based on the spectral index reconstruction was proposed in this study. First, six types of initial spectral indices were reconstructed based on healthy samples in the training set. Then, the severity sequences corresponding to the reconstructed initial spectral indices (RISIs) were sorted and compared with the ideal sequence. After attaining sequences most consistent with the ideal one, the ratio between the inter- and intra- levels was calculated to select the sensitive RISI. Moreover, the range of each severity level was established by the thresholds between adjacent grades of the selected sensitive RISI, which was finally used to determine the disease severity level caused by cotton aphids. Results of the cotton aphids showed that the proposed approach achieved a grading performance with OA = 0.944, AA = 0.900, and Kappa coefficient = 0.928. Hence, the proposed approach based on hyperspectral index reconstruction is effective and has potential application in grading the aphid infestation severity of cotton.

Combined effects of root-associated entomopathogenic and mycorrhizal fungi on the foliar pathogen Botrytis cinerea in tomato

Many fungi live intimately associated with plants and may benefit or harm the host plant. Improved knowledge of such interactions is needed for increasing plant health and crop productivity by implementation of fungal inoculants. Co-inoculations of different beneficial fungi offer the possibility to understand complex plant–microbe interactions that may be functionally complementary for improved plant production and protection. Here, we studied the individual and combined effects of the arbuscular mycorrhizal fungus (AMF) Funneliformis mosseae with three isolates of entomopathogenic fungi (EPF), representing Metarhizium brunneum, M. robertsii and Beauveria bassiana, on protection against the foliar phytopathogen Botrytis cinerea and on plant growth. Seedlings of tomato (Solanum lycopersicum L. var. Moneymaker) were inoculated in the substrate with AMF or EPF alone and in dual combinations under greenhouse conditions. Inoculation with the different EPF isolates reduced lesion sizes of B. cinerea on inoculated tomato leaves, but only in the experimental repetition that showed highest level of disease severity. The AMF F. mosseae had no additional effect on B. cinerea lesion size in combinations with EPF. In the experimental repetition with least disease severity, the AMF treatment led to limited increase of B. cinerea lesion sizes. In general, F. mosseae caused an increase in plant biomass, and the co-inoculations of AMF and EPF did in some combinations increase plant growth. Below-ground interactions between AMF and EPF were observed, as the presence of AMF in the roots was associated with a decrease of EPF root colonization densities. However, AMF colonization rates were unaffected by EPF presence. The study indicated a functional complementarity between EPF and AMF by suppressing phytopathogens and increasing plant growth, respectively. However, it further revealed the challenge of obtaining consistent results of plant–microbe-phytopathogen interactions, which must be overcome for future implementation of beneficial fungi as inoculants in plant production.

The Relationship Between COVID-19 Disease Severity and Zonulin Levels.

IntroductionZonulin is a protein that plays a role in the reversible regulation of epithelial permeability. As zonulin is released in large amounts into the intestinal lumen, it disrupts the integrity of the tight junctions and causes continuous migration of antigens to the submucosa. Consequently, it can trigger inflammatory processes and severe immune reactions. In severe cases, SARS-CoV-2 may have a major impact on the clinical manifestations of the disease by directly or indirectly affecting intestinal cells and triggering systemic inflammation. Therefore, our study aimed to investigate the role of one of the possible mediators, zonulin, in the severity of the COVID-19 infection.Methods Thirty COVID-19 patients and 35 healthy controls were included in the study. Blood samples were taken from the patients on the 1st, 4th, and 8th days of hospitalization. Serum zonulin levels were determined by enzyme-linked immunosorbent assay (ELISA). Complete blood count (white blood cell [WBC], neutrophil, lymphocyte, and platelet), biochemical parameters (serum lactic acid dehydrogenase [LDH], erythrocyte sedimentation rate [ESR], C-reactive protein [CRP], D-dimer, ferritin, fibrinogen levels) were determined and chronic systemic disease states of the patients were assessed.Results Serum zonulin levels were notably higher in the healthy control group compared to the patient group (p=0.003). Although there was an increase in the zonulin values by time in hospitalization, this rising was not significant. Conversely, ESR and CRP levels were significantly higher in the patient group (p<0.001). There was no significant difference between the two groups regarding gender, age, and WBC counts.Conclusion The serum zonulin levels of COVID-19 patients with the mild clinical course were lower than the healthy control group. Moreover, serum zonulin levels were not correlated with ESR, CRP, and other inflammation markers. Our results suggest that low serum zonulin levels in COVID-19 patients might represent a mild disease course.

Quantifying the effects of stripe rust disease on wheat canopy spectrum based on eliminating non-physiological stresses

The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage. Separating the effects of disease stress on the crop from the observed mixed signals is crucial for increasing the precision of remote sensing monitoring of wheat stripe rust. The canopy spectrum of winter wheat infected by stripe rust was processed with the difference-in-differences (DID) algorithm used in econometrics. The monitoring accuracies of wheat stripe rust before and after processing with the DID algorithm were compared in the presence of various external factors, disease severity, and several simulated satellite sensors. The correlation between the normalized difference vegetation index processed by the DID algorithm (NDVI-DID) and the disease severity level (SL) increased in comparison with the NDVI before processing. The increase in precision in the natural disease area in the field in the presence of large differences in growth stage, growth, planting, and management of the crop was greater than that in the controlled experiment. For low disease levels (SL < 20%), the R2 of the regression of NDVI-DID on SL was 38.8% higher than that of the NDVI and the root mean square error (RMSE) was reduced by 11.1%. The increase in precision was greater than that for the severe level (SL > 40%). According to the measured hyperspectral data, the spectral reflectance of three satellite sensor levels was simulated. The wide-band NDVI was calculated. Compared with the wide-band NDVI and vegetation indexes (VIS) before DID processing, there were increases in the correlation between SL and the various types of VIS-DID, as well as in the correlation between SL and NDVI-DID. It is feasible to apply the DID algorithm to multispectral satellite data and diverse types of VIS for monitoring wheat stripe rust. Our results improve the quantification of independent effects of stripe rust infection on canopy reflectance spectrum, increase the precision of remote sensing monitoring of wheat stripe rust, and provide a reference for remote sensing monitoring of other crop diseases.

Detection of Eucalyptus Leaf Disease with UAV Multispectral Imagery

Forest disease is one of the most important factors affecting tree growth and product quality, reducing economic values of forest ecosystem goods and services. In order to prevent and control forest diseases, accurate detection in a timely manner is essential. Unmanned aerial vehicles (UAVs) are becoming an important tool for acquiring multispectral imagery, but have not been extensively used for detection of forest diseases. This research project selected a eucalyptus forest as a case study to explore the performance of leaf disease detection using high spatial resolution multispectral imagery that had been acquired by UAVs. The key variables sensitive to eucalyptus leaf diseases, including spectral bands and vegetation indices, were identified by using a mutual information–based feature selection method, then distinguishing disease levels using random forest and spectral angle mapper approaches. The results show that green, red edge, and near-infrared wavelengths, nitrogen reflectance index, and greenness index are sensitive to forest diseases. The random forest classifier, based on a combination of sensitive spectral bands (green, red edge, and near-infrared wavelengths) and a nitrogen reflectance index, provided the best differentiation results for healthy and three disease severity levels (mild, moderate, and severe) with overall accuracy of 90.1% and kappa coefficient of 0.87. This research provides a new way to detect eucalyptus leaf diseases, and the proposed method may be suitable for other forest types.

Comparison of pneumonia and nonpneumonia-related Acinetobacter baumannii complex bacteremia: A single-center retrospective study

Acinetobacter baumannii complex (ABC) is a group of increasingly prevalent opportunistic pathogens that cause a variety of life-threatening nosocomial infections, especially in the intensive care unit (ICU). This study assessed the differences between pneumonia- and non-pneumonia-related ABC bacteremia and possible independent risk factors for 30-day mortality. The clinical data of ICU patients diagnosed with ABC bacteremia at a tertiary care hospital from January 2009 to December 2020 were collected, and sorted into groups of ABC bacteremia with and without pneumonia. Significant changes in the incidence of ABC bacteremia and antibiotic resistance were observed over the 12-year study. Compared with nonpneumonia-related ABC bacteremia, pneumonia-related ABC bacteremia was associated with a higher rate of hypertension, less prior tigecycline use, more carbapenem-resistant (CR) strains, and a higher 30-day mortality rate. In multivariate analysis, immunosuppression, higher APACHE II score, and SOFA score were independent risk factors for 30-day mortality. Moreover, the risk of death was 1.919 times higher in the pneumonia-related group. Although pneumonia-related ABC bacteremia had worse outcomes, it was not an independent risk factor for death statistically. Immunosuppression and disease severity levels increased the risks of death in ICU patients with ABC bacteremia.

Developing and Validating an Emergency Triage Model Using Machine Learning Algorithms with Medical Big Data.

ObjectiveTo establish an emergency triage model through the statistical analysis of big data during a particular time period from a hospital information system to improve the accuracy of triage in emergency department (ED).MethodsA total of 276,164 patients who visited the Emergency Medicine Department of Beijing Chao-Yang Hospital from 2017 to 2020 were included in this study, including 123,392 men and 152,772 women aged from 14 to 112 years. The baseline characteristics (age and gender) and medical records (patient’s condition, body temperature, heart rate, breathing, blood pressure, consciousness, and oxygen saturation) of the patients was collected. The data samples were randomly allocated, with 80% as the training set and 20% as the testing set. The patients were divided into levels I, II, III, and IV in accordance with a four-level triage standard. We selected the effective Extreme Gradient Boosting (XGBoost) algorithm as our emergency classification prediction model. The XGBoost model was applied to simulate the thinking process of triage nurses, and the De Long’s test was used to compare the receiver operating characteristic (ROC) curve of different models. The P value was obtained by calculating the variance and covariance of area under the curve (AUC) values of different ROC curves.ResultsLevel I had 4960 (1.8%) patients, level II had 25,646 (9.29%), level III had 130,664 (47.31%), and level IV had 114,894 (41.6%). The XGBoost model was built following a logic exercise based on the traditional manual pre-inspection and triage results. After verification, the prediction accuracy was 82.57%. The AUC of each disease severity level (levels I, II, III, and IV) was 0.9629, 0.9554, 0.9120, and 0.9296, respectively.ConclusionThe emergency triage prediction model, which achieved a relatively strong accuracy rate, can reduce the work intensity of medical workers and improve their working efficiency.

SIRS tanılı hastalarda enflamasyon ve kemik döngüsü arasındaki ilişkinin sklerostin ve Dickkopf-1 (DKK-1) düzeyleri ile değerlendirilmesi

Introduction: In intensive care units (ICU), patients remain bedridden for a long time. In addition, severe infections are frequently seen in ICUs. Both prolonged immobilization and serious infections are associated with bone tissue loss. The Wnt pathway has recently been focused on evaluating bone tissue loss. The Wnt pathway participates in both infections and the formation of bone tissue. Wnt pathway inhibitors sclerostin and Dickkopf-1 (DKK-1) inhibit bone formation and increase osteoclastic activity. In this study, we aimed to examine bone turnover by the Wnt inhibitors sclerostin and DKK-1 and their possible associations with inflammation in SIRS patients.Methods: We included 30 patients diagnosed with systemic inflammatory response syndrome (SIRS) in the study group and 16 in the control group. Serum sclerostin, DKK-1, white blood cell (WBC), and C-Reactive Protein (CRP) levels on the day of SIRS diagnosis (basal), the 7th, 14th, and 21stdays were evaluated in the study group, and the results were compared with the control group.Results: When the control group was compared with the basal SIRS, there was a significant elevation in both sclerostin (p=0.003) and DKK-1 (p=0.001). Statistical analysis showed significant decreases in sclerostin levels between basal and the 7th, 14th, and 21st days (p=0.033, p=0.003, p=0.002, respectively). Similarly, significant decreases in DKK-1 levels between basal and the 7th and 21st days (p=0.015, p=0.001, respectively) and an insignificant decrease on the 14th day (p=0.191) was observed. Sclerostin was positively and significantly correlated with WBC and CRP in basal and 7th-day measurements and WBC in 7th and 14th days. DKK-1 is positively and significantly correlated with WBC in basal and 7th-daymeasurements, while DKK-1 negatively correlates with CRP in basal-7th-day measurements.Conclusion: In this study, it was shown for the first time that the Wnt antagonists sclerostin and DKK-1 values are high in SIRS patients in ICU. Both biomarker levels decreased in parallel with the treatment. However, it could not be associated with disease severity and inflammatory marker levels. We believe that monitoring the change of Wnt antagonists will be useful in demonstrating bone turnover in patients with SIRS.Keywords: Dickkopf-1, Intensive care unit, Sclerostin, Systemic inflammatory response syndrome, Wnt signaling pathway, Bone turnover

Integrate the Canopy SIF and Its Derived Structural and Physiological Components for Wheat Stripe Rust Stress Monitoring

Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIFcanopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIRVP, which can characterize the canopy structure, and SIFtot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIFcanopy, NIRVP, and SIFtot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIRVP was more sensitive to variation in SIFcanopy than SIFtot, and the correlation between SIFtot and SL was 6.6% higher than that of SIFcanopy. Using the decomposed SIFtot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% &lt; SL ≤ 45%), the variation in SIFcanopy was affected by both NIRVP and SIFtot, and the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components. When the severity level (SL) of disease reached a severe level (SL &gt; 45%), SIFtot was more sensitive to the variation in SIFcanopy, and NIRVP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIFcanopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress.

Assessing rice blast disease severity through hyperspectral remote sensing

Remote sensing is being increasingly used in stress management in different agricultural practices. It is useful for real time analysis for crop stress which is not possible for visual observation alone. Rice blast caused by fungus Pyricularia Oryzae is a serious constrain in rice production in India. There is hardly any basic information available for spectral characteristics of rice blast disease for its real-time detection and management. Present study is to characterize spectral reflectance of blast affected rice in order to identify the sensitive spectral range. Disease severity of 10 different genotypes of rice was graded 0 to 9 based on the extent of host organ covered by symptom or lesion. Result shows that severely infected plant (score 9) have higher reflectance at visible region and lower reflectance at NIR region. Change in the reflectance for the infected plant as compare to the healthy plant was more pronounced in the VNIR, 550 to 760 nm and 1140 and 1300 nm having correlation coefficient above 0.6. The study of change in the reflectance with the change in wavelength (1st derivative) revealed that VNIR region have high correlation with the disease severity. Maximum rate of change value at red edge position (REP) is called as red edge value (REV) which has good relation with disease severity levels. Amplitude of the red edge peak decreases with the increase in severity levels. Amplitude of score 0 and 9 was 0.00929 and 0.002301, respectively for upland land condition whereas the amplitude of the score 0 and 9 was 0.010421 and 0.00193, respectively for upland land rice. This study identifies that VNIR and red edge region are sensitive for detecting rice blast, which could be utilized to aerial or satellite based monitoring blast affected rice cropping region.

Relationships of Serum Homocysteine, Vitamin B12, and Folic Acid Levels with Papulopustular Rosacea Severity: A Case-Control Study.

Background Rosacea is a chronic inflammatory skin disease with a multifactorial etiology. Recently, associations between serum homocysteine (Hcy) levels and inflammatory skin diseases, such as psoriasis and hidradenitis suppurativa, have been reported. However, no study has explored the levels of serum Hcy, folic acid, and vitamin B12 in patients with rosacea. Objective To investigate serum Hcy, vitamin B12, and folic acid levels in patients with papulopustular rosacea (PPR), we characterized the association of these levels with PPR severity. Methods This case-control study included 138 PPR patients and 58 healthy controls. The serum levels of Hcy, vitamin B12, and folic acid were measured. A correlation was assessed between disease severity and serum levels of Hcy, vitamin B12, and folic acid. Results Serum vitamin B12 and folic acid levels were significantly lower in PPR patients than in the healthy controls (p = 0.011 and p = 0.0173, respectively). Although serum Hcy levels did not significantly differ between PPR patients and healthy controls, PPR severity was positively correlated with serum Hcy levels (p < 0.001). Conclusions Our results suggest a possible association between hyperhomocysteinemia and vitamin B12 deficiency in patients with PPR.

IL-17A )rs2275913; G197A) gene polymorphism as predictor for disease severity and its correlation with IL-17 serum levels in COVID-19 patients

Severity of symptoms in COVID-19 has been shown to result from a cytokine storm. Interleukin (IL)-17 is one of these various cytokines, which results in a proinflammatory response, systemic inflammatory symptoms, inflammatory cell infiltration of lung tissue and thus leads to the massive lung pathology and multiorgan failure. Gene polymorphisms in the regulatory regions of cytokine-encoding genes affect the amounts of cytokines produced and possess a fundamental role in infectious diseases. This study aimed to investigate the role of IL-17A (rs2275913; G197A) gene polymorphism as predictor of disease severity and its correlation with IL-17 serum levels in COVID-19 patients. A group of 70 COVID-19 patients and 17 age and sex-matched control subjects were enrolled in the present work. Patients were classified into two groups moderate, severe and acute respiratory distress (ARDS) cases, defined according to the criteria established by the world health organization. Quantitative real time-polymerase chain reaction was done to detect IL-17A (rs2275913; G197A). Serum IL-17 levels were assessed by an enzyme-linked immunosorbent assay in both patients and controls. The distribution of different IL-17A G/A genotypes among COVID-19 patients were 44.3% for GG genotype, 44.3% for AG genotype and 11.4% for AA genotype. Genotypes among the control group were 43.8% for GG genotype, 50% for AG genotype and 6.3% for AA genotype. G allele distribution was 66.4%, 68.8% in patient and control group, respectively, and A allele was 33.6% and 31.3%, respectively. There was no association between the different genotypes, disease severity or IL-17 serum levels in the patient group. In conclusion, despite the possible role of IL-17 in the pathogenesis of inflammation, there was no association between IL-17 polymorphism and disease severity or IL-17 serum levels among Egyptian COVID-19 patients.

Use of Anticoagulants in COVID-19: A Review.

Coronavirus disease 2019 (COVID-19) is associated with elevated rates of major and fatal thrombotic events, postulated to be the result of a hypercoagulable state mediated through inflammatory and immunomodulatory mechanisms. Early observational studies showed that disease severity and elevated serum D-dimer levels can predict thrombotic risk in patients hospitalized with COVID-19 and reported an alarming phenomenon of breakthrough thrombosis despite standard-of-care prophylaxis, suggesting the need for enhanced thromboprophylactic strategies. Data on anticoagulant agent selection, dosing, and duration for COVID-19 inpatients are now poised to inform updated professional society guidance. However, there remains limited high-quality data regarding postdischarge and especially ambulatory patients with COVID-19. This review includes published, peer-reviewed, observational, and randomized controlled trial data and major professional society guidance informing thrombosis prevention and treatment in patients with COVID-19. There remains great variability in the approach to anticoagulation in COVID-19. This article will review pathogenesis of COVID-related thrombosis and the evidence guiding thromboprophylaxis particularly in inpatients, with attention to the INSPIRATION, ACTION, RAPID, HEP-COVID, and multiplatform trials. Emerging thromboprophylaxis data from the postdischarge setting (particularly the recently published MICHELLE trial), and the outpatient setting, will be examined. Finally, thrombosis treatment considerations will briefly be reviewed. Substantial high-quality data support practice changes to COVID-19 thromboprophylaxis. Risk stratification by setting, disease severity, and biomarkers such as D-dimer is critical in considering choice, dose, and duration of anticoagulants.